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<h1>Overview</h1>

</div> <div class=”row variablerow”> <div class=”col-md-6 namecol”>

<p class=”h4”>Dataset info</p> <table class=”stats” style=”margin-left: 1em;”>

<tbody> <tr>

<th>Number of variables</th> <td>328 </td>

</tr> <tr>

<th>Number of observations</th> <td>3210 </td>

</tr> <tr>

<th>Total Missing (%)</th> <td>6.9% </td>

</tr> <tr>

<th>Total size in memory</th> <td>7.9 MiB </td>

</tr> <tr>

<th>Average record size in memory</th> <td>2.5 KiB </td>

</tr> </tbody>

</table>

</div> <div class=”col-md-6 namecol”>

<p class=”h4”>Variables types</p> <table class=”stats” style=”margin-left: 1em;”>

<tbody> <tr>

<th>Numeric</th> <td>193 </td>

</tr> <tr>

<th>Categorical</th> <td>0 </td>

</tr> <tr>

<th>Boolean</th> <td>3 </td>

</tr> <tr>

<th>Date</th> <td>0 </td>

</tr> <tr>

<th>Text (Unique)</th> <td>0 </td>

</tr> <tr>

<th>Rejected</th> <td>132 </td>

</tr> <tr>

<th>Unsupported</th> <td>0 </td>

</tr> </tbody>

</table>

</div> <div class=”col-md-12” style=”padding-left: 1em;”>

<p class=”h4”>Warnings</p> <ul class=”list-unstyled”><li><a href=”#pp_var_2010 Census Population”><code>2010 Census Population</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_AGRITRSM_OPS07”><code>AGRITRSM_OPS07</code></a> has 134 / 4.2% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_AGRITRSM_OPS07”><code>AGRITRSM_OPS07</code></a> has 362 / 11.3% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_AGRITRSM_OPS12”><code>AGRITRSM_OPS12</code></a> has 134 / 4.2% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_AGRITRSM_OPS12”><code>AGRITRSM_OPS12</code></a> has 263 / 8.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_AGRITRSM_RCT07”><code>AGRITRSM_RCT07</code></a> has 1239 / 38.6% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_AGRITRSM_RCT07”><code>AGRITRSM_RCT07</code></a> has 362 / 11.3% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_AGRITRSM_RCT12”><code>AGRITRSM_RCT12</code></a> has 1095 / 34.1% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_AGRITRSM_RCT12”><code>AGRITRSM_RCT12</code></a> has 263 / 8.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_BERRY_ACRES07”><code>BERRY_ACRES07</code></a> has 909 / 28.3% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_BERRY_ACRES07”><code>BERRY_ACRES07</code></a> has 843 / 26.3% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_BERRY_ACRES12”><code>BERRY_ACRES12</code></a> is highly correlated with <a href=”#pp_var_BERRY_ACRES07”><code>BERRY_ACRES07</code></a> (ρ = 0.98128) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_BERRY_ACRESPTH07”><code>BERRY_ACRESPTH07</code></a> has 909 / 28.3% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_BERRY_ACRESPTH07”><code>BERRY_ACRESPTH07</code></a> has 843 / 26.3% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_BERRY_ACRESPTH07”><code>BERRY_ACRESPTH07</code></a> is highly skewed (γ1 = 32.243) <span class=”label label-info”>Skewed</span></li><li><a href=”#pp_var_BERRY_ACRESPTH12”><code>BERRY_ACRESPTH12</code></a> is highly correlated with <a href=”#pp_var_BERRY_ACRESPTH07”><code>BERRY_ACRESPTH07</code></a> (ρ = 0.98479) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_BERRY_FARMS07”><code>BERRY_FARMS07</code></a> has 134 / 4.2% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_BERRY_FARMS07”><code>BERRY_FARMS07</code></a> has 843 / 26.3% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_BERRY_FARMS12”><code>BERRY_FARMS12</code></a> is highly correlated with <a href=”#pp_var_BERRY_FARMS07”><code>BERRY_FARMS07</code></a> (ρ = 0.93937) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_CHILDPOVRATE15”><code>CHILDPOVRATE15</code></a> is highly correlated with <a href=”#pp_var_POVRATE15”><code>POVRATE15</code></a> (ρ = 0.93822) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_CHIPSTAX_STORES14”><code>CHIPSTAX_STORES14</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_CHIPSTAX_STORES14”><code>CHIPSTAX_STORES14</code></a> has 2138 / 66.6% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_CHIPSTAX_VENDM14”><code>CHIPSTAX_VENDM14</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_CHIPSTAX_VENDM14”><code>CHIPSTAX_VENDM14</code></a> has 713 / 22.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_CH_FOODINSEC_12_15”><code>CH_FOODINSEC_12_15</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_CH_FOODINSEC_12_15”><code>CH_FOODINSEC_12_15</code></a> has 163 / 5.1% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_CH_VLFOODSEC_12_15”><code>CH_VLFOODSEC_12_15</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_CH_VLFOODSEC_12_15”><code>CH_VLFOODSEC_12_15</code></a> has 327 / 10.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_CONVS09”><code>CONVS09</code></a> is highly correlated with <a href=”#pp_var_Population Estimate, 2016”><code>Population Estimate, 2016</code></a> (ρ = 0.92485) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_CONVS14”><code>CONVS14</code></a> is highly correlated with <a href=”#pp_var_CONVS09”><code>CONVS09</code></a> (ρ = 0.99646) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_CONVSPTH09”><code>CONVSPTH09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_CONVSPTH09”><code>CONVSPTH09</code></a> has 46 / 1.4% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_CONVSPTH14”><code>CONVSPTH14</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_CONVSPTH14”><code>CONVSPTH14</code></a> has 38 / 1.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_CSA07”><code>CSA07</code></a> has 134 / 4.2% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_CSA07”><code>CSA07</code></a> has 732 / 22.8% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_CSA12”><code>CSA12</code></a> has 134 / 4.2% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_CSA12”><code>CSA12</code></a> has 944 / 29.4% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_Child and Adult Care participants, FY 2012”><code>Child and Adult Care participants, FY 2012</code></a> is highly correlated with <a href=”#pp_var_Child and Adult Care particpants FY 2011”><code>Child and Adult Care particpants FY 2011</code></a> (ρ = 0.99474) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_Child and Adult Care participants, FY 2013”><code>Child and Adult Care participants, FY 2013</code></a> is highly correlated with <a href=”#pp_var_Child and Adult Care participants, FY 2012”><code>Child and Adult Care participants, FY 2012</code></a> (ρ = 0.99338) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_Child and Adult Care participants, FY 2014”><code>Child and Adult Care participants, FY 2014</code></a> is highly correlated with <a href=”#pp_var_Child and Adult Care participants, FY 2013”><code>Child and Adult Care participants, FY 2013</code></a> (ρ = 0.99269) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_Child and Adult Care participants, FY 2015”><code>Child and Adult Care participants, FY 2015</code></a> is highly correlated with <a href=”#pp_var_Child and Adult Care participants, FY 2014”><code>Child and Adult Care participants, FY 2014</code></a> (ρ = 0.9954) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_Child and Adult Care particpants FY 2009”><code>Child and Adult Care particpants FY 2009</code></a> is highly correlated with <a href=”#pp_var_National School Lunch Program participants, FY 2015”><code>National School Lunch Program participants, FY 2015</code></a> (ρ = 0.95709) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_Child and Adult Care particpants FY 2011”><code>Child and Adult Care particpants FY 2011</code></a> is highly correlated with <a href=”#pp_var_Child and Adult Care particpants FY 2009”><code>Child and Adult Care particpants FY 2009</code></a> (ρ = 0.99714) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_DIRSALES07”><code>DIRSALES07</code></a> has 359 / 11.2% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_DIRSALES07”><code>DIRSALES07</code></a> has 60 / 1.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_DIRSALES12”><code>DIRSALES12</code></a> is highly correlated with <a href=”#pp_var_DIRSALES07”><code>DIRSALES07</code></a> (ρ = 0.90428) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_DIRSALES_FARMS07”><code>DIRSALES_FARMS07</code></a> has 134 / 4.2% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_DIRSALES_FARMS07”><code>DIRSALES_FARMS07</code></a> has 60 / 1.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_DIRSALES_FARMS12”><code>DIRSALES_FARMS12</code></a> is highly correlated with <a href=”#pp_var_DIRSALES_FARMS07”><code>DIRSALES_FARMS07</code></a> (ρ = 0.96228) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FARM_TO_SCHOOL09”><code>FARM_TO_SCHOOL09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FARM_TO_SCHOOL09”><code>FARM_TO_SCHOOL09</code></a> has 2939 / 91.6% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_FARM_TO_SCHOOL13”><code>FARM_TO_SCHOOL13</code></a> has 286 / 8.9% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FARM_TO_SCHOOL13”><code>FARM_TO_SCHOOL13</code></a> has 1117 / 34.8% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_FDPIR12”><code>FDPIR12</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FDPIR12”><code>FDPIR12</code></a> has 2911 / 90.7% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_FFR09”><code>FFR09</code></a> is highly correlated with <a href=”#pp_var_SNAPS16”><code>SNAPS16</code></a> (ρ = 0.95189) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FFR14”><code>FFR14</code></a> is highly correlated with <a href=”#pp_var_FFR09”><code>FFR09</code></a> (ρ = 0.99882) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FFRPTH09”><code>FFRPTH09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FFRPTH09”><code>FFRPTH09</code></a> has 141 / 4.4% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_FFRPTH14”><code>FFRPTH14</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FFRPTH14”><code>FFRPTH14</code></a> has 153 / 4.8% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_FMRKT09”><code>FMRKT09</code></a> has 79 / 2.5% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FMRKT09”><code>FMRKT09</code></a> has 1360 / 42.4% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_FMRKT16”><code>FMRKT16</code></a> is highly correlated with <a href=”#pp_var_FMRKT09”><code>FMRKT09</code></a> (ρ = 0.91012) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FMRKTPTH09”><code>FMRKTPTH09</code></a> has 79 / 2.5% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FMRKTPTH09”><code>FMRKTPTH09</code></a> has 1360 / 42.4% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_FMRKTPTH16”><code>FMRKTPTH16</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FMRKTPTH16”><code>FMRKTPTH16</code></a> has 889 / 27.7% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_FMRKT_ANMLPROD16”><code>FMRKT_ANMLPROD16</code></a> is highly correlated with <a href=”#pp_var_FMRKT_FRVEG16”><code>FMRKT_FRVEG16</code></a> (ρ = 0.96896) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FMRKT_BAKED16”><code>FMRKT_BAKED16</code></a> is highly correlated with <a href=”#pp_var_FMRKT_ANMLPROD16”><code>FMRKT_ANMLPROD16</code></a> (ρ = 0.98152) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FMRKT_CREDIT16”><code>FMRKT_CREDIT16</code></a> is highly correlated with <a href=”#pp_var_FMRKT_SNAP16”><code>FMRKT_SNAP16</code></a> (ρ = 0.92193) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FMRKT_FRVEG16”><code>FMRKT_FRVEG16</code></a> is highly correlated with <a href=”#pp_var_FMRKT_CREDIT16”><code>FMRKT_CREDIT16</code></a> (ρ = 0.97114) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FMRKT_OTHERFOOD16”><code>FMRKT_OTHERFOOD16</code></a> is highly correlated with <a href=”#pp_var_FMRKT_BAKED16”><code>FMRKT_BAKED16</code></a> (ρ = 0.98659) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FMRKT_SFMNP16”><code>FMRKT_SFMNP16</code></a> is highly correlated with <a href=”#pp_var_FMRKT_WIC16”><code>FMRKT_WIC16</code></a> (ρ = 0.93896) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FMRKT_SNAP16”><code>FMRKT_SNAP16</code></a> has 967 / 30.1% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FMRKT_SNAP16”><code>FMRKT_SNAP16</code></a> has 1289 / 40.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_FMRKT_WIC16”><code>FMRKT_WIC16</code></a> has 967 / 30.1% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FMRKT_WIC16”><code>FMRKT_WIC16</code></a> has 1416 / 44.1% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_FMRKT_WICCASH16”><code>FMRKT_WICCASH16</code></a> has 967 / 30.1% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FMRKT_WICCASH16”><code>FMRKT_WICCASH16</code></a> has 1730 / 53.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_FOODHUB16”><code>FOODHUB16</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FOODHUB16”><code>FOODHUB16</code></a> has 2991 / 93.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_FOODINSEC_10_12”><code>FOODINSEC_10_12</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FOODINSEC_13_15”><code>FOODINSEC_13_15</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FOODINSEC_CHILD_01_07”><code>FOODINSEC_CHILD_01_07</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FOODINSEC_CHILD_03_11”><code>FOODINSEC_CHILD_03_11</code></a> is highly correlated with <a href=”#pp_var_FOODINSEC_CHILD_01_07”><code>FOODINSEC_CHILD_01_07</code></a> (ρ = 0.94773) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FOOD_TAX14”><code>FOOD_TAX14</code></a> is highly correlated with <a href=”#pp_var_CHIPSTAX_STORES14”><code>CHIPSTAX_STORES14</code></a> (ρ = 1) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FRESHVEG_ACRES07”><code>FRESHVEG_ACRES07</code></a> has 1370 / 42.7% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FRESHVEG_ACRES07”><code>FRESHVEG_ACRES07</code></a> has 300 / 9.3% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_FRESHVEG_ACRES07”><code>FRESHVEG_ACRES07</code></a> is highly skewed (γ1 = 22.249) <span class=”label label-info”>Skewed</span></li><li><a href=”#pp_var_FRESHVEG_ACRES12”><code>FRESHVEG_ACRES12</code></a> is highly correlated with <a href=”#pp_var_FRESHVEG_ACRES07”><code>FRESHVEG_ACRES07</code></a> (ρ = 0.99083) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FRESHVEG_ACRESPTH07”><code>FRESHVEG_ACRESPTH07</code></a> has 1370 / 42.7% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FRESHVEG_ACRESPTH07”><code>FRESHVEG_ACRESPTH07</code></a> has 300 / 9.3% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_FRESHVEG_ACRESPTH12”><code>FRESHVEG_ACRESPTH12</code></a> is highly correlated with <a href=”#pp_var_FRESHVEG_ACRESPTH07”><code>FRESHVEG_ACRESPTH07</code></a> (ρ = 0.94863) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FRESHVEG_FARMS07”><code>FRESHVEG_FARMS07</code></a> is highly correlated with <a href=”#pp_var_VEG_FARMS07”><code>VEG_FARMS07</code></a> (ρ = 0.97465) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FRESHVEG_FARMS12”><code>FRESHVEG_FARMS12</code></a> is highly correlated with <a href=”#pp_var_FRESHVEG_FARMS07”><code>FRESHVEG_FARMS07</code></a> (ρ = 0.90251) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FSR09”><code>FSR09</code></a> is highly correlated with <a href=”#pp_var_FFR14”><code>FFR14</code></a> (ρ = 0.97433) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FSR14”><code>FSR14</code></a> is highly correlated with <a href=”#pp_var_FSR09”><code>FSR09</code></a> (ρ = 0.99878) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FSRPTH09”><code>FSRPTH09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FSRPTH09”><code>FSRPTH09</code></a> has 57 / 1.8% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_FSRPTH14”><code>FSRPTH14</code></a> is highly correlated with <a href=”#pp_var_FSRPTH09”><code>FSRPTH09</code></a> (ρ = 0.90099) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_GHVEG_FARMS07”><code>GHVEG_FARMS07</code></a> has 134 / 4.2% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_GHVEG_FARMS07”><code>GHVEG_FARMS07</code></a> has 1878 / 58.5% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_GHVEG_FARMS12”><code>GHVEG_FARMS12</code></a> has 134 / 4.2% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_GHVEG_FARMS12”><code>GHVEG_FARMS12</code></a> has 1412 / 44.0% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_GHVEG_SQFT07”><code>GHVEG_SQFT07</code></a> has 913 / 28.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_GHVEG_SQFT07”><code>GHVEG_SQFT07</code></a> has 1878 / 58.5% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_GHVEG_SQFT12”><code>GHVEG_SQFT12</code></a> has 966 / 30.1% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_GHVEG_SQFT12”><code>GHVEG_SQFT12</code></a> has 1412 / 44.0% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_GHVEG_SQFT12”><code>GHVEG_SQFT12</code></a> is highly skewed (γ1 = 21.474) <span class=”label label-info”>Skewed</span></li><li><a href=”#pp_var_GHVEG_SQFTPTH07”><code>GHVEG_SQFTPTH07</code></a> has 913 / 28.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_GHVEG_SQFTPTH07”><code>GHVEG_SQFTPTH07</code></a> has 1878 / 58.5% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_GHVEG_SQFTPTH12”><code>GHVEG_SQFTPTH12</code></a> has 966 / 30.1% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_GHVEG_SQFTPTH12”><code>GHVEG_SQFTPTH12</code></a> has 1412 / 44.0% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_GROC09”><code>GROC09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_GROC09”><code>GROC09</code></a> has 56 / 1.7% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_GROC14”><code>GROC14</code></a> is highly correlated with <a href=”#pp_var_GROC09”><code>GROC09</code></a> (ρ = 0.9926) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_GROCPTH09”><code>GROCPTH09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_GROCPTH09”><code>GROCPTH09</code></a> has 56 / 1.7% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_GROCPTH14”><code>GROCPTH14</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_GROCPTH14”><code>GROCPTH14</code></a> has 67 / 2.1% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_LACCESS_BLACK15”><code>LACCESS_BLACK15</code></a> has 97 / 3.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_LACCESS_BLACK15”><code>LACCESS_BLACK15</code></a> has 189 / 5.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_LACCESS_CHILD10”><code>LACCESS_CHILD10</code></a> is highly correlated with <a href=”#pp_var_LACCESS_LOWI15”><code>LACCESS_LOWI15</code></a> (ρ = 0.93398) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_LACCESS_CHILD15”><code>LACCESS_CHILD15</code></a> is highly correlated with <a href=”#pp_var_LACCESS_CHILD10”><code>LACCESS_CHILD10</code></a> (ρ = 0.994) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_LACCESS_CHILD_10_15”><code>LACCESS_CHILD_10_15</code></a> has 106 / 3.3% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_LACCESS_CHILD_10_15”><code>LACCESS_CHILD_10_15</code></a> has 278 / 8.7% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_LACCESS_CHILD_10_15”><code>LACCESS_CHILD_10_15</code></a> is highly skewed (γ1 = 50.355) <span class=”label label-info”>Skewed</span></li><li><a href=”#pp_var_LACCESS_HHNV10”><code>LACCESS_HHNV10</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_LACCESS_HHNV15”><code>LACCESS_HHNV15</code></a> is highly correlated with <a href=”#pp_var_LACCESS_HHNV10”><code>LACCESS_HHNV10</code></a> (ρ = 0.97302) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_LACCESS_HISP15”><code>LACCESS_HISP15</code></a> has 97 / 3.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_LACCESS_HISP15”><code>LACCESS_HISP15</code></a> has 53 / 1.7% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_LACCESS_LOWI10”><code>LACCESS_LOWI10</code></a> is highly correlated with <a href=”#pp_var_LACCESS_POP15”><code>LACCESS_POP15</code></a> (ρ = 0.907) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_LACCESS_LOWI15”><code>LACCESS_LOWI15</code></a> is highly correlated with <a href=”#pp_var_LACCESS_LOWI10”><code>LACCESS_LOWI10</code></a> (ρ = 0.98469) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_LACCESS_MULTIR15”><code>LACCESS_MULTIR15</code></a> has 97 / 3.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_LACCESS_MULTIR15”><code>LACCESS_MULTIR15</code></a> has 38 / 1.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_LACCESS_NHASIAN15”><code>LACCESS_NHASIAN15</code></a> has 97 / 3.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_LACCESS_NHASIAN15”><code>LACCESS_NHASIAN15</code></a> has 230 / 7.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_LACCESS_NHNA15”><code>LACCESS_NHNA15</code></a> has 97 / 3.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_LACCESS_NHNA15”><code>LACCESS_NHNA15</code></a> has 170 / 5.3% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_LACCESS_NHPI15”><code>LACCESS_NHPI15</code></a> has 97 / 3.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_LACCESS_NHPI15”><code>LACCESS_NHPI15</code></a> has 1175 / 36.6% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_LACCESS_NHPI15”><code>LACCESS_NHPI15</code></a> is highly skewed (γ1 = 40.47) <span class=”label label-info”>Skewed</span></li><li><a href=”#pp_var_LACCESS_POP10”><code>LACCESS_POP10</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_LACCESS_POP15”><code>LACCESS_POP15</code></a> is highly correlated with <a href=”#pp_var_LACCESS_POP10”><code>LACCESS_POP10</code></a> (ρ = 0.99406) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_LACCESS_SENIORS10”><code>LACCESS_SENIORS10</code></a> is highly correlated with <a href=”#pp_var_LACCESS_CHILD15”><code>LACCESS_CHILD15</code></a> (ρ = 0.91878) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_LACCESS_SENIORS15”><code>LACCESS_SENIORS15</code></a> is highly correlated with <a href=”#pp_var_LACCESS_SENIORS10”><code>LACCESS_SENIORS10</code></a> (ρ = 0.9937) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_LACCESS_SNAP15”><code>LACCESS_SNAP15</code></a> is highly correlated with <a href=”#pp_var_LACCESS_HHNV15”><code>LACCESS_HHNV15</code></a> (ρ = 0.90806) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_LACCESS_WHITE15”><code>LACCESS_WHITE15</code></a> is highly correlated with <a href=”#pp_var_LACCESS_SENIORS15”><code>LACCESS_SENIORS15</code></a> (ρ = 0.95929) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_MEDHHINC15”><code>MEDHHINC15</code></a> has 79 / 2.5% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_METRO13”><code>METRO13</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_METRO13”><code>METRO13</code></a> has 1968 / 61.3% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_MILK_PRICE10”><code>MILK_PRICE10</code></a> has 106 / 3.3% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_MILK_SODA_PRICE10”><code>MILK_SODA_PRICE10</code></a> is highly correlated with <a href=”#pp_var_MILK_PRICE10”><code>MILK_PRICE10</code></a> (ρ = 0.91655) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_National School Lunch Program participants FY 2009”><code>National School Lunch Program participants FY 2009</code></a> is highly correlated with <a href=”#pp_var_WIC participants, FY 2015”><code>WIC participants, FY 2015</code></a> (ρ = 0.96736) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_National School Lunch Program participants FY 2011”><code>National School Lunch Program participants FY 2011</code></a> is highly correlated with <a href=”#pp_var_National School Lunch Program participants FY 2009”><code>National School Lunch Program participants FY 2009</code></a> (ρ = 0.99972) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_National School Lunch Program participants, FY 2012”><code>National School Lunch Program participants, FY 2012</code></a> is highly correlated with <a href=”#pp_var_National School Lunch Program participants FY 2011”><code>National School Lunch Program participants FY 2011</code></a> (ρ = 0.99994) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_National School Lunch Program participants, FY 2013”><code>National School Lunch Program participants, FY 2013</code></a> is highly correlated with <a href=”#pp_var_National School Lunch Program participants, FY 2012”><code>National School Lunch Program participants, FY 2012</code></a> (ρ = 0.99978) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_National School Lunch Program participants, FY 2014”><code>National School Lunch Program participants, FY 2014</code></a> is highly correlated with <a href=”#pp_var_National School Lunch Program participants, FY 2013”><code>National School Lunch Program participants, FY 2013</code></a> (ρ = 0.9999) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_National School Lunch Program participants, FY 2015”><code>National School Lunch Program participants, FY 2015</code></a> is highly correlated with <a href=”#pp_var_National School Lunch Program participants, FY 2014”><code>National School Lunch Program participants, FY 2014</code></a> (ρ = 0.99976) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_ORCHARD_ACRES07”><code>ORCHARD_ACRES07</code></a> has 619 / 19.3% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_ORCHARD_ACRES07”><code>ORCHARD_ACRES07</code></a> has 359 / 11.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_ORCHARD_ACRES12”><code>ORCHARD_ACRES12</code></a> is highly correlated with <a href=”#pp_var_ORCHARD_ACRES07”><code>ORCHARD_ACRES07</code></a> (ρ = 0.99592) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_ORCHARD_ACRESPTH07”><code>ORCHARD_ACRESPTH07</code></a> has 619 / 19.3% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_ORCHARD_ACRESPTH07”><code>ORCHARD_ACRESPTH07</code></a> has 359 / 11.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_ORCHARD_ACRESPTH12”><code>ORCHARD_ACRESPTH12</code></a> is highly correlated with <a href=”#pp_var_ORCHARD_ACRESPTH07”><code>ORCHARD_ACRESPTH07</code></a> (ρ = 0.96957) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_ORCHARD_FARMS07”><code>ORCHARD_FARMS07</code></a> has 134 / 4.2% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_ORCHARD_FARMS07”><code>ORCHARD_FARMS07</code></a> has 359 / 11.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_ORCHARD_FARMS12”><code>ORCHARD_FARMS12</code></a> is highly correlated with <a href=”#pp_var_ORCHARD_FARMS07”><code>ORCHARD_FARMS07</code></a> (ρ = 0.99031) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_AGRITRSM_OPS_07_12”><code>PCH_AGRITRSM_OPS_07_12</code></a> has 496 / 15.5% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_AGRITRSM_OPS_07_12”><code>PCH_AGRITRSM_OPS_07_12</code></a> has 243 / 7.6% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_AGRITRSM_RCT_07_12”><code>PCH_AGRITRSM_RCT_07_12</code></a> has 1945 / 60.6% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_BERRY_ACRESPTH_07_12”><code>PCH_BERRY_ACRESPTH_07_12</code></a> is highly correlated with <a href=”#pp_var_PCH_BERRY_ACRES_07_12”><code>PCH_BERRY_ACRES_07_12</code></a> (ρ = 0.99863) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_BERRY_ACRES_07_12”><code>PCH_BERRY_ACRES_07_12</code></a> has 1939 / 60.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_BERRY_ACRES_07_12”><code>PCH_BERRY_ACRES_07_12</code></a> has 69 / 2.1% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_BERRY_FARMS_07_12”><code>PCH_BERRY_FARMS_07_12</code></a> has 977 / 30.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_BERRY_FARMS_07_12”><code>PCH_BERRY_FARMS_07_12</code></a> has 218 / 6.8% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_CACFP_09_15”><code>PCH_CACFP_09_15</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_CONVSPTH_09_14”><code>PCH_CONVSPTH_09_14</code></a> is highly correlated with <a href=”#pp_var_PCH_CONVS_09_14”><code>PCH_CONVS_09_14</code></a> (ρ = 0.98861) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_CONVS_09_14”><code>PCH_CONVS_09_14</code></a> has 97 / 3.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_CONVS_09_14”><code>PCH_CONVS_09_14</code></a> has 542 / 16.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_CSA_07_12”><code>PCH_CSA_07_12</code></a> has 866 / 27.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_CSA_07_12”><code>PCH_CSA_07_12</code></a> has 219 / 6.8% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_DIRSALES_07_12”><code>PCH_DIRSALES_07_12</code></a> has 529 / 16.5% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_DIRSALES_07_12”><code>PCH_DIRSALES_07_12</code></a> is highly skewed (γ1 = 22.614) <span class=”label label-info”>Skewed</span></li><li><a href=”#pp_var_PCH_DIRSALES_FARMS_07_12”><code>PCH_DIRSALES_FARMS_07_12</code></a> has 194 / 6.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_DIRSALES_FARMS_07_12”><code>PCH_DIRSALES_FARMS_07_12</code></a> has 148 / 4.6% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_FFRPTH_09_14”><code>PCH_FFRPTH_09_14</code></a> is highly correlated with <a href=”#pp_var_PCH_FFR_09_14”><code>PCH_FFR_09_14</code></a> (ρ = 0.9901) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_FFR_09_14”><code>PCH_FFR_09_14</code></a> has 124 / 3.9% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_FFR_09_14”><code>PCH_FFR_09_14</code></a> has 547 / 17.0% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_FMRKTPTH_09_16”><code>PCH_FMRKTPTH_09_16</code></a> is highly correlated with <a href=”#pp_var_PCH_FMRKT_09_16”><code>PCH_FMRKT_09_16</code></a> (ρ = 0.99444) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_FMRKT_09_16”><code>PCH_FMRKT_09_16</code></a> has 628 / 19.6% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_FMRKT_09_16”><code>PCH_FMRKT_09_16</code></a> has 1505 / 46.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_FRESHVEG_ACRESPTH_07_12”><code>PCH_FRESHVEG_ACRESPTH_07_12</code></a> is highly correlated with <a href=”#pp_var_PCH_FRESHVEG_ACRES_07_12”><code>PCH_FRESHVEG_ACRES_07_12</code></a> (ρ = 0.99841) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_FRESHVEG_ACRES_07_12”><code>PCH_FRESHVEG_ACRES_07_12</code></a> has 2160 / 67.3% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_FRESHVEG_FARMS_07_12”><code>PCH_FRESHVEG_FARMS_07_12</code></a> is highly correlated with <a href=”#pp_var_PCH_VEG_FARMS_07_12”><code>PCH_VEG_FARMS_07_12</code></a> (ρ = 0.96454) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_FSRPTH_09_14”><code>PCH_FSRPTH_09_14</code></a> is highly correlated with <a href=”#pp_var_PCH_FSR_09_14”><code>PCH_FSR_09_14</code></a> (ρ = 0.98863) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_FSR_09_14”><code>PCH_FSR_09_14</code></a> has 109 / 3.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_FSR_09_14”><code>PCH_FSR_09_14</code></a> has 401 / 12.5% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_GHVEG_FARMS_07_12”><code>PCH_GHVEG_FARMS_07_12</code></a> has 2012 / 62.7% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_GHVEG_FARMS_07_12”><code>PCH_GHVEG_FARMS_07_12</code></a> has 187 / 5.8% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_GHVEG_SQFTPTH_07_12”><code>PCH_GHVEG_SQFTPTH_07_12</code></a> is highly correlated with <a href=”#pp_var_PCH_GHVEG_SQFT_07_12”><code>PCH_GHVEG_SQFT_07_12</code></a> (ρ = 0.99987) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_GHVEG_SQFT_07_12”><code>PCH_GHVEG_SQFT_07_12</code></a> has 2870 / 89.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_GROCPTH_09_14”><code>PCH_GROCPTH_09_14</code></a> is highly correlated with <a href=”#pp_var_PCH_GROC_09_14”><code>PCH_GROC_09_14</code></a> (ρ = 0.99149) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_GROC_09_14”><code>PCH_GROC_09_14</code></a> has 93 / 2.9% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_GROC_09_14”><code>PCH_GROC_09_14</code></a> has 961 / 29.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_LACCESS_HHNV_10_15”><code>PCH_LACCESS_HHNV_10_15</code></a> has 91 / 2.8% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_LACCESS_HHNV_10_15”><code>PCH_LACCESS_HHNV_10_15</code></a> is highly skewed (γ1 = 52.642) <span class=”label label-info”>Skewed</span></li><li><a href=”#pp_var_PCH_LACCESS_LOWI_10_15”><code>PCH_LACCESS_LOWI_10_15</code></a> is highly correlated with <a href=”#pp_var_PCH_LACCESS_POP_10_15”><code>PCH_LACCESS_POP_10_15</code></a> (ρ = 0.99999) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_LACCESS_POP_10_15”><code>PCH_LACCESS_POP_10_15</code></a> has 104 / 3.2% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_LACCESS_POP_10_15”><code>PCH_LACCESS_POP_10_15</code></a> has 253 / 7.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_LACCESS_POP_10_15”><code>PCH_LACCESS_POP_10_15</code></a> is highly skewed (γ1 = 55.725) <span class=”label label-info”>Skewed</span></li><li><a href=”#pp_var_PCH_LACCESS_SENIORS_10_15”><code>PCH_LACCESS_SENIORS_10_15</code></a> is highly correlated with <a href=”#pp_var_PCH_LACCESS_LOWI_10_15”><code>PCH_LACCESS_LOWI_10_15</code></a> (ρ = 0.99981) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_NSLP_09_15”><code>PCH_NSLP_09_15</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_ORCHARD_ACRESPTH_07_12”><code>PCH_ORCHARD_ACRESPTH_07_12</code></a> is highly correlated with <a href=”#pp_var_PCH_ORCHARD_ACRES_07_12”><code>PCH_ORCHARD_ACRES_07_12</code></a> (ρ = 0.99826) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_ORCHARD_ACRES_07_12”><code>PCH_ORCHARD_ACRES_07_12</code></a> has 1192 / 37.1% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_ORCHARD_FARMS_07_12”><code>PCH_ORCHARD_FARMS_07_12</code></a> has 493 / 15.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_ORCHARD_FARMS_07_12”><code>PCH_ORCHARD_FARMS_07_12</code></a> has 223 / 6.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_PC_DIRSALES_07_12”><code>PCH_PC_DIRSALES_07_12</code></a> is highly correlated with <a href=”#pp_var_PCH_DIRSALES_07_12”><code>PCH_DIRSALES_07_12</code></a> (ρ = 0.99896) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_PC_SNAPBEN_10_15”><code>PCH_PC_SNAPBEN_10_15</code></a> has 158 / 4.9% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_PC_WIC_REDEMP_08_12”><code>PCH_PC_WIC_REDEMP_08_12</code></a> has 1273 / 39.7% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_RECFACPTH_09_14”><code>PCH_RECFACPTH_09_14</code></a> is highly correlated with <a href=”#pp_var_PCH_RECFAC_09_14”><code>PCH_RECFAC_09_14</code></a> (ρ = 0.9968) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_RECFAC_09_14”><code>PCH_RECFAC_09_14</code></a> has 201 / 6.3% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_RECFAC_09_14”><code>PCH_RECFAC_09_14</code></a> has 1285 / 40.0% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_REDEMP_SNAPS_12_16”><code>PCH_REDEMP_SNAPS_12_16</code></a> has 351 / 10.9% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_REDEMP_WICS_08_12”><code>PCH_REDEMP_WICS_08_12</code></a> has 1273 / 39.7% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_SBP_09_15”><code>PCH_SBP_09_15</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_SFSP_09_15”><code>PCH_SFSP_09_15</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_SLHOUSE_07_12”><code>PCH_SLHOUSE_07_12</code></a> has 243 / 7.6% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_SLHOUSE_07_12”><code>PCH_SLHOUSE_07_12</code></a> has 2464 / 76.8% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_SNAPSPTH_12_16”><code>PCH_SNAPSPTH_12_16</code></a> is highly correlated with <a href=”#pp_var_PCH_SNAPS_12_16”><code>PCH_SNAPS_12_16</code></a> (ρ = 0.98953) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_SNAPS_12_16”><code>PCH_SNAPS_12_16</code></a> has 107 / 3.3% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_SNAPS_12_16”><code>PCH_SNAPS_12_16</code></a> has 120 / 3.7% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_SNAP_12_16”><code>PCH_SNAP_12_16</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_SPECSPTH_09_14”><code>PCH_SPECSPTH_09_14</code></a> is highly correlated with <a href=”#pp_var_PCH_SPECS_09_14”><code>PCH_SPECS_09_14</code></a> (ρ = 0.99719) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_SPECS_09_14”><code>PCH_SPECS_09_14</code></a> has 266 / 8.3% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_SPECS_09_14”><code>PCH_SPECS_09_14</code></a> has 1435 / 44.7% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_SUPERCPTH_09_14”><code>PCH_SUPERCPTH_09_14</code></a> is highly correlated with <a href=”#pp_var_PCH_SUPERC_09_14”><code>PCH_SUPERC_09_14</code></a> (ρ = 0.99027) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_SUPERC_09_14”><code>PCH_SUPERC_09_14</code></a> has 216 / 6.7% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_SUPERC_09_14”><code>PCH_SUPERC_09_14</code></a> has 2596 / 80.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_VEG_ACRESPTH_07_12”><code>PCH_VEG_ACRESPTH_07_12</code></a> has 968 / 30.2% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_VEG_ACRES_07_12”><code>PCH_VEG_ACRES_07_12</code></a> has 1153 / 35.9% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_VEG_ACRES_07_12”><code>PCH_VEG_ACRES_07_12</code></a> is highly skewed (γ1 = 37.151) <span class=”label label-info”>Skewed</span></li><li><a href=”#pp_var_PCH_VEG_FARMS_07_12”><code>PCH_VEG_FARMS_07_12</code></a> has 420 / 13.1% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_VEG_FARMS_07_12”><code>PCH_VEG_FARMS_07_12</code></a> has 230 / 7.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_WICSPTH_08_12”><code>PCH_WICSPTH_08_12</code></a> is highly correlated with <a href=”#pp_var_PCH_WICS_08_12”><code>PCH_WICS_08_12</code></a> (ρ = 0.98279) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_WICS_08_12”><code>PCH_WICS_08_12</code></a> has 97 / 3.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_WICS_08_12”><code>PCH_WICS_08_12</code></a> has 1325 / 41.3% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_WIC_09_15”><code>PCH_WIC_09_15</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_18YOUNGER10”><code>PCT_18YOUNGER10</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_65OLDER10”><code>PCT_65OLDER10</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_CACFP09”><code>PCT_CACFP09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_CACFP15”><code>PCT_CACFP15</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_DIABETES_ADULTS08”><code>PCT_DIABETES_ADULTS08</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_DIABETES_ADULTS13”><code>PCT_DIABETES_ADULTS13</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_FMRKT_ANMLPROD16”><code>PCT_FMRKT_ANMLPROD16</code></a> has 967 / 30.1% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_FMRKT_ANMLPROD16”><code>PCT_FMRKT_ANMLPROD16</code></a> has 636 / 19.8% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_FMRKT_BAKED16”><code>PCT_FMRKT_BAKED16</code></a> has 967 / 30.1% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_FMRKT_BAKED16”><code>PCT_FMRKT_BAKED16</code></a> has 576 / 17.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_FMRKT_CREDIT16”><code>PCT_FMRKT_CREDIT16</code></a> has 967 / 30.1% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_FMRKT_CREDIT16”><code>PCT_FMRKT_CREDIT16</code></a> has 726 / 22.6% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_FMRKT_FRVEG16”><code>PCT_FMRKT_FRVEG16</code></a> has 967 / 30.1% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_FMRKT_FRVEG16”><code>PCT_FMRKT_FRVEG16</code></a> has 488 / 15.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_FMRKT_OTHERFOOD16”><code>PCT_FMRKT_OTHERFOOD16</code></a> is highly correlated with <a href=”#pp_var_PCT_FMRKT_FRVEG16”><code>PCT_FMRKT_FRVEG16</code></a> (ρ = 0.90806) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCT_FMRKT_SFMNP16”><code>PCT_FMRKT_SFMNP16</code></a> has 967 / 30.1% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_FMRKT_SFMNP16”><code>PCT_FMRKT_SFMNP16</code></a> has 1329 / 41.4% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_FMRKT_SNAP16”><code>PCT_FMRKT_SNAP16</code></a> has 967 / 30.1% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_FMRKT_SNAP16”><code>PCT_FMRKT_SNAP16</code></a> has 1289 / 40.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_FMRKT_WIC16”><code>PCT_FMRKT_WIC16</code></a> has 967 / 30.1% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_FMRKT_WIC16”><code>PCT_FMRKT_WIC16</code></a> has 1416 / 44.1% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_FMRKT_WICCASH16”><code>PCT_FMRKT_WICCASH16</code></a> has 967 / 30.1% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_FMRKT_WICCASH16”><code>PCT_FMRKT_WICCASH16</code></a> has 1730 / 53.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_FREE_LUNCH09”><code>PCT_FREE_LUNCH09</code></a> has 153 / 4.8% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_FREE_LUNCH14”><code>PCT_FREE_LUNCH14</code></a> has 314 / 9.8% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_HISP10”><code>PCT_HISP10</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_HSPA15”><code>PCT_HSPA15</code></a> has 1196 / 37.3% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_LACCESS_BLACK15”><code>PCT_LACCESS_BLACK15</code></a> has 97 / 3.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_LACCESS_BLACK15”><code>PCT_LACCESS_BLACK15</code></a> has 189 / 5.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_LACCESS_CHILD10”><code>PCT_LACCESS_CHILD10</code></a> is highly correlated with <a href=”#pp_var_PCT_LACCESS_POP10”><code>PCT_LACCESS_POP10</code></a> (ρ = 0.96149) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCT_LACCESS_CHILD15”><code>PCT_LACCESS_CHILD15</code></a> is highly correlated with <a href=”#pp_var_PCT_LACCESS_POP15”><code>PCT_LACCESS_POP15</code></a> (ρ = 0.95773) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCT_LACCESS_HHNV10”><code>PCT_LACCESS_HHNV10</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_LACCESS_HHNV15”><code>PCT_LACCESS_HHNV15</code></a> has 80 / 2.5% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_LACCESS_HISP15”><code>PCT_LACCESS_HISP15</code></a> has 97 / 3.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_LACCESS_HISP15”><code>PCT_LACCESS_HISP15</code></a> has 53 / 1.7% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_LACCESS_LOWI10”><code>PCT_LACCESS_LOWI10</code></a> is highly correlated with <a href=”#pp_var_PCT_LACCESS_POP10”><code>PCT_LACCESS_POP10</code></a> (ρ = 0.90295) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCT_LACCESS_LOWI15”><code>PCT_LACCESS_LOWI15</code></a> is highly correlated with <a href=”#pp_var_PCT_LACCESS_POP15”><code>PCT_LACCESS_POP15</code></a> (ρ = 0.90394) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCT_LACCESS_MULTIR15”><code>PCT_LACCESS_MULTIR15</code></a> has 97 / 3.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_LACCESS_MULTIR15”><code>PCT_LACCESS_MULTIR15</code></a> has 38 / 1.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_LACCESS_NHASIAN15”><code>PCT_LACCESS_NHASIAN15</code></a> has 97 / 3.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_LACCESS_NHASIAN15”><code>PCT_LACCESS_NHASIAN15</code></a> has 230 / 7.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_LACCESS_NHNA15”><code>PCT_LACCESS_NHNA15</code></a> has 97 / 3.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_LACCESS_NHNA15”><code>PCT_LACCESS_NHNA15</code></a> has 170 / 5.3% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_LACCESS_NHPI15”><code>PCT_LACCESS_NHPI15</code></a> has 97 / 3.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_LACCESS_NHPI15”><code>PCT_LACCESS_NHPI15</code></a> has 1175 / 36.6% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_LACCESS_NHPI15”><code>PCT_LACCESS_NHPI15</code></a> is highly skewed (γ1 = 25.235) <span class=”label label-info”>Skewed</span></li><li><a href=”#pp_var_PCT_LACCESS_POP10”><code>PCT_LACCESS_POP10</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_LACCESS_POP15”><code>PCT_LACCESS_POP15</code></a> has 97 / 3.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_LACCESS_SENIORS10”><code>PCT_LACCESS_SENIORS10</code></a> is highly correlated with <a href=”#pp_var_PCT_LACCESS_POP10”><code>PCT_LACCESS_POP10</code></a> (ρ = 0.92085) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCT_LACCESS_SENIORS15”><code>PCT_LACCESS_SENIORS15</code></a> is highly correlated with <a href=”#pp_var_PCT_LACCESS_POP15”><code>PCT_LACCESS_POP15</code></a> (ρ = 0.91044) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCT_LACCESS_SNAP15”><code>PCT_LACCESS_SNAP15</code></a> has 97 / 3.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_LACCESS_WHITE15”><code>PCT_LACCESS_WHITE15</code></a> is highly correlated with <a href=”#pp_var_PCT_LACCESS_SENIORS15”><code>PCT_LACCESS_SENIORS15</code></a> (ρ = 0.93066) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCT_LOCLFARM07”><code>PCT_LOCLFARM07</code></a> has 139 / 4.3% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_LOCLFARM07”><code>PCT_LOCLFARM07</code></a> has 55 / 1.7% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_LOCLFARM12”><code>PCT_LOCLFARM12</code></a> has 138 / 4.3% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_LOCLFARM12”><code>PCT_LOCLFARM12</code></a> has 72 / 2.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_LOCLSALE07”><code>PCT_LOCLSALE07</code></a> has 416 / 13.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_LOCLSALE07”><code>PCT_LOCLSALE07</code></a> has 48 / 1.5% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_LOCLSALE12”><code>PCT_LOCLSALE12</code></a> has 357 / 11.1% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_LOCLSALE12”><code>PCT_LOCLSALE12</code></a> has 65 / 2.0% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_NHASIAN10”><code>PCT_NHASIAN10</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_NHBLACK10”><code>PCT_NHBLACK10</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_NHBLACK10”><code>PCT_NHBLACK10</code></a> has 33 / 1.0% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_NHNA10”><code>PCT_NHNA10</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_NHPI10”><code>PCT_NHPI10</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_NHPI10”><code>PCT_NHPI10</code></a> has 484 / 15.1% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_NHPI10”><code>PCT_NHPI10</code></a> is highly skewed (γ1 = 45.127) <span class=”label label-info”>Skewed</span></li><li><a href=”#pp_var_PCT_NHWHITE10”><code>PCT_NHWHITE10</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_NSLP09”><code>PCT_NSLP09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_NSLP15”><code>PCT_NSLP15</code></a> is highly correlated with <a href=”#pp_var_PCT_NSLP09”><code>PCT_NSLP09</code></a> (ρ = 0.98444) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCT_OBESE_ADULTS08”><code>PCT_OBESE_ADULTS08</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_OBESE_ADULTS13”><code>PCT_OBESE_ADULTS13</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_REDUCED_LUNCH09”><code>PCT_REDUCED_LUNCH09</code></a> has 153 / 4.8% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_REDUCED_LUNCH14”><code>PCT_REDUCED_LUNCH14</code></a> has 314 / 9.8% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_REDUCED_LUNCH14”><code>PCT_REDUCED_LUNCH14</code></a> has 151 / 4.7% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_SBP09”><code>PCT_SBP09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_SBP15”><code>PCT_SBP15</code></a> is highly correlated with <a href=”#pp_var_PCT_SBP09”><code>PCT_SBP09</code></a> (ρ = 0.92649) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCT_SFSP09”><code>PCT_SFSP09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_SFSP15”><code>PCT_SFSP15</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_SNAP12”><code>PCT_SNAP12</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_SNAP16”><code>PCT_SNAP16</code></a> is highly correlated with <a href=”#pp_var_PCT_SNAP12”><code>PCT_SNAP12</code></a> (ρ = 0.91642) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCT_WIC09”><code>PCT_WIC09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_WIC15”><code>PCT_WIC15</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PC_DIRSALES07”><code>PC_DIRSALES07</code></a> has 359 / 11.2% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PC_DIRSALES07”><code>PC_DIRSALES07</code></a> has 60 / 1.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PC_DIRSALES12”><code>PC_DIRSALES12</code></a> has 313 / 9.8% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PC_DIRSALES12”><code>PC_DIRSALES12</code></a> has 76 / 2.4% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PC_FFRSALES07”><code>PC_FFRSALES07</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PC_FFRSALES12”><code>PC_FFRSALES12</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PC_FSRSALES07”><code>PC_FSRSALES07</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PC_FSRSALES12”><code>PC_FSRSALES12</code></a> is highly correlated with <a href=”#pp_var_PC_FSRSALES07”><code>PC_FSRSALES07</code></a> (ρ = 0.91163) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PC_SNAPBEN10”><code>PC_SNAPBEN10</code></a> has 158 / 4.9% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PC_SNAPBEN15”><code>PC_SNAPBEN15</code></a> is highly correlated with <a href=”#pp_var_PC_SNAPBEN10”><code>PC_SNAPBEN10</code></a> (ρ = 0.94136) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PC_WIC_REDEMP08”><code>PC_WIC_REDEMP08</code></a> has 1136 / 35.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PC_WIC_REDEMP12”><code>PC_WIC_REDEMP12</code></a> is highly correlated with <a href=”#pp_var_PC_WIC_REDEMP08”><code>PC_WIC_REDEMP08</code></a> (ρ = 0.931) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PERCHLDPOV10”><code>PERCHLDPOV10</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PERCHLDPOV10”><code>PERCHLDPOV10</code></a> has 2426 / 75.6% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PERPOV10”><code>PERPOV10</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PERPOV10”><code>PERPOV10</code></a> has 2781 / 86.6% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_POPLOSS10”><code>POPLOSS10</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_POPLOSS10”><code>POPLOSS10</code></a> has 2606 / 81.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_POVRATE15”><code>POVRATE15</code></a> has 79 / 2.5% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_Population Estimate, 2011”><code>Population Estimate, 2011</code></a> is highly correlated with <a href=”#pp_var_2010 Census Population”><code>2010 Census Population</code></a> (ρ = 0.99996) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_Population Estimate, 2012”><code>Population Estimate, 2012</code></a> is highly correlated with <a href=”#pp_var_Population Estimate, 2011”><code>Population Estimate, 2011</code></a> (ρ = 0.99998) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_Population Estimate, 2013”><code>Population Estimate, 2013</code></a> is highly correlated with <a href=”#pp_var_Population Estimate, 2012”><code>Population Estimate, 2012</code></a> (ρ = 0.99998) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_Population Estimate, 2014”><code>Population Estimate, 2014</code></a> is highly correlated with <a href=”#pp_var_Population Estimate, 2013”><code>Population Estimate, 2013</code></a> (ρ = 0.99997) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_Population Estimate, 2015”><code>Population Estimate, 2015</code></a> is highly correlated with <a href=”#pp_var_Population Estimate, 2014”><code>Population Estimate, 2014</code></a> (ρ = 0.99997) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_Population Estimate, 2016”><code>Population Estimate, 2016</code></a> is highly correlated with <a href=”#pp_var_Population Estimate, 2015”><code>Population Estimate, 2015</code></a> (ρ = 0.99997) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_RECFAC09”><code>RECFAC09</code></a> is highly correlated with <a href=”#pp_var_FSR14”><code>FSR14</code></a> (ρ = 0.95735) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_RECFAC14”><code>RECFAC14</code></a> is highly correlated with <a href=”#pp_var_RECFAC09”><code>RECFAC09</code></a> (ρ = 0.99353) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_RECFACPTH09”><code>RECFACPTH09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_RECFACPTH09”><code>RECFACPTH09</code></a> has 885 / 27.6% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_RECFACPTH14”><code>RECFACPTH14</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_RECFACPTH14”><code>RECFACPTH14</code></a> has 1007 / 31.4% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_REDEMP_SNAPS12”><code>REDEMP_SNAPS12</code></a> has 317 / 9.9% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_REDEMP_SNAPS16”><code>REDEMP_SNAPS16</code></a> has 250 / 7.8% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_REDEMP_WICS08”><code>REDEMP_WICS08</code></a> has 1136 / 35.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_REDEMP_WICS12”><code>REDEMP_WICS12</code></a> is highly correlated with <a href=”#pp_var_REDEMP_WICS08”><code>REDEMP_WICS08</code></a> (ρ = 0.91221) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_SLHOUSE07”><code>SLHOUSE07</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SLHOUSE07”><code>SLHOUSE07</code></a> has 1979 / 61.7% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_SLHOUSE12”><code>SLHOUSE12</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SLHOUSE12”><code>SLHOUSE12</code></a> has 2023 / 63.0% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_SNAPS12”><code>SNAPS12</code></a> is highly correlated with <a href=”#pp_var_SPECS14”><code>SPECS14</code></a> (ρ = 0.92214) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_SNAPS16”><code>SNAPS16</code></a> is highly correlated with <a href=”#pp_var_SNAPS12”><code>SNAPS12</code></a> (ρ = 0.99663) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_SNAPSPTH12”><code>SNAPSPTH12</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SNAPSPTH16”><code>SNAPSPTH16</code></a> has 104 / 3.2% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SNAP_BBCE09”><code>SNAP_BBCE09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SNAP_BBCE09”><code>SNAP_BBCE09</code></a> has 1673 / 52.1% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_SNAP_BBCE16”><code>SNAP_BBCE16</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SNAP_BBCE16”><code>SNAP_BBCE16</code></a> has 819 / 25.5% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_SNAP_CAP09”><code>SNAP_CAP09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SNAP_CAP09”><code>SNAP_CAP09</code></a> has 1968 / 61.3% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_SNAP_CAP16”><code>SNAP_CAP16</code></a> is highly correlated with <a href=”#pp_var_SNAP_CAP09”><code>SNAP_CAP09</code></a> (ρ = 0.94179) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_SNAP_OAPP09”><code>SNAP_OAPP09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SNAP_OAPP09”><code>SNAP_OAPP09</code></a> has 1343 / 41.8% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_SNAP_OAPP16”><code>SNAP_OAPP16</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SNAP_OAPP16”><code>SNAP_OAPP16</code></a> has 342 / 10.7% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_SNAP_PART_RATE08”><code>SNAP_PART_RATE08</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SNAP_PART_RATE13”><code>SNAP_PART_RATE13</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SNAP_REPORTSIMPLE09”><code>SNAP_REPORTSIMPLE09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SNAP_REPORTSIMPLE09”><code>SNAP_REPORTSIMPLE09</code></a> has 81 / 2.5% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_SNAP_REPORTSIMPLE16”><code>SNAP_REPORTSIMPLE16</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SODATAX_STORES14”><code>SODATAX_STORES14</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SODATAX_STORES14”><code>SODATAX_STORES14</code></a> has 686 / 21.4% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_SODATAX_VENDM14”><code>SODATAX_VENDM14</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SODATAX_VENDM14”><code>SODATAX_VENDM14</code></a> has 332 / 10.3% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_SODA_PRICE10”><code>SODA_PRICE10</code></a> has 106 / 3.3% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SPECS09”><code>SPECS09</code></a> is highly correlated with <a href=”#pp_var_GROC14”><code>GROC14</code></a> (ρ = 0.9563) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_SPECS14”><code>SPECS14</code></a> is highly correlated with <a href=”#pp_var_SPECS09”><code>SPECS09</code></a> (ρ = 0.99301) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_SPECSPTH09”><code>SPECSPTH09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SPECSPTH09”><code>SPECSPTH09</code></a> has 1132 / 35.3% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_SPECSPTH14”><code>SPECSPTH14</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SPECSPTH14”><code>SPECSPTH14</code></a> has 1208 / 37.6% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_SUPERC09”><code>SUPERC09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SUPERC09”><code>SUPERC09</code></a> has 1480 / 46.1% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_SUPERC14”><code>SUPERC14</code></a> is highly correlated with <a href=”#pp_var_SUPERC09”><code>SUPERC09</code></a> (ρ = 0.98347) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_SUPERCPTH09”><code>SUPERCPTH09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SUPERCPTH09”><code>SUPERCPTH09</code></a> has 1480 / 46.1% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_SUPERCPTH14”><code>SUPERCPTH14</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SUPERCPTH14”><code>SUPERCPTH14</code></a> has 1346 / 41.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_School Breakfast Program participants FY 2009”><code>School Breakfast Program participants FY 2009</code></a> is highly correlated with <a href=”#pp_var_National School Lunch Program participants, FY 2015”><code>National School Lunch Program participants, FY 2015</code></a> (ρ = 0.92183) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_School Breakfast Program participants FY 2011”><code>School Breakfast Program participants FY 2011</code></a> has 3210 / 100.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_School Breakfast Program participants FY 2011”><code>School Breakfast Program participants FY 2011</code></a> has constant value <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_School Breakfast Program participants, FY 2012”><code>School Breakfast Program participants, FY 2012</code></a> has 3210 / 100.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_School Breakfast Program participants, FY 2012”><code>School Breakfast Program participants, FY 2012</code></a> has constant value <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_School Breakfast Program participants, FY 2013”><code>School Breakfast Program participants, FY 2013</code></a> is highly correlated with <a href=”#pp_var_School Breakfast Program participants FY 2009”><code>School Breakfast Program participants FY 2009</code></a> (ρ = 0.98406) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_School Breakfast Program participants, FY 2014”><code>School Breakfast Program participants, FY 2014</code></a> is highly correlated with <a href=”#pp_var_School Breakfast Program participants, FY 2013”><code>School Breakfast Program participants, FY 2013</code></a> (ρ = 0.99862) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_School Breakfast Program participants, FY 2015”><code>School Breakfast Program participants, FY 2015</code></a> is highly correlated with <a href=”#pp_var_School Breakfast Program participants, FY 2014”><code>School Breakfast Program participants, FY 2014</code></a> (ρ = 0.99676) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_State Population, 2011”><code>State Population, 2011</code></a> is highly correlated with <a href=”#pp_var_State Population, 2010”><code>State Population, 2010</code></a> (ρ = 0.99998) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_State Population, 2013”><code>State Population, 2013</code></a> is highly correlated with <a href=”#pp_var_State Population, 2012”><code>State Population, 2012</code></a> (ρ = 0.99999) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_State Population, 2014”><code>State Population, 2014</code></a> is highly correlated with <a href=”#pp_var_State Population, 2013”><code>State Population, 2013</code></a> (ρ = 0.99998) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_State Population, 2015”><code>State Population, 2015</code></a> is highly correlated with <a href=”#pp_var_State Population, 2014”><code>State Population, 2014</code></a> (ρ = 0.99997) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_State Population, 2016”><code>State Population, 2016</code></a> is highly correlated with <a href=”#pp_var_State Population, 2015”><code>State Population, 2015</code></a> (ρ = 0.99996) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_State Population, 2009”><code>State Population, 2009</code></a> is highly correlated with <a href=”#pp_var_Child and Adult Care participants, FY 2015”><code>Child and Adult Care participants, FY 2015</code></a> (ρ = 0.92968) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_State Population, 2010”><code>State Population, 2010</code></a> is highly correlated with <a href=”#pp_var_State Population, 2009”><code>State Population, 2009</code></a> (ρ = 0.9999) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_State Population, 2012”><code>State Population, 2012</code></a> is highly correlated with <a href=”#pp_var_State Population, 2011”><code>State Population, 2011</code></a> (ρ = 0.99998) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_StateFIPS “><code>StateFIPS </code></a> has 555 / 17.3% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_Summer Food participants FY 2011”><code>Summer Food participants FY 2011</code></a> is highly correlated with <a href=”#pp_var_Summer Food particpants FY 2009”><code>Summer Food particpants FY 2009</code></a> (ρ = 0.98245) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_Summer Food participants, FY 2012”><code>Summer Food participants, FY 2012</code></a> is highly correlated with <a href=”#pp_var_Summer Food participants FY 2011”><code>Summer Food participants FY 2011</code></a> (ρ = 0.99308) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_Summer Food participants, FY 2013”><code>Summer Food participants, FY 2013</code></a> is highly correlated with <a href=”#pp_var_Summer Food participants, FY 2012”><code>Summer Food participants, FY 2012</code></a> (ρ = 0.98386) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_Summer Food participants, FY 2014”><code>Summer Food participants, FY 2014</code></a> is highly correlated with <a href=”#pp_var_Summer Food participants, FY 2013”><code>Summer Food participants, FY 2013</code></a> (ρ = 0.98127) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_Summer Food participants, FY 2015”><code>Summer Food participants, FY 2015</code></a> is highly correlated with <a href=”#pp_var_Summer Food participants, FY 2014”><code>Summer Food participants, FY 2014</code></a> (ρ = 0.99324) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_Summer Food particpants FY 2009”><code>Summer Food particpants FY 2009</code></a> has 555 / 17.3% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_USDA Model And”><code>USDA Model And</code></a> is highly correlated with <a href=”#pp_var_USDA Model Percent”><code>USDA Model Percent</code></a> (ρ = 0.99358) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_USDA Model Or”><code>USDA Model Or</code></a> is highly correlated with <a href=”#pp_var_USDA Model Count”><code>USDA Model Count</code></a> (ρ = 0.99064) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_VEG_ACRES07”><code>VEG_ACRES07</code></a> has 682 / 21.2% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_VEG_ACRES07”><code>VEG_ACRES07</code></a> has 286 / 8.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_VEG_ACRES12”><code>VEG_ACRES12</code></a> is highly correlated with <a href=”#pp_var_VEG_ACRES07”><code>VEG_ACRES07</code></a> (ρ = 0.9886) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_VEG_ACRESPTH07”><code>VEG_ACRESPTH07</code></a> has 682 / 21.2% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_VEG_ACRESPTH07”><code>VEG_ACRESPTH07</code></a> has 286 / 8.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_VEG_ACRESPTH12”><code>VEG_ACRESPTH12</code></a> is highly correlated with <a href=”#pp_var_VEG_ACRESPTH07”><code>VEG_ACRESPTH07</code></a> (ρ = 0.99906) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_VEG_FARMS07”><code>VEG_FARMS07</code></a> has 134 / 4.2% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_VEG_FARMS07”><code>VEG_FARMS07</code></a> has 286 / 8.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_VEG_FARMS12”><code>VEG_FARMS12</code></a> is highly correlated with <a href=”#pp_var_VEG_FARMS07”><code>VEG_FARMS07</code></a> (ρ = 0.91017) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_VLFOODSEC_10_12”><code>VLFOODSEC_10_12</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_VLFOODSEC_13_15”><code>VLFOODSEC_13_15</code></a> is highly correlated with <a href=”#pp_var_FOODINSEC_13_15”><code>FOODINSEC_13_15</code></a> (ρ = 0.93787) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_WIC participants FY 2009”><code>WIC participants FY 2009</code></a> has 555 / 17.3% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_WIC participants FY 2011”><code>WIC participants FY 2011</code></a> is highly correlated with <a href=”#pp_var_WIC participants FY 2009”><code>WIC participants FY 2009</code></a> (ρ = 0.99857) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_WIC participants, FY 2012”><code>WIC participants, FY 2012</code></a> is highly correlated with <a href=”#pp_var_WIC participants FY 2011”><code>WIC participants FY 2011</code></a> (ρ = 0.9754) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_WIC participants, FY 2013”><code>WIC participants, FY 2013</code></a> is highly correlated with <a href=”#pp_var_WIC participants, FY 2012”><code>WIC participants, FY 2012</code></a> (ρ = 0.99975) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_WIC participants, FY 2014”><code>WIC participants, FY 2014</code></a> is highly correlated with <a href=”#pp_var_WIC participants, FY 2013”><code>WIC participants, FY 2013</code></a> (ρ = 0.99951) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_WIC participants, FY 2015”><code>WIC participants, FY 2015</code></a> is highly correlated with <a href=”#pp_var_WIC participants, FY 2014”><code>WIC participants, FY 2014</code></a> (ρ = 0.9992) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_WICS08”><code>WICS08</code></a> is highly correlated with <a href=”#pp_var_SNAPS16”><code>SNAPS16</code></a> (ρ = 0.91514) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_WICS12”><code>WICS12</code></a> is highly correlated with <a href=”#pp_var_WICS08”><code>WICS08</code></a> (ρ = 0.98574) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_WICSPTH08”><code>WICSPTH08</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_WICSPTH08”><code>WICSPTH08</code></a> has 141 / 4.4% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_WICSPTH12”><code>WICSPTH12</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_WICSPTH12”><code>WICSPTH12</code></a> has 149 / 4.6% zeros <span class=”label label-info”>Zeros</span></li> </ul>

</div>

</div>
<div class=”row headerrow highlight”>

<h1>Variables</h1>

</div> <div class=”row variablerow”> <div class=”col-md-3 namecol”>

<p class=”h4 pp-anchor” id=”pp_var_2010 Census Population”>2010 Census Population<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3081</td>

</tr> <tr>

<th>Unique (%)</th> <td>96.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>98449</td>

</tr> <tr>

<th>Minimum</th> <td>82</td>

</tr> <tr>

<th>Maximum</th> <td>9818600</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram7793734927789615807”>

<img src=”data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAAtFJREFUeJzt2r1KI1EchvHXTVCxFivRSrAJiKTwIwHBQq1slCCIqOAFWAfL3EAEEbwCISo2GgsrCy1EhRRBsVIE8RKCMlssq8y6eSWQzIg8P0iRyZzwbx7OmZC2IAgCAfivX3EPAHxnybgH%2BFc6X254zWVhugWTAOwggEUggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggJGMe4BmSOfLDa%2B5LEy3YBL8NG1BEARxDwF8VxyxAINAAINAAINAAINA0HRnZ2caGxvT%2Bvp6Q%2BumpqaUSqVCr8HBQR0cHLRo0q/9iJ958X3s7OyoVCqpv7%2B/4bUnJyeh94%2BPj8rlcspms80ar2HsIGiqjo4OG8jR0ZFmZ2c1NDSkyclJ7e7u1v2uQqGg1dVVdXd3t2rcL7GDoKmWlpbqflapVJTP57W5uanR0VFdX19rbW1NAwMDGh4eDt17cXGharWqYrHY6pEtdhBEZn9/XxMTE8pkMkokEkqn05qZmdHh4eGne7e3t7WysqL29vYYJv3ADoLIPDw86Pz8XKlU6v1aEATKZDKh%2B%2B7u7nRzc6Otra2oR/yEQBCZzs5OLSwsaGNjw95XLpc1MjKirq6uiCarjyMWItPX16fb29vQtefnZ729vYWunZ6eanx8PMrR6iIQRGZubk5XV1fa29tTrVZTtVrV/Px86OfdWq2m%2B/t79fb2xjjpB/7Ni6b6%2B3zx%2BvoqSUom/5ziK5WKJOn4%2BFjFYlFPT0/q6enR4uKilpeX39e/vLwom82qVCqFnlXiQiCAwRELMAgEMAgEMAgEMAgEMAgEMAgEMAgEMAgEMAgEMH4DDSqdXFxHngwAAAAASUVORK5CYII%3D”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives7793734927789615807,#minihistogram7793734927789615807”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives7793734927789615807”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles7793734927789615807”

aria-controls=”quantiles7793734927789615807” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram7793734927789615807” aria-controls=”histogram7793734927789615807”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common7793734927789615807” aria-controls=”common7793734927789615807”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme7793734927789615807” aria-controls=”extreme7793734927789615807”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles7793734927789615807”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>82</td>

</tr> <tr>

<th>5-th percentile</th> <td>2963.8</td>

</tr> <tr>

<th>Q1</th> <td>11155</td>

</tr> <tr>

<th>Median</th> <td>25902</td>

</tr> <tr>

<th>Q3</th> <td>66618</td>

</tr> <tr>

<th>95-th percentile</th> <td>423250</td>

</tr> <tr>

<th>Maximum</th> <td>9818600</td>

</tr> <tr>

<th>Range</th> <td>9818500</td>

</tr> <tr>

<th>Interquartile range</th> <td>55463</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>313410</td>

</tr> <tr>

<th>Coef of variation</th> <td>3.1835</td>

</tr> <tr>

<th>Kurtosis</th> <td>344.32</td>

</tr> <tr>

<th>Mean</th> <td>98449</td>

</tr> <tr>

<th>MAD</th> <td>117020</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>14.335</td>

</tr> <tr>

<th>Sum</th> <td>308340000</td>

</tr> <tr>

<th>Variance</th> <td>98227000000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram7793734927789615807”>

<img 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vihxo0bpwceeEAnTpyQJK1evVobN25UZmamdu3apZiYGKWlpcntdvtyuQAAwEZsGbCKi4sVFxen6dOnKygoSFdccYWGDx%2Bujz/%2BWLt379b58%2Bc1ceJENWnSRJ06ddKdd96prKwsSdKaNWuUlJSkpKQkBQYGaujQoWrfvr02bNggScrKytK4cePUpk0bBQcHKz09XXl5eTpw4IAvlwwAAGzElgErJCRE8%2BbNU/PmzavGjh8/rsjISOXk5KhDhw4KCAiomouNjVV2drYkKScnR7GxsR77i42NldPp1NmzZ5Wbm%2BsxHxwcrFatWsnpdHp5VQAAoL5w%2BLoAE5xOp1atWqVly5Zp69atCgkJ8ZgPCwuTy%2BVSZWWlXC6XQkNDPeZDQ0OVm5urU6dOye12X3C%2BqKioxvUUFBSosLDQY8zhaKLIyMharuziAgLslY8dDnvVK/3YY7v12k7osXfRX%2B%2Bjx95nxx7bPmB98sknmjhxoqZPn67ExERt3br1gtv5%2BflV/fel7qeq6/1WWVlZWrJkicdYWlqaJk%2BeXKf92l14eJCvS/jZQkIa%2B7qEeo8eexf99T567H126rGtA9bOnTv10EMPafbs2br99tslSREREfrqq688tnO5XAoLC5O/v7/Cw8PlcrmqzUdERFRtc6H5Zs2a1biulJQUJScne4w5HE1UVFRai9Vdmp2SvCTj67dCQIC/QkIaq7i4TBUVlb4up16ix95Ff72PHntfXXrsq1/ubRuwPv30U82cOVPPP/%2B8%2BvTpUzUeFxen1157TeXl5XI4vl%2Be0%2BlUly5dquZ/uB/rB06nU4MHD1ZgYKDatWunnJwc9erVS9L3N9QfPXpUnTt3rnFtkZGR1S4HFhaeVnn5L/sbz87rr6iotHX9dkCPvYv%2Beh899j479dhep0D%2BT3l5uR5//HHNmDHDI1xJUlJSkoKDg7Vs2TKVlZXpwIEDWrt2rVJTUyVJo0aN0gcffKDdu3fru%2B%2B%2B09q1a/XVV19p6NChkqTU1FStXLlSeXl5KikpUUZGhjp27Kj4%2BHjL1wkAAOzJ8jNYycnJGjFihO644w5deeWVP2sf%2B/fvV15enubOnau5c%2Bd6zG3btk0vvPCCnnjiCWVmZqp58%2BZKT09Xv379JEnt27dXRkaG5s2bp/z8fLVt21bLly9XixYtJEmjR49WYWGhxowZo9LSUiUkJFS7nwoAAOBi/NwWP0Fz6dKl2rx5s/75z3/q%2Buuv16hRo5ScnFx1Oa%2B%2BKiw8bXyfDoe/bs7Ya3y/3rJ16g2%2BLqHWHA5/hYcHqaio1Danpe2GHnsX/fU%2Beux9delxixZNvVTVxVl%2BiTAtLU1btmzRG2%2B8oXbt2un3v/%2B9kpKSNH/%2BfB05csTqcgAAAIzz2T1YnTp10syZM7Vr1y499thjeuONN3Trrbdq/Pjx%2Buyzz3xVFgAAQJ35LGCdP39eW7Zs0X333aeZM2cqKipKjz76qDp27Khx48Zp48aNvioNAACgTiy/8SkvL09r167V%2BvXrVVpaqoEDB%2Bovf/mLunfvXrVNz549NWfOHA0ZMsTq8gAAAOrM8oA1ePBgtW7dWhMmTNDtt9%2BusLCwatskJSXp5MmTVpcGAABghOUBa%2BXKlVUP8byYAwcOWFANAACAeZbfg9WhQwfdf//92rFjR9XYK6%2B8ovvuu6/an6gBAACwI8sD1rx583T69Gm1bdu2aqxfv36qrKzUs88%2Ba3U5AAAAxll%2BifD999/Xxo0bFR4eXjUWExOjjIwM3XbbbVaXAwAAYJzlZ7DOnj2rwMDA6oX4%2B6usrMzqcgAAAIyzPGD17NlTzz77rE6dOlU19vXXX%2BvJJ5/0eFQDAACAXVl%2BifCxxx7TPffco%2Buvv17BwcGqrKxUaWmpWrZsqVdffdXqcgAAAIyzPGC1bNlSmzdv1nvvvaejR4/K399frVu3Vp8%2BfRQQEGB1OQAAAMZZHrAkqWHDhrrpppt8cWgAAACvszxgHTt2TAsWLNAXX3yhs2fPVpt/9913rS4JAADAKJ/cg1VQUKA%2BffqoSZMmVh8eAADA6ywPWNnZ2Xr33XcVERFh9aEBAAAsYfljGpo1a8aZKwAAUK9ZHrAmTJigJUuWyO12W31oAAAAS1h%2BifC9997Tp59%2BqnXr1umqq66Sv79nxnv99detLgkAAMAoywNWcHCw%2Bvbta/VhAQAALGN5wJo3b57VhwQAALCU5fdgSdKXX36pxYsX69FHH60a%2B8c//uGLUgAAAIyzPGDt27dPQ4cO1TvvvKNNmzZJ%2Bv7ho2PHjuUhowAAoF6wPGAtXLhQDz30kDZu3Cg/Pz9J3/99wmeffVZLly61uhwAAADjLA9Yn3/%2BuVJTUyWpKmBJ0qBBg5SXl2d1OQAAAMZZHrCaNm16wb9BWFBQoIYNG1pdDgAAgHGWB6xu3brp97//vUpKSqrGjhw5opkzZ%2Br666%2B3uhwAAADjLH9Mw6OPPqq77rpLCQkJqqioULdu3VRWVqZ27drp2WeftbocAAAA4ywPWFdccYU2bdqkPXv26MiRI2rUqJFat26tG264weOeLAAAALuyPGBJUoMGDXTTTTf54tAAAABeZ3nASk5OvuiZKp6FBQAA7M7ygHXrrbd6BKyKigodOXJETqdTd911l9XlAAAAGGd5wJoxY8YFx7dv366//e1vFlcDAABgnk/%2BFuGF3HTTTdq8ebOvywAAAKizyyZgHTx4UG6329dlAAAA1JnllwhHjx5dbaysrEx5eXm65ZZbrC4HAADAOMsDVkxMTLV3EQYGBmrkyJG68847rS4HAADAOMsDFk9rBwAA9Z3lAWv9%2BvU13vb222%2B/6PzevXs1c%2BZMJSQkaOHChVXj69at02OPPaYGDRp4bL969Wp17txZlZWVev7557Vp0yYVFxerc%2BfOmjNnjlq2bClJcrlcmjNnjv7%2B97/L399fSUlJmj17tho1alSLlQIAgF8qywPWrFmzVFlZWe2Gdj8/P48xPz%2B/iwasFStWaO3atWrVqtUF53v27KlXX331gnOrV6/Wxo0btWLFCkVFRWnhwoVKS0vT22%2B/LT8/P82ePVvnzp3Tpk2bdP78eU2ZMkUZGRl6/PHHf8aKAQDAL43l7yJ88cUX1adPH61evVoff/yxPvroI61atUp9%2B/bVihUr9Nlnn%2Bmzzz7TgQMHLrqfwMDAiwasi8nKytK4cePUpk0bBQcHKz09XXl5eTpw4IC%2B%2BeYb7dixQ%2Bnp6YqIiFBUVJQmTZqkN998U%2BfPn/%2B5ywYAAL8gPrkHKzMzU1FRUVVjPXr0UMuWLTV%2B/Hht2rSpRvsZO3bsReePHz%2Buu%2B%2B%2BW9nZ2QoJCdHkyZM1bNgwnT17Vrm5uYqNja3aNjg4WK1atZLT6dTp06cVEBCgDh06VM136tRJZ86c0Zdffukx/p8UFBSosLDQY8zhaKLIyMgara2mAgIum6ds1IjDYa96pR97bLde2wk99i7663302Pvs2GPLA9ZXX32l0NDQauMhISHKz883coyIiAjFxMRo2rRpatu2rf7617/q4YcfVmRkpK655hq53e5qNYSGhqqoqEhhYWEKDg72eKfjD9sWFRXV6PhZWVlasmSJx1haWpomT55cx5XZW3h4kK9L%2BNlCQhr7uoR6jx57F/31PnrsfXbqseUBKzo6Ws8%2B%2B6ymTJmi8PBwSVJxcbEWLVqkq6%2B%2B2sgx%2BvXrp379%2BlV9PnjwYP31r3/VunXrqv5Uz8UealrXB56mpKQoOTnZY8zhaKKiotI67fen7JTkJRlfvxUCAvwVEtJYxcVlqqio9HU59RI99i7663302Pvq0mNf/XJvecB67LHHNH36dGVlZSkoKEj%2B/v4qKSlRo0aNtHTpUq8dNzo6WtnZ2QoLC5O/v79cLpfHvMvlUrNmzRQREaGSkhJVVFQoICCgak6SmjVrVqNjRUZGVrscWFh4WuXlv%2BxvPDuvv6Ki0tb12wE99i7663302Pvs1GPLA1afPn20e/du7dmzRydOnJDb7VZUVJRuvPFGNW3a1MgxXnvtNYWGhurWW2%2BtGsvLy1PLli0VGBiodu3aKScnR7169ZL0/Rm0o0ePqnPnzoqOjpbb7dbhw4fVqVMnSZLT6VRISIhat25tpD4AAFC/WR6wJKlx48YaMGCATpw4UfXsKZPOnTunp59%2BWi1bttS1116r7du367333tMbb7whSUpNTVVmZqb69u2rqKgoZWRkqGPHjoqPj5ckDRw4UH/84x/13HPP6dy5c1q6dKlGjhwph8Mn7QIAADZjeWI4e/asnnjiCW3evFmSlJ2dreLiYk2bNk1/%2BMMfFBISUqP9/BCGysvLJUk7duyQ9P3ZprFjx6q0tFRTpkxRYWGhrrrqKi1dulRxcXGSvv97iIWFhRozZoxKS0uVkJDgcVP6U089pSeeeEIDBgxQgwYNdNtttyk9Pd1YDwAAQP3m567rHd219PTTT%2Bujjz7SpEmT9PDDD%2Buzzz5TcXGxpkyZopYtW%2Bqpp56yshzLFBaeNr5Ph8NfN2fsNb5fb9k69QZfl1BrDoe/wsODVFRUapvr/nZDj72L/nofPfa%2BuvS4RQsztx/VluVvQ9u%2BfbsWLVqkQYMGVT0KISQkRPPmzdM777xjdTkAAADGWR6wSktLFRMTU208IiJCZ86csbocAAAA4ywPWFdffbX%2B9re/SfJ83tS2bdv0q1/9yupyAAAAjLP8Jvff/OY3evDBB3XHHXeosrJSL7/8srKzs7V9%2B3bNmjXL6nIAAACMszxgpaSkyOFwaNWqVQoICNALL7yg1q1bKyMjQ4MGDbK6HAAAAOMsD1gnT57UHXfcoTvuuMPqQwMAAFjC8nuwBgwYUOe/9QcAAHA5szxgJSQkaOvWrVYfFgAAwDKWXyK88sor9cwzzygzM1NXX321GjRo4DG/YMECq0sCAAAwyvKAlZubq2uuuUaSVFRUZPXhAQAAvM6ygJWenq6FCxfq1VdfrRpbunSp0tLSrCoBAADAEpbdg7Vz585qY5mZmVYdHgAAwDKWBawLvXOQdxMCAID6yLKA9cMfdr7UGAAAgN1Z/pgGAACA%2Bo6ABQAAYJhl7yI8f/68pk%2BffskxnoMFAADszrKA1b17dxUUFFxyDAAAwO4sC1j//vwrAACA%2Box7sAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMMzWAWvv3r1KTExUenp6tbktW7ZoyJAh6tq1q0aMGKH333%2B/aq6yslILFy7UgAED1LNnT40fP17Hjh2rmne5XJo6daoSExPVp08fzZo1S2fPnrVkTQAAwP5sG7BWrFihuXPnqlWrVtXmDh06pJkzZ2rGjBn68MMPNW7cOD3wwAM6ceKEJGn16tXauHGjMjMztWvXLsXExCgtLU1ut1uSNHv2bJWVlWnTpk168803lZeXp4yMDEvXBwAA7Mu2ASswMFBr1669YMBas2aNkpKSlJSUpMDAQA0dOlTt27fXhg0bJElZWVkaN26c2rRpo%2BDgYKWnpysvL08HDhzQN998ox07dig9PV0RERGKiorSpEmT9Oabb%2Br8%2BfNWLxMAANiQw9cF/Fxjx479j3M5OTlKSkryGIuNjZXT6dTZs2eVm5ur2NjYqrng4GC1atVKTqdTp0%2BfVkBAgDp06FA136lTJ505c0Zffvmlx/h/UlBQoMLCQo8xh6OJIiMja7q8GgkIsFc%2BdjjsVa/0Y4/t1ms7ocfeRX%2B9jx57nx17bNuAdTEul0uhoaEeY6GhocrNzdWpU6fkdrsvOF9UVKSwsDAFBwfLz8/PY06SioqKanT8rKwsLVmyxGMsLS1NkydP/jnLqTfCw4N8XcLPFhLS2Ncl1Hv02Lvor/fRY%2B%2BzU4/rZcCSVHU/1c%2BZv9RrLyUlJUXJyckeYw5HExUVldZpvz9lpyQvyfj6rRAQ4K%2BQkMYqLi5TRUWlr8upl%2Bixd9Ff76PH3leXHvvql/t6GbDCw8Plcrk8xlwulyIiIhQWFiZ/f/8Lzjdr1kwREREqKSlRRUWFAgICquYkqVmzZjU6fmRkZLXLgYWFp1Ve/sv%2BxrPz%2BisqKm1dvx3QY%2B%2Biv95Hj73PTj221ymQGoqLi1N2drbHmNPpVJcuXRQYGKh27dopJyenaq64uFhHjx5V586d1bFjR7ndbh0%2BfNjjtSEhIWrdurVlawAAAPZVLwPWqFGj9MEHH2j37t367rvvtHbtWn2YLp2bAAARI0lEQVT11VcaOnSoJCk1NVUrV65UXl6eSkpKlJGRoY4dOyo%2BPl4REREaOHCg/vjHP%2BrkyZM6ceKEli5dqpEjR8rhqJcn/AAAgGG2TQzx8fGSpPLycknSjh07JH1/tql9%2B/bKyMjQvHnzlJ%2Bfr7Zt22r58uVq0aKFJGn06NEqLCzUmDFjVFpaqoSEBI%2Bb0p966ik98cQTGjBggBo0aKDbbrvtgg8zBQAAuBA/d13v6EaNFBaeNr5Ph8NfN2fsNb5fb9k69QZfl1BrDoe/wsODVFRUapvr/nZDj72L/nofPfa%2BuvS4RYumXqrq4urlJUIAAABfImABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGH1NmB16NBBcXFxio%2BPr/p4%2BumnJUn79u3TyJEj1a1bNw0ePFgbNmzweO3KlSs1cOBAdevWTampqcrOzvbFEgAAgE05fF2AN23btk1XXXWVx1hBQYEmTZqkWbNmaciQIfrkk080ceJEtW7dWvHx8dq5c6cWL16sF198UR06dNDKlSt1//3365133lGTJk18tBIAAGAn9fYM1n%2ByceNGxcTEaOTIkQoMDFRiYqKSk5O1Zs0aSVJWVpZGjBihLl26qFGjRrr33nslSbt27fJl2QAAwEbqdcBasGCB%2BvXrpx49emj27NkqLS1VTk6OYmNjPbaLjY2tugz403l/f3917NhRTqfT0toBAIB91dtLhNddd50SExP13HPP6dixY5o6daqefPJJuVwuRUVFeWwbFhamoqIiSZLL5VJoaKjHfGhoaNV8TRQUFKiwsNBjzOFoosjIyJ%2B5mgsLCLBXPnY47FWv9GOP7dZrO6HH3kV/vY8ee58de1xvA1ZWVlbVf7dp00YzZszQxIkT1b1790u%2B1u121/nYS5Ys8RhLS0vT5MmT67RfuwsPD/J1CT9bSEhjX5dQ79Fj76K/3kePvc9OPa63AeunrrrqKlVUVMjf318ul8tjrqioSBEREZKk8PDwavMul0vt2rWr8bFSUlKUnJzsMeZwNFFRUenPrP7C7JTkJRlfvxUCAvwVEtJYxcVlqqio9HU59RI99i7663302Pvq0mNf/XJfLwPWwYMHtWHDBj3yyCNVY3l5eWrYsKGSkpL01ltveWyfnZ2tLl26SJLi4uKUk5Oj4cOHS5IqKip08OBBjRw5ssbHj4yMrHY5sLDwtMrLf9nfeHZef0VFpa3rtwN67F301/vosffZqcf2OgVSQ82aNVNWVpYyMzN17tw5HTlyRM8//7xSUlI0bNgw5efna82aNfruu%2B%2B0Z88e7dmzR6NGjZIkpaamav369dq/f7/Kysq0bNkyNWzYUP369fPtogAAgG3UyzNYUVFRyszM1IIFC6oC0vDhw5Wenq7AwEAtX75cc%2BfO1ZNPPqno6GjNnz9f1157rSSpb9%2B%2BmjZtmqZOnapvv/1W8fHxyszMVKNGjXy8KgAAYBd%2B7rre0Y0aKSw8bXyfDoe/bs7Ya3y/3rJ16g2%2BLqHWHA5/hYcHqaio1Danpe2GHnsX/fU%2Beux9delxixZNvVTVxdXLS4QAAAC%2BRMACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsC4gPz9fv/3tb5WQkKD%2B/ftr/vz5qqys9HVZAADAJhy%2BLuBy9OCDD6pTp07asWOHvv32W02YMEHNmzfX3Xff7evSbO3Xf/x/vi6hVrZOvcHXJQAAbIozWD/hdDp1%2BPBhzZgxQ02bNlVMTIzGjRunrKwsX5cGAABsgjNYP5GTk6Po6GiFhoZWjXXq1ElHjhxRSUmJgoODL7mPgoICFRYWeow5HE0UGRlptNaAAPKxN9ntjNtfZ9zo6xJ%2Blh%2B%2Bjvl69g7663302Pvs2GMC1k%2B4XC6FhIR4jP0QtoqKimoUsLKysrRkyRKPsQceeEAPPviguUL1fZC764ovlJKSYjy84XsFBQXKysqix15UUFCgv/zlRXrsJfTX%2B%2Bix99mxx/aJghZyu911en1KSorWrVvn8ZGSkmKouh8VFhZqyZIl1c6WwRx67H302Lvor/fRY%2B%2BzY485g/UTERERcrlcHmMul0t%2Bfn6KiIio0T4iIyNtk7ABAIB5nMH6ibi4OB0/flwnT56sGnM6nWrbtq2CgoJ8WBkAALALAtZPxMbGKj4%2BXgsWLFBJSYny8vL08ssvKzU11delAQAAmwiYM2fOHF8Xcbm58cYbtWnTJj399NPavHmzRo4cqfHjx8vPz8/XpVUTFBSkXr16cXbNi%2Bix99Fj76K/3kePvc9uPfZz1/WObgAAAHjgEiEAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQSsy1x%2Bfr5%2B%2B9vfKiEhQf3799f8%2BfNVWVl5wW1XrlypgQMHqlu3bkpNTVV2drbF1dpTbXr82muvaeDAgeratauGDRumHTt2WFyt/dSmvz/4%2Buuv1bVrVy1evNiiKu2tNj3Oy8vTmDFj1KVLFyUlJemVV16xtlibqmmPKysrtWjRIiUnJ6tr164aMmSItmzZ4oOK7Wnv3r1KTExUenr6RberrKzUwoULNWDAAPXs2VPjx4/XsWPHLKqyhty4rA0fPtz9%2BOOPu4uLi91Hjhxx33LLLe6XXnqp2nbvvvuuu0ePHu79%2B/e7y8rK3MuXL3ffcMMN7tLSUh9UbS817fG2bdvc3bt3d3/88cfuc%2BfOud944w13p06d3EePHvVB1fZR0/7%2BuwceeMDdvXt396JFiyyq0t5q2uOysjJ3v3793CtWrHCfOXPGfeDAAffgwYPdubm5PqjaXmra41WrVrn79OnjzsvLc5eXl7t37tzpjo2NdR86dMgHVdtLZmam%2B5ZbbnGPHj3aPXXq1Ituu3LlSnf//v3dubm57tOnT7ufeuop95AhQ9yVlZUWVXtpnMG6jDmdTh0%2BfFgzZsxQ06ZNFRMTo3HjxikrK6vatllZWRoxYoS6dOmiRo0a6d5775Uk7dq1y%2BqybaU2PT579qymTZum7t27q0GDBrrzzjsVFBSk/fv3%2B6Bye6hNf3%2BwZ88e5ebmql%2B/ftYVamO16fHWrVsVHByse%2B%2B9V40bN1bnzp21adMmtWnTxgeV20dtepyTk6Pu3bvrmmuuUUBAgPr376%2BwsDD9z//8jw8qt5fAwECtXbtWrVq1uuS2WVlZGjdunNq0aaPg4GClp6crLy9PBw4csKDSmiFgXcZycnIUHR2t0NDQqrFOnTrpyJEjKikpqbZtbGxs1ef%2B/v7q2LGjnE6nZfXaUW16PGzYMP3mN7%2Bp%2Bry4uFilpaWKioqyrF67qU1/pe9D7FNPPaUnnnhCDofDylJtqzY9/uSTT9S%2BfXs9%2Buij6tGjhwYNGqQNGzZYXbLt1KbH/fr109///ncdOnRI586d07vvvquysjL16tXL6rJtZ%2BzYsWratOkltzt79qxyc3M9fuYFBwerVatWl9XPPALWZczlcikkJMRj7Idv8KKiomrb/vs3/w/b/nQ7eKpNj/%2Bd2%2B3W448/ri5duvA/zouobX%2BXLl2q6667Tr1797akvvqgNj0%2BceKE3n33XSUmJmrv3r2aMGGCZs6cqYMHD1pWrx3Vpse33HKLUlJSdPvttys%2BPl7Tp0/XvHnzdOWVV1pWb3136tQpud3uy/5nHr8iXubcbrdXtsWPatu38%2BfP65FHHlFubq5Wrlzpparqj5r2Nzc3V2vWrNHGjRu9XFH9U9Meu91uderUSUOGDJEkDR8%2BXK%2B//rq2bdvmcTYA1dW0x%2BvXr9f69eu1Zs0adejQQfv27dP06dN15ZVXqnPnzl6u8pflcv%2BZxxmsy1hERIRcLpfHmMvlkp%2BfnyIiIjzGw8PDL7jtT7eDp9r0WPr%2B1PSECRP0r3/9S6tXr1bz5s2tKtWWatpft9utOXPm6MEHH1SLFi2sLtPWavM13KJFi2qXYKKjo1VYWOj1Ou2sNj1etWqVUlJS1LlzZwUGBqpfv37q3bs3l2INCgsLk7%2B//wX/TZo1a%2BajqqojYF3G4uLidPz4cZ08ebJqzOl0qm3btgoKCqq2bU5OTtXnFRUVOnjwoLp06WJZvXZUmx673W6lp6fL4XDolVdeUXh4uNXl2k5N%2B/uvf/1LH330kRYtWqSEhAQlJCRo8%2BbNevHFFzV8%2BHBflG4btfkabtOmjT7//HOP3/zz8/MVHR1tWb12VJseV1ZWqqKiwmPs3LlzltT5SxEYGKh27dp5/MwrLi7W0aNHL6uzhASsy1hsbKzi4%2BO1YMEClZSUKC8vTy%2B//LJSU1MlSYMGDdLHH38sSUpNTdX69eu1f/9%2BlZWVadmyZWrYsCHvxLqE2vR448aNys3N1fPPP6/AwEBflm0bNe3vFVdcoT179ujtt9%2Bu%2BkhOTtbo0aOVmZnp41Vc3mrzNTx06FAVFRXphRde0NmzZ7Vp0ybl5ORo6NChvlzCZa82PU5OTtbatWt1%2BPBhlZeX6/3339e%2Bffs0YMAAXy7B9r7%2B%2BmsNGjSo6llXqampWrlypfLy8lRSUqKMjAx17NhR8fHxPq70R9yDdZlbtGiRZs%2BerRtuuEHBwcEaPXp01TvZjhw5ojNnzkiS%2Bvbtq2nTpmnq1Kn69ttvFR8fr8zMTDVq1MiX5dtCTXv85ptvKj8/v9pN7cOGDdPcuXMtr9suatLfgIAAXXHFFR6va9y4sYKDg7lkWAM1/RqOiorS8uXL9cwzz%2BhPf/qTfvWrX2np0qW6%2BuqrfVm%2BLdS0xxMmTFB5ebnS0tJ08uRJRUdHa%2B7cubr%2B%2But9Wb4t/BCOysvLJanqQc5Op1Pnz5/XkSNHqs4Gjh49WoWFhRozZoxKS0uVkJCgJUuW%2BKbw/8DPfbnfJQYAAGAzXCIEAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQCAy8LevXuVmJio9PT0Wr1u4MCBio%2BP9/i49tpr9dZbb3mp0kvjSe4AAMDnVqxYobVr16pVq1a1fu327ds9Pj927JhSUlJ04403miqv1jiDBQAAfC4wMPCiAWvLli0aNmyYrrvuOg0YMEBZWVn/cV/PPPOM7rnnHjVv3txb5V4SZ7AAAIDPjR079j/OOZ1OzZo1S4sXL9b111%2Bvf/zjH7rvvvvUrl07devWzWPbDz/8UIcOHdKiRYu8XfJFcQYLAABc1tatW6d%2B/fqpT58%2BCggIUI8ePfTrX/9ab7/9drVtX3jhBd19991q2LChDyr9EWewAADAZe3o0aPat2%2Bf4uPjq8bcbrf69Onjsd3nn3%2Bu/fv3609/%2BpPVJVbz/wENn0nzbGyjVQAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common7793734927789615807”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>21720.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11539.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>23370.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>32727.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>45058.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6970.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7042.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9980.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2756.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>783.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3070)</td> <td class=”number”>3111</td> <td class=”number”>96.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme7793734927789615807”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>82.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>90.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>286.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>416.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>460.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>3095313.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3817117.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4092459.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5194675.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9818605.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_AGRITRSM_OPS07”>AGRITRSM_OPS07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>77</td>

</tr> <tr>

<th>Unique (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>4.2%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>134</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>7.578</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>161</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>11.3%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-3415885602319052952”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-3415885602319052952”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-3415885602319052952”

aria-controls=”quantiles-3415885602319052952” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-3415885602319052952” aria-controls=”histogram-3415885602319052952”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-3415885602319052952” aria-controls=”common-3415885602319052952”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-3415885602319052952” aria-controls=”extreme-3415885602319052952”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-3415885602319052952”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>2</td>

</tr> <tr>

<th>Median</th> <td>5</td>

</tr> <tr>

<th>Q3</th> <td>9</td>

</tr> <tr>

<th>95-th percentile</th> <td>24</td>

</tr> <tr>

<th>Maximum</th> <td>161</td>

</tr> <tr>

<th>Range</th> <td>161</td>

</tr> <tr>

<th>Interquartile range</th> <td>7</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>10.412</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.3739</td>

</tr> <tr>

<th>Kurtosis</th> <td>42.06</td>

</tr> <tr>

<th>Mean</th> <td>7.578</td>

</tr> <tr>

<th>MAD</th> <td>6.0738</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>4.9777</td>

</tr> <tr>

<th>Sum</th> <td>23310</td>

</tr> <tr>

<th>Variance</th> <td>108.4</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-3415885602319052952”>

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q37%2B/fv/736tbt27uY7p3767Zs2dr4MCB3h4PAACgzrxesO6//361atVKY8eO1eDBgxUREXHJMcnJyTp16pS3RwMAADDC6wVr%2BfLl6tGjx1WP27dvnxemAQAAMM/r92C1a9dO48aN05YtW9xr//3f/60nn3xSLpfL2%2BMAAAAY5/WClZGRoTNnzqhNmzbutd69e6u6ulrz5s3z9jgAAADGef0S4eeff661a9cqMjLSvdayZUtlZmbqgQce8PY4AAAAxnn9DFZFRYWCg4MvHSQwUOXl5d4eBwAAwDivF6zu3btr3rx5On36tHvt%2B%2B%2B/10svveTxVg0AAAB25fVLhNOnT9d//Md/6I477lBISIiqq6tVVlam5s2b6w9/%2BIO3xwEAADDO6wWrefPm%2Buijj/Tpp5/q6NGjCgwMVKtWrdSrVy8FBQV5exwAAADjvF6wJKl%2B/fq65557fPHUAAAA153XC9axY8c0f/58ffPNN6qoqLhk/5NPPvH2SAAAAEb55B6swsJC9erVSw0bNvT20wMAAFx3Xi9Yubm5%2BuSTTxQVFeXtpwYAAPAKr79NQ%2BPGjTlzBQAA/JrXC9bYsWO1ePFiWZbl7acGAADwCq9fIvz000/15ZdfatWqVbrlllsUGOjZ8d577z1vjwQAAGCU1wtWSEiI7rrrLm8/LQAAgNd4vWBlZGQYe6zPPvtMU6dOVWJiohYsWOBeX7VqlaZPn6569ep5HP/OO%2B%2BoY8eOqq6u1sKFC7Vu3TqVlJSoY8eOmj17tpo3by5Jcrlcmj17tv7yl78oMDBQycnJmjlzpho0aGBsdgAA4L%2B8fg%2BWJH377bdatGiRXnjhBffaV199VavHeOONNzR37ly1aNHisvvdu3eX0%2Bn0%2BNWxY0dJPxWttWvXKisrS9u2bVPLli2Vlpbmvi9s5syZKi8v17p16/TBBx8oPz9fmZmZ15gWAAD8s/F6wdq1a5cGDRqkzZs3a926dZJ%2BevPRUaNG1epNRoODg5WTk/OzBetKsrOzlZqaqtatWyskJETp6enKz8/Xvn379MMPP2jLli1KT09XVFSUYmJi9NRTT%2BmDDz7Q%2BfPna/1cAADgn4/XLxEuWLBAzz33nB5//HH3GaXmzZtr3rx5WrJkifr27Vujxxk1atQV948fP64nnnhCubm5CgsL08SJE/Xggw%2BqoqJChw4dUlxcnPvYkJAQtWjRQk6nU2fOnFFQUJDatWvn3u/QoYN%2B/PFHffvttx7rP6ewsFBFRUUeaw5HQ0VHR9coW00FBfnkBOQ1czhqNu%2BFXHbLdzXksh9/zUYueyGXPXm9YP3973/XihUrJEkBAQHu9fvuu0/Tp0838hxRUVFq2bKlnn32WbVp00Yff/yxnn/%2BeUVHR%2Bu2226TZVkKDw/3%2BJjw8HAVFxcrIiJCISEhHrNdOLa4uLhGz5%2Bdna3Fixd7rKWlpWnixIl1TGZvkZGNanV8WNhN12kS3yKX/fhrNnLZC7nsxesFKzQ0VBUVFapfv77HemFh4SVr16p3797q3bu3%2B/f333%2B/Pv74Y61atUpTpkyRpCu%2BD1dd36NrxIgR6tOnj8eaw9FQxcVldXrci9mt9dc0f1BQoMLCblJJSbmqqqqv81TeQy778dds5LIXctVNbb%2B5N8XrBatr16565ZVXNGPGDPfa4cOHNWvWLN1xxx3X7XmbNWum3NxcRUREKDAwUC6Xy2Pf5XKpcePGioqKUmlpqaqqqhQUFOTek356F/qaiI6OvuRyYFHRGVVW%2Bs8nxrWobf6qqmq//DMjl/34azZy2Qu57MXrp0BeeOEFffXVV0pMTNTZs2fVtWtXDRgwQC6XS9OmTTPyHO%2B%2B%2B67Wr1/vsZafn6/mzZsrODhYbdu2VV5ennuvpKRER48eVceOHdW%2BfXtZlqWDBw%2B6951Op8LCwtSqVSsj8wEAAP/m9TNYTZs21bp167Rjxw4dPnxYDRo0UKtWrdSzZ0%2BP%2B57q4ty5c5ozZ46aN2%2BuX/ziF9q0aZM%2B/fRTvf/%2B%2B5KklJQUZWVl6a677lJMTIwyMzPVvn17JSQkSJL69%2B%2Bv3/72t3r11Vd17tw5LVmyRMOGDZPD4fU/LgAAYEM%2BaQz16tXTPffcU6fHuFCGKisrJUlbtmyR9NPZplGjRqmsrEzPPPOMioqKdMstt2jJkiWKj4%2BXJI0cOVJFRUV67LHHVFZWpsTERI%2Bb0l9%2B%2BWXNmjVLffv2Vb169fTAAw8oPT29TvMCAIB/HgGWl//V5T59%2BlzxTFVt3gvLToqKzhh/TIcjUPdmfmb8ca%2BXDZN61ug4hyNQkZGNVFxc5lfX5cllP/6ajVz2Qq66adIk9Lo99pV4/QzWgAEDPApWVVWVDh8%2BLKfTqccff9zb4wAAABjn9YJ14W0SLrZp0yb9%2Bc9/9vI0AAAA5t0wb6R0zz336KOPPvL1GAAAAHV2wxSs/fv31/kNPgEAAG4EXr9EOHLkyEvWysvLlZ%2Bfr379%2Bnl7HAAAAOO8XrBatmx5yU8RBgcHa9iwYXr44Ye9PQ4AAIBxXi9Y8%2BbN8/ZTAgAAeJXXC9aHH35Y42MHDx58HScBAAC4PrxesF588UVVV1dfckN7QECAx1pAQAAFCwAA2JLXC9abb76pt956S%2BPGjVO7du1kWZa%2B/vprvfHGG3r00UeVmJjo7ZEAAACM8sk9WFlZWYqJiXGv3X777WrevLlGjx6tdevWeXskAAAAo7z%2BPlhHjhxReHj4JethYWEqKCjw9jgAAADGeb1gNWvWTPPmzVNxcbF7raSkRPPnz9ett97q7XEAAACM8/olwunTp2vy5MnKzs5Wo0aNFBgYqNLSUjVo0EBLlizx9jgAAADGeb1g9erVS9u3b9eOHTt04sQJWZalmJgY3XnnnQoNDfX2OAAAAMZ5vWBJ0k033aS%2BffvqxIkTat68uS9GAAAAuG68fg9WRUWFpk6dqi5duuiXv/ylpJ/uwRozZoxKSkq8PQ4AAIBxXi9Yr732mg4cOKDMzEwFBv7f01dVVSkzM9Pb4wAAABjn9YK1adMm/dd//Zfuu%2B8%2B9z/6HBYWpoyMDG3evNnb4wAAABjn9YJVVlamli1bXrIeFRWlH3/80dvjAAAAGOf1gnXrrbfqz3/%2BsyR5/NuDGzdu1L/%2B6796exwAAADjvP5ThI888ogmTJighx56SNXV1Xr77beVm5urTZs26cUXX/T2OAAAAMZ5vWCNGDFCDodDK1asUFBQkF5//XW1atVKmZmZuu%2B%2B%2B7w9DgAAgHFeL1inTp3SQw89pIceesjbTw0AAOAVXr8Hq2/fvh73XgEAAPgbrxesxMREbdiwwdtPCwAA4DVev0T4L//yL/rNb36jrKws3XrrrapXr57H/vz58709EgAAgFFeL1iHDh3SbbfdJkkqLi729tMDAABcd14rWOnp6VqwYIH%2B8Ic/uNeWLFmitLQ0b40AAADgFV67B2vr1q2XrGVlZXnr6QEAALzGawXrcj85yE8TAgAAf%2BS1gnXhH3a%2B2hoAAIDdef1tGgAAAPwdBQsAAMAwr/0U4fnz5zV58uSrrvE%2BWAAAwO68VrC6deumwsLCq64BAADYndcK1j%2B%2B/xUAAIA/4x4sAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDBbF6zPPvtMSUlJSk9Pv2Rv/fr1GjhwoLp06aKhQ4fq888/d%2B9VV1drwYIF6tu3r7p3767Ro0fr2LFj7n2Xy6VJkyYpKSlJvXr10osvvqiKigqvZAIAAPZn24L1xhtvaO7cuWrRosUlewcOHNDUqVM1ZcoUffHFF0pNTdXTTz%2BtEydOSJLeeecdrV27VllZWdq2bZtatmyptLQ0WZYlSZo5c6bKy8u1bt06ffDBB8rPz1dmZqZX8wEAAPuybcEKDg5WTk7OZQvWypUrlZycrOTkZAUHB2vQoEGKjY3VmjVrJEnZ2dlKTU1V69atFRISovT0dOXn52vfvn364YcftGXLFqWnpysqKkoxMTF66qmn9MEHH%2Bj8%2BfPejgkAAGzItgVr1KhRCg0NvexeXl6e4uLiPNbi4uLkdDpVUVGhQ4cOeeyHhISoRYsWcjqdOnDggIKCgtSuXTv3focOHfTjjz/q22%2B/vT5hAACAX/Hav0XoTS6XS%2BHh4R5r4eHhOnTokE6fPi3Lsi67X1xcrIiICIWEhCggIMBjT5KKi4tr9PyFhYUqKiryWHM4Gio6Ovpa4vysoCB79WOHo2bzXshlt3xXQy778dds5LIXctmTXxYsSe77qa5l/2ofezXZ2dlavHixx1paWpomTpxYp8e1u8jIRrU6Pizspus0iW%2BRy378NRu57IVc9uKXBSsyMlIul8tjzeVyKSoqShEREQoMDLzsfuPGjRUVFaXS0lJVVVUpKCjIvSdJjRs3rtHzjxgxQn369PFYczgaqri47FojXZbdWn9N8wcFBSos7CaVlJSrqqr6Ok/lPeSyH3/NRi57IVfd1Pabe1P8smDFx8crNzfXY83pdOr%2B%2B%2B9XcHCw2rZtq7y8PPXo0UOSVFJSoqNHj6pjx45q1qyZLMvSwYMH1aFDB/fHhoWFqVWrVjV6/ujo6EsuBxYVnVFlpf98YlyL2uavqqr2yz8zctmPv2Yjl72Qy17sdQqkhoYPH66dO3dq%2B/btOnv2rHJycnTkyBENGjRIkpSSkqLly5crPz9fpaWlyszMVPv27ZWQkKCoqCj1799fv/3tb3Xq1CmdOHFCS5Ys0bBhw%2BRw%2BGUfBQAAhtm2MSQkJEiSKisrJUlbtmyR9NPZptjYWGVmZiojI0MFBQVq06aNli1bpiZNmkiSRo4cqaKiIj322GMqKytTYmKixz1TL7/8smbNmqW%2BffuqXr16euCBBy77ZqYAAACXE2DV9Y5u1EhR0Rnjj%2BlwBOrezM%2BMP%2B71smFSzxod53AEKjKykYqLy/zqtDG57Mdfs5HLXshVN02aXP4tna43v7xECAAA4EsULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADPPbgtWuXTvFx8crISHB/WvOnDmSpF27dmnYsGHq2rWr7r//fq1Zs8bjY5cvX67%2B/fura9euSklJUW5uri8iAAAAm3L4eoDraePGjbrllls81goLC/XUU0/pxRdf1MCBA7Vnzx6NHz9erVq1UkJCgrZu3apFixbpzTffVLt27bR8%2BXKNGzdOmzdvVsOGDX2UBAAA2InfnsH6OWvXrlXLli01bNgwBQcHKykpSX369NHKlSslSdnZ2Ro6dKg6deqkBg0aaMyYMZKkbdu2%2BXJsAABgI35dsObPn6/evXvr9ttv18yZM1VWVqa8vDzFxcV5HBcXF%2Be%2BDHjxfmBgoNq3by%2Bn0%2BnV2QEAgH357SXCzp07KykpSa%2B%2B%2BqqOHTumSZMm6aWXXpLL5VJMTIzHsRERESouLpYkuVwuhYeHe%2ByHh4e792uisLBQRUVFHmsOR0NFR0dfY5rLCwqyVz92OGo274Vcdst3NeSyH3/NRi57IZc9%2BW3Bys7Odv9369atNWXKFI0fP17dunW76sdallXn5168eLHHWlpamiZOnFinx7W7yMhGtTo%2BLOym6zSJb5HLfvw1G7nshVz24rcF62K33HKLqqqqFBgYKJfL5bFXXFysqKgoSVJkZOQl%2By6XS23btq3xc40YMUJ9%2BvTxWHM4Gqq4uOwap788u7X%2BmuYPCgpUWNhNKikpV1VV9XWeynvIZT/%2Bmo1c9kKuuqntN/em%2BGXB2r9/v9asWaNp06a51/Lz81W/fn0lJyfrj3/8o8fxubm56tSpkyQpPj5eeXl5GjJkiCSpqqpK%2B/fv17Bhw2r8/NHR0ZdcDiwqOqPKSv/5xLgWtc1fVVXtl39m5LIff81GLnshl73Y6xRIDTVu3FjZ2dnKysrSuXPndPjwYS1cuFAjRozQgw8%2BqIKCAq1cuVJnz57Vjh07tGPHDg0fPlySlJKSog8//FB79%2B5VeXm5li5dqvrqoJe/AAAPiklEQVT166t3796%2BDQUAAGzDL89gxcTEKCsrS/Pnz3cXpCFDhig9PV3BwcFatmyZ5s6dq5deeknNmjXTa6%2B9pl/84heSpLvuukvPPvusJk2apJMnTyohIUFZWVlq0KCBj1MBAAC78MuCJUndu3fXe%2B%2B997N7q1ev/tmPfeSRR/TII49cr9EAAICf88tLhAAAAL5EwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIY5fD0A/nn88rd/8vUItbJhUk9fjwAAsCnOYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEOXw9wIyooKNBLL72kffv2qWHDhhowYIAmT56swED66D%2BTX/72T74eoVY2TOrp6xEAAP8fBesyJkyYoA4dOmjLli06efKkxo4dq5tvvllPPPGEr0cDAAA2QMG6iNPp1MGDB/X2228rNDRUoaGhSk1N1e9//3sKFm5odjrjxtk2AP6OgnWRvLw8NWvWTOHh4e61Dh066PDhwyotLVVISMhVH6OwsFBFRUUeaw5HQ0VHRxudNSiIS5awJzuVQUnaOjXZ1yMYdeFrh799DSGXvfhrrgsoWBdxuVwKCwvzWLtQtoqLi2tUsLKzs7V48WKPtaeffloTJkwwN6h%2BKnKPN/1GI0aMMF7efKmwsFDZ2dnksgl/zSX9X7aKiq5%2Bla2wsFC///2bfveakcte/DXXBf5ZG%2BvIsqw6ffyIESO0atUqj18jRowwNN3/KSoq0uLFiy85W2Z35LIXf80l%2BW82ctkLueyJM1gXiYqKksvl8lhzuVwKCAhQVFRUjR4jOjraL9s4AACoGc5gXSQ%2BPl7Hjx/XqVOn3GtOp1Nt2rRRo0aNfDgZAACwCwrWReLi4pSQkKD58%2BertLRU%2Bfn5evvtt5WSkuLr0QAAgE0EzZ49e7avh7jR3HnnnVq3bp3mzJmjjz76SMOGDdPo0aMVEBDg69Eu0ahRI/Xo0cPvzq6Ry178NZfkv9nIZS/ksp8Aq653dAMAAMADlwgBAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNg2VBBQYF%2B9atfKTExUXfffbdee%2B01VVdX%2B3qsa1JQUKC0tDQlJiYqKSlJ06ZNU0lJiSTpwIEDevTRR9WtWzf169dPb731lo%2BnvTavvPKK2rVr5/79rl27NGzYMHXt2lX333%2B/1qxZ48Ppam/p0qXq1auXOnfurNTUVH333XeS7J9r//79GjVqlG6//Xb17NlTU6ZMcf%2Bj73bK9tlnnykpKUnp6emX7K1fv14DBw5Uly5dNHToUH3%2B%2Befuverqai1YsEB9%2B/ZV9%2B7dNXr0aB07dsybo1/RlXJt3rxZgwYNUpcuXdS/f3%2B9//77HvvLly9X//791bVrV6WkpCg3N9dbY9fIlbJdUFZWpt69e2vatGnuNTu/Zt9//73Gjx%2Bvzp07KykpSfPnz3f/PXaj56oxC7YzZMgQa8aMGVZJSYl1%2BPBhq1%2B/ftZbb73l67GuyQMPPGBNmzbNKi0ttY4fP24NHTrUmj59ulVeXm7deeed1qJFi6yysjIrNzfX6tGjh7Vp0yZfj1wr%2B/fvt3r06GHFxsZalmVZ33//vdW5c2dr5cqVVkVFhfWnP/3J6tixo/W3v/3Nx5PWzIoVK6z77rvPys/Pt86cOWPNmTPHmjNnju1znT9/3urZs6c1f/586%2BzZs9apU6esJ554wpowYYKtsmVlZVn9%2BvWzRo4caU2aNMljb//%2B/VZ8fLy1fft2q6Kiwlq9erXVqVMn6/jx45ZlWdby5cutu%2B%2B%2B2zp06JB15swZ6%2BWXX7YGDhxoVVdX%2ByKKhyvl2rdvn5WQkGB9/PHH1vnz563t27dbHTp0sP76179almVZn3zyiXX77bdbe/futcrLy61ly5ZZPXv2tMrKynwR5RJXyvaPMjIyrG7dullTp051r9n1NauurraGDRtmzZkzxzpz5ox16NAh66GHHrJ27txpWdaNnas2OINlM06nUwcPHtSUKVMUGhqqli1bKjU1VdnZ2b4erdZKSkoUHx%2BvyZMnq1GjRmratKmGDBmi3bt3a/v27Tp//rzGjx%2Bvhg0bqkOHDnr44YdtlbO6ulqzZs1Samqqe23t2rVq2bKlhg0bpuDgYCUlJalPnz5auXKl7wathbfeekvp6em67bbbFBISohkzZmjGjBm2z1VUVKSioiI9%2BOCDql%2B/viIjI3XvvffqwIEDtsoWHBysnJwctWjR4pK9lStXKjk5WcnJyQoODtagQYMUGxvrPhuXnZ2t1NRUtW7dWiEhIUpPT1d%2Bfr727dvn7RiXuFIul8ulsWPH6p577pHD4VBycrJiY2O1e/duST/lGjp0qDp16qQGDRpozJgxkqRt27Z5NcPPuVK2Cw4ePKh169ZpyJAhHut2fc3%2B%2Bte/6tixY3r%2B%2BecVEhKi1q1bKycnR3fccYekGztXbVCwbCYvL0/NmjVTeHi4e61Dhw46fPiwSktLfThZ7YWFhSkjI0M333yze%2B348eOKjo5WXl6e2rVrp6CgIPdeXFzcDXdq/0ree%2B89BQcHa%2BDAge61vLw8xcXFeRxnl1zff/%2B9vvvuO50%2BfVoDBgxQYmKiJk6cqFOnTtk6lyTFxMSoffv2ys7OVllZmU6ePKnNmzerd%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%2B%2B%2B7R79259%2BumnCg0N1ZQpU3w9mlFX%2B9ph568tlmXptdde07p167R06VIFBwd77NnRyZMntXDhQs2ePftnv/bZMZtlWYqKitKYMWN00003KTk5Wffee682bNjgcYzdUbBsJioqSi6Xy2PN5XIpICBAUVFRPpqqbrZu3apf/epXmj59ukaNGiXpp5wXf7ficrkUERGhwMAb%2B3/bXbt26auvvvK4bHFBZGTkJa9fcXGxLV67C5dy//FsTrNmzWRZls6fP2/bXNJPr9l3332nZ599VqGhoYqJidHEiRP18ccfKzAw0NbZLrjc/3sul0tRUVHuz6vL7Tdu3NibY16T6upqTZs2TVu3btW7776r2267zb13pdw3unnz5mnw4MEeP4V8gZ1fsyZNmlxy%2BbBZs2YqKiqyda6L3dh/U%2BES8fHxOn78uPvHx6WfLq21adNGjRo18uFk1%2BbLL7/U1KlTtXDhQg0ePNi9Hh8fr6%2B//lqVlZXuNafTqU6dOvlizFpZs2aNTp48qbvvvluJiYnuU/uJiYmKjY295N6d3NxcW%2BRq2rSpQkJCdODAAfdaQUGB6tWrp%2BTkZNvmkqSqqipVV1d7fNd87tw5SVJSUpKts10QHx9/SY4Ln1PBwcFq27at8vLy3HslJSU6evSoOnbs6O1Ra%2B2VV17RN998o3fffVfNmzf32IuPj/fIVVVVpf3799vi9VuzZo1ycnKUmJioxMREvfnmm/roo4%2BUmJho69esdevWOnbsmMrKytxrBQUFatasma1zXYyCZTNxcXFKSEjQ/PnzVVpaqvz8fL399ttKSUnx9Wi1VllZqRkzZmjKlCnq1auXx15ycrJCQkK0dOlSlZeXa9%2B%2BfcrJybFFzmnTpmnTpk1avXq1Vq9eraysLEnS6tWrNXDgQBUUFGjlypU6e/asduzYoR07dmj48OE%2BnvrqHA6Hhg0bptdff13/%2B7//q5MnT2rJkiUaOHCghgwZYttcktSlSxc1bNhQixYtUnl5uYqLi7V06VJ1795dDz74oK2zXTB8%2BHDt3LlT27dv19mzZ5WTk6MjR45o0KBBkqSUlBQtX75c%2Bfn5Ki0tVWZmptq3b6%2BEhAQfT35le/bs0Zo1a5SVlaWIiIhL9lNSUvThhx9q7969Ki8v19KlS1W/fn317t3b%2B8PW0o4dO7R27Vr315KRI0eqT58%2BWr16tST7vmZ9%2BvRRWFiY/vM//1M//vijdu3apS1btri/GbVrrosFWP5wofOfzIkTJzRz5kz95S9/UUhIiEaOHKmnn37aNvcnXbB79279%2B7//u%2BrXr3/J3saNG1VWVqZZs2YpNzdXN998s5588kk98sgjPpi0br777jv17dtXX3/9taSffkR57ty5ys/PV7NmzTR58mT169fPx1PWzLlz55SRkaGPPvpI58%2BfV//%2B/TVz5kw1atTI1rmkn85Kvfrqqzp48KDq16%2BvHj16aNq0aYqJibFNtgt/AV048%2BtwOCTJ/dNXmzdv1vz581VQUKA2bdroxRdfVPfu3SX9dM/LokWL9N5776msrEyJiYl6%2BeWX1bRpUx8k8XSlXNOnT9cf//hH99oF3bt3d/9k2v/8z/8oKytLJ0%2BeVEJCgmbPnq3Y2FgvJvh5V3vN/tGiRYtUUFCgefPmSbLvayZJf//73zVr1izl5eUpKipKzzzzjPttKG7kXLVBwQIAADCMS4QAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYNj/A8ZzlcWC14%2BYAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-3415885602319052952”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>362</td> <td class=”number”>11.3%</td> <td>

<div class=”bar” style=”width:48%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>314</td> <td class=”number”>9.8%</td> <td>

<div class=”bar” style=”width:42%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>291</td> <td class=”number”>9.1%</td> <td>

<div class=”bar” style=”width:39%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>241</td> <td class=”number”>7.5%</td> <td>

<div class=”bar” style=”width:32%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>232</td> <td class=”number”>7.2%</td> <td>

<div class=”bar” style=”width:31%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>231</td> <td class=”number”>7.2%</td> <td>

<div class=”bar” style=”width:31%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>193</td> <td class=”number”>6.0%</td> <td>

<div class=”bar” style=”width:26%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>173</td> <td class=”number”>5.4%</td> <td>

<div class=”bar” style=”width:23%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>163</td> <td class=”number”>5.1%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>122</td> <td class=”number”>3.8%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (66)</td> <td class=”number”>754</td> <td class=”number”>23.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>134</td> <td class=”number”>4.2%</td> <td>

<div class=”bar” style=”width:18%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-3415885602319052952”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>362</td> <td class=”number”>11.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>291</td> <td class=”number”>9.1%</td> <td>

<div class=”bar” style=”width:80%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>314</td> <td class=”number”>9.8%</td> <td>

<div class=”bar” style=”width:86%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>241</td> <td class=”number”>7.5%</td> <td>

<div class=”bar” style=”width:66%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>231</td> <td class=”number”>7.2%</td> <td>

<div class=”bar” style=”width:64%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>108.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>113.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>116.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>130.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>161.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_AGRITRSM_OPS12”>AGRITRSM_OPS12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>104</td>

</tr> <tr>

<th>Unique (%)</th> <td>3.2%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>4.2%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>134</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>10.754</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>236</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>8.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram8702936312644719015”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives8702936312644719015,#minihistogram8702936312644719015”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives8702936312644719015”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles8702936312644719015”

aria-controls=”quantiles8702936312644719015” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram8702936312644719015” aria-controls=”histogram8702936312644719015”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common8702936312644719015” aria-controls=”common8702936312644719015”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme8702936312644719015” aria-controls=”extreme8702936312644719015”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles8702936312644719015”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>3</td>

</tr> <tr>

<th>Median</th> <td>6</td>

</tr> <tr>

<th>Q3</th> <td>13</td>

</tr> <tr>

<th>95-th percentile</th> <td>36</td>

</tr> <tr>

<th>Maximum</th> <td>236</td>

</tr> <tr>

<th>Range</th> <td>236</td>

</tr> <tr>

<th>Interquartile range</th> <td>10</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>15.44</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.4358</td>

</tr> <tr>

<th>Kurtosis</th> <td>35.943</td>

</tr> <tr>

<th>Mean</th> <td>10.754</td>

</tr> <tr>

<th>MAD</th> <td>8.9273</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>4.7087</td>

</tr> <tr>

<th>Sum</th> <td>33078</td>

</tr> <tr>

<th>Variance</th> <td>238.38</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram8702936312644719015”>

<img 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64oorAttkZmbqN7/5jcaOHWt1eQAAAG1mecAaM2aMevfurWnTpumWW25RYmJiq21ycnJ0%2BPBhq0sDAAAwwvKAtWrVKg0fPvyc2%2B3evduCagAAAMyz/B6sAQMG6L777tPmzZsDY//xH/%2Bhe%2B65Rz6fz%2BpyAAAAjLM8YC1atEhHjx5V3759A2MjR45US0uLFi9ebHU5AAAAxll%2BifCDDz7Q2rVrlZSUFBhLTU1VSUmJbr75ZqvLAQAAMM7yM1iNjY2Kjo5uXUhkpBoaGqwuBwAAwDjLA1ZmZqYWL16sI0eOBMb%2B/ve/64knngh6VAMAAIBdWX6JcO7cufrXf/1X/fKXv1RsbKxaWlpUX1%2Bvnj176o9//KPV5QAAABhnecDq2bOn3n33Xb3//vs6cOCAIiMj1bt3b40YMUJRUVFWlwMAAGCc5QFLkjp06KBrr702FIcGAABod5YHrIMHD6q0tFRff/21GhsbW83/%2Bc9/trokAAAAo0JyD1ZNTY1GjBihjh07Wn14AACAdmd5wKqoqNCf//xnJScnW31oAAAAS1j%2BmIZOnTpx5goAAIQ1ywPWtGnTtGzZMvn9fqsPDQAAYAnLLxG%2B//77%2Buyzz7RmzRr16NFDkZHBGe/111%2B3uiQAAACjLA9YsbGxuvrqq60%2BLAAAgGUsD1iLFi2y%2BpAAAACWsvweLEn65ptv9Nxzz%2Bmxxx4LjP3tb3/7wfvZsWOHsrOzVVRUFDS%2BZs0aXXbZZcrIyAj6%2BvzzzyVJLS0tWrp0qXJzc5WZmampU6fq4MGDgdf7fD7NnDlT2dnZGjFihObNm3faZ3YBAACcjuUBa%2BfOnRo3bpw2bdqkdevWSfr%2B4aNTpkz5QQ8Zff7557Vw4UL16tXrtPOZmZlyuVxBX4MHD5Ykvfrqq1q7dq3Kysq0detWpaamqrCwMHDj/fz589XQ0KB169bpzTfflNvtVklJSRtXDgAALhSWB6ylS5fqkUce0dq1axURESHp%2B79PuHjxYi1fvvy89xMdHa3y8vIzBqyzcTqdKigoUJ8%2BfRQbG6uioiK53W7t3r1b3377rTZv3qyioiIlJyera9euuv/%2B%2B/Xmm2/qxIkTP/hYAADgwmN5wPqv//ov5efnS1IgYEnSDTfcILfbfd77mTJliuLi4s44X11drbvuukuZmZnKzc3V22%2B/LUlqbGzU3r17lZaWFtg2NjZWvXr1ksvl0p49exQVFaUBAwYE5gcNGqTvvvtO33zzzXnXBwAALlyW3%2BQeFxenxsZGdejQIWi8pqam1diPlZycrNTUVD388MPq27ev/vSnP%2BnRRx9VSkqKfvazn8nv9yshISHoNQkJCfJ6vUpMTFRsbGxQ%2BDu5rdfrPa/j19TUyOPxBI05HB2VkpLSxpUFi4oKyS10P5rDYa96T3Wy33bru53Rc2vRb%2BvR8/BlecAaNmyYfve73%2Bnxxx8PjO3bt08LFizQL3/5SyPHGDlypEaOHBn47zFjxuhPf/qT1qxZo%2BLiYkk664NO2/oQVKfTqWXLlgWNFRYWasaMGW3ar90lJcWEugQj4uMvCXUJFxx6bi36bT16Hn4sD1iPPfaY7rzzTmVlZam5uVnDhg1TQ0OD%2BvXrp8WLF7fbcbt3766KigolJiYqMjJSPp8vaN7n86lTp05KTk5WXV2dmpubFRUVFZiTvv8zP%2BcjLy9Po0aNChpzODrK6603sJL/Y7dPPKbXb7WoqEjFx1%2Bi2toGNTe3hLqcCwI9txb9th49b3%2Bh%2BnBvecDq1q2b1q1bp%2B3bt2vfvn26%2BOKL1bt3b1155ZVBl%2BXa4rXXXlNCQoJuuummwJjb7VbPnj0VHR2tfv36qbKyUsOHD5ck1dbW6sCBAxo8eLC6d%2B8uv9%2BvL7/8UoMGDZIkuVwuxcfHq3fv3ud1/JSUlFaXAz2eo2pqurDfPOGy/ubmlrBZi13Qc2vRb%2BvR8/BjecCSpIsuukjXXnttu%2B3/%2BPHjeuqpp9SzZ09ddtlleu%2B99/T%2B%2B%2B/rjTfekCTl5%2BerrKxMV199tbp27aqSkhINHDhQGRkZkqTrr79ezzzzjJ5%2B%2BmkdP35cy5cv18SJE%2BVwhKRdAADAZixPDKNGjTrrmarzfRbWyTDU1NQkSdq8ebOk7882TZkyRfX19XrooYfk8XjUo0cPLV%2B%2BXOnp6ZKkyZMny%2BPx6I477lB9fb2ysrKC7pl68skntWDBAuXm5uqiiy7SzTff3OphpgAAAGcS4W/rHd0/UElJSVDAam5u1r59%2B%2BRyuXTnnXfqnnvusbIcy3g8R43v0%2BGI1HUlO4zvt71smHllqEtoE4cjUklJMfJ66zmVbxF6bi36bT163v66dDnzI53ak%2BVnsE7%2BFt%2Bp3nvvPf3lL3%2BxuBoAAADzfjK/hnbttdfq3XffDXUZAAAAbfaTCVhffPFFm58/BQAA8FNg%2BSXCyZMntxpraGiQ2%2B3W6NGjrS4HAADAOMsDVmpqaqvfIoyOjtbEiRN12223WV0OAACAcZYHrPZ8WjsAAMBPgeUB66233jrvbW%2B55ZZ2rAQAAKB9WB6w5s2bp5aWllY3tEdERASNRUREELAAAIAtWR6wXnjhBb344ou67777NGDAAPn9fn311Vd6/vnndfvttysrK8vqkgAAAIwKyT1YZWVl6tq1a2Ds5z//uXr27KmpU6dq3bp1VpcEAABglOXPwdq/f78SEhJajcfHx6uqqsrqcgAAAIyzPGB1795dixcvltfrDYzV1taqtLRUl156qdXlAAAAGGf5JcK5c%2Bdq1qxZcjqdiomJUWRkpOrq6nTxxRdr%2BfLlVpcDAABgnOUBa8SIEdq2bZu2b9%2BuQ4cOye/3q2vXrrrqqqsUFxeav3gNAABgkuUBS5IuueQS5ebm6tChQ%2BrZs2coSgAAAGg3lt%2BD1djYqNmzZ2vo0KG68cYbJX1/D9bdd9%2Bt2tpaq8sBAAAwzvKAtWTJEu3Zs0clJSWKjPy/wzc3N6ukpMTqcgAAAIyzPGC99957%2Bvd//3fdcMMNgT/6HB8fr0WLFmnTpk1WlwMAAGCc5QGrvr5eqamprcaTk5P13XffWV0OAACAcZYHrEsvvVR/%2BctfJCnobw9u3LhR//zP/2x1OQAAAMZZ/luEv/71r/Xggw/q1ltvVUtLi1566SVVVFTovffe07x586wuBwAAwDjLA1ZeXp4cDodeeeUVRUVF6fe//7169%2B6tkpIS3XDDDVaXAwAAYJzlAevw4cO69dZbdeutt1p9aAAAAEtYfg9Wbm5u0L1XAAAA4cbygJWVlaUNGzZYfVgAAADLWH6J8J/%2B6Z/029/%2BVmVlZbr00kt10UUXBc2XlpZaXRIAAIBRlgesvXv36mc/%2B5kkyev1Wn14AACAdmdZwCoqKtLSpUv1xz/%2BMTC2fPlyFRYWWlUCAACAJSy7B2vLli2txsrKyqw6PAAAgGUsC1in%2B81BfpsQAACEI8sC1sk/7HyuMQAAALuz/DENAAAA4Y6ABQAAYJhlv0V44sQJzZo165xjPAcLAADYnWUB64orrlBNTc05xwAAAOzOsoD1/59/BQAAEM64BwsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgmK0D1o4dO5Sdna2ioqJWc%2BvXr9fYsWM1dOhQTZgwQR988EFgrqWlRUuXLlVubq4yMzM1depUHTx4MDDv8/k0c%2BZMZWdna8SIEZo3b54aGxstWRMAALA/2was559/XgsXLlSvXr1aze3Zs0ezZ89WcXGxPv74YxUUFOiBBx7QoUOHJEmvvvqq1q5dq7KyMm3dulWpqakqLCyU3%2B%2BXJM2fP18NDQ1at26d3nzzTbndbpWUlFi6PgAAYF%2B2DVjR0dEqLy8/bcBavXq1cnJylJOTo%2BjoaI0bN079%2B/fXO%2B%2B8I0lyOp0qKChQnz59FBsbq6KiIrndbu3evVvffvutNm/erKKiIiUnJ6tr1666//779eabb%2BrEiRNWLxMAANiQZX%2BL0LQpU6acca6yslI5OTlBY2lpaXK5XGpsbNTevXuVlpYWmIuNjVWvXr3kcrl09OhRRUVFacCAAYH5QYMG6bvvvtM333wTNH4mNTU18ng8QWMOR0elpKSc7/LOS1SUvfKxw2Gvek91st9267ud0XNr0W/r0fPwZduAdTY%2Bn08JCQlBYwkJCdq7d6%2BOHDkiv99/2nmv16vExETFxsYqIiIiaE6SvF7veR3f6XRq2bJlQWOFhYWaMWPGj1lO2EhKigl1CUbEx18S6hIuOPTcWvTbevQ8/IRlwJIUuJ/qx8yf67XnkpeXp1GjRgWNORwd5fXWt2m/p7LbJx7T67daVFSk4uMvUW1tg5qbW0JdzgWBnluLfluPnre/UH24D8uAlZSUJJ/PFzTm8/mUnJysxMRERUZGnna%2BU6dOSk5OVl1dnZqbmxUVFRWYk6ROnTqd1/FTUlJaXQ70eI6qqenCfvOEy/qbm1vCZi12Qc%2BtRb%2BtR8/Dj71OgZyn9PR0VVRUBI25XC4NGTJE0dHR6tevnyorKwNztbW1OnDggAYPHqyBAwfK7/fryy%2B/DHptfHy8evfubdkaAACAfYVlwJo0aZI%2B%2Bugjbdu2TceOHVN5ebn279%2BvcePGSZLy8/O1atUqud1u1dXVqaSkRAMHDlRGRoaSk5N1/fXX65lnntHhw4d16NAhLV%2B%2BXBMnTpTDEZYn/AAAgGG2TQwZGRmSpKamJknS5s2bJX1/tql///4qKSnRokWLVFVVpb59%2B2rlypXq0qWLJGny5MnyeDy64447VF9fr6ysrKCb0p988kktWLBAubm5uuiii3TzzTef9mGmAAAApxPhb%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%2Beg0bNkz5%2BfmqqKgIxRIAAIBNOUJdQHvauHGjevToETRWU1Oj%2B%2B%2B/X/PmzdPYsWP16aefavr06erdu7cyMjK0ZcsWPffcc3rhhRc0YMAArVq1Svfdd582bdqkjh07hmglAADATsL2DNaZrF27VqmpqZo4caKio6OVnZ2tUaNGafXq1ZIkp9OpCRMmaMiQIbr44ot19913S5K2bt0ayrIBAICNhPUZrNLSUv3tb39TXV2dbrzxRs2ZM0eVlZVKS0sL2i4tLU0bNmyQJFVWVuqmm24KzEVGRmrgwIFyuVwaM2bMeR23pqZGHo8naMzh6KiUlJQ2rihYVJS98rHDYa96T3Wy33bru53Rc2vRb%2BvR8/AVtgHr8ssvV3Z2tp5%2B%2BmkdPHhQM2fO1BNPPCGfz6euXbsGbZuYmCiv1ytJ8vl8SkhICJpPSEgIzJ8Pp9OpZcuWBY0VFhZqxowZP3I14SEpKSbUJRgRH39JqEu44NBza9Fv69Hz8BO2AcvpdAb%2B3adPHxUXF2v69Om64oorzvlav9/fpmPn5eVp1KhRQWMOR0d5vfVt2u%2Bp7PaJx/T6rRYVFan4%2BEtUW9ug5uaWUJdzQaDn1qLf1qPn7S9UH%2B7DNmCdqkePHmpublZkZKR8Pl/QnNfrVXJysiQpKSmp1bzP51O/fv3O%2B1gpKSmtLgd6PEfV1HRhv3nCZf3NzS1hsxa7oOfWot/Wo%2Bfhx16nQM7TF198ocWLFweNud1udejQQTk5Oa0eu1BRUaEhQ4ZIktLT01VZWRmYa25u1hdffBGYBwAAOJewDFidOnWS0%2BlUWVmZjh8/rn379unZZ59VXl6efvWrX6mqqkqrV6/WsWPHtH37dm3fvl2TJk2SJOXn5%2Butt97Srl271NDQoBUrVqhDhw4aOXJkaBcFAABsIywvEXbt2lVlZWUqLS0NBKTx48erqKhI0dHRWrlypRYuXKgnnniSxlP0AAAK%2B0lEQVRC3bt315IlS3TZZZdJkq6%2B%2Bmo9/PDDmjlzpv7xj38oIyNDZWVluvjii0O8KgAAYBcR/rbe0Y3z4vEcNb5PhyNS15XsML7f9rJh5pWhLqFNHI5IJSXFyOut514Ji9Bza9Fv69Hz9telS1xIjhuWlwgBAABCiYAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwzBHqAnDhuPGZD0Ndwg%2ByYeaVoS4BAGBTnMECAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADDMEeoCgJ%2BqG5/5MNQl/CAbZl4Z6hIAAP%2BLM1inUVVVpXvvvVdZWVm65pprtGTJErW0tIS6LAAAYBOcwTqNBx98UIMGDdLmzZv1j3/8Q9OmTVPnzp111113hbo0AABgAwSsU7hcLn355Zd66aWXFBcXp7i4OBUUFOjll18mYOEnzW6XNO2Ey68AfigC1ikqKyvVvXt3JSQkBMYGDRqkffv2qa6uTrGxsefcR01NjTweT9CYw9FRKSkpRmuNiuIKL2AFu4XXPxVf9aNed/JnCj9brEPPwxcB6xQ%2Bn0/x8fFBYyfDltfrPa%2BA5XQ6tWzZsqCxBx54QA8%2B%2BKC5QvV9kLuz29fKy8szHt7QWk1NjZxOJ/22ED23Vk1NjV5%2B%2BQX6bSF6Hr6IzKfh9/vb9Pq8vDytWbMm6CsvL89Qdf/H4/Fo2bJlrc6WoX3Qb%2BvRc2vRb%2BvR8/DFGaxTJCcny%2BfzBY35fD5FREQoOTn5vPaRkpLCJxEAAC5gnME6RXp6uqqrq3X48OHAmMvlUt%2B%2BfRUTExPCygAAgF0QsE6RlpamjIwMlZaWqq6uTm63Wy%2B99JLy8/NDXRoAALCJqN/85je/CXURPzVXXXWV1q1bp6eeekrvvvuuJk6cqKlTpyoiIiLUpbUSExOj4cOHc3bNIvTbevTcWvTbevQ8PEX423pHNwAAAIJwiRAAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAKWDVVVVenee%2B9VVlaWrrnmGi1ZskQtLS2hLiusDBgwQOnp6crIyAh8PfXUU5KknTt3auLEiRo2bJjGjBmjd955J8TV2tOOHTuUnZ2toqKiVnPr16/X2LFjNXToUE2YMEEffPBBYK6lpUVLly5Vbm6uMjMzNXXqVB08eNDK0m3rTD1fs2aNLrvssqDv94yMDH3%2B%2BeeS6PmPVVVVpcLCQmVlZSk7O1tz5sxRbW2tJGnPnj26/fbbdcUVV2j06NF68cUXg157tvcAbMIP2xk/frz/8ccf99fW1vr37dvnHz16tP/FF18MdVlhpX///v6DBw%2B2Gv/73//uv/zyy/2rV6/2NzY2%2Bj/88EP/4MGD/Z9//nkIqrSvsrIy/%2BjRo/2TJ0/2z5w5M2juiy%2B%2B8Kenp/u3bdvmb2xs9L/99tv%2BIUOG%2BKurq/1%2Bv9%2B/atUq/zXXXOPfu3ev/%2BjRo/4nn3zSP3bsWH9LS0solmIbZ%2Bv5m2%2B%2B6b/99tvP%2BFp6/uPcfPPN/jlz5vjr6ur81dXV/gkTJvjnzp3rb2ho8F911VX%2B5557zl9fX%2B%2BvqKjwDx8%2B3P/ee%2B/5/f5zvwdgD5zBshmXy6Uvv/xSxcXFiouLU2pqqgoKCuR0OkNd2gVh7dq1Sk1N1cSJExUdHa3s7GyNGjVKq1evDnVpthIdHa3y8nL16tWr1dzq1auVk5OjnJwcRUdHa9y4cerfv3/gTKHT6VRBQYH69Omj2NhYFRUVye12a/fu3VYvw1bO1vNzoec/XG1trdLT0zVr1izFxMSoW7duGj9%2BvD755BNt27ZNJ06c0PTp09WxY0cNGjRIt912W%2BDn%2BLneA7AHApbNVFZWqnv37kpISAiMDRo0SPv27VNdXV0IKws/paWlGjlypH7%2B859r/vz5qq%2BvV2VlpdLS0oK2S0tLU0VFRYiqtKcpU6YoLi7utHNn6rHL5VJjY6P27t0bNB8bG6tevXrJ5XK1a812d7aeS1J1dbXuuusuZWZmKjc3V2%2B//bYk0fMfKT4%2BXosWLVLnzp0DY9XV1UpJSVFlZaUGDBigqKiowNz//zlytvcA7IOAZTM%2Bn0/x8fFBYyfDltfrDUVJYenyyy9Xdna2Nm3aJKfTqV27dumJJ544bf8TExPpvUE%2Bny/oA4T0/fe41%2BvVkSNH5Pf7zziPHyc5OVmpqal65JFH9OGHH%2Brhhx/W3LlztXPnTnpuiMvl0iuvvKLp06ef8eeIz%2BdTS0vLWd8DsA8Clg35/f5QlxD2nE6nbrvtNnXo0EF9%2BvRRcXGx1q1bpxMnToS6tAvCub7HeQ%2BYNXLkSL3wwgtKS0tThw4dNGbMGF133XVas2ZNYBt6/uN9%2Bumnmjp1qmbNmqXs7OwzbhcRERH4N/22PwKWzSQnJ8vn8wWN%2BXw%2BRUREKDk5OURVhb8ePXqoublZkZGRrfrv9XrpvUFJSUmn/R5PTk5WYmLiaf838Pl86tSpk5Vlhr3u3burpqaGnrfRli1bdO%2B992ru3LmaMmWKpO9/jp96Nsrn8wV6fbb3AOyDgGUz6enpqq6u1uHDhwNjLpdLffv2VUxMTAgrCx9ffPGFFi9eHDTmdrvVoUMH5eTktLrfqqKiQkOGDLGyxLCWnp7eqscul0tDhgxRdHS0%2BvXrp8rKysBcbW2tDhw4oMGDB1tdath47bXXtH79%2BqAxt9utnj170vM2%2BOyzzzR79mw9%2B%2ByzuuWWWwLj6enp%2Buqrr9TU1BQYO/k9fnL%2BTO8B2AcBy2bS0tKUkZGh0tJS1dXVye1266WXXlJ%2Bfn6oSwsbnTp1ktPpVFlZmY4fP659%2B/bp2WefVV5enn71q1%2BpqqpKq1ev1rFjx7R9%2B3Zt375dkyZNCnXZYWPSpEn66KOPtG3bNh07dkzl5eXav3%2B/xo0bJ0nKz8/XqlWr5Ha7VVdXp5KSEg0cOFAZGRkhrty%2Bjh8/rqeeekoul0snTpzQunXr9P7772vy5MmS6PmP0dTUpMcff1zFxcUaMWJE0FxOTo5iY2O1YsUKNTQ0aPfu3SovLw/8HD/XewD2EOHnQq/tHDp0SPPnz9d//ud/KjY2VpMnT9YDDzwQdP0ebfPXv/5VpaWl%2Buqrr9ShQweNHz9eRUVFio6O1l//%2BlctXLhQbrdb3bt316xZszR69OhQl2wrJ/%2BP%2BeQneIfDIUmB35LatGmTSktLVVVVpb59%2B2revHnKzMyU9P29Kc8995xef/111dfXKysrS08%2B%2BaS6desWgpXYx9l67vf7tWLFCpWXl8vj8ahHjx569NFHdc0110ii5z/GJ598on/5l39Rhw4dWs1t3LhR9fX1WrBggSoqKtS5c2fdc889%2BvWvfx3Y5mzvAdgDAQsAAMAwLhECAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGH/A%2BCDQRaFsHo5AAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common8702936312644719015”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>263</td> <td class=”number”>8.2%</td> <td>

<div class=”bar” style=”width:24%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>241</td> <td class=”number”>7.5%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>238</td> <td class=”number”>7.4%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>229</td> <td class=”number”>7.1%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>219</td> <td class=”number”>6.8%</td> <td>

<div class=”bar” style=”width:20%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>196</td> <td class=”number”>6.1%</td> <td>

<div class=”bar” style=”width:18%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>170</td> <td class=”number”>5.3%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>157</td> <td class=”number”>4.9%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>144</td> <td class=”number”>4.5%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>123</td> <td class=”number”>3.8%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (93)</td> <td class=”number”>1096</td> <td class=”number”>34.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>134</td> <td class=”number”>4.2%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme8702936312644719015”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>263</td> <td class=”number”>8.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>241</td> <td class=”number”>7.5%</td> <td>

<div class=”bar” style=”width:91%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>229</td> <td class=”number”>7.1%</td> <td>

<div class=”bar” style=”width:87%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>238</td> <td class=”number”>7.4%</td> <td>

<div class=”bar” style=”width:90%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>219</td> <td class=”number”>6.8%</td> <td>

<div class=”bar” style=”width:83%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>139.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>141.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>147.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>181.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>236.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_AGRITRSM_RCT07”>AGRITRSM_RCT07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>540</td>

</tr> <tr>

<th>Unique (%)</th> <td>16.8%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>38.6%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>1239</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>211000</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>8464000</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>11.3%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-96081159557793503”>

<img src=”data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAAtpJREFUeJzt2r1KI1EchvHXNahYi5VoJdgERFL4kYBgoVY2iggiKngB1sEyNxBBBK9AiIqNxsLKQgtRIUVQrBRBvISgzBbLKrNuXokkM677/CBFJueEf/NwZkKagiAIBOCvfsQ9APCVJeIe4E%2BpbLHmPee5iQZMAnCCABaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAEYi7gHqIZUt1rznPDfRgEnw3XyLQD7jM1HVigj/fU1BEARxDwF8VTyDAAaBAAaBAAaBAAaBoO5OTk40PDys1dXVmvaNj48rmUyGXn19fdrb22vQpB/7b3/mRWNsbW2pUCiop6en5r1HR0eh9/f395qdnVUmk6nXeDXjBEFdtba22kAODg40NTWl/v5%2BjY2NaXt7u%2Bp35XI5LS8vq6Ojo1HjfogTBHW1sLBQ9bNSqaRsNqv19XUNDQ3p8vJSKysr6u3t1cDAQGjt2dmZyuWy8vl8o0e2OEEQmd3dXY2OjiqdTqu5uVmpVEqTk5Pa399/t3Zzc1NLS0tqaWmJYdI3nCCIzN3dnU5PT5VMJl%2BvBUGgdDodWndzc6OrqyttbGxEPeI7BILItLW1aW5uTmtra3ZdsVjU4OCg2tvbI5qsOm6xEJnu7m5dX1%2BHrj0%2BPurl5SV07fj4WCMjI1GOVhWBIDLT09O6uLjQzs6OKpWKyuWyZmZmQj/vVioV3d7eqqurK8ZJ3/BvXtTV7%2BeL5%2BdnSVIi8esuvlQqSZIODw%2BVz%2Bf18PCgzs5Ozc/Pa3Fx8XX/09OTMpmMCoVC6FklLgQCGNxiAQaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAAaBAMZPEdqgXv5cI%2BAAAAAASUVORK5CYII%3D”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-96081159557793503,#minihistogram-96081159557793503”
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</div> <div class=”row collapse col-md-12” id=”descriptives-96081159557793503”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-96081159557793503”

aria-controls=”quantiles-96081159557793503” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-96081159557793503” aria-controls=”histogram-96081159557793503”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-96081159557793503” aria-controls=”common-96081159557793503”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-96081159557793503” aria-controls=”extreme-96081159557793503”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-96081159557793503”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>7000</td>

</tr> <tr>

<th>Median</th> <td>42000</td>

</tr> <tr>

<th>Q3</th> <td>168500</td>

</tr> <tr>

<th>95-th percentile</th> <td>1048500</td>

</tr> <tr>

<th>Maximum</th> <td>8464000</td>

</tr> <tr>

<th>Range</th> <td>8464000</td>

</tr> <tr>

<th>Interquartile range</th> <td>161500</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>542750</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.5722</td>

</tr> <tr>

<th>Kurtosis</th> <td>71.001</td>

</tr> <tr>

<th>Mean</th> <td>211000</td>

</tr> <tr>

<th>MAD</th> <td>261930</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>6.9714</td>

</tr> <tr>

<th>Sum</th> <td>415880000</td>

</tr> <tr>

<th>Variance</th> <td>294570000000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-96081159557793503”>

<img src=”data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3XtcVPW%2B//E3MArKTVDBIlLzlgKat8jLFkNT09IsC93nZFZ2tMgLapmav7Qs7YTHUizDdhe37iIvx5S8bc3MzrFdtrNG1NqiHcs0xmRCEZPL/P7oIbsJLdh8WTMDr%2BfjweMR3%2B%2BatT5fP9DjPWutWfi5XC6XAAAAYIy/pwsAAACobQhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwm6cLqCscjjPG9%2Bnv76fIyGCdPl2osjKX8f3jX0dvvBe98V70xnv5cm%2BaNg31yHE5g%2BXD/P395OfnJ39/P0%2BXgl%2BhN96L3ngveuO96E3VEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDCbpwtA9XSbtcXTJVTa5sm9PF0CAACW4AwWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIb59N8i3L17t6ZPn67ExEQtWrSofPzxxx/XO%2B%2B847ZtaWmphg0bpvnz5%2Buxxx7Thg0bFBAQUD4fGBiovXv3SpKcTqfmzJmjjz/%2BWP7%2B/kpKStLs2bMVFBRkzcIAAIBP89mAtXz5cq1Zs0bNmzevMDdv3jzNmzev/PuSkhLddtttGjRoUPnYgw8%2BqAkTJlxy37Nnz9aFCxeUnZ2t4uJiTZo0Senp6Xr88cfNLwQAANQ6PnuJMDAw8LIB69feeOMNXXnllUpKSvrdbU%2BdOqXt27crLS1NkZGRio6O1kMPPaS1a9equLjYROkAAKCW89kzWKNHj67UdgUFBVq2bJn%2B8pe/uI1/9NFH2rFjh/7v//5PrVq10pw5cxQfH6%2BDBw8qICBA7dq1K982Li5O586d05EjR9zGLycvL08Oh8NtzGZrqKioqErVXFkBAb6Vj20236q3Oi72xtd6VBfQG%2B9Fb7wXvak6nw1YlbVy5Up1795dbdq0KR%2BLjY2Vv7%2B/Jk2apODgYGVkZOi%2B%2B%2B7T1q1b5XQ6FRISIj8/v/Ltw8PDJUn5%2BfmVOmZWVpYyMjLcxlJTUzVx4kQDK/JdERHBni7BcmFhDTxdAi6D3ngveuO96E3l1eqAVVpaqlWrVmnhwoVu46mpqW7fP/LII8rOztb27dsVFBQkl8tVreOmpKQoOTnZbcxma6j8/MJq7ffXfO2dhOn1e7OAAH%2BFhTVQQUGRSkvLPF0OfoHeeC964718uTeeenNfqwPWJ598ogsXLqhbt26/uV1AQICuuOIK5eXl6brrrtPZs2dVWlpa/ilDp9MpSWrcuHGljhsVFVXhcqDDcUYlJb71Q2laXVx/aWlZnVy3L6A33oveeC96U3m%2BdQqkinbs2KEbbrhBNts/c6TL5dL8%2BfN16NCh8rELFy7o2LFjio2NVfv27eVyudzm7Xa7wsLC1LJlS0vrBwAAvqlWB6yDBw/qqquuchvz8/PTt99%2Bq7lz5%2Br7779XYWGh0tPTVa9ePfXv31%2BRkZEaOHCgnn/%2BeZ0%2BfVonT57U0qVLNWLECLegBgAAcDk%2BmxgSEhIk/fyMK0navn27pJ/PNl3kcDjUpEmTCq99%2Bumn9eyzz%2Br222/X2bNn1bFjR73xxhtq2LChJOnJJ5/UE088oX79%2BqlevXq65ZZblJaWVtNLAgAAtYSfq7p3dKNSHI4zxvdps/nrpvTdxvdbUzZP7uXpEixjs/krIiJY%2BfmF3K/gZeiN96I33suXe9O0aahHjlurLxECAAB4AgELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADPPpgLV792717NlTaWlpbuPr1q3Ttddeq4SEBLevL774QpJUVlamRYsWqV%2B/furevbvuv/9%2BffPNN%2BWvdzqdmjx5snr27KnevXtr1qxZOn/%2BvKVrAwAAvstnA9by5cs1b948NW/e/JLz3bt3l91ud/vq2LGjJGnVqlXauHGjMjMztXPnTrVo0UKpqalyuVySpNmzZ6uoqEjZ2dlau3atcnNzlZ6ebtnaAACAb/PZgBUYGKg1a9ZcNmD9lqysLI0ZM0atWrVSSEiI0tLSlJubq88//1ynTp3S9u3blZaWpsjISEVHR%2Buhhx7S2rVrVVxcXAMrAQAAtY3N0wX8q0aPHv2b8ydOnNC9996r/fv3KywsTBMnTtSwYcN0/vx5HT58WB06dCjfNiQkRM2bN5fdbteZM2cUEBCgdu3alc/HxcXp3LlzOnLkiNv45eTl5cnhcLiN2WwNFRUVVcVV/raAAN/Kxzabb9VbHRd742s9qgvojfeiN96L3lSdzwas3xIZGakWLVpoypQpat26tf7617/q0UcfVVRUlK655hq5XC6Fh4e7vSY8PFz5%2Bflq1KiRQkJC5Ofn5zYnSfn5%2BZU6flZWljIyMtzGUlNTNXHixGquzLdFRAR7ugTLhYU18HQJuAx6473ojfeiN5VXKwNW37591bdv3/LvhwwZor/%2B9a9at26dpk2bJknl91tdym/NVUZKSoqSk5Pdxmy2hsrPL6zWfn/N195JmF6/NwsI8FdYWAMVFBSptLTM0%2BXgF%2BiN96I33suXe%2BOpN/e1MmBdSkxMjPbv369GjRrJ399fTqfTbd7pdKpx48aKjIzU2bNnVVpaqoCAgPI5SWrcuHGljhUVFVXhcqDDcUYlJb71Q2laXVx/aWlZnVy3L6A33oveeC96U3m%2BdQqkkt58801t2rTJbSw3N1exsbEKDAxUmzZtlJOTUz5XUFCgY8eOqWPHjmrfvr1cLpcOHTpUPm%2B32xUWFqaWLVtatgYAAOC7amXAunDhgp566inZ7XYVFxcrOztbH3zwgUaOHClJGjVqlFasWKHc3FydPXtW6enpat%2B%2BvRISEhQZGamBAwfq%2Beef1%2BnTp3Xy5EktXbpUI0aMkM1WZ074AQCAavDZxJCQkCBJKikpkSRt375d0s9nm0aPHq3CwkJNmjRJDodDV111lZYuXar4%2BHhJ0siRI%2BVwOHT33XersLBQiYmJbjelP/nkk3riiSfUr18/1atXT7fcckuFh5kCAABcjp%2Brund0o1IcjjPG92mz%2Beum9N3G91tTNk/u5ekSLGOz%2BSsiIlj5%2BYXcr%2BBl6I33ojfey5d707RpqEeOWysvEQIAAHgSAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAM89mAtXv3bvXs2VNpaWkV5rZt26ahQ4eqc%2BfOGjhwoN5%2B%2B%2B3yuSVLlqh9%2B/ZKSEhw%2Bzp16pQk6aefftL/%2B3//T3369FFiYqImTpyo/Px8y9YFAAB8n08GrOXLl2vevHlq3rx5hbkvvvhC06ZN08SJE/XJJ59o5syZevLJJ7V3797ybYYNGya73e721aRJE0nSokWLlJOTo6ysLG3dulUul0szZsywbG0AAMD3%2BWTACgwM1Jo1ay4ZsJxOp8aNG6f%2B/fvLZrMpKSlJbdu2dQtYl1NSUqI1a9booYce0hVXXKFGjRpp8uTJev/99/X999/XxFIAAEAt5JMBa/To0QoNDb3kXJ8%2BfZSamlr%2BfUlJiRwOh6Kjo8vHvvzyS40cOVJdunTRkCFD9OGHH0qSjh07pjNnziguLq5821atWikoKEg5OTk1tBoAAFDb2DxdQE1LT09Xw4YNNXjwYElSs2bNFBsbq6lTpyoqKkpZWVkaP368NmzYIKfTKUkKCwtz20dYWFiV7sPKy8uTw%2BFwG7PZGioqKqqaq3EXEOBb%2Bdhm8616q%2BNib3ytR3UBvfFe9MZ70Zuqq7UBy%2BVyKT09XdnZ2VqxYoUCAwMlSXfeeafuvPPO8u3GjBmjd999Vxs2bFCfPn3KX1sdWVlZysjIcBtLTU3VxIkTq7VfXxcREezpEiwXFtbA0yXgMuiN96I33oveVF6tDFhlZWWaMWOGvvjiC7355puKjY39ze1jYmKUl5enyMhIST/fxxUc/M8w8OOPP6px48aVPn5KSoqSk5Pdxmy2hsrPL6zCKn6fr72TML1%2BbxYQ4K%2BwsAYqKChSaWmZp8vBL9Ab70VvvJcv98ZTb%2B5rZcB65pln9I9//ENvvvmmGjVq5Db34osvqnPnzurRo0f5WG5urgYPHqzY2FiFh4crJydHMTExkqSvvvpKFy5cUHx8fKWPHxUVVeFyoMNxRiUlvvVDaVpdXH9paVmdXLcvoDfei954L3pTeb51CqQSPv30U23YsEGZmZkVwpX089mpuXPn6siRI/rpp5/06quv6tixYxo%2BfLgCAgJ01113admyZTpx4oTy8/P1X//1X7rpppvKH%2BMAAADwe3zyDFZCQoKknz8hKEnbt2%2BXJNntdq1du1ZnzpzRjTfe6Paa7t2769VXX9XUqVMl/XzvldPpVOvWrfX666%2BrWbNmkqSJEyeqsLBQw4YNU0lJiW688UbNmTPHopUBAIDawM9V3Tu6USkOxxnj%2B7TZ/HVT%2Bm7j%2B60pmyf38nQJlrHZ/BUREaz8/EJOp3sZeuO96I338uXeNG166cc61bRad4kQAADA0whYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDDLA1ZycrIyMjJ04sQJqw8NAABgCcsD1h133KFNmzapf//%2BGjt2rLZt21b%2BRHYAAIDawPKAlZqaqk2bNuntt99WmzZt9MwzzygpKUnPPfecjh49anU5AAAAxnnsHqy4uDhNnz5dO3fu1MyZM/X2229r8ODBuv/%2B%2B/XFF194qiwAAIBq81jAKi4u1qZNm/TAAw9o%2BvTpio6O1owZM9S%2BfXuNGTNGGzdu9FRpAAAA1WKz%2BoC5ublas2aN1q9fr8LCQg0cOFBvvPGGunbtWr5N9%2B7dNWfOHN16661WlwcAAFBtlgesIUOGqGXLlho3bpxuu%2B02NWrUqMI2SUlJOn36tNWlAQAAGGF5wFqxYoWuv/76393u888/t6AaAAAA8yy/B6tdu3YaP368tm/fXj72%2Buuv64EHHpDT6bS6HAAAAOMsD1jz58/XmTNn1Lp16/Kxvn37qqysTAsWLLC6HAAAAOMsv0T44YcfauPGjYqIiCgfa9GihdLT03XLLbdYXQ4AAIBxlp/BOn/%2BvAIDAysW4u%2BvoqIiq8sBAAAwzvKA1b17dy1YsEA//vhj%2Bdj333%2BvuXPnuj2qAQAAwFdZfolw5syZuu%2B%2B%2B9SjRw%2BFhISorKxMhYWFio2N1Z///GerywEAADDO8oAVGxurd999Vx988IGOHTsmf39/tWzZUr1791ZAQIDV5QAAABhnecCSpPr166t///6eODQAAECNszxgffPNN1q4cKH%2B8Y9/6Pz58xXmd%2BzYYXVJAAAARnnkHqy8vDz17t1bDRs2tPrwAAAANc7ygLV//37t2LFDkZGRVh8aAADAEpY/pqFx48acuQIAALWa5QFr3LhxysjIkMvlsvrQAAAAlrD8EuEHH3ygv//971q3bp2uuuoq%2Bfu7Z7y33nrL6pIAAACMsjxghYSEqE%2BfPlYfFgAAwDKWB6z58%2Bcb29fu3bs1ffp0JSYmatGiRW5zmzZt0ksvvaRvv/1WLVu21JQpU9S7d29JUllZmV544QVlZ2eroKBAHTt21Jw5cxQbGytJcjqdmjNnjj7%2B%2BGP5%2B/srKSlJs2fPVlBQkLHaAQBA7WX5PViSdOTIES1ZskQzZswoH/vss8%2BqtI/ly5dr3rx5at68eYW5gwcPavr06Zo2bZo%2B%2BugjjRkzRg8//LBOnjwpSVq1apU2btyozMxM7dy5Uy1atFBqamr5fWGzZ89WUVGRsrOztXbtWuXm5io9Pb0aKwYAAHWJ5QFrz549Gjp0qLZt26bs7GxJPz98dPTo0VV6yGhgYKDWrFlzyYC1evVqJSUlKSkpSYGBgRo6dKjatm2rDRs2SJKysrI0ZswYtWrVSiEhIUpLS1Nubq4%2B//xznTp1Stu3b1daWpoiIyMVHR2thx56SGvXrlVxcbGZfwQAAFCrWR6wFi1apEceeUQbN26Un5%2BfpJ//PuGCBQu0dOnSSu9n9OjRCg0NveRcTk6OOnTo4DbWoUMH2e12nT9/XocPH3abDwkJUfPmzWW323Xw4EEFBASoXbt25fNxcXE6d%2B6cjhw5UpWlAgCAOsrye7C%2B%2BuorrVy5UpLKA5YkDRo0SDNnzjRyDKfTqfDwcLex8PBwHT58WD/%2B%2BKNcLtcl5/Pz89WoUSOFhIS41XZx2/z8/EodPy8vTw6Hw23MZmuoqKiof2U5lxUQ4JErvP8ym8236q2Oi73xtR7VBfTGe9Eb70Vvqs7ygBUaGqrz58%2Brfv36buN5eXkVxqrj956z9Vvz1X1GV1ZWljIyMtzGUlNTNXHixGrt19dFRAR7ugTLhYU18HQJuAx6473ojfeiN5VnecDq0qWLnnnmGT3%2B%2BOPlY0ePHtUTTzyhHj16GDlGRESEnE6n25jT6VRkZKQaNWokf3//S843btxYkZGROnv2rEpLSxUQEFA%2BJ/38FPrKSElJUXJystuYzdZQ%2BfmF/%2BqSLsnX3kmYXr83CwjwV1hYAxUUFKm0tMzT5eAX6I33ojfey5d746k395YHrBkzZuiee%2B5RYmKiSktL1aVLFxUVFalNmzZasGCBkWPEx8dr//79bmN2u11DhgxRYGCg2rRpo5ycHF1//fWSpIKCAh07dkwdO3ZUTEyMXC6XDh06pLi4uPLXhoWFqWXLlpU6flRUVIXLgQ7HGZWU%2BNYPpWl1cf2lpWV1ct2%2BgN54L3rjvehN5VkesJo1a6bs7Gzt2rVLR48eVVBQkFq2bKlevXq53fdUHXfddZdGjBih999/Xz169NDGjRv19ddfa%2BjQoZKkUaNGKTMzU3369FF0dLTS09PVvn17JSQkSJIGDhyo559/Xs8%2B%2B6wuXLigpUuXasSIEbLZLP/nAgAAPsgjiaFevXrq379/tfZxMQyVlJRIkrZv3y7p57NNbdu2VXp6uubPn6/jx4%2BrdevWevnll9W0aVNJ0siRI%2BVwOHT33XersLBQiYmJbvdMPfnkk3riiSfUr18/1atXT7fccovS0tKqVS8AAKg7/FwW/9Xl5OTk3zxTVZVnYfkSh%2BOM8X3abP66KX238f3WlM2Te3m6BMvYbP6KiAhWfn4hp9O9DL3xXvTGe/lyb5o2vfQjnWqa5WewBg8e7BawSktLdfToUdntdt1zzz1WlwMAAGCc5QFr2rRplxzfunWr/va3v1lcDQAAgHle8zn//v3769133/V0GQAAANXmNQHrwIED1X7AJwAAgDew/BLhyJEjK4wVFRUpNzdXAwYMsLocAAAA4ywPWC1atKjwKcLAwECNGDFCd955p9XlAAAAGGd5wDL1tHYAAABvZXnAWr9%2BfaW3ve2222qwEgAAgJphecCaNWuWysrKKtzQ7ufn5zbm5%2BdHwAIAAD7J8oD1yiuv6NVXX9X48ePVrl07uVwuffnll1q%2BfLn%2B/d//XYmJiVaXBAAAYJRH7sHKzMxUdHR0%2BVi3bt0UGxur%2B%2B%2B/X9nZ2VaXBAAAYJTlz8H6%2BuuvFR4eXmE8LCxMx48ft7ocAAAA4ywPWDExMVqwYIHy8/PLxwoKCrRw4UJdffXVVpcDAABgnOWXCGfOnKmpU6cqKytLwcHB8vf319mzZxUUFKSlS5daXQ4AAIBxlges3r176/3339euXbt08uRJuVwuRUdH6w9/%2BINCQ0OtLgcAAMA4ywOWJDVo0ED9%2BvXTyZMnFRsb64kSAAAAaozl92CdP39e06dPV%2BfOnXXzzTdL%2BvkerLFjx6qgoMDqcgAAAIyzPGA999xzOnjwoNLT0%2BXv/8/Dl5aWKj093epyAAAAjLM8YG3dulWLFy/WoEGDyv/oc1hYmObPn69t27ZZXQ4AAIBxlgeswsJCtWjRosJ4ZGSkzp07Z3U5AAAAxlkesK6%2B%2Bmr97W9/kyS3vz24ZcsWXXnllVaXAwAAYJzlnyL84x//qAkTJuiOO%2B5QWVmZXnvtNe3fv19bt27VrFmzrC4HAADAOMsDVkpKimw2m1auXKmAgAAtW7ZMLVu2VHp6ugYNGmR1OQAAAMZZHrBOnz6tO%2B64Q3fccYfVhwYAALCE5fdg9evXz%2B3eKwAAgNrG8oCVmJiozZs3W31YAAAAy1h%2BifCKK67Q008/rczMTF199dWqV6%2Be2/zChQutLgkAAMAoywPW4cOHdc0110iS8vPzrT48AABAjbMsYKWlpWnRokX685//XD62dOlSpaamWlUCAACAJSy7B%2Bu9996rMJaZmWnV4QEAACxjWcC61CcH%2BTQhAACojSy7RHjxDzv/3pgJn3zyie677z63MZfLpeLiYq1YsUKjR49W/fr13eb/8z//UzfffLMkacWKFVq1apUcDofatWunWbNmKT4%2BvkZqBQAAtY/lN7lboXv37rLb7W5jy5Yt06FDhyRJMTExl7xkKf18KXPJkiV65ZVX1K5dO61YsULjx4/Xtm3b1LBhwxqvHQAA%2BD7Ln4PlCd99951ee%2B01Pfroo7%2B7bVZWlm6//XZ16tRJQUFBGjt2rCRp586dNV0mAACoJSw7g1VcXKypU6f%2B7lhNPAfrhRde0B133KErr7xS33zzjQoLC5Wamqq9e/eqfv36uu%2B%2B%2BzRmzBj5%2BfkpJydHgwcPLn%2Btv7%2B/2rdvL7vdriFDhlTqeHl5eXI4HG5jNltDRUVFGV1XQIBv5WObzbfqrY6LvfG1HtUF9MZ70RvvRW%2BqzrKA1bVrV%2BXl5f3umGnffvuttm3bpm3btkmSQkJC1LZtW91zzz1atGiRPv74Y02aNEmhoaEaMWKEnE6nwsPD3fYRHh5epWd2ZWVlKSMjw20sNTVVEydOrP6CfFhERLCnS7BcWFgDT5eAy6A33oveeC96U3mWBaxfPv/KSqtWrdKAAQPUtGlTSVJcXJxbLb1799bIkSO1bt06jRgxQlL1P92YkpKi5ORktzGbraHy8wurtd9f87V3EqbX780CAvwVFtZABQVFKi0t83Q5%2BAV6473ojffy5d546s19rbzJ/Ze2bt2q6dOn/%2BY2MTEx2rp1qyQpIiJCTqfTbd7pdKpNmzaVPmZUVFSFy4EOxxmVlPjWD6VpdXH9paVldXLdvoDeeC96473oTeX51imQKjp48KCOHz%2BuXr16lY9t3rxZf/nLX9y2O3LkiGJjYyVJ8fHxysnJKZ8rLS3VgQMH1KlTJ2uKBgAAPq9WB6wDBw6oUaNGCgkJKR%2BrV6%2Benn32WX344YcqLi7W//zP/2jt2rUaNWqUJGnUqFFav3699u3bp6KiIr300kuqX7%2B%2B%2Bvbt66FVAAAAX1OrLxGeOnWq/N6ri/r376%2BZM2fqqaee0okTJ9SkSRPNnDlTAwYMkCT16dNHU6ZM0eTJk/XDDz8oISFBmZmZCgoK8sQSAACAD/Jz8fdqLOFwnDG%2BT5vNXzel7za%2B35qyeXKv39%2BolrDZ/BUREaz8/ELuV/Ay9MZ70Rvv5cu9ado01CPHrdWXCAEAADyBgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACG1dqA1a5dO8XHxyshIaH866mnnpIk7dmzRyNGjFCXLl00ZMgQbdiwwe21K1as0MCBA9WlSxeNGjVK%2B/fv98QSAACAj7J5uoCatGXLFl111VVuY3l5eXrooYc0a9Ys3Xrrrfr000/14IMPqmXLlkpISNB7772nJUuW6JVXXlG7du20YsUKjR8/Xtu2bVPDhg09tBIAAOBLau0ZrMvZuHGjWrRooREjRigwMFA9e/ZUcnKyVq9eLUnKysrS7bffrk6dOikoKEhjx46VJO3cudOTZQMAAB9Sq89gLVy4UJ999pnOnj2rm2%2B%2BWY899phycnLUoUMHt%2B06dOigzZs3S5JycnI0ePDg8jl/f3%2B1b99edrtdQ4YMqdRx8/Ly5HA43MZstoaKioqq5orcBQT4Vj622Xyr3uq42Btf61FdQG%2B8F73xXvSm6mptwLruuuvUs2dPPfvss/rmm280efJkzZ07V06nU9HR0W7bNmrUSPn5%2BZIkp9Op8PBwt/nw8PDy%2BcrIyspSRkaG21jkRaF5AAAQxElEQVRqaqomTpz4L66mdoiICPZ0CZYLC2vg6RJwGfTGe9Eb70VvKq/WBqysrKzy/27VqpWmTZumBx98UF27dv3d17pcrmodOyUlRcnJyW5jNltD5ecXVmu/v%2BZr7yRMr9%2BbBQT4KyysgQoKilRaWubpcvAL9MZ70Rvv5cu98dSb%2B1obsH7tqquuUmlpqfz9/eV0Ot3m8vPzFRkZKUmKiIioMO90OtWmTZtKHysqKqrC5UCH44xKSnzrh9K0urj%2B0tKyOrluX0BvvBe98V70pvJ86xRIJR04cEALFixwG8vNzVX9%2BvWVlJRU4bEL%2B/fvV6dOnSRJ8fHxysnJKZ8rLS3VgQMHyucBAAB%2BT60MWI0bN1ZWVpYyMzN14cIFHT16VC%2B88IJSUlI0bNgwHT9%2BXKtXr9ZPP/2kXbt2adeuXbrrrrskSaNGjdL69eu1b98%2BFRUV6aWXXlL9%2BvXVt29fzy4KAAD4jFp5iTA6OlqZmZlauHBheUAaPny40tLSFBgYqJdfflnz5s3T3LlzFRMTo%2Beee07XXnutJKlPnz6aMmWKJk%2BerB9%2B%2BEEJCQnKzMxUUFCQh1cFAAB8hZ%2Brund0o1IcjjPG92mz%2Beum9N3G91tTNk/u5ekSLGOz%2BSsiIlj5%2BYXcr%2BBl6I33ojfey5d707RpqEeOWysvEQIAAHgSAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMq7UB6/jx40pNTVViYqJ69uypxx57TAUFBfr222/Vrl07JSQkuH396U9/Kn/tpk2bdOutt6pz5866/fbb9eGHH3pwJQAAwNfYPF1ATRk/frzi4%2BP13nvv6cyZM0pNTdWzzz6rBx98UJJkt9sv%2BbqDBw9q%2BvTpysjI0A033KCtW7fq4Ycf1pYtW9SsWTMrlwAAAHxUrTyDVVBQoPj4eE2dOlXBwcFq1qyZhg8frr179/7ua1evXq2kpCQlJSUpMDBQQ4cOVdu2bbVhwwYLKgcAALVBrQxYYWFhmj9/vpo0aVI%2BduLECUVFRZV//%2Bijj6p379664YYbtHDhQhUXF0uScnJy1KFDB7f9dejQ4bJnvAAAAH6t1l4i/CW73a6VK1fqpZdeUv369dW5c2fddNNNevrpp3Xw4EFNmDBBNptNkyZNktPpVHh4uNvrw8PDdfjw4UofLy8vTw6Hw23MZmvoFvBMCAjwrXxss/lWvdVxsTe%2B1qO6gN54L3rjvehN1dX6gPXpp5/qwQcf1NSpU9WzZ09J0ltvvVU%2B37FjR40bN04vv/yyJk2aJElyuVzVOmZWVpYyMjLcxlJTUzVx4sRq7dfXRUQEe7oEy4WFNfB0CbgMeuO96I33ojeVV6sD1nvvvadHHnlEs2fP1m233XbZ7WJiYnTq1Cm5XC5FRETI6XS6zTudTkVGRlb6uCkpKUpOTnYbs9kaKj%2B/sGoL%2BB2%2B9k7C9Pq9WUCAv8LCGqigoEilpWWeLge/QG%2B8F73xXr7cG0%2B9ua%2B1Aevvf/%2B7pk%2BfrhdeeEG9e/cuH9%2BzZ4/27dtX/mlCSTpy5IhiYmLk5%2Ben%2BPh47d%2B/321fdrtdQ4YMqfSxo6KiKlwOdDjOqKTEt34oTauL6y8tLauT6/YF9MZ70RvvRW8qz7dOgVRSSUmJHn/8cU2bNs0tXElSaGioli5dqnfeeUfFxcWy2%2B3605/%2BpFGjRkmS7rrrLv3v//6v3n//ff30009as2aNvv76aw0dOtQTSwEAAD7Iz1XdG4680N69e/Vv//Zvql%2B/foW5LVu26MCBA8rIyNDXX3%2Bt0NBQ3X333XrggQfk7/9z3ty2bZsWLlyo48ePq3Xr1po1a5a6d%2B9erZocjjPVev2l2Gz%2Buil9t/H91pTNk3t5ugTL2Gz%2BiogIVn5%2BIe/2vAy98V70xnv5cm%2BaNg31yHFr5SXCbt266csvv7zsfExMjG666abLzg8YMEADBgyoidIAAEAdUCsvEQIAAHgSAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIbZPF0A6o6bn/8fT5dQJZsn9/J0CQAAH8UZLAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDeJL7JRw/flxz587V559/roYNG2rw4MGaOnWq/P3Jo3UJT54HAPyrCFiXMGHCBMXFxWn79u364YcfNG7cODVp0kT33nuvp0sDAAA%2BgID1K3a7XYcOHdJrr72m0NBQhYaGasyYMXrjjTcIWPBqvnTGjbNtAGo7Atav5OTkKCYmRuHh4eVjcXFxOnr0qM6ePauQkJDf3UdeXp4cDofbmM3WUFFRUUZrDQjgkiV8k83muZ/di783/P54H3rjvehN1RGwfsXpdCosLMxt7GLYys/Pr1TAysrKUkZGhtvYww8/rAkTJpgrVD8HuXua/UMpKSnGwxuqJy8vT1lZWfTGC%2BXl5emNN16hN16I3ngvelN1RNFLcLlc1Xp9SkqK1q1b5/aVkpJiqLp/cjgcysjIqHC2DJ5Hb7wXvfFe9MZ70Zuq4wzWr0RGRsrpdLqNOZ1O%2Bfn5KTIyslL7iIqKIuEDAFCHcQbrV%2BLj43XixAmdPn26fMxut6t169YKDg72YGUAAMBXELB%2BpUOHDkpISNDChQt19uxZ5ebm6rXXXtOoUaM8XRoAAPARAXPmzJnj6SK8zR/%2B8AdlZ2frqaee0rvvvqsRI0bo/vvvl5%2Bfn6dLqyA4OFjXX389Z9e8EL3xXvTGe9Eb70VvqsbPVd07ugEAAOCGS4QAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwvNzx48f1H//xH0pMTNSNN96o5557TmVlZZfcdsWKFRo4cKC6dOmiUaNGaf/%2B/RZXW7dUpTdvvvmmBg4cqM6dO2vYsGHavn27xdXWLVXpzUXff/%2B9OnfurCVLllhUZd1Uld7k5ubq7rvvVqdOnZSUlKTXX3/d2mLrmMr2pqysTIsXL1ZycrI6d%2B6sW2%2B9VZs2bfJAxV7OBa82fPhw1%2BOPP%2B4qKChwHT161DVgwADXq6%2B%2BWmG7HTt2uLp16%2Bbat2%2Bfq6ioyPXyyy%2B7evXq5SosLPRA1XVDZXuzZcsWV9euXV179%2B51XbhwwfX222%2B74uLiXMeOHfNA1XVDZXvzSw8//LCra9eursWLF1tUZd1U2d4UFRW5%2Bvbt61q%2BfLnr3Llzrs8//9w1ZMgQ1%2BHDhz1Qdd1Q2d6sXLnS1bt3b1dubq6rpKTE9d5777k6dOjgOnjwoAeq9l6cwfJidrtdhw4d0rRp0xQaGqoWLVpozJgxysrKqrBtVlaWbr/9dnXq1ElBQUEaO3asJGnnzp1Wl10nVKU358%2Bf15QpU9S1a1fVq1dPd955p4KDg7Vv3z4PVF77VaU3F%2B3atUuHDx9W3759rSu0DqpKbzZv3qyQkBCNHTtWDRo0UMeOHZWdna1WrVp5oPLaryq9ycnJUdeuXXXNNdcoICBAN954oxo1aqQvv/zSA5V7LwKWF8vJyVFMTIzCw8PLx%2BLi4nT06FGdPXu2wrYdOnQo/97f31/t27eX3W63rN66pCq9GTZsmP74xz%2BWf19QUKDCwkJFR0dbVm9dUpXeSD8H4CeffFJPPPGEbDablaXWOVXpzaeffqq2bdtqxowZ6tatmwYNGqQNGzZYXXKdUZXe9O3bVx9//LEOHjyoCxcuaMeOHSoqKtL1119vddlejYDlxZxOp8LCwtzGLv7w5%2BfnV9j2l78YF7f99XYwoyq9%2BSWXy6XHH39cnTp14n9GNaSqvVm6dKmuu%2B463XDDDZbUV5dVpTcnT57Ujh071LNnT%2B3evVvjxo3T9OnTdeDAAcvqrUuq0psBAwYoJSVFt912mxISEjR16lTNnz9fV1xxhWX1%2BgLernk5l8tVI9ui%2Bqr6711cXKzHHntMhw8f1ooVK2qoKkiV783hw4e1evVqbdy4sYYrwkWV7Y3L5VJcXJxuvfVWSdLw4cP11ltvacuWLW5n62FOZXuzfv16rV%2B/XqtXr1a7du20Z88eTZ06VVdccYU6duxYw1X6Ds5gebHIyEg5nU63MafTKT8/P0VGRrqNR0REXHLbX28HM6rSG%2Bnny1Djxo3Td999p1WrVqlJkyZWlVrnVLY3LpdLc%2BbM0YQJE9S0aVOry6yTqvJ707RpU4WGhrqNxcTEyOFw1HiddVFVerNy5UqlpKSoY8eOCgwMVN%2B%2BfXXDDTdwCfdXCFheLD4%2BXidOnNDp06fLx%2Bx2u1q3bq3g4OAK2%2Bbk5JR/X1paqgMHDqhTp06W1VuXVKU3LpdLaWlpstlsev311xUREWF1uXVKZXvz3Xff6ZNPPtHixYuVmJioxMREvfvuu3rllVc0fPhwT5Re61Xl96ZVq1b66quv3M6qHD9%2BXDExMZbVW5dUpTdlZWUqLS11G7tw4YIldfoSApYX69ChgxISErRw4UKdPXtWubm5eu211zRq1ChJ0qBBg7R3715J0qhRo7R%2B/Xrt27dPRUVFeumll1S/fn0%2BFVVDqtKbjRs36vDhw3rhhRcUGBjoybLrhMr2plmzZtq1a5feeeed8q/k5GSNHDlSmZmZHl5F7VSV35uhQ4cqPz9fy5Yt0/nz55Wdna2cnBwNHTrUk0uotarSm%2BTkZK1Zs0aHDh1SSUmJPvzwQ%2B3Zs0f9%2BvXz5BK8DvdgebnFixdr9uzZ6tWrl0JCQjRy5MjyT6QdPXpU586dkyT16dNHU6ZM0eTJk/XDDz8oISFBmZmZCgoK8mT5tVple7N27VodP368wk3tw4YN07x58yyvuy6oTG8CAgLUrFkzt9c1aNBAISEhXDKsQZX9vYmOjtbLL7%2Bsp59%2BWi%2B%2B%2BKKuvPJKLV26VFdffbUny6/VKtubcePGqaSkRKmpqTp9%2BrRiYmI0b9489ejRw5Plex0/F3dGAwAAGMUlQgAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAPAKu3fvVs%2BePZWWllal1w0cOFAJCQluX9dee63%2B%2B7//u4Yq/X08yR0AAHjc8uXLtWbNGjVv3rzKr926davb9998841SUlL0hz/8wVR5VcYZLAAA4HGBgYG/GbA2bdqkYcOG6brrrlO/fv2UlZV12X09/fTTuu%2B%2B%2B9SkSZOaKvd3cQYLAAB43OjRoy87Z7fbNWvWLC1ZskQ9evTQZ599pgceeEBt2rRRly5d3Lb96KOPdPDgQS1evLimS/5NnMECAABebd26derbt6969%2B6tgIAAdevWTTfffLPeeeedCtsuW7ZM9957r%2BrXr%2B%2BBSv%2BJM1gAAMCrHTt2THv27FFCQkL5mMvlUu/evd22%2B%2Bqrr7Rv3z69%2BOKLVpdYwf8H%2Bj2ziU/rh3gAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-96081159557793503”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>362</td> <td class=”number”>11.3%</td> <td>

<div class=”bar” style=”width:26%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10000.0</td> <td class=”number”>31</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5000.0</td> <td class=”number”>28</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2000.0</td> <td class=”number”>26</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7000.0</td> <td class=”number”>25</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4000.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>21000.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8000.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>15000.0</td> <td class=”number”>20</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>26000.0</td> <td class=”number”>20</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (529)</td> <td class=”number”>1393</td> <td class=”number”>43.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>1239</td> <td class=”number”>38.6%</td> <td>

<div class=”bar” style=”width:89%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-96081159557793503”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>362</td> <td class=”number”>11.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1000.0</td> <td class=”number”>18</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2000.0</td> <td class=”number”>26</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3000.0</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4000.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>5727000.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5896000.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6647000.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6929000.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8464000.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_AGRITRSM_RCT12”>AGRITRSM_RCT12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>643</td>

</tr> <tr>

<th>Unique (%)</th> <td>20.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>34.1%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>1095</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>276830</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>23723000</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>8.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-3361272401294183502”>

<img src=”data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAAtFJREFUeJzt2r1KI1EchvHXTVCxFivRSrAJiKTwIwHBQq1slCCIqOAFWAfL3EAEEbwCISo2GgsrCy1EhRRBsVIE8RKCMlssq8y6eSWQzIg8P0iRyZzwbx7OmZC2IAgCAfivX3EPAHxnybgH%2BFc6X254zWVhugWTAOwggEUggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggEEggJGMe4BmSOfLDa%2B5LEy3YBL8NG1BEARxDwF8VxyxAINAAINAAINAAINA0HRnZ2caGxvT%2Bvp6Q%2BumpqaUSqVCr8HBQR0cHLRo0q/9iJ958X3s7OyoVCqpv7%2B/4bUnJyeh94%2BPj8rlcspms80ar2HsIGiqjo4OG8jR0ZFmZ2c1NDSkyclJ7e7u1v2uQqGg1dVVdXd3t2rcL7GDoKmWlpbqflapVJTP57W5uanR0VFdX19rbW1NAwMDGh4eDt17cXGharWqYrHY6pEtdhBEZn9/XxMTE8pkMkokEkqn05qZmdHh4eGne7e3t7WysqL29vYYJv3ADoLIPDw86Pz8XKlU6v1aEATKZDKh%2B%2B7u7nRzc6Otra2oR/yEQBCZzs5OLSwsaGNjw95XLpc1MjKirq6uiCarjyMWItPX16fb29vQtefnZ729vYWunZ6eanx8PMrR6iIQRGZubk5XV1fa29tTrVZTtVrV/Px86OfdWq2m%2B/t79fb2xjjpB/7Ni6b6%2B3zx%2BvoqSUom/5ziK5WKJOn4%2BFjFYlFPT0/q6enR4uKilpeX39e/vLwom82qVCqFnlXiQiCAwRELMAgEMAgEMAgEMAgEMAgEMAgEMAgEMAgEMAgEMH4DDSqdXFxHngwAAAAASUVORK5CYII%3D”>

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<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-3361272401294183502”

aria-controls=”quantiles-3361272401294183502” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-3361272401294183502” aria-controls=”histogram-3361272401294183502”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-3361272401294183502” aria-controls=”common-3361272401294183502”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-3361272401294183502” aria-controls=”extreme-3361272401294183502”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-3361272401294183502”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>13000</td>

</tr> <tr>

<th>Median</th> <td>65000</td>

</tr> <tr>

<th>Q3</th> <td>233000</td>

</tr> <tr>

<th>95-th percentile</th> <td>1284200</td>

</tr> <tr>

<th>Maximum</th> <td>23723000</td>

</tr> <tr>

<th>Range</th> <td>23723000</td>

</tr> <tr>

<th>Interquartile range</th> <td>220000</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>775710</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.8021</td>

</tr> <tr>

<th>Kurtosis</th> <td>404.2</td>

</tr> <tr>

<th>Mean</th> <td>276830</td>

</tr> <tr>

<th>MAD</th> <td>332680</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>15.141</td>

</tr> <tr>

<th>Sum</th> <td>585500000</td>

</tr> <tr>

<th>Variance</th> <td>601730000000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-3361272401294183502”>

<img 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JioiooKVVdXy263%2B%2BYkqXHjxnU6VlZWlvr06eM35nBEqays0sBK/i3YPvmYXr/V7Hab4uIaqrz8uKqrawJdTsij39ai39ai39YJ1If7kAxYr7/%2BuuLj43Xrrbf6xkpKSpSSkqKIiAi1bdtWxcXFuuaaayRJ5eXlOnDggDp37qzk5GR5vV7t3r1bnTp1kiS5XC7FxcWpVatWdTp%2BUlJSrcuBbvcxVVVd3j9EobL%2B6uqakFlLMKDf1qLf1qLfoSu4ToHU0alTp/TMM8/I5XLp9OnTWrVqlf76179qxIgRkqTs7GwtXrxYJSUlqqioUEFBgTp06KC0tDQlJiaqf//%2BeuGFF3T06FEdOXJEc%2BfO1dChQ%2BVwhGQeBQAAhgVtYkhLS5MkVVVVSZI2bNgg6buzTSNHjlRlZaXGjRsnt9utK6%2B8UnPnzlVqaqokacSIEXK73brrrrtUWVmp9PR0v3umnn76aT311FPq27evGjRooNtuu63Ww0wBAADOJ8xb3zu6USdu9zHj%2B3Q4bLqxYIvx/V4sa8b3CnQJ9eJw2JSQEK2yskpO6VuAfluLfluLflunadPzP9LpYgrJS4QAAACBRMACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwywNWnz59VFhYqMOHD1t9aAAAAEtYHrDuvPNOrV69Wv369dO9996r9evXq6qqyuoyAAAALhrLA1Zubq5Wr16tP/3pT2rbtq2ee%2B45ZWZm6vnnn9e%2BffusLgcAAMC4gN2D1alTJ%2BXn52vTpk164okn9Kc//Um33nqrRo0apU8//TRQZQEAANRbwALW6dOntXr1at13333Kz89Xs2bN9Pjjj6tDhw7KycnRypUrA1UaAABAvTisPmBJSYmKior05ptvqrKyUv3799drr72mq6%2B%2B2rdNjx49NGXKFN1%2B%2B%2B1WlwcAAFBvlgesAQMGqFWrVrr//vs1aNAgNWrUqNY2mZmZOnr06PfuZ8uWLcrPz1d6erpmz57tN7d%2B/XoVFhbq4MGDSkpK0qhRozR8%2BHBJ0pw5c/Tyyy/L4fBf%2BqZNm9SkSROdPHlSzz77rDZv3qyTJ08qPT1dU6dOVUJCQj1XDgAALheWB6zFixfrmmuu%2BcHttm/fft65RYsWqaioSC1atKg19%2Bmnn2rixIn69a9/rd69e%2Bv9999Xbm6urrrqKnXv3l2SdMcdd2jGjBnn3Pfs2bNVXFwsp9Ophg0bavLkyXr88cc1f/78Oq4QAABc7iy/B6t9%2B/YaPXq0NmzY4Bv73e9%2Bp/vuu08ej6dO%2B4iIiDhvwPJ4PLr//vvVr18/ORwOZWZmql27dtq2bdsP7reqqkpFRUV64IEHdMUVV6hRo0YaP368Nm/erC%2B//LLuiwQAAJc1y89gTZ8%2BXceOHVObNm18Y71799aWLVs0Y8aM855Z%2Br9Gjhx53rnrr79e119/ve/7qqoqud1uNWvWzDf2r3/9SyNGjNBnn32mK664Qo8//rgyMjJ04MABHTt2TJ06dfJt27p1a0VGRqq4uNhvH9%2BntLRUbrfbb8zhiFJSUlKdXl9XdntwPYjf4Qiues92pt/B1vdgRb%2BtRb%2BtRb9Dn%2BUB67333tPKlSv97mlq2bKlCgoKdNtttxk/XkFBgaKionTrrbdKkpo3b66UlBRNmDBBSUlJcjqdGj16tFasWOE7gxYXF%2Be3j7i4OJWVldX5mE6nU4WFhX5jubm5Gjt2bD1XE9wSEqIDXYIRcXENA13CZYV%2BW4t%2BW4t%2Bhy7LA9aJEycUERFRa9xms%2Bn48ePGjuP1elVQUKBVq1Zp8eLFvmMOGzZMw4YN822Xk5Ojt99%2BWytWrPCd%2BfJ6vfU6dlZWlvr06eM35nBEqayssl77PVuwffIxvX6r2e02xcU1VHn5cVVX1wS6nJBHv61Fv61Fv60TqA/3lgesHj16aMaMGZowYYLi4%2BMlSV9%2B%2BaVmzpzp96iG%2BqipqdHjjz%2BuTz/9VK%2B//rpSUlK%2Bd/vk5GSVlpYqMTFR0nf3cUVH//tfyDfffKPGjRvX%2BfhJSUm1Lge63cdUVXV5/xCFyvqrq2tCZi3BgH5bi35bi36HLssD1hNPPKF77rlH1157rWJiYlRTU6PKykqlpKTo97//vZFjPPfcc/r888/1%2Buuv13oMxMsvv6yuXbvq2muv9Y2VlJTo1ltvVUpKiuLj41VcXKzk5GRJ0meffaZTp04pNTXVSG0AACD0WR6wUlJS9Pbbb%2Buvf/2rDhw4IJvNplatWikjI0N2u73e%2B//oo4%2B0YsUKrV69%2BpzP2PJ4PJo6dapefvllJScna%2BnSpTpw4IAGDx4su92u4cOHa/78%2BUpLS1NkZKR%2B/etf68Ybb1STJk3qXRsAALg8WB6wJCk8PFz9%2BvX7j1%2BflpYm6bvfEJTke%2BSDy%2BXSsmXLdOzYMd1www1%2Br%2BnRo4d%2B%2B9vfasKECZK%2Bu/fK4/GoTZs2%2Bt3vfqfmzZtLksaOHavKykrdcccdqqqq0g033KApU6b8x7UCAIDLT5i3vnd0X6CDBw9q1qxZ%2Bvzzz3XixIla8%2B%2B8846V5VjG7T5mfJ8Oh003Fmwxvt%2BLZc34XoEuoV4cDpsSEqJVVlbJPRMWoN/Wot/Wot/Wado0NiDHDcg9WKWlpcrIyFBUVJTVhwcAALjoLA9YO3bs0DvvvOP7jT0AAIBQY/mDlBo3bsyZKwAAENIsD1j333%2B/CgsL6/0wTwAAgEuV5ZcI//rXv%2Brjjz/W8uXLdeWVV8pm8894f/zjH60uCQAAwCjLA1ZMTIzfH2MGAAAINZYHrOnTp1t9SAAAAEsF5K8F7927V3PmzNHjjz/uG/vnP/8ZiFIAAACMszxgffjhhxo4cKDWr1%2BvVatWSfru4aMjR44M2YeMAgCAy4vlAWv27Nl65JFHtHLlSoWFhUn67u8TzpgxQ3PnzrW6HAAAAOMsD1ifffaZsrOzJckXsCTp5ptvVklJidXlAAAAGGd5wIqNjT3n3yAsLS1VeHi41eUAAAAYZ3nA6tatm5577jlVVFT4xvbt26f8/Hxde%2B21VpcDAABgnOWPaXj88cd19913Kz09XdXV1erWrZuOHz%2Butm3basaMGVaXAwAAYJzlAat58%2BZatWqV3n33Xe3bt0%2BRkZFq1aqVevXq5XdPFgAAQLCyPGBJUoMGDdSvX79AHBoAAOCiszxg9enT53vPVPEsLAAAEOwsD1i33nqrX8Cqrq7Wvn375HK5dPfdd1tdDgAAgHGWB6yJEyeec3zdunX629/%2BZnE1AAAA5gXkbxGeS79%2B/fT2228HugwAAIB6u2QC1s6dO%2BX1egNdBgAAQL1ZfolwxIgRtcaOHz%2BukpIS3XTTTVaXAwAAYJzlAatly5a1foswIiJCQ4cO1bBhw6wuBwAAwDjLAxZPawcAAKHO8oD15ptv1nnbQYMGXcRKAAAALg7LA9akSZNUU1NT64b2sLAwv7GwsLAfDFhbtmxRfn6%2B0tPTNXv2bL%2B51atXa968efriiy/UqlUrPfzww8rIyJAk1dTU6MUXX9SqVatUXl6uzp07a8qUKUpJSZEkeTweTZkyRX//%2B99ls9mUmZmpyZMnKzIy0kQLAABAiLP8twhfeeUVZWRkaOnSpdq2bZv%2B8Y9/aMmSJbr%2B%2Buu1aNEiffrpp/r000%2B1ffv2793PokWLNG3aNLVo0aLW3K5du5Sfn6%2BJEydq69atysnJ0YMPPqgjR45IkpYuXaqVK1dq4cKF2rRpk1q2bKnc3FxfwJs8ebKOHz%2BuVatWadmyZSopKVFBQYH5ZgAAgJBkecCaMWOGpk2bpquvvloxMTGKjY1V9%2B7d9fTTT2vmzJkKDw/3fX2fiIgIFRUVnTNgvfHGG8rMzFRmZqYiIiI0cOBAtWvXTitWrJAkOZ1O5eTkqHXr1oqJiVFeXp5KSkq0fft2ffXVV9qwYYPy8vKUmJioZs2a6YEHHtCyZct0%2BvTpi9ITAAAQWiwPWPv371d8fHyt8bi4OB06dKjO%2Bxk5cqRiY2PPOVdcXKyOHTv6jXXs2FEul0snTpzQnj17/OZjYmLUokULuVwu7dq1S3a7Xe3bt/fNd%2BrUSd9%2B%2B6327t1b5/oAAMDly/J7sJKTkzVjxgyNGzdOCQkJkqTy8nK99NJL%2BvGPf2zkGB6Pp1aIi4%2BP1549e/TNN9/I6/Wec76srEyNGjVSTEyM36MkzmxbVlZWp%2BOXlpbK7Xb7jTkcUUpKSvpPlnNedvsl85zYOnE4gqves53pd7D1PVjRb2vRb2vR79BnecB64oknNGHCBDmdTkVHR8tms6miokKRkZGaO3euseP80FPhv2%2B%2Bvk%2BUdzqdKiws9BvLzc3V2LFj67XfYJeQEB3oEoyIi2sY6BIuK/TbWvTbWvQ7dFkesDIyMrR582a9%2B%2B67OnLkiLxer5o1a6brrrvuvJf8LlRCQoI8Ho/fmMfjUWJioho1aiSbzXbO%2BcaNGysxMVEVFRWqrq6W3W73zUlS48aN63T8rKws9enTx2/M4YhSWVnlf7qkcwq2Tz6m1281u92muLiGKi8/rurqmkCXE/Lot7Xot7Xot3UC9eHe8oAlSQ0bNlTfvn115MgR36MRTEpNTdWOHTv8xlwulwYMGKCIiAi1bdtWxcXFuuaaayR9d4nywIED6ty5s5KTk%2BX1erV792516tTJ99q4uDi1atWqTsdPSkqqdTnQ7T6mqqrL%2B4coVNZfXV0TMmsJBvTbWvTbWvQ7dFl%2BCuTEiRPKz89X165ddcstt0j6LuDce%2B%2B9Ki8vN3KM4cOH64MPPtDmzZt18uRJFRUVaf/%2B/Ro4cKAkKTs7W4sXL1ZJSYkqKipUUFCgDh06KC0tTYmJierfv79eeOEFHT16VEeOHNHcuXM1dOhQORwByaMAACDIWJ4Ynn/%2Bee3atUsFBQV69NFHfePV1dUqKCjQ008/Xaf9pKWlSZKqqqokSRs2bJD03dmmdu3aqaCgQNOnT9ehQ4fUpk0bLViwQE2bNpX03R%2Bcdrvduuuuu1RZWan09HS/e6aefvppPfXUU%2Brbt68aNGig2267TXl5eUbWDwAAQl%2BYt753dF%2BgjIwMLVmyRC1btlSXLl18DxQ9cuSIBg0apK1bt1pZjmXc7mPG9%2Blw2HRjwRbj%2B71Y1ozvFegS6sXhsCkhIVplZZWc0rcA/bYW/bYW/bZO06Zm7u%2B%2BUJZfIqysrFTLli1rjScmJurbb7%2B1uhwAAADjLA9YP/7xj/W3v/1Nkv/jENauXasf/ehHVpcDAABgnOX3YP385z/XQw89pDvvvFM1NTV69dVXtWPHDq1bt06TJk2yuhwAAADjLA9YWVlZcjgcWrJkiex2u%2BbPn69WrVqpoKBAN998s9XlAAAAGGd5wDp69KjuvPNO3XnnnVYfGgAAwBKW34PVt2/fev8pGgAAgEuZ5QErPT1da9assfqwAAAAlrH8EuEVV1yhZ599VgsXLtSPf/xjNWjQwG9%2B1qxZVpcEAABglOUBa8%2BePbrqqqskSWVlZVYfHgAA4KKzLGDl5eVp9uzZ%2Bv3vf%2B8bmzt3rnJzc60qAQAAwBKW3YO1cePGWmMLFy606vAAAACWsSxgnes3B/ltQgAAEIosC1hhYWF1GgMAAAh2lj%2BmAQAAINQRsAAAAAyz7LcIT58%2BrQkTJvzgGM/BAgAAwc6ygHX11VertLT0B8cAAACCnWUB6/8%2B/woAACCUcQ8WAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDDLHjRqpX/84x%2B65557/Ma8Xq9Onz6txYsXa%2BTIkQoPD/eb/9WvfqVbbrlFkrR48WItXbpUbrdb7du316RJk5SammpZ/QAAILiFZMDq0aOHXC6X39j8%2BfO1e/duSVJycrI2btx4ztdu3LhRc%2BbM0SuvvKL27dtr8eLFGj16tNavX6%2BoqKiLXjsAAAh%2Bl8Ulwv/93//Vq6%2B%2BqkcfffQHt3U6nRoyZIi6dOmiyMhI3XvvvZKkTZs2XewyAQBAiAjJM1hne/HFF3XnnXfqRz/6kQ4ePKjKykrl5uZq27ZtCg8P1z333KOcnByFhYWpuLhYt956q%2B%2B1NptNHTp0kMvl0oABA%2Bp0vNLSUrndbr8xhyNKSUlJRtdltwdXPnY4gqves53pd7D1PVjRb2sIagISAAAU4klEQVTRb2vR79AX8gHriy%2B%2B0Pr167V%2B/XpJUkxMjNq1a6e7775bs2fP1t///neNGzdOsbGxGjp0qDwej%2BLj4/32ER8fr7Kysjof0%2Bl0qrCw0G8sNzdXY8eOrf%2BCglhCQnSgSzAiLq5hoEu4rNBva9Fva9Hv0BXyAWvp0qW66aab1LRpU0lSp06d9Pvf/943n5GRoREjRmj58uUaOnSopO9uiK%2BPrKws9enTx2/M4YhSWVllvfZ7tmD75GN6/Vaz222Ki2uo8vLjqq6uCXQ5IY9%2BW4t%2BW4t%2BWydQH%2B5DPmCtW7dO%2Bfn537tNcnKy1q1bJ0lKSEiQx%2BPxm/d4PGrbtm2dj5mUlFTrcqDbfUxVVZf3D1GorL%2B6uiZk1hIM6Le16Le16HfoCq5TIBdo165dOnTokHr16uUbW7Nmjf7whz/4bbd3716lpKRIklJTU1VcXOybq66u1s6dO9WlSxdrigYAAEEvpAPWzp071ahRI8XExPjGGjRooJkzZ%2Bq9997T6dOn9f7772vZsmXKzs6WJGVnZ%2BvNN9/UJ598ouPHj2vevHkKDw9X7969A7QKAAAQbEL6EuFXX33lu/fqjH79%2BumJJ57QM888o8OHD6tJkyZ64okndNNNN0mSrr/%2Bej388MMaP368vv76a6WlpWnhwoWKjIwMxBIAAEAQCvPW945u1Inbfcz4Ph0Om24s2GJ8vxfLmvG9fnijS5jDYVNCQrTKyiq5Z8IC9Nta9Nta9Ns6TZvGBuS4IX2JEAAAIBAIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMNCNmC1b99eqampSktL830988wzkqQPP/xQQ4cOVbdu3TRgwACtWLHC77WLFy9W//791a1bN2VnZ2vHjh2BWAIAAAhSjkAXcDGtXbtWV155pd9YaWmpHnjgAU2aNEm33367PvroI40ZM0atWrVSWlqaNm7cqDlz5uiVV15R%2B/bttXjxYo0ePVrr169XVFRUgFYCAACCSciewTqflStXqmXLlho6dKgiIiLUs2dP9enTR2%2B88YYkyel0asiQIerSpYsiIyN17733SpI2bdoUyLIBAEAQCemANWvWLPXu3Vvdu3fX5MmTVVlZqeLiYnXs2NFvu44dO/ouA549b7PZ1KFDB7lcLktrBwAAwStkLxH%2B9Kc/Vc%2BePTVz5kwdPHhQ48eP19SpU%2BXxeNSsWTO/bRs1aqSysjJJksfjUXx8vN98fHy8b74uSktL5Xa7/cYcjiglJSX9h6s5N7s9uPKxwxFc9Z7tTL%2BDre/Bin5bi35bi36HvpANWE6n0/fPrVu31sSJEzVmzBhdffXVP/har9db72MXFhb6jeXm5mrs2LH12m%2BwS0iIDnQJRsTFNQx0CZcV%2Bm0t%2Bm0t%2Bh26QjZgne3KK69UdXW1bDabPB6P31xZWZkSExMlSQkJCbXmPR6P2rZtW%2BdjZWVlqU%2BfPn5jDkeUysoq/8Pqzy3YPvmYXr/V7Hab4uIaqrz8uKqrawJdTsij39ai39ai39YJ1If7kAxYO3fu1IoVK/TYY4/5xkpKShQeHq7MzEz9%2Bc9/9tt%2Bx44d6tKliyQpNTVVxcXFGjx4sCSpurpaO3fu1NChQ%2Bt8/KSkpFqXA93uY6qqurx/iEJl/dXVNSGzlmBAv61Fv61Fv0NXcJ0CqaPGjRvL6XRq4cKFOnXqlPbt26cXX3xRWVlZuuOOO3To0CG98cYbOnnypN599129%2B%2B67Gj58uCQpOztbb775pj755BMdP35c8%2BbNU3h4uHr37h3YRQEAgKARkmewmjVrpoULF2rWrFm%2BgDR48GDl5eUpIiJCCxYs0LRp0zR16lQlJyfr%2Beef109%2B8hNJ0vXXX6%2BHH35Y48eP19dff620tDQtXLhQkZGRAV4VAAAIFmHe%2Bt7RjTpxu48Z36fDYdONBVuM7/diWTO%2BV6BLqBeHw6aEhGiVlVVySt8C9Nta9Nta9Ns6TZvGBuS4IXmJEAAAIJAIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIaFbMA6dOiQcnNzlZ6erp49e%2Bqxxx5TeXm5vvjiC7Vv315paWl%2BX7/5zW98r129erVuv/12de3aVUOGDNF7770XwJUAAIBg4wh0ARfL6NGjlZqaqo0bN%2BrYsWPKzc3VzJkzNWbMGEmSy%2BU65%2Bt27dql/Px8FRYW6mc/%2B5nWrVunBx98UGvXrlXz5s2tXAIAAAhSIXkGq7y8XKmpqZowYYKio6PVvHlzDR48WNu2bfvB177xxhvKzMxUZmamIiIiNHDgQLVr104rVqywoHIAABAKQvIMVlxcnKZPn%2B43dvjwYSUlJfm%2Bf/TRR/XBBx%2BoqqpKw4YN09ixY9WgQQMVFxcrMzPT77UdO3Y87xmvcyktLZXb7fYbczii/I5vgt0eXPnY4Qiues92pt/B1vdgRb%2BtRb%2BtRb9DX0gGrLO5XC4tWbJE8%2BbNU3h4uLp27aobb7xRzz77rHbt2qWHHnpIDodD48aNk8fjUXx8vN/r4%2BPjtWfPnjofz%2Bl0qrCw0G8sNzdXY8eONbKeYJWQEB3oEoyIi2sY6BIuK/TbWvTbWvQ7dIV8wProo480ZswYTZgwQT179pQk/fGPf/TNd%2B7cWffff78WLFigcePGSZK8Xm%2B9jpmVlaU%2Bffr4jTkcUSorq6zXfs8WbJ98TK/fana7TXFxDVVeflzV1TWBLifk0W9r0W9r0W/rBOrDfUgHrI0bN%2BqRRx7R5MmTNWjQoPNul5ycrK%2B%2B%2Bkper1cJCQnyeDx%2B8x6PR4mJiXU%2BblJSUq3LgW73MVVVXd4/RKGy/urqmpBZSzCg39ai39ai36EruE6BXICPP/5Y%2Bfn5evHFF/3C1Ycffqh58%2Bb5bbt3714lJycrLCxMqamp2rFjh9%2B8y%2BVSly5dLKkbAAAEv5AMWFVVVXryySc1ceJEZWRk%2BM3FxsZq7ty5euutt3T69Gm5XC795je/UXZ2tiRp%2BPDh%2BuCDD7R582adPHlSRUVF2r9/vwYOHBiIpQAAgCAU5q3vDUeXoG3btum//uu/FB4eXmtu7dq12rlzpwoLC7V//37Fxsbqrrvu0n333Seb7bu8uX79es2aNUuHDh1SmzZtNGnSJPXo0aNeNbndx%2Br1%2BnNxOGy6sWCL8f1eLGvG9wp0CfXicNiUkBCtsrJKTulbgH5bi35bi35bp2nT2IAcNyTvwerevbv%2B9a9/nXc%2BOTlZN95443nnb7rpJt10000XozQAAHAZCMlLhAAAAIFEwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgHUOhw4d0i9/%2BUulp6frhhtu0PPPP6%2BamppAlwUAAIKEI9AFXIoeeughderUSRs2bNDXX3%2Bt%2B%2B%2B/X02aNNEvfvGLQJcW1G554f1Al3BB1ozvFegSAABBijNYZ3G5XNq9e7cmTpyo2NhYtWzZUjk5OXI6nYEuDQAABAnOYJ2luLhYycnJio%2BP94116tRJ%2B/btU0VFhWJiYn5wH6WlpXK73X5jDkeUkpKSjNZqt5OPL6ZgO%2BP2l4nXBboEo868v3mfW4N%2BW4t%2Bhz4C1lk8Ho/i4uL8xs6ErbKysjoFLKfTqcLCQr%2BxBx98UA899JC5QvVdkLu7%2BefKysoyHt5QW2lpqZxOJ/22SGlpqV577RX6bRH6bS36HfqIzufg9Xrr9fqsrCwtX77c7ysrK8tQdf/mdrtVWFhY62wZLg76bS36bS36bS36Hfo4g3WWxMREeTwevzGPx6OwsDAlJibWaR9JSUl8IgEA4DLGGayzpKam6vDhwzp69KhvzOVyqU2bNoqOjg5gZQAAIFgQsM7SsWNHpaWladasWaqoqFBJSYleffVVZWdnB7o0AAAQJOxTpkyZEugiLjXXXXedVq1apWeeeUZvv/22hg4dqlGjRiksLCzQpdUSHR2ta665hrNrFqHf1qLf1qLf1qLfoS3MW987ugEAAOCHS4QAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwLnGHDh3SL3/5S6Wnp%2BuGG27Q888/r5qamnNuu3jxYvXv31/dunVTdna2duzYYXG1wa%2Bu/Z4zZ446dOigtLQ0v6%2BvvvoqAFUHry1btqhnz57Ky8v73u1qamo0e/Zs9e3bVz169NCoUaN08OBBi6oMHXXt92OPPeb7u6xnvrp3725RlaHj0KFDys3NVXp6unr27KnHHntM5eXl59x29erVuv3229W1a1cNGTJE7733nsXVwjQC1iXuoYceUrNmzbRhwwa9%2Buqr2rBhg1577bVa223cuFFz5szRr371K33wwQe64YYbNHr0aH377bcBqDp41bXfknTHHXfI5XL5fTVp0sTiioPXokWLNG3aNLVo0eIHt126dKlWrlyphQsXatOmTWrZsqVyc3PFX/qquwvptySNGTPG7729bdu2i1xh6Bk9erTi4uK0ceNGLV%2B%2BXJ9//rlmzpxZa7tdu3YpPz9fEydO1NatW5WTk6MHH3xQR44cCUDVMIWAdQlzuVzavXu3Jk6cqNjYWLVs2VI5OTlyOp21tnU6nRoyZIi6dOmiyMhI3XvvvZKkTZs2WV120LqQfqP%2BIiIiVFRUVKf/4TudTuXk5Kh169aKiYlRXl6eSkpKtH37dgsqDQ0X0m/UX3l5uVJTUzVhwgRFR0erefPmGjx48DmD6htvvKHMzExlZmYqIiJCAwcOVLt27bRixYoAVA5TCFiXsOLiYiUnJys%2BPt431qlTJ%2B3bt08VFRW1tu3YsaPve5vNpg4dOsjlcllWb7C7kH5L0r/%2B9S%2BNGDFC3bp104ABAzilf4FGjhyp2NjYH9zuxIkT2rNnj9/7OyYmRi1atOD9fQHq2u8ztm7dqkGDBqlr164aOnQotxxcoLi4OE2fPt3vrPbhw4eVlJRUa9uz//stSR07duT9HeQIWJcwj8ejuLg4v7Ez//MvKyurte3/DQZntj17O5zfhfS7efPmSklJ0cyZM/X%2B%2B%2B9r2LBhGj16tPbu3WtZvZeLb775Rl6vl/e3hVJSUtSiRQstWLBAW7ZsUffu3XXPPffQ73pwuVxasmSJxowZU2uO/36HJgLWJe5C7jHhfpT6q2sPhw0bppdeekktWrRQw4YNlZOTow4dOnBK/yLi/W2d3NxcPffcc2rWrJliYmL0yCOPKDw8XBs2bAh0aUHpo48%2B0qhRozRhwgT17NnznNvw/g49BKxLWGJiojwej9%2BYx%2BNRWFiYEhMT/cYTEhLOue3Z2%2BH8LqTf55KcnKzS0tKLVd5lq1GjRrLZbOf8d9O4ceMAVXV5sdvtuuKKK3h//wc2btyoX/7yl3riiSc0cuTIc27Df79DEwHrEpaamqrDhw/r6NGjvjGXy6U2bdooOjq61rbFxcW%2B76urq7Vz50516dLFsnqD3YX0%2B%2BWXX9aHH37oN1ZSUqKUlBRLar2cREREqG3btn7v7/Lych04cECdO3cOYGWhyev1avr06dq9e7dv7NSpUzpw4ADv7wv08ccfKz8/Xy%2B%2B%2BKIGDRp03u1SU1Nr3ePmcrn473eQI2Bdws48h2bWrFmqqKhQSUmJXn31VWVnZ0uSbr75Zt9vpGRnZ%2BvNN9/UJ598ouPHj2vevHkKDw9X7969A7iC4HIh/fZ4PJo6dar27t2rkydP6re//a0OHDigwYMHB3IJIePLL7/UzTff7HvWVXZ2thYvXqySkhJVVFSooKDA9xwy1N//7XdYWJi%2B%2BOILTZ06VV9%2B%2BaUqKytVUFCgBg0aqF%2B/foEuNWhUVVXpySef1MSJE5WRkVFr/u6779bq1aslScOHD9cHH3ygzZs36%2BTJkyoqKtL%2B/fs1cOBAq8uGQY5AF4Dv99JLL2ny5Mnq1auXYmJiNGLECP385z%2BXJO3bt8/3nKvrr79eDz/8sMaPH6%2Bvv/5aaWlpWrhwoSIjIwNZftCpa78nTJggScrJyZHH41GbNm30u9/9Ts2bNw9Y7cHmTDiqqqqSJN/9PS6XS6dPn9a%2Bfft06tQpSdKIESPkdrt11113qbKyUunp6SosLAxM4UHqQvr97LPPaubMmRoyZIgqKirUuXNnvfbaa4qKigpM8UHok08%2BUUlJiaZNm6Zp06b5za1du1YHDx7UN998I0lq166dCgoKNH36dB06dEht2rTRggUL1LRp00CUDkPCvNxZBwAAYBSXCAEAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAOCSsGXLFvXs2VN5eXkX9Lr%2B/fsrLS3N7%2BsnP/mJ/vznP1%2BkSn8YT3IHAAABt2jRIhUVFalFixYX/Np169b5fX/w4EFlZWXpuuuuM1XeBeMMFgAACLiIiIjvDVirV6/WHXfcoZ/%2B9Kfq27evnE7neff17LPP6p577lGTJk0uVrk/iDNYAAAg4EaOHHneOZfLpUmTJmnOnDm69tpr9c9//lP33Xef2rZtq27duvltu3XrVu3atUsvvfTSxS75e3EGCwAAXNKWL1%2Bu3r17KyMjQ3a7Xd27d9ctt9yit956q9a28%2BfP1y9%2B8QuFh4cHoNJ/4wwWAAC4pB04cEAffvih0tLSfGNer1cZGRl%2B23322Wf65JNP9PLLL1tdYi3/D6xTOlJ7HSD9AAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-3361272401294183502”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>263</td> <td class=”number”>8.2%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6000.0</td> <td class=”number”>31</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14000.0</td> <td class=”number”>26</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3000.0</td> <td class=”number”>26</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10000.0</td> <td class=”number”>25</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11000.0</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2000.0</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4000.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16000.0</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9000.0</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (632)</td> <td class=”number”>1634</td> <td class=”number”>50.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>1095</td> <td class=”number”>34.1%</td> <td>

<div class=”bar” style=”width:67%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-3361272401294183502”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>263</td> <td class=”number”>8.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1000.0</td> <td class=”number”>12</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2000.0</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3000.0</td> <td class=”number”>26</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4000.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>5393000.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5433000.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6416000.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6449000.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>23723000.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_BERRY_ACRES07”>BERRY_ACRES07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>235</td>

</tr> <tr>

<th>Unique (%)</th> <td>7.3%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>28.3%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>909</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>108.83</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>28904</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>26.3%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram654084034793660679”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives654084034793660679,#minihistogram654084034793660679”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives654084034793660679”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles654084034793660679”

aria-controls=”quantiles654084034793660679” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram654084034793660679” aria-controls=”histogram654084034793660679”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common654084034793660679” aria-controls=”common654084034793660679”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme654084034793660679” aria-controls=”extreme654084034793660679”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles654084034793660679”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>5</td>

</tr> <tr>

<th>Q3</th> <td>22</td>

</tr> <tr>

<th>95-th percentile</th> <td>137</td>

</tr> <tr>

<th>Maximum</th> <td>28904</td>

</tr> <tr>

<th>Range</th> <td>28904</td>

</tr> <tr>

<th>Interquartile range</th> <td>22</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>925.52</td>

</tr> <tr>

<th>Coef of variation</th> <td>8.504</td>

</tr> <tr>

<th>Kurtosis</th> <td>465.01</td>

</tr> <tr>

<th>Mean</th> <td>108.83</td>

</tr> <tr>

<th>MAD</th> <td>179.01</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>18.587</td>

</tr> <tr>

<th>Sum</th> <td>250430</td>

</tr> <tr>

<th>Variance</th> <td>856600</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram654084034793660679”>

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TJ07UsGHD1KxZszq3ycjI0OHDh51eGgAAgBWOF6zFixerV69e573d1q1bHVgNAACAfY5fg5WQkKAHHnjA%2B6dtJOl3v/ud7r//fnk8HqeXAwAAYJ3jBWv27Nk6evSo2rdv7x3r27evamtrNWfOHKeXAwAAYJ3jbxF%2B9tlnWrFihZo3b%2B4di4%2BPV15enm6//XanlwMAAGCd42ewqqurFRUVVXch4eGqqqpyejkAAADWOV6wevbsqTlz5ujIkSPesX/%2B85%2BaOXOmz0c1AAAAhCrH3yKcNm2afvnLX%2Br6669XdHS0amtrVVlZqdatW%2BvNN990ejkAAADWOV6wWrdurQ8//FCffvqp9u3bp/DwcLVt21Z9%2BvRRRESE08sBAACwzvGCJUmNGjXSzTffHIxDAwAA1DvHC9b%2B/fs1b948/e1vf1N1dXWd%2BU8%2B%2BcTpJQEAAFgVlGuwSkpK1KdPHzVu3NjpwwMAANQ7xwvW9u3b9cknn8jlcjl9aAAAAEc4/jENLVq04MwVAAC4qDlesCZOnKgFCxbIGOP0oQEAABzh%2BFuEn376qb766istX75cV199tcLDfTveO%2B%2B84/SSAAAArHK8YEVHR%2BvGG290%2BrAAAACOcbxgzZ492%2BlDAgAAOMrxa7Akac%2BePXrppZf05JNPese%2B/vrrYCwFAADAOscL1ubNmzV06FCtWbNGK1eulHT6w0fHjBnDh4wCAICLguMFa/78%2BXrssce0YsUKhYWFSTr99wnnzJmjhQsXOr0cAAAA6xwvWH/961%2BVlZUlSd6CJUm33nqrioqKnF4OAACAdY4XrKZNm57xbxCWlJSoUaNGTi8HAADAOscLVvfu3fXcc8%2BpoqLCO7Z3715NnTpV119/vdPLAQAAsM7xj2l48sknde%2B99yotLU01NTXq3r27qqqq1KFDB82ZM8fp5QAAAFjneMH62c9%2BppUrV2rjxo3au3evLr/8crVt21a9e/f2uSYLAAAgVDlesCTpsssu08033xyMQwMAANQ7xwtWv379znmmis/CAgAAoc7xgjVo0CCfglVTU6O9e/fK7Xbr3nvvdXo5AAAA1jlesKZMmXLG8Y8//lhffPGFw6sBAACwLyh/i/BMbr75Zn344YfBXgYAAMAFazAFa8eOHTLGBHsZAAAAF8zxtwhHjRpVZ6yqqkpFRUUaMGCA08sBAACwzvGCFR8fX%2Be3CKOiojRixAiNHDnS6eUAAABY53jB4tPaAQDAxc7xgvX%2B%2B%2B/7fdthw4bV40oAAADqh%2BMFa/r06aqtra1zQXtYWJjPWFhYGAULAACEJMcL1muvvabXX39dDzzwgBISEmSM0TfffKNXX31Vo0ePVlpamtNLAgAAsCoo12Dl5%2BerZcuW3rFrr71WrVu31rhx47Ry5UqnlwQAAGCV45%2BD9e233yo2NrbOeExMjA4cOOD0cgAAAKxzvGC1atVKc%2BbMUVlZmXesvLxc8%2BbN0zXXXOP0cgAAAKxz/C3CadOmafLkySooKFCTJk0UHh6uiooKXX755Vq4cKHTywEAALDO8YLVp08fbdiwQRs3btTBgwdljFHLli11ww03qGnTpk4vBwAAwDrHC5YkXXHFFerfv78OHjyo1q1bB2MJAAAA9cbxa7Cqq6s1depUdevWTbfddpuk09dgjR8/XuXl5U4vBwAAwDrHC9bcuXO1c%2BdO5eXlKTz8/w5fU1OjvLw8p5cDAABgneMF6%2BOPP9aLL76oW2%2B91ftHn2NiYjR79mytWbPG6eUAAABY53jBqqysVHx8fJ1xl8ulY8eOOb0cAAAA6xwvWNdcc42%2B%2BOILSfL524MfffSR/vVf/9Xp5QAAAFjneMH6xS9%2BoUceeUTPP/%2B8amtr9dvf/laTJ0/WtGnTdO%2B99wb0WJs2bVJ6erpyc3PrzK1atUpDhgxRt27dNHz4cH322WfeudraWs2fP1/9%2B/dXz549NW7cOO3fv9877/F4lJOTo/T0dPXp00fTp09XdXX1T980AAC4pDhesDIzMzV16lR9/vnnioiI0CuvvKIDBw4oLy9PWVlZfj/Oq6%2B%2BqmeffVZt2rSpM7dz505NnTpVU6ZM0eeff66xY8fq4Ycf1sGDByVJS5Ys0YoVK5Sfn6/169crPj5e2dnZ3jNqM2bMUFVVlVauXKn33ntPRUVFXIAPAAD85njBOnz4sO666y79/ve/19atW/XFF1/onXfe0a233hrQ40RFRWnZsmVnLFhLly5VRkaGMjIyFBUVpaFDh6pjx4764IMPJEkFBQUaO3as2rVrp%2BjoaOXm5qqoqEhbt27Vd999p7Vr1yo3N1cul0stW7bUQw89pPfee08nT560kgEAALi4Of5Bo/3799dXX33l/Q3Cn2rMmDFnnSssLFRGRobPWGJiotxut6qrq7V7924lJiZ656Kjo9WmTRu53W4dPXpUERERSkhI8M4nJSXp2LFj2rNnj8/42ZSUlKi0tNRnLDKyseLi4vzdnl8iIhzvxxckMjJ46/0%2Bq1DLLFjIy39kFRjy8h9ZBaah5eV4wUpLS9Pq1as1aNCgejuGx%2BNRbGysz1hsbKx2796tI0eOyBhzxvmysjI1a9ZM0dHRPgXw%2B9v%2B8A9Un0tBQYEWLFjgM5adna1Jkyb9lO1cNJo3bxLsJSgm5opgLyGkkJf/yCow5OU/sgpMQ8nL8YL1L//yL/r1r3%2Bt/Px8XXPNNbrssst85ufNm2flOD/8DcVA58933/PJzMxUv379fMYiIxurrKzygh73xxpKS/eX7f0HIiIiXDExV6i8vEo1NbVBW0eoIC//kVVgyMt/ZBWYs%2BUVrB/uHS9Yu3fv1s9//nNJ/p8RClTz5s3l8Xh8xjwej1wul5o1a6bw8PAzzrdo0UIul0sVFRWqqalRRESEd06SWrRo4dfx4%2BLi6rwdWFp6VKdOXdpfIA1h/zU1tQ1iHaGCvPxHVoEhL/%2BRVWAaSl6OFazc3FzNnz9fb775pnds4cKFys7Otn6s5ORkbd%2B%2B3WfM7XZr8ODBioqKUocOHVRYWKhevXpJOv23EPft26fU1FS1atVKxhjt2rVLSUlJ3vvGxMSobdu21tcKAAAuPo69x7Ru3bo6Y/n5%2BfVyrLvvvlt//vOftWHDBh0/flzLli3Tt99%2Bq6FDh0qSsrKytHjxYhUVFamiokJ5eXnq3LmzUlJS5HK5NHDgQP3mN7/R4cOHdfDgQS1cuFAjRoxQZKTjJ/wAAEAIcqwxnOm6pgu51iklJUWSdOrUKUnS2rVrJZ0%2B29SxY0fl5eVp9uzZOnDggNq3b69FixbpqquukiSNGjVKpaWluueee1RZWam0tDSfi9KfeeYZPfXUU%2Brfv78uu%2Bwy3X777Wf8MFMAAIAzcaxgneljGS7koxrcbvc55wcMGKABAwacdS2TJk0662/1NW3aVP/xH//xk9cGAAAubaH1a2gAAAAhgIIFAABgmWNvEZ48eVKTJ08%2B75itz8ECAAAIFscKVo8ePVRSUnLeMQAAgFDnWMH64edfAQAAXMy4BgsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLLtqClZCQoOTkZKWkpHj/zZo1S5K0efNmjRgxQt27d9fgwYP1wQcf%2BNx38eLFGjhwoLp3766srCxt3749GFsAAAAhKjLYC6hPH330ka6%2B%2BmqfsZKSEj300EOaPn26hgwZoi%2B//FIPPvig2rZtq5SUFK1bt04vvfSSXnvtNSUkJGjx4sV64IEHtGbNGjVu3DhIOwEAAKHkoj2DdTYrVqxQfHy8RowYoaioKKWnp6tfv35aunSpJKmgoEDDhw9Xly5ddPnll2v8%2BPGSpPXr1wdz2QAAIIRc1Gew5s2bp6%2B//loVFRW67bbb9MQTT6iwsFCJiYk%2Bt0tMTNTq1aslSYWFhRo0aJB3Ljw8XJ07d5bb7dbgwYP9Om5JSYlKS0t9xiIjGysuLu4Cd%2BQrIiK0%2BnFkZPDW%2B31WoZZZsJCX/8gqMOTlP7IKTEPL66ItWF27dlV6erqef/557d%2B/Xzk5OZo5c6Y8Ho9atmzpc9tmzZqprKxMkuTxeBQbG%2BszHxsb6533R0FBgRYsWOAzlp2drUmTJv3E3VwcmjdvEuwlKCbmimAvIaSQl//IKjDk5T%2ByCkxDyeuiLVgFBQXe/27Xrp2mTJmiBx98UD169DjvfY0xF3TszMxM9evXz2csMrKxysoqL%2Bhxf6yhtHR/2d5/ICIiwhUTc4XKy6tUU1MbtHWECvLyH1kFhrz8R1aBOVtewfrh/qItWD929dVXq6amRuHh4fJ4PD5zZWVlcrlckqTmzZvXmfd4POrQoYPfx4qLi6vzdmBp6VGdOnVpf4E0hP3X1NQ2iHWECvLyH1kFhrz8R1aBaSh5hdYpED/t2LFDc%2BbM8RkrKipSo0aNlJGRUedjF7Zv364uXbpIkpKTk1VYWOidq6mp0Y4dO7zzAAAA53NRFqwWLVqooKBA%2Bfn5OnHihPbu3asXXnhBmZmZuuOOO3TgwAEtXbpUx48f18aNG7Vx40bdfffdkqSsrCy9//772rJli6qqqvTyyy%2BrUaNG6tu3b3A3BQAAQsZF%2BRZhy5YtlZ%2Bfr3nz5nkL0p133qnc3FxFRUVp0aJFevbZZzVz5ky1atVKc%2BfOVadOnSRJN954ox599FHl5OTo0KFDSklJUX5%2Bvi6//PIg7woAAISKMHOhV3TDL6WlR60/ZmRkuG7J22T9cevL6pzeQTt2ZGS4mjdvorKyygbx3nxDR17%2BI6vAkJf/yCowZ8vrqquaBmU9F%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%2BI6vANLS8KFg/4vF4FBMT4zP2fdkqKyvzq2AVFBRowYIFPmMPP/ywHnnkEXsL1ekid%2B/P/qbMzEzr5e1iU1JSooKCArLyE3n5r6SkRG%2B88RpZ%2BYm8/EdWgWloeTWMmtfAGGMu6P6ZmZlavny5z7/MzExLq/s/paWlWrBgQZ2zZaiLrAJDXv4jq8CQl//IKjANLS/OYP2Iy%2BWSx%2BPxGfN4PAoLC5PL5fLrMeLi4hpEewYAAMHBGawfSU5OVnFxsQ4fPuwdc7vdat%2B%2BvZo0aRLElQEAgFBBwfqRxMREpaSkaN68eaqoqFBRUZF%2B%2B9vfKisrK9hLAwAAISLi6aeffjrYi2hobrjhBq1cuVKzZs3Shx9%2BqBEjRmjcuHEKCwsL9tLqaNKkiXr16sXZNT%2BQVWDIy39kFRhndP09AAALpElEQVTy8h9ZBaYh5RVmLvSKbgAAAPjgLUIAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYIejAgQOaMGGC0tLSdNNNN2nu3Lmqra0N9rIck5CQoOTkZKWkpHj/zZo1S5K0efNmjRgxQt27d9fgwYP1wQcf%2BNx38eLFGjhwoLp3766srCxt377dO3f8%2BHH9%2B7//u2688UalpaVp0qRJKisrc3RvNmzatEnp6enKzc2tM7dq1SoNGTJE3bp10/Dhw/XZZ59552prazV//nz1799fPXv21Lhx47R//37vvMfjUU5OjtLT09WnTx9Nnz5d1dXV3vmdO3dq9OjR6tGjhwYMGKDXX3%2B9fjdqydnyWr58uTp16uTzPEtJSdG2bdskXZp5HThwQNnZ2UpLS1N6erqeeOIJlZeXSzr/furzuddQnS2vv//970pISKjz3Pqv//ov730vtbx27dqle%2B%2B9Vz169FB6erpycnJUWloqqX5f1%2Bv1%2B6lByLnzzjvNr371K1NeXm727t1rBgwYYF5//fVgL8sxHTt2NPv3768z/s9//tN07drVLF261FRXV5s//elPJjU11Wzbts0YY8wnn3xirr32WrNlyxZTVVVlFi1aZHr37m0qKyuNMcbMnj3bDB8%2B3PzjH/8wZWVl5uGHHzYTJ050dG8XKj8/3wwYMMCMGjXK5OTk%2BMzt2LHDJCcnmw0bNpjq6mrzhz/8wXTp0sUUFxcbY4xZvHixuemmm8zu3bvN0aNHzTPPPGOGDBliamtrjTHGPPzww2bChAnm0KFD5uDBgyYzM9PMmjXLGGNMVVWVueGGG8xLL71kKisrzfbt202vXr3Mxx9/7GwAATpXXu%2B9954ZPXr0We97KeZ1%2B%2B23myeeeMJUVFSY4uJiM3z4cDNt2rTz7qc%2Bn3sN2dny2r9/v%2BnYseNZ73ep5XX8%2BHFz/fXXmwULFpjjx4%2BbQ4cOmdGjR5uHHnqo3l/X6/P7KQUrxGzbts107tzZeDwe79h///d/m4EDBwZxVc46W8F67bXXzLBhw3zGcnJyzIwZM4wxxkyYMME899xz3rmamhrTu3dvs3LlSnPy5EnTo0cPs3btWu/87t27TUJCgjl48GA97cS%2BN954w5SXl5upU6fWKQwzZ8402dnZPmMjR440ixYtMsYYM3jwYPPGG294544ePWoSExPN119/bUpLS02nTp3Mzp07vfMbN240Xbt2NSdOnDCrV6821113nTl16pR3fu7cueaXv/xlfWzTmnPldb6CdanldeTIEfPEE0%2BY0tJS79ibb75pBgwYcN791Odzr6E6V17nK1iXWl4ej8e8%2B%2B675uTJk96xN954w9xyyy31%2Brpe399PeYswxBQWFqpVq1aKjY31jiUlJWnv3r2qqKgI4sqcNW/ePPXt21fXXnutZsyYocrKShUWFioxMdHndomJid7TxT%2BeDw8PV%2BfOneV2u7Vv3z4dPXpUSUlJ3vl27drp8ssvV2FhoTObsmDMmDFq2rTpGefOlo/b7VZ1dbV2797tMx8dHa02bdrI7XZr586dioiIUEJCgnc%2BKSlJx44d0549e1RYWKiEhARFRET4PPYPT9U3ROfKS5KKi4t13333qWfPnurfv7/%2B8Ic/SNIlmVdMTIxmz56tK6%2B80jtWXFysuLi48%2B6nPp97DdW58vre448/rj59%2Bui6667TvHnzdPLkSUmXXl6xsbEaOXKkIiMjJUl79uzR73//e9122231%2Brpe399PKVghxuPxKCYmxmfs%2BydHKF4v9FN07dpV6enpWrNmjQoKCrRlyxbNnDnzjNk0a9bMm4vH4/H5QpJOZ1dWViaPxyNJde4fExNz0eR6rv0fOXJExphz5hMdHa2wsDCfOUne%2BTNl7/F4Qvb6QJfLpfj4eD322GP605/%2BpEcffVTTpk3T5s2byUuS2%2B3WW2%2B9pQcffPC8%2B6nP516o%2BGFejRo1Urdu3XTLLbdo/fr1ys/P1wcffKD//M//lFS/X6sN2YEDB5ScnKxBgwYpJSVFkyZNqtfX9fr%2BfkrBCkHGmGAvIagKCgo0cuRINWrUSO3atdOUKVO0cuVK709/53K%2B7C72bC9k/z8lmx%2B%2ByIeavn376rXXXlNiYqIaNWqkwYMH65ZbbtHy5cu9t7lU8/ryyy81btw4TZ48Wenp6We93Q/34/RzryH5cV5xcXF65513dMstt%2Biyyy5TamqqJk6c6Pdz63zzoZpXq1at5Ha79dFHH%2Bnbb7/V448/7tf9GmpWFKwQ43K5vK38ex6PR2FhYXK5XEFaVXBdffXVqqmpUXh4eJ1sysrKvLk0b978jNm5XC7vbX48f%2BTIEbVo0aIeV%2B%2Bcc%2B2/WbNmZ8zP4/GoRYsWcrlcqqioUE1Njc%2BcJO/8j3/i83g83se9WLRq1UolJSWXdF7r1q3ThAkTNG3aNI0ZM0aSzruf%2BnzuNXRnyutMWrVqpe%2B%2B%2B07GmEs6r7CwMMXHxys3N1crV65UZGRkvb2u1/f304b9lYw6kpOTVVxcrMOHD3vH3G632rdvryZNmgRxZc7YsWOH5syZ4zNWVFSkRo0aKSMjo841LNu3b1eXLl0knc7uh9dT1dTUaMeOHerSpYtat26t2NhYn/m//vWvOnHihJKTk%2BtxR85JTk6uk4/b7VaXLl0UFRWlDh06%2BOy/vLxc%2B/btU2pqqjp37ixjjHbt2uVz35iYGLVt21bJycn65ptvdOrUqTqPHarefvttrVq1ymesqKhIrVu3vmTz%2BuqrrzR16lS98MILGjZsmHf8fPupz%2BdeQ3a2vDZv3qyXX37Z57Z79uxRq1atFBYWdsnltXnzZg0cONDn7fHvf9BITU2tt9f1ev9%2BauVSeThq5MiRZtq0aebo0aNm9%2B7dpl%2B/fuatt94K9rIccfDgQdO1a1ezaNEic/z4cbNnzx4zaNAgM2vWLPPdd9%2BZbt26mXfffddUV1ebDRs2mNTUVO9v02zcuNH06NHDfP311%2BbYsWPmpZdeMhkZGaaqqsoYc/q3nu68807zj3/8wxw%2BfNhMnDjRPPLII8Hc7k92pt%2BK%2B%2Babb0xKSopZv369qa6uNkuXLjXdunUzJSUlxpjTvz3Tt29f769%2Bz5gxw9x1113e%2B%2Bfk5Jjx48ebQ4cOmeLiYnPXXXeZOXPmGGNO/5r1TTfdZF588UVz7Ngxs2XLFnPttdea9evXO7bnC3GmvH73u9%2BZ6667zmzbts2cOHHCrFixwnTu3Nm43W5jzKWX18mTJ81tt91m3nnnnTpz59tPfT73Gqpz5eV2u01SUpJ5//33zYkTJ8y2bdtM7969vR8PcKnlVV5ebtLT082cOXPMsWPHzKFDh8y4cePML37xi3p/Xa/P76cUrBBUXFxsxo8fb1JTU016erp58cUXvZ9/cin4y1/%2BYjIzM03Xrl1Nr169zOzZs011dbV3bujQoSYpKckMGDCgzucKLVmyxGRkZJjk5GSTlZVlvvnmG%2B/c8ePHzdNPP2169uxpunXrZh599FFTXl7u6N4uVHJysklOTjadOnUynTp18v7/9z7%2B%2BGMzYMAAk5SUZO644w7zl7/8xTtXW1trXnjhBXP99deb1NRUc//993s/d8eY0y%2BCubm5pmvXrqZnz55m5syZ5vjx4975b775xowaNcokJyebvn37miVLljiz6Qtwrrxqa2vNwoULzU033WSSk5PNrbfeatatW%2Be976WW1//8z/%2BYjh07ejP64b%2B///3v591PfT73GqLz5bVmzRozdOhQk5qaanr37m1eeeUVU1NT473/pZbXrl27zOjRo01qaqq57rrrTE5Ojvcjcurzdb0%2Bv5%2BGGROiV8MBAAA0UFyDBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACW/T%2BPlNkSThVMKwAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common654084034793660679”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>843</td> <td class=”number”>26.3%</td> <td>

<div class=”bar” style=”width:92%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>80</td> <td class=”number”>2.5%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>79</td> <td class=”number”>2.5%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>63</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>56</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>55</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>54</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>54</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>51</td> <td class=”number”>1.6%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (224)</td> <td class=”number”>899</td> <td class=”number”>28.0%</td> <td>

<div class=”bar” style=”width:98%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>909</td> <td class=”number”>28.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme654084034793660679”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>843</td> <td class=”number”>26.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>54</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>79</td> <td class=”number”>2.5%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>80</td> <td class=”number”>2.5%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>11190.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11241.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11379.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12335.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>28904.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_BERRY_ACRES12”><s>BERRY_ACRES12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_BERRY_ACRES07”><code>BERRY_ACRES07</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.98128</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_BERRY_ACRESPTH07”>BERRY_ACRESPTH07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>1459</td>

</tr> <tr>

<th>Unique (%)</th> <td>45.5%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>28.3%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>909</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1.7288</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>864.77</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>26.3%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram3363896216418564230”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives3363896216418564230,#minihistogram3363896216418564230”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives3363896216418564230”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles3363896216418564230”

aria-controls=”quantiles3363896216418564230” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram3363896216418564230” aria-controls=”histogram3363896216418564230”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common3363896216418564230” aria-controls=”common3363896216418564230”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme3363896216418564230” aria-controls=”extreme3363896216418564230”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles3363896216418564230”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0.10081</td>

</tr> <tr>

<th>Q3</th> <td>0.40021</td>

</tr> <tr>

<th>95-th percentile</th> <td>2.5569</td>

</tr> <tr>

<th>Maximum</th> <td>864.77</td>

</tr> <tr>

<th>Range</th> <td>864.77</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.40021</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>21.171</td>

</tr> <tr>

<th>Coef of variation</th> <td>12.246</td>

</tr> <tr>

<th>Kurtosis</th> <td>1233.6</td>

</tr> <tr>

<th>Mean</th> <td>1.7288</td>

</tr> <tr>

<th>MAD</th> <td>2.815</td>

</tr> <tr class=”alert”>

<th>Skewness</th> <td>32.243</td>

</tr> <tr>

<th>Sum</th> <td>3978</td>

</tr> <tr>

<th>Variance</th> <td>448.21</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram3363896216418564230”>

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dO8n7wuSf/5n/%2BpX/7ylxf8AWYAAIBg5HhgLV68WKdOnVL37t29a8OHD1dTU5OWLFni9HYAAACsc/wlwnfeeUdbtmxRXFycdy0pKUkFBQW69dZbnd4OAACAdY5fwaqvr1dkZOSFG3G5VFdX5/R2AAAArHM8sAYPHqwlS5boq6%2B%2B8q598cUXevzxx30%2BqgEAACBYOf4S4bx58/Tv//7v%2BslPfqKoqCg1NTWptrZWXbp00Z///GentwMAAGCd44HVpUsXvfbaa3rrrbf02WefyeVyqVu3bho2bJjCw8Od3g4AAIB1jgeWJLVu3Vo33XSTP54aAACgxTkeWMePH9eyZcv0ySefqL6%2B/oLjb775ptNbAgAAsMov78EqLy/XsGHD1LZtW6efHgAAoMU5HlhFRUV68803FR8f7/RTAwAAOMLxj2no0KEDV64AAEBIczywHnjgAa1cuVLGGKefGgAAwBGOv0T41ltv6f3339emTZt0zTXXyOXybbwXX3zR6S0BAABY5XhgRUVF6ac//anTTwsAAOAYxwNr8eLFTj8lAACAoxx/D5Ykffrpp1qxYoUeeeQR79oHH3zgj60AAABY53hg7d%2B/X%2BPGjdPOnTu1detWSV9/%2BOikSZP4kFEAABASHA%2Bs5cuX69e//rW2bNmisLAwSV//fcIlS5Zo1apVTm8HAADAOscD63//93%2BVk5MjSd7AkqSbb75ZJSUlTm8HAADAOscDq3379hf9G4Tl5eVq3bq109sBAACwzvHAGjBggJ588knV1NR4144ePar8/Hz95Cc/cXo7AAAA1jn%2BMQ2PPPKI7r33XqWnp6uxsVEDBgxQXV2devTooSVLlji9HQAAAOscD6xOnTpp69at2rdvn44ePao2bdqoW7duGjp0qM97sgAAAIKV44ElSa1atdJNN93kj6cGAABocY4H1ogRI77zShWfhQUAAIKd44E1ZswYn8BqbGzU0aNH5Xa7de%2B99zq9HQAAAOscD6w5c%2BZcdH3Hjh36%2B9//7vBuAAAA7PPL3yK8mJtuukmvvfaav7cBAADQbAETWAcPHpQxxt/bAAAAaDbHXyKcOHHiBWt1dXUqKSnRqFGjnN4OAACAdY4HVlJS0gW/RRgZGamsrCzdeeedTm8HAADAOscDi09rBwAAoc7xwHrllVcu%2Bba33357C%2B4EAACgZTgeWPPnz1dTU9MFb2gPCwvzWQsLCyOwAABAUHI8sJ577jmtXbtW06ZNU69evWSM0eHDh/Xss8/q7rvvVnp6utNbAgAAsMov78Fas2aNEhMTvWuDBg1Sly5dNGXKFG3dutXpLQEAAFjl%2BOdgHTt2TDExMResR0dHq7S01OntAAAAWOd4YHXu3FlLlixRVVWVd626ulrLli3Ttdde6/R2AAAArHP8JcJ58%2BZp9uzZKiwsVLt27eRyuVRTU6M2bdpo1apVTm8HAADAOscDa9iwYdq7d6/27dunEydOyBijxMRE3XDDDWrfvr3T2wEAALDO8cCSpKuuukojR47UiRMn1KVLF39sAQAAoMU4/h6s%2Bvp65efnq3///vr5z38u6ev3YE2dOlXV1dVObwcAAMA6xwNr6dKlOnTokAoKCuRy/d/TNzY2qqCgwOntAAAAWOd4YO3YsUNPP/20br75Zu8ffY6OjtbixYu1c%2BdOp7cDAABgneOBVVtbq6SkpAvW4%2BPjdfr0aae3AwAAYJ3jgXXttdfq73//uyT5/O3B7du361//9V%2Bd3g4AAIB1jv8W4S9%2B8QvNmDFDd9xxh5qamvT888%2BrqKhIO3bs0Pz5853eDgAAgHWOX8HKzs5Wfn6%2B/va3vyk8PFx//OMfVVpaqoKCAuXk5Pygx3r77beVkZGhvLy8C45t27ZNY8eOVf/%2B/TVhwgS988473mNNTU1avny5Ro4cqcGDB2vKlCk6fvy497jH49GsWbOUkZGhYcOGaf78%2Baqvr7/8kwYAAFcUx69gVVZW6o477tAdd9zRrMd59tlntXHjRnXt2vWCY4cOHVJ%2Bfr5WrlypH//4x9qxY4ceeughbd%2B%2BXZ06ddL69eu1ZcsWPfvss0pMTNTy5cuVm5urV199VWFhYXr00Ud19uxZbd26VQ0NDfrVr36lgoICLViwoFl7BgAAVwbHr2CNHDnS571XlysyMvJbA2vDhg3KzMxUZmamIiMjNW7cOPXs2VObN2%2BWJBUWFmry5Mm6/vrrFRUVpby8PJWUlOjAgQM6efKkdu3apby8PMXHxysxMVEPPvigXn75ZTU0NDR73wAAIPQ5fgUrPT1dr7/%2BusaMGdOsx5k0adK3HisuLlZmZqbPWnJystxut%2Brr63XkyBElJyd7j0VFRalr165yu906deqUwsPD1atXL%2B/xlJQUnT59Wp9%2B%2BqnP%2BrcpLy9XRUWFz1pERFslJCRc6uldkvBwx/u4WSIigmu/l%2BObmQTbbEIZMwk8zCSwMI%2BW4Xhg/cu//It%2B%2B9vfas2aNbr22mvVqlUrn%2BPLli1r9nN4PB7FxMT4rMXExOjIkSP66quvZIy56PGqqirFxsYqKirK%2Bxld3xyTpKqqqkt6/sLCQq1cudJnLTc3VzNnzryc0wkZcXHt/L0Fx0RHX%2BXvLeA8zCTwMJPAwjzscjywjhw5ouuuu07SpQfL5fi%2BlyG/63hzX8LMzs7WiBEjfNYiItqqqqq2WY97vmD7acP2%2BQei8HCXoqOvUnV1nRobm/y9HYiZBCJmElhCfR7%2B%2BuHescDKy8vT8uXL9ec//9m7tmrVKuXm5lp/rri4OHk8Hp81j8ej%2BPh4xcbGyuVyXfR4hw4dFB8fr5qaGjU2Nio8PNx7TJI6dOhwSc%2BfkJBwwcuBFRWndO5c6P3D/SGupPNvbGy6os43GDCTwMNMAgvzsMuxSyC7d%2B%2B%2BYG3NmjUt8lypqakqKiryWXO73erbt68iIyPVo0cPFRcXe49VV1frs88%2BU58%2BfdS7d28ZY/Txxx/73Dc6OlrdunVrkf0CAIDQ4lhgXexlNxu/TXgxd911l/76179q7969OnPmjDZu3Khjx45p3LhxkqScnBytW7dOJSUlqqmpUUFBgXr37q20tDTFx8dr9OjR%2BsMf/qDKykqdOHFCq1atUlZWliIiHH9FFQAABCHHiuGf3zT%2BXWuXKi0tTZJ07tw5SdKuXbskfX21qWfPniooKNDixYtVWlqq7t27a/Xq1erYsaMkaeLEiaqoqNA999yj2tpapaen%2B7wp/Te/%2BY0WLlyokSNHqlWrVrr11lsv%2BmGmAAAAFxO0l2Tcbvd3Hh81apRGjRp10WNhYWGaOXPmt/5WX/v27fX73/%2B%2B2XsEAABXpuD6NTQAAIAg4NgVrIaGBs2ePft712x8DhYAAIA/ORZYAwcOVHl5%2BfeuAQAABDvHAuufP/8KAAAglPEeLAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMsILAAAAMtCNrB69eql1NRUpaWleb8WLVokSdq/f7%2BysrI0YMAA3XLLLdq8ebPPfdetW6fRo0drwIABysnJUVFRkT9OAQAABKkIf2%2BgJW3fvl3XXHONz1p5ebkefPBBzZ8/X2PHjtV7772n6dOnq1u3bkpLS9Pu3bu1YsUKPffcc%2BrVq5fWrVunadOmaefOnWrbtq2fzgQAAASTkL2C9W22bNmipKQkZWVlKTIyUhkZGRoxYoQ2bNggSSosLNSECRPUt29ftWnTRlOnTpUk7dmzx5/bBgAAQSSkA2vZsmUaPny4Bg0apEcffVS1tbUqLi5WcnKyz%2B2Sk5O9LwOef9zlcql3795yu92O7h0AAASvkH2JsF%2B/fsrIyNDvfvc7HT9%2BXLNmzdLjjz8uj8ejxMREn9vGxsaqqqpKkuTxeBQTE%2BNzPCYmxnv8UpSXl6uiosJnLSKirRISEi7zbC4uPDy4%2BjgiIrj2ezm%2BmUmwzSaUMZPAw0wCC/NoGSEbWIWFhd7/vv766zVnzhxNnz5dAwcO/N77GmOa/dwrV670WcvNzdXMmTOb9bjBLi6unb%2B34Jjo6Kv8vQWch5kEHmYSWJiHXSEbWOe75ppr1NjYKJfLJY/H43OsqqpK8fHxkqS4uLgLjns8HvXo0eOSnys7O1sjRozwWYuIaKuqqtrL3P3FBdtPG7bPPxCFh7sUHX2Vqqvr1NjY5O/tQMwkEDGTwBLq8/DXD/chGVgHDx7U5s2bNXfuXO9aSUmJWrdurczMTP3lL3/xuX1RUZH69u0rSUpNTVVxcbHGjx8vSWpsbNTBgweVlZV1yc%2BfkJBwwcuBFRWndO5c6P3D/SGupPNvbGy6os43GDCTwMNMAgvzsCu4LoFcog4dOqiwsFBr1qzR2bNndfToUT311FPKzs7WbbfdptLSUm3YsEFnzpzRvn37tG/fPt11112SpJycHL3yyiv68MMPVVdXp2eeeUatW7fW8OHD/XtSAAAgaITkFazExEStWbNGy5Yt8wbS%2BPHjlZeXp8jISK1evVpPPPGEHn/8cXXu3FlLly7Vj370I0nST3/6Uz388MOaNWuWvvzyS6WlpWnNmjVq06aNn88KAAAEizDT3Hd045JUVJyy/pgRES79rOBt64/bUl6fNdTfW2hxEREuxcW1U1VVLZfaAwQzCTzMJLCE%2Bjw6dmzvl%2BcNyZcIAQAA/InAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAAgAAsIzAuojS0lLdf//9Sk9P14033qilS5eqqanJ39sCAABBIsLfGwhEM2bMUEpKinbt2qUvv/xSDzzwgK6%2B%2Bmrdd999/t4aAAAIAlzBOo/b7dbHH3%2BsOXPmqH379kpKStLkyZNVWFjo760BAIAgwRWs8xQXF6tz586KiYnxrqWkpOjo0aOqqalRVFTU9z5GeXm5KioqfNYiItoqISHB6l7Dw4OrjyMigmu/l%2BObmQTbbEIZMwk8zCSwMI%2BWQWCdx%2BPxKDo62mftm9iqqqq6pMAqLCzUypUrfdYeeughzZgxw95G9XXI3dvpE2VnZ1uPN1ye8vJy/elPzzGTAMJMAg8zCSzMo2WQqxdhjGnW/bOzs7Vp0yafr%2BzsbEu7%2Bz8VFRVauXLlBVfL4D/MJPAwk8DDTAIL82gZXME6T3x8vDwej8%2Bax%2BNRWFiY4uPjL%2BkxEhIS%2BCkAAIArGFewzpOamqqysjJVVlZ619xut7p376527dr5cWcAACBYEFjnSU5OVlpampYtW6aamhqVlJTo%2BeefV05Ojr%2B3BgAAgkT4Y4899pi/NxFobrjhBm3dulWLFi3Sa6%2B9pqysLE2ZMkVhYWH%2B3toF2rVrpyFDhnB1LYAwk8DDTAIPMwkszMO%2BMNPcd3QDAADABy8RAgAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgBaHS0lLdf//9Sk9P14033qilS5eqqanJ39sKeaWlpcrNzVV6eroyMjI0d%2B5cVVdXS5IOHTqku%2B%2B%2BWwMHDtSoUaO0du1an/tu27ZNY8eOVf80xbBNAAAHWklEQVT%2B/TVhwgS98847/jiFkPXkk0%2BqV69e3v/fv3%2B/srKyNGDAAN1yyy3avHmzz%2B3XrVun0aNHa8CAAcrJyVFRUZHTWw5pzzzzjIYNG6Z%2B/fpp8uTJ%2BvzzzyUxF384ePCgJk2apEGDBmno0KGaM2eOKisrJTGPFmcQdMaPH28WLFhgqqurzdGjR82oUaPM2rVr/b2tkHfrrbeauXPnmpqaGlNWVmYmTJhg5s2bZ%2Brq6swNN9xgVqxYYWpra01RUZEZMmSI2bFjhzHGmIMHD5rU1FSzd%2B9eU19fb1599VXTt29fU1ZW5uczCg0HDx40Q4YMMT179jTGGPPFF1%2BYfv36mQ0bNpj6%2Bnrz3//936ZPnz7mo48%2BMsYY8%2Babb5pBgwaZDz/80NTV1ZnVq1eboUOHmtraWn%2BeRsh44YUXzM0332xKSkrMqVOnzKJFi8yiRYuYix80NDSYoUOHmmXLlpkzZ86YyspKc99995kZM2YwDwdwBSvIuN1uffzxx5ozZ47at2%2BvpKQkTZ48WYWFhf7eWkirrq5WamqqZs%2BerXbt2qlTp04aP3683n33Xe3du1cNDQ2aPn262rZtq5SUFN15553emWzYsEGZmZnKzMxUZGSkxo0bp549e17w0yJ%2BuKamJi1cuFCTJ0/2rm3ZskVJSUnKyspSZGSkMjIyNGLECG3YsEGSVFhYqAkTJqhv375q06aNpk6dKknas2ePP04h5Kxdu1Z5eXm67rrrFBUVpQULFmjBggXMxQ8qKipUUVGh2267Ta1bt1ZcXJx%2B9rOf6dChQ8zDAQRWkCkuLlbnzp0VExPjXUtJSdHRo0dVU1Pjx52FtujoaC1evFhXX321d62srEwJCQkqLi5Wr169FB4e7j2WnJzsvZxeXFys5ORkn8dLTk6W2%2B12ZvMh7MUXX1RkZKTGjh3rXfu27/e3zcPlcql3797Mw4IvvvhCn3/%2Bub766iuNGTNG6enpmjlzpiorK5mLHyQmJqp3794qLCxUbW2tvvzyS%2B3cuVPDhw9nHg4gsIKMx%2BNRdHS0z9o3sVVVVeWPLV2R3G63XnjhBU2fPv2iM4mNjZXH41FTU5M8Ho9PEEtfz4x5Nc/Jkye1YsUKLVy40Gf92%2BbxzfebebScEydOSJK2b9%2Bu559/Xq%2B%2B%2BqpOnDihBQsWMBc/cLlcWrFihd58800NGDBAGRkZOnfunGbPns08HEBgBSFjjL%2B3cEV77733NGXKFM2ePVsZGRnferuwsDDvfzMz%2BxYvXqwJEyaoe/fuP/i%2BzKNlfPN9nTp1qhITE9WpUyfNmDFDu3fv/kH3hx1nz57VtGnTdPPNN%2Bvdd9/VW2%2B9pfbt22vOnDmXdH/m0TwEVpCJj4%2BXx%2BPxWfN4PAoLC1N8fLyfdnXl2L17t%2B6//37NmzdPkyZNkvT1TM7/qc7j8Sg2NlYul0txcXEXnRnzunz79%2B/XBx98oNzc3AuOXez7XVVV5f1%2BM4%2BW881L6P98ZaRz584yxqihoYG5OGz//v36/PPP9fDDD6t9%2B/ZKTEzUzJkz9cYbb8jlcjGPFkZgBZnU1FSVlZV5f81W%2Bvrlqu7du6tdu3Z%2B3Fnoe//995Wfn6%2BnnnpKt99%2Bu3c9NTVVhw8f1rlz57xrbrdbffv29R4//9eb//k4frjNmzfryy%2B/1I033qj09HRNmDBBkpSenq6ePXte8P0uKirymUdxcbH3WGNjow4ePMg8LOjUqZOioqJ06NAh71ppaalatWqlzMxM5uKwxsZGNTU1%2BVyJOnv2rCQpIyODebQ0//0CIy7XnXfeaebNm2dOnTpljhw5YkaMGGFeeOEFf28rpDU0NJif//zn5sUXX7zg2JkzZ8yNN95onn76aXP69Gnz4YcfmkGDBpk9e/YYY4w5fPiwSUtLM3v27DH19fVmw4YNpn///qa8vNzhswgdHo/HlJWVeb8%2B%2BOAD07NnT1NWVmZKS0tN//79zUsvvWTq6%2BvN3r17TZ8%2BfcyhQ4eMMcbs27fPDBw40HzwwQfm9OnTZsWKFSYzM9PU1dX5%2BaxCw5NPPmlGjhxpjh07Zk6ePGmys7PN3LlzzcmTJ5mLwyorK82QIUPM73//e3P69GlTWVlppk2bZv7t3/6NeTiAwApCZWVlZurUqaZPnz4mIyPDPP3006apqcnf2wpp//M//2N69uxpUlNTL/j6/PPPzeHDh83EiRNNamqqGT58uFm/fr3P/Xfs2GFGjRplUlJSzG233Wb%2B8Y9/%2BOlMQtPx48e9n4NljDH/%2BMc/zLhx40xKSooZNWqU9zPJvrF%2B/XqTmZlpUlNTTU5Ojjl8%2BLDTWw5ZZ86cMY899pgZPHiw6devn8nPzzc1NTXGGObiD26329x9991m0KBBJiMjw8yaNcucOHHCGMM8WlqYMbyLDQAAwCbegwUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGAZgQUAAGDZ/wOsBei29yG%2BxAAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common3363896216418564230”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>843</td> <td class=”number”>26.3%</td> <td>

<div class=”bar” style=”width:58%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.2540327702273593</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.022822713164140952</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.03142875102143441</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.09522513943681131</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.23300492827404898</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5083884087442806</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.8164598301763554</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.44671864847303444</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.13294615680649338</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (1448)</td> <td class=”number”>1448</td> <td class=”number”>45.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>909</td> <td class=”number”>28.3%</td> <td>

<div class=”bar” style=”width:63%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme3363896216418564230”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>843</td> <td class=”number”>26.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0001940152127328304</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0005387997803852095</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.000777250801734202</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0010713359773905256</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>151.13462203984773</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>206.07734806629836</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>276.2582056892779</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>289.7045919859608</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>864.7678314983245</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_BERRY_ACRESPTH12”><s>BERRY_ACRESPTH12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_BERRY_ACRESPTH07”><code>BERRY_ACRESPTH07</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.98479</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_BERRY_FARMS07”>BERRY_FARMS07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>99</td>

</tr> <tr>

<th>Unique (%)</th> <td>3.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>4.2%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>134</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>8.0832</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>395</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>26.3%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram4550917930795069320”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives4550917930795069320,#minihistogram4550917930795069320”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives4550917930795069320”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles4550917930795069320”

aria-controls=”quantiles4550917930795069320” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram4550917930795069320” aria-controls=”histogram4550917930795069320”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common4550917930795069320” aria-controls=”common4550917930795069320”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme4550917930795069320” aria-controls=”extreme4550917930795069320”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles4550917930795069320”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>3</td>

</tr> <tr>

<th>Q3</th> <td>8</td>

</tr> <tr>

<th>95-th percentile</th> <td>31</td>

</tr> <tr>

<th>Maximum</th> <td>395</td>

</tr> <tr>

<th>Range</th> <td>395</td>

</tr> <tr>

<th>Interquartile range</th> <td>8</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>19.528</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.4159</td>

</tr> <tr>

<th>Kurtosis</th> <td>117.26</td>

</tr> <tr>

<th>Mean</th> <td>8.0832</td>

</tr> <tr>

<th>MAD</th> <td>8.7311</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>8.9245</td>

</tr> <tr>

<th>Sum</th> <td>24864</td>

</tr> <tr>

<th>Variance</th> <td>381.35</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram4550917930795069320”>

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EcXFxGjFihHJyciRJy5YtU0pKilJSUhQUFKQhQ4YoJiZGK1eulCTl5ORo7Nixatu2rYKDg5WRkaH8/Hxt3769PiMDAAAbcdT3Ai5GSEiIZs2a5TFWWFioyMhI5eXlqUOHDgoICHDPxcbGatmyZZKkvLw8paSkeOwbGxsrp9OpyspK7dmzR7Gxse654OBgtW7dWk6nU507d67T%2BoqKilRcXOwx5nA0UWRk5AXlrE1AgL36scNR9/Wezma3jHXhy9kk385HNnsim33ZOZ8tC9bPOZ1Ovf3221q4cKHWrl2rkJAQj/mwsDC5XC7V1NTI5XIpNDTUYz40NFR79uzR0aNHZVnWWedLSkrqvJ6cnBxlZWV5jKWlpSk9Pf0Ck/mW8PCmF7xPSMgVl2ElDYMvZ5N8Ox/Z7Ils9mXHfLYvWF9%2B%2BaUmTJigyZMnKzk5WWvXrj3rdn5%2Bfu7/ru15qkt93mrUqFHq27evx5jD0UQlJeWXdNyfs1ujv5D8AQH%2BCgm5QqWlFaqurrmMq/I%2BX84m%2BXY%2BstkT2ezLRL6L%2BebeBFsXrA0bNuiJJ57Q9OnTdeedd0qSIiIi9N1333ls53K5FBYWJn9/f4WHh8vlcp0xHxER4d7mbPPNmzev87oiIyPPuB1YXHxMVVW%2B98l/IS4mf3V1jc/%2BvflyNsm385HNnshmX3bMZ69LID/x1VdfaerUqXr11Vfd5UqS4uPj9c0336iqqso95nQ61alTJ/d8bm6ux7FOzwcFBal9%2B/bKy8tzz5WWlmr//v1KTEy8zIkAAICvsGXBqqqq0rPPPqspU6aoV69eHnMpKSkKDg7WwoULVVFRoe3bt2v58uVKTU2VJI0cOVKffPKJNm3apBMnTmj58uX67rvvNGTIEElSamqqFi9erPz8fJWVlSkzM1MdO3ZUQkKC13MCAAB7suUtwm3btik/P18zZ87UzJkzPeY%2B%2BOADvfbaa3r%2B%2BeeVnZ2tK6%2B8UhkZGerdu7ckKSYmRpmZmZo1a5YKCgrUrl07LVq0SC1atJAkjR49WsXFxbr33ntVXl6upKSkMx5YBwAAOB8/i3fQ9Iri4mPGj%2Blw%2BKt/5hbjx71c1k66oc7bOhz%2BCg9vqpKSctvdd6%2BNL2eTfDsf2eyJbPZlIl%2BLFs0Mr6pubHmLEAAAoCGjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwrxesvn37KisrS4WFhd5%2BaQAAAK/wesG66667tGbNGt1888164IEHtH79eo93XQcAALA7rxestLQ0rVmzRu%2B8847at2%2BvV155RSkpKZozZ4727dvn7eUAAAAYV2/PYMXFxWnq1KnauHGjpk2bpnfeeUe33Xabxo0bpx07dtTXsgAAAC5ZvRWsU6dOac2aNXrwwQc1depUtWzZUk8//bQ6duyosWPHatWqVfW1NAAAgEvi9d9FmJ%2Bfr%2BXLl%2Bu9995TeXm5Bg4cqD/%2B8Y/q1q2be5sePXpoxowZGjx4sLeXBwAAcMm8XrAGDRqkNm3aaPz48brzzjsVFhZ2xjYpKSk6cuSIt5cGAABghNcL1uLFi9WzZ89at9u%2BfbsXVgMAAGCe15/B6tChgx5%2B%2BGF99NFH7rE//OEPevDBB%2BVyuby9HAAAAOO8XrBmzZqlY8eOqV27du6x3r17q6amRrNnz/b2cgAAAIzz%2Bi3Cjz/%2BWKtWrVJ4eLh7LDo6WpmZmbr99tu9vRwAAADjvH4Fq7KyUkFBQWcuxN9fFRUV3l4OAACAcV4vWD169NDs2bN19OhR99gPP/ygF154weOtGgAAAOzK67cIp02bpl/96le6/vrrFRwcrJqaGpWXl6tVq1b605/%2B5O3lAAAAGOf1gtWqVSv97W9/0z/%2B8Q/t379f/v7%2BatOmjXr16qWAgABvLwcAAMA4rxcsSQoMDNTNN99cHy8NAABw2Xm9YB04cEBz587Vt99%2Bq8rKyjPm//73v3t7SQAAAEbVyzNYRUVF6tWrl5o0aeLtlwcAALjsvF6wcnNz9fe//10RERHefmkAAACv8PrbNDRv3pwrVwAAwKd5vWCNHz9eWVlZsizL2y8NAADgFV6/RfiPf/xDX331lVasWKGrr75a/v6eHW/p0qXeXhIAAIBRXi9YwcHBuummm7z9sgAAAF7j9YI1a9Ysb78kAACAV3n9GSxJ2rt3r%2BbPn6%2Bnn37aPfb111/Xx1IAAACM83rB2rp1q4YMGaL169dr9erVkn5889H77ruPNxkFAAA%2BwesFa968eXriiSe0atUq%2Bfn5Sfrx9xPOnj1bCxYs8PZyAAAAjPN6wfr3v/%2Bt1NRUSXIXLEm65ZZblJ%2Bf7%2B3lAAAAGOf1gtWsWbOz/g7CoqIiBQYGens5AAAAxnm9YHXt2lWvvPKKysrK3GP79u3T1KlTdf3113t7OQAAAMZ5/W0ann76aY0ZM0ZJSUmqrq5W165dVVFRofbt22v27NneXg4AAIBxXi9YV111lVavXq3Nmzdr3759aty4sdq0aaMbbrjB45ksAAAAu/J6wZKkRo0a6eabb66PlwYAALjsvF6w%2Bvbte94rVbwXFgAAsDuvF6zbbrvNo2BVV1dr3759cjqdGjNmjLeXAwAAYJzXC9aUKVPOOr5u3Tp99tlnF3SsLVu2aOrUqUpKStK8efPc4ytWrNC0adPUqFEjj%2B2XLFmixMRE1dTU6NVXX9Xq1atVWlqqxMREzZgxQ61atZIkuVwuzZgxQ59//rn8/f2VkpKi6dOnq3HjxheYFgAA/Ceql99FeDY333yz/va3v9V5%2B9dff10zZ85U69atzzrfo0cPOZ1Oj4/ExERJPxatVatWKTs7Wxs3blR0dLTS0tJkWZYkafr06aqoqNDq1av17rvvKj8/X5mZmZceEgAA/EdoMAVr586d7oJTF0FBQVq%2BfPk5C9b55OTkaOzYsWrbtq2Cg4OVkZGh/Px8bd%2B%2BXYcOHdJHH32kjIwMRUREqGXLlpo4caLeffddnTp16oJfCwAA/Ofx%2Bi3C0aNHnzFWUVGh/Px8DRgwoM7Hue%2B%2B%2B847X1hYqPvvv1%2B5ubkKCQlRenq67rjjDlVWVmrPnj2KjY11bxscHKzWrVvL6XTq2LFjCggIUIcOHdzzcXFxOn78uPbu3esxfi5FRUUqLi72GHM4migyMrLO%2BeoiIKDB9OM6cTjqvt7T2eyWsS58OZvk2/nIZk9ksy875/N6wYqOjj7jpwiDgoI0fPhwjRgxwshrREREKDo6Wo8//rjatWunDz/8UE8%2B%2BaQiIyP1y1/%2BUpZlKTQ01GOf0NBQlZSUKCwsTMHBwR5rPL1tSUlJnV4/JydHWVlZHmNpaWlKT0%2B/xGT2Fh7e9IL3CQm54jKspGHw5WySb%2Bcjmz2Rzb7smM/rBcsb79beu3dv9e7d2/3nQYMG6cMPP9SKFSvcD9mf73bkhdyqPJtRo0apb9%2B%2BHmMORxOVlJRf0nF/zm6N/kLyBwT4KyTkCpWWVqi6uuYyrsr7fDmb5Nv5yGZPZLMvE/ku5pt7E7xesN577706b3vnnXcae92oqCjl5uYqLCxM/v7%2BcrlcHvMul0vNmzdXRESEysrKVF1drYCAAPecJDVv3rxOrxUZGXnG7cDi4mOqqvK9T/4LcTH5q6trfPbvzZezSb6dj2z2RDb7smM%2BrxesZ555RjU1NWdcJfLz8/MY8/Pzu%2BiC9Ze//EWhoaG67bbb3GP5%2Bflq1aqVgoKC1L59e%2BXl5alnz56SpNLSUu3fv1%2BJiYmKioqSZVnavXu34uLiJElOp1MhISFq06bNRa0HAAD8Z/H6PaY33nhDvXr10pIlS/TFF1/oX//6l95%2B%2B23ddNNNev3117Vjxw7t2LFD27dvv%2BjXOHnypF566SU5nU6dOnVKq1ev1j/%2B8Q/3A/apqalavHix8vPzVVZWpszMTHXs2FEJCQmKiIjQwIED9dvf/lZHjhzRwYMHtWDBAg0fPlwOR738ZiEAAGAz9fIMVnZ2tlq2bOke6969u1q1aqVx48Zp9erVdTpOQkKCJKmqqkqS9NFHH0n68WrTfffdp/Lycj322GMqLi7W1VdfrQULFig%2BPl7Sjz/JWFxcrHvvvVfl5eVKSkryeCj9xRdf1PPPP69%2B/fqpUaNGuv3225WRkWEkPwAA8H1eL1jffffdGT/BJ0khISEqKCio83GcTuc55/z8/DRx4kRNnDjxnPPp6enn/Km%2BZs2a6Te/%2BU2d1wIAAPBTXr9FGBUVpdmzZ3u85UFpaanmzp2ra665xtvLAQAAMM7rV7CmTZumyZMnKycnR02bNpW/v7/KysrUuHFjLViwwNvLAQAAMM7rBatXr17atGmTNm/erIMHD8qyLLVs2VI33nijmjVr5u3lAAAAGFcvPxZ3xRVXqF%2B/fjp48KBatWpVH0sAAAC4bLz%2BDFZlZaWmTp2qLl266NZbb5X04zNYDzzwgEpLS729HAAAAOO8XrDmzJmjXbt2KTMzU/7%2B///y1dXVyszM9PZyAAAAjPN6wVq3bp1%2B97vf6ZZbbnH/QuWQkBDNmjVL69ev9/ZyAAAAjPN6wSovL1d0dPQZ4xERETp%2B/Li3lwMAAGCc1wvWNddco88%2B%2B0ySPH734AcffKD/%2Bq//8vZyAAAAjPP6TxHefffdevTRR3XXXXeppqZGv//975Wbm6t169bpmWee8fZyAAAAjPN6wRo1apQcDofefvttBQQE6LXXXlObNm2UmZmpW265xdvLAQAAMM7rBevIkSO66667dNddd3n7pQEAALzC689g9evXz%2BPZKwAAAF/j9YKVlJSktWvXevtlAQAAvMbrtwh/8Ytf6OWXX1Z2drauueYaNWrUyGN%2B7ty53l4SAACAUV4vWHv27NEvf/lLSVJJSYm3Xx4AAOCy81rBysjI0Lx58/SnP/3JPbZgwQKlpaV5awkAAABe4bVnsDZs2HDGWHZ2trdeHgAAwGu8VrDO9pOD/DQhAADwRV4rWKd/sXNtYwAAAHbn9bdpAAAA8HUULAAAAMO89lOEp06d0uTJk2sd432wAACA3XmtYHXr1k1FRUW1jgEAANid1wrWT9//CgAAwJfxDBYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYJitC9aWLVuUnJysjIyMM%2BbWrFmjwYMHq0uXLho2bJg%2B/vhj91xNTY3mzZunfv36qUePHho3bpwOHDjgnne5XJo0aZKSk5PVq1cvPfPMM6qsrPRKJgAAYH%2B2LVivv/66Zs6cqdatW58xt2vXLk2dOlVTpkzRp59%2BqrFjx%2BqRRx7RwYMHJUlLlizRqlWrlJ2drY0bNyo6OlppaWmyLEuSNH36dFVUVGj16tV69913lZ%2Bfr8zMTK/mAwAA9mXbghUUFKTly5eftWAtW7ZMKSkpSklJUVBQkIYMGaKYmBitXLlSkpSTk6OxY8eqbdu2Cg4OVkZGhvLz87V9%2B3YdOnRIH330kTIyMhQREaGWLVtq4sSJevfdd3Xq1ClvxwQAADZk24J13333qVmzZmedy8vLU2xsrMdYbGysnE6nKisrtWfPHo/54OBgtW7dWk6nU7t27VJAQIA6dOjgno%2BLi9Px48e1d%2B/eyxMGAAD4FEd9L%2BBycLlcCg0N9RgLDQ3Vnj17dPToUVmWddb5kpIShYWFKTg4WH5%2Bfh5zklRSUlKn1y8qKlJxcbHHmMPRRJGRkRcT55wCAuzVjx2Ouq/3dDa7ZawLX84m%2BXY%2BstkT2ezLzvl8smBJcj9PdTHzte1bm5ycHGVlZXmMpaWlKT09/ZKOa3fh4U0veJ%2BQkCsuw0oaBl/OJvl2PrLZE9nsy475fLJghYeHy%2BVyeYy5XC5FREQoLCxM/v7%2BZ51v3ry5IiIiVFZWpurqagUEBLjnJKl58%2BZ1ev1Ro0apb9%2B%2BHmMORxOVlJRfbKSzslujv5D8AQH%2BCgm5QqWlFaqurrmMq/I%2BX84m%2BXY%2BstkT2ezLRL6L%2BebeBJ8sWPHx8crNzfUYczqdGjRokIKCgtS%2BfXvl5eWpZ8%2BekqTS0lLt379fiYmJioqKkmVZ2r17t%2BLi4tz7hoSEqE2bNnV6/cjIyDNuBxYXH1NVle998l%2BIi8lfXV3js39vvpxN8u18ZLMnstmXHfPZ6xJIHY0cOVKffPKJNm3apBMnTmj58uX67rvvNGTIEElSamqqFi9erPz8fJWVlSkzM1MdO3ZUQkKCIiIiNHDgQP32t7/VkSNHdPDgQS1YsEDqNZzpAAATmklEQVTDhw%2BXw%2BGTfRQAABhm28aQkJAgSaqqqpIkffTRR5J%2BvNoUExOjzMxMzZo1SwUFBWrXrp0WLVqkFi1aSJJGjx6t4uJi3XvvvSovL1dSUpLHM1Mvvviinn/%2BefXr10%2BNGjXS7bffftY3MwUAADgbP%2BtSn%2BhGnRQXHzN%2BTIfDX/0ztxg/7uWydtINdd7W4fBXeHhTlZSU2%2B6ycG18OZvk2/nIZk9ksy8T%2BVq0OPtbOl1uPnmLEAAAoD5RsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAw3y2YHXo0EHx8fFKSEhwf7z00kuSpK1bt2r48OHq2rWrBg0apJUrV3rsu3jxYg0cOFBdu3ZVamqqcnNz6yMCAACwKUd9L%2BBy%2BuCDD3T11Vd7jBUVFWnixIl65plnNHjwYH355ZeaMGGC2rRpo4SEBG3YsEHz58/XG2%2B8oQ4dOmjx4sV6%2BOGHtX79ejVp0qSekgAAADvx2StY57Jq1SpFR0dr%2BPDhCgoKUnJysvr27atly5ZJknJycjRs2DB16tRJjRs31gMPPCBJ2rhxY30uGwAA2IhPF6y5c%2Beqd%2B/e6t69u6ZPn67y8nLl5eUpNjbWY7vY2Fj3bcCfz/v7%2B6tjx45yOp1eXTsAALAvn71F2LlzZyUnJ%2BvXv/61Dhw4oEmTJumFF16Qy%2BVSy5YtPbYNCwtTSUmJJMnlcik0NNRjPjQ01D1fF0VFRSouLvYYcziaKDIy8iLTnF1AgL36scNR9/Wezma3jHXhy9kk385HNnsim33ZOZ/PFqycnBz3f7dt21ZTpkzRhAkT1K1bt1r3tSzrkl87KyvLYywtLU3p6emXdFy7Cw9vesH7hIRccRlW0jD4cjbJt/ORzZ7IZl92zOezBevnrr76alVXV8vf318ul8tjrqSkRBEREZKk8PDwM%2BZdLpfat29f59caNWqU%2Bvbt6zHmcDRRSUn5Ra7%2B7OzW6C8kf0CAv0JCrlBpaYWqq2su46q8z5ezSb6dj2z2RDb7MpHvYr65N8EnC9bOnTu1cuVKPfXUU%2B6x/Px8BQYGKiUlRX/96189ts/NzVWnTp0kSfHx8crLy9PQoUMlSdXV1dq5c6eGDx9e59ePjIw843ZgcfExVVX53if/hbiY/NXVNT779%2BbL2STfzkc2eyKbfdkxn70ugdRR8%2BbNlZOTo%2BzsbJ08eVL79u3Tq6%2B%2BqlGjRumOO%2B5QQUGBli1bphMnTmjz5s3avHmzRo4cKUlKTU3Ve%2B%2B9p23btqmiokILFy5UYGCgevfuXb%2BhAACAbfjkFayWLVsqOztbc%2BfOdRekoUOHKiMjQ0FBQVq0aJFmzpypF154QVFRUZozZ46uvfZaSdJNN92kxx9/XJMmTdLhw4eVkJCg7OxsNW7cuJ5TAQAAu/DJgiVJPXr00NKlS8859/77759z37vvvlt333335VoaAADwcT55ixAAAKA%2BUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDBHfS8A/zlu/e0/63sJF2TtpBvqewkAAJviChYAAIBhFCwAAADDKFhnUVBQoIceekhJSUnq06eP5syZo5qamvpeFgAAsAmewTqLRx99VHFxcfroo490%2BPBhjR8/XldeeaXuv//%2B%2Bl4aAACwAa5g/YzT6dTu3bs1ZcoUNWvWTNHR0Ro7dqxycnLqe2kAAMAmuIL1M3l5eYqKilJoaKh7LC4uTvv27VNZWZmCg4NrPUZRUZGKi4s9xhyOJoqMjDS61oAA%2BvHlZLefesTl8%2BGUGyX9/785X/y3RzZ78uVskr3zUbB%2BxuVyKSQkxGPsdNkqKSmpU8HKyclRVlaWx9gjjzyiRx991NxC9WORG3PVtxo1apTx8lbfioqKlJOTQzYb8uV8RUVF%2BuMf3yCbzZDNvuycz36V0Assy7qk/UeNGqUVK1Z4fIwaNcrQ6v5fcXGxsrKyzrha5gvIZl%2B%2BnI9s9kQ2%2B7JzPq5g/UxERIRcLpfHmMvlkp%2BfnyIiIup0jMjISNs1bQAAYA5XsH4mPj5ehYWFOnLkiHvM6XSqXbt2atq0aT2uDAAA2AUF62diY2OVkJCguXPnqqysTPn5%2Bfr973%2Bv1NTU%2Bl4aAACwiYAZM2bMqO9FNDQ33nijVq9erZdeekl/%2B9vfNHz4cI0bN05%2Bfn71vbQzNG3aVD179vTJq2tksy9fzkc2eyKbfdk1n591qU90AwAAwAO3CAEAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2DZUEFBgR566CElJSWpT58%2BmjNnjmpqaup7WRetQ4cOio%2BPV0JCgvvjpZdekiRt3bpVw4cPV9euXTVo0CCtXLmynldbuy1btig5OVkZGRlnzK1Zs0aDBw9Wly5dNGzYMH388cfuuZqaGs2bN0/9%2BvVTjx49NG7cOB04cMCbS6/VubKtWLFC1157rcc5TEhI0I4dOyTZI1tBQYHS0tKUlJSk5ORkPfXUUyotLZUk7dq1S/fcc4%2B6deumAQMG6K233vLY93zntSE4V7bvv/9eHTp0OOO8vfnmm%2B59G3q23bt3a8yYMerWrZuSk5M1adIkFRcXS6r968fixYs1cOBAde3aVampqcrNza2PCOd0rmyfffbZWc/b2rVr3fs29Gw/9corr6hDhw7uP9v9vLlZsJ2hQ4dazz77rFVaWmrt27fPGjBggPXWW2/V97IuWkxMjHXgwIEzxn/44Qerc%2BfO1rJly6zKykrrn//8p5WYmGjt2LGjHlZZN9nZ2daAAQOs0aNHW5MmTfKY27lzpxUfH29t2rTJqqystN5//32rU6dOVmFhoWVZlrV48WKrT58%2B1p49e6xjx45ZL774ojV48GCrpqamPqKc4XzZ3n33Xeuee%2B45574NPZtlWdbtt99uPfXUU1ZZWZlVWFhoDRs2zJo2bZpVUVFh3Xjjjdb8%2BfOt8vJyKzc31%2BrZs6e1bt06y7JqP68NwbmyHThwwIqJiTnnfg0924kTJ6zrr7/eysrKsk6cOGEdPnzYuueee6yJEyfW%2BvXj73//u9W9e3dr27ZtVkVFhbVo0SLrhhtusMrLy%2Bs51Y/Ol%2B3TTz%2B1%2BvTpc859G3q2n9q5c6fVs2dP9%2Beh3c/bT3EFy2acTqd2796tKVOmqFmzZoqOjtbYsWOVk5NT30szbtWqVYqOjtbw4cMVFBSk5ORk9e3bV8uWLavvpZ1TUFCQli9frtatW58xt2zZMqWkpCglJUVBQUEaMmSIYmJi3N%2Bd5eTkaOzYsWrbtq2Cg4OVkZGh/Px8bd%2B%2B3dsxzup82WrT0LOVlpYqPj5ekydPVtOmTXXVVVdp6NCh%2BuKLL7Rp0yadOnVKEyZMUJMmTRQXF6cRI0a4/83Vdl7r2/my1aahZ6uoqFBGRobGjx%2BvwMBARUREqH///vr2229r/fqRk5OjYcOGqVOnTmrcuLEeeOABSdLGjRvrM5Lb%2BbLVpqFnO62mpkbPP/%2B8xo4d6x6z%2B3n7KQqWzeTl5SkqKkqhoaHusbi4OO3bt09lZWX1uLJLM3fuXPXu3Vvdu3fX9OnTVV5erry8PMXGxnpsFxsb23AvB0u677771KxZs7POnSuP0%2BlUZWWl9uzZ4zEfHBys1q1by%2Bl0XtY119X5sklSYWGh7r//fvXo0UP9%2BvXT%2B%2B%2B/L0m2yBYSEqJZs2bpyiuvdI8VFhYqMjJSeXl56tChgwICAtxzP/08PN95bQjOl%2B20J598Ur169dJ1112nuXPn6tSpU5IafrbQ0FCNGDFCDodDkrR371799a9/1a233lrr14%2Bfz/v7%2B6tjx462yCZJ5eXl7tu%2BN954o37/%2B9/LsixJDT/baUuXLlVQUJAGDx7sHrP7efspCpbNuFwuhYSEeIydLlslJSX1saRL1rlzZyUnJ2v9%2BvXKycnRtm3b9MILL5w1a1hYmG1zulwuj2Is/XjuSkpKdPToUVmWdc75hi4iIkLR0dF64okn9M9//lOPP/64pk2bpq1bt9oym9Pp1Ntvv60JEyac8/PQ5XKppqbmvOe1IfpptsDAQHXp0kX9%2B/fXxo0blZ2drZUrV%2Bp//ud/JJ3/c7YhKSgoUHx8vG677TYlJCQoPT291q8fds4WHBysmJgYjRkzRlu2bNGsWbOUlZWld999V5I9sh06dEjz58/X888/7zHuK%2BdNomDZ0unvUnxFTk6ORowYocDAQLVt21ZTpkzR6tWr3d9F%2B5Lazp1dz23v3r31xhtvKDY2VoGBgRo0aJD69%2B%2BvFStWuLexS7Yvv/xS48aN0%2BTJk5WcnHzO7fz8/Nz/bddskZGRWrp0qfr3769GjRopMTFR48ePt915i4qKktPp1AcffKDvvvtOTz75ZJ32s2u2uLg4/elPf1LPnj0VGBioXr16afTo0bY6b7NmzdKwYcPUrl27C963oWc7jYJlMxEREXK5XB5jLpdLfn5%2BioiIqKdVmXX11Verurpa/v7%2BZ2QtKSmxbc7w8PCznruIiAiFhYWdNa/L5VLz5s29uUxjoqKiVFRUZKtsGzZs0EMPPaRp06bpvvvuk/Tjv7mff3fscrncuc53XhuSs2U7m6ioKB06dEiWZdkmm/Rj4Y2OjlZGRoZWr14th8Nx3q8fds525MiRM7Y5/e9NavjZtm7dqq%2B//lppaWlnzJ1t7XY9bxQsm4mPj1dhYaHHPzCn06l27dqpadOm9biyi7Nz507Nnj3bYyw/P1%2BBgYFKSUk543mr3NxcderUyZtLNCY%2BPv6MPE6nU506dVJQUJDat2%2BvvLw891xpaan279%2BvxMREby/1gv3lL3/RmjVrPMby8/PVqlUr22T76quvNHXqVL366qu688473ePx8fH65ptvVFVV5R47fd5Oz5/rvDYU58q2detWLVy40GPbvXv3KioqSn5%2Bfg0%2B29atWzVw4ECPt6nx9//x/9YSExPP%2B/UjPj7e43OyurpaO3futEW2zZs3689//rPH9nv37lWrVq0kNfxsK1eu1OHDh9WnTx8lJSVp2LBhkqSkpCTFxMTY%2Brx5qJefXcQlGTFihDVt2jTr2LFj1p49e6y%2Bfftab7/9dn0v66IcPHjQ6ty5s7Vo0SLrxIkT1t69e63bbrvNeumll6xDhw5ZXbp0sd555x2rsrLS2rRpk5WYmGjt2rWrvpddq6lTp57xVgbffPONlZCQYG3cuNGqrKy0li1bZnXp0sUqKiqyLMuy/vznP1u9e/d2v5XB9OnTrbvuuqs%2Bln9eZ8v2hz/8wbruuuusHTt2WCdPnrRWrVpldezY0XI6nZZlNfxsp06dsm699VZr6dKlZ8ydOHHC6tOnj/W73/3OOn78uLVt2zare/fu1saNGy3Lqv281rfzZXM6nVZcXJz13nvvWSdPnrR27Nhh3XDDDe63fWno2UpLS63k5GRr9uzZ1vHjx63Dhw9b48aNs%2B6%2B%2B%2B5av35s3rzZ6tatm/X1119bx48ft%2BbPn2%2BlpKRYFRUV9ZzqR%2BfL9uGHH1qJiYnWli1brJMnT1off/yx1blzZ/dbhzT0bC6XyyosLHR/fP3111ZMTIxVWFhoFRQU2Pq8/RQFy4YKCwutBx54wEpMTLSSk5Ot3/3udw3q/YQu1Oeff26NGjXK6ty5s9WzZ09r1qxZVmVlpXtuyJAhVlxcnDVgwAD3F5CGKj4%2B3oqPj7euvfZa69prr3X/%2BbR169ZZAwYMsOLi4qw77rjD%2Bvzzz91zNTU11quvvmpdf/31VmJiovXggw82mPcbsqzzZ6upqbEWLFhg9enTx4qPj7duueUWa8OGDe59G3q2f/3rX1ZMTIw7008/vv/%2Be%2Bubb76xRo8ebcXHx1u9e/e2lixZ4rH/%2Bc5rfast2/r1660hQ4ZYiYmJ1g033GC99tprVnV1tXv/hpzNsixr9%2B7d1j333GMlJiZa1113nTVp0iTr4MGDlmXV/vVjyZIlVkpKihUfH2%2BlpqZa33zzTX1EOKfzZVu6dKk1YMAAKyEhwerTp4/1zjvveOzb0LP91M/fj83u5%2B00P8uyydNiAAAANsEzWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABg2P8BZQicPgFDiFYAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common4550917930795069320”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>843</td> <td class=”number”>26.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>315</td> <td class=”number”>9.8%</td> <td>

<div class=”bar” style=”width:37%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>275</td> <td class=”number”>8.6%</td> <td>

<div class=”bar” style=”width:33%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>218</td> <td class=”number”>6.8%</td> <td>

<div class=”bar” style=”width:26%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>189</td> <td class=”number”>5.9%</td> <td>

<div class=”bar” style=”width:23%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>138</td> <td class=”number”>4.3%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>136</td> <td class=”number”>4.2%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>107</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>92</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (88)</td> <td class=”number”>685</td> <td class=”number”>21.3%</td> <td>

<div class=”bar” style=”width:81%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>134</td> <td class=”number”>4.2%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme4550917930795069320”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>843</td> <td class=”number”>26.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>315</td> <td class=”number”>9.8%</td> <td>

<div class=”bar” style=”width:37%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>275</td> <td class=”number”>8.6%</td> <td>

<div class=”bar” style=”width:33%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>218</td> <td class=”number”>6.8%</td> <td>

<div class=”bar” style=”width:26%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>189</td> <td class=”number”>5.9%</td> <td>

<div class=”bar” style=”width:23%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>247.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>256.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>295.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>301.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>395.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_BERRY_FARMS12”><s>BERRY_FARMS12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_BERRY_FARMS07”><code>BERRY_FARMS07</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.93937</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_CHILDPOVRATE15”><s>CHILDPOVRATE15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_POVRATE15”><code>POVRATE15</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.93822</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_CHIPSTAX_STORES14”>CHIPSTAX_STORES14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>12</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1.1044</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>7</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>66.6%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram4447323976218706339”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives4447323976218706339,#minihistogram4447323976218706339”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives4447323976218706339”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles4447323976218706339”

aria-controls=”quantiles4447323976218706339” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram4447323976218706339” aria-controls=”histogram4447323976218706339”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common4447323976218706339” aria-controls=”common4447323976218706339”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme4447323976218706339” aria-controls=”extreme4447323976218706339”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles4447323976218706339”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>1.225</td>

</tr> <tr>

<th>95-th percentile</th> <td>6.15</td>

</tr> <tr>

<th>Maximum</th> <td>7</td>

</tr> <tr>

<th>Range</th> <td>7</td>

</tr> <tr>

<th>Interquartile range</th> <td>1.225</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>2.0149</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.8245</td>

</tr> <tr>

<th>Kurtosis</th> <td>1.6051</td>

</tr> <tr>

<th>Mean</th> <td>1.1044</td>

</tr> <tr>

<th>MAD</th> <td>1.5145</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.7407</td>

</tr> <tr>

<th>Sum</th> <td>3458.9</td>

</tr> <tr>

<th>Variance</th> <td>4.0598</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram4447323976218706339”>

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72Z0f0ChVVzS9bNbtXcknWzkzvwBWXB6tOnj/r06eP58w033KC//OUvWrVqlaZMmSJJntdbncv3HauPjIwM9e3b12vNZotUeXlVg/b9rkBr%2Bqbyh4WFym5vpoqKatXW1hnZMxBYNbdk3exWzS1ZNzu5zec2/c19fQVlwTqXhIQE7dixQy1atFBoaKhcLpfXcZfLpZYtWyouLk6VlZWqra1VWFiY55gktWzZsl7nio%2BPP%2Bt2oNN5TDU11vliORfT%2BWtr6yz5ObVqbsm62a2aW7JudnIHvsC6BFJPr7zyitavX%2B%2B1VlJSovbt2ysiIkKdO3dWcXGx51hFRYUOHDigSy%2B9VF26dJHb7dbu3bs9xx0Oh%2Bx2uzp27OizDAAAIHAFZcE6efKkHn30UTkcDp06dUpFRUV69913NWrUKEnS6NGjtXTpUpWUlKiyslJ5eXnq0qWLUlJSFBcXp4EDB%2Brpp5/W0aNHdfjwYS1atEgjRoyQzWaZC34AAKABArYxpKSkSJJqamokSVu2bJF0%2BmrTmDFjVFVVpYkTJ8rpdKpdu3ZatGiRkpOTJUmjRo2S0%2BnUbbfdpqqqKqWmpnq9KP2RRx7RrFmz1K9fPzVp0kQ33njjWW9mCgAAcD4h7oa%2Bohv14nQeM76nzRaq6/K2Gd%2B3sWyYdLWRfWy2UMXGRqm8vCpo7tXXh1VzS9bNbtXcknWzk9t87tatmxvdr76C8hYhAACAP1GwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwnxesvn37Kj8/X4cOHfL1qQEAAHzC5wXr5ptv1vr169W/f3/deeed2rx5s2pqanw9BgAAQKPxecHKysrS%2BvXr9ec//1mdO3fW448/rvT0dD355JPat2%2Bfr8cBAAAwzm%2BvwerWrZtyc3P11ltvadq0afrzn/%2BsQYMGaezYsfrss8/8NRYAAECD%2Ba1gnTp1SuvXr9cf/vAH5ebmqk2bNnrwwQfVpUsXZWZmau3atf4aDQAAoEFsvj5hSUmJVqxYoTfeeENVVVUaOHCgXnrpJV1xxRWex/Tq1UsPPfSQBg8e7OvxAAAAGsznBeuGG25Qx44ddffdd2vo0KFq0aLFWY9JT0/X0aNHfT0aAACAET4vWEuXLtWVV175g4/79NNPfTANAACAeT5/DVZSUpLGjRunLVu2eNZefPFF/eEPf5DL5fL1OAAAAMb5vGDNmTNHx44dU6dOnTxrffr0UV1dnebOnevrcQAAAIzzecF67733lJ%2Bfr8TERM9aYmKi8vLytG3btnrvs23bNqWlpSknJ%2BesY5s3b9aQIUPUo0cPDRw4UH/%2B8589xxYuXKguXbooJSXF6%2BPrr7%2BWJJ04cUL/9V//pWuuuUapqanKzs5WeXn5Tw8MAAAsx%2BcF6/jx44qIiDh7kNBQVVdX12uP5557TrNnz1aHDh3OOvbZZ59pypQpys7O1ocffqhp06bpkUce0fbt2z2Puemmm%2BRwOLw%2BWrVqJUmaP3%2B%2BiouLVVhYqE2bNsntduvBBx/8iWkBAIAV%2Bbxg9erVS3PnztU333zjWfvqq6/08MMPe71Vw/eJiIjQihUrzlmwXC6X7r77bvXv3182m03p6em65JJLvArW%2BdTU1GjFihW65557dMEFF6hFixaaNGmS3n77bX311Vf1DwkAACzN5z9FOG3aNN1xxx361a9%2BpejoaNXV1amqqkrt27fXyy%2B/XK89xowZc95j11xzja655hrPn2tqauR0OtWmTRvP2ueff65Ro0bpn//8py644AI9%2BOCD6t27tw4cOKBjx46pW7dunsdefPHFatq0qYqLi732AAAAOB%2BfF6z27dtr3bp1evfdd3XgwAGFhoaqY8eO6t27t8LCwoyfLy8vT5GRkRo0aJAkqW3btmrfvr0mT56s%2BPh4FRYWaty4cVqzZo3npxjtdrvXHna7/Ue9DqusrExOp9NrzWaLVHx8fAPTeAsL89sb8f8kNpuZec/kDrT8DWXV3JJ1s1s1t2Td7OQOntw%2BL1iSFB4erv79%2BzfqOdxut/Ly8lRUVKSlS5d6Xvc1cuRIjRw50vO4zMxMrVu3TmvWrPFc%2BXK73Q06d2FhofLz873WsrKylJ2d3aB9A11sbJTR/ez2Zkb3CxRWzS1ZN7tVc0vWzU7uwOfzgvXll19q3rx5%2BuKLL3T8%2BPGzjr/55psNPkddXZ0efPBBffbZZ3rllVfUvn377318QkKCysrKFBcXJ%2Bn067iiov5TBr755hu1bNmy3ufPyMhQ3759vdZstkiVl1f9iBQ/LNCavqn8YWGhstubqaKiWrW1dUb2DARWzS1ZN7tVc0vWzU5u87lNf3NfX355DVZZWZl69%2B6tyMjIRjnH448/ri%2B%2B%2BEKvvPLKWb%2BK549//KN69OihX/3qV561kpISDRo0SO3bt1dMTIyKi4uVkJAgSfrnP/%2BpkydPKjk5ud7nj4%2BPP%2Bt2oNN5TDU11vliORfT%2BWtr6yz5ObVqbsm62a2aW7JudnIHPp8XrB07dujNN9/0XC0y7aOPPtKaNWu0fv36c/6eQ5fLpYcfflh//OMflZCQoOXLl%2BvAgQMaNmyYwsLCdMstt%2BiZZ55RSkqKmjZtqqeeekrXXXed520cAAAAfojPC1bLli0bfOUqJSVF0umfEJTk%2BbU7DodDK1eu1LFjx3Tttdd6PadXr1564YUXNHnyZEmnX3vlcrnUqVMnvfjii2rbtq0kKTs7W1VVVbrppptUU1Oja6%2B9Vg899FCD5gUAANYS4m7oK7p/pNdee03/%2Bte/NHnyZIWEhPjy1H7ldB4zvqfNFqrr8ur/7vf%2BtmHS1Ub2sdlCFRsbpfLyqqC5lFwfVs0tWTe7VXNL1s1ObvO5W7dubnS/%2BvL5Fax3331XH3/8sVatWqV27dopNNT7hdqvvvqqr0cCAAAwyucFKzo62uuNQAEAAIKNzwvWnDlzfH1KAAAAn/LLGynt3btXCxcu9Polyv/4xz/8MQoAAIBxPi9YH3zwgYYMGaLNmzerqKhI0uk3Hx0zZoyRNxkFAADwN58XrPnz5%2Bv%2B%2B%2B/X2rVrPT9F2L59e82dO1eLFi3y9TgAAADG%2Bbxg/fOf/9To0aMlyettGq6//nqVlJT4ehwAAADjfF6wmjdvfs7fQVhWVqbw8HBfjwMAAGCczwvW5Zdfrscff1yVlZWetX379ik3N9fr9wMCAAAEKp%2B/TcODDz6o22%2B/XampqaqtrdXll1%2Bu6upqde7cWXPnzvX1OAAAAMb5vGC1bdtWRUVFeuedd7Rv3z41bdpUHTt21NVXX22pX50DAACCl88LliQ1adJE/fv398epAQAAGp3PC1bfvn2/90oV74UFAAACnc8L1qBBg7wKVm1trfbt2yeHw6Hbb7/d1%2BMAAAAY5/OCNWXKlHOub9q0SX/72998PA0AAIB5fvldhOfSv39/rVu3zt9jAAAANNjPpmDt3LlTbrfb32MAAAA0mM9vEY4aNeqsterqapWUlGjAgAG%2BHgcAAMA4nxesxMTEs36KMCIiQiNGjNDIkSN9PQ4AAIBxPi9YvFs7AAAIdj4vWG%2B88Ua9Hzt06NBGnAQAAKBx%2BLxgTZ8%2BXXV1dWe9oD0kJMRrLSQkhIIFAAACks8L1vPPP68XXnhB48aNU1JSktxutz7//HM999xz%2Bt3vfqfU1FRfjwQAAGCUX16DVVBQoDZt2njWevbsqfbt22vs2LEqKiry9UgAAABG%2Bfx9sPbv36%2BYmJiz1u12u0pLS309DgAAgHE%2BL1gJCQmaO3euysvLPWsVFRWaN2%2BefvGLX/yovbZt26a0tDTl5OScdWz9%2BvUaPHiwevTooeHDh%2Bu9997zHKurq9P8%2BfPVr18/9erVS2PHjtWXX37pOe5yuTRp0iSlpaWpd%2B/emj59uo4fP/4T0gIAACvyecGaNm2aNmzYoLS0NPXs2VNXXnmlrrrqKq1atUpTp06t9z7PPfecZs%2BerQ4dOpx1bNeuXcrNzdWUKVP017/%2BVZmZmbr33nt1%2BPBhSdLy5cu1du1aFRQU6K233lJiYqKysrI8L7KfOXOmqqurVVRUpJUrV6qkpER5eXlmPgEAACDo%2Bfw1WL1799bbb7%2Btd955R4cPH5bb7VabNm3061//Ws2bN6/3PhEREVqxYoUee%2BwxnThxwuvYa6%2B9pvT0dKWnp0uShgwZomXLlmnNmjW66667VFhYqMzMTF188cWSpJycHKWmpurTTz9Vu3bttGXLFr3%2B%2BuuKi4uTJN1zzz2aOHGicnNz1aRJE0OfCQAAEKx8XrAkqVmzZurXr58OHz6s9u3b/6Q9xowZc95jxcXFnnJ1RteuXeVwOHT8%2BHHt2bNHXbt29RyLjo5Whw4d5HA4dOzYMYWFhSkpKclzvFu3bvr222%2B1d%2B9er/XzKSsrk9Pp9Fqz2SIVHx9f33j1Ehb2s/lVkvVis5mZ90zuQMvfUFbNLVk3u1VzS9bNTu7gye3zgnX8%2BHHNmjVL69atkyTt2LFDFRUVuu%2B%2B%2B/TUU0/Jbrc3%2BBwul%2BusF9LHxMRoz549%2Buabb%2BR2u895vLy8XC1atFB0dLTXr/M589j/%2B7qx71NYWKj8/HyvtaysLGVnZ/%2BUOEEjNjbK6H52ezOj%2BwUKq%2BaWrJvdqrkl62Ynd%2BDzecF68skntWvXLuXl5emBBx7wrNfW1iovL0%2BPPPKIkfN8941Mf8zxH3ruD8nIyFDfvn291my2SJWXVzVo3%2B8KtKZvKn9YWKjs9maqqKhWbW2dkT0DgVVzS9bNbtXcknWzk9t8btPf3NeXzwvWpk2btGzZMiUmJio3N1fS6bdomDNnjoYOHWqkYMXGxsrlcnmtuVwuxcXFqUWLFgoNDT3n8ZYtWyouLk6VlZWqra1VWFiY55gktWzZsl7nj4%2BPP%2Bt2oNN5TDU11vliORfT%2BWtr6yz5ObVqbsm62a2aW7JudnIHPp9fAqmqqlJiYuJZ63Fxcfr222%2BNnCM5OVk7duzwWnM4HOrevbsiIiLUuXNnFRcXe45VVFTowIEDuvTSS9WlSxe53W7t3r3b67l2u10dO3Y0Mh8AAAhuPi9Yv/jFL/S3v/1NkvetuI0bN%2BrCCy80co5bbrlF77//vt5%2B%2B22dOHFCK1as0P79%2BzVkyBBJ0ujRo7V06VKVlJSosrJSeXl56tKli1JSUhQXF6eBAwfq6aef1tGjR3X48GEtWrRII0aMkM3ml58JAAAAAcbnjeHWW2/VhAkTdPPNN6uurk5LlizRjh07tGnTJk2fPr3e%2B6SkpEiSampqJElbtmyRdPpq0yWXXKK8vDzNmTNHpaWl6tSpk5599lm1bt1akjRq1Cg5nU7ddtttqqqqUmpqqteL0h955BHNmjVL/fr1U5MmTXTjjTee881MAQAAziXE3dBXdP8EK1eu1LJly7R37141bdpUHTt2VGZmpq6//npfj%2BIzTucx43vabKG6Lm%2Bb8X0by4ZJVxvZx2YLVWxslMrLq4LmXn19WDW3ZN3sVs0tWTc7uc3nbt26/u%2BxaZLPr2AdPXpUN998s26%2B%2BWZfnxoAAMAnfP4arH79%2BjX4bRAAAAB%2BznxesFJTU7VhwwZfnxYAAMBnfH6L8IILLtBjjz2mgoIC/eIXvzjrd/vNmzfP1yMBAAAY5fOCtWfPHl100UWS6v%2BrZwAAAAKJzwpWTk6O5s%2Bfr5dfftmztmjRImVlZflqBAAAAJ/w2Wuwtm7detZaQUGBr04PAADgMz4rWOf6yUF%2BmhAAAAQjnxWskJCQeq0BAAAEOp%2B/TQMAAECwo2ABAAAY5rOfIjx16pQmT578g2u8DxYAAAh0PitYV1xxhcrKyn5wDQAAIND5rGD93/e/AgAACGa8BgsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAw3z2uwh96cMPP9Qdd9zhteZ2u3Xq1CktXbpUY8aMUXh4uNfx//7v/9ZvfvMbSdLSpUu1fPlyOZ1OJSUlafr06UpOTvbZ/AAAILAFZcHq1auXHA6H19ozzzyj3bt3S5ISEhK0devWcz5369atWrhwoZ5//nklJSVp6dKlGjdunDZv3qzIyMhGnx0AAAQ%2BS9wi/Pe//60lS5bogQce%2BMHHFhYWavjw4erevbuaNm2qO%2B%2B8U5L01ltvNfaYAAAgSATlFazvWrBggW6%2B%2BWZdeOGF%2BvKbQYVYAAAUeUlEQVTLL1VVVaWsrCxt375d4eHhuuOOO5SZmamQkBAVFxdr0KBBnueGhoaqS5cucjgcuuGGG%2Bp1vrKyMjmdTq81my1S8fHxRnOFhQVWP7bZzMx7Jneg5W8oq%2BaWrJvdqrkl62Ynd/DkDvqCdfDgQW3evFmbN2%2BWJEVHR%2BuSSy7R7bffrvnz5%2Bvvf/%2B7Jk6cqObNm2vEiBFyuVyKiYnx2iMmJkbl5eX1PmdhYaHy8/O91rKyspSdnd3wQAEsNjbK6H52ezOj%2BwUKq%2BaWrJvdqrkl62Ynd%2BAL%2BoK1fPlyDRgwQK1bt5YkdevWTS%2B//LLneO/evTVq1CitWrVKI0aMkHT6BfENkZGRob59%2B3qt2WyRKi%2BvatC%2B3xVoTd9U/rCwUNntzVRRUa3a2jojewYCq%2BaWrJvdqrkl62Ynt/ncpr%2B5r6%2BgL1ibNm1Sbm7u9z4mISFBmzZtkiTFxsbK5XJ5HXe5XOrcuXO9zxkfH3/W7UCn85hqaqzzxXIupvPX1tZZ8nNq1dySdbNbNbdk3ezkDnyBdQnkR9q1a5dKS0t19dVXe9Y2bNig//mf//F63N69e9W%2BfXtJUnJysoqLiz3HamtrtXPnTnXv3t03QwMAgIAX1AVr586datGihaKjoz1rTZo00RNPPKH33ntPp06d0v/%2B7/9q5cqVGj16tCRp9OjReuONN/TJJ5%2BourpaixcvVnh4uPr06eOnFAAAINAE9S3Cr7/%2B2vPaqzP69%2B%2BvadOm6dFHH9WhQ4fUqlUrTZs2TQMGDJAkXXPNNbrvvvs0adIkHTlyRCkpKSooKFDTpk39EQEAAASgEHdDX9GNenE6jxnf02YL1XV524zv21g2TLr6hx9UDzZbqGJjo1ReXhU09%2Brrw6q5Jetmt2puybrZyW0%2Bd%2BvWzY3uV19BfYsQAADAHyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYUFbsJKSkpScnKyUlBTPx6OPPipJ%2BuCDDzRixAhdfvnluuGGG7RmzRqv5y5dulQDBw7U5ZdfrtGjR2vHjh3%2BiAAAAAKUzd8DNKaNGzeqXbt2XmtlZWW65557NH36dA0ePFgfffSRxo8fr44dOyolJUVbt27VwoUL9fzzzyspKUlLly7VuHHjtHnzZkVGRvopCQAACCRBewXrfNauXavExESNGDFCERERSktLU9%2B%2BffXaa69JkgoLCzV8%2BHB1795dTZs21Z133ilJeuutt/w5NgAACCBBXbDmzZunPn36qGfPnpo5c6aqqqpUXFysrl27ej2ua9euntuA3z0eGhqqLl26yOFw%2BHR2AAAQuIL2FuFll12mtLQ0PfHEE/ryyy81adIkPfzww3K5XGrTpo3XY1u0aKHy8nJJksvlUkxMjNfxmJgYz/H6KCsrk9Pp9Fqz2SIVHx//E9OcW1hYYPVjm83MvGdyB1r%2BhrJqbsm62a2aW7JudnIHT%2B6gLViFhYWef7744os1ZcoUjR8/XldcccUPPtftdjf43Pn5%2BV5rWVlZys7ObtC%2BgS42NsrofnZ7M6P7BQqr5pasm92quSXrZid34AvagvVd7dq1U21trUJDQ%2BVyubyOlZeXKy4uTpIUGxt71nGXy6XOnTvX%2B1wZGRnq27ev15rNFqny8qqfOP25BVrTN5U/LCxUdnszVVRUq7a2zsiegcCquSXrZrdqbsm62cltPrfpb%2B7rKygL1s6dO7VmzRpNnTrVs1ZSUqLw8HClp6fr9ddf93r8jh071L17d0lScnKyiouLNWzYMElSbW2tdu7cqREjRtT7/PHx8WfdDnQ6j6mmxjpfLOdiOn9tbZ0lP6dWzS1ZN7tVc0vWzU7uwBdYl0DqqWXLliosLFRBQYFOnjypffv2acGCBcrIyNBNN92k0tJSvfbaazpx4oTeeecdvfPOO7rlllskSaNHj9Ybb7yhTz75RNXV1Vq8eLHCw8PVp08f/4YCAAABIyivYLVp00YFBQWaN2%2BepyANGzZMOTk5ioiI0LPPPqvZs2fr4YcfVkJCgp588kn98pe/lCRdc801uu%2B%2B%2BzRp0iQdOXJEKSkpKigoUNOmTf2cCgAABIqgLFiS1KtXL7366qvnPbZ69erzPvfWW2/Vrbfe2lijAQCAIBeUtwgBAAD8iYIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwLGgLVmlpqbKyspSamqq0tDRNnTpVFRUVOnjwoJKSkpSSkuL18ac//cnz3PXr12vw4MHq0aOHhg8frvfee8%2BPSQAAQKCx%2BXuAxjJu3DglJydr69atOnbsmLKysvTEE09o/PjxkiSHw3HO5%2B3atUu5ubnKz8/XVVddpU2bNunee%2B/Vxo0b1bZtW19GAAAAASoor2BVVFQoOTlZkydPVlRUlNq2bathw4Zp%2B/btP/jc1157Tenp6UpPT1dERISGDBmiSy65RGvWrPHB5AAAIBgE5RUsu92uOXPmeK0dOnRI8fHxnj8/8MADev/991VTU6ORI0cqOztbTZo0UXFxsdLT072e27Vr1/Ne8TqXsrIyOZ1OrzWbLdLr/CaEhQVWP7bZzMx7Jneg5W8oq%2BaWrJvdqrkl62Ynd/DkDsqC9V0Oh0PLli3T4sWLFR4erh49eui6667TY489pl27dmnChAmy2WyaOHGiXC6XYmJivJ4fExOjPXv21Pt8hYWFys/P91rLyspSdna2kTyBKjY2yuh%2Bdnszo/sFCqvmlqyb3aq5JetmJ3fgC/qC9dFHH2n8%2BPGaPHmy0tLSJEmvvvqq5/ill16qu%2B%2B%2BW88%2B%2B6wmTpwoSXK73Q06Z0ZGhvr27eu1ZrNFqry8qkH7flegNX1T%2BcPCQmW3N1NFRbVqa%2BuM7BkIrJpbsm52q%2BaWrJud3OZzm/7mvr6CumBt3bpV999/v2bOnKmhQ4ee93EJCQn6%2Buuv5Xa7FRsbK5fL5XXc5XIpLi6u3ueNj48/63ag03lMNTXW%2BWI5F9P5a2vrGvVz%2Bpun/7fR9m4MGyZd7e8RGl1j/53/XFk1t2Td7OQOfIF1CeRH%2BPjjj5Wbm6sFCxZ4lasPPvhAixcv9nrs3r17lZCQoJCQECUnJ2vHjh1exx0Oh7p37%2B6TuQEAQOALyoJVU1OjGTNmaMqUKerdu7fXsebNm2vRokVavXq1Tp06JYfDoT/96U8aPXq0JOmWW27R%2B%2B%2B/r7ffflsnTpzQihUrtH//fg0ZMsQfUQAAQAAKcTf0BUc/Q9u3b9dvf/tbhYeHn3Vs48aN2rlzp/Lz87V//341b95ct912m/7whz8oNPR039y8ebPmzZun0tJSderUSdOnT1evXr0aNJPTeaxBzz8Xmy1U1%2BVtM74vAlMw3yK02UIVGxul8vKqoLl9UB9WzS1ZNzu5zedu3bq50f3qKyhfg9WzZ099/vnn5z2ekJCg66677rzHBwwYoAEDBjTGaAAAwAKC8hYhAACAP1GwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMOC8o1GAQBoDPwSeNQXBQsAfgD/UwXwY3GLEAAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAzjpwgBAH4TaD%2BhCdQXV7AAAAAM4woWAABBKpCuEG5/7Hp/j2AUBQsAgkwg/U8VCFbcIgQAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbDOobS0VHfddZdSU1N17bXX6sknn1RdXZ2/xwIAAAGC98E6hwkTJqhbt27asmWLjhw5orvvvlutWrXS73//e3%2BPBgAAAgBXsL7D4XBo9%2B7dmjJlipo3b67ExERlZmaqsLDQ36MBAIAAwRWs7yguLlZCQoJiYmI8a926ddO%2BfftUWVmp6OjoH9yjrKxMTqfTa81mi1R8fLzRWcPC6Mf4D5steP99OPPvOv/OA8EtmL7GKVjf4XK5ZLfbvdbOlK3y8vJ6FazCwkLl5%2Bd7rd17772aMGGCuUF1usjd3vYLZWRkGC9vP2dlZWUqLCwkt4WUlZXppZee91t2f/2ONKv/nVsxu5VzL1y4MKhyB09VNMjtdjfo%2BRkZGVq1apXXR0ZGhqHp/sPpdCo/P/%2Bsq2XBjtzWyi1ZN7tVc0vWzU7u4MnNFazviIuLk8vl8lpzuVwKCQlRXFxcvfaIj48PmgYOAAB%2BPK5gfUdycrIOHTqko0ePetYcDoc6deqkqKgoP04GAAACBQXrO7p27aqUlBTNmzdPlZWVKikp0ZIlSzR69Gh/jwYAAAJE2EMPPfSQv4f4ufn1r3%2BtoqIiPfroo1q3bp1GjBihsWPHKiQkxN%2BjnSUqKkpXXnml5a6ukdtauSXrZrdqbsm62ckdHLlD3A19RTcAAAC8cIsQAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKVgAqLS3VXXfdpdTUVF177bV68sknVVdX5%2B%2BxfGLbtm1KS0tTTk6Ov0fxqdLSUmVlZSk1NVVpaWmaOnWqKioq/D2WT%2BzevVu33367rrjiCqWlpWnSpElyOp3%2BHsunHn/8cSUlJfl7DJ9ISkpScnKyUlJSPB%2BPPvqov8fymcWLF6t379667LLLlJmZqYMHD/p7pEb14Ycfev1dp6SkKDk5OSj%2BfadgBaAJEyaoTZs22rJli5YsWaItW7bopZde8vdYje65557T7Nmz1aFDB3%2BP4nPjxo2T3W7X1q1btWrVKn3xxRd64okn/D1Wozt58qTuuOMOXXnllfrggw9UVFSkI0eOyEq/QnXXrl1avXq1v8fwqY0bN8rhcHg%2BZs6c6e%2BRfGL58uVas2aNli5dqvfee0%2BdOnXSiy%2B%2B6O%2BxGlWvXr28/q4dDofuvfde/eY3v/H3aA1GwQowDodDu3fv1pQpU9S8eXMlJiYqMzNThYWF/h6t0UVERGjFihWWK1gVFRVKTk7W5MmTFRUVpbZt22rYsGHavn27v0drdNXV1crJydHdd9%2Bt8PBwxcXF6brrrtMXX3zh79F8oq6uTrNmzVJmZqa/R4EPvPDCC8rJydFFF12k6OhozZgxQzNmzPD3WD7173//W0uWLNEDDzzg71EajIIVYIqLi5WQkKCYmBjPWrdu3bRv3z5VVlb6cbLGN2bMGDVv3tzfY/ic3W7XnDlz1KpVK8/aoUOHFB8f78epfCMmJkYjR46UzWaTJO3du1evv/56UHx3Wx%2BvvvqqIiIiNHjwYH%2BP4lPz5s1Tnz591LNnT82cOVNVVVX%2BHqnRffXVVzp48KC%2B%2BeYbDRo0SKmpqcrOztbRo0f9PZpPLViwQDfffLMuvPBCf4/SYBSsAONyuWS3273WzpSt8vJyf4wEH3M4HFq2bJnGjx/v71F8prS0VMnJyRo0aJBSUlKUnZ3t75Ea3ddff62FCxdq1qxZ/h7Fpy677DKlpaVp8%2BbNKiws1CeffKKHH37Y32M1usOHD0s6fXt0yZIlWr16tQ4fPmypK1gHDx7U5s2b9fvf/97foxhBwQpAbrfb3yPATz766CONHTtWkydPVlpamr/H8ZmEhAQ5HA5t3LhR%2B/fvD4rbBz9kzpw5Gj58uDp16uTvUXyqsLBQI0eOVHh4uC6%2B%2BGJNmTJFRUVFOnnypL9Ha1Rn/rt%2B5513qk2bNmrbtq0mTJigrVu36sSJE36ezjeWL1%2BuAQMGqHXr1v4exQgKVoCJi4uTy%2BXyWnO5XAoJCVFcXJyfpoIvbN26VXfddZemTZumMWPG%2BHscnwsJCVFiYqJycnJUVFQU1LdOPvjgA/3jH/9QVlaWv0fxu3bt2qm2tlZHjhzx9yiN6sxLAP7vHYqEhAS53e6gz37Gpk2b1LdvX3%2BPYQwFK8AkJyfr0KFDXv9zcTgc6tSpk6Kiovw4GRrTxx9/rNzcXC1YsEBDhw719zg%2B88EHH2jgwIFeb0MSGnr6P1tNmjTx11iNbs2aNTpy5IiuvfZapaamavjw4ZKk1NRUrVu3zs/TNZ6dO3dq7ty5XmslJSUKDw8P%2Btcctm3bVtHR0dq1a5dnrbS0VE2aNAn67NLpn5YtLS3V1Vdf7e9RjKFgBZiuXbsqJSVF8%2BbNU2VlpUpKSrRkyRKNHj3a36OhkdTU1GjGjBmaMmWKevfu7e9xfCo5OVmVlZV68sknVV1draNHj2rhwoXq2bNnUP/Aw9SpU7Vp0yatXr1aq1evVkFBgSRp9erVQfUd/ne1bNlShYWFKigo0MmTJ7Vv3z4tWLBAGRkZCgsL8/d4jcpms2nEiBF65pln9K9//UtHjhzRokWLNHjwYM8PeQSznTt3qkWLFoqOjvb3KMaEuHlBT8A5fPiwZs6cqb///e%2BKjo7WqFGjdO%2B99yokJMTfozWqlJQUSacLhyTPf3QcDoffZvKF7du367e//a3Cw8PPOrZx40YlJCT4YSrf%2BfzzzzV79mx99tlnioyM1FVXXaWpU6eqTZs2/h7NZw4ePKh%2B/frp888/9/coje7DDz/UvHnz9Pnnnys8PFzDhg1TTk6OIiIi/D1aozt58qTmzJmjdevW6dSpUxo4cKBmzpxpibsTzz77rNauXauioiJ/j2IMBQsAAMAwbhECAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGH/Dw0gzJFe9r8XAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common4447323976218706339”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2138</td> <td class=”number”>66.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.5</td> <td class=”number”>208</td> <td class=”number”>6.5%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>137</td> <td class=”number”>4.3%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.225</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.15</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.000000000000001</td> <td class=”number”>82</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.5</td> <td class=”number”>77</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>44</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme4447323976218706339”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2138</td> <td class=”number”>66.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.225</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.5</td> <td class=”number”>208</td> <td class=”number”>6.5%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.7500000000000002</td> <td class=”number”>29</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>4.5</td> <td class=”number”>77</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:73%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:90%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>44</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:42%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.15</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.000000000000001</td> <td class=”number”>82</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:78%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_CHIPSTAX_VENDM14”>CHIPSTAX_VENDM14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>22</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.7%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>3.8955</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>8</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>22.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram8098190314900289194”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives8098190314900289194,#minihistogram8098190314900289194”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives8098190314900289194”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles8098190314900289194”

aria-controls=”quantiles8098190314900289194” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram8098190314900289194” aria-controls=”histogram8098190314900289194”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common8098190314900289194” aria-controls=”common8098190314900289194”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme8098190314900289194” aria-controls=”extreme8098190314900289194”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles8098190314900289194”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>1</td>

</tr> <tr>

<th>Median</th> <td>4.75</td>

</tr> <tr>

<th>Q3</th> <td>6</td>

</tr> <tr>

<th>95-th percentile</th> <td>7</td>

</tr> <tr>

<th>Maximum</th> <td>8</td>

</tr> <tr>

<th>Range</th> <td>8</td>

</tr> <tr>

<th>Interquartile range</th> <td>5</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>2.6282</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.67468</td>

</tr> <tr>

<th>Kurtosis</th> <td>-1.3539</td>

</tr> <tr>

<th>Mean</th> <td>3.8955</td>

</tr> <tr>

<th>MAD</th> <td>2.3128</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.44778</td>

</tr> <tr>

<th>Sum</th> <td>12201</td>

</tr> <tr>

<th>Variance</th> <td>6.9077</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram8098190314900289194”>

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0aJBmzZqlmTNnqrq6WvHx8SosLFRgYKDJHQEAAG9h2YDl7%2B%2BvhQsXauHChS3mkpKSWlwm/GeZmZnKzMxsz/IAAICFWfISIQAAgJkIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABrPsV%2BUAgFH6zdtudgmtsm3mALNLAH7xOIMFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDDLBqyYmBjFxcUpPj6%2B%2Befxxx%2BXJJWUlCgtLU2JiYkaOXKkNm3a5PLYoqIiDR8%2BXImJicrIyFBpaakZLQAAAC9lM7uA9rR9%2B3ZdffXVLmOVlZWaPn265s2bp1GjRumjjz7StGnT1K1bN8XHx2vXrl3Kz8/Xc889p5iYGBUVFSkrK0s7d%2B5UUFCQSZ0AAABvYtkzWOezefNmRUdHKy0tTQEBAUpJSVFqaqqKi4slSXa7XWPHjlVCQoICAwM1ZcoUSdLu3bvNLBsAAHgRS5/BWrlypT755BMdP35cd955p%2BbOnauysjLFxsa6LBcbG6tt27ZJksrKyjRixIjmOV9fX/Xq1UsOh0MjR45063krKytVVVXlMmazBSkyMrKNHbny8/OufGyzuVfv2b68rT93WLU3q/YleWdP7Gvtx93Xtr1YdZtZsS%2BPC1ipqakaO3asxo0bpyuvvPKi19OnTx%2BlpKToiSee0Ndff62ZM2dq0aJFcjqdioqKclk2LCxMNTU1kiSn06nQ0FCX%2BdDQ0OZ5d9jtdhUUFLiMZWdnKycn5yK7sYbw8I6tWj4kpEM7VWI%2Bq/Zm1b68Dfta%2B2nta9terLrNrNSXxwWscePGaevWrVq9erVuvvlmTZgwQampqbLZWleq3W5v/v/rr79eeXl5mjZtmvr27XvBxzY1NbW67n%2BWnp6u1NRUlzGbLUg1NSfatN6f87ak727/fn6%2BCgnpoNraOjU0NLZzVZeWVXuzal%2BS9%2B1nEvtaezL6ON5aVt1m7dmXWaHY4wJWdna2srOzVVZWpi1btmjJkiVatGiR7rnnHqWlpalbt24Xtd6rr75aDQ0N8vX1ldPpdJmrqalRRESEJCk8PLzFvNPpVI8ePdx%2BrsjIyBaXA6uqflB9vXV2hovR2v4bGhot%2B5pZtTer9uVt2Nfaj6e8TlbdZlbqy2P/ada7d2/NmTNHu3fv1qOPPqr//u//1ogRI3T//ffrr3/96798bHl5uZYtW%2BYydujQIfn7%2B2vw4MEtPnahtLRUCQkJkqS4uDiVlZU1zzU0NKi8vLx5HgAA4EI8NmD99NNPeuONN/TAAw9ozpw5ioqK0iOPPKJevXpp8uTJ2rx583kf27lzZ9ntdhUWFur06dM6fPiwVq1apfT0dN19992qqKhQcXGxTp06pT179mjPnj2aMGGCJCkjI0MbNmzQvn37VFdXp9WrV8vf319Dhgy5RJ0DAABv53GXCA8dOqT169drw4YNOnHihIYPH66XXnrJ5d6ppKQkPfbYYxo1atQ51xEVFaXCwkKtXLmyOSCNGTNGubm5CggI0Jo1a7R48WItWrRIXbt21fLly9WzZ09J0qBBgzRr1izNnDlT1dXVio%2BPV2FhoQIDAy9J/wAAwPt5XMAaOXKkunXrpqlTp%2Bqee%2B5RWFhYi2UGDx6sY8eO/cv1JCUl6dVXXz3v3MaNG8/72MzMTGVmZraucAAAgP/P4wJWUVGR%2Bvfvf8HlPv3000tQDQAAQOt53D1YMTExysrK0ptvvtk89uKLL%2BqBBx5o8e4%2BAAAAT%2BRxAWvp0qX64Ycf1L179%2BaxIUOGqLGxscU7AwEAADyRx10ifO%2B997R582aFh4c3j0VHR2vFihW66667TKwMAADAPR53BuvkyZMKCAhoMe7r66u6ujoTKgIAAGgdjwtYSUlJWrZsmb7//vvmsW%2B//VaLFi1y62tuAAAAzOZxlwgfffRR3Xfffbr55psVHBysxsZGnThxQtdcc41efvlls8sDAAC4II8LWNdcc422bt2qd955R3/729/k6%2Burbt26aeDAgfLz8zO7PAAAgAvyuIAlSf7%2B/rrtttvMLgMAAOCieFzA%2Bvrrr7Vy5Up99tlnOnnyZIv5t956y4SqAAAA3OdxAevRRx9VZWWlBg4cqKCgILPLAQAAaDWPC1ilpaV66623FBERYXYpAAAAF8XjPqahc%2BfOnLkCAABezeMC1tSpU1VQUKCmpiazSwEAALgoHneJ8J133tHHH3%2Bs1157TVdffbV8fV0z4KuvvmpSZQAAAO7xuIAVHBysQYMGmV0GAADARfO4gLV06VKzSwAAAGgTj7sHS5K%2B%2BOIL5efn65FHHmke%2B%2BSTT0ysCAAAwH0eF7BKSko0evRo7dy5U1u2bJF05sNHJ02axIeMAgAAr%2BBxAeupp57S7373O23evFk%2BPj6Sznw/4bJly/T000%2BbXB0AAMCFeVzA%2Br//%2Bz9lZGRIUnPAkqQ77rhDhw4dMqssAAAAt3lcwOrUqdM5v4OwsrJS/v7%2BJlQEAADQOh4XsBITE7VkyRIdP368eezw4cOaM2eObr75ZhMrAwAAcI/HfUzDI488onvvvVfJyclqaGhQYmKi6urq1KNHDy1btszs8gAAAC7I4wJWly5dtGXLFu3Zs0eHDx9WYGCgunXrpgEDBrjckwUAAOCpPC5gSdJll12m2267zewyAAAALorHBazU1NR/eaaKz8ICAACezuMC1ogRI1wCVkNDgw4fPiyHw6F77733ota5ZMkSvfTSSzp48KCkMx9munLlSn3xxRe68sorNXXqVI0ePbp5%2BaKiIq1bt05VVVWKiYnRvHnzFBcX17bGAADAL4bHBay8vLxzju/YsUN/%2BctfWr2%2B/fv3a%2BPGjc1/rqys1PTp0zVv3jyNGjVKH330kaZNm6Zu3bopPj5eu3btUn5%2Bvp577jnFxMSoqKhIWVlZ2rlzp4KCgi66LwAA8MvhcR/TcD633Xabtm7d2qrHNDY2auHChZo8eXLz2ObNmxUdHa20tDQFBAQoJSVFqampKi4uliTZ7XaNHTtWCQkJCgwM1JQpUyRJu3fvNqwXAABgbV4TsMrLy9XU1NSqx7z66qsKCAjQqFGjmsfKysoUGxvrslxsbKxKS0vPOe/r66tevXrJ4XC0oXoAAPBL4nGXCCdOnNhirK6uTocOHdKwYcPcXs93332n/Px8vfzyyy7jTqdTUVFRLmNhYWGqqalpng8NDXWZDw0NbZ53R2VlpaqqqlzGbLYgRUZGur0Od/j5eU0%2BliTZbO7Ve7Yvb%2BvPHVbtzap9Sd7ZE/ta%2B3H3tW0vVt1mVuzL4wJWdHR0i3cRBgQEKC0tTePHj3d7PUuXLtXYsWPVvXt3HTlypFU1tPZM2c/Z7XYVFBS4jGVnZysnJ6dN6/V24eEdW7V8SEiHdqrEfFbtzap9eRv2tfbT2te2vVh1m1mpL48LWEZ8WntJSYk%2B%2BeQTbdmypcVceHi4nE6ny1hNTY0iIiLOO%2B90OtWjRw%2B3nz89PV2pqakuYzZbkGpqTri9Dnd4W9J3t38/P1%2BFhHRQbW2dGhoa27mqS8uqvVm1L8n79jOJfa09GX0cby2rbrP27MusUOxxAWvDhg1uL3vPPfecc3zTpk2qrq7W0KFDJf3jjFRycrLuu%2B%2B%2BFsGrtLRUCQkJkqS4uDiVlZVpzJgxks58TER5ebnS0tLcrisyMrLF5cCqqh9UX2%2BdneFitLb/hoZGy75mVu3Nqn15G/a19uMpr5NVt5mV%2BvK4gDVv3jw1Nja2uEzn4%2BPjMubj43PegDV37lz9x3/8R/Ofjx49qvT0dG3cuFGNjY1as2aNiouLNXr0aL3//vvas2eP7Ha7JCkjI0OzZs3SXXfdpZiYGD3//PPy9/fXkCFDjG8WAABYkscFrOeee04vvPCCsrKyFBMTo6amJh08eFDPPvusfvOb3yg5OfmC6wgNDXW5Ub2%2Bvl7Sme85lKQ1a9Zo8eLFWrRokbp27arly5erZ8%2BekqRBgwZp1qxZmjlzpqqrqxUfH6/CwkIFBga2Q7cAAMCKPC5gLVu2TIWFhS7v9OvXr5%2BuueYa3X///ee8r%2BpCrr766uZPcZekpKQklw8f/bnMzExlZma2%2BnkAAAAkD/wcrC%2B//LLFxyRIUkhIiCoqKkyoCAAAoHU8LmB17dpVy5Ytc/ncqdraWq1cuVK/%2BtWvTKwMAADAPR53ifDRRx/V7NmzZbfb1bFjR/n6%2Bur48eMKDAzU008/bXZ5AAAAF%2BRxAWvgwIF6%2B%2B23tWfPHh09elRNTU2KiorSLbfcok6dOpldHgAAwAV5XMCSpA4dOujWW2/V0aNHdc0115hdDgAAQKt4XMA6efKkFi5cqK1bt0o68yGgtbW1mjVrlv7whz8oJCTE5AoBwLPd%2Bcc/m10C8IvncTe5L1%2B%2BXPv379eKFSvk6/uP8hoaGrRixQoTKwMAAHCPxwWsHTt26L/%2B6790xx13NH/pc0hIiJYuXaqdO3eaXB0AAMCFeVzAOnHihKKjo1uMR0RE6Mcff7z0BQEAALSSxwWsX/3qV/rLX/4iSS7fPbh9%2B3ZdddVVZpUFAADgNo%2B7yT0zM1MzZszQuHHj1NjYqLVr16q0tFQ7duzQvHnzzC4PAADggjwuYKWnp8tms%2BmVV16Rn5%2BfnnnmGXXr1k0rVqzQHXfcYXZ5AAAAF%2BRxAevYsWMaN26cxo0bZ3YpAAAAF8Xj7sG69dZbXe69AgAA8DYeF7CSk5O1bds2s8sAAAC4aB53ifDKK6/U73//exUWFupXv/qVLrvsMpf5lStXmlQZAACAezwuYH3%2B%2Bee67rrrJEk1NTUmVwMAANB6HhOwcnNz9dRTT%2Bnll19uHnv66aeVnZ1tYlUAAACt5zH3YO3atavFWGFhoQmVAAAAtI3HBKxzvXOQdxMCAABv5DEB6%2BwXO19oDAAAwNN5TMACAACwCgIWAACAwTzmXYQ//fSTZs%2BefcExPgcLAAB4Oo8JWH379lVlZeUFxwAAADydxwSsf/78KwAAAG/GPVgAAAAGI2ABAAAYzLIB68CBA7r33nvVt29fpaSkaObMmaqqqpIklZSUKC0tTYmJiRo5cqQ2bdrk8tiioiINHz5ciYmJysjIUGlpqRktAAAAL2XJgHX69Gndd9996t%2B/v0pKSrRlyxZVV1frscceU2VlpaZPn66JEyeqpKRE8%2BbN04IFC%2BRwOCSd%2Bcqe/Px8Pfnkk9q7d6%2BGDh2qrKws/fjjjyZ3BQAAvIUlA1ZdXZ1yc3M1depU%2Bfv7KyIiQrfffrs%2B%2B%2Bwzbd68WdHR0UpLS1NAQIBSUlKUmpqq4uJiSZLdbtfYsWOVkJCgwMBATZkyRZK0e/duM1sCAABexGPeRWik0NBQjR8/vvnPX3zxhV5//XXdeeedKisrU2xsrMvysbGx2rZtmySprKxMI0aMaJ7z9fVVr1695HA4NHLkSLeev7Kysvly5Fk2W5AiIyMvtqVz8vPzrnxss7lX79m%2BvK0/d1i1N6v2JVmzJ1w8d49j7cWq%2B5oV%2B7JkwDqroqJCw4cPV319vSZMmKCcnBw98MADioqKclkuLCxMNTU1kiSn06nQ0FCX%2BdDQ0OZ5d9jtdhUUFLiMZWdnKycn5yI7sYbw8I6tWj4kpEM7VWI%2Bq/Zm1b6As1p7HGsvVt3XrNSXpQNW165d5XA49NVXX%2Bk///M/9fDDD7v1uKampjY9b3p6ulJTU13GbLYg1dScaNN6f87bkr67/fv5%2BSokpINqa%2BvU0NDYzlVdWlbtzap9Sd63n6F9GX0cby2r7mvt2ZdZodjSAUuSfHx8FB0drdzcXE2cOFGDBw%2BW0%2Bl0WaampkYRERGSpPDw8BbzTqdTPXr0cPs5IyMjW1wOrKr6QfX11tkZLkZr%2B29oaLTsa2bV3qzaF3DW7SveNbuEVtk2c4DZJbSKlY4hlgxYJSUleuyxx7Rt2zb5%2Bp751%2BfZ//7617/Wjh07XJYvLS1VQkKCJCkuLk5lZWUaM2aMJKmhoUHl5eVKS0u7hB1Y051//LPZJbSKtx2YAACew5LnvuPi4nT8%2BHEtX75cdXV1OnbsmPLz89WvXz9lZGSooqJCxcXFOnXqlPbs2aM9e/ZowoQJkqSMjAxt2LBB%2B/btU11dnVavXi1/f38NGTLE3KYAAIDXsGTA6tSpk15zKztjAAAS/UlEQVR44QWVlpbqpptu0siRI9WpUyf94Q9/UOfOnbVmzRq98sor6tu3r5YsWaLly5erZ8%2BekqRBgwZp1qxZmjlzpvr376%2B9e/eqsLBQgYGBJncFAAC8hSUvEUpSTEzMeb9AOikpSRs3bjzvYzMzM5WZmdlepQEAAIuz5BksAAAAMxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMJhlA1ZFRYWys7OVnJyslJQUzZ07V7W1tZKk/fv36ze/%2BY369u2rYcOG6YUXXnB57BtvvKFRo0bpxhtv1NixY/Xee%2B%2BZ0QIAAPBSlg1YWVlZCgkJ0a5du/Taa6/ps88%2B0xNPPKGTJ09q6tSpuummm/Tuu%2B/qqaee0po1a7Rz505JZ8LXnDlzlJeXp/fff1%2BTJ0/WQw89pKNHj5rcEQAA8BaWDFi1tbWKi4vT7Nmz1bFjR3Xp0kVjxozRhx9%2BqLfffls//fSTpk2bpqCgIPXu3Vvjx4%2BX3W6XJBUXF2vw4MEaPHiwAgICNHr0aN1www3atGmTyV0BAABvYcmAFRISoqVLl%2Bryyy9vHvvmm28UGRmpsrIyxcTEyM/Pr3kuNjZWpaWlkqSysjLFxsa6rC82NlYOh%2BPSFA8AALyezewCLgWHw6FXXnlFq1ev1rZt2xQSEuIyHxYWJqfTqcbGRjmdToWGhrrMh4aG6vPPP3f7%2BSorK1VVVeUyZrMFKTIy8uKbOAc/P0vmY49hsxn/%2Bp7dZlbbdlbtS7JmT/jlaI/jWHuw4jHE8gHro48%2B0rRp0zR79mylpKRo27Zt51zOx8en%2Bf%2Bbmpra9Jx2u10FBQUuY9nZ2crJyWnTenFphYd3bLd1h4R0aLd1m8mqfQHeqj2PY%2B3BSscQSwesXbt26Xe/%2B50WLFige%2B65R5IUERGhL7/80mU5p9OpsLAw%2Bfr6Kjw8XE6ns8V8RESE28%2Bbnp6u1NRUlzGbLUg1NScurpHzsFLS90RGby/pzDYLCemg2to6NTQ0Gr5%2Bs1i1L4n9DN6tPY5j7aE9jyFmhUzLBqyPP/5Yc%2BbM0apVqzRw4MDm8bi4OP3pT39SfX29bLYz7TscDiUkJDTPn70f6yyHw6GRI0e6/dyRkZEtLgdWVf2g%2Bnpr/cVjde25vRoaGi35%2B2DVvgBv5W37o5WOIZb8p1l9fb3mz5%2BvvLw8l3AlSYMHD1ZwcLBWr16turo6ffrpp1q/fr0yMjIkSRMmTNDevXv19ttv69SpU1q/fr2%2B/PJLjR492oxWAACAF7LkGax9%2B/bp0KFDWrx4sRYvXuwyt337dj3zzDNauHChCgsLdfnllys3N1dDhgyRJN1www1asWKFli5dqoqKCnXv3l1r1qzRFVdcYUInAADAG1kyYPXr108HDx78l8v86U9/Ou/csGHDNGzYMKPLAgAAvxCWvEQIAABgJgIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGMyyAevdd99VSkqKcnNzW8y98cYbGjVqlG688UaNHTtW7733XvNcY2OjnnrqKd16661KSkrS/fffr6%2B//vpSlg4AALycJQPWs88%2Bq8WLF%2Bvaa69tMbd//37NmTNHeXl5ev/99zV58mQ99NBDOnr0qCRp3bp12rx5swoLC7V7925FR0crOztbTU1Nl7oNAADgpSwZsAICArR%2B/fpzBqzi4mINHjxYgwcPVkBAgEaPHq0bbrhBmzZtkiTZ7XZNnjxZ119/vYKDg5Wbm6tDhw7p008/vdRtAAAAL2Uzu4D2MGnSpPPOlZWVafDgwS5jsbGxcjgcOnnypD7//HPFxsY2zwUHB%2Bvaa6%2BVw%2BFQnz593Hr%2ByspKVVVVuYzZbEGKjIxsRRcX5udnyXzsMWw241/fs9vMatvOqn1J1uwJvxztcRxrD1Y8hlgyYP0rTqdToaGhLmOhoaH6/PPP9f3336upqemc8zU1NW4/h91uV0FBgctYdna2cnJyLr5wXHLh4R3bbd0hIR3abd1msmpfgLdqz%2BNYe7DSMeQXF7AkXfB%2Bqrbeb5Wenq7U1FSXMZstSDU1J9q03p%2BzUtL3REZvL%2BnMNgsJ6aDa2jo1NDQavn6zWLUvif0M3q09jmPtoT2PIWaFzF9cwAoPD5fT6XQZczqdioiIUFhYmHx9fc8537lzZ7efIzIyssXlwKqqH1Rfb62/eKyuPbdXQ0OjJX8frNoX4K28bX%2B00jHkF/dPs7i4OJWWlrqMORwOJSQkKCAgQD169FBZWVnzXG1trf72t7/p17/%2B9aUuFQAAeKlfXMCaMGGC9u7dq7ffflunTp3S%2BvXr9eWXX2r06NGSpIyMDBUVFenQoUM6fvy4VqxYoV69eik%2BPt7kygEAgLew5CXCs2Govr5ekvTmm29KOnOm6oYbbtCKFSu0dOlSVVRUqHv37lqzZo2uuOIKSdLEiRNVVVWlf//3f9eJEyeUnJzc4oZ1AACAf8WSAcvhcPzL%2BWHDhmnYsGHnnPPx8VFOTg7v%2BAMAABfNkgELAABId/7xz2aX4LYPf3%2BH2SUY6hd3DxYAAEB7I2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMFsZhcAeKo7//hns0tolW0zB5hdAgDg/%2BMMFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2CdQ0VFhR588EElJydr6NChWr58uRobG80uCwAAeAk%2BB%2BscZsyYod69e%2BvNN99UdXW1pk6dqssvv1y//e1vzS4NAAB4Ac5g/YzD4dCBAweUl5enTp06KTo6WpMnT5bdbje7NAAA4CU4g/UzZWVl6tq1q0JDQ5vHevfurcOHD%2Bv48eMKDg6%2B4DoqKytVVVXlMmazBSkyMtLQWv38yMf4B5vNvN%2BHs7%2BLVvydtGJPgKey0v5GwPoZp9OpkJAQl7GzYaumpsatgGW321VQUOAy9tBDD2nGjBnGFaozQe7eLp8pPT3d8PBmpsrKStntdsv1JVm3t8rKSr300nOW60uy7n4mWfv30Yp9SdbtrbKyUvn5%2BZbqyzpR0UBNTU1tenx6erpee%2B01l5/09HSDqvuHqqoqFRQUtDhb5u2s2pdk3d6s2pdEb97Iqn1J1u3Nin1xButnIiIi5HQ6XcacTqd8fHwUERHh1joiIyMtk8ABAEDrcQbrZ%2BLi4vTNN9/o2LFjzWMOh0Pdu3dXx44dTawMAAB4CwLWz8TGxio%2BPl4rV67U8ePHdejQIa1du1YZGRlmlwYAALyE32OPPfaY2UV4mltuuUVbtmzR448/rq1btyotLU3333%2B/fHx8zC6thY4dO6p///6WO7tm1b4k6/Zm1b4kevNGVu1Lsm5vVuvLp6mtd3QDAADABZcIAQAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsDyQhUVFXrwwQeVnJysoUOHavny5WpsbDS7LMO8%2B%2B67SklJUW5urtmlGKqiokLZ2dlKTk5WSkqK5s6dq9raWrPLarMDBw7o3nvvVd%2B%2BfZWSkqKZM2eqqqrK7LIMtWTJEsXExJhdhmFiYmIUFxen%2BPj45p/HH3/c7LIMs3r1ag0cOFB9%2BvTR5MmTdeTIEbNLarMPPvjAZXvFx8crLi7OEr%2BX5eXlmjRpkvr166cBAwYoLy9Px44dM7usNiNgeaEZM2YoKipKb775ptauXas333xTL730ktllGeLZZ5/V4sWLde2115pdiuGysrIUEhKiXbt26bXXXtNnn32mJ554wuyy2uT06dO677771L9/f5WUlGjLli2qrq6Wlb7idP/%2B/dq4caPZZRhu%2B/btcjgczT8LFiwwuyRDrFu3Tps2bVJRUZHee%2B89de/eXS%2B%2B%2BKLZZbVZUlKSy/ZyOBx66KGHdOedd5pdWpvU19frwQcfVJ8%2BfbR3715t2bJFx44ds8QxhIDlZRwOhw4cOKC8vDx16tRJ0dHRmjx5sux2u9mlGSIgIEDr16%2B3XMCqra1VXFycZs%2BerY4dO6pLly4aM2aMPvzwQ7NLa5O6ujrl5uZq6tSp8vf3V0REhG6//XZ99tlnZpdmiMbGRi1cuFCTJ082uxS46YUXXlBubq6uu%2B46BQcHa/78%2BZo/f77ZZRnu73//u9auXauHH37Y7FLapKqqSlVVVbr77rvl7%2B%2Bv8PBw3X777dq/f7/ZpbUZAcvLlJWVqWvXrgoNDW0e6927tw4fPqzjx4%2BbWJkxJk2apE6dOpldhuFCQkK0dOlSXX755c1j33zzjSIjI02squ1CQ0M1fvx42Ww2SdIXX3yh119/3ev/VX3Wq6%2B%2BqoCAAI0aNcrsUgy3cuVKDRkyRP369dOCBQt04sQJs0tqs2%2B//VZHjhzR999/rxEjRig5OVk5OTmWuNz0c6tWrdK4ceN01VVXmV1Km0RFRalXr16y2%2B06ceKEqqurtXPnTg0ZMsTs0tqMgOVlnE6nQkJCXMbOhq2amhozSsJFcDgceuWVVzRt2jSzSzFERUWF4uLiNGLECMXHxysnJ8fsktrsu%2B%2B%2BU35%2BvhYuXGh2KYbr06ePUlJStHPnTtntdu3bt0%2BLFi0yu6w2O3r0qKQzlz/Xrl2rjRs36ujRo5Y7g3XkyBHt3LlTv/3tb80upc18fX2Vn5%2Bvt956S4mJiUpJSVF9fb1mz55tdmltRsDyQk1NTWaXgDb46KOPdP/992v27NlKSUkxuxxDdO3aVQ6HQ9u3b9eXX37p9ZctJGnp0qUaO3asunfvbnYphrPb7Ro/frz8/f11/fXXKy8vT1u2bNHp06fNLq1Nzh4bp0yZoqioKHXp0kUzZszQrl27dOrUKZOrM866des0bNgwXXHFFWaX0manT59WVlaW7rjjDn344Yd655131KlTJ%2BXl5ZldWpsRsLxMRESEnE6ny5jT6ZSPj48iIiJMqgru2rVrlx588EE9%2BuijmjRpktnlGMrHx0fR0dHKzc1tvlHVW5WUlOiTTz5Rdna22aVcEldffbUaGhpUXV1tdiltcvYS/D%2Bf5e/atauampq8vrd/tmPHDqWmpppdhiFKSkp05MgRzZo1S506dVJUVJRycnL0P//zPy3%2BrvM2BCwvExcXp2%2B%2B%2BcblLy%2BHw6Hu3burY8eOJlaGC/n44481Z84crVq1Svfcc4/Z5RiipKREw4cPd/mYEF/fM4eVyy67zKyy2mzTpk2qrq7W0KFDlZycrLFjx0qSkpOTtXXrVpOra5vy8nItW7bMZezQoUPy9/f3%2BnsCu3TpouDgYJcbpCsqKnTZZZd5fW9n7d%2B/XxUVFRowYIDZpRiioaFBjY2NLldmvP1M6lkELC8TGxur%2BPh4rVy5UsePH9ehQ4e0du1aZWRkmF0a/oX6%2BnrNnz9feXl5GjhwoNnlGCYuLk7Hjx/X8uXLVVdXp2PHjik/P1/9%2BvXz6jcrzJ07Vzt27NDGjRu1ceNGFRYWSpI2btzo9WcOOnfuLLvdrsLCQp0%2BfVqHDx/WqlWrlJ6eLj8/P7PLaxObzaa0tDQ988wz%2Buqrr1RdXa2nn35ao0aNan4jhrcrLy9XWFiYgoODzS7FEDfeeKOCgoKUn5%2Bvuro61dTUaPXq1UpKSlJYWJjZ5bWJTxM39Hido0ePasGCBfrf//1fBQcHa%2BLEiXrooYfk4%2BNjdmltFh8fL%2BlMIJHUfFB0OBym1WSEDz/8UP/2b/8mf3//FnPbt29X165dTajKGAcPHtTixYv117/%2BVUFBQbrppps0d%2B5cRUVFmV2aYY4cOaJbb71VBw8eNLsUQ3zwwQdauXKlDh48KH9/f40ZM0a5ubkKCAgwu7Q2O336tJYuXaqtW7fqp59%2B0vDhw7VgwQLLnOFfs2aNNm/erC1btphdimFKS0v1xBNP6MCBA/L391f//v0tcQwhYAEAABiMS4QAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAG%2B3/lChOO9pGgygAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common8098190314900289194”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>713</td> <td class=”number”>22.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>431</td> <td class=”number”>13.4%</td> <td>

<div class=”bar” style=”width:60%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>381</td> <td class=”number”>11.9%</td> <td>

<div class=”bar” style=”width:53%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.25</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:36%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.3</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.225</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.5</td> <td class=”number”>109</td> <td class=”number”>3.4%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.15</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.75</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (11)</td> <td class=”number”>689</td> <td class=”number”>21.5%</td> <td>

<div class=”bar” style=”width:96%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme8098190314900289194”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>713</td> <td class=”number”>22.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.225</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.5</td> <td class=”number”>75</td> <td class=”number”>2.3%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.7500000000000002</td> <td class=”number”>29</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>6.5</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.875</td> <td class=”number”>87</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:89%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>92</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:94%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.000000000000001</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>82</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:84%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_CH_FOODINSEC_12_15”>CH_FOODINSEC_12_15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>38</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.2%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>-0.75811</td>

</tr> <tr>

<th>Minimum</th> <td>-4.3</td>

</tr> <tr>

<th>Maximum</th> <td>2.7</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>5.1%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram5560798530495592168”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives5560798530495592168”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles5560798530495592168”

aria-controls=”quantiles5560798530495592168” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram5560798530495592168” aria-controls=”histogram5560798530495592168”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common5560798530495592168” aria-controls=”common5560798530495592168”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme5560798530495592168” aria-controls=”extreme5560798530495592168”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles5560798530495592168”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-4.3</td>

</tr> <tr>

<th>5-th percentile</th> <td>-3</td>

</tr> <tr>

<th>Q1</th> <td>-2</td>

</tr> <tr>

<th>Median</th> <td>-0.8</td>

</tr> <tr>

<th>Q3</th> <td>0.2</td>

</tr> <tr>

<th>95-th percentile</th> <td>2</td>

</tr> <tr>

<th>Maximum</th> <td>2.7</td>

</tr> <tr>

<th>Range</th> <td>7</td>

</tr> <tr>

<th>Interquartile range</th> <td>2.2</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>1.4965</td>

</tr> <tr>

<th>Coef of variation</th> <td>-1.974</td>

</tr> <tr>

<th>Kurtosis</th> <td>-0.58956</td>

</tr> <tr>

<th>Mean</th> <td>-0.75811</td>

</tr> <tr>

<th>MAD</th> <td>1.247</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.35002</td>

</tr> <tr>

<th>Sum</th> <td>-2374.4</td>

</tr> <tr>

<th>Variance</th> <td>2.2395</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram5560798530495592168”>

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TrUOA8AAHAtPhmw5syZox07dmjjxo3auHGj8vPzJUkbN27UsGHDVF5erqKiIl24cEHFxcUqLi7WqFGjJEmZmZnasGGDDh48qNraWq1YsULBwcEaNGiQhR0BAIDmxCcPEYaHh7ucqF5XVydJ6tSpkyRp5cqVysnJ0aJFi9S5c2fl5ubqrrvukiQNHDhQzz77rGbMmKHTp08rPj5e%2Bfn5atmypecbAQAAzZJPBqzLdenSRUeOHGn8/379%2BrlcfPRyY8aM0ZgxYzxRGgAA8EE%2BeYgQAADASgQsAAAAw7wuYKWkpCgvL08nT560uhQAAIAb4nUB6/HHH9fWrVv14IMPavz48dq5c2fjSeoAAADNgdcFrKysLG3dulW///3v1b17d7388stKTk5Wbm6ujh07ZnV5AAAA1%2BR1Aet7vXr1UnZ2tvbu3avnnntOv//97zV06FCNGzdOn332mdXlAQAA/CCvDViXLl3S1q1bNWHCBGVnZ6tjx46aO3euevbsqbFjx2rz5s1WlwgAAHBVXncdrLKyMq1bt04bNmzQuXPnNGTIEL399tvq06dP4zL9%2BvXTwoULNWzYMAsrBQAAuDqvC1ipqanq2rWrJk6cqMcee0zt2rW7Ypnk5GSdOXPGguoAAACuzesCVkFBgfr373/N5T799FMPVAMAAHD9vO4crJiYGE2aNEm7du1qHPvtb3%2BrCRMmyOFwWFgZAABA03hdwFq8eLHOnj2rbt26NY4NGjRIDQ0NWrJkiYWVAQAANI3XHSLct2%2BfNm/erIiIiMax6OhoLV26VD/72c8srAwAAKBpvG4P1vnz5xUSEnLFeGBgoGpray2oCAAA4Pp4XcDq16%2BflixZom%2B%2B%2BaZx7Ouvv9aiRYtcLtUAAADgrbzuEOFzzz2np59%2BWvfdd5/atGmjhoYGnTt3Trfeeqveeecdq8sDAAC4Jq8LWLfeeqv%2B8Ic/6I9//KP%2B/ve/KzAwUF27dtWAAQMUFBRkdXkAAADX5HUBS5KCg4P14IMPWl0GAADADfG6gPXVV19p2bJl%2BuKLL3T%2B/Pkr5nfv3m1BVQAAAE3ndQHrueeeU0VFhQYMGKDWrVtbXQ4AAMB187qAVVJSot27dysyMtLqUgAAAG6I112m4ZZbbmHPFQAAaNa8LmBNnDhReXl5cjqdVpcCAABwQ7zuEOEf//hHffLJJ1q/fr26dOmiwEDXDPjuu%2B9aVBkAAEDTeF3AatOmjQYOHGh1GQAAADfM6wLW4sWLrS4BAADgpnjdOViS9OWXX2r58uWaO3du49hf/vIXCysCAABoOq8LWPv379fw4cO1c%2BdObdmyRdJ3Fx998sknucgoAABoFrwuYL366qv6xS9%2Boc2bNysgIEDSd99PuGTJEr3xxhsWVwcAAHBtXhew/vrXvyozM1OSGgOWJD3yyCMqKyuzqiwAAIAm87qA1bZt26t%2BB2FFRYWCg4MtqAgAAOD6eF3ASkhI0Msvv6yamprGsWPHjik7O1v33XefhZUBAAA0jdddpmHu3Ll66qmnlJiYqPr6eiUkJKi2tlbdu3fXkiVLrC4PAADgmrwuYHXq1ElbtmxRcXGxjh07ppYtW6pr1666//77Xc7JAgAA8FZeF7AkqUWLFnrwwQetLgMAJEmPvvYnq0sA0Mx4XcBKSUn50T1VXAsLAAB4O68LWEOHDnUJWPX19Tp27JjsdrueeuopCysDAABoGq8LWLNnz77q%2BI4dO/TnP//Zw9UAAABcP68LWD/kwQcf1PPPP6/nn3/e6lJwg5rbeSzbZtxvdQkAgGbK666D9UMOHTokp9PZ5OU///xzPfXUU%2BrTp4%2BSkpI0Y8YMVVZWSvru%2Bw7T09OVkJCg1NRUbdq0yeW2BQUFGjJkiBISEpSZmamSkhKjvQAAAN/mdXuwRo8efcVYbW2tysrK9PDDDzdpHRcvXtTTTz%2Btn//851q1apVqamr07//%2B71q4cKFeeOEFTZkyRfPmzdOwYcN04MABTZ48WV27dlV8fLz27Nmj5cuX66233lJMTIwKCgo0adIk7dy5U61btzbdLgAA8EFeF7Cio6Ov%2BBRhSEiI0tPTNXLkyCato7a2VjNnztSIESNks9kUGRmphx56SGvXrtXmzZsVHR2t9PR0SVJSUpJSUlJUVFSk%2BPh4FRYWKi0tTb1795YkjR8/XgUFBdq7d69SU1PNNgsAAHyS1wUsE1drDw8PdwljX375pd577z09%2BuijKi0tVWxsrMvysbGx2rZtmySptLRUQ4cObZwLDAxUz549ZbfbCVgAAKBJvC5gbdiwocnLPvbYYz86X15eriFDhqiurk6jRo3S9OnTNWHCBHXs2NFluXbt2qmqqkqS5HA4FB4e7jIfHh7eON8UFRUVjed7fc9ma62oqKgmr8PTgoICXX5Cstl4LAB3c/d25s/vbf7au7f07XUBa968eWpoaLjihPaAgACXsYCAgGsGrM6dO8tut%2Btvf/ubnn/%2Bef3yl79sUg3XczL91RQWFiovL89lLCsrS9OnT7%2Bp9XpCWFgrq0vwGhERoVaXAPg8T21n/vze5q%2B9W9231wWst956S6tXr9akSZMUExMjp9OpI0eOaNWqVXriiSeUmJh4XesLCAhQdHS0Zs6cqdGjRys5OVkOh8NlmaqqKkVGRkqSIiIirph3OBzq3r17k%2B8zIyNDKSkpLmM2W2tVVZ27rto9KSgoUGFhrVRdXav6%2Bgary/EK3vx8Ab7C3duZP7%2B3%2BWvvl/dt1R/LXhewlixZovz8fJfDeH379tWtt96qcePGacuWLddcx/79%2B7Vw4UJt27ZNgYHf7SL8/ufdd9%2BtHTt2uCxfUlLSeFJ7XFycSktLNWLECEnfXUn%2B0KFDjSfFN0VUVNQVhwMrK8%2Bqrs77X%2BD19Q3Nok5P4HEA3M9T25k/v7f5a%2B9W9%2B11B2aPHz9%2BxTlQkhQWFqby8vImrSMuLk41NTXKzc1VbW2tzpw5o%2BXLl6tv377KzMxUeXm5ioqKdOHCBRUXF6u4uFijRo2SJGVmZmrDhg06ePCgamtrtWLFCgUHB2vQoEEm2wQAAD7M6wJW586dtWTJEpeTyqurq7Vs2TLddtttTVpH27ZttXr1apWUlOjee%2B9Vamqq2rZtq//4j//QLbfcopUrV2rt2rXq06ePXn75ZeXm5uquu%2B6SJA0cOFDPPvusZsyYof79%2B%2BvDDz9Ufn6%2BWrZs6ZZ%2BAQCA7wlw3uwZ3Ybt27dPs2bNUnV1tUJDQxUYGKiamhq1bNlSb7zxhu677z6rS7whlZVnrS7hR9lsgYqICFVV1Tm37VLlq3Lcq7k9voDk/u3ME%2B9t3spfe7%2B87w4d2lpThyX3%2BiMGDBig999/X8XFxTp16pScTqc6duyon/70p2rb1poHCQAA4Hp4XcCSpFatWumBBx7QqVOndOutt1pdDgAAwHXxunOwzp8/r%2BzsbN1zzz169NFHJX13Dtb48eNVXV1tcXUAAADX5nUBKzc3V4cPH9bSpUsbL60gfXe5hKVLl1pYGQAAQNN4XcDasWOHfvOb3%2BiRRx5p/NLnsLAwLV68WDt37rS4OgAAgGvzuoB17tw5RUdHXzEeGRmpb7/91vMFAQAAXCevC1i33Xab/vznP0ty/U7A7du365//%2BZ%2BtKgsAAKDJvO5ThGPGjNG0adP0%2BOOPq6GhQWvWrFFJSYl27NihefPmWV0eAADANXldwMrIyJDNZtPatWsVFBSkN998U127dtXSpUv1yCOPWF0eAADANXldwDpz5owef/xxPf7441aXAgAAcEO87hysBx54QF727T0AAADXxesCVmJiorZt22Z1GQAAADfM6w4R/tM//ZNeeukl5efn67bbblOLFi1c5pctW2ZRZQAAAE3jdQHr6NGjuuOOOyRJVVVVFlcDAABw/bwmYM2cOVOvvvqq3nnnncaxN954Q1lZWRZWBQAAcP285hysPXv2XDGWn59vQSUAAAA3x2sC1tU%2BOcinCQEAQHPkNQHr%2By92vtYYAACAt/OagAUAAOArCFgAAACGec2nCC9duqRZs2Zdc4zrYAEAAG/nNQGrT58%2BqqiouOYYAACAt/OagPX/X/8KAACgOeMcLAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAM85rvIgQAAGb1nbfd6hKabNuM%2B60uwSj2YAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhPhuwysvLlZWVpcTERCUlJWnOnDmqrq6WJB0%2BfFhPPPGE%2BvTpo4cfflirV692ue3WrVs1bNgw3XPPPUpLS9O%2BffusaAEAADRTPhuwJk2apLCwMO3Zs0fr16/XF198oVdeeUXnz5/XxIkTde%2B99%2BqDDz7Qq6%2B%2BqpUrV2rnzp2Svgtf2dnZmj17tj766CONHTtWU6dO1alTpyzuCAAANBc%2BGbCqq6sVFxenWbNmKTQ0VJ06ddKIESP08ccf6/3339elS5c0efJktW7dWr169dLIkSNVWFgoSSoqKlJycrKSk5MVEhKi4cOHq0ePHtq0aZPFXQEAgObCJy80GhYWpsWLF7uMnTx5UlFRUSotLVVMTIyCgoIa52JjY1VUVCRJKi0tVXJyssttY2NjZbfbm3z/FRUVqqysdBmz2VorKirqelvxmKCgQJefkGw2HgvA3dy9nfnze1tz69nUa8FbnnOfDFiXs9vtWrt2rVasWKFt27YpLCzMZb5du3ZyOBxqaGiQw%2BFQeHi4y3x4eLiOHj3a5PsrLCxUXl6ey1hWVpamT59%2B4014SFhYK6tL8BoREaFWlwD4PE9tZ7y3eT/TrwWrn3OfD1gHDhzQ5MmTNWvWLCUlJWnbtm1XXS4gIKDxv51O503dZ0ZGhlJSUlzGbLbWqqo6d1PrdaegoECFhbVSdXWt6usbrC7HK3jz8wX4CndvZ/783mb1HpzrZeq1cPlzbtUfyz4dsPbs2aNf/OIXWrBggR577DFJUmRkpI4fP%2B6ynMPhULt27RQYGKiIiAg5HI4r5iMjI5t8v1FRUVccDqysPKu6Ou/fuOvrG5pFnZ7A4wC4n6e2M97bvJ/p58fq57x5xdvr8Mknnyg7O1uvv/56Y7iSpLi4OB05ckR1dXWNY3a7Xb17926cLykpcVnX/z8PAABwLT65B6uurk7z58/X7NmzNWDAAJe55ORktWnTRitWrND48eP117/%2BVevWrVNubq4kadSoUUpPT9f777%2Bv%2B%2B67T5s3b9bx48c1fPhwK1oBAHiRR1/7k9UloJnwyYB18OBBlZWVKScnRzk5OS5z27dv15tvvqkXXnhB%2Bfn5at%2B%2BvWbOnKlBgwZJknr06KGlS5dq8eLFKi8vV7du3bRy5Up16NDBgk4AAEBz5JMBq2/fvjpy5MiPLvPf//3fPzj38MMP6%2BGHHzZdFgAA8BM%2Bew4WAACAVQhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMMwnP0UIAGgeuK4UfBV7sAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjO8iBH4A35EGALhR7MECAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDfDZgffDBB0pKStLMmTOvmNu6dauGDRume%2B65R2lpadq3b1/jXENDg1599VU98MAD6tevn8aNG6evvvrKk6UDAIBmzicD1qpVq5STk6Pbb7/9irnDhw8rOztbs2fP1kcffaSxY8dq6tSpOnXqlCTpd7/7nTZv3qz8/Hzt3btX0dHRysrKktPp9HQbAACgmfLJgBUSEqJ169ZdNWAVFRUpOTlZycnJCgkJ0fDhw9WjRw9t2rRJklRYWKixY8fqzjvvVJs2bTRz5kyVlZXp008/9XQbAACgmfLJgPXkk0%2Bqbdu2V50rLS1VbGysy1hsbKzsdrvOnz%2Bvo0ePusy3adNGt99%2Bu%2Bx2u1trBgAAvsNmdQGe5nA4FB4e7jIWHh6uo0eP6ptvvpHT6bzqfFVVVZPvo6KiQpWVlS5jNltrRUVF3XjhbhYUFOjyEwAAT7LZzPz%2B8ZbfZ34XsCRd83yqmz3fqrCwUHl5eS5jWVlZmj59%2Bk2t1xPCwlpZXQIAwA9YVZCSAAAK70lEQVRFRIQaXZ/Vv8/8LmBFRETI4XC4jDkcDkVGRqpdu3YKDAy86vwtt9zS5PvIyMhQSkqKy5jN1lpVVeduvHA3CwoKVFhYK1VX16q%2BvsHqcgAAfsbU78jLf5%2BZDm5N5XcBKy4uTiUlJS5jdrtdqampCgkJUffu3VVaWqr%2B/ftLkqqrq/X3v/9dd999d5PvIyoq6orDgZWVZ1VX5/3Bpb6%2BoVnUCQDwLaZ/91j9%2B8zvTrgZNWqUPvzwQ73//vu6cOGC1q1bp%2BPHj2v48OGSpMzMTBUUFKisrEw1NTVaunSpevbsqfj4eIsrBwAAzYVP7sH6PgzV1dVJknbt2iXpuz1VPXr00NKlS7V48WKVl5erW7duWrlypTp06CBJGj16tCorK/Wv//qvOnfunBITE684nwoAAODHBDi5gqZHVFaetbqEH2WzBSoiIlRVVefctkv10df%2B5Jb1AgCav20z7jeynst/n3XocPXLNrmb3x0iBAAAcDcCFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwzGZ1Abg5j772J6tLAAAAl2EP1lWUl5frmWeeUWJiogYPHqzc3Fw1NDRYXRYAAGgm2IN1FdOmTVOvXr20a9cunT59WhMnTlT79u31b//2b1aXBgAAmgH2YF3Gbrfr888/1%2BzZs9W2bVtFR0dr7NixKiwstLo0AADQTLAH6zKlpaXq3LmzwsPDG8d69eqlY8eOqaamRm3atLnmOioqKlRZWekyZrO1VlRUlPF6AQDwBTabmX0%2BQUGBLj%2BtQsC6jMPhUFhYmMvY92GrqqqqSQGrsLBQeXl5LmNTp07VtGnTzBX6f3380iNG1lNRUaHCwkJlZGT4VRCkb//qW/Lf3unbv/qW/Lf3iooKvf32W5b3zSHCq3A6nTd1%2B4yMDK1fv97lX0ZGhqHq3KOyslJ5eXlX7HnzdfTtX31L/ts7fftX35L/9u4tfbMH6zKRkZFyOBwuYw6HQwEBAYqMjGzSOqKiovzqrwUAAOCKPViXiYuL08mTJ3XmzJnGMbvdrm7duik0NNTCygAAQHNBwLpMbGys4uPjtWzZMtXU1KisrExr1qxRZmam1aUBAIBmImjhwoULrS7C2/z0pz/Vli1b9OKLL%2BoPf/iD0tPTNW7cOAUEBFhdmluFhoaqf//%2Bfrenjr79q2/Jf3unb//qW/Lf3r2h7wDnzZ7RDQAAABccIgQAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjICFq3r77bcVExOjEydOWF2KR5w4cUJTpkxR//79lZiYqAkTJujYsWNWl%2BV2VVVVys7O1v3336/ExERNnTpVJ0%2BetLosj7Db7XrooYc0atQoq0txq/Lycj3zzDNKTEzU4MGDlZubq4aGBqvL8ogPPvhASUlJmjlzptWleFR5ebmysrKUmJiopKQkzZkzR9XV1VaX5RGff/65nnrqKfXp00dJSUmaMWOGKisrLamFgIUrfP3111q9erXVZXhUVlaW2rdvr71792r37t1q06aNX7wpz507V//4xz%2B0efNm7dixQ5cuXdLcuXOtLsvtNm3apGnTpun222%2B3uhS3mzZtmjp27Khdu3ZpzZo12rVrl95%2B%2B22ry3K7VatWKScnxy%2Be48tNmjRJYWFh2rNnj9avX68vvvhCr7zyitVlud3Fixf19NNPq3///tq/f7%2B2bNmi06dPy6qvXCZg4QovvfSSRo8ebXUZHnPx4kU98cQTmjVrlkJDQ9WmTRv97Gc/09GjR%2BXLX9XpdDrVsWNHZWdnKzIyUu3atdPo0aN14MABn%2B5bki5cuKDCwkL17t3b6lLcym636/PPP9fs2bPVtm1bRUdHa%2BzYsSosLLS6NLcLCQnRunXr/C5gVVdXKy4urvH9rFOnThoxYoQ%2B/vhjq0tzu9raWs2cOVMTJ05UcHCwIiMj9dBDD%2BmLL76wpB4CFlwUFxfryJEjGjdunNWleExwcLBGjhyp8PBwSdLJkyf1X//1X3rkkUcUEBBgcXXuExAQoEWLFqlHjx6NYydPnlSHDh18um9JGjlypDp27Gh1GW5XWlqqzp07N762JalXr146duyYampqLKzM/Z588km1bdvW6jI8LiwsTIsXL1b79u0bx06ePKmoqCgLq/KM8PBwjRw5UjabTZL05Zdf6r333tOjjz5qST0ELDQ6f/68XnzxRT3//PMKDg62uhxLxMXFadCgQWrVqpV%2B9atfWV2OR504cUKvv/66Jk%2BebHUpMMThcCgsLMxl7PuwVVVVZUVJ8DC73a61a9f61XZdXl6uuLg4DR06VPHx8Zo%2BfboldRCw/MjGjRsVExNz1X/r16/XihUrFBcXp/vvv9/qUo27Vu/fKykpUXFxsVq0aKFx48Y1%2B5OBm9p3WVmZnnjiCY0YMUIjR460sGIzmtq3P/D1w734YQcOHNC4ceM0a9YsJSUlWV2Ox3Tu3Fl2u13bt2/X8ePH9ctf/tKaQpyA0%2Bk8evSo895773WeOnWqcaxHjx7Or776ysKqrPP11187e/To4fzss8%2BsLsXtPv30U2f//v2db775ptWleNxvfvMb58iRI60uw20KCwudgwcPdhk7ePCgMyYmxllTU2NRVZ6VnZ3tnDFjhtVleNzu3budCQkJzvfee8/qUiz1ySefOHv06OE8ffq0x%2B%2BbPViQJG3btk1nz57V8OHDlZiYqMTERElSWlqaVq1aZXF17vXll18qOTnZ5ZBJYOB3m0aLFi2sKssjjh8/rmeeeUbZ2dmaOHGi1eXAsLi4OJ08eVJnzpxpHLPb7erWrZtCQ0MtrAzu9Mknnyg7O1uvv/66HnvsMavL8Zj9%2B/dryJAhLkcerHwvJ2BBkjR27Fjt2rVLGzdubPwnSfn5%2BcrMzLS4Ove6/fbb1bZtW%2BXk5Ki6ulo1NTVatmyZbrvtNt1xxx1Wl%2BdWv/rVrzRq1CilpaVZXQrcIDY2VvHx8Vq2bJlqampUVlamNWvW%2BPw27c/q6uo0f/58zZ49WwMGDLC6HI%2BKi4tTTU2NcnNzVVtbqzNnzmj58uXq27evJR94CHA6OUCPq4uJidHu3bvVpUsXq0txu/LycuXk5Oijjz5ScHCw7r77bs2ZM0d33nmn1aW5zcmTJzVo0CC1aNHiik8Nrl69Wv369bOoMvcbMmSI/vd//1f19fVqaGho/Ot2%2B/bt6ty5s8XVmXXq1CktWLBA//M//6M2bdpo9OjRmjp1qs9/UjQ%2BPl7Sd4FDUuMny%2Bx2u2U1ecLHH3%2Bsn//851f9oJIvvr4vd%2BTIEeXk5Oizzz5T69atde%2B992rOnDmWfGqYgAUAAGAYhwgBAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwLD/A5XJ8OCvOhbbAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common5560798530495592168”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-3.0</td> <td class=”number”>312</td> <td class=”number”>9.7%</td> <td>

<div class=”bar” style=”width:23%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-2.0</td> <td class=”number”>267</td> <td class=”number”>8.3%</td> <td>

<div class=”bar” style=”width:20%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.2</td> <td class=”number”>203</td> <td class=”number”>6.3%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.1</td> <td class=”number”>188</td> <td class=”number”>5.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>163</td> <td class=”number”>5.1%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.9</td> <td class=”number”>148</td> <td class=”number”>4.6%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.3</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.1</td> <td class=”number”>122</td> <td class=”number”>3.8%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.5</td> <td class=”number”>117</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (27)</td> <td class=”number”>1359</td> <td class=”number”>42.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme5560798530495592168”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-4.3</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-3.6</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-3.0</td> <td class=”number”>312</td> <td class=”number”>9.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-2.9</td> <td class=”number”>29</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-2.4</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:32%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.4</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.5</td> <td class=”number”>83</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:69%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.5</td> <td class=”number”>36</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:30%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.7</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:53%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_CH_VLFOODSEC_12_15”>CH_VLFOODSEC_12_15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>28</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.9%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>-0.10093</td>

</tr> <tr>

<th>Minimum</th> <td>-2.6</td>

</tr> <tr>

<th>Maximum</th> <td>2.9</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>10.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-2401873375237977370”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-2401873375237977370,#minihistogram-2401873375237977370”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-2401873375237977370”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-2401873375237977370”

aria-controls=”quantiles-2401873375237977370” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-2401873375237977370” aria-controls=”histogram-2401873375237977370”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-2401873375237977370” aria-controls=”common-2401873375237977370”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-2401873375237977370” aria-controls=”extreme-2401873375237977370”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-2401873375237977370”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-2.6</td>

</tr> <tr>

<th>5-th percentile</th> <td>-1.1</td>

</tr> <tr>

<th>Q1</th> <td>-0.5</td>

</tr> <tr>

<th>Median</th> <td>-0.2</td>

</tr> <tr>

<th>Q3</th> <td>0</td>

</tr> <tr>

<th>95-th percentile</th> <td>1.1</td>

</tr> <tr>

<th>Maximum</th> <td>2.9</td>

</tr> <tr>

<th>Range</th> <td>5.5</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.5</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.79923</td>

</tr> <tr>

<th>Coef of variation</th> <td>-7.919</td>

</tr> <tr>

<th>Kurtosis</th> <td>2.4345</td>

</tr> <tr>

<th>Mean</th> <td>-0.10093</td>

</tr> <tr>

<th>MAD</th> <td>0.57788</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.2103</td>

</tr> <tr>

<th>Sum</th> <td>-316.1</td>

</tr> <tr>

<th>Variance</th> <td>0.63878</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-2401873375237977370”>

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%2BM3HxMT0zDfFIWFhVq%2BfLnfWE5OjnJzc5uzOUEtOvoKu0twNPpnjbi4KLtLaFHY7wJD/5rPSb0LyoD17bffasqUKbrtttsarp964oknlJeX16Tn19fXB7R%2BVlaWMjIy/MZcrkh5vVUBvW4wCQsLVXT0FaqoqFZtbZ3d5TgO/bMW792z2O8CQ/%2BaL5De2fUDUlAGrOLiYn355ZeaNWuWwsLC1LZtW%2BXm5mrUqFG6%2Beab5fP5/B7v9XoVHx8vSYqLi2s07/P51KVLlyavn5CQ0Oh0oMdzQjU1vKHOVVtbR18CQP%2BsQY/9sd8Fhv41n5N655yTmRehtrZWdXV1fkeivv32W0lSenp6o9sulJSUqEePHpKklJQUlZaW%2Br3WgQMHGuYBAAAuxPKAlZGRoeXLl%2BvIkSOXbI0bb7xRkZGRWrZsmaqrq%2BX1erVy5Ur17t1bo0aNUllZmdavX6/Tp09rz5492rNnj8aNGydJys7O1saNG/XBBx%2BourpaK1euVOvWrTVw4MBLVi8AAAgulgesO%2B64Q2%2B%2B%2BaYGDx6sSZMmafv27aqpqTG6RlxcnH7729/q/fffV//%2B/XX77bcrIiJCS5YsUbt27bRq1SqtW7dOvXr10lNPPaVFixbphhtukCT1799fs2bN0owZM9SnTx/t3btXq1evVkREhNEaAQBA8AqpD/SK7mYqLS1VUVGRtm7dqjNnzmj06NHKzMxUp06d7CjnkvN4TthdQovicoUqLi5KXm%2BVY86ntyTn69/Qpe/aXFXw2jqjr90ltAi8bwND/5ovkN61b9/2ElX1/Wy7Bqt79%2B7Kz8/Xrl279Oijj%2BoPf/iDhg0bpvvuu0/79%2B%2B3qywAAICA2Rawzpw5ozfffFP333%2B/8vPzlZiYqDlz5qhbt26aOHGitmzZYldpAAAAAbH8Ng0HDx7Uhg0btHHjRlVVVWnIkCF66aWX1KtXr4bH9O7dW/Pnz9eIESOsLg8AACBglges4cOHq1OnTpo8ebJGjx6t2NjYRo8ZMGCAjh8/bnVpAAAARlgesAoKCtSnT58LPu7DDz%2B0oBoAAADzLL8Gq2vXrpoyZYp27NjRMPa73/1O999/f6M7qAMAADiR5QFr4cKFOnHihDp37twwNnDgQNXV1enpp5%2B2uhwAAADjLD9F%2BM4772jLli2Ki4trGEtKStLixYt1%2B%2B23W10OAACAcZYfwTp16pTCw8MbFxIaqurqaqvLAQAAMM7ygNW7d289/fTT%2BuabbxrGvvrqKz3xxBN%2Bt2oAAABwKstPET766KO699579ZOf/ERt2rRRXV2dqqqq1LFjR7388stWlwMAAGCc5QGrY8eOeuONN/SnP/1JX3zxhUJDQ9WpUyf169dPYWFhVpcDAABgnOUBS5Jat26twYMH27E0AADAJWd5wPrHP/6hJUuW6NNPP9WpU6cazf/xj3%2B0uiQAAACjbLkGq7y8XP369VNkZKTVywMAAFxylgeskpIS/fGPf1R8fLzVSwMAAFjC8ts0tGvXjiNXAAAgqFkesCZPnqzly5ervr7e6qUBAAAsYfkpwj/96U96//339frrr%2Buaa65RaKh/xvv9739vdUkAAABGWR6w2rRpo/79%2B1u9LAAAgGUsD1gLFy60ekkAAABLWX4NliR9/vnnWrZsmebMmdMw9re//c2OUgAAAIyzPGAVFxdr5MiR2r59u4qKiiSdvfnohAkTuMkoAAAICpYHrGeffVYPP/ywtmzZopCQEEln/z7h008/rRUrVlhdDgAAgHGWB6y///3vys7OlqSGgCVJt912mw4ePGh1OQAAAMZZHrDatm173r9BWF5ertatW1tdDgAAgHGWB6yePXvqqaeeUmVlZcPYoUOHlJ%2Bfr5/85CdWlwMAAGCc5bdpmDNnju655x6lpaWptrZWPXv2VHV1tbp06aKnn37a6nIAAACMszxgXXXVVSoqKtKePXt06NAhRUREqFOnTurbt6/fNVkAAABOZXnAkqRWrVpp8ODBdiwNAABwyVkesDIyMr73SBX3wgIAAE5necAaNmyYX8Cqra3VoUOH5Ha7dc8991hdDgAAgHGWB6y8vLzzjm/btk1/%2BctfLK4GAADAPFv%2BFuH5DB48WG%2B88YbdZQAAAASsxQSsAwcOqL6%2B3u4yAAAAAmb5KcLx48c3GquurtbBgwf105/%2B1OpyAAAAjLM8YCUlJTX6LcLw8HBlZmbqzjvvtLocAAAA4ywPWNytHQAABDvLA9bGjRub/NjRo0cHtNbKlSv1yiuvqLKyUj/60Y%2B0YMECXXPNNSouLtaSJUv0%2Beef6%2Bqrr9bkyZM1cuTIhucVFBTolVdekcfjUdeuXTV37lylpKQEVAsAALh8WB6w5s6dq7q6ukYXtIeEhPiNhYSEBBSwXnnlFW3evFkFBQVKSEjQ0qVL9bvf/U4PPPCApk6dqrlz52rEiBF677339OCDD6pTp05KTU3Vzp07tWzZMr344ovq2rWrCgoKNGXKFG3fvl2RkZHNrgcAAFw%2BLA9YL774otasWaMpU6aoa9euqq%2Bv1yeffKIXXnhBd999t9LS0oyss2bNGuXn5%2BsHP/iBJOmxxx6TJP32t79VUlKSMjMzJUnp6enKyMjQ%2BvXrlZqaqsLCQo0dO1Y9evSQJE2aNEkFBQXatWuXhg8fbqQ2AAAQ3Gy5Bmv16tVKTExsGLvpppvUsWNH3XfffSoqKgp4ja%2B%2B%2BkpffvmlvvnmGw0bNkzHjh1TWlqa5s%2Bfr9LSUiUnJ/s9Pjk5WVu3bpUklZaWatiwYQ1zoaGh6tatm9xud5MDVnl5uTwej9%2BYyxWphISEALcseISFhfp9xcWhf9ZyueizxH4XKPrXfE7sneUB6/Dhw4qJiWk0Hh0drbKyMiNrHD16VJL01ltvae3ataqvr1dubq4ee%2BwxnTp1yi/cSVJsbKy8Xq8kyefzNaovJiamYb4pCgsLtXz5cr%2BxnJwc5ebmNmdzglp09BV2l%2BBo9M8acXFRdpfQorDfBYb%2BNZ%2BTemd5wOrQoYOefvppPfTQQ4qLi5MkVVRU6De/%2BY2uvfZaI2t8dy3XpEmTGsLU9OnTdf/99ys9Pb3Jz2%2BurKwsZWRk%2BI25XJHyeqsCet1gEhYWqujoK1RRUa3a2jq7y3Ec%2Bmct3rtnsd8Fhv41XyC9s%2BsHJMsD1qOPPqrZs2ersLBQUVFRCg0NVWVlpSIiIrRixQoja1x55ZWSzh4V%2B06HDh1UX1%2BvM2fOyOfz%2BT3e6/UqPj5ekhQXF9do3ufzqUuXLk1ePyEhodHpQI/nhGpqeEOdq7a2jr4EgP5Zgx77Y78LDP1rPif1zvKA1a9fP%2B3evVt79uzR0aNHVV9fr8TERN18881q27atkTWuuuoqtWnTRh999JG6d%2B8uSSorK1OrVq00YMAAbdq0ye/xJSUlDRe1p6SkqLS0VGPGjJEk1dbW6sCBAw0XxQMAAFyILVeLXXHFFbrlllt0yy236Oc//7mGDRtmLFxJksvlUmZmpp5//nn953/%2Bp44dO6YVK1ZoxIgRGjNmjMrKyrR%2B/XqdPn1ae/bs0Z49ezRu3DhJUnZ2tjZu3KgPPvhA1dXVWrlypVq3bq2BAwcaqw8AAAQ3ywPWqVOnlJ%2BfrxtvvFFDhw6VdPYarEmTJqmiosLYOrNnz9bNN9%2BsO%2B%2B8U4MHD1ZSUpIee%2BwxtWvXTqtWrdK6devUq1cvPfXUU1q0aJFuuOEGSVL//v01a9YszZgxQ3369NHevXu1evVqRUREGKsNAAAEt5D6QK/ovki//OUv9de//lVTp07VL37xC%2B3fv18VFRV66KGH1LFjRz355JNWlmMZj%2BeE3SW0KC5XqOLiouT1VjnmfHpLcr7%2BDV36rs1VBa%2BtM/raXUKLwPs2MPSv%2BQLpXfv25s6QXQzLj2Bt27ZNv/nNb3Tbbbc1/NHn6OhoLVy4UNu3b7e6HAAAAOMsD1hVVVVKSkpqNB4fH6%2BTJ09aXQ4AAIBxlgesa6%2B9Vn/5y18k%2Bd9v6q233tI//dM/WV0OAACAcZbfpuGuu%2B7S9OnTdccdd6iurk5r165VSUmJtm3bprlz51pdDgAAgHGWB6ysrCy5XC6tW7dOYWFhev7559WpUyctXrxYt912m9XlAAAAGGd5wDp%2B/LjuuOMO3XHHHVYvDQAAYAnLr8G65ZZbAv5bfwAAAC2Z5QErLS1NW7dutXpZAAAAy1h%2BivDqq6/Wr371K61evVrXXnutWrVq5Te/ZMkSq0sCAAAwyvKA9dlnn%2BkHP/iBJMnr9Vq9PAAAwCVnWcCaOXOmnn32Wb388ssNYytWrFBOTo5VJQAAAFjCsmuwdu7c2Whs9erVVi0PAABgGcsC1vl%2Bc5DfJgQAAMHIsoD13R92vtAYAACA01l%2BmwYAAIBgR8ACAAAwzLLfIjxz5oxmz559wTHugwUAAJzOsoDVq1cvlZeXX3AMAADA6SwLWP/7/lcAAADBjGuwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAw7LIIWE899ZS6du3a8H1xcbEyMzPVs2dPDR8%2BXJs3b/Z7fEFBgYYMGaKePXsqOztbJSUlVpcMAAAcLOgD1kcffaRNmzY1fF9eXq6pU6dq/PjxKi4u1ty5czVv3jy53W5J0s6dO7Vs2TI988wz2rt3rwYNGqQpU6bo5MmTdm0CAABwmKAOWHV1dXr88cc1ceLEhrEtW7YoKSlJmZmZCg8PV3p6ujIyMrR%2B/XpJUmFhocaOHasePXooIiJCkyZNkiTt2rXLjk0AAAAOFNQB6/e//73Cw8M1YsSIhrHS0lIlJyf7PS45ObnhNOC586GhoerWrVvDES4AAIALcdldwKXy9ddfa9myZXr55Zf9xn0%2BnxITE/3GYmNj5fV6G%2BZjYmL85mNiYhrmm6K8vFwej8dvzOWKVEJCwsVsQlALCwv1%2B4qLQ/%2Bs5XLRZ4n9LlD0r/mc2LugDVgLFy7U2LFj1blzZ3355ZcX9dz6%2BvqA1i4sLNTy5cv9xnJycpSbmxvQ6waj6Ogr7C7B0eifNeLiouwuoUVhvwsM/Ws%2BJ/UuKANWcXGx/va3v6moqKjRXFxcnHw%2Bn9%2BY1%2BtVfHz8/znv8/nUpUuXJq%2BflZWljIwMvzGXK1Jeb1WTXyPYhYWFKjr6ClVUVKu2ts7uchyH/lmL9%2B5Z7HeBoX/NF0jv7PoBKSgD1ubNm3Xs2DENGjRI0v8ckUpLS9O9997bKHiVlJSoR48ekqSUlBSVlpZqzJgxkqTa2lodOHBAmZmZTV4/ISGh0elAj%2BeEamp4Q52rtraOvgSA/lmDHvtjvwsM/Ws%2BJ/XOOSczL8Ijjzyibdu2adOmTdq0aZNWr14tSdq0aZNGjBihsrIyrV%2B/XqdPn9aePXu0Z88ejRs3TpKUnZ2tjRs36oMPPlB1dbVWrlyp1q1ba%2BDAgTZuEQAAcJKgPIIVExPjd6F6TU2NJOmqq66SJK1atUoLFizQE088oQ4dOmjRokW64YYbJEn9%2B/fXrFmzNGPGDB07dkypqalavXq1IiIirN8QAADgSEEZsM51zTXX6JNPPmn4vnfv3n43Hz3XXXfdpbvuusuK0gAAQBAKylOEAAAAdrosjmABwOVk6NJ37S6hybbO6Gt3CcAlwREsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGCYy%2B4CgJZq6NJ37S4BAOBQHMECAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhLrsLAICWbujSd%2B0uAYDDBO0RrLKyMuXk5CgtLU3p6el65JFHVFFRIUn66KOPdPfdd6tXr1766U9/qjVr1vg9980339SIESN04403auzYsXrnnXfs2AQAAOBQQRuwpkyZoujoaO3cuVOvv/66Pv30U/3617/WqVOnNHnyZP34xz/W22%2B/rWeffVarVq3S9u3bJZ0NX/n5%2BcrLy9Of//xnTZw4UdOmTdPRo0dt3iIAAOAUQRmwKioqlJKSotmzZysqKkpXXXWVxowZo30Mz%2B/3AAAK1ElEQVT79mn37t06c%2BaMHnzwQUVGRqp79%2B668847VVhYKElav369BgwYoAEDBig8PFwjR47U9ddfr82bN9u8VQAAwCmCMmBFR0dr4cKFuvLKKxvGjhw5ooSEBJWWlqpr164KCwtrmEtOTlZJSYkkqbS0VMnJyX6vl5ycLLfbbU3xAADA8S6Li9zdbrfWrVunlStXauvWrYqOjvabj42Nlc/nU11dnXw%2Bn2JiYvzmY2Ji9NlnnzV5vfLycnk8Hr8xlytSCQkJzd%2BIIBMWFur3FcDlyeW6fD4D%2BNxrPif2LugD1nvvvacHH3xQs2fPVnp6urZu3Xrex4WEhDT8u76%2BPqA1CwsLtXz5cr%2BxnJwc5ebmBvS6wSg6%2Bgq7SwBgo7i4KLtLsByfe83npN4FdcDauXOnHn74Yc2bN0%2BjR4%2BWJMXHx%2Bvw4cN%2Bj/P5fIqNjVVoaKji4uLk8/kazcfHxzd53aysLGVkZPiNuVyR8nqrmrchQSgsLFTR0VeooqJatbV1dpcDwCaX0%2Bcin3vNF0jv7ArxQRuw3n//feXn5%2Bu5555Tv379GsZTUlL06quvqqamRi7X2c13u93q0aNHw/x312N9x%2B12a/jw4U1eOyEhodHpQI/nhGpqeEOdq7a2jr4Al7HL8f3P517zOal3zjmZeRFqamr02GOPKS8vzy9cSdKAAQPUpk0brVy5UtXV1frwww%2B1YcMGZWdnS5LGjRunvXv3avfu3Tp9%2BrQ2bNigw4cPa%2BTIkXZsCgAAcKCQ%2BkAvOGqB9u3bp5/97Gdq3bp1o7m33npLVVVVevzxx1VSUqIrr7xS999/v%2B66666Gx2zfvl1LlixRWVmZOnfurLlz56p3794B1eTxnAjo%2BcHG5QpVXFyUvN6qFvvTCHfvBi69rTP62l2CZZzwuddSBdK79u3bXqKqvl9QBqyWiIDlzwkfNAQs4NIjYKEpnBiwgvIUIQAAgJ0IWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDCX3QUAAC5fQ5e%2Ba3cJF2XrjL52lwCH4AgWAACAYQQsAAAAwwhYAAAAhnENFgAAQcpJ17gF2/VtHMECAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDAC1nmUlZXpgQceUFpamgYNGqRFixaprq7O7rIAAIBD8Meez2P69Onq3r27duzYoWPHjmny5Mm68sor9fOf/9zu0gAAgANwBOscbrdbH3/8sfLy8tS2bVslJSVp4sSJKiwstLs0AADgEBzBOkdpaak6dOigmJiYhrHu3bvr0KFDqqysVJs2bS74GuXl5fJ4PH5jLlekEhISjNd76%2BK3jb/mpfL/8m5u%2BHdYWKjfVwBwAper%2BZ9ZfO59v%2B/rrRN7R8A6h8/nU3R0tN/Yd2HL6/U2KWAVFhZq%2BfLlfmPTpk3T9OnTzRX63/b96jbjr2mF8vJyvfTSi8rKyrokwdOEltzb8vJyFRYWtuj%2BtVT0rvnoXWDs%2BNxryZ9jF8MJ/884l3OioIXq6%2BsDen5WVpZef/11v/%2BysrIMVRccPB6Pli9f3uhIH5qG/jUfvWs%2BehcY%2Btd8TuwdR7DOER8fL5/P5zfm8/kUEhKi%2BPj4Jr1GQkKCYxI2AAAwjyNY50hJSdGRI0d0/PjxhjG3263OnTsrKirKxsoAAIBTELDOkZycrNTUVC1ZskSVlZU6ePCg1q5dq%2BzsbLtLAwAADhE2f/78%2BXYX0dLcfPPNKioq0i9/%2BUu98cYbyszM1H333aeQkBC7SwsqUVFR6tOnD0cGm4n%2BNR%2B9az56Fxj613xO611IfaBXdAMAAMAPpwgBAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgwVZer1f5%2Bfnq27ev0tLSNG3aNB05csTushzF7Xbr1ltv1bhx4%2BwupcUrKyvTAw88oLS0NA0aNEiLFi1SXV2d3WU5xttvv6309HTNnDnT7lIcp6ysTDk5OUpLS1N6eroeeeQRVVRU2F2WI3z88ce655571KtXL6Wnp2vGjBnyeDx2l3VBBCzYas6cOfr666%2B1ZcsWbdu2TWfOnNGcOXPsLssxNm/erOnTp%2Bu6666zuxRHmD59uhITE7Vjxw6tXbtWO3bs0EsvvWR3WY7wwgsvaMGCBexrzTRlyhRFR0dr586dev311/Xpp5/q17/%2Btd1ltXjffvut7r33XvXp00fFxcUqKirSsWPH5IQ/o0zAgm3q6%2BuVmJio/Px8xcfHKzY2VuPHj9d7770n/kRm05w%2BfVqFhYXq0aOH3aW0eG63Wx9//LHy8vLUtm1bJSUlaeLEiSosLLS7NEcIDw/Xhg0bCFjNUFFRoZSUFM2ePVtRUVG66qqrNGbMGO3bt8/u0lq86upqzZw5U5MnT1br1q0VHx%2BvW2%2B9VZ9%2B%2BqndpV2Qy%2B4CcPkKCQnRE0884Td25MgRtW/fXiEhITZV5Sx33nmn3SU4RmlpqTp06KCYmJiGse7du%2BvQoUOqrKxUmzZtbKyu5ZswYYLdJThWdHS0Fi5c6Dd25MgRJSQk2FSRc8TExPh9zn3%2B%2Bef693//dw0dOtTGqpqGI1hoMb788ks999xzevDBB%2B0uBUHI5/MpOjrab%2By7sOX1eu0oCZcpt9utdevW8Vl3EcrKypSSkqJhw4YpNTVVubm5dpd0QQQsXFKbNm1S165dz/vf66%2B/3vC4gwcP6u6779aYMWM4KvO/NLV/aBpOPcNu7733nu677z7Nnj1b6enpdpfjGB06dJDb7dZbb72lw4cP6xe/%2BIXdJV0QpwhxSY0aNUqjRo363sfs379f999/v%2B69915NnjzZosqcoSn9Q9PEx8fL5/P5jfl8PoWEhCg%2BPt6mqnA52blzpx5%2B%2BGHNmzdPo0ePtrscxwkJCVFSUpJmzpyp8ePHa%2B7cuS36vcsRLNjq8OHDeuCBB5Sfn0%2B4wiWVkpKiI0eO6Pjx4w1jbrdbnTt3VlRUlI2V4XLw/vvvKz8/X8899xzh6iIUFxdryJAhfrdTCQ09G11atWplV1lNQsCCrZ588kmNGzdOY8eOtbsUBLnk5GSlpqZqyZIlqqys1MGDB7V27VplZ2fbXRqCXE1NjR577DHl5eWpX79%2BdpfjKCkpKaqsrNSiRYtUXV2t48ePa9myZbrpppvUtm1bu8v7XiH1XJQAmxw5ckQDBw5Uq1atGv3W4Jo1a9S7d2%2BbKnOOIUOG6L/%2B679UW1ururq6hp/o3nrrLXXo0MHm6lqeo0ePat68efqP//gPtWnTRuPHj9e0adP4rdUmSE1NlXQ2LEiSy3X2ChO3221bTU6xb98%2B/exnP1Pr1q0bzfFevbBPPvlECxYs0P79%2BxUZGakf//jHeuSRR5SYmGh3ad%2BLgAUAAGAYpwgBAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwLD/D3q2GO3ypi54AAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-2401873375237977370”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-0.2</td> <td class=”number”>686</td> <td class=”number”>21.4%</td> <td>

<div class=”bar” style=”width:93%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.9</td> <td class=”number”>336</td> <td class=”number”>10.5%</td> <td>

<div class=”bar” style=”width:46%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>327</td> <td class=”number”>10.2%</td> <td>

<div class=”bar” style=”width:45%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.5</td> <td class=”number”>243</td> <td class=”number”>7.6%</td> <td>

<div class=”bar” style=”width:33%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.1</td> <td class=”number”>217</td> <td class=”number”>6.8%</td> <td>

<div class=”bar” style=”width:30%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.3</td> <td class=”number”>140</td> <td class=”number”>4.4%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.4</td> <td class=”number”>140</td> <td class=”number”>4.4%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.1</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.7</td> <td class=”number”>93</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (17)</td> <td class=”number”>735</td> <td class=”number”>22.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-2401873375237977370”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-2.6</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.7</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.3</td> <td class=”number”>63</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:63%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.2</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:58%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.1</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:53%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.1</td> <td class=”number”>217</td> <td class=”number”>6.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.3</td> <td class=”number”>72</td> <td class=”number”>2.2%</td> <td>

<div class=”bar” style=”width:33%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.4</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.9</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:30%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_CONVS09”><s>CONVS09</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_Population Estimate, 2016”><code>Population Estimate, 2016</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.92485</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_CONVS14”><s>CONVS14</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_CONVS09”><code>CONVS09</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99646</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_CONVSPTH09”>CONVSPTH09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3053</td>

</tr> <tr>

<th>Unique (%)</th> <td>95.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.60158</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>3.1217</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>1.4%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram8994136379234386822”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives8994136379234386822,#minihistogram8994136379234386822”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives8994136379234386822”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles8994136379234386822”

aria-controls=”quantiles8994136379234386822” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram8994136379234386822” aria-controls=”histogram8994136379234386822”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common8994136379234386822” aria-controls=”common8994136379234386822”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme8994136379234386822” aria-controls=”extreme8994136379234386822”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles8994136379234386822”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.24987</td>

</tr> <tr>

<th>Q1</th> <td>0.40638</td>

</tr> <tr>

<th>Median</th> <td>0.55159</td>

</tr> <tr>

<th>Q3</th> <td>0.72818</td>

</tr> <tr>

<th>95-th percentile</th> <td>1.0915</td>

</tr> <tr>

<th>Maximum</th> <td>3.1217</td>

</tr> <tr>

<th>Range</th> <td>3.1217</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.3218</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.30918</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.51396</td>

</tr> <tr>

<th>Kurtosis</th> <td>10.812</td>

</tr> <tr>

<th>Mean</th> <td>0.60158</td>

</tr> <tr>

<th>MAD</th> <td>0.21523</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.2134</td>

</tr> <tr>

<th>Sum</th> <td>1884.1</td>

</tr> <tr>

<th>Variance</th> <td>0.095595</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram8994136379234386822”>

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bbDZb/ceRkf/3F3L69Gm1adOmwcePj4/3uh3ocJxRTU3jLLza2rpG23dzFqw9Zb14oyfG6Isx%2BmKMvngKyhumL7zwgr788kuvcCVJycnJKi4urv%2B4trZW%2B/fvV69evdShQwfFxMR4zP/zn//U%2BfPnlZyc7LP6AQBAYAu6gPX5559r48aNKigoUGxsrNd8Zmam1q9frz179qiqqkrLly9Xy5YtNWjQIIWFhWnMmDH63e9%2Bp%2BPHj8vpdOo//uM/dM899%2BiGG27ww9kAAIBAFJC3CFNSUiR99xOCkrRlyxZJkt1u19q1a3XmzBkNHjzY4zV9%2B/bVypUrNWDAAE2dOlW5ubk6efKkUlJSVFBQoIiICElSTk6OKisr9eCDD6qmpkaDBw/W3LlzfXdyAAAg4IW4r/eJbjSIw3HG9H1aLKGKi4uU01kZEPe973/lE3%2BXcFU2597l7xJMFWjrxRfoiTH6Yoy%2BGGvqfWnbNtovxw26W4QAAAD%2BRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAEdsHbu3KnU1FTl5eV5zb3//vsaMWKEevfurfT0dH388cf1c3V1dVq8eLGGDBmivn37auLEiTp27Fj9vMvlUm5urlJTU9W/f3/NmjVL1dXVPjknAAAQ%2BAI2YL322muaP3%2B%2BOnbs6DV34MABzZgxQ9OnT9df/vIXZWVl6cknn9SJEyckSe%2B88442bdqkgoICbdu2TZ06dVJ2drbcbrckac6cOaqqqlJRUZHWrl2rkpIS5efn%2B/T8AABA4ArYgBUeHq41a9YYBqzVq1dr4MCBGjhwoMLDwzVy5Eh1795dGzdulCQVFhYqKytLXbp0UVRUlPLy8lRSUqK9e/fq22%2B/1ZYtW5SXlyebzaZ27dppypQpWrt2rS5cuODr0wQAAAEoYAPWhAkTFB0dbThXXFysxMREj7HExETZ7XZVV1fr0KFDHvNRUVHq2LGj7Ha7Dhw4oLCwMPXo0aN%2BPikpSWfPntXhw4cb52QAAEBQsfj6gGlpaUpPT9dDDz2kG2%2B8sVGO4XK5FBMT4zEWExOjQ4cO6fTp03K73YbzTqdTsbGxioqKUkhIiMecJDmdzgYdv6ysTA6Hw2PMYmmt%2BPj4azmdywoLC/X4E%2BayWIKrr6wXb/TEGH0xRl%2BM0RdjPg9YDz30kN577z0tX75cd955p8aMGaO0tDRZLOaWcvF5qmuZv9Jrr6SwsFBLly71GMvOzlZOTs517fdyrNZWjbLf5i4uLtLfJTQK1os3emKMvhijL8boiyefB6zs7GxlZ2eruLhYRUVFeuGFFzRv3jyNGjVKGRkZ6ty583UfIy4uTi6Xy2PM5XLJZrMpNjZWoaGhhvNt2rSRzWZTRUWFamtrFRYWVj8nSW3atGnQ8ceOHau0tDSPMYultZzOyms9JUNhYaGyWlupvLxKtbV1pu4bMv3vy99YL97oiTH6Yoy%2BGGvqffHXN8s%2BD1gXJSUlKSkpSb/61a/0/vvva%2B7cuVq5cqVSU1P1y1/%2BUrfccss17zs5OVn79u3zGLPb7Ro%2BfLjCw8PVrVs3FRcX6/bbb5cklZeX6%2BjRo7rllluUkJAgt9utgwcPKikpqf61Vqu1weEvPj7e63agw3FGNTWNs/Bqa%2Bsabd/NWbD2lPXijZ4Yoy/G6Isx%2BuLJbzdML1y4oPfff1%2BPPfaYZsyYoXbt2unZZ59Vz549lZWVpU2bNl3zvseMGaNdu3Zp%2B/btOnfunNasWaOvvvpKI0eOlCRlZmZq1apVKikpUUVFhfLz89WzZ0%2BlpKTIZrNp6NCheuWVV3Tq1CmdOHFCy5YtU0ZGhum3MQEAQHDyeWIoKSnRmjVrtH79elVWVmro0KF68803deutt9Zv07dvX82dO1cjRoy47H5SUlIkSTU1NZKkLVu2SPrualP37t2Vn5%2BvBQsWqLS0VF27dtWKFSvUtm1bSdK4cePkcDj0yCOPqLKyUv369fN4Zur555/Xc889pyFDhqhFixZ64IEHDN/MFAAAwEiI%2B3qf6L5KN998szp37qyxY8dq1KhRio2NNdyuV69e2rt3ry9La1QOxxnT92mxhCouLlJOZ2VAXJa9/5VP/F3CVdmce5e/SzBVoK0XX6AnxuiLMfpirKn3pW1b47d0amw%2Bv4K1atWq%2Bmefvk8whSsAANC8%2BPwZrB49emjy5Mn1t/Qk6Y033tBjjz3m9ZN9AAAAgcjnAWvBggU6c%2BaMunbtWj82aNAg1dXVaeHChb4uBwAAwHQ%2Bv0X48ccfa9OmTYqLi6sf69Spk/Lz8/XAAw/4uhwAAADT%2BfwKVnV1tcLDw70LCQ1VVVWVr8sBAAAwnc8DVt%2B%2BfbVw4UKdPn26fuybb77RvHnzPN6qAQAAIFD5/BbhzJkz9fOf/1x33nmnoqKiVFdXp8rKSnXo0EFvvfWWr8sBAAAwnc8DVocOHfTee%2B/pz3/%2Bs44eParQ0FB17txZ/fv3r//dfwAAAIHML7/7pWXLlrr77rv9cWgAAIBG5/OAdezYMS1atEhffvmlqqurveb/9Kc/%2BbokAAAAU/nlGayysjL1799frVu39vXhAQAAGp3PA9a%2Bffv0pz/9STabzdeHBgAA8Amfv01DmzZtuHIFAACCms%2BvYD3%2B%2BONaunSppk2bppCQEF8fHmiw%2B1/5xN8lXJXNuXf5uwQAwP/j84D15z//WX/729%2B0bt06/eAHP1BoqOdFtD/84Q%2B%2BLgkAAMBUPg9YUVFRGjBggK8PCwAA4DM%2BD1gLFizw9SEBAAB8yucPuUvS4cOHtWTJEj377LP1Y3//%2B9/9UQoAAIDpfB6wdu/erZEjR%2Bqjjz5SUVGRpO/efHTChAm8ySgAAAgKPg9Yixcv1tNPP61NmzbV/xRhhw4dtHDhQi1btszX5QAAAJjO5wHrn//8pzIzMyXJ420a7rvvPpWUlPi6HAAAANP5PGBFR0cb/g7CsrIytWzZ0tflAAAAmM7nAatPnz564YUXVFFRUT925MgRzZgxQ3feeaevywEAADCdz9%2Bm4dlnn9Wjjz6qfv36qba2Vn369FFVVZW6deumhQsX%2BrocAAAA0/k8YLVv315FRUXasWOHjhw5ooiICHXu3Fl33XUXvzoHAAAEBZ8HLElq0aKF7r77bn8cGgAAoNH5PGClpaV975Uq3gsLAAAEOp8HrGHDhnkErNraWh05ckR2u12PPvqor8sBAAAwnc8D1vTp0w3HP/zwQ3366aemHWf//v1auHCh9u/fr/DwcN15552aOXOmbDabdu/erUWLFunw4cO68cYb9fjjj2vkyJH1r121apXeeecdORwO9ejRQ7NmzVJycrJptQEAgODml99FaOTuu%2B/We%2B%2B9Z8q%2Bampq9Itf/EI/%2BtGPtGvXLhUVFenUqVOaO3euysrKNGXKFI0bN067d%2B/WrFmzNGfOHNntdknS1q1btWTJEr300kvatWuXBg8erMmTJ%2Bvs2bOm1AYAAIJfkwlY%2B/fvl9vtNmVfDodDDodDDz74oFq2bKm4uDjdc889OnDggDZt2qROnTopIyND4eHhSk1NVVpamlavXi1JKiwsVHp6unr16qWIiAhNmjRJkrRt2zZTagMAAMHP57cIx40b5zVWVVWlkpIS3XvvvaYco127durZs6cKCwv1y1/%2BUtXV1froo480aNAgFRcXKzEx0WP7xMREbd68WZJUXFysYcOG1c%2BFhoaqZ8%2BestvtGj58eIOOX1ZWJofD4TFmsbRWfHz8dZ6Zp7CwUI8/0bxZLN%2B/Dlgv3uiJMfpijL4Yoy/GfB6wOnXq5PVThOHh4crIyNDDDz9syjFCQ0O1ZMkSZWVl6c0335Qk3X777Zo2bZqmTJmidu3aeWwfGxsrp9MpSXK5XIqJifGYj4mJqZ9viMLCQi1dutRjLDs7Wzk5OddyOldktbZqlP0isMTFRTZoO9aLN3pijL4Yoy/G6IsnnwcsX7xb%2B/nz5zV58mTdd9999c9PzZs377IP2F/qem9Vjh07VmlpaR5jFktrOZ2V17XfS4WFhcpqbaXy8irV1taZum8EniutL9aLN3pijL4Yoy/GmnpfGvrNp9l8HrDWr1/f4G1HjRp1TcfYvXu3vv76a02dOlVhYWGKjo5WTk6OHnzwQf34xz%2BWy%2BXy2N7pdMpms0mS4uLivOZdLpe6devW4OPHx8d73Q50OM6opqZxFl5tbV2j7RuBo6FrgPXijZ4Yoy/G6Isx%2BuLJ5wFr1qxZqqur87pKFBIS4jEWEhJyzQGrtrbW6xjnz5%2BXJKWmpuqPf/yjx/b79u1Tr169JEnJyckqLi7W6NGj6/e1f/9%2BZWRkXFMtAACg%2BfH5E2m///3v1b9/f73zzjv67LPP9Ne//lVvv/22BgwYoNdee01ffPGFvvjiC%2B3du/eaj9G7d2%2B1bt1aS5YsUVVVlZxOp5YvX66%2BffvqwQcfVGlpqVavXq1z585px44d2rFjh8aMGSNJyszM1Pr167Vnzx5VVVVp%2BfLlatmypQYNGmRSBwAAQLDzyzNYBQUFHg%2Ba33bbberQoYMmTpyooqKi6z5GXFyc/uu//ksvvviiBgwYoJYtW%2Br222/X3Llz1aZNG61YsULz58/XvHnzlJCQoJdfflk333yzJGnAgAGaOnWqcnNzdfLkSaWkpKigoEARERHXXRcAAGgefB6wvvrqK6%2Bf0pMkq9Wq0tJS046TnJyst956y3Cub9%2B%2B2rBhw2VfO378eI0fP960WgAAQPPi81uECQkJWrhwocfbHpSXl2vRokW66aabfF0OAACA6Xx%2BBWvmzJmaNm2aCgsLFRkZqdDQUFVUVCgiIkLLli3zdTkAAACm83nA6t%2B/v7Zv364dO3boxIkTcrvdateunX784x8rOjra1%2BUAAACYzucBS5JatWqlIUOG6MSJE%2BrQoYM/SgAAAGg0Pn8Gq7q6WjNmzFDv3r11//33S/ruGaxJkyapvLzc1%2BUAAACYzucB6%2BWXX9aBAweUn5%2Bv0ND/O3xtba3y8/N9XQ4AAIDpfB6wPvzwQ7366qu677776n/ps9Vq1YIFC/TRRx/5uhwAAADT%2BTxgVVZWqlOnTl7jNptNZ8%2Be9XU5AAAApvN5wLrpppv06aefSpLH7wr84IMP9G//9m%2B%2BLgcAAMB0Pv8pwvHjx%2Bupp57SQw89pLq6Or3%2B%2Buvat2%2BfPvzwQ82aNcvX5QAAAJjO5wFr7NixslgsevvttxUWFqbf/e536ty5s/Lz83Xffff5uhwAAADT%2BTxgnTp1Sg899JAeeughXx8aAADAJ3z%2BDNaQIUM8nr0CAAAINj4PWP369dPmzZt9fVgAAACf8fktwhtvvFG/%2Bc1vVFBQoJtuukktWrTwmF%2B0aJGvSwIAADCVzwPWoUOH9MMf/lCS5HQ6fX14AACARuezgJWXl6fFixfrrbfeqh9btmyZsrOzfVUCAACAT/jsGaytW7d6jRUUFPjq8AAAAD7js4Bl9JOD/DQhAAAIRj4LWBd/sfOVxgAAAAKdz9%2BmAQAAINgRsAAAAEzms58ivHDhgqZNm3bFMd4HCwAABDqfBaxbb71VZWVlVxwDAAAIdD4LWP/6/lcAAADBjGewAAAATEbAAgAAMFlQB6zly5erf//%2B%2BtGPfqSsrCx9/fXXkqTdu3crIyNDffr00fDhw7Vx40aP161atUpDhw5Vnz59lJmZqX379vmjfAAAEKCCNmC988472rhxo1atWqWPP/5YXbt21RtvvKGysjJNmTJF48aN0%2B7duzVr1izNmTNHdrtd0ne/0mfJkiV66aWXtGvXLg0ePFiTJ0/W2bNn/XxGAAAgUARtwFq5cqXy8vL0wx/%2BUFFRUZo9e7Zmz56tTZs2qVOnTsrIyFB4eLhSU1OVlpam1atXS5IKCwuVnp6uXr16KSIiQpMmTZIkbdu2zZ%2BnAwAAAkhQBqxvvvlGX3/9tU6fPq1hw4apX79%2BysnJ0alTp1RcXKzExESP7RMTE%2BtvA146Hxoaqp49e9Zf4QIAALgSn71Ngy%2BdOHFCkvTBBx/o9ddfl9vtVk5OjmbPnq3q6mq1a9fOY/vY2Fg5nU5JksvlUkxMjMd8TExM/XxDlJWVyeFweIxZLK0VHx9/LaexJZP8AAAWLElEQVRzWWFhoR5/onmzWL5/HbBevNETY/TFGH0xRl%2BMBWXAcrvdkqRJkybVh6mnnnpKjz32mFJTUxv8%2BmtVWFiopUuXeoxlZ2crJyfnuvZ7OVZrq0bZLwJLXFxkg7ZjvXijJ8boizH6Yoy%2BeArKgHXDDTdIkqxWa/1YQkKC3G63Lly4IJfL5bG90%2BmUzWaTJMXFxXnNu1wudevWrcHHHzt2rNLS0jzGLJbWcjorr%2Bo8riQsLFRWayuVl1eptrbO1H0j8FxpfbFevNETY/TFGH0x1tT70tBvPs0WlAGrffv2ioqK0oEDB5SUlCRJKi0tVYsWLTRw4EBt2LDBY/t9%2B/apV69ekqTk5GQVFxdr9OjRkqTa2lrt379fGRkZDT5%2BfHy81%2B1Ah%2BOMamoaZ%2BHV1tY12r4ROBq6Blgv3uiJMfpijL4Yoy%2BegvKGqcViUUZGhn73u9/pf//3f3Xy5EktW7ZMI0aM0OjRo1VaWqrVq1fr3Llz2rFjh3bs2KExY8ZIkjIzM7V%2B/Xrt2bNHVVVVWr58uVq2bKlBgwb596QAAEDACMorWJI0bdo0nT9/Xg8//LAuXLigoUOHavbs2YqMjNSKFSs0f/58zZs3TwkJCXr55Zd18803S5IGDBigqVOnKjc3VydPnlRKSooKCgoUERHh5zMCAACBIsR9vU90o0EcjjOm79NiCVVcXKSczsqAuCx7/yuf%2BLuEoLY5967vnQ%2B09eIL9MQYfTFGX4w19b60bRvtl%2BMG5S1CAAAAfyJgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMmaRcB64YUX1KNHj/qPd%2B/erYyMDPXp00fDhw/Xxo0bPbZftWqVhg4dqj59%2BigzM1P79u3zdckAACCABX3AOnDggDZs2FD/cVlZmaZMmaJx48Zp9%2B7dmjVrlubMmSO73S5J2rp1q5YsWaKXXnpJu3bt0uDBgzV58mSdPXvWX6cAAAACTFAHrLq6Oj333HPKysqqH9u0aZM6deqkjIwMhYeHKzU1VWlpaVq9erUkqbCwUOnp6erVq5ciIiI0adIkSdK2bdv8cQoAACAAWfxdQGP6wx/%2BoPDwcI0YMUKvvPKKJKm4uFiJiYke2yUmJmrz5s3188OGDaufCw0NVc%2BePWW32zV8%2BPAGHbesrEwOh8NjzGJprfj4%2BOs5HS9hYaEef6J5s1i%2Bfx2wXrzRE2P0xRh9MUZfjAVtwPr222%2B1ZMkSvfXWWx7jLpdL7dq18xiLjY2V0%2Bmsn4%2BJifGYj4mJqZ9viMLCQi1dutRjLDs7Wzk5OVdzCg1mtbZqlP0isMTFRTZoO9aLN3pijL4Yoy/G6IunoA1YCxYsUHp6urp27aqvv/76ql7rdruv69hjx45VWlqax5jF0lpOZ%2BV17fdSYWGhslpbqby8SrW1dabuG4HnSuuL9eKNnhijL8boi7Gm3peGfvNptqAMWLt379bf//53FRUVec3FxcXJ5XJ5jDmdTtlstsvOu1wudevWrcHHj4%2BP97od6HCcUU1N4yy82tq6Rts3AkdD1wDrxRs9MUZfjNEXY/TFU1DeMN24caNOnjypwYMHq1%2B/fkpPT5ck9evXT927d/d624V9%2B/apV69ekqTk5GQVFxfXz9XW1mr//v318wAAAFcSlAHrmWee0YcffqgNGzZow4YNKigokCRt2LBBI0aMUGlpqVavXq1z585px44d2rFjh8aMGSNJyszM1Pr167Vnzx5VVVVp%2BfLlatmypQYNGuTHMwIAAIEkKG8RxsTEeDyoXlNTI0lq3769JGnFihWaP3%2B%2B5s2bp4SEBL388su6%2BeabJUkDBgzQ1KlTlZubq5MnTyolJUUFBQWKiIjw/YkAAICAFJQB61I/%2BMEP9I9//KP%2B4759%2B3q8%2Beilxo8fr/Hjx/uiNAAAEISC8hYhAACAPxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADBZs3gfrGB226wP/F0CAAC4BFewAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExm8XcBAMxx/yuf%2BLuEBtuce5e/SwCARsUVLAAAAJMFbcAqLS1Vdna2%2BvXrp9TUVD3zzDMqLy%2BXJB04cEA//elPdeutt%2Bree%2B/VypUrPV77/vvva8SIEerdu7fS09P18ccf%2B%2BMUAABAgAragDV58mRZrVZt3bpV69at05dffqkXX3xR1dXVevzxx3XHHXdo586dWrx4sVasWKGPPvpI0nfha8aMGZo%2Bfbr%2B8pe/KCsrS08%2B%2BaROnDjh5zMCAACBIigDVnl5uZKTkzVt2jRFRkaqffv2Gj16tD777DNt375dFy5c0BNPPKHWrVsrKSlJDz/8sAoLCyVJq1ev1sCBAzVw4ECFh4dr5MiR6t69uzZu3OjnswIAAIEiKAOW1WrVggULdMMNN9SPHT9%2BXPHx8SouLlaPHj0UFhZWP5eYmKh9%2B/ZJkoqLi5WYmOixv8TERNntdt8UDwAAAl6z%2BClCu92ut99%2BW8uXL9fmzZtltVo95mNjY%2BVyuVRXVyeXy6WYmBiP%2BZiYGB06dKjBxysrK5PD4fAYs1haKz4%2B/tpPwkBYWFDmYzQDFkvTWLsX/w3xb8kTfTFGX4zRF2NBH7A%2B//xzPfHEE5o2bZpSU1O1efNmw%2B1CQkLq/9/tdl/XMQsLC7V06VKPsezsbOXk5FzXfoFgERcX6e8SPFitrfxdQpNEX4zRF2P0xVNQB6ytW7fq6aef1pw5czRq1ChJks1m01dffeWxncvlUmxsrEJDQxUXFyeXy%2BU1b7PZGnzcsWPHKi0tzWPMYmktp7Py2k7kMvhuAYHK7H8L1yosLFRWayuVl1eptrbO3%2BU0GfTFGH0x1tT74q9v6II2YP3tb3/TjBkz9Nvf/lb9%2B/evH09OTta7776rmpoaWSzfnb7dblevXr3q5y8%2Bj3WR3W7X8OHDG3zs%2BPh4r9uBDscZ1dQ0vYUH%2BENT%2B7dQW1vX5GpqCuiLMfpijL54CspLIDU1NZo9e7amT5/uEa4kaeDAgYqKitLy5ctVVVWlvXv3as2aNcrMzJQkjRkzRrt27dL27dt17tw5rVmzRl999ZVGjhzpj1MBAAABKMR9vQ8cNUGfffaZfvKTn6hly5Zecx988IEqKyv13HPPad%2B%2Bfbrhhhv02GOPafz48fXbfPTRR1q0aJFKS0vVtWtXzZo1S3379r2umhyOM9f1eiMWS6juyd9p%2Bn6BxtZUflWOxRKquLhIOZ2VfOf9L%2BiLMfpirKn3pW3baL8cNyhvEd522236xz/%2B8b3bvPvuu5edu/fee3XvvfeaXRYAAGgmgvIWIQAAgD8RsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQWfxcAoPm5/5VP/F3CVdmce5e/SwAQYLiCBQAAYDICFgAAgMkIWAAAACbjGSwDpaWlmjdvnvbu3avWrVtr2LBhmjZtmkJDyaNAc8QzYwCuFgHLwFNPPaWkpCRt2bJFJ0%2Be1OOPP64bbrhBP/vZz/xdGgAACAAErEvY7XYdPHhQr7/%2BuqKjoxUdHa2srCy9%2BeabBCwAASGQrrhxtQ3BioB1ieLiYiUkJCgmJqZ%2BLCkpSUeOHFFFRYWioqKuuI%2BysjI5HA6PMYulteLj402tNSyMW5YAAlsghUE0rv9v%2Bo/9XYKpCFiXcLlcslqtHmMXw5bT6WxQwCosLNTSpUs9xp588kk99dRT5hWq74Lco%2B2/1NixY00Pb4GsrKxMhYWF9OUS9MUbPTFGX4zRF2P0xRiXQAy43e7rev3YsWO1bt06j//Gjh1rUnX/x%2BFwaOnSpV5Xy5o7%2BmKMvnijJ8boizH6Yoy%2BGOMK1iVsNptcLpfHmMvlUkhIiGw2W4P2ER8fT4oHAKAZ4wrWJZKTk3X8%2BHGdOnWqfsxut6tr166KjIz0Y2UAACBQELAukZiYqJSUFC1atEgVFRUqKSnR66%2B/rszMTH%2BXBgAAAkTY3Llz5/q7iKbmxz/%2BsYqKivTrX/9a7733njIyMjRx4kSFhIT4uzQvkZGRuv3227m6dgn6Yoy%2BeKMnxuiLMfpijL54C3Ff7xPdAAAA8MAtQgAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwmrjS0lL94he/UL9%2B/TR48GC9/PLLqqurM9x21apVGjp0qPr06aPMzEzt27fPx9X6TkP7smTJEvXs2VMpKSke/3377bd%2BqLrx7dy5U6mpqcrLy/ve7erq6rR48WINGTJEffv21cSJE3Xs2DEfVel7De3LM888U//7SC/%2Bd9ttt/moSt8qLS1Vdna2%2BvXrp9TUVD3zzDMqLy833Pb999/XiBEj1Lt3b6Wnp%2Bvjjz/2cbW%2B09C%2BrFu3TjfffLPX55YvvvjCD1U3voMHD%2BrRRx/VrbfeqtTUVOXm5srhcBhu25y%2BFn0vN5q00aNHu2fPnu0uLy93HzlyxH3vvfe6V65c6bXdn/70J/dtt93m3rNnj7uqqsq9YsUK91133eWurKz0Q9WNr6F9efXVV90zZszwQ4W%2BV1BQ4L733nvd48aNc%2Bfm5n7vtqtWrXIPHjzYfejQIfeZM2fczz//vHvEiBHuuro6H1XrO1fTlxkzZrhfffVVH1XmXw888ID7mWeecVdUVLiPHz/uTk9Pd8%2BcOdNru/3797uTk5Pd27dvd1dXV7s3bNjg7tWrl/v48eN%2BqLrxNbQva9eudf/0pz/1Q4W%2Bd%2B7cOfedd97pXrp0qfvcuXPukydPun/605%2B6p0yZ4rVtc/ta9H24gtWE2e12HTx4UNOnT1d0dLQ6deqkrKwsFRYWem1bWFio9PR09erVSxEREZo0aZIkadu2bb4uu9FdTV%2Bak/DwcK1Zs0YdO3a84raFhYXKyspSly5dFBUVpby8PJWUlGjv3r0%2BqNS3rqYvzUV5ebmSk5M1bdo0RUZGqn379ho9erQ%2B%2B%2Bwzr21Xr16tgQMHauDAgQoPD9fIkSPVvXt3bdy40Q%2BVN66r6UtzUlVVpby8PD3%2B%2BONq2bKlbDab7rnnHn355Zde2zanr0VXQsBqwoqLi5WQkKCYmJj6saSkJB05ckQVFRVe2yYmJtZ/HBoaqp49e8put/usXl%2B5mr5I0j/%2B8Q%2BNGzdOffr00fDhw4P29saECRMUHR19xe2qq6t16NAhj/USFRWljh07BuV6aWhfLvrLX/6iUaNGqXfv3srIyAjK2xtWq1ULFizQDTfcUD92/PhxxcfHe2176ecWSUpMTAzKtXI1fbk497Of/Ux9%2B/bVkCFDtGHDBl%2BV6lMxMTF6%2BOGHZbFYJEmHDx/WH//4R91///1e2zanr0VXQsBqwlwul6xWq8fYxVDhdDq9tv3XwHFx20u3CwZX05f27durQ4cOevHFF/XJJ5/o4Ycf1uTJk3X48GGf1dvUnD59Wm63u9msl6vRoUMHdezYUStWrNDOnTt122236ec//3nQ98Vut%2Bvtt9/WE0884TXXnD63XOr7%2BmKz2dSpUyc9/fTT%2BuSTTzR16lTNnDlTu3fv9kOlvlFaWqrk5GQNGzZMKSkpysnJ8dqmOa%2BXSxGwmji3290o2wa6hp7rww8/rFdffVUdO3ZUq1atlJWVpZ49ewbl7Y2r1ZzWS0NlZ2frhRdeULt27RQVFaWnn35aLVu21JYtW/xdWqP5/PPPNXHiRE2bNk2pqamG2zTHtXKlvgwaNEi///3vlZiYqJYtW2r48OG65557tG7dOj9U6xsJCQmy2%2B364IMP9NVXX%2BlXv/qV4XbNcb0YIWA1YTabTS6Xy2PM5XIpJCRENpvNYzwuLs5w20u3CwZX0xcjCQkJKisra6zymrzY2FiFhoYa9rBNmzZ%2BqqppCgsL04033hi062Xr1q36xS9%2BoZkzZ2rChAmG2zSnzy0XNaQvRprD55aQkBB16tRJeXl5Kioq0qlTpzzmm%2BN6uRwCVhOWnJys48ePeyxgu92url27KjIy0mvb4uLi%2Bo9ra2u1f/9%2B9erVy2f1%2BsrV9OU///M/vS7Zl5SUqEOHDj6ptSkKDw9Xt27dPNZLeXm5jh49qltuucWPlfmX2%2B3WggULdPDgwfqx8%2BfP6%2BjRo0G5Xv72t79pxowZ%2Bu1vf6tRo0Zddrvk5GSv59DsdntQfm6RGt6Xd999V%2B%2B//77HWLB%2Bbtm9e7eGDh3q8VY4oaHfxYcWLVp4bNucvhZdCQGrCbv4fjyLFi1SRUWFSkpK9PrrryszM1OSdN9999X/dEtmZqbWr1%2BvPXv2qKqqSsuXL1fLli01aNAgP55B47iavrhcLs2bN0%2BHDx/WuXPntHLlSh09elSjR4/25yn43DfffKP77ruv/r2uMjMztWrVKpWUlKiiokL5%2Bfn17xfWnPxrX0JCQvT1119r3rx5%2Buabb1RZWan8/Hy1aNFCd999t79LNVVNTY1mz56t6dOnq3///l7zjz76aH14GDNmjHbt2qXt27fr3LlzWrNmjb766iuNHDnS12U3uqvpy/nz5/XrX/9adrtdFy5cUFFRkf785z9r3Lhxvi670SUnJ6uiokIvv/yyqqqqdOrUKS1ZskS33XaboqOjm%2B3Xoiux%2BLsAfL9XX31Vc%2BbM0V133aWoqCiNGzdO48ePlyQdOXJEZ8%2BelSQNGDBAU6dOVW5urk6ePKmUlBQVFBQoIiLCn%2BU3mob2Zdq0aZKkrKwsuVwude3aVW%2B88Ybat2/vt9oby8VwVFNTI0n1zw1d/AJw5MgRnT9/XpI0btw4ORwOPfLII6qsrFS/fv20dOlS/xTeyK6mL7/5zW/04osvKj09XRUVFbrlllv05ptvqnXr1v4pvpHs2bNHJSUlmj9/vubPn%2B8x98EHH%2BjYsWM6ffq0JKl79%2B7Kz8/XggULVFpaqq5du2rFihVq27atP0pvVFfTlwkTJqiyslK//OUv5XA49IMf/EDLli1TcnKyP0pvVNHR0Vq5cqXmz5%2BvO%2B64Q61bt9Ydd9yh3/zmN5Ka99ei7xPi5mk0AAAAU3GLEAAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGT/Pz1dqzK%2BlQGwAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common8994136379234386822”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>46</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.452284034</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.529661017</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.599655198</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.542299349</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.677659815</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.47692858</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.418760469</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.571183778</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.432900433</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3042)</td> <td class=”number”>3067</td> <td class=”number”>95.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme8994136379234386822”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>46</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.074046649</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.082731739</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.08364701</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.095620578</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>2.861230329</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.962336014</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.03030303</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.04414003</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.121748179</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_CONVSPTH14”>CONVSPTH14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3050</td>

</tr> <tr>

<th>Unique (%)</th> <td>95.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.5968</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>4.6012</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>1.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram2087637263134865150”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives2087637263134865150,#minihistogram2087637263134865150”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives2087637263134865150”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles2087637263134865150”

aria-controls=”quantiles2087637263134865150” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram2087637263134865150” aria-controls=”histogram2087637263134865150”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common2087637263134865150” aria-controls=”common2087637263134865150”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme2087637263134865150” aria-controls=”extreme2087637263134865150”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles2087637263134865150”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.25984</td>

</tr> <tr>

<th>Q1</th> <td>0.40079</td>

</tr> <tr>

<th>Median</th> <td>0.54456</td>

</tr> <tr>

<th>Q3</th> <td>0.72369</td>

</tr> <tr>

<th>95-th percentile</th> <td>1.0759</td>

</tr> <tr>

<th>Maximum</th> <td>4.6012</td>

</tr> <tr>

<th>Range</th> <td>4.6012</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.3229</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.31053</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.52032</td>

</tr> <tr>

<th>Kurtosis</th> <td>22.687</td>

</tr> <tr>

<th>Mean</th> <td>0.5968</td>

</tr> <tr>

<th>MAD</th> <td>0.21151</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>3.0191</td>

</tr> <tr>

<th>Sum</th> <td>1869.2</td>

</tr> <tr>

<th>Variance</th> <td>0.096429</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram2087637263134865150”>

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KqqSkOHDlVKSori4uJUWVmp5ORkSdI///lPHT58WOnp6c3ef2Ji4jGXA12uA2poaJ0fjMbGplbbdlvWnJ7Se3vQd/vQe/vQ%2B8ASdBd0P/jgA61Zs0YlJSXHhCvpyNmphx56SDt37tQPP/ygZ599Vrt379Z1112nsLAw3XjjjXrqqae0d%2B9eud1u/fGPf9Tll1/ue4wDAADAyQTkGayMjAxJR35DUJIqKiokSU6nUytXrtSBAwd02WWX%2Ba3Tr18/Pfvss5oyZYqkI/deeTwe9ejRQ3/%2B85/VpUsXSVJBQYHq6up07bXXqqGhQZdddpkefPBBi44MAAAEgxBvS%2B/oRrO4XAeMb9PhCFV8fJTc7rqAOG181ePv2l3CKVk3%2BdITzgVa74MFfbcPvbcPvW%2BZzp2P/1in1hZ0lwgBAADsRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwywNWTk6OiouLtXfvXqt3DQAAYAnLA9b111%2Bv1157TYMHD9att96qDRs2qKGhweoyAAAAWo3lASs/P1%2BvvfaaXnrpJfXs2VOPPfaYsrOzNX/%2BfO3atcvqcgAAAIyz7R6stLQ0TZs2TW%2B88YZmzJihl156SUOHDtW4ceP0ySef2FUWAABAi9kWsOrr6/Xaa6/ptttu07Rp05SUlKT77rtPvXr10tixY7V27Vq7SgMAAGgRh9U7rKqq0ooVK/TKK6%2Borq5OQ4YM0bJly3TBBRf4lunXr58efPBBXXPNNVaXF3AunLne7hIAAMBRLA9Yw4YNU/fu3TV%2B/HiNGDFCHTt2PGaZ7Oxs7d%2B/3%2BrSAAAAjLA8YC1fvlwXXXTRSZf7%2BOOPLagGAADAPMvvwUpNTdWECRNUUVHhG/vzn/%2Bs2267TR6Px%2BpyAAAAjLM8YM2ZM0cHDhxQjx49fGMDBw5UU1OT5s6da3U5AAAAxll%2BifCdd97R2rVrFR8f7xvr1q2bioqKdPXVV1tdDgAAgHGWn8E6dOiQIiIiji0kNFQHDx60uhwAAADjLA9Y/fr109y5c/Xdd9/5xr755hs99NBDfo9qAAAACFSWB6wZM2Zo8%2BbNuuSSS3TRRRfpwgsv1MCBA7V161bNnj37lLa1adMmZWVlqbCw8Ji51157Tddcc4369u2rkSNH6p133vHNNTU1aeHChRo0aJD69euncePG6csvv/TNezweTZ48WVlZWerfv79mzpypQ4cO/fKDBgAAbYrl92ClpKTo1Vdf1dtvv63du3crNDRU3bt3V//%2B/RUWFtbs7SxZskQrVqxQ165dj5nbvn27pk2bpuLiYl188cUqLy/XXXfdpfXr16tLly56/vnntXbtWi1ZskRJSUlauHCh8vPztXr1aoWEhGjWrFk6fPiwysrKVF9fr0mTJqmoqEj333%2B/yVYAAIAgZctH5YSHh2vw4MG65ZZbNHbsWGVnZ59SuJKkiIiIEwasl19%2BWdnZ2crOzlZERISGDx%2Buc845R2vWrJEklZaWauzYsTr77LMVHR2twsJCVVVV6eOPP9a%2BfftUUVGhwsJCJSQkKCkpSXfeeadWrlyp%2Bvp6I8cPAACCm%2BVnsL788kstWLBAn3322XEvu73%2B%2BuvN2s6YMWNOOFdZWans7Gy/sd69e8vpdOrQoUP6/PPP1bt3b99cdHS0unbtKqfTqQMHDigsLEypqam%2B%2BbS0NH3//ffauXOn3/iJVFdXy%2BVy%2BY05HJFKTExs1rE1V1iYbR8l2SY4HCfu74%2B95zWwFn23D723D70PTJYHrBkzZqi6ulr9%2B/dXZGRkq%2BzD4/EoLi7ObywuLk6ff/65vvvuO3m93uPOu91udezYUdHR0QoJCfGbkyS3292s/ZeWlqq4uNhvLD8/XwUFBb/kcGCT%2BPioky4TG9vBgkpwNPpuH3pvH3ofWCwPWFu3btXrr7%2BuhISEVt2P1%2Bv9xfMnW/dk8vLylJOT4zfmcETK7a5r0XaPxruZ1vVzr1dYWKhiYzuopuagGhubLKyqbaPv9qH39qH3LdOcN8utwfKA1alTp1Y7c/Wj%2BPj4Yz52x%2BPxKCEhQR07dlRoaOhx5zt16qSEhATV1taqsbHRd1/Yj8t26tSpWftPTEw85nKgy3VADQ38YASS5rxejY1NvK42oO/2off2ofeBxfJTIOPHj1dxcXGLzxL9nPT0dG3dutVvzOl0qk%2BfPoqIiFDPnj1VWVnpm6upqdHu3bt13nnnqVevXvJ6vdqxY4ffurGxserevXur1QwAAIKH5Wew3n77bX344YdatWqVzjzzTIWG%2Bme8F198scX7uPHGG5Wbm6s333xTl1xyidauXasvvvhCw4cPlySNHj1aJSUlGjBggJKSklRUVKRevXopIyNDkjRkyBA9/vjjmjdvng4fPqzFixcrNzdXDofl7QIAAAHI8sQQHR2tAQMGtHg7P4ahhoYGSVJFRYWkI2ebzjnnHBUVFWnOnDnas2ePevTooaefflqdO3eWJI0aNUoul0s33XST6urqlJmZ6XdT%2BsMPP6wHHnhAgwYNUrt27XT11Vcf92GmAAAAxxPibc1rdfBxuQ4Y36bDEarLizYZ3y6OWDf50hPOORyhio%2BPkttdxz0RFqLv9qH39qH3LdO5c4wt%2B7Xl19B27typRYsW6b777vONffTRR3aUAgAAYJzlAWvz5s0aPny4NmzYoLKyMklHHj46ZsyYZj9kFAAA4NfM8oC1cOFC3XPPPVq7dq3vYZ4pKSmaO3euFi9ebHU5AAAAxlkesP75z39q9OjRkuT3tPQrr7xSVVVVVpcDAABgnOUBKyYm5rifQVhdXa3w8HCrywEAADDO8oB1/vnn67HHHlNtba1vbNeuXZo2bZouueQSq8sBAAAwzvLnYN133326%2BeablZmZqcbGRp1//vk6ePCgevbsqblz51pdDgAAgHGWB6wuXbqorKxMb731lnbt2qX27dure/fuuvTSS/3uyQIAAAhUtnz2S7t27TR48GA7dg0AANDqLA9YOTk5P3umimdhAQCAQGd5wBo6dKhfwGpsbNSuXbvkdDp18803W10OAACAcZYHrKlTpx53vLy8XH//%2B98trgYAAMA8Wz6L8HgGDx6sV1991e4yAAAAWuxXE7C2bdsmr9drdxkAAAAtZvklwlGjRh0zdvDgQVVVVemKK66wuhwAAADjLA9Y3bp1O%2Ba3CCMiIpSbm6sbbrjB6nIAAACMszxg8bR2AAAQ7CwPWK%2B88kqzlx0xYkQrVgIAANA6LA9YM2fOVFNT0zE3tIeEhPiNhYSEELAAAEBAsjxgPfPMM3r22Wc1YcIEpaamyuv16tNPP9WSJUv0r//6r8rMzLS6JAAAAKNsuQerpKRESUlJvrELL7xQKSkpGjdunMrKyqwuCQAAwCjLn4P1xRdfKC4u7pjx2NhY7dmzx%2BpyAAAAjLM8YCUnJ2vu3Llyu92%2BsZqaGi1YsEBnnXWW1eUAAAAYZ/klwhkzZmjKlCkqLS1VVFSUQkNDVVtbq/bt22vx4sVWlwMAAGCc5QGrf//%2BevPNN/XWW2/p66%2B/ltfrVVJSkn73u98pJibG6nIAAACMszxgSVKHDh00aNAgff3110pJSbGjBAAAgFZj%2BT1Yhw4d0rRp09S3b19dddVVko7cg3XrrbeqpqbG6nIAAACMszxgzZ8/X9u3b1dRUZFCQ3/afWNjo4qKiqwuBwAAwDjLA1Z5ebn%2B4z/%2BQ1deeaXvQ59jY2M1Z84cbdiwwepyAAAAjLM8YNXV1albt27HjCckJOj777%2B3uhwAAADjLA9YZ511lv7%2B979Lkt9nD65fv15nnHGG1eUAAAAYZ/lvEf7Lv/yLJk6cqOuvv15NTU1aunSptm7dqvLycs2cOdPqcgAAAIyzPGDl5eXJ4XDoueeeU1hYmJ566il1795dRUVFuvLKK60uBwAAwDjLA9b%2B/ft1/fXX6/rrr2%2B1fbz//vu65ZZb/Ma8Xq/q6%2Bu1fPlyjRkzRuHh4X7z//7v/%2B57bMTy5cv1/PPPy%2BVyKTU1VTNnzlR6enqr1QsAAIKL5QFr0KBB%2BvDDD32/Qdga%2BvXrJ6fT6Tf21FNPaceOHZKOfB7ixo0bj7vuxo0btWjRIj3zzDNKTU3V8uXLNWHCBG3YsEGRkZGtVjMAAAgelt/knpmZqXXr1lm6z//93//V0qVLde%2B995502dLSUo0cOVJ9%2BvRR%2B/btdeutt0qS3njjjdYuEwAABAnLz2CdfvrpevTRR1VSUqKzzjpL7dq185tfsGCB8X0%2B8cQTuv7663XGGWfoyy%2B/VF1dnfLz87VlyxaFh4frlltu0dixYxUSEqLKykoNHTrUt25oaKh69eolp9OpYcOGNWt/1dXVcrlcfmMOR6QSExONHldYmOX5uE1xOE7c3x97z2tgLfpuH3pvH3ofmCwPWJ9//rl%2B85vfSJLcbner7%2B%2Brr77Shg0bfA8xjY6O1jnnnKObb75ZCxcu1HvvvadJkyYpJiZGubm58ng8iouL89tGXFzcKdVaWlqq4uJiv7H8/HwVFBS0/IBgmfj4qJMuExvbwYJKcDT6bh96bx96H1gsC1iFhYVauHCh/vKXv/jGFi9erPz8/Fbd7/PPP68rrrhCnTt3liSlpaX51dC/f3%2BNGjVKq1atUm5uriT/53P9Enl5ecrJyfEbczgi5XbXtWi7R%2BPdTOv6udcrLCxUsbEdVFNzUI2NTRZW1bbRd/vQe/vQ%2B5Zpzpvl1mBZwDreTeUlJSWtHrDKy8s1bdq0n10mOTlZ5eXlkqT4%2BHh5PB6/eY/Ho549ezZ7n4mJicdcDnS5DqihgR%2BMQNKc16uxsYnX1Qb03T703j70PrBYdgrkeGeFWnqm6GS2b9%2BuPXv26NJLL/WNrVu3Tv/1X//lt9zOnTuVkpIiSUpPT1dlZaVvrrGxUdu2bVOfPn1atVYAABA8LAtYx3ssQ2s%2BqkGStm3bpo4dOyo6Oto31q5dO82bN0/vvPOO6uvr9e6772rlypUaPXq0JGn06NF65ZVX9I9//EMHDx7Uk08%2BqfDwcA0cOLBVawUAAMHD8pvcrbRv3z7fvVc/Gjx4sGbMmKFHHnlEe/fu1WmnnaYZM2boiiuukCQNGDBAd999tyZPnqxvv/1WGRkZKikpUfv27e04BAAAEIBCvK19ne7/69Onjz7%2B%2BOOTjgUrl%2BuA8W06HKG6vGiT8e3iiHWTLz3hnMMRqvj4KLndddwTYSH6bh96bx963zKdO8fYsl/LzmDV19drypQpJx1rjedgAQAAWMmygHXBBReourr6pGMAAACBzrKA9X%2BfPQUAABDMeFIlAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMCxoA1ZqaqrS09OVkZHh%2B3rkkUckSZs3b1Zubq7OP/98DRs2TGvWrPFbd/ny5RoyZIjOP/98jR49Wlu3brXjEAAAQIBy2F1Aa1q/fr3OPPNMv7Hq6mrdeeedmjlzpq655hp98MEHuuOOO9S9e3dlZGRo48aNWrRokZ555hmlpqZq%2BfLlmjBhgjZs2KDIyEibjgQAAASSoD2DdSJr165Vt27dlJubq4iICGVlZSknJ0cvv/yyJKm0tFQjR45Unz591L59e916662SpDfeeMPOsgEAQAAJ6jNYCxYs0EcffaTa2lpdddVVmj59uiorK9W7d2%2B/5Xr37q1169ZJkiorKzV06FDfXGhoqHr16iWn06lhw4Y1a7/V1dVyuVx%2BYw5HpBITE1t4RP7CwtpcPraUw3Hi/v7Ye14Da9F3%2B9B7%2B9D7wBS0Aeu3v/2tsrKyNG/ePH355ZeaPHmyHnroIXk8HiUlJfkt27FjR7ndbkmSx%2BNRXFyc33xcXJxvvjlKS0tVXFzsN5afn6%2BCgoJfeDSwQ3x81EmXiY3tYEElOBp9tw%2B9tw%2B9DyxBG7BKS0t9/3/22Wdr6tSpuuOOO3TBBRecdF2v19uifefl5SknJ8dvzOGIlNtd16LtHo13M63r515iCnyPAAANLElEQVSvsLBQxcZ2UE3NQTU2NllYVdtG3%2B1D7%2B1D71umOW%2BWW0PQBqyjnXnmmWpsbFRoaKg8Ho/fnNvtVkJCgiQpPj7%2BmHmPx6OePXs2e1%2BJiYnHXA50uQ6ooYEfjEDSnNersbGJ19UG9N0%2B9N4%2B9D6wBOUpkG3btmnu3Ll%2BY1VVVQoPD1d2dvYxj13YunWr%2BvTpI0lKT09XZWWlb66xsVHbtm3zzQMAAJxMUAasTp06qbS0VCUlJTp8%2BLB27dqlJ554Qnl5ebr22mu1Z88evfzyy/rhhx/01ltv6a233tKNN94oSRo9erReeeUV/eMf/9DBgwf15JNPKjw8XAMHDrT3oAAAQMAIykuESUlJKikp0YIFC3wB6brrrlNhYaEiIiL09NNPa/bs2XrooYeUnJys%2BfPn69xzz5UkDRgwQHfffbcmT56sb7/9VhkZGSopKVH79u1tPioAABAoQrwtvaMbzeJyHTC%2BTYcjVJcXbTK%2BXRyxbvKlJ5xzOEIVHx8lt7uOeyIsRN/tQ%2B/tQ%2B9bpnPnGFv2G5SXCAEAAOxEwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYFrQBa8%2BePcrPz1dmZqaysrI0ffp01dTU6KuvvlJqaqoyMjL8vv70pz/51n3ttdd0zTXXqG/fvho5cqTeeecdG48EAAAEGofdBbSWCRMmKD09XRs3btSBAweUn5%2BvefPm6Y477pAkOZ3O4663fft2TZs2TcXFxbr44otVXl6uu%2B66S%2BvXr1eXLl2sPATY7KrH37W7hFOybvKldpcAAPj/gvIMVk1NjdLT0zVlyhRFRUWpS5cuuu6667Rly5aTrvvyyy8rOztb2dnZioiI0PDhw3XOOedozZo1FlQOAACCQVCewYqNjdWcOXP8xvbu3avExETfn%2B%2B991797W9/U0NDg2644QYVFBSoXbt2qqysVHZ2tt%2B6vXv3PuEZr%2BOprq6Wy%2BXyG3M4Iv32b0JYWFDmY/xCDkfwfz/8%2BD3P97716L196H1gCsqAdTSn06nnnntOTz75pMLDw9W3b19dfvnlevTRR7V9%2B3ZNnDhRDodDkyZNksfjUVxcnN/6cXFx%2Bvzzz5u9v9LSUhUXF/uN5efnq6CgwMjxAMcTHx9ldwmWiY3tYHcJbRa9tw%2B9DyxBH7A%2B%2BOAD3XHHHZoyZYqysrIkSS%2B%2B%2BKJv/rzzztP48eP19NNPa9KkSZIkr9fbon3m5eUpJyfHb8zhiJTbXdei7R6NdzP4v0x/f/0ahYWFKja2g2pqDqqxscnuctoUem8fet8ydr35DOqAtXHjRt1zzz2aNWuWRowYccLlkpOTtW/fPnm9XsXHx8vj8fjNezweJSQkNHu/iYmJx1wOdLkOqKGBHwy0nrb0/dXY2NSmjvfXhN7bh94HlqA9BfLhhx9q2rRpeuKJJ/zC1ebNm/Xkk0/6Lbtz504lJycrJCRE6enp2rp1q9%2B80%2BlUnz59LKkbAAAEvqAMWA0NDbr//vs1depU9e/f328uJiZGixcv1urVq1VfXy%2Bn06k//elPGj16tCTpxhtv1N/%2B9je9%2Beab%2BuGHH7RixQp98cUXGj58uB2HAgAAAlCIt6U3HP0KbdmyRb///e8VHh5%2BzNz69eu1bds2FRcX64svvlBMTIxuuukm3XbbbQoNPZI3N2zYoAULFmjPnj3q0aOHZs6cqX79%2BrWoJpfrQIvWPx6HI1SXF20yvl0EprbwHCyHI1Tx8VFyu%2Bu4VGIxem8fet8ynTvH2LLfoAxYv0YELLQ2AhZaE723D71vGbsCVlBeIgQAALATAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwzGF3AQDMuOrxd%2B0uodnWTb7U7hIAoFVxBus49uzZo9tvv12ZmZm67LLLNH/%2BfDU1NdldFgAACBCcwTqOiRMnKi0tTRUVFfr22281fvx4nXbaafrDH/5gd2kAACAAELCO4nQ6tWPHDi1dulQxMTGKiYnR2LFjtWzZMgIWYEggXc6UuKQJ4NQRsI5SWVmp5ORkxcXF%2BcbS0tK0a9cu1dbWKjo6%2BqTbqK6ulsvl8htzOCKVmJhotNawMK7wAlYItEAYSP469Xd2l/Cr9%2BPf9fydH1gIWEfxeDyKjY31G/sxbLnd7mYFrNLSUhUXF/uN3XXXXZo4caK5QnUkyN3c5TPl5eUZD2/4edXV1SotLaX3FqPv9qH39qmurtayZc/Q%2BwBDHD4Or9fbovXz8vK0atUqv6%2B8vDxD1f3E5XKpuLj4mLNlaH303h703T703j70PjBxBusoCQkJ8ng8fmMej0chISFKSEho1jYSExN5lwEAQBvGGayjpKena%2B/evdq/f79vzOl0qkePHoqKirKxMgAAECgIWEfp3bu3MjIytGDBAtXW1qqqqkpLly7V6NGj7S4NAAAEiLAHH3zwQbuL%2BLX53e9%2Bp7KyMj3yyCN69dVXlZubq3HjxikkJMTu0o4RFRWliy66iLNrNqD39qDv9qH39qH3gSfE29I7ugEAAOCHS4QAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAtCePXt0%2B%2B23KzMzU5dddpnmz5%2BvpqYmu8tqMzZt2qSsrCwVFhbaXUqbsmfPHuXn5yszM1NZWVmaPn26ampq7C6rTdixY4duvvlmXXDBBcrKytLkyZPlcrnsLqvNeeyxx5Sammp3GWgmAlYAmjhxopKSklRRUaGlS5eqoqJCy5Yts7usNmHJkiWaPXu2unbtancpbc6ECRMUGxurjRs3atWqVfrss880b948u8sKeocPH9Ytt9yiiy66SJs3b1ZZWZm%2B/fZb8TG21tq%2BfbtWr15tdxk4BQSsAON0OrVjxw5NnTpVMTEx6tatm8aOHavS0lK7S2sTIiIitGLFCgKWxWpqapSenq4pU6YoKipKXbp00XXXXactW7bYXVrQO3jwoAoLCzV%2B/HiFh4crISFBl19%2BuT777DO7S2szmpqa9MADD2js2LF2l4JTQMAKMJWVlUpOTlZcXJxvLC0tTbt27VJtba2NlbUNY8aMUUxMjN1ltDmxsbGaM2eOTjvtNN/Y3r17lZiYaGNVbUNcXJxuuOEGORwOSdLOnTv13//937rqqqtsrqztePHFFxUREaFrrrnG7lJwChx2F4BT4/F4FBsb6zf2Y9hyu92Kjo62oyzAUk6nU88995yefPJJu0tpM/bs2aMhQ4aooaFBN954owoKCuwuqU3Yt2%2BfFi1apL/85S92l4JTxBmsAOT1eu0uAbDNBx98oHHjxmnKlCnKysqyu5w2Izk5WU6nU%2BvXr9cXX3yhe%2B%2B91%2B6S2oQ5c%2BZo5MiR6tGjh92l4BQRsAJMQkKCPB6P35jH41FISIgSEhJsqgqwxsaNG3X77bdrxowZGjNmjN3ltDkhISHq1q2bCgsLVVZWpv3799tdUlDbvHmzPvroI%2BXn59tdCn4BAlaASU9P1969e/3%2BYnM6nerRo4eioqJsrAxoXR9%2B%2BKGmTZumJ554QiNGjLC7nDZj8%2BbNGjJkiN%2BjYEJDj/zT0a5dO7vKahPWrFmjb7/9VpdddpkyMzM1cuRISVJmZqZeffVVm6vDyRCwAkzv3r2VkZGhBQsWqLa2VlVVVVq6dKlGjx5td2lAq2loaND999%2BvqVOnqn///naX06akp6ertrZW8%2BfP18GDB7V//34tWrRIF154Ib/w0cqmT5%2Bu8vJyrV69WqtXr1ZJSYkkafXq1crJybG5OpxMiJcbegLO119/rVmzZum9995TdHS0Ro0apbvuukshISF2lxb0MjIyJB35B1%2BS7zernE6nbTW1BVu2bNHvf/97hYeHHzO3fv16JScn21BV2/Hpp59q9uzZ%2BuSTTxQZGamLL75Y06dPV1JSkt2ltSlfffWVBg0apE8//dTuUtAMBCwAAADDuEQIAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIb9P%2B2tw7qo%2BPdNAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common2087637263134865150”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>38</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.696864111</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.465441005</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.81300813</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.869565217</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.645399226</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.768639508</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.4004004</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.432664756</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.654212401</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3039)</td> <td class=”number”>3075</td> <td class=”number”>95.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme2087637263134865150”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>38</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.071500072</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.07536931</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.088082445</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.101864113</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>2.923976608</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.023758099</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.726708075</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.816793893</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.601226994</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_CSA07”>CSA07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>44</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>4.2%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>134</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>4.0543</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>79</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>22.8%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram6787255574279489228”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives6787255574279489228,#minihistogram6787255574279489228”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives6787255574279489228”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles6787255574279489228”

aria-controls=”quantiles6787255574279489228” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram6787255574279489228” aria-controls=”histogram6787255574279489228”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common6787255574279489228” aria-controls=”common6787255574279489228”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme6787255574279489228” aria-controls=”extreme6787255574279489228”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles6787255574279489228”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>1</td>

</tr> <tr>

<th>Median</th> <td>2</td>

</tr> <tr>

<th>Q3</th> <td>6</td>

</tr> <tr>

<th>95-th percentile</th> <td>13</td>

</tr> <tr>

<th>Maximum</th> <td>79</td>

</tr> <tr>

<th>Range</th> <td>79</td>

</tr> <tr>

<th>Interquartile range</th> <td>5</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>5.4609</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.3469</td>

</tr> <tr>

<th>Kurtosis</th> <td>30.694</td>

</tr> <tr>

<th>Mean</th> <td>4.0543</td>

</tr> <tr>

<th>MAD</th> <td>3.5302</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>4.0409</td>

</tr> <tr>

<th>Sum</th> <td>12471</td>

</tr> <tr>

<th>Variance</th> <td>29.821</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram6787255574279489228”>

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lZmZqw4YNvhoNAADggrjsfsOysjKtW7dOr732murq6nTDDTfoj3/8owYMGNDymEGDBunxxx/X6NGj7R4PAADggtlesEaNGqXu3btr2rRpuvXWWxUREXHGY9LS0nTs2DG7RwMAADDC9oK1atUqDR48%2BCcft2vXLhumAQAAMM/2a7Di4uI0ffp0vf322y1r//Vf/6Vf//rX8ng8do8DAABgnO0FKy8vT8ePH1fPnj1b1oYNG6bm5mYtXrzY7nEAAACMs/0U4QcffKANGzYoMjKyZS0mJkb5%2Bfm6%2Beab7R4HAADAONuPYNXX1ys4OPjMQQIDdfLkyXN6rffff1%2BpqanKzs72Wn/11Vd11VVXKSkpyevj9P21mpubtXTpUg0fPlyDBg3S1KlTdfDgwZbnezwezZ49W6mpqRo6dKgeffRR1dfXn0daAADwf5HtBWvQoEFavHixvv3225a1b775Rk888YTXrRp%2ByvPPP69FixapW7duP/o%2Bbrfb6yM5OVmStGbNGm3YsEGFhYXavn27YmJilJWVJcuyJEkLFizQyZMntXHjRr3yyisqKytTfn7%2BBaQGAAD/l9hesObNm6edO3fq6quv1uDBgzVw4EANGzZMxcXFWrRoUatfJzg4WOvWrfvRgvWvFBUVKTMzUz169FBISIiys7NVVlamXbt26ciRI3r77beVnZ2tqKgoderUSffdd59eeeUVNTQ0nPN7AQCA/3tsvwara9eueuONN/Tee%2B/p66%2B/VmBgoLp3766hQ4cqKCio1a8zZcqUf7lfUVGhu%2B%2B%2BW8XFxQoLC9OsWbN0yy23qL6%2BXnv37lV8fHzLY0NCQtStWze53W4dP35cQUFBiouLa9lPSEjQiRMntG/fPq91AACAs7G9YElS27Ztdd11112014%2BKilJMTIwefPBB9ezZU2%2B99ZYefvhhRUdH68orr5RlWQoPD/d6Tnh4uKqrqxUREaGQkBAFBAR47UlSdXV1q96/srJSVVVVXmsuVztFR0dfYDJvQUE%2B%2B01H58Xlav28p7M5LWNr%2BHM2yb/zkc2ZyOZcTs5ne8E6ePCglixZoq%2B%2B%2BuqsF46/8847F/wew4YN07Bhw1r%2BPGrUKL311lt69dVXlZOTI0kt11udzb/aa42ioiIVFBR4rWVlZWnWrFkX9LpOFxnZ/pyfExZ26UWY5OfBn7NJ/p2PbM5ENudyYj7bC9a8efNUWVmpoUOHql27dra9b5cuXVRcXKyIiAgFBgaecVNTj8ejDh06KCoqSrW1tWpqamo5ZXn6sR06dGjVe2VkZCg9Pd1rzeVqp%2BrqOgNJ/sFpjf5c8gcFBSos7FLV1JxUU1PzRZzKfv6cTfLvfGRzJrI5l4l85/PNvQm2F6zi4mK98847ioqKumjv8ac//Unh4eEaOXJky1pZWZm6du2q4OBg9erVSyUlJS2/sqempkZff/21kpOT1aVLF1mWpT179ighIUGS5Ha7FRYWpu7du7fq/aOjo884HVhVdVyNjf73xX8uzid/U1Oz3/69%2BXM2yb/zkc2ZyOZcTsxn%2ByGQDh06XPQjV999950WLlwot9uthoYGbdy4Ue%2B9954mTpwoSZo0aZJWrVqlsrIy1dbWKj8/X71791ZSUpKioqJ0ww036He/%2B52OHTumw4cPa/ny5Ro/frxcLp9csgYAABzG9sYwbdo0FRQUaM6cOV4Xkp%2BrpKQkSVJjY6MktfxuQ7fbrSlTpqiurk4PPPCAqqqqdPnll2v58uVKTEyUJE2cOFFVVVW68847VVdXp5SUFK9rpn7zm9/oscce0/Dhw9WmTRvdfPPNZ9zMFAAA4McEWBd6Rfc5mjlzpj777DNZlqXLL79cgYHeB9FeeuklO8exTVXVceOv6XIF6vr8942/7sXy5uwhrX6syxWoyMj2qq6uc9xh4Z/iz9kk/85HNmcim3OZyNexY6jhqVrH9iNYISEh%2BtWvfmX32wIAANjG9oKVl5dn91sCAADYyic/579v3z49%2B%2ByzeuSRR1rWPv/8c1%2BMAgAAYJztBWvnzp0aM2aMtm7dqo0bN0r6/uajU6ZMMXKTUQAAAF%2BzvWAtXbpUDz30kDZs2NDyU4Rdu3bV4sWLtXz5crvHAQAAMM72gvU///M/mjRpkiR53abhxhtvVFlZmd3jAAAAGGd7wQoNDT3r7yCsrKxU27Zt7R4HAADAONsLVv/%2B/fXkk0%2Bqtra2ZW3//v3Kzc3V1Vdfbfc4AAAAxtl%2Bm4ZHHnlEd911l1JSUtTU1KT%2B/fvr5MmT6tWrlxYvXmz3OAAAAMbZXrA6d%2B6sjRs36t1339X%2B/ft1ySWXqHv37hoyZMgF/eocAACAnwuf/PbiNm3a6LrrrvPFWwMAAFx0thes9PT0f3mkinthAQAAp7O9YI0cOdKrYDU1NWn//v1yu92666677B4HAADAONsLVk5OzlnXt2zZor/%2B9a82TwMAAGCeT34X4dlcd911euONN3w9BgAAwAX72RSs3bt3y7IsX48BAABwwWw/RThx4sQz1k6ePKmysjKNGDHC7nEAAACMs71gxcTEnPFThMHBwRo/frwmTJhg9zgAAADG2V6wuFs7AADwd7YXrNdee63Vj7311lsv4iQAAAAXh%2B0F69FHH1Vzc/MZF7QHBAR4rQUEBFCwAACAI9lesH7/%2B9/rhRde0PTp0xUXFyfLsvTll1/q%2Beef1%2BTJk5WSkmL3SAAAAEb55BqswsJCderUqWVt4MCB6tq1q6ZOnaqNGzfaPRIAAIBRtt8H68CBAwoPDz9jPSwsTOXl5XaPAwAAYJztBatLly5avHixqqurW9Zqamq0ZMkSXXHFFXaPAwAAYJztpwjnzZunOXPmqKioSO3bt1dgYKBqa2t1ySWXaPny5XaPAwAAYJztBWvo0KHasWOH3n33XR0%2BfFiWZalTp0665pprFBoaavc4AAAAxtlesCTp0ksv1fDhw3X48GF17drVFyMAAABcNLZfg1VfX6/c3Fz169dPN910k6Tvr8G65557VFNTY/c4AAAAxtlesJ5%2B%2BmmVlpYqPz9fgYH/ePumpibl5%2BfbPQ4AAIBxthesLVu26JlnntGNN97Y8kufw8LClJeXp61bt9o9DgAAgHG2F6y6ujrFxMScsR4VFaUTJ07YPQ4AAIBxthesK664Qn/9618lyet3D27evFm/%2BMUv7B4HAADAONt/ivCOO%2B7QzJkzddttt6m5uVkvvviiiouLtWXLFj366KN2jwMAAGCc7QUrIyNDLpdLq1evVlBQkJ577jl1795d%2Bfn5uvHGG%2B0eBwAAwDjbC9axY8d022236bbbbrP7rQEAAGxh%2BzVYw4cP97r2CgAAwN/YXrBSUlL05ptv2v22AAAAtrH9FOG//du/6be//a0KCwt1xRVXqE2bNl77S5YssXskAAAAo2wvWHv37tWVV14pSaqurrb77QEAAC462wpWdna2li5dqv/%2B7/9uWVu%2BfLmysrLsGgEAAMAWtl2DtW3btjPWCgsL7Xp7AAAA29hWsM72k4P8NCEAAPBHthWs07/Y%2BafWAAAAnM722zQAAAD4OwoWAACAYbb9FGFDQ4PmzJnzk2vcBwsAADidbQVrwIABqqys/Mk1AAAAp7OtYP3z/a9Mef/995Wbm6uUlBQtXbrUa2/Tpk1asWKFDh06pO7du%2BvBBx/U0KFDJUnNzc1atmyZNm7cqJqaGiUnJ%2Bvxxx9X165dJUkej0ePP/64Pv74YwUGBiotLU0LFizQJZdcYjwDAADwP469Buv555/XokWL1K1btzP2SktLlZubq5ycHH300UfKzMzU/fffr8OHD0uS1qxZow0bNqiwsFDbt29XTEyMsrKyWm4bsWDBAp08eVIbN27UK6%2B8orKyMuXn59uaDwAAOJdjC1ZwcLDWrVt31oK1du1apaWlKS0tTcHBwRozZoxiY2O1fv16SVJRUZEyMzPVo0cPhYSEKDs7W2VlZdq1a5eOHDmit99%2BW9nZ2YqKilKnTp1033336ZVXXlFDQ4PdMQEAgAM5tmBNmTJFoaGhZ90rKSlRfHy811p8fLzcbrfq6%2Bu1d%2B9er/2QkBB169ZNbrdbpaWlCgoKUlxcXMt%2BQkKCTpw4oX379l2cMAAAwK/Y/sue7eDxeBQeHu61Fh4err179%2Brbb7%2BVZVln3a%2BurlZERIRCQkK8boJ6%2BrGt/eXUlZWVqqqq8lpzudopOjr6fOL8qKAgZ/Vjl6v1857O5rSMreHP2ST/zkc2ZyKbczk5n18WLOmnfw3Pv9q/0F/hU1RUpIKCAq%2B1rKwszZo164Je1%2BkiI9uf83PCwi69CJP8PPhzNsm/85HNmcjmXE7M55cFKzIyUh6Px2vN4/EoKipKERERCgwMPOt%2Bhw4dFBUVpdraWjU1NSkoKKhlT5I6dOjQqvfPyMhQenq615rL1U7V1XXnG%2BmsnNbozyV/UFCgwsIuVU3NSTU1NV/Eqeznz9kk/85HNmcim3OZyHc%2B39yb4JcFKzExUcXFxV5rbrdbo0aNUnBwsHr16qWSkhINHjxYklRTU6Ovv/5aycnJ6tKliyzL0p49e5SQkNDy3LCwMHXv3r1V7x8dHX3G6cCqquNqbPS/L/5zcT75m5qa/fbvzZ%2BzSf6dj2zORDbncmI%2BZx0CaaXbb79dH374oXbs2KFTp05p3bp1OnDggMaMGSNJmjRpklatWqWysjLV1tYqPz9fvXv3VlJSkqKionTDDTfod7/7nY4dO6bDhw9r%2BfLlGj9%2BvFwuv%2ByjAADAMMc2hqSkJElSY2OjJOntt9%2BW9P3RptjYWOXn5ysvL0/l5eXq2bOnVq5cqY4dO0qSJk6cqKqqKt15552qq6tTSkqK1zVTv/nNb/TYY49p%2BPDhatOmjW6%2B%2BWZlZ2fbnBAAADhVgHWhV3SjVaqqjht/TZcrUNfnv2/8dS%2BWN2cPafVjXa5ARUa2V3V1neMOC/8Uf84m%2BXc%2BsjkT2ZzLRL6OHc9%2BS6eLzS9PEQIAAPgSBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADDMbwtWXFycEhMTlZSU1PKxcOFCSdLOnTs1fvx49e/fX6NGjdL69eu9nrtq1SrdcMMN6t%2B/vyZNmqTi4mJfRAAAAA7l8vUAF9PmzZt1%2BeWXe61VVlbqvvvu06OPPqrRo0fr008/1YwZM9S9e3clJSVp27ZtevbZZ/X73/9ecXFxWrVqlaZPn66tW7eqXbt2PkoCAACcxG%2BPYP2YDRs2KCYmRuPHj1dwcLBSU1OVnp6utWvXSpKKioo0btw49enTR5dcconuueceSdL27dt9OTYAAHAQvy5YS5Ys0bBhwzRw4EAtWLBAdXV1KikpUXx8vNfj4uPjW04D/nA/MDBQvXv3ltvttnV2AADgXH57irBv375KTU3VU089pYMHD2r27Nl64okn5PF41KlTJ6/HRkREqLq6WpLk8XgUHh7utR8eHt6y3xqVlZWqqqryWnO52ik6Ovo805xdUJCz%2BrHL1fp5T2dzWsbW8Odskn/nI5szkc25nJzPbwtWUVFRy3/36NFDOTk5mjFjhgYMGPCTz7Us64Lfu6CgwGstKytLs2bNuqDXdbrIyPbn/JywsEsvwiQ/D/6cTfLvfGRzJrI5lxPz%2BW3B%2BqHLL79cTU1NCgwMlMfj8dqrrq5WVFSUJCkyMvKMfY/Ho169erX6vTIyMpSenu615nK1U3V13XlOf3ZOa/Tnkj8oKFBhYZeqpuakmpqaL%2BJU9vPnbJJ/5yObM5HNuUzkO59v7k3wy4K1e/durV%2B/XnPnzm1ZKysrU9u2bZWWlqY///nPXo8vLi5Wnz59JEmJiYkqKSnR2LFjJUlNTU3avXu3xo8f3%2Br3j46OPuN0YFXVcTU2%2Bt8X/7k4n/xNTc1%2B%2B/fmz9kk/85HNmcim3M5MZ%2BzDoG0UocOHVRUVKTCwkJ999132r9/v5YtW6aMjAzdcsstKi/LQ8MqAAAN/0lEQVQv19q1a3Xq1Cm9%2B%2B67evfdd3X77bdLkiZNmqTXXntNX3zxhU6ePKkVK1aobdu2GjZsmG9DAQAAx/DLI1idOnVSYWGhlixZ0lKQxo4dq%2BzsbAUHB2vlypVatGiRnnjiCXXp0kVPP/20rrrqKknSr371Kz344IOaPXu2jh49qqSkJBUWFuqSSy7xcSoAAOAUflmwJGnQoEF66aWXfnTv9ddf/9Hn3nHHHbrjjjsu1mgAAMDP%2BeUpQgAAAF%2BiYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADD/PY%2BWPj5uel3f/H1COfkzdlDfD0CAMChOIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEuXw8A/Fzd9Lu/%2BHqEc/Lm7CG%2BHgEA8P9xBAsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2CdRXl5ue69916lpKTo2muv1dNPP63m5mZfjwUAAByC2zScxcyZM5WQkKC3335bR48e1bRp03TZZZfp7rvv9vVoAADAAShYP%2BB2u7Vnzx69%2BOKLCg0NVWhoqDIzM/XHP/6RgoWfNSfdt4t7dgHwdxSsHygpKVGXLl0UHh7espaQkKD9%2B/ertrZWISEhP/kalZWVqqqq8lpzudopOjra6KxBQZzhhTM5qQxK0ls510j6x785f/y3RzZn8udskrPzUbB%2BwOPxKCwszGvtdNmqrq5uVcEqKipSQUGB19r999%2BvmTNnmhtU3xe5uzp/pYyMDOPlzdcqKytVVFRENgfy53yVlZX64x9/TzaHIZtzOTmf8yqhDSzLuqDnZ2Rk6NVXX/X6yMjIMDTdP1RVVamgoOCMo2X%2BgGzO5c/5yOZMZHMuJ%2BfjCNYPREVFyePxeK15PB4FBAQoKiqqVa8RHR3tuKYNAADM4QjWDyQmJqqiokLHjh1rWXO73erZs6fat2/vw8kAAIBTULB%2BID4%2BXklJSVqyZIlqa2tVVlamF198UZMmTfL1aAAAwCGCHn/88cd9PcTPzTXXXKONGzdq4cKFeuONNzR%2B/HhNnTpVAQEBvh7tDO3bt9fgwYP98uga2ZzLn/ORzZnI5lxOzRdgXegV3QAAAPDCKUIAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYDlReXq57771XKSkpuvbaa/X000%2BrubnZ12Odt/fff1%2BpqanKzs4%2BY2/Tpk0aPXq0%2BvXrp3HjxumDDz7wwYTnr7y8XFlZWUpJSVFqaqrmzp2rmpoaSVJpaakmT56sAQMGaMSIEXrhhRd8PO252bNnj%2B666y4NGDBAqampmj17tqqqqiRJO3fu1Pjx49W/f3%2BNGjVK69ev9/G05%2B/JJ59UXFxcy5/9IVtcXJwSExOVlJTU8rFw4UJJ/pFvxYoVGjp0qPr27avMzEwdOnRIkrOz/e1vf/P6fCUlJSkxMbHla9PJ2U7bvXu3pkyZooEDB2rIkCHKycnRsWPHJDk0nwXHGTt2rDV//nyrpqbG2r9/vzVixAjrhRde8PVY56WwsNAaMWKENXHiRGv27Nlee7t377YSExOtHTt2WPX19dbrr79u9enTx6qoqPDRtOfu5ptvtubOnWvV1tZaFRUV1rhx46x58%2BZZJ0%2BetK655hrr2Wefterq6qzi4mJr8ODB1pYtW3w9cqucOnXKuvrqq62CggLr1KlT1tGjR63Jkydb9913n/XNN99Yffv2tdauXWvV19dbf/nLX6zk5GTr73//u6/HPme7d%2B%2B2Bg8ebMXGxlqWZflNttjYWOvgwYNnrPtDvtWrV1s33nijVVZWZh0/ftxauHChtXDhQr/I9kMrVqywHnjgAb/I1tDQYA0ZMsRasmSJderUKevYsWPW3Xffbc2cOdOx%2BTiC5TBut1t79uxRTk6OQkNDFRMTo8zMTBUVFfl6tPMSHBysdevWqVu3bmfsrV27VmlpaUpLS1NwcLDGjBmj2NhYZ3znIqmmpkaJiYmaM2eO2rdvr86dO2vs2LH65JNPtGPHDjU0NGjGjBlq166dEhISNGHCBMd8Hk%2BePKns7GxNmzZNbdu2VVRUlK6//np99dVX2rBhg2JiYjR%2B/HgFBwcrNTVV6enpWrt2ra/HPifNzc167LHHlJmZ2bLmL9l%2BjD/ke%2BGFF5Sdna0rr7xSISEhmj9/vubPn%2B8X2f7Z//7v/%2BrFF1/Uww8/7BfZqqqqVFVVpVtuuUVt27ZVZGSkrr/%2BepWWljo2HwXLYUpKStSlSxeFh4e3rCUkJGj//v2qra314WTnZ8qUKQoNDT3rXklJieLj473W4uPj5Xa77RjtgoWFhSkvL0%2BXXXZZy1pFRYWio6NVUlKiuLg4BQUFtezFx8eruLjYF6Oes/DwcE2YMEEul0uStG/fPv35z3/WTTfd9KOfN6dkO%2B2ll15ScHCwRo8e3bLmL9kkacmSJRo2bJgGDhyoBQsWqK6uzvH5vvnmGx06dEjffvutRo4cqZSUFM2aNUvHjh1zfLYfWrZsmW677Tb94he/8ItsnTp1Uu/evVVUVKS6ujodPXpUW7du1bBhwxybj4LlMB6PR2FhYV5rp8tWdXW1L0a6aDwej1eRlL7P6tScbrdbq1ev1owZM876eYyIiJDH43HU9XTl5eVKTEzUyJEjlZSUpFmzZv1oNid93o4cOaJnn31Wjz32mNe6P2STpL59%2Byo1NVVbt25VUVGRvvjiCz3xxBOOz3f48GFJ0ubNm/Xiiy/q9ddf1%2BHDhzV//nzHZ/tnhw4d0tatW3X33XdL8o%2Bvy8DAQD377LN655131L9/f6WmpqqxsVFz5sxxbD4KlgNZluXrEWzjL1k//fRTTZ06VXPmzFFqauqPPi4gIMDGqS5cly5d5Ha7tXnzZh04cEAPP/ywr0cyIi8vT%2BPGjVPPnj19PcpFUVRUpAkTJqht27bq0aOHcnJytHHjRjU0NPh6tAty%2Bv8X99xzjzp16qTOnTtr5syZ2rZtm48nM2vNmjUaMWKEOnbs6OtRjPnuu%2B80ffp03Xjjjfrkk0/03nvvKTQ0VDk5Ob4e7bxRsBwmKipKHo/Ha83j8SggIEBRUVE%2BmuriiIyMPGtWp%2BXctm2b7r33Xs2bN09TpkyR9P3n8YfffXk8HkVERCgw0Fn/LAMCAhQTE6Ps7Gxt3LhRLpfrjM9bdXW1Yz5vO3fu1Oeff66srKwz9s72NemkbD/m8ssvV1NTkwIDAx2d7/Tp%2BH8%2B2tGlSxdZlqWGhgZHZ/tnW7ZsUXp6esuf/eHrcufOnTp06JAefPBBhYaGqlOnTpo1a5beeustx35dOuv/5FBiYqIqKipafnRV%2Bv7UU8%2BePdW%2BfXsfTmZeYmLiGefY3W63%2BvTp46OJzt1nn32m3NxcLVu2TLfeemvLemJior788ks1Nja2rDkp286dO3XDDTd4nc48XQyTk5PP%2BLwVFxc7Jtv69et19OhRXXvttUpJSdG4ceMkSSkpKYqNjXV0Nun7H4VfvHix11pZWZnatm2rtLQ0R%2Bfr3LmzQkJCVFpa2rJWXl6uNm3aOD7baaWlpSovL9eQIUNa1pKSkhyframpSc3NzV5nLb777jtJUmpqqjPz%2BfRnGHFeJkyYYM2bN886fvy4tXfvXis9Pd1avXq1r8e6ILm5uWfcpuHLL7%2B0kpKSrO3bt1v19fXW2rVrrX79%2BlmVlZU%2BmvLcNDQ0WDfddJP10ksvnbF36tQp69prr7WeeeYZ68SJE9YXX3xhDRw40Nq%2Bfbv9g56HmpoaKzU11Vq8eLF14sQJ6%2BjRo9bUqVOtO%2B64wzpy5IjVr18/6%2BWXX7bq6%2ButHTt2WMnJyVZpaamvx24Vj8djVVRUtHx8/vnnVmxsrFVRUWGVl5c7OptlWdbhw4etvn37WitXrrROnTpl7du3zxo5cqS1cOFCx3/uLMuynnzySWv48OHWgQMHrCNHjlgZGRnW3Llz/SKbZVnWunXrrMGDB3ut%2BUO2Y8eOWYMHD7b%2B8z//0zpx4oR17Ngxa/r06da///u/OzYfBcuBKioqrHvuucdKTk62UlNTrWeeecZqbm729VjnJTEx0UpMTLSuuuoq66qrrmr582lbtmyxRowYYSUkJFi33HKL9fHHH/tw2nPzt7/9zYqNjW3J9M8fhw4dsr788ktr4sSJVmJiojVs2DBrzZo1vh75nOzZs8eaPHmylZycbP3yl7%2B0Zs%2BebR0%2BfNiyLMv6%2BOOPrTFjxlgJCQnWiBEjHHN/r7M5ePBgy32wLMs/sn388cdWRkaG1bdvX2vw4MFWXl6eVV9f37Ln5HynTp2yHn/8cWvQoEFW3759rdzcXKu2ttayLOdnsyzLeu6556xRo0adse4P2dxutzV58mRr4MCBVmpqquP/nxJgWX5yFTEAAMDPBNdgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBh/w%2BQ9GVS1mUY/QAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common6787255574279489228”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>732</td> <td class=”number”>22.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>449</td> <td class=”number”>14.0%</td> <td>

<div class=”bar” style=”width:61%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>365</td> <td class=”number”>11.4%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>311</td> <td class=”number”>9.7%</td> <td>

<div class=”bar” style=”width:43%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>233</td> <td class=”number”>7.3%</td> <td>

<div class=”bar” style=”width:32%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>205</td> <td class=”number”>6.4%</td> <td>

<div class=”bar” style=”width:28%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>162</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>121</td> <td class=”number”>3.8%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>89</td> <td class=”number”>2.8%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>84</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (33)</td> <td class=”number”>325</td> <td class=”number”>10.1%</td> <td>

<div class=”bar” style=”width:44%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>134</td> <td class=”number”>4.2%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme6787255574279489228”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>732</td> <td class=”number”>22.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>449</td> <td class=”number”>14.0%</td> <td>

<div class=”bar” style=”width:61%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>365</td> <td class=”number”>11.4%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>311</td> <td class=”number”>9.7%</td> <td>

<div class=”bar” style=”width:43%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>233</td> <td class=”number”>7.3%</td> <td>

<div class=”bar” style=”width:32%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>52.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>54.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>59.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>61.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>79.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_CSA12”>CSA12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>54</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.7%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>4.2%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>134</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>4.0514</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>88</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>29.4%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram7600181567450927190”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives7600181567450927190,#minihistogram7600181567450927190”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives7600181567450927190”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles7600181567450927190”

aria-controls=”quantiles7600181567450927190” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram7600181567450927190” aria-controls=”histogram7600181567450927190”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common7600181567450927190” aria-controls=”common7600181567450927190”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme7600181567450927190” aria-controls=”extreme7600181567450927190”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles7600181567450927190”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>2</td>

</tr> <tr>

<th>Q3</th> <td>5</td>

</tr> <tr>

<th>95-th percentile</th> <td>16</td>

</tr> <tr>

<th>Maximum</th> <td>88</td>

</tr> <tr>

<th>Range</th> <td>88</td>

</tr> <tr>

<th>Interquartile range</th> <td>5</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>6.9362</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.7121</td>

</tr> <tr>

<th>Kurtosis</th> <td>33.277</td>

</tr> <tr>

<th>Mean</th> <td>4.0514</td>

</tr> <tr>

<th>MAD</th> <td>4.0204</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>4.6227</td>

</tr> <tr>

<th>Sum</th> <td>12462</td>

</tr> <tr>

<th>Variance</th> <td>48.111</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram7600181567450927190”>

<img 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0qH7729%2BqT58%2B9a/p27evfvnLX2rEiBFWlwcAAHDLLA9Yw4cPV4cOHTRlyhSNGjVK0dHR170mLS1NFy5csLo0AAAAIywPWOvWrVO/fv2%2B83VHjhyxoBoAAADzLN%2BD1bVrV02dOlV79%2B6tH/v3f/93PfHEE3K73VaXAwAAYJzlASs3N1eXLl1Sp06d6scGDhyouro6LV261OpyAAAAjLP8FuG7776rrVu3KiYmpn4sISFBeXl5euCBB6wuBwAAwDjLr2BdvnxZISEh1xdis6mqqsrqcgAAAIyzPGD17dtXS5cu1cWLF%2BvHPv/8cz3zzDNej2oAAAAIVJbfIpw3b57%2B%2BZ//WT/60Y8UHh6uuro6VVZWql27dvrd735ndTkAAADGWR6w2rVrp%2B3bt%2Bvtt9/W6dOnZbPZ1KFDBw0YMEDBwcFWlwMAAGCc5QFLkpo3b657773XF6cGAABocJYHrDNnzmjZsmX69NNPdfny5evm33rrLatLAgAAMMone7DKyso0YMAAhYaGWn16AACABmd5wCosLNRbb72l2NhYq08NAABgCcsf09CiRQuuXAEAgEbN8oA1ZcoUrVy5Uh6P55aP9c477yg1NVVZWVle45s2bdIPf/hDJScne319/PHHkqS6ujotX75cgwcPVt%2B%2BfTV58mSdOXOm/v1ut1uzZ89WamqqBgwYoPnz599wvxgAAMCNWH6L8O2339aHH36oTZs2qW3btrLZvDPe66%2B/flPHeeWVV7Rx40a1b9/%2BhvN9%2B/b9xudqvfbaa9q6dateeeUVxcfHa/ny5crMzNTmzZsVFBSkhQsX6urVq9q2bZuqq6s1a9Ys5eXlacGCBd9vsQAAoEmyPGCFh4frxz/%2B8S0fJyQkRBs3btSzzz6rK1eufK/3FhQUKCMjQx07dpQkZWVlKSUlRUeOHFHbtm21d%2B9e/elPf6rfJ/bkk09q1qxZmjNnjpo1a3bLtQMAgMbN8oCVm5tr5DiTJk361vlz587pscceU2FhoSIjIzVz5kw9%2BOCDunz5sk6cOKHExMT614aHh6t9%2B/ZyOBy6dOmSgoOD1bVr1/r57t2768svv9Rnn33mNf5NysrK5HQ6vcbs9lDFxcV9z1V%2Bu%2BBgy%2B/w3hK7PbDq/T6u9SLQetIY0Qv/QS/8C/2wlk8eNPrZZ59p%2B/bt%2Bp//%2BZ/6wPXXv/5VvXr1MnL82NhYJSQk6KmnnlKnTp305ptv6he/%2BIXi4uJ05513yuPxKCoqyus9UVFRcrlcio6OVnh4uIKCgrzmJMnlct3U%2BQsKCrRy5UqvsczMTM2cOfMWVxbYYmLCfF1Cg4uMvN3XJeBv6IX/oBf%2BhX5Yw/KAdejQIT3xxBPq0KGDTp06pdzcXJ05c0aTJk3Siy%2B%2BqMGDB9/yOQYOHKiBAwfW/3348OF68803tWnTJuXk5EjSt26yv9UN%2BOPHj9egQYO8xuz2ULlclbd03K8LtE8hptfvT4KDbYqMvF3l5VWqra3zdTlNGr3wH/TCvzTVfvjqw73lAWv58uV6%2Bumn9eijj6pHjx6Svvr9hEuXLtWqVauMBKwbadOmjQoLCxUdHS2bzSa32%2B0173a71aJFC8XGxqqiokK1tbX1vxvx2mtbtGhxU%2BeKi4u77nag03lJNTVN5xv6RprC%2Bmtr65rEOgMBvfAf9MK/0A9rWH4J5L/%2B67%2BUnp4uSV634YYNG6bi4mIj5/j973%2BvHTt2eI0VFxerXbt2CgkJUefOnVVUVFQ/V15ertOnT6tHjx7q1q2bPB6Pjh8/Xj/vcDgUGRmpDh06GKkPAAA0bpYHrIiIiBs%2BU6qsrEzNmzc3co6rV69q8eLFcjgcqq6u1rZt2/T2229rwoQJkqT09HStW7dOxcXFqqioUF5enrp166bk5GTFxsZq6NChevHFF3XhwgWVlpZq1apVGjt2rOx2n2xZAwAAAcbyxNC7d28999xzXs%2BUOnnypBYtWqQf/ehHN32c5ORkSVJNTY0kae/evZK%2Buto0adIkVVZWatasWXI6nWrbtq1WrVqlpKQkSdKECRPkdDo1ceJEVVZWKiUlxWtT%2Bq9%2B9SstWrRIgwcPVrNmzfTAAw9c9zBTAACAbxLkMfFI9e%2BhtLRUjz76qM6ePava2lqFhoaqqqpKnTt31ssvv6wf/OAHVpZjGafzkvFj2u023Zf3jvHjNpSds/v7uoQGY7fbFBMTJperkr0NPkYv/Ae98C9NtR%2BtWkX45LyWX8Fq3bq1tm3bpoMHD%2BrkyZO67bbb1KFDB/Xv399rTxYAAECg8smmombNmunee%2B/1xakBAAAanOUBa9CgQd96peqtt96ysBoAAADzLA9Y999/v1fAqq2t1cmTJ%2BVwOPToo49aXQ4AAIBxlgesa09S/7rdu3frL3/5i8XVAAAAmOc3v2vl3nvv1fbt231dBgAAwC3zm4B19OjRW/4dgAAAAP7A8luE156m/v9VVVWpuLhYQ4YMsbocAAAA4ywPWAkJCdf9FGFISIjGjh2rhx9%2B2OpyAAAAjLM8YC1dutTqUwIAAFjK8oD1xhtv3PRrR40a1YCVAAAANAzLA9b8%2BfNVV1d33Yb2oKAgr7GgoCACFgAACEiWB6xf//rXWrt2raZOnaquXbvK4/Hok08%2B0SuvvKJHHnlEKSkpVpcEAABglE/2YOXn5ys%2BPr5%2B7O6771a7du00efJkbdu2zeqSAAAAjLL8OVinTp1SVFTUdeORkZEqKSmxuhwAAADjLA9Ybdq00dKlS%2BVyuerHysvLtWzZMt1xxx1WlwMAAGCc5bcI582bp%2BzsbBUUFCgsLEw2m00VFRW67bbbtGrVKqvLAQAAMM7ygDVgwAAdOHBABw8eVGlpqTwej%2BLj43XPPfcoIiLC6nIAAACMszxgSdLtt9%2BuwYMHq7S0VO3atfNFCQAAAA3G8j1Yly9f1pw5c9SrVy/99Kc/lfTVHqzHH39c5eXlVpcDAABgnOUB64UXXtCxY8eUl5cnm%2B3/Tl9bW6u8vDyrywEAADDO8oC1e/du/du//ZuGDRtW/0ufIyMjlZubqz179lhdDgAAgHGWB6zKykolJCRcNx4bG6svv/zS6nIAAACMszxg3XHHHfrLX/4iSV6/e3DXrl36wQ9%2BYHU5AAAAxln%2BU4Q/%2B9nPNGPGDD300EOqq6vTq6%2B%2BqsLCQu3evVvz58%2B3uhwAAADjLA9Y48ePl91u1/r16xUcHKyXX35ZHTp0UF5enoYNG2Z1OQAAAMZZHrAuXLighx56SA899JDVpwYAALCE5XuwBg8e7LX3CgAAoLGxPGClpKRo586dVp8WAADAMpbfIvyHf/gHPfvss8rPz9cdd9yhZs2aec0vW7bM6pIAAACMsjxgnThxQnfeeackyeVyWX16AACABmdZwMrKytLy5cv1u9/9rn5s1apVyszMtKoEAAAAS1i2B2vfvn3XjeXn51t1egAAAMtYFrBu9JOD/DQhAABojCwLWNd%2BsfN3jQEAAAQ6yx/TAAAA0NgRsAAAAAyz7KcIq6urlZ2d/Z1jPAcLAAAEOssCVp8%2BfVRWVvadYwAAAIHOsoD1/59/BQAA0JixBwsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMC%2BiA9c477yg1NVVZWVnXze3YsUMjRoxQr169NGbMGL377rv1c3V1dVq%2BfLkGDx6svn37avLkyTpz5kz9vNvt1uzZs5WamqoBAwZo/vz5unz5siVrAgAAgS9gA9Yrr7yiJUuWqH379tfNHTt2THPmzFFOTo7ef/99ZWRkaPr06SotLZUkvfbaa9q6davy8/O1f/9%2BJSQkKDMzs/6XTy9cuFBVVVXatm2b/vjHP6q4uFh5eXmWrg8AAASugA1YISEh2rhx4w0D1oYNG5SWlqa0tDSFhIRo5MiR6tKli7Zs2SJJKigoUEZGhjp27Kjw8HBlZWWpuLhYR44c0RdffKG9e/cqKytLsbGxio%2BP15NPPqk//vGPqq6utnqZAAAgAFn2oFHTJk2a9I1zRUVFSktL8xpLTEyUw%2BHQ5cuXdeLECSUmJtbPhYeHq3379nI4HLp06ZKCg4PVtWvX%2Bvnu3bvryy%2B/1GeffeY1/k3KysrkdDq9xuz2UMXFxd3s8m5KcHBg5WO7PbDq/T6u9SLQetIY0Qv/QS/8C/2wVsAGrG/jdrsVFRXlNRYVFaUTJ07o4sWL8ng8N5x3uVyKjo5WeHi4goKCvOYkyeVy3dT5CwoKtHLlSq%2BxzMxMzZw58%2B9ZTqMRExPm6xIaXGTk7b4uAX9DL/wHvfAv9MMajTJgSarfT/X3zH/Xe7/L%2BPHjNWjQIK8xuz1ULlflLR336wLtU4jp9fuT4GCbIiNvV3l5lWpr63xdTpNGL/wHvfAvTbUfvvpw3ygDVkxMjNxut9eY2%2B1WbGysoqOjZbPZbjjfokULxcbGqqKiQrW1tQoODq6fk6QWLVrc1Pnj4uKuux3odF5STU3T%2BYa%2Bkaaw/trauiaxzkBAL/wHvfAv9MMagXUJ5CYlJSWpsLDQa8zhcKhnz54KCQlR586dVVRUVD9XXl6u06dPq0ePHurWrZs8Ho%2BOHz/u9d7IyEh16NDBsjUAAIDA1SgD1rhx4/Tee%2B/pwIEDunLlijZu3KhTp05p5MiRkqT09HStW7dOxcXFqqioUF5enrp166bk5GTFxsZq6NChevHFF3XhwgWVlpZq1apVGjt2rOz2RnnBDwAAGBawiSE5OVmSVFNTI0nau3evpK%2BuNnXp0kV5eXnKzc1VSUmJOnXqpDVr1qhVq1aSpAkTJsjpdGrixImqrKxUSkqK16b0X/3qV1q0aJEGDx6sZs2a6YEHHrjhw0wBAABuJMhzqzu6cVOczkvGj2m323Rf3jvGj9tQds7u7%2BsSGozdblNMTJhcrkr2NvgYvfAf9MK/NNV%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%2B%2BOOSpP379/uybAAAEEAadcBatmyZBg4cqLvvvlsLFy5UZWWlioqKlJiY6PW6xMTE%2BtuAX5%2B32Wzq1q2bHA6HpbUDAIDA1WhvEd51111KTU3V888/rzNnzmj27Nl65pln5Ha7FR8f7/Xa6OhouVwuSZLb7VZUVJTXfFRUVP38zSgrK5PT6fQas9tDFRcX93eu5saCgwMrH9vtgVXv93GtF4HWk8aIXvgPeuFf6Ie1Gm3AKigoqP9zx44dlZOTo2nTpqlPnz7f%2BV6Px3PL5165cqXXWGZmpmbOnHlLxw10MTFhvi6hwUVG3u7rEvA39MJ/0Av/Qj%2Bs0WgD1te1bdtWtbW1stlscrvdXnMul0uxsbGSpJiYmOvm3W63OnfufNPnGj9%2BvAYNGuQ1ZreHyuWq/Durv7FA%2BxRiev3%2BJDjYpsjI21VeXqXa2jpfl9Ok0Qv/QS/8S1Pth68%2B3DfKgHX06FFt2bJFc%2BfOrR8rLi5W8%2BbNlZaWpj/96U9ery8sLFTPnj0lSUlJSSoqKtLo0aMlSbW1tTp69KjGjh170%2BePi4u77nag03lJNTVN5xv6RprC%2Bmtr65rEOgMBvfAf9MK/0A9rBNYlkJvUokULFRQUKD8/X1evXtXJkyf10ksvafz48XrwwQdVUlKiDRs26MqVKzp48KAOHjyocePGSZLS09P1xhtv6KOPPlJVVZVWr16t5s2ba%2BDAgb5dFAAACBiN8gpWfHy88vPztWzZsvoKSxuTAAAKY0lEQVSANHr0aGVlZSkkJERr1qzRkiVL9Mwzz6hNmzZ64YUX9MMf/lCS9OMf/1hPPfWUZs%2BerfPnzys5OVn5%2Bfm67bbbfLwqAAAQKII8t7qjGzfF6bxk/Jh2u0335b1j/LgNZefs/r4uocHY7TbFxITJ5ark0ruP0Qv/QS/8S1PtR6tWET45b6O8RQgAAOBLBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGCY3dcFoOn46Yt/9nUJ38vO2f19XQIAIEBxBQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGF2XxcA%2BKufvvhnX5fwveyc3d/XJQAA/oYrWAAAAIYRsAAAAAwjYAEAABjGHqwbKCkp0TPPPKMjR44oNDRU999/v7Kzs2WzkUfhvwJpzxj7xQA0dgSsG5gxY4a6d%2B%2BuvXv36vz585oyZYpatmypxx57zNelAQCAAMAlma9xOBw6fvy4cnJyFBERoYSEBGVkZKigoMDXpQEAgADBFayvKSoqUps2bRQVFVU/1r17d508eVIVFRUKDw//zmOUlZXJ6XR6jdntoYqLizNaa3Aw%2BRiBKZBuZwaiN3Pu8XUJkv7v/yj%2Br/IP9MNaBKyvcbvdioyM9Bq7FrZcLtdNBayCggKtXLnSa2z69OmaMWOGuUL1VZB7tPWnGj9%2BvPHwhu%2BnrKxMBQUF9MIP0Av/UVZWpt/%2B9tf0wk/QD2sRY2/A4/Hc0vvHjx%2BvTZs2eX2NHz/eUHX/x%2Bl0auXKldddLYP16IX/oBf%2Bg174F/phLa5gfU1sbKzcbrfXmNvtVlBQkGJjY2/qGHFxcXw6AACgCeMK1tckJSXp3LlzunDhQv2Yw%2BFQp06dFBYW5sPKAABAoCBgfU1iYqKSk5O1bNkyVVRUqLi4WK%2B%2B%2BqrS09N9XRoAAAgQwb/85S9/6esi/M0999yjbdu2afHixdq%2BfbvGjh2ryZMnKygoyNelXScsLEz9%2BvXj6pofoBf%2Bg174D3rhX%2BiHdYI8t7qjGwAAAF64RQgAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAErAJWUlOjnP/%2B5UlJS9JOf/EQvvPCC6urqfF1Wk1FSUqLMzEylpKQoNTVVc%2BfOVXl5uSTp2LFjeuSRR9SnTx8NGTJEa9eu9XG1Tcdzzz2nrl271v/90KFDGjt2rHr37q3hw4dry5YtPqyuaVi9erUGDBigu%2B66SxkZGTp79qwkemG1o0ePatKkSbr77rvVv39/5eTk6MKFC5LohaU8CDijR4/2LFiwwFNeXu45efKkZ8iQIZ61a9f6uqwm44EHHvDMnTvXU1FR4Tl37pxnzJgxnnnz5nmqqqo899xzj2fFihWeyspKT2Fhoadfv36e3bt3%2B7rkRu/o0aOefv36ebp06eLxeDyezz//3HPXXXd5NmzY4Ll8%2BbLnz3/%2Bs6dHjx6ejz/%2B2MeVNl7r16/3DBs2zFNcXOy5dOmSZ/HixZ7FixfTC4tVV1d7%2Bvfv71m2bJnnypUrngsXLngee%2Bwxz4wZM%2BiFxbiCFWAcDoeOHz%2BunJwcRUREKCEhQRkZGSooKPB1aU1CeXm5kpKSlJ2drbCwMLVu3VqjR4/W4cOHdeDAAVVXV2vatGkKDQ1V9%2B7d9fDDD9ObBlZXV6dFixYpIyOjfmzr1q1KSEjQ2LFjFRISotTUVA0aNEgbNmzwXaGN3Nq1a5WVlaU777xT4eHhWrBggRYsWEAvLOZ0OuV0OvXggw%2BqefPmiomJ0X333adjx47RC4sRsAJMUVGR2rRpo6ioqPqx7t276%2BTJk6qoqPBhZU1DZGSkcnNz1bJly/qxc%2BfOKS4uTkVFReratauCg4Pr5xITE1VYWOiLUpuM119/XSEhIRoxYkT9WFFRkRITE71eRy8azueff66zZ8/q4sWLuv/%2B%2B5WSkqKZM2fqwoUL9MJi8fHx6tatmwoKClRZWanz589rz549GjhwIL2wGAErwLjdbkVGRnqNXQtbLpfLFyU1aQ6HQ%2BvXr9e0adNu2Jvo6Gi53W72yDWQL774QitWrNCiRYu8xr%2BpF/wbaRilpaWSpF27dunVV1/V5s2bVVpaqgULFtALi9lsNq1YsUJvvfWWevfurdTUVNXU1Cg7O5teWIyAFYA8Ho%2BvS4CkDz74QJMnT1Z2drZSU1O/8XVBQUEWVtW05ObmasyYMerUqZOvS2nSrv2f9Pjjjys%2BPl6tW7fWjBkztG/fPh9X1vRcvXpVU6dO1bBhw3T48GG9/fbbioiIUE5Ojq9La3IIWAEmNjZWbrfba8ztdisoKEixsbE%2Bqqrp2bdvn37%2B859r3rx5mjRpkqSvevP1T4Jut1vR0dGy2finZtqhQ4f017/%2BVZmZmdfNxcTEXPfvxOVy8W%2BkgVy7Zf7/r460adNGHo9H1dXV9MJChw4d0tmzZ/XUU08pIiJC8fHxmjlzpt58803ZbDZ6YSH%2B1w8wSUlJOnfuXP2P3Epf3abq1KmTwsLCfFhZ0/Hhhx9qzpw5eumllzRq1Kj68aSkJH3yySeqqampH3M4HOrZs6cvymz0tmzZovPnz%2BsnP/mJUlJSNGbMGElSSkqKunTpct2%2BksLCQnrRQFq3bq3w8HAdO3asfqykpETNmjVTWloavbBQbW2t6urqvO50XL16VZKUmppKLyxEwAowiYmJSk5O1rJly1RRUaHi4mK9%2BuqrSk9P93VpTUJNTY0WLFignJwcDRgwwGsuLS1N4eHhWr16taqqqnTkyBFt3LiR3jSQuXPnavfu3dq8ebM2b96s/Px8SdLmzZs1YsQIlZSUaMOGDbpy5YoOHjyogwcPaty4cT6uunGy2%2B0aO3asXn75Zf33f/%2B3zp8/r1WrVmnEiBEaPXo0vbBQr169FBoaqhUrVqiqqkoul0urV69W37599eCDD9ILCwV52NATcEpLS7Vw4UL9x3/8h8LDwzVhwgRNnz6dvT4WOHz4sP7pn/5JzZs3v25u165dqqys1KJFi1RYWKiWLVvqiSee0M9%2B9jMfVNr0nD17VoMHD9Ynn3wiSfrP//xPLVmyRMXFxWrTpo2ys7M1ZMgQH1fZeF29elW5ubnavn27qqurNXToUC1cuFBhYWH0wmKFhYV6/vnndfz4cTVv3lz9%2BvXT3LlzFR8fTy8sRMACAAAwjFuEAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGDY/wIWMvYXMHUESwAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common7600181567450927190”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>944</td> <td class=”number”>29.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>412</td> <td class=”number”>12.8%</td> <td>

<div class=”bar” style=”width:44%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>396</td> <td class=”number”>12.3%</td> <td>

<div class=”bar” style=”width:42%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>264</td> <td class=”number”>8.2%</td> <td>

<div class=”bar” style=”width:28%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>230</td> <td class=”number”>7.2%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>160</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>110</td> <td class=”number”>3.4%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>94</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>54</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (43)</td> <td class=”number”>317</td> <td class=”number”>9.9%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>134</td> <td class=”number”>4.2%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme7600181567450927190”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>944</td> <td class=”number”>29.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>412</td> <td class=”number”>12.8%</td> <td>

<div class=”bar” style=”width:44%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>396</td> <td class=”number”>12.3%</td> <td>

<div class=”bar” style=”width:42%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>264</td> <td class=”number”>8.2%</td> <td>

<div class=”bar” style=”width:28%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>230</td> <td class=”number”>7.2%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>61.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>65.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>75.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>76.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>88.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Child and Adult Care participants, FY 2012”><s>Child and Adult Care participants, FY 2012</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_Child and Adult Care particpants FY 2011”><code>Child and Adult Care particpants FY 2011</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99474</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Child and Adult Care participants, FY 2013”><s>Child and Adult Care participants, FY 2013</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_Child and Adult Care participants, FY 2012”><code>Child and Adult Care participants, FY 2012</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99338</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Child and Adult Care participants, FY 2014”><s>Child and Adult Care participants, FY 2014</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_Child and Adult Care participants, FY 2013”><code>Child and Adult Care participants, FY 2013</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99269</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Child and Adult Care participants, FY 2015”><s>Child and Adult Care participants, FY 2015</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_Child and Adult Care participants, FY 2014”><code>Child and Adult Care participants, FY 2014</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.9954</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Child and Adult Care particpants FY 2009”><s>Child and Adult Care particpants FY 2009</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_National School Lunch Program participants, FY 2015”><code>National School Lunch Program participants, FY 2015</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.95709</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Child and Adult Care particpants FY 2011”><s>Child and Adult Care particpants FY 2011</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_Child and Adult Care particpants FY 2009”><code>Child and Adult Care particpants FY 2009</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99714</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_DIRSALES07”>DIRSALES07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>887</td>

</tr> <tr>

<th>Unique (%)</th> <td>27.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>11.2%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>359</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>400.34</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>17170</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>1.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram482336829505820325”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives482336829505820325,#minihistogram482336829505820325”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives482336829505820325”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles482336829505820325”

aria-controls=”quantiles482336829505820325” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram482336829505820325” aria-controls=”histogram482336829505820325”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common482336829505820325” aria-controls=”common482336829505820325”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme482336829505820325” aria-controls=”extreme482336829505820325”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles482336829505820325”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>7</td>

</tr> <tr>

<th>Q1</th> <td>44</td>

</tr> <tr>

<th>Median</th> <td>123</td>

</tr> <tr>

<th>Q3</th> <td>338.5</td>

</tr> <tr>

<th>95-th percentile</th> <td>1640.5</td>

</tr> <tr>

<th>Maximum</th> <td>17170</td>

</tr> <tr>

<th>Range</th> <td>17170</td>

</tr> <tr>

<th>Interquartile range</th> <td>294.5</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>993.08</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.4806</td>

</tr> <tr>

<th>Kurtosis</th> <td>87.534</td>

</tr> <tr>

<th>Mean</th> <td>400.34</td>

</tr> <tr>

<th>MAD</th> <td>450.46</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>7.8306</td>

</tr> <tr>

<th>Sum</th> <td>1141400</td>

</tr> <tr>

<th>Variance</th> <td>986200</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram482336829505820325”>

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WAABAmVVYwSosLNS6dev0xBNPaMKECapfv76ee%2B45tWrVSkOGDNGaNWsqamkAAABlYnP3HWZmZmr58uX68MMPlZ%2Bfrx49eujvf/%2B77rjjDudtYmJi9MILL6hXr17uXh4AAECZub1g9ezZU02bNtWwYcPUp08fBQcHl7pNfHy8Tp486e6lAQAAGOH2grV48WK1a9fuirfbvXu3G1YDAABgntvPwQoLC9Pw4cO1adMm59h//dd/6YknnpDD4XD3cgAAAIxze8FKSUnR6dOn1bx5c%2BdYx44dVVJSopkzZ7p7OQAAAMa5/SPCHTt2aM2aNapTp45zrEmTJkpNTdX999/v7uUAAAAY5/YjWAUFBfL39y%2B9EF9fnTlzxt3LAQAAMM7tBSsmJkYzZ87UqVOnnGO//PKLpk2b5nKpBgAAAE/l9o8IJ02apL/85S%2B66667FBAQoJKSEuXn56tRo0Z699133b0cAAAA49xesBo1aqSPPvpIn332mQ4fPixfX181bdpUHTp0kJ%2Bfn7uXAwAAYJzbC5YkVa9eXV27dq2IuwYAACh3bi9YR44c0ezZs/XTTz%2BpoKCg1Pynn37q7iUBAAAYVSHnYGVlZalDhw6qWbOmu%2B8eAACg3Lm9YO3Zs0effvqpQkJC3H3XAAAAbuH2yzTUrVuXI1cAAKBKc3vBGjZsmObNmyfLssq8r%2B3btysuLk7Jycku4ytXrtStt96qqKgol6/vv/9eklRSUqI5c%2BaoS5cuiomJ0dChQ3XkyBHn9g6HQ2PGjFFcXJw6dOigyZMnX/R8MQAAgItx%2B0eEn332mb777jutXLlSDRs2lK%2Bva8d7//33r2o/b7zxhpYvX67GjRtfdD4mJuaS19VaunSp1qxZozfeeEP169fXnDlzlJSUpFWrVsnHx0dTp07VuXPntHbtWhUWFmr06NFKTU3VlClT/r2wAADAK7m9YAUEBOiee%2B4p8378/f21fPlyvfTSSzp79uy/tW16erqGDBmiZs2aSZKSk5MVGxur3bt3q2HDhtq0aZP%2B8Y9/OM8Te%2BqppzR69GhNmDBB1apVK/PaAQBA1eb2gpWSkmJkP4MHD77s/PHjx/XYY49pz549CgwM1KhRo/TAAw%2BooKBA%2B/fvV3h4uPO2AQEBaty4sex2u06fPi0/Pz%2BFhYU55yMiIvT777/rwIEDLuMAAAAXUyEXGj1w4IA%2B%2Bugj/fzzz87C9T//8z9q06aNkf2HhISoSZMmeuaZZ9S8eXN98sknevbZZxUaGqpbbrlFlmUpKCjIZZugoCDl5OQoODhYAQEB8vHxcZmTpJycnKu6/6ysLGVnZ7uM2Ww1FRoaWsZkrvz83H4KXZnYbObWeyG7pz0Gpnhzfm/OLnl3fm/OLpHf0/K7vWDt3LlTTzzxhJo2bapDhw4pJSVFR44c0eDBg/Xqq6%2BqS5cuZb6Pjh07qmPHjs5/9%2BzZU5988olWrlypcePGSdJlT7Iv6wn46enpmjdvnstYUlKSRo0aVab9ero6dWoZ32dg4HXG9%2BlJvDm/N2eXvDu/N2eXyO8p%2Bd1esObMmaPx48fr0UcfVXR0tKTzf59w5syZmj9/vpGCdTENGjTQnj17FBwcLF9fXzkcDpd5h8OhunXrKiQkRHl5eSouLnb%2BbcQLt61bt%2B5V3VdCQoI6d%2B7sMmaz1VROTr6BJP/PU1r8BSbz%2B/n5KjDwOuXmnlFxcYmx/XoKb87vzdkl787vzdkl8l9r/vL44f5quL1g/etf/9KSJUskyeVjuHvvvVeTJk0ych/vvfeegoKCdN999znHMjMz1ahRI/n7%2B6tFixbKyMhQu3btJEm5ubk6fPiwoqOj1aBBA1mWpR9%2B%2BEERERGSJLvdrsDAQDVt2vSq7j80NLTUx4HZ2adVVOR93xB/VB75i4tLvPpx9eb83pxd8u783pxdIr%2Bn5Hf7IZDatWtf9JpSWVlZql69upH7OHfunKZPny673a7CwkKtXbtWn332mQYNGiRJSkxM1OLFi5WZmam8vDylpqaqVatWioqKUkhIiHr06KFXX31VJ0%2Be1IkTJzR//nz1799fNluFnLIGAAA8jNsbw%2B23366XX37Z5ZpSBw8e1PPPP6%2B77rrrqvcTFRUlSSoqKpIkbdq0SdL5o02DBw9Wfn6%2BRo8erezsbDVs2FDz589XZGSkJGnQoEHKzs7WI488ovz8fMXGxrqcM/Xiiy/q%2BeefV5cuXVStWjXdf//9pS5mCgAAcCk%2BlolLqv8bTpw4oUcffVRHjx5VcXGxatasqTNnzqhFixZauHChbrrpJncux22ys08b36fN5qtuqduN77e8rB/T3ti%2BbDZf1alTSzk5%2BR5xqNg0b87vzdkl787vzdkl8l9r/nr1apfjqi7N7UewbrjhBq1du1bbtm3TwYMHVaNGDTVt2lTt27d3OScLAADAU1XISUXVqlVT165dK%2BKuAQAAyp3bC1bnzp0ve6Tq008/deNqAAAAzHN7wbrvvvtcClZxcbEOHjwou92uRx991N3LAQAAMM7tBevCldT/bMOGDfrqq6/cvBoAAADzKs2lwLt27aqPPvqoopcBAABQZpWmYO3du7fMfwMQAACgMnD7R4QXrqb%2BR2fOnFFmZqa6d%2B/u7uUAAAAY5/aC1aRJk1K/Rejv76/%2B/ftrwIAB7l4OAACAcW4vWDNnznT3XQIAALiV2wvWhx9%2BeNW37dOnTzmuBAAAoHy4vWBNnjxZJSUlpU5o9/HxcRnz8fGhYAEAAI/k9oL15ptv6u2339bw4cMVFhYmy7L0448/6o033tDDDz%2Bs2NhYdy8JAADAqAo5BystLU3169d3jrVt21aNGjXS0KFDtXbtWncvCQAAwCi3Xwfr0KFDCgoKKjUeGBioY8eOuXs5AAAAxrm9YDVo0EAzZ85UTk6Ocyw3N1ezZ8/WzTff7O7lAAAAGOf2jwgnTZqksWPHKj09XbVq1ZKvr6/y8vJUo0YNzZ8/393LAQAAMM7tBatDhw7aunWrtm3bphMnTsiyLNWvX1933323ateu7e7lAAAAGOf2giVJ1113nbp06aITJ06oUaNGFbEEAACAcuP2c7AKCgo0YcIEtWnTRv/xH/8h6fw5WI8//rhyc3PdvRwAAADj3F6wZs2apX379ik1NVW%2Bvv9/98XFxUpNTXX3cgAAAIxze8HasGGDXnvtNd17773OP/ocGBiolJQUbdy40d3LAQAAMM7tBSs/P19NmjQpNR4SEqLff//d3csBAAAwzu0F6%2Babb9ZXX30lSS5/e/Djjz/WTTfd5O7lAAAAGOf23yJ86KGH9PTTT%2BvBBx9USUmJ3nnnHe3Zs0cbNmzQ5MmT3b0cAAAA49xesBISEmSz2bRkyRL5%2Bflp4cKFatq0qVJTU3Xvvfe6ezkAAADGub1gnTx5Ug8%2B%2BKAefPBBd981AACAW7j9HKwuXbq4nHsFAABQ1bi9YMXGxmr9%2BvXuvlsAAAC3cftHhDfeeKNeeuklpaWl6eabb1a1atVc5mfPnu3uJQEAABjl9oK1f/9%2B3XLLLZKknJwcd989AABAuXNbwUpOTtacOXP07rvvOsfmz5%2BvpKQkdy0BAADALdx2DtbmzZtLjaWlpbnr7gEAANzGbQXrYr85yG8TAgCAqshtBevCH3a%2B0hgAAICnc/tlGgAAAKo6ChYAAIBhbvstwsLCQo0dO/aKY1wHCwAAeDq3Faw77rhDWVlZVxwDAADwdG4rWH%2B8/hUAAEBVxjlYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYJhHF6zt27crLi5OycnJpebWrVunXr16qU2bNurXr5927NjhnCspKdGcOXPUpUsXxcTEaOjQoTpy5Ihz3uFwaMyYMYqLi1OHDh00efJkFRQUuCUTAADwfB5bsN544w3NmDFDjRs3LjW3b98%2BTZgwQePGjdOXX36pIUOGaOTIkTpx4oQkaenSpVqzZo3S0tK0ZcsWNWnSRElJSc4/Pj116lSdOXNGa9eu1YoVK5SZmanU1FS35gMAAJ7LYwuWv7%2B/li9fftGCtWzZMsXHxys%2BPl7%2B/v7q3bu3WrZsqdWrV0uS0tPTNWTIEDVr1kwBAQFKTk5WZmamdu/erV9//VWbNm1ScnKyQkJCVL9%2BfT311FNasWKFCgsL3R0TAAB4II8tWIMHD1bt2rUvOpeRkaHw8HCXsfDwcNntdhUUFGj//v0u8wEBAWrcuLHsdrv27dsnPz8/hYWFOecjIiL0%2B%2B%2B/68CBA%2BUTBgAAVCluu5K7OzkcDgUFBbmMBQUFaf/%2B/Tp16pQsy7rofE5OjoKDgxUQECAfHx%2BXOUnKycm5qvvPyspSdna2y5jNVlOhoaHXEueS/Pw8qx/bbObWeyG7pz0Gpnhzfm/OLnl3fm/OLpHf0/JXyYIlyXk%2B1bXMX2nbK0lPT9e8efNcxpKSkjRq1Kgy7dfT1alTy/g%2BAwOvM75PT%2BLN%2Bb05u%2BTd%2Bb05u0R%2BT8lfJQtWnTp15HA4XMYcDodCQkIUHBwsX1/fi87XrVtXISEhysvLU3Fxsfz8/JxzklS3bt2ruv%2BEhAR17tzZZcxmq6mcnPxrjXRRntLiLzCZ38/PV4GB1yk394yKi0uM7ddTeHN%2Bb84ueXd%2Bb84ukf9a85fHD/dXo0oWrMjISO3Zs8dlzG63q2fPnvL391eLFi2UkZGhdu3aSZJyc3N1%2BPBhRUdHq0GDBrIsSz/88IMiIiKc2wYGBqpp06ZXdf%2BhoaGlPg7Mzj6toiLv%2B4b4o/LIX1xc4tWPqzfn9%2Bbsknfn9%2BbsEvk9Jb9nHQK5SgMHDtQXX3yhrVu36uzZs1q%2BfLkOHTqk3r17S5ISExO1ePFiZWZmKi8vT6mpqWrVqpWioqIUEhKiHj166NVXX9XJkyd14sQJzZ8/X/3795fNViX7KAAAMMxjG0NUVJQkqaioSJK0adMmSeePNrVs2VKpqalKSUnRsWPH1Lx5cy1atEj16tWTJA0aNEjZ2dl65JFHlJ%2Bfr9jYWJdzpl588UU9//zz6tKli6pVq6b777//ohczBQAAuBgfq6xndOOqZGefNr5Pm81X3VK3G99veVk/pr2xfdlsvqpTp5ZycvI94lCxad6c35uzS96d35uzS%2BS/1vz16l38kk7lrUp%2BRAgAAFCRKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCsyhassLAwRUZGKioqyvk1ffp0SdLOnTvVv39/3X777erZs6dWr17tsu3ixYvVo0cP3X777UpMTNSePXsqIgIAAPBQtopeQHn6%2BOOP1bBhQ5exrKwsPfXUU5o8ebJ69eqlb7/9ViNGjFDTpk0VFRWlzZs36/XXX9ebb76psLAwLV68WMOHD9fGjRtVs2bNCkoCAAA8SZU9gnUpa9asUZMmTdS/f3/5%2B/srLi5OnTt31rJlyyRJ6enp6tevn1q3bq0aNWro8ccflyRt2bKlIpcNAAA8SJUuWLNnz1bHjh3Vtm1bTZ06Vfn5%2BcrIyFB4eLjL7cLDw50fA/553tfXV61atZLdbnfr2gEAgOeqsh8R3nbbbYqLi9Mrr7yiI0eOaMyYMZo2bZocDofq16/vctvg4GDl5ORIkhwOh4KCglzmg4KCnPNXIysrS9nZ2S5jNltNhYaGXmOai/Pz86x%2BbLOZW%2B%2BF7J72GJjizfm9Obvk3fm9ObtEfk/LX2ULVnp6uvO/mzVrpnHjxmnEiBG64447rritZVllvu958%2Ba5jCUlJWnUqFFl2q%2Bnq1OnlvF9BgZeZ3yfnsSb83tzdsm783tzdon8npK/yhasP2vYsKGKi4vl6%2Bsrh8PhMpeTk6OQkBBJUp06dUrNOxwOtWjR4qrvKyEhQZ07d3YZs9lqKicn/xpXf3Ge0uIvMJnfz89XgYHXKTf3jIqLS4zt11N4c35vzi55d35vzi6R/1rzl8cP91ejShasvXv3avXq1Zo4caJzLDMzU9WrV1d8fLz%2B8Y9/uNx%2Bz54p9H55AAASsElEQVQ9at26tSQpMjJSGRkZ6tu3rySpuLhYe/fuVf/%2B/a/6/kNDQ0t9HJidfVpFRd73DfFH5ZG/uLjEqx9Xb87vzdkl787vzdkl8ntKfs86BHKV6tatq/T0dKWlpencuXM6ePCg5s6dq4SEBD3wwAM6duyYli1bprNnz2rbtm3atm2bBg4cKElKTEzUhx9%2BqF27dunMmTNasGCBqlevro4dO1ZsKAAA4DGq5BGs%2BvXrKy0tTbNnz3YWpL59%2Byo5OVn%2B/v5atGiRZsyYoWnTpqlBgwaaNWuWbr31VknSPffco2eeeUZjxozRb7/9pqioKKWlpalGjRoVnAoAAHiKKlmwJCkmJkbvv//%2BJedWrVp1yW0feughPfTQQ%2BW1NAAAUMVVyY8IAQAAKhIFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwzFbRC4D3%2BI9XP6/oJfxb1o9pX9FLAAB4KI5gAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEUrIs4duyYnnzyScXGxqpTp06aNWuWSkpKKnpZAADAQ3CZhot4%2BumnFRERoU2bNum3337TsGHDdP311%2Buxxx6r6KXBjbisBADgWnEE60/sdrt%2B%2BOEHjRs3TrVr11aTJk00ZMgQpaenV/TSAACAh%2BAI1p9kZGSoQYMGCgoKco5FRETo4MGDysvLU0BAwBX3kZWVpezsbJcxm62mQkNDja7Vz49%2BjP/nSUfcPhl39zVve%2BF1762vf2/O783ZJfJ7Wn4K1p84HA4FBga6jF0oWzk5OVdVsNLT0zVv3jyXsZEjR%2Brpp582t1CdL3KP3vCTEhISjJe3yi4rK0vp6elemV3y7vxZWVn6%2B9/f9Mrsknfn9%2BbsEvk9Lb9n1EA3syyrTNsnJCRo5cqVLl8JCQmGVvf/srOzNW/evFJHy7yBN2eXvDu/N2eXvDu/N2eXyO9p%2BTmC9SchISFyOBwuYw6HQz4%2BPgoJCbmqfYSGhnpEuwYAAOWDI1h/EhkZqePHj%2BvkyZPOMbvdrubNm6tWrVoVuDIAAOApKFh/Eh4erqioKM2ePVt5eXnKzMzUO%2B%2B8o8TExIpeGgAA8BB%2BL7zwwgsVvYjK5u6779batWs1ffp0ffTRR%2Brfv7%2BGDh0qHx%2Bfil5aKbVq1VK7du288uiaN2eXvDu/N2eXvDu/N2eXyO9J%2BX2ssp7RDQAAABd8RAgAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAXLAx07dkxPPvmkYmNj1alTJ82aNUslJSUVvawyOXbsmJKSkhQbG6u4uDhNnDhRubm5Onr0qMLCwhQVFeXy9dZbbzm3XbdunXr16qU2bdqoX79%2B2rFjh3OupKREc%2BbMUZcuXRQTE6OhQ4fqyJEjFRHxksLCwhQZGemSb/r06ZKknTt3qn///rr99tvVs2dPrV692mXbxYsXq0ePHrr99tuVmJioPXv2OOfOnj2rv/71r7rnnnsUGxurUaNGKScnx63ZruTrr78u9dxGRkYqLCxMX3311UWf%2B/Xr1zu398T827dvV1xcnJKTk0vNleW17HA4NGbMGMXFxalDhw6aPHmyCgoKnPP79u3Tww8/rDvuuEPdu3fX22%2B/Xb5BL%2BJy2Tdu3KjevXurTZs26tGjhz744APn3Ouvv65WrVqVei38%2Buuvkq78XFeW98xL5V%2B5cqVuvfXWUvm%2B//57SVX7uZ8yZUqp3OHh4XruueckSRMnTnT%2BjeALX23btnVuX6mzW/A4ffv2taZMmWLl5uZaBw8etLp37269/fbbFb2sMrn//vutiRMnWnl5edbx48etfv36WZMmTbKOHDlitWzZ8pLb7d2714qMjLS2bt1qFRQUWKtWrbJat25tHT9%2B3LIsy1q8eLHVqVMna//%2B/dbp06etF1980erVq5dVUlLirmhX1LJlS%2BvIkSOlxn/55Rfrtttus5YtW2YVFBRYn3/%2BuRUdHW19//33lmVZ1qeffmq1bdvW2rVrl3XmzBlr0aJFVvv27a38/HzLsiwrJSXF6tevn/Xzzz9bOTk51siRI61hw4a5Ndu1WLBggTV69Gjryy%2B/tDp16nTJ23li/rS0NKt79%2B7WoEGDrDFjxrjMlfW1PHLkSOvJJ5%2B0fvvtN%2BvEiRNWQkKCNX36dMuyLOvMmTPW3Xffbb3%2B%2ButWfn6%2BtWfPHqtdu3bWhg0bKkX23bt3W1FRUdYnn3xiFRYWWlu3brUiIiKsr7/%2B2rIsy3rttdesCRMmXHLfV3quK8N75uXyr1ixwnr44YcvuW1Vfu7/rLCw0OrZs6e1detWy7Isa8KECdZrr712ydtX5uwcwfIwdrtdP/zwg8aNG6fatWurSZMmGjJkiNLT0yt6adcsNzdXkZGRGjt2rGrVqqUbbrhBffv21TfffHPFbZctW6b4%2BHjFx8fL399fvXv3VsuWLZ1HetLT0zVkyBA1a9ZMAQEBSk5OVmZmpnbv3l3escpszZo1atKkifr37y9/f3/FxcWpc%2BfOWrZsmaTz2fr166fWrVurRo0aevzxxyVJW7ZsUVFRkZYvX66nnnpKN954o4KDgzVmzBht3bpVv/zyS0XGuqyff/5Z77zzjp599tkr3tYT8/v7%2B2v58uVq3LhxqbmyvJZ//fVXbdq0ScnJyQoJCVH9%2BvX11FNPacWKFSosLNTWrVtVWFioESNGqGbNmoqIiNCAAQPc%2Br5xuewOh0PDhg1T165dZbPZFB8fr5YtW17Ve8CVnuvK8p55ufxXUpWf%2Bz/7%2B9//rptuuknx8fFXvG1lz07B8jAZGRlq0KCBgoKCnGMRERE6ePCg8vLyKnBl1y4wMFApKSm6/vrrnWPHjx9XaGio89/PPvusOnTooDvvvFOzZ89WYWGhpPOPR3h4uMv%2BwsPDZbfbVVBQoP3797vMBwQEqHHjxrLb7eWc6t8ze/ZsdezYUW3bttXUqVOVn59/yWwXPgb787yvr69atWolu92uw4cP6/Tp04qIiHDON2vWTDVq1FBGRoZ7Ql2DuXPn6sEHH9RNN90kScrPz3d%2BdHz33XfrnXfekfV/f5/eE/MPHjxYtWvXvuhcWV7L%2B/btk5%2Bfn8LCwpzzERER%2Bv3333XgwAFlZGQoLCxMfn5%2BLvv%2B40eq5e1y2e%2B55x4lJSU5/11UVKTs7GzVr1/fOfbjjz9q0KBBzo/LL3x8eqXnurK8Z14uv3T%2BPe%2Bxxx5TTEyMunTpolWrVklSlX/u/yg3N1cLFy7U%2BPHjXca//PJL9enTR23atFH//v2da6/s2SlYHsbhcCgwMNBl7MIbR2U4v8QEu92uJUuWaMSIEapevbratGmjbt26acuWLUpLS9Pq1av1t7/9TdL5x%2BOPb5zS%2BccjJydHp06dkmVZl5yvLG677TbFxcVp48aNSk9P165duzRt2rSLPtfBwcHOtV8uu8PhkKRS2wcGBlaq7H909OhRbdy4UY899pik8/8TadmypR599FFt375dKSkpmjdvnlasWCGp6uUvy2vZ4XAoICBAPj4%2BLnOSnPMXey05HI5Kef5mamqqatasqfvuu0%2BSdMMNN6hRo0Z65ZVX9Pnnn2vAgAEaPny4Dhw4cMXn2hPeM0NCQtSkSRONHz9en3/%2BuZ555hlNmjRJO3fu9KrnfsmSJYqJiVGLFi2cY40aNVLjxo21aNEibd%2B%2BXW3bttVf/vIXj8hOwfJAF36Cr4q%2B/fZbDR06VGPHjlVcXJxCQ0P1/vvvq1u3bqpWrZqio6M1bNgwrVy50rnNlR6Pyv54paena8CAAapevbqaNWumcePGae3atc6jdJfj6dn/aOnSperevbvq1asn6fxPou%2B%2B%2B67atWun6tWrq0OHDho0aFCVeu7/rCx5riXrH//HVBlYlqVZs2Zp7dq1WrBggfz9/SVJAwYM0GuvvabGjRvruuuu05AhQ9SqVSuXX/ow/di4U8eOHfXmm28qPDxc1atXV8%2BePdWtW7erfq1Xhee%2BuLhYS5cu1eDBg13Gk5KS9PLLL6t%2B/foKCAjQ%2BPHjVb16dW3atElS5c5OwfIwISEhzp/YLnA4HPLx8VFISEgFrcqMzZs368knn9SkSZNKfZP9UYMGDfTrr7/KsizVqVPnoo9HSEiIgoOD5evre9H5unXrlksGExo2bKji4uKLrj0nJ8f5PF8u%2B4Xb/Hn%2B1KlTlTb7hg0b1Llz58vepkGDBsrKypJU9fKX5bUcEhKivLw8FRcXu8xJcs7/%2BWiNw%2BFw7rcyKCkp0cSJE7V582a99957uuWWWy57%2BwuvhSs91576nnkhnzc899L53yg%2Bd%2B6cy28IXoyfn59uvPFG53NfmbNXnkcXVyUyMlLHjx/XyZMnnWN2u13NmzdXrVq1KnBlZfPdd99pwoQJmjt3rvr06eMc37lzpxYsWOBy2wMHDqhBgwby8fFRZGRkqc/T7Xa7WrduLX9/f7Vo0cLlnJvc3FwdPnxY0dHR5RvoKu3du1czZ850GcvMzFT16tUVHx9fKtuePXvUunVrSedfC3/MVlxcrL1796p169Zq1KiRgoKCXOb/9a9/6dy5c4qMjCzHRNdm3759OnbsmNq3b%2B8cW79%2Bvf77v//b5XYHDhxQo0aNJFWt/JLK9Fpu1aqVLMvSDz/84LJtYGCgmjZtqsjISP34448qKioqte/K4uWXX9ZPP/2k9957z/kcX/C3v/1NO3fudBnLzMxUo0aNrvhce8J75nvvvad169a5jF3I5w3PvSR9%2BumnuvPOO2Wz2ZxjlmUpJSXFJdu5c%2Bd0%2BPBhNWrUqNJnp2B5mAvXA5k9e7by8vKUmZmpd955R4mJiRW9tGtWVFSkKVOmaNy4cerQoYPLXO3atTV//nytWrVKhYWFstvteuutt5x5Bw4cqC%2B%2B%2BEJbt27V2bNntXz5ch06dEi9e/eWJCUmJmrx4sXKzMxUXl6eUlNTndfTqQzq1q2r9PR0paWl6dy5czp48KDmzp2rhIQEPfDAAzp27JiWLVums2fPatu2bdq2bZsGDhwo6Xy2Dz/8ULt27dKZM2e0YMECVa9eXR07dpSfn58GDhyohQsX6vjx48rJydF//ud/qlu3bi6/TFBZ7N27V8HBwQoICHCOVatWTa%2B88op27NihwsJCff7551qxYoXzua9K%2BaWyvZZDQkLUo0cPvfrqqzp58qROnDih%2BfPnq3///s7fygsICNCCBQt05swZ7d69W8uXL6807xvffvutVq9erbS0NAUHB5eadzgcmjZtmg4cOKCzZ8/q7bff1uHDh9W3b98rPtee8J557tw5TZ8%2BXXa7XYWFhVq7dq0%2B%2B%2BwzDRo0SFLVfu4v2Ldvnxo2bOgy5uPjo6NHj2ratGn65ZdflJ%2Bfr9TUVFWrVk1du3at/NndcjEIGHX8%2BHHr8ccft6Kjo624uDjrtddeq1TXdfp3ff3111bLli2tyMjIUl9Hjx61Nm7caPXu3duKjo622rdvby1cuNAqLi52br9hwware/fuVkREhPXAAw9Y//znP51zJSUl1ty5c6277rrLio6Otp544gnndYUqi3/%2B859WQkKCddttt1nt2rWzUlJSrIKCAudc7969rYiICKt79%2B6lrt%2BydOlSKz4%2B3oqMjLQSExOtH3/80Tl39uxZ64UXXrBiYmKsNm3aWM8884yVm5vr1mxXa%2BHChVbPnj1Ljb///vtW9%2B7draioKKtTp07WBx984DLvafkvvK5vvfVW69Zbb3X%2B%2B4KyvJZzc3Ot5ORk67bbbrNiYmKsadOmWWfPnnXO//jjj9agQYOsyMhIq2PHjtbSpUvdE/r/XC77c8895zJ24euxxx6zLMuyCgoKrJdeesm6%2B%2B67raioKKtv377Wd99959z3lZ7ryvCeebn8JSUl1vz5861OnTpZkZGR1r333mtt3rzZuW1Vfu4v6N69u/Xmm2%2BW2jYnJ8eaOHGiFRcXZ0VHR1sPP/ywtX//fud8Zc7uY1mV/Ow/AAAAD8NHhAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABg2P8C/YIF96ScCbgAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common482336829505820325”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>60</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>18.0</td> <td class=”number”>28</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>40.0</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>37.0</td> <td class=”number”>20</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.0</td> <td class=”number”>20</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16.0</td> <td class=”number”>20</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>32.0</td> <td class=”number”>20</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>54.0</td> <td class=”number”>19</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (876)</td> <td class=”number”>2598</td> <td class=”number”>80.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>359</td> <td class=”number”>11.2%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme482336829505820325”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>60</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>12</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:20%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>16</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:27%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>15</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>11677.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11837.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13920.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14245.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17170.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_DIRSALES12”><s>DIRSALES12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_DIRSALES07”><code>DIRSALES07</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.90428</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_DIRSALES_FARMS07”>DIRSALES_FARMS07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>237</td>

</tr> <tr>

<th>Unique (%)</th> <td>7.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>4.2%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>134</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>44.322</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>787</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>1.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram1506662521867213660”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives1506662521867213660,#minihistogram1506662521867213660”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives1506662521867213660”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles1506662521867213660”

aria-controls=”quantiles1506662521867213660” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram1506662521867213660” aria-controls=”histogram1506662521867213660”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common1506662521867213660” aria-controls=”common1506662521867213660”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme1506662521867213660” aria-controls=”extreme1506662521867213660”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles1506662521867213660”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>3</td>

</tr> <tr>

<th>Q1</th> <td>11</td>

</tr> <tr>

<th>Median</th> <td>26</td>

</tr> <tr>

<th>Q3</th> <td>56</td>

</tr> <tr>

<th>95-th percentile</th> <td>142</td>

</tr> <tr>

<th>Maximum</th> <td>787</td>

</tr> <tr>

<th>Range</th> <td>787</td>

</tr> <tr>

<th>Interquartile range</th> <td>45</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>57.006</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.2862</td>

</tr> <tr>

<th>Kurtosis</th> <td>34.636</td>

</tr> <tr>

<th>Mean</th> <td>44.322</td>

</tr> <tr>

<th>MAD</th> <td>35.678</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>4.3875</td>

</tr> <tr>

<th>Sum</th> <td>136340</td>

</tr> <tr>

<th>Variance</th> <td>3249.6</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram1506662521867213660”>

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/119evXT2PHjtXXX39dV2MBAACctzorWBUVFVq7dq3uvfdeZWRkqGnTpnrsscfUvn17jRkzRqtXr66r0QAAAM6LJ9hPmJ%2Bfr5UrV%2Bqtt95SWVmZ%2Bvbtq1dffVVdunTx36Zbt2566qmnNGDAgGCPBwAAcN6CXrD69%2B%2Bvli1b6v7779egQYPUqFGj026TmpqqI0eOBHs0AAAAK4JesJYuXaqkpKSz3m779u1BmAYAAMC%2BoJ%2BD1a5dO40bN07vvfeef%2B0///M/de%2B998rn8wV7HAAAAOuCXrBmzZqlY8eOqXXr1v61nj17qrq6WrNnzw72OAAAANYF/SXCjz/%2BWKtXr1Z0dLR/rUWLFsrMzNQtt9wS7HEAAACsC/oRrPLycoWHh58%2BSGioTpw4cU6P9dFHHyklJUXp6ekB62%2B%2B%2BaauuuoqJSQkBHydur5WdXW15s2bp969e6tbt24aO3as9u3b57%2B/z%2BfTpEmTlJKSoh49eujxxx9XeXn5r0gLAAD%2BLwp6werWrZtmz56to0eP%2Bte%2B//57Pf300wGXajibF198UTNnzlTz5s1/9nm8Xm/AV2JioiRp%2BfLlWr16tbKysrR582a1aNFCaWlpMsZIkqZPn64TJ05ozZo1euONN5Sfn6/MzMzzSA0AAP4vCXrBmjZtmrZu3aprr71WSUlJ6tq1q3r27KmcnBzNnDmzxo8THh6ulStX/mzB%2BiXZ2dkaM2aMWrVqpYiICKWnpys/P1/bt2/XoUOH9N577yk9PV0xMTFq2rSpHnjgAb3xxhuqqKg45%2BcCAAD/9wT9HKwrrrhC77zzjj788EPt3btXoaGhatmypXr06KGwsLAaP87o0aN/cb%2BwsFB33323cnJyFBkZqYkTJ%2BrWW29VeXm58vLyFBcX579tRESEmjdvLq/Xq2PHjiksLEzt2rXz73fo0EHHjx/Xrl27AtYBAADOJOgFS5Lq1aunG264odYePyYmRi1atNDDDz%2Bs1q1b691339Wjjz6q2NhY/fa3v5UxRlFRUQH3iYqKUnFxsRo1aqSIiAiFhIQE7ElScXFxjZ7/4MGDKioqCljzeOorNjb2PJMFCgurs086%2BlU8nprPeyqb0zLWlJvzuTmb5O58ZHMuN%2BdzaragF6x9%2B/Zp7ty5%2Bu6778544vj7779/3s/Rs2dP9ezZ0//7/v37691339Wbb76pKVOmSJL/fKsz%2BaW9msjOztbChQsD1tLS0jRx4sTzelyni45ucM73iYy8pBYmuXC4OZ%2Bbs0nuzkc253JzPqdlC3rBmjZtmg4ePKgePXqofv36QXveZs2aKScnR40aNVJoaOhpFzX1%2BXxq3LixYmJiVFpaqqqqKv9Llqdu27hx4xo91/Dhw9WrV6%2BANY%2BnvoqLyywk%2BV9Oa/Pnkj8sLFSRkZeopOSEqqqqa3GquuHmfG7OJrk7H9mcy835zjfbr/nh3oagF6ycnBy9//77iomJqbXn%2BMtf/qKoqCj169fPv5afn68rrrhC4eHhatOmjXJzc/0f2VNSUqK9e/cqMTFRzZo1kzFG33zzjTp06CBJ8nq9ioyMVMuWLWv0/LGxsae9HFhUdEyVle76pj9XvyZ/VVW1q//c3JzPzdkkd%2Bcjm3O5OZ/TsgX9EEjjxo1r/cjVDz/8oBkzZsjr9aqiokJr1qzRhx9%2BqBEjRkiSRo4cqaVLlyo/P1%2BlpaXKzMxU%2B/btlZCQoJiYGPXt21d/%2BMMfdOTIER04cECLFi3S0KFD5fHUySlrAADAYYLeGO6//34tXLhQkydPDjiR/FwlJCRIkiorKyXJ/9mGXq9Xo0ePVllZmR566CEVFRXp8ssv16JFixQfHy9JGjFihIqKinTnnXeqrKxMycnJAedMPfPMM3ryySfVu3dvXXTRRbrllltOu5gpAADAzwkx53tG9zmaMGGCvvzySxljdPnllys0NPAg2muvvRbMcYKmqOiY9cf0eEJ1Y%2BZH1h%2B3tqyb1L3Gt/V4QhUd3UDFxWWOOiRcU27O5%2BZskrvzkc253JzvfLM1adKwFqY6u6AfwYqIiNDvfve7YD8tAABA0AS9YM2aNSvYTwkAABBUdfI%2B/127dmnBggV67LHH/GtfffVVXYwCAABgXdAL1tatWzVw4EBt3LhRa9askfTjxUdHjx5t5SKjAAAAdS3oBWvevHl65JFHtHr1av%2B7CK%2B44grNnj1bixYtCvY4AAAA1gW9YP3Xf/2XRo4cKUkBl2m46aablJ%2BfH%2BxxAAAArAt6wWrYsOEZP4Pw4MGDqlevXrDHAQAAsC7oBatz58569tlnVVpa6l/bvXu3MjIydO211wZ7HAAAAOuCfpmGxx57THfddZeSk5NVVVWlzp0768SJE2rTpo1mz54d7HEAAACsC3rBuuyyy7RmzRp98MEH2r17ty6%2B%2BGK1bNlS3bt3P6%2BPzgEAALhQ1MmnF1900UW64YYb6uKpAQAAal3QC1avXr1%2B8UgV18ICAABOF/SC1a9fv4CCVVVVpd27d8vr9equu%2B4K9jgAAADWBb1gTZky5YzrGzZs0N///vcgTwMAAGBfnXwW4ZnccMMNeuedd%2Bp6DAAAgPN2wRSsHTt2yBhT12MAAACct6C/RDhixIjT1k6cOKH8/Hz16dMn2OMAAABYF/SC1aJFi9PeRRgeHq6hQ4dq2LBhwR4HAADAuqAXLK7WDgAA3C7oBeutt96q8W0HDRpUi5MAAADUjqAXrMcff1zV1dWnndAeEhISsBYSEkLBAgAAjhT0gvXSSy/p5Zdf1rhx49SuXTsZY/Ttt9/qxRdf1KhRo5ScnBzskQAAAKyqk3OwsrKy1LRpU/9a165ddcUVV2js2LFas2ZNsEcCAACwKujXwdqzZ4%2BioqJOW4%2BMjFRBQUGwxwEAALAu6AWrWbNmmj17toqLi/1rJSUlmjt3rq688spgjwMAAGBd0F8inDZtmiZPnqzs7Gw1aNBAoaGhKi0t1cUXX6xFixYFexwAAADrgl6wevTooS1btuiDDz7QgQMHZIxR06ZNdd1116lhw4bBHgcAAMC6oBcsSbrkkkvUu3dvHThwQFdccUVdjAAAAFBrgn4OVnl5uTIyMtSpUyfdfPPNkn48B%2Buee%2B5RSUlJsMcBAACwLugFa86cOdq5c6cyMzMVGvq/T19VVaXMzMxgjwMAAGBd0AvWhg0b9Pzzz%2Bumm27yf%2BhzZGSkZs2apY0bNwZ7HAAAAOuCXrDKysrUokWL09ZjYmJ0/PjxYI8DAABgXdAL1pVXXqm///3vkhTw2YPr16/Xb37zm2CPAwAAYF3Q30V4xx13aMKECRoyZIiqq6v1yiuvKCcnRxs2bNDjjz8e7HEAAACsC3rBGj58uDwej5YtW6awsDC98MILatmypTIzM3XTTTcFexwAAADrgl6wjhw5oiFDhmjIkCHBfmoAAICgCPo5WL179w449woAAMBtgl6wkpOTtW7dumA/LQAAQNAE/SXCf/mXf9Hvf/97ZWVl6corr9RFF10UsD937txgjwQAAGBV0AtWXl6efvvb30qSiouLg/30AAAAtS5oBSs9PV3z5s3Tn/70J//aokWLlJaWFqwRAAAAgiJo52Bt2rTptLWsrKxgPT0AAEDQBK1gnemdg7ybEAAAuFHQCtapD3Y%2B2xoAAIDTBf0yDQAAAG5HwQIAALAsaO8irKio0OTJk8%2B6xnWwAACA0wWtYHXp0kUHDx486xoAAIDTBa1g/fP1r2z56KOPlJGRoeTkZM2bNy9gb%2B3atVq8eLH279%2Bvli1b6uGHH1aPHj0kSdXV1Zo/f77WrFmjkpISJSYm6qmnntIVV1whSfL5fHrqqaf02WefKTQ0VKmpqZo%2Bfbouvvhi6xkAAID7OPYcrBdffFEzZ85U8%2BbNT9vbuXOnMjIyNGXKFH366acaM2aMHnzwQR04cECStHz5cq1evVpZWVnavHmzWrRoobS0NP9lI6ZPn64TJ05ozZo1euONN5Sfn6/MzMyg5gMAAM7l2IIVHh6ulStXnrFgrVixQqmpqUpNTVV4eLgGDhyotm3batWqVZKk7OxsjRkzRq1atVJERITS09OVn5%2Bv7du369ChQ3rvvfeUnp6umJgYNW3aVA888IDeeOMNVVRUBDsmAABwIMcWrNGjR6thw4Zn3MvNzVVcXFzAWlxcnLxer8rLy5WXlxewHxERoebNm8vr9Wrnzp0KCwtTu3bt/PsdOnTQ8ePHtWvXrtoJAwAAXCXoH/YcDD6fT1FRUQFrUVFRysvL09GjR2WMOeN%2BcXGxGjVqpIiIiICLoJ66bU0/nPrgwYMqKioKWPN46is2NvbXxPlZYWHO6sceT83nPZXNaRlrys353JxNcnc%2BsjmXm/M5NZsrC5Z09o/h%2BaX98/0In%2BzsbC1cuDBgLS0tTRMnTjyvx3W66OgG53yfyMhLamGSC4eb87k5m%2BTufGRzLjfnc1o2Vxas6Oho%2BXy%2BgDWfz6eYmBg1atRIoaGhZ9xv3LixYmJiVFpaqqqqKoWFhfn3JKlx48Y1ev7hw4erV69eAWseT30VF5f92khn5LQ2fy75w8JCFRl5iUpKTqiqqroWp6obbs7n5mySu/ORzbncnO98s/2aH%2B5tcGXBio%2BPV05OTsCa1%2BtV//79FR4erjZt2ig3N1dJSUmSpJKSEu3du1eJiYlq1qyZjDH65ptv1KFDB/99IyMj1bJlyxo9f2xs7GkvBxYVHVNlpbu%2B6c/Vr8lfVVXt6j83N%2BdzczbJ3fnI5lxuzue0bM46BFJDt99%2Buz755BNt2bJFJ0%2Be1MqVK7Vnzx4NHDhQkjRy5EgtXbpU%2Bfn5Ki0tVWZmptq3b6%2BEhATFxMSob9%2B%2B%2BsMf/qAjR47owIEDWrRokYYOHSqPx5V9FAAAWObYxpCQkCBJqqyslCS99957kn482tS2bVtlZmZq1qxZKigoUOvWrbVkyRI1adJEkjRixAgVFRXpzjvvVFlZmZKTkwPOmXrmmWf05JNPqnfv3rrooot0yy23KD09PcgJAQCAU4WY8z2jGzVSVHTM%2BmN6PKG6MfMj649bW9ZN6l7j23o8oYqObqDi4jJHHRKuKTfnc3M2yd35yOZcbs53vtmaNDnzJZ1qmytfIgQAAKhLFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAy1xasdu3aKT4%2BXgkJCf6vGTNmSJK2bt2qoUOHqnPnzurfv79WrVoVcN%2BlS5eqb9%2B%2B6ty5s0aOHKmcnJy6iAAAABzKU9cD1Kb169fr8ssvD1g7ePCgHnjgAT3%2B%2BOMaMGCAvvjiC40fP14tW7ZUQkKCNm3apAULFuill15Su3bttHTpUo0bN04bN25U/fr16ygJAABwEtcewfo5q1evVosWLTR06FCFh4crJSVFvXr10ooVKyRJ2dnZGjx4sK6%2B%2BmpdfPHFuueeeyRJmzdvrsuxAQCAg7i6YM2dO1c9e/ZU165dNX36dJWVlSk3N1dxcXEBt4uLi/O/DPjT/dDQULVv315erzeoswMAAOdy7UuEHTt2VEpKip577jnt27dPkyZN0tNPPy2fz6emTZsG3LZRo0YqLi6WJPl8PkVFRQXsR0VF%2Bfdr4uDBgyoqKgpY83jqKzY29lemObOwMGf1Y4%2Bn5vOeyua0jDXl5nxuzia5Ox/ZnMvN%2BZyazbUFKzs72//rVq1aacqUKRo/fry6dOly1vsaY877uRcuXBiwlpaWpokTJ57X4zpddHSDc75PZOQltTDJhcPN%2BdycTXJ3PrI5l5vzOS2bawvWT11%2B%2BeWqqqpSaGiofD5fwF5xcbFiYmIkSdHR0aft%2B3w%2BtWnTpsbPNXz4cPXq1StgzeOpr%2BLisl85/Zk5rc2fS/6wsFBFRl6ikpITqqqqrsWp6oab87k5m%2BTufGRzLjfnO99sv%2BaHextcWbB27NihVatWaerUqf61/Px81atXT6mpqfrrX/8acPucnBxdffXVkqT4%2BHjl5ubqtttukyQeKXqjAAAPqUlEQVRVVVVpx44dGjp0aI2fPzY29rSXA4uKjqmy0l3f9Ofq1%2BSvqqp29Z%2Bbm/O5OZvk7nxkcy4353NaNmcdAqmhxo0bKzs7W1lZWfrhhx%2B0e/duzZ8/X8OHD9ett96qgoICrVixQidPntQHH3ygDz74QLfffrskaeTIkXrrrbe0bds2nThxQosXL1a9evXUs2fPug0FAAAcw5VHsJo2baqsrCzNnTvXX5Buu%2B02paenKzw8XEuWLNHMmTP19NNPq1mzZpozZ46uuuoqSdLvfvc7Pfzww5o0aZIOHz6shIQEZWVl6eKLL67jVAAAwClcWbAkqVu3bnrttdd%2Bdu/tt9/%2B2fvecccduuOOO2prNAAA4HKufIkQAACgLlGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMtce6FRXHhu/sPf6nqEc7JuUve6HgEA4FAcwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMk9dDwBcqG7%2Bw9/qeoRzsm5S97oeAQDwPziCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWGdQUFCg%2B%2B67T8nJybr%2B%2Bus1Z84cVVdX1/VYAADAIXgX4RlMmDBBHTp00HvvvafDhw/r/vvv16WXXqq77767rkcDAAAOQMH6Ca/Xq2%2B%2B%2BUavvPKKGjZsqIYNG2rMmDF69dVXKVi4oDntshJOwiUwAJwrCtZP5ObmqlmzZoqKivKvdejQQbt371ZpaakiIiLO%2BhgHDx5UUVFRwJrHU1%2BxsbFWZw0L4xVeIBg8nv/9t3bq350b//2RzbncnM%2Bp2ShYP%2BHz%2BRQZGRmwdqpsFRcX16hgZWdna%2BHChQFrDz74oCZMmGBvUP1Y5O667DsNHz7cenmrawcPHlR2drYrs0nuzufmbNKP%2BV599SVX5iObc7k5n1OzOasOBokx5rzuP3z4cL355psBX8OHD7c03f8qKirSwoULTzta5gZuzia5O5%2Bbs0nuzkc253JzPqdm4wjWT8TExMjn8wWs%2BXw%2BhYSEKCYmpkaPERsb66iWDQAA7OII1k/Ex8ersLBQR44c8a95vV61bt1aDRo0qMPJAACAU1CwfiIuLk4JCQmaO3euSktLlZ%2Bfr1deeUUjR46s69EAAIBDhD311FNP1fUQF5rrrrtOa9as0YwZM/TOO%2B9o6NChGjt2rEJCQup6tNM0aNBASUlJrjy65uZskrvzuTmb5O58ZHMuN%2BdzYrYQc75ndAMAACAALxECAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBcqCCggLdd999Sk5O1vXXX685c%2Baourq6rsc6Jx999JFSUlKUnp5%2B2t7atWs1YMAAderUSYMHD9bHH3/s36uurta8efPUu3dvdevWTWPHjtW%2BffuCOfpZFRQUKC0tTcnJyUpJSdHUqVNVUlIiSdq5c6dGjRqlLl26qE%2BfPnr55ZcD7vtL2S8E33zzje666y516dJFKSkpmjRpkoqKiiRJW7du1dChQ9W5c2f1799fq1atCrjv0qVL1bdvX3Xu3FkjR45UTk5OXUSokWeffVbt2rXz/94N2dq1a6f4%2BHglJCT4v2bMmCHJHfkkafHixerRo4c6duyoMWPGaP/%2B/ZKcne8f//hHwN9ZQkKC4uPj/d%2BfTs4mSTt27NDo0aPVtWtXde/eXVOmTNGRI0ckOT%2BbDBzntttuM0888YQpKSkxu3fvNn369DEvv/xyXY9VY1lZWaZPnz5mxIgRZtKkSQF7O3bsMPHx8WbLli2mvLzcvP322%2Bbqq682hYWFxhhjli5daq6//nqTl5dnjh07Zp555hkzYMAAU11dXRdRzuiWW24xU6dONaWlpaawsNAMHjzYTJs2zZw4ccJcd911ZsGCBaasrMzk5OSYpKQks2HDBmPM2bPXtZMnT5prr73WLFy40Jw8edIcPnzYjBo1yjzwwAPm%2B%2B%2B/Nx07djQrVqww5eXl5m9/%2B5tJTEw0X3/9tTHGmPfff9907drVbNu2zZw4ccIsWbLEdO/e3ZSVldVxqtPt2LHDJCUlmbZt2xpjjGuytW3b1uzbt%2B%2B0dbfkW7ZsmbnppptMfn6%2BOXbsmJkxY4aZMWOGa/L9s8WLF5uHHnrI8dkqKipM9%2B7dzdy5c83JkyfNkSNHzN13320mTJjg%2BGzGGEPBcpivv/7atG/f3vh8Pv/an//8Z9O3b986nOrcvPrqq6akpMRkZGScVrCefvppk5aWFrA2bNgws2TJEmOMMf379zevvvqqf%2B/YsWMmLi7OfPXVV7U/eA0cPXrUTJ061RQVFfnX/vSnP5k%2BffqYdevWmWuuucZUVlb69%2BbMmWP%2B7d/%2BzRhz9ux1zefzmddff91UVFT411599VVz4403mpdeeskMGjQo4PaTJk0y06dPN8YYc99995lnn33Wv1dVVWW6d%2B9u1qxZE5zha6iqqsoMGzbM/Md//Ie/YLkl288VLLfk69Wrl/%2BHlX/mlnynFBQUmKSkJFNQUOD4bP/93/9t2rZta/Ly8vxrf/7zn80NN9zg%2BGzGGMNLhA6Tm5urZs2aKSoqyr/WoUMH7d69W6WlpXU4Wc2NHj1aDRs2PONebm6u4uLiAtbi4uLk9XpVXl6uvLy8gP2IiAg1b95cXq%2B3VmeuqcjISM2aNUuXXnqpf62wsFCxsbHKzc1Vu3btFBYW5t%2BLi4vzH9b%2BpewXgqioKA0bNkwej0eStGvXLv31r3/VzTff/LOz/1y20NBQtW/f/oLJdsprr72m8PBwDRgwwL/mlmySNHfuXPXs2VNdu3bV9OnTVVZW5op833//vfbv36%2BjR4%2BqX79%2BSk5O1sSJE3XkyBFX5Ptn8%2BfP15AhQ/Sb3/zG8dmaNm2q9u3bKzs7W2VlZTp8%2BLA2btyonj17Oj6bxDlYjuPz%2BRQZGRmwdqpsFRcX18VIVvl8voDyKP2Yr7i4WEePHpUx5mf3L0Rer1fLli3T%2BPHjz/h316hRI/l8PlVXV/9i9gtJQUGB4uPj1a9fPyUkJGjixIk/m%2B3U7E7IdujQIS1YsEBPPvlkwLobsklSx44dlZKSoo0bNyo7O1vbtm3T008/7Yp8Bw4ckCStX79er7zyit5%2B%2B20dOHBATzzxhCvynbJ//35t3LhRd999tyTnf2%2BGhoZqwYIFev/999W5c2elpKSosrJSkydPdnw2iYLlSMaYuh6hVp0tn1Pyf/HFFxo7dqwmT56slJSUn71dSEiI/9dOyNasWTN5vV6tX79ee/bs0aOPPlqj%2B13o2WbNmqXBgwerdevW53zfCz2bJGVnZ2vYsGGqV6%2BeWrVqpSlTpmjNmjWqqKg4630v9Hyn5rvnnnvUtGlTXXbZZZowYYI2bdp0Tve/0C1fvlx9%2BvRRkyZNanyfCznbDz/8oHHjxummm27S559/rg8//FANGzbUlClTanT/CzmbRMFynJiYGPl8voA1n8%2BnkJAQxcTE1NFU9kRHR58xX0xMjBo1aqTQ0NAz7jdu3DiYY57Vpk2bdN9992natGkaPXq0pB//7n7605XP5/Pn%2BqXsF5qQkBC1aNFC6enpWrNmjTwez2mzFxcX%2B2e/0LNt3bpVX331ldLS0k7bO9PsTsr2cy6//HJVVVWd8d%2BU0/Kdekn%2Bn494NGvWTMYYVVRUOD7fKRs2bFCvXr38v3f69%2BbWrVu1f/9%2BPfzww2rYsKGaNm2qiRMn6t1333XF9yUFy2Hi4%2BNVWFjofxur9OPLUK1bt1aDBg3qcDI74uPjT3urrdfr1dVXX63w8HC1adNGubm5/r2SkhLt3btXiYmJwR71Z3355ZfKyMjQ/PnzNWjQIP96fHy8vv32W1VWVvrXTmU7tf9z2S8EW7duVd%2B%2BfQMuCRIa%2BuN/IYmJiafNnpOTE5Dtn//eqqqqtGPHjgsm26pVq3T48GFdf/31Sk5O1uDBgyVJycnJatu2raOzST%2B%2BFX727NkBa/n5%2BapXr55SU1Mdn%2B%2Byyy5TRESEdu7c6V8rKCjQRRdd5Ip80o%2BXeCkoKFD37t39awkJCY7OVlVVperq6oAjUT/88IMkKSUlxdHZJHGZBicaNmyYmTZtmjl27JjJy8szvXr1MsuWLavrsc7Zmd5F%2BO2335qEhASzefNmU15eblasWGE6depkDh48aIz58R0mPXv29F%2BmYfr06WbIkCF1Mf4ZVVRUmJtvvtm89tprp%2B2dPHnSXH/99eb55583x48fN9u2bTNdu3Y1mzdvNsacPXtdKykpMSkpKWb27Nnm%2BPHj5vDhw2bs2LHmjjvuMIcOHTKdOnUyr7/%2BuikvLzdbtmwxiYmJZufOncYYYz744APTpUsX89VXX5njx4%2BbBQsWmNTUVHPixIk6TvUjn89nCgsL/V9fffWVadu2rSksLDQFBQWOzmaMMQcOHDAdO3Y0S5YsMSdPnjS7du0y/fr1MzNmzHD8390pzz77rOndu7fZs2ePOXTokBk%2BfLiZOnWqa/KtXLnSJCUlBaw5PduRI0dMUlKS%2Bfd//3dz/Phxc%2BTIETNu3Djzr//6r47PZgyXaXCkwsJCc88995jExESTkpJinn/%2B%2BQvqOlBnEx8fb%2BLj481VV11lrrrqKv/vT9mwYYPp06eP6dChg7n11lvNZ5995t%2Brrq428%2BfPN9dee61JTEw099577wVznShjjPnHP/5h2rZt68/0z1/79%2B833377rRkxYoSJj483PXv2NMuXLw%2B4/y9lvxB88803ZtSoUSYxMdFcc801ZtKkSebAgQPGGGM%2B%2B%2BwzM3DgQNOhQwfTp0%2Bf094yv3z5cpOammri4%2BPNyJEjzbffflsXEWpk3759/ss0GOOObJ999pkZPny46dixo0lKSjKzZs0y5eXl/j2n5zt58qR56qmnTLdu3UzHjh1NRkaGKS0tNca4I98LL7xg%2Bvfvf9q607N5vV4zatQo07VrV5OSkuKq/1NCjLnAzxIDAABwGM7BAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADL/j9KHilTifNCwQAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common1506662521867213660”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>89</td> <td class=”number”>2.8%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.0</td> <td class=”number”>76</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>75</td> <td class=”number”>2.3%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>72</td> <td class=”number”>2.2%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>70</td> <td class=”number”>2.2%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.0</td> <td class=”number”>69</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>15.0</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.0</td> <td class=”number”>63</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17.0</td> <td class=”number”>63</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (226)</td> <td class=”number”>2365</td> <td class=”number”>73.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>134</td> <td class=”number”>4.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme1506662521867213660”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>60</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>42</td> <td class=”number”>1.3%</td> <td>

<div class=”bar” style=”width:70%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>44</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:73%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>59</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:98%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>60</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>553.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>620.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>695.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>753.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>787.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_DIRSALES_FARMS12”><s>DIRSALES_FARMS12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_DIRSALES_FARMS07”><code>DIRSALES_FARMS07</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.96228</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FARM_TO_SCHOOL09”>FARM_TO_SCHOOL09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.061622</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>91.6%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-725797269478885061”>

</div> <div class=”col-md-12 text-right”>

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<li role=”presentation” class=”active”><a href=”#quantiles-725797269478885061”

aria-controls=”quantiles-725797269478885061” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-725797269478885061” aria-controls=”histogram-725797269478885061”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-725797269478885061” aria-controls=”common-725797269478885061”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-725797269478885061” aria-controls=”extreme-725797269478885061”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-725797269478885061”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>0</td>

</tr> <tr>

<th>95-th percentile</th> <td>1</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr>

<th>Range</th> <td>1</td>

</tr> <tr>

<th>Interquartile range</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.24051</td>

</tr> <tr>

<th>Coef of variation</th> <td>3.9029</td>

</tr> <tr>

<th>Kurtosis</th> <td>11.314</td>

</tr> <tr>

<th>Mean</th> <td>0.061622</td>

</tr> <tr>

<th>MAD</th> <td>0.11565</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>3.6478</td>

</tr> <tr>

<th>Sum</th> <td>193</td>

</tr> <tr>

<th>Variance</th> <td>0.057843</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-725797269478885061”>

<img 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Kz8/X5MmT1blzZ4WHhysrK0vFxcXat2%2BfPy8ZAAA4iCMLVkREhBYsWKC2bds2rB09elSxsbEqLCxU9%2B7dFRIS0rCXkJCggoICSVJhYaESEhJ8zpeQkCC3262amhoVFRX57IeHh6tjx45yu90WXxUAAAgULn8PYILb7dYbb7yh5cuXa/PmzYqIiPDZj4qKksfjUX19vTwejyIjI332IyMjVVRUpBMnTsjr9V5wv7y8vNHzlJaWqqyszGfN5Wql2NjYJl7ZDwsJcVY/drmcM%2B%2B5bJ2WsROQrbXI1zpka51AzNbxBeuzzz7TjBkzNHv2bKWkpGjz5s0XPC4oKKjhny/2PNWlPm%2BVn5%2Bv3Nxcn7XMzEzNnDnzks7rdNHRYf4eockiIlr6e4SARbbWIl/rkK11AilbRxes7du366GHHtLcuXN1%2B%2B23S5JiYmL09ddf%2Bxzn8XgUFRWl4OBgRUdHy%2BPxnLcfExPTcMyF9tu0adPoudLT05WWluaz5nK1Unl5VROu7uKc1vRNX7%2BVQkKCFRHRUhUV1aqrq/f3OAGFbK1FvtYhW%2BtYma2//nDv2IL1%2BeefKzs7Wy%2B88IIGDRrUsJ6YmKg333xTtbW1crm%2Buzy3263evXs37J97Husct9utkSNHKjQ0VF27dlVhYaEGDBgg6bsH6g8fPqxevXo1erbY2Njz3g4sKzup2tof9y9IJ15/XV29I%2Bd2ArK1Fvlah2ytE0jZOusWyP9TW1urJ554QnPmzPEpV5KUmpqq8PBwLV%2B%2BXNXV1dq3b5/WrFmjjIwMSdKECRP08ccfa%2BfOnTp9%2BrTWrFmjr7/%2BWqNHj5YkZWRkaOXKlSouLlZlZaVycnLUo0cPJSUl2X6dAADAmRx5B2vv3r0qLi7W/PnzNX/%2BfJ%2B9LVu26KWXXtJTTz2lvLw8tW3bVllZWRoyZIgkqVu3bsrJydGCBQtUUlKiLl26aMWKFWrXrp0kaeLEiSorK9OkSZNUVVWl5OTk856nAgAA%2BCFBXj5B0xZlZSeNn9PlCtZNObuNn9cqm2fd4O8RGs3lClZ0dJjKy6sC5nb15YJsrUW%2B1iFb61iZbbt2rY2er7Ec%2BRYhAADA5YyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhme8FKS0tTbm6ujh49avdLAwAA2ML2gnXHHXdo06ZNuvHGGzV16lS9//77qq2ttXsMAAAAy9hesDIzM7Vp0ya9/fbb6tq1q37zm98oNTVVixYt0qFDh%2BweBwAAwDi/PYPVs2dPZWdna8eOHXrsscf09ttv69Zbb9WUKVP0X//1X/4aCwAA4JL5rWCdPXtWmzZt0j333KPs7Gy1b99ejz76qHr06KHJkydrw4YN/hoNAADgkrjsfsHi4mKtWbNG69atU1VVlYYPH64//OEP6tevX8Mx/fv317x58zRq1Ci7xwMAALhktheskSNHqlOnTpo2bZpuv/12RUVFnXdMamqqjh8/bvdoAAAARthesFauXKkBAwZc9Lh9%2B/bZMA0AAIB5tj%2BD1b17d02fPl3btm1rWHvttdd0zz33yOPx2D0OAACAcbYXrAULFujkyZPq0qVLw9qQIUNUX1%2BvhQsX2j0OAACAcba/RfjRRx9pw4YNio6ObliLj49XTk6ObrvtNrvHAQAAMM72O1g1NTUKDQ09f5DgYFVXV9s9DgAAgHG2F6z%2B/ftr4cKFOnHiRMPaN998o6efftrnoxoAAACcyva3CB977DH96le/0vXXX6/w8HDV19erqqpKHTp00Ouvv273OAAAAMbZXrA6dOig9957Tx9%2B%2BKEOHz6s4OBgderUSYMGDVJISIjd4wAAABhne8GSpObNm%2BvGG2/0x0sDAABYzvaCdeTIES1evFhffvmlampqztv/4IMP7B4JAADAKL88g1VaWqpBgwapVatWdr88AACA5WwvWAUFBfrggw8UExNj90sDAADYwvaPaWjTpg13rgAAQECzvWBNmzZNubm58nq9dr80AACALWx/i/DDDz/U559/rrVr1%2Bqqq65ScLBvx3vrrbfsHgkAAMAo2wtWeHi4Bg8ebPfLAgAA2Mb2grVgwQK7XxIAAMBWtj%2BDJUlfffWVli5dqkcffbRh7a9//as/RgEAADDO9oK1Z88ejR49Wu%2B//742btwo6bsPH73rrrv4kFEAABAQbC9YS5Ys0UMPPaQNGzYoKChI0nd/P%2BHChQu1bNkyu8cBAAAwzvaC9cUXXygjI0OSGgqWJI0YMULFxcV2jwMAAGCc7QWrdevWF/w7CEtLS9W8eXO7xwEAADDO9oLVt29f/eY3v1FlZWXD2qFDh5Sdna3rr7/e7nEAAACMs/1jGh599FHdfffdSk5OVl1dnfr27avq6mp17dpVCxcutHscAAAA42wvWFdccYU2btyoXbt26dChQ2rRooU6deqkG264weeZLAAAAKeyvWBJUrNmzXTjjTf646UBAAAsZ3vBSktL%2B8E7VU35LKzdu3crOztbycnJWrJkScP62rVr9dhjj6lZs2Y%2Bx69atUq9evVSfX29XnjhBW3cuFEVFRXq1auX5s2bpw4dOkiSPB6P5s2bp7/85S8KDg5Wamqq5s6dqxYtWjTxagEAwI%2BR7QXr1ltv9SlYdXV1OnTokNxut%2B6%2B%2B%2B5Gn%2Bfll1/WmjVr1LFjxwvu9%2B/fX6%2B//voF91atWqUNGzbo5ZdfVvv27bVkyRJlZmbq3XffVVBQkObOnaszZ85o48aNOnv2rB544AHl5OToiSeeaNrFAgCAHyXbC9acOXMuuL5161b9%2Bc9/bvR5QkNDtWbNGj333HM6ffp0k2bIz8/X5MmT1blzZ0lSVlaWkpOTtW/fPl111VXatm2b/vjHPyomJkaSdO%2B99%2BqBBx5Qdnb2eXfFAAAAvs8vz2BdyI033qgnn3xSTz75ZKOOv%2Buuu35w/%2BjRo/rlL3%2BpgoICRUREaObMmRozZoxqampUVFSkhISEhmPDw8PVsWNHud1unTx5UiEhIerevXvDfs%2BePXXq1Cl99dVXPuv/TGlpqcrKynzWXK5Wio2NbdS1NVZIiF/%2BKsl/mcvlnHnPZeu0jJ2AbK1FvtYhW%2BsEYraXTcHav3%2B/vF6vkXPFxMQoPj5eDz74oLp06aI//elPevjhhxUbG6trrrlGXq9XkZGRPl8TGRmp8vJyRUVFKTw83OdtzHPHlpeXN%2Br18/PzlZub67OWmZmpmTNnXuKVOVt0dJi/R2iyiIiW/h4hYJGttcjXOmRrnUDK1vaCNXHixPPWqqurVVxcrJtvvtnIawwZMkRDhgxp%2BPnIkSP1pz/9SWvXrm14i/KHytylFr309HSlpaX5rLlcrVReXnVJ5/0%2BpzV909dvpZCQYEVEtFRFRbXq6ur9PU5AIVtrka91yNY6Vmbrrz/c216w4uPjz/suwtDQUI0fP1533nmnZa8bFxengoICRUVFKTg4WB6Px2ff4/GoTZs2iomJUWVlperq6hQSEtKwJ0lt2rRp1GvFxsae93ZgWdlJ1db%2BuH9BOvH66%2BrqHTm3E5CttcjXOmRrnUDK1vaCZcentb/55puKjIzUrbfe2rBWXFysDh06KDQ0VF27dlVhYaEGDBggSaqoqNDhw4fVq1cvxcXFyev16uDBg%2BrZs6ckye12KyIiQp06dbJ8dgAA4Hy2F6x169Y1%2Btjbb7/9X3qNM2fO6Nlnn1WHDh107bXXauvWrfrwww/19ttvS5IyMjKUl5enwYMHq3379srJyVGPHj2UlJQkSRo%2BfLh%2B%2B9vf6vnnn9eZM2e0bNkyjR8/Xi7XZfPIGgAAuIzZ3hgef/xx1dfXn/ecU1BQkM9aUFDQDxasc2WotrZWkrRt2zZJ391tuuuuu1RVVaUHHnhAZWVluuqqq7Rs2TIlJiZK%2Bu45sLKyMk2aNElVVVVKTk72eSj9mWee0VNPPaVhw4apWbNmuu2225SVlWUmAAAAEPCCvKa%2Bda%2BR9uzZo1deeUXTp09X9%2B7d5fV69be//U0vv/yyfvGLXyg5Obnh2ObNm9s5mqXKyk4aP6fLFaybcnYbP69VNs%2B6wd8jNJrLFazo6DCVl1cFzPMAlwuytRb5WodsrWNltu3atTZ6vsbyyzNYeXl5at%2B%2BfcPaddddpw4dOmjKlCnauHGj3SMBAAAYZfv3%2BX/99dfnfQaVJEVERKikpMTucQAAAIyzvWDFxcVp4cKFPh/aWVFRocWLF%2Bvqq6%2B2exwAAADjbH%2BL8LHHHtPs2bOVn5%2BvsLAwBQcHq7KyUi1atNCyZcvsHgcAAMA42wvWoEGDtHPnTu3atUvHjh2T1%2BtV%2B/bt9bOf/UytW/vnQTQAAACT/PLBTi1bttSwYcN07NgxdejQwR8jAAAAWMb2Z7BqamqUnZ2tPn366JZbbpH03TNYU6dOVUVFhd3jAAAAGGd7wVq0aJEOHDignJwcBQf/78vX1dUpJyfH7nEAAACMs71gbd26VS%2B%2B%2BKJGjBjR8Jc%2BR0REaMGCBXr//fftHgcAAMA42wtWVVWV4uPjz1uPiYnRqVOn7B4HAADAONsL1tVXX60///nPkuTzdw9u2bJFP/nJT%2BweBwAAwDjbv4vw5z//ue6//37dcccdqq%2Bv16uvvqqCggJt3bpVjz/%2BuN3jAAAAGGd7wUpPT5fL5dIbb7yhkJAQvfTSS%2BrUqZNycnI0YsQIu8cBAAAwzvaCdfz4cd1xxx2644477H5pAAAAW9j%2BDNawYcN8nr0CAAAINLYXrOTkZG3evNnulwUAALCN7W8RXnnllXruueeUl5enq6%2B%2BWs2aNfPZX7x4sd0jAQAAGGV7wSoqKtI111wjSSovL7f75QEAACxnW8HKysrSkiVL9PrrrzesLVu2TJmZmXaNAAAAYAvbnsHavn37eWt5eXl2vTwAAIBtbCtYF/rOQb6bEAAABCLbCta5v9j5YmsAAABOZ/vHNAAAAAQ6ChYAAIBhtn0X4dmzZzV79uyLrvE5WAAAwOlsK1j9%2BvVTaWnpRdcAAACczraC9X8//woAACCQ8QwWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADDM0QVr9%2B7dSklJUVZW1nl7mzZt0qhRo9SnTx%2BNGzdOH330UcNefX29lixZomHDhql///6aMmWKjhw50rDv8Xg0a9YspaSkaNCgQXr88cdVU1NjyzUBAADnc2zBevnllzV//nx17NjxvL0DBw4oOztbc%2BbM0SeffKLJkyfrvvvu07FjxyRJq1at0oYNG5SXl6cdO3YoPj5emZmZ8nq9kqS5c%2BequrpaGzdu1DvvvKPi4mLl5OTYen0AAMC5HFuwQkNDtWbNmgsWrNWrVys1NVWpqakKDQ3V6NGj1a1bN61fv16SlJ%2Bfr8mTJ6tz584KDw9XVlaWiouLtW/fPv3jH//Qtm3blJWVpZiYGLVv31733nuv3nnnHZ09e9buywQAAA7k8vcA/6q77rrrn%2B4VFhYqNTXVZy0hIUFut1s1NTUqKipSQkJCw154eLg6duwot9utkydPKiQkRN27d2/Y79mzp06dOqWvvvrKZ/2fKS0tVVlZmc%2Bay9VKsbGxjb28RgkJcVY/drmcM%2B%2B5bJ2WsROQrbXI1zpka51AzNaxBeuHeDweRUZG%2BqxFRkaqqKhIJ06ckNfrveB%2BeXm5oqKiFB4erqCgIJ89SSovL2/U6%2Bfn5ys3N9dnLTMzUzNnzvxXLidgREeH%2BXuEJouIaOnvEQIW2VqLfK1DttYJpGwDsmBJanie6l/Zv9jXXkx6errS0tJ81lyuViovr7qk836f05q%2B6eu3UkhIsCIiWqqiolp1dfX%2BHiegkK21yNc6ZGsdK7P11x/uA7JgRUdHy%2BPx%2BKx5PB7FxMQoKipKwcHBF9xv06aNYmJiVFlZqbq6OoWEhDTsSVKbNm0a9fqxsbHnvR1YVnZStbU/7l%2BQTrz%2Burp6R87tBGRrLfK1DtlaJ5CyddYtkEZKTExUQUGBz5rb7Vbv3r0VGhqqrl27qrCwsGGvoqJChw8fVq9evdSjRw95vV4dPHjQ52sjIiLUqVMn264BAAA4V0AWrAkTJujjjz/Wzp07dfr0aa1Zs0Zff/21Ro8eLUnKyMjQypUrVVxcrMrKSuXk5KhHjx5KSkpSTEyMhg8frt/%2B9rc6fvy4jh07pmXLlmn8%2BPFyuQLyhh8AADDMsY3%2BCBjYAAAQCUlEQVQhKSlJklRbWytJ2rZtm6Tv7jZ169ZNOTk5WrBggUpKStSlSxetWLFC7dq1kyRNnDhRZWVlmjRpkqqqqpScnOzzUPozzzyjp556SsOGDVOzZs102223XfDDTAEAAC4kyHupT3SjUcrKTho/p8sVrJtydhs/r1U2z7rB3yM0mssVrOjoMJWXVwXM8wCXC7K1Fvlah2ytY2W27dq1Nnq%2BxgrItwgBAAD8iYIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADAsYAtW9%2B7dlZiYqKSkpIYfzz77rCRpz549Gj9%2BvPr27auRI0dq/fr1Pl%2B7cuVKDR8%2BXH379lVGRoYKCgr8cQkAAMChXP4ewEpbtmzRVVdd5bNWWlqqe%2B%2B9V48//rhGjRqlzz77TDNmzFCnTp2UlJSk7du3a%2BnSpfrd736n7t27a%2BXKlZo%2Bfbref/99tWrVyk9XAgAAnCRg72D9Mxs2bFB8fLzGjx%2Bv0NBQpaSkKC0tTatXr5Yk5efna9y4cerdu7datGihqVOnSpJ27Njhz7EBAICDBPQdrMWLF%2Buvf/2rKisrdcstt%2BiRRx5RYWGhEhISfI5LSEjQ5s2bJUmFhYW69dZbG/aCg4PVo0cPud1ujRw5slGvW1paqrKyMp81l6uVYmNjL/GKfIWEOKsfu1zOmfdctk7L2AnI1lrkax2ytU4gZhuwBeunP/2pUlJS9Pzzz%2BvIkSOaNWuWnn76aXk8HrVv397n2KioKJWXl0uSPB6PIiMjffYjIyMb9hsjPz9fubm5PmuZmZmaOXPmv3g1gSE6OszfIzRZRERLf48QsMjWWuRrHbK1TiBlG7AFKz8/v%2BGfO3furDlz5mjGjBnq16/fRb/W6/Ve0munp6crLS3NZ83laqXy8qpLOu/3Oa3pm75%2BK4WEBCsioqUqKqpVV1fv73ECCtlai3ytQ7bWsTJbf/3hPmAL1vddddVVqqurU3BwsDwej89eeXm5YmJiJEnR0dHn7Xs8HnXt2rXRrxUbG3ve24FlZSdVW/vj/gXpxOuvq6t35NxOQLbWIl/rkK11AilbZ90CaaT9%2B/dr4cKFPmvFxcVq3ry5UlNTz/vYhYKCAvXu3VuSlJiYqMLCwoa9uro67d%2B/v2EfAADgYgKyYLVp00b5%2BfnKy8vTmTNndOjQIb3wwgtKT0/XmDFjVFJSotWrV%2Bv06dPatWuXdu3apQkTJkiSMjIytG7dOu3du1fV1dVavny5mjdvriFDhvj3ogAAgGME5FuE7du3V15enhYvXtxQkMaOHausrCyFhoZqxYoVmj9/vp5%2B%2BmnFxcVp0aJFuvbaayVJgwcP1oMPPqhZs2bp22%2B/VVJSkvLy8tSiRQs/XxUAAHCKIO%2BlPtGNRikrO2n8nC5XsG7K2W38vFbZPOsGf4/QaC5XsKKjw1ReXhUwzwNcLsjWWuRrHbK1jpXZtmvX2uj5Gisg3yIEAADwJwoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMJe/BwAAANa45bf/n79HaLRPnxvh7xGM4g4WAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBesCSkpK9Otf/1rJyckaOnSoFi1apPr6en%2BPBQAAHMLl7wEuR/fff7969uypbdu26dtvv9W0adPUtm1b/fKXv/T3aAAAwAG4g/U9brdbBw8e1Jw5c9S6dWvFx8dr8uTJys/P9/doAADAIbiD9T2FhYWKi4tTZGRkw1rPnj116NAhVVZWKjw8/KLnKC0tVVlZmc%2Bay9VKsbGxRmcNCXFWP3a5nDPvuWydlrETkK21yNc6ZGu9QMqWgvU9Ho9HERERPmvnylZ5eXmjClZ%2Bfr5yc3N91u677z7df//95gbVd0Xu7iu%2BVHp6uvHy9mNXWlqqP/zhd2RrAbK1Fvlax4nZfvrcCH%2BP0CilpaVaunSpo7K9mMCpigZ5vd5L%2Bvr09HStXbvW50d6erqh6f5XWVmZcnNzz7tbhktHttYhW2uRr3XI1jqBmC13sL4nJiZGHo/HZ83j8SgoKEgxMTGNOkdsbGzANHAAANB03MH6nsTERB09elTHjx9vWHO73erSpYvCwsL8OBkAAHAKCtb3JCQkKCkpSYsXL1ZlZaWKi4v16quvKiMjw9%2BjAQAAhwiZN2/ePH8Pcbn52c9%2Bpo0bN%2BrZZ5/Ve%2B%2B9p/Hjx2vKlCkKCgry92jnCQsL04ABA7i7ZgGytQ7ZWot8rUO21gm0bIO8l/pENwAAAHzwFiEAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRSsy1xJSYl%2B/etfKzk5WUOHDtWiRYtUX19/wWNXrlyp4cOHq2/fvsrIyFBBQYHN0zpLU7J98803NXz4cPXp00djxozRtm3bbJ7WWZqS7TnffPON%2BvTpo6VLl9o0pXM1Jd/i4mJNmjRJvXv3Vmpqql577TV7h3WYxmZbX1%2BvF198UWlpaerTp49GjRqlTZs2%2BWFiZ9m9e7dSUlKUlZX1g8fV19dryZIlGjZsmPr3768pU6boyJEjNk1piBeXtbFjx3qfeOIJb0VFhffQoUPem2%2B%2B2fvKK6%2Bcd9wHH3zgve6667x79%2B71VldXe1esWOG94YYbvFVVVX6Y2hkam%2B2WLVu8/fr183766afeM2fOeN9%2B%2B21vz549vYcPH/bD1M7Q2Gz/r/vuu8/br18/74svvmjTlM7V2Hyrq6u9Q4YM8b788sveU6dOefft2%2BcdOXKkt6ioyA9TO0Njs33jjTe8gwYN8hYXF3tra2u927dv9yYkJHgPHDjgh6mdIS8vz3vzzTd7J06c6J01a9YPHrty5Urv0KFDvUVFRd6TJ096n3nmGe%2BoUaO89fX1Nk176biDdRlzu906ePCg5syZo9atWys%2BPl6TJ09Wfn7%2Becfm5%2Bdr3Lhx6t27t1q0aKGpU6dKknbs2GH32I7QlGxramr04IMPql%2B/fmrWrJnuvPNOhYWFae/evX6Y/PLXlGzP2bVrl4qKijRkyBD7BnWopuS7efNmhYeHa%2BrUqWrZsqV69eqljRs3qnPnzn6Y/PLXlGwLCwvVr18/XXPNNQoJCdHQoUMVFRWlv/3tb36Y3BlCQ0O1Zs0adezY8aLH5ufna/LkyercubPCw8OVlZWl4uJi7du3z4ZJzaBgXcYKCwsVFxenyMjIhrWePXvq0KFDqqysPO/YhISEhp8HBwerR48ecrvdts3rJE3JdsyYMfr5z3/e8POKigpVVVWpffv2ts3rJE3JVvquwD7zzDN66qmn5HK57BzVkZqS72effaZu3brp0Ucf1XXXXacRI0Zo/fr1do/sGE3JdsiQIfrLX/6iAwcO6MyZM/rggw9UXV2tAQMG2D22Y9x1111q3br1RY%2BrqalRUVGRz%2B9p4eHh6tixo6N%2BT6NgXcY8Ho8iIiJ81s79wi8vLz/v2P/7H4Vzx37/OHynKdn%2BX16vV0888YR69%2B7Nf0j/iaZmu2zZMv30pz/VwIEDbZnP6ZqS77Fjx/TBBx8oJSVFu3fv1rRp05Sdna39%2B/fbNq%2BTNCXbm2%2B%2BWenp6br99tuVlJSk2bNna8GCBbryyittmzdQnThxQl6v1/G/p/HHxcuc1%2Bu15Fg0Pa%2BzZ8/qkUceUVFRkVauXGnRVIGhsdkWFRVp9erV2rBhg8UTBZbG5uv1etWzZ0%2BNGjVKkjR27Fi99dZb2rJli8/dAfyvxma7bt06rVu3TqtXr1b37t21Z88ezZ49W1deeaV69epl8ZQ/Dk7/PY07WJexmJgYeTwenzWPx6OgoCDFxMT4rEdHR1/w2O8fh%2B80JVvpu1vW06ZN09///netWrVKbdu2tWtUx2lstl6vV/PmzdP999%2Bvdu3a2T2mYzXl39127dqd95ZMXFycysrKLJ/TiZqS7RtvvKH09HT16tVLoaGhGjJkiAYOHMhbsAZERUUpODj4gv9ftGnTxk9TNR0F6zKWmJioo0eP6vjx4w1rbrdbXbp0UVhY2HnHFhYWNvy8rq5O%2B/fvV%2B/evW2b10makq3X61VWVpZcLpdee%2B01RUdH2z2uozQ227///e/6z//8T7344otKTk5WcnKy3nvvPf3ud7/T2LFj/TG6IzTl393OnTvriy%2B%2B8LkTUFJSori4ONvmdZKmZFtfX6%2B6ujqftTNnztgyZ6ALDQ1V165dfX5Pq6io0OHDhx11d5CCdRlLSEhQUlKSFi9erMrKShUXF%2BvVV19VRkaGJGnEiBH69NNPJUkZGRlat26d9u7dq%2Brqai1fvlzNmzfnu7L%2BiaZku2HDBhUVFemFF15QaGioP8d2hMZme8UVV2jXrl169913G36kpaVp4sSJysvL8/NVXL6a8u/u6NGjVV5erpdeekk1NTXauHGjCgsLNXr0aH9ewmWrKdmmpaVpzZo1OnjwoGpra/XRRx9pz549GjZsmD8vwbG%2B%2BeYbjRgxouGzrjIyMrRy5UoVFxersrJSOTk56tGjh5KSkvw8aePxDNZl7sUXX9TcuXN1ww03KDw8XBMnTmz4jrZDhw7p1KlTkqTBgwfrwQcf1KxZs/Ttt98qKSlJeXl5atGihT/Hv6w1Ntt33nlHJSUl5z3UPmbMGM2fP9/2uZ2gMdmGhIToiiuu8Pm6li1bKjw8nLcML6Kx/%2B62b99eK1as0HPPPaf/%2BI//0E9%2B8hMtW7ZMV199tT/Hv6w1Nttp06aptrZWmZmZOn78uOLi4jR//nxdf/31/hz/snauHNXW1kpSwwc2u91unT17VocOHWq4Czhx4kSVlZVp0qRJqqqqUnJysnJzc/0z%2BL8oyOv0p8gAAAAuM7xFCAAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACG/f%2Bk9/3w6kWn%2BQAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-725797269478885061”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2939</td> <td class=”number”>91.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>193</td> <td class=”number”>6.0%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-725797269478885061”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2939</td> <td class=”number”>91.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>193</td> <td class=”number”>6.0%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2939</td> <td class=”number”>91.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>193</td> <td class=”number”>6.0%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FARM_TO_SCHOOL13”>FARM_TO_SCHOOL13<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>8.9%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>286</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.61799</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>34.8%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram7843680539760830756”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives7843680539760830756,#minihistogram7843680539760830756”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives7843680539760830756”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles7843680539760830756”

aria-controls=”quantiles7843680539760830756” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram7843680539760830756” aria-controls=”histogram7843680539760830756”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common7843680539760830756” aria-controls=”common7843680539760830756”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme7843680539760830756” aria-controls=”extreme7843680539760830756”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles7843680539760830756”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>1</td>

</tr> <tr>

<th>Q3</th> <td>1</td>

</tr> <tr>

<th>95-th percentile</th> <td>1</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr>

<th>Range</th> <td>1</td>

</tr> <tr>

<th>Interquartile range</th> <td>1</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.48596</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.78636</td>

</tr> <tr>

<th>Kurtosis</th> <td>-1.7651</td>

</tr> <tr>

<th>Mean</th> <td>0.61799</td>

</tr> <tr>

<th>MAD</th> <td>0.47216</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.48592</td>

</tr> <tr>

<th>Sum</th> <td>1807</td>

</tr> <tr>

<th>Variance</th> <td>0.23616</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram7843680539760830756”>

<img 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/h/zwww9q2LBhtc8fExNT5XGg03lS5eXW/IasqKi07NiXO7K1Dtlai3ytQ7bWCaRsA%2Bdh5y889dRT%2Bte//nXOcvXnP/9ZO3bs8ForKChQXFyc4uLiFBERofz8fM%2B2r776SmfPnlVCQoItswMAAP8XcAXrs88%2B05o1a5Sdna3IyMgq210ul5544gkdOHBAP/74o1555RUdOnRIw4YNU0hIiO68804tWbJER48eVVFRkf70pz/p5ptv9nwbBwAAgAvxy0eEiYmJkn76CkFJ2rJliyQpLy9Pq1at0smTJ9WnTx%2Bvz%2BnWrZteeeUVTZ06VdJP7165XC61bt1ar732mpo2bSpJmjhxokpLSzVkyBCVl5erT58%2Bevzxx226MgAAEAiC3Bf7Rjeqxek8afyYDkewoqLqq6ioNGCeWV8qyNY6ZGst8rWOP2b7u2f/5usRqm3nvIGWZNu4cQOjx6uugHtECAAA4GsULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACG2V6wUlJSlJWVpaNHj9p9agAAAFvYXrBuv/12bdiwQf369dPYsWO1efNmlZeX2z0GAACAZWwvWOnp6dqwYYPefvtttWnTRk899ZSSk5P1zDPP6ODBg3aPAwAAYJzP3sGKj4/X9OnT9cEHH2jGjBl6%2B%2B23dcstt2jMmDH64osvfDUWAADARfNZwSorK9OGDRt03333afr06WrSpIkeffRRtW/fXqNHj9batWt9NRoAAMBFcdh9woKCAq1cuVKrV69WaWmpBgwYoNdff11dunTx7NOtWzc9/vjjGjRokN3j%2BZ2uMzf6eoRqe2/yjb4eAQAAW9hesG699Va1bNlS48aN09ChQxUZGVlln%2BTkZJ04ccLu0QAAAIywvWDl5OTo%2Buuvv%2BB%2Bn3/%2BuQ3TAAAAmGf7O1jt2rXT%2BPHjtWXLFs/aa6%2B9pvvuu08ul8vucQAAAIyzvWDNnz9fJ0%2BeVOvWrT1rvXv3VmVlpRYsWGD3OAAAAMbZ/ojw448/1tq1axUVFeVZa9GihTIzM3XbbbfZPQ4AAIBxtt/BOnPmjEJDQ6sOEhys06dP2z0OAACAcbYXrG7dumnBggX64YcfPGvfffednnjiCa9v1QAAAOCvbH9EOGPGDN1777264YYbFBYWpsrKSpWWliouLk5/%2Bctf7B4HAADAONsLVlxcnNavX6%2BPPvpIhw4dUnBwsFq2bKmePXsqJCTE7nEAAACMs71gSVLt2rXVr18/X5waAADAcrYXrMOHD2vhwoX617/%2BpTNnzlTZvnXrVrtHAgAAMMon72AVFhaqZ8%2Beqlevnt2nBwAAsJztBWv37t3aunWroqOj7T41AACALWz/Ng0NGzbkzhUAAAhothescePGKSsrS263%2B6KPtX37dvXo0UMZGRlVtm3YsEGDBg1S586dNXz4cH388ceebZWVlVq0aJH69u2rbt26acyYMTp8%2BLBnu8vl0uTJk9WjRw/17NlTM2fOPOf7YgAAAOdi%2ByPCjz76SP/85z/1zjvv6Morr1RwsHfHe%2Butt6p1nGXLlmnlypVq3rx5lW179%2B7V9OnTlZWVpe7du2vTpk168MEHtXHjRjVt2lTLly/X2rVrtWzZMjVp0kSLFi1Senq63n33XQUFBWn27Nk6e/as1q1bp7KyMk2aNEmZmZmaNWuWkQwAAEBgs/0OVlhYmHr16qXk5GS1atVKLVu29PqortDQ0PMWrBUrVig5OVnJyckKDQ3V4MGD1bZtW61Zs0aSlJubq9GjR6tVq1YKCwtTRkaGCgoK9Pnnn%2Bv48ePasmWLMjIyFB0drSZNmuiBBx7QqlWrVFZWZiwHAAAQuGy/gzV//nwjxxk1atR5t%2BXn5ys5OdlrrUOHDsrLy9OZM2e0f/9%2BdejQwbMtLCxMzZs3V15enk6ePKmQkBC1a9fOsz0%2BPl6nTp3SgQMHvNbPp7CwUE6n02vN4ainmJiY6l5etYSE2N6PL4rD4T/z/pytv2XsD8jWWuRrHbK1XiBl65NvNHrgwAGtX79e3377radw/d///Z86d%2B5s5Pgul0sRERFeaxEREdq/f79%2B%2BOEHud3uc24vKipSZGSkwsLCFBQU5LVNkoqKiqp1/tzcXGVlZXmtpaena%2BLEif/J5QSMqKj6vh6hxsLD6/p6hIBFttYiX%2BuQrXUCKVvbC9aOHTt03333qWXLlvr66681f/58HT58WKNGjdKzzz6rvn37GjnPhV6i/63tF/sCfmpqqlJSUrzWHI56Kioqvajj/pq/NX3T12%2BlkJBghYfXVXHxaVVUVPp6nIBCttYiX%2BuQrfWsyNZXf7m3vWAtWrRIDz30kO655x517NhR0k8/n3DBggVavHixkYIVFRUll8vlteZyuRQdHa3IyEgFBwefc3vDhg0VHR2tkpISVVRUeH424s/7NmzYsFrnj4mJqfI40Ok8qfLyy/s3pD9ef0VFpV/O7Q/I1lrkax2ytU4gZWv7LZCvvvpKaWlpkuT1GG7gwIEqKCgwco6EhATt3r3bay0vL0%2BdOnVSaGio2rRpo/z8fM%2B24uJiHTp0SB07dlT79u3ldru1b98%2Br88NDw%2Bv0Uv4AADg8mV7wWrQoME5v6dUYWGhateubeQcd955p/7%2B97/rww8/1I8//qiVK1fq66%2B/1uDBgyVJaWlpysnJUUFBgUpKSpSZman27dsrMTFR0dHRGjBggJ599lmdOHFCx44d0%2BLFizVixAg5HD55ZQ0AAPgZ2xvDddddp6eeesrre0odPHhQjz32mG644YZqHycxMVGSVF5eLknasmWLpJ/uNrVt21aZmZmaP3%2B%2Bjhw5otatW2vp0qVq3LixJGnkyJFyOp26%2B%2B67VVpaqqSkJK%2BX0ufMmaPHHntMffv2Va1atXTbbbed85uZAgAAnEuQ28S3VK%2BBY8eO6Z577tE333yjiooK1atXT6dPn1abNm20ZMkSXXHFFXaOYxun86TxYzocwbo5c7vx41rlvck3%2BnqEanM4ghUVVV9FRaUB8z7ApYJsrUW%2B1vHHbH/37N98PUK17Zw30JJsGzduYPR41WX7HaymTZtq3bp12rZtmw4ePKg6deqoZcuWuvHGG73eyQIAAPBXPnmpqFatWurXr58vTg0AAGA52wtWSkrKb96p2rp1q43TAAAAmGd7wbrlllu8ClZFRYUOHjyovLw83XPPPXaPAwAAYJztBWvatGnnXN%2B0aZP%2B8Y9/2DwNAACAeZfMz1rp16%2Bf1q9f7%2BsxAAAALtolU7D27Nlz0T8DEAAA4FJg%2ByPCkSNHVlk7ffq0CgoK1L9/f7vHAQAAMM72gtWiRYsqX0UYGhqqESNG6I477rB7HAAAAONsL1gLFiyw%2B5QAAAC2sr1grV69utr7Dh061MJJAAAArGF7wZo5c6YqKyurvNAeFBTktRYUFETBAgAAfsn2gvXSSy/plVde0fjx49WuXTu53W59%2BeWXWrZsmf7rv/5LSUlJdo8EAABglE/ewcrOzlaTJk08a127dlVcXJzGjBmjdevW2T0SAACAUbZ/H6yvv/5aERERVdbDw8N15MgRu8cBAAAwzvaCFRsbqwULFqioqMizVlxcrIULF%2Bqqq66yexwAAADjbH9EOGPGDE2dOlW5ubmqX7%2B%2BgoODVVJSojp16mjx4sV2jwMAAGCc7QWrZ8%2Be%2BvDDD7Vt2zYdO3ZMbrdbTZo00U033aQGDRrYPQ4AAIBxthcsSapbt6769u2rY8eOKS4uzhcjAAAAWMb2d7DOnDmj6dOnq3Pnzvrd734n6ad3sMaOHavi4mK7xwEAADDO9oL1zDPPaO/evcrMzFRw8L9PX1FRoczMTLvHAQAAMM72grVp0yY9//zzGjhwoOeHPoeHh2v%2B/PnavHmz3eMAAAAYZ3vBKi0tVYsWLaqsR0dH69SpU3aPAwAAYJztBeuqq67SP/7xD0ny%2BtmDGzdu1BVXXGH3OAAAAMbZ/lWEd911lyZMmKDbb79dlZWVevXVV7V7925t2rRJM2fOtHscAAAA42wvWKmpqXI4HHrjjTcUEhKiJUuWqGXLlsrMzNTAgQPtHgcAAMA42wvWiRMndPvtt%2Bv222%2B3%2B9QAAAC2sP0drL59%2B3q9ewUAABBobC9YSUlJeu%2B99%2Bw%2BLQAAgG1sf0TYrFkzzZs3T9nZ2brqqqtUq1Ytr%2B0LFy60eyQAAACjbC9Y%2B/fv19VXXy1JKioqsvv0AAAAlrOtYGVkZGjRokX6y1/%2B4llbvHix0tPTjZ/r008/1b333uu15na7VVZWppycHI0aNUq1a9f22v7f//3fnp%2BNmJOTo%2BXLl8vpdKpdu3aaOXOmEhISjM8JAAACk20F6/3336%2Bylp2dbUnB6tatm/Ly8rzWlixZon379kmSYmNjzznPz3O%2B8MILeumll9SuXTvl5ORo/Pjx2rx5s%2BrVq2d8VgAAEHhse8n9XF85aNdXE3777bd69dVX9fDDD19w39zcXA0fPlydOnVSnTp1NHbsWEnSBx98YPWYAAAgQNhWsH7%2Bwc4XWrPCc889p9tvv93zo3hKS0uVnp6upKQk3XTTTXr11Vc9ZS8/P18dOnTwfG5wcLDat29f5Y4YAADA%2Bdj%2BkrvdvvnmG23evFmbN2%2BWJIWFhalt27a65557tGjRIn3yySeaNGmSGjRooBEjRsjlcikiIsLrGBERETV6Ib%2BwsFBOp9NrzeGop5iYmIu/oF8ICbH9u2xcFIfDf%2Bb9OVt/y9gfkK21yNc6ZGu9QMo24AvW8uXL1b9/fzVu3FiSFB8f7/Wifc%2BePTVy5Ei98847GjFihKSLf3SZm5urrKwsr7X09HRNnDjxoo7r76Ki6vt6hBoLD6/r6xECFtlai3ytQ7bWCaRsbStYZWVlmjp16gXXTH8frE2bNmn69Om/uU9sbKw2bdokSYqKipLL5fLa7nK51KZNm2qfMzU1VSkpKV5rDkc9FRWVVvsY1eFvTd/09VspJCRY4eF1VVx8WhUVlb4eJ6CQrbXI1zpkaz0rsvXVX%2B5tK1hdunRRYWHhBddM2rt3r44cOaIbb7zRs/bee%2B%2BpqKhId911l2ftwIEDiouLkyQlJCQoPz9fw4YNkyRVVFRoz549nrtb1RETE1PlcaDTeVLl5Zf3b0h/vP6Kikq/nNsfkK21yNc6ZGudQMrWtoL1y8dydtmzZ48iIyMVFhbmWatVq5aefvppXXXVVUpKStInn3yiVatW6emnn5YkpaWlacqUKbrtttvUrl07vfzyy6pdu7Z69%2B5t%2B/wAAMA/BfQ7WMePH/e8e/Wzfv36acaMGXryySd19OhRNWrUSDNmzFD//v0lSb169dKUKVM0efJkff/990pMTFR2drbq1Knji0sAAAB%2BKMht1zejusw5nSeNH9PhCNbNmduNH9cq702%2B8cI7XSIcjmBFRdVXUVFpwNyuvlSQrbXI1zr%2BmO3vnv2br0eotp3zBlqSbePGDYwer7r86y1pAAAAP0DBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIYFbMFq166dEhISlJiY6Pl48sknJUk7duzQiBEjdN111%2BnWW2/VmjVrvD43JydHAwYM0HXXXae0tDTt3r3bF5cAAAD8lMPXA1hp48aNuvLKK73WCgsL9cADD2jmzJkaNGiQPvvsM91///1q2bKlEhMT9f777%2BuFF17QSy%2B9pHbt2iknJ0fjx4/X5s2bVa9ePR9dCQAA8CcBewfrfNauXasWLVpoxIgRCg0NVY8ePZSSkqIVK1ZIknJzczV8%2BHB16tRJderU0dixYyVJH3zwgS/HBgAAfiSgC9bChQvVu3dvde3aVbNnz1Zpaany8/PVoUMHr/06dOjgeQz46%2B3BwcFq37698vLybJ0dAAD4r4B9RHjttdeqR48eevrpp3X48GFNnjxZTzzxhFwul5o0aeK1b2RkpIqKiiRJLpdLERERXtsjIiI826ujsLBQTqfTa83hqKeYmJj/8GrOLSTEv/qxw%2BE/8/6crb9l7A/I1lrkax2ytV4gZRuwBSs3N9fzv1u1aqVp06bp/vtbCOp8AAAQtElEQVTvV5cuXS74uW63%2B6LPnZWV5bWWnp6uiRMnXtRx/V1UVH1fj1Bj4eF1fT1CwCJba5GvdcjWOoGUbcAWrF%2B78sorVVFRoeDgYLlcLq9tRUVFio6OliRFRUVV2e5yudSmTZtqnys1NVUpKSleaw5HPRUVlf6H05%2BbvzV909dvpZCQYIWH11Vx8WlVVFT6epyAQrbWIl/rkK31rMjWV3%2B5D8iCtWfPHq1Zs0aPPPKIZ62goEC1a9dWcnKy/ud//sdr/927d6tTp06SpISEBOXn52vYsGGSpIqKCu3Zs0cjRoyo9vljYmKqPA50Ok%2BqvPzy/g3pj9dfUVHpl3P7A7K1Fvlah2ytE0jZ%2BtctkGpq2LChcnNzlZ2drbNnz%2BrgwYN67rnnlJqaqiFDhujIkSNasWKFfvzxR23btk3btm3TnXfeKUlKS0vT6tWrtWvXLp0%2BfVovvviiateurd69e/v2ogAAgN8IyDtYTZo0UXZ2thYuXOgpSMOGDVNGRoZCQ0O1dOlSzZ07V0888YRiY2P1zDPP6JprrpEk9erVS1OmTNHkyZP1/fffKzExUdnZ2apTp46PrwoAAPiLgCxYktStWze99dZb59327rvvnvdz77rrLt11111WjQYAAAJcQD4iBAAA8CUKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADAsYAvWkSNHlJ6erqSkJPXo0UOPPPKIiouL9c0336hdu3ZKTEz0%2Bnj55Zc9n7thwwYNGjRInTt31vDhw/Xxxx/78EoAAIC/cfh6AKuMHz9eCQkJev/993Xy5Emlp6fr6aef1v333y9JysvLO%2Bfn7d27V9OnT1dWVpa6d%2B%2BuTZs26cEHH9TGjRvVtGlTOy8BAAD4qYC8g1VcXKyEhARNnTpV9evXV9OmTTVs2DDt3Lnzgp%2B7YsUKJScnKzk5WaGhoRo8eLDatm2rNWvW2DA5AAAIBAFZsMLDwzV//nw1atTIs3b06FHFxMR4fv3www%2BrZ8%2Be6t69uxYuXKiysjJJUn5%2Bvjp06OB1vA4dOpz3jhcAAMCvBewjwl/Ky8vTG2%2B8oRdffFG1a9dW586ddfPNN2vevHnau3evJkyYIIfDoUmTJsnlcikiIsLr8yMiIrR///5qn6%2BwsFBOp9NrzeGo51XwTAgJ8a9%2B7HD4z7w/Z%2BtvGfsDsrUW%2BVqHbK0XSNkGfMH67LPPdP/992vq1Knq0aOHJOmtt97ybO/YsaPGjRunpUuXatKkSZIkt9t9UefMzc1VVlaW11p6eromTpx4Ucf1d1FR9X09Qo2Fh9f19QgBi2ytRb7WIVvrBFK2AV2w3n//fT300EOaPXu2hg4det79YmNjdfz4cbndbkVFRcnlcnltd7lcio6OrvZ5U1NTlZKS4rXmcNRTUVFpzS7gAvyt6Zu%2BfiuFhAQrPLyuiotPq6Ki0tfjBBSytRb5WodsrWdFtr76y33AFqx//vOfmj59up577jn17NnTs75jxw7t2rXL89WEknTgwAHFxsYqKChICQkJ2r17t9ex8vLydOutt1b73DExMVUeBzqdJ1Vefnn/hvTH66%2BoqPTLuf0B2VqLfK1DttYJpGz96xZINZWXl2vWrFmaNm2aV7mSpAYNGmjx4sV69913VVZWpry8PL388stKS0uTJN155536%2B9//rg8//FA//vijVq5cqa%2B//lqDBw/2xaUAAAA/FJB3sHbt2qWCggLNnTtXc%2BfO9dq2ceNGLVq0SFlZWfrjH/%2BoBg0a6O6779Y999wjSWrbtq0yMzM1f/58HTlyRK1bt9bSpUvVuHFjX1wKAADwQwFZsLp27aovv/zyvNtjY2N18803n3d7//791b9/fytGAwAAl4GAfEQIAADgSxQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAK1jkcOXJEf/jDH5SUlKQ%2BffromWeeUWVlpa/HAgAAfsLh6wEuRRMmTFB8fLy2bNmi77//XuPGjVOjRo30%2B9//3tejAQAAP8AdrF/Jy8vTvn37NG3aNDVo0EAtWrTQ6NGjlZub6%2BvRAACAn%2BAO1q/k5%2BcrNjZWERERnrX4%2BHgdPHhQJSUlCgsLu%2BAxCgsL5XQ6vdYcjnqKiYkxOmtIiH/1Y4fDf%2Bb9OVt/y9gfkK21yNc6ZGu9QMqWgvUrLpdL4eHhXms/l62ioqJqFazc3FxlZWV5rT344IOaMGGCuUH1U5G7p%2Bm/lJqaary8Xe4KCwv1%2Busvka0FyNZa5Gsdf8x257yBvh6hWgoLC/XCCy/4VbYXEjhV0SC3231Rn5%2Bamqp33nnH6yM1NdXQdP/mdDqVlZVV5W4ZLh7ZWodsrUW%2B1iFb6wRittzB%2BpXo6Gi5XC6vNZfLpaCgIEVHR1frGDExMQHTwAEAQM1xB%2BtXEhISdPToUZ04ccKzlpeXp9atW6t%2B/fo%2BnAwAAPgLCtavdOjQQYmJiVq4cKFKSkpUUFCgV199VWlpab4eDQAA%2BImQxx9//HFfD3Gpuemmm7Ru3To9%2BeSTWr9%2BvUaMGKExY8YoKCjI16NVUb9%2BfV1//fXcXbMA2VqHbK1FvtYhW%2BsEWrZB7ot9oxsAAABeeEQIAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgF6xJ35MgR/eEPf1BSUpL69OmjZ555RpWVlefcNycnRwMGDNB1112ntLQ07d692%2BZp/UtNsn3zzTc1YMAAde7cWUOGDNGWLVtsnta/1CTbn3333Xfq3LmzXnjhBZum9F81ybegoEB33323OnXqpOTkZL322mv2DutnqpttZWWlnn/%2BeaWkpKhz584aNGiQNmzY4IOJ/cv27dvVo0cPZWRk/OZ%2BlZWVWrRokfr27atu3bppzJgxOnz4sE1TGuLGJW3YsGHuWbNmuYuLi90HDx509%2B/f3/3KK69U2W/r1q3url27unft2uU%2Bffq0e%2BnSpe4bb7zRXVpa6oOp/UN1s924caO7S5cu7p07d7rPnj3rfvvtt93x8fHuQ4cO%2BWBq/1DdbH/pwQcfdHfp0sX9/PPP2zSl/6puvqdPn3b37t3bvWzZMvepU6fcn3/%2BufvWW29179%2B/3wdT%2B4fqZvvGG2%2B4e/bs6S4oKHCXl5e733//fXeHDh3ce/fu9cHU/iE7O9vdv39/98iRI92TJ0/%2BzX1zcnLcffr0ce/fv9998uRJ95w5c9yDBg1yV1ZW2jTtxeMO1iUsLy9P%2B/bt07Rp09SgQQO1aNFCo0ePVm5ubpV9c3NzNXz4cHXq1El16tTR2LFjJUkffPCB3WP7hZpke%2BbMGU2ZMkVdunRRrVq1dMcdd6h%2B/fratWuXDya/9NUk259t27ZN%2B/fvV%2B/eve0b1E/VJN/33ntPYWFhGjt2rOrWrauOHTtq3bp1atWqlQ8mv/TVJNv8/Hx16dJFV199tUJCQtSnTx9FRkbqyy%2B/9MHk/iE0NFQrV65U8%2BbNL7hvbm6uRo8erVatWiksLEwZGRkqKCjQ559/bsOkZlCwLmH5%2BfmKjY1VRESEZy0%2BPl4HDx5USUlJlX07dOjg%2BXVwcLDat2%2BvvLw82%2Bb1JzXJdsiQIbrrrrs8vy4uLlZpaamaNGli27z%2BpCbZSj8V2Dlz5uixxx6Tw%2BGwc1S/VJN8P/vsM7Vt21aPPvqounbtqoEDB2rNmjV2j%2Bw3apJt79699cknn2jv3r06e/astm7dqtOnT%2Bv666%2B3e2y/MWrUKDVo0OCC%2B505c0b79%2B/3%2BjMtLCxMzZs396s/0yhYlzCXy6Xw8HCvtZ9/4xcVFVXZ95f/Ufh531/vh5/UJNtfcrvdmjVrljp16sR/SM%2BjptkuXrxY1157rbp3727LfP6uJvkeO3ZMW7duVY8ePbR9%2B3aNGzdO06dP1549e2yb15/UJNv%2B/fsrNTVVQ4cOVWJioqZOnar58%2BerWbNmts0bqH744Qe53W6//zONvy5e4txutyX7ouZ5lZWV6ZFHHtH%2B/fuVk5Nj0VSBobrZ7t%2B/XytWrNDatWstniiwVDdft9ut%2BPh4DRo0SJI0bNgwvfXWW9q4caPX3QH8W3WzXb16tVavXq0VK1aoXbt22rFjh6ZOnapmzZqpY8eOFk95efD3P9O4g3UJi46Olsvl8lpzuVwKCgpSdHS013pUVNQ59/31fvhJTbKVfrplPW7cOH377bdavny5GjVqZNeofqe62brdbj3%2B%2BOOaMGGCGjdubPeYfqsm/%2B42bty4yiOZ2NhYOZ1Oy%2Bf0RzXJ9o033lBqaqo6duyo0NBQ9e7dW927d%2BcRrAGRkZEKDg4%2B5/8XDRs29NFUNUfBuoQlJCTo6NGjOnHihGctLy9PrVu3Vv369avsm5%2Bf7/l1RUWF9uzZo06dOtk2rz%2BpSbZut1sZGRlyOBx67bXXFBUVZfe4fqW62X777bf69NNP9fzzzyspKUlJSUlav369XnrpJQ0bNswXo/uFmvy726pVK3311VdedwKOHDmi2NhY2%2Bb1JzXJtrKyUhUVFV5rZ8%2BetWXOQBcaGqo2bdp4/ZlWXFysQ4cO%2BdXdQQrWJaxDhw5KTEzUwoULVVJSooKCAr366qtKS0uTJA0cOFA7d%2B6UJKWlpWn16tXatWuXTp8%2BrRdffFG1a9fmq7LOoybZrl27Vvv379dzzz2n0NBQX47tF6qbbdOmTbVt2za9%2B%2B67no%2BUlBSNHDlS2dnZPr6KS1dN/t0dPHiwioqKtGTJEp05c0br1q1Tfn6%2BBg8e7MtLuGTVJNuUlBStXLlS%2B/btU3l5uT7%2B%2BGPt2LFDffv29eUl%2BK3vvvtOAwcO9Hyvq7S0NOXk5KigoEAlJSXKzMxU%2B/btlZiY6ONJq493sC5xzz//vGbPnq0bb7xRYWFhGjlypOcr2g4ePKhTp05Jknr16qUpU6Zo8uTJ%2Bv7775WYmKjs7GzVqVPHl%2BNf0qqb7apVq3TkyJEqL7UPGTJEc%2BfOtX1uf1CdbENCQtS0aVOvz6tbt67CwsJ4ZHgB1f13t0mTJlq6dKnmzZunP//5z7riiiu0ePFiXXXVVb4c/5JW3WzHjRun8vJypaen68SJE4qNjdXcuXN1ww03%2BHL8S9rP5ai8vFySPN%2BwOS8vT2VlZTp48KDnLuDIkSPldDp19913q7S0VElJScrKyvLN4P%2BhILe/v0UGAABwieERIQAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAY9v882xT7ljlG%2BwAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common7843680539760830756”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>1807</td> <td class=”number”>56.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1117</td> <td class=”number”>34.8%</td> <td>

<div class=”bar” style=”width:62%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>286</td> <td class=”number”>8.9%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme7843680539760830756”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1117</td> <td class=”number”>34.8%</td> <td>

<div class=”bar” style=”width:62%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>1807</td> <td class=”number”>56.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1117</td> <td class=”number”>34.8%</td> <td>

<div class=”bar” style=”width:62%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>1807</td> <td class=”number”>56.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FDPIR12”>FDPIR12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>18</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.14974</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>22</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>90.7%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-6094336966893244097”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-6094336966893244097,#minihistogram-6094336966893244097”
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</div> <div class=”row collapse col-md-12” id=”descriptives-6094336966893244097”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-6094336966893244097”

aria-controls=”quantiles-6094336966893244097” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-6094336966893244097” aria-controls=”histogram-6094336966893244097”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-6094336966893244097” aria-controls=”common-6094336966893244097”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-6094336966893244097” aria-controls=”extreme-6094336966893244097”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-6094336966893244097”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>0</td>

</tr> <tr>

<th>95-th percentile</th> <td>1</td>

</tr> <tr>

<th>Maximum</th> <td>22</td>

</tr> <tr>

<th>Range</th> <td>22</td>

</tr> <tr>

<th>Interquartile range</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.95921</td>

</tr> <tr>

<th>Coef of variation</th> <td>6.4056</td>

</tr> <tr>

<th>Kurtosis</th> <td>239.91</td>

</tr> <tr>

<th>Mean</th> <td>0.14974</td>

</tr> <tr>

<th>MAD</th> <td>0.27836</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>13.751</td>

</tr> <tr>

<th>Sum</th> <td>469</td>

</tr> <tr>

<th>Variance</th> <td>0.92008</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-6094336966893244097”>

<img 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asWOHkpOTlZeXJ4/H48/lAgCAIBKUAau6ulppaWmaOXOmIiMjdckll2jkyJF67733tHPnTp0%2BfVqTJ09W27ZtlZqaqttvv10lJSWSpDVr1igrK0tZWVmKiIjQiBEj1KNHD61bt06SVFJSovHjx6tr166KiopSfn6%2BKioqtG/fPn8uGQAABJGgDFh2u10LFy5Uu3btvGOHDx9WYmKiysvL1bNnT4WFhXnnUlJSVFZWJkkqLy9XSkqKz/FSUlLkcDh04sQJHThwwGc%2BKipKnTt3lsPhaOZVAQCAlsLm7wJMcDgceumll7RixQpt2rRJdrvdZz42NlZut1uNjY1yu92KiYnxmY%2BJidGBAwf05ZdfyuPxnHPe5XI1uZ6qqio5nU6fMZutrRITE3/gys4vLCy48rHNFlz1/hBnehFsPWmJ6EXgoBeBhX5YK%2BgD1vvvv6/Jkydr5syZyszM1KZNm875upCQEO9/f99%2Bqgvdb1VSUqKioiKfsby8PE2bNu2Cjhvs4uIi/V1Cs7PbL/Z3CfgGvQgc9CKw0A9rBHXA2r59ux544AHNnTtXt956qyQpPj5ehw4d8nmd2%2B1WbGysQkNDFRcXJ7fbfdZ8fHy89zXnmk9ISGhyXTk5OcrOzvYZs9nayuWq/QGr%2B37B9inE9PoDSVhYqOz2i1VdXaeGhkZ/l9Oq0YvAQS8CS2vth78%2B3AdtwPrggw9UUFCgZcuWacCAAd7xtLQ0vfzyy6qvr5fN9vXyHA6H%2BvTp450/sx/rDIfDoWHDhikiIkLdu3dXeXm5rrrqKklfb6j/7LPP1Lt37ybXlpiYeNbtQKfzuOrrW88X9Lm0hvU3NDS2inUGA3oROOhFYKEf1giuSyDfqK%2Bv1yOPPKJZs2b5hCtJysrKUlRUlFasWKG6ujrt27dPpaWlys3NlSSNGTNGb7/9tnbu3KmTJ0%2BqtLRUhw4d0ogRIyRJubm5WrVqlSoqKlRTU6PCwkL16tVL6enplq8TAAAEJ8uvYGVnZ2vUqFG67bbbdOmll/6oY%2Bzdu1cVFRVasGCBFixY4DO3efNmPfPMM3rsscdUXFysdu3aKT8/X4MGDZIk9ejRQ4WFhVq4cKEqKyvVrVs3rVy5Uu3bt5ckjR07Vk6nU3fccYdqa2uVkZFx1n4qAACA8wnxWPwEzeXLl%2BuNN97QP//5T11zzTUaM2aMsrOzvbfzWiqn87jxY9psoRpSuNv4cZvLphnX%2BruEZmOzhSouLlIuVy2X3v2MXgQOehFYWms/2reP9st5Lb9FmJeXp40bN%2BqVV15R9%2B7d9etf/1pZWVlavHixDh48aHU5AAAAxvltD1ZqaqoKCgq0Y8cOzZ49W6%2B88opuuukmTZgwQR9%2B%2BKG/ygIAALhgfgtYp0%2Bf1saNG3XPPfeooKBAHTp00MMPP6xevXpp/PjxWr9%2Bvb9KAwAAuCCWb3yqqKhQaWmpXnvtNdXW1mro0KH6wx/%2BoH79%2Bnlf079/f82bN0/Dhw%2B3ujwAAIALZnnAGjZsmLp06aKJEyfq1ltvVWxs7FmvycrK0rFjx6wuDQAAwAjLA9aqVau8D/E8n3379llQDQAAgHmW78Hq2bOnJk2apG3btnnH/vM//1P33HPPWb%2BiBgAAIBhZHrAWLlyo48ePq1u3bt6xQYMGqbGxUYsWLbK6HAAAAOMsv0X41ltvaf369YqLi/OOJScnq7CwUDfffLPV5QAAABhn%2BRWsEydOKCIi4uxCQkNVV1dndTkAAADGWR6w%2Bvfvr0WLFunLL7/0jv3rX//S448/7vOoBgAAgGBl%2BS3C2bNn6%2B6779Y111yjqKgoNTY2qra2Vp06ddKLL75odTkAAADGWR6wOnXqpDfeeENvvvmmPvvsM4WGhqpLly4aMGCAwsLCrC4HAADAOMsDliSFh4fr%2Buuv98epAQAAmp3lAevzzz/XkiVL9Mknn%2BjEiRNnzf/lL3%2BxuiQAAACj/LIHq6qqSgMGDFDbtm2tPj0AAECzszxglZWV6S9/%2BYvi4%2BOtPjUAAIAlLH9MQ0JCAleuAABAi2Z5wJo4caKKiork8XisPjUAAIAlLL9F%2BOabb%2BqDDz7Q2rVr1bFjR4WG%2Bma8P/7xj1aXBAAAYJTlASsqKkoDBw60%2BrQAAACWsTxgLVy40OpTAgAAWMryPViS9Omnn%2Brpp5/Www8/7B377//%2Bb3%2BUAgAAYJzlAWvPnj0aMWKEtm7dqg0bNkj6%2BuGj48aN4yGjAACgRbA8YC1dulQPPPCA1q9fr5CQEElf/37CRYsWafny5VaXAwAAYJzlAesf//iHcnNzJckbsCTpxhtvVEVFhdXlAAAAGGd5wIqOjj7n7yCsqqpSeHi41eUAAAAYZ3nA6tu3r37961%2BrpqbGO3bw4EEVFBTommuusbocAAAA4yx/TMPDDz%2BsO%2B%2B8UxkZGWpoaFDfvn1VV1en7t27a9GiRVaXAwAAYJzlAeuSSy7Rhg0btGvXLh08eFBt2rRRly5ddO211/rsyQIAAAhWlgcsSbrooot0/fXX%2B%2BPUAAAAzc7ygJWdnX3eK1U8CwsAAAQ7ywPWTTfd5BOwGhoadPDgQTkcDt15551WlwMAAGCc5QFr1qxZ5xzfsmWL/vrXv1pcDQAAgHl%2B%2BV2E53L99dfrjTfe8HcZAAAAFyxgAtZHH30kj8fj7zIAAAAumOW3CMeOHXvWWF1dnSoqKnTDDTdYXQ4AAIBxlges5OTks36KMCIiQqNHj9btt99udTkAAADGWR6weFo7AABo6SwPWK%2B99lqTX3vrrbeed3737t0qKChQRkaGli5d6h1fu3atZs%2BerYsuusjn9atXr1bv3r3V2NioZcuWacOGDaqurlbv3r01b948derUSZLkdrs1b948/e1vf1NoaKiysrI0d%2B5ctWnT5gesFAAAtFaWB6w5c%2BaosbHxrA3tISEhPmMhISHnDVjPPvusSktL1blz53PO9%2B/fXy%2B%2B%2BOI551avXq3169fr2WefVYcOHbR06VLl5eXp9ddfV0hIiObOnatTp05pw4YNOn36tKZPn67CwkI98sgjP2LFAACgtbH8pwife%2B45DRgwQKtXr9Z7772nd999Vy%2B99JIGDhyoZ599Vh9%2B%2BKE%2B/PBD7du377zHiYiIOG/AOp%2BSkhKNHz9eXbt2VVRUlPLz81VRUaF9%2B/bpiy%2B%2B0LZt25Sfn6/4%2BHh16NBBU6ZM0auvvqrTp0//2GUDAIBWxC97sIqLi9WhQwfv2JVXXqlOnTppwoQJ2rBhQ5OOM27cuPPOHz58WHfddZfKyspkt9s1bdo03XLLLTpx4oQOHDiglJQU72ujoqLUuXNnORwOHT9%2BXGFhYerZs6d3PjU1VV999ZU%2B/fRTn/HvUlVVJafT6TNms7VVYmJik9bWVGFhAfOUjSax2YKr3h/iTC%2BCrSctEb0IHPQisNAPa1kesA4dOqSYmJizxu12uyorK42cIz4%2BXsnJybr//vvVrVs3/fnPf9aDDz6oxMRE/eQnP5HH4zmrhpiYGLlcLsXGxioqKsrnJx3PvNblcjXp/CUlJSoqKvIZy8vL07Rp0y5wZcEtLi7S3yU0O7v9Yn%2BXgG/Qi8BBLwIL/bCG5QErKSlJixYt0vTp0xUXFydJqq6u1u9%2B9ztddtllRs4xaNAgDRo0yPv3YcOG6c9//rPWrl3r/VU953uo6YU%2B8DQnJ0fZ2dk%2BYzZbW7lctRd03G8Ltk8hptcfSMLCQmW3X6zq6jo1NDT6u5xWjV4EDnoRWFprP/z14d7ygDV79mzNnDlTJSUlioyMVGhoqGpqatSmTRstX7682c6blJSksrIyxcbGKjQ0VG6322fe7XYrISFB8fHxqqmpUUNDg8LCwrxzkpSQkNCkcyUmJp51O9DpPK76%2BtbzBX0urWH9DQ2NrWKdwYBeBA56EVjohzUsD1gDBgzQzp07tWvXLh05ckQej0cdOnTQz372M0VHRxs5x8svv6yYmBjddNNN3rGKigp16tRJERER6t69u8rLy3XVVVdJ%2BvoK2meffabevXsrKSlJHo9HH3/8sVJTUyVJDodDdrtdXbp0MVIfAABo2SwPWJJ08cUXa/DgwTpy5Ij32VMmnTp1SvPnz1enTp10%2BeWXa8uWLXrzzTf1yiuvSJJyc3NVXFysgQMHqkOHDiosLFSvXr2Unp4uSRo6dKh%2B%2B9vf6qmnntKpU6e0fPlyjR49WjabX/53AQCAIGN5Yjhx4oQee%2BwxvfHGG5KksrIyVVdX6/7779dvfvMb2e32Jh3nTBiqr6%2BXJG3btk3S11ebxo0bp9raWk2fPl1Op1MdO3bU8uXLlZaWJunr34fodDp1xx13qLa2VhkZGT6b0p944gk99thjGjx4sC666CLdfPPNys/PN/b/AAAAtGwhngvd0f0DzZ8/X%2B%2B%2B%2B66mTJmiBx98UB9%2B%2BKGqq6s1ffp0derUSU888YSV5VjG6Txu/Jg2W6iGFO42ftzmsmnGtf4uodnYbKGKi4uUy1XL3gY/oxeBg14Eltbaj/btzWw/%2BqEs/zG0LVu26He/%2B51uvPFG76MQ7Ha7Fi5cqK1bt1pdDgAAgHGWB6za2lolJyefNR4fH6%2BvvvrK6nIAAACMszxgXXbZZfrrX/8qyfd5U5s3b9a//du/WV0OAACAcZZvcv/FL36h%2B%2B67T7fddpsaGxv1wgsvqKysTFu2bNGcOXOsLgcAAMA4ywNWTk6ObDabXnrpJYWFhemZZ55Rly5dVFhYqBtvvNHqcgAAAIyzPGAdO3ZMt912m2677TarTw0AAGAJy/dgDR48%2BIJ/1x8AAEAgszxgZWRkaNOmTVafFgAAwDKW3yK89NJL9eSTT6q4uFiXXXaZLrroIp/5JUuWWF0SAACAUZYHrAMHDugnP/mJJMnlcll9egAAgGZnWcDKz8/X0qVL9eKLL3rHli9frry8PKtKAAAAsIRle7C2b99%2B1lhxcbFVpwcAALCMZQHrXD85yE8TAgCAlsiygHXmFzt/3xgAAECws/wxDQAAAC0dAQsAAMAwy36K8PTp05o5c%2Bb3jvEcLAAAEOwsC1j9%2BvVTVVXV944BAAAEO8sC1v9//hUAAEBLxh4sAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMC%2BqAtXv3bmVmZio/P/%2BsuY0bN2r48OG64oorNGrUKL311lveucbGRi1dulSDBw9W//79NWHCBH3%2B%2BefeebfbrRkzZigzM1MDBgzQnDlzdOLECUvWBAAAgl/QBqxnn31WCxYsUOfOnc%2Ba279/vwoKCjRr1iy98847Gj9%2BvKZOnaojR45IklavXq3169eruLhYO3bsUHJysvLy8uTxeCRJc%2BfOVV1dnTZs2KBXX31VFRUVKiwstHR9AAAgeAVtwIqIiFBpaek5A9aaNWuUlZWlrKwsRUREaMSIEerRo4fWrVsnSSopKdH48ePVtWtXRUVFKT8/XxUVFdq3b5%2B%2B%2BOILbdu2Tfn5%2BYqPj1eHDh00ZcoUvfrqqzp9%2BrTVywQAAEHI5u8Cfqxx48Z951x5ebmysrJ8xlJSUuRwOHTixAkdOHBAKSkp3rmoqCh17txZDodDx48fV1hYmHr27OmdT01N1VdffaVPP/3UZ/y7VFVVyel0%2BozZbG2VmJjY1OU1SVhYcOVjmy246v0hzvQi2HrSEtGLwEEvAgv9sFbQBqzzcbvdiomJ8RmLiYnRgQMH9OWXX8rj8Zxz3uVyKTY2VlFRUQoJCfGZkySXy9Wk85eUlKioqMhnLC8vT9OmTfsxy2kx4uIi/V1Cs7PbL/Z3CfgGvQgc9CKw0A9rtMiAJcm7n%2BrHzH/fe79PTk6OsrOzfcZstrZyuWov6LjfFmyfQkyvP5CEhYXKbr9Y1dV1amho9Hc5rRq9CBz0IrC01n7468N9iwxYcXFxcrvdPmNut1vx8fGKjY1VaGjoOecTEhIUHx%2BvmpoaNTQ0KCwszDsnSQkJCU06f2Ji4lm3A53O46qvbz1f0OfSGtbf0NDYKtYZDOhF4KAXgYV%2BWCO4LoE0UVpamsrKynzGHA6H%2BvTpo4iICHXv3l3l5eXeuerqan322Wfq3bu3evXqJY/Ho48//tjnvXa7XV26dLFsDQAAIHi1yIA1ZswYvf3229q5c6dOnjyp0tJSHTp0SCNGjJAk5ebmatWqVaqoqFBNTY0KCwvVq1cvpaenKz4%2BXkOHDtVvf/tbHTt2TEeOHNHy5cs1evRo2WwjH3IUAAAN30lEQVQt8oIfAAAwLGgTQ3p6uiSpvr5ekrRt2zZJX19t6tGjhwoLC7Vw4UJVVlaqW7duWrlypdq3by9JGjt2rJxOp%2B644w7V1tYqIyPDZ1P6E088occee0yDBw/WRRddpJtvvvmcDzMFAAA4lxDPhe7oRpM4nceNH9NmC9WQwt3Gj9tcNs241t8lNBubLVRxcZFyuWrZ2%2BBn9CJw0IvA0lr70b59tF/O2yJvEQIAAPgTAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADGuxAatnz55KS0tTenq698/8%2BfMlSXv27NHo0aPVt29fDRs2TOvWrfN576pVqzR06FD17dtXubm5Kisr88cSAABAkLL5u4DmtHnzZnXs2NFnrKqqSlOmTNGcOXM0fPhwvf/%2B%2B5o8ebK6dOmi9PR0bd%2B%2BXU8//bSee%2B459ezZU6tWrdKkSZO0detWtW3b1k8rAQAAwaTFXsH6LuvXr1dycrJGjx6tiIgIZWZmKjs7W2vWrJEklZSUaNSoUerTp4/atGmjX/3qV5KkHTt2%2BLNsAAAQRFp0wFqyZIkGDRqkK6%2B8UnPnzlVtba3Ky8uVkpLi87qUlBTvbcBvz4eGhqpXr15yOByW1g4AAIJXi71F%2BNOf/lSZmZl66qmn9Pnnn2vGjBl6/PHH5Xa71aFDB5/XxsbGyuVySZLcbrdiYmJ85mNiYrzzTVFVVSWn0%2BkzZrO1VWJi4o9czbmFhQVXPrbZgqveH%2BJML4KtJy0RvQgc9CKw0A9rtdiAVVJS4v3vrl27atasWZo8ebL69ev3ve/1eDwXfO6ioiKfsby8PE2bNu2Cjhvs4uIi/V1Cs7PbL/Z3CfgGvQgc9CKw0A9rtNiA9W0dO3ZUQ0ODQkND5Xa7feZcLpfi4%2BMlSXFxcWfNu91ude/evcnnysnJUXZ2ts%2BYzdZWLlftj6z%2B3ILtU4jp9QeSsLBQ2e0Xq7q6Tg0Njf4up1WjF4GDXgSW1toPf324b5EB66OPPtK6dev00EMPeccqKioUHh6urKws/elPf/J5fVlZmfr06SNJSktLU3l5uUaOHClJamho0EcffaTRo0c3%2BfyJiYln3Q50Oo%2Brvr71fEGfS2tYf0NDY6tYZzCgF4GDXgQW%2BmGN4LoE0kQJCQkqKSlRcXGxTp06pYMHD2rZsmXKycnRLbfcosrKSq1Zs0YnT57Url27tGvXLo0ZM0aSlJubq9dee0179%2B5VXV2dVqxYofDwcA0aNMi/iwIAAEGjRV7B6tChg4qLi7VkyRJvQBo5cqTy8/MVERGhlStXasGCBXr88ceVlJSkxYsX6/LLL5ckDRw4UPfff79mzJiho0ePKj09XcXFxWrTpo2fVwUAAIJFiOdCd3SjSZzO48aPabOFakjhbuPHbS6bZlzr7xKajc0Wqri4SLlctVx69zN6ETjoRWBprf1o3z7aL%2BdtkbcIAQAA/ImABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2CdQ2Vlpe69915lZGTouuuu0%2BLFi9XY2OjvsgAAQJCw%2BbuAQHTfffcpNTVV27Zt09GjRzVx4kS1a9dOd911l79LAwAAQYCA9S0Oh0Mff/yxXnjhBUVHRys6Olrjx4/XH/7wBwLWBfr5b//L3yX8IJtmXOvvEgAAQYqA9S3l5eVKSkpSTEyMdyw1NVUHDx5UTU2NoqKivvcYVVVVcjqdPmM2W1slJiYarTUsjDu8zSnYAuGfZ/3M3yUEhDPfF3x/%2BB%2B9CCz0w1oErG9xu92y2%2B0%2BY2fClsvlalLAKikpUVFRkc/Y1KlTdd9995krVF8HuTsv%2BUQ5OTnGwxt%2BmKqqKpWUlNCLAFBVVaU//OE5ehEA6EVgoR/WIsaeg8fjuaD35%2BTkaO3atT5/cnJyDFX3f5xOp4qKis66Wgbr0YvAQS8CB70ILPTDWlzB%2Bpb4%2BHi53W6fMbfbrZCQEMXHxzfpGImJiXw6AACgFeMK1rekpaXp8OHDOnbsmHfM4XCoW7duioyM9GNlAAAgWBCwviUlJUXp6elasmSJampqVFFRoRdeeEG5ubn%2BLg0AAASJsHnz5s3zdxGB5mc/%2B5k2bNig%2BfPn64033tDo0aM1YcIEhYSE%2BLu0s0RGRuqqq67i6loAoBeBg14EDnoRWOiHdUI8F7qjGwAAAD64RQgAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAErCFVWVuree%2B9VRkaGrrvuOi1evFiNjY3%2BLqvV6tmzp9LS0pSenu79M3/%2BfH%2BX1Srs3r1bmZmZys/PP2tu48aNGj58uK644gqNGjVKb731lh8qbF2%2Bqx9r167V5Zdf7vM9kp6erg8//NBPlbZslZWVysvLU0ZGhjIzM/XQQw%2BpurpakrR//3798pe/VL9%2B/XTDDTfo%2Beef93O1LZfN3wXgh7vvvvuUmpqqbdu26ejRo5o4caLatWunu%2B66y9%2BltVqbN29Wx44d/V1Gq/Lss8%2BqtLRUnTt3Pmtu//79KigoUFFRka6%2B%2Bmpt2bJFU6dO1ebNm3XJJZf4odqW73z9kKT%2B/fvrxRdftLiq1mnSpElKS0vT9u3bdfz4ceXl5empp57S3LlzNXHiRI0ZM0bFxcU6ePCg7r77bnXs2FE33HCDv8tucbiCFWQcDoc%2B/vhjzZo1S9HR0UpOTtb48eNVUlLi79IAS0VERHznP%2Bhr1qxRVlaWsrKyFBERoREjRqhHjx5at26dHyptHc7XD1inurpaaWlpmjlzpiIjI3XJJZdo5MiReu%2B997Rz506dPn1akydPVtu2bZWamqrbb7%2Bdfz%2BaCQEryJSXlyspKUkxMTHesdTUVB08eFA1NTV%2BrKx1W7JkiQYNGqQrr7xSc%2BfOVW1trb9LavHGjRun6Ojoc86Vl5crJSXFZywlJUUOh8OK0lql8/VDkg4fPqy77rpL/fv31%2BDBg/X6669bWF3rYbfbtXDhQrVr1847dvjwYSUmJqq8vFw9e/ZUWFiYdy4lJUVlZWX%2BKLXFI2AFGbfbLbvd7jN2Jmy5XC5/lNTq/fSnP1VmZqa2bt2qkpIS7d27V48//ri/y2rV3G63z4cQ6evvE75H/CM%2BPl7Jycl64IEH9F//9V%2B6//77NXv2bO3Zs8ffpbV4DodDL730kiZPnnzOfz9iY2PldrvZx9sMCFhByOPx%2BLsE/D8lJSW6/fbbFR4erq5du2rWrFnasGGDTp065e/SWjW%2BTwLHoEGD9NxzzyklJUXh4eEaNmyYhgwZorVr1/q7tBbt/fff14QJEzRz5kxlZmZ%2B5%2BtCQkIsrKr1IGAFmfj4eLndbp8xt9utkJAQxcfH%2B6kq/H8dO3ZUQ0ODjh496u9SWq24uLhzfp/wPRI4kpKSVFVV5e8yWqzt27fr3nvv1ezZszVu3DhJX//78e2ruG63W7GxsQoNJQ6Yxv/RIJOWlqbDhw/r2LFj3jGHw6Fu3bopMjLSj5W1Th999JEWLVrkM1ZRUaHw8HAlJib6qSqkpaWdta/E4XCoT58%2BfqqodXv55Ze1ceNGn7GKigp16tTJTxW1bB988IEKCgq0bNky3Xrrrd7xtLQ0/f3vf1d9fb13jO%2BL5kPACjIpKSlKT0/XkiVLVFNTo4qKCr3wwgvKzc31d2mtUkJCgkpKSlRcXKxTp07p4MGDWrZsmXJycnw2ksJaY8aM0dtvv62dO3fq5MmTKi0t1aFDhzRixAh/l9YqnTp1SvPnz5fD4dDp06e1YcMGvfnmmxo7dqy/S2tx6uvr9cgjj2jWrFkaMGCAz1xWVpaioqK0YsUK1dXVad%2B%2BfSotLeXfj2YS4mGjQtA5cuSI5s6dq7/97W%2BKiorS2LFjNXXqVO6j%2B8m7776rJUuW6O9//7vCw8M1cuRI5efnKyIiwt%2BltWjp6emS5P00brN9/Vi/Mz8puHXrVi1ZskSVlZXq1q2b5syZo/79%2B/un2FbgfP3weDxasWKFSktL5XQ61bFjRz344IO67rrr/FZvS/Xee%2B/p3//93xUeHn7W3ObNm1VbW6vHHntMZWVlateune655x794he/8EOlLR8BCwAAwDBuEQIAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYf8LD8cFv8pf6eMAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-6094336966893244097”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2911</td> <td class=”number”>90.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>152</td> <td class=”number”>4.7%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>31</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>11</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>15.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (7)</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-6094336966893244097”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2911</td> <td class=”number”>90.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>152</td> <td class=”number”>4.7%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>31</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>11</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>15.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>18.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>19.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>22.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FFR09”><s>FFR09</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_SNAPS16”><code>SNAPS16</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.95189</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FFR14”><s>FFR14</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_FFR09”><code>FFR09</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99882</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FFRPTH09”>FFRPTH09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2955</td>

</tr> <tr>

<th>Unique (%)</th> <td>92.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.56274</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>6.0883</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>4.4%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram6778464823414465149”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives6778464823414465149,#minihistogram6778464823414465149”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives6778464823414465149”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles6778464823414465149”

aria-controls=”quantiles6778464823414465149” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram6778464823414465149” aria-controls=”histogram6778464823414465149”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common6778464823414465149” aria-controls=”common6778464823414465149”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme6778464823414465149” aria-controls=”extreme6778464823414465149”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles6778464823414465149”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.10513</td>

</tr> <tr>

<th>Q1</th> <td>0.40478</td>

</tr> <tr>

<th>Median</th> <td>0.56605</td>

</tr> <tr>

<th>Q3</th> <td>0.70435</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.94875</td>

</tr> <tr>

<th>Maximum</th> <td>6.0883</td>

</tr> <tr>

<th>Range</th> <td>6.0883</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.29957</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.30023</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.53351</td>

</tr> <tr>

<th>Kurtosis</th> <td>43.309</td>

</tr> <tr>

<th>Mean</th> <td>0.56274</td>

</tr> <tr>

<th>MAD</th> <td>0.20323</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>3.1513</td>

</tr> <tr>

<th>Sum</th> <td>1762.5</td>

</tr> <tr>

<th>Variance</th> <td>0.090135</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram6778464823414465149”>

<img 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Xr9aqVau8Z748Hs8ZrZ2RkaE%2Bffr4jDkczVVeXnVG%2Bz1RY3gHYCfTr1dDEhYWqujoc1RRcVg1NbV2l9Pg0B//6E/d6I1/dvQnNjbCknVOFJQBq7a2VpMnT9aXX36pv/71r0pMTPS7fUJCgsrKyhQXFyfpp/u4IiL%2B9YL88MMPatmyZb3Xj4%2BPP%2BlyoMt1SNXV/LIFksbwetXU1DaK4zxd9Mc/%2BlM3euNfY%2BhPUJ4CefLJJ/X111%2BfMlz993//tzZt2uQzVlpaqsTERCUmJiomJkYlJSXeua%2B%2B%2BkrHjh1TcnKyJbUDAIDAF3QB65NPPtGqVatUUFCgFi1anDTvdrs1Y8YM7dixQ0ePHtXzzz%2BvXbt2aeDAgQoLC9Mdd9yhZ555Rnv37lV5ebn%2B9Kc/6dprr/V%2BjQMAAMAvCchLhCkpKZJ%2B%2BoSgJBUXF0uSnE6nli9frkOHDumaa67xeU6PHj30/PPPa8KECZJ%2BuvfK7XarU6dOeuGFF9SmTRtJUlZWlqqqqnTrrbequrpa11xzjaZPn27RkQEAgGAQ4jnTO7pRLy7XIeP7dDhCA%2B6TeYHkjfFX2V3CWeNwhCo2NkLl5VVBfx/E6aA//tGfutEb/%2BzoT6tWUZasc6Kgu0QIAABgNwIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDDLA1afPn2Un5%2BvvXv3Wr00AACAJSwPWLfffrvWrFmjfv366Z577tH69eu9f1MQAAAgGFgesDIzM7VmzRq98sor6ty5s5588kmlp6dr9uzZ2rlzp9XlAAAAGGfbPVjdunVTTk6O3nrrLU2ZMkWvvPKKBgwYoLvvvltffvmlXWUBAACcMdsC1vHjx7VmzRrde%2B%2B9ysnJUevWrTV58mR16dJFI0eO1Ouvv25XaQAAAGfEYfWCpaWlWrZsmV577TVVVVWpf//%2BevHFF3XZZZd5t%2BnRo4emT5%2Bum2%2B%2B2eryAAAAzpjlAevGG29Uhw4dNGrUKN12221q0aLFSdukp6fr4MGDVpcGAABghOUBa8mSJbriiit%2BcbsvvvjCgmoAAADMs/werKSkJI0ePVrFxcXesRdeeEH33nuv3G631eUAAAAYZ3nAys3N1aFDh9SpUyfvWO/evVVbW6tZs2ZZXQ4AAIBxll8ifO%2B99/T6668rNjbWO9a%2BfXvl5eXppptusrocAAAA4yw/g3XkyBGFh4efXEhoqA4fPmx1OQAAAMZZHrB69OihWbNm6YcffvCOff/995oxY4bPVzUAAAAEKssvEU6ZMkV33XWXfve73ykyMlK1tbWqqqpSYmKi/vKXv1hdDgAAgHGWB6zExEStXr1a7777rnbt2qXQ0FB16NBBPXv2VFhYmNXlAAAAGGd5wJKkpk2bql%2B/fnYsDQAAcNZZHrC%2B%2B%2B47zZkzR19//bWOHDly0vybb75pdUkAAABG2XIPVllZmXr27KnmzZtbvTwAAMBZZ3nA2rx5s958803FxcVZvTQAAIAlLP%2BahpYtW3LmCgAABDXLA9aoUaOUn58vj8dj9dIAAACWsPwS4bvvvqtPP/1UK1asUNu2bRUa6pvxXn755Xrva%2BPGjcrJyVFqaqrmzp3rM7dmzRotXLhQu3fvVocOHfTggw%2BqZ8%2BekqTa2lrNmzdPRUVFqqioUPfu3TV9%2BnQlJiZKktxut6ZPn64PP/xQoaGhSk9P17Rp09SsWbMzPHoAANAYWH4GKzIyUr169VJ6ero6duyoDh06%2BDzqa9GiRZo5c6batWt30tzWrVuVk5OjiRMn6h//%2BIdGjhypBx54QPv27ZMkvfTSS3r99ddVUFCgt956S%2B3bt1dmZqb3rNq0adN0%2BPBhFRUVafny5SotLVVeXp6ZBgAAgKBn%2BRms3NxcI/sJDw/XsmXL9MQTT%2Bjo0aM%2Bc6%2B%2B%2BqrS09OVnp4uSbrlllu0dOlSrVq1Svfdd58KCws1cuRIdezYUZKUnZ2t1NRUffHFF2rbtq2Ki4v1t7/9zXsj/v33369x48YpJydHTZo0MVI/AAAIXpafwZKkHTt2aP78%2BZo8ebJ37LPPPvtV%2Bxg%2BfLiioqJOOVdSUqKuXbv6jHXt2lVOp1NHjhzRN9984zMfGRmpdu3ayel0auvWrQoLC1NSUpJ3vlu3bvrxxx%2B1Y8eOX1UjAABonCw/g7Vp0ybde%2B%2B96tChg7799lvl5ubqu%2B%2B%2B0/Dhw/X000%2Brb9%2B%2BZ7yG2%2B1WTEyMz1hMTIy%2B%2BeYb/fDDD/J4PKecLy8vV4sWLRQZGamQkBCfOUkqLy%2Bv1/plZWVyuVw%2BYw5Hc8XHx5/O4dQpLMyWfNxoOBzB29%2Bff3b4GTo1%2BuMf/akbvfGvMfXH8oA1d%2B5cPfTQQxoxYoS6d%2B8u6ae/Tzhr1iwtWLDASMCS9IufUvQ3f6afcCwsLFR%2Bfr7PWGZmprKyss5ov7BWbGyE3SWcddHR59hdQoNGf/yjP3WjN/41hv5YHrC%2B%2BuorLV26VJJ8zhJdf/31mjJlipE1YmNj5Xa7fcbcbrfi4uLUokULhYaGnnK%2BZcuWiouLU2VlpWpqarx/fPrnbVu2bFmv9TMyMtSnTx%2BfMYejucrLq073kE6pMbwDsJPp16shCQsLVXT0OaqoOKyamlq7y2lw6I9/9Kdu9MY/O/pj15tlywNWVFSUjhw5oqZNm/qMl5WVnTR2upKTk7V582afMafTqRtvvFHh4eHq3LmzSkpKdMUVV0iSKioqtGvXLnXv3l0JCQnyeDzatm2bunXr5n1udHR0vT/lGB8ff9LlQJfrkKqr%2BWULJI3h9aqpqW0Ux3m66I9/9Kdu9Ma/xtAfy0%2BBXHrppXryySdVWVnpHdu5c6dycnL0u9/9zsgad9xxh95//329/fbbOnr0qJYtW6Zvv/1Wt9xyiyRp2LBhWrJkiUpLS1VZWam8vDx16dJFKSkpiouLU//%2B/fX000/r4MGD2rdvnxYsWKDBgwfL4bA8jwIAgABkeWKYPHmyRowYodTUVNXU1OjSSy/V4cOH1blzZ82aNave%2B0lJSZEkVVdXS5KKi4sl/XS26cILL1ReXp5yc3O1Z88ederUSc8%2B%2B6xatWolSRo6dKhcLpfuvPNOVVVVKTU11eeeqccee0yPPvqo%2BvbtqyZNmuimm25Sdna2qRYAAIAgF%2BKx4W/WHD9%2BXO%2B884527typZs2aqUOHDrrqqqt87skKNi7XIeP7dDhCdW3eRuP7xU/eGH%2BV3SWcNQ5HqGJjI1ReXhX0p%2BlPB/3xj/7Ujd74Z0d/WrU69Vc6nW22XPNq0qSJ%2BvXrZ8fSAAAAZ53lAatPnz5%2Bz1S9%2BeabFlYDAABgnuUBa8CAAT4Bq6amRjt37pTT6dSIESOsLgcAAMA4ywPWxIkTTzm%2Bbt06ffDBBxZXAwAAYF6D%2BabKfv36afXq1XaXAQAAcMYaTMDasmXLGf%2BJGgAAgIbA8kuEQ4cOPWns8OHDKi0t1XXXXWd1OQAAAMZZHrDat29/0qcIw8PDNXjwYA0ZMsTqcgAAAIyzPGD9mm9rBwAACESWB6zXXnut3tvedtttZ7ESAACAs8PygPXII4%2Botrb2pBvaQ0JCfMZCQkIIWAAAICBZHrCee%2B45Pf/88xo9erSSkpLk8Xi0fft2LVq0SP/5n/%2Bp1NRUq0sCAAAwypZ7sAoKCtS6dWvv2OWXX67ExETdfffdKioqsrokAAAAoyz/Hqxvv/1WMTExJ41HR0drz549VpcDAABgnOUBKyEhQbNmzVJ5ebl3rKKiQnPmzNH5559vdTkAAADGWX6JcMqUKZowYYIKCwsVERGh0NBQVVZWqlmzZlqwYIHV5QAAABhnecDq2bOn3n77bb3zzjvat2%2BfPB6PWrdurauvvlpRUVFWlwMAAGCc5QFLks455xz17dtX%2B/btU2Jioh0lAAAAnDWW34N15MgR5eTk6JJLLtENN9wg6ad7sO655x5VVFRYXQ4AAIBxlges2bNna%2BvWrcrLy1No6L%2BWr6mpUV5entXlAAAAGGd5wFq3bp3%2B67/%2BS9dff733jz5HR0crNzdX69evt7ocAAAA4ywPWFVVVWrfvv1J43Fxcfrxxx%2BtLgcAAMA4ywPW%2Beefrw8%2B%2BECSfP724Nq1a/Wb3/zG6nIAAACMs/xThL///e81duxY3X777aqtrdXixYu1efNmrVu3To888ojV5QAAABhnecDKyMiQw%2BHQ0qVLFRYWpmeeeUYdOnRQXl6err/%2BeqvLAQAAMM7ygHXw4EHdfvvtuv32261eGgAAwBKW34PVt29fn3uvAAAAgo3lASs1NVVvvPGG1csCAABYxvJLhOedd56eeOIJFRQU6Pzzz1eTJk185ufMmWN1SQAAAEZZHrC%2B%2BeYbXXDBBZKk8vJyq5cHAAA46ywLWNnZ2Zo7d67%2B8pe/eMcWLFigzMxMq0oAAACwhGX3YG3YsOGksYKCgrOy1kcffaSUlBSfR3JyspKSkvTBBx8oKSnppPl/vy9syZIl6t%2B/vy699FINGzZMmzdvPit1AgCA4GTZGaxTfXLwbH2asEePHnI6nT5jzzzzjLZt2yZJSkhIOGXgk34KgvPnz9dzzz2npKQkLVmyRKNHj9b69evVvHnzs1IvAAAILpadwfr5Dzv/0tjZ8H//939avHixHn744V/ctrCwUIMGDdJFF12kZs2a6Z577pEkvfXWW2e7TAAAECQs/5oGO8ybN0%2B33367928dVlVVKTMzU6mpqbr66qu1ePFi79m0kpISde3a1fvc0NBQdenS5aQzYgAAAHWx/FOEVtu9e7fWr1%2Bv9evXS5IiIyN14YUXasSIEZo7d64%2B/PBDjRs3TlFRURo8eLDcbrdiYmJ89hETE/OrPvFYVlYml8vlM%2BZwNFd8fPyZH9C/CQtrFPnYNg5H8Pb3558dfoZOjf74R3/qRm/8a0z9sSxgHT9%2BXBMmTPjFMdPfg/XSSy/puuuuU6tWrSRJ3bp18/kkY8%2BePTV06FCtWLFCgwcPlnTm94YVFhYqPz/fZywzM1NZWVlntF9YKzY2wu4Szrro6HPsLqFBoz/%2B0Z%2B60Rv/GkN/LAtYl112mcrKyn5xzLR169YpJyfH7zYJCQlat26dJCk2NlZut9tn3u12q3PnzvVeMyMjQ3369PEZcziaq7y8qt77qI/G8A7ATqZfr4YkLCxU0dHnqKLisGpqau0up8GhP/7Rn7rRG//s6I9db5YtC1j/ftbIKlu3btWePXt01VVXecfeeOMNlZeX6/e//713bMeOHUpMTJQkJScnq6SkRAMHDpQk1dTUaMuWLd6zW/URHx9/0uVAl%2BuQqqv5ZQskjeH1qqmpbRTHebroj3/0p270xr/G0J%2BgPgWyZcsWtWjRQpGRkd6xJk2a6KmnntJ7772n48eP63//93%2B1fPlyDRs2TJI0bNgwvfbaa/r88891%2BPBhLVy4UE2bNlXv3r1tOgoAABBogvom9/3793vvvfpZv379NGXKFD3%2B%2BOPau3evzj33XE2ZMkXXXXedJKlXr1568MEHNX78eB04cEApKSkqKChQs2bN7DgEAAAQgEI8Z%2BvbPuHD5TpkfJ8OR6iuzdtofL/4yRvjr/rljQKUwxGq2NgIlZdXBf1p%2BtNBf/yjP3WjN/7Z0Z9WraIsWedEQX2JEAAAwA4ELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGBY0AaspKQkJScnKyUlxft4/PHHJUmbNm3S4MGDdemll%2BrGG2/UqlWrfJ67ZMkS9e/fX5deeqmGDRumzZs323EIAAAgQDnsLuBsWrt2rdq2beszVlZWpvvvv1%2BPPPKIbr75Zn3yyScaM2aMOnTooJSUFG3YsEHz58/Xc889p6SkJC1ZskSjR4/W%2BvXr1bx5c5uOBAAABJKgPYNVl9dff13t27fX4MGDFR4errS0NPXp00evvvqqJKmwsFCDBg3SRRddpGbNmumee%2B6RJL311lt2lg0AAAJIUJ/BmjNnjj777DNVVlbqhhtu0KRJk1RSUqKuXbv6bNe1a1e98cYbkqSSkhINGDDAOxcaGqouXbrI6XTqxhtvrNe6ZWVlcrlcPmMOR3PFx8ef4RH5CgtrdPnYUg5H8Pb3558dfoZOjf7Bk/%2BJAAAPiElEQVT4R3/qRm/8a0z9CdqAdfHFFystLU1PPfWUvvvuO40fP14zZsyQ2%2B1W69atfbZt0aKFysvLJUlut1sxMTE%2B8zExMd75%2BigsLFR%2Bfr7PWGZmprKysk7zaGCH2NgIu0s466Kjz7G7hAaN/vhHf%2BpGb/xrDP0J2oBVWFjo/d8dO3bUxIkTNWbMGF122WW/%2BFyPx3NGa2dkZKhPnz4%2BYw5Hc5WXV53Rfk/UGN4B2Mn069WQhIWFKjr6HFVUHFZNTa3d5TQ49Mc/%2BlM3euOfHf2x681y0AasE7Vt21Y1NTUKDQ2V2%2B32mSsvL1dcXJwkKTY29qR5t9utzp0713ut%2BPj4ky4HulyHVF3NL1sgaQyvV01NbaM4ztNFf/yjP3WjN/41hv4E5SmQLVu2aNasWT5jpaWlatq0qdLT00/62oXNmzfroosukiQlJyerpKTEO1dTU6MtW7Z45wEAAH5JUAasli1bqrCwUAUFBTp27Jh27typefPmKSMjQ7feeqv27NmjV199VUePHtU777yjd955R3fccYckadiwYXrttdf0%2Beef6/Dhw1q4cKGaNm2q3r1723tQAAAgYATlJcLWrVuroKBAc%2BbM8QakgQMHKjs7W%2BHh4Xr22Wc1c%2BZMzZgxQwkJCZo9e7Z%2B%2B9vfSpJ69eqlBx98UOPHj9eBAweUkpKigoICNWvWzOajAgAAgSLEc6Z3dKNeXK5DxvfpcITq2ryNxveLn7wx/iq7SzhrHI5QxcZGqLy8Kujvgzgd9Mc/%2BlM3euOfHf1p1SrKknVOFJSXCAEAAOxEwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGBW3A2rNnjzIzM5Wamqq0tDRNmjRJFRUV2r17t5KSkpSSkuLz%2BPOf/%2Bx97po1a3TzzTfrkksu0aBBg/Tee%2B/ZeCQAACDQOOwu4GwZPXq0kpOTtWHDBh06dEiZmZl66qmnNGbMGEmS0%2Bk85fO2bt2qnJwc5efn68orr9S6dev0wAMPaO3atWrTpo2VhwAAAAJUUJ7BqqioUHJysiZMmKCIiAi1adNGAwcO1Mcff/yLz3311VeVnp6u9PR0hYeH65ZbbtGFF16oVatWWVA5AAAIBkF5Bis6Olq5ubk%2BY3v37lV8fLz33w8//LDef/99VVdXa8iQIcrKylKTJk1UUlKi9PR0n%2Bd27dq1zjNep1JWViaXy%2BUz5nA091nfhLCwoMzHDYbDEbz9/flnh5%2BhU6M//tGfutEb/xpTf4IyYJ3I6XRq6dKlWrhwoZo2bapLLrlE1157rZ544glt3bpVY8eOlcPh0Lhx4%2BR2uxUTE%2BPz/JiYGH3zzTf1Xq%2BwsFD5%2Bfk%2BY5mZmcrKyjJyPLBGbGyE3SWcddHR59hdQoNGf/yjP3WjN/41hv4EfcD65JNPNGbMGE2YMEFpaWmSpJdfftk73717d40aNUrPPvusxo0bJ0nyeDxntGZGRob69OnjM%2BZwNFd5edUZ7fdEjeEdgJ1Mv14NSVhYqKKjz1FFxWHV1NTaXU6DQ3/8oz91ozf%2B2dEfu94sB3XA2rBhgx566CFNmzZNt912W53bJSQkaP/%2B/fJ4PIqNjZXb7faZd7vdiouLq/e68fHxJ10OdLkOqbqaX7ZA0hher5qa2kZxnKeL/vhHf%2BpGb/xrDP0J2lMgn376qXJycjRv3jyfcLVp0yYtXLjQZ9sdO3YoISFBISEhSk5O1ubNm33mnU6nLrroIkvqBgAAgS8oA1Z1dbWmTp2qiRMnqmfPnj5zUVFRWrBggVauXKnjx4/L6XTqz3/%2Bs4YNGyZJuuOOO/T%2B%2B%2B/r7bff1tGjR7Vs2TJ9%2B%2B23uuWWW%2Bw4FAAAEICC8hLh559/rtLSUs2cOVMzZ870mVu7dq3mzp2r/Px8/fGPf1RUVJTuvPNOjRgxQpJ04YUXKi8vT7m5udqzZ486deqkZ599Vq1atbLjUAAAQAAKyoB1%2BeWXa/v27XXOJyQk6Nprr61z/rrrrtN11113NkoDAACNQFBeIgQAALATAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDHHYXADRUNzz9v3aX8Ku8Mf4qu0sAAPx/nMECAAAwjIAFAABgGAHrFPbs2aP77rtPqampuuaaazR79mzV1tbaXRYAAAgQ3IN1CmPHjlW3bt1UXFysAwcOaNSoUTr33HP1hz/8we7SAABAACBgncDpdGrbtm1avHixoqKiFBUVpZEjR%2BrFF18kYKFBC6Sb8rkhH0CwI2CdoKSkRAkJCYqJifGOdevWTTt37lRlZaUiIyN/cR9lZWVyuVw%2BYw5Hc8XHxxutNSyMK7wITA5Hw/7Z/fl3i9%2BxU6M/daM3/jWm/hCwTuB2uxUdHe0z9nPYKi8vr1fAKiwsVH5%2Bvs/YAw88oLFjx5orVD8FuRFtvlZGRobx8BboysrKVFhYSG/qQH/8Kysr04svPkd/6kB/6kZv/GtM/Qn%2BCHkaPB7PGT0/IyNDK1as8HlkZGQYqu5fXC6X8vPzTzpbBnrzS%2BiPf/THP/pTN3rjX2PqD2ewThAXFye32%2B0z5na7FRISori4uHrtIz4%2BPuiTOQAAqBtnsE6QnJysvXv36uDBg94xp9OpTp06KSIiwsbKAABAoCBgnaBr165KSUnRnDlzVFlZqdLSUi1evFjDhg2zuzQAABAgwqZPnz7d7iIamquvvlpFRUV6/PHHtXr1ag0ePFh33323QkJC7C7tJBEREbriiis4u3YK9MY/%2BuMf/fGP/tSN3vjXWPoT4jnTO7oBAADgg0uEAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsALQnj17dN999yk1NVXXXHONZs%2BerdraWrvLalA2btyotLQ0ZWdn211Kg7Nnzx5lZmYqNTVVaWlpmjRpkioqKuwuq8HYtm2bRowYocsuu0xpaWkaP368XC6X3WU1OE8%2B%2BaSSkpLsLqNBSUpKUnJyslJSUryPxx9/3O6yGpSFCxeqZ8%2BeuvjiizVy5Ejt3r3b7pLOGgJWABo7dqxat26t4uJiLV68WMXFxXrxxRftLqvBWLRokWbOnKl27drZXUqDNHr0aEVHR2vDhg1asWKFvv76az311FN2l9UgHDt2THfddZeuuOIKbdq0SUVFRTpw4ID4k62%2Btm7dqpUrV9pdRoO0du1aOZ1O72PatGl2l9RgvPTSS1q1apWWLFmi9957T506ddILL7xgd1lnDQErwDidTm3btk0TJ05UVFSU2rdvr5EjR6qwsNDu0hqM8PBwLVu2jIB1ChUVFUpOTtaECRMUERGhNm3aaODAgfr444/tLq1BOHz4sLKzszVq1Cg1bdpUcXFxuvbaa/X111/bXVqDUVtbq0cffVQjR460uxQEmOeff17Z2dm64IILFBkZqalTp2rq1Kl2l3XWELACTElJiRISEhQTE%2BMd69atm3bu3KnKykobK2s4hg8frqioKLvLaJCio6OVm5urc8891zu2d%2B9excfH21hVwxETE6MhQ4bI4XBIknbs2KG//e1vuuGGG2yurOF4%2BeWXFR4erptvvtnuUhqkOXPmqHfv3rr88ss1bdo0VVVV2V1Sg/D9999r9%2B7d%2BuGHHzRgwAClpqYqKytLBw8etLu0s4aAFWDcbreio6N9xn4OW%2BXl5XaUhADmdDq1dOlSjRkzxu5SGpQ9e/YoOTlZAwYMUEpKirKysuwuqUHYv3%2B/5s%2Bfr0cffdTuUhqkiy%2B%2BWGlpaVq/fr0KCwv1%2Beefa8aMGXaX1SDs27dP0k%2BXUBcvXqyVK1dq3759nMFCw%2BLxeOwuAUHgk08%2B0d13360JEyYoLS3N7nIalISEBDmdTq1du1bffvutHn74YbtLahByc3M1aNAgderUye5SGqTCwkINGTJETZs2VceOHTVx4kQVFRXp2LFjdpdmu5//f%2Buee%2B5R69at1aZNG40dO1YbNmzQ0aNHba7u7CBgBZi4uDi53W6fMbfbrZCQEMXFxdlUFQLNhg0bdN9992nKlCkaPny43eU0SCEhIWrfvr2ys7NVVFQU1Jcy6mPTpk367LPPlJmZaXcpAaNt27aqqanRgQMH7C7Fdj/flvDvV2ASEhLk8XiCtj8ErACTnJysvXv3%2BvzH3ul0qlOnToqIiLCxMgSKTz/9VDk5OZo3b55uu%2B02u8tpUDZt2qT%2B/fv7fO1JaOhP/5ls0qSJXWU1CKtWrdKBAwd0zTXXKDU1VYMGDZIkpaamavXq1TZXZ78tW7Zo1qxZPmOlpaVq2rQp9zhKatOmjSIjI7V161bv2J49e9SkSZOg7Q8BK8B07dpVKSkpmjNnjiorK1VaWqrFixdr2LBhdpeGAFBdXa2pU6dq4sSJ6tmzp93lNDjJycmqrKzU7NmzdfjwYR08eFDz58/X5Zdf3ug/ODFp0iStW7dOK1eu1MqVK1VQUCBJWrlypfr06WNzdfZr2bKlCgsLVVBQoGPHjmnnzp2aN2%2BeMjIyFBYWZnd5tnM4HBo8eLCeeeYZ/fOf/9SBAwe0YMEC3Xzzzd4PlQSbEA839AScffv2adq0afrwww8VGRmpoUOH6oEHHlBISIjdpTUIKSkpkn4KE5K8v7xOp9O2mhqKjz/%2BWP/xH/%2Bhpk2bnjS3du1aJSQk2FBVw7J9%2B3bNnDlTX375pZo3b64rr7xSkyZNUuvWre0urUHZvXu3%2Bvbtq%2B3bt9tdSoPx0Ucfac6cOdq%2BfbuaNm2qgQMHKjs7W%2BHh4XaX1iAcO3ZMubm5Wr16tY4fP67%2B/ftr2rRpQXv1hYAFAABgGJcIAQAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMCw/wcWV%2BRdeegZIAAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common6778464823414465149”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>141</td> <td class=”number”>4.4%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.452284034</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.447227191</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.438020149</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.942211055</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.437062937</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.826446281</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.453514739</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.580088463</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.419375131</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2944)</td> <td class=”number”>2972</td> <td class=”number”>92.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme6778464823414465149”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>141</td> <td class=”number”>4.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0583158</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.068031839</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.069208942</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.077375426</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>2.647837599</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.988669819</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.074558032</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.139051497</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.088280061</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FFRPTH14”>FFRPTH14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2952</td>

</tr> <tr>

<th>Unique (%)</th> <td>92.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.57739</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>5.5556</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>4.8%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-7643221741425744054”>

</div> <div class=”col-md-12 text-right”>

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<li role=”presentation” class=”active”><a href=”#quantiles-7643221741425744054”

aria-controls=”quantiles-7643221741425744054” role=”tab” data-toggle=”tab”>Statistics</a></li>

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<li role=”presentation”><a href=”#extreme-7643221741425744054” aria-controls=”extreme-7643221741425744054”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-7643221741425744054”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.089371</td>

</tr> <tr>

<th>Q1</th> <td>0.41406</td>

</tr> <tr>

<th>Median</th> <td>0.5783</td>

</tr> <tr>

<th>Q3</th> <td>0.73295</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.97678</td>

</tr> <tr>

<th>Maximum</th> <td>5.5556</td>

</tr> <tr>

<th>Range</th> <td>5.5556</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.31889</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.30513</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.52846</td>

</tr> <tr>

<th>Kurtosis</th> <td>30.248</td>

</tr> <tr>

<th>Mean</th> <td>0.57739</td>

</tr> <tr>

<th>MAD</th> <td>0.21058</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.5717</td>

</tr> <tr>

<th>Sum</th> <td>1808.4</td>

</tr> <tr>

<th>Variance</th> <td>0.093102</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-7643221741425744054”>

<img 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qpa1dc3WF1OyKBvgaN3gaN3jaz6sByWAWvmzJn65JNP9MorryghIcFvLj09XWVlZRo%2BfLgkqb6%2BXnv27FFOTo7OP/98xcfHq6ysTMnJyZKkf/7znzp69KjS09Obvf2kpKQTLge63YdUV3fuHuChiPfr1OrrG%2BhPAOhb4Ohd4OidNcLuFMj777%2BvNWvWqLi4%2BIRwJUl5eXlatWqVdu7cqdraWi1atEjR0dEaMGCAoqKidPvtt%2BvZZ5/VN998I4/Ho//6r//S4MGDdd5551mwNwAAIBSF5BmsjIwMSY3fEJSkTZs2SZJcLpdWrlypQ4cOaeDAgX6v6d27t5YsWaJ%2B/frpoYce0vjx43XgwAFlZGSouLhYrVu3liQVFBSopqZGN998s%2Brq6jRw4EBNmzYteDsHAABCXoTvTO/oRrO43YeMr9Nmi9Tgou3G14tG68ZfbXUJLY7NFqnExFh5PDVccjgN9C1w9C5w9K5Rhw4nf6zT2RZ2lwgBAACsRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgWEgHrO3btysrK0uFhYUnzL3xxhsaNmyYLrvsMo0YMUJvvfVW01xDQ4PmzZunQYMGqXfv3rr77rv11VdfNc17vV6NHz9eWVlZ6tu3ryZPnqwjR44EZZ8AAEDoC9mAtXjxYs2YMUOdO3c%2BYW7v3r2aNGmSJk6cqLffflujR4/W/fffr/3790uSXn75Za1du1bFxcXasmWLUlJSlJ%2BfL5/PJ0maMmWKamtrVVJSopUrV6q8vFxFRUVB3T8AABC6gh6wsrOztWDBAn3zzTdntJ6YmBitWLHipAFr%2BfLl6t%2B/v/r376%2BYmBjddNNNuuiii7RmzRpJktPp1OjRo9WlSxfFxcWpsLBQ5eXl2rVrl7777jtt2rRJhYWFcjgc6tixo8aNG6eVK1fq2LFjZ1QzAAA4NwQ9YN1666164403dO2112rMmDHauHGj6urqTns9o0aNUrt27U46V1ZWptTUVL%2Bx1NRUuVwuHTlyRJ9%2B%2BqnffFxcnDp37iyXy6W9e/cqKipK3bt3b5pPS0vT4cOH9dlnn512nQAA4NxjC/YG8/PzlZ%2Bfr7KyMpWUlGjmzJmaPn26brnlFuXk5OjCCy884214vV7Fx8f7jcXHx%2BvTTz/V999/L5/Pd9J5j8ejhIQExcXFKSIiwm9OkjweT7O2X1lZKbfb7Tdms7VVUlJSILtzSlFRIXuFNyTYbPT3x44fcxx7p4e%2BBY7eBY7eWSvoAeu4tLQ0paWl6eGHH9Ybb7yhadOmacmSJcrKytKDDz6oSy655IzWf/x%2BqkDmf%2B61P8fpdGrBggV%2BY/n5%2BSooKDij9SK4EhNjrS6hxbLb21hdQkiib4Gjd4Gjd9awLGAdO3ZMf/3rX/Xaa6/p7bffVkpKih544AFVVlZq9OjRmj59uoYNGxbQuhMTE%2BX1ev3GvF6vHA6HEhISFBkZedL59u3by%2BFwqLq6WvX19YqKimqak6T27ds3a/u5ubnKzs72G7PZ2srjqQlof06FTyVnl%2Bn3KxxERUXKbm%2Bjqqpa1dc3WF1OyKBvgaN3gaN3jaz6sBz0gFVeXq4VK1Zo1apVqqmp0ZAhQ/TCCy/o8ssvb1qmd%2B/emjZtWsABKz09Xbt37/Ybc7lcGjp0qGJiYtStWzeVlZWpT58%2BkqSqqip9%2BeWXuuSSS5ScnCyfz6d9%2B/YpLS2t6bV2u73Zly%2BTkpJOuBzodh9SXd25e4CHIt6vU6uvb6A/AaBvgaN3gaN31gj6KZChQ4dq69atuvfee/Xmm29qzpw5fuFKkvr376%2BDBw8GvI3bb79dO3bs0NatW/XDDz9oxYoV%2BuKLL3TTTTdJkvLy8rRs2TKVl5erurpaRUVF6tGjhzIyMuRwODRkyBA9/fTTOnjwoPbv36%2BFCxcqJydHNptlJ/wAAEAICXpiWLZsWdOZo5%2Bya9eun5zPyMiQpKZvIG7atElS49mmiy66SEVFRZo1a5YqKirUtWtXPffcc%2BrQoYMkaeTIkXK73brjjjtUU1OjzMxMv3umnnjiCU2dOlWDBg1Sq1atdOONN570YaYAAAAnE%2BE70zu6T9P333%2BvSZMmKScnR9dee60k6fnnn9ff//53zZkzRwkJCcEsJ2jc7kPG12mzRWpw0Xbj60WjdeOvtrqEFsdmi1RiYqw8nhouOZwG%2BhY4ehc4eteoQ4eTP9LpbAv6JcJZs2bp0KFD6tq1a9PYgAED1NDQoNmzZwe7HAAAAOOCfonwrbfe0tq1a5WYmNg0lpKSoqKiIt14443BLgcAAMC4oJ/BOnLkiGJiYk4sJDJStbW1wS4HAADAuKAHrN69e2v27Nn6/vvvm8a%2B/fZbTZ8%2B/YRvEwIAAISioF8ifOyxx3TXXXfpqquuUlxcnBoaGlRTU6Pzzz9fL774YrDLAQAAMC7oAev888/X66%2B/rjfffFNffvmlIiMjdeGFF6pv375NT04HAAAIZZY8OTM6OrrpEQ0AAADhJugB66uvvtLcuXP1ySef6MiRIyfM/%2B1vfwt2SQAAAEZZcg9WZWWl%2Bvbtq7Zt2wZ78wAAAGdd0APW7t279be//U0OhyPYmwYAAAiKoD%2BmoX379py5AgAAYS3oAevee%2B/VggULFOSfQAQAAAiaoF8ifPPNN/XBBx/otdde0y9/%2BUtFRvpnvFdffTXYJQEAABgV9IAVFxenfv36BXuzAAAAQRP0gDVr1qxgbxIAACCogn4PliR99tlnmj9/vh599NGmsQ8//NCKUgAAAIwLesAqLS3VTTfdpI0bN6qkpERS48NHR40axUNGAQBAWAh6wJo3b57%2B8Ic/aO3atYqIiJDU%2BPuEs2fP1sKFC4NdDgAAgHFBD1j//Oc/lZeXJ0lNAUuSrr/%2BepWXlwe7HAAAAOOCHrDatWt30t8grKysVHR0dLDLAQAAMC7oAatXr16aOXOmqqurm8Y%2B//xzTZo0SVdddVWwywEAADAu6I9pePTRR3XnnXcqMzNT9fX16tWrl2pra9WtWzfNnj072OUAAAAYF/SA1alTJ5WUlGjbtm36/PPP1bp1a1144YW6%2Buqr/e7JAgAACFVBD1iS1KpVK1177bVWbBoAAOCsC3rAys7O/skzVTwLCwAAhLqgB6wbbrjBL2DV19fr888/l8vl0p133hnscgAAAIwLesCaOHHiScc3bNigd955J8jVAAAAmGfJbxGezLXXXqvXX3/d2Pr27NmjUaNG6YorrtDVV1%2BtiRMn6uDBg5Iaf64nJydHvXr10tChQ7VmzRq/1y5btkxDhgxRr169lJeXp927dxurCwAAhL8WE7D27Nkjn89nZF11dXW65557dOmll2rHjh0qKSnRwYMHNW3aNFVWVmrcuHEaOXKkSktLNXnyZE2ZMkUul0uStHnzZs2fP19PPfWUduzYoYEDB2rs2LE6fPiwkdoAAED4C/olwpEjR54wVltbq/Lycl133XVGtuF2u%2BV2u3XzzTcrOjpa0dHRGjx4sJYsWaK1a9cqJSVFOTk5kqSsrCxlZ2dr%2BfLlysjIkNPp1IgRI9SzZ09J0pgxY7Rs2TJt2bJFQ4cONVIfAAAIb0EPWCkpKSd8izAmJkY5OTm67bbbjGyjY8eO6tGjh5xOpx588EEdOXJEGzdu1IABA1RWVqbU1FS/5VNTU7Vu3TpJUllZmW644YamucjISPXo0UMul4uABQAAmiXoASsYT2uPjIzU/PnzNXr0aL3wwguSpD59%2BmjChAkaN26cOnbs6Ld8QkKCPB6PJMnr9So%2BPt5vPj4%2Bvmm%2BOSorK%2BV2u/3GbLa2SkpKCmR3TikqqsVc4Q1LNhv9/bHjxxzH3umhb4Gjd4Gjd9YKesBatWpVs5e95ZZbAtrG0aNHNXbsWF1//fVN909Nnz79lN9g/LEzvRfM6XRqwYIFfmP5%2BfkqKCg4o/UiuBITY60uocWy29tYXUJIom%2BBo3eBo3fWCHrAmjx5shoaGk4IMREREX5jERERAQes0tJSff3113rooYcUFRWldu3aqaCgQDfffLOuueYaeb1ev%2BU9Ho8cDockKTEx8YR5r9erbt26NXv7ubm5ys7O9huz2drK46kJaH9OhU8lZ5fp9yscREVFym5vo6qqWtXXN1hdTsigb4Gjd4Gjd42s%2BrAc9ID15z//WUuWLNHYsWPVvXt3%2BXw%2Bffzxx1q8eLF%2B97vfKTMz84y3UV9ff0KIO3r0qKTGm9r/8pe/%2BC2/e/fuppva09PTVVZWpuHDhzeta8%2BePU03xTdHUlLSCZcD3e5Dqqs7dw/wUMT7dWr19Q30JwD0LXD0LnD0zhpBPwUye/ZszZgxQ5dffrni4uLUrl07XXHFFXriiSf05JNPNn3rLzo6OuBtXHbZZWrbtq3mz5%2Bv2tpaeTweLVq0SL1799bNN9%2BsiooKLV%2B%2BXD/88IO2bdumbdu26fbbb5ck5eXladWqVdq5c6dqa2u1aNEiRUdHa8CAAYY6AAAAwl3QA9YXX3xxwk3kkmS321VRUWFkG4mJifrv//5vffDBB%2BrXr59uvPFGtW7dWnPnzlX79u313HPP6aWXXtLll1%2BumTNnas6cObr44oslSf369dNDDz2k8ePHq0%2BfPtqxY4eKi4vVunVrI7UBAIDwF%2BEz9XTPZrrhhhvUp08fPfjgg0pMTJQkVVVV6U9/%2BpPeffddrV69OpjlBI3bfcj4Om22SA0u2m58vWi0bvzVVpfQ4thskUpMjJXHU8Mlh9NA3wJH7wJH7xp16NDOku0G/R6sxx57TBMmTJDT6VRsbKwiIyNVXV2t1q1ba%2BHChcEuBwAAwLigB6y%2Bfftq69at2rZtm/bv3y%2Bfz6eOHTvqmmuuUbt21qRMAAAAk4IesCSpTZs2GjRokPbv36/zzz/fihIAAADOmqDf5H7kyBFNmjRJl112mX79619LarwHa8yYMaqqqgp2OQAAAMYFPWDNmTNHe/fuVVFRkSIj/7X5%2Bvp6FRUVBbscAAAA44IesDZs2KA//elPuv7665t%2B9Nlut2vWrFnauHFjsMsBAAAwLugBq6amRikpKSeMOxwOHT58ONjlAAAAGBf0gHXBBRfonXfekeT/o8rr16/Xv/3bvwW7HAAAAOOC/i3C3/zmN3rggQd06623qqGhQUuXLtXu3bu1YcMGTZ48OdjlAAAAGBf0gJWbmyubzaaXXnpJUVFRevbZZ3XhhReqqKhI119/fbDLAQAAMC7oAevgwYO69dZbdeuttwZ70wAAAEER9HuwBg0apCD//CEAAEBQBT1gZWZmat26dcHeLAAAQNAE/RLhL37xC/3nf/6niouLdcEFF6hVq1Z%2B83Pnzg12SQAAAEYFPWB9%2Bumn%2BtWvfiVJ8ng8wd48AADAWRe0gFVYWKh58%2BbpxRdfbBpbuHCh8vPzg1UCAABAUATtHqzNmzefMFZcXByszQMAAARN0ALWyb45yLcJAQBAOApawDr%2Bw84/NwYAABDqgv6YBgAAgHBHwAIAADAsaN8iPHbsmCZMmPCzYzwHCwAAhLqgBazLL79clZWVPzsGAAAQ6oIWsP7v868AAADCGfdgAQAAGEbAAgAAMIyABQAAYFhYB6xFixapb9%2B%2BuvTSSzV69Gh9/fXXkqTS0lLl5OSoV69eGjp0qNasWeP3umXLlmnIkCHq1auX8vLytHv3bivKBwAAISpsA9bLL7%2BsNWvWaNmyZXrrrbfUtWtXPf/886qsrNS4ceM0cuRIlZaWavLkyZoyZYpcLpekxt9MnD9/vp566int2LFDAwcO1NixY3X48GGL9wgAAISKsA1YS5YsUWFhoX71q18pLi5Ojz/%2BuB5//HGtXbtWKSkpysnJUUxMjLKyspSdna3ly5dLkpxOp0aMGKGePXuqdevWGjNmjCRpy5YtVu4OAAAIIWEZsL799lt9/fXX%2Bv7773XDDTcoMzNTBQUFOnjwoMrKypSamuq3fGpqatNlwB/PR0ZGqkePHk1nuAAAAH5O0J6DFUz79%2B%2BXJK1fv15Lly6Vz%2BdTQUGBHn/8cR05ckQdO3b0Wz4hIUEej0eS5PV6FR8f7zcfHx/fNN8clZWVcrvdfmM2W1slJSUFsjunFBUVlvm4xbDCcRPPAAASVUlEQVTZ6O%2BPHT/mOPZOD30LHL0LHL2zVlgGLJ/PJ0kaM2ZMU5h64IEH9Pvf/15ZWVnNfn2gnE6nFixY4DeWn5%2BvgoKCM1ovgisxMdbqElosu72N1SWEJPoWOHoXOHpnjbAMWOedd54kyW63N40lJyfL5/Pp2LFj8nq9fst7PB45HA5JUmJi4gnzXq9X3bp1a/b2c3NzlZ2d7Tdms7WVx1NzWvvxc/hUcnaZfr/CQVRUpOz2NqqqqlV9fYPV5YQM%2BhY4ehc4etfIqg/LYRmwOnXqpLi4OO3du1dpaWmSpIqKCrVq1Ur9%2B/fX6tWr/ZbfvXu3evbsKUlKT09XWVmZhg8fLkmqr6/Xnj17lJOT0%2BztJyUlnXA50O0%2BpLq6c/cAD0W8X6dWX99AfwJA3wJH7wJH76wRlqdAbDabcnJy9Oyzz%2Bp///d/deDAAS1cuFDDhg3T8OHDVVFRoeXLl%2BuHH37Qtm3btG3bNt1%2B%2B%2B2SpLy8PK1atUo7d%2B5UbW2tFi1apOjoaA0YMMDanQIAACEjLM9gSdKECRN09OhR3XbbbTp27JiGDBmixx9/XLGxsXruuec0Y8YMTZ8%2BXcnJyZozZ44uvvhiSVK/fv300EMPafz48Tpw4IAyMjJUXFys1q1bW7xHAAAgVET4zvSObjSL233I%2BDpttkgNLtpufL1otG781VaX0OLYbJFKTIyVx1PDJYfTQN8CR%2B8CR%2B8adejQzpLthuUlQgAAACsRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABh2TgSsmTNnqnv37k1/Ly0tVU5Ojnr16qWhQ4dqzZo1fssvW7ZMQ4YMUa9evZSXl6fdu3cHu2QAABDCwj5g7d27V6tXr276e2VlpcaNG6eRI0eqtLRUkydP1pQpU%2BRyuSRJmzdv1vz58/XUU09px44dGjhwoMaOHavDhw9btQsAACDEhHXAamho0NSpUzV69OimsbVr1yolJUU5OTmKiYlRVlaWsrOztXz5ckmS0%2BnUiBEj1LNnT7Vu3VpjxoyRJG3ZssWKXQAAACEorAPWq6%2B%2BqpiYGA0bNqxprKysTKmpqX7LpaamNl0G/PF8ZGSkevTo0XSGCwAA4OfYrC7gbPnuu%2B80f/58vfjii37jXq9XHTt29BtLSEiQx%2BNpmo%2BPj/ebj4%2BPb5pvjsrKSrndbr8xm62tkpKSTmcXflZUVFjnY8vZbPT3x44fcxx7p4e%2BBY7eBY7eWStsA9asWbM0YsQIde3aVV9//fVpvdbn853Rtp1OpxYsWOA3lp%2Bfr4KCgjNaL4IrMTHW6hJaLLu9jdUlhCT6Fjh6Fzh6Z42wDFilpaX68MMPVVJScsJcYmKivF6v35jH45HD4TjlvNfrVbdu3Zq9/dzcXGVnZ/uN2Wxt5fHUNHsdzcGnkrPL9PsVDqKiImW3t1FVVa3q6xusLidk0LfA0bvA0btGVn1YDsuAtWbNGh04cEADBw6U9K8zUpmZmbrrrrtOCF67d%2B9Wz549JUnp6ekqKyvT8OHDJUn19fXas2ePcnJymr39pKSkEy4Hut2HVFd37h7goYj369Tq6xvoTwDoW%2BDoXeDonTXC8hTII488og0bNmj16tVavXq1iouLJUmrV6/WsGHDVFFRoeXLl%2BuHH37Qtm3btG3bNt1%2B%2B%2B2SpLy8PK1atUo7d%2B5UbW2tFi1apOjoaA0YMMDCPQIAAKEkLM9gxcfH%2B92oXldXJ0nq1KmTJOm5557TjBkzNH36dCUnJ2vOnDm6%2BOKLJUn9%2BvXTQw89pPHjx%2BvAgQPKyMhQcXGxWrduHfwdAQAAISnCd6Z3dKNZ3O5Dxtdps0VqcNF24%2BtFo3Xjr7a6hBbHZotUYmKsPJ4aLjmcBvoWOHoXOHrXqEOHdpZsNywvEQIAAFiJgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMCwsA1YFRUVys/PV2ZmprKysvTII4%2BoqqpKkrR371797ne/0%2BWXX67rrrtOS5Ys8XvtG2%2B8oWHDhumyyy7TiBEj9NZbb1mxCwAAIESFbcAaO3as7Ha7Nm/erNdee02ffPKJnnzySR05ckT33nuvrrzySm3fvl3z5s3Tc889p40bN0pqDF%2BTJk3SxIkT9fbbb2v06NG6//77tX//fov3CAAAhIqwDFhVVVVKT0/XhAkTFBsbq06dOmn48OF67733tHXrVh07dkz33Xef2rZtq7S0NN12221yOp2SpOXLl6t///7q37%2B/YmJidNNNN%2Bmiiy7SmjVrLN4rAAAQKsIyYNntds2aNUvnnXde09g333yjpKQklZWVqXv37oqKimqaS01N1e7duyVJZWVlSk1N9VtfamqqXC5XcIoHAAAhz2Z1AcHgcrn00ksvadGiRVq3bp3sdrvffEJCgrxerxoaGuT1ehUfH%2B83Hx8fr08//bTZ26usrJTb7fYbs9naKikpKfCdOImoqLDMxy2GzUZ/f%2Bz4Mcexd3roW%2BDoXeDonbXCPmC9//77uu%2B%2B%2BzRhwgRlZWVp3bp1J10uIiKi6f/7fL4z2qbT6dSCBQv8xvLz81VQUHBG60VwJSbGWl1Ci2W3t7G6hJBE3wJH7wJH76wR1gFr8%2BbN%2BsMf/qApU6bolltukSQ5HA598cUXfst5vV4lJCQoMjJSiYmJ8nq9J8w7HI5mbzc3N1fZ2dl%2BYzZbW3k8NYHtyCnwqeTsMv1%2BhYOoqEjZ7W1UVVWr%2BvoGq8sJGfQtcPQucPSukVUflsM2YH3wwQeaNGmSnnnmGfXt27dpPD09Xa%2B88orq6upkszXuvsvlUs%2BePZvmj9%2BPdZzL5dLQoUObve2kpKQTLge63YdUV3fuHuChiPfr1OrrG%2BhPAOhb4Ohd4OidNcLyFEhdXZ0ef/xxTZw40S9cSVL//v0VFxenRYsWqba2Vrt27dKKFSuUl5cnSbr99tu1Y8cObd26VT/88INWrFihL774QjfddJMVuwIAAEJQhO9Mbzhqgd577z399re/VXR09Alz69evV01NjaZOnardu3frvPPO0%2B9//3v95je/aVpm48aNmjt3rioqKtS1a1dNnjxZvXv3PqOa3O5DZ/T6k7HZIjW4aLvx9aLRuvFXW11Ci2OzRSoxMVYeTw2fiE8DfQscvQscvWvUoUM7S7YblpcIr7jiCn388cc/ucwrr7xyyrnrrrtO1113nemyAADAOSIsLxECAABYiYAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACG2awuAGipfv30360u4bSsG3%2B11SUAAP4/zmABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQSsk6ioqNA999yjzMxMDRw4UHPmzFFDQ4PVZQEAgBDBYxpO4oEHHlBaWpo2bdqkAwcO6N5779V5552nf//3f7e6NOCUQumxEjxSAkC44wzWj7hcLu3bt08TJ05Uu3btlJKSotGjR8vpdFpdGgAACBGcwfqRsrIyJScnKz4%2BvmksLS1Nn3/%2BuaqrqxUXF/ez66isrJTb7fYbs9naKikpyWitUVHkY4SmUDrbFor%2BOvEaq0sw5vi/c/x7d/ronbUIWD/i9Xplt9v9xo6HLY/H06yA5XQ6tWDBAr%2Bx%2B%2B%2B/Xw888IC5QtUY5O7s9Ilyc3ONh7dwVllZKafTSd8CQO8CQ98CV1lZqRde%2BDO9CwC9sxax9iR8Pt8ZvT43N1evvfaa35/c3FxD1f2L2%2B3WggULTjhbhp9G3wJH7wJD3wJH7wJH76zFGawfcTgc8nq9fmNer1cRERFyOBzNWkdSUhKfFgAAOIdxButH0tPT9c033%2BjgwYNNYy6XS127dlVsbKyFlQEAgFBBwPqR1NRUZWRkaO7cuaqurlZ5ebmWLl2qvLw8q0sDAAAhImratGnTrC6ipbnmmmtUUlKiP/7xj3r99deVk5Oju%2B%2B%2BWxEREVaXdoLY2Fj16dOHs2unib4Fjt4Fhr4Fjt4Fjt5ZJ8J3pnd0AwAAwA%2BXCAEAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2CFoIqKCt1zzz3KzMzUwIEDNWfOHDU0NFhdVsjYvn27srKyVFhYaHUpIaWiokL5%2BfnKzMxUVlaWHnnkEVVVVVldVou3b98%2B3Xnnnbr88suVlZWl8ePHy%2B12W11WSJk5c6a6d%2B9udRkho3v37kpPT1dGRkbTnz/%2B8Y9Wl3XOIWCFoAceeEAdO3bUpk2btHTpUm3atEkvvPCC1WWFhMWLF2vGjBnq3Lmz1aWEnLFjx8put2vz5s167bXX9Mknn%2BjJJ5%2B0uqwW7ejRo7rrrrvUp08flZaWqqSkRAcOHBA/Adt8e/fu1erVq60uI%2BSsX79eLper6c%2BUKVOsLumcQ8AKMS6XS/v27dPEiRPVrl07paSkaPTo0XI6nVaXFhJiYmK0YsUKAtZpqqqqUnp6uiZMmKDY2Fh16tRJw4cP13vvvWd1aS1abW2tCgsLde%2B99yo6OloOh0ODBw/WJ598YnVpIaGhoUFTp07V6NGjrS4FOG0ErBBTVlam5ORkxcfHN42lpaXp888/V3V1tYWVhYZRo0apXbt2VpcRcux2u2bNmqXzzjuvaeybb75RUlKShVW1fPHx8brttttks9kkSZ999pn%2B8pe/6Ne//rXFlYWGV199VTExMRo2bJjVpYScuXPnasCAAbriiis0ZcoU1dTUWF3SOYeAFWK8Xq/sdrvf2PGw5fF4rCgJ5yCXy6WXXnpJ9913n9WlhISKigqlp6frhhtuUEZGhgoKCqwuqcX77rvvNH/%2BfE2dOtXqUkLOpZdeqqysLG3cuFFOp1M7d%2B7U9OnTrS7rnEPACkE%2Bn8/qEnAOe//993X33XdrwoQJysrKsrqckJCcnCyXy6X169friy%2B%2B0MMPP2x1SS3erFmzNGLECHXt2tXqUkKO0%2BnUbbfdpujoaHXp0kUTJ05USUmJjh49anVp5xQCVohxOBzyer1%2BY16vVxEREXI4HBZVhXPF5s2bdc899%2Bixxx7TqFGjrC4npERERCglJUWFhYUqKSnRwYMHrS6pxSotLdWHH36o/Px8q0sJC7/85S9VX1%2BvAwcOWF3KOYWAFWLS09P1zTff%2BP3j7HK51LVrV8XGxlpYGcLdBx98oEmTJumZZ57RLbfcYnU5IaG0tFRDhgzxe4xKZGTjP7utWrWyqqwWb82aNTpw4IAGDhyozMxMjRgxQpKUmZmp119/3eLqWrY9e/Zo9uzZfmPl5eWKjo7mnskgI2CFmNTUVGVkZGju3Lmqrq5WeXm5li5dqry8PKtLQxirq6vT448/rokTJ6pv375WlxMy0tPTVV1drTlz5qi2tlYHDx7U/PnzdcUVV/Bli5/wyCOPaMOGDVq9erVWr16t4uJiSdLq1auVnZ1tcXUtW/v27eV0OlVcXKyjR4/q888/1zPPPKPc3FxFRUVZXd45JcLHDT0hZ//%2B/ZoyZYr%2B8Y9/KC4uTiNHjtT999%2BviIgIq0tr8TIyMiQ1BgZJTd/ucrlcltUUCt577z399re/VXR09Alz69evV3JysgVVhYaPP/5YM2bM0EcffaS2bdvqyiuv1COPPKKOHTtaXVrI%2BPrrrzVo0CB9/PHHVpcSEt59913NnTtXH3/8saKjozV8%2BHAVFhYqJibG6tLOKQQsAAAAw7hECAAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACG/T/GJ/1VXD%2BHjgAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-7643221741425744054”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>153</td> <td class=”number”>4.8%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.385728062</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.377073906</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.477099237</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.388349515</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.555447552</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.491364273</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.550660793</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.74906367</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.424268137</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2941)</td> <td class=”number”>2960</td> <td class=”number”>92.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-7643221741425744054”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>153</td> <td class=”number”>4.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.066640011</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.070731362</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.080729797</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.089349535</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>2.786624204</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.976928802</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.052769298</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.512641425</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.555555556</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FMRKT09”>FMRKT09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>39</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.2%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.5%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>79</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1.6643</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>94</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>42.4%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram7468366662828954450”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives7468366662828954450,#minihistogram7468366662828954450”
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</div> <div class=”row collapse col-md-12” id=”descriptives7468366662828954450”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles7468366662828954450”

aria-controls=”quantiles7468366662828954450” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram7468366662828954450” aria-controls=”histogram7468366662828954450”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common7468366662828954450” aria-controls=”common7468366662828954450”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme7468366662828954450” aria-controls=”extreme7468366662828954450”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles7468366662828954450”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>1</td>

</tr> <tr>

<th>Q3</th> <td>2</td>

</tr> <tr>

<th>95-th percentile</th> <td>7</td>

</tr> <tr>

<th>Maximum</th> <td>94</td>

</tr> <tr>

<th>Range</th> <td>94</td>

</tr> <tr>

<th>Interquartile range</th> <td>2</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>3.9986</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.4025</td>

</tr> <tr>

<th>Kurtosis</th> <td>169</td>

</tr> <tr>

<th>Mean</th> <td>1.6643</td>

</tr> <tr>

<th>MAD</th> <td>1.821</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>10.02</td>

</tr> <tr>

<th>Sum</th> <td>5211</td>

</tr> <tr>

<th>Variance</th> <td>15.989</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram7468366662828954450”>

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EPt3r1btbW1mjp1qtq2bavevXvrrrvuUnFxsSRpzZo1Sk9PV3p6usLCwjR69Gj16NFDGzZskCQVFxcrKytLXbt2VUREhHJyclRWVqZ9%2B/b5c8kAACCABGTAcjgcys/PV/v27RvHjh07pri4OJWWlqpnz54KDQ1tnEtKSlJJSYkkqbS0VElJST77S0pKktPp1JkzZ3To0CGf%2BYiICHXu3FlOp7OFVwUAAIKF3d8FmOB0OvXqq69q%2BfLl2rJlixwOh898dHS0PB6PGhoa5PF4FBUV5TMfFRWlQ4cO6dSpU/J6vU3Ou93uZtdTUVEhl8vlM2a3t1VcXNw/ubLvFxoaWPnYbg%2BseptyvueB1vtARs%2BtRb%2BtR8%2BDU8AHrI8%2B%2BkhTp07VzJkzlZaWpi1btjS5XUhISOO//9D9VBd7v1VxcbEKCwt9xrKzszV9%2BvSL2m%2Bgi4kJ93cJxjgcl/m7hEsOPbcW/bYePQ8uAR2wdu7cqccee0zz5s3THXfcIUmKjY3V559/7rOdx%2BNRdHS0bDabYmJi5PF4LpiPjY1t3Kap%2BXbt2jW7rvHjx2vo0KE%2BY3Z7W7nd1f/E6n5YoL3bMb1%2BfwgNtcnhuEyVlTWqr2/wdzmXBHpuLfptPXresvz15j5gA9bHH3%2BsvLw8Pf/88xo8eHDjeHJysn73u9%2Bprq5Odvu3y3M6nerbt2/j/Pn7sc5zOp0aOXKkwsLC1L17d5WWlmrgwIGSvr2h/ujRo%2BrTp0%2Bza4uLi7vgcqDLdVp1dZf2D04wrb%2B%2BviGo1hMI6Lm16Lf16HlwCaxTIP%2Bnrq5Oc%2BfOVW5urk%2B4kqT09HRFRERo%2BfLlqqmp0b59%2B7R27VplZmZKksaNG6f3339fu3fv1tmzZ7V27Vp9/vnnGj16tCQpMzNTq1atUllZmaqqqlRQUKBevXopJSXF8nUCAIDAFJBnsD755BOVlZVp4cKFWrhwoc/c1q1b9cILL2j%2B/PkqKipS%2B/btlZOToyFDhkiSevTooYKCAuXn56u8vFzdunXTihUr1KFDB0nShAkT5HK5dM8996i6ulqpqakX3E8FAADwfUK8PEHTEi7XaeP7tNttGl7wrvH9tpQtMwb5u4SLZrfbFBMTLre7mlP5FqHn1qLf1qPnLatDh0i/HDcgLxECAAD8K7M8YA0dOlSFhYU6duyY1YcGAACwhOUB684779TmzZt100036f7779f27dtVV1dndRkAAAAtxvKAlZ2drc2bN%2Bv3v/%2B9unfvrmeeeUbp6elatGiRjhw5YnU5AAAAxvntHqzevXsrLy9Pu3bt0uzZs/X73/9et956qyZNmqS//OUv/ioLAADgovktYNXW1mrz5s164IEHlJeXp/j4eD3xxBPq1auXsrKytHHjRn%2BVBgAAcFEsfw5WWVmZ1q5dqzfeeEPV1dUaMWKEXn75ZV1zzTWN2wwYMEALFizQqFGjrC4PAADgolkesEaOHKkuXbpo8uTJuuOOOxQdHX3BNunp6Tp58qTVpQEAABhhecBatWpV4%2B/5%2Bz779u2zoBoAAADzLL8Hq2fPnpoyZYp27NjROPbf//3feuCBB%2BTxeKwuBwAAwDjLA1Z%2Bfr5Onz6tbt26NY4NGTJEDQ0NevbZZ60uBwAAwDjLLxG%2B99572rhxo2JiYhrHEhMTVVBQoNtuu83qcgAAAIyz/AzWmTNnFBYWdmEhNptqamqsLgcAAMA4ywPWgAED9Oyzz%2BrUqVONY1999ZWefPJJn0c1AAAABCrLLxHOnj1bv/jFL/TTn/5UERERamhoUHV1tTp16qRXXnnF6nIAAACMszxgderUSW%2B%2B%2BabeeecdHT16VDabTV26dNHgwYMVGhpqdTkAAADGWR6wJKl169a66aab/HFoAACAFmd5wPriiy%2B0ePFiffbZZzpz5swF82%2B//bbVJQEAABjll3uwKioqNHjwYLVt29bqwwMAALQ4ywNWSUmJ3n77bcXGxlp9aAAAAEtY/piGdu3aceYKAAAENcsD1uTJk1VYWCiv12v1oQEAACxh%2BSXCd955Rx9//LHWrVunK664Qjabb8Z77bXXrC4JAADAKMsDVkREhG644QarDwsAAGAZywNWfn6%2B1YcEAACwlOX3YEnS4cOHtXTpUj3xxBONY3/%2B85/9UQoAAIBxlgesvXv3avTo0dq%2Bfbs2bdok6duHj06cOJGHjAIAgKBgecBasmSJHnvsMW3cuFEhISGSvv39hM8%2B%2B6yWLVtmdTkAAADGWR6w/vrXvyozM1OSGgOWJN1yyy0qKyuzuhwAAADjLA9YkZGRTf4OwoqKCrVu3drqcgAAAIyzPGD1799fzzzzjKqqqhrHjhw5ory8PP30pz%2B1uhwAAADjLH9MwxNPPKF7771Xqampqq%2BvV//%2B/VVTU6Pu3bvr2WeftbocAAAA4ywPWB07dtSmTZu0Z88eHTlyRG3atFGXLl00aNAgn3uyAAAAApXlAUuSWrVqpZtuuskfhwYAAGhxlgesoUOHfu%2BZKp6FBQAAAp3lAevWW2/1CVj19fU6cuSInE6n7r33XqvLAQAAMM7ygJWbm9vk%2BLZt2/THP/7R4moAAADM88vvImzKTTfdpDfffNPfZQAAAFy0f5mAtX//fnm9Xn%2BXAQAAcNEsv0Q4YcKEC8ZqampUVlamm2%2B%2B%2BZ/a17vvvqu8vDylpqZqyZIljePr1q3T7Nmz1apVK5/tV69erT59%2BqihoUHPP/%2B8Nm3apMrKSvXp00cLFixQp06dJEkej0cLFizQn/70J9lsNqWnp2vevHlq06bNj1gxAAC41FgesBITEy/4FGFYWJgyMjJ01113NXs/L774otauXavOnTs3OT9gwAC98sorTc6tXr1aGzdu1Isvvqj4%2BHgtWbJE2dnZWr9%2BvUJCQjRv3jydO3dOmzZtUm1trR555BEVFBRo7ty5zV8oAAC4ZFkesEw9rT0sLExr167V008/rbNnz/5Try0uLlZWVpa6du0qScrJyVFqaqr27dunK664Qjt27NDrr7%2Bu2NhYSdJDDz2kRx55RHl5eRecFQMAAPguywPWG2%2B80ext77jjjn84N3HixO997bFjx3TfffeppKREDodD06dP1%2B23364zZ87o0KFDSkpKatw2IiJCnTt3ltPp1OnTpxUaGqqePXs2zvfu3VvffPONDh8%2B7DMOAADQFMsD1pw5c9TQ0HDBDe0hISE%2BYyEhId8bsL5PbGysEhMT9eijj6pbt25666239PjjjysuLk5XXXWVvF6voqKifF4TFRUlt9ut6OhoRURE%2BFzGPL%2Bt2%2B1u1vErKirkcrl8xuz2toqLi/tR6/lHQkP/ZT6j0Cx2e2DV25TzPQ%2B03gcyem4t%2Bm09eh6cLA9Yv/nNb7Ry5UpNmTJFPXv2lNfr1aeffqoXX3xRd999t1JTUy/6GEOGDNGQIUMa/zxy5Ei99dZbWrduXeNzuL7vE4sX%2B2nG4uJiFRYW%2BoxlZ2dr%2BvTpF7XfQBcTE%2B7vEoxxOC7zdwmXHHpuLfptPXoeXPxyD1ZRUZHi4%2BMbx6699lp16tRJkyZN0qZNm1rkuAkJCSopKVF0dLRsNps8Ho/PvMfjUbt27RQbG6uqqirV19crNDS0cU6S2rVr16xjjR8/XkOHDvUZs9vbyu2uNrCSvwu0dzum1%2B8PoaE2ORyXqbKyRvX1Df4u55JAz61Fv61Hz1uWv97cWx6wPv/88wsuz0mSw%2BFQeXm5kWP87ne/U1RUlG699dbGsbKyMnXq1ElhYWHq3r27SktLNXDgQElSZWWljh49qj59%2BighIUFer1cHDx5U7969JUlOp1MOh0NdunRp1vHj4uIuuBzocp1WXd2l/YMTTOuvr28IqvUEAnpuLfptPXoeXCw/BZKQkKBnn33W536myspKLV68WFdeeaWRY5w7d05PPfWUnE6namtrtWnTJr3zzjuNz%2BDKzMzUqlWrVFZWpqqqKhUUFKhXr15KSUlRbGysRowYoV//%2Btc6efKkjh8/rmXLlikjI0N2u%2BV5FAAABCDLE8Ps2bM1c%2BZMFRcXKzw8XDabTVVVVWrTpo2WLVvW7P2kpKRIkurq6iRJO3bskPTt2aaJEyequrpajzzyiFwul6644gotW7ZMycnJkr592KnL5dI999yj6upqpaam%2Btwz9atf/Urz58/XsGHD1KpVK912223Kyckx1QIAABDkQrx%2B%2BP00NTU12rNnj44fPy6v16v4%2BHhdf/31ioyMtLoUy7hcp43v0263aXjBu8b321K2zBjk7xIumt1uU0xMuNzuak7lW4SeW4t%2BW4%2Bet6wOHfyTLfxyzeuyyy7TsGHDdPz48cZfTwMAABAsLL8H68yZM8rLy1O/fv30s5/9TNK392Ddf//9qqystLocAAAA4ywPWIsWLdKBAwdUUFAgm%2B3vh6%2Bvr1dBQYHV5QAAABhnecDatm2b/uu//ku33HJL49PSHQ6H8vPztX37dqvLAQAAMM7ygFVdXa3ExMQLxmNjY/XNN99YXQ4AAIBxlgesK6%2B8Un/84x8l%2Bf5Kmq1bt%2Brf/u3frC4HAADAOMs/Rfjzn/9c06ZN05133qmGhga99NJLKikp0bZt2zRnzhyrywEAADDO8oA1fvx42e12vfrqqwoNDdULL7ygLl26qKCgQLfccovV5QAAABhnecA6efKk7rzzTt15551WHxoAAMASlt%2BDNWzYMPnh4fEAAACWsTxgpaamasuWLVYfFgAAwDKWXyK8/PLL9fTTT6uoqEhXXnmlWrVq5TO/ePFiq0sCAAAwyvKAdejQIV111VWSJLfbbfXhAQAAWpxlASsnJ0dLlizRK6%2B80ji2bNkyZWdnW1UCAACAJSy7B2vnzp0XjBUVFVl1eAAAAMtYFrCa%2BuQgnyYEAADByLKAdf4XO//QGAAAQKCz/DENAAAAwY6ABQAAYJhlnyKsra3VzJkzf3CM52ABAIBAZ1nAuuaaa1RRUfGDYwAAAIHOsoD1/z//CgAAIJhxDxYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEBHbDeffddpaWlKScn54K5zZs3a9SoUerXr5/Gjh2r9957r3GuoaFBS5Ys0bBhwzRgwABNmjRJX3zxReO8x%2BPRjBkzlJaWpsGDB2vOnDk6c%2BaMJWsCAACBL2AD1osvvqiFCxeqc%2BfOF8wdOHBAeXl5ys3N1QcffKCsrCw9/PDDOn78uCRp9erV2rhxo4qKirRr1y4lJiYqOztbXq9XkjRv3jzV1NRo06ZN%2BsMf/qCysjIVFBRYuj4AABC4AjZghYWFae3atU0GrDVr1ig9PV3p6ekKCwvT6NGj1aNHD23YsEGSVFxcrKysLHXt2lURERHKyclRWVmZ9u3bp6%2B//lo7duxQTk6OYmNjFR8fr4ceekh/%2BMMfVFtba/UyAQBAAArYgDVx4kRFRkY2OVdaWqqkpCSfsaSkJDmdTp05c0aHDh3ymY%2BIiFDnzp3ldDp14MABhYaGqmfPno3zvXv31jfffKPDhw%2B3zGIAAEBQsfu7gJbg8XgUFRXlMxYVFaVDhw7p1KlT8nq9Tc673W5FR0crIiJCISEhPnOS5Ha7m3X8iooKuVwunzG7va3i4uJ%2BzHL%2BodDQwMrHdntg1duU8z0PtN4HMnpuLfptPXoenIIyYElqvJ/qx8z/0Gt/SHFxsQoLC33GsrOzNX369Ivab6CLiQn3dwnGOByX%2BbuESw49txb9th49Dy5BGbBiYmLk8Xh8xjwej2JjYxUdHS2bzdbkfLt27RQbG6uqqirV19crNDS0cU6S2rVr16zjjx8/XkOHDvUZs9vbyu2u/rFLalKgvdsxvX5/CA21yeG4TJWVNaqvb/B3OZcEem4t%2Bm09et6y/PXmPigDVnJyskpKSnzGnE6nRo4cqbCwMHXv3l2lpaUaOHCgJKmyslJHjx5Vnz59lJCQIK/Xq4MHD6p3796Nr3U4HOrSpUuzjh8XF3fB5UCX67Tq6i7tH5xgWn99fUNQrScQ0HNr0W/r0fPgElinQJpp3Lhxev/997V7926dPXtWa9eu1eeff67Ro0dLkjIzM7Vq1SqVlZWpqqpKBQUF6tWrl1JSUhQbG6sRI0bo17/%2BtU6ePKnjx49r2bJlysjIkN0elHkUAAAYFrCJISUlRZJUV1cnSdqxY4ekb8829ejRQwUFBcrPz1d5ebm6deu4aTWCAAAOf0lEQVSmFStWqEOHDpKkCRMmyOVy6Z577lF1dbVSU1N97pn61a9%2Bpfnz52vYsGFq1aqVbrvttiYfZgoAANCUEO/F3tGNZnG5Thvfp91u0/CCd43vt6VsmTHI3yVcNLvdppiYcLnd1ZzKtwg9txb9th49b1kdOjT9SKeWFpSXCAEAAPyJgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADAvagNWzZ08lJycrJSWl8eupp56SJO3du1cZGRnq37%2B/Ro4cqQ0bNvi8dtWqVRoxYoT69%2B%2BvzMxMlZSU%2BGMJAAAgQNn9XUBL2rp1q6644gqfsYqKCj300EOaM2eORo0apY8%2B%2BkhTp05Vly5dlJKSop07d2rp0qX6zW9%2Bo549e2rVqlWaMmWKtm/frrZt2/ppJQAAIJAE7Rmsf2Tjxo1KTExURkaGwsLClJaWpqFDh2rNmjWSpOLiYo0dO1Z9%2B/ZVmzZtdP/990uSdu3a5c%2ByAQBAAAnqgLV48WINGTJE1157rebNm6fq6mqVlpYqKSnJZ7ukpKTGy4DfnbfZbOrVq5ecTqeltQMAgMAVtJcIr776aqWlpem5557TF198oRkzZujJJ5%2BUx%2BNRfHy8z7bR0dFyu92SJI/Ho6ioKJ/5qKioxvnmqKiokMvl8hmz29sqLi7uR66maaGhgZWP7fbAqrcp53seaL0PZPTcWvTbevQ8OAVtwCouLm78965duyo3N1dTp07VNddc84Ov9Xq9F33swsJCn7Hs7GxNnz79ovYb6GJiwv1dgjEOx2X%2BLuGSQ8%2BtRb%2BtR8%2BDS9AGrO%2B64oorVF9fL5vNJo/H4zPndrsVGxsrSYqJiblg3uPxqHv37s0%2B1vjx4zV06FCfMbu9rdzu6h9ZfdMC7d2O6fX7Q2ioTQ7HZaqsrFF9fYO/y7kk0HNr0W/r0fOW5a8390EZsPbv368NGzZo1qxZjWNlZWVq3bq10tPT9frrr/tsX1JSor59%2B0qSkpOTVVpaqjFjxkiS6uvrtX//fmVkZDT7%2BHFxcRdcDnS5Tquu7tL%2BwQmm9dfXNwTVegIBPbcW/bYePQ8ugXUKpJnatWun4uJiFRUV6dy5czpy5Iief/55jR8/XrfffrvKy8u1Zs0anT17Vnv27NGePXs0btw4SVJmZqbeeOMNffLJJ6qpqdHy5cvVunVrDRkyxL%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%2BnUwYMH9dJLLykyMlKRkZHKysrSyy%2B/TMC6xHDPGADgxyJgfUdpaakSEhIUFRXVONa7d28dOXJEVVVVioiI%2BMF9VFRUyOVy%2BYzZ7W0VFxdntNbQUK7w4u8CKRC%2BlXt9s7c9/33O97s16Lf16HlwImB9h8fjkcPh8Bk7H7bcbnezAlZxcbEKCwt9xh5%2B%2BGFNmzbNXKH6Nsjd2/EzjR8/3nh4Q9MqKipUXFxMzy1UUVGhl1/%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%2BvLLLyXR85ayf/9%2BTZw4Uddee60GDRqk3NxcnTx5UhI9DzpeBJwxY8Z4586d662srPQeOXLEe/PNN3tXrlzp77KCzm233eadNWuWt6qqynvs2DHv2LFjvbNnz/bW1NR4r7/%2Beu/SpUu91dXV3pKSEu/AgQO927Zt83fJQWH//v3egQMHenv06OH1er3er776ynv11Vd716xZ4z1z5oz3f/7nf7x9%2BvTx/uUvf/FzpYHv1Vdf9d5yyy3esrIy7%2BnTp71PPfWU96mnnqLnLaS2ttY7aNAg7%2BLFi71nz571njx50nvfffd5p02bRs%2BDEGewAozT6dTBgweVm5uryMhIJSYmKisrS8XFxf4uLahUVlYqOTlZM2fOVHh4uDp27KgxY8boww8/1O7du1VbW6upU6eqbdu26t27t%2B666y7%2BDgxoaGjQ/PnzlZWV1Ti2ceNGJSYmKiMjQ2FhYUpLS9PQoUO1Zs0a/xUaJFauXKmcnBxdddVVioiI0Ny5czV37lx63kJcLpdcLpduv/12tW7dWjExMRo%2BfLgOHDhAz4MQASvAlJaWKiEhQVFRUY1jvXv31pEjR1RVVeXHyoKLw%2BFQfn6%2B2rdv3zh27NgxxcXFqbS0VD179lRoaGjjXFJSkkpKSvxRalB57bXXFBYWplGjRjWOlZaWKikpyWc7%2Bn3xvvrqK3355Zc6deqUbr31VqWmpmr69Ok6efIkPW8h8fHx6tWrl4qLi1VdXa0TJ05o%2B/btGjJkCD0PQgSsAOPxeORwOHzGzoctt9vtj5IuCU6nU6%2B%2B%2BqqmTp3a5N9BdHS0PB4P98JdhK%2B//lpLly7V/Pnzfcb/Ub/5fr84x48flyRt3bpVL730ktavX6/jx49r7ty59LyF2Gw2LV26VG%2B//bb69%2B%2BvtLQ01dXVaebMmfQ8CBGwApDX6/V3CZeUjz76SJMmTdLMmTOVlpb2D7cLCQmxsKrgk5%2Bfr7Fjx6pbt27%2BLuWScP6/I/fff7/i4%2BPVsWNHTZs2TTt37vRzZcHr3LlzmjJlim655RZ9%2BOGHeueddxQZGanc3Fx/l4YWQMAKMLGxsfJ4PD5jHo9HISEhio2N9VNVwWvnzp168MEHNXv2bE2cOFHSt38H331X6fF4FB0dLZuNH6kfY%2B/evfrzn/%2Bs7OzsC%2BZiYmIu%2BJ53u918v1%2Bk85e///%2BzJgkJCfJ6vaqtraXnLWDv3r368ssv9eijjyoyMlLx8fGaPn263nrrLdlsNnoeZPi/QYBJTk7WsWPHGj/WK317%2Bapbt24KDw/3Y2XB5%2BOPP1ZeXp6ef/553XHHHY3jycnJ%2BvTTT1VXV9c45nQ61bdvX3%2BUGRQ2bNigEydO6MYbb1RqaqrGjh0rSUpNTVWPHj0uuA%2BlpKSEfl%2Bkjh07KiIiQgcOHGgcKy8vV6tWrZSenk7PW0B9fb0aGhp8rkKcO3dOkpSWlkbPgwwBK8AkJSUpJSVFixcvVlVVlcrKyvTSSy8pMzPT36UFlbq6Os2dO1e5ubkaPHiwz1x6eroiIiK0fPly1dTUaN%2B%2BfVq7di1/Bxdh1qxZ2rZtm9avX6/169erqKhIkrR%2B/XqNGjVK5eXlWrNmjc6ePas9e/Zoz549GjdunJ%2BrDmx2u10ZGRl64YUX9Le//U0nTpzQsmXLNGrUKI0ZM4aet4B%2B/fqpbdu2Wrp0qWpqauR2u7V8%2BXINGDBAt99%2BOz0PMiFebugJOMePH9e8efP0pz/9SREREZowYYIefvhh7gEy6MMPP9S///u/q3Xr1hfMbd26VdXV1Zo/f75KSkrUvn17PfDAA/r5z3/uh0qD05dffqlhw4bp008/lST97//%2BrxYuXKiysjIlJCRo5syZuvnmm/1cZeA7d%2B6c8vPz9eabb6q2tlYjRozQvHnzFB4eTs9bSElJiZ577jkdPHhQrVu31sCBAzVr1izFx8fT8yBDwAIAADCMS4QAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYNj/A6%2Baqdiust4LAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common7468366662828954450”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1360</td> <td class=”number”>42.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>884</td> <td class=”number”>27.5%</td> <td>

<div class=”bar” style=”width:65%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>371</td> <td class=”number”>11.6%</td> <td>

<div class=”bar” style=”width:28%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>173</td> <td class=”number”>5.4%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>85</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>54</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>41</td> <td class=”number”>1.3%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>39</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>19</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>18</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (28)</td> <td class=”number”>87</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>79</td> <td class=”number”>2.5%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme7468366662828954450”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1360</td> <td class=”number”>42.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>884</td> <td class=”number”>27.5%</td> <td>

<div class=”bar” style=”width:65%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>371</td> <td class=”number”>11.6%</td> <td>

<div class=”bar” style=”width:28%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>173</td> <td class=”number”>5.4%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>85</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>39.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>42.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>57.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>82.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>94.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FMRKT16”><s>FMRKT16</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_FMRKT09”><code>FMRKT09</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.91012</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FMRKTPTH09”>FMRKTPTH09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>1765</td>

</tr> <tr>

<th>Unique (%)</th> <td>55.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.5%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>79</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.035911</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1.0199</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>42.4%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-2949764489412629236”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-2949764489412629236,#minihistogram-2949764489412629236”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-2949764489412629236”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-2949764489412629236”

aria-controls=”quantiles-2949764489412629236” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-2949764489412629236” aria-controls=”histogram-2949764489412629236”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-2949764489412629236” aria-controls=”common-2949764489412629236”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-2949764489412629236” aria-controls=”extreme-2949764489412629236”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-2949764489412629236”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0.010186</td>

</tr> <tr>

<th>Q3</th> <td>0.04389</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.14706</td>

</tr> <tr>

<th>Maximum</th> <td>1.0199</td>

</tr> <tr>

<th>Range</th> <td>1.0199</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.04389</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.070202</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.9549</td>

</tr> <tr>

<th>Kurtosis</th> <td>33.863</td>

</tr> <tr>

<th>Mean</th> <td>0.035911</td>

</tr> <tr>

<th>MAD</th> <td>0.040894</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>4.7004</td>

</tr> <tr>

<th>Sum</th> <td>112.44</td>

</tr> <tr>

<th>Variance</th> <td>0.0049283</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-2949764489412629236”>

<img 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ZmZkqLCz0VWkAAACXxWb1AR0Oh9asWaN169apsrJSI0aM0Ntvv61BgwbVbzN48GA988wzGjNmjNXlAQAAXDbLA9bo0aPVvXt3TZ06VePGjVNkZGSDbVJSUnTixAmrSwMAADDC8oC1YsUK3XjjjZfcbv/%2B/RZUAwAAYJ7l92D16dNH999/v7Zu3Vo/9tZbb%2Bk3v/mNXC6X1eUAAAAYZ3nAmj9/vk6dOqWePXvWjw0bNkx1dXVasGCB1eUAAAAYZ/klwj179qiwsFBRUVH1Y3FxccrNzdWdd95pdTkAAADGWX4Gq7q6WsHBwQ0LCQxUVVWV1eUAAAAYZ3nAGjx4sBYsWKCTJ0/Wj3333Xd69tlnPR7VAAAA4K8sv0T4%2BOOP67/%2B67908803KzQ0VHV1daqsrFTXrl31xz/%2B0epyAAAAjLM8YHXt2lUffPCBPvroIx05ckSBgYHq3r27hg4dqqCgIKvLAQAAMM7ygCVJbdu21W233eaLQwMAAHid5QHr6NGjWrRokb7%2B%2BmtVV1c3mN%2B2bZvVJQEAABjlk3uwSktLNXToUHXo0MHqwwMAAHid5QHrwIED2rZtm6Kjo60%2BNAAAgCUsf0xDx44dOXMFAABaNMsD1tSpU5WXlye32231oQEAACxh%2BSXCjz76SJ9//rnWrl2rq666SoGBnhnvnXfesbokAAAAoywPWKGhobrlllusPiwAAIBlLA9Y8%2BfPt/qQAAAAlrL8HixJ%2Buabb/TKK6/oscceqx/74osvfFEKAACAcZYHrL1792rs2LHasmWLNmzYIOnHh49OnjyZh4wCAIAWwfKAtXjxYj366KMqLCxUQECApB//PuGCBQu0dOlSq8sBAAAwzvKA9fe//10ZGRmSVB%2BwJGnkyJFyOBxN2tfu3buVnJysnJwcj/G1a9fq2muvVWJiosfH3/72N0lSXV2dFi9erLS0NA0ePFhZWVk6evRo/etdLpdmzJih5ORkDR06VHPnzr3on/UBAAC4GMsDVlhY2EXDSmlpqdq2bdvo/bz22muaN2%2BeunXrdtH5wYMHy263e3z0799fkrRq1SoVFhYqPz9fO3bsUFxcnLKzs%2BufzfXkk0%2BqqqpKGzZs0HvvvSeHw6Hc3NyfsFoAANAaWR6wrr/%2Bev3%2B979XRUVF/djhw4c1e/Zs3XzzzY3eT3BwsNasWfNvA9Z/UlBQoMzMTPXo0UOhoaHKycmRw%2BHQ/v379f3332vr1q3KyclRdHS0OnfurAceeEDvvfeezp071%2BRjAQCA1sfygPXYY4/piy%2B%2BUFJSks6cOaPrr79eo0aNksvl0pw5cxq9n8mTJyssLOzfzh87dky//vWvNXjwYKWlpen999%2BXJFVXV6u4uFjx8fH124aGhqpbt26y2%2B06ePCggoKC1KdPn/r5hIQEnT59Wt98881PWDEAAGhtLH8OVpcuXbRhwwbt2rVLhw8fVrt27dS9e3cNGTLE456syxEdHa24uDg98sgj6tmzp/785z/rd7/7nWJiYnTNNdfI7XYrIiLC4zUREREqKytTZGSkQkNDPWo5v21ZWVmjjl9aWiqn0%2BkxZrN1UExMzGWuzFNQkE%2BesvGT2WzNv97zPfW33jZ39NU76Kt59NQ7WmNfLQ9YktSmTRvddtttXtv/sGHDNGzYsPrPR48erT//%2Bc9au3atZs2aJUn/8W8hXu7fSSwoKFBeXp7HWHZ2tqZPn35Z%2B/V3UVEhvi6h0cLD2/u6hBaJvnoHfTWPnnpHa%2Bqr5QErNTX1P56p8tazsGJjY3XgwAFFRkYqMDBQLpfLY97lcqljx46Kjo5WRUWFamtrFRQUVD8nSR07dmzUsdLT05WamuoxZrN1UFlZpYGV/JO/vRMwvX5vCAoKVHh4e5WXV6m2ts7X5bQY9NU76Kt59NQ7fNlXX725tzxgjRo1yiNg1dbW6vDhw7Lb7frVr35l5Bh/%2BtOfFBERoVGjRtWPORwOde3aVcHBwerVq5eKiop04403SpLKy8t15MgR9e/fX7GxsXK73Tp06JASEhIkSXa7XeHh4erevXujjh8TE9PgcqDTeUo1Na37i9Wf1l9bW%2BdX9foL%2Buod9NU8euodramvlges85foLvThhx/qL3/5i5FjnD17Vs8//7y6du2qa6%2B9Vh9%2B%2BKE%2B%2Bugjvfvuu5KkjIwM5efn65ZbblHnzp2Vm5urvn37KjExUZI0YsQIvfTSS3rxxRd19uxZLV26VBMmTJDN5pMrqgAAwM80m8Rw22236amnntJTTz3VqO3Ph6GamhpJ0tatWyX9eLZp8uTJqqys1MMPPyyn06mrrrpKS5cuVb9%2B/SRJEydOlNPp1KRJk1RZWamkpCSPe6aee%2B45Pf3000pLS1ObNm105513NniYKQAAwL8T4L7cO7oNsdvtysrK0l//%2Bldfl%2BIVTucp4/u02QJ1e%2B5u4/v1lk0zhvi6hEuy2QIVFRWisrLKVnMa2wr01Tvoq3n01Dt82ddOnf79I528yfIzWBMnTmwwVlVVJYfDoeHDh1tdDgAAgHGWB6y4uLgGv0UYHBysCRMm6N5777W6HAAAAOMsD1gLFiyw%2BpAAAACWsjxgrVu3rtHbjhs3zouVAAAAeIflAWvu3Lmqq6tr8LT0gIAAj7GAgAACFgAA8EuWB6zXX39db7zxhu6//3716dNHbrdbX331lV577TX98pe/VFJSktUlAQAAGOWTe7Dy8/PVuXPn%2BrEbbrhBXbt2VVZWljZs2GB1SQAAAEZZ/sfsvv32W0VERDQYDw8PV0lJidXlAAAAGGd5wIqNjdWCBQtUVlZWP1ZeXq5Fixbp6quvtrocAAAA4yy/RPj4449r5syZKigoUEhIiAIDA1VRUaF27dpp6dKlVpcDAABgnOUBa%2BjQodq5c6d27dql48ePy%2B12q3Pnzvr5z3%2BusDDfPM4eAADAJJ/8sef27dsrLS1Nx48fV9euXX1RAgAAgNdYfg9WdXW1Zs%2BerYEDB%2BqOO%2B6Q9OM9WFOmTFF5ebnV5QAAABhnecBauHChDh48qNzcXAUG/vPwtbW1ys3NtbocAAAA4ywPWB9%2B%2BKGWLFmikSNH1v/R5/DwcM2fP19btmyxuhwAAADjLA9YlZWViouLazAeHR2t06dPW10OAACAcZYHrKuvvlp/%2BctfJMnjbw9u3rxZP/vZz6wuBwAAwDjLf4vwF7/4hR566CHdc889qqur05tvvqkDBw7oww8/1Ny5c60uBwAAwDjLA1Z6erpsNptWrlypoKAgvfrqq%2Brevbtyc3M1cuRIq8sBAAAwzvKAdeLECd1zzz265557rD40AACAJSy/BystLc3j3isAAICWxvKAlZSUpE2bNll9WAAAAMtYfonwyiuv1AsvvKD8/HxdffXVatOmjcf8okWLrC4JAADAKMsDVnFxsa655hpJUllZmdWHBwAA8DrLAlZOTo4WL16sP/7xj/VjS5cuVXZ2tlUlAAAAWMKye7C2b9/eYCw/P9%2BqwwMAAFjGsoB1sd8c5LcJAQBAS2RZwDr/h50vNQYAAODvLH9MAwAAQEtHwAIAADDMst8iPHfunGbOnHnJMZ6DBQAA/J1lAWvQoEEqLS295BgAAIC/syxg/evzrwAAAFoy7sECAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYJhfB6zdu3crOTlZOTk5DeY2btyoMWPGaODAgbr77ru1Z8%2Be%2Brm6ujotXrxYaWlpGjx4sLKysnT06NH6eZfLpRkzZig5OVlDhw7V3LlzVV1dbcmaAACA//PbgPXaa69p3rx56tatW4O5gwcPavbs2Zo1a5Y%2B%2BeQTZWZm6sEHH9Tx48clSatWrVJhYaHy8/O1Y8cOxcXFKTs7W263W5L05JNPqqqqShs2bNB7770nh8Oh3NxcS9cHAAD8l98GrODgYK1Zs%2BaiAWv16tVKSUlRSkqKgoODNXbsWPXu3Vvr16%2BXJBUUFCgzM1M9evRQaGiocnJy5HA4tH//fn3//ffaunWrcnJyFB0drc6dO%2BuBBx7Qe%2B%2B9p3Pnzlm9TAAA4If8NmBNnjxZYWFhF50rKipSfHy8x1h8fLzsdruqq6tVXFzsMR8aGqpu3brJbrfr4MGDCgoKUp8%2BfernExISdPr0aX3zzTfeWQwAAGhRbL4uwBtcLpciIiI8xiIiIlRcXKyTJ0/K7XZfdL6srEyRkZEKDQ1VQECAx5wklZWVNer4paWlcjqdHmM2WwfFxMT8lOX8W0FB/pWPbbbmX%2B/5nvpbb5s7%2Buod9NU8euodrbGvLTJgSaq/n%2BqnzF/qtZdSUFCgvLw8j7Hs7GxNnz79svbr76KiQnxdQqOFh7f3dQktEn31DvpqHj31jtbU1xYZsKKiouRyuTzGXC6XoqOjFRkZqcDAwIvOd%2BzYUdHR0aqoqFBtba2CgoLq5ySpY8eOjTp%2Benq6UlNTPcZstg4qK6v8qUu6KH97J2B6/d4QFBSo8PD2Ki%2BvUm1tna/LaTHoq3fQV/PoqXf4sq%2B%2BenPfIgNWv379dODAAY8xu92u0aNHKzg4WL169VJRUZFuvPFGSVJ5ebmOHDmi/v37KzY2Vm63W4cOHVJCQkL9a8PDw9W9e/dGHT8mJqbB5UCn85Rqalr3F6s/rb%2B2ts6v6vUX9NU76Kt59NQ7WlNf/esUSCPdd999%2Bvjjj7Vz506dOXNGa9as0bfffquxY8dKkjIyMrRixQo5HA5VVFQoNzdXffv2VWJioqKjozVixAi99NJLOnHihI4fP66lS5dqwoQJstlaZB4FAACG%2BW1iSExMlCTV1NRIkrZu3Srpx7NNvXv3Vm5urubPn6%2BSkhL17NlTy5cvV6dOnSRJEydOlNPp1KRJk1RZWamkpCSPe6aee%2B45Pf3000pLS1ObNm105513XvRhpgAAABcT4L7cO7rRKE7nKeP7tNkCdXvubuP79ZZNM4b4uoRLstkCFRUVorKyylZzGtsK9NU76Kt59NQ7fNnXTp0u/kgnb2uRlwgBAAB8iYAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMCwFhuw%2BvTpo379%2BikxMbH%2B4/nnn5ck7d27VxMmTND111%2Bv0aNHa/369R6vXbFihUaMGKHrr79eGRkZOnDggC%2BWAAAA/JTN1wV40%2BbNm3XVVVd5jJWWluqBBx7Q3LlzNWbMGH322WeaNm2aunfvrsTERG3fvl2vvPKKXn/9dfXp00crVqzQ/fffry1btqhDhw4%2BWgkAAPAnLfYM1r9TWFiouLg4TZgwQcHBwUpOTlZqaqpWr14tSSooKNDdd9%2BtAQMGqF27dpoyZYokaceOHb4sGwAA%2BJEWfQZr0aJF%2BuKLL1RRUaE77rhDc%2BbMUVFRkeLj4z22i4%2BP16ZNmyRJRUVFGjVqVP1cYGCg%2BvbtK7vdrtGjRzfquKWlpXI6nR5jNlsHxcTEXOaKPAUF%2BVc%2Bttmaf73ne%2BpvvW3u6Kt30Ffz6Kl3tMa%2BttiAdd111yk5OVkvvviijh49qhkzZujZZ5%2BVy%2BVS586dPbaNjIxUWVmZJMnlcikiIsJjPiIion6%2BMQoKCpSXl%2Bcxlp2drenTp//E1bQMUVEhvi6h0cLD2/u6hBaJvnoHfTWPnnpHa%2Bpriw1YBQUF9f/do0cPzZo1S9OmTdOgQYMu%2BVq3231Zx05PT1dqaqrHmM3WQWVllZe13wv52zsB0%2Bv3hqCgQIWHt1d5eZVqa%2Bt8XU6LQV%2B9g76aR0%2B9w5d99dWb%2BxYbsC501VVXqba2VoGBgXK5XB5zZWVlio6OliRFRUU1mHe5XOrVq1ejjxUTE9PgcqDTeUo1Na37i9Wf1l9bW%2BdX9foL%2Buod9NU8euodramv/nUKpJG%2B/PJLLViwwGPM4XCobdu2SklJafDYhQMHDmjAgAGSpH79%2BqmoqKh%2Brra2Vl9%2B%2BWX9PAAAwKW0yIDVsWNHFRQUKD8/X2fPntXhw4f18ssvKz09XXfddZdKSkop/mLCAAAL1ElEQVS0evVqnTlzRrt27dKuXbt03333SZIyMjK0bt067du3T1VVVVq2bJnatm2rYcOG%2BXZRAADAb7TIS4SdO3dWfn6%2BFi1aVB%2BQxo8fr5ycHAUHB2v58uWaN2%2Benn32WcXGxmrhwoW69tprJUm33HKLHnnkEc2YMUM//PCDEhMTlZ%2Bfr3bt2vl4VQAAwF8EuC/3jm40itN5yvg%2BbbZA3Z672/h%2BvWXTjCG%2BLuGSbLZARUWFqKysstXcJ2AF%2Buod9NU8euodvuxrp05hlh7vvBZ5iRAAAMCXCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDbL4uAK3HHS/9j69LaJJNM4b4ugQAgJ/iDBYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDCbrwsAmqs7XvofX5fQJJtmDPF1CQCA/48zWBdRUlKi3/72t0pKStKtt96qhQsXqq6uztdlAQAAP8EZrIt46KGHlJCQoK1bt%2BqHH37Q1KlTdcUVV%2BjXv/61r0sDAAB%2BgIB1AbvdrkOHDunNN99UWFiYwsLClJmZqbfffpuAhWbNny5pcjkTQEtHwLpAUVGRYmNjFRERUT%2BWkJCgw4cPq6KiQqGhoZfcR2lpqZxOp8eYzdZBMTExRmsNCuIKL/yTP4VBf/TnWT/3dQl%2B6/z3Vb6/mtUa%2B0rAuoDL5VJ4eLjH2PmwVVZW1qiAVVBQoLy8PI%2BxBx98UA899JC5QvVjkPtVl6%2BVnp5uPLy1VqWlpSooKKCnhtFX76Cv5pWWlurtt1%2Bnp4a1xr62nijZBG63%2B7Jen56errVr13p8pKenG6run5xOp/Ly8hqcLcNPR0%2B9g756B301j556R2vsK2ewLhAdHS2Xy%2BUx5nK5FBAQoOjo6EbtIyYmptUkdAAA0BBnsC7Qr18/HTt2TCdOnKgfs9vt6tmzp0JCQnxYGQAA8BcErAvEx8crMTFRixYtUkVFhRwOh958801lZGT4ujQAAOAngp555plnfF1Ec/Pzn/9cGzZs0PPPP68PPvhAEyZMUFZWlgICAnxdWgMhISG68cYbObtmED31DvrqHfTVPHrqHa2trwHuy72jGwAAAB64RAgAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAGrmSspKdFvf/tbJSUl6dZbb9XChQtVV1d30W1XrFihESNG6Prrr1dGRoYOHDhgcbX%2BoSk9/dOf/qQRI0Zo4MCBuuuuu7R161aLq/UfTenred99950GDhyoV155xaIq/UtTeupwODRp0iQNGDBAKSkpeuutt6wt1o80tq91dXVasmSJUlNTNXDgQI0ZM0YbN270QcX%2BYffu3UpOTlZOTs5/3K6urk6LFy9WWlqaBg8erKysLB09etSiKi3kRrM2fvx49xNPPOEuLy93Hz582D18%2BHD3G2%2B80WC7bdu2uW%2B44Qb3vn373FVVVe7ly5e7hwwZ4q6srPRB1c1bY3u6efNm96BBg9yffvqp%2B%2BzZs%2B53333XnZCQ4D5y5IgPqm7%2BGtvXf/Xggw%2B6Bw0a5F6yZIlFVfqXxva0qqrKPWzYMPdrr73mPn36tHv//v3u0aNHu4uLi31QdfPX2L6uXLnSPXToULfD4XDX1NS4t2/f7o6Pj3cfPHjQB1U3b/n5%2Be7hw4e7J06c6J4xY8Z/3HbFihXuW2%2B91V1cXOw%2BdeqU%2B7nnnnOPGTPGXVdXZ1G11uAMVjNmt9t16NAhzZo1S2FhYYqLi1NmZqYKCgoabFtQUKC7775bAwYMULt27TRlyhRJ0o4dO6wuu1lrSk%2Brq6v1yCOPaNCgQWrTpo3uvfdehYSEaN%2B%2BfT6ovHlrSl/P27Vrl4qLizVs2DDrCvUjTenppk2bFBoaqilTpqh9%2B/bq37%2B/NmzYoB49evig8uatKX0tKirSoEGDdM011ygoKEi33nqrIiMj9dVXX/mg8uYtODhYa9asUbdu3S65bUFBgTIzM9WjRw%2BFhoYqJydHDodD%2B/fvt6BS6xCwmrGioiLFxsYqIiKifiwhIUGHDx9WRUVFg23j4%2BPrPw8MDFTfvn1lt9stq9cfNKWnd911l37xi1/Uf15eXq7Kykp17tzZsnr9RVP6Kv0YXp977jk9/fTTstlsVpbqN5rS088%2B%2B0y9e/fWY489phtuuEEjR47U%2BvXrrS7ZLzSlr8OGDdNf//pXHTx4UGfPntW2bdtUVVWlG2%2B80eqym73JkycrLCzskttVV1eruLjY4%2BdVaGiounXr1uJ%2BXhGwmjGXy6Xw8HCPsfPfFMrKyhps%2B6/fMM5ve%2BF2rV1Tevqv3G63nnjiCQ0YMIBvrhfR1L4uXbpU1113nW666SZL6vNHTenp8ePHtW3bNiUnJ2v37t2aOnWqZs%2BerS%2B//NKyev1FU/o6fPhwpaena9y4cUpMTNTMmTM1f/58XXnllZbV29KcPHlSbre7Vfy84q1jM%2Bd2u72ybWvW1D6dO3dOc%2BbMUXFxsVasWOGlqvxfY/taXFys1atXq7Cw0MsV%2Bb/G9tTtdishIUFjxoyRJI0fP17vvPOONm/e7HGmAD9qbF/XrVundevWafXq1erTp4/27t2rmTNn6sorr1T//v29XGXL1hp%2BXnEGqxmLjo6Wy%2BXyGHO5XAoICFB0dLTHeFRU1EW3vXC71q4pPZV%2BPJ09depU/eMf/9CqVat0xRVXWFWqX2lsX91ut5555hk99NBD6tSpk9Vl%2BpWm/Fvt1KlTg8szsbGxcjqdXq/T3zSlrytXrlR6err69%2B%2Bv4OBgDRs2TDfddBOXXy9DZGSkAgMDL/r/oGPHjj6qyjsIWM1Yv379dOzYMZ04caJ%2BzG63q2fPngoJCWmwbVFRUf3ntbW1%2BvLLLzVgwADL6vUHTemp2%2B1WTk6ObDab3nrrLUVFRVldrt9obF//8Y9/6H//93%2B1ZMkSJSUlKSkpSR988IFef/11jR8/3helN1tN%2Bbfao0cP/f3vf/c4K1BSUqLY2FjL6vUXTelrXV2damtrPcbOnj1rSZ0tVXBwsHr16uXx86q8vFxHjhxpcWcFCVjNWHx8vBITE7Vo0SJVVFTI4XDozTffVEZGhiRp5MiR%2BvTTTyVJGRkZWrdunfbt26eqqiotW7ZMbdu25Te0LtCUnhYWFqq4uFgvv/yygoODfVl2s9fYvnbp0kW7du3S%2B%2B%2B/X/%2BRmpqqiRMnKj8/38eraF6a8m917NixKisr06uvvqrq6mpt2LBBRUVFGjt2rC%2BX0Cw1pa%2Bpqalas2aNDh06pJqaGu3Zs0d79%2B5VWlqaL5fgd7777juNHDmy/llXGRkZWrFihRwOhyoqKpSbm6u%2BffsqMTHRx5WaxT1YzdySJUv05JNPasiQIQoNDdXEiRPrf7Pt8OHDOn36tCTplltu0SOPPKIZM2bohx9%2BUGJiovLz89WuXTtflt8sNban7733nkpKShrc1H7XXXdp3rx5ltfd3DWmr0FBQerSpYvH69q3b6/Q0FAuGV5EY/%2Btdu7cWcuXL9cLL7yg//7v/9bPfvYzLV26VFdffbUvy2%2B2GtvXqVOnqqamRtnZ2Tpx4oRiY2M1b9483Xzzzb4sv1k6H45qamokqf6hzHa7XefOndPhw4frz/5NnDhRTqdTkyZNUmVlpZKSkpSXl%2Bebwr0owN0a7jQDAACwEJcIAQAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMCw/wdVfUECbUDAqAAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-2949764489412629236”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1360</td> <td class=”number”>42.4%</td> <td>

<div class=”bar” style=”width:77%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.083633018315631</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0851136266916333</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0501027105566411</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0418130122093996</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.111308993766696</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.118793062485151</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.141063619692481</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.146756677428823</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.105887335874629</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (1754)</td> <td class=”number”>1754</td> <td class=”number”>54.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>79</td> <td class=”number”>2.5%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-2949764489412629236”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1360</td> <td class=”number”>42.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.000736921666946287</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.000815750510863757</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.000920403504896547</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.000967346259658952</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.616903146206046</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.618429189857761</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.662690523525514</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.744047619047619</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.01988781234064</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FMRKTPTH16”>FMRKTPTH16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2226</td>

</tr> <tr>

<th>Unique (%)</th> <td>69.3%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.05841</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1.4451</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>27.7%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram510150311854432857”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives510150311854432857,#minihistogram510150311854432857”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives510150311854432857”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles510150311854432857”

aria-controls=”quantiles510150311854432857” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram510150311854432857” aria-controls=”histogram510150311854432857”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common510150311854432857” aria-controls=”common510150311854432857”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme510150311854432857” aria-controls=”extreme510150311854432857”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles510150311854432857”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0.029681</td>

</tr> <tr>

<th>Q3</th> <td>0.071945</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.21455</td>

</tr> <tr>

<th>Maximum</th> <td>1.4451</td>

</tr> <tr>

<th>Range</th> <td>1.4451</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.071945</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.098157</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.6805</td>

</tr> <tr>

<th>Kurtosis</th> <td>45.472</td>

</tr> <tr>

<th>Mean</th> <td>0.05841</td>

</tr> <tr>

<th>MAD</th> <td>0.056582</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>5.1903</td>

</tr> <tr>

<th>Sum</th> <td>182.94</td>

</tr> <tr>

<th>Variance</th> <td>0.0096348</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram510150311854432857”>

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LTUW6MBAAC0i83qAzocDq1atUpr165VXV2dxowZoxdffFEjRoxwP2bkyJG67777NG7cOKvHAwAAaDfLC9bll1%2Bufv36aerUqbrqqqsUFRXV7DHp6ek6ePCg1aMBAAAYYXnBWr58uc4999xffdyOHTssmAYAAMA8y%2B/BGjx4sKZNm6aNGze611544QX9%2Bc9/ltPptHocAAAA4ywvWAsWLNCPP/6oAQMGuNdGjx6tpqYmLVy40OpxAAAAjLP8EuF7772n0tJSRUdHu9cSEhJUWFioK664wupxAAAAjLP8DFZ9fb1CQ0ObDxIcrEOHDlk9DgAAgHGWF6yRI0dq4cKF%2BuGHH9xr3377re6//36Pj2oAAADwVZZfIrzrrrv0pz/9Seeff77CwsLU1NSkuro69enTR3/961%2BtHgcAAMA4ywtWnz599MYbb%2Bjdd9/V3r17FRwcrH79%2BmnUqFEKCQmxehwAAADjLC9YktS1a1ddfPHF3jg0AABAh7O8YO3bt0%2BLFi3SV199pfr6%2Bmbb33nnHatHAgAAMMor92BVVlZq1KhR6tGjh9WHBwAA6HCWF6ydO3fqnXfeUUxMjNWHBgAAsITlH9PQq1cvzlwBAAC/ZnnBmjp1qoqKiuRyuaw%2BNAAAgCUsv0T47rvv6tNPP9WaNWt0%2BumnKzjYs%2BO98sorVo8EAABglOUFKywsTBdeeKHVhwUAALCM5QVrwYIFVh8SAADAUpbfgyVJX3/9tZ588kndeeed7rXPPvvMG6MAAAAYZ3nB%2BuCDDzR%2B/Hi9/fbbWrdunaSfP3z0pptu4kNGAQCAX7C8YC1evFi33XabSktLFRQUJOnn30%2B4cOFCLVmyxOpxAAAAjLO8YH355ZfKzs6WJHfBkqSxY8fK4XC0aV/btm1TWlqa8vLyPNbXrFmjs846SykpKR5f//znPyVJTU1NWrx4sTIzMzVy5EhNnjxZ%2B/btcz/f6XRq9uzZSktL06hRozRv3rwWf60PAABASywvWOHh4S2WlcrKSnXt2rXV%2B3n66adVUFCgvn37trh95MiRstvtHl9Dhw6VJK1YsUKlpaUqLi7W5s2blZCQoNzcXPdnc82fP1%2BHDh3SunXrtHr1ajkcDhUWFp5EWgAAEIgsL1jnnHOOHn74YdXW1rrX9uzZo/z8fJ1//vmt3k9oaKhWrVr1iwXr3ykpKVFOTo769%2B%2BvsLAw5eXlyeFwaMeOHfruu%2B%2B0ceNG5eXlKSYmRnFxcZoxY4ZWr16to0ePtvlYAAAg8FhesO6880599tlnSk1N1eHDh3XOOefosssuk9Pp1B133NHq/dx0000KDw//xe379%2B/XH//4R40cOVKZmZl67bXXJEn19fUqLy9XYmKi%2B7FhYWHq27ev7Ha7du3apZCQEA0ePNi9PSkpST/99JO%2B/vrrk0gMAAACjeWfg3Xqqadq3bp12rp1q/bs2aNu3bqpX79%2BuuCCCzzuyWqPmJgYJSQk6NZbb9WAAQP0t7/9TbfffrtiY2N15plnyuVyKTIy0uM5kZGRqq6uVlRUlMLCwjxmOfbY6urqVh2/srJSVVVVHms2Ww/Fxsa2M5mnkBCvfMrGSbPZ2jfvsby%2Blrs9Ai0zef1foGUOtLxSYGZuieUFS5K6dOmiiy%2B%2BuMP2P3r0aI0ePdr958svv1x/%2B9vftGbNGs2dO1eS/u3vQmzv70ksKSlRUVGRx1pubq5mzZrVrv36uujonkb2ExHR3ch%2BfEmgZSav/wu0zIGWVwrMzMezvGBlZGT82zNVHfVZWPHx8dq5c6eioqIUHBwsp9Ppsd3pdKpXr16KiYlRbW2tGhsbFRIS4t4mSb169WrVsbKyspSRkeGxZrP1UHV1nYEk/8fX3h20N39ISLAiIrqrpuaQGhubDE3VuQVaZvL6v0DLHGh5pc6X2dSb%2B7ayvGBddtllHgWrsbFRe/bskd1u1x/%2B8Acjx3j55ZcVGRmpyy67zL3mcDjUp08fhYaGauDAgSorK9O5554rSaqpqdHevXs1dOhQxcfHy%2BVyaffu3UpKSpIk2e12RUREqF%2B/fq06fmxsbLPLgVVVP6qhwfv/0LzJVP7GxqaA%2B14GWmby%2Br9AyxxoeaXAzHw8ywvWsUt0J3rrrbf00UcfGTnGkSNH9OCDD6pPnz4666yz9NZbb%2Bndd9/Vq6%2B%2BKknKzs5WcXGxLrzwQsXFxamwsFBDhgxRSkqKJGnMmDF67LHH9Mgjj%2BjIkSNasmSJJkyYIJvNK1dUAQCAj%2Bk0jeHiiy/WPffco3vuuadVjz9WhhoaGiRJGzdulPTz2aabbrpJdXV1uuWWW1RVVaXTTz9dS5YsUXJysiRp4sSJqqqq0qRJk1RXV6fU1FSPe6YeeOAB3XvvvcrMzFSXLl10xRVXNPswUwAAgF/SaQrW559/3qaby%2B12%2By9uCwoK0owZMzRjxoxf3D5r1qxfvOk8PDxc//mf/9nqWQAAAI5necGaOHFis7VDhw7J4XDokksusXocAAAA4ywvWAkJCc1%2BijA0NFQTJkzQddddZ/U4AAAAxllesBYuXGj1IQEAACxlecFau3Ztqx971VVXdeAkAAAAHcPygjVv3jw1NTU1u6E9KCjIYy0oKIiCBQAAfJLlBeuZZ57Rc889p2nTpmnw4MFyuVz64osv9PTTT%2BvGG29Uamqq1SMBAAAY5ZV7sIqLixUXF%2Bde%2B%2B1vf6s%2Bffpo8uTJWrdundUjAQAAGGX5L7P75ptvFBkZ2Ww9IiJCFRUVVo8DAABgnOUFKz4%2BXgsXLlR1dbV7raamRosWLdIZZ5xh9TgAAADGWX6J8K677tKcOXNUUlKinj17Kjg4WLW1terWrZuWLFli9TgAAADGWV6wRo0apS1btmjr1q06cOCAXC6X4uLi9Lvf/U7h4eFWjwMAAGCcV34XYffu3ZWZmakDBw6oT58%2B3hgBAACgw1h%2BD1Z9fb3y8/M1fPhwXXrppZJ%2BvgdrypQpqqmpsXocAAAA4ywvWI8%2B%2Bqh27dqlwsJCBQf/3%2BEbGxtVWFho9TgAAADGWV6w3nrrLT3xxBMaO3as%2B5c%2BR0REaMGCBXr77betHgcAAMA4ywtWXV2dEhISmq3HxMTop59%2BsnocAAAA4ywvWGeccYY%2B%2BugjSfL43YNvvvmmfvOb31g9DgAAgHGW/xThDTfcoJkzZ%2Braa69VU1OTnn/%2Bee3cuVNvvfWW5s2bZ/U4AAAAxllesLKysmSz2fTSSy8pJCRETz31lPr166fCwkKNHTvW6nEAAACMs7xgHTx4UNdee62uvfZaqw8NAABgCcvvwcrMzPS49woAAMDfWF6wUlNTtWHDBqsPCwAAYBnLLxGedtppeuihh1RcXKwzzjhDXbp08di%2BaNEiq0cCAAAwyvKCVV5erjPPPFOSVF1dbfXhAQAAOpxlBSsvL0%2BLFy/WX//6V/fakiVLlJuba9UIAAAAlrDsHqxNmzY1WysuLrbq8AAAAJaxrGC19JOD/DQhAADwR5YVrGO/2PnX1gAAAHyd5R/TAAAA4O8oWAAAAIZZ9lOER48e1Zw5c351jc/BAgAAvs6ygjVixAhVVlb%2B6hoAAICvs6xgHf/5VwAAAP6Me7AAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGObTBWvbtm1KS0tTXl5es23r16/XuHHjNHz4cF1zzTV677333Nuampq0ePFiZWZmauTIkZo8ebL27dvn3u50OjV79mylpaVp1KhRmjdvnurr6y3JBAAAfJ/PFqynn35aBQUF6tu3b7Ntu3btUn5%2BvubOnasPP/xQOTk5uvnmm3XgwAFJ0ooVK1RaWqri4mJt3rxZCQkJys3NlcvlkiTNnz9fhw4d0rp167R69Wo5HA4VFhZamg8AAPguny1YoaGhWrVqVYsFa%2BXKlUpPT1d6erpCQ0M1fvx4DRo0SK%2B//rokqaSkRDk5Oerfv7/CwsKUl5cnh8OhHTt26LvvvtPGjRuVl5enmJgYxcXFacaMGVq9erWOHj1qdUwAAOCDfLZg3XTTTQoPD29xW1lZmRITEz3WEhMTZbfbVV9fr/Lyco/tYWFh6tu3r%2Bx2u3bt2qWQkBANHjzYvT0pKUk//fSTvv76644JAwAA/IrN2wN0BKfTqcjISI%2B1yMhIlZeX64cffpDL5Wpxe3V1taKiohQWFqagoCCPbZJUXV3dquNXVlaqqqrKY81m66HY2NiTifOLQkJ8qx/bbO2b91heX8vdHoGWmbz%2BL9AyB1peKTAzt8QvC5Yk9/1UJ7P91577a0pKSlRUVOSxlpubq1mzZrVrv74uOrqnkf1ERHQ3sh9fEmiZyev/Ai1zoOWVAjPz8fyyYEVHR8vpdHqsOZ1OxcTEKCoqSsHBwS1u79Wrl2JiYlRbW6vGxkaFhIS4t0lSr169WnX8rKwsZWRkeKzZbD1UXV13spFa5GvvDtqbPyQkWBER3VVTc0iNjU2GpurcAi0zef1foGUOtLxS58ts6s19W/llwUpOTtbOnTs91ux2uy6//HKFhoZq4MCBKisr07nnnitJqqmp0d69ezV06FDFx8fL5XJp9%2B7dSkpKcj83IiJC/fr1a9XxY2Njm10OrKr6UQ0N3v%2BH5k2m8jc2NgXc9zLQMpPX/wVa5kDLKwVm5uP51imQVrr%2B%2Buv1/vvva8uWLTp8%2BLBWrVqlb775RuPHj5ckZWdna/ny5XI4HKqtrVVhYaGGDBmilJQUxcTEaMyYMXrsscd08OBBHThwQEuWLNGECRNks/llHwUAAIb5bGNISUmRJDU0NEiSNm7cKOnns02DBg1SYWGhFixYoIqKCg0YMEDLli1T7969JUkTJ05UVVWVJk2apLq6OqWmpnrcM/XAAw/o3nvvVWZmprp06aIrrriixQ8zBQAAaEmQq713dKNVqqp%2BNL5Pmy1Yvy/cZny/HWXD7Ava9XybLVjR0T1VXV0XMKedAy0zef1foGUOtLxS58vcu3fLH%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%2BvXrp5SUFG3atElPPvmknnnmGQ0ePFjLly/XtGnT9Pbbb6tHjx5eSgIAAHyJ357B%2BiWlpaVKSEjQhAkTFBoaqrS0NGVkZGjlypWSpJKSEl1zzTUaNmyYunXrpilTpkiSNm/e7M2xAQCAD/HrM1iLFi3SZ599ptraWl166aW64447VFZWpsTERI/HJSYmasOGDZKksrIyXXbZZe5twcHBGjJkiOx2uy6//PJWHbeyslJVVVUeazZbD8XGxrYzkaeQEN/qxzZb%2B%2BY9ltfXcrdHoGUmr/8LtMyBllcKzMwt8duCdfbZZystLU2PPPKI9u3bp9mzZ%2Bv%2B%2B%2B%2BX0%2BlUXFycx2OjoqJUXV0tSXI6nYqMjPTYHhkZ6d7eGiUlJSoqKvJYy83N1axZs04yjX%2BIju5pZD8REd2N7MeXBFpm8vq/QMscaHmlwMx8PL8tWCUlJe7/7t%2B/v%2BbOnavp06drxIgRv/pcl8vVrmNnZWUpIyPDY81m66Hq6rp27fdEvvbuoL35Q0KCFRHRXTU1h9TY2GRoqs4t0DKT1/8FWuZAyyt1vsym3ty3ld8WrBOdfvrpamxsVHBwsJxOp8e26upqxcTESJKio6ObbXc6nRo4cGCrjxUbG9vscmBV1Y9qaPD%2BPzRvMpW/sbEp4L6XgZaZvP4v0DIHWl4pMDMfz7dOgbTS559/roULF3qsORwOde3aVenp6c0%2BdmHnzp0aNmyYJCk5OVllZWXubY2Njfr888/d2wEAAH6NXxasXr16qaTwcdDSAAANZUlEQVSkRMXFxTpy5Ij27Nmjxx9/XFlZWbryyitVUVGhlStX6vDhw9q6dau2bt2q66%2B/XpKUnZ2ttWvXavv27Tp06JCWLl2qrl27avTo0d4NBQAAfIZfXiKMi4tTcXGxFi1a5C5IV199tfLy8hQaGqply5apoKBA999/v%2BLj4/Xoo4/qrLPOkiRdeOGFuvXWWzV79mx9//33SklJUXFxsbp16%2BblVAAAwFf4ZcGSpJEjR%2BqVV175xW2vvfbaLz73hhtu0A033NBRowEAAD/nl5cIAQAAvImCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGGbz9gAIHJc%2B9ndvj9AmG2Zf4O0RAAA%2BijNYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACG2bw9ANBZXfrY3709QptsmH2Bt0cAAPx/nMFqQUVFhf7yl78oNTVVF110kR599FE1NTV5eywAAOAjOIPVgpkzZyopKUkbN27U999/r6lTp%2BqUU07RH//4R2%2BPBvwiXzrjxtk2AP6OM1gnsNvt2r17t%2BbOnavw8HAlJCQoJydHJSUl3h4NAAD4CM5gnaCsrEzx8fGKjIx0ryUlJWnPnj2qra1VWFjYr%2B6jsrJSVVVVHms2Ww/FxsYanTUkhH4M3%2BRLZ9t80d/m/s7bI/yiY69bgfL6FWh5pcDM3BIK1gmcTqciIiI81o6Vrerq6lYVrJKSEhUVFXms3XzzzZo5c6a5QfVzkfvDqV8pKyvLeHnrjCorK1VSUhIweaXAy0xe/1dZWakXX3wmYDIHWl4pMDO3JLDr5S9wuVzten5WVpbWrFnj8ZWVlWVouv9TVVWloqKiZmfL/FWg5ZUCLzN5/V%2BgZQ60vFJgZm4JZ7BOEBMTI6fT6bHmdDoVFBSkmJiYVu0jNjY2oFs7AACBjjNYJ0hOTtb%2B/ft18OBB95rdbteAAQPUs2dPL04GAAB8BQXrBImJiUpJSdGiRYtUW1srh8Oh559/XtnZ2d4eDQAA%2BIiQ%2B%2B677z5vD9HZ/O53v9O6dev04IMP6o033tCECRM0efJkBQUFeXu0Znr27Klzzz03YM6uBVpeKfAyk9f/BVrmQMsrBWbmEwW52ntHNwAAADxwiRAAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMApWJ1dRUaG//OUvSk1N1UUXXaRHH31UTU1NLT52%2BfLlGjNmjM455xxlZ2dr586dFk/bfm3J%2B/LLL2vMmDEaPny4rrzySm3cuNHiaduvLXmP%2BfbbbzV8%2BHA9%2BeSTFk1pVlsyOxwOTZo0ScOGDVN6erpeeOEFa4c1oLV5m5qa9MQTTygjI0PDhw/XuHHjtH79ei9M3H7btm1TWlqa8vLy/u3jmpqatHjxYmVmZmrkyJGaPHmy9u3bZ9GUZrUlc1FRkfvvOSsrSx9//LFFU5rT2rzHKysrU2JiotasWdOBk3UeFKxObubMmYqLi9PGjRv1/PPPa%2BPGjXrxxRebPW7Tpk168skn9R//8R96//33ddFFF2natGn66aefvDD1yWtt3rfeekuLFi3Sww8/rH/84x%2B68cYbNXv2bJ97cW5t3uMVFBQoJCTEognNa23m%2Bvp6TZkyRenp6frwww/15JNPatWqVXI4HF6Y%2BuS1Nu/LL7%2BslStX6plnntHHH3%2BsW2%2B9Vbfddpt2797thalP3tNPP62CggL17dv3Vx%2B7YsUKlZaWqri4WJs3b1ZCQoJyc3Pla7/BrS2ZX3jhBa1evVrLli3TRx99pFGjRik3N1e1tbUWTGpGW/Ie09TUpHvvvVc9evTowMk6FwpWJ2a327V7927NnTtX4eHhSkhIUE5OjkpKSpo9tqSkRNdcc42GDRumbt26acqUKZKkzZs3Wz32SWtL3vr6et16660aMWKEunTpouuuu049e/bU9u3bvTD5yWlL3mO2bt2q8vJyjR492rpBDWpL5g0bNigsLExTpkxR9%2B7dNXToUK1bt079%2B/f3wuQnpy15y8rKNGLECJ155pkKCQnRRRddpKioKH3xxRdemPzkhYaGatWqVa36n29JSYlycnLUv39/hYWFKS8vTw6HQzt27LBgUnPakjk4OFi33367Bg4cqK5du%2BpPf/qTnE6nvvzySwsmNaMteY95%2BeWXFR4eriFDhnTgZJ0LBasTKysrU3x8vCIjI91rSUlJ2rNnT7N3O8dOvR4THBysIUOGyG63WzZve7Ul75VXXqkbbrjB/eeamhrV1dUpLi7Osnnbqy15pZ9L5QMPPKB7771XNpvNylGNaUvmTz75RIMGDdKdd96p3/72txo7dqxef/11q0dul7bkHT16tP7xj39o165dOnLkiN555x0dOnRI5557rtVjt8tNN92k8PDwX31cfX29ysvLPV63wsLC1LdvX5963ZJan1mScnJydOmll7r/fODAAUlSbGxsh8zWEdqSV5Kqqqq0ZMkSzZ8/vwOn6nwoWJ2Y0%2BlURESEx9qxF%2Brq6upmjz3%2BRfzYY098XGfWlrzHc7lcuvvuuzVs2DCf%2Bp9RW/MuWbJEZ599ts477zxL5usIbcl84MABvfPOO0pLS9O2bds0depU5efn6/PPP7ds3vZqS95LLrlEWVlZuuqqq5SSkqI5c%2BZowYIFOu200yyb10o//PCDXC6Xz79utceRI0c0b948jR8/Xqeffrq3x%2BkwCxYs0HXXXaczzzzT26NYyjffBgeQttyL4Gv3LbSkrRmOHj2qO%2B64Q%2BXl5Vq%2BfHkHTdVxWpu3vLxcK1euVGlpaQdP1PFam9nlcikpKUnjxo2TJF199dV65ZVX9Oabb3qc9ejsWpt37dq1Wrt2rVauXKnBgwfrgw8%2B0Jw5c3Taaadp6NChHTyl9/jD69bJqK2tVW5urkJCQnT//fd7e5wO8/e//13bt2/Xww8/7O1RLMcZrE4sJiZGTqfTY83pdCooKEgxMTEe69HR0S0%2B9sTHdWZtySv9fIlh6tSp%2Bte//qUVK1bolFNOsWpUI1qb1%2BVy6b777tPMmTPVu3dvq8c0qi1/x7179252GSI%2BPl5VVVUdPqcpbcn70ksvKSsrS0OHDlVoaKhGjx6t8847z%2Bcui7ZWVFSUgoODW/z%2B9OrVy0tTWePgwYO68cYbFR4ermeffdZvb/w%2BcuSIHnjgAd1zzz3q1q2bt8exHAWrE0tOTtb%2B/ft18OBB95rdbteAAQPUs2fPZo8tKytz/7mxsVGff/65hg0bZtm87dWWvC6XS3l5ebLZbHrhhRcUHR1t9bjt1tq8//rXv/Tf//3feuKJJ5SamqrU1FS98cYbeuaZZ3T11Vd7Y/ST1pa/4/79%2B%2BvLL7/0OMNRUVGh%2BPh4y%2BZtr7bkbWpqUmNjo8fakSNHLJnTG0JDQzVw4ECP162amhrt3bvXr8/YHT58WFOnTlVSUpKeeOIJvy4e27dv1//8z/8oPz/f/dr16aef6sEHH9T06dO9PV6Ho2B1YomJiUpJSdGiRYtUW1srh8Oh559/XtnZ2ZKksWPHuj8/JTs7W2vXrtX27dt16NAhLV26VF27dvWpnzZrS97S0lKVl5fr8ccfV2hoqDfHPmmtzXvqqadq69ateu2119xfGRkZmjhxooqLi72com3a8nc8fvx4VVdX66mnnlJ9fb3WrVunsrIyjR8/3psR2qQteTMyMrRq1Srt3r1bDQ0Neu%2B99/TBBx8oMzPTmxGM%2BvbbbzV27Fj3x6lkZ2dr%2BfLlcjgcqq2tVWFhoYYMGaKUlBQvT2rOiZmfe%2B45denSRQ8%2B%2BKCCg/3vf8HH5z377LO1ZcsWj9eu5ORk3XLLLXrooYe8PWqH4x6sTu6JJ57Q/PnzdcEFFygsLEwTJ050//Tcnj173J9zdeGFF%2BrWW2/V7Nmz9f333yslJUXFxcU%2B9%2B6otXlXr16tioqKZje1X3nllSooKLB87pPVmrwhISE69dRTPZ7XvXt3hYWF%2BeQlw9b%2BHcfFxWnZsmV66KGH9F//9V/6zW9%2BoyVLluiMM87w5vht1tq8U6dOVUNDg3Jzc3Xw4EHFx8eroKBA559/vjfHb7Nj5aihoUGS3B8AbLfbdfToUe3Zs8d9Zm7ixImqqqrSpEmTVFdXp9TUVBUVFXln8HZoS%2BbVq1dr//79za4uTJ8%2BXTNmzLBw6pPX2rxdu3Zt9trVtWtXRURE%2BNTtKycryBWodxgCAAB0EP87PwkAAOBlFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGPb/AKUgAziOBtLHAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common510150311854432857”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>889</td> <td class=”number”>27.7%</td> <td>

<div class=”bar” style=”width:40%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.1396843134515994</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.11904761904761904</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.1644195988161789</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.11566042100393245</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.10354110581901015</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.1268284433922381</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.3700962250185048</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0279814203368963</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0465614378171998</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2215)</td> <td class=”number”>2223</td> <td class=”number”>69.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme510150311854432857”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>889</td> <td class=”number”>27.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.002389257896497348</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.002614420095478622</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.002698192346037772</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0028233035474809075</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.9099181073703367</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.1695906432748537</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.2531328320802004</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.366120218579235</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.445086705202312</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FMRKT_ANMLPROD16”><s>FMRKT_ANMLPROD16</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_FMRKT_FRVEG16”><code>FMRKT_FRVEG16</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.96896</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FMRKT_BAKED16”><s>FMRKT_BAKED16</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_FMRKT_ANMLPROD16”><code>FMRKT_ANMLPROD16</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.98152</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FMRKT_CREDIT16”><s>FMRKT_CREDIT16</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_FMRKT_SNAP16”><code>FMRKT_SNAP16</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.92193</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FMRKT_FRVEG16”><s>FMRKT_FRVEG16</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_FMRKT_CREDIT16”><code>FMRKT_CREDIT16</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.97114</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FMRKT_OTHERFOOD16”><s>FMRKT_OTHERFOOD16</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_FMRKT_BAKED16”><code>FMRKT_BAKED16</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.98659</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FMRKT_SFMNP16”><s>FMRKT_SFMNP16</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_FMRKT_WIC16”><code>FMRKT_WIC16</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.93896</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FMRKT_SNAP16”>FMRKT_SNAP16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>33</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>30.1%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>967</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1.2073</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>52</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>40.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-620853937255008612”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-620853937255008612,#minihistogram-620853937255008612”
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</div> <div class=”row collapse col-md-12” id=”descriptives-620853937255008612”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-620853937255008612”

aria-controls=”quantiles-620853937255008612” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-620853937255008612” aria-controls=”histogram-620853937255008612”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-620853937255008612” aria-controls=”common-620853937255008612”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-620853937255008612” aria-controls=”extreme-620853937255008612”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-620853937255008612”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>1</td>

</tr> <tr>

<th>95-th percentile</th> <td>5</td>

</tr> <tr>

<th>Maximum</th> <td>52</td>

</tr> <tr>

<th>Range</th> <td>52</td>

</tr> <tr>

<th>Interquartile range</th> <td>1</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>3.309</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.7408</td>

</tr> <tr>

<th>Kurtosis</th> <td>76.445</td>

</tr> <tr>

<th>Mean</th> <td>1.2073</td>

</tr> <tr>

<th>MAD</th> <td>1.4847</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>7.4843</td>

</tr> <tr>

<th>Sum</th> <td>2708</td>

</tr> <tr>

<th>Variance</th> <td>10.949</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-620853937255008612”>

<img 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msAAABGWB5Y69at05gxY7716w4ePGjBbgAAAMyz/B6sxMREPfTQQ9q1a5dv7T//8z/1wAMPyOPxWL0dAAAA4ywPrBUrVujs2bMaNGiQb23cuHFqbW3VypUrrd4OAACAcZa/Rfjee%2B9py5Ytio2N9a0lJCSoqKhId999t9XbAQAAMM7yK1hNTU2KiIhou5HQUDU2Nlq9HQAAAOMsD6zRo0dr5cqV%2BuKLL3xrf//73/X000/7fVQDAABAsLL8LcLFixfr3//933XLLbcoMjJSra2tamhoUL9%2B/fTb3/7W6u0AAAAYZ3lg9evXT2%2B//bbeffddffbZZwoNDdWAAQM0duxYhYWFWb0dAAAA4ywPLEkKDw/XHXfcEYinBgAAuOosD6yTJ0%2BquLhYn376qZqamtoc/%2BMf/2j1lgAAAIwKyD1YNTU1Gjt2rLp162b10wMAAFx1lgdWRUWF/vjHP8put1v91AAAAJaw/GMaevbsyZUrAADQpVkeWHPnzlVJSYm8Xq/VTw0AAGAJy98ifPfdd/XRRx9p48aN6tu3r0JD/Rvv97//vdVbAgAAMMrywIqMjNQPfvADq58WAADAMpYH1ooVK6x%2BSgAAAEtZfg%2BWJB0/flxr1qzRE0884Vv729/%2BFoitAAAAGGd5YB04cEBTpkzRzp07VV5eLunrDx%2BdPXs2HzIKAAC6BMsDa/Xq1frFL36hLVu2KCQkRNLXfz/hypUrtXbtWqu3AwAAYJzlgfXf//3fysnJkSRfYEnSD3/4Q7lcLqu3AwAAYJzlgdWjR492/w7CmpoahYeHW70dAAAA4ywPrBEjRui5555TfX29b62qqkqFhYW65ZZbrN4OAACAcZZ/TMMTTzyh%2B%2B%2B/X2lpaWppadGIESPU2NiowYMHa%2BXKlVZvBwAAwDjLA%2Bv6669XeXm59u3bp6qqKl177bUaMGCAbr31Vr97sgAAAIKV5YElSddcc43uuOOOQDw1AADAVWd5YI0fP/6SV6r4LCwAABDsLA%2Bsu%2B66yy%2BwWlpaVFVVJafTqfvvv9/q7QAAABhneWAVFBS0u75jxw795S9/sXg3AAAA5gXk7yJszx133KG333470NsAAAC4Yp0msA4dOiSv1xvobQAAAFwxy98inDVrVpu1xsZGuVwuTZgwwertAAAAGGd5YCUkJLT5LcKIiAhlZWVpxowZVm8HAADAOMsDi09rBwAAXZ3lgfXWW291%2BGunTp16FXcCAABwdVgeWEuWLFFra2ubG9pDQkL81kJCQggsAAAQlCwPrFdeeUWvvvqqHnroISUmJsrr9ero0aN6%2BeWXde%2B99yotLc3qLQEAABgVkHuwSktL1adPH9/aqFGj1K9fP82ZM0fl5eVWbwkAAMAoyz8H68SJE4qOjm6zHhUVperqaqu3AwAAYJzlgRUfH6%2BVK1eqtrbWt1ZXV6fi4mLdcMMN3%2Bmx9u/fr/T0dOXn57c5tnXrVk2ePFk33XSTpk%2Bfrvfee893rLW1VatXr1ZmZqZGjx6tOXPm6OTJk77jHo9HCxYsUHp6usaOHaslS5aoqanpMs4WAAD8M7I8sBYvXqxt27YpPT1do0aN0pgxY3TzzTdr48aNWrRoUYcf5%2BWXX9by5cvVv3//NscOHz6swsJCFRQU6M9//rNyc3P1yCOP6PTp05Kk119/XVu2bFFpaan27NmjhIQE5eXl%2BW6yX7p0qRobG1VeXq433nhDLpdLRUVFZgYAAAC6PMvvwRo7dqz27t2rffv26fTp0/J6verTp49uu%2B029ejRo8OPExERobKyMv3yl7/UuXPn/I5t2LBBGRkZysjIkCRNmTJF69ev1%2BbNm/Xggw/K4XAoNzdXAwcOlCTl5%2BcrLS1NBw8eVN%2B%2BfbVr1y69%2BeabstvtkqSHH35YP//5z1VYWKhrrrnG0CQAAEBXZXlgSdJ1112nzMxMnT59Wv369busx5g9e/ZFj1VWVvri6htJSUlyOp1qamrSsWPHlJSU5DsWGRmp/v37y%2Bl06uzZswoLC1NiYqLveHJysr788ksdP37cbx0AAKA9lgdWU1OTli1bprfffluSVFFRobq6Oj322GP69a9/raioqCt%2BDo/H0%2BZG%2BujoaB07dkxffPGFvF5vu8dra2sVExOjyMhIv7/O55uv/b/3jV1KTU2N3G6335rN1k1xcXGXczoXFRbWaf6u7g6x2Tr3fr%2BZZ7DNtbNinmYxT3OYpVnMs32WB9aqVat0%2BPBhFRUV6fHHH/ett7S0qKioSM8884yR57nwg0y/y/Fv%2B95v43A4VFJS4reWl5en%2BfPnX9HjBrvY2O6B3kKHREVdF%2BgtdCnM0yzmaQ6zNIt5%2BrM8sHbs2KH169crISFBhYWFkr7%2BiIYVK1Zo6tSpRgIrNjZWHo/Hb83j8chutysmJkahoaHtHu/Zs6fsdrvq6%2BvV0tKisLAw3zFJ6tmzZ4eePzs7W%2BPHj/dbs9m6qba24XJPqV3B9tOC6fM3LSwsVFFR16murlEtLa2B3k7QY55mMU9zmKVZnX2egfrh3vLAamhoUEJCQpt1u92uL7/80shzpKSkqKKiwm/N6XRq0qRJioiI0ODBg1VZWakxY8ZI%2BvpjIj777DMNGzZM8fHx8nq9OnLkiJKTk33fGxUVpQEDBnTo%2BePi4tq8Heh2n1Vzc%2Bd74VkpWM6/paU1aPYaDJinWczTHGZpFvP0Z/klkBtuuEF/%2BctfJPm/Fbd9%2B3b967/%2Bq5HnmDlzpt5//33t3btX586dU1lZmU6cOKEpU6ZIknJycrRu3Tq5XC7V19erqKhIQ4cOVWpqqux2uyZOnKjnn39eZ86c0enTp7V27VplZWXJZgvI7wQAAIAgY3kx/OQnP9Gjjz6qe%2B65R62trXrttddUUVGhHTt2aMmSJR1%2BnNTUVElSc3OzJGnXrl2Svr7aNGTIEBUVFWnFihWqrq7WoEGD9NJLL6l3796SpFmzZsntduu%2B%2B%2B5TQ0OD0tLS/O6ZeuaZZ7Rs2TJlZmbqmmuu0d13393uh5kCAAC0J8R7pXd0X4Y33nhD69ev1/Hjx3XttddqwIABys3N1Q9/%2BEOrt2IZt/us8ce02UJ1Z9F%2B4497tWxbcGugt3BJNluoYmO7q7a2gcvcBjBPs5inOczSrM4%2Bz969O/4ZmyZZfgXrzJkzuueee3TPPfdY/dQAAACWsPwerMzMzCv%2BGAQAAIDOzPLASktL07Zt26x%2BWgAAAMtY/hbhv/zLv%2BiXv/ylSktLdcMNN7T5u/2Ki4ut3hIAAIBRlgfWsWPH9L3vfU9Sx//qGQAAgGBiWWDl5%2Bdr9erV%2Bu1vf%2BtbW7t2rfLy8qzaAgAAgCUsuwdr9%2B7dbdZKS0utenoAAADLWBZY7f3mIL9NCAAAuiLLAiskJKRDawAAAMHO8o9pAAAA6OoILAAAAMMs%2By3C8%2BfPa%2BHChd%2B6xudgAQCAYGdZYI0cOVI1NTXfugYAABDsLAus//v5VwAAAF0Z92ABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAY1mUDKzExUSkpKUpNTfX9efbZZyVJBw4cUFZWlkaMGKFJkyZp8%2BbNft%2B7bt06TZw4USNGjFBOTo4qKioCcQoAACBI2QK9gatp%2B/bt6tu3r99aTU2NHn74YS1ZskSTJ0/Whx9%2BqHnz5mnAgAFKTU3V7t27tWbNGr3yyitKTEzUunXr9NBDD2nnzp3q1q1bgM4EAAAEky57BetitmzZooSEBGVlZSkiIkLp6ekaP368NmzYIElyOByaPn26hg8frmuvvVY/%2B9nPJEl79uwJ5LYBAEAQ6dKBVVxcrHHjxmnUqFFaunSpGhoaVFlZqaSkJL%2BvS0pK8r0NeOHx0NBQDR06VE6n09K9AwCA4NVl3yK88cYblZ6erl/96lc6efKkFixYoKeffloej0d9%2BvTx%2B9qYmBjV1tZKkjwej6Kjo/2OR0dH%2B453RE1Njdxut9%2BazdZNcXFxl3k27QsLC64%2Bttk6936/mWewzbWzYp5mMU9zmKVZzLN9XTawHA6H758HDhyogoICzZs3TyNHjvzW7/V6vVf83CUlJX5reXl5mj9//hU9brCLje0e6C10SFTUdYHeQpfCPM1inuYwS7OYp78uG1gX6tu3r1paWhQaGiqPx%2BN3rLa2Vna7XZIUGxvb5rjH49HgwYM7/FzZ2dkaP36835rN1k21tQ2Xufv2BdtPC6bP37SwsFBFRV2nurpGtbS0Bno7QY95msU8zWGWZnX2eQbqh/suGViHDh3S5s2btWjRIt%2Bay%2BVSeHi4MjIy9Oabb/p9fUVFhYYPHy5JSklJUWVlpaZNmyZJamlp0aFDh5SVldXh54%2BLi2vzdqDbfVbNzZ3vhWelYDn/lpbWoNlrMGCeZjFPc5ilWczTX3BdAumgnj17yuFwqLS0VF999ZWqqqr0wgsvKDs7Wz/%2B8Y9VXV2tDRs26Ny5c9q3b5/27dunmTNnSpJycnL01ltv6eOPP1ZjY6NefPFFhYeHa9y4cYE9KQAAEDS65BWsPn36qLS0VMXFxb5AmjZtmvLz8xUREaGXXnpJy5cv19NPP634%2BHitWrVK3//%2B9yVJP/jBD/TYY49pwYIF%2Bvzzz5WamqrS0lJde%2B21AT4rAAAQLEK8V3pHNzrE7T5r/DFttlDdWbTf%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%2BNHzfwr0Fr6TbQtuDfQWAABBiitYAAAAhhFYAAAAhhFYAAAAhhFYAAAAhhFY7aiurtaDDz6otLQ03X777Vq1apVaW1sDvS0AABAk%2BC3Cdjz66KNKTk7Wrl279Pnnn2vu3Lnq1auXfvrTnwZ6a7AQv/UIALhcXMG6gNPp1JEjR1RQUKAePXooISFBubm5cjgcgd4aAAAIElzBukBlZaXi4%2BMVHR3tW0tOTlZVVZXq6%2BsVGRn5rY9RU1Mjt9vtt2azdVNcXJzRvYaF0cf4X8F2xS2YvFNwW6C34Pvvnf/urxyzNIt5to/AuoDH41FUVJTf2jexVVtb26HAcjgcKikp8Vt75JFH9Oijj5rbqL4Oufuv/1TZ2dnG4%2B2fUU1NjRwOB/M0hHmaVVNTo9/85hXmaQCzNIt5to/cbIfX672i78/OztbGjRv9/mRnZxva3f9yu90qKSlpc7UMl4d5msU8zWKe5jBLs5hn%2B7iCdQG73S6Px%2BO35vF4FBISIrvd3qHHiIuLo%2BIBAPgnxhWsC6SkpOjUqVM6c%2BaMb83pdGrQoEHq3r17AHcGAACCBYF1gaSkJKWmpqq4uFj19fVyuVx67bXXlJOTE%2BitAQCAIBH21FNPPRXoTXQ2t912m8rLy/Xss8/q7bffVlZWlubMmaOQkJBAb62N7t27a8yYMVxdM4R5msU8zWKe5jBLs5hnWyHeK72jGwAAAH54ixAAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAisIVVdX68EHH1RaWpphzVtDAAAHFElEQVRuv/12rVq1Sq2trYHeVlDZv3%2B/0tPTlZ%2Bf3%2BbY1q1bNXnyZN10002aPn263nvvvQDsMLhUV1crLy9PaWlpSk9P16JFi1RXVydJOnz4sO69916NHDlSEyZM0Kuvvhrg3XZuR44c0f3336%2BRI0cqPT1dCxYskNvtliQdOHBAWVlZGjFihCZNmqTNmzcHeLfB5bnnnlNiYqLv35nnd5eYmKiUlBSlpqb6/jz77LOSmGcbXgSdadOmeZ988klvXV2dt6qqyjthwgTvq6%2B%2BGuhtBY3S0lLvhAkTvLNmzfIuWLDA79ihQ4e8KSkp3r1793qbmpq8mzZt8g4fPtx76tSpAO02ONx9993eRYsWeevr672nTp3yTp8%2B3bt48WJvY2Oj97bbbvOuWbPG29DQ4K2oqPCOGTPGu2PHjkBvuVM6d%2B6c95ZbbvGWlJR4z5075/3888%2B99957r/fhhx/2/v3vf/feeOON3g0bNnibmpq8f/rTn7zDhg3zfvLJJ4HedlA4dOiQd8yYMd4hQ4Z4vV4v87xMQ4YM8Z48ebLNOvNsiytYQcbpdOrIkSMqKChQjx49lJCQoNzcXDkcjkBvLWhERESorKxM/fv3b3Nsw4YNysjIUEZGhiIiIjRlyhQNGTKEn8Quoa6uTikpKVq4cKG6d%2B%2Bu66%2B/XtOmTdMHH3ygvXv36vz585o3b566deum5ORkzZgxg9frRTQ2Nio/P19z585VeHi47Ha77rzzTn366afasmWLEhISlJWVpYiICKWnp2v8%2BPHasGFDoLfd6bW2tmrZsmXKzc31rTFPs5hnWwRWkKmsrFR8fLyio6N9a8nJyaqqqlJ9fX0AdxY8Zs%2BerR49erR7rLKyUklJSX5rSUlJcjqdVmwtKEVFRWnFihXq1auXb%2B3UqVOKi4tTZWWlEhMTFRYW5juWlJSkioqKQGy104uOjtaMGTNks9kkScePH9ebb76pH/3oRxd9bTLLb/f73/9eERERmjx5sm%2BNeV6%2B4uJijRs3TqNGjdLSpUvV0NDAPNtBYAUZj8ejqKgov7VvYqu2tjYQW%2BpSPB6PX7xKX8%2BX2Xac0%2BnU%2BvXrNW/evHZfrzExMfJ4PNw3eAnV1dVKSUnRXXfdpdTUVM2fP/%2Bis%2BS1eWn/%2BMc/tGbNGi1btsxvnXlenhtvvFHp6enauXOnHA6HPv74Yz399NPMsx0EVhDyer2B3kKXxnwv34cffqg5c%2BZo4cKFSk9Pv%2BjXhYSEWLir4BMfHy%2Bn06nt27frxIkTevzxxwO9paC1YsUKTZ8%2BXYMGDQr0VroEh8OhGTNmKDw8XAMHDlRBQYHKy8t1/vz5QG%2Bt0yGwgozdbpfH4/Fb83g8CgkJkd1uD9Cuuo7Y2Nh258tsv93u3bv14IMPavHixZo9e7akr1%2BvF/4E6/F4FBMTo9BQ/vdzKSEhIUpISFB%2Bfr7Ky8tls9navDZra2t5bV7CgQMH9Le//U15eXltjrX33zrz/O769u2rlpYWhYaGMs8L8H%2B4IJOSkqJTp07pzJkzvjWn06lBgwape/fuAdxZ15CSktLmngGn06nhw4cHaEfB4aOPPlJhYaFeeOEFTZ061beekpKio0ePqrm52bfGPC/uwIEDmjhxot/bp9%2BE6LBhw9q8NisqKpjlJWzevFmff/65br/9dqWlpWn69OmSpLS0NA0ZMoR5fkeHDh3SypUr/dZcLpfCw8OVkZHBPC9AYAWZpKQkpaamqri4WPX19XK5XHrttdeUk5MT6K11CTNnztT777%2BvvXv36ty5cyorK9OJEyc0ZcqUQG%2Bt02pubtaTTz6pgoICjR071u9YRkaGIiMj9eKLL6qxsVEHDx5UWVkZr9eLSElJUX19vVatWqXGxkadOXNGa9as0ahRo5STk6Pq6mpt2LBB586d0759%2B7Rv3z7NnDkz0NvutBYtWqQdO3Zo06ZN2rRpk0pLSyVJmzZt0uTJk5nnd9SzZ085HA6Vlpbqq6%2B%2BUlVVlV544QVlZ2frxz/%2BMfO8QIiXG06CzunTp7V06VL913/9lyIjIzVr1iw98sgj3NfSQampqZLku6ryzW9sffObgjt37lRxcbGqq6s1aNAgLVmyRKNHjw7MZoPABx98oH/7t39TeHh4m2Pbt29XQ0ODli1bpoqKCvXq1UsPPPCAfvKTnwRgp8Hh6NGjWr58uT755BN169ZNN998sxYtWqQ%2Bffror3/9q5YvXy6Xy6X4%2BHgtXLhQEyZMCPSWg8b//M//KDMzU0ePHpUk5nkZ/vrXv6q4uFhHjx5VeHi4pk2bpvz8fEVERDDPCxBYAAAAhvEWIQAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGH/D1JIyXwUiLcOAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-620853937255008612”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1289</td> <td class=”number”>40.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>525</td> <td class=”number”>16.4%</td> <td>

<div class=”bar” style=”width:41%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>171</td> <td class=”number”>5.3%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>49</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>31</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>18</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (22)</td> <td class=”number”>45</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>967</td> <td class=”number”>30.1%</td> <td>

<div class=”bar” style=”width:75%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-620853937255008612”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1289</td> <td class=”number”>40.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>525</td> <td class=”number”>16.4%</td> <td>

<div class=”bar” style=”width:41%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>171</td> <td class=”number”>5.3%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>49</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>36.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>38.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>41.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>43.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>52.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FMRKT_WIC16”>FMRKT_WIC16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>34</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>30.1%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>967</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1.1694</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>48</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>44.1%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-610503464288004810”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-610503464288004810”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-610503464288004810”

aria-controls=”quantiles-610503464288004810” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-610503464288004810” aria-controls=”histogram-610503464288004810”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-610503464288004810” aria-controls=”common-610503464288004810”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-610503464288004810” aria-controls=”extreme-610503464288004810”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-610503464288004810”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>1</td>

</tr> <tr>

<th>95-th percentile</th> <td>5</td>

</tr> <tr>

<th>Maximum</th> <td>48</td>

</tr> <tr>

<th>Range</th> <td>48</td>

</tr> <tr>

<th>Interquartile range</th> <td>1</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>3.4149</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.9202</td>

</tr> <tr>

<th>Kurtosis</th> <td>68.142</td>

</tr> <tr>

<th>Mean</th> <td>1.1694</td>

</tr> <tr>

<th>MAD</th> <td>1.5392</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>7.1692</td>

</tr> <tr>

<th>Sum</th> <td>2623</td>

</tr> <tr>

<th>Variance</th> <td>11.662</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-610503464288004810”>

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8Z73yyiuaM2eOJHnut2rOd821REZGhkaMGOE1ZrOFq7KyplX7/ba2kNB/CNPrt1JISLDs9g6qqqpVQ0Ojr8sJaPTaOvTaGvTZOs312le/3AdkwGpOQkKCSkpKFBMTo%2BDgYLlcLq95l8uljh07Ki4uTtXV1WpoaFBISIhnTpI6duzYomPFx8c3uRzodB5Tff25/YMVCOtvaGgMiHX4A3ptHXptDfpsnbbQa/86BdJCL730kjZs2OA1VlZWpm7duiksLEx9%2BvRRaWmpZ66qqkoHDx7UgAED1K9fP7ndbu3evdsz73A4ZLfb1bNnT8vWAAAA/FdABqyTJ0/qwQcflMPhUF1dndavX6%2B//OUvmjJliiQpMzNTK1euVFlZmaqrq5Wfn69%2B/fopJSVFcXFxGjNmjB577DEdPXpUhw8f1ooVKzRx4kTZbOfMCT8AANAKfpsYUlJSJEn19fWSpK1bt0r6%2BmzT1KlTVVNTozvuuENOp1Pnn3%2B%2BVqxYoeTkZEnSlClT5HQ6ddNNN6mmpkapqaleN6U/8MADWrRokUaOHKl27drp2muvbfIwUwAAgNMJcrf2jm60iNN5zPg%2BbbZgXZm/w/h%2Bz5aNsy71dQlnzGYLVmxshCora3x%2BXT/Q0Wvr0Gtr0GfrNNfrzp2jfFJLQF4iBAAA8CUCFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMMsD1ogRI1RQUKBDhw5ZfWgAAABLWB6wbrjhBm3YsEGjRo3Sr371K23ZskX19fVWlwEAAHDWWB6wsrKytGHDBv3xj39Unz599PDDDys9PV2PPPKI9u/fb3U5AAAAxvnsHqz%2B/ftr7ty5evPNNzV//nz98Y9/1NVXX61bbrlFn3zyia/KAgAAaDWfBay6ujpt2LBBv/71rzV37lx16dJF99xzj/r166dp06Zp3bp1vioNAACgVWxWH7CsrEzFxcV69dVXVVNTozFjxuj555/X4MGDPdsMHTpU9913n8aOHWt1eQAAAK1mecC65ppr1LNnT02fPl3XX3%2B9YmJimmyTnp6uo0ePWl0aAACAEZYHrJUrV%2Bqiiy763u0%2B/vhjC6oBAAAwz/J7sBITEzVjxgxt3brVM/aHP/xBv/71r%2BVyuVq8nx07digtLU05OTlN5rZs2aJx48Zp0KBBGjNmjP74xz965pYvX65%2B/fopJSXF6%2BvIkSOSpBMnTui3v/2tLr/8cqWmpio7O1uVlZWtWDEAADjXWB6w8vLydOzYMfXu3dszNnz4cDU2Nmrx4sUt2sfTTz%2Bt3Nxcde/evcncJ598ojlz5ig7O1vvvfee5s%2BfrwceeEDvv/%2B%2BZ5vrrrtODofD66tTp06SpGXLlqm0tFRFRUXavHmz3G637rnnnlauGgAAnEssD1hvv/22CgoK1KNHD89Yjx49lJ%2Bfrx07drRoH2FhYSouLm42YLlcLk2fPl2jRo2SzWZTenq6%2Bvbt6xWwTqe%2Bvl7FxcW69dZbdd555ykmJkazZs3S9u3b9cUXX7R4jQAA4Nxm%2BT1Yx48fV1hYWJPx4OBg1dbWtmgfU6dOPe3c5Zdfrssvv9zzfX19vZxOp7p06eIZ27Nnj6ZMmaJ//etfOu%2B883TPPfdo2LBhOnjwoI4dO6b%2B/ft7tu3Vq5fat2%2Bv0tJSr318l4qKCjmdTq8xmy1c8fHxLXp9S4WE%2BNefkrTZ/KvebzrVa3/ruT%2Bi19ah19agz9ZpS722PGANHTpUixcv1uzZsxUdHS1J%2BuKLL7RkyRKvRzWYkp%2Bfr/DwcF199dWSpK5du6pbt26aPXu24uPjVVRUpBkzZmjt2rWee8DsdrvXPux2%2Bw%2B6D6uoqEgFBQVeY1lZWcrOzm7lavxbbGyEr0toNbu9g69LOGfQa%2BvQa2vQZ%2Bu0hV5bHrDmz5%2BvX/7yl7rkkksUGRmpxsZG1dTUqFu3bnrhhReMHcftdis/P1/r16/XypUrPWfNJk2apEmTJnm2mzZtml5//XWtXbvWc%2BbL7Xa36tgZGRkaMWKE15jNFq7KyppW7ffb2kJC/yFMr99KISHBsts7qKqqVg0Njb4uJ6DRa%2BvQa2vQZ%2Bs012tf/XJvecDq1q2bXn/9df3lL3/RwYMHFRwcrJ49e2rYsGEKCQkxcozGxkbdc889%2BuSTT/TSSy%2BpW7du37l9QkKCKioqFBcXJ%2Bnr%2B7giIv7vf8i///1vdezYscXHj4%2BPb3I50Ok8pvr6c/sHKxDW39DQGBDr8Af02jr02hr02TptodeWByxJCg0N1ahRo87a/h9%2B%2BGF9%2Bumneumll5o8yPR3v/udBg0apEsuucQzVlZWpquvvlrdunVTdHS0SktLlZCQIEn617/%2BpZMnTyo5Ofms1QsAAAKL5QHrs88%2B09KlS/Xpp5/q%2BPHjTebfeOONVu3/gw8%2B0Nq1a7Vhw4ZmnxLvcrl0//3363e/%2B50SEhK0atUqHTx4UOPHj1dISIgmT56sJ598UikpKWrfvr0effRRXXnllZ7HOAAAAHwfn9yDVVFRoWHDhik8PPyM9pGSkiLp608ISvI8tNThcGjNmjU6duyYrrjiCq/XDB06VM8%2B%2B6xmz54t6et7r1wul3r37q0//OEP6tq1qyQpOztbNTU1uu6661RfX68rrrhC99133xnVCQAAzk1B7tbe0f0DDRo0SG%2B88YbnfqdzhdN5zPg%2BbbZgXZnfsmeHtQUbZ13q6xLOmM0WrNjYCFVW1vj8un6go9fWodfWoM/Waa7XnTtH%2BaQWyz%2BG1rFjxzM%2BcwUAAOAPLA9Y06dPV0FBQasfhQAAANBWWX4P1l/%2B8hd9%2BOGHeuWVV3T%2B%2BecrONg747388stWlwQAAGCU5QErMjLS60/ZAAAABBrLA1ZeXp7VhwQAALCUT/7Wyr59%2B7R8%2BXLdc889nrF//vOfvigFAADAOMsD1rvvvqtx48Zpy5YtWr9%2BvaSvHz46derUVj9kFAAAoC2wPGAtW7ZMd911l9atW6egoCBJX/99wsWLF2vFihVWlwMAAGCc5QHrX//6lzIzMyXJE7Ak6aqrrlJZWZnV5QAAABhnecCKiopq9m8QVlRUKDQ01OpyAAAAjLM8YF144YV6%2BOGHVV1d7Rnbv3%2B/5s6dq0suucTqcgAAAIyz/DEN99xzj26%2B%2BWalpqaqoaFBF154oWpra9WnTx8tXrzY6nIAAACMszxgde3aVevXr9dbb72l/fv3q3379urZs6cuvfRSr3uyAAAA/JXlAUuS2rVrp1GjRvni0AAAAGed5QFrxIgR33mmimdhAQAAf2d5wLr66qu9AlZDQ4P2798vh8Ohm2%2B%2B2epyAAAAjLM8YM2ZM6fZ8c2bN%2Bvvf/%2B7xdUAAACY55O/RdicUaNG6fXXX/d1GQAAAK3WZgLWzp075Xa7fV0GAABAq1l%2BiXDKlClNxmpra1VWVqbRo0dbXQ4AAIBxlgesHj16NPkUYVhYmCZOnKhJkyZZXQ4AAIBxlgcsntYOAAACneUB69VXX23xttdff/1ZrAQAAODssDxgLViwQI2NjU1uaA8KCvIaCwoKImABAAC/ZHnAeuaZZ/Tss89qxowZSkxMlNvt1p49e/T000/rxhtvVGpqqtUlAQAAGGX5YxoWL16s3NxcDR48WJGRkYqKitKQIUP0wAMPaMmSJQoNDfV8fZ8dO3YoLS1NOTk5TeY2bNigsWPHatCgQZowYYLefvttz1xjY6OWLVumkSNHaujQobrlllv02WefeeZdLpdmzZqltLQ0DRs2TAsWLNDx48fNNAAAAAQ8ywPWgQMHFB0d3WTcbrervLy8xft5%2BumnlZubq%2B7duzeZ27Vrl%2BbOnas5c%2Bbob3/7m6ZNm6bbbrtNhw8fliStWrVK69atU2Fhod5880316NFDWVlZnkuUCxcuVG1trdavX681a9aorKxM%2Bfn5Z7hiAABwrrE8YCUkJGjx4sWqrKz0jFVVVWnp0qX68Y9/3OL9hIWFqbi4uNmAtXr1aqWnpys9PV1hYWEaN26c%2Bvbtq7Vr10qSioqKNG3aNPXq1UuRkZHKyclRWVmZPv74Yx05ckRbt25VTk6O4uLi1KVLF916661as2aN6urqWt8AAAAQ8Cy/B2v%2B/PmaPXu2ioqKFBERoeDgYFVXV6t9%2B/ZasWJFi/czderU086VlpYqPT3daywpKUkOh0PHjx/X3r17lZSU5JmLjIxU9%2B7d5XA4dOzYMYWEhCgxMdEz379/f3311Vfat2%2Bf1/jpVFRUyOl0eo3ZbOGKj49v6fJaJCSkzTyIv0VsNv%2Bq95tO9drfeu6P6LV16LU16LN12lKvLQ9Yw4YN0/bt2/XWW2/p8OHDcrvd6tKliy677DJFRUUZOYbL5WpyGTI6Olp79%2B7Vv//9b7nd7mbnKysrFRMTo8jISK%2BHoZ7a9ptn3b5LUVGRCgoKvMaysrKUnZ19JssJGLGxEb4uodXs9g6%2BLuGcQa%2BtQ6%2BtQZ%2Bt0xZ6bXnAkqQOHTpo5MiROnz4sLp163ZWjvF9f9fwu%2BZb%2BzcRMzIyNGLECK8xmy1clZU1rdrvt7WFhP5DmF6/lUJCgmW3d1BVVa0aGhp9XU5Ao9fWodfWoM/Waa7Xvvrl3vKAdfz4cS1atEivv/66JKmkpERVVVW688479eijj8put7f6GLGxsXK5XF5jLpdLcXFxiomJUXBwcLPzHTt2VFxcnKqrq9XQ0KCQkBDPnCR17NixRcePj49vcjnQ6Tym%2Bvpz%2BwcrENbf0NAYEOvwB/TaOvTaGvTZOm2h15afAnnkkUe0a9cu5efnKzj4/w7f0NBg7JN6ycnJKikp8RpzOBwaOHCgwsLC1KdPH5WWlnrmqqqqdPDgQQ0YMED9%2BvWT2%2B3W7t27vV5rt9vVs2dPI/UBAIDAZnnA2rx5s/7rv/5LV111lec%2BJ7vdrry8PG3ZssXIMSZPnqx33nlH27dv14kTJ1RcXKwDBw5o3LhxkqTMzEytXLlSZWVlqq6uVn5%2Bvvr166eUlBTFxcVpzJgxeuyxx3T06FEdPnxYK1as0MSJE2Wz%2BeSKKgAA8DOWJ4aamhr16NGjyXhcXJy%2B%2BuqrFu8nJSVFklRfXy9J2rp1q6Svzzb17dtX%2Bfn5ysvLU3l5uXr37q2nnnpKnTt3liRNmTJFTqdTN910k2pqapSamup1U/oDDzygRYsWaeTIkWrXrp2uvfbaZh9mCgAA0BzLA9aPf/xj/f3vf1dqaqrXzeSbNm3Sj370oxbvx%2BFwfOf86NGjNXr06GbngoKClJ2dfdpP9UVFRenRRx9tcS0AAADfZHnA%2BvnPf67bb79dN9xwgxobG/Xcc8%2BppKREmzdv1oIFC6wuBwAAwDjLA1ZGRoZsNptefPFFhYSE6Mknn1TPnj2Vn5%2Bvq666yupyAAAAjLM8YB09elQ33HCDbrjhBqsPDQAAYAnLP0U4cuTIVj/IEwAAoC2zPGClpqZq48aNVh8WAADAMpZfIjzvvPP00EMPqbCwUD/%2B8Y/Vrl07r/mlS5daXRIAAIBRlgesvXv36ic/%2BYmklv/xZAAAAH9iWcDKycnRsmXL9MILL3jGVqxYoaysLKtKAAAAsIRl92Bt27atyVhhYaFVhwcAALCMZQGruU8O8mlCAAAQiCwLWKf%2BsPP3jQEAAPg7yx/TAAAAEOgIWAAAAIZZ9inCuro6zZ49%2B3vHeA4WAADwd5YFrMGDB6uiouJ7xwAAAPydZQHrm8%2B/AgAACGTcgwUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgmGVPcrfSe%2B%2B9p1/%2B8pdeY263W3V1dVq5cqWmTp2q0NBQr/n//M//1M9%2B9jNJ0sqVK7Vq1So5nU4lJiZqwYIFSk5Otqx%2BAADg3wIyYA0dOlQOh8Nr7Mknn9Tu3bslSQkJCdq2bVuzr922bZuWL1%2BuZ555RomJiVq5cqVmzJihLVu2KDw8/KzXDgAA/N85cYnwf//3f/Xcc8/p7rvv/t5ti4qKNGHCBA0cOFDt27fXr371K0nSm2%2B%2BebbLBAAAAeKcCFiPP/64brjhBv3oRz%2BSJNXU1CgrK0upqam67LLL9Nxzz8ntdkuSSktLlZSU5HltcHCw%2BvXr1%2BSMGAAAwOkE5CXCb/r888%2B1ZcsWbdmyRZIUGRmpvn376uabb9ayZcv0j3/8Q3fccYeioqI0ceJEuVwuRUeeolIOAAAUYklEQVRHe%2B0jOjpalZWVLT5mRUWFnE6n15jNFq74%2BPjWL%2BgbQkL8Kx/bbP5V7zed6rW/9dwf0Wvr0Gtr0GfrtKVeB3zAWrVqlUaPHq3OnTtLkvr3768XXnjBMz9s2DBNmTJFr7zyiiZOnChJnrNZZ6qoqEgFBQVeY1lZWcrOzm7Vfv1dbGyEr0toNbu9g69LOGfQa%2BvQa2vQZ%2Bu0hV4HfMDavHmz5s6d%2B53bJCQkaPPmzZKk2NhYuVwur3mXy6U%2Bffq0%2BJgZGRkaMWKE15jNFq7KypoW76Ml2kJC/yFMr99KISHBsts7qKqqVg0Njb4uJ6DRa%2BvQa2vQZ%2Bs012tf/XIf0AFr165dKi8v16WXXuoZ27hxoyorK/Xzn//cM7Zv3z5169ZNkpScnKzS0lKNHz9ektTQ0KCdO3d6zm61RHx8fJPLgU7nMdXXn9s/WIGw/oaGxoBYhz%2Bg19ah19agz9ZpC732r1MgP9DOnTsVExOjyMhIz1i7du20ZMkSvf3226qrq9Nf//pXrVmzRpmZmZKkzMxMvfrqq/roo49UW1urJ554QqGhoRo%2BfLiPVgEAAPxNQJ/BOnLkiOfeq1NGjRql%2BfPn68EHH9ShQ4fUqVMnzZ8/X6NHj5YkXX755brzzjs1a9Ysffnll0pJSVFhYaHat2/viyUAAAA/FORu7R3daBGn85jxfdpswboyf4fx/Z4tG2dd%2Bv0btVE2W7BiYyNUWVnj89POgY5eW4deW4M%2BW6e5XnfuHOWTWgL6EiEAAIAvELAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGBWzASkxMVHJyslJSUjxfDz74oCTp3Xff1cSJE3XhhRfqmmuu0dq1a71eu3LlSo0ZM0YXXnihMjMzVVJS4oslAAAAP2XzdQFn06ZNm3T%2B%2Bed7jVVUVOjWW2/VggULNHbsWH3wwQeaOXOmevbsqZSUFG3btk3Lly/XM888o8TERK1cuVIzZszQli1bFB4e7qOVAAAAfxKwZ7BOZ926derRo4cmTpyosLAwpaWlacSIEVq9erUkqaioSBMmTNDAgQPVvn17/epXv5Ikvfnmm74sGwAA%2BJGADlhLly7V8OHDNWTIEC1cuFA1NTUqLS1VUlKS13ZJSUmey4Dfng8ODla/fv3kcDgsrR0AAPivgL1EeMEFFygtLU1LlizRZ599plmzZun%2B%2B%2B%2BXy%2BVSly5dvLaNiYlRZWWlJMnlcik6OtprPjo62jPfEhUVFXI6nV5jNlu44uPjz3A1zQsJ8a98bLP5V73fdKrX/tZzf0SvrUOvrUGfrdOWeh2wAauoqMjz37169dKcOXM0c%2BZMDR48%2BHtf63a7W33sgoICr7GsrCxlZ2e3ar/%2BLjY2wtcltJrd3sHXJZwz6LV16LU16LN12kKvAzZgfdv555%2BvhoYGBQcHy%2BVyec1VVlYqLi5OkhQbG9tk3uVyqU%2BfPi0%2BVkZGhkaMGOE1ZrOFq7Ky5gyrb15bSOg/hOn1WykkJFh2ewdVVdWqoaHR1%2BUENHptHXptDfpsneZ67atf7gMyYO3cuVNr167VvHnzPGNlZWUKDQ1Venq6/vSnP3ltX1JSooEDB0qSkpOTVVpaqvHjx0uSGhoatHPnTk2cOLHFx4%2BPj29yOdDpPKb6%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%2Bx%2BbqAs2XGjBlKTk7Wtm3bdOzYMWVlZWnJkiWaOXOmJMnhcDT7ul27dmnu3LkqKCjQxRdfrM2bN%2Bu2227Tpk2b1LVrVyuXAAAA/FRAnsGqqqpScnKyZs%2BerYiICHXt2lXjx4/X%2B%2B%2B//72vXb16tdLT05Wenq6wsDCNGzdOffv21dq1ay2oHAAABIKADFh2u115eXnq1KmTZ%2BzQoUOKj4/3fH/33Xdr2LBhuvjii7V06VLV1dVJkkpLS5WUlOS1v6SkpNOe8QIAAPi2gL1E%2BE0Oh0MvvviinnjiCYWGhmrQoEG68sor9dBDD2nXrl26/fbbZbPZdMcdd8jlcik6Otrr9dHR0dq7d2%2BLj1dRUSGn0%2Bk1ZrOFewU8E0JC/Csf22z%2BVe83neq1v/XcH9Fr69Bra9Bn67SlXgd8wPrggw80c%2BZMzZ49W2lpaZKkl19%2B2TM/YMAATZ8%2BXU899ZTuuOMOSZLb7W7VMYuKilRQUOA1lpWVpezs7Fbt19/Fxkb4uoRWs9s7%2BLqEcwa9tg69tgZ9tk5b6HVAB6xt27bprrvu0sKFC3X99defdruEhAQdOXJEbrdbsbGxcrlcXvMul0txcXEtPm5GRoZGjBjhNWazhauysuaHLeB7tIWE/kOYXr%2BVQkKCZbd3UFVVrRoaGn1dTkCj19ah19agz9Zprte%2B%2BuU%2BYAPWhx9%2BqLlz5%2Brxxx/XsGHDPOPvvvuuPvroI8%2BnCSVp3759SkhIUFBQkJKTk1VSUuK1L4fDoWuuuabFx46Pj29yOdDpPKb6%2BnP7BysQ1t/Q0BgQ6/AH9No69Noa9Nk6baHX/nUKpIXq6%2Bt17733as6cOV7hSpKioqK0YsUKvfbaa6qrq5PD4dDvf/97ZWZmSpImT56sd955R9u3b9eJEydUXFysAwcOaNy4cb5YCgAA8EMBeQbro48%2BUllZmXJzc5Wbm%2Bs1t2nTJi1btkwFBQX67W9/q6ioKN100026%2BeabJUl9%2B/ZVfn6%2B8vLyVF5ert69e%2Bupp55S586dfbEUAADghwIyYA0ZMkR79uw57XxCQoKuvPLK086PHj1ao0ePPhulAQCAc0BAXiIEAADwJQIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwm68LwLnjZ4/91dcl/CAbZ13q6xIAAH6KM1gAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYzSgvL9dvfvMbpaam6oorrtAjjzyixsZGX5cFAAD8BE9yb8btt9%2Bu/v37a%2BvWrfryyy81ffp0derUSb/4xS98XRosxJPnAQBnioD1LQ6HQ7t379Zzzz2nqKgoRUVFadq0aXr%2B%2BecJWGjT/CkQEgYBBDoC1reUlpYqISFB0dHRnrH%2B/ftr//79qq6uVmRk5Pfuo6KiQk6n02vMZgtXfHy80VpDQrjCC/9ks53%2BvXvqfc37%2B%2Byj19agz9ZpS70mYH2Ly%2BWS3W73GjsVtiorK1sUsIqKilRQUOA1dtttt%2Bn22283V6i%2BDnI3d/1UGRkZxsMbvFVUVKioqIheW6CiokLPP/8MvbYAvbYGfbZOW%2Bq17yNeG%2BR2u1v1%2BoyMDL3yyiteXxkZGYaq%2Bz9Op1MFBQVNzpbBPHptHXptHXptDfpsnbbUa85gfUtcXJxcLpfXmMvlUlBQkOLi4lq0j/j4eJ8nZwAA4DucwfqW5ORkHTp0SEePHvWMORwO9e7dWxERET6sDAAA%2BAsC1rckJSUpJSVFS5cuVXV1tcrKyvTcc88pMzPT16UBAAA/EXLffffd5%2Bsi2prLLrtM69ev14MPPqjXX39dEydO1C233KKgoCBfl9ZERESELrroIs6uWYBeW4deW4deW4M%2BW6et9DrI3do7ugEAAOCFS4QAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGw/FB5ebl%2B85vfKDU1VVdccYUeeeQRNTY2%2BrqsgLFjxw6lpaUpJyenydyGDRs0duxYDRo0SBMmTNDbb7/tgwoDR3l5ubKyspSamqq0tDTNmzdPVVVVkqRdu3bpxhtv1ODBgzV69Gg9%2B%2ByzPq7Wf%2B3evVs333yzBg8erLS0NM2aNUtOp1OS9O6772rixIm68MILdc0112jt2rU%2BrjZwPPzww0pMTPR8T6/NSkxMVHJyslJSUjxfDz74oKQ20ms3/M748ePd9957r7uqqsq9f/9%2B9%2BjRo93PPvusr8sKCIWFhe7Ro0e7p0yZ4p41a5bX3M6dO93Jycnu7du3u48fP%2B5%2B7bXX3AMHDnQfOnTIR9X6v2uvvdY9b948d3V1tfvQoUPuCRMmuOfPn%2B%2Bura11X3bZZe7ly5e7a2pq3CUlJe6LLrrIvXnzZl%2BX7HdOnDjhvuSSS9wFBQXuEydOuL/88kv3jTfe6L711lvdX3zxhfuCCy5wr1692n38%2BHH3X//6V/eAAQPcn3zyia/L9ns7d%2B50X3TRRe6%2Bffu63W43vT4L%2Bvbt6/7ss8%2BajLeVXnMGy884HA7t3r1bc%2BbMUVRUlHr06KFp06apqKjI16UFhLCwMBUXF6t79%2B5N5lavXq309HSlp6crLCxM48aNU9%2B%2Bffkt9AxVVVUpOTlZs2fPVkREhLp27arx48fr/fff1/bt21VXV6eZM2cqPDxc/fv316RJk3ifn4Ha2lrl5ORo%2BvTpCg0NVVxcnK688kp9%2BumnWrdunXr06KGJEycqLCxMaWlpGjFihFavXu3rsv1aY2OjFi1apGnTpnnG6LV12kqvCVh%2BprS0VAkJCYqOjvaM9e/fX/v371d1dbUPKwsMU6dOVVRUVLNzpaWlSkpK8hpLSkqSw%2BGworSAY7fblZeXp06dOnnGDh06pPj4eJWWlioxMVEhISGeuaSkJJWUlPiiVL8WHR2tSZMmyWazSZL27dunP/3pT/rZz3522vc0fW6dl19%2BWWFhYRo7dqxnjF6fHUuXLtXw4cM1ZMgQLVy4UDU1NW2m1wQsP%2BNyuWS3273GToWtyspKX5R0znC5XF7BVvq69/TdDIfDoRdffFEzZ85s9n0eExMjl8vF/YZnqLy8XMnJybr66quVkpKi7Ozs0/aZ9/SZO3LkiJYvX65FixZ5jdNr8y644AKlpaVpy5YtKioq0kcffaT777%2B/zfSagOWH3G63r0s4Z9H7s%2BODDz7QLbfcotmzZystLe202wUFBVlYVWBJSEiQw%2BHQpk2bdODAAd19992%2BLikg5eXlacKECerdu7evSwl4RUVFmjRpkkJDQ9WrVy/NmTNH69evV11dna9Lk0TA8jtxcXFyuVxeYy6XS0FBQYqLi/NRVeeG2NjYZntP31tn27Zt%2Bs1vfqP58%2Bdr6tSpkr5%2Bn3/7t02Xy6WYmBgFB/PP1pkKCgpSjx49lJOTo/Xr18tmszV5T1dWVvKePkPvvvuu/vnPfyorK6vJXHP/ftBrs84//3w1NDQoODi4TfSaf6n8THJysg4dOqSjR496xhwOh3r37q2IiAgfVhb4kpOTm1zDdzgcGjhwoI8q8n8ffvih5s6dq8cff1zXX3%2B9Zzw5OVl79uxRfX29Z4xen5l3331XY8aM8bq0eiqkDhgwoMl7uqSkhD6fobVr1%2BrLL7/UFVdcodTUVE2YMEGSlJqaqr59%2B9Jrg3bu3KnFixd7jZWVlSk0NFTp6eltotcELD%2BTlJSklJQULV26VNXV1SorK9Nzzz2nzMxMX5cW8CZPnqx33nlH27dv14kTJ1RcXKwDBw5o3Lhxvi7NL9XX1%2Bvee%2B/VnDlzNGzYMK%2B59PR0RUZG6oknnlBtba0%2B/vhjFRcX8z4/A8nJyaqurtYjjzyi2tpaHT16VMuXL9eQIUOUmZmp8vJyrV69WidOnNBbb72lt956S5MnT/Z12X5p3rx52rx5s1577TW99tprKiwslCS99tprGjt2LL02qGPHjioqKlJhYaFOnjyp/fv36/HHH1dGRoauu%2B66NtHrIDc3lfidw4cPa%2BHChfrHP/6hyMhITZkyRbfddhv3pxiQkpIiSZ4zJ6c%2BeXXqk4JbtmzR0qVLVV5ert69e2vBggUaOnSob4r1c%2B%2B//77%2B4z/%2BQ6GhoU3mNm3apJqaGi1atEglJSXq1KmTfv3rX%2BvnP/%2B5Dyr1f3v27FFubq4%2B%2BeQThYeH6%2BKLL9a8efPUpUsXvffee8rNzVVZWZkSEhI0e/ZsjR492tclB4TPP/9cI0eO1J49eySJXhv23nvvaenSpdqzZ49CQ0M1fvx45eTkKCwsrE30moAFAABgGJcIAQAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMCw/wfRkO6uVMfjAQAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-610503464288004810”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1416</td> <td class=”number”>44.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>415</td> <td class=”number”>12.9%</td> <td>

<div class=”bar” style=”width:30%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>147</td> <td class=”number”>4.6%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>92</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>39</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>19</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>14</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>13</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>9</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (23)</td> <td class=”number”>46</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>967</td> <td class=”number”>30.1%</td> <td>

<div class=”bar” style=”width:68%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-610503464288004810”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1416</td> <td class=”number”>44.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>415</td> <td class=”number”>12.9%</td> <td>

<div class=”bar” style=”width:30%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>147</td> <td class=”number”>4.6%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>92</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>39</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>35.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>39.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>41.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>44.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>48.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FMRKT_WICCASH16”>FMRKT_WICCASH16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>22</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.7%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>30.1%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>967</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.55729</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>39</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>53.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram6844169616561339459”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives6844169616561339459,#minihistogram6844169616561339459”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives6844169616561339459”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles6844169616561339459”

aria-controls=”quantiles6844169616561339459” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram6844169616561339459” aria-controls=”histogram6844169616561339459”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common6844169616561339459” aria-controls=”common6844169616561339459”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme6844169616561339459” aria-controls=”extreme6844169616561339459”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles6844169616561339459”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>0</td>

</tr> <tr>

<th>95-th percentile</th> <td>3</td>

</tr> <tr>

<th>Maximum</th> <td>39</td>

</tr> <tr>

<th>Range</th> <td>39</td>

</tr> <tr>

<th>Interquartile range</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>1.9752</td>

</tr> <tr>

<th>Coef of variation</th> <td>3.5444</td>

</tr> <tr>

<th>Kurtosis</th> <td>136.62</td>

</tr> <tr>

<th>Mean</th> <td>0.55729</td>

</tr> <tr>

<th>MAD</th> <td>0.85966</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>9.6551</td>

</tr> <tr>

<th>Sum</th> <td>1250</td>

</tr> <tr>

<th>Variance</th> <td>3.9016</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram6844169616561339459”>

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nnBGjFihDp16qRp06Zp9OjRatWqVZ3HJCYm6vz581aPBgAAYITlBWvdunXq16/fzz7u4MGDFkwDAABgnuX3YEVFRenJJ5/Url27atf%2B%2B7//W48//ricTqfV4wAAABhnecFasmSJfvjhB3Xp0qV2bcCAAaqpqdHSpUutHgcAAMA4yy8RfvTRR9qyZYtat25duxYZGan09HQ98MADVo8DAABgnOVnsCorKxUYGFh3EF9fVVRUWD0OAACAcZYXrL59%2B2rp0qX6/vvva9f%2B/ve/66WXXnL7qAYAAAC7svwS4bx58/Tv//7vuueeexQUFKSamhqVl5erQ4cOeuutt6weBwAAwDjLC1aHDh30wQcf6MMPP9Q333wjX19fderUSf3795efn5/V4wAAABhnecGSpICAAN1///2e2DUAAMBNZ3nBOn36tDIyMvT111%2BrsrKyzvY///nPVo8EAABglEfuwSoqKlL//v3VokULq3cPAABw01lesPLy8vTnP/9ZYWFhVu8aAADAEpZ/TEObNm04cwUAALya5QVr2rRpyszMlMvlsnrXAAAAlrD8EuGHH36oL7/8Uhs3blT79u3l6%2Bve8d577z2rRwIAADDK8oIVFBSkX//611bvFgAAwDKWF6wlS5ZYvUsAAABLWX4PliSdOHFCK1eu1PPPP1%2B79re//c0TowAAABhnecE6cOCARo0apZ07dyo3N1fS5Q8fnTx5Mh8yCgAAvILlBWv58uV67rnntGXLFvn4%2BEi6/PsJly5dqlWrVlk9DgAAgHGWF6z//d//VXJysiTVFixJ%2Bpd/%2BRcVFBRYPQ4AAIBxlhes4ODgq/4OwqKiIgUEBFg9DgAAgHGWF6zevXvrlVdeUVlZWe3ayZMnlZaWpnvuucfqcQAAAIyz/GMann/%2BeT3yyCOKj49XdXW1evfurYqKCnXt2lVLly61ehwAAADjLC9Yt912m3Jzc7Vv3z6dPHlSzZs3V6dOnXTvvfe63ZMFAABgV5YXLElq1qyZ7r//fk/sGgAA4KazvGANGjToumeq%2BCwsAABgd5YXrOHDh7sVrOrqap08eVIOh0OPPPKI1eMAAAAYZ3nBmj179lXXd%2BzYoc8%2B%2B8ziaQAAAMzzyO8ivJr7779fH3zwgafHAAAAuGFNpmAdPnxYLpfL02MAAADcMMsvEU6cOLHOWkVFhQoKCjR06FCrxwEAADDO8oIVGRlZ56cIAwMDNW7cOI0fP97qcQAAAIyzvGDxae0AAMDbWV6w3n///Xo/dvTo0TdxEgAAgJvD8oI1f/581dTU1Lmh3cfHx23Nx8eHggUAAGzJ8oL1xhtvaM2aNXryyScVFRUll8ulY8eO6fXXX9ekSZMUHx9v9UgAAABGeeQerKysLLVr16527a677lKHDh00depU5ebmWj0SAACAUZZ/DtapU6cUGhpaZz0kJESFhYVWjwMAAGCc5QUrIiJCS5cuVUlJSe1aaWmpMjIydPvtt1s9DgAAgHGWXyKcN2%2BeZs2apezsbLVs2VK%2Bvr4qKytT8%2BbNtWrVKqvHAQAAMM7ygtW/f3/t3btX%2B/bt09mzZ%2BVyudSuXTvdd999Cg4ObtBr7d%2B/X2lpaYqPj9fy5cvdtm3dulWrV6/Wt99%2Bq06dOunZZ59V//79JUk1NTVasWKFcnNzVVpaqh49eujFF19Uhw4dJElOp1Mvvvii/vKXv8jX11eJiYlasGCBmjdvbuaLAAAAvJpHfhfhLbfcosGDB2vw4MF69NFHNXz48AaXq9dff12LFy9Wx44d62w7cuSI0tLSNHv2bH366aeaMmWKnn76aZ09e1aS9M4772jLli3KysrSnj17FBkZqZSUlNqPiViwYIEqKiqUm5urDRs2qKCgQOnp6TceHAAA/CJYXrAqKyuVlpamXr166Te/%2BY2ky/dgPfbYYyotLa336wQGBionJ%2BeqBWv9%2BvVKTExUYmKiAgMDNWrUKHXr1k2bN2%2BWJGVnZ2vKlCnq3LmzgoKClJqaqoKCAh08eFDfffeddu3apdTUVIWFhaldu3Z66qmntGHDBlVVVZn5IgAAAK9m%2BSXCZcuW6ciRI0pPT9ecOXNq16urq5Wenq6XX365Xq8zefLka27Lz89XYmKi21p0dLQcDocqKyt1/PhxRUdH124LCgpSx44d5XA49MMPP8jPz09RUVG122NiYvTjjz/qxIkTbuvXUlRUpOLiYrc1f/8WCg8Pr1e2%2BvLz88gJyEbz92/YvD/ls1vO%2BiKfvZHPvrw5m0S%2BpsLygrVjxw69/fbbioyMVFpamqTLH9GwZMkSjR49ut4F63qcTmedj4IIDQ3V8ePH9f3338vlcl11e0lJiVq1aqWgoCC3X0j902P/708%2BXk92drYyMzPd1lJSUjRjxozGxPEarVu3bNTzQkJuMTxJ00I%2BeyOffXlzNol8nmZ5wSovL1dkZGSd9bCwMP3444/G9nPlr%2BJpyPafe%2B7PSUpK0qBBg9zW/P1bqKSk/IZe90pNvb1fqaH5/fx8FRJyi0pLK1RdXXOTpvIc8tkb%2BezLm7NJ5LtSY7%2B5v1GWF6zbb79dn332meLj492KzPbt2/XP//zPRvbRunVrOZ1OtzWn06mwsDC1atVKvr6%2BV93epk0bhYWFqaysTNXV1fLz86vdJklt2rSp1/7Dw8PrXA4sLv5Bly5531/0hmhs/urqGq/%2B2pHP3shnX96cTSKfp1lesB566CE988wzevDBB1VTU6O1a9cqLy9PO3bs0Pz5843sIzY2Vnl5eW5rDodDI0aMUGBgoLp27ar8/Hz169dP0uWb7L/55hv16NFDERERcrlcOnr0qGJiYmqfGxISok6dOhmZDwAAeDfLrzElJSUpLS1Nn376qfz8/PS73/1OhYWFSk9PV3JyspF9TJgwQZ988on27t2rCxcuKCcnR6dOndKoUaMkScnJyVq3bp0KCgpUVlam9PR0de/eXXFxcQoLC9OwYcP029/%2BVufPn9fZs2e1atUqjRs3Tv7%2BlvdRAABgQ5Y3hvPnz%2BvBBx/Ugw8%2BeEOvExcXJ0m6dOmSJGnXrl2SLp9t6tatm9LT07VkyRIVFhaqS5cueu2113TrrbdKkiZOnKji4mI9/PDDKi8vV3x8vNtN6S%2B//LIWLlyowYMHq1mzZnrggQeUmpp6Q/MCAIBfDh/Xjd7R3UC9evXSl19%2B6fZTer8ExcU/GH9Nf39fDUnfb/x1b5ZtM%2B9t0OP9/X3VunVLlZSUN%2Bnr7I1FPnsjn315czaJfFe69daGfZC5KZZfIoyPj9e2bdus3i0AAIBlLL9E%2BE//9E/6z//8T2VlZen2229Xs2bN3LZnZGRYPRIAAIBRlhes48eP61e/%2BpWk%2Bn9wJwAAgJ1YVrBSU1O1fPlyvfXWW7Vrq1atUkpKilUjAAAAWMKye7B2795dZy0rK8uq3QMAAFjGsoJ1tR9WtPgHGAEAACxhWcG62scy/NI%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%2Bu9u3bu60VFRXpqaee0vz58zVy5Eh98cUXmj59ujp16qS4uDjt3r1bK1eu1BtvvKGoqCitW7dOTz75pHbu3KkWLVp4KAkAALATrz2DdS1btmxRZGSkxo0bp8DAQCUkJGjQoEFav369JCk7O1tjx45Vz5491bx5cz322GOSpD179nhybAAAYCNeXbAyMjI0YMAA3XXXXVqwYIHKy8uVn5%2Bv6Ohot8dFR0fXXga8cruvr6%2B6d%2B8uh8Nh6ewAAMC%2BvPYS4Z133qmEhAS9%2BuqrOn36tGbOnKmXXnpJTqdT7dq1c3tsq1atVFJSIklyOp0KDQ112x4aGlq7vT6KiopUXFzstubv30Lh4eGNTHN1fn726sf%2B/g2b96d8dstZX%2BSzN/LZlzdnk8jXVHhtwcrOzq79586dO2v27NmaPn26%2BvTp87PPdblcN7zvzMxMt7WUlBTNmDHjhl7X7lq3btmo54WE3GJ4kqaFfPZGPvvy5mwS%2BTzNawvWldq3b6/q6mr5%2BvrK6XS6bSspKVFYWJgkqXXr1nW2O51Ode3atd77SkpK0qBBg9zW/P1bqKSkvJHTX11Tb%2B9Xamh%2BPz9fhYTcotLSClVX19ykqTyHfPZGPvvy5mwS%2Ba7U2G/ub5RXFqzDhw9r8%2BbNmjt3bu1aQUGBAgIClJiYqD/%2B8Y9uj8/Ly1PPnj0lSbGxscrPz9eYMWMkSdXV1Tp8%2BLDGjRtX7/2Hh4fXuRxYXPyDLl3yvr/oDdHY/NXVNV79tSOfvZHPvrw5m0Q%2BT7PXKZB6atOmjbKzs5WVlaWLFy/q5MmTWrFihZKSkvSv//qvKiws1Pr163XhwgXt27dP%2B/bt04QJEyRJycnJev/99/XVV1%2BpoqJCq1evVkBAgAYMGODZUAAAwDa88gxWu3btlJWVpYyMjNqCNGbMGKWmpiowMFCvvfaaFi9erJdeekkRERFatmyZ7rjjDknSr3/9az377LOaOXOmzp07p7i4OGVlZal58%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%2BkB8Mvxm99%2B7OkRGmTbzHs9PQIAwKY4gwUAAGAYBQsAAMAwCtZVFBYW6oknnlB8fLwGDhyoZcuWqaamxtNjAQAAm%2BAerKt45plnFBMTo127duncuXOaNm2a2rZtq0cffdTTowEAABugYF3B4XDo6NGjWrt2rYKDgxUcHKwpU6bozTffpGD9wnBTPgCgsShYV8jPz1dERIRCQ0Nr12JiYnTy5EmVlZUpKCjoZ1%2BjqKhIxcXFbmv%2B/i0UHh5udFY/P67w4h/sVAj/NPs%2BT49ww356/3nr%2B9Cb83lzNol8TQUF6wpOp1MhISFuaz%2BVrZKSknoVrOzsbGVmZrqtPf3003rmmWfMDarLRe6R275WUlKS8fLWFBQVFSk7O5t8NvVLyPfmm2%2BQz4a8OZtEvqaiadc/D3G5XDf0/KSkJG3cuNHtT1JSkqHp/qG4uFiZmZl1zpZ5C/LZG/nszZvzeXM2iXxNBWewrhAWFian0%2Bm25nQ65ePjo7CwsHq9Rnh4eJNu1QAA4ObiDNYVYmNjdebMGZ0/f752zeFwqEuXLmrZsqUHJwMAAHZBwbpCdHS04uLilJGRobKyMhUUFGjt2rVKTk729GgAAMAm/F588cUXPT1EU3PfffcpNzdXixYt0gcffKBx48Zp6tSp8vHx8fRodbRs2VL9%2BvXz2rNr5LM38tmbN%2Bfz5mwS%2BZoCH9eN3tENAAAAN1wiBAAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWABiDyYgAAAJr0lEQVQAAIZRsAAAAAyjYAEAABhGwQIAADCMgmVDhYWFeuKJJxQfH6%2BBAwdq2bJlqqmp8fRYxkRFRSk2NlZxcXG1fxYtWuTpsW7I/v37lZCQoNTU1Drbtm7dqpEjR6pXr14aO3asPvroIw9MeGOulW/jxo2644473I5lXFycDh065KFJG6ewsFApKSmKj49XQkKC5s6dq9LSUknSkSNHNGnSJPXp00dDhw7VmjVrPDxtw1wr27fffquoqKg6x%2B73v/%2B9p0dukKNHj%2BqRRx5Rnz59lJCQoJkzZ6q4uFiSdODAAY0bN069e/fWiBEjtHnzZg9P23DXyvfZZ59d9fht27bN0yM32iuvvKKoqKjaf2/yx88F2xkzZozrhRdecJWWlrpOnjzpGjp0qGvNmjWeHsuYbt26uU6fPu3pMYzJyspyDR061DVx4kTXzJkz3bYdPnzYFRsb69q7d6%2BrsrLStWnTJlfPnj1dZ86c8dC0DXe9fBs2bHBNmjTJQ5OZ88ADD7jmzp3rKisrc505c8Y1duxY17x581wVFRWu%2B%2B67z7Vy5UpXeXm5Ky8vz9WvXz/Xjh07PD1yvV0r2%2BnTp13dunXz9Hg35MKFC6577rnHlZmZ6bpw4YLr3LlzrkmTJrmeeuop19///nfXnXfe6Vq/fr2rsrLS9fHHH7t69OjhOnTokKfHrrfr5fv0009dAwcO9PSIxhw%2BfNjVr1%2B/2r%2BTdjh%2BnMGyGYfDoaNHj2r27NkKDg5WZGSkpkyZouzsbE%2BPhmsIDAxUTk6OOnbsWGfb%2BvXrlZiYqMTERAUGBmrUqFHq1q1b0/tO7Dqul88blJaWKjY2VrNmzVLLli112223acyYMfr888%2B1d%2B9eVVVVafr06WrRooViYmI0fvx427wfr5fNG1RUVCg1NVXTpk1TQECAwsLCNGTIEH399dfasmWLIiMjNW7cOAUGBiohIUGDBg3S%2BvXrPT12vV0vnzepqanRwoULNWXKlNo1Oxw/CpbN5OfnKyIiQqGhobVrMTExOnnypMrKyjw4mVkZGRkaMGCA7rrrLi1YsEDl5eWeHqnRJk%2BerODg4Ktuy8/PV3R0tNtadHS0HA6HFaMZcb18knTmzBk9%2Buij6tu3rwYPHqxNmzZZON2NCwkJ0ZIlS9S2bdvatTNnzig8PFz5%2BfmKioqSn59f7bbo6Gjl5eV5YtQGu162n8yZM0f9%2B/fX3XffrYyMDFVVVXli1EYJDQ3V%2BPHj5e/vL0k6ceKE/vjHP%2Bo3v/nNNd97djl20vXzSVJ5eXnt5d/77rtPa9eulcvl8uTIjfLee%2B8pMDBQI0eOrF2zw/GjYNmM0%2BlUSEiI29pPZaukpMQTIxl35513KiEhQTt37lR2dra%2B%2BuorvfTSS54e66ZwOp1uZVm6fDy95ViGhYUpMjJSzz33nD7%2B%2BGM9%2B%2Byzmjdvng4cOODp0RrN4XDo7bff1vTp06/6fmzVqpWcTqct74v8v9kCAgLUq1cvDRkyRHv27FFWVpY2b96s//qv//L0mA1WWFio2NhYDR8%2BXHFxcZoxY8Y1j50d33tXyxcUFKRu3brpkUce0f79%2B7VkyRJlZmZqw4YNnh63Qb777jutXLlSCxcudFu3w/GjYNmQHb8DaYjs7GyNHz9eAQEB6ty5s2bPnq3c3FxdvHjR06PdFN58PAcMGKA33nhD0dHRCggI0IgRIzRkyBBt3LjR06M1yhdffKGpU6dq1qxZSkhIuObjfHx8LJzKjCuzhYeH67333tOQIUPUrFkz9ejRQ9OmTbPlsYuIiJDD4dD27dt16tQpzZkzx9MjGXW1fDExMXrrrbfUr18/BQQEqH///po4caLtjt%2BSJUs0duxYdenSxdOjNBgFy2bCwsLkdDrd1pxOp3x8fBQWFuahqW6u9u3bq7q6WufOnfP0KMa1bt36qsfTW4%2BldPl/BkVFRZ4eo8F2796tJ554QvPmzdPkyZMlXX4/Xvkds9PpVKtWreTra5//vF4t29VERETou%2B%2B%2Bs%2BU3BT4%2BPoqMjFRqaqpyc3Pl7%2B9f571XUlJi2/felfnOnz9f5zF2e%2B8dOHBAf/vb35SSklJn29X%2B29nUjp99/gsASVJsbKzOnDnj9uZxOBzq0qWLWrZs6cHJzDh8%2BLCWLl3qtlZQUKCAgAC3%2B0K8RWxsbJ17BhwOh3r27Omhicx69913tXXrVre1goICdejQwUMTNc6XX36ptLQ0rVixQqNHj65dj42N1bFjx3Tp0qXaNbsdv2tlO3DggFavXu322BMnTigiIsI2Z%2BgOHDigYcOGuV2u/an49ujRo857Ly8vz1bH7nr59u3bp//5n/9xe/yJEyds9d7bvHmzzp07p4EDByo%2BPl5jx46VJMXHx6tbt25N/vhRsGwmOjpacXFxysjIUFlZmQoKCrR27VolJyd7ejQj2rRpo%2BzsbGVlZenixYs6efKkVqxYoaSkJLcbib3FhAkT9Mknn2jv3r26cOGCcnJydOrUKY0aNcrToxlx8eJFLVq0SA6HQ1VVVcrNzdWHH36oiRMnenq0ert06ZJeeOEFzZ49W/3793fblpiYqKCgIK1evVoVFRU6ePCgcnJybPN%2BvF624OBgrVq1Sps2bVJVVZUcDod%2B//vf2yabdLkAl5WVadmyZaqoqND58%2Be1cuVK3XXXXUpOTlZhYaHWr1%2BvCxcuaN%2B%2Bfdq3b58mTJjg6bHr7Xr5goOD9eqrr%2Bqjjz5SVVWVPv74Y23YsMFWx2/u3LnasWOHNm3apE2bNikrK0uStGnTJo0cObLJHz8flx3P9f7CnT17VgsWLNBf/vIXBQUFaeLEiXr66adt813lz/nrX/%2BqjIwMHTt2TAEBARozZoxSU1MVGBjo6dEaJS4uTpJqz3L89BM/P/2k4M6dO5WRkaHCwkJ16dJF8%2BfPV9%2B%2BfT0zbCNcL5/L5dLq1auVk5Oj4uJitW/fXnPmzNHAgQM9Nm9Dff755/q3f/s3BQQE1Nm2fft2lZeXa%2BHChcrLy1Pbtm31%2BOOP66GHHvLApA33c9kOHz6szMxMnTp1SsHBwXr44Yf1%2BOOP2%2Bry57Fjx7R48WIdOnRILVq00N133625c%2BeqXbt2%2Butf/6rFixeroKBAERERmjVrloYOHerpkRvkevmys7O1Zs0anTlzRm3bttX06dM1fvx4T4/caN9%2B%2B60GDx6sY8eOSVKTP34ULAAAAMPs820IAACATVCwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGDY/wNNf7uvoIqwpwAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common6844169616561339459”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1730</td> <td class=”number”>53.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>302</td> <td class=”number”>9.4%</td> <td>

<div class=”bar” style=”width:18%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>87</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>47</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>16</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>11</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (11)</td> <td class=”number”>16</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>967</td> <td class=”number”>30.1%</td> <td>

<div class=”bar” style=”width:56%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme6844169616561339459”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1730</td> <td class=”number”>53.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>302</td> <td class=”number”>9.4%</td> <td>

<div class=”bar” style=”width:18%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>87</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>47</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>21.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>22.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>28.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>34.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>39.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FOODHUB16”>FOODHUB16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>6</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.2%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.054598</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>6</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>93.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-8264277048430566014”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-8264277048430566014”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-8264277048430566014”

aria-controls=”quantiles-8264277048430566014” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-8264277048430566014” aria-controls=”histogram-8264277048430566014”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-8264277048430566014” aria-controls=”common-8264277048430566014”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-8264277048430566014” aria-controls=”extreme-8264277048430566014”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-8264277048430566014”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>0</td>

</tr> <tr>

<th>95-th percentile</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>6</td>

</tr> <tr>

<th>Range</th> <td>6</td>

</tr> <tr>

<th>Interquartile range</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.29019</td>

</tr> <tr>

<th>Coef of variation</th> <td>5.3151</td>

</tr> <tr>

<th>Kurtosis</th> <td>129.12</td>

</tr> <tr>

<th>Mean</th> <td>0.054598</td>

</tr> <tr>

<th>MAD</th> <td>0.10428</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>8.884</td>

</tr> <tr>

<th>Sum</th> <td>171</td>

</tr> <tr>

<th>Variance</th> <td>0.084211</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-8264277048430566014”>

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vr9cbzMsFAAAhJCQLVkVFhdLS0jRp0iTFxsaqTZs2uuOOO/Thhx9q69atOnPmjMaPH69mzZqpe/fuGjFihBwOhyRp2bJlysrKUlZWlmJiYjR06FB16dJFq1atkiQ5HA7l5eWpY8eOiouLU2FhoUpLS7Vz585gXjIAAAghtmAP8FPY7XbNnDnTb%2B3w4cNKSkpSSUmJunbtqqioKN9eamqqli1bJkkqKSlRVlaW32tTU1PldDp18uRJ7du3T6mpqb69uLg4tW/fXk6nU1dddVWD5isvL5fL5fJbs9maKSkp6UddZyBRUaHVj2026%2Bc9m1GoZWUV8gmMjAIjo/qRT2DhmFFIFqzvczqdevXVV7Vo0SKtW7dOdrvdb79FixbyeDyqq6uTx%2BNRfHy83358fLz27dun48ePy%2Bv1nnff7XY3eB6Hw6EFCxb4reXn56ugoOBHXll4SUiIDdq57fbLgnbuUEA%2BgZFRYGRUP/IJLJwyCvmC9dFHH2n8%2BPGaNGmSMjMztW7duvN%2BX0REhO%2B/B3qe6kKft8rJyVF2drbfms3WTG531QUd9/tCrembvv6GiIqKlN1%2BmSoqqlVbW2f5%2BRs78gmMjAIjo/qRT2AXM6Ng/eE%2BpAvW5s2b9cgjj2j69Om6/fbbJUmJiYk6ePCg3/d5PB61aNFCkZGRSkhIkMfjOWc/MTHR9z3n22/ZsmWD50pKSjrndqDLdUI1NZf2D1Ywr7%2B2tu6Sz78%2B5BMYGQVGRvUjn8DCKaPQegvk33z88ceaMmWKXnjhBV%2B5kqS0tDR98cUXqqmp8a05nU717NnTt79r1y6/Y53dj4mJUefOnVVSUuLbq6io0JdffqkePXpc5CsCAADhIiQLVk1NjZ544glNnjxZ/fr189vLyspSXFycFi1apOrqau3cuVPLly9Xbm6uJGnkyJF6//33tXXrVp06dUrLly/XwYMHNXToUElSbm6uli5dqtLSUlVWVqqoqEjdunVTenq65dcJAABCU0jeIvz0009VWlqqGTNmaMaMGX5769ev14svvqinnnpKxcXFuvzyy1VYWKj%2B/ftLkrp06aKioiLNnDlTZWVl6tSpkxYvXqxWrVpJkkaNGiWXy6W77rpLVVVVysjIOOeBdQAAgPpEePkETUu4XCeMH9Nmi9TAou3Gj3uxrJt4neXntNkilZAQK7e7Kmzu65tEPoGRUWBkVD/yCexiZtSqVXOjx2uokLxFCAAA0JhRsAAAAAyjYAEAABhGwQIAADDM8oKVnZ2tBQsW6PDhw1afGgAAwBKWF6w777xTa9eu1Y033qh7771XGzdu9PtQUAAAgFBnecHKz8/X2rVr9cYbb6hz5856/vnnlZWVpTlz5ujAgQNWjwMAAGBc0J7B6t69u6ZMmaItW7bo8ccf1xtvvKHBgwdrzJgx%2Buyzz4I1FgAAwAULWsE6c%2BaM1q5dq/vuu09TpkxR69at9dhjj6lbt27Ky8vT6tWrgzUaAADABbH8V%2BWUlpZq%2BfLlevvtt1VVVaVBgwbplVdeUe/evX3f06dPHz399NMaMmSI1eMBAABcMMsL1i233KIOHTpo7Nixuv3229WiRYtzvicrK0vHjh2zejQAAAAjLC9YS5cuVd%2B%2BfQN%2B386dOy2YBgAAwDzLn8Hq2rWrxo0bp02bNvnW/vSnP%2Bm%2B%2B%2B6Tx%2BOxehwAAADjLC9YM2fO1IkTJ9SpUyffWv/%2B/VVXV6dZs2ZZPQ4AAIBxlt8ifO%2B997R69WolJCT41lJSUlRUVKRbb73V6nEAAACMs/wdrJMnTyomJubcQSIjVV1dbfU4AAAAxllesPr06aNZs2bp%2BPHjvrWvv/5azzzzjN9HNQAAAIQqy28RPv7447rnnnv0i1/8QnFxcaqrq1NVVZXatWunP//5z1aPAwAAYJzlBatdu3Z655139O677%2BrLL79UZGSkOnTooH79%2BikqKsrqcQAAAIyzvGBJUnR0tG688cZgnBoAAOCis7xgHTp0SHPnztXevXt18uTJc/b/%2Bte/Wj0SAACAUUF5Bqu8vFz9%2BvVTs2bNrD49AADARWd5wdq1a5f%2B%2Bte/KjEx0epTAwAAWMLyj2lo2bIl71wBAICwZnnBGjt2rBYsWCCv12v1qQEAACxh%2BS3Cd999Vx9//LFWrFihtm3bKjLSv%2BO9/vrrVo8EAABglOUFKy4uTtdff73VpwUAALCM5QVr5syZVp8SAADAUpY/gyVJ%2B/fv1/z58/XYY4/51j755JNgjAIAAGCc5QVrx44dGjp0qDZu3Kg1a9ZI%2Bu7DR0ePHs2HjAIAgLBgecGaN2%2BeHnnkEa1evVoRERGSvvv9hLNmzdLChQutHgcAAMA4ywvWP/7xD%2BXm5kqSr2BJ0k033aTS0lKrxwEAADDO8oLVvHnz8/4OwvLyckVHR1s9DgAAgHGWF6xevXrp%2BeefV2VlpW/twIEDmjJlin7xi19YPQ4AAIBxln9Mw2OPPaa7775bGRkZqq2tVa9evVRdXa3OnTtr1qxZVo8DAABgnOUFq02bNlqzZo22bdumAwcOqGnTpurQoYOuu%2B46v2eyAAAAQpXlBUuSmjRpohtvvDEYpwYAALjoLC9Y2dnZ9b5TxWdhAQCAUGd5wRo8eLBfwaqtrdWBAwfkdDp19913Wz0OAACAcZYXrMmTJ593fcOGDfrb3/5m8TQAAADmBeV3EZ7PjTfeqHfeeedHvWb79u3KzMxUYWGh3/qKFSt05ZVXKj093e/rs88%2BkyTV1dVp3rx5GjBggPr06aMxY8bo0KFDvtd7PB5NnDhRmZmZ6tevn6ZNm3bez%2B4CAAA4n0ZTsD7//HN5vd4Gf/9LL72kGTNmqH379ufd79Onj5xOp99Xjx49JEmvvfaaVq9ereLiYm3ZskUpKSnKz8/3nX/69Omqrq7WmjVr9Oabb6q0tFRFRUUXfpEAAOCSYPktwlGjRp2zVl1drdLSUv3qV79q8HFiYmK0fPlyPffcczp16tSPmsHhcCgvL08dO3aUJBUWFiojI0M7d%2B5U27ZttWnTJr311ltKTEyUJD3wwAN66KGHNGXKFDVp0uRHnQsAAFx6LC9YKSkp5/wtwpiYGA0fPlwjRoxo8HFGjx5d7/7hw4f1m9/8Rrt27ZLdbldBQYFuu%2B02nTx5Uvv27VNqaqrve%2BPi4tS%2BfXs5nU6dOHFCUVFR6tq1q2%2B/e/fu%2Bvbbb7V//36/9R9SXl4ul8vlt2azNVNSUlKDr68hoqIazRuQDWKzWT/v2YxCLSurkE9gZBQYGdWPfAILx4wsL1hWfFp7YmKiUlJS9PDDD6tTp076y1/%2BokcffVRJSUm64oor5PV6FR8f7/ea%2BPh4ud1utWjRQnFxcX4l8Oz3ut3uBp3f4XBowYIFfmv5%2BfkqKCi4wCsLbQkJsUE7t91%2BWdDOHQrIJzAyCoyM6kc%2BgYVTRpYXrLfffrvB33v77bf/pHP0799f/fv39/3zLbfcor/85S9asWKF728x1ve81495Fux8cnJylJ2d7bdmszWT2111Qcf9vlBr%2BqavvyGioiJlt1%2Bmiopq1dbWWX7%2Bxo58AiOjwMiofuQT2MXMKFh/uLe8YE2bNk11dXXnlJiIiAi/tYiIiJ9csM4nOTlZu3btUosWLRQZGSmPx%2BO37/F41LJlSyUmJqqyslK1tbWKiory7UlSy5YtG3SupKSkc24HulwnVFNzaf9gBfP6a2vrLvn860M%2BgZFRYGRUP/IJLJwysrxg/eEPf9DLL7%2BscePGqWvXrvJ6vfriiy/00ksv6de//rUyMjIu%2BBz/%2B7//q/j4eA0ePNi3Vlpaqnbt2ikmJkadO3dWSUmJ%2BvbtK0mqqKjQl19%2BqR49eig5OVler1d79uxR9%2B7dJUlOp1N2u10dOnS44NkAAED4C8ozWMXFxWrdurVv7ZprrlG7du00ZswYrVmz5oLPcfr0aT377LNq166drrzySm3YsEHvvvuu3njjDUlSbm6uiouLdf3116t169YqKipSt27dlJ6eLkkaNGiQfve732n27Nk6ffq0Fi5cqOHDh8tmC8qvbgQAACHG8sZw8ODBcx4wlyS73a6ysrIGH%2BdsGaqpqZEkbdq0SdJ37zaNHj1aVVVVeuihh%2BRyudS2bVstXLhQaWlpkr77qAiXy6W77rpLVVVVysjI8Hso/be//a2eeuopDRgwQE2aNNGtt956zoeZAgAA/JAI74U%2B0f0jDR48WH379tVDDz2khIQESd/dovv973%2Bvv//971q5cqWV41jG5Tph/Jg2W6QGFm03ftyLZd3E6yw/p80WqYSEWLndVWFzX98k8gmMjAIjo/qRT2AXM6NWrZobPV5DWf4O1uOPP65JkybJ4XAoNjZWkZGRqqysVNOmTbVw4UKrxwEAADDO8oLVr18/bd26Vdu2bdORI0fk9XrVunVr/fKXv1Tz5sFpmQAAACYF5antyy67TAMGDNCRI0fUrl27YIwAAABw0Vj%2BSZUnT57UlClTdPXVV%2Bvmm2%2BW9N0zWPfee68qKiqsHgcAAMA4ywvWnDlztHv3bhUVFSky8l%2Bnr62tVVFRkdXjAAAAGGd5wdqwYYN%2B//vf66abbvL9vj%2B73a6ZM2dq48aNVo8DAABgnOUFq6qqSikpKeesJyYm6ttvv7V6HAAAAOMsL1g///nP9be//U2S/y9VXr9%2Bvf7jP/7D6nEAAACMs/xvEf7nf/6nJkyYoDvvvFN1dXVasmSJdu3apQ0bNmjatGlWjwMAAGCc5QUrJydHNptNr776qqKiovTiiy%2BqQ4cOKioq0k033WT1OAAAAMZZXrCOHTumO%2B%2B8U3feeafVpwYAALCE5c9gDRgwQBb/%2BkMAAABLWV6wMjIytG7dOqtPCwAAYBnLbxH%2B7Gc/03PPPafi4mL9/Oc/V5MmTfz2586da/VIAAAARllesPbt26crrrhCkuR2u60%2BPQAAwEVnWcEqLCzUvHnz9Oc//9m3tnDhQuXn51s1AgAAgCUsewZr8%2BbN56wVFxdbdXoAAADLWFawzvc3B/nbhAAAIBxZVrDO/mLnQGsAAAChzvKPaQAAAAh3FCwAAADDLPtbhGfOnNGkSZMCrvE5WAAAINRZVrB69%2B6t8vLygGsAAAChzrKC9e%2BffwUAABDOeAYLAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGBYSBes7du3KzMzU4WFhefsrV27VkOGDNHVV1%2BtYcOG6b333vPt1dXVad68eRowYID69OmjMWPG6NChQ759j8ejiRMnKjMzU/369dO0adN08uRJS64JAACEvpAtWC%2B99JJmzJih9u3bn7O3e/duTZkyRZMnT9YHH3ygvLw8Pfjggzpy5Igk6bXXXtPq1atVXFysLVu2KCUlRfn5%2BfJ6vZKk6dOnq7q6WmvWrNGbb76p0tJSFRUVWXp9AAAgdIVswYqJidHy5cvPW7CWLVumrKwsZWVlKSYmRkOHDlWXLl20atUqSZLD4VBeXp46duyouLg4FRYWqrS0VDt37tQ333yjTZs2qbCwUImJiWrdurUeeOABvfnmmzpz5ozVlwkAAEKQLdgD/FSjR4/%2Bwb2SkhJlZWX5raWmpsrpdOrkyZPat2%2BfUlNTfXtxcXFq3769nE6nTpw4oaioKHXt2tW33717d3377bfav3%2B/3/oPKS8vl8vl8luz2ZopKSmpoZfXIFFRodWPbTbr5z2bUahlZRXyCYyMAiOj%2BpFPYOGYUcgWrPp4PB7Fx8f7rcXHx2vfvn06fvy4vF7veffdbrdatGihuLg4RURE%2BO1JktvtbtD5HQ6HFixY4LeWn5%2BvgoKCn3I5YSMhITZo57bbLwvauUMB%2BQRGRoGRUf3IJ7BwyigsC5Yk3/NUP2U/0GsDycnJUXZ2tt%2BazdZMbnfVBR33%2B0Kt6Zu%2B/oaIioqU3X6ZKiqqVVtbZ/n5GzvyCYyMAiOj%2BpFPYBczo2D94T4sC1ZCQoI8Ho/fmsfjUWJiolq0aKHIyMjz7rds2VKJiYmqrKxUbW2toqKifHuS1LJlywadPykp6ZzbgS7XCdXUXNo/WMG8/trauks%2B//qQT2BkFBgZ1Y98AgunjELrLZAGSktL065du/zWnE6nevbsqZiYGHXu3FklJSW%2BvYqKCn355Zfq0aOHunXrJq/Xqz179vi91m63q0OHDpZdAwAACF1hWbBGjhyp999/X1u3btWpU6e0fPlyHTx4UEOHDpUk5ebmaunSpSotLVVlZaWKioq504thAAAOpUlEQVTUrVs3paenKzExUYMGDdLvfvc7HTt2TEeOHNHChQs1fPhw2Wxh%2BYYfAAAwLGQbQ3p6uiSppqZGkrRp0yZJ373b1KVLFxUVFWnmzJkqKytTp06dtHjxYrVq1UqSNGrUKLlcLt11112qqqpSRkaG30Ppv/3tb/XUU09pwIABatKkiW699dbzfpgpAADA%2BUR4L/SJbjSIy3XC%2BDFttkgNLNpu/LgXy7qJ11l%2BTpstUgkJsXK7q8Lmvr5J5BMYGQVGRvUjn8AuZkatWjU3eryGCstbhAAAAMFEwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADAvbgtW1a1elpaUpPT3d9/Xss89Kknbs2KHhw4erV69euuWWW7Rq1Sq/1y5dulSDBg1Sr169lJubq127dgXjEgAAQIiyBXuAi2n9%2BvVq27at31p5ebkeeOABTZs2TUOGDNFHH32k8ePHq0OHDkpPT9fmzZs1f/58/eEPf1DXrl21dOlSjRs3Ths3blSzZs2CdCUAACCUhO07WD9k9erVSklJ0fDhwxUTE6PMzExlZ2dr2bJlkiSHw6Fhw4apZ8%2Beatq0qe69915J0pYtW4I5NgAACCFh/Q7W3Llz9cknn6iyslI333yzpk6dqpKSEqWmpvp9X2pqqtatWydJKikp0eDBg317kZGR6tatm5xOp2655ZYGnbe8vFwul8tvzWZrpqSkpAu8In9RUaHVj2026%2Bc9m1GoZWUV8gmMjAIjo/qRT2DhmFHYFqyrrrpKmZmZmj17tg4dOqSJEyfqmWeekcfjUevWrf2%2Bt0WLFnK73ZIkj8ej%2BPh4v/34%2BHjffkM4HA4tWLDAby0/P18FBQU/8WrCQ0JCbNDObbdfFrRzhwLyCYyMAiOj%2BpFPYOGUUdgWLIfD4fvvHTt21OTJkzV%2B/Hj17t074Gu9Xu8FnTsnJ0fZ2dl%2BazZbM7ndVRd03O8LtaZv%2BvobIioqUnb7ZaqoqFZtbZ3l52/syCcwMgqMjOpHPoFdzIyC9Yf7sC1Y39e2bVvV1tYqMjJSHo/Hb8/tdisxMVGSlJCQcM6%2Bx%2BNR586dG3yupKSkc24HulwnVFNzaf9gBfP6a2vrLvn860M%2BgZFRYGRUP/IJLJwyCq23QBro888/16xZs/zWSktLFR0draysrHM%2BdmHXrl3q2bOnJCktLU0lJSW%2BvdraWn3%2B%2Bee%2BfQAAgEDCsmC1bNlSDodDxcXFOn36tA4cOKAXXnhBOTk5uu2221RWVqZly5bp1KlT2rZtm7Zt26aRI0dKknJzc/X222/r008/VXV1tRYtWqTo6Gj1798/uBcFAABCRljeImzdurWKi4s1d%2B5cX0G64447VFhYqJiYGC1evFgzZszQM888o%2BTkZM2ZM0dXXnmlJOn666/Xww8/rIkTJ%2Bro0aNKT09XcXGxmjZtGuSrAgAAoSLCe6FPdKNBXK4Txo9ps0VqYNF248e9WNZNvM7yc9pskUpIiJXbXRU29/VNIp/AyCgwMqof%2BQR2MTNq1aq50eM1VFjeIgQAAAgmChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgmC3YA%2BDScfPv/r9gj/CjrJt4XbBHAACEKN7BAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYJ1HWVmZ7r//fmVkZOiGG27QnDlzVFdXF%2ByxAABAiOCDRs9jwoQJ6t69uzZt2qSjR49q7Nixuvzyy/Wb3/wm2KMBAIAQQMH6HqfTqT179mjJkiVq3ry5mjdvrry8PL3yyisULDRqofRJ%2BXxKPoBwR8H6npKSEiUnJys%2BPt631r17dx04cECVlZWKi4sLeIzy8nK5XC6/NZutmZKSkozOGhXFHd6LyWYj34sllLI9%2B3PGz9sPI6P6kU9g4ZgRBet7PB6P7Ha739rZsuV2uxtUsBwOhxYsWOC39uCDD2rChAnmBtV3Re7uNnuVk5NjvLyFi/Lycjkcjksiow%2Bfu%2BlHv%2BZSyuenKi8v1yuv/IGM6kFG9SOfwMIxo/CpigZ5vd4Len1OTo5WrFjh95WTk2Noun9xuVxasGDBOe%2BW4V/IqH7kExgZBUZG9SOfwMIxI97B%2Bp7ExER5PB6/NY/Ho4iICCUmJjboGElJSWHTwAEAwI/HO1jfk5aWpsOHD%2BvYsWO%2BNafTqU6dOik2NjaIkwEAgFBBwfqe1NRUpaena%2B7cuaqsrFRpaamWLFmi3NzcYI8GAABCRNTTTz/9dLCHaGx%2B%2Bctfas2aNXr22Wf1zjvvaPjw4RozZowiIiKCPdo5YmNj1bdvX95dqwcZ1Y98AiOjwMiofuQTWLhlFOG90Ce6AQAA4IdbhAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbBCUFlZme6//35lZGTohhtu0Jw5c1RXVxfssRqd7du3KzMzU4WFhcEepVEqKytTfn6%2BMjIylJmZqalTp6qioiLYYzUqe/bs0d13363evXsrMzNTEydOlMvlCvZYjdLzzz%2Bvrl27BnuMRqdr165KS0tTenq67%2BvZZ58N9liNzqJFi9SvXz9dddVVysvL01dffRXskS4YBSsETZgwQa1bt9amTZu0ZMkSbdq0Sa%2B88kqwx2pUXnrpJc2YMUPt27cP9iiN1rhx42S327V582atWLFCe/fu1ezZs4M9VqNx%2BvRp3XPPPerbt6927NihNWvW6OjRo%2BLXt55r9%2B7dWrlyZbDHaLTWr18vp9Pp%2B5o%2BfXqwR2pUXnvtNa1atUpLly7Ve%2B%2B9p06dOulPf/pTsMe6YBSsEON0OrVnzx5NnjxZzZs3V0pKivLy8uRwOII9WqMSExOj5cuXU7B%2BQEVFhdLS0jRp0iTFxsaqTZs2uuOOO/Thhx8Ge7RGo7q6WoWFhRo7dqyio6OVmJiogQMHau/evcEerVGpq6vTU089pby8vGCPghD18ssvq7CwUFdccYXi4uL0xBNP6Iknngj2WBeMghViSkpKlJycrPj4eN9a9%2B7ddeDAAVVWVgZxssZl9OjRat68ebDHaLTsdrtmzpypyy%2B/3Ld2%2BPBhJSUlBXGqxiU%2BPl4jRoyQzWaTJO3fv19vvfWWbr755iBP1ri8/vrriomJ0ZAhQ4I9SqM1d%2B5c9e/fX9dcc42mT5%2BuqqqqYI/UaHz99df66quvdPz4cQ0ePFgZGRkqKCjQsWPHgj3aBaNghRiPxyO73e63drZsud3uYIyEMOB0OvXqq69q/PjxwR6l0SkrK1NaWpoGDx6s9PR0FRQUBHukRuObb77R/Pnz9dRTTwV7lEbrqquuUmZmpjZu3CiHw6FPP/1UzzzzTLDHajSOHDki6bvbqEuWLNHKlSt15MgR3sFCcHi93mCPgDDy0UcfacyYMZo0aZIyMzODPU6jk5ycLKfTqfXr1%2BvgwYN69NFHgz1SozFz5kwNGzZMnTp1CvYojZbD4dCIESMUHR2tjh07avLkyVqzZo1Onz4d7NEahbP/f3bvvfeqdevWatOmjSZMmKDNmzfr1KlTQZ7uwlCwQkxiYqI8Ho/fmsfjUUREhBITE4M0FULV5s2bdf/99%2Bvxxx/X6NGjgz1OoxUREaGUlBQVFhZqzZo1YXH74kLt2LFDn3zyifLz84M9Skhp27atamtrdfTo0WCP0iicfUzh3%2B/MJCcny%2Bv1hnxGFKwQk5aWpsOHD/v9C97pdKpTp06KjY0N4mQINR9//LGmTJmiF154Qbfffnuwx2l0duzYoUGDBvl9BEpk5Hf/ymzSpEmwxmo0Vq1apaNHj%2BqGG25QRkaGhg0bJknKyMjQO%2B%2B8E%2BTpGofPP/9cs2bN8lsrLS1VdHQ0zzv%2BP23atFFcXJx2797tWysrK1OTJk1CPiMKVohJTU1Venq65s6dq8rKSpWWlmrJkiXKzc0N9mgIITU1NXriiSc0efJk9evXL9jjNEppaWmqrKzUnDlzVF1drWPHjmn%2B/Pm65ppr%2BAsUkqZOnaoNGzZo5cqVWrlypYqLiyVJK1euVHZ2dpCnaxxatmwph8Oh4uJinT59WgcOHNALL7ygnJwcRUVFBXu8RsFms2n48OF68cUX9c9//lNHjx7VwoULNWTIEN9fMAlVEV4e6Ak5R44c0fTp0/V///d/iouL06hRo/Tggw8qIiIi2KM1Gunp6ZK%2BKxKSfD%2BoTqczaDM1Jh9%2B%2BKH%2B67/%2BS9HR0efsrV%2B/XsnJyUGYqvH54osvNGPGDH322Wdq1qyZrr32Wk2dOlWtW7cO9miNzldffaUBAwboiy%2B%2BCPYojcrf//53zZ07V1988YWio6N1xx13qLCwUDExMcEerdE4ffq0Zs6cqXfeeUdnzpzRoEGDNH369JC/K0PBAgAAMIxbhAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABg2P8PfSYOTDVWe30AAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-8264277048430566014”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2991</td> <td class=”number”>93.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>18</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-8264277048430566014”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2991</td> <td class=”number”>93.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>18</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2991</td> <td class=”number”>93.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>18</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FOODINSEC_10_12”>FOODINSEC_10_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>43</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.3%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>15.032</td>

</tr> <tr>

<th>Minimum</th> <td>8.7</td>

</tr> <tr>

<th>Maximum</th> <td>20.9</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram1212649561706876396”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives1212649561706876396,#minihistogram1212649561706876396”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives1212649561706876396”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles1212649561706876396”

aria-controls=”quantiles1212649561706876396” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram1212649561706876396” aria-controls=”histogram1212649561706876396”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common1212649561706876396” aria-controls=”common1212649561706876396”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme1212649561706876396” aria-controls=”extreme1212649561706876396”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles1212649561706876396”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>8.7</td>

</tr> <tr>

<th>5-th percentile</th> <td>11.2</td>

</tr> <tr>

<th>Q1</th> <td>13.4</td>

</tr> <tr>

<th>Median</th> <td>14.9</td>

</tr> <tr>

<th>Q3</th> <td>16.7</td>

</tr> <tr>

<th>95-th percentile</th> <td>18.4</td>

</tr> <tr>

<th>Maximum</th> <td>20.9</td>

</tr> <tr>

<th>Range</th> <td>12.2</td>

</tr> <tr>

<th>Interquartile range</th> <td>3.3</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>2.3785</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.15823</td>

</tr> <tr>

<th>Kurtosis</th> <td>0.10833</td>

</tr> <tr>

<th>Mean</th> <td>15.032</td>

</tr> <tr>

<th>MAD</th> <td>1.8931</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.21839</td>

</tr> <tr>

<th>Sum</th> <td>47080</td>

</tr> <tr>

<th>Variance</th> <td>5.6574</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram1212649561706876396”>

<img 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aNm2atm3bpvXr12v9%2BvXKyMiQJK1fv14DBgxQfn6%2BMjMzdeHCBWVnZys7O1tDhgyRJKWlpWndunX64osvVFJSouXLl6tBgwbq1auXhXsEAADqEttdIkxKSlJycrKeeOIJ3Xzzzde1jrCwMJ8b1UtLSyVJLVu2lCStWLFCc%2BfO1Zw5cxQdHa0FCxbozjvvlCT17NlTkyZN0oQJE3TmzBnFxcUpIyNDDRs2rOGeAQCA%2BiLAW9M7ug1btmyZNm/erG%2B%2B%2BUbdu3fXkCFDlJSUJKfTdlnwmrjdZ60uwTacTociIkJUWFjMtf7LuN7%2B9H394xtYFXBjZE14oFa2w3Gnav7cn%2BbNm1iyXdtdIkxPT9eWLVv0wQcfqF27dnrllVeUmJioBQsW6Pjx41aXBwAAcFW2C1g/69Spk6ZOnapdu3Zp%2BvTp%2BuCDD9SvXz%2BNGDFChw4dsro8AACAK7JtwLp06ZK2bNmiUaNGaerUqWrRooVefPFFdezYUcOGDdPGjRutLhEAAOCybHdj07Fjx7R27VqtW7dOxcXF6tOnj95991116dKlYpmuXbtq9uzZGjBggIWVAgAAXJ7tAlb//v3Vpk0bjR49Wo8//rjCw8MrLZOYmKjvv//eguoAAACuznYBa9WqVbrvvvuuutzBgwdroRoAAIBrZ7t7sDp06KAxY8Zox44dFWN/%2BtOfNGrUqEq/wgYAAMCObBew5s2bp7Nnz6pt27YVY7169VJ5ebnmz59vYWUAAADVY7tLhHv27NHGjRsVERFRMda6dWstXLhQv/rVryysDAAAoHpsdwbr/PnzCgoKqjTucDhUUlJiQUUAAADXxnYBq2vXrpo/f75%2B%2BOGHirHTp09rzpw5Po9qAAAAsCvbXSKcPn26hg8fru7du6tx48YqLy9XcXGxWrVqpffee8/q8gAAAK7KdgGrVatW2rx5s/72t7/pn//8pxwOh9q0aaMePXooMDDQ6vIAAACuynYBS5IaNGigRx55xOoyAAAArovtAtbJkye1aNEiffXVVzp//nyl%2Bb/%2B9a8WVAUAAFB9tgtY06dPV0FBgXr06KHg4GCrywEAALhmtgtYubm5%2Butf/6rIyEirSwEAALgutntMQ9OmTTlzBQAA6jTbBazRo0dr6dKl8nq9VpcCAABwXWx3ifBvf/ubPvvsM3344Ye65ZZb5HD4ZsA///nPFlUGAABQPbYLWI0bN1bPnj2tLgMAAOC62S5gzZs3z%2BoSAAAAasR292BJ0tdff60lS5boxRdfrBj7/PPPLawIAACg%2BmwXsPbt26eBAwdq%2B/bt2rRpk6SfHj76zDPP8JBRAABQJ9guYC1evFi/%2Bc1vtHHjRgUEBEj66fcTzp8/X8uWLbO4OgAAgKuzXcD6xz/%2BobS0NEmqCFiS9Nhjj%2BnYsWNWlQUAAFBttgtYTZo0uezvICwoKFCDBg0sqAgAAODa2C5gde7cWa%2B88orOnTtXMXb8%2BHFNnTpV3bt3t7AyAACA6rHdYxpefPFFPfvss%2BrWrZvKysrUuXNnlZSUqF27dpo/f77V5QEAAFyV7QJWy5YttWnTJmVnZ%2Bv48eNq2LCh2rRpowceeMDnniwAAAC7sl3AkqSbbrpJjzzyiNVlAAAAXBfbBaykpKQqz1TxLCwAAGB3tgtY/fr18wlYZWVlOn78uHJycvTss89aWBkAwLS%2Br39sdQnXJGvCA1aXgDrCdgFrypQplx3ftm2bPv3001quBgAA4NrZ7jENV/LII49o8%2BbNVpcBAABwVXUmYB0%2BfFher9fqMgAAAK7KdpcIhw4dWmmspKREx44dU%2B/evS2oCAAA4NrYLmC1bt260rcIg4KClJKSosGDB1tUFQAAQPXZLmCZelr7l19%2BqXnz5ik3N1dBQUG67777NGPGDDVv3lz79u3TokWL9PXXX%2Bvmm2/W6NGjNXDgwIrXrlq1SmvWrJHb7VaHDh00Y8YMxcbGGqkLAAD4P9sFrHXr1lV72ccff/yy4xcvXtTw4cP11FNP6a233tK5c%2Bf0wgsvaPbs2Zo1a5aee%2B45zZgxQwMGDNCBAwc0duxYtWnTRnFxcdq5c6eWLFmit99%2BWx06dNCqVas0ZswYbd%2B%2BXcHBwaZ2EwAA%2BDHbBawZM2aovLy80g3tAQEBPmMBAQFXDFglJSWaOHGiBg0aJKfTqcjISD366KNavXq1Nm7cqNatWyslJUWSlJCQoKSkJGVmZiouLk4ul0vJycmKj4%2BXJI0cOVKrVq3Srl271L9//xu01wAAwJ/Y7luEb7/9tnr06KE1a9Zo//79%2Bvvf/67Vq1erZ8%2Beeuutt3To0CEdOnRIBw8evOI6wsLCNHjwYDmdP%2BXHr7/%2BWn/5y1/Ut29f5eXlKSYmxmf5mJgY5ebmSlKleYfDoY4dOyonJ%2BcG7C0AAPBHtjuDNX/%2BfGVkZKhFixYVY/fee69atWqlESNGaNOmTdVeV35%2Bvvr06aPS0lINGTJE48eP16hRo3zWLUnh4eEqLCyUJHk8HoWFhfnMh4WFVcxXR0FBgdxut8%2BY0xmsqKioaq/DnwUGOnx%2Bwhf9AezL6fTPzyXHHfNsF7BOnDhRKeBIUmhoqPLz869pXdHR0crJydE333yj3/3ud/rtb39brdfV9HlbLpdLS5cu9RlLT0/X%2BPHja7RefxMa2sjqEmyN/gD2ExERYnUJNxTHHXNsF7Cio6M1f/58vfDCC4qIiJAkFRUV6Y9//KNuvfXWa15fQECAWrdurYkTJ2ro0KFKTEyUx%2BPxWaawsFCRkZGSpIiIiErzHo9H7dq1q/Y2U1NTlZSU5DPmdAarsLD4muv3R4GBDoWGNlJRUYnKysqtLsd26A9gX/56HPfn445Vodh2AWv69OmaPHmyXC6XQkJC5HA4dO7cOTVs2FDLli2r1jr27dun2bNnKysrSw7HT6c7f/551113adu2bT7L5%2BbmVtzUHhsbq7y8PA0aNEjST79s%2BvDhwxU3xVdHVFRUpcuBbvdZlZb615u2psrKyulJFegPYD/%2B/pnkuGOO7QJWjx49tHv3bmVnZ%2BvUqVPyer1q0aKFHnzwQTVp0qRa64iNjdW5c%2Be0YMECjR8/XiUlJVqyZInuvfdepaWlaeXKlcrMzNTAgQP1ySefKDs7Wy6XS5KUlpamSZMm6Ve/%2BpU6dOig//qv/1KDBg3Uq1evG7jXAADAn9guYElSo0aN9PDDD%2BvUqVNq1arVNb%2B%2BSZMmWrlypebOnav7779fwcHBuv/%2B%2B/Xyyy%2BradOmWrFihebOnas5c%2BYoOjpaCxYs0J133ilJ6tmzpyZNmqQJEybozJkziouLU0ZGhho2bGh6NwEAgJ8K8NrsNyifP39es2bN0ubNmyX9dPmuqKhIkyZN0h/%2B8AeFhoZaXOH1cbvPWl2CbTidDkVEhKiwsJhT0Zdxvf3p%2B/rHN7AqAJKUNeEBq0u4Ifz5uNy8efWufplmu%2B9jLliwQEeOHNHChQsr7puSfroXauHChRZWBgAAUD22C1jbtm3TH//4Rz322GMVv/Q5NDRU8%2BbN0/bt2y2uDgAA4OpsF7CKi4vVunXrSuORkZH68ccfa78gAACAa2S7gHXrrbfq008/leT7wM%2BtW7fq3/7t36wqCwAAoNps9y3CJ598Us8//7yeeOIJlZeX65133lFubq62bdumGTNmWF0eAADAVdkuYKWmpsrpdGr16tUKDAzUm2%2B%2BqTZt2mjhwoV67LHHrC4PAADgqmwXsL7//ns98cQTeuKJJ6wuBQAA4LrY7h6shx9%2BuMa/bBkAAMBKtgtY3bp1U1ZWltVlAAAAXDfbXSK8%2Beab9fLLLysjI0O33nqrbrrpJp/5RYsWWVQZAABA9dguYB09elS33367JKmwsNDiagAAAK6dbQLWxIkTtXjxYr333nsVY8uWLVN6erqFVQEAAFw729yDtXPnzkpjGRkZFlQCAABQM7YJWJf75iDfJgQAAHWRbQLWz7/Y%2BWpjAAAAdmebgAUAAOAvCFgAAACG2eZbhJcuXdLkyZOvOsZzsAAAgN3ZJmB16dJFBQUFVx0DAACwO9sErH99/hUAAEBdxj1YAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAY5rcBKz8/X%2Bnp6erWrZsSEhI0bdo0FRUVSZKOHDmip59%2BWl26dFHv3r21cuVKn9du2bJFAwYM0D333KPk5GTt2bPHil0AAAB1lN8GrDFjxig0NFQ7d%2B7Uhx9%2BqK%2B%2B%2Bkqvvvqqzp8/r9GjR%2Bv%2B%2B%2B/XRx99pMWLF2vFihXavn27pJ/C19SpUzVlyhR98sknGjZsmMaNG6dTp05ZvEcAAKCu8MuAVVRUpNjYWE2ePFkhISFq2bKlBg0apP3792v37t26dOmSxo4dq%2BDgYHXq1EmDBw%2BWy%2BWSJGVmZioxMVGJiYkKCgrSwIED1b59e23YsMHivQIAAHWF0%2BoCboTQ0FDNmzfPZ%2By7775TVFSU8vLy1KFDBwUGBlbMxcTEKDMzU5KUl5enxMREn9fGxMQoJyen2tsvKCiQ2%2B32GXM6gxUVFXWtu%2BKXAgMdPj/hi/4A9uV0%2BufnkuOOeX4ZsH4pJydHq1ev1vLly5WVlaXQ0FCf%2BfDwcHk8HpWXl8vj8SgsLMxnPiwsTEePHq329lwul5YuXeozlp6ervHjx1//Tvih0NBGVpdga/QHsJ%2BIiBCrS7ihOO6Y4/cB68CBAxo7dqwmT56shIQEZWVlXXa5gICAin/3er012mZqaqqSkpJ8xpzOYBUWFtdovf4iMNCh0NBGKioqUVlZudXlXNGjCz%2ByugQANuOvx/G6cly%2BHlaFYr8OWDt37tRvfvMbvfTSS3r88cclSZGRkTpx4oTPch6PR%2BHh4XI4HIqIiJDH46k0HxkZWe3tRkVFVboc6HafVWmpf71pa6qsrJyeAKhT/P2YxXHZHL%2B92PrZZ59p6tSpeuONNyrClSTFxsbqf/7nf1RaWloxlpOTo/j4%2BIr53Nxcn3X96zwAAMDV%2BGXAKi0t1cyZMzVlyhT16NHDZy4xMVGNGzfW8uXLVVJSooMHD2rt2rVKS0uTJA0ZMkR79%2B7V7t27deHCBa1du1YnTpzQwIEDrdgVAABQBwV4a3rDkQ3t379fTz31lBo0aFBpbuvWrSouLtasWbOUm5urZs2aadSoUXryyScrltm%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%2BrYsWM6ePBgbe8GAACoo/wyYD3zzDNq0qTJZefy8vIUExPjMxYTE6OcnBydP39eR48e9Zlv3LixbrvtNuXk5NzQmgEAgP9wWl1AbfN4PAoLC/MZCwsL09GjR/XDDz/I6/Vedr6wsLDa2ygoKJDb7fYZczqDFRUVdf2F%2B5HAQIfPTwCoK5xOjls3ir/1tt4FLElXvZ%2BqpvdbuVwuLV261GcsPT1d48ePr9F6/U1oaCOrSwCAaxIREWJ1CX7L33pb7wJWRESEPB6Pz5hA8EHWAAALhUlEQVTH41FkZKTCw8PlcDguO9%2B0adNqbyM1NVVJSUk%2BY05nsAoLi6%2B/cD8SGOhQaGgjFRWVqKys3OpyAKDaOI7fODeqt1YFt3oXsGJjY5Wbm%2BszlpOTo/79%2BysoKEjt2rVTXl6e7rvvPklSUVGR/vnPf%2Bquu%2B6q9jaioqIqXQ50u8%2BqtJQw8a/KysrpCYA6hWPWjeNvvfWvC57VMGTIEO3du1e7d%2B/WhQsXtHbtWp04cUIDBw6UJKWlpWnVqlU6duyYzp07p4ULF6pjx46Ki4uzuHIAAFBX%2BOUZrJ/DUGlpqSRpx44dkn46U9W%2BfXstXLhQ8%2BbNU35%2Bvtq2basVK1aoefPmkqShQ4fK7XbrP/7jP1RcXKxu3bpVup8KAACgKgFenqBZK9zus1aXYBtOp0MRESEqLCy29Snhvq9/bHUJAGwma8IDVpdwTerScexG9bZ588s/tulG88szWLCnuvRBBwCgJurdPVgAAAA3GgELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADDMaXUBqJm%2Br39sdQkAAOAXOIMFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAuIz8/X7/%2B9a/VrVs3PfTQQ1qwYIHKy8utLgsAANQRTqsLsKPnn39enTp10o4dO3TmzBmNHj1azZo103/%2B539aXRoAAKgDOIP1Czk5Ofryyy81ZcoUNWnSRK1bt9awYcPkcrmsLg0AANQRnMH6hby8PEVHRyssLKxirFOnTjp%2B/LjOnTunxo0bX3UdBQUFcrvdPmNOZ7CioqKM1wsAqD1OJ%2BclbhR/6y0B6xc8Ho9CQ0N9xn4OW4WFhdUKWC6XS0uXLvUZGzdunJ5//nlzhf6f/S8/ZnydN1pBQYFcLpdSU1MJnZdBf66M3lSN/lxZfe1Ndf8fUV/7cyP5V1w0xOv11uj1qamp%2BvDDD33%2BSU1NNVRd3ed2u7V06dJKZ/nwE/pzZfSmavTnyuhN1eiPeZzB%2BoXIyEh5PB6fMY/Ho4CAAEVGRlZrHVFRUfwNAACAeowzWL8QGxur7777Tt9//33FWE5Ojtq2bauQkBALKwMAAHUFAesXYmJiFBcXp0WLFuncuXM6duyY3nnnHaWlpVldGgAAqCMCZ8%2BePdvqIuzmwQcf1KZNm/T73/9emzdvVkpKikaMGKGAgACrS/MbISEhuu%2B%2B%2BzgreAX058roTdXoz5XRm6rRH7MCvDW9oxsAAAA%2BuEQIAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCzfcRx99pISEBE2cOLHS3JYtWzRgwADdc889Sk5O1p49eyyo0DpV9Wb79u0aOHCg7rnnHvXp00cffPCBBRVaq6r%2B/Ky4uFi9evXStGnTarEy61XVm9OnT2vs2LG6%2B%2B67lZCQoEWLFqm8vNyCKq1TVX/WrFmjPn36VHy23nvvPQsqtE5%2Bfr7S09PVrVs3JSQkaNq0aSoqKpIkHTlyRE8//bS6dOmi3r17a%2BXKlRZXW3c5rS4A/u2tt97S2rVrddttt1WaO3LkiKZOnaqlS5fq/vvv17Zt2zRu3Dht3bpVLVu2tKDa2lVVbw4dOqQpU6boD3/4g3r16qWPP/5Y6enpuv3223XvvfdaUG3tq6o//2rJkiU6d%2B5cLVVlD1X1xuv1aty4cYqPj9eePXt0%2BvRpTZ06VQkJCerevbsF1da%2BqvqTnZ2tBQsW6N1331VcXJxycnL07LPPqlWrVurVq1ftF2uBMWPGKDY2Vjt37tTZs2eVnp6uV199VS%2B99JJGjx6tIUOGKCMjQ8ePH9fw4cN1yy23qHfv3laXXedwBgs3VFBQ0BUPdJmZmUpMTFRiYqKCgoI0cOBAtW/fXhs2bLCg0tpXVW88Ho9Gjx6tRx55RE6nU4mJiWrfvr32799vQaXWqKo/P/vyyy%2B1adMmDRo0qBYrs15Vvfn73/%2BukydP6re//a0aN26sO%2B64Q2vXrq034Uqquj%2B5ublq166d4uPj5XA4FB8fr/bt2%2Bvw4cMWVFr7ioqKFBsbq8mTJyskJEQtW7bUoEGDtH//fu3evVuXLl3S2LFjFRwcrE6dOmnw4MFyuVxWl10nEbBwQz3zzDNq0qTJZefy8vIUExPjMxYTE6OcnJzaKM1yVfWmZ8%2BeSk9Pr/hzaWmp3G63WrRoUVvlWa6q/kg/namZPXu2Jk6cqNDQ0FqszHpV9ebAgQNq3769Fi9erG7duunhhx%2Bud5d5qurPgw8%2BqKNHj%2BrTTz/VxYsX9fnnn%2BvYsWPq0aNHLVdpjdDQUM2bN0/NmjWrGPvuu%2B8UFRWlvLw8dejQQYGBgRVzMTExys3NtaLUOo%2BABct4PB6FhYX5jIWFhamwsNCiiuxr4cKFCg4OVr9%2B/awuxTZcLpcCAgKUnJxsdSm2curUKX3xxRdq2rSpdu/erd/97ndavHixduzYYXVptnDXXXfpxRdf1PDhwxUXF6enn35aEyZM0F133WV1aZbIycnR6tWrNXbsWHk8nkp/WQkPD5fH46l39/CZQMCCpbxer9Ul2JrX69WCBQu0adMmLV%2B%2BXEFBQVaXZAtnzpzRG2%2B8odmzZysgIMDqcmzF6/UqMjJSI0eOVKNGjZSYmKhHH31UWVlZVpdmC5988okWLVqkt99%2BW4cOHdK7776rN998s14G0AMHDmjEiBGaPHmyEhISrrgcn7HrQ8CCZSIiIuTxeHzGPB6PIiMjLarIXsrLyzVt2jTt3LlT77//vm6//XarS7KN%2BfPn6/HHH1eHDh2sLsV2mjdvXunyWHR0tNxut0UV2cv777%2Bv3r17q3v37goKCtK9996r/v37a%2B3atVaXVqt27typX//615o%2BfbqeeeYZSVJkZGSlKwgej0fh4eFyOIgL14pvEcIysbGxla7t5%2BTkqH///hZVZC%2BvvPKKvvrqK73//vsKDw%2B3uhxb2bBhg0JDQ/Xhhx9Kks6fP6/y8nLt2rVLn376qcXVWeuOO%2B7QyZMnVVxcrJCQEEk/fS0/Ojra4srsoby8XGVlZT5jFy9etKgaa3z22WeaOnWq3njjDZ97z2JjY/X%2B%2B%2B%2BrtLRUTudP8SAnJ0fx8fFWlVqnEUlhmSFDhmjv3r3avXu3Lly4oLVr1%2BrEiRMaOHCg1aVZ7sCBA9qwYYMyMjIIV5eRnZ2tjRs3av369Vq/fr2GDh2qpKQkrV%2B/3urSLJeUlKTQ0FC99tpr%2BvHHH7Vv3z7t2LGDe9X%2BT1JSkrZt26b9%2B/ertLRUhw4dUlZWlh599FGrS6sVpaWlmjlzpqZMmVLpxv7ExEQ1btxYy5cvV0lJiQ4ePKi1a9cqLS3NomrrtgAvN8HgBoqLi5P004daks/fiqSfHqa5aNEi5efnq23btpoxY4a6du1qTbG1rKreTJ8%2BXX/5y18qxn7WtWvXevONsKu9d/7VkiVLlJ%2Bfr/nz59degRa6Wm/%2B8Y9/aNasWcrLy1NkZKReeOGFevUoi6v1591339V///d/6/Tp02rRooWGDBmi4cOH14t7jfbv36%2BnnnpKDRo0qDS3detWFRcXa9asWcrNzVWzZs00atQoPfnkkxZUWvcRsAAAAAzjEiEAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGPb/AECPC2V/NpJ0AAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common1212649561706876396”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>18.4</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>15.6</td> <td class=”number”>178</td> <td class=”number”>5.5%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16.9</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.7</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14.1</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.0</td> <td class=”number”>116</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.9</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14.4</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14.3</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.4</td> <td class=”number”>101</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (32)</td> <td class=”number”>1749</td> <td class=”number”>54.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme1212649561706876396”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>8.7</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:20%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.2</td> <td class=”number”>14</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.9</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:24%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.6</td> <td class=”number”>87</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.2</td> <td class=”number”>55</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:63%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>17.0</td> <td class=”number”>93</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:37%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17.9</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:27%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>18.4</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>19.7</td> <td class=”number”>15</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.9</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:45%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FOODINSEC_13_15”>FOODINSEC_13_15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>42</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.3%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>14.274</td>

</tr> <tr>

<th>Minimum</th> <td>8.5</td>

</tr> <tr>

<th>Maximum</th> <td>20.8</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-3949235167948981369”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-3949235167948981369,#minihistogram-3949235167948981369”
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</div> <div class=”row collapse col-md-12” id=”descriptives-3949235167948981369”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-3949235167948981369”

aria-controls=”quantiles-3949235167948981369” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-3949235167948981369” aria-controls=”histogram-3949235167948981369”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-3949235167948981369” aria-controls=”common-3949235167948981369”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-3949235167948981369” aria-controls=”extreme-3949235167948981369”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-3949235167948981369”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>8.5</td>

</tr> <tr>

<th>5-th percentile</th> <td>10.1</td>

</tr> <tr>

<th>Q1</th> <td>12.4</td>

</tr> <tr>

<th>Median</th> <td>14.8</td>

</tr> <tr>

<th>Q3</th> <td>15.4</td>

</tr> <tr>

<th>95-th percentile</th> <td>18.4</td>

</tr> <tr>

<th>Maximum</th> <td>20.8</td>

</tr> <tr>

<th>Range</th> <td>12.3</td>

</tr> <tr>

<th>Interquartile range</th> <td>3</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>2.4612</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.17242</td>

</tr> <tr>

<th>Kurtosis</th> <td>0.32044</td>

</tr> <tr>

<th>Mean</th> <td>14.274</td>

</tr> <tr>

<th>MAD</th> <td>1.9092</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.33494</td>

</tr> <tr>

<th>Sum</th> <td>44706</td>

</tr> <tr>

<th>Variance</th> <td>6.0573</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-3949235167948981369”>

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BffvUqVMn/e53v1NJSYmmTZumMWPGKD8/X/Pnz9czzzyjgoICSdLOnTuVkZGhZcuWaf/%2B/br//vs1ZcoUnTt3zlhtAADAu1kesKqqqhQQENC4EF9fVVZWGlvn1Vdf1axZs3T77bcrKChICxYs0IIFC7RlyxZ16NBBSUlJCggIUFxcnBISEpSdnS1JysrK0siRIxUdHa0WLVpo4sSJkqRdu3YZqw0AAHg3y09yj4mJ0dKlS5Wamqrg4GBJ0tdff63nn3/e7VINTfH111/r5MmT%2Bvvf/66hQ4fq9OnTio2N1aJFi1RUVKTIyEi320dGRmrbtm2SpKKiIg0dOrRhztfXV926dVNBQYESExOvaP2SkhKVlpa6jTkcgQoPD5ck%2Bfn5uv3Ed%2BiNZ/TGM3rjzuH4rg/0xjN64xm9aTrLA9a8efP0xBNPqG/fvgoKClJtba0qKip0yy23aP369UbWOHXqlCTp3Xff1Wuvvaa6ujrNmDFDCxYsUFVVlSIiItxuHxISorKyMkmSy%2BVqCH4XBQcHN8xfiaysLGVmZrqNpaSkaMaMGW5jTmfLK/6dNxp64xm98Yze1AsNbdVojN54Rm88ozdXz/KAdcstt%2Bidd97R%2B%2B%2B/r7/%2B9a/y9fVVx44d1a9fP/n5%2BRlZo66uTpI0ceLEhjD11FNPadKkSYqLi7vi%2B1%2Bt0aNHKyEhwW3M4QhUWVmFpPp3BE5nS5WXV6qmprZJa3kbeuMZvfGM3ri7%2BFoj0ZvLoTeeeVNvLvWGwwqWByxJ8vf316BBg67Z72/btq0kyel0Noy1b99edXV1unDhQqPLQZSVlSksLEySFBoa2mje5XKpc%2BfOV7x%2BeHh4w%2BHAi0pLv1F1tfuDtKamttEY6tEbz%2BiNZ/Sm3qV6QG88ozee0ZurZ3nA%2BvLLL5Wenq4vvvhCVVVVjebfe%2B%2B9Jq/Rrl07BQUF6ciRI%2Brevbskqbi4WDfddJPi4%2BO1efNmt9sXFhYqOjpakhQVFaWioiKNGDFCklRTU6PDhw8rKSmpyXUBAIAbgy3nYJWUlKhfv34KDAy8Jms4HA4lJSXppZdeUkxMjIKCgrRq1SoNGzZMI0aM0H//938rOztbw4cP14EDB7Rnzx5lZWVJkpKTkzV79mw99NBD6tq1q37729/K399fAwYMuCa1AgAA72N5wCosLNR7773XcEjuWklNTdX58%2Bf12GOP6cKFCxo8eLAWLFigVq1aac2aNXruuef07LPPqn379lq%2BfLnuvPNOSVL//v01e/ZszZw5U6dPn1aPHj20du1atWjR4prWCwAAvIflAatNmzbXbM/V9/n7%2B2vhwoVauHBho7mYmJhGhwm/b%2BzYsRo7duy1LA8AAHgxyy9wMXnyZGVmZjb5k3oAAADXK8v3YL3//vv68MMPtWnTJv3kJz%2BRr697xvv9739vdUkAAABGWR6wgoKC1L9/f6uXBQAAsIzlAWvJkiVWLwkAAGApW75k6C9/%2BYsyMjL09NNPN4x99NFHdpQCAABgnOUBKz8/X8OHD1deXp5yc3Ml1V98dNy4cUYuMgoAAGA3ywPWypUr9atf/UpbtmyRj4%2BPpPrvJ1y6dKlWrVpldTkAAADGWR6wPv/8cyUnJ0tSQ8CSpCFDhujYsWNWlwMAAGCc5QGrdevWl/wOwpKSEvn7%2B1tdDgAAgHGWB6xevXpp8eLFOnv2bMPY8ePHNWfOHPXt29fqcgAAAIyz/DINTz/9tH7xi18oNjZWNTU16tWrlyorK9W5c2ctXbrU6nIAAACMszxgtWvXTrm5udqzZ4%2BOHz%2BuFi1aqGPHjrr33nvdzskCAABoriwPWJJ00003adCgQXYsDQAAcM1ZHrASEhIuu6eKa2EBAIDmzvKANXToULeAVVNTo%2BPHj6ugoEC/%2BMUvrC4HAADAOMsDVlpa2iXHt2/frg8%2B%2BMDiagAAAMyz5bsIL2XQoEF655137C4DAACgya6bgHX48GHV1dXZXQYAAECTWX6IcMyYMY3GKisrdezYMT344INWlwMAAGCc5QGrQ4cOjT5FGBAQoKSkJD322GNWlwMAAGCc5QGLq7UDAABvZ3nAevvtt6/4to888sg1rAQAAODasDxgzZ8/X7W1tY1OaPfx8XEb8/HxIWABAIBmyfKA9corr%2BjVV1/VlClT1LVrV9XV1enPf/6zXn75ZT3%2B%2BOOKjY21uiQAAACjbDkHa%2B3atYqIiGgYu%2Beee3TLLbdowoQJys3NtbokAAAAoyy/DtaJEycUHBzcaNzpdKq4uNjqcgAAAIyzPGC1b99eS5cuVVlZWcNYeXm50tPTdeutt1pdDgAAgHGWHyKcN2%2BeUlNTlZWVpVatWsnX11dnz55VixYttGrVKqvLAQAAMM7ygNWvXz/t3r1be/bs0alTp1RXV6eIiAjdd999at26tdXlAAAAGGd5wJKkli1bauDAgTp16pRuueUWO0oAAAC4ZiwPWFVVVVq4cKHeeecdSVJhYaHKy8s1e/Zs/ed//qecTqfVJQGX9LMX/mh3Cf%2BQbTPvtbsEAMD/Z/lJ7suXL9eRI0e0YsUK%2Bfp%2Bt3xNTY1WrFhhdTkAAADGWR6wtm/frv/6r//SkCFDGr702el0asmSJcrLy7O6HAAAAOMsD1gVFRXq0KFDo/GwsDCdO3fO6nIAAACMszxg3Xrrrfrggw8kye27B99991398z//s9XlAAAAGGf5Se5jx47VU089pUcffVS1tbV67bXXVFhYqO3bt2v%2B/PlWlwMAAGCc5QFr9OjRcjgceuONN%2BTn56eXXnpJHTt21IoVKzRkyBCrywEAADDO8oB15swZPfroo3r00UetXhoAAMASlp%2BDNXDgQLdzrwAAALyN5QErNjZW27Zts3pZAAAAy1h%2BiPCf/umf9Jvf/EZr167VrbfeqptuusltPj093eqSAAAAjLI8YB09elS33367JKmsrMzq5QEAAK45ywLWrFmztHLlSq1fv75hbNWqVUpJSbGqBAAAAEtYdg7Wzp07G42tXbvWquUBAAAsY1nAutQnB/k0IQAA8EaWBayLX%2Bz8Y2MAAADNneWXaQAAAPB2ln%2BKEAB%2B9sIf7S4BAK4pywLWhQsXlJqa%2BqNjXAcLAAA0d5YdIuzdu7dKSkrc/lxq7FpYvHixunbt2vDv/Px8JSUlqVevXkpMTFROTo7b7detW6fBgwerV69eSk5OVmFh4TWpCwAAeCfL9mB9//pXVjpy5Ig2b97c8O%2BSkhJNmzZN8%2BfP17Bhw3To0CFNnTpVHTt2VI8ePbRz505lZGTolVdeUdeuXbVu3TpNmTJFeXl5CgwMtGUbAABA8%2BLVJ7nX1tZq4cKFGj9%2BfMPYli1b1KFDByUlJSkgIEBxcXFKSEhQdna2JCkrK0sjR45UdHS0WrRooYkTJ0qSdu3aZccmAACAZsirT3L//e9/r4CAAA0bNkwvvPCCJKmoqEiRkZFut4uMjGz4AuqioiINHTq0Yc7X11fdunVTQUGBEhMTr2jdkpISlZaWuo05HIEKDw%2BXJPn5%2Bbr9xHfozdVzOOgZ6n3/scBzyjN64xm9aTqvDVh/%2B9vflJGR0ejQpMvlUkREhNtYSEhIw/ciulwuBQcHu80HBwf/Q9%2BbmJWVpczMTLexlJQUzZgxw23M6Wx5xb/zRkNv/nGhoa3sLgHXiUs9FnhOeUZvPKM3V89rA9aSJUs0cuRIderUSSdPnvyH7tvUK8yPHj1aCQkJbmMOR6DKyiok1b8jcDpbqry8UjU1tU1ay9vQm6t38fEFfP%2BxwHPKM3rjmTf1xq43n14ZsPLz8/XRRx8pNze30VxoaKhcLpfbWFlZmcLCwjzOu1wude7c%2BYrXDw8PbzgceFFp6TeqrnZ/kNbU1DYaQz1684%2BjX7joUo8FnlOe0RvP6M3V88qAlZOTo9OnT%2Bv%2B%2B%2B%2BX9N0eqdjYWD3xxBONgldhYaGio6MlSVFRUSoqKtKIESMkSTU1NTp8%2BLCSkpIs3ALgH8fFOwHg%2BuGVZ6/NnTtX27dv1%2BbNm7V582atXbtWkrR582YNGzZMxcXFys7O1rfffqs9e/Zoz549GjVqlCQpOTlZb7/9tj7%2B%2BGNVVlZq9erV8vf314ABA2zcIgAA0Jx45R6s4OBgtxPVq6urJUnt2rWTJK1Zs0bPPfecnn32WbVv317Lly/XnXfeKUnq37%2B/Zs%2BerZkzZ%2Br06dPq0aOH1q5dqxYtWli/IQAAoFnyyoD1Qz/5yU/05z//ueHfMTExbhcf/aGxY8dq7NixVpQGAAC8kFceIgQAALATAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADDMYXcBAACzfvbCH%2B0u4Yptm3mv3SUA14TX7sEqLi5WSkqKYmNjFRcXp7lz56q8vFySdOTIET3%2B%2BOPq3bu3HnzwQb366qtu9926dauGDRumnj17auTIkdq3b58dmwAAAJoprw1YU6ZMkdPp1M6dO7Vp0yZ98cUXev7551VVVaXJkyfrpz/9qfbu3auVK1dqzZo1ysvLk1QfvubMmaO0tDQdOHBA48eP1/Tp03Xq1CmbtwgAADQXXhmwysvLFRUVpdTUVLVq1Urt2rXTiBEjdPDgQe3evVsXLlzQ1KlTFRgYqO7du%2Buxxx5TVlaWJCk7O1vx8fGKj49XQECAhg8fri5duignJ8fmrQIAAM2FV56D5XQ6tWTJErexr776SuHh4SoqKlLXrl3l5%2BfXMBcZGans7GxJUlFRkeLj493uGxkZqYKCgitev6SkRKWlpW5jDkegwsPDJUl%2Bfr5uP/EdegPcWBwO%2B57rvN54Rm%2BazisD1g8VFBTojTfe0OrVq7Vt2zY5nU63%2BZCQELlcLtXW1srlcik4ONhtPjg4WEePHr3i9bKyspSZmek2lpKSohkzZriNOZ0t/8EtuXHQG%2BDGEBrayu4SeL25DHpz9bw%2BYB06dEhTp05Vamqq4uLitG3btkvezsfHp%2BHvdXV1TVpz9OjRSkhIcBtzOAJVVlYhqf4dgdPZUuXllaqpqW3SWt6G3gA3louvi3bg9cYzb%2BqNXSHeqwPWzp079atf/UrPPPOMHnnkEUlSWFiYTpw44XY7l8ulkJAQ%2Bfr6KjQ0VC6Xq9F8WFjYFa8bHh7ecDjwotLSb1Rd7f4grampbTSGevQGuDFcD89zXm88ozdXz2sD1ocffqg5c%2BboxRdfVL9%2B/RrGo6KitGHDBlVXV8vhqN/8goICRUdHN8wXFha6/a6CggIlJiZaV7yXak7X5gEAoCm8MmBVV1drwYIFSktLcwtXkhQfH6%2BgoCCtXr1aEydO1Oeff66NGzdq%2BfLlkqRRo0YpKSlJu3fvVt%2B%2BfbVlyxadOHFCw4cPt2NTAMCrNbc3XlwYFVfKp66pJxxdhw4ePKif//zn8vf3bzT37rvvqqKiQgsXLlRhYaHatm2rSZMmaezYsQ23ycvLU3p6uoqLi9WpUyfNnz9fMTExTaqptPSbhr87HL4KDW2lsrKKG2rXa3N7IQWAH7pRApY3/T91882tbVnXKwPW9YiARcAC0PwRsJofuwIWF7gAAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwh90FAACAa%2BNnL/zR7hKu2LaZ99pdglHswQIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjCu5AwBwhZrTldFhL/ZgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwvuy5meOLRwEAuP6wBwursXpIAAAKRklEQVQAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsC6hOLiYj355JOKjY3V/fffr%2BXLl6u2ttbusgAAQDPBdbAu4amnnlL37t21Y8cOnT59WpMnT1bbtm31r//6r3aXBgAAmgH2YP1AQUGBPvvsM6Wlpal169bq0KGDxo8fr6ysLLtLAwAAzQR7sH6gqKhI7du3V3BwcMNY9%2B7ddfz4cZ09e1ZBQUE/%2BjtKSkpUWlrqNuZwBCo8PFyS5Ofn6/YTAIAbncPhXf8nErB%2BwOVyyel0uo1dDFtlZWVXFLCysrKUmZnpNjZ9%2BnQ99dRTkuoD2Ouvv6LRo0c3hK6rdfA3Q5p0/%2BtNSUmJsrKyjPTG29Abz%2BiNZ/TGM3rjGb1pOu%2BKi4bU1dU16f6jR4/Wpk2b3P6MHj26Yb60tFSZmZmN9nKB3lwOvfGM3nhGbzyjN57Rm6ZjD9YPhIWFyeVyuY25XC75%2BPgoLCzsin5HeHg4iR8AgBsYe7B%2BICoqSl999ZXOnDnTMFZQUKBOnTqpVatWNlYGAACaCwLWD0RGRqpHjx5KT0/X2bNndezYMb322mtKTk62uzQAANBM%2BC1atGiR3UVcb%2B677z7l5ubqP/7jP/TOO%2B8oKSlJEyZMkI%2BPj7E1WrVqpT59%2BrBX7BLojWf0xjN64xm98YzeeEZvmsanrqlndAMAAMANhwgBAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgWezw4cMaN26c7rnnHt17771KS0tz%2B2LpG8nevXsVFxenWbNmNZrbunWrhg0bpp49e2rkyJHat2%2BfDRXa53K9ycvL0/Dhw9WzZ08NHjxYb731lg0V2udyvbmooqJCAwYM0Ny5cy2szH6X683XX3%2BtqVOn6u6771ZcXJzS09NVW1trQ5X2uFxv3nzzTQ0ePLjhObV%2B/XobKrRPcXGxUlJSFBsbq7i4OM2dO1fl5eWSpCNHjujxxx9X79699eCDD%2BrVV1%2B1udrmg4Bloerqaj355JO6%2B%2B67tX//fuXm5urMmTO6Eb8O8uWXX9Zzzz2n2267rdHckSNHNGfOHKWlpenAgQMaP368pk%2BfrlOnTtlQqfUu15tPP/1UaWlpmjFjhv70pz9p3rx5%2BvWvf62DBw/aUKn1Lteb78vIyNDZs2ctqur6cLne1NXVafr06Wrfvr327dun9evXKz8/Xx988IENlVrvcr3Zs2ePli9frmXLlunQoUNatmyZ0tPTtXv3busLtcmUKVPkdDq1c%2BdObdq0SV988YWef/55VVVVafLkyfrpT3%2BqvXv3auXKlVqzZo3y8vLsLrlZIGBZqLS0VKWlpXr44Yfl7%2B%2Bv0NBQPfDAAzpy5IjdpVkuICBAGzduvOQLXnZ2tuLj4xUfH6%2BAgAANHz5cXbp0UU5Ojg2VWu9yvXG5XJo8ebIGDRokh8Oh%2BPh4denS5YYJWJfrzUWfffaZcnNzNWLECAsrs9/levOnP/1JX375pf7t3/5NQUFBuuOOO7Rx40b17dvXhkqtd7neFBYWqnPnzoqOjpavr6%2Bio6PVpUsXHT582IZKrVdeXq6oqCilpqaqVatWateunUaMGKGDBw9q9%2B7dunDhgqZOnarAwEB1795djz32mLKysuwuu1kgYFkoIiJC3bp1U1ZWlioqKnT69Gnl5eVpwIABdpdmuXHjxql169aXnCsqKlJkZKTbWGRkpAoKCqwozXaX603//v2VkpLS8O/q6mqVlpYqIiLCqvJsdbneSPV7ahYtWqRZs2bJ6XRaWJn9LtebQ4cOqUuXLlq5cqViY2M1cODAG%2BpQz%2BV6c9999%2Bno0aP64IMPdP78eX300Uc6duyY%2BvXrZ3GV9nA6nVqyZInatm3bMPbVV18pPDxcRUVF6tq1q/z8/BrmIiMjVVhYaEepzQ4By0K%2Bvr7KyMjQe%2B%2B9p169eikuLk7V1dVKTU21u7TrisvlUnBwsNtYcHCwysrKbKro%2BrVixQoFBgZq6NChdpdyXcjKypKPj49GjhxpdynXlVOnTunjjz9WmzZttHv3bv37v/%2B7Vq5cqR07dthdmu3uuusuPf3003riiSfUo0cPPf7445o5c6buuusuu0uzRUFBgd544w1NnTpVLper0RuVkJAQuVyuG%2Br8vatFwLLQ%2BfPnNWXKFA0ZMkQHDx7U%2B%2B%2B/r9atWystLc3u0q47dXV1dpdwXaurq9Py5cuVm5ur1atXKyAgwO6SbHf69Gm9%2BOKLWrRokXx8fOwu57pSV1ensLAwTZw4US1btlR8fLweeOABbdu2ze7SbHfgwAGlp6frlVde0aeffqrXX39dL7300g0ZPg8dOqQJEyYoNTVVcXFxHm/H8%2BvKELAslJ%2Bfr5MnT2r27Nlq3bq1IiIiNGPGDP3f//2fXC6X3eVdN0JDQxv1w%2BVyKSwszKaKri%2B1tbWaO3eudu7cqQ0bNuj222%2B3u6TrwtKlS/XII4%2Boa9eudpdy3bn55psbHSJr3769SktLbaro%2BrFhwwY9%2BOCD6tu3rwICAnTPPfcoMTFRGzdutLs0S%2B3cuVNPPvmk5s2bp3HjxkmSwsLCGh05cLlcCgkJka8v8eHHOOwu4EZSU1Oj2tpat70z58%2Bft7Gi61NUVFSjY/wFBQVKTEy0qaLry%2BLFi/XFF19ow4YNCgkJsbuc60ZOTo6cTqc2bdokSaqqqlJtba127dp1w3xazpM77rhDX375pSoqKtSqVStJ9R/Nb9%2B%2Bvc2V2a%2B2tlY1NTVuYzfa6/KHH36oOXPm6MUXX3Q79ywqKkobNmxQdXW1HI76uFBQUKDo6Gi7Sm1WiKAW6tmzpwIDA5WRkaHKykqVlZVp9erViomJ4T/K7xk1apT279%2Bv3bt369tvv9XGjRt14sQJDR8%2B3O7SbHfo0CHl5ORo7dq1PGZ%2BYM%2BePdqyZYs2b96szZs3a8yYMUpISNDmzZvtLs12CQkJcjqdWrZsmc6dO6f8/Hzt2LGDc9VU35vt27fr4MGDqq6u1qeffqpt27bpgQcesLs0S1RXV2vBggVKS0trdGJ/fHy8goKCtHr1alVWVuqTTz7Rxo0blZycbFO1zYtPHSe7WKqwsFDPP/%2B8PvvsM/n7%2B6tPnz6aO3fuDfMpsIt69Oghqf7JLcnt3ZFUfzHN9PR0FRcXq1OnTpo/f75iYmLsKdZil%2BvNvHnz9L//%2B78NYxfFxMTcEJ8K%2B7HHzfdlZGSouLhYS5cuta5AG/1Ybz7//HMtXLhQRUVFCgsL0y9/%2Bcsb5lIWP9ab119/Xf/zP/%2Bjr7/%2BWhERERo1apSeeOKJG%2BJco4MHD%2BrnP/%2B5/P39G829%2B%2B67qqio0MKFC1VYWKi2bdtq0qRJGjt2rA2VNj8ELAAAAMM4RAgAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhv0/DLhBXgDlchwAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-3949235167948981369”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>14.9</td> <td class=”number”>317</td> <td class=”number”>9.9%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>15.4</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17.6</td> <td class=”number”>187</td> <td class=”number”>5.8%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.4</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.1</td> <td class=”number”>125</td> <td class=”number”>3.9%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16.1</td> <td class=”number”>124</td> <td class=”number”>3.9%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.8</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14.8</td> <td class=”number”>109</td> <td class=”number”>3.4%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14.6</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.8</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (31)</td> <td class=”number”>1561</td> <td class=”number”>48.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-3949235167948981369”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>8.5</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:20%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.7</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:24%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.8</td> <td class=”number”>14</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.9</td> <td class=”number”>87</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.1</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:24%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>16.1</td> <td class=”number”>124</td> <td class=”number”>3.9%</td> <td>

<div class=”bar” style=”width:66%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17.6</td> <td class=”number”>187</td> <td class=”number”>5.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>18.4</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>19.2</td> <td class=”number”>15</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.8</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:61%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FOODINSEC_CHILD_01_07”>FOODINSEC_CHILD_01_07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>40</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.2%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>8.5867</td>

</tr> <tr>

<th>Minimum</th> <td>4.8</td>

</tr> <tr>

<th>Maximum</th> <td>12.6</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram2453349319675928087”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives2453349319675928087,#minihistogram2453349319675928087”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives2453349319675928087”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles2453349319675928087”

aria-controls=”quantiles2453349319675928087” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram2453349319675928087” aria-controls=”histogram2453349319675928087”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common2453349319675928087” aria-controls=”common2453349319675928087”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme2453349319675928087” aria-controls=”extreme2453349319675928087”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles2453349319675928087”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>4.8</td>

</tr> <tr>

<th>5-th percentile</th> <td>6.2</td>

</tr> <tr>

<th>Q1</th> <td>7.2</td>

</tr> <tr>

<th>Median</th> <td>8.3</td>

</tr> <tr>

<th>Q3</th> <td>9.5</td>

</tr> <tr>

<th>95-th percentile</th> <td>12.6</td>

</tr> <tr>

<th>Maximum</th> <td>12.6</td>

</tr> <tr>

<th>Range</th> <td>7.8</td>

</tr> <tr>

<th>Interquartile range</th> <td>2.3</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>1.8944</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.22063</td>

</tr> <tr>

<th>Kurtosis</th> <td>-0.27613</td>

</tr> <tr>

<th>Mean</th> <td>8.5867</td>

</tr> <tr>

<th>MAD</th> <td>1.5052</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.56804</td>

</tr> <tr>

<th>Sum</th> <td>26893</td>

</tr> <tr>

<th>Variance</th> <td>3.5889</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram2453349319675928087”>

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u0jnr5/0h/qwuVOuCYnJ2vnzp3685//rA4dOmjx4sWKi4vT8uXLdeLEiVqtY86cOdqzZ4%2B2bt2qrVu3KjMzU5K0detWjRgxQoWFhcrKytKlS5d08OBBHTx4UOPGjZMkJSUlacuWLTp69KjKysqUkZEhX19fDRw4sKFaBgAAHsbljmD9qGvXrurataueeuop7dy5UwsWLND69esVGxur3/72t7rzzjt/8bHBwcFOF6qXl5dLklq1aiVJWrdunRYtWqSFCxeqdevWWr58uTp37ixJGjBggGbOnKkZM2bozJkz6tatmzIzM%2BXv79%2BA3QIAAE/isgHrypUr%2Bstf/qI33nhD7733niIiIjR9%2BnQVFRVp8uTJWrhwodPtF66mTZs2%2Bvzzz6u/7t27t9PNR39qwoQJmjBhQr17AAAANyeXC1gFBQXavHmztmzZotLSUg0ZMkT/%2BZ//qZ49e1Yv07t3by1YsKDWAQsAAOBGcrmANXz4cLVt21ZTp07VQw89pJCQkBrLxMXF6ezZsxZUB7iuoS8ctroEAMD/c7mAtXHjRvXp0%2Beay33yySc3oBoAAIC6c7lXEXbq1EnTpk3TW2%2B9VT32yiuv6NFHH61xfyoAAABX5HIBa8mSJfruu%2B/Uvn376rGBAweqsrJSS5cutbAyAACA2nG5U4SHDh3Stm3bFBoaWj0WERGhFStW6F/%2B5V8srAw3G65pAgBcL5c7gnXx4kX5%2BfnVGPf29lZZWZkFFQEAANSNywWs3r17a%2BnSpTp37lz12LfffquFCxc63aoBAADAVbncKcKnn35aDz/8sO6%2B%2B24FBASosrJSpaWluvXWW/Xqq69aXR4AAMA1uVzAuvXWW7Vjxw698847%2Bvrrr%2BXt7a22bduqf//%2B8vHxsbo8AACAa3K5gCVJvr6%2Buu%2B%2B%2B6wuAwAA4Lq4XMD65ptvtHLlSn3xxRe6ePFijfm3337bgqoAAABqz%2BUC1tNPP62ioiL1799fTZo0sbocAACAOnO5gJWXl6e3335bYWFhVpcCAABwXVzuNg3NmjXjyBUAAHBrLhewpk6dqvT0dFVVVVldCgAAwHVxuVOE77zzjj766CO98cYbatOmjby9nTPgn/70J4sqAwAAqB2XC1gBAQEaMGCA1WUAAABcN5cLWEuWLLG6BAAAgHpxuWuwJOlvf/ubVq9erd/97nfVYx9//LGFFQEAANSeywWsnJwcjRw5Unv37tX27dsl/XDz0YkTJ3KTUQAA4BZcLmA9//zzevLJJ7Vt2zZ5eXlJ%2BuH9CZcuXao1a9ZYXB0AAMC1uVzA%2Bu///m8lJSVJUnXAkqQHHnhABQUFVpUFAABQay4XsAIDA3/2PQiLiork6%2BtrQUUAAAB143IBKyYmRosXL9aFCxeqx06cOKHZs2fr7rvvtrAyAACA2nG52zT87ne/06RJk9S3b19VVFQoJiZGZWVl6tChg5YuXWp1eQAAANfkcgGrVatW2r59uw4ePKgTJ07I399fbdu2Vb9%2B/ZyuyQIAAHBVLhewJKlRo0a67777rC4DAADgurhcwIqPj7/qkSruhQUAAFydywWsYcOGOQWsiooKnThxQrm5uZo0aZKFlQEAANSOywWstLS0nx3fs2eP/vrXv97gagAAAOrO5W7T8Evuu%2B8%2B7dixw%2BoyAAAArsltAtaxY8dUVVVldRkAAADX5HKnCMePH19jrKysTAUFBRo8eLAFFQEAANSNywWsiIiIGq8i9PPzU0JCgsaOHWtRVQAAALXncgGLu7V7rqEvHLa6BAAAbgiXC1hbtmyp9bIPPfRQA1YCAABwfVwuYM2dO1eVlZU1Lmj38vJyGvPy8iJgAQAAl%2BRyryJ8%2BeWX1b9/f23atEkffPCB3n//fb322msaMGCAXnrpJX366af69NNP9cknn1x1PZ999pkmTZqknj17KjY2VjNmzFBxcbEkKScnRwkJCYqJidHw4cOVnZ3t9NiNGzdqyJAhiomJUVJSkvLy8hqsXwAA4HlcLmAtXbpUixYtUs%2BePRUQEKDAwED16tVLv//977Vs2TL5%2BvpWf/ySy5cv6%2BGHH1afPn2Uk5Oj7du368yZM1qwYIGKior0%2BOOPa/z48crJydHcuXM1f/585ebmSpL27dun1atX67nnntORI0c0aNAgTZs2Td9///2N%2Bi8AAABuzuUC1smTJxUcHFxjPCgoSIWFhbVaR1lZmVJTUzV16lT5%2BvoqLCxM999/v7744gtt27ZNERERSkhIkJ%2Bfn2JjYxUfH6%2BsrCxJkt1u1%2BjRoxUdHS1/f39NmTJFkrR//35zTQIAAI/mctdgtW7dWkuXLtVvf/tbhYaGSpLOnz%2BvF198Ubfddlut1hEcHOx0S4e//e1vevPNNzV06FDl5%2BcrMjLSafnIyEjt2rVLkpSfn69hw4ZVz3l7e6tLly7Kzc3V8OHDa7X9oqKi6tORP7LZmig8PLxWj28IPj7eTp8BeC6b7cb%2BnHv68wv94Xq4XMB6%2BumnNWvWLNntdjVt2lTe3t66cOGC/P39tWbNmjqtq7CwUEOGDFF5ebnGjRunlJQUPfroo2rZsqXTciEhISopKZEkORyOGkfQgoODq%2Bdrw263Kz093WksOTlZKSkpdaq/IQQFNba6BAANLDS0qSXb9fTnF/pDXbhcwOrfv78OHDiggwcP6vTp06qqqlLLli11zz33KDAwsE7rat26tXJzc/XVV1/p3//93/XUU0/V6nH1fUuexMRExcfHO43ZbE1UUlJar/XWh4%2BPt4KCGuv8%2BTJVVFRaVgeAhnejn2s8/fmF/tybVX9wuFzAkqTGjRvr3nvv1enTp3XrrbfWa11eXl6KiIhQamqqxo8fr7i4ODkcDqdlSkpKFBYWJkkKDQ2tMe9wONShQ4dabzM8PLzG6cDi4u9UXm79jltRUekSdQBoOFb9jHv68wv9oS5c7oTrxYsXNXv2bPXo0UNDhw6V9MM1WFOmTNH58%2BdrtY6cnBwNGTJElZX/2FG8vX9o9c4776xx24W8vDxFR0dLkqKiopSfn189V1FRoWPHjlXPAwAAXIvLBazly5fr%2BPHjWrFiRXUokn4IOitWrKjVOqKionThwgUtX75cZWVlOnv2rFavXq1evXopKSlJhYWFysrK0qVLl3Tw4EEdPHhQ48aNkyQlJSVpy5YtOnr0qMrKypSRkSFfX18NHDiwIdoFAAAeyOUC1p49e/Tiiy/qgQceqH7T56CgIC1ZskR79%2B6t1ToCAwO1fv165eXl6a677tLw4cMVGBioP/7xj2rWrJnWrVun1157TT179tTixYu1fPlyde7cWZI0YMAAzZw5UzNmzFCfPn105MgRZWZmyt/fv8F6BgAAnsXlrsEqLS1VREREjfGwsLA63eyzU6dOevXVV392rnfv3tq6desvPnbChAmaMGFCrbcFAADwz1wuYN12223661//qr59%2Bzq9mm/37t265ZZbLKwMAAD3MvSFw1aXUGu7ZvSzugSjXC5gTZgwQdOnT9eYMWNUWVmpDRs2KC8vT3v27NHcuXOtLg8AAOCaXC5gJSYmymaz6bXXXpOPj4/Wrl2rtm3basWKFXrggQesLg8AAOCaXC5gnT17VmPGjNGYMWOsLgUAAOC6uNyrCO%2B9995630kdAADASi4XsPr27Vv9xssAAADuyOVOEf7qV7/SH/7wB2VmZuq2225To0aNnOZXrlxpUWUAAAC143IB68svv1S7du0k/fAegQAAAO7GZQJWamqqnn/%2Beaebg65Zs0bJyckWVgUAAFB3LnMN1r59%2B2qMZWZmWlAJAABA/bhMwPq5Vw7yakIAAOCOXCZg/fjGztcaAwAAcHUuE7AAAAA8BQELAADAMJd5FeGVK1c0a9asa45xHywAAODqXCZg9ezZU0VFRdccAwAAcHUuE7D%2B%2Bf5XAAAA7oxrsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMJvVBQAAzBr6wmGrS6i1XTP6WV0C0CA4ggUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAM89iAVVhYqOTkZPXt21exsbGaM2eOzp8/L0k6fvy4/vVf/1U9e/bU4MGDtX79eqfH7ty5UyNGjFCPHj00evRoHTp0yIoWAACAm/LYgDVt2jQFBQVp3759euONN/TFF19o2bJlunjxoqZOnaq77rpL7777rp5//nmtW7dOe/fulfRD%2BJo9e7bS0tL03nvvafLkyXriiSd0%2BvRpizsCAADuwiMD1vnz5xUVFaVZs2apadOmatWqlUaNGqUPPvhABw4c0JUrV/TYY4%2BpSZMm6tq1q8aOHSu73S5JysrKUlxcnOLi4uTn56eRI0eqY8eOys7OtrgrAADgLjwyYAUFBWnJkiVq3rx59djf//53hYeHKz8/X506dZKPj0/1XGRkpPLy8iRJ%2Bfn5ioyMdFpfZGSkcnNzb0zxAADA7d0Ub5WTm5ur1157TRkZGdq1a5eCgoKc5kNCQuRwOFRZWSmHw6Hg4GCn%2BeDgYH355Ze13l5RUZGKi4udxmy2JgoPD7/%2BJurJx8fb6TMAuAKbzfWfk3j%2BvDHcYV%2BoC48PWB9%2B%2BKEee%2BwxzZo1S7Gxsdq1a9fPLufl5VX976qqqnpt0263Kz093WksOTlZKSkp9VqvCUFBja0uAQCqhYY2tbqEWuP5s2G5075QGx4dsPbt26cnn3xS8%2BfP10MPPSRJCgsL08mTJ52WczgcCgkJkbe3t0JDQ%2BVwOGrMh4WF1Xq7iYmJio%2BPdxqz2ZqopKT0%2BhoxwMfHW0FBjXX%2BfJkqKiotqwMA/pmVz4u1xfPnjdFQ%2B4JVwc1jA9ZHH32k2bNna9WqVerfv3/1eFRUlF5//XWVl5fLZvuh/dzcXEVHR1fP/3g91o9yc3M1fPjwWm87PDy8xunA4uLvVF5u/Q9mRUWlS9QBAJLc6vmI58%2BG5Wn/t551wvP/lZeXa968eUpLS3MKV5IUFxengIAAZWRkqKysTJ988ok2b96spKQkSdK4ceN05MgRHThwQJcuXdLmzZt18uRJjRw50opWAACAG/LII1hHjx5VQUGBFi1apEWLFjnN7d69W2vXrtUzzzyjzMxMNW/eXKmpqRo4cKAkqWPHjlqxYoWWLFmiwsJCtW/fXuvWrVOLFi0s6AQAALgjjwxYvXr10ueff37VZV5//fVfnBs8eLAGDx5suiwAAHCT8MhThAAAAFYiYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAw2xWFwAAgLsY%2BsJhq0uAm%2BAIFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBh3MkdAGAZ7owOT8URLAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAY5rEB691331VsbKxSU1NrzO3cuVMjRoxQjx49NHr0aB06dKh6rrKyUs8//7zuvfde9e7dW4888oi%2B%2BeabG1k6AABwcx4ZsF566SUtWrRIt99%2Be42548ePa/bs2UpLS9N7772nyZMn64knntDp06clSZs2bdK2bduUmZmp/fv3KyIiQsnJyaqqqrrRbQAAADflkQHLz89Pmzdv/tmAlZWVpbi4OMXFxcnPz08jR45Ux44dlZ2dLUmy2%2B2aPHmy7rjjDgUEBCg1NVUFBQX65JNPbnQbAADATdmsLqAhTJw48Rfn8vPzFRcX5zQWGRmp3NxcXbx4UV9%2B%2BaUiIyOr5wICAnT77bcrNzdX3bt3r9X2i4qKVFxc7DRmszVReHh4Hbowy8fH2%2BkzAACuxGbzrN9PHhmwrsbhcCg4ONhpLDg4WF9%2B%2BaXOnTunqqqqn50vKSmp9TbsdrvS09OdxpKTk5WSknL9hRsSFNTY6hIAAKghNLSp1SUYddMFLEnXvJ6qvtdbJSYmKj4%2B3mnMZmuikpLSeq23Pnx8vBUU1Fjnz5epoqLSsjoAAPg5DfU70qrgdtMFrNDQUDkcDqcxh8OhsLAwhYSEyNvb%2B2fnmzVrVutthIeH1zgdWFz8ncrLrQ82FRWVLlEHAAD/zNN%2BN3nWCc9aiIqKUl5entNYbm6uoqOj5ef4JgXAAAALO0lEQVTnpw4dOig/P7967vz58/r6669155133uhSAQCAm7rpAta4ceN05MgRHThwQJcuXdLmzZt18uRJjRw5UpKUlJSkjRs3qqCgQBcuXNCKFSvUpUsXdevWzeLKAQCAu/DIU4Q/hqHy8nJJ0ltvvSXphyNVHTt21IoVK7RkyRIVFhaqffv2WrdunVq0aCFJGj9%2BvIqLi/Vv//ZvKi0tVd%2B%2BfWtcsA4AAHA1XlXcQfOGKC7%2BztLt22zeCg1tqpKSUsvOcw994bAl2wUAuL5dM/o1yHpbtAhskPVey013ihAAAKChEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGOaRb/Z8M%2BH9/QAAcD0cwQIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAetnFBYW6je/%2BY369u2rQYMGafny5aqsrLS6LAAA4CZsVhfgiqZPn66uXbvqrbfe0pkzZzR16lQ1b95cv/71r60uDQAAuAGOYP1Ebm6uPvvsM6WlpSkwMFARERGaPHmy7Ha71aUBAAA3wRGsn8jPz1fr1q0VHBxcPda1a1edOHFCFy5cUEBAwDXXUVRUpOLiYqcxm62JwsPDjdcLAIAnsNk865gPAesnHA6HgoKCnMZ%2BDFslJSW1Clh2u13p6elOY0888YSmT59urtD/98EfHqjVckVFRbLb7UpMTPTIoEd/7o3%2B3Bv9uTdP788qnhUXDamqqqrX4xMTE/XGG284fSQmJhqq7voUFxcrPT29xpE1T0F/7o3%2B3Bv9uTdP788qHMH6ibCwMDkcDqcxh8MhLy8vhYWF1Wod4eHh/BUAAMBNjCNYPxEVFaW///3vOnv2bPVYbm6u2rdvr6ZNm1pYGQAAcBcErJ%2BIjIxUt27dtHLlSl24cEEFBQXasGGDkpKSrC4NAAC4CZ8FCxYssLoIV3PPPfdo%2B/btevbZZ7Vjxw4lJCTokUcekZeXl9Wl1UvTpk3Vp08fjz0SR3/ujf7cG/25N0/vzwpeVfW9ohsAAABOOEUIAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgB6ybQqVMnRUVFqVu3btUfzz77rNVlGZWRkaH%2B/fure/fumjx5sk6dOmV1SUa8//77Tt%2B3bt26KSoqSp06dbK6NGOOHTumiRMnqlevXurXr5/S0tKc3mzd3eXl5WnixInq2bOn7rnnHv3Hf/yH1SXVy7vvvqvY2FilpqbWmNu5c6dGjBihHj16aPTo0Tp06JAFFdbP1fq7cuWKli1bps6dO%2Budd96xoLr6u1p/e/fu1ciRI9WjRw8NGTJEf/7zny2o0HPYrC4AN8bu3bvVpk0bq8toEJs2bVJ2drY2btyo8PBwvfDCC3rllVc0b948q0urt969eys3N9dpbO3atfrss88sqsis8vJy/eY3v9Ho0aP18ssvq7S0VLNmzdKCBQv04osvWl1evTkcDk2ZMkVjx47VunXrdOrUKU2dOlW33HKLhg4danV5dfbSSy9p8%2BbNuv3222vMHT9%2BXLNnz1Z6erruuusu7dmzR0888YR2796tVq1aWVBt3V2tv%2B%2B//16TJk1S%2B/bt5a7vMHe1/j799FOlpaXpj3/8owYOHKjDhw8rOTlZ7dq1U69evSyo1v1xBAtub/369UpNTVW7du0UEBCgefPmeUS4%2Bjn/8z//ow0bNuipp56yuhQjiouLVVxcrAcffFC%2Bvr4KDQ3V/fffr%2BPHj1tdmhFHjx5VaWmpZsyYocaNG6tDhw565JFHtHnzZqtLuy5%2Bfn6/%2BAs6KytLcXFxiouLk5%2Bfn0aOHKmOHTsqOzvbgkqvz9X6%2B/777zVmzBgtWbLEgsrMuFp/DodDU6dO1X333Sebzaa4uDh17NhRH3zwgQWVegYC1k1i5cqVGjhwoHr16qX58%2BertLTU6pKM%2BPbbb3Xq1CmdO3dOw4YNU9%2B%2BfZWSkuJRp5j%2B2apVqzRmzBjdcsstVpdiRMuWLdWlSxfZ7XaVlpbqzJkz2rt3rwYOHGh1acZ4eXk5fR0cHOy2AXLixIkKDAz82bn8/HxFRkY6jUVGRtY4AuvKrtZf8%2BbNNX78%2BBtckVlX62/AgAFKTk6u/rq8vFzFxcVq2bLljSrP4xCwbgLdu3dXbGys9u7dK7vdrqNHj2rhwoVWl2XE6dOnJf1wCnTDhg3aunWrTp8%2B7ZFHsE6dOqW9e/fq17/%2BtdWlGOPt7a3Vq1fr7bffVkxMjGJjY1VeXq5Zs2ZZXZoRPXr0UOPGjbVq1SqVlZXp66%2B/1n/913/p3LlzVpdmnMPhUHBwsNNYcHCwSkpKLKoI9bFixQo1adJEw4YNs7oUt0XAugnY7XaNHTtWvr6%2BuuOOO5SWlqbt27fr8uXLVpdWbz9eCzFlyhS1bNlSrVq10vTp07Vv3z5dunTJ4urM2rRpkwYPHqwWLVpYXYoxly9f1rRp0/TAAw/ogw8%2B0DvvvKPAwEClpaVZXZoRwcHBWrNmjXJyctSvXz89%2BeSTevDBB%2BXj42N1aQ3CXa9Nwj9UVVVp%2BfLl2r59uzIyMuTn52d1SW6LgHUTatOmjSoqKnTmzBmrS6m35s2bS5KCgoKqx1q3bq2qqiqP6O%2Bf7dmzR/Hx8VaXYVROTo5OnTqlmTNnKjAwUC1btlRKSor%2B8pe/yOFwWF2eEb169VJWVpY%2B%2Bugj2e12hYSEeORpl9DQ0BrfM4fDobCwMIsqQl1VVlZqzpw52rdvn15//XW1a9fO6pLcGgHLwx07dkxLly51GisoKJCvr6/Cw8MtqsqcVq1aKSAgwOmalsLCQjVq1Mgj%2BvvR8ePHVVhYqH79%2BlldilEVFRWqrKx0OvLhCUdWf3Tp0iW9%2BeabunDhQvXY4cOH1aNHDwurahhRUVHKy8tzGsvNzVV0dLRFFaGuFi9erC%2B%2B%2BEKvv/66br31VqvLcXsELA/XrFkz2e12ZWZm6vLlyzpx4oRWrVqlxMREjzhNYbPZlJCQoLVr1%2Bqrr77SmTNntGbNGo0YMUI2m%2BfcheTYsWMKCQlRQECA1aUY1aNHDzVp0kSrV69WWVmZSkpKlJGRod69eyskJMTq8uqtUaNGSk9PV0ZGhsrLy3Xo0CFlZ2dr0qRJVpdm3Lhx43TkyBEdOHBAly5d0ubNm3Xy5EmNHDnS6tJQCx9%2B%2BKGys7OVmZnpET97rsCripPmHu/999/XypUr9fnnn8vX11ejRo1Samqqx5xbv3z5spYsWaIdO3boypUrGjJkiObPn6%2BmTZtaXZox69at07Zt27R9%2B3arSzEuLy9Py5Yt02effSZfX1/16dNHc%2BbM8ZjTaLm5uXrmmWdUUFCgVq1aKS0tTffff7/VZV2Xbt26SfrhFWaSqv%2BI%2BfGVgnv37tXKlStVWFio9u3ba%2B7cuerdu7c1xV6Hq/W3ZcsWzZ8/X9IPzzmNGjWSl5eXHnzwQS1atMiaguvoav09/fTTevPNN2v8Ydq7d2%2BtX7/%2BxhbqIQhYAAAAhnGKEAAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAM%2Bz9Le1BOj9zmwAAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common2453349319675928087”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>12.6</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>246</td> <td class=”number”>7.7%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.2</td> <td class=”number”>225</td> <td class=”number”>7.0%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.2</td> <td class=”number”>205</td> <td class=”number”>6.4%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.3</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.5</td> <td class=”number”>106</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.8</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.0</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.5</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (29)</td> <td class=”number”>1575</td> <td class=”number”>49.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme2453349319675928087”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>4.8</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.3</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:24%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.7</td> <td class=”number”>87</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:39%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.2</td> <td class=”number”>225</td> <td class=”number”>7.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.3</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>10.5</td> <td class=”number”>77</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:31%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.2</td> <td class=”number”>75</td> <td class=”number”>2.3%</td> <td>

<div class=”bar” style=”width:30%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.3</td> <td class=”number”>82</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:32%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.4</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.6</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FOODINSEC_CHILD_03_11”><s>FOODINSEC_CHILD_03_11</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_FOODINSEC_CHILD_01_07”><code>FOODINSEC_CHILD_01_07</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.94773</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FOOD_TAX14”><s>FOOD_TAX14</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_CHIPSTAX_STORES14”><code>CHIPSTAX_STORES14</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>1</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FRESHVEG_ACRES07”>FRESHVEG_ACRES07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>631</td>

</tr> <tr>

<th>Unique (%)</th> <td>19.7%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>42.7%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>1370</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1084.1</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>246870</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>9.3%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-4572769429236068892”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-4572769429236068892,#minihistogram-4572769429236068892”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-4572769429236068892”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-4572769429236068892”

aria-controls=”quantiles-4572769429236068892” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-4572769429236068892” aria-controls=”histogram-4572769429236068892”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-4572769429236068892” aria-controls=”common-4572769429236068892”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-4572769429236068892” aria-controls=”extreme-4572769429236068892”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-4572769429236068892”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>10</td>

</tr> <tr>

<th>Median</th> <td>49.5</td>

</tr> <tr>

<th>Q3</th> <td>256.25</td>

</tr> <tr>

<th>95-th percentile</th> <td>3716.5</td>

</tr> <tr>

<th>Maximum</th> <td>246870</td>

</tr> <tr>

<th>Range</th> <td>246870</td>

</tr> <tr>

<th>Interquartile range</th> <td>246.25</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>7487.8</td>

</tr> <tr>

<th>Coef of variation</th> <td>6.907</td>

</tr> <tr>

<th>Kurtosis</th> <td>657.87</td>

</tr> <tr>

<th>Mean</th> <td>1084.1</td>

</tr> <tr>

<th>MAD</th> <td>1697.2</td>

</tr> <tr class=”alert”>

<th>Skewness</th> <td>22.249</td>

</tr> <tr>

<th>Sum</th> <td>1994700</td>

</tr> <tr>

<th>Variance</th> <td>56068000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-4572769429236068892”>

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2B0qKhIxcaeo4qKo6qvbwj1OGGJjJ1Fvs4iX%2BfZyDhUf7kPy4LV0NCgu%2B66S1u3btWf//xnpaSknPLxycnJKi0tVWJioqQv38fVqtV/vyFffPGF2rRp0%2BTzJyUlNXo5sKysUnV1Z/cfQLdef319g2tndwsydhb5Oot8nefGjN11C6SJ7r//fv373/8%2BYbn6/e9/ry1btgSslZSUKCUlRSkpKYqLi1NxcbF/7%2BOPP9bx48eVnp4elNkBAID7hV3Bev/997VmzRotX75c8fHxjfZ9Pp/mzZun3bt369ixY3ryySe1d%2B9ejRo1SlFRUbrmmmv02GOP6cCBAyovL9dvf/tbDR061P8xDgAAAKfjypcIMzIyJH35E4KStHHjRkmS1%2BvVqlWrVFlZqUGDBgU8JysrS08%2B%2BaRmzJgh6cv3Xvl8PnXu3Fl//OMfdd5550mSpkyZourqal199dWqq6vToEGDdO%2B99wbpygAAQDiIMGf6jm40SVlZpfVjejyRGlqw2fpxnfLqtH6hHuFb8XgilZDQSuXl1a577d8tyNhZ5Oss8nWejYzbtm1teaqmCbuXCAEAAEKNggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwLOgFa/DgwSosLNSBAweCfWoAAICgCHrBGjNmjNatW6dLL71U119/vTZs2OD/NwUBAADCQdALVn5%2BvtatW6fnn39eXbp00f3336/c3Fw9%2BOCD2rNnT7DHAQAAsC5k78FKS0vTrFmztGnTJs2ZM0fPP/%2B8Lr/8ck2aNElbt24N1VgAAABnLGQFq7a2VuvWrdMNN9ygWbNmqV27drrrrrvUvXt3XXfddXr55ZdDNRoAAMAZ8QT7hCUlJVq5cqVefPFFVVdXa/jw4Xr66ad14YUX%2Bh%2BTlZWle%2B%2B9V1dddVWwxwMAADhjQS9YV1xxhTp27KjJkydr5MiRio%2BPb/SY3NxcHT58ONijAQAAWBH0grVixQpddNFFp33cv/71ryBMAwAAYF/Q34OVmpqqm266SRs3bvSv/fGPf9QNN9wgn88X7HEAAACsC3rBWrhwoSorK9W5c2f/2sCBA9XQ0KBFixYFexwAAADrgv4S4dtvv62XX35ZCQkJ/rUOHTqooKBAV155ZbDHAQAAsC7od7BqamoUHR3deJDISB09ejTY4wAAAFgX9IKVlZWlRYsW6YsvvvCvffbZZ5o3b17ARzUAAAC4VdBfIpwzZ45%2B%2Bctf6sc//rFiYmLU0NCg6upqpaSk6E9/%2BlOwxwEAALAu6AUrJSVFr7zyiv76179q7969ioyMVMeOHdW/f39FRUUFexwAAADrgl6wJKl58%2Ba69NJLQ3FqAAAAxwW9YO3bt0%2BLFy/Wv//9b9XU1DTaf/3114M9EgAAgFUheQ9WaWmp%2Bvfvr5YtWwb79AAAAI4LesHatm2bXn/9dSUmJgb71AAAAEER9I9paNOmDXeuAABAWAt6wZo8ebIKCwtljAn2qQEAAIIi6C8R/vWvf9U///lPrV69WhdccIEiIwM73nPPPRfskQAAAKwK%2Bh2smJgYDRgwQLm5uerUqZM6duwY8PVtbN68WTk5OZo%2BfXqjvXXr1umqq65S7969NXr0aL399tv%2BvYaGBi1ZskRDhgxRVlaWJk2apH379vn3fT6fpk2bppycHPXv31933333CX/iEQAA4ESCfgdr4cKFVo7z%2BOOPa%2BXKlWrfvn2jvR07dmjWrFkqLCzUxRdfrPXr1%2BvWW2/Va6%2B9pvPOO0/PPvusXn75ZT3%2B%2BONq166dlixZovz8fL300kuKiIjQ3Llzdfz4ca1du1a1tbWaOnWqCgoKdM8991iZHQAAhLeg38GSpN27d%2BuRRx7RXXfd5V/74IMPvtUxoqOjT1qwXnjhBeXm5io3N1fR0dEaMWKEunbtqjVr1kiSioqKdN1116lTp06KiYnR9OnTVVJSon/96186dOiQNm7cqOnTpysxMVHt2rXTLbfcolWrVqm2tvbMLhwAAJwVgn4Ha8uWLbrhhhvUsWNHffLJJ1q4cKH27duniRMn6qGHHtKQIUOadJyJEyeedK%2B4uFi5ubkBaz169JDX61VNTY127dqlHj16%2BPdiYmLUvn17eb1eVVZWKioqSqmpqf79tLQ0HTlyRLt37w5YP5nS0lKVlZUFrHk8LZWUlNSka2uqqKiQ9OPvzONx17xf5eu2nN2EjJ1Fvs4iX%2Be5OeOgF6wlS5bojjvu0LXXXqvMzExJX/77hIsWLdLSpUubXLBOxefzKS4uLmAtLi5Ou3bt0hdffCFjzAn3y8vLFR8fr5iYGEVERATsSVJ5eXmTzl9UVKTCwsKAtfz8fE2ZMuW7XE7YSEhoFeoRvpPY2HNCPULYI2Nnka%2BzyNd5bsw46AXr448/1jPPPCNJASXmsssu05w5c6yd53QfA3Gq/TP9CIm8vDwNHjw4YM3jaany8uozOu43ua3R275%2Bp0VFRSo29hxVVBxVfX1DqMcJS2TsLPJ1Fvk6z0bGofrLfdALVuvWrVVTU6PmzZsHrJeWljZa%2B64SEhLk8/kC1nw%2BnxITExUfH6/IyMgT7rdp00aJiYmqqqpSfX29oqKi/HvSlx%2BS2hRJSUmNXg4sK6tUXd3Z/QfQrddfX9/g2tndgoydRb7OIl/nuTHjoN8C6dOnj%2B6//35VVVX51/bs2aNZs2bpxz/%2BsZVzpKena9u2bQFrXq9XPXv2VHR0tLp06aLi4mL/XkVFhfbu3avMzEx1795dxhh99NFHAc%2BNjY391h8jAQAAzk5BL1h33XWXPvjgA2VnZ%2BvYsWPq06ePLr/8cvl8Ps2ePdvKOa655hr97W9/05tvvqljx45p5cqV%2BuSTTzRixAhJ0oQJE7RixQqVlJSoqqpKBQUF6t69uzIyMpSYmKjhw4froYce0uHDh3Xw4EEtXbpUY8eOlccT9Bt%2BAADAhYLeGM477zytXbtWb731lvbs2aMWLVqoY8eO6tevX8B7sk4nIyNDklRXVydJ2rhxo6Qv7zZ17dpVBQUFWrhwofbv36/OnTtr2bJlatu2rSRp/PjxKisr089//nNVV1crOzs74E3p9913n379619ryJAhatasma688soTfpgpAADAiUQY/lHAoCgrq7R%2BTI8nUkMLNls/rlNendYv1CN8Kx5PpBISWqm8vNp1r/27BRk7i3ydRb7Os5Fx27atLU/VNEG/gzV48OBT3ql6/fXXgzgNAACAfUEvWJdffnlAwaqvr9eePXvk9Xp17bXXBnscAAAA64JesGbOnHnC9fXr1%2Bvvf/97kKcBAACw73vzSZWXXnqpXnnllVCPAQAAcMa%2BNwVr%2B/btZ/wJ6gAAAN8HQX%2BJcPz48Y3Wjh49qpKSEg0bNizY4wAAAFgX9ILVoUOHRj9FGB0drbFjx2rcuHHBHgcAAMC6oBesRYsWBfuUAAAAQRX0gvXiiy82%2BbEjR450cBIAAABnBL1g3X333WpoaGj0hvaIiIiAtYiICAoWAABwpaAXrCeeeEJPPvmkbrrpJqWmpsoYo507d%2Brxxx/Xz372M2VnZwd7JAAAAKtC8h6s5cuXq127dv61vn37KiUlRZMmTdLatWuDPRIAAIBVQf8crE8%2B%2BURxcXGN1mNjY7V///5gjwMAAGBd0AtWcnKyFi1apPLycv9aRUWFFi9erB/%2B8IfBHgcAAMC6oL9EOGfOHM2YMUNFRUVq1aqVIiMjVVVVpRYtWmjp0qXBHgcAAMC6oBes/v37680339Rbb72lgwcPyhijdu3a6ZJLLlHr1q2DPQ4AAIB1QS9YknTOOedoyJAhOnjwoFJSUkIxAgAAgGOC/h6smpoazZo1S71799ZPfvITSV%2B%2BB%2Bv6669XRUVFsMcBAACwLugF68EHH9SOHTtUUFCgyMj/nr6%2Bvl4FBQXBHgcAAMC6oBes9evX63e/%2B50uu%2Bwy/z/6HBsbq4ULF2rDhg3BHgcAAMC6oBes6upqdejQodF6YmKijhw5EuxxAAAArAt6wfrhD3%2Bov//975IU8G8Pvvbaa/rBD34Q7HEAAACsC/pPEf70pz/VbbfdpjFjxqihoUFPPfWUtm3bpvXr1%2Bvuu%2B8O9jgAAADWBb1g5eXlyePx6JlnnlFUVJQee%2BwxdezYUQUFBbrsssuCPQ4AAIB1QS9Yhw8f1pgxYzRmzJhgnxoAACAogv4erCFDhgS89woAACDcBL1gZWdn69VXXw32aQEAAIIm6C8Rnn/%2B%2BfrNb36j5cuX64c//KGaNWsWsL948eJgjwQAAGBV0AvWrl279KMf/UiSVF5eHuzTAwAAOC5oBWv69OlasmSJ/vSnP/nXli5dqvz8/GCNAAAAEBRBew/WG2%2B80Wht%2BfLljpzrvffeU0ZGRsBXenq6UlNT9fe//12pqamN9r/%2BvrAVK1Zo%2BPDh6tOnjyZMmKBt27Y5MicAAAhPQbuDdaKfHHTqpwmzsrLk9XoD1h577DF99NFHkqTk5OQTFj7pyyL4yCOP6IknnlBqaqpWrFihm266SRs2bFDLli0dmRcAAISXoN3B%2Buofdj7dmhP%2B85//6KmnntKdd9552scWFRVp9OjR6tmzp1q0aKHrr79ekrRp0yanxwQAAGEi6B/TEAoPP/ywxowZ4/%2B3Dqurq5Wfn6/s7Gxdcskleuqpp/x304qLi9WjRw//cyMjI9W9e/dGd8QAAABOJug/RRhsn376qTZs2KANGzZIkmJiYtS1a1dde%2B21WrJkid59911NnTpVrVu31tixY%2BXz%2BRQXFxdwjLi4uG/1E4%2BlpaUqKysLWPN4WiopKenML%2BhroqLc1Y89HnfN%2B1W%2BbsvZTcjYWeTrLPJ1npszDlrBqq2t1YwZM067ZvtzsJ599lkNGzZMbdu2lSSlpaUF/CRj//79NX78eK1evVpjx46VdObvDSsqKlJhYWHAWn5%2BvqZMmXJGx3W7hIRWoR7hO4mNPSfUI4Q9MnYW%2BTqLfJ3nxoyDVrAuvPBClZaWnnbNtvXr12vWrFmnfExycrLWr18vSUpISJDP5wvY9/l86tKlS5PPmZeXp8GDBweseTwtVV5e3eRjNIXbGr3t63daVFSkYmPPUUXFUdXXN4R6nLBExs4iX2eRr/NsZByqv9wHrWB9/a5RsOzYsUP79%2B9Xv379/GuvvvqqysvL9dOf/tS/tnv3bqWkpEiS0tPTVVxcrFGjRkmS6uvrtX37dv/draZISkpq9HJgWVml6urO7j%2BAbr3%2B%2BvoG187uFmTsLPJ1Fvk6z40Zu%2BsWyLe0fft2xcfHKyYmxr/WrFkzPfDAA3r77bdVW1urd955R6tWrdKECRMkSRMmTNCLL76oDz/8UEePHtWjjz6q5s2ba%2BDAgSG6CgAA4DZh/Sb3Q4cO%2Bd979ZVLL71Uc%2BbM0fz583XgwAGde%2B65mjNnjoYNGyZJGjBggG6//XZNmzZNn3/%2BuTIyMrR8%2BXK1aNEiFJcAAABcKMI49WmfCFBWVmn9mB5PpIYWbLZ%2BXKe8Oq3f6R/0PeLxRCohoZXKy6tdd2vaLcjYWeTrLPJ1no2M27ZtbXmqpgnrlwgBAABCgYIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALAvbgpWamqr09HRlZGT4v%2BbPny9J2rJli8aOHas%2Bffroiiuu0Jo1awKeu2LFCg0fPlx9%2BvTRhAkTtG3btlBcAgAAcClPqAdw0muvvaYLLrggYK20tFS33HKL7r77bl111VV6//33dfPNN6tjx47KyMjQG2%2B8oUceeURPPPGEUlNTtWLFCt10003asGGDWrZsGaIrAQAAbhK2d7BO5uWXX1aHDh00duxYRUdHKycnR4MHD9YLL7wgSSoqKtLo0aPVs2dPtWjRQtdff70kadOmTaEcGwAAuEhYF6zFixdr4MCB6tu3r%2BbOnavq6moVFxerR48eAY/r0aOH/2XAb%2B5HRkaqe/fu8nq9QZ0dAAC4V9i%2BRNirVy/l5OTogQce0L59%2BzRt2jTNmzdPPp9P7dq1C3hsfHy8ysvLJUk%2Bn09xcXEB%2B3Fxcf79pigtLVVZWVnAmsfTUklJSd/xak4sKspd/djjcdeixbfsAAATh0lEQVS8X%2BXrtpzdhIydRb7OIl/nuTnjsC1YRUVF/v/dqVMnzZw5UzfffLMuvPDC0z7XGHPG5y4sLAxYy8/P15QpU87ouG6XkNAq1CN8J7Gx54R6hLBHxs4iX2eRr/PcmHHYFqxvuuCCC1RfX6/IyEj5fL6AvfLyciUmJkqSEhISGu37fD516dKlyefKy8vT4MGDA9Y8npYqL6/%2BjtOfmNsave3rd1pUVKRiY89RRcVR1dc3hHqcsETGziJfZ5Gv82xkHKq/3Idlwdq%2BfbvWrFmj2bNn%2B9dKSkrUvHlz5ebm6n//938DHr9t2zb17NlTkpSenq7i4mKNGjVKklRfX6/t27dr7NixTT5/UlJSo5cDy8oqVVd3dv8BdOv119c3uHZ2tyBjZ5Gvs8jXeW7M2F23QJqoTZs2Kioq0vLly3X8%2BHHt2bNHDz/8sPLy8nT11Vdr//79euGFF3Ts2DG99dZbeuutt3TNNddIkiZMmKAXX3xRH374oY4ePapHH31UzZs318CBA0N7UQAAwDXC8g5Wu3bttHz5ci1evNhfkEaNGqXp06crOjpay5Yt04IFCzRv3jwlJyfrwQcfVLdu3SRJAwYM0O23365p06bp888/V0ZGhpYvX64WLVqE%2BKoAAIBbRJgzfUc3mqSsrNL6MT2eSA0t2Gz9uE55dVq/UI/wrXg8kUpIaKXy8mrX3Zp2CzJ2Fvk6i3ydZyPjtm1bW56qacLyJUIAAIBQomABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAy8K2YO3fv1/5%2BfnKzs5WTk6OZs%2BerYqKCn366adKTU1VRkZGwNcf/vAH/3PXrVunq666Sr1799bo0aP19ttvh/BKAACA23hCPYBTbrrpJqWnp%2BuNN95QZWWl8vPz9cADD%2Bjmm2%2BWJHm93hM%2Bb8eOHZo1a5YKCwt18cUXa/369br11lv12muv6bzzzgvmJQAAAJcKyztYFRUVSk9P14wZM9SqVSudd955GjVqlP7xj3%2Bc9rkvvPCCcnNzlZubq%2BjoaI0YMUJdu3bVmjVrgjA5AAAIB2F5Bys2NlYLFy4MWDtw4ICSkpL8v77zzjv1t7/9TXV1dRo3bpymTJmiZs2aqbi4WLm5uQHP7dGjx0nveJ1IaWmpysrKAtY8npYB57chKspd/djjcde8X%2BXrtpzdhIydRb7OIl/nuTnjsCxY3%2BT1evXMM8/o0UcfVfPmzdW7d28NHTpUv/nNb7Rjxw7ddttt8ng8mjp1qnw%2Bn%2BLi4gKeHxcXp127djX5fEVFRSosLAxYy8/P15QpU6xcj1slJLQK9QjfSWzsOaEeIeyRsbPI11nk6zw3Zhz2Bev999/XzTffrBkzZignJ0eS9Nxzz/n3MzMzNXnyZC1btkxTp06VJBljzuiceXl5Gjx4cMCax9NS5eXVZ3Tcb3Jbo7d9/U6LiopUbOw5qqg4qvr6hlCPE5bI2Fnk6yzydZ6NjEP1l/uwLlhvvPGG7rjjDs2dO1cjR4486eOSk5N16NAhGWOUkJAgn88XsO/z%2BZSYmNjk8yYlJTV6ObCsrFJ1dWf3H0C3Xn99fYNrZ3cLMnYW%2BTqLfJ3nxozddQvkW/jnP/%2BpWbNm6eGHHw4oV1u2bNGjjz4a8Njdu3crOTlZERERSk9P17Zt2wL2vV6vevbsGZS5AQCA%2B4Vlwaqrq9M999yjmTNnqn///gF7rVu31tKlS/XSSy%2BptrZWXq9Xf/jDHzRhwgRJ0jXXXKO//e1vevPNN3Xs2DGtXLlSn3zyiUaMGBGKSwEAAC4Uli8RfvjhhyopKdGCBQu0YMGCgL3XXntNS5YsUWFhoX71q1%2BpdevW%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%2BCcPvRPqEcLaX2ZeEuoRvrWvfg%2B77feyW5Cvs8jXeW7OmIL1DT6fT7GxsQFrX5Wt8vLyJhWsoqIiFRYWBqzdeuutuu222%2BwNqi%2BL3LXn/Vt5eXnWyxu%2BzLeoqIh8HVRaWqqnn36CjB1Cvs4iX%2Be5OWP3VcIgMMac0fPz8vK0evXqgK%2B8vDxL0/1XWVmZCgsLG90tgx3k6zwydhb5Oot8nefmjLmD9Q2JiYny%2BXwBaz6fTxEREUpMTGzSMZKSklzXtAEAgD3cwfqG9PR0HThwQIcPH/aveb1ede7cWa1atQrhZAAAwC0oWN/Qo0cPZWRkaPHixaqqqlJJSYmeeuopTZgwIdSjAQAAl4i699577w31EN83l1xyidauXav58%2BfrlVde0dixYzVp0iRFRESEerRGWrVqpYsuuoi7aw4hX%2BeRsbPI11nk6zy3ZhxhzvQd3QAAAAjAS4QAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwXGj//v268cYblZ2drUGDBunBBx9UQ0NDqMf6XklNTVV6eroyMjL8X/Pnz5ckbdmyRWPHjlWfPn10xRVXaM2aNQHPXbFihYYPH64%2BffpowoQJ2rZtm3/v2LFj%2BtWvfqUBAwYoOztbU6ZMUXl5uX8/nL83mzdvVk5OjqZPn95ob926dbrqqqvUu3dvjR49Wm%2B//bZ/r6GhQUuWLNGQIUOUlZWlSZMmad%2B%2Bff59n8%2BnadOmKScnR/3799fdd9%2Btmpoa//6OHTv0s5/9TBdeeKGGDRumJ598ssnndpuTZbx69Wp169Yt4PdzRkaGtm7dKomMm2r//v3Kz89Xdna2cnJyNHv2bFVUVEg6swyczt8tTpbvp59%2BqtTU1Ea/f//whz/4nxuW%2BRq4zqhRo8w999xjKioqzJ49e8ywYcPMk08%2BGeqxvle6du1q9u3b12j9s88%2BM7169TIvvPCCqampMe%2B8847JzMw0W7duNcYY8/rrr5u%2BffuaDz/80Bw9etQsW7bM9OvXz1RXVxtjjFm4cKEZPXq0%2Bc9//mPKy8vNrbfeaiZPnuw/frh%2Bb5YvX26GDRtmxo8fb6ZNmxawt337dpOenm7efPNNU1NTY1566SXTs2dPc%2BDAAWOMMStWrDCDBg0yu3btMpWVlea%2B%2B%2B4zV111lWloaDDGGHPrrbeaG2%2B80Xz%2B%2Befm4MGDJi8vz8yfP98YY8zRo0fNJZdcYh555BFTXV1ttm3bZi666CKzfv36Jp3bTU6V8apVq8zPfvazkz6XjJvmyiuvNLNnzzZVVVXmwIEDZvTo0WbOnDlnnIGT%2BbvJyfLdt2%2Bf6dq160mfF675UrBcZuvWraZ79%2B7G5/P51/7nf/7HDB8%2BPIRTff%2BcrGA98cQTZuTIkQFr06ZNM3PnzjXGGHPjjTea%2B%2B%2B/379XX19v%2BvXrZ9auXWtqa2vNhRdeaDZu3Ojf37Vrl0lNTTUHDx4M6%2B/N008/bSoqKsysWbMa/Z//vHnzTH5%2BfsDauHHjzLJly4wxxlxxxRXm6aef9u9VVlaaHj16mA8%2B%2BMCUlZWZbt26mR07dvj333rrLdOrVy9z/Phx8%2Bqrr5qLL77Y1NXV%2BfcffPBB88tf/rJJ53aTU2V8uoJFxqf3xRdfmNmzZ5uysjL/2p/%2B9CczbNiwM87Ayfzd4lT5nq5ghWu%2BvEToMsXFxUpOTlZcXJx/LS0tTXv27FFVVVUIJ/v%2BWbx4sQYOHKi%2Bfftq7ty5qq6uVnFxsXr06BHwuB49evhfBvzmfmRkpLp37y6v16u9e/eqsrJSaWlp/v1OnTqpRYsWKi4uDuvvzcSJE9W6desT7p0sU6/Xq5qaGu3atStgPyYmRu3bt5fX69WOHTsUFRWl1NRU/35aWpqOHDmi3bt3q7i4WKmpqYqKigo49sm%2BX18/t9ucKmNJOnDggH7xi18oKytLQ4YM0UsvvSRJZNxEsbGxWrhwoc4991z/2oEDB5SUlHRGGTidv1ucKt%2Bv3Hnnnerfv78uvvhiLV68WLW1tZLCN18Klsv4fD7FxsYGrH31f%2Bhffy/Q2a5Xr17KycnRhg0bVFRUpA8//FDz5s07YX7x8fH%2B7Hw%2BX0BBkr7Mt7y8XD6fT5IaPT82Nta/fzZ%2Bb06V2RdffCFjzCkzjYmJUURERMCepJNmGh8fL5/Pp4aGhlOeO5wkJiaqQ4cOuuOOO/TOO%2B/o9ttv15w5c7RlyxYy/o68Xq%2BeeeYZ3XzzzWeUgdP5u9XX823evLl69%2B6toUOHatOmTVq%2BfLnWrFmj3//%2B95JC%2B98QJ1GwXMgYE%2BoRvveKioo0btw4NW/eXJ06ddLMmTO1du1a/9%2BYTuV0%2BZ5q/2z93gQ7s6//x/RsyHzgwIF64okn1KNHDzVv3lxXXHGFhg4dqtWrV/sfQ8ZN9/7772vSpEmaMWOGcnJyTvq4b5OBk/m7zTfzTUpK0nPPPaehQ4eqWbNmyszM1OTJk5v8%2B/d0%2B9/XfClYLpOYmOi/k/IVn8%2BniIgIJSYmhmiq778LLrhA9fX1ioyMbJRfeXm5P7uEhIQT5puYmOh/zDf3v/jiC7Vp0%2Bas/d6cKrP4%2BPgTZu7z%2BfyZVVVVqb6%2BPmBPkn//m3dKfD6f/7inOne4S05OVmlpKRl/S2%2B88YZuvPFGzZkzRxMnTpSkM8rA6fzd5kT5nkhycrIOHTokY0zY5uu%2B795ZLj09XQcOHNDhw4f9a16vV507d1arVq1CONn3x/bt27Vo0aKAtZKSEjVv3ly5ubmNXnvftm2bevbsKenLfIuLi/179fX12r59u3r27KmUlBTFxcUF7H/88cc6fvy40tPTz9rvTXp6eqNMvV6vevbsqejoaHXp0iUgs4qKCu3du1eZmZnq3r27jDH66KOPAp4bGxurjh07Kj09XTt37lRdXV2jY5/u3OHkz3/%2Bs9atWxewVlJSopSUFDL%2BFv75z39q1qxZevjhhzVy5Ej/%2Bplk4HT%2BbnKyfLds2aJHH3004LG7d%2B9WcnKyIiIiwjdfx99GD%2BvGjRtn5syZYyorK82uXbvM4MGDzTPPPBPqsb43Dh48aHr16mWWLVtmjh07Znbv3m0uv/xyM3/%2BfHPo0CHTu3dv8/zzz5uamhrz5ptvmszMTP9PoLz11lvmwgsvNB988IE5cuSIeeSRR0xubq45evSoMebLnz4ZNWqU%2Bc9//mMOHz5sJk%2BebG677Tb/ucP9e3Oin3DbuXOnycjIMJs2bTI1NTXmhRdeML179zalpaXGmC9/knLgwIH%2BH7GeO3euGTNmjP/506ZNM9dff735/PPPzYEDB8yYMWPMokWLjDHGHDt2zAwaNMj87ne/M0eOHDEffvih6du3r9m0aVOTzu1GJ8r4j3/8o7n44ovN1q1bzfHjx83LL79sunfvbrxerzGGjJuitrbW/OQnPzHPPfdco70zzcDJ/N3iVPl6vV6TlpZmXnzxRXP8%2BHGzdetW069fP/9H2IRrvhQsFzpw4IC5/vrrTWZmpsnJyTG/%2B93v/J8Hgi%2B9%2B%2B67Ji8vz/Tq1ctcdNFFZuHChaampsa/N2LECJOWlmaGDRvW6PNQnn32WZObm2vS09PNhAkTzM6dO/17x44dM/fee6/JysoyvXv3NrfffrupqKjw74fr9yY9Pd2kp6ebbt26mW7duvl//ZX169ebYcOGmbS0NHP11Vebd99917/X0NBgHn74YfPjH//YZGZmmhtuuCHgM5QqKirM9OnTTa9evUxWVpaZN2%2BeOXbsmH9/586dZvz48SY9Pd0MHDjQPPvsswGznercbnKqjBsaGszSpUvNoEGDTHp6urnsssvMG2%2B84X8uGZ/ee%2B%2B9Z7p27erP9etfn3766Rll4HT%2BbnC6fDds2GBGjBhhMjMzTb9%2B/cxjjz1m6uvr/c8Px3wjjAmzdy8CAACEGO/BAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADL/h90aBTLyYTMjAAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-4572769429236068892”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>300</td> <td class=”number”>9.3%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>25</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.0</td> <td class=”number”>20</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.0</td> <td class=”number”>20</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>22.0</td> <td class=”number”>19</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>19</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (620)</td> <td class=”number”>1351</td> <td class=”number”>42.1%</td> <td>

<div class=”bar” style=”width:98%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>1370</td> <td class=”number”>42.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-4572769429236068892”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>300</td> <td class=”number”>9.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>63543.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>73924.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>74791.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>89856.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>246867.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FRESHVEG_ACRES12”><s>FRESHVEG_ACRES12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_FRESHVEG_ACRES07”><code>FRESHVEG_ACRES07</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99083</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FRESHVEG_ACRESPTH07”>FRESHVEG_ACRESPTH07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>1542</td>

</tr> <tr>

<th>Unique (%)</th> <td>48.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>42.7%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>1370</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>19.919</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1831.9</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>9.3%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-8314971339799685321”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-8314971339799685321,#minihistogram-8314971339799685321”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-8314971339799685321”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-8314971339799685321”

aria-controls=”quantiles-8314971339799685321” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-8314971339799685321” aria-controls=”histogram-8314971339799685321”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-8314971339799685321” aria-controls=”common-8314971339799685321”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-8314971339799685321” aria-controls=”extreme-8314971339799685321”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-8314971339799685321”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0.31513</td>

</tr> <tr>

<th>Median</th> <td>1.4401</td>

</tr> <tr>

<th>Q3</th> <td>4.833</td>

</tr> <tr>

<th>95-th percentile</th> <td>66.799</td>

</tr> <tr>

<th>Maximum</th> <td>1831.9</td>

</tr> <tr>

<th>Range</th> <td>1831.9</td>

</tr> <tr>

<th>Interquartile range</th> <td>4.5179</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>102.5</td>

</tr> <tr>

<th>Coef of variation</th> <td>5.1457</td>

</tr> <tr>

<th>Kurtosis</th> <td>132.64</td>

</tr> <tr>

<th>Mean</th> <td>19.919</td>

</tr> <tr>

<th>MAD</th> <td>31.121</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>10.401</td>

</tr> <tr>

<th>Sum</th> <td>36651</td>

</tr> <tr>

<th>Variance</th> <td>10506</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-8314971339799685321”>

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VVkFBcZW2%2B0ue9q6hKvkDAvwVGlpLhYUnVVZWbnBVVydfyutLWSXyejNfyip5Tl4Tb%2B4vh1cWrPLycj322GP68ssv9frrrys6OvqCt4%2BKilJeXp4iIiIk/XwdV506/3lCfvzxR9WrV6/S%2B4%2BMjKxwOjA//4RKS6/eF6A7mMhfVlbuU4%2BjL%2BX1pawSeb2ZL2WVfC9vZXnWIZBKeuqpp/Tvf//7nOXqT3/6k3bs2OEylpubq%2BjoaEVHRyssLEw5OTnOua%2B//lpnzpxRbGysW9YOAAA8n9cVrM8%2B%2B0yrV69WVlaWwsPDK8w7HA7NmjVL%2B/fv1%2BnTp7V48WIdPHhQAwYMUEBAgIYMGaIXX3xRR44cUUFBgZ555hn17NnT%2BTUOAAAAF%2BORpwjj4uIk/fwJQUnatGmTJMlut2vlypU6ceKEunbt6nKf%2BPh4LV68WBMnTpT087VXDodDLVq00J///Gc1atRIkjRu3DgVFxfr9ttvV2lpqbp27aqZM2e6KRkAAPAGflZVr%2BhGpeTnnzC%2BTZvNXz3Ttxvf7pWyfsLNl31fm81fdevWUUFBsU%2Bc6/elvL6UVSKvN/OlrJLn5G3QIKRa9ut1pwgBAACqGwULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMPcXrC6deumzMxMHTlyxN27BgAAcAu3F6w777xT69atU48ePXT//fdr48aNKi0tdfcyAAAArhi3F6yUlBStW7dOb775plq2bKmnnnpKSUlJevrpp3XgwAF3LwcAAMC4arsGq23btpo8ebK2bt2qqVOn6s0331SfPn00cuRIffnll9W1LAAAgCqrtoJVUlKidevW6YEHHtDkyZPVsGFDPfbYY2rdurVGjBihNWvWVNfSAAAAqsTm7h3m5uZqxYoVevvtt1VcXKzevXvr1Vdf1W9%2B8xvnbeLj4zVz5kzddttt7l4eAABAlbm9YPXt21fNmjXTqFGjdMcddyg8PLzCbZKSknT8%2BHF3Lw0AAMAItxespUuX6sYbb7zo7b744gs3rAYAAMA8t1%2BDFRMTo9GjR2vTpk3OsT//%2Bc964IEH5HA43L0cAAAA49xesNLS0nTixAm1aNHCOdalSxeVl5dr7ty57l4OAACAcW4/Rfjhhx9qzZo1qlu3rnOsadOmSk9PV79%2B/dy9HAAAAOPcfgTr1KlTCgoKqrgQf3%2BdPHnS3csBAAAwzu0FKz4%2BXnPnztWPP/7oHPv%2B%2B%2B81a9Ysl69qAAAA8FRuP0U4depU3Xffffrtb3%2Br4OBglZeXq7i4WNHR0frLX/7i7uUAAAAY5/aCFR0drXfeeUcffPCBDh48KH9/fzVr1kydOnVSQECAu5cDAABgnNsLliQFBgaqR48e1bFrAACAK87tBevQoUPKyMjQv//9b506darC/ObNm929JAAAAKOq5RqsvLw8derUSbVr13b37gEAAK44txesXbt2afPmzYqIiHD3rgEAANzC7V/TUK9ePWNHrrZv367ExESlpqZWmFu3bp1uu%2B02dejQQQMHDtSHH37onCsvL9f8%2BfPVvXt3xcfHa%2BTIkTp06JBz3uFwaMKECUpMTFSnTp00bdq0c57OBAAAOBe3F6xRo0YpMzNTlmVVaTsvvfSS5syZoyZNmlSY27NnjyZPnqxJkybp448/1ogRI/Tggw/q6NGjkqRly5ZpzZo1ysrK0tatW9W0aVOlpKQ41zRjxgydPHlSa9eu1cqVK5Wbm6v09PQqrRcAAPgOtxesDz74QH/729908803a8iQIRo6dKjLT2UFBQVpxYoV5yxYy5cvV1JSkpKSkhQUFKT%2B/furVatWWr16tSQpOztbI0aMUPPmzRUcHKzU1FTl5ubqiy%2B%2B0LFjx7Rp0yalpqYqIiJCDRs21NixY7Vy5UqVlJQYexwAAID3cvs1WMHBwercuXOVtzN8%2BPDzzuXk5CgpKcllrE2bNrLb7Tp16pT27dunNm3auKypSZMmstvtOnHihAICAhQTE%2BOcb9u2rX766Sft37/fZfx88vLylJ%2Bf7zJms9VWZGRkZeNVSkCA2/txldhsl7/es1k9LfPl8qW8vpRVIq8386Wsku/lvVRuL1hpaWlXfB8Oh0NhYWEuY2FhYdq3b59%2B/PFHWZZ1zvmCggKFh4crODhYfn5%2BLnOSVFBQUKn9Z2dnKzMz02UsJSVF48aNu5w4XqNu3TpV3kZoaC0DK/EcvpTXl7JK5PVmvpRV8r28lVUtXzS6f/9%2BvfPOO/ruu%2B%2Bchetf//qXOnToYGwfF7vG60LzVb0%2BLDk5Wd26dXMZs9lqq6CguErb/SVPe9dQlfwBAf4KDa2lwsKTKisrN7iqq5Mv5fWlrBJ5vZkvZZU8J6%2BJN/eXw%2B0Fa8eOHXrggQfUrFkzffPNN0pLS9OhQ4c0fPhwPfvss%2BrevXuV91G3bl05HA6XMYfDoYiICIWHh8vf3/%2Bc8/Xq1VNERISKiopUVlbm/NU9Z29br169Su0/MjKywunA/PwTKi29el%2BA7mAif1lZuU89jr6U15eySuT1Zr6UVfK9vJXl9kMg8%2BfP1yOPPKI1a9Y4T8NFR0dr7ty5WrBggZF9xMbGateuXS5jdrtd7du3V1BQkFq2bKmcnBznXGFhoQ4ePKh27dqpdevWsixLe/fudblvaGiomjVrZmR9AADAu7m9YH399dcaNmyYJLlc53TrrbcqNzfXyD6GDBmiv//979q2bZtOnz6tFStW6JtvvlH//v0lScOGDdPSpUuVm5uroqIipaenq3Xr1oqLi1NERIR69%2B6tZ599VsePH9fRo0e1YMECDRo0SDZbtZxRBQAAHsbtjSEkJESnTp1SYGCgy3heXl6FsQuJi4uTJJWWlkqSNm3aJOnno02tWrVSenq60tLSdPjwYbVo0UKLFi1SgwYNJElDhw5Vfn6%2B7r77bhUXFyshIcHlovQnnnhCjz/%2BuLp3764aNWqoX79%2B5/wyUwAAgHNxe8G64YYb9NRTT2n69OnOsQMHDujxxx/Xb3/720pvx263X3C%2BV69e6tWr1znn/Pz8NG7cuPN%2Bqi8kJETPPPNMpdcCAADw39xesB577DHdc889SkhIUFlZmW644QadPHlSLVu21Ny5c929HAAAAOPcXrAaNWqktWvX6v3339eBAwdUs2ZNNWvWTDfffLPLNVkAAACeqlqu2q5Ro4Z69OhRHbsGAAC44txesLp163bBI1WbN29242oAAADMc3vB6tOnj0vBKisr04EDB2S323XPPfe4ezkAAADGub1gTZo06ZzjGzZs0D/%2B8Q83rwYAAMC8q%2BaX2fXo0UPvvPNOdS8DAACgyq6agrV79%2B4q/5JlAACAq4HbTxEOHTq0wtjJkyeVm5t73i8GBQAA8CRuL1hNmzat8CnCoKAgDRo0SIMHD3b3cgAAAIxze8Hi29oBAIC3c3vBevvttyt92zvuuOMKrgQAAODKcHvBmjZtmsrLyytc0O7n5%2Bcy5ufnR8ECAAAeye0F6%2BWXX9bixYs1evRoxcTEyLIsffXVV3rppZf0%2B9//XgkJCe5eEgAAgFHVcg1WVlaWGjZs6Bzr2LGjoqOjNXLkSK1du9bdSwIAADDK7d%2BD9c033ygsLKzCeGhoqA4fPuzu5QAAABjn9oIVFRWluXPnqqCgwDlWWFiojIwMXXfdde5eDgAAgHFuP0U4depUTZw4UdnZ2apTp478/f1VVFSkmjVrasGCBe5eDgAAgHFuL1idOnXStm3b9P777%2Bvo0aOyLEsNGzbULbfcopCQEHcvBwAAwDi3FyxJqlWrlrp3766jR48qOjq6OpYAAABwxbj9GqxTp05p8uTJ6tChg373u99J%2BvkarPvvv1%2BFhYXuXg4AAIBxbi9YTz/9tPbs2aP09HT5%2B/9n92VlZUpPT3f3cgAAAIxze8HasGGDnn/%2Bed16663OX/ocGhqqtLQ0bdy40d3LAQAAMM7tBau4uFhNmzatMB4REaGffvrJ3csBAAAwzu0F67rrrtM//vEPSXL53YPvvvuurr32WncvBwAAwDi3f4rwrrvu0kMPPaQ777xT5eXlWrJkiXbt2qUNGzZo2rRp7l4OAACAcW4vWMnJybLZbHrttdcUEBCgF198Uc2aNVN6erpuvfVWdy8HAADAOLcXrOPHj%2BvOO%2B/UnXfe6e5dAwAAuIXbr8Hq3r27y7VXAAAA3sbtBSshIUHr1693924BAADcxu2nCK%2B55ho9%2BeSTysrK0nXXXacaNWq4zGdkZLh7SQAAAEa5vWDt27dPv/rVryRJBQUFV2QfO3fu1H333ecyZlmWSkpKtHTpUg0fPlyBgYEu8//7v//r/NU9S5cu1bJly5Sfn6%2BYmBhNmzZNsbGxV2StAADA%2B7itYKWmpmr%2B/Pn6y1/%2B4hxbsGCBUlJSjO8rPj5edrvdZezFF1/U3r17JUlRUVHasmXLOe%2B7ZcsWvfDCC3r55ZcVExOjpUuXavTo0dq4caNq165tfK0AAMD7uO0arHMVmqysLLfs%2B7vvvtOSJUv06KOPXvS22dnZGjhwoNq3b6%2BaNWvq/vvvlyRt3br1Si8TAAB4CbcVrHN9ctBdnyZ87rnndOeddzq/Kb64uFgpKSlKSEjQLbfcoiVLljjXkpOTozZt2jjv6%2B/vr9atW1c4IgYAAHA%2BbjtFePYXO19szLRvv/1WGzdudP4i6eDgYLVq1Ur33HOP5s%2Bfr08%2B%2BUTjx49XSEiIBg0aJIfDobCwMJdthIWFXdL1Ynl5ecrPz3cZs9lqKzIysuqB/ktAgNs/BFolNtvlr/dsVk/LfLl8Ka8vZZXI6818Kavke3kvldsvcne3ZcuWqVevXmrQoIEkqW3bti7XgXXq1ElDhw7VW2%2B9pUGDBkmq%2BpG17OxsZWZmuoylpKRo3LhxVdqup6tbt06VtxEaWsvASjyHL%2BX1pawSeb2ZL2WVfC9vZXl9wdqwYYMmT558wdtERUVpw4YNkqS6devK4XC4zDscDrVs2bLS%2B0xOTla3bt1cxmy22iooKK70NirD0941VCV/QIC/QkNrqbDwpMrKyg2u6urkS3l9KatEXm/mS1klz8lr4s395XBbwSopKdHEiRMvOmbye7D27Nmjw4cP6%2Babb3aOrV%2B/XgUFBbrrrrucY/v371d0dLQkKTY2Vjk5ORowYIAkqaysTLt373Ye3aqMyMjICqcD8/NPqLT06n0BuoOJ/GVl5T71OPpSXl/KKpHXm/lSVsn38laW2wrWb37zG%2BXl5V10zKTdu3crPDxcwcHBzrEaNWpo3rx5uu6665SQkKBPPvlEK1eu1Lx58yRJw4YN08MPP6x%2B/fopJiZGr7zyigIDA9WlS5crtk4AAOBd3Faw/vu6J3c5duyY89qrs3r06KGpU6dq9uzZOnLkiOrXr6%2BpU6eqV69ekqTOnTvr4Ycf1oQJE/TDDz8oLi5OWVlZqlmzptvXDwAAPJOfxW9edov8/BPGt2mz%2Batn%2Bnbj271S1k%2B4%2BeI3Og%2BbzV9169ZRQUGxTxyK9qW8vpRVIq8386WskufkbdAgpFr261lXSQMAAHgAChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgmNcWrJiYGMXGxiouLs75M3v2bEnSjh07NGjQIN1www3q27evVq9e7XLfpUuXqnfv3rrhhhs0bNgw7dq1qzoiAAAAD2Wr7gVcSe%2B%2B%2B64aN27sMpaXl6exY8dq2rRpuu222/TZZ59pzJgxatasmeLi4rRlyxa98MILevnllxUTE6OlS5dq9OjR2rhxo2rXrl1NSQAAgCfx2iNY57NmzRo1bdpUgwYNUlBQkBITE9WtWzctX75ckpSdna2BAweqffv2qlmzpu6//35J0tatW6tz2QAAwIN49RGsjIwM/etf/1JRUZF%2B97vfacqUKcrJyVGbNm1cbtemTRutX79ekpSTk6M%2Bffo45/z9/dW6dWvZ7Xb17du3UvvNy8tTfn6%2By5jNVluRkZFVTOQqIMCz%2BrHNdvnrPZvV0zJfLl/K60tZJfJ6M1/KKvle3kvltQXr%2BuuvV2LSXxxEAAAXC0lEQVRioubNm6dDhw5pwoQJmjVrlhwOhxo2bOhy2/DwcBUUFEiSHA6HwsLCXObDwsKc85WRnZ2tzMxMl7GUlBSNGzfuMtN4h7p161R5G6GhtQysxHP4Ul5fyiqR15v5UlbJ9/JWltcWrOzsbOd/N2/eXJMmTdKYMWP0m9/85qL3tSyrSvtOTk5Wt27dXMZsttoqKCiu0nZ/ydPeNVQlf0CAv0JDa6mw8KTKysoNrurq5Et5fSmrRF5v5ktZJc/Ja%2BLN/eXw2oL1S40bN1ZZWZn8/f3lcDhc5goKChQRESFJqlu3boV5h8Ohli1bVnpfkZGRFU4H5uefUGnp1fsCdAcT%2BcvKyn3qcfSlvL6UVSKvN/OlrJLv5a0szzoEUkm7d%2B/W3LlzXcZyc3MVGBiopKSkCl%2B7sGvXLrVv316SFBsbq5ycHOdcWVmZdu/e7ZwHAAC4GK8sWPXq1VN2draysrJ05swZHThwQM8995ySk5N1%2B%2B236/Dhw1q%2BfLlOnz6t999/X%2B%2B//76GDBkiSRo2bJjefvttff755zp58qQWLlyowMBAdenSpXpDAQAAj%2BGVpwgbNmyorKwsZWRkOAvSgAEDlJqaqqCgIC1atEhz5szRrFmzFBUVpaefflq//vWvJUmdO3fWww8/rAkTJuiHH35QXFycsrKyVLNmzWpOBQAAPIVXFixJio%2BP1xtvvHHeuVWrVp33vnfddZfuuuuuK7U0AADg5bzyFCEAAEB1omABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAw7y2YB0%2BfFgpKSlKSEhQYmKipkyZosLCQn377beKiYlRXFycy88rr7zivO%2B6det02223qUOHDho4cKA%2B/PDDakwCAAA8ja26F3CljB49WrGxsdqyZYtOnDihlJQUzZs3T2PGjJEk2e32c95vz549mjx5sjIzM3XTTTdpw4YNevDBB/Xuu%2B%2BqUaNG7owAAAA8lFcewSosLFRsbKwmTpyoOnXqqFGjRhowYIA%2B/fTTi953%2BfLlSkpKUlJSkoKCgtS/f3%2B1atVKq1evdsPKAQCAN/DKghUaGqq0tDTVr1/fOXbkyBFFRkY6//zoo4%2BqU6dOuummm5SRkaGSkhJJUk5Ojtq0aeOyvTZt2pz3iBcAAMAvee0pwv9mt9v12muvaeHChQoMDFSHDh3Us2dPPfnkk9qzZ48eeugh2Ww2jR8/Xg6HQ2FhYS73DwsL0759%2Byq9v7y8POXn57uM2Wy1XQqeCQEBntWPbbbLX%2B/ZrJ6W%2BXL5Ul5fyiqR15v5UlbJ9/JeKq8vWJ999pnGjBmjiRMnKjExUZL0xhtvOOfbtWunUaNGadGiRRo/frwkybKsKu0zOztbmZmZLmMpKSkaN25clbbr6erWrVPlbYSG1jKwEs/hS3l9KatEXm/mS1kl38tbWV5dsLZs2aJHHnlEM2bM0B133HHe20VFRenYsWOyLEt169aVw%2BFwmXc4HIqIiKj0fpOTk9WtWzeXMZuttgoKii8twEV42ruGquQPCPBXaGgtFRaeVFlZucFVXZ18Ka8vZZXI6818KavkOXlNvLm/HF5bsP75z39q8uTJeu6559SpUyfn%2BI4dO/T55587P00oSfv371dUVJT8/PwUGxurXbt2uWzLbrerb9%2B%2Bld53ZGRkhdOB%2BfknVFp69b4A3cFE/rKycp96HH0pry9llcjrzXwpq%2BR7eSvLsw6BVFJpaammT5%2BuSZMmuZQrSQoJCdGCBQu0atUqlZSUyG6365VXXtGwYcMkSUOGDNHf//53bdu2TadPn9aKFSv0zTffqH///tURBQAAeCCvPIL1%2BeefKzc3V3PmzNGcOXNc5t59913Nnz9fmZmZ%2BuMf/6iQkBDdfffduueeeyRJrVq1Unp6utLS0nT48GG1aNFCixYtUoMGDaojCgAA8EBeWbA6duyor7766rzzUVFR6tmz53nne/XqpV69el2JpQEAAB/glacIAQAAqhMFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADDMVt0LuBodPnxYs2bN0hdffKHatWurT58%2Bmjhxovz96aNV8btnP6ruJVyS9RNuru4lAAA8FAXrHB566CG1bdtWmzZt0g8//KBRo0apfv36uvfee6t7aQAAwANwSOYX7Ha79u7dq0mTJikkJERNmzbViBEjlJ2dXd1LAwAAHoIjWL%2BQk5OjqKgohYWFOcfatm2rAwcOqKioSMHBwRfdRl5envLz813GbLbaioyMNLrWgAD68ZXkaac035t0S3UvodJ6pm%2Bv7iVcEpOP7dm/t77y99eX8vpSVsn38l4qCtYvOBwOhYaGuoydLVsFBQWVKljZ2dnKzMx0GXvwwQf10EMPmVuofi5y9zT6t5KTk42Xt6tNXl6esrOzfSKr5P15P33yVud/e3vWX8rLy9Orr75MXi/kS1kl38t7qaid52BZVpXun5ycrLfeesvlJzk52dDq/iM/P1%2BZmZkVjpZ5I1/KKvlWXl/KKpHXm/lSVsn38l4qjmD9QkREhBwOh8uYw%2BGQn5%2BfIiIiKrWNyMhI2jwAAD6MI1i/EBsbqyNHjuj48ePOMbvdrhYtWqhOnTrVuDIAAOApKFi/0KZNG8XFxSkjI0NFRUXKzc3VkiVLNGzYsOpeGgAA8BABM2fOnFndi7ja3HLLLVq7dq1mz56td955R4MGDdLIkSPl5%2BdX3UuroE6dOrrxxht94uiaL2WVfCuvL2WVyOvNfCmr5Ht5L4WfVdUrugEAAOCCU4QAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwPNDhw4f1hz/8QQkJCeratauefvpplZeXV/eyLtvhw4eVkpKihIQEJSYmasqUKSosLNS3336rmJgYxcXFufy88sorzvuuW7dOt912mzp06KCBAwfqww8/rMYklRMTE6PY2FiXTLNnz5Yk7dixQ4MGDdINN9ygvn37avXq1S73Xbp0qXr37q0bbrhBw4YN065du6ojQqXt3LmzwvMXGxurmJgY/eMf/zjn87t%2B/Xrn/T0h7/bt25WYmKjU1NQKcxd6fZaXl2v%2B/Pnq3r274uPjNXLkSB06dMg573A4NGHCBCUmJqpTp06aNm2aTp065ZZM53OhrBs3blT//v3VoUMH9e7dW2%2B%2B%2BaZz7oUXXlDr1q0rPNfHjh2TJJ0%2BfVp//OMf1blzZyUkJGjcuHEqKChwW67zOV/et956S7/%2B9a8r5Pnyyy8leddzO3369Ao527Rpo8cee0ySNGXKFOfv8D3707FjR%2Bf9r8asbmPB4wwYMMCaPn26VVhYaB04cMDq1auXtXjx4upe1mXr16%2BfNWXKFKuoqMg6cuSINXDgQGvq1KnWoUOHrFatWp33frt377ZiY2Otbdu2WadOnbJWrVpltW/f3jpy5IgbV3/pWrVqZR06dKjC%2BPfff29df/311vLly61Tp05ZH330kdWuXTvryy%2B/tCzLsjZv3mx17NjR%2Bvzzz62TJ09aixYtsm6%2B%2BWaruLjY3RGqZOHChdb48eOtjz/%2B2Oratet5b%2BcJebOysqxevXpZQ4cOtSZMmOAyd7HX59KlS62uXbta%2B/bts06cOGE98cQT1m233WaVl5dblmVZDz74oPWHP/zB%2BuGHH6yjR49aycnJ1uzZs92e8awLZf3iiy%2BsuLg467333rNKSkqsbdu2WW3btrV27txpWZZlPf/889bkyZPPu%2B20tDRr4MCB1nfffWcVFBRYDz74oDVq1KgrmudiLpR35cqV1u9///vz3tebnttfKikpsfr27Wtt27bNsizLmjx5svX888%2Bf9/ZXW1Z34giWh7Hb7dq7d68mTZqkkJAQNW3aVCNGjFB2dnZ1L%2B2yFBYWKjY2VhMnTlSdOnXUqFEjDRgwQJ9%2B%2BulF77t8%2BXIlJSUpKSlJQUFB6t%2B/v1q1alXhqI%2BnWLNmjZo2bapBgwYpKChIiYmJ6tatm5YvXy5Jys7O1sCBA9W%2BfXvVrFlT999/vyRp69at1bnsS/Ldd99pyZIlevTRRy96W0/IGxQUpBUrVqhJkyYV5i72%2BszOztaIESPUvHlzBQcHKzU1Vbm5ufriiy907Ngxbdq0SampqYqIiFDDhg01duxYrVy5UiUlJe6OKenCWR0Oh0aNGqUePXrIZrMpKSlJrVq1qtTf49LSUq1YsUJjx47VNddco/DwcE2YMEHbtm3T999/fyWiVMqF8l6MNz23v/Tqq6/q2muvVVJS0kVvezVmdScKlofJyclRVFSUwsLCnGNt27bVgQMHVFRUVI0ruzyhoaFKS0tT/fr1nWNHjhxRZGSk88%2BPPvqoOnXqpJtuukkZGRnOv5g5OTlq06aNy/batGkju93unsVXQUZGhrp06aKOHTtqxowZKi4uPm%2Bes6fFfjnv7%2B%2Bv1q1be0Tes5577jndeeeduvbaayVJxcXFztPDt9xyi5YsWSLr///%2BeU/IO3z4cIWEhJxz7kKvz1OnTmnfvn0u88HBwWrSpInsdrv27NmjgIAAxcTEOOfbtm2rn376Sfv3778yYS7iQlk7d%2B6slJQU559LS0uVn5%2Bvhg0bOse%2B%2BuorDR061Hn6%2B%2Bzp0oMHD%2BrEiRNq27at87bNmzdXzZo1lZOTc4XSXNyF8ko//3/q3nvvVXx8vLp3765Vq1ZJktc9t/%2BtsLBQL774oh555BGX8Y8//lh33HGHOnTooEGDBjn/n3U1ZnUnCpaHcTgcCg0NdRk7W7auhmsWqsput%2Bu1117TmDFjFBgYqA4dOqhnz57aunWrsrKytHr1av3pT3%2BS9PNj8d9FU/r5sbjaH4frr79eiYmJ2rhxo7Kzs/X5559r1qxZ53xuw8PDnXk8Ne9Z3377rTZu3Kh7771X0s//6LRq1Ur33HOPtm/frrS0NGVmZmrlypWSPD/vhdb/448/yrKs8847HA4FBwfLz8/PZU7yjL/n6enpql27tvr06SNJatSokaKjozVv3jx99NFHGjx4sEaPHq39%2B/fL4XBIUoXXfmho6FWbNSIiQk2bNtUjjzyijz76SA8//LCmTp2qHTt2ePVz%2B9prryk%2BPl4tW7Z0jkVHR6tJkyZatGiRtm/fro4dO%2Bq%2B%2B%2B7z%2BKwmULA80Nl3%2BN7ms88%2B08iRIzVx4kQlJiYqMjJSb7zxhnr27KkaNWqoXbt2GjVqlN566y3nfTzxscjOztbgwYMVGBio5s2ba9KkSVq7dm2lDpl7Yt6zli1bpl69eqlBgwaSfn4n%2B5e//EU33nijAgMD1alTJw0dOtTjn9//drH1X2jeE7NblqWnn35aa9eu1cKFCxUUFCRJGjx4sJ5//nk1adJEtWrV0ogRI9S6dWuX0/melLdLly56%2BeWX1aZNGwUGBqpv377q2bNnpV%2B7npT1rLKyMi1btkzDhw93GU9JSdFTTz2lhg0bKjg4WI888ogCAwO1adMmSZ6Z1RQKloeJiIhwvuM7y%2BFwyM/PTxEREdW0qqrbsmWL/vCHP2jq1KkV/gL/t6ioKB07dkyWZalu3brnfCw87XFo3LixysrK5O/vXyFPQUGBM4%2Bn592wYYO6det2wdtERUUpLy9PkufnvdD6w8PDz/l8OxwO1atXTxERESoqKlJZWZnLnCTVq1fvyi/%2BMpSXl2vKlCnasmWLXn/9df3qV7%2B64O3PPtdnn89fPhY//vjjVZv1XM7m8cbnVvr5E8Fnzpxx%2BYTguQQEBOiaa65xPreemNUUCpaHiY2N1ZEjR3T8%2BHHnmN1uV4sWLVSnTp1qXNnl%2B%2Bc//6nJkyfrueee0x133OEc37FjhxYuXOhy2/379ysqKkp%2Bfn6KjY2t8LF9u92u9u3bu2Xdl2P37t2aO3euy1hubq4CAwOVlJRUIc%2BuXbuceWJjY12uSSkrK9Pu3buv6rxn7dmzR4cPH9bNN9/sHFu/fr3%2B%2Bte/utxu//79io6OluTZeSVd8PUZFBSkli1buuQrLCzUwYMH1a5dO7Vu3VqWZWnv3r0u9w0NDVWzZs3cluFSPPXUU/r3v/%2Bt119/3fkcnvWnP/1JO3bscBnLzc1VdHS0oqOjFRYW5vJYfP311zpz5oxiY2PdsvZL9frrr2vdunUuY2fzeONzK0mbN2/WTTfdJJvN5hyzLEtpaWkuWc6cOaODBw8qOjraY7OaQsHyMGe/byQjI0NFRUXKzc3VkiVLNGzYsOpe2mUpLS3V9OnTNWnSJHXq1MllLiQkRAsWLNCqVatUUlIiu92uV155xZl1yJAh%2Bvvf/65t27bp9OnTWrFihb755hv179%2B/OqJUSr169ZSdna2srCydOXNGBw4c0HPPPafk5GTdfvvtOnz4sJYvX67Tp0/r/fff1/vvv68hQ4ZIkoYNG6a3335bn3/%2BuU6ePKmFCxcqMDBQXbp0qd5QlbB7926Fh4crODjYOVajRg3NmzdPH374oUpKSvTRRx9p5cqVzufXk/NKF399Dhs2TEuXLlVubq6KioqUnp7u/K6oiIgI9e7dW88%2B%2B6yOHz%2Buo0ePasGCBRo0aJDLP3BXi88%2B%2B0yrV69WVlaWwsPDK8w7HA7NmjVL%2B/fv1%2BnTp7V48WIdPHhQAwYMUEBAgIYMGaIXX3xRR44cUUFBgZ555hn17NnT5cMvV5MzZ85o9uzZstvtKikp0dq1a/XBBx9o6NChkrzruT1rz549aty4scuYn5%2Bfvv32W82aNUvff/%2B9iouLlZ6erho1aqhHjx4em9UUP8uXT5B6qKNHj2rGjBn65JNPFBwcrKFDh%2BrBBx90uZDQU3z66af6n//5HwUGBlaYe/fdd7V7925lZmbqm2%2B%2BUUhIiO6%2B%2B2498MAD8vf/%2Bb3Bxo0blZGRocOHD6tFixaaNm2a4uPj3R3jkuzcuVMZGRn66quvFBgYqAEDBig1NVVBQUHauXOn5syZo9zcXEVFRWnixInq1auX875//etflZWVpR9%2B%2BEFxcXGaOXOmWrVqVY1pKmfRokVas2aN1q5d6zKenZ2txYsX68iRI6pfv77GjBmjwYMHO%2Bev9rxxcXGSfn6jIMn5j8bZTzpe6PVpWZZeeOEFvfHGGyouLlZCQoKeeOIJNWrUSJJ04sQJPf7449q6datq1Kihfv36acqUKef8u%2BIOF8o6depU/e1vf6vwj2Z8fLwWL16s06dPKyMjQ%2B%2B%2B%2B64cDodatGihGTNmqEOHDpJ%2BLixpaWl65513VFpaqq5du2rmzJmV%2BmTblXKhvJZlaeHChVqxYoXy8/PVuHFjPfroo%2Bratask73puz%2Brdu7eGDBmikSNHutzX4XBo3rx5%2BuCDD1RUVKR27dpp5syZat68uaSrL6s7UbAAAAAM4xQhAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABj2/wAx1BpZfNnS2wAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-8314971339799685321”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>300</td> <td class=”number”>9.3%</td> <td>

<div class=”bar” style=”width:20%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.958456177968373</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.62267701717243</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0220990996663097</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.56256036234056</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.1319471434123591</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.931842385516507</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6005645306588193</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>118.08359707519371</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.1622378361244652</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (1531)</td> <td class=”number”>1531</td> <td class=”number”>47.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>1370</td> <td class=”number”>42.7%</td> <td>

<div class=”bar” style=”width:89%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-8314971339799685321”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>300</td> <td class=”number”>9.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.005838126272833154</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.006026883920206736</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.011715384115227833</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.01868605416655886</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1042.4707135250267</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1207.211782630777</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1290.5797101449275</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1625.9900038446751</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1831.9271009112385</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FRESHVEG_ACRESPTH12”><s>FRESHVEG_ACRESPTH12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_FRESHVEG_ACRESPTH07”><code>FRESHVEG_ACRESPTH07</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.94863</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FRESHVEG_FARMS07”><s>FRESHVEG_FARMS07</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_VEG_FARMS07”><code>VEG_FARMS07</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.97465</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FRESHVEG_FARMS12”><s>FRESHVEG_FARMS12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_FRESHVEG_FARMS07”><code>FRESHVEG_FARMS07</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.90251</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FSR09”><s>FSR09</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_FFR14”><code>FFR14</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.97433</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FSR14”><s>FSR14</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_FSR09”><code>FSR09</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99878</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FSRPTH09”>FSRPTH09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3057</td>

</tr> <tr>

<th>Unique (%)</th> <td>95.2%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.776</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>13.699</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>1.8%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-5502087873696669715”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-5502087873696669715,#minihistogram-5502087873696669715”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-5502087873696669715”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-5502087873696669715”

aria-controls=”quantiles-5502087873696669715” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-5502087873696669715” aria-controls=”histogram-5502087873696669715”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-5502087873696669715” aria-controls=”common-5502087873696669715”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-5502087873696669715” aria-controls=”extreme-5502087873696669715”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-5502087873696669715”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.22976</td>

</tr> <tr>

<th>Q1</th> <td>0.49485</td>

</tr> <tr>

<th>Median</th> <td>0.66888</td>

</tr> <tr>

<th>Q3</th> <td>0.89804</td>

</tr> <tr>

<th>95-th percentile</th> <td>1.6054</td>

</tr> <tr>

<th>Maximum</th> <td>13.699</td>

</tr> <tr>

<th>Range</th> <td>13.699</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.40319</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.58415</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.75277</td>

</tr> <tr>

<th>Kurtosis</th> <td>100.32</td>

</tr> <tr>

<th>Mean</th> <td>0.776</td>

</tr> <tr>

<th>MAD</th> <td>0.33317</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>6.5936</td>

</tr> <tr>

<th>Sum</th> <td>2430.4</td>

</tr> <tr>

<th>Variance</th> <td>0.34123</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-5502087873696669715”>

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fr444%2B1detWnT17VmPHjlVQUJC6dOmie%2B%2B9VwUFBZKk5cuXKyUlRSkpKQoMDNSgQYPUsWNHrV69WpJUUFCg9PR0tWvXTiEhIcrKylJxcbF27drlycgAAMCL2Dw9wKWw2%2B2aPXu229rhw4cVFRWloqIiderUSQEBAXV7cXFxWr58uSSpqKhIKSkpbs%2BNi4uTw%2BHQqVOntG/fPsXFxdXthYSEqE2bNnI4HLr%2B%2BuvrNV9paanKysrc1my2IEVFRf2inBcTEOBd/dhmMzvvufze9nEwwZezS%2BT35fy%2BnF0ivzfl98qC9WMOh0OvvPKKFi9erPXr18tut7vth4eHy%2Bl0qra2Vk6nU2FhYW77YWFh2rdvn44fPy6Xy3XB/fLy8nrPU1BQoLy8PLe1jIwMZWZm/sJkjUtERHCDHNdub94gx/UGvpxdIr8v5/fl7BL5vSG/1xesnTt3auzYsZo0aZKSk5O1fv36Cz7Oz8%2Bv7t8vdj/V5d5vlZqaqj59%2Brit2WxBKi%2Bvuqzj/pg3NPgfaoj8dntzVVScVE1NrdFjX%2Bl8ObtEfl/O78vZJfJfSv6G%2BuL%2BYry6YG3evFmPPvqopk%2BfrsGDB0uSIiMjdeDAAbfHOZ1OhYeHy9/fXxEREXI6neftR0ZG1j3mQvstWrSo91xRUVHnXQ4sKzuh6mrf%2B8vwQw2Vv6am1mc/tr6cXSK/L%2Bf35ewS%2Bb0hv3edAvmBTz75RDk5OXruuefqypUkxcfH68svv1R1dXXdmsPh0HXXXVe3X1hY6Hasc/uBgYHq0KGDioqK6vYqKip08OBBde3atYETAQCAxsIrC1Z1dbWmTZum7Oxs9ezZ020vJSVFISEhWrx4sU6ePKldu3ZpxYoVSktLkyQNHz5cH3zwgbZu3arTp09rxYoVOnDggAYNGiRJSktL07Jly1RcXKzKykrl5uaqc%2BfOSkhIsDwnAADwTl55ifCzzz5TcXGxZs2apVmzZrntbdiwQc8//7xmzJih/Px8XXXVVcrKylLv3r0lSR07dlRubq5mz56tkpIStW/fXkuWLFHLli0lSSNGjFBZWZnuv/9%2BVVVVKSkp6bwb1gEAAH6On4t30LREWdkJ48e02fx1e%2B77xo/bUNZPvMXo8Ww2f0VEBKu8vOqKvxZvmi9nl8jvy/l9ObtE/kvJ37JlaANPdWFeeYkQAADgSkbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGCY5QWrT58%2BysvL0%2BHDh61%2BaQAAAEtYXrDuuecerVu3TrfddpsefPBBbdy4UdXV1VaPAQAA0GAsL1gZGRlat26d3njjDXXo0EHPPvusUlJSNG/ePO3fv9/qcQAAAIzz2D1YXbp0UU5OjrZs2aLHH39cb7zxhvr3769Ro0bp888/99RYAAAAl81jBevs2bNat26dHnroIeXk5Cg6OlpTpkxR586dlZ6erjVr1nhqNAAAgMtis/oFi4uLtWLFCr311luqqqpSv3799Pe//1033HBD3WMSExM1c%2BZMDRw40OrxAAAALpvlBWvAgAFq27atRo8ercGDBys8PPy8x6SkpOjYsWNWjwYAAGCE5QVr2bJl6tGjx0Uft2vXLgumAQAAMM/ye7A6deqkMWPGaNOmTXVrf/vb3/TQQw/J6XRaPQ4AAIBxlhes2bNn68SJE2rfvn3dWu/evVVbW6s5c%2BZYPQ4AAIBxll8i3L59u9asWaOIiIi6tdjYWOXm5uquu%2B6yehwAAADjLD%2BDderUKQUGBp4/iL%2B/Tp48afU4AAAAxllesBITEzVnzhwdP368bu2bb77Rk08%2B6fZWDQAAAN7K8kuEjz/%2BuP70pz/p5ptvVkhIiGpra1VVVaXWrVvr5ZdftnocAAAA4ywvWK1bt9bbb7%2Bt9957TwcPHpS/v7/atm2rnj17KiAgwOpxAAAAjLO8YElS06ZNddttt3nipQEAABqc5QXr0KFDmj9/vvbu3atTp06dt//uu%2B9aPRIAAIBRHrkHq7S0VD179lRQUJDVLw8AANDgLC9YhYWFevfddxUZGWn1SwMAAFjC8rdpaNGiBWeuAABAo2Z5wRo9erTy8vLkcrmsfmkAAABLWH6J8L333tMnn3yilStX6uqrr5a/v3vHe/31160eCQAAwCjLC1ZISIh69epl9csCAABYxvKCNXv2bKtfEgAAwFKW34MlSV999ZUWLlyoKVOm1K19%2BumnnhgFAADAOMsL1o4dOzRo0CBt3LhRa9eulfT9m4%2BOHDmSNxkFAACNguUFa8GCBXr00Ue1Zs0a%2Bfn5Sfr%2B5xPOmTNHixYtsnocAAAA4ywvWP/%2B97%2BVlpYmSXUFS5LuuOMOFRcXWz0OAACAcZYXrNDQ0Av%2BDMLS0lI1bdrU6nEAAACMs7xgde/eXc8%2B%2B6wqKyvr1vbv36%2BcnBzdfPPNVo8DAABgnOUFa8qUKfr000%2BVlJSk06dPq3v37urfv7%2BcTqcmT578i471/vvvKzk5WVlZWW7rK1eu1LXXXquEhAS3X59//rkkqba2VgsWLFDfvn2VmJioUaNG6dChQ3XPdzqdmjhxopKTk9WzZ09NnTr1gmfdAAAALsTy98Fq1aqV1q5dq23btmn//v1q1qyZ2rZtq1tuucXtnqyLeeGFF7RixQq1adPmgvuJiYl6%2BeWXL7j36quvas2aNXrhhRcUHR2tBQsWKCMjQ6tWrZKfn5%2BmT5%2BuM2fOaO3atTp79qwmTJig3NxcTZs27ZIyAwAA32J5wZKkJk2a6LbbbrusYwQGBmrFihV65plndPr06V/03IKCAqWnp6tdu3aSpKysLCUlJWnXrl26%2BuqrtWnTJr355puKjIyUJD388MOaMGGCcnJy1KRJk8uaGwAANH6WF6w%2Bffr87Jmq%2Br4X1siRI392//Dhw/rjH/%2BowsJC2e12ZWZm6u6779apU6e0b98%2BxcXF1T02JCREbdq0kcPh0IkTJxQQEKBOnTrV7Xfp0kXfffedvvrqK7d1AACAC7G8YPXv39%2BtYNXU1Gj//v1yOBx64IEHjLxGZGSkYmNj9cgjj6h9%2B/b65z//qccee0xRUVG65ppr5HK5FBYW5vacsLAwlZeXKzw8XCEhIW4znntseXl5vV6/tLRUZWVlbms2W5CioqIuM5m7gACPvBH/JbPZzM57Lr%2B3fRxM8OXsEvl9Ob8vZ5fI7035LS9Y2dnZF1x/55139NFHHxl5jd69e6t37951vx8wYID%2B%2Bc9/auXKlXWv73K5fvL5P7dXHwUFBcrLy3Nby8jIUGZm5mUd19tFRAQ3yHHt9uYNclxv4MvZJfL7cn5fzi6R3xvye%2BQerAu57bbb9MQTT%2BiJJ55okOPHxMSosLBQ4eHh8vf3l9PpdNt3Op1q0aKFIiMjVVlZqZqaGgUEBNTtSVKLFi3q9Vqpqanq06eP25rNFqTy8ioDSf7DGxr8DzVEfru9uSoqTqqmptbosa90vpxdIr8v5/fl7BL5LyV/Q31xfzFXTMH64osvLvvM0TmvvfaawsLC1L9//7q14uJitW7dWoGBgerQoYOKiorUo0cPSVJFRYUOHjyorl27KiYmRi6XS3v27FGXLl0kSQ6HQ3a7XW3btq3X60dFRZ13ObCs7ISqq33vL8MPNVT%2Bmppan/3Y%2BnJ2ify%2BnN%2BXs0vk94b8lhesESNGnLd28uRJFRcX63e/%2B52R1zhz5oyefvpptW7dWtdee63eeecdvffee3rjjTckSWlpacrPz1evXr0UHR2t3Nxcde7cWQkJCZKkfv366S9/%2BYvmzp2rM2fOaNGiRRo2bJhstiumjwIAgCuY5Y0hNjb2vO8iDAwM1LBhw3TvvffW%2BzjnylB1dbUkadOmTZK%2BP9s0cuRIVVVVacKECSorK9PVV1%2BtRYsWKT4%2BXtL3Ja%2BsrEz333%2B/qqqqlJSU5HbP1FNPPaUZM2aob9%2B%2BatKkie66667z3swUAADgp/i5TF2Xw88qKzth/Jg2m79uz33f%2BHEbyvqJtxg9ns3mr4iIYJWXV13xp4pN8%2BXsEvl9Ob8vZ5fIfyn5W7YMbeCpLszyM1hvvfVWvR87ePDgBpwEAACgYVhesKZOnara2trzbmj38/NzW/Pz86NgAQAAr2R5wXrxxRf10ksvacyYMerUqZNcLpe%2B/PJLvfDCC/rDH/6gpKQkq0cCAAAwyvKCNWfOHOXn5ys6Orpu7cYbb1Tr1q01atQorV271uqRAAAAjLL8nSoPHDhw3o%2BpkSS73a6SkhKrxwEAADDO8oIVExOjOXPmuP1cv4qKCs2fP1%2B/%2Bc1vrB4HAADAOMsvET7%2B%2BOOaNGmSCgoKFBwcLH9/f1VWVqpZs2ZatGiR1eMAAAAYZ3nB6tmzp7Zu3apt27bpyJEjcrlcio6O1m9/%2B1uFhnrmvSoAAABM8sjPfmnevLn69u2rI0eOqHXr1p4YAQAAoMFYfg/WqVOnlJOTo27duunOO%2B%2BU9P09WA8%2B%2BKAqKiqsHgcAAMA4ywvWvHnztHv3buXm5srf/z8vX1NTo9zcXKvHAQAAMM7ygvXOO%2B/ov//7v3XHHXfU/dBnu92u2bNna%2BPGjVaPAwAAYJzlBauqqkqxsbHnrUdGRuq7776zehwAAADjLC9Yv/nNb/TRRx9JktvPHtywYYN%2B/etfWz0OAACAcZZ/F%2BF9992n8ePH65577lFtba2WLl2qwsJCvfPOO5o6darV4wAAABhnecFKTU2VzWbTK6%2B8ooCAAD3//PNq27atcnNzdccdd1g9DgAAgHGWF6xjx47pnnvu0T333GP1SwMAAFjC8nuw%2Bvbt63bvFQAAQGNjecFKSkrS%2BvXrrX5ZAAAAy1h%2BifBXv/qVnnnmGeXn5%2Bs3v/mNmjRp4rY/f/58q0cCAAAwyvKCtW/fPl1zzTWSpPLycqtfHgAAoMFZVrCysrK0YMECvfzyy3VrixYtUkZGhlUjAAAAWMKye7A2b9583lp%2Bfr5VLw8AAGAZywrWhb5zkO8mBAAAjZFlBevcD3a%2B2BoAAIC3s/xtGgAAABo7ChYAAIBhln0X4dmzZzVp0qSLrvE%2BWAAAwNtZVrBuuOEGlZaWXnQNAADA21lWsH74/lcAAACNGfdgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwry5Y77//vpKTk5WVlXXe3rp16zRw4EB169ZNQ4cO1fbt2%2Bv2amtrtWDBAvXt21eJiYkaNWqUDh06VLfvdDo1ceJEJScnq2fPnpo6dapOnTplSSYAAOD9vLZgvfDCC5o1a5batGlz3t7u3buVk5Oj7Oxsffjhh0pPT9e4ceN05MgRSdKrr76qNWvWKD8/X1u2bFFsbKwyMjLkcrkkSdOnT9fJkye1du1a/eMf/1BxcbFyc3MtzQcAALyX1xaswMBArVix4oIFa/ny5UpJSVFKSooCAwM1aNAgdezYUatXr5YkFRQUKD09Xe3atVNISIiysrJUXFysXbt26dtvv9WmTZuUlZWlyMhIRUdH6%2BGHH9Y//vEPnT171uqYAADAC3ltwRo5cqRCQ0MvuFdUVKS4uDi3tbi4ODkcDp06dUr79u1z2w8JCVGbNm3kcDi0e/duBQQEqFOnTnX7Xbp00XfffaevvvqqYcIAAIBGxebpARqC0%2BlUWFiY21pYWJj27dun48ePy%2BVyXXC/vLxc4eHhCgkJkZ%2Bfn9ueJJWXl9fr9UtLS1VWVua2ZrMFKSoq6lLi/KSAAO/qxzab2XnP5fe2j4MJvpxdIr8v5/fl7BL5vSl/oyxYkurup7qU/Ys992IKCgqUl5fntpaRkaHMzMzLOq63i4gIbpDj2u3NG%2BS43sCXs0vk9%2BX8vpxdIr835G%2BUBSsiIkJOp9Ntzel0KjIyUuHh4fL397/gfosWLRQZGanKykrV1NQoICCgbk%2BSWrRoUa/XT01NVZ8%2BfdzWbLYglZdXXWqkC/KGBv9DDZHfbm%2BuioqTqqmpNXrsK50vZ5fI78v5fTm7RP5Lyd9QX9xfTKMsWPHx8SosLHRbczgcGjBggAIDA9WhQwcVFRWpR48ekqSKigodPHhQXbt2VUxMjFwul/bs2aMuXbrUPddut6tt27b1ev2oqKjzLgeWlZ1QdbXv/WX4oYbKX1NT67MfW1/OLpHfl/P7cnaJ/N6Q37tOgdTT8OHD9cEHH2jr1q06ffq0VqxYoQMHDmjQoEGSpLS0NC1btkzFxcWqrKxUbm6uOnfurISEBEVGRqpfv376y1/%2BomPHjunIkSOoqgjvAAAQH0lEQVRatGiRhg0bJputUfZRAABgmNc2hoSEBElSdXW1JGnTpk2Svj/b1LFjR%2BXm5mr27NkqKSlR%2B/bttWTJErVs2VKSNGLECJWVlen%2B%2B%2B9XVVWVkpKS3O6ZeuqppzRjxgz17dtXTZo00V133XXBNzMFAAC4ED/X5d7RjXopKzth/Jg2m79uz33f%2BHEbyvqJtxg9ns3mr4iIYJWXV13xp4pN8%2BXsEvl9Ob8vZ5fIfyn5W7a88Fs6NbRGeYkQAADAkyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGNdqC1alTJ8XHxyshIaHu19NPPy1J2rFjh4YNG6bu3btrwIABWr16tdtzly1bpn79%2Bql79%2B5KS0tTYWGhJyIAAAAvZfP0AA1pw4YNuvrqq93WSktL9fDDD2vq1KkaOHCgdu7cqbFjx6pt27ZKSEjQ5s2btXDhQr344ovq1KmTli1bpjFjxmjjxo0KCgryUBIAAOBNGu0ZrJ%2ByZs0axcbGatiwYQoMDFRycrL69Omj5cuXS5IKCgo0dOhQXXfddWrWrJkefPBBSdKWLVs8OTYAAPAijfoM1vz58/Xpp5%2BqsrJSd955pyZPnqyioiLFxcW5PS4uLk7r16%2BXJBUVFal///51e/7%2B/urcubMcDocGDBhQr9ctLS1VWVmZ25rNFqSoqKjLTOQuIMC7%2BrHNZnbec/m97eNggi9nl8jvy/l9ObtEfm/K32gL1vXXX6/k5GTNnTtXhw4d0sSJE/Xkk0/K6XQqOjra7bHh4eEqLy%2BXJDmdToWFhbnth4WF1e3XR0FBgfLy8tzWMjIylJmZeYlpGoeIiOAGOa7d3rxBjusNfDm7RH5fzu/L2SXye0P%2BRluwCgoK6v69Xbt2ys7O1tixY3XDDTdc9Lkul%2BuyXjs1NVV9%2BvRxW7PZglReXnVZx/0xb2jwP9QQ%2Be325qqoOKmamlqjx77S%2BXJ2ify%2BnN%2BXs0vkv5T8DfXF/cU02oL1Y1dffbVqamrk7%2B8vp9PptldeXq7IyEhJUkRExHn7TqdTHTp0qPdrRUVFnXc5sKzshKqrfe8vww81VP6amlqf/dj6cnaJ/L6c35ezS%2BT3hvzedQqknr744gvNmTPHba24uFhNmzZVSkrKeW%2B7UFhYqOuuu06SFB8fr6Kiorq9mpoaffHFF3X7AAAAF9MoC1aLFi1UUFCg/Px8nTlzRvv379dzzz2n1NRU3X333SopKdHy5ct1%2BvRpbdu2Tdu2bdPw4cMlSWlpaXrrrbf02Wef6eTJk1q8eLGaNm2q3r17ezYUAADwGo3yEmF0dLTy8/M1f/78uoI0ZMgQZWVlKTAwUEuWLNGsWbP05JNPKiYmRvPmzdO1114rSerVq5ceeeQRTZw4UUePHlVCQoLy8/PVrFkzD6cCAADeolEWLElKTEzU66%2B//pN7q1at%2Bsnn3nfffbrvvvsaajQAANDINcpLhAAAAJ5EwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhm8/QA8B13/uV/PD3CL7J%2B4i2eHgEA4KU4gwUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMMzm6QGuRCUlJXryySe1a9cuBQUFqX///po0aZL8/emjvuTOv/yPp0f4RdZPvMXTIwAA/j8K1gWMHz9eXbp00aZNm3T06FGNHj1aV111lf74xz96ejQAAOAFOCXzIw6HQ3v27FF2drZCQ0MVGxur9PR0FRQUeHo0AADgJTiD9SNFRUWKiYlRWFhY3VqXLl20f/9%2BVVZWKiQk5KLHKC0tVVlZmduazRakqKgoo7MGBNCP8R82m%2B/8eTj3Z99X/w74cn5fzi6R35vyU7B%2BxOl0ym63u62dK1vl5eX1KlgFBQXKy8tzWxs3bpzGjx9vblB9X%2BQeaLVXqampxsubNygtLVVBQYFP5vfl7NL3%2Bf/%2B9xfJ74P5fTm7RH5vyn/lV0APcLlcl/X81NRUrVy50u1Xamqqoen%2Bo6ysTHl5eeedLfMVvpzfl7NL5Pfl/L6cXSK/N%2BXnDNaPREZGyul0uq05nU75%2BfkpMjKyXseIioq64ps1AABoOJzB%2BpH4%2BHgdPnxYx44dq1tzOBxq3769goODPTgZAADwFhSsH4mLi1NCQoLmz5%2BvyspKFRcXa%2BnSpUpLS/P0aAAAwEsEzJw5c6anh7jS/Pa3v9XatWv19NNP6%2B2339awYcM0atQo%2Bfn5eXq08wQHB6tHjx4%2Be3bNl/P7cnaJ/L6c35ezS%2BT3lvx%2Brsu9oxsAAABuuEQIAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFywuVlJToz3/%2Bs5KSknTrrbdq3rx5qq2t9fRYlikpKVFGRoaSkpKUnJysyZMnq6KiwtNjecSzzz6rTp06eXoMyy1evFg9e/bU9ddfr/T0dH399deeHskSX3zxhUaOHKkbb7xRt9xyi7Kzs91%2BMH1j9P777ys5OVlZWVnn7a1bt04DBw5Ut27dNHToUG3fvt0DEzacn8u%2BceNGDRo0SN26dVO/fv30xhtveGDChvVz%2Bc%2BpqqpS7969NXnyZAsnqx8KlhcaP368oqOjtWnTJi1dulSbNm3S3//%2Bd0%2BPZZkxY8bIbrdr8%2BbNWrlypfbu3au5c%2Bd6eizL7d69W6tWrfL0GJZ79dVXtXr1ai1btkzbt29X%2B/bt9be//c3TYzW46upq/fnPf9b111%2BvDz74QGvXrtWxY8fUmH%2Bc7AsvvKBZs2apTZs25%2B3t3r1bOTk5ys7O1ocffqj09HSNGzdOR44c8cCk5v1c9s8//1zZ2dnKzMzUv/71Lz3%2B%2BON66qmn9PHHH3tg0obxc/l/aOHChaqsrLRoql%2BGguVlHA6H9uzZo%2BzsbIWGhio2Nlbp6ekqKCjw9GiWqKioUHx8vCZNmqTg4GC1atVKQ4YMaVSfWOqjtrZWM2bMUHp6uqdHsdxLL72krKwsXXPNNQoJCdG0adM0bdo0T4/V4MrKylRWVqa7775bTZs2VUREhG6//Xbt3r3b06M1mMDAQK1YseKC/5Ndvny5UlJSlJKSosDAQA0aNEgdO3bU6tWrPTCpeT%2BX3el0avTo0brttttks9mUkpKijh07NqrPgz%2BX/5w9e/Zo7dq1GjJkiIWT1R8Fy8sUFRUpJiZGYWFhdWtdunTR/v37r9gWb5Ldbtfs2bN11VVX1a0dPnxYUVFRHpzKeq%2B//roCAwM1cOBAT49iqW%2B%2B%2BUZff/21jh8/rv79%2ByspKUmZmZmN/jKZJEVHR6tz584qKChQVVWVjh49qo0bN6p3796eHq3BjBw5UqGhoRfcKyoqUlxcnNtaXFycHA6HFaM1uJ/L3qtXL2VkZNT9vrq6WmVlZYqOjrZqvAb3c/klyeVyaebMmcrKypLdbrdwsvqjYHkZp9N53h%2Bmc2WrvLzcEyN5lMPh0CuvvKKxY8d6ehTLfPvtt1q4cKFmzJjh6VEsd%2B7yz4YNG7R06VKtWrVKR44c8YkzWP7%2B/lq4cKHeffddde/eXcnJyaqurtakSZM8PZpHOJ1Oty80pe8/F/ri58Hc3FwFBQWpf//%2Bnh7FMgUFBfLz89PQoUM9PcpPomB5IZfL5ekRrgg7d%2B7UqFGjNGnSJCUnJ3t6HMvMnj1bQ4cOVfv27T09iuXO/dl/8MEHFR0drVatWmn8%2BPHavHmzTp8%2B7eHpGtaZM2c0ZswY3XHHHfr444/13nvvKTQ0VNnZ2Z4ezWN8/XOhy%2BXSvHnztHbtWi1evFiBgYGeHskSR48e1XPPPaeZM2fKz8/P0%2BP8JJunB8AvExkZKafT6bbmdDrl5%2BenyMhID01lvc2bN%2BvRRx/V9OnTNXjwYE%2BPY5kdO3bo008/1dq1az09ikecuzT8w7O4MTExcrlcOnr0qH796197arQGt2PHDn399dd65JFHFBAQoNDQUGVmZuruu%2B%2BW0%2BlUeHi4p0e0VERExAU/F/rK58Ha2lpNmTJFn3/%2BuV577TW1bt3a0yNZZs6cORo8ePAV/x3UFCwvEx8fr8OHD%2BvYsWN1n0gcDofat2%2Bv4OBgD09njU8%2B%2BUQ5OTl67rnn1LNnT0%2BPY6nVq1fr6NGjuvXWWyX95yv4pKQkPfHEExowYIAnx2twrVq1UkhIiHbv3q0uXbpI%2Bv5tO5o0adLo78OrqalRbW2t21mbM2fOeHAiz4qPj1dhYaHbmsPhaPR/B8559tlntXfvXr322ms%2BV65Xr14tu92ulStXSpJOnTql2tpabdmyRR999JGHp/sPCpaXiYuLU0JCgubPn68pU6bom2%2B%2B0dKlS/WnP/3J06NZorq6WtOmTVN2drbPlStJmjx5siZMmFD3%2ByNHjig1NVWrVq06736Uxshms2nYsGF6/vnnlZiYqJCQEC1atEgDBw6Uzda4P51169ZNQUFBWrhwocaMGaNTp05p8eLFSkxM9Ln/wUrS8OHDNWzYMG3dulU333yz1qxZowMHDmjQoEGeHq3B7dy5U6tXr9a6det88r/9tm3b3H6/dOlSHTlyRFOmTPHQRBfm5/L1i9he6MiRI5o%2Bfbr%2B93//VyEhIRoxYoTGjRt3RV%2BLNuXjjz/W73//ezVt2vS8vQ0bNigmJsYDU3nO119/rb59%2B%2BrLL7/09CiWOXPmjGbPnq23335bZ8%2BeVb9%2B/TR9%2BnSfOINbWFiouXPnas%2BePWratKl69OihyZMnN6rvHvuhhIQESd9/YSWprkSf%2B07BjRs3av78%2BSopKVH79u01depUJSYmemZYw34u%2B%2BOPP64333zzvC8qEhMT9dJLL1k7aAO52H/7H1q4cKFKSko0Z84c6wasBwoWAACAYXwXIQAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAY9v8A29YXwSDTsrQAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-5502087873696669715”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>57</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.049180328</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.509683996</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.744463056</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.776699029</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.415282392</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.876040298</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.883392226</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.694444444</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.130710086</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3046)</td> <td class=”number”>3057</td> <td class=”number”>95.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-5502087873696669715”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>57</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0425387</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.058924</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0604485</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0609645</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>5.025125628</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.688282139</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.196721311</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.393285372</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.698630137</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FSRPTH14”><s>FSRPTH14</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_FSRPTH09”><code>FSRPTH09</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.90099</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_GHVEG_FARMS07”>GHVEG_FARMS07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>24</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.7%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>4.2%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>134</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1.3153</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>80</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>58.5%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram1044561578819287183”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives1044561578819287183”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles1044561578819287183”

aria-controls=”quantiles1044561578819287183” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram1044561578819287183” aria-controls=”histogram1044561578819287183”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common1044561578819287183” aria-controls=”common1044561578819287183”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme1044561578819287183” aria-controls=”extreme1044561578819287183”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles1044561578819287183”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>2</td>

</tr> <tr>

<th>95-th percentile</th> <td>6</td>

</tr> <tr>

<th>Maximum</th> <td>80</td>

</tr> <tr>

<th>Range</th> <td>80</td>

</tr> <tr>

<th>Interquartile range</th> <td>2</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>3.1534</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.3974</td>

</tr> <tr>

<th>Kurtosis</th> <td>207.81</td>

</tr> <tr>

<th>Mean</th> <td>1.3153</td>

</tr> <tr>

<th>MAD</th> <td>1.6885</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>10.212</td>

</tr> <tr>

<th>Sum</th> <td>4046</td>

</tr> <tr>

<th>Variance</th> <td>9.9441</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram1044561578819287183”>

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GfOHHXs2FFdu3bVrbfeqnfffVe7d%2B9WQ0ODZs2apQ4dOigxMVGTJk1SUVGRJGn9%2BvVKT09Xenq6QkNDNX78ePXp00cbN26UJBUVFWnatGnq2bOnwsLClJubq9LSUn344YeBjAwAAGzElgUrPDxcixcv1mWXXeZdKy8vV0xMjEpKStS3b1%2BFhIR49xISElRcXCxJKikpUUJCgs/xEhIS5Ha7VV9fr4MHD/rsh4WFqXv37nK73W2cCgAAOIUr0AOY4Ha79fzzz2v16tXaunWrwsPDffYjIyPl8XjU3Nwsj8ejiIgIn/2IiAgdPHhQX3zxhSzLOud%2BVVVVq%2BepqKhQZWWlz5rL1UExMTHnmeybhYTYqx%2B7XK2b92wuu%2BVrDadmc2ouiWx25NRcknOzOTGX7QvWe%2B%2B9p1mzZmnOnDlKS0vT1q1bz/m4oKAg7/9/2/1UF3u/VVFRkVauXOmzlp2drZycnIs6rt1FRXU8r8eHh1/aRpMEnlOzOTWXRDY7cmouybnZnJTL1gVr586d%2BslPfqL58%2BfrlltukSRFR0fryJEjPo/zeDyKjIxUcHCwoqKi5PF4WuxHR0d7H3Ou/c6dO7d6rszMTGVkZPisuVwdVFVVex7pvp3dmn5r84eEBCs8/FJVV9epqam5jafyL6dmc2ouiWx25NRcknOztWWu8/3m3hTbFqz3339f%2Bfn5euqppzRs2DDvelJSkn7/%2B9%2BrsbFRLteX8dxut/r37%2B/dP3s/1llut1tjx45VaGioevfurZKSEg0dOlTSlzfUf/LJJ0pJSWn1bDExMS0uB1ZWnlRjo3O%2BGC7E%2BeZvamp27N%2BZU7M5NZdENjtyai7JudmclMtep0D%2Bv8bGRs2bN095eXk%2B5UqS0tPTFRYWptWrV6uurk4ffvihNmzYoKysLEnS5MmT9fbbb2v37t06ffq0NmzYoCNHjmj8%2BPGSpKysLK1du1alpaWqqalRQUGB%2BvXrp%2BTkZL/nBAAA9mTLM1h79%2B5VaWmpFi1apEWLFvnsbdu2TU8//bQee%2BwxFRYW6rLLLlNubq6GDx8uSerTp48KCgq0ePFilZWVqVevXlqzZo26dOkiSZoyZYoqKyt15513qra2VqmpqS3upwIAAPgmQRavoOkXlZUnjR/T5QrWyII3jR%2B3rWydfXWrHudyBSsqqqOqqmodc6r4LKdmc2ouiWx25NRcknOztWWuLl06GT1ea9nyEiEAAMC/MgoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMMzvBSsjI0MrV65UeXm5v58aAADAL/xesG677TZt2bJF119/ve655x7t2LFDjY2N/h4DAACgzfi9YGVnZ2vLli168cUX1bt3bz3xxBNKT0/X0qVLdfjwYX%2BPAwAAYFzA7sFKTExUfn6%2Bdu3apblz5%2BrFF1/UmDFjNH36dP39738P1FgAAAAXLWAFq6GhQVu2bNGPfvQj5efnKzY2Vo888oj69eunadOmadOmTYEaDQAA4KK4/P2EpaWl2rBhg15%2B%2BWXV1tZq9OjR%2Bu1vf6vBgwd7HzNkyBAtWLBA48aN8/d4AAAAF83vBWvs2LHq0aOHZsyYoVtuuUWRkZEtHpOenq4TJ074ezQAAAAj/F6w1q5dq6FDh37r4z788EM/TAMAAGCe3%2B/B6tu3r2bOnKnXXnvNu/Zf//Vf%2BtGPfiSPx%2BPvcQAAAIzze8FavHixTp48qV69ennXhg8frubmZi1ZssTf4wAAABjn90uEb731ljZt2qSoqCjvWnx8vAoKCnTTTTf5exwAAADj/H4Gq76%2BXqGhoS0HCQ5WXV2dv8cBAAAwzu8Fa8iQIVqyZIm%2B%2BOIL79pnn32mxx9/3OelGgAAAOzK75cI586dqx/%2B8If6wQ9%2BoLCwMDU3N6u2tlbdunXT7373O3%2BPAwAAYJzfC1a3bt30pz/9SW%2B88YY%2B%2BeQTBQcHq0ePHho2bJhCQkL8PQ4AAIBxfi9YktSuXTtdf/31gXhqAACANuf3gnX06FEtW7ZMH3/8serr61vs//nPf/b3SAAAAEYF5B6siooKDRs2TB06dPD30wMAALQ5vxes4uJi/fnPf1Z0dLS/nxoAAMAv/P4yDZ07d%2BbMFQAAcDS/F6wZM2Zo5cqVsizL308NAADgF36/RPjGG2/o/fff10svvaTLL79cwcG%2BHe%2BFF17w90gAAABG%2Bb1ghYWF6dprr/X30wIAAPiN3wvW4sWL/f2UAAAAfuX3e7Ak6dChQ1qxYoUeeeQR79oHH3wQiFEAAACM83vB2rNnj8aPH68dO3Zo8%2BbNkr588dGpU6fyIqMAAMAR/F6wli9frp/85CfatGmTgoKCJH35%2BwmXLFmiVatW%2BXscAAAA4/xesP7xj38oKytLkrwFS5JuuOEGlZaW%2BnscAAAA4/xesDp16nTO30FYUVGhdu3a%2BXscAAAA4/xesAYNGqQnnnhCNTU13rXDhw8rPz9fP/jBD/w9DgAAgHF%2Bf5mGRx55RHfddZdSU1PV1NSkQYMGqa6uTr1799aSJUv8PQ4AAIBxfi9YXbt21ebNm/X666/r8OHDat%2B%2BvXr06KGrr77a554sAAAAu/J7wZKkSy65RNdff30gnhoAAKDN%2Bb1gZWRkfOOZKl4LCwAA2J3fC9aYMWN8ClZTU5MOHz4st9utu%2B6667yO9eabbyo/P1%2Bpqalavny5d/2ll17S3Llzdckll/g8ft26dUpJSVFzc7Oeeuopbd68WdXV1UpJSdGCBQvUrVs3SZLH49GCBQv0zjvvKDg4WOnp6Zo/f77at29/EckBAMC/C78XrLy8vHOub9%2B%2BXX/9619bfZxnnnlGGzZsUPfu3c%2B5P2TIEP3ud7875966deu0adMmPfPMM4qNjdXy5cuVnZ2tV155RUFBQZo/f77OnDmjzZs3q6GhQQ888IAKCgo0b968Vs8HAAD%2BfQXkdxGey/XXX68//elPrX58aGjoNxasb1JUVKRp06apZ8%2BeCgsLU25urkpLS/Xhhx/q888/12uvvabc3FxFR0crNjZW9957r/7whz%2BooaHhvJ8LAAD8%2BwnITe7nsm/fPlmW1erHT5069Rv3y8vLdffdd6u4uFjh4eHKycnRzTffrPr6eh08eFAJCQnex4aFhal79%2B5yu906efKkQkJC1LdvX%2B9%2BYmKiTp06pUOHDvmsf52KigpVVlb6rLlcHRQTE9PqfK0REvIv049bxeVq3bxnc9ktX2s4NZtTc0lksyOn5pKcm82JufxesKZMmdJira6uTqWlpRo1apSR54iOjlZ8fLwefPBB9erVS6%2B%2B%2BqoeeughxcTE6Pvf/74sy1JERITP%2B0RERKiqqkqRkZEKCwvzuU/s7GOrqqpa9fxFRUVauXKlz1p2drZycnIuMpm9RUV1PK/Hh4df2kaTBJ5Tszk1l0Q2O3JqLsm52ZyUy%2B8FKz4%2BvsVPEYaGhmrixImaNGmSkecYPny4hg8f7v3z2LFj9eqrr%2Bqll17y3gP2TWfLzudM2rlkZmYqIyPDZ83l6qCqqtqLOu5X2a3ptzZ/SEiwwsMvVXV1nZqamtt4Kv9yajan5pLIZkdOzSU5N1tb5jrfb%2B5N8XvBCtSrtcfFxam4uFiRkZEKDg6Wx%2BPx2fd4POrcubOio6NVU1OjpqYmhYSEePckqXPnzq16rpiYmBaXAysrT6qx0TlfDBfifPM3NTU79u/Mqdmcmksimx05NZfk3GxOyuX3gvXyyy%2B3%2BrG33HLLBT3H73//e0VERGjMmDHetdLSUnXr1k2hoaHq3bu3SkpKNHToUElSdXW1PvnkE6WkpCguLk6WZenAgQNKTEyUJLndboWHh6tHjx4XNA8AAPj34veC9eijj6q5ubnFZbigoCCftaCgoAsuWGfOnNHChQvVrVs3XXHFFdq%2BfbveeOMNvfjii5KkrKwsFRYW6tprr1VsbKwKCgrUr18/JScnS5JGjx6tX/7yl3ryySd15swZrVq1ShMnTpTL9S/zMwEAAOBfmN8bw69//Ws9%2B%2Byzmjlzpvr27SvLsvTRRx/pmWee0R133KHU1NRWHedsGWpsbJQkvfbaa5K%2BPNs0depU1dbW6oEHHlBlZaUuv/xyrVq1SklJSZK%2BvNG%2BsrJSd955p2pra5WamupzU/rPfvYzPfbYYxoxYoQuueQS3XTTTcrNzTX51wAAABwsyLrYO7rP080336zCwkLFxsb6rH/22WeaPn26Nm/e7M9x/Kay8qTxY7pcwRpZ8Kbx47aVrbOvbtXjXK5gRUV1VFVVrWOuxZ/l1GxOzSWRzY6cmktybra2zNWlSyejx2stv/8Y2pEjR1q8RIIkhYeHq6yszN/jAAAAGOf3ghUXF6clS5b4vKZUdXW1li1bpu9973v%2BHgcAAMA4v9%2BDNXfuXM2ZM0dFRUXq2LGjgoODVVNTo/bt22vVqlX%2BHgcAAMA4vxesYcOGaffu3Xr99dd17NgxWZal2NhYXXPNNerUKTDXSQEAAEwKyOsOXHrppRoxYoSOHTumbt26BWIEAACANuP3e7Dq6%2BuVn5%2BvgQMH6sYbb5T05T1Y99xzj6qrq/09DgAAgHF%2BL1hLly7V/v37VVBQoODg/3v6pqYmFRQU%2BHscAAAA4/xesLZv365f/epXuuGGG7y/9Dk8PFyLFy/Wjh07/D0OAACAcX4vWLW1tYqPj2%2BxHh0drVOnTvl7HAAAAOP8XrC%2B973v6a9//ask%2BfzuwW3btum73/2uv8cBAAAwzu8/RXj77bfr/vvv12233abm5mY999xzKi4u1vbt2/Xoo4/6exwAAADj/F6wMjMz5XK59PzzzyskJERPP/20evTooYKCAt1www3%2BHgcAAMA4vxesEydO6LbbbtNtt93m76cGAADwC7/fgzVixAife68AAACcxu8FKzU1VVu3bvX30wIAAPiN3y8Rfuc739HPf/5zFRYW6nvf%2B54uueQSn/1ly5b5eyQAAACj/F6wDh48qO9///uSpKqqKn8/PQAAQJvzW8HKzc3V8uXL9bvf/c67tmrVKmVnZ/trBAAAAL/w2z1YO3fubLFWWFjor6cHAADwG78VrHP95CA/TQgAAJzIbwXr7C92/rY1AAAAu/P7yzQAAAA4HQULAADAML/9FGFDQ4PmzJnzrWu8DhYAALA7vxWswYMHq6Ki4lvXAAAA7M5vBeufX/8KAADAybgHCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYZuuC9eabbyotLU25ubkt9rZs2aJx48Zp4MCBmjBhgt566y3vXnNzs5YvX64RI0ZoyJAhmj59uo4ePerd93g8mj17ttLS0jRs2DA9%2Buijqq%2Bv90smAABgf7YtWM8884wWLVqk7t27t9jbv3%2B/8vPzlZeXp7/85S%2BaNm2a7rvvPh07dkyStG7dOm3atEmFhYXatWuX4uPjlZ2dLcuyJEnz589XXV2dNm/erD/84Q8qLS1VQUGBX/MBAAD7sm3BCg0N1YYNG85ZsNavX6/09HSlp6crNDRU48ePV58%2BfbRx40ZJUlFRkaZNm6aePXsqLCxMubm5Ki0t1YcffqjPP/9cr732mnJzcxUdHa3Y2Fjde%2B%2B9%2BsMf/qCGhgZ/xwQAADZk24I1depUderU6Zx7JSUlSkhI8FlLSEiQ2%2B1WfX29Dh486LMfFham7t27y%2B12a//%2B/QoJCVHfvn29%2B4mJiTp16pQOHTrUNmEAAICjuAI9QFvweDyKiIjwWYuIiNDBgwf1xRdfyLKsc%2B5XVVUpMjJSYWFhCgoK8tmTpKqqqlY9f0VFhSorK33WXK4OiomJuZA4XyskxF792OVq3bxnc9ktX2s4NZtTc0lksyOn5pKcm82JuRxZsCR576e6kP1ve99vU1RUpJUrV/qsZWdnKycn56KOa3dRUR3P6/Hh4Ze20SSB59RsTs0lkc2OnJpLcm42J%2BVyZMGKioqSx%2BPxWfN4PIqOjlZkZKSCg4PPud%2B5c2dFR0erpqZGTU1NCgkJ8e5JUufOnVv1/JmZmcrIyPBZc7k6qKqq9kIjnZPdmn5r84eEBCs8/FJVV9epqam5jafyL6dmc2ouiWx25NRcknOztWWu8/3m3hRHFqykpCQVFxf7rLndbo0dO1ahoaHq3bu3SkpKNHToUElSdXW1PvnkE6WkpCguLk6WZenAgQNKTEz0vm94eLh69OjRquePiYlpcTn%2BA%2BeVAAASwUlEQVSwsvKkGhud88VwIc43f1NTs2P/zpyazam5JLLZkVNzSc7N5qRc9joF0kqTJ0/W22%2B/rd27d%2Bv06dPasGGDjhw5ovHjx0uSsrKytHbtWpWWlqqmpkYFBQXq16%2BfkpOTFR0drdGjR%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%2B/Vo48%2BqnHjxum9997TrFmz1KNHDyUnJ2vnzp1asWKFfv3rX6tv375au3atZs6cqR07dqhDhw4BSgIAAOzEsWewvs6mTZsUHx%2BviRMnKjQ0VGlpacrIyND69eslSUVFRZowYYL69%2B%2Bv9u3b65577pEk7dq1K5BjAwAAG3H0Gaxly5bpgw8%2BUE1NjW688UY9/PDDKikpUUJCgs/jEhIStHXrVklSSUmJxowZ490LDg5Wv3795Ha7NXbs2FY9b0VFhSorK33WXK4OiomJuchEvkJC7NWPXa7WzXs2l93ytYZTszk1l0Q2O3JqLsm52ZyYy7EFa8CAAUpLS9OTTz6po0ePavbs2Xr88cfl8XgUGxvr89jIyEhVVVVJkjwejyIiInz2IyIivPutUVRUpJUrV/qsZWdnKycn5wLTOENUVMfzenx4%2BKVtNEngOTWbU3NJZLMjp%2BaSnJvNSbkcW7CKioq8/9%2BzZ0/l5eVp1qxZGjx48Le%2Br2VZF/XcmZmZysjI8FlzuTqoqqr2oo77VXZr%2Bq3NHxISrPDwS1VdXaempuY2nsq/nJrNqbkkstmRU3NJzs3WlrnO95t7UxxbsL7q8ssvV1NTk4KDg%2BXxeHz2qqqqFB0dLUmKiopqse/xeNS7d%2B9WP1dMTEyLy4GVlSfV2OicL4YLcb75m5qaHft35tRsTs0lkc2OnJpLcm42J%2BWy1ymQVtq3b5%2BWLFnis1ZaWqp27dopPT29xcsuFBcXq3///pKkpKQklZSUePeampq0b98%2B7z4AAMC3cWTB6ty5s4qKilRYWKgzZ87o8OHDeuqpp5SZmambb75ZZWVlWr9%2BvU6fPq3XX39dr7/%2BuiZPnixJysrK0ssvv6y9e/eqrq5Oq1evVrt27TR8%2BPDAhgIAALbhyEuEsbGxKiws1LJly7wF6dZbb1Vubq5CQ0O1Zs0aLVq0SI8//rji4uK0dOlSXXHFFZKka6%2B9Vg8%2B%2BKBmz56t48ePKzk5WYWFhWrfvn2AUwEAALtwZMGSpCFDhuiFF1742r1XXnnla9/39ttv1%2B23395WowEAAIdz5CVCAACAQKJgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAxzBXoA/Pu48Zf/HegRzsvW2VcHegQAgE1xBgsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIJ1DmVlZfrxj3%2Bs1NRUXXfddVq6dKmam5sDPRYAALAJXmj0HO6//34lJibqtdde0/HjxzVjxgxddtlluvvuuwM9GgAAsAHOYH2F2%2B3WgQMHlJeXp06dOik%2BPl7Tpk1TUVFRoEcDAAA2wRmsrygpKVFcXJwiIiK8a4mJiTp8%2BLBqamoUFhb2rceoqKhQZWWlz5rL1UExMTFGZw0JoR%2B3Jbv9ap9X864J2HOf/Vx04uck2ezHqbkk52ZzYi4K1ld4PB6Fh4f7rJ0tW1VVVa0qWEVFRVq5cqXP2n333af777/f3KD6ssjd1fVjZWZmGi9vgVRRUaGioiLH5ZKcm62iokK//e2vHZdLIpsdOTWX5NxsTszlnKpokGVZF/X%2BmZmZeumll3zeMjMzDU33fyorK7Vy5coWZ8vszqm5JOdmc2ouiWx25NRcknOzOTEXZ7C%2BIjo6Wh6Px2fN4/EoKChI0dHRrTpGTEyMYxo4AAA4f5zB%2BoqkpCSVl5frxIkT3jW3261evXqpY8eOAZwMAADYBQXrKxISEpScnKxly5appqZGpaWleu6555SVlRXo0QAAgE2ELFiwYEGgh/hXc80112jz5s1auHCh/vSnP2nixImaPn26goKCAj1aCx07dtTQoUMdd3bNqbkk52Zzai6JbHbk1FySc7M5LVeQdbF3dAMAAMAHlwgBAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNg2VBZWZl%2B/OMfKzU1Vdddd52WLl2q5ubmQI91wd58802lpaUpNze3xd6WLVs0btw4DRw4UBMmTNBbb70VgAkvTFlZmbKzs5Wamqq0tDQ9/PDDqq6uliTt379fd9xxhwYPHqxRo0bp2WefDfC0rXfgwAHdddddGjx4sNLS0jR79mxVVlZKkvbs2aOJEydq0KBBGjt2rDZu3BjgaS/cE088ob59%2B3r/bOdsffv2VVJSkpKTk71vCxculGTvXGetXr1aw4YN04ABAzRt2jR9%2Bumnkuyd7W9/%2B5vPxys5OVlJSUnez0k7Z9u3b5%2BmTp2qK6%2B8UldffbXy8vJ04sQJSfbO1YIF27n11lutefPmWdXV1dbhw4etUaNGWc8%2B%2B2ygx7oghYWF1qhRo6wpU6ZYs2fP9tnbt2%2BflZSUZO3evduqr6%2B3XnnlFat///5WeXl5gKY9PzfddJP18MMPWzU1NVZ5ebk1YcIEa%2B7cuVZdXZ11zTXXWCtWrLBqa2ut4uJia%2BjQodb27dsDPfK3On36tPWDH/zAWrlypXX69Gnr%2BPHj1h133GHde%2B%2B91meffWYNGDDAWr9%2BvVVfX2/993//t5WSkmL9/e9/D/TY523fvn3W0KFDrT59%2BliWZdk%2BW58%2BfayjR4%2B2WLd7LsuyrOeff9664YYbrNLSUuvkyZPWwoULrYULFzoi21etXr3aeuCBB2ydraGhwbr66qutZcuWWadPn7ZOnDhh3X333db9999v61znwhksm3G73Tpw4IDy8vLUqVMnxcfHa9q0aSoqKgr0aBckNDRUGzZsUPfu3VvsrV%2B/Xunp6UpPT1doaKjGjx%2BvPn362OI7murqaiUlJWnOnDnq2LGjunbtqltvvVXvvvuudu/erYaGBs2aNUsdOnRQYmKiJk2aZIuPYV1dnXJzczVjxgy1a9dO0dHRGjlypD7%2B%2BGNt2rRJ8fHxmjhxokJDQ5WWlqaMjAytX78%2B0GOfl%2BbmZj322GOaNm2ad80p2b7KCbmeffZZ5ebm6vvf/77CwsI0b948zZs3zxHZ/tn//u//6rnnntNDDz1k62yVlZWqrKzUzTffrHbt2ikqKkojR47U/v37bZ3rXChYNlNSUqK4uDhFRER41xITE3X48GHV1NQEcLILM3XqVHXq1OmceyUlJUpISPBZS0hIkNvt9sdoFyU8PFyLFy/WZZdd5l0rLy9XTEyMSkpK1LdvX4WEhHj3EhISVFxcHIhRz0tERIQmTZokl8slSTp06JD%2B%2BMc/6sYbb/zaj5cdcv2zF154QaGhoRo3bpx3zQnZli1bpuHDh%2BvKK6/U/PnzVVtba/tcn332mT799FN98cUXGjNmjFJTU5WTk6MTJ07YPttXPfXUU7rtttv03e9%2B19bZYmNj1a9fPxUVFam2tlbHjx/Xjh07NHz4cFvnOhcKls14PB6Fh4f7rJ0tW1VVVYEYqc14PB6fIil9mdWOOd1ut55//nnNmjXrnB/DyMhIeTwe29xLV1ZWpqSkJI0ZM0bJycnKycn52lx2%2Bnh9/vnnWrFihR577DGfdbtnGzBggNLS0rRjxw4VFRVp7969evzxx22f69ixY5Kkbdu26bnnntMrr7yiY8eOad68ebbP9s8%2B/fRT7dixQ3fffbcke38%2BBgcHa8WKFfrzn/%2BsQYMGKS0tTY2NjZozZ46tc50LBcuGLMsK9Ah%2B44Ss7733nqZPn645c%2BYoLS3tax8XFBTkx6kuTlxcnNxut7Zt26YjR47ooYceCvRIRixevFgTJkxQr169Aj2KUUVFRZo0aZLatWunnj17Ki8vT5s3b1ZDQ0OgR7soZ/99uOeeexQbG6uuXbvq/vvv186dOwM8mVnr1q3TqFGj1KVLl0CPctHOnDmjmTNn6oYbbtC7776rN954Q506dVJeXl6gRzOOgmUz0dHR8ng8Pmsej0dBQUGKjo4O0FRtIyoq6pxZ7ZRz586d%2BvGPf6y5c%2Bdq6tSpkr78GH71OzKPx6PIyEgFB9vnSzIoKEjx8fHKzc3V5s2b5XK5Wny8qqqqbPPx2rNnjz744ANlZ2e32DvX56Kdsn3V5ZdfrqamJgUHB9s619lL8P981iMuLk6WZamhocHW2f7Z9u3blZGR4f2znT8f9%2BzZo08//VQPPvigOnXqpNjYWOXk5OjVV1%2B1/efjV9nnX3NIkpKSklReXu79kVbpy8tPvXr1UseOHQM4mXlJSUktrr273W71798/QBOdn/fff1/5%2Bfl66qmndMstt3jXk5KS9NFHH6mxsdG7Zpdce/bs0ejRo30uZZ4thSkpKS0%2BXsXFxbbIJUkbN27U8ePHdd111yk1NVUTJkyQJKWmpqpPnz62zbZv3z4tWbLEZ620tFTt2rVTenq6bXNJUteuXRUWFqb9%2B/d718rKynTJJZfYPttZ%2B/fvV1lZma6%2B%2BmrvWnJysm2zNTU1qbm52efqxJkzZyRJaWlpts11TgH9GUZckEmTJllz5861Tp48aR08eNDKyMiwnn/%2B%2BUCPdVHy8/NbvEzDRx99ZCUnJ1u7du2y6uvrrfXr11sDBw60KioqAjRl6zU0NFg33nij9cILL7TYO336tHXddddZv/rVr6xTp05Ze/futa688kpr165d/h/0PFVXV1tpaWnWkiVLrFOnTlnHjx%2B3pk%2Bfbt1%2B%2B%2B3W559/bg0cONB68cUXrfr6emv37t1WSkqKtX///kCP3Soej8cqLy/3vn3wwQdWnz59rPLycqusrMy22Y4dO2YNGDDAWrNmjXX69Gnr0KFD1pgxY6yFCxfa/mNmWZb1xBNPWCNGjLCOHDliff7551ZmZqb18MMPOyKbZVnWhg0brKFDh/qs2TnbiRMnrKFDh1q/%2BMUvrFOnTlknTpywZs6caf3Hf/yHrXOdCwXLhsrLy6177rnHSklJsdLS0qxf/epXVnNzc6DHuiBJSUlWUlKSdcUVV1hXXHGF989nbd%2B%2B3Ro1apSVmJho3XzzzdY777wTwGlb729/%2B5vVp08fb55/fvv000%2Btjz76yJoyZYqVlJRkDR8%2B3Fq3bl2gR261AwcOWHfccYeVkpJiXXXVVdbs2bOtY8eOWZZlWe%2B88441fvx4KzEx0Ro1apQtXtvr6xw9etT7OliWZe9s77zzjpWZmWkNGDDAGjp0qLV48WKrvr7eu2fXXJb15TcsCxYssIYMGWINGDDAys/Pt2pqaizLsn82y7Ksp59%2B2ho7dmyLdTtnc7vd1h133GFdeeWVVlpammP/DQmyLAfcRQwAAPAvhHuwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMCw/wcWzUstmxX18gAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common1044561578819287183”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1878</td> <td class=”number”>58.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>402</td> <td class=”number”>12.5%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>296</td> <td class=”number”>9.2%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>160</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>84</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>61</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>46</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>30</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>25</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (13)</td> <td class=”number”>70</td> <td class=”number”>2.2%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>134</td> <td class=”number”>4.2%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme1044561578819287183”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1878</td> <td class=”number”>58.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>402</td> <td class=”number”>12.5%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>296</td> <td class=”number”>9.2%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>160</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>84</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>21.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>22.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>24.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>71.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>80.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_GHVEG_FARMS12”>GHVEG_FARMS12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>47</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.5%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>4.2%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>134</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>2.8079</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>150</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>44.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram8370457339354730174”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives8370457339354730174”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles8370457339354730174”

aria-controls=”quantiles8370457339354730174” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram8370457339354730174” aria-controls=”histogram8370457339354730174”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common8370457339354730174” aria-controls=”common8370457339354730174”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme8370457339354730174” aria-controls=”extreme8370457339354730174”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles8370457339354730174”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>1</td>

</tr> <tr>

<th>Q3</th> <td>3</td>

</tr> <tr>

<th>95-th percentile</th> <td>12</td>

</tr> <tr>

<th>Maximum</th> <td>150</td>

</tr> <tr>

<th>Range</th> <td>150</td>

</tr> <tr>

<th>Interquartile range</th> <td>3</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>5.991</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.1336</td>

</tr> <tr>

<th>Kurtosis</th> <td>133.23</td>

</tr> <tr>

<th>Mean</th> <td>2.8079</td>

</tr> <tr>

<th>MAD</th> <td>3.215</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>7.8305</td>

</tr> <tr>

<th>Sum</th> <td>8637</td>

</tr> <tr>

<th>Variance</th> <td>35.892</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram8370457339354730174”>

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0atUqpaWlKS0tTWFhYRo0aJDatWuntWvXSpJyc3OVkZGh1q1bKyIiQllZWSooKNCuXbt8GRkAAPgRu68HuBhRUVGaO3eux9qhQ4cUHx%2Bv/Px8tW/fXqGhoe69xMRErVq1SpKUn5%2BvtLQ0j%2BcmJibK4XDoxIkT2rdvnxITE917ERERatGihRwOh66%2B%2BuoazVdUVKTi4mKPNbu9oeLj4y8o5/mEhvpXP7bbL27e0zn9Le%2BFImfgCZas5AwswZKzrvllwfolh8OhV199VcuWLdP777%2BvqKgoj/2YmBi5XC5VV1fL5XIpOjraYz86Olr79u3TsWPHZFnWWfedTmeN58nNzdWSJUs81jIzMzVx4sQLTBZYYmPDa/X8qKjLDE1yaSNn4AmWrOQMLMGSs674fcH64osvNH78eE2ePFmpqal6//33z/q4kJAQ9/8/3/1Utb3fasSIEerdu7fHmt3eUE5nWa2O%2B0v%2B9t3FxeYPDbUpKuoylZSUq6qq2vBUlw5yBp5gyUrOwBJoOWv7zf3F8uuCtXXrVj3yyCOaNWuWBg8eLEmKi4vTgQMHPB7ncrkUExMjm82m2NhYuVyuM/bj4uLcjznbfqNGjWo8V3x8/BkvBxYXH1dlpf9/odZGbfNXVVUHxceQnIEnWLKSM7AES8664l%2BXQP7Nl19%2BqalTp2rRokXuciVJSUlJ%2Bvrrr1VZWeleczgc6ty5s3s/Ly/P41in98PCwtS2bVvl5%2Be790pKSnTw4EF16tSpjhMBAIBA4ZcFq7KyUjNnztSUKVPUs2dPj720tDRFRERo2bJlKi8v165du7R69Wqlp6dLkoYPH65PP/1U27dv18mTJ7V69WodOHBAgwYNkiSlp6dr5cqVKigoUGlpqbKzs9WhQwclJyd7PScAAPBPfvkS4VdffaWCggLNmTNHc%2BbM8djbuHGjnn/%2BeT3xxBPKyclR48aNlZWVpV69ekmS2rVrp%2BzsbM2dO1eFhYVq06aNli9friZNmkiSRo4cqeLiYt19990qKytTSkrKGTesAwAAnEuIxTtoekVx8XHjx7Tbbbo5%2B2Pjx60r70%2B67qKeZ7fbFBsbLqezLKDvByBn4AmWrOQMLIGWs0mTSJ%2Bc1y9fIgQAALiUUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMK8XrN69e2vJkiU6dOiQt08NAADgFV4vWLfffrs2bNigm266SWPGjNHmzZs9fq0NAACAv/N6wcrMzNSGDRv05ptvqm3btnr22WeVlpam%2BfPna//%2B/d4eBwAAwDif3YPVsWNHTZ06Vdu2bdP06dP15ptvqn///ho9erT%2B/ve/%2B2osAACAWvNZwaqoqNCGDRt03333aerUqUpISNBjjz2mDh06KCMjQ%2BvWrfPVaAAAALXi9V/2XFBQoNWrV%2Bvdd99VWVmZ%2BvXrpz/%2B8Y/q1q2b%2BzHdu3fX7NmzNXDgQG%2BPBwAAUGteL1gDBgxQq1atNHbsWA0ePFgxMTFnPCYtLU1Hjx719mgAAABGeL1grVy5Uj169Djv43bt2uWFaQAAAMzz%2Bj1Y7du317hx47Rlyxb32v/%2B7//qvvvuk8vl8vY4AAAAxnm9YM2dO1fHjx9XmzZt3Gu9evVSdXW15s2b5%2B1xAAAAjPP6S4SffPKJ1q1bp9jYWPday5YtlZ2drVtvvdXb4wAAABjn9StYJ06cUFhY2JmD2GwqLy/39jgAAADGeb1gde/eXfPmzdOxY8fcaz/88IOefPJJj7dqAAAA8Fdef4lw%2BvTp%2Bv3vf6/f/va3ioiIUHV1tcrKytS8eXO98sor3h4HAADAOK8XrObNm%2Bu9997TRx99pIMHD8pms6lVq1bq2bOnQkNDvT0OAACAcV4vWJJUv3593XTTTb44NQAAQJ3zesH67rvvtGDBAn3zzTc6ceLEGfsffviht0cCAAAwyif3YBUVFalnz55q2LCht08PAABQ57xesPLy8vThhx8qLi7O26cGAADwCq%2B/TUOjRo24cgUAAAKa1wvW2LFjtWTJElmW5e1TAwAAeIXXXyL86KOP9OWXX%2Brtt9/W5ZdfLpvNs%2BO98cYb3h4JAADAKK8XrIiICN1www3ePi0AAIDXeL1gzZ0719unBAAA8Cqv34MlSd9%2B%2B60WL16sxx57zL32t7/9zRejAAAAGOf1grVz504NGjRImzdv1vr16yX9/Oajo0aN4k1GAQBAQPB6wVq4cKEeeeQRrVu3TiEhIZJ%2B/v2E8%2BbN09KlS709DgAAgHFeL1j/%2BMc/lJ6eLknugiVJt9xyiwoKCrw9DgAAgHFeL1iRkZFn/R2ERUVFql%2B/vrfHAQAAMM7rBatr16569tlnVVpa6l7bv3%2B/pk6dqt/%2B9rfeHgcAAMA4r79Nw2OPPaZ77rlHKSkpqqqqUteuXVVeXq62bdtq3rx53h4HAADAOK8XrKZNm2r9%2BvXasWOH9u/frwYNGqhVq1a67rrrPO7JAgAA8FdeL1iSVK9ePd10002%2BODUAAECd83rB6t279zmvVPFeWAAAwN95vWD179/fo2BVVVVp//79cjgcuueeey7oWB9//LGmTp2qlJQULVy40L3%2B9ttva/r06apXr57H41977TV16tRJ1dXVWrRokdavX6%2BSkhJ16tRJs2fPVvPmzSVJLpdLs2fP1l/%2B8hfZbDalpaVp1qxZatCgQS2SAwCAYOH1gjVlypSzrm/atEl//vOfa3ycF154QatXr1aLFi3Out%2B9e3e98sorZ9177bXXtG7dOr3wwgtKSEjQwoULlZmZqTVr1igkJESzZs3SqVOntH79elVUVOihhx5Sdna2Zs6cWeP5AABA8PLJ7yI8m5tuuknvvfdejR8fFhZ2zoJ1Lrm5ucrIyFDr1q0VERGhrKwsFRQUaNeuXfrxxx%2B1ZcsWZWVlKS4uTgkJCXrggQf01ltvqaKi4oLPBQAAgs8lU7B2794ty7Jq/PhRo0YpMjLyV/cPHTqke%2B%2B9V927d1efPn20Zs0aSdKJEye0b98%2BJSYmuh8bERGhFi1ayOFwaM%2BePQoNDVX79u3d%2Bx07dtRPP/2kb7/99iKSAQCAYOP1lwhHjhx5xlp5ebkKCgrUt29fI%2BeIi4tTy5Yt9fDDD6tNmzb64IMP9Oijjyo%2BPl5XXnmlLMtSdHS0x3Oio6PldDoVExOjiIgIj/vETj/W6XTW6PxFRUUqLi72WLPbGyo%2BPr6WyTyFhl4y/bhG7PaLm/d0Tn/Le6HIGXiCJSs5A0uw5KxrXi9YLVu2POOnCMPCwjRs2DDdcccdRs7Rq1cv9erVy/3nAQMG6IMPPtDbb7/tvgfsXFfLLuRK2tnk5uZqyZIlHmuZmZmaOHFirY7r72Jjw2v1/KioywxNcmkjZ%2BAJlqzkDCzBkrOueL1g%2Berd2ps1a6a8vDzFxMTIZrPJ5XJ57LtcLjVq1EhxcXEqLS1VVVWVQkND3XuS1KhRoxqda8SIEerdu7fHmt3eUE5nmYEk/%2BJv311cbP7QUJuioi5TSUm5qqqqDU916SBn4AmWrOQMLIGWs7bf3F8srxesd999t8aPHTx48EWd4/XXX1d0dLT69%2B/vXisoKFDz5s0VFhamtm3bKj8/Xz169JAklZSU6ODBg%2BrUqZOaNWsmy7K0d%2B9edezYUZLkcDgUFRWlVq1a1ej88fHxZ7wcWFx8XJWV/v%2BFWhu1zV9VVR0UH0NyBp5gyUrOwBIsOeuK1wvWjBkzVF1dfcbLcCEhIR5rISEhF12wTp06paefflrNmzfXVVddpU2bNumjjz7Sm2%2B%2BKUlKT09XTk6ObrjhBiUkJCg7O1sdOnRQcnKyJKlfv376wx/%2BoOeee06nTp3S0qVLNWzYMNntPnnjewAA4Ge83hhefPFFrVixQuPGjVP79u1lWZa%2B/vprvfDCC7rrrruUkpJSo%2BOcLkOVlZWSpC1btkj6%2BWrTqFGjVFZWpoceekjFxcW6/PLLtXTpUiUlJUn6%2BUb74uJi3X333SorK1NKSorHPVNPPfWUnnjiCfXp00f16tXTrbfeqqysLJMfBgAAEMBCrNre0X2BbrvtNuXk5CghIcFj/YcfftDo0aO1fv16b47jNcXFx40f02636ebsj40ft668P%2Bm6i3qe3W5TbGy4nM6ygL5cTc7AEyxZyRlYAi1nkya//pZOdcnrd0kfOHDgjLdIkKSoqCgVFhZ6exwAAADjvF6wmjVrpnnz5nm8p1RJSYkWLFigK664wtvjAAAAGOf1e7CmT5%2BuyZMnKzc3V%2BHh4bLZbCotLVWDBg20dOlSb48DAABgnNcLVs%2BePbV9%2B3bt2LFDhw8flmVZSkhI0PXXX3/OX30DAADgL3zyvgOXXXaZ%2BvTpo8OHD6t58%2Ba%2BGAEAAKDOeP0erBMnTmjq1Knq0qWLfve730n6%2BR6sMWPGqKSkxNvjAAAAGOf1gjV//nzt2bNH2dnZstn%2BdfqqqiplZ2d7exwAAADjvF6wNm3apP/5n//RLbfc4v6lz1FRUZo7d642b97s7XEAAACM83rBKisrU8uWLc9Yj4uL008//eTtcQAAAIzzesG64oor9Oc//1mSPH734MaNG/Uf//Ef3h4HAADAOK//FOGdd96pCRMm6Pbbb1d1dbVefvll5eXladOmTZoxY4a3xwEAADDO6wVrxIgRstvtevXVVxUaGqrnn39erVq1UnZ2tm655RZvjwMAAGCc1wvW0aNHdfvtt%2Bv222/39qkBAAC8wuv3YPXp08fj3isAAIBA4/WClZKSovfff9/bpwUAAPAar79E%2BJvf/EbPPPOMcnJydMUVV6hevXoe%2BwsWLPD2SAAAAEZ5vWDt27dPV155pSTJ6XR6%2B/QAAAB1zmsFKysrSwsXLtQrr7ziXlu6dKkyMzO9NQIAAIBXeO0erK1bt56xlpOT463TAwAAeI3XCtbZfnKQnyYEAACByGsF6/Qvdj7fGgAAgL/z%2Bts0AAAABDoKFgAAgGFe%2BynCiooKTZ48%2BbxrvA8WAADwd14rWN26dVNRUdF51wAAAPyd1wrWv7//FQAAQCDjHiwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDC/Llgff/yxUlNTlZWVdcbehg0bNHDgQHXp0kVDhw7VJ5984t6rrq7WwoUL1adPH3Xv3l2jR4/Wd9995953uVyaNGmSUlNT1bNnT82YMUMnTpzwSiYAAOD//LZgvfDCC5ozZ45atGhxxt6ePXs0depUTZkyRZ999pkyMjL04IMP6vDhw5Kk1157TevWrVNOTo62bdumli1bKjMzU5ZlSZJmzZql8vJyrV%2B/Xm%2B99ZYKCgqUnZ3t1XwAAMB/%2BW3BCgsL0%2BrVq89asFatWqW0tDSlpaUpLCxMgwYNUrt27bR27VpJUm5urjIyMtS6dWtFREQoKytLBQUF2rVrl3788Udt2bJFWVlZiouLU0JCgh544AG99dZbqqio8HZMAADgh%2By%2BHuBijRo16lf38vPzlZaW5rGWmJgoh8OhEydOaN%2B%2BfUpMTHTvRUREqEWLFnI4HDp%2B/LhCQ0PVvn17937Hjh31008/6dtvv/VY/zVFRUUqLi72WLPbGyo%2BPr6m8WokNNS/%2BrHdfnHzns7pb3kvFDkDT7BkJWdgCZacdc1vC9a5uFwuRUdHe6xFR0dr3759OnbsmCzLOuu%2B0%2BlUTEyMIiIiFBIS4rEnSU6ns0bnz83N1ZIlSzzWMjMzNXHixIuJEzBiY8Nr9fyoqMsMTXJpI2fgCZas5AwswZKzrgRkwZLkvp/qYvbP99zzGTFihHr37u2xZrc3lNNZVqvj/pK/fXdxsflDQ22KirpMJSXlqqqqNjzVpYOcgSdYspIzsARaztp%2Bc3%2BxArJgxcbGyuVyeay5XC7FxcUpJiZGNpvtrPuNGjVSXFycSktLVVVVpdDQUPeeJDVq1KhG54%2BPjz/j5cDi4uOqrPT/L9TaqG3%2BqqrqoPgYkjPwBEtWcgaWYMlZV/zrEkgNJSUlKS8vz2PN4XCoc%2BfOCgsLU9u2bZWfn%2B/eKykp0cGDB9WpUyd16NBBlmVp7969Hs%2BNiopSq1atvJYBAAD4r4AsWMOHD9enn36q7du36%2BTJk1q9erUOHDigQYMGSZLS09O1cuVKFRQUqLS0VNnZ2erQoYOSk5MVFxenfv366Q9/%2BIOOHj2qw4cPa%2BnSpRo2bJjs9oC84AcAAAzz28aQnJwsSar4sUw4AAASu0lEQVSsrJQkbdmyRdLPV5vatWun7OxszZ07V4WFhWrTpo2WL1%2BuJk2aSJJGjhyp4uJi3X333SorK1NKSorHTelPPfWUnnjiCfXp00f16tXTrbfeetY3MwUAADibEKu2d3SjRoqLjxs/pt1u083ZHxs/bl15f9J1F/U8u92m2NhwOZ1lAX0/ADkDT7BkJWdgCbScTZpE%2BuS8AfkSIQAAgC9RsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwwK2YLVv315JSUlKTk52//P0009Lknbu3Klhw4apa9euGjBggNauXevx3JUrV6pfv37q2rWr0tPTlZeX54sIAADAT9l9PUBd2rhxoy6//HKPtaKiIj3wwAOaMWOGBg4cqC%2B%2B%2BELjx49Xq1atlJycrK1bt2rx4sV68cUX1b59e61cuVLjxo3T5s2b1bBhQx8lAQAA/iRgr2D9mnXr1qlly5YaNmyYwsLClJqaqt69e2vVqlWSpNzcXA0dOlSdO3dWgwYNNGbMGEnStm3bfDk2AADwIwFdsBYsWKBevXrpmmuu0axZs1RWVqb8/HwlJiZ6PC4xMdH9MuAv9202mzp06CCHw%2BHV2QEAgP8K2JcIr776aqWmpuq5557Td999p0mTJunJJ5%2BUy%2BVSQkKCx2NjYmLkdDolSS6XS9HR0R770dHR7v2aKCoqUnFxscea3d5Q8fHxF5nm7EJD/asf2%2B0XN%2B/pnP6W90KRM/AES1ZyBpZgyVnXArZg5ebmuv9/69atNWXKFI0fP17dunU773Mty6r1uZcsWeKxlpmZqYkTJ9bquP4uNja8Vs%2BPirrM0CSXNnIGnmDJSs7AEiw560rAFqxfuvzyy1VVVSWbzSaXy%2BWx53Q6FRcXJ0mKjY09Y9/lcqlt27Y1PteIESPUu3dvjzW7vaGczrKLnP7s/O27i4vNHxpqU1TUZSopKVdVVbXhqS4d5Aw8wZKVnIEl0HLW9pv7ixWQBWv37t1au3atpk2b5l4rKChQ/fr1lZaWpnfeecfj8Xl5eercubMkKSkpSfn5%2BRoyZIgkqaqqSrt379awYcNqfP74%2BPgzXg4sLj6uykr//0Ktjdrmr6qqDoqPITkDT7BkJWdgCZacdcW/LoHUUKNGjZSbm6ucnBydOnVK%2B/fv16JFizRixAjddtttKiws1KpVq3Ty5Ent2LFDO3bs0PDhwyVJ6enpevfdd/XVV1%2BpvLxcy5YtU/369dWrVy/fhgIAAH4jIK9gJSQkKCcnRwsWLHAXpCFDhigrK0thYWFavny55syZoyeffFLNmjXT/PnzddVVV0mSbrjhBj388MOaNGmSjhw5ouTkZOXk5KhBgwY%2BTgUAAPxFQBYsSerevbveeOONX91bs2bNrz73zjvv1J133llXowEAgAAXkC8RAgAA%2BBIFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADD7L4eAMHjd3/4k69HuCDvT7rO1yMAAPwUV7AAAAAMo2ABAAAYRsECAAAwjIIFAABgGAXrLAoLC3X//fcrJSVFN954o%2BbPn6/q6mpfjwUAAPwEP0V4FhMmTFDHjh21ZcsWHTlyRGPHjlXjxo117733%2Bno0AADgByhYv%2BBwOLR37169/PLLioyMVGRkpDIyMvTHP/6RghVkeFsJAMDFomD9Qn5%2Bvpo1a6bo6Gj3WseOHbV//36VlpYqIiLivMcoKipScXGxx5rd3lDx8fFGZw0N5RVe/Ivd7h9fD6e/boPh6zdYspIzsARLzrpGwfoFl8ulqKgoj7XTZcvpdNaoYOXm5mrJkiUeaw8%2B%2BKAmTJhgblD9XOTuafqNRowYYby8XUqKioqUm5tLzgBRVFSkP/7xxYDPKQVPVnIGlmDJWdeop2dhWVatnj9ixAi9/fbbHv%2BMGDHC0HT/UlxcrCVLlpxxtSzQkDOwBEtOKXiykjOwBEvOusYVrF%2BIi4uTy%2BXyWHO5XAoJCVFcXFyNjhEfH0/rBwAgiHEF6xeSkpJ06NAhHT161L3mcDjUpk0bhYeH%2B3AyAADgLyhYv5CYmKjk5GQtWLBApaWlKigo0Msvv6z09HRfjwYAAPxE6OzZs2f7eohLzfXXX6/169fr6aef1nvvvadhw4Zp9OjRCgkJ8fVoZwgPD1ePHj0C/uoaOQNLsOSUgicrOQNLsOSsSyFWbe/oBgAAgAdeIgQAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjILlhwoLC3X//fcrJSVFN954o%2BbPn6/q6mpfj2VEYWGhMjMzlZKSotTUVE2bNk0lJSWSpD179uiuu%2B5St27d1LdvX61YscLH05rx7LPPqn379u4/79y5U8OGDVPXrl01YMAArV271ofT1d6yZcvUs2dPXX311crIyND3338vKbBy7t69W6NGjdI111yj6667TlOmTHH/wnh/z/nxxx8rNTVVWVlZZ%2Bxt2LBBAwcOVJcuXTR06FB98skn7r3q6motXLhQffr0Uffu3TV69Gh999133hz9gpwr5%2BbNmzVo0CB16dJF/fr105tvvumxv3LlSvXr109du3ZVenq68vLyvDX2RTlX1tPKysrUq1cvTZs2zb3mb59Tn7Pgd4YMGWLNnDnTKikpsfbv32/17dvXWrFiha/HMuLWW2%2B1pk2bZpWWllqHDh2yhg4dak2fPt0qLy%2B3rr/%2Bemvx4sVWWVmZlZeXZ/Xo0cPatGmTr0euld27d1s9evSw2rVrZ1mWZf3www/W1Vdfba1atco6ceKE9ac//cnq1KmT9fe//93Hk16cV1991brlllusgoIC6/jx49bTTz9tPf300wGVs6KiwrruuuusBQsWWCdPnrSOHj1q3XvvvdaECRP8PmdOTo7Vt29fa%2BTIkdakSZM89nbv3m0lJSVZ27dvt06cOGGtWbPG6ty5s3Xo0CHLsixr5cqV1o033mjt27fPOn78uPXUU09ZAwcOtKqrq30R5ZzOlXPXrl1WcnKy9cEHH1gVFRXW9u3brY4dO1p//etfLcuyrA8//NC65pprrK%2B%2B%2BsoqLy%2B3li9fbl133XVWWVmZL6Kc17my/ru5c%2Bda3bp1s6ZOnepe86fP6aWAK1h%2BxuFwaO/evZoyZYoiIyPVsmVLZWRkKDc319ej1VpJSYmSkpI0efJkhYeHq2nTphoyZIg%2B//xzbd%2B%2BXRUVFRo/frwaNmyojh076o477vDr3NXV1XriiSeUkZHhXlu3bp1atmypYcOGKSwsTKmpqerdu7dWrVrlu0FrYcWKFcrKytKVV16piIgIzZw5UzNnzgyonMXFxSouLtZtt92m%2BvXrKzY2VjfffLP27Nnj9znDwsK0evVqtWjR4oy9VatWKS0tTWlpaQoLC9OgQYPUrl079xW63NxcZWRkqHXr1oqIiFBWVpYKCgq0a9cub8c4r3PldLlcGjt2rG666SbZ7XalpaWpXbt2%2BvzzzyX9nHPo0KHq3LmzGjRooDFjxkiStm3b5tUMNXWurKft3btX69ev15AhQzzW/elzeimgYPmZ/Px8NWvWTNHR0e61jh07av/%2B/SotLfXhZLUXFRWluXPnqnHjxu61Q4cOKT4%2BXvn5%2BWrfvr1CQ0Pde4mJiZf8pfhzeeONNxQWFqaBAwe61/Lz85WYmOjxOH/N%2BcMPP%2Bj777/XsWPH1L9/f6WkpGjixIk6evRoQOVMSEhQhw4dlJubq7KyMh05ckSbN29Wr169/D7nqFGjFBkZeda9X8vmcDh04sQJ7du3z2M/IiJCLVq0kMPhqNOZL8a5ct5www3KzMx0/7myslLFxcVKSEiQdObHwWazqUOHDpdkTuncWSXJsizNnj1bWVlZioqKcq/72%2Bf0UkDB8jMul8vji16Su2w5nU5fjFRnHA6HXn31VY0fP/6suWNiYuRyufzy/rMff/xRixcv1hNPPOGx/ms5/fFze/jwYUnSxo0b9fLLL2vNmjU6fPiwZs6cGVA5bTabFi9erA8//FBdu3ZVamqqKisrNXny5IDK%2BUsul8vjGz3p57%2BLnE6njh07JsuyfnXfn2VnZ6thw4bq37%2B/pHN/HPxRbm6uQkJCNHToUI/1QP6c1hUKlh%2ByLMvXI9S5L774QqNHj9bkyZOVmpr6q48LCQnx4lTmzJ07V0OHDlWbNm18PUqdOf11OmbMGCUkJKhp06aaMGGCtm7d6uPJzDp16pTGjRunW265RZ9//rk%2B%2BugjRUZGasqUKb4erc6d7%2B%2BiQPq7yrIszZ8/X%2BvXr9eyZcsUFhbmsRcIjhw5okWLFmn27Nm/%2BndroGT1BgqWn4mLi5PL5fJYc7lcCgkJUVxcnI%2BmMmvr1q26//77NX36dI0aNUrSz7l/%2BV2Sy%2BVSTEyMbDb/%2BjLeuXOn/va3v3m87HBabGzsGZ9fp9Ppl5/b0y/1/vsVnGbNmsmyLFVUVARMzp07d%2Br777/Xww8/rMjISCUkJGjixIn64IMPZLPZAibnL53ta9XlcikuLs797%2BXZ9hs1auTNMY2orq7WtGnTtHXrVr3%2B%2Buu68sor3Xvn%2Bjj4m3nz5mnw4MEeP9V8WqB9Tr3Bv/7LBCUlJenQoUPuHwGXfn4prU2bNgoPD/fhZGZ8%2BeWXmjp1qhYtWqTBgwe715OSkvT111%2BrsrLSveZwONS5c2dfjFkra9eu1ZEjR3TjjTcqJSXFfSk%2BJSVF7dq1O%2BP%2BnLy8PL/M2bRpU0VERGjPnj3utcLCQtWrV09paWkBk7OqqkrV1dUe39mfOnVKkpSamhowOX8pKSnpjGyn/50MCwtT27ZtlZ%2Bf794rKSnRwYMH1alTJ2%2BPWmvPPvusvvnmG73%2B%2Butq3ry5x15SUpJHzqqqKu3evdsvP8dr167V6tWrlZKSopSUFL344ot67733lJKSEnCfU2%2BgYPmZxMREJScna8GCBSotLVVBQYFefvllpaen%2B3q0WqusrNTMmTM1ZcoU9ezZ02MvLS1NERERWrZsmcrLy7Vr1y6tXr3aL3NPmzZNmzZt0po1a7RmzRrl5ORIktasWaOBAweqsLBQq1at0smTJ7Vjxw7t2LFDw4cP9/HUF85ut2vYsGF6/vnn9c9//lNHjhzR0qVLNXDgQA0ZMiRgcnbp0kUNGzbU4sWLVV5eLqfTqWXLlql79%2B667bbbAibnLw0fPlyffvqptm/frpMnT2r16tU6cOCABg0aJElKT0/XypUrVVBQoNLSUmVnZ6tDhw5KTk728eQX5osvvtDatWuVk5OjmJiYM/bT09P17rvv6quvvlJ5ebmWLVum%2BvXrq1evXt4ftpZ27NihdevWuf9uGjlypHr37q01a9ZICpzPqbeEWLyg6ncOHz6sWbNm6S9/%2BYsiIiI0cuRIPfjgg357P9Jpn3/%2Buf7zP/9T9evXP2Nv48aNKisr0xNPPKG8vDw1btxY9913n%2B68804fTGrW999/rz59%2Bujrr7%2BWJP31r3/VnDlzVFBQoGbNmmny5Mnq27evj6e8OKdOndLcuXP13nvvqaKiQv369dOsWbMUHh4eUDnz8vL03HPPae/evapfv7569OihadOmKSEhwa9znv4P5%2Bkrx3a7XZLcPzW2efNmLViwQIWFhWrTpo1mzJih7t27S/r5Xp3FixfrjTfeUFlZmVJSUvTUU0%2BpadOmPkhybufKOX36dL3zzjvutdO6d%2B/ufrPj//u//1NOTo6OHDmi5ORkzZ49W%2B3atfNigpo73%2Bf03y1evFiFhYWaN2%2BeJP/6nF4KKFgAAACG8RIhAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABj2/wAWI2NPrmxzcwAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common8370457339354730174”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1412</td> <td class=”number”>44.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>404</td> <td class=”number”>12.6%</td> <td>

<div class=”bar” style=”width:29%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>309</td> <td class=”number”>9.6%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>233</td> <td class=”number”>7.3%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>155</td> <td class=”number”>4.8%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>92</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>40</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>39</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (36)</td> <td class=”number”>208</td> <td class=”number”>6.5%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>134</td> <td class=”number”>4.2%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme8370457339354730174”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1412</td> <td class=”number”>44.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>404</td> <td class=”number”>12.6%</td> <td>

<div class=”bar” style=”width:29%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>309</td> <td class=”number”>9.6%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>233</td> <td class=”number”>7.3%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>155</td> <td class=”number”>4.8%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>46.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>53.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>56.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>57.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>150.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_GHVEG_SQFT07”>GHVEG_SQFT07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>384</td>

</tr> <tr>

<th>Unique (%)</th> <td>12.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>28.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>913</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>6341.8</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>785600</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>58.5%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram312383075173520244”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives312383075173520244,#minihistogram312383075173520244”
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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives312383075173520244”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles312383075173520244”

aria-controls=”quantiles312383075173520244” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram312383075173520244” aria-controls=”histogram312383075173520244”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common312383075173520244” aria-controls=”common312383075173520244”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme312383075173520244” aria-controls=”extreme312383075173520244”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles312383075173520244”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>0</td>

</tr> <tr>

<th>95-th percentile</th> <td>29075</td>

</tr> <tr>

<th>Maximum</th> <td>785600</td>

</tr> <tr>

<th>Range</th> <td>785600</td>

</tr> <tr>

<th>Interquartile range</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>36705</td>

</tr> <tr>

<th>Coef of variation</th> <td>5.7879</td>

</tr> <tr>

<th>Kurtosis</th> <td>247.25</td>

</tr> <tr>

<th>Mean</th> <td>6341.8</td>

</tr> <tr>

<th>MAD</th> <td>10644</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>14.192</td>

</tr> <tr>

<th>Sum</th> <td>14567000</td>

</tr> <tr>

<th>Variance</th> <td>1347300000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram312383075173520244”>

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IknntCwYcP8PR4AAEC9%2Bb1gDR06VB06dNCkSZN0%2B%2B23q0WLFrUek5KSoqNHj/p7NAAAACv8XrBWrFihvn37XvBxX3/9tR%2BmAQAAsM/v92DFxMRo8uTJnt8dKEn/%2BZ//qfvuu09ut9vf4wAAAFjn94I1d%2B5cHT9%2BXJ06dfKsDRgwQDU1NZo3b56/xwEAALDO75cIP/nkE61du1YtW7b0rLVv314LFizQrbfe6u9xAAAArPP7O1iVlZUKCwurPUhwsCoqKvw9DgAAgHV%2BL1h9%2BvTRvHnzdOzYMc/a999/r9mzZ3t9VAMAAECg8vslwpkzZ%2Bq3v/2trrvuOoWHh6umpkbl5eVq27atXn31VX%2BPAwAAYJ3fC1bbtm317rvv6qOPPtLBgwcVHBysDh06qH///goJCfH3OAAAANb5vWBJUpMmTXTTTTc1xqkBAAAanN8L1qFDh7Rw4UL94x//UGVlZa39Dz/80N8jAQAAWNUo92AVFRWpf//%2Batq0qb9PDwAA0OD8XrB27NihDz/8UFFRUf4%2BNQAAgF/4/WMaWrVqxTtXAADA0fxesCZNmqQlS5bIGOPvUwMAAPiF3y8RfvTRR/riiy/01ltv6aqrrlJwsHfHe/311/09EgAAgFV%2BL1jh4eG64YYb/H1aAAAAv/F7wZo7d66/TwkAAOBXfr8HS5L27dunxYsX65FHHvGsffnll40xCgAAgHV%2BL1jbt2/X8OHDtXHjRq1bt07Sjx8%2BOm7cOD5kFAAAOILfC9aiRYv0%2B9//XmvXrlVQUJCkH38/4bx587R06VJ/jwMAAGCd3wvW3//%2Bd6Wnp0uSp2BJ0s0336z8/Hx/jwMAAGCd3wtW8%2BbNz/o7CIuKitSkSRN/jwMAAGCd3wtWz5499fTTT6usrMyztn//fmVnZ%2Bu6667z9zgAAADW%2Bf1jGh555BHdc889SkpKUnV1tXr27KmKigp17txZ8%2BbN8/c4AAAA1vm9YF155ZVat26dtm3bpv379%2BvSSy9Vhw4d1K9fP697sgAAAAKV3wuWJF1yySW66aabGuPUAAAADc7vBWvgwIHnfaeKz8ICAACBzu8Fa8iQIV4Fq7q6Wvv375fL5dI999zj73EAAACs83vBmj59%2BlnX33//ff3lL3/x8zQAAAD2NcrvIjybm266Se%2B%2B%2B25jjwEAAFBvv5iCtXPnThljGnsMAACAevP7JcIxY8bUWquoqFB%2Bfr5SU1P9PQ4AAIB1fi9Y7du3r/VThGFhYRo1apRGjx7t73EAAACs83vB4tPaAQCA0/m9YL399ts%2BP/b2229vwEkAAAAaht8L1qOPPqqamppaN7QHBQV5rQUFBVGwAABAQPJ7wXrppZf08ssva/LkyYqJiZExRnv27NGLL76osWPHKikpyd8jAQAAWNUo92Dl5OSodevWnrXevXurbdu2mjBhgtatW%2BfvkQAAAKzy%2B%2BdgHThwQJGRkbXWIyIiVFBQ4O9xAAAArPN7wWrTpo3mzZunkpISz1ppaakWLlyoq6%2B%2B2t/jAAAAWOf3S4QzZ87UtGnTlJubq2bNmik4OFhlZWW69NJLtXTpUn%2BPAwAAYJ3fC1b//v21detWbdu2TYcPH5YxRq1bt9b111%2Bv5s2b%2B3scAAAA6/xesCTpsssu06BBg3T48GG1bdu2MUYAAABoMH6/B6uyslLZ2dnq0aOHbrnlFkk/3oM1ceJElZaW%2BnscAAAA6/xesObPn69du3ZpwYIFCg7%2B/9NXV1drwYIF/h4HAADAOr8XrPfff1/PPfecbr75Zs8vfY6IiNDcuXO1ceNGf48DAABgnd8LVnl5udq3b19rPSoqSidOnPD3OAAAANb5vWBdffXV%2Bstf/iJJXr978L333tOvfvWrOh3r448/VnJysrKysmrtrV%2B/XsOGDVOPHj00YsQIffLJJ569mpoaLVq0SIMGDVKfPn00YcIEHTp0yLPvdruVmZmp5ORk9e/fX48%2B%2BqgqKyvrGhUAAPyT8nvBuuuuuzRlyhQ988wzqqmp0SuvvKJp06Zp5syZuueee3w%2BzosvvqinnnpK7dq1q7W3a9cuZWdna/r06fr00081fvx4PfTQQzp8%2BLAkaeXKlVq7dq1ycnK0ZcsWtW/fXhkZGZ7CN2vWLFVUVGjdunV68803lZ%2Bfz/1hAADAZ34vWGlpacrOztann36qkJAQvfDCCyooKNCCBQuUnp7u83HCwsK0evXqsxasVatWKSUlRSkpKQoLC9Pw4cPVpUsXrVmzRpKUm5ur8ePHq2PHjgoPD1dWVpby8/P19ddf64cfftCmTZuUlZWlqKgotW7dWg8%2B%2BKDefPNNVVVVWfs%2BAAAA5/L752AdPXpUI0eO1MiRI%2Bt1nHHjxp1zLy8vTykpKV5rsbGxcrlcqqys1N69exUbG%2BvZCw8PV7t27eRyuXT8%2BHGFhIQoJibGsx8XF6cTJ05o3759XusAAABn4/eCNWjQIH3xxReenyBsCG63u9YvlI6MjNTevXt17NgxGWPOul9SUqIWLVooPDzca74zj/3p7088n6KiIhUXF3uthYY2VXR09MXEOaeQEL%2B/AVkvoaG%2Bz3smW6Bl9JWT8zk5m0S%2BQObkbJKz8wViNr8XrKSkJG3YsEFDhgxp0PP89Ab6uu5f6LkXkpubqyVLlnitZWRkaOrUqfU6bqBr2bJZnZ8TEXFZA0zyy%2BHkfE7OJpEvkDk5m%2BTsfIGUze8F61/%2B5V/07//%2B78rJydHVV1%2BtSy65xGt/4cKF9T5Hy5Yt5Xa7vdbcbreioqLUokULBQcHn3W/VatWioqKUllZmaqrqxUSEuLZk6RWrVr5dP60tDQNHDjQay00tKlKSsovNtJZBVKTl1Sn/CEhwYqIuEylpRWqrq5pwKkah5PzOTmbRL5A5uRskrPz1Sfbxfzj3ga/F6y9e/fq17/%2BtSTfL7nVVXx8vHbs2OG15nK5NHToUIWFhalz587Ky8tT3759Jf34q3oOHjyoxMREtWnTRsYY7d69W3FxcZ7nRkREqEOHDj6dPzo6utblwOLi4zp92lkv%2BLq6mPzV1TWO/r45OZ%2BTs0nkC2ROziY5O18gZfNbwcrKytKiRYv06quvetaWLl2qjIwM6%2Be68847NWrUKG3dulXXXXed1q5dqwMHDmj48OGSpPT0dOXk5OiGG25Q69attWDBAnXt2lUJCQmSpMGDB%2BtPf/qTnnnmGZ06dUpLly7VqFGjFBraKL8bGwAABBi/NYbNmzfXWsvJybnognWmDJ0%2BfVqStGnTJkk/vtvUpUsXLViwQHPnzlVBQYE6deqkZcuW6YorrpAkjRkzRsXFxbr77rtVXl6upKQkr3umnnzyST3%2B%2BOMaNGiQLrnkEt16661n/TBTAACAs/FbwTrbjeP1uZnc5XKddz81NVWpqaln3QsKCtLUqVPPedN58%2BbN9cc//vGiZwMAAP/c/HaX9Nk%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%2BPV0JCgudrzpw5kqTt27dr1KhR6tmzp4YOHao1a9Z4PXfFihUaPHiwevbsqfT0dO3YsaMxIgAAgAAV2tgDNKT33ntPV111lddaUVGRHnzwQT366KMaNmyYPv/8cz3wwAPq0KGDEhIStHnzZi1evFgvvfSSYmJitGLFCk2ePFkbN25U06ZNGykJAAAIJI59B%2Btc1q5dq/bt22vUqFEKCwtTcnKyBg4cqFWrVkmScnNzNWLECHXr1k2XXnqpJk6cKEnasmVLY44NAAACiKML1sKFCzVgwAD17t1bs2bNUnl5ufLy8hQbG%2Bv1uNjYWM9lwJ/vBwcHq2vXrnK5XH6dHQAABC7HXiLs3r27kpOT9cwzz%2BjQoUPKzMzU7Nmz5Xa71bp1a6/HtmjRQiUlJZIkt9utyMhIr/3IyEjPvi%2BKiopUXFzstRYa2lTR0dEXmebsQkICqx%2BHhvo%2B75lsgZbRV07O5%2BRsEvkCmZOzSc7OF4jZHFuwcnNzPf/dsWNHTZ8%2BXQ888IB69ep1wecaY%2Bp97iVLlnitZWRkaOrUqfU6bqBr2bJZnZ8TEXFZA0zyy%2BHkfE7OJpEvkDk5m%2BTsfIGUzbEF6%2BeuuuoqVVdXKzg4WG6322uvpKREUVFRkqSWLVvW2ne73ercubPP50pLS9PAgQO91kJDm6qkpPwipz%2B7QGrykuqUPyQkWBERl6m0tELV1TUNOFXjcHI%2BJ2eTyBfInJxNcna%2B%2BmS7mH/c2%2BDIgrVz506tWbNGM2bM8Kzl5%2BerSZMmSklJ0Z///Gevx%2B/YsUPdunWTJMXHxysvL0933HGHJKm6ulo7d%2B7UqFGjfD5/dHR0rcuBxcXHdfq0s17wdXUx%2Bauraxz9fXNyPidnk8gXyJycTXJ2vkDKFlhvgfioVatWys3NVU5Ojk6dOqX9%2B/fr2WefVVpamm677TYVFBRo1apVOnnypLZt26Zt27bpzjvvlCSlp6fr7bff1ldffaWKigo9//zzatKkiQYMGNC4oQAAQMBw5DtYrVu3Vk5OjhYuXOgpSHfccYeysrIUFhamZcuW6amnntLs2bPVpk0bzZ8/X9dcc40k6YYbbtDDDz%2BszMxMHTlyRAkJCcrJydGll17ayKkAAECgcGTBkqQ%2Bffro9ddfP%2BfeO%2B%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%2BfNXU1DT2WAAAIECENvYAv0RTpkxRXFycNm3apCNHjmjSpEm6/PLLde%2B99zb2aAAAIABQsH7G5XJp9%2B7deuWVV9S8eXM1b95c48eP1/LlyylY9XTLn/6nsUeokw2Z/Rp7BABAgKJg/UxeXp7atGmjyMhIz1pcXJz279%2BvsrIyhYeHX/AYRUVFKi4u9loLDW2q6Ohoq7OGhHCFtyEFWiH8YPr1jT2CpP9/XTr19Um%2BwOXkbJKz8wViNgrWz7jdbkVERHitnSlbJSUlPhWs3NxcLVmyxGvtoYce0pQpU%2BwNqh%2BL3D1X/kNpaWnWy1tjKyoqUm5uriOzSc7OV1RUpOXLX3JkNol8gczJ2SRn5wvEbIFTBf3IGFOv56elpemtt97y%2BkpLS7M03f8rLi7WkiVLar1b5gROziY5O5%2BTs0nkC2ROziY5O18gZuMdrJ%2BJioqS2%2B32WnO73QoKClJUVJRPx4iOjg6Yhg0AAOzjHayfiY%2BPV2FhoY4ePepZc7lc6tSpk5o1a9aIkwEAgEBBwfqZ2NhYJSQkaOHChSorK1N%2Bfr5eeeUVpaenN/ZoAAAgQIQ88cQTTzT2EL80119/vdatW6c5c%2Bbo3Xff1ahRozRhwgQFBQU19mi1NGvWTH379nXku2sDPMWKAAANEUlEQVROziY5O5%2BTs0nkC2ROziY5O1%2BgZQsy9b2jGwAAAF64RAgAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQUrABUUFOj%2B%2B%2B9XUlKSbrzxRs2fP181NTWNOtPHH3%2Bs5ORkZWVl1dpbv369hg0bph49emjEiBH65JNPPHs1NTVatGiRBg0apD59%2BmjChAk6dOiQZ9/tdiszM1PJycnq37%2B/Hn30UVVWVnr2d%2B3apbFjx6pXr15KTU3Vyy%2B/7PO5fVVQUKCMjAwlJSUpOTlZM2bMUGlpab3P39DZfbV7927dc8896tWrl5KTk5WZmani4mJJ0vbt2zVq1Cj17NlTQ4cO1Zo1a7yeu2LFCg0ePFg9e/ZUenq6duzY4dk7efKk/u3f/k033HCDkpKSNHXqVJWUlHh9X8/3Or7Quevq6aefVkxMjM/HD4RsMTExio%2BPV0JCgudrzpw5jsknSc8//7z69%2B%2Bv7t27a/z48fr2228DPt9f//pXr7%2BzhIQExcfHe16fgZztjJ07d2rcuHHq3bu3%2BvXrp%2BnTp%2Bvo0aOOyecTg4Bzxx13mMcee8yUlpaa/fv3m9TUVPPyyy832jw5OTkmNTXVjBkzxmRmZnrt7dy508THx5utW7eayspK884775hu3bqZwsJCY4wxK1asMDfeeKPZu3evOX78uHnyySfNsGHDTE1NjTHGmIceesjcf//95siRI%2Bbw4cMmLS3NzJkzxxhjTEVFhbn%2B%2BuvN4sWLTXl5udmxY4fp27evef/99306t69uvfVWM2PGDFNWVmYKCwvNiBEjzMyZM%2Bt9/obM7quTJ0%2Ba6667zixZssScPHnSHDlyxIwdO9Y8%2BOCD5vvvvzfdu3c3q1atMpWVleZ//ud/TGJiovnmm2%2BMMcZ8%2BOGHpnfv3uarr74yFRUVZtmyZaZfv36mvLzcGGPM3LlzzYgRI8x3331nSkpKzEMPPWQmTZrkOff5XscXOndd7dy50/Tt29d06dLFp%2BMHSrYuXbqYQ4cO1Vp3Sr7XXnvN3HzzzSY/P98cP37czJkzx8yZM8cx%2BX7q%2BeefN7/73e8cka2qqsr069fPLFy40Jw8edIcPXrU3HvvvWbKlCmOyOcrClaA%2Beabb0zXrl2N2%2B32rP3Xf/2XGTx4cKPNtHz5clNaWmqys7NrFazZs2ebjIwMr7XRo0ebZcuWGWOMGTp0qFm%2BfLln7/jx4yY2NtZ8%2BeWXpri42FxzzTVm165dnv1t27aZ7t27m1OnTpkNGzaYa6%2B91pw%2BfdqzP3/%2BfPPb3/7Wp3P74tixY2bGjBmmuLjYs/bqq6%2Ba1NTUep%2B/IbP7yu12mzfeeMNUVVV51pYvX25%2B85vfmJdeesncfvvtXo/PzMw0s2bNMsYYc//995unn37as1ddXW369etn1q1bZ6qqqkyvXr3Mpk2bPPt79%2B41MTEx5vDhwxd8HV/o3HVRXV1tRo8ebf7jP/7DU7Ccku1cBcsp%2BQYOHHjWfzQ4Jd8ZBQUFpm/fvqagoMAR2b777jvTpUsXs3fvXq/z3HTTTY7I5ysuEQaYvLw8tWnTRpGRkZ61uLg47d%2B/X2VlZY0y07hx49S8efOz7uXl5Sk2NtZrLTY2Vi6XS5WVldq7d6/Xfnh4uNq1ayeXy6Vdu3YpJCTE67JOXFycTpw4oX379ikvL08xMTEKCQnxOvaZt5PPd25fRUREaO7cubr88ss9a4WFhYqOjq7X%2BRs6u68iIyM1evRohYaGSpL27dunP//5z7rlllvOOf%2B58gUHB6tr165yuVw6ePCgjh8/rri4OM9%2Bx44ddemllyovL%2B%2BCr%2BMLnbsuXn/9dYWFhWnYsGGeNadkk6SFCxdqwIAB6t27t2bNmqXy8nJH5Pv%2B%2B%2B/17bff6tixYxoyZIjnctDRo0cdke%2Bnnn32WY0cOVK/%2BtWvHJGtdevW6tq1q3Jzc1VeXq4jR45o48aNGjBggCPy%2BYqCFWDcbrciIiK81s68mH56HfqXwu12e73YpR/nLSkp0bFjx2SMOee%2B2%2B1WeHi4goKCvPYkefZ//r1o0aKF3G63ampqznvui%2BVyufTaa6/pgQceqNf5Gzp7XRUUFCg%2BPl5DhgxRQkKCpk6des5znPn%2BnS%2Bf2%2B2WpFrPj4iIOOf8vuSr69/dDz/8oMWLF%2Bvxxx/3WndCNknq3r27kpOTtXHjRuXm5uqrr77S7NmzHZHv8OHDkqT33ntPr7zyit555x0dPnxYjz32mCPynfHtt99q48aNuvfeez2zB3q24OBgLV68WB9%2B%2BKF69uyp5ORknT59WtOmTXNEPl9RsAKQMaaxR6iTC817vv2LyfrTUmLze/X5559rwoQJmjZtmpKTk62cvyGz10WbNm3kcrn03nvv6cCBA/rDH/7g0/P8na%2Bu5s6dqxEjRqhTp051fu4vPZsk5ebmavTo0WrSpIk6duyo6dOna926daqqqrrgc3/p%2Bc6cY%2BLEiWrdurWuvPJKTZkyRZs3b67T8y9m35//j125cqVSU1N1xRVX%2BPycX3q2U6dOafLkybr55pv1t7/9TR999JGaN2%2Bu6dOn%2B/T8X3o%2BX1GwAkxUVJSnxZ/hdrsVFBSkqKioRprq3Fq2bHnWeaOiotSiRQsFBwefdb9Vq1aKiopSWVmZqqurvfYkefZ//i8Pt9vtOe75zl1Xmzdv1v3336%2BZM2dq3LhxklSv8zd09osRFBSk9u3bKysrS%2BvWrVNoaGit%2BUpKSjzfv/PlO/OYn%2B8fO3bMM//5XsdnO/ZPz%2B2L7du368svv1RGRkatvQsd/5ee7VyuuuoqVVdXn/W1FWj5zlyW/%2Bk7Dm3atJExRlVVVQGf74z3339fAwcO9PzZCa/N7du369tvv9XDDz%2Bs5s2bq3Xr1po6dao%2B%2BOADR7w2fUXBCjDx8fEqLCz0/Lir9ONlq06dOqlZs2aNONnZxcfH17q%2B7XK51K1bN4WFhalz587Ky8vz7JWWlurgwYNKTExU165dZYzR7t27vZ4bERGhDh06KD4%2BXnv27NHp06drHftC566LL774QtnZ2Xr22Wd1%2B%2B23e2W72PM3dHZfbd%2B%2BXYMHD/a6rHimoCUmJtaaf8eOHV75fjp/dXW1du7cqW7duqlt27aKjIz02v/73/%2BuU6dOKT4%2B/oKv44SEhPOe2xdr1qzRkSNHdOONNyopKUkjRoyQJCUlJalLly4BnU368cfg582b57WWn5%2BvJk2aKCUlJeDzXXnllQoPD9euXbs8awUFBbrkkksckU/68aNWCgoK1K9fP8/ahY4fCNmqq6tVU1Pj9W7SqVOnJEnJyckBn89nDXLrPBrU6NGjzcyZM83x48fN3r17zcCBA81rr73W2GOd9acI9%2BzZYxISEsyWLVtMZWWlWbVqlenRo4cpKioyxvz4Ex4DBgzwfFTBrFmzzMiRIz3Pz8zMNBMnTjRHjhwxhYWFZuTIkWbevHnGmB8/YuDGG280zz33nDlx4oT56quvTO/evc2WLVt8OrcvqqqqzC233GJef/31Wnv1PX9DZvdVaWmpSU5ONvPmzTMnTpwwR44cMRMmTDB33XWX%2BeGHH0yPHj3MG2%2B8YSorK83WrVtNYmKi5ycbt23bZnr16mW%2B/PJLc%2BLECbN48WKTkpJiKioqjDE//lTjHXfcYb777jtz9OhRM2nSJDNlyhTPuc/3Or7QuX3hdrtNYWGh5%2BvLL780Xbp0MYWFhaagoCCgsxljzOHDh0337t3NsmXLzMmTJ82%2BffvMkCFDzJw5cwL%2B7%2B6Mp59%2B2gwaNMgcOHDA/PDDDyYtLc3MmDHDMflWr15t%2Bvbt67XmhGxHjx41ffv2NX/84x/NiRMnzNGjR83kyZPNv/7rvzoin68oWAGosLDQTJw40SQmJprk5GTz3HPPeT47qTHEx8eb%2BPh4c80115hrrrnG8%2Bcz3n//fZOammri4uLMbbfdZj777DPPXk1NjXn22WfNddddZxITE819993n9TlVpaWlJisry3Tv3t306dPHzJ4925w8edKzv2fPHjNmzBgTHx9vBgwYYFauXOk12/nO7Yu//vWvpkuXLp5MP/369ttv63X%2Bhs7uq927d5uxY8eaxMREc%2B2115rMzExz%2BPBhY4wxn332mRk%2BfLiJi4szqamptX5kfuXKlSYlJcXEx8eb9PR0s2fPHs/eyZMnzRNPPGH69OljevToYR5%2B%2BGFTWlrq2b/Q6/hC566rQ4cOeT6mwSnZPvvsM5OWlma6d%2B9u%2Bvbta%2BbOnWsqKysdk%2B%2Bnc3Tv3t1kZ2ebsrIyx%2BR74YUXzNChQ2utOyGby%2BUyY8eONb179zbJycmO/f/K%2BQQZ8wu6IwwAAMABuAcLAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACz7P3hO6rbnpoHCAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common312383075173520244”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1878</td> <td class=”number”>58.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8400.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4200.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>24000.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5200.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2160.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>18300.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7950.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8640.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>22030.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (373)</td> <td class=”number”>396</td> <td class=”number”>12.3%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>913</td> <td class=”number”>28.4%</td> <td>

<div class=”bar” style=”width:49%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme312383075173520244”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1878</td> <td class=”number”>58.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>21.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>85.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>90.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>171.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>439552.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>541749.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>613540.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>773748.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>785600.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_GHVEG_SQFT12”>GHVEG_SQFT12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>783</td>

</tr> <tr>

<th>Unique (%)</th> <td>24.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>30.1%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>966</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>21460</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>4650000</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>44.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram2541325018267275574”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives2541325018267275574,#minihistogram2541325018267275574”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives2541325018267275574”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles2541325018267275574”

aria-controls=”quantiles2541325018267275574” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram2541325018267275574” aria-controls=”histogram2541325018267275574”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common2541325018267275574” aria-controls=”common2541325018267275574”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme2541325018267275574” aria-controls=”extreme2541325018267275574”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles2541325018267275574”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>10662</td>

</tr> <tr>

<th>95-th percentile</th> <td>69646</td>

</tr> <tr>

<th>Maximum</th> <td>4650000</td>

</tr> <tr>

<th>Range</th> <td>4650000</td>

</tr> <tr>

<th>Interquartile range</th> <td>10662</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>177490</td>

</tr> <tr>

<th>Coef of variation</th> <td>8.2711</td>

</tr> <tr>

<th>Kurtosis</th> <td>505.06</td>

</tr> <tr>

<th>Mean</th> <td>21460</td>

</tr> <tr>

<th>MAD</th> <td>31948</td>

</tr> <tr class=”alert”>

<th>Skewness</th> <td>21.474</td>

</tr> <tr>

<th>Sum</th> <td>48155000</td>

</tr> <tr>

<th>Variance</th> <td>31504000000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram2541325018267275574”>

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IMHDzpQDQAAgH2OX4MVGxurp556Sjt37vSO/du//ZuefPJJeTwep8sBAACwzvGAtWTJEp0/f14dO3b0jvXv3181NTVaunSp0%2BUAAABY5/hHhJ999pk2b96sqKgo71i7du2UlZWlRx991OlyAAAArHP8DFZVVZVCQ0NrFxIcrMrKSqfLAQAAsM7xgNW7d28tXbpU586d84799a9/1aJFi3xu1QAAABCoHP%2BIcP78%2BfrpT3%2Bqvn37KiwsTDU1NaqoqFCbNm30zjvvOF0OAACAdY4HrDZt2uijjz7Sp59%2BqpMnTyo4OFjt27dXv379FBIS4nQ5AAAA1jkesCTp9ttv14MPPuiPpQEAABqc4wHr1KlTWr58uf785z%2Brqqqq1vwnn3zidEkAAABW%2BeUarOLiYvXr109NmzZ1enkAAIAG53jAys3N1SeffKLo6GinlwYAAHCE47dpaNGiBWeuAABAo%2BZ4wJo%2BfbpWrVolY4zTSwMAADjC8Y8IP/30U33xxRfauHGj7rrrLgUH%2B2a89957z%2BmSAAAArHI8YIWFhemBBx5welkAAADHOB6wlixZ4vSSAAAAjnL8GixJOnbsmFauXKnnnnvOO3bgwAF/lAIAAGCd4wFr//79GjFihHbs2KEtW7ZI%2Bvbmo5MmTeImowAAoFFwPGCtWLFCP/vZz7R582YFBQVJ%2BvbvEy5dulSrV692uhwAAADrHA9Yf/rTn5SWliZJ3oAlSQ8//LAKCgqcLgcAAMA6xwNW8%2BbNr/k3CIuLi3X77bc7XQ4AAIB1jgesnj176uWXX1Z5ebl37Pjx45o7d6769u3rdDkAAADWOX6bhueee05PPPGEkpOTVV1drZ49e6qyslKdOnXS0qVLnS4HAADAOscD1g9%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%2B/PmaPXu2cnJy1KxZMwUHB6u8vFxNmjTR6tWrnS4HAADAOscDVr9%2B/bRnzx7t3btXp0%2BfljFGrVq10v3336/mzZs7XQ4AAIB1jgcsSbrjjjs0aNAgnT59Wm3atPFHCQAAAA3G8WuwqqqqNHfuXPXo0UOPPPKIpG%2BvwZo6darKysqcLgcAAMA6xwPWsmXLdPjwYWVlZSk4%2BP%2BXr66uVlZWltPlAAAAWOd4wNq%2Bfbtee%2B01Pfzww94/%2BhweHq4lS5Zox44df9ex9u3bp5SUFGVmZtaa27p1q4YPH64ePXpo9OjR%2Buyzz7xzNTU1WrFihQYNGqTevXtrypQpOnXqlHfe4/EoIyNDKSkp6tevnxYsWKCqqqrvuWMAAHCrcTxgVVRUqF27drXGo6OjdeHChTof54033tDixYvVtm3bWnOHDx/W3LlzNWfOHP3%2B97/X5MmTNXPmTJ0%2BfVqStG7dOm3evFnZ2dnavXu32rVrp/T0dO%2Bf6lm4cKEqKyu1ZcsWffDBByooKODsGgAAqDPHA9bdd9%2BtP/zhD5Lk87cHP/74Y/3jP/5jnY8TGhqqDRs2XDNgrV%2B/XqmpqUpNTVVoaKhGjBihzp07a9OmTZKknJwcTZ48WR06dFBYWJgyMzNVUFCggwcP6uuvv9bOnTuVmZmp6OhotWrVSk8//bQ%2B%2BOADXbp0qZ67BwAAtwLHf4vwxz/%2BsZ555hmNGTNGNTU1%2BvWvf63c3Fxt375dCxYsqPNxJk2adN25vLw8paam%2BozFxcXJ7XarqqpK%2Bfn5iouL886FhYWpbdu2crvdOn/%2BvEJCQhQbG%2Budj4%2BP14ULF3Ts2DGfcQAAgGtxPGCNHz9eLpdL7777rkJCQvSrX/1K7du3V1ZWlh5%2B%2BGEra3g8nlp/jiciIkL5%2Bfk6d%2B6cjDHXnC8tLVVkZKTCwsK814ddmZPkc/f571JcXKySkhKfMZerqWJiYr7Pdq4rJMTxE5D14nIFVr3Xc6Xvgdb/QEffnUfP/YO%2BNw6OB6yzZ89qzJgxGjNmTIOu87cfP/698zd67o3k5ORo1apVPmPp6emaNWtWvY4b6KKimvm7BKvCw%2B/wdwm3JPruPHruH/Q9sDkesAYNGqQvvvjC5wyRbVFRUfJ4PD5jHo9H0dHRioyMVHBw8DXnW7RooejoaJWXl6u6ulohISHeOUlq0aJFndYfP368Bg4c6DPmcjVVaWnF993SNQXauxvb%2B/eXkJBghYffobKySlVX1/i7nFsGfXcePfcP%2Bm6Xv97cOx6wkpOTtW3bNg0dOrTB1khISFBubq7PmNvt1rBhwxQaGqpOnTopLy9Pffr0kfTtjU5PnjyppKQktW7dWsYYHTlyRPHx8d7nhoeHq3379nVaPyYmptbHgSUl53X58q39jdLY9l9dXdPo9hQI6Lvz6Ll/0PfA5njA%2Bod/%2BAf9y7/8i7Kzs3X33Xfrtttu85lfvnx5vdd47LHHNHbsWO3Zs0d9%2B/bV5s2bdeLECY0YMUKSlJaWpuzsbD3wwANq1aqVsrKy1LVrVyUmJkqShgwZol/%2B8pd65ZVX9M0332j16tUaO3asXC6//GUhAAAQYBxPDPn5%2BfrhD38oqe4XjV/LlTB0%2BfJlSdLOnTslfXu2qXPnzsrKytKSJUtUWFiojh07as2aNWrZsqUkacKECSopKdHjjz%2BuiooKJScn%2B1wz9eKLL%2Br555/XoEGDdNttt%2BnRRx%2B95s1MAQAAriXI1PeK7jrKzMzUihUrfMZWr16t9PR0J5b3u5KS89aP6XIF66GsfdaP21C2Zdzn7xKscLmCFRXVTKWlFZy%2BdxB9dx499w/6blfLls39sq5jV0nv2rWr1lh2drZTywMAADjGsYB1rRNlDp08AwAAcJRjAetat2VoyFs1AAAA%2BEtg3UgJAAAgABCwAAAALHPsNg2XLl3S7Nmzbzhm4z5YAAAA/uRYwOrVq5eKi4tvOAYAABDoHAtY77zzjlNLAQAA%2BBXXYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFjWaANWbGysEhISlJiY6P166aWXJEn79%2B/X2LFj1bNnTw0bNkybNm3yee7atWs1ZMgQ9ezZU2lpacrNzfXHFgAAQIBy%2BbuAhvTxxx/rrrvu8hkrLi7W008/rQULFmj48OH6/PPPNWPGDLVv316JiYnatWuXVq5cqTfffFOxsbFau3atnnrqKe3YsUNNmzb1004AAEAgabRnsK5n8%2BbNateuncaOHavQ0FClpKRo4MCBWr9%2BvSQpJydHo0ePVrdu3dSkSRNNnTpVkrR7925/lg0AAAJIow5Yy5cvV//%2B/XXPPfdo4cKFqqioUF5enuLi4nweFxcX5/0Y8Or54OBgde3aVW6329HaAQBA4Gq0HxF2795dKSkpeuWVV3Tq1CllZGRo0aJF8ng8atWqlc9jIyMjVVpaKknyeDyKiIjwmY%2BIiPDO10VxcbFKSkp8xlyupoqJifmeu7m2kJDAyscuV2DVez1X%2Bh5o/Q909N159Nw/6Hvj0GgDVk5Ojve/O3TooDlz5mjGjBnq1avXDZ9rjKn32qtWrfIZS09P16xZs%2Bp13EAXFdXM3yVYFR5%2Bh79LuCXRd%2BfRc/%2Bg74Gt0Qasq911112qrq5WcHCwPB6Pz1xpaamio6MlSVFRUbXmPR6POnXqVOe1xo8fr4EDB/qMuVxNVVpa8T2rv7ZAe3dje//%2BEhISrPDwO1RWVqnq6hp/l3PLoO/Oo%2Bf%2BQd/t8teb%2B0YZsA4dOqRNmzZp3rx53rGCggLdfvvtSk1N1W9%2B8xufx%2Bfm5qpbt26SpISEBOXl5WnUqFGSpOrqah06dEhjx46t8/oxMTG1Pg4sKTmvy5dv7W%2BUxrb/6uqaRrenQEDfnUfP/YO%2BB7bAOgVSRy1atFBOTo6ys7P1zTff6Pjx43r11Vc1fvx4/ehHP1JhYaHWr1%2Bvixcvau/evdq7d68ee%2BwxSVJaWpo%2B/PBDffnll6qsrNTrr7%2Bu22%2B/Xf379/fvpgAAQMBolGewWrVqpezsbC1fvtwbkEaNGqXMzEyFhoZqzZo1Wrx4sRYtWqTWrVtr2bJl6tKliyTpgQce0LPPPquMjAydOXNGiYmJys7OVpMmTfy8KwAAECiCTH2v6EadlJSct35MlytYD2Xts37chrIt4z5/l2CFyxWsqKhmKi2t4PS9g%2Bi78%2Bi5f9B3u1q2bO6XdRvlR4QAAAD%2BRMACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyAdQ2FhYWaNm2akpOTNWDAAC1btkw1NTX%2BLgsAAAQIl78LuBk988wzio%2BP186dO3XmzBlNnz5dd955p37yk5/4uzQAABAAOIN1FbfbrSNHjmjOnDlq3ry52rVrp8mTJysnJ8ffpQEAgADBGayr5OXlqXXr1oqIiPCOxcfH6/jx4yovL1dYWNgNj1FcXKySkhKfMZerqWJiYqzWGhISWPnY5Qqseq/nSt8Drf%2BBjr47j577B31vHAhYV/F4PAoPD/cZuxK2SktL6xSwcnJytGrVKp%2BxmTNn6plnnrFXqL4Nck/84M8aP3689fCG6ysuLtbbb79J3x1G351Hz/2DvjcOxONrMMbU6/njx4/Xxo0bfb7Gjx9vqbr/V1JSolWrVtU6W4aGRd/9g747j577B31vHDiDdZXo6Gh5PB6fMY/Ho6CgIEVHR9fpGDExMbzrAADgFsYZrKskJCSoqKhIZ8%2Be9Y653W517NhRzZo182NlAAAgUBCwrhIXF6fExEQtX75c5eXlKigo0K9//WulpaX5uzQAABAgQl544YUX/F3Ezeb%2B%2B%2B/Xli1b9NJLL%2Bmjjz7S2LFjNWXKFAUFBfm7tFqaNWumPn36cHbNYfTdP%2Bi78%2Bi5f9D3wBdk6ntFNwAAAHzwESEAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQSsAFRYWKhp06YpOTlZAwYM0LJly1RTU%2BPvsm4a%2B/btU0pKijIzM2vNbd26VcOHD1ePHj00evRoffbZZ965mpoarVixQoMGDVLv3r01ZcoUnTonPxOEAAAJM0lEQVR1yjvv8XiUkZGhlJQU9evXTwsWLFBVVZV3/vDhw5o4caJ69eqlwYMH66233rK29s2usLBQ6enpSk5OVkpKiubNm6eysjJJDduXhnxNAsGRI0f0xBNPqFevXkpJSVFGRoZKSkokSfv379fYsWPVs2dPDRs2TJs2bfJ57tq1azVkyBD17NlTaWlpys3N9c5dvHhR//zP/6wHHnhAycnJmjVrlkpLS73zN/oZVJ%2B1A8nLL7%2Bs2NhY77/pOXwYBJxRo0aZX/ziF6asrMwcP37cDB482Lz11lv%2BLuumkJ2dbQYPHmwmTJhgMjIyfOYOHTpkEhISzJ49e0xVVZX57W9/a7p162aKioqMMcasXbvWDBgwwOTn55vz58%2BbF1980QwfPtzU1NQYY4yZOXOmmTZtmjlz5ow5ffq0GT9%2BvHnppZeMMcZUVlaa%2B%2B%2B/36xcudJUVFSY3Nxc06dPH7N9%2B3Yra9/sHn30UTNv3jxTXl5uioqKzOjRo838%2BfMbvC8N%2BZrc7C5evGj69u1rVq1aZS5evGjOnDljJk6caJ5%2B%2Bmnz17/%2B1XTv3t2sX7/eVFVVmf/8z/80SUlJ5quvvjLGGPPJJ5%2BYe%2B65x3z55ZemsrLSrFmzxtx3332moqLCGGPMkiVLzOjRo81f/vIXU1paambOnGmmT5/uXfu7fgbVd%2B1AcejQIdOnTx/TuXNnY0z9903PGx8CVoD56quvTNeuXY3H4/GO/fu//7sZMmSIH6u6ebz99tumrKzMzJ07t1bAWrRokUlPT/cZGzdunFmzZo0xxphhw4aZt99%2B2zt3/vx5ExcXZw4cOGBKSkpMly5dzOHDh73ze/fuNd27dzfffPON2bZtm7n33nvN5cuXvfPLli0zP/3pT%2Bu99s3u3LlzZt68eaakpMQ79s4775jBgwc3aF8a%2BjW52Xk8HvP%2B%2B%2B%2BbS5cuecfefvtt89BDD5k333zTjBw50ufxGRkZZuHChcYYY6ZNm2Zefvll71x1dbW57777zJYtW8ylS5dMr169zM6dO73z%2Bfn5JjY21pw%2BffqGP4Pqs3agqK6uNuPGjTP/%2Bq//6g1Y9BxX4yPCAJOXl6fWrVsrIiLCOxYfH6/jx4%2BrvLzcj5XdHCZNmqTmzZtfcy4vL09xcXE%2BY3FxcXK73aqqqlJ%2Bfr7PfFhYmNq2bSu3263Dhw8rJCTE5%2BOA%2BPh4XbhwQceOHVNeXp5iY2MVEhLic%2Bwrp%2BHrs/bNLjw8XEuWLNGdd97pHSsqKlJMTEyD9qUhX5NAEBERoXHjxsnlckmSjh07pt/85jd65JFHrru36%2B09ODhYXbt2ldvt1smTJ3X%2B/HnFx8d75zt06KAmTZooLy/vhj%2BD6rN2oHjvvfcUGhqq4cOHe8foOa5GwAowHo9H4eHhPmNXvun%2B9vN61ObxeHx%2BQEnf9q60tFTnzp2TMea68x6PR2FhYQoKCvKZk%2BSdv/p1iYyMlMfjUU1NTb3WDjRut1vvvvuuZsyY0aB9acjXJJAUFhYqISFBQ4cOVWJiombNmnXdvV/Z23ft3ePxSFKt54eHh1%2B3r3Xpe13WDgRff/21Vq5cqeeff95nnJ7jagSsAGSM8XcJAetGvfuu%2Be/T97/9n3991g4Un3/%2BuaZMmaLZs2crJSXluo%2Bz1ZeGfk0CQevWreV2u/Xxxx/rxIkT%2BvnPf16n5zndd5vP96clS5Zo9OjR6tix49/9XHp%2BayFgBZjo6Gjvu50rPB6PgoKCFB0d7aeqAkNUVNQ1excdHa3IyEgFBwdfc75FixaKjo5WeXm5qqurfeYkeeevfjfo8Xi8x63P2oFi165dmjZtmubPn69JkyZJUoP2pSFfk0ATFBSkdu3aKTMzU1u2bJHL5aq1t9LSUu/evmvvVx5z9fy5c%2Be8ff2un0HXOnZd177Z7d%2B/XwcOHFB6enqtufrsm543TgSsAJOQkKCioiKdPXvWO%2BZ2u9WxY0c1a9bMj5Xd/BISEmr9arLb7Va3bt0UGhqqTp06KS8vzztXVlamkydPKikpSV27dpUxRkeOHPF5bnh4uNq3b6%2BEhAQdPXpUly9frnXs%2Bq4dCL744gvNnTtXr776qkaOHOkdb8i%2BNORrEgj279%2BvIUOG%2BPyqfnDwtz/Sk5KSau0tNzfXZ%2B9/29fq6modOnRI3bp1U5s2bRQREeEz/6c//UnffPONEhISbvgzKDEx8XuvfbPbtGmTzpw5owEDBig5OVmjR4%2BWJCUnJ6tz5870HL4cvKAelowbN87Mnz/fnD9/3uTn55uBAwead999199l3VSu9VuER48eNYmJiWb37t2mqqrKrF%2B/3vTo0cMUFxcbY779rZz%2B/ft7bwmwcOFCM2bMGO/zMzIyzNSpU82ZM2dMUVGRGTNmjFm6dKkx5ttfmR8wYIB57bXXzIULF8yXX35p7rnnHrN7924ra9/MLl26ZB555BHz3nvv1Zpr6L405GtysysrKzMpKSlm6dKl5sKFC%2BbMmTNmypQp5sc//rH5%2BuuvTY8ePcz7779vqqqqzJ49e0xSUpL3Ny737t1revXqZQ4cOGAuXLhgVq5caVJTU01lZaUx5tvfthw1apT5y1/%2BYs6ePWumT59unnnmGe/a3/UzqL5r38w8Ho8pKiryfh04cMB07tzZFBUVmcLCQnoOHwSsAFRUVGSmTp1qkpKSTEpKinnttdcC5n5JDS0hIcEkJCSYLl26mC5dunj/fcX27dvN4MGDTXx8vPnRj35k/vjHP3rnampqzKuvvmr69u1rkpKSzJNPPulzT6SysjKTmZlpunfvbnr37m0WLVpkLl686J0/evSomTBhgklISDD9%2B/c369at86mtPmvfzP77v//bdO7c2dvrv/363//93wbtS0O%2BJoHgyJEjZuLEiSYpKcnce%2B%2B9JiMjw5w%2BfdoYY8wf//hHM2LECBMfH28GDx7svf/XFevWrTOpqakmISHBpKWlmaNHj3rnLl68aF544QXTu3dv06NHD/Pss8%2BasrIy7/yNfgbVZ%2B1AcurUKe9tGoyh5/AVZAxXvgEAANjENVgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYNn/Abm%2B/X5%2BWWDqAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common2541325018267275574”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1412</td> <td class=”number”>44.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3000.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>600.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14400.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6000.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8760.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13600.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10740.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2920.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17500.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (772)</td> <td class=”number”>808</td> <td class=”number”>25.2%</td> <td>

<div class=”bar” style=”width:57%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>966</td> <td class=”number”>30.1%</td> <td>

<div class=”bar” style=”width:68%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme2541325018267275574”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1412</td> <td class=”number”>44.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>60.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>66.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>134.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1582888.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2599822.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3826760.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4508936.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4649990.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_GHVEG_SQFTPTH07”>GHVEG_SQFTPTH07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>421</td>

</tr> <tr>

<th>Unique (%)</th> <td>13.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>28.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>913</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>92.898</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>19024</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>58.5%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram6153235250663255763”>

<img src=”data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAABCpJREFUeJzt2r9rU3sYx/FPekwwUJFSk5pU4yJdhJKhLq1BxMVo9v4DgS6lIIj4A1GRRlCKQ9vFWZC6%2BAMF3YIOJW0nhVJw0KCQkKIWosGk2Odu5fYqz0V7TXvl/RrP4Xu%2Bz/LmfBNOyMxMAH6oY6sHALazHVs9wD8NXHz602sWxk/8hkkA3iCAi0AAB4EADgIBHAQCOAgEcBAI4CAQwEEggINAAAeBAA4CARwEAjgIBHAQCOAgEMBBIICDQAAHgQAOAgEcBAI4CARwEAjgIBDAQSCAg0AAB4EADgIBHAQCOAgEcBAI4CAQwEEggINAAAeBAA4CARwEAjgIBHAQCOAgEMBBIICDQAAHgQAOAgEcBAI4dmz1AP%2BFgYtPf3rNwviJ3zAJ/jQhM7OtHgLw1Go1zczMaHh4WPF4vK17c8TCtre8vKypqSktLy%2B3fW8CARwEAjgIBHAQCLa9WCym0dFRxWKxtu/Nv1iAgzcI4CAQwEEggINAAAeBAA4CARwEAjj%2BiM/d8f9x%2B/ZtPXv2TEEQKJVKqVAoaHZ2VlNTUwqHw9q1a5du3Lih3bt369WrVxofH1cQBAqCQIVCQfv27dO7d%2B904cIFffv2TWtra7p06ZIOHTqkT58%2B6dy5c6rX61pdXdXY2JgymczmBjagTRYWFiyXy1mr1TIzs9HRUbtz544NDQ1ZuVw2M7PJyUm7du2amZlls1mbn583M7P79%2B/byMiImZnl83l78OCBmZnNzc1ZLpczM7PLly/b9PS0mZm9ffvWMpmMNZvNTc3MEQttk06ndffuXYXDYUlSV1eXvnz5ov379yuVSkmScrmcisWi3r9/r8%2BfP2tgYECSdPLkSc3Ozmp1dVWlUknZbFaSdPjwYa2srKhSqej58%2Bc6deqUJOnAgQNKJpN6%2BfLlpmYmELRNEATq7OyUJJXLZRWLRa2trW34xioWi6larapWq2nPnj3r1yORiHbu3KmPHz8qGo0qEon865p4PK5qtbqpmQkEbbe0tKR8Pq9CoaDe3t4N98xMoVDop5/5ozW/%2Bqy/IxC01eLiosbGxnTz5k0dOXJEiURCtVpt/X61WlUymfzueqPRULPZVFdXl75%2B/apms7lhTSKR0N69ezesqVQqSiQSm5qXQNA2jUZDp0%2Bf1uTkpNLptCSpv79flUpFb968kSQ9fPhQx48fVyKRUHd3t0qlkiTp0aNHOnr0qCKRiIaGhvTkyRNJ0osXL5RMJtXT06Njx47p8ePHkqTXr1/rw4cP6u/v39TMfO6Otrl3754mJibU19e3fm1wcFDpdFq3bt1SEASKxWIqFArq7OzU0tKSrl69qlAopGg0quvXrysej6tSqej8%2BfNqtVrq6OjQlStXdPDgQdXrdZ09e1YrKysyM505c2b9R/6vIhDAwRELcBAI4CAQwPEXVSMMFwt7TWEAAAAASUVORK5CYII%3D”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives6153235250663255763,#minihistogram6153235250663255763”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives6153235250663255763”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles6153235250663255763”

aria-controls=”quantiles6153235250663255763” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram6153235250663255763” aria-controls=”histogram6153235250663255763”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common6153235250663255763” aria-controls=”common6153235250663255763”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme6153235250663255763” aria-controls=”extreme6153235250663255763”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles6153235250663255763”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>0</td>

</tr> <tr>

<th>95-th percentile</th> <td>431</td>

</tr> <tr>

<th>Maximum</th> <td>19024</td>

</tr> <tr>

<th>Range</th> <td>19024</td>

</tr> <tr>

<th>Interquartile range</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>582.4</td>

</tr> <tr>

<th>Coef of variation</th> <td>6.2692</td>

</tr> <tr>

<th>Kurtosis</th> <td>532.19</td>

</tr> <tr>

<th>Mean</th> <td>92.898</td>

</tr> <tr>

<th>MAD</th> <td>158.55</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>19.318</td>

</tr> <tr>

<th>Sum</th> <td>213390</td>

</tr> <tr>

<th>Variance</th> <td>339190</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram6153235250663255763”>

<img src=”data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3XtUVXXC//EPF8UUAY%2BFlZKYF5Kbl0KKLLyFlWlpKtIqs7EyoxxJC7N6ymzCljhm6ajU5JPlU%2BTlKTUtMy9ZWdPUZEc0J9GWxmhQckIIFGH//vDneTrina8bzuH9Wou19Ps9%2B5zvh33ED3tvNn6WZVkCAACAMf51vQAAAABfQ8ECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIYF1vUCGoqiooPGn9Pf308ORzMdOFCm6mrL%2BPPXBw0ho9QwcpLRdzSEnGT0HRdd1LxOXpcjWF7M399Pfn5%2B8vf3q%2BulnDcNIaPUMHKS0Xc0hJxkRG1RsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAsMC6XgBq56onPqjrJZyx1eOvreslAABgC45gAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAzz2oJVUFCg9PR0JSYmKikpSZMmTVJJSYkkafv27brzzjt15ZVXKiUlRa%2B99prHtqtWrdLAgQPVrVs3DRkyRJ9%2B%2Bql7rrq6WjNnzlTfvn2VkJCg0aNHa%2B/evbZmAwAA3s1rC9YDDzygkJAQrVu3TsuWLdMPP/ygF154QRUVFRozZoyuvvpqbdq0STNnztT8%2BfO1Zs0aSUfLV2ZmpiZOnKgvvvhCo0aN0kMPPaT9%2B/dLkhYtWqQVK1YoJydH69evV2RkpNLT02VZVl3GBQAAXsQrC1ZJSYliY2M1YcIENWvWTBdffLEGDx6sf/7zn9qwYYMqKys1duxYNW3aVDExMRo2bJhyc3MlSYsXL1ZycrKSk5MVFBSkQYMGqVOnTlq%2BfLkkKTc3V6NGjVL79u0VHBysjIwM5efna8uWLXUZGQAAeJHAul7AuQgJCVFWVpbH2L59%2BxQeHq68vDxFRUUpICDAPRcdHa3FixdLkvLy8pScnOyxbXR0tJxOpyoqKrRz505FR0e754KDg9W2bVs5nU517dr1jNZXWFiooqIij7HAwKYKDw8/q5ynExDgXf04MPDs13sso7dlPVsNIScZfUdDyElG1JZXFqzjOZ1Ovfnmm5o7d65Wr16tkJAQj/mwsDC5XC5VV1fL5XIpNDTUYz40NFQ7d%2B7Ub7/9JsuyTjhfXFx8xuvJzc3V7NmzPcbS09M1bty4s0zmW1q0aHbO24aEXGBwJfVXQ8hJRt/REHKSEefK6wvW119/rbFjx2rChAlKSkrS6tWrT/g4Pz8/959Pdz1Vba%2B3Sk1NVZ8%2BfTzGAgObqri4rFbPezxv%2B67jXPIHBPgrJOQClZSUq6qq%2Bjysqn5oCDnJ6DsaQk4y%2Bo7afHNfG15dsNatW6dHH31UTz31lG677TZJksPh0I8//ujxOJfLpbCwMPn7%2B6tFixZyuVw15h0Oh/sxJ5pv2bLlGa8rPDy8xunAoqKDOnLEd9/AZ6I2%2BauqqhvE568h5CSj72gIOcmIc%2BVdh0D%2B4JtvvlFmZqZmzZrlLleSFBsbqx07dujIkSPuMafTqS5durjnt27d6vFcx%2BaDgoLUsWNH5eXluedKSkq0Z88excfHn%2BdEAADAV3hlwTpy5IiefPJJTZw4UT179vSYS05OVnBwsObOnavy8nJt2bJFS5YsUVpamiRp%2BPDh%2Bvzzz7VhwwYdOnRIS5Ys0Y8//qhBgwZJktLS0rRw4ULl5%2BertLRU2dnZ6ty5s%2BLi4mzPCQAAvJNXniL89ttvlZ%2Bfr%2Beee07PPfecx9wHH3ygefPm6emnn1ZOTo4uvPBCZWRkqFevXpKkTp06KTs7W1lZWSooKFCHDh00f/58XXTRRZKkESNGqKioSHfddZfKysqUmJhY44J1AACAU/GzuIOmLYqKDhp/zsBAf92Qvcn4854vq8dfe9bbBAb6q0WLZiouLvPpawQaQk4y%2Bo6GkJOMvuOii5rXyet65SlCAACA%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%2BbMmerbt68SEhI0evRo7d271729y%2BXS%2BPHjlZSUpJ49e%2BqJJ55QRUWFrdkAAID38tqC9corr%2Bi5555T27ZtTzifkJAgp9Pp8REfHy9JWrRokVasWKGcnBytX79ekZGRSk9Pl2VZkqSnnnpK5eXlWrlypZYuXar8/HxlZ2fblg0AAHg3ry1YQUFBWrJkyUkL1qnk5uZq1KhRat%2B%2BvYKDg5WRkaH8/Hxt2bJFv/zyi9auXauMjAw5HA61atVKDz74oJYuXarKysrzkAQAAPiawLpewLkaOXLkKef37dune%2B65R1u3blVISIjGjRunW2%2B9VRUVFdq5c6eio6Pdjw0ODlbbtm3ldDp18OBBBQQEKCoqyj0fExOj33//Xbt27fIYP5nCwkIVFRV5jAUGNlV4ePhZpjy1gADv6seBgWe/3mMZvS3r2WoIOcnoOxpCTjKitry2YJ2Kw%2BFQZGSkHnnkEXXo0EEfffSRHnvsMYWHh%2Bvyyy%2BXZVkKDQ312CY0NFTFxcUKCwtTcHCw/Pz8POYkqbi4%2BIxePzc3V7Nnz/YYS09P17hx42qZzLu1aNHsnLcNCbnA4Erqr4aQk4y%2BoyHkJCPOlU8WrF69eqlXr17uvw8YMEAfffSRli1bpokTJ0qS%2B3qrEznV3JlITU1Vnz59PMYCA5uquLisVs97PG/7ruNc8gcE%2BCsk5AKVlJSrqqr6PKyqfmgIOcnoOxpCTjL6jtp8c18bPlmwTqR169baunWrwsLC5O/vL5fL5THvcrnUsmVLORwOlZaWqqqqSgEBAe45SWrZsuUZvVZ4eHiN04FFRQd15IjvvoHPRG3yV1VVN4jPX0PISUbf0RBykhHnyrsOgZyht956S6tWrfIYy8/PV0REhIKCgtSxY0fl5eW550pKSrRnzx7Fx8erc%2BfOsixL33//vXve6XQqJCRE7dq1sy0DAADwXj5ZsA4fPqypU6fK6XSqsrJSK1eu1CeffKIRI0ZIktLS0rRw4ULl5%2BertLRU2dnZ6ty5s%2BLi4uRwONS/f3%2B9%2BOKLOnDggPbv3685c%2BZo6NChCgxsMAf8AABALXhtY4iLi5MkHTlyRJK0du1aSUePNo0cOVJlZWX685//rKKiIrVp00Zz5sxRbGysJGnEiBEqKirSXXfdpbKyMiUmJnpclP7ss8/q6aefVt%2B%2BfdWoUSPdcsstNW5mCgAAcDJ%2BVm2v6MYZKSo6aPw5AwP9dUP2JuPPe76sHn/tWW8TGOivFi2aqbi4zKevEWgIOcnoOxpCTjL6josual4nr%2BuTpwgBAADqku0Fq0%2BfPpo9e7b27dtn90sDAADYwvaCdfvtt2vVqlXq16%2Bf7r33Xq1Zs8Z9HRUAAIAvsL1gpaena9WqVXrnnXfUsWNHPf/880pOTtb06dO1e/duu5cDAABgXJ1dgxUTE6PMzEytX79ekydP1jvvvKObb75Zo0eP1nfffVdXywIAAKi1OitYlZWVWrVqle677z5lZmaqVatWevzxx9W5c2eNGjVKK1asqKulAQAA1Irt98HKz8/XkiVL9O6776qsrEz9%2B/fX66%2B/riuvvNL9mISEBD3zzDMaOHCg3csDAACoNdsL1oABA9SuXTuNGTNGt912m8LCwmo8Jjk5WQcOHLB7aQAAAEbYXrAWLlyoHj16nPZxW7ZssWE1AAAA5tl%2BDVZUVJQeeOAB96%2B2kaT//u//1n333SeXy2X3cgAAAIyzvWBlZWXp4MGD6tChg3usV69eqq6u1rRp0%2BxeDgAAgHG2nyL89NNPtWLFCrVo0cI9FhkZqezsbN1yyy12LwcAAMA4249gVVRUKCgoqOZC/P1VXl5u93IAAACMs71gJSQkaNq0afrtt9/cYz///LOmTJnicasGAAAAb2X7KcLJkyfrT3/6k6655hoFBwerurpaZWVlioiI0BtvvGH3cgAAAIyzvWBFRETo/fff1yeffKI9e/bI399f7dq1U8%2BePRUQEGD3cgAAAIyzvWBJUuPGjdWvX7%2B6eGkAAIDzzvaCtXfvXs2YMUM//PCDKioqasx//PHHdi8JAADAqDq5BquwsFA9e/ZU06ZN7X55AACA8872grV161Z9/PHHcjgcdr80AACALWy/TUPLli05cgUAAHya7QVrzJgxmj17tizLsvulAQAAbGH7KcJPPvlE33zzjZYtW6Y2bdrI39%2Bz47399tt2LwkAAMAo2wtWcHCwrr/%2BertfFgAAwDa2F6ysrCy7XxIAAMBWtl%2BDJUm7du3Syy%2B/rMcff9w99q9//asulgIAAGCc7QVr8%2BbNGjRokNasWaOVK1dKOnrz0ZEjR3KTUQAA4BNsL1gzZ87Uo48%2BqhUrVsjPz0/S0d9POG3aNM2ZM8fu5QAAABhne8H697//rbS0NElyFyxJuvHGG5Wfn2/3cgAAAIyzvWA1b978hL%2BDsLCwUI0bN7Z7OQAAAMbZXrC6d%2B%2Bu559/XqWlpe6x3bt3KzMzU9dcc43dywEAADDO9ts0PP7447r77ruVmJioqqoqde/eXeXl5erYsaOmTZtm93IAAACMs71gXXzxxVq5cqU2btyo3bt3q0mTJmrXrp2uvfZaj2uyAAAAvJXtBUuSGjVqpH79%2BtXFSwMAAJx3thesPn36nPJIFffCAgAA3s72gnXzzTd7FKyqqirt3r1bTqdTd999t93LAQAAMM72gjVx4sQTjn/44Yf68ssvbV4NAACAeXXyuwhPpF%2B/fnr//ffrehkAAAC1Vm8K1rZt22RZVl0vAwAAoNZsP0U4YsSIGmPl5eXKz89XSkqK3csBAAAwzvaCFRkZWeOnCIOCgjR06FANGzbM7uUAAAAYZ3vB4m7tAADA19lesN59990zfuxtt912HlcCAABwfthesJ544glVV1fXuKDdz8/PY8zPz4%2BCBQAAvJLtBevVV1/Va6%2B9pgceeEBRUVGyLEs7duzQK6%2B8ojvvvFOJiYl2LwkAAMCoOrkGKycnR61atXKPXXXVVYqIiNDo0aO1cuVKu5cEAABglO33wfrxxx8VGhpaYzwkJEQFBQV2LwcAAMA42wtW69atNW3aNBUXF7vHSkpKNGPGDF122WV2LwcAAMA4208RTp48WRMmTFBubq6aNWsmf39/lZaWqkmTJpozZ47dywEAADDO9oLVs2dPbdiwQRs3btT%2B/ftlWZZatWql6667Ts2bN7d7OQAAAMbZXrAk6YILLlDfvn21f/9%2BRURE1MUSAAAAzhvbr8GqqKhQZmamunXrpptuuknS0Wuw7r33XpWUlNi9HAAAAONsL1jTp0/X9u3blZ2dLX///3v5qqoqZWdn270cAAAA42wvWB9%2B%2BKFeeukl3Xjjje5f%2BhwSEqKsrCytWbPG7uUAAAAYZ3vBKisrU2RkZI1xh8Oh33//3e7lAAAAGGd7wbrsssv05ZdfSpLH7x784IMPdOmll9q9HAAAAONsL1h33HGHHn74Yb3wwguqrq7WggULNGHCBE2ePFl33333WT3Xpk2blJSUpIyMjBpzq1at0sCBA9WtWzcNGTJEn376qXuuurpaM2fOVN%2B%2BfZWQkKDRo0dr79697nmXy6Xx48crKSlJPXv21BNPPKGKiopzDw0AABoU2wtWamqqMjMz9cUXXyggIEDz5s1TQUGBsrOzlZaWdsbP88orr%2Bi5555T27Zta8xt375dmZmZmjhxor744guNGjVKDz30kPbv3y9JWrRokVasWKGcnBytX79ekZGRSk9Pdx9Re%2Bqpp1ReXq6VK1dq6dKlys/P5wJ8AABwxmwvWAcOHNDtt9%2Bu//3f/9WWLVv05Zdf6u2339aNN954Vs8TFBSkJUuWnLBgLV68WMnJyUpOTlZQUJAGDRqkTp06afny5ZKk3NxcjRo1Su3bt1dwcLAyMjKUn5%2BvLVu26JdfftHatWuVkZEhh8OhVq1a6cEHH9TSpUtVWVlp5HMAAAB8m%2B03Gu3bt6%2B%2B%2BeYb908QnquRI0eedC4vL0/JyckeY9HR0XI6naqoqNDOnTsVHR3tngsODlbbtm3ldDp18OBBBQQEKCoqyj0fExOj33//Xbt27fIYP5nCwkIVFRV5jAUGNlV4ePiZxjsjAQG29%2BNaCQw8%2B/Uey%2BhtWc9WQ8hJRt/REHKSEbVle8FKTEzU6tWrdfPNN5%2B313C5XAoNDfUYCw0N1c6dO/Xbb7/JsqwTzhcXFyssLEzBwcEeBfDYY//4C6pPJTc3V7Nnz/YYS09P17hx484ljs9o0aLZOW8bEnKBwZXUXw0hJxl9R0PISUacK9sL1iWXXKK//OUvysnJ0WWXXaZGjRp5zM%2BYMcPI6/zxJxTPdv50255Oamqq%2BvTp4zEWGNhUxcVltXre43nbdx3nkj8gwF8hIReopKRcVVXV52FV9UNDyElG39EQcpLRd9Tmm/vasL1g7dy5U5dffrmkMz8idLZatGghl8vlMeZyueRwOBQWFiZ/f/8Tzrds2VIOh0OlpaWqqqpSQECAe06SWrZseUavHx4eXuN0YFHRQR054rtv4DNRm/xVVdUN4vPXEHKS0Xc0hJxkxLmyrWBlZGRo5syZeuONN9xjc%2BbMUXp6uvHXio2N1datWz3GnE6nBgwYoKCgIHXs2FF5eXnq0aOHpKO/C3HPnj2Kj49X69atZVmWvv/%2Be8XExLi3DQkJUbt27YyvFQAA%2BB7bzjGtW7euxlhOTs55ea3hw4fr888/14YNG3To0CEtWbJEP/74owYNGiRJSktL08KFC5Wfn6/S0lJlZ2erc%2BfOiouLk8PhUP/%2B/fXiiy/qwIED2r9/v%2BbMmaOhQ4cqMND2A34AAMAL2dYYTnRdU22udYqLi5MkHTlyRJK0du1aSUePNnXq1EnZ2dnKyspSQUGBOnTooPnz5%2Buiiy6SJI0YMUJFRUW66667VFZWpsTERI%2BL0p999lk9/fTT6tu3rxo1aqRbbrnlhDczBQAAOBHbCtaJbstQm1s1OJ3OU86npKQoJSXlpGsZN27cSX%2Bqr3nz5vrrX/96zmsDAAANm3f9GBoAAIAXoGABAAAYZtspwsrKSk2YMOG0Y6bugwUAAFBXbCtYV155pQoLC087BgAA4O1sK1h/vP8VAACAL%2BMaLAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAzz2YIVFRWl2NhYxcXFuT%2BmTp0qSdq8ebOGDh2q7t27a8CAAVq%2BfLnHtgsXLlT//v3VvXt3paWlaevWrXURAQAAeKnAul7A%2BfTBBx%2BoTZs2HmOFhYV68MEH9cQTT2jgwIH6%2BuuvNXbsWLVr105xcXFat26dXn75Zb366quKiorSwoUL9cADD2jNmjVq2rRpHSUBAADexGePYJ3MihUrFBkZqaFDhyooKEhJSUnq06ePFi9eLEnKzc3VkCFD1KVLFzVp0kT33nuvJGn9%2BvV1uWwAAOBFfPoI1owZM/Svf/1LpaWluummmzRp0iTl5eUpOjra43HR0dFavXq1JCkvL08333yze87f31%2BdO3eW0%2BnUgAEDzuh1CwsLVVRU5DEWGNhU4eHhtUzkKSDAu/pxYODZr/dYRm/LerYaQk4y%2Bo6GkJOMqC2fLVhdu3ZVUlKSXnjhBe3du1fjx4/XlClT5HK51KpVK4/HhoWFqbi4WJLkcrkUGhrqMR8aGuqePxO5ubmaPXu2x1h6errGjRt3jml8Q4sWzc5525CQCwyupP5qCDnJ6DsaQk4y4lz5bMHKzc11/7l9%2B/aaOHGixo4dqyuvvPK021qWVavXTk1NVZ8%2BfTzGAgObqri4rFbPezxv%2B67jXPIHBPgrJOQClZSUq6qq%2Bjysqn5oCDnJ6DsaQk4y%2Bo7afHNfGz5bsI7Xpk0bVVVVyd/fXy6Xy2OuuLhYDodDktSiRYsa8y6XSx07djzj1woPD69xOrCo6KCOHPHdN/CZqE3%2BqqrqBvH5awg5yeg7GkJOMuJcedchkDO0bds2TZs2zWMsPz9fjRs3VnJyco3bLmzdulVdunSRJMXGxiovL889V1VVpW3btrnnAQAATscnC1bLli2Vm5urnJwcHT58WLt379asWbOUmpqqW2%2B9VQUFBVq8eLEOHTqkjRs3auPGjRo%2BfLgkKS0tTe%2B%2B%2B66%2B/fZblZeXa%2B7cuWrcuLF69epVt6EAAIDX8MlThK1atVJOTo5mzJjhLkiDBw9WRkaGgoKCNH/%2BfD333HOaMmWKWrdurenTp%2BuKK66QJF1//fV65JFHNH78eP3666%2BKi4tTTk6OmjRpUsepAACAt/DJgiVJCQkJevvtt086995775102zvuuEN33HHH%2BVoaAADwcT55ihAAAKAuUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYJ1BQUKD7779fiYmJ6t27t6ZPn67q6uq6XhYAAPASgXW9gPro4YcfVkxMjNauXatff/1VY8aM0YUXXqh77rmnrpcGAAC8AAXrOE6nU99//70WLFig5s2bq3nz5ho1apRef/11ClYt3fTiZ3W9hLOyevy1db0EAICXomAdJy8vT61bt1ZoaKh7LCYmRrt371ZpaamCg4NP%2BxyFhYUqKiryGAsMbKrw8HCjaw0I4Azv%2BeRthfCjidfV9RJO6dj71Zfftw0ho9QwcpIRtUXBOo7L5VJISIjH2LGyVVxcfEYFKzc3V7Nnz/YYe%2Bihh/Twww%2BbW6iOFrm7L/5BqampxstbfVFYWKjc3Fyfzig1jJyFhYV6/fVXyegDGkJOMqK2qK0nYFlWrbZPTU3VsmXLPD5SU1MNre7/FBUVafbs2TWOlvmShpBRahg5yeg7GkJOMqK2OIJ1HIfDIZfL5THmcrnk5%2Bcnh8NxRs8RHh7OdwMAADRgHME6TmxsrPbt26cDBw64x5xOpzp06KBmzZrV4coAAIC3oGAdJzo6WnFxcZoxY4ZKS0uVn5%2BvBQsWKC0tra6XBgAAvETAM88880xdL6K%2Bue6667Ry5UpNnTpV77//voYOHarRo0fLz8%2BvrpdWQ7NmzdSjRw%2BfPrrWEDJKDSMnGX1HQ8hJRtSGn1XbK7oBAADggVOEAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsLxQQUGB7r//fiUmJqp3796aPn26qqur63pZp1VQUKD09HQlJiYqKSlJkyZNUklJiX4FtmyjAAAN7ElEQVT66SdFRUUpLi7O4%2BPvf/%2B7e9tVq1Zp4MCB6tatm4YMGaJPP/3UPVddXa2ZM2eqb9%2B%2BSkhI0OjRo7V37966iChJioqKUmxsrEeWqVOnSpI2b96soUOHqnv37howYICWL1/use3ChQvVv39/de/eXWlpadq6dat77tChQ/qv//ovXX/99UpMTNS4ceNUXFxsazZJ%2Buqrr2rsq9jYWEVFRenLL7884b5cvXq1V2TctGmTkpKSlJGRUWOuNu9Bl8ul8ePHKykpST179tQTTzyhiooK9/z27dt155136sorr1RKSopee%2B21Osm4Zs0aDRo0SN26dVP//v31zjvvuOdefvllde7cuca%2B/eWXXySdft/Z/XXrZDmXLVumK664okaO7777TpJv7Msnn3yyRr7o6Gg9/vjjkqRJkya5f%2B/usY%2BrrrqqXmb0aha8zuDBg60nn3zSKikpsXbv3m2lpKRYr732Wl0v67RuueUWa9KkSVZpaam1b98%2Ba8iQIdbkyZOtvXv3Wp06dTrpdtu2bbNiY2OtDRs2WBUVFdZ7771ndenSxdq3b59lWZa1cOFCq3fv3tbOnTutgwcPWs8%2B%2B6w1cOBAq7q62q5oHjp16mTt3bu3xvjPP/9sde3a1Vq8eLFVUVFhffbZZ1Z8fLz13XffWZZlWR9//LF11VVXWd9%2B%2B61VXl5uzZ8/37r22mutsrIyy7IsKysryxoyZIj1n//8xyouLrYeeugha8yYMbZmO5m5c%2Bdaf/7zn60vvvjC6t2790kfV58z5uTkWCkpKdaIESOs8ePHe8zV9j340EMPWffff7/166%2B/Wvv377dSU1OtqVOnWpZlWeXl5dZ1111nvfzyy1ZZWZm1detWq0ePHtaHH35oa8YtW7ZYcXFx1kcffWRVVlZaGzZssGJiYqyvvvrKsizLeumll6zMzMyTPvfp9p2dX7dOlXPp0qXWnXfeedJtfWFfHq%2BystIaMGCAtWHDBsuyLCszM9N66aWXTvr4%2BpLR23EEy8s4nU59//33mjhxopo3b67IyEiNGjVKubm5db20UyopKVFsbKwmTJigZs2a6eKLL9bgwYP1z3/%2B87TbLl68WMnJyUpOTlZQUJAGDRqkTp06uY/%2B5ObmatSoUWrfvr2Cg4OVkZGh/Px8bdmy5XzHOisrVqxQZGSkhg4dqqCgICUlJalPnz5avHixpKM5hgwZoi5duqhJkya69957JUnr16/XkSNHtGTJEj344IO65JJLFBYWpvHjx2vDhg36%2Beef6zKW/vOf/2jBggV67LHHTvvY%2BpwxKChIS5YsUdu2bWvM1eY9%2BMsvv2jt2rXKyMiQw%2BFQq1at9OCDD2rp0qWqrKzUhg0bVFlZqbFjx6pp06aKiYnRsGHDzsu/6VNldLlcGjNmjPr166fAwEAlJyerU6dOZ/Rv9HT7zu6vW6fKeTq%2BsC%2BP9/rrr%2BvSSy9VcnLyaR9bnzJ6OwqWl8nLy1Pr1q0VGhrqHouJidHu3btVWlpahys7tZCQEGVlZenCCy90j%2B3bt0/h4eHuvz/22GPq2bOnrr76as2YMUOVlZWSjmaOjo72eL7o6Gg5nU5VVFRo586dHvPBwcFq27atnE7neU51cjNmzFCvXr101VVX6amnnlJZWdlJcxw7RXb8vL%2B/vzp37iyn06k9e/bo4MGDiomJcc%2B3b99eTZo0UV5enj2hTmLWrFm6/fbbdemll0qSysrK3KeCr7vuOi1YsEDW//%2Bd8vU548iRI9W8efMTztXmPbh9%2B3YFBAQoKirKPR8TE6Pff/9du3btUl5enqKiohQQEODx3H88dWrKqTJef/31Sk9Pd//9yJEjKioqUqtWrdxjO3bs0IgRI9ynuI%2BdJj3dvrP769apckpHv/bcc889SkhIUN%2B%2BffXee%2B9Jks/syz8qKSnRvHnz9Oijj3qMf/HFF7rtttvUrVs3DR061L3G%2BpTR21GwvIzL5VJISIjH2LEvWnVxPc65cjqdevPNNzV27Fg1btxY3bp10w033KD169crJydHy5cv19/%2B9jdJRzP/8QuzdDRzcXGxfvvtN1mWddL5utC1a1clJSVpzZo1ys3N1bfffqspU6accN%2BFhYW513mqnC6XS5JqbB8SElKn%2B/2nn37SmjVrdM8990g6%2Bp9Rp06ddPfdd2vTpk3KysrS7NmztXTpUknemVGq3XvQ5XIpODhYfn5%2BHnOS3PMnel%2B4XK46vbYyOztbTZs21c033yxJuvjiixUREaEXXnhBn332mYYNG6YHHnhAu3btOu2%2Bq09ftxwOhyIjI/Xoo4/qs88%2B0yOPPKLJkydr8%2BbNPrkv33zzTSUkJKhjx47usYiICLVt21bz58/Xpk2bdNVVV%2BlPf/qT12asryhYXujY0QBv9fXXX2v06NGaMGGCkpKSFB4errfffls33HCDGjVqpPj4eI0ZM0bLli1zb3O6zPXpc5Kbm6thw4apcePGat%2B%2BvSZOnKiVK1e6j8idijfllKRFixYpJSVFF110kaSj3%2Bm%2B8cYb6tGjhxo3bqyePXtqxIgRXrsv/6g26z6XTH/8D85OlmVp%2BvTpWrlypebOnaugoCBJ0rBhw/TSSy%2Bpbdu2uuCCCzRq1Ch17tzZ4wc1TH8OzodevXrp1VdfVXR0tBo3bqwBAwbohhtuOOP3qDfty6qqKi1atEgjR470GE9PT9fzzz%2BvVq1aKTg4WI8%2B%2BqgaN26stWvXSvKujPUZBcvLOBwO93eLx7hcLvn5%2BcnhcNTRqs7cunXrdP/992vy5Mk1/tH/UevWrfXLL7/Isiy1aNHihJkdDofCwsLk7%2B9/wvmWLVuelwxnq02bNqqqqjrhOouLi9377VQ5jz3m%2BPnffvutTnN%2B%2BOGH6tOnzykf07p1axUWFkryzozSqdd9uvegw%2BFQaWmpqqqqPOYkueePP4rjcrncz2un6upqTZo0SevWrdNbb72lyy%2B//JSPP7ZvT7fv6vvXrWM5fGlfSkd/4vfw4cMePyF4IgEBAbrkkkvc%2B9KbMtZnfDa8TGxsrPbt26cDBw64x5xOpzp06KBmzZrV4cpO75tvvlFmZqZmzZql2267zT2%2BefNmzZ071%2BOxu3btUuvWreXn56fY2Nga5/edTqe6dOmioKAgdezY0eManZKSEu3Zs0fx8fHnN9AJbNu2TdOmTfMYy8/PV%2BPGjZWcnFwjx9atW9WlSxdJR/ftH3NUVVVp27Zt6tKliyIiIhQaGuox/%2B9//1uHDx9WbGzseUx0ctu3b1dBQYGuvfZa99jq1av1P//zPx6P27VrlyIiIiR5X8ZjavMe7Ny5syzL0vfff%2B%2BxbUhIiNq1a6fY2Fjt2LFDR44cqfHcdnv%2B%2Bef1ww8/6K233nLvs2P%2B9re/afPmzR5j%2Bfn5ioiIOO2%2Bq09ft9566y2tWrXKY%2BxYDl/al5L08ccf6%2Bqrr1ZgYKB7zLIsZWVleWQ4fPiw9uzZo4iICK/LWJ9RsLzMsXuXzJgxQ6WlpcrPz9eCBQuUlpZW10s7pSNHjujJJ5/UxIkT1bNnT4%2B55s2ba86cOXrvvfdUWVkpp9Opv//97%2B5Mw4cP1%2Beff64NGzbo0KFDWrJkiX788UcNGjRIkpSWlqaFCxcqPz9fpaWlys7Odt%2Bvx24tW7ZUbm6ucnJydPjwYe3evVuzZs1Samqqbr31VhUUFGjx4sU6dOiQNm7cqI0bN2r48OHuHO%2B%2B%2B66%2B/fZblZeXa%2B7cuWrcuLF69eqlgIAADR8%2BXPPmzdO%2BfftUXFysv/71r7rhhhs8fnDATtu2bVNYWJiCg4PdY40aNdILL7ygTz/9VJWVlfrss8%2B0dOlS9770tozH1OY96HA41L9/f7344os6cOCA9u/frzlz5mjo0KHun9YLDg7W3LlzVV5eri1btmjJkiW2/5v%2B%2BuuvtXz5cuXk5CgsLKzGvMvl0pQpU7Rr1y4dOnRIr732mvbs2aPBgwefdt/Vp69bhw8f1tSpU%2BV0OlVZWamVK1fqk08%2B0YgRIyT5xr48Zvv27WrTpo3HmJ%2Bfn3766SdNmTJFP//8s8rKypSdna1GjRqpX79%2BXpexXrPthhAwZt%2B%2Bfda9995rxcfHW0lJSdZLL71UZ/d8OlNfffWV1alTJys2NrbGx08//WStWbPGGjRokBUfH29de%2B211rx586yqqir39h9%2B%2BKGVkpJixcTEWLfeeqv1j3/8wz1XXV1tzZo1y7rmmmus%2BPh467777nPfn6gu/OMf/7BSU1Otrl27Wj169LCysrKsiooK99ygQYOsmJgYKyUlpca9YxYtWmQlJydbsbGxVlpamrVjxw733KFDh6xnnnnGSkhIsLp162Y98sgjVklJia3Z/mjevHnWgAEDaoy//fbbVkpKihUXF2f17t3beueddzzm62vGY%2B/HK664wrriiivcfz%2BmNu/BkpISKyMjw%2BratauVkJBgTZkyxTp06JB7fseOHdaIESOs2NhYq1evXtaiRYtsz/j44497jB37uOeeeyzLsqyKigrrL3/5i3XddddZcXFx1uDBg61vvvnG/dyn23d2ft06Vc7q6mprzpw5Vu/eva3Y2FjrxhtvtNatW%2Bfe1hf25TEpKSnWq6%2B%2BWmPb4uJia9KkSVZSUpIVHx9v3XnnndbOnTvrXUZv52dZ9eTKQwAAAB/BKUIAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMOz/AWnWB4IohcZgAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common6153235250663255763”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1878</td> <td class=”number”>58.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>212.27943992452617</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>84.90221642764016</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>667.9375790805567</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>243.36214158684595</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>386.8969499711271</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>315.66128115612565</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>181.96856906534327</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>58.20302292637978</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.13172426194852</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (410)</td> <td class=”number”>410</td> <td class=”number”>12.8%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>913</td> <td class=”number”>28.4%</td> <td>

<div class=”bar” style=”width:49%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme6153235250663255763”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1878</td> <td class=”number”>58.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.3049651374020426</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.3879620905614366</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.4121032294348368</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9667778461189703</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>4725.162265187678</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6867.913352513116</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7289.204097714736</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8717.656106576696</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>19024.429602402615</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_GHVEG_SQFTPTH12”>GHVEG_SQFTPTH12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>834</td>

</tr> <tr>

<th>Unique (%)</th> <td>26.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>30.1%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>966</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>302.14</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>42244</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>44.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram4716180065706233798”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives4716180065706233798,#minihistogram4716180065706233798”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives4716180065706233798”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles4716180065706233798”

aria-controls=”quantiles4716180065706233798” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram4716180065706233798” aria-controls=”histogram4716180065706233798”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common4716180065706233798” aria-controls=”common4716180065706233798”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme4716180065706233798” aria-controls=”extreme4716180065706233798”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles4716180065706233798”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>139.82</td>

</tr> <tr>

<th>95-th percentile</th> <td>1246</td>

</tr> <tr>

<th>Maximum</th> <td>42244</td>

</tr> <tr>

<th>Range</th> <td>42244</td>

</tr> <tr>

<th>Interquartile range</th> <td>139.82</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>1404.2</td>

</tr> <tr>

<th>Coef of variation</th> <td>4.6476</td>

</tr> <tr>

<th>Kurtosis</th> <td>387.95</td>

</tr> <tr>

<th>Mean</th> <td>302.14</td>

</tr> <tr>

<th>MAD</th> <td>452.82</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>15.885</td>

</tr> <tr>

<th>Sum</th> <td>678000</td>

</tr> <tr>

<th>Variance</th> <td>1971800</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram4716180065706233798”>

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eekpbt271jv37v/%2B7nnzySXk8HqfLAQAAsM7xgLVgwQKdPn1aHTp08I717dtXdXV1WrhwodPlAAAAWOf4KcJPP/1U69evV0xMjHcsPj5eubm5evjhh50uBwAAwDrHj2DV1NQoPDy8fiGhoaqurna6HAAAAOscD1i9evXSwoULderUKe/Y3/72N82bN8/nVg0AAADByvFThLNnz9ZPf/pT3X333YqIiFBdXZ2qqqrUpk0bvf32206XAwAAYJ3jAatNmzb68MMP9cknn%2Bjo0aMKDQ1Vu3bt1KdPH4WFhTldDgAAgHWOByxJuvnmm3Xffff5Y9cAAACNzvGAdezYMS1evFh/%2BctfVFNTU2/%2B448/drokAAAAq/xyDVZpaan69Omjpk2bOr17AACARud4wCooKNDHH3%2Bs2NhYp3cNAADgCMdv09CiRQuOXAEAgOua4wFr8uTJWrZsmYwxTu8aAADAEY6fIvzkk0/0xRdfaO3atbrtttsUGuqb8d59912nSwIAALDK8YAVERGhe%2B%2B91%2BndAgAAOMbxgLVgwQKndwkAAOAox6/BkqRDhw5p6dKleu6557xju3fv9kcpAAAA1jkesD777DMNHTpUW7Zs0YYNGyR9e/PRcePGcZNRAABwXXA8YC1ZskQ/%2B9nPtH79eoWEhEj69vcTLly4UMuXL3e6HAAAAOscD1h//vOfNWbMGEnyBixJeuCBB1RUVOR0OQAAANY5HrCaN29%2B0d9BWFpaqptvvtnpcgAAAKxzPGD16NFDL7/8siorK71jhw8f1syZM3X33Xc7XQ4AAIB1jt%2Bm4bnnntMTTzyhtLQ01dbWqkePHqqurlbHjh21cOFCp8sBAACwzvGA9aMf/UgbNmzQzp07dfjwYTVp0kTt2rVT7969fa7JAgAACFaOByxJuummm3Tffff5Y9cAAACNzvGA1b9//8seqeJeWAAAINg5HrAeeughn4BVW1urw4cPy%2B1264knnnC6HAAAAOscD1gzZsy46PjmzZv1xz/%2B0eFqAAAA7PPL7yK8mPvuu08ffvihv8sAAAC4ZgETsPbu3StjjL/LAAAAuGaOnyIcPXp0vbHq6moVFRVp4MCBTpcDAABgneMBKz4%2Bvt5PEYaHh2vkyJEaNWqU0%2BUAAABY53jA4m7tAADgeud4wPrggw8a/Nhhw4Y1YiUAAACNw/GANWfOHNXV1dW7oD0kJMRnLCQkhIAFAACCkuMB64033tCbb76pp556SgkJCTLG6MCBA3r99dc1duxYpaWlOV0SAACAVX65BisvL0%2BtWrXyjt1xxx1q06aNJkyYoA0bNjhdEgAAgFWO3wfryJEjioqKqjceGRmp4uJip8sBAACwzvGA1bp1ay1cuFDl5eXesYqKCi1evFi333670%2BUAAABY5/gpwtmzZ2v69OnKz89Xs2bNFBoaqsrKSjVp0kTLly93uhwAAADrHA9Yffr00Y4dO7Rz504dP35cxhi1atVK99xzj5o3b%2B50OQAAANY5HrAk6ZZbbtGAAQN0/PhxtWnTxh8lAAAANBrHr8GqqanRzJkz1b17dz344IOSvr0Ga%2BLEiaqoqHC6HAAAAOscD1iLFi3Svn37lJubq9DQv%2B%2B%2BtrZWubm5V7WtXbt2KT09XTk5OfXmNm7cqCFDhqh79%2B4aPny4Pv30U%2B9cXV2dlixZogEDBqhXr16aMGGCjh075p33eDzKzs5Wenq6%2BvTpozlz5qimpuYHrBYAANyIHA9Ymzdv1quvvqoHHnjA%2B0ufIyMjtWDBAm3ZsqXB23n99dc1f/58tW3btt7cvn37NHPmTM2YMUN/%2BMMfNH78eE2dOlXHjx%2BXJK1atUrr169XXl6etm/frvj4eGVlZXnvJD937lxVV1drw4YNev/991VUVHTV4Q8AANy4HA9YVVVVio%2BPrzceGxurM2fONHg74eHhWrNmzUUD1urVq5WRkaGMjAyFh4dr6NCh6tSpk9atWydJys/P1/jx49W%2BfXtFREQoJydHRUVF2rNnj77%2B%2Bmtt3bpVOTk5io2NVatWrfT000/r/fff17lz537wugEAwI3D8Yvcb7/9dv3xj39UWlqaz%2B8e/Oijj/SP//iPDd7OuHHjLjlXWFiojIwMn7HExES53W7V1NTo4MGDSkxM9M5FRESobdu2crvdOn36tMLCwpSQkOCdT0pK0pkzZ3To0CGf8UspLS1VWVmZz5jL1VRxcXENXV6DhIU5no%2BvicsVXPVei%2B96E2w9uhHQm8BGfwIXvbk6jgesH//4x3rmmWc0YsQI1dXV6Te/%2BY0KCgq0efNmzZkzx8o%2BPB5PvbvFR0VF6eDBgzp16pSMMRedLy8vV3R0tCIiIrynL7%2Bbk%2BRzc9TLyc/P17Jly3zGsrKyNG3atB%2BynOtGTEwzf5fguMjIW/xdAi6B3gQ2%2BhO46E3DOB6wMjMz5XK59M477ygsLEy//vWv1a5dO%2BXm5uqBBx6wtp/vHx272vkrPfdKMjMz1b9/f58xl6upysurrmm7Fwq2TxG21x/IwsJCFRl5iyoqqlVbW%2BfvcvA99Caw0Z/AFay98deHe8cD1smTJzVixAiNGDGi0fYRExMjj8fjM%2BbxeBQbG6vo6GiFhoZedL5FixaKjY1VZWWlamtrFRYW5p2TpBYtWjRo/3FxcfVOB5aVndb588HzDdkYbsT119bW3ZDrDgb0JrDRn8BFbxrG8UMgAwYMuOYjRFeSnJysgoICnzG3262uXbsqPDxcHTt2VGFhoXeuoqJCR48eVWpqqrp06SJjjPbv3%2B/z3MjISLVr165R6wYAANcHxwNWWlqaNm3a1Kj7eOyxx/T73/9eO3bs0NmzZ7VmzRodOXJEQ4cOlSSNGTNGK1euVFFRkSorK5Wbm6suXbooJSVFsbGxGjRokH71q1/p5MmTOn78uJYvX66RI0fK5fLLje8BAECQcTwx/MM//IP%2B9V//VXl5ebr99tt10003%2BcwvXry4QdtJSUmRJJ0/f16StHXrVknfHm3q1KmTcnNztWDBAhUXF6tDhw5asWKFWrZsKUkaPXq0ysrK9Pjjj6uqqkppaWk%2BF6W/%2BOKLev755zVgwADddNNNevjhhy96M1MAAICLCTGNfb7uAo8//vhl599%2B%2B22HKnFWWdlp69t0uUJ1f%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%2Bull16SJH322WcaOXKkevToocGDB2vdunU%2Bz125cqUGDRqkHj16aMyYMSooKPDHEgAAQJBy%2BbuAxvTRRx/ptttu8xkrLS3V008/rTlz5mjIkCH6/PPPNWXKFLVr104pKSnatm2bli5dqjfeeEMJCQlauXKlnnrqKW3ZskVNmzb100oAAEAwuW6PYF3K%2BvXrFR8fr5EjRyo8PFzp6enq37%2B/Vq9eLUnKz8/X8OHD1bVrVzVp0kQTJ06UJG3fvt2fZQMAgCByXR/BWrx4sXbv3q3Kyko9%2BOCDmjVrlgoLC5WYmOjzuMTERG3atEmSVFhYqIceesg7Fxoaqi5dusjtdmvw4MEN2m9paanKysp8xlyupoqLi7vGFfkKCwuufOxyBVe91%2BK73gRbj24E9Caw0Z/ARW%2BuznUbsLp166b09HT98pe/1LFjx5Sdna158%2BbJ4/GoVatWPo%2BNjo5WeXm5JMnj8SgqKspnPioqyjvfEPn5%2BVq2bJnPWFZWlqZNm/YDV3N9iIlp5u8SHBcZeYu/S8Al0JvARn8CF71pmOs2YOXn53v/3L59e82YMUNTpkxRz549r/hcY8w17TszM1P9%2B/f3GXO5mqq8vOqatnuhYPsUYXv9gSwsLFSRkbeooqJatbV1/i4H30NvAhv9CVzB2ht/fbi/bgPWhW677TbV1tYqNDRUHo/HZ668vFyxsbGSpJiYmHrzHo9HHTt2bPC%2B4uLi6p0OLCs7rfPng%2BcbsjHciOuvra27IdcdDOhNYKM/gYveNExwHQJpoL1792rhwoU%2BY0VFRbr55puVkZFR77YLBQUF6tq1qyQpOTlZhYWF3rna2lrt3bvXOw8AAHAl12XAatGihfLz85WXl6dvvvlGhw8f1iuvvKLMzEw98sgjKi4u1urVq3X27Fnt3LlTO3fu1GOPPSZJGjNmjD744AN9%2BeWXqq6u1muvvaabb75Zffv29e%2BiAABA0LguTxG2atVKeXl5Wrx4sTcgPfroo8rJyVF4eLhWrFih%2BfPna968eWrdurUWLVqkzp07S5LuvfdePfvss8rOztaJEyeUkpKivLw8NWnSxM%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%2Bp2/S7gqm7J7%2B7sEAECQ4gjWBdxut/bv368ZM2aoefPmio%2BP1/jx45Wfn%2B/v0gAAQJDgCNYFCgsL1bp1a0VFRXnHkpKSdPjwYVVWVioiIuKK2ygtLVVZWZnPmMvVVHFxcVZrDQsjHzemYDviFkx%2BO%2BMev%2B37u383/PsJTPQncNGbq0PAuoDH41FkZKTP2Hdhq7y8vEEBKz8/X8uWLfMZmzp1qp555hl7herbIPfEj/6izMxM6%2BEN16a0tFT5%2Bfn0JgCVlpbqrbfeoDcBiv4ELnpzdYihF2GMuabnZ2Zmau3atT5fmZmZlqr7u7KyMi1btqze0TL4H70JXPQmsNGfwEVvrg5HsC4QGxsrj8fjM%2BbxeBQSEqLY2NgGbSMuLo50DwDADYwjWBdITk5WSUmJTp486R1zu93q0KGDmjVr5sfKAABAsCBgXSAxMVEpKSlavHixKisrVVRUpN/85jcaM2aMv0sDAABBIuyFF154wd9FBJp77rlHGzZs0EsvvaQPP/xQI0eO1IQJExQSEuLv0upp1qyZ7rzzTo6uBSB6E7joTWCjP4GL3jRciLnWK7oBAADgg1OEAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsIJQcXGxJk2apLS0NPXr10%2BLFi1SXV2dv8u6ruzatUvp6enKycmpN7dx40YNGTJE3bt31/DLapbhAAAJFUlEQVThw/Xpp5965%2Brq6rRkyRINGDBAvXr10oQJE3Ts2DHvvMfjUXZ2ttLT09WnTx/NmTNHNTU13vl9%2B/Zp7Nix6tmzpwYOHKg333yzcRcahIqLi5WVlaW0tDSlp6dr1qxZqqiokHTl168xewdp//79euKJJ9SzZ0%2Blp6crOztbZWVlkqTPPvtMI0eOVI8ePTR48GCtW7fO57krV67UoEGD1KNHD40ZM0YFBQXeubNnz%2Bpf/uVfdO%2B99yotLU3Tpk1TeXm5d573xKvz8ssvKyEhwft3etNIDILOo48%2Ban7xi1%2BYiooKc/jwYTNw4EDz5ptv%2Brus60ZeXp4ZOHCgGT16tMnOzvaZ27t3r0lOTjY7duwwNTU15r//%2B79N165dTUlJiTHGmJUrV5p%2B/fqZgwcPmtOnT5sXX3zRDBkyxNTV1RljjJk6daqZNGmSOXHihDl%2B/LjJzMw0L730kjHGmOrqanPPPfeYpUuXmqqqKlNQUGDuvPNOs3nzZmdfgAD38MMPm1mzZpnKykpTUlJihg8fbmbPnn3F168xewdjzp49a%2B6%2B%2B26zbNkyc/bsWXPixAkzduxY8/TTT5u//e1vplu3bmb16tWmpqbG/O53vzOpqanmq6%2B%2BMsYY8/HHH5s77rjDfPnll6a6utqsWLHC9O7d21RVVRljjFmwYIEZPny4%2Betf/2rKy8vN1KlTzeTJk7375j2x4fbu3WvuvPNO06lTJ2OMoTeNiIAVZL766ivTpUsX4/F4vGP/8R//YQYNGuTHqq4vb731lqmoqDAzZ86sF7DmzZtnsrKyfMZGjRplVqxYYYwxZvDgweatt97yzp0%2BfdokJiaa3bt3m7KyMtO5c2ezb98%2B7/zOnTtNt27dzDfffGM2bdpk7rrrLnP%2B/Hnv/KJFi8xPf/rTxlhmUDp16pSZNWuWKSsr8469/fbbZuDAgVd8/RqzdzDG4/GY9957z5w7d8479tZbb5n777/fvPHGG2bYsGE%2Bj8/OzjZz5841xhgzadIk8/LLL3vnamtrTe/evc2GDRvMuXPnTM%2BePc3WrVu98wcPHjQJCQnm%2BPHjvCdehdraWjNq1Cjzb//2b96ARW8aD6cIg0xhYaFat26tqKgo71hSUpIOHz6syspKP1Z2/Rg3bpyaN29%2B0bnCwkIlJib6jCUmJsrtdqumpkYHDx70mY%2BIiFDbtm3ldru1b98%2BhYWF%2BRyaT0pK0pkzZ3To0CEVFhYqISFBYWFhPtv%2B/uH4G11kZKQWLFigW2%2B91TtWUlKiuLi4K75%2Bjdk7SFFRURo1apRcLpck6dChQ/qv//ovPfjgg5d87S/Vm9DQUHXp0kVut1tHjx7V6dOnlZSU5J1v3769mjRposLCQt4Tr8K7776r8PBwDRkyxDtGbxoPASvIeDweRUZG%2Box99837/fPeaBwej8fnzUL69vUvLy/XqVOnZIy55LzH41FERIRCQkJ85iR55y/sbXR0tDwez41zzcJVcrvdeueddzRlypQrvn6N2Tv8XXFxsZKTk/XQQw8pJSVF06ZNu2RvvnvtLtcbj8cjSfWeHxkZecl/N/Smvq%2B//lpLly7V888/7zNObxoPASsIGWP8XcIN7Uqv/%2BXmf0jvvv%2BfOv7u888/14QJEzR9%2BnSlp6df8nHff/2c7t2NqHXr1nK73froo4905MgR/fznP2/Q8%2BhN41qwYIGGDx%2BuDh06XPVz6c0PQ8AKMrGxsd5PDd/xeDwKCQlRbGysn6q6ccTExFz09Y%2BNjVV0dLRCQ0MvOt%2BiRQvFxsaqsrJStbW1PnOSvPMXfqrzeDze7eLvtm3bpkmTJmn27NkaN26cJF3x9WvM3sFXSEiI4uPjlZOTow0bNsjlctV7bcvLy73vWZfrzXePuXD%2B1KlT3t7wnnh5n332mXbv3q2srKx6cxd77emNHbxrB5nk5GSVlJTo5MmT3jG3260OHTqoWbNmfqzsxpCcnFzvmii3262uXbsqPDxcHTt2VGFhoXeuoqJCR48eVWpqqrp06SJjjPbv3%2B/z3MjISLVr107Jyck6cOCAzp8/X2/b%2BLsvvvhCM2fO1CuvvKJhw4Z5x6/0%2BjVm7/Dtf%2BKDBg3yOZ393QeD1NTUeq99QUGBT2%2B%2B/9rX1tZq79696tq1q9q0aaOoqCif%2BT//%2Bc/65ptvlJyczHtiA6xbt04nTpxQv379lJaWpuHDh0uS0tLS1KlTJ3rTWBy/rB7XbNSoUWb27Nnm9OnT5uDBg6Z///7mnXfe8XdZ152L/RThgQMHTEpKitm%2Bfbupqakxq1evNt27dzelpaXGmG9/QqZv377eH/WfO3euGTFihPf52dnZZuLEiebEiROmpKTEjBgxwixcuNAY8%2B2Puffr18%2B8%2Buqr5syZM%2BbLL780d9xxh9m%2Bfbtjaw50586dMw8%2B%2BKB59913681d6fVrzN7BmIqKCpOenm4WLlxozpw5Y06cOGEmTJhgfvzjH5uvv/7adO/e3bz33numpqbG7Nixw6Smpnp/KnPnzp2mZ8%2BeZvfu3ebMmTNm6dKlJiMjw1RXVxtjvv1p0EcffdT89a9/NSdPnjSTJ082zzzzjHffvCdensfjMSUlJd6v3bt3m06dOpmSkhJTXFxMbxoJASsIlZSUmIkTJ5rU1FSTnp5uXn31Ve%2B9enDtkpOTTXJysuncubPp3Lmz9%2B/f2bx5sxk4cKBJSkoyjzzyiPnTn/7knaurqzOvvPKKufvuu01qaqp58sknvfdZMubb/4RycnJMt27dTK9evcy8efPM2bNnvfMHDhwwo0ePNsnJyaZv375m1apVziw6SPzP//yP6dSpk7cn3//6v//7vyu%2Bfo3ZOxizf/9%2BM3bsWJOammruuusuk52dbY4fP26MMeZPf/qTGTp0qElKSjIDBw6sd3%2B3VatWmYyMDJOcnGzGjBljDhw44J07e/aseeGFF0yvXr1M9%2B7dzbPPPmsqKiq887wnXp1jx455b9NgDL1pLCHG3MBXoAEAADQCrsECAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMv%2BHzTC2hPKgtkTAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common4716180065706233798”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1412</td> <td class=”number”>44.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>39.5663740281769</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>28.085931844012006</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>81.71538078185769</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1044.2988204456094</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>455.85733000717767</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>55.22515423162826</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1672.7987872331707</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1706.9956875898417</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>210.62450337989566</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (823)</td> <td class=”number”>823</td> <td class=”number”>25.6%</td> <td>

<div class=”bar” style=”width:58%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>966</td> <td class=”number”>30.1%</td> <td>

<div class=”bar” style=”width:68%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme4716180065706233798”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1412</td> <td class=”number”>44.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.04855401294061553</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0733370816730633</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.2569122233697635</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.8523069189145818</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>13692.117524008749</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14644.931609518457</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16274.54303353105</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16577.621931781956</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>42244.15676224981</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_GROC09”>GROC09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>188</td>

</tr> <tr>

<th>Unique (%)</th> <td>5.9%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>20.276</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>2027</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>1.7%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-6139428519808234832”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-6139428519808234832,#minihistogram-6139428519808234832”
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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-6139428519808234832”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-6139428519808234832”

aria-controls=”quantiles-6139428519808234832” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-6139428519808234832” aria-controls=”histogram-6139428519808234832”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-6139428519808234832” aria-controls=”common-6139428519808234832”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-6139428519808234832” aria-controls=”extreme-6139428519808234832”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-6139428519808234832”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>1</td>

</tr> <tr>

<th>Q1</th> <td>3</td>

</tr> <tr>

<th>Median</th> <td>6</td>

</tr> <tr>

<th>Q3</th> <td>13</td>

</tr> <tr>

<th>95-th percentile</th> <td>70.45</td>

</tr> <tr>

<th>Maximum</th> <td>2027</td>

</tr> <tr>

<th>Range</th> <td>2027</td>

</tr> <tr>

<th>Interquartile range</th> <td>10</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>79.809</td>

</tr> <tr>

<th>Coef of variation</th> <td>3.9361</td>

</tr> <tr>

<th>Kurtosis</th> <td>283.77</td>

</tr> <tr>

<th>Mean</th> <td>20.276</td>

</tr> <tr>

<th>MAD</th> <td>24.251</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>14.596</td>

</tr> <tr>

<th>Sum</th> <td>63505</td>

</tr> <tr>

<th>Variance</th> <td>6369.5</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-6139428519808234832”>

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N133306cuSIJGnlypVau3atcnNztXXrVsXExCgzM1OGYXgzLgAAsBBLFqyysjLFx8dr2rRpatmypS677DKNGDFCf/vb37Rt2zbV1NRo4sSJatGiheLi4jRq1Cjl5eVJklatWqWUlBSlpKQoKChIw4cPV5cuXbRmzRpJUl5ensaOHauOHTsqODhYWVlZKioq0u7du70ZGQAAWIglC1ZISIiys7N16aWXusYOHz6syMhIFRYWqmvXrgoICHDNxcbGqqCgQJJUWFio2NhYt%2BeLjY2V3W5XVVWV9u3b5zYfHBys9u3by263X%2BBUAADAV9i8vQAz2O12vfbaa1q6dKk2bNigkJAQt/mwsDA5nU7V19fL6XQqNDTUbT40NFT79u3TsWPHZBjGKecdDkeT11NSUqLS0lK3MZuthSIjI88x2ZkFBFirH9tsTVtvQy6r5TsTX8wkkctqyGUtvpjLFzOdjuUL1ueff66JEydq2rRpSk5O1oYNG065nZ%2Bfn%2Bv/n%2B16qvO93iovL0%2BLFy92G8vMzNTkyZPP63mtLjy85TltHxJyyQVaiff4YiaJXFZDLmvxxVy%2BmOlkli5YW7Zs0YMPPqjZs2frlltukSRFRETom2%2B%2BcdvO6XQqLCxM/v7%2BCg8Pl9PpbDQfERHh2uZU861bt27yutLT05Wamuo2ZrO1kMNRcQ7pzs5qPwE0NX9AgL9CQi5RWVml6urqL/CqPMMXM0nkshpyWYsv5vJGpnP94d4sli1YX3zxhWbMmKFFixapb9%2B%2BrvH4%2BHi9/vrrqq2tlc32Uzy73a7u3bu75huux2pgt9s1dOhQBQUFqXPnziosLFSfPn0k/XRB/cGDB5WYmNjktUVGRjY6HVhaely1tb7xBvmlzjV/XV29z/2d%2BWImiVxWQy5r8cVcvpjpZNY6BPJ/amtr9eijj2r69Olu5UqSUlJSFBwcrKVLl6qyslK7d%2B9Wfn6%2BMjIyJEmjR4/WJ598om3btqm6ulr5%2Bfn65ptvNHz4cElSRkaGVqxYoaKiIpWXlysnJ0fdunVTQkKCx3MCAABr8vgRrNTUVI0cOVK33nqrLr/88l/0HLt27VJRUZHmzZunefPmuc299957WrZsmR5//HHl5ubq0ksvVVZWlvr37y9J6tKli3JycpSdna3i4mJ16tRJy5cvV5s2bSRJY8aMUWlpqW6//XZVVFQoKSmp0fVUAAAAZ%2BJnePgOmkuWLNG7776rf/7zn7rmmms0evRopaamuk7n%2BarS0uOmP6fN5q9BOTtMf94LZcPUa5u0nc3mr/DwlnI4KnzmELIvZpLIZTXkshZfzOWNTG3atPLIfk7m8VOEmZmZWr9%2Bvd5880117txZTz31lFJSUjR//nwdOHDA08sBAAAwndeuwYqLi9OMGTO0detWzZo1S2%2B%2B%2BaZuvPFGjRs3Tn//%2B9%2B9tSwAAIDz5rWCVVNTo/Xr1%2Buee%2B7RjBkz1LZtWz388MPq1q2bxo4dq7Vr13praQAAAOfF4xc%2BFRUVKT8/X2%2B//bYqKio0ZMgQ/elPf9JVV13l2qZ3796aM2eOhg0b5unlAQAAnDePF6yhQ4eqQ4cOGj9%2BvG655RaFhYU12iYlJUVHjx719NIAAABM4fGCtWLFCtdNPM9k9%2B7dHlgNAACA%2BTx%2BDVbXrl01YcIEbd682TX2X//1X7rnnnsa/YoaAAAAK/J4wcrOztbx48fVqVMn11j//v1VX1%2Bvp59%2B2tPLAQAAMJ3HTxF%2B9NFHWrt2rcLDw11jMTExysnJ0U033eTp5QAAAJjO40ewqqqqFBQU1Hgh/v6qrKz09HIAAABM5/GC1bt3bz399NM6duyYa%2By7777T3Llz3W7VAAAAYFUeP0U4a9Ys/e53v9M111yj4OBg1dfXq6KiQtHR0Xr11Vc9vRwAAADTebxgRUdH691339WHH36ogwcPyt/fXx06dFDfvn0VEBDg6eUAAACYzuMFS5ICAwN1/fXXe2PXAAAAF5zHC9ahQ4e0YMECff3116qqqmo0/8EHH3h6SQAAAKbyyjVYJSUl6tu3r1q0aOHp3QMAAFxwHi9YBQUF%2BuCDDxQREeHpXQMAAHiEx2/T0Lp1a45cAQAAn%2BbxgjV%2B/HgtXrxYhmF4etcAAAAe4fFThB9%2B%2BKG%2B%2BOILrV69Wu3atZO/v3vHe%2BONNzy9JAAAAFN5vGAFBwerX79%2Bnt4tAACAx3i8YGVnZ3t6lwAAAB7l8WuwJGn//v16/vnn9fDDD7vG/ud//scbSwEAADCdxwvWzp07NXz4cG3atEnr1q2T9NPNR%2B%2B44w5uMgoAAHyCxwvWwoUL9eCDD2rt2rXy8/OT9NPvJ3z66ae1ZMkSTy8HAADAdB4vWP/4xz%2BUkZEhSa6CJUk33HCDioqKPL0cAAAA03m8YLVq1eqUv4OwpKREgYGBnl4OAACA6TxesHr27KmnnnpK5eXlrrEDBw5oxowZuuaaazy9HAAAANN5/DYNDz/8sO68804lJSWprq5OPXv2VGVlpTp37qynn37a08sBAAAwnccL1mWXXaZ169Zp%2B/btOnDggJo3b64OHTro2muvdbsmCwAAwKo8XrAkqVmzZrr%2B%2Buu9sWsAAIALzuMFKzU19YxHqrgXFgAAsDqPF6wbb7zRrWDV1dXpwIEDstvtuvPOOz29HAAAANN5vGBNnz79lOMbN27UX/7yFw%2BvBgAAwHxe%2BV2Ep3L99dfr3Xff9fYyAAAAzttFU7D27NkjwzC8vQwAAIDz5vFThGPGjGk0VllZqaKiIg0ePNjTywEAADCdxwtWTExMo39FGBQUpLS0NI0aNcrTywEAADCdxwuWmXdr37Fjh2bMmKGkpCQtXLjQNb569WrNmjVLzZo1c9t%2B5cqVSkxMVH19vRYtWqR169aprKxMiYmJmjNnjqKjoyVJTqdTc%2BbM0V//%2Blf5%2B/srJSVFs2fPVvPmzU1bOwAA8F0eL1hvv/12k7e95ZZbTjv3wgsvKD8/X%2B3btz/lfO/evfXqq6%2Becm7lypVau3atXnjhBbVt21YLFy5UZmam3nnnHfn5%2BWn27Nk6ceKE1q1bp5qaGk2ZMkU5OTl69NFHm7x2AADw/5fHC9Yjjzyi%2Bvr6Rhe0%2B/n5uY35%2BfmdsWAFBQUpPz9fTz75pKqrq89pDXl5eRo7dqw6duwoScrKylJSUpJ2796tdu3aafPmzfrzn/%2BsiIgISdKkSZM0ZcoUzZgxo9FRMQAAgJN5vGC9%2BOKLevnllzVhwgR17dpVhmHoq6%2B%2B0gsvvKDbbrtNSUlJTXqeO%2B6444zzhw8f1l133aWCggKFhIRo8uTJuvnmm1VVVaV9%2B/YpNjbWtW1wcLDat28vu92u48ePKyAgQF27dnXNx8XF6ccff9T%2B/fvdxgEAAE7FK9dg5ebmqm3btq6xXr16KTo6WuPGjdO6devOex8RERGKiYnRAw88oE6dOun999/XQw89pMjISP3mN7%2BRYRgKDQ11e0xoaKgcDofCwsIUHBzsdiF%2Bw7YOh6NJ%2By8pKVFpaanbmM3WQpGRkeeZzF1AwEVzl40msdmatt6GXFbLdya%2BmEkil9WQy1p8MZcvZjodjxesb775plG5kaSQkBAVFxebso/%2B/furf//%2Brj8PHTpU77//vlavXu26k/yZ7rl1vvfjysvL0%2BLFi93GMjMzNXny5PN6XqsLD295TtuHhFxygVbiPb6YSSKX1ZDLWnwxly9mOpnHC1ZUVJSefvppTZkyReHh4ZKksrIyPffcc/r1r399QfdbUFCgsLAw%2Bfv7y%2Bl0us07nU61bt1aERERKi8vV11dnQICAlxzktS6desm7Ss9PV2pqaluYzZbCzkcFSYk%2BTer/QTQ1PwBAf4KCblEZWWVqqurv8Cr8gxfzCSRy2rIZS2%2BmMsbmc71h3uzeLxgzZo1S9OmTVNeXp5atmwpf39/lZeXq3nz5lqyZIkp%2B3j99dcVGhqqG2%2B80TVWVFSk6OhoBQUFqXPnziosLFSfPn0k/VTwDh48qMTEREVFRckwDH355ZeKi4uTJNntdoWEhKhDhw5N2n9kZGSj04GlpcdVW%2Bsbb5Bf6lzz19XV%2B9zfmS9mkshlNeSyFl/M5YuZTubxgtW3b19t27ZN27dv15EjR2QYhtq2bavrrrtOrVq1MmUfJ06c0BNPPKHo6GhdccUV2rhxoz788EO9%2BeabkqSMjAzl5uaqX79%2Batu2rXJyctStWzclJCRIkoYMGaJnn31WzzzzjE6cOKElS5YoLS1NNpvH/7oAAIAFeaUxXHLJJRo4cKCOHDniurnnuWooQ7W1tZKkzZs3S/rpaNMdd9yhiooKTZkyRaWlpWrXrp2WLFmi%2BPh4ST/9up7S0lLdfvvtqqioUFJSkts1U7///e/1%2BOOPa%2BDAgWrWrJluuukmZWVlnU9kAADw/4if4eHfsFxVVaXHH39c7777riSpoKBAZWVleuCBB/SHP/xBISEhnlyOx5SWHjf9OW02fw3K2WH6814oG6Ze26TtbDZ/hYe3lMNR4TOHkH0xk0QuqyGXtfhiLm9katPGnLNj58rjV0nPnz9fe/fuVU5Ojvz9/737uro65eTkeHo5AAAApvN4wdq4caOee%2B453XDDDa57TYWEhCg7O1ubNm3y9HIAAABM5/GCVVFRoZiYmEbjERER%2BvHHHz29HAAAANN5vGD9%2Bte/1l/%2B8hdJ7jf0fO%2B99/SrX/3K08sBAAAwncf/FeFvf/tb3X///br11ltVX1%2BvV155RQUFBdq4caMeeeQRTy8HAADAdB4vWOnp6bLZbHrttdcUEBCgZcuWqUOHDsrJydENN9zg6eUAAACYzuMF6%2BjRo7r11lt16623enrXAAAAHuHxa7AGDhx43r9MGQAA4GLm8YKVlJSkDRs2eHq3AAAAHuPxU4SXX365nnzySeXm5urXv/61mjVr5ja/YMECTy8JAADAVB4vWPv27dNvfvMbSZLD4fD07gEAAC44jxWsrKwsLVy4UK%2B%2B%2BqprbMmSJcrMzPTUEgAAADzCY9dgbdmypdFYbm6up3YPAADgMR4rWKf6l4P8a0IAAOCLPFawGn6x89nGAAAArM7jt2kAAADwdRQsAAAAk3nsXxHW1NRo2rRpZx3jPlgAAMDqPFawrrrqKpWUlJx1DAAAwOo8VrB%2Bfv8rAAAAX8Y1WAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMksXrB07dig5OVlZWVmN5tavX69hw4apR48eGjlypD766CPXXH19vRYuXKiBAweqd%2B/eGjdunA4dOuSadzqdmjp1qpKTk9W3b1898sgjqqqq8kgmAABgfZYtWC%2B88ILmzZun9u3bN5rbu3evZsyYoenTp%2BvTTz/V2LFjdd999%2BnIkSOSpJUrV2rt2rXKzc3V1q1bFRMTo8zMTBmGIUmaPXu2KisrtW7dOr311lsqKipSTk6OR/MBAADrsmzBCgoKUn5%2B/ikL1qpVq5SSkqKUlBQFBQVp%2BPDh6tKli9asWSNJysvL09ixY9WxY0cFBwcrKytLRUVF2r17t77//ntt3rxZWVlZioiIUNu2bTVp0iS99dZbqqmp8XRMAABgQTZvL%2BCXuuOOO047V1hYqJSUFLex2NhY2e12VVVVad%2B%2BfYqNjXXNBQcHq3379rLb7Tp%2B/LgCAgLUtWtX13xcXJx%2B/PFH7d%2B/3238dEpKSlRaWuo2ZrO1UGRkZFPjNUlAgLX6sc3WtPU25LJavjPxxUwSuayGXNbii7l8MdPpWLZgnYnT6VRoaKjbWGhoqPbt26djx47JMIxTzjscDoWFhSk4OFh%2Bfn5uc5LkcDiatP%2B8vDwtXrzYbSwzM1OTJ0/%2BJXF8Rnh4y3PaPiTkkgu0Eu/xxUwSuayGXNbii7l8MdPJfLJgSXJdT/VL5s/22LNJT09Xamqq25jN1kIOR8V5Pe/JrPYTQFPzBwT4KyTkEpWVVaqurv4Cr8ozfDGTRC6rIZe1%2BGIub2Q61x/uzeKTBSs8PFxOp9NtzOl0KiIiQmFhYfL39z/lfOvWrRUREaHy8nLV1dUpICDANSdJrVu3btL%2BIyMjG50OLC09rtpa33iD/FLnmr%2Burt7n/s58MZNELqshl7X4Yi5fzHQyax0CaaL4%2BHgVFBS4jdntdnXv3l1BQUHq3LmzCgsLXXNlZWWqFV2wAAAXBElEQVQ6ePCgEhMT1a1bNxmGoS%2B//NLtsSEhIerQoYPHMgAAAOvyyYI1evRoffLJJ9q2bZuqq6uVn5%2Bvb775RsOHD5ckZWRkaMWKFSoqKlJ5eblycnLUrVs3JSQkKCIiQkOGDNGzzz6ro0eP6siRI1qyZInS0tJks/nkAT8AAGAyyzaGhIQESVJtba0kafPmzZJ%2BOtrUpUsX5eTkKDs7W8XFxerUqZOWL1%2BuNm3aSJLGjBmj0tJS3X777aqoqFBSUpLbRem///3v9fjjj2vgwIFq1qyZbrrpplPezBQAAOBU/IzzvaIbTVJaetz057TZ/DUoZ4fpz3uhbJh6bZO2s9n8FR7eUg5Hhc%2Bco/fFTBK5rIZc1uKLubyRqU2bVh7Zz8l88hQhAACAN1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATOazBatr166Kj49XQkKC6%2BuJJ56QJO3cuVNpaWnq2bOnhg4dqjVr1rg9dsWKFRoyZIh69uypjIwMFRQUeCMCAACwKJu3F3Ahvffee2rXrp3bWElJiSZNmqRHHnlEw4YN0%2Beff66JEyeqQ4cOSkhI0JYtW/T888/rxRdfVNeuXbVixQpNmDBBmzZtUosWLbyUBAAAWInPHsE6nbVr1yomJkZpaWkKCgpScnKyUlNTtWrVKklSXl6eRo4cqe7du6t58%2Ba6%2B%2B67JUlbt2715rIBAICF%2BHTBWrBggfr3769evXpp9uzZqqioUGFhoWJjY922i42NdZ0GPHne399f3bp1k91u9%2BjaAQCAdfnsKcIrr7xSycnJeuaZZ3To0CFNnTpVc%2BfOldPpVNu2bd22DQsLk8PhkCQ5nU6Fhoa6zYeGhrrmm6KkpESlpaVuYzZbC0VGRv7CNKcWEGCtfmyzNW29Dbmslu9MfDGTRC6rIZe1%2BGIuX8x0Oj5bsPLy8lz/v2PHjpo%2BfbomTpyoq6666qyPNQzjvPe9ePFit7HMzExNnjz5vJ7X6sLDW57T9iEhl1yglXiPL2aSyGU15LIWX8zli5lO5rMF62Tt2rVTXV2d/P395XQ63eYcDociIiIkSeHh4Y3mnU6nOnfu3OR9paenKzU11W3MZmshh6PiF67%2B1Kz2E0BT8wcE%2BCsk5BKVlVWqrq7%2BAq/KM3wxk0QuqyGXtfhiLm9kOtcf7s3ikwVrz549WrNmjWbOnOkaKyoqUmBgoFJSUvTnP//ZbfuCggJ1795dkhQfH6/CwkKNGDFCklRXV6c9e/YoLS2tyfuPjIxsdDqwtPS4amt94w3yS51r/rq6ep/7O/PFTBK5rIZc1uKLuXwx08msdQikiVq3bq28vDzl5ubqxIkTOnDggBYtWqT09HTdfPPNKi4u1qpVq1RdXa3t27dr%2B/btGj16tCQpIyNDb7/9tnbt2qXKykotXbpUgYGB6t%2B/v3dDAQAAy/DJI1ht27ZVbm6uFixY4CpII0aMUFZWloKCgrR8%2BXLNmzdPc%2BfOVVRUlObPn68rrrhCktSvXz898MADmjp1qn744QclJCQoNzdXzZs393IqAABgFT5ZsCSpd%2B/eeuONN047984775z2sb/97W/129/%2B9kItDQAA%2BDifPEUIAADgTRQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBOoXi4mLde%2B%2B9SkpK0oABAzR//nzV19d7e1kAAMAibN5ewMXo/vvvV1xcnDZv3qwffvhB48eP16WXXqq77rrL20uztP949mNvL%2BGcbJh6rbeXAACwKI5gncRut%2BvLL7/U9OnT1apVK8XExGjs2LHKy8vz9tIAAIBFcATrJIWFhYqKilJoaKhrLC4uTgcOHFB5ebmCg4PP%2BhwlJSUqLS11G7PZWigyMtLUtQYE0I8vJKsdcXt/%2BnUe32fD96CvfS%2BSy1rIZR2%2BmOl0KFgncTqdCgkJcRtrKFsOh6NJBSsvL0%2BLFy92G7vvvvt0//33m7dQ/VTk7rzsa6Wnp5te3ryppKREeXl5PpXLFzNJP%2BX6059eJJdFkMtafDGXL2Y6Hd%2BvkL%2BAYRjn9fj09HStXr3a7Ss9Pd2k1f1baWmpFi9e3OhomdX5Yi5fzCSRy2rIZS2%2BmMsXM50OR7BOEhERIafT6TbmdDrl5%2BeniIiIJj1HZGSkzzdzAABwehzBOkl8fLwOHz6so0ePusbsdrs6deqkli1benFlAADAKihYJ4mNjVVCQoIWLFig8vJyFRUV6ZVXXlFGRoa3lwYAACwiYM6cOXO8vYiLzXXXXad169bpiSee0Lvvvqu0tDSNGzdOfn5%2B3l5aIy1btlSfPn187uiaL%2BbyxUwSuayGXNbii7l8MdOp%2BBnne0U3AAAA3HCKEAAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsCyouLta9996rpKQkDRgwQPPnz1d9fb23l9UkxcXFyszMVFJSkpKTkzVz5kyVlZXp22%2B/VdeuXZWQkOD29dJLL7keu379eg0bNkw9evTQyJEj9dFHH3kxyb917dpV8fHxbut%2B4oknJEk7d%2B5UWlqaevbsqaFDh2rNmjVuj12xYoWGDBminj17KiMjQwUFBd6I0Mhnn33W6LWIj49X165d9Ze//OWUr9WGDRtcj7/Ycu3YsUPJycnKyspqNHem76v6%2BnotXLhQAwcOVO/evTVu3DgdOnTINe90OjV16lQlJyerb9%2B%2BeuSRR1RVVeX1TJs2bdLw4cPVo0cPDRkyRG%2B%2B%2BaZr7vnnn1e3bt0avX7ff/%2B9JKm6ulqPPfaY%2BvXrp6SkJE2ePFkOh8Mjmc6Ua/Xq1briiisarfvvf/%2B7pIv7tTpTrkcffbRRptjYWD388MOSpJkzZ7p%2BR27DV69evS6aXKf7TJekvXv36rbbbtNVV12lwYMH6%2BWXX3Z77Pm89yzBgOWMGDHCePTRR42ysjLjwIEDxuDBg42XX37Z28tqkptuusmYOXOmUV5ebhw%2BfNgYOXKkMWvWLOPQoUNGly5dTvu4PXv2GPHx8ca2bduMqqoq45133jG6d%2B9uHD582IOrP7UuXboYhw4dajT%2B3XffGVdeeaWxatUqo6qqyvj444%2BNxMRE4%2B9//7thGIbxwQcfGL169TJ27dplVFZWGsuXLzeuvfZao6KiwtMRmmTp0qXGlClTjE8//dQYMGDAabe72HLl5uYagwcPNsaMGWNMnTrVbe5s31crVqwwBgwYYOzbt884fvy48fvf/94YNmyYUV9fbxiGYdx3333Gvffea/zwww/GkSNHjPT0dOOJJ57waqbdu3cbCQkJxvvvv2/U1NQY27ZtM%2BLi4ozPPvvMMAzDeO6554wZM2ac9rmzs7ONkSNHGv/6178Mh8Nh3Hfffcb48eMvaJ4GZ8r11ltvGbfddttpH3uxvlaGceZcJ6upqTGGDh1qbNu2zTAMw5gxY4bx3HPPnXZ7b%2BYyjNN/pldWVhrXXXed8fzzzxsVFRVGQUGB0adPH2Pjxo2GYZz/e88KOIJlMXa7XV9%2B%2BaWmT5%2BuVq1aKSYmRmPHjlVeXp63l3ZWZWVlio%2BP17Rp09SyZUtddtllGjFihP72t7%2Bd9bGrVq1SSkqKUlJSFBQUpOHDh6tLly6NjghdTNauXauYmBilpaUpKChIycnJSk1N1apVqyRJeXl5GjlypLp3767mzZvr7rvvliRt3brVm8s%2BpX/961965ZVX9NBDD51124stV1BQkPLz89W%2BfftGc2f7vsrLy9PYsWPVsWNHBQcHKysrS0VFRdq9e7e%2B//57bd68WVlZWYqIiFDbtm01adIkvfXWW6qpqfFaJqfTqfHjx%2Bv666%2BXzWZTSkqKunTp0qT3WW1trfLz8zVp0iRdfvnlCgsL09SpU7Vt2zZ99913FyKKmzPlOpuL9bWSzi3Xn/70J/3qV79SSkrKWbf1dq4zfaZv27ZNNTU1mjhxolq0aKG4uDiNGjXK9d%2Bq83nvWQUFy2IKCwsVFRWl0NBQ11hcXJwOHDig8vJyL67s7EJCQpSdna1LL73UNXb48GFFRka6/vzQQw%2Bpb9%2B%2Buvrqq7VgwQLXh0RhYaFiY2Pdni82NlZ2u90ziz%2BLBQsWqH///urVq5dmz56tioqK06654XTZyfP%2B/v7q1q3bRZPp5xYtWqRbb71Vv/rVryRJFRUVrtMC1113nV555RUZ//d74y%2B2XHfccYdatWp1yrkzfV9VVVVp3759bvPBwcFq37697Ha79u7dq4CAAHXt2tU1HxcXpx9//FH79%2B%2B/MGH%2Bz5ky9evXT5mZma4/19bWqrS0VG3btnWNffXVVxozZozr1HXDqZmDBw/q%2BPHjiouLc23bsWNHNW/eXIWFhRcozb%2BdKZf00%2BfFXXfdpd69e2vgwIF65513JOmifq2ks%2BdqUFZWpmXLlunBBx90G//00091yy23qEePHkpLS3N9hng715k%2B0wsLC9W1a1cFBAS45s70%2Bdcw35T3nlVQsCzG6XQqJCTEbayhbHnyOgkz2O12vfbaa5o4caICAwPVo0cPDRo0SFu3blVubq7WrFmjP/7xj5J%2Byv3zUin9lPtiyHzllVcqOTlZmzZtUl5ennbt2qW5c%2Bee8rUKCwtzrflizvRz3377rTZt2qS77rpL0k8fdF26dNGdd96pHTt2KDs7W4sXL9Zbb70lyTq5pDOv9dixYzIM47TzTqdTwcHB8vPzc5uTLq73Yk5Ojlq0aKEbb7xRknTZZZcpOjpazzzzjD7%2B%2BGONGjVKEyZM0P79%2B%2BV0OiWp0fdtSEiI1zNFREQoJiZGDz74oD7%2B%2BGM98MADmjVrlnbu3Okzr9Vrr72m3r17q3Pnzq6x6OhotW/fXsuXL9eOHTvUq1cv/e53v7soc/38M/10n39Op1P19fXn9d6zCgqWBTUcKbCyzz//XOPGjdO0adOUnJysyMhIvfHGGxo0aJCaNWumxMREjR8/XqtXr3Y95mLNnZeXp1GjRikwMFAdO3bU9OnTtW7duiYdor9YM/3cypUrNXjwYLVp00bSTz8hv/rqq%2BrTp48CAwPVt29fjRkzxhKv1amcba1nmr%2BYcxqGofnz52vdunVaunSpgoKCJEmjRo3Sc889p/bt2%2BuSSy7R2LFj1a1bN7fT7Rdjrv79%2B%2BvFF19UbGysAgMDNXToUA0aNKjJ33cXY6afq6ur08qVK3XHHXe4jWdmZuqpp55S27ZtFRwcrAcffFCBgYHavHmzpIsn18mf6afz8zJ4Pu89K6BgWUxERITrp8wGTqdTfn5%2BioiI8NKqzs2WLVt07733atasWY0%2BTH4uKipK33//vQzDUHh4%2BClzX4yZ27Vrp7q6Ovn7%2Bzdas8PhcK3ZKpk2btyo1NTUM24TFRWlkpISSdbJJZ15rWFhYad8DZ1Op1q3bq2IiAiVl5errq7ObU6SWrdufeEXfwb19fWaOXOmtmzZotdff12/%2Bc1vzrh9w%2BvX8BqdnPnYsWNez3QqDeu28mvV4LPPPtOJEyfc/oXgqQQEBOjyyy93vV4XQ65TfaZHREQ0OtrkdDpdr9X5vPesgoJlMfHx8Tp8%2BLCOHj3qGrPb7erUqZNatmzpxZU1zRdffKEZM2Zo0aJFuuWWW1zjO3fu1NKlS9223b9/v6KiouTn56f4%2BPhG/9Tfbrere/fuHln36ezZs0dPP/2021hRUZECAwOVkpLSaM0FBQWuNcfHx7td11JXV6c9e/Z4PdPP7d27V8XFxbr22mtdYxs2bNB///d/u223f/9%2BRUdHS7JGrgZn%2Br4KCgpS586d3bKUlZXp4MGDSkxMVLdu3WQYhr788ku3x4aEhKhDhw4ey3AqTz31lL7%2B%2Bmu9/vrrrtelwR//%2BEft3LnTbayoqEjR0dGKjo5WaGioW%2BZ//OMfOnHihOLj4z2y9tN5/fXXtX79erexhnVb%2BbVq8MEHH%2Bjqq6%2BWzWZzjRmGoezsbLd1nzhxQgcPHlR0dPRFket0n%2Bnx8fH66quvVFtb67a2n3/%2B/dL3nlVQsCym4X4oCxYsUHl5uYqKivTKK68oIyPD20s7q9raWj366KOaPn26%2Bvbt6zbXqlUrLVmyRO%2B8845qampkt9v10ksvuXKNHj1an3zyibZt26bq6mrl5%2Bfrm2%2B%2B0fDhw70RxaV169bKy8tTbm6uTpw4oQMHDmjRokVKT0/XzTffrOLiYq1atUrV1dXavn27tm/frtGjR0uSMjIy9Pbbb2vXrl2qrKzU0qVLFRgYqP79%2B3s108/t2bNHYWFhCg4Odo01a9ZMzzzzjD766CPV1NTo448/1ltvveV6rayQq8HZvq8yMjK0YsUKFRUVqby8XDk5Oa57SEVERGjIkCF69tlndfToUR05ckRLlixRWlqa238kPe3zzz/XmjVrlJubq7CwsEbzTqdTc%2BfO1f79%2B1VdXa2XX35ZBw8e1IgRIxQQEKDRo0dr2bJlOnz4sBwOh/7whz9o0KBBbhcye8OJEyf0xBNPyG63q6amRuvWrdOHH36oMWPGSLLma/Vze/fuVbt27dzG/Pz89O2332ru3Ln67rvvVFFRoZycHDVr1kzXX3%2B913Od6TM9JSVFwcHBWrp0qSorK7V7927l5%2Bc3%2BTP9TK%2BnZXjwlhAwyeHDh427777bSExMNJKTk43nnnvOEvcG%2Beyzz4wuXboY8fHxjb6%2B/fZbY9OmTcbw4cONxMRE49prrzWWLVtm1NXVuR6/ceNGY/DgwUZcXJxx8803G3/961%2B9mObf/vrXvxrp6enGlVdeafTp08fIzs42qqqqXHPDhw834uLijMGDB7vuAdNg5cqVRkpKihEfH29kZGQYX331lTcinNayZcuMoUOHNhp/4403jMGDBxsJCQnGgAEDjDfffNNt/mLK1fA9dsUVVxhXXHGF688NzvR9VV9fbyxatMi45pprjMTEROOee%2B5xu/daWVmZkZWVZVx55ZVG7969jblz5xrV1dVezfTwww%2B7jTV83XXXXYZhGEZVVZXx5JNPGtddd52RkJBgjBgxwvjiiy9cz11dXW3MmTPH6N27t9GjRw/jgQceMMrKyi54prPlqq%2BvN5YsWWIMGDDAiI%2BPN2644QZjy5YtrsderK/V2XI1GDx4sPHiiy82eqzD4TBmzpxpJCcnG4mJicZtt91m7Nu3zzXvzVxn%2B0z/6quvjDFjxhjx8fFG//79jZUrV7o9/nzee1bgZxgWv4oMAADgIsMpQgAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJP9Lw%2BDrs9YmrxYAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-6139428519808234832”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>344</td> <td class=”number”>10.7%</td> <td>

<div class=”bar” style=”width:35%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>340</td> <td class=”number”>10.6%</td> <td>

<div class=”bar” style=”width:35%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>301</td> <td class=”number”>9.4%</td> <td>

<div class=”bar” style=”width:31%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>265</td> <td class=”number”>8.3%</td> <td>

<div class=”bar” style=”width:27%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>243</td> <td class=”number”>7.6%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>214</td> <td class=”number”>6.7%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>142</td> <td class=”number”>4.4%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>131</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>103</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.0</td> <td class=”number”>74</td> <td class=”number”>2.3%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (177)</td> <td class=”number”>975</td> <td class=”number”>30.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-6139428519808234832”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>56</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>265</td> <td class=”number”>8.3%</td> <td>

<div class=”bar” style=”width:77%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>344</td> <td class=”number”>10.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>340</td> <td class=”number”>10.6%</td> <td>

<div class=”bar” style=”width:98%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>301</td> <td class=”number”>9.4%</td> <td>

<div class=”bar” style=”width:87%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1230.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1261.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1400.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1804.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2027.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_GROC14”><s>GROC14</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_GROC09”><code>GROC09</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.9926</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_GROCPTH09”>GROCPTH09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3033</td>

</tr> <tr>

<th>Unique (%)</th> <td>94.5%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.26804</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>3.0738</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>1.7%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-5256192353242295326”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-5256192353242295326,#minihistogram-5256192353242295326”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-5256192353242295326”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-5256192353242295326”

aria-controls=”quantiles-5256192353242295326” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-5256192353242295326” aria-controls=”histogram-5256192353242295326”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-5256192353242295326” aria-controls=”common-5256192353242295326”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-5256192353242295326” aria-controls=”extreme-5256192353242295326”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-5256192353242295326”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.084503</td>

</tr> <tr>

<th>Q1</th> <td>0.14991</td>

</tr> <tr>

<th>Median</th> <td>0.20692</td>

</tr> <tr>

<th>Q3</th> <td>0.30563</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.68157</td>

</tr> <tr>

<th>Maximum</th> <td>3.0738</td>

</tr> <tr>

<th>Range</th> <td>3.0738</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.15572</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.2245</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.83754</td>

</tr> <tr>

<th>Kurtosis</th> <td>26.154</td>

</tr> <tr>

<th>Mean</th> <td>0.26804</td>

</tr> <tr>

<th>MAD</th> <td>0.13847</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>3.8496</td>

</tr> <tr>

<th>Sum</th> <td>839.51</td>

</tr> <tr>

<th>Variance</th> <td>0.050398</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-5256192353242295326”>

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GCE7QFrw4YNuvnmm6%2B47sCBAzZUAwAAYJ7tN7n36dNHs2fP9t8zJUl/%2BMMf9B//8R%2B1Hp0AAAAQimwPWMuWLdPZs2fVs2dP/9jw4cNVU1Oj5cuX210OAACAcbZfIvzkk0%2B0ZcsWxcXF%2BccSEhKUm5ure%2B%2B91%2B5yAAAAjLP9DFZVVZUiIyNrFxIersrKSrvLAQAAMM72gDVkyBAtX75cZ86c8Y99%2B%2B23WrJkScCjGgAAAEKV7ZcIFy1apN/85je69dZbFRUVpZqaGlVUVKhr167605/%2BZHc5AAAAxtkesLp27ar3339fH3/8sY4fP67w8HB1795dw4YN8/9xZQAAgFAWlD%2BV07JlS91xxx3B2DUAAECDsz1gnThxQitXrtQ333yjqqqqWvMfffSR3SUBAAAYFZR7sEpKSjRs2DC1adPG7t0DAAA0ONsD1sGDB/XRRx/J6XTavWsAAABb2P6Yhvbt23PmCgAANGm2B6xZs2YpLy9PPp/P7l0DAADYwvZLhB9//LG%2B%2Buorbd68WV26dFF4eGDG%2B6//%2Bi%2B7SwIAADDK9oAVFRWl2267ze7dAgAA2Mb2gLVs2TK7dwkAAGAr2%2B/BkqSjR4/qlVde0e9%2B9zv/2N///vdglAIAAGCc7QHr888/17hx4/Thhx9q69atkn58%2BOiMGTN4yCgAAGgSbA9Yq1at0hNPPKEtW7YoLCxM0o9/n3D58uVas2aN3eUAAAAYZ3vA%2Bp//%2BR9lZGRIkj9gSdLo0aPldrvtLgcAAMA42wNWdHR0nX%2BDsKSkRC1btrS7HAAAAONsD1iDBg3SCy%2B8oPLycv9YUVGRFixYoFtvvdXucgAAAIyz/TENv/vd73T//fcrJSVF1dXVGjRokCorK9WrVy8tX77c7nIAAACMsz1gderUSVu3btXevXtVVFSkVq1aqXv37ho6dGjAPVkAAAChyvaAJUktWrTQHXfcEYxdAwAANDjbA9aIESMue6aKZ2EBAIBQZ3vAuueeewICVnV1tYqKiuRyuXT//ffbXQ4AAIBxtges%2BfPn1zn%2BwQcf6K9//avN1QAAAJgXlL9FWJc77rhD77//frDLAAAAuGaNJmAdOnRIPp8v2GUAAABcM9svEaanp9caq6yslNvt1l133WV3OQAAAMbZHrASEhJq/RZhZGSkJk%2BerClTpthdDgAAgHG2Byye1g4AAJo62wPWO%2B%2B8U%2B%2B148ePb8BKAAAAGobtAWvx4sWqqampdUN7WFhYwFhYWBgBCwAAhCTbA9Zrr72m9evXa/bs2erTp498Pp/%2B%2Bc9/6tVXX9V9992nlJQUu0sCAAAwKij3YOXn56tjx47%2BscGDB6tr166aOXOmtm7dandJAAAARtn%2BHKxjx44pNja21nhMTIyKi4vtLgcAAMA42wNW586dtXz5cpWWlvrHysrKtHLlSt1www12lwMAAGCc7ZcIFy1apHnz5qmgoEBt27ZVeHi4ysvL1apVK61Zs8bucgAAAIyzPWANGzZMe/bs0d69e3Xq1Cn5fD517NhRv/zlLxUdHW13OQAAAMbZHrAkqXXr1ho5cqROnTqlrl27BqMEAACABmP7PVhVVVVasGCBBg4cqLvvvlvSj/dgPfjggyorK7O7HAAAAONsD1grVqzQ119/rdzcXIWH/9/uq6urlZuba3c5AAAAxtkesD744AOtXr1ao0eP9v/R55iYGC1btkwffvih3eUAAAAYZ3vAqqioUEJCQq1xp9OpH374we5yAAAAjLM9YN1www3661//KkkBf3twx44d%2Brd/%2Bze7ywEAADDO9t8i/PWvf61HH31UkyZNUk1NjV5//XUdPHhQH3zwgRYvXmx3OQAAAMbZHrCmTZsmh8OhN954QxEREfr973%2Bv7t27Kzc3V6NHj7a7HAAAAONsD1inT5/WpEmTNGnSJLt3DQAAYAvb78EaOXJkwL1XAAAATY3tASslJUXbt283sq19%2B/YpNTVVOTk5tea2bdumsWPHauDAgZo4caI%2B%2BeQT/1xNTY1WrVqlkSNHasiQIZo5c6ZOnDjhn/d6vcrOzlZqaqqGDRumxYsXq6qqykjNAACg6bP9EuH111%2Bv559/Xvn5%2BbrhhhvUokWLgPmVK1fWazuvvvqqNm3apG7dutWa%2B/rrr7VgwQLl5eXplltu0QcffKBHHnlEO3bsUKdOnfTmm29qy5YtevXVV9WxY0etWrVKWVlZevfddxUWFqannnpK58%2Bf19atW3XhwgU99thjys3N1ZNPPmmkBwAAoGmz/QzWkSNH9Itf/ELR0dEqLS1VSUlJwEd9RUZGWgasjRs3Ki0tTWlpaYqMjNS4cePUu3dvvffee5KkgoICZWZmqkePHoqKilJOTo7cbrcOHDig7777Tjt37lROTo6cTqc6duyohx9%2BWG%2B//bYuXLhgrA8AAKDpsu0MVk5OjlatWqU//elP/rE1a9YoKyvrZ21vxowZlnOFhYVKS0sLGEtMTJTL5VJVVZWOHDmixMRE/1xUVJS6desml8uls2fPKiIiQn369PHP9%2BvXTz/88IOOHj0aMA4AAFAX2wLWrl27ao3l5%2Bf/7IB1OV6vV7GxsQFjsbGxOnLkiM6cOSOfz1fnfGlpqdq1a6eoqCj/n/G5NCdJpaWl9dp/SUmJPB5PwJjD0Ubx8fE/53AsRUTYfgLymjgc9tV7qTeh1iM70Btr9MYavbFGb6w1597YFrDq%2Bs3Bhvxtwitt%2B3Lz11pXQUGB8vLyAsaysrI0d%2B7ca9puqIuLa2v7PmNiWtu%2Bz1BBb6zRG2v0xhq9sdYce2NbwPrXM0KXGzMhLi5OXq83YMzr9crpdKpdu3YKDw%2Bvc759%2B/ZyOp0qLy9XdXW1IiIi/HOS1L59%2B3rtf9q0aRoxYkTAmMPRRqWlFT/3kOoUaj8RmD7%2By4mICFdMTGuVlVWqurrGtv2GAnpjjd5YozfW6I21xtCbYPxwLwXhtwjtkJSUpIMHDwaMuVwujRkzRpGRkerVq5cKCwt18803S5LKysp0/Phx9e/fX507d5bP59Phw4fVr18//%2BfGxMSoe/fu9dp/fHx8rcuBHs9ZXbzYvL/wgnH81dU1zb7vVuiNNXpjjd5YozfWmmNvQusUSD1NnTpVn332mfbs2aNz585p06ZNOnbsmMaNGydJysjI0IYNG%2BR2u1VeXq7c3Fz17dtXycnJcjqdGjVqlF566SWdPn1ap06d0po1azR58mQ5HE0yjwIAAMNsSwwXLlzQvHnzrjhW3%2BdgJScnS5IuXrwoSdq5c6ekH8829e7dW7m5uVq2bJmKi4vVs2dPrVu3Th06dJAkpaeny%2BPxaPr06aqoqFBKSkrAPVPPPvusnn76aY0cOVItWrTQvffeW%2BfDTAEAAOoS5rPp79ZMnz69Xuv%2B9TEOTYnHc9b4Nh2OcN2Zu8/4dhvK9uyhtu3L4QhXXFxblZZWNLvT0ldCb6zRG2v0xhq9sdYYetOhQ3RQ9mvbGaymGpwAAAB%2BqknegwUAABBMBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADDMEewC0Hzc/dKnwS7hqmzPHhrsEgAAIYozWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMCwJhuw%2BvTpo6SkJCUnJ/s/nnvuOUnS559/rsmTJ2vQoEEaM2aM3nvvvYDP3bBhg0aNGqVBgwYpIyNDBw8eDMYhAACAENWkn4O1Y8cOdenSJWCspKREDz/8sBYvXqyxY8fqyy%2B/1Jw5c9S9e3clJydr165deuWVV/Taa6%2BpT58%2B2rBhg2bPnq0PP/xQbdq0CdKRAACAUNJkz2BZ2bJlixISEjR58mRFRkYqNTVVI0aM0MaNGyVJBQUFmjhxogYMGKBWrVrpwQcflCTt3r07mGUDAIAQ0qTPYK1cuVJ///vfVV5errvvvlsLFy5UYWGhEhMTA9YlJiZq%2B/btkqTCwkLdc889/rnw8HD17dtXLpdLY8aMqdd%2BS0pK5PF4AsYcjjaKj4%2B/xiMKFBHR7PKxrRyOptnfS68bXj%2B10Rtr9MYavbHWnHvTZAPWv//7vys1NVUvvviiTpw4oezsbC1ZskRer1cdO3YMWNuuXTuVlpZKkrxer2JjYwPmY2Nj/fP1UVBQoLy8vICxrKwszZ0792ceDYIhLq5tsEtoUDExrYNdQqNFb6zRG2v0xlpz7E2TDVgFBQX%2B/%2B7Ro4fmz5%2BvOXPm6Kabbrri5/p8vmva97Rp0zRixIiAMYejjUpLK65puz/VHH8isJPp/1%2BNRUREuGJiWqusrFLV1TXBLqdRoTfW6I01emOtMfQmWD8sN9mA9VNdunRRdXW1wsPD5fV6A%2BZKS0vldDolSXFxcbXmvV6vevXqVe99xcfH17oc6PGc1cWLfOGFkqb%2B/6u6uqbJH%2BPPRW%2Bs0Rtr9MZac%2BxNkzwFcujQIS1fvjxgzO12q2XLlkpLS6v12IWDBw9qwIABkqSkpCQVFhb656qrq3Xo0CH/PAAAwJU0yYDVvn17FRQUKD8/X%2BfPn1dRUZFefvllTZs2Tb/61a9UXFysjRs36ty5c9q7d6/27t2rqVOnSpIyMjL0zjvvaP/%2B/aqsrNTatWvVsmVLDR8%2BPLgHBQAAQkaTvETYsWNH5efna%2BXKlf6ANGHCBOXk5CgyMlLr1q3T0qVLtWTJEnXu3FkrVqzQjTfeKEm67bbb9Pjjjys7O1vff/%2B9kpOTlZ%2Bfr1atWgX5qAAAQKgI813rHd2oF4/nrPFtOhzhujN3n/Ht4kfbs4cGu4QG4XCEKy6urUpLK5rdPRFXQm%2Bs0Rtr9MZaY%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%2BvXrp507d%2Br777/XrFmzdN111%2BmBBx4IdmlAkxBKZ9skzrgBuHqcwfoJl8ulw4cPa/78%2BYqOjlZCQoIyMzNVUFAQ7NIAAECI4AzWTxQWFqpz586KjY31j/Xr109FRUUqLy9XVFTUFbdRUlIij8cTMOZwtFF8fLzRWiMiyMeAHRwOvtYuvd/wvlMbvbHWnHtDwPoJr9ermJiYgLFLYau0tLReAaugoEB5eXkBY4888ogeffRRc4XqxyB3f6dvNG3aNOPhLdSVlJSooKCA3tSB3lijN9ZKSkr0xz%2B%2BRm/qQG%2BsNefeNL9IWQ8%2Bn%2B%2BaPn/atGnavHlzwMe0adMMVfd/PB6P8vLyap0tA725HHpjjd5YozfW6I215twbzmD9hNPplNfrDRjzer0KCwuT0%2Bms1zbi4%2BObXVIHAAD/hzNYP5GUlKSTJ0/q9OnT/jGXy6WePXuqbdu2QawMAACECgLWTyQmJio5OVkrV65UeXm53G63Xn/9dWVkZAS7NAAAECIinnnmmWeCXURj88tf/lJbt27Vc889p/fff1%2BTJ0/WzJkzFRYWFuzSamnbtq1uvvlmzq7Vgd5YozfW6I01emON3lhrrr3npWpAAAAI40lEQVQJ813rHd0AAAAIwCVCAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWI1ccXGxHnroIaWkpOj222/XihUrVFNTU%2BfaDRs2aNSoURo0aJAyMjJ08OBBm6u1V31788orr6hv375KTk4O%2BPjuu%2B%2BCULU99u3bp9TUVOXk5Fx2XU1NjVatWqWRI0dqyJAhmjlzpk6cOGFTlcFR394sXLjQ/7dJL30MHjzYpiqDo7i4WFlZWUpJSVFqaqoWLlyosrKyOtdu27ZNY8eO1cCBAzVx4kR98sknNldrr/r2ZvPmzbrxxhtrvd/84x//CELV9jh8%2BLDuv/9%2B3XTTTUpNTVV2drY8Hk%2Bda5vV9ykfGrUJEyb4nnzySV9ZWZmvqKjId9ddd/nWr19fa91HH33kGzx4sG///v2%2ByspK37p163xDhw71VVRUBKFqe9S3N6tXr/YtWLAgCBUGR35%2Bvu%2Buu%2B7ypaen%2B7Kzsy%2B7dsOGDb7bb7/dd%2BTIEd/Zs2d9zz77rG/s2LG%2Bmpoam6q119X0ZsGCBb7Vq1fbVFnjcO%2B99/oWLlzoKy8v9508edI3ceJE36JFi2qtO3TokC8pKcm3Z88eX1VVle/dd9/1DRgwwHfy5MkgVG2P%2Bvbm7bff9t13331BqDA4zp0757v11lt9eXl5vnPnzvm%2B//5733333ed7%2BOGHa61tbt%2BnOIPViLlcLh0%2BfFjz589XdHS0EhISlJmZqYKCglprCwoKNHHiRA0YMECtWrXSgw8%2BKEnavXu33WXb4mp609xERkZq06ZN6tat2xXXFhQUKDMzUz169FBUVJRycnLkdrt14MABGyq139X0prkpKytTUlKS5s2bp7Zt26pTp06aMGGC/va3v9Vau3HjRqWlpSktLU2RkZEaN26cevfurffeey8IlTe8q%2BlNc1NZWamcnBzNmjVLLVu2lNPp1J133qlvvvmm1trm9n2KgNWIFRYWqnPnzoqNjfWP9evXT0VFRSovL6%2B1NjEx0f/v8PBw9e3bVy6Xy7Z67XQ1vZGkf/7zn0pPT9egQYM0ZsyYJn05Y8aMGYqOjr7iuqqqKh05ciTgdRMVFaVu3bo12ddNfXtzyRdffKHx48dr4MCBmjx5cpO%2BnBETE6Nly5bpuuuu84%2BdPHlS8fHxtdb%2B9P1GkhITE5vs6%2BZqenNp7oEHHtCQIUM0cuRIvfvuu3aVarvY2FhNmTJFDodDknT06FH993//t%2B6%2B%2B%2B5aa5vb9ykCViPm9XoVExMTMHYpUJSWltZa%2B69h49Lan65rKq6mN506dVLXrl314osv6tNPP9WUKVM0e/ZsHT161LZ6G6MzZ87I5/M1q9fN1ejatau6deumdevWad%2B%2BfRo8eLB%2B85vfNJveuFwuvfHGG5ozZ06tueb2fvNTl%2BuN0%2BlUQkKCnnjiCX366ad6/PHHtWjRIn3%2B%2BedBqNQ%2BxcXFSkpK0j333KPk5GTNnTu31prm9rohYDVyPp%2BvQdY2BfU93ilTpmj16tXq1q2bWrdurczMTPXt27fJXs64Ws3tdVNfWVlZeuGFF9SxY0dFRUXpiSeeUMuWLbVz585gl9bgvvzyS82cOVPz5s1TampqnWua6%2BvmSr0ZPny4XnvtNSUmJqply5YaM2aM7rzzTm3evDkI1dqnc%2BfOcrlc2rFjh44dO6bf/va3da5rTq8bAlYj5nQ65fV6A8a8Xq/CwsLkdDoDxuPi4upc%2B9N1TcXV9KYunTt3VklJSUOVFxLatWun8PDwOvvYvn37IFXVeEVEROj6669v8q%2BbXbt26aGHHtKiRYs0Y8aMOtc0t/ebS%2BrTm7o0l/ebsLAwJSQkKCcnR1u3btXp06cD5pvb64aA1YglJSXp5MmTAS9Sl8ulnj17qm3btrXWFhYW%2Bv9dXV2tQ4cOacCAAbbVa6er6c1//ud/1jo973a71bVrV1tqbawiIyPVq1evgNdNWVmZjh8/rv79%2BwexsuDz%2BXxatmyZDh8%2B7B87f/68jh8/3qRfN1999ZUWLFigl19%2BWePHj7dcl5SUVOt%2BNJfL1WTfb6T69%2Batt97Stm3bAsaa8vvN559/rlGjRgU8Iic8/Mdo0aJFi4C1ze37FAGrEbv0DJ6VK1eqvLxcbrdbr7/%2BujIyMiRJo0eP9v8WS0ZGht555x3t379flZWVWrt2rVq2bKnhw4cH8QgaztX0xuv1asmSJTp69KjOnTun9evX6/jx45owYUIwDyEovv32W40ePdr/rKuMjAxt2LBBbrdb5eXlys3N9T8zrLn5196EhYXpf//3f7VkyRJ9%2B%2B23qqioUG5urlq0aKE77rgj2KU2iIsXL%2BrJJ5/U/PnzNWzYsFrz999/vz84TJ06VZ999pn27Nmjc%2BfOadOmTTp27JjGjRtnd9m2uJrenD9/Xs8995xcLpcuXLigrVu36uOPP1Z6errdZdsiKSlJ5eXlWrFihSorK3X69Gm98sorGjx4sKKjo5v19ylHsAvA5a1evVpPPfWUhg4dqqioKKWnp%2BvXv/61JKmoqEg//PCDJOm2227T448/ruzsbH3//fdKTk5Wfn6%2BWrVqFczyG1R9ezNv3jxJUmZmprxer3r27Kk//OEP6tSpU9Bqb0iXwtHFixclyX/P0KU3/KKiIp0/f16SlJ6eLo/Ho%2BnTp6uiokIpKSnKy8sLTuE2uJrePP/883rxxRc1ceJElZeXq3///vrjH/%2BoNm3aBKf4BrZ//3653W4tXbpUS5cuDZjbsWOHTpw4oTNnzkiSevfurdzcXC1btkzFxcXq2bOn1q1bpw4dOgSj9AZ3Nb2ZMWOGKioq9Nhjj8nj8ahLly5as2aNkpKSglF6g4uOjtb69eu1dOlS3XLLLWrTpo1uueUWPf/885Ka9/epMF9zuuMMAADABlwiBAAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADD/h8FBQ8yMGaJPgAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-5256192353242295326”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>56</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.4952947</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.452284034</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.288850376</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.126823082</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.166112957</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.288683603</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.264830508</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.394632991</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.181521147</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3022)</td> <td class=”number”>3054</td> <td class=”number”>95.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-5256192353242295326”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>56</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0138614</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0313834</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0315169</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0348311</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>2.049180328</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.183803458</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.262443439</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.007518797</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.073770492</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_GROCPTH14”>GROCPTH14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3024</td>

</tr> <tr>

<th>Unique (%)</th> <td>94.2%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.25192</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>3.1496</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>2.1%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-6175607757451177990”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-6175607757451177990,#minihistogram-6175607757451177990”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-6175607757451177990”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-6175607757451177990”

aria-controls=”quantiles-6175607757451177990” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-6175607757451177990” aria-controls=”histogram-6175607757451177990”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-6175607757451177990” aria-controls=”common-6175607757451177990”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-6175607757451177990” aria-controls=”extreme-6175607757451177990”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-6175607757451177990”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.071782</td>

</tr> <tr>

<th>Q1</th> <td>0.13884</td>

</tr> <tr>

<th>Median</th> <td>0.19453</td>

</tr> <tr>

<th>Q3</th> <td>0.28548</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.64199</td>

</tr> <tr>

<th>Maximum</th> <td>3.1496</td>

</tr> <tr>

<th>Range</th> <td>3.1496</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.14664</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.21937</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.87081</td>

</tr> <tr>

<th>Kurtosis</th> <td>26.76</td>

</tr> <tr>

<th>Mean</th> <td>0.25192</td>

</tr> <tr>

<th>MAD</th> <td>0.1323</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>3.9671</td>

</tr> <tr>

<th>Sum</th> <td>789.02</td>

</tr> <tr>

<th>Variance</th> <td>0.048125</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-6175607757451177990”>

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1P5%2BflB42vXrtUVV1yh9PT0oK/Twa2hoUHLly/X8OHDNXjwYE2ZMkUHDx4MvN7n82nWrFnKzMzUkCFDNH/%2BfNXV1ZltAAAAaLacdi/o8Xi0Zs0avfbaa6qpqdGIESP04osv6sorrwxsM3jwYP32t7/V6NGjf3A/zz77rNasWaOuXbuGnB88eLD%2B9Kc/hZx7%2BeWXtX79ej377LPq0KGDli9frry8PL3%2B%2BuuKiorSggULdOLECW3YsEEnT57UzJkzVVRUpIcffvjCDh4AAFwSbD%2BDNWrUKG3btk1Tp07Vu%2B%2B%2Bq6VLlwaFK0nKysrSkSNHzrqfmJiYswassyktLVVubq66d%2B%2BuuLg45efny%2BPxaOfOnfr666%2B1efNm5efny%2BVyqUOHDrrnnnv0yiuv6OTJk%2Be9FgAAuPTYfgZr1apVuuqqq8653c6dO886P2nSpLPOHzp0SHfeead27dqlhIQEzZgxQ7/61a9UV1enPXv2KDU1NbBtXFycunbtKrfbrWPHjik6Olq9e/cOzPft21fffvut9u7dGzQOAAAQiu0Bq3fv3po2bZrGjx%2Bv66%2B/XpL0xz/%2BUe%2B//76WLl2qtm3bXvAaLpdLKSkpuv/%2B%2B9WjRw/9/e9/14MPPqjk5GT99Kc/ld/vV2JiYtBrEhMTVVlZqbZt2youLk5RUVFBc5JUWVnZqPUrKirk9XqDxpzONkpOTr7AIwsWHR1Zz4l1Opu%2B3tM9ibTeNCV6Ehp9saInVvTEip40ju0Bq7CwUMeOHVOPHj0CY0OHDtX27du1ZMkSLVmy5ILXGDp0qIYOHRr491GjRunvf/%2B71q5dqzlz5kiS/H7/D77%2BbHONUVpaquLi4qCxvLw8zZgx44L2G%2BmSkmJtWyshobVta0UKehIafbGiJ1b0xIqenJ3tAeu9997T%2BvXrlZSUFBhLSUlRUVGRbr755iZbt1OnTtq1a5fatm0rh8Mhn88XNO/z%2BdSuXTu5XC5VV1ervr5e0dHRgTlJateuXaPWmjBhgoYNGxY05nS2UWVljYEj%2BY9I%2B%2B3B9PGHEh3tUEJCa1VV1aq%2BvqHJ14sE9CQ0%2BmJFT6zoiVWk9cTOX%2B6/z/aAVVdXp5iYGMu4w%2BFQbW2tkTX%2B/Oc/KzExUTfddFNgzOPxqEuXLoqJiVHPnj1VVlYWuBesqqpKBw4cUL9%2B/dSpUyf5/X7t3r1bffv2lSS53W4lJCSoW7dujVo/OTnZcjnQ6z2mU6cu/jdiU7Lz%2BOvrGy75fp%2BJnoRGX6zoiRU9saInZ2f7KZDBgwdryZIlOnr0aGDsq6%2B%2B0sKFCy1/TfhjnThxQo899pjcbrdOnjypDRs26N1331V2drYkKScnR6tWrZLH41F1dbWKiorUp08fpaeny%2BVyacSIEfrd736nI0eO6PDhw1qxYoXGjx8vp9P2PAoAACKQ7Ylh3rx5%2Bs1vfqNrrrlGcXFxamhoUE1Njbp06fKDz60KJT09XZICn2O4efNmSd%2BdbZo0aZJqamo0c%2BZMeb1ede7cWStWrFBaWpokKTs7W16vVxMnTlRNTY0yMjKC7pl69NFH9cgjj2j48OFq0aKFbr75ZsvDTAEAAH5IlP9C7%2Bj%2BEU6cOKF3331XBw4ckMPhULdu3TRkyJDAPU/Nkdd7zPg%2BnU6Hbijabny/TeWNWdc2%2BRpOp0NJSbGqrKzh1PW/0ZPQ6IsVPbGiJ1aR1pP27ePDsm5Yrnm1bNky8IgGAACA5sb2gHXw4EEtW7ZMX375ZcjP93v77bftLgkAAMCosNyDVVFRoSFDhqhNmzZ2Lw8AANDkbA9Yu3bt0ttvvy2Xy2X30gAAALaw/TEN7dq148wVAABo1mwPWFOnTlVxcfEFfxwNAADAxcr2S4TvvvuuPv30U61du1adO3eWwxGc8f7yl7/YXRIAAIBRtgesuLg4XXfddXYvCwAAYBvbA1ZhYaHdSwIAANjK9nuwJGnv3r16%2Bumn9dBDDwXG/vd//zccpQAAABhne8D68MMPNWbMGL311lvasGGDpO8ePjpp0iQeMgoAAJoF2wPW8uXL9cADD2j9%2BvWKioqSJHXp0kVLlizRihUr7C4HAADAONsD1v/93/8pJydHkgIBS5JGjhwpj8djdzkAAADG2R6w4uPjQ34GYUVFhVq2bGl3OQAAAMbZHrAGDhyoxx9/XNXV1YGxffv2qaCgQNdcc43d5QAAABhn%2B2MaHnroIU2ePFkZGRmqr6/XwIEDVVtbq549e2rJkiV2lwMAAGCc7QGrY8eO2rBhg9555x3t27dPrVq1Urdu3XTttdcG3ZMFAAAQqWwPWJLUokULXX/99eFYGgAAoMnZHrCGDRt21jNVPAsLAABEOtsD1k033RQUsOrr67Vv3z653W5NnjzZ7nIAAACMsz1gzZkzJ%2BT4m2%2B%2BqX/84x82VwMAAGBeWD6LMJTrr79ef/vb38JdBgAAwAW7aALWZ599Jr/fH%2B4yAAAALpjtlwizs7MtY7W1tfJ4PLrxxhvtLgcAAMA42wNWSkqK5a8IY2JiNH78eN122212lwMAAGCc7QGLp7UDAIDmzvaA9dprrzV621tuuaUJKwEAAGgatges%2BfPnq6GhwXJDe1RUVNBYVFQUAQsAAEQk2wPWc889p%2Beff17Tpk1T79695ff79cUXX%2BjZZ5/VHXfcoYyMDLtLAgAAMCos92CVlJSoQ4cOgbFBgwapS5cumjJlijZs2GB3SQAAAEbZ/hys/fv3KzEx0TKekJCg8vJyu8sBAAAwzvaA1alTJy1ZskSVlZWBsaqqKi1btkyXX3653eUAAAAYZ/slwnnz5mn27NkqLS1VbGysHA6Hqqur1apVK61YscLucgAAAIyzPWANGTJE27Zt0zvvvKPDhw/L7/erQ4cO%2BvnPf674%2BHi7ywEAADDO9oAlSa1bt9bw4cN1%2BPBhdenSJRwlAAAANBnb78Gqq6tTQUGBBgwYoF/%2B8peSvrsH66677lJVVZXd5QAAABhne8BaunSpPv/8cxUVFcnh%2BM/y9fX1KioqsrscAAAA42wPWG%2B%2B%2BaaeeuopjRw5MvChzwkJCSosLNRbb71ldzkAAADG2R6wampqlJKSYhl3uVz69ttv7S4HAADAONsD1uWXX65//OMfkhT02YObNm3Sf/3Xf9ldDgAAgHG2/xXhr3/9a91333269dZb1dDQoBdeeEG7du3Sm2%2B%2Bqfnz59tdDgAAgHG2B6wJEybI6XTqpZdeUnR0tH7/%2B9%2BrW7duKioq0siRI%2B0uBwAAwDjbA9aRI0d066236tZbb7V7aQAAAFvYfg/W8OHDg%2B69AgAAaG5sD1gZGRl644037F4WAADANrZfIvzJT36ixYsXq6SkRJdffrlatGgRNL9s2TK7SwIAADDK9oC1Z88e/fSnP5UkVVZW2r08AABAk7MtYOXn52v58uX605/%2BFBhbsWKF8vLy7CoBAADAFrbdg7VlyxbLWElJiV3LAwAA2Ma2gBXqLwf5a0IAANAc2RawTn%2Bw87nGAAAAIp3tj2kwafv27crMzFR%2Bfr5lbuPGjRo9erQGDBigcePG6b333gvMNTQ0aPny5Ro%2BfLgGDx6sKVOm6ODBg4F5n8%2BnWbNmKTMzU0OGDNH8%2BfNVV1dnyzEBAIDIF7EB69lnn9WiRYvUtWtXy9znn3%2BugoICzZkzRx999JFyc3N177336vDhw5Kkl19%2BWevXr1dJSYm2bt2qlJQU5eXlBS5ZLliwQLW1tdqwYYNeeeUVeTweFRUV2Xp8AAAgctn2V4QnT57U7NmzzznW2OdgxcTEaM2aNVq8eLGOHz8eNLd69WplZWUpKytLkjRmzBi99NJLWrdune6%2B%2B26VlpYqNzdX3bt3l/TdXzhmZGRo586d6ty5szZv3qxXX31VLpdLknTPPfdo5syZKigosDy3CwAA4Ey2Bawrr7xSFRUV5xxrrEmTJv3gXFlZWSBcnZaamiq32626ujrt2bNHqampgbm4uDh17dpVbrdbx44dU3R0tHr37h2Y79u3r7799lvt3bs3aPyHVFRUyOv1Bo05nW2UnJzc2MNrlOjoyDoB6XQ2fb2nexJpvWlK9CQ0%2BmJFT6zoiRU9aRzbAtb3n3/V1Hw%2BnxITE4PGEhMTtWfPHh09elR%2Bvz/kfGVlpdq2bau4uLigG/BPb9vYB6OWlpaquLg4aCwvL08zZsz4MYfTbCQlxdq2VkJCa9vWihT0JDT6YkVPrOiJFT05O9uf5G6Xcz0C4mzzF/r4iAkTJmjYsGFBY05nG1VW1lzQfs8Uab89mD7%2BUKKjHUpIaK2qqlrV1zc0%2BXqRgJ6ERl%2Bs6IkVPbGKtJ7Y%2Bcv99zXLgJWUlCSfzxc05vP55HK51LZtWzkcjpDz7dq1k8vlUnV1terr6xUdHR2Yk6R27do1av3k5GTL5UCv95hOnbr434hNyc7jr69vuOT7fSZ6Ehp9saInVvTEip6cXWSdAmmktLQ07dq1K2jM7Xarf//%2BiomJUc%2BePVVWVhaYq6qq0oEDB9SvXz/16dNHfr9fu3fvDnptQkKCunXrZtsxAACAyNUsA9btt9%2BuDz74QNu2bdPx48e1Zs0a7d%2B/X2PGjJEk5eTkaNWqVfJ4PKqurlZRUZH69Omj9PR0uVwujRgxQr/73e905MgRHT58WCtWrND48ePldDbLE34AAMCwiE0M6enpkqRTp05JkjZv3izpu7NNvXr1UlFRkQoLC1VeXq4ePXpo5cqVat%2B%2BvSQpOztbXq9XEydOVE1NjTIyMoJuSn/00Uf1yCOPaPjw4WrRooVuvvnmkA8zBQAACCXKzwcC2sLrPWZ8n06nQzcUbTe%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%2B65R/Pnz9fo0aP1ySefaPr06erWrZvS09O1ZcsWPf3003ruuefUu3dvrVq1StOmTdNbb72lNm3ahOlIAABAJGm2Z7B%2ByPr165WSkqLx48crJiZGmZmZGjZsmFavXi1JKi0t1bhx49S/f3%2B1atVKd911lyRp69at4SwbAABEkGYdsJYtW6ahQ4dq0KBBWrBggWpqalRWVqbU1NSg7VJTUwOXAc%2Bcdzgc6tOnj9xut621AwCAyNVsLxH%2B7Gc/U2Zmpp544gkdPHhQs2bN0sKFC%2BXz%2BdShQ4egbdu2bavKykpJks/nU2JiYtB8YmJiYL4xKioq5PV6g8aczjZKTk7%2BkUcTWnR0ZOVjp7Pp6z3dk0jrTVOiJ6HRFyt6YkVPrOhJ4zTbgFVaWhr45%2B7du2vOnDmaPn26rrzyynO%2B1u/3X/DaxcXFQWN5eXmaMWPGBe030t1QtD3cJZyXjxePDHcJRiUktA53CRcl%2BmJFT6zoiRU9ObtmG7DO1LlzZ9XX18vhcMjn8wXNVVZWyuVySZKSkpIs8z6fTz179mz0WhMmTNCwYcOCxpzONqqsrPmR1YfGbw9Ny/R/r3CJjnYoIaG1qqpqVV/fEO5yLhr0xYqeWNETq0jrSVJSbFjWbZYB67PPPtO6des0d%2B7cwJjH41HLli2VlZWlV199NWj7Xbt2qX///pKktLQ0lZWVaezYsZKk%2Bvp6ffbZZxo/fnyj109OTrZcDvR6j%2BnUqYv/jYj/aG7/verrG5rdMZlAX6zoiRU9saInZ9csT4G0a9dOpaWlKikp0YkTJ7Rv3z49%2BeSTmjBhgn71q1%2BpvLxcq1ev1vHjx/XOO%2B/onXfe0e233y5JysnJ0WuvvaYdO3aotrZWzzzzjFq2bKmhQ4eG96AAAEDEaJZnsDp06KDaay7UAAAMwElEQVSSkhItW7YsEJDGjh2r/Px8xcTEaOXKlVq0aJEWLlyoTp06aenSpbriiiskSdddd53uv/9%2BzZo1S998843S09NVUlKiVq1ahfmoAABApIjyX%2Bgd3WgUr/eY8X06nY6Iu3E8krwx69pwl2CE0%2BlQUlKsKitrOJ3/PfTFip5Y0ROrSOtJ%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%2Bvbtq3379qm6ulpxcXHn3EdFRYW8Xm/QmNPZRsnJyUZrjY7mkiVgB6fz0vj/2unvKXxv%2BQ96YkVPGoeAdQafz6eEhISgsdNhq7KyslEBq7S0VMXFxUFj9957r%2B677z5zheq7IDe545eaMGGC8fAWqSoqKlRaWkpPvoeehEZfrCoqKvTii8/Rk%2B%2BhJ1b0pHGInyH4/f4Lev2ECRO0du3aoK8JEyYYqu4/vF6viouLLWfLLmX0xIqehEZfrOiJFT2xoieNwxmsM7hcLvl8vqAxn8%2BnqKgouVyuRu0jOTmZVA8AwCWMM1hnSEtL06FDh3TkyJHAmNvtVo8ePRQbGxvGygAAQKQgYJ0hNTVV6enpWrZsmaqrq%2BXxePTCCy8oJycn3KUBAIAIEf3b3/72t%2BEu4mLz85//XBs2bNBjjz2mv/3tbxo/frymTJmiqKiocJdmERsbq6uuuoqza99DT6zoSWj0xYqeWNETK3pyblH%2BC72jGwAAAEG4RAgAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAHrIldeXq67775bGRkZ%2BsUvfqGlS5eqoaEh5LarVq3SiBEjNHDgQOXk5GjXrl02V2uPxvbk6aefVp8%2BfZSenh709fXXX4eh6qa3fft2ZWZmKj8//6zbNTQ0aPny5Ro%2BfLgGDx6sKVOm6ODBgzZVaa/G9mTu3LmBzyE9/TVo0CCbqrRXeXm58vLylJGRoczMTM2dO1dVVVUht924caNGjx6tAQMGaNy4cXrvvfdsrtYeje3J2rVrdcUVV1i%2Bp/zzn/8MQ9VNa/fu3Zo8ebKuvPJKZWZmatasWfJ6vSG3vVR%2B9pw3Py5qY8eO9T/88MP%2Bqqoq/759%2B/w33nij//nnn7ds9/bbb/sHDRrk37Fjh7%2B2tta/cuVK/7XXXuuvqakJQ9VNq7E9eeqpp/wFBQVhqNB%2BJSUl/htvvNGfnZ3tnzVr1lm3XbVqlf8Xv/iFf8%2BePf5jx475H330Uf/o0aP9DQ0NNlVrj/PpSUFBgf%2Bpp56yqbLwuvnmm/1z5871V1dX%2Bw8dOuQfN26cf968eZbtPvvsM39aWpp/27Zt/rq6Ov/rr7/u79%2B/v//QoUNhqLppNbYnr7zyiv%2BOO%2B4IQ4X2On78uP%2Baa67xFxcX%2B48fP%2B7/5ptv/HfccYf/nnvusWx7Kf3sOV%2BcwbqIud1u7d69W3PmzFF8fLxSUlKUm5ur0tJSy7alpaUaN26c%2Bvfvr1atWumuu%2B6SJG3dutXuspvU%2BfTkUhITE6M1a9aoa9eu59y2tLRUubm56t69u%2BLi4pSfny%2BPx6OdO3faUKl9zqcnl4qqqiqlpaVp9uzZio2NVceOHTV27Fh9/PHHlm1Xr16trKwsZWVlKSYmRmPGjFGvXr20bt26MFTedM6nJ5eK2tpa5efna%2BrUqWrZsqVcLpduuOEGffnll5ZtL5WfPT8GAesiVlZWpk6dOikxMTEw1rdvX%2B3bt0/V1dWWbVNTUwP/7nA41KdPH7ndbtvqtcP59ESSvvjiC2VnZ2vgwIEaNWpUs73EMWnSJMXHx59zu7q6Ou3ZsyfovRIXF6euXbs2u/dKY3ty2kcffaRbbrlFAwYM0Pjx45vlZY6EhAQVFhbqsssuC4wdOnRIycnJlm3P/J4iSampqc3ufXI%2BPTk9d%2Bedd2rw4MEaPny4Xn/9dbtKtU1iYqJuu%2B02OZ1OSdLevXv16quv6pe//KVl20vlZ8%2BPQcC6iPl8PiUkJASNnQ4WlZWVlm2/HzpOb3vmdpHufHrSsWNHdenSRU888YTef/993XbbbZo2bZr27t1rW70Xm6NHj8rv918S75Xz0aVLF3Xt2lUrV67U9u3bNWjQIP3mN79p9j1xu9166aWXNH36dMvcpfI95Uxn64nL5VJKSooeeOABvf/%2B%2B7r//vs1b948ffjhh2GotOmVl5crLS1NN910k9LT0zVjxgzLNpfq%2B6QxCFgXOb/f3yTbRrLGHudtt92mp556Sl27dlXr1q2Vm5urPn36NLtLHD/GpfJeaay8vDw9/vjj6tChg%2BLi4vTAAw%2BoZcuW2rx5c7hLazKffPKJpkyZotmzZyszMzPkNpfa%2B%2BRcPRk6dKiee%2B45paamqmXLlho1apRuuOEGrV27NgzVNr1OnTrJ7XZr06ZN2r9/vx588MGQ211q75PGImBdxFwul3w%2BX9CYz%2BdTVFSUXC5X0HhSUlLIbc/cLtKdT09C6dSpkyoqKpqqvIte27Zt5XA4QvawXbt2Yarq4hMdHa2f/OQnzfa9smXLFt19992aN2%2BeJk2aFHKbS%2BV7ymmN6Ukozf17SlRUlFJSUpSfn68NGzboyJEjQfOX2vvkfBCwLmJpaWk6dOhQ0Bva7XarR48eio2NtWxbVlYW%2BPf6%2Bnp99tln6t%2B/v2312uF8evI///M/llP3Ho9HXbp0saXWi1FMTIx69uwZ9F6pqqrSgQMH1K9fvzBWFj5%2Bv1%2BFhYXavXt3YOzEiRM6cOBAs3yvfPrppyooKNCTTz6pW2655Qe3S0tLs9yH5na7m933FKnxPfnzn/%2BsjRs3Bo01x%2B8pH374oUaMGBH0%2BBuH47u40KJFi6BtL5WfPT8GAesidvq5PMuWLVN1dbU8Ho9eeOEF5eTkSJJGjhwZ%2BEuXnJwcvfbaa9qxY4dqa2v1zDPPqGXLlho6dGgYj8C88%2BmJz%2BfTwoULtXfvXh0/flzPP/%2B8Dhw4oLFjx4bzEGz31VdfaeTIkYFnXeXk5GjVqlXyeDyqrq5WUVFR4Hlhl4rv9yQqKkr/%2Bte/tHDhQn311VeqqalRUVGRWrRooeuvvz7cpRp16tQpPfzww5ozZ46GDBlimZ88eXIgQNx%2B%2B%2B364IMPtG3bNh0/flxr1qzR/v37NWbMGLvLblLn05MTJ07osccek9vt1smTJ7Vhwwa9%2B%2B67ys7OtrvsJpWWlqbq6motXbpUtbW1OnLkiJ5%2B%2BmkNGjRI8fHxl%2BTPnh/DGe4CcHZPPfWUFixYoGuvvVZxcXHKzs7Wr3/9a0nSvn379O2330qSrrvuOt1///2aNWuWvvnmG6Wnp6ukpEStWrUKZ/lNorE9mT17tiQpNzdXPp9PPXr00B//%2BEd17NgxbLU3ldPh6NSpU5IUuHfo9A%2BCffv26cSJE5Kk7Oxseb1eTZw4UTU1NcrIyFBxcXF4Cm9C59OTxYsX64knntC4ceNUXV2tfv366cUXX1SbNm3CU3wT2bFjhzwejxYtWqRFixYFzW3atEkHDx7U0aNHJUm9evVSUVGRCgsLVV5erh49emjlypVq3759OEpvMufTk0mTJqmmpkYzZ86U1%2BtV586dtWLFCqWlpYWj9CYTHx%2Bv559/XosWLdLVV1%2BtNm3a6Oqrr9bixYslXbo/e85XlJ%2B70wAAAIziEiEAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGPb/AVD70obfKjrwAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-6175607757451177990”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.166002656</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.274951883</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.240124865</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.176460208</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.096432015</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.138861888</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.16307893</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.645577792</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.187038249</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3013)</td> <td class=”number”>3046</td> <td class=”number”>94.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-6175607757451177990”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0264194</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0304294</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0316797</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0368772</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>2.06185567</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.089864159</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.207505519</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.544529262</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.149606299</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_BLACK15”>LACCESS_BLACK15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2885</td>

</tr> <tr>

<th>Unique (%)</th> <td>89.9%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>97</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>2276.5</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>167910</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>5.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-7203202038511554232”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-7203202038511554232,#minihistogram-7203202038511554232”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-7203202038511554232”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-7203202038511554232”

aria-controls=”quantiles-7203202038511554232” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-7203202038511554232” aria-controls=”histogram-7203202038511554232”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-7203202038511554232” aria-controls=”common-7203202038511554232”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-7203202038511554232” aria-controls=”extreme-7203202038511554232”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-7203202038511554232”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>7</td>

</tr> <tr>

<th>Median</th> <td>87.873</td>

</tr> <tr>

<th>Q3</th> <td>1013.5</td>

</tr> <tr>

<th>95-th percentile</th> <td>10754</td>

</tr> <tr>

<th>Maximum</th> <td>167910</td>

</tr> <tr>

<th>Range</th> <td>167910</td>

</tr> <tr>

<th>Interquartile range</th> <td>1006.5</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>8808.4</td>

</tr> <tr>

<th>Coef of variation</th> <td>3.8692</td>

</tr> <tr>

<th>Kurtosis</th> <td>122.71</td>

</tr> <tr>

<th>Mean</th> <td>2276.5</td>

</tr> <tr>

<th>MAD</th> <td>3335.9</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>9.4904</td>

</tr> <tr>

<th>Sum</th> <td>7086800</td>

</tr> <tr>

<th>Variance</th> <td>77588000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-7203202038511554232”>

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YqLi9PUqVPVuHFjXXHFFRoxYoT%2B9a9/afv27SorK9N9992nRo0aKTY2VqNHj1ZWVpYkadWqVUpKSlJSUpKCgoI0bNgwdejQQWvXrpUkZWVlady4cWrbtq2Cg4OVlpam3Nxc7d2715tbBgAAPsQnC1ZISIjmzp2ryy%2B/3DV29OhRRUZGKicnR9HR0QoICHDNxcTEKDs7W5KUk5OjmJgYt%2BPFxMTIbrertLRUBw4ccJsPDg5Wq1atZLfba3lXAACgvrB5ewEm2O12vf7661q6dKk2btyokJAQt/mwsDA5nU5VVlbK6XQqNDTUbT40NFQHDhzQiRMnZFnWWecdDkeN15Ofn6%2BCggK3MZutkSIjIy9wZ%2BcWEOBb/dhmM7/eqgx8LQuTyIAMJDKQyEAiA6nuZODzBeuzzz7Tfffdp6lTpyoxMVEbN2486%2BP8/Pxc//1891Nd7P1WWVlZWrx4sdtYamqqJk2adFHH9XXh4Y1r7dghIZfV2rF9BRmQgUQGEhlIZCB5PwOfLlhbt27Vww8/rJkzZ2r48OGSpIiICH3zzTduj3M6nQoLC5O/v7/Cw8PldDqrzUdERLgec7b5pk2b1nhdycnJ6t%2B/v9uYzdZIDkfxBezu/Lzdzi%2BU6f1LZzIICblMhYUlqqioNH58X0AGZCCRgUQGEhlI1TOozR/uz8VnC9bnn3%2Bu9PR0LVq0SH369HGNx8XF6Y033lB5eblstjPbs9vt6tKli2u%2B6n6sKna7XUOGDFFQUJDat2%2BvnJwc9erVS9KZG%2BoPHz6szp0713htkZGR1d4OLCg4qfLyS/Nir1Kb%2B6%2BoqLzk8yUDMpDIQCIDiQwk72fgWy%2BB/K/y8nI9/vjjmjZtmlu5kqSkpCQFBwdr6dKlKikp0d69e7V69WqlpKRIksaMGaOPP/5Y27dv16lTp7R69Wp98803GjZsmCQpJSVFK1asUG5uroqKipSRkaFOnTopPj7e4/sEAAC%2BySdfwdqzZ49yc3M1Z84czZkzx23uvffe07Jly/THP/5RmZmZuvzyy5WWlqa%2BfftKkjp06KCMjAzNnTtXeXl5ateunZYvX65mzZpJksaOHauCggLddtttKi4uVkJCQrX7qQAAAM7Fz%2BITND2ioOCk8WPabP4amLHT%2BHFry8YpvY0f02bzV3h4YzkcxZfsy%2BFkQAYSGUhkIJGBVD2DZs2aeGUdPvkWIQAAQF1GwQIAADCMggUAAGCYxwtW//79tXjxYh09etTTpwYAAPAIjxesW265RRs2bNB1112nu%2B%2B%2BW5s3b1Z5ebmnlwEAAFBrPF6wUlNTtWHDBr311ltq3769nn76aSUlJWn%2B/Pk6dOiQp5cDAABgnNfuwYqNjVV6erq2bdumxx57TG%2B99ZZuvPFGjR8/Xl988YW3lgUAAHDRvFawysrKtGHDBt1zzz1KT09X8%2BbN9eijj6pTp04aN26c1q1b562lAQAAXBSPf5J7bm6uVq9erXfeeUfFxcUaPHiwXn31VfXo0cP1mJ49e2rWrFkaOnSop5cHAABw0TxesIYMGaI2bdpowoQJGj58uMLCwqo9JikpScePH/f00gAAAIzweMFasWKFevXqdd7H7d271wOrAQAAMM/j92BFR0dr4sSJ2rJli2vsv//7v3XPPffI6XR6ejkAAADGebxgzZ07VydPnlS7du1cY3379lVlZaXmzZvn6eUAAAAY5/G3CHft2qV169YpPDzcNda6dWtlZGTopptu8vRyAAAAjPP4K1ilpaUKCgqqvhB/f5WUlHh6OQAAAMZ5vGD17NlT8%2BbN04kTJ1xj3333nWbPnu32UQ0AAAC%2ByuNvET722GO66667dPXVVys4OFiVlZUqLi5Wy5Yt9dprr3l6OQAAAMZ5vGC1bNlSf//73/Xhhx/q8OHD8vf3V5s2bdSnTx8FBAR4ejkAAADGebxgSVJgYKCuu%2B46b5waAACg1nm8YB05ckQLFizQ119/rdLS0mrzH3zwgaeXBAAAYJRX7sHKz89Xnz591KhRI0%2BfHgAAoNZ5vGBlZ2frgw8%2BUEREhKdPDQAA4BEe/5iGpk2b8soVAACo1zxesCZMmKDFixfLsixPnxoAAMAjPP4W4YcffqjPP/9ca9asUYsWLeTv797x3nzzTU8vCQAAwCiPF6zg4GBde%2B21nj4tAACAx3i8YM2dO9fTpwQAAPAoj9%2BDJUkHDx7U888/r0cffdQ19u9//9sbSwEAADDO4wVr9%2B7dGjZsmDZv3qz169dLOvPho7fffjsfMgoAAOoFjxeshQsX6uGHH9a6devk5%2Bcn6czvJ5w3b56WLFni6eUAAAAY5/GC9Z///EcpKSmS5CpYknT99dcrNzfX08sBAAAwzuMFq0mTJmf9HYT5%2BfkKDAz09HIAAACM83jB6t69u55%2B%2BmkVFRW5xg4dOqT09HRdffXVnl4OAACAcR7/mIZHH31Ud9xxhxISElRRUaHu3burpKRE7du317x58zy9HAAAAOM8XrCuuOIKrV%2B/Xjt27NChQ4fUsGFDtWnTRr1793a7JwsAAMBXebxgSVKDBg103XXXeePUAAAAtc7jBat///7nfKWKz8ICAAC%2BzuMF68Ybb3QrWBUVFTp06JDsdrvuuOMOTy8HAADAOI8XrGnTpp11fNOmTfrHP/7h4dUAAACY55XfRXg21113nf7%2B9797exkAAAAXrc4UrH379smyrAt6zs6dO5WYmKi0tDS38TVr1qhjx46Kj493%2B/PFF19IkiorK7Vw4UINGDBAPXv21Pjx43XkyBHX851Op6ZMmaLExET16dNHM2bMOOuHowIAAJyNx98iHDt2bLWxkpIS5ebmatCgQTU%2BzgsvvKDVq1erVatWZ53v2bOnXnvttbPOrVy5UuvWrdMLL7yg5s2ba%2BHChUpNTdW7774rPz8/zZw5U6dPn9b69etVVlamyZMnKyMjQ48//niN1wcAAC5dHi9YrVu3rvavCIOCgjRq1CiNHj26xscJCgrS6tWr9dRTT%2BnUqVMXtIasrCyNGzdObdu2lSSlpaUpISFBe/fuVYsWLbRlyxb97W9/U0REhCTp/vvv1%2BTJk5Wenq4GDRpc0LkAAMClx%2BMFy9Sntd9%2B%2B%2B3nnD969KjuvPNOZWdnKyQkRJMmTdLNN9%2Bs0tJSHThwQDExMa7HBgcHq1WrVrLb7Tp58qQCAgIUHR3tmo%2BNjdWPP/6ogwcPuo3/kvz8fBUUFLiN2WyNFBkZeYG7PLeAgDrzDm%2BN2Gzm11uVga9lYRIZkIFEBhIZSGQg1Z0MPF6w3nnnnRo/dvjw4b/qHBEREWrdurUeeughtWvXTu%2B//74eeeQRRUZG6ve//70sy1JoaKjbc0JDQ%2BVwOBQWFqbg4GC3V9mqHutwOGp0/qysLC1evNhtLDU1VZMmTfpV%2B6kvwsMb19qxQ0Iuq7Vj%2BwoyIAOJDCQykMhA8n4GHi9YM2bMUGVlZbUb2v38/NzG/Pz8fnXB6tu3r/r27ev6%2B5AhQ/T%2B%2B%2B9rzZo1ro%2BJONcN9Rd6s/3PJScnq3///m5jNlsjORzFF3Xcn/N2O79QpvcvnckgJOQyFRaWqKKi0vjxfQEZkIFEBhIZSGQgVc%2BgNn%2B4PxePF6wXX3xRL7/8siZOnKjo6GhZlqWvvvpKL7zwgm699VYlJCTUynmjoqKUnZ2tsLAw%2Bfv7y%2Bl0us07nU41bdpUERERKioqUkVFhQICAlxzktS0adManSsyMrLa24EFBSdVXn5pXuxVanP/FRWVl3y%2BZEAGEhlIZCCRgeT9DLxyD1ZmZqaaN2/uGrvyyivVsmVLjR8/XuvXr7/oc7zxxhsKDQ3VjTfe6BrLzc1Vy5YtFRQUpPbt2ysnJ0e9evWSJBUWFurw4cPq3LmzoqKiZFmWvvzyS8XGxkqS7Ha7QkJC1KZNm4teGwAAqP88/h7TN998U%2B3%2BJ0kKCQlRXl6ekXOcPn1aTz75pOx2u8rKyrR%2B/Xp9%2BOGHro%2BISElJ0YoVK5Sbm6uioiJlZGSoU6dOio%2BPV0REhAYPHqxnn31Wx48f17Fjx7RkyRKNGjVKNptXfjc2AADwMR5vDFFRUZo3b54mT56s8PBwSWdeQXruuef0u9/9rsbHiY%2BPlySVl5dLkrZs2SLpzKtNt99%2Bu4qLizV58mQVFBSoRYsWWrJkieLi4iSd%2BSyugoIC3XbbbSouLlZCQoLbTel/%2BtOf9Mc//lEDBgxQgwYNdNNNN1X7MFMAAIBf4mdd7B3dF2jXrl2aOnWqCgsL1bhxY/n7%2B6uoqEgNGzbUkiVLdPXVV3tyOR5TUHDS%2BDFtNn8NzNhp/Li1ZeOU3saPabP5Kzy8sRyO4kv2fgMyIAOJDCQykMhAqp5Bs2ZNvLMOT5%2BwT58%2B2r59u3bs2KFjx47Jsiw1b95c11xzjZo08U4IAAAAJnnlpqLLLrtMAwYM0LFjx9SyZUtvLAEAAKDWePwm99LSUqWnp6tbt2664YYbJJ25B%2Bvuu%2B9WYWGhp5cDAABgnMcL1vz587V//35lZGTI3///Tl9RUaGMjAxPLwcAAMA4jxesTZs26bnnntP111/v%2BnU0ISEhmjt3rjZv3uzp5QAAABjn8YJVXFys1q1bVxuPiIjQjz/%2B6OnlAAAAGOfxgvW73/1O//jHPyS5/86/9957T7/97W89vRwAAADjPP6vCP/whz/owQcf1C233KLKykq98sorys7O1qZNmzRjxgxPLwcAAMA4jxes5ORk2Ww2vf766woICNCyZcvUpk0bZWRk6Prrr/f0cgAAAIzzeME6fvy4brnlFt1yyy2ePjUAAIBHePwerAEDBsjDv50HAADAozxesBISErRx40ZPnxYAAMBjPP4W4W9%2B8xs99dRTyszM1O9%2B9zs1aNDAbX7BggWeXhIAAIBRHi9YBw4c0O9//3tJksPh8PTpAQAAap3HClZaWpoWLlyo1157zTW2ZMkSpaamemoJAAAAHuGxe7C2bt1abSwzM9NTpwcAAPAYjxWss/3LQf41IQAAqI88VrCqfrHz%2BcYAAAB8ncc/pgEAAKC%2Bo2ABAAAY5rF/RVhWVqapU6eed4zPwQIAAL7OYwWrR48eys/PP%2B8YAACAr/NYwfrp518BAADUZ9yDBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwzKcL1s6dO5WYmKi0tLRqcxs2bNDQoUPVrVs3jRw5Urt27XLNVVZWauHChRowYIB69uyp8ePH68iRI655p9OpKVOmKDExUX369NGMGTNUWlrqkT0BAADf57MF64UXXtCcOXPUqlWranP79%2B9Xenq6pk2bpk8%2B%2BUTjxo3TAw88oGPHjkmSVq5cqXXr1ikzM1Pbtm1T69atlZqaKsuyJEkzZ85USUmJ1q9fr7ffflu5ubnKyMjw6P4AAIDv8tmCFRQUpNWrV5%2B1YK1atUpJSUlKSkpSUFCQhg0bpg4dOmjt2rWSpKysLI0bN05t27ZVcHCw0tLSlJubq7179%2Br777/Xli1blJaWpoiICDVv3lz333%2B/3n77bZWVlXl6mwAAwAf5bMG6/fbb1aRJk7PO5eTkKCYmxm0sJiZGdrtdpaWlOnDggNt8cHCwWrVqJbvdrv379ysgIEDR0dGu%2BdjYWP344486ePBg7WwGAADUKzZvL6A2OJ1OhYaGuo2FhobqwIEDOnHihCzLOuu8w%2BFQWFiYgoOD5efn5zYnSQ6Ho0bnz8/PV0FBgduYzdZIkZGRv2Y7vyggwLf6sc1mfr1VGfhaFiaRARlIZCCRgUQGUt3JoF4WLEmu%2B6l%2Bzfz5nns%2BWVlZWrx4sdtYamqqJk2adFHH9XXh4Y1r7dghIZfV2rF9BRmQgUQGEhlIZCB5P4N6WbDCw8PldDrdxpxOpyIiIhQWFiZ/f/%2Bzzjdt2lQREREqKipSRUWFAgICXHOS1LRp0xqdPzk5Wf3793cbs9kayeEo/rVbOitvt/MLZXr/0pkMQkIuU2FhiSoqKo0f3xeQARlIZCCRgUQGUvUMavOH%2B3OplwUrLi5O2dnZbmN2u11DhgxRUFCQ2rdvr5ycHPXq1UuSVFhYqMOHD6tz586KioqSZVn68ssvFRsb63puSEiI2rRpU6PzR0ZGVns7sKDgpMrLL82LvUpt7r%2BiovKSz5cMyEAiA4kMJDKQvJ%2BBb70EUkNjxozRxx9/rO3bt%2BvUqVNavXq1vvnmGw0bNkySlJKFnsdLAAAW0ElEQVSSohUrVig3N1dFRUXKyMhQp06dFB8fr4iICA0ePFjPPvusjh8/rmPHjmnJkiUaNWqUbLZ62UcBAIBhPtsY4uPjJUnl5eWSpC1btkg682pThw4dlJGRoblz5yovL0/t2rXT8uXL1axZM0nS2LFjVVBQoNtuu03FxcVKSEhwu2fqT3/6k/74xz9qwIABatCggW666aazfpgpAADA2fhZF3tHN2qkoOCk8WPabP4amLHT%2BHFry8YpvY0f02bzV3h4YzkcxZfsy%2BFkQAYSGUhkIJGBVD2DZs3O/pFOta1evkUIAADgTRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMCweluwoqOjFRcXp/j4eNefJ598UpK0e/dujRo1St27d9eQIUO0du1at%2BeuWLFCgwcPVvfu3ZWSkqLs7GxvbAEAAPgom7cXUJvee%2B89tWjRwm0sPz9f999/v2bMmKGhQ4fqs88%2B03333ac2bdooPj5eW7du1fPPP68XX3xR0dHRWrFihSZOnKjNmzerUaNGXtoJAADwJfX2Faxfsm7dOrVu3VqjRo1SUFCQEhMT1b9/f61atUqSlJWVpZEjR6pLly5q2LCh7r77bknStm3bvLlsAADgQ%2Br1K1gLFizQv//9bxUVFemGG27Q9OnTlZOTo5iYGLfHxcTEaOPGjZKknJwc3Xjjja45f39/derUSXa7XUOGDKnRefPz81VQUOA2ZrM1UmRk5EXuyF1AgG/1Y5vN/HqrMvC1LEwiAzKQyEAiA4kMpLqTQb0tWF27dlViYqKeeeYZHTlyRFOmTNHs2bPldDrVvHlzt8eGhYXJ4XBIkpxOp0JDQ93mQ0NDXfM1kZWVpcWLF7uNpaamatKkSb9yN/VDeHjjWjt2SMhltXZsX0EGZCCRgUQGEhlI3s%2Bg3hasrKws139v27atpk2bpvvuu089evQ473Mty7qocycnJ6t///5uYzZbIzkcxRd13J/zdju/UKb3L53JICTkMhUWlqiiotL48X0BGZCBRAYSGUhkIFXPoDZ/uD%2BXeluwfq5FixaqqKiQv7%2B/nE6n25zD4VBERIQkKTw8vNq80%2BlU%2B/bta3yuyMjIam8HFhScVHn5pXmxV6nN/VdUVF7y%2BZIBGUhkIJGBRAaS9zPwrZdAamjfvn2aN2%2Be21hubq4CAwOVlJRU7WMXsrOz1aVLF0lSXFyccnJyXHMVFRXat2%2Bfax4AAOB86mXBatq0qbKyspSZmanTp0/r0KFDWrRokZKTk3XzzTcrLy9Pq1at0qlTp7Rjxw7t2LFDY8aMkSSlpKTonXfe0Z49e1RSUqKlS5cqMDBQffv29e6mAACAz6iXbxE2b95cmZmZWrBggasgjRgxQmlpaQoKCtLy5cs1Z84czZ49W1FRUZo/f746duwoSbr22mv10EMPacqUKfrhhx8UHx%2BvzMxMNWzY0Mu7AgAAvqJeFixJ6tmzp958881fnHv33Xd/8bl/%2BMMf9Ic//KG2lgYAAOq5evkWIQAAgDdRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMJu3F4BLxw3PfuTtJVyQjVN6e3sJAAAfxStYAAAAhlGwziIvL0/33nuvEhIS1K9fP82fP1%2BVlZXeXhYAAPARvEV4Fg8%2B%2BKBiY2O1ZcsW/fDDD5owYYIuv/xy3Xnnnd5eGgAA8AEUrJ%2Bx2%2B368ssv9corr6hJkyZq0qSJxo0bp1dffZWCdYnxtXvG3p92jbeXAAD4XxSsn8nJyVFUVJRCQ0NdY7GxsTp06JCKiooUHBx83mPk5%2BeroKDAbcxma6TIyEijaw0I4B1e/J%2BBGTu9vYQaM10Gq74XLuXvCTIgA4kMpLqTAQXrZ5xOp0JCQtzGqsqWw%2BGoUcHKysrS4sWL3cYeeOABPfjgg%2BYWqjNF7o4rvlZycrLx8uYr8vPzlZWVRQZkoFdffZEMyIAMyKDOZHDpVtxzsCzrop6fnJysNWvWuP1JTk42tLr/U1BQoMWLF1d7texSQgZkIJGBRAYSGUhkINWdDHgF62ciIiLkdDrdxpxOp/z8/BQREVGjY0RGRl6yPzkAAABewaomLi5OR48e1fHjx11jdrtd7dq1U%2BPGjb24MgAA4CsoWD8TExOj%2BPh4LViwQEVFRcrNzdUrr7yilJQUby8NAAD4iIBZs2bN8vYi6pprrrlG69ev15NPPqm///3vGjVqlMaPHy8/Pz9vL62axo0bq1evXpf0q2tkQAYSGUhkIJGBRAZS3cjAz7rYO7oBAADghrcIAQAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYPmgvLw83XvvvUpISFC/fv00f/58VVZWentZFywvL0%2BpqalKSEhQYmKipk%2BfrsLCQn377beKjo5WfHy825%2BXXnrJ9dwNGzZo6NCh6tatm0aOHKldu3a55iorK7Vw4UINGDBAPXv21Pjx43XkyBHXvNPp1JQpU5SYmKg%2BffpoxowZKi0t9ejeq0RHRysuLs5tn08%2B%2BaQkaffu3Ro1apS6d%2B%2BuIUOGaO3atW7PXbFihQYPHqzu3bsrJSVF2dnZrrlTp07piSee0LXXXquEhARNmjRJDofDNV9XrqFPP/202tc5Li5O0dHR%2Bsc//nHW62Djxo2u5/tyBjt37lRiYqLS0tKqzdXm9b1//37deuut6tGjhwYNGqSXX365xuc26Vz737x5s4YNG6Zu3bpp8ODBeuutt1xzzz//vDp16lTtuvj%2B%2B%2B8lXfzX/Xzfd57IYM2aNerYsWO1PX7xxReS6s81cK4MHn/88Wr7j4mJ0aOPPipJmj59uut3B1f9ufLKK%2BtWBhZ8zogRI6zHH3/cKiwstA4dOmQNGjTIevnll729rAt20003WdOnT7eKioqso0ePWiNHjrQee%2Bwx68iRI1aHDh1%2B8Xn79u2z4uLirO3bt1ulpaXWu%2B%2B%2Ba3Xp0sU6evSoZVmWtWLFCqtfv37WgQMHrJMnT1p/%2BtOfrKFDh1qVlZWWZVnWAw88YN17773WDz/8YB07dsxKTk62nnzySY/s%2Bec6dOhgHTlypNr4d999Z3Xt2tVatWqVVVpaan300UdW586drS%2B%2B%2BMKyLMv64IMPrCuvvNLas2ePVVJSYi1fvtzq3bu3VVxcbFmWZc2dO9caOXKk9f/%2B3/%2BzHA6H9cADD1gTJkxwHb8uX0NLly61Jk%2BebH3yySdWv379fvFxvpxBZmamNWjQIGvs2LHWlClT3OZq8/ouKSmxrrnmGuv555%2B3iouLrezsbKtXr17Wpk2banRuT%2Bx/7969Vnx8vPX%2B%2B%2B9bZWVl1vbt263Y2Fjr008/tSzLsp577jkrPT39F499MV/3833feSqDt99%2B27r11lt/8bn14Ro4XwY/V1ZWZg0ZMsTavn27ZVmWlZ6ebj333HO/%2BPi6kAEFy8d88cUXVqdOnSyn0%2Bka%2B%2Btf/2oNHjzYi6u6cCdOnLCmT59uFRQUuMZee%2B01a9CgQectWLNnz7ZSU1PdxkaPHm0tX77csizLGjJkiPXqq6%2B65k6ePGnFxMRY//73v62CggKrY8eO1v79%2B13zO3bssLp27WqdPn3a1PZq7JcK1osvvmgNHz7cbWzKlCnWzJkzLcuyrHvvvdd6%2BumnXXMVFRVW7969rfXr11tlZWVWjx49rC1btrjmDxw4YEVHR1vHjh2r09dQXl6e1atXLysvL%2B%2B8BcuXM3j11VetwsJCKz09vdr/sdTm9b1x40brqquussrLy13z8%2BfPt%2B66664anduUc%2B1/x44d1uLFi93GRowYYS1dutSyrHMXrIv9up/v%2B86kc2VwvoJVH64Byzp3Bj/34osvWvfcc4/r7%2BcqWHUlA94i9DE5OTmKiopSaGioayw2NlaHDh1SUVGRF1d2YUJCQjR37lxdfvnlrrGjR48qMjLS9fdHHnlEffr00VVXXaUFCxaorKxM0pkMYmJi3I4XExMju92u0tJSHThwwG0%2BODhYrVq1kt1u1/79%2BxUQEKDo6GjXfGxsrH788UcdPHiwtrZ7TgsWLFDfvn115ZVXaubMmSouLv7FPVa9BfbzeX9/f3Xq1El2u12HDx/WyZMnFRsb65pv27atGjZsqJycnDp9DS1atEi33HKLfvvb30qSiouLXW8jX3PNNXrllVdk/e/vp/flDG6//XY1adLkrHO1eX3n5OQoOjpaAQEBbsf%2Bpevqp%2Bc26Vz7v/baa5Wamur6e3l5uQoKCtS8eXPX2FdffaWxY8e63sarevvmYr/u5/u%2BM%2BlcGUhn/vfwzjvvVM%2BePTVgwAC9%2B%2B67klRvrgHp/BlUKSws1LJly/Twww%2B7jX/yyScaPny4unXrplGjRrn2UFcyoGD5GKfTqZCQELexqv%2Bx%2BOl9Br7Gbrfr9ddf13333afAwEB169ZNAwcO1LZt25SZmam1a9fqL3/5i6QzGfz0fyClMxk4HA6dOHFClmX94rzT6VRwcLD8/Pzc5iTv5Ne1a1clJiZq8%2BbNysrK0p49ezR79uyzfp3DwsJcazxXBk6nU5KqPT8kJMQ1XxevoW%2B//VabN2/WnXfeKenM/2l06NBBd9xxh3bu3Km5c%2Bdq8eLFevvttyXVzwyk2r2%2Bf%2Bm6cjqdqqysPOe5vSUjI0ONGjXSjTfeKEm64oor1LJlSz3zzDP66KOPNHr0aE2cOFEHDx686K/7%2Bb7vPCUiIkKtW7fWww8/rI8%2B%2BkgPPfSQHnvsMe3evfuSvAZef/119ezZU%2B3bt3eNtWzZUq1atdLy5cu1c%2BdOXXnllbrrrrvqVAYULB9U9RN8ffHZZ59p/Pjxmjp1qhITExUZGak333xTAwcOVIMGDdS5c2dNmDBBa9ascT3nfBmca74u5ZeVlaXRo0crMDBQbdu21bRp07R%2B/XrXq3XnUl8yqLJy5UoNGjRIzZo1k3TmJ87XXntNvXr1UmBgoPr06aOxY8fWy%2Bvg5zy9r5/%2BH1FdycWyLM2fP1/r16/X0qVLFRQUJEkaPXq0nnvuObVq1UqXXXaZxo0bp06dOrndjO6rX/cqffv21YsvvqiYmBgFBgZqyJAhGjhwYI2v/fpyDUhSRUWFVq5cqdtvv91tPDU1VU8//bSaN2%2Bu4OBgPfzwwwoMDNSWLVsk1Y0MKFg%2BJiIiwvVTWhWn0yk/Pz9FRER4aVW/3tatW3Xvvffqscceq/YN9FNRUVH6/vvvZVmWwsPDz5pBRESEwsLC5O/vf9b5pk2bKiIiQkVFRaqoqHCbk6SmTZsa3Nmv06JFC1VUVJx1Dw6Hw/U1PlcGVY/5%2BfyJEydcGdTFa2jTpk3q37//OR8TFRWl/Px8SfUzA%2Bnc%2B7rY6zsiIqLaT%2BFOp9N13HOd25MqKys1ffp0bd26VW%2B88YZ%2B//vfn/PxVdfFxX7dz7b/n37feVPVHi%2BVa6DKp59%2BqtOnT7v9C8GzCQgI0G9%2B8xvXdVAXMqBg%2BZi4uDgdPXpUx48fd43Z7Xa1a9dOjRs39uLKLtznn3%2Bu9PR0LVq0SMOHD3eN7969W0uXLnV77MGDBxUVFSU/Pz/FxcVVuyfCbrerS5cuCgoKUvv27ZWTk%2BOaKyws1OHDh9W5c2d16tRJlmXpyy%2B/dHtuSEiI2rRpU0s7Pbt9%2B/Zp3rx5bmO5ubkKDAxUUlJStT1mZ2erS5cuks5cBz/dY0VFhfbt26cuXbqoZcuWCg0NdZv/z3/%2Bo9OnTysuLq5OXkP79%2B9XXl6eevfu7RrbuHGj/vrXv7o97uDBg2rZsqWk%2BpdBldq8vuPi4vTVV1%2BpvLy82rHPd25Pevrpp/X111/rjTfecH29q/zlL3/R7t273cZyc3PVsmXLi/66x8fHn/P7zlPeeOMNbdiwwW2sao%2BXyjVQ5YMPPtBVV10lm83mGrMsS3PnznXb4%2BnTp3X48GG1bNmyzmRAwfIxVZ/7sWDBAhUVFSk3N1evvPKKUlJSvL20C1JeXq7HH39c06ZNU58%2BfdzmmjRpoiVLlujdd99VWVmZ7Ha7XnrpJdcex4wZo48//ljbt2/XqVOntHr1an3zzTcaNmyYJCklJUUrVqxQbm6uioqKlJGR4frcnIiICA0ePFjPPvusjh8/rmPHjmnJkiUaNWqU2zewJzRt2lRZWVnKzMzU6dOndejQIS1atEjJycm6%2BeablZeXp1WrVunUqVPasWOHduzYoTFjxrj2%2BM4772jPnj0qKSnR0qVLFRgYqL59%2ByogIEBjxozRsmXLdPToUTkcDv35z3/WwIEDdfnll9fJa2jfvn0KCwtTcHCwa6xBgwZ65plntGvXLpWVlemjjz7S22%2B/7VpnfcugSm1e30lJSQoODtbSpUtVUlKivXv3avXq1TX%2B3vKEzz77TGvXrlVmZqbCwsKqzTudTs2ePVsHDx7UqVOn9PLLL%2Bvw4cMaMWLERX/dhw4des7vO085ffq0nnzySdntdpWVlWn9%2BvX68MMPNXbsWEn1/xr4qf3796tFixZuY35%2Bfvr22281e/ZsfffddyouLlZGRoYaNGig6667ru5kcEH/5hB1wtGjR627777b6ty5s5WYmGg999xzrs8/8RWffvqp1aFDBysuLq7an2%2B//dbavHmzNWzYMKtz585W7969rWXLllkVFRWu52/atMkaNGiQFRsba918883WP//5T9dcZWWltWjRIuvqq6%2B2OnfubN1zzz1un19SWFhopaWlWV27drV69uxpzZ492zp16pRH91/ln//8p5WcnGx17drV6tWrlzV37lyrtLTUNTds2DArNjbWGjRokOszWqqsXLnSSkpKsuLi4qyUlBTrq6%2B%2Bcs2dOnXKmjVrltWzZ0%2BrW7du1kMPPWQVFha65uvaNbRs2TJryJAh1cbffPNNa9CgQVZ8fLzVr18/66233nKb99UMqq71jh07Wh07dnT9vUptXt9fffWVNXbsWCsuLs7q27evtXLlSre1nevcntj/o48%2B6jZW9efOO%2B%2B0LMuySktLraeeesq65pprrPj4eGvEiBHW559/7jr2xX7dz/d954kMKisrrSVLllj9%2BvWz4uLirOuvv97aunWr67n14Ro4XwZVBg0aZL344ovVnutwOKzp06dbiYmJVufOna1bb73VOnDggGu%2BLmTgZ1l16G42AACAeoC3CAEAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAsP8PYMp0s/LeY%2BsAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-7203202038511554232”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>189</td> <td class=”number”>5.9%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>18</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.999999970197678</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.00000001490116</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.99999997019768</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.00000002933666</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.99999999627471</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2874)</td> <td class=”number”>2879</td> <td class=”number”>89.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-7203202038511554232”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>189</td> <td class=”number”>5.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.26221166852e-08</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0002072595839309</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0010663634166122</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0013897542376071</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>123766.895145556</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>125888.219965812</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>136917.020467991</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>137560.129882083</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>167913.190404415</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_CHILD10”><s>LACCESS_CHILD10</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_LACCESS_LOWI15”><code>LACCESS_LOWI15</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.93398</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_CHILD15”><s>LACCESS_CHILD15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_LACCESS_CHILD10”><code>LACCESS_CHILD10</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.994</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_CHILD_10_15”>LACCESS_CHILD_10_15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2826</td>

</tr> <tr>

<th>Unique (%)</th> <td>88.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.3%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>106</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>278.32</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>592130</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>8.7%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram7117110502744204773”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives7117110502744204773,#minihistogram7117110502744204773”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives7117110502744204773”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles7117110502744204773”

aria-controls=”quantiles7117110502744204773” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram7117110502744204773” aria-controls=”histogram7117110502744204773”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common7117110502744204773” aria-controls=”common7117110502744204773”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme7117110502744204773” aria-controls=”extreme7117110502744204773”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles7117110502744204773”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-51.396</td>

</tr> <tr>

<th>Q1</th> <td>-11.103</td>

</tr> <tr>

<th>Median</th> <td>-0.052298</td>

</tr> <tr>

<th>Q3</th> <td>7.2269</td>

</tr> <tr>

<th>95-th percentile</th> <td>84.676</td>

</tr> <tr>

<th>Maximum</th> <td>592130</td>

</tr> <tr>

<th>Range</th> <td>592230</td>

</tr> <tr>

<th>Interquartile range</th> <td>18.33</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>11080</td>

</tr> <tr>

<th>Coef of variation</th> <td>39.81</td>

</tr> <tr>

<th>Kurtosis</th> <td>2644.5</td>

</tr> <tr>

<th>Mean</th> <td>278.32</td>

</tr> <tr>

<th>MAD</th> <td>544.32</td>

</tr> <tr class=”alert”>

<th>Skewness</th> <td>50.355</td>

</tr> <tr>

<th>Sum</th> <td>863900</td>

</tr> <tr>

<th>Variance</th> <td>122760000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram7117110502744204773”>

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wclSStXrtTatWu1bNkybd68We3bt1dmZqaMMcE8LgAACCEhWbDKy8uVmJioKVOmqFmzZrrssst044036u9//7u2bNmimpoa3X333WratKkSEhI0duxY5eXlSZJWrVqltLQ0paWlKTIyUiNHjlSXLl20Zs0aSVJeXp4mTJigjh07KioqSjk5OSosLNRHH30UzCMDAIAQEpIFy%2B12a968ebr00kt9Y8XFxYqLi1NBQYG6du2qiIgI31x8fLzy8/MlSQUFBYqPj/e7Xnx8vDwej6qrq7V7926/%2BaioKLVr104ej%2BccnwoAAFwoXMHegA0ej0crVqzQkiVLtH79erndbr/5Fi1ayOv1qr6%2BXl6vV9HR0X7z0dHR2r17t44cOSJjzGnny8rKHO%2BnpKREpaWlfmMuV1PFxcV9y5OdWUREaPVjlys4%2Bz2ZU6jlFUhk5Aw5OUNODSMjZ0I5p5AvWO%2B//77uvvtuTZkyRampqVq/fv1pHxcWFub774bupzrb%2B63y8vK0aNEiv7HMzExlZWWd1XVDXUxMs6Cu73ZfEtT1QwEZOUNOzpBTw8jImVDMKaQL1qZNm/Tzn/9cM2fO1KhRoyRJsbGx2rdvn9/jvF6vWrRoofDwcMXExMjr9Z4yHxsb63vM6eZbtmzpeF/p6ekaNGiQ35jL1VRlZZXf4nQNC7VGb/v8TkVEhMvtvkTl5VWqq6sPyh7Od2TkDDk5Q04NIyNnbOQUrG/uQ7ZgffDBB5o2bZqefvpp9evXzzeemJio3/3ud6qtrZXL9eXxPB6Punfv7ps/eT/WSR6PR8OHD1dkZKQ6d%2B6sgoIC9e3bV9KXN9Tv379fycnJjvcWFxd3ytuBpaVHVVt7cX8RBfv8dXX1Qd/D%2BY6MnCEnZ8ipYWTkTCjmFPCXQAYNGqRFixapuLj4O1%2BjtrZWDz/8sKZOnepXriQpLS1NUVFRWrJkiaqqqvTRRx9p9erVysjIkCSNGzdO7777rrZs2aLjx49r9erV2rdvn0aOHClJysjI0PLly1VYWKiKigrl5uaqW7duSkpK%2Bu6HBgAAF5WAv4J100036fXXX9eSJUt09dVXa9y4cRo0aJDv1SYntm/frsLCQs2dO1dz5871m3vzzTf161//Wo8%2B%2BqiWLVumSy%2B9VDk5ORowYIAkqUuXLsrNzdW8efNUVFSkTp06aenSpWrVqpUkafz48SotLdUtt9yiyspKpaSknHI/FQAAwJmEmSB9gmZBQYHWrVun9evXq6amRqNGjdKYMWPUoUOHYGznnCstPWr9mi5XuK7Nfdv6dc%2BV9dnXBGVdlytcMTHNVFZWGXIvMQcKGTlDTs6QU8PIyBkbObVq1dzyrpwJ2l3SCQkJmjZtmjZv3qwZM2bo5Zdf1rBhwzRx4kR9/PHHwdoWAADAWQtawaqpqdEbb7yhO%2B64Q9OmTVPr1q314IMPqlu3bpowYYLWrl0brK0BAACclYDfg1VYWKjVq1fr1VdfVWVlpYYOHaoXXnhBvXv39j2mT58%2BmjVrlkaMGBHo7QEAAJy1gBes4cOHq0OHDpo0aZJGjRqlFi1anPKYtLQ0HT58ONBbAwAAsCLgBWv58uW%2Bz5g6E365MgAACFUBvwera9euuuuuu7Rx40bf2H//93/rjjvuOOUT1AEAAEJRwAvWvHnzdPToUXXq1Mk3NmDAANXX12v%2B/PmB3g4AAIB1AX%2BL8J133tHatWsVExPjG2vfvr1yc3N1/fXXB3o7AAAA1gX8Fazq6mpFRkaeupHwcFVVVQV6OwAAANYFvGD16dNH8%2BfP15EjR3xjn3/%2BuWbPnu33UQ0AAAChKuBvEc6YMUM/%2B9nPdPXVVysqKkr19fWqrKxU27Zt9eKLLwZ6OwAAANYFvGC1bdtWr7/%2Buv785z9r//79Cg8PV4cOHdSvXz9FREQEejsAAADWBbxgSVLjxo31ox/9KBhLAwAAnHMBL1gHDhzQU089pU8//VTV1dWnzP/pT38K9JYAAACsCso9WCUlJerXr5%2BaNm0a6OUBAADOuYAXrPz8fP3pT39SbGxsoJcGAAAIiIB/TEPLli155QoAAFzQAl6wJk2apEWLFskYE%2BilAQAAAiLgbxH%2B%2Bc9/1gcffKBXXnlFl19%2BucLD/TveSy%2B9FOgtAQAAWBXwghUVFaX%2B/fsHelkAAICACXjBmjdvXqCXBAAACKiA34MlSXv27NHChQv14IMP%2BsY%2B/PDDYGwFAADAuoAXrG3btmnkyJF66623tG7dOklffvjorbfeyoeMAgCAC0LAC9aCBQv085//XGvXrlVYWJikL38/4fz587V48eJAbwcAAMC6gBesf/zjH8rIyJAkX8GSpOuuu06FhYWB3g4AAIB1AS9YzZs3P%2B3vICwpKVHjxo0DvR0AAADrAl6wevXqpccff1wVFRW%2Bsb1792ratGm6%2BuqrA70dAAAA6wL%2BMQ0PPvigbrvtNqWkpKiurk69evVSVVWVOnfurPnz5wd6OwAAANYFvGBddtllWrdunbZu3aq9e/eqSZMm6tChg6655hq/e7IAAABCVcALliQ1atRIP/rRj4KxNAAAwDkX8II1aNCgM75SxWdhAQCAUBfwgjVs2DC/glVXV6e9e/fK4/HotttuC/R2AAAArAt4wZo6deppxzds2KC//vWvAd4NAACAfUH5XYSn86Mf/Uivv/56sLcBAABw1s6bgrVjxw4ZY4K9DQAAgLMW8LcIx48ff8pYVVWVCgsLNWTIkEBvBwAAwLqAF6z27duf8lOEkZGRGjNmjMaOHRvo7QAAAFgX8ILFp7UDAIALXcAL1quvvur4saNGjTrj/Ntvv61p06YpJSVFCxYs8I2/8sormjFjhho1auT3%2BJUrVyo5OVn19fV6%2BumntW7dOpWXlys5OVmzZs1S27ZtJUler1ezZs3Se%2B%2B9p/DwcKWlpWnmzJlq0qTJtzgpAAC4WAW8YD300EOqr68/5Yb2sLAwv7GwsLAzFqxnn31Wq1evVrt27U4736dPH7344ounnVu5cqXWrl2rZ599Vq1bt9aCBQuUmZmp1157TWFhYZo5c6ZOnDihdevWqaamRvfdd59yc3P18MMPf4cTAwCAi03Af4rwueeeU79%2B/bRy5Ur9/e9/19/%2B9jetWLFC/fv317PPPquPP/5YH3/8sT766KMzXicyMvKMBetM8vLyNGHCBHXs2FFRUVHKyclRYWGhPvroI33xxRfauHGjcnJyFBsbq9atW2vy5Mn6/e9/r5qamu96bAAAcBEJyj1Yy5YtU%2BvWrX1jV155pdq2bauJEydq3bp1jq5z6623nnG%2BuLhYP/3pT5Wfny%2B3262srCzdcMMNqq6u1u7duxUfH%2B97bFRUlNq1ayePx6OjR48qIiJCXbt29c0nJCTo2LFj2rNnj984AADA6QS8YO3bt0/R0dGnjLvdbhUVFVlZIzY2Vu3bt9f999%2BvTp066Y9//KMeeOABxcXF6Qc/%2BIGMMafsITo6WmVlZWrRooWioqL8ftLx5GPLysocrV9SUqLS0lK/MZerqeLi4s7yZP4iIs6bjzFzxOUKzn5P5hRqeQUSGTlDTs6QU8PIyJlQzingBatNmzaaP3%2B%2B7rvvPsXExEiSysvL9cwzz%2Bj73/%2B%2BlTUGDBigAQMG%2BP4%2BfPhw/fGPf9Qrr7zi%2B1U9Z/pQ07P9wNO8vDwtWrTIbywzM1NZWVlndd1QFxPTLKjru92XBHX9UEBGzpCTM%2BTUMDJyJhRzCnjBmjFjhqZMmaK8vDw1a9ZM4eHhqqioUJMmTbR48eJztm6bNm2Un5%2BvFi1aKDw8XF6v12/e6/WqZcuWio2NVUVFherq6hQREeGbk6SWLVs6Wis9PV2DBg3yG3O5mqqsrNLCSf4t1Bq97fM7FRERLrf7EpWXV6murj4oezjfkZEz5OQMOTWMjJyxkVOwvrkPeMHq16%2BftmzZoq1bt%2BrgwYMyxqh169b64Q9/qObNm1tZ43e/%2B52io6M1bNgw31hhYaHatm2ryMhIde7cWQUFBerbt6%2BkL19B279/v5KTk9WmTRsZY7Rr1y4lJCRIkjwej9xutzp06OBo/bi4uFPeDiwtPara2ov7iyjY56%2Brqw/6Hs53ZOQMOTlDTg0jI2dCMaeAFyxJuuSSSzR48GAdPHjQ99lTNp04cUJz5sxR27ZtdcUVV2jDhg3685//rJdfflmSlJGRoWXLlql///5q3bq1cnNz1a1bNyUlJUmShg4dql/96ld64okndOLECS1evFhjxoyRyxWUuAAAQIgJeGOorq7Wo48%2Bqtdff12SlJ%2Bfr/Lyct1///365S9/Kbfb7eg6J8tQbW2tJGnjxo2Svny16dZbb1VlZaXuu%2B8%2BlZaW6vLLL9fixYuVmJgo6cvfh1haWqpbbrlFlZWVSklJ8btn6rHHHtOjjz6qwYMHq1GjRrr%2B%2BuuVk5NjLQMAAHBhCzNne0f3tzRnzhz97W9/0%2BTJk/XAAw/o448/Vnl5ue677z61bdtWjz32WCC3EzClpUetX9PlCte1uW9bv%2B65sj77mqCs63KFKyammcrKKkPuJeZAISNnyMkZcmoYGTljI6dWrezcfvRtBfwu6Q0bNuiZZ57Rdddd5/soBLfbrXnz5umtt94K9HYAAACsC3jBqqysVPv27U8Zj42N1bFjxwK9HQAAAOsCXrC%2B//3v669//ask/8%2BbevPNN/Uf//Efgd4OAACAdQG/yf0nP/mJ7r33Xt10002qr6/Xb3/7W%2BXn52vDhg166KGHAr0dAAAA6wJesNLT0%2BVyubRixQpFRETo17/%2BtTp06KDc3Fxdd911gd4OAACAdQEvWIcPH9ZNN92km266KdBLAwAABETA78EaPHjwWf%2BuPwAAgPNZwAtWSkqK1q9fH%2BhlAQAAAibgbxF%2B73vf0y9%2B8QstW7ZM3//%2B99WoUSO/%2BaeeeirQWwIAALAq4AVr9%2B7d%2BsEPfiBJKisrC/TyAAAA51zAClZOTo4WLFigF1980Te2ePFiZWZmBmoLAAAAARGwe7A2bdp0ytiyZcsCtTwAAEDABKxgne4nB/lpQgAAcCEKWME6%2BYudGxoDAAAIdQH/mAYAAIALHQULAADAsoD9FGFNTY2mTJnS4BifgwUAAEJdwApW7969VVJS0uAYAABAqAtYwfrq518BAABcyLgHCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAy0K6YL399ttKTU1VTk7OKXNvvPGGRowYoZ49e2r06NF65513fHP19fVasGCBBg8erD59%2BmjixIk6cOCAb97r9So7O1upqanq16%2BfHnroIVVXVwfkTAAAIPSFbMF69tlnNXfuXLVr1%2B6UuZ07d2ratGmaOnWq/vKXv2jChAm65557dPDgQUnSypUrtXbtWi1btkybN29W%2B/btlZmZKWOMJGnmzJmqqqrSunXr9Pvf/16FhYXKzc0N6PkAAEDoCtmCFRkZqdWrV5%2B2YK1atUppaWlKS0tTZGSkRo4cqS5dumjNmjWSpLy8PE2YMEEdO3ZUVFSUcnJyVFhYqI8%2B%2BkhffPGFNm7cqJycHMXGxqp169aaPHmyfv/736umpibQxwQAACEoZAvWrbfequbNm592rqCgQPHx8X5j8fHx8ng8qq6u1u7du/3mo6Ki1K5dO3k8Hu3cuVMRERHq2rWrbz4hIUHHjh3Tnj17zs1hAADABcUV7A2cC16vV9HR0X5j0dHR2r17t44cOSJjzGnny8rK1KJFC0VFRSksLMxvTpLKysocrV9SUqLS0lK/MZerqeLi4r7Lcb5RRERo9WOXKzj7PZlTqOUVSGTkDDk5Q04NIyNnQjmnC7JgSfLdT/Vd5ht6bkPy8vK0aNEiv7HMzExlZWWd1XVDXUxMs6Cu73ZfEtT1QwEZOUNOzpBTw8jImVDM6YIsWDExMfJ6vX5jXq9XsbGxatGihcLDw08737JlS8XGxqqiokJ1dXWKiIjwzUlSy5YtHa2fnp6uQYMG%2BY25XE1VVlb5XY90WqHW6G2f36mIiHC53ZeovLxKdXX1QdnD%2BY6MnCEnZ8ipYWTkjI2cgvXN/QVZsBITE5Wfn%2B835vF4NHz4cEVGRqpz584qKChQ3759JUnl5eXav3%2B/kpOT1aZNGxljtGvXLiUkJPie63a71aFDB0frx8XFnfJ2YGka06kJAAAT4ElEQVTpUdXWXtxfRME%2Bf11dfdD3cL4jI2fIyRlyahgZOROKOYXWSyAOjRs3Tu%2B%2B%2B662bNmi48ePa/Xq1dq3b59GjhwpScrIyNDy5ctVWFioiooK5ebmqlu3bkpKSlJsbKyGDh2qX/3qVzp8%2BLAOHjyoxYsXa8yYMXK5Lsg%2BCgAALAvZxpCUlCRJqq2tlSRt3LhR0pevNnXp0kW5ubmaN2%2BeioqK1KlTJy1dulStWrWSJI0fP16lpaW65ZZbVFlZqZSUFL97ph577DE9%2BuijGjx4sBo1aqTrr7/%2BtB9mCgAAcDph5mzv6IYjpaVHrV/T5QrXtblvW7/uubI%2B%2B5qgrOtyhSsmppnKyipD7iXmQCEjZ8jJGXJqGBk5YyOnVq1O/5FO59oF%2BRYhAABAMFGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMCyC7Zgde3aVYmJiUpKSvL9mTNnjiRp27ZtGjNmjHr16qXhw4drzZo1fs9dvny5hg4dql69eikjI0P5%2BfnBOAIAAAhRrmBv4Fx68803dfnll/uNlZSUaPLkyXrooYc0YsQIvf/%2B%2B7r77rvVoUMHJSUladOmTVq4cKGee%2B45de3aVcuXL9ddd92lt956S02bNg3SSQAAQCi5YF/B%2BiZr165V%2B/btNWbMGEVGRio1NVWDBg3SqlWrJEl5eXkaPXq0unfvriZNmuj222%2BXJG3evDmY2wYAACHkgi5YTz31lAYMGKArr7xSM2fOVGVlpQoKChQfH%2B/3uPj4eN/bgF%2BfDw8PV7du3eTxeAK6dwAAELou2LcIe/ToodTUVD3xxBM6cOCAsrOzNXv2bHm9XrVu3drvsS1atFBZWZkkyev1Kjo62m8%2BOjraN%2B9ESUmJSktL/cZcrqaKi4v7jqc5vYiI0OrHLldw9nsyp1DLK5DIyBlycoacGkZGzoRyThdswcrLy/P9d8eOHTV16lTdfffd6t27d4PPNcac9dqLFi3yG8vMzFRWVtZZXTfUxcQ0C%2Br6bvclQV0/FJCRM%2BTkDDk1jIycCcWcLtiC9XWXX3656urqFB4eLq/X6zdXVlam2NhYSVJMTMwp816vV507d3a8Vnp6ugYNGuQ35nI1VVlZ5Xfc/emFWqO3fX6nIiLC5XZfovLyKtXV1QdlD%2Bc7MnKGnJwhp4aRkTM2cgrWN/cXZMHasWOH1qxZo%2BnTp/vGCgsL1bhxY6WlpekPf/iD3%2BPz8/PVvXt3SVJiYqIKCgp04403SpLq6uq0Y8cOjRkzxvH6cXFxp7wdWFp6VLW1F/cXUbDPX1dXH/Q9nO/IyBlycoacGkZGzoRiTqH1EohDLVu2VF5enpYtW6YTJ05o7969evrpp5Wenq4bbrhBRUVFWrVqlY4fP66tW7dq69atGjdunCQpIyNDr776qrZv366qqiotWbJEjRs31oABA4J7KAAAEDIuyFewWrdurWXLlumpp57yFaQbb7xROTk5ioyM1NKlSzV37lzNnj1bbdq00ZNPPqkrrrhCktS/f3/df//9ys7O1qFDh5SUlKRly5apSZMmQT4VAAAIFWHmbO/ohiOlpUetX9PlCte1uW9bv%2B65sj77mqCs63KFKyammcrKKkPuJeZAISNnyMkZcmoYGTljI6dWrZpb3pUzF%2BRbhAAAAMFEwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwTqOoqEh33nmnUlJSNHDgQD355JOqr68P9rYAAECIcAV7A%2Beje%2B%2B9VwkJCdq4caMOHTqkSZMm6dJLL9VPf/rTYG8NAACEAF7B%2BhqPx6Ndu3Zp6tSpat68udq3b68JEyYoLy8v2FsDAAAhglewvqagoEBt2rRRdHS0bywhIUF79%2B5VRUWFoqKiGrxGSUmJSktL/cZcrqaKi4uzuteIiNDqxy5XcPZ7MqdQyyuQyMgZcnKGnBpGRs6Eck4UrK/xer1yu91%2BYyfLVllZmaOClZeXp0WLFvmN3XPPPbr33nvtbVRfFrnbLvtU6enp1svbhaSkpEQvvPAcOZ0BGTlDTs6QU8PIyJlQzin0KmEAGGPO6vnp6el65ZVX/P6kp6db2t2/lZaWatGiRae8WgZ/5NQwMnKGnJwhp4aRkTOhnBOvYH1NbGysvF6v35jX61VYWJhiY2MdXSMuLi7kmjYAALCHV7C%2BJjExUcXFxTp8%2BLBvzOPxqFOnTmrWrFkQdwYAAEIFBetr4uPjlZSUpKeeekoVFRUqLCzUb3/7W2VkZAR7awAAIEREzJo1a1awN3G%2B%2BeEPf6h169Zpzpw5ev311zVmzBhNnDhRYWFhwd7aKZo1a6a%2Bffvy6loDyKlhZOQMOTlDTg0jI2dCNacwc7Z3dAMAAMAPbxECAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBClFFRUW68847lZKSooEDB%2BrJJ59UfX19sLd11t5%2B%2B22lpqYqJyfnlLk33nhDI0aMUM%2BePTV69Gi98847vrn6%2BnotWLBAgwcPVp8%2BfTRx4kQdOHDAN%2B/1epWdna3U1FT169dPDz30kKqrq33zO3fu1M0336zevXtryJAhev755x2vHQxFRUXKzMxUSkqKUlNTNX36dJWXl0s6u7Oc6xwDadeuXbrtttvUu3dvpaamKjs7W6WlpZKkbdu2acyYMerVq5eGDx%2BuNWvW%2BD13%2BfLlGjp0qHr16qWMjAzl5%2Bf75o4fP65HHnlE/fv3V0pKirKyslRWVuabb%2Bhrs6G1g%2Bnxxx9X165dfX8np3/r2rWrEhMTlZSU5PszZ84cR3u9mHJasmSJ%2BvXrpx49emjChAn67LPPHO3zgszIICTdeOON5uGHHzbl5eVm7969ZsiQIeb5558P9rbOyrJly8yQIUPM%2BPHjTXZ2tt/cjh07TGJiotmyZYuprq42r732munevbspLi42xhizfPlyM3DgQLN7925z9OhR89hjj5kRI0aY%2Bvp6Y4wx99xzj7nzzjvNoUOHzMGDB016erqZM2eOMcaYqqoq88Mf/tAsXLjQVFZWmvz8fNO3b1%2BzYcMGR2sHw/XXX2%2BmT59uKioqTHFxsRk9erSZMWPGWZ/lXOYYSMePHzdXX321WbRokTl%2B/Lg5dOiQufnmm83kyZPN559/bnr06GFWrVplqqurzf/%2B7/%2Ba5ORk8/HHHxtjjPnTn/5krrzySrN9%2B3ZTVVVlli5daq655hpTWVlpjDFm3rx5ZvTo0eaf//ynKSsrM/fcc4%2BZNGmSb%2B0zfW02tHYw7dixw/Tt29d06dLFGNPwXi%2B2nLp06WIOHDhwyjg5/duKFSvMddddZwoLC83Ro0fNnDlzzJw5cy7ajChYIejjjz823bp1M16v1zf2P//zP2bo0KFB3NXZe%2BGFF0x5ebmZNm3aKQVr9uzZJjMz029s7NixZunSpcYYY4YPH25eeOEF39zRo0dNfHy8%2BfDDD01paam54oorzM6dO33zW7duNT169DAnTpww69evN1dddZWpra31zT/55JPmZz/7maO1A%2B3IkSNm%2BvTpprS01Df24osvmiFDhpz1Wc5ljoHk9XrNyy%2B/bGpqanxjL7zwgrn22mvNc889Z0aNGuX3%2BOzsbDNz5kxjjDF33nmnefzxx31zdXV15pprrjHr1q0zNTU1pnfv3mbjxo2%2B%2Bd27d5uuXbuagwcPNvi12dDawVJXV2fGjh1r/uu//stXsMjJ3zcVLHL6t0GDBp32G6qLNSPeIgxBBQUFatOmjaKjo31jCQkJ2rt3ryoqKoK4s7Nz6623qnnz5qedKygoUHx8vN9YfHy8PB6PqqurtXv3br/5qKgotWvXTh6PRzt37lRERITfWx8JCQk6duyY9uzZo4KCAnXt2lURERF%2B1z75EvWZ1g4Gt9utefPm6dJLL/WNFRcXKy4u7qzOcq5zDKTo6GiNHTtWLpdLkrRnzx794Q9/0I9//ONvzOCbMgoPD1e3bt3k8Xi0f/9%2BHT16VAkJCb75jh07qkmTJiooKGjwa7OhtYPlpZdeUmRkpEaMGOEbI6dTPfXUUxowYICuvPJKzZw5U5WVleT0L59//rk%2B%2B%2BwzHTlyRMOGDfO9lXf48OGLNiMKVgjyer1yu91%2BYyf/5/rq%2B9IXEq/X6/cFJH155rKyMh05ckTGmG%2Bc93q9ioqKUlhYmN%2BcJN/81/Ns0aKFvF6v6uvrz7j2%2BcDj8WjFihW6%2B%2B67z%2Bos5zrHYCgqKlJiYqKGDRumpKQkZWVlfeM%2BT/57nikjr9crSac83%2B12f2MGTjIK5v9LX3zxhRYuXKhHH33Ub5yc/PXo0UOpqal66623lJeXp%2B3bt2v27Nnk9C8HDx6UJL355pv67W9/q9dee00HDx7Uww8/fNFmRMEKUcaYYG8h4Bo685nmv0teXy0S52ve77//viZOnKgpU6YoNTX1Gx/3bc5yLnMMtDZt2sjj8ejNN9/Uvn379MADDzh6XqAzCqZ58%2BZp9OjR6tSp07d%2B7sWUU15ensaOHavGjRurY8eOmjp1qtatW6eampoGn3sx5HRyn7fffrtat26tyy67TPfee682bdr0rZ7/XebP14woWCEoNjbW1%2BpP8nq9CgsLU2xsbJB2dW7FxMSc9syxsbFq0aKFwsPDTzvfsmVLxcbGqqKiQnV1dX5zknzzX/9uxuv1%2Bq57prWDadOmTbrzzjs1Y8YM3XrrrZJ0Vmc51zkGS1hYmNq3b6%2BcnBytW7dOLpfrlDOWlZX5/j3PlNHJx3x9/siRI74MzvS1ebprf3XtQNu2bZs%2B/PBDZWZmnjLX0F4vppxO5/LLL1ddXd1pv2YuxpxO3rLw1VeL2rRpI2OMampqLsqMKFghKDExUcXFxTp8%2BLBvzOPxqFOnTmrWrFkQd3buJCYmnvKeucfjUffu3RUZGanOnTuroKDAN1deXq79%2B/crOTlZ3bp1kzFGu3bt8nuu2%2B1Whw4dlJiYqE8%2B%2BUS1tbWnXLuhtYPlgw8%2B0LRp0/T0009r1KhRvvGzOcu5zjGQtm3bpqFDh/q9NXmy5CUnJ5%2BSQX5%2Bvl9GX82grq5OO3bsUPfu3dW2bVtFR0f7zf/jH//QiRMnlJiY2ODXZlJS0hnXDrQ1a9bo0KFDGjhwoFJSUjR69GhJUkpKirp06UJO/7Jjxw7Nnz/fb6ywsFCNGzdWWloaOUm67LLLFBUVpZ07d/rGioqK1KhRo4s3o3N%2BGz3OibFjx5oZM2aYo0ePmt27d5tBgwaZFStWBHtbVpzupwg/%2BeQTk5SUZDZv3myqq6vNqlWrTM%2BePU1JSYkx5sufGhkwYIDv4wVmzpxpbrrpJt/zs7Ozze23324OHTpkiouLzU033WTmz59vjPnyR/oHDhxonnnmGXPs2DGzfft2c%2BWVV5rNmzc7WjvQampqzI9//GPz0ksvnTJ3tmc5lzkGUnl5uUlNTTXz5883x44dM4cOHTITJ040P/nJT8wXX3xhevbsaV5%2B%2BWVTXV1ttmzZYpKTk30/Hbl161bTu3dv8%2BGHH5pjx46ZhQsXmrS0NFNVVWWM%2BfInI2%2B88Ubzz3/%2B0xw%2BfNhMmjTJ3Hvvvb61z/S12dDageb1ek1xcbHvz4cffmi6dOliiouLTVFRETn9y8GDB02PHj3M0qVLzfHjx82ePXvMsGHDzJw5c/j/6Ssef/xxM3jwYLNv3z7zxRdfmPT0dDN9%2BvSLNiMKVogqLi42t99%2Bu0lOTjapqanmmWee8X1WUahKTEw0iYmJ5oorrjBXXHGF7%2B8nbdiwwQwZMsQkJCSYG264wbz33nu%2Bufr6evP000%2Bbq6%2B%2B2iQnJ5s77rjD73OqysvLTU5OjunRo4fp06ePmT17tjl%2B/Lhv/pNPPjHjx483iYmJZsCAAWblypV%2BezvT2oH2t7/9zXTp0sWXz1f/fPbZZ2d1lnOdYyDt2rXL3HzzzSY5OdlcddVVJjs72xw8eNAYY8x7771nRo4caRISEsyQIUNO%2BdHylStXmrS0NJOYmGgyMjLMJ5984ps7fvy4mTVrlunTp4/p2bOnuf/%2B%2B015eblvvqGvzYbWDqYDBw74PqbBGHL6qvfee8%2Bkp6ebHj16mL59%2B5p58%2BaZ6upqR3u9WHL66ll69Ohhpk2bZioqKhzt80LMKMyY8/TuMAAAgBDFPVgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYNn/AxnotUchHIcRAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common7117110502744204773”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>278</td> <td class=”number”>8.7%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-30.099407437116625</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.7839555030772787</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>53.63651925237697</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>34.0078713358341</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-11.29537905097039</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-55.06549827843521</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-21.537191558696197</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-23.175864713673935</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2815)</td> <td class=”number”>2815</td> <td class=”number”>87.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>106</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme7117110502744204773”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-99.94242310762233</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-98.8895369395067</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-98.08895762460443</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-95.78266718621464</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>8282.707749903071</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11232.759410102526</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>26128.162530164445</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>171961.8566608403</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>592130.5637640554</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_HHNV10”>LACCESS_HHNV10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3115</td>

</tr> <tr>

<th>Unique (%)</th> <td>97.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>661.07</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>16334</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.6%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram2289936167295975588”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives2289936167295975588,#minihistogram2289936167295975588”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives2289936167295975588”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles2289936167295975588”

aria-controls=”quantiles2289936167295975588” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram2289936167295975588” aria-controls=”histogram2289936167295975588”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common2289936167295975588” aria-controls=”common2289936167295975588”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme2289936167295975588” aria-controls=”extreme2289936167295975588”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles2289936167295975588”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>18.665</td>

</tr> <tr>

<th>Q1</th> <td>118.22</td>

</tr> <tr>

<th>Median</th> <td>320.27</td>

</tr> <tr>

<th>Q3</th> <td>706.12</td>

</tr> <tr>

<th>95-th percentile</th> <td>2540.6</td>

</tr> <tr>

<th>Maximum</th> <td>16334</td>

</tr> <tr>

<th>Range</th> <td>16334</td>

</tr> <tr>

<th>Interquartile range</th> <td>587.9</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>1122.5</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.698</td>

</tr> <tr>

<th>Kurtosis</th> <td>40.813</td>

</tr> <tr>

<th>Mean</th> <td>661.07</td>

</tr> <tr>

<th>MAD</th> <td>618.56</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>5.1237</td>

</tr> <tr>

<th>Sum</th> <td>2070500</td>

</tr> <tr>

<th>Variance</th> <td>1260000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram2289936167295975588”>

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dWGDRt07733KjU1VY0bN9Yjjzyidu3aafTo0Vq3bl1tLQ0AAKBaQp3eYXZ2tlavXq033nhDxcXF6tu3r1566SV16dLFd5uuXbvqySef1IABA5xeHgAAQLU5XrD69%2B%2BvFi1aaNy4cRo0aJDq169/xm2SkpJ05MgRp5cGAABgheMFa/ny5erWrVult9u1a5cDqwEAALDP8Wuw2rZtq/Hjx2vz5s2%2Bsf/5n//RvffeK6/X6/RyAAAArHO8YKWlpenYsWNq1aqVb6xnz56qqKjQnDlznF4OAACAdY6fIvzwww%2B1bt06NWjQwDfWvHlzpaen69Zbb3V6OQAAANY5/g5WSUmJwsLCzlxIcLBOnDjh9HIAAACsc7xgde3aVXPmzNHRo0d9Yz/88IOeeuopv69qAAAACFSOnyJ89NFH9Yc//EHXXnutwsPDVVFRoeLiYjVt2lQvv/yy08sBAACwzvGC1bRpU7311lt6//33deDAAQUHB6tFixbq0aOHQkJCnF4OAACAdY4XLEm6%2BOKLdeONN9bGrgEAAGqc4wXr4MGDmjdvnr7%2B%2BmuVlJScMf/ee%2B85vSQAAACrauUarLy8PPXo0UN16tRxevcAAAA1zvGCtXv3br333nuKiopyetcAAACOcPxrGho2bMg7VwAAwNUcL1jjxo3TokWLZIxxetcAAACOcPwU4fvvv68vvvhCa9as0RVXXKHgYP%2BO9%2Bqrrzq9JAAAAKscL1jh4eG6/vrrnd4tAACAYxwvWGlpaU7vEgAAwFGOX4MlSd98840WLlyoRx55xDe2Y8eO2lgKAACAdY4XrI8//lgDBw7Upk2btH79ekk/ffnoqFGj%2BJJRAADgCo4XrPnz5%2Bvhhx/WunXrFBQUJOmnv084Z84cLV682OnlAAAAWOd4wfrXv/6lkSNHSpKvYEnSzTffrOzsbKeXAwAAYJ3jBatevXpn/RuEeXl5uvjiiy9oWx988IESExOVkpLiN75mzRpdddVVio%2BP9/v58ssvJUkVFRWaP3%2B%2Bevfura5du2rMmDE6ePCg7/5er1eTJ09WYmKievTooenTp591zQAAAGfjeMHq3LmzZs%2BeraKiIt/Y/v37lZqaqmuvvbbK23nuuec0a9YsNWvW7KzzXbt2lcfj8ftp3769JGnlypVat26dli1bpq1bt6p58%2BZKTk72ffnpjBkzdOLECa1fv16vv/66srOzlZ6eXo3UAADg34njBeuRRx7Rjh07lJCQoJMnT6pz587q16%2BfvF6vpk2bVuXthIWFafXq1ecsWOeTkZGh0aNHq2XLlgoPD1dKSoqys7O1a9cu/fjjj9q8ebNSUlIUFRWlxo0b6/7779frr7%2Bu0tLSC94XAAD49%2BP492BdfvnlWr9%2BvbZv3679%2B/frkksuUYsWLdS9e3e/a7IqM2rUqPPO5%2Bbm6p577tHu3bsVERGhSZMm6bbbblNJSYmysrIUExPju214eLiaNWsmj8ejY8eOKSQkRG3btvXNx8bG6vjx4/rmm2/8xs8lLy9P%2Bfn5fmOhoXUUHR1d5XxVERJSK9%2By8auFhla%2B3tOZAi1bZdyYy42ZJHfmIlPgcGMuN2aqCscLliRddNFFuvHGG2ts%2B1FRUWrevLkeeughtWrVSu%2B%2B%2B67%2B%2BMc/Kjo6WldeeaWMMYqMjPS7T2RkpAoKClS/fn2Fh4f7lb3Tty0oKKjS/jMyMrRo0SK/seTkZE2aNKmayQJbgwZ1q3zbiIhLa3AltceNudyYSXJnLjIFDjfmcmOm83G8YPXq1eu871TZ%2BC6snj17qmfPnr5/9%2B/fX%2B%2B%2B%2B67WrFmjqVOnStJ5/9h0df8Q9fDhw9WrVy%2B/sdDQOiooKK7Wdn8p0F4NVCV/SEiwIiIuVWHhCZWXVziwKme4MZcbM0nuzEWmwOHGXLWd6UJe3NvkeMHq16%2BfX8EqLy/X/v375fF49Pvf/77G9tukSRPt3r1b9evXV3BwsLxer9%2B81%2BtVw4YNFRUVpaKiIpWXlyskJMQ3J0kNGzas0r6io6PPOB2Yn39MZWXuOFh%2BrQvJX15e4crflxtzuTGT5M5cZAocbszlxkzn43jBOv0O0i%2B98847%2BvTTT63s45VXXlFkZKT69evnG8vOzlbTpk0VFham1q1bKzMzU926dZMkFRYW6sCBA2rfvr2aNGkiY4y%2B%2BuorxcbGSpI8Ho8iIiLUokULK%2BsDAADu9ps5x3TjjTfqrbfesrKtU6dOaebMmfJ4PCotLdX69ev1/vvva8SIEZKkkSNHavny5crOzlZRUZHS09PVrl07xcfHKyoqSn379tVf/vIXHTlyRIcOHdLixYs1bNgwhYbWyiVrAAAgwPxmGsOePXsu6Nqn%2BPh4SVJZWZkkafPmzZJ%2Berdp1KhRKi4u1oMPPqj8/HxdccUVWrx4seLi4iRJI0aMUH5%2Bvu6%2B%2B24VFxcrISHB76L0p59%2BWk888YR69%2B6tiy66SLfeeusZX2YKAABwLo4XrNPvIv3ciRMnlJ2drT59%2BlR5Ox6P55xzQUFBuv/%2B%2B3X//fefc37SpEnn/FRfvXr19Oc//7nKawEAAPg5xwtW8%2BbNz/gUYVhYmIYNG6bbb7/d6eUAAABY53jBmjNnjtO7BAAAcJTjBeuNN96o8m0HDRpUgysBAACoGY4XrOnTp6uiouKMC9qDgoL8xoKCgihYAAAgIDlesJ5//nm98MILGj9%2BvNq2bStjjPbt26fnnntOd911lxISEpxeEgAAgFW1cg3WsmXL1LhxY9/Y1VdfraZNm2rMmDFav36900sCAACwyvEvGv3222/P%2BEPLkhQREaGcnBynlwMAAGCd4wWrSZMmmjNnjgoKCnxjhYWFmjdvnn73u985vRwAAADrHD9F%2BOijj2rKlCnKyMhQ3bp1FRwcrKKiIl1yySVavHix08sBAACwzvGC1aNHD23btk3bt2/XoUOHZIxR48aNdd1116levXpOLwcAAMC6WvlbhJdeeql69%2B6tQ4cOqWnTprWxBAAAgBrj%2BDVYJSUlSk1NVadOnXTLLbdI%2BukarLFjx6qwsNDp5QAAAFjneMGaO3eu9u7dq/T0dAUH/9/uy8vLlZ6e7vRyAAAArHO8YL3zzjt69tlndfPNN/v%2B6HNERITS0tK0adMmp5cDAABgneMFq7i4WM2bNz9jPCoqSsePH3d6OQAAANY5XrB%2B97vf6dNPP5Ukv789%2BPbbb%2Bs///M/nV4OAACAdY5/ivDOO%2B/UxIkTNXToUFVUVOjFF1/U7t279c4772j69OlOLwcAAMA6xwvW8OHDFRoaqhUrVigkJER//etf1aJFC6Wnp%2Bvmm292ejkAAADWOV6wjhw5oqFDh2ro0KFO7xoAAMARjl%2BD1bt3b79rrwAAANzG8YKVkJCgjRs3Or1bAAAAxzh%2BivA//uM/9Kc//UnLli3T7373O1100UV%2B8/PmzXN6SQAAAFY5XrCysrJ05ZVXSpIKCgqc3j0AAECNc6xgpaSkaP78%2BXr55Zd9Y4sXL1ZycrJTSwAAAHCEY9dgbdmy5YyxZcuWObV7AAAAxzhWsM72yUE%2BTQgAANzIsYJ1%2Bg87VzYGAAAQ6Bz/mgYAAAC3o2ABAABY5tinCEtLSzVlypRKx/geLAAAEOgcK1hdunRRXl5epWMAAACBzrGC9fPvvwIAAHAzrsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYFlAF6wPPvhAiYmJSklJOWNuw4YNGjBggDp16qQhQ4boww8/9M1VVFRo/vz56t27t7p27aoxY8bo4MGDvnmv16vJkycrMTFRPXr00PTp01VSUuJIJgAAEPgCtmA999xzmjVrlpo1a3bG3N69e5WamqqpU6fqk08%2B0ejRo/XAAw/o0KFDkqSVK1dq3bp1WrZsmbZu3armzZsrOTlZxhhJ0owZM3TixAmtX79er7/%2BurKzs5Wenu5oPgAAELgCtmCFhYVp9erVZy1Yq1atUlJSkpKSkhQWFqaBAweqTZs2Wrt2rSQpIyNDo0ePVsuWLRUeHq6UlBRlZ2dr165d%2BvHHH7V582alpKQoKipKjRs31v3336/XX39dpaWlTscEAAABKGAL1qhRo1SvXr2zzmVmZiomJsZvLCYmRh6PRyUlJcrKyvKbDw8PV7NmzeTxeLR3716FhISobdu2vvnY2FgdP35c33zzTc2EAQAArhJa2wuoCV6vV5GRkX5jkZGRysrK0tGjR2WMOet8QUGB6tevr/DwcAUFBfnNSVJBQUGV9p%2BXl6f8/Hy/sdDQOoqOjv41cc4pJCSw%2BnFoaOXrPZ0p0LJVxo253JhJcmcuMgUON%2BZyY6aqcGXBkuS7nurXzFd238pkZGRo0aJFfmPJycmaNGlStbYb6Bo0qFvl20ZEXFqDK6k9bszlxkySO3ORKXC4MZcbM52PKwtWgwYN5PV6/ca8Xq%2BioqJUv359BQcHn3W%2BYcOGioqKUlFRkcrLyxUSEuKbk6SGDRtWaf/Dhw9Xr169/MZCQ%2BuooKD410Y6q0B7NVCV/CEhwYqIuFSFhSdUXl7hwKqc4cZcbswkuTMXmQKHG3PVdqYLeXFvkysLVlxcnHbv3u035vF41L9/f4WFhal169bKzMxUt27dJEmFhYU6cOCA2rdvryZNmsgYo6%2B%2B%2BkqxsbG%2B%2B0ZERKhFixZV2n90dPQZpwPz84%2BprMwdB8uvdSH5y8srXPn7cmMuN2aS3JmLTIHDjbncmOl8AustkCq644479NFHH2nbtm06efKkVq9erW%2B//VYDBw6UJI0cOVLLly9Xdna2ioqKlJ6ernbt2ik%2BPl5RUVHq27ev/vKXv%2BjIkSM6dOiQFi9erGHDhik01JV9FAAAWBawjSE%2BPl6SVFZWJknavHmzpJ/ebWrTpo3S09OVlpamnJwctWrVSkuXLlWjRo0kSSNGjFB%2Bfr7uvvtuFRcXKyEhwe%2BaqaefflpPPPGEevfurYsuuki33nrrWb/MFAAA4GyCTHWv6EaV5Ocfs77N0NBg3ZT%2BgfXt1pSNk7tXepvQ0GA1aFBXBQXFrnor2Y253JhJcmcuMgUON%2Baq7UyNGp39K51qmitPEQIAANQmChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMtcW7Datm2ruLg4xcfH%2B35mzpwpSfr44481bNgwde7cWf3799fatWv97rt8%2BXL17dtXnTt31siRI7V79%2B7aiAAAAAJUaG0voCa9/fbbuuKKK/zG8vLydP/992v69OkaMGCAPv/8c02YMEEtWrRQfHy8tmzZooULF%2Br5559X27ZttXz5co0fP16bNm1SnTp1aikJAAAIJK59B%2Btc1q1bp%2BbNm2vYsGEKCwtTYmKievXqpVWrVkmSMjIyNGTIEHXo0EGXXHKJxo4dK0naunVrbS4bAAAEEFe/gzVv3jzt2LFDRUVFuuWWWzRt2jRlZmYqJibG73YxMTHauHGjJCkzM1P9%2BvXzzQUHB6tdu3byeDzq379/lfabl5en/Px8v7HQ0DqKjo6uZiJ/ISGB1Y9DQytf7%2BlMgZatMm7M5cZMkjtzkSlwuDGXGzNVhWsLVseOHZWYmKhnnnlGBw8e1OTJk/XUU0/J6/WqcePGfretX7%2B%2BCgoKJEler1eRkZF%2B85GRkb75qsjIyNCiRYv8xpKTkzVp0qRfmcYdGjSoW%2BXbRkRcWoMrqT1uzOXGTJI7c5EpcLgxlxsznY9rC1ZGRobvf7ds2VJTp07VhAkT1KVLl0rva4yp1r6HDx%2BuXr16%2BY2FhtZRQUFxtbb7S4H2aqAq%2BUNCghURcakKC0%2BovLzCgVU5w4253JhJcmcuMgUON%2Baq7UwX8uLeJtcWrF%2B64oorVF5eruDgYHm9Xr%2B5goICRUVFSZIaNGhwxrzX61Xr1q2rvK/o6OgzTgfm5x9TWZk7DpZf60Lyl5dXuPL35cZcbswiVVZGAAARZElEQVQkuTMXmQKHG3O5MdP5BNZbIFW0Z88ezZkzx28sOztbF198sZKSks742oXdu3erQ4cOkqS4uDhlZmb65srLy7Vnzx7fPAAAQGVcWbAaNmyojIwMLVu2TKdOndL%2B/fu1YMECDR8%2BXLfddptycnK0atUqnTx5Utu3b9f27dt1xx13SJJGjhypN954Qzt37tSJEye0ZMkSXXzxxerZs2fthgIAAAHDlacIGzdurGXLlmnevHm%2BgjR48GClpKQoLCxMS5cu1axZs/TUU0%2BpSZMmmjt3rq666ipJ0vXXX6%2BHHnpIkydP1uHDhxUfH69ly5bpkksuqeVUAAAgULiyYElS165d9eqrr55z7s033zznfe%2B8807deeedNbU0AADgcq48RQgAAFCbKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAsC63tBeDfxy1/%2Bd/aXsIF2Ti5e20vAQAQoHgHCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACW8ceegXPgj1MDAH4t3sECAACwjIIFAABgGQXrLHJycnTfffcpISFBN9xwg%2BbOnauKioraXhYAAAgQXIN1FhMnTlRsbKw2b96sw4cPa9y4cbrssst0zz331PbSAABAAKBg/YLH49FXX32lF198UfXq1VO9evU0evRovfTSSxQs/KYF2kX5gYQPEAC4UBSsX8jMzFSTJk0UGRnpG4uNjdX%2B/ftVVFSk8PDwSreRl5en/Px8v7HQ0DqKjo62utaQEM7wAk4IDQ3sY%2B30c4WbnjPcmElyZy43ZqoKCtYveL1eRURE%2BI2dLlsFBQVVKlgZGRlatGiR39gDDzygiRMn2luofipyv7/8aw0fPtx6easteXl5ysjIcFUmyZ253JhJcmeuvLw8vfTS82QKAG7M5cZMVfHvVSeryBhTrfsPHz5ca9as8fsZPny4pdX9n/z8fC1atOiMd8sCmRszSe7M5cZMkjtzkSlwuDGXGzNVBe9g/UJUVJS8Xq/fmNfrVVBQkKKioqq0jejo6H%2Brlg4AAPzxDtYvxMXFKTc3V0eOHPGNeTwetWrVSnXr1q3FlQEAgEBBwfqFmJgYxcfHa968eSoqKlJ2drZefPFFjRw5sraXBgAAAkTIk08%2B%2BWRtL%2BK35rrrrtP69es1c%2BZMvfXWWxo2bJjGjBmjoKCg2l7aGerWratu3bq56t01N2aS3JnLjZkkd%2BYiU%2BBwYy43ZqpMkKnuFd0AAADwwylCAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAEoJydH9913nxISEnTDDTdo7ty5qqioqO1lnSEnJ0fJyclKSEhQYmKipk2bpsLCQknS3r17ddddd6lLly7q06ePXnjhBb/7btiwQQMGDFCnTp00ZMgQffjhh765iooKzZ8/X71791bXrl01ZswYHTx40NFsp82ePVtt27b1/fvjjz/WsGHD1LlzZ/Xv319r1671u/3y5cvVt29fde7cWSNHjtTu3bt9cydPntTjjz%2Bu66%2B/XgkJCZo0aZIKCgocy7JkyRL16NFDHTt21OjRo/Xdd98FfKY9e/Zo1KhRuvrqq9W9e3dNnTrV94fcAynXBx98oMTERKWkpJwxV51jxev1avLkyUpMTFSPHj00ffp0lZSU%2BOYrO05rKtOmTZs0cOBAderUSX379tVrr73mN1%2Bdx6amnz/Pl%2Bu04uJi9ezZU9OmTfONBepj9cMPP2jChAnq2LGjEhMTNW/ePN/v87ecyREGAWfw4MHmscceM4WFhWb//v2mT58%2B5oUXXqjtZZ3h1ltvNdOmTTNFRUUmNzfXDBkyxDz66KPmxIkT5rrrrjMLFy40xcXFZvfu3aZbt27mnXfeMcYYs2fPHhMXF2e2bdtmSkpKzJtvvmk6dOhgcnNzjTHGLF%2B%2B3Nxwww0mKyvLHDt2zDz99NNmwIABpqKiwtF8e/bsMd26dTNt2rQxxhjzww8/mI4dO5pVq1aZkpIS87//%2B7%2Bmffv25ssvvzTGGPPee%2B%2BZq6%2B%2B2uzcudOcOHHCLF261HTv3t0UFxcbY4xJS0szQ4YMMd9//70pKCgwDzzwgBk3bpwjWVasWGFuvvlmk52dbY4dO2ZmzpxpZs6cGdCZSktLTffu3c28efPMyZMnzZEjR8w999xjJk6cGFC5li1bZvr06WNGjBhhJk%2Be7DdX3WPlgQceMPfdd585fPiwOXTokBk%2BfLiZOXOmMcZUepzWVKZdu3aZ%2BPh48%2B6775rS0lKzbds2Exsba/75z38aY6r/2NTk8%2Bf5cv1cWlqa6dKli0lNTfWNBeJjVVFRYYYNG2Zmzpxpjh07ZrKysszQoUPNRx999JvO5BQKVoD58ssvTbt27YzX6/WN/f3vfzd9%2B/atxVWd6ejRo2batGkmPz/fN/byyy%2BbPn36mI0bN5prrrnGlJWV%2Bebmzp1r/vCHPxhjjHnqqadMcnKy3/Zuv/12s3TpUmOMMf379zcvvfSSb%2B7YsWMmJibG7NixoyYj%2BSkvLze33367%2Be///m9fwXr%2B%2BefNoEGD/G43efJkM2PGDGOMMffdd5%2BZPXu23za6d%2B9u1q9fb0pLS02XLl3M5s2bffNZWVmmbdu25tChQzWep1evXmd94grkTN9//71p06aNycrK8o39/e9/NzfeeGNA5XrppZdMYWGhSU1NPeM/cNU5VvLz881VV11l9u7d65vfvn276dixozl16lSlx2lNZdq%2BfbtZtGiR39jgwYPNkiVLjDHVe2xq%2BvnzfLlO27t3r%2BnevbuZNWuWX8EKxMfq008/NQkJCebkyZNnve9vNZNTOEUYYDIzM9WkSRNFRkb6xmJjY7V//34VFRXV4sr8RUREKC0tTZdddplvLDc3V9HR0crMzFTbtm0VEhLim4uJifG9zZ%2BZmamYmBi/7cXExMjj8aikpERZWVl%2B8%2BHh4WrWrJk8Hk8Np/o/r776qsLCwjRgwADf2LnWfa5cwcHBateunTwejw4cOKBjx44pNjbWN9%2ByZUtdcsklyszMrNEsP/zwg7777jsdPXpU/fr1851WOXLkSMBmkqTGjRurXbt2ysjIUHFxsQ4fPqxNmzapZ8%2BeAZVr1KhRqlev3lnnqnOs7N27VyEhIX6nuGNjY3X8%2BHF98803lR6nNZXp%2BuuvV3Jysu/fZWVlys/PV%2BPGjc%2Ba%2BUIem5p%2B/jxfLkkyxujJJ59USkqKIiIifOOB%2Blh9/vnnatOmjebPn6%2BEhAT17t3bdxrvt5zJKRSsAOP1ev0OTEm%2BJwsnr225UB6PRytWrNCECRPOmqF%2B/fryer2qqKiQ1%2Bv1ewKUfspYUFCgo0ePyhhzznkn/Pjjj1q4cKGeeOIJv/Fz5Tq9rvPl8nq9knTG/SMiImo816FDhyRJb7/9tl588UW9%2BeabOnTokB577LGAzST99B/ehQsX6r333lPnzp2VmJiosrIyTZkyJaBz/Vx1jhWv16vw8HAFBQX5zUnyzZ/vOHVKenq66tSpo379%2Bkmq3mNT28%2BfGRkZCgoK0pAhQ/zGA/WxOnTokHbu3KmGDRtq27ZtevzxxzV//nxt3rw5YDPZRMEKQMaY2l7CBfn88881ZswYTZkyRYmJiee83c8PtMoy1ubvIC0tTUOGDFGrVq0u%2BL6/xVyn9zl27Fg1btxYl19%2BuSZOnKgtW7Zc0P1/7XxNOXXqlMaPH6%2Bbb75Zn332md5//33Vq1dPU6dOrdL9f6u5fqk66/w1GX5%2BnNYkY4zmzp2r9evXa8mSJQoLC/Obq%2By%2Bv2auJh0%2BfFgLFizQk08%2Bec7fYaA9VsYYRUVFaezYsbr00kuVlJSkm266SRs3bvS7zfnuf6Gc%2Bv%2BfDRSsABMVFeV7lXaa1%2BtVUFCQoqKiamlV57Zlyxbdd999evTRRzVq1ChJP2X45atFr9er%2BvXrKzg4WA0aNDhrxqioKN9tzjbfsGHDmg2jnz55tmPHDr9TGKedbd0FBQW%2Bx%2BV8uU7f5pfzR48erfFcp0/j/vzVYpMmTWSMUWlpaUBmkn56rL777js99NBDqlevnho3bqxJkybp3XffPev/hwIl189V51iJiopSUVGRysvL/eYk%2BebPd5zWpIqKCk2bNk1btmzRK6%2B8oiuvvNI3V53HpjafP%2BfMmaNBgwb5nRI7LVAfq0aNGp1x%2BrBJkybKz88P2Ew2BcYq4RMXF6fc3FzfR82ln06/tWrVSnXr1q3FlZ3piy%2B%2BUGpqqhYsWKBBgwb5xuPi4rRv3z6VlZX5xjwejzp06OCb/%2BV59tPzYWFhat26td%2B1LoWFhTpw4IDat29fw4mktWvX6vDhw7rhhhuUkJDge6s/ISFBbdq0OWPdu3fv9sv183WXl5drz5496tChg5o2barIyEi/%2BX/96186deqU4uLiajTT5ZdfrvDwcO3du9c3lpOTo4suukhJSUkBmen0WioqKvxeJZ86dUqSlJiYGLC5fq46x0q7du1kjNFXX33ld9%2BIiAi1aNGi0uO0Js2ePVtff/21XnnlFTVt2tRvrjqPTW0%2Bf65du1arV69WQkKCEhIS9Pzzz%2Butt95SQkJCwD5WLVu21MGDB1VcXOwby8nJUZMmTQI2k1U1fx09bLv99tvNo48%2B6vtYbK9evcyKFStqe1l%2BSktLzS233GJeffXVM%2BZOnjxpbrjhBvPss8%2Ba48ePm507d5qrr77abN261RhjzL59%2B0x8fLzZunWrKSkpMatWrTKdOnUyeXl5xpifPvXTs2dP30d/Z8yYYYYOHepILq/Xa3Jzc30/O3bsMG3atDG5ubkmJyfHdOrUybz22mumpKTEbNu2zbRv3973KZnt27ebLl26mB07dpjjx4%2BbhQsXmqSkJHPixAljzE%2BfkBk8eLD5/vvvzZEjR8y4cePMxIkTHck1e/Zs07t3b/Ptt9%2BaH3/80QwfPtxMmzbN/PjjjwGb6ciRI6Zbt27mz3/%2Bszl%2B/Lg5cuSIGT9%2BvPmv//qvgMx1tk9xVfdYmTx5shk7dqw5fPiwyc3NNUOHDjVz5swxxlR%2BnNZUps8%2B%2B8x07drV7xPIP1fdx8aJ58%2Bz5fr580Zubq6ZPXu2mTRpku8rNQLxsTr9VQqPP/64KS4uNh999JGJj483//jHPwIiU02jYAWg3NxcM3bsWNO%2BfXuTmJhonn32Wce/A6oy//znP02bNm1MXFzcGT/fffed2bdvnxkxYoSJi4szPXv2NCtXrvS7/zvvvGP69OljYmNjzW233eY7YI356btXFixYYK699lrTvn17c%2B%2B99/qepJx28OBB39c0GGPMP/7xDzNw4EATGxtr%2BvTpc8ZXH6xcudIkJSWZuLg4M3LkSLNv3z7f3MmTJ82TTz5punbtajp16mQeeughU1hY6EiOn%2B%2B7Y8eOJjU11RQVFQV0JmOM8Xg85q677jJXX321SUxMNJMnT/Z9lUKg5Dp93Fx11VXmqquu8v37tOocK4WFhSYlJcV07NjRdO3a1Tz11FN%2BH7mv7DitiUyPPPKI39jpn3vuucd3/%2Bo8NjX5/FnZY/Vzzz77rN/XNATiY/Xz/cbHx5ukpCSzZs2a33wmpwQZ8xu5UhMAAMAluAYLAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACz7f4UrXnzCGlK%2BAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common2289936167295975588”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>19</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1233.55979709597</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>112.065380839541</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>957.677673913419</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>193.181529933526</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3150.6949512274</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>329.812011624733</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>341.98406489593</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>800.788733405248</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>277.616486268864</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3104)</td> <td class=”number”>3104</td> <td class=”number”>96.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme2289936167295975588”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>19</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0899097505334794</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.249055350564959</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.51227935496778</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.623552825694787</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>11074.4328249868</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12394.9195636111</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13813.4935438988</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13907.3064750753</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16334.4231756195</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_HHNV15”><s>LACCESS_HHNV15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_LACCESS_HHNV10”><code>LACCESS_HHNV10</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.97302</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_HISP15”>LACCESS_HISP15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3062</td>

</tr> <tr>

<th>Unique (%)</th> <td>95.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>97</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>2332.7</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>263440</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>1.7%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-1970107093991670559”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-1970107093991670559”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-1970107093991670559”

aria-controls=”quantiles-1970107093991670559” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-1970107093991670559” aria-controls=”histogram-1970107093991670559”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-1970107093991670559” aria-controls=”common-1970107093991670559”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-1970107093991670559” aria-controls=”extreme-1970107093991670559”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-1970107093991670559”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>2.899</td>

</tr> <tr>

<th>Q1</th> <td>31.162</td>

</tr> <tr>

<th>Median</th> <td>136.78</td>

</tr> <tr>

<th>Q3</th> <td>883.65</td>

</tr> <tr>

<th>95-th percentile</th> <td>8461.8</td>

</tr> <tr>

<th>Maximum</th> <td>263440</td>

</tr> <tr>

<th>Range</th> <td>263440</td>

</tr> <tr>

<th>Interquartile range</th> <td>852.49</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>12181</td>

</tr> <tr>

<th>Coef of variation</th> <td>5.2217</td>

</tr> <tr>

<th>Kurtosis</th> <td>250.98</td>

</tr> <tr>

<th>Mean</th> <td>2332.7</td>

</tr> <tr>

<th>MAD</th> <td>3449.5</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>14.263</td>

</tr> <tr>

<th>Sum</th> <td>7261800</td>

</tr> <tr>

<th>Variance</th> <td>148370000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-1970107093991670559”>

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VlJSkGTNmqHXr1rr00kt100036e9//7t27NihM2fOaNq0aWrVqpUSExN18803Kz8/X5K0evVqpaenKz09XeHh4Ro7dqy6d%2B%2BudevWSZLy8/M1efJkdenSRREREcrJyVFRUZE%2B/vjjQF4yAAAIIkFZsJxOpxYuXKhLLrnEu3bkyBHFxsaqsLBQ8fHxCgsL8%2B4lJCSooKBAklRYWKiEhASf4yUkJMjlcqmyslL79u3z2Y%2BIiFDHjh3lcrka%2BaoAAEBz4Qj0ADa4XC699tprev7557Vp0yY5nU6f/aioKHk8HtXU1Mjj8SgyMtJnPzIyUvv27dPx48dljDnnvtvtrvc8xcXFKikp8VlzOFopNjb2B17ZhYWFBVc/djia7rxnswy2TJsacrSHLO0hS3vIsv6CvmB98MEHmjZtmmbMmKG0tDRt2rTpnI8LCQnx/ue67qdq6P1W%2Bfn5WrZsmc9aVlaWpk%2Bf3qDjBrvo6NaBHqFOTufFgR6hWSBHe8jSHrK0hyzrFtQFa9u2bXrwwQc1d%2B5c3XjjjZKkmJgYffnllz6P83g8ioqKUmhoqKKjo%2BXxeGrtx8TEeB9zrv22bdvWe66MjAwNGzbMZ83haCW3u/wHXF3dgu0nCNvXb1NYWKiczotVWlqh6uqaQI8TtMjRHrK0hyztCcYsA/XDfdAWrA8//FCzZs3SM888o8GDB3vXk5KS9Nvf/lZVVVVyOL67PJfLpV69enn3z96PdZbL5dLo0aMVHh6ubt26qbCwUAMGDJD03Q31Bw8eVEpKSr1ni42NrfV2YEnJCVVVBcc3Y2MJhuuvrq4JijmbOnK0hyztIUt7yLJuwfUSyP%2BoqqrSo48%2BqpkzZ/qUK0lKT09XRESEnn/%2BeVVUVOjjjz/WmjVrlJmZKUmaOHGi3n//fe3YsUOnTp3SmjVr9OWXX2rs2LGSpMzMTK1cuVJFRUUqKytTbm6uevbsqeTkZL9fJwAACE5B%2BQrW7t27VVRUpAULFmjBggU%2Be%2B%2B8845eeOEFPf7448rLy9Mll1yinJwcDRkyRJLUvXt35ebmauHChTp8%2BLC6du2qFStWqF27dpKkSZMmqaSkRLfeeqvKy8uVmppa634qAACACwkxfv4EzWHDhmncuHEaP368LrvsMn%2BeOqBKSk5YP6bDEaoRue9aP25j2ZQ9KNAjnJfDEaro6NZyu8t52bsByNEesrSHLO0JxizbtWsTkPP6/S3C8ePHa%2BPGjbr66qt1xx13aMuWLaqqqvL3GAAAAI3G7wUrKytLGzdu1BtvvKFu3brp5z//udLT07V48WIdOHDA3%2BMAAABYF7Cb3BMTEzVr1ixt375ds2fP1htvvKHrrrtOU6ZM0SeffBKosQAAABosYAXrzJkz2rhxo%2B68807NmjVL7du31yOPPKKePXtq8uTJWr9%2BfaBGAwAAaBC//xZhUVGR1qxZozfffFPl5eUaNWqUXnnlFfXr18/7mP79%2B2vevHkaM2aMv8cDAABoML8XrNGjR6tz586aOnWqbrzxRkVFRdV6THp6uo4dO%2Bbv0QAAAKzwe8FauXKl91PSL%2BTjjz/2wzQAAAD2%2Bf0erPj4eN19993aunWrd%2B03v/mN7rzzzlr/BiAAAEAw8nvBWrhwoU6cOKGuXbt614YMGaKamhotWrTI3%2BMAAABY5/e3CN977z2tX79e0dHR3rVOnTopNzdX119/vb/HAQAAsM7vr2BVVlYqPDy89iChoaqoqPD3OAAAANb5vWD1799fixYt0vHjx71rX3/9tebPn%2B/zUQ0AAADByu9vEc6ePVs/%2B9nP9JOf/EQRERGqqalReXm5OnTooFdffdXf4wAAAFjn94LVoUMHvf322/rTn/6kgwcPKjQ0VJ07d9bgwYMVFhbm73EAAACs83vBkqQWLVro6quvDsSpAQAAGp3fC9ahQ4e0ZMkSffHFF6qsrKy1/8c//tHfIwEAAFgVkHuwiouLNXjwYLVq1crfpwcAAGh0fi9YBQUF%2BuMf/6iYmBh/nxoAAMAv/P4xDW3btuWVKwAA0Kz5vWBNnTpVy5YtkzHG36cGAADwC7%2B/RfinP/1JH374odauXavLL79coaG%2BHe/111/390gAAABW%2Bb1gRURE6KqrrvL3aQEAAPzG7wVr4cKF/j4lAACAX/n9HixJ2r9/v5577jk98sgj3rWPPvooEKMAAABY5/eCtWvXLo0dO1ZbtmzRhg0bJH334aO33XYbHzIKAACaBb8XrKVLl%2BrBBx/U%2BvXrFRISIum7f59w0aJFWr58ub/HAQAAsM7vBevzzz9XZmamJHkLliRdc801Kioq8vc4AAAA1vm9YLVp0%2Bac/wZhcXGxWrRo4e9xAAAArPN7werbt69%2B/vOfq6yszLt24MABzZo1Sz/5yU/8PQ4AAIB1fv%2BYhkceeUS33367UlNTVV1drb59%2B6qiokLdunXTokWL/D0OAACAdX4vWJdeeqk2bNignTt36sCBA2rZsqU6d%2B6sQYMG%2BdyTBQAAEKz8XrAk6aKLLtLVV18diFMDAAA0Or8XrGHDhl3wlSo%2BCwsAAAQ7vxes6667zqdgVVdX68CBA3K5XLr99tv9PQ4AAIB1fi9YM2fOPOf65s2b9de//tXP0wAAANgXkH%2BL8Fyuvvpqvf3224EeAwAAoMGaTMHas2ePjDGBHgMAAKDB/P4W4aRJk2qtVVRUqKioSCNHjvT3OAAAANb5vWB16tSp1m8RhoeHa8KECbr55pv9PQ4AAIB1fi9YNj%2Bt/d1339WsWbOUmpqqpUuXetfXrl2r2bNn66KLLvJ5/KpVq5SSkqKamho988wz2rBhg0pLS5WSkqJ58%2BapQ4cOkiSPx6N58%2Bbpb3/7m0JDQ5Wenq65c%2BeqZcuW1mYHAADNl98L1ptvvlnvx954443n3XvxxRe1Zs0adezY8Zz7/fv316uvvnrOvVWrVmn9%2BvV68cUX1b59ey1dulRZWVl66623FBISorlz5%2Br06dPasGGDzpw5o/vvv1%2B5ubl69NFH6z07AAD41%2BX3gjVnzhzV1NTUuqE9JCTEZy0kJOSCBSs8PFxr1qzRU089pVOnTv2gGfLz8zV58mR16dJFkpSTk6PU1FR9/PHHuvzyy7V161b9/ve/V0xMjCTpnnvu0f33369Zs2bVelUMAADg%2B/xesF566SW9/PLLuvvuuxUfHy9jjD777DO9%2BOKLuuWWW5Samlqv49x2220X3D9y5Ih%2B%2BtOfqqCgQE6nU9OnT9cNN9ygyspK7du3TwkJCd7HRkREqGPHjnK5XDpx4oTCwsIUHx/v3U9MTNTJkye1f/9%2Bn/XzKS4uVklJic%2Baw9FKsbGx9bq2%2BgoLazK/BFovDkfTnfdslsGWaVNDjvaQpT1kaQ9Z1l9A7sHKy8tT%2B/btvWtXXHGFOnTooClTpmjDhg0NPkdMTIw6deqkBx54QF27dtUf/vAHPfTQQ4qNjdWPf/xjGWMUGRnp85zIyEi53W5FRUUpIiLC50b8s491u931On9%2Bfr6WLVvms5aVlaXp06c38MqCW3R060CPUCen8%2BJAj9AskKM9ZGkPWdpDlnXze8H68ssva5UbSXI6nTp8%2BLCVcwwZMkRDhgzx/n306NH6wx/%2BoLVr13o/Sf5Cn7nV0M/jysjI0LBhw3zWHI5WcrvLG3Tc7wu2nyBsX79NYWGhcjovVmlphaqrawI9TtAiR3vI0h6ytCcYswzUD/d%2BL1hxcXFatGiR7r//fkVHR0uSSktL9eyzz%2BpHP/pRo563oKBAUVFRCg0Nlcfj8dn3eDxq27atYmJiVFZWpurqaoWFhXn3JKlt27b1OldsbGyttwNLSk6oqio4vhkbSzBcf3V1TVDM2dSRoz1kaQ9Z2kOWdfN7wZo9e7ZmzJih/Px8tW7dWqGhoSorK1PLli21fPlyK%2Bf47W9/q8jISF133XXetaKiInXo0EHh4eHq1q2bCgsLNWDAAEnfFbyDBw8qJSVFcXFxMsbo008/VWJioiTJ5XLJ6XSqc%2BfOVuYDAADNm98L1uDBg7Vjxw7t3LlTR48elTFG7du315VXXqk2bdpYOcfp06f15JNPqkOHDurRo4c2b96sP/3pT3rjjTckSZmZmcrLy9NVV12l9u3bKzc3Vz179lRycrIkadSoUfrlL3%2Bpp59%2BWqdPn9by5cs1YcIEORx%2BjwsAAAShgDSGiy%2B%2BWMOHD9fRo0e9H%2B75Q50tQ1VVVZKkrVu3Svru1abbbrtN5eXluv/%2B%2B1VSUqLLL79cy5cvV1JSkqTv/rmekpIS3XrrrSovL1dqaqrPTelPPPGEHn/8cQ0fPlwXXXSRrr/%2BeuXk5DTkkgEAwL%2BQEOPnf2G5srJSjz/%2BuN5%2B%2B21JUkFBgUpLS/XAAw/oF7/4hZxOpz/H8ZuSkhPWj%2BlwhGpE7rvWj9tYNmUPCvQI5%2BVwhCo6urXc7nLuK2gAcrSHLO0hS3uCMct27ey8O/ZD%2Bf3X0BYvXqy9e/cqNzdXoaH/e/rq6mrl5ub6exwAAADr/F6wNm/erGeffVbXXHON97OmnE6nFi5cqC1btvh7HAAAAOv8XrDKy8vVqVOnWusxMTE6efKkv8cBAACwzu8F60c/%2BpH%2B%2Bte/SvL9QM933nlH//Zv/%2BbvcQAAAKzz%2B28R/vu//7vuu%2B8%2BjR8/XjU1Nfr1r3%2BtgoICbd68WXPmzPH3OAAAANb5vWBlZGTI4XDotddeU1hYmF544QV17txZubm5uuaaa/w9DgAAgHV%2BL1jHjh3T%2BPHjNX78eH%2BfGgAAwC/8fg/W8OHDG/yPKQMAADRlfi9Yqamp2rRpk79PCwAA4Dd%2Bf4vwsssu01NPPaW8vDz96Ec/0kUXXeSzv2TJEn%2BPBAAAYJXfC9a%2Bffv04x//WJLkdrv9fXoAAIBG57eClZOTo6VLl%2BrVV1/1ri1fvlxZWVn%2BGgEAAMAv/HYP1rZt22qt5eXl%2Bev0AAAAfuO3gnWu3xzktwkBAEBz5LeCdfYfdq5rDQAAINj5/WMaAAAAmjsKFgAAgGV%2B%2By3CM2fOaMaMGXWu8TlYAAAg2PmtYPXr10/FxcV1rgEAAAQ7vxWsf/78KwAAgOaMe7AAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFgW1AXr3XffVVpamnJycmrtbdy4UWPGjFGfPn00btw4vffee969mpoaLV26VMOHD1f//v01ZcoUHTp0yLvv8XiUnZ2ttLQ0DR48WHPmzFFlZaVfrgkAAAS/oC1YL774ohYsWKCOHTvW2tu7d69mzZqlmTNn6i9/%2BYsmT56se%2B%2B9V0ePHpUkrVq1SuvXr1deXp62b9%2BuTp06KSsrS8YYSdLcuXNVUVGhDRs26He/%2B52KioqUm5vr1%2BsDAADBK2gLVnh4uNasWXPOgrV69Wqlp6crPT1d4eHhGjt2rLp3765169ZJkvLz8zV58mR16dJFERERysnJUVFRkT7%2B%2BGN988032rp1q3JychQTE6P27dvrnnvu0e9%2B9zudOXPG35cJAACCUNAWrNtuu01t2rQ5515hYaESEhJ81hISEuRyuVRZWal9%2B/b57EdERKhjx45yuVzau3evwsLCFB8f791PTEzUyZMntX///sa5GAAA0Kw4Aj1AY/B4PIqMjPRZi4yM1L59%2B3T8%2BHEZY86573a7FRUVpYiICIWEhPjsSZLb7a7X%2BYuLi1VSUuKz5nC0Umxs7P/lcs4rLCy4%2BrHD0XTnPZtlsGXa1JCjPWRpD1naQ5b11ywLliTv/VT/l/26nluX/Px8LVu2zGctKytL06dPb9Bxg110dOtAj1Anp/PiQI/QLJCjPWRpD1naQ5Z1a5YFKzo6Wh6Px2fN4/EoJiZGUVFRCg0NPed%2B27ZtFRMTo7KyMlVXVyssLMy7J0lt27at1/kzMjI0bNgwnzWHo5Xc7vL/6yWdU7D9BGH7%2Bm0KCwuV03mxSksrVF1dE%2BhxghY52kOW9pClPcGYZaB%2BuG%2BWBSspKUkFBQU%2Bay6XS6NHj1Z4eLi6deumwsJCDRgwQJJUWlqqgwcPKiUlRXFxcTLG6NNPP1ViYqL3uU6nU507d67X%2BWNjY2u9HVhSckJVVcHxzdhYguH6q6trgmLOpo4c7SFLe8jSHrKsW3C9BFJPEydO1Pvvv68dO3bo1KlTWrNmjb788kuNHTtWkpSZmamVK1eqqKgoUlIGAAASs0lEQVRIZWVlys3NVc%2BePZWcnKyYmBiNGjVKv/zlL3Xs2DEdPXpUy5cv14QJE%2BRwNMs%2BCgAALAvaxpCcnCxJqqqqkiRt3bpV0nevNnXv3l25ublauHChDh8%2BrK5du2rFihVq166dJGnSpEkqKSnRrbfeqvLycqWmpvrcM/XEE0/o8ccf1/Dhw3XRRRfp%2BuuvP%2BeHmQIAAJxLiGnoHd2ol5KSE9aP6XCEakTuu9aP21g2ZQ8K9Ajn5XCEKjq6tdzucl72bgBytIcs7SFLe4Ixy3btzv2RTo2tWb5FCAAAEEgULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgWbMtWPHx8UpKSlJycrL3z5NPPilJ2rVrlyZMmKC%2Bfftq9OjRWrdunc9zV65cqVGjRqlv377KzMxUQUFBIC4BAAAEKUegB2hM77zzji6//HKfteLiYt1zzz2aM2eOxowZow8%2B%2BEDTpk1T586dlZycrG3btum5557TSy%2B9pPj4eK1cuVJ33323tmzZolatWgXoSgAAQDBptq9gnc/69evVqVMnTZgwQeHh4UpLS9OwYcO0evVqSVJ%2Bfr7GjRunXr16qWXLlrrjjjskSdu3bw/k2AAAIIg061ewlixZoo8%2B%2BkhlZWW69tpr9fDDD6uwsFAJCQk%2Bj0tISNCmTZskSYWFhbruuuu8e6GhoerZs6dcLpdGjx5dr/MWFxerpKTEZ83haKXY2NgGXpGvsLDg6scOR9Od92yWwZZpU0OO9pClPWRpD1nWX7MtWL1791ZaWpqefvppHTp0SNnZ2Zo/f748Ho/at2/v89ioqCi53W5JksfjUWRkpM9%2BZGSkd78%2B8vPztWzZMp%2B1rKwsTZ8%2B/f94Nc1DdHTrQI9QJ6fz4kCP0CyQoz1kaQ9Z2kOWdWu2BSs/P9/7n7t06aKZM2dq2rRp6tevX53PNcY06NwZGRkaNmyYz5rD0Upud3mDjvt9wfYThO3rtyksLFRO58UqLa1QdXVNoMcJWuRoD1naQ5b2BGOWgfrhvtkWrO%2B7/PLLVV1drdDQUHk8Hp89t9utmJgYSVJ0dHStfY/Ho27dutX7XLGxsbXeDiwpOaGqquD4ZmwswXD91dU1QTFnU0eO9pClPWRpD1nWLbheAqmnPXv2aNGiRT5rRUVFatGihdLT02t97EJBQYF69eolSUpKSlJhYaF3r7q6Wnv27PHuAwAA1KVZFqy2bdsqPz9feXl5On36tA4cOKBnnnlGGRkZuuGGG3T48GGtXr1ap06d0s6dO7Vz505NnDhRkpSZmak333xTu3fvVkVFhZ5//nm1aNFCQ4YMCexFAQCAoNEs3yJs37698vLytGTJEm9Buummm5STk6Pw8HCtWLFCCxYs0Pz58xUXF6fFixerR48ekqSrrrpKDzzwgLKzs/Xtt98qOTlZeXl5atmyZYCvCgAABIsQ09A7ulEvJSUnrB/T4QjViNx3rR%2B3sWzKHhToEc7L4QhVdHRrud3l3FfQAORoD1naQ5b2BGOW7dq1Cch5m%2BVbhAAAAIFEwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwzuHw4cO66667lJqaqqFDh2rx4sWqqakJ9FgAACBIOAI9QFN03333KTExUVu3btW3336rqVOn6pJLLtFPf/rTQI8GAACCAAXre1wulz799FP9%2Bte/Vps2bdSmTRtNnjxZr7zyCgWrga795Z8DPcIPsil7UKBHAAAEKQrW9xQWFiouLk6RkZHetcTERB04cEBlZWWKiIio8xjFxcUqKSnxWXM4Wik2NtbqrGFhvMPbmIKtEP5h5pWBHsH7Pcn3ZsORpT1kaQ9Z1h8F63s8Ho%2BcTqfP2tmy5Xa761Ww8vPztWzZMp%2B1e%2B%2B9V/fdd5%2B9QfVdkbv90i%2BUkZFhvbz9qykuLlZ%2Bfj5ZNlBxcbFeeeUlcrSALO0hS3vIsv6ooOdgjGnQ8zMyMrR27VqfPxkZGZam%2B18lJSVatmxZrVfL8MORpR3kaA9Z2kOW9pBl/fEK1vfExMTI4/H4rHk8HoWEhCgmJqZex4iNjaXZAwDwL4xXsL4nKSlJR44c0bFjx7xrLpdLXbt2VevWrQM4GQAACBYUrO9JSEhQcnKylixZorKyMhUVFenXv/61MjMzAz0aAAAIEmHz5s2bF%2Bghmporr7xSGzZs0JNPPqm3335bEyZM0JQpUxQSEhLo0Wpp3bq1BgwYwKtrFpClHeRoD1naQ5b2kGX9hJiG3tENAAAAH7xFCAAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBSsIHT58WHfddZdSU1M1dOhQLV68WDU1NYEeK2Di4%2BOVlJSk5ORk758nn3xSkrRr1y5NmDBBffv21ejRo7Vu3Tqf565cuVKjRo1S3759lZmZqYKCAu/eqVOn9Nhjj%2Bmqq65Samqqpk%2BfLrfb7d1vDl%2BHd999V2lpacrJyam1t3HjRo0ZM0Z9%2BvTRuHHj9N5773n3ampqtHTpUg0fPlz9%2B/fXlClTdOjQIe%2B%2Bx%2BNRdna20tLSNHjwYM2ZM0eVlZXe/b179%2BqWW25Rv379NHLkSL388sv1PndTdb4s165dqx49evh8fyYnJ%2BuTTz6RRJbncvjwYWVlZSk1NVVpaWl6%2BOGHVVpaKqlh19vYWTc158vxq6%2B%2BUnx8fK3vyV/96lfe55KjBQZB56abbjKPPvqoKS0tNQcOHDAjR440L7/8cqDHCpju3bubQ4cO1Vr/%2BuuvTe/evc3q1atNZWWl%2BfOf/2xSUlLMJ598Yowx5o9//KO54oorzO7du01FRYVZsWKFGTRokCkvLzfGGLNw4UIzbtw4849//MO43W5z7733mqlTp3qPH%2Bxfh7y8PDNy5EgzadIkk52d7bO3Z88ek5SUZHbs2GEqKyvNW2%2B9ZXr16mWOHDlijDFm5cqVZujQoWbfvn3mxIkT5oknnjBjxowxNTU1xhhj7r33XnPXXXeZb7/91hw9etRkZGSYJ5980hhjTEVFhbnyyivNc889Z8rLy01BQYEZMGCA2bx5c73O3RRdKMvf/e535pZbbjnvc8mytuuvv948/PDDpqyszBw5csSMGzfOzJ49u8HX25hZN0Xny/HQoUOme/fu530eOdpBwQoyn3zyienZs6fxeDzetf/6r/8yo0aNCuBUgXW%2BgvXSSy%2BZG2%2B80WctOzvbzJ071xhjzF133WV%2B/vOfe/eqq6vNoEGDzIYNG8yZM2dMv379zNatW737%2B/btM/Hx8ebo0aPN4uvwyiuvmNLSUjNr1qxapWD%2B/PkmKyvLZ%2B3mm282K1asMMYYM3r0aPPKK694906cOGESEhLMRx99ZEpKSkyPHj3M3r17vfs7d%2B40vXv3NqdPnzabNm0yAwcONFVVVd79xYsXm5/97Gf1OndTdKEs6ypYZOnr%2BPHj5uGHHzYlJSXetVdffdWMHDmywdfbmFk3NRfKsa6CRY528BZhkCksLFRcXJwiIyO9a4mJiTpw4IDKysoCOFlgLVmyREOGDNEVV1yhuXPnqry8XIWFhUpISPB5XEJCgvdtwO/vh4aGqmfPnnK5XDp48KBOnDihxMRE736XLl3UsmVLFRYWNouvw2233aY2bdqcc%2B982blcLlVWVmrfvn0%2B%2BxEREerYsaNcLpf27t2rsLAwxcfHe/cTExN18uRJ7d%2B/X4WFhYqPj1dYWJjPsc/3dfnnczdVF8pSko4cOaKf/vSn6t%2B/v4YPH6633npLksjyHJxOpxYuXKhLLrnEu3bkyBHFxsY26HobO%2Bum5kI5nvXQQw9p8ODBGjhwoJYsWaIzZ85IIkdbKFhBxuPxyOl0%2Bqyd/T/5f74/6F9J7969lZaWpi1btig/P1%2B7d%2B/W/Pnzz5lVVFSUNyePx%2BNTkKTvsnS73fJ4PJJU6/lOp9O735y/DhfK5vjx4zLGXDC7iIgIhYSE%2BOxJOm92UVFR8ng8qqmpueC5g1FMTIw6deqkBx98UH/%2B85/1wAMPaPbs2dq1axdZ1oPL5dJrr72madOmNeh6Gzvrpu6fc2zRooX69OmjESNGaPv27crLy9O6dev0n//5n5IC%2B9//5oSCFYSMMYEeoUnJz8/XzTffrBYtWqhLly6aOXOmNmzY4P1p7ELqyvJC%2B8396%2BDvbP75f5CbU7ZDhgzRSy%2B9pISEBLVo0UKjR4/WiBEjtHbtWu9jyPLcPvjgA02ZMkUzZsxQWlraeR/3Q663MbNuqr6fY2xsrF5//XWNGDFCF110kVJSUjR16tR6f0/Wtd9cc/yhKFhBJiYmxvvqylkej0chISGKiYkJ0FRNy%2BWXX67q6mqFhobWysrtdntzio6OPmeWMTEx3sd8f//48eNq27Zts/86XCibqKioc2br8Xi82ZSVlam6utpnT5J3//uvoHg8Hu9xL3Tu5iIuLk7FxcVkeQHbtm3TXXfdpdmzZ%2Bu2226TpAZdb2Nn3VSdK8dziYuL0zfffCNjDDla0ryu5l9AUlKSjhw5omPHjnnXXC6XunbtqtatWwdwssDYs2ePFi1a5LNWVFSkFi1aKD09vdb7%2BgUFBerVq5ek77IsLCz07lVXV2vPnj3q1auXOnTooMjISJ/9zz//XKdPn1ZSUlKz/zokJSXVys7lcqlXr14KDw9Xt27dfLIpLS3VwYMHlZKSop49e8oYo08//dTnuU6nU507d1ZSUpI%2B%2B%2BwzVVVV1Tp2XecORr/97W%2B1ceNGn7WioiJ16NCBLM/jww8/1KxZs/TMM8/oxhtv9K435HobO%2Bum6Hw57tq1S88//7zPY/fv36%2B4uDiFhISQoy3%2Bupse9tx8881m9uzZ5sSJE2bfvn1m2LBh5rXXXgv0WAFx9OhR07t3b7NixQpz6tQps3//fnPdddeZJ5980nzzzTemT58%2B5o033jCVlZVmx44dJiUlxfvbLTt37jT9%2BvUzH330kTl58qR57rnnTHp6uqmoqDDGfPebLTfddJP5xz/%2BYY4dO2amTp1q7rvvPu%2B5m8vX4Vy/%2BfbZZ5%2BZ5ORks337dlNZWWlWr15t%2BvTpY4qLi40x3/3G5JAhQ7y/pj137lwzfvx47/Ozs7PNHXfcYb799ltz5MgRM378eLNo0SJjjDGnTp0yQ4cONc8%2B%2B6w5efKk2b17t7niiivM9u3b63XupuxcWf7mN78xAwcONJ988ok5ffq0Wb9%2BvenZs6dxuVzGGLL8vjNnzphrr73WvP7667X2Gnq9jZl1U3OhHF0ul0lMTDRvvvmmOX36tPnkk0/MoEGDvB8zQ452ULCC0JEjR8wdd9xhUlJSTFpamnn22We9nz/yr%2Bhvf/ubycjIML179zYDBgwwCxcuNJWVld69sWPHmsTERDNy5Mhan7WyatUqk56ebpKSkkxmZqb57LPPvHunTp0y8%2BbNM/379zd9%2BvQxDzzwgCktLfXuB/vXISkpySQlJZkePXqYHj16eP9%2B1ubNm83IkSNNYmKiueGGG8zf/vY3715NTY155plnzE9%2B8hOTkpJi7rzzTp/PViotLTU5OTmmd%2B/epn///mb%2B/Pnm1KlT3v3PPvvMTJo0ySQlJZkhQ4aYVatW%2Bcx2oXM3RRfKsqamxixfvtwMHTrUJCUlmWuuucZs27bN%2B1yy9PXf//3fpnv37t4M//nPV1991aDrbeysm5K6ctyyZYsZO3asSUlJMYMGDTIvvPCCqa6u9j6fHBsuxJggvgMSAACgCeIeLAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACw7P8DzEP7obsXEq4AAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-1970107093991670559”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>780.101462879589</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>32.8746741015684</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>972.640103023006</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>192.730928686085</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>63.1425761251288</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>196.624887172406</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>254.521274996716</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>47.1522246400029</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>280.835001175464</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3051)</td> <td class=”number”>3051</td> <td class=”number”>95.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-1970107093991670559”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0004283733724151</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0013897542376071</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.002359034217136</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0036824005073868</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>187655.499169663</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>217460.415981226</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>246274.004039954</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>254482.018349479</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>263440.500069803</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_LOWI10”><s>LACCESS_LOWI10</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_LACCESS_POP15”><code>LACCESS_POP15</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.907</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_LOWI15”><s>LACCESS_LOWI15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_LACCESS_LOWI10”><code>LACCESS_LOWI10</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.98469</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_MULTIR15”>LACCESS_MULTIR15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3076</td>

</tr> <tr>

<th>Unique (%)</th> <td>95.8%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>97</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1293.9</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>115780</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>1.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram1893777315355736608”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives1893777315355736608,#minihistogram1893777315355736608”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives1893777315355736608”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles1893777315355736608”

aria-controls=”quantiles1893777315355736608” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram1893777315355736608” aria-controls=”histogram1893777315355736608”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common1893777315355736608” aria-controls=”common1893777315355736608”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme1893777315355736608” aria-controls=”extreme1893777315355736608”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles1893777315355736608”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>3.857</td>

</tr> <tr>

<th>Q1</th> <td>33.394</td>

</tr> <tr>

<th>Median</th> <td>132.64</td>

</tr> <tr>

<th>Q3</th> <td>659.32</td>

</tr> <tr>

<th>95-th percentile</th> <td>5400.9</td>

</tr> <tr>

<th>Maximum</th> <td>115780</td>

</tr> <tr>

<th>Range</th> <td>115780</td>

</tr> <tr>

<th>Interquartile range</th> <td>625.93</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>5423.5</td>

</tr> <tr>

<th>Coef of variation</th> <td>4.1916</td>

</tr> <tr>

<th>Kurtosis</th> <td>222.49</td>

</tr> <tr>

<th>Mean</th> <td>1293.9</td>

</tr> <tr>

<th>MAD</th> <td>1802.3</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>13.067</td>

</tr> <tr>

<th>Sum</th> <td>4027900</td>

</tr> <tr>

<th>Variance</th> <td>29415000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram1893777315355736608”>

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5SvrKwszZo1S3/5y180adIk3X///Tp06JAkac2aNdqwYYNycnK0Y8cOdezYUenp6TLG%2BPNwAQBAAAnIglVeXq6EhATNnDlTrVq10mWXXaZbb71V77//vnbu3KmTJ0/q3nvvVcuWLdWjRw%2BNGzdOubm5kqS1a9cqJSVFKSkpCg0N1ejRo9WtWzetX79ekpSbm6tJkyapc%2BfOCgsLU2ZmpgoLC7Vnzx5/HjIAAAggDn8v4PsIDw/XwoULPcaKi4sVExOj/Px8de/eXSEhIe65%2BPh4rV27VpKUn5%2BvlJQUj%2BfGx8fL6XSqpqZGBQUFio%2BPd8%2BFhYWpQ4cOcjqd6t27t1frKykpUWlpqceYw9FSMTExDTrO8wkJCax%2B7HD4b72nsgq0zPyFvLxHVt4jq4YhL%2B81xawCsmCdzul06qWXXtLKlSu1efNmhYeHe8xHRkbK5XKpvr5eLpdLERERHvMREREqKCjQ0aNHZYw563xZWZnX68nNzdXy5cs9xtLT0zVjxowGHtnFJSqqlb%2BXoPDwS/y9hIBCXt4jK%2B%2BRVcOQl/eaUlYBX7A%2B%2BOAD3XvvvZo5c6aSk5O1efPmsz4uKCjI/d/nu5/qQu%2B3Sk1N1ZAhQzzGHI6WKiuruqDtnq4pNXVv2D7%2BhggJCVZ4%2BCUqL69WXV2939YRKMjLe2TlPbJqGPLy3rmy8tcP9wFdsLZv366f//znmjt3rm655RZJUnR0tL744guPx7lcLkVGRio4OFhRUVFyuVxnzEdHR7sfc7b5Nm3aeL2umJiYMy4HlpZWqLb2h/0N0hSOv66uvkmsI1CQl/fIyntk1TDk5b2mlFVgvQXyLR9%2B%2BKGysrL0zDPPuMuVJCUkJOjTTz9VbW2te8zpdKpXr17u%2Bby8PI9tnZoPDQ1V165dlZ%2Bf754rLy/XgQMH1LNnz0Y%2BIgAAcLEIyIJVW1urRx55RLNmzdLAgQM95lJSUhQWFqaVK1equrpae/bs0bp165SWliZJGj9%2BvN577z3t3LlTx48f17p16/TFF19o9OjRkqS0tDStXr1ahYWFqqysVHZ2tuLi4pSYmOjz4wQAAIEpIC8RfvzxxyosLNSCBQu0YMECj7k33nhDv/nNb/TYY48pJydHl156qTIzMzV48GBJUrdu3ZSdna2FCxeqqKhIXbp00apVq9S2bVtJ0oQJE1RaWqo77rhDVVVVSkpKOuOGdQAAgHMJMnyCpk%2BUllZY36bDEazrs9%2Bxvt3GsjljgN/27XAEKyqqlcrKqprM9fmmjLy8R1beI6uGIS/vnSurtm1b%2B2VNPr9EOGTIEC1fvlzFxcW%2B3jUAAIBP%2BLxg3Xbbbdq0aZOuu%2B46TZkyRVu3bvW4IR0AACDQ%2Bbxgpaena9OmTXrllVfUtWtXPfXUU0pJSdHixYu1f/9%2BXy8HAADAOr/9FmGPHj2UlZWlHTt2aM6cOXrllVc0YsQITZ48Wf/4xz/8tSwAAIAL5reCdfLkSW3atEl33323srKyFBsbq4ceekhxcXGaNGmSNmzY4K%2BlAQAAXBCff0xDYWGh1q1bp9dee01VVVUaPny4XnjhBfXr18/9mP79%2B2vevHkaNWqUr5cHAABwwXxesEaOHKlOnTpp6tSpuuWWWxQZGXnGY1JSUnTkyBFfLw0AAMAKnxes1atX66qrrjrv4/bs2eOD1QAAANjn83uwunfvrmnTpmnbtm3usf/%2B7//W3XfffcYfWQYAAAhEPi9YCxcuVEVFhbp06eIeGzx4sOrr67Vo0SJfLwcAAMA6n18ifPfdd7VhwwZFRUW5xzp27Kjs7GzddNNNvl4OAACAdT5/B6umpkahoaFnLiQ4WNXV1b5eDgAAgHU%2BL1j9%2B/fXokWLdPToUffYV199pfnz53t8VAMAAECg8vklwjlz5uhnP/uZfvrTnyosLEz19fWqqqpS%2B/bt9eKLL/p6OQAAANb5vGC1b99ef/7zn/X222/rwIEDCg4OVqdOnTRw4ECFhIT4ejkAAADW%2BbxgSVLz5s113XXX%2BWPXAAAAjc7nBevgwYNasmSJPvvsM9XU1Jwx/9Zbb/l6SQAAAFb55R6skpISDRw4UC1btvT17gEAABqdzwtWXl6e3nrrLUVHR/t61wAAAD7h849paNOmDe9cAQCAi5rPC9bUqVO1fPlyGWN8vWsAAACf8PklwrffflsffvihXn31VV1%2B%2BeUKDvbseC%2B//LKvlwQAAGCVzwtWWFiYBg0a5OvdAgAA%2BIzPC9bChQt9vUsAAACf8vk9WJL0%2Beefa9myZXrooYfcYx999JE/lgIAAGCdzwvW7t27NXr0aG3dulUbN26U9M2Hj06cOJEPGQUAABcFnxespUuX6uc//7k2bNigoKAgSd/8fcJFixZpxYoVvl4OAACAdT4vWP/85z%2BVlpYmSe6CJUk33HCDCgsLfb0cAAAA63xesFq3bn3Wv0FYUlKi5s2b%2B3o5AAAA1vm8YPXt21dPPfWUKisr3WP79%2B9XVlaWfvrTn/p6OQAAANb5/GMaHnroId15551KSkpSXV2d%2Bvbtq%2BrqanXt2lWLFi3y9XIAAACs83nBuuyyy7Rx40bt2rVL%2B/fvV4sWLdSpUycNGDDA454sAACAQOXzgiVJzZo103XXXeePXQMAADQ6nxesIUOGnPOdKj4LCwAABDqfF6wRI0Z4FKy6ujrt379fTqdTd955p6%2BXAwAAYJ3PC9asWbPOOr5lyxb99a9/9fFqAAAA7PPL3yI8m%2Buuu05//vOf/b0MAACAC9ZkCtbevXtljPH3MgAAAC6Yzy8RTpgw4Yyx6upqFRYWatiwYb5eDgAAgHU%2BL1gdO3Y847cIQ0NDNXbsWI0bN65B23rnnXeUlZWlpKQkLV261D3%2B6quvas6cOWrWrJnH49esWaOePXuqvr5ezzzzjDZu3Kjy8nL17NlT8%2BbNU/v27SVJLpdL8%2BbN09/%2B9jcFBwcrJSVFc%2BfOVYsWLb7nUQMAgB8SnxcsW5/W/uyzz2rdunXq0KHDWef79%2B%2BvF1988axza9as0YYNG/Tss88qNjZWS5cuVXp6ul5//XUFBQVp7ty5OnHihDZu3KiTJ0/qgQceUHZ2th555BErawcAABc3nxes1157zevH3nLLLd85FxoaqnXr1unJJ5/U8ePHG7SG3NxcTZo0SZ07d5YkZWZmKikpSXv27NHll1%2Bubdu26U9/%2BpOio6MlSffdd58eeOABZWVlnfGuGAAAwOl8XrAefvhh1dfXn3FDe1BQkMdYUFDQOQvWxIkTz7mf4uJi3XXXXcrLy1N4eLhmzJihm2%2B%2BWTU1NSooKFB8fLz7sWFhYerQoYOcTqcqKioUEhKi7t27u%2Bd79OihY8eO6fPPP/cY/y4lJSUqLS31GHM4WiomJua8z22IkJAm8zsKXnE4/LfeU1kFWmb%2BQl7eIyvvkVXDkJf3mmJWPi9Yzz33nJ5//nlNmzZN3bt3lzFGn376qZ599lndfvvtSkpKuuB9REdHq2PHjnrwwQfVpUsXvfnmm/rFL36hmJgY/eQnP5ExRhERER7PiYiIUFlZmSIjIxUWFuZxn9ipx5aVlXm1/9zcXC1fvtxjLD09XTNmzLjAIwtsUVGt/L0EhYdf4u8lBBTy8h5ZeY%2BsGoa8vNeUsvLLPVg5OTmKjY11j1155ZVq3769Jk%2BerI0bN17wPgYPHqzBgwe7/z1y5Ei9%2BeabevXVV90fdHquj4S40I%2BLSE1N1ZAhQzzGHI6WKiuruqDtnq4pNXVv2D7%2BhggJCVZ4%2BCUqL69WXV2939YRKMjLe2TlPbJqGPLy3rmy8tcP9z4vWF988cUZ7x5JUnh4uIqKihptv%2B3atVNeXp4iIyMVHBwsl8vlMe9yudSmTRtFR0ersrJSdXV1CgkJcc9JUps2bbzaV0xMzBmXA0tLK1Rb%2B8P%2BBmkKx19XV98k1hEoyMt7ZOU9smoY8vJeU8rK52%2BBtGvXTosWLfK43FZeXq4lS5boxz/%2BsZV9/OEPf9CmTZs8xgoLC9W%2BfXuFhoaqa9euys/P99j/gQMH1LNnT8XFxckYo08%2B%2BcQ973Q6FR4erk6dOllZHwAAuLj5/B2sOXPmaObMmcrNzVWrVq0UHBysyspKtWjRQitWrLCyjxMnTuiJJ55Q%2B/btdcUVV2jLli16%2B%2B239corr0iS0tLSlJOTo0GDBik2NlbZ2dmKi4tTYmKiJGn48OH61a9%2BpaefflonTpzQihUrNHbsWDkcPo8LAAAEIJ83hoEDB2rnzp3atWuXDh06JGOMYmNjdc0116h169Zeb%2BdUGaqtrZUkbdu2TdI37zZNnDhRVVVVeuCBB1RaWqrLL79cK1asUEJCgqRvPk2%2BtLRUd9xxh6qqqpSUlORxU/rjjz%2Buxx57TEOHDlWzZs100003KTMz01YEAADgIhdk/PQHAE%2BePKlDhw65Pz39YldaWmF9mw5HsK7Pfsf6dhvL5owBftu3wxGsqKhWKiurajLX55sy8vIeWXmPrBqGvLx3rqzatvX%2BzRubfH4PVk1NjbKystSnTx/deOONkr65B2rKlCkqLy/39XIAAACs83nBWrx4sfbt26fs7GwFB//f7uvq6pSdne3r5QAAAFjn84K1ZcsW/frXv9YNN9zg/jDP8PBwLVy4UFu3bvX1cgAAAKzzecGqqqpSx44dzxiPjo7WsWPHfL0cAAAA63xesH784x/rr3/9qyTPT0x/44039G//9m%2B%2BXg4AAIB1Pv%2BYhn//93/X9OnTddttt6m%2Bvl6/%2B93vlJeXpy1btujhhx/29XIAAACs83nBSk1NlcPh0EsvvaSQkBD95je/UadOnZSdna0bbrjB18sBAACwzucF68iRI7rtttt02223%2BXrXAAAAPuHze7CGDh0qP322KQAAgE/4vGAlJSVp8%2BbNvt4tAACAz/j8EuGPfvQjPfnkk8rJydGPf/xjNWvWzGN%2ByZIlvl4SAACAVT4vWAUFBfrJT34iSSorK/P17gEAABqdzwpWZmamli5dqhdffNE9tmLFCqWnp/tqCQAAAD7hs3uwtm/ffsZYTk6Or3YPAADgMz4rWGf7zUF%2BmxAAAFyMfFawTv1h5/ONAQAABDqff0wDAADAxY6CBQAAYJnPfovw5MmTmjlz5nnH%2BBwsAAAQ6HxWsPr166eSkpLzjgEAAAQ6nxWsb3/%2BFQAAwMWMe7AAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFgW0AXrnXfeUXJysjIzM8%2BY27Rpk0aNGqU%2BffpozJgxevfdd91z9fX1Wrp0qYYOHar%2B/ftr8uTJOnjwoHve5XIpIyNDycnJGjhwoB5%2B%2BGHV1NT45JgAAEDgC9iC9eyzz2rBggXq0KHDGXP79u1TVlaWZs2apb/85S%2BaNGmS7r//fh06dEiStGbNGm3YsEE5OTnasWOHOnbsqPT0dBljJElz585VdXW1Nm7cqD/%2B8Y8qLCxUdna2T48PAAAEroAtWKGhoVq3bt1ZC9batWuVkpKilJQUhYaGavTo0erWrZvWr18vScrNzdWkSZPUuXNnhYWFKTMzU4WFhdqzZ4%2B%2B/vprbdu2TZmZmYqOjlZsbKzuu%2B8%2B/fGPf9TJkyd9fZgAACAAOfy9gO9r4sSJ3zmXn5%2BvlJQUj7H4%2BHg5nU7V1NSooKBA8fHx7rmwsDB16NBBTqdTFRUVCgkJUffu3d3zPXr00LFjx/T55597jH%2BXkpISlZaWeow5HC0VExPj7eF5JSQksPqxw%2BG/9Z7KKtAy8xfy8h5ZeY%2BsGoa8vNcUswrYgnUuLpdLERERHmMREREqKCjQ0aNHZYw563xZWZkiIyMVFhamoKAgjzlJKisr82r/ubm5Wr58ucdYenq6ZsyY8X0O56IRFdXK30tQePgl/l5CQCEv75GV98iqYcjLe00pq4uyYEly30/1febP99zzSU1N1ZAhQzzGHI6WKiuruqDtnq4pNXVv2D7%2BhggJCVZ4%2BCUqL69WXV2939YRKMjLe2TlPbJqGPLy3rmy8tcP9xdlwYqKipLL5fIYc7lcio6OVmRkpIKDg88636ZNG0VHR6uyslJ1dXUKCQlxz0lSmzZtvNp/TEzMGZcDS0srVFv7w/4GaQrHX1dX3yTWESjIy3tk5T2yahjy8l5Tyiqw3gLxUkJCgvLy8jzGnE6nevXqpdDQUHXt2lX5%2BfnuufLych04cEA9e/ZUXFycjDH65JNPPJ4bHh6uTp2IHCwKAAAUOUlEQVQ6%2BewYAABA4LooC9b48eP13nvvaefOnTp%2B/LjWrVunL774QqNHj5YkpaWlafXq1SosLFRlZaWys7MVFxenxMRERUdHa/jw4frVr36lI0eO6NChQ1qxYoXGjh0rh%2BOifMMPAABYFrCNITExUZJUW1srSdq2bZukb95t6tatm7Kzs7Vw4UIVFRWpS5cuWrVqldq2bStJmjBhgkpLS3XHHXeoqqpKSUlJHjelP/7443rsscc0dOhQNWvWTDfddNNZP8wUAADgbILMhd7RDa%2BUllZY36bDEazrs9%2Bxvt3GsjljgN/27XAEKyqqlcrKqprM9fmmjLy8R1beI6uGIS/vnSurtm1b%2B2VNF%2BUlQgAAAH%2BiYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyy7agtW9e3clJCQoMTHR/fXEE09Iknbv3q2xY8eqb9%2B%2BGjlypNavX%2B/x3NWrV2v48OHq27ev0tLSlJeX549DAAAAAcrh7wU0pjfeeEOXX365x1hJSYnuu%2B8%2BPfzwwxo1apQ%2B%2BOAD3XvvverUqZMSExO1fft2LVu2TM8995y6d%2B%2Bu1atXa9q0adq6datatmzppyMBAACB5KJ9B%2Bu7bNiwQR07dtTYsWMVGhqq5ORkDRkyRGvXrpUk5ebmasyYMerVq5datGihKVOmSJJ27Njhz2UDAIAAclG/g7VkyRJ99NFHqqys1I033qjZs2crPz9f8fHxHo%2BLj4/X5s2bJUn5%2BfkaMWKEey44OFhxcXFyOp0aOXKkV/stKSlRaWmpx5jD0VIxMTEXeESeQkICqx87HP5b76msAi0zfyEv75GV98iqYcjLe00xq4u2YPXu3VvJycl6%2BumndfDgQWVkZGj%2B/PlyuVyKjY31eGxkZKTKysokSS6XSxERER7zERER7nlv5Obmavny5R5j6enpmjFjxvc8motDVFQrfy9B4eGX%2BHsJAYW8vEdW3iOrhiEv7zWlrC7agpWbm%2Bv%2B786dO2vWrFm699571a9fv/M%2B1xhzQftOTU3VkCFDPMYcjpYqK6u6oO2erik1dW/YPv6GCAkJVnj4JSovr1ZdXb3f1hEoyMt7ZOU9smoY8vLeubLy1w/3F23BOt3ll1%2Buuro6BQcHy%2BVyecyVlZUpOjpakhQVFXXGvMvlUteuXb3eV0xMzBmXA0tLK1Rb%2B8P%2BBmkKx19XV98k1hEoyMt7ZOU9smoY8vJeU8oqsN4C8dLevXu1aNEij7HCwkI1b95cKSkpZ3zsQl5ennr16iVJSkhIUH5%2Bvnuurq5Oe/fudc8DAACcz0VZsNq0aaPc3Fzl5OToxIkT2r9/v5555hmlpqbq5ptvVlFRkdauXavjx49r165d2rVrl8aPHy9JSktL02uvvaaPP/5Y1dXVWrlypZo3b67Bgwf796AAAEDAuCgvEcbGxionJ0dLlixxF6Rbb71VmZmZCg0N1apVq7RgwQLNnz9f7dq10%2BLFi3XFFVdIkgYNGqQHH3xQGRkZOnz4sBITE5WTk6MWLVr4%2BagAAECgCDIXekc3vFJaWmF9mw5HsK7Pfsf6dhvL5owBftu3wxGsqKhWKiurajLX55sy8vIeWXmPrBqGvLx3rqzatm3tlzVdlJcIAQAA/ImCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAsc/h7AU1RUVGR5s%2Bfrz179qhly5YaMWKEZs6cqeBg%2BuiFuPFX/%2BPvJTTI5owB/l4CACBAUbDOYvr06erRo4e2bdumw4cPa%2BrUqbr00kt11113%2BXtpAAAgAPCWzGmcTqc%2B%2BeQTzZo1S61bt1bHjh01adIk5ebm%2BntpAAAgQPAO1mny8/PVrl07RUREuMd69Oih/fv3q7KyUmFhYefdRklJiUpLSz3GHI6WiomJsbrWkBD6cWMKtEuab866xtq2Tp1bnGPnR1beI6uGIS/vNcWsKFincblcCg8P9xg7VbbKysq8Kli5ublavny5x9j999%2Bv6dOn21uovilyd172mVJTU62Xt4tNSUmJcnNzycpLJSUleuGF58jLC2TlPbJqGPLyXlPMqulUvSbEGHNBz09NTdWrr77q8ZWammppdf%2BntLRUy5cvP%2BPdMpyJrBqGvLxHVt4jq4YhL%2B81xax4B%2Bs00dHRcrlcHmMul0tBQUGKjo72ahsxMTFNpkEDAADf4x2s0yQkJKi4uFhHjhxxjzmdTnXp0kWtWrXy48oAAECgoGCdJj4%2BXomJiVqyZIkqKytVWFio3/3ud0pLS/P30gAAQIAImTdv3jx/L6Kpueaaa7Rx40Y98cQT%2BvOf/6yxY8dq8uTJCgoK8vfSztCqVStdddVVvLvmBbJqGPLyHll5j6wahry819SyCjIXekc3AAAAPHCJEAAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyClYAKioq0j333KOkpCRde%2B21Wrx4serr6/29rEZVVFSk9PR0JSUlKTk5WbNnz1Z5ebkkad%2B%2Bfbr99tvVr18/DRs2TM8//7zHczdt2qRRo0apT58%2BGjNmjN599133XH19vZYuXaqhQ4eqf//%2Bmjx5sg4ePOied7lcysjIUHJysgYOHKiHH35YNTU1vjloC5566il1797d/e/du3dr7Nix6tu3r0aOHKn169d7PH716tUaPny4%2Bvbtq7S0NOXl5bnnjh8/rkcffVSDBg1SUlKSZsyYobKyMvd8oJ6XK1eu1MCBA9W7d29NmjRJX375pSSyOpu9e/dq4sSJuvLKKzVgwADNmjVLR44ckURekvTOO%2B8oOTlZmZmZZ8w15uvQhbwG%2Bsu5stq6datGjx6tPn36aPjw4XrllVc85hvzXDrfedwgBgHn1ltvNY888ogpLy83%2B/fvN8OGDTPPP/%2B8v5fVqG666SYze/ZsU1lZaYqLi82YMWPMnDlzTHV1tbnmmmvMsmXLTFVVlcnLyzNXXXWV2bJlizHGmL1795qEhASzc%2BdOU1NTY15//XXTq1cvU1xcbIwxZvXq1ebaa681BQUFpqKiwjz%2B%2BONm1KhRpr6%2B3hhjzP3332/uuecec/jwYXPo0CGTmppqnnjiCb/l0BB79%2B41V111lenWrZsxxpivvvrK9O7d26xdu9bU1NSY//mf/zE9e/Y0//jHP4wxxrz11lvmyiuvNB9//LGprq42q1atMgMGDDBVVVXGGGMWLlxoxowZY/71r3%2BZsrIyc//995upU6e69xeI5%2BVLL71kbrjhBlNYWGgqKirME088YZ544gmyOouTJ0%2BaAQMGmCVLlpjjx4%2BbI0eOmLvuustMnz6dvIwxOTk5ZtiwYWbChAkmIyPDY64xX4cu9DXQH86V1Z49e0xiYqJ58803zcmTJ83OnTtNjx49zN///ndjTOOeS%2Bc7jxuKghVg/vGPf5i4uDjjcrncY7///e/N8OHD/biqxnX06FEze/ZsU1pa6h578cUXzbBhw8zmzZvN1VdfbWpra91zixcvNj/72c%2BMMcbMnz/fpKene2xv3LhxZtWqVcYYY0aOHGleeOEF91xFRYWJj483H330kSktLTVXXHGF2bdvn3t%2B165dpnfv3ubEiRONcqy21NXVmXHjxpn/%2Bq//ches5557ztxyyy0ej8vIyDBz5841xhhzzz33mKeeespjGwMGDDAbN240J0%2BeNP369TPbtm1zzxcUFJju3bubQ4cOBex5OWTIEPf/EX0bWZ3pX//6l%2BnWrZspKChwj/3%2B97831113HXkZY1544QVTXl5usrKyzigNjfk6dKGvgf5wrqx27dplli9f7jF26623mpUrVxpjGvdcOt953FBcIgww%2Bfn5ateunSIiItxjPXr00P79%2B1VZWenHlTWe8PBwLVy4UJdeeql7rLi4WDExMcrPz1f37t0VEhLinouPj3e/ZZyfn6/4%2BHiP7cXHx8vpdKqmpkYFBQUe82FhYerQoYOcTqf27dunkJAQj0tsPXr00LFjx/T555831uFa8fLLLys0NFSjRo1yj31XFt%2BVVXBwsOLi4uR0OnXgwAFVVFSoR48e7vnOnTurRYsWys/PD8jz8quvvtKXX36po0ePasSIEe7LCUeOHCGrs4iNjVVcXJxyc3NVVVWlw4cPa%2BvWrRo8eDB5SZo4caJat2591rnGfB26kNdAfzlXVoMGDVJ6err737W1tSotLVVsbKykxj2XznceNxQFK8C4XC6Fh4d7jJ06Wb59nfli5nQ69dJLL%2Bnee%2B89ax6RkZFyuVyqr6%2BXy%2BXy%2BGaSvsmrrKxMR48elTHmO%2BddLpfCwsIUFBTkMSc17ay//vprLVu2TI899pjH%2BHdldepYzpWVy%2BWSpDOeHx4e7p4PtPPy0KFDkqQ33nhDv/vd7/T666/r0KFDeuSRR8jqLIKDg7Vs2TK99dZb6tu3r5KTk1VbW6uZM2eS13k05uvQhbwGBoLs7Gy1bNlSI0aMkNS459L5zuOGomAFIGOMv5fgNx988IEmT56smTNnKjk5%2BTsf9%2B0Xo/Plda75QMx64cKFGjNmjLp06dLg5/6Qsjq13ilTpig2NlaXXXaZpk%2Bfru3btzfo%2Bd9nPtCykqQTJ05o2rRpuuGGG/T%2B%2B%2B/r7bffVuvWrTVr1iyvnv9Dy%2Bt0vj7%2BhrwGNkXGGC1evFgbN27UypUrFRoa6jF3vud%2BnznbKFgBJjo62t3ST3G5XAoKClJ0dLSfVuUb27dv1z333KM5c%2BZo4sSJkr7J4/SfLlwulyIjIxUcHKyoqKiz5hUdHe1%2BzNnm27Rpo%2BjoaFVWVqqurs5jTpLatGnTGId4wXbv3q2PPvrI4y32U86WRVlZmfu8OVdWpx5z%2BvzRo0fdWQXaeXnqkvO3f2Jt166djDE6efIkWZ1m9%2B7d%2BvLLL/Xggw%2BqdevWio2N1YwZM/Tmm2%2Be9fvoh57XtzXm69CFvAY2VfX19Zo9e7a2b9%2BuP/zhD/rJT37inmvMc%2Bl8r5ENRcEKMAkJCSouLnb/arT0zSWzLl26qFWrVn5cWeP68MMPlZWVpWeeeUa33HKLezwhIUGffvqpamtr3WNOp1O9evVyz59%2B/fzUfGhoqLp27ar8/Hz3XHl5uQ4cOKCePXsqLi5Oxhh98sknHs8NDw9Xp06dGutQL8j69et1%2BPBhXXvttUpKStKYMWMkSUlJSerWrdsZWeTl5Xlk9e0s6urqtHfvXvXq1Uvt27dXRESEx/w///lPnThxQgkJCQF5Xl522WUKCwvTvn373GNFRUVq1qyZUlJSyOo0dXV1qq%2Bv93gH4MSJE5Kk5ORk8jqHxnwdupDXwKbqqaee0meffaY//OEPat%2B%2BvcdcY55LiYmJ5zyPG%2Bx73RoPvxo3bpyZM2eOqaioMAUFBWbIkCHmpZde8veyGs3JkyfNjTfeaF5%2B%2BeUz5o4fP26uvfZa8%2Btf/9ocO3bMfPzxx%2BbKK680O3bsMMYY8%2Bmnn5rExESzY8cOU1NTY9auXWv69OljSkpKjDHf/AbJ4MGD3b8ePXfuXHPbbbe5t5%2BRkWGmTJliDh8%2BbIqLi81tt91mFi1a5JPj/j5cLpcpLi52f3300UemW7dupri42BQVFZk%2BffqYV155xdTU1JidO3eanj17un87adeuXaZfv37mo48%2BMseOHTPLli0zKSkpprq62hjzzW8m3XrrreZf//qXOXLkiJk6daqZPn26e9%2BBeF4%2B9dRTZujQoeaLL74wX3/9tUlNTTWzZ882X3/9NVmd5siRI%2Baqq64yv/zlL82xY8fMkSNHzLRp08x//Md/kNe3nO034xrzdehCXwP96WxZvf/%2B%2B6Z///4evzX%2BbY15Lp3vPG4oClYAKi4uNlOmTDE9e/Y0ycnJ5te//rX781IuRn//%2B99Nt27dTEJCwhlfX375pfn000/NhAkTTEJCghk8eLBZs2aNx/O3bNlihg0bZnr06GFuvvlm87e//c09V19fb5555hnz05/%2B1PTs2dPcfffdHp8PU15ebjIzM03v3r1N//79zfz5883x48d9duwX6uDBg%2B6PaTDGmL/97W9m9OjRpkePHmbYsGFnfETBmjVrTEpKiklISDBpaWnm008/dc8dP37czJs3z/Tv39/06dPHPPjgg6a8vNw9H4jn5bePqXfv3iYrK8tUVlYaY8jqbJxOp7n99tvNlVdeaZKTk01GRoY5dOiQMYa8Tr0mXXHFFeaKK65w//uUxnwdupDXQH84V1YPPfSQx9ipr7vuusv9/MY8l853HjdEkDEBePcbAABAE8Y9WAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABg2f8DQmKWvG03LrcAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common1893777315355736608”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>38</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>138.308562242177</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1443.3933485213</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>232.978171267488</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16251.0407088714</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>269.46526443334</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.13923115398529</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>102.512523248839</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>80.2061345196998</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3065)</td> <td class=”number”>3065</td> <td class=”number”>95.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme1893777315355736608”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>38</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.78710449493e-05</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.30253205902e-05</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.000632869778201</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0017772159771994</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>73145.3450067915</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>94996.7956478329</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>102836.110546633</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>113699.486120656</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>115781.021512587</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_NHASIAN15”>LACCESS_NHASIAN15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2836</td>

</tr> <tr>

<th>Unique (%)</th> <td>88.3%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>97</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>696.05</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>76998</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>7.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-1555056792734630075”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-1555056792734630075,#minihistogram-1555056792734630075”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-1555056792734630075”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-1555056792734630075”

aria-controls=”quantiles-1555056792734630075” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-1555056792734630075” aria-controls=”histogram-1555056792734630075”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-1555056792734630075” aria-controls=”common-1555056792734630075”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-1555056792734630075” aria-controls=”extreme-1555056792734630075”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-1555056792734630075”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>3.3309</td>

</tr> <tr>

<th>Median</th> <td>16.554</td>

</tr> <tr>

<th>Q3</th> <td>130.89</td>

</tr> <tr>

<th>95-th percentile</th> <td>2954.4</td>

</tr> <tr>

<th>Maximum</th> <td>76998</td>

</tr> <tr>

<th>Range</th> <td>76998</td>

</tr> <tr>

<th>Interquartile range</th> <td>127.56</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>3471.9</td>

</tr> <tr>

<th>Coef of variation</th> <td>4.988</td>

</tr> <tr>

<th>Kurtosis</th> <td>182.87</td>

</tr> <tr>

<th>Mean</th> <td>696.05</td>

</tr> <tr>

<th>MAD</th> <td>1109</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>11.465</td>

</tr> <tr>

<th>Sum</th> <td>2166800</td>

</tr> <tr>

<th>Variance</th> <td>12054000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-1555056792734630075”>

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02YMEGPP/64jh8/Lklas2aNNmzYoFWrVmnHjh1q166dkpOTZYypz7gAAMCH%2BGTBKioqUnR0tKZNm6ZmzZrpuuuu07333qu///3v2rlzp86dO6dHH31UTZs2VdeuXTVmzBhlZmZKktauXauEhAQlJCQoMDBQI0aMUOfOnbV%2B/XpJUmZmpiZMmKAOHTooKChIaWlpysnJ0d69e%2BszMgAA8CGO%2Bh7gSgQHB2v%2B/Pkea3l5eYqIiFB2dra6dOmigIAA915UVJTWrl0rScrOzlZCQoLHsVFRUXI6nSorK9PBgwcVFRXl3gsKClLbtm3ldDrVvXv3Gs2Xn5%2BvgoICjzWHo6kiIiJqlfNyAgJ8qx87HLWb93w%2BX8tZG2S0B7tntHs%2BiYx20NDy%2BWTBupDT6dRbb72lFStWaPPmzQoODvbYDw0NlcvlUlVVlVwul0JCQjz2Q0JCdPDgQZ06dUrGmIvuFxYW1niezMxMLVu2zGMtOTlZKSkptUxmL2Fhza7ouODgayyepOEhoz3YPaPd80lktIOGks/nC9YXX3yhRx99VNOmTVN8fLw2b9580cf5%2Bfm5//ly91Nd7f1WiYmJGjhwoMeaw9FUhYUlV/W8F2ooLb2maps/IMBfwcHXqKioVJWVVXU0Vf0ioz3YPaPd80lktINL5bvSb%2B6vlk8XrO3bt%2BvXv/61Zs2apXvuuUeSFB4eriNHjng8zuVyKTQ0VP7%2B/goLC5PL5aq2Hx4e7n7MxfZbtGhR47kiIiKqXQ4sKDitigr7vaBr40rzV1ZW2f5rR0Z7sHtGu%2BeTyGgHDSWfb70F8iNffvml0tPTtWTJEne5kqTo6Gh9/fXXqqiocK85nU5169bNvZ%2BVleXxXOf3AwMD1alTJ2VnZ7v3ioqKdPToUcXGxtZxIgAAYBc%2BWbAqKir07LPPavr06erfv7/HXkJCgoKCgrRixQqVlpZq7969WrdunZKSkiRJY8eO1WeffaadO3eqvLxc69at05EjRzRixAhJUlJSklavXq2cnBwVFxcrIyNDkZGRiomJ8XpOAADgm3zyEuGePXuUk5OjefPmad68eR57H3zwgV555RU9//zzWrVqla699lqlpaVpwIABkqTOnTsrIyND8%2BfPV25urjp27KiVK1eqZcuWkqRx48apoKBADzzwgEpKShQXF1fthnUAAICf4mf4BE2vKCg4bflzOhz%2Buj3jE8uft65sTu1Xq8c7HP4KC2umwsKSBnE9vS6Q0R7sntHu%2BSQy2sGl8rVs2bxe5vH6JcKBAwdq2bJlysvL8/apAQAAvMLrBWvUqFHatGmTbrvtNk2aNElbt271uCEdAADA13m9YCUnJ2vTpk1655131KlTJ7300ktKSEjQwoULdfjwYW%2BPAwAAYLl6%2BynCrl27Kj09XTt27NDMmTP1zjvvaOjQoZo4caL%2B8Y9/1NdYAAAAV63eCta5c%2Be0adMmPfzww0pPT1erVq309NNPKzIyUhMmTNCGDRvqazQAAICr4vWPacjJydG6dev03nvvqaSkREOGDNEbb7yhXr16uR/Tp08fzZ49W8OHD/f2eAAAAFfN6wVr2LBhat%2B%2BvSZPnqx77rlHoaGh1R6TkJCgkydPens0AAAAS3i9YK1evVp9%2B/a97OP27t3rhWkAAACs5/V7sLp06aIpU6Zo27Zt7rX//M//1MMPP1ztL1kGAADwRV4vWPPnz9fp06fVsWNH99qAAQNUVVWlBQsWeHscAAAAy3n9EuGnn36qDRs2KCwszL3Wrl07ZWRk6K677vL2OAAAAJbz%2BjtYZWVlCgwMrD6Iv79KS0u9PQ4AAIDlvF6w%2BvTpowULFujUqVPute%2B%2B%2B05z5szx%2BKgGAAAAX%2BX1S4QzZ87Ur371K910000KCgpSVVWVSkpK1KZNG7355pveHgcAAMByXi9Ybdq00Z///Gd9/PHHOnr0qPz9/dW%2BfXv1799fAQEB3h4HAADAcl4vWJLUuHFj3XbbbfVxagAAgDrn9YJ17NgxLVq0SN98843Kysqq7X/00UfeHgkAAMBS9XIPVn5%2Bvvr376%2BmTZt6%2B/QAAAB1zusFKysrSx999JHCw8O9fWoAAACv8PrHNLRo0YJ3rgAAgK15vWBNnjxZy5YtkzHG26cGAADwCq9fIvz444/15Zdf6t1339X1118vf3/Pjvf22297eyQAAABLeb1gBQUF6ZZbbvH2aQEAALzG6wVr/vz53j4lAACAV3n9HixJOnTokJYuXaqnn37avfbVV1/VxygAAACW83rB2r17t0aMGKGtW7dq48aNkn748NHx48fzIaMAAMAWvF6wFi9erF//%2BtfasGGD/Pz8JP3w9xMuWLBAy5cv9/Y4AAAAlvN6wfrnP/%2BppKQkSXIXLEm64447lJOT4%2B1xAAAALOf1gtW8efOL/h2E%2Bfn5aty4sbfHAQAAsJzXC1bPnj310ksvqbi42L12%2BPBhpaen66abbvL2OAAAAJbz%2Bsc0PP3003rwwQcVFxenyspK9ezZU6WlperUqZMWLFjg7XEAAAAs5/WCdd1112njxo3atWuXDh8%2BrCZNmqh9%2B/bq16%2Bfxz1ZAAAAvsrrBUuSGjVqpNtuu60%2BTg0AAFDnvF6wBg4c%2BJPvVPFZWAAAwNd5vWANHTrUo2BVVlbq8OHDcjqdevDBB709DgAAgOW8XrCmT59%2B0fUtW7bor3/9q5enAQAAsF69/F2EF3Pbbbfpz3/%2Bc32PAQAAcNUaTMHat2%2BfjDH1PQYAAMBV8/olwnHjxlVbKy0tVU5OjgYPHuztcQAAACzn9YLVrl27aj9FGBgYqNGjR2vMmDG1eq5PPvlE6enpiouL0%2BLFi93r7777rmbOnKlGjRp5PH7NmjWKjY1VVVWVlixZoo0bN6qoqEixsbGaPXu22rRpI0lyuVyaPXu2Pv/8c/n7%2ByshIUGzZs1SkyZNrjA1AAD4f4nXC5ZVn9b%2B6quvat26dWrbtu1F9/v06aM333zzontr1qzRhg0b9Oqrr6pVq1ZavHixkpOT9f7778vPz0%2BzZs3S2bNntXHjRp07d05PPPGEMjIy9Oyzz1oyOwAAsDevF6z33nuvxo%2B95557LrkXGBiodevW6cUXX1R5eXmtZsjMzNSECRPUoUMHSVJaWpri4uK0d%2B9eXX/99dq2bZv%2B9Kc/KTw8XJL02GOP6YknnlB6enq1d8UAAAAu5PWC9cwzz6iqqqraDe1%2Bfn4ea35%2Bfj9ZsMaPH/%2BT58nLy9NDDz2krKwsBQcHKyUlRXfffbfKysp08OBBRUVFuR8bFBSktm3byul06vTp0woICFCXLl3c%2B127dtWZM2d06NAhj/VLyc/PV0FBgceaw9FUERERlz22NgICGszPKNSIw1G7ec/n87WctUFGe7B7Rrvnk8hoBw0tn9cL1muvvabXX39dU6ZMUZcuXWSM0ddff61XX31V999/v%2BLi4q76HOHh4WrXrp2efPJJdezYUR9%2B%2BKGeeuopRURE6Je//KWMMQoJCfE4JiQkRIWFhQoNDVVQUJDHfWLnH1tYWFij82dmZmrZsmUea8nJyUpJSbnKZL4tLKzZFR0XHHyNxZM0PGS0B7tntHs%2BiYx20FDy1cs9WKtWrVKrVq3ca71791abNm00ceJEbdy48arPMWDAAA0YMMD9%2B2HDhunDDz/Uu%2B%2B%2B6/6g05/6SIir/biIxMREDRw40GPN4WiqwsKSq3reCzWUll5Ttc0fEOCv4OBrVFRUqsrKqjqaqn6R0R7sntHu%2BSQy2sGl8l3pN/dXy%2BsF68iRI9XePZKk4OBg5ebm1tl5W7duraysLIWGhsrf318ul8tj3%2BVyqUWLFgoPD1dxcbEqKysVEBDg3pOkFi1a1OhcERER1S4HFhScVkWF/V7QtXGl%2BSsrq2z/tSOjPdg9o93zSWS0g4aSz%2BtvgbRu3VoLFizwuNxWVFSkRYsW6Re/%2BIUl5/jv//5vbdq0yWMtJydHbdq0UWBgoDp16qTs7GyP8x89elSxsbGKjIyUMUYHDhxw7zudTgUHB6t9%2B/aWzAcAAOzN6%2B9gzZw5U9OmTVNmZqaaNWsmf39/FRcXq0mTJlq%2BfLkl5zh79qzmzp2rNm3a6IYbbtCWLVv08ccf65133pEkJSUladWqVbrlllvUqlUrZWRkKDIyUjExMZKkIUOG6Le//a1efvllnT17VsuXL9fo0aPlcHj9ywUAAHyQ1xtD//79tXPnTu3atUvHjx%2BXMUatWrXSzTffrObNm9f4ec6XoYqKCknStm3bJP3wbtP48eNVUlKiJ554QgUFBbr%2B%2Buu1fPlyRUdHS/rh0%2BQLCgr0wAMPqKSkRHFxcR43pb/wwgt6/vnnNWjQIDVq1Eh33XWX0tLSrPoSAAAAm/Mz9fQXAJ47d07Hjx93f3q63RUUnLb8OR0Of92e8Ynlz1tXNqf2q9XjHQ5/hYU1U2FhSYO4nl4XyGgPds9o93wSGe3gUvlatqz5mzdW8vo9WGVlZUpPT1ePHj105513SvrhHqhJkyapqKjI2%2BMAAABYzusFa%2BHChdq/f78yMjLk7/9/p6%2BsrFRGRoa3xwEAALCc1wvWli1b9Lvf/U533HGH%2B8M8g4ODNX/%2BfG3dutXb4wAAAFjO6wWrpKRE7dq1q7YeHh6uM2fOeHscAAAAy3m9YP3iF7/QX//6V0men5j%2BwQcf6Oc//7m3xwEAALCc1z%2Bm4b777tPUqVM1atQoVVVV6Q9/%2BIOysrK0ZcsWPfPMM94eBwAAwHJeL1iJiYlyOBx66623FBAQoFdeeUXt27dXRkaG7rjjDm%2BPAwAAYDmvF6yTJ09q1KhRGjVqlLdPDQAA4BVevwdr0KBBqqfPNgUAAPAKrxesuLg4bd682dunBQAA8BqvXyL82c9%2BphdffFGrVq3SL37xCzVq1Mhjf9GiRd4eCQAAwFJeL1gHDx7UL3/5S0lSYWGht08PAABQ57xWsNLS0rR48WK9%2Beab7rXly5crOTnZWyMAAAB4hdfuwdq%2BfXu1tVWrVnnr9AAAAF7jtYJ1sZ8c5KcJAQCAHXmtYJ3/i50vtwYAAODrvP4xDQAAAHZHwQIAALCY136K8Ny5c5o2bdpl1/gcLAAA4Ou8VrB69eql/Pz8y64BAAD4Oq8VrB9//hUAAICdcQ8WAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABbz6YL1ySefKD4%2BXmlpadX2Nm3apOHDh6tHjx4aOXKkPv30U/deVVWVFi9erEGDBqlPnz6aOHGijh075t53uVxKTU1VfHy8%2Bvfvr2eeeUZlZWVeyQQAAHyfzxasV199VfPmzVPbtm2r7e3fv1/p6emaPn26/vKXv2jChAl6/PHHdfz4cUnSmjVrtGHDBq1atUo7duxQu3btlJycLGOMJGnWrFkqLS3Vxo0b9cc//lE5OTnKyMjwaj4AAOC7fLZgBQYGat26dRctWGvXrlVCQoISEhIUGBioESNGqHPnzlq/fr0kKTMzUxMmTFCHDh0UFBSktLQ05eTkaO/evfr%2B%2B%2B%2B1bds2paWlKTw8XK1atdJjjz2mP/7xjzp37py3YwIAAB/kqO8BrtT48eMvuZedna2EhASPtaioKDmdTpWVlengwYOKiopy7wUFBalt27ZyOp06ffq0AgIC1KVLF/d%2B165ddebMGR06dMhj/VLy8/NVUFDgseZwNFVERERN49VIQIBv9WOHo3bzns/nazlrg4z2YPeMds8nkdEOGlo%2Bny1YP8XlcikkJMRjLSQkRAcPHtSpU6dkjLnofmFhoUJDQxUUFCQ/Pz%2BPPUkqLCys0fkzMzO1bNkyj7Xk5GSlpKRcSRzbCAtrdkXHBQdfY/EkDQ8Z7cHuGe2eTyKjHTSUfLYsWJLc91Ndyf7ljr2cxMREDRw40GPN4WiqwsKSq3reCzWUll5Ttc0fEOCv4OBrVFRUqsrKqjqaqn6R0R7sntHu%2BSQy2sGl8l3pN/dXy5YFKywsTC6Xy2PN5XIpPDxcoaGh8vf3v%2Bh%2BixYtFB4eruLiYlVWViogIMC9J0ktWrSo0fkjIiKqXQ4sKDitigr7vaBr40rzV1ZW2f5rR0Z7sHtGu%2BeTyGgHDSWfb70FUkPR0dHKysryWHM6nerWrZsCAwPVqVMnZWdnu/eKiop09OhRxcbGKjIyUsYYHThwwOPY4OBgtW/f3msZAACA77JlwRo7dqw%2B%2B%2Bx3SgFxAAAU/0lEQVQz7dy5U%2BXl5Vq3bp2OHDmiESNGSJKSkpK0evVq5eTkqLi4WBkZGYqMjFRMTIzCw8M1ZMgQ/fa3v9XJkyd1/PhxLV%2B%2BXKNHj5bDYcs3/AAAgMV8tjHExMRIkioqKiRJ27Ztk/TDu02dO3dWRkaG5s%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%2B9fcuXMlSbt379bo0aPVs2dPDRs2TOvXr/c4dvXq1RoyZIh69uyppKQkZWVl1UcEAADgoxz1PUBd%2BuCDD3T99dd7rOXn5%2Buxxx7TM888o%2BHDh%2BuLL77Qo48%2Bqvbt2ysmJkbbt2/X0qVL9dprr6lLly5avXq1pkyZoq1bt6pp06b1lAQAAPgS276DdSkbNmxQu3btNHr0aAUGBio%2BPl4DBw7U2rVrJUmZmZkaOXKkunXrpiZNmmjSpEmSpB07dtTn2AAAwIfY%2Bh2sRYsW6auvvlJxcbHuvPNOzZgxQ9nZ2YqKivJ4XFRUlDZv3ixJys7O1tChQ917/v7%2BioyMlNPp1LBhw2p03vz8fBUUFHisORxNFRERcZWJPAUE%2BFY/djhqN%2B/5fL6WszbIaA92z2j3fBIZ7aCh5bNtwerevbvi4%2BP18ssv69ixY0pNTdWcOXPkcrnUqlUrj8eGhoaqsLBQkuRyuRQSEuKxHxIS4t6viczMTC1btsxjLTk5WSkpKVeYxh7Cwppd0XHBwddYPEnDQ0Z7sHtGu%2BeTyGgHDSWfbQtWZmam%2B587dOig6dOn69FHH1WvXr0ue6wx5qrOnZiYqIEDB3qsORxNVVhYclXPe6GG0tJrqrb5AwL8FRx8jYqKSlVZWVVHU9UvMtqD3TPaPZ9ERju4VL4r/eb%2Batm2YF3o%2BuuvV2Vlpfz9/eVyuTz2CgsLFR4eLkkKCwurtu9yudSpU6canysiIqLa5cCCgtOqqLDfC7o2rjR/ZWWV7b92ZLQHu2e0ez6JjHbQUPL51lsgNbRv3z4tWLDAYy0nJ0eNGzdWQkJCtY9dyMrKUrdu3SRJ0dHRys7Odu9VVlZq37597n0AAIDLsWXBatGihTIzM7Vq1SqdPXtWhw8f1pIlS5SYmKi7775bubm5Wrt2rcrLy7Vr1y7t2rVLY8eOlSQlJSXpvffe0549e1RaWqoVK1aocePGGjBgQP2GAgAAPsOWlwhbtWqlVatWadGiRe6CdO%2B99yotLU2BgYFauXKl5s2bpzlz5qh169ZauHChbrjhBknSLbfcoieffFKpqak6ceKEYmJitGrVKjVp0qSeUwEAAF9hy4IlSX369NHbb799yb3333//ksfed999uu%2B%2B%2B%2BpqNAAAYHO2vEQIAABQnyhYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWMxR3wM0RLm5uZozZ4727t2rpk2baujQoZo2bZr8/emjV%2BPO3/5PfY9QK5tT%2B9X3CAAAH0XBuoipU6eqa9eu2rZtm06cOKHJkyfr2muv1UMPPVTfowEAAB/AWzIXcDqdOnDggKZPn67mzZurXbt2mjBhgjIzM%2Bt7NAAA4CN4B%2BsC2dnZat26tUJCQtxrXbt21eHDh1VcXKygoKDLPkd%2Bfr4KCgo81hyOpoqIiLB01oAA%2BnFd8rVLmr7kw%2Bk31/cIljr/76Jd/520ez6JjHbQ0PJRsC7gcrkUHBzssXa%2BbBUWFtaoYGVmZmrZsmUea48//rimTp1q3aD6ocg9eN03SkxMtLy8NQT5%2BfnKzMy0bT6JjHaRn5%2BvN954zbYZ7Z5PIqMdNLR8DaPmNTDGmKs6PjExUe%2B%2B%2B67Hr8TERIum%2Bz8FBQVatmxZtXfL7MLu%2BSQy2oXdM9o9n0RGO2ho%2BXgH6wLh4eFyuVweay6XS35%2BfgoPD6/Rc0RERDSI9gwAAOoH72BdIDo6Wnl5eTp58qR7zel0qmPHjmrWrFk9TgYAAHwFBesCUVFRiomJ0aJFi1RcXKycnBz94Q9/UFJSUn2PBgAAfETA7NmzZ9f3EA3NzTffrI0bN2ru3Ln685//rNGjR2vixIny8/Or79Gqadasmfr27Wvbd9fsnk8io13YPaPd80lktIOGlM/PXO0d3QAAAPDAJUIAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsHxQbm6uHnnkEcXFxenWW2/VwoULVVVVVd9jVfPJJ58oPj5eaWlp1fY2bdqk4cOHq0ePHho5cqQ%2B/fRT915VVZUWL16sQYMGqU%2BfPpo4caKOHTvm3ne5XEpNTVV8fLz69%2B%2BvZ555RmVlZe79/fv36/7771evXr00ePBgvf7663WSLzc3V8nJyYqLi1N8fLxmzJihoqKiGs1Ql/mtdODAAT344IPq1auX4uPjlZqaqoKCAknS7t27NXr0aPXs2VPDhg3T%2BvXrPY5dvXq1hgwZop49eyopKUlZWVnuvfLycj333HO65ZZbFBcXp5SUFBUWFrr36%2Bs1/tJLL6lLly7u39shY5cuXRQdHa2YmBj3r7lz59om33krVqxQ//791b17d02YMEHffvutbTL%2B7W9/8/jzi4mJUXR0tPu1aoeM%2B/bt0/jx49W7d2/169dP06dP18mTJ307n4HPuffee82zzz5rioqKzOHDh83gwYPN66%2B/Xt9jeVi1apUZPHiwGTdunElNTfXY27dvn4mOjjY7d%2B40ZWVl5v333zfdunUzeXl5xhhjVq9ebW699VZz8OBBc/r0afPCCy%2BY4cOHm6qqKmOMMY8//rh55JFHzIkTJ8zx48dNYmKimTt3rjHGmNLSUnPzzTebpUuXmpKSEpOVlWX69u1rtmzZYnnGu%2B66y8yYMcMUFxebvLw8M3LkSDNz5szLzlCX%2Ba1UXl5ubrrpJrNs2TJTXl5uTpw4Ye6//37z2GOPme%2B%2B%2B850797drF271pSVlZn/%2BZ//MbGxseYf//iHMcaYjz76yPTu3dvs2bPHlJaWmpUrV5p%2B/fqZkpISY4wx8%2BfPNyNHjjT/%2Bte/TGFhoXn88cfN5MmT3eeuj9f4vn37TN%2B%2BfU3nzp2NMcY2GTt37myOHTtWbd0u%2BYwx5q233jJ33HGHycnJMadPnzZz5841c%2BfOtVXGC61YscI88cQTtsh47tw5069fP7No0SJTXl5uTp48aR566CEzdepUn85HwfIx//jHP0xkZKRxuVzutf/6r/8yQ4YMqcepqnvjjTdMUVGRSU9Pr1aw5syZY5KTkz3WxowZY1auXGmMMWbYsGHmjTfecO%2BdPn3aREVFma%2B%2B%2BsoUFBSYG264wezfv9%2B9v2vXLtO9e3dz9uxZs3nzZnPjjTeaiooK9/7ChQvNr371K0vznTp1ysyYMcMUFBS41958800zePDgy85Ql/mt5HK5zDvvvGPOnTvnXnvjjTfM7bffbl577TVzzz33eDw%2BNTXVzJo1yxhjzCOPPGJeeukl915lZaXp16%2Bf2bhxozl37pzp1auX2bZtm3v/4MGDpkuXLub48eP18hqvrKw0Y8aMMf/xH//hLlh2yXipgmWXfMYYM3DgwIt%2BE2WnjD%2BWm5tr%2Bvbta3Jzc22R8V//%2Bpfp3LmzOXjwoMd5brvtNp/OxyVCH5Odna3WrVsrJCTEvda1a1cdPnxYxcXF9TiZp/Hjx6t58%2BYX3cvOzlZUVJTHWlRUlJxOp8rKynTw4EGP/aCgILVt21ZOp1P79%2B9XQECAx2Wcrl276syZMzp06JCys7PVpUsXBQQEeDz3j98ytkJwcLDmz5%2Bva6%2B91r2Wl5eniIiIy85Ql/mtFBISojFjxsjhcEiSDh06pD/96U%2B68847L5nhUhn9/f0VGRkpp9Opo0eP6vTp0%2Bratat7v0OHDmrSpImys7Pr5TX%2B9ttvKzAwUMOHD3ev2SnjokWLNGDAAPXu3VuzZs1SSUmJbfJ99913%2Bvbbb3Xq1CkNHTrUfRno5MmTtsl4oSVLlmjUqFH6%2Bc9/bouMrVq1UmRkpDIzM1VSUqITJ05o69atGjBggE/no2D5GJfLpeDgYI%2B18y%2BOH19XbshcLpfHC1r6IUNhYaFOnTolY8wl910ul4KCguTn5%2BexJ8m9f%2BHXJzQ0VC6Xq07v/3A6nXrrrbf06KOPXnaGusxfF3JzcxUdHa2hQ4cqJiZGKSkpl8x4foafyuhyuSSp2vHBwcGX/DOsy4zff/%2B9li5dqueff95j3S4Zu3fvrvj4eG3dulWZmZnas2eP5syZY5t8x48flyR98MEH%2BsMf/qD3339fx48f17PPPmubjD/27bffauvWrXrooYck2eN16u/vr6VLl%2Bqjjz5Sz549FR8fr4qKCk2bNs2n81GwfJAxpr5HuGqXy/BT%2B1eS/8eFxGpffPGFJk6cqGnTpik%2BPr5GM3g7/9Vo3bq1nE6nPvjgAx05ckRPPfVUjY7zlYzz58/XyJEj1bFjx1of6wsZMzMzNWbMGDVu3FgdOnTQ9OnTtXHjRp07d%2B6yx/pCvvPnmTRpklq1aqXrrrtOU6dO1fbt22t1/JXs18d/i9esWaPBgwerZcuWNT6moWc8e/aspkyZojvuuEN///vf9fHHH6t58%2BaaPn16jY5vqPkoWD4mPDzc3crPc7lc8vPzU3h4eD1NVTthYWEXzRAeHq7Q0FD5%2B/tfdL9FixYKDw9XcXGxKisrPfYkufcv/M7D5XK5n9dq27dv1yOPPKKZM2dq/PjxknTZGeoyf13x8/NTu3btlJaWpo0bN8rhcFSbsbCw0P0a/KmM5x9z4f6pU6fcGb31Gt%2B9e7e%2B%2BuorJScnV9u7WAZfzHih66%2B/XpWVlRd9nflivvOX6X/8TkTr1q1ljNG5c%2BdskfHHtmzZooEDB7p/b4fX6e7du/Xtt9/qySefVPPmzdWqVSulpKToww8/9OnXKQXLx0RHRysvL8/946vSD5enOnbsqGbNmtXjZDUXHR1d7Z4op9Opbt26KTAwUJ06dVJ2drZ7r6ioSEePHlVsbKwiIyNljNGBAwc8jg0ODlb79u0VHR2tr7/%2BWhUVFdWe22pffvml0tPTtWTJEt1zzz0e%2BX5qhrrMb6Xdu3dryJAhHpdWz5fU2NjYahmysrI8Mv44Q2Vlpfbt26du3bqpTZs2CgkJ8dj/5z//qbNnzyo6Otqrr/H169frxIkTuvXWWxUXF6eRI0dKkuLi4tS5c2efz7hv3z4tWLDAYy0nJ0eNGzdWQkKCz%2BeTpOuuu05BQUHav3%2B/ey03N1eNGjWyTcbz9u/fr9zcXPXr18%2B9FhMT4/MZKysrVVVV5fFu0tmzZyVJ8fHxvpvPklvl4VVjxowxM2fONKdPnzYHDx40AwcONG%2B99VZ9j3VRF/spwq%2B//trExMSYHTt2mLKyMrN27VrTo0cPk5%2Bfb4z54ac4BgwY4P6YglmzZplRo0a5j09NTTWTJk0yJ06cMHl5eWbUqFFmwYIFxpgfPlrg1ltvNb/73e/MmTNnzJ49e0zv3r3Njh07LM117tw5c%2Bedd5q333672t7lZqjL/FYqKioy8fHxZsGCBebMmTPmxIkTZuLEiea%2B%2B%2B4z33//venRo4d55513TFlZmdm5c6eJjY11/3Tjrl27TK9evcxXX31lzpw5Y5YuXWoSEhJMaWmpMeaHn6q89957zb/%2B9S9z8uRJM3nyZDN16lT3ub31Gne5XCYvL8/966uvvjKdO3c2eXl5Jjc31%2BczHj9%2B3HTv3t2sXLnSlJeXm0OHDpmhQ4eauXPn2ubP0BhjXnrpJTNo0CBz5MgR8/3335vExEQzY8YMW2U0xph169aZvn37eqzZIePJkydN3759zW9%2B8xtz5swZc/LkSTNlyhTzb//2bz6dj4Llg/Ly8sykSZNMbGysiY%2BPN7/73e/cn5HUUERHR5vo6Ghzww03mBtuuMH9%2B/O2bNliBg8ebLp27Wruvvtu8/nnn7v3qqqqzJIlS8xNN91kYmNjzcMPP%2Bz%2BjChjfvgff1pamunevbvp06ePmTNnjikvL3fvf/3112bcuHEmOjraDBgwwKxZs8byfH/7299M586d3bl%2B/Ovbb7%2B97Ax1md9KBw4cMPfff7%2BJjY01N954o0lNTTXHjx83xhjz%2BeefmxEjRpiuXbuawYMHV/sx%2BTVr1piEhAQTHR1tkpKSzNdff%2B3eKy8vN7NnzzZ9%2BvQxPXr0ME8%2B%2BaQpKipy79fXa/zYsWPuj2mwS8bPP//cJCYmmu7du5u%2Bffua%2BfPnm7KyMtvku3CW7t27m/T0dFNcXGyrjMYY88orr5hhw4ZVW7dDRqfTae6//37Tu3dvEx8fb4v/1vgZY4M7pgEAABoQ7sECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACw2P8HQurkDKww9YcAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-1555056792734630075”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>230</td> <td class=”number”>7.2%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>19</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>12</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.00000002980232</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.999999970197678</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.00000001490116</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.999999992549419</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.00000000186265</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2825)</td> <td class=”number”>2831</td> <td class=”number”>88.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-1555056792734630075”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>230</td> <td class=”number”>7.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0010663634166122</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0011702766641974</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0012762330006808</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0017601195722818</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>40484.6192974398</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>40907.3637539073</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>41894.6005324653</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>74726.7598218825</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>76998.3485618334</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_NHNA15”>LACCESS_NHNA15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2922</td>

</tr> <tr>

<th>Unique (%)</th> <td>91.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>97</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>225.84</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>40351</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>5.3%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-3292891295225806937”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-3292891295225806937,#minihistogram-3292891295225806937”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-3292891295225806937”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-3292891295225806937”

aria-controls=”quantiles-3292891295225806937” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-3292891295225806937” aria-controls=”histogram-3292891295225806937”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-3292891295225806937” aria-controls=”common-3292891295225806937”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-3292891295225806937” aria-controls=”extreme-3292891295225806937”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-3292891295225806937”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>5.2623</td>

</tr> <tr>

<th>Median</th> <td>20.806</td>

</tr> <tr>

<th>Q3</th> <td>100.88</td>

</tr> <tr>

<th>95-th percentile</th> <td>794.64</td>

</tr> <tr>

<th>Maximum</th> <td>40351</td>

</tr> <tr>

<th>Range</th> <td>40351</td>

</tr> <tr>

<th>Interquartile range</th> <td>95.616</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>1376.3</td>

</tr> <tr>

<th>Coef of variation</th> <td>6.0942</td>

</tr> <tr>

<th>Kurtosis</th> <td>480.57</td>

</tr> <tr>

<th>Mean</th> <td>225.84</td>

</tr> <tr>

<th>MAD</th> <td>322.25</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>19.888</td>

</tr> <tr>

<th>Sum</th> <td>703050</td>

</tr> <tr>

<th>Variance</th> <td>1894300</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-3292891295225806937”>

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cXFSkhI0IQJE1S/fn1de%2B21uuOOO/SPf/xDmzZt0unTpzV27FjVq1dP8fHxGjZsmLKzsyVJy5YtU2pqqlJTUxUaGqrBgwerdevWWrlypSQpOztbo0aNUosWLRQWFqbMzEzl5eVp%2B/btNRkZAAA4iCMLVnh4uGbOnKlrrrnGO3bw4EHFxMQoNzdXbdq0UUhIiHcuLi5OOTk5kqTc3FzFxcX57C8uLk5ut1tlZWXas2ePz3xYWJiaNm0qt9vt51QAAKC2cNX0Amxwu916%2B%2B239corr2jt2rUKDw/3mY%2BMjJTH41FlZaU8Ho8iIiJ85iMiIrRnzx4dPXpUxphzzhcVFVV7PQUFBSosLPQZc7nqKSYm5hKTXVhIiLP6sctVvfWezeW0fBdDLmchl7OQy1lqa67vcnzB%2BvzzzzV27FhNmDBBKSkpWrt27Tm3CwoK8v73xa6nutzrrbKzs7VgwQKfsfT0dI0fP/6y9ut0UVH1L2n78PCr/bSSmkUuZyGXs5DLWWprLsnhBWvDhg16/PHHNWXKFN1%2B%2B%2B2SpOjoaH3zzTc%2B23k8HkVGRio4OFhRUVHyeDxV5qOjo73bnGu%2BYcOG1V7XiBEj1KdPH58xl6ueiopKLiHdxTmt%2BVc3f0hIsMLDr1ZxcakqKir9vKrAIZezkMtZyOUsgcx1qT/c2%2BLYgvXFF19o0qRJeumll9SjRw/veEJCgv7whz%2BovLxcLteZeG63W%2B3bt/fOn70e6yy3262BAwcqNDRUrVq1Um5urrp27SrpzAX1%2B/fvV1JSUrXXFhMTU%2BV0YGHhMZWX155vjh/iUvNXVFTWyq8ZuZyFXM5CLmeprbkkh17kXl5ermeeeUYTJ070KVeSlJqaqrCwML3yyisqLS3V9u3btXz5cqWlpUmShg8frk8//VSbNm3SyZMntXz5cn3zzTcaPHiwJCktLU2LFy9WXl6ejh8/rjlz5qhdu3ZKTEwMeE4AAOBMAX8Hq0%2BfPhoyZIjuvPNO/ehHP/pB%2B9i2bZvy8vI0Y8YMzZgxw2fuww8/1KuvvqrnnntOWVlZuuaaa5SZmalevXpJklq3bq05c%2BZo5syZys/PV8uWLbVo0SI1atRIkjRy5EgVFhbqnnvuUUlJiZKTk6tcTwUAAHAhQSbAd9BcuHChPvjgA/3rX//SDTfcoOHDh6tPnz7e03m1VWHhMev7dLmC1W/OFuv79Ze1Gd2rtZ3LFayoqPoqKiqpVW8dk8tZyOUs5HKWQOZq1KiBX/d/PgE/RZienq41a9bonXfeUatWrfTCCy8oNTVVs2fP1r59%2BwK9HAAAAOtq7Bqs%2BPh4TZo0SRs3btTkyZP1zjvv6JZbbtHo0aO1Y8eOmloWAADAZauxgnX69GmtWbNGDzzwgCZNmqTGjRvrqaeeUrt27TRq1CitWrWqppYGAABwWQJ%2B4VNeXp6WL1%2Bu9957TyUlJRowYIDefPNNde7c2btNly5dNHXqVA0aNCjQywMAALhsAS9YAwcOVPPmzTVmzBjdfvvtioyMrLJNamqqjhw5EuilAQAAWBHwgrV48WLvTTwvZPv27QFYDQAAgH0BvwarTZs2euihh7R%2B/Xrv2O9%2B9zs98MADVX5FDQAAgBMFvGDNnDlTx44dU8uWLb1jvXr1UmVlpWbNmhXo5QAAAFgX8FOEn3zyiVatWqWoqCjvWLNmzTRnzhzdeuutgV4OAACAdQF/B6usrEyhoaFVFxIcrNLS0kAvBwAAwLqAF6wuXbpo1qxZOnr0qHfsv//9r6ZNm%2BZzqwYAAACnCvgpwsmTJ%2BvnP/%2B5brjhBoWFhamyslIlJSVq0qSJ3nrrrUAvBwAAwLqAF6wmTZrogw8%2B0J///Gft379fwcHBat68uXr06KGQkJBALwcAAMC6gBcsSapTp45%2B%2BtOf1sShAQAA/C7gBevAgQOaO3euvv76a5WVlVWZ/9Of/hToJQEAAFhVI9dgFRQUqEePHqpXr16gDw8AAOB3AS9YOTk5%2BtOf/qTo6OhAHxoAACAgAn6bhoYNG/LOFQAAqNUCXrDGjBmjBQsWyBgT6EMDAAAERMBPEf75z3/WF198oRUrVui6665TcLBvx1u6dGmglwQAAGBVwAtWWFiYevbsGejDAgAABEzAC9bMmTMDfUgAAICACvg1WJK0d%2B9ezZ8/X0899ZR37Msvv6yJpQAAAFgX8IK1detWDR48WB999JFWr14t6czNR%2B%2B9915uMgoAAGqFgBesefPm6fHHH9eqVasUFBQk6czvJ5w1a5YWLlwY6OUAAABYF/CC9c9//lNpaWmS5C1YknTTTTcpLy8v0MsBAACwLuAFq0GDBuf8HYQFBQWqU6dOoJcDAABgXcALVqdOnfTCCy/o%2BPHj3rF9%2B/Zp0qRJuuGGGwK9HAAAAOsCfpuGp556Svfdd5%2BSk5NVUVGhTp06qbS0VK1atdKsWbMCvRwAAADrAl6wrr32Wq1evVqbN2/Wvn37VLduXTVv3lzdu3f3uSYLAADAqQJesCTpqquu0k9/%2BtOaODQAAIDfBbxg9enT54LvVHEvLAAA4HQBL1i33HKLT8GqqKjQvn375Ha7dd999wV6OQAAANYFvGBNnDjxnOPr1q3TX//61wCvBgAAwL4a%2BV2E5/LTn/5UH3zwQU0vAwAA4LJdMQVr586dMsbU9DIAAAAuW8BPEY4cObLKWGlpqfLy8tS/f/9ALwcAAMC6gBesZs2aVfkUYWhoqIYOHaphw4YFejkAAADWBbxgcbd2AABQ2wW8YL333nvV3vb222%2B/4PyWLVs0adIkJScna968ed7xFStWaPLkybrqqqt8tl%2ByZImSkpJUWVmpl156SatXr1ZxcbGSkpI0depUNWnSRJLk8Xg0depU/e1vf1NwcLBSU1M1ZcoU1a1b9xKSAgCA/6sCXrCefvppVVZWVrmgPSgoyGcsKCjoggXrtdde0/Lly9W0adNzznfp0kVvvfXWOeeWLFmiVatW6bXXXlPjxo01b948paen6/3331dQUJCmTJmiU6dOafXq1Tp9%2BrQeffRRzZkzR88888wPSAwAAP6vCfinCF9//XX16NFDS5Ys0T/%2B8Q/9/e9/19tvv62ePXvqtdde044dO7Rjxw5t3779gvsJDQ29YMG6kOzsbI0aNUotWrRQWFiYMjMzlZeXp%2B3bt%2Bvbb7/V%2BvXrlZmZqejoaDVu3Fjjxo3Tu%2B%2B%2Bq9OnT//Q2AAA4P%2BQGrkGKysrS40bN/aOXX/99WrSpIlGjx6t1atXV2s/99577wXnDx48qJ/97GfKyclReHi4xo8fr9tuu01lZWXas2eP4uLivNuGhYWpadOmcrvdOnbsmEJCQtSmTRvvfHx8vE6cOKG9e/f6jJ9PQUGBCgsLfcZcrnqKiYmpVrbqCgm5Yu6yUS0uV/XWezaX0/JdDLmchVzOQi5nqa25vivgBeubb75RRERElfHw8HDl5%2BdbOUZ0dLSaNWumxx57TC1bttTHH3%2BsJ554QjExMfrJT34iY0yVNURERKioqEiRkZEKCwvz%2BaTj2W2Lioqqdfzs7GwtWLDAZyw9PV3jx4%2B/zGTOFhVV/5K2Dw%2B/2k8rqVnkchZyOQu5nKW25pJqoGDFxsZq1qxZevTRRxUVFSVJKi4u1ssvv6wf//jHVo7Rq1cv9erVy/v3gQMH6uOPP9aKFSu8v6rnQjc1vdwbno4YMUJ9%2BvTxGXO56qmoqOSy9vt9Tmv%2B1c0fEhKs8PCrVVxcqoqKSj%2BvKnDI5SzkchZyOUsgc13qD/e2BLxgTZ48WRMmTFB2drbq16%2Bv4OBgHT9%2BXHXr1tXChQv9dtzY2Fjl5OQoMjJSwcHB8ng8PvMej0cNGzZUdHS0jh8/roqKCoWEhHjnJKlhw4bVOlZMTEyV04GFhcdUXl57vjl%2BiEvNX1FRWSu/ZuRyFnI5C7mcpbbmkmqgYPXo0UObNm3S5s2bdejQIRlj1LhxY914441q0KCBlWP84Q9/UEREhG655RbvWF5enpo0aaLQ0FC1atVKubm56tq1q6Qz76Dt379fSUlJio2NlTFGu3fvVnx8vCTJ7XYrPDxczZs3t7I%2BAABQuwW8YEnS1Vdfrb59%2B%2BrQoUPee0/ZdOrUKU2fPl1NmjRR27ZttW7dOv35z3/WO%2B%2B8I0lKS0tTVlaWevbsqcaNG2vOnDlq166dEhMTJUkDBgzQr371K7344os6deqUFi5cqKFDh8rlqpEvFwAAcJiAN4aysjI999xz%2BuCDDyRJOTk5Ki4u1mOPPaZf/vKXCg8Pr9Z%2Bzpah8vJySdL69eslnXm36d5771VJSYkeffRRFRYW6rrrrtPChQuVkJAg6czvQywsLNQ999yjkpISJScn%2B1yU/vzzz%2Bu5555T3759ddVVV%2BnWW29VZmamta8BAACo3YLM5V7RfYmmT5%2Buv//97xo3bpyeeOIJ7dixQ8XFxXr00UfVpEkTPf/884FcTsAUFh6zvk%2BXK1j95myxvl9/WZvRvVrbuVzBioqqr6Kiklp1bp5czkIuZyGXswQyV6NGdi4/ulQB/xjaunXr9PLLL%2Bumm27y3gohPDxcM2fO1EcffRTo5QAAAFgX8IJVUlKiZs2aVRmPjo7WiRMnAr0cAAAA6wJesH784x/rr3/9qyTf%2B019%2BOGH%2Bn//7/8FejkAAADWBfwi97vuukuPPPKI7rzzTlVWVuq3v/2tcnJytG7dOj399NOBXg4AAIB1AS9YI0aMkMvl0ttvv62QkBC9%2Buqrat68uebMmaObbrop0MsBAACwLuAF68iRI7rzzjt15513BvrQAAAAARHwa7D69u172b/rDwAA4EoW8IKVnJystWvXBvqwAAAAARPwU4Q/%2BtGP9Itf/EJZWVn68Y9/rKuuuspnfu7cuYFeEgAAgFUBL1h79uzRT37yE0lSUVFRoA8PAADgdwErWJmZmZo3b57eeust79jChQuVnp4eqCUAAAAERMCuwdqwYUOVsaysrEAdHgAAIGACVrDO9clBPk0IAABqo4AVrLO/2PliYwAAAE4X8Ns0AAAA1HYULAAAAMsC9inC06dPa8KECRcd4z5YAADA6QJWsDp37qyCgoKLjgEAADhdwArWd%2B9/BQAAUJtxDRYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJY5umBt2bJFKSkpyszMrDK3Zs0aDRo0SB07dtSQIUP0ySefeOcqKys1b9489e3bV126dNHo0aN14MAB77zH41FGRoZSUlLUo0cPPf300yorKwtIJgAA4HyOLVivvfaaZsyYoaZNm1aZ27VrlyZNmqSJEyfqs88%2B06hRo/Twww/r0KFDkqQlS5Zo1apVysrK0saNG9WsWTOlp6fLGCNJmjJlikpLS7V69Wq9%2B%2B67ysvL05w5cwKaDwAAOJdjC1ZoaKiWL19%2BzoK1bNkypaamKjU1VaGhoRo8eLBat26tlStXSpKys7M1atQotWjRQmFhYcrMzFReXp62b9%2Bub7/9VuvXr1dmZqaio6PVuHFjjRs3Tu%2B%2B%2B65Onz4d6JgAAMCBXDW9gB/q3nvvPe9cbm6uUlNTfcbi4uLkdrtVVlamPXv2KC4uzjsXFhampk2byu1269ixYwoJCVGbNm288/Hx8Tpx4oT27t3rM34%2BBQUFKiws9BlzueopJiamuvGqJSTEWf3Y5arees/mclq%2BiyGXs5DLWcjlLLU113c5tmBdiMfjUUREhM9YRESE9uzZo6NHj8oYc875oqIiRUZGKiwsTEFBQT5zklRUVFSt42dnZ2vBggU%2BY%2Bnp6Ro/fvwPiVNrREXVv6Ttw8Ov9tNKaha5nIVczkIuZ6mtuaRaWrAkea%2Bn%2BiHzF3vsxYwYMUJ9%2BvTxGXO56qmoqOSy9vt9Tmv%2B1c0fEhKs8PCrVVxcqoqKSj%2BvKnDI5SzkchZyOUsgc13qD/e21MqCFRUVJY/H4zPm8XgUHR2tyMhIBQcHn3O%2BYcOGio6O1vHjx1VRUaGQkBDvnCQ1bNiwWsePiYmpcjqwsPCYystrzzfHD3Gp%2BSsqKmvl14xczkIuZyGXs9TWXJKDL3K/kISEBOXk5PiMud1utW/fXqGhoWrVqpVyc3O9c8XFxdq/f7%2BSkpLUrl07GWO0e/dun8eGh4erefPmAcsAAACcq1YWrOHDh%2BvTTz/Vpk2bdPLkSS1fvlzffPONBg8eLElKS0uGZoTzAAAU2UlEQVTT4sWLlZeXp%2BPHj2vOnDlq166dEhMTFR0drQEDBuhXv/qVjhw5okOHDmnhwoUaOnSoXK5a%2BYYfAACwzLGNITExUZJUXl4uSVq/fr2kM%2B82tW7dWnPmzNHMmTOVn5%2Bvli1batGiRWrUqJEkaeTIkSosLNQ999yjkpISJScn%2B1yU/vzzz%2Bu5555T3759ddVVV%2BnWW289581MAQAAziXIXO4V3aiWwsJj1vfpcgWr35wt1vfrL2szuldrO5crWFFR9VVUVFKrzs2Ty1nI5SzkcpZA5mrUqIFf938%2BtfIUIQAAQE2iYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZbW2YLVp00YJCQlKTEz0/pk%2BfbokaevWrRo6dKg6deqkgQMHauXKlT6PXbx4sQYMGKBOnTopLS1NOTk5NREBAAA4lKumF%2BBPH374oa677jqfsYKCAo0bN05PP/20Bg0apM8//1xjx45V8%2BbNlZiYqA0bNmj%2B/Pl6/fXX1aZNGy1evFgPPfSQPvroI9WrV6%2BGkgAAACepte9gnc%2BqVavUrFkzDR06VKGhoUpJSVGfPn20bNkySVJ2draGDBmi9u3bq27durr//vslSRs3bqzJZQMAAAep1QVr7ty56tWrl66//npNmTJFJSUlys3NVVxcnM92cXFx3tOA358PDg5Wu3bt5Ha7A7p2AADgXLX2FGGHDh2UkpKiF198UQcOHFBGRoamTZsmj8ejxo0b%2B2wbGRmpoqIiSZLH41FERITPfEREhHe%2BOgoKClRYWOgz5nLVU0xMzA9Mc24hIc7qxy5X9dZ7NpfT8l0MuZyFXM5CLmeprbm%2Bq9YWrOzsbO9/t2jRQhMnTtTYsWPVuXPniz7WGHPZx16wYIHPWHp6usaPH39Z%2B3W6qKj6l7R9ePjVflpJzSKXs5DLWcjlLLU1l1SLC9b3XXfddaqoqFBwcLA8Ho/PXFFRkaKjoyVJUVFRVeY9Ho9atWpV7WONGDFCffr08RlzueqpqKjkB67%2B3JzW/KubPyQkWOHhV6u4uFQVFZV%2BXlXgkMtZyOUs5HKWQOa61B/ubamVBWvnzp1auXKlnnzySe9YXl6e6tSpo9TUVP3xj3/02T4nJ0ft27eXJCUkJCg3N1d33HGHJKmiokI7d%2B7U0KFDq338mJiYKqcDCwuPqby89nxz/BCXmr%2BiorJWfs3I5SzkchZyOUttzSXV0ovcGzZsqOzsbGVlZenUqVPat2%2BfXnrpJY0YMUK33Xab8vPztWzZMp08eVKbN2/W5s2bNXz4cElSWlqa3nvvPW3btk2lpaV65ZVXVKdOHfXq1atmQwEAAMeole9gNW7cWFlZWZo7d663IN1xxx3KzMxUaGioFi1apBkzZmjatGmKjY3V7Nmz1bZtW0lSz5499dhjjykjI0OHDx9WYmKisrKyVLdu3RpOBQAAnKJWFixJ6tKli5YuXXreuffff/%2B8j73rrrt01113%2BWtpAACglquVpwgBAABqEgULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwTqH/Px8Pfjgg0pOTlbv3r01e/ZsVVZW1vSyAACAQ7hqegFXokceeUTx8fFav369Dh8%2BrDFjxuiaa67Rz372s5peGgAAcADewfoet9ut3bt3a%2BLEiWrQoIGaNWumUaNGKTs7u6aXBgAAHIJ3sL4nNzdXsbGxioiI8I7Fx8dr3759On78uMLCwi66j4KCAhUWFvqMuVz1FBMTY3WtISHO6sc3/%2BovNb2ES/LxxBut7u/s8%2BW05%2B1iyOUs5HIWcjkXBet7PB6PwsPDfcbOlq2ioqJqFazs7GwtWLDAZ%2Bzhhx/WI488Ym%2BhOlPk7rv2a40YMcJ6eatJBQUFys7OrpW53nzzdXI5BLmchVzOUltzfVftrY6XwRhzWY8fMWKEVqxY4fNnxIgRllb3vwoLC7VgwYIq75Y5HbmchVzOQi5nIZdz8Q7W90RHR8vj8fiMeTweBQUFKTo6ulr7iImJqbWNHAAAXBzvYH1PQkKCDh48qCNHjnjH3G63WrZsqfr169fgygAAgFNQsL4nLi5OiYmJmjt3ro4fP668vDz99re/VVpaWk0vDQAAOETI1KlTp9b0Iq40N954o1avXq3p06frgw8%2B0NChQzV69GgFBQXV9NKqqF%2B/vrp27Vrr3l0jl7OQy1nI5SzkcqYgc7lXdAMAAMAHpwgBAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgOVB%2Bfr4efPBBJScnq3fv3po9e7YqKytrelnn1KZNGyUkJCgxMdH7Z/r06ZKkrVu3aujQoerUqZMGDhyolStX%2Bjx28eLFGjBggDp16qS0tDTl5OR4506ePKlnn31WPXv2VHJyssaPH6%2BioiK/ZtmyZYtSUlKUmZlZZW7NmjUaNGiQOnbsqCFDhuiTTz7xzlVWVmrevHnq27evunTpotGjR%2BvAgQPeeY/Ho4yMDKWkpKhHjx56%2BumnVVZW5p3ftWuX7r77bnXu3Fn9%2B/fXG2%2B8EZBcK1asUNu2bX2eu8TERO3YseOKz5Wfn6/09HQlJycrJSVFTz75pIqLi6t1XH8%2Bl/7K9e9//1tt2rSp8lz95je/cUSu3bt367777lPnzp2VkpKijIwMFRYWSvLv60QgXkvPl%2B2vf/3rOZ%2BztWvXOiabJL3wwgtq06aN9%2B9Of76sMnCcO%2B64wzzzzDOmuLjY7Nu3z/Tv39%2B88cYbNb2sc2rdurU5cOBAlfH//ve/pkOHDmbZsmWmrKzM/OUvfzFJSUlmx44dxhhj/vSnP5nrr7/ebNu2zZSWlppFixaZ7t27m5KSEmOMMTNnzjRDhgwx//nPf0xRUZF5%2BOGHzZgxY/yWIysry/Tv39%2BMHDnSZGRk%2BMzt3LnTJCQkmE2bNpmysjLz/vvvm/bt25uDBw8aY4xZvHix6d27t9mzZ485duyYef75582gQYNMZWWlMcaYhx9%2B2Dz44IPm8OHD5tChQ2bEiBFm%2BvTpxhhjSktLzY033mjmz59vSkpKTE5OjunatatZt26d33O9%2B%2B675u677z7vY6/kXLfeeqt58sknzfHjx83BgwfNkCFDzOTJky96XH8%2Bl/7MdeDAAdO6devzPu5KznXy5Elzww03mAULFpiTJ0%2Baw4cPm7vvvtuMGzfO768T/n4tvVC2zz77zPTu3fu8j73Ssxlz5t9V165dvf/2nP582UbBcpgdO3aYdu3aGY/H4x37/e9/bwYMGFCDqzq/8xWs119/3dx%2B%2B%2B0%2BYxkZGWbKlCnGGGMefPBB88ILL3jnKioqTPfu3c3q1avN6dOnTefOnc369eu983v27DFt2rQxhw4d8kuON9980xQXF5tJkyZVKSLTpk0z6enpPmPDhg0zixYtMsYYM3DgQPPmm296544dO2bi4uLMl19%2BaQoLC03btm3Nrl27vPObN282HTp0MKdOnTJr16413bp1M%2BXl5d752bNnm5///Od%2Bz3WxgnWl5jp69Kh58sknTWFhoXfsrbfeMv3797/ocf35XPoz18UK1pWcy%2BPxmHfeececPn3aO/bmm2%2Bafv36%2BfV1IhCvpRfKdrGCdaVnq6ioMMOGDTO//vWvvf/2nP582cYpQofJzc1VbGysIiIivGPx8fHat2%2Bfjh8/XoMrO7%2B5c%2BeqV69euv766zVlyhSVlJQoNzdXcXFxPtvFxcV53y7%2B/nxwcLDatWsnt9ut/fv369ixY4qPj/fOt2jRQnXr1lVubq5fMtx7771q0KDBOefOl8XtdqusrEx79uzxmQ8LC1PTpk3ldru1a9cuhYSE%2BLzFHh8frxMnTmjv3r3Kzc1VmzZtFBIS4rPv776t7q9cknTw4EH97Gc/U5cuXdS3b1%2B9//77knRF5woPD9fMmTN1zTXX%2BOSIiYm56HH9%2BVz6M9dZTzzxhHr06KFu3bpp7ty5On369BWfKyIiQsOGDZPL5ZIk7d27V3/84x918803%2B/V1IhCvpRfKJkklJSXeU7433nijfvvb38oY44hsS5cuVWhoqAYNGuQdc/rzZRsFy2E8Ho/Cw8N9xs7%2Bg/P3NUg/RIcOHZSSkqKPPvpI2dnZ2rZtm6ZNm3bOHJGRkd4MHo/H5xtJOpOzqKhIHo9Hkqo8Pjw8vEa%2BBhda69GjR2WMuWCWsLAwBQUF%2BcxJ8s6f6%2Bvk8Xj8fu1BdHS0mjVrpscff1x/%2Bctf9Nhjj2ny5MnaunWro3K53W69/fbbGjt27EWP68/n0rbv5qpTp446duyofv36aePGjcrKytLKlSv161//WpJ//43akp%2Bfr4SEBN1yyy1KTEzU%2BPHj/fo6EcjX0nNlCwsLU%2BvWrXXfffdpy5YtmjlzphYsWKB33333is/27bffav78%2BXruued8xmvL82ULBcuBzv6E4wTZ2dkaNmyY6tSpoxYtWmjixIlavXq19yfrC7lYzivp63A5a/0hOb77Pzt/6dWrl15//XXFxcWpTp06GjhwoPr166cVK1Z4t7nSc33%2B%2BecaPXq0JkyYoJSUlGodN9DP5Q/x/VwxMTFaunSp%2BvXrp6uuukpJSUkaM2ZMtZ%2Bri80HIldsbKzcbrc%2B/PBDffPNN3riiSeq9bgrPZd07mzx8fF666231LVrV9WpU0c9evTQyJEjHfGczZw5U0OGDFHLli0v%2BbFXaiZ/oGA5THR0tLfpn%2BXxeBQUFKTo6OgaWlX1XXfddaqoqFBwcHCVHEVFRd4MUVFR58wZHR3t3eb780ePHlXDhg39uPpzu9BaIyMjz5nV4/GoYcOGio6O1vHjx1VRUeEzJ8k7//2fzjwej3e/gRYbG6uCggJH5NqwYYMefPBBTZ48Wffee68kXfS4/nwubTlXrnOJjY3Vt99%2BK2OMI3JJZ4pus2bNlJmZqdWrV8vlcvntdSLQr6Xfz3bkyJEq25z9/pKu3Gxbt27Vl19%2BqfT09Cpz51qzU58vGyhYDpOQkKCDBw/6fHO63W61bNlS9evXr8GVVbVz507NmjXLZywvL0916tRRampqlettcnJy1L59e0lncn73eqqKigrt3LlT7du3V5MmTRQREeEz/89//lOnTp1SQkKCHxOdW0JCQpUsbrdb7du3V2hoqFq1auWz1uLiYu3fv19JSUlq166djDHavXu3z2PDw8PVvHlzJSQk6KuvvlJ5eXmVffvbH/7wB61Zs8ZnLC8vT02aNLnic33xxReaNGmSXnrpJd1%2B%2B%2B3e8Ysd15/PpT9zbd26Va%2B88orPtnv37lVsbKyCgoKu6Fxbt27VgAEDfE4Nny3ZSUlJfnudCMRr6YWybd68Wb///e99tt%2B7d6%2BaNGlyRWdbuXKlDh8%2BrN69eys5OVlDhgyRJCUnJ6t169aOfr6s8/tl9LBu2LBhZvLkyebYsWNmz549pk%2BfPubtt9%2Bu6WVVcejQIdOhQwezaNEic/LkSbN3715zyy23mOnTp5tvv/3WdOzY0bzzzjumrKzMbNq0ySQlJXk/qbR582bTuXNn8%2BWXX5oTJ06Y%2BfPnm9TUVFNaWmqMOfPJrzvuuMP85z//MUeOHDFjxowxjzzyiN8znevTdl999ZVJTEw0GzduNGVlZWbZsmWmY8eOpqCgwBhz5pMuvXr18n4EfsqUKebOO%2B/0Pj4jI8Pcf//95vDhw%2BbgwYPmzjvvNLNmzTLGnPmYd%2B/evc3LL79sTpw4YbZt22auv/56s3HjRr/n%2Bt3vfme6detmduzYYU6dOmVWrVpl2rVrZ9xu9xWd6/Tp0%2Bbmm282S5curTJ3seP687n0Zy63223i4%2BPNe%2B%2B9Z06dOmV27Nhhunfv7v0I%2B5Wcq7i42KSkpJhZs2aZEydOmMOHD5vRo0ebu%2B66y%2B%2BvE/5%2BLb1Qto8//tgkJSWZLVu2mFOnTplPPvnEdOjQwXvLkCs1m8fjMQcPHvT%2B%2BfLLL03r1q3NwYMHTX5%2BvqOfL9soWA508OBBc//995ukpCSTkpJiXn75Ze/9aq40f/vb38yIESNMhw4dTNeuXc3MmTNNWVmZd27w4MEmPj7e9O/fv8o9kJYsWWJSU1NNQkKCSUtLM1999ZV37uTJk2bq1KmmS5cupmPHjuaxxx4zxcXFfsuRkJBgEhISTNu2bU3btm29fz9r3bp1pn///iY%2BPt7cdttt5m9/%2B5t3rrKy0rz00kvmhhtuMElJSeaBBx7w3n/ImDMvwpmZmaZDhw6mS5cuZtq0aebkyZPe%2Ba%2B%2B%2BsqMHDnSJCQkmF69epklS5YEJFdlZaVZuHCh6d27t0lISDA33XST2bBhwxWf6%2B9//7tp3bq1N8t3//z73/%2B%2B6HH9%2BVz6M9dHH31kBg8ebJKSkkz37t3Nq6%2B%2BaioqKq74XMYYs3v3bnP33XebpKQk061bN5ORkeG95Yo/XycC8Vp6oWxLly41/fv3N4mJiaZ3797mnXfecVQ2Y0yVW4Q4/fmyKcgYh101BgAAcIXjGiwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsOz/A5HM0HonFwrAAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-3292891295225806937”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>170</td> <td class=”number”>5.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.00000002980232</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.00000004470348</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.999999970197678</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.99999998137355</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2911)</td> <td class=”number”>2912</td> <td class=”number”>90.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-3292891295225806937”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>170</td> <td class=”number”>5.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0024060137802735</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0044683939777315</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.005254138906821</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0065442048944533</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>18956.7011005882</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25152.1782851138</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>27048.7705948986</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>36272.9042347345</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>40350.8004293314</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_NHPI15”>LACCESS_NHPI15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>1829</td>

</tr> <tr>

<th>Unique (%)</th> <td>57.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>97</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>32.305</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>21387</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>36.6%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram3837598272648955424”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives3837598272648955424,#minihistogram3837598272648955424”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives3837598272648955424”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles3837598272648955424”

aria-controls=”quantiles3837598272648955424” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram3837598272648955424” aria-controls=”histogram3837598272648955424”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common3837598272648955424” aria-controls=”common3837598272648955424”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme3837598272648955424” aria-controls=”extreme3837598272648955424”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles3837598272648955424”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>1</td>

</tr> <tr>

<th>Q3</th> <td>7.1206</td>

</tr> <tr>

<th>95-th percentile</th> <td>71.12</td>

</tr> <tr>

<th>Maximum</th> <td>21387</td>

</tr> <tr>

<th>Range</th> <td>21387</td>

</tr> <tr>

<th>Interquartile range</th> <td>7.1206</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>436.61</td>

</tr> <tr>

<th>Coef of variation</th> <td>13.515</td>

</tr> <tr>

<th>Kurtosis</th> <td>1885.6</td>

</tr> <tr>

<th>Mean</th> <td>32.305</td>

</tr> <tr>

<th>MAD</th> <td>51.668</td>

</tr> <tr class=”alert”>

<th>Skewness</th> <td>40.47</td>

</tr> <tr>

<th>Sum</th> <td>100570</td>

</tr> <tr>

<th>Variance</th> <td>190630</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram3837598272648955424”>

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b5cLgAACCABGbBKSkqUkJCgadOmqWnTprr88st1880364svvtD27dt1%2BvRpjR8/Xk2aNFF8fLxuvfVW5eTkSJLWrl2rlJQUpaSkKDQ0VMOHD1eHDh20YcMGSVJOTo7GjBmjtm3bKiwsTJmZmcrPz9fu3bt9uWQAABBAAjJg2e12LVy4UJdddpl7rLCwUDExMcrLy1PHjh0VEhLinouLi1Nubq4kKS8vT3FxcR77i4uLk8PhUEVFhfbv3%2B8xHxYWplatWsnhcNTzqgAAQENh83UBJjgcDr366qtauXKlNm/eLLvd7jEfEREhl8ul6upquVwuhYeHe8yHh4dr//79On78uCzLqnHe6XTWup6ioiIVFxd7jNlsTRQTE/NvruzcQkICKx/bbIFV74U605dA609DR1/8E33xT/Sl7gI%2BYH355ZcaP368pk2bpuTkZG3evLnG7YKCgtz/fr77qep6v1VOTo6WL1/uMZaRkaFJkybVab%2BBLjKyqa9L8Cq7/VJfl4Aa0Bf/RF/8E325cAEdsLZt26YHH3xQc%2BbM0YgRIyRJUVFROnTokMd2LpdLERERCg4OVmRkpFwu11nzUVFR7m1qmo%2BOjq51XWlpaRowYIDHmM3WRE5n2b%2BxuvMLtE8Wptfvr0JCgmW3X6qSknJVVVX7uhz8A33xT/TFPzWkvvjqw33ABqyvvvpKM2bM0LJly9SnTx/3eEJCgl577TVVVlbKZvt5eQ6HQ127dnXPn7kf6wyHw6GhQ4cqNDRU7du3V15ennr37i3p5xvqDx8%2BrC5dutS6tpiYmLMuBxYXn1BlZWB/k9bVxbb%2Bqqrqi27NgYC%2B%2BCf64p/oy4Xz%2BimQAQMGaPny5SosLLzgfVRWVuqRRx7R9OnTPcKVJKWkpCgsLEwrV65UeXm5du/erXXr1ik9PV2SNGrUKH3yySfavn27Tp48qXXr1unQoUMaPny4JCk9PV2rV69Wfn6%2BSktLlZWVpc6dOysxMfHCFw0AAC4qQZaXH/C0YsUKvf322/rb3/6mq6%2B%2BWqNGjdKAAQPcZ5tq44svvtB//dd/qVGjRmfNvfvuuyorK9Njjz2m3NxcXXbZZbr33nv129/%2B1r3Ne%2B%2B9pyVLlqigoEDt2rXT7Nmz1atXL0k/33/17LPP6vXXX1dZWZmSkpL0%2BOOP6/LLL6/TuouLT9Tp9TWx2YI1KOsj4/utL5unXOPrErzCZgtWZGRTOZ1lfPLzI/TFP9EX/9SQ%2BtK8eTOfHNfrAeuMvLw8bdq0SZs3b9bp06c1YsQIpaamqk2bNr4op94RsAhY8C364p/oi39qSH3xVcDy2V3S8fHxmjFjhj744APNmjVLf/jDH3TDDTdo7Nix2rNnj6/KAgAAqDOfBazTp0/rnXfe0b333qsZM2aoRYsWevjhh9W5c2eNGTNGGzdu9FVpAAAAdeL13yLMz8/XunXr9Oabb6qsrExDhgzRyy%2B/rJ49e7q36dWrl%2BbOnathw4Z5uzwAAIA683rAGjp0qNq0aaNx48ZpxIgRioiIOGublJQUHTt2zNulAQAAGOH1gLV69Wr3M6bOhT%2BuDAAAApXX78Hq2LGj7r//fm3dutU99j//8z%2B69957z3qCOgAAQCDyesBauHChTpw4oXbt2rnH%2BvXrp%2Brqai1atMjb5QAAABjn9UuEH3/8sTZu3KjIyEj3WOvWrZWVlaUbb7zR2%2BUAAAAY5/UzWBUVFQoNDT27kOBglZeXe7scAAAA47wesHr16qVFixbp%2BPHj7rHvv/9e8%2BbN83hUAwAAQKDy%2BiXCWbNm6e6779bVV1%2BtsLAwVVdXq6ysTC1bttQrr7zi7XIAAACM83rAatmypd5%2B%2B219%2BOGHOnz4sIKDg9WmTRv16dNHISEh3i4HAADAOK8HLElq1KiRrrvuOl8cGgAAoN55PWAdOXJES5Ys0XfffaeKioqz5t9//31vlwQAAGCUT%2B7BKioqUp8%2BfdSkSRNvHx4AAKDeeT1g5ebm6v3331dUVJS3Dw0AAOAVXn9MQ3R0NGeuAABAg%2Bb1gDVu3DgtX75clmV5%2B9AAAABe4fVLhB9%2B%2BKG%2B%2BuorrV%2B/XldccYWCgz0z3uuvv%2B7tkgAAAIzyesAKCwtT3759vX1YAAAAr/F6wFq4cKG3DwkAAOBVXr8HS5IOHDigZ599Vg8//LB77K9//asvSgEAADDO6wFr586dGj58uN577z1t2rRJ0s8PH73jjjt4yCgAAGgQvB6wli5dqgcffFAbN25UUFCQpJ//PuGiRYu0YsUKb5cDAABgnNcD1rfffqv09HRJcgcsSbr%2B%2BuuVn5/v7XIAAACM83rAatasWY1/g7CoqEiNGjXydjkAAADGeT1g9ejRQ7/73e9UWlrqHjt48KBmzJihq6%2B%2B2tvlAAAAGOf1xzQ8/PDDuvPOO5WUlKSqqir16NFD5eXlat%2B%2BvRYtWuTtcgAAAIzzesC6/PLLtWnTJu3YsUMHDx5U48aN1aZNG11zzTUe92QBAAAEKq8HLEm65JJLdN111/ni0AAAAPXO6wFrwIAB5zxTxbOwAABAoPN6wLrhhhs8AlZVVZUOHjwoh8OhO%2B%2B809vlAAAAGOf1gDV9%2BvQax7ds2aK//OUvXq4GAADAPJ/8LcKaXHfddXr77bd9XQYAAECd%2BU3A2rt3ryzL8nUZAAAAdeb1S4SjR48%2Ba6y8vFz5%2BfkaPHiwt8sBAAAwzusBq3Xr1mf9FmFoaKhSU1N16623erscAAAA47wesHhaOwAAaOi8HrDefPPNWm87YsSIc85/9NFHmjFjhpKSkrR06VL3%2BPr16zVr1ixdcsklHtuvWbNGXbp0UXV1tZYtW6ZNmzappKREXbp00dy5c9WyZUtJksvl0ty5c/XZZ58pODhYKSkpmjNnjho3bvxvrBQAAFysvB6wZs%2Bererq6rNuaA8KCvIYCwoKOmfAev7557Vu3Tq1atWqxvlevXrplVdeqXFuzZo12rhxo55//nm1aNFCS5cuVUZGht566y0FBQVpzpw5OnXqlDZt2qTTp09r8uTJysrK0iOPPHIBKwYAABcbr/8W4QsvvKA%2BffpozZo1%2BuKLL/T555/r1VdfVd%2B%2BffX8889rz5492rNnj3bv3n3O/YSGhp4zYJ1LTk6OxowZo7Zt2yosLEyZmZnKz8/X7t279cMPP2jr1q3KzMxUVFSUWrRooQkTJuiNN97Q6dOnL3TZAADgIuKTe7Cys7PVokUL99iVV16pli1bauzYsdq0aVOt9nPHHXecc76wsFB33XWXcnNzZbfbNWnSJN10002qqKjQ/v37FRcX5942LCxMrVq1ksPh0IkTJxQSEqKOHTu65%2BPj4/XTTz/pwIEDHuMAAAA18XrAOnTokMLDw88at9vtKigoMHKMqKgotW7dWlOnTlW7du30pz/9SQ899JBiYmL0m9/8RpZlnVVDeHi4nE6nIiIiFBYW5vGbjme2dTqdtTp%2BUVGRiouLPcZstiaKiYmp48o8hYT4zWPMasVmC6x6L9SZvgRafxo6%2BuKf6It/oi915/WAFRsbq0WLFmny5MmKjIyUJJWUlOiZZ57Rr3/9ayPH6Nevn/r16%2Bf%2B76FDh%2BpPf/qT1q9f7/5TPed6qGldH3iak5Oj5cuXe4xlZGRo0qRJddpvoIuMbOrrErzKbr/U1yWgBvTFP9EX/0RfLpzXA9asWbM0bdo05eTkqGnTpgoODlZpaakaN26sFStW1NtxY2NjlZubq4iICAUHB8vlcnnMu1wuRUdHKyoqSqWlpaqqqlJISIh7TpKio6Nrday0tDQNGDDAY8xmayKns8zASv5PoH2yML1%2BfxUSEiy7/VKVlJSrqqra1%2BXgH%2BiLf6Iv/qkh9cVXH%2B69HrD69Omj7du3a8eOHTp69Kgsy1KLFi107bXXqlmzZkaO8dprryk8PFw33HCDeyw/P18tW7ZUaGio2rdvr7y8PPXu3VvSz2fQDh8%2BrC5duig2NlaWZenrr79WfHy8JMnhcMhut6tNmza1On5MTMxZlwOLi0%2BosjKwv0nr6mJbf1VV9UW35kBAX/wTffFP9OXCeT1gSdKll16qgQMH6ujRo%2B5nT5l06tQpzZ8/Xy1btlSnTp20ZcsWffjhh/rDH/4gSUpPT1d2drb69u2rFi1aKCsrS507d1ZiYqIkaciQIXr66af15JNP6tSpU1qxYoVSU1Nls/nk7QIAAAHG64mhoqJCjz32mN5%2B%2B21JUm5urkpKSjR16lQ99dRTstvttdrPmTBUWVkpSdq6daukn8823XHHHSorK9PkyZNVXFysK664QitWrFBCQoKkn/8eYnFxsW6//XaVlZUpKSnJ456pxx9/XI899pgGDhyoSy65RDfeeKMyMzONvQcAAKBhC7Lqekf3v2n%2B/Pn6/PPPNWHCBD300EPas2ePSkpKNHnyZLVs2VKPP/64N8vxmuLiE8b3abMFa1DWR8b3W182T7nG1yV4hc0WrMjIpnI6yzi17kfoi3%2BiL/6pIfWleXMztx/9u7x%2Bl/SWLVv0zDPP6Prrr3c/CsFut2vhwoV67733vF0OAACAcV4PWGVlZWrduvVZ41FRUfrpp5%2B8XQ4AAIBxXg9Yv/71r/WXv/xFkufzpt599139x3/8h7fLAQAAMM7rN7n/9re/1QMPPKBbbrlF1dXVeumll5Sbm6stW7Zo9uzZ3i4HAADAOK8HrLS0NNlsNr366qsKCQnRc889pzZt2igrK0vXX3%2B9t8sBAAAwzusB69ixY7rlllt0yy23ePvQAAAAXuH1e7AGDhxY57/1BwAA4M%2B8HrCSkpK0efNmbx8WAADAa7x%2BifBXv/qVnnjiCWVnZ%2BvXv/61LrnkEo/5JUuWeLskAAAAo7wesPbv36/f/OY3kiSn0%2BntwwMAANQ7rwWszMxMLV26VK%2B88op7bMWKFcrIyPBWCQAAAF7htXuwtm3bdtZYdna2tw4PAADgNV4LWDX95iC/TQgAABoirwWsM3/Y%2BXxjAAAAgc7rj2kAAABo6AhYAAAAhnnttwhPnz6tadOmnXeM52ABAIBA57WA1bNnTxUVFZ13DAAAINB5LWD98/OvAAAAGjLuwQIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwLCADlgfffSRkpOTlZmZedbcO%2B%2B8o2HDhql79%2B4aOXKkPv74Y/dcdXW1li5dqoEDB6pXr14aO3asjhw54p53uVyaMmWKkpOT1adPH82ePVsVFRVeWRMAAAh8ARuwnn/%2BeS1YsECtWrU6a27fvn2aMWOGpk%2Bfrk8//VRjxozRxIkTdfToUUnSmjVrtHHjRmVnZ%2BuDDz5Q69atlZGRIcuyJElz5sxReXm5Nm3apDfeeEP5%2BfnKysry6voAAEDgCtiAFRoaqnXr1tUYsNauXauUlBSlpKQoNDRUw4cPV4cOHbRhwwZJUk5OjsaMGaO2bdsqLCxMmZmZys/P1%2B7du/XDDz9o69atyszMVFRUlFq0aKEJEybojTfe0OnTp729TAAAEIACNmDdcccdatasWY1zeXl5iouL8xiLi4uTw%2BFQRUWF9u/f7zEfFhamVq1ayeFwaN%2B%2BfQoJCVHHjh3d8/Hx8frpp5904MCB%2BlkMAABoUGy%2BLqA%2BuFwuhYeHe4yFh4dr//79On78uCzLqnHe6XQqIiJCYWFhCgoK8piTJKfTWavjFxUVqbi42GPMZmuimJiYC1nOvxQSElj52GYLrHov1Jm%2BBFp/Gjr64p/oi3%2BiL3XXIAOWJPf9VBcyf77Xnk9OTo6WL1/uMZaRkaFJkybVab%2BBLjKyqa9L8Cq7/VJfl4Aa0Bf/RF/8E325cA0yYEVGRsrlcnmMuVwuRUVFKSIiQsHBwTXOR0dHKyoqSqWlpaqqqlJISIh7TpKio6Nrdfy0tDQNGDDAY8xmayKns%2BxCl1SjQPtkYXr9/iokJFh2%2B6UqKSlXVVW1r8vBP9AX/0Rf/FND6ouvPtw3yICVkJCg3NxcjzGHw6GhQ4cqNDRU7du3V15ennr37i1JKikp0eHDh9WlSxfFxsbKsix9/fXXio%2BPd7/WbrerTZs2tTp%2BTEzMWZcDi4tPqLIysL9J6%2BpiW39VVfVFt%2BZAQF/8E33xT/TlwgXWKZBaGjVqlD755BNt375dJ0%2Be1Lp163To0CENHz5ckpSenq7Vq1crPz9fpaWlysrKUufOnZVltEsKAAARhElEQVSYmKioqCgNGTJETz/9tI4dO6ajR49qxYoVSk1Nlc3WIPMoAAAwLGATQ2JioiSpsrJSkrR161ZJP59t6tChg7KysrRw4UIVFBSoXbt2WrVqlZo3by5JGj16tIqLi3X77berrKxMSUlJHvdMPf7443rsscc0cOBAXXLJJbrxxhtrfJgpAABATYKsut7RjVopLj5hfJ82W7AGZX1kfL/1ZfOUa3xdglfYbMGKjGwqp7OMU%2Bt%2BhL74J/rinxpSX5o3r/mRTvWtQV4iBAAA8CUCFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAY1mADVseOHZWQkKDExET31/z58yVJO3fuVGpqqnr06KGhQ4dqw4YNHq9dvXq1hgwZoh49eig9PV25ubm%2BWAIAAAhQNl8XUJ/effddXXHFFR5jRUVFmjBhgmbPnq1hw4bpyy%2B/1Pjx49WmTRslJiZq27ZtevbZZ/XCCy%2BoY8eOWr16te6//3699957atKkiY9WAgAAAkmDPYP1r2zcuFGtW7dWamqqQkNDlZycrAEDBmjt2rWSpJycHI0cOVJdu3ZV48aNdc8990iSPvjgA1%2BWDQAAAkiDDlhLlixRv379dOWVV2rOnDkqKytTXl6e4uLiPLaLi4tzXwb85XxwcLA6d%2B4sh8Ph1doBAEDgarCXCLt166bk5GQ9%2BeSTOnLkiKZMmaJ58%2BbJ5XKpRYsWHttGRETI6XRKklwul8LDwz3mw8PD3fO1UVRUpOLiYo8xm62JYmJiLnA1NQsJCax8bLMFVr0X6kxfAq0/DR198U/0xT/Rl7prsAErJyfH/e9t27bV9OnTNX78ePXs2fO8r7Usq87HXr58ucdYRkaGJk2aVKf9BrrIyKa%2BLsGr7PZLfV0CakBf/BN98U/05cI12ID1S1dccYWqqqoUHBwsl8vlMed0OhUVFSVJioyMPGve5XKpffv2tT5WWlqaBgwY4DFmszWR01l2gdXXLNA%2BWZhev78KCQmW3X6pSkrKVVVV7ety8A/0xT/RF//UkPriqw/3DTJg7d27Vxs2bNDMmTPdY/n5%2BWrUqJFSUlL0xz/%2B0WP73Nxcde3aVZKUkJCgvLw83XzzzZKkqqoq7d27V6mpqbU%2BfkxMzFmXA4uLT6iyMrC/SevqYlt/VVX1RbfmQEBf/BN98U/05cIF1imQWoqOjlZOTo6ys7N16tQpHTx4UMuWLVNaWppuuukmFRQUaO3atTp58qR27NihHTt2aNSoUZKk9PR0vfnmm9q1a5fKy8u1cuVKNWrUSP369fPtogAAQMBokGewWrRooezsbC1ZssQdkG6%2B%2BWZlZmYqNDRUq1at0oIFCzRv3jzFxsZq8eLF6tSpkySpb9%2B%2Bmjp1qqZMmaIff/xRiYmJys7OVuPGjX28KgAAECiCrLre0Y1aKS4%2BYXyfNluwBmV9ZHy/9WXzlGt8XYJX2GzBioxsKqezjFPrfoS%2B%2BCf64p8aUl%2BaN2/mk%2BM2yEuEAAAAvkTAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbBqUFBQoPvuu09JSUnq37%2B/Fi9erOrqal%2BXBQAAAoTN1wX4owceeEDx8fHaunWrfvzxR40bN06XXXaZ7rrrLl%2BXBgAAAgBnsH7B4XDo66%2B/1vTp09WsWTO1bt1aY8aMUU5Ojq9LAwAAAYIzWL%2BQl5en2NhYhYeHu8fi4%2BN18OBBlZaWKiws7Lz7KCoqUnFxsceYzdZEMTExRmsNCQmsfGyzBVa9F%2BpMXwKtPw0dffFP9MU/0Ze6I2D9gsvlkt1u9xg7E7acTmetAlZOTo6WL1/uMTZx4kQ98MAD5grVz0Huzsu/U1pamvHwhgtXVFSkl19%2Bgb74Gfrin%2BiLf6IvdUc0rYFlWXV6fVpamtavX%2B/xlZaWZqi6/1NcXKzly5efdbYMvkVf/BN98U/0xT/Rl7rjDNYvREVFyeVyeYy5XC4FBQUpKiqqVvuIiYkh8QMAcBHjDNYvJCQkqLCwUMeOHXOPORwOtWvXTk2bNvVhZQAAIFAQsH4hLi5OiYmJWrJkiUpLS5Wfn6%2BXXnpJ6enpvi4NAAAEiJC5c%2BfO9XUR/ubaa6/Vpk2bNH/%2BfL399ttKTU3V2LFjFRQU5OvSztK0aVP17t2bs2t%2Bhr74J/rin%2BiLf6IvdRNk1fWObgAAAHjgEiEAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQSsAFRQUKD77rtPSUlJ6t%2B/vxYvXqzq6mpfl9UgdezYUQkJCUpMTHR/zZ8/X5K0c%2BdOpaamqkePHho6dKg2bNjg8drVq1dryJAh6tGjh9LT05Wbm%2BueO3nypB599FH17dtXSUlJmjRpkpxOp1fXFmg%2B%2BugjJScnKzMz86y5d955R8OGDVP37t01cuRIffzxx%2B656upqLV26VAMHDlSvXr00duxYHTlyxD3vcrk0ZcoUJScnq0%2BfPpo9e7YqKirc8/v27dNtt92mnj17avDgwXrxxRfrd6EB5l/1Zf369erUqZPHz05iYqL27Nkjib7Up4KCAmVkZCgpKUnJycmaOXOmSkpKJJ3/favPn6WLjoWAc/PNN1uPPPKIVVJSYh08eNAaPHiw9eKLL/q6rAapQ4cO1pEjR84a//77761u3bpZa9eutSoqKqw///nPVpcuXaw9e/ZYlmVZ77//vnXllVdau3btssrLy61Vq1ZZ11xzjVVWVmZZlmUtXLjQGjlypPX//t//s5xOpzVx4kRr3LhxXl1bIMnOzrYGDx5sjR492poyZYrH3N69e62EhARr%2B/btVkVFhfXWW29ZXbt2tQoLCy3LsqzVq1db/fv3t/bv32%2BdOHHCevzxx61hw4ZZ1dXVlmVZ1sSJE6377rvP%2BvHHH62jR49aaWlp1vz58y3Lsqzy8nLr2muvtZ599lmrrKzMys3NtXr37m1t2bLFu2%2BAnzpXX9544w3rtttu%2B5evpS/158Ybb7RmzpxplZaWWoWFhdbIkSOtWbNmnfd9q8%2BfpYsRASvA7Nmzx%2BrcubPlcrncY//7v/9rDRkyxIdVNVz/KmC98MIL1ogRIzzGpkyZYs2ZM8eyLMu67777rN/97nfuuaqqKuuaa66xNm3aZJ0%2Bfdrq2bOntXXrVvf8/v37rY4dO1pHjx6tp5UEtpdfftkqKSmxZsyYcdb/yOfNm2dlZGR4jN16663WqlWrLMuyrKFDh1ovv/yye%2B7EiRNWXFyc9de//tUqLi62OnXqZO3bt889v2PHDqtbt27WqVOnrM2bN1tXXXWVVVlZ6Z5fvHixdffdd9fHMgPOufpyvoBFX%2BrH8ePHrZkzZ1rFxcXusVdeecUaPHjwed%2B3%2BvxZuhhxiTDA5OXlKTY2VuHh4e6x%2BPh4HTx4UKWlpT6srOFasmSJ%2BvXrpyuvvFJz5sxRWVmZ8vLyFBcX57FdXFyc%2BzLgL%2BeDg4PVuXNnORwOHT58WCdOnFB8fLx7vm3btmrcuLHy8vK8s6gAc8cdd6hZs2Y1zv2rXjgcDlVUVGj//v0e82FhYWrVqpUcDof27dunkJAQdezY0T0fHx%2Bvn376SQcOHFBeXp46duyokJAQj33/8%2BXei9m5%2BiJJhYWFuuuuu9SrVy8NHDhQb731liTRl3pkt9u1cOFCXXbZZe6xwsJCxcTEnPd9q8%2BfpYsRASvAuFwu2e12j7EzYYt7eMzr1q2bkpOT9d577yknJ0e7du3SvHnzauxDRESEuwcul8sjBEs/98npdMrlcknSWa%2B32%2B308AKc670%2Bfvy4LMs6Zy/CwsIUFBTkMSfJPV9Tn10uF/c9nkdUVJRat26tBx98UH/%2B8581depUzZo1Szt37qQvXuRwOPTqq69q/Pjx533f6vNn6WJEwApAlmX5uoSLRk5Ojm699VY1atRIbdu21fTp07Vp0yadPn36vK89X5/oozl1ea8vpA///D8R1Kxfv3564YUXFBcXp0aNGmno0KEaNGiQ1q9f796GvtSvL7/8UmPHjtW0adOUnJz8L7f75/fN2z9LDRkBK8BERUW5z4Cc4XK5FBQUpKioKB9VdfG44oorVFVVpeDg4LP64HQ63T2IjIyssU9RUVHubX45f/z4cUVHR9dj9Q3Tud7riIiIGnvlcrkUHR2tqKgolZaWqqqqymNOknv%2Bl5%2B%2BXS6Xe7/498TGxqqoqIi%2BeMG2bdt03333adasWbrjjjsk6bzvW33%2BLF2M%2BE4MMAkJCSosLNSxY8fcYw6HQ%2B3atVPTpk19WFnDs3fvXi1atMhjLD8/X40aNVJKSspZ93vk5uaqa9eukn7u0z/fT1VVVaW9e/eqa9euatmypcLDwz3mv/32W506dUoJCQn1uKKGKSEh4axeOBwOde3aVaGhoWrfvr3He11SUqLDhw%2BrS5cu6ty5syzL0tdff%2B3xWrvdrjZt2ighIUHffPONKisrz9o3zu21117TO%2B%2B84zGWn5%2Bvli1b0pd69tVXX2nGjBlatmyZRowY4R4/3/tWnz9LFyMCVoCJi4tTYmKilixZotLSUuXn5%2Bull15Senq6r0trcKKjo5WTk6Ps7GydOnVKBw8e1LJly5SWlqabbrpJBQUFWrt2rU6ePKkdO3Zox44dGjVqlCQpPT1db775pnbt2qXy8nKtXLlSjRo1Ur9%2B/RQSEqJRo0bpueeeU2FhoZxOp5566ikNGjTI48ZU1M6oUaP0ySefaPv27Tp58qTWrVunQ4cOafjw4ZJ%2B7sXq1auVn5%2Bv0tJSZWVlqXPnzkpMTFRUVJSGDBmip59%2BWseOHdPRo0e1YsUKpaamymazKSUlRWFhYVq5cqXKy8u1e/durVu3jp%2B3Wjh16pTmz58vh8Oh06dPa9OmTfrwww81evRoSfSlvlRWVuqRRx7R9OnT1adPH4%2B5871v9fmzdFHy%2Bu8tos4KCwute%2B65x%2BrSpYuVnJxsPfPMM%2B7nkMCszz77zEpLS7O6detm9e7d21q4cKFVUVHhnhs%2BfLgVHx9vDR48%2BKxn8KxZs8ZKSUmxEhISrPT0dOubb75xz508edKaO3eu1atXL6t79%2B7W1KlTrZKSEq%2BuLZAkJCRYCQkJVqdOnaxOnTq5//uMLVu2WIMHD7bi4%2BOtm266yfrss8/cc9XV1dayZcusq6%2B%2B2urSpYt17733up/rY1mWVVJSYmVmZlrdunWzevXqZc2bN886efKke/6bb76xRo8ebSUkJFj9%2BvWz1qxZ451FB4Bz9aW6utpasWKF1b9/fyshIcG6/vrrrW3btrlfS1/qx%2Beff2516NDB3Yt//vr73/9%2B3vetPn%2BWLjZBlsVdaQAAACZxiRAAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADPv/3GBBbNZCN/MAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common3837598272648955424”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1175</td> <td class=”number”>36.6%</td> <td>

<div class=”bar” style=”width:64%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>54</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.999999970197678</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.00000002980232</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.999999940395355</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.00000002980232</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.999999985098839</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (1818)</td> <td class=”number”>1829</td> <td class=”number”>57.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme3837598272648955424”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1175</td> <td class=”number”>36.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.3930619692e-05</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0003300452954136</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0005951898056082</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0006521593895741</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>2727.39028008784</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3573.3913626684</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4297.90149323097</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8272.19155922628</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>21387.0630610889</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_POP10”>LACCESS_POP10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3106</td>

</tr> <tr>

<th>Unique (%)</th> <td>96.8%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>20192</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>886070</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-1044419425427747796”>

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<li role=”presentation” class=”active”><a href=”#quantiles-1044419425427747796”

aria-controls=”quantiles-1044419425427747796” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-1044419425427747796” aria-controls=”histogram-1044419425427747796”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-1044419425427747796” aria-controls=”common-1044419425427747796”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-1044419425427747796” aria-controls=”extreme-1044419425427747796”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

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<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>252.7</td>

</tr> <tr>

<th>Q1</th> <td>1668.9</td>

</tr> <tr>

<th>Median</th> <td>4100.8</td>

</tr> <tr>

<th>Q3</th> <td>12962</td>

</tr> <tr>

<th>95-th percentile</th> <td>101570</td>

</tr> <tr>

<th>Maximum</th> <td>886070</td>

</tr> <tr>

<th>Range</th> <td>886070</td>

</tr> <tr>

<th>Interquartile range</th> <td>11293</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>51387</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.5449</td>

</tr> <tr>

<th>Kurtosis</th> <td>55.718</td>

</tr> <tr>

<th>Mean</th> <td>20192</td>

</tr> <tr>

<th>MAD</th> <td>25315</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>6.1021</td>

</tr> <tr>

<th>Sum</th> <td>63241000</td>

</tr> <tr>

<th>Variance</th> <td>2640600000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

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2Bfn5kqR169YpMzNTmZmZioyM1OjRo9W9e3dt2LBBkpSfn6%2BJEyeqS5cuioqKUm5uroqLi/Xhhx8G8pABAEAICcmA5XQ6tXDhQl144YXesdLSUsXHx6uwsFA9evRQRESEdy4pKUkFBQWSpMLCQiUlJfnsLykpSS6XS7W1tSoqKvKZj4qKUseOHeVyuZr4qAAAQHPhCHQBNrhcLj3zzDNauXKltmzZIqfT6TMfExMjj8ejhoYGeTweRUdH%2B8xHR0erqKhIR44ckTHmtPNut7vR9ZSVlam8vNxnzOForfj4%2BO94ZGcWERFa%2BdjhCK16G%2BtkH0KtH80RvQgO9CF40IvACfmA9e677%2Br222/XzJkzlZGRoS1btpx2u7CwMO%2B/n%2B1%2BqnO93yo/P1/Lly/3GcvJydH06dPPab%2BhLja2TaBLaFJO5wWBLgH/RC%2BCA30IHvTC/0I6YG3fvl2/%2BMUvNG/ePF133XWSpLi4OH3%2B%2Bec%2B23k8HsXExCg8PFyxsbHyeDynzMfFxXm3Od18u3btGl1XVlaWhg4d6jPmcLSW2139HY7u7ELtE4nt4w8WERHhcjovUEVFjerrGwJdznmNXgQH%2BhA86EXgPtyHbMB67733NHv2bD322GMaNGiQdzwlJUV//OMfVVdXJ4fj68NzuVzq1auXd/7k/VgnuVwujRw5UpGRkerWrZsKCws1YMAASV/fUL9//36lpaU1urb4%2BPhTLgeWl1eqru78/OI%2Bqbkff319Q7M/xlBBL4IDfQge9ML/QusUyD/V1dXpvvvu06xZs3zClSRlZmYqKipKK1euVE1NjT788EOtX79e2dnZkqTx48fr7bff1s6dO3Xs2DGtX79en3/%2BuUaPHi1Jys7O1po1a1RcXKyqqirl5eWpZ8%2BeSk1N9ftxAgCA0BSSZ7A%2B%2BOADFRcXa8GCBVqwYIHP3CuvvKLf/e53euCBB7R69WpdeOGFys3N1eDBgyVJ3bt3V15enhYuXKiSkhJ17dpVq1atUvv27SVJEyZMUHl5uW666SZVV1crPT39lPupAAAAziTM8ARNvygvr7S%2BT4cjXFfkvWl9v01ly4yBgS6hSTgc4YqNbSO3u5pT8AFGL4IDfQge9EJq375tQNYNyUuEAAAAwYyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACzze8AaOnSoli9frtLSUn8vDQAA4Bd%2BD1g33HCDNm/erMsvv1yTJ0/Wq6%2B%2Bqrq6On%2BXAQAA0GT8HrBycnK0efNmPf/88%2BrWrZseeeQRZWZmavHixdq3b5%2B/ywEAALAuYPdgJScna/bs2dqxY4fmzp2r559/XldffbUmTZqkjz76KFBlAQAAnLOABawTJ05o8%2BbNuvXWWzV79mwlJCTonnvuUc%2BePTVx4kRt3LgxUKUBAACcE4e/FywuLtb69ev14osvqrq6WiNGjNAf/vAH9evXz7tN//79NX/%2BfI0aNcrf5QEAAJwzvweskSNHqnPnzpoyZYquu%2B46xcTEnLJNZmamDh8%2B7O/SAAAArPB7wFqzZo0GDBhw1u0%2B/PBDP1QDAABgn9/vwerRo4emTp2qbdu2ecf%2B%2B7//W7feeqs8Ho%2B/ywEAALDO7wFr4cKFqqysVNeuXb1jgwcPVkNDgxYtWuTvcgAAAKzz%2ByXCt956Sxs3blRsbKx3rFOnTsrLy9M111zj73IAAACs8/sZrNraWkVGRp5aSHi4ampq/F0OAACAdX4PWP3799eiRYt05MgR79iXX36pBx980OdRDQAAAKHK75cI586dq5///Of66U9/qqioKDU0NKi6ulodOnTQ008/7e9yAAAArPN7wOrQoYNefvllvfHGG9q/f7/Cw8PVuXNnDRo0SBEREf4uBwAAwDq/ByxJatmypS6//PJALA0AANDk/B6wDhw4oCVLlujTTz9VbW3tKfOvv/66v0sCAACwKiD3YJWVlWnQoEFq3bq1v5cHAABocn4PWAUFBXr99dcVFxfn76UBAAD8wu%2BPaWjXrh1nrgAAQLPm94A1ZcoULV%2B%2BXMYYfy8NAADgF36/RPjGG2/ovffe0wsvvKCLL75Y4eG%2BGe%2B5557zd0kAAABW%2BT1gRUVF6bLLLvP3sgAAAH7j94C1cOFCfy8JAADgV36/B0uSPvvsMy1btkz33HOPd%2Bz9998PRCkAAADW%2BT1g7d69W6NHj9arr76qTZs2Sfr64aM333wzDxkFAADNgt8D1tKlS/WLX/xCGzduVFhYmKSvfz/hokWLtGLFCn%2BXAwAAYJ3fA9bf//53ZWdnS5I3YEnSlVdeqeLiYn%2BXAwAAYJ3fA1bbtm1P%2BzsIy8rK1LJlS3%2BXAwAAYJ3fA1bfvn31yCOPqKqqyju2b98%2BzZ49Wz/96U/9XQ4AAIB1fn9Mwz333KNbbrlF6enpqq%2BvV9%2B%2BfVVTU6Nu3bpp0aJF/i4HAADAOr8HrIsuukibNm3Srl27tG/fPrVq1UqdO3fWwIEDfe7JAgAACFV%2BD1iS1KJFC11%2B%2BeWBWBoAAKDJ%2BT1gDR069IxnqngWFgAACHV%2BD1hXX321T8Cqr6/Xvn375HK5dMstt3ynfb355puaPXu20tPTtXTpUu/4Cy%2B8oLlz56pFixY%2B269du1ZpaWlqaGjQY489pk2bNqmiokJpaWmaP3%2B%2BOnToIEnyeDyaP3%2B%2B/vrXvyo8PFyZmZmaN2%2BeWrVqdQ5HDgAAzhd%2BD1izZs067fjWrVv1l7/8pdH7efzxx7V%2B/Xp17NjxtPP9%2B/fX008/fdq5tWvXauPGjXr88ceVkJCgpUuXKicnRy%2B99JLCwsI0b948HT9%2BXJs2bdKJEyd01113KS8vT/fdd1%2Bj6wMAAOevgPwuwtO5/PLL9fLLLzd6%2B8jIyDMGrDPJz8/XxIkT1aVLF0VFRSk3N1fFxcX68MMP9dVXX2nbtm3Kzc1VXFycEhISdMcdd%2BhPf/qTTpw48Z3XAgAA55%2BA3OR%2BOnv27JExptHb33zzzWecLy0t1c9%2B9jMVFBTI6XRq%2BvTpuvbaa1VbW6uioiIlJSV5t42KilLHjh3lcrlUWVmpiIgI9ejRwzufnJyso0eP6rPPPvMZ/zZlZWUqLy/3GXM4Wis%2BPr7Rx9cYERFBk48bxeEIrXob62QfQq0fzRG9CA70IXjQi8Dxe8CaMGHCKWM1NTUqLi7W8OHDrawRFxenTp066e6771bXrl312muv6Ze//KXi4%2BP1ox/9SMYYRUdH%2B7wmOjpabrdbMTExioqK8rlP7OS2bre7Uevn5%2Bdr%2BfLlPmM5OTmaPn36OR5ZaIuNbRPoEpqU03lBoEvAP9GL4EAfgge98D%2B/B6xOnTqd8lOEkZGRGjt2rMaNG2dljcGDB2vw4MHe/x45cqRee%2B01vfDCC957wM50tuy7nEk7naysLA0dOtRnzOFoLbe7%2Bpz2%2B02h9onE9vEHi4iIcDmdF6iiokb19Q2BLue8Ri%2BCA30IHvQicB/u/R6wAvW09sTERBUUFCgmJkbh4eHyeDw%2B8x6PR%2B3atVNcXJyqqqpUX1%2BviIgI75wktWvXrlFrxcfHn3I5sLy8UnV15%2BcX90nN/fjr6xua/TGGCnoRHOhD8KAX/uf3gPXiiy82etvrrrvue63xxz/%2BUdHR0br66qu9Y8XFxerQoYMiIyPVrVs3FRYWasCAAZKkiooK7d%2B/X2lpaUpMTJQxRh9//LGSk5MlSS6XS06nU507d/5e9QAAgPOL3wPWvffeq4aGhlMuw4WFhfmMhYWFfe%2BAdfz4cT388MPq0KGDfvzjH2vr1q1644039Pzzz0uSsrOztXr1al122WVKSEhQXl6eevbsqdTUVEnSiBEj9Jvf/EaPPvqojh8/rhUrVmjs2LFyOILmZwIAAEAQ83tieOKJJ/Tkk09q6tSp6tGjh4wx%2BuSTT/T444/rxhtvVHp6eqP2czIM1dXVSZK2bdsm6euzTTfffLOqq6t11113qby8XBdffLFWrFihlJQUSV/faF9eXq6bbrpJ1dXVSk9P97kp/aGHHtIDDzygYcOGqUWLFrrmmmuUm5tr820AAADNWJg51zu6v6Nrr71Wq1evVkJCgs/4l19%2BqUmTJmnTpk3%2BLMdvyssrre/T4QjXFXlvWt9vU9kyY2CgS2gSDke4YmPbyO2u5h6HAKMXwYE%2BBA96IbVv3zYg6/r9x9A%2B//zzUx6RIElOp1MlJSX%2BLgcAAMA6vwesxMRELVq0yOeZUhUVFVqyZIl%2B%2BMMf%2BrscAAAA6/x%2BD9bcuXM1c%2BZM5efnq02bNgoPD1dVVZVatWqlFStW%2BLscAAAA6/wesAYNGqSdO3dq165dOnjwoIwxSkhI0KWXXqq2bQNznRQAAMCmgDx34IILLtCwYcN08OBBdejQIRAlAAAANBm/34NVW1ur2bNnq0%2BfPrrqqqskfX0P1uTJk1VRUeHvcgAAAKzze8BavHix9u7dq7y8PIWH/9/y9fX1ysvL83c5AAAA1vk9YG3dulW//e1vdeWVV3p/6bPT6dTChQv16quv%2BrscAAAA6/wesKqrq9WpU6dTxuPi4nT06FF/lwMAAGCd3wPWD3/4Q/3lL3%2BRJJ/fPfjKK6/o3/7t3/xdDgAAgHV%2B/ynCf//3f9e0adN0ww03qKGhQU899ZQKCgq0detW3Xvvvf4uBwAAwDq/B6ysrCw5HA4988wzioiI0O9%2B9zt17txZeXl5uvLKK/1dDgAAgHV%2BD1iHDx/WDTfcoBtuuMHfSwMAAPiF3%2B/BGjZsmM%2B9VwAAAM2N3wNWenq6tmzZ4u9lAQAA/Mbvlwh/8IMf6Fe/%2BpVWr16tH/7wh2rRooXP/JIlS/xdEgAAgFV%2BD1hFRUX60Y9%2BJElyu93%2BXh4AAKDJ%2BS1g5ebmaunSpXr66ae9YytWrFBOTo6/SgAAAPALv92DtX379lPGVq9e7a/lAQAA/MZvAet0PznITxMCAIDmyG8B6%2BQvdj7bGAAAQKjz%2B2MaAAAAmjsCFgAAgGV%2B%2BynCEydOaObMmWcd4zlYAAAg1PktYPXr109lZWVnHQMAAAh1fgtY//r8KwAAgOaMe7AAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUhHbDefPNNZWRkKDc395S5zZs3a9SoUerTp4/GjBmjt956yzvX0NCgpUuXatiwYerfv78mTZqkAwcOeOc9Ho9mzJihjIwMDRo0SPfee69qa2v9ckwAACD0hWzAevzxx7VgwQJ17NjxlLm9e/dq9uzZmjVrlv785z9r4sSJuvPOO3Xw4EFJ0tq1a7Vx40atXr1aO3bsUKdOnZSTkyNjjCRp3rx5qqmp0aZNm/SnP/1JxcXFysvL8%2BvxAQCA0BWyASsyMlLr168/bcBat26dMjMzlZmZqcjISI0ePVrdu3fXhg0bJEn5%2BfmaOHGiunTpoqioKOXm5qq4uFgffvihvvrqK23btk25ubmKi4tTQkKC7rjjDv3pT3/SiRMn/H2YAAAgBIVswLr55pvVtm3b084VFhYqKSnJZywpKUkul0u1tbUqKirymY%2BKilLHjh3lcrm0d%2B9eRUREqEePHt755ORkHT16VJ999lnTHAwAAGhWHIEuoCl4PB5FR0f7jEVHR6uoqEhHjhyRMea08263WzExMYqKilJYWJjPnCS53e5GrV9WVqby8nKfMYejteLj47/P4XyriIjQyscOR2jV21gn%2BxBq/WiO6EVwoA/Bg14ETrMMWJK891N9n/mzvfZs8vPztXz5cp%2BxnJwcTZ8%2B/Zz2G%2BpiY9sEuoQm5XReEOgS8E/0IjjQh%2BBBL/yvWQas2NhYeTwenzGPx6O4uDjFxMQoPDz8tPPt2rVTXFycqqqqVF9fr4iICO%2BcJLVr165R62dlZWno0KE%2BYw5Ha7nd1d/3kE4r1D6R2D7%2BYBERES6n8wJVVNSovr4h0OWc1%2BhFcKAPwYNeBO7DfbMMWCkpKSooKPAZc7lcGjlypCIjI9WtWzcVFhZqwIABkqSKigrt379faWlpSkxMlDFGH3/8sZKTk72vdTqd6ty5c6PWj4%2BPP%2BVyYHl5perqzs8v7pOa%2B/HX1zc0%2B2MMFfQiONCH4EEv/C%2B0ToE00vjx4/X2229r586dOnbsmNavX6/PP/9co0ePliRlZ2drzZo1Ki4uVlVVlfLy8tSzZ0%2BlpqYqLi5OI0aM0G9%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%2Bvbtq5EjR2rDhg0%2Br12zZo1GjBihvn37Kjs7WwUFBYE4BAAAEKIcgS6gKb3yyiu6%2BOKLfcbKysp0xx136N5779WoUaP07rvv6vbbb1fnzp2Vmpqq7du3a9myZXriiSfUo0cPrVmzRlOnTtWrr76q1q1bB%2BhIAABAKGm2Z7C%2BzcaNG9WpUyeNHTtWkZGRysjI0NChQ7Vu3TpJUn5%2BvsaMGaNevXqpVatWmjx5siRpx44dgSwbAACEkGZ9BmvJkiV6//33VVVVpauuukpz5sxRYWGhkpKSfLZLSkrSli1bJEmFhYW6%2BuqrvXPh4eHq2bOnXC6XRo4c2ah1y8rKVF5e7jPmcLRWfHz8OR6Rr4iI0MrHDkdo1dtYJ/sQav1ojuhFcKAPwYNeBE6zDVi9e/dWRkaGHn30UR04cEAzZszQgw8%2BKI/Ho4SEBJ9tY2Ji5Ha7JUkej0fR0dE%2B89HR0d75xsjPz9fy5ct9xnJycjR9%2BvTveTTNQ2xsm0CX0KSczgsCXQL%2BiV4EB/oQPOiF/zXbgJWfn%2B/99y5dumjWrFm6/fbb1a9fv7O%2B1hhzTmtnZWVp6NChPmMOR2u53dXntN9vCrVPJLaPP1hERITL6bxAFRU1qq9vCHQ55zV6ERzoQ/CgF4H7cN9sA9Y3XXzxxaqvr1d4eLg8Ho/PnNvtVlxcnCQpNjb2lHmPx6Nu3bo1eq34%2BPhTLgeWl1eqru78/OI%2Bqbkff319Q7M/xlBBL4IDfQge9ML/QusUSCPt2bNHixYt8hkrLi5Wy5YtlZmZecpjFwoKCtSrVy9JUkpKigoLC71z9fX12rNnj3ceAADgbJplwGrXrp3y8/O1evVqHT9%2BXPv27dNjjz2mrKwsXXvttSopKdG6det07Ngx7dq1S7t27dL48eMlSdnZ2XrxxRf1wQcfqKamRitXrlTLli01ePDgwB4UAAAIGc3yEmFCQoJWr16tJUuWeAPS9ddfr9zcXEVGRmrVqlVasGCBHnzwQSUmJmrx4sX68Y9/LEm67LLLdPfdd2vGjBk6dOiQUlNTtXr1arVq1SrARwUAAEJFmDnXO7rRKOXlldb36XCE64q8N63vt6lsmTEw0CU0CYcjXLGxbeR2V3OPQ4DRi%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%2BPt5qrRER5GP8n1C6Z%2By1WZc2yX5Pfk/Y/t64Iu9Nq/trak31/jZWU/UB3x29CBwC1jd4PB45nU6fsZNhy%2B12Nypg5efna/ny5T5jd955p6ZNm2avUH0d5G656FNlZWVZD29ovLKyMuXn59OHIFBWVqY//OEJ671451dXWtvX%2BaCp%2BoDvjl4EDpH2NIwx5/T6rKwsvfDCCz5/srKyLFX3f8rLy7V8%2BfJTzpbBv%2BhD8KAXwYE%2BBA96ETicwfqGuLg4eTwenzGPx6OwsDDFxcU1ah/x8fF8UgAA4DzGGaxvSElJUWlpqQ4fPuwdc7lc6tq1q9q0aRPAygAAQKggYH1DUlKSUlNTtWTJElVVVam4uFhPPfWUsrOzA10aAAAIERHz58%2BfH%2Bgigs2ll16qTZs26eGHH9bLL7%2BssWPHatKkSQoLCwt0aado06aNBgwYwNm1AKMPwYNeBAf6EDzoRWCEmXO9oxsAAAA%2BuEQIAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBKwSVlJTotttuU3p6uoYMGaLFixeroaEh0GWFjJKSEuXk5Cg9PV0ZGRmaM2eOKioqJEl79%2B7VjTfeqH79%2Bmn48OF68sknfV67efNmjRo1Sn369NGYMWP01ltveecaGhq0dOlSDRs2TP3799ekSZN04MAB77zH49GMGTOUkZGhQYMG6d5771Vtba13/mxrN2ePPPKIevTo4f3v3bt3a%2BzYserbt69GjhypDRs2%2BGy/Zs0ajRgxQn379lV2drYKCgq8c8eOHdP999%2Bvyy67TOnp6Zo%2Bfbrcbrd3/mzfP2dbu7lauXKlBg0apN69e2vixIn64osvJNELf9qzZ49uvvlmXXLJJRo4cKBmzZqlw4cPS6IPIckg5Fx//fXmvvvuMxUVFWbfvn1m%2BPDh5sknnwx0WSHjmmuuMXPmzDFVVVWmtLTUjBkzxsydO9fU1NSYSy%2B91CxbtsxUV1ebgoICM2DAALN161ZjjDF79uwxKSkpZufOnaa2tta89NJLplevXqa0tNQYY8yaNWvMkCFDTFFRkamsrDQPPfSQGTVqlGloaDDGGHPnnXea2267zRw6dMgcPHjQZGVlmYcfftgYY866dnO2Z88eM2DAANO9e3djjDFffvml6d27t1m3bp2pra01//u//2vS0tLMRx99ZIwx5vXXXzeXXHKJ%2BeCDD0xNTY1ZtWqVGThwoKmurjbGGLNw4UIzZswY849//MO43W5z5513milTpnjXO9P3z9nWbq6eeeYZc%2BWVV5ri4mJTWVlpHn74YfPwww/TCz86ceKEGThwoFmyZIk5duyYOXz4sPnZz35mpk2bRh9CFAErxHz00UemZ8%2BexuPxeMeeffZZM2LEiABWFTqOHDli5syZY8rLy71jTz/9tBk%2BfLjZsmWL%2BclPfmLq6uq8c4sXLzY///nPjTHGPPjggyYnJ8dnf%2BPGjTOrVq0yxhgzcuRI84c//ME7V1lZaZKSksz7779vysvLzY9//GOzd%2B9e7/yuXbtM7969zfHjx8%2B6dnNVX19vxo0bZ/7rv/7LG7CeeOIJc9111/lsN2PGDDNv3jxjjDG33XabeeSRR3z2MXDgQLNp0yZz4sQJ069fP7Nt2zbvfFFRkenRo4c5ePDgWb9/zrZ2czV06NDThnl64T//%2BMc/TPfu3U1RUZF37NlnnzWXX345fQhRXCIMMYWFhUpMTFR0dLR3LDk5Wfv27VNVVVUAKwsNTqdTCxcu1IUXXugdKy0tVXx8vAoLC9WjRw9FRER455KSkryn2gsLC5WUlOSzv6SkJLlcLtXW1qqoqMhnPioqSh07dpTL5dLevXsVERHhcxksOTlZR48e1WeffXbWtZur5557TpGRkRo1apR37Nve52/rQ3h4uHr27CmXy6X9%2B/ersrJSycnJ3vkuXbqoVatWKiwsPOv3z9nWbo6%2B/PJLffHFFzpy5Iiuvvpq7yWkw4cP0ws/SkhIUM%2BePZWfn6/q6modOnRIr776qgYPHkwfQhQBK8R4PB45nU6fsZPfGP96TR2N43K59Mwzz%2Bj2228/7XsbExMjj8ejhoYGeTwen7%2BEpK/fe7fbrSNHjsgY863zHo9HUVFRCgsL85mT5J0/09rN0VdffaVly5bpgQce8Bn/tvfi5Nf3mfrg8Xgk6ZTXO53Ob32fG9OH5vy9dfDgQUnSK6%2B8oqeeekovvfSSDh48qPvuu49e%2BFF4eLiWLVum119/XX379lVGRobq6uo0c%2BZM%2BhCiCFghyBgT6BKahXfffVeTJk3SzJkzlZGR8a3b/WsoOtt7f6b579O3f127uVm4cKHGjBmjrl27fufX%2BrsPzdnJ92Py5MlKSEjQRRddpGnTpmn79u3f6fXfZ55e/J/jx49r6tSpuvLKK/XOO%2B/ojTfeUNu2bTVr1qxGvZ4%2BBB8CVoiJi4vzfiI5yePxKCwsTHFxcQGqKvRs375dt912m%2BbOnaubb75Z0tfv7Tc/lXk8HsXExCg8PFyxsbGnfe/j4uK825xuvl27doqLi1NVVZXq6%2Bt95iR558%2B0dnOze/duvf/%2B%2B8rJyTll7nTvs9vt9n59n6kPJ7f55vyRI0e87/OZvn/OtnZzdPJy%2Bb%2BepUhMTJQxRidOnKAXfrJ792598cUXuvvuu9W2bVslJCRo%2BvTpeu211077dwt9CH7N72/uZi4lJUWlpaXeH92Vvr7M1bVrV7Vp0yaAlYWO9957T7Nnz9Zjjz2m6667zjuekpKiTz75RHV1dd4xl8ulXr16eee/ed/ByfnIyEh169ZNhYWF3rmKigrt379faWlp6tmzp4wx%2Bvjjj31e63Q61blz57Ou3dxs2LBBhw4d0pAhQ5Senq4xY8ZIktLT09W9e/dT3ueCggKfPvzr%2B1xfX689e/aoV69e6tChg6Kjo33m//73v%2Bv48eNKSUk56/dPamrqGdduji666CJFRUVp79693rGSkhK1aNFCmZmZ9MJP6uvr1dDQ4HM26fjx45KkjIwM%2BhCK/H1XPc7duHHjzNy5c01lZaUpKioyQ4cONc8880ygywoJJ06cMFdddZV57rnnTpk7duyYGTJkiPntb39rjh49aj744ANzySWXmB07dhhjjPnkk09Mamqq2bFjh6mtrTXr1q0zffr0MWVlZcaYr3/yZvDgwd7HNMybN8/ccMMN3v3PmDHDTJ482Rw6dMiUlpaaG264wSxatKhRazc3Ho/HlJaWev%2B8//77pnv37qa0tNSUlJSYPn36mOeff97U1taanTt3mrS0NO9PYO7atcv069fPvP/%2B%2B%2Bbo0aNm2bJlJjMz09TU1Bhjvv7py%2Buvv9784x//MIcPHzZTpkwx06ZN8659pu%2Bfr7766oxrN1ePPPKIGTZsmPn888/NV199ZbKyssycOXPO%2Bn7QC3sOHz5sBgwYYH7961%2Bbo0ePmsOHD5upU6ea//iP/6APIYqAFYJKS0vN5MmTTVpamsnIyDC//e1vvc9awpn97W9/M927dzcpKSmn/Pniiy/MJ598YiZMmGBSUlLM4MGDzdq1a31ev3XrVjN8%2BHCTnJxsrr32WvPXv/7VO9fQ0GAee%2Bwx89Of/tSkpaWZW2%2B91fuMLGOMqaioMLm5uaZ3796mf//%2B5sEHHzTHjh3zzp9t7ebswIED3sc0GGPMX//6VzN69GiTnJxshg8ffsojBNauXWsyMzNNSkqKyc7ONp988ol37tixY2b%2B/Pmmf//%2Bpk%2BfPubuu%2B82FRUV3vmzff%2Bcbe3m6F/fs969e5vZs2ebqqoqYwy98CeXy2VuvPFGc8kll5iMjAwzY8YMc/DgQWMMfQhFYcZwdxsAAIBN3IMFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJb9fx8FqzDz4h1JAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-1044419425427747796”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>28</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>641.000000070198</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2939.75031268952</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>740.834081486508</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1035.31608657788</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>39853.8469149006</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3038.85778064322</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2346.71934712042</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2189.89111615632</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>101.416347637773</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3095)</td> <td class=”number”>3095</td> <td class=”number”>96.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-1044419425427747796”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>28</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00296986475586891</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0441346429288387</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0955020561814308</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.312787592411041</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>492281.154146267</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>503753.083526738</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>537897.612200725</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>613459.048963576</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>886068.668386497</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_POP15”><s>LACCESS_POP15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_LACCESS_POP10”><code>LACCESS_POP10</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99406</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_SENIORS10”><s>LACCESS_SENIORS10</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_LACCESS_CHILD15”><code>LACCESS_CHILD15</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.91878</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_SENIORS15”><s>LACCESS_SENIORS15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_LACCESS_SENIORS10”><code>LACCESS_SENIORS10</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.9937</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_SNAP15”><s>LACCESS_SNAP15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_LACCESS_HHNV15”><code>LACCESS_HHNV15</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.90806</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_WHITE15”><s>LACCESS_WHITE15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_LACCESS_SENIORS15”><code>LACCESS_SENIORS15</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.95929</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_MEDHHINC15”>MEDHHINC15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2994</td>

</tr> <tr>

<th>Unique (%)</th> <td>93.3%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.5%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>79</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>48591</td>

</tr> <tr>

<th>Minimum</th> <td>22894</td>

</tr> <tr>

<th>Maximum</th> <td>125900</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram4732728103973025122”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives4732728103973025122,#minihistogram4732728103973025122”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives4732728103973025122”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles4732728103973025122”

aria-controls=”quantiles4732728103973025122” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram4732728103973025122” aria-controls=”histogram4732728103973025122”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common4732728103973025122” aria-controls=”common4732728103973025122”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme4732728103973025122” aria-controls=”extreme4732728103973025122”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles4732728103973025122”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>22894</td>

</tr> <tr>

<th>5-th percentile</th> <td>32596</td>

</tr> <tr>

<th>Q1</th> <td>40393</td>

</tr> <tr>

<th>Median</th> <td>46786</td>

</tr> <tr>

<th>Q3</th> <td>54134</td>

</tr> <tr>

<th>95-th percentile</th> <td>71912</td>

</tr> <tr>

<th>Maximum</th> <td>125900</td>

</tr> <tr>

<th>Range</th> <td>103010</td>

</tr> <tr>

<th>Interquartile range</th> <td>13741</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>12357</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.25431</td>

</tr> <tr>

<th>Kurtosis</th> <td>3.4056</td>

</tr> <tr>

<th>Mean</th> <td>48591</td>

</tr> <tr>

<th>MAD</th> <td>9135.8</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.3657</td>

</tr> <tr>

<th>Sum</th> <td>152140000</td>

</tr> <tr>

<th>Variance</th> <td>152700000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram4732728103973025122”>

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eXLl3t9VAMAAEBT5fgtwsWLF%2Buee%2B7RTTfdpNDQUNXW1qq8vFwdOnTQc8895/R0AAAArHM8YHXo0EGvvvqq3nrrLX3xxRcKDAxUp06dNHjwYAUFBTk9HQAAAOscD1iS1KxZM91yyy2%2BGBoAAKDROR6wvvzyS61du1affvqpKisr6/S/%2BeabTk8JAADAKp88g1VUVKTBgwerRYsWTg8PAADQ6BwPWHl5eXrzzTcVGRnp9NAAAACOcPxjGtq0acOVKwAA4NccD1gzZ87UunXrZIxxemgAAABHOH6L8K233tIHH3ygl19%2BWf/yL/%2BiwEDvjPfCCy84PSUAAACrHA9YoaGhGjJkiNPDAgAAOMbxgLVy5UqnhwQAAHCU489gSdJnn32mzMxMPfjgg562AwcO%2BGIqAAAA1jkesPbv36%2BxY8dq165d2r59u6RvP3x0ypQpfMgoAADwC44HrMcff1w/%2B9nPtG3bNgUEBEj69vsJV61apaeeesrp6QAAAFjneMD6%2B9//rrS0NEnyBCxJuvXWW3X06FGnpwMAAGCd4wGrVatWF/wOwqKiIjVr1szqWOvXr9fgwYPVu3dvTZ06VcePH5f07W3K1NRU9e3bV6NHj9bWrVu91tu0aZNGjhypvn37Ki0tTXl5eVbnBQAA/JvjAatv375asWKFTp8%2B7Wk7duyYFi5cqJtuusnaOM8//7y2bt2qTZs26Z133lHnzp31u9/9TkVFRbr//vs1efJk7d%2B/X0uWLNGyZcuUm5srSdq9e7cyMzP12GOPad%2B%2BfRo2bJhmzZqlM2fOWJsbAADwb44HrAcffFAHDhxQQkKCqqqq1LdvX40aNUput1uLFi2yNs7TTz%2BtuXPn6sYbb1RoaKiWLl2qpUuXatu2berYsaNSU1MVEhKixMREJScnKzs7W5KUlZWllJQUxcfHq3nz5po%2Bfbokac%2BePdbmBgAA/Jvjn4PVrl07bd%2B%2BXTk5OTp27JiaN2%2BuTp06adCgQV7PZF2Jr7/%2BWsePH9fJkyc1atQolZSUKCEhQQ8//LDy8/MVExPjtXxMTIx27twpScrPz9eoUaM8fYGBgerRo4dyc3M1evToBo1fVFSk4uJir7bg4BaKioq66HpBQYFeP4FLERzc%2BPsN%2B6h91NQu6mkfNb08jgcsSbrmmmt0yy23NNrrFxYWSpJee%2B01PfPMMzLGaM6cOVq6dKkqKysVHR3ttXzr1q3lcrkkSW63W%2BHh4V794eHhnv6GyMrK0rp167za0tPTNWfOnAatHxZ2bYPHAs6LiGjp2Fjso/ZRU7uop33U9NI4HrCSk5MveqXKxmdhnf8i6enTp3vC1OzZs3XvvfcqMTGxwetfrkmTJik5OdmrLTi4hVyu8ouuFxQUqLCwa1VWVqGamtormgN%2BeOrbv2xgH7WPmtpFPe1r6jV18o/P73I8YI0aNcorYNXU1OjYsWPKzc3Vv//7v1sZ47rrrpMkhYWFedrat28vY4zOnTsnt9vttbzL5VJkZKQkKSIiok6/2%2B1Wly5dGjx%2BVFRUnduBxcWnVF3dsB2zpqa2wcsC5zm5z7CP2kdN7aKe9lHTS%2BN4wFqwYMEF219//XX9%2Bc9/tjJGu3btFBoaqkOHDik2NlaSVFBQoGuuuUZJSUnasmWL1/J5eXmKj4%2BXJMXFxSk/P1/jx4%2BX9G0APHjwoFJTU63MDQAA%2BL%2Br5om1W265Ra%2B%2B%2BqqV1woODlZqaqp%2B/etf63//939VUlKip556SmPGjNH48eNVUFCg7OxsVVVVKScnRzk5ObrzzjslSWlpaXrllVf04YcfqqKiQuvXr1ezZs00dOhQK3MDAAD%2BzycPuV/IwYMHr/jZp%2B%2BaP3%2B%2Bzp49q4kTJ%2BrcuXMaOXKkli5dqpYtW2rDhg169NFHtXz5crVv316rV69W9%2B7dJUlDhgzRvHnzlJGRoZKSEvXs2VMbN25U8%2BbNrc0NAAD4twBjM9U0wOTJk%2Bu0VVRU6OjRoxoxYoR%2B%2BctfOjkdxxQXn6p3meDgQEVEtJTLVe6X97lve%2BJdX0/Br%2B3MGNToY/j7PuoL1NQu6mlfU69p27atfDKu41ewOnbsWOddhCEhIUpNTdXEiROdng4AAIB1jgesVatWOT0kAACAoxwPWK%2B88kqDlx03blwjzgQAAKBxOB6wlixZotra2joPtAcEBHi1BQQEELAAAECT5HjA%2Bu1vf6unn35as2bNUrdu3WSM0eHDh/Wb3/xGd911lxISEpyeEgAAgFU%2BeQZr48aNXt8H2L9/f3Xo0EHTpk3T9u3bnZ4SAACAVY5/0Ojnn39e58uUpW%2B/1qagoMDp6QAAAFjneMBq3769Vq1aJZfL5WkrKyvT2rVrdf311zs9HQAAAOscv0W4ePFizZ8/X1lZWWrZsqUCAwN1%2BvRpNW/eXE899ZTT0wEAALDO8YA1ePBg7d27Vzk5OSosLJQxRtHR0br55pvVqpVvPm0VAADAJp98F%2BG1116r4cOHq7CwUB06dPDFFAAAABqN489gVVZWauHCherTp49uu%2B02Sd8%2BgzV9%2BnSVlZU5PR0AAADrHA9Yq1ev1qFDh7RmzRoFBv7f8DU1NVqzZo3T0wEAALAuwHz/I9Ub2eDBg7V582Z17NhR8fHx%2BuijjyRJhYWFGjdunN577z0np%2BOY4uJT9S7T1L%2BxvD63PfGur6eAq8TOjEG%2BnsJVw9%2BPe6dRT/uaek3btvXN892OX8EqLy9Xx44d67RHRkbqzJkzTk8HAADAOscD1vXXX68///nPkuT13YOvvfaa/vmf/9np6QAAAFjn%2BLsIf/KTn2j27NmaMGGCamtr9cwzzygvL0%2Bvv/66lixZ4vR0AAAArHM8YE2aNEnBwcHavHmzgoKC9Otf/1qdOnXSmjVrdOuttzo9HQAAAOscD1ilpaWaMGGCJkyY4PTQAAAAjnD8Gazhw4fL4TcuAgAAOMrxgJWQkKCdO3c6PSwAAIBjHL9F%2BE//9E/6xS9%2BoY0bN%2Br666/XNddc49W/du1ap6cEAABgleMB68iRI7rxxhslSS6Xy%2BnhAQAAGp1jAWvu3Ll6/PHH9dxzz3nannrqKaWnpzs1BQAAAEc49gzW7t2767Rt3LjRqeEBAAAc41jAutA7B3k3IQAA8EeOBayAgIAGtQEAADR1jn9MAwAAgL8jYAEAAFjm2LsIz507p/nz59fbxudgAQCAps6xgNWvXz8VFRXV2wYAANDUORawvvv5VwAAAP6MZ7AAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWPaDCFgrVqxQt27dPL/v379fqamp6tu3r0aPHq2tW7d6Lb9p0yaNHDlSffv2VVpamvLy8pyeMgAAaML8PmAdOnRIW7Zs8fxeVFSk%2B%2B%2B/X5MnT9b%2B/fu1ZMkSLVu2TLm5uZKk3bt3KzMzU4899pj27dunYcOGadasWTpz5oyvNgEAADQxfh2wamtr9dBDD2nq1Kmetm3btqljx45KTU1VSEiIEhMTlZycrOzsbElSVlaWUlJSFB8fr%2BbNm2v69OmSpD179vhiEwAAQBPk1wHrhRdeUEhIiMaMGeNpy8/PV0xMjNdyMTExntuA3%2B8PDAxUjx49PFe4AAAA6hPs6wk0lm%2B%2B%2BUaZmZl67rnnvNrdbreio6O92lq3bi2Xy%2BXpDw8P9%2BoPDw/39DdEUVGRiouLvdqCg1soKirqousFBQV6/QT8VXAw%2B/h5HPd2UU/7qOnl8duAtXLlSqWkpKhz5846fvz4Ja1rjLmisbOysrRu3TqvtvT0dM2ZM6dB64eFXXtF4wNXu4iIlr6ewlWH494u6mkfNb00fhmw9u/frwMHDmj79u11%2BiIiIuR2u73aXC6XIiMj/2G/2%2B1Wly5dGjz%2BpEmTlJyc7NUWHNxCLlf5RdcLCgpUWNi1KiurUE1NbYPHA5qa%2Bo6FHxKOe7uop31Nvaa%2B%2BoPOLwPW1q1bVVJSomHDhkn6vytSCQkJuueee%2BoEr7y8PMXHx0uS4uLilJ%2Bfr/Hjx0uSampqdPDgQaWmpjZ4/KioqDq3A4uLT6m6umE7Zk1NbYOXBZoi9u%2B6OO7top72UdNL45c3VBctWqTXX39dW7Zs0ZYtW7Rx40ZJ0pYtWzRmzBgVFBQoOztbVVVVysnJUU5Oju68805JUlpaml555RV9%2BOGHqqio0Pr169WsWTMNHTrUh1sEAACaEr%2B8ghUeHu71oHp1dbUkqV27dpKkDRs26NFHH9Xy5cvVvn17rV69Wt27d5ckDRkyRPPmzVNGRoZKSkrUs2dPbdy4Uc2bN3d%2BQwAAQJMUYK70iW40SHHxqXqXCQ4OVERES7lc5X55Gfa2J9719RRwldiZMcjXU7hq%2BPtx7zTqaV9Tr2nbtq18Mq5f3iIEAADwJQIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADL/DZgFRQUKD09XQkJCUpMTNSiRYtUVlYmSTp06JDuuusu9evXTyNGjNDTTz/tte6OHTs0ZswY9enTRykpKXrnnXd8sQkAAKCJ8tuANWvWLIWFhWn37t16%2BeWX9emnn%2Bo///M/VVlZqZkzZ%2BrHP/6x3n77bT3%2B%2BOPasGGDdu3aJenb8LVw4UItWLBA7733nqZOnaoHHnhAhYWFPt4iAADQVPhlwCorK1NcXJzmz5%2Bvli1bql27dho/frzef/997d27V%2BfOndN9992nFi1aKDY2VhMnTlRWVpYkKTs7W0lJSUpKSlJISIjGjh2rrl27auvWrT7eKgAA0FQE%2B3oCjSEsLEwrV670ajtx4oSioqKUn5%2Bvbt26KSgoyNMXExOj7OxsSVJ%2Bfr6SkpK81o2JiVFubm6Dxy8qKlJxcbFXW3BwC0VFRV10vaCgQK%2BfgL8KDmYfP4/j3i7qaR81vTx%2BGbC%2BLzc3V5s3b9b69eu1c%2BdOhYWFefW3bt1abrdbtbW1crvdCg8P9%2BoPDw/XkSNHGjxeVlaW1q1b59WWnp6uOXPmNGj9sLBrGzwW0BRFRLT09RSuOhz3dlFP%2B6jppfH7gPW3v/1N9913n%2BbPn6/ExETt3LnzgssFBAR4/tsYc0VjTpo0ScnJyV5twcEt5HKVX3S9oKBAhYVdq7KyCtXU1F7RHICrWX3Hwg8Jx71d1NO%2Bpl5TX/1B59cBa/fu3frZz36mZcuWady4cZKkyMhIff75517Lud1utW7dWoGBgYqIiJDb7a7THxkZ2eBxo6Ki6twOLC4%2Bperqhu2YNTW1DV4WaIrYv%2BviuLeLetpHTS%2BN395Q/eCDD7Rw4UI9%2BeSTnnAlSXFxcTp8%2BLCqq6s9bbm5uYqPj/f05%2BXleb3Wd/sBAADq45cBq7q6WkuXLtWCBQs0ePBgr76kpCSFhoZq/fr1qqio0EcffaSXXnpJaWlpkqQ777xT%2B/bt0969e1VVVaWXXnpJn3/%2BucaOHeuLTQEAAE1QgLnSB46uQu%2B//75%2B%2BtOfqlmzZnX6XnvtNZWXl%2Buhhx5SXl6errvuOt177736yU9%2B4llm165dWrt2rQoKCtS5c2ctWbJEAwYMuKI5FRefqneZ4OBARUS0lMtV7peXYW974l1fTwFXiZ0Zg3w9hauGvx/3TqOe9jX1mrZt28on4/plwLoaNVbAIrSgKSJg/Z%2Bm/j%2Bvqw31tK%2Bp19RXAcsvbxECAAD4EgELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALPPrr8oBcHVqah8vwsdKALhUXMECAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALAs2NcTAICr3W1PvOvrKVySnRmDfD0F4AePK1gAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywjy9lPsAAANuUlEQVRYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwLJgX08AAGDXbU%2B86%2BspNNjOjEG%2BngLQKLiCBQAAYBkBCwAAwDJuEQIAfKYp3c6UuKWJhuMK1gUUFBRoxowZSkhI0LBhw7R69WrV1tb6eloAAKCJ4ArWBcyePVuxsbF64403VFJSopkzZ%2Bq6667T3Xff7eupAQCAJoArWN%2BTm5urTz75RAsWLFCrVq3UsWNHTZ06VVlZWb6eGgAAaCK4gvU9%2Bfn5at%2B%2BvcLDwz1tsbGxOnbsmE6fPq3Q0NB6X6OoqEjFxcVebcHBLRQVFXXR9YKCAr1%2BAgCuLk3tmbGm5E8Lbvb1FKwiYH2P2%2B1WWFiYV9v5sOVyuRoUsLKysrRu3TqvtgceeECzZ8%2B%2B6HpFRUV69tnfatKkSfWGsfPe/8WtDVruh6ioqEhZWVmXVE9cHDW1j5raRT3to6aXh0slF2CMuaL1J02apJdfftnr36RJk%2Bpdr7i4WOvWratz9QuXh3raR03to6Z2UU/7qOnl4QrW90RGRsrtdnu1ud1uBQQEKDIyskGvERUVRcoHAOAHjCtY3xMXF6cTJ06otLTU05abm6vOnTurZcuWPpwZAABoKghY3xMTE6OePXtq7dq1On36tI4ePapnnnlGaWlpvp4aAABoIoIefvjhh309iavNzTffrO3bt%2BuRRx7Rq6%2B%2BqtTUVE2bNk0BAQGNPnbLli01cOBArpZZQj3to6b2UVO7qKd91PTSBZgrfaIbAAAAXrhFCAAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAcuSgoICpaenKyEhQYmJiVq0aJHKysokSYcOHdJdd92lfv36acSIEXr66ae91t2xY4fGjBmjPn36KCUlRe%2B8846nr7a2Vo8//riGDx%2BuAQMGaNq0afryyy89/W63WxkZGUpMTNTgwYO1ZMkSVVZWOrPRDlmxYoW6devm%2BX3//v1KTU1V3759NXr0aG3dutVr%2BU2bNmnkyJHq27ev0tLSlJeX5%2BmrqqrSf/zHf2jIkCFKSEjQnDlz5HK5PP0FBQWaMWOGEhISNGzYMK1evVq1tbWNv5EOWb9%2BvQYPHqzevXtr6tSpOn78uCRqerkOHjyoKVOmqH///ho0aJAWLFjg%2BaJ4atowb7/9thITEzV37tw6fY15bryS8/LV7mI13bVrl8aOHas%2Bffpo5MiRevHFF736G3O/rO%2BY8DsGVtx%2B%2B%2B1m0aJF5vTp0%2BbEiRMmJSXFLF682FRUVJibb77ZZGZmmvLycpOXl2cGDhxoXn/9dWOMMQcPHjRxcXFm7969prKy0mzZssXEx8ebEydOGGOM2bRpkxk2bJg5cuSIOXXqlPn5z39uxowZY2pra40xxjzwwANmxowZpqSkxBQWFppJkyaZRx55xGd1sO3gwYNm4MCBpmvXrsYYY77%2B%2BmvTu3dvk52dbSorK827775revXqZT7%2B%2BGNjjDFvvvmm6d%2B/v/nwww9NRUWF2bBhgxk0aJApLy83xhizcuVKk5KSYr766ivjcrnMAw88YGbOnOkZb/z48Wbp0qWmrKzMHDt2zIwYMcI8/fTTzm94I9i8ebO59dZbzdGjR82pU6fMI488Yh555BFqepnOnTtnBg0aZNauXWuqqqpMaWmpufvuu83s2bOpaQNt3LjRjBgxwkyePNlkZGR49TXmufFKz8tXs4vV9KOPPjI9e/Y0f/rTn8y5c%2BfM3r17TWxsrPnrX/9qjGnc/bK%2BY8IfEbAsOHnypFm0aJEpLi72tD333HNmxIgRZufOnebHP/6xqa6u9vStXr3a3HPPPcYYY5YvX27S09O9Xm/ixIlmw4YNxhhjRo8ebZ599llP36lTp0xMTIw5cOCAKS4uNt27dzeHDh3y9Ofk5JjevXubs2fPNsq2OqmmpsZMnDjR/Nd//ZcnYP32t78148aN81ouIyPDLFu2zBhjzIwZM8yKFSu8XmPQoEFm%2B/bt5ty5c6Zfv37mjTfe8PQfOXLEdOvWzRQWFpqPP/7Y9OjRw7jdbk//73//ezNy5MjG3EzHJCcne/4H8l3U9PJ89dVXpmvXrubIkSOett///vfmlltuoaYN9Oyzz5qysjKzcOHCOmGgMc%2BNV3pevppdrKY5OTlm3bp1Xm3jx48369evN8Y07n5Z3zHhj7hFaEFYWJhWrlyp6667ztN24sQJRUVFKT8/X926dVNQUJCnLyYmxnPZNT8/XzExMV6vFxMTo9zcXFVWVurIkSNe/aGhobrhhhuUm5urQ4cOKSgoyOv2WWxsrM6cOaPPPvussTbXMS%2B88IJCQkI0ZswYT9s/qtc/qmdgYKB69Oih3NxcffHFFzp16pRiY2M9/T/60Y/UvHlz5efnKz8/X%2B3bt1d4eLinPzY2VseOHdPp06cbazMd8fXXX%2Bv48eM6efKkRo0a5bm8X1paSk0vU3R0tHr06KGsrCyVl5erpKREu3bt0tChQ6lpA02ZMkWtWrW6YF9jnhuv5Lx8tbtYTYcMGaL09HTP79XV1SouLlZ0dLSkxt0v6zsm/BEBqxHk5uZq8%2BbNuu%2B%2B%2B%2BR2uxUWFubV37p1a7ndbtXW1srtdnvtkJIUHh4ul8ulkydPyhjzD/vdbrdCQ0MVEBDg1SfJ6754U/TNN98oMzNTDz30kFf7P6rn%2Be29WD3dbrck1Vk/LCzM0//9Pn%2BpZ2FhoSTptdde0zPPPKMtW7aosLBQS5cupaaXKTAwUJmZmXrzzTfVt29fJSYmqrq6WvPnz6emFjTmufFKzsv%2BZM2aNWrRooVGjRolqXH3y/qOCX9EwLLsb3/7m6ZNm6b58%2BcrMTHxHy733QPfGHPR17xYf33rNlUrV65USkqKOnfufMnrUs%2B6zm/X9OnTFR0drXbt2mn27NnavXv3Ja1/Of3%2BWtOzZ89q1qxZuvXWW/X%2B%2B%2B/rrbfeUqtWrbRgwYIGrU9N6%2Bd0jS7lvNyUGWO0evVqbd%2B%2BXevXr1dISIhXX33rXk7fDxEBy6Ldu3drxowZWrx4saZMmSJJioyMrJPQ3W63WrdurcDAQEVERHj%2BMvhuf2RkpGeZC/W3adNGkZGROn36tGpqarz6JKlNmzaNsYmO2L9/vw4cOOB1Kfu8C9XL5XIpMjLyH/afr%2Bf5Zb7ff/LkSU89L7RuQECAZ92m6vzt6%2B/%2BBdm%2BfXsZY3Tu3Dlqehn279%2Bv48ePa968eWrVqpWio6M1Z84c/elPf7rgcUtNL01jnhuv5Lzc1NXW1mrRokXavXu3/vCHP%2BjGG2/09DXmflnfudsfEbAs%2BeCDD7Rw4UI9%2BeSTGjdunKc9Li5Ohw8fVnV1tactNzdX8fHxnv7v34M%2B3x8SEqIuXbooPz/f01dWVqYvvvhCvXr1Uo8ePWSM0SeffOK1blhYmDp16tRYm9rotm7dqpKSEg0bNkwJCQlKSUmRJCUkJKhr16516pWXl%2BdVz%2B/Wq6amRgcPHlR8fLw6dOig8PBwr/6///3vOnv2rOLi4hQXF6cTJ0543mYvfVvPzp07q2XLlo25yY2uXbt2Cg0N1aFDhzxtBQUFuuaaa5SUlERNL0NNTY1qa2u9/mo/e/asJCkxMZGaXqHGPDdeyXm5qVuxYoU%2B/fRT/eEPf1CHDh28%2Bhpzv%2BzZs%2BdFjwm/5NDD9H7t3Llz5rbbbjMvvPBCnb6qqiozbNgw86tf/cqcOXPGfPjhh6Z///5mz549xhhjDh8%2BbHr27Gn27NljKisrTXZ2tunTp48pKioyxnz7LoyhQ4d63oq8bNkyM2HCBM/rZ2RkmOnTp5uSkhJz4sQJM2HCBLNq1SpHtruxuN1uc%2BLECc%2B/AwcOmK5du5oTJ06YgoIC06dPH/Piiy%2BayspKs3fvXtOrVy/Pu4VycnJMv379zIEDB8yZM2dMZmamSUpKMhUVFcaYb98pNH78ePPVV1%2BZ0tJSM3PmTDN79mzP2BMnTjSLFy82p06dMkeOHDHJyclm8%2BbNPqmDbStWrDDDhw83n3/%2Bufnmm2/MpEmTzKJFi8w333xDTS9DaWmpGThwoPnlL39pzpw5Y0pLS82sWbPMT3/6U2p6iS70jrfGPDde6Xm5KbhQTd9//30zYMAAr3e8f1dj7pf1HRP%2BiIBlwV//%2BlfTtWtXExcXV%2Bff8ePHzeHDh83kyZNNXFycGTp0qHn%2B%2Bee91n/99dfNiBEjTGxsrLnjjjvMX/7yF09fbW2tefLJJ81NN91kevXqZe69916vz2IpKyszc%2BfONb179zYDBgwwy5cvN1VVVY5tuxO%2B/PJLz8c0GGPMX/7yFzN27FgTGxtrRowYUeejB55//nmTlJRk4uLiTFpamjl8%2BLCnr6qqyjz88MNmwIABpk%2BfPmbevHmmrKzM03/ixAkzffp006tXL5OYmGh%2B9atfeT5Xp6n77rb37t3bLFy40Jw%2BfdoYQ00vV25urrnrrrtM//79TWJiosnIyDCFhYXGGGraEOfPk927dzfdu3f3/H5eY54br%2BS8fDW7WE0ffPBBr7bz/%2B6%2B%2B27P%2Bo25X9Z3TPibAGN4Kg0AAMAmnsECAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMv%2BHxaD09BOUd4CAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common4732728103973025122”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>37085.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>42120.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>57862.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>48127.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>44221.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>43428.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>49664.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>57367.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>46067.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50335.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2983)</td> <td class=”number”>3105</td> <td class=”number”>96.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>79</td> <td class=”number”>2.5%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme4732728103973025122”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>22894.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>23014.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>23341.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>23968.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>24001.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>109926.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>110224.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>112844.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>122092.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>125900.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_METRO13”>METRO13<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.37165</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>61.3%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram104538311934215190”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives104538311934215190,#minihistogram104538311934215190”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives104538311934215190”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles104538311934215190”

aria-controls=”quantiles104538311934215190” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram104538311934215190” aria-controls=”histogram104538311934215190”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common104538311934215190” aria-controls=”common104538311934215190”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme104538311934215190” aria-controls=”extreme104538311934215190”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles104538311934215190”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>1</td>

</tr> <tr>

<th>95-th percentile</th> <td>1</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr>

<th>Range</th> <td>1</td>

</tr> <tr>

<th>Interquartile range</th> <td>1</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.48332</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.3005</td>

</tr> <tr>

<th>Kurtosis</th> <td>-1.7186</td>

</tr> <tr>

<th>Mean</th> <td>0.37165</td>

</tr> <tr>

<th>MAD</th> <td>0.46705</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.53147</td>

</tr> <tr>

<th>Sum</th> <td>1164</td>

</tr> <tr>

<th>Variance</th> <td>0.2336</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram104538311934215190”>

<img 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rHbK1jpXZBusv9yFZsP74xz8qNjZWN910k2%2BtrKxMHTp0UEREhLp166aSkhJdffXVkqTKykodOnRIvXr1UlJSkrxer/bv36%2BUlBRJksvlUkxMjDp37tyo8ycmJp72dqDbfUx1dRf3F6Qdr7%2B%2BvsGWc9sB2VqLfK1DttYJpWztdQukkU6cOKHHHntMLpdLtbW12rBhg/785z9r7NixkqScnBwVFRWprKxMVVVVys/PV8%2BePZWWlqaEhAQNGTJETz/9tI4eParPPvtMS5Ys0ZgxY%2BR0hmQfBQAAhtm2MaSlpUmS6urqJElbt26VdPJu07hx41RdXa1p06bJ7Xbrsssu05IlS5SamipJGjt2rNxut%2B68805VV1crPT3d75mpefPm6eGHH9agQYPUrFkz3XLLLad9mCkAAMDZhHnP94luNIrbfcz4MZ3OcN2Yv9P4ca2ycfp1wR6h0ZzOcMXHR6qiojpkbldfKMjWWuRrHbK1jpXZtmlz9o90slJIvkUIAAAQTBQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADLNtwdq5c6cyMjKUl5d32rYtW7Zo%2BPDh6tOnj4YMGaJXX33Vt23x4sXq2bOn0tLS/H598cUXkqRvvvlGv/rVrzRgwAClp6dr6tSpqqioCNh1AQAA%2B7NlwVq%2BfLnmz5%2Bvjh07nrbtgw8%2B0MyZMzV16lS9%2B%2B67euihhzRv3jzt2rXLt8%2BIESPkcrn8fl1yySWSpEWLFqmkpETFxcXavHmzvF6vHnzwwYBdGwAAsD9bFqyIiAitWrXqjAXL4/Fo4sSJuuGGG%2BR0OpWZmanu3bv7Fayzqaur06pVq3TPPfeoffv2iouL0/Tp0/XWW2/p888/t%2BJSAABACHIGe4DvY9y4cWfdNmDAAA0YMMD3%2B7q6OrndbrVt29a39uGHH2rs2LH66KOP1L59ez344IPq37%2B/Dh06pGPHjiklJcW3b5cuXdSiRQuVlJT4HeO7lJeXy%2B12%2B605na2UmJjY2EtsFIfDXv3Y6bTPvKeytVvGdkC21iJf65CtdUIxW1sWrKbIz89Xq1atdNNNN0mS2rVrpw4dOmjGjBlKTExUcXGxJk2apHXr1snj8UiSYmJi/I4RExPTpOewiouLVVBQ4LeWm5urqVOnnufV2Ft8fGSwR2iymJiWwR4hZJGttcjXOmRrnVDKNuAFKysrS6NHj9att96q9u3bW3Yer9er/Px8bdiwQUVFRYqIiJAk3Xbbbbrtttt8%2B40fP16vv/661q1b57vz5fV6z%2Bvc2dnZysrK8ltzOlupoqL6vI77bXZr%2Bqav30oOR7hiYlqqsrJG9fUNwR4npJCttcjXOmRrHSuzDdZf7gNesG699Va9/vrrWrp0qa699lrdfvvtysrKktNpbpSGhgY9%2BOCD%2BuCDD/THP/5RHTp0%2BM79k5KSVF5eroSEBEknn%2BOKjPzPP5CvvvpKrVu3bvT5ExMTT3s70O0%2Bprq6i/sL0o7XX1/fYMu57YBsrUW%2B1iFb64RStgG/BZKbm6s33nhDr776qrp166Zf//rXyszM1FNPPaWDBw8aOcevf/1rffzxx2csV7/97W/1zjvv%2BK2VlZWpQ4cO6tChg2JjY1VSUuLb9tFHH%2BnEiRNKTU01MhsAAAh9QXuPKSUlRbNmzdL27dv10EMP6dVXX9VNN92kCRMm6IMPPvjex33vvfe0bt06FRYWKi4u7rTtHo9Hjz76qA4cOKBvvvlGL7zwgg4dOqRRo0bJ4XDo9ttv13PPPacjR46ooqJCv/nNb3TjjTf6PsYBAADgXIL2kHttba3%2B9Kc/ac2aNfrb3/6mTp06acqUKSovL9f48eP16KOPatiwYWd8bVpamqST3yEoSVu3bpUkuVwurV69WseOHdP111/v95p%2B/frphRde0IwZMySdfPbK4/Goa9eueumll9SuXTtJ0tSpU1VdXa0RI0aorq5O119/vR555BErIgAAACEqzHu%2BT3Q3UVlZmVatWqW1a9equrpaQ4YM0dixY9W3b1/fPjt27NAjjzyi7du3B3I0S7ndx4wf0%2BkM1435O40f1yobp18X7BEazekMV3x8pCoqqkPmeYALBdlai3ytQ7bWsTLbNm2ijR6vsQJ%2BB%2Bvmm29W586dNXHiRI0cOfKMb%2BNlZmbq6NGjgR4NAADAiIAXrKKiIl199dXn3G/37t0BmAYAAMC8gD/k3qNHD02aNMn33JQkvfTSS/r5z3/u%2B6BPAAAAOwt4wVqwYIGOHTumrl27%2BtYGDhyohoYGPfHEE4EeBwAAwLiAv0X49ttva/369YqPj/etderUSfn5%2BbrlllsCPQ4AAIBxAb%2BDdfz4cd%2BPrfEbJDxcNTU1gR4HAADAuIAXrH79%2BumJJ57QV1995Vv7/PPP9eijj/p9VAMAAIBdBfwtwoceekg/%2B9nPdO211yoqKkoNDQ2qrq5Whw4d9Ic//CHQ4wAAELJ%2B/PRfgj1Co%2B16fGiwRzAq4AWrQ4cOev311/XnP/9Zhw4dUnh4uDp37qz%2B/fvL4XAEehwAAADjgvKjcpo3b64bbrghGKcGAACwXMAL1qeffqqFCxfq448/1vHjx0/b/uabbwZ6JAAAAKOC8gxWeXm5%2Bvfvr1atWgX69AAAAJYLeMHas2eP3nzzTSUkJAT61AAAAAER8I9paN26NXeuAABASAt4wZo4caIKCgrk9XoDfWoAAICACPhbhH/%2B85/1z3/%2BU2vWrNFll12m8HD/jvfKK68EeiQAAACjAl6woqKiNGDAgECfFgAAIGACXrAWLFgQ6FMCAAAEVMCfwZKkAwcOaPHixXrwwQd9a//v//2/YIwCAABgXMAL1jvvvKPhw4dry5Yt2rBhg6STHz46btw4PmQUAACEhIAXrEWLFun%2B%2B%2B/X%2BvXrFRYWJunkzyd84okntGTJkkCPAwAAYFzAC9ZHH32knJwcSfIVLEkaOnSoysrKAj0OAACAcQEvWNHR0Wf8GYTl5eVq3rx5oMcBAAAwLuAF68orr9Svf/1rVVVV%2BdYOHjyoWbNm6dprrw30OAAAAMYF/GMaHnzwQd11111KT09XfX29rrzyStXU1Khbt2564oknAj0OAACAcQEvWO3atdOGDRu0Y8cOHTx4UC1atFDnzp113XXX%2BT2TBQAAYFcBL1iS1KxZM91www3BODUAAIDlAl6wsrKyvvNOFZ%2BFBQAA7C7gD7nfdNNNfr%2BGDBmi7t2765tvvtHYsWObdKydO3cqIyNDeXl5p2174403NGzYMPXp00ejR4/W22%2B/7dvW0NCgRYsWadCgQerXr58mTJigTz/91Lfd4/Fo%2BvTpysjIUP/%2B/TV79uwzfucjAADAmQT8DtbMmTPPuL5582b9/e9/b/Rxli9frlWrVqljx46nbdu3b59mzZqlgoICXXPNNdq8ebPuvfdebdq0Se3atdOKFSu0fv16LV%2B%2BXG3bttWiRYuUm5ur1157TWFhYZo7d65OnDihDRs2qLa2VtOmTVN%2Bfr7mzJnzva8bAABcPILyswjP5IYbbtDrr7/e6P0jIiLOWrBWrlypzMxMZWZmKiIiQsOHD1f37t21bt06SVJxcbHGjx%2BvLl26KCoqSnl5eSorK9Pu3bv1xRdfaOvWrcrLy1NCQoLatm2re%2B65R6tXr1Ztba2x6wUAAKErKA%2B5n8nevXvl9Xobvf%2B4cePOuq2kpESZmZl%2Ba8nJyXK5XDp%2B/LhKS0uVnJzs2xYVFaWOHTvK5XLp2LFjcjgc6tGjh297SkqKvv76ax04cMBv/WzKy8vldrv91pzOVkpMTGzs5TWKw3HB9ONGcTrtM%2B%2BpbO2WsR2QrbXI1zpka71QyjbgBetMz1nV1NSorKxMgwcPNnIOj8ej2NhYv7XY2FiVlpbqq6%2B%2BktfrPeP2iooKxcXFKSoqyu9B/FP7VlRUNOr8xcXFKigo8FvLzc3V1KlTv8/lhIz4%2BMhgj9BkMTEtgz1CyCJba5GvdcjWOqGUbcALVqdOnU77LsKIiAiNGTNGt912m7HznOtu2Hdtb8qdtDPJzs5WVlaW35rT2UoVFdXnddxvs1vTN339VnI4whUT01KVlTWqr28I9jghhWytRb7WIVvrWZFtsP5yH/CCFYhPa4%2BPj5fH4/Fb83g8SkhIUFxcnMLDw8%2B4vXXr1kpISFBVVZXq6%2BvlcDh82ySpdevWjTp/YmLiaW8Hut3HVFd3cX9B2vH66%2BsbbDm3HZCttcjXOmRrnVDKNuAFa%2B3atY3ed%2BTIkd/rHKmpqdqzZ4/fmsvl0s0336yIiAh169ZNJSUluvrqqyVJlZWVOnTokHr16qWkpCR5vV7t379fKSkpvtfGxMSoc%2BfO32seAABwcQl4wZo9e7YaGhpOexsuLCzMby0sLOx7F6zbb79dY8aM0VtvvaVrr71W69ev1yeffKLhw4dLknJyclRYWKgBAwaobdu2ys/PV8%2BePZWWliZJGjJkiJ5%2B%2Bmk9%2BeSTOnHihJYsWaIxY8bI6bxgvicAAABcwALeGJ5//nm98MILmjRpknr06CGv16sPP/xQy5cv109%2B8hOlp6c36jinylBdXZ0kaevWrZJO3m3q3r278vPztWDBAh0%2BfFhdu3bVsmXL1KZNG0knH7R3u9268847VV1drfT0dL%2BH0ufNm6eHH35YgwYNUrNmzXTLLbec8cNMAQAAziTMe75PdDfRiBEjVFhYqLZt2/qtf/7555owYYI2bNgQyHECxu0%2BZvyYTme4bszfafy4Vtk4/bpgj9BoTme44uMjVVFRHTLPA1woyNZa5GsdO2b746f/EuwRGm3X40MtybZNm2ijx2usgH8b2ieffHLaRyRIUkxMjA4fPhzocQAAAIwLeMFKSkrSE0884feZUpWVlVq4cKEuv/zyQI8DAABgXMCfwXrooYc0Y8YMFRcXKzIyUuHh4aqqqlKLFi20ZMmSQI8DAABgXMALVv/%2B/fXWW29px44d%2Buyzz%2BT1etW2bVv96Ec/UnR0cN4nBQAAMCkonzvQsmVLDRo0SJ999pk6dOgQjBEAAAAsE/BnsI4fP65Zs2apT58%2B%2BvGPfyzp5DNYd999tyorKwM9DgAAgHEBL1hPPfWU9u3bp/z8fIWH/%2Bf09fX1ys/PD/Q4AAAAxgW8YG3evFnPPvushg4d6vuhzzExMVqwYIG2bNkS6HEAAACMC3jBqq6uVqdOnU5bT0hI0Ndffx3ocQAAAIwLeMG6/PLL9fe//12S/H724KZNm3TppZcGehwAAADjAv5dhHfccYemTJmiW2%2B9VQ0NDXrxxRe1Z88ebd68WbNnzw70OAAAAMYFvGBlZ2fL6XTq5ZdflsPh0HPPPafOnTsrPz9fQ4cODfQ4AAAAxgW8YB09elS33nqrbr311kCfGgAAICAC/gzWoEGD/J69AgAACDUBL1jp6enauHFjoE8LAAAQMAF/i7B9%2B/Z6/PHHVVhYqMsvv1zNmjXz275w4cJAjwQAAGBUwAtWaWmpfvCDH0iSKioqAn16AAAAywWsYOXl5WnRokX6wx/%2B4FtbsmSJcnNzAzUCAABAQATsGaxt27adtlZYWBio0wMAAARMwArWmb5zkO8mBAAAoShgBevUD3Y%2B1xoAAIDdBfxjGgAAAEIdBQsAAMCwgH0XYW1trWbMmHHONT4HCwAA2F3AClbfvn1VXl5%2BzjUAAAC7C1jB%2Bu/PvwIAAAhlPIMFAABgWMB/VE4gvPvuu/rZz37mt%2Bb1elVbW6uioiKNGzdOzZs399v%2Bv//7v/rxj38sSSoqKtKKFSvkdrvVo0cPzZ49W6mpqQGbHwAA2FtIFqx%2B/frJ5XL5rT333HPav3%2B/JCkpKemMnywvnfzE%2BcWLF%2Bv5559Xjx49VFRUpEmTJmnLli1q1aqV5bMDAAD7uyjeIvz3v/%2BtF198Ub/85S/PuW9xcbFGjx6t3r17q0WLFrr77rslSdu3b7d6TAAAECIuioL1zDPP6NZbb9Wll14qSaqurlZubq7S09P1ox/9SC%2B%2B%2BKLvx/aUlJQoOTnZ99rw8HD17NnztDtiAAAAZxOSbxH%2Bt3/961/asmWLtmzZIkmKiopS9%2B7dddddd2nRokX6xz/%2BoWnTpik6OlpjxoyRx%2BPn0ueZAAAU6klEQVRRbGys3zFiY2NVUVHR6HOWl5fL7Xb7rTmdrZSYmHj%2BF/RfHA579WOn0z7znsrWbhnbAdlai3ytQ7bWC6VsQ75grVixQoMHD1abNm0kSSkpKX4fGdG/f3%2BNHTtWa9as0ZgxYySd/w%2BhLi4uVkFBgd9abm6upk6del7Htbv4%2BMhgj9BkMTEtgz1CyCJba5GvdcjWOqGUbcgXrM2bN2vWrFnfuU9SUpI2b94sSYqPj5fH4/Hb7vF41K1bt0afMzs7W1lZWX5rTmcrVVRUN/oYjWG3pm/6%2Bq3kcIQrJqalKitrVF/fEOxxQgrZWot8rUO21rMi22D95T6kC9a%2Bfft0%2BPBhXXfddb61jRs3qqKiQnfccYdv7cCBA%2BrQoYMkKTU1VSUlJRo1apQkqb6%2BXnv37vXd3WqMxMTE094OdLuPqa7u4v6CtOP119c32HJuOyBba5GvdcjWOqGUrb1ugTTR3r17FRcXp6ioKN9as2bN9OSTT%2Brtt99WbW2t/vKXv2j16tXKycmRJOXk5Gjt2rV6//33VVNTo6VLl6p58%2BYaOHBgkK4CAADYTUjfwfriiy98z16dcsMNN%2Bihhx7SY489piNHjuiSSy7RQw89pMGDB0uSBgwYoPvuu0/Tp0/Xl19%2BqbS0NBUWFqpFixbBuAQAAGBDYd7zfaIbjeJ2HzN%2BTKczXDfm7zR%2BXKtsnH7duXe6QDid4YqPj1RFRXXI3K6%2BUJCttcjXOnbM9sdP/yXYIzTarseHWpJtmzbRRo/XWCH9FiEAAEAwULAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwLGQLVo8ePZSamqq0tDTfr8cee0yS9M4772jMmDG68sordfPNN2vdunV%2Bry0qKtKQIUN05ZVXKicnR3v27AnGJQAAAJtyBnsAK23atEmXXXaZ31p5ebnuuecezZ49W8OGDdN7772nyZMnq3PnzkpLS9O2bdu0ePFiPf/88%2BrRo4eKioo0adIkbdmyRa1atQrSlQAAADsJ2TtYZ7N%2B/Xp16tRJY8aMUUREhDIyMpSVlaWVK1dKkoqLizV69Gj17t1bLVq00N133y1J2r59ezDHBgAANhLSBWvhwoUaOHCgrrrqKs2dO1fV1dUqKSlRcnKy337Jycm%2BtwG/vT08PFw9e/aUy%2BUK6OwAAMC%2BQvYtwiuuuEIZGRl68skn9emnn2r69Ol69NFH5fF41LZtW7994%2BLiVFFRIUnyeDyKjY312x4bG%2Bvb3hjl5eVyu91%2Ba05nKyUmJn7Pqzkzh8Ne/djptM%2B8p7K1W8Z2QLbWIl/rkK31QinbkC1YxcXFvv/fpUsXzZw5U5MnT1bfvn3P%2BVqv13ve5y4oKPBby83N1dSpU8/ruHYXHx8Z7BGaLCamZbBHCFlkay3ytQ7ZWieUsg3ZgvVtl112merr6xUeHi6Px%2BO3raKiQgkJCZKk%2BPj407Z7PB5169at0efKzs5WVlaW35rT2UoVFdXfc/ozs1vTN339VnI4whUT01KVlTWqr28I9jghhWytRb7WIVvrWZFtsP5yH5IFa%2B/evVq3bp0eeOAB31pZWZmaN2%2BuzMxM/d///Z/f/nv27FHv3r0lSampqSopKdGoUaMkSfX19dq7d6/GjBnT6PMnJiae9nag231MdXUX9xekHa%2B/vr7BlnPbAdlai3ytQ7bWCaVs7XULpJFat26t4uJiFRYW6sSJEzp48KCeeeYZZWdna8SIETp8%2BLBWrlypb775Rjt27NCOHTt0%2B%2B23S5JycnK0du1avf/%2B%2B6qpqdHSpUvVvHlzDRw4MLgXBQAAbCMk72C1bdtWhYWFWrhwoa8gjRo1Snl5eYqIiNCyZcs0f/58Pfroo0pKStJTTz2lH/7wh5KkAQMG6L777tP06dP15ZdfKi0tTYWFhWrRokWQrwoAANhFSBYsSerXr59eeeWVs2577bXXzvraO%2B64Q3fccYdVowEAgBAXkm8RAgAABBMFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMNCtmAdPnxYubm5Sk9PV0ZGhh544AFVVlbqX//6l3r06KG0tDS/X7/73e98r33jjTc0bNgw9enTR6NHj9bbb78dxCsBAAB24wz2AFaZNGmSUlNTtW3bNh07dky5ubl68sknNXnyZEmSy%2BU64%2Bv27dunWbNmqaCgQNdcc402b96se%2B%2B9V5s2bVK7du0CeQkAAMCmQvIOVmVlpVJTUzVjxgxFRkaqXbt2GjVqlHbt2nXO165cuVKZmZnKzMxURESEhg8fru7du2vdunUBmBwAAISCkLyDFRMTowULFvitHTlyRImJib7f//KXv9Rf//pX1dXV6bbbbtPUqVPVrFkzlZSUKDMz0%2B%2B1ycnJZ73jdSbl5eVyu91%2Ba05nK7/zm%2BBw2KsfO532mfdUtnbL2A7I1lrkax2ytV4oZRuSBevbXC6XXn75ZS1dulTNmzdXnz59dOONN%2Brxxx/Xvn37NGXKFDmdTk2bNk0ej0exsbF%2Br4%2BNjVVpaWmjz1dcXKyCggK/tdzcXE2dOtXI9dhVfHxksEdospiYlsEeIWSRrbXI1zpka51QyjbkC9Z7772nyZMna8aMGcrIyJAkvfLKK77tvXr10sSJE7Vs2TJNmzZNkuT1es/rnNnZ2crKyvJbczpbqaKi%2BryO%2B212a/qmr99KDke4YmJaqrKyRvX1DcEeJ6SQrbXI1zpkaz0rsg3WX%2B5DumBt27ZN999/v%2BbOnauRI0eedb%2BkpCR98cUX8nq9io%2BPl8fj8dvu8XiUkJDQ6PMmJiae9nag231MdXUX9xekHa%2B/vr7BlnPbAdlai3ytQ7bWCaVs7XULpAn%2B%2Bc9/atasWXrmmWf8ytU777yjpUuX%2Bu174MABJSUlKSwsTKmpqdqzZ4/fdpfLpd69ewdkbgAAYH8hWbDq6uo0Z84czZw5U/379/fbFh0drSVLlui1115TbW2tXC6Xfve73yknJ0eSdPvtt%2Buvf/2r3nrrLX3zzTdatWqVPvnkEw0fPjwYlwIAAGwoJN8ifP/991VWVqb58%2Bdr/vz5fts2bdqkRYsWqaCgQL/61a8UHR2tO%2B%2B8U3fddZckqXv37srPz9eCBQt0%2BPBhde3aVcuWLVObNm2CcSkAAMCGQrJgXXXVVfrwww/Puj0pKUk33njjWbcPHjxYgwcPtmI0AABwEQjJtwgBAACCiYIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRSsMzh8%2BLB%2B8YtfKD09Xddff72eeuopNTQ0BHssAABgE85gD3AhmjJlilJSUrR161Z9%2BeWXmjhxoi655BL99Kc/DfZoAADABriD9S0ul0v79%2B/XzJkzFR0drU6dOmn8%2BPEqLi4O9mgAAMAmuIP1LSUlJUpKSlJsbKxvLSUlRQcPHlRVVZWioqLOeYzy8nK53W6/NaezlRITE43O6nDYqx87nfaZ91S2dsvYDsjWWuRrHbK1XihlS8H6Fo/Ho5iYGL%2B1U2WroqKiUQWruLhYBQUFfmv33nuvpkyZYm5QnSxyd7X7WNnZ2cbL28WuvLxcv//982RrAbK1Fvlax47Z7np8aLBHaJTy8nItXrzYVtmeS%2BhURYO8Xu95vT47O1tr1qzx%2B5WdnW1ouv9wu90qKCg47W4Zzh/ZWodsrUW%2B1iFb64RittzB%2BpaEhAR5PB6/NY/Ho7CwMCUkJDTqGImJiSHTwAEAQNNxB%2BtbUlNTdeTIER09etS35nK51LVrV0VGRgZxMgAAYBcUrG9JTk5WWlqaFi5cqKqqKpWVlenFF19UTk5OsEcDAAA24XjkkUceCfYQF5of/ehH2rBhgx577DG9/vrrGjNmjCZMmKCwsLBgj3aayMhIXX311dxdswDZWodsrUW%2B1iFb64RatmHe832iGwAAAH54ixAAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMArWBe7w4cP6xS9%2BofT0dF1//fV66qmn1NDQcMZ9i4qKNGTIEF155ZXKycnRnj17AjytvTQl2z/%2B8Y8aMmSI%2BvTpoxEjRmjr1q0BntZempLtKZ9//rn69OmjxYsXB2hK%2B2pKvmVlZbrzzjvVu3dvZWZm6qWXXgrssDbT2GwbGhr07LPPKisrS3369NGwYcP0xhtvBGFie9m5c6cyMjKUl5f3nfs1NDRo0aJFGjRokPr166cJEybo008/DdCUhnhxQRs1apR3zpw53srKSu/Bgwe9gwcP9r7wwgun7ffmm296r7rqKu/777/vramp8S5btsx73XXXeaurq4MwtT00NttNmzZ5%2B/bt6921a5f3xIkT3ldffdWbkpLiPXToUBCmtofGZvvf7r33Xm/fvn29zz77bICmtK/G5ltTU%2BMdOHCgd/ny5d6vv/7au3v3bu/NN9/sLS0tDcLU9tDYbF9%2B%2BWVv//79vWVlZd66ujrvtm3bvMnJyd59%2B/YFYWp7KCws9A4ePNg7duxY7/Tp079z36KiIu/111/vLS0t9R47dsw7b94877Bhw7wNDQ0Bmvb8cQfrAuZyubR//37NnDlT0dHR6tSpk8aPH6/i4uLT9i0uLtbo0aPVu3dvtWjRQnfffbckafv27YEe2xaaku3x48d13333qW/fvmrWrJluu%2B02RUZG6v333w/C5Be%2BpmR7yo4dO1RaWqqBAwcGblCbakq%2BGzduVFRUlO6%2B%2B261bNlSvXr10oYNG9SlS5cgTH7ha0q2JSUl6tu3r37wgx/I4XDo%2BuuvV1xcnD788MMgTG4PERERWrVqlTp27HjOfYuLizV%2B/Hh16dJFUVFRysvLU1lZmXbv3h2ASc2gYF3ASkpKlJSUpNjYWN9aSkqKDh48qKqqqtP2TU5O9v0%2BPDxcPXv2lMvlCti8dtKUbEeMGKE77rjD9/vKykpVV1erbdu2AZvXTpqSrXSywM6bN08PP/ywnE5nIEe1pabk%2B95776l79%2B568MEHddVVV2no0KFat25doEe2jaZkO3DgQP3jH//Qvn37dOLECb355puqqanR1VdfHeixbWPcuHGKjo4%2B537Hjx9XaWmp359pUVFR6tixo63%2BTKNgXcA8Ho9iYmL81k594VdUVJy273//R%2BHUvt/eDyc1Jdv/5vV6NWfOHPXu3Zv/kJ5FU7NdsmSJrrjiCl1zzTUBmc/umpLvZ599pjfffFMZGRnauXOnJk6cqFmzZmnv3r0Bm9dOmpLt4MGDlZ2drZEjRyotLU0zZszQggUL1L59%2B4DNG6q%2B%2Buoreb1e2/%2BZxl8XL3Ber9eSfdH0vGpra/XAAw%2BotLRURUVFFk0VGhqbbWlpqVauXKn169dbPFFoaWy%2BXq9XKSkpGjZsmCRp1KhReuWVV7Rp0ya/uwP4j8Zmu3btWq1du1YrV65Ujx499M4772jGjBlq3769evXqZfGUFwe7/5nGHawLWEJCgjwej9%2Bax%2BNRWFiYEhIS/Nbj4%2BPPuO%2B398NJTclWOnnLeuLEifr3v/%2BtFStW6JJLLgnUqLbT2Gy9Xq8eeeQRTZkyRW3atAn0mLbVlH9327Rpc9pbMklJSXK73ZbPaUdNyfbll19Wdna2evXqpYiICA0cOFDXXHMNb8EaEBcXp/Dw8DP%2Bs2jdunWQpmo6CtYFLDU1VUeOHNHRo0d9ay6XS127dlVkZORp%2B5aUlPh%2BX19fr71796p3794Bm9dOmpKt1%2BtVXl6enE6nXnrpJcXHxwd6XFtpbLb//ve/9e677%2BrZZ59Venq60tPT9frrr%2Bv555/XqFGjgjG6LTTl390uXbroo48%2B8rsTcPjwYSUlJQVsXjtpSrYNDQ2qr6/3Wztx4kRA5gx1ERER6tatm9%2BfaZWVlTp06JCt7g5SsC5gycnJSktL08KFC1VVVaWysjK9%2BOKLysnJkSQNHTpUu3btkiTl5ORo7dq1ev/991VTU6OlS5eqefPmfFfWWTQl2/Xr16u0tFTPPPOMIiIigjm2LTQ223bt2mnHjh167bXXfL%2BysrI0duxYFRYWBvkqLlxN%2BXd3%2BPDhqqio0HPPPafjx49rw4YNKikp0fDhw4N5CRespmSblZWlVatWaf/%2B/aqrq9Pbb7%2Btd955R4MGDQrmJdjW559/rqFDh/o%2B6yonJ0dFRUUqKytTVVWV8vPz1bNnT6WlpQV50sbjGawL3LPPPqu5c%2BfquuuuU1RUlMaOHev7jraDBw/q66%2B/liQNGDBA9913n6ZPn64vv/xSaWlpKiwsVIsWLYI5/gWtsdmuXr1ahw8fPu2h9hEjRmj%2B/PkBn9sOGpOtw%2BFQu3bt/F7XsmVLRUVF8ZbhOTT23922bdtq2bJlevzxx/Xb3/5Wl156qZYsWaLLL788mONf0Bqb7cSJE1VXV6fc3FwdPXpUSUlJmj9/vq699tpgjn9BO1WO6urqJMn3gc0ul0u1tbU6ePCg7y7g2LFj5Xa7deedd6q6ulrp6ekqKCgIzuDfU5jX7k%2BRAQAAXGB4ixAAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADPv/uxLXTS4dWU4AAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common104538311934215190”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1968</td> <td class=”number”>61.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>1164</td> <td class=”number”>36.3%</td> <td>

<div class=”bar” style=”width:59%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme104538311934215190”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1968</td> <td class=”number”>61.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>1164</td> <td class=”number”>36.3%</td> <td>

<div class=”bar” style=”width:59%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1968</td> <td class=”number”>61.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>1164</td> <td class=”number”>36.3%</td> <td>

<div class=”bar” style=”width:59%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_MILK_PRICE10”>MILK_PRICE10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>36</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.3%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>106</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.97257</td>

</tr> <tr>

<th>Minimum</th> <td>0.72199</td>

</tr> <tr>

<th>Maximum</th> <td>1.3951</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-585755305967145292”>

</div> <div class=”col-md-12 text-right”>

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<li role=”presentation” class=”active”><a href=”#quantiles-585755305967145292”

aria-controls=”quantiles-585755305967145292” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-585755305967145292” aria-controls=”histogram-585755305967145292”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-585755305967145292” aria-controls=”common-585755305967145292”

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<li role=”presentation”><a href=”#extreme-585755305967145292” aria-controls=”extreme-585755305967145292”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-585755305967145292”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0.72199</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.76951</td>

</tr> <tr>

<th>Q1</th> <td>0.87364</td>

</tr> <tr>

<th>Median</th> <td>0.9703</td>

</tr> <tr>

<th>Q3</th> <td>1.082</td>

</tr> <tr>

<th>95-th percentile</th> <td>1.1367</td>

</tr> <tr>

<th>Maximum</th> <td>1.3951</td>

</tr> <tr>

<th>Range</th> <td>0.67309</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.20838</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.12024</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.12363</td>

</tr> <tr>

<th>Kurtosis</th> <td>-0.6628</td>

</tr> <tr>

<th>Mean</th> <td>0.97257</td>

</tr> <tr>

<th>MAD</th> <td>0.099678</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.14003</td>

</tr> <tr>

<th>Sum</th> <td>3018.9</td>

</tr> <tr>

<th>Variance</th> <td>0.014458</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-585755305967145292”>

<img 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mjM6dO6dBgwZpwYIFatOmjdasWaPFixdr0aJFat%2B%2BvbKystS5c2dJUp8%2BfTR79mzNnDlTJ06cUHx8vHJychQUFOTiHQEAAE/htQErICBAjz76qB599NE6cz169KhzmfCfZWRkKCMjoznLAwAAXswrLxECAAC4EgELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwmNsFrJSUFGVnZ%2Bv77793dSkAAAC/i9sFrNGjR2vbtm265ZZbNGnSJO3YsUOVlZWuLgsAAMBpbhewMjMztW3bNv3nf/6nOnXqpCVLlqhv377KysrSsWPHnH6emJgYxcXFKT4%2BvvbXY489JkkqKChQamqqEhMTNWzYMG3evNnhsbm5uRo0aJASExOVnp6uoqIiQ/cIAAC8m9nVBVxI165d1bVrVz3wwAPatm2bFi5cqLVr1yo5OVn33Xefrrvuukaf4%2B2339aVV17pMFZaWqpp06Zp/vz5Gj58uD7%2B%2BGNNnTpVHTp0UHx8vHbt2qWVK1fqxRdfVExMjHJzczVlyhTt2LFDrVu3bq7tAgAAL%2BJ2Z7B%2Bc%2B7cOW3btk1333235s2bp3bt2umhhx5Sly5dNH78eG3ZsuV3Pe%2BWLVsUHR2t1NRUBQYGKjk5WSkpKcrLy5MkWSwWjRo1SgkJCQoKCtKkSZMkSbt37zZsbwAAwLu53Rmso0ePasOGDdq4caNOnTqlQYMG6ZVXXlH37t1r1/To0UMLFy7U8OHDG3yuFStW6NNPP1V5ebmGDBmiBx98UMXFxYqNjXVYFxsbq%2B3bt0uSiouLNXTo0No5k8mkLl26qLCwUMOGDXNqD6WlpbJarQ5jZnNrRUVF1bve39/k8NXX%2BPr%2BJd/rgdlcd5%2B%2B1oPmVF9/PQWvA3rgLft3u4A1bNgwdejQQZMnT9btt9%2Bu8PDwOmv69u2rsrKyBp/n%2BuuvV3Jysp544gl9%2B%2B23mjlzphYtWiS73a527do5rA0PD5fNZpMk2e12hYWFOcyHhYXVzjvDYrEoOzvbYSwzM1MzZsxo8HGhoa2cPoY38vX9S77Tg4iINhec85UeNKeG%2BuspeB3QA0/fv9sFrNzcXPXs2bPRdZ999lmD8xaLpfa/r732Ws2dO1dTp051OBN2ITU1NY0X2oC0tDSlpKQ4jJnNrWWznap3vb%2B/SaGhrXTyZIWqqqov6tieyNf3L/leD%2Br7XvC1HjSnC73XeAJeB/TA6P276i8cbhewYmJiNGXKFKWmpuqWW26RJL388svau3evsrKy6j2j5Ywrr7xSVVVVMplMstvtDnM2m02RkZGSpIiIiDrzdrtdnTp1cvpYUVFRdS4HWq0/q7Ky4RdKVVV1o2u8ma/vX/KdHjS0R1/pQXPyhv7xOqAHnr5/t7vAuXTpUv3888/q2LFj7Vi/fv1UXV2tZcuWOfUcBw4cqLP26NGjCggIUN%2B%2Bfet87EJRUZESEhIkSXFxcSouLq6dq6qq0oEDB2rnAQAAGuN2AWvPnj3Kzs5WdHR07Vh0dLSWL1%2Bu999/36nnaNu2rSwWi3JycnT27FkdO3ZMzz77rNLS0nTbbbeppKREeXl5OnPmjPLz85Wfn6%2BxY8dKktLT07Vx40bt379fFRUVWr16tQICAtSvX79m2C0AAPBGbneJ8PTp0woMDKwzbjKZVFFR4dRztGvXTjk5OVqxYkVtQBo5cqRmzZqlwMBArVmzRosXL9aiRYvUvn17ZWVlqXPnzpKkPn36aPbs2Zo5c6ZOnDih%2BPh45eTkKCgoyNB9AgAA7%2BV2AatHjx5atmyZ5syZU/vTfD/88IOeeOIJp25Q/%2Bfn%2Bdvf/nbBuU2bNl3wsRkZGcrIyGha4QAAAP%2Bf2wWshx9%2BWBMmTNBNN92k4OBgVVdX69SpU7rqqqv06quvuro8AACARrldwLrqqqv01ltv6e9//7u%2B%2BeYbmUwmdejQQb1795a/v7%2BrywMAAGiU2wUsSQoICKj9iAYAAABP43YB69tvv9WKFSv0xRdf6PTp03Xmd%2B7c6YKqAAAAnOd2Aevhhx9WaWmpevfurdatW7u6HAAAgCZzu4BVVFSknTt31n6yOgAAgKdxuw8abdu2LWeuAACAR3O7gDV58mRlZ2df9D%2B4DAAA4Cpud4nw73//uz755BO9%2BeabuvLKK2UyOWbAC314KAAAgLtwu4AVHBysPn36uLoMAACA383tAtbSpUtdXQIAAMBFcbt7sCTpyy%2B/1MqVK/XQQw/Vjn366acurAgAAMB5bhewCgoKNGLECO3YsUNbt26V9OuHj9555518yCgAAPAIbhewnn76ad1///3asmWL/Pz8JP367xMuW7ZMq1atcnF1AAAAjXO7gPX5558rPT1dkmoDliQNHjxYR48edVVZAAAATnO7gBUSElLvv0FYWlqqgIAAF1QEAADQNG4XsBITE7VkyRKVl5fXjh07dkzz5s3TTTfd5MLKAAAAnON2H9Pw0EMP6a677lJSUpKqqqqUmJioiooKderUScuWLXN1eQAAAI1yu4B1%2BeWXa%2BvWrcrPz9exY8cUFBSkDh06qFevXg73ZAEAALgrtwtYknTJJZfolltucXUZ8HFDntnr6hIAAB7K7QJWSkpKg2eq%2BCwsAADg7twuYA0dOtQhYFVVVenYsWMqLCzUXXfd5cLKAAAAnON2AWvu3Ln1jr/zzjv68MMPW7gaAACApnO7j2m4kFtuuUVvvfWWq8sAAABolMcErAMHDqimpsbVZQAAADTK7S4Rjhs3rs5YRUWFjh49qoEDB7qgIgAAgKZxu4AVHR1d56cIAwMDlZqaqjFjxvyu51yyZIleeeUVHT58WJJUUFCgFStW6Msvv9QVV1yhyZMna8SIEbXrc3Nz9dprr8lqtSomJkbz589XXFzc798UAADwKW4XsIz%2BtPaDBw9q06ZNtb8vLS3VtGnTNH/%2BfA0fPlwff/yxpk6dqg4dOig%2BPl67du3SypUr9eKLLyomJka5ubmaMmWKduzYodatWxtaGwAA8E5uF7A2btzo9Nrbb7%2B9wfnq6mo9%2BuijGj9%2BvJ555hlJ0pYtWxQdHa3U1FRJUnJyslJSUpSXl6f4%2BHhZLBaNGjVKCQkJkqRJkyYpNzdXu3fv1rBhw37nrgAAgC9xu4A1f/58VVdX17mh3c/Pz2HMz8%2Bv0YD1t7/9TYGBgRo%2BfHhtwCouLlZsbKzDutjYWG3fvr12fujQobVzJpNJXbp0UWFhIQELAAA4xe0C1osvvqi1a9dqypQpiomJUU1NjQ4fPqwXXnhBd9xxh5KSkpx6nh9//FErV67Uq6%2B%2B6jBut9vVrl07h7Hw8HDZbLba%2BbCwMIf5sLCw2nlnlJaWymq1OoyZza0VFRVV73p/f5PDV1/j6/v3RWZz3f/XvA6MU19/PQWvA3rgLft3u4C1bNky5eTkOISgG264QVdddZUmTpyorVu3OvU8S5cu1ahRo9SxY0d99913TarhYj8OwmKxKDs722EsMzNTM2bMaPBxoaGtLuq4ns7X9%2B9LIiLaXHCO18HFa6i/noLXAT3w9P27XcD66quv6pxBkqTQ0FCVlJQ49RwFBQX69NNP6w1jERERstvtDmM2m02RkZEXnLfb7erUqZOzW1BaWppSUlIcxszm1rLZTtW73t/fpNDQVjp5skJVVdVOH8db%2BPr%2BfVF93wu8DoxzofcaT8DrgB4YvX9X/YXD7QJW%2B/bttWzZMt13332KiIiQJJ08eVLPPfec/vjHPzr1HJs3b9aJEyfUv39/Sf93RiopKUkTJkyoE7yKiopqb2qPi4tTcXGxRo4cKenXfwvxwIEDtTfFOyMqKqrO5UCr9WdVVjb8Qqmqqm50jTfz9f37kob%2BP/M6uHje0D9eB/TA0/fvdgHr4Ycf1pw5c2SxWNSmTRuZTCaVl5crKChIq1atcuo5HnzwQd133321vz9%2B/LjS0tK0adMmVVdXa82aNcrLy9OIESP0wQcfKD8/XxaLRZKUnp6u2bNn689//rNiYmL00ksvKSAgQP369WuO7QIAAC/kdgGrd%2B/eeu%2B995Sfn6/jx4%2BrpqZG7dq1080336yQkBCnniMsLMzhMmNlZaUk6fLLL5ckrVmzRosXL9aiRYvUvn17ZWVlqXPnzpKkPn36aPbs2Zo5c6ZOnDih%2BPh45eTkKCgoyOCdAgAAb%2BV2AUuSWrVqpQEDBuj48eO66qqrLvr5rrzyytpPcZekHj16OHz46PkyMjKUkZFx0ccFAAC%2Bye1%2BBvL06dOaN2%2BeunXrpiFDhkj69R6sSZMm6eTJky6uDgAAoHFuF7CysrJ08OBBLV%2B%2BXCbT/5VXVVWl5cuXu7AyAAAA57hdwHrnnXf03HPPafDgwbX/6HNoaKiWLl2qHTt2uLg6AACAxrldwDp16pSio6PrjEdGRuqXX35p%2BYIAAACayO0C1h//%2BEd9%2BOGHkhw/Uf3tt9/WH/7wB1eVBQAA4DS3%2BynCjIwMTZ8%2BXaNHj1Z1dbXWrVunoqIivfPOO5o/f76rywMAAGiU2wWstLQ0mc1mrV%2B/Xv7%2B/nr%2B%2BefVoUMHLV%2B%2BXIMHD3Z1eQAAAI1yu4BVVlam0aNHa/To0a4uBQAA4Hdxu3uwBgwY4HDvFQAAgKdxuzNYSUlJ2r59u4YOHerqUgA0kyHP7HV1CQDQrNwuYF1xxRV6/PHHlZOToz/%2B8Y%2B65JJLHOZXrFjhosoAAACc43YB68iRI7rmmmskSTabzcXVAAAANJ3bBKxZs2bp6aef1quvvlo7tmrVKmVmZrqwKgAAgKZzm5vcd%2B3aVWcsJyfHBZUAAABcHLcJWPX95CA/TQgAADyR2wSs3/5h58bGAAAA3J3bBCwAAABvQcACAAAwmNv8FOG5c%2Bc0Z86cRsf4HCwAAODu3CZgde/eXaWlpY2OAQAAuDu3CVj//PlXAAAAnox7sAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAM5rUB69ChQ7rrrrvUvXt3JScna%2BbMmbJarZKkgoICpaamKjExUcOGDdPmzZsdHpubm6tBgwYpMTFR6enpKioqcsUWAACAh/LKgHX27FlNmDBBPXv2VEFBgbZu3aoTJ05o4cKFKi0t1bRp0zRu3DgVFBRo/vz5euSRR1RYWChJ2rVrl1auXKknn3xS%2B/btU//%2B/TVlyhT98ssvLt4VAADwFF4ZsCoqKjRr1ixNnjxZAQEBioyM1K233qovvvhCW7ZsUXR0tFJTUxUYGKjk5GSlpKQoLy9PkmSxWDRq1CglJCQoKChIkyZNkiTt3r3blVsCAAAexCsDVlhYmMaMGSOz%2BdfPUf3yyy/1X//1XxoyZIiKi4sVGxvrsD42Nrb2MuD58yaTSV26dKk9wwUAANAYt/kk9%2BZQUlKiQYMGqbKyUmPHjtWMGTN09913q127dg7rwsPDZbPZJEl2u11hYWEO82FhYbXzzigtLa293%2Bs3ZnNrRUVF1bve39/k8NXX%2BPr%2BAaOZzZ77vcT7AT3wlv17dcBq3769CgsL9fXXX%2Buvf/2rHnjgAaceV1NTc1HHtVgsys7OdhjLzMzUjBkzGnxcaGirizqup/P1/QNGiYho4%2BoSLhrvB/TA0/fv1QFLkvz8/BQdHa1Zs2Zp3Lhx6tu3r%2Bx2u8Mam82myMhISVJERESdebvdrk6dOjl9zLS0NKWkpDiMmc2tZbOdqne9v79JoaGtdPJkhaqqqp0%2Bjrfw9f0DRrvQe40n4P2AHhi9f1f9hcMrA1ZBQYEWLlyo7du3y2T69RTjb1%2Bvu%2B46vfPOOw7ri4qKlJCQIEmKi4tTcXGxRo4cKUmqqqrSgQMHlJqa6vTxo6Ki6lwOtFp/VmVlwy%2BUqqrqRtd4M1/fP2AUb/g%2B4v2AHnj6/j37AucFxMXFqby8XFlZWaqoqFBZWZlWrlypG264Qenp6SopKVFeXp7OnDmj/Px85efna%2BzYsZKk9PR0bdy4Ufv371dFRYVWr16tgIAA9evXz7WbAgAAHsMrA1ZISIjWrl2roqIi3XjjjRo2bJhCQkL01FNPqW3btlqzZo3Wr1%2Bv7t27a8mSJcrKylLnzp0lSX369NHs2bM1c%2BZM9ezZU/v27VNOTo6CgoJcvCsAAOAp/Gou9o5uOMVq/fmCc2azSRERbWSznfLo06EBS85tAAATvElEQVS/l7vuf8gze11dAvC7bJ/Zy9Ul/G7u%2Bn7Qkny9B0bv/7LLQgyoqum88h4suCcCCwDAV3jlJUIAAABXImABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyPaQAAL%2BNJH4niyZ/ZBTSEM1gAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDCvDVglJSXKzMxUUlKSkpOT9eCDD%2BrkyZOSpIMHD%2BqOO%2B5Q9%2B7dNXDgQK1du9bhsdu2bdPw4cPVrVs3jRo1Snv27HHFFgAAgIfy2oA1ZcoUhYaGateuXXrzzTf1xRdf6IknntDp06c1efJk3XjjjXr//ff19NNPa82aNdqxY4ekX8PXvHnzNHfuXH3wwQcaP3687r33Xh0/ftzFOwIAAJ7C7OoCmsPJkycVFxenOXPmqE2bNmrTpo1GjhypV199Ve%2B9957OnTunqVOnyt/fX127dtWYMWNksVg0cOBA5eXlqW/fvurbt68kacSIEVq/fr02b96se%2B65x8U7q2vIM3tdXQIAADiPVwas0NBQLV261GHs%2B%2B%2B/V1RUlIqLixUTEyN/f//audjYWOXl5UmSiouLa8PVP88XFhY6ffzS0lJZrVaHMbO5taKioupd7%2B9vcvgKAL7CbHZ83%2BP9kB54y/69MmCdr7CwUOvXr9fq1au1fft2hYaGOsyHh4fLbrerurpadrtdYWFhDvNhYWE6cuSI08ezWCzKzs52GMvMzNSMGTMafFxoaCunjwEA3iAiok2947wf0gNP37/XB6yPP/5YU6dO1Zw5c5ScnKzt27fXu87Pz6/2v2tqai7qmGlpaUpJSXEYM5tby2Y7Ve96f3%2BTQkNb6eTJClVVVV/UsQHAk5z/vsj7IT0wev8XCvHNzasD1q5du3T//ffrkUce0e233y5JioyM1FdffeWwzm63Kzw8XCaTSREREbLb7XXmIyMjnT5uVFRUncuBVuvPqqxs%2BIVSVVXd6BoA8CYXes/j/ZAeePr%2BPfsCZwM%2B%2BeQTzZs3T88%2B%2B2xtuJKkuLg4HT58WJWVlbVjhYWFSkhIqJ0vKipyeK5/ngcAAGiMVwasyspKLViwQHPnzlXv3r0d5vr27avg4GCtXr1aFRUV%2Buyzz7Rhwwalp6dLksaOHat9%2B/bpvffe05kzZ7RhwwZ99dVXGjFihCu2AgAAPJBfzcXecOSGPvroI/3Lv/yLAgIC6sy9/fbbOnXqlB599FEVFRXp0ksv1d13362MjIzaNTt27NCKFStUUlKijh07av78%2BerRo8dF1WS1/nzBObPZpIiINrLZTjX5dCgf0wDAk22f2cvh9xfzfugtfL0HRu//sstCDKiq6bzyHqwbbrhBhw8fbnDN66%2B/fsG5gQMHauDAgUaXBQAAfIRXXiIEAABwJQIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwbw2YL3//vtKTk7WrFmz6sxt27ZNw4cPV7du3TRq1Cjt2bOndq66ulpPP/20BgwYoB49emjixIn69ttvW7J0AADg4bwyYL3wwgtavHixrr766jpzBw8e1Lx58zR37lx98MEHGj9%2BvO69914dP35ckvTaa69py5YtysnJ0e7duxUdHa3MzEzV1NS09DYAAICH8sqAFRgYqA0bNtQbsPLy8tS3b1/17dtXgYGBGjFihP70pz9p8%2BbNkiSLxaLx48fr2muvVXBwsGbNmqWjR4/qs88%2Ba%2BltAAAAD%2BWVAevOO%2B9USEhIvXPFxcWKjY11GIuNjVVhYaFOnz6tI0eOOMwHBwfr6quvVmFhYbPWDAAAvIfZ1QW0NLvdrrCwMIexsLAwHTlyRD/99JNqamrqnbfZbE4fo7S0VFar1WHMbG6tqKioetf7%2B5scvgKArzCbHd/3eD%2BkB96yf58LWJIavZ/qYu%2B3slgsys7OdhjLzMzUjBkzGnxcaGirizouAHiaiIg29Y7zfkgPPH3/PhewIiIiZLfbHcbsdrsiIyMVHh4uk8lU73zbtm2dPkZaWppSUlIcxszm1rLZTtW73t/fpNDQVjp5skJVVdVOHwcAPN3574u8H9IDo/d/oRDf3HwuYMXFxamoqMhhrLCwUMOGDVNgYKA6deqk4uJi9ezZU5J08uRJffPNN7ruuuucPkZUVFSdy4FW68%2BqrGz4hVJVVd3oGgDwJhd6z%2BP9kB54%2Bv49%2BwLn7zB27Fjt27dP7733ns6cOaMNGzboq6%2B%2B0ogRIyRJ6enpys3N1dGjR1VeXq7ly5erS5cuio%2BPd3HlAADAU3jlGazfwlBlZaUk6d1335X065mqP/3pT1q%2BfLmWLl2qkpISdezYUWvWrNFll10mSRo3bpysVqv%2B8pe/6NSpU0pKSqpzPxUAAEBD/Gr4BM0WYbX%2BfME5s9mkiIg2stlONfl06JBn9l5saQDgMttn9nL4/cW8H3oLX%2B%2BB0fu/7LL6P7apufncJUIAAIDmRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYGZXFwAA8F1Dntnr6hKaZPvMXq4uAR6CM1gAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAlY9SkpKdM899ygpKUn9%2B/dXVlaWqqurXV0WAADwEPxjz/WYPn26unbtqnfffVcnTpzQ5MmTdemll%2Bpf//VfXV0aAADwAASs8xQWFurQoUNat26dQkJCFBISovHjx%2BuVV14hYAEAPMqQZ/a6ugSnbZ/Zy9UlGIqAdZ7i4mK1b99eYWFhtWNdu3bVsWPHVF5eruDg4Eafo7S0VFar1WHMbG6tqKioetf7%2B5scvgIA3JPZ3Pzv0776Z8JvvfWW/ROwzmO32xUaGuow9lvYstlsTgUsi8Wi7Oxsh7F7771X06dPr3d9aWmpXnnlRaWlpV0whF3IR48PbtJ6d1RaWiqLxfK79u8t6AE9kOiBRA%2Bki/sz4Xye%2BGeEkft3Jc%2BOh82kpqbmoh6flpamN9980%2BFXWlraBddbrVZlZ2fXOevlK3x9/xI9kOiBRA8keiDRA2/ZP2ewzhMZGSm73e4wZrfb5efnp8jISKeeIyoqyqNTNwAAuDicwTpPXFycvv/%2Be5WVldWOFRYWqmPHjmrTpo0LKwMAAJ6CgHWe2NhYxcfHa8WKFSovL9fRo0e1bt06paenu7o0AADgIfwXLly40NVFuJubb75ZW7du1WOPPaa33npLqampmjhxovz8/JrtmG3atFHPnj199iyZr%2B9fogcSPZDogUQPJHrgDfv3q7nYO7oBAADggEuEAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2C1gJKSEt1zzz1KSkpS//79lZWVperq6jrrJkyYoPj4eIdfXbp0UXZ2tguqNpazPaiurtZzzz2nlJQUdevWTcOHD9e2bdtcULHxnO3BuXPn9Oyzz2rAgAG6/vrrdeedd%2Brbb791QcXGe//995WcnKxZs2Y1uK66ulpPP/20BgwYoB49emjixIk%2B1wNJ%2BvHHHzVx4kTFxMTozJkzLVBdy2jK6yA7O7v2/SAtLU0fffRRC1XZvJztwdmzZ7V48WL17t1b3bp106hRo5Sfn99CVTavpnwv/Ka4uFixsbF68803m7EyYxCwWsD06dPVrl07vfvuu1q3bp3effddvfLKK3XWrV27VoWFhbW/9u7dq7Zt2%2BrWW291QdXGcrYHr7/%2BuvLy8vTiiy/qo48%2B0uzZs3X//ffr0KFDLqjaWM72ICcnRxs3btSqVav0wQcfqHv37po2bVq9YcyTvPDCC1q8eLGuvvrqRte%2B9tpr2rJli3JycrR7925FR0crMzNTnv4vezWlB4cPH1ZqaqrCw8NboLKW05QevPzyy3rjjTe0Zs0affjhh%2Brdu7cyMzNVXl7eApU2n6b0ICsrS//zP/%2BjDRs26B//%2BIdGjBih6dOny2q1tkClzacpPfhNdXW1Hn30UbVu3boZKzMOAauZFRYW6tChQ5o7d65CQkIUHR2t8ePHy2KxNPrYZ555RrfeeqtiYmJaoNLm05QeFBcXq3v37rrmmmvk7%2B%2Bv/v37Kzw8XIcPH3ZB5cZpSg927dqlMWPGqHPnzgoKCtL06dNVVlamzz77zAWVGycwMFAbNmxw6g3VYrFo/PjxuvbaaxUcHKxZs2bp6NGjPtWDsrIyPfXUUxo7dmwLVNZymtIDk8mkBx54QJ06dVJAQIAmTJggu92uzz//vAUqbT5N6cGNN96oxx9/XJdffrnMZrNSU1N15swZffPNNy1QafNpSg9%2B8/rrryskJERdunRpxsqMQ8BqZsXFxWrfvr3CwsJqx7p27apjx441%2BLewr7/%2BWhs3btT06dNbosxm1ZQe9OvXT//93/%2BtgwcP6uzZs9q5c6cqKirUs2fPli7bUE19Hfj5%2BdX%2Bt8lkUnBwsA4ePNgitTaXO%2B%2B8UyEhIY2uO336tI4cOaLY2NjaseDgYF199dUqLCxszhKbnbM9kKSbbrpJiYmJzVxRy2tKD8aPH68hQ4bU/v748eOSpKioqGapraU0pQcDBgxQp06dJEnl5eVas2aNoqOj1bVr1%2BYssdk1pQeSZLVatWrVKj3yyCPNWJWxzK4uwNvZ7XaFhoY6jP32h6zNZlNwcHC9j8vJydHo0aMVGRnZ7DU2t6b0YODAgTp48KBuv/12SVKrVq30xBNP6Iorrmi5gptBU3rQv39/WSwWpaSkqEOHDsrLy9Px48f1008/tWjNrvLTTz%2BppqbGIYxKv/bLZrO5qCq42tmzZzV//nyNGDFCV155pavLaXETJkzQ3r17FRMTo3//939XUFCQq0tqUUuXLtWYMWN0zTXXuLoUpxGwWkBT7xux2%2B3atGmTtm/f3kwVtTxne7Bx40Zt3LhReXl5iomJUUFBgebMmaMrrrhC1113XTNX2byc7cHdd98tu92uiRMnqrq6WqmpqerRo4f8/f2buUL34un3W8E45eXlyszMlL%2B/vxYtWuTqclxi7dq1Ki8v13/8x3/ojjvu0MaNG9WuXTtXl9Ui9u7dq/3792vJkiWuLqVJuETYzCIjI2W32x3G7Ha7/Pz8Lnh2aufOnerQoYOuuuqqliix2TWlB%2BvXr1daWpquu%2B46BQYGql%2B/frrxxhu1efPmlizZcE3pQWBgoBYsWKA9e/Zo3759mj17tn744QefeTMNDw%2BXyWSqt19t27Z1UVVwlbKyMt1xxx0KCQnRSy%2B95DE3ODeH4OBg3XPPPQoNDdXWrVtdXU6LOHv2rP7t3/5Nf/3rXz3urB0Bq5nFxcXp%2B%2B%2B/V1lZWe1YYWGhOnbsqDZt2tT7mJ07d6pXr14tVWKza0oPqqurVVVV5TB29uzZFqmzOTWlB8XFxSooKKj9/Q8//KAjR4545f049QkMDFSnTp1UXFxcO3by5El98803Hn8WE01z5swZTZ48WV27dtVzzz3ncX/AGuH222/Xzp07HcZMJpPMZt%2B4ALV//359/fXXmjdvnpKSkpSUlKRPPvlEjz32mKZOnerq8hpEwGpmsbGxio%2BP14oVK1ReXq6jR49q3bp1Sk9PlyQNHjy4zue6HDx40KvuMWhKD1JSUrRhwwYdOnRIlZWV2rNnjwoKCjRgwABXbuGiNaUHhw8f1ty5c/X111%2BrvLxcCxcu1IABA7zmjGZ9fvjhBw0ePLj2s67S09OVm5uro0ePqry8XMuXL1eXLl0UHx/v4kqbz/k98EXn92Dt2rW65JJL9Nhjj8lk8o0/rs7vQUJCgp599ll98803OnfunCwWi7799lv17t3bxZU2n3/uwfXXX6/33ntPmzZtqv0VFxen%2B%2B67T48//rirS22Qb0RgF3vuuef0yCOPqFevXgoODta4ceOUkZEhSTp27Jh%2B%2BeUXh/VWq1WXXnqpK0ptNs72YPLkyaqsrFRmZqbKysrUvn17LV68WDfddJMryzeEsz0YOXKkPv/8c40dO1aVlZXq16%2BfFi5c6MLKjfFbOKqsrJQkvfvuu5J%2BPZN37tw5HTt2rPZs5bhx42S1WvWXv/xFp06dUlJSkld84G5TerBgwQJt2rSp9l60G264QZL02GOP1f4QiCdqSg/eeOMNff/990pISHB4jqlTp2ratGktWLWxmtKDBx98UE899ZTGjBmjs2fPqkOHDlq1apWuvfZa1xRvEGd7EBAQoMsvv9zhsQEBAQoNDXX7HwLzq%2BFOUgAAAEP5xjlXAACAFkTAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADPb/ACTW5O%2B%2BC9QXAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-585755305967145292”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.9485941</td> <td class=”number”>438</td> <td class=”number”>13.6%</td> <td>

<div class=”bar” style=”width:45%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.082025</td> <td class=”number”>275</td> <td class=”number”>8.6%</td> <td>

<div class=”bar” style=”width:28%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.795895</td> <td class=”number”>231</td> <td class=”number”>7.2%</td> <td>

<div class=”bar” style=”width:24%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.095938</td> <td class=”number”>225</td> <td class=”number”>7.0%</td> <td>

<div class=”bar” style=”width:23%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9703046</td> <td class=”number”>209</td> <td class=”number”>6.5%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.873642</td> <td class=”number”>200</td> <td class=”number”>6.2%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9400685</td> <td class=”number”>169</td> <td class=”number”>5.3%</td> <td>

<div class=”bar” style=”width:18%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.136671</td> <td class=”number”>143</td> <td class=”number”>4.5%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.062503</td> <td class=”number”>131</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.053402</td> <td class=”number”>106</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (25)</td> <td class=”number”>977</td> <td class=”number”>30.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>106</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-585755305967145292”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.7219861</td> <td class=”number”>68</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:30%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.7460754</td> <td class=”number”>45</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:20%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.7695072</td> <td class=”number”>93</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:40%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.795895</td> <td class=”number”>231</td> <td class=”number”>7.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.8388169</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.163639</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:29%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.191763</td> <td class=”number”>12</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.216942</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.217211</td> <td class=”number”>19</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:79%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.395075</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_MILK_SODA_PRICE10”><s>MILK_SODA_PRICE10</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_MILK_PRICE10”><code>MILK_PRICE10</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.91655</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_National School Lunch Program participants FY 2009”><s>National School Lunch Program participants FY 2009</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_WIC participants, FY 2015”><code>WIC participants, FY 2015</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.96736</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_National School Lunch Program participants FY 2011”><s>National School Lunch Program participants FY 2011</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_National School Lunch Program participants FY 2009”><code>National School Lunch Program participants FY 2009</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99972</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_National School Lunch Program participants, FY 2012”><s>National School Lunch Program participants, FY 2012</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_National School Lunch Program participants FY 2011”><code>National School Lunch Program participants FY 2011</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99994</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_National School Lunch Program participants, FY 2013”><s>National School Lunch Program participants, FY 2013</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_National School Lunch Program participants, FY 2012”><code>National School Lunch Program participants, FY 2012</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99978</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_National School Lunch Program participants, FY 2014”><s>National School Lunch Program participants, FY 2014</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_National School Lunch Program participants, FY 2013”><code>National School Lunch Program participants, FY 2013</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.9999</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_National School Lunch Program participants, FY 2015”><s>National School Lunch Program participants, FY 2015</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_National School Lunch Program participants, FY 2014”><code>National School Lunch Program participants, FY 2014</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99976</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_ORCHARD_ACRES07”>ORCHARD_ACRES07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>776</td>

</tr> <tr>

<th>Unique (%)</th> <td>24.2%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>19.3%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>619</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1933.3</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>471820</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>11.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-630505268760076789”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-630505268760076789,#minihistogram-630505268760076789”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-630505268760076789”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-630505268760076789”

aria-controls=”quantiles-630505268760076789” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-630505268760076789” aria-controls=”histogram-630505268760076789”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-630505268760076789” aria-controls=”common-630505268760076789”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-630505268760076789” aria-controls=”extreme-630505268760076789”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-630505268760076789”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>13</td>

</tr> <tr>

<th>Median</th> <td>54</td>

</tr> <tr>

<th>Q3</th> <td>232.5</td>

</tr> <tr>

<th>95-th percentile</th> <td>4262.5</td>

</tr> <tr>

<th>Maximum</th> <td>471820</td>

</tr> <tr>

<th>Range</th> <td>471820</td>

</tr> <tr>

<th>Interquartile range</th> <td>219.5</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>16332</td>

</tr> <tr>

<th>Coef of variation</th> <td>8.4476</td>

</tr> <tr>

<th>Kurtosis</th> <td>462.14</td>

</tr> <tr>

<th>Mean</th> <td>1933.3</td>

</tr> <tr>

<th>MAD</th> <td>3245.7</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>19.379</td>

</tr> <tr>

<th>Sum</th> <td>5009300</td>

</tr> <tr>

<th>Variance</th> <td>266730000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-630505268760076789”>

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QEAADSY4wFr8ODB6tChgyZOnKhhw4apRYsWdW6Tmpqqo0ePOl0aAACAFY4HrGXLlunqq68%2B7%2B127tzpQDUAAAD2OX4OVteuXfXAAw9o48aN3rH//M//1H333SePx%2BN0OQAAANY5HrDmzJmj48ePq1OnTt6xfv36qba2VnPnznW6HAAAAOscD1gff/yxFi1apPbt23vH2rdvr6ysLG3btu1HHWvbtm1KSUlRRkaGz/iqVavUrVs3JSYm%2BvycvvxDbW2tFixYoAEDBqhPnz6aMGGCDh065L2/x%2BPR1KlTlZKSor59%2B%2BqJJ55QVVXVT980AAC4oDgesKqqqhQeHl63kNBQVVZW1vs4r7zyimbPnq127dqddb5Pnz5yu90%2BP0lJSZKk5cuXa82aNcrOztbmzZvVvn17paenyxgjSZo1a5YqKyu1du1avfPOOyooKFBWVtZP2C0AALgQOR6w%2BvTpo7lz5%2BrYsWPesX/%2B85965plnfC7VcD7h4eFauXLlDwasc8nJydH48ePVsWNHRUREKCMjQwUFBdq5c6e%2B/fZbbdy4URkZGYqJiVHr1q314IMP6p133tGpU6d%2B9FoAAODC4/inCGfOnKlf//rXuvbaaxUREaHa2lpVVFSobdu2euONN%2Bp9nHHjxp1zvqioSL/61a%2BUm5uryMhITZkyRXfccYeqqqqUn5%2BvuLg4720jIiLUrl07ud1uHT9%2BXGFhYeratat3Pj4%2BXidOnNC%2Bfft8xgEAAM7G8YDVtm1bvffee/roo4908OBBhYaGqkOHDurbt6/CwsKsrBETE6P27dvrkUceUadOnfTnP/9Zjz32mGJjY/WLX/xCxhhFRUX53CcqKkqlpaVq0aKFIiIiFBIS4jMnSaWlpfVav7i4WCUlJT5jLldTxcbGNnBnvsLC/HYh/p/E5Qqses90ut%2BB1vdARs%2BdRb%2BdR8%2BDl%2BMBS5Iuvvhi3XTTTY12/H79%2Bqlfv37efw8ePFh//vOftWrVKk2fPl2SvOdbnc255uojJydHixYt8hlLT0/XlClTGnTcQBcd3czfJVgRGXmJv0u44NBzZ9Fv59Hz4ON4wDp06JDmz5%2Bvr7/%2B%2BqyfzPvwww8bZd02bdooNzdXLVq0UGhoaJ1rbnk8HrVs2VIxMTEqLy9XTU2N9xW107dt2bJlvdYaM2aM%2Bvfv7zPmcjVVaWmFhZ38n0B7xmN7/04LCwtVZOQlKiurVE1Nrb/LuSDQc2fRb%2BfR88bnryf3fjkHq7i4WH379lXTpk0bZY3//u//VlRUlG677TbvWEFBgdq2bavw8HB17txZeXl53ivKl5WV6eDBg0pKSlKbNm1kjNGePXsUHx8vSXK73YqMjFSHDh3qtX5sbGydtwNLSo6ruvrC/uUJlv3X1NQGzV4CBT13Fv12Hj0PPo4HrNzcXH344YeKiYlptDW%2B%2B%2B47Pffcc2rbtq26deumDRs26KOPPtLbb78tSUpLS1N2drZuuOEGtW7dWllZWerevbsSExMlSYMGDdLvfvc7vfDCC/ruu%2B%2B0ePFijRo1Si6XX95RBQAAAcbxxNCyZUsrr1ydDkPV1dWS5P3qHbfbrXHjxqmiokIPP/ywSkpKdPnll2vx4sVKSEiQJI0dO1YlJSW6%2B%2B67VVFRoeTkZJ9zpp599lk99dRTGjBggC666CLdfvvtdS5mCgAA8ENCTEPP6P6RVqxYof/93//VtGnTfD6pF%2BxKSo5bP6bLFaqbs37c1e/9af3U6/xdQoO4XKGKjm6m0tIKXsp3CD13Fv12Hj1vfK1aNffLuo6/gvXRRx/piy%2B%2B0KpVq3T55ZcrNNT3RO233nrL6ZIAAACscjxgRURE6IYbbnB6WQAAAMc4HrDmzJnj9JIAAACO8suFlPbt26eXXnpJjz/%2BuHfsyy%2B/9EcpAAAA1jkesLZv366hQ4fqgw8%2B0Nq1ayV9f/HRcePGNdpFRgEAAJzkeMBasGCBHn30Ua1Zs8b7KcK2bdtq7ty5Wrx4sdPlAAAAWOd4wPr73/%2ButLQ0SfK5TMMtt9yigoICp8sBAACwzvGA1bx587N%2BB2FxcbEuvvhip8sBAACwzvGA1atXLz3//PMqLy/3ju3fv1%2BZmZm69tprnS4HAADAOscv0/D444/rnnvuUXJysmpqatSrVy9VVlaqc%2BfOmjt3rtPlAAAAWOd4wLrsssu0du1abd26Vfv371eTJk3UoUMHXXfddRfUV%2BcAAIDg5XjAkqSLLrpIN910kz%2BWBgAAaHSOB6z%2B/fuf85UqroUFAAACneMB67bbbvMJWDU1Ndq/f7/cbrfuuecep8sBAACwzvGANX369LOOb9iwQX/9618drgYAAMA%2Bv3wX4dncdNNNeu%2B99/xdBgAAQIP9bALWrl27ZIzxdxkAAAAN5vhbhGPHjq0zVllZqYKCAg0cONDpcgAAAKxzPGC1b9%2B%2BzqcIw8PDNWrUKI0ePdrpcgAAAKxzPGBxtXYAABDsHA9Y7777br1vO2zYsEasBAAAoHE4HrCeeOIJ1dbW1jmhPSQkxGcsJCSEgAUAAAKS4wHr1Vdf1WuvvaYHHnhAXbt2lTFGe/fu1SuvvKK77rpLycnJTpcEAABglV/OwcrOzlbr1q29Y1dddZXatm2rCRMmaO3atU6XBAAAYJXj18E6cOCAoqKi6oxHRkaqsLDQ6XIAAACsczxgtWnTRnPnzlVpaal3rKysTPPnz9cVV1zhdDkAAADWOf4W4cyZMzVt2jTl5OSoWbNmCg0NVXl5uZo0aaLFixc7XQ4AAIB1jgesvn37asuWLdq6dasOHz4sY4xat26t66%2B/Xs2bN3e6HAAAAOscD1iSdMkll2jAgAE6fPiw2rZt648SAAAAGo3j52BVVVUpMzNTPXv21K233irp%2B3Ow7r33XpWVlTldDgAAgHWOB6x58%2BZp9%2B7dysrKUmjo/y1fU1OjrKwsp8sBAACwzvGAtWHDBr344ou65ZZbvF/6HBkZqTlz5uiDDz5wuhwAAADrHA9YFRUVat%2B%2BfZ3xmJgYnThxwulyAAAArHM8YF1xxRX661//Kkk%2B3z34/vvv69///d%2BdLgcAAMA6xz9F%2BMtf/lIPPfSQRo4cqdraWr3%2B%2BuvKzc3Vhg0b9MQTTzhdDgAAgHWOB6wxY8bI5XLpzTffVFhYmH7/%2B9%2BrQ4cOysrK0i233OJ0OQAAANY5HrCOHj2qkSNHauTIkU4vDQAA4AjHz8EaMGCAz7lXAAAAwcbxgJWcnKz169c7vSwAAIBjHH%2BL8N/%2B7d/029/%2BVtnZ2briiit00UUX%2BczPnz/f6ZIAAACscjxg5efn6xe/%2BIUkqbS01OnlAQAAGp1jASsjI0MLFizQG2%2B84R1bvHix0tPTnSoBAADAEY6dg7Vp06Y6Y9nZ2U4tDwAA4BjHAtbZPjnIpwkBAEAwcixgnf5i5/ONAQAABDrHL9MAAAAQ7AhYAAAAljn2KcJTp05p2rRp5x3jOlgAACDQORawevfureLi4vOOAQAABDrHAta/Xv/Klm3btikzM1PJyclasGCBz9y6dev08ssv65tvvlGHDh30yCOPqG/fvpKk2tpaLVy4UGvXrlVZWZmSkpL09NNPq23btpIkj8ejp59%2BWp999plCQ0OVmpqqWbNmqUmTJtb3AAAAgk/AnoP1yiuvaPbs2WrXrl2dud27dyszM1PTp0/Xp59%2BqvHjx2vy5Mk6fPiwJGn58uVas2aNsrOztXnzZrVv317p6eney0bMmjVLlZWVWrt2rd555x0VFBQoKyvL0f0BAIDAFbABKzw8XCtXrjxrwFqxYoVSU1OVmpqq8PBwDR06VF26dNHq1aslSTk5ORo/frw6duyoiIgIZWRkqKCgQDt37tS3336rjRs3KiMjQzExMWrdurUefPBBvfPOOzp16pTT2wQAAAHI8e8itGXcuHE/OJeXl6fU1FSfsbi4OLndblVVVSk/P19xcXHeuYiICLVr105ut1vHjx9XWFiYunbt6p2Pj4/XiRMntG/fPp/xH1JcXKySkhKfMZerqWJjY%2Bu7vXoJCwusfOxyBVa9Zzrd70DreyCj586i386j58ErYAPWuXg8HkVFRfmMRUVFKT8/X8eOHZMx5qzzpaWlatGihSIiInwugnr6tvX9cuqcnBwtWrTIZyw9PV1Tpkz5KdsJGtHRzfxdghWRkZf4u4QLDj13Fv12Hj0PPkEZsKTzfw3PueYb%2BhU%2BY8aMUf/%2B/X3GXK6mKi2taNBxzxRoz3hs799pYWGhioy8RGVllaqpqfV3ORcEeu4s%2Bu08et74/PXkPigDVnR0tDwej8%2BYx%2BNRTEyMWrRoodDQ0LPOt2zZUjExMSovL1dNTY3CwsK8c5LUsmXLeq0fGxtb5%2B3AkpLjqq6%2BsH95gmX/NTW1QbOXQEHPnUW/nUfPg09gvQRSTwkJCcrNzfUZc7vd6tGjh8LDw9W5c2fl5eV558rKynTw4EElJSWpe/fuMsZoz549PveNjIxUhw4dHNsDAAAIXEEZsO6880598skn2rJli06ePKmVK1fqwIEDGjp0qCQpLS1Ny5YtU0FBgcrLy5WVlaXu3bsrMTFRMTExGjRokH73u9/p6NGjOnz4sBYvXqxRo0bJ5QrKF/wAAIBlAZsYEhMTJUnV1dWSpI0bN0r6/tWmLl26KCsrS3PmzFFhYaE6deqkJUuWqFWrVpKksWPHqqSkRHfffbcqKiqUnJzsc1L6s88%2Bq6eeekoDBgzQRRddpNtvv10ZGRkO7xAAAASqENPQM7pRLyUlx60f0%2BUK1c1Z26wft7Gsn3qdv0toEJcrVNHRzVRaWsG5Eg6h586i386j542vVavmflk3KN8iBAAA8CcCFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYFnQBqyuXbsqISFBiYmJ3p/nnntOkrR9%2B3aNGjVKvXr10uDBg7V69Wqf%2By5btkyDBg1Sr169lJaWptzcXH9sAQAABCiXvwtoTO%2B//74uv/xyn7Hi4mI9%2BOCDeuKJJzRkyBB9/vnnmjRpkjp06KDExERt2rRJL730kl599VV17dpVy5Yt0wMPPKAPPvhATZs29dNOAABAIAnaV7B%2ByJo1a9S%2BfXuNGjVK4eHhSklJUf/%2B/bVixQpJUk5OjkaMGKEePXqoSZMmuvfeeyVJmzdv9mfZAAAggAR1wJo/f7769eunq666SrNmzVJFRYXy8vIUFxfnc7u4uDjv24BnzoeGhqp79%2B5yu92O1g4AAAJX0L5FeOWVVyolJUUvvPCCDh06pKlTp%2BqZZ56Rx%2BNR69atfW7bokULlZaWSpI8Ho%2BioqJ85qOiorzz9VFcXKySkhKfMZerqWJjY3/ibs4uLCyw8rHLFVj1nul0vwOt74GMnjuLfjuPngevoA1YOTk53v/u2LGjpk%2BfrkmTJql3797nva8xpsFrL1q0yGcsPT1dU6ZMadBxA110dDN/l2BFZOQl/i7hgkPPnUW/nUfPg0/QBqwzXX755aqpqVFoaKg8Ho/PXGlpqWJiYiRJ0dHRdeY9Ho86d%2B5c77XGjBmj/v37%2B4y5XE1VWlrxE6s/u0B7xmN7/04LCwtVZOQlKiurVE1Nrb/LuSDQc2fRb%2BfR88bnryf3QRmwdu3apdWrV2vGjBnesYKCAl188cVKTU3VH//4R5/b5%2BbmqkePHpKkhIQE5eXlafjw4ZKkmpoa7dq1S6NGjar3%2BrGxsXXeDiwpOa7q6gv7lydY9l9TUxs0ewkU9NxZ9Nt59Dz4BNZLIPXUsmVL5eTkKDs7W999953279%2BvhQsXasyYMbrjjjtUWFioFStW6OTJk9q6dau2bt2qO%2B%2B8U5KUlpamd999Vzt27FBlZaVefvllXXzxxerXr59/NwUAAAJGUL6C1bp1a2VnZ2v%2B/PnegDR8%2BHBlZGQoPDxcS5Ys0ezZs/XMM8%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%2BHht3LhRR44c0cSJE3XppZfqV7/6lb9LAwAAAYCAdQa32609e/bo9ddfV/PmzdW8eXONHz9eS5cuJWA10K2/%2B4u/S/hR1k%2B9zt8lAAACFAHrDHl5eWrTpo2ioqK8Y/Hx8dq/f7/Ky8sVERFx3mMUFxerpKTEZ8zlaqrY2FirtYaF8Q5vY3K5fPt7ut/03Tn03Fn023n0PHgRsM7g8XgUGRnpM3Y6bJWWltYrYOXk5GjRokU%2BY5MnT9ZDDz1kr1B9H%2BTuuexrjRkzxnp4Q13FxcVauvRV%2Bu0geu4s%2Bu08eh68iMxnYYxp0P3HjBmjVatW%2BfyMGTPGUnX/p6SkRIsWLarzahkaB/12Hj13Fv12Hj0PXryCdYaYmBh5PB6fMY/Ho5CQEMXExNTrGLGxsTwTAQDgAsYrWGdISEhQUVGRjh496h1zu93q1KmTmjVr5sfKAABAoCBgnSEuLk6JiYmaP3%2B%2BysvLVVBQoNdff11paWn%2BLg0AAASIsKeffvppfxfxc3P99ddr7dq1eu655/Tee%2B9p1KhRmjBhgkJCQvxdWh3NmjXT1VdfzatrDqHfzqPnzqLfzqPnwSnENPSMbgAAAPjgLUIAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAaiwsFD333%2B/kpOTdeONN2revHmqra31d1k/O9u2bVNKSooyMjLqzK1bt05DhgxRz549NWLECH388cfeudraWi1YsEADBgxQnz59NGHCBB06dMg77/F4NHXqVKWkpKhv37564oknVFVV5Z3fvXu37rrrLvXu3VsDBw7Ua6%2B9Vu%2B1A1lhYaHS09OVnJyslJQUzZgxQ2VlZZIa1pPGfjwC2Z49e3TPPfeod%2B/eSklJ0dSpU1VSUiJJ2r59u0aNGqVevXpp8ODBWr16tc99ly1bpkGDBqlXr15KS0tTbm6ud%2B7kyZP6zW9%2BoxtuuEHJycmaMmWKSktLvfPn%2Bxt0vrWDwfPPP6%2BuXbt6/02/UYdBwBk%2BfLh58sknTVlZmdm/f78ZOHCgee211/xd1s9Kdna2GThwoBk7dqyZOnWqz9yuXbtMQkKC2bJli6mqqjJ/%2BtOfTI8ePUxRUZExxphly5aZG2%2B80eTn55vjx4%2BbZ5991gwZMsTU1tYaY4yZPHmyuf/%2B%2B82RI0fM4cOHzZgxY8xzzz1njDGmsrLSXH/99eall14yFRUVJjc311x99dVmw4YN9Vo7kN1%2B%2B%2B1mxowZpry83BQVFZkRI0aYmTNnNrgnjfl4BLKTJ0%2Baa6%2B91ixatMicPHnSHDlyxNx1113mwQcfNP/85z/NlVdeaVasWGGqqqrMX/7yF5OUlGS%2B%2BuorY4wxH374obnqqqvMjh07TGVlpVmyZIm57rrrTEVFhTHGmDlz5pgRI0aYf/zjH6a0tNRMnjzZTJw40bv2uf4GnW/tYLBr1y5z9dVXmy5duhhjzr9n%2Bn1hImAFmK%2B%2B%2Bsp0797deDwe79h//dd/mUGDBvmxqp%2BfpUuXmrKyMpOZmVknYD3zzDMmPT3dZ2z06NFmyZIlxhhjBg8ebJYuXeqdO378uImLizNffvmlKSkpMd26dTO7d%2B/2zm/dutVceeWV5rvvvjPr168311xzjamurvbOz5s3z/z617%2Bu19qB6tixY2bGjBmmpKTEO/bGG2%2BYgQMHNrgnjfl4BDKPx2Pefvttc%2BrUKe/Y0qVLzc0332xeffVVM2zYMJ/bT5061cyaNcsYY8z9999vnn/%2Bee9cTU2Nue6668zatWvNqVOnTO/evc3GjRu98/n5%2BaZr167m8OHD5/0bdL61A11NTY0ZPXq0%2BY//%2BA9vwKLfOBveIgwweXl5atOmjaKiorxj8fHx2r9/v8rLy/1Y2c/LuHHj1Lx587PO5eXlKS4uzmcsLi5ObrdbVVVVys/P95mPiIhQu3bt5Ha7tXv3boWFhfm8NRAfH68TJ05o3759ysvLU9euXRUWFuZz7NNvB5xr7UAWGRmpOXPm6NJLL/WOFRUVKTY2tkE9aezHI5BFRUVp9OjRcrlckqR9%2B/bpj3/8o2699dYf7OkP9Tw0NFTdu3eX2%2B3WwYMHdfz4ccXHx3vnO3bsqCZNmigvL%2B%2B8f4POt3age%2ButtxQeHq4hQ4Z4x%2Bg3zoaAFWA8Ho8iIyN9xk7/4v3re/b4YR6Px%2BePlfR9D0tLS3Xs2DEZY35w3uPxKCIiQiEhIT5zkrzzZz4%2BLVq0kMfjUW1t7TnXDiZut1tvvvmmJk2a1KCeNPbjEQwKCwuVkJCg2267TYmJiZoyZcoP7vv0/2fn6rnH45GkOvePjIz8wZ7Wp%2BfB8P/4t99%2Bq5deeklPPfWUzzj9xtkQsAKQMcbfJQS88/XwXPM/pf//GgCC/fH7/PPPNWHCBE2bNk0pKSk/eLsf05PGfDwCXZs2beR2u/X%2B%2B%2B/rwIEDeuyxx%2Bp1P6d7HgzmzJmjESNGqFOnTj/6vvT7wkPACjAxMTHeZzyneTwehYSEKCYmxk9VBZbo6Oiz9jAmJkYtWrRQaGjoWedbtmypmJgYlZeXq6amxmdOknf%2BzGeOHo/He9xzrR0MNm3apPvvv18zZ87UuHHjJKlBPWnsxyNYhISEqH379srIyNDatWvlcrnq9Ky0tNT7/9m5en76NmfOHzt2zNvTc/0NOtux/3XtQLV9%2B3Z9%2BeWXSk9PrzN3vj3T7wtT8PyFuUAkJCSoqKhIR48e9Y653W516tRJzZo182NlgSMhIaHO%2BQlut1s9evRQeHi4OnfurLy8PO9cWVmZDh48qKSkJHXv3l3GGO3Zs8fnvpGRkerQoYMSEhK0d%2B9eVVdX1zn2%2BdYOdF988YUyMzO1cOFCDRs2zDvekJ409uMRyLZv365Bgwb5vNV5OjQmJSXV6Wlubq5Pz/%2B1pzU1Ndq1a5d69Oihtm3bKioqymf%2B73//u7777jslJCSc929QYmLiOdcOVKtXr9aRI0d04403Kjk5WSNGjJAkJScnq0uXLvQbdTl9Vj0abvTo0WbmzJnm%2BPHjJj8/3/Tv39%2B8%2Beab/i7rZ%2BlsnyLcu3evSUxMNJs3bzZVVVVmxYoVpmfPnqa4uNgY8/0ndPr16%2Be9LMCsWbPMyJEjvfefOnWquffee82RI0dMUVGRGTlypJk7d64x5vuPzt94443mxRdfNCdOnDA7duwwV111ldm8eXO91g5Up06dMrfeeqt566236sw1tCeN%2BXgEsrKyMpOSkmLmzp1rTpw4YY4cOWImTJhgfvnLX5pvv/3W9OzZ07z99tumqqrKbNmyxSQlJXk/bbl161bTu3dv8%2BWXX5oTJ06Yl156yaSmpprKykpjzPeftBw%2BfLj5xz/%2BYY4ePWomTpxoHnroIe/a5/obdL61A5XH4zFFRUXeny%2B//NJ06dLFFBUVmcLCQvqNOghYAaioqMjce%2B%2B9JikpyaSkpJgXX3zRe00gfC8hIcEkJCSYbt26mW7dunn/fdqGDRvMwIEDTXx8vLnjjjvMZ5995p2rra01CxcuNNdee61JSkoy9913n891qsrKykxGRoa58sorTZ8%2BfcwzzzxjTp486Z3fu3evGTt2rElISDD9%2BvUzy5cv96ntXGsHqv/5n/8xXbp08fb5X3%2B%2B%2BeabBvWksR%2BPQLZnzx5z1113maSkJHPNNdeYqVOnmsOHDxtjjPnss8/M0KFDTXx8vBk4cGCda38tX77cpKammoSEBJOWlmb27t3rnTt58qR5%2BumnTZ8%2BfUzPnj3NI488YsrKyrzz5/sbdL61g8GhQ4e8l2kwhn6jrhBjOHsOAADAJs7BAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADL/h/VKpHajRVbWQAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-630505268760076789”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>359</td> <td class=”number”>11.2%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.0</td> <td class=”number”>35</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>32</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14.0</td> <td class=”number”>29</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16.0</td> <td class=”number”>28</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>21.0</td> <td class=”number”>28</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17.0</td> <td class=”number”>27</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>27</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>27</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (765)</td> <td class=”number”>1966</td> <td class=”number”>61.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>619</td> <td class=”number”>19.3%</td> <td>

<div class=”bar” style=”width:32%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-630505268760076789”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>359</td> <td class=”number”>11.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>18</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>187613.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>191155.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>274351.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>407208.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>471825.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_ORCHARD_ACRES12”><s>ORCHARD_ACRES12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_ORCHARD_ACRES07”><code>ORCHARD_ACRES07</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99592</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_ORCHARD_ACRESPTH07”>ORCHARD_ACRESPTH07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2234</td>

</tr> <tr>

<th>Unique (%)</th> <td>69.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>19.3%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>619</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>26.027</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>2943.1</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>11.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram4963076682427918467”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives4963076682427918467,#minihistogram4963076682427918467”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives4963076682427918467”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles4963076682427918467”

aria-controls=”quantiles4963076682427918467” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram4963076682427918467” aria-controls=”histogram4963076682427918467”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common4963076682427918467” aria-controls=”common4963076682427918467”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme4963076682427918467” aria-controls=”extreme4963076682427918467”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles4963076682427918467”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0.33058</td>

</tr> <tr>

<th>Median</th> <td>1.3103</td>

</tr> <tr>

<th>Q3</th> <td>5.2492</td>

</tr> <tr>

<th>95-th percentile</th> <td>111.15</td>

</tr> <tr>

<th>Maximum</th> <td>2943.1</td>

</tr> <tr>

<th>Range</th> <td>2943.1</td>

</tr> <tr>

<th>Interquartile range</th> <td>4.9186</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>133.33</td>

</tr> <tr>

<th>Coef of variation</th> <td>5.1229</td>

</tr> <tr>

<th>Kurtosis</th> <td>214.59</td>

</tr> <tr>

<th>Mean</th> <td>26.027</td>

</tr> <tr>

<th>MAD</th> <td>40.939</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>12.754</td>

</tr> <tr>

<th>Sum</th> <td>67435</td>

</tr> <tr>

<th>Variance</th> <td>17777</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram4963076682427918467”>

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LU0AACAOnF4%2B4D5%2BflavXq1PvroI5WXl2vQoEF655131LNnT/djevXqpRdeeEFDhw719vIAAADqzOsFa8iQIWrXrp0mTpyo4cOHq0WLFuc9JiUlRSdOnPD20gAAAIzwesFasWKFbrrppss%2Bbu/evV5YDQAAgHlevwYrJiZGjz76qLZu3eoe%2B8///E89/PDDcrlc3l4OAACAcV4vWPPnz9epU6fUoUMH91jfvn1VW1urBQsW/FOvtWvXLiUnJysjI8NjfM2aNercubMSEhI8vs7%2BdmJtba0WL16s/v37q1evXpowYYKOHTvmfr7L5dLUqVOVnJysPn36aPbs2aqsrKzDrgEAwNXE6wXr888/V1ZWlqKjo91j0dHRyszM1K5du2y/zuuvv6558%2Bapbdu2F5zv1auXnE6nx1diYqIkaeXKlVq3bp2ys7O1fft2RUdHKz09XZZlSZKee%2B45VVRUaP369frwww%2BVn5%2BvzMzMK980AAC4qni9YFVWVio4OPj8hQQGqqKiwvbrBAcHa/Xq1RctWJeSk5Oj8ePHq3379goJCVFGRoby8/O1d%2B9e/fDDD9q6dasyMjIUERGh1q1b67HHHtOHH36oqqqqf/pYAADg6uP1i9x79eqlBQsWaNq0aQoLC5Mk/f3vf9fLL7/scauGyxk3btwl5wsLC/Xggw8qNzdXoaGhmjJliu655x5VVlbq4MGDio2NdT82JCREbdu2ldPp1KlTpxQUFKSYmBj3fFxcnH766ScdOnTIY/xiioqKVFxc7DHmcDRVZGSk7f3ZERTkX3%2Br2%2BHwzXrP5uRveXkbOdlDTvaQkz3kZJ%2B/ZeX1gjVr1iz9%2Bte/1i233KKQkBDV1taqvLxcbdq00bvvvmvkGBEREYqOjtaTTz6pDh066A9/%2BIOefvppRUZG6le/%2BpUsy3KXu7PCwsJUUlKiFi1aKCQkRAEBAR5zklRSUmLr%2BDk5OcrKyvIYS09P15QpU%2Bq4M/8WHt7Mp8cPDb3Gp8f3F%2BRkDznZQ072kJN9/pKV1wtWmzZt9Mknn%2Bizzz7T0aNHFRgYqHbt2qlPnz4KCgoycoy%2Bffuqb9%2B%2B7v8fMmSI/vCHP2jNmjWaPn26JLmvt7qQS83ZkZqaqn79%2BnmMORxNVVJSXqfXPZe/tPizTO/frqCgQIWGXqPS0grV1NT6ZA3%2BgJzsISd7yMkecrLvSrPy1T/uvV6wJKlx48a64447vHrMqKgo5ebmqkWLFgoMDDzvlhAul0stW7ZURESEysrKVFNT4y58Zx/bsmVLW8eKjIw873RgcfEpVVdf3d88vt5/TU2tz9fgD8jJHnKyh5zsISf7/CUrrxesY8eOadGiRfruu%2B8ueOuDTz/9tM7H%2BP3vf6%2BwsDANHjzYPZafn682bdooODhYHTt2VF5envuGp6WlpTp69KgSExMVFRUly7J04MABxcXFSZKcTqdCQ0PVrl27Oq8NAAA0fD65BquoqEh9%2BvRR06ZN6%2BUYZ86c0dy5c9WmTRt17txZmzdv1meffaYPPvhAkpSWlqbs7Gzddtttat26tTIzM9WlSxclJCRIkgYNGqRXXnlFL7/8ss6cOaOlS5dq5MiRcjh88oEfAADwM15vDLm5ufr0008VERFRp9c5W4aqq6slyX1neKfTqXHjxqm8vFxPPPGEiouLdf3112vp0qWKj4%2BXJI0ZM0bFxcW6//77VV5erqSkJI%2BL0l988UU9//zz6t%2B/vxo1aqS77777vJuZAgAAXEyAVdcruv9Jd9xxh9avX68mTZp487A%2BV1x8yvhrOhyBGpBp/%2BasvrZxam%2BfHNfhCFR4eDOVlJT7xXl7XyEne8jJHnKyh5zsu9KsWrVqXo%2Brujiv/xraxIkTlZWVVeff1AMAAPil8vopws8%2B%2B0xfffWV1qxZo%2Buvv16BgZ4d7/333/f2kgAAAIzyesEKCQnRbbfd5u3DAgAAeI3XC9b8%2BfO9fUgAAACv8smtwA8dOqRXX31VzzzzjHvs66%2B/9sVSAAAAjPN6wdq9e7eGDRumLVu2aP369ZJ%2BvvnouHHjjNxkFAAAwNe8XrAWL16sp556SuvWrXP/QeU2bdpowYIFWrp0qbeXAwAAYJzXC9bf/vY3paWlSZK7YEnSnXfeqfz8fG8vBwAAwDivF6zmzZtf8G8QFhUVqXHjxt5eDgAAgHFeL1g9evTQSy%2B9pLKyMvfY4cOHNWPGDN1yyy3eXg4AAIBxXr9NwzPPPKMHHnhASUlJqqmpUY8ePVRRUaGOHTtqwYIF3l4OAACAcV4vWNddd53Wr1%2BvnTt36vDhw2rSpInatWun3r17e1yTBQAA4K%2B8XrAkqVGjRrrjjjt8cWgAAIB65/WC1a9fv0t%2BUsW9sAAAgL/zesEaPHiwR8GqqanR4cOH5XQ69cADD3h7OQAAAMZ5vWBNnz79guObN2/Wn//8Zy%2BvBgAAwDyf/C3CC7njjjv0ySef%2BHoZAAAAdfaLKVj79u2TZVm%2BXgYAAECdef0U4ZgxY84bq6ioUH5%2BvgYOHOjt5QAAABjn9YIVHR193m8RBgcHa%2BTIkRo1apS3lwMAAGCc1wsWd2sHAAANndcL1kcffWT7scOHD6/HlQAAANQPrxes2bNnq7a29rwL2gMCAjzGAgICKFgAAMAveb1gvfHGG3rrrbf06KOPKiYmRpZl6dtvv9Xrr7%2BusWPHKikpydtLAgAAMMon12BlZ2erdevW7rEbb7xRbdq00YQJE7R%2B/XpvLwkAAMAor98H68iRIwoLCztvPDQ0VAUFBd5eDgAAgHFeL1hRUVFasGCBSkpK3GOlpaVatGiRbrjhBm8vBwAAwDivnyKcNWuWpk2bppycHDVr1kyBgYEqKytTkyZNtHTpUm8vBwAAwDivF6w%2Bffpox44d2rlzp44fPy7LstS6dWvdeuutat68ubeXAwAAYJzXC5YkXXPNNerfv7%2BOHz%2BuNm3a%2BGIJAAAA9cbr12BVVlZqxowZ6t69u%2B666y5JP1%2BD9dBDD6m0tNTbywEAADDO6wVr4cKF2r9/vzIzMxUY%2BP%2BHr6mpUWZmpreXAwAAYJzXC9bmzZv1u9/9Tnfeeaf7jz6HhoZq/vz52rJli7eXAwAAYJzXC1Z5ebmio6PPG4%2BIiNBPP/3k7eUAAAAY5/WCdcMNN%2BjPf/6zJHn87cFNmzbpX//1X729HAAAAOO8/luE//Zv/6bHH39c9913n2pra/X2228rNzdXmzdv1uzZs729HAAAAOO8XrBSU1PlcDj03nvvKSgoSK%2B99pratWunzMxM3Xnnnd5eDgAAgHFeL1gnTpzQfffdp/vuu8/bhwYAAPAKr1%2BD1b9/f49rrwAAABoarxespKQkbdy40duHBQAA8BqvnyL8l3/5F/32t79Vdna2brjhBjVq1MhjftGiRd5eEgAAgFFeL1gHDx7Ur371K0lSSUmJtw8PAABQ77xWsDIyMrR48WK9%2B%2B677rGlS5cqPT3dW0sAAADwCq9dg7Vt27bzxrKzs711eAAAAK/xWsG60G8O8tuEAACgIfJawTr7h50vNwYAAODvvH6bBgAAgIaOggUAAGCY136LsKqqStOmTbvsGPfBAgAA/s5rn2D17NlTRUVFHl8XGvtn7Nq1S8nJycrIyDhvbsOGDRo6dKi6d%2B%2BuESNG6PPPP3fP1dbWavHixerfv7969eqlCRMm6NixY%2B55l8ulqVOnKjk5WX369NHs2bNVWVl55ZsHAABXFa99gvWP978y4fXXX9fq1avVtm3b8%2Bb279%2BvGTNmKCsrSzfffLM2b96syZMna9OmTbruuuu0cuVKrVu3Tq%2B//rpat26txYsXKz09XR9//LECAgL03HPP6cyZM1q/fr2qqqr0xBNPKDMzU88%2B%2B6zRPQAAgIbJb6/BCg4OvmjBWrVqlVJSUpSSkqLg4GANGzZMnTp10tq1ayVJOTk5Gj9%2BvNq3b6%2BQkBBlZGQoPz9fe/fu1Q8//KCtW7cqIyNDERERat26tR577DF9%2BOGHqqqq8vY2AQCAH/L6n8oxZdy4cRedy8vLU0pKisdYbGysnE6nKisrdfDgQcXGxrrnQkJC1LZtWzmdTp06dUpBQUGKiYlxz8fFxemnn37SoUOHPMYvpqioSMXFxR5jDkdTRUZG2t2eLUFB/tWPHQ7frPdsTv6Wl7eRkz3kZA852UNO9vlbVn5bsC7F5XIpLCzMYywsLEwHDx7UyZMnZVnWBedLSkrUokULhYSEeNyj6%2Bxj7f7txJycHGVlZXmMpaena8qUKVeynQYjPLyZT48fGnqNT4/vL8jJHnKyh5zsISf7/CWrBlmwpMvfJf5S83W9w3xqaqr69evnMeZwNFVJSXmdXvdc/tLizzK9f7uCggIVGnqNSksrVFNT65M1%2BANysoec7CEne8jJvivNylf/uG%2BQBSs8PFwul8tjzOVyKSIiQi1atFBgYOAF51u2bKmIiAiVlZWppqZGQUFB7jlJatmypa3jR0ZGnnc6sLj4lKqrr%2B5vHl/vv6am1udr8AfkZA852UNO9pCTff6SlX99BGJTfHy8cnNzPcacTqe6du2q4OBgdezYUXl5ee650tJSHT16VImJierSpYssy9KBAwc8nhsaGqp27dp5bQ8AAMB/NciCNXr0aH3xxRfasWOHTp8%2BrdWrV%2BvIkSMaNmyYJCktLU0rVqxQfn6%2BysrKlJmZqS5duighIUEREREaNGiQXnnlFZ04cULHjx/X0qVLNXLkSDkcDfIDPwAAYJjfNoaEhARJUnV1tSRp69atkn7%2BtKlTp07KzMzU/PnzVVBQoA4dOmj58uVq1aqVJGnMmDEqLi7W/fffr/LyciUlJXlclP7iiy/q%2BeefV//%2B/dWoUSPdfffdF7yZKQAAwIUEWHW9ohu2FBefMv6aDkegBmTuMv669WXj1N4%2BOa7DEajw8GYqKSn3i/P2vkJO9pCTPeRkDznZd6VZtWrVvB5XdXEN8hQhAACAL1GwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhjXYghUTE6P4%2BHglJCS4v%2BbOnStJ2r17t0aOHKkePXpoyJAhWrt2rcdzV6xYoUGDBqlHjx5KS0tTbm6uL7YAAAD8lMPXC6hPmzZt0vXXX%2B8xVlRUpMcee0yzZ8/W0KFD9eWXX2rSpElq166dEhIStG3bNr366qt64403FBMToxUrVujRRx/Vli1b1LRpUx/tBAAA%2BJMG%2BwnWxaxbt07R0dEaOXKkgoODlZycrH79%2BmnVqlWSpJycHI0YMUJdu3ZVkyZN9NBDD0mStm/f7stlAwAAP9KgP8FatGiRvv76a5WVlemuu%2B7SzJkzlZeXp9jYWI/HxcbGauPGjZKkvLw8DR482D0XGBioLl26yOl0asiQIbaOW1RUpOLiYo8xh6OpIiMj67gjT0FB/tWPHQ7frPdsTv6Wl7eRkz3kZA852UNO9vlbVg22YHXr1k3Jycl6%2BeWXdezYMU2dOlVz5syRy%2BVS69atPR7bokULlZSUSJJcLpfCwsI85sPCwtzzduTk5CgrK8tjLD09XVOmTLnC3TQM4eHNfHr80NBrfHp8f0FO9pCTPeRkDznZ5y9ZNdiClZOT4/7v9u3ba/r06Zo0aZJ69ux52edallWnY6empqpfv34eYw5HU5WUlNfpdc/lLy3%2BLNP7tysoKFChodeotLRCNTW1PlmDPyAne8jJHnKyh5zsu9KsfPWP%2BwZbsM51/fXXq6amRoGBgXK5XB5zJSUlioiIkCSFh4efN%2B9yudSxY0fbx4qMjDzvdGBx8SlVV1/d3zy%2B3n9NTa3P1%2BAPyMkecrKHnOwhJ/v8JSv/%2BgjEpn379mnBggUeY/n5%2BWrcuLFSUlLOu%2B1Cbm6uunbtKkmKj49XXl6ee66mpkb79u1zzwMAAFxOgyxYLVu2VE5OjrKzs3XmzBkdPnxYS5Z5CMrjAAAPlUlEQVQsUWpqqu655x4VFBRo1apVOn36tHbu3KmdO3dq9OjRkqS0tDR99NFH2rNnjyoqKrRs2TI1btxYffv29e2mAACA32iQpwhbt26t7OxsLVq0yF2Q7r33XmVkZCg4OFjLly/XvHnzNGfOHEVFRWnhwoXq3LmzJOm2227Tk08%2BqalTp%2BrHH39UQkKCsrOz1aRJEx/vCgAA%2BIsAq65XdMOW4uJTxl/T4QjUgMxdxl%2B3vmyc2tsnx3U4AhUe3kwlJeV%2Bcd7eV8jJHnKyh5zsISf7rjSrVq2a1%2BOqLq5BniIEAADwJQoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMIevF/BLVFBQoDlz5mjv3r1q2rSpBg8erGnTpikwkD5aF3e98kdfL%2BGfsnFqb18vAQDgpyhYF/D4448rLi5OW7du1Y8//qiJEyfq2muv1YMPPujrpQEAAD/ARzLncDqdOnDggKZPn67mzZsrOjpa48ePV05Ojq%2BXBgAA/ASfYJ0jLy9PUVFRCgsLc4/FxcXp8OHDKisrU0hIyGVfo6ioSMXFxR5jDkdTRUZGGl1rUBD9uD752ynNP0y/tU7PP/t%2B4n11aeRkDznZQ072%2BVtWFKxzuFwuhYaGeoydLVslJSW2ClZOTo6ysrI8xiZPnqzHH3/c3EL1c5F74LrvlJqaary8NSRFRUXKyckhp8soKirSO%2B%2B8QU6XQU72kJM95GSfv2XlHzXQyyzLqtPzU1NTtWbNGo%2Bv1NRUQ6v7f8XFxcrKyjrv0zJ4Iid7yMkecrKHnOwhJ/v8LSs%2BwTpHRESEXC6Xx5jL5VJAQIAiIiJsvUZkZKRftGsAAFA/%2BATrHPHx8SosLNSJEyfcY06nUx06dFCzZs18uDIAAOAvKFjniI2NVUJCghYtWqSysjLl5%2Bfr7bffVlpamq%2BXBgAA/ETQCy%2B88IKvF/FLc%2Butt2r9%2BvWaO3euPvnkE40cOVITJkxQQECAr5d2nmbNmummm27i07XLICd7yMkecrKHnOwhJ/v8KasAq65XdAMAAMADpwgBAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNg%2BaGCggI98sgjSkpK0u23366FCxeqtrbW18vyiZiYGMXHxyshIcH9NXfuXEnS7t27NXLkSPXo0UNDhgzR2rVrPZ67YsUKDRo0SD169FBaWppyc3N9sYV6s2vXLiUnJysjI%2BO8uQ0bNmjo0KHq3r27RowYoc8//9w9V1tbq8WLF6t///7q1auXJkyYoGPHjrnnXS6Xpk6dquTkZPXp00ezZ89WZWWlV/ZUHy6W05o1a9S5c2eP91ZCQoK%2B%2BeYbSVdfTgUFBUpPT1dSUpKSk5M1c%2BZMlZaWSpL279%2BvsWPHqmfPnho4cKDeeustj%2BfW5f3mby6W0/fff6%2BYmJjz3k9vvvmm%2B7lXU04HDhzQAw88oJ49eyo5OVlTp05VcXGxpLr97D59%2BrR%2B85vf6LbbblNSUpKmTJmikpISr%2B7NzYLfuffee61nn33WKi0ttQ4fPmwNHDjQeuutt3y9LJ/o1KmTdezYsfPG//73v1vdunWzVq1aZVVWVlp//OMfrcTEROubb76xLMuyPv30U%2BvGG2%2B09uzZY1VUVFjLly%2B3evfubZWXl3t7C/UiOzvbGjhwoDVmzBhr6tSpHnP79u2z4uPjrR07dliVlZXWxx9/bHXt2tUqLCy0LMuyVqxYYd1%2B%2B%2B3WwYMHrVOnTlkvvviiNXToUKu2ttayLMuaPHmy9cgjj1g//vijdfz4cSs1NdWaO3eu1/dowqVy%2BvDDD62xY8de9LlXU06WZVl33323NXPmTKusrMwqLCy0RowYYc2aNcuqqKiwbr31VuvVV1%2B1ysvLrdzcXOumm26yNm/ebFlW3d9v/uZiOR07dszq1KnTRZ93NeV0%2BvRp65ZbbrGysrKs06dPWz/%2B%2BKM1duxY67HHHqvzz%2B758%2BdbI0aMsP73f//XKikpsSZPnmxNnDjRJ/ukYPmZb775xurSpYvlcrncY//1X/9lDRo0yIer8p2LFaw33njDGj58uMfY1KlTreeee86yLMt65JFHrJdeesk9V1NTY/Xu3dtav359/S7YS9555x2rtLTUmjFjxnnFYc6cOVZ6errH2KhRo6zly5dblmVZQ4YMsd555x333KlTp6zY2Fjr66%2B/toqLi63OnTtb%2B/fvd8/v3LnT6tatm3XmzJl63FH9uFROlytYV1NOJ0%2BetGbOnGkVFxe7x959911r4MCB1saNG62bb77Zqq6uds8tXLjQ%2BvWvf21ZVt3eb/7mUjldrmBdTTm5XC7rgw8%2BsKqqqtxj77zzjjVgwIA6/eyuqqqyevbsaW3dutU9f/DgQSsmJsY6fvx4Pe/qfJwi9DN5eXmKiopSWFiYeywuLk6HDx9WWVmZD1fmO4sWLVLfvn1144036rnnnlN5ebny8vIUGxvr8bjY2Fj3R8nnzgcGBqpLly5yOp1eXXt9GTdunJo3b37BuYtl43Q6VVlZqYMHD3rMh4SEqG3btnI6ndq/f7%2BCgoIUExPjno%2BLi9NPP/2kQ4cO1c9m6tGlcpKkwsJCPfjgg%2BrVq5f69%2B%2Bvjz/%2BWJKuupxCQ0M1f/58XXvtte6xwsJCRUZGKi8vTzExMQoKCnLPXep77ey8nfebv7lUTmc9/fTT6tOnj26%2B%2BWYtWrRIVVVVkq6unMLCwjRq1Cg5HA5J0qFDh/Tf//3fuuuuu%2Br0s/vo0aM6deqU4uLi3PPt27dXkyZNlJeX54WdeaJg%2BRmXy6XQ0FCPsbNly2fnmX2oW7duSk5O1pYtW5STk6M9e/Zozpw5F8ypRYsW7oxcLpdHSZV%2BzvFqyPBSez958qQsy7rovMvlUkhIiAICAjzmpIb3/ouIiFB0dLSeeuop/fGPf9STTz6pWbNmaffu3Vd9Tk6nU%2B%2B9954mTZp00e81l8ul2traOr3f/N0/5tS4cWN1795dAwYM0Pbt25Wdna21a9fqP/7jPyTV7fvSXxUUFCg%2BPl6DBw9WQkKCpkyZUqef3S6XS5LOe35oaKhPcqJg%2BSHLsny9hF%2BMnJwcjRo1So0bN1b79u01ffp0rV%2B/3v2vwku5mnO83N4vNX%2B15Na3b1%2B98cYbio2NVePGjTVkyBANGDBAa9ascT/maszpyy%2B/1IQJEzRt2jQlJydf9HH/WC7r8n7zV%2BfmFBkZqffff18DBgxQo0aNlJiYqIkTJ9p%2BP9mZ9zdRUVFyOp3atGmTjhw5oqefftrW8/wlJwqWn4mIiHC39LNcLpcCAgIUERHho1X9clx//fWqqalRYGDgeTmVlJS4MwoPD79gjldDhpfae4sWLS6YncvlUsuWLRUREaGysjLV1NR4zElSy5Yt63/xPhYVFaWioqKrNqdt27bpkUce0axZszRu3DhJP/9MOvfTAZfL5c6oLu83f3WhnC4kKipKP/zwgyzLuipzkn4u4tHR0crIyND69evlcDiu%2BGf32cecO3/y5Emf5ETB8jPx8fEqLCzUiRMn3GNOp1MdOnRQs2bNfLgy79u3b58WLFjgMZafn6/GjRsrJSXlvNsu5ObmqmvXrpJ%2BzvEfz8nX1NRo37597vmGLD4%2B/rxsnE6nunbtquDgYHXs2NEjm9LSUh09elSJiYnq0qWLLMvSgQMHPJ4bGhqqdu3aeW0P3vD73/9eGzZs8BjLz89XmzZtrsqcvvrqK82YMUNLlizR8OHD3ePx8fH69ttvVV1d7R47%2B346O3%2Bl7zd/dLGcdu/erWXLlnk89tChQ4qKilJAQMBVldPu3bs1aNAgj9sLBQb%2BXEcSExOv%2BGd3mzZtFBYW5jH/t7/9TWfOnFF8fHx9bumCKFh%2BJjY2VgkJCVq0aJHKysqUn5%2Bvt99%2BW2lpab5emte1bNlSOTk5ys7O1pkzZ3T48GEtWbJEqampuueee1RQUKBVq1bp9OnT2rlzp3bu3KnRo0dLktLS0vTRRx9pz549qqio0LJly9S4cWP17dvXt5vygtGjR%2BuLL77Qjh07dPr0aa1evVpHjhzRsGHDJP2czYoVK5Sfn6%2BysjJlZmaqS5cuSkhIUEREhAYNGqRXXnlFJ06c0PHjx7V06VKNHDnSfcFqQ3HmzBnNnTtXTqdTVVVVWr9%2BvT777DONGTNG0tWVU3V1tZ599llNnz5dffr08ZhLSUlRSEiIli1bpoqKCu3du1erV692/0yqy/vN31wqp%2BbNm2vp0qX6%2BOOPVVVVJafTqTfffPOqzCk%2BPl5lZWVauHChKioqdOLECb366qu68cYblZaWdsU/u4OCgjR69Gi99tprKiwsVElJif793/9dAwYM8PjFA6/x7i8twoTCwkLroYceshITE63k5GTrd7/7nV/eC8WEv/zlL1ZqaqrVrVs366abbrLmz59vVVZWuueGDRtmxcXFWQMHDnTfl%2BeslStXWikpKVZ8fLyVlpZmffvtt77YQr2Ij4%2B34uPjrc6dO1udO3d2//9ZmzdvtgYOHGjFxcVZ99xzj/WXv/zFPVdbW2stWbLEuuWWW6zExETr4Ycfdt%2BLx7Isq7S01MrIyLC6detm9erVy5ozZ451%2BvRpr%2B7PlEvlVFtbay1dutS6/fbbrfj4eOvOO%2B%2B0tm3b5n7u1ZTT//zP/1idOnVy5/OPX99//7317bffWmPGjLHi4%2BOtvn37WitXrvR4fl3eb/7kcjlt2bLFGjZsmJWYmGj17t3beu2116yamhr386%2BWnCzLsg4cOGCNHTvWSkxMtG6%2B%2BWZr6tSp7lsp1OVn9%2BnTp60XXnjB6tWrl9W9e3frySeftEpLS726t7MCLOsXcjUYAABAA8EpQgAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAw7P8AyR7OFKkfBW0AAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common4963076682427918467”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>359</td> <td class=”number”>11.2%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6005645306588193</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.5086395533086847</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0431723989003414</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5136826375268515</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>27.541057621761947</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.7037402671846886</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>172.9202830818534</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.3981125480601189</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.6364764267990073</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2223)</td> <td class=”number”>2223</td> <td class=”number”>69.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>619</td> <td class=”number”>19.3%</td> <td>

<div class=”bar” style=”width:28%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme4963076682427918467”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>359</td> <td class=”number”>11.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0036862890419237775</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.005715254586015535</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.007714127653384408</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.009053962749733756</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1512.6675739062455</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1591.6457811194653</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2488.3511269276396</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2588.838883888389</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2943.0600420448136</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_ORCHARD_ACRESPTH12”><s>ORCHARD_ACRESPTH12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_ORCHARD_ACRESPTH07”><code>ORCHARD_ACRESPTH07</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.96957</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_ORCHARD_FARMS07”>ORCHARD_FARMS07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>234</td>

</tr> <tr>

<th>Unique (%)</th> <td>7.3%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>4.2%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>134</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>37.657</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>4685</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>11.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-2591283239813382839”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-2591283239813382839”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-2591283239813382839”

aria-controls=”quantiles-2591283239813382839” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-2591283239813382839” aria-controls=”histogram-2591283239813382839”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-2591283239813382839” aria-controls=”common-2591283239813382839”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-2591283239813382839” aria-controls=”extreme-2591283239813382839”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-2591283239813382839”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>3</td>

</tr> <tr>

<th>Median</th> <td>9</td>

</tr> <tr>

<th>Q3</th> <td>22</td>

</tr> <tr>

<th>95-th percentile</th> <td>108.25</td>

</tr> <tr>

<th>Maximum</th> <td>4685</td>

</tr> <tr>

<th>Range</th> <td>4685</td>

</tr> <tr>

<th>Interquartile range</th> <td>19</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>186.84</td>

</tr> <tr>

<th>Coef of variation</th> <td>4.9616</td>

</tr> <tr>

<th>Kurtosis</th> <td>289.24</td>

</tr> <tr>

<th>Mean</th> <td>37.657</td>

</tr> <tr>

<th>MAD</th> <td>47.799</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>15.217</td>

</tr> <tr>

<th>Sum</th> <td>115830</td>

</tr> <tr>

<th>Variance</th> <td>34909</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-2591283239813382839”>

<img 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7p3767s7GwZY0K5XAAAEEbCMmCVl5crJSVFM2fOVPv27XX%2B%2Befrpptu0p///Gdt375dp06d0vTp09WuXTslJydrwoQJKigokCStXbtWmZmZyszMVHR0tMaOHas%2Bffpo/fr1kqSCggJNmTJFPXv2VExMjHJzc1VcXKyPP/44lEsGAABhJCwDltvt1sKFC3XeeecFxkpLS5WYmKiioiL17dtXUVFRgbmkpCQVFhZKkoqKipSUlBS0v6SkJHk8HlVXV2vfvn1B8zExMerWrZs8Hk8zrwoAALQUrlAXYIPH49FLL72kp556Sps3b5bb7Q6aj4uLk9/vV319vfx%2Bv2JjY4PmY2NjtW/fPh07dkzGmAbnfT5fo%2BspKyuT1%2BsNGnO52ikxMfFfXNnXi4oKr3zscoVXvQ050/Nw6304o%2BfOo%2BfOo%2BctT9gHrA8%2B%2BEDTp0/XzJkzlZGRoc2bNzf4uoiIiMD//qb7qZp6v1VBQYGWL18eNJadna0ZM2Y0ab/hLj6%2BfahLsMbtPjfUJbQ69Nx59Nx59LzlCOuAtXXrVv3kJz/R3LlzdeONN0qSEhIS9Pnnnwe9zu/3Ky4uTpGRkYqPj5ff7z9rPiEhIfCahuY7duzY6LomTZqk4cOHB425XO3k81X%2BC6v7ZuH2Scf2%2BkMhKipSbve5Ki%2BvUl1dfajLaRXoufPoufPoefMJ1Yf7sA1YH374ofLy8vTkk09qyJAhgfGUlBT9%2Bte/Vm1trVyu08vzeDzq379/YP7M/VhneDwejR49WtHR0erdu7eKioo0ePBgSadvqD948KDS0tIaXVtiYuJZlwO93uOqrW3dPzQtaf11dfUtaj3hgJ47j547j563HOF1CuT/1NbW6pFHHtGsWbOCwpUkZWZmKiYmRk899ZSqqqr08ccfa926dcrKypIkTZw4Ue%2B//762b9%2BumpoarVu3Tp9//rnGjh0rScrKytLq1atVXFysiooK5efnq1%2B/fkpNTXV8nQAAIDyF5RmsXbt2qbi4WAsWLNCCBQuC5t544w09/fTTevTRR7Vq1Sqdd955ys3N1dChQyVJffr0UX5%2BvhYuXKiSkhL16tVLK1euVKdOnSRJkydPltfr1a233qrKykqlp6efdT8VAADA14kwPEHTEV7vcev7dLkidU3%2Bu9b321w251wR6hKazOWKVHx8e/l8lZzGdwg9dx49dx49bz6dOnUIyXHD8hIhAADAd5njAWv48OFavny5SktLnT40AACAIxwPWDfffLM2bdqkq6%2B%2BWnfccYfeeust1dbWOl0GAABAs3E8YGVnZ2vTpk165ZVX1Lt3bz3xxBPKzMzU4sWLdeDAAafLAQAAsC5k92AlJycrLy9P27Zt0%2BzZs/XKK6/ouuuu09SpU/WXv/wlVGUBAAA0WcgC1qlTp7Rp0ybdeeedysvLU%2BfOnfXwww%2BrX79%2BmjJlijZs2BCq0gAAAJrE8edgFRcXa926dXr99ddVWVmpUaNG6YUXXtDAgQMDrxk0aJDmzZunMWPGOF0eAABAkzkesEaPHq0ePXpo2rRpuvHGGxUXF3fWazIzM3X06FGnSwMAALDC8YC1evXqwO/5%2Bzoff/yxA9UAAADY5/g9WH379tXdd9%2BtLVu2BMb%2B67/%2BS3feeaf8fr/T5QAAAFjneMBauHChjh8/rl69egXGhg4dqvr6ei1atMjpcgAAAKxz/BLhe%2B%2B9pw0bNig%2BPj4w1r17d%2BXn5%2Bv66693uhwAAADrHD%2BDVV1drejo6LMLiYxUVVWV0%2BUAAABY53jAGjRokBYtWqRjx44Fxv7%2B979r/vz5QY9qAAAACFeOXyKcPXu2fvzjH%2Bvyyy9XTEyM6uvrVVlZqa5du%2BrFF190uhwAAADrHA9YXbt21e9%2B9zu98847OnjwoCIjI9WjRw8NGTJEUVFRTpcDAABgneMBS5LatGmjq6%2B%2BOhSHBgAAaHaOB6xDhw5pyZIl%2Buyzz1RdXX3W/Ntvv%2B10SQAAAFaF5B6ssrIyDRkyRO3atXP68AAAAM3O8YBVWFiot99%2BWwkJCU4fGgAAwBGOP6ahY8eOnLkCAAAtmuMBa9q0aVq%2BfLmMMU4fGgAAwBGOXyJ855139OGHH%2BrVV1/VhRdeqMjI4Iz38ssvO10SAACAVY4HrJiYGF111VVOHxYAAMAxjgeshQsXOn1IAAAARzl%2BD5Yk7d%2B/X8uWLdPDDz8cGPvoo49CUQoAAIB1jgesnTt3auzYsXrrrbe0ceNGSacfPnrbbbfxkFEAANAiOB6wli5dqp/85CfasGGDIiIiJJ3%2B/YSLFi3SihUrnC4HAADAOscD1qeffqqsrCxJCgQsSfrBD36g4uJip8sBAACwzvGA1aFDhwZ/B2FZWZnatGnjdDkAAADWOR6wLrnkEj3xxBOqqKgIjB04cEB5eXm6/PLLnS4HAADAOscf0/Dwww/r9ttvV3p6uurq6nTJJZeoqqpKvXv31qJFi5wuBwAAwDrHA9b555%2BvjRs3aseOHTpw4IDatm2rHj166Iorrgi6JwsAACBcOR6wJOmcc87R1VdfHYpDAwAANDvHA9bw4cO/9kwVz8ICAADhzvGAdd111wUFrLq6Oh04cEAej0e333670%2BUAAABY53jAmjVrVoPjb775pv74xz86XA0AAIB9IfldhA25%2Buqr9bvf/S7UZQAAADTZdyZg7d69W8aYUJcBAADQZI5fIpw8efJZY1VVVSouLtbIkSP/pX29%2B%2B67ysvLU3p6upYuXRoYf/XVVzV79mydc845Qa9fs2aN0tLSVF9fryeffFIbN25UeXm50tLSNG/ePHXt2lWS5Pf7NW/ePP3pT39SZGSkMjMzNXfuXLVt2/ZbrBgAALQ2jges7t27n/UtwujoaI0fP14TJkxo9H6eeeYZrVu3Tt26dWtwftCgQXrxxRcbnFuzZo02bNigZ555Rp07d9bSpUuVnZ2t3/72t4qIiNDcuXN18uRJbdy4UadOndL999%2Bv/Px8PfLII41fKAAAaLUcD1i2ntYeHR2tdevW6fHHH1dNTc2/tG1BQYGmTJminj17SpJyc3OVnp6ujz/%2BWBdeeKG2bNmi1157TQkJCZKke%2B65R/fff7/y8vLOOisGAADwZY4HrNdff73Rr73xxhu/cu6222772m1LS0v1ox/9SIWFhXK73ZoxY4ZuuOEGVVdXa9%2B%2BfUpKSgq8NiYmRt26dZPH49Hx48cVFRWlvn37BuaTk5N14sQJ7d%2B/P2gcAACgIY4HrDlz5qi%2Bvv6sG9ojIiKCxiIiIr42YH2dhIQEde/eXQ888IB69eql3//%2B93rwwQeVmJio73//%2BzLGKDY2Nmib2NhY%2BXw%2BxcXFKSYmJugy5pnX%2Bny%2BRh2/rKxMXq83aMzlaqfExMRvtZ6vEhX1nfmOQqO4XOFVb0PO9Dzceh/O6Lnz6Lnz6HnL43jAevbZZ/Xcc8/p7rvvVt%2B%2BfWWM0SeffKJnnnlGt9xyi9LT05t8jKFDh2ro0KGBv48ePVq///3v9eqrrwaew/V131hs6rcZCwoKtHz58qCx7OxszZgxo0n7DXfx8e1DXYI1bve5oS6h1aHnzqPnzqPnLUdI7sFatWqVOnfuHBi79NJL1bVrV02dOlUbN25sluN26dJFhYWFiouLU2RkpPx%2Bf9C83%2B9Xx44dlZCQoIqKCtXV1SkqKiowJ0kdO3Zs1LEmTZqk4cOHB425XO3k81VaWMk/hNsnHdvrD4WoqEi53eeqvLxKdXX1oS6nVaDnzqPnzqPnzSdUH%2B4dD1iff/75WZfnJMntdqukpMTKMX79618rNjZW1113XWCsuLhYXbt2VXR0tHr37q2ioiINHjxYklReXq6DBw8qLS1NXbp0kTFGe/fuVXJysiTJ4/HI7XarR48ejTp%2BYmLiWZcDvd7jqq1t3T80LWn9dXX1LWo94YCeO4%2BeO4%2BetxyOnwLp0qWLFi1aFHQ/U3l5uZYsWaLvfe97Vo5x8uRJPfbYY/J4PDp16pQ2btyod955J/AMrqysLK1evVrFxcWqqKhQfn6%2B%2BvXrp9TUVCUkJGjUqFH6xS9%2BoaNHj%2Brw4cNasWKFxo8fL5fL8TwKAADCkOOJYfbs2Zo5c6YKCgrUvn17RUZGqqKiQm3bttWKFSsavZ/U1FRJUm1trSRpy5Ytkk6fbbrttttUWVmp%2B%2B%2B/X16vVxdeeKFWrFihlJQUSacfdur1enXrrbeqsrJS6enpQfdM/exnP9Ojjz6qESNG6JxzztH111%2Bv3NxcWy0AAAAtXIQJwe%2Bnqaqq0o4dO3T48GEZY9S5c2ddeeWV6tChg9OlOMbrPW59ny5XpK7Jf9f6fpvL5pwrQl1Ck7lckYqPby%2Bfr5LT%2BA6h586j586j582nU6fQZIuQXPM699xzNWLECB0%2BfDjw62kAAABaCsfvwaqurlZeXp4GDBiga6%2B9VtLpe7DuuOMOlZeXO10OAACAdY4HrMWLF2vPnj3Kz89XZOQ/Dl9XV6f8/HynywEAALDO8YD15ptv6pe//KV%2B8IMfBJ6W7na7tXDhQr311ltOlwMAAGCd4wGrsrJS3bt3P2s8ISFBJ06ccLocAAAA6xwPWN/73vf0xz/%2BUVLwr6R544039G//9m9OlwMAAGCd498i/OEPf6j77rtPN998s%2Brr6/X888%2BrsLBQb775pubMmeN0OQAAANY5HrAmTZokl8ull156SVFRUXr66afVo0cP5efn6wc/%2BIHT5QAAAFjneMA6evSobr75Zt18881OHxoAAMARjt%2BDNWLECIXg4fEAAACOcTxgpaena/PmzU4fFgAAwDGOXyK84IIL9Pjjj2vVqlX63ve%2Bp3POOSdofsmSJU6XBAAAYJXjAWvfvn36/ve/L0ny%2BXxOHx4AAKDZORawcnNztXTpUr344ouBsRUrVig7O9upEgAAABzh2D1YW7duPWts1apVTh0eAADAMY4FrIa%2BOci3CQEAQEvkWMA684udv2kMAAAg3Dn%2BmAYAAICWjoAFAABgmWPfIjx16pRmzpz5jWM8BwsAAIQ7xwLWwIEDVVZW9o1jAAAA4c6xgPXPz78CAABoybgHCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAsrAOWO%2B%2B%2B64yMjKUm5t71tymTZs0ZswYDRgwQOPGjdN7770XmKuvr9fSpUs1YsQIDRo0SFOnTtWhQ4cC836/Xzk5OcrIyNCQIUM0Z84cVVdXO7ImAAAQ/sI2YD3zzDNasGCBunXrdtbcnj17lJeXp1mzZukPf/iDpkyZonvvvVeHDx%2BWJK1Zs0YbNmzQqlWrtG3bNnXv3l3Z2dkyxkiS5s6dq6qqKm3cuFG/%2Bc1vVFxcrPz8fEfXBwAAwlfYBqzo6GitW7euwYC1du1aZWZmKjMzU9HR0Ro7dqz69Omj9evXS5IKCgo0ZcoU9ezZUzExMcrNzVVxcbE%2B/vhjffHFF9qyZYtyc3OVkJCgzp0765577tFvfvMbnTp1yullAgCAMBS2Aeu2225Thw4dGpwrKipSUlJS0FhSUpI8Ho%2Bqq6u1b9%2B%2BoPmYmBh169ZNHo9He/bsUVRUlPr27RuYT05O1okTJ7R///7mWQwAAGhRXKEuoDn4/X7FxsYGjcXGxmrfvn06duyYjDENzvt8PsXFxSkmJkYRERFBc5Lk8/kadfyysjJ5vd6gMZernRITE7/Ncr5SVFR45WOXK7zqbciZnodb78MZPXcePXcePW95WmTAkhS4n%2BrbzH/Ttt%2BkoKBAy5cvDxrLzs7WjBkzmrTfcBcf3z7UJVjjdp8b6hJaHXruPHruPHrecrTIgBUfHy%2B/3x805vf7lZCQoLi4OEVGRjY437FjRyUkJKiiokJ1dXWKiooKzElSx44dG3X8SZMmafjw4UFjLlc7%2BXyV33ZJDQq3Tzq21x8KUVGRcrvPVXl5lerq6kNdTqtAz51Hz51Hz5tPqD7ct8iAlZKSosLCwqAxj8ej0aNHKzo6Wr1791ZRUZEGDx4sSSovL9fBgweVlpamLl26yBijvXv3Kjk5ObCt2%2B1Wjx49GnX8xMTEsy4Her3HVVvbun9oWtL66%2BrqW9R6wgE9dx49dx49bznC6xRII02cOFHvv/%2B%2Btm/frpqaGq1bt06ff/65xo4dK0nKysrS6tWrVVxcrIqKCuXn56tfv35KTU1VQkKCRo0apV/84hc6evSoDh8%2BrBUrVmj8%2BPFyuVpkHgUAAJaFbWJITU2VJNXW1kqStmzZIun02abFTkgNAAARSUlEQVQ%2BffooPz9fCxcuVElJiXr16qWVK1eqU6dOkqTJkyfL6/Xq1ltvVWVlpdLT04PumfrZz36mRx99VCNGjNA555yj66%2B/vsGHmQIAADQkwjT1jm40itd73Po%2BXa5IXZP/rvX9NpfNOVeEuoQmc7kiFR/fXj5fJafxHULPnUfPnUfPm0%2BnTg0/0qm5tchLhAAAAKFEwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlrXYgNW3b1%2BlpKQoNTU18Oexxx6TJO3cuVPjx4/XJZdcotGjR2v9%2BvVB265evVqjRo3SJZdcoqysLBUWFoZiCQAAIEy5Ql1Ac3rjjTd04YUXBo2VlZXpnnvu0Zw5czRmzBh98MEHmj59unr06KHU1FRt3bpVy5Yt07PPPqu%2Bfftq9erVuvvuu/XWW2%2BpXbt2IVoJAAAIJy32DNZX2bBhg7p3767x48crOjpaGRkZGj58uNauXStJKigo0Lhx49S/f3%2B1bdtWd9xxhyRp27ZtoSwbAACEkRYdsJYsWaKhQ4fq0ksv1dy5c1VZWamioiIlJSUFvS4pKSlwGfDL85GRkerXr588Ho%2BjtQMAgPDVYi8RXnzxxcrIyNDPf/5zHTp0SDk5OZo/f778fr86d%2B4c9Nq4uDj5fD5Jkt/vV2xsbNB8bGxsYL4xysrK5PV6g8ZcrnZKTEz8lqtpWFRUeOVjlyu86m3ImZ6HW%2B/DGT13Hj13Hj1veVpswCooKAj87549e2rWrFmaPn26Bg4c%2BI3bGmOafOzly5cHjWVnZ2vGjBlN2m%2B4i49vH%2BoSrHG7zw11Ca0OPXcePXcePW85WmzA%2BrILL7xQdXV1ioyMlN/vD5rz%2BXxKSEiQJMXHx5817/f71bt370Yfa9KkSRo%2BfHjQmMvVTj5f5besvmHh9knH9vpDISoqUm73uSovr1JdXX2oy2kV6Lnz6Lnz6HnzCdWH%2BxYZsHbv3q3169froYceCowVFxerTZs2yszM1GuvvRb0%2BsLCQvXv31%2BSlJKSoqKiIt10002SpLq6Ou3evVvjx49v9PETExPPuhzo9R5XbW3r/qFpSeuvq6tvUesJB/TcefTcefS85QivUyCN1LFjRxUUFGjVqlU6efKkDhw4oCeffFKTJk3SDTfcoJKSEq1du1Y1NTXasWOHduzYoYkTJ0qSsrKy9Prrr2vXrl2qqqrSU089pTZt2mjo0KGhXRQAAAgbLfIMVufOnbVq1SotWbIkEJBuuukm5ebmKjo6WitXrtSCBQs0f/58denSRYsXL9ZFF10kSbrqqqv0wAMPKCcnR0eOHFFqaqpWrVqltm3bhnhVAAAgXESYpt7RjUbxeo9b36fLFalr8t%2B1vt/msjnnilCX0GQuV6Ti49vL56vkNL5D6Lnz6Lnz6Hnz6dSpQ0iO2yIvEQIAAIQSAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyA1YCSkhLdddddSk9P17Bhw7R48WLV19eHuiwAABAmXKEu4LvovvvuU3JysrZs2aIjR45o2rRpOu%2B88/SjH/0o1KUBAIAwQMD6Eo/Ho7179%2Br5559Xhw4d1KFDB02ZMkUvvPACAauJrv3F/4S6hH/J5pwrQl0CACBMEbC%2BpKioSF26dFFsbGxgLDk5WQcOHFBFRYViYmK%2BcR9lZWXyer1BYy5XOyUmJlqtNSqKK7zNKdwCYTj5/awrG/3aM//O%2BffuHHruPHre8hCwvsTv98vtdgeNnQlbPp%2BvUQGroKBAy5cvDxq79957dd9999krVKeD3O3nf6ZJkyZZD29oWFlZmQoKCui5g8rKyvTCC8/ScwfRc%2BfR85aHqNwAY0yTtp80aZJeffXVoD%2BTJk2yVN0/eL1eLV%2B%2B/KyzZWg%2B9Nx59Nx59Nx59Lzl4QzWlyQkJMjv9weN%2Bf1%2BRUREKCEhoVH7SExM5BMIAACtGGewviQlJUWlpaU6evRoYMzj8ahXr15q3759CCsDAADhgoD1JUlJSUpNTdWSJUtUUVGh4uJiPf/888rKygp1aQAAIExEzZs3b16oi/iuufLKK7Vx40Y99thj%2Bt3vfqfx48dr6tSpioiICHVpZ2nfvr0GDx7M2TUH0XPn0XPn0XPn0fOWJcI09Y5uAAAABOESIQAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBKwwVFJSorvuukvp6ekaNmyYFi9erPr6%2BlCXFZbeffddZWRkKDc396y5TZs2acyYMRowYIDGjRun9957LzBXX1%2BvpUuXasSIERo0aJCmTp2qQ4cOBeb9fr9ycnKUkZGhIUOGaM6cOaqurnZkTd9lJSUlys7OVnp6ujIyMvTQQw%2BpvLxckrRnzx7dcsstGjhwoEaOHKnnnnsuaNumvB%2Bt2d69e3X77bdr4MCBysjIUE5OjrxeryRp586dGj9%2BvC655BKNHj1a69evD9p29erVGjVqlC655BJlZWWpsLAwMFdTU6Of/vSnuuqqq5Senq4ZM2bI5/M5urZw8MQTT6hv376Bv9PzVsQg7Nx0003mkUceMeXl5ebAgQNm5MiR5rnnngt1WWFn1apVZuTIkWby5MkmJycnaG737t0mJSXFbN%2B%2B3VRXV5vf/va3pn///qa0tNQYY8zq1avNsGHDzL59%2B8zx48fNz372MzNmzBhTX19vjDHm3nvvNXfddZc5cuSIOXz4sJk0aZJ57LHHHF/jd831119vHnroIVNRUWFKS0vNuHHjzOzZs01VVZW58sorzbJly0xlZaUpLCw0gwcPNm%2B%2B%2BaYxpunvR2tVU1NjLr/8crN8%2BXJTU1Njjhw5Ym655RZzzz33mL///e/m4osvNmvXrjXV1dXmf/7nf0xaWpr5y1/%2BYowx5u233zaXXnqp2bVrl6mqqjIrV640V1xxhamsrDTGGLNw4UIzbtw487e//c34fD5z7733mmnTpoVyud85u3fvNoMHDzZ9%2BvQxxhh63soQsMLMX/7yF9OvXz/j9/sDY//93/9tRo0aFcKqwtMLL7xgysvLTV5e3lkBa/78%2BSY7OztobMKECWblypXGGGNGjx5tXnjhhcDc8ePHTVJSkvnoo4%2BM1%2Bs1F110kdmzZ09gfseOHebiiy82J0%2BebMYVfbcdO3bMPPTQQ8br9QbGXnzxRTNy5EizefNmc9lll5na2trA3OLFi82Pf/xjY0zT3o/WzO/3m1deecWcOnUqMPbCCy%2BYa665xjz77LPmxhtvDHp9Tk6OmTt3rjHGmLvuuss88cQTgbm6ujpzxRVXmI0bN5pTp06ZgQMHmi1btgTm9%2B3bZ/r27WsOHz7czKsKD3V1dWbChAnmP//zPwMBi563LlwiDDNFRUXq0qWLYmNjA2PJyck6cOCAKioqQlhZ%2BLntttvUoUOHBueKioqUlJQUNJaUlCSPx6Pq6mrt27cvaD4mJkbdunWTx%2BPRnj17FBUVFXRZIDk5WSdOnND%2B/fubZzFhwO12a%2BHChTrvvPMCY6WlpUpMTFRRUZH69u2rqKiowFxSUlLg8khT3o/WLDY2VhMmTJDL5ZIk7d%2B/X6%2B99pquvfbar%2BzpV/U8MjJS/fr1k8fj0cGDB3X8%2BHElJycH5nv27Km2bduqqKjIgZV997388suKjo7WmDFjAmP0vHUhYIUZv98vt9sdNHYmbHEt3h6/3x8UYqXTffb5fDp27JiMMV857/f7FRMTo4iIiKA5iffon3k8Hr300kuaPn16g/%2Bu4%2BLi5Pf7VV9f36T3A6fvfUtJSdF1112n1NRUzZgx4yt7fqZnX9dzv98vSWdt73a76bmkL774QsuWLdOjjz4aNE7PWxcCVhgyxoS6hFbhm/r8dfO8R1/vgw8%2B0NSpUzVz5kxlZGR85ev%2BOaQ25f1o7bp06SKPx6M33nhDn3/%2BuR588MFGbUfPv52FCxdq3Lhx6tWr17%2B8LT1vOQhYYSYhISHwSeYMv9%2BviIgIJSQkhKiqlic%2BPr7BPickJCguLk6RkZENznfs2FEJCQmqqKhQXV1d0JwkdezYsfmL/47bunWr7rrrLs2ePVu33XabpNP/rr/8Kdzv9wd63ZT3A6dFRESoe/fuys3N1caNG%2BVyuc7qmc/nC/x35Ot6fuY1X54/duxYq%2B/5zp079dFHHyk7O/usuYZ6Ss9bLgJWmElJSVFpaamOHj0aGPN4POrVq5fat28fwspalpSUlKCvR0un%2B9y/f39FR0erd%2B/eQfc9lJeX6%2BDBg0pLS1O/fv1kjNHevXuDtnW73erRo4dja/gu%2BvDDD5WXl6cnn3xSN954Y2A8JSVFn3zyiWprawNjZ/p9Zv7bvh%2Bt2c6dOzVq1Kigx7hERp7%2Bz35aWtpZPS0sLAzq%2BT/3tK6uTrt371b//v3VtWtXxcbGBs1/%2BumnOnnypFJSUppzSd9569ev15EjRzRs2DClp6dr3LhxkqT09HT16dOHnrcmIbm1Hk0yYcIEM3v2bHP8%2BHGzb98%2BM3z4cPPSSy%2BFuqyw1dC3CD/55BOTmppqtm3bZqqrq83atWvNgAEDTFlZmTHm9Dc3hw4dGngswNy5c83NN98c2D4nJ8fccccd5siRI6a0tNTcfPPNZtGiRY6u67vm1KlT5tprrzUvv/zyWXM1NTVm2LBh5pe//KU5ceKE2bVrl7n00kvNtm3bjDFNfz9aq/LycpORkWEWLVpkTpw4YY4cOWKmTp1qfvjDH5ovvvjCDBgwwLzyyiumurrabN%2B%2B3aSlpQW%2B/bpjxw4zcOBA89FHH5kTJ06YZcuWmczMTFNVVWWMOf0tz5tuusn87W9/M0ePHjXTpk0z9913XyiX%2B53g9/tNaWlp4M9HH31k%2BvTpY0pLS01JSQk9b0UIWGGotLTU3HHHHSYtLc1kZGSYX/7yl63%2BeT/fRkpKiklJSTEXXXSRueiiiwJ/P%2BPNN980I0eONMnJyeaGG24wf/rTnwJz9fX15sknnzSXX365SUtLM3feeWfgmUzGnP4/ttzcXHPxxRebQYMGmfnz55uamhpH1/dd87//%2B7%2BmT58%2BgT7/85%2B//vWv5pNPPjGTJ082KSkpZujQoWbNmjVB2zfl/WjN9u7da2655RaTlpZmLrvsMpOTkxP4Wv%2Bf/vQnM3bsWJOcnGxGjhwZeO7YGWvWrDGZmZkmJSXFZGVlmU8%2B%2BSQwV1NTY%2BbNm2cGDRpkBgwYYB544AFTXl7u6NrCwaFDhwKPaTCGnrcmEcZwxxwAAIBN3IMFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJb9f8IqA2l5CXsNAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-2591283239813382839”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>359</td> <td class=”number”>11.2%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>163</td> <td class=”number”>5.1%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>160</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>153</td> <td class=”number”>4.8%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>149</td> <td class=”number”>4.6%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>147</td> <td class=”number”>4.6%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>132</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>127</td> <td class=”number”>4.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>109</td> <td class=”number”>3.4%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (223)</td> <td class=”number”>1472</td> <td class=”number”>45.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>134</td> <td class=”number”>4.2%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-2591283239813382839”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>359</td> <td class=”number”>11.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>160</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:45%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>163</td> <td class=”number”>5.1%</td> <td>

<div class=”bar” style=”width:45%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>153</td> <td class=”number”>4.8%</td> <td>

<div class=”bar” style=”width:43%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>149</td> <td class=”number”>4.6%</td> <td>

<div class=”bar” style=”width:42%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>2334.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3124.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3671.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4008.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4685.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_ORCHARD_FARMS12”><s>ORCHARD_FARMS12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_ORCHARD_FARMS07”><code>ORCHARD_FARMS07</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99031</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_AGRITRSM_OPS_07_12”>PCH_AGRITRSM_OPS_07_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>445</td>

</tr> <tr>

<th>Unique (%)</th> <td>13.9%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>15.5%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>496</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>75.616</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>3900</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>7.6%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-3907171561227265531”>

</div> <div class=”col-md-12 text-right”>

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<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-3907171561227265531”

aria-controls=”quantiles-3907171561227265531” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-3907171561227265531” aria-controls=”histogram-3907171561227265531”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-3907171561227265531” aria-controls=”common-3907171561227265531”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-3907171561227265531” aria-controls=”extreme-3907171561227265531”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

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<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-3907171561227265531”>
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<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-100</td>

</tr> <tr>

<th>Q1</th> <td>-28.571</td>

</tr> <tr>

<th>Median</th> <td>25</td>

</tr> <tr>

<th>Q3</th> <td>100</td>

</tr> <tr>

<th>95-th percentile</th> <td>400</td>

</tr> <tr>

<th>Maximum</th> <td>3900</td>

</tr> <tr>

<th>Range</th> <td>4000</td>

</tr> <tr>

<th>Interquartile range</th> <td>128.57</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>194.75</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.5755</td>

</tr> <tr>

<th>Kurtosis</th> <td>68.329</td>

</tr> <tr>

<th>Mean</th> <td>75.616</td>

</tr> <tr>

<th>MAD</th> <td>117.27</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>5.4671</td>

</tr> <tr>

<th>Sum</th> <td>205220</td>

</tr> <tr>

<th>Variance</th> <td>37927</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-3907171561227265531”>

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1VVpblz56pXr15KSkrSmDFjtG/fPs/2brdbEydOVEpKirp166YnnnhCFRUV9sIDAABHq7WCdfz4ca1evVq///3vNWXKFDVv3lyPP/642rdvr9GjR2vlypVn3P7VV1/VzJkz1bJly1POJyUlyeVyeX0kJiZKkhYvXqyVK1cqKytLGzZsUHR0tNLS0mSMkSRNmzZNR44c0apVq/Tee%2B8pPz9fmZmZdj8BAADAsYJ9/YT5%2BflatmyZ3n//fZWXl6tPnz5666231LlzZ89jkpKS9PTTT2vAgAGn3U9ISIiWLVumZ599VkePHj2vNWRnZ2v06NFq3bq1JCk9PV3Jycnavn27rrjiCq1bt05//etfFRERIUl64IEH9NBDD2nKlCmqV6/eBaQGAAD/TXxesPr3769WrVpp7NixGjRokBo3blztMampqTp48OAZ9zNq1KgzzhcWFuqee%2B5RTk6OwsLCNGHCBN16662qqKhQXl6eYmNjPY8NDQ1Vy5Yt5XK5dOjQIQUFBSkmJsYzHxcXp8OHD2v37t1e4wAAAKfi84K1aNEidenS5ayP2759%2BwU/R0REhKKjo/Xwww%2BrTZs2%2BuSTT/Too48qMjJSv/3tb2WMUXh4uNc24eHhKikpUePGjRUaGqqAgACvOUkqKSk5p%2BcvKipScXGx11hwcANFRkZecCYnCA62f0Y6KCjQ608ncWo2p%2BaSnJvNqbkksvkjf8nl84IVExOj%2B%2B%2B/X0OGDNGNN94oSfrTn/6kL774QrNnzz7lK1rnq3v37urevbvn7/3799cnn3yi5cuXa/LkyZLkud7qVM40dy6ys7M1f/58r7G0tDRNmDDhovbr75o0aVhj%2Bw4Lu7TG9l3bnJrNqbkk52Zzai6JbP6orufyecHKyMjQoUOH1KZNG89Y9%2B7dtXnzZs2aNUuzZs2qkeeNiopSTk6OGjdurMDAQLndbq95t9utpk2bKiIiQmVlZaqsrFRQUJBnTpKaNm16Ts81bNgw9ezZ02ssOLiBSkrKLSTxXzWRPygoUGFhl6q09IgqK6us7782OTWbU3NJzs3m1FwS2fzR%2BeaqyR/uz8TnBevzzz/XypUr1aRJE89YdHS0MjMzdcstt1h5jr/85S8KDw9Xv379PGP5%2Bflq0aKFQkJC1LZtW%2BXm5npOVZaWlmrv3r1KTExUVFSUjDHatWuX4uLiJEkul0thYWFq1arVOT1/ZGRktdOBxcWHdOKEc77AL0RN5q%2BsrHLs59ep2ZyaS3JuNqfmksjmj%2Bp6Lp%2BfwKyoqFBISEj1hQQG6siRI1ae49ixY5oxY4ZcLpeOHz%2BuVatW6bPPPtPw4cMlSSNGjNCiRYuUn5%2BvsrIyZWZmqn379kpISFBERIT69OmjF154QQcPHtT%2B/fu1YMECDRkyRMHBPu%2BjAADAD/m8MSQlJWnWrFmaNGmS5%2BLxf//733r%2B%2Bee9btVwNgkJCZLk%2BTU769atk/TLq02jRo1SeXm5HnroIRUXF%2BuKK67QggULFB8fL0kaPny4iouLddddd6m8vFzJycle10w988wzeuqpp9SrVy/Vq1dPt9xyS7WbmQIAAJxOgLnYK7rP0759%2B/S73/1OBQUFCg0NVVVVlcrLy9WiRQu9/fbbat68uS%2BX4zPFxYdqZL99X/iiRvZbE9ZM7Gp9n8HBgWrSpKFKSsrr9EvFF8Kp2ZyaS3JuNqfmksjmj843V7NmjXywqup8/gpWixYt9OGHH%2Bqzzz7T3r17FRgYqFatWqlbt26ei8oBAAD8Wa1cVFS/fn3PLRoAAACcxucFa9%2B%2BfZozZ46%2B//77U/4C5U8//dTXSwIAALDK5wVr6tSpKioqUrdu3dSgQQNfPz0AAECN83nBysnJ0aeffur5RcoAAABO4/P7YDVt2pRXrgAAgKP5vGCNHTtW8%2BfPv%2Bjf9wcAAFBX%2BfwU4WeffaZvvvlGy5cv1xVXXKHAQO%2BOt2TJEl8vCQAAwCqfF6zQ0FDdcMMNvn5aAAAAn/F5wcrIyPD1UwIAAPiUz6/BkqTdu3dr3rx5evzxxz1jW7durY2lAAAAWOfzgrVlyxYNHDhQa9eu1apVqyT9cvPRUaNGcZNRAADgCD4vWHPnztUjjzyilStXKiAgQNIvv59w1qxZWrBgga%2BXAwAAYJ3PC9Y///lPjRgxQpI8BUuSbr75ZuXn5/t6OQAAANb5vGA1atTolL%2BDsKioSPXr1/f1cgAAAKzzecHq1KmTnnvuOZWVlXnG9uzZoylTpui6667z9XIAAACs8/ltGh5//HHdfffdSk5OVmVlpTp16qQjR46obdu2mjVrlq%2BXAwAAYJ3PC9bll1%2BuVatWadOmTdqzZ48uueQStWrVSl27dvW6JgsAAMBf%2BbxgSVK9evV044031sZTAwAA1DifF6yePXue8ZUq7oUFAAD8nc8LVr9%2B/bwKVmVlpfbs2SOXy6W7777b18sBAACwzucFa/Lkyacc//jjj/XVV1/5eDUAAAD21crvIjyVG2%2B8UR9%2B%2BGFtLwMAAOCi1ZmCtWPHDhljansZAAAAF83npwiHDx9ebezIkSPKz89X7969fb0cAAAA63xesKKjo6u9izAkJERDhgzR0KFDfb0cAAAA63xesLhbOwAAcDqfF6z333//nB87aNCgGlwJAABAzfB5wXriiSdUVVVV7YL2gIAAr7GAgAAKFgAA8Es%2BL1ivvfaa3njjDd1///2KiYmRMUbfffedXn31VY0cOVLJycm%2BXhIAAIBVtXINVlZWlpo3b%2B4Zu%2Baaa9SiRQuNGTNGq1at8vWSAAAArPL5fbB%2B%2BOEHhYeHVxsPCwtTQUGBr5cDAABgnc8LVlRUlGbNmqWSkhLPWGlpqebMmaMrr7zS18sBAACwzuenCKdOnapJkyYpOztbDRs2VGBgoMrKynTJJZdowYIFvl4OAACAdT4vWN26ddPGjRu1adMm7d%2B/X8YYNW/eXNdff70aNWrk6%2BUAAABY5/OCJUmXXnqpevXqpf3796tFixa1sQQAAIAa4/NrsCoqKjRlyhR17NhRffv2lfTLNVj33nuvSktLfb0cAAAA63xesGbPnq2dO3cqMzNTgYH/9/SVlZXKzMz09XIAAACs83nB%2Bvjjj/XSSy/p5ptv9vzS57CwMGVkZGjt2rW%2BXg4AAIB1Pi9Y5eXlio6OrjYeERGhw4cP%2B3o5AAAA1vm8YF155ZX66quvJMnrdw9%2B9NFH%2Bs1vfuPr5QAAAFjn83cR3nnnnXrwwQd1%2B%2B23q6qqSm%2B%2B%2BaZycnL08ccf64knnvD1cgAAAKzzecEaNmyYgoOD9c477ygoKEivvPKKWrVqpczMTN18882%2BXg4AAIB1Pi9YBw8e1O23367bb7/d108NAADgEz6/BqtXr15e114BAAA4jc8LVnJystasWePrpwUAAPAZn58i/J//%2BR89%2B%2ByzysrK0pVXXql69ep5zc%2BZM8fXSwIAALDK5wUrLy9Pv/3tbyVJJSUlvn56AACAGuezgpWenq65c%2Bfq7bff9owtWLBAaWlpvloCAACAT/jsGqz169dXG8vKyvLV0wMAAPiMzwrWqd45yLsJAQCAE/msYJ38xc5nGzsfmzdvVkpKitLT06vNrV69WgMGDFDHjh01ePBgff755565qqoqzZ07V7169VJSUpLGjBmjffv2eebdbrcmTpyolJQUdevWTU888YQqKiouaq0AAOC/h89v02DLq6%2B%2BqpkzZ6ply5bV5nbu3KkpU6Zo8uTJ%2BvLLLzV69GiNHz9e%2B/fvlyQtXrxYK1euVFZWljZs2KDo6GilpaV5XlGbNm2ajhw5olWrVum9995Tfn6%2BMjMzfZoPAAD4L78tWCEhIVq2bNkpC9bSpUuVmpqq1NRUhYSEaODAgWrXrp1WrFghScrOztbo0aPVunVrhYaGKj09Xfn5%2Bdq%2Bfbt%2B%2BuknrVu3Tunp6YqIiFDz5s31wAMP6L333tPx48d9HRMAAPghn72L8Pjx45o0adJZx871PlijRo067Vxubq5SU1O9xmJjY%2BVyuVRRUaG8vDzFxsZ65kJDQ9WyZUu5XC4dOnRIQUFBiomJ8czHxcXp8OHD2r17t9c4AADAqfisYHXu3FlFRUVnHbPB7XYrPDzcayw8PFx5eXn6%2BeefZYw55XxJSYkaN26s0NBQr%2BvDTj72XO/bVVRUpOLiYq%2Bx4OAGioyMvJA4jhEcbP8F06CgQK8/ncSp2ZyaS3JuNqfmksjmj/wll88K1n/e/8oXzvYOxTPNX%2By7G7OzszV//nyvsbS0NE2YMOGi9uvvmjRpWGP7Dgu7tMb2Xducms2puSTnZnNqLols/qiu5/L5ndx9oUmTJnK73V5jbrdbERERaty4sQIDA08537RpU0VERKisrEyVlZUKCgryzElS06ZNz%2Bn5hw0bpp49e3qNBQc3UElJ%2BYVGcoSayB8UFKiwsEtVWnpElZVV1vdfm5yazam5JOdmc2ouiWz%2B6Hxz1eQP92fiyIIVHx%2BvnJwcrzGXy6X%2B/fsrJCREbdu2VW5urrp06SJJKi0t1d69e5WYmKioqCgZY7Rr1y7FxcV5tg0LC1OrVq3O6fkjIyOrnQ4sLj6kEyec8wV%2BIWoyf2VllWM/v07N5tRcknOzOTWXRDZ/VNdz1e0TmBfojjvu0N/%2B9jdt3LhRR48e1bJly/TDDz9o4MCBkqQRI0Zo0aJFys/PV1lZmTIzM9W%2BfXslJCQoIiJCffr00QsvvKCDBw9q//79WrBggYYMGaLgYEf2UQAAYJnfNoaEhARJ0okTJyRJ69atk/TLq03t2rVTZmamMjIyVFBQoDZt2mjhwoVq1qyZJGn48OEqLi7WXXfdpfLyciUnJ3tdM/XMM8/oqaeeUq9evVSvXj3dcsstp7yZKQAAwKkEGH5fjU8UFx%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%2BOVkJDg%2BZgxY4YkacuWLRoyZIg6deqk/v37a8WKFV7bLlq0SH369FGnTp00YsQI5eTk1EYEAADgp4JrewE16aOPPtIVV1zhNVZUVKQHHnhATzzxhAYMGKCvv/5a48aNU6tWrZSQkKD169dr3rx5eu211xQTE6NFixbp/vvv19q1a9WgQYNaSgIAAPyJY1/BOp2VK1cqOjpaQ4YMUUhIiFJSUtSzZ08tXbpUkpSdna3Bgwfr6quv1iWXXKJ7771XkrRhw4baXDYAAPAjjn4Fa86cOdq6davKysrUt29fPfbYY8rNzVVsbKzX42JjY7VmzRpJUm5urvr16%2BeZCwwMVPv27eVyudS/f/9zet6ioiIVFxd7jQUHN1BkZORFJvJvwcH2%2B3xQUKDXn07i1GxOzSU5N5tTc0lk80f%2BksuxBatDhw5KSUnR888/r3379mnixImaPn263G63mjdv7vXYxo0bq6SkRJLkdrsVHh7uNR8eHu6ZPxfZ2dmaP3%2B%2B11haWpomTJhwgWmcoUmThjW277CwS2ts37XNqdmcmktybjan5pLI5o/qei7HFqzs7GzPf7du3VqTJ0/WuHHj1Llz57Nua4y5qOceNmyYevbs6TUWHNxAJSXlF7Vff1cT%2BYOCAhUWdqlKS4%2BosrLK%2Bv5rk1OzOTWX5NxsTs0lkc0fnW%2Bumvzh/kwcW7B%2B7YorrlBlZaUCAwPldru95kpKShQRESFJatKkSbV5t9uttm3bnvNzRUZGVjsdWFx8SCdOOOcL/ELUZP7KyirHfn6dms2puSTnZnNqLols/qiu56rbJzAv0I4dOzRr1iyvsfz8fNWvX1%2BpqanVbruQk5Ojq6%2B%2BWpIUHx%2Bv3Nxcz1xlZaV27NjhmQcAADgbRxZP%2BS25AAARDElEQVSspk2bKjs7W1lZWTp27Jj27NmjF198UcOGDdOtt96qgoICLV26VEePHtWmTZu0adMm3XHHHZKkESNG6P3339e2bdt05MgRvfzyy6pfv766d%2B9eu6EAAIDfcOQpwubNmysrK0tz5szxFKTbbrtN6enpCgkJ0cKFCzVz5kxNnz5dUVFRmj17tq666ipJ0g033KCHH35YEydO1IEDB5SQkKCsrCxdcskltZwKAAD4C0cWLElKSkrSkiVLTjv3wQcfnHbbO%2B%2B8U3feeWdNLQ0AADicI08RAgAA1CYKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALAsuLYXgP8efV/4oraXcF7WTOxa20sAAPgpXsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMCy4NpeQF1UUFCg6dOna/v27WrQoIH69eunSZMmKTCQPvrfpO8LX9T2Es7Lmolda3sJAID/j4J1Cg8%2B%2BKDi4uK0bt06HThwQGPHjtVll12me%2B65p7aXBgAA/AAvyfyKy%2BXSrl27NHnyZDVq1EjR0dEaPXq0srOza3tpAADAT/AK1q/k5uYqKipK4eHhnrG4uDjt2bNHZWVlCg0NPes%2BioqKVFxc7DUWHNxAkZGR1tcLnORPpzT/8ezNCgpy3s93JzM5LZtTc0lk80f%2BkouC9Stut1thYWFeYyfLVklJyTkVrOzsbM2fP99rbPz48XrwwQftLfT/%2B8ezN5/T44qKipSdna1hw4Y5qug5NZfk3GxFRUWaN2%2Be43JJv2R7663XHJfNqbkksvkjf8lVt%2BtfLTHGXNT2w4YN0/Lly70%2Bhg0bZml1F6a4uFjz58%2Bv9sqav3NqLsm52ZyaS3JuNqfmksjmj/wlF69g/UpERITcbrfXmNvtVkBAgCIiIs5pH5GRkXW6VQMAgJrFK1i/Eh8fr8LCQh08eNAz5nK51KZNGzVs2LAWVwYAAPwFBetXYmNjlZCQoDlz5qisrEz5%2Bfl68803NWLEiNpeGgAA8BNBTz/99NO1vYi65vrrr9eqVas0Y8YMffjhhxoyZIjGjBmjgICA2l7aRWnYsKG6dOniuFfinJpLcm42p%2BaSnJvNqbkksvkjf8gVYC72im4AAAB44RQhAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULIcrKCjQfffdp%2BTkZPXo0UOzZ89WVVVVbS/rnMTExCg%2BPl4JCQmejxkzZkiStmzZoiFDhqhTp07q37%2B/VqxY4bXtokWL1KdPH3Xq1EkjRoxQTk5ObUTw2Lx5s1JSUpSenl5tbvXq1RowYIA6duyowYMH6/PPP/fMVVVVae7cuerVq5eSkpI0ZswY7du3zzPvdrs1ceJEpaSkqFu3bnriiSdUUVHhk0wnnS7b8uXLddVVV3kdv4SEBH377bd%2Bka2goEBpaWlKTk5WSkqKHnvsMZWWlkqSdu7cqZEjR6pz587q3bu33njjDa9tL%2BaY1ma2H3/8UTExMdWO2euvv%2B4X2Xbt2qW7775bnTt3VkpKiiZOnKji4mJJF/c94%2BjRo/rDH/6gG264QcnJyZowYYJKSkp8lutM2b766qtTHrM1a9b4TbaTnnvuOcXExHj%2B7u/HTAaOdtttt5knn3zSlJaWmj179pjevXubN954o7aXdU7atWtn9u3bV2383//%2Bt%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%2B99ZYpLS01U6ZMqVZCpk%2BfbtLS0rzGhg4dahYuXGiMMaZ///7mrbfe8swdOnTIxMbGmq1bt5ri4mJz1VVXmZ07d3rmN23aZDp06GCOHTtWg4n%2Bz5myna1g1eVsP//8s3nsscdMcXGxZ%2Bztt982vXv3NmvWrDHXXnutOXHihGdu9uzZ5ne/%2B50x5uKOqS%2BcKdvZClZdzuZ2u827775rjh8/7hl76623zE033XRR3zOOHz9uOnfubNatW%2BeZz8vLMzExMWb//v01nOoXZ8p2toJV17OdXNPQoUPNH//4R8/Xn78fM2OM4RShg%2BXm5ioqKkrh4eGesbi4OO3Zs0dlZWW1uLJzN2fOHHXv3l3XXHONpk2bpvLycuXm5io2NtbrcbGxsZ6Xh389HxgYqPbt28vlcvl07SeNGjVKjRo1OuXc6bK4XC5VVFQoLy/Paz40NFQtW7aUy%2BXSzp07FRQU5PWSelxcnA4fPqzdu3fXTJhfOVM2SSosLNQ999yjpKQk9erVSx988IEk1flsYWFhysjI0GWXXeaVJTIyUrm5uYqJiVFQUJBn7kxffyfnz%2BWY%2BsKZsp306KOPqlu3brr22ms1Z84cHT9%2BXFLdzhYeHq6hQ4cqODhYkrR792799a9/Vd%2B%2BfS/qe8bevXt16NAhxcXFeeZbt26tSy65RLm5uTWeSzpzNkkqLy/3nPK9/vrr9eabb8oY4xfZJGnJkiUKCQnRgAEDPGP%2BfswkrsFyNLfbrbCwMK%2Bxk2Wrts6xn48OHTooJSVFa9euVXZ2trZt26bp06efMlfjxo09mdxut1eplH7JXRczn2mtP//8s4wxp513u90KDQ1VQECA15xUN45vRESEoqOj9cgjj%2BiLL77Qww8/rKlTp2rLli1%2Bl83lcumdd97RuHHjTvv153a7VVVVdVHHtDb8Z7b69eurY8eOuummm7RhwwZlZWVpxYoV%2BuMf/yjp4r5efaWgoEDx8fHq16%2BfEhISNGHChIv6nuF2uyWp2vZhYWE%2BP2anyhYaGqp27drp7rvv1ubNm5WRkaH58%2Bfrvffek1T3s/3000%2BaN2%2BennrqKa9xJxwzCpbDnfwpxh9lZ2dr6NChql%2B/vlq3bq3Jkydr1apVnp%2Bmz8Sfcp9trWear8s5u3fvrtdee02xsbGqX7%2B%2B%2Bvfvr5tuuknLly/3PMYfsn399dcaM2aMJk2apJSUlNM%2B7j/L4MUcU1/6dbbIyEgtWbJEN910k%2BrVq6fExESNHTv2nI/ZuczXtKioKLlcLn300Uf64Ycf9Oijj57TdnU9l3TqbHFxcXr77bfVpUsX1a9fX926ddPw4cP95phlZGRo8ODBatOmzXlvW5dzSRQsR4uIiPA0%2BZPcbrcCAgIUERFRS6u6cFdccYUqKysVGBhYLVdJSYknU5MmTU6Zuy5mPtNaGzdufMqsbrdbTZs2VUREhMrKylRZWek1J0lNmzat%2BcVfgKioKBUVFflNtvXr1%2Bu%2B%2B%2B7T1KlTNWrUKEm//Lv69U/Bbrfbk%2BlijqkvnSrbqURFRemnn36SMcZvsgUEBCg6Olrp6elatWqVgoODL/h7xsnH/Hr%2B559/rpV/Z7/OdvDgwWqPOfnvTKrb2bZs2aKtW7cqLS2t2typ1u1vx4yC5WDx8fEqLCz0%2BgfocrnUpk0bNWzYsBZXdnY7duzQrFmzvMby8/NVv359paamVrvtQk5Ojq6%2B%2BmpJv%2BT%2Bz/PslZWV2rFjh2e%2BLomPj6%2BWxeVy6eqrr1ZISIjatm3rlaW0tFR79%2B5VYmKi2rdvL2OMdu3a5bVtWFiYWrVq5bMMp/OXv/xFq1ev9hrLz89XixYt/CLbN998oylTpujFF1/UoEGDPOPx8fH67rvvdOLECa%2B1/efX34UeU185XbYtW7bo5Zdf9nrs7t27FRUVpYCAgDqdbcuWLerTp4/XbWgCA3/5X1xiYuIFf89o0aKFwsPDveb/%2Bc9/6tixY4qPj6/JSB5nyrZp0yb9%2Bc9/9nr87t271aJFC0l1O9uKFSt04MAB9ejRQ8nJyRo8eLAkKTk5We3atfPrYyaJ2zQ43dChQ83UqVPNoUOHTF5enunZs6d55513antZZ7V//37ToUMHs3DhQnP06FGze/du069fPzNjxgzz008/mY4dO5p3333XVFRUmI0bN5rExETPO842bdpkOnfubLZu3WoOHz5s5s2bZ1JTU82RI0dqNdOp3mn33XffmYSEBLNhwwZTUVFhli5dajp27GiKioqMMb%2B867N79%2B6et71PmzbN3H777Z7tJ06caO69915z4MABU1hYaG6//XYza9Ysn%2BYy5tTZ/vSnP5lrr73WfPvtt%2BbYsWNm5cqVpn379sblchlj6na248ePm759%2B5olS5ZUmzt69Kjp0aOHeemll8zhw4fNtm3bzDXXXGM2bNhgjLn4Y1qb2Vwul4mLizPvv/%2B%2BOXbsmPn2229N165dPbd2qcvZSktLTUpKipk1a5Y5fPiwOXDggBkzZoy58847L/p7xuzZs81tt91m/vWvf5mDBw%2BasWPHmgcffNAnuc6W7ZNPPjGJiYlm8%2BbN5tixY%2Bbzzz83HTp08Nw2pC5nc7vdprCw0POxdetW065dO1NYWGgKCgr8%2BpgZw20aHK%2BwsNDce%2B%2B9JjEx0aSkpJiXXnrJp/fbuRh///vfzbBhw0yHDh1Mly5dTEZGhqmoqPDMDRw40MTFxZnevXt7vpmctHjxYpOammri4%2BPNiBEjzHfffVcbEYwxxsTHx5v4%2BHhz1VVXmauuusrz95M%2B/vhj07t3bxMXF2duvfVW8/e//90zV1VVZV588UVz3XXXmcTERPP73//ec88hY375xpuenm46dOhgkpKSzPTp083Ro0frRLaqqiqzYMEC06NHDxMfH29uvvlms379er/I9r//%2B7%2BmXbt2njz/%2BfHjjz%2Ba7777zgwfPtzEx8eb7t27m8WLF3ttfzHHtLazrV271gwcONAkJiaarl27mldeecVUVlb6RbZdu3aZkSNHmsTERHPttdeaiRMnet6WfzHfM44ePWqefvppk5SUZDp27GgefvhhU1pa6rNcZ8u2ZMkS07t3b5OQkGB69Ohh3n33Xb/KdtKvbxPi78cswJg6cOUeAACAg3ANFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABY9v8Akxd81V3baUAAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-3907171561227265531”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>243</td> <td class=”number”>7.6%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>145</td> <td class=”number”>4.5%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>139</td> <td class=”number”>4.3%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>96</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>77</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.33333333333333</td> <td class=”number”>63</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>300.0</td> <td class=”number”>57</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-66.66666666666666</td> <td class=”number”>55</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.33333333333333</td> <td class=”number”>55</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (434)</td> <td class=”number”>1682</td> <td class=”number”>52.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>496</td> <td class=”number”>15.5%</td> <td>

<div class=”bar” style=”width:30%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-3907171561227265531”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>139</td> <td class=”number”>4.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-93.75</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-90.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-87.5</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-85.71428571428571</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1400.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1500.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1700.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2200.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3900.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_AGRITRSM_RCT_07_12”>PCH_AGRITRSM_RCT_07_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>1122</td>

</tr> <tr>

<th>Unique (%)</th> <td>35.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>60.6%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>1945</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>232.45</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>20557</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.3%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram5264902312674463017”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives5264902312674463017”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles5264902312674463017”

aria-controls=”quantiles5264902312674463017” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram5264902312674463017” aria-controls=”histogram5264902312674463017”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common5264902312674463017” aria-controls=”common5264902312674463017”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme5264902312674463017” aria-controls=”extreme5264902312674463017”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles5264902312674463017”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-90.503</td>

</tr> <tr>

<th>Q1</th> <td>-40.783</td>

</tr> <tr>

<th>Median</th> <td>22.768</td>

</tr> <tr>

<th>Q3</th> <td>146.76</td>

</tr> <tr>

<th>95-th percentile</th> <td>1036.3</td>

</tr> <tr>

<th>Maximum</th> <td>20557</td>

</tr> <tr>

<th>Range</th> <td>20657</td>

</tr> <tr>

<th>Interquartile range</th> <td>187.54</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>1026.9</td>

</tr> <tr>

<th>Coef of variation</th> <td>4.4176</td>

</tr> <tr>

<th>Kurtosis</th> <td>163.95</td>

</tr> <tr>

<th>Mean</th> <td>232.45</td>

</tr> <tr>

<th>MAD</th> <td>358.47</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>11.081</td>

</tr> <tr>

<th>Sum</th> <td>294040</td>

</tr> <tr>

<th>Variance</th> <td>1054400</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram5264902312674463017”>

<img 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FTZ06VUOGDHHdP7Vw4ULNmjXrit5vWVad9p%2Benq7%2B/fu7jdlsjeRwlNdpu/6uoa8/JCRYERFXq7S0QtXVNd4uB5dAj/wDffJ9vtQjb/1wH5AB68CBA/r22281Y8YMhYSEqGnTpsrKytLIkSN16623yul0ur3e4XAoJiZGkhQdHV1r3ul0qn379le8/9jY2FqXA%2B32s6qqatjfCBr6%2Bn9QXV3DsfBx9Mg/0Cff15B7FJAXR6urq1VTU%2BN2JurChQuSpJSUlFqPXSgsLFRycrIkKTExUUVFRW7bOnz4sGseAADgcjwesPr3769Vq1bpxIkT9baPLl26qFGjRlq5cqUqKirkcDi0Zs0ade/eXSNHjlRxcbE2bNig8%2BfPa9%2B%2Bfdq3b5/Gjh0rScrIyNCmTZt08OBBVVRUaM2aNQoNDVXfvn3rrV4AABBYPB6w7rzzTm3btk0DBw7UpEmTtHPnTlVVVRndR3R0tP74xz/qo48%2BUp8%2BfXTHHXcoPDxcy5YtU7NmzbR27Vq98sor6tatmxYtWqSnn35anTp1kiT16dNHM2bMUHZ2tnr06KH9%2B/crJydH4eHhRmsEAACBK8iq6x3dv1BRUZHy8/O1fft2Xbx4UaNGjVJaWpratm3rjXLqnd1%2Btl62e/uK9%2Bplu/Vhe3Yvb5fgVTZbsKKjG8vhKG%2Bw9yT4OnrkH%2BiT7/OlHrVo0dQr%2B/XaPVgJCQmaPXu29uzZozlz5ugvf/mLhg4dqokTJ%2BrQoUPeKgsAAKDOvBawLl68qG3btum%2B%2B%2B7T7Nmz1bJlSz366KOKi4vThAkTtHXrVm%2BVBgAAUCcef0zDsWPHtHHjRm3atEnl5eUaPHiwXnrpJXXr1s31mu7du2vBggUaPny4p8sDAACoM48HrGHDhqlt27aaMmWKRo0apaioqFqvSU1N1enTpz1dGgAAgBEeD1i5ubnq0aPHZV/3ySefeKAaAAAA8zx%2BD1bHjh01depU7dq1yzX2pz/9Sffdd1%2BtJ6gDAAD4I48HrMWLF%2Bvs2bNq166da6xv376qqanRkiVLPF0OAACAcR6/RPjuu%2B9q69atio6Odo21adNGS5cu1R133OHpcgAAAIzz%2BBmsyspKhYWF1S4kOFgVFRWeLgcAAMA4jwes7t27a8mSJTpz5oxr7LvvvtPChQvdHtUAAADgrzx%2BiXDOnDm69957dcstt6hJkyaqqalReXm5WrdurZdfftnT5QAAABjn8YDVunVrvfHGG3r77bf19ddfKzg4WG3btlXv3r0VEhLi6XIAAACM83jAkqTQ0FANHDjQG7sGAACodx4PWN98842WLVumL774QpWVlbXm33rrLU%2BXBAAAYJRX7sEqKSlR79691ahRI0/vHgAAoN55PGAVFhbqrbfeUkxMjKd3DQAA4BEef0xDs2bNOHMFAAACmscD1pQpU7Rq1SpZluXpXQMAAHiExy8Rvv322/roo4/0%2Buuv67rrrlNwsHvG%2B/Of/%2BzpkgAAAIzyeMBq0qSJ%2BvTp4%2BndAgAAeIzHA9bixYs9vUsAAACP8vg9WJL05ZdfauXKlXr00UddYx9//LE3SgEAADDO4wHrwIEDGjFihHbu3Kn8/HxJ3z98dPz48TxkFAAABASPB6zly5fr4Ycf1tatWxUUFCTp%2B3%2BfcMmSJVq9erWnywEAADDO4wHr888/V0ZGhiS5ApYkDRkyRMeOHfN0OQAAAMZ5PGA1bdr0kv8GYUlJiUJDQz1dDgAAgHEeD1hdu3bVokWLVFZW5ho7fvy4Zs%2BerVtuucXT5QAAABjn8cc0PProo/rtb3%2Brnj17qrq6Wl27dlVFRYXat2%2BvJUuWeLocAAAA4zwesK655hrl5%2Bdr3759On78uMLDw9W2bVv16tXL7Z4sAAAAf%2BXxgCVJV111lQYOHOiNXQMAANQ7jwes/v37/%2BSZKp6FBQAA/J3HA9bQoUPdAlZ1dbWOHz%2BugoIC/fa3v/V0OQAAAMZ5PGDNmjXrkuM7duzQ3/72Nw9XAwAAYJ5X/i3CSxk4cKDeeOMNb5cBAABQZz4TsA4fPizLsrxdBgAAQJ15/BLhuHHjao1VVFTo2LFjGjRokKfLAQAAMM7jAatNmza1foswLCxMaWlpuuuuuzxdDgAAgHEeD1g8rR0AAAQ6jwesTZs2XfFrR40aVad9rVmzRuvXr1dZWZk6d%2B6sJ598Utddd50OHDigZcuW6csvv9S1116rKVOmaMSIEa735ebmav369bLb7erYsaPmzp2rxMTEOtUCAAAaDo8HrLlz56qmpqbWDe1BQUFuY0FBQXUKWOvXr9eWLVuUm5ur2NhYrVixQn/60580efJkTZs2TXPnztXw4cP14Ycf6v7771fbtm2VlJSk3bt3a%2BXKlXrhhRfUsWNH5ebmaurUqdq5c6caNWr0i%2BsBAAANh8cD1gsvvKB169Zp6tSp6tixoyzL0meffabnn39ed999t3r27GlkP%2BvWrdPs2bP1q1/9SpI0b948SdIf//hHtWnTRmlpaZKklJQU9e/fXxs2bFBSUpLy8vI0ZswYJScnS5ImTZqk3Nxc7dmzR8OGDTNSGwAACGxeuQcrJydHLVu2dI3dfPPNat26tSZOnKj8/Pw67%2BO7777Tt99%2BqzNnzmjo0KE6deqUevbsqQULFqioqEjx8fFur4%2BPj9f27dslSUVFRRo6dKhrLjg4WHFxcSooKLjigFVSUiK73e42ZrM1UmxsbB1X5t9sNp95KohXhIQEu32F76FH/oE%2B%2BT565IWA9dVXXykyMrLWeEREhIqLi43s4%2BTJk5KkN998Uy%2B%2B%2BKIsy1JWVpbmzZunyspKt3AnSVFRUXI4HJIkp9NZq77IyEjX/JXIy8vTqlWr3MYyMzOVlZX1S5YTMKKjG3u7BJ8QEXG1t0vAZdAj/0CffF9D7pHHA1arVq20ZMkSPfTQQ4qOjpYklZaW6tlnn9X1119vZB8/3Ms1adIkV5h68MEHdd999yklJeWK3/9Lpaenq3///m5jNlsjORzlddquv2vo6w8JCVZExNUqLa1QdXWNt8vBJdAj/0CffJ8v9chbP9x7PGDNmTNHM2fOVF5enho3bqzg4GCVlZUpPDxcq1evNrKP5s2bS/r%2BrNgPWrVqJcuydPHiRTmdTrfXOxwOxcTESJKio6NrzTudTrVv3/6K9x8bG1vrcqDdflZVVQ37G0FDX/8PqqtrOBY%2Bjh75B/rk%2BxpyjzwesHr37q29e/dq3759OnnypCzLUsuWLXXrrbeqadOmRvZxzTXXqEmTJjpy5IgSEhIkScXFxbrqqquUmpqqzZs3u72%2BsLDQdVN7YmKiioqKNHr0aElSdXW1Dh8%2B7LopHgAA4HK8cvfZ1VdfrQEDBmjAgAG65557NHToUGPhSpJsNpvS0tL03HPP6X/%2B53906tQprV69WsOHD9fo0aNVXFysDRs26Pz589q3b5/27dunsWPHSpIyMjK0adMmHTx4UBUVFVqzZo1CQ0PVt29fY/UBAIDA5vGAVVlZqdmzZ6tLly66/fbbJX1/D9akSZNUWlpqbD8zZ87UrbfeqrvuuksDBw5UmzZtNG/ePDVr1kxr167VK6%2B8om7dumnRokV6%2Bumn1alTJ0lSnz59NGPGDGVnZ6tHjx7av3%2B/cnJyFB4ebqw2AAAQ2IKsut7R/TM98cQTev/99zVt2jT953/%2Bpw4dOqTS0lI99NBDat26tR5//HFPluMxdvvZetnu7Sveq5ft1oft2b28XYJX2WzBio5uLIejvMHek%2BDr6JF/oE%2B%2Bz5d61KKFuStkP4fHz2Dt2LFDzz77rIYMGeL6R58jIiK0ePFi7dy509PlAAAAGOfxgFVeXq42bdrUGo%2BJidG5c%2Bc8XQ4AAIBxHg9Y119/vf72t79Jcn/e1Jtvvql/%2B7d/83Q5AAAAxnn8MQ2/%2Bc1v9OCDD%2BrOO%2B9UTU2NXnzxRRUWFmrHjh2aO3eup8sBAAAwzuMBKz09XTabTa%2B88opCQkL03HPPqW3btlq6dKmGDBni6XIAAACM83jAOn36tO68807deeednt41AACAR3j8HqwBAwbU%2Bd/6AwAA8GUeD1g9e/bU9u3bPb1bAAAAj/H4JcJrr71Wv//975WTk6Prr79eV111ldv8smXLPF0SAACAUR4PWEePHtWvfvUrSZLD4fD07gEAAOqdxwLW9OnTtXz5cr388suusdWrVyszM9NTJQAAAHiEx%2B7B2r17d62xnJwcT%2B0eAADAYzwWsC71m4P8NiEAAAhEHgtYP/zDzpcbAwAA8Hcef0wDAABAoCNgAQAAGOax3yK8ePGiZs6cedkxnoMFAAD8nccCVrdu3VRSUnLZMQAAAH/nsYD1z8%2B/AgAACGTcgwUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGENImAtWrRIHTt2dP39wIEDSktLU9euXTVs2DBt2bLF7fW5ubkaPHiwunbtqoyMDBUWFnq6ZAAA4McCPmAdOXJEmzdvdv29pKRE06ZN07hx43TgwAHNnTtX8%2BfPV0FBgSRp9%2B7dWrlypZ566int379f/fr109SpU3Xu3DlvLQEAAPiZgA5YNTU1euyxxzRhwgTX2NatW9WmTRulpaUpLCxMKSkp6t%2B/vzZs2CBJysvL05gxY5ScnKzw8HBNmjRJkrRnzx5vLAEAAPihgA5Yf/7znxUWFqbhw4e7xoqKihQfH%2B/2uvj4eNdlwH%2BdDw4OVlxcnOsMFwAAwOXYvF1AffnHP/6hlStX6uWXX3YbdzqdatmypdtYVFSUHA6Haz4yMtJtPjIy0jV/JUpKSmS3293GbLZGio2N/TlLCDg2W0Dn%2BcsKCQl2%2BwrfQ4/8A33yffQogAPW4sWLNWbMGLVr107ffvvtz3qvZVl12ndeXp5WrVrlNpaZmamsrKw6bdffRUc39nYJPiEi4mpvl4DLoEf%2BgT75vobco4AMWAcOHNDHH3%2Bs/Pz8WnPR0dFyOp1uYw6HQzExMT8673Q61b59%2Byvef3p6uvr37%2B82ZrM1ksNRfsXbCEQNff0hIcGKiLhapaUVqq6u8XY5uAR65B/ok%2B/zpR5564f7gAxYW7Zs0alTp9SvXz9J/3dGqmfPnrr33ntrBa/CwkIlJydLkhITE1VUVKTRo0dLkqqrq3X48GGlpaVd8f5jY2NrXQ6028%2BqqqphfyNo6Ov/QXV1DcfCx9Ej/0CffF9D7lFAXhx95JFHtGPHDm3evFmbN29WTk6OJGnz5s0aPny4iouLtWHDBp0/f1779u3Tvn37NHbsWElSRkaGNm3apIMHD6qiokJr1qxRaGio%2Bvbt68UVAQAAfxKQZ7AiIyPdblSvqqqSJF1zzTWSpLVr1%2BrJJ5/UwoUL1apVKz399NPq1KmTJKlPnz6aMWOGsrOzderUKSUlJSknJ0fh4eGeXwgAAPBLQVZd7%2BjGFbHbz9bLdm9f8V69bLc%2BbM/u5e0SvMpmC1Z0dGM5HOUN9pS5r6NH/oE%2B%2BT5f6lGLFk29st%2BAvEQIAADgTQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADAsYANWcXGxMjMz1bNnT6WkpOiRRx5RaWmpJOnIkSO6%2B%2B671a1bNw0aNEjr1q1ze%2B%2B2bds0fPhwdenSRWPGjNG7777rjSUAAAA/FbABa%2BrUqYqIiNDu3bv1%2Buuv64svvtB//dd/qbKyUlOmTNGvf/1rvfPOO1q%2BfLnWrl2rnTt3Svo%2BfM2ePVuzZs3SX//6V02YMEEPPPCATp486eUVAQAAfxGQAau0tFSJiYmaOXOmGjdurGuuuUajR4/WBx98oL179%2BrixYu6//771ahRIyUkJOiuu%2B5SXl6eJGnDhg1KTU1VamqqwsLCNGLECHXo0EFbtmzx8qoAAIC/CMiAFRERocWLF6t58%2BausRMnTig2NlZFRUXq2LGjQkJCXHPx8fEqLCzxVxJIAAAPCUlEQVSUJBUVFSk%2BPt5te/Hx8SooKPBM8QAAwO/ZvF2AJxQUFOiVV17RmjVrtH37dkVERLjNR0VFyel0qqamRk6nU5GRkW7zkZGROnr06BXvr6SkRHa73W3MZmuk2NjYX76IAGCzBWSev2IhIcFuX%2BF76JF/oE%2B%2Bjx41gID14Ycf6v7779fMmTOVkpKi7du3X/J1QUFBrv9tWVad9pmXl6dVq1a5jWVmZiorK6tO2/V30dGNvV2CT4iIuNrbJeAy6JF/oE%2B%2BryH3KKAD1u7du/Xwww9r/vz5GjVqlCQpJiZGX331ldvrnE6noqKiFBwcrOjoaDmdzlrzMTExV7zf9PR09e/f323MZmskh6P8ly0kQDT09YeEBCsi4mqVllaourrG2%2BXgEuiRf6BPvs%2BXeuStH%2B4DNmB99NFHmj17tp555hn17t3bNZ6YmKhXX31VVVVVstm%2BX35BQYGSk5Nd8z/cj/WDgoICDRs27Ir3HRsbW%2BtyoN1%2BVlVVDfsbQUNf/w%2Bqq2s4Fj6OHvkH%2BuT7GnKPAvLiaFVVlebNm6dZs2a5hStJSk1NVZMmTbRmzRpVVFTok08%2B0caNG5WRkSFJGjt2rPbv36%2B9e/fq/Pnz2rhxo7766iuNGDHCG0sBAAB%2BKMiq6w1HPuiDDz7Qv//7vys0NLTW3Jtvvqny8nI99thjKiwsVPPmzXXffffpN7/5jes1O3fu1LJly1RcXKx27dpp7ty56t69e51qstvP1un9P%2Bb2Fe/Vy3brw/bsXt4uwatstmBFRzeWw1HeYH%2Bi83X0yD/QJ9/nSz1q0aKpV/YbkJcIb775Zn322Wc/%2BZpXX331R%2BcGDRqkQYMGmS4LAAA0EAF5iRAAAMCbCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDCbtwvwRcXFxVq4cKE%2B%2BeQTNWrUSEOHDtXMmTMVHEwerYvbV7zn7RJ%2Blu3ZvbxdAgDATxGwLuHBBx9UQkKCdu3apVOnTmnKlClq3ry57rnnHm%2BXBgAA/ACnZP5FQUGBPv30U82aNUtNmzZVmzZtNGHCBOXl5Xm7NAAA4Cc4g/UvioqK1KpVK0VGRrrGEhISdPz4cZWVlalJkyaX3UZJSYnsdrvbmM3WSLGxscbrRf3xt0ua/9%2BsW71dwhW7bek73i7hZ/GnY9sQhIQEu32F76FHBKxanE6nIiIi3MZ%2BCFsOh%2BOKAlZeXp5WrVrlNvbAAw/owQcfNFfo//PB74eopKREeXl5Sk9PJ8T5MPr0fz74/RBvl3BJ9Mg/lJSU6KWXXqBPPowecYnwkizLqtP709PT9frrr7v9SU9PN1RdbXa7XatWrap11gy%2BhT75PnrkH%2BiT76NHnMGqJSYmRk6n023M6XQqKChIMTExV7SN2NjYBpvYAQAAZ7BqSUxM1IkTJ3T69GnXWEFBgdq1a6fGjRt7sTIAAOAvCFj/Ij4%2BXklJSVq2bJnKysp07Ngxvfjii8rIyPB2aQAAwE%2BELFiwYIG3i/A1t956q/Lz8/XEE0/ojTfeUFpamiZOnKigoCBvl/ajGjdurB49enCWzcfRJ99Hj/wDffJ9Db1HQVZd7%2BgGAACAGy4RAgAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwPJzxcXFmjx5snr27Kl%2B/frp6aefVk1NjbfLCngdO3ZUYmKikpKSXH%2BeeOIJSdKBAweUlpamrl27atiwYdqyZYvbe3NzczV48GB17dpVGRkZKiwsdM2dP39ev/vd79SnTx/17NlTWVlZcjgcHl2bP3vnnXeUkpKi6dOn15rbtm2bhg8fri5dumjMmDF69913XXM1NTVavny5BgwYoO7du2vixIn65ptvXPNOp1PZ2dlKSUlR7969NXfuXFVWVrrmjxw5orvvvlvdunXToEGDtG7duvpdqB/7sR69/vrr6tSpk9tnKikpSYcOHZJEjzytuLhYmZmZ6tmzp1JSUvTII4%2BotLRU0uWPZX1%2B1vyKBb82evRoa968eVZpaal1/Phxa9CgQda6deu8XVbA69Chg/XNN9/UGv/uu%2B%2Bszp07Wxs2bLAqKyut9957z7rpppusQ4cOWZZlWW%2B99ZZ18803WwcPHrQqKiqstWvXWr169bLKy8sty7KsxYsXW2PGjLH%2B93//13I4HNYDDzxgTZkyxaNr81c5OTnWoEGDrHHjxlnZ2dluc4cPH7YSExOtvXv3WpWVldbmzZut5ORk68SJE5ZlWVZubq7Vr18/6%2BjRo9bZs2etxx9/3Bo%2BfLhVU1NjWZZlPfDAA9bkyZOtU6dOWSdPnrTS09OtJ554wrIsy6qoqLBuvfVWa%2BXKlVZ5eblVWFho9ejRw9qxY4dnD4Af%2BKkevfbaa9bdd9/9o%2B%2BlR551xx13WI888ohVVlZmnThxwhozZow1Z86cyx7L%2Bvys%2BRsClh87dOiQFRcXZzmdTtfYf//3f1uDBw/2YlUNw48FrBdeeMEaNWqU21h2drY1f/58y7Isa/LkydaiRYtcc9XV1VavXr2s/Px86%2BLFi1a3bt2sXbt2ueaPHj1qdezY0Tp58mQ9rSRwvPTSS1Zpaak1e/bsWv/xXrhwoZWZmek2dtddd1lr1661LMuyhg0bZr300kuuubNnz1rx8fHWxx9/bNntdqtTp07WkSNHXPP79u2zOnfubF24cMHavn279etf/9qqqqpyzT/99NPWvffeWx/L9Gs/1aPLBSx65DlnzpyxHnnkEctut7vGXn75ZWvQoEGXPZb1%2BVnzN1wi9GNFRUVq1aqVIiMjXWMJCQk6fvy4ysrKvFhZw7Bs2TL17dtXN998s%2BbPn6/y8nIVFRUpPj7e7XXx8fGuy4D/Oh8cHKy4uDgVFBTo66%2B/1tmzZ5WQkOCav/HGGxUeHq6ioiLPLMqPjR8/Xk2bNr3k3I/1paCgQJWVlTp69KjbfJMmTXTDDTeooKBAR44cUUhIiDp27OiaT0hI0Llz5/Tll1%2BqqKhIHTt2VEhIiNu2//nSL773Uz2SpBMnTuiee%2B5R9%2B7dNWDAAG3evFmS6JGHRUREaPHixWrevLlr7MSJE4qNjb3ssazPz5q/IWD5MafTqYiICLexH8IW9%2B3Ur86dOyslJUU7d%2B5UXl6eDh48qIULF16yJ1FRUa5%2BOJ1Ot0Asfd8zh8Mhp9MpSbXeHxERQT/r6KeO%2B5kzZ2RZ1k/2pUmTJgoKCnKbk%2BSav1TPnU4n90P%2BDDExMWrTpo0efvhhvffee5oxY4bmzJmjAwcO0CMvKygo0CuvvKL777//sseyPj9r/oaA5ecsy/J2CQ1SXl6e7rrrLoWGhurGG2/UrFmzlJ%2Bfr4sXL172vZfrGT2tH3U57r%2BkJ//8HwlcXt%2B%2BffXCCy8oPj5eoaGhGjZsmG677Ta9/vrrrtfQI8/78MMPNXHiRM2cOVMpKSk/%2Brp/Ppae/qz5KgKWH4uJiXGd9fiB0%2BlUUFCQYmJivFRVw3TdddepurpawcHBtXricDhc/YiOjr5kz2JiYlyv%2Bdf5M2fOqFmzZvVYfeD7qeMeFRV1yb45nU41a9ZMMTExKisrU3V1tducJNf8v/507XQ6XdvFL9eqVSuVlJTQIy/ZvXu3Jk%2BerDlz5mj8%2BPGSdNljWZ%2BfNX/D/7P8WGJiok6cOKHTp0%2B7xgoKCtSuXTs1btzYi5UFtsOHD2vJkiVuY8eOHVNoaKhSU1Nr3ddRWFio5ORkSd/37J/vp6qurtbhw4eVnJys1q1bKzIy0m3%2B888/14ULF5SYmFiPKwp8iYmJtfpSUFCg5ORkhYWFqX379m7HvbS0VF9//bVuuukmxcXFybIsffrpp27vjYiIUNu2bZWYmKjPPvtMVVVVtbaNK/fqq69q27ZtbmPHjh1T69at6ZEXfPTRR5o9e7aeeeYZjRo1yjV%2BuWNZn581f0PA8mPx8fFKSkrSsmXLVFZWpmPHjunFF19URkaGt0sLaM2aNVNeXp5ycnJ04cIFHT9%2BXM8884zS09M1cuRIFRcXa8OGDTp//rz27dunffv2aezYsZKkjIwMbdq0SQcPHlRFRYXWrFmj0NBQ9e3bVyEhIRo7dqyee%2B45nThxQg6HQ3/4wx902223ud1sip9v7Nix2r9/v/bu3avz589r48aN%2BuqrrzRixAhJ3/clNzdXx44dU1lZmZYuXaq4uDglJSUpJiZGgwcP1ooVK3T69GmdPHlSq1evVlpammw2m1JTU9WkSROtWbNGFRUV%2BuSTT7Rx40Y%2Bhz/ThQsX9MQTT6igoEAXL15Ufn6%2B3n77bY0bN04SPfKkqqoqzZs3T7NmzVLv3r3d5i53LOvzs%2BZ3PP57izDqxIkT1qRJk6ybbrrJSklJsZ599lnX80RQf/7%2B979b6enpVufOna0ePXpYixcvtiorK11zI0aMsBISEqxBgwbVetbO%2BvXrrdTUVCsxMdHKyMiwPvvsM9fc%2BfPnrQULFljdu3e3unTpYs2YMcMqLS316Nr8VWJiopWYmGh16tTJ6tSpk%2BvvP9ixY4c1aNAgKyEhwRo5cqT197//3TVXU1NjPfPMM9Ytt9xi3XTTTdZ9993nem6PZVlWaWmpNX36dKtz585W9%2B7drYULF1rnz593zX/22WfWuHHjrMTERKtv377W%2BvXrPbNoP/NTPaqpqbFWr15t9evXz0pMTLSGDBli7d692/VeeuQ577//vtWhQwdXf/75z7fffnvZY1mfnzV/EmRZAXRHGQAAgA/gEiEAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGPb/Aw8TReHsM21KAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common5264902312674463017”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>31</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>11</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.33333333333333</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>400.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>60.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>40.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>450.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-75.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (1111)</td> <td class=”number”>1191</td> <td class=”number”>37.1%</td> <td>

<div class=”bar” style=”width:61%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>1945</td> <td class=”number”>60.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme5264902312674463017”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>31</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-98.85714285714286</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-98.07692307692307</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-98.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-96.93877551020408</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>10158.333333333332</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10200.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10585.714285714286</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11661.111111111111</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20557.14285714286</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_BERRY_ACRESPTH_07_12”><s>PCH_BERRY_ACRESPTH_07_12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_BERRY_ACRES_07_12”><code>PCH_BERRY_ACRES_07_12</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99863</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_BERRY_ACRES_07_12”>PCH_BERRY_ACRES_07_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>667</td>

</tr> <tr>

<th>Unique (%)</th> <td>20.8%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>60.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>1939</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>55.641</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>4400</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>2.1%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-8894917047836830092”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-8894917047836830092”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-8894917047836830092”

aria-controls=”quantiles-8894917047836830092” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-8894917047836830092” aria-controls=”histogram-8894917047836830092”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-8894917047836830092” aria-controls=”common-8894917047836830092”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-8894917047836830092” aria-controls=”extreme-8894917047836830092”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-8894917047836830092”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-78.49</td>

</tr> <tr>

<th>Q1</th> <td>-30.602</td>

</tr> <tr>

<th>Median</th> <td>4.318</td>

</tr> <tr>

<th>Q3</th> <td>66.667</td>

</tr> <tr>

<th>95-th percentile</th> <td>300</td>

</tr> <tr>

<th>Maximum</th> <td>4400</td>

</tr> <tr>

<th>Range</th> <td>4500</td>

</tr> <tr>

<th>Interquartile range</th> <td>97.269</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>222.51</td>

</tr> <tr>

<th>Coef of variation</th> <td>3.9991</td>

</tr> <tr>

<th>Kurtosis</th> <td>142.18</td>

</tr> <tr>

<th>Mean</th> <td>55.641</td>

</tr> <tr>

<th>MAD</th> <td>102.95</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>9.373</td>

</tr> <tr>

<th>Sum</th> <td>70720</td>

</tr> <tr>

<th>Variance</th> <td>49513</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-8894917047836830092”>

<img 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VQeT0WD3jfQNdb1h4WFKjLyUpWVVaq2ts7pcholeuA8euC8xtoDp365D8qAtXfvXn399deaNm2awsLC1KJFC2VnZ%2BuOO%2B7QjTfeKK/X67e9x%2BNRbGysJCkmJqbevNfrVceOHS94/3FxcfUuB7rd5aqpaTw/0OfT2NdfW1vX6I%2BB0%2BiB8%2BiB8%2BiBPYLyQmxtba3q6ur8zkSdOXNGkpSamlrvsQuFhYVKTk6WJCUlJamoqMjvvQ4cOOCbBwAA%2BDFBGbC6d%2B%2Bupk2batmyZaqsrJTH49Hy5cvVq1cv3XHHHSouLtaaNWtUXV2t3bt3a/fu3Ro1apQkKTMzU%2BvWrdP%2B/ftVWVmp5cuXq0mTJurXr5%2BziwIAAAEjKANWTEyMfvvb3%2BrDDz9U3759dfvttysiIkJLlixRy5YttWLFCr388svq2bOn5s%2Bfr0WLFqlLly6SpL59%2B2ratGnKyclR7969tWfPHq1cuVIREREOrwoAAASKENPQO7pxQdzu8ovyvrc9%2Bd5Fed%2BLYUtOH6dLcITLFaqYmGbyeCq478Eh9MB59MB5jbUHl13WwpH9BuUZLAAAACfZHrDS09OVm5urY8eO2b1rAAAAW9gesIYPH67Nmzfr5ptv1oQJE7Rt2zbV1NTYXQYAAMBFY3vAysrK0ubNm/Xaa6%2BpY8eOmj9/vtLS0rRo0SIdPXrU7nIAAAAs59g9WImJiZo5c6Z27typWbNm6bXXXtOgQYM0fvx4ffzxx06VBQAA0GCOBayzZ89q8%2BbNuu%2B%2B%2BzRz5ky1bt1aDz/8sOLj4zVu3Dht3LjRqdIAAAAaxPY/lXPkyBGtXbtW69atU0VFhQYOHKgXX3xRPXv29G3Tq1cvzZ07V0OGDLG7PAAAgAazPWANHjxY7du316RJk3TnnXcqOjq63jZpaWk6ceKE3aUBAABYwvaAlZeXp969e//odh999JEN1QAAAFjP9nuwOnfurMmTJ2v79u2%2Bsd/97ne677775PV67S4HAADAcrYHrAULFqi8vFwdOnTwjfXr1091dXVauHCh3eUAAABYzvZLhO%2B%2B%2B642btyomJgY31i7du20ePFi3X777XaXAwAAYDnbz2BVVVUpPDy8fiGhoaqsrLS7HAAAAMvZHrB69eqlhQsX6uTJk76xb775RvPmzfN7VAMAAECgsv0S4axZs3Tvvffq%2BuuvV/PmzVVXV6eKigq1bdtWL730kt3lAAAAWM72gNW2bVu9%2Beab%2Bv3vf68vv/xSoaGhat%2B%2BvW644QaFhYXZXQ4AAIDlbA9YktSkSRPdfPPNTuwaAADgorM9YH311VdasmSJPvvsM1VVVdWbf/vtt%2B0uCQAAwFKO3INVUlKiG264QU2bNrV79wAAABed7QGrsLBQb7/9tmJjY%2B3eNQAAgC1sf0xDy5YtOXMFAACCmu0Ba9KkScrNzZUxxu5dAwAA2ML2S4S///3v9eGHH%2BqNN97QL37xC4WG%2Bme8V1991e6SAAAALGV7wGrevLn69u1r924BAABsY3vAWrBggd27BAAAsJXt92BJ0ueff65ly5bp4Ycf9o3t27fPiVIAAAAsZ3vA2rt3r4YOHapt27Zp06ZNks49fHTs2LE8ZBQAAAQF2wPW0qVL9atf/UobN25USEiIpHN/n3DhwoV65pln7C4HAADAcrYHrE8//VSZmZmS5AtYknTrrbfqyJEjdpcDAABgOdsDVosWLc77NwhLSkrUpEkTu8sBAACwnO0Bq0ePHpo/f75OnTrlGzt69Khmzpyp66%2B/3u5yAAAALGf7Yxoefvhh3XPPPUpJSVFtba169OihyspKdezYUQsXLrS7HAAAAMvZHrAuv/xybdq0Sbt379bRo0cVERGh9u3bq0%2BfPn73ZAEAAAQq2wOWJF1yySW6%2Beabndg1AADARWd7wEpPT/%2B7Z6p4FhYAAAh0tgesQYMG%2BQWs2tpaHT16VAUFBbrnnnvsLgcAAMBytgesGTNmnHd869at%2BuMf/2hzNQAAANZz5G8Rns/NN9%2BsN9980%2BkyAAAAGuxnE7AOHDggY4zTZQAAADSY7ZcIx4wZU2%2BssrJSR44c0YABAyzd1/Lly7V69WqdOnVK11xzjR5//HH94he/0N69e7VkyRJ9/vnnuuKKKzRp0iQNHTrU97q8vDytXr1abrdbnTt31uzZs5WUlGRpbQAAIHjZHrDatWtX71OE4eHhGjFihEaOHGnZflavXq0NGzYoLy9PcXFxevLJJ/W73/1OEydO1JQpUzR79mwNGTJEH3zwge6//361b99eXbt21Y4dO7Rs2TI9//zz6ty5s/Ly8jR58mRt27ZNTZs2taw%2BAAAQvGwPWHY9rX3VqlWaOXOmfvnLX0qSHnnkEUnSb3/7W7Vr104jRoyQJKWmpio9PV1r1qxR165dlZ%2Bfr4yMDCUnJ0uSJkyYoLy8PO3cuVODBw%2B2pXYAABDYbA9Y69atu%2BBt77zzzp%2B0j2%2B%2B%2BUZff/21Tp48qUGDBqm0tFQpKSmaO3euioqKlJCQ4Ld9QkKCtmzZIkkqKirSoEGDfHOhoaGKj49XQUEBAQsAAFwQ2wPW7NmzVVdXV%2B%2BG9pCQEL%2BxkJCQnxywjh8/Lkl666239MILL8gYo%2BzsbD3yyCOqqqpS69at/baPjo6Wx%2BORJHm9XkVFRfnNR0VF%2BeYvRElJidxut9%2BYy9VUcXFxP2U5QcPl%2Btl8psJWYWGhfl9hP3rgPHrgPHpgL9sD1vPPP69Vq1Zp8uTJ6ty5s4wxOnTokJ577jndfffdSklJafA%2BvgtqEyZM8IWpBx98UPfdd59SU1Mv%2BPU/VX5%2BvnJzc/3GsrKylJ2d3aD3DXQxMc2cLsFRkZGXOl1Co0cPnEcPnEcP7OHIPVgrV670O4t07bXXqm3btho/frw2bdrU4H20atVKkhQZGekba9OmjYwxOnv2rLxer9/2Ho9HsbGxkqSYmJh6816vVx07drzg/Y8ePVrp6el%2BYy5XU3k8Ff/QOoJNY11/WFioIiMvVVlZpWpr65wup1GiB86jB85rrD1w6pd72wPWF198Ue8SnHQuDBUXF1uyj8svv1zNmzfXwYMHlZiYKEkqLi7WJZdcorS0NK1fv95v%2B8LCQt9N7UlJSSoqKtKwYcMknftTPgcOHPDdFH8h4uLi6l0OdLvLVVPTeH6gz6exr7%2B2tq7RHwOn0QPn0QPn0QN72H4htk2bNlq4cKHfPU1lZWVasmSJrrzySkv24XK5NGLECD377LP6v//7P5WWluqZZ57RkCFDNGzYMBUXF2vNmjWqrq7W7t27tXv3bo0aNUqSlJmZqXXr1mn//v2qrKzU8uXL1aRJE/Xr18%2BS2gAAQPCz/QzWrFmzNH36dOXn56tZs2YKDQ3VqVOnFBERoWeeecay/UyfPl1nzpzRyJEjdfbsWQ0cOFCPPPKImjVrphUrVujxxx/XvHnz1KZNGy1atEhdunSRJPXt21fTpk1TTk6OSktL1bVrV61cuVIRERGW1QYAAIJbiHHg79NUVlZq9%2B7dOn78uIwxat26tW688Ua1aNHC7lJs43aXX5T3ve3J9y7K%2B14MW3L6OF2CI1yuUMXENJPHU8FpeYfQA%2BfRA%2Bc11h5cdpkz2cL2M1iSdOmll%2Bqmm27S8ePH1bZtWydKAAAAuGhsvwerqqpKM2fOVPfu3XXbbbdJOncP1oQJE1RWVmZ3OQAAAJazPWAtWrRIBw8e1OLFixUa%2Btfd19bWavHixXaXAwAAYDnbA9bWrVv19NNP69Zbb/X90efIyEgtWLBA27Zts7scAAAAy9kesCoqKtSuXbt647GxsTp9%2BrTd5QAAAFjO9oB15ZVX6o9//KMk/z9J89Zbb%2Bmf/umf7C4HAADAcrZ/ivCuu%2B7Sgw8%2BqOHDh6uurk4vvPCCCgsLtXXrVs2ePdvucgAAACxne8AaPXq0XC6XXn75ZYWFhenZZ59V%2B/bttXjxYt166612lwMAAGA52wPWiRMnNHz4cA0fPtzuXQMAANjC9nuwbrrpJjnw8HgAAADb2B6wUlJStGXLFrt3CwAAYBvbLxFeccUV%2Bs1vfqOVK1fqyiuv1CWXXOI3v2TJErtLAgAAsJTtAevw4cP65S9/KUnyeDx27x4AAOCisy1gTZ06VUuXLtVLL73kG3vmmWeUlZVlVwkAAAC2sO0erB07dtQbW7lypV27BwAAsI1tAet8nxzk04QAACAY2RawvvvDzj82BgAAEOhsf0wDAABAsCNgAQAAWMy2TxGePXtW06dP/9ExnoMFAAACnW0Bq2fPniopKfnRMQAAgEBnW8D62%2BdfAQAABDPuwQIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwWKMIWPPnz1fnzp193%2B/du1cjRoxQjx49NHjwYG3YsMFv%2B7y8PA0cOFA9evRQZmamCgsL7S4ZAAAEsKAPWAcPHtT69et935eUlGjKlCkaM2aM9u7dq9mzZ2vOnDkqKCiQJO3YsUPLli3TE088oT179qh///6aPHmyTp8%2B7dQSAABAgAnqgFVXV6dHH31U48aN841t3LhR7dq104gRIxQeHq7U1FSlp6drzZo1kqT8/HxlZGQoOTlZERERmjBhgiRp586dTiwBAAAEoKAOWK%2B%2B%2BqrCw8M1ZMgQ31hRUZESEhL8tktISPBdBvz%2BfGhoqOLj431nuAAAAH6My%2BkCLpZvv/1Wy5Yt00svveQ37vV61bp1a7%2Bx6OhoeTwe33xUVJTffFRUlG/%2BQpSUlMjtdvuNuVxNFRcX948sIei4XEGd539QWFio31fYjx44jx44jx7YK2gD1oIFC5SRkaEOHTro66%2B//odea4xp0L7z8/OVm5vrN5aVlaXs7OwGvW%2Bgi4lp5nQJjoqMvNTpEho9euA8euA8emCPoAxYe/fu1b59%2B7Rp06Z6czExMfJ6vX5jHo9HsbGxPzjv9XrVsWPHC97/6NGjlZ6e7jfmcjWVx1Nxwe8RjBrr%2BsPCQhUZeanKyipVW1vndDmNEj1wHj1wXmPtgVO/3AdlwNqwYYNKS0vVv39/SX89I5WSkqJ77723XvAqLCxUcnKyJCkpKUlFRUUaNmyYJKm2tlYHDhzQiBEjLnj/cXFx9S4Hut3lqqlpPD/Q59PY119bW9foj4HT6IHz6IHz6IE9gvJC7EMPPaStW7dq/fr1Wr9%2BvVauXClJWr9%2BvYYMGaLi4mKtWbNG1dXV2r17t3bv3q1Ro0ZJkjIzM7Vu3Trt379flZWVWr58uZo0aaJ%2B/fo5uCIAABBIgvIMVlRUlN%2BN6jU1NZKkyy%2B/XJK0YsUKPf7445o3b57atGmjRYsWqUuXLpKkvn37atq0acrJyVFpaam6du2qlStXKiIiwv6FAACAgBRiGnpHNy6I211%2BUd73tiffuyjvezFsyenjdAmOcLlCFRPTTB5PBaflHUIPnEcPnNdYe3DZZS0c2W9QXiIEAABwEgELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAiwVtwCouLlZWVpZSUlKUmpqqhx56SGVlZZKkgwcP6u6771bPnj01YMAArVq1yu%2B1mzdv1pAhQ9S9e3dlZGTo3XffdWIJAAAgQAVtwJo8ebIiIyO1Y8cOvfHGG/rss8/0n//5n6qqqtKkSZN03XXX6Z133tHSpUu1YsUKbdu2TdK58DVz5kzNmDFDf/jDHzRu3Dg98MADOn78uMMrAgAAgSIoA1ZZWZmSkpI0ffp0NWvWTJdffrmGDRum999/X7t27dLZs2d1//33q2nTpkpMTNTIkSOVn58vSVqzZo3S0tKUlpam8PBwDR06VJ06ddKGDRscXhUAAAgUQRmwIiMjtWDBArVq1co3duzYMcXFxamoqEidO3dWWFiYby4hIUGFhYWSpKKiIiUkJPi9X0JCggoKCuwpHgAABDyX0wXYoaCgQC%2B//LKWL1/gzyxIAAAOtUlEQVSuLVu2KDIy0m8%2BOjpaXq9XdXV18nq9ioqK8puPiorS4cOHL3h/JSUlcrvdfmMuV1PFxcX99EUEAZcrKPP8jwoLC/X7CvvRA%2BfRA%2BfRA3sFfcD64IMPdP/992v69OlKTU3Vli1bzrtdSEiI738bYxq0z/z8fOXm5vqNZWVlKTs7u0HvG%2BhiYpo5XYKjIiMvdbqERo8eOI8eOI8e2COoA9aOHTv0q1/9SnPmzNGdd94pSYqNjdUXX3zht53X61V0dLRCQ0MVExMjr9dbbz42NvaC9zt69Gilp6f7jblcTeXxVPy0hQSJxrr%2BsLBQRUZeqrKyStXW1jldTqNED5xHD5zXWHvg1C/3QRuwPvzwQ82cOVNPPfWUbrjhBt94UlKSXnnlFdXU1MjlOrf8goICJScn%2B%2Ba/ux/rOwUFBRo8ePAF7zsuLq7e5UC3u1w1NY3nB/p8Gvv6a2vrGv0xcBo9cB49cB49sEdQXoitqanRI488ohkzZviFK0lKS0tT8%2BbNtXz5clVWVuqjjz7S2rVrlZmZKUkaNWqU9uzZo127dqm6ulpr167VF198oaFDhzqxFAAAEIBCTENvOPoZev/99/XP//zPatKkSb25t956SxUVFXr00UdVWFioVq1a6b777tNdd93l22bbtm1asmSJiouL1aFDB82ePVu9evVqUE1ud3mDXv9DbnvyvYvyvhfDlpw%2BTpfgCJcrVDExzeTxVPBbo0PogfPogfMaaw8uu6yFI/sNykuE1157rQ4dOvR3t3nllVd%2BcG7AgAEaMGCA1WUBAIBGIigvEQIAADiJgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDGX0wWg8bjtyfecLuEfsiWnj9MlAAACFGewAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwDqP4uJiTZw4USkpKerfv78WLVqkuro6p8sCAAABgsc0nMeDDz6oxMREbd%2B%2BXaWlpZo0aZJatWqlf/3Xf3W6NNiIx0oAAH4qzmB9T0FBgT755BPNmDFDLVq0ULt27TRu3Djl5%2Bc7XRoAAAgQnMH6nqKiIrVp00ZRUVG%2BscTERB09elSnTp1S8%2BbNf/Q9SkpK5Ha7/cZcrqaKi4uzvF7gO4F0xu3/m3GjrfsLCwv1%2Bwr70QPn0QN7EbC%2Bx%2Bv1KjIy0m/su7Dl8XguKGDl5%2BcrNzfXb%2ByBBx7Qgw8%2BaF2h/7/3f3Prj25TUlKi/Px8jR49mpDnAI6/80pKSvTii8/TAwfRA%2BfRA3sRY8/DGNOg148ePVpvvPGG37/Ro0dbVN0/zu12Kzc3t95ZNdiD4%2B88euA8euA8emAvzmB9T2xsrLxer9%2BY1%2BtVSEiIYmNjL%2Bg94uLi%2BO0AAIBGjDNY35OUlKRjx47pxIkTvrGCggJ16NBBzZo1c7AyAAAQKAhY35OQkKCuXbtqyZIlOnXqlI4cOaIXXnhBmZmZTpcGAAACRNjcuXPnOl3Ez82NN96oTZs26de//rXefPNNjRgxQuPHj1dISIjTpf1kzZo1U%2B/evTkL5xCOv/PogfPogfPogX1CTEPv6AYAAIAfLhECAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgBXEiouLNXHiRKWkpKh///5atGiR6urqnC4r4L3zzjtKTU3V1KlT681t3rxZQ4YMUffu3ZWRkaF3333XN1dXV6elS5fqpptuUq9evTR%2B/Hh99dVXvnmv16ucnBylpqbqhhtu0OzZs1VVVWXLmgJNcXGxsrKylJKSotTUVD300EMqKyuTJB08eFB33323evbsqQEDBmjVqlV%2Br21Ij/BXn3zyie655x717NlTqampysnJkdvtliTt3btXI0aMUI8ePTR48GBt2LDB77V5eXkaOHCgevTooczMTBUWFvrmqqur9R//8R/q27evUlJSlJ2dLY/HY%2BvaAs38%2BfPVuXNn3/cc/58Jg6A1bNgw88gjj5iysjJz9OhRM2DAALNq1SqnywpoK1euNAMGDDBjxowxOTk5fnMHDhwwSUlJZteuXaaqqsqsX7/eJCcnm2PHjhljjMnLyzP9%2B/c3hw8fNuXl5eaxxx4zQ4YMMXV1dcYYYx544AEzceJEU1paao4fP25Gjx5tfv3rX9u%2BxkBw%2B%2B23m4ceesicOnXKHDt2zGRkZJhZs2aZyspKc%2BONN5ply5aZiooKU1hYaHr37m22bt1qjGl4j3BOdXW1uf76601ubq6prq42paWl5u677zZTpkwx33zzjbnmmmvMmjVrTFVVlXnvvfdMt27dzMcff2yMMebtt9821157rdm/f7%2BprKw0K1asMH369DEVFRXGGGMWLFhgMjIyzF/%2B8hfj8XjMAw88YCZNmuTkcn/WDhw4YHr37m06depkjDEc/58RAlaQ%2Bvjjj018fLzxer2%2Bsf/5n/8xAwcOdLCqwPfiiy%2BasrIyM3PmzHoBa968eSYrK8tvbOTIkWbFihXGGGMGDx5sXnzxRd9ceXm5SUhIMPv27TNut9t06dLFHDx40De/e/duc80115gzZ85cxBUFnpMnT5qHHnrIuN1u39hLL71kBgwYYLZs2WKuu%2B46U1NT45tbtGiRuffee40xDesR/srr9ZrXXnvNnD171jf24osvmltuucU8//zz5s477/TbPicnx8yZM8cYY8zEiRPN/PnzfXO1tbWmT58%2BZtOmTebs2bOmZ8%2BeZvv27b75w4cPm86dO5vjx49f5FUFntraWjNy5Ejz3//9376AxfH/%2BeASYZAqKipSmzZtFBUV5RtLTEzU0aNHderUKQcrC2xjx45VixYtzjtXVFSkhIQEv7GEhAQVFBSoqqpKhw8f9ptv3ry5rrrqKhUUFOjgwYMKCwvzO82fmJio06dP6/PPP784iwlQkZGRWrBggVq1auUbO3bsmOLi4lRUVKTOnTsrLCzMN5eQkOC7BNKQHuGvoqKiNHLkSLlcLknS559/rv/93//Vbbfd9oPH%2BId6EBoaqvj4eBUUFOjLL79UeXm5EhMTffNXX321IiIiVFRUZMPKAsurr76q8PBwDRkyxDfG8f/5IGAFKa/Xq8jISL%2Bx78IW19MvDq/X6xdopXPH3OPx6OTJkzLG/OC81%2BtV8%2BbNFRIS4jcn0a8fU1BQoJdffln333//eX/uo6Oj5fV6VVdX16Aeob7i4mIlJSVp0KBB6tq1q7Kzs3%2BwB98dw7/XA6/XK0n1Xh8ZGUkPvufbb7/VsmXL9Oijj/qNc/x/PghYQcwY43QJjc6PHfO/N0%2B//nEffPCBxo8fr%2BnTpys1NfUHt/vb4NqQHsFfmzZtVFBQoLfeektffPGF/v3f//2CXkcPGm7BggXKyMhQhw4d/uHXcvztQcAKUrGxsb7fRr7j9XoVEhKi2NhYh6oKbjExMec95rGxsYqOjlZoaOh551u2bKnY2FidOnVKtbW1fnOS1LJly4tffADasWOHJk6cqFmzZmns2LGSzv3cf/83ba/X6zv%2BDekRzi8kJETt2rXT1KlTtWnTJrlcrnrH0OPx%2BP5/5%2B/14Lttvj9/8uRJevA39u7dq3379ikrK6ve3PmOL8ffGQSsIJWUlKRjx47pxIkTvrGCggJ16NBBzZo1c7Cy4JWUlOT3cWfp3DFPTk5WeHi4Onbs6HcfQ1lZmb788kt169ZN8fHxMsbok08%2B8XttZGSk2rdvb9saAsWHH36omTNn6qmnntKdd97pG09KStKhQ4dUU1PjG/uuB9/N/9Qe4a/27t2rgQMH%2Bj32JTT03H9OunXrVu8YFxYW%2BvXgb49xbW2tDhw4oOTkZLVt21ZRUVF%2B859%2B%2BqnOnDmjpKSki7mkgLJhwwaVlpaqf//%2BSklJUUZGhiQpJSVFnTp14vj/XDhyaz1sMXLkSDNr1ixTXl5uDh8%2BbNLT083LL7/sdFlB4XyfIjx06JDp2rWr2blzp6mqqjJr1qwx3bt3NyUlJcaYc5/i7Nevn%2B8RAHPmzDHDhw/3vT4nJ8dMmDDBlJaWmmPHjpnhw4ebhQsX2rquQHD27Flz2223mVdffbXeXHV1tenfv795%2BumnzenTp83%2B/fvNtddea3bu3GmMaXiPcE5ZWZlJTU01CxcuNKdPnzalpaVm/Pjx5q677jLffvut6d69u3nttddMVVWV2bVrl%2BnWrZvvE7K7d%2B82PXv2NPv27TOnT582y5YtM2lpaaaystIYc%2B5Tn8OGDTN/%2BctfzIkTJ8ykSZPMgw8%2B6ORyf3a8Xq85duyY79%2B%2BfftMp06dzLFjx0xxcTHH/2eCgBXEjh07ZiZMmGC6detmUlNTzdNPP83zfBooKSnJJCUlmS5dupguXbr4vv/O1q1bzYABA0xiYqK54447zJ/%2B9CffXF1dnXnqqafM9ddfb7p162buu%2B8%2B3/OXjDn3H62pU6eaa665xvTq1cvMmzfPVFdX27q%2BQPDnP//ZdOrUyXfs//bf119/bQ4dOmTGjBljkpKSTL9%2B/czq1av9Xt%2BQHuGvPvnkE3P33Xebbt26meuuu87k5OT4Psr/pz/9yQwdOtQkJiaaAQMG%2BJ5D9p3Vq1ebtLQ0k5SUZDIzM82hQ4d8c9XV1Wbu3LmmV69epnv37mbatGmmrKzM1rUFmq%2B%2B%2Bsr3mAZjOP4/FyHGcDcbAACAlbgHCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGL/DyhmJ8fjLLHJAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-8894917047836830092”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>69</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>35</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>30</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>27</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.33333333333333</td> <td class=”number”>19</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-25.0</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>150.0</td> <td class=”number”>11</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>300.0</td> <td class=”number”>11</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (656)</td> <td class=”number”>1014</td> <td class=”number”>31.6%</td> <td>

<div class=”bar” style=”width:52%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>1939</td> <td class=”number”>60.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-8894917047836830092”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>35</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-94.48818897637796</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-93.42105263157895</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-92.10526315789474</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-90.9090909090909</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1400.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1500.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2033.3333333333333</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2800.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4400.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_BERRY_FARMS_07_12”>PCH_BERRY_FARMS_07_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>432</td>

</tr> <tr>

<th>Unique (%)</th> <td>13.5%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>30.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>977</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>44.479</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>1700</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>6.8%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-7299798339557518474”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-7299798339557518474,#minihistogram-7299798339557518474”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-7299798339557518474”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-7299798339557518474”

aria-controls=”quantiles-7299798339557518474” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-7299798339557518474” aria-controls=”histogram-7299798339557518474”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-7299798339557518474” aria-controls=”common-7299798339557518474”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-7299798339557518474” aria-controls=”extreme-7299798339557518474”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-7299798339557518474”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-100</td>

</tr> <tr>

<th>Q1</th> <td>-32</td>

</tr> <tr>

<th>Median</th> <td>14.286</td>

</tr> <tr>

<th>Q3</th> <td>77.778</td>

</tr> <tr>

<th>95-th percentile</th> <td>300</td>

</tr> <tr>

<th>Maximum</th> <td>1700</td>

</tr> <tr>

<th>Range</th> <td>1800</td>

</tr> <tr>

<th>Interquartile range</th> <td>109.78</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>136.04</td>

</tr> <tr>

<th>Coef of variation</th> <td>3.0585</td>

</tr> <tr>

<th>Kurtosis</th> <td>24.465</td>

</tr> <tr>

<th>Mean</th> <td>44.479</td>

</tr> <tr>

<th>MAD</th> <td>86.28</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>3.5841</td>

</tr> <tr>

<th>Sum</th> <td>99321</td>

</tr> <tr>

<th>Variance</th> <td>18506</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-7299798339557518474”>

<img 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Q8/ptwTIMQ7m5uVq7dq2WLVum0NBQt7nz3bY%2BhgwZovT0dLex4OAmKi2tqNd%2BfZ0v5A8KCpTN1lhlZZWqrq7x9nIuOqvllayX2Wp5JTJbIfOF5PXWk3u/LFg1NTV65JFH9MUXX%2BjVV19Vy5YtXXNRUVGuV6pOczqdat%2B%2BvaKjo13fN236f3fIDz/8oObNm9f5%2BDExMbVOB5aUHFVVlf8/6M/Fl/JXV9f41Hrry2p5JetltlpeicxW0JDzNtyTl/XwxBNP6Ouvv65VriQpMTFRBQUFru%2Brq6u1a9cudenSRS1btlRERITb/L///W%2BdPHlSiYmJHls/AADwbX5XsD777DOtXr1aeXl5ioyMrDWfmZmpN998U9u3b1dlZaWWLFmikJAQ9ejRQ0FBQRo8eLCeffZZHTx4UKWlpXrqqafUu3dvXXLJJV5IAwAAfJFPniLs3LmzpB/fIShJGzZskCQ5HA6tXLlSR48eVc%2BePd1uk5SUpOeff16pqal68MEHNW7cOB0%2BfFidO3dWXl6ewsLCJEnZ2dmqqKjQ7bffrqqqKvXs2VMzZszwXDgAAODzAoz6XtGNOikpOXpR9vubBR9elP1eDOvG3ejtJZxXcHCgoqKaqrS0osGe1zeT1fJK1ststbwSma2Q%2BULyXnppMw%2Btyp3fnSIEAADwNgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMk8XrDS09O1aNEiHTx40NOHBgAA8AiPF6w777xTb7/9tm6%2B%2BWaNHDlS69evV1VVlaeXAQAAcNF4vGBlZWXp7bff1uuvv6727dvriSeeUFpamubOnat9%2B/Z5ejkAAACm89o1WJ06ddLEiRO1adMmTZ48Wa%2B//rpuvfVWjRgxQl988YW3lgUAAFBvXitYp06d0ttvv617771XEydOVGxsrB555BHFx8dr%2BPDhWrNmjbeWBgAAUC/Bnj5gYWGhVqxYoTfffFMVFRXq27evXnzxRV1zzTWubZKSkjRjxgz179/f08sDAACoN48XrH79%2BqlNmzYaNWqU7rjjDkVGRtbaJi0tTUeOHPH00gAAAEzh8VOEy5Yt07p16zR8%2BPAzlqvTduzYcd59bd26VSkpKcrJyak19/bbb6t///7q2rWrBg0apA8%2B%2BMA1V1NTo/nz56tXr15KSkrSiBEjdODAAde80%2BnUuHHjlJKSou7du2vKlCk6fvz4BSYFAABW5fGC1bFjR40ePVobNmxwjf3tb3/TvffeK6fTWef9PPfcc5o5c6ZatWpVa2737t2aOHGiJkyYoI8%2B%2BkjDhw/X2LFjdejQIUnSK6%2B8ojVr1igvL0%2BbNm1S69atlZWVJcMwJEnTpk1TZWWl1q5dq5UrV6qwsFC5ubn1TA4AAKzC4wVr1qxZOnr0qNq1a%2Bca69Gjh2pqajR79uw67yc0NFQrVqw4Y8Favny50tLSlJaWptDQUA0YMEAdOnTQ6tWrJUl2u13Dhw9X27ZtFR4erpycHBUWFmrHjh36/vvvtWHDBuXk5Cg6OlqxsbEaM2aMVq5cqVOnTtX/BwAAAPyex6/B%2BuCDD7RmzRpFRUW5xlq3bq3c3Fzddtttdd7PsGHDzjpXUFCgtLQ0t7GEhAQ5HA4dP35ce/bsUUJCgmsuPDxcrVq1ksPh0NGjRxUUFKSOHTu65jt16qRjx45p7969buNnU1xcrJKSErex4OAmiomJqWs8vxQc3PB/M1NQUKDbV39ntbyS9TJbLa9EZivwhbweL1jHjx9XaGhorfHAwEBVVlaacgyn06mIiAi3sYiICO3Zs0c//PCDDMM443xpaakiIyMVHh6ugIAAtzlJKi0trdPx7Xa7Fi1a5DaWlZWl7OzsXxLHb0RFNfX2EurMZmvs7SV4lNXyStbLbLW8EpmtoCHn9XjBSkpK0uzZszV%2B/HhXcfnuu%2B80Z84ct49qqK/T11P9kvnz3fZ8hgwZovT0dLex4OAmKi2tqNd%2BfZ0v5A8KCpTN1lhlZZWqrq7x9nIuOqvllayX2Wp5JTJbIfOF5PXWk3uPF6zJkyfrj3/8o2644QaFh4erpqZGFRUVatmypV566SVTjhEVFVXrgnmn06no6GhFRkYqMDDwjPPNmzdXdHS0ysvLVV1draCgINecJDVv3rxOx4%2BJial1OrCk5Kiqqvz/QX8uvpS/urrGp9ZbX1bLK1kvs9XySmS2goac1%2BMFq2XLlnrrrbf0/vvva//%2B/QoMDFSbNm3UvXt3V6Gpr8TERO3cudNtzOFwqF%2B/fgoNDVX79u1VUFCg6667TpJUVlam/fv366qrrlJcXJwMw9CXX36pTp06uW5rs9nUpk0bU9YHAAD8m8cLliSFhITo5ptvvmj7Hzx4sDIyMrR582bdcMMNWrNmjb755hsNGDBAkpSZmam8vDylpqYqNjZWubm5io%2BPV%2BfOnSVJffv21YIFCzRnzhydPHlSixcvVkZGhoKDvfLjAgAAPsbjjeHAgQOaN2%2Bevv766zN%2BeOd7771Xp/2cLkNVVVWS5PpcLYfDoQ4dOig3N1ezZs1SUVGR2rVrp6VLl%2BrSSy%2BVJA0dOlQlJSW65557VFFRoeTkZLeL0h977DFNnz5dvXr1UqNGjXTbbbed8cNMAQAAzsQr12AVFxere/fuatKkyS/ej8PhOOd8nz591KdPnzPOBQQEKDs7%2B6zv6mvWrJmeeuqpX7w2AABgbR4vWDt37tR7772n6OhoTx8aAADAIzz%2BCV3Nmzev1ytXAAAADZ3HC9aoUaO0aNGien/WFAAAQEPl8VOE77//vj7//HO98cYbuvzyyxUY6N7xXnvtNU8vCQAAwFQeL1jh4eFKTU319GEBAAA8xuMFa9asWZ4%2BJAAAgEd55ddQ7927VwsXLtQjjzziGvvXv/7ljaUAAACYzuMFKz8/XwMGDND69eu1du1aST9%2B%2BOiwYcPq/CGjAAAADZnHC9b8%2BfP10EMPac2aNQoICJD04%2B8nnD17thYvXuzp5QAAAJjO4wXr3//%2BtzIzMyXJVbAk6ZZbblFhYaGnlwMAAGA6jxesZs2anfF3EBYXFyskJMTTywEAADCdxwtWt27d9MQTT6i8vNw1tm/fPk2cOFE33HCDp5cDAABgOo9/TMMjjzyi3//%2B90pOTlZ1dbW6deumyspKtW/fXrNnz/b0cgAAAEzn8YLVokULrV27Vlu2bNG%2BffsUFhamNm3a6MYbb3S7JgsAAMBXebxgSVKjRo108803e%2BPQAAAAF53HC1Z6evo5X6nis7AAAICv83jBuvXWW90KVnV1tfbt2yeHw6Hf//73nl4OAACA6TxesCZMmHDG8XfffVcff/yxh1cDAABgPq/8LsIzufnmm/XWW295exkAAAD11mAK1q5du2QYhreXAQAAUG8eP0U4dOjQWmOVlZUqLCxUnz59PL0cAAAA03m8YLVu3brWuwhDQ0OVkZGhu%2B66y9PLAQAAMJ3HCxaf1g4AAPydxwvWm2%2B%2BWedt77jjjou4EgAAgIvD4wVrypQpqqmpqXVBe0BAgNtYQEAABQsAAPgkj7%2BL8C9/%2BYu6d%2B%2BuV155RZ9%2B%2Bqk%2B%2BeQTvfzyy0pNTdVzzz2nL774Ql988YV27NhRr%2BPs2rVLw4YN07XXXqsbb7xREyZM0JEjRyRJ%2Bfn5ysjIULdu3dSvXz%2BtXr3a7bbLli1T37591a1bN2VmZmrnzp31WgsAALAWjxes2bNna%2BbMmbrmmmsUHh6uZs2a6dprr9Vjjz2mOXPmKCQkxPXnl6qqqtJ9992nq6%2B%2BWtu2bdPatWt15MgRzZgxQ8XFxRozZoyGDh2q/Px8TZkyRdOmTZPD4ZAkbdy4UQsXLtSTTz6pbdu2qWfPnho9erSOHTtm1o8AAAD4OY8XrG%2B%2B%2BUYRERG1xm02m4qKikw5RklJiUpKSnT77bcrJCREUVFR6t27t3bv3q01a9aodevWysjIUGhoqFJSUpSenq7ly5dLkux2uwYNGqQuXbooLCxMI0eOlCRt2rTJlLUBAAD/5/GCFRcXp9mzZ6u0tNQ1VlZWpnnz5umKK64w5RixsbGKj4%2BX3W5XRUWFDh8%2BrPXr16tHjx4qKChQQkKC2/YJCQmu04A/nw8MDFR8fLzrFS4AAIDz8fhF7pMnT9b48eNlt9vVtGlTBQYGqry8XGFhYVq8eLEpxwgMDNTChQs1fPhwvfjii5Kk6667TuPHj9eYMWMUGxvrtn1kZKSr8DmdzlqvsEVERLgVwvMpLi5WSUmJ21hwcBPFxMT8kjh%2BIzi4wfzigLMKCgp0%2B%2BrvrJZXsl5mq%2BWVyGwFvpDX4wWre/fu2rx5s7Zs2aJDhw7JMAzFxsbqpptuUrNmzUw5xsmTJzV69GjdcsstruunHn300bP%2Boumfq%2B%2Bv7LHb7Vq0aJHbWFZWlrKzs%2Bu1X18XFdXU20uoM5utsbeX4FFWyytZL7PV8kpktoKGnNfjBUuSGjdurF69eunQoUNq2bKl6fvPz8/Xt99%2BqwcffFBBQUFq1qyZsrOzdfvtt%2Bumm26S0%2Bl02760tFTR0dGSpKioqFrzTqdT7du3r/PxhwwZovT0dLex4OAmKi2t%2BIWJ/IMv5A8KCpTN1lhlZZWqrq7x9nIuOqvllayX2Wp5JTJbIfOF5PXWk3uPF6zjx49r%2BvTpeuuttyRJO3fuVFlZmR588EE99dRTstls9T5GdXV1rc/aOnnypCQpJSVF//jHP9y237lzp7p06SJJSkxMVEFBgQYOHOja165du5SRkVHn48fExNQ6HVhSclRVVf7/oD8XX8pfXV3jU%2ButL6vllayX2Wp5JTJbQUPO6/GTl3PnztXu3buVm5urwMD/O3x1dbVyc3NNOUbXrl3VpEkTLVy4UJWVlSotLdWSJUuUlJSk22%2B/XUVFRVq%2BfLlOnDihLVu2aMuWLRo8eLAkKTMzU2%2B%2B%2Baa2b9%2BuyspKLVmyRCEhIerRo4cpawMAAP7P4wXr3Xff1TPPPKNbbrnF9UufbTabZs2apfXr15tyjKioKP31r3/V559/rtTUVN12220KCwvTvHnz1Lx5cy1dulQvv/yyrrnmGj3xxBOaO3eurrzySklSamqqHnzwQY0bN07XXXedtm3bpry8PIWFhZmyNgAA4P88foqwoqJCrVu3rjUeHR1t6od5JiYm6qWXXjrjXFJSklatWnXW29599926%2B%2B67TVsLAACwFo%2B/gnXFFVfo448/luT%2Bbr133nlHv/rVrzy9HAAAANN5/BWsu%2B%2B%2BWw888IDuvPNO1dTU6IUXXtDOnTv17rvvasqUKZ5eDgAAgOk8XrCGDBmi4OBgvfzyywoKCtKzzz6rNm3aKDc3V7fccounlwMAAGA6jxesI0eO6M4779Sdd97p6UMDAAB4hMevwerVq1e9PykdAACgIfN4wUpOTta6des8fVgAAACP8fgpwssuu0z//d//rby8PF1xxRVq1KiR2/y8efM8vSQAAABTebxg7dmzR7/%2B9a8l/fg7AAEAAPyNxwpWTk6O5s%2Bf7/bhn4sXL1ZWVpanlgAAAOARHrsGa%2BPGjbXG8vLyPHV4AAAAj/FYwTrTOwd5NyEAAPBHHitYp3%2Bx8/nGAAAAfJ3HP6YBAADA31GwAAAATOaxdxGeOnVK48ePP%2B8Yn4MFAAB8nccK1jXXXKPi4uLzjgEAAPg6jxWsn37%2BFQAAgD/jGiwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJP5dcFasmSJunfvrquvvlrDhw/Xt99%2BK0nKz89XRkaGunXrpn79%2Bmn16tVut1u2bJn69u2rbt26KTMzUzt37vTG8gEAgI/y24L1yiuvaPXq1Vq2bJk%2B%2BOADtWvXTn/7299UXFysMWPGaOjQocrPz9eUKVM0bdo0ORwOSdLGjRu1cOFCPfnkk9q2bZt69uyp0aNH69ixY15OBAAAfIXfFqznn39eOTk5%2BvWvf63w8HBNnTpVU6dO1Zo1a9S6dWtlZGQoNDRUKSkpSk9P1/LlyyVJdrtdgwYNUpcuXRQWFqaRI0dKkjZt2uTNOAAAwId47Jc9e9J3332nb7/9Vj/88INuvfVWHT58WMnJyZoxY4YKCgqUkJDgtn1CQoLWrVsnSSooKNCtt97qmgsMDFR8fLwcDof69etXp%2BMXFxerpKTEbSw4uIliYmLqmcy3BQc3/D4fFBTo9tXfWS2PKjLIAAAa4klEQVSvZL3MVssrkdkKfCGvXxasQ4cOSZLeeecdvfDCCzIMQ9nZ2Zo6daqOHz%2Bu2NhYt%2B0jIyNVWloqSXI6nYqIiHCbj4iIcM3Xhd1u16JFi9zGsrKylJ2d/Uvi%2BI2oqKbeXkKd2WyNvb0Ej7JaXsl6ma2WVyKzFTTkvH5ZsAzDkCSNHDnSVaYeeOAB3XvvvUpJSanz7X%2BpIUOGKD093W0sOLiJSksr6rVfX%2BcL%2BYOCAmWzNVZZWaWqq2u8vZyLzmp5JetltlpeicxWyHwheb315N4vC9Yll1wiSbLZbK6xuLg4GYahU6dOyel0um1fWlqq6OhoSVJUVFSteafTqfbt29f5%2BDExMbVOB5aUHFVVlf8/6M/Fl/JXV9f41Hrry2p5JetltlpeicxW0JDzNtyTl/XQokULhYeHa/fu3a6xoqIiNWrUSGlpabU%2BdmHnzp3q0qWLJCkxMVEFBQWuuerqau3atcs1DwAAcD5%2BWbCCg4OVkZGhZ599Vv/v//0/HT58WIsXL1b//v01cOBAFRUVafny5Tpx4oS2bNmiLVu2aPDgwZKkzMxMvfnmm9q%2BfbsqKyu1ZMkShYSEqEePHt4NBQAAfIZfniKUpPHjx%2BvkyZO66667dOrUKfXt21dTp05V06ZNtXTpUs2cOVOPPvqo4uLiNHfuXF155ZWSpNTUVD344IMaN26cDh8%2BrM6dOysvL09hYWFeTgQAAHxFgFHfK7pRJyUlRy/Kfn%2Bz4MOLst%2BLYd24G729hPMKDg5UVFRTlZZWNNjz%2BmayWl7JepmtllcisxUyX0jeSy9t5qFVufPLU4QAAADeRMECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTBXt7AbCO3yz40NtLuCDrxt3o7SUAAHyUJV7BeuKJJ9SxY0fX9/n5%2BcrIyFC3bt3Ur18/rV692m37ZcuWqW/fvurWrZsyMzO1c%2BdOTy8ZAAD4ML8vWLt379aqVatc3xcXF2vMmDEaOnSo8vPzNWXKFE2bNk0Oh0OStHHjRi1cuFBPPvmktm3bpp49e2r06NE6duyYtyIAAAAf49cFq6amRtOnT9fw4cNdY2vWrFHr1q2VkZGh0NBQpaSkKD09XcuXL5ck2e12DRo0SF26dFFYWJhGjhwpSdq0aZM3IgAAAB/k19dgvfbaawoNDVX//v21YMECSVJBQYESEhLctktISNC6detc87feeqtrLjAwUPHx8XI4HOrXr1%2BdjltcXKySkhK3seDgJoqJialPHHhYcLBfP/%2BQJAUFBbp9tQKrZbZaXonMVuALef22YH3//fdauHChXnrpJbdxp9Op2NhYt7HIyEiVlpa65iMiItzmIyIiXPN1YbfbtWjRIrexrKwsZWdnX0gEeFlUVFNvL8FjbLbG3l6Cx1kts9XySmS2goac128L1qxZszRo0CC1a9dO33777QXd1jCMeh17yJAhSk9PdxsLDm6i0tKKeu0XnmWF%2BysoKFA2W2OVlVWqurrG28vxCKtltlpeicxWyHwheb31ZNkvC1Z%2Bfr7%2B9a9/ae3atbXmoqKi5HQ63cZKS0sVHR191nmn06n27dvX%2BfgxMTG1TgeWlBxVVZX/P%2Bj9iZXur%2BrqGkvllayX2Wp5JTJbQUPO23BPXtbD6tWrdfjwYfXs2VPJyckaNGiQJCk5OVkdOnSo9bELO3fuVJcuXSRJiYmJKigocM1VV1dr165drnkAAIDz8cuCNWnSJL377rtatWqVVq1apby8PEnSqlWr1L9/fxUVFWn58uU6ceKEtmzZoi1btmjw4MGSpMzMTL355pvavn27KisrtWTJEoWEhKhHjx5eTAQAAHyJX54ijIiIcLtQvaqqSpLUokULSdLSpUs1c%2BZMPfroo4qLi9PcuXN15ZVXSpJSU1P14IMPaty4cTp8%2BLA6d%2B6svLw8hYWFeT4IAADwSX5ZsH7u8ssv11dffeX6Pikpye3DR3/u7rvv1t133%2B2JpQEAAD/kl6cIAQAAvImCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmMxvC1ZRUZGysrKUnJyslJQUTZo0SWVlZZKk3bt363e/%2B52uueYa9enTR88//7zbbd9%2B%2B231799fXbt21aBBg/TBBx94IwIAAPBRfluwRo8eLZvNpo0bN%2BqNN97Q119/rTlz5uj48eMaNWqUrr/%2Bem3dulXz58/X0qVLtX79ekk/lq%2BJEydqwoQJ%2BuijjzR8%2BHCNHTtWhw4d8nIiAADgK/yyYJWVlSkxMVHjx49X06ZN1aJFCw0cOFCffvqpNm/erFOnTun%2B%2B%2B9XkyZN1KlTJ911112y2%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%2BqqqpKwcE/xnc4HOrSpYtr/vT1WKc5HA7169evzseOiYmpdTqwpOSoqqr8/0HvT6x0f1VX11gqr2S9zFbLK5HZChpy3oZ78rIeqqqqNHXqVE2YMMGtXElSWlqawsPDtWTJElVWVmrHjh1asWKFMjMzJUmDBw/Wtm3btHnzZp04cUIrVqzQN998owEDBngjCgAA8EF%2B%2BQrW9u3bVVhYqJkzZ2rmzJluc%2B%2B8846effZZTZ8%2BXXl5ebrkkkuUk5OjHj16SJI6dOig3NxczZo1S0VFRWrXrp2WLl2qSy%2B91AtJAACAL/LLgnXttdfqq6%2B%2BOuc2r7766lnn%2BvTpoz59%2Bpi9LAAAYBF%2BeYoQAADAmyhYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmCzY2wsAGqrfLPjQ20u4IOvG3ejtJQAA/hevYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIxPcgf8hC998jyfOg/A3/EK1hkUFRXpvvvuU3Jysnr27Km5c%2BeqpqbG28sCAAA%2BglewzuCBBx5Qp06dtGHDBh0%2BfFijRo3SJZdcoj/84Q/eXhoAAPABvIL1Mw6HQ19%2B%2BaUmTJigZs2aqXXr1ho%2BfLjsdru3lwYAAHwEr2D9TEFBgeLi4hQREeEa69Spk/bt26fy8nKFh4efdx/FxcUqKSlxGwsObqKYmBjT1wv4Il%2B6XswX/c%2BEm1z/HRQU6Pa1oemdu9XbS7ggP/3ZNiQN/X42my/kpWD9jNPplM1mcxs7XbZKS0vrVLDsdrsWLVrkNjZ27Fg98MAD5i30f33637eYvs/TiouLZbfbNWTIEMuUQ6tltlpeyXqZi4uL9eKLf2mweS/G/8Osdh9LDf9%2BNpsv5G241c%2BLDMOo1%2B2HDBmiN954w%2B3PkCFDTFqd55SUlGjRokW1Xo3zZ1bLbLW8kvUyWy2vRGYr8IW8vIL1M9HR0XI6nW5jTqdTAQEBio6OrtM%2BYmJiGmyjBgAAFx%2BvYP1MYmKiDh48qCNHjrjGHA6H2rVrp6ZNm3pxZQAAwFdQsH4mISFBnTt31rx581ReXq7CwkK98MILyszM9PbSAACAjwiaMWPGDG8voqG56aabtHbtWj3%2B%2BON66623lJGRoREjRiggIMDbS/O4pk2b6rrrrrPUq3dWy2y1vJL1Mlstr0RmK2joeQOM%2Bl7RDQAAADecIgQAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFC2dUVFSk%2B%2B67T8nJyerZs6fmzp2rmpoaby%2BrXoqKipSVlaXk5GSlpKRo0qRJKisr07fffquOHTuqc%2BfObn/%2B%2Bte/um779ttvq3///uratasGDRqkDz74wItJ6qZjx45KTEx0y/T4449LkvLz85WRkaFu3bqpX79%2BWr16tdttly1bpr59%2B6pbt27KzMzUzp07vRHhgnzyySe17sPExER17NhRH3/88Rnv43Xr1rlu7yuZt27dqpSUFOXk5NSaO9fjtKamRvPnz1evXr2UlJSkESNG6MCBA655p9OpcePGKSUlRd27d9eUKVN0/Phxj2Q6n3NlXr9%2BvQYMGKCuXbuqb9%2B%2Bev31111zCxcuVHx8fK37/fvvv5cknThxQn/605%2BUmpqq5ORkZWdnq7S01GO5zuZsed944w1deeWVtfJ88cUXkvzzPp46dWqtvAkJCXrkkUckSZMmTXL9DuHTf6699lrX7b2a2QDOYODAgcbUqVONsrIyY9%2B%2BfUafPn2M559/3tvLqpfbbrvNmDRpklFeXm4cPHjQGDRokDF58mTjwIEDRocOHc56u127dhmJiYnG5s2bjePHjxurVq0yunTpYhw8eNCDq79wHTp0MA4cOFBr/LvvvjOuvvpqY/ny5cbx48eNDz/80LjqqquML774wjAMw3jvvfeMa6%2B91ti%2BfbtRWVlpLF261LjxxhuNiooKT0eotyVLlhj/9V//ZXz00UdGz549z7qdr2TOy8sz%2BvTpYwwdOtQYN26c29z5HqfLli0zevbsaezZs8c4evSo8dhjjxn9%2B/c3ampqDMMwjLFjxxr33XefcfjwYePQoUPGkCFDjMcff9zjGX/uXJl37NhhdO7c2fif//kf49SpU8bmzZuNTp06GZ988olhGIbxzDPPGBMnTjzrvmfNmmUMGjTI%2BM9//mOUlpYaY8eONUaNGnVR85zPufKuXLnS%2BN3vfnfW2/rjffxzp06dMvr162ds3rzZMAzDmDhxovHMM8%2BcdXtvZuYVLNTicDj05ZdfasKECWrWrJlat26t4cOHy263e3tpv1hZWZkSExM1fvx4NW3aVC1atNDAgQP16aefnve2y5cvV1pamtLS0hQaGqoBAwaoQ4cOtV718RVr1qxR69atlZGRodDQUKWkpCg9PV3Lly%2BXJNntdg0aNEhdunRRWFiYRo4cKUnatGmTN5d9wf7zn//ohRde0MMPP3zebX0lc2hoqFasWKFWrVrVmjvf49Rut2v48OFq27atwsPDlZOTo8LCQu3YsUPff/%2B9NmzYoJycHEVHRys2NlZjxozRypUrderUKU/HdHOuzE6nU6NGjdLNN9%2Bs4OBgpaWlqUOHDnX6e11VVaUVK1ZozJgxuuyyyxQZGalx48Zp8%2BbN%2Bu677y5GlDo5V97z8cf7%2BOdefPFF/epXv1JaWtp5t/V2ZgoWaikoKFBcXJwiIiJcY506ddK%2BfftUXl7uxZX9cjabTbNmzdIll1ziGjt48KBiYmJc3z/88MPq3r27rr/%2Bes2bN8/1F7CgoEAJCQlu%2B0tISJDD4fDM4uth3rx56tGjh6699lpNmzZNFRUVZ81z%2BpTYz%2BcDAwMVHx/vE3l/6umnn9add96pX/3qV5KkiooK1ynim266SS%2B88IKM//1d976SediwYWrWrNkZ5871OD1%2B/Lj27NnjNh8eHq5WrVrJ4XBo9%2B7dCgoKUseOHV3znTp10rFjx7R3796LE6aOzpU5NTVVWVlZru%2BrqqpUUlKi2NhY19hXX32loUOHuk6Hnz5tun//fh09elSdOnVybdu2bVuFhYWpoKDgIqU5v3PllX78/9Yf/vAHJSUlqVevXlq1apUk%2Be19/FNlZWV69tln9dBDD7mNf/TRR7rjjjvUtWtXZWRkuP5f5u3MFCzU4nQ6ZbPZ3MZOl62GcH2CGRwOh15%2B%2BWXdf//9CgkJUdeuXdW7d29t2rRJeXl5Wr16tf785z9L%2BvHn8dOyKf3482joP4urr75aKSkpWr9%2Bvex2u7Zv365HH330jPdvZGSkK4%2Bv5v2pb7/9VuvXr9cf/vAHST/%2BQ9OhQwf9/ve/19atWzVr1iwtWrRIK1eulOQfmc%2BV4YcffpBhGGeddzqdCg8PV0BAgNuc5Ft/53Nzc9WkSRPdeuutkqQWLVqoZcuWmjNnjj788EPdddddGj16tPbu3Sun0ylJtf4u2Gy2Bps5OjparVu31kMPPaQPP/xQDz74oCZPnqz8/HxL3Mcvv/yykpKS1L59e9dYy5Yt1apVKy1dulRbt27Vtddeqz/%2B8Y8NIjMFC2d0%2Bpm9P/rss880YsQIjR8/XikpKYqJidFrr72m3r17q1GjRrrqqqs0atQovfHGG67b%2BOLPw26366677lJISIjatm2rCRMmaO3atXV6adwX8/7UK6%2B8oj59%2BujSSy%2BV9OOz1pdeeknXXXedQkJC1L17dw0dOtTn7%2BOfO1%2BGc837cn7DMDR37lytXbtWS5YsUWhoqCTprrvu0jPPPKNWrVqpcePGGj58uOLj491O7/tS7h49eugvf/mLEhISFBISon79%2Bql37951fhz7Utafq66u1iuvvKJhw4a5jWdlZemJJ55QbGyswsPD9dBDDykkJEQbNmyQ5N3MFCzUEh0d7Xp2d5rT6VRAQICio6O9tCpzbNy4Uffdd58mT55c6y/qT8XFxen777%2BXYRiKioo648/D134Wl19%2BuaqrqxUYGFgrT2lpqSuPP%2BR99913lZ6efs5t4uLiVFxcLMk/Mp8rQ2Rk5Bnvd6fTqebNmys6Olrl5eWqrq52m5Ok5s2bX/zF10NNTY0mTZqkjRs36tVXX9Wvf/3rc25/%2Bn4/fd/%2B/Gfyww8/NPjMP3U6jz/fx9KP7xI%2BefKk2zsEzyQoKEiXXXaZ6z72ZmYKFmpJTEzUwYMHdeTIEdeYw%2BFQu3bt1LRpUy%2BurH4%2B//xzTZw4UU8//bTuuOMO13h%2Bfr6WLFnitu3evXsVFxengIAAJSYm1nrLvsPhUJcuXTyy7l9i165dmj17tttYYWGhQkJClJaWVivPzp07XXkSExPdrkGprq7Wrl27GnTen9q9e7eKiop04403usbWrVunv//9727b7d27Vy1btpTk%2B5klnfNxGhoaqvbt27tlLCsr0/79%2B3XVVVcpPj5ehmHoyy%2B/dLutzWZTmzZtPJbhl3jiiSf09ddf69VXX3Xdn6f9%2Bc9/Vn5%2BvttYYWGhWrZsqZYtWyoiIsLtZ/Lvf/9bJ0%2BeVGJiokfWfqFeffVVvf32225jp/P4830sSe%2B9956uv/56BQcHu8YMw9CsWbPcMp08eVL79%2B9Xy5YtvZ6ZgoVaTn%2BmyLx581ReXq7CwkK98MILyszM9PbSfrGqqipNnTpVEyZMUPfu3d3mmjVrpsWLF2vVqlU6deqUHA6H/vrXv7ryDh48WNu2bdPmzZt14sQJrVixQt98840GDBjgjSh10rx5c9ntduXl5enkyZPat2%2Bfnn76aQ0ZMkS33367ioqKtHz5cp04cUJbtmzRli1bNHjwYElSZmam3nzzTW3fvl2VlZVasmSJQkJC1KNHD%2B%2BGqqNdu3YpMjJS4eHhrrFGjRppzpw5%2BuCDD3Tq1Cl9%2BOGHWrlypes%2B9vXM0vkfp5mZmVq2bJkKCwtVXl6u3Nxc12dERUdHq2/fvlqwYIGOHDmiQ4cOafHixcrIyHD7B62h%2Beyzz7R69Wrl5eUpMjKy1rzT6dSjjz6qvXv36sSJE3r%2B%2Bee1f/9%2BDRw4UEFBQRo8eLCeffZZHTx4UKWlpXrqqafUu3dvtzfDNCQnT57U448/LofDoVOnTmnt2rV6//33NXToUEn%2BeR%2Bftnv3bl1%2B%2BeVuYwEBAfr222/16KOP6rvvvlNFRYVyc3PVqFEj3XzzzV7PHGD48klZXDSHDh3StGnT9M9//lPh4eEaOnSoxo4d63axoC/59NNP9dvf/lYhISG15t555x3t2rVLixYt0jfffKNmzZrpnnvu0b333qvAwB%2Bfg6xfv17z5s1TUVGR2rVrpylTpigpKcnTMS7IJ598onnz5umrr75SSEiIBg4cqJycHIWGhuqTTz7RzJkzVVhYqLi4OI0fP159%2BvRx3fbvf/%2B78vLydPjwYXXu3FkzZsxQhw4dvJim7pYuXao1a9Zo7dq1buN2u13PP/%2B8Dh48qEsuuUT333%2B/7rrrLte8L2Tu3LmzpB%2BfMEhy/SNx%2Bt2O53qcGoahhQsX6rXXXlNFRYWSk5P12GOPqUWLFpKko0ePavr06dq0aZMaNWqk2267TZMmTTrj3xlPOlfmyZMn6x//%2BEetfyyTkpL0/PPP68SJE5o3b57eeecdOZ1OtWvXTtOmTVPXrl0l/VhYZs2apbfeektVVVXq2bOnZsyYUad3tF0s58prGIaWLFmiFStWqKSkRJdffrkefvhh9ezZU5J/3sen9e3bV4MHD9aIESPcbut0OjVnzhy9//77Ki8v11VXXaUZM2aobdu2krybmYIFAABgMk4RAgAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmOz/AwHuD4p%2BOc2HAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-7299798339557518474”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>218</td> <td class=”number”>6.8%</td> <td>

<div class=”bar” style=”width:18%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>189</td> <td class=”number”>5.9%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>137</td> <td class=”number”>4.3%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>96</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>85</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>79</td> <td class=”number”>2.5%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.33333333333333</td> <td class=”number”>56</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.33333333333333</td> <td class=”number”>50</td> <td class=”number”>1.6%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>47</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>300.0</td> <td class=”number”>42</td> <td class=”number”>1.3%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (421)</td> <td class=”number”>1234</td> <td class=”number”>38.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>977</td> <td class=”number”>30.4%</td> <td>

<div class=”bar” style=”width:79%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-7299798339557518474”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>189</td> <td class=”number”>5.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-87.5</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-85.71428571428571</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-83.33333333333334</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-81.81818181818183</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>900.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1100.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1200.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1300.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1700.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_CACFP_09_15”>PCH_CACFP_09_15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>52</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.15572</td>

</tr> <tr>

<th>Minimum</th> <td>-0.37468</td>

</tr> <tr>

<th>Maximum</th> <td>0.92495</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram4385781818992168142”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives4385781818992168142,#minihistogram4385781818992168142”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives4385781818992168142”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles4385781818992168142”

aria-controls=”quantiles4385781818992168142” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram4385781818992168142” aria-controls=”histogram4385781818992168142”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common4385781818992168142” aria-controls=”common4385781818992168142”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme4385781818992168142” aria-controls=”extreme4385781818992168142”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles4385781818992168142”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-0.37468</td>

</tr> <tr>

<th>5-th percentile</th> <td>-0.18379</td>

</tr> <tr>

<th>Q1</th> <td>0.035319</td>

</tr> <tr>

<th>Median</th> <td>0.12288</td>

</tr> <tr>

<th>Q3</th> <td>0.22377</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.76085</td>

</tr> <tr>

<th>Maximum</th> <td>0.92495</td>

</tr> <tr>

<th>Range</th> <td>1.2996</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.18845</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.24843</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.5954</td>

</tr> <tr>

<th>Kurtosis</th> <td>1.0908</td>

</tr> <tr>

<th>Mean</th> <td>0.15572</td>

</tr> <tr>

<th>MAD</th> <td>0.1811</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.0035</td>

</tr> <tr>

<th>Sum</th> <td>487.72</td>

</tr> <tr>

<th>Variance</th> <td>0.061719</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram4385781818992168142”>

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smTJ3X33XerX79%2B6tOnj%2BLi4pSXl6cVK1YY3dfGjRs1cOBA9ejRQ1OmTNFnn30mSTp58qQSExPVq1cvjRw5Ujk5OW7rZWVlKSEhQb169VJycrLy8vKM1gUAAPyb7fdgtW3bVi%2B99JL%2B9re/6ZNPPlFgYKDat2%2BvgQMHKigoyNh%2BnnvuOeXk5CgrK0uRkZFat26dtmzZomnTpmnmzJlasmSJRo8erbfeeksPP/yw2rdvr27duunIkSNKT0/Xn//8Z3Xu3FlZWVmaMWOGDh48qCZNmhirDwAA%2BC/bA5YkNWzYUEOHDq3XfWzevFmpqan66U9/KklaunSpJOnpp59Wu3btlJiYKEmKiYlRfHy8tm/frm7duik7O1sTJkxQ9%2B7dJUlTp05VVlaWjh49qpEjR9ZrzQAAwD/YHrA%2B/fRTrV27Vh9%2B%2BKEqKipqzR8%2BfPim9/H555/rs88%2B0xdffKERI0bowoUL6t%2B/v5YvX678/HxFRUW5LR8VFaX9%2B/dLkvLz8zVixAjXXGBgoLp06aLc3FwCFgAA8IjtAWvx4sUqLi7WwIED6%2B2S2/nz5yVJBw4c0DPPPCPLsjRnzhwtXbpUFRUVatWqldvyzZs3V2lpqSTJ6XQqLCzMbT4sLMw174ni4mKVlJS4jTkcTRQZGenxNoKCAt1%2B3srohTt/6IfDYaZ2f%2BiFKfSiBr2oQS%2B8x/aAlZeXp8OHDysiIqLe9mFZlqSvLu99HaZmz56thx56SDExMR6v/31lZ2crIyPDbSwlJUVz5syp87ZCQxvfVC3%2BhF648%2BV%2BhIc3Nbo9X%2B6FafSiBr2oQS/sZ3vAatGiRb3fLH7bbbdJkkJDQ11jbdq0kWVZun79eq2v5CktLXUFvvDw8FrzTqdTHTt29Hj/SUlJio%2BPdxtzOJqotPSKx9sICgpUaGhjlZWVq6qq2uP1/BG9cOcP/ajLc%2BFf8YdemEIvatCLGvTC/B90nrI9YE2fPl0ZGRlasGCBAgIC6mUfrVu3VkhIiAoKChQdHS1JKioqUoMGDRQbG6vdu3e7LZ%2BXl%2Be6qb1r167Kz8/X%2BPHjJUlVVVU6ffq066Z4T0RGRta6HFhSckmVlXV/cFdVVX%2Bv9fwRvXDny/34Rdor3i6hTvbPHeDtEjzmy48L0%2BhFDXphP9sD1t/%2B9je9/fbb2rlzp3784x8rMND9uvDzzz9/0/twOBxKTEzUk08%2Bqb59%2ByokJEQbNmzQ6NGjNX78eD3xxBPavn27xowZo9dff13Hjx9Xdna2JCk5OVnz58/XqFGj1LlzZz399NNq2LCh4uLibrouAABwa7A9YIWEhGjQoEH1vp8FCxbo2rVruu%2B%2B%2B3T9%2BnUlJCRo6dKlatq0qTZt2qQVK1boscceU5s2bbRmzRrdeeedkqRBgwZp/vz5mjt3ri5cuKBu3bopMzNTjRo1qveaAQCAfwiwbvaObnikpORSnZZ3OAIVHt5UpaVXbvnTuvTC3bf14951r3mxKv/mC5cIeZ7UoBc16IXUsmUzr%2BzXK%2B/b/Oijj5Senq5Fixa5xt555x1vlAIAAGCc7QHr5MmTGjNmjA4ePKi9e/dK%2BurDRx944AEjHzIKAADgbbYHrMcff1y//e1vtWfPHte7CNu2bavVq1drw4YNdpcDAABgnO0B63/%2B53%2BUnJwsSW4f0zB8%2BHAVFhbaXQ4AAIBxtgesZs2a3fA7CIuLi9WwYUO7ywEAADDO9oDVq1cvrVy5UpcvX3aNnT17Vqmpqbr77rvtLgcAAMA42z8Ha9GiRfr1r3%2Bt/v37q6qqSr169VJ5ebk6duyo1atX210OAACAcbYHrNatW2vv3r06fvy4zp49q0aNGql9%2B/YaMGBAvX11DgAAgJ1sD1iS1KBBAw0dOtQbuwYAAKh3tges%2BPj4f3mmis/CAgAAvs72gDVixAi3gFVVVaWzZ88qNzdXv/71r%2B0uBwAAwDjbA9bChQtvOP7yyy/rjTfesLkaAAAA87zyXYQ3MnToUL300kveLgMAAOCm/WAC1unTp2VZlrfLAAAAuGm2XyKcNGlSrbHy8nIVFhZq2LBhdpcDAABgnO0Bq127drXeRRgcHKzExETdd999dpcDAABgnO0Bi09rBwAA/s72gLVr1y6Plx03blw9VgIAAFA/bA9YS5YsUXV1da0b2gMCAtzGAgICCFgAAMAn2R6w/vznP2vz5s2aMWOGOnfuLMuy9MEHH%2Bipp57SL3/5S/Xv39/ukgAAAIzyyj1YmZmZatWqlWusT58%2Batu2rR588EHt3bvX7pIAAACMsv1zsD7%2B%2BGOFhYXVGg8NDVVRUZHd5QAAABhne8Bq06aNVq9erdLSUtdYWVmZ1q5dqzvuuMPucgAAAIyz/RLh4sWLtWDBAmVnZ6tp06YKDAzU5cuX1ahRI23YsMHucgAAAIyzPWANHDhQx44d0/Hjx3X%2B/HlZlqVWrVrpnnvuUbNmzewuBwAAwDjbA5YkNW7cWEOGDNH58%2BfVtm1bb5QAfKd7173m7RIAAD7K9nuwKioqlJqaqp49e%2Bree%2B%2BV9NU9WFOnTlVZWZnd5QAAABhne8Bas2aNCgoKlJaWpsDAmt1XVVUpLS3N7nIAAACMsz1gvfzyy1q/fr2GDx/u%2BtLn0NBQrVq1SgcPHrS7HAAAAONsD1hXrlxRu3btao1HRETo6tWrdpcDAABgnO0B64477tAbb7whSW7fPXjgwAH96Ec/srscAAAA42x/F%2BHkyZM1e/ZsTZw4UdXV1XrmmWeUl5enl19%2BWUuWLLG7HAAAAONsD1hJSUlyOBzaunWrgoKC9OSTT6p9%2B/ZKS0vT8OHD7S4HAADAONsD1sWLFzVx4kRNnDjR7l0DAADYwvZ7sIYMGeJ27xUAAIC/sT1g9e/fX/v377d7twAAALax/RLh7bffrj/84Q/KzMzUHXfcoQYNGrjNr1271u6SAAAAjLI9YJ05c0Y//elPJUmlpaV27x4AAKDe2Raw5s2bp8cff1x/%2BctfXGMbNmxQSkqKXSUAAADYwrZ7sI4cOVJrLDMz067dAwAA2Ma2gHWjdw7ybkIAAOCPbAtYX3%2Bx83eNAQAA%2BDrbP6YBAADA3xGwAAAADLPtXYTXr1/XggULvnOMz8ECAAC%2BzraA1bt3bxUXF3/nGAAAgK%2BzLWD98%2BdfAQAA%2BDPuwQIAADDslghYK1euVOfOnV2/nzx5UomJierVq5dGjhypnJwct%2BWzsrKUkJCgXr16KTk5WXl5eXaXDAAAfJjfB6yCggLt3r3b9XtxcbFmzpypSZMm6eTJk1qyZImWLVum3NxcSV994nx6err%2B%2BMc/6sSJExo8eLBmzJihq1eveusQAACAj/HrgFVdXa1HH31UU6ZMcY3t2bNH7dq1U2JiooKDgxUTE6P4%2BHht375dkpSdna0JEyaoe/fuatSokaZOnSpJOnr0qDcOAQAA%2BCDbbnL3hueff17BwcEaPXq01q1bJ0nKz89XVFSU23JRUVHav3%2B/a37EiBGuucDAQHXp0kW5ubkaOXKkR/stLi5WSUmJ25jD0USRkZEe1x4UFOj281ZGL%2BBtDscP/7HH86QGvahBL7zHbwPWP/7xD6Wnp9d696LT6VSrVq3cxpo3b67S0lLXfFhYmNt8WFiYa94T2dnZysjIcBtLSUnRnDlz6nIIkqTQ0MZ1Xsdf0Qt4S3h4U2%2BX4DGeJzXoRQ16YT%2B/DVirVq3ShAkT1KFDB3322Wd1Wvdmv4Q6KSlJ8fHxbmMORxOVll7xeBtBQYEKDW2ssrJyVVVV31Q9vo5ewNvq8tz1Fp4nNehFDXrhvT%2BQ/DJgnTx5Uu%2B884727t1bay48PFxOp9NtrLS0VBEREd8673Q61bFjR4/3HxkZWetyYEnJJVVW1v3BXVVV/b3W80f0At7iS487nic16EUNemE/v7wom5OTowsXLmjw4MHq37%2B/JkyYIEnq37%2B/OnXqVOtjF/Ly8tS9e3dJUteuXZWfn%2B%2Baq6qq0unTp13zAAAA38UvA9Yjjzyil19%2BWbt379bu3buVmZkpSdq9e7dGjx6toqIibd%2B%2BXV9%2B%2BaWOHz%2Bu48eP6/7775ckJScna9euXXr33XdVXl6ujRs3qmHDhoqLi/PiEQEAAF/il5cIw8LC3G5Ur6yslCS1bt1akrRp0yatWLFCjz32mNq0aaM1a9bozjvvlCQNGjRI8%2BfP19y5c3XhwgV169ZNmZmZatSokf0HAgAAfJJfBqxv%2BvGPf6wPPvjA9Xvfvn3dPnz0myZPnqzJkyfbURoAAPBDfnmJEAAAwJsIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwzOHtAgAAZt277jVvl%2BCx/XMHeLsEoF5wBgsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMc3i7ANw67l33mrdLAADAFpzBAgAAMIyABQAAYBgBCwAAwDC/DVhFRUVKSUlR//79FRMTo0ceeURlZWWSpIKCAv3yl79U7969NWzYMG3evNlt3X379mn06NHq2bOnJkyYoFdffdUbhwAAAHyU3wasGTNmKDQ0VEeOHNHOnTv14Ycf6j/%2B4z9UUVGh6dOn66677tIrr7yixx9/XJs2bdLBgwclfRW%2BUlNTtXDhQr3%2B%2BuuaMmWKZs2apfPnz3v5iAAAgK/wy4BVVlamrl27asGCBWratKlat26t8ePH680339SxY8d0/fp1Pfzww2rSpImio6N13333KTs7W5K0fft2xcbGKjY2VsHBwRozZow6deqknJwcLx8VAADwFX4ZsEJDQ7Vq1SrddtttrrFz584pMjJS%2Bfn56ty5s4KCglxzUVFRysvLkyTl5%2BcrKirKbXtRUVHKzc21p3gAAODzbonPwcrNzdXWrVu1ceNG7d%2B/X6GhoW7zzZs3l9PpVHV1tZxOp8LCwtzmw8LCdObMGY/3V1xcrJKSErcxh6OJIiMjPd5GUFCg208A3uNw/PCfh776mlEfvfXVXtQHeuE9fh%2Bw3nrrLT388MNasGCBYmJitH///hsuFxAQ4Pp/y7Juap/Z2dnKyMhwG0tJSdGcOXPqvK3Q0MY3VQuAmxce3tTbJXjM114z6rO3vtaL%2BkQv7OfXAevIkSP67W9/q2XLlmncuHGSpIiICH388cduyzmdTjVv3lyBgYEKDw%2BX0%2BmsNR8REeHxfpOSkhQfH%2B825nA0UWnpFY%2B3ERQUqNDQxiorK1dVVbXH6wEwry7PXW/559cMX1IfveX1swa98N4fSH4bsN5%2B%2B22lpqbqT3/6kwYOHOga79q1q7Zt26bKyko5HF8dfm5urrp37%2B6a//p%2BrK/l5uZq5MiRHu87MjKy1uXAkpJLqqys%2B4O7qqr6e60HwBxfeg762j%2Bi9dlbXj9r0Av7%2BeVF2crKSi1dulQLFy50C1eSFBsbq5CQEG3cuFHl5eU6deqUduzYoeTkZEnS/fffrxMnTujYsWP68ssvtWPHDn388ccaM2aMNw4FAAD4IL88g/Xuu%2B%2BqsLBQK1as0IoVK9zmDhw4oCeffFKPPvqoMjMzddttt2nevHmKi4uTJHXq1ElpaWlatWqVioqK1KFDB23atEktW7b0wpEAAABf5JcBq0%2BfPvrggw/%2B5TLbtm371rlhw4Zp2LBhpssCAAC3CL%2B8RAgAAOBNBCwAAADDCFgAAACGEbAAAAAMI2ABAAAY5pfvIgQAk%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%2Bu2267Tf/2b//m7dIAAIAP4AzWN%2BTm5ur999/XwoUL1axZM7Vr105TpkxRdna2t0sDAAA%2BgjNY35Cfn682bdooLCzMNRYdHa2zZ8/q8uXLCgkJ%2Bc5tFBcXq6SkxG3M4WiiyMhIj%2BsICgp0%2BwkAgD9zOPzr3zsC1jc4nU6Fhoa6jX0dtkpLSz0KWNnZ2crIyHAbmzVrlmbPnu1xHcXFxXr22T8rKSnpXwazN/8w3ONt%2Bqri4mJlZ2d/Zy9uFfSjBr2oQS9q0Isa9MJ7/CsuGmJZ1k2tn5SUpJ07d7r9l5SUVKdtlJSUKCMjo9aZsFsRvXBHP2rQixr0oga9qEEvvIczWN8QEREhp9PpNuZ0OhUQEKCIiAiPthEZGclfCgAA3MI4g/UNXbt21blz53Tx4kXXWG5urjp06KCmTZt6sTIAAOArCFjfEBUVpW7dumnt2rW6fPmyCgsL9cwzzyg5OdnbpQEAAB8RtHz58uXeLuKH5p577tHevXv1%2B9//Xi%2B99JISExP14IMPKiAgwNY6mjZtqn79%2BnHmTPTim%2BhHDXpRg17UoBc16IV3BFg3e0c3AAAA3HCJEAAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAtYPhNPp1Ny5cxUTE6OBAwdqyZIlqqio%2BM71rly5ori4OD3yyCM2VGmPuvbi4MGDGjNmjHr27KmEhAS98MILNlZrXlFRkaZNm6b%2B/ftr8ODBWrNmjaqrq2%2B4bFZWlhISEtSrVy8lJycrLy/P5mrrX136sW3bNiUkJKhnz54aO3asDh06ZHO19asuvfja559/rp49eyo9Pd2mKu1Rl14UFhbqV7/6lbp3767Y2Fht2bLF3mLrmae9qK6u1vr16xUfH6%2BePXtq9OjR2rdvnxcqvkVY%2BEGYNWuWNW3aNOvChQvW%2BfPnraSkJOv3v//9d663atUqq3fv3lZqaqoNVdqjLr04deqU1a1bN%2Buvf/2rdf36devYsWNWdHS09fe//93mqs0ZP368tXTpUqusrMw6e/asNWzYMGvz5s21ljt8%2BLDVp08f691337XKy8utTZs2WQMGDLCuXLniharrj6f9OHDggNW7d2/rzTfftK5du2a98MILVnR0tPXJJ594oer64Wkv/tmsWbOs3r17W%2BvXr7epSnt42ovy8nIrLi7Oeuqpp6yrV69ap06dskaOHGmdOXPGC1XXD097sXXrVmvgwIFWYWGhVVlZaR05csSKioqyCgoKvFC1/yNg/QCUlJRYd955p9uD/Pjx41aPHj2sa9eufet6BQUF1oABA6wVK1b4TcCqay%2BOHz9uZWRkuI2NHz/e2rhxY73XWh/ee%2B89q0uXLpbT6XSN/ed//qeVkJBQa9lp06ZZK1eudP1eVVVlDRgwwNq7d68ttdqhLv3YtWuX9dxzz7mN9evXz8rJyan3Ou1Ql1587dixY9bw4cOtBQsW%2BFXAqksvdu7caY0aNcrO8mxVl14sWrTI%2Bs1vfuM2FhMTY%2B3atave67wVcYnwB6CgoEBBQUHq3Lmzayw6OlpXr17VRx99dMN1LMvS8uXLNW/ePIWGhtpVar2ray8GDRqklJQU1%2B%2BVlZUqKSlRq1atbKnXtPz8fLVp00ZhYWGusejoaJ09e1aXL1%2ButWxUVJTr98DAQHXp0kW5ubm21Vvf6tKPsWPHavLkya7fy8rKdOXKFZ99LHxTXXohSRUVFfrd736nRx99VA6Hw85S611devHWW2%2BpU6dOWrRokfr06aPhw4crJyfH7pLrTV16ERcXp//%2B7/9WQUGBrl27psOHD6u8vFz9%2BvWzu%2BxbAgHrB8DpdCokJEQBAQGusa%2BfLKWlpTdcJzs7WwEBAZowYYItNdrl%2B/Tin6WlpalJkyYaMWJEvdVYn5xOZ63A/G3H73Q63V5Uv17Wkz75irr0459ZlqWlS5eqe/fufvOPR117sWHDBvXo0UN33XWXLfXZqS69OH/%2BvA4fPqyYmBi98sormj59ulJTU3X69Gnb6q1PdenFsGHDlJSUpHHjxqlbt25asGCBVq1apdtvv922em8l/vVnzQ/Y7t279e///u83nJs3b54sy/J4WxcuXNCf/vQnbdmyxS2I%2BAqTvfiaZVlKS0vT3r17lZWVpeDg4Jst02vqcvzfp1e%2Bpq7HeP36dT3yyCM6c%2BaMsrKy6qkq7/C0F2fOnNH27du1Z8%2Beeq7IezzthWVZio6O1ujRoyVJ48eP1/PPP68DBw64nQH2ZZ72YteuXdq1a5e2b9%2Buzp076%2BTJk1qwYIFuv/12/fznP6/nKm89BCybjB07VmPHjr3h3GuvvabLly%2BrqqpKQUFBkr76q0SSWrRoUWv51atXa9y4cW6X0XyJyV5IX70zZtGiRXrvvfe0bds2tW3btn4Kt0FERITreL/mdDoVEBCgiIgIt/Hw8PAbLtuxY8d6r9MudemH9NVlsZkzZ6q8vFzPPfecwsPD7Sq13nnai69vH5g9e7Zatmxpd5m2qMvjomXLlrWWbdOmjUpKSuq9TjvUpRdbt25VUlKSK0zFxcXprrvuUk5ODgGrHhCwfgC6dOkiy7L0/vvvKzo6WpKUm5ur0NBQtW/fvtbyOTk5Cg0N1c6dOyV99Y9KdXW1jh49qjfeeMPW2k2ray8kaeXKlfrwww%2B1bds2NW/e3M5yjevatavOnTunixcvul4cc3Nz1aFDBzVt2rTWsvn5%2BRo/frwkqaqqSqdPn1ZiYqLtddeXuvTDsizNmzdPDodDW7Zs8emzmDfiaS/%2B7//%2BT3//%2B9/14Ycfav369ZKkq1evKjAwUEeOHNF//dd/eaV%2Bk%2BryuPjZz36mbdu2ybIs1xn/oqIi3XPPPbbXXR/q0ovq6mpVVVW5jV27ds22Wm85tt9WjxuaO3euNXXqVOvChQvWuXPnrIkTJ1qrV692zT/wwAPWSy%2B9ZFmWZZ07d87tv5UrV1pz5syxzp07563yjapLL958802rb9%2B%2BVklJibfKNe6%2B%2B%2B6zFi9ebF26dMk6c%2BaMFR8fb23dutWyLMtKSEhwfQTF8ePHrd69e1vvvPOOdfXqVSs9Pd2KjY21ysvLvVm%2BcZ72Y/fu3dbQoUOtq1everPceuVJLyorK2u9RsyZM8dauXKlVVxc7OUjMMfTx8X58%2BetHj16WE888YRVXl5u7dmzx4qOjrb%2B93//15vlG%2BVpL9LT0624uDiroKDAun79uvXKK69Y0dHR1okTJ7xZvt/iDNYPxNfv9hkyZIgaNGigUaNGad68ea75Tz/9VF988YUkqXXr1m7rhoSEqHHjxrXGfVVdevHiiy/q0qVLGjx4sNs2%2Bvbtq82bN9tatynr16/XsmXLNGDAAIWEhGjSpEmud8edPXtWV69elfTVOyjnz5%2BvuXPn6sKFC%2BrWrZsyMzPVqFEjb5ZvnKf9ePHFF1VUVFTrpvaxY8dqxYoVttddHzzpRVBQUK3XgsaNGyskJMSvLhl6%2Brho1aqVNm3apD/84Q964okn9KMf/UgbNmzQHXfc4c3yjfK0F9OnT1dlZaVSUlJ08eJFtWnTRitWrNDdd9/tzfL9VoBl3QJ3yQIAANiIj2kAAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMP%2BH3qNBgjFCI5lAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common4385781818992168142”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.504188649377471</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.037501523481868304</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.06979255283625063</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.2237696603925874</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0486443082022352</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.03531898846845771</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.13831482126624062</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.11938814094361927</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.16494664181334517</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.16679358189872617</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (41)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme4385781818992168142”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-0.37468134376752626</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:30%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.30016237495327425</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:43%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.20829892508883452</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:69%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.18378842957478048</td> <td class=”number”>77</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.16617867453602786</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.5064546537266801</td> <td class=”number”>14</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5515094754631577</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:77%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.7608535025514243</td> <td class=”number”>75</td> <td class=”number”>2.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.7667722877815244</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:32%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9249518226067124</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:85%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_CONVSPTH_09_14”><s>PCH_CONVSPTH_09_14</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_CONVS_09_14”><code>PCH_CONVS_09_14</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.98861</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_CONVS_09_14”>PCH_CONVS_09_14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>738</td>

</tr> <tr>

<th>Unique (%)</th> <td>23.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>97</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>2.7404</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>350</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>16.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram769797248661251286”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives769797248661251286,#minihistogram769797248661251286”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives769797248661251286”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles769797248661251286”

aria-controls=”quantiles769797248661251286” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram769797248661251286” aria-controls=”histogram769797248661251286”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common769797248661251286” aria-controls=”common769797248661251286”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme769797248661251286” aria-controls=”extreme769797248661251286”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles769797248661251286”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-33.333</td>

</tr> <tr>

<th>Q1</th> <td>-11.765</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>11.765</td>

</tr> <tr>

<th>95-th percentile</th> <td>50</td>

</tr> <tr>

<th>Maximum</th> <td>350</td>

</tr> <tr>

<th>Range</th> <td>450</td>

</tr> <tr>

<th>Interquartile range</th> <td>23.529</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>30.596</td>

</tr> <tr>

<th>Coef of variation</th> <td>11.165</td>

</tr> <tr>

<th>Kurtosis</th> <td>24.301</td>

</tr> <tr>

<th>Mean</th> <td>2.7404</td>

</tr> <tr>

<th>MAD</th> <td>18.449</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>3.1309</td>

</tr> <tr>

<th>Sum</th> <td>8531</td>

</tr> <tr>

<th>Variance</th> <td>936.13</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram769797248661251286”>

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erdraOlPXDf9j9vHlLzgPrEcPrEcPGmbVD58BGbD%2B%2BMc/KjIyUjfffLNnrKSkRD169FBISIj69u2r4uJiXXXVVZKk8vJynThxQgMGDFBsbKwMw9Dhw4eVkJAgSXI4HIqIiFDv3r0btf2YmJh6twOdzgrV1DTPQV9bW9ds64b/aO3HAOeB9eiB9ehByxGQN2vPnj2rxx9/XA6HQ%2BfOndPWrVv117/%2BVZMnT5YkZWRkKD8/XyUlJaqsrFROTo769%2B%2BvpKQkRUdHa9SoUVq1apVOnTqlzz77TGvWrFFaWprs9oDMowAAwGR%2BmxiSkpIkSTU1NZKk3bt3Szp/tWnKlCmqqqrSrFmz5HQ61b17d61Zs0aJiYmSpMmTJ8vpdOrOO%2B9UVVWVkpOTvZ6Zeuyxx/Too49q%2BPDhatOmjW699dZ6b2YKAADQkCCjqU90o1GczgrT12m32xQVFSaXq4pLws1g9Ko3rS7he9k%2B%2B1qrS7AE54H16IH16EHDunRp%2BC2dmlNA3iIEAACwEgELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAk/k8YA0bNky5ubk6efJkk9azb98%2BpaSkKDs7u97crl27NHbsWA0aNEijRo3Sn//8Z8/c6tWr1b9/fyUlJXl9fPHFF5KkM2fO6D/%2B4z90/fXXKzk5WVlZWXK5XE2qFQAAtC4%2BD1i33367tm3bphEjRuiee%2B7Rrl27VFNT873WsW7dOi1dulQ9e/asN/fhhx9q7ty5ysrK0rvvvqtHHnlEjz32mPbv3%2B9Z5rbbbpPD4fD66Ny5syRp5cqVKi4uVkFBgXbu3CnDMPTwww83bacBAECr4vOAlZmZqW3btunPf/6z%2Bvbtq1//%2BtdKTU3VU089pWPHjjVqHSEhISosLLxgwHK73Zo%2BfbpGjBghu92u1NRU9evXzytgNaSmpkaFhYW69957dckll6hjx46aPXu29u7dq88///x77ysAAGid7FZtOCEhQQkJCXrooYe0bds2LV68WOvXr1dKSopmzZqlAQMGNPjaKVOmNDh3/fXX6/rrr/f8u6amRk6nU127dvWMffTRR5o8ebL%2B8Y9/6JJLLtHDDz%2BsIUOG6MSJE6qoqFBCQoJn2UsvvVTt2rVTcXGx1zq%2BS1lZmZxOp9eY3R6qmJiYRr2%2BsYKDbV6f0brZ7a3zOOA8sB49sB49aHksC1jnzp3TK6%2B8oo0bN%2Bpvf/ubevXqpfvvv19lZWWaOnWqlixZojFjxjR5Ozk5OQoNDdXNN98sSerWrZt69OihOXPmKCYmRgUFBZoxY4Y2b94st9stSYqIiPBaR0RExPd6DqugoEC5ubleY5mZmcrKymri3lxYRET7Zlkv/EtUVJjVJViK88B69MB69KDl8HnAKikpUWFhoV566SVVVVVp1KhR%2Bv3vf68rrrjCs8zgwYO1ePHiJgUswzCUk5OjrVu3Kj8/XyEhIZKkiRMnauLEiZ7lpk6dqpdfflmbN2/2XPkyDOMHb1eS0tPTNWzYMK8xuz1ULldVk9b7bcHBNkVEtFd5ebVqa%2BtMXTf8j9nHl7/gPLAePbAePWiYVT98%2Bjxg3XLLLerdu7emT5%2BucePGqWPHjvWWSU1N1alTp37wNurq6vTwww/rww8/1B//%2BEf16NHjO5ePjY1VWVmZoqOjJZ1/jiss7F8N%2Beqrr9SpU6dGbz8mJqbe7UCns0I1Nc1z0NfW1jXbuuE/WvsxwHlgPXpgPXrQcvg8YOXn5%2Buqq6666HIHDhz4wdv49a9/rY8//lh//OMf6wW4//mf/9GgQYN0zTXXeMZKSkp08803q0ePHoqMjFRxcbFiY2MlSf/4xz909uxZJSYm/uB6AABA6%2BLzp%2BHi4uI0Y8YM7d692zP2u9/9Tr/85S89z0A1xXvvvafNmzcrLy/vglfH3G63lixZoqNHj%2BrMmTNav369Tpw4ofHjxys4OFiTJk3SM888o5MnT8rlcum//uu/dOONN3rexgEAAOBifH4Fa9myZaqoqFCfPn08Y0OHDtW%2Bffu0fPlyLV%2B%2B/KLrSEpKkiTP%2B2d9E9YcDodefPFFVVRU6IYbbvB6zeDBg7V%2B/XrNmTNH0vlnr9xut/r06aPf/e536tatmyQpKytLVVVVuu2221RTU6MbbrhBixcvbvJ%2BAwCA1iPIaOoT3d/TkCFDtGXLFkVFRXmNu1wu3XrrrXrzzTd9WY7POJ0Vpq/TbrcpKipMLlcV99ybwehV/nUsbp99rdUlWILzwHr0wHr0oGFdunSwZLs%2Bv0V4%2BvRpz2/0eRVis6m6utrX5QAAAJjO5wFr8ODBWr58ub766ivP2Oeff64lS5Z4vVUDAACAv/L5M1iPPPKI7r77bl1zzTUKDw9XXV2dqqqq1KNHD/3hD3/wdTkAAACm83nA6tGjh15%2B%2BWX99a9/1YkTJ2Sz2dS7d28NGTJEwcHBvi4HAADAdJb8qZy2bdtqxIgRVmwaAACg2fk8YH3yySdasWKFPv74Y50%2Bfbre/KuvvurrkgAAAExlyTNYZWVlGjJkiEJDQ329eQAAgGbn84BVVFSkV1991fN3/wAAAAKNz9%2BmoVOnTly5AgAAAc3nAWv69OnKzc2Vj99AHgAAwGd8fovwr3/9q95//31t3LhR3bt3l83mnfH%2B9Kc/%2BbokAAAAU/k8YIWHh%2Bv666/39WYBAAB8xucBa9myZb7eJAAAgE/5/BksSTp69KhWr16thx9%2B2DP2f//3f1aUAgAAYDqfB6y3335bY8eO1a5du7R161ZJ5998dMqUKbzJKAAACAg%2BD1grV67Ugw8%2BqC1btigoKEjS%2Bb9PuHz5cq1Zs8bX5QAAAJjO5wHrH//4hzIyMiTJE7Ak6aabblJJSYmvywEAADCdzwNWhw4dLvg3CMvKytS2bVtflwMAAGA6nwesyy%2B/XL/%2B9a9VWVnpGTt27JjmzZuna665xtflAAAAmM7nb9Pw8MMP66677lJycrJqa2t1%2BeWXq7q6Wn379tXy5ct9XQ4AAIDpfB6wunXrpq1bt%2Br111/XsWPH1K5dO/Xu3VvXXnut1zNZAAAA/srnAUuS2rRpoxEjRlixaQAAgGbn84A1bNiw77xSxXthAQAAf%2BfzgHXzzTd7Baza2lodO3ZMDodDd911l6/LAQAAMJ3PA9bcuXMvOL5z5079/e9/93E1AAAA5rPkbxFeyIgRI/Tyyy9bXQYAAECTtZiAdfDgQRmGYXUZAAAATebzW4STJ0%2BuN1ZdXa2SkhKNHDnye61r3759mjdvnpKTk7Vy5UqvuW3btmnt2rX69NNP1bt3bz3wwAMaMmSIJKmurk5PP/20tm7dqvLycg0YMECLFy9Wjx49JElut1uLFy/WO%2B%2B8I5vNptTUVC1atEjt2rX7gXsNAABaE58HrF69etX7LcKQkBClpaVp4sSJjV7PunXrVFhYqJ49e9abO3TokObNm6fc3FxdffXV2rlzp%2B677z7t2LFD3bp10wsvvKAtW7Zo3bp16tq1q1auXKnMzExt2rRJQUFBWrRokc6ePautW7fq3LlzmjVrlnJycrRw4cIm7z8AAAh8Pg9YZr1be0hIiAoLC/XEE0/ozJkzXnMbNmxQamqqUlNTJUljx47V888/r82bN%2BtXv/qVCgoKNHXqVF166aWSpOzsbCUnJ%2BvAgQPq3r27du/erb/85S%2BKjo6WJN17772aNWuW5s2bpzZt2phSPwAACFw%2BD1gvvfRSo5cdN25cg3NTpkxpcK64uNgTrr4RHx8vh8Oh06dP68iRI4qPj/fMhYeHq2fPnnI4HKqoqFBwcLDi4uI88wkJCfr666919OhRr/GGlJWVyel0eo3Z7aGKiYm56Gu/j%2BBgm9dntG52e%2Bs8DjgPrEcPrEcPWh6fB6wFCxaorq6u3gPtQUFBXmNBQUHfGbC%2Bi9vtVmRkpNdYZGSkjhw5oq%2B%2B%2BkqGYVxw3uVyqWPHjgoPD/e6jfnNsi6Xq1HbLygoUG5urtdYZmamsrKyfsjuXFRERPtmWS/8S1RUmNUlWIrzwHr0wHr0oOXwecD6zW9%2Bo/Xr12vGjBmKi4uTYRj66KOPtG7dOt1xxx1KTk42ZTsX%2B43E75pv6m8zpqena9iwYV5jdnuoXK6qJq3324KDbYqIaK/y8mrV1taZum74H7OPL3/BeWA9emA9etAwq374tOQZrLy8PHXt2tUzduWVV6pHjx6aNm2atm7d2uRtREVFye12e4253W5FR0erY8eOstlsF5zv1KmToqOjVVlZqdraWgUHB3vmJKlTp06N2n5MTEy924FOZ4VqaprnoK%2BtrWu2dcN/tPZjgPPAevTAevSg5fD5zdrjx4/Xuz0nSRERESotLTVlG4mJiSoqKvIaczgcGjhwoEJCQtS3b18VFxd75srLy3XixAkNGDBA/fv3l2EYOnz4sNdrIyIi1Lt3b1PqAwAAgc3nASs2NlbLly/3ep6pvLxcK1as0I9//GNTtjFp0iS99dZb2rt3r86cOaPCwkIdP35cY8eOlSRlZGQoPz9fJSUlqqysVE5Ojvr376%2BkpCRFR0dr1KhRWrVqlU6dOqXPPvtMa9asUVpamux2n1/wAwAAfsjnieGRRx7RnDlzVFBQoLCwMNlsNlVWVqpdu3Zas2ZNo9eTlJQkSaqpqZEk7d69W9L5q039%2BvVTTk6Oli1bptLSUvXp00fPPvusunTpIun8m506nU7deeedqqqqUnJystdD6Y899pgeffRRDR8%2BXG3atNGtt96q7Oxss74EAAAgwAUZFvx9murqar3%2B%2Buv67LPPZBiGunbtquuuu04dOnTwdSk%2B43RWmL5Ou92mqKgwuVxV3HNvBqNXvWl1Cd/L9tnXWl2CJTgPrEcPrEcPGtalizXZwpJ7Xu3bt9fw4cP12Wefef48DQAAQKDw%2BTNYp0%2Bf1rx58zRo0CCNHj1a0vlnsO655x6Vl5f7uhwAAADT%2BTxgPfXUUzp06JBycnJks/1r87W1tcrJyfF1OQAAAKbzecDauXOn/vu//1s33XST593SIyIitGzZMu3atcvX5QAAAJjO5wGrqqpKvXr1qjceHR2tr7/%2B2tflAAAAmM7nAevHP/6x/v73v0vy/pM0O3bs0I9%2B9CNflwMAAGA6n/8W4c9%2B9jPdf//9uv3221VXV6fnnntORUVF2rlzpxYsWODrcgAAAEzn84CVnp4uu92u559/XsHBwXrmmWfUu3dv5eTk6KabbvJ1OQAAAKbzecA6deqUbr/9dt1%2B%2B%2B2%2B3jQAAIBP%2BPwZrOHDh8uCN48HAADwGZ8HrOTkZG3fvt3XmwUAAPAZn98ivOSSS/TEE08oLy9PP/7xj9WmTRuv%2BRUrVvi6JAAAAFP5PGAdOXJEP/nJTyRJLpfL15sHAABodj4LWNnZ2Vq5cqX%2B8Ic/eMbWrFmjzMxMX5UAAADgEz57BmvPnj31xvLy8ny1eQAAAJ/xWcC60G8O8tuEAAAgEPksYH3zh50vNgYAAODvfP42DQAAAIGOgAUAAGAyn/0W4blz5zRnzpyLjvE%2BWAAAwN/5LGBdccUVKisru%2BgYAtfoVW9aXQIAAD7hs4D17%2B9/BQAAEMh4BgsAAMBkBCwAAACTEbAAAABMRsACAAAwmc8ecveld999V3fffbfXmGEYOnfunPLz8zVlyhS1bdvWa/4///M/NXr0aElSfn6%2BXnjhBTmdTsXFxWnBggVKTEz0Wf0AAMC/BWTAGjx4sBwOh9fYM888o8OHD0uSYmNjL/jHp6Xzf5R69erV%2Bs1vfqO4uDjl5%2BdrxowZ2rVrl0JDQ5u9dgAA4P9axS3Cf/7zn3ruuef00EMPXXTZgoICTZgwQQMHDlS7du10zz33SJJee%2B215i4TAAAEiFYRsJ5%2B%2Bmndfvvt%2BtGPfiRJqqqqUmZmppKTk3Xdddfpueeek2EYkqTi4mLFx8d7Xmuz2dS/f/96V8QAAAAaEpC3CP/dp59%2Bql27dmnXrl2SpPDwcPXr10933XWXVq5cqXfeeUezZs1Shw4dlJaWJrfbrcjISK91REZGyuVyNXqbZWVlcjqdXmN2e6hiYmKavkP/JjjY5vUZrZvd3jqPA84D69ED69GDlifgA9YLL7ygkSNHqkuJFtBJAAAUwklEQVSXLpKkhIQEr3eVHzJkiCZPnqyNGzcqLS1NkjxXs36ogoIC5ebmeo1lZmYqKyurSettSERE%2B2ZZL/xLVFSY1SVYivPAevTAevSg5Qj4gLVz507NmzfvO5eJjY3Vzp07JUlRUVFyu91e8263W3379m30NtPT0zVs2DCvMbs9VC5XVaPX0RjBwTZFRLRXeXm1amvrTF03/I/Zx5e/4DywHj2wHj1omFU/fAZ0wDp06JBKS0t17bXXesa2b98ul8uln/3sZ56xo0ePqkePHpKkxMREFRcXa/z48ZKk2tpaHTx40HN1qzFiYmLq3Q50OitUU9M8B31tbV2zrRv%2Bo7UfA5wH1qMH1qMHLUdA36w9ePCgOnbsqPDwcM9YmzZt9OSTT%2BqNN97QuXPn9Oabb%2BrFF19URkaGJCkjI0MvvfSSPvjgA1VXV2vt2rVq27athg4datFeAAAAfxPQV7C%2B%2BOILz7NX3xgxYoQeeeQRPf744zp58qQ6d%2B6sRx55RCNHjpQkXX/99XrggQc0e/Zsffnll0pKSlJeXp7atWtnxS4AAAA/FGQ09YluNIrTWWH6Ou12m6KiwuRyVfnFJeHRq960uoSAtn32tRdfKAD523kQiOiB9ehBw7p06WDJdgP6FiEAAIAVCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYLGADVlxcnBITE5WUlOT5ePzxxyVJb7/9ttLS0nT55Zfrlltu0ebNm71em5%2Bfr1GjRunyyy9XRkaGioqKrNgFAADgp%2BxWF9CcduzYoe7du3uNlZWV6d5779WCBQs0ZswYvffee5o5c6Z69%2B6tpKQk7dmzR6tXr9ZvfvMbxcXFKT8/XzNmzNCuXbsUGhpq0Z4AAAB/ErBXsBqyZcsW9erVS2lpaQoJCVFKSoqGDRumDRs2SJIKCgo0YcIEDRw4UO3atdM999wjSXrttdesLBsAAPiRgA5YK1as0NChQ3XllVdq0aJFqqqqUnFxseLj472Wi4%2BP99wG/Pa8zWZT//795XA4fFo7AADwXwF7i/Cyyy5TSkqKnnzySX3yySeaPXu2lixZIrfbra5du3ot27FjR7lcLkmS2%2B1WZGSk13xkZKRnvjHKysrkdDq9xuz2UMXExPzAvbmw4GCb12e0bnZ76zwOOA%2BsRw%2BsRw9anoANWAUFBZ7/vvTSSzV37lzNnDlTV1xxxUVfaxhGk7edm5vrNZaZmamsrKwmrbchERHtm2W98C9RUWFWl2ApzgPr0QPr0YOWI2AD1rd1795dtbW1stlscrvdXnMul0vR0dGSpKioqHrzbrdbffv2bfS20tPTNWzYMK8xuz1ULlfVD6z%2BwoKDbYqIaK/y8mrV1taZum74H7OPL3/BeWA9emA9etAwq374DMiAdfDgQW3evFnz58/3jJWUlKht27ZKTU3VX/7yF6/li4qKNHDgQElSYmKiiouLNX78eElSbW2tDh48qLS0tEZvPyYmpt7tQKezQjU1zXPQ19bWNdu64T9a%2BzHAeWA9emA9etByBOTN2k6dOqmgoEB5eXk6e/asjh07pqefflrp6em67bbbVFpaqg0bNujMmTN6/fXX9frrr2vSpEmSpIyMDL300kv64IMPVF1drbVr16pt27YaOnSotTsFAAD8RkBeweratavy8vK0YsUKT0AaP368srOzFRISomeffVZLly7VkiVLFBsbq6eeeko//elPJUnXX3%2B9HnjgAc2ePVtffvmlkpKSlJeXp3bt2lm8VwAAwF8EGU19ohuN4nRWmL5Ou92mqKgwuVxVfnFJePSqN60uIaBtn32t1SVYwt/Og0BED6xHDxrWpUsHS7YbkLcIAQAArETAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQBG7BKS0uVmZmp5ORkpaSkaP78%2BSovL9enn36quLg4JSUleX389re/9bx227ZtGjNmjAYNGqQJEybojTfesHBPAACAv7FbXUBzmTFjhhITE7Vnzx5VVFQoMzNTTz75pGbOnClJcjgcF3zdoUOHNG/ePOXm5urqq6/Wzp07dd9992nHjh3q1q2bL3cBAAD4qYC8glVeXq7ExETNmTNHYWFh6tatm8aPH6/9%2B/df9LUbNmxQamqqUlNTFRISorFjx6pfv37avHmzDyoHAACBICCvYEVERGjZsmVeYydPnlRMTIzn3w899JDeeust1dTUaOLEicrKylKbNm1UXFys1NRUr9fGx8c3eMXrQsrKyuR0Or3G7PZQr%2B2bITjY5vUZrZvd3jqPA84D69ED69GDlicgA9a3ORwOPf/881q7dq3atm2rQYMG6cYbb9QTTzyhQ4cO6f7775fdbtesWbPkdrsVGRnp9frIyEgdOXKk0dsrKChQbm6u11hmZqaysrJM2Z9vi4ho3yzrhX%2BJigqzugRLcR5Yjx5Yjx60HAEfsN577z3NnDlTc%2BbMUUpKiiTpT3/6k2d%2BwIABmj59up599lnNmjVLkmQYRpO2mZ6ermHDhnmN2e2hcrmqmrTebwsOtikior3Ky6tVW1tn6rrhf8w%2BvvwF54H16IH16EHDrPrhM6AD1p49e/Tggw9q0aJFGjduXIPLxcbG6osvvpBhGIqKipLb7faad7vdio6ObvR2Y2Ji6t0OdDorVFPTPAd9bW1ds60b/qO1HwOcB9ajB9ajBy1HwN6sff/99zVv3jw9/fTTXuHq7bff1tq1a72WPXr0qGJjYxUUFKTExEQVFRV5zTscDg0cONAndQMAAP8XkAGrpqZGCxcu1Ny5czVkyBCvuQ4dOmjNmjXatGmTzp07J4fDod/%2B9rfKyMiQJE2aNElvvfWW9u7dqzNnzqiwsFDHjx/X2LFjrdgVAADgh4KMpj5w1ALt379fP//5z9W2bdt6czt27NDBgweVm5ur48ePq0OHDrrzzjv1y1/%2BUjbb%2Bby5a9curVixQqWlperTp48WLFigwYMHN6kmp7OiSa%2B/ELvdpqioMLlcVX5xSXj0qjetLiGgbZ99rdUlWMLfzoNARA%2BsRw8a1qVLB0u2G5DPYF155ZX66KOPGpyPjY3VjTfe2OD8yJEjNXLkyOYoDQAAtAIBeYsQAADASgQsAAAAkxGwAAAATEbAAgAAMFlAPuQOtEb%2B9FuarfU3HgG0HlzBAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMZre6AACtz%2BhVb1pdwveyffa1VpcAwM9wBQsAAMBkXMG6gNLSUi1ZskQHDhxQaGiobr75Zs2ZM0c2W8vLo1cu2GF1CQAA4FsIWBdw//33KyEhQbt379aXX36p6dOnq3PnzvrFL35hdWkAAMAPtLxLMhZzOBw6fPiw5s6dqw4dOqhXr16aOnWqCgoKrC4NAAD4Ca5gfUtxcbFiY2MVGRnpGUtISNCxY8dUWVmp8PDwi66jrKxMTqfTa8xuD1VMTIyptQYHk48BX/C3h/L9yStzr7O6hO/lxpx9Vpfwvfjb1zeQELC%2Bxe12KyIiwmvsm7DlcrkaFbAKCgqUm5vrNXbffffp/vvvN69QnQ9yd3X7WOnp6aaHNzROWVmZCgoK6IGF6IH1WlMP9j9xk9UlXFBr6oG/4BLIBRiG0aTXp6ena%2BPGjV4f6enpJlX3L06nU7m5ufWulsF36IH16IH16IH16EHLwxWsb4mOjpbb7fYac7vdCgoKUnR0dKPWERMTw08QAAC0YlzB%2BpbExESdPHlSp06d8ow5HA716dNHYWFhFlYGAAD8BQHrW%2BLj45WUlKQVK1aosrJSJSUleu6555SRkWF1aQAAwE8EL168eLHVRbQ01113nbZu3arHH39cL7/8stLS0jRt2jQFBQVZXVo9YWFhuuqqq7i6ZiF6YD16YD16YD160LIEGU19ohsAAABeuEUIAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyApafcDgcuvHGGzVp0qR6c2%2B//bbS0tJ0%2BeWX65ZbbtHmzZu95vPz8zVq1ChdfvnlysjIUFFRka/KDlilpaX61a9%2BpeTkZN1www166qmnVFdXZ3VZAWffvn1KSUlRdnZ2vblt27ZpzJgxGjRokCZMmKA33njDM1dXV6eVK1dq%2BPDhGjx4sKZNm6ZPPvnEl6UHjNLSUmVmZio5OVkpKSmaP3%2B%2BysvLJUmHDh3SHXfcoSuuuEIjR47U%2BvXrvV77XT1C4x0%2BfFh33XWXrrjiCqWkpGj27NlyOp2S%2BP7fohlo8TZt2mSkpqYa06ZNMyZOnOg19/nnnxuXXXaZsWHDBuP06dPGm2%2B%2BaQwYMMD48MMPDcMwjFdffdW48sorjQ8%2B%2BMCorq42nn32WePaa681qqqqrNiVgDF%2B/Hhj4cKFRnl5uXHs2DFj5MiRxvr1660uK6Dk5eUZI0eONCZPnmzMnj3ba%2B7gwYNGYmKisXfvXuP06dPGpk2bjIEDBxonT540DMMw8vPzjRtuuME4cuSIUVFRYTz22GPGmDFjjLq6Oit2xa/deuutxvz5843Kykrj5MmTxoQJE4xHHnnEqK6uNq677jpj9erVRlVVlVFUVGRcddVVxs6dOw3DuHiP0DhnzpwxrrnmGiM3N9c4c%2BaM8eWXXxp33HGHce%2B99/L9v4XjCpYfOHPmjAoKCjRw4MB6c1u2bFGvXr2UlpamkJAQpaSkaNiwYdqwYYMkqaCgQBMmTNDAgQPVrl073XPPPZKk1157zaf7EEgcDocOHz6suXPnqkOHDurVq5emTp2qgoICq0sLKCEhISosLFTPnj3rzW3YsEGpqalKTU1VSEiIxo4dq379%2Bnl%2Bei8oKNDUqVN16aWXKjw8XNnZ2SopKdGBAwd8vRt%2Brby8XImJiZozZ47CwsLUrVs3jR8/Xvv379fevXt17tw5zZw5U6GhoUpISNDEiRM958HFeoTGqa6uVnZ2tqZPn662bdsqOjpaN954oz7%2B%2BGO%2B/7dwBCw/MHHiRHXt2vWCc8XFxYqPj/cai4%2BP91wG/va8zWZT//795XA4mq/gAFdcXKzY2FhFRkZ6xhISEnTs2DFVVlZaWFlgmTJlijp06HDBuYaOe4fDodOnT%2BvIkSNe8%2BHh4erZsyfH/fcUERGhZcuWqXPnzp6xkydPKiYmRsXFxYqLi1NwcLBn7ru%2B93wzTw%2B%2Bn8jISE2cOFF2u12SdPToUf3lL3/R6NGj%2Bf7fwhGw/Jzb7VZERITXWMeOHeVyuTzz/x4EpPMn7Dfz%2BP4u9DX/5mvM19U3vuu4/uqrr2QYBsd9M3A4HHr%2B%2Bec1c%2BbMBr/3uN1u1dXV8b3HZKWlpUpMTNTNN9%2BspKQkZWVl8f2/hSNgtQCbNm1SXFzcBT82btzY5PUbhmFClfh3fE2td7Ee0CNzvffee5o2bZrmzJmjlJSUBpcLCgry/Dc9ME9sbKwcDod27Nih48eP66GHHmrU6%2BiBdexWFwDptttu02233faDXhsVFSW32%2B015nK5FB0d3eC82%2B1W3759f1ixUHR09AW/pkFBQZ6vO5pXQ8d1dHS0OnbsKJvNdsH5Tp06%2BbLMgLFnzx49%2BOCDWrRokcaNGyfp/Hlw/Phxr%2BXcbrfn6/9dPcIPExQUpF69eik7O1uTJ09Wamoq3/9bMK5g%2BbmkpKR6v3ZbVFTkeSA%2BMTFRxcXFnrna2lodPHjwgg/Mo3ESExN18uRJnTp1yjPmcDjUp08fhYWFWVhZ65GYmFjvuHc4HBo4cKBCQkLUt29fr%2BO%2BvLxcJ06c0IABA3xdqt97//33NW/ePD399NOecCWd78FHH32kmpoaz9g3PfhmvqEeofHefvttjRo1yuttYGy28//rHjBgAN//WzAClp8bM2aMSktLtWHDBp05c0avv/66Xn/9dc/7ZWVkZOill17SBx98oOrqaq1du1Zt27bV0KFDrS3cj8XHxyspKUkrVqxQZWWlSkpK9NxzzykjI8Pq0lqNSZMm6a233tLevXt15swZFRYW6vjx4xo7dqyk88d9fn6%2BSkpKVFlZqZycHPXv319JSUkWV%2B5fampqtHDhQs2dO1dDhgzxmktNTVV4eLjWrl2r6upqHThwQIWFhZ7z4GI9QuMkJiaqsrJSTz31lKqrq3Xq1CmtXr1aV155pTIyMvj%2B34IFGdygbfFGjRqlf/7zn6qtrVVdXZ3atGkjSdqxY4diY2P17rvvaunSpSopKVFsbKzmzJmjkSNHel7/v//7v8rLy9OXX36ppKQkLV68WP369bNqdwLCZ599pkWLFumdd95ReHi4Jk%2BerPvuu8/r%2BRM0zTdh6JsrJN/8FtU3vwG1a9curVixQqWlperTp48WLFigwYMHSzr/3Mnq1av1pz/9SVVVVUpOTtZjjz2mbt26WbAn/mv//v36%2Bc9/rrZt29ab27Fjh6qqqvToo4%2BqqKhInTt31i9/%2BUv97Gc/8yzzXT1C43300UdaunSpPvzwQ4WGhurqq6/W/Pnz1bVrV77/t2AELAAAAJNxixAAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBk/w/YqyTdUZd3AQAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common769797248661251286”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>542</td> <td class=”number”>16.9%</td> <td>

<div class=”bar” style=”width:26%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-25.0</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.333333333</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-20.0</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.333333333</td> <td class=”number”>51</td> <td class=”number”>1.6%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-16.666666667</td> <td class=”number”>50</td> <td class=”number”>1.6%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-14.285714286</td> <td class=”number”>49</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-12.5</td> <td class=”number”>46</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>42</td> <td class=”number”>1.3%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (727)</td> <td class=”number”>2094</td> <td class=”number”>65.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme769797248661251286”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>11</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-80.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-66.666666667</td> <td class=”number”>11</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-60.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-57.142857143</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>166.666666667</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>9</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>250.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>300.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:45%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>350.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_CSA_07_12”>PCH_CSA_07_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>235</td>

</tr> <tr>

<th>Unique (%)</th> <td>7.3%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>27.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>866</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>11.355</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>1700</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>6.8%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram7373189057644289415”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives7373189057644289415,#minihistogram7373189057644289415”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives7373189057644289415”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles7373189057644289415”

aria-controls=”quantiles7373189057644289415” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram7373189057644289415” aria-controls=”histogram7373189057644289415”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common7373189057644289415” aria-controls=”common7373189057644289415”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme7373189057644289415” aria-controls=”extreme7373189057644289415”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles7373189057644289415”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-100</td>

</tr> <tr>

<th>Q1</th> <td>-83.333</td>

</tr> <tr>

<th>Median</th> <td>-33.333</td>

</tr> <tr>

<th>Q3</th> <td>50</td>

</tr> <tr>

<th>95-th percentile</th> <td>300</td>

</tr> <tr>

<th>Maximum</th> <td>1700</td>

</tr> <tr>

<th>Range</th> <td>1800</td>

</tr> <tr>

<th>Interquartile range</th> <td>133.33</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>153.55</td>

</tr> <tr>

<th>Coef of variation</th> <td>13.522</td>

</tr> <tr>

<th>Kurtosis</th> <td>21.862</td>

</tr> <tr>

<th>Mean</th> <td>11.355</td>

</tr> <tr>

<th>MAD</th> <td>97.209</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>3.6487</td>

</tr> <tr>

<th>Sum</th> <td>26617</td>

</tr> <tr>

<th>Variance</th> <td>23578</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram7373189057644289415”>

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fT9ewYQN3L6HKQkLquXsJLmW1vJL1Mlstr0RmK6jNeb2yYFVUVOjxxx/X119/rbfffltRUVGX3T4yMlL5%2BfkKDw%2BXdP46rgYN/r8M/Pjjj2rUqFGV95%2BcnKxevXo5jfn711dhYclVpPA%2BnpDfz89XISH1VFR0WuXlFe5eTo2zWl7JepmtllcisxUyX01ed/1y75UF69lnn9W///1vvf322woLC3Oa%2B%2BMf/6hOnTrp5ptvdozl5eVpwIABioqKUmhoqHJzcxUZGSlJ%2Bte//qVz584pNja2yvuPiIiodDqwoOCUysq8/0l/OZ6Uv7y8wqPWW11WyytZL7PV8kpktoLanLf2nrz8lb788kutXbtWWVlZlcqVdP7o1Ny5c3Xw4EGdPXtWy5Yt0%2BHDhzVkyBD5%2Bflp%2BPDheuWVV3Ts2DEVFhbq%2BeefV9%2B%2BfR1v4wAAAHAlHnkEKy4uTtL5VwhK0ubNmyVJNptNq1ev1qlTp9SzZ0%2Bn28THx2vZsmWaMmWKpPPXXtntdrVp00Z/%2BtOf1KxZM0nSxIkTVVJSorvuuktlZWXq2bOn5syZ46JkAADAG/gY1b2iG1VSUHCqRu739hd21Mj91oQNk29x9xKuyN/fVw0bNlBhYUmtPexsJqvllayX2Wp5JTJbIfPV5G3S5OJv61TTvO4UIQAAgLtRsAAAAExGwQIAADCZywtWr169lJmZqWPHjrl61wAAAC7h8oJ19913a/369erTp48efPBBbdq0yfFqQAAAAG/g8oKVkpKi9evX691331Xbtm317LPPKjExUc8995wOHTrk6uUAAACYzm3XYMXExGj69OnasmWLZs6cqXfffVcDBgzQmDFj9PXXX7trWQAAANXmtoJVWlqq9evX6w9/%2BIOmT5%2Bupk2b6vHHH1f79u01evRorVu3zl1LAwAAqBaXv5N7Xl6eVq1apffee08lJSXq37%2B/3nzzTf3ud79zbBMfH685c%2BbozjvvdPXyAAAAqs3lBWvgwIFq1aqVxo4dq8GDB1/07wUmJibq5MmTrl4aAACAKVxesJYvX64bb7zxitt99dVXLlgNAACA%2BVx%2BDVZ0dLTGjRvn%2BAPNkvSnP/1Jf/jDH2S32129HAAAANO5vGClpaXp1KlTatOmjWOsR48eqqio0IIFC1y9HAAAANO5/BTh9u3btW7dOjVs2NAx1rJlS6Wnp%2BuOO%2B5w9XIAAABM5/IjWGfOnFFgYGDlhfj66vTp065eDgAAgOlcXrDi4%2BO1YMEC/fjjj46x77//XnPnznV6qwYAAABP5fJThDNnztQDDzygm2%2B%2BWUFBQaqoqFBJSYmioqL05z//2dXLAQAAMJ3LC1ZUVJQ%2B%2BOADffbZZzp8%2BLB8fX3VqlUrdevWTX5%2Bfq5eDgAAgOlcXrAkKSAgQH369HHHrgEAAGqcywvWkSNHlJGRoX//%2B986c%2BZMpfmPP/7Y1UsCAAAwlVuuwcrPz1e3bt1Uv359V%2B8eAACgxrm8YO3Zs0cff/yxwsPDXb1rAAAAl3D52zQ0atSII1cAAMCrubxgjR07VpmZmTIMw9W7BgAAcAmXnyL87LPP9I9//ENr1qxR8%2BbN5evr3PHeeecdVy8JAADAVC4vWEFBQerevburdwsAAOAyLi9YaWlprt4lAACAS7n8GixJOnjwoF5%2B%2BWU9/vjjjrF//vOf7lgKAACA6VxesHbu3KlBgwZp06ZNysnJkXT%2BzUdHjRrFm4wCAACv4PKCtWjRIk2bNk3r1q2Tj4%2BPpPN/n3DBggVavHixq5cDAABgOpcXrH/9618aOXKkJDkKliTddtttysvLu6r72rZtmxISEpSamlppbv369brzzjvVqVMnDR06VNu3b3fMVVRUaNGiRerdu7fi4%2BM1ZswYHTlyxDFvt9s1efJkJSQkqFu3bpo1a9ZF/6wPAADAxbi8YAUHB1%2B0rOTn5ysgIKDK9/Pqq69q/vz5atGiRaW5ffv2afr06Zo6dao%2B//xzjR49Wg8//LCOHz8uSVqxYoXWrVunrKwsbdmyRS1btlRKSorjvblmz56t06dPKycnR6tXr1ZeXp7S09N/ZWIAAGA1Li9YnTt31rPPPqvi4mLH2KFDhzR9%2BnTdfPPNVb6fwMBArVq16qIFa%2BXKlUpMTFRiYqICAwM1aNAgtWvXTmvXrpUkZWdna/To0WrdurWCgoKUmpqqvLw8ffXVVzpx4oQ2b96s1NRUhYeHq2nTppowYYJWr16t0tLS6n8DAACA13P52zQ8/vjjuu%2B%2B%2B9S1a1eVl5erc%2BfOOn36tNq2basFCxZU%2BX5GjRp1ybnc3FwlJiY6jXXo0EE2m01nzpzRgQMH1KFDB8dcUFCQWrRoIZvNplOnTsnPz0/R0dGO%2BZiYGP300086ePCg0/il5Ofnq6CgwGnM37%2B%2BIiIiqhrPK/n7u%2BVFq1fFz8/X6bO3s1peyXqZrZZXIrMVeEJelxesZs2aKScnR59%2B%2BqkOHTqkunXrqlWrVrrlllucrsmqDrvdrtDQUKex0NBQHThwQD/%2B%2BKMMw7jofGFhocLCwhQUFOS0lgvbFhYWVmn/2dnZyszMdBpLSUnRxIkTf00cr9GwYQN3L6HKQkLquXsJLmW1vJL1Mlstr0RmK6jNeV1esCSpTp066tOnT43u40p/6/By89X9O4nJycnq1auX05i/f30VFpZU6349nSfk9/PzVUhIPRUVnVZ5eYW7l1PjrJZXsl5mq%2BWVyGyFzFeT112/3Lu8YPXq1euyR6rMeC%2Bshg0bym63O43Z7XaFh4crLCxMvr6%2BF51v1KiRwsPDVVxcrPLycvn5%2BTnmJKlRo0ZV2n9ERESl04EFBadUVub9T/rL8aT85eUVHrXe6rJaXsl6ma2WVyKzFdTmvC4vWAMGDHAqWOXl5Tp06JBsNpvuu%2B8%2BU/YRGxurPXv2OI3ZbDYNHDhQgYGBatu2rXJzc3XjjTdKkoqKinT48GF17NhRkZGRMgxD%2B/fvV0xMjOO2ISEhatWqlSnrAwAA3s3lBWvq1KkXHd%2B4caP%2B9re/mbKP4cOHKykpSVu3btXNN9%2BsdevW6dtvv9WgQYMkSSNHjlRWVpa6d%2B%2Bupk2bKj09Xe3bt1dcXJwkqX///nrhhRe0cOFCnTt3TosXL1ZSUpL8/d1yRhUAAHiYWtMY%2BvTpoyeffFJPPvlklba/UIbKysokSZs3b5Z0/mhTu3btlJ6errS0NB09elRt2rTR0qVL1aRJE0nSiBEjVFBQoHvvvVclJSXq2rWr00XpTz/9tJ566in17t1bderU0R133HHRNzMFAAC4mFpTsPbu3XtVF5fbbLbLzvfr10/9%2BvW76JyPj48mTpx4yVf1BQcH6/nnn6/yWgAAAH7O5QVrxIgRlcZOnz6tvLy8SxYiAAAAT%2BLygtWyZctKryIMDAxUUlKShg0b5urlAAAAmM7lBetq3q0dAADAE7m8YL333ntV3nbw4ME1uBIAAICa4fKCNWvWLFVUVFS6oN3Hx8dpzMfHh4IFAAA8kssL1muvvaZly5Zp3Lhxio6OlmEY%2Buabb/Tqq6/q97//vbp27erqJQEAAJjKLddgZWVlqWnTpo6xLl26KCoqSmPGjFFOTo6rlwQAAGAqX1fv8Ntvv1VoaGil8ZCQEB09etTVywEAADCdywtWZGSkFixYoMLCQsdYUVGRMjIy9Jvf/MbVywEAADCdy08Rzpw5U1OmTFF2drYaNGggX19fFRcXq27dulq8eLGrlwMAAGA6lxesbt26aevWrfr00091/PhxGYahpk2b6tZbb1VwcLCrlwMAAGA6t/wtwnr16ql37946fvy4oqKi3LEEAACAGuPya7DOnDmj6dOnq1OnTrr99tslnb8G68EHH1RRUZGrlwMAAGA6lxes5557Tvv27VN6erp8ff9/9%2BXl5UpPT3f1cgAAAEzn8oK1ceNGvfTSS7rtttscf/Q5JCREaWlp2rRpk6uXAwAAYDqXF6ySkhK1bNmy0nh4eLh%2B%2BuknVy8HAADAdC4vWL/5zW/0t7/9TZKc/vbghx9%2BqGuvvdbVywEAADCdy19FeM899%2BiRRx7R3XffrYqKCr3xxhvas2ePNm7cqFmzZrl6OQAAAKZzecFKTk6Wv7%2B/3nrrLfn5%2BemVV15Rq1atlJ6erttuu83VywEAADCdywvWyZMndffdd%2Bvuu%2B929a4BAABcwuXXYPXu3dvp2isAAABv4/KC1bVrV23YsMHVuwUAAHAZl58ivOaaa/TMM88oKytLv/nNb1SnTh2n%2BYyMDFcvCQAAwFQuL1gHDhzQb3/7W0lSYWGhq3cPAABQ41xWsFJTU7Vo0SL9%2Bc9/dowtXrxYKSkprloCAACAS7jsGqxPPvmk0lhWVpardg8AAOAyLitYF3vlIK8mBAAA3shlBevCH3a%2B0hgAAICnc/nbNAAAAHg7l7%2BK0BV27dqlBx54wGnMMAyVlpZq%2BfLlGjVqlAICApzm//u//1u33367JGn58uVasWKFCgoKFB0drVmzZik2NtZl6wcAAJ7NZQWrtLRUU6ZMueKYGe%2BDFR8fL5vN5jT2yiuvaP/%2B/ZKkyMjIi150L52/GP/ll1/Wa6%2B9pujoaC1fvlzjxo3Tpk2bVL9%2B/WqvDQAAeD%2BXnSL83e9%2Bp/z8fKePi43VhP/85z9644039Nhjj11x2%2BzsbA0dOlTXX3%2B96tatqwcffFCStGXLlhpZGwAA8D4uO4L18/e/crUXX3xRd999t6699lodOXJEJSUlSklJ0RdffKGAgAA98MADGj16tHx8fJSbm6sBAwY4buvr66v27dvLZrNp4MCBbssAAAA8h1deg/Vz3333nTZt2qRNmzZJkoKCgtSuXTvdd999WrRokf7%2B979r0qRJCg4OVlJSkux2u0JDQ53uIzQ09KredT4/P18FBQVOY/7%2B9RUREVH9QB7M37/2v6bCz8/X6bO3s1peyXqZrZZXIrMVeEJery9YK1asUL9%2B/dSkSRNJUkxMjNPRtG7dumnEiBFas2aNkpKSJFX//bmys7OVmZnpNJaSkqKJEydW6349XcOGDdy9hCoLCann7iW4lNXyStbLbLW8EpmtoDbn9fqCtXHjRk2fPv2y20RGRmrjxo2SpIYNG8putzvN2%2B12tW3btsr7TE5OVq9evZzG/P3rq7CwpMr34Y08Ib%2Bfn69CQuqpqOi0yssr3L2cGme1vJL1Mlstr0RmK2S%2Bmrzu%2BuXeqwvWvn37dPToUd1yyy2OsQ0bNqiwsFD33HOPY%2BzgwYOKioqSJMXGxio3N1dDhgyRJJWXl2vv3r2Oo1tVERERUel0YEHBKZWVef%2BT/nI8KX95eYVHrbe6rJZXsl5mq%2BWVyGwFtTlv7T15aYK9e/cqLCxMQUFBjrE6depo4cKF2r59u0pLS7Vjxw6tXr1aI0eOlCSNHDlS7733nnbv3q3Tp09ryZIlCggIUI8ePdyUAgAAeBqvPoJ14sQJx7VXF/Tp00czZ87UvHnzdOzYMTVu3FgzZ85Uv379JEndu3fXo48%2BqsmTJ%2BuHH35QXFycsrKyVLduXXdEAAAAHsjH4C8uu0RBwakaud/bX9hRI/dbEzZMvuXKG7mZv7%2BvGjZsoMLCklp72NlMVssrWS%2Bz1fJKZLZC5qvJ26RJsItW5cyrTxECAAC4AwULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGReW7Cio6MVGxuruLg4x8e8efMkSTt37lRSUpI6d%2B6sgQMHau3atU63Xb58ufr376/OnTtr5MiR2rNnjzsiAAAAD%2BXv7gXUpA8//FDNmzd3GsvPz9eECRM0a9Ys3Xnnnfryyy81fvx4tWrVSnFxcfrkk0/08ssv67XXXlN0dLSWL1%2BucePGadOmTapfv76bkgAAAE/itUewLmXdunVq2bKlkpKSFBgYqISEBPXq1UsrV66UJGVnZ2vo0KG6/vrrVbduXT344IOSpC1btrhz2QAAwIN49RGsjIwM/fOf/1RxcbFuv/12zZgxQ7m5uerQoYPTdh06dNCGDRskSbm5uRowYIBjztfXV%2B3bt5fNZtPAgQOrtN/8/HwVFBQ4jfn711dEREQ1E3k2f//a3%2Bf9/HydPns7q%2BWVrJfuhq%2BYAAAW10lEQVTZanklMluBJ%2BT12oJ1ww03KCEhQQsXLtSRI0c0efJkzZ07V3a7XU2bNnXaNiwsTIWFhZIku92u0NBQp/nQ0FDHfFVkZ2crMzPTaSwlJUUTJ078lWm8Q8OGDdy9hCoLCann7iW4lNXyStbLbLW8EpmtoDbn9dqClZ2d7fjv1q1ba%2BrUqRo/frx%2B97vfXfG2hmFUa9/Jycnq1auX05i/f30VFpZU6349nSfk9/PzVUhIPRUVnVZ5eYW7l1PjrJZXsl5mq%2BWVyGyFzFeT112/3Httwfql5s2bq7y8XL6%2BvrLb7U5zhYWFCg8PlyQ1bNiw0rzdblfbtm2rvK%2BIiIhKpwMLCk6prMz7n/SX40n5y8srPGq91WW1vJL1Mlstr0RmK6jNeWvvyctq2Lt3rxYsWOA0lpeXp4CAACUmJlZ624U9e/bo%2BuuvlyTFxsYqNzfXMVdeXq69e/c65gEAAK7EKwtWo0aNlJ2draysLJ07d06HDh3Siy%2B%2BqOTkZN111106evSoVq5cqbNnz%2BrTTz/Vp59%2BquHDh0uSRo4cqffee0%2B7d%2B/W6dOntWTJEgUEBKhHjx7uDQUAADyGV54ibNq0qbKyspSRkeEoSEOGDFFqaqoCAwO1dOlSzZ8/X3PnzlVkZKSee%2B45XXfddZKk7t2769FHH9XkyZP1ww8/KC4uTllZWapbt66bUwEAAE/hlQVLkuLj4/XOO%2B9ccu7999%2B/5G3vuece3XPPPTW1NAAA4OW88hQhAACAO1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABM5u/uBcA6bn9hh7uXcFU2TL7F3UsAAHgojmABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDKvLVhHjx5VSkqKunbtqoSEBM2YMUNFRUX67rvvFB0drbi4OKeP119/3XHb9evX684771SnTp00dOhQbd%2B%2B3Y1JAACAp/Ha98EaN26cYmNj9cknn%2BjUqVNKSUnRwoULNX78eEmSzWa76O327dun6dOnKzMzUzfddJM2btyohx9%2BWB9%2B%2BKGaNWvmyggAAMBDeeURrKKiIsXGxmrKlClq0KCBmjVrpiFDhuiLL7644m1XrlypxMREJSYmKjAwUIMGDVK7du20du1aF6wcAAB4A68sWCEhIUpLS1Pjxo0dY8eOHVNERITj68cee0zdunXTTTfdpIyMDJWWlkqScnNz1aFDB6f769ChwyWPeAEAAPyS154i/Dmbzaa33npLS5YsUUBAgDp16qS%2BffvqmWee0b59%2B/TII4/I399fkyZNkt1uV2hoqNPtQ0NDdeDAgSrvLz8/XwUFBU5j/v71nQoeaj9/f6/8/cOJn5%2Bv02crsFpmq%2BWVyGwFnpDX6wvWl19%2BqfHjx2vKlClKSEiQJL3zzjuO%2BY4dO2rs2LFaunSpJk2aJEkyDKNa%2B8zOzlZmZqbTWEpKiiZOnFit%2B4VrNWzYwN1LcJmQkHruXoLLWS2z1fJKZLaC2pzXqwvWJ598omnTpmn27NkaPHjwJbeLjIzUiRMnZBiGGjZsKLvd7jRvt9sVHh5e5f0mJyerV69eTmP%2B/vVVWFhydQHgVlZ4vPz8fBUSUk9FRadVXl7h7uW4hNUyWy2vRGYrZL6avO76ZdlrC9Y//vEPTZ8%2BXS%2B%2B%2BKK6devmGN%2B5c6d2797teDWhJB08eFCRkZHy8fFRbGys9uzZ43RfNptNAwcOrPK%2BIyIiKp0OLCg4pbIy73/SexMrPV7l5RWWyitZL7PV8kpktoLanLf2nryshrKyMj3xxBOaOnWqU7mSpODgYC1evFjvv/%2B%2BSktLZbPZ9Prrr2vkyJGSpOHDh%2Buvf/2rtm7dqrNnz2rVqlX69ttvNWjQIHdEAQAAHsgrj2Dt3r1beXl5mj9/vubPn%2B809%2BGHH2rRokXKzMzUk08%2BqeDgYN1777267777JEnt2rVTenq60tLSdPToUbVp00ZLly5VkyZN3BEFAAB4IK8sWF26dNE333xzyfnIyEj17dv3kvP9%2BvVTv379amJpAADAArzyFCEAAIA7UbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJP5u3sBQG11%2Bws73L2Eq7Jh8i3uXgIA4P9wBAsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk/E2DYCX8KS3leAtJQB4O45gAQAAmIyCdRFHjx7VQw89pK5du6pnz5567rnnVFFR4e5lAQAAD8Epwot45JFHFBMTo82bN%2BuHH37Q2LFj1bhxY91///3uXhoAAPAAHMH6BZvNpv3792vq1KkKDg5Wy5YtNXr0aGVnZ7t7aQAAwENwBOsXcnNzFRkZqdDQUMdYTEyMDh06pOLiYgUFBV3xPvLz81VQUOA05u9fXxEREaavF/BEnnRBvif6aOqtjv/28/N1%2BmwFZPZ%2BnpCXgvULdrtdISEhTmMXylZhYWGVClZ2drYyMzOdxh5%2B%2BGE98sgj5i30/3zxzG2m3%2BcF%2Bfn5ys7OVnJysmXKodUyWy2vZL3M%2Bfn5evPN1yyTVyKzFTJ7Qt7aW/3cyDCMat0%2BOTlZa9ascfpITk42aXWuU1BQoMzMzEpH47yZ1TJbLa9kvcxWyyuR2Qo8IS9HsH4hPDxcdrvdacxut8vHx0fh4eFVuo%2BIiIha26gBAEDN4wjWL8TGxurYsWM6efKkY8xms6lNmzZq0KCBG1cGAAA8BQXrFzp06KC4uDhlZGSouLhYeXl5euONNzRy5Eh3Lw0AAHgIvzlz5sxx9yJqm1tvvVU5OTmaN2%2BePvjgAyUlJWnMmDHy8fFx99JcrkGDBrrxxhstdfTOapmtlleyXmar5ZXIbAW1Pa%2BPUd0rugEAAOCEU4QAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYOGijh49qoceekhdu3ZVz5499dxzz6miosLdy6qWo0ePKiUlRV27dlVCQoJmzJihoqIifffdd4qOjlZcXJzTx%2Buvv%2B647fr163XnnXeqU6dOGjp0qLZv3%2B7GJFUTHR2t2NhYp0zz5s2TJO3cuVNJSUnq3LmzBg4cqLVr1zrddvny5erfv786d%2B6skSNHas%2BePe6IcFV27dpV6TGMjY1VdHS0/va3v130Md6wYYPj9p6Sedu2bUpISFBqamqlucs9TysqKrRo0SL17t1b8fHxGjNmjI4cOeKYt9vtmjx5shISEtStWzfNmjVLZ86ccUmmK7lc5k2bNmnQoEHq1KmT%2Bvfvr3fffdcx9/LLL6t9%2B/aVHvcTJ05Iks6ePasnn3xS3bt3V9euXTVx4kQVFha6LNelXCrvmjVrdN1111XK8/XXX0vyzsf4iSeeqJS3Q4cOevzxxyVJM2bMcPwN4QsfXbp0cdzerZkN4CKGDBliPPHEE0ZRUZFx6NAho1%2B/fsayZcvcvaxqueOOO4wZM2YYxcXFxrFjx4yhQ4caM2fONI4cOWK0a9fukrfbu3evERsba2zdutU4c%2BaM8f777xvXX3%2B9cezYMReu/uq1a9fOOHLkSKXx77//3rjhhhuMlStXGmfOnDF27NhhdOzY0fj6668NwzCMjz/%2B2OjSpYuxe/du4/Tp08bSpUuNW265xSgpKXF1hGpbsmSJMWnSJOPzzz83evbsecntPCVzVlaW0a9fP2PEiBHG5MmTneau9Dxdvny50bNnT%2BPAgQPGqVOnjKefftq48847jYqKCsMwDOPhhx82HnroIeOHH34wjh8/biQnJxvz5s1zecZfulzmr776yoiLizM%2B%2Bugjo7S01Ni6dasRExNj7Nq1yzAMw3jppZeM6dOnX/K%2B09LSjKFDhxr/%2Bc9/jMLCQuPhhx82xo4dW6N5ruRyeVevXm38/ve/v%2BRtvfEx/qXS0lJj4MCBxtatWw3DMIzp06cbL7300iW3d2dmjmChEpvNpv3792vq1KkKDg5Wy5YtNXr0aGVnZ7t7ab9aUVGRYmNjNWXKFDVo0EDNmjXTkCFD9MUXX1zxtitXrlRiYqISExMVGBioQYMGqV27dpWO%2BniKdevWqWXLlkpKSlJgYKASEhLUq1cvrVy5UpKUnZ2toUOH6vrrr1fdunX14IMPSpK2bNnizmVftf/85z9644039Nhjj11xW0/JHBgYqFWrVqlFixaV5q70PM3Oztbo0aPVunVrBQUFKTU1VXl5efrqq6904sQJbd68WampqQoPD1fTpk01YcIErV69WqWlpa6O6eRyme12u8aOHas%2BffrI399fiYmJateuXZV%2BrsvKyrRq1SpNmDBB11xzjcLCwjR58mRt3bpV33//fU1EqZLL5b0Sb3yMf%2BnNN9/Utddeq8TExCtu6%2B7MFCxUkpubq8jISIWGhjrGYmJidOjQIRUXF7txZb9eSEiI0tLS1LhxY8fYsWPHFBER4fj6scceU7du3XTTTTcpIyPD8QOYm5urDh06ON1fhw4dZLPZXLP4asjIyFCPHj3UpUsXzZ49WyUlJZfMc%2BGU2C/nfX191b59e4/I%2B3Mvvvii7r77bl177bWSpJKSEscp4ltvvVVvvPGGjP/7W/eeknnUqFEKDg6%2B6NzlnqdnzpzRgQMHnOaDgoLUokUL2Ww27du3T35%2BfoqOjnbMx8TE6KefftLBgwdrJkwVXS5z9%2B7dlZKS4vi6rKxMBQUFatq0qWPsm2%2B%2B0YgRIxynwy%2BcNj18%2BLBOnTqlmJgYx7atW7dW3bp1lZubW0NpruxyeaXz/9%2B6//77FR8fr969e%2Bv999%2BXJK99jH%2BuqKhIr7zyiqZNm%2BY0/vnnn2vw4MHq1KmTkpKSHP8vc3dmChYqsdvtCgkJcRq7ULZqw/UJZrDZbHrrrbc0fvx4BQQEqFOnTurbt6%2B2bNmirKwsrV27Vn/84x8lnf9%2B/LxsSue/H7X9e3HDDTcoISFBmzZtUnZ2tnbv3q25c%2Bde9PENCwtz5PHUvD/33XffadOmTbr//vslnf%2BHpl27drrvvvu0bds2paWlKTMzU6tXr5bkHZkvl%2BHHH3%2BUYRiXnLfb7QoKCpKPj4/TnORZP/Pp6emqX7%2B%2BBgwYIElq1qyZoqKitHDhQu3YsUPDhg3TuHHjdPDgQdntdkmq9LMQEhJSazOHh4erZcuWmjZtmnbs2KFHH31UM2fO1M6dOy3xGL/11luKj49X27ZtHWNRUVFq0aKFli5dqm3btqlLly564IEHakVmChYu6sJv9t7oyy%2B/1JgxYzRlyhQlJCQoIiJC77zzjvr27as6deqoY8eOGjt2rNasWeO4jSd%2BP7KzszVs2DAFBASodevWmjp1qnJycqp0aNwT8/7cihUr1K9fPzVp0kTS%2Bd9a//znP%2BvGG29UQECAunXrphEjRnj8Y/xLV8pwuXlPzm8Yhp577jnl5ORoyZIlCgwMlCQNGzZML730klq0aKF69epp9OjRat%2B%2BvdPpfU/K3aNHD7322mvq0KGDAgICNHDgQPXt27fKz2NPyvpL5eXlWrFihUaNGuU0npKSomeffVZNmzZVUFCQpk2bpoCAAG3evFmSezNTsFBJeHi447e7C%2Bx2u3x8fBQeHu6mVZnjk08%2B0UMPPaSZM2dW%2BkH9ucjISJ04cUKGYahhw4YX/X542veiefPmKi8vl6%2Bvb6U8hYWFjjzekHfjxo3q1avXZbeJjIxUfn6%2BJO/IfLkMYWFhF33c7Xa7GjVqpPDwcBUXF6u8vNxpTpIaNWpU84uvhoqKCs2YMUOffPKJ3n77bf32t7%2B97PYXHvcLj%2B0vvyc//vhjrc/8cxfyePNjLJ1/lfC5c%2BecXiF4MX5%2Bfrrmmmscj7E7M1OwUElsbKyOHTumkydPOsZsNpvatGmjBg0auHFl1fOPf/xD06dP14svvqjBgwc7xnfu3KklS5Y4bXvw4EFFRkbKx8dHsbGxlV6yb7PZdP3117tk3b/G3r17tWDBAqexvLw8BQQEKDExsVKePXv2OPLExsY6XYNSXl6uvXv31uq8P7dv3z4dPXpUt9xyi2Nsw4YN%2Bstf/uK03cGDBxUVFSXJ8zNLuuzzNDAwUG3btnXKWFRUpMOHD6tjx45q3769DMPQ/v37nW4bEhKiVq1auSzDr/Hss8/q3//%2Bt95%2B%2B23H43nBH//4R%2B3cudNpLC8vT1FRUYqKilJoaKjT9%2BRf//qXzp07p9jYWJes/Wq9/fbbWr9%2BvdPYhTze/BhL0scff6ybbrpJ/v7%2BjjHDMJSWluaU6dy5czp8%2BLCioqLcnpmChUouvKdIRkaGiouLlZeXpzfeeEMjR45099J%2BtbKyMj3xxBOaOnWqunXr5jQXHBysxYsX6/3331dpaalsNptef/11R97hw4frr3/9q7Zu3aqzZ89q1apV%2BvbbbzVo0CB3RKmSRo0aKTs7W1lZWTp37pwOHTqkF198UcnJybrrrrt09OhRrVy5UmfPntWnn36qTz/9VMOHD5ckjRw5Uu%2B99552796t06dPa8mSJQoICFCPHj3cG6qK9u7dq7CwMAUFBTnG6tSpo4ULF2r79u0qLS3Vjh07tHr1asdj7OmZpSs/T0eOHKnly5crLy9PxcXFSk9Pd7xHVHh4uPr3768XXnhBJ0%2Be1PHjx7V48WIlJSU5/YNW23z55Zdau3atsrKyFBYWVmnebrdr7ty5OnjwoM6ePatly5bp8OHDGjJkiPz8/DR8%2BHC98sorOnbsmAoLC/X888%2Brb9%2B%2BTi%2BGqU3OnTunefPmyWazqbS0VDk5Ofrss880YsQISd75GF%2Bwb98%2BNW/e3GnMx8dH3333nebOnavvv/9eJSUlSk9PV506ddSnTx%2B3Z/YxPPmkLGrM8ePHNXv2bP39739XUFCQRowYoYcfftjpYkFP8sUXX%2Bi//uu/FBAQUGnuww8/1N69e5WZmalvv/1WwcHBuvfee/WHP/xBvr7nfwfZtGmTMjIydPToUbVp00azZs1SfHy8q2NclV27dikjI0PffPONAgICNGTIEKWmpiowMFC7du3S/PnzlZeXp8jISE2ZMkX9%2BvVz3PYvf/mLsrKy9MMPPyguLk5z5sxRu3bt3Jim6pYuXap169YpJyfHaTw7O1vLli3TsWPH1LhxY40fP17Dhg1zzHtC5ri4OEnnf2GQ5PhH4sKrHS/3PDUMQy%2B//LLeeecdlZSUqGvXrnr66afVrFkzSdKpU6f01FNPacuWLapTp47uuOMOzZgx46I/M650ucwzZ87U//zP/1T6xzI%2BPl7Lli3T2bNnlZGRoQ8//FB2u11t2rTR7Nmz1alTJ0nnC0taWpo%2B%2BOADlZWVqWfPnpozZ06VXtFWUy6X1zAMLVmyRKtWrVJBQYGaN2%2Buxx57TD179pTknY/xBf3799fw4cM1ZswYp9va7XYtXLhQn332mYqLi9WxY0fNmTNHrVu3luTezBQsAAAAk3GKEAAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGT/C6GiHzyIVGPpAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common7373189057644289415”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>535</td> <td class=”number”>16.7%</td> <td>

<div class=”bar” style=”width:56%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>219</td> <td class=”number”>6.8%</td> <td>

<div class=”bar” style=”width:23%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>146</td> <td class=”number”>4.5%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>127</td> <td class=”number”>4.0%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.33333333333333</td> <td class=”number”>84</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-66.66666666666666</td> <td class=”number”>74</td> <td class=”number”>2.3%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>60</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>56</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-75.0</td> <td class=”number”>52</td> <td class=”number”>1.6%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.33333333333333</td> <td class=”number”>41</td> <td class=”number”>1.3%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (224)</td> <td class=”number”>950</td> <td class=”number”>29.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>866</td> <td class=”number”>27.0%</td> <td>

<div class=”bar” style=”width:91%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme7373189057644289415”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>535</td> <td class=”number”>16.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-94.73684210526315</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-94.11764705882352</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-92.85714285714286</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-92.3076923076923</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1200.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1300.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1400.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1500.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1700.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_DIRSALES_07_12”>PCH_DIRSALES_07_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2347</td>

</tr> <tr>

<th>Unique (%)</th> <td>73.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>16.5%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>529</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>54.31</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>11585</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.8%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram989401167782385801”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives989401167782385801,#minihistogram989401167782385801”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives989401167782385801”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles989401167782385801”

aria-controls=”quantiles989401167782385801” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram989401167782385801” aria-controls=”histogram989401167782385801”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common989401167782385801” aria-controls=”common989401167782385801”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme989401167782385801” aria-controls=”extreme989401167782385801”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles989401167782385801”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-77.907</td>

</tr> <tr>

<th>Q1</th> <td>-33.333</td>

</tr> <tr>

<th>Median</th> <td>5.2486</td>

</tr> <tr>

<th>Q3</th> <td>64.205</td>

</tr> <tr>

<th>95-th percentile</th> <td>293.09</td>

</tr> <tr>

<th>Maximum</th> <td>11585</td>

</tr> <tr>

<th>Range</th> <td>11685</td>

</tr> <tr>

<th>Interquartile range</th> <td>97.538</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>313.12</td>

</tr> <tr>

<th>Coef of variation</th> <td>5.7654</td>

</tr> <tr>

<th>Kurtosis</th> <td>738.22</td>

</tr> <tr>

<th>Mean</th> <td>54.31</td>

</tr> <tr>

<th>MAD</th> <td>103.83</td>

</tr> <tr class=”alert”>

<th>Skewness</th> <td>22.614</td>

</tr> <tr>

<th>Sum</th> <td>145600</td>

</tr> <tr>

<th>Variance</th> <td>98042</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram989401167782385801”>

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732msrKyjRgwAA9//zz6t69u%2Bc2PXr00MMPP6xBgwb5ujwAAIAa83nAGjhwoNq0aaPx48frpptuUpMmTc64TVpamo4cOeLr0gAAAKzwecBavny5evbsed7b7dixwwfVAAAA2Ofzc7A6duyoCRMmaNOmTZ6x//7v/9Ydd9wht9vt63IAAACs83nAmjt3ro4dO6Z27dp5xvr06aPq6mo99thjvi4HAADAOp8fInz//fe1du1axcTEeMZat26t7Oxs3XDDDb4uBwAAwDqfv4JVXl6uiIiIMwsJDdWJEyd8XQ4AAIB1Pg9YPXr00GOPPaajR496xr7//ns98sgjXpdqAAAACFY%2BP0Q4Y8YM/fGPf9QVV1yhyMhIVVdXq6ysTC1bttQLL7zg63IAAACs83nAatmypd544w29%2B%2B672r9/v0JDQ9WmTRv17t1bYWFhvi4HAADAOp8HLEmqX7%2B%2BrrnmGn/sGgAAoNb5PGAdOHBA8%2BfP19dff63y8vIz5t955x1flwQAAGCVX87BKiwsVO/evdWwYcMabeu9995TVlaWUlJStGDBAs/46tWrNWPGDNWrV8/r9itWrFBycrKqq6v15JNPat26dSopKVFycrIefvhhtWzZUpLkdrv18MMP6%2BOPP1ZoaKjS0tI0c%2BZMNWjQoEb1AgCA3wefB6ydO3fqnXfeUWxsbI228/TTT2vVqlVq1arVWed79OjxiyfNr1ixQmvXrtXTTz%2Bt5s2ba8GCBcrIyNDrr7%2BukJAQzZw5U6dOndK6detUUVGhe%2B65R9nZ2XrwwQdrVDMAAPh98PllGpo2bVrjV64kKSIi4pwB61xycnI0ZswYtW3bVpGRkcrMzFR%2Bfr527NihH374QZs2bVJmZqZiY2PVvHlz3X333Xr11VdVUVFR47oBAEDd5/NXsMaPH69FixZp6tSpCgkJ%2Bc3bGT169DnnCwoKdPvtt2vnzp2KiorS5MmTdeONN6q8vFx5eXmKj4/33DYyMlKtWrWSy%2BXSsWPHFBYWpo4dO3rmExISdPz4cX3zzTde47%2BksLBQRUVFXmPh4Q0VFxf3K1dZt4SH%2BzzPn1VYWKjXv/hl9Mo5euUMfXKOXjkXiL3yecB699139dlnn2n16tW6%2BOKLFRrq3YyXX365xvuIjY1V69atde%2B996pdu3Z6%2B%2B239ac//UlxcXG69NJLZYxRdHS0132io6NVXFysJk2aKDIy0iv8nb5tcXGxo/3n5ORo0aJFXmMZGRmaPHlyDVcW3GJiGvm7BC9RURf4u4SgQa%2Bco1fO0Cfn6JVzgdQrnwesyMhIXXXVVbW6jz59%2BqhPnz6e7wcOHKi3335bq1ev1rRp0yRJxphfvP%2B55pxIT09X3759vcbCwxuquLisRtsNdoGy/rCwUEVFXaCSkhOqqqr2dzkBjV45R6%2BcoU/O0SvnztUrf/1x7/OANXfuXF/vUpLUokUL7dy5U02aNFFoaKjcbrfXvNvtVtOmTRUbG6vS0lJVVVV5Lnx6%2BrZNmzZ1tK%2B4uLgzDgcWFR1TZeXv%2Bwck0NZfVVUdcDUFKnrlHL1yhj45R6%2BcC6Re%2BeVg5TfffKOFCxfq/vvv94x9/vnn1rb/0ksvaf369V5j%2Bfn5atmypSIiItS%2BfXvl5uZ65kpKSrR//34lJyerU6dOMsboq6%2B%2B8sy7XC5FRUWpTZs21moEAAB1l88D1vbt2zV48GBt3LhR69atk/TjxUdHjx5t7SKjp06d0qxZs%2BRyuVRRUaF169bp3Xff1ciRIyVJo0aN0vLly5Wfn6/S0lJlZ2erU6dOSkpKUmxsrAYMGKAnnnhCR44c0aFDh7R48WINHz5c4eF%2BufA9AAAIMj5PDAsWLNB9992n2267TcnJyZJ%2B/HzCxx57TIsXL1a/fv0cbScpKUmSVFlZKUnatGmTpB9fbRo9erTKysp0zz33qKioSBdffLEWL16sxMRESdLIkSNVVFSkW2%2B9VWVlZUpJSfE6Kf3RRx/VQw89pH79%2BqlevXq64YYblJmZaa0HAACgbgsxNT2j%2B1fq0qWLPv74Y9WvX1%2BdO3fWjh07JElVVVXq1q2b5/u6pqjoWK1s97onPqiV7daGDVN6%2BbsEST9eLiImppGKi8sC5lh9oKJXztErZ%2BiTc/TKuXP1qlmzxn6pyeeHCBs3bnzWzyAsLCxU/fr1fV0OAACAdT4PWN26ddOcOXNUWlrqGdu7d6%2BysrJ0xRVX%2BLocAAAA63x%2BDtb999%2Bv2267TSkpKZ7DgidOnFD79u312GOP%2BbocAAAA63wesC666CKtW7dO27Zt0969e9WgQQO1adNGvXr1qtFH5wAAAAQKv1x3oF69errmmmv8sWsAAIBa5/OA1bdv33O%2BUmXrWlgAAAD%2B4vOAdf3113sFrKqqKu3du1cul0u33Xabr8sBAACwzucB6/SHLf/cW2%2B9pX/84x8%2BrgYAAMA%2Bv3wW4dlcc801euONN/xdBgAAQI0FTMDatWuXfHxReQAAgFrh80OEpz9w%2BadOnDih/Px89e/f39flAAAAWOfzgNW6desz3kUYERGh4cOHa8SIEb4uBwAAwDqfByyu1g4AAOo6nwes1157zfFtb7rpplqsBAAAoHb4PGA98MADqq6uPuOE9pCQEK%2BxkJAQAhYAAAhKPg9YzzzzjJ599llNmDBBHTt2lDFGe/bs0dNPP61bbrlFKSkpvi4JAADAKr%2Bcg7Vs2TI1b97cM3b55ZerZcuWGjt2rNatW%2BfrkgAAAKzy%2BXWw9u3bp%2Bjo6DPGo6KidPDgQV%2BXAwAAYJ3PA1aLFi302GOPqbi42DNWUlKi%2BfPn65JLLvF1OQAAANb5/BDhjBkzNHXqVOXk5KhRo0YKDQ1VaWmpGjRooMWLF/u6HAAAAOt8HrB69%2B6trVu3atu2bTp06JCMMWrevLmuvPJKNW7c2NflAAAAWOfzgCVJF1xwgfr166dDhw6pZcuW/igBAACg1vj8HKzy8nJlZWWpa9euuu666yT9eA7WuHHjVFJS4utyAAAArPN5wJo3b552796t7OxshYb%2B/91XVVUpOzvb1%2BUAAABY5/OA9dZbb%2Bmpp57Stdde6/nQ56ioKM2dO1cbN270dTkAAADW%2BTxglZWVqXXr1meMx8bG6vjx474uBwAAwDqfB6xLLrlE//jHPyTJ67MH33zzTf3rv/6rr8sBAACwzufvIvz3f/93TZo0ScOGDVN1dbWee%2B457dy5U2%2B99ZYeeOABX5cDAABgnc8DVnp6usLDw/Xiiy8qLCxMf/3rX9WmTRtlZ2fr2muv9XU5AAAA1vk8YB05ckTDhg3TsGHDfL1rAAAAn/D5OVj9%2BvXzOvcKAACgrvF5wEpJSdGGDRt8vVsAAACf8fkhwn/5l3/RX/7yFy1btkyXXHKJ6tWr5zU/f/58X5cEAABglc8DVl5eni699FJJUnFxsa93DwAAUOt8FrAyMzO1YMECvfDCC56xxYsXKyMjw1clAAAA%2BITPzsHavHnzGWPLli3z1e4BAAB8xmcB62zvHOTdhAAAoC7yWcA6/cHO5xsDAAAIdj6/TAMAAEBdR8ACAACwzGfvIqyoqNDUqVPPO8Z1sAAAQLDzWcDq3r27CgsLzzsGAAAQ7HwWsH56/SsAAIC6jHOwAAAALCNgAQAAWEbAAgAAsCyoA9Z7772n1NRUZWZmnjG3fv16DRo0SF27dtXQoUP1/vvve%2Baqq6u1YMEC9evXTz169NDYsWN14MABz7zb7daUKVOUmpqq3r1764EHHlB5eblP1gQAAIJf0Aasp59%2BWrNnz1arVq3OmNu9e7eysrI0bdo0ffTRRxozZowmTpyoQ4cOSZJWrFihtWvXatmyZdqyZYtat26tjIwMz0f3zJw5UydOnNC6dev06quvKj8/X9nZ2T5dHwAACF5BG7AiIiK0atWqswaslStXKi0tTWlpaYqIiNDgwYPVoUMHrVmzRpKUk5OjMWPGqG3btoqMjFRmZqby8/O1Y8cO/fDDD9q0aZMyMzMVGxur5s2b6%2B6779arr76qiooKXy8TAAAEoaANWKNHj1bjxo3POpebm6v4%2BHivsfj4eLlcLpWXlysvL89rPjIyUq1atZLL5dLu3bsVFhamjh07euYTEhJ0/PhxffPNN7WzGAAAUKf47DpYvuR2uxUdHe01Fh0drby8PB09elTGmLPOFxcXq0mTJoqMjPT6IOrTty0uLna0/8LCQhUVFXmNhYc3VFxc3G9ZTp0RHh4YeT4sLNTrX/wyeuUcvXKGPjlHr5wLxF7VyYAlyXM%2B1W%2BZP999zycnJ0eLFi3yGsvIyNDkyZNrtN1gFxPTyN8leImKusDfJQQNeuUcvXKGPjlHr5wLpF7VyYAVExMjt9vtNeZ2uxUbG6smTZooNDT0rPNNmzZVbGysSktLVVVVpbCwMM%2BcJDVt2tTR/tPT09W3b1%2BvsfDwhiouLvutS6oTAmX9YWGhioq6QCUlJ1RVVe3vcgIavXKOXjlDn5yjV86dq1f%2B%2BuO%2BTgasxMRE7dy502vM5XJp4MCBioiIUPv27ZWbm6uePXtKkkpKSrR//34lJyerRYsWMsboq6%2B%2BUkJCgue%2BUVFRatOmjaP9x8XFnXE4sKjomCorf98/IIG2/qqq6oCrKVDRK%2BfolTP0yTl65Vwg9SpwDlZadPPNN%2BvDDz/U1q1bdfLkSa1atUr79u3T4MGDJUmjRo3S8uXLlZ%2Bfr9LSUmVnZ6tTp05KSkpSbGysBgwYoCeeeEJHjhzRoUOHtHjxYg0fPlzh4XUyjwIAAMuCNjEkJSVJkiorKyVJmzZtkvTjq00dOnRQdna25s6dq4MHD6pdu3ZaunSpmjVrJkkaOXKkioqKdOutt6qsrEwpKSle50w9%2Buijeuihh9SvXz/Vq1dPN9xww1kvZgoAAHA2IaamZ3TDkaKiY7Wy3eue%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%2BoY8eOWr58uSZMmKCNGzeqYcOGfloJAAAIJnX2FaxfsnbtWrVu3VrDhw9XRESEUlNT1bdvX61cuVKSlJOTo6FDh6pz585q0KCBxo0bJ0nasmWLP8sGAABBpE4HrPnz56tPnz66/PLLNXPmTJWVlSk3N1fx8fFet4uPj/ccBvz5fGhoqDp16iSXy%2BXT2gEAQPCqs4cIu3TpotTUVD3%2B%2BOM6cOCApkyZokceeURut1vNmzf3um2TJk1UXFwsSXK73YqOjvaaj46O9sw7UVhYqKKiIq%2Bx8PCGiouL%2B42rqRvCwwMjz4eFhXr9i19Gr5yjV87QJ%2BfolXOB2Ks6G7BycnI8/2/btq2mTZumu%2B66S927dz/vfY0xNd73okWLvMYyMjI0efLkGm032MXENPJ3CV6ioi7wdwlBg145R6%2BcoU/O0SvnAqlXdTZg/dzFF1%2BsqqoqhYaGyu12e80VFxcrNjZWkhQTE3PGvNvtVvv27R3vKz09XX379vUaCw9vqOList9Yfd0QKOsPCwtVVNQFKik5oaqqan%2BXE9DolXP0yhn65By9cu5cvfLXH/d1MmDt2rVLa9as0fTp0z1j%2Bfn5ql%2B/vtLS0vQ///M/XrffuXOnOnfuLElKTExUbm6uhgwZIkmqqqrSrl27NHz4cMf7j4uLO%2BNwYFHRMVVW/r5/QAJt/VVV1QFXU6CiV87RK2fok3P0yrlA6lXgHKy0qGnTpsrJydHDQC%2BkAAAPoElEQVSyZct06tQp7d27V08%2B%2BaTS09N144036uDBg1q5cqVOnjypbdu2adu2bbr55pslSaNGjdJrr72mL774QidOnNCSJUtUv3599enTx7%2BLAgAAQaNOvoLVvHlzLVu2TPPnz/cEpCFDhigzM1MRERFaunSpZs%2BerUceeUQtWrTQvHnzdNlll0mSrrrqKt17772aMmWKDh8%2BrKSkJC1btkwNGjTw86oAAECwCDE1PaMbjhQVHauV7V73xAe1st3asGFKL3%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%2BPG68MILdfvtt/u7NAAAEAR4BetnXC6XvvrqK02bNk2NGzdW69atNWbMGOXk5Pi7NAAAECR4BetncnNz1aJFC0VHR3vGEhIStHfvXpWWlioyMvK82ygsLFRRUZHXWHh4Q8XFxVmvN5hc98QH/i7hV3l72pX%2BLsHvwsJCvf7FL6NXztAn5%2BiVc4HYKwLWz7jdbkVFRXmNnQ5bxcXFjgJWTk6OFi1a5DU2ceJETZo0yV6h/%2BeTv1zr%2BX9hYaFycnKUnp7%2Buw9z50KfnCssLNTzzz9DrxygV87QJ%2BfolXOB2KvAiXoBxBhTo/unp6dr9erVXl/p6emWqvtlRUVFWrRo0RmvnsEbfXKOXjlHr5yhT87RK%2BcCsVe8gvUzsbGxcrvdXmNut1shISGKjY11tI24uLiASdAAAMD3eAXrZxITE1VQUKAjR454xlwul9q1a6dGjRr5sTIAABAsCFg/Ex8fr6SkJM2fP1%2BlpaXKz8/Xc889p1GjRvm7NAAAECTCHn744Yf9XUSgufLKK7Vu3TrNmjVLb7zxhoYPH66xY8cqJCTE36WdV6NGjdSzZ09ebTsP%2BuQcvXKOXjlDn5yjV84FWq9CTE3P6AYAAIAXDhECAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAqgMOHjyoO%2B%2B8UykpKbr66qs1b948VVdX%2B7ssnzh48KAyMjKUkpKi1NRUTZ8%2BXSUlJZKk3bt365ZbblH37t3Vv39/Pfvss173Xb9%2BvQYNGqSuXbtq6NChev/99z1z1dXVWrBggfr166cePXpo7NixOnDggE/XVpvmzJmjjh07er7fvn27hg8frm7dumngwIFas2aN1%2B2XL1%2BuAQMGqFu3bho1apR27tzpmTt58qT%2B/Oc/66qrrlJKSoomT56s4uJin62ltixZskS9e/dWly5dNGbMGH377beS6NVP7dq1S6NHj9bll1%2BuXr16adq0aTpy5Igk%2BiRJ7733nlJTU5WZmXnGXE2ef9xut6ZMmaLU1FT17t1bDzzwgMrLyz3z53vuCzTn6tPGjRs1ePBgde3aVQMGDNArr7ziNV%2BTx1Gt/%2B40CHpDhgwxDz74oCkpKTF79%2B41/fv3N88%2B%2B6y/y/KJG264wUyfPt2UlpaagoICM3ToUDNjxgxz4sQJc%2BWVV5qFCxeasrIys3PnTtOzZ0/z1ltvGWOM2bVrl0lMTDRbt2415eXl5vXXXzedO3c2BQUFxhhjli9fbq6%2B%2BmqTl5dnjh07Zh599FEzaNAgU11d7c/lWrFr1y7Ts2dP06FDB2OMMd9//73p0qWLWblypSkvLzcffPCBSU5ONl9%2B%2BaUxxph33nnHXH755eaLL74wJ06cMEuXLjW9evUyZWVlxhhj5s6da4YOHWq%2B%2B%2B47U1xcbCZOnGjGjx/vt/XZ8OKLL5prr73W5Ofnm2PHjplZs2aZWbNm0aufqKioML169TLz5883J0%2BeNEeOHDG33367mTRpEn0yxixbtsz079/fjBw50kyZMsVrrqbPPxMnTjR33nmnOXz4sDl06JBJT083s2bNMsaY8z73BZpz9WnHjh0mKSnJvP3226aiosJs3brVJCQkmH/%2B85/GmJo/jmr7dycBK8h9%2BeWXplOnTsbtdnvG/v73v5sBAwb4sSrfOHr0qJk%2BfbopKiryjL3wwgumf//%2BZsOGDeYPf/iDqays9MzNmzfP/PGPfzTGGPPII4%2BYjIwMr%2B2NGDHCLF261BhjzMCBA83zzz/vmTt27JiJj483n3/%2BeW0uqdZVVVWZESNGmP/6r//yBKxnnnnG3HTTTV63mzJlipk5c6Yxxpg777zTzJkzx2sbvXr1MuvWrTMVFRWme/fuZtOmTZ75vLw807FjR3Po0CEfrKh29O3b96y/kOjV//fdd9%2BZDh06mLy8PM/Y3//%2Bd3PNNdfQJ2PM888/b0pKSkxWVtYZwaEmzz9FRUXmsssuM7t37/bMb9u2zXTp0sWcOnXqvM99geZcfdq2bZtZtGiR19iQIUPMkiVLjDE1exz54ncnhwiDXG5urlq0aKHo6GjPWEJCgvbu3avS0lI/Vlb7oqKiNHfuXF144YWesYKCAsXFxSk3N1cdO3ZUWFiYZy4%2BPt7z8nFubq7i4%2BO9thcfHy%2BXy6Xy8nLl5eV5zUdGRqpVq1ZyuVy1vKra9fLLLysiIkKDBg3yjP1SL36pV6GhoerUqZNcLpf279%2BvY8eOKSEhwTPftm1bNWjQQLm5ubW8mtrx/fff69tvv9XRo0d1/fXXew4tHDlyhF79RPPmzdWpUyfl5OSorKxMhw8f1saNG9WnTx/6JGn06NFq3LjxWedq8vyze/duhYWFeR3iT0hI0PHjx/XNN9%2Bc97kv0JyrT1dddZUyMjI831dWVqqoqEjNmzeXVLPHkS9%2BdxKwgpzb7VZUVJTX2OkHTDCes1ATLpdLL774ou66666z9qVJkyZyu92qrq6W2%2B32%2BsGSfuxbcXGxjh49KmPML84Hqx9%2B%2BEELFy7UQw895DX%2BS706vdZz9crtdkvSGfePiooK2l4dOnRIkvTmm2/queee0%2Buvv65Dhw7pwQcfpFc/ERoaqoULF%2Bqdd95Rt27dlJqaqsrKSk2dOpU%2BnUdNnn/cbrciIyMVEhLiNSfJM3%2Bu575glp2drYYNG%2Br666%2BXVLPHkS9%2BdxKw6gBjjL9L8LtPP/1UY8eO1dSpU5WamvqLt/vpk9L5%2BlbX%2Bjp37lwNHTpU7dq1%2B9X3/T316vRaxo0bp%2BbNm%2Buiiy7SpEmTtHnz5l91/986HyxOnTqlCRMm6Nprr9Unn3yid999V40bN9a0adMc3f/30qdfUpP1/5be/PS5L9gYYzRv3jytW7dOS5YsUUREhNfc%2Be77W%2BZsIGAFudjYWE9SP83tdiskJESxsbF%2Bqsq3Nm/erDvvvFMzZszQ6NGjJf3Yl5//FeJ2u9WkSROFhoYqJibmrH2LjY313OZs802bNq3dxdSS7du36/PPP/d6uf20s/WiuLjY8/g5V69O3%2Bbn80ePHg3aXp0%2B5PzTv25btGghY4wqKiro1f/Zvn27vv32W917771q3LixmjdvrsmTJ%2Bvtt98%2B68/P77VPZ1OT55/Y2FiVlpaqqqrKa06SZ/5cz33Bprq6WtOnT9fmzZv10ksv6dJLL/XM1eRx5IvfncHXbXhJTExUQUGB563R0o%2BHytq1a6dGjRr5sTLf%2BOyzz5SVlaUnn3xSN910k2c8MTFRe/bsUWVlpWfM5XKpc%2BfOnvmfn5Nwej4iIkLt27f3Ot%2BjpKRE%2B/fvV3Jyci2vqHasWbNGhw8f1tVXX62UlBQNHTpUkpSSkqIOHTqc0YudO3d69eqnvaiqqtKuXbvUuXNntWzZUtHR0V7z//u//6tTp04pMTHRByuz76KLLlJkZKR2797tGTt48KDq1auntLQ0evV/qqqqVF1d7fUqwKlTpyRJqamp9OkcavL806lTJxlj9NVXX3ndNyoqSm3atDnvc1%2BwmTNnjr7%2B%2Bmu99NJLatmypddcTR5HPvndae10efjNiBEjzIwZM8yxY8dMXl6e6du3r3nxxRf9XVatq6ioMNddd515%2BeWXz5g7efKkufrqq81TTz1ljh8/br744gtz%2BeWXmy1bthhjjNmzZ49JSkoyW7ZsMeXl5WblypWma9euprCw0Bjz47tJ%2BvTp43mb9MyZM82wYcN8uTyr3G63KSgo8Hx9/vnnpkOHDqagoMAcPHjQdO3a1bzyyiumvLzcbN261SQnJ3vepbRt2zbTvXt38/nnn5vjx4%2BbhQsXmrS0NHPixAljzI/vUBoyZIj57rvvzJEjR8z48ePNpEmT/LncGpszZ47p16%2Bf2bdvn/nhhx9Menq6mT59uvnhhx/o1f85cuSI6dmzp/nP//xPc/z4cXPkyBEzYcIE8x//8R/06SfO9u64mj7/TJkyxYwbN84cPnzYFBQUmGHDhpnHHnvMGHP%2B575AdbY%2BffLJJ6ZHjx5e7xT/qZo%2Bjmr7dycBqw4oKCgw48aNM8nJySY1NdU89dRTdeJ6Tefzz3/%2B03To0MEkJiae8fXtt9%2BaPXv2mJEjR5rExETTp08fs2LFCq/7v/XWW6Z///4mISHB3Hjjjebjjz/2zFVXV5snn3zSXHHFFSY5OdnccccdnmvU1AUHDhzwXKbBGGM%2B/vhjM3jwYJOQkGD69%2B9/xiUKVqxYYdLS0kxiYqIZNWqU2bNnj2fu5MmT5uGHHzY9evQwXbt2Nffee68pKSnx2Vpqw0/X1KVLF5OVlWVKS0uNMfTqp1wul7nlllvM5ZdfblJTU82UKVM8l1L4vffp9HPRZZddZi677DLP96fV5PmnpKTEZGZmmi5dupgePXqYRx55xJw8edIzf77nvkByrj7df//9XmOnv26//XbP/WvyOKrt350hxtTxMwkBAAB8jHOwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMCy/wef6pFxdd%2BYdQAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common989401167782385801”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>25</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>20</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>11</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.33333333333333</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>9</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>66.66666666666666</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.33333333333333</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-90.0</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2336)</td> <td class=”number”>2575</td> <td class=”number”>80.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>529</td> <td class=”number”>16.5%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme989401167782385801”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>20</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-97.03588143525741</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-96.90721649484536</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-96.66666666666667</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-96.49122807017544</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1800.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3144.4444444444443</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4600.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5355.555555555556</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11584.615384615385</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_DIRSALES_FARMS_07_12”>PCH_DIRSALES_FARMS_07_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>1277</td>

</tr> <tr>

<th>Unique (%)</th> <td>39.8%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>6.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>194</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>16.348</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>1500</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>4.6%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram8446293992153190033”>

</div> <div class=”col-md-12 text-right”>

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aria-expanded=”false” aria-controls=”collapseExample”>

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</div> <div class=”row collapse col-md-12” id=”descriptives8446293992153190033”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles8446293992153190033”

aria-controls=”quantiles8446293992153190033” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram8446293992153190033” aria-controls=”histogram8446293992153190033”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common8446293992153190033” aria-controls=”common8446293992153190033”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme8446293992153190033” aria-controls=”extreme8446293992153190033”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles8446293992153190033”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-60</td>

</tr> <tr>

<th>Q1</th> <td>-22.222</td>

</tr> <tr>

<th>Median</th> <td>1.1912</td>

</tr> <tr>

<th>Q3</th> <td>32.059</td>

</tr> <tr>

<th>95-th percentile</th> <td>125</td>

</tr> <tr>

<th>Maximum</th> <td>1500</td>

</tr> <tr>

<th>Range</th> <td>1600</td>

</tr> <tr>

<th>Interquartile range</th> <td>54.281</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>87.291</td>

</tr> <tr>

<th>Coef of variation</th> <td>5.3394</td>

</tr> <tr>

<th>Kurtosis</th> <td>72.496</td>

</tr> <tr>

<th>Mean</th> <td>16.348</td>

</tr> <tr>

<th>MAD</th> <td>45.663</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>6.4638</td>

</tr> <tr>

<th>Sum</th> <td>49307</td>

</tr> <tr>

<th>Variance</th> <td>7619.7</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram8446293992153190033”>

<img 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Fawzp07pw0bNuihhx5Samqq6tevryeeeEKtW7fW8OHDtW7dOl%2BNBgAAcFmCvf2EOTk5Wr16td5%2B%2B20VFxerV69eevXVV9WhQwf3bTp16qSnn35affv29fZ4AAAAl83rBatPnz5q0qSJRo0apf79%2B6tOnToX3CYxMVHHjx/39mgAAABWeL1gLV%2B%2BXJ07d77o7fbs2eOFaQAAAOzz%2BjlYLVu21OjRo7Vlyxb32v/%2B7//qoYceksvl8vY4AAAA1nm9YM2ePVsnT55Us2bN3GvdunVTeXm55syZ4%2B1xAAAArPP6IcKPP/5Y69atU0REhHutcePGSktL05133untcQAAAKzz%2BjtYJSUlCgkJuXCQwECdPn3a2%2BMAAABY5/WC1alTJ82ZM0cnTpxwr/3www%2BaPn26x6UaAAAA/JXXC9aUKVO0c%2BdO3XTTTercubM6duyobt26KTMzUzNnzvxNj/XRRx8pISFBKSkpHutr1qxRq1atFBcX5/Fx/grx5eXlmj9/vnr06KFOnTppxIgROnz4sPv%2BLpdLEyZMUEJCgrp27aqpU6eqpKTk8sMDAIB/CV4/B6thw4Z699139eGHH%2BrQoUMKDAxUkyZN1LVrVwUFBV3y47z44otavXq1GjVqVOF%2Bp06d9Nprr1W4t3LlSq1bt04vvvii6tevr/nz5ys5OVnvvPOOAgICNG3aNJ09e1br16/XuXPn9OijjyotLU1PPvnk78oMAAD%2BtXi9YEnSFVdcodtuu%2B2yHiMkJESrV6/Ws88%2BqzNnzvym%2B6anp2v48OFq2rSpJCklJUXx8fHas2ePrr32Wm3ZskX/93//p8jISEnSI488okcffVSpqamqUaPGZc0NAACcz%2BsF6/Dhw5o3b56%2B%2BeabCg%2B7ffDBB5f0OMOGDat0Pzc3Vw8%2B%2BKAyMzMVFham8ePH66677lJJSYmys7MVHR3tvm1oaKgaNWqkjIwMnTx5UkFBQWrZsqV7PyYmRqdOndK3337rsf5r8vLylJ%2Bf77EWHFxTUVFRl5TNqYKDffarLysVFBTo8adTODWX5NxsTs0lOTebU3NJzs7mDV4vWFOmTFFeXp66du2qmjVrVslzREZGqnHjxnrsscfUrFkzvf/%2B%2B/rDH/6gqKgoXX/99TLGKDw83OM%2B4eHhKigoUJ06dRQaGqqAgACPPUkqKCi4pOdPT0/XokWLPNaSk5M1fvz4y0zm3yIiavl6hEqFhV3l6xGqhFNzSc7N5tRcknOzOTWX5OxsVcnrBSszM1MffPCB%2B/BbVejWrZu6devm/nufPn30/vvva82aNZo0aZIkyRjzq/evbO9SDBkyRN27d/dYCw6uqYKC4st6XH9XXfMHBQUqLOwqFRaeVllZua/HscapuSTnZnNqLsm52ZyaS3JONl99c%2B/1glW3bt0qe%2BeqMg0aNFBmZqbq1KmjwMDAC34tj8vlUt26dRUZGamioiKVlZW5T7o/f9u6dete0nNFRUVdcDgwP/%2BkSkv99xPUhuqev6ysvNrP%2BHs4NZfk3GxOzSU5N5tTc0nOzlaVvH5gddSoUVq0aNFlv0tUmTfeeEMbNmzwWMvJyVHDhg0VEhKi5s2bKysry71XWFioQ4cOqU2bNmrdurWMMfr666/d%2BxkZGQoLC1OTJk2qbGYAAOAcXn8H68MPP9SXX36pNWvW6Nprr1VgoGfHe/PNNy/7Oc6ePasZM2aoYcOGatWqlTZt2qQPP/xQb731liQpKSlJy5Yt0y233KL69esrLS1NrVu3VlxcnCSpV69eev755/Xcc8/p7NmzWrx4sQYPHqzgYJ/80CUAAPAzXm8MoaGhuuWWWy77cc6XodLSUknSli1bJP30btOwYcNUXFysRx99VPn5%2Bbr22mu1ePFixcbGSpKGDh2q/Px83X///SouLlZ8fLzHSenPPPOMnnrqKfXo0UM1atTQnXfeecHFTAEAAH5NgKnKY3Vwy88/WSWPe8fzn1TJ41aFjRO6%2BHqECgUHByoiopYKCooddZ6BU3NJzs3m1FySc7M5NZfknGz16tX2yfP65OIW3377rRYuXKgnnnjCvbZr1y5fjAIAAGCd1wvWzp071a9fP23evFnr16%2BX9NPFR4cNG3bJFxkFAACozrxesObPn6/HH39c69atc1/Ms2HDhpozZ44WL17s7XEAAACs83rB%2Bsc//qGkpCRJ8rha%2Bu23366cnBxvjwMAAGCd1wtW7dq1K/wdhHl5ebriiiu8PQ4AAIB1Xi9Y7du316xZs1RUVOReO3DggFJTU3XTTTd5exwAAADrvH4drCeeeEIPPPCA4uPjVVZWpvbt2%2Bv06dNq3ry55syZ4%2B1xAAAArPN6wbrmmmu0fv167dixQwcOHNCVV16pJk2aqEuXLh7nZAEAAPgrn/zulxo1aui2227zxVMDAABUOa8XrO7du1f6ThXXwgIAAP7O6wWrd%2B/eHgWrrKxMBw4cUEZGhh544AFvjwMAAGCd1wvWpEmTKlzftGmT/vrXv3p5GgAAAPt88rsIK3Lbbbfp3Xff9fUYAAAAl63aFKy9e/fKGOPrMQAAAC6b1w8RDh069IK106dPKycnRz179vT2OAAAANZ5vWA1btz4gp8iDAkJ0eDBg3X33Xd7exwAAADrvF6wuFo7AABwOq8XrLfffvuSb9u/f/8qnAQAAKBqeL1gTZ06VeXl5Rec0B4QEOCxFhAQQMECAAB%2ByesF66WXXtLLL7%2Bs0aNHq2XLljLGaP/%2B/XrxxRd13333KT4%2B3tsjAQAAWOWTc7CWLVum%2BvXru9c6duyohg0basSIEVq/fr23RwIAALDK69fBOnjwoMLDwy9YDwsL05EjR7w9DgAAgHVeL1gNGjTQnDlzVFBQ4F4rLCzUvHnzdN1113l7HAAAAOu8fohwypQpmjhxotLT01WrVi0FBgaqqKhIV155pRYvXuztcQAAAKzzesHq2rWrtm/frh07dujo0aMyxqh%2B/fq6%2BeabVbt2bW%2BPAwAAYJ3XC5YkXXXVVerRo4eOHj2qhg0b%2BmIEAACAKuP1c7BKSkqUmpqqdu3a6Y477pD00zlYI0eOVGFhobfHAQAAsM7rBWvu3Lnat2%2Bf0tLSFBj4z6cvKytTWlqat8cBAACwzusFa9OmTXrhhRd0%2B%2B23u3/pc1hYmGbPnq3Nmzd7exwAAADrvF6wiouL1bhx4wvWIyMjderUKW%2BPAwAAYJ3XC9Z1112nv/71r5Lk8bsH33vvPf37v/%2B7t8cBAACwzus/RXjvvfdq3LhxGjRokMrLy/XKK68oMzNTmzZt0tSpU709DgAAgHVeL1hDhgxRcHCwVqxYoaCgIP3pT39SkyZNlJaWpttvv93b4wAAAFjn9YJ1/PhxDRo0SIMGDfL2UwMAAHiF18/B6tGjh8e5VwAAAE7j9YIVHx%2BvjRs3evtpAQAAvMbrhwj/7d/%2BTc8%2B%2B6yWLVum6667TjVq1PDYnzdvnrdHAgAAsMrrBSs7O1vXX3%2B9JKmgoMDbTw8AAFDlvFawUlJSNH/%2BfL322mvutcWLFys5OdlbIwAAAHiF187B2rp16wVry5Yt89bTAwAAeI3XClZFPznITxMCAAAn8lrBOv%2BLnS%2B2BgAA4O%2B8fpkGAAAAp6NgAQAAWOa1nyI8d%2B6cJk6ceNE1roMFAAD8ndcKVocOHZSXl3fRNQAAAH/ntYL18%2BtfAQAAOJlfn4P10UcfKSEhQSkpKRfsbdiwQX379lW7du00cOBAffzxx%2B698vJyzZ8/Xz169FCnTp00YsQIHT582L3vcrk0YcIEJSQkqGvXrpo6dapKSkq8kgkAAPg/vy1YL774ombOnKlGjRpdsLdv3z6lpqZq0qRJ%2BuyzzzR8%2BHCNHTtWR48elSStXLlS69at07Jly7Rt2zY1btxYycnJ7utyTZs2TadPn9b69ev1l7/8RTk5OUpLS/NqPgAA4L/8tmCFhIRo9erVFRasVatWKTExUYmJiQoJCVG/fv3UokULrV27VpKUnp6u4cOHq2nTpgoNDVVKSopycnK0Z88e/fjjj9qyZYtSUlIUGRmp%2BvXr65FHHtFf/vIXnTt3ztsxAQCAH/LbgjVs2DDVrl27wr2srCxFR0d7rEVHRysjI0MlJSXKzs722A8NDVWjRo2UkZGhffv2KSgoSC1btnTvx8TE6NSpU/r222%2BrJgwAAHAUr53k7k0ul0vh4eEea%2BHh4crOztaJEydkjKlwv6CgQHXq1FFoaKjHVebP37agoOCSnj8vL0/5%2Bfkea8HBNRUVFfV74jhGcHD17PNBQYEefzqFU3NJzs3m1FySc7M5NZfk7Gze4MiCJV389xxWtn%2B5vyMxPT1dixYt8lhLTk7W%2BPHjL%2Btx/V1ERC1fj1CpsLCrfD1ClXBqLsm52ZyaS3JuNqfmkpydrSo5smBFRETI5XJ5rLlcLkVGRqpOnToKDAyscL9u3bqKjIxUUVGRysrKFBQU5N6TpLp1617S8w8ZMkTdu3f3WAsOrqmCguLfG8kRqmv%2BoKBAhYVdpcLC0yorK/f1ONY4NZfk3GxOzSU5N5tTc0nOyearb%2B4dWbBiY2OVmZnpsZaRkaE%2BffooJCREzZs3V1ZWljp37ixJKiws1KFDh9SmTRs1aNBAxhh9/fXXiomJcd83LCxMTZo0uaTnj4qKuuBwYH7%2BSZWW%2Bu8nqA3VPX9ZWXm1n/H3cGouybnZnJpLcm42p%2BaSnJ2tKjnywOo999yjTz/9VNu3b9eZM2e0evVqHTx4UP369ZMkJSUlafny5crJyVFRUZHS0tLUunVrxcXFKTIyUr169dLzzz%2Bv48eP6%2BjRo1q8eLEGDx6s4GBH9lEAAGCZ3zaGuLg4SVJpaakkacuWLZJ%2BerepRYsWSktL0%2BzZs3XkyBE1a9ZMS5cuVb169SRJQ4cOVX5%2Bvu6//34VFxcrPj7e45ypZ555Rk899ZR69OihGjVq6M4776zwYqYAAAAVCTCXe0Y3Lkl%2B/skqedw7nv%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%2BfXv16dNHa9eu9bjv8uXL1atXL7Vv315JSUnKzMz0RQQAAOCngn09QFV67733dO2113qs5eXl6ZFHHtHUqVPVt29fffHFFxozZoyaNGmiuLg4bd26VQsXLtRLL72kli1bavny5Ro9erQ2b96smjVr%2BigJAADwJ459B%2BvXrFu3To0bN9bgwYMVEhKihIQEde/eXatWrZIkpaena%2BDAgbrhhht05ZVXauTIkZKkbdu2%2BXJsAADgRxz9Dta8efO0a9cuFRUV6Y477tDkyZOVlZWl6Ohoj9tFR0dr48aNkqSsrCz17t3bvRcYGKjWrVsrIyNDffr0uaTnzcvLU35%2BvsdacHBNRUVFXWYi/xYcXD37fFBQoMefTuHUXJJzszk1l%2BTcbE7NJTk7mzc4tmC1bdtWCQkJeu6553T48GFNmDBB06dPl8vlUv369T1uW6dOHRUUFEiSXC6XwsPDPfbDw8Pd%2B5ciPT1dixYt8lhLTk7W%2BPHjf2caZ4iIqOXrESoVFnaVr0eoEk7NJTk3m1NzSc7N5tRckrOzVSXHFqz09HT3fzdt2lSTJk3SmDFj1KFDh4ve1xhzWc89ZMgQde/e3WMtOLimCgqKL%2Btx/V11zR8UFKiwsKtUWHhaZWXlvh7HGqfmkpybzam5JOdmc2ouyTnZfPXNvWML1i9de%2B21KisrU2BgoFwul8deQUGBIiMjJUkREREX7LtcLjVv3vySnysqKuqCw4H5%2BSdVWuq/n6A2VPf8ZWXl1X7G38OpuSTnZnNqLsm52ZyaS3J2tqrkyAOre/fu1Zw5czz1W4eCAAAQWElEQVTWcnJydMUVVygxMfGCyy5kZmbqhhtukCTFxsYqKyvLvVdWVqa9e/e69wEAAC7GkQWrbt26Sk9P17Jly3T27FkdOHBACxYs0JAhQ3TXXXfpyJEjWrVqlc6cOaMdO3Zox44duueeeyRJSUlJevvtt7V7926dPn1aS5Ys0RVXXKFu3br5NhQAAPAbjjxEWL9%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%2BePapZs6Z69%2B6tiRMnKjCQPvqv5I7nP/H1CL/JxgldfD0CAOD/o2BVYNy4cYqJidGWLVt07NgxjRo1SldffbUefPBBX48GAAD8AG/J/EJGRoa%2B/vprTZo0SbVr11bjxo01fPhwpaen%2B3o0AADgJ3gH6xeysrLUoEEDhYeHu9diYmJ04MABFRUVKTQ09KKPkZeXp/z8fI%2B14OCaioqKsj4vcJ6/HdL0J%2B9PutkrzxMUFOjxp5M4NZtTc0nOzuYNFKxfcLlcCgsL81g7X7YKCgouqWClp6dr0aJFHmtjx47VuHHj7A36/33%2B7O3WH7MieXl5Sk9P15AhQxxXFJ2azam5JOdmy8vL06uvvuS4XJJzszk1l%2BTsbN5ALa2AMeay7j9kyBCtWbPG42PIkCGWpvON/Px8LVq06IJ35pzAqdmcmktybjan5pKcm82puSRnZ/MG3sH6hcjISLlcLo81l8ulgIAARUZGXtJjREVF0fYBAPgXxjtYvxAbG6vc3FwdP37cvZaRkaFmzZqpVq1aPpwMAAD4CwrWL0RHRysuLk7z5s1TUVGRcnJy9MorrygpKcnXowEAAD8R9PTTTz/t6yGqm5tvvlnr16/XjBkz9O6772rw4MEaMWKEAgICfD2aT9WqVUudO3d25Dt5Ts3m1FySc7M5NZfk3GxOzSU5O1tVCzCXe0Y3AAAAPHCIEAAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYu6siRI3r44YcVHx%2BvW2%2B9VXPnzlV5ebmvx7okR44cUXJysuLj45WQkKDJkyersLBQkrRv3z7dd9996tChg3r27KmXX37Z474bNmxQ37591a5dOw0cOFAff/yxLyJc1KxZs9SyZUv333fu3KnBgwerffv26tOnj9auXetx%2B%2BXLl6tXr15q3769kpKSlJmZ6e2RL2rJkiXq2rWr2rZtq%2BHDh%2Bu7776T5N/Z9u7dq2HDhqljx47q0qWLJk2a5P6l8v6W66OPPlJCQoJSUlIu2Kvs66a8vFzz589Xjx491KlTJ40YMUKHDx9277tcLk2YMEEJCQnq2rWrpk6dqpKSEq9kOq%2BybJs3b1a/fv3Url079erVS2%2B99ZbHfmWv05kzZ/THP/5Rt9xyi%2BLj4zV%2B/HgVFBRUeZ7zKst1XnFxsbp166bJkye71/zhNau2DHARAwYMME8%2B%2BaQpLCw0Bw4cMD179jQvv/yyr8e6JHfeeaeZPHmyKSoqMrm5uWbgwIFmypQp5vTp0%2Bbmm282CxcuNMXFxSYzM9N07tzZbNq0yRhjzN69e01sbKzZvn27KSkpMe%2B884654YYbTG5uro8Tedq7d6/p3LmzadGihTHGmB9%2B%2BMG0bdvWrFq1ypSUlJhPPvnEtGnTxnz11VfGGGM%2B%2BOAD07FjR7N7925z%2BvRps3TpUtOlSxdTXFzsyxgeVqxYYW6//XaTk5NjTp48aWbMmGFmzJjh19nOnTtnunTpYubNm2fOnDljjh8/bh588EEzbtw4v8u1bNky07NnTzN06FAzYcIEj72Lfd0sX77c3HrrrSY7O9ucPHnSPPPMM6Zv376mvLzcGGPM2LFjzcMPP2yOHTtmjh49aoYMGWJmzJhRLbLt2bPHxMXFmffff9%2BcO3fObN%2B%2B3cTExJi///3vxpiLv06zZ882AwcONN9//70pKCgwY8eONaNGjfJ5rp%2BbPXu26dChg0lNTXWvVffXrDqjYKFSX331lWndurVxuVzutddff9306tXLh1NdmhMnTpjJkyeb/Px899prr71mevbsaTZu3GhuvPFGU1pa6t6bO3eu%2Ba//%2Bi9jjDHTp083ycnJHo939913m6VLl3pn%2BEtQVlZm7r77bvM///M/7oL10ksvmf79%2B3vcbsKECWbatGnGGGMefvhhM2vWLI/H6NKli1m/fr33Br%2BI7t27u4vuz/lztu%2B//960aNHCZGdnu9def/11c9ttt/ldrldffdUUFhaa1NTUC/5nfbGvmz59%2BphXX33VvXfy5EkTHR1tdu3aZfLz802rVq3Mvn373Ps7duwwbdu2NWfPnq3CRP9UWbYdO3aYRYsWeawNGDDALFmyxBhT%2Bet07tw506FDB7Nlyxb3fnZ2tmnZsqU5evRoFSb6SWW5ztu3b5/p0qWLmTlzpkfBqu6vWXXGIUJUKisrSw0aNFB4eLh7LSYmRgcOHFBRUZEPJ7u4sLAwzZ49W1dffbV7LTc3V1FRUcrKylLLli0VFBTk3ouOjna/pZ%2BVlaXo6GiPx4uOjlZGRoZ3hr8Eb775pkJCQtS3b1/32q/N/Wu5AgMD1bp162qT64cfftB3332nEydOqHfv3u5DKcePH/frbPXr11fr1q2Vnp6u4uJiHTt2TJs3b1a3bt38LtewYcNUu3btCvcq%2B7opKSlRdna2x35oaKgaNWqkjIwM7du3T0FBQR6Hu2NiYnTq1Cl9%2B%2B23VRPmFyrLdssttyg5Odn999LSUuXn56t%2B/fqSKn%2BdDh06pJMnTyomJsa937RpU1155ZXKysqqojT/VFkuSTLG6Omnn1ZKSorCwsLc6/7wmlVnFCxUyuVyeXzBSXKXLW%2BeP2BDRkaGVqxYoTFjxlSYq06dOnK5XCovL5fL5fIoldJPuatL5h9//FELFy7UU0895bH%2Ba7nOz13dcx09elSS9N577%2BmVV17RO%2B%2B8o6NHj%2BrJJ5/062yBgYFauHChPvjgA7Vv314JCQkqLS3VxIkT/TrXL1U264kTJ2SM%2BdV9l8ul0NBQBQQEeOxJ1fPfmrS0NNWsWVO9e/eWVHl2l8slSRe8zmFhYdUiW3p6ugICAjRw4ECPdae9Zt5GwcJFGWN8PcJl%2B%2BKLLzRixAhNnDhRCQkJv3q7n/9DUZ1zz549WwMHDlSzZs1%2B832rc67zs40cOVL169fXNddco3Hjxmnr1q2/6f7VzdmzZzV69Gjdfvvt%2Bvzzz/Xhhx%2Bqdu3amjRp0iXdv7rmqsjFZq1s3x9yGmM0d%2B5crV%2B/XkuWLFFISIjH3sXuW90cO3ZMCxYs0NNPP%2B3x79/P%2Bftr5isULFQqMjLS/d3XeS6XSwEBAYqMjPTRVL/N1q1b9fDDD2vKlCkaNmyYpJ9y/fI7LJfLpTp16igwMFAREREV5q4OmXfu3Kldu3Z5HK44r6K5CwoK3HNX51yS3Idzf/6dfoMGDWSM0blz5/w2286dO/Xdd9/pscceU%2B3atVW/fn2NHz9e77//vgIDA/021y9VNuv5r62K9uvWravIyEgVFRWprKzMY0%2BS6tatW/XDX4Ly8nJNnjxZW7du1RtvvKHrr7/evVdZ9vOv1S/3T5w44fNsc%2BbMUf/%2B/T0O853nhNfMlyhYqFRsbKxyc3PdP04u/XSorVmzZqpVq5YPJ7s0X375pVJTU7VgwQL179/fvR4bG6v9%2B/ertLTUvZaRkaEbbrjBvf/LH4X/%2Bb4vrV27VseOHdOtt96q%2BPh499v68fHxatGixQVzZ2ZmeuT6%2BTkfZWVl2rt3b7XIJUnXXHONQkNDtW/fPvfakSNHVKNGDSUmJvpttrKyMpWXl3t8t3/27FlJUkJCgt/m%2BqXKvm5CQkLUvHlzjyyFhYU6dOiQ2rRpo9atW8sYo6%2B//trjvmFhYWrSpInXMlRm1qxZ%2Buabb/TGG2%2BoYcOGHnuVvU4NGzZUeHi4x/4//vEPnT17VrGxsV6bvyJr167V6tWrFR8fr/j4eL300kt69913FR8f74jXzKe8eko9/NLdd99tpkyZYk6ePGmys7NN9%2B7dzYoVK3w91kWdO3fO3HHHHebNN9%2B8YO/MmTPm1ltvNS%2B88II5deqU2b17t%2BnYsaPZtm2bMcaY/fv3m7i4OLNt2zZTUlJiVq1aZdq1a2fy8vK8nOJCLpfL5Obmuj927dplWrRoYXJzc82RI0dMu3btzFtvvWVKSkrM9u3bTZs2bdw/5bNjxw7ToUMHs2vXLnPq1CmzcOFCk5iYaE6fPu3jVP80a9Ys06NHD3Pw4EHz448/miFDhpjJkyebH3/80W%2BzHT9%2B3HTu3Nn893//tzl16pQ5fvy4GT16tPnP//xPv81V0U%2BkXezr5vXXXzfdunVz/8j/tGnTzKBBg9z3nzBhghk5cqQ5duyYyc3NNYMGDTJz5szxai5jKs72%2Beefm06dOnn8VPLPXex1mjt3rhkwYID5/vvvzfHjx82oUaPMuHHjqjzLz1WU6%2Bf/luTm5ppZs2aZ8ePHuy%2Bt4S%2BvWXVEwcJF5ebmmpEjR5o2bdqYhIQE88ILL7ivgVKd/f3vfzctWrQwsbGxF3x89913Zv/%2B/Wbo0KEmNjbWdOvWzaxcudLj/ps2bTI9e/Y0MTEx5q677jJ/%2B9vffJSkcocPH3ZfpsEYY/72t7%2BZfv36mZiYGNOzZ88LLnmwcuVKk5iYaGJjY01SUpLZv3%2B/t0eu1JkzZ8zTTz9tOnXqZNq2bWtSU1NNUVGRMca/s2VkZJj77rvPdOzY0SQkJJgJEya4f0Tfn3Kd/xpq1aqVadWqlfvv51X2dVNeXm4WLFhgbrrpJtOmTRvz0EMPeVxbrrCw0KSkpJi2bduaTp06menTp5szZ85Ui2xPPPGEx9r5jwcffNB9/8pep59/Xrdr18489thjprCw0Oe5fumFF17wuExDdX/NqrMAYzhDDQAAwCbOwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAy/4fNgGOq3fLY9sAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common8446293992153190033”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>148</td> <td class=”number”>4.6%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>49</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>46</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.33333333333333</td> <td class=”number”>39</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>37</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>30</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.33333333333333</td> <td class=”number”>29</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-20.0</td> <td class=”number”>27</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-25.0</td> <td class=”number”>26</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (1266)</td> <td class=”number”>2561</td> <td class=”number”>79.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>194</td> <td class=”number”>6.0%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme8446293992153190033”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>49</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-91.66666666666666</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-90.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-88.88888888888889</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-87.5</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>900.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1000.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1100.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1300.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1500.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_FFRPTH_09_14”><s>PCH_FFRPTH_09_14</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_FFR_09_14”><code>PCH_FFR_09_14</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.9901</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_FFR_09_14”>PCH_FFR_09_14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>855</td>

</tr> <tr>

<th>Unique (%)</th> <td>26.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.9%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>124</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>5.6048</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>300</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>17.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-2507517649779266139”>

</div> <div class=”col-md-12 text-right”>

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<li role=”presentation” class=”active”><a href=”#quantiles-2507517649779266139”

aria-controls=”quantiles-2507517649779266139” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-2507517649779266139” aria-controls=”histogram-2507517649779266139”

role=”tab” data-toggle=”tab”>Histogram</a></li>

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role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-2507517649779266139” aria-controls=”extreme-2507517649779266139”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

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<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-40</td>

</tr> <tr>

<th>Q1</th> <td>-7.1429</td>

</tr> <tr>

<th>Median</th> <td>1.3992</td>

</tr> <tr>

<th>Q3</th> <td>16.667</td>

</tr> <tr>

<th>95-th percentile</th> <td>57.143</td>

</tr> <tr>

<th>Maximum</th> <td>300</td>

</tr> <tr>

<th>Range</th> <td>400</td>

</tr> <tr>

<th>Interquartile range</th> <td>23.81</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>34.369</td>

</tr> <tr>

<th>Coef of variation</th> <td>6.1321</td>

</tr> <tr>

<th>Kurtosis</th> <td>10.021</td>

</tr> <tr>

<th>Mean</th> <td>5.6048</td>

</tr> <tr>

<th>MAD</th> <td>21.221</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.319</td>

</tr> <tr>

<th>Sum</th> <td>17296</td>

</tr> <tr>

<th>Variance</th> <td>1181.2</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

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XevXq5fr/QYMG6Z///KdWrVqlqVOnSpLrequaXGquNtLS0tSnTx%2B3MZutkYqKSuu03QsFBQUqIuIqFReXqbKyytRt%2B5JVc3ma2Y%2BvK2HlY0Y2/2PVXJJ1s3kyl69%2B%2BLRkwapJbGys9uzZo8aNGyswMFBOp9Nt3ul0qkmTJoqOjlZJSYkqKysVFBTkmpOkJk2a1GpfMTEx1U4HOhxnVFHhmSdDZWWVx7btS1bN5Sn14XNl5WNGNv9j1VySdbNZKZd1Tnb%2BlzfeeEPr1693GysoKFDLli0VEhKi9u3bKz8/3zVXXFyso0ePKikpSR07dpRhGNq/f79r3m63KyIiQm3atPFaBgAA4L8sWbDOnz%2BvJ598Una7XeXl5Vq3bp0%2B/PBDjR49WpKUnp6uZcuWqaCgQCUlJcrOzlbHjh2VmJio6OhoDRw4UM8995xOnz6tU6dOafHixRoxYoRstl/MC34AAKAO/LYxJCYmSpIqKiokSZs3b5b046tNY8aMUWlpqSZNmiSHw6EWLVpo8eLFSkhIkCSNHj1aDodDd999t0pLS5WcnOx2UfoTTzyhxx9/XH379lWDBg10%2B%2B23V3szUwAAgIsJMOp6RTdqxeE4Y/o2bbZARUWFqqio1DLnrKX6k%2BvW5z722b5/jvcm3%2ByzfdeXY%2BYJZPM/Vs0lWTebJ3M1bRpu6vZqy5KnCAEAAHyJggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyrxesPn36KCcnRydPnqzTdrZv366UlBRlZWVVm9u0aZMGDx6sLl26aODAgXrrrbdcc88//7w6duyoxMREt49vv/1WknTu3Dn96U9/Us%2BePZWcnKzMzEwVFRXVaa0AAOCXxesF684779T69evVr18/3Xfffdq0aZMqKiquaBsvvvii5s6dq1atWlWb%2B%2BqrrzR16lRlZmbq888/14wZM/TEE09o586drtsMGTJEdrvd7ePqq6%2BWJC1cuFD5%2BfnKy8vTxo0bZRiGHn300bqFBgAAvyheL1gZGRlav3693nrrLbVv315PP/20UlNT9eyzz%2Brw4cO12kZISIhWrlxZY8FyOp0aP368%2BvXrJ5vNptTUVHXo0MGtYF1MRUWFVq5cqfvvv1/XXHONGjdurMmTJ2vbtm365ptvrjgrAAD4ZfLZNVjx8fGaPn26tm7dqhkzZuitt97SbbfdpnHjxumrr7665H3HjBmj8PDwGud69uypjIwM1/9XVFTI4XCoWbNmrrEDBw5o9OjRuv766zVo0CB99NFHkqSjR4/qzJkzio%2BPd922bdu2atiwofLz8%2BsSFwAA/ILYfLXj8vJy/fOf/9SqVav06aefqnXr1nrwwQdVWFiosWPHas6cObrjjjvqvJ/s7Gw1atRIt912mySpefPmatmypaZMmaKYmBjl5eVpwoQJWrNmjZxOpyQpIiLCbRsRERFXdB1WYWGhHA6H25jN1kgxMTF1TOMuKCjQ7V%2BrsGouT7PZfPf5svIxI5v/sWouybrZrJjL6wWroKBAK1eu1DvvvKPS0lINHDhQr732mrp27eq6Tffu3TV79uw6FSzDMJSdna1169Zp2bJlCgkJkSSNHDlSI0eOdN1u7Nixevfdd7VmzRr17NnTdd%2B6yMvLU05OjttYRkaGMjMz67Tdi4mIuMoj2/U1q%2BbylKioUF8vwdLHjGz%2Bx6q5JOtms1IurxesQYMGqU2bNho/fryGDh2qxo0bV7tNamqqTp8%2B/bP3UVVVpUcffVRfffWV3njjDbVs2fKSt4%2BNjVVhYaGio6Ml/XgdV2jof75Zff/992rSpEmt95%2BWlqY%2Bffq4jdlsjVRUVHoFKS4vKChQERFXqbi4TJWVVaZu25esmsvTzH58XQkrHzOy%2BR%2Br5pKsm82TuXz1w6fXC9ayZct0ww03XPZ2u3fv/tn7ePrpp/X111/rjTfeqFbg/vrXv6pLly666aabXGMFBQW67bbb1LJlS0VGRio/P1%2BxsbGSpH//%2B986f/68EhISar3/mJiYaqcDHY4zqqjwzJOhsrLKY9v2Javm8pT68Lmy8jEjm/%2Bxai7JutmslMvrJzvj4uI0YcIEbd682TX2t7/9TX/4wx9c10DVxRdffKE1a9YoNze3xlfHnE6n5syZo0OHDuncuXN65ZVXdPToUQ0bNkxBQUEaNWqUXnjhBZ08eVJFRUX685//rP79%2B7vexgEAAOByvP4K1rx583TmzBm1a9fONdarVy9t375d8%2BfP1/z58y%2B7jcTERElyvX/WT2XNbrfr7bff1pkzZ9S7d2%2B3%2B3Tv3l2vvPKKpkyZIunHa6%2BcTqfatWunv/3tb2revLkkKTMzU6WlpRoyZIgqKirUu3dvzZ49u865AQDAL0eAUdcruq9Qjx49tHbtWkVFRbmNFxUV6fbbb9fHH3/szeV4jcNxxvRt2myBiooKVVFRqWVeUpXqT65bn/Ovx%2BJ7k2/22b7ryzHzBLL5H6vmkqybzZO5mjat%2BW2dPM3rpwjPnj3r%2Bo0%2Bt4UEBqqsrMzbywEAADCd1wtW9%2B7dNX/%2BfH3//feusW%2B%2B%2BUZz5sxxe6sGAAAAf%2BX1a7BmzJihe%2B%2B9VzfddJPCwsJUVVWl0tJStWzZUn//%2B9%2B9vRwAAADTeb1gtWzZUu%2B%2B%2B64%2B/PBDHT16VIGBgWrTpo169OihoKAgby8HAADAdD75UznBwcHq16%2BfL3YNAADgcV4vWMeOHdOCBQv09ddf6%2BzZs9Xm33//fW8vCQAAwFQ%2BuQarsLBQPXr0UKNGjby9ewAAAI/zesHas2eP3n//fdff/QMAALAar79NQ5MmTXjlCgAAWJrXC9b48eOVk5MjL7%2BBPAAAgNd4/RThhx9%2BqC%2B//FKrVq1SixYtFBjo3vHefPNNby8JAADAVF4vWGFhYerZs6e3dwsAAOA1Xi9Y8%2BbN8/YuAQAAvMrr12BJ0qFDh/T888/r0UcfdY393//9ny%2BWAgAAYDqvF6wdO3Zo8ODB2rRpk9atWyfpxzcfHTNmDG8yCgAALMHrBWvhwoWaNm2a1q5dq4CAAEk//n3C%2BfPna/Hixd5eDgAAgOm8XrD%2B/e9/Kz09XZJcBUuSbrnlFhUUFHh7OQAAAKbzesEKDw%2Bv8W8QFhYWKjg42NvLAQAAMJ3XC9b111%2Bvp59%2BWiUlJa6xw4cPa/r06brpppu8vRwAAADTef1tGh599FHdc889Sk5OVmVlpa6//nqVlZWpffv2mj9/vreXAwAAYDqvF6zmzZtr3bp1%2BuCDD3T48GE1bNhQbdq00c033%2Bx2TRYAAIC/8nrBkqQGDRqoX79%2Bvtg1AACAx3m9YPXp0%2BeSr1TxXlgAAMDfeb1g3XbbbW4Fq7KyUocPH5bdbtc999zj7eUAAACYzusFa%2BrUqTWOb9y4Uf/617%2B8vBoAAADz%2BeRvEdakX79%2Bevfdd329DAAAgDqrNwVr7969MgzD18sAAACoM6%2BfIhw9enS1sbKyMhUUFGjAgAFXtK3t27dr%2BvTpSk5O1sKFC93m1q9fryVLluj48eNq06aNHnroIfXo0UOSVFVVpUWLFmndunUqLi5WUlKSZs%2BerZYtW0qSnE6nZs%2Berc8%2B%2B0yBgYFKTU3VrFmz1LBhw5%2BZGgAA/JJ4vWC1bt262m8RhoSEaMSIERo5cmStt/Piiy9q5cqVatWqVbW5ffv2afr06crJydGNN96ojRs36oEHHtCGDRvUvHlzLV%2B%2BXGvXrtWLL76oZs2aaeHChcrIyNDq1asVEBCgWbNm6fz581q3bp3Ky8s1adIkZWdna%2BbMmXXODwAArM/rBcusd2sPCQnRypUr9dRTT%2BncuXNucytWrFBqaqpSU1MlSYMHD9brr7%2BuNWvW6I9//KPy8vI0duxYtW3bVpKUlZWl5ORk7d69Wy1atNDmzZv1j3/8Q9HR0ZKk%2B%2B%2B/X5MmTdL06dPVoEEDU9YPAACsy%2BsF65133qn1bYcOHXrRuTFjxlx0Lj8/31WuftKpUyfZ7XadPXtWBw8eVKdOnVxzYWFhatWqlex2u86cOaOgoCDFxcW55uPj4/XDDz/o0KFDbuMAAAA18XrBeuyxx1RVVVXtgvaAgAC3sYCAgEsWrEtxOp2KjIx0G4uMjNTBgwf1/fffyzCMGueLiorUuHFjhYWFuZ3G/Om2RUVFtdp/YWGhHA6H25jN1kgxMTE/J85FBQUFuv1rFVbN5Wk2m%2B8%2BX1Y%2BZmTzP1bNJVk3mxVzeb1gvfTSS3rllVc0YcIExcXFyTAMHThwQC%2B%2B%2BKLuuusuJScnm7Kfy/1G4qXm6/rbjHl5ecrJyXEby8jIUGZmZp22ezEREVd5ZLu%2BZtVcnhIVFerrJVj6mJHN/1g1l2TdbFbK5ZNrsHJzc9WsWTPXWLdu3dSyZUuNGzdO69atq/M%2BoqKi5HQ63cacTqeio6PVuHFjBQYG1jjfpEkTRUdHq6SkRJWVlQoKCnLNSVKTJk1qtf%2B0tDT16dPHbcxma6SiotKfG6lGQUGBioi4SsXFZaqsrDJ1275k1VyeZvbj60pY%2BZiRzf9YNZdk3WyezOWrHz69XrCOHDlS7fScJEVEROjEiROm7CMhIUF79uxxG7Pb7Ro0aJBCQkLUvn175efn64YbbpAkFRcX6%2BjRo0pKSlJsbKwMw9D%2B/fsVHx/vum9ERITatGlTq/3HxMRUOx3ocJxRRYVnngyVlVUe27YvWTWXp9SHz5WVjxnZ/I9Vc0nWzWalXF4/2RkbG6v58%2Be7Xc9UXFysBQsW6Ne//rUp%2Bxg1apQ%2B%2BeQTbdu2TefOndPKlSt15MgRDR48WJKUnp6uZcuWqaCgQCUlJcrOzlbHjh2VmJio6OhoDRw4UM8995xOnz6tU6dOafHixRoxYoRsNq/3UQAA4Ie83hhmzJihKVOmKC8vT6GhoQoMDFRJSYkaNmyoxYsX13o7iYmJkqSKigpJ0ubNmyX9%2BGpThw4dlJ2drXnz5unEiRNq166dli5dqqZNm0r68c1OHQ6H7r77bpWWlio5OdntmqknnnhCjz/%2BuPr27asGDRro9ttvV1ZWllmfAgAAYHEBhg/%2BPk1ZWZk%2B%2BOADnTp1SoZhqFmzZvrtb3%2Br8PBwby/FaxyOM6Zv02YLVFRUqIqKSi3zkqpUf3Ld%2BtzHPtv3z/He5Jt9tu/6csw8gWz%2Bx6q5JOtm82Supk190y18cs7rqquuUt%2B%2BfXXq1CnXn6cBAACwCq9fg3X27FlNnz5dXbp00a233irpx2uw7rvvPhUXF3t7OQAAAKbzesF69tlntW/fPmVnZysw8D%2B7r6ysVHZ2treXAwAAYDqvF6yNGzfqL3/5i2655RbXu6VHRERo3rx52rRpk7eXAwAAYDqvF6zS0lK1bt262nh0dLR%2B%2BOEHby8HAADAdF4vWL/%2B9a/1r3/9S5L7n6TZsGGDfvWrX3l7OQAAAKbz%2Bm8R/u53v9ODDz6oO%2B%2B8U1VVVXr11Ve1Z88ebdy4UY899pi3lwMAAGA6rxestLQ02Ww2vf766woKCtILL7ygNm3aKDs7W7fccou3lwMAAGA6rxes06dP684779Sdd97p7V0DAAB4hdevwerbt6988ObxAAAAXuP1gpWcnKz33nvP27sFAADwGq%2BfIrzmmmv01FNPKTc3V7/%2B9a/VoEEDt/kFCxZ4e0kAAACm8nrBOnjwoH7zm99IkoqKiry9ewAAAI/zWsHKysrSwoUL9fe//901tnjxYmVkZHhrCQAAAF7htWuwtmzZUm0sNzfXW7sHAADwGq8VrJp%2Bc5DfJgQAAFbktYL10x92vtwYAACAv/P62zQAAABYHQULAADAZF77LcLy8nJNmTLlsmO8DxYAAPB3XitYXbt2VWFh4WXHAAAA/J3XCtZ/v/8VAACAlXENFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJvPZbhN70%2Beef695773UbMwxD5eXlWrZsmcaMGaPg4GC3%2Bf/5n//RrbfeKklatmyZli9fLofDobi4OD322GNKSEjw2voBAIB/s2TB6t69u%2Bx2u9vYCy%2B8oP3790uSYmNjtWXLlhrvu2XLFj3//PN66aWXFBcXp2XLlmnChAnatGmTGjVq5PG1AwAA//eLOEX4//7f/9Orr76qhx9%2B%2BLK3zcvL0/Dhw9W5c2c1bNiWk%2BiLAAAZUklEQVRQ9913nyRp69atnl4mAACwCEu%2BgnWhRYsW6c4779SvfvUrHTt2TKWlpcrIyNDOnTsVHByse%2B%2B9V2PHjlVAQIDy8/N12223ue4bGBiojh07ym63a9CgQbXaX2FhoRwOh9uYzdZIMTExpuYKCgp0%2B9cqrJrL02w2332%2BrHzMyOZ/rJpLsm42K%2BayfME6fvy4Nm3apE2bNkmSwsLC1KFDB91zzz1auHChPvvsM02aNEnh4eEaMWKEnE6nIiMj3bYRGRmpoqKiWu8zLy9POTk5bmMZGRnKzMyse6AaRERc5ZHt%2BppVc3lKVFSor5dg6WNGNv9j1VySdbNZKZflC9by5cs1YMAANW3aVJIUHx/v9md7evToodGjR2vVqlUaMWKEpB8viK%2BLtLQ09enTx23MZmukoqLSOm33QkFBgYqIuErFxWWqrKwyddu%2BZNVcnmb24%2BtKWPmYkc3/WDWXZN1snszlqx8%2BLV%2BwNm7cqOnTp1/yNrGxsdq4caMkKSoqSk6n023e6XSqffv2td5nTExMtdOBDscZVVR45slQWVnlsW37klVzeUp9%2BFxZ%2BZiRzf9YNZdk3WxWymWdk5012Ldvn06cOKGbb77ZNfbee%2B/pf//3f91ud%2BjQIbVs2VKSlJCQoPz8fNdcZWWl9u7dq86dO3tn0QAAwO9ZumDt3btXjRs3VlhYmGusQYMGeuaZZ/TRRx%2BpvLxcH3/8sd5%2B%2B22lp6dLktLT0/XOO%2B9o165dKisr05IlSxQcHKxevXr5KAUAAPA3lj5F%2BO2337quvfpJv379NGPGDD355JM6efKkrr76as2YMUMDBgyQJPXs2VMPPfSQJk%2BerO%2B%2B%2B06JiYnKzc1Vw4YNfREBAAD4oQCjrld0o1YcjjOmb9NmC1RUVKiKikotc85aqj%2B5bn3uY5/t%2B%2Bd4b/LNl7%2BRh9SXY%2BYJZPM/Vs0lWTebJ3M1bRpu6vZqy9KnCAEAAHyBggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAym68X4ClxcXFq0KCBAgICXGOjRo3SrFmztGPHDi1YsECHDh3SNddco/Hjx2vw4MGu2y1btkzLly%2BXw%2BFQXFycHnvsMSUkJPgiBlBrtz73sa%2BXUGvvTb7Z10sAAI%2BybMGSpA0bNqhFixZuY4WFhbr//vv12GOP6Y477tAXX3yhiRMnqk2bNkpMTNSWLVv0/PPP66WXXlJcXJyWLVumCRMmaNOmTWrUqJGPkgAAAH/yiztFuHbtWrVu3VojRoxQSEiIUlJS1KdPH61YsUKSlJeXp%2BHDh6tz585q2LCh7rvvPknS1q1bfblsAADgRyxdsBYsWKBevXqpW7dumjVrlkpLS5Wfn69OnTq53a5Tp07as2ePJFWbDwwMVMeOHWW32726dgAA4L8se4rwuuuuU0pKip555hkdO3ZMkydP1pw5c%2BR0OtWsWTO32zZu3FhFRUWSJKfTqcjISLf5yMhI13xtFBYWyuFwuI3ZbI0UExPzM9PULCgo0O1fq7BqLvyHzeY/x9bKj0erZrNqLsm62ayYy7IFKy8vz/Xfbdu21dSpUzVx4kR17dr1svc1DKPO%2B87JyXEby8jIUGZmZp22ezEREVd5ZLu%2BZtVckKKiQn29hCtm5cejVbNZNZdk3WxWymXZgnWhFi1aqLKyUoGBgXI6nW5zRUVFio6OliRFRUVVm3c6nWrfvn2t95WWlqY%2Bffq4jdlsjVRUVPozV1%2BzoKBARURcpeLiMlVWVpm6bV%2Byai78h9nPBU%2By8uPRqtmsmkuybjZP5vLVD3SWLFh79%2B7VmjVr9Mgjj7jGCgoKFBwcrNTUVP3jH/9wu/2ePXvUuXNnSVJCQoLy8/M1bNgwSVJlZaX27t2rESNG1Hr/MTEx1U4HOhxnVFHhmSdDZWWVx7btS1bNBfnlcbXy49Gq2ayaS7JuNivlss7Jzv/SpEkT5eXlKTc3V%2BfPn9fhw4e1aNEipaWlaciQITpx4oRWrFihc%2BfO6YMPPtAHH3ygUaNGSZLS09P1zjvvaNeuXSorK9OSJUsUHBysXr16%2BTYUAADwG5Z8BatZs2bKzc3VggULXAVp2LBhysrKUkhIiJYuXaq5c%2Bdqzpw5io2N1bPPPqtrr71WktSzZ0899NBDmjx5sr777jslJiYqNzdXDRs29HEqAADgLyxZsCSpe/fuevPNNy86t3r16ove93e/%2B51%2B97vfeWppAADA4ix5ihAAAMCXKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAym68XgF%2BOW5/72NdLAADAK3gFCwAAwGQULAAAAJNZtmCdOHFCGRkZSk5OVkpKih555BEVFxfr%2BPHjiouLU2JiotvHyy%2B/7Lrv%2BvXrdccdd6hLly4aPny4PvroIx8mAQAA/say12BNmDBBCQkJ2rJli86cOaOMjAw988wzmjhxoiTJbrfXeL99%2B/Zp%2BvTpysnJ0Y033qiNGzfqgQce0IYNG9S8eXNvRgAAAH7Kkq9gFRcXKyEhQVOmTFFoaKiaN2%2BuYcOGaefOnZe974oVK5SamqrU1FSFhIRo8ODB6tChg9asWeOFlQMAACuw5CtYERERmjdvntvYyZMnFRMT4/r/hx9%2BWJ988okqKio0cuRIZWZmqkGDBsrPz1dqaqrbfTt16nTRV7xqUlhYKIfD4TZmszVy278ZgoIC3f4F/IXN5j%2BPWSs/z6yazaq5JOtms2IuSxasC9ntdr3%2B%2ButasmSJgoOD1aVLF/Xv319PPfWU9u3bpwcffFA2m02TJk2S0%2BlUZGSk2/0jIyN18ODBWu8vLy9POTk5bmMZGRnKzMw0Jc%2BFIiKu8sh2AU%2BJigr19RKumJWfZ1bNZtVcknWzWSmX5QvWF198oYkTJ2rKlClKSUmRJL355puu%2BaSkJI0fP15Lly7VpEmTJEmGYdRpn2lpaerTp4/bmM3WSEVFpXXa7oWCggIVEXGViovLVFlZZeq2AU8y%2B7ngSVZ%2Bnlk1m1VzSdbN5slcvvqBztIFa8uWLZo2bZpmzZqloUOHXvR2sbGx%2Bvbbb2UYhqKiouR0Ot3mnU6noqOja73fmJiYaqcDHY4zqqjwzJOhsrLKY9sGPMEfH69Wfp5ZNZtVc0nWzWalXNY52XmBL7/8UtOnT9eiRYvcytWOHTu0ZMkSt9seOnRIsbGxCggIUEJCgvbs2eM2b7fb1blzZ6%2BsGwAA%2BD9LFqyKigrNnDlTU6dOVY8ePdzmwsPDtXjxYq1evVrl5eWy2%2B16%2BeWXlZ6eLkkaNWqUPvnkE23btk3nzp3TypUrdeTIEQ0ePNgXUQAAgB%2By5CnCXbt2qaCgQHPnztXcuXPd5jZs2KCFCxcqJydHf/rTnxQeHq67775b99xzjySpQ4cOys7O1rx583TixAm1a9dOS5cuVdOmTX0RBQAA%2BCFLFqxu3brpwIEDF52PjY1V//79Lzo/YMAADRgwwBNLAwAAvwCWPEUIAADgSxQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExm8/UCUDfdHtvg6yUAludvz7P3Jt/s6yUAv3i8ggUAAGAyChYAAIDJKFgAAAAm4xosAF5363Mf%2B3oJAOBRvIIFAABgMgoWAACAyShYAAAAJqNg1eDEiRP64x//qOTkZPXu3VvPPvusqqqqfL0sAADgJ7jIvQYPPvig4uPjtXnzZn333XcaP368rr76av3%2B97/39dIAAIAfoGBdwG63a//%2B/Xr11VcVHh6u8PBwjR07Vq%2B99hoFCwBM5m%2B/Ucq75KO2KFgXyM/PV2xsrCIjI11j8fHxOnz4sEpKShQWFnbZbRQWFsrhcLiN2WyNFBMTY%2Bpag4I4wwugOn8rLf7EZvPt192fvu5b7eu/FXNRsC7gdDoVERHhNvZT2SoqKqpVwcrLy1NOTo7b2AMPPKAHH3zQvIXqxyJ3T/OvlZaWZnp586XCwkLl5eVZLpdk3WxWzSWRzR9ZNZf0Y7bXXnvJctmsmMs6VdFEhmHU6f5paWlatWqV20daWppJq/sPh8OhnJycaq%2BW%2BTur5pKsm82quSSy%2BSOr5pKsm82KuXgF6wLR0dFyOp1uY06nUwEBAYqOjq7VNmJiYizTwAEAwJXjFawLJCQk6OTJkzp9%2BrRrzG63q127dgoNDfXhygAAgL%2BgYF2gU6dOSkxM1IIFC1RSUqKCggK9%2BuqrSk9P9/XSAACAnwiaPXv2bF8vor757W9/q3Xr1unJJ5/Uu%2B%2B%2BqxEjRmjcuHEKCAjw9dKqCQ0N1Q033GC5V9esmkuybjar5pLI5o%2Bsmkuybjar5Qow6npFNwAAANxwihAAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULD9ht9vVv39/jRo1qtrcjh07NGLECF1//fUaNGiQ1qxZ4za/bNkyDRw4UNdff73S09O1Z88eby37ivTp00cJCQlKTEx0fUyYMME1v2/fPt11113q2rWrBgwYoFdeecWHq71yJ06c0B//%2BEclJyerd%2B/eevbZZ1VVVeXrZV2xuLi4asfpySeflHT5x2J9s337dqWkpCgrK6va3Pr163XHHXeoS5cuGj58uD766CPXXFVVlRYuXKi%2Bffuqe/fuGjdunI4dO%2BbNpV/WxbKtWrVK1157rdvxS0xM1FdffSWp/mc7ceKEMjIylJycrJSUFD3yyCMqLi6WdPmvEZc6pvXBxbIdP35ccXFx1Y7Zyy%2B/7Lpvfc62f/9%2B3XPPPeratatSUlI0efJkORwOSdb5/lUjA/Xe6tWrjdTUVGPcuHHGyJEj3ea%2B%2BeYb47rrrjNWrFhhnD171vj444%2BNpKQk46uvvjIMwzDef/99o1u3bsauXbuMsrIyY%2BnSpcbNN99slJaW%2BiLKJfXu3dv49NNPa5wrKyszfvvb3xrPP/%2B8UVpaauzZs8e44YYbjI0bN3p5lT/fsGHDjJkzZxrFxcXG4cOHjQEDBhivvPKKr5d1xTp06GAcO3as2vjlHov1TW5urjFgwABj9OjRxuTJk93m9u7dayQkJBjbtm0zzp49a6xevdro3LmzcfLkScMwDGPZsmVG7969jYMHDxpnzpwxnnjiCeOOO%2B4wqqqqfBGlmktle/vtt4277rrrovet79luv/1245FHHjFKSkqMkydPGsOHDzdmzJhx2a8Rlzum9cHFsh07dszo0KHDRe9Xn7OdO3fOuOmmm4ycnBzj3LlzxnfffWfcddddxv3332%2Bp71814RUsP3Du3Dnl5eWpc%2BfO1ebWrl2r1q1ba8SIEQoJCVFKSor69OmjFStWSJLy8vI0fPhwde7cWQ0bNtR9990nSdq6datXM9TVtm3bVF5erokTJ6pRo0aKj4/XyJEjlZeX5%2Bul1Yrdbtf%2B/fs1depUhYeHq3Xr1ho7dqzfrL82LvdYrG9CQkK0cuVKtWrVqtrcihUrlJqaqtTUVIWEhGjw4MHq0KGD66frvLw8jR07Vm3btlVYWJiysrJUUFCg3bt3eztGjS6V7XLqc7bi4mIlJCRoypQpCg0NVfPmzTVs2DDt3Lnzsl8jLndMfe1S2S6nPmcrKytTVlaWxo8fr%2BDgYEVHR6t///76%2BuuvLf/9i4LlB0aOHKlmzZrVOJefn69OnTq5jXXq1Mn1MuqF84GBgerYsaPsdrvnFlwHy5YtU79%2B/dSlSxdlZmbqu%2B%2B%2Bk/Rjjri4OAUFBblu%2B98567v8/HzFxsYqMjLSNRYfH6/Dhw%2BrpKTEhyv7eRYsWKBevXqpW7dumjVrlkpLSy/7WKxvxowZo/Dw8BrnLpbFbrfr7NmzOnjwoNt8WFiYWrVqVW%2BeV5fKJkknT57U73//e3Xv3l19%2B/bV6tWrJaneZ4uIiNC8efN09dVXu8ZOnjypmJiYy36NuNQxrQ8ule0nDz/8sHr06KEbb7xRCxYsUHl5uaT6nS0yMlIjR46UzWaTJB06dEj/%2BMc/dOutt1ru%2B9eFKFh%2Bzul0KiIiwm2scePGKioqcs3/9zd16ccH/E/z9UnHjh2VlJSk1atXa/369XI6nZo0aZKki%2Bd0Op1%2BcR1TTev/6bjUx2NxKdddd51SUlK0adMm5eXladeuXZozZ85lH4v%2B5FLPm%2B%2B//16GYfjN8%2BpC0dHRat26taZNm6aPP/5YDz30kGbMmKEdO3b4XTa73a7XX39dEydOvOzXCH/6Wii5ZwsODlaXLl3Uv39/bd26Vbm5uVqzZo3%2B%2Bte/SvKPr/MnTpxQQkKCbrvtNiUmJiozM9NS379qQsGqB1avXq24uLgaP1atWlXn7RuGYcIq6%2B5yORcvXqzx48crNDRU11xzjR5//HF9/vnnOnr06EW3GRAQ4MUEdVNfjkNd5eXlaeTIkQoODlbbtm01depUrVu3zvXTtFVc7nj56/Hs1auXXnrpJXXq1EnBwcEaNGiQ%2Bvfv7/a1xh%2ByffHFFxo3bpymTJmilJSUi97uv79G%2BEMuqXq2mJgYvfnmm%2Brfv78aNGigpKQkjR8/3q%2BOWWxsrOx2uzZs2KAjR47o4YcfrtX96nuuS7H5egGQhgwZoiFDhvys%2B0ZFRcnpdLqNFRUVKTo6%2BqLzTqdT7du3/3mLrYMrzRkbGytJKiwsVHR0tI4cOeI273Q61bhxYwUG1v%2BfE6Kjo2s8DgEBAa5j5a9atGihyspKBQYGXvKx6E8u9ryJjo52PeZqmm/SpIk3l2ma2NhY7dmzx2%2BybdmyRdOmTdOsWbM0dOhQSbrs14hLHdP6pKZsNYmNjdW3334rwzD8JltAQIBat26trKwsjR49WqmpqX7z/evnqP/fmXBJiYmJ1a5x2bNnj%2BuC%2BISEBOXn57vmKisrtXfv3hovmPelEydO6PHHH9f58%2BddYwUFBZKkli1bKiEhQQcOHFBFRYVr3m6317scF5OQkKCTJ0/q9OnTrjG73a527dopNDTUhyu7Mnv37tX8%2BfPdxgoKChQcHKzU1NRLPhb9SUJCQrUsPz3eQkJC1L59e7fnVXFxsY4ePaqkpCRvL/WKvfHGG1q/fr3bWEFBgVq2bOkX2b788ktNnz5dixYtcisgl/sacaljWl9cLNuOHTu0ZMkSt9seOnRIsbGxCggIqNfZduzYoYEDB7pdyvHTD8VJSUmW%2BP51MRQsP3fHHXfoxIkTWrFihc6dO6cPPvhAH3zwgev9stLT0/XOO%2B9o165dKisr05IlSxQcHKxevXr5duEXaNKkibZs2aL58%2Bfrhx9%2B0DfffKN58%2Bapd%2B/eatasmVJTUxUWFqYlS5aorKxMu3fv1sqVK5Wenu7rpddKp06dlJiYqAULFqikpEQFBQV69dVX/Wb9P2nSpIny8vKUm5ur8%2BfP6/Dhw1q0aJHS0tI0ZMiQSz4W/cmoUaP0ySefaNu2bTp37pxWrlypI0eOaPDgwZJ%2BfF4tW7ZMBQUFKikpUXZ2tjp27KjExEQfr/zyzp8/ryeffFJ2u13l5eVat26dPvzwQ40ePVpS/c5WUVGhmTNnaurUqerRo4fb3OW%2BRlzumPrapbKFh4dr8eLFWr16tcrLy2W32/Xyyy/7RbaEhASVlJTo2WefVVlZmU6fPq3nn39e3bp1U3p6uiW%2Bf12Uj94eAldgwIABRkJCgtGxY0cjLi7OSEhIMBISEozjx48bhmEYn332mTF48GAjPj7eGDBgQLX3hlq%2BfLmRmppqJCQkGOnp6caBAwd8EeOy9u/fb4wdO9bo2rWr0bVrV%2BORRx4xvv/%2Be9f8gQMHjNGjRxsJCQlGr169jOXLl/twtVfu5MmTxn333WckJSUZKSkpxl/%2B8pd6895CV%2BKzzz4z0tLSjOuuu8644YYbjHnz5hlnz551zV3qsVif/PQ8uvbaa41rr73W9f8/2bhxozFgwAAjPj7eGDJkiPHZZ5%2B55qqqqoxFixYZN910k5GUlGT84Q9/qBfvOfSTS2WrqqoyFi9ebPTu3dtISEgwbrnlFmPLli2u%2B9bnbJ9//rnRoUMHV57//jh%2B/Phlv0Zc6pj62uWybdq0yRg8eLCRlJRk3HzzzcYLL7xgVFZWuu5fn7Pt37/fuOuuu4ykpCTjxhtvNCZPnmycOnXKMAzrfP%2BqSYBh%2BPEVZAAAAPUQpwgBAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABM9v8BwcXYqYAQqx8AAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-2507517649779266139”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>547</td> <td class=”number”>17.0%</td> <td>

<div class=”bar” style=”width:27%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>75</td> <td class=”number”>2.3%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.333333333</td> <td class=”number”>71</td> <td class=”number”>2.2%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.333333333</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>55</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>51</td> <td class=”number”>1.6%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-25.0</td> <td class=”number”>51</td> <td class=”number”>1.6%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-16.666666667</td> <td class=”number”>46</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>44</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (844)</td> <td class=”number”>2030</td> <td class=”number”>63.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>124</td> <td class=”number”>3.9%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-2507517649779266139”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-80.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-75.0</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-66.666666667</td> <td class=”number”>12</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-60.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>166.666666667</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>183.333333333</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>12</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>250.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>300.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_FMRKTPTH_09_16”><s>PCH_FMRKTPTH_09_16</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_FMRKT_09_16”><code>PCH_FMRKT_09_16</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99444</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_FMRKT_09_16”>PCH_FMRKT_09_16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>126</td>

</tr> <tr>

<th>Unique (%)</th> <td>3.9%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>19.6%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>628</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>37.589</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>1600</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>46.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-7581544685750232050”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-7581544685750232050,#minihistogram-7581544685750232050”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-7581544685750232050”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-7581544685750232050”

aria-controls=”quantiles-7581544685750232050” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-7581544685750232050” aria-controls=”histogram-7581544685750232050”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-7581544685750232050” aria-controls=”common-7581544685750232050”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-7581544685750232050” aria-controls=”extreme-7581544685750232050”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-7581544685750232050”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-50</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>50</td>

</tr> <tr>

<th>95-th percentile</th> <td>200</td>

</tr> <tr>

<th>Maximum</th> <td>1600</td>

</tr> <tr>

<th>Range</th> <td>1700</td>

</tr> <tr>

<th>Interquartile range</th> <td>50</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>99.005</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.6339</td>

</tr> <tr>

<th>Kurtosis</th> <td>40.191</td>

</tr> <tr>

<th>Mean</th> <td>37.589</td>

</tr> <tr>

<th>MAD</th> <td>62.163</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>4.4185</td>

</tr> <tr>

<th>Sum</th> <td>97055</td>

</tr> <tr>

<th>Variance</th> <td>9801.9</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-7581544685750232050”>

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Lt27Ws6VkRExEWXA222Uyor87wXppk8PX95eYXHZ6gM8pOf/N6bX%2BIcXK8qeUH1vffe06pVq5zG8vPzFRUVpYCAADVr1kx5eXmOuaKiIh0%2BfFgtW7ZU8%2BbNZRiG9u3b55i3Wq0KDg5W48aNXZYBAAB4ripZsM6dO6dp06bJarWqtLRUubm5%2BuyzzzRgwABJUkpKihYtWqT8/HwVFxcrMzNTzZs3V3x8vMLDw9WtWze98sorOnnypI4fP665c%2Beqb9%2B%2Bsli85g0/AABQCR7bGOLj4yVJZWVlkqT169dLOv9u08CBA1VSUqKRI0fKZrOpQYMGmjt3ruLi4iRJAwYMkM1m0yOPPKKSkhIlJCQ43ZT%2B/PPP67nnnlOnTp1UrVo1Pfjggxd9mCkAAMDl%2BBiVvaMb18RmO3VT9nv/K1tvyn5vhtWj7nX3Em6IxeKrsLBaKiws8cr7D8hPfvJ7b37J889B3bpBbjlulbxECAAA4E4ULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAEzmsQVr8%2BbNSkxMVHp6%2BkVz69atU8%2BePdW6dWt169ZNf//73x1zc%2BbMUfPmzRUfH%2B/0deLECUnS2bNn9ec//1nt2rVTQkKC0tLSVFhY6LJcAADA87m8YHXs2FFZWVk6duzYDe/jjTfe0PTp09WwYcOL5r7%2B%2BmuNGTNGaWlp2r59uyZMmKDnn39eO3bscGzz0EMPyWq1On3VqVNHkjRr1izl5eUpJydHa9eulWEYevbZZ294rQAAwPu4vGA9/PDDWrVqlTp37qwnnnhC69atU1lZ2XXtIyAgQEuXLr1kwbLb7RoyZIg6d%2B4si8WipKQkRUdHOxWsyykrK9PSpUs1fPhw3XLLLQoNDdWoUaO0adMm/fDDD9e1RgAA4L0srj5gamqqUlNTlZeXp9zcXL344ouaOnWqevXqpb59%2B6px48ZX3cfAgQMvO9euXTu1a9fO8eeysjLZbDbVq1fPMfbNN99owIAB%2Bvbbb3XLLbfo2WefVdu2bXX48GGdOnVKsbGxjm2bNGmi6tWrKy8vz2kfV1JQUCCbzeY0ZrHUVERExDU9vqqyWDzzirSfn6/Td29DfvL/8ru38fb8EufgRrm8YF0QGxur2NhYjR07VqtWrdKUKVO0YMECJSYmauTIkWrZsqUpx8nMzFTNmjX1wAMPSJLq16%2BvqKgojR49WhEREcrJydHQoUO1YsUK2e12SVJwcLDTPoKDg6/rPqycnBxlZWU5jaWmpiotLa2SaTxbWFgtdy%2BhUoKDa7h7CW5FfvJ7M2/PL3EOrpfbClZpaak%2B/vhjLV%2B%2BXF988YUaNWqkp556SgUFBRo0aJCmTp2qHj163PD%2BDcNQZmamcnNztWjRIgUEBEiS%2BvXrp379%2Bjm2GzRokD766COtWLHC8c6XYRiVypacnKyOHTs6jVksNVVYWFKp/Xo6T83v5%2Ber4OAaKio6rfLyCncvx%2BXIT37ye29%2ByfPPgbt%2BuHd5wcrPz9fSpUv1wQcfqKSkRN26ddPbb7%2BtO%2B64w7FNmzZtNGXKlBsuWBUVFXr22Wf19ddf67333lNUVNQVt4%2BMjFRBQYHCw8Mlnb%2BPq1at/3tCfvrpJ9WuXfuajx8REXHR5UCb7ZTKyjzvhWkmT89fXl7h8Rkqg/zkJ7/35pc4B9fL5QWre/fuaty4sYYMGaJevXopNDT0om2SkpJ08uTJGz7Giy%2B%2BqO%2B%2B%2B07vvffeRft/7bXX1Lp1a91zzz2Osfz8fD3wwAOKiopSSEiI8vLyFBkZKUn69ttvde7cOcXFxd3wegAAgHdxecFatGiR7rrrrqtut2vXrhva/5dffqkVK1Zo1apVlyxvdrtdU6dO1WuvvabIyEgtXrxYhw8fVu/eveXn56f%2B/fvr9ddfV3x8vKpXr66//OUv6tKli%2BNjHAAAAK7G5QUrJiZGQ4cOVd%2B%2BfdW5c2dJ0l//%2Bldt3bpVL7/88iVL0a/Fx8dLkuPjHdavXy9JslqtWrZsmU6dOqUOHTo4PaZNmzZasGCBRo8eLen8vVd2u11NmzbVX//6V9WvX1%2BSlJaWppKSEj300EMqKytThw4dNGXKFFOyAwAA7%2BBjVPaO7us0fvx4HTlyRC%2B88IIaNWokSTp06JCmTZumunXr6qWXXnLlclzGZjt1U/Z7/ytbb8p%2Bb4bVo%2B519xJuiMXiq7CwWiosLPHK%2Bw/IT37ye29%2ByfPPQd26QW45rsvfwdqyZYtWrlypsLAwx1ijRo2UmZmpBx980NXLAQAAMJ3LPzXszJkzjo9McFqIr69Onz7t6uUAAACYzuUFq02bNnrppZf0008/OcZ%2B%2BOEHTZ061emjGgAAADyVyy8RTpgwQY8//rjuueceBQYGqqKiQiUlJYqKitLf/vY3Vy8HAADAdC4vWFFRUfroo4/02Wef6fDhw/L19VXjxo3Vtm1b%2Bfn5uXo5AAAApnPLr8rx9/d3fEQDAABAVePygnXkyBHNnDlT3333nc6cOXPR/CeffOLqJQEAAJjKLfdgFRQUqG3btqpZs6arDw8AAHDTubxg7d69W5988onjFysDAABUNS7/mIbatWvzzhUAAKjSXF6whgwZoqysLLn4N/QAAAC4jMsvEX722Wf66quvtHz5cjVo0EC%2Bvs4d7/3333f1kgAAAEzl8oIVGBiodu3aufqwAAAALuPygpWRkeHqQwIAALiUy%2B/BkqQDBw5ozpw5evbZZx1j//M//%2BOOpQAAAJjO5QVr27Zt6tmzp9atW6fc3FxJ5z98dODAgXzIKAAAqBJcXrBmzZqlZ555RitXrpSPj4%2Bk87%2Bf8KWXXtLcuXNdvRwAAADTubxgffvtt0pJSZEkR8GSpPvuu0/5%2BfmuXg4AAIDpXF6wgoKCLvk7CAsKCuTv7%2B/q5QAAAJjO5QXr9ttv14svvqji4mLH2MGDBzVu3Djdc889rl4OAACA6Vz%2BMQ3PPvusHn30USUkJKi8vFy33367Tp8%2BrWbNmumll15y9XIAAABM5/KCVb9%2BfeXm5urTTz/VwYMHVb16dTVu3Fj33nuv0z1ZAAAAnsrlBUuSqlWrps6dO7vj0AAAADedywtWx44dr/hOFZ%2BFBQAAPJ3LC9YDDzzgVLDKy8t18OBBWa1WPfroo65eDgAAgOlcXrDGjBlzyfG1a9fqn//8p4tXAwAAYD63/C7CS%2BncubM%2B%2Bugjdy8DAACg0n4zBWvPnj0yDOO6HrN582YlJiYqPT39orlVq1apR48eat26tfr06aMtW7Y45ioqKjRr1ix16tRJbdq00eDBg3XkyBHHvN1u16hRo5SYmKi2bdtq4sSJl/xwVAAAgEtx%2BSXCAQMGXDR2%2BvRp5efnq2vXrte8nzfeeENLly5Vw4YNL5rbu3evxo0bp6ysLN19991au3atRowYoTVr1qh%2B/fpavHixVq5cqTfeeEP16tXTrFmzlJqaqg8//FA%2BPj6aPHmyzp07p9zcXJWWlmrkyJHKzMzUpEmTKpUdAAB4B5e/g9WoUSM1btzY6at169YaN26cXnzxxWveT0BAwGUL1pIlS5SUlKSkpCQFBASoZ8%2Beio6O1ooVKyRJOTk5GjRokJo0aaLAwEClp6crPz9fu3bt0okTJ7R%2B/Xqlp6crPDxc9erV0/Dhw7Vs2TKVlpaadh4AAEDV5fJ3sMz6tPaBAwdedi4vL09JSUlOYy1atJDVatWZM2e0f/9%2BtWjRwjEXGBiohg0bymq16tSpU/Lz81NMTIxjPjY2Vj///LMOHDjgNH45BQUFstlsTmMWS01FRERca7wqyWL5zVyRvi5%2Bfr5O370N%2Bcn/y%2B/extvzS5yDG%2BXygvXBBx9c87a9evW6oWPY7XaFhIQ4jYWEhGj//v366aefZBjGJecLCwsVGhqqwMBAp4%2BSuLBtYWHhNR0/JydHWVlZTmOpqalKS0u7kThVRlhYLXcvoVKCg2u4ewluRX7yezNvzy9xDq6XywvWxIkTVVFRcdEN7T4%2BPk5jPj4%2BN1ywJF31hvkrzV/vzfa/lpycrI4dOzqNWSw1VVhYUqn9ejpPze/n56vg4BoqKjqt8vIKdy/H5chPfvJ7b37J88%2BBu364d3nBevPNN7VgwQINHTpUMTExMgxD33zzjd544w396U9/UkJCQqWPERYWJrvd7jRmt9sVHh6u0NBQ%2Bfr6XnK%2Bdu3aCg8PV3FxscrLy%2BXn5%2BeYk6TatWtf0/EjIiIuuhxos51SWZnnvTDN5On5y8srPD5DZZCf/OT33vwS5%2BB6ueUerOzsbNWrV88xdueddyoqKkqDBw9Wbm5upY8RFxen3bt3O41ZrVZ1795dAQEBatasmfLy8nTXXXdJkoqKinT48GG1bNlSkZGRMgxD%2B/btU2xsrOOxwcHBaty4caXXBgAAqj6X37F26NChi%2B5/kqTg4GAdPXrUlGP0799fn3/%2BuTZt2qSzZ89q6dKlOnTokHr27ClJSklJ0aJFi5Sfn6/i4mJlZmaqefPmio%2BPV3h4uLp166ZXXnlFJ0%2Be1PHjxzV37lz17dtXFotbfjc2AADwMC5vDJGRkXrppZc0cuRIhYWFSTr/DtKrr76q3/3ud9e8n/j4eElSWVmZJGn9%2BvWSzr/bFB0drczMTGVkZOjo0aNq2rSp5s%2Bfr7p160o6/1lcNptNjzzyiEpKSpSQkOB0U/rzzz%2Bv5557Tp06dVK1atX04IMPXvLDTAEAAC7Fx6jsHd3XacuWLRo9erSKiopUq1Yt%2Bfr6qri4WNWrV9fcuXN1zz33uHI5LmOznbop%2B73/la03Zb83w%2BpR97p7CTfEYvFVWFgtFRaWeOX9B%2BQnP/m9N7/k%2Beegbt0gtxzX5e9gtW3bVps2bdKnn36q48ePyzAM1atXT3/84x8VFOSekwAAAGAmt9xUVKNGDXXq1EnHjx9XVFSUO5YAAABw07j8JvczZ85o3Lhxat26te6//35J5%2B/BeuKJJ1RUVOTq5QAAAJjO5QXr5Zdf1t69e5WZmSlf3/87fHl5uTIzM129HAAAANO5vGCtXbtWr776qu677z7Hr6MJDg5WRkaG1q1b5%2BrlAAAAmM7lBaukpESNGjW6aDw8PFw///yzq5cDAABgOpcXrN/97nf65z//Kcn5d/6tWbNGt956q6uXAwAAYDqX/y3C//iP/9BTTz2lhx9%2BWBUVFVq4cKF2796ttWvXauLEia5eDgAAgOlcXrCSk5NlsVj0zjvvyM/PT6%2B//roaN26szMxM3Xfffa5eDgAAgOlcXrBOnjyphx9%2BWA8//LCrDw0AAOASLr8Hq1OnTnLxb%2BcBAABwKZcXrISEBK1evdrVhwUAAHAZl18ivOWWW/TCCy8oOztbv/vd71StWjWn%2BZkzZ7p6SQAAAKZyecHav3%2B/fv/730uSCgsLXX14AACAm85lBSs9PV2zZs3S3/72N8fY3LlzlZqa6qolAAAAuITL7sHasGHDRWPZ2dmuOjwAAIDLuKxgXepvDvK3CQEAQFXksoJ14Rc7X20MAADA07n8YxoAAACqOgoWAACAyVz2twhLS0s1evToq47xOVgAAMDTuaxg3XHHHSooKLjqGAAAgKdzWcH65edfAQAAVGXcgwUAAGAyChYAAIDJKFgAAAAmo2ABAACYzGU3ubvS9u3b9fjjjzuNGYah0tJSLVq0SAMHDpS/v7/T/H//93/r/vvvlyQtWrRIixcvls1mU0xMjCZOnKi4uDiXrR8AAHi2Klmw2rRpI6vV6jT2%2Buuva9%2B%2BfZKkyMjIS/7yaen8L6WeM2eO3nzzTcXExGjRokUaOnSo1q1bp5o1a970tQMAAM/nFZcI//3vf2vhwoUaO3bsVbfNyclRnz591KpVK1WvXl1PPPGEJGnjxo03e5kAAKCKqJLvYP3a7Nmz9fDDD%2BvWW2/VkSNHVFJSotTUVO3YsUP%2B/v56/PHHNWjQIPn4%2BCgvL08PPPCA47G%2Bvr5q3ry5rFarunfvfk3HKygokM1mcxqzWGoqIiLC1FyexmLxzD7v5%2Bfr9N3bkJ/8v/zubbw9v8Q5uFFVvmB9//33WrdundatWydJCgwMVHR0tB599FHNmjVL//rXvzRy5EgFBQWpb9%2B%2BstvtCgkJcdpHSEiICgtiDpr9AAAZNElEQVQLr/mYOTk5ysrKchpLTU1VWlpa5QN5sLCwWu5eQqUEB9dw9xLcivzk92benl/iHFyvKl%2BwFi9erK5du6pu3bqSpNjYWKdPlW/btq0GDBig5cuXq2/fvpLO3xBfGcnJyerYsaPTmMVSU4WFJZXar6fz1Px%2Bfr4KDq6hoqLTKi%2BvcPdyXI785Ce/9%2BaXPP8cuOuH%2BypfsNauXatx48ZdcZvIyEitXbtWkhQWFia73e40b7fb1axZs2s%2BZkRExEWXA222Uyor87wXppk8PX95eYXHZ6gM8pOf/N6bX%2BIcXK8qfUF17969Onr0qO69917H2OrVq/Xuu%2B86bXfgwAFFRUVJkuLi4pSXl%2BeYKy8v1549e9SqVSvXLBoAAHi8Kl2w9uzZo9DQUAUGBjrGqlWrphkzZmjLli0qLS3V1q1btWzZMqWkpEiSUlJS9MEHH2jnzp06ffq05s2bJ39/f7Vv395NKQAAgKep0pcIT5w44bj36oLOnTtrwoQJmjZtmo4dO6Y6depowoQJ6tq1qySpXbt2evrppzVq1Cj9%2BOOPio%2BPV3Z2tqpXr%2B6OCAAAwAP5GJW9oxvXxGY7dVP2e/8rW2/Kfm%2BG1aPuvfpGv0EWi6/CwmqpsLDEK%2B8/ID/5ye%2B9%2BSXPPwd16wa55bhV%2BhIhAACAO1CwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMFmVLVgxMTGKi4tTfHy842vatGmSpG3btqlv3766/fbb1b17d61YscLpsYsWLVK3bt10%2B%2B23KyUlRbt373ZHBAAA4KEs7l7AzbRmzRo1aNDAaaygoEDDhw/XxIkT1aNHD3355ZcaNmyYGjdurPj4eG3YsEFz5szRm2%2B%2BqZiYGC1atEhDhw7VunXrVLNmTTclAQAAnqTKvoN1OStXrlSjRo3Ut29fBQQEKDExUR07dtSSJUskSTk5OerTp49atWql6tWr64knnpAkbdy40Z3LBgAAHqRKv4M1c%2BZM/c///I%2BKi4t1//33a/z48crLy1OLFi2ctmvRooVWr14tScrLy9MDDzzgmPP19VXz5s1ltVrVvXv3azpuQUGBbDab05jFUlMRERGVTOTZLBbP7PN%2Bfr5O370N%2Bcn/y%2B/extvzS5yDG1VlC9Ztt92mxMREzZgxQ0eOHNGoUaM0depU2e121atXz2nb0NBQFRYWSpLsdrtCQkKc5kNCQhzz1yInJ0dZWVlOY6mpqUpLS7vBNFVDWFgtdy%2BhUoKDa7h7CW5FfvJ7M2/PL3EOrleVLVg5OTmOf27SpInGjBmjYcOG6Y477rjqYw3DqNSxk5OT1bFjR6cxi6WmCgtLKrVfT%2Bep%2Bf38fBUcXENFRadVXl7h7uW4HPnJT37vzS95/jlw1w/3VbZg/VqDBg1UXl4uX19f2e12p7nCwkKFh4dLksLCwi6at9vtatas2TUfKyIi4qLLgTbbKZWVed4L00yenr%2B8vMLjM1QG%2BclPfu/NL3EOrleVLFh79uzRihUrNH78eMdYfn6%2B/P39lZSUpH/84x9O2%2B/evVutWrWSJMXFxSkvL0%2B9e/eWJJWXl2vPnj3q27ev6wJUUfe/stXdS7guq0fd6%2B4lAAA8VJW8Y6127drKyclRdna2zp07p4MHD2r27NlKTk7WQw89pKNHj2rJkiU6e/asPv30U3366afq37%2B/JCklJUUffPCBdu7cqdOnT2vevHny9/dX%2B/bt3RsKAAB4jCr5Dla9evWUnZ2tmTNnOgpS7969lZ6eroCAAM2fP1/Tp0/X1KlTFRkZqZdffll/%2BMMfJEnt2rXT008/rVGjRunHH39UfHy8srOzVb16dTenAgAAnsLHqOwd3bgmNtupm7JfT7vs5kkuXCK0WHwVFlZLhYUlXnn/AfnJT37vzS95/jmoWzfILcetkpcIAQAA3ImCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMmqbME6evSoUlNTlZCQoMTERI0fP15FRUX6/vvvFRMTo/j4eKevt956y/HYVatWqUePHmrdurX69OmjLVu2uDEJAADwNBZ3L%2BBmGTp0qOLi4rRhwwadOnVKqampmjFjhoYNGyZJslqtl3zc3r17NW7cOGVlZenuu%2B/W2rVrNWLECK1Zs0b169d3ZQQAAOChquQ7WEVFRYqLi9Po0aNVq1Yt1a9fX71799aOHTuu%2BtglS5YoKSlJSUlJCggIUM%2BePRUdHa0VK1a4YOUAAKAqqJLvYAUHBysjI8Np7NixY4qIiHD8eezYsfr8889VVlamfv36KS0tTdWqVVNeXp6SkpKcHtuiRYvLvuN1KQUFBbLZbE5jFktNp%2BPjt89iOf/zh5%2Bf83dvQ37y//K7t/H2/BLn4EZVyYL1a1arVe%2B8847mzZsnf39/tW7dWl26dNELL7ygvXv36qmnnpLFYtHIkSNlt9sVEhLi9PiQkBDt37//mo%2BXk5OjrKwsp7HU1FSlpaWZkgeuERZWy%2BnPwcE13LSS3wbyk9%2BbeXt%2BiXNwvap8wfryyy81bNgwjR49WomJiZKk999/3zHfsmVLDRkyRPPnz9fIkSMlSYZhVOqYycnJ6tixo9OYxVJThYUlldovXOvC8%2BXn56vg4BoqKjqt8vIKN6/K9chPfvJ7b37J88/Br39YdpUqXbA2bNigZ555RpMnT1avXr0uu11kZKROnDghwzAUFhYmu93uNG%2B32xUeHn7Nx42IiLjocqDNdkplZZ73wvRmv36%2ByssrvPo5JD/5ye%2B9%2BSXOwfWqshdUv/rqK40bN06zZ892Klfbtm3TvHnznLY9cOCAIiMj5ePjo7i4OO3evdtp3mq1qlWrVi5ZNwAA8HxVsmCVlZVp0qRJGjNmjNq2bes0FxQUpLlz5%2BrDDz9UaWmprFar3nrrLaWkpEiS%2Bvfvr88//1ybNm3S2bNntXTpUh06dEg9e/Z0RxQAAOCBquQlwp07dyo/P1/Tp0/X9OnTnebWrFmjWbNmKSsrS3/%2B858VFBSkRx55RI8%2B%2BqgkKTo6WpmZmcrIyNDRo0fVtGlTzZ8/X3Xr1nVHFAAA4IGqZMG688479c0331x2PjIyUl26dLnsfNeuXdW1a9ebsTQAAOAFquQlQgAAAHeiYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMou7FwD8Vt3/ylZ3L%2BG6rB51r7uXAAD4/3gHCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsG6hKNHj%2BrJJ59UQkKCOnTooJdfflkVFRXuXhYAAPAQfNDoJTz11FOKjY3V%2BvXr9eOPP2rIkCGqU6eOHnvsMXcvDQAAeAAK1q9YrVbt27dPCxcuVFBQkIKCgjRo0CC9/fbbFCz8pnnSJ8/zqfMAqjoK1q/k5eUpMjJSISEhjrHY2FgdPHhQxcXFCgwMvOo%2BCgoKZLPZnMYslpqKiIgwfb2AJ/KkMihJO164T35%2B3nlHxYXc5PfO/BLn4EZRsH7FbrcrODjYaexC2SosLLymgpWTk6OsrCynsREjRuipp54yb6H/344X7jN9n79UUFCgnJwcJScne21B9PZzQP4CzZkzx6vzv/32m%2BT30vwS5%2BBGUUcvwTCMSj0%2BOTlZy5cvd/pKTk42aXWuZbPZlJWVddE7ct7E288B%2BclPfu/NL3EObhTvYP1KeHi47Ha705jdbpePj4/Cw8OvaR8RERG0fAAAvBjvYP1KXFycjh07ppMnTzrGrFarmjZtqlq1arlxZQAAwFNQsH6lRYsWio%2BP18yZM1VcXKz8/HwtXLhQKSkp7l4aAADwEH5TpkyZ4u5F/Nb88Y9/VG5urqZNm6aPPvpIffv21eDBg%2BXj4%2BPupblFrVq1dNddd3n1O3jefg7IT37ye29%2BiXNwI3yMyt7RDQAAACdcIgQAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFC5d19OhRPfnkk0pISFCHDh308ssvq6Kiwt3LMtXRo0eVmpqqhIQEJSYmavz48SoqKtL333%2BvmJgYxcfHO3299dZbjseuWrVKPXr0UOvWrdWnTx9t2bLFjUluTExMjOLi4pwyTps2TZK0bds29e3bV7fffru6d%2B%2BuFStWOD120aJF6tatm26//XalpKRo9%2B7d7ohww7Zv337R8xsXF6eYmBj985//vOTzv3r1asfjPTX/5s2blZiYqPT09IvmrvSarqio0KxZs9SpUye1adNGgwcP1pEjRxzzdrtdo0aNUmJiotq2bauJEyfqzJkzLsl0Pa6Uf926derZs6dat26tbt266e9//7tjbs6cOWrevPlFr4kTJ05Iks6ePas///nPateunRISEpSWlqbCwkKX5bpWl8u/fPly/eEPf7go39dffy2p6jz/LmUAl9G7d29j0qRJRlFRkXHw4EGja9euxoIFC9y9LFM9%2BOCDxvjx443i4mLj2LFjRp8%2BfYwJEyYYR44cMaKjoy/7uD179hhxcXHGpk2bjDNnzhgffvih0apVK%2BPYsWMuXH3lRUdHG0eOHLlo/IcffjBuu%2B02Y8mSJcaZM2eMrVu3Gi1btjS%2B/vprwzAM45NPPjHuvPNOY%2BfOncbp06eN%2BfPnG/fee69RUlLi6gimmjdvnjFy5Ejjiy%2B%2BMDp06HDZ7Tw1f3Z2ttG1a1djwIABxqhRo5zmrvaaXrRokdGhQwdj//79xqlTp4znn3/e6NGjh1FRUWEYhmGMGDHCePLJJ40ff/zROH78uJGcnGxMmzbN5Rmv5Er5d%2B3aZcTHxxsff/yxUVpaamzatMmIjY01tm/fbhiGYbz66qvGuHHjLrvvjIwMo0%2BfPsa///1vo7Cw0BgxYoQxZMiQm5rnel0p/7Jly4w//elPl31sVXj%2BXY13sHBJVqtV%2B/bt05gxYxQUFKRGjRpp0KBBysnJcffSTFNUVKS4uDiNHj1atWrVUv369dW7d2/t2LHjqo9dsmSJkpKSlJSUpICAAPXs2VPR0dEXvcvjqVauXKlGjRqpb9%2B%2BCggIUGJiojp27KglS5ZIknJyctSnTx%2B1atVK1atX1xNPPCFJ2rhxozuXXSn//ve/tXDhQo0dO/aq23pq/oCAAC1dulQNGza8aO5qr%2BmcnBwNGjRITZo0UWBgoNLT05Wfn69du3bpxIkTWr9%2BvdLT0xUeHq569epp%2BPDhWrZsmUpLS10d87KulN9ut2vIkCHq3LmzLBaLkpKSFB0dfU3/PSgrK9PSpUs1fPhw3XLLLQoNDdWoUaO0adMm/fDDDzcjyg25Uv6rqQrPv6tRsHBJeXl5ioyMVEhIiGMsNjZWBw8eVHFxsRtXZp7g4GBlZGSoTp06jrFjx44pIiLC8eexY8eqbdu2uvvuuzVz5kzHfyzy8vLUokULp/21aNFCVqvVNYs30cyZM9W%2BfXvdeeedmjx5skpKSi6b78JlsF/P%2B/r6qnnz5h6Z/4LZs2fr4Ycf1q233ipJKikpcVw%2B/uMf/6iFCxfKMAxJnpt/4MCBCgoKuuTclV7TZ86c0f79%2B53mAwMD1bBhQ1mtVu3du1d%2Bfn6KiYlxzMfGxurnn3/WgQMHbk6YG3Cl/O3atVNqaqrjz2VlZbLZbKpXr55j7JtvvtGAAQMcl80vXEI9fPiwTp06pdjYWMe2TZo0UfXq1ZWXl3eT0ly/K%2BWXzv/377HHHlObNm3UqVMnffjhh5JUZZ5/V6Ng4ZLsdruCg4Odxi6Urd/ifQVmsFqteueddzRs2DD5%2B/urdevW6tKlizZu3Kjs7GytWLFCr732mqTz5%2BeX5VM6f3487dzcdtttSkxM1Lp165STk6OdO3dq6tSpl3z%2BQ0NDHfmqSv4Lvv/%2Be61bt06PPfaYpPP/84iOjtajjz6qzZs3KyMjQ1lZWVq2bJmkqpdfunKmn376SYZhXHbebrcrMDBQPj4%2BTnOS5/73IjMzUzVr1tQDDzwgSapfv76ioqI0Y8YMbd26Vf369dPQoUN14MAB2e12Sbro35ng4GCPyR8eHq5GjRrpmWee0datW/X0009rwoQJ2rZtm1c%2B/2agYOGyLvy07g2%2B/PJLDR48WKNHj1ZiYqIiIiL0/vvvq0uXLqpWrZpatmypIUOGaPny5Y7HVIXzk5OTo379%2Bsnf319NmjTRmDFjlJube01v61eF/BcsXrxYXbt2Vd26dSWd/%2Bn7b3/7m%2B666y75%2B/urbdu2GjBgQJV7/n/tapmuNF9VzodhGHr55ZeVm5urefPmKSAgQJLUr18/vfrqq2rYsKFq1KihQYMGqXnz5k63BXjyOWjfvr3efPNNtWjRQv7%2B/urevbu6dOlyza95T85%2Bs1CwcEnh4eGOn8ousNvt8vHxUXh4uJtWdXNs2LBBTz75pCZMmKCBAwdedrvIyEidOHFChmEoLCzskufH089NgwYNVF5eLl9f34vyFRYWOvJVtfxr165Vx44dr7hNZGSkCgoKJFW9/NKVM4WGhl7yNWG321W7dm2Fh4eruLhY5eXlTnOSVLt27Zu/eJNUVFRo/Pjx2rBhg9577z39/ve/v%2BL2F14TF573X5%2Bfn376yaPy/9qFfN7y/JuNgoVLiouL07Fjx3Ty5EnHmNVqVdOmTVWrVi03rsxcX331lcaNG6fZs2erV69ejvFt27Zp3rx5TtseOHBAkZGR8vHxUVxc3EV/Ld9qtapVq1YuWbcZ9uzZo5deeslpLD8/X/7%2B/kpKSroo3%2B7dux354uLinO4tKS8v1549ezwq/wV79%2B7V0aNHde%2B99zrGVq9erXfffddpuwMHDigqKkpS1cp/wZVe0wEBAWrWrJlT5qKiIh0%2BfFgtW7ZU8%2BbNZRiG9u3b5/TY4OBgNW7c2GUZKuvFF1/Ud999p/fee8/xXF/w2muvadu2bU5j%2Bfn5ioqKUlRUlEJCQpzOz7fffqtz584pLi7OJWuvrPfee0%2BrVq1yGruQz1uef7NRsHBJLVq0UHx8vGbOnKni4mLl5%2Bdr4cKFSklJcffSTFNWVqZJkyZpzJgxatu2rdNcUFCQ5s6dqw8//FClpaWyWq166623HPn79%2B%2Bvzz//XJs2bdLZs2e1dOlSHTp0SD179nRHlBtSu3Zt5eTkKDs7W%2BfOndPBgwc1e/ZsJScn66GHHtLRo0e1ZMkSnT17Vp9%2B%2Bqk%2B/fRT9e/fX5KUkpKiDz74QDt37tTp06c1b948%2Bfv7q3379u4NdQP27Nmj0NBQBQYGOsaqVaumGTNmaMuWLSotLdXWrVu1bNkyx/NflfJfcLXXdEpKihYtWqT8/HwVFxcrMzPT8blQ4eHh6tatm1555RWdPHlSx48f19y5c9W3b19ZLBY3J7s2X375pVasWKHs7GyFhoZeNG%2B32zV16lQdOHBAZ8%2Be1YIFC3T48GH17t1bfn5%2B6t%2B/v15//XUdO3ZMhYWF%2Bstf/qIuXbo4/SWa37Jz585p2rRpslqtKi0tVW5urj777DMNGDBAUtV//m8GH4MLp7iM48ePa/LkyfrXv/6lwMBADRgwQCNGjHC6kdGT7dixQ//5n/8pf3//i%2BbWrFmjPXv2KCsrS4cOHVJQUJAeeeQR/dd//Zd8fc//XLJu3TrNnDlTR48eVdOmTTVx4kS1adPG1TEqZfv27Zo5c6a%2B%2BeYb%2Bfv7q3fv3kpPT1dAQIC2b9%2Bu6dOnKz8/X5GRkRo9erS6du3qeOy7776r7Oxs/fjjj4qPj9eUKVMUHR3txjQ3Zv78%2BVq5cqVyc3OdxnNycrRgwQIdO3ZMderU0bBhw9SvXz/HvCfmj4%2BPl3T%2BhwtJjv/5Xfjbj1d6TRuGoTlz5uj9999XSUmJEhIS9Pzzz6t%2B/fqSpFOnTum5557Txo0bVa1aNT344IMaP378Jf/9cpcr5Z8wYYL%2B8Y9/XFQI2rRpowULFujs2bOaOXOm1qxZI7vdrqZNm2ry5Mlq3bq1pPMFJSMjQx999JHKysrUoUMHTZky5Yp/a8/VrpTfMAzNmzdPS5culc1mU4MGDTR27Fh16NBBUtV4/l2NggUAAGAyLhECAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACY7P8BoS4Z3r2KsXwAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-7581544685750232050”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1505</td> <td class=”number”>46.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>282</td> <td class=”number”>8.8%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>96</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>72</td> <td class=”number”>2.2%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>300.0</td> <td class=”number”>43</td> <td class=”number”>1.3%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.3333333333333</td> <td class=”number”>37</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>66.6666666666667</td> <td class=”number”>25</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.3333333333333</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (115)</td> <td class=”number”>320</td> <td class=”number”>10.0%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>628</td> <td class=”number”>19.6%</td> <td>

<div class=”bar” style=”width:42%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-7581544685750232050”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-75.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-66.6666666666667</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>72</td> <td class=”number”>2.2%</td> <td>

<div class=”bar” style=”width:92%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-45.4545454545455</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>750.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>900.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1000.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1100.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1600.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_FRESHVEG_ACRESPTH_07_12”><s>PCH_FRESHVEG_ACRESPTH_07_12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_FRESHVEG_ACRES_07_12”><code>PCH_FRESHVEG_ACRES_07_12</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99841</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_FRESHVEG_ACRES_07_12”>PCH_FRESHVEG_ACRES_07_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>880</td>

</tr> <tr>

<th>Unique (%)</th> <td>27.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>67.3%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>2160</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>18.501</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>1933.3</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.5%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram938530226900838921”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives938530226900838921,#minihistogram938530226900838921”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives938530226900838921”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles938530226900838921”

aria-controls=”quantiles938530226900838921” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram938530226900838921” aria-controls=”histogram938530226900838921”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common938530226900838921” aria-controls=”common938530226900838921”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme938530226900838921” aria-controls=”extreme938530226900838921”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles938530226900838921”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-85</td>

</tr> <tr>

<th>Q1</th> <td>-43.139</td>

</tr> <tr>

<th>Median</th> <td>-6.8323</td>

</tr> <tr>

<th>Q3</th> <td>38.374</td>

</tr> <tr>

<th>95-th percentile</th> <td>182.98</td>

</tr> <tr>

<th>Maximum</th> <td>1933.3</td>

</tr> <tr>

<th>Range</th> <td>2033.3</td>

</tr> <tr>

<th>Interquartile range</th> <td>81.513</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>137.11</td>

</tr> <tr>

<th>Coef of variation</th> <td>7.4108</td>

</tr> <tr>

<th>Kurtosis</th> <td>83.308</td>

</tr> <tr>

<th>Mean</th> <td>18.501</td>

</tr> <tr>

<th>MAD</th> <td>68.614</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>7.3649</td>

</tr> <tr>

<th>Sum</th> <td>19426</td>

</tr> <tr>

<th>Variance</th> <td>18798</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram938530226900838921”>

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AAABq5biC1bRpU7355pt677339M033ygwMFDNmzdXz549FRQUZPd4AAAAtXJcwZKkkJAQ9evXz%2B4xAAAAzonjCta3336rhQsX6ssvv1R5eXmN/XfeeceGqQAAAOrOcQVr%2BvTpKioqUs%2BePVW/fn27xwEAADhrjitY%2Bfn5eueddxQTE2P3KAAAAOfEcR/TcOmll/LKFQAA8GuOK1ijR4/W4sWLZVmW3aMAAACcE8cdInzvvff02WefafXq1brqqqsUGOjbAV977TWbJgMAAKgbxxWs8PBw9e7d2%2B4xAAAAzpnjCtbcuXPtHgEAAOC8OO49WJK0b98%2BLVq0SI899ph37e9//7uNEwEAANSd4wpWbm6uBg0apC1btmjDhg2STn746F133cWHjAIAAL/guIL19NNP65FHHtH69esVEBAg6eTfJ5w3b56WLFli83QAAAC1c1zB%2Buc//6mMjAxJ8hYsSbrlllu0d%2B9eu8YCAACoM8cVrIYNG/7q3yAsKipSSEiIDRMBAACcHccVrM6dO2vOnDk6evSod23//v2aOnWqbrzxRhsnAwAAqBvHfUzDY489prvvvlvdu3dXVVWVOnfurLKyMrVs2VLz5s2zezwAAIBaOa5gNW7cWBs2bND27du1f/9%2BhYWFqXnz5urRo4fPe7IAAACcynEFS5Lq1aunfv362T0GAADAOXFcwUpOTj7jK1V8FhYAAHA6xxWsAQMG%2BBSsqqoq7d%2B/X3l5ebr77rttnAwAAKBuHFewpkyZ8qvrmzdv1scff3yRpwEAADh7jvuYhtPp16%2Bf3nzzTbvHAAAAqJXfFKxdu3bJsiy7xwAAAKiV4w4RjhgxosZaWVmZ9u7dq5SUFBsmAgAAODuOK1jNmjWr8VuEoaGhSk9P19ChQ22aCgAAoO4cV7D4tHYAAODvHFew1qxZU%2BfT3n777RdwEgAAgHPjuII1Y8YMVVdX13hDe0BAgM9aQEAABQsAADiS4wrW888/r%2BXLl2vMmDFq3bq1LMvSnj179L//%2B7%2B688471b17d7tHBAAAOCPHFax58%2BYpKytLjRo18q516dJFTZs21b333qsNGzbYOB0AAEDtHPc5WF9//bUiIyNrrEdERKiwsNCGiQAAAM6O4wpWkyZNNG/ePJWUlHjXSktLtXDhQl199dU2TgYAAFA3jjtEOH36dE2ePFnZ2dlq0KCBAgMDdfToUYWFhWnJkiV2jwcAAFArxxWsnj17atu2bdq%2BfbsOHToky7LUqFEj9erVSw0bNrR7PAAAgFo5rmBJ0iWXXKKbbrpJhw4dUtOmTe0eBwAA4Kw47j1Y5eXlmjp1qjp16qTf/OY3kk6%2BB2vUqFEqLS21eToAAIDaOa5gzZ8/X7t379aCBQsUGPj/x6uqqtKCBQtsnAwAAKBuHFewNm/erOeee0633HKL948%2BR0REaO7cudqyZYvN0wEAANTOcQXr2LFjatasWY31mJgY/fTTTxd/IAAAgLPkuIJ19dVX6%2BOPP5Ykn789%2BNZbb%2BnKK6%2B0aywAAIA6c9xvEd5xxx166KGHNGTIEFVXV2vFihXKz8/X5s2bNWPGDLvHAwAAqJXjCtbw4cMVHBysl19%2BWUFBQVq6dKmaN2%2BuBQsW6JZbbrF7PAAAgFo5rmAdOXJEQ4YM0ZAhQ%2BweBQAA4Jw47j1YN910k897rwAAAPyN4wpW9%2B7dtWnTJqOXOWfOHLVu3dr7fW5urtLT09W5c2elpqZq3bp1PqdfuXKl%2Bvfvr86dOysjI0P5%2BflG5wEAAO7muEOEV1xxhX7/%2B98rKytLV199terVq%2Bezv3DhwrO6vN27d2vt2rXe74uKijR27FjNmDFDAwcO1KeffqoHHnhAzZs3V7t27bR161YtWrRIzz//vFq3bq2VK1dqzJgx2rJli%2BrXr28kIwAAcDfHvYL11Vdf6dprr1XDhg1VUlKioqIin6%2BzUV1drd/97ncaOXKkd239%2BvVq1qyZ0tPTFRoaqsTERCUnJysnJ0eSlJ2drbS0NHXo0EFhYWEaNWqUJOndd981lhEAALibY17Bmjhxop5%2B%2Bmm99NJL3rUlS5Zo3Lhx53yZr732mkJDQzVw4EA988wzkqSCggLFxcX5nC4uLs57WLKgoEADBgzw7gUGBqpt27bKy8tTampqna63qKhIxcXFPmvBwfUVGxt7zlncIDjYvj4fFBTo869buDGXGzNJ5PInbswkuTOXkzM5pmBt3bq1xlpWVtY5F6zvv/9eixYt8ilskuTxeNSoUSOftaioKJWUlHj3IyMjffYjIyO9%2B3WRnZ2txYsX%2B6yNGzdO48ePP5sIrhMd3cDuERQRcYndI1wQbszlxkwSufyJGzNJ7szlxEyOKVi/9puD5/PbhHPnzlVaWppatGihAwcOnPcsZ2P48OFKTk72WQsOrq%2BSkmPndbn%2Bzs78QUGBioi4RKWlZaqqqrZtDtPcmMuNmSRy%2BRM3ZpLcmasumez64d4xBevUH3auba0ucnNz9fe//10bNmyosRcdHS2Px%2BOzVlJSopiYmNPuezwetWzZss7XHxsbW%2BNwYHHxj6qsdMcd%2Blw5IX9VVbUj5jDNjbncmEkilz9xYybJnbmcmMl5By0NWLdunQ4fPqy%2Bffuqe/fuSktLk3TyIyBatWpV42MX8vPz1aFDB0lSQkKCCgoKvHtVVVXatWuXdx8AAKA2rixY06ZN0%2BbNm7V27VqtXbtWWVlZkqS1a9dq4MCBKiwsVE5Ojo4fP67t27dr%2B/btGjZsmCQpIyNDa9as0c6dO1VWVqbMzEyFhISoT58%2BNiYCAAD%2BxDGHCCsqKjR58uRa1%2BryOViRkZE%2Bb1SvrKyUJDVu3FiStGzZMj355JOaPXu2mjRpovnz56tNmzaSpN69e2vSpEmaMGGCDh8%2BrHbt2ikrK0thYWHnlQ8AAPzncEzBuv7662t8ztWvrZ2Lq666Snv27PF%2B37VrV58PH/2lO%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%2BeWXeuqpp1ReXq7Ro0frhhtu0Pvvv6%2Bnn35ay5Yt05YtWySdLF9Tp07VlClT9NFHH2nkyJF68MEHdejQIZsTAQAAf%2BHKglVaWqqEhARNnjxZDRo0UOPGjTV48GD97W9/07Zt21RRUaEHHnhA9evXV3x8vIYOHars7GxJUk5OjpKSkpSUlKTQ0FANGjRIrVq10rp162xOBQAA/EWw3QNcCBEREZo7d67P2sGDBxUbG6uCggK1bt1aQUFB3r24uDjl5ORIkgoKCpSUlORz3ri4OOXl5dX5%2BouKilRcXOyzFhxcX7GxsWcbxVWCg%2B3r80FBgT7/uoUbc7kxk0Quf%2BLGTJI7czk5kysL1i/l5eXp5ZdfVmZmpjZt2qSIiAif/aioKHk8HlVXV8vj8SgyMtJnPzIyUl999VWdry87O1uLFy/2WRs3bpzGjx9/7iFcIDq6gd0jKCLiErtHuCDcmMuNmSRy%2BRM3ZpLcmcuJmVxfsD799FM98MADmjx5shITE7Vp06ZfPV1AQID3vy3LOq/rHD58uJKTk33WgoPrq6Tk2Hldrr%2BzM39QUKAiIi5RaWmZqqqqbZvDNDfmcmMmiVz%2BxI2ZJHfmqksmu364d3XB2rp1qx555BE9/vjjuv322yVJMTEx%2Bvrrr31O5/F4FBUVpcDAQEVHR8vj8dTYj4mJqfP1xsbG1jgcWFz8oyor3XGHPldOyF9VVe2IOUxzYy43ZpLI5U/cmElyZy4nZnLeQUtDPvvsM02dOlXPPvust1xJUkJCgvbs2aPKykrvWl5enjp06ODdz8/P97msn%2B8DAADUxpUFq7KyUjNnztSUKVPUs2dPn72kpCSFh4crMzNTZWVl%2Bvzzz7Vq1SplZGRIkoYNG6YdO3Zo27ZtOn78uFatWqWvv/5agwYNsiMKAADwQ648RLhz507t3btXTz75pJ588kmfvbfeektLly7V7373O2VlZemyyy7TxIkT1adPH0lSq1attGDBAs2dO1eFhYVq0aKFli1bpssvv9yGJAAAwB%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%2B/unfvrr59%2B2r%2B/Pmqrq62eywAAOAngu0ewIkeeughxcfH6%2B2339bhw4c1evRoXXbZZbrnnnvsHg0X0W%2Be%2BdDuEc7Kpgk97B4BAPBvvIL1C3l5efriiy80ZcoUNWzYUM2aNdPIkSOVnZ1t92gAAMBP8ArWLxQUFKhJkyaKjIz0rsXHx2v//v06evSowsPDa72MoqIiFRcX%2B6wFB9dXbGys8XmBU/ztFTd/8pcpvc77MoKCAn3%2BdQs35nJTppsXvG/3CHV2Lo8zJ99WFKxf8Hg8ioiI8Fk7VbZKSkrqVLCys7O1ePFin7UHH3xQDz30kLlB/%2B1vv7/F6OUVFRUpOztbw4cPd1UhJJf/cGMm6WSuF198nlx%2BwE2Zfv7/CDc%2Btpx8Wzmv8jmAZVnndf7hw4dr9erVPl/Dhw83NN2FVVxcrMWLF9d4Bc7fkct/uDGTRC5/4sZMkjtzOTkTr2D9QkxMjDwej8%2Bax%2BNRQECAYmJi6nQZsbGxjmvSAADg4uEVrF9ISEjQwYMHdeTIEe9aXl6eWrRooQYNGtg4GQAA8BcUrF%2BIi4tTu3bttHDhQh09elR79%2B7VihUrlJGRYfdoAADATwTNmjVrlt1DOE2vXr20YcMGPfHEE3rzzTeVnp6ue%2B%2B9VwEBAXaPdlE0aNBA3bp1c90rduTyH27MJJHLn7gxk%2BTOXE7NFGCd7zu6AQAA4INDhAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbDgVVhYqPvvv1/du3dX3759NX/%2BfFVXV9s9Vq0KCws1btw4de/eXYmJiZo2bZpKS0t14MABtW7dWu3atfP5euGFF7zn3bhxowYOHKhOnTopLS1NH3zwgY1JfLVu3VoJCQk%2Bsz/xxBOSpNzcXKWnp6tz585KTU3VunXrfM67cuVK9e/fX507d1ZGRoby8/PtiODjk08%2BqXFbJCQkqHXr1vr4449/9bbatGmT9/xOyvT%2B%2B%2B8rMTFREydOrLF3pvtUdXW1nn76ad10003q2rWr7r33Xn377bfefY/HowkTJigxMVE9e/bUjBkzVF5eflEySWfOtWXLFg0aNEidOnVS//799frrr3v3Fi1apLZt29a4/b7//ntJ0vHjx/Vf//Vf6t27t7p3767x48cvgWs9AAANOElEQVSrpKTE9lyrV69WmzZtasz9j3/8Q5Kzb6/TZZo5c2aNPHFxcXrsscckSdOmTfP%2Bzd1TX126dHFEJun0z%2BeStHv3bt155526/vrrlZKSouXLl/uc93weexeEBfzb4MGDrZkzZ1qlpaXW/v37rZSUFGv58uV2j1WrW2%2B91Zo2bZp19OhR6%2BDBg1ZaWpo1ffp069tvv7VatWp12vPt2rXLSkhIsLZt22aVl5dba9eutTp06GAdPHjwIk5/eq1atbK%2B/fbbGuvfffed1bFjRysnJ8cqLy%2B3PvzwQ6t9%2B/bWP/7xD8uyLOudd96xunTpYu3cudMqKyuzli1bZvXo0cM6duzYxY5Qq8zMTOvhhx%2B2PvroI6tv376nPZ2TMmVlZVkpKSnWiBEjrAkTJvjs1XafWrlypdW3b1/rq6%2B%2Bsn788Ufrv//7v62BAwda1dXVlmVZ1oMPPmjdf//91uHDh61Dhw5Zw4cPt5544gnbc33%2B%2BedWu3btrL/85S9WRUWFtW3bNis%2BPt765JNPLMuyrOeee86aOnXqaS977ty5VlpamvWvf/3LKikpsR588EFr9OjRFzTPKWfK9cYbb1h33nnnac/r1NvrTJl%2BqaKiwkpNTbW2bdtmWZZlTZ061XruuedOe3o774OWdfrn87KyMqtXr17WokWLrGPHjln5%2BflWt27drM2bN1uWdf6PvQuBV7AgScrLy9MXX3yhKVOmqGHDhmrWrJlGjhyp7Oxsu0c7o9LSUiUkJGjy5Mlq0KCBGjdurMGDB%2Btvf/tbrefNyclRUlKSkpKSFBoaqkGDBqlVq1Y1Xg1ymvXr16tZs2ZKT09XaGioEhMTlZycrJycHElSdna20tLS1KFDB4WFhWnUqFGSpHfffdfOsWv417/%2BpRUrVujRRx%2Bt9bROyhQaGqpVq1bpmmuuqbFX230qOztbI0eO1HXXXafw8HBNnDhRe/fu1eeff67vv/9eb7/9tiZOnKiYmBg1atRIY8eO1RtvvKGKigpbc3k8Ho0ePVr9%2BvVTcHCwkpKS1KpVqzo9ziorK7Vq1SqNHTtWV1xxhaKiojRhwgRt27ZN33333YWI4uNMuWrj1NvrbDK9%2BOKLuvLKK5WUlFTrae2%2BD57p%2BXzbtm2qqKjQAw88oPr16ys%2BPl5Dhw71/j/qfB57FwoFC5KkgoICNWnSRJGRkd61%2BPh47d%2B/X0ePHrVxsjOLiIjQ3Llzddlll3nXDh48qNjYWO/3jz76qHr27KkbbrhBCxcu9D5RFBQUKC4uzufy4uLilJeXd3GGr4OFCxeqT58%2B6tKlix5//HEdO3bstHOfOmT2y/3AwEC1bdvWUbkk6dlnn9WQIUN05ZVXSpKOHTvmPTTQq1cvrVixQta//xa9kzLdddddatiw4a/unek%2BVV5erq%2B%2B%2BspnPzw8XNdcc43y8vK0e/duBQUFqXXr1t79%2BPh4/fTTT9q3b9%2BFCfMzZ8rVu3dvjRs3zvt9ZWWliouL1ahRI%2B/anj17NGLECO9h61OHZ7755hv9%2BOOPio%2BP9572uuuuU1hYmAoKCi5Qmv/vTLmkk88X99xzj7p27aqbbrpJa9eulSRH3161ZTqltLRUS5cu1SOPPOKz/tFHH%2Bn2229Xp06dlJ6e7n3usPs%2BeKbn84KCArVu3VpBQUHevTM9753ar8tj70KhYEHSyZ9QIyIifNZOla2L%2BV6J85WXl6eXX35ZDzzwgEJCQtSpUyfdfPPNevfdd5WVlaV169bpj3/8o6STmX9eKKWTmZ2St2PHjkpMTNSWLVuUnZ2tnTt3avbs2b96W0VFRXnndnouSTpw4IC2bNmie%2B65R9LJJ7tWrVrp7rvv1vvvv6%2B5c%2Bdq8eLFeuONNyT5RybpzHP%2B8MMPsizrtPsej0fh4eEKCAjw2ZOc9xhcsGCB6tevrwEDBkiSGjdurKZNm%2Bqpp57Shx9%2BqKFDh2rMmDHat2%2BfPB6PJNW4z0ZERNieKyYmRs2aNdMjjzyiDz/8UJMmTdL06dOVm5vritvr5ZdfVteuXdWyZUvvWtOmTXXNNddo2bJlev/999WlSxf99re/dWSmnz%2Bfn%2B55z%2BPxqLq6%2BrweexcKBQtep14t8Feffvqp7r33Xk2ePFmJiYmKjY3Va6%2B9pptvvln16tVT%2B/btNXr0aK1evdp7Hidnzs7O1tChQxUSEqLrrrtOU6ZM0YYNG%2Br0Ur2Tc0nSK6%2B8opSUFF1%2B%2BeWSTv6U/NJLL6lbt24KCQlRz549NWLECL%2B5rX6utjnPtO/0jJZlaf78%2BdqwYYMyMzMVGhoqSRo6dKiee%2B45XXPNNbrkkks0cuRItW3b1udwuxOz9enTR88//7zi4uIUEhKi1NRU3XzzzXW%2B3zkx0ylVVVV65ZVXdNddd/msjxs3TnPmzFGjRo0UHh6uRx55RCEhIXr77bclOSfTL5/PT%2BfnZfB8HnsXAgULkk7%2BJHfqJ81TPB6PAgICFBMTY9NUdbd161bdf//9mj59eo0nlJ9r0qSJvv/%2Be1mWpejo6F/N7NS8V111laqqqhQYGFhj7pKSEu/c/pBr8%2BbNSk5OPuNpmjRpoqKiIkn%2BkUk685xRUVG/ett5PB5deumliomJ0dGjR1VVVeWzJ0mXXnrphR%2B%2BFtXV1Zo2bZq2bt2qV199Vddee%2B0ZT3/q9jt1G/0y9w8//OCIXL90am5/v70%2B%2BeQTnThxwuc3BH9NUFCQrrjiCu9t5YRMv/Z8HhMTU%2BPVJo/H472dzuexd6FQsCBJSkhI0MGDB3XkyBHvWl5enlq0aKEGDRrYOFntPvvsM02dOlXPPvusbr/9du96bm6uMjMzfU67b98%2BNWnSRAEBAUpISKjxq/55eXnq0KHDRZn7THbt2qV58%2Bb5rO3du1chISFKSkqqMXd%2Bfr537oSEBJ/3tlRVVWnXrl2OyCWdfJ9HYWGhevTo4V3btGmT/vznP/ucbt%2B%2BfWratKkk52c65Uz3qdDQULVs2dInR2lpqb755hu1b99ebdu2lWVZ%2BuKLL3zOGxERoebNm1%2B0DKczZ84cffnll3r11Ve9t8spf/zjH5Wbm%2BuztnfvXjVt2lRNmzZVZGSkT%2B5//vOfOnHihBISEi7K7Kfz6quvauPGjT5rp%2Bb299vrnXfe0Q033KDg4GDvmmVZmjt3rs/MJ06c0DfffKOmTZs6ItPpns8TEhK0Z88eVVZW%2Bsz28%2Be9c33sXSgULEiS93NRFi5cqKNHj2rv3r1asWKFMjIy7B7tjCorKzVz5kxNmTJFPXv29Nlr2LChlixZorVr16qiokJ5eXl64YUXvJmGDRumHTt2aNu2bTp%2B/LhWrVqlr7/%2BWoMGDbIjio9LL71U2dnZysrK0okTJ7R//349%2B%2ByzGj58uG677TYVFhYqJydHx48f1/bt27V9%2B3YNGzZMkpSRkaE1a9Zo586dKisrU2ZmpkJCQtSnTx97Q/3brl27FBUVpfDwcO9avXr19NRTT%2BmDDz5QRUWFPvzwQ73xxhve28rpmU6p7T6VkZGhlStXau/evTp69KgWLFjg/fyomJgY9e/fX88884yOHDmiQ4cOacmSJUpPT/f5n6QdPv30U61bt05ZWVmKioqqse/xeDR79mzt27dPx48f1/Lly/XNN99o8ODBCgoK0rBhw7R06VIdPHhQJSUl%2Bp//%2BR/dfPPNPm9mtsOJEyf0xBNPKC8vTxUVFdqwYYPee%2B89jRgxQpL/3l7SyR9krrrqKp%2B1gIAAHThwQLNnz9Z3332nY8eOacGCBapXr5769etne6YzPZ8nJSUpPDxcmZmZKisr0%2Beff65Vq1bV%2Bfn8TLflBXPBPgACfufgwYPWqFGjrPbt21uJiYnWc889d0E/I8SETz75xGrVqpWVkJBQ4%2BvAgQPWli1brEGDBlnt27e3evToYS1dutSqqqrynn/z5s1WSkqKFR8fb912223WX//6VxvT%2BPrrX/9qDR8%2B3OrYsaPVrVs3a%2B7cuVZ5ebl3b9CgQVZ8fLyVkpLi/SyYU1555RUrKSnJSkhIsDIyMqw9e/bYEeFXLV261EpNTa2x/tprr1kpKSlWu3btrL59%2B1qvv/66z75TMp26f7Vp08Zq06aN9/tTznSfqq6utp599lnrxhtvtNq3b2/dd999Pp%2B7Vlpaak2cONHq2LGj1bVrV2v27NnW8ePHbc/12GOP%2Bayd%2Brrnnnssy7Ks8vJy6/e//73Vq1cvq127dtbgwYOtzz77zHvZx48ft2bNmmV17drV6tSpkzVp0iSrtLTU9lzV1dXWkiVLrL59%2B1oJCQnWLbfcYm3dutV7XqfeXrXdBy3LslJSUqznn3%2B%2BxnlLSkqsadOmWYmJiVb79u2tO%2B%2B80/rqq69sz2RZtT%2Bf79mzxxoxYoSVkJBg9enTx3rllVd8zn8%2Bj70LIcCyHPKONgAAAJfgECEAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGPb/ALQ59U0WYyRyAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common938530226900838921”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>27</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>15</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.33333333333333</td> <td class=”number”>13</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-60.0</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-25.0</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>40.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (869)</td> <td class=”number”>950</td> <td class=”number”>29.6%</td> <td>

<div class=”bar” style=”width:44%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>2160</td> <td class=”number”>67.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme938530226900838921”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>27</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-97.3568281938326</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-96.84210526315789</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-96.10894941634241</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-94.11764705882352</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>750.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1000.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1400.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1887.5</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1933.3333333333333</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_FRESHVEG_FARMS_07_12”><s>PCH_FRESHVEG_FARMS_07_12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_VEG_FARMS_07_12”><code>PCH_VEG_FARMS_07_12</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.96454</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_FSRPTH_09_14”><s>PCH_FSRPTH_09_14</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_FSR_09_14”><code>PCH_FSR_09_14</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.98863</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_FSR_09_14”>PCH_FSR_09_14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>928</td>

</tr> <tr>

<th>Unique (%)</th> <td>28.9%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>109</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>4.5323</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>300</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>12.5%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram3099546844654126325”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives3099546844654126325,#minihistogram3099546844654126325”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives3099546844654126325”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles3099546844654126325”

aria-controls=”quantiles3099546844654126325” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram3099546844654126325” aria-controls=”histogram3099546844654126325”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common3099546844654126325” aria-controls=”common3099546844654126325”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme3099546844654126325” aria-controls=”extreme3099546844654126325”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles3099546844654126325”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-40</td>

</tr> <tr>

<th>Q1</th> <td>-10.345</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>15</td>

</tr> <tr>

<th>95-th percentile</th> <td>50</td>

</tr> <tr>

<th>Maximum</th> <td>300</td>

</tr> <tr>

<th>Range</th> <td>400</td>

</tr> <tr>

<th>Interquartile range</th> <td>25.345</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>33.865</td>

</tr> <tr>

<th>Coef of variation</th> <td>7.472</td>

</tr> <tr>

<th>Kurtosis</th> <td>14.59</td>

</tr> <tr>

<th>Mean</th> <td>4.5323</td>

</tr> <tr>

<th>MAD</th> <td>20.908</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.2386</td>

</tr> <tr>

<th>Sum</th> <td>14055</td>

</tr> <tr>

<th>Variance</th> <td>1146.9</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram3099546844654126325”>

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WPmZk8z9WzSVZN5uVclmyYNUlJiZGu3fvVrNmzRQYGCin0%2Bk273Q61bx5c0VFRamsrEzV1dUKCgpyzUlS8%2BbN67WvlJQUDRgwwG3MZmuikpJyE5L8n6CgQIWHX6XS0gpVV9eYum1fsmouTzP78XU5rHzMyOZ/rJpLsm42T%2Bby1Q%2BflixYb7zxhiIiInTrrbe6xgoLCxUbG6uQkBB16tRJBQUF6t27tySptLRUhw8fVmJiomJiYmQYhvbt26f4%2BHhJksPhUHh4uNq3b1%2Bv/UdHR9c6HVhcfFpVVZ55MlRX13hs275k1Vye0hA%2BV1Y%2BZmTzP1bNJVk3m5VyWedk5384d%2B6cnnrqKTkcDlVWVio/P1/vv/%2B%2Bxo0bJ0lKTU1VTk6OCgsLVVZWpqysLHXp0kUJCQmKiorS0KFD9fzzz%2BvkyZM6ceKEli5dqtGjR8tms2QfBQAAJvPbxpCQkCBJqqqqkiRt3rxZ0g%2BvNo0fP17l5eWaOnWqiouL1aZNGy1dulR2u12SNG7cOBUXF%2Bvuu%2B9WeXm5%2BvTp43bN1JNPPqknnnhCAwcOVKNGjXTbbbfVejNTAACACwkwrvSKbtRLcfFp07dpswUqMjJUJSXllnlJVWo4uW55/iOf7funeGfajT7bd0M5Zp5ANv9j1VySdbN5MlfLlhd%2BSydPsuQpQgAAAF%2BiYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJjM6wVrwIABys7O1vHjx729awAAAK/wesG68847tX79eg0aNEj33XefNm3apKqqqsvezgcffKCkpCRlZGTUmtu0aZOGDx%2Bu7t27a%2BjQofr73//umluyZIm6dOmihIQEt49vv/1WknT27Fn9/ve/10033aQ%2BffooPT1dJSUlPz0wAAD42fF6wUpLS9P69ev197//XZ06ddIzzzyj5ORkPffcczp48GC9tvHiiy9q/vz5atu2ba25L7/8UjNmzFB6ero%2B%2B%2BwzzZo1S08%2B%2BaR27Njhus2IESPkcDjcPlq0aCFJWrRokQoKCpSbm6uNGzfKMAw99thj5oQHAAA/Cz67Bis%2BPl4zZ87U1q1bNWvWLP3973/XrbfeqokTJ%2BrLL7%2B86H1DQkKUl5dXZ8FyOp2aNGmSBg0aJJvNpuTkZHXu3NmtYF1IVVWV8vLydP/99%2Bvqq69Ws2bNNG3aNG3btk3ffPPNT84KAAB%2BXmy%2B2nFlZaX%2B%2Bc9/avXq1frkk0/Url07PfjggyoqKtKECRM0b9483X777XXed/z48Rfc7k033aSbbrrJ9f9VVVUqLi5Wq1atXGNfffWVxo0bp3//%2B9%2B6%2Buqr9dhjj6lv3746fPiwTp8%2Brfj4eNdtO3TooMaNG6ugoMBtGxdTVFSk4uJitzGbrYmio6Prdf/6CgoKdPvXKqyay9NsNt99vqx8zMjmf6yaS7JuNivm8nrBKiwsVF5ent566y2Vl5dr6NCheu2119SjRw/XbXr16qW5c%2BdesGBdjqysLDVp0kS33nqrJKl169aKjY3V9OnTFR0drdzcXE2ePFlr166V0%2BmUJIWHh7ttIzw8/LKuw8rNzVV2drbbWFpamtLT068wTd3Cw6/yyHZ9zaq5PCUyMtTXS7D0MSOb/7FqLsm62ayUy%2BsFa9iwYWrfvr0mTZqkO%2B64Q82aNat1m%2BTkZJ08efKK9mMYhrKyspSfn6%2BcnByFhIRIksaMGaMxY8a4bjdhwgS9/fbbWrt2reuVL8MwrmjfKSkpGjBggNuYzdZEJSXlV7Td8wUFBSo8/CqVllaourrG1G37klVzeZrZj6/LYeVjRjb/Y9VcknWzeTKXr3749HrBysnJUe/evS95u127dv3kfdTU1Oixxx7Tl19%2BqTfeeEOxsbEXvX1MTIyKiooUFRUl6YfruEJD/%2B%2BAnDp1Ss2bN6/3/qOjo2udDiwuPq2qKs88Gaqrazy2bV%2Byai5PaQifKysfM7L5H6vmkqybzUq5vH6yMy4uTpMnT9bmzZtdY3/%2B85/129/%2B1nWK7ko988wz%2Bvrrr%2BssV3/605%2B0fft2t7HCwkLFxsYqNjZWERERKigocM39%2B9//1rlz52S3201ZGwAAsD6vF6zMzEydPn1aHTt2dI3169dPNTU1WrBgwRVv//PPP9fatWu1YsWKOk8/Op1OzZs3TwcOHNDZs2f1yiuv6PDhwxo5cqSCgoI0duxYvfDCCzp%2B/LhKSkr0hz/8QYMHD3a9jQMAAMCleP0U4Ycffqh169YpMjLSNdauXTtlZWXptttuq9c2EhISJMn1BqU/vhrmcDj05ptv6vTp0%2Brfv7/bfXr16qVXXnlF06dPl/TDtVdOp1MdO3bUn//8Z7Vu3VqSlJ6ervLyco0YMUJVVVXq37%2B/5s6de0WZAQDAz0uAcaVXdF%2Bmnj176v3331eTJk3cxk%2BdOqX%2B/fvriy%2B%2B8OZyvKa4%2BLTp27TZAhUZGaqSknLLnLOWGk6uW57/yGf7/inemXajz/bdUI6ZJ5DN/1g1l2TdbJ7M1bJlU1O3V19eP0XYq1cvLViwQKdOnXKNffPNN5o3b57bWzUAAAD4K6%2BfIpw1a5buvfde3XDDDQoLC1NNTY3Ky8sVGxurv/zlL95eDgAAgOm8XrBiY2P19ttv6/3339fhw4cVGBio9u3bq2/fvgoKCvL2cgAAAEznkz%2BVExwcrEGDBvli1wAAAB7n9YJ15MgRLVy4UF9//bXOnDlTa/7dd9/19pIAAABM5ZNrsIqKitS3b99av0kIAABgBV4vWLt379a7777r%2BrM0AAAAVuP1t2lo3rw5r1wBAABL83rBmjRpkrKzs%2BXl9zcFAADwGq%2BfInz//ff1xRdfaPXq1WrTpo0CA9073t/%2B9jdvLwkAAMBUXi9YYWFhuummm7y9WwAAAK/xesHKzMz09i4BAAC8yuvXYEnSgQMHtGTJEj322GOusf/5n//xxVIAAABM5/WCtX37dg0fPlybNm1Sfn6%2BpB/efHT8%2BPG8ySgAALAErxesRYsW6eGHH9a6desUEBAg6Ye/T7hgwQItXbrU28sBAAAwndcL1r///W%2BlpqZKkqtgSdLNN9%2BswsJCby8HAADAdF4vWE2bNq3zbxAWFRUpODjY28sBAAAwndcL1nXXXadnnnlGZWVlrrGDBw9q5syZuuGGG7y9HAAAANN5/W0aHnvsMd1zzz3q06ePqqurdd1116miokKdOnXSggULvL0cAAAA03m9YLVu3Vr5%2Bfl67733dPDgQTVu3Fjt27fXjTfe6HZNFgAAgL/yesGSpEaNGmnQoEG%2B2DUAAIDHeb1gDRgw4KKvVPFeWAAAwN95vWDdeuutbgWrurpaBw8elMPh0D333OPt5QAAAJjO6wVrxowZdY5v3LhR//rXv7y8GgAAAPP55G8R1mXQoEF6%2B%2B23fb0MAACAK9ZgCtaePXtkGIavlwEAAHDFvH6KcNy4cbXGKioqVFhYqCFDhlzWtj744APNnDlTffr00aJFi9zm1q9fr2XLluno0aNq3769HnroIfXt21eSVFNTo8WLFys/P1%2BlpaVKTEzU3LlzFRsbK0lyOp2aO3euPv30UwUGBio5OVlz5sxR48aNf2JqAADwc%2BL1gtWuXbtav0UYEhKi0aNHa8yYMfXezosvvqi8vDy1bdu21tzevXs1c%2BZMZWdn6/rrr9fGjRv1wAMPaMOGDWrdurVWrlypdevW6cUXX1SrVq20aNEipaWlac2aNQoICNCcOXN07tw55efnq7KyUlOnTlVWVpZmz559xfkBAID1eb1gmfVu7SEhIcrLy9PTTz%2Bts2fPus2tWrVKycnJSk5OliQNHz5cr7/%2ButauXavf/e53ys3N1YQJE9ShQwdJUkZGhvr06aNdu3apTZs22rx5s/7xj38oKipKknT//fdr6tSpmjlzpho1amTK%2BgEAgHV5vWC99dZb9b7tHXfcccG58ePHX3CuoKDAVa5%2B1LVrVzkcDp05c0b79%2B9X165dXXNhYWFq27atHA6HTp8%2BraCgIMXFxbnm4%2BPj9f333%2BvAgQNu4wAAAHXxesF6/PHHVVNTU%2BuC9oCAALexgICAixasi3E6nYqIiHAbi4iI0P79%2B3Xq1CkZhlHnfElJiZo1a6awsDC305g/3rakpKRe%2By8qKlJxcbHbmM3WRNHR0T8lzgUFBQW6/WsVVs3laTab7z5fVj5mZPM/Vs0lWTebFXN5vWC99NJLeuWVVzR58mTFxcXJMAx99dVXevHFF3XXXXepT58%2BpuznUr%2BReLH5K/1txtzcXGVnZ7uNpaWlKT09/Yq2eyHh4Vd5ZLu%2BZtVcnhIZGerrJVj6mJHN/1g1l2TdbFbK5ZNrsFasWKFWrVq5xnr27KnY2FhNnDhR%2Bfn5V7yPyMhIOZ1OtzGn06moqCg1a9ZMgYGBdc43b95cUVFRKisrU3V1tYKCglxzktS8efN67T8lJUUDBgxwG7PZmqikpPynRqpTUFCgwsOvUmlphaqra0zdti9ZNZenmf34uhxWPmZk8z9WzSVZN5snc/nqh0%2BvF6xDhw7VOj0nSeHh4Tp27Jgp%2B7Db7dq9e7fbmMPh0LBhwxQSEqJOnTqpoKBAvXv3liSVlpbq8OHDSkxMVExMjAzD0L59%2BxQfH%2B%2B6b3h4uNq3b1%2Bv/UdHR9c6HVhcfFpVVZ55MlRX13hs275k1Vye0hA%2BV1Y%2BZmTzP1bNJVk3m5Vyef1kZ0xMjBYsWOB2PVNpaakWLlyoX/7yl6bsY%2BzYsfr444%2B1bds2nT17Vnl5eTp06JCGDx8uSUpNTVVOTo4KCwtVVlamrKwsdenSRQkJCYqKitLQoUP1/PPP6%2BTJkzpx4oSWLl2q0aNHy2bzeh8FAAB%2ByOuNYdasWZo%2Bfbpyc3MVGhqqwMBAlZWVqXHjxlq6dGm9t5OQkCBJqqqqkiRt3rxZ0g%2BvNnXu3FlZWVnKzMzUsWPH1LFjRy1fvlwtW7aU9MObnRYXF%2Bvuu%2B9WeXm5%2BvTp43bN1JNPPqknnnhCAwcOVKNGjXTbbbcpIyPDrE8BAACwuADDB3%2BfpqKiQu%2B9955OnDghwzDUqlUr/frXv1bTpk29vRSvKS4%2Bbfo2bbZARUaGqqSk3DIvqUoNJ9ctz3/ks33/FO9Mu9Fn%2B24ox8wTyOZ/rJpLsm42T%2BZq2dI33cIn57yuuuoqDRw4UCdOnHD9eRoAAACr8Po1WGfOnNHMmTPVvXt33XLLLZJ%2BuAbrvvvuU2lpqbeXAwAAYDqvF6znnntOe/fuVVZWlgID/2/31dXVysrK8vZyAAAATOf1grVx40b98Y9/1M033%2Bx6t/Tw8HBlZmZq06ZN3l4OAACA6bxesMrLy9WuXbta41FRUfr%2B%2B%2B%2B9vRwAAADTeb1g/fKXv9S//vUvSe5/kmbDhg36xS9%2B4e3lAAAAmM7rv0X4X//1X3rwwQd15513qqamRq%2B%2B%2Bqp2796tjRs36vHHH/f2cgAAAEzn9YKVkpIim82m119/XUFBQXrhhRfUvn17ZWVl6eabb/b2cgAAAEzn9YJ18uRJ3Xnnnbrzzju9vWsAAACv8Po1WAMHDpQP3jweAADAa7xesPr06aN33nnH27sFAADwGq%2BfIrz66qv19NNPa8WKFfrlL3%2BpRo0auc0vXLjQ20sCAAAwldcL1v79%2B/WrX/1KklRSUuLt3QMAAHic1wpWRkaGFi1apL/85S%2BusaVLlyotLc1bSwAAAPAKr12DtWXLllpjK1as8NbuAQAAvMZrBauu3xzktwkBAIAVea1g/fiHnS81BgAA4O%2B8/jYNAAAAVkfBAgAAMJnXfouwsrJS06dPv%2BQY74MFAAD8ndcKVo8ePVRUVHTJMQAAAH/ntYL1n%2B9/BQAAYGVcgwUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMq/9FqE3ffbZZ7r33nvdxgzDUGVlpXJycjR%2B/HgFBwe7zf/3f/%2B3brnlFklSTk6OVq5cqeLiYsXFxenxxx%2BX3W732voBAIB/s2TB6tWrlxwOh9vYCy%2B8oH379kmSYmJitGXLljrvu2XLFi1ZskQvvfSS4uLilJOTo8mTJ2vTpk1q0qSJx9cOAAD838/iFOH/%2B3//T6%2B%2B%2BqoeeeSRS942NzdXo0a18iUGAAAZeUlEQVSNUrdu3dS4cWPdd999kqStW7d6epkAAMAiLPkK1vkWL16sO%2B%2B8U7/4xS905MgRlZeXKy0tTTt27FBwcLDuvfdeTZgwQQEBASooKNCtt97qum9gYKC6dOkih8OhYcOG1Wt/RUVFKi4udhuz2ZooOjra1FxBQYFu/1qFVXN5ms3mu8%2BXlY8Z2fyPVXNJ1s1mxVyWL1hHjx7Vpk2btGnTJklSWFiYOnfurHvuuUeLFi3Sp59%2BqqlTp6pp06YaPXq0nE6nIiIi3LYRERGhkpKSeu8zNzdX2dnZbmNpaWlKT0%2B/8kB1CA%2B/yiPb9TWr5vKUyMhQXy/B0seMbP7Hqrkk62azUi7LF6yVK1dqyJAhatmypSQpPj7e7c/29O3bV%2BPGjdPq1as1evRoST9cEH8lUlJSNGDAALcxm62JSkrKr2i75wsKClR4%2BFUqLa1QdXWNqdv2Javm8jSzH1%2BXw8rHjGz%2Bx6q5JOtm82QuX/3wafmCtXHjRs2cOfOit4mJidHGjRslSZGRkXI6nW7zTqdTnTp1qvc%2Bo6Oja50OLC4%2BraoqzzwZqqtrPLZtX7JqLk9pCJ8rKx8zsvkfq%2BaSrJvNSrmsc7KzDnv37tWxY8d04403usbeeecd/fWvf3W73YEDBxQbGytJstvtKigocM1VV1drz5496tatm3cWDQAA/J6lC9aePXvUrFkzhYWFucYaNWqkZ599Vh9%2B%2BKEqKyv10Ucf6c0331RqaqokKTU1VW%2B99ZZ27typiooKLVu2TMHBwerXr5%2BPUgAAAH9j6VOE3377revaqx8NGjRIs2bN0lNPPaXjx4%2BrRYsWmjVrloYMGSJJuummm/TQQw9p2rRp%2Bu6775SQkKAVK1aocePGvogAAAD8UIBxpVd0o16Ki0%2Bbvk2bLVCRkaEqKSm3zDlrqeHkuuX5j3y275/inWk3XvpGHtJQjpknkM3/WDWXZN1snszVsmVTU7dXX5Y%2BRQgAAOALFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTWbZgxcXFyW63KyEhwfXx1FNPSZK2b9%2Bu0aNH67rrrtOwYcO0du1at/vm5ORo6NChuu6665Samqrdu3f7IgIAAPBTNl8vwJM2bNigNm3auI0VFRXp/vvv1%2BOPP67bb79dn3/%2BuaZMmaL27dsrISFBW7Zs0ZIlS/TSSy8pLi5OOTk5mjx5sjZt2qQmTZr4KAkAAPAnln0F60LWrVundu3aafTo0QoJCVFSUpIGDBigVatWSZJyc3M1atQodevWTY0bN9Z9990nSdq6dasvlw0AAPyIpQvWwoUL1a9fP/Xs2VNz5sxReXm5CgoK1LVrV7fbde3a1XUa8Pz5wMBAdenSRQ6Hw6trBwAA/suypwivvfZaJSUl6dlnn9WRI0c0bdo0zZs3T06nU61atXK7bbNmzVRSUiJJcjqdioiIcJuPiIhwzddHUVGRiouL3cZstiaKjo7%2BiWnqFhQU6PavVVg1l6fZbL77fFn5mJHN/1g1l2TdbFbMZdmClZub6/rvDh06aMaMGZoyZYp69OhxyfsahnHF%2B87OznYbS0tLU3p6%2BhVt90LCw6/yyHZ9zaq5PCUyMtTXS7D0MSOb/7FqLsm62ayUy7IF63xt2rRRdXW1AgMD5XQ63eZKSkoUFRUlSYqMjKw173Q61alTp3rvKyUlRQMGDHAbs9maqKSk/Ceuvm5BQYEKD79KpaUVqq6uMXXbvmTVXJ5m9uPrclj5mJHN/1g1l2TdbJ7M5asfPi1ZsPbs2aO1a9fq0UcfdY0VFhYqODhYycnJ%2Bsc//uF2%2B927d6tbt26SJLvdroKCAo0cOVKSVF1drT179mj06NH13n90dHSt04HFxadVVeWZJ0N1dY3Htu1LVs3lKQ3hc2XlY0Y2/2PVXJJ1s1kpl3VOdv6H5s2bKzc3VytWrNC5c%2Bd08OBBLV68WCkpKRoxYoSOHTumVatW6ezZs3rvvff03nvvaezYsZKk1NRUvfXWW9q5c6cqKiq0bNkyBQcHq1%2B/fr4NBQAA/IYlX8Fq1aqVVqxYoYULF7oK0siRI5WRkaGQkBAtX75c8%2BfP17x58xQTE6PnnntO11xzjSTppptu0kMPPaRp06bpu%2B%2B%2BU0JCglasWKHGjRv7OBUAAPAXAcaVXtGNeikuPm36Nm22QEVGhqqkpNwyL6lKDSfXLc9/5LN9W90702709RLqraE8Hj3BqtmsmkuybjZP5mrZsqmp26svS54iBAAA8CUKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyS75NAxomfisPAPBzwStYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAms2zBOnbsmNLS0tSnTx8lJSXp0UcfVWlpqY4ePaq4uDglJCS4fbz88suu%2B65fv1633367unfvrlGjRunDDz/0YRIAAOBvbL5egKdMnjxZdrtdW7Zs0enTp5WWlqZnn31WU6ZMkSQ5HI4677d3717NnDlT2dnZuv7667Vx40Y98MAD2rBhg1q3bu3NCAAAwE9Z8hWs0tJS2e12TZ8%2BXaGhoWrdurVGjhypHTt2XPK%2Bq1atUnJyspKTkxUSEqLhw4erc%2BfOWrt2rRdWDgAArMCSr2CFh4crMzPTbez48eOKjo52/f8jjzyijz/%2BWFVVVRozZozS09PVqFEjFRQUKDk52e2%2BXbt2veArXnUpKipScXGx25jN1sRt/2YICgp0%2BxfwFzab/zxmrfw8s2o2q%2BaSrJvNirksWbDO53A49Prrr2vZsmUKDg5W9%2B7dNXjwYD399NPau3evHnzwQdlsNk2dOlVOp1MRERFu94%2BIiND%2B/fvrvb/c3FxlZ2e7jaWlpSk9Pd2UPOcLD7/KI9sFPCUyMtTXS7hsVn6eWTWbVXNJ1s1mpVyWL1iff/65pkyZounTpyspKUmS9Le//c01n5iYqEmTJmn58uWaOnWqJMkwjCvaZ0pKigYMGOA2ZrM1UUlJ%2BRVt93xBQYEKD79KpaUVqq6uMXXbgCeZ/VzwJCs/z6yazaq5JOtm82QuX/1AZ%2BmCtWXLFj388MOaM2eO7rjjjgveLiYmRt9%2B%2B60Mw1BkZKScTqfbvNPpVFRUVL33Gx0dXet0YHHxaVVVeebJUF1d47FtA57gj49XKz/PrJrNqrkk62azUi7rnOw8zxdffKGZM2dq8eLFbuVq%2B/btWrZsmdttDxw4oJiYGAUEBMhut2v37t1u8w6HQ926dfPKugEAgP%2BzZMGqqqrS7NmzNWPGDPXt29dtrmnTplq6dKnWrFmjyspKORwOvfzyy0pNTZUkjR07Vh9//LG2bdums2fPKi8vT4cOHdLw4cN9EQUAAPghS54i3LlzpwoLCzV//nzNnz/fbW7Dhg1atGiRsrOz9fvf/15NmzbV3XffrXvuuUeS1LlzZ2VlZSkzM1PHjh1Tx44dtXz5crVs2dIXUQAAgB%2ByZMHq2bOnvvrqqwvOx8TEaPDgwRecHzJkiIYMGeKJpQEAgJ8BS54iBAAA8CUKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyWy%2BXgAANHQ9H9/g6yVclnem3ejrJQA/exQsAF53y/Mf%2BXoJAOBRFCw/528/WQMA8HPANVgAAAAmo2ABAACYjIJVh2PHjul3v/ud%2BvTpo/79%2B%2Bu5555TTU2Nr5cFAAD8BNdg1eHBBx9UfHy8Nm/erO%2B%2B%2B06TJk1SixYt9Jvf/MbXSwMAAH6AgnUeh8Ohffv26dVXX1XTpk3VtGlTTZgwQa%2B99hoFCwBM5m%2B/UcpbYKC%2BKFjnKSgoUExMjCIiIlxj8fHxOnjwoMrKyhQWFnbJbRQVFam4uNhtzGZroujoaFPXGhTEGV4AtflbafEnNptvv%2B7%2B%2BHW/vl//B2d94MnlmGrH0zdb6vsaBes8TqdT4eHhbmM/lq2SkpJ6Fazc3FxlZ2e7jT3wwAN68MEHzVuofihy97T%2BWikpKaaXN18qKipSbm6u5XJJ1s1m1VwS2fyRVXNJP2R77bWX6p1tx9M3e2FVV66oqEhLliyx1DGzTlU0kWEYV3T/lJQUrV692u0jJSXFpNX9n%2BLiYmVnZ9d6tczfWTWXZN1sVs0lkc0fWTWXZN1sVszFK1jniYqKktPpdBtzOp0KCAhQVFRUvbYRHR1tmQYOAAAuH69gncdut%2Bv48eM6efKka8zhcKhjx44KDQ314coAAIC/oGCdp2vXrkpISNDChQtVVlamwsJCvfrqq0pNTfX10gAAgJ8Imjt37lxfL6Kh%2BfWvf638/Hw99dRTevvttzV69GhNnDhRAQEBvl5aLaGhoerdu7flXl2zai7Jutmsmksimz%2Byai7JutmslivAuNIrugEAAOCGU4QAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYPkJh8OhwYMHa%2BzYsbXmtm/frtGjR%2Bu6667TsGHDtHbtWrf5nJwcDR06VNddd51SU1O1e/duby37sgwYMEB2u10JCQmuj8mTJ7vm9%2B7dq7vuuks9evTQkCFD9Morr/hwtZfv2LFj%2Bt3vfqc%2Bffqof//%2Beu6551RTU%2BPrZV22uLi4WsfpqaeeknTpx2JD88EHHygpKUkZGRm15tavX6/bb79d3bt316hRo/Thhx%2B65mpqarRo0SINHDhQvXr10sSJE3XkyBFvLv2SLpRt9erVuuaaa9yOX0JCgr788ktJDT/bsWPHlJaWpj59%2BigpKUmPPvqoSktLJV36a8TFjmlDcKFsR48eVVxcXK1j9vLLL7vu25Cz7du3T/fcc4969OihpKQkTZs2TcXFxZKs8/2rTgYavDVr1hjJycnGxIkTjTFjxrjNffPNN8a1115rrFq1yjhz5ozx0UcfGYmJicaXX35pGIZhvPvuu0bPnj2NnTt3GhUVFcby5cuNG2%2B80SgvL/dFlIvq37%2B/8cknn9Q5V1FRYfz61782lixZYpSXlxu7d%2B82evfubWzcuNHLq/zpRo4cacyePdsoLS01Dh48aAwZMsR45ZVXfL2sy9a5c2fjyJEjtcYv9VhsaFasWGEMGTLEGDdunDFt2jS3uT179hh2u93Ytm2bcebMGWPNmjVGt27djOPHjxuGYRg5OTlG//79jf379xunT582nnzySeP22283ampqfBGllotle/PNN4277rrrgvdt6Nluu%2B0249FHHzXKysqM48ePG6NGjTJmzZp1ya8RlzqmDcGFsh05csTo3LnzBe/XkLOdPXvWuOGGG4zs7Gzj7NmzxnfffWfcddddxv3332%2Bp71914RUsP3D27Fnl5uaqW7dutebWrVundu3aafTo0QoJCVFSUpIGDBigVatWSZJyc3M1atQodevWTY0bN9Z9990nSdq6datXM1ypbdu2qbKyUlOmTFGTJk0UHx%2BvMWPGKDc319dLqxeHw6F9%2B/ZpxowZatq0qdq1a6cJEyb4zfrr41KPxYYmJCREeXl5atu2ba25VatWKTk5WcnJyQoJCdHw4cPVuXNn10/Xubm5mjBhgjp06KCwsDBlZGSosLBQu3bt8naMOl0s26U05GylpaWy2%2B2aPn26QkND1bp1a40cOVI7duy45NeISx1TX7tYtktpyNkqKiqUkZGhSZMmKTg4WFFRURo8eLC%2B/vpry3//omD5gTFjxqhVq1Z1zhUUFKhr165uY127dnW9jHr%2BfGBgoLp06SKHw%2BG5BV%2BBnJwcDRo0SN27d1d6erq%2B%2B%2B47ST/kiIuLU1BQkOu2/5mzoSsoKFBMTIwiIiJcY/Hx8Tp48KDKysp8uLKfZuHCherXr5969uypOXPmqLy8/JKPxYZm/Pjxatq0aZ1zF8ricDh05swZ7d%2B/320%2BLCxMbdu2bTDPq4tlk6Tjx4/rN7/5jXr16qWBAwdqzZo1ktTgs4WHhyszM1MtWrRwjR0/flzR0dGX/BpxsWPaEFws248eeeQR9e3bV9dff70WLlyoyspKSQ07W0REhMaMGSObzSZJOnDggP7xj3/olltusdz3r/NRsPyc0%2BlUeHi421izZs1UUlLimv/Pb%2BrSDw/4H%2Bcbki5duigxMVFr1qzR%2BvXr5XQ6NXXqVEkXzul0Ov3iOqa61v/jcWmIx%2BJirr32WiUlJWnTpk3Kzc3Vzp07NW/evEs%2BFv3JxZ43p06dkmEYfvO8Ol9UVJTatWunhx9%2BWB999JEeeughzZo1S9u3b/e7bA6HQ6%2B//rqmTJlyya8R/vS1UHLPFhwcrO7du2vw4MHaunWrVqxYobVr1%2BpPf/qTJP/4On/s2DHZ7XbdeuutSkhIUHp6uqW%2Bf9WFgtUArFmzRnFxcXV%2BrF69%2Boq3bxiGCau8cpfKuXTpUk2aNEmhoaG6%2Buqr9cQTT%2Bizzz7T4cOHL7jNgIAALya4Mg3lOFyp3NxcjRkzRsHBwerQoYNmzJih/Px810/TVnGp4%2BWvx7Nfv3566aWX1LVrVwUHB2vYsGEaPHiw29caf8j2%2Beefa%2BLEiZo%2BfbqSkpIueLv//BrhD7mk2tmio6P1t7/9TYMHD1ajRo2UmJioSZMm%2BdUxi4mJkcPh0IYNG3To0CE98sgj9bpfQ891MTZfLwDSiBEjNGLEiJ9038jISDmdTrexkpISRUVFXXDe6XSqU6dOP22xV%2BByc8bExEiSioqKFBUVpUOHDrnNO51ONWvWTIGBDf/nhKioqDqPQ0BAgOtY%2Bas2bdqourpagYGBF30s%2BpMLPW%2BioqJcj7m65ps3b%2B7NZZomJiZGu3fv9ptsW7Zs0cMPP6w5c%2BbojjvukKRLfo242DFtSOrKVpeYmBh9%2B%2B23MgzDb7IFBASoXbt2ysjI0Lhx45ScnOw3379%2Biob/nQkXlZCQUOsal927d7suiLfb7SooKHDNVVdXa8%2BePXVeMO9Lx44d0xNPPKFz5865xgoLCyVJsbGxstvt%2Buqrr1RVVeWadzgcDS7Hhdjtdh0/flwnT550jTkcDnXs2FGhoaE%2BXNnl2bNnjxYsWOA2VlhYqODgYCUnJ1/0sehP7HZ7rSw/Pt5CQkLUqVMnt%2BdVaWmpDh8%2BrMTERG8v9bK98cYbWr9%2BvdtYYWGhYmNj/SLbF198oZkzZ2rx4sVuBeRSXyMudkwbigtl2759u5YtW%2BZ22wMHDigmJkYBAQENOtv27ds1dOhQt0s5fvyhODEx0RLfvy6EguXnbr/9dh07dkyrVq3S2bNn9d577%2Bm9995zvV9Wamqq3nrrLe3cuVMVFRVatmyZgoOD1a9fP98u/DzNmzfXli1btGDBAn3//ff65ptvlJmZqf79%2B6tVq1ZKTk5WWFiYli1bpoqKCu3atUt5eXlKTU319dLrpWvXrkpISNDChQtVVlamwsJCvfrqq36z/h81b95cubm5WrFihc6dO6eDBw9q8eLFSklJ0YgRIy76WPQnY8eO1ccff6xt27bp7NmzysvL06FDhzR8%2BHBJPzyvcnJyVFhYqLKyMmVlZalLly5KSEjw8cov7dy5c3rqqafkcDhUWVmp/Px8vf/%2B%2Bxo3bpykhp2tqqpKs2fP1owZM9S3b1%2B3uUt9jbjUMfW1i2Vr2rSpli5dqjVr1qiyslIOh0Mvv/yyX2Sz2%2B0qKyvTc889p4qKCp08eVJLlixRz549lZqaaonvXxfko7eHwGUYMmSIYbfbjS5duhhxcXGG3W437Ha7cfToUcMwDOPTTz81hg8fbsTHxxtDhgyp9d5QK1euNJKTkw273W6kpqYaX331lS9iXNK%2BffuMCRMmGD169DB69OhhPProo8apU6dc81999ZUxbtw4w263G/369TNWrlzpw9VevuPHjxv33XefkZiYaCQlJRl//OMfG8x7C12OTz/91EhJSTGuvfZao3fv3kZmZqZx5swZ19zFHosNyY/Po2uuuca45pprXP//o40bNxpDhgwx4uPjjREjRhiffvqpa66mpsZYvHixccMNNxiJiYnGb3/72wbxnkM/uli2mpoaY%2BnSpUb//v0Nu91u3HzzzcaWLVtc923I2T777DOjc%2BfOrjz/%2BXH06NFLfo242DH1tUtl27RpkzF8%2BHAjMTHRuPHGG40XXnjBqK6udt2/IWfbt2%2BfcddddxmJiYnG9ddfb0ybNs04ceKEYRjW%2Bf5VlwDD8OMryAAAABogThECAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACY7P8DikgTdIIHhSoAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common3099546844654126325”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>401</td> <td class=”number”>12.5%</td> <td>

<div class=”bar” style=”width:18%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.333333333</td> <td class=”number”>72</td> <td class=”number”>2.2%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>60</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-25.0</td> <td class=”number”>55</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>54</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>52</td> <td class=”number”>1.6%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>49</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-20.0</td> <td class=”number”>48</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.333333333</td> <td class=”number”>42</td> <td class=”number”>1.3%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16.666666667</td> <td class=”number”>40</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (917)</td> <td class=”number”>2228</td> <td class=”number”>69.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>109</td> <td class=”number”>3.4%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme3099546844654126325”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>19</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-80.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-75.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-71.428571429</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-66.666666667</td> <td class=”number”>14</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:73%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>133.333333333</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>150.0</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>166.666666667</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>12</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>300.0</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:42%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_GHVEG_FARMS_07_12”>PCH_GHVEG_FARMS_07_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>147</td>

</tr> <tr>

<th>Unique (%)</th> <td>4.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>62.7%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>2012</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>93.699</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>1900</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>5.8%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-6870500319386779148”>

<img src=”data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAA7BJREFUeJzt3M8rfHscx/HXONdEkcTQjB8r2SjNwneDsrC5o9n7B5SNlCU3RTKKZMHKWomNH6WunbKYBjslO4kajZIF8iPed6fc773vLg3j3vt8LM905rw3zz6fTueckJmZAPylokIPAHxnvxR6gD9r%2B%2B33d59zMPnrJ0wCsIIALgIBHAQCOAgEcBAI4CAQwEEggINAAAeBAA4CARwEAjgIBHAQCOAgEMBBIICDQAAHgQAOAgEcBAI4CARwEAjgIBDAQSCAg0AAx7f7cNxH8LE5fBZWEMBBIICDQAAHgQAOAgEcBAI4CARwEAjgIBDAQSCAg0AAB4EADgIBHAQCOAgEcPwn3gf5CN4hwT/BCgI4CARw/G%2B3WB/x3m0ZW7J/P1YQwMEK8om%2B6kYAK9vnCZmZFXoIwJPL5bSysqLe3l7V1NR86bXZYuHbu7y81MLCgi4vL7/82gQCOAgEcBAI4CAQfHuRSEQDAwOKRCJffm3uYgEOVhDAQSCAg0AAB4EADgIBHAQCOAgEcPC4OwpmcXFR29vbCoJAjY2NSqVSSqfTWlhYUHFxscrLyzU9Pa2KigodHh5qcnJSQRAoCAKlUinV19fr7OxMIyMjen5%2B1svLi0ZHR9XS0pK/IQ0ogIODA0smk/b4%2BGhmZgMDA7a0tGQdHR12enpqZmbz8/M2MTFhZmaJRML29/fNzGxtbc36%2B/vNzKyvr8/W19fNzGxvb8%2BSyWRe52SLhYKIx%2BNaXl5WcXGxJKmyslK3t7dqaGhQY2OjJCmZTGpnZ0fn5%2Be6ublRW1ubJKmnp0fpdFpPT0/KZDJKJBKSpB8/fuj6%2BlrZbDZvcxIICiIIApWVlUmSTk9PtbOzo5eXlzfPW0UiEV1cXCiXy6m6uvr1eDgcVklJia6urlRaWqpwOPzTOflCICio4%2BNj9fX1KZVKqa6u7s1vZqZQKPTu//zIOX%2BHQFAwR0dHGhwc1MzMjDo7OxWNRpXL5V5/v7i4UCwW%2B%2Bn43d2dHh4eVFlZqfv7ez08PLw5JxqN5m1GAkFB3N3daWhoSPPz84rH45Kk1tZWZbNZnZycSJI2NjbU3d2taDSqqqoqZTIZSdLm5qa6uroUDofV0dGhra0tSdLu7q5isZhqa2vzNiePu6MgVldXNTs7q%2Bbm5tdj7e3tisfjmpubUxAEikQiSqVSKisr0/HxscbHxxUKhVRaWqqpqSnV1NQom81qeHhYj4%2BPKioq0tjYmJqamvI2J4EADrZYgINAAAeBAI4/AEmr536lanA7AAAAAElFTkSuQmCC”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-6870500319386779148”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-6870500319386779148”

aria-controls=”quantiles-6870500319386779148” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-6870500319386779148” aria-controls=”histogram-6870500319386779148”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-6870500319386779148” aria-controls=”common-6870500319386779148”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-6870500319386779148” aria-controls=”extreme-6870500319386779148”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-6870500319386779148”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-100</td>

</tr> <tr>

<th>Q1</th> <td>-25</td>

</tr> <tr>

<th>Median</th> <td>50</td>

</tr> <tr>

<th>Q3</th> <td>166.67</td>

</tr> <tr>

<th>95-th percentile</th> <td>450</td>

</tr> <tr>

<th>Maximum</th> <td>1900</td>

</tr> <tr>

<th>Range</th> <td>2000</td>

</tr> <tr>

<th>Interquartile range</th> <td>191.67</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>201.7</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.1526</td>

</tr> <tr>

<th>Kurtosis</th> <td>16.936</td>

</tr> <tr>

<th>Mean</th> <td>93.699</td>

</tr> <tr>

<th>MAD</th> <td>135.13</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>3.0691</td>

</tr> <tr>

<th>Sum</th> <td>112250</td>

</tr> <tr>

<th>Variance</th> <td>40683</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-6870500319386779148”>

<img 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SgAANBs2PIVrDZt2ignJ0cLFizwFKShQ4dq0qRJCgoK0vLlyzVnzhzNnj1bMTExmjdvnjp16iRJ6tevnyZPnqyJEyfqyJEjSkhIUE5OjoKDgy1OBQAAmgtbFixJ6tWrl1599dVz7q1du/ac173nnnt0zz33XKrRAACAzdnyFCEAAICVKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYzOcKVkpKihYvXqxDhw5ZPQoAAMBF8bmCdffdd2vjxo267bbbNHr0aBUUFKimpsbqsQAAABrN5wpWVlaWNm7cqNdee00dOnTQs88%2Bq%2BTkZM2bN0/79%2B%2B3ejwAAIAG%2BVzBOq1Lly6aNm2atm7dqhkzZui1117ToEGD9NBDD%2Bnvf/%2B71eMBAACck88WrFOnTmnjxo16%2BOGHNW3aNLVp00ZPPPGEOnfurFGjRmn9%2BvVWjwgAAHBWDqsHONPevXu1evVqrVmzRlVVVRo4cKBeeukl3XjjjZ7L9OrVS0899ZSGDBli4aQAAABn53MFa/DgwWrXrp3GjBmju%2B66SxEREfUuk5ycrG%2B%2B%2BcaC6QAAABrmcwUrNzdXvXv3bvByO3bsuAzTAAAAXDifew9WXFycxo4dq7ffftuz9vvf/14PP/yw3G63hZMBAAA0js8VrOzsbB09elTt27f3rPXv3191dXWaO3euhZMBAAA0js%2BdInzvvfe0fv16RUZGetZiY2M1f/58/fSnP7VwMgAAgMbxuVewjh8/rqCgoHrr/v7%2Bqq6utmAiAACAC%2BNzBatXr16aO3euvv32W8/aV199pdmzZ3t9VAMAAICv8rlThDNmzNCDDz6on/zkJwoNDVVdXZ2qqqrUtm1b/eEPf7B6PAAAgAb5XMFq27at3nzzTf3pT3/SF198IX9/f7Vr1059%2B/ZVQECA1eMBAAA0yOcKliQFBgbqtttus3oMAACAi%2BJzBevAgQNasGCBPvvsMx0/frze/jvvvGPBVAAAAI3ncwVrxowZKi8vV9%2B%2BfRUSEmLKMZ999lm99NJL2r17tySpqKhICxYs0L59%2B3TNNddozJgxSktL81w%2BNzdXq1atUkVFheLi4jRz5kw5nU5TZgEAAPbncwWrpKRE77zzjqKiokw53q5du7R27VrP38vLyzV%2B/HjNnDlTQ4YM0ccff6xx48apXbt2SkhI0JYtW7Ro0SK9%2BOKLiouLU25ursaOHauCggLTCh8AALA3n/uYhtatW5tWZOrq6vTLX/5So0aN8qytX79esbGxSk9PV1BQkJKSkpSSkqL8/HxJUl5enoYNG6bExEQFBwdr9OjRkqStW7eaMhMAALA/nytYY8aM0eLFi2UYRpOP9eqrryooKEhDhgzxrJWWlio%2BPt7rcvHx8SopKTnrvr%2B/vzp37qzi4uImzwMAAP4z%2BNwpwj/96U/65JNP9MYbb%2Bi6666Tv793B3z11VcbdZyvv/5aixYtqvfZWW63W23atPFai4iIkMvl8uyHh4d77YeHh3v2G6O8vFwVFRVeaw5HiKKjoxt9DDtyOKzt8wEB/l5f7cSu2eyaS7JvNrvmkuybza65JGsz%2BVzBCg0NVb9%2B/Zp8nOzsbA0bNkzt27fXwYMHL%2Bi6TX31LC8vT4sXL/Zay8rK0oQJE5p03OYuMrKl1SNIksLCWlg9wiVj12x2zSXZN5tdc0n2zWbXXFbxuYKVnZ3d5GMUFRXpb3/7mzZs2FBvLzIyUm6322vN5XJ53lR/tn23260OHTo0%2BvYzMjKUkpLiteZwhMjlqmr0MezI6vwBAf4KC2uhyspq1dbWWTqL2eyaza65JPtms2suyb7Z7JpL%2Bj6bFXyuYEnSvn379Oabb%2BrLL7/0FK6//e1v6t69e6Ouv27dOh05ckQDBgyQ9P0rUn369NGDDz5Yr3iVlJQoMTFRkuR0OlVaWqqhQ4dKkmpra7Vz506lp6c3ev7o6Oh6pwMrKo6qpsZeD9wL5Sv5a2vrfGYWs9k1m11zSfbNZtdckn2z2TWXVXzuhGtRUZHS0tJUUFDgKUIHDhzQfffd1%2BgPGZ0%2Bfbo2b96stWvXau3atcrJyZEkrV27VkOGDFFZWZny8/N14sQJFRYWqrCwUCNGjJAkZWZmas2aNdq%2Bfbuqq6u1dOlSBQYGqn///pckLwAAsB%2BfewXr%2Beef12OPPab7779fXbt2lfTd7yecO3eulixZoltvvbXBY4SHh3u9Ub2mpkaSdPXVV0uSli9frjlz5mj27NmKiYnRvHnz1KlTJ0lSv379NHnyZE2cOFFHjhxRQkKCcnJyFBwcbHZUAABgUz5XsP7xj3/o5ZdfliT5%2Bfl51u%2B44w7NmDHjoo553XXXeT7FXZJ69erl9eGjZ7rnnnt0zz33XNRtAQAA%2BNwpwlatWp31dxCWl5crMDDQgokAAAAujM8VrB49eujZZ5/VsWPHPGv79%2B/XtGnT9JOf/MTCyQAAABrH504RPvHEE7r//vvVp08f1dbWqkePHqqurlaHDh00d%2B5cq8cDAABokM8VrKuvvlobNmxQYWGh9u/fr%2BDgYLVr104333yz13uyAAAAfJXPFSxJuuKKK3TbbbdZPQYAAMBF8bmClZKSct5Xqhr7WVgAAABW8bmCNWjQIK%2BCVVtbq/3796u4uFj333%2B/hZMBAAA0js8VrKlTp551ffPmzfrzn/98macBAAC4cD73MQ3nctttt%2BnNN9%2B0egwAAIAGNZuCtXPnTs8vbQYAAPBlPneKcOTIkfXWqqurtXfvXqWmplowEQAAwIXxuYIVGxtb718RBgUFKT09XcOHD7doKgAAgMbzuYLFp7UDAIDmzucK1po1axp92bvuuusSTgIAAHBxfK5gzZw5U3V1dfXe0O7n5%2Be15ufnR8ECAAA%2ByecK1osvvqgVK1Zo7NixiouLk2EY2r17t37729/q3nvvVZ8%2BfaweEQAA4Lx8rmDNnTtXOTk5atOmjWetZ8%2Beatu2rR566CFt2LDBwukAAAAa5nOfg/X5558rPDy83npYWJjKysosmAgAAODC%2BFzBiomJ0dy5c%2BVyuTxrlZWVWrBggX70ox9ZOBkAAEDj%2BNwpwhkzZmjKlCnKy8tTy5Yt5e/vr2PHjik4OFhLliyxejwAAIAG%2BVzB6tu3r7Zt26bCwkIdPnxYhmGoTZs2uuWWW9SqVSurxwMAAGiQzxUsSWrRooVuvfVWHT58WG3btrV6HJjkzl%2B/b/UIF2TTxJutHgEA0Ez53Huwjh8/rmnTpql79%2B668847JX33HqzRo0ersrLS4ukAAAAa5nMFa968edq1a5fmz58vf//vx6utrdX8%2BfMtnAwAAKBxfK5gbd68WS%2B88ILuuOMOzy99DgsLU3Z2tgoKCiyeDgAAoGE%2BV7CqqqoUGxtbbz0qKkr/%2Bte/Lv9AAAAAF8jnCtaPfvQj/fnPf5Ykr989%2BNZbb%2Bnaa6%2B1aiwAAIBG87l/RXjPPffo0Ucf1d133626ujqtXLlSJSUl2rx5s2bOnGn1eAAAAA3yuYKVkZEhh8Ohl19%2BWQEBAVq2bJnatWun%2BfPn64477rB6PAAAgAb5XMH65ptvdPfdd%2Bvuu%2B%2B2ehQAAICL4nPvwbr11lu93nsFAADQ3PhcwerTp482bdpk9RgAAAAXzedOEV5zzTV65plnlJOTox/96Ee64oorvPYXLFjQqON8%2Bumnys7OVklJiYKCgtS7d2/NnDlTV111lYqKirRgwQLt27dP11xzjcaMGaO0tDTPdXNzc7Vq1SpVVFQoLi5OM2fOlNPpNDUnAACwL597BWvPnj368Y9/rFatWsnlcqm8vNzrT2OcPHlSDz74oHr37q2ioiJt2LBBR44c0VNPPaXy8nKNHz9eI0eOVFFRkWbOnKknn3xSxcXFkqQtW7Zo0aJF%2BtWvfqUPPvhAAwYM0NixY/kMLgAA0Gg%2B8wrWpEmT9Pzzz%2BsPf/iDZ23JkiXKysq64GNVV1dr0qRJGjp0qBwOh6KionT77bfr5Zdf1vr16xUbG6v09HRJUlJSklJSUpSfn6%2BEhATl5eVp2LBhSkxMlCSNHj1aubm52rp1qwYPHmxOWAAAYGs%2B8wrWli1b6q3l5ORc1LHCw8M1fPhwORzf9cd9%2B/bpj3/8o%2B68806VlpYqPj7e6/Lx8fEqKSmRpHr7/v7%2B6ty5s%2BcVLgAAgIb4zCtYZ/uXg03914RlZWUaOHCgampqNGLECE2YMEEPP/yw2rRp43W5iIgIuVwuSZLb7VZ4eLjXfnh4uGe/McrLy1VRUeG15nCEKDo6%2BiKTwAoOh8/8/NGggAB/r692Yddckn2z2TWXZN9sds0lWZvJZwrW6V/s3NDahYiJiVFxcbH%2B%2Bc9/6he/%2BIUef/zxRl2vqcUuLy9Pixcv9lrLysrShAkTmnRcXF6RkS2tHuGChYW1sHqES8KuuST7ZrNrLsm%2B2eyayyo%2BU7AuFT8/P8XGxmrSpEkaOXKkkpOT5Xa7vS7jcrkUFRUlSYqMjKy373a71aFDh0bfZkZGhlJSUrzWHI4QuVxVF5kCVmhO91dAgL/CwlqosrJatbV1Vo9jGrvmkuybza65JPtms2su6ftsVrBlwSoqKtJTTz2lTZs2yd//u5cHT3/t2rWrNm/e7HX5kpISz5vanU6nSktLNXToUElSbW2tdu7c6XlTfGNER0fXOx1YUXFUNTX2euDaXXO8v2pr65rl3A2xay7Jvtnsmkuybza75rKKzxSsU6dOacqUKQ2uNeZzsJxOp44dO6Z58%2BZpwoQJqq6u1qJFi9SzZ09lZmZqxYoVys/PV1pamj788EMVFhYqLy9PkpSZmanJkyfrpz/9qeLi4vS73/1OgYGB6t%2B/v2lZAQCAvflMwbrxxhvrfc7V2dYao1WrVlqxYoXmzJmjm266SSEhIbrpppv0zDPPqHXr1lq%2BfLnmzJmj2bNnKyYmRvPmzVOnTp0kSf369dPkyZM1ceJEHTlyRAkJCcrJyVFwcLApOQEAgP35Gfziv8uiouLoJTnunb9%2B/5IcF9KmiTdbPUKjORz%2BioxsKZerylYv8ds1l2TfbHbNJdk3m11zSd9ns4L9/k0mAACAxShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAy2xassrIyZWVlqU%2BfPkpKStL06dNVWVkpSdq1a5fuvfde3XjjjUpNTdWKFSu8rrtx40YNGTJE3bt317Bhw/Tee%2B9ZEQEAADRTti1YY8eOVVhYmLZs2aI33nhDn332mZ577jkdP35cY8aM0U033aR3331Xzz//vJYvX66CggJJ35WvadOmaerUqfrwww81atQoPfLIIzp8%2BLDFiQAAQHNhy4JVWVkpp9OpKVOmqGXLlrr66qs1dOhQ/fWvf9W2bdt06tQpjRs3TiEhIerSpYuGDx%2BuvLw8SVJ%2Bfr6Sk5OVnJysoKAgpaWlqWPHjlq3bp3FqQAAQHPhsHqASyEsLEzZ2dlea4cOHVJ0dLRKS0sVFxengIAAz158fLzy8/MlSaWlpUpOTva6bnx8vIqLixt9%2B%2BXl5aqoqPBaczhCFB0dfaFRYCGHo/n8/BEQ4O/11S7smkuybza75pLsm82uuSRrM9myYJ2puLhYL7/8spYuXapNmzYpLCzMaz8iIkJut1t1dXVyu90KDw/32g8PD9eePXsafXt5eXlavHix11pWVpYmTJhw8SFw2UVGtrR6hAsWFtbC6hEuCbvmkuybza65JPtms2suq9i%2BYH388ccaN26cpkyZoqSkJG3atOmsl/Pz8/P8b8MwmnSbGRkZSklJ8VpzOELkclU16bi4vJrT/RUQ4K%2BwsBaqrKxWbW2d1eOYxq65JPtms2suyb7Z7JpL%2Bj6bFWxdsLZs2aLHHntMTz75pO666y5JUlRUlD7//HOvy7ndbkVERMjf31%2BRkZFyu9319qOiohp9u9HR0fVOB1ZUHFVNjb0euHbXHO%2Bv2tq6Zjl3Q%2ByaS7JvNrvmkuybza65rGK/E67YGMwtAAAWfklEQVT/9sknn2jatGlauHChp1xJktPp1O7du1VTU%2BNZKy4uVmJiome/pKTE61g/3AcAAGiILQtWTU2NZs2apalTp6pv375ee8nJyQoNDdXSpUtVXV2tHTt2aPXq1crMzJQkjRgxQh988IG2bdumEydOaPXq1fr888%2BVlpZmRRQAANAM2fIU4fbt27V3717NmTNHc%2BbM8dp76623tGzZMv3yl79UTk6OrrzySk2aNEn9%2B/eXJHXs2FHz589Xdna2ysrK1L59ey1fvlxXXXWVBUkAAEBzZMuC1bNnT%2B3evfu8l3nllVfOuZeamqrU1FSzxwIAAP8hbHmKEAAAwEoULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwmW0L1rvvvqukpCRNmjSp3t7GjRs1ZMgQde/eXcOGDdN7773n2aurq9Pzzz%2BvW2%2B9Vb169dJDDz2kAwcOXM7RAQBAM2fLgvXb3/5Wc%2BbM0fXXX19vb9euXZo2bZqmTp2qDz/8UKNGjdIjjzyiw4cPS5JWrVql9evXKycnR1u3blVsbKyysrJkGMbljgEAAJopWxasoKAgrV69%2BqwFKz8/X8nJyUpOTlZQUJDS0tLUsWNHrVu3TpKUl5enUaNG6YYbblBoaKgmTZqkvXv3aseOHZc7BgAAaKYcVg9wKdx3333n3CstLVVycrLXWnx8vIqLi3X8%2BHHt2bNH8fHxnr3Q0FBdf/31Ki4uVrdu3Rp1%2B%2BXl5aqoqPBaczhCFB0dfQEpYDWHo/n8/BEQ4O/11S7smkuybza75pLsm82uuSRrM9myYJ2P2%2B1WeHi411p4eLj27Nmjb7/9VoZhnHXf5XI1%2Bjby8vK0ePFir7WsrCxNmDDh4gfHZRcZ2dLqES5YWFgLq0e4JOyaS7JvNrvmkuybza65rPIfV7AkNfh%2Bqqa%2B3yojI0MpKSleaw5HiFyuqiYdF5dXc7q/AgL8FRbWQpWV1aqtrbN6HNPYNZdk32x2zSXZN5tdc0nfZ7PCf1zBioyMlNvt9lpzu92KiopSRESE/P39z7rfunXrRt9GdHR0vdOBFRVHVVNjrweu3TXH%2B6u2tq5Zzt0Qu%2BaS7JvNrrkk%2B2azay6r/McVLKfTqZKSEq%2B14uJiDR48WEFBQerQoYNKS0vVu3dvSVJlZaW%2B%2BOILde3a1YpxYaE7f/2%2B1SNckL8%2Bc4fVIwAA/s1%2B72hrwIgRI/TBBx9o27ZtOnHihFavXq3PP/9caWlpkqTMzEzl5uZq7969OnbsmObPn6/OnTsrISHB4skBAEBzYctXsE6XoZqaGknS22%2B/Lem7V6o6duyo%2BfPnKzs7W2VlZWrfvr2WL1%2Buq666SpI0cuRIVVRU6L/%2B679UVVWlPn361HvDOgAAwPn4GXyC5mVRUXH0khy3uZ3GwqXz12fukMtVZav3UDgc/oqMbGm7XJJ9s9k1l2TfbHbNJX2fzQr/cacIAQAALjUKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMofVAwAwR8%2BZb1k9QqNtmniz1SMAwCXFK1gAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMX5VzFmVlZZo9e7Z27NihkJAQDRo0SFOmTJG/P30UMMOdv37f6hEuCL/aB8CFomCdxaOPPqouXbro7bff1pEjRzRmzBhdeeWVeuCBB6weDQAANAMUrDMUFxfr008/1cqVK9WqVSu1atVKo0aN0ksvvUTBAtAsNKdXCHl1EHZFwTpDaWmpYmJiFB4e7lnr0qWL9u/fr2PHjik0NLTBY5SXl6uiosJrzeEIUXR0tOnzArj0HA7z3x4QEODv9fU/1aX4//ZSCQjwV8%2BZb1k9hm3939RbTD%2Bmld9fFKwzuN1uhYWFea2dLlsul6tRBSsvL0%2BLFy/2WnvkkUf06KOPmjfov/31mTtMP2Z5ebny8vKUkZFhq1Jo11ySfbPZNZf0XbaXXnrxkmS7FM8LjWX3%2B%2Bz%2Bqz%2BzXTa732eX6vusIc3nR4fLyDCMJl0/IyNDb7zxhtefjIwMk6a79CoqKrR48eJ6r8I1d3bNJdk3m11zSfbNZtdckn2z2TWXZG02XsE6Q1RUlNxut9ea2%2B2Wn5%2BfoqKiGnWM6Oho2/0UAAAAGo9XsM7gdDp16NAhffPNN5614uJitW/fXi1btrRwMgAA0FxQsM4QHx%2BvhIQELViwQMeOHdPevXu1cuVKZWZmWj0aAABoJgKeeuqpp6wewtfccsst2rBhg55%2B%2Bmm9%2BeabSk9P10MPPSQ/Pz%2BrR7tsWrZsqd69e9vuVTu75pLsm82uuST7ZrNrLsm%2B2eyaS7Ium5/R1Hd0AwAAwAunCAEAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBgkdZWZl%2B/vOfq0%2BfPhowYIDmzZunuro6q8dqlLKyMmVlZalPnz5KSkrS9OnTVVlZqYMHDyouLk4JCQlef373u995rrtx40YNGTJE3bt317Bhw/Tee%2B9ZmKS%2BuLg4OZ1Or/mffvppSVJRUZHS09PVo0cPDR48WOvWrfO6bm5urgYOHKgePXooMzNTJSUlVkSo56OPPqp3nzidTsXFxenPf/7zWe%2BzTZs2ea7va7neffddJSUladKkSfX2zvf4qqur0/PPP69bb71VvXr10kMPPaQDBw549t1utyZOnKikpCT17dtXM2fO1PHjxy9LptPOl62goEBpaWnq3r27Bg4cqNdee82zt2jRInXu3Lne/fj1119Lkk6cOKFf/OIX6tevn/r06aMJEybI5XJZnuuNN95Qp06d6s3997//XVLzvs9mzZpVL1d8fLyeeOIJSdL06dM9v4/39J%2BePXv6TLZzPc9L0q5du3TvvffqxhtvVGpqqlasWOF13aZ8H140A/i3oUOHGrNmzTIqKyuN/fv3G6mpqcaKFSusHqtRfvrTnxrTp083jh07Zhw6dMgYNmyYMWPGDOPAgQNGx44dz3m9nTt3Gk6n09i2bZtx/PhxY%2B3atUZiYqJx6NChyzj9%2BXXs2NE4cOBAvfWvvvrK6Natm5Gfn28cP37ceP/9942uXbsaf//73w3DMIx33nnH6Nmzp7F9%2B3ajurraWL58uXHzzTcbVVVVlztCoyxdutT4f//v/xkffvihMWDAgHNeztdy5eTkGKmpqcbIkSONiRMneu019PjKzc01BgwYYOzZs8c4evSo8d///d/GkCFDjLq6OsMwDOORRx4xfv7znxtHjhwxDh8%2BbGRkZBhPP/20T2TbsWOHkZCQYPzf//2fcerUKWPbtm1Gly5djI8%2B%2BsgwDMN44YUXjGnTpp3z2NnZ2cawYcOML7/80nC5XMYjjzxijBkz5pLmOe18uV5//XXj3nvvPed1m/N9dqZTp04ZgwcPNrZt22YYhmFMmzbNeOGFF855eauznet5vrq62rjllluMRYsWGVVVVUZJSYnRu3dvY/PmzYZhNP378GLxChYkScXFxfr00081depUtWrVSrGxsRo1apTy8vKsHq1BlZWVcjqdmjJlilq2bKmrr75aQ4cO1V//%2BtcGr5ufn6/k5GQlJycrKChIaWlp6tixY71XgnzR%2BvXrFRsbq/T0dAUFBSkpKUkpKSnKz8%2BXJOXl5WnYsGFKTExUcHCwRo8eLUnaunWrlWOf1ZdffqmVK1fq8ccfb/CyvpYrKChIq1ev1vXXX19vr6HHV15enkaNGqUbbrhBoaGhmjRpkvbu3asdO3bo66%2B/1ttvv61JkyYpKipKbdq00fjx4/X666/r1KlTlmdzu90aM2aMbrvtNjkcDiUnJ6tjx46N%2Br6rqanR6tWrNX78eF1zzTWKiIjQxIkTtW3bNn311VeXIoqX8%2BVqSHO%2Bz8700ksv6dprr1VycnKDl7U62/me57dt26ZTp05p3LhxCgkJUZcuXTR8%2BHDPf7%2Ba8n3YFBQsSJJKS0sVExOj8PBwz1qXLl20f/9%2BHTt2zMLJGhYWFqbs7GxdeeWVnrVDhw4pOjra8/fHH39cffv21U033aQFCxZ4nhBKS0sVHx/vdbz4%2BHgVFxdfnuEbacGCBerfv7969uypJ598UlVVVeec/fTpsjP3/f391blzZ5/LJkkLFy7U3XffrWuvvVaSVFVV5TkVcMstt2jlypUy/v176X0t13333adWrVqdde98j6/jx49rz549XvuhoaG6/vrrVVxcrF27dikgIEBxcXGe/S5duuhf//qX9u3bd2nCnOF82fr166esrCzP32tqalRRUaE2bdp41nbv3q2RI0d6TmGfPi3zxRdf6OjRo%2BrSpYvnsjfccIOCg4NVWlp6idJ873y5pO%2BePx544AH16tVLt956q9auXStJzf4%2B%2B6HKykotW7ZMjz32mNf6hx9%2BqLvuukvdu3dXenq65/nE6mzne54vLS1VXFycAgICPHvney48vd%2BY78OmoGBB0nc/jYaFhXmtnS5bl/N9EWYoLi7Wyy%2B/rHHjxikwMFDdu3fX7bffrq1btyonJ0fr1q3Tb37zG0nf5f5hqZS%2By%2B1Lmbt166akpCQVFBQoLy9P27dv1%2BzZs896n0VERHhmbw7ZJOngwYMqKCjQAw88IOm7J7eOHTvq/vvv17vvvqvs7GwtXrxYr7/%2BuqTmk0s6/6zffvutDMM4577b7VZoaKj8/Py89iTf/J6cP3%2B%2BQkJCNGjQIEnS1VdfrbZt2%2Bq5557T%2B%2B%2B/r%2BHDh2vs2LHat2%2Bf3G63JNV7/IaFhVmeLSoqSrGxsXrsscf0/vvva/LkyZoxY4aKiopsdZ%2B9/PLL6tWrlzp06OBZa9u2ra6//notX75c7777rnr27KkHH3zQJ7P98Hn%2BXM%2BFbrdbdXV1Tfo%2BbAoKFjxOv0LQnH388cd66KGHNGXKFCUlJSk6Olqvvvqqbr/9dl1xxRXq2rWrxowZozfeeMNzHV/PnZeXp%2BHDhyswMFA33HCDpk6dqg0bNjTqZXlfzyZJq1atUmpqqq666ipJ3/1U/Ic//EG9e/dWYGCg%2Bvbtq5EjRzar%2B%2ByHGpr1fPvNIadhGJo3b542bNigpUuXKigoSJI0fPhwvfDCC7r%2B%2BuvVokULjRo1Sp07d/Y6/e6L%2Bfr3768XX3xR8fHxCgwM1ODBg3X77bc3%2BvHni5nOVFtbq1WrVum%2B%2B%2B7zWs/KytKzzz6rNm3aKDQ0VI899pgCAwP19ttvS/KdbGc%2Bz5/LD8tgU74PLxYFC5K%2B%2B6nt9E%2BVp7ndbvn5%2BSkqKsqiqS7Mli1b9POf/1wzZsyo98TxQzExMfr6669lGIYiIyPPmtuXM1933XWqra2Vv79/vdldLpdn9uaSbfPmzUpJSTnvZWJiYlReXi6p%2BeSSzj9rRETEWe9Dt9ut1q1bKyoqSseOHVNtba3XniS1bt360g/fCHV1dZo%2Bfbq2bNmiV155RT/%2B8Y/Pe/nT9%2BPp%2B%2BrM7N9%2B%2B63PZPuh03Pb4T6TvvtXvCdPnvT6F4JnExAQoGuuucZzn/lCtrM9z0dFRdV7tcntdnvur6Z8HzYFBQuSJKfTqUOHDumbb77xrBUXF6t9%2B/Zq2bKlhZM1zieffKJp06Zp4cKFuuuuuzzrRUVFWrp0qddl9%2B3bp5iYGPn5%2BcnpdNb7J/7FxcVKTEy8LHM3ZOfOnZo7d67X2t69exUYGKjk5OR6s5eUlHhmdzqdXu9nqa2t1c6dO30mm/Td%2BzrKysp08803e9Y2bdqk//3f//W63L59%2B9S2bVtJzSPXaed7fAUFBalDhw5eWSorK/XFF1%2Boa9eu6ty5swzD0Keffup13bCwMLVr1%2B6yZTifZ599Vp999pleeeUVz/1z2m9%2B8xsVFRV5re3du1dt27ZV27ZtFR4e7pX9H//4h06ePCmn03lZZj%2BXV155RRs3bvRaOz23He4zSXrnnXd00003yeFweNYMw1B2drbX7CdPntQXX3yhtm3b%2BkS2cz3PO51O7d69WzU1NV6z/fC58GK/D5uCggVJ8nz2yYIFC3Ts2DHt3btXK1euVGZmptWjNaimpkazZs3S1KlT1bdvX6%2B9Vq1aacmSJVq7dq1OnTql4uJi/e53v/PkGjFihD744ANt27ZNJ06c0OrVq/X5558rLS3Niij1tG7dWnl5ecrJydHJkye1f/9%2BLVy4UBkZGfrZz36msrIy5efn68SJEyosLFRhYaFGjBghScrMzNSaNWu0fft2VVdXa%2BnSpQoMDFT//v2tDfUDO3fuVEREhEJDQz1rV1xxhZ577jm99957OnXqlN5//329/vrrnvusOeQ6raHHV2ZmpnJzc7V3714dO3ZM8%2BfP93x2VFRUlAYOHKhf//rX%2Buabb3T48GEtWbJE6enpXv9htMrHH3%2BsdevWKScnRxEREfX23W63Zs%2BerX379unEiRNasWKFvvjiCw0dOlQBAQEaMWKEli1bpkOHDsnlcul//ud/dPvtt3u9idkKJ0%2Be1NNPP63i4mKdOnVKGzZs0J/%2B9CeNHDlSUvO%2Bz07btWuXrrvuOq81Pz8/HTx4ULNnz9ZXX32lqqoqzZ8/X1dccYVuu%2B02y7Od73k%2BOTlZoaGhWrp0qaqrq7Vjxw6tXr260c/z57tPm6RJH/IAWzl06JAxevRoo2vXrkZSUpLxwgsvNPlzQC6Hjz76yOjYsaPhdDrr/Tl48KBRUFBgpKWlGV27djVuvvlmY9myZUZtba3n%2Bps3bzZSU1ONLl26GD/72c%2BMv/zlLxamqe8vf/mLkZGRYXTr1s3o3bu3kZ2dbRw/ftyzl5aWZnTp0sVITU31fO7LaatWrTKSk5MNp9NpZGZmGrt377YiwjktW7bMGDx4cL31V1991UhNTTUSEhKMAQMGGK%2B99prXvi/lOv1Y69Spk9GpUyfP30873%2BOrrq7OWLhwofGTn/zE6Nq1q/Hwww97fQZbZWWlMWnSJKNbt25Gr169jNmzZxsnTpzwiWxPPPGE19rpPw888IBhGIZx/Phx45lnnjFuueUWIyEhwRg6dKjxySefeI594sQJ46mnnjJ69epldO/e3Zg8ebJRWVlpea66ujpjyZIlxoABAwyn02nccccdxpYtWzzXbc732WmpqanGiy%2B%2BWO%2B6LpfLmD59upGUlGR07drVuPfee409e/Z49q3M1tDz/O7du42RI0caTqfT6N%2B/v7Fq1Sqv6zfl%2B/Bi%2BRmGj7xrDQAAwCY4RQgAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGCy/w9IYcMmKIejYgAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-6870500319386779148”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>187</td> <td class=”number”>5.8%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>165</td> <td class=”number”>5.1%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>113</td> <td class=”number”>3.5%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>87</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>51</td> <td class=”number”>1.6%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>300.0</td> <td class=”number”>41</td> <td class=”number”>1.3%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>400.0</td> <td class=”number”>30</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>150.0</td> <td class=”number”>27</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.33333333333333</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (136)</td> <td class=”number”>409</td> <td class=”number”>12.7%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>2012</td> <td class=”number”>62.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-6870500319386779148”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>165</td> <td class=”number”>5.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-88.88888888888889</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-87.5</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-85.71428571428571</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-83.33333333333334</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1200.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1500.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1600.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1800.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1900.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_GHVEG_SQFTPTH_07_12”><s>PCH_GHVEG_SQFTPTH_07_12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_GHVEG_SQFT_07_12”><code>PCH_GHVEG_SQFT_07_12</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99987</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_GHVEG_SQFT_07_12”>PCH_GHVEG_SQFT_07_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>325</td>

</tr> <tr>

<th>Unique (%)</th> <td>10.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>89.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>2870</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>293.41</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>27641</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-5318577132920805567”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-5318577132920805567,#minihistogram-5318577132920805567”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-5318577132920805567”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-5318577132920805567”

aria-controls=”quantiles-5318577132920805567” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-5318577132920805567” aria-controls=”histogram-5318577132920805567”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-5318577132920805567” aria-controls=”common-5318577132920805567”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-5318577132920805567” aria-controls=”extreme-5318577132920805567”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-5318577132920805567”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-93.897</td>

</tr> <tr>

<th>Q1</th> <td>5.1369</td>

</tr> <tr>

<th>Median</th> <td>91.527</td>

</tr> <tr>

<th>Q3</th> <td>247.16</td>

</tr> <tr>

<th>95-th percentile</th> <td>932.4</td>

</tr> <tr>

<th>Maximum</th> <td>27641</td>

</tr> <tr>

<th>Range</th> <td>27741</td>

</tr> <tr>

<th>Interquartile range</th> <td>242.03</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>1552.9</td>

</tr> <tr>

<th>Coef of variation</th> <td>5.2926</td>

</tr> <tr>

<th>Kurtosis</th> <td>285.97</td>

</tr> <tr>

<th>Mean</th> <td>293.41</td>

</tr> <tr>

<th>MAD</th> <td>368.76</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>16.301</td>

</tr> <tr>

<th>Sum</th> <td>99759</td>

</tr> <tr>

<th>Variance</th> <td>2411400</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-5318577132920805567”>

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NmyZcrIyNCTTz6p0NBQ3XvvvRo3bpwkqW3btlqyZIkWLlyo/Px8tW7dWqtXr9a1115rx1YAAIAfqpcFq0ePHjp06NBFj0dHR2vgwIEXPZ6UlKSkpKTaGA0AADQA9fIjQgAAADtRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALCYzxWs/v37KyMjQwUFBXaPAgAAYIrPFay7775b27Zt02233aaJEydq586dqqiosHssAACAGvO5gpWamqpt27bpjTfeUJs2bfTMM88oMTFRixcv1tGjR%2B0eDwAAoFo%2BV7B%2B0rFjR82cOVPvv/%2B%2BZs%2BerTfeeEN33nmnJkyYoC%2B%2B%2BMLu8QAAAC7KZwvWuXPntG3bNj3wwAOaOXOmWrRooccff1wdOnTQ%2BPHjtWXLFrtHBAAAuCCH3QP8Ul5enjZs2KC33npLZWVlGjRokF5%2B%2BWV1797d85iePXtq3rx5GjJkiI2TAgAAXJjPFazBgwerVatWmjRpkoYNG6ZmzZqd95jExESdPHnShukAAACq53MfEa5du1bbt2/X%2BPHjL1iufvL5559f8jz5%2BflKTU1VfHy8EhISNGvWLJWUlEiSDh48qHvuuUfdu3dXUlKS1qxZ4/Xcbdu2aciQIeratatGjBihvXv3XvnGAABAg%2BFzBatdu3aaPHmy3n33Xc/af/3Xf%2BmBBx6Q2%2B2u8XkmT56ssLAw7dq1Sxs3btTXX3%2BtZ599VqdPn9akSZPUu3dvffjhh1q2bJlWr16tnTt3SvqxfM2cOVMzZszQX//6V40fP15Tp07ViRMnLN8rAACon3yuYC1cuFCnTp1S69atPWt9%2B/ZVVVWVFi1aVKNzlJSUyOl0avr06WrSpImuu%2B46DR8%2BXH/729%2B0e/dunTt3TlOmTFHjxo3VsWNHjRo1SllZWZKk9evXKzExUYmJiQoJCdHQoUPVtm1bbd68uVb2CwAA6h%2BfK1h79%2B5VRkaGYmJiPGsxMTFasmSJPvzwwxqdIywsTAsXLtQ111zjWSsoKFBUVJRyc3PVrl07BQUFeY7FxsYqJydHkpSbm6vY2Fiv88XGxsrlcl3BrgAAQEPicze5nz59WiEhIeetBwYGqry83NQ5XS6X1q1bp1WrVmn79u0KCwvzOt6sWTO53W5VVVXJ7XYrPDzc63h4eLgOHz5c49crLCxUUVGR15rD0VhRUVGm5q8vHA7f6vNBQYFeP1FzZGcOuZlHduaRnT18rmD17NlTixYt0vTp0z1F55///KeeffZZr69qqKlPP/1UU6ZM0fTp05WQkKDt27df8HEBAQGe/2wYhrnh/09WVpYyMjK81lJTU5WWlnZF5/V3ERFN7B7hgsLCrrZ7BL9FduaQm3lkZx7Z1S2fK1izZ8/W/fffr5tuuklNmzZVVVWVysrK1LJlS73yyiuXda5du3bp0Ucf1dy5czVs2DBJUmRkpI4dO%2Bb1OLfbrWbNmikwMFARERHn3UzvdrsVGRlZ49dNTk5W//79vdYcjsYqLi67rPnrG1/bf1BQoMLCrlZJSbkqK6vsHsevkJ055GYe2ZnX0LOz65d7nytYLVu21Ntvv60PPvhA33zzjQIDA9WqVSv16dPH676p6nz22WeaOXOmli9frj59%2BnjWnU6nXnvtNVVUVMjh%2BHH7LpdLnTt39hz/6X6sn7hcLg0ePLjGrx0VFXXex4FFRadUUdHw3tg/56v7r6ys8tnZfB3ZmUNu5pGdeWRXt3yuYElScHCwbrvtNtPPr6io0BNPPKEZM2Z4lSvpxy8pbdq0qVatWqWJEyfqq6%2B%2B0oYNG7R48WJJ0ujRozVy5Ejt3r1bN910k7Zs2aJjx45p6NChV7QnAADQcPhcwTp%2B/LiWLl2qr7/%2BWqdPnz7v%2BHvvvVftOfbv36%2B8vDwtWLBACxYs8Dr2zjvv6LnnntP/%2B3//T5mZmbrmmmuUnp6uvn37SpLatm2rJUuWaOHChcrPz1fr1q21evVqXXvttZbsDwAA1H8%2BV7Bmz56twsJC9enTR40bNzZ1jh49eujQoUOXfMxrr7120WNJSUlKSkoy9doAAAA%2BV7BycnL03nvvXdZN5QAAAL7E574Uo3nz5qavXAEAAPgCnytYkyZNUkZGxhV/FxUAAIBdfO4jwg8%2B%2BECfffaZNm7cqOuvv16Bgd4d8PXXX7dpMgAAgJrxuYLVtGlT3XrrrXaPAQAAYJrPFayFCxfaPQIAAMAV8bl7sCTpyJEjWrFihR5//HHP2t///ncbJwIAAKg5nytY2dnZGjp0qHbu3KmtW7dK%2BvHLR8eOHVujLxkFAACwm88VrGXLlunRRx/Vli1bFBAQIOnHv59w0aJFWrlypc3TAQAAVM/nCtZXX32llJQUSfIULEm6/fbblZeXZ9dYAAAANeZzBSs0NPSCfwdhYWGhgoODbZgIAADg8vhcwerWrZueeeYZlZaWetaOHj2qmTNn6qabbrJxMgAAgJrxua9pePzxxzVu3DjFx8ersrJS3bp1U3l5udq0aaNFixbZPR4AAEC1fK5gXXfdddq6dav27Nmjo0ePqlGjRmrVqpVuvvlmr3uyAAAAfJXPFSxJuuqqq3TbbbfZPQYAAIApPlew%2Bvfvf8krVXwXFgAA8HU%2BV7DuvPNOr4JVWVmpo0ePyuVyady4cTZOBgAAUDM%2BV7BmzJhxwfUdO3bo448/ruNpAAAALp/PfU3Dxdx22216%2B%2B237R4DAACgWn5TsA4cOCDDMOweAwAAoFo%2B9xHhmDFjzlsrLy9XXl6ekpKSbJgIAADg8vhcwYqJiTnv3yIMCQnRyJEjNWrUKJumAgAAqDmfK1h8WzsAAPB3Plew3nrrrRo/dtiwYbU4CQAAgDk%2BV7DmzJmjqqqq825oDwgI8FoLCAigYAEAAJ/kcwXrhRde0Jo1azR58mS1a9dOhmHo0KFDev7553XPPfcoPj7e7hEBAAAuyecK1qJFi5SZmakWLVp41nr06KGWLVtqwoQJ2rp1q43TAQAAVM/nvgfr2LFjCg8PP289LCxM%2Bfn5NkwEAABweXyuYEVHR2vRokUqLi72rJWUlGjp0qX69a9/beNkAAAANeNzHxHOnj1b06dPV1ZWlpo0aaLAwECVlpaqUaNGWrlypd3jAQAAVMvnClafPn20e/du7dmzRydOnJBhGGrRooVuueUWhYaG2j0eAABAtXyuYEnS1VdfrQEDBujEiRNq2bKl3eMAAABcFp%2B7B%2Bv06dOaOXOmunbtqjvuuEPSj/dgTZw4USUlJTZPBwAAUD2fK1iLFy/WwYMHtWTJEgUG/v/jVVZWasmSJTZOBgAAUDM%2BV7B27NihP/3pT7r99ts9f%2BlzWFiYFi5cqJ07d9b4PB9%2B%2BKESEhKUnp7utb5x40a1b99ecXFxXn%2B%2B%2BOILSVJVVZWWLVumAQMGqGfPnpowYYKOHz9u3QYBAEC953P3YJWVlSkmJua89cjISP3www81Osfzzz%2BvDRs26IYbbrjg8Z49e%2BqVV1654LFXX31VW7Zs0fPPP68WLVpo2bJlSk1N1aZNmzyFDwAA4FJ87grWr3/9a3388ceS5PV3D77zzjv6t3/7txqdIyQk5JIF61KysrI0fvx43XjjjWratKnS09OVl5enzz///LLPBQAAGiafu4L129/%2BVg899JDuvvtuVVVV6aWXXlJOTo527NihOXPm1OgcY8eOveTxgoIC3XfffcrJyVFYWJjS0tJ011136fTp0zp8%2BLBiY2M9j23atKluuOEGuVwudenS5Yr2BgAAGgafK1jJyclyOBxat26dgoKC9Nxzz6lVq1ZasmSJbr/99is%2Bf2RkpGJiYvTII4%2BodevW%2Bstf/qLHHntMUVFR%2Bs1vfiPDMM77q3rCw8O9vlm%2BOoWFhSoqKvJaczgaKyoq6orn92cOh29dMA0KCvT6iZojO3PIzTyyM4/s7OFzBevkyZO6%2B%2B67dffdd9fK%2Bfv27au%2Bfft6/nnw4MH6y1/%2Boo0bN2rGjBmSvD%2BaNCMrK0sZGRlea6mpqUpLS7ui8/q7iIgmdo9wQWFhV9s9gt8iO3PIzTyyM4/s6pbPFawBAwbos88%2Bq9MbyqOjo5WTk6NmzZopMDBQbrfb67jb7Vbz5s1rfL7k5GT179/fa83haKzi4jJL5vVXvrb/oKBAhYVdrZKSclVWVtk9jl8hO3PIzTyyM6%2BhZ2fXL/c%2BV7Di4%2BO1fft23XnnnbVy/tdee03h4eFe58/Ly1PLli0VEhKiNm3aKDc3V7169ZL045ecfvPNN%2BrUqVONXyMqKuq8jwOLik6poqLhvbF/zlf3X1lZ5bOz%2BTqyM4fczCM788iubvlcwfrVr36lp59%2BWpmZmfr1r3%2Btq666yuv40qVLr%2Bj8Z8%2Be1fz589WyZUu1b99eO3bs0AcffKA33nhDkpSSkqLMzEzdeuutatGihZYsWaIOHTooLi7uil4XAAA0HD5XsA4fPqzf/OY3knRZN5b/3E9lqKKiQpL07rvvSpJcLpfGjh2rsrIyPfzwwyoqKtL111%2BvlStXyul0SpLGjBmjoqIi3XvvvSorK1N8fPx591MBAABcSoBxpXd0WyQ9PV3Lli3zWlu5cqVSU1NtmshaRUWnauW8d/zxo1o5b23YPu1mu0fw4nAEKiKiiYqLy7hsfpnIzhxyM4/szGvo2V17bagtr%2Bsz/87mrl27zlvLzMy0YRIAAIAr4zMF60IX0nzk4hoAAMBl8ZmCdaGvZeDv/gMAAP7IZwoWAABAfUHBAgAAsJjPfE3DuXPnNH369GrXrvR7sAAAAGqbzxSs7t27q7CwsNo1AAAAX%2BczBeuVV16xewQAAABLcA8WAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWKzeFqwPP/xQCQkJSk9PP%2B/Ytm3bNGTIEHXt2lUjRozQ3r17Pceqqqq0bNkyDRgwQD179tSECRN0/PjxuhwdAAD4uXpZsJ5//nktWLBAN9xww3nHDh48qJkzZ2rGjBn661//qvHjx2vq1Kk6ceKEJOnVV1/Vli1blJmZqffff18xMTFKTU2VYRh1vQ0AAOCn6mXBCgkJ0YYNGy5YsNavX6/ExEQlJiYqJCREQ4cOVdu2bbV582ZJUlZWlsaPH68bb7xRTZs2VXp6uvLy8vT555/X9TYAAICfctg9QG0YO3bsRY/l5uYqMTHRay02NlYul0unT5/W4cOHFRsb6znWtGlT3XDDDXK5XOrSpUuNXr%2BwsFBFRUVeaw5HY0VFRV3GLuofh8O3%2BnxQUKDXT9Qc2ZlDbuaRnXlkZ496WbAuxe12Kzw83GstPDxchw8f1vfffy/DMC54vLi4uMavkZWVpYyMDK%2B11NRUpaWlmR%2B8HoiIaGL3CBcUFna13SP4LbIzh9zMIzvzyK5uNbiCJana%2B6mu9H6r5ORk9e/f32vN4Wis4uKyKzqvv/O1/QcFBSos7GqVlJSrsrLK7nH8CtmZQ27mkZ15DT07u365b3AFKyIiQm6322vN7XYrMjJSzZo1U2Bg4AWPN2/evMavERUVdd7HgUVFp1RR0fDe2D/nq/uvrKzy2dl8HdmZQ27mkZ15ZFe3GtwHsk6nUzk5OV5rLpdLnTt3VkhIiNq0aaPc3FzPsZKSEn3zzTfq1KlTXY8KAAD8VIMrWKNHj9a%2Bffu0e/dunTlzRhs2bNCxY8c0dOhQSVJKSorWrl2rvLw8lZaWasmSJerQoYPi4uJsnhwAAPiLevkR4U9lqKKiQpL07rvvSvrxSlXbtm21ZMkSLVy4UPn5%2BWrdurVWr16ta6%2B9VpI0ZswYFRUV6d5771VZWZni4%2BPPu2EdAADgUgIMvkGzThQVnaqV897xx49q5by1Yfu0m%2B0ewYvDEaiIiCYqLi7jvoTLRHbmkJt5ZGdeQ8/u2mtDbXndBvcRIQAAQG2jYAEAAFiMggUAAGAxChZOacdrAAAQr0lEQVQAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWMxh9wB2aNeuna666ioFBAR41kaPHq25c%2BcqOztbS5cu1ZEjR/SrX/1KkyZN0tChQ22cFgAA%2BJsGWbAk6Z133tH111/vtVZYWKjf/e53mjNnjoYMGaJPP/1UU6ZMUatWrRQXF2fTpAAAwN/wEeHPbNmyRTExMRo5cqRCQkKUkJCg/v37a/369XaPBgAA/EiDLVhLly5V37591aNHD82dO1dlZWXKzc1VbGys1%2BNiY2OVk5Nj05QAAMAfNciPCLt06aKEhAQ9%2B%2ByzOn78uKZNm6annnpKbrdbLVq08Hpss2bNVFxcfFnnLywsVFFRkdeaw9FYUVFRVzy7P3M4fKvPBwUFev1EzZGdOeRmHtmZR3b2aJAFKysry/Ofb7zxRs2YMUNTpkxR9%2B7dLTt/RkaG11pqaqrS0tIsOb%2B/iohoYvcIFxQWdrXdI/gtsjOH3MwjO/PIrm41yIL1S9dff70qKysVGBgot9vtday4uFiRkZGXdb7k5GT179/fa83haKzi4rIrntWf%2Bdr%2Bg4ICFRZ2tUpKylVZWWX3OH6F7MwhN/PIzryGnp1dv9w3uIJ14MABbd68WbNmzfKs5eXlKTg4WImJiXrzzTe9Hp%2BTk6POnTtf1mtERUWd93FgUdEpVVQ0vDf2z/nq/isrq3x2Nl9HduaQm3lkZx7Z1a0G94Fs8%2BbNlZWVpczMTJ09e1ZHjx7V8uXLlZycrLvuukv5%2Bflav369zpw5oz179mjPnj0aPXq03WMDAAA/0uAKVosWLZSZmaldu3YpPj5eY8aM0S233KJHH31UzZs31%2BrVq7Vu3Tp1795dzzzzjBYvXqz27dvbPTYAAPAjDe4jQknq2bOnXn/99Yse27RpUx1PBAAA6pMGdwULAACgtlGwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAXrAvLz8/Xggw8qPj5e/fr10%2BLFi1VVVWX3WAAAwE847B7AFz300EPq2LGj3n33XX333XeaNGmSrrnmGt133312j%2BbX7vjjR3aPcFm2T7vZ7hEAAH6KK1i/4HK59OWXX2rGjBkKDQ1VTEyMxo8fr6ysLLtHAwAAfoIrWL%2BQm5ur6OhohYeHe9Y6duyoo0ePqrS0VE2bNq32HIWFhSoqKvJaczgaKyoqyvJ5UXscDn7/uJigoECvn6gZcjOP7MwZuORDu0eosb/MuMXuESxFwfoFt9utsLAwr7WfylZxcXGNClZWVpYyMjK81qZOnaqHHnrIukH/z7aHuykrK0vJyckUuMtUWFhIdiYVFhbq5ZdfILvLRG7mkZ05f3v6dv6/zib8KnABhmFc0fOTk5O1ceNGrz/JyckWTeetqKhIGRkZ510xQ/XIzjyyM4fczCM788jOHlzB%2BoXIyEi53W6vNbfbrYCAAEVGRtboHFFRUfyWAABAA8YVrF9wOp0qKCjQyZMnPWsul0utW7dWkyZNbJwMAAD4CwrWL8TGxiouLk5Lly5VaWmp8vLy9NJLLyklJcXu0QAAgJ8Imjdv3jy7h/A1t9xyi7Zu3ar58%2Bfr7bff1siRIzVhwgQFBATYPdoFNWnSRL169eIKmwlkZx7ZmUNu5pGdeWRX9wKMK72jGwAAAF74iBAAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULD%2BWn5%2BvBx98UPHx8erXr58WL16sqqoqu8eyRbt27eR0OhUXF%2Bf5M3/%2BfElSdna2Ro4cqW7dumnw4MHavHmz13PXrl2rQYMGqVu3bkpJSVFOTo7n2JkzZ/Tkk0/q1ltvVXx8vNLS0lRcXFyne7Pahx9%2BqISEBKWnp593bNu2bRoyZIi6du2qESNGaO/evZ5jVVVVWrZsmQYMGKCePXtqwoQJOn78uOe42%2B3WtGnTlJCQoD59%2BmjOnDk6ffq05/jBgwd1zz33qHv37kpKStKaNWtqd6O14GLZbdy4Ue3bt/d6/8XFxemLL76QRHb5%2BflKTU1VfHy8EhISNGvWLJWUlEiqfm%2B1%2BZ70BxfL7ttvv1W7du3Oe8%2B9%2BOKLnuc29OxsZ8BvDR8%2B3HjiiSeMkpIS4%2BjRo0ZSUpKxZs0au8eyRdu2bY3jx4%2Bft/7Pf/7T6NKli7F%2B/Xrj9OnTxkcffWR06tTJ%2BOKLLwzDMIz33nvP6NGjh7F//36jvLzcWL16tXHzzTcbZWVlhmEYxsKFC40RI0YY//u//2sUFxcbU6dONSZNmlSne7NSZmamkZSUZIwZM8aYNm2a17EDBw4YTqfT2L17t3H69Glj06ZNRufOnY2CggLDMAxj7dq1Rr9%2B/YzDhw8bp06dMv7jP/7DGDJkiFFVVWUYhmFMnTrVePDBB43vvvvOOHHihJGcnGzMnz/fMAzDKC8vN2655RZjxYoVRllZmZGTk2P06tXL2LFjR90GcAUuld2f//xn45577rnocxt6dv/%2B7/9uzJo1yygtLTUKCgqMESNGGLNnz652b7X5nvQXF8vu%2BPHjRtu2bS/6PLKzHwXLT33xxRdGhw4dDLfb7Vn77//%2Bb2PQoEE2TmWfixWsF154wRg2bJjX2rRp04y5c%2BcahmEYDz74oPHMM894jlVWVho333yzsXXrVuPcuXNG9%2B7djXfffddz/PDhw0a7du2MEydO1NJOatfLL79slJSUGDNnzjyvJDz11FNGamqq19qoUaOM1atXG4ZhGIMHDzZefvllz7FTp04ZsbGxxt///nejqKjIaN%2B%2BvXHw4EHP8T179hhdunQxzp49a2zfvt3o3bu3UVFR4Tm%2BePFi4/7776%2BNbdaKS2VXXcFqyNl9//33xqxZs4yioiLP2iuvvGIkJSVVu7fafE/6g0tlV13BaujZ%2BQI%2BIvRTubm5io6OVnh4uGetY8eOOnr0qEpLS22czD5Lly5V37591aNHD82dO1dlZWXKzc1VbGys1%2BNiY2M9HwP%2B8nhgYKA6dOggl8ulb775RqdOnVLHjh09x2%2B88UY1atRIubm5dbMpi40dO1ahoaEXPHaxrFwul06fPq3Dhw97HW/atKluuOEGuVwuHTx4UEFBQWrXrp3neMeOHfXDDz/oyJEjys3NVbt27RQUFOR17p9/HOvrLpWdJBUUFOi%2B%2B%2B5Tz549NWDAAG3atEmSGnx2YWFhWrhwoa655hrPWkFBgaKioqrdW22%2BJ/3BpbL7yWOPPaY%2Bffqod%2B/eWrp0qc6dOyeJ7HwBBctPud1uhYWFea39VLb8/R4hM7p06aKEhATt3LlTWVlZ2r9/v5566qkL5tSsWTNPRm6326ukSj/mWFxcLLfbLUnnPT8sLKxeZnypLL7//nsZhnHJrJo2baqAgACvY5I8xy/034Pb7a4X9w1GRkYqJiZGjz76qD766CM98sgjmj17trKzs8nuF1wul9atW6cpU6ZUu7fafE/6o59nFxwcrK5du2rgwIF6//33lZmZqc2bN%2Bs///M/JdXu/55RMxQsP2YYht0j%2BIysrCyNGjVKwcHBuvHGGzVjxgxt3brV89vcpVSXY0PK%2BUqyMJPTz/8P3J/17dtXL7zwgmJjYxUcHKzBgwdr4MCB2rhxo%2BcxZCd9%2BumnmjBhgqZPn66EhISLPu7ne6vr96Sv%2BmV2UVFRev311zVw4EBdddVV6tSpkyZNmlTj91x1x%2BtTdnahYPmpyMhIzxWWn7jdbgUEBCgyMtKmqXzH9ddfr8rKSgUGBp6XU3FxsSejiIiIC%2BYYGRnpecwvj3///fdq3rx5LU5vj0tl0axZswtm6Xa71bx5c0VGRqq0tFSVlZVexyR5jv/yN1%2B32%2B05b30UHR2twsJCsvs/u3bt0oMPPqjZs2dr7NixklTt3mrzPelPLpTdhURHR%2Btf//qXDMMgOx/gP//rhBen06mCggKdPHnSs%2BZyudS6dWs1adLExsnq3oEDB7Ro0SKvtby8PAUHBysxMfG8e1VycnLUuXNnST/m%2BPP7qSorK3XgwAF17txZLVu2VHh4uNfxr776SmfPnpXT6azFHdnD6XSel5XL5VLnzp0VEhKiNm3aeGVRUlKib775Rp06dVKHDh1kGIa%2B/PJLr%2BeGhYWpVatWcjqdOnTokCoqKs47d33w2muvadu2bV5reXl5atmyJdlJ%2BuyzzzRz5kwtX75cw4YN86xXt7fafE/6i4tll52drVWrVnk99siRI4qOjlZAQADZ%2BQAKlp%2BKjY1VXFycli5dqtLSUuXl5emll15SSkqK3aPVuebNmysrK0uZmZk6e/asjh49quXLlys5OVl33XWX8vPztX79ep05c0Z79uzRnj17NHr0aElSSkqK3nrrLe3fv1/l5eVatWqVgoOD1bdvXwUFBWn06NF67rnnVFBQoOLiYv3hD3/QwIEDvW46rS9Gjx6tffv2affu3Tpz5ow2bNigY8eOaejQoZJ%2BzGrt2rXKy8tTaWmplixZog4dOiguLk6RkZEaNGiQ/vjHP%2BrkyZM6ceKEVq5cqZEjR8rhcCgxMVFNmzbVqlWrVF5ers8//1wbNmyoN%2B/Xs2fPav78%2BXK5XDp37py2bt2qDz74QGPGjJHUsLOrqKjQE088oRkzZqhPnz5ex6rbW22%2BJ/3BpbILDQ3VypUrtWnTJp07d04ul0svvvgi2fmSOvw3FmGxgoICY%2BLEiUanTp2MhIQE409/%2BpPnO0wamk8%2B%2BcRITk42unTpYvTq1ctYuHChcfr0ac%2BxoUOHGh07djSSkpLO%2B/6gV1991UhMTDScTqeRkpJiHDp0yHPszJkzxrx584yePXsaXbt2NR555BGjpKSkTvdmJafTaTidTqN9%2B/ZG%2B/btPf/8kx07dhhJSUlGx44djbvuusv45JNPPMeqqqqM5cuXGzfddJPRqVMn44EHHvB8p45hGEZJSYmRnp5udOnSxejZs6fx1FNPGWfOnPEcP3TokDFmzBjD6XQaffv2NV599dW62bRFLpVdVVWVsXLlSqNfv36G0%2Bk0br/9dmPXrl2e5zbk7P7nf/7HaNu2rSevn//59ttvq91bbb4nfV112e3cudMYOnSo0alTJ%2BPmm282nnvuOaOystLz/IacnS8IMAzuZAMAALASHxECAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABY7P8DSR88hCFfAbYAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-5318577132920805567”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>159.98360655737704</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>171.60493827160494</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-45.36423841059602</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>344.91148433953697</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.075</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>39.004825468446455</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-26.141754094387316</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-6.315789473684211</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>166.07610729881472</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (314)</td> <td class=”number”>314</td> <td class=”number”>9.8%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>2870</td> <td class=”number”>89.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-5318577132920805567”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-93.57604878950286</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-91.17075428042573</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-85.05833333333334</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-79.96918335901387</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>2266.447368421053</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2983.333333333333</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3536.238709677419</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3774.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>27641.176470588238</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_GROCPTH_09_14”><s>PCH_GROCPTH_09_14</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_GROC_09_14”><code>PCH_GROC_09_14</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99149</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_GROC_09_14”>PCH_GROC_09_14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>415</td>

</tr> <tr>

<th>Unique (%)</th> <td>12.9%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.9%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>93</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>-1.9576</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>300</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>29.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram6540833462032197023”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives6540833462032197023,#minihistogram6540833462032197023”
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</div> <div class=”row collapse col-md-12” id=”descriptives6540833462032197023”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles6540833462032197023”

aria-controls=”quantiles6540833462032197023” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram6540833462032197023” aria-controls=”histogram6540833462032197023”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common6540833462032197023” aria-controls=”common6540833462032197023”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme6540833462032197023” aria-controls=”extreme6540833462032197023”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles6540833462032197023”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-50</td>

</tr> <tr>

<th>Q1</th> <td>-20</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>6.6429</td>

</tr> <tr>

<th>95-th percentile</th> <td>50</td>

</tr> <tr>

<th>Maximum</th> <td>300</td>

</tr> <tr>

<th>Range</th> <td>400</td>

</tr> <tr>

<th>Interquartile range</th> <td>26.643</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>33.615</td>

</tr> <tr>

<th>Coef of variation</th> <td>-17.171</td>

</tr> <tr>

<th>Kurtosis</th> <td>13.687</td>

</tr> <tr>

<th>Mean</th> <td>-1.9576</td>

</tr> <tr>

<th>MAD</th> <td>21.072</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.9976</td>

</tr> <tr>

<th>Sum</th> <td>-6101.9</td>

</tr> <tr>

<th>Variance</th> <td>1130</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram6540833462032197023”>

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vueCZfLpYKCAp%2BxnJwc5ebmntF2TyY29iy/bDfY7JrLX9q3bxvsJdj6mJEt9Ng1l2TfbHbKZcuCVV9fr7vvvluffPKJXnnlFSUlJZ3y9omJiSotLVV8fLyk717H1bbtf75ZffPNN%2BrQoUOz95%2BVlaXBgwf7jDkcbVReXnUaKZoWERGu2NizVFFRrbq6eqPbDia75vI304%2Bv02HnY0a20GPXXJJ9s/kzV7B%2B%2BLRlwXr00Uf12Wef6ZVXXlG7du185v7whz%2BoV69euvzyy71jJSUlGjlypJKSkhQXF6fi4mIlJiZKkv7973/rxIkTcjqdzd5/QkJCg8uBZWVHVVvrnydDXV2937YdTHbN5S8t4XNl52NGttBj11ySfbPZKZd9Lnb%2Bfx999JHWrl2rwsLCBuVK%2Bu7s1IMPPqi9e/fq%2BPHjeuGFF7R//36NGzdOERERuv766/X000/r0KFDKi8v1%2B9%2B9zsNGzbM%2BzYOAAAATQnJM1ipqamSvvsNQUnavHmzJMntdmvVqlU6evSoBg0a5HOfvn376oUXXtCsWbMkfffaK4/Ho65du%2BqPf/yjzj33XElSbm6uqqqqNHbsWNXW1mrQoEF64IEHApQMAADYQZh1pq/oRrOUlR01vk2HI1zt27dVeXmVbU6pSi0n19VPvBe0ff8Ur8%2B8Imj7binHzB/IFnrsmkuybzZ/5jrnnMbf1snfbHeJEAAAINgoWAAAAIZRsAAAAAwLeMEaPHiwCgoKdOjQoUDvGgAAICACXrCuu%2B46bdiwQUOHDtUtt9yiTZs2eX8bEAAAwA4CXrBycnK0YcMG/fnPf1a3bt306KOPKiMjQ48//rj27dsX6OUAAAAYF7TXYKWkpGjOnDnaunWr5s6dqz//%2Bc8aOXKkpkyZok8%2B%2BSRYywIAADhjQStYNTU12rBhg379619rzpw56tixo%2B6%2B%2B2716NFDkydP1rp164K1NAAAgDMS8HdyLykp0cqVK/Xaa6%2BpqqpKI0aM0EsvvaQ%2Bffp4b9O3b1898MADGj16dKCXBwAAcMYCXrBGjRqlLl26aOrUqbr22msb/XuBGRkZOnLkSKCXBgAAYETAC9ayZct06aWXNnm7jz/%2BOACrAQAAMC/gr8FKTk7WtGnTvH%2BgWZL%2B%2BMc/6te//rU8Hk%2BglwMAAGBcwAvWggULdPToUXXt2tU7NnDgQNXX12vhwoWBXg4AAIBxAb9E%2BO6772rdunVq3769d6xz587Kz8/XNddcE%2BjlAAAAGBfwM1jHjh1TVFRUw4WEh6u6ujrQywEAADAu4AWrb9%2B%2BWrhwob755hvv2FdffaUHH3zQ560aAAAAQlXALxHOnTtXN998sy6//HJFR0ervr5eVVVVSkpK0p/%2B9KdALwcAAMC4gBespKQk/eUvf9Hbb7%2Bt/fv3Kzw8XF26dFH//v0VERER6OUAAAAYF/CCJUmRkZEaOnRoMHYNAADgdwEvWAcOHNCiRYv02Wef6dixYw3m33zzzUAvCQAAwKigvAartLRU/fv3V5s2bQK9ewAAAL8LeMEqKirSm2%2B%2Bqfj4%2BEDvGgAAICAC/jYNHTp04MwVAACwtYAXrKlTp6qgoECWZQV61wAAAAER8EuEb7/9tv75z39q9erVOv/88xUe7tvxXn311UAvCQAAwKiAF6zo6GgNGDAg0LsFAAAImIAXrAULFgR6lwAAAAEV8NdgSdLevXu1ZMkS3X333d6xf/3rX8FYCgAAgHEBL1jbt2/XmDFjtGnTJq1fv17Sd28%2BOmnSJN5kFAAA2ELAC9bixYt1xx13aN26dQoLC5P03d8nXLhwoZYuXRro5QAAABgX8IL173//W9nZ2ZLkLViSdNVVV6mkpOS0tvXOO%2B8oPT1deXl5DeY2bNig0aNHq1evXho/frzeffdd71x9fb0WL16sIUOGqG/fvpoyZYoOHDjgnfd4PJo5c6bS09PVv39/3XPPPY3%2BWR8AAIDGBLxgxcTENFpWSktLFRkZ2eztPPvss5o/f746derUYG7Xrl2aM2eOZs%2Berb///e%2BaPHmyfvvb3%2BrLL7%2BUJC1fvlzr1q1TYWGhtm7dqs6dOysnJ8f73lzz5s1TdXW11q9fr1WrVqmkpET5%2Bfk/MTEAAPhvE/CC1bt3bz366KOqrKz0ju3bt09z5szR5Zdf3uztREVFaeXKlY0WrBUrVigjI0MZGRmKiorSmDFj1L17d61du1aS5HK5NHnyZF1wwQWKjo5WXl6eSkpK9PHHH%2Bvw4cPavHmz8vLyFB8fr44dO%2BrWW2/VqlWrVFNTc%2BafAAAAYHsBf5uGu%2B%2B%2BWzfddJP69eunuro69e7dW9XV1erWrZsWLlzY7O1MmjTppHPFxcXKyMjwGevZs6fcbreOHTumPXv2qGfPnt656OhoderUSW63W0ePHlVERISSk5O98ykpKfr222%2B1d%2B9en/GTKS0tVVlZmc%2BYw9FGCQkJzY3XLBER4T7/2oVdc/mbwxG8z5edjxnZQo9dc0n2zWbHXAEvWOeee67Wr1%2Bvt956S/v27VPr1q3VpUsXXXHFFT6vyToTHo9HcXFxPmNxcXHas2ePvvnmG1mW1eh8eXm52rVrp%2BjoaJ%2B1fH/b8vLyZu3f5XKpoKDAZywnJ0e5ubk/JU6TYmPP8st2g82uufylffu2wV6CrY8Z2UKPXXNJ9s1mp1wBL1iS1KpVKw0dOtSv%2B2jqbx2eav5M/05iVlaWBg8e7DPmcLRReXnVGW33xyIiwhUbe5YqKqpVV1dvdNvBZNdc/mb68XU67HzMyBZ67JpLsm82f%2BYK1g%2BfAS9YgwcPPuWZKhPvhdW%2BfXt5PB6fMY/Ho/j4eLVr107h4eGNznfo0EHx8fGqrKxUXV2dIiIivHOS1KFDh2btPyEhocHlwLKyo6qt9c%2BToa6u3m/bDia75vKXlvC5svMxI1vosWsuyb7Z7JQr4AVr5MiRPgWrrq5O%2B/btk9vt1k033WRkH06nU0VFRT5jbrdbo0aNUlRUlLp166bi4mJdeumlkqSKigrt379faWlpSkxMlGVZ2r17t1JSUrz3jY2NVZcuXYysDwAA2FvAC9bs2bMbHd%2B4caP%2B8Y9/GNnH9ddfr8zMTG3btk2XX3651q1bp88//1xjxoyRJGVnZ6uwsFADBgxQx44dlZ%2Bfrx49eig1NVWSNGLECD3xxBN67LHHdOLECS1dulSZmZlyOIJyRRUAAISYFtMYhg4dqvvuu0/33Xdfs27/fRmqra2VJG3evFnSd2ebunfvrvz8fC1YsEAHDx5U165d9cwzz%2Bicc86RJE2cOFFlZWX65S9/qaqqKvXr18/nRekPPfSQ7r//fg0ZMkStWrXSNddc0%2BibmQIAADSmxRSsnTt3ntaLy91u9ynnhw8fruHDhzc6FxYWptzc3JP%2BVl9MTIx%2B97vfNXstAAAAPxTwgjVx4sQGY9XV1SopKTlpIQIAAAglAS9YnTt3bvBbhFFRUcrMzNSECRMCvRwAAADjAl6wTufd2gEAAEJRwAvWa6%2B91uzbXnvttX5cCQAAgH8EvGDdc889qq%2Bvb/CC9rCwMJ%2BxsLAwChYAAAhJAS9Yzz33nF544QVNmzZNycnJsixLn376qZ599lndeOON6tevX6CXBAAAYFRQXoNVWFiojh07escuueQSJSUlacqUKVq/fn2glwQAAGBUeKB3%2BPnnnysuLq7BeGxsrA4ePBjo5QAAABgX8IKVmJiohQsXqry83DtWUVGhRYsW6ec//3mglwMAAGBcwC8Rzp07V7NmzZLL5VLbtm0VHh6uyspKtW7dWkuXLg30cgAAAIwLeMHq37%2B/tm3bprfeektffvmlLMtSx44ddeWVVyomJibQywEAADAuKH%2BL8KyzztKQIUP05ZdfKikpKRhLAAAA8JuAvwbr2LFjmjNnjnr16qWrr75a0nevwbrllltUUVER6OUAAAAYF/CC9fjjj2vXrl3Kz89XePh/dl9XV6f8/PxALwcAAMC4gBesjRs36ve//72uuuoq7x99jo2N1YIFC7Rp06ZALwcAAMC4gBesqqoqde7cucF4fHy8vv3220AvBwAAwLiAF6yf//zn%2Bsc//iFJPn978I033tDPfvazQC8HAADAuID/FuENN9yg2267Tdddd53q6%2Bv14osvqqioSBs3btQ999wT6OUAAAAYF/CClZWVJYfDoZdfflkRERF6%2Bumn1aVLF%2BXn5%2Buqq64K9HIAAACMC3jBOnLkiK677jpdd911gd41AABAQAT8NVhDhgzxee0VAACA3QS8YPXr10%2Bvv/56oHcLAAAQMAG/RHjeeefpkUceUWFhoX7%2B85%2BrVatWPvOLFi0K9JIAAACMCnjB2rNnj37xi19IksrLywO9ewAAAL8LWMHKy8vT4sWL9ac//ck7tnTpUuXk5ARqCQAAAAERsNdgbdmypcFYYWFhoHYPAAAQMAErWI395iC/TQgAAOwoYAXr%2Bz/s3NQYAABAqAv42zQAAADYXcB/izAQPvjgA918880%2BY5ZlqaamRsuWLdOkSZMUGRnpM/8///M/uvrqqyVJy5Yt0/Lly1VWVqbk5GTdc889cjqdAVs/AAAIbQErWDU1NZo1a1aTYybeB6tv375yu90%2BY08//bR2794tSUpMTGz0RffSdy/GX7JkiZ577jklJydr2bJlmjZtmjZt2qQ2bdqc8doAAID9BewSYZ8%2BfVRaWurz0diYP/zf//2fXnzxRd15551N3tblcmn8%2BPG66KKL1Lp1a91yyy2SpK1bt/plbQAAwH4Cdgbrh%2B9/FWhPPvmkrrvuOv3sZz/TgQMHVFVVpZycHH344YeKjIzUzTffrMmTJyssLEzFxcUaOXKk977h4eHq0aOH3G63Ro0aFbQMAAAgdNjyNVg/9MUXX2jTpk3atGmTJCk6Olrdu3fXTTfdpMWLF%2Bv999/XjBkzFBMTo8zMTHk8HsXFxflsIy4u7rTedb60tFRlZWU%2BYw5HGyUkJJx5oB%2BIiAj3%2Bdcu7JrL3xyO4H2%2B7HzMyBZ67JpLsm82O%2BayfcFavny5hg8frnPOOUeSlJKS4nM2rX///po4caJWr16tzMxMSWf%2B/lwul0sFBQU%2BYzk5OcrNzT2j7Z5MbOxZftlusNk1l7%2B0b9822Euw9TEjW%2Bixay7JvtnslMv2BWvjxo2aM2fOKW%2BTmJiojRs3SpLat28vj8fjM%2B/xeNStW7dm7zMrK0uDBw/2GXM42qi8vKrZ22iOiIhwxcaepYqKatXV1RvddjDZNZe/mX58nQ47HzOyhR675pLsm82fuYL1w6etC9auXbt08OBBXXHFFd6x119/XeXl5brhhhu8Y3v37lVSUpIkyel0qri4WOPGjZMk1dXVaefOnd6zW82RkJDQ4HJgWdlR1db658lQV1fvt20Hk11z%2BUtL%2BFzZ%2BZiRLfTYNZdk32x2ymWfi52N2Llzp9q1a6fo6GjvWKtWrfTYY4/p3XffVU1Njd577z2tWrVK2dnZkqTs7Gy99tpr2rFjh6qrq/XUU08pMjJSAwcODFIKAAAQamx9Buvw4cPe1159b%2BjQoZo7d64efvhhHTp0SGeffbbmzp2r4cOHS5IGDBig22%2B/XTNnztTXX3%2Bt1NRUFRYWqnXr1sGIAAAAQlCYxV9cDoiysqPGt%2BlwhKt9%2B7YqL6%2ByzSlVqeXkuvqJ94K275/i9ZlXNH0jP2kpx8wfyBZ67JpLsm82f%2BY655wYo9trLltfIgQAAAgGChYAAIBhFCwAAADDKFgAAACGUbAAAAAMs/XbNKBlCbXfygMA4KfiDBYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAw2xas5ORkOZ1Opaamej8efvhhSdL27duVmZmp3r17a9SoUVq7dq3PfZctW6YRI0aod%2B/eys7OVlFRUTAiAACAEOUI9gL86Y033tD555/vM1ZaWqpbb71V99xzj0aPHq2PPvpI06dPV5cuXZSamqotW7ZoyZIleu6555ScnKxly5Zp2rRp2rRpk9q0aROkJAAAIJTY9gzWyaxbt06dO3dWZmamoqKilJ6ersGDB2vFihWSJJfLpfHjx%2Buiiy5S69atdcstt0iStm7dGsxlAwCAEGLrM1iLFi3Sv/71L1VWVurqq6/WXXfdpeLiYvXs2dO3DVM1AAAVGUlEQVTndj179tTrr78uSSouLtbIkSO9c%2BHh4erRo4fcbrdGjRrVrP2WlpaqrKzMZ8zhaKOEhIQzTOQrIiLc51/8d3M4gvc4sPNjkWyhx665JPtms2Mu2xasiy%2B%2BWOnp6Xrsscd04MABzZw5Uw8%2B%2BKA8Ho86duzoc9t27dqpvLxckuTxeBQXF%2BczHxcX551vDpfLpYKCAp%2BxnJwc5ebm/sQ0pxYbe5ZftovQ0r5922AvwdaPRbKFHrvmkuybzU65bFuwXC6X978vuOACzZ49W9OnT1efPn2avK9lWWe076ysLA0ePNhnzOFoo/LyqjPa7o9FRIQrNvYsVVRUq66u3ui2EXpMP75Oh50fi2QLPXbNJdk3mz9zBeuHT9sWrB87//zzVVdXp/DwcHk8Hp%2B58vJyxcfHS5Lat2/fYN7j8ahbt27N3ldCQkKDy4FlZUdVW%2BufJ0NdXb3fto3Q0RIeA3Z%2BLJIt9Ng1l2TfbHbKZZ%2BLnT%2Bwc%2BdOLVy40GespKREkZGRysjIaPC2C0VFRbroooskSU6nU8XFxd65uro67dy50zsPAADQFFsWrA4dOsjlcqmwsFAnTpzQvn379OSTTyorK0tjx47VwYMHtWLFCh0/flxvvfWW3nrrLV1//fWSpOzsbL322mvasWOHqqur9dRTTykyMlIDBw4MbigAABAybHmJsGPHjiosLNSiRYu8BWncuHHKy8tTVFSUnnnmGc2fP18PPvigEhMT9fjjj%2BvCCy%2BUJA0YMEC33367Zs6cqa%2B//lqpqakqLCxU69atg5wKAACEClsWLEnq27evXn311ZPOrVmz5qT3veGGG3TDDTf4a2kAAMDmbHmJEAAAIJgoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwzBHsBQAw4%2Bon3gv2Eprt9ZlXBHsJAOBXnMECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyzbcE6ePCgcnJy1K9fP6Wnp%2Buuu%2B5SRUWFvvjiCyUnJys1NdXn4/nnn/fed8OGDRo9erR69eql8ePH69133w1iEgAAEGocwV6Av0ybNk1Op1NbtmzR0aNHlZOTo8cee0zTp0%2BXJLnd7kbvt2vXLs2ZM0cFBQW67LLLtHHjRv32t7/VG2%2B8oXPPPTeQEQAAQIiy5RmsiooKOZ1OzZo1S23bttW5556rcePG6cMPP2zyvitWrFBGRoYyMjIUFRWlMWPGqHv37lq7dm0AVg4AAOzAlgUrNjZWCxYs0Nlnn%2B0dO3TokBISErz/f%2Bedd6p///667LLLtGjRItXU1EiSiouL1bNnT5/t9ezZ86RnvAAAAH7MtpcIf8jtduvll1/WU089pcjISPXq1UvDhg3TI488ol27dum2226Tw%2BHQjBkz5PF4FBcX53P/uLg47dmzp9n7Ky0tVVlZmc%2BYw9HGp%2BCZEBER7vMvECocjtB5zNr5eWbXbHbNJdk3mx1z2b5gffTRR5o%2BfbpmzZql9PR0SdKrr77qnU9LS9PUqVP1zDPPaMaMGZIky7LOaJ8ul0sFBQU%2BYzk5OcrNzT2j7Z5MbOxZftku4C/t27cN9hJOm52fZ3bNZtdckn2z2SmXrQvWli1bdMcdd2jevHm69tprT3q7xMREHT58WJZlqX379vJ4PD7zHo9H8fHxzd5vVlaWBg8e7DPmcLRReXnV6QVoQkREuGJjz1JFRbXq6uqNbhvwJ9PPBX%2By8/PMrtnsmkuybzZ/5grWD3S2LVj//Oc/NWfOHD355JPq37%2B/d3z79u3asWOH97cJJWnv3r1KTExUWFiYnE6nioqKfLbldrs1atSoZu87ISGhweXAsrKjqq31z5Ohrq7eb9sG/CEUH692fp7ZNZtdc0n2zWanXPa52PkDtbW1uvfeezV79myfciVJMTExWrp0qdasWaOamhq53W49//zzys7OliRdf/31%2Btvf/qZt27bp%2BPHjWrlypT7//HONGTMmGFEAAEAIsuUZrB07dqikpETz58/X/PnzfebeeOMNLV68WAUFBbrvvvsUExOjX/7yl7rpppskSd27d1d%2Bfr4WLFiggwcPqmvXrnrmmWd0zjnnBCMKAAAIQbYsWJdccok%2B/fTTk84nJiZq2LBhJ50fPny4hg8f7o%2BlAQCA/wK2vEQIAAAQTBQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgmCPYCwCAlu6Se94I9hJOy%2Bszrwj2EoD/ehSsEBdqX/gBAPhvwCVCAAAAwyhYAAAAhlGwAAAADKNgAQAAGMaL3AEE3NVPvBfsJQCAX3EGqxEHDx7Ub37zG/Xr10%2BDBg3S448/rvr6%2BmAvCwAAhAjOYDXitttuU0pKijZv3qyvv/5aU6dO1dlnn61f/epXwV4aAAAIAZzB%2BhG3263du3dr9uzZiomJUefOnTV58mS5XK5gLw0AAIQIzmD9SHFxsRITExUXF%2BcdS0lJ0b59%2B1RZWano6Ogmt1FaWqqysjKfMYejjRISEoyuNSKCfgygIYejeV8bvv8aEsyvJcPy3wnavn%2BKv86%2BMqj7P91jFkqf3w8fucpW39coWD/i8XgUGxvrM/Z92SovL29WwXK5XCooKPAZ%2B%2B1vf6vbbrvN3EL1XZG76dzPlJWVZby8BVNpaalcLpftckn2zWbXXJL9s7300nNBzfbhI1cZ3ybH7D/88fn1h9LSUi1ZssRWx8w%2BVdEgy7LO6P5ZWVlavXq1z0dWVpah1f1HWVmZCgoKGpwtC3V2zSXZN5tdc0lkC0V2zSXZN5sdc3EG60fi4%2BPl8Xh8xjwej8LCwhQfH9%2BsbSQkJNimgQMAgNPHGawfcTqdOnTokI4cOeIdc7vd6tq1q9q2bRvElQEAgFBBwfqRnj17KjU1VYsWLVJlZaVKSkr04osvKjs7O9hLAwAAISLigQceeCDYi2hprrzySq1fv14PP/yw/vKXvygzM1NTpkxRWFhYsJfWQNu2bXXppZfa7uyaXXNJ9s1m11wS2UKRXXNJ9s1mt1xh1pm%2BohsAAAA%2BuEQIAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFK0S43W4NGzZM119/fYO57du3KzMzU71799aoUaO0du1an/lly5ZpxIgR6t27t7Kzs1VUVBSoZZ%2BWwYMHy%2Bl0KjU11fsxbdo07/yuXbt04403qk%2BfPho%2BfLheeOGFIK729B08eFC/%2Bc1v1K9fPw0aNEiPP/646uvrg72s05acnNzgOD388MOSmn4stjTvvPOO0tPTlZeX12Buw4YNGj16tHr16qXx48fr3Xff9c7V19dr8eLFGjJkiPr27aspU6bowIEDgVx6k06WbfXq1brwwgt9jl9qaqo%2B%2BeQTSS0/28GDB5WTk6N%2B/fopPT1dd911lyoqKiQ1/TXiVMe0JThZti%2B%2B%2BELJyckNjtnzzz/vvW9LzrZ7927ddNNN6tOnj9LT0zVz5kyVlZVJss/3r0ZZaPHWrFljZWRkWFOmTLEmTJjgM/fVV19ZF198sbVixQrr2LFj1nvvvWelpaVZn3zyiWVZlvXmm29al1xyibVjxw6rurraeuaZZ6wrrrjCqqqqCkaUUxo0aJD197//vdG56upq68orr7SWLFliVVVVWUVFRdall15qbdy4McCr/OnGjRtn3XvvvVZFRYW1b98%2Ba/jw4dYLL7wQ7GWdtu7du1sHDhxoMN7UY7GlKSwstIYPH25NnDjRmjlzps/czp07LafTaW3bts06duyYtWbNGuuiiy6yDh06ZFmWZS1btswaNGiQtWfPHuvo0aPWQw89ZI0ePdqqr68PRpQGTpVt1apV1o033njS%2B7b0bNdcc4111113WZWVldahQ4es8ePHW3Pnzm3ya0RTx7QlOFm2AwcOWN27dz/p/VpytuPHj1uXX365VVBQYB0/ftz6%2BuuvrRtvvNG69dZbbfX9qzGcwQoBx48fl8vl0kUXXdRgbt26dercubMyMzMVFRWl9PR0DR48WCtWrJAkuVwujR8/XhdddJFat26tW265RZK0devWgGY4U9u2bVNNTY2mT5%2BuNm3aKCUlRRMmTJDL5Qr20prF7XZr9%2B7dmj17tmJiYtS5c2dNnjw5ZNbfHE09FluaqKgorVy5Up06dWowt2LFCmVkZCgjI0NRUVEaM2aMunfv7v3p2uVyafLkybrgggsUHR2tvLw8lZSU6OOPPw50jEadKltTWnK2iooKOZ1OzZo1S23bttW5556rcePG6cMPP2zya0RTxzTYTpWtKS05W3V1tfLy8jR16lRFRkYqPj5ew4YN02effWb7718UrBAwYcIEdezYsdG54uJi9ezZ02esZ8%2Be3tOoP54PDw9Xjx495Ha7/bfgM7Bs2TINHTpUvXr1Um5urr7%2B%2BmtJ3%2BVITk5WRESE97Y/zNnSFRcXKzExUXFxcd6xlJQU7du3T5WVlUFc2U%2BzaNEiDRw4UJdcconmzZunqqqqJh%2BLLc2kSZMUExPT6NzJsrjdbh07dkx79uzxmY%2BOjlanTp1azPPqVNkk6dChQ/rVr36lvn37asiQIVqzZo0ktfhssbGxWrBggc4%2B%2B2zv2KFDh5SQkNDk14hTHdOW4FTZvnfnnXeqf//%2Buuyyy7Ro0SLV1NRIatnZ4uLiNGHCBDkcDknS3r179b//%2B7%2B6%2Buqrbff968coWCHO4/EoNjbWZ6xdu3YqLy/3zv/wm7r03QP%2B%2B/mWpEePHkpLS9OaNWu0YcMGeTwezZgxQ9LJc3o8npB4HVNj6//%2BuLTEY3EqF198sdLT07Vp0ya5XC7t2LFDDz74YJOPxVByqufNN998I8uyQuZ59WPx8fHq3Lmz7rjjDr333nu6/fbbNXfuXG3fvj3ksrndbr388suaPn16k18jQulroeSbLTIyUr169dKwYcO0detWFRYWau3atfrDH/4gKTS%2Bzh88eFBOp1MjR45UamqqcnNzbfX9qzEUrBZgzZo1Sk5ObvRj9erVZ7x9y7IMrPLMNZVz6dKlmjp1qtq2bavzzjtP999/vz744APt37//pNsMCwsLYIIz01KOw5lyuVyaMGGCIiMjdcEFF2j27Nlav36996dpu2jqeIXq8Rw4cKCee%2B459ezZU5GRkRo1apSGDRvm87UmFLJ99NFHmjJlimbNmqX09PST3u6HXyNCIZfUMFtCQoJeffVVDRs2TK1atVJaWpqmTp0aUscsMTFRbrdbb7zxhj7//HPdeeedzbpfS891Ko5gLwDS2LFjNXbs2J903/bt28vj8fiMlZeXKz4%2B/qTzHo9H3bp1%2B2mLPQOnmzMxMVGSVFpaqvj4eH3%2B%2Bec%2B8x6PR%2B3atVN4eMv/OSE%2BPr7R4xAWFuY9VqHq/PPPV11dncLDw0/5WAwlJ3vexMfHex9zjc136NAhkMs0JjExUUVFRSGTbcuWLbrjjjs0b948XXvttZLU5NeIUx3TlqSxbI1JTEzU4cOHZVlWyGQLCwtT586dlZeXp4kTJyojIyNkvn/9FC3/OxNOKTU1tcFrXIqKirwviHc6nSouLvbO1dXVaefOnY2%2BYD6YDh48qPvvv18nTpzwjpWUlEiSkpKS5HQ69emnn6q2ttY773a7W1yOk3E6nTp06JCOHDniHXO73eratavatm0bxJWdnp07d2rhwoU%2BYyUlJYqMjFRGRsYpH4uhxOl0Nsjy/eMtKipK3bp183leVVRUaP/%2B/UpLSwv0Uk/bK6%2B8og0bNviMlZSUKCkpKSSy/fOf/9ScOXP05JNP%2BhSQpr5GnOqYthQny7Z9%2B3Y99dRTPrfdu3evEhMTFRYW1qKzbd%2B%2BXSNGjPB5Kcf3PxSnpaXZ4vvXyVCwQtzo0aN18OBBrVixQsePH9dbb72lt956y/t%2BWdnZ2Xrttde0Y8cOVVdX66mnnlJkZKQGDhwY3IX/SIcOHbRlyxYtXLhQ3377rb766istWLBAgwYNUseOHZWRkaHo6Gg99dRTqq6u1scff6yVK1cqOzs72Etvlp49eyo1NVWLFi1SZWWlSkpK9OKLL4bM%2Br/XoUMHuVwuFRYW6sSJE9q3b5%2BefPJJZWVlaezYsad8LIaS66%2B/Xn/729%2B0bds2HT9%2BXCtXrtTnn3%2BuMWPGSPruebVs2TKVlJSosrJS%2Bfn56tGjh1JTU4O88qadOHFCDz/8sNxut2pqarR%2B/Xq9/fbbmjhxoqSWna22tlb33nuvZs%2Berf79%2B/vMNfU1oqljGmynyhYTE6OlS5dqzZo1qqmpkdvt1vPPPx8S2ZxOpyorK/X444%2BrurpaR44c0ZIlS3TJJZcoOzvbFt%2B/TipIbw%2BB0zB8%2BHDL6XRaPXr0sJKTky2n02k5nU7riy%2B%2BsCzLst5//31rzJgxVkpKijV8%2BPAG7w21fPlyKyMjw3I6nVZ2drb16aefBiNGk3bv3m1NnjzZ6tOnj9WnTx/rrrvusr755hvv/KeffmpNnDjRcjqd1sCBA63ly5cHcbWn79ChQ9Ytt9xipaWlWenp6dbvf//7FvPeQqfj/ffft7KysqyLL77YuvTSS60FCxZYx44d886d6rHYknz/PLrwwgutCy%2B80Pv/39u4caM1fPhwKyUlxRo7dqz1/vvve%2Bfq6%2ButJ5980rr88suttLQ069e//nWLeM%2Bh750qW319vbV06VJr0KBBltPptK666ipry5Yt3vu25GwffPCB1b17d2%2BeH3588cUXTX6NONUxDbamsm3atMkaM2aMlZaWZl1xxRXW008/bdXV1Xnv35Kz7d6927rxxhuttLQ067LLLrNmzpxpffnll5Zl2ef7V2PCLCuEX0EGAADQAnGJEAAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAM%2B39L46mgBj82fgAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common6540833462032197023”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>961</td> <td class=”number”>29.9%</td> <td>

<div class=”bar” style=”width:76%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.333333333</td> <td class=”number”>165</td> <td class=”number”>5.1%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-25.0</td> <td class=”number”>144</td> <td class=”number”>4.5%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>129</td> <td class=”number”>4.0%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>86</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.333333333</td> <td class=”number”>85</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-20.0</td> <td class=”number”>84</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-16.666666667</td> <td class=”number”>77</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (404)</td> <td class=”number”>1261</td> <td class=”number”>39.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>93</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme6540833462032197023”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>26</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-80.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-75.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-71.428571429</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-66.666666667</td> <td class=”number”>26</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>125.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>133.333333333</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>150.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>300.0</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:83%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_LACCESS_HHNV_10_15”>PCH_LACCESS_HHNV_10_15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3113</td>

</tr> <tr>

<th>Unique (%)</th> <td>97.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.8%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>91</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>33.086</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>43191</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-7019968200483418534”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-7019968200483418534,#minihistogram-7019968200483418534”
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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-7019968200483418534”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-7019968200483418534”

aria-controls=”quantiles-7019968200483418534” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-7019968200483418534” aria-controls=”histogram-7019968200483418534”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-7019968200483418534” aria-controls=”common-7019968200483418534”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-7019968200483418534” aria-controls=”extreme-7019968200483418534”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-7019968200483418534”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-46.422</td>

</tr> <tr>

<th>Q1</th> <td>-14.628</td>

</tr> <tr>

<th>Median</th> <td>4.7531</td>

</tr> <tr>

<th>Q3</th> <td>28.984</td>

</tr> <tr>

<th>95-th percentile</th> <td>100.94</td>

</tr> <tr>

<th>Maximum</th> <td>43191</td>

</tr> <tr>

<th>Range</th> <td>43291</td>

</tr> <tr>

<th>Interquartile range</th> <td>43.612</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>789.88</td>

</tr> <tr>

<th>Coef of variation</th> <td>23.873</td>

</tr> <tr>

<th>Kurtosis</th> <td>2863.6</td>

</tr> <tr>

<th>Mean</th> <td>33.086</td>

</tr> <tr>

<th>MAD</th> <td>61.353</td>

</tr> <tr class=”alert”>

<th>Skewness</th> <td>52.642</td>

</tr> <tr>

<th>Sum</th> <td>103200</td>

</tr> <tr>

<th>Variance</th> <td>623910</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-7019968200483418534”>

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BbNdf7552vEiBH65JNPtG3bNlVVVWnKlClq1qyZunbtqjFjxignJ0eStHr1aqWlpSktLU3h4eEaNmyYOnXqpHXr1kmScnJyNGHCBLVv314RERHKyspSfn6%2Bdu7c6c8lAwCAIOLwdwG/RmRkpObNm%2Bc1VlhYqLi4OOXl5alz584KCwvzzCUkJGj16tWSpLy8PKWlpXk9NyEhQU6nU5WVldq7d68SEhI8cxEREWrbtq2cTqcuueSSOtVXVFSk4uJirzGHo5ni4uJ%2B0TobGocjKPP8LxYWFur1Hf5FPwIPPQk89MS%2BoAxYP%2BZ0OrVy5Uo999xz2rRpkyIjI73mo6Oj5Xa7VVtbK7fbraioKK/5qKgo7d27V0ePHpUx5rTzLperzvXk5ORoyZIlXmMZGRmaOnXqL1xZwxIT09zfJfhUZOS5/i4BP0A/Ag89CTz0xJ6gD1iffvqppkyZounTpys1NVWbNm067eNCQkI8/32m66nO9nqrcePGqX///l5jDkczuVzlZ7XdYNdY1h8WFqrIyHNVUlKhmppaf5fT6NGPwENPAk9D7om/frkP6oC1ZcsW3XvvvZo9e7aGDx8uSYqNjdU333zj9Ti3263o6GiFhoYqJiZGbrf7lPnY2FjPY04337JlyzrXFRcXd8rpwOLiUlVXN6wf2l%2Bqsa2/pqa20a05kNGPwENPAg89scfnJ1v79%2B%2BvJUuWqLCw8Ky289lnn2nmzJl65plnPOFKkhITE/Xll1%2BqurraM%2BZ0OtWtWzfPfG5urte2vp8PDw9Xx44dlZeX55krKSnRgQMHlJycfFb1AgCAxsPnAWvUqFHauHGjrr76at122216%2B%2B23vcJQXVRXV%2Buhhx7SjBkz1KdPH6%2B5tLQ0RURE6LnnnlNFRYV27typNWvWKD09XZI0duxYffjhh9q2bZuOHz%2BuNWvW6JtvvtGwYcMkSenp6VqxYoXy8/NVVlam7OxsdenSRUlJSXZeAAAA0OCFGD/d4CkvL08bNmzQpk2bVFVVpeHDh2v06NFq167dGZ/7ySef6F//9V/VpEmTU%2BbefPNNlZeX65FHHlFubq7OO%2B883X777brhhhs8j3n77be1cOFCFRQUqEOHDpo1a5Z69eol6eT1V4sXL9Yrr7yi8vJypaSk6LHHHtP5559/VustLi49q%2Bf/lMFPf1Av260PmzKv8HcJPuFwhComprlcrnIOtQcA%2BhF46Engacg9adWqhV/267eA9T1jjDZu3Kg5c%2BaorKxMqampmjZtWoM7JUfAImDBP%2BhH4KEngach98RfActvN7yoqqrSxo0bdfvtt2vmzJlq3bq1HnjgAXXp0kUTJkzQ%2BvXr/VUaAADAWfH5pwjz8/O1Zs0avfbaayovL9egQYP00ksvqWfPnp7H9OrVS3PmzNHQoUN9XR4AAMBZ83nAGjJkiNq1a6dJkyZp%2BPDhio6OPuUxaWlpOnLkiK9LAwAAsMLnAWvFihXq3bv3GR/H3/4DAADByufXYHXu3FmTJ0/W5s2bPWP/%2BZ//qdtvv/2UG3wCAAAEI58HrHnz5qm0tFQdOnTwjPXt21e1tbWaP3%2B%2Br8sBAACwzuenCN9//32tX79eMTExnrH4%2BHhlZ2fruuuu83U5AAAA1vn8CFZlZaXCw8NPLSQ0VBUVFb4uBwAAwDqfB6xevXpp/vz5Onr0qGfs22%2B/1aOPPup1qwYAAIBg5fNThA8%2B%2BKBuvfVWXX755YqIiFBtba3Ky8t14YUX6uWXX/Z1OQAAANb5PGBdeOGFeuONN/Tuu%2B/qwIEDCg0NVbt27dSnTx%2BFhYX5uhwAAADrfB6wJKlJkya6%2Buqr/bFrAACAeufzgHXw4EEtXLhQX3/9tSorK0%2BZf%2Bedd3xdEgAAgFV%2BuQarqKhIffr0UbNmzXy9ewAAgHrn84CVm5urd955R7Gxsb7eNQAAgE/4/DYNLVu25MgVAABo0HwesCZNmqQlS5bIGOPrXQMAAPiEz08Rvvvuu/rss8%2B0du1aXXDBBQoN9c54r7zyiq9LAgAAsMrnASsiIkJXXXWVr3cLAADgMz4PWPPmzfP1LgEAAHzK59dgSdK%2Bffu0ePFiPfDAA56xzz//3B%2BlAAAAWOfzgLVjxw4NGzZMb7/9tjZs2CDp5M1Hb775Zm4yCgAAGgSfB6xFixbp3nvv1fr16xUSEiLp5N8nnD9/vpYuXerrcgAAAKzzecD66quvlJ6eLkmegCVJv/vd75Sfn%2B/rcgAAAKzzecBq0aLFaf8GYVFRkZo0aeLrcgAAAKzzecDq0aOHnnzySZWVlXnG9u/fr5kzZ%2Bryyy/3dTkAAADW%2Bfw2DQ888IBuueUWpaSkqKamRj169FBFRYU6duyo%2BfPn%2B7ocAAAA63wesM4//3xt2LBB27dv1/79%2B9W0aVO1a9dOV1xxhdc1WQAAAMHK5wFLks455xxdffXV/tg1AABAvfN5wOrfv//PHqniXlgAACDY%2BTxgXXvttV4Bq6amRvv375fT6dQtt9zi63IAAACs83nAmjFjxmnH33rrLf3tb3/zcTUAAAD2%2BeVvEZ7O1VdfrTfeeMPfZQAAAJy1gAlYu3btkjHG32UAAACcNZ%2BfIhw/fvwpYxUVFcrPz9fAgQN9XQ4AAIB1Pg9Y8fHxp3yKMDw8XKNHj9aYMWN8XQ4AAIB1Pg9Y3K0dAAA0dD4PWK%2B99lqdHzt8%2BPB6rAQAAKB%2B%2BDxgzZo1S7W1tadc0B4SEuI1FhIScsaA9d5772nmzJlKSUnRokWLPONr167Vgw8%2BqHPOOcfr8atWrVJycrJqa2v1zDPPaMOGDSopKVFycrLmzJmjCy%2B8UJLkdrs1Z84cffzxxwoNDVVaWppmz56tpk2bnu3yAQBAI%2BDzgPXCCy/oxRdf1OTJk9W5c2cZY/Tll1/q%2Beef14033qiUlJQ6bef555/XmjVr1LZt29PO9%2BrVSy%2B//PJp51atWqX169fr%2BeefV%2BvWrbVo0SJlZGTo9ddfV0hIiGbPnq0TJ05ow4YNqqqq0rRp05Sdna2HHnroV68bAAA0Hj6/TcP8%2BfM1d%2B5c9ezZUxEREWrRooUuvfRSPfbYY3rqqafUpEkTz9fPCQ8P/9mA9XNycnI0YcIEtW/fXhEREcrKylJ%2Bfr527typ7777Tps3b1ZWVpZiY2PVunVr3XnnnXr11VdVVVX1a5cNAAAaEZ8fwfrmm28UFRV1ynhkZKQKCgrqvJ2bb775Z%2BcLCwv1%2B9//Xrm5uYqMjNTUqVN1/fXXq7KyUnv37lVCQoLnsREREWrbtq2cTqdKS0sVFhamzp07e%2Ba7du2qY8eOad%2B%2BfV7jP6WoqEjFxcVeYw5HM8XFxdV5fQ2RwxEwt12rV2FhoV7f4V/0I/DQk8BDT%2BzzecBq06aN5s%2Bfr2nTpikmJkaSVFJSomeffVYXXXSRlX3ExsYqPj5e99xzjzp06KC//vWvuu%2B%2B%2BxQXF6ff/va3MsacEvKioqLkcrkUHR2tiIgIr1tJfP9Yl8tVp/3n5ORoyZIlXmMZGRmaOnXqWa4suMXENPd3CT4VGXmuv0vAD9CPwENPAg89scfnAevBBx/U9OnTlZOTo%2BbNmys0NFRlZWVq2rSpli5damUfffv2Vd%2B%2BfT3/HjJkiP76179q7dq1nr%2BF%2BHN3jT/bO8qPGzdO/fv39xpzOJrJ5So/q%2B0Gu8ay/rCwUEVGnquSkgrV1NT6u5xGj34EHnoSeBpyT/z1y73PA1afPn20bds2bd%2B%2BXYcOHZIxRq1bt9aVV16pFi1a1Nt%2B27Rpo9zcXEVHRys0NFRut9tr3u12q2XLloqNjVVZWZlqamoUFhbmmZOkli1b1mlfcXFxp5wOLC4uVXV1w/qh/aUa2/pramob3ZoDGf0IPPQk8NATe3wesCTp3HPP1YABA3To0CHPrRFs%2Bu///m9FRUXp2muv9Yzl5%2BfrwgsvVHh4uDp27Ki8vDz17t1b0slTlAcOHFBycrLatGkjY4z27Nmjrl27SpKcTqciIyPVrl0767UCAICGx%2BdXs1VWVmrmzJnq3r27Bg8eLOlkwLnttttUUlJiZR8nTpzQ448/LqfTqaqqKm3YsEHvvvuu5%2B8gpqena8WKFcrPz1dZWZmys7PVpUsXJSUlKTY2VoMGDdLTTz%2BtI0eO6NChQ1q6dKlGjx4th8MveRQAAAQZnyeGBQsWaPfu3crOztZ9993nGa%2BpqVF2drYee%2ByxOm0nKSlJklRdXS1J2rx5s6STR5tuvvlmlZeXa9q0aSouLtYFF1ygpUuXKjExUdLJPzhdXFysm266SeXl5UpJSfG6KP2xxx7TI488ogEDBuicc87Rddddp6ysLCvrBwAADV%2BIOdsrun%2BhPn36aOXKlYqPj1e3bt20c%2BdOSdKhQ4c0fPhwffTRR74sx2eKi0vrZbuDn/6gXrZbHzZlXuHvEnzC4QhVTExzuVzlXMsQAOhH4KEngach96RVq/q7vvvn%2BPwUYXl5ueLj408Zj42N1bFjx3xdDgAAgHU%2BD1gXXXSR/va3v0nyvh3Cm2%2B%2BqX/5l3/xdTkAAADW%2BfwarBtuuEF33323Ro0apdraWv3pT39Sbm6u3nrrLc2aNcvX5QAAAFjn84A1btw4ORwOrVy5UmFhYfrDH/6gdu3aKTs7W7/73e98XQ4AAIB1Pg9YR44c0ahRozRq1Chf7xoAAMAnfH4N1oABA876T9EAAAAEMp8HrJSUFG3atMnXuwUAAPAZn58i/M1vfqMnnnhCy5cv10UXXaRzzjnHa37hwoW%2BLgkAAMAqnwesvXv36re//a0kyeVy%2BXr3AAAA9c5nASsrK0uLFi3Syy%2B/7BlbunSpMjIyfFUCAACAT/jsGqwtW7acMrZ8%2BXJf7R4AAMBnfBawTvfJQT5NCAAAGiKfBayQkJA6jQEAAAQ7n9%2BmAQAAoKEjYAEAAFjms08RVlVVafr06Wcc4z5YAAAg2PksYPXs2VNFRUVnHAMAAAh2PgtYP7z/FQAAQEPGNVgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALAsqAPWe%2B%2B9p9TUVGVlZZ0yt3HjRg0dOlTdu3fXyJEj9f7773vmamtrtWjRIg0YMEC9evXSxIkTdfDgQc%2B82%2B1WZmamUlNT1adPH82aNUuVlZU%2BWRMAAAh%2BQRuwnn/%2Bec2dO1dt27Y9ZW737t2aOXOmZsyYoY8%2B%2BkgTJkzQXXfdpUOHDkmSVq1apfXr12v58uXaunWr4uPjlZGRIWOMJGn27NmqqKjQhg0b9Oqrryo/P1/Z2dk%2BXR8AAAheQRuwwsPDtWbNmtMGrNWrVystLU1paWkKDw/XsGHD1KlTJ61bt06SlJOTowkTJqh9%2B/aKiIhQVlaW8vPztXPnTn333XfavHmzsrKyFBsbq9atW%2BvOO%2B/Uq6%2B%2BqqqqKl8vEwAABCGHvwv4tW6%2B%2BeafnMvLy1NaWprXWEJCgpxOpyorK7V3714lJCR45iIiItS2bVs5nU6VlpYqLCxMnTt39sx37dpVx44d0759%2B7zGf0pRUZGKi4u9xhyOZoqLi6vr8hokhyNo8/wvEhYW6vUd/kU/Ag89CTz0xL6gDVg/x%2B12KyoqymssKipKe/fu1dGjR2WMOe28y%2BVSdHS0IiIiFBIS4jUnSS6Xq077z8nJ0ZIlS7zGMjIyNHXq1F%2BznAYjJqa5v0vwqcjIc/1dAn6AfgQeehJ46Ik9DTJgSfJcT/Vr5s/03DMZN26c%2Bvfv7zXmcDSTy1V%2BVtsNdo1l/WFhoYqMPFclJRWqqan1dzmNHv0IPPQk8DTknvjrl/sGGbBiYmLkdru9xtxut2JjYxUdHa3Q0NDTzrds2VKxsbEqKytTTU2NwsLCPHOS1LJlyzrtPy4u7pTTgcXFpaqublg/tL9UY1t/TU1to1tzIKMfgYeeBB56Yk%2BDPNmamJio3NxcrzGn06lu3bopPDxcHTt2VF5enmeupKREBw4cUHJysrp06SJjjPbs2eP13MjISLVr185nawAAAMGrQQassWPH6sMPP9S2bdt0/PhxrVmzRt98842GDRsmSUpPT9eKFSuUn5%2BvsrIyZWdnq0uXLkpKSlJsbKwGDRqkp59%2BWkeOHNGhQ4e0dOlSjR49Wg5HgzzgBwAALAvaxJCUlCRJqq6uliRt3rxZ0smjTZ06dVJ2drbmzZungoICdehS5BHSAAARFklEQVTQQcuWLVOrVq0kSePHj1dxcbFuuukmlZeXKyUlxeui9Mcee0yPPPKIBgwYoHPOOUfXXXfdaW9mCgAAcDoh5myv6EadFBeX1st2Bz/9Qb1stz5syrzC3yX4hMMRqpiY5nK5yrmWIQDQj8BDTwJPQ%2B5Jq1Yt/LLfBnmKEAAAwJ8IWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgWYMNWJ07d1ZiYqKSkpI8X48//rgkaceOHRo9erR69OihIUOGaN26dV7PXbFihQYNGqQePXooPT1dubm5/lgCAAAIUg5/F1Cf3nzzTV1wwQVeY0VFRbrzzjs1a9YsDR06VJ9%2B%2BqmmTJmidu3aKSkpSVu2bNHixYv1wgsvqHPnzlqxYoUmT56st99%2BW82aNfPTSgAAQDBpsEewfsr69esVHx%2Bv0aNHKzw8XKmpqerfv79Wr14tScrJydHIkSPVrVs3NW3aVLfddpskaevWrf4sGwAABJEGfQRr4cKF%2Bvzzz1VWVqbBgwfr/vvvV15enhISErwel5CQoE2bNkmS8vLydO2113rmQkND1aVLFzmdTg0ZMqRO%2By0qKlJxcbHXmMPRTHFxcWe5ouDmcDSOPB8WFur1Hf5FPwIPPQk89MS%2BBhuwLrnkEqWmpuqpp57SwYMHlZmZqUcffVRut1utW7f2emx0dLRcLpckye12Kyoqyms%2BKirKM18XOTk5WrJkiddYRkaGpk6d%2BitX0zDExDT3dwk%2BFRl5rr9LwA/Qj8BDTwIPPbGnwQasnJwcz3%2B3b99eM2bM0JQpU9SzZ88zPtcYc1b7HjdunPr37%2B815nA0k8tVflbbDXaNZf1hYaGKjDxXJSUVqqmp9Xc5jR79CDz0JPA05J7465f7BhuwfuyCCy5QTU2NQkND5Xa7veZcLpdiY2MlSTExMafMu91udezYsc77iouLO%2BV0YHFxqaqrG9YP7S/V2NZfU1Pb6NYcyOhH4KEngYee2NMgT7bu2rVL8%2BfP9xrLz89XkyZNlJaWdsptF3Jzc9WtWzdJUmJiovLy8jxzNTU12rVrl2ceAADgTBpkwGrZsqVycnK0fPlynThxQvv379czzzyjcePG6frrr1dBQYFWr16t48ePa/v27dq%2BfbvGjh0rSUpPT9drr72mL774QhUVFXruuefUpEkT9e3b17%2BLAgAAQaNBniJs3bq1li9froULF3oC0ogRI5SVlaXw8HAtW7ZMc%2BfO1aOPPqo2bdpowYIFuvjiiyVJV111le655x5lZmbq8OHDSkpK0vLly9W0aVM/rwoAAASLEHO2V3SjToqLS%2Btlu4Of/qBetlsfNmVe4e8SfMLhCFVMTHO5XOVcyxAA6EfgoSeBpyH3pFWrFn7Zb4M8RQgAAOBPBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyAdRoFBQW64447lJKSon79%2BmnBggWqra31d1kAACBIOPxdQCC6%2B%2B671bVrV23evFmHDx/WpEmTdN555%2Bn3v/%2B9v0sDAABBgCNYP%2BJ0OrVnzx7NmDFDLVq0UHx8vCZMmKCcnBx/lwYAAIIER7B%2BJC8vT23atFFUVJRnrGvXrtq/f7/KysoUERFxxm0UFRWpuLjYa8zhaKa4uDjr9QYTh6Nx5PmwsFCv7/Av%2BhF46EngoSf2EbB%2BxO12KzIy0mvs%2B7DlcrnqFLBycnK0ZMkSr7G77rpLd999t71C/8/GaT2Uk5OjcePGNfoAFyiKior00ksv0JMAQT8CDz0JPPTEPqLqaRhjzur548aN09q1a72%2Bxo0bZ6k6b8XFxVqyZMkpR8zgP/QksNCPwENPAg89sY8jWD8SGxsrt9vtNeZ2uxUSEqLY2Ng6bSMuLo7fAAAAaMQ4gvUjiYmJKiws1JEjRzxjTqdTHTp0UPPmzf1YGQAACBYErB9JSEhQUlKSFi5cqLKyMuXn5%2BtPf/qT0tPT/V0aAAAIEmFz5syZ4%2B8iAs2VV16pDRs26PHHH9cbb7yh0aNHa%2BLEiQoJCfF3aafVvHlz9e7dmyNsAYSeBBb6EXjoSeChJ3aFmLO9ohsAAABeOEUIAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBK0gVFBTojjvuUEpKivr166cFCxaotrbW32UFvffee0%2BpqanKyso6ZW7jxo0aOnSounfvrpEjR%2Br999/3zNXW1mrRokUaMGCAevXqpYkTJ%2BrgwYOeebfbrczMTKWmpqpPnz6aNWuWKisrPfO7d%2B/WjTfeqJ49e2rgwIF68cUX63ehQaSgoEAZGRlKSUlRamqq7r//fpWUlEg68%2BtWnz1rrPbs2aNbbrlFPXv2VGpqqjIzM1VcXCxJ2rFjh0aPHq0ePXpoyJAhWrdunddzV6xYoUGDBqlHjx5KT09Xbm6uZ%2B748eN6%2BOGHddVVVyklJUVTp06Vy%2BXyzPOeVzdPPvmkOnfu7Pk3PfEjg6A0YsQI89BDD5mSkhKzf/9%2BM3DgQPPiiy/6u6ygtnz5cjNw4EAzfvx4k5mZ6TW3a9cuk5iYaLZt22YqKyvN66%2B/brp162YKCwuNMcasWLHC9OvXz%2Bzdu9eUlpaaxx57zAwdOtTU1tYaY4y56667zB133GEOHz5sDh06ZMaNG2cef/xxY4wxFRUV5sorrzSLFy825eXlJjc31/Tu3du89dZbvn0BAtR1111n7r//flNWVmYKCwvNyJEjzYMPPnjG160%2Be9ZYHT9%2B3Fx%2B%2BeVmyZIl5vjx4%2Bbw4cPmxhtvNHfeeaf59ttvzSWXXGJWr15tKisrzQcffGCSk5PN3//%2Bd2OMMe%2B884659NJLzRdffGEqKirMsmXLzBVXXGHKy8uNMcbMmzfPjBw50vzzn/80LpfL3HXXXWbSpEmeffOed2a7du0yvXv3Np06dTLGGHriZwSsIPT3v//ddOnSxbjdbs/Yf/3Xf5lBgwb5sarg99JLL5mSkhIzc%2BbMUwLWo48%2BajIyMrzGxowZY5YtW2aMMWbIkCHmpZde8syVlpaahIQE8/nnn5vi4mJz8cUXm927d3vmt2/fbi655BJz4sQJs2nTJnPZZZeZ6upqz/yCBQvMrbfeWh/LDCpHjx41999/vykuLvaMvfzyy2bgwIFnfN3qs2eNldvtNn/%2B859NVVWVZ%2Byll14y11xzjXnhhRfM8OHDvR6fmZlpZs%2BebYwx5o477jBPPvmkZ66mpsZcccUVZsOGDaaqqsr07NnTbN682TO/d%2B9e07lzZ3Po0CHe8%2BqgpqbGjBkzxvzHf/yHJ2DRE//iFGEQysvLU5s2bRQVFeUZ69q1q/bv36%2BysjI/Vhbcbr75ZrVo0eK0c3l5eUpISPAaS0hIkNPpVGVlpfbu3es1HxERobZt28rpdGr37t0KCwvzOmzftWtXHTt2TPv27VNeXp46d%2B6ssLAwr23/8FB9YxUZGal58%2BbpvPPO84wVFhYqLi7ujK9bffassYqKitKYMWPkcDgkSfv27dNf/vIXDR48%2BCdf75/qR2hoqLp06SKn06kDBw6otLRUXbt29cy3b99eTZs2VV5eHu95dfDKK68oPDxcQ4cO9YzRE/8iYAUht9utyMhIr7Hvf8h/eH4c9rjdbq83Eunka%2B5yuXT06FEZY35y3u12KyIiQiEhIV5zkjzzP%2B5ndHS03G431zP8iNPp1MqVKzVlypQzvm712bPGrqCgQImJibr22muVlJSkqVOn/mQ/vn%2B9fq4fbrdbkk55fmRk5E/%2Bb4R%2B/H/fffedFi9erEceecRrnJ74FwErSBlj/F1Co3Om1/zn5n9Nv374f%2B6QPv30U02cOFHTp09XamrqTz7uh6%2Bbr3vWWLRp00ZOp1NvvvmmvvnmG9133311eh79qB/z5s3TyJEj1aFDh1/8XHpSfwhYQSg2Ntbz28X33G63QkJCFBsb66eqGraYmJjTvuaxsbGKjo5WaGjoaedbtmyp2NhYlZWVqaamxmtOkmf%2Bx7/xud1uz3YhbdmyRXfccYcefPBB3XzzzZJ0xtetPnuGk0E2Pj5eWVlZ2rBhgxwOxymvp8vl8rwn/Vw/vn/Mj%2BePHj3q6Qfveae3Y8cOff7558rIyDhl7nSvOT3xHd69g1BiYqIKCwt15MgRz5jT6VSHDh3UvHlzP1bWcCUmJp5yTZTT6VS3bt0UHh6ujh07Ki8vzzNXUlKiAwcOKDk5WV26dJExRnv27PF6bmRkpNq1a6fExER9%2BeWXqq6uPmXbkD777DPNnDlTzzzzjIYPH%2B4ZP9PrVp89a6x27NihQYMGeZ26/v6XgOTk5FNe79zcXK9%2B/PD1rqmp0a5du9StWzddeOGFioqK8pr/6quvdOLECSUmJvKe9zPWrVunw4cPq1%2B/fkpJSdHIkSMlSSkpKerUqRM98SefX1YPK8aMGWMefPBBU1paavbu3Wv69%2B9vVq5c6e%2ByGoTTfYrwyy%2B/NElJSWbr1q2msrLSrF692nTv3t0UFRUZY05%2BeqZv376ej/zPnj3bjBo1yvP8zMxMc9ttt5nDhw%2BbwsJCM2rUKDN//nxjzMmPvvfr1888%2B%2Byz5tixY%2BaLL74wl156qdm6davP1hyoqqqqzODBg80rr7xyytyZXrf67FljVVJSYlJTU838%2BfPNsWPHzOHDh83EiRPNDTfcYL777jvTvXt38%2Bc//9lUVlaabdu2meTkZM8nMbdv32569uxpPv/8c3Ps2DGzePFik5aWZioqKowxJz8BOmLECPPPf/7THDlyxEyaNMncfffdnn3znnd6brfbFBYWer4%2B//xz06lTJ1NYWGgKCgroiR8RsIJUYWGhue2220xycrJJTU01zz77rOf%2BPfh1EhMTTWJiorn44ovNxRdf7Pn399566y0zcOBA07VrV3P99debjz/%2B2DNXW1trnnnmGXP55Zeb5ORkc/vtt3vut2TMyf9jysrKMpdcconp1auXefTRR83x48c9819%2B%2BaUZP368SUxMNH379jWrVq3yzaID3P/8z/%2BYTp06eXrxw69//OMfZ3zd6rNnjdWePXvMjTfeaJKTk81ll11mMjMzzaFDh4wxxnz88cdm2LBhpmvXrmbgwIGn3Mtt1apVJi0tzSQmJpr09HTz5ZdfeuaOHz9u5syZY3r16mW6d%2B9u7rnnHlNSUuKZ5z2vbg4ePOi5TYMx9MSfQozhKjUAAACbuAYLAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACz7f1OyUoQ8kD%2B8AAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-7019968200483418534”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-21.145756979844002</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-24.130394091201644</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-5.123585725463743</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>61.975448491610585</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-18.115846071191786</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>28.56100758898044</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>80.53604079131686</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.366185591722498</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3102)</td> <td class=”number”>3102</td> <td class=”number”>96.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>91</td> <td class=”number”>2.8%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-7019968200483418534”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-97.3923858227572</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-94.86540134812407</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-94.43642778059058</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-92.62676300352646</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1414.9742534899246</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1418.1396799536421</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2566.029297037542</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7527.600269913827</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>43191.275083753615</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_LACCESS_LOWI_10_15”><s>PCH_LACCESS_LOWI_10_15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_LACCESS_POP_10_15”><code>PCH_LACCESS_POP_10_15</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99999</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_LACCESS_POP_10_15”>PCH_LACCESS_POP_10_15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2854</td>

</tr> <tr>

<th>Unique (%)</th> <td>88.9%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.2%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>104</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>7205.4</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>22084000</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>7.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram6935661258462143894”>

<img src=”data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAAsJJREFUeJzt2r1KI1EchvHXNWiwFivRSrAJiKTwIwHBwrWyUUQQUcELsA6WuYEIIngFQlRsNBZWFrEQI6QIipUiiJcQlNliWWXWzSuBZEaW5wcpMjkn%2BTcPZxLSEQRBIAD/9CPuAYDvLBH3AH9L50pN77nK/2zDJAAnCGARCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGB0BEEQxD0E8F1xggAGgQAGgQAGgQAGgaDlLi4uNDExoc3Nzab2zczMKJVKhR7Dw8M6Ojpq06RfS8T2yfgv7e3tqVgsanBwsOm9Z2dnoeePj49aXFxUNptt1XhN4wRBS3V3d9tATk5ONDc3p5GREU1PT2t/f7/he%2BXzea2vr6u3t7dd436JEwQttbKy0vC1arWqXC6n7e1tjY%2BPq1KpaGNjQ0NDQxodHQ2tvby8VK1WU6FQaPfIFicIInN4eKipqSllMhl1dnYqnU5rdnZWx8fHn9bu7u5qbW1NXV1dMUz6gRMEkXl4eFC5XFYqlXq/FgSBMplMaN3d3Z1ubm60s7MT9YifEAgik0wmtbS0pK2tLbuuVCppbGxMPT09EU3WGLdYiMzAwIBub29D156fn/X29ha6dn5%2BrsnJyShHa4hAEJn5%2BXldX1/r4OBA9XpdtVpNCwsLoZ936/W67u/v1d/fH%2BOkH/g3L1rqz/eL19dXSVIi8fsuvlqtSpJOT09VKBT09PSkvr4%2BLS8va3V19X3/y8uLstmsisVi6LtKXAgEMLjFAgwCAQwCAQwCAQwCAQwCAQwCAQwCAQwCAQwCAYxfiYSaWtBZErUAAAAASUVORK5CYII%3D”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives6935661258462143894”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles6935661258462143894”

aria-controls=”quantiles6935661258462143894” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram6935661258462143894” aria-controls=”histogram6935661258462143894”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common6935661258462143894” aria-controls=”common6935661258462143894”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme6935661258462143894” aria-controls=”extreme6935661258462143894”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles6935661258462143894”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-50.821</td>

</tr> <tr>

<th>Q1</th> <td>-11.029</td>

</tr> <tr>

<th>Median</th> <td>-0.066231</td>

</tr> <tr>

<th>Q3</th> <td>7.2319</td>

</tr> <tr>

<th>95-th percentile</th> <td>82.44</td>

</tr> <tr>

<th>Maximum</th> <td>22084000</td>

</tr> <tr>

<th>Range</th> <td>22084000</td>

</tr> <tr>

<th>Interquartile range</th> <td>18.261</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>396270</td>

</tr> <tr>

<th>Coef of variation</th> <td>54.996</td>

</tr> <tr>

<th>Kurtosis</th> <td>3105.5</td>

</tr> <tr>

<th>Mean</th> <td>7205.4</td>

</tr> <tr>

<th>MAD</th> <td>14356</td>

</tr> <tr class=”alert”>

<th>Skewness</th> <td>55.725</td>

</tr> <tr>

<th>Sum</th> <td>22380000</td>

</tr> <tr>

<th>Variance</th> <td>157030000000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram6935661258462143894”>

<img 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AassrIyJSYmatq0aQoLC1Pbtm01cuRIffzxx9q2bZvOnTuniRMnqkWLFurevbvuvPNO5efnS5JWrlyp1NRUpaamKjQ0VMOHD1eXLl20Zs0aSVJ%2Bfr4yMzPVsWNHhYeHKzs7W0VFRdq7d68vlwwAAAJIQAYsu92uefPmqXXr1p6xY8eOKSYmRoWFheratatCQkI8cwkJCSooKJAkFRYWKiEhwWt/CQkJcjgcOn36tA4cOOA1Hx4ervbt28vhcNTzqgAAQENh83UBJjgcDr355ptasmSJNmzYILvd7jUfGRkpl8ulmpoauVwuRUREeM1HRETowIEDOnnypNxu9wXnnU5nnespKSlRaWmp15jN1kIxMTGXuLIfFxISWPnYZgusei/F%2BV4EWk8aGvrgP%2BiF/6AXvhHwAeuTTz7RxIkTNW3aNKWkpGjDhg0X3C4oKMjz/xe7nupyr7fKz89Xbm6u11hWVpYmT558WfsNdFFRYb4uod7Z7c19XQJEH/wJvfAf9MJaAR2wtmzZokcffVSzZs3SiBEjJEnR0dE6fPiw13Yul0uRkZEKDg5WVFSUXC5Xrfno6GjPNheab9WqVZ3rSk9P16BBg7zGbLYWcjorLmF1Fxdor0ZMr9%2BfhIQEy25vrrKySlVX1/i6nEaLPvgPeuE/GnsvfPXiPmAD1qeffqoZM2bopZdeUv/%2B/T3jiYmJ%2BtOf/qSqqirZbN8tz%2BFwqGfPnp7589djnedwODR06FCFhoaqc%2BfOKiwsVL9%2B/SR9d0H9kSNH1KNHjzrXFhMTU%2BvtwNLSU6qqanw/2N/XGNZfXV3TKNbp7%2BiD/6AX/oNeWMvyUyCDBg1Sbm6ujh079pP3UVVVpaeeekrTp0/3CleSlJqaqvDwcC1ZskSVlZXau3evVq1apYyMDEnS6NGjtXPnTm3btk1nzpzRqlWrdPjwYQ0fPlySlJGRoeXLl6uoqEjl5eXKyclRt27dlJSU9NMXDQAAGpUgt8U3eFq8eLHef/99/fOf/9R1112n0aNHa9CgQZ6zTXXx8ccf6z//8z/VtGnTWnMbN25URUWFnnnmGRUUFKh169Z64IEHdNddd3m2%2BeCDD7RgwQIVFxerU6dOmjlzpvr27Svpu%2BuvFi1apLfeeksVFRVKTk7Ws88%2Bq7Zt217WuktLT13W4y/EZgvWzTk7jO%2B3vmyYer2vS6g3NluwoqLC5HRW8ArRh%2BiD/6AX/qOx96JNm5Y%2BOa7lAeu8wsJCrVu3Ths2bNC5c%2Bc0YsQIpaWlqUOHDr4op94RsAhYqH/0wX/QC//R2Hvhq4Dls6uku3fvrhkzZmjr1q168skn9ec//1m33nqrxo0bp88%2B%2B8xXZQEAAFw2nwWsc%2BfOaf369XrggQc0Y8YMxcbG6oknnlC3bt2UmZmptWvX%2Bqo0AACAy2L5XxEWFRVp1apVevfdd1VRUaEhQ4bo9ddf1zXXXOPZpm/fvpo9e7aGDRtmdXkAAACXzfKANXToUHXo0EHjx4/XiBEjFBkZWWub1NRUnThxwurSAAAAjLA8YC1fvtxzj6kfw4crAwCAQGX5NVhdu3bVhAkTtHnzZs/YH//4Rz3wwAO17qAOAAAQiCwPWPPmzdOpU6fUqVMnz9jAgQNVU1OjF154wepyAAAAjLP8LcKPPvpIa9euVVRUlGcsPj5eOTk5uu2226wuBwAAwDjLz2CdPn1aoaGhtQsJDlZlZaXV5QAAABhnecDq27evXnjhBZ08edIz9tVXX2nOnDlet2oAAAAIVJa/Rfjkk0/qvvvu03XXXafw8HDV1NSooqJC7dq10xtvvGF1OQAAAMZZHrDatWun999/Xx9%2B%2BKGOHDmi4OBgdejQQf3791dISIjV5QAAABhnecCSpKZNm%2Bqmm27yxaEBAADqneUB6%2BjRo1qwYIG%2B/PJLnT59utb8X//6V6tLAgAAMMon12CVlJSof//%2BatGihdWHBwAAqHeWB6yCggL99a9/VXR0tNWHBgAAsITlt2lo1aoVZ64AAECDZnnAGj9%2BvHJzc%2BV2u60%2BNAAAgCUsf4vwww8/1KeffqrVq1fryiuvVHCwd8Z76623rC4JAADAKMsDVnh4uAYMGGD1YQEAACxjecCaN2%2Be1YcEAACwlOXXYEnSwYMHtWjRIj3xxBOesX/84x%2B%2BKAUAAMA4ywPWrl27NHz4cH3wwQdat26dpO9uPjp27FhuMgoAABoEywPWwoUL9eijj2rt2rUKCgqS9N3nE77wwgtavHix1eUAAAAYZ3nA%2BuKLL5SRkSFJnoAlSbfccouKioqsLgcAAMA4ywNWy5YtL/gZhCUlJWratKnV5QAAABhnecDq3bu3fvWrX6m8vNwzdujQIc2YMUPXXXed1eUAAAAYZ/ltGp544gndc889Sk5OVnV1tXr37q3Kykp17txZL7zwgtXlAAAAGGd5wGrbtq3WrVun7du369ChQ2rWrJk6dOig66%2B/3uuaLAAAgEBlecCSpCZNmuimm27yxaEBAADqneUBa9CgQT96pop7YQEAgEBnecC69dZbvQJWdXW1Dh06JIfDoXvuucfqcgAAAIyzPGBNnz79guObNm3S3/72N4urAQAAMM8nn0V4ITfddJPef/99X5cBAABw2fwmYH3%2B%2Bedyu92%2BLgMAAOCyWf4W4ZgxY2qNVVZWqqioSL/4xS%2BsLgcAAMA4ywNWfHx8rb8iDA0NVVpamu68806rywEAADDO8oDF3doBAEBDZ3nAevfdd%2Bu87YgRI350fseOHZoxY4aSk5O1cOFCz/jq1av15JNPqkmTJl7br1ixQj169FBNTY1eeuklrVu3TmVlZerRo4dmz56tdu3aSZJcLpdmz56t//mf/1FwcLBSU1M1a9YsNWvW7BJWCgAAGivLA9bMmTNVU1NT64L2oKAgr7GgoKAfDVjLli3TqlWr1L59%2BwvO9%2B3bV2%2B88cYF51asWKG1a9dq2bJlio2N1cKFC5WVlaX33ntPQUFBmjVrls6ePat169bp3LlzmjJlinJycvTUU0/9hBUDAIDGxvK/Ivz973%2Bv/v37a8WKFfr444/197//XW%2B%2B%2BaYGDBigZcuW6bPPPtNnn32mvXv3/uh%2BQkNDfzRg/Zj8/HxlZmaqY8eOCg8PV3Z2toqKirR37159/fXX2rx5s7KzsxUdHa3Y2FhNmjRJb7/9ts6dO/dTlw0AABoRn1yDlZeXp9jYWM9Ynz591K5dO40bN07r1q2r037Gjh37o/PHjh3Tvffeq4KCAtntdk2ePFm33367Tp8%2BrQMHDighIcGzbXh4uNq3by%2BHw6FTp04pJCREXbt29cx3795d3377rQ4ePOg1DgAAcCGWB6zDhw8rIiKi1rjdbldxcbGRY0RHRys%2BPl6PPPKIOnXqpL/85S967LHHFBMTo5/97Gdyu921aoiIiJDT6VRkZKTCw8O9/tLx/LZOp7NOxy8pKVFpaanXmM3WQjExMZe5Mm8hIX5zG7M6sdkCq95Lcb4XgdaThoY%2B%2BA964T/ohW9YHrDi4uL0wgsvaMqUKYqKipIklZWV6eWXX9ZVV11l5BgDBw7UwIEDPd8PHTpUf/nLX7R69WrPR/X82E1NL/eGp/n5%2BcrNzfUay8rK0uTJky9rv4EuKirM1yXUO7u9ua9LgOiDP6EX/oNeWMvygPXkk09q2rRpys/PV1hYmIKDg1VeXq5mzZpp8eLF9XbcuLg4FRQUKDIyUsHBwXK5XF7zLpdLrVq1UnR0tMrLy1VdXa2QkBDPnCS1atWqTsdKT0/XoEGDvMZsthZyOisMrOT/BNqrEdPr9ychIcGy25urrKxS1dU1vi6n0aIP/oNe%2BI/G3gtfvbi3PGD1799f27Zt0/bt23X8%2BHG53W7FxsbqhhtuUMuWLY0c409/%2BpMiIiJ06623esaKiorUrl07hYaGqnPnziosLFS/fv0kfXcG7ciRI%2BrRo4fi4uLkdru1f/9%2Bde/eXZLkcDhkt9vVoUOHOh0/Jiam1tuBpaWnVFXV%2BH6wv68xrL%2B6uqZRrNPf0Qf/QS/8B72wluUBS5KaN2%2BuwYMH6/jx4557T5l09uxZPffcc2rXrp2uvvpqbdq0SR9%2B%2BKH%2B/Oc/S5IyMjKUl5enAQMGKDY2Vjk5OerWrZuSkpIkSUOGDNFvf/tbzZ8/X2fPntXixYuVlpYmm80n/1wAACDAWJ4YTp8%2BrWeeeUbvv/%2B%2BJKmgoEBlZWV65JFH9Jvf/EZ2u71O%2BzkfhqqqqiRJmzdvlvTd2aaxY8eqoqJCU6ZMUWlpqa688kotXrxYiYmJkr77PMTS0lLdfffdqqioUHJystc1U88%2B%2B6yeeeYZDR48WE2aNNFtt92m7OxsY/8GAACgYQtyX%2B4V3Zfoueee09///ndNmjRJjz32mD777DOVlZVpypQpateunZ599lkry7FMaekp4/u02YJ1c84O4/utLxumXu/rEuqNzRasqKgwOZ0VnIL3IfrgP%2BiF/2jsvWjTxszlR5fK8qukN23apJdfflm33HKL51YIdrtd8%2BbN0wcffGB1OQAAAMZZHrAqKioUHx9fazw6Olrffvut1eUAAAAYZ3nAuuqqq/S3v/1Nkvf9pjZu3Kj/%2BI//sLocAAAA4yy/yP2uu%2B7Sww8/rDvuuEM1NTV67bXXVFBQoE2bNmnmzJlWlwMAAGCc5QErPT1dNptNb775pkJCQvTKK6%2BoQ4cOysnJ0S233GJ1OQAAAMZZHrBOnDihO%2B64Q3fccYfVhwYAALCE5ddgDR48%2BLI/6w8AAMCfWR6wkpOTtWHDBqsPCwAAYBnL3yK84oor9PzzzysvL09XXXWVmjRp4jW/YMECq0sCAAAwyvKAdeDAAf3sZz%2BTJDmdTqsPDwAAUO8sC1jZ2dlauHCh3njjDc/Y4sWLlZWVZVUJAAAAlrDsGqwtW7bUGsvLy7Pq8AAAAJaxLGBd6C8H%2BWtCAADQEFkWsM5/sPPFxgAAAAKd5bdpAAAAaOgIWAAAAIZZ9leE586d07Rp0y46xn2wAABAoLMsYF1zzTUqKSm56BgAAECgsyxgff/%2BVwAAAA0Z12ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGBYQAesHTt2KCUlRdnZ2bXm1q9fr2HDhqlXr14aNWqUPvroI89cTU2NFi5cqMGDB6tv374aN26cjh496pl3uVyaOnWqUlJS1L9/f82cOVOnT5%2B2ZE0AACDwBWzAWrZsmebOnav27dvXmtu3b59mzJih6dOna/fu3crMzNRDDz2k48ePS5JWrFihtWvXKi8vT1u3blV8fLyysrLkdrslSbNmzVJlZaXWrVunt99%2BW0VFRcrJybF0fQAAIHAFbMAKDQ3VqlWrLhiwVq5cqdTUVKWmpio0NFTDhw9Xly5dtGbNGklSfn6%2BMjMz1bFjR4WHhys7O1tFRUXau3evvv76a23evFnZ2dmKjo5WbGysJk2apLffflvnzp2zepkAACAABWzAGjt2rFq2bHnBucLCQiUkJHiNJSQkyOFw6PTp0zpw4IDXfHh4uNq3by%2BHw6F9%2B/YpJCREXbt29cx3795d3377rQ4ePFg/iwEAAA2KzdcF1AeXy6WIiAivsYiICB04cEAnT56U2%2B2%2B4LzT6VRkZKTCw8MVFBTkNSdJTqezTscvKSlRaWmp15jN1kIxMTE/ZTn/VkhIYOVjmy2w6r0U53sRaD1paOiD/6AX/oNe%2BEaDDFiSPNdT/ZT5iz32YvLz85Wbm%2Bs1lpWVpcmTJ1/WfgNdVFSYr0uod3Z7c1%2BXANEHf0Iv/Ae9sFaDDFhRUVFyuVxeYy6XS9HR0YqMjFRwcPAF51u1aqXo6GiVl5erurpaISEhnjlJatWqVZ2On56erkGDBnmN2Wwt5HRW/NQlXVCgvRoxvX5/EhISLLu9ucrKKlVdXePrchot%2BuA/6IX/aOy98NWL%2BwYZsBITE1VQUOA15nA4NHToUIWGhqpz584qLCxUv379JEllZWU6cuSIevToobi4OLndbu3fv1/du3f3PNZut6tDhw51On5MTEyttwNLS0%2Bpqqrx/WB/X2NYf3V1TaNYp7%2BjD/6DXvgPemGtwDoFUkejR4/Wzp07tW3bNp05c0arVq3S4cOHNXz4cElSRkaGli9frqKiIpWXlysnJ0fdunVTUlKSoqOjNWTIEP32t7/ViRMndPwGosdHAAAPtUlEQVT4cS1evFhpaWmy2RpkHgUAAIYFbGJISkqSJFVVVUmSNm/eLOm7s01dunRRTk6O5s2bp%2BLiYnXq1ElLly5VmzZtJEljxoxRaWmp7r77blVUVCg5Odnrmqlnn31WzzzzjAYPHqwmTZrotttuu%2BDNTAEAAC4kyH25V3SjTkpLTxnfp80WrJtzdhjfb33ZMPV6X5dQb2y2YEVFhcnprOAUvA/RB/9BL/xHY%2B9FmzYXvqVTfWuQbxECAAD4EgELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAxrsAGra9euSkxMVFJSkufrueeekyTt2rVLaWlp6t27t4YOHao1a9Z4PXb58uUaMmSIevfurYyMDBUUFPhiCQAAIEDZfF1Afdq4caOuvPJKr7GSkhJNmjRJM2fO1LBhw/TJJ59o4sSJ6tChg5KSkrRlyxYtWrRIv//979W1a1ctX75cEyZM0AcffKAWLVr4aCUAACCQNNgzWP/O2rVrFR8fr7S0NIWGhiolJUWDBg3SypUrJUn5%2BfkaNWqUevbsqWbNmun%2B%2B%2B%2BXJG3dutWXZQMAgADSoAPWggULNHDgQPXp00ezZs1SRUWFCgsLlZCQ4LVdQkKC523AH84HBwerW7ducjgcltYOAAACV4N9i/DnP/%2B5UlJSNH/%2BfB09elRTp07VnDlz5HK5FBsb67VtZGSknE6nJMnlcikiIsJrPiIiwjNfFyUlJSotLfUas9laKCYm5ieu5sJCQgIrH9tsgVXvpTjfi0DrSUNDH/wHvfAf9MI3GmzAys/P9/x/x44dNX36dE2cOFHXXHPNRR/rdrsv%2B9i5ubleY1lZWZo8efJl7TfQRUWF%2BbqEeme3N/d1CRB98Cf0wn/QC2s12ID1Q1deeaWqq6sVHBwsl8vlNed0OhUdHS1JioqKqjXvcrnUuXPnOh8rPT1dgwYN8hqz2VrI6az4idVfWKC9GjG9fn8SEhIsu725ysoqVV1d4%2BtyGi364D/ohf9o7L3w1Yv7BhmwPv/8c61Zs0aPP/64Z6yoqEhNmzZVamqq3nnnHa/tCwoK1LNnT0lSYmKiCgsLNXLkSElSdXW1Pv/8c6WlpdX5%2BDExMbXeDiwtPaWqqsb3g/19jWH91dU1jWKd/o4%2B%2BA964T/ohbUC6xRIHbVq1Ur5%2BfnKy8vT2bNndejQIb300ktKT0/X7bffruLiYq1cuVJnzpzR9u3btX37do0ePVqSlJGRoXfffVd79uxRZWWllixZoqZNm2rgwIG%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%2BOCDSk5O1o033qgXX3xRNTU1vi4LAAAECJuvC/BHDz/8sLp3767Nmzfrm2%2B%2B0fjx49W6dWvde%2B%2B9vi4NAAAEAM5g/YDD4dD%2B/fs1ffp0tWzZUvHx8crMzFR%2Bfr6vSwMAAAGCM1g/UFhYqLi4OEVERHjGunfvrkOHDqm8vFzh4eEX3UdJSYlKS0u9xmy2FoqJiTFaa0hIYOVjmy2w6r0U53sRaD1paOiD/6AX/oNe%2BAYB6wdcLpfsdrvX2Pmw5XQ66xSw8vPzlZub6zX20EMP6eGHHzZXqL4Lcve0/VLp6enGwxsuTUlJiV5//ff0wsfog/%2BgF/6DXvgGcfYC3G73ZT0%2BPT1dq1ev9vpKT083VN3/KS0tVW5ubq2zZbAevfAP9MF/0Av/QS98gzNYPxAdHS2Xy%2BU15nK5FBQUpOjo6DrtIyYmhlcJAAA0YpzB%2BoHExEQdO3ZMJ06c8Iw5HA516tRJYWFhPqwMAAAECgLWDyQkJCgpKUkLFixQeXm5ioqK9NprrykjI8PXpQEAgAARMnv27Nm%2BLsLf3HDDDVq3bp2ee%2B45vf/%2B%2B0pLS9O4ceMUFBTk69JqCQsLU79%2B/Ti75gfohX%2BgD/6DXvgPemG9IPflXtENAAAAL7xFCAAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAcvPFRcX68EHH1RycrJuvPFGvfjii6qpqbngtsuXL9eQIUPUu3dvZWRkqKCgwOJqG6669mHRokXq1q2bkpKSvL6%2B/vprH1TdcO3YsUMpKSnKzs7%2B0e1qamq0cOFCDR48WH379tW4ceN09OhRi6psHOrai8cff9zzWa/nv/r06WNRlQ1fcXGxsrKylJycrJSUFD3%2B%2BOMqKyu74Lbr16/XsGHD1KtXL40aNUofffSRxdU2DgQsP/fwww8rNjZWmzdv1muvvabNmzfr9ddfr7Xdli1btGjRIv3617/Wzp07deONN2rChAn69ttvfVB1w1PXPkjS7bffLofD4fXVunVriytuuJYtW6a5c%2Beqffv2F912xYoVWrt2rfLy8rR161bFx8crKytLfEKYGZfSC0maOHGi1/Pi448/rucKG48JEybIbrdry5YtWr16tb788kvNnz%2B/1nb79u3TjBkzNH36dO3evVuZmZl66KGHdPz4cR9U3bARsPyYw%2BHQ/v37NX36dLVs2VLx8fHKzMxUfn5%2BrW3z8/M1atQo9ezZU82aNdP9998vSdq6davVZTc4l9IH1L/Q0FCtWrWqTr/U8/PzlZmZqY4dOyo8PFzZ2dkqKirS3r17Lai04buUXqD%2BlJWVKTExUdOmTVNYWJjatm2rkSNHXjDArly5UqmpqUpNTVVoaKiGDx%2BuLl26aM2aNT6ovGEjYPmxwsJCxcXFKSIiwjPWvXt3HTp0SOXl5bW2TUhI8HwfHBysbt26yeFwWFZvQ3UpfZCk//3f/9WYMWPUu3dvDR06lNPvho0dO1YtW7a86HanT5/WgQMHvJ4X4eHhat%2B%2BPc8LQ%2Brai/N2796tESNGqFevXkpLS%2BMyBkPsdrvmzZvndab82LFjiomJqbXtD39XSFJCQgLPiXpAwPJjLpdLdrvda%2Bz8L3mn01lr2%2B8HgPPb/nA7XLpL6UPbtm3Vrl07zZ8/X//93/%2BtO%2B%2B8UxMmTNDBgwctqxffOXnypNxuN88LP9GuXTu1b99eS5cu1Y4dO9SnTx/dd9999KIeOBwOvfnmm5o4cWKtOX5XWIeA5ecu5VoRriupP3X9t73zzjv18ssvq3379mrevLkyMzPVrVs3Tr/7EM8L/5CVlaVf/epXio2NVXh4uB599FE1bdpUmzdv9nVpDconn3yicePGadq0aUpJSbngNjwnrEHA8mPR0dFyuVxeYy6XS0FBQYqOjvYaj4qKuuC2P9wOl%2B5S%2BnAhcXFxKikpqa/y8G9ERkYqODj4gr1r1aqVj6rCeSEhIbriiit4bhi0ZcsWPfjgg3ryySc1duzYC27D7wrrELD8WGJioo4dO6YTJ054xhwOhzp16qSwsLBa2xYWFnq%2Br66u1ueff66ePXtaVm9DdSl9%2BN3vfqddu3Z5jRUVFaldu3aW1Ir/Exoaqs6dO3s9L8rKynTkyBH16NHDh5U1Pm63W/PmzdP%2B/fs9Y2fPntWRI0d4bhjy6aefasaMGXrppZc0YsSIf7tdYmJirWvfHA4HvyvqAQHLj52/Z8yCBQtUXl6uoqIivfbaa8rIyJAk3XLLLZ6/EsnIyNC7776rPXv2qLKyUkuWLFHTpk01cOBAH66gYbiUPrhcLs2ZM0cHDx7UmTNn9Oqrr%2BrIkSMaOXKkL5fQaHz11Ve65ZZbPPe6ysjI0PLly1VUVKTy8nLl5OR47lOG%2BvX9XgQFBelf//qX5syZo6%2B%2B%2BkoVFRXKyclRkyZNdNNNN/m61IBXVVWlp556StOnT1f//v1rzd9zzz1av369JGn06NHauXOntm3bpjNnzmjVqlU6fPiwhg8fbnXZDZ7N1wXgx7388suaNWuWrr/%2BeoWHh2vMmDG66667JEmHDh3y3OdqwIABeuSRRzR16lR98803SkpKUl5enpo1a%2BbL8huMuvZh2rRpkqTMzEy5XC516tRJf/zjH9W2bVuf1d7QnA9HVVVVkuS5hsfhcOjcuXM6dOiQzp49K0kaM2aMSktLdffdd6uiokLJycnKzc31TeEN0KX04vnnn9f8%2BfM1atQolZeXq0ePHnr99dfVokUL3xTfgOzZs0dFRUWaO3eu5s6d6zW3ceNGHT16VCdPnpQkdenSRTk5OZo3b56Ki4vVqVMnLV26VG3atPFF6Q1akJur3QAAAIziLUIAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAADwCzt27FBKSoqys7Mv6XFDhgxRUlKS19fVV1%2Btd955p54qvTju5A4AAHxu2bJlWrVqldq3b3/Jj920aZPX90ePHlV6erpuuOEGU%2BVdMs5gAQAAnwsNDf3RgLV%2B/Xrdfvvt%2BvnPf67BgwcrPz//3%2B7r%2Beef13333afWrVvXV7kXxRksAADgc2PHjv23cw6HQzNnztSiRYt03XXX6R//%2BIceeOABde7cWb179/badvfu3dq3b59efvnl%2Bi75R3EGCwAA%2BLXVq1dr4MCB6t%2B/v0JCQtSnTx/98pe/1HvvvVdr21deeUX33nuvmjZt6oNK/w9nsAAAgF87cuSIdu3apaSkJM%2BY2%2B1W//79vbb74osvtGfPHv3ud7%2BzusRa/h8OUGEwtIIa7gAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common6935661258462143894”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>253</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-13.02214348099776</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.2795109011547191</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-32.911122307261365</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.778184617906631</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9264387408107527</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>108.78233620264051</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>40.908729681773295</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-28.130063456816035</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2843)</td> <td class=”number”>2843</td> <td class=”number”>88.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>104</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme6935661258462143894”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-99.9396082716682</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-98.4748552102837</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-96.81572711751195</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-96.31525184384067</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>4684.809925818614</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>15117.3265672713</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>26147.419425788114</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>198601.9453146752</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>22083757.707267486</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_LACCESS_SENIORS_10_15”><s>PCH_LACCESS_SENIORS_10_15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_LACCESS_LOWI_10_15”><code>PCH_LACCESS_LOWI_10_15</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99981</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_NSLP_09_15”>PCH_NSLP_09_15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>52</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>-0.85431</td>

</tr> <tr>

<th>Minimum</th> <td>-1.6969</td>

</tr> <tr>

<th>Maximum</th> <td>0.52972</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-3442596609541139049”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-3442596609541139049”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-3442596609541139049”

aria-controls=”quantiles-3442596609541139049” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-3442596609541139049” aria-controls=”histogram-3442596609541139049”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-3442596609541139049” aria-controls=”common-3442596609541139049”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-3442596609541139049” aria-controls=”extreme-3442596609541139049”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-3442596609541139049”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-1.6969</td>

</tr> <tr>

<th>5-th percentile</th> <td>-1.3805</td>

</tr> <tr>

<th>Q1</th> <td>-1.0448</td>

</tr> <tr>

<th>Median</th> <td>-0.83969</td>

</tr> <tr>

<th>Q3</th> <td>-0.66507</td>

</tr> <tr>

<th>95-th percentile</th> <td>-0.27875</td>

</tr> <tr>

<th>Maximum</th> <td>0.52972</td>

</tr> <tr>

<th>Range</th> <td>2.2266</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.37972</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.33679</td>

</tr> <tr>

<th>Coef of variation</th> <td>-0.39423</td>

</tr> <tr>

<th>Kurtosis</th> <td>0.9196</td>

</tr> <tr>

<th>Mean</th> <td>-0.85431</td>

</tr> <tr>

<th>MAD</th> <td>0.25071</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.36881</td>

</tr> <tr>

<th>Sum</th> <td>-2675.7</td>

</tr> <tr>

<th>Variance</th> <td>0.11343</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-3442596609541139049”>

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qXf72229bMBUAAEDt2S6w5s6dq4KCAvXs2VNBQUFWjwMAAFBntgus3Nxcvf322woPD7d6FAAAgHqx3ds0XHvttZy5AgAAXs12gTVp0iStXLlSlZWVVo8CAABQL7Z7ivDdd9/V4cOHtXnzZl1//fVyODwb8LXXXrNoMgAAgNqxXWC1aNFCvXr1snoMAACAerNdYC1evNjqEQAAABrEdq/BkqSvvvpKmZmZeuyxx6q2ffTRRxZOBAAAUHu2C6zs7GwNHz5cWVlZ2rFjh6Sf3nz07rvv5k1GAQCAV7BdYD377LN65JFHtH37dvn5%2BUn66fcTLlmyRKtWrbJ4OgAAgJrZLrD%2B9a9/KS0tTZKqAkuSBg4cqGPHjlk1FgAAQK3ZLrBatmx5xd9BWFBQoICAAAsmAgAAqBvbBVZSUpIWLVqkM2fOVG07fvy45syZo1tvvdXCyQAAAGrHdm/T8Nhjj%2Bmee%2B5Rt27dVF5erqSkJJWVlalDhw5asmSJ1eMBAADUyHaB1aZNG%2B3YsUP79%2B/X8ePHFRgYqHbt2qlHjx4er8kCAACwK9sFliRdc8016t%2B/v9VjAAAA1IvtAis5Ofl/nqnivbAAAIDd2S6wBg8e7BFY5eXlOn78uHJycnTPPfdYOBkAAEDt2C6wZs%2BefcXtb731lt5///1GngYAAKDubPc2Db%2Bkf//%2BevPNN60eAwAAoEZeE1hHjhxRZWWl1WMAAADUyHZPEY4bN67atrKyMh07dkx33HGHBRMBAADUje0CKzo6utpPETZr1kwpKSkaM2aMRVMBAADUnu0Ci3drBwAA3s52gbVly5Za73vnnXdexUkAAADqx3aBNW/ePFVUVFR7Qbufn5/HNj8/PwILAADYku0Ca926dVq/fr0yMjIUExOjyspKff755/rrX/%2Bqu%2B66S926dbN6RAAAgP/JdoG1ZMkSrV27VpGRkVXbbrnlFrVt21b333%2B/duzYYeF0AAAANbPd%2B2B9/fXXCg0NrbY9JCRE%2Bfn5FkwEAABQN7YLrKioKC1ZskQlJSVV206fPq3ly5frhhtusHAyAACA2rHdU4Rz587VrFmz5HK5FBwcLIfDoTNnzigwMFCrVq2yejwAAIAa2S6wevbsqXfeeUf79%2B/XqVOnVFlZqcjISN12221q2bKl1eMBAADUyHaBJUnNmzdXv379dOrUKbVt29bqcQAAAOrEdq/BOnfunObMmaObb75ZgwYNkvTTa7AmTJig06dPWzwdAABAzWwXWEuXLtXRo0e1bNkyORz/N155ebmWLVtm4WQAAAC1Y7vAeuutt/Tcc89p4MCBVb/0OSQkRIsXL1ZWVpbF0wEAANTMdoFVWlqq6OjoatvDw8N19uzZxh8IAACgjmwXWDfccIPef/99SfL43YO7d%2B/Wr3/963pd56JFixQTE1P19%2BzsbKWkpCgpKUlDhgzRtm3bPPbfuHGjBgwYoKSkJKWlpSk3N7detwsAAJom2/0U4fjx4zV16lSNHj1aFRUV2rBhg3Jzc/XWW29p3rx5db6%2Bo0ePauvWrVV/Lygo0OTJkzVv3jwNGzZMH374oR588EG1a9dOCQkJ2rt3rzIzM7Vu3TrFxMRo48aNysjIUFZWloKCgkweKgAA8FG2O4OVmpqqOXPm6L333pO/v7%2Bef/555efna9myZUpLS6vTdVVUVOjxxx9Xenp61bbt27crOjpaKSkpatasmbp3767k5GRt2rRJkuRyuTRq1CglJiYqMDBQEyZMkCTt27fP2DECAADfZrszWMXFxRo9erRGjx7d4Ot67bXX1KxZMw0bNkwrVqyQJOXl5SkuLs5jv7i4OO3atavq8sGDB1dd5nA4FBsbq5ycHA0ZMqRWt1tQUKDCwkKPbU5nkCIiIhpyOF7D39/h8Sfg7ZzO%2Bt%2BXeTzYA%2BtgD01pHWwXWP369dPhw4erfoKwvr7//ntlZmbq5Zdf9tjudrsVGRnpsa1Vq1ZVv/vQ7XZX%2B2XToaGhHr8bsSYul0srV6702DZlyhRNmzatLofg9UJCmls9AmBEWFhwg6%2BDx4M9sA720BTWwXaB1a1bN%2B3atcvjLFJ9LF68WKNGjVL79u114sSJOn3sf7%2B4vj5SU1OVnJzssc3pDFJJSWmDrtdb%2BPs7FBLSXKdPl6m8vMLqcYAGa8hjl8eDPbAO9mDFOpj4B1J92C6wfvWrX%2BmPf/yj1q5dqxtuuEHXXHONx%2BXLly%2Bv8Tqys7P10UcfaceOHdUuCwsLk9vt9thWUlKi8PDwX7zc7XarQ4cOtT6GiIiIak8HFhb%2BqEuXmtaDury8oskdM3yTifsxjwd7YB3soSmsg%2B0C68svv9SNN94oSXV6Wu6/bdu2TUVFRerbt6%2Bk/zsj1a1bN913333Vwis3N1eJiYmSpPj4eOXl5WnkyJGSfnoH%2BSNHjiglJaVeswAAgKbHNoE1Y8YMPfvssx6vmVq1apWmTJlS5%2Bt69NFH9fDDD1f9/dSpU0pNTdXWrVtVUVGhNWvWaNOmTRo%2BfLjee%2B897d%2B/Xy6XS5KUlpammTNnaujQoYqJidELL7yggIAA9enTp8HHCAAAmgbbBNbevXurbVu7dm29Ais0NNTjheqXLl2SJLVp00aStGbNGj311FNauHChoqKitHTpUnXs2FGS1KtXL82cOVPTp09XUVGREhIStHbtWgUGBtbnsAAAQBNkm8C60gvLG/pi859df/31%2Bvzzz6v%2B3qVLF483H73c%2BPHjNX78eCO3DQAAmh7bvBHFld6WoaFv1QAAAGAF2wQWAACAryCwAAAADLPNa7AuXryoWbNm1bitNu%2BDBQAAYCXbBFbnzp1VUFBQ4zYAAAC7s01gXf47AwEAALwVr8ECAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwzDa/7Bm%2Bb9CKg1aPAABAo%2BAMFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGFOqwcAAJg1aMVBq0eotV3Te1g9AnBVcAYLAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMH7ZMwDUwJt%2BeTIAe%2BAMFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGE%2BG1j5%2BfmaMmWKunXrpu7du%2BvRRx/V6dOnJUlHjx7VXXfdpc6dO%2BuOO%2B7Q%2BvXrPT52586dGjZsmG6%2B%2BWaNGjVKBw4csOIQAACAl/LZwMrIyFBISIj27t2rzZs364svvtDTTz%2Btc%2BfOadKkSfrd736nf/7zn3r22We1Zs0aZWVlSfopvubMmaPZs2frvffeU3p6uh566CGdOnXK4iMCAADewicD6/Tp04qPj9esWbMUHBysNm3aaOTIkfrggw/0zjvv6OLFi3rwwQcVFBSkTp06acyYMXK5XJKkTZs2qXfv3urdu7eaNWum4cOH67e//a22bdtm8VEBAABv4ZOBFRISosWLF6t169ZV206ePKmIiAjl5eUpJiZG/v7%2BVZfFxcUpNzdXkpSXl6e4uDiP64uLi1NOTk7jDA8AALye0%2BoBGkNOTo5eeeUVrV69Wrt27VJISIjH5a1atZLb7VZFRYXcbrdCQ0M9Lg8NDdWXX35Z69srKChQYWGhxzanM0gRERH1Pwgv4u/v8PgTAH6J09k4Xyf4umQPTWkdfD6wPvzwQz344IOaNWuWunfvrl27dl1xPz8/v6r/r6ysbNBtulwurVy50mPblClTNG3atAZdr7cJCWlu9QgAbC4sLLhRb4%2BvS/bQFNbBpwNr7969euSRR7RgwQLdeeedkqTw8HB9/fXXHvu53W61atVKDodDYWFhcrvd1S4PDw%2Bv9e2mpqYqOTnZY5vTGaSSktL6HYiX8fd3KCSkuU6fLlN5eYXV4wCwscb6usjXJXuwYh0aO%2BJ/5rOBdfjwYc2ZM0d//vOf1bNnz6rt8fHxevXVV3Xp0iU5nT8dfk5OjhITE6su//n1WD/LycnRkCFDan3bERER1Z4OLCz8UZcuNa0HdXl5RZM7ZgB109hfI/i6ZA9NYR188knQS5cuaf78%2BZo9e7ZHXElS79691aJFC61evVplZWX65JNP9MYbbygtLU2SNHbsWB06dEjvvPOOzp8/rzfeeENff/21hg8fbsWhAAAAL%2BRX2dAXHNnQBx98oN///vcKCAiodtnu3btVWlqqxx9/XLm5uWrdurUmTpyo8ePHV%2B2TlZWl5cuXKz8/X%2B3bt9e8efPUpUuXBs1UWPhjgz7emzidDoWFBaukpNTjXyiDVhy0cCoAdrRreo9GuZ1f%2BrqExmXFOlx3XctGuZ3L%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%2BuBBx5Qt27d1LdvXy1dulQVFRVWjwUAALwETxFewdSpU9WpUyft2bNHRUVFmjRpklq3bq17773X6tEAAIAX4AzWZXJycvTZZ59p9uzZatmypaKjo5Weni6Xy2X1aAAAwEtwBusyeXl5ioqKUmhoaNW2Tp066fjx4zpz5oxatGhR43UUFBSosLDQY5vTGaSIiAjj8wKAN3M6G%2Bff%2Bf7%2BDo8/6%2Bv2Zf80MQ6uoLHuC42FwLqM2%2B1WSEiIx7afY6ukpKRWgeVyubRy5UqPbQ899JCmTp1qbtD/74M/DjR%2BnQ1VUFAgl8ul1NRUotJCrIM9sA72UFBQoJdeWtfgdbDj11xv0pQeD76Vi4ZUVlY26ONTU1O1efNmj/9SU1MNTWd/hYWFWrlyZbWzeGhcrIM9sA72wDrYQ1NaB85gXSY8PFxut9tjm9vtlp%2Bfn8LDw2t1HRERET5f5gAA4JdxBusy8fHxOnnypIqLi6u25eTkqH379goODrZwMgAA4C0IrMvExcUpISFBy5cv15kzZ3Ts2DFt2LBBaWlpVo8GAAC8hP8TTzzxhNVD2M1tt92mHTt26Mknn9Sbb76plJQU3X///fLz87N6NK8RHBysrl27ctbPYqyDPbAO9sA62ENTWQe/yoa%2BohsAAAAeeIoQAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILRuTk5Oj222/X2LFj/%2Bd%2BmZmZio2NVUJCgsd/33//fSNN6ttquw6StHHjRg0YMEBJSUlKS0tTbm5uI0zo%2B9xut6ZPn67u3burZ8%2Bemjdvns6dO3fFfTdv3qyOHTtWezx8%2BumnjTy1b8jPz9cDDzygbt26qW/fvlq6dKkqKiquuC/3/6untuvg698PnFYPAO%2B3bds2/elPf1L79u11%2BvTpGvcfMWKElixZ0giTNS11WYe9e/cqMzNT69atU0xMjDZu3KiMjAxlZWUpKCiokSb2TQsWLNCFCxe0Y8cOXbx4UQ8//LCWLVum%2BfPnX3H/Ll266OWXX27kKX3T1KlT1alTJ%2B3Zs0dFRUWaNGmSWrdurXvvvddjP%2B7/V1dt10Hy7e8HnMFCg50/f14ul0uJiYlWj9Kk1WUdXC6XRo0apcTERAUGBmrChAmSpH379l3tMX3a999/rz179mjGjBkKDw9XZGSkJk%2BerL///e%2B6ePGi1eP5tJzMLaRrAAAEU0lEQVScHH322WeaPXu2WrZsqejoaKWnp8vlclXbl/v/1VOXdfB1BBYabMyYMYqMjKz1/p9//rnGjRunpKQkDRkyRAcOHLiK0zUddVmHvLw8xcXFVf3d4XAoNjZWOTk5V2u8JuHo0aPy9/dXTExM1bZOnTrp7Nmz%2Buqrr674MSdPntS9996rLl26qF%2B/ftq6dWtjjetT8vLyFBUVpdDQ0KptnTp10vHjx3XmzJlq%2B3L/vzrqsg6Sb38/ILDQqNq0aaO2bdvq6aef1sGDBzVmzBhlZGT84jcfXB1ut9vjC6AkhYaGqqSkxKKJfIPb7VaLFi3k5%2BdXte3nz/OVPrfh4eGKjo7WI488ooMHD2rmzJmaO3eusrOzG21mX%2BF2uxUSEuKx7Zc%2B99z/r566rIOvfz8gsFCjrVu3KiYm5or/bd68uU7XNWbMGD333HP6zW9%2Bo%2BbNmys9PV2xsbHatm3bVZred5hcB0mqrKy8ClP6vv%2B1Dvn5%2BXX6vPbp00fr1q1TXFycAgICNGTIEN1%2B%2B%2B31Wk/U7T7N/f/qqe3n1te/H/Aid9RoxIgRGjFixFW7/qioKBUUFFy16/cVJtchLCxMbrfbY5vb7VaHDh2MXL8v%2B1/rcPDgQZ05c0bl5eXy9/eXpKrP87XXXlur64%2BKiuIn2uohPDz8ivdpPz8/hYeHe2zn/n/11GUdrsSXvh9wBguN6i9/%2BUu1pz%2BOHTumtm3bWjRR0xQfH6%2B8vLyqv5eXl%2BvIkSP8oEIDxcbGqrKyUp999lnVtpycHIWEhKhdu3bV9n/11Ve1c%2BdOj208HuonPj5eJ0%2BeVHFxcdW2nJwctW/fXsHBwdX25f5/ddRlHXz9%2BwGBhatu4MCB%2BuCDDyT99C%2BZhQsX6quvvtL58%2Be1fv16ffPNNxo5cqTFU/q%2B/16HtLQ0bdmyRR9//LHKysq0evVqBQQEqE%2BfPtYO6eXCw8M1YMAArVixQsXFxTp16pRWrVqllJQUOZ0/PWFwzz33VEXVhQsX9OSTTyonJ0cXL17Ujh079O6772rcuHFWHoZXiouLU0JCgpYvX64zZ87o2LFj2rBhg9LS0iRx/28sdVkHX/9%2BwFOEaLABAwboP//5j8rLy1VRUaGEhARJ0u7duxUVFaXjx4/r7NmzkqRZs2ZJktLT0%2BV2u9W%2BfXu9%2BOKLatOmjWXz%2B4q6rEOvXr00c%2BZMTZ8%2BXUVFRUpISNDatWsVGBho5SH4hD/84Q96/PHH1a9fP11zzTUaOnSoZsyYUXX5t99%2Bqx9%2B%2BEGSdPfdd6u0tFQPP/ywCgsLdf3112vVqlWKj4%2B3anyv9txzz2nBggXq0aOHWrRooXHjxmn8%2BPGSxP2/EdV2HXz9%2B4FfJa/0AwAAMIqnCAEAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAz7f%2BVuGsJWNJopAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-3442596609541139049”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-0.8292550292280048</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.032756923426117</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.1669174732523278</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.3597551462116115</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.8534170176788809</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.7846266669309205</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.2787497928955176</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.2498273848264496</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.8930345156151489</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.7381798930509191</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (41)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-3442596609541139049”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-1.6968605681597184</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.5197014184803113</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:31%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.511422340362337</td> <td class=”number”>44</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:59%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.4023717697451108</td> <td class=”number”>75</td> <td class=”number”>2.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.380526082488867</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:89%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-0.2318128380258475</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:86%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.15074467934100255</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.18146206578609814</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.400809876497803</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.529722771676318</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:26%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_ORCHARD_ACRESPTH_07_12”><s>PCH_ORCHARD_ACRESPTH_07_12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_ORCHARD_ACRES_07_12”><code>PCH_ORCHARD_ACRES_07_12</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99826</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_ORCHARD_ACRES_07_12”>PCH_ORCHARD_ACRES_07_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>1609</td>

</tr> <tr>

<th>Unique (%)</th> <td>50.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>37.1%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>1192</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>25.148</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>3712.5</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-5032278172989357121”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-5032278172989357121”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-5032278172989357121”

aria-controls=”quantiles-5032278172989357121” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-5032278172989357121” aria-controls=”histogram-5032278172989357121”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-5032278172989357121” aria-controls=”common-5032278172989357121”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-5032278172989357121” aria-controls=”extreme-5032278172989357121”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-5032278172989357121”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-72.167</td>

</tr> <tr>

<th>Q1</th> <td>-31.092</td>

</tr> <tr>

<th>Median</th> <td>-2.6667</td>

</tr> <tr>

<th>Q3</th> <td>37.886</td>

</tr> <tr>

<th>95-th percentile</th> <td>188.15</td>

</tr> <tr>

<th>Maximum</th> <td>3712.5</td>

</tr> <tr>

<th>Range</th> <td>3812.5</td>

</tr> <tr>

<th>Interquartile range</th> <td>68.978</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>152.63</td>

</tr> <tr>

<th>Coef of variation</th> <td>6.0692</td>

</tr> <tr>

<th>Kurtosis</th> <td>213.16</td>

</tr> <tr>

<th>Mean</th> <td>25.148</td>

</tr> <tr>

<th>MAD</th> <td>67.675</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>11.365</td>

</tr> <tr>

<th>Sum</th> <td>50749</td>

</tr> <tr>

<th>Variance</th> <td>23296</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-5032278172989357121”>

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srEzV1dUKCgpyzUlS06ZNL2mtyMjIsy4HOhwnVVXlW1%2BUZvNU/urqGkt8rK2Qk4z%2BwQoZJWvktEJGs/jlxdRXXnlFb731lttYUVGRoqOjFRwcrA4dOqiwsNA1V1paqkOHDikxMVGdO3eWYRjau3eva95utys0NFRt27b1WAYAAOC7/LJgnTlzRnPnzpXdbldlZaUKCgr0zjvvaOzYsZKktLQ0LV%2B%2BXEVFRSorK1NOTo46d%2B6shIQERUREaPDgwXrqqad0/PhxHTt2TIsXL9aoUaNks1nmCT8AAFALPtsYEhISJElVVVWSpM2bN0v64dmmcePGqby8XJMnT5bD4VCrVq20ePFixcfHS5LGjh0rh8Oh2267TeXl5UpKSnK7Kf3RRx/VI488ogEDBqhevXq68cYbz3oxUwAAgPMJMGp7RzcuicNxsk7Oe/1T79fJeevC%2Bim96vT8NlugwsMbqaSk3K/vEbBCTjL6BytklKyR05czNm/e2Cvr%2BuUlQgAAAG%2BiYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJfLZgvfvuu0pOTlZmZuZZc5s2bdLQoUPVrVs3DR48WK%2B99ppr7umnn1bnzp2VkJDg9vbtt99Kkk6fPq0//vGP6tOnj5KSkpSRkaGSkhKP5QIAAL7PJwvWM888o3nz5ql169ZnzX3xxReaNm2aMjIy9PHHH2vGjBl69NFH9cknn7iOGTZsmOx2u9tbs2bNJEkLFy5UYWGh8vPztXHjRhmGoYceeshj2QAAgO/zyYIVHBysVatWnbNgOZ1OTZgwQddee61sNptSUlLUsWNHt4J1PlVVVVq1apXuueceXX755WrSpImmTJmibdu26ZtvvqmLKAAAwA/ZvL2BX2LcuHHnnevTp4/69Onj%2BndVVZUcDodatGjhGvvyyy81duxYffXVV7r88sv10EMPqXfv3jp06JBOnjypuLg417Ht2rVTgwYNVFhY6HaOCykuLpbD4XAbs9kaKjIy8lIj%2BiWbrW77fFBQoNt7f2WFnGT0D1bIKFkjpxUyms0nC9bPkZOTo4YNG%2BqGG26QJLVs2VLR0dGaOnWqIiMjlZ%2Bfr4kTJ2rdunVyOp2SpNDQULdzhIaG/qz7sPLz85Wbm%2Bs2lp6eroyMjFqm8W3h4Y08sk5o6GUeWcfbrJCTjP7BChkla%2BS0QkazeLxg9e/fXyNHjtTNN9%2Bsyy%2B/vM7WMQxDOTk5Kigo0PLlyxUcHCxJGj16tEaPHu067o477tCbb76pdevWuZ75MgyjVmunpqaqf//%2BbmM2W0OVlJTX6ry%2Brq7zBwUFKjT0MpWWVqi6uqZO1/ImK%2BQko3%2BwQkbJGjl9OaOnfrj/KY8XrJtvvllvvvmmlixZoquvvlpjxoxR//79ZbOZt5Wamho99NBD%2BuKLL/TKK68oOjr6gsdHRUWpuLhYERERkn64j6tRo/9%2BQk6cOKGmTZte8vqRkZFnXQ50OE6qqsq3vijN5qn81dU1lvhYWyEnGf2DFTJK1shphYxm8fjF1PT0dL311lt67bXX1KFDBz3%2B%2BONKSUnRk08%2BqQMHDpiyxuOPP65///vf5yxXf/nLX7Rjxw63saKiIkVHRys6OlphYWEqLCx0zX311Vc6c%2BaM4uPjTdkbAADwf167Wy0uLk5ZWVnaunWrZsyYoddee0033HCDxo8fry%2B%2B%2BOIXn/fTTz/VunXrlJeXpyZNmpw173Q6NWfOHO3fv1%2BnT5/W888/r0OHDmnEiBEKCgrSmDFjtHTpUh09elQlJSX605/%2BpIEDB7pexgEAAOBivHaTe2Vlpf7xj39ozZo1%2BvDDD9WmTRvdd999Ki4u1h133KE5c%2BbopptuOudjExISJP3wG4KStHnzZkmS3W7X6tWrdfLkSfXr18/tMT169NDzzz%2BvqVOnSvrh3iun06n27dvrr3/9q1q2bClJysjIUHl5uYYNG6aqqir169dPs2fProsPAQAA8FMBRm3v6P6ZioqKtGrVKr3%2B%2BusqLy/X4MGDNXbsWHXv3t11zPbt2zV79mxt3brVk1urUw7HyTo57/VPvV8n560L66f0qtPz22yBCg9vpJKScr%2B%2BR8AKOcnoH6yQUbJGTl/O2Lx5Y6%2Bs6/FnsIYMGaK2bdtqwoQJGj58%2BDkv46WkpOj48eOe3hoAAIApPF6wli9frquuuuqix33%2B%2Bece2A0AAID5PH6Te0xMjCZOnOi6b0qS/vrXv%2BoPf/iD64U%2BAQAAfJnHC1Z2drZOnjyp9u3bu8b69u2rmpoazZ8/39PbAQAAMJ3HLxG%2B9957euONNxQeHu4aa9OmjXJycnTjjTd6ejsAAACm8/gzWKdOnXL92Rq3jQQGqqKiwtPbAQAAMJ3HC1aPHj00f/58nThxwjX2zTffaM6cOW4v1QAAAOCrPH6JcMaMGbrzzjt19dVXKyQkRDU1NSovL1d0dLReeuklT28HAADAdB4vWNHR0XrzzTf1zjvv6NChQwoMDFTbtm3Vu3dvBQUFeXo7AAAApvPKn8qpX7%2B%2Brr32Wm8sDQAAUOc8XrAOHz6sBQsW6N///rdOnTp11vzbb7/t6S0BAACYyiv3YBUXF6t3795q2LChp5cHAACocx4vWLt27dLbb7%2BtiIgITy8NAADgER5/mYamTZvyzBUAAPBrHi9YEyZMUG5urgzD8PTSAAAAHuHxS4TvvPOOPvvsM61Zs0atWrVSYKB7x3v11Vc9vSUAAABTebxghYSEqE%2BfPp5eFgAAwGM8XrCys7M9vSQAAIBHefweLEnav3%2B/nn76aT300EOusX/961/e2AoAAIDpPF6wduzYoaFDh2rTpk0qKCiQ9MOLj44bN44XGQUAAH7B4wVr4cKFeuCBB/TGG28oICBA0g9/n3D%2B/PlavHixp7cDAABgOo8XrK%2B%2B%2BkppaWmS5CpYknTdddepqKjI09sBAAAwnccLVuPGjc/5NwiLi4tVv359T28HAADAdB4vWFdccYUef/xxlZWVucYOHDigrKwsXX311Z7eDgAAgOk8/jINDz30kG6//XYlJSWpurpaV1xxhSoqKtShQwfNnz/f09sBAAAwnccLVsuWLVVQUKDt27frwIEDatCggdq2batevXq53ZMFAADgqzxesCSpXr16uvbaa72xNAAAQJ3zeMHq37//BZ%2Bp%2BjmvhfXuu%2B8qKytLSUlJWrhwodvcW2%2B9pSVLlujrr79W27Ztdf/996t3796SpJqaGi1atEgFBQUqLS1VYmKiZs%2BerejoaEmS0%2BnU7Nmz9dFHHykwMFApKSmaNWuWGjRo8AsSAwAAq/F4wbrhhhvcClZ1dbUOHDggu92u22%2B//ZLP88wzz2jVqlVq3br1WXN79uxRVlaWcnNz1bNnT23cuFH33nuvNmzYoJYtW2rFihV644039Mwzz6hFixZauHCh0tPTtXbtWgUEBGjWrFk6c%2BaMCgoKVFlZqcmTJysnJ0czZ8405WMAAAD8m8cL1rRp0845vnHjRv3zn/%2B85PMEBwdr1apVeuyxx3T69Gm3uZUrVyolJUUpKSmSpKFDh%2Brll1/WunXrdPfddys/P1933HGH2rVrJ0nKzMxUUlKSPv/8c7Vq1UqbN2/W3//%2Bd0VEREiS7rnnHk2ePFlZWVmqV6/eL4kNAAAsxCt/i/Bcrr32Wr355puXfPy4cePUuHHjc84VFhYqNjbWbSw2NlZ2u12nTp3Svn373OZDQkLUunVr2e127dmzR0FBQYqJiXHNx8XF6fvvv9f%2B/ft/ZioAAGBFXrnJ/Vx2794twzBMOZfT6VRYWJjbWFhYmPbt26cTJ07IMIxzzpeUlKhJkyYKCQlxu4z547ElJSWXtH5xcbEcDofbmM3WUJGRkb8kjt%2Bw2eq2zwcFBbq991dWyElG/2CFjJI1cloho9k8XrDGjh171lhFRYWKioo0aNAg09a5WFm70Hxti15%2Bfr5yc3PdxtLT05WRkVGr8/q68PBGHlknNPQyj6zjbVbISUb/YIWMkjVyWiGjWTxesNq0aXPWbxEGBwdr1KhRGj16tClrhIeHy%2Bl0uo05nU5FRESoSZMmCgwMPOd806ZNFRERobKyMlVXVysoKMg1J0lNmza9pPVTU1PVv39/tzGbraFKSsp/aSS/UNf5g4ICFRp6mUpLK1RdXVOna3mTFXKS0T9YIaNkjZy%2BnNFTP9z/lMcLliderT0%2BPl67du1yG7Pb7RoyZIiCg4PVoUMHFRYW6qqrrpIklZaW6tChQ0pMTFRUVJQMw9DevXsVFxfnemxoaKjatm17SetHRkaedTnQ4Tipqirf%2BqI0m6fyV1fXWOJjbYWcZPQPVsgoWSOnFTKaxeMF6/XXX7/kY4cPH/6L1hgzZoxGjRqlbdu26eqrr9Ybb7yhgwcPaujQoZKktLQ05eXlqU%2BfPmrRooVycnLUuXNnJSQkSJIGDx6sp556Sk888YTOnDmjxYsXa9SoUbLZfjW3rAEAgF8xjzeGhx9%2BWDU1NWfd5xQQEOA2FhAQcMGC9WMZqqqqkiRt3rxZ0g/PNnXs2FE5OTnKzs7WkSNH1L59ey1btkzNmzeX9MN9YA6HQ7fddpvKy8uVlJTkds/Uo48%2BqkceeUQDBgxQvXr1dOONNyozM9OcDwAAAPB7AYZZv7p3iXbs2KHnn39eEydOVExMjAzD0JdffqlnnnlGt956q5KSklzH1q9f35Nbq1MOx8k6Oe/1T71fJ%2BetC%2Bun9KrT89tsgQoPb6SSknK/fgrbCjnJ6B%2BskFGyRk5fzti8%2Bblf0qmueeUerLy8PLVo0cI1duWVVyo6Olrjx49XQUGBp7cEAABgKo%2B/oMXBgwfPeg0qSQoNDdWRI0c8vR0AAADTebxgRUVFaf78%2BW4v2llaWqoFCxbot7/9rae3AwAAYDqPXyKcMWOGpk6dqvz8fDVq1EiBgYEqKytTgwYNtHjxYk9vBwAAwHQeL1i9e/fWtm3btH37dh07dkyGYahFixa65pprzvu3BQEAAHyJV17Y6bLLLtOAAQN07NgxRUdHe2MLAAAAdcbj92CdOnVKWVlZ6tatm66//npJP9yDddddd6m0tNTT2wEAADCdxwvWk08%2BqT179ignJ0eBgf9dvrq6Wjk5OZ7eDgAAgOk8XrA2btyoP//5z7ruuutcf/Q5NDRU2dnZ2rRpk6e3AwAAYDqPF6zy8nK1adPmrPGIiAh9//33nt4OAACA6TxesH7729/qn//8pyS5/e3BDRs26De/%2BY2ntwMAAGA6j/8W4S233KL77rtPN998s2pqavTCCy9o165d2rhxox5%2B%2BGFPbwcAAMB0Hi9YqampstlsevnllxUUFKSlS5eqbdu2ysnJ0XXXXefp7QAAAJjO4wXr%2BPHjuvnmm3XzzTd7emkAAACP8Pg9WAMGDHC79woAAMDfeLxgJSUlaf369Z5eFgAAwGM8fonw8ssv12OPPaa8vDz99re/Vb169dzmFyxY4OktAQAAmMrjBWvfvn363e9%2BJ0kqKSnx9PIAAAB1zmMFKzMzUwsXLtRLL73kGlu8eLHS09M9tQUAAACP8Ng9WFu2bDlrLC8vz1PLAwAAeIzHCta5fnOQ3yYEAAD%2ByGMF68c/7HyxMQAAAF/n8ZdpAAAA8HcULAAAAJN57LcIKysrNXXq1IuO8TpYAADA13msYHXv3l3FxcUXHQMAAPB1HitY//v6VwAAAP6Me7AAAABM5vE/leMJH3/8se688063McMwVFlZqeXLl2vcuHGqX7%2B%2B2/z//d//6frrr5ckLV%2B%2BXCtWrJDD4VBMTIwefvhhxcfHe2z/AADAt/llwerRo4fsdrvb2NKlS7V3715JUlRU1DlfWV764RXnn376aT377LOKiYnR8uXLNXHiRG3atEkNGzas870DAADfZ4lLhP/5z3/0wgsv6MEHH7zosfn5%2BRo5cqS6dOmiBg0a6K677pIkbd26ta63CQAA/IQlCtaiRYt088036ze/%2BY0kqby8XOnp6UpKStI111yjF154wfVnewoLCxUbG%2Bt6bGBgoDp37nzWM2IAAADn45eXCP/X119/rU2bNmnTpk2SpJCQEHXs2FG33367Fi5cqI8%2B%2BkiTJ09W48aNNWrUKDnbY14LAAAZYElEQVSdToWFhbmdIywsTCUlJZe8ZnFxsRwOh9uYzdZQkZGRtQ/kw2y2uu3zQUGBbu/9lRVyktE/WCGjZI2cVshoNr8vWCtWrNCgQYPUvHlzSVJcXJzbS0b07t1bY8eO1Zo1azRq1ChJtf8j1Pn5%2BcrNzXUbS09PV0ZGRq3O6%2BvCwxt5ZJ3Q0Ms8so63WSEnGf2DFTJK1shphYxm8fuCtXHjRmVlZV3wmKioKG3cuFGSFB4eLqfT6TbvdDrVoUOHS14zNTVV/fv3dxuz2RqqpKT8ks/hj%2Bo6f1BQoEJDL1NpaYWqq2vqdC1vskJOMvoHK2SUrJHTlzN66of7n/LrgrVnzx4dOXJEvXr1co2tX79eJSUluuWWW1xj%2B/fvV3R0tCQpPj5ehYWFGjFihCSpurpau3fvdj27dSkiIyPPuhzocJxUVZVvfVGazVP5q6trLPGxtkJOMvoHK2SUrJHTChnN4tcXU3fv3q0mTZooJCTENVavXj098cQTeu%2B991RZWan3339fq1evVlpamiQpLS1Nr7/%2Bunbu3KmKigotWbJE9evXV9%2B%2Bfb2UAgAA%2BBq/fgbr22%2B/dd179aNrr71WM2bM0Ny5c3X06FE1a9ZMM2bM0KBBgyRJffr00f33368pU6bou%2B%2B%2BU0JCgvLy8tSgQQNvRAAAAD4owKjtHd24JA7HyTo57/VPvV8n560L66f0uvhBtWCzBSo8vJFKSsr9%2BilsK%2BQko3%2BwQkbJGjl9OWPz5o29sq5fXyIEAADwBgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmMxvC1ZMTIzi4%2BOVkJDgeps7d64kaceOHRo1apSuuOIKDRkyROvWrXN77PLlyzV48GBdccUVSktL065du7wRAQAA%2BCibtzdQlzZs2KBWrVq5jRUXF%2Buee%2B7Rww8/rJtuukmffvqpJk2apLZt2yohIUFbtmzR008/rWeffVYxMTFavny5Jk6cqE2bNqlhw4ZeSgIAAHyJ3z6DdT5vvPGG2rRpo1GjRik4OFjJycnq37%2B/Vq5cKUnKz8/XyJEj1aVLFzVo0EB33XWXJGnr1q3e3DYAAPAhfv0M1oIFC/Svf/1LZWVluv766zV9%2BnQVFhYqNjbW7bjY2FitX79eklRYWKgbbrjBNRcYGKjOnTvLbrdryJAhl7RucXGxHA6H25jN1lCRkZG1TOTbbLa67fNBQYFu7/2VFXKS0T9YIaNkjZxWyGg2vy1YXbt2VXJysp544gkdPnxYU6ZM0Zw5c%2BR0OtWiRQu3Y5s0aaKSkhJJktPpVFhYmNt8WFiYa/5S5OfnKzc3120sPT1dGRkZvzCNfwgPb%2BSRdUJDL/PIOt5mhZxk9A9WyChZI6cVMprFbwtWfn6%2B63%2B3a9dO06ZN06RJk9S9e/eLPtYwjFqtnZqaqv79%2B7uN2WwNVVJSXqvz%2Brq6zh8UFKjQ0MtUWlqh6uqaOl3Lm6yQk4z%2BwQoZJWvk9OWMnvrh/qf8tmD9VKtWrVRdXa3AwEA5nU63uZKSEkVEREiSwsPDz5p3Op3q0KHDJa8VGRl51uVAh%2BOkqqp864vSbJ7KX11dY4mPtRVyktE/WCGjZI2cVshoFr%2B8mLp7927Nnz/fbayoqEj169dXSkrKWS%2B7sGvXLnXp0kWSFB8fr8LCQtdcdXW1du/e7ZoHAAC4GL8sWE2bNlV%2Bfr7y8vJ05swZHThwQIsWLVJqaqqGDRumI0eOaOXKlTp9%2BrS2b9%2Bu7du3a8yYMZKktLQ0vf7669q5c6cqKiq0ZMkS1a9fX3379vVuKAAA4DP88hJhixYtlJeXpwULFrgK0ogRI5SZmang4GAtW7ZM8%2BbN05w5cxQVFaUnn3xSnTp1kiT16dNH999/v6ZMmaLvvvtOCQkJysvLU4MGDbycCgAA%2BAq/LFiS1KNHD7366qvnnVu7du15H3vLLbfolltuqautAQAAP%2BeXlwgBAAC8iYIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJvPbgnXkyBGlp6crKSlJycnJmj59ukpLS/X1118rJiZGCQkJbm/PPfec67FvvfWWbrrpJnXr1k0jR47Ue%2B%2B958UkAADA19i8vYG6MnHiRMXHx2vLli06efKk0tPT9cQTT2jSpEmSJLvdfs7H7dmzR1lZWcrNzVXPnj21ceNG3XvvvdqwYYNatmzpyQgAAMBH%2BeUzWKWlpYqPj9fUqVPVqFEjtWzZUiNGjNAnn3xy0ceuXLlSKSkpSklJUXBwsIYOHaqOHTtq3bp1Htg5AADwB375DFZoaKiys7Pdxo4eParIyEjXvx988EF98MEHqqqq0ujRo5WRkaF69eqpsLBQKSkpbo%2BNjY097zNe51JcXCyHw%2BE2ZrM1dFvfimy2uu3zQUGBbu/9lRVyktE/WCGjZI2cVshoNr8sWD9lt9v18ssva8mSJapfv766deumgQMH6rHHHtOePXt03333yWazafLkyXI6nQoLC3N7fFhYmPbt23fJ6%2BXn5ys3N9dtLD09XRkZGabk8VXh4Y08sk5o6GUeWcfbrJCTjP7BChkla%2BS0Qkaz%2BH3B%2BvTTTzVp0iRNnTpVycnJkqRXX33VNZ%2BYmKgJEyZo2bJlmjx5siTJMIxarZmamqr%2B/fu7jdlsDVVSUl6r8/q6us4fFBSo0NDLVFpaoerqmjpdy5uskJOM/sEKGSVr5PTljJ764f6n/LpgbdmyRQ888IBmzZql4cOHn/e4qKgoffvttzIMQ%2BHh4XI6nW7zTqdTERERl7xuZGTkWZcDHY6TqqryrS9Ks3kqf3V1jSU%2B1lbISUb/YIWMkjVyWiGjWfz2Yupnn32mrKwsLVq0yK1c7dixQ0uWLHE7dv/%2B/YqKilJAQIDi4%2BO1a9cut3m73a4uXbp4ZN8AAMD3%2BWXBqqqq0syZMzVt2jT17t3bba5x48ZavHix1q5dq8rKStntdj333HNKS0uTJI0ZM0YffPCBtm3bptOnT2vVqlU6ePCghg4d6o0oAADAB/nlJcKdO3eqqKhI8%2BbN07x589zmNmzYoIULFyo3N1d//OMf1bhxY9122226/fbbJUkdO3ZUTk6OsrOzdeTIEbVv317Lli1T8%2BbNvREFAAD4IL8sWFdeeaW%2B/PLL885HRUVp4MCB550fNGiQBg0aVBdbAwAAFuCXlwgBAAC8iYIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMpu3NwDruP6p9729hZ9l/ZRe3t4CAMBH8QwWAACAyShY53DkyBHdfffdSkpKUr9%2B/fTkk0%2BqpqbG29sCAAA%2BgkuE53DfffcpLi5Omzdv1nfffacJEyaoWbNm%2Bv3vf%2B/trQEAAB/AM1g/YbfbtXfvXk2bNk2NGzdWmzZtdMcddyg/P9/bWwMAAD6CZ7B%2BorCwUFFRUQoLC3ONxcXF6cCBAyorK1NISMhFz1FcXCyHw%2BE2ZrM1VGRkpOn7Rd3xtZvy/zHtGm9vwTRBQYFu7/0RGf2HFXJaIaPZKFg/4XQ6FRoa6jb2Y9kqKSm5pIKVn5%2Bv3Nxct7F7771X9913n3kb/f8%2Beey6SzquuLhY%2Bfn5Sk1N9duiZ4WMkjVyFhcX68UXnyWjj7NCRskaOa2Q0WxU0XMwDKNWj09NTdWaNWvc3lJTU03a3S/jcDiUm5t71jNr/sQKGSVr5CSjf7BCRskaOa2Q0Ww8g/UTERERcjqdbmNOp1MBAQGKiIi4pHNERkbS8AEAsDCewfqJ%2BPh4HT16VMePH3eN2e12tW/fXo0aNfLizgAAgK%2BgYP1EbGysEhIStGDBApWVlamoqEgvvPCC0tLSvL01AADgI4Jmz54929ub%2BLW55pprVFBQoLlz5%2BrNN9/UqFGjNH78eAUEBHh7a7XSqFEjXXXVVX79TJwVMkrWyElG/2CFjJI1cloho5kCjNre0Q0AAAA3XCIEAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBcsCjhw5orvvvltJSUnq16%2BfnnzySdXU1Hh7Wz9LTEyM4uPjlZCQ4HqbO3euJGnHjh0aNWqUrrjiCg0ZMkTr1q1ze%2Bzy5cs1ePBgXXHFFUpLS9OuXbu8EeGc3n33XSUnJyszM/Osubfeeks33XSTunXrppEjR%2Bq9995zzdXU1GjhwoUaMGCAevToofHjx%2Bvw4cOueafTqSlTpig5OVm9e/fWww8/rFOnTnkk07mcL%2BeaNWvUqVMnt89rQkKCvvjiC0m%2Bk/PIkSNKT09XUlKSkpOTNX36dJWWlkqS9uzZo1tvvVXdu3fXoEGD9Pzzz7s9tjafZ087X86vv/5aMTExZ30en3vuOddjfSXn3r17dfvtt6t79%2B5KTk7WlClT5HA4JNXue83p06f1xz/%2BUX369FFSUpIyMjJUUlLi0Ww/Ol/Gf/7zn%2Bf8PK5fv971WF/J%2BKtgwO%2BNGDHCmDlzplFaWmocOHDAGDRokPH88897e1s/S8eOHY3Dhw%2BfNf7NN98YXbt2NVauXGmcOnXKeP/9943ExETjiy%2B%2BMAzDMN5%2B%2B23jyiuvNHbu3GlUVFQYy5YtM3r16mWUl5d7OsJZ8vLyjEGDBhljx441pkyZ4ja3e/duIz4%2B3ti2bZtx6tQpY%2B3atUaXLl2Mo0ePGoZhGMuXLzf69etn7Nu3zzh58qTx6KOPGjfddJNRU1NjGIZh3Hvvvcbdd99tfPfdd8axY8eM1NRUY%2B7cuR7PaBgXzrl69Wrj1ltvPe9jfSXnjTfeaEyfPt0oKyszjh49aowcOdKYMWOGUVFRYVxzzTXG008/bZSXlxu7du0yrrrqKmPjxo2GYdT%2B8/xryXn48GGjY8eO532cr%2BQ8ffq0cfXVVxu5ubnG6dOnje%2B%2B%2B8649dZbjXvuuafW32uys7ONkSNHGv/5z3%2BMkpIS49577zUmTJjg0XwXy/jhhx8a/fr1O%2B9jfSXjrwUFy8998cUXRufOnQ2n0%2Bka%2B9vf/mYMHjzYi7v6%2Bc5XsJ599llj%2BPDhbmNTpkwxZs2aZRiGYdx9993G448/7pqrrq42evXqZRQUFNTthi/Biy%2B%2BaJSWlhpZWVlnFY85c%2BYY6enpbmOjR482li1bZhiGYQwZMsR48cUXXXMnT540YmNjjX/961%2BGw%2BEwOnXqZOzZs8c1v337dqNr167GmTNn6jDRuV0o58UKli/kPHHihDF9%2BnTD4XC4xl566SVj0KBBxvr1642ePXsaVVVVrrknn3zSuPPOOw3DqN3n2dMulPNiBctXcjqdTuO1114zKisrXWMvvviiMXDgwFp9r6msrDS6d%2B9ubN682TW/b98%2BIyYmxjh27Fgdp3J3oYwXK1i%2BkvHXgkuEfq6wsFBRUVEKCwtzjcXFxenAgQMqKyvz4s5%2BvgULFqhv37668sorNWvWLJWXl6uwsFCxsbFux8XGxrqetv7pfGBgoDp37iy73e7RvZ/LuHHj1Lhx43POnS%2BX3W7XqVOntG/fPrf5kJAQtW7dWna7XXv27FFQUJBiYmJc83Fxcfr%2B%2B%2B%2B1f//%2BuglzARfKKUlHjx7V73//e/Xo0UMDBgzQ2rVrJclncoaGhio7O1vNmjVzyxQZGanCwkLFxMQoKCjINXehr88f5y/l8%2BxpF8r5owcffFC9e/dWz549tWDBAlVWVkrynZxhYWEaPXq0bDabJGn//v36%2B9//ruuvv75W32sOHTqkkydPKi4uzjXfrl07NWjQQIWFhR5I9l8XyihJ5eXlrsvA11xzjV544QUZhiHJdzL%2BWlCw/JzT6VRoaKjb2I9ly5eujXft2lXJycnatGmT8vPztXPnTs2ZM%2Bec%2BZo0aeLK5nQ63cql9EP%2BX3v2C%2B37xIkTMgzjvPNOp1MhISEKCAhwm5N%2BfZ/ziIgItWnTRg888IDef/993X///ZoxY4Z27NjhszntdrtefvllTZo06bxfn06nUzU1NbX6PHvb/%2BasX7%2B%2BunXrpoEDB2rr1q3Ky8vTunXr9Je//EVS7b6eveHIkSOKj4/XDTfcoISEBGVkZNTqe43T6ZSksx4fGhr6q8oYEhKijh076vbbb9e7776r7Oxs5ebmavXq1ZJ8L6O3UbAs4MefPnxZfn6%2BRo8erfr166tdu3aaNm2aCgoKXD8hX4iv5r/Yvi807yuZ%2B/btq2effVaxsbGqX7%2B%2BhgwZooEDB2rNmjWuY3wp56effqrx48dr6tSpSk5OPu9x/1sKa/N59paf5oyMjNSrr76qgQMHql69ekpMTNSECRMu%2BfN4KfOeFBUVJbvdrg0bNujgwYN68MEHL%2Blxvp4xLi5OL730kq666irVr19fvXv31tixY3328%2BhtFCw/FxER4frJ4kdOp1MBAQGKiIjw0q5qr1WrVqqurlZgYOBZ%2BUpKSlzZwsPDz5n/1579Qvtu0qTJOXM7nU41bdpUERERKisrU3V1tducJDVt2rTuN19LUVFRKi4u9rmcW7Zs0d13360ZM2Zo3Lhxkn747%2B%2BnP707nU5Xttp8nr3lXDnPJSoqSt9%2B%2B60Mw/DJnAEBAWrTpo0yMzNVUFAgm832i7/X/HjMT%2BdPnDjxq8p4/Pjxs4758b9HyTczehMFy8/Fx8fr6NGjbv/h2O12tW/fXo0aNfLizi7d7t27NX/%2BfLexoqIi1a9fXykpKWe97MKuXbvUpUsXST/k/9/r/9XV1dq9e7dr/tcqPj7%2BrFx2u11dunRRcHCwOnTo4JartLRUhw4dUmJiojp37izDMLR37163x4aGhqpt27Yey3ApXnnlFb311ltuY0VFRYqOjvapnJ999pmysrK0aNEiDR8%2B3DUeHx%2BvL7/8UlVVVW57/N%2Bvz1/6efaG8%2BXcsWOHlixZ4nbs/v37FRUVpYCAAJ/JuWPHDg0ePNjtZWwCA3/4v8nExMRf/L0mOjpaYWFhbvNfffWVzpw5o/j4%2BLqMdJYLZdy%2Bfbv%2B9re/uR2/f/9%2BRUdHS/KdjL8WFCw/Fxsbq4SEBC1YsEBlZWUqKirSCy%2B8oLS0NG9v7ZI1bdpU%2Bfn5ysvL05kzZ3TgwAEtWrRIqampGjZsmI4cOaKVK1fq9OnT2r59u7Zv364xY8ZIktLS0vT6669r586dqqio0JIlS1S/fn317dvXu6EuYsyYMfrggw%2B0bds2nT59WqtWrdLBgwc1dOhQST/kWr58uYqKilRWVqacnBx17txZCQkJioiI0ODBg/XUU0/p%2BPHjOnbsmBYvXqxRo0a5bmz9tThz5ozmzp0ru92uyspKFRQU6J133tHYsWMl%2BUbOqqoqzZw5U9OmTVPv3r3d5lJSUhQSEqIlS5aooqJCn3/%2BuVatWuX67682n2dPu1DOxo0ba/HixVq7dq0qKytlt9v13HPP%2BVzO%2BPh4lZWV6cknn1RFRYWOHz%2Bup59%2BWldeeaXS0tJ%2B8feaoKAgjRkzRkuXLtXRo0dVUlKiP/3pTxo4cKDbLw14O2Pjxo31xBNP6L333lNlZaXef/99rV692vV59JWMvxoe/I1FeMnRo0eNu%2B66y0hMTDSSk5ONP//5z157HZ1f6qOPPjJSU1ONrl27GldddZWRnZ1tnDp1yjU3dOhQIy4uzhg0aJDrNYZ%2BtGLFCiMlJcWIj4830tLSjC%2B//NIbEc4SHx9vxMfHG506dTI6derk%2BvePNm7caAwaNMiIi4szhg0bZnz00UeuuZqaGmPRokXG1VdfbSQmJhp/%2BMMfXK8pZBiGUVpaamRmZhpdu3Y1evToYcyZM8c4ffq0R/P96EI5a2pqjMWLFxv9%2BvUz4uPjjeuuu87YsmWL67G%2BkPPjjz82Onbs6Mr1v29ff/218eWXXxpjx4414uPjjb59%2BxorVqxwe3xtPs%2BedLGcmzZtMoYOHWokJiYavXr1MpYuXWpUV1e7Hu8rOffu3WvceuutRmJiotGzZ09jypQprpcZqM33mtOnTxuzZ882evToYXTr1s24//77jdLSUo9m%2B9GFMr766qvGoEGDjISEBKNfv37Ga6%2B95vZYX8n4axBgGNyRBgAAYCYuEQIAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJjs/wHPb3fsCyQsuwAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-5032278172989357121”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>30</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.33333333333333</td> <td class=”number”>14</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>11</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16.666666666666664</td> <td class=”number”>9</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-11.11111111111111</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>66.66666666666666</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-20.0</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (1598)</td> <td class=”number”>1879</td> <td class=”number”>58.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>1192</td> <td class=”number”>37.1%</td> <td>

<div class=”bar” style=”width:63%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-5032278172989357121”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-93.92405063291139</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-93.89312977099237</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-93.75</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-93.24324324324324</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1383.6448598130842</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1434.4827586206898</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1700.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2350.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3712.5</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_ORCHARD_FARMS_07_12”>PCH_ORCHARD_FARMS_07_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>727</td>

</tr> <tr>

<th>Unique (%)</th> <td>22.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>15.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>493</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>7.1518</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>1000</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>6.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram7131436052254597327”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives7131436052254597327,#minihistogram7131436052254597327”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives7131436052254597327”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles7131436052254597327”

aria-controls=”quantiles7131436052254597327” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram7131436052254597327” aria-controls=”histogram7131436052254597327”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common7131436052254597327” aria-controls=”common7131436052254597327”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme7131436052254597327” aria-controls=”extreme7131436052254597327”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles7131436052254597327”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-81.925</td>

</tr> <tr>

<th>Q1</th> <td>-36</td>

</tr> <tr>

<th>Median</th> <td>-8.3333</td>

</tr> <tr>

<th>Q3</th> <td>26.923</td>

</tr> <tr>

<th>95-th percentile</th> <td>143.17</td>

</tr> <tr>

<th>Maximum</th> <td>1000</td>

</tr> <tr>

<th>Range</th> <td>1100</td>

</tr> <tr>

<th>Interquartile range</th> <td>62.923</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>82.3</td>

</tr> <tr>

<th>Coef of variation</th> <td>11.508</td>

</tr> <tr>

<th>Kurtosis</th> <td>21.758</td>

</tr> <tr>

<th>Mean</th> <td>7.1518</td>

</tr> <tr>

<th>MAD</th> <td>51.331</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>3.417</td>

</tr> <tr>

<th>Sum</th> <td>19432</td>

</tr> <tr>

<th>Variance</th> <td>6773.3</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram7131436052254597327”>

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9/WcjLCxUUVHNVVZ2VLW1df4uJ6DQu4ahf96jd95rzN7564/7oAxYDz/8sP71r3/pT3/6k2JiYjzmfvvb36pnz5766U9/6h4rKirS8OHD1a5dO0VHR6uwsFAJCQmSpH/%2B8586fvy4kpOT6338%2BPj4Ey4HOp3lqqk5v3/gzvf1e6O2to6%2BeYneNQz98x69814w9S54Lnb%2Bn08%2B%2BURr1qzRsmXLTghX0rdnp%2BbOnatdu3bp2LFjevbZZ7Vnzx7deOONCgsL080336wnn3xSxcXFKi0t1W9%2B8xtdffXV7rdxAAAAOJOAPIOVkpIi6dtXCErSpk2bJEkFBQVavXq1ysvLNWDAAI/n9OnTR88%2B%2B6ymTZsm6dt7r1wulzp16qQ//OEPatu2rSQpKytLlZWVuv7661VTU6MBAwbogQce8NHKAABAMAgxDb2jG/XidJY3yn6veez9RtlvY1if3dffJQQMhyNUsbEtVVpaGTSny32F3jUM/fMevfNeY/audeuTv61TYwu6S4QAAAD%2BRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDKfB6yBAwcqLy9PxcXFvj40AACAT/g8YI0ePVrr1q3T4MGDNWnSJG3cuFE1NTW%2BLgMAAKDR%2BDxgZWZmat26dXrxxRfVuXNnPfzww0pPT9ejjz6q3bt3%2B7ocAAAA6/x2D1ZSUpJmzJihLVu2aNasWXrxxRc1fPhwTZw4UV988YW/ygIAAGgwvwWs6upqrVu3TrfddptmzJihNm3a6N5771XXrl01YcIEvfrqq/4qDQAAoEEcvj5gUVGRVq1apb/85S%2BqrKzU0KFDtXz5cl122WXubfr06aMHHnhAI0eO9HV5AAAADebzgDVixAh17NhRd9xxh2644QbFxMScsE16eroOHz7s69IAAACs8HnAWrFihS6//PIzbvf555/7oBoAAAD7fH4PVmJioiZPnqxNmza5x/7whz/otttuk8vl8nU5AAAA1vk8YC1YsEDl5eXq1KmTe6x///6qq6vTwoULfV0OAACAdT6/RPjee%2B/p1VdfVWxsrHusQ4cOys3N1bXXXuvrcgAAAKzz%2BRmsqqoqhYeHn1hIaKiOHj3q63IAAACs83nA6tOnjxYuXKhvvvnGPfb1119r7ty5Hm/VAAAAEKh8folw1qxZuvXWW/XTn/5UERERqqurU2Vlpdq1a6c//vGPvi4HAADAOp8HrHbt2um1117TO%2B%2B8oz179ig0NFQdO3ZUv379FBYW5utyAAAArPN5wJKkpk2bavDgwf44NAAAQKPzecDau3evFi1apH/961%2Bqqqo6YX7z5s2%2BLgkAAMAqv9yDVVJSon79%2BqlFixa%2BPjwAAECj83nA2rp1qzZv3qy4uDhfHxoAAMAnfP42Da1atbJ25urdd99VWlqacnJyTphbt26dRo4cqZ49e2rUqFF677333HN1dXVavHixBg0apD59%2BmjixInau3eve97lcik7O1tpaWnq16%2BfZs%2BefdLLmQAAACfj84B1xx13KC8vT8aYBu3n6aef1vz589W%2BffsT5rZv364ZM2Zo%2BvTp%2Btvf/qYJEybo7rvv1oEDByRJK1eu1Kuvvqply5Zpy5Yt6tChgzIzM901zZkzR0ePHtXatWu1evVqFRUVKTc3t0H1AgCA84fPA9Y777yj//7v/1bfvn118803a%2BzYsR6P%2BgoPD9eqVatOGrBeeuklpaenKz09XeHh4bruuuvUpUsXrVmzRpKUn5%2BvCRMm6OKLL1ZERIRycnJUVFSkzz//XAcPHtSmTZuUk5OjuLg4tWnTRnfddZdWr16t6upqa30AAADBy%2Bf3YEVEROiqq65q8H7Gjx9/yrnCwkKlp6d7jHXr1k0FBQWqqqrSzp071a1bN4%2Ba2rdvr4KCApWXlyssLEyJiYnu%2BaSkJB05ckS7du3yGAcAADgZnwesBQsWNPoxXC6XoqOjPcaio6O1c%2BdOffPNNzLGnHS%2BtLRUMTExioiIUEhIiMecJJWWltbr%2BCUlJXI6nR5jDkcLxcfHe7OcoOFw%2BPyEacAKCwv1%2BIr6o3cNQ/%2B8R%2B%2B8F4y988sbje7atUuvvfaa/v3vf7sD16effqqePXtaO8aZ7vE63XxD7w/Lz89XXl6ex1hmZqaysrIatN9AFxvb0t8lBJyoqOb%2BLiFg0buGoX/eo3feC6be%2BTxgffDBB7rtttvUsWNHffXVV1qwYIH27t2r8ePH67HHHtOgQYMafIzY2Fi5XC6PMZfLpbi4OMXExCg0NPSk861atVJcXJwqKipUW1vr/uie77Zt1apVvY6fkZGhgQMHeow5HC1UWlrp7ZKCwvm%2B/rMRFhaqqKjmKis7qtraOn%2BXE1DoXcPQP%2B/RO%2B81Zu/89ce9zwPW4sWL9ctf/lK33HKLunfvLunbzydcuHChli5daiVgJScna%2BvWrR5jBQUFGjFihMLDw9W5c2cVFhbq8ssvlySVlZVpz5496t69uxISEmSM0ZdffqmkpCT3c6OiotSxY8d6HT8%2BPv6Ey4FOZ7lqas7vH7jzff3eqK2to29eoncNQ/%2B8R%2B%2B8F0y98/nFzn/%2B858aN26cJHnc5zRs2DAVFRVZOcbNN9%2Bsv/71r3rrrbd07NgxrVq1Sl999ZWuu%2B46SdK4ceO0YsUKFRUVqaKiQrm5ueratatSUlIUFxenoUOH6rHHHtPhw4d14MABLV26VGPGjJHD4ZcrqgAAIMD4PDFERkaqqqpKTZs29RgvKSk5Yex0UlJSJEk1NTWSpE2bNkn69mxTly5dlJubqwULFmj//v3q1KmTnnrqKbVu3VqSNHbsWDmdTv385z9XZWWlUlNTPe6ZevDBB3X//fdr0KBBatKkia699tqTvpkpAADAyYSYht7RfZamTp2q5s2b67777lPfvn31%2Beefa/fu3br//vsVExOjJ554wpfl%2BIzTWd4o%2B73msfcbZb%2BNYX12X3%2BXEDAcjlDFxrZUaWll0Jwu9xV61zD0z3v0znuN2bvWrSOt7q%2B%2BfH4G695779Utt9yi1NRU1dbWqlevXjp69Kg6d%2B6shQsX%2BrocAAAA63wesNq2bau1a9fq7bff1u7du9WsWTN17NhRffv29bgnCwAAIFD55a7tJk2aaPDgwf44NAAAQKPzecAaOHDgac9Ubd682YfVAAAA2OfzgDV8%2BHCPgFVbW6vdu3eroKBAt9xyi6/LAQAAsM7nAWv69OknHd%2BwYYP%2B/ve/%2B7gaAAAA%2B86ZT1UcPHiwXnvtNX%2BXAQAA0GDnTMDatm1bgz9kGQAA4Fzg80uEY8eOPWHs6NGjKioq0pAhQ3xdDgAAgHU%2BD1gdOnQ44VWE4eHhGjNmjG666SZflwMAAGCdzwMW79YOAACCnc8D1l/%2B8pd6b3vDDTc0YiUAAACNw%2BcBa/bs2aqrqzvhhvaQkBCPsZCQEAIWAAAISD4PWM8884yeffZZTZ48WYmJiTLGaMeOHXr66af1s5/9TKmpqb4uCQAAwCq/3IO1bNkytWnTxj3Wu3dvtWvXThMnTtTatWt9XRIAAIBVPn8frK%2B%2B%2BkrR0dEnjEdFRWn//v2%2BLgcAAMA6nweshIQELVy4UKWlpe6xsrIyLVq0SBdddJGvywEAALDO55cIZ82apWnTpik/P18tW7ZUaGioKioq1KxZMy1dutTX5QAAAFjn84DVr18/vfXWW3r77bd14MABGWPUpk0bXXnllYqMjPR1OQAAANb5PGBJUvPmzTVo0CAdOHBA7dq180cJAAAAjcbn92BVVVVpxowZ6tmzp6655hpJ396DNWnSJJWVlfm6HAAAAOt8HrAeffRRbd%2B%2BXbm5uQoN/f/D19bWKjc319flAAAAWOfzgLVhwwY98cQTGjZsmPtDn6OiorRgwQJt3LjR1%2BUAAABY5/OAVVlZqQ4dOpwwHhcXpyNHjvi6HAAAAOt8HrAuuugi/f3vf5ckj88efP311/XjH//Y1%2BUAAABY5/NXEf7Hf/yHpkyZotGjR6uurk7PPfectm7dqg0bNmj27Nm%2BLgcAAMA6nwesjIwMORwOPf/88woLC9OTTz6pjh07Kjc3V8OGDfN1OQAAANb5PGAdPnxYo0eP1ujRo319aAAAAJ/w%2BT1YgwYN8rj3CgAAINj4PGClpqZq/fr1vj4sAACAz/j8EuGPfvQjPfTQQ1q2bJkuuugiNWnSxGN%2B0aJFvi4JAADAKp8HrJ07d%2BonP/mJJKm0tNTXhwcAAGh0PgtYOTk5Wrx4sf74xz%2B6x5YuXarMzEzrx/roo4906623eowZY1RdXa0VK1Zo/Pjxatq0qcf8f/3Xf7k/G3HFihVauXKlnE6nEhMTNXv2bCUnJ1uvEwAABCefBaw333zzhLFly5Y1SsDq06ePCgoKPMaefPJJffnll5KkhISEk9bzXZ1LlizRM888o8TERK1YsUKTJ0/Wxo0b1aJFC%2Bu1AgCA4OOzm9xP9spBX72a8N///reee%2B45/epXvzrjtvn5%2BRo1apQuvfRSNWvWTJMmTZIkbdmypbHLBAAAQcJnZ7C%2B%2B2DnM401hscff1yjR4/Wj3/8Y%2B3du1eVlZXKzMzUxx9/rKZNm%2BrWW2/VhAkTFBISosLCQg0fPtz93NDQUHXt2lUFBQUaMWJEvY5XUlIip9PpMeZwtFB8fLzVdQUah8PnL1oNWGFhoR5fUX/0rmHon/fonfeCsXc%2Bv8nd1/bt26eNGzdq48aNkqSIiAh16dJFt9xyixYvXqwPP/xQU6dOVWRkpMaMGSOXy6Xo6GiPfURHR5/VDfn5%2BfnKy8vzGMvMzFRWVlbDFxTAYmNb%2BruEgBMV1dzfJQQsetcw9M979M57wdS7oA9YK1eu1JAhQ9S6dWtJUlJSkseN9v369dPYsWP18ssva8yYMZIafukyIyNDAwcO9BhzOFqotLSyQfsNdL1nv%2B7vEs7KG9Ov9Nuxw8JCFRXVXGVlR1VbW%2Be3OgIRvWsY%2Buc9eue9xuydv/6491nAqq6u1rRp0844Zvt9sDZs2KAZM2acdpuEhARt2LBBkhQbGyuXy%2BUx73K51Llz53ofMz4%2B/oTLgU5nuWpq%2BIELJOfCv1dtbd05UUcgoncNQ/%2B8R%2B%2B8F0y989nFzssuu0wlJSUej5ON2bR9%2B3bt379fffv2dY%2BtX79eL7zwgsd2u3btUrt27SRJycnJKiwsdM/V1tZq27ZtuvTSS63WBgAAgpfPzmB9/7Kcr2zbtk0xMTGKiIhwjzVp0kSPPPKILrroIqWmpurDDz/U6tWr9cgjj0iSxo0bp3vuuUfXXnutEhMT9fvf/15NmzZV//79fV4/AAAITEF9D9bBgwfd9159Z/DgwZo1a5bmzZun4uJiXXDBBZo1a5aGDBkiSbrqqqt0zz33KDs7W4cOHVJKSoqWLVumZs2a%2BWMJAAAgAIUYX70Z1XnO6SxvlP1e89j7jbJfSOuz%2B555o0bicIQqNralSksrg%2BZ%2BBF%2Bhdw1D/7xH77zXmL1r3TrS6v7qK3jecAIAAOAcQcACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlgVtwEpMTFRycrJSUlLcj3nz5kmSPvjgA40ZM0a9evXSiBEjtGbNGo/nrlixQkOHDlWvXr00btw4bd261R9LAAAAAcrh7wIa0%2Buvv64LL7zQY6ykpER33XWXZs%2BerZEjR%2BqTTz7RnXfeqY4dOyolJUVvvvmmlixZomeeeUaJiYlasWKFJk%2BerI0bN6pFixZ%2BWgkAAAgkQXsG61ReffVVdejQQWPGjFF4eLjS0tI0cOBAvfTSS5Kk/Px8jRo1SpdeeqmaNWumSZMmSZK2bNniz7IBAEAACeozWIsWLdKnn36qiooKXXPNNZo5c6YKCwvVrVs3j%2B26deum9evXS5IKCws1fPhw91xoaKi6du2qgoICjRgxol7HLSkpkdPp9BhzOFooPj6%2BgSuCLzkc/vv7Iyws1OMr6o/eNQz98x69814w9i5oA1aPHj2UlpamRx55RHv37lV2drbmzp0rl8ulNm3aeGwbExOj0tJSSZLL5VJ0dLRxaif5AAASxklEQVTHfHR0tHu%2BPvLz85WXl%2BcxlpmZqaysLC9XA3%2BIjW3p7xIUFdXc3yUELHrXMPTPe/TOe8HUu6ANWPn5%2Be7/vvjiizV9%2BnTdeeeduuyyy874XGNMg46dkZGhgQMHeow5HC1UWlrZoP3Ct/z57xUWFqqoqOYqKzuq2to6v9URiOhdw9A/79E77zVm7/z1x3LQBqwfuvDCC1VbW6vQ0FC5XC6PudLSUsXFxUmSYmNjT5h3uVzq3LlzvY8VHx9/wuVAp7NcNTX8wAWSc%2BHfq7a27pyoIxDRu4ahf96jd94Lpt4Fz8XO79m2bZsWLlzoMVZUVKSmTZsqPT39hLdd2Lp1qy699FJJUnJysgoLC91ztbW12rZtm3seAADgTIIyYLVq1Ur5%2BflatmyZjh8/rt27d%2Bvxxx9XRkaGrr/%2Beu3fv18vvfSSjh07prfffltvv/22br75ZknSuHHj9Je//EWfffaZjh49qt/97ndq2rSp%2Bvfv799FAQCAgBGUlwjbtGmjZcuWadGiRe6AdOONNyonJ0fh4eF66qmnNH/%2BfM2dO1cJCQl69NFHdckll0iSrrrqKt1zzz3Kzs7WoUOHlJKSomXLlqlZs2Z%2BXhUAAAgUIaahd3SjXpzO8kbZ7zWPvd8o%2B4W0Pruv347tcIQqNralSksrg%2BZ%2BBF%2Bhdw1D/7xH77zXmL1r3TrS6v7qKygvEQIAAPgTAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYFrQBa//%2B/crMzFRqaqrS0tI0c%2BZMlZWVad%2B%2BfUpMTFRKSorH4/e//737uevWrdPIkSPVs2dPjRo1Su%2B9954fVwIAAAKNw98FNJbJkycrOTlZb775psrLy5WZmalHHnlEd955pySpoKDgpM/bvn27ZsyYoby8PF1xxRXasGGD7r77br3%2B%2Butq27atL5cAAAACVFCewSorK1NycrKmTZumli1bqm3btrrxxhv18ccfn/G5L730ktLT05Wenq7w8HBdd9116tKli9asWeODygEAQDAIyoAVFRWlBQsW6IILLnCPFRcXKz4%2B3v39r371K/Xr109XXHGFFi1apOrqaklSYWGhunXr5rG/bt26nfKMFwAAwA8F7SXC7ysoKNDzzz%2Bv3/3ud2ratKl69uypq6%2B%2BWg899JC2b9%2BuKVOmyOFwaOrUqXK5XIqOjvZ4fnR0tHbu3Fnv45WUlMjpdHqMORwtPAIezn0Oh//%2B/ggLC/X4ivqjdw1D/7xH77wXjL0L%2BoD1ySef6M4779S0adOUlpYmSfrzn//snu/evbvuuOMOPfXUU5o6daokyRjToGPm5%2BcrLy/PYywzM1NZWVkN2i98Kza2pb9LUFRUc3%2BXELDoXcPQP%2B/RO%2B8FU%2B%2BCOmC9%2Beab%2BuUvf6k5c%2BbohhtuOOV2CQkJOnjwoIwxio2Nlcvl8ph3uVyKi4ur93EzMjI0cOBAjzGHo4VKSyvPbgHwK3/%2Be4WFhSoqqrnKyo6qtrbOb3UEInrXMPTPe/TOe43ZO3/9sRy0Aesf//iHZsyYoccff1z9%2BvVzj3/wwQf67LPP3K8mlKRdu3YpISFBISEhSk5O1tatWz32VVBQoBEjRtT72PHx8SdcDnQ6y1VTww9cIDkX/r1qa%2BvOiToCEb1rGPrnPXrnvWDqXfBc7Pyempoa3XfffZo%2BfbpHuJKkyMhILV26VK%2B88oqqq6tVUFCg3//%2B9xo3bpwk6eabb9Zf//pXvfXWWzp27JhWrVqlr776Stddd50/lgIAAAJQUJ7B%2Buyzz1RUVKT58%2Bdr/vz5HnOvv/66Fi9erLy8PP36179WZGSkfv7zn%2BuWW26RJHXp0kW5ublasGCB9u/fr06dOumpp55S69at/bEUAAAQgIIyYPXu3Vs7duw45XxCQoKuvvrqU84PGTJEQ4YMaYzSAADAeSAoLxECAAD4EwELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyh78LAM5V1zz2vr9LOCvrs/v6uwQAwP/hDBYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAy/ioHCBIBNJH%2B/CxPgCCHWewAAAALCNgAQAAWEbAOon9%2B/fr9ttvV2pqqgYMGKBHH31UdXV1/i4LAAAECO7BOokpU6YoKSlJmzZt0qFDh3THHXfoggsu0C9%2B8Qt/lwYAAAIAAesHCgoK9OWXX%2Bq5555TZGSkIiMjNWHCBC1fvpyABVgSSDfkS9yUD%2BDsEbB%2BoLCwUAkJCYqOjnaPJSUlaffu3aqoqFBERMQZ91FSUiKn0%2Bkx5nC0UHx8vPV6ATS%2BQAuEaDxvTL/ylHNhYaEeX1F/wdg7AtYPuFwuRUVFeYx9F7ZKS0vrFbDy8/OVl5fnMXb33XdrypQp9gr9Px8/NMz6Pm0oKSlRfn6%2BMjIyCJZeoH/eo3cNQ/%2B8V1JSouXLn6F3XgjG3gVPVLTIGNOg52dkZOjll1/2eGRkZFiqLjA4nU7l5eWdcCYP9UP/vEfvGob%2BeY/eeS8Ye8cZrB%2BIi4uTy%2BXyGHO5XAoJCVFcXFy99hEfHx80CRwAAJw9zmD9QHJysoqLi3X48GH3WEFBgTp16qSWLVv6sTIAABAoCFg/0K1bN6WkpGjRokWqqKhQUVGRnnvuOY0bN87fpQEAgAAR9sADDzzg7yLONVdeeaXWrl2refPm6bXXXtOYMWM0ceJEhYSE%2BLu0gNKyZUtdfvnlnPnzEv3zHr1rGPrnPXrnvWDrXYhp6B3dAAAA8MAlQgAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFiwbv/%2B/br99tuVmpqqAQMG6NFHH1VdXZ2/yzpn7N%2B/X5mZmUpNTVVaWppmzpypsrIySdL27dv1s5/9TJdddpmGDBmiZ5991uO569at08iRI9WzZ0%2BNGjVK7733nj%2BWcE54%2BOGHlZiY6P7%2Bgw8%2B0JgxY9SrVy%2BNGDFCa9as8dh%2BxYoVGjp0qHr16qVx48Zp69atvi75nPC73/1O/fr1U48ePTRhwgTt27dPEv07k23btmn8%2BPHq3bu3%2Bvbtq%2BnTp%2Bvw4cOS6N3JvPvuu0pLS1NOTs4Jc6f7PVZXV6fFixdr0KBB6tOnjyZOnKi9e/e6510ul7Kzs5WWlqZ%2B/fpp9uzZqqqq8smazpoBLLvxxhvNfffdZ8rKyszu3bvNkCFDzLPPPuvvss4Z1157rZk5c6apqKgwxcXFZtSoUWbWrFnm6NGj5sorrzRLliwxlZWVZuvWrebyyy83GzZsMMYYs23bNpOcnGzeeustU1VVZV555RVz6aWXmuLiYj%2BvyPe2bdtmLr/8ctOlSxdjjDFff/216dGjh3nppZdMVVWVef/990337t3NF198YYwxZvPmzaZ3797ms88%2BM0ePHjVPPfWU6du3r6msrPTnMnzu%2BeefN8OGDTNFRUWmvLzczJs3z8ybN4/%2BnUF1dbXp27evWbRokTl27Jg5fPiw%2BcUvfmGmTJlC705i2bJlZsiQIWbs2LEmOzvbY%2B5Mv8dWrFhhBgwYYHbu3GnKy8vNgw8%2BaEaOHGnq6uqMMcbcfffd5vbbbzeHDh0yBw4cMBkZGWbevHk%2BX2N9ELBg1RdffGG6du1qXC6Xe%2ByFF14wQ4cO9WNV545vvvnGzJw50zidTvfYH//4RzNkyBCzfv16c8UVV5iamhr33KOPPmpuvfVWY4wxc%2BfONZmZmR77u%2Bmmm8xTTz3lm%2BLPEbW1teamm24yv/3tb90B65lnnjE33HCDx3bZ2dlmzpw5xhhjbr/9dvPwww977KNv375m7dq1viv8HDBw4EB3YP8%2B%2Bnd6//73v02XLl3Mzp073WMvvPCCGTx4ML07ieXLl5uysjIzY8aMEwLWmX6PjRgxwixfvtw9V15ebrp162Y%2B/fRT43Q6zSWXXGK2b9/unn/77bdNjx49zPHjxxtxRd7hEiGsKiwsVEJCgqKjo91jSUlJ2r17tyoqKvxY2bkhKipKCxYs0AUXXOAeKy4uVnx8vAoLC5WYmKiwsDD3XLdu3dyXEwoLC9WtWzeP/XXr1k0FBQW%2BKf4c8ec//1nh4eEaOXKke%2BxUvTlV70JDQ9W1a9fzqndff/219u3bp2%2B%2B%2BUbDhw9XamqqsrKydPjwYfp3Bm3atFHXrl2Vn5%2BvyspKHTp0SBs3blT//v3p3UmMHz9ekZGRJ5073e%2Bxqqoq7dy502M%2BIiJC7du3V0FBgbZv366wsDCPWwOSkpJ05MgR7dq1q3EW0wAELFjlcrkUFRXlMfZd2CotLfVHSee0goICPf/887rzzjtP2ruYmBi5XC7V1dXJ5XJ5BFfp296eT309ePCglixZovvvv99j/FS9%2B6439E46cOCAJOn111/Xc889p1deeUUHDhzQfffdR//OIDQ0VEuWLNHmzZvVq1cvpaWlqaamRtOmTaN3Z%2Bl0/fjmm29kjDnlvMvlUkREhEJCQjzmpHPz/y8ELFhnjPF3CQHhk08%2B0cSJEzVt2jSlpaWdcrvv/zI533u7YMECjRo1Sp06dTrr557vvftu/ZMmTVKbNm3Utm1bTZkyRW%2B%2B%2BeZZPf98dPz4cU2ePFnDhg3Txx9/rHfeeUeRkZGaPn16vZ5/PvfuZM7Uj9PNB1IvCViwKi4uTi6Xy2PM5XIpJCREcXFxfqrq3PPmm2/q9ttv16xZszR%2B/HhJ3/buh3%2BFuVwuxcTEKDQ0VLGxsSft7fnS1w8%2B%2BECffvqpMjMzT5g7WW9KS0vdvTnfeyfJfVn6%2B2dbEhISZIxRdXU1/TuNDz74QPv27dM999yjyMhItWnTRllZWXrjjTcUGhpK787C6frx3e%2B6k823atVKcXFxqqioUG1trcecJLVq1arxiz9LBCxYlZycrOLiYvfLl6VvL4N16tRJLVu29GNl545//OMfmjFjhh5//HHdcMMN7vHk5GTt2LFDNTU17rGCggJdeuml7vkfvrz7%2B/PBbs2aNTp06JAGDBig1NRUjRo1SpKUmpqqLl26nNCbrVu3evSusLDQPVdbW6tt27adN72TpLZt2yoiIkLbt293j%2B3fv19NmjRReno6/TuN2tpa1dXVeZw9OX78uCQpLS2N3p2F0/0eCw8PV%2BfOnT36VVZWpj179qh79%2B7q2rWrjDH68ssvPZ4bFRWljh07%2BmwN9eaXW%2BsR1G666SYza9YsU15ebnbu3GkGDhxonn/%2BeX%2BXdU6orq4211xzjfnzn/98wtyxY8fMgAEDzBNPPGGOHDliPvvsM9O7d2%2BzZcsWY4wxO3bsMCkpKWbLli2mqqrKvPTSS6Znz56mpKTEx6vwD5fLZYqLi92PTz/91HTp0sUUFxeb/fv3m549e5oXX3zRVFVVmbfeest0797d/Wqjt99%2B21x22WXm008/NUeOHDFLliwx6enp5ujRo35elW89/PDDZtCgQearr74yBw8eNBkZGWbmzJnm4MGD9O80Dh8%2BbC6//HLzm9/8xhw5csQcPnzYTJ482fznf/4nvTuNk72K8Ey/x1544QXTv39/99s0zJkzx4wePdr9/OzsbDNp0iRz6NAhU1xcbEaPHm0WLlzo03XVFwEL1hUXF5tJkyaZ7t27m7S0NPPEE0%2B438PkfPfRRx%2BZLl26mOTk5BMe%2B/btMzt27DBjx441ycnJpn///mblypUez9%2BwYYMZMmSISUpKMtdff7358MMP/bQS/9u7d6/7bRqMMebDDz801113nUlKSjJDhgw54e0IVq5cadLT001ycrIZN26c2bFjh69L9rtjx46ZBx54wPTp08f06NHDzJgxw1RUVBhj6N%2BZFBQUmJ/97Gemd%2B/eJi0tzWRnZ5sDBw4YY%2BjdD333O%2B2SSy4xl1xyifv775zu91hdXZ15/PHHzU9/%2BlPTvXt3c9ttt3m8119ZWZnJyckxPXr0MH369DFz5841x44d8%2Bn66ivEmAC6YwwAACAAcA8WAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFj2v80ZwHnduk8NAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common7131436052254597327”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>223</td> <td class=”number”>6.9%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>118</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>114</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>85</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.33333333333333</td> <td class=”number”>59</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.33333333333333</td> <td class=”number”>54</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-25.0</td> <td class=”number”>54</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-40.0</td> <td class=”number”>45</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-20.0</td> <td class=”number”>42</td> <td class=”number”>1.3%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (716)</td> <td class=”number”>1865</td> <td class=”number”>58.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>493</td> <td class=”number”>15.4%</td> <td>

<div class=”bar” style=”width:27%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme7131436052254597327”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>114</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-95.06493506493506</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-91.00719424460432</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-90.9090909090909</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-90.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>450.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>500.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>600.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:75%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>700.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1000.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_PC_DIRSALES_07_12”><s>PCH_PC_DIRSALES_07_12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_DIRSALES_07_12”><code>PCH_DIRSALES_07_12</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99896</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_PC_SNAPBEN_10_15”>PCH_PC_SNAPBEN_10_15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3051</td>

</tr> <tr>

<th>Unique (%)</th> <td>95.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>4.9%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>158</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>-4.9067</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>166.49</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram1804534458517092932”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives1804534458517092932,#minihistogram1804534458517092932”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives1804534458517092932”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles1804534458517092932”

aria-controls=”quantiles1804534458517092932” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram1804534458517092932” aria-controls=”histogram1804534458517092932”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common1804534458517092932” aria-controls=”common1804534458517092932”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme1804534458517092932” aria-controls=”extreme1804534458517092932”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles1804534458517092932”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-27.823</td>

</tr> <tr>

<th>Q1</th> <td>-15.55</td>

</tr> <tr>

<th>Median</th> <td>-6.5059</td>

</tr> <tr>

<th>Q3</th> <td>3.8755</td>

</tr> <tr>

<th>95-th percentile</th> <td>22.646</td>

</tr> <tr>

<th>Maximum</th> <td>166.49</td>

</tr> <tr>

<th>Range</th> <td>266.49</td>

</tr> <tr>

<th>Interquartile range</th> <td>19.425</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>17.043</td>

</tr> <tr>

<th>Coef of variation</th> <td>-3.4735</td>

</tr> <tr>

<th>Kurtosis</th> <td>7.727</td>

</tr> <tr>

<th>Mean</th> <td>-4.9067</td>

</tr> <tr>

<th>MAD</th> <td>12.497</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.0591</td>

</tr> <tr>

<th>Sum</th> <td>-14975</td>

</tr> <tr>

<th>Variance</th> <td>290.47</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram1804534458517092932”>

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W1ds43dklBH61Dnc2MtmkEdzaCOjROQF1b/%2BMc/auPGjV5tpaWlSkxMVGhoqLp3766SkhJPX2VlpQ4ePKjevXurZ8%2Becrvd%2Buyzzzz9DodDkZGR6tKli2XHAAAA/FdABqyTJ0/qiSeekMPhUHV1tTZs2KB33nlH48ePlyTl5ORoxYoVKi0t1dGjR1VQUKCePXsqNTVVsbGxGjZsmJ5%2B%2Bml9%2B%2B23%2BvLLL7V06VJlZWXJZmsxJ/wAAEAT%2BG1iSE1NlSTV1NRIkrZt2ybp1NmmCRMmqKqqSlOnTpXT6VTHjh21dOlSpaSkSJLGjx8vp9OpO%2B%2B8U1VVVUpLS/O6Kf3xxx/XvHnzNHjwYLVq1Uq33XbbGQ8zBQAAOJsgd1Pv6EaDOJ1HjI9pswUrJiZMFRVVXB9vgkCp481Pv%2B/rKTTYpmnX%2B3oKF6VAWYu%2BRh3NCJQ6tm8f4ZP9BuQlQgAAAF8iYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGCY3wasd999V%2Bnp6crPzz%2Bjb%2BvWrRoxYoT69u2rYcOG6c9//rOn79lnn1XPnj2Vmprq9fH1119Lkk6cOKFf/vKXGjBggNLS0pSXl6eKigrLjgsAAPg/vwxYzz//vBYsWKBOnTqd0ffpp59qxowZysvL04cffqjZs2fr8ccf10cffeTZZuTIkXI4HF4fl156qSRpyZIlKikpkd1u15YtW%2BR2uzVr1izLjg0AAPg/vwxYoaGhWrVqVb0By%2BVy6d5779WQIUNks9mUkZGhHj16eAWss6mpqdGqVat033336bLLLlN0dLSmTZumHTt26KuvvmqOQwEAAAHI5usJNMaECRPO2jdgwAANGDDA83VNTY2cTqfi4%2BM9bXv27NH48eP1%2Beef67LLLtOsWbPUv39/HTx4UEeOHFFycrJn265du6pNmzYqKSnxGuNcysvL5XQ6vdpstraKi4tr6CE2SEhIsNdnNA51tJ7NRq3rw1o0gzqaQR2bxvKAlZmZqTFjxuj222/XZZdd1uz7KygoUNu2bXXLLbdIkjp06KDExERNnz5dcXFxstvtmjx5statWyeXyyVJioyM9BojMjLygu7DstvtKiws9GrLzc1VXl5eE4%2BmfpGRlzTLuC0NdbROTEyYr6dwUWMtmkEdzaCOjWN5wLr99tv1%2Buuva9myZbruuus0btw4ZWZmymYzOxW3262CggJt2LBBK1asUGhoqCRp7NixGjt2rGe7iRMn6vXXX9e6des8Z77cbneT9p2dna3MzEyvNputrSoqqpo07ulCQoIVGXmJKiuPqba2zujYLQl1tJ7p90KgYC2aQR3NCJQ6%2BuoHOssDVm5urnJzc1VSUqINGzbo17/%2BtebPn69Ro0YpKytLXbp0afI%2B6urqNGvWLH366af64x//qMTExHNun5CQoPLycsXGxko6dR9XWNi//4d89913ateuXYP3HxcXd8blQKfziGpqmmeB1tbWNdvYLQl1tA51PjfWohnU0Qzq2Dg%2Bu7CanJysmTNn6q233tLs2bP15z//WbfccosmTZqkTz/9tElj//rXv9YXX3xRb7j6n//5H33wwQdebaWlpUpMTFRiYqKioqJUUlLi6fv888918uRJpaSkNGlOAACg5fBZwKqurtbGjRv185//XDNnzlR8fLxmzZqlnj17auLEiVq/fn2jxv3444%2B1bt06FRUVKTo6%2Box%2Bl8ul%2BfPna9%2B%2BfTpx4oRefPFFHTx4UKNHj1ZISIjGjRun5557TocPH1ZFRYV%2B85vf6MYbb/Q8xgEAAOB8LL9EWFpaqlWrVum1115TVVWVhg0bppdffllXXXWVZ5t%2B/frpscce0/Dhw%2BsdIzU1VdKp3xCUpG3btkmSHA6HVq9erSNHjmjQoEFer%2BnXr59efPFFTZ8%2BXdKpe69cLpe6deum3/3ud%2BrQoYMkKS8vT1VVVRo5cqRqamo0aNAgPfbYY0ZrAAAAAluQu6l3dF%2Bgyy%2B/XF26dFF2drZGjRpV71kmSerTp48%2B%2BeQTK6fWrJzOI8bHtNmCFRMTpoqKKq6PN0Gg1PHmp9/39RQabNO06309hYtSoKxFX6OOZgRKHdu3j/DJfi0/g7VixQpdc801590ukMIVAABoWSy/ByspKUmTJ0/2XNaTpN/97nf6%2Bc9/7nkOFQAAgD%2BzPGAtXLhQR44cUbdu3TxtAwcOVF1dnRYtWmT1dAAAAIyz/BLhe%2B%2B9p/Xr1ysmJsbT1rlzZxUUFOi2226zejoAAADGWX4G6/jx456nqntNJDhYx44ds3o6AAAAxlkesPr166dFixbpu%2B%2B%2B87R99dVXmj9/vtejGgAAAPyV5ZcIZ8%2BerbvvvlvXXXedwsPDVVdXp6qqKiUmJur3v/%2B91dMBAAAwzvKAlZiYqNdff13vvPOODh48qODgYHXp0kX9%2B/dXSEiI1dMBAAAwzvKAJUmtW7fWkCFDfLFrAACAZmd5wDp06JAWL16sL774QsePHz%2Bj/80337R6SgAAAEb55B6s8vJy9e/fX23btrV69wAAAM3O8oBVXFysN998U7GxsVbvGgAAwBKWP6ahXbt2nLkCAAABzfKAde%2B996qwsFBut9vqXQMAAFjC8kuE77zzjv7xj39ozZo16tixo4KDvTPen/70J6unBAAAYJTlASs8PFwDBgywercAAACWsTxgLVy40OpdAgAAWMrye7Akad%2B%2BfXr22Wc1a9YsT9v//d//%2BWIqAAAAxlkesD744AONGDFCW7du1YYNGySdevjohAkTeMgoAAAICJYHrCVLluihhx7S%2BvXrFRQUJOnU3ydctGiRli5davV0AAAAjLM8YH3%2B%2BefKycmRJE/AkqSbbrpJpaWlVk8HAADAOMsDVkRERL1/g7C8vFytW7e2ejoAAADGWR6wrrzySv3617/W0aNHPW379%2B/XzJkzdd1111k9HQAAAOMsf0zDrFmzdNdddyktLU21tbW68sordezYMXXv3l2LFi2yejoAAADGWR6wOnTooA0bNujtt9/W/v371aZNG3Xp0kXXX3%2B91z1ZAAAA/srygCVJrVq10pAhQ3yxawAAgGZnecDKzMw855kqnoUFAAD8neU3ud9yyy1eH8OGDVOPHj104sQJjR8//oLGevfdd5Wenq78/Pwz%2BjZu3Kjhw4erb9%2B%2BGjNmjN577z1PX11dnZYsWaLBgwerX79%2BmjRpkg4dOuTpd7lcmjZtmtLT09W/f3/NmTOn3t98BAAAqI/lZ7BmzJhRb/uWLVv0t7/9rcHjPP/881q1apU6dep0Rt/u3bs1c%2BZMFRYW6tprr9WWLVt0//33a/PmzerQoYNeffVVrV%2B/Xs8//7zi4%2BO1ZMkS5ebmau3atQoKCtLcuXN18uRJbdiwQdXV1Zo6daoKCgr06KOPNvq4AQBAy%2BGTv0VYnyFDhuj1119v8PahoaFnDVgrV65URkaGMjIyFBoaqhEjRqhHjx5at26dJMlut2vixInq2rWrwsPDlZ%2Bfr9LSUn3yySf6%2BuuvtW3bNuXn5ys2Nlbx8fG67777tHr1alVXVxs7XgAAELgumoC1a9cuud3uBm8/YcIERURE1NtXUlKiXr16ebX16tVLDodDx48f1969e736w8PD1alTJzkcDu3evVshISFKSkry9CcnJ%2Bv777/Xvn37LvCoAABAS2T5JcL67rM6duyYSktLNXToUCP7cLlcioqK8mqLiorS3r179d1338ntdtfbX1FRoejoaIWHh3vdiP/DthUVFQ3af3l5uZxOp1ebzdZWcXFxjTmcswoJCfb6jMahjtaz2ah1fViLZlBHM6hj01gesDp37nzGbxGGhoYqKytLY8eONbaf850NO1f/hZxJq4/dbldhYaFXW25urvLy8po07tlERl7SLOO2NNTROjExYb6ewkWNtWgGdTSDOjaO5QHLiqe1x8TEyOVyebW5XC7FxsYqOjpawcHB9fa3a9dOsbGxOnr0qGpraxUSEuLpk6R27do1aP/Z2dnKzMz0arPZ2qqioqqxh1SvkJBgRUZeosrKY6qtrTM6dktCHa1n%2Br0QKFiLZlBHMwKljr76gc7ygPXaa681eNtRo0Y1ah8pKSkqLi72anM4HLr11lsVGhqq7t27q6SkRNdcc40kqbKyUgcPHlTv3r2VkJAgt9utzz77TMnJyZ7XRkZGqkuXLg3af1xc3BmXA53OI6qpaZ4FWltb12xjtyTU0TrU%2BdxYi2ZQRzOoY%2BNYHrDmzJmjurq6My7DBQUFebUFBQU1OmCNGzdOWVlZ2rFjh6677jqtX79eBw4c0IgRIyRJOTk5Kioq0oABAxQfH6%2BCggL17NlTqampkqRhw4bp6aef1pNPPqmTJ09q6dKlysrKks3mkwffAwAAP2N5YnjhhRf04osvavLkyUpKSpLb7daePXv0/PPP64477lBaWlqDxvkhDNXU1EiStm3bJunU2aYePXqooKBACxcuVFlZmbp166bly5erffv2kk7daO90OnXnnXeqqqpKaWlpXvdMPf7445o3b54GDx6sVq1a6bbbbqv3YaYAAAD1CXI39Y7uCzRy5EgVFRUpPj7eq/2rr77SpEmTtGHDBiunYxmn84jxMW22YMXEhKmioorTt00QKHW8%2Ben3fT2FBts07XpfT%2BGiFChr0deooxmBUsf27et/pFNzs/x3Lw8cOHDGIxIkKTIyUmVlZVZPBwAAwDjLA1ZCQoIWLVrk9UypyspKLV68WD/%2B8Y%2Btng4AAIBxlt%2BDNXv2bE2fPl12u11hYWEKDg7W0aNH1aZNGy1dutTq6QAAABhnecDq37%2B/duzYobfffltffvml3G634uPjdcMNN5z1T98AAAD4E588d%2BCSSy7R4MGD9eWXXyoxMdEXUwAAAGg2lt%2BDdfz4cc2cOVN9%2B/bVzTffLOnUPVj33HOPKisrrZ4OAACAcZYHrKeeekq7d%2B9WQUGBgoP/vfva2loVFBRYPR0AAADjLA9YW7Zs0X//93/rpptu8vzR58jISC1cuFBbt261ejoAAADGWR6wqqqq1Llz5zPaY2Nj9f3331s9HQAAAOMsD1g//vGP9be//U2SvP724ObNm/WjH/3I6ukAAAAYZ/lvEf70pz/VAw88oNtvv111dXV66aWXVFxcrC1btmjOnDlWTwcAAMA4ywNWdna2bDabXnnlFYWEhOi5555Tly5dVFBQoJtuusnq6QAAABhnecD69ttvdfvtt%2Bv222%2B3etcAAACWsPwerMGDB3vdewUAABBoLA9YaWlp2rRpk9W7BQAAsIzllwgvu%2Bwy/epXv1JRUZF%2B/OMfq1WrVl79ixcvtnpKAAAARlkesPbu3auf/OQnkqSKigqrdw8AANDsLAtY%2Bfn5WrJkiX7/%2B9972pYuXarc3FyrpgAAAGAJy%2B7B2r59%2BxltRUVFVu0eAADAMpYFrPp%2Bc5DfJgQAAIHIsoD1wx92Pl8bAACAv7P8MQ0AAACBjoAFAABgmGW/RVhdXa3p06eft43nYAEAAH9nWcC66qqrVF5eft42AAAAf2dZwPrP518BAAAEMu7BAgAAMIyABQAAYJjlf4vQCh9%2B%2BKHuvvturza3263q6mqtWLFCEyZMUOvWrb36/%2Bu//ks333yzJGnFihV69dVX5XQ6lZSUpDlz5iglJcWy%2BQMAAP8WkAGrX79%2BcjgcXm3PPfecPvvsM0lSQkJCvX%2B6Rzr1J32effZZvfDCC0pKStKKFSs0efJkbd26VW3btm32uQMAAP/XIi4R/utf/9JLL72khx9%2B%2BLzb2u12jRkzRn369FGbNm10zz33SJLeeuut5p4mAAAIEAF5But0zzzzjG6//Xb96Ec/0qFDh1RVVaXc3Fx99NFHat26te6%2B%2B25NnDhRQUFBKikp0S233OJ5bXBwsHr27CmHw6Fbb721QfsrLy%2BX0%2Bn0arPZ2iouLs7ocYWEBHt9RuNQR%2BvZbNS6PqxFM6ijGdSxaQI%2BYP3zn//U1q1btXXrVklSeHi4evToobvuuktLlizR3//%2Bd02dOlURERHKysqSy%2BVSVFSU1xhRUVGqqKho8D7tdrsKCwu92nJzc5WXl9f0A6pHZOQlzTJuS0MdrRMTE%2BbrKVzUWItFVqtlAAAVP0lEQVRmUEczqGPjBHzAevXVVzV06FC1b99ekpScnOz1TK7%2B/ftr/PjxWrNmjbKysiSduiG%2BKbKzs5WZmenVZrO1VUVFVZPGPV1ISLAiIy9RZeUx1dbWGR27JaGO1jP9XggUrEUzqKMZgVJHX/1AF/ABa8uWLZo5c%2BY5t0lISNCWLVskSTExMXK5XF79LpdL3bt3b/A%2B4%2BLizrgc6HQeUU1N8yzQ2tq6Zhu7JaGO1qHO58ZaNIM6mkEdGyegL6zu3r1bZWVluv766z1tmzZt0h/%2B8Aev7fbt26fExERJUkpKikpKSjx9tbW12rVrl/r06WPNpAEAgN8L6IC1a9cuRUdHKzw83NPWqlUrPfnkk3rvvfdUXV2t999/X6tXr1ZOTo4kKScnR6%2B99pp27typY8eOadmyZWrdurUGDhzoo6MAAAD%2BJqAvEX799deee69%2BMGTIEM2ePVtPPPGEDh8%2BrEsvvVSzZ8/W0KFDJUkDBgzQgw8%2BqGnTpumbb75RamqqioqK1KZNG18cAgAA8ENB7qbe0Y0GcTqPGB/TZgtWTEyYKiqquD7eBIFSx5ufft/XU2iwTdOuP/9GLVCgrEVfo45mBEod27eP8Ml%2BA/oSIQAAgC8QsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYJjN1xMA0PLc/PT7vp7CBdk07XpfTwGAn%2BEMFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABgWsAErKSlJKSkpSk1N9Xw88cQTkqQPPvhAWVlZuvLKK3Xrrbdq3bp1Xq9dsWKFhg0bpiuvvFI5OTkqLi72xSEAAAA/FdCPadi8ebM6duzo1VZeXq777rtPc%2BbM0fDhw/Xxxx9rypQp6tKli1JTU7V9%2B3Y9%2B%2ByzeuGFF5SUlKQVK1Zo8uTJ2rp1q9q2beujIwEAAP4kYM9gnc369evVuXNnZWVlKTQ0VOnp6crMzNTKlSslSXa7XWPGjFGfPn3Upk0b3XPPPZKkt956y5fTBgAAfiSgA9bixYs1cOBAXX311Zo7d66qqqpUUlKiXr16eW3Xq1cvz2XA0/uDg4PVs2dPORwOS%2BcOAAD8V8BeIrziiiuUnp6uJ598UocOHdK0adM0f/58uVwuxcfHe20bHR2tiooKSZLL5VJUVJRXf1RUlKe/IcrLy%2BV0Or3abLa2iouLa%2BTR1C8kJNjrMxqHOuJ8bDZr1gZr0QzqaAZ1bJqADVh2u93z3127dtWMGTM0ZcoUXXXVVed9rdvtbvK%2BCwsLvdpyc3OVl5fXpHHPJjLykmYZt6WhjjibmJgwS/fHWjSDOppBHRsnYAPW6Tp27Kja2loFBwfL5XJ59VVUVCg2NlaSFBMTc0a/y%2BVS9%2B7dG7yv7OxsZWZmerXZbG1VUVHVyNnXLyQkWJGRl6iy8phqa%2BuMjt2SUEecj%2Bn37tmwFs2gjmYESh2t/gHpBwEZsHbt2qV169bpkUce8bSVlpaqdevWysjI0P/%2B7/96bV9cXKw%2BffpIklJSUlRSUqLRo0dLkmpra7Vr1y5lZWU1eP9xcXFnXA50Oo%2BopqZ5FmhtbV2zjd2SUEecjdXrgrVoBnU0gzo2TkBeWG3Xrp3sdruKiop08uRJ7d%2B/X88884yys7M1cuRIlZWVaeXKlTpx4oTefvttvf322xo3bpwkKScnR6%2B99pp27typY8eOadmyZWrdurUGDhzo24MCAAB%2BIyDPYMXHx6uoqEiLFy/2BKTRo0crPz9foaGhWr58uRYsWKD58%2BcrISFBTz31lC6//HJJ0oABA/Tggw9q2rRp%2Buabb5SamqqioiK1adPGx0cFAAD8RZC7qXd0o0GcziPGx7TZghUTE6aKiipO3zZBoNTx5qff9/UUAtamaddbsp9AWYu%2BRh3NCJQ6tm8f4ZP9BuQlQgAAAF8iYAEAABhGwAIAADCMgAUAAGBYQP4WIWACN40DABqLM1gAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhARuwysrKlJubq7S0NKWnp%2BuRRx5RZWWl/vnPfyopKUmpqaleH7/97W89r924caOGDx%2Buvn37asyYMXrvvfd8eCQAAMDf2Hw9geYyefJkpaSkaPv27Tpy5Ihyc3P15JNPasqUKZIkh8NR7%2Bt2796tmTNnqrCwUNdee622bNmi%2B%2B%2B/X5s3b1aHDh2sPAQAAOCnAvIMVmVlpVJSUjR9%2BnSFhYWpQ4cOGj16tD766KPzvnblypXKyMhQRkaGQkNDNWLECPXo0UPr1q2zYOYAACAQBOQZrMjISC1cuNCr7fDhw4qLi/N8/fDDD%2Bsvf/mLampqNHbsWOXl5alVq1YqKSlRRkaG12t79ep11jNe9SkvL5fT6fRqs9naeu3fhJCQYK/PaBzqiPOx2axZG6xFM6ijGdSxaQIyYJ3O4XDolVde0bJly9S6dWv17dtXN954o371q19p9%2B7deuCBB2Sz2TR16lS5XC5FRUV5vT4qKkp79%2B5t8P7sdrsKCwu92nJzc5WXl2fkeE4XGXlJs4zb0lBHnE1MTJil%2B2MtmkEdzaCOjRPwAevjjz/WlClTNH36dKWnp0uS/vSnP3n6e/furXvvvVfLly/X1KlTJUlut7tJ%2B8zOzlZmZqZXm83WVhUVVU0a93QhIcGKjLxElZXHVFtbZ3TsloQ64nxMv3fPhrVoBnU0I1DqaPUPSD8I6IC1fft2PfTQQ5o7d65GjRp11u0SEhL09ddfy%2B12KyYmRi6Xy6vf5XIpNja2wfuNi4s743Kg03lENTXNs0Bra%2BuabeyWhDribKxeF6xFM6ijGdSxcQL2wuo//vEPzZw5U88884xXuPrggw%2B0bNkyr2337dunhIQEBQUFKSUlRcXFxV79DodDffr0sWTeAADA/wVkwKqpqdGjjz6qGTNmqH///l59ERERWrp0qdauXavq6mo5HA799re/VU5OjiRp3Lhx%2Bstf/qIdO3boxIkTWrVqlQ4cOKARI0b44lAAAIAfCshLhDt37lRpaakWLFigBQsWePVt3rxZS5YsUWFhoX75y18qIiJCd955p%2B666y5JUo8ePVRQUKCFCxeqrKxM3bp10/Lly9W%2BfXtfHAoAAPBDARmwrr76au3Zs%2Bes/QkJCbrxxhvP2j906FANHTq0OaYGAABagIC8RAgAAOBLBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDbL6eAABc7G5%2B%2Bn1fT%2BGCbJp2va%2BnALR4nMECAAAwjIBVj7KyMv3iF79QWlqaBg0apKeeekp1dXW%2BnhYAAPATXCKsxwMPPKDk5GRt27ZN33zzje69915deuml%2BtnPfubrqQEAAD9AwDqNw%2BHQZ599ppdeekkRERGKiIjQxIkT9fLLLxOwAPgFf7pnjPvFEKgIWKcpKSlRQkKCoqKiPG3Jycnav3%2B/jh49qvDw8POOUV5eLqfT6dVms7VVXFyc0bmGhATr6jmbjY4JAFbypzAoSW/MuMHXU7BMSEiw12dcGALWaVwulyIjI73afghbFRUVDQpYdrtdhYWFXm3333%2B/HnjgAXMT1akgd1eHL5SdnW08vLUk5eXlstvt1LGJqGPTUUMzqKMZ5eXlevnlF6hjIxFL6%2BF2u5v0%2BuzsbK1Zs8brIzs729Ds/s3pdKqwsPCMs2W4MNTRDOrYdNTQDOpoBnVsGs5gnSY2NlYul8urzeVyKSgoSLGxsQ0aIy4ujrQPAEALxhms06SkpOjw4cP69ttvPW0Oh0PdunVTWFiYD2cGAAD8BQHrNL169VJqaqoWL16so0ePqrS0VC%2B99JJycnJ8PTUAAOAnQh577LHHfD2Ji80NN9ygDRs26IknntDrr7%2BurKwsTZo0SUFBQb6e2hnCwsJ0zTXXcHatiaijGdSx6aihGdTRDOrYeEHupt7RDQAAAC9cIgQAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIDlJxwOh2688UaNGzfujL4PPvhAWVlZuvLKK3Xrrbdq3bp1Xv0rVqzQsGHDdOWVVyonJ0fFxcVWTfuilpmZqZSUFKWmpno%2BJk%2Be7OnfvXu37rjjDl111VUaOnSoXnzxRR/O9uJVVlamX/ziF0pLS9OgQYP01FNPqa6uztfTuuglJSWdsf6eeOIJSed/T7d07777rtLT05Wfn39G38aNGzV8%2BHD17dtXY8aM0Xvvvefpq6ur05IlSzR48GD169dPkyZN0qFDh6yc%2BkXlbHVcs2aNLr/8cq%2B1mZqaqk8//VQSdWwwNy56a9eudWdkZLgnTZrkHjt2rFffV1995b7iiivcK1eudB8/ftz9/vvvu3v37u3%2B9NNP3W632/3mm2%2B6r776avfOnTvdx44dcy9fvtx9/fXXu6uqqnxxKBeVQYMGuf/617/W23fs2DH3DTfc4H722WfdVVVV7uLiYvc111zj3rJli8WzvPiNHj3a/eijj7orKyvd%2B/fvdw8dOtT94osv%2BnpaF70ePXq4Dx06dEb7%2Bd7TLV1RUZF76NCh7vHjx7unTZvm1bdr1y53SkqKe8eOHe7jx4%2B7165d6%2B7Tp4/78OHDbrfb7V6xYoV70KBB7r1797qPHDnifvzxx93Dhw9319XV%2BeJQfOpcdVy9erX7jjvuOOtrqWPDcAbLD5w4cUJ2u119%2BvQ5o2/9%2BvXq3LmzsrKyFBoaqvT0dGVmZmrlypWSJLvdrjFjxqhPnz5q06aN7rnnHknSW2%2B9Zekx%2BJsdO3aourpaU6ZMUdu2bZWcnKyxY8fKbrf7emoXFYfDoc8%2B%2B0wzZsxQRESEOnfurIkTJ1KnJjjfe7qlCw0N1apVq9SpU6cz%2BlauXKmMjAxlZGQoNDRUI0aMUI8ePTxnAO12uyZOnKiuXbsqPDxc%2Bfn5Ki0t1SeffGL1Yfjcuep4PtSxYQhYfmDs2LGKj4%2Bvt6%2BkpES9evXyauvVq5fnMuDp/cHBwerZs6ccDkfzTdiPrFixQkOGDFHfvn2Vl5enb775RtKpuiUlJSkkJMSz7X/WFaeUlJQoISFBUVFRnrbk5GTt379fR48e9eHM/MPixYs1cOBAXX311Zo7d66qqqrO%2B55u6SZMmKCIiIh6%2B85WO4fDoePHj2vv3r1e/eHh4erUqVOL/H54rjpK0uHDh/Wzn/1M/fr10%2BDBg7V27VpJoo4XgIDl51wulyIjI73aoqOjVVFR4en/z3/8JCkqKsrT35L17NlTvXv31tq1a7Vx40a5XC5NnTpV0tnr6nK5uL/oP9RXpx/WG2vs3K644gqlp6dr69atstvt2rlzp%2BbPn3/e9zTO7lzf77777ju53W6%2BHzZAbGysOnfurIceekjvv/%2B%2BHnzwQc2ePVsffPABdbwANl9PANLatWv18MMP19u3cOFCjRkzpknju93uJr3eX52vrkuXLvV8HRYWpnnz5umWW27RwYMHzzpmUFCQ8Xn6u5a6vprqPy%2Bjdu3aVTNmzNCUKVN01VVX%2BXBW/u9865H1en4DBw7UwIEDPV/feuuteuONN7RmzRrNmDFDEnVsCALWRWDkyJEaOXJko14bExMjl8vl1VZRUaHY2Niz9rtcLnXv3r1xk/UjF1rXhIQESVJ5ebliY2N14MABr36Xy6Xo6GgFB3Pi9wexsbH1rq%2BgoCDPGkTDdOzYUbW1tQoODj7nexpnd7bvd7GxsZ73bn397dq1s3KafikhIUHFxcXU8QLwL4WfS01NPePejOLiYs8N8SkpKSopKfH01dbWateuXfXeMN%2BSlJWVad68eTp58qSnrbS0VJKUmJiolJQU7dmzRzU1NZ5%2Bh8PR4ut2upSUFB0%2BfFjffvutp83hcKhbt24KCwvz4cwubrt27dKiRYu82kpLS9W6dWtlZGSc8z2Ns0tJSTmjdj%2B8b0NDQ9W9e3ev74eVlZU6ePCgevfubfVUL2p//OMftXHjRq%2B20tJSJSYmUscLQMDyc8OHD1dZWZlWrlypEydO6O2339bbb7/teV5WTk6OXnvtNe3cuVPHjh3TsmXL1Lp1a6/Tvy1Ru3bttH37di1atEjff/%2B9vvrqKy1cuFCDBg1SfHy8MjIyFB4ermXLlunYsWP65JNPtGrVKuXk5Ph66heVXr16KTU1VYsXL9bRo0dVWlqql156iTqdR7t27WS321VUVKSTJ09q//79euaZZ5Sdna2RI0ee8z2Nsxs3bpz%2B8pe/aMeOHTpx4oRWrVqlAwcOaMSIEZJOfT9csWKFSktLdfToURUUFKhnz55KTU318cwvLidPntQTTzwhh8Oh6upqbdiwQe%2B8847Gjx8viTo2VJCbC6kXvWHDhulf//qXamtrVVdXp1atWkmSNm/erISEBH344YdasGCBSktLlZCQoOnTp2vo0KGe1//hD39QUVGRvvnmG6Wmpuqxxx5Tjx49fHU4F409e/Zo0aJFnt98ufHGGzVr1izPDcaff/655s2bp%2BLiYl166aX6%2Bc9/rp/%2B9Ke%2BnPJF6csvv9TcuXP197//XeHh4Ro/frzuv/9%2B7lc7jw8//FCLFy/Wnj171Lp1a40ePVr5%2BfkKDQ0973u6JfvhH/Efzi7bbKfudPnhfbx161YtXrxYZWVl6tatm%2BbMmaN%2B/fpJOnXf0LPPPqs//elPqqqqUlpamh5//HF16NDBB0fiW%2Beqo9vt1rJly7Rq1So5nU517NhRDz/8sAYNGiSJOjYUAQsAAMAwLhECAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGH/D9gyk00wCqeOAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common1804534458517092932”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.682493475205764</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.29771772747727</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-13.57801767815846</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.387415467318316</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-9.073673528673588</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-11.130195221490796</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-5.691265469605406</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-2.770588291605267</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>18.072969571232555</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3040)</td> <td class=”number”>3040</td> <td class=”number”>94.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>158</td> <td class=”number”>4.9%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme1804534458517092932”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-82.71216451839076</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-61.52902111790639</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-57.204265324055385</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-56.604511109806644</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>99.02247160480412</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.11795059490913</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>102.32075078491461</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>113.7509175434304</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>166.49403029539158</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_PC_WIC_REDEMP_08_12”>PCH_PC_WIC_REDEMP_08_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>1938</td>

</tr> <tr>

<th>Unique (%)</th> <td>60.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>39.7%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>1273</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>-7.956</td>

</tr> <tr>

<th>Minimum</th> <td>-99.498</td>

</tr> <tr>

<th>Maximum</th> <td>249.95</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-5465219224016524376”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-5465219224016524376,#minihistogram-5465219224016524376”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-5465219224016524376”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-5465219224016524376”

aria-controls=”quantiles-5465219224016524376” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-5465219224016524376” aria-controls=”histogram-5465219224016524376”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-5465219224016524376” aria-controls=”common-5465219224016524376”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-5465219224016524376” aria-controls=”extreme-5465219224016524376”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-5465219224016524376”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-99.498</td>

</tr> <tr>

<th>5-th percentile</th> <td>-28.396</td>

</tr> <tr>

<th>Q1</th> <td>-17.196</td>

</tr> <tr>

<th>Median</th> <td>-8.9853</td>

</tr> <tr>

<th>Q3</th> <td>-0.4449</td>

</tr> <tr>

<th>95-th percentile</th> <td>14.959</td>

</tr> <tr>

<th>Maximum</th> <td>249.95</td>

</tr> <tr>

<th>Range</th> <td>349.45</td>

</tr> <tr>

<th>Interquartile range</th> <td>16.751</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>15.969</td>

</tr> <tr>

<th>Coef of variation</th> <td>-2.0072</td>

</tr> <tr>

<th>Kurtosis</th> <td>39.096</td>

</tr> <tr>

<th>Mean</th> <td>-7.956</td>

</tr> <tr>

<th>MAD</th> <td>10.761</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.9396</td>

</tr> <tr>

<th>Sum</th> <td>-15411</td>

</tr> <tr>

<th>Variance</th> <td>255</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-5465219224016524376”>

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xZLa9ntlVe13W8LCgpUeHgrlZdXqba2zrDt%2BgIzZ2%2BMkefXtcTMx9us2c2aWzJv9ubM7a0fPv2yYNXV1emJJ57Qxx9/rN///vfq2LGjay4qKsr1TtU3HA6HunXrpujoaNffQ0P/74B89dVXatu2bZP3HxMTU%2B9yoM12VjU1xn%2Bx1NbWNct2fYGZszfE318LMx9vs2Y3a27JvNn9Kbf/XOz8lkWLFumf//xnvXIlSYmJiSouLnb9vba2VocOHVLv3r3VsWNHRUREuM3/4x//0IULF5SYmOix9QMAAN/mdwXrww8/1ObNm5WXl6fIyMh685mZmdq4caMOHDigqqoqrV69Wi1bttSgQYMUFBSkMWPG6IUXXtDJkydlt9v1n//5n7rjjjt03XXXeSENAADwRT55iTApKUnS1/9CUJJ27twpSbJarVq/fr3Onj2rwYMHuz2nX79%2BWrt2rQYOHKjp06dr2rRpOn36tJKSkpSXl6eQkBBJUnZ2tiorKzVq1CjV1NRo8ODBmj9/vufCAQAAnxfgvNo7utEkNttZQ7dnsQQqKipUdnul31yvbipPZf/Jc%2B8027abw/Zpt3p7Cc2Cc9182c2aWzJv9ubM3a5dG0O311R%2Bd4kQAADA2yhYAAAABqNgAQAAGIyCBQAAYDAKFgAAgMEoWAAAAAajYAEAABiMggUAAGAwChYAAIDBKFgAAAAGo2ABAAAYjIIFAABgMAoWAACAwShYAAAABqNgAQAAGIyCBQAAYDAKFgAAgMEoWAAAAAajYAEAABiMggUAAGAwChYAAIDBKFgAAAAGo2ABAAAYjIIFAABgMAoWAACAwShYAAAABvPpgvXWW28pJSVFOTk59ea2bdumESNGqE%2BfPkpPT9fbb7/tmqurq9Py5cs1ZMgQ9evXTxMnTtSJEydc8w6HQ9OmTVNKSooGDBig2bNnq7q62iOZAACA7/N4wUpLS9PKlSt18uTJq9rOiy%2B%2BqIULF6pTp0715g4fPqxZs2Zp5syZevfddzVhwgQ99NBDOnXqlCTptdde05YtW5SXl6fdu3crLi5OWVlZcjqdkqS5c%2BeqqqpKW7du1fr161VSUqLc3NyrWi8AADAPjxese%2B%2B9V9u2bdPtt9%2BuSZMmaceOHaqpqfne2wkODlZhYWGDBWvdunVKTU1VamqqgoODNXLkSHXv3l2bN2%2BWJBUUFGjChAnq0qWLwsLClJOTo5KSEh08eFBffvmldu7cqZycHEVHR6t9%2B/aaOnWq1q9fr4sXL151fgAA4P88XrCysrK0bds2/eEPf1C3bt20aNEipaamaunSpTp27FiTtzN%2B/Hi1adOmwbni4mLFx8e7jcXHx8tqtaq6ulpHjx51mw8LC1OnTp1ktVp1%2BPBhBQUFqUePHq75hIQEnTt3Tp9%2B%2Bun3TAsAAMzI4q0dJyQkKCEhQY899pi2bdum%2BfPna%2B3atUpJSdEjjzyiXr16XfG2HQ6HIiIi3MYiIiJ09OhRffXVV3I6nQ3O2%2B12RUZGKiwsTAEBAW5zkmS325u0/7KyMtlsNrcxi6W1YmJiriROg4KCAt3%2BNBMzZ2%2BMxeKfr4eZj7dZs5s1t2Te7P6Y22sF6%2BLFi/rzn/%2BsDRs26N1331VcXJwefvhhlZWVacKECVqwYIFGjBhxxdv/5n6qK5m/3HMvp6CgQCtXrnQby8rKUnZ29lVttyHh4a0M36avMHP2hkRFhXp7Cc3KzMfbrNnNmlsyb3Z/yu3xglVSUqLCwkJt3LhRlZWVGjZsmH73u9/p5ptvdj2mX79%2Bmj9//hUXrKioKDkcDrcxh8Oh6OhoRUZGKjAwsMH5tm3bKjo6WhUVFaqtrVVQUJBrTpLatm3bpP2PHTtWaWlpbmMWS2vZ7ZVXlKchQUGBCg9vpfLyKtXW1hm2XV9g5uyNMfL8upaY%2BXibNbtZc0vmzd6cub31w6fHC9bw4cPVuXNnPfjgg7rnnnsUGRlZ7zGpqak6c%2BbMFe8jMTFRRUVFbmNWq1XDhw9XcHCwunXrpuLiYvXv31%2BSVF5ers8%2B%2B0y9evVSbGysnE6nPvnkEyUkJLieGx4ers6dOzdp/zExMfUuB9psZ1VTY/wXS21tXbNs1xeYOXtD/P21MPPxNmt2s%2BaWzJvdn3J7/GJnfn6%2Btm/frgkTJjRYrr5x8ODBK97HmDFjtG/fPu3Zs0fnz59XYWGhjh8/rpEjR0qSMjMzlZ%2Bfr5KSElVUVCg3N1c9e/ZUUlKSoqOjNWzYMD333HM6c%2BaMTp06pVWrVikjI0MWi9euqAIAAB/i8cbQo0cPTZ48WRkZGbr99tslSb/97W/1zjvvaOnSpY2Wrm9LSkqSJNdHPOzcuVPS1%2B82de/eXbm5uVq8eLFKS0vVtWtXrVmzRu3atZMkjRs3TjabTQ888IAqKyuVnJzsds/UU089pXnz5mnIkCFq0aKF7r777gY/zBQAAKAhAc6rvaP7e3r88cd14sQJPfPMM4qLi5MkHT9%2BXE8//bTatWunJUuWeHI5HmOznTV0exZLoKKiQmW3V/rN26lN5ansP3nunWbbdnPYPu1Wby%2BhWXCumy%2B7WXNL5s3enLnbtWv4I52am8ffwXr77be1ZcsWRUVFucbi4uKUm5uru%2B%2B%2B29PLAQAAMJzH78Gqrq5WcHBw/YUEBqqqqsrTywEAADCcxwtWv379tGTJEn311VeusS%2B%2B%2BEILFixw%2B6gGAAAAX%2BXxS4RPPvmkfvGLX%2BjHP/6xwsLCVFdXp8rKSnXs2FGvvPKKp5cDAABgOI8XrI4dO%2BpPf/qT/vrXv%2Bqzzz5TYGCgOnfurAEDBrg%2B2BMAAMCXeeWDnVq2bOn6iAYAAAB/4/GCdeLECS1btkz//Oc/VV1dXW/%2BL3/5i6eXBAAAYCiv3INVVlamAQMGqHXr1p7ePQAAQLPzeMEqKirSX/7yF0VHR3t61wAAAB7h8Y9paNu2Le9cAQAAv%2BbxgvXggw9q5cqV8vBv6AEAAPAYj18i/Otf/6qPPvpIGzZs0PXXX6/AQPeO9/rrr3t6SQAAAIbyeMEKCwvTwIEDPb1bAAAAj/F4wVq8eLGndwkAAOBRHr8HS5I%2B/fRTrVixQk888YRr7O9//7s3lgIAAGA4jxes/fv3a%2BTIkdqxY4e2bt0q6esPHx0/fjwfMgoAAPyCxwvW8uXL9eijj2rLli0KCAiQ9PXvJ1yyZIlWrVrl6eUAAAAYzuMF6x//%2BIcyMzMlyVWwJOnOO%2B9USUmJp5cDAABgOI8XrDZt2jT4OwjLysrUsmVLTy8HAADAcB4vWH379tWiRYtUUVHhGjt27JhmzZqlH//4x55eDgAAgOE8/jENTzzxhH72s58pOTlZtbW16tu3r6qqqtStWzctWbLE08sBAAAwnMcLVocOHbR161bt3btXx44dU0hIiDp37qxbb73V7Z4sAAAAX%2BXxgiVJLVq00O233%2B6NXQMAADQ7jxestLS0Rt%2Bp4rOwAACAr/N4wbrrrrvcClZtba2OHTsmq9Wqn/3sZ55eDgAAgOE8XrBmzpzZ4Pibb76p9957z8OrAQAAMJ5XfhdhQ26//Xb96U9/8vYyAAAArto1U7AOHTokp9Np6PbGjx%2BvW265RbfeeqtmzpypM2fOSPr69yFmZGSob9%2B%2BGj58uDZv3uz23Pz8fA0bNkx9%2B/ZVZmamioqKDFsXAADwfx6/RDhu3Lh6Y1VVVSopKdHQoUMN2UdNTY1%2B9atfKT09XS%2B99JIqKys1Y8YMzZ8/X3PmzNHUqVM1e/ZsjRgxQh9%2B%2BKGmTJmizp07KykpSbt27dKKFSv00ksvqUePHsrPz9fkyZO1Y8cOtW7d2pD1AQAA/%2Bbxd7Di4uLUuXNnt//16dNHs2bN0qJFiwzZh81mk81m06hRo9SyZUtFRUXpjjvu0OHDh7VlyxbFxcUpIyNDwcHBSklJUVpamtatWydJKigoUHp6unr37q2QkBBNmjRJkrR7925D1gYAAPyfx9/B8sSntbdv3149e/ZUQUGBHnnkEVVXV2vHjh0aNGiQiouLFR8f7/b4%2BPh4bd%2B%2BXZJUXFysu%2B66yzUXGBionj17ymq1avjw4c2%2BdgAA4Ps8XrA2btzY5Mfec889V7SPwMBArVixQhMmTNDvfvc7SVL//v01Y8YMTZ06Ve3bt3d7fGRkpOx2uyTJ4XAoIiLCbT4iIsI13xRlZWWy2WxuYxZLa8XExFxJnAYFBQW6/WkmZs7eGIvFP18PMx9vs2Y3a27JvNn9MbfHC9bs2bNVV1dX74b2gIAAt7GAgIArLlgXLlzQ5MmTdeedd2ry5Mk6d%2B6cFixYcMmPiPiuq73ZvqCgQCtXrnQby8rKUnZ29lVttyHh4a0M36avMHP2hkRFhXp7Cc3KzMfbrNnNmlsyb3Z/yu3xgvXSSy9p7dq1mjx5snr06CGn06kjR47oxRdf1E9/%2BlMlJydf9T7279%2Bvzz//XNOnT1dQUJDatGmj7OxsjRo1SrfddpscDofb4%2B12u6KjoyVJUVFR9eYdDoe6devW5P2PHTtWaWlpbmMWS2vZ7ZVXmKi%2BoKBAhYe3Unl5lWpr6wzbri8wc/bGGHl%2BXUvMfLzNmt2suSXzZm/O3N764dMr92Dl5eW5Xaa75ZZb1LFjR02cOFFbt2696n3U1tbWe5fswoULkqSUlBT98Y9/dHt8UVGRevfuLUlKTExUcXGxRo8e7drWoUOHlJGR0eT9x8TE1LscaLOdVU2N8V8stbV1zbJdX2Dm7A3x99fCzMfbrNnNmlsyb3Z/yu3xi53Hjx%2Bvd4%2BTJIWHh6u0tNSQffTp00etW7fWihUrVFVVJbvdrtWrV6tfv34aNWqUSktLtW7dOp0/f1579%2B7V3r17NWbMGElSZmamNm7cqAMHDqiqqkqrV69Wy5YtNWjQIEPWBgAA/J/HC1ZsbKyWLFnidtN4eXm5li1bphtuuMGQfURFRek3v/mNPvroIw0cOFB33323QkJCtGzZMrVt21Zr1qzRq6%2B%2BqptvvlmLFi3S0qVLdeONN0qSBg4cqOnTp2vatGnq37%2B/9u3bp7y8PIWEhBiyNgAA4P8CnEZ%2BfHoTvP3225oxY4bKy8sVGhqqwMBAVVRUKCQkRKtWrdKPf/xjTy7HY2y2s4Zuz2IJVFRUqOz2Sr95O7WpPJX9J8%2B902zbbg7bp93q7SU0C85182U3a27JvNmbM3e7dm0M3V5TefwerAEDBmjPnj3au3evTp06JafTqfbt2%2Bu2225TmzbeeREAAACM5PGCJUmtWrXSkCFDdOrUKXXs2NEbSwAAAGg2Hr8Hq7q6WrNmzVKfPn30k5/8RNLX92BNmjRJ5eXlnl4OAACA4TxesJYuXarDhw8rNzdXgYH/t/va2lrl5uZ6ejkAAACG83jBevPNN/Vf//VfuvPOOxUQECDp649oWLx4sXbs2OHp5QAAABjO4wWrsrJScXFx9cajo6N17tw5Ty8HAADAcB4vWDfccIPee%2B89Se6/8%2B%2BNN97Qv/3bv3l6OQAAAIbz%2BL8ivP/%2B%2B/Xwww/r3nvvVV1dnV5%2B%2BWUVFRXpzTff1OzZsz29HAAAAMN5vGCNHTtWFotFr776qoKCgvTCCy%2Boc%2BfOys3N1Z133unp5QAAABjO4wXrzJkzuvfee3Xvvfd6etcAAAAe4fF7sIYMGSIP/3YeAAAAj/J4wUpOTtb27ds9vVsAAACP8fglwh/84Ad65plnlJeXpxtuuEEtWrRwm1%2B2bJmnlwQAAGAojxeso0eP6oc//KEkyW63e3r3AAAAzc5jBSsnJ0fLly/XK6%2B84hpbtWqVsrKyPLUEAAAAj/DYPVi7du2qN5aXl%2Bep3QMAAHiMxwpWQ/9ykH9NCAAA/JHHCtY3v9j5cmMAAAC%2BzuMf0wAAAODvKFgAAAAG89i/Irx48aJmzJhx2TE%2BBwsAAPg6jxWsm2%2B%2BWWVlZZcdAwAA8HUeK1jf/vwrAAAAf8Y9WAAAAAajYAEAABiMggUAAGAwChYAAIDB/LpgrV69WgMGDNBNN92kCRMm6PPPP5ck7d%2B/XxkZGerbt6%2BGDx%2BuzZs3uz0vPz9fw4YNU9%2B%2BfZWZmamioiJvLB8AAPgovy1Yr732mjZv3qz8/Hy9/fbb6tq1q37729%2BqrKxMU6dO1bhx47R//37Nnj1bc%2BfOldVqlfT1L6VesWKFnn32We3bt0%2BDBw/W5MmTde7cOS8nAgAAvsJvC9batWuVk5OjH/7whwoLC9OcOXM0Z84cbdmyRXFxccrIyFBwcLBSUlKUlpamdevWSZIKCgqUnp6u3r17KyQkRJMmTZIk7d6mjNNzAAAYR0lEQVS925txAACAD/HLgvXFF1/o888/11dffaW77rpLycnJys7O1pkzZ1RcXKz4%2BHi3x8fHx7suA353PjAwUD179nS9wwUAAHA5HvugUU86deqUJOmNN97Qyy%2B/LKfTqezsbM2ZM0fV1dVq37692%2BMjIyNlt9slSQ6HQxEREW7zERERrvmmKCsrk81mcxuzWForJibmSuI0KCgo0O1PMzFz9sZYLP75epj5eJs1u1lzS%2BbN7o%2B5/bJgOZ1OSdKkSZNcZerhhx/WL3/5S6WkpDT5%2BVeqoKBAK1eudBvLyspSdnb2VW23IeHhrQzfpq8wc/aGREWFensJzcrMx9us2c2aWzJvdn/K7ZcF67rrrpMkhYeHu8ZiY2PldDp18eJFORwOt8fb7XZFR0dLkqKiourNOxwOdevWrcn7Hzt2rNLS0tzGLJbWstsrv1eOxgQFBSo8vJXKy6tUW1tn2HZ9gZmzN8bI8%2BtaYubjbdbsZs0tmTd7c%2Bb21g%2BfflmwOnTooLCwMB0%2BfFgJCQmSpNLSUrVo0UKpqanatGmT2%2BOLiorUu3dvSVJiYqKKi4s1evRoSVJtba0OHTqkjIyMJu8/Jiam3uVAm%2B2samqM/2Kpra1rlu36AjNnb4i/vxZmPt5mzW7W3JJ5s/tTbv%2B52PktFotFGRkZeuGFF/S///u/On36tFatWqURI0Zo9OjRKi0t1bp163T%2B/Hnt3btXe/fu1ZgxYyRJmZmZ2rhxow4cOKCqqiqtXr1aLVu21KBBg7wbCgAA%2BAy/fAdLkmbMmKELFy7ovvvu08WLFzVs2DDNmTNHoaGhWrNmjRYuXKgFCxYoNjZWS5cu1Y033ihJGjhwoKZPn65p06bp9OnTSkpKUl5enkJCQrycCAAA%2BIoA59Xe0Y0msdnOGro9iyVQUVGhstsr/ebt1KbyVPafPPdOs227OWyfdqu3l9AsONfNl92suSXzZm/O3O3atTF0e03ll5cIAQAAvImCBQAAYDAKFgAAgMEoWAAAAAajYAEAABiMggUAAGAwChYAAIDBKFgAAAAGo2ABAAAYjIIFAABgMAoWAACAwShYAAAABqNgAQAAGIyCBQAAYDAKFgAAgMEoWAAAAAajYAEAABiMggUAAGAwChYAAIDBKFgAAAAGo2ABAAAYjIIFAABgMAoWAACAwShYAAAABqNgAQAAGIyCBQAAYDAKFgAAgMFMUbAWLVqkHj16uP6%2Bf/9%2BZWRkqG/fvho%2BfLg2b97s9vj8/HwNGzZMffv2VWZmpoqKijy9ZAAA4MP8vmAdPnxYmzZtcv29rKxMU6dO1bhx47R//37Nnj1bc%2BfOldVqlSTt2rVLK1as0LPPPqt9%2B/Zp8ODBmjx5ss6dO%2BetCAAAwMf4dcGqq6vTvHnzNGHCBNfYli1bFBcXp4yMDAUHByslJUVpaWlat26dJKmgoEDp6enq3bu3QkJCNGnSJEnS7t27vREBAAD4IIu3F9CcXn/9dQUHB2vEiBF67rnnJEnFxcWKj493e1x8fLy2b9/umr/rrrtcc4GBgerZs6esVquGDx/epP2WlZXJZrO5jVksrRUTE3M1cdwEBQW6/WkmZs7eGIvFP18PMx9vs2Y3a27JvNn9MbffFqwvv/xSK1as0CuvvOI27nA41L59e7exyMhI2e1213xERITbfEREhGu%2BKQoKCrRy5Uq3saysLGVnZ3%2BfCE0SHt7K8G36CjNnb0hUVKi3l9CszHy8zZrdrLkl82b3p9x%2BW7AWL16s9PR0de3aVZ9//vn3eq7T6byqfY8dO1ZpaWluYxZLa9ntlVe13W8LCgpUeHgrlZdXqba2zrDt%2BgIzZ2%2BMkefXtcTMx9us2c2aWzJv9ubM7a0fPv2yYO3fv19///vftXXr1npzUVFRcjgcbmN2u13R0dGXnHc4HOrWrVuT9x8TE1PvcqDNdlY1NcZ/sdTW1jXLdn2BmbM3xN9fCzMfb7NmN2tuybzZ/Sm3/1zs/JbNmzfr9OnTGjx4sJKTk5Weni5JSk5OVvfu3et97EJRUZF69%2B4tSUpMTFRxcbFrrra2VocOHXLNAwAAXI5fFqzHH39cb775pjZt2qRNmzYpLy9PkrRp0yaNGDFCpaWlWrdunc6fP6%2B9e/dq7969GjNmjCQpMzNTGzdu1IEDB1RVVaXVq1erZcuWGjRokBcTAQAAX%2BKXlwgjIiLcblSvqamRJHXo0EGStGbNGi1cuFALFixQbGysli5dqhtvvFGSNHDgQE2fPl3Tpk3T6dOnlZSUpLy8PIWEhHg%2BCAAA8El%2BWbC%2B6/rrr9eRI0dcf%2B/Xr5/bh49%2B1/3336/777/fE0sDAAB%2ByC8vEQIAAHgTBQsAAMBgFCwAAACDUbAAAAAMRsECAAAwGAULAADAYBQsAAAAg1GwAAAADEbBAgAAMBgFCwAAwGAULAAAAINRsAAAAAxGwQIAADAYBQsAAMBgFCwAAACDUbAAAAAMRsECAAAwGAULAADAYBQsAAAAg1GwAAAADEbBAgAAMBgFCwAAwGAULAAAAINRsAAAAAxGwQIAADAYBQsAAMBgFCwAAACDWby9gOZSWlqqRYsW6YMPPlBQUJAGDhyoJ598UuHh4Tp8%2BLCeeeYZHT58WG3bttW4ceP0i1/8wvXcbdu2afXq1fr888/VuXNnTZ8%2BXQMGDPBiGuDyfvLcO95eQpNtn3art5cAAM3Kb9/Bmjx5ssLDw7Vr1y5t2LBB//znP/XrX/9a1dXVevDBB/WjH/1Ib731lpYvX641a9Zox44dkqTDhw9r1qxZmjlzpt59911NmDBBDz30kE6dOuXlRAAAwFf4ZcEqLy9XYmKiZsyYodDQUHXo0EGjR4/WBx98oD179ujixYuaMmWKWrdurYSEBN13330qKCiQJK1bt06pqalKTU1VcHCwRo4cqe7du2vz5s1eTgUAAHyFX14iDA8P1%2BLFi93GTp48qZiYGBUXF6tHjx4KCgpyzcXHx2vdunWSpOLiYqWmpro9Nz4%2BXlartcn7Lysrk81mcxuzWForJibm%2B0a5pKCgQLc/zcTM2f2FxdL0Y2fm423W7GbNLZk3uz/m9suC9V1Wq1WvvvqqVq9ere3btys8PNxtPjIyUg6HQ3V1dXI4HIqIiHCbj4iI0NGjR5u8v4KCAq1cudJtLCsrS9nZ2Vce4hLCw1sZvk1fYebsvi4qKvR7P8fMx9us2c2aWzJvdn/K7fcF68MPP9SUKVM0Y8YMpaSkaPv27Q0%2BLiAgwPX/nU7nVe1z7NixSktLcxuzWFrLbq%2B8qu1%2BW1BQoMLDW6m8vEq1tXWGbdcXmDm7v/g%2BXwtmPt5mzW7W3JJ5szdn7iv5gc4Ifl2wdu3apUcffVRz587VPffcI0mKjo7W8ePH3R7ncDgUGRmpwMBARUVFyeFw1JuPjo5u8n5jYmLqXQ602c6qpsb4L5ba2rpm2a4vMHN2X3clx83Mx9us2c2aWzJvdn/K7T8XO7/jo48%2B0qxZs/T888%2B7ypUkJSYm6siRI6qpqXGNWa1W9e7d2zVfVFTktq1vzwMAAFyOXxasmpoazZkzRzNnzqz3%2BVWpqakKCwvT6tWrVVVVpYMHD6qwsFCZmZmSpDFjxmjfvn3as2ePzp8/r8LCQh0/flwjR470RhQAAOCD/PIS4YEDB1RSUqKFCxdq4cKFbnNvvPGGXnjhBc2bN095eXm67rrrlJOTo0GDBkmSunfvrtzcXC1evFilpaXq2rWr1qxZo3bt2nkhCQAA8EV%2BWbBuueUWHTlypNHH/P73v7/k3NChQzV06FCjlwUAAEzCLy8RAgAAeBMFCwAAwGAULAAAAINRsAAAAAxGwQIAADAYBQsAAMBgFCwAAACDUbAAAAAMRsECAAAwGAULAADAYBQsAAAAg1GwAAAADEbBAgAAMBgFCwAAwGAULAAAAINRsAAAAAxm8fYCYB4/ee4dby8BAACP4B0sAAAAg1GwAAAADEbBAgAAMBgFCwAAwGAULAAAAINRsAAAAAxGwQIAADAYBQsAAMBgFCwAAACDUbAAAAAMRsFqQGlpqX71q18pOTlZgwcP1tKlS1VXV%2BftZQEAAB/B7yJswMMPP6yEhATt3LlTp0%2Bf1oMPPqjrrrtOP//5z729NMAv%2BNrvpdw%2B7VZvLwGAj%2BEdrO%2BwWq365JNPNHPmTLVp00ZxcXGaMGGCCgoKvL00AADgI3gH6zuKi4sVGxuriIgI11hCQoKOHTumiooKhYWFXXYbZWVlstlsbmMWS2vFxMQYts6goEC3PwE0H197x%2B3PM2/z9hKuipm/v5k1uz/mpmB9h8PhUHh4uNvYN2XLbrc3qWAVFBRo5cqVbmMPPfSQHn74YcPWWVZWpt/97iWNHTvW0OLWnD545k5DtlNWVqaCggKfym4Ecpsrt2Te7L74/c0oZs3uj7n9pyoayOl0XtXzx44dqw0bNrj9b%2BzYsQat7ms2m00rV66s906ZGZg1O7nNlVsyb3az5pbMm90fc/MO1ndER0fL4XC4jTkcDgUEBCg6OrpJ24iJifGbBg4AAL4/3sH6jsTERJ08eVJnzpxxjVmtVnXt2lWhoaFeXBkAAPAVFKzviI%2BPV1JSkpYtW6aKigqVlJTo5ZdfVmZmpreXBgAAfETQ/Pnz53t7Edea2267TVu3btXTTz%2BtP/3pT8rIyNDEiRMVEBDg7aW5CQ0NVf/%2B/U35zppZs5PbXLkl82Y3a27JvNn9LXeA82rv6AYAAIAbLhECAAAYjIIFAABgMAoWAACAwShYAAAABqNgAQAAGIyCBQAAYDAKFgAAgMEoWAAAAAajYAEAABiMguUDrFar7rjjDo0ZM6be3P79%2B5WRkaG%2Bfftq%2BPDh2rx5s9t8fn6%2Bhg0bpr59%2ByozM1NFRUWeWrah0tLSlJiYqKSkJNf/Jk%2Be7Jo/fPiwfvrTn%2Brmm2/W0KFDtXbtWi%2Bu1nilpaX61a9%2BpeTkZA0ePFhLly5VXV2dt5dluB49etQ7zk8//bSky5/rvuatt95SSkqKcnJy6s1t27ZNI0aMUJ8%2BfZSenq63337bNVdXV6fly5dryJAh6tevnyZOnKgTJ054culX7VLZN2zYoBtvvNHt%2BCclJenjjz%2BW5PvZS0tLlZWVpeTkZKWkpOjxxx9XeXm5pMt/D2vsnLjWXSr3559/rh49etQ73r/5zW9cz/Xl3HLimrZp0yZnamqqc%2BLEic777rvPbe6LL75w3nTTTc5169Y5q6urne%2B8846zV69ezo8//tjpdDqdf/nLX5y33HKL88CBA86qqirnmjVrnLfeequzsrLSG1GuyuDBg53vvvtug3NVVVXO2267zblixQpnZWWls6ioyNm/f3/nm2%2B%2B6eFVNp/Ro0c758yZ4ywvL3ceO3bMOXToUOfatWu9vSzDde/e3XnixIl645c7131NXl6ec%2BjQoc5x48Y5p02b5jZ36NAhZ2JionPPnj3O6upq56ZNm5y9e/d2njx50ul0Op35%2BfnOwYMHO48ePeo8e/as86mnnnKOGDHCWVdX540o31tj2devX%2B/86U9/esnn%2Bnr2u%2B%2B%2B2/n44487KyoqnCdPnnSmp6c7n3zyyct%2BD7vcOXGtu1TuEydOOLt3737J5/l6bt7BusadP39eBQUF6t27d725LVu2KC4uThkZGQoODlZKSorS0tK0bt06SVJBQYHS09PVu3dvhYSEaNKkSZKk3bt3ezRDc9uzZ48uXryoKVOmqHXr1kpISNB9992ngoICby/NEFarVZ988olmzpypNm3aKC4uThMmTPCbfE1xuXPd1wQHB6uwsFCdOnWqN7du3TqlpqYqNTVVwcHBGjlypLp37%2B56x66goEATJkxQly5dFBYWppycHJWUlOjgwYOejnFFGst%2BOb6cvby8XImJiZoxY4ZCQ0PVoUMHjR49Wh988MFlv4dd7py4ljWW%2B3J8ObfEJcJr3n333af27ds3OFdcXKz4%2BHi3sfj4eNdlwO/OBwYGqmfPnrJarc234GaUn5%2Bv22%2B/XX369FF2drZOnz4t6eucPXr0UFBQkOux334dfF1xcbFiY2MVERHhGktISNCxY8dUUVHhxZU1j2XLlmnQoEG65ZZbNHfuXFVWVl72XPc148ePV5s2bRqcu1RWq9Wq6upqHT161G0%2BLCxMnTp18pmv68ayS9LJkyf185//XP369dOQIUO0adMmSfL57OHh4Vq8eLGuu%2B4619jJkycVExNz2e9hjZ0T17rGcn/jscce04ABA/SjH/1Iy5Yt08WLFyX5dm6JguXTHA6HwsPD3cYiIyNlt9td89/%2Bj7IkRUREuOZ9Sc%2BePdWrVy9t2rRJ27Ztk8Ph0COPPCLp0q%2BDw%2BHwi/uUGsr3zXH1xWPZmJtuukkpKSnasWOHCgoKdODAAS1YsOCy57o/aezr9quvvpLT6fSbr%2Bvvio6OVlxcnB599FG98847mj59up588knt37/f77JbrVa9%2BuqrmjJlymW/h/nT9/Jv527ZsqX69OmjO%2B64Q7t371ZeXp42b96s//7v/5bk%2B/8Ns3h7AWa3adMmPfbYYw3OLV68WOnp6Ve1fafTeVXP95TLvQ6rVq1y/T00NFTz5s3TXXfdpc8%2B%2B%2ByS2wwICDB8nd7iK8fxan37smeXLl00c%2BZMTZkyRTfffLMXV%2BV5lzve/no%2BDBo0SIMGDXL9ffjw4frzn/%2BsDRs2aObMmZL8I/uHH36oKVOmaMaMGUpJSdH27dsbfNy3v4f5Y25Jev31113zvXr10oMPPqg1a9a4foD25dwULC8bNWqURo0adUXPjYqKksPhcBuz2%2B2Kjo6%2B5LzD4VC3bt2ubLHN6Pu%2BDrGxsZKksrIyRUdH6/jx427zDodDkZGRCgz0/Tdpo6OjGzyOAQEBrmPtr66//nrV1tYqMDCw0XPdn1zq6zY6Otp1Tjc037ZtW08u02NiY2NVVFTkN9l37dqlRx99VHPnztU999wjSZf9HtbYOeErGsrdkNjYWH355ZdyOp0%2Bn9v3/%2BtjYklJSfXuQSkqKnLdEJ%2BYmKji4mLXXG1trQ4dOtTgDfPXstLSUs2bN08XLlxwjZWUlEiSOnbsqMTERB05ckQ1NTWueavV6nM5LyUxMVEnT57UmTNnXGNWq1Vdu3ZVaGioF1dmrEOHDmnJkiVuYyUlJWrZsqVSU1MbPdf9SWJiYr2s35zPwcHB6tatm9vXdXl5uT777DP16tXL00s13O9//3tt27bNbaykpEQdO3b0i%2BwfffSRZs2apeeff96tZFzue1hj54QvuFTu/fv3a/Xq1W6P/fTTTxUbG6uAgACfz03B8mEjRoxQaWmp1q1bp/Pnz2vv3r3au3ev6/OyMjMztXHjRh04cEBVVVVavXq1WrZs6fYWvC9o27atdu3apSVLlujcuXP64osvtHjxYg0ePFjt27dXamqqwsLCtHr1alVVVengwYMqLCxUZmamt5duiPj4eCUlJWnZsmWqqKhQSUmJXn75Zb/J9422bduqoKBAeXl5unDhgo4dO6bnn39eY8eO1ahRoxo91/3JmDFjtG/fPu3Zs0fnz59XYWGhjh8/rpEjR0r6%2Bus6Pz9fJSUlqqioUG5urnr27KmkpCQvr/zqXbhwQU8//bSsVqsuXryorVu36q9//avGjRsnybez19TUaM6cOZo5c6YGDBjgNne572GXOyeuZY3lbtOmjVatWqVNmzbp4sWLslqt%2Bs1vfuMXuSUpwOnLFzhNYNiwYfrXv/6l2tpa1dXVqUWLFpKkN954Q7GxsXr//fe1cOFClZSUKDY2VjNmzNDQoUNdz/%2Bf//kf5eXl6fTp00pKStL8%2BfPVvXt3b8W5YkeOHNGSJUtc/3rkjjvu0BNPPOG6MfQf//iH5s2bp6KiIl133XX65S9/qfvvv9%2BbSzbUqVOnNHfuXP3tb39TWFiYxo0bp4ceesiv7jOTpPfff1/Lli3TkSNH1LJlS40ePVo5OTkKDg6%2B7LnuS74pBN%2B8Y2GxfH23xjfn944dO7Rs2TKVlpaqa9eumj17tvr16yfp63tSVqxYoddff12VlZVKTk7WU089pQ4dOnghyffXWHan06nVq1ersLBQNptN119/vR577DENHjxYkm9n/%2BCDD/Tv//7vatmyZb25N954Q5WVlY1%2BD2vsnLiWXS73oUOHtHLlSh0/flxt2rTRAw88oF/%2B8peu2zt8NbdEwQIAADAclwgBAAAMRsECAAAwGAULAADAYBQsAAAAg1GwAAAADEbBAgAAMBgFCwAAwGAULAAAAINRsAAAAAxGwQIAADAYBQsAAMBgFCwAAACDUbAAAAAM9v8AHAxR8HRFB6IAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-5465219224016524376”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-18.78033</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-38.21899</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-8.161306</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-7.718587</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-20.58828</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-21.7779</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-9.155752</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-6.134289</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-9.662831</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-11.70654</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (1927)</td> <td class=”number”>1927</td> <td class=”number”>60.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>1273</td> <td class=”number”>39.7%</td> <td>

<div class=”bar” style=”width:66%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-5465219224016524376”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-99.4984</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-70.83068</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-61.84319</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-61.54203</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-60.67674</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>75.29898</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>76.18424</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>78.47611</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>95.01101</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>249.9504</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_RECFACPTH_09_14”><s>PCH_RECFACPTH_09_14</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_RECFAC_09_14”><code>PCH_RECFAC_09_14</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.9968</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_RECFAC_09_14”>PCH_RECFAC_09_14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>355</td>

</tr> <tr>

<th>Unique (%)</th> <td>11.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>6.3%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>201</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>-5.7627</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>400</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>40.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram8994879604975734326”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives8994879604975734326,#minihistogram8994879604975734326”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives8994879604975734326”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles8994879604975734326”

aria-controls=”quantiles8994879604975734326” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram8994879604975734326” aria-controls=”histogram8994879604975734326”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common8994879604975734326” aria-controls=”common8994879604975734326”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme8994879604975734326” aria-controls=”extreme8994879604975734326”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles8994879604975734326”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-100</td>

</tr> <tr>

<th>Q1</th> <td>-25</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>0</td>

</tr> <tr>

<th>95-th percentile</th> <td>71.092</td>

</tr> <tr>

<th>Maximum</th> <td>400</td>

</tr> <tr>

<th>Range</th> <td>500</td>

</tr> <tr>

<th>Interquartile range</th> <td>25</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>50.374</td>

</tr> <tr>

<th>Coef of variation</th> <td>-8.7413</td>

</tr> <tr>

<th>Kurtosis</th> <td>9.47</td>

</tr> <tr>

<th>Mean</th> <td>-5.7627</td>

</tr> <tr>

<th>MAD</th> <td>30.968</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.5426</td>

</tr> <tr>

<th>Sum</th> <td>-17340</td>

</tr> <tr>

<th>Variance</th> <td>2537.5</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram8994879604975734326”>

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fvmrTpo0KCwvVpUsXJScnS5IyMjL03HPP6de//rW%2B/PJLLV%2B%2BXFlZWbLZgrJcAADAZAGbGL4KQzU1NZKkrVu3Srp8tumWW27x2jYiIkLNmjXztI8ZM0ZOp1OPPvqoKisrlZKS4nVT%2Bty5czVr1iwNGDBAN910kx544IHvfJgpAADA14UYjb2jGw3idJ4zfUybLVTR0S3kclUGzTXr64XNFqqBhbv8PY1rsjn/Hn9PoUFYt9aivtahttaxsratW7c0dbyGCrxflQIAALjOEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkARuwdu3apdTUVBUUFNTre/fddzVs2DB1795dGRkZev311736V61apYyMDPXo0UPZ2dkqKSnx9F28eFH//u//rr59%2ByolJUV5eXlyuVyWHw8AAAgeARmwXnzxRc2bN09t27at1/fJJ59o8uTJysvL09///ndNnz5dc%2BfO1QcffCBJ2rZtm5YuXaqFCxdq9%2B7d6t%2B/vyZMmKALFy5IkpYsWaLS0lIVFxdry5YtMgxD06ZN8%2BnxAQCAwBaQASs8PFxr1669YsByu916/PHHdd9998lmsyktLU2dOnXyBKzi4mJlZmaqW7duatq0qcaPHy9J2r59u2pqarR27VpNnDhRP/jBDxQVFaX8/Hzt2LFDp06d8ukxAgCAwBWQAWvs2LFq2bLlFfv69u2r3Nxcz/c1NTVyOp1q06aNJKm0tFQJCQme/tDQUHXp0kUOh0NHjx7VuXPnlJiY6Olv3769mjZtqtLSUouOBgAABBubvydgtcLCQjVv3lyDBw%2BWdPkMV2RkpNc2kZGRcrlccrvdkiS73e7Vb7fbr%2Bk%2BrPLycjmdTq82m625YmNjv88hfKuwsFCvrzBPINbUZguMObNurUV9rUNtrROMtQ3agGUYhgoLC7Vp0yatWrVK4eHhXn1Xe21jFBcXa9myZV5tubm5ysvLa9S438Zub2bJuAgs0dEt/D2Fa8K6tRb1tQ61tU4w1TYoA1ZdXZ2mTZumTz75RK%2B%2B%2BqpuvfVWT190dLTnTNVX3G63OnbsqJiYGM/3LVr83z9WX3zxhVq1atXg/Y8ePVrp6elebTZbc7lcld/ncL5VWFio7PZmqqioUm1tnalj3%2BgC8VOU2evLKqxba1Ff61Bb61hZW399%2BAzKgPXss8/q008/1auvvqqoqCivvqSkJJWWlmrEiBGSpNraWu3bt09ZWVm69dZbFRkZqdLSUsXFxUmS/vnPf%2BrLL79UUlJSg/cfGxtb73Kg03lONTXW/EDW1tZZNjYCR6CtAdattaivdaitdYKptoH3Mf0qPvzwQ23YsEFFRUX1wpUkZWdn680339THH3%2BsqqoqrVixQk2aNFG/fv0UFhamUaNG6Xe/%2B51OnDghl8ul3/zmNxo4cKBuvvlmPxwNAAAIRAF5Bis5OVnS5d8QlKStW7dKkhwOh9atW6dz586pf//%2BXq/p1auXVq5cqb59%2B%2Brpp59Wfn6%2Bzpw5o%2BTkZBUVFalp06aSpLy8PFVWVmr48OGqqalR//79NXv2bN8dHAAACHghRmPv6EaDOJ3nTB/TZgtVdHQLuVyVQXNK9Xphs4VqYOEuf0/jmmzOv8ffU2gQ1q21qK91qK11rKxt69ZXfqyT1YLuEiEAAIC/EbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABM5vOAlZ6ermXLlunEiRO%2B3jUAAIBP%2BDxgPfTQQ3r77bd13333afz48Xr33XdVU1Pj62kAAABYxucBKzc3V2%2B//bZef/11dezYUc8%2B%2B6zS0tK0aNEiHTlyxNfTAQAAMJ3f7sFKTEzUlClTtH37dk2fPl2vv/66Bg8erHHjxumTTz7x17QAAAAazW8B69KlS3r77bf12GOPacqUKWrTpo2mTZumLl26KCcnRxs3bvTX1AAAABrF5usdlpWVae3atXrzzTdVWVmpjIwM/fGPf9Sdd97p2aZXr16aPXu2hg4d6uvpAQAANJrPA9aQIUPUrl07Pf7443rwwQcVFRVVb5u0tDSdPXvW11MDAAAwhc8D1qpVq9S7d%2B%2Brbrd3714fzAYAAMB8Pr8Hq3PnzpowYYK2bt3qafvDH/6gxx57TG6329fTAQAAMJ3PA9b8%2BfN17tw5dejQwdPWr18/1dXVacGCBb6eDgAAgOl8fonwvffe08aNGxUdHe1pi4%2BPV2FhoR544AFfTwcAAMB0Pj%2BDVV1drfDw8PoTCQ1VVVXVNY21a9cupaamqqCgoF7f22%2B/raFDh6p79%2B7KzMzUe%2B%2B95%2Bmrq6vTkiVLNGDAAPXq1Uvjxo3TsWPHPP1ut1v5%2BflKTU1Vnz59NGPGDFVXV1/T3AAAwI3L5wGrV69eWrBggb744gtP26lTpzRnzhyvRzVczYsvvqh58%2Bapbdu29fr279%2BvKVOmaPLkyfrrX/%2BqnJwcPfnkkzp58qQkafXq1dq4caOKioq0fft2xcfHKzc3V4ZhSJJmzpypqqoqbdq0SevWrVNZWZkKCwsbeeQAAOBG4fOANX36dO3Zs0d33323evfurZ49e6pfv34qKSnRvHnzGjxOeHi41q5de8WAtWbNGqWlpSktLU3h4eEaNmyYOnXqpA0bNkiSiouLlZOTo/bt2ysiIkIFBQUqKyvT3r17dfr0aW3dulUFBQWKiYlRmzZtNHHiRK1bt06XLl0yrQ4AACB4%2BfwerFtvvVVvvfWW/vu//1tHjx5VaGio2rVrpz59%2BigsLKzB44wdO/Zb%2B0pLS5WWlubVlpCQIIfDoerqah06dEgJCQmevoiICLVt21YOh0Pnzp1TWFiYOnfu7OlPTEzUhQsXdPjwYa/2b1NeXi6n0%2BnVZrM1V2xsbEMPr0HCwkK9vsI8gVhTmy0w5sy6tRb1tQ61tU4w1tbnAUuSmjRpovvuu8%2By8d1utyIjI73aIiMjdejQIX3xxRcyDOOK/S6XS1FRUYqIiFBISIhXnyS5XK4G7b%2B4uFjLli3zasvNzVVeXt73OZyrstubWTIuAkt0dAt/T%2BGasG6tRX2tQ22tE0y19XnAOnbsmBYvXqxPP/30ijeO//nPfzZlP1/dT/V9%2Bq/22qsZPXq00tPTvdpstuZyuSobNe43hYWFym5vpoqKKtXW1pk69o0uED9Fmb2%2BrMK6tRb1tQ61tY6VtfXXh0%2BfB6zp06ervLxcffr0UfPmzS3ZR3R0dL2HlrrdbsXExCgqKkqhoaFX7G/VqpViYmJ0/vx51dbWei5ZfrVtq1atGrT/2NjYepcDnc5zqqmx5geytrbOsrEROAJtDbBurUV9rUNtrRNMtfV5wCopKdGf//xnxcTEWLaPpKQklZSUeLU5HA4NGTJE4eHh6tixo0pLSz1/sqeiokJHjx5V165dFRcXJ8MwdODAASUmJnpea7fb1a5dO8vmDAAAgofPr4O0atXKsjNXXxk1apR2796tHTt26OLFi1q7dq0%2B%2B%2BwzDRs2TJKUnZ2tVatWqaysTOfPn1dhYaG6dOmi5ORkxcTEKCMjQ88995zOnj2rkydPavny5crKypLN5pdb1gAAQIDxeWJ4/PHHtWzZMk2aNMnrRvJrlZycLEmqqamRJM/fNnQ4HOrUqZMKCws1f/58HT9%2BXB06dNALL7yg1q1bS5LGjBkjp9OpRx99VJWVlUpJSfG6KX3u3LmaNWuWBgwYoJtuukkPPPDAFR9mCgAAcCUhRmPv6L5GTz31lP7xj3/IMAz98Ic/VGio90m01157zZfT8Rmn85zpY9psoYqObiGXqzJorllfL2y2UA0s3OXvaVyTzfn3%2BHsKDcK6tRb1tQ61tY6VtW3duqWp4zWUz89gRUREqG/fvr7eLQAAgM/4PGDNnz/f17sEAADwKb887Ofw4cNaunSppk2b5mn76KOP/DEVAAAA0/k8YO3Zs0fDhg3Tu%2B%2B%2Bq02bNkm6/PDRsWPHmvaQUQAAAH/yecBasmSJfvGLX2jjxo2e3yK89dZbtWDBAi1fvtzX0wEAADCdzwPWP//5T2VnZ0uS12Ma7r//fpWVlfl6OgAAAKbzecBq2bLlFf8GYXl5uZo0aeLr6QAAAJjO5wGrR48eevbZZ3X%2B/HlP25EjRzRlyhTdfffdvp4OAACA6Xz%2BmIZp06bpJz/5iVJSUlRbW6sePXqoqqpKHTt21IIFC3w9HQAAANP5PGDdcsst2rRpk3bu3KkjR46oadOmateune65555G/ekcAACA64Vf/nrxTTfdpPvuu88fuwYAALCczwNWenr6d56p4llYAAAg0Pk8YA0ePNgrYNXW1urIkSNyOBz6yU9%2B4uvpAAAAmM7nAWvy5MlXbN%2ByZYvef/99H88GAADAfH75W4RXct999%2Bmtt97y9zQAAAAa7boJWPv27ZNhGP6eBgAAQKP5/BLhmDFj6rVVVVWprKxMgwYN8vV0AAAATOfzgBUfH1/vtwjDw8OVlZWlkSNH%2Bno6AAAApvN5wOJp7QAAINj5PGC9%2BeabDd72wQcftHAmAAAA1vB5wJoxY4bq6urq3dAeEhLi1RYSEkLAAgAAAcnnAev3v/%2B9Vq5cqQkTJqhz584yDEMHDx7Uiy%2B%2BqEceeUQpKSm%2BnhIAAICp/HIPVlFRkdq0aeNp69mzp2699VaNGzdOmzZt8vWUAAAATOXz52B99tlnioyMrNdut9t1/PhxX08HAADAdD4PWHFxcVqwYIFcLpenraKiQosXL9Ztt91m2n727dunsWPHqmfPnrrnnns0efJknT17VpK0Z88eZWVlqUePHhoyZIg2bNjg9dpVq1YpIyNDPXr0UHZ2tkpKSkybFwAACH4%2BD1jTp0/X5s2blZqaqp49e6p3796666679MYbb2jq1Kmm7KOmpkY///nPdccdd2j37t3atGmTzp49q9mzZ6u8vFwTJ07UmDFjtGfPHs2YMUMzZ86Uw%2BGQJG3btk1Lly7VwoULtXv3bvXv318TJkzQhQsXTJkbAAAIfj6/B6tPnz7asWOHdu7cqZMnT8owDLVp00b33nuvWrZsaco%2BnE6nnE6nhg8friZNmqhJkyYaOHCgVq5cqY0bNyo%2BPl5ZWVmSpNTUVKWnp2vNmjVKTk5WcXGxMjMz1a1bN0nS%2BPHjtWrVKm3fvl1DhgwxZX4AACC4%2BeVvETZr1kwDBgzQgAED9NOf/lSDBw82LVxJUps2bdSlSxcVFxersrJSZ86c0bvvvqt%2B/fqptLRUCQkJXtsnJCR4LgN%2Bsz80NFRdunTxnOECAAC4Gp%2BfwaqurtasWbP01ltvSZJKSkpUUVGhp59%2BWr/5zW9kt9sbvY/Q0FAtXbpUOTk5%2BuMf/yhJ6t27tyZNmqSJEyd6/QajJEVFRXnuCXO73fVuwo%2BMjPS6Z%2BxqysvL5XQ6vdpstuaKjY39PofzrcLCQr2%2BwjyBWFObLTDmzLq1FvW1DrW1TjDW1ucBa9GiRdq/f78KCwv1y1/%2B0tNeW1urwsJCzZ07t9H7%2BPLLLzVhwgTdf//9nvun5syZo8mTJzfo9d98COq1Ki4u1rJly7zacnNzlZeX16hxv43d3syScRFYoqNb%2BHsK14R1ay3qax1qa51gqq3PA9aWLVv0yiuvKD4%2BXlOmTJF0%2BREN8%2BfP14MPPmhKwNqzZ48%2B//xzPf300woLC1PLli2Vl5en4cOH695775Xb7fba3uVyKSYmRpIUHR1dr9/tdqtjx44N3v/o0aOVnp7u1WazNZfLVfk9j%2BjKwsJCZbc3U0VFlWpr60wd%2B0YXiJ%2BizF5fVmHdWov6WofaWsfK2vrrw6fPA1ZlZaXi4%2BPrtcfExJj2m3q1tbX1/hzPl19%2BKenyTe1/%2BtOfvLYvKSnx3NSelJSk0tJSjRgxwjPWvn37PDfFN0RsbGy9y4FO5znV1FjzA1lbW2fZ2AgcgbYGWLfWor7WobbWCaba%2Bvxj%2Bm233ab3339fkveluHfeeUf/8i//Yso%2BunfvrubNm2vp0qWqqqqSy%2BXSihUr1KtXLw0fPlzHjx/XmjVrdPHiRe3cuVM7d%2B7UqFGjJEnZ2dl688039fFUqSG4AAAZYklEQVTHH6uqqkorVqxQkyZN1K9fP1PmBgAAgp/Pz2A9/PDDeuqpp/TQQw%2Bprq5OL730kkpKSrRlyxbNmDHDlH1ER0frP//zP/XrX/9affv2VZMmTdS7d2/Nnj1brVq10gsvvKB58%2BZpzpw5iouL06JFi3T77bdLkvr27aunn35a%2Bfn5OnPmjJKTk1VUVKSmTZuaMjcAABD8QozG3tH9Paxbt06vvPKKDh8%2BrKZNm6pdu3bKycnR/fff7%2Bup%2BIzTec70MW22UEVHt5DLVRk0p1SvFzZbqAYW7vL3NK7J5vx7/D2FBmHdWov6WofaWsfK2rZubd5joK6Fz89gnT17Vg899JAeeughX%2B8aAADAJ3x%2BD9aAAQMa/RgEAACA65nPA1ZKSoo2b97s690CAAD4jM8vEf7gBz/Qr371KxUVFem2227TTTfd5NW/ePFiX08JAADAVD4PWIcOHdKPfvQjSbqmPz8DAAAQKHwWsAoKCrRkyRK9/PLLnrbly5crNzfXV1MAAADwCZ/dg7Vt27Z6bUVFRb7aPQAAgM/4LGBd6TcH%2BW1CAAAQjHwWsEJCQhrUBgAAEOh8/pgGAACAYEfAAgAAMJnPfovw0qVLmjRp0lXbeA4WAAAIdD4LWHfeeafKy8uv2gYAABDofBawvv78KwAAgGDGPVgAAAAm8/mfysGN68fP/cXfUwAAwCc4gwUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYLKgDlgrVqxQnz59dMcddygnJ0eff/65JGnPnj3KyspSjx49NGTIEG3YsMHrdatWrVJGRoZ69Oih7OxslZSU%2BGP6AAAgQAVtwFq9erU2bNigVatW6b333lOHDh30hz/8QeXl5Zo4caLGjBmjPXv2aMaMGZo5c6YcDockadu2bVq6dKkWLlyo3bt3q3///powYYIuXLjg5yMCAACBImgD1sqVK1VQUKAf/ehHioiI0DPPPKNnnnlGGzduVHx8vLKyshQeHq7U1FSlp6drzZo1kqTi4mJlZmaqW7duatq0qcaPHy9J2r59uz8PBwAABJCg/GPPp06d0ueff64vvvhCgwcP1pkzZ5SSkqLZs2ertLRUCQkJXtsnJCRo8%2BbNkqTS0lINHjzY0xcaGqouXbrI4XBoyJAhDdp/eXm5nE6nV5vN1lyxsbGNPDJvYWGhXl9xY7PZAmMdsG6tRX2tQ22tE4y1DcqAdfLkSUnSO%2B%2B8o5deekmGYSgvL0/PPPOMqqur1aZNG6/to6Ki5HK5JElut1uRkZFe/ZGRkZ7%2BhiguLtayZcu82nJzc5WXl/d9Dueq7PZmloyLwBId3cLfU7gmrFtrUV/rUFvrBFNtgzJgGYYhSRo/frwnTD311FN67LHHlJqa2uDXf1%2BjR49Wenq6V5vN1lwuV2Wjxv2msLBQ2e3NVFFRpdraOlPHRuAxe31ZhXVrLeprHWprHStr668Pn0EZsG6%2B%2BWZJkt1u97TFxcXJMAxdunRJbrfba3uXy6WYmBhJUnR0dL1%2Bt9utjh07Nnj/sbGx9S4HOp3nVFNjzQ9kbW2dZWMjcATaGmDdWov6WofaWieYahs8Fzu/5pZbblFERIT279/vaTt%2B/LhuuukmpaWl1XvsQklJibp16yZJSkpKUmlpqaevtrZW%2B/bt8/QDAABcTVAGLJvNpqysLP3ud7/T//zP/%2BjMmTNavny5hg4dqhEjRuj48eNas2aNLl68qJ07d2rnzp0aNWqUJCk7O1tvvvmmPv74Y1VVVWnFihVq0qSJ%2BvXr59%2BDAgAAASMoLxFK0qRJk/Tll19q5MiRunTpkjIyMvTMM8%2BoRYsWeuGFFzRv3jzNmTNHcXFxWrRokW6//XZJUt%2B%2BffX0008rPz9fZ86cUXJysoqKitS0aVM/HxEAAAgUIUZj7%2BhGgzid50wf02YLVXR0C7lclQFxzfrHz/3F31MIapvz7/H3FBok0NZtoKG%2B1qG21rGytq1btzR1vIYKykuEAAAA/kTAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAk90QAevZZ59V586dPd/v2bNHWVlZ6tGjh4YMGaINGzZ4bb9q1SplZGSoR48eys7OVklJia%2BnDAAAAljQB6z9%2B/dr/fr1nu/Ly8s1ceJEjRkzRnv27NGMGTM0c%2BZMORwOSdK2bdu0dOlSLVy4ULt371b//v01YcIEXbhwwV%2BHAAAAAkxQB6y6ujrNmjVLOTk5nraNGzcqPj5eWVlZCg8PV2pqqtLT07VmzRpJUnFxsTIzM9WtWzc1bdpU48ePlyRt377dH4cAAAACUFAHrNdee03h4eEaOnSop620tFQJCQle2yUkJHguA36zPzQ0VF26dPGc4QIAALgam78nYJXTp09r6dKlevnll73a3W632rRp49UWFRUll8vl6Y%2BMjPTqj4yM9PQ3RHl5uZxOp1ebzdZcsbGx13IIVxUWFur1FTc2my0w1gHr1lrU1zrU1jrBWNugDVjz589XZmamOnTooM8///yaXmsYRqP2XVxcrGXLlnm15ebmKi8vr1Hjfhu7vZkl4yKwREe38PcUrgnr1lrU1zrU1jrBVNugDFh79uzRRx99pE2bNtXri46Oltvt9mpzuVyKiYn51n63262OHTs2eP%2BjR49Wenq6V5vN1lwuV2WDx2iIsLBQ2e3NVFFRpdraOlPHRuAxe31ZhXVrLeprHWprHStr668Pn0EZsDZs2KAzZ86of//%2Bkv7vjFRKSop%2B9rOf1QteJSUl6tatmyQpKSlJpaWlGjFihCSptrZW%2B/btU1ZWVoP3HxsbW%2B9yoNN5TjU11vxA1tbWWTY2AkegrQHWrbWor3WorXWCqbbBc7Hza6ZOnaotW7Zo/fr1Wr9%2BvYqKiiRJ69ev19ChQ3X8%2BHGtWbNGFy9e1M6dO7Vz506NGjVKkpSdna0333xTH3/8saqqqrRixQo1adJE/fr18%2BMRAQCAQBKUZ7AiIyO9blSvqamRJN1yyy2SpBdeeEHz5s3TnDlzFBcXp0WLFun222%2BXJPXt21dPP/208vPzdebMGSUnJ6uoqEhNmzb1/YEAAICAFJQB65t%2B%2BMMf6uDBg57ve/Xq5fXw0W96%2BOGH9fDDD/tiagAAIAgF5SVCAAAAfyJgAQAAmIyABQAAYLIb4h6sYNZzxjv%2BngIAAPgGzmABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJgsaAPW8ePHlZubq5SUFKWmpmrq1KmqqKiQJO3fv1%2BPPPKI7rzzTg0aNEgrV670eu3bb7%2BtoUOHqnv37srMzNR7773nj0MAAAABKmgD1oQJE2S327Vt2za98cYb%2BvTTT/XrX/9a1dXVevzxx3XXXXdp165dWrJkiV544QW9%2B%2B67ki6HrylTpmjy5Mn661//qpycHD355JM6efKkn48IAAAEiqAMWBUVFUpKStKkSZPUokUL3XLLLRoxYoQ%2B%2BOAD7dixQ5cuXdITTzyh5s2bKzExUSNHjlRxcbEkac2aNUpLS1NaWprCw8M1bNgwderUSRs2bPDzUQEAgEBh8/cErGC32zV//nyvthMnTig2NlalpaXq3LmzwsLCPH0JCQlas2aNJKm0tFRpaWler01ISJDD4Wjw/svLy%2BV0Or3abLbmio2NvdZD%2BU5hYUGZj/E92WyBsR6%2BWresX2tQX%2BtQW%2BsEY22DMmB9k8Ph0CuvvKIVK1Zo8%2BbNstvtXv1RUVFyu92qq6uT2%2B1WZGSkV39kZKQOHTrU4P0VFxdr2bJlXm25ubnKy8v7/gcBXEV0dAt/T%2BGa2O3N/D2FoEZ9rUNtrRNMtQ36gPXhhx/qiSee0KRJk5SamqrNmzdfcbuQkBDP/xuG0ah9jh49Wunp6V5tNltzuVyVjRr3m4Ip6aPxzF5fVgkLC5Xd3kwVFVWqra3z93SCDvW1DrW1jpW19deHz6AOWNu2bdMvfvELzZw5Uw8%2B%2BKAkKSYmRp999pnXdm63W1FRUQoNDVV0dLTcbne9/piYmAbvNzY2tt7lQKfznGpq%2BIGEdQJtfdXW1gXcnAMJ9bUOtbVOMNU2aE%2BB/OMf/9CUKVP0/PPPe8KVJCUlJengwYOqqanxtDkcDnXr1s3TX1JS4jXW1/sBAACuJigDVk1NjZ555hlNnjxZffr08epLS0tTRESEVqxYoaqqKu3du1dr165Vdna2JGnUqFHavXu3duzYoYsXL2rt2rX67LPPNGzYMH8cCgAACEAhRmNvOLoOffDBB/rXf/1XNWnSpF7fO%2B%2B8o8rKSs2aNUslJSW6%2Beab9dhjj%2Bnhhx/2bPPuu%2B9q8eLFOn78uDp06KAZM2aoV69ejZqT03muUa%2B/EpstVAMLd5k%2BLgLT5vx7/D2FBrHZQhUd3UIuV2XQXAq4nlBf61Bb61hZ29atW5o6XkMF5T1YPXv21MGDB79zm1dfffVb%2BwYNGqRBgwaZPS0AAHCDCMpLhAAAAP5EwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZEH5HCzgRvTj5/7i7yk02Ae/ut/fUwAAS3EGCwAAwGQELAAAAJMRsAAAAExGwAIAADAZN7kD8LmeM97x9xSuyeb8e/w9BQABhjNYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyfhjz1dw/PhxzZkzR3v37lXz5s01ePBgTZo0SaGh5FEA179A%2BmPa/CFtBCsC1hU89dRTSkxM1NatW3XmzBk9/vjjuvnmm/XTn/7U31MDAAABgFMy3%2BBwOHTgwAFNnjxZLVu2VHx8vHJyclRcXOzvqQEAgADBGaxvKC0tVVxcnCIjIz1tiYmJOnLkiM6fP6%2BIiIirjlFeXi6n0%2BnVZrM1V2xsrKlzDQsjHwO%2B8OPn/uLvKQQtmy1w3sfCwkID6vJroPngV/cH1b9rBKxvcLvdstvtXm1fhS2Xy9WggFVcXKxly5Z5tT355JN66qmnzJuoLge5n9zyqUaPHm16eLvRlZeXq7i4mNpagNpai/pah/dc65SXl2vp0qVBVdvgiYomMgyjUa8fPXq03njjDa//Ro8ebdLs/o/T6dSyZcvqnS1D41Fb61Bba1Ff61Bb6wRjbTmD9Q0xMTFyu91ebW63WyEhIYqJiWnQGLGxsUGTwAEAwLXjDNY3JCUl6cSJEzp79qynzeFwqEOHDmrRooUfZwYAAAIFAesbEhISlJycrMWLF%2Bv8%2BfMqKyvTSy%2B9pOzsbH9PDQAABIiw2bNnz/b3JK439957rzZt2qT/%2BI//0FtvvaWsrCyNGzdOISEh/p5aPS1atFDv3r05u2YBamsdamst6msdamudYKttiNHYO7oBAADghUuEAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2AFCIfDoYEDB2rUqFH1%2Bvbs2aOsrCz16NFDQ4YM0YYNG7z6V61apYyMDPXo0UPZ2dkqKSnx1bQD0vHjx/Xzn/9cKSkp6t%2B/vxYtWqS6ujp/Tytg7Nq1S6mpqSooKKjX9/bbb2vo0KHq3r27MjMz9d5773n66urqtGTJEg0YMEC9evXSuHHjdOzYMV9O/bp3/Phx5ebmKiUlRampqZo6daoqKiokSfv379cjjzyiO%2B%2B8U4MGDdLKlSu9XvtdtYd04MAB/eQnP9Gdd96p1NRU5efny%2Bl0SuI91kzPPvusOnfu7Pk%2BqGtr4Lq3fv16Iy0tzRg3bpwxcuRIr75Tp04Zd9xxh7FmzRqjurra%2BMtf/mJ07drV%2BOSTTwzDMIw///nPRs%2BePY2PP/7YqKqqMl544QXjnnvuMSorK/1xKAFhxIgRxjPPPGNUVFQYR44cMQYNGmSsXLnS39MKCEVFRcagQYOMMWPGGPn5%2BV59%2B/btM5KSkowdO3YY1dXVxvr1641u3boZJ06cMAzDMFatWmX079/fOHTokHHu3Dlj7ty5xtChQ426ujp/HMp16YEHHjCmTp1qnD9/3jhx4oSRmZlpTJ8%2B3aiqqjLuvfdeY%2BnSpUZlZaVRUlJi9O7d29iyZYthGFev/Y3u4sWLxt13320sW7bMuHjxonHmzBnjkUceMSZOnMh7rIn27dtn9O7d2%2BjUqZNhGMH/7xdnsALAxYsXVVxcrG7dutXr27hxo%2BLj45WVlaXw8HClpqYqPT1da9askSQVFxcrMzNT3bp1U9OmTTV%2B/HhJ0vbt2316DIHC4XDowIEDmjx5slq2bKn4%2BHjl5OSouLjY31MLCOHh4Vq7dq3atm1br2/NmjVKS0tTWlqawsPDNWzYMHXq1MnzibW4uFg5OTlq3769IiIiVFBQoLKyMu3du9fXh3FdqqioUFJSkiZNmqQWLVrolltu0YgRI/TBBx9ox44dunTpkp544gk1b95ciYmJGjlypGfdXq32N7qqqioVFBTo8ccfV5MmTRQTE6OBAwfq008/5T3WJHV1dZo1a5ZycnI8bcFeWwJWABg5cqTatGlzxb7S0lIlJCR4tSUkJHhOo36zPzQ0VF26dJHD4bBuwgGstLRUcXFxioyM9LQlJibqyJEjOn/%2BvB9nFhjGjh2rli1bXrHv29aqw%2BFQdXW1Dh065NUfERGhtm3bslb/P7vdrvnz5%2Bvmm2/2tJ04cUKxsbEqLS1V586dFRYW5un7rveBr/qp7WWRkZEaOXKkbDabJOnw4cP605/%2BpB//%2BMe8x5rktddeU3h4uIYOHeppC/baErACnNvtlt1u92qLioqSy%2BXy9H89LEiX30y%2B6oe3K9Xzq/pRs8b5rrX4xRdfyDAM1uo1cDgceuWVV/TEE0986/uA2%2B1WXV0d7wMNdPz4cSUlJWnw4MFKTk5WXl4e77EmOH36tJYuXapZs2Z5tQd7bQlY14H169erc%2BfOV/zvjTfeaPT4hmGYMMsbB/WyztVqS%2B0b5sMPP9S4ceM0adIkpaamfut2ISEhnv%2BntlcXFxcnh8Ohd955R5999pl%2B%2BctfNuh11Pa7zZ8/X5mZmerQocM1vzaQa2vz9wQgDR8%2BXMOHD/9er42Ojpbb7fZqc7lciomJ%2BdZ%2Bt9utjh07fr/JBrmYmJgr1iskJMRTU3w/37YWY2JiFBUVpdDQ0Cv2t2rVypfTvO5t27ZNv/jFLzRz5kw9%2BOCDki6v288%2B%2B8xrO7fb7anrd9Ue3kJCQhQfH6%2BCggKNGTNGaWlpvMc2wp49e/TRRx9p06ZN9fqC/d8vzmAFuOTk5Hq/tlpSUuK5IT4pKUmlpaWevtraWu3bt%2B%2BKN8zjcr1OnDihs2fPetocDoc6dOigFi1a%2BHFmgS8pKaneWnU4HOrWrZvCw8PVsWNHr7VaUVGho0ePqmvXrr6e6nXrH//4h6ZMmaLnn3/eE66ky7U9ePCgampqPG1f1far/m%2BrPS6HgIyMDK/HsYSGXv7nsWvXrrzHNsKGDRt05swZ9e/fXykpKcrMzJQkpaSkqFOnTsFdW//9AiOu1W9/%2B9t6j2k4ffq00b17d%2BP11183qqurjR07dhhdu3Y19u/fbxiGYezcudO48847jY8%2B%2Bsi4cOGCsXTpUiMtLc2oqqryxyEEhJEjRxrTp083zp07Zxw6dMhIT083XnnlFX9PK6BMmTKl3mMaDh48aCQnJxvbt283qqurjTVr1hjdu3c3ysvLDcMwjP/6r/8y%2BvXr53lMw8yZM42HHnrIH9O/Ll26dMn48Y9/bLz22mv1%2Bi5evGj079/f%2BO1vf2tcuHDB%2BPjjj42ePXsa27dvNwzj6rW/0VVUVBipqanGggULjAsXLhhnzpwxxo0bZzz88MO8xzaS2%2B02Tpw44fnvo48%2BMjp16mScOHHCOH78eFDXloAVAAYNGmQkJSUZXbp0MTp37mwkJSUZSUlJxueff24YhmH87W9/M4YNG2YkJiYagwYN8jz75iurV6820tLSjKSkJCM7O9s4ePCgPw4jYJw4ccIYP3680bVrVyM1NdX47W9/y7OYGuirtXn77bcbt99%2Bu%2Bf7r2zZssUYNGiQkZiYaAwfPtz429/%2B5umrq6sznn/%2BeePuu%2B82unbtajz22GM8p%2Blr/v73vxudOnXy1PTr/33%2B%2BefGwYMHjTFjxhhJSUlGv379jNWrV3u9/rtqD8M4cOCA8cgjjxhdu3Y17rrrLiM/P984efKkYRi8x5rp2LFjnudgGUZw1zbEMAL4DjIAAIDrEPdgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmOz/AantYq8GRk9GAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common8994879604975734326”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1285</td> <td class=”number”>40.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>245</td> <td class=”number”>7.6%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>191</td> <td class=”number”>6.0%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.333333333</td> <td class=”number”>96</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>87</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>72</td> <td class=”number”>2.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-25.0</td> <td class=”number”>65</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-66.666666667</td> <td class=”number”>54</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.333333333</td> <td class=”number”>46</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>34</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (344)</td> <td class=”number”>834</td> <td class=”number”>26.0%</td> <td>

<div class=”bar” style=”width:65%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>201</td> <td class=”number”>6.3%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme8994879604975734326”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>245</td> <td class=”number”>7.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-85.714285714</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-83.333333333</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-80.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-75.0</td> <td class=”number”>11</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>166.666666667</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>250.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>300.0</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:37%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>400.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_REDEMP_SNAPS_12_16”>PCH_REDEMP_SNAPS_12_16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2860</td>

</tr> <tr>

<th>Unique (%)</th> <td>89.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>10.9%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>351</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>-19.942</td>

</tr> <tr>

<th>Minimum</th> <td>-93.354</td>

</tr> <tr>

<th>Maximum</th> <td>405.98</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-3728935783486102555”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-3728935783486102555,#minihistogram-3728935783486102555”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-3728935783486102555”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-3728935783486102555”

aria-controls=”quantiles-3728935783486102555” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-3728935783486102555” aria-controls=”histogram-3728935783486102555”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-3728935783486102555” aria-controls=”common-3728935783486102555”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-3728935783486102555” aria-controls=”extreme-3728935783486102555”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-3728935783486102555”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-93.354</td>

</tr> <tr>

<th>5-th percentile</th> <td>-45.427</td>

</tr> <tr>

<th>Q1</th> <td>-32.324</td>

</tr> <tr>

<th>Median</th> <td>-22.087</td>

</tr> <tr>

<th>Q3</th> <td>-10.496</td>

</tr> <tr>

<th>95-th percentile</th> <td>11.866</td>

</tr> <tr>

<th>Maximum</th> <td>405.98</td>

</tr> <tr>

<th>Range</th> <td>499.34</td>

</tr> <tr>

<th>Interquartile range</th> <td>21.827</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>20.923</td>

</tr> <tr>

<th>Coef of variation</th> <td>-1.0492</td>

</tr> <tr>

<th>Kurtosis</th> <td>71.923</td>

</tr> <tr>

<th>Mean</th> <td>-19.942</td>

</tr> <tr>

<th>MAD</th> <td>13.965</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>4.6556</td>

</tr> <tr>

<th>Sum</th> <td>-57015</td>

</tr> <tr>

<th>Variance</th> <td>437.77</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-3728935783486102555”>

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vX3/GdXz88cdKT09Xbm6uz/jatWt12WWXKSUlxefrx%2BDW1NSkpUuXavTo0Ro2bJiys7O1f/9%2B7/Iej0ezZs1Senq6RowYoXnz5qmhocFsAwAAQIcVZvcGy8vLtWbNGr355puqq6vT2LFj9Yc//EFDhw71vmbYsGF64oknNH78%2BFOu55VXXtGaNWvUu3fvFueHDRumP/7xjy3OrVq1SuvXr9crr7yi7t27a%2BnSpcrJydFbb72lkJAQzZ8/Xz/88IM2bNig48eP66GHHlJBQYEee%2Byx9u08AAA4L9h%2BBuumm27Sli1bNH36dH300UdavHixT7iSpIyMDB06dOi06wkPDz9twDqd4uJiZWVlqW/fvoqMjFRubq7Ky8u1fft2ff/993r//feVm5srp9Op7t276/7779ef/vQnHT9%2B/Ky3BQAAzj%2B2n8FasWKFhg8ffsbXbd%2B%2B/bTzd91112nnKysr9ctf/lIlJSWKiorSzJkzdcstt6ihoUFlZWVKTEz0vjYyMlK9e/eWy%2BXSkSNHFBoaqoSEBO98UlKSjh49qm%2B//dZnHAAAoCW2B6yEhATdd999mjx5sq677jpJ0n/913/p008/1eLFixUTE9PubTidTsXHx%2BvXv/61%2BvXrp7/85S965JFHFBcXp5/97GeyLEvR0dE%2By0RHR6u6uloxMTGKjIxUSEiIz5wkVVdXt2r7VVVVcrvdPmNhYZ0VFxfXpv0JDXX4fIc9wsLod3tx7PoPvfUv%2Bus/50tvbQ9YCxcu1JEjR9SvXz/v2MiRI/Xxxx9r0aJFWrRoUbu3MXLkSI0cOdL795tuukl/%2BctftHbtWuXl5UmSLMs65fKnm2uN4uJiFRYW%2Bozl5ORo5syZ7VpvVNRF7VoeZyc2NiLQJXQYHLv%2BQ2/9i/76T0fvre0B65NPPtH69esVGxvrHYuPj1dBQYFuvvlmv223R48eKikpUUxMjBwOhzwej8%2B8x%2BNR165d5XQ6VVtbq8bGRoWGhnrnJKlr166t2tbUqVM1atQon7GwsM6qrq5rU%2B2hoQ5FRV2kmpp6NTY2tWkdOHtt/ffCP3Hs%2Bg%2B99S/66z929zZQPyzbHrAaGhoUHh7ebNzhcKi%2Bvt7INv7nf/5H0dHRuvHGG71j5eXl6tWrl8LDw9W/f3%2BVlpZ67wWrqanRvn37lJqaqh49esiyLO3atUtJSUmSJJfLpaioKPXp06dV24%2BLi2t2OdDtPqITJ9p3IDU2NrV7HWg9em0Ox67/0Fv/or/%2B09F7a/sF0GHDhmnRokU6fPiwd%2Bwf//iHnnzyyWa/TdhWP/zwg55%2B%2Bmm5XC4dP35cGzZs0EcffaRp06ZJkjIzM7VixQqVl5ertrZWBQUFGjhwoFJSUuR0OjV27Fg9//zzOnTokA4cOKBly5Zp8uTJCguzPY8CAIAgZHtimDt3rv7jP/5DV111lSIjI9XU1KS6ujr16tXrlM%2BtaklKSookeT/H8P3335d08mzTXXfdpbq6Oj300ENyu93q2bOnli1bpuTkZEnStGnT5Ha7deedd6qurk5paWk%2B90w99dRTevzxxzV69GhdcMEFuvnmm5s9zBQAAOBUQqz23tHdBj/88IM%2B%2Bugj7du3Tw6HQ3369NGIESO89zx1RG73kTYvGxbmUGxshKqr64L6dOq45z8NdAln5e1ZVwe6hKDXUY7dcxG99S/66z9297Zbty5%2B30ZLAnLNq1OnTt5HNAAAAHQ0tges/fv3a8mSJfrmm29a/Hy/Dz74wO6SAAAAjArIPVhVVVUaMWKEOnfubPfmAQAA/M72gFVSUqIPPvhATqfT7k0DAADYwvbHNHTt2pUzVwAAoEOzPWBNnz5dhYWF7f44GgAAgHOV7ZcIP/roI3311Vdau3atevbsKYfDN%2BO99tprdpcEAABglO0BKzIyUj//%2Bc/t3iwAAIBtbA9YCxcutHuTAAAAtrL9HixJ%2Bvbbb/XSSy/p0Ucf9Y59/fXXgSgFAADAONsD1tatWzVhwgS999572rBhg6STDx%2B96667eMgoAADoEGwPWEuXLtXDDz%2Bs9evXKyQkRJLUq1cvLVq0SMuWLbO7HAAAAONsD1j/93//p8zMTEnyBixJuuGGG1ReXm53OQAAAMbZHrC6dOnS4mcQVlVVqVOnTnaXAwAAYJztAWvIkCF69tlnVVtb6x3bs2eP8vPzddVVV9ldDgAAgHG2P6bh0Ucf1d133620tDQ1NjZqyJAhqq%2BvV//%2B/bVo0SK7ywEAADDO9oB1ySWXaMOGDfrwww%2B1Z88eXXjhherTp4%2Buvvpqn3uyAAAAgpXtAUuSLrjgAl133XWB2DQAAIDf2R6wRo0addozVTwLCwAABDvbA9aNN97oE7AaGxu1Z88euVwu3X333XaXAwAAYJztASsvL6/F8XfffVd//etfba4GAADAvIB8FmFLrrvuOv35z38OdBkAAADtds4ErB07dsiyrECXAQAA0G62XyKcNm1as7H6%2BnqVl5drzJgxdpcDAABgnO0BKz4%2BvtlvEYaHh2vy5MmaMmWK3eUAAAAYZ3vA4mntAACgo7M9YL355putfu2tt97qx0oAAAD8w/aANW/ePDU1NTW7oT0kJMRnLCQkhIAFAACCku0B67e//a1effVV3XfffUpISJBlWdq9e7deeeUV3XHHHUpLS7O7JAAAAKMCcg9WUVGRunfv7h274oor1KtXL2VnZ2vDhg12lwQAAGCU7c/B2rt3r6Kjo5uNR0VFqaKiwu5yAAAAjLM9YPXo0UOLFi1SdXW1d6ympkZLlizRpZdeanc5AAAAxtl%2BiXDu3LmaPXu2iouLFRERIYfDodraWl144YVatmyZ3eUAAAAYZ3vAGjFihLZs2aIPP/xQBw4ckGVZ6t69u6655hp16dLF7nIAAACMsz1gSdJFF12k0aNH68CBA%2BrVq1cgSgAAAPAb2%2B/BamhoUH5%2BvgYPHqxx48ZJOnkP1j333KOamhq7ywEAADDO9oC1ePFi7dy5UwUFBXI4/rn5xsZGFRQU2F0OAACAcbYHrHfffVcvvviibrjhBu%2BHPkdFRWnhwoV677337C4HAADAONsDVl1dneLj45uNO51OHT161O5yAAAAjLM9YF166aX661//Kkk%2Bnz34zjvv6N///d/tLgcAAMA423%2BL8Be/%2BIUefPBB3XbbbWpqatLvf/97lZSU6N1339W8efPsLgcAAMA42wPW1KlTFRYWppUrVyo0NFS/%2Bc1v1KdPHxUUFOiGG26wuxwAAADjbA9Yhw4d0m233abbbrvN7k0DAADYwvZ7sEaPHu1z7xUAAEBHY3vASktL09tvv233ZgEAAGxj%2ByXCf/u3f9MzzzyjoqIiXXrppbrgggt85pcsWWJ3SQAAAEbZHrDKysr0s5/9TJJUXV1t9%2BYBAAD8zraAlZubq6VLl%2BqPf/yjd2zZsmXKycmxqwQAAABb2HYP1qZNm5qNFRUV2bV5AAAA29gWsFr6zUF%2BmxAAAHREtgWsHz/Y%2BUxjAAAAwc72xzSY9PHHHys9PV25ubnN5jZu3Kjx48dr8ODBmjRpkj755BPvXFNTk5YuXarRo0dr2LBhys7O1v79%2B73zHo9Hs2bNUnp6ukaMGKF58%2BapoaHBln0CAADBL2gD1iuvvKIFCxaod%2B/ezeZ27typ/Px85eXl6fPPP1dWVpYeeOABHThwQJK0atUqrV%2B/XkVFRdq8ebPi4%2BOVk5PjvWQ5f/581dfXa8OGDfrTn/6k8vJyFRQU2Lp/AAAgeNn2W4THjx/X7NmzzzjW2udghYeHa82aNXrmmWd07Ngxn7nVq1crIyNDGRkZkqQJEyZo5cqVWrdune69914VFxcrKytLffv2lXTyNxzT0tK0fft29ezZU%2B%2B//77eeOMNOZ1OSdL999%2Bvhx56SPn5%2Bc2e2wUAAPBTtgWsoUOHqqqq6oxjrXXXXXedcq60tNQbrn6UmJgol8ulhoYGlZWVKTEx0TsXGRmp3r17y%2BVy6ciRIwoNDVVCQoJ3PikpSUePHtW3337rM34qVVVVcrvdPmNhYZ0VFxfX2t3zERrq8PkOe4SF0e/24tj1H3rrX/TXf86X3toWsP71%2BVf%2B5vF4FB0d7TMWHR2tsrIyHT58WJZltThfXV2tmJgYRUZG%2BtyA/%2BNrW/tg1OLiYhUWFvqM5eTkaObMmW3ZHa%2BoqIvatTzOTmxsRKBL6DA4dv2H3voX/fWfjt5b25/kbpczPQLidPPtfXzE1KlTNWrUKJ%2BxsLDOqq6ua9P6QkMdioq6SDU19WpsbGpXbWi9tv574Z84dv2H3voX/fUfu3sbqB%2BWO2TAio2Nlcfj8RnzeDxyOp2KiYmRw%2BFocb5r165yOp2qra1VY2OjQkNDvXOS1LVr11ZtPy4urtnlQLf7iE6caN%2BB1NjY1O51oPXotTkcu/5Db/2L/vpPR%2B9th7wAmpycrJKSEp8xl8ulQYMGKTw8XP3791dpaal3rqamRvv27VNqaqoGDhwoy7K0a9cun2WjoqLUp08f2/YBAAAErw4ZsG6//XZ99tln2rJli44dO6Y1a9Zo7969mjBhgiQpMzNTK1asUHl5uWpra1VQUKCBAwcqJSVFTqdTY8eO1fPPP69Dhw7pwIEDWrZsmSZPnqywsA55wg8AABgWtIkhJSVFknTixAlJ0vvvvy/p5NmmAQMGqKCgQAsXLlRFRYX69eun5cuXq1u3bpKkadOmye12684771RdXZ3S0tJ8bkp/6qmn9Pjjj2v06NG64IILdPPNN7f4MFMAAICWhFh8IKAt3O4jbV42LMyh2NgIVVfXBfX16nHPfxroEs7K27OuDnQJQa%2BjHLvnInrrX/TXf%2BzubbduXfy%2BjZZ0yEuEAAAAgUTAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMKzDBqyEhAQlJycrJSXF%2B/X0009LkrZu3arJkydryJAhuummm7Ru3TqfZVesWKGxY8dqyJAhyszMVElJSSB2AQAABKmwQBfgT%2B%2B884569uzpM1ZVVaX7779f8%2BbN0/jx4/Xll19qxowZ6tOnj1JSUrRp0ya99NJL%2Bu1vf6uEhAStWLFC9913n9577z117tw5QHsCAACCSYc9g3Uq69evV3x8vCZPnqzw8HClp6dr1KhRWr16tSSpuLhYkyZN0qBBg3ThhRfqnnvukSRt3rw5kGUDAIAg0qED1pIlSzRy5EhdccUVmj9/vurq6lRaWqrExESf1yUmJnovA/503uFwaODAgXK5XLbWDgAAgleHvUR4%2BeWXKz09Xc8995z279%2BvWbNm6cknn5TH41H37t19XhsTE6Pq6mpJksfjUXR0tM98dHS0d741qqqq5Ha7fcbCwjorLi6uTfsSGurw%2BQ57hIXR7/bi2PUfeutf9Nd/zpfedtiAVVxc7P1z3759lZeXpxkzZmjo0KFnXNayrHZvu7Cw0GcsJydHM2fObNd6o6IuatfyODuxsRGBLqHD4Nj1H3rrX/TXfzp6bztswPqpnj17qrGxUQ6HQx6Px2euurpaTqdTkhQbG9ts3uPxqH///q3e1tSpUzVq1CifsbCwzqqurmtT7aGhDkVFXaSamno1Nja1aR04e23998I/cez6D731L/rrP3b3NlA/LHfIgLVjxw6tW7dOc%2BbM8Y6Vl5erU6dOysjI0BtvvOHz%2BpKSEg0aNEiSlJycrNLSUk2cOFGS1NjYqB07dmjy5Mmt3n5cXFyzy4Fu9xGdONG%2BA6mxsand60Dr0WtzOHb9h976F/31n47e2w55AbRr164qLi5WUVGRfvjhB%2B3Zs0cvvPCCpk6dqltuuUUVFRVavXq1jh07pg8//FAffvihbr/9dklSZmam3nzzTW3btk319fV6%2BeWX1alTJ40cOTKwOwUAAIJGhzyD1b17dxUVFWnJkiXegDRx4kTl5uYqPDxcy5cv14IFC/Tkk0%2BqR48eWrx4sS677DJJ0s9//nP9%2Bte/1qxZs3Tw4EGlpKSoqKhIF154YYC9ySnfAAAMNUlEQVT3CgAABIsQq713dKNV3O4jbV42LMyh2NgIVVfXBfXp1HHPfxroEs7K27OuDnQJQa%2BjHLvnInrrX/TXf%2BzubbduXfy%2BjZZ0yEuEAAAAgUTAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwLC3QBwLlq3POfBrqEs/L2rKsDXQIA4P8jYAW5YAsBAACcD7hECAAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAGrBRUVFbr33nuVlpama6%2B9VosXL1ZTU1OgywIAAEEiLNAFnIsefPBBJSUl6f3339fBgwc1ffp0XXzxxfrlL38Z6NIAAEAQ4AzWT7hcLu3atUt5eXnq0qWL4uPjlZWVpeLi4kCXBgAAggRnsH6itLRUPXr0UHR0tHcsKSlJe/bsUW1trSIjI8%2B4jqqqKrndbp%2BxsLDOiouLa1NNoaEOn%2B9AS8Y9/2mgS2i1v%2BRdE%2BgSgh7vC/5Ff/3nfOktAesnPB6PoqKifMZ%2BDFvV1dWtCljFxcUqLCz0GXvggQf04IMPtqmmqqoq/eEPv9XUqVObhbQvnrmhTevESVVVVSouLm6xt2g/%2Bus/p3tfQPvRX/85X3rbseNjG1mW1a7lp06dqrVr1/p8TZ06tc3rc7vdKiwsbHZWDO1Hb/2L/voPvfUv%2Bus/50tvOYP1E06nUx6Px2fM4/EoJCRETqezVeuIi4vr0KkcAACcHmewfiI5OVmVlZU6dOiQd8zlcqlfv36KiIgIYGUAACBYELB%2BIjExUSkpKVqyZIlqa2tVXl6u3//%2B98rMzAx0aQAAIEiEPvHEE08EuohzzTXXXKMNGzbo6aef1p///GdNnjxZ2dnZCgkJCVhNERERGj58OGfR/IDe%2Bhf99R9661/013/Oh96GWO29oxsAAAA%2BuEQIAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgB6xzjcrl0/fXX6/bbb282t3XrVk2ePFlDhgzRTTfdpHXr1vnMr1ixQmPHjtWQIUOUmZmpkpISu8oOShUVFbr33nuVlpama6%2B9VosXL1ZTU1OgywoaH3/8sdLT05Wbm9tsbuPGjRo/frwGDx6sSZMm6ZNPPvHONTU1aenSpRo9erSGDRum7Oxs7d%2B/387Sz3kVFRXKyclRWlqa0tPTNWfOHNXU1EiSdu7cqTvuuENDhw7VmDFj9Oqrr/ose7re46Rdu3bp7rvv1tChQ5Wenq5Zs2bJ7XZL4n3WpGeffVYJCQnev593vbVwznjrrbesjIwMKzs725oyZYrP3D/%2B8Q/r8ssvt1avXm01NDRYn376qZWammr9/e9/tyzLsj744APriiuusLZt22bV19dby5cvt66%2B%2Bmqrrq4uELsSFCZOnGg99thjVk1NjbVnzx5rzJgx1quvvhrosoJCUVGRNWbMGGvatGnWrFmzfOZ27NhhJScnW1u2bLEaGhqst956yxo0aJBVWVlpWZZlrVixwrr22mutsrIy68iRI9ZTTz1ljR8/3mpqagrErpyTbr75ZmvOnDlWbW2tVVlZaU2aNMmaO3euVV9fb11zzTXWSy%2B9ZNXV1VklJSXW8OHDrXfffdeyrDP3HpZ17Ngx66qrrrIKCwutY8eOWQcPHrTuuOMO6/777%2Bd91qAdO3ZYw4cPtwYMGGBZ1vn5fxhnsM4hx44dU3FxsQYNGtRsbv369YqPj9fkyZMVHh6u9PR0jRo1SqtXr5YkFRcXa9KkSRo0aJAuvPBC3XPPPZKkzZs327oPwcLlcmnXrl3Ky8tTly5dFB8fr6ysLBUXFwe6tKAQHh6uNWvWqHfv3s3mVq9erYyMDGVkZCg8PFwTJkzQgAEDvD%2BtFhcXKysrS3379lVkZKRyc3NVXl6u7du3270b56SamholJydr9uzZioiI0CWXXKKJEyfqiy%2B%2B0JYtW3T8%2BHHNmDFDnTt3VlJSkqZMmeI9bs/Ue0j19fXKzc3V9OnT1alTJzmdTl1//fX65ptveJ81pKmpSY8//riysrK8Y%2BdjbwlY55ApU6aoe/fuLc6VlpYqMTHRZywxMdF7CvWn8w6HQwMHDpTL5fJfwUGstLRUPXr0UHR0tHcsKSlJe/bsUW1tbQArCw533XWXunTp0uLcqY5Vl8ulhoYGlZWV%2BcxHRkaqd%2B/eHKv/X1RUlBYuXKiLL77YO1ZZWam4uDiVlpYqISFBoaGh3rnTvQ/8OE9v/yk6OlpTpkxRWFiYJOnbb7/VG2%2B8oXHjxvE%2Ba8hrr72m8PBwjR8/3jt2PvaWgBUkPB6PoqKifMZiYmJUXV3tnf/XsCCdfCP5cR6%2BWurnj/2jZ%2B1zumPx8OHDsiyLY/UsuFwurVy5UjNmzDjl%2B4DH41FTUxPvA2ehoqJCycnJuvHGG5WSkqKZM2fyPmvA999/r5deekmPP/64z/j52FsClo3eeustJSQktPi1du3adq/fsiwDVZ4/6Jf/nKm39L51vvzyS2VnZ2v27NlKT08/5etCQkK8f6a3rdOjRw%2B5XC6988472rt3rx555JFWLUd/T2/hwoWaNGmS%2BvXrd9bLdrTehgW6gPPJLbfcoltuuaVNy8bGxsrj8fiMVVdXy%2Bl0nnLe4/Gof//%2BbSu2g3M6nS32KyQkxNtTtM2pjkWn06mYmBg5HI4W57t27Wpnmee8TZs26eGHH9b8%2BfN16623Sjp53O7du9fndR6Px9vX0/UezYWEhCg%2BPl65ubmaNm2aMjIyeJ9th61bt%2Brrr7/Whg0bms2dj/%2BHcQYrSKSkpDT7ldWSkhLvDfHJyckqLS31zjU2NmrHjh0t3jCPk/2qrKzUoUOHvGMul0v9%2BvVTREREACsLfsnJyc2OVZfLpUGDBik8PFz9%2B/f3OVZramq0b98%2Bpaam2l3qOeurr75Sfn6%2BXnjhBW%2B4kk72dvfu3Tpx4oR37Mfe/jh/qt7jpK1bt2rs2LE%2Bj2RxOE7%2BV5iamsr7bDusW7dOBw8e1LXXXqu0tDRNmjRJkpSWlqYBAwacf70N3C8w4lRefPHFZo9p%2BP77763Bgwdbr7/%2ButXQ0GBt2bLFSk1NtXbu3GlZlmV9%2BOGH1tChQ62vv/7aOnr0qPXSSy9ZGRkZVn19fSB2IShMmTLFmjt3rnXkyBGrrKzMGjVqlLVy5cpAlxVU8vPzmz2mYffu3VZKSoq1efNmq6GhwVq9erU1ePBgq6qqyrIsy/rv//5va%2BTIkd7HNMyfP9%2B67bbbAlH%2BOen48ePWuHHjrNdee63Z3LFjx6xrr73WevHFF62jR49a27Zts6644gpr8%2BbNlmWdufewrJqaGis9Pd1atGiRdfToUevgwYNWdna29Ytf/IL32XbyeDxWZWWl9%2Bvrr7%2B2BgwYYFVWVloVFRXnXW8JWOeQMWPGWMnJydbAgQOthIQEKzk52UpOTra%2B%2B%2B47y7Is629/%2B5s1YcIEKykpyRozZoz32Tc/WrVqlZWRkWElJydbmZmZ1u7duwOxG0GjsrLSuueee6zU1FQrPT3devHFF3kWUyv9eGxedtll1mWXXeb9%2B4/effdda8yYMVZSUpJ1yy23WH/729%2B8c01NTdYLL7xgXXXVVVZqaqr1q1/9iuc0/Yv//d//tQYMGODt6b9%2Bfffdd9bu3butadOmWcnJydbIkSOtVatW%2BSx/ut7jpF27dll33HGHlZqaal155ZXWrFmzrAMHDliWxfusSfv37/c%2BB8uyzr/ehlhWB7urDAAAIMC4BwsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADPt/bK8BA194tZgAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-3728935783486102555”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-31.382259185584143</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14.766414693954195</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.727560652703104</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>40.66110047898739</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.7861944103078</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-24.25459242659929</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-20.872452066960705</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-23.756639958918576</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-14.49355335018637</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-22.139024954817586</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2849)</td> <td class=”number”>2849</td> <td class=”number”>88.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>351</td> <td class=”number”>10.9%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-3728935783486102555”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-93.3539482297769</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-85.90756052007816</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-71.82452044481902</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-67.7326095706506</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-67.3805592808927</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>107.43031920457118</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>146.6120379870547</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>175.46705483314022</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>227.4641245673449</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>405.9816390895147</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_REDEMP_WICS_08_12”>PCH_REDEMP_WICS_08_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>1938</td>

</tr> <tr>

<th>Unique (%)</th> <td>60.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>39.7%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>1273</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>-3.5951</td>

</tr> <tr>

<th>Minimum</th> <td>-98.127</td>

</tr> <tr>

<th>Maximum</th> <td>254.62</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-6563243437933534735”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-6563243437933534735,#minihistogram-6563243437933534735”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-6563243437933534735”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-6563243437933534735”

aria-controls=”quantiles-6563243437933534735” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-6563243437933534735” aria-controls=”histogram-6563243437933534735”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-6563243437933534735” aria-controls=”common-6563243437933534735”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-6563243437933534735” aria-controls=”extreme-6563243437933534735”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-6563243437933534735”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-98.127</td>

</tr> <tr>

<th>5-th percentile</th> <td>-34.5</td>

</tr> <tr>

<th>Q1</th> <td>-17.359</td>

</tr> <tr>

<th>Median</th> <td>-5.6912</td>

</tr> <tr>

<th>Q3</th> <td>7.6577</td>

</tr> <tr>

<th>95-th percentile</th> <td>33.164</td>

</tr> <tr>

<th>Maximum</th> <td>254.62</td>

</tr> <tr>

<th>Range</th> <td>352.75</td>

</tr> <tr>

<th>Interquartile range</th> <td>25.016</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>22.289</td>

</tr> <tr>

<th>Coef of variation</th> <td>-6.1998</td>

</tr> <tr>

<th>Kurtosis</th> <td>12.001</td>

</tr> <tr>

<th>Mean</th> <td>-3.5951</td>

</tr> <tr>

<th>MAD</th> <td>16.148</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.6249</td>

</tr> <tr>

<th>Sum</th> <td>-6963.7</td>

</tr> <tr>

<th>Variance</th> <td>496.79</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-6563243437933534735”>

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Z4jQ2YcIEZWVl/ZI45xUWdrXR7XkCX8zcWJGRTd29BON89XiT27f4Ym5fyOyVBevUqVMaO3as7rzzTsf9U0899ZSys7Mb9Xy73X5J%2B8/IyFBqaqrTWGBgE1VUVF3Sds8ICPBXWNjVqqysVm1tnZFtXul8MfPFMnV%2BXQl89XiTm9zezh2Z3fWPT5cXrNTUVKWlpenee%2B/Vtddee1n2UVhYqO%2B%2B%2B06TJ09WQECAmjVrpqysLA0ZMkR33HGHbDab0%2BMrKioUFRUlSYqMjKw3b7PZ1LZt20bvPzo6ut7lwPLyo6qpMXsy1dbWGd/mlc4XMzeWN35efPV4k9u3%2BGJuX8js8oug9957r9avX68%2Bffpo9OjR2rRpk2pqaozuo7a2VnV1dU6vRJ06dUqSlJSUVO9tF4qKipSYmChJio%2BPV3FxsdO2du/e7ZgHAABoiMsL1oQJE7R%2B/Xq9%2Beabatu2rebOnavk5GQtWLBA%2B/fvN7KPTp06qUmTJlq8eLGqq6tVUVGh3Nxcde3aVUOGDFFpaakKCgp08uRJbdu2Tdu2bdPw4cMlSZmZmVq1apV27Nih6upq5ebmKigoSL169TKyNgAA4P3cdht/x44dNXXqVG3ZskXTpk3Tm2%2B%2BqQEDBuiBBx7Qrl27LmnbkZGReumll/T555%2BrZ8%2BeuvvuuxUSEqKFCxeqefPmWrZsmZYvX64uXbpo7ty5WrBggW6%2B%2BWZJUs%2BePTV58mRNnDhR3bp10/bt25WXl6eQkBATsQEAgA/ws1/qHd2/0OnTp/WPf/xDK1eu1Mcff6zY2FgNHz5cZWVl%2Bvvf/66nnnpKgwYNcsfSLovy8qPGthUY6K/IyKaqqKjy%2BmvYZ7gj813PfeSS/ZiyYeLt7l6CMb54jkvkJrf3c0fma65p5pL9nM3lN7mXlJRoxYoVWrVqlaqqqtS/f3/97W9/U5cuXRyP6dq1q2bPnu1VBQsAAPgOlxesgQMH6sYbb9SYMWN0zz33KCIiot5jkpOTHb83EAAAwNO4vGDl5%2BerW7duDT5u586dLlgNAACAeS6/yb19%2B/YaO3as3n33XcfYX//6Vz344IP13n8KAADAE7m8YM2bN09Hjx5VmzZtHGO9evVSXV2d5s%2Bf7%2BrlAAAAGOfyS4Qffvih1q5dq8jISMdYbGyscnJydPfdd7t6OQAAAMa5/BWsEydOKDg4uP5C/P1VXV3t6uUAAAAY5/KC1bVrV82fP18//vijY%2Bz777/XU0895fRWDQAAAJ7K5ZcIp02bpj/84Q/6zW9%2Bo9DQUNXV1amqqkqtW7fWq6%2B%2B6urlAAAAGOfygtW6dWu9/fbbev/99/XNN9/I399fN954o3r06KGAgABXLwcAAMA4lxcsSQoKClKfPn3csWsAAIDLzuUF69tvv9XChQv11Vdf6cSJE/Xm33vvPVcvCQAAwCi33INVVlamHj16qEmTJq7ePQAAwGXn8oJVVFSk9957T1FRUa7eNQAAgEu4/G0amjdvzitXAADAq7m8YI0ZM0ZLliyR3W539a4BAABcwuWXCN9//319/vnnWrlypa677jr5%2Bzt3vDfeeMPVSwIAADDK5QUrNDRUPXv2dPVuAQAAXMblBWvevHmu3iUAAIBLufweLEn6%2BuuvtXjxYj355JOOsX/961/uWAoAAIBxLi9YhYWFGjx4sDZt2qR169ZJ%2BunNR%2B%2B//37eZBQAAHgFlxesRYsW6bHHHtPatWvl5%2Bcn6affTzh//nwtXbrU1csBAAAwzuUF68svv1RmZqYkOQqWJN15550qKSlx9XIAAACMc3nBatas2Tl/B2FZWZmCgoJcvRwAAADjXF6wOnfurLlz5%2BrYsWOOsf3792vq1Kn6zW9%2B4%2BrlAAAAGOfyt2l48skn9bvf/U7du3dXbW2tOnfurOrqarVt21bz58939XIAAACMc3nBatWqldatW6dt27Zp//79CgkJ0Y033qjbb7/d6Z4sAAAAT%2BXygiVJV111lfr06eOOXQMAAFx2Li9YqampF3ylivfCAgAAns7lBWvAgAFOBau2tlb79%2B%2BX1WrV7373O1cvBwAAwDiXF6zs7Oxzjm/cuFH//Oc/XbwaAAAA89zyuwjPpU%2BfPnr77bfdvQwAAIBLdsUUrN27d8tut7t7GQAAAJfM5ZcIR4wYUW%2BsurpaJSUl6tevn6uXAwAAYJzLC1ZsbGy9nyIMDg5Wenq6hg0b5urlAAAAGOfygsW7tQMAAG/n8oK1atWqRj/2nnvuuYwrAQAAuDxcXrCmT5%2Buurq6eje0%2B/n5OY35%2BflRsAAAgEdy%2BU8R/uUvf1GPHj302muv6dNPP9Unn3yi5cuXq2fPnnrxxRe1a9cu7dq1Szt37rzkfeXm5qpHjx669dZbNWrUKH333XeSpMLCQqWnp6tz584aOHCg1qxZ4/S8/Px89e/fX507d1ZmZqaKiooueS0AAMB3uLxgzZ8/X3PmzFGXLl0UGhqqZs2a6bbbbtPTTz%2BtP/3pTwoKCnL8dylee%2B01rVmzRvn5%2Bfrwww/Vpk0b/fWvf1VZWZnGjx%2BvESNGqLCwUNOnT9fMmTNltVolSZs3b9bixYv17LPPavv27UpJSdHYsWN1/PhxE/EBAIAPcHnBOnDggMLDw%2BuNh4WFqbS01Nh%2BXn75ZU2aNEm/%2BtWvFBoaqhkzZmjGjBlau3atYmNjlZ6eruDgYCUlJSk1NVUFBQWSJIvForS0NCUmJiokJESjR4%2BWJG3ZssXY2gAAgHdz%2BT1YMTExmj9/vh599FFFRkZKkiorK/XnP/9Z119/vZF9fP/99/ruu%2B/0448/asCAATp8%2BLC6d%2B%2Bu2bNnq7i4WHFxcU6Pj4uL04YNGyRJxcXFGjBggGPO399fHTp0kNVq1cCBAxu1/7KyMpWXlzuNBQY2UXR09CUm%2B0lAgL/Tn77AFzNfrMBA7/nc%2BOrxJje5vZ0vZXZ5wZo2bZqmTJkii8Wipk2byt/fX8eOHVNISIiWLl1qZB%2BHDh2SJL3zzjt65ZVXZLfblZWVpRkzZujEiRNq2bKl0%2BMjIiJUUVEhSbLZbPVeYQsPD3fMN4bFYtGSJUucxiZMmKCsrKxfEue8wsKuNro9T%2BCLmRsrMrKpu5dgnK8eb3L7Fl/M7QuZXV6wevTooa1bt2rbtm06dOiQ7Ha7WrZsqTvuuEPNmjUzso8zP404evRoR5l65JFH9OCDDyopKanRz/%2BlMjIylJqa6jQWGNhEFRVVl7TdMwIC/BUWdrUqK6tVW1tnZJtXOl/MfLFMnV9XAl893uQmt7dzR2Z3/ePT5QVLkq6%2B%2Bmr17t1bhw4dUuvWrY1vv0WLFpJ%2Buq/rjJiYGNntdp0%2BfVo2m83p8RUVFYqKipIkRUZG1pu32Wxq27Zto/cfHR1d73JgeflR1dSYPZlqa%2BuMb/NK54uZG8sbPy%2B%2BerzJ7Vt8MbcvZHb5RdATJ05o6tSp6tSpk%2B666y5JP92DNXr0aFVWVhrZR6tWrRQaGqo9e/Y4xkpLS3XVVVcpOTm53tsuFBUVKTExUZIUHx%2Bv4uJix1xtba12797tmAcAAGiIywvWggULtGfPHuXk5Mjf//92X1tbq5ycHCP7CAwMVHp6ul544QX9z//8jw4fPqylS5dq0KBBGjp0qEpLS1VQUKCTJ09q27Zt2rZtm4YPHy5JyszM1KpVq7Rjxw5VV1crNzdXQUFB6tWrl5G1AQAA7%2BfygrVx40b9%2Bc9/1p133un4pc9hYWGaN2%2BeNm3aZGw/U6ZM0R133KFhw4apT58%2Bio2N1YwZM9S8eXMtW7ZMy5cvV5cuXTR37lwtWLBAN998sySpZ8%2Bemjx5siZOnKhu3bpp%2B/btysvLU0hIiLG1AQAA7%2Bbye7CqqqoUGxtbbzwqKsrom3kGBQVp1qxZmjVrVr25rl27avXq1ed97siRIzVy5EhjawEAAL7F5a9gXX/99frnP/8pyfmn9d555x3927/9m6uXAwAAYJzLX8EaOXKkHnnkEd17772qq6vTK6%2B8oqKiIm3cuFHTp0939XIAAACMc3nBysjIUGBgoJYvX66AgAC98MILuvHGG5WTk6M777zT1csBAAAwzuUF68iRI7r33nt17733unrXAAAALuHye7B69%2B59ye%2BUDgAAcCVzecHq3r274xcrAwAAeCOXXyK89tpr9cc//lF5eXm6/vrrddVVVznNL1y40NVLAgAAMMrlBWvfvn361a9%2BJemn3wEIAADgbVxWsCZNmqRFixbp1VdfdYwtXbpUEyZMcNUSAAAAXMJl92Bt3ry53lheXp6rdg8AAOAyLitY5/rJQX6aEAAAeCOXFawzv9i5oTEAAABP5/K3aQAAAPB2FCwAAADDXPZThKdPn9aUKVMaHON9sAAAgKdzWcHq0qWLysrKGhwDAADwdC4rWD9//ysAAABvxj1YAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYFujuBQAw467nPnL3Ehptw8Tb3b0EALisfOIVrLlz56p9%2B/aOjwsLC5Wenq7OnTtr4MCBWrNmjdPj8/Pz1b9/f3Xu3FmZmZkqKipy9ZIBAIAH8/qCtWfPHq1evdrxcVlZmcaPH68RI0aosLBQ06dP18yZM2W1WiVJmzdv1uLFi/Xss89q%2B/btSklJ0dixY3X8%2BHF3RQAAAB7GqwtWXV2dZs2apVGjRjnG1q5dq9jYWKWnpys4OFhJSUlKTU1VQUGBJMlisSgtLU2JiYkKCQnR6NGjJUlbtmxxRwQAAOCBvLpgvfHGGwoODtagQYMcY8XFxYqLi3N6XFxcnOMy4Nnz/v7%2B6tChg%2BMVLgAAgIZ47U3uP/zwgxYvXqxXX33Vadxms6lly5ZOYxEREaqoqHDMh4eHO82Hh4c75hujrKxM5eXlTmOBgU0UHR19MRHOKyDA3%2BlPX%2BCLmb1ZYOCFj6OvHm9yk9vb%2BVJmry1Y8%2BbNU1pamtq0aaPvvvvuop5rt9svad8Wi0VLlixxGpswYYKysrIuabtnCwu72uj2PIEvZvZGkZFNG/U4Xz3e5PYtvpjbFzJ7ZcEqLCzUv/71L61bt67eXGRkpGw2m9NYRUWFoqKizjtvs9nUtm3bRu8/IyNDqampTmOBgU1UUVHV6G1cSECAv8LCrlZlZbVqa%2BuMbPNK54uZvVlDXwu%2BerzJTW5v547Mjf0HnWleWbDWrFmjw4cPKyUlRdL/vSLVvXt3/eEPf6hXvIqKipSYmChJio%2BPV3FxsYYOHSpJqq2t1e7du5Went7o/UdHR9e7HFheflQ1NWZPptraOuPbvNL5YmZv1Nhj6KvHm9y%2BxRdz%2B0Jmr7wI%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%2B%2BupKQkPfHEE6qsrJQk7dmzR/fdd5%2B6dOmifv366eWXX3Z67vr16zVo0CB16tRJaWlp%2BvDDD90RAQAAeCivLVhjx45VWFiYNm/erJUrV%2Bqrr77Sn/70J504cUJjxozRr3/9a33wwQdatGiRli1bpk2bNkn6qXxNnTpV2dnZ%2BvjjjzVq1Cg9/PDDOnTokJsTAQAAT%2BGVBauyslLx8fGaMmWKmjZtqlatWmno0KH69NNPtXXrVp0%2BfVrjxo1TkyZN1LFjRw0bNkwWi0WSVFDAM%2BOWAAAQB0lEQVRQoOTkZCUnJys4OFiDBw9Wu3bttGbNGjenAgAAniLQ3Qu4HMLCwjRv3jynsYMHDyo6OlrFxcVq3769AgICHHNxcXEqKCiQJBUXFys5OdnpuXFxcbJarY3ef1lZmcrLy53GAgObKDo6%2BmKjnFNAgL/Tn77AFzN7s8DACx9HXz3e5Ca3t/OlzF5ZsM5mtVq1fPly5ebmasOGDQoLC3Oaj4iIkM1mU11dnWw2m8LDw53mw8PDtW/fvkbvz2KxaMmSJU5jEyZMUFZW1i8PcQ5hYVcb3Z4n8MXM3igysmmjHuerx5vcvsUXc/tCZq8vWJ999pnGjRunKVOmKCkpSRs2bDjn4/z8/Bz/b7fbL2mfGRkZSk1NdRoLDGyiioqqS9ruGQEB/goLu1qVldWqra0zsk1X6JvzgbuXgCtEQ18LnnqOXypyk9vbuSNzY/9BZ5pXF6zNmzfrscce08yZM3XPPfdIkqKionTgwAGnx9lsNkVERMjf31%2BRkZGy2Wz15qOiohq93%2Bjo6HqXA8vLj6qmxuzJVFtbZ3ybgCs09rz11XOc3L7FF3P7QmavvQj6%2Beefa%2BrUqXr%2B%2Becd5UqS4uPjtXfvXtXU1DjGrFarEhMTHfNFRUVO2/r5PAAAQEO8smDV1NRoxowZys7OVo8ePZzmkpOTFRoaqtzcXFVXV2vnzp1asWKFMjMzJUnDhw/X9u3btXXrVp08eVIrVqzQgQMHNHjwYHdEAQAAHsgrLxHu2LFDJSUlmjNnjubMmeM098477%2BiFF17QrFmzlJeXpxYtWmjSpEnq1auXJKldu3bKycnRvHnzVFpaqjZt2mjZsmW65ppr3JAEAAB4Iq8sWLfddpv27t17wce8/vrr553r16%2Bf%2BvXrZ3pZAADAR3jlJUIAAAB3omABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYYHuXgAA33PXcx%2B5ewkXZcPE2929BAAehlewAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWOdQWlqqhx56SN27d1dKSooWLFiguro6dy8LAAB4CN5o9BweeeQRdezYUe%2B%2B%2B64OHz6sMWPGqEWLFvr973/v7qUBAAAPwCtYZ7Farfriiy%2BUnZ2tZs2aKTY2VqNGjZLFYnH30gAAgIfgFayzFBcXKyYmRuHh4Y6xjh07av/%2B/Tp27JhCQ0Mb3EZZWZnKy8udxgIDmyg6OtrIGgMC/B1/9s35wMg2AZyfp/1qn39k3%2BHuJVyUn39P8yW%2BmNuXMlOwzmKz2RQWFuY0dqZsVVRUNKpgWSwWLVmyxGns4Ycf1iOPPGJkjWVlZfrb3/6ijIwMffrHO41s80pXVlYmi8WijIwMY0XVE5Cb3L7g59/TyO3dfCmz91fIX8But1/S8zMyMrRy5Uqn/zIyMgytTiovL9eSJUvqvUrmzXwxs0RucvsGcvtObl/KzCtYZ4mKipLNZnMas9ls8vPzU1RUVKO2ER0d7fXNHAAAnB%2BvYJ0lPj5eBw8e1JEjRxxjVqtVbdq0UdOmTd24MgAA4CkoWGeJi4tTQkKCFi5cqGPHjqmkpESvvPKKMjMz3b00AADgIQJmz549292LuNLccccdWrdunZ555hm9/fbbSk9P1wMPPCA/Pz93L82hadOm6tatm0%2B9quaLmSVyk9s3kNt3cvtKZj/7pd7RDQAAACdcIgQAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIJ1hbNarerbt6%2BGDx9eb66wsFDp6enq3LmzBg4cqDVr1jjN5%2Bfnq3///urcubMyMzNVVFTkqmUbk5qaqvj4eCUkJDj%2BGzt2rGN%2Bz549uu%2B%2B%2B9SlSxf169dPL7/8shtXa1Zpaakeeughde/eXSkpKVqwYIHq6urcvSzj2rdvX%2B8YP/PMM5IaPsc9yQcffKCkpCRNmjSp3tz69es1aNAgderUSWlpafrwww8dc3V1dVq0aJF69%2B6trl276oEHHtC3337ryqVfkvPlXrlypW6%2B%2BWan456QkKBdu3ZJ8vzcpaWlmjBhgrp3766kpCQ98cQTqqyslNTw960LnQ9XuvPl/u6779S%2Bfft6x/ull15yPNeTc5%2BTHVes1atX25OTk%2B0PPPCAfdiwYU5z33//vf3WW2%2B1FxQU2E%2BcOGH/6KOP7Lfccot9165ddrvdbn/vvffst912m33Hjh326upq%2B7Jly%2By33367vaqqyh1RfrGUlBT7xx9/fM656upq%2Bx133GFfvHixvaqqyl5UVGTv1q2bfePGjS5e5eUxdOhQ%2B4wZM%2ByVlZX2/fv32/v162d/%2BeWX3b0s49q1a2f/9ttv6403dI57kry8PHu/fv3sI0aMsE%2BcONFpbvfu3fb4%2BHj71q1b7SdOnLCvXr3anpiYaD948KDdbrfb8/Pz7SkpKfZ9%2B/bZjx49an/66aftgwYNstfV1bkjykW5UO633nrLft999533uZ6c22632%2B%2B%2B%2B277E088YT927Jj94MGD9rS0NPu0adMa/L7V0PlwpTtf7m%2B//dberl278z7P03OfC69gXcFOnjwpi8WixMTEenNr165VbGys0tPTFRwcrKSkJKWmpqqgoECSZLFYlJaWpsTERIWEhGj06NGSpC1btrg0w%2BW0detWnT59WuPGjVOTJk3UsWNHDRs2TBaLxd1Lu2RWq1VffPGFsrOz1axZM8XGxmrUqFFeka2xGjrHPUlwcLBWrFihG264od5cQUGBkpOTlZycrODgYA0ePFjt2rVzvFpnsVg0atQo3XTTTQoNDdWkSZNUUlKinTt3ujrGRbtQ7oZ4cu7KykrFx8drypQpatq0qVq1aqWhQ4fq008/bfD7VkPnw5XsQrkb4sm5z4eCdQUbNmyYWrZsec654uJixcXFOY3FxcU5LgOePe/v768OHTrIarVevgVfJvn5%2BerTp486deqkrKwsHT58WNJPGdu3b6%2BAgADHY3/%2BOfBkxcXFiomJUXh4uGOsY8eO2r9/v44dO%2BbGlV0eCxcuVK9evXTbbbdp5syZqqqqavAc9yT333%2B/mjVrds658%2BW0Wq06ceKE9u3b5zQfGhqqG264wSO%2Bli%2BUW5IOHjyo3//%2B9%2Bratat69%2B6t1atXS5LH5w4LC9O8efPUokULx9jBgwcVHR3d4PetC50PV7oL5T7j8ccfV48ePfTrX/9aCxcu1OnTpyV5du7zoWB5KJvNprCwMKexiIgIVVRUOOZ//pezJIWHhzvmPUWHDh10yy23aPXq1Vq/fr1sNpseffRRSef/HNhsNo%2B/V%2Blc2c4cT087hg259dZblZSUpE2bNslisWjHjh166qmnGjzHvcWFvlZ//PFH2e12r/haPltUVJRiY2P12GOP6aOPPtLkyZM1bdo0FRYWel1uq9Wq5cuXa9y4cQ1%2B3/KW792Sc%2B6goCB16tRJffv21ZYtW5SXl6c1a9bov/7rvyR5z99ZPxfo7gX4stWrV%2Bvxxx8/59y8efOUlpZ2Sdu32%2B2X9HxXaOhzsHTpUsfHTZs21axZszRgwAB98803592mn5%2Bf8XW6gyccPxN%2BftnzpptuUnZ2tsaNG6cuXbq4cVWu1dCx9sZzoVevXurVq5fj44EDB%2Bof//iHVq5cqezsbEnekfuzzz7TuHHjNGXKFCUlJWnDhg3nfNzPv295Y25JeuONNxzzt9xyi8aMGaNly5Y5/tHsDbl/joLlRkOGDNGQIUN%2B0XMjIyNls9mcxioqKhQVFXXeeZvNprZt2/6yxV4mF/s5iImJkSSVlZUpKipKBw4ccJq32WyKiIiQv79nvzgbFRV1zuPn5%2BfnOMbe6rrrrlNtba38/f0veI57i/N9rUZFRTnO5XPNN2/e3JXLdImYmBgVFRV5Te7Nmzfrscce08yZM3XPPfdIUoPfty50PniKc%2BU%2Bl5iYGP3www%2By2%2B1ekftsnv23kA9LSEiody9KUVGR44b4%2BPh4FRcXO%2BZqa2u1e/fuc94wf6UqLS3VrFmzdOrUKcdYSUmJJKl169aKj4/X3r17VVNT45i3Wq0elfF84uPjdfDgQR05csQxZrVa1aZNGzVt2tSNKzNr9%2B7dmj9/vtNYSUmJgoKClJycfMFz3FvEx8fXy3nmPA4ODlbbtm2dvpYrKyv1zTff6JZbbnH1Uo16/fXXtX79eqexkpIStW7d2ityf/7555o6daqef/55p5LR0PetC50PnuB8uQsLC5Wbm%2Bv02K%2B//loxMTHy8/Pz%2BNznQsHyUIMGDVJpaakKCgp08uRJbdu2Tdu2bXO8X1ZmZqZWrVqlHTt2qLq6Wrm5uQoKCnJ6Sf5K17x5c23evFnz58/X8ePH9f3332vevHlKSUlRy5YtlZycrNDQUOXm5qq6ulo7d%2B7UihUrlJmZ6e6lX7K4uDglJCRo4cKFOnbsmEpKSvTKK694Rbafa968uSwWi/Ly8nTq1Cnt379fzz//vDIyMjRkyJALnuPeYvjw4dq%2Bfbu2bt2qkydPasWKFTpw4IAGDx4s6aev5fz8fJWUlOjYsWPKyclRhw4dlJCQ4OaVX5pTp07pmWeekdVq1enTp7Vu3Tq9//77GjFihCTPzl1TU6MZM2YoOztbPXr0cJpr6PtWQ%2BfDlexCuZs1a6alS5dq9erVOn36tKxWq1566SWvyH0%2BfnZvu%2BjpRfr376///d//VW1trerq6nTVVVdJkt555x3FxMTok08%2B0Zw5c1RSUqKYmBhNmTJF/fr1czz/73//u/Ly8nT48GElJCRo9uzZateunbvi/CJ79%2B7V/PnzHT9J0rdvXz355JOOm0S//PJLzZo1S0VFRWrRooUefPBBjRw50p1LNubQoUOaOXOm/vu//1uhoaEaMWKEHn74Ya%2B5x%2ByMTz75RAsXLtTevXsVFBSkoUOHatKkSQoODm7wHPcUZ0rBmVctAgN/ujvjzHm9adMmLVy4UKWlpWrTpo2mT5%2Burl27SvrpvpTFixfrjTfeUFVVlbp3766nn35arVq1ckOSi3Oh3Ha7Xbm5uVqxYoXKy8t13XXX6fHHH1dKSookz8796aef6t///d8VFBRUb%2B6dd95RVVXVBb9vXeh8uJI1lHv37t1asmSJDhw4oGbNmum3v/2tHnzwQcctHZ6a%2B3woWAAAAIZxiRAAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADPt/DUarmrcw37QAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-6563243437933534735”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-16.67612</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-25.6952</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-7.139538</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>31.35116</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-21.91461</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-20.48456</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-16.64413</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-12.98182</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-5.848339</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.236936</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (1927)</td> <td class=”number”>1927</td> <td class=”number”>60.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>1273</td> <td class=”number”>39.7%</td> <td>

<div class=”bar” style=”width:66%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-6563243437933534735”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-98.12657</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-68.79174</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-64.7597</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-63.54496</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-56.97397</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>94.64105</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>104.2254</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>131.2125</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>150.3263</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>254.6232</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_SBP_09_15”>PCH_SBP_09_15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>52</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.68642</td>

</tr> <tr>

<th>Minimum</th> <td>-0.13397</td>

</tr> <tr>

<th>Maximum</th> <td>4.2368</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-4063947978983909475”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-4063947978983909475”>

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<li role=”presentation” class=”active”><a href=”#quantiles-4063947978983909475”

aria-controls=”quantiles-4063947978983909475” role=”tab” data-toggle=”tab”>Statistics</a></li>

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role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-4063947978983909475” aria-controls=”common-4063947978983909475”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-4063947978983909475” aria-controls=”extreme-4063947978983909475”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-4063947978983909475”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-0.13397</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.044096</td>

</tr> <tr>

<th>Q1</th> <td>0.39306</td>

</tr> <tr>

<th>Median</th> <td>0.65002</td>

</tr> <tr>

<th>Q3</th> <td>0.86191</td>

</tr> <tr>

<th>95-th percentile</th> <td>1.2584</td>

</tr> <tr>

<th>Maximum</th> <td>4.2368</td>

</tr> <tr>

<th>Range</th> <td>4.3708</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.46886</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.5472</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.79718</td>

</tr> <tr>

<th>Kurtosis</th> <td>12.664</td>

</tr> <tr>

<th>Mean</th> <td>0.68642</td>

</tr> <tr>

<th>MAD</th> <td>0.3521</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.7143</td>

</tr> <tr>

<th>Sum</th> <td>2149.9</td>

</tr> <tr>

<th>Variance</th> <td>0.29943</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-4063947978983909475”>

<img 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mau/evWFjXq9XI0aMUHx8vHr06KGysjL1799fklRRUaFPP/1UP//5z9WpUycFg0EdOHBAvXv3Dt3W7XarW7duDVo/LS2t3ulAn%2B%2Bkamqc84Ssra1zVD1OZOXjQz%2BchX44C/1wFvrRMFF5InXs2LHavXu3Xn31VX399ddav369Dh8%2BrFGjRkmS8vPztXbtWh08eFCnTp1ScXGxevXqpaysLKWmpmrYsGFasWKFTpw4oWPHjmnVqlUaM2aMXK6ozKMAAMCwiE0MWVlZkqSamhpJ0vbt2yWdO9p08cUXq7i4WIsXL9aRI0fUvXt3PfTQQ%2BrQoYMkafz48fL5fPrd736nyspKZWdnh10zde%2B99%2Bquu%2B7S0KFD1aJFC1133XXf%2BWGmAAAA3yUm2NgrutEgPt9Ju0uQdO6dcSkpCfL7Ky0/xHvtir9aul5jbZl2RZOvYWc/UB/9cBb64SyR2o8OHb77I52aWlSeIgQAALATAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDDLA1Zubq5KSkp09OhRq5cGAACwhOUB68Ybb9RLL72kq666SpMmTdK2bdtUU1NjdRkAAABNxvKAVVBQoJdeeknPPvusevTooUWLFmnQoEFaunSpDh06ZHU5AAAAxtl2DVbv3r01a9Ys7dy5U3PnztWzzz6r4cOHa%2BLEifrggw/sKgsAAKDRbAtYZ8%2Be1UsvvaRbb71Vs2bNUnp6uubMmaNevXppwoQJ2rRpk12lAQAANIrL6gUPHjyo9evX64UXXlBlZaWGDRumJ554Qpdddllom379%2Bunuu%2B/WyJEjrS4PAACg0SwPWCNGjFC3bt00efJk3XDDDUpOTq63zaBBg3TixAmrSwMAADDC8oC1du1a9e/f/0e3e//99y2oBgAAwDzLr8Hq2bOnpkyZou3bt4fGHn/8cd16660KBAJWlwMAAGCc5QFr8eLFOnnypLp37x4aGzx4sOrq6rRkyRKrywEAADDO8lOEr7/%2BujZt2qSUlJTQWNeuXVVcXKzrrrvO6nIAAACMs/wIVnV1teLj4%2BsXEhurqqoqq8sBAAAwzvKA1a9fPy1ZskRfffVVaOyLL77QPffcE/ZRDQAAAJHK8lOEc%2BfO1e9//3tdfvnlSkxMVF1dnSorK9W5c2c9%2BeSTVpcDAABgnOUBq3PnznrxxRf1l7/8RZ9%2B%2BqliY2PVrVs3DRgwQHFxcVaXAwAAYJzlAUuSWrZsqauuuqpJ19i3b5%2BWLFmiffv2KT4%2BXpdffrnmzp2r1NRU7dmzR8uWLdMnn3yiCy64QJMnT9aoUaNCt127dq2efvpp%2BXw%2B9ezZU/PmzVNmZmaT1gsAAKKH5QHrs88%2B07Jly/TRRx%2Bpurq63vwrr7zS6DVqamp02223KS8vT4888ogqKys1Y8YM3X333Zo/f76mTp2qefPmaeTIkXr77bd1%2B%2B23q1u3bsrKytKOHTu0cuVKPfLII%2BrZs6fWrl2rKVOmaNu2bWrTpk2jawMAANHPlmuwysvLNWDAgCYLLD6fTz6fT9dff71atmypli1b6uqrr9Zjjz2mTZs2qWvXrhozZowkKScnR7m5uVq3bp2ysrLk8XiUl5enPn36SJImTZqktWvXaufOnRoxYkST1AsAAKKL5QFr7969euWVV5Samtpka6Snp6tXr17yeDz6l3/5F1VXV2vbtm0aPHiwysrKlJGREbZ9RkaGtmzZIkkqKyvT8OHDQ3OxsbHq1auXvF5vgwNWeXm5fD5f2JjL1UZpaWmN3LPGi4uLDfuJ7%2BdyNf1jRD%2BchX44C/1wFvpxfiwPWO3atWvyU22xsbFauXKlJkyYoCeeeEKS1L9/f82YMUNTp05Venp62PbJycny%2B/2SpEAgoKSkpLD5pKSk0HxDeDwelZSUhI0VFBSosLDwp%2BxOk3C7W9tdguOlpCRYthb9cBb64Sz0w1noR8NYHrAmT56skpISzZgxQzExMU2yxpkzZzRlyhRdc801mjJlik6fPq177rlHM2fObNDtg8Fgo9YfN26ccnNzw8Zcrjby%2Bysbdb8mxMXFyu1urYqKKtXW1tldjqNZ0S/64Sz0w1noh7NEaj%2BsfLH8bZYHrL/85S9655139Pzzz%2Bsf//EfFRsbfqjxmWeeafQae/bs0eeff67p06crLi5Obdu2VWFhoa6//npdeeWV9b5U2u/3h05ZpqSk1JsPBALq0aNHg9dPS0urdzrQ5zupmhrnPCFra%2BscVY8TWfn40A9noR/OQj%2BchX40jOUBKzExUQMHDmzSNWpra1VXVxd2JOrMmTOSzl3U/uc//zls%2B71794Yuas/MzFRZWZlGjx4duq99%2B/aFLooHAAD4MZYHrMWLFzf5GpdeeqnatGmjlStXasqUKaqurtbq1avVr18/XX/99SopKdG6des0atQovfHGG9q1a5c8Ho8kKT8/X9OnT9d1112nnj176tFHH1XLli01ePDgJq8bAABEB1veCvDJJ59o5cqVmjNnTmjs3XffNXb/KSkpevTRR/XOO%2B9o4MCBuu6669SqVSstW7ZM7dq100MPPaSnnnpKl112mRYtWqSlS5fqkksukSQNHDhQ06dP17Rp09S/f3/t3r1bpaWlatWqlbH6AABAdIsJNvaK7vO0Z88e3XrrrerWrZsOHz4sr9erzz77TMOHD9eKFSs0dOhQK8uxjM930u4SJJ376IGUlAT5/ZWWn0O/dsVfLV2vsbZMu6LJ17CzH6iPfjgL/XCWSO1Hhw5tbVnX8iNYy5cv1x/%2B8Adt2rQp9C7Czp07a8mSJVq1apXV5QAAABhnecD629/%2Bpvz8fEkK%2B5iGa665RgcPHrS6HAAAAOMsv8i9bdu2qq6uVsuWLcPGy8vL640BduKUJgDgp7L8CFbfvn21aNEinTp1KjR26NAhzZo1S5dffrnV5QAAABhn%2BRGsOXPm6JZbblF2drZqa2vVt29fVVVVqUePHlqyZInV5QAAABhnecDq2LGjNm/erF27dunQoUNq1aqVunXrpiuuuKLJvjoHAADASpYHLElq0aKFrrrqKjuWBgAAaHKWB6zc3NwfPFL1yiuvWFgNAACAeZYHrOHDh4cFrNraWh06dEher1e33HKL1eUAAAAYZ3nAmjlz5neOb926VW%2B%2B%2BabF1QAAAJhny3cRfperrrpKL774ot1lAAAANJpjAta%2Bfftk8dciAgAANAnLTxGOHz%2B%2B3lhVVZUOHjyoX/3qV1aXAwAAYJzlAatr16713kUYHx%2BvMWPG6KabbrK6HAAAAOMsD1h8WjsAAIh2lgesF154ocHb3nDDDU1YCQAAQNOwPGDNmzdPdXV19S5oj4mJCRuLiYkhYAEAgIhkecB65JFH9Nhjj2nKlCnq2bOngsGgPvzwQz388MP67W9/q%2BzsbKtLAgAAMMqWa7BKS0uVnp4eGvvFL36hzp07a%2BLEidq8ebPVJQEAABhl%2BedgHT58WElJSfXG3W63jhw5YnU5AAAAxlkesDp16qQlS5bI7/eHxioqKrRs2TJdeOGFVpcDAABgnOWnCOfOnasZM2bI4/EoISFBsbGxOnXqlFq1aqVVq1ZZXQ4AAIBxlgesAQMG6NVXX9WuXbt07NgxBYNBpaen68orr1Tbtm2tLgcAAMA4ywOWJLVu3VpDhw7VsWPH1LlzZztKAAAAaDKWX4NVXV2tWbNm6dJLL9W1114r6dw1WJMmTVJFRYXV5QAAABhnecBaunSp9u/fr%2BLiYsXG/t/ytbW1Ki4utrocAAAA4ywPWFu3btV//ud/6pprrgl96bPb7dbixYu1bds2q8sBAAAwzvKAVVlZqa5du9YbT01N1enTp60uBwAAwDjLA9aFF16oN998U5LCvnvw5Zdf1j/8wz9YXQ4AAIBxlr%2BL8Ne//rXuvPNO3Xjjjaqrq9OaNWu0d%2B9ebd26VfPmzbO6HAAAAOMsD1jjxo2Ty%2BXSU089pbi4OD344IPq1q2biouLdc0111hdDgAAgHGWB6wTJ07oxhtv1I033mj10gAAAJaw/BqsoUOHhl171ZRWr16tAQMG6J/%2B6Z80YcIEff7555KkPXv2aMyYMerbt69GjBihjRs3ht1u7dq1GjZsmPr27av8/Hzt3bvXknoBAEB0sDxgZWdna8uWLU2%2BztNPP62NGzdq7dq1ev3119W9e3c9/vjjKi8v19SpUzV%2B/Hjt2bNH8%2BbN04IFC%2BT1eiVJO3bs0MqVK3X//fdr9%2B7dGjJkiKZMmcI7HAEAQINZforwggsu0B//%2BEeVlpbqwgsvVIsWLcLmly1bZmSdxx57TLNmzdLPfvYzSdL8%2BfMlSY8%2B%2Bqi6du2qMWPGSJJycnKUm5urdevWKSsrSx6PR3l5eerTp48kadKkSVq7dq127typESNGGKkNAABEN8uPYH388cf62c9%2BprZt28rv96u8vDzsHxO%2B%2BOILff755/rqq680fPhwZWdnq7CwUCdOnFBZWZkyMjLCts/IyAidBvz7%2BdjYWPXq1St0hAsAAODHWHYEq6ioSMuXL9eTTz4ZGlu1apUKCgqMr3Xs2DFJ5z5ba82aNQoGgyosLNT8%2BfNVXV2t9PT0sO2Tk5Pl9/slSYFAQElJSWHzSUlJofmGKC8vl8/nCxtzudooLS3tp%2ByOUXFxsWE/ET1cLnraWPx9OAv9cBb6cX4sC1g7duyoN1ZaWtokAeubi%2BgnTZoUClN33nmnbr31VuXk5DT49j%2BVx%2BNRSUlJ2FhBQYEKCwsbdb8mud2t7S4BhqWkJNhdQtTg78NZ6Iez0I%2BGsSxgfVdoaap3E7Zv317Sue84/EanTp0UDAZ19uxZBQKBsO39fr9SU1MlSSkpKfXmA4GAevTo0eD1x40bp9zc3LAxl6uN/P7K89qPphAXFyu3u7UqKqpUW1tndzkwyAnPr0jH34ez0A9nidR%2B2PXi07KA9c0XO//YmAkdO3ZUYmKi9u/fr969e0uSjhw5ohYtWmjQoEHasGFD2PZ79%2B4NXdSemZmpsrIyjR49WpJUW1urffv2hS6Kb4i0tLR6pwN9vpOqqXHOE7K2ts5R9aDx6Kc5/H04C/1wFvrRMFF5ItXlcmnMmDF68MEH9T//8z86fvy4Vq1apZEjR2r06NE6cuSI1q1bp6%2B//lq7du3Srl27NHbsWElSfn6%2BXnjhBb333nuqqqrS6tWr1bJlSw0ePNjenQIAABHD8o9psMqMGTN05swZ3XTTTTp79qyGDRum%2BfPnKyEhQQ899JAWLlyoe%2B65R506ddLSpUt1ySWXSJIGDhyo6dOna9q0aTp%2B/LiysrJUWlqqVq1a2bxHAAAgUsQELfpY9YyMDF177bVhY1u2bKk3ZupzsJzG5ztpdwmSzr3TLCUlQX5/peWHeK9d8VdL12tutky7wu4SIp6dfx%2Boj344S6T2o0OHtrasa9kRrMsuu6ze51x91xgAAECksyxgffvzrwAAAKJZVF7kDgAAYCcCFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGBY1H4XYXPB188AAOA8HMECAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGNYuAtWjRIvXs2TP0%2B549ezRmzBj17dtXI0aM0MaNG8O2X7t2rYYNG6a%2BffsqPz9fe/futbpkAAAQwaI%2BYO3fv18bNmwI/V5eXq6pU6dq/Pjx2rNnj%2BbNm6cFCxbI6/VKknbs2KGVK1fq/vvv1%2B7duzVkyBBNmTJFp0%2BftmsXAABAhInqgFVXV6e77rpLEyZMCI1t2rRJXbt21ZgxYxQfH6%2BcnBzl5uZq3bp1kiSPx6O8vDz16dNHrVq10qRJkyRJO3futGMXAABABHLZXUBTeuaZZxQfH6%2BRI0dqxYoVkqSysjJlZGSEbZeRkaEtW7aE5ocPHx6ai42NVa9eveT1ejVixIgGrVteXi6fzxc25nK1UVpaWmN2B/hBLldUv16yRFxcbNhP2It%2BOAv9OD9RG7C%2B/PJLrVy5Uk8%2B%2BWTYeCAQUHp6ethYcnKy/H5/aD4pKSlsPikpKTTfEB6PRyUlJWFjBQXqS2e/AAANeElEQVQFKiwsPJ9dAM5LSkqC3SVEDbe7td0l4Fvoh7PQj4aJ2oC1ePFi5eXlqXv37vr888/P67bBYLBRa48bN065ublhYy5XG/n9lY26X%2BCH8PxqvLi4WLndrVVRUaXa2jq7y2n26IezRGo/7HrxGZUBa8%2BePXr33Xe1efPmenMpKSkKBAJhY36/X6mpqd87HwgE1KNHjwavn5aWVu90oM93UjU1kfOEROTh%2BWVObW0dj6eD0A9noR8NE5UnUjdu3Kjjx49ryJAhys7OVl5eniQpOztbF198cb2PXdi7d6/69OkjScrMzFRZWVlorra2Vvv27QvNAwAA/JioDFizZ8/W1q1btWHDBm3YsEGlpaWSpA0bNmjkyJE6cuSI1q1bp6%2B//lq7du3Srl27NHbsWElSfn6%2BXnjhBb333nuqqqrS6tWr1bJlSw0ePNjGPQIAAJEkKk8RJiUlhV2oXlNTI0nq2LGjJOmhhx7SwoULdc8996hTp05aunSpLrnkEknSwIEDNX36dE2bNk3Hjx9XVlaWSktL1apVK%2Bt3BAAARKSYYGOv6EaD%2BHwnm%2BR%2Br13x1ya5X0SeLdOusLuEiOdyxSolJUF%2BfyXXmDgA/XCWSO1Hhw5tbVk3Kk8RAgAA2ImABQAAYBgBCwAAwLCovMgdaI4i6Xo8rhcDEO04ggUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDojZgHTlyRAUFBcrOzlZOTo5mz56tiooKSdL%2B/fv129/%2BVpdddpl%2B9atf6bHHHgu77UsvvaSRI0fq0ksvVV5enl5//XU7dgEAAESoqA1YU6ZMkdvt1o4dO/T888/ro48%2B0n333afq6mpNnjxZv/zlL/Xaa69p%2BfLleuihh7Rt2zZJ58LXrFmzNHPmTL3xxhuaMGGC7rjjDh07dszmPQIAAJEiKgNWRUWFMjMzNWPGDCUkJKhjx44aPXq03nrrLb366qs6e/asbr/9drVp00a9e/fWTTfdJI/HI0lat26dBg0apEGDBik%2BPl6jRo3SxRdfrI0bN9q8VwAAIFJEZcByu91avHix2rdvHxo7evSo0tLSVFZWpp49eyouLi40l5GRob1790qSysrKlJGREXZ/GRkZ8nq91hQPAAAinsvuAqzg9Xr11FNPafXq1dqyZYvcbnfYfHJysgKBgOrq6hQIBJSUlBQ2n5SUpI8//rjB65WXl8vn84WNuVxtlJaW9tN3AogiLpczX9vFxcWG/YS96Iez0I/zE/UB6%2B2339btt9%2BuGTNmKCcnR1u2bPnO7WJiYkL/HgwGG7Wmx%2BNRSUlJ2FhBQYEKCwsbdb9AtEhJSbC7hB/kdre2uwR8C/1wFvrRMFEdsHbs2KE//OEPWrBggW644QZJUmpqqg4fPhy2XSAQUHJysmJjY5WSkqJAIFBvPjU1tcHrjhs3Trm5uWFjLlcb%2Bf2VP21HgCjj1L%2BFuLhYud2tVVFRpdraOrvLafboh7NEaj/sekEXtQHrnXfe0axZs/TAAw9owIABofHMzEz96U9/Uk1NjVyuc7vv9XrVp0%2Bf0Pw312N9w%2Bv1asSIEQ1eOy0trd7pQJ/vpGpqIucJCTQlp/8t1NbWOb7G5oR%2BOAv9aJioPJFaU1Oj%2BfPna%2BbMmWHhSpIGDRqkxMRErV69WlVVVXr//fe1fv165efnS5LGjh2r3bt369VXX9XXX3%2Bt9evX6/Dhwxo1apQduwIAACJQTLCxFxw50FtvvaXf/OY3atmyZb25l19%2BWZWVlbrrrru0d%2B9etW/fXrfeeqt%2B/etfh7bZtm2bli1bpiNHjqh79%2B6aN2%2Be%2BvXr16iafL6Tjbr997l2xV%2Bb5H6BprRl2hV2l/CdXK5YpaQkyO%2Bv5BW6A9APZ4nUfnTo0NaWdaMyYDkRAQv4PwQsNAT9cJZI7YddASsqTxECAADYiYAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwzGV3AQCA5uvaFX%2B1u4TzsmXaFXaXgAjBESwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGB/TAMByvDUfQLTjCNZ3OHLkiG677TZlZ2dryJAhWrp0qerq6uwuCwAARAiOYH2HO%2B%2B8U71799b27dt1/PhxTZ48We3bt9c///M/210aAAANFklHi6PtSDFHsP6O1%2BvVgQMHNHPmTLVt21Zdu3bVhAkT5PF47C4NAABECI5g/Z2ysjJ16tRJSUlJobHevXvr0KFDOnXqlBITE3/0PsrLy%2BXz%2BcLGXK42SktLM14vgKbncvFaFOfwXGg60fbYErD%2BTiAQkNvtDhv7Jmz5/f4GBSyPx6OSkpKwsTvuuEN33nmnuUL/11t/vOa8ti8vL5fH49G4ceMIfA5AP5yFfljvh/4bRj8a73z/H/FD6Mf5ia64aEgwGGzU7ceNG6fnn38%2B7J9x48YZqq5xfD6fSkpK6h1hgz3oh7PQD2ehH85CP84PR7D%2BTmpqqgKBQNhYIBBQTEyMUlNTG3QfaWlppHsAAJoxjmD9nczMTB09elQnTpwIjXm9XnXv3l0JCQk2VgYAACIFAevvZGRkKCsrS8uWLdOpU6d08OBBrVmzRvn5%2BXaXBgAAIkTc3XfffbfdRTjNlVdeqc2bN%2Bvf//3f9eKLL2rMmDGaOHGiYmJi7C7NiISEBPXv358jcg5BP5yFfjgL/XAW%2BtFwMcHGXtENAACAMJwiBAAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgNWMHDlyRLfddpuys7M1ZMgQLV26VHV1dXaX1Wy99tprysnJUVFRkd2lQOf%2BPgoKCpSdna2cnBzNnj1bFRUVdpfVLB04cEC33HKLLrvsMuXk5GjatGny%2BXx2lwVJixYtUs%2BePe0uIyIQsJqRO%2B%2B8U%2Bnp6dq%2BfbvWrFmj7du364knnrC7rGbp4Ycf1sKFC9WlSxe7S8H/mjJlitxut3bs2KHnn39eH330ke677z67y2p2zpw5o9///vfq37%2B/9uzZo82bN%2Bv48ePia3Ptt3//fm3YsMHuMiIGAauZ8Hq9OnDggGbOnKm2bduqa9eumjBhgjwej92lNUvx8fFav349AcshKioqlJmZqRkzZighIUEdO3bU6NGj9dZbb9ldWrNTVVWloqIiTZ48WS1btlRqaqquvvpqffTRR3aX1qzV1dXprrvu0oQJE%2BwuJWIQsJqJsrIyderUSUlJSaGx3r1769ChQzp16pSNlTVPN998s9q2bWt3GfhfbrdbixcvVvv27UNjR48eVVpamo1VNU9JSUm66aab5HK5JEmffPKJ/vznP%2Bvaa6%2B1ubLm7ZlnnlF8fLxGjhxpdykRw2V3AbBGIBCQ2%2B0OG/smbPn9fiUmJtpRFuBIXq9XTz31lFavXm13Kc3WkSNHNGzYMNXU1Gjs2LEqLCy0u6Rm68svv9TKlSv15JNP2l1KROEIVjMSDAbtLgFwvLffflsTJ07UjBkzlJOTY3c5zVanTp3k9Xr18ssv6/Dhw/rXf/1Xu0tqthYvXqy8vDx1797d7lIiCgGrmUhNTVUgEAgbCwQCiomJUWpqqk1VAc6yY8cO3XbbbZo7d65uvvlmu8tp9mJiYtS1a1cVFRVp8%2BbNOnHihN0lNTt79uzRu%2B%2B%2Bq4KCArtLiTgErGYiMzNTR48eDfsPlNfrVffu3ZWQkGBjZYAzvPPOO5o1a5YeeOAB3XDDDXaX02zt2bNHw4YNC/sImdjYc/%2BratGihV1lNVsbN27U8ePHNWTIEGVnZysvL0%2BSlJ2drRdffNHm6pyNgNVMZGRkKCsrS8uWLdOpU6d08OBBrVmzRvn5%2BXaXBtiupqZG8%2BfP18yZMzVgwAC7y2nWMjMzderUKS1dulRVVVU6ceKEVq5cqV/84he8McQGs2fP1tatW7VhwwZt2LBBpaWlkqQNGzYoNzfX5uqcLSbIhTnNxrFjx7RgwQL913/9lxITEzV%2B/HjdcccdiomJsbu0ZicrK0vSuf%2BxSwq9Y8rr9dpWU3P21ltv6Te/%2BY1atmxZb%2B7ll19Wp06dbKiq%2Bfrwww%2B1cOFCffDBB2rTpo1%2B%2Bctfavbs2UpPT7e7tGbv888/19ChQ/Xhhx/aXYrjEbAAAAAM4xQhAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABj2/wFTizXuasjo8QAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-4063947978983909475”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.7572224013779127</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.39305586107764334</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.4240674302214602</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.8619116579790758</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6787441323913024</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5616871155140446</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0191839651297627</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6265330254922419</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.3343375363551049</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.2553703762687425</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (41)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-4063947978983909475”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-0.13397324917975917</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.1177125976924489</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.11492635956941344</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.10415315822124027</td> <td class=”number”>36</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:78%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.03103797836582345</td> <td class=”number”>46</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.3978601063838512</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:38%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.699209651935957</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:44%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.7248856799309928</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0418616197607866</td> <td class=”number”>55</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.236801603228066</td> <td class=”number”>16</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:29%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_SFSP_09_15”>PCH_SFSP_09_15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>52</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.094027</td>

</tr> <tr>

<th>Minimum</th> <td>-1.615</td>

</tr> <tr>

<th>Maximum</th> <td>0.84929</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-609837850269236373”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-609837850269236373,#minihistogram-609837850269236373”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-609837850269236373”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-609837850269236373”

aria-controls=”quantiles-609837850269236373” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-609837850269236373” aria-controls=”histogram-609837850269236373”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-609837850269236373” aria-controls=”common-609837850269236373”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-609837850269236373” aria-controls=”extreme-609837850269236373”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-609837850269236373”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-1.615</td>

</tr> <tr>

<th>5-th percentile</th> <td>-0.37704</td>

</tr> <tr>

<th>Q1</th> <td>-0.041263</td>

</tr> <tr>

<th>Median</th> <td>0.11501</td>

</tr> <tr>

<th>Q3</th> <td>0.26852</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.46222</td>

</tr> <tr>

<th>Maximum</th> <td>0.84929</td>

</tr> <tr>

<th>Range</th> <td>2.4643</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.30978</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.28236</td>

</tr> <tr>

<th>Coef of variation</th> <td>3.0029</td>

</tr> <tr>

<th>Kurtosis</th> <td>1.3315</td>

</tr> <tr>

<th>Mean</th> <td>0.094027</td>

</tr> <tr>

<th>MAD</th> <td>0.20701</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.24314</td>

</tr> <tr>

<th>Sum</th> <td>294.49</td>

</tr> <tr>

<th>Variance</th> <td>0.079726</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-609837850269236373”>

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h499i/6C3%2BJj29uyTqcw8EvJAPW5cuXNXHiRA0cOND7/NT8%2BfM1ffr0Br3e4/Fc1/p5eXnKzs72GbPZouVyVV3Xfv0tIiJcsbHNdPZstWpr6wJdTkiix/5Ff%2BFv/n4f5xw2z6pQ/H0hGbAOHjyor7/%2BWlOnTlVERIRatGihwsJCDRs2THfddZfcbrfP9i6XSwkJCZKk%2BPj4evNut1udO3du8PqJiYn1bgc6nedUUxMcPyy1tXVBU2uwosf%2BRX/hL1adV5zDwS8kb/LW1taqrq7O50rU5cuXJUmZmZn1PnahvLxcXbp0kSSlp6fL4XD47Ovw4cPeeQAAgGsJyYDVtWtXRUdHa%2BXKlaqurpbL5dKaNWvUvXt3DRs2TBUVFVq/fr0uXbqkffv2ad%2B%2BfRo9erQkKT8/X5s2bdKhQ4dUXV2tNWvWqGnTpurbt29gDwoAAASNkAxY8fHx%2Bv3vf68PP/xQffr00b333quoqCgtW7ZMrVq10tq1a/Xqq6/qjjvu0MKFC7VkyRLdfvvtkqQ%2Bffpo6tSpKioqUo8ePXTgwAEVFxcrKioqwEcFAACCRZjnep/obqTs7GyNGDFCI0eO1E033WTl0gHldJ4LdAnXZLOFKz6%2BuVyuKu79%2Bwk99i/6%2B517VuwPdAkha3tRL7/un3PYvDZtWgRkXcuvYI0cOVLbtm3TgAEDNH78eO3cuVM1NTVWlwEAAOA3lgesgoICbdu2TW%2B88YY6d%2B6shQsXKisrS0uWLNHx48etLgcAAMC4gD2DlZaWphkzZmjPnj2aNWuW3njjDQ0aNEgPPfSQPv7440CVBQAAcN0CFrCuXLmibdu26eGHH9aMGTPUtm1bzZw5UykpKRo3bpy2bt0aqNIAAACui%2BUfNHr06FFt2LBBmzZtUlVVlXJycvTyyy/rjjvu8G7TvXt3zZs3T0OGDLG6PAAAgOtmecAaPHiwOnbsqAkTJui%2B%2B%2B5Ty5Yt622TlZWl06dPW10aAACAEZYHrJKSEvXo0eOa23300UcWVAMAAGCe5c9gJScna%2BLEidq1a5d37KWXXtLDDz9c728AAgAABCPLA9aiRYt07tw5derUyTvWt29f1dXVafHixVaXAwAAYJzltwjfe%2B89bd26VfHx8d6xDh06aOnSpbr33nutLgcAAMA4y69gXbx4UZGRkfULCQ9XdXW11eUAAAAYZ3nA6t69uxYvXqwzZ854x7755hvNnz/f56MaAAAAgpXltwhnzZqlX//61/rFL36hmJgY1dXVqaqqSu3bt9crr7xidTkAAADGWR6w2rdvr7feekvvvPOOvvzyS4WHh6tjx47q3bu3IiIirC4HAADAOMsDliQ1bdpUAwYMCMTSAAAAfmd5wPrqq6%2B0bNkyffbZZ7p48WK9%2BT/%2B8Y9WlwQAAGBUQJ7BqqysVO/evRUdHW318gAAAH5necAqLy/XH//4RyUkJFi9NAAAgCUs/5iGVq1aceUKAACENMsD1oQJE7Rq1Sp5PB6rlwYAALCE5bcI33nnHX344YfauHGjbr75ZoWH%2B2a8P/zhD1aXBAAAYJTlASsmJkZ9%2BvSxelkAAADLWB6wFi1aZPWSAAAAlrL8GSxJOnbsmFauXKmZM2d6x/7yl78EohQAAADjLA9YBw8e1NChQ7Vz506VlZVJ%2Bu7DR8eOHcuHjAIAgJBgecBavny5nnjiCW3dulVhYWGSvvv7hIsXL9bq1autLgcAAMA4ywPWX//6V%2BXn50uSN2BJ0sCBA3X06FGrywEAADDO8oDVokWLq/4NwsrKSjVt2tTqcgAAAIyzPGB169ZNCxcu1Pnz571jx48f14wZM/SLX/zC6nIAAACMs/xjGmbOnKkHHnhAPXv2VG1trbp166bq6mp17txZixcvtrocAAAA4ywPWO3atVNZWZn27dun48ePKyoqSh07dlSvXr18nskCAAAIVpYHLElq0qSJBgwYEIilAQAA/M7ygJWdnf0Pr1TxWVgAACDYWR6wBg0a5BOwamtrdfz4cdntdj3wwANWlwMAAGCc5QFr%2BvTpVx3fsWOH/vznP1tcDQAAgHkB%2BVuEVzNgwAC99dZbgS4DAADguv1oAtbhw4fl8XgCXQYAAMB1s/wW4ZgxY%2BqNVVdX6%2BjRo/rlL39pdTkAAADGWR6wOnToUO%2B3CCMjI5Wbm6tRo0YZXWvNmjV67bXXdP78ef3sZz/TggULdPPNN%2BvgwYNatmyZjh07pptuukkTJkzQ0KFDva8rKSnRa6%2B9JqfTqeTkZM2ePVvp6elGawMAAKHL8oBl1ae1v/baa9qyZYtKSkqUmJioFStW6KWXXtIjjzyiSZMmafbs2RoyZIg%2B%2BOADPfroo%2BrYsaMyMjK0e/durVy5Ui%2B88IKSk5NVUlKiiRMnaufOnYqOjrakdgAAENwsD1ibNm1q8Lb33XffD15n3bp1mjFjhn7yk59IkubMmSNJ%2Bv3vf68OHTooNzdXkpSZmans7GytX79eGRkZKi0t1YgRI9SlSxdJ0vjx41VSUqI9e/Zo8ODBP7geAABw47A8YM2ePVt1dXX1HmgPCwvzGQsLC/vBAeubb77R119/rTNnzmjQoEE6deqUevbsqXnz5snhcCg1NdVn%2B9TUVG3fvl2S5HA4NGjQIO9ceHi4UlJSZLfbCVgAAKBBLA9YL7zwgtatW6eJEycqOTlZHo9Hn376qZ5//nn96le/Us%2BePa97jZMnT0qS3n77bb344ovyeDwqLCzUnDlzdPHiRbVt29Zn%2B5YtW8rlckmS3G634uLifObj4uK88w1RWVkpp9PpM2azRSsxMfGHHI5lIiLCfb7CPHrsX/QX/maz%2Bffc4hwOHQF5Bqu4uNgn5Nx5551q3769HnroIZWVlV33Gn%2B7EjZ%2B/HjvOpMnT9bDDz%2BszMzMBr/%2BhyotLdWqVat8xgoKClRYWHhd%2B7VKbGyzQJcQ8uixf9Ff%2BEt8fHNL1uEcDn6WB6wvvvii3hUiSYqNjVVFRYWRNVq3bu3d598kJSXJ4/HoypUrcrvdPtu7XC4lJCRIkuLj4%2BvNu91ude7cucHr5%2BXlKTs722fMZouWy1XVqOOwWkREuGJjm%2Bns2WrV1tYFupyQRI/9i/7C3/z9Ps45bJ5Vofj7LA9YSUlJWrx4sR5//HHFx8dLks6ePatnn31Wt9xyi5E12rVrp5iYGB05ckRpaWmSpIqKCjVp0kRZWVnavHmzz/bl5eXeh9rT09PlcDg0fPhwSd/9rcTDhw97H4pviMTExHq3A53Oc6qpCY4fltrauqCpNVjRY/%2Biv/AXq84rzuHgZ/lN3lmzZmn79u3KzMzUnXfeqR49eujnP/%2B5Nm7cqCeffNLIGjabTbm5uXruuef03//93zp16pRWr16tIUOGaPjw4aqoqND69et16dIl7du3T/v27dPo0aMlSfn5%2Bdq0aZMOHTqk6upqrVmzRk2bNlXfvn2N1AYAAEKf5Vewevfurb1792rfvn06efKkPB6P2rZtq7vuukstWrQwts60adN0%2BfJljRo1SleuXFFOTo7mzJmj5s2ba%2B3atVqwYIHmz5%2BvpKQkLVmyRLfffrskqU%2BfPpo6daqKiop06tQpZWRkqLi4WFFRUcZqAwAAoS3ME6A/AHjlyhWdPHlS7du3D8TylnM6zwW6hGuy2cIVH99cLlcVl6b9hB77F/39zj0r9ge6hJC1vaiXX/fPOWxemzbmLt40huW3CC9evKgZM2aoa9euuueeeyR99wzW%2BPHjdfbsWavLAQAAMM7ygLVkyRIdOXJES5cuVXj4/y5fW1urpUuXWl0OAACAcZYHrB07dujZZ5/VwIEDvX/0OTY2VosWLdLOnTutLgcAAMA4ywNWVVWVOnToUG88ISFBFy5csLocAAAA4ywPWLfccov%2B/Oc/S/L9xPS3335b//RP/2R1OQAAAMZZ/jEN999/vyZPnqyRI0eqrq5OL774osrLy7Vjxw7Nnj3b6nIAAACMszxg5eXlyWaz6dVXX1VERISee%2B45dezYUUuXLtXAgQOtLgcAAMA4ywPW6dOnNXLkSI0cOdLqpQEAACxh%2BTNY/fv3V4A%2B2xQAAMASlgesnj17avv27VYvCwAAYBnLbxHedNNN%2Bvd//3cVFxfrlltuUZMmTXzmly1bZnVJAAAARlkesD7//HP95Cc/kSS5XC6rlwcAAPA7ywLWlClTtHz5cr3yyivesdWrV6ugoMCqEgAAACxh2TNYu3fvrjdWXFxs1fIAAACWsSxgXe03B/ltQgAAEIosC1h/%2B8PO1xoDAAAIdpZ/TAMAAECoI2ABAAAYZtlvEV65ckXTpk275hifgwUAAIKdZQHrjjvuUGVl5TXHAAAAgp1lAev/fv4VAABAKOMZLAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGWfancgAgWN2zYn%2BgSwAQZLiCBQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABh2QwSshQsXKjk52fv9wYMHlZubq27dumnw4MHasmWLz/YlJSXKyclRt27dlJ%2Bfr/LycqtLBgAAQSzkA9aRI0e0efNm7/eVlZWaNGmSxowZo4MHD2r27NmaO3eu7Ha7JGn37t1auXKlnnnmGR04cED9%2BvXTxIkTdeHChUAdAgAACDIhHbDq6ur01FNPady4cd6xrVu3qkOHDsrNzVVkZKQyMzOVnZ2t9evXS5JKS0s1YsQIdenSRVFRURo/frwkac%2BePYE4BAAAEIRCOmD94Q9/UGRkpIYMGeIdczgcSk1N9dkuNTXVexvw%2B/Ph4eFKSUnxXuECAAC4lpD9W4TffvutVq5cqVdeecVn3O12q23btj5jLVu2lMvl8s7HxcX5zMfFxXnnG6KyslJOp9NnzGaLVmJiYmMOwXIREeE%2BX2EePfYv%2Bgt/s9n8e25xDoeOkA1YixYt0ogRI9SpUyd9/fXXjXqtx%2BO5rrVLS0u1atUqn7GCggIVFhZe136tEhvbLNAlhDx67F/0F/4SH9/cknU4h4NfSAasgwcP6i9/%2BYvKysrqzcXHx8vtdvuMuVwuJSQk/N15t9utzp07N3j9vLw8ZWdn%2B4zZbNFyuaoavI9AiIgIV2xsM509W63a2rpAlxOS6LF/0V/4m7/fxzmHzbMqFH9fSAasLVu26NSpU%2BrXr5%2Bk/70i1bNnT/3617%2BuF7zKy8vVpUsXSVJ6erocDoeGDx8uSaqtrdXhw4eVm5vb4PUTExPr3Q50Os%2BppiY4flhqa%2BuCptZgRY/9i/7CX6w6rziHg19I3uR98skntWPHDm3evFmbN29WcXGxJGnz5s0aMmSIKioqtH79el26dEn79u3Tvn37NHr0aElSfn6%2BNm3apEOHDqm6ulpr1qxR06ZN1bdv3wAeEQAACCYheQUrLi7O50H1mpoaSVK7du0kSWvXrtWCBQs0f/58JSUlacmSJbr99tslSX369NHUqVNVVFSkU6dOKSMjQ8XFxYqKirL%2BQAAAQFAK81zvE91oEKfzXKBLuCabLVzx8c3lclVxadpP6LF/%2Bau/96zYb2xfCG7bi3r5df%2B8R5jXpk2LgKwbkrcIAQAAAomABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGGYLdAEAAASLe1bsD3QJjbK9qFegS7hhcQULAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABgWsgGroqJCBQUF6tmzpzIzM/Xkk0/q7NmzkqQjR47oV7/6le644w798pe/1Lp163xeu23bNg0ZMkRdu3bViBEj9N577wXiEAAAQJAK2YA1ceJExcbGavfu3dq4caM%2B%2B%2Bwz/fa3v9XFixc1YcIE/fznP9e7776r5cuXa%2B3atdq5c6ek78LXjBkzNH36dP3pT3/SuHHj9Nhjj%2BnkyZMBPiIAABAsQjJgnT17Vunp6Zo2bZqaN2%2Budu3aafjw4Xr//fe1d%2B9eXblyRY8%2B%2Bqiio6OVlpamUaNGqbS0VJK0fv16ZWVlKSsrS5GRkRo6dKhuu%2B02bdmyJcBHBQAAgoUt0AX4Q2xsrBYtWuTmoHjPAAALXElEQVQzduLECSUmJsrhcCg5OVkRERHeudTUVK1fv16S5HA4lJWV5fPa1NRU2e32Bq9fWVkpp9PpM2azRSsxMbGxh2KpiIhwn68wjx77F/0FfNls/CwESkgGrO%2Bz2%2B169dVXtWbNGm3fvl2xsbE%2B8y1btpTb7VZdXZ3cbrfi4uJ85uPi4vT55583eL3S0lKtWrXKZ6ygoECFhYU//CAsFBvbLNAlhDx67F/0F/hOfHzzQJdwwwr5gPXBBx/o0Ucf1bRp05SZmant27dfdbuwsDDv//Z4PNe1Zl5enrKzs33GbLZouVxV17Vff4uICFdsbDOdPVut2tq6QJcTkuixf9FfwNeP/d87VghUyAzpgLV792498cQTmjt3ru677z5JUkJCgr744guf7dxut1q2bKnw8HDFx8fL7XbXm09ISGjwuomJifVuBzqd51RTExxv%2BLW1dUFTa7Cix/5Ff4Hv8HMQOCF7c/bDDz/UjBkz9B//8R/ecCVJ6enp%2BvTTT1VTU%2BMds9vt6tKli3e%2BvLzcZ1//dx4AAOBaQjJg1dTUaM6cOZo%2Bfbp69%2B7tM5eVlaWYmBitWbNG1dXV%2Buijj7Rhwwbl5%2BdLkkaPHq0DBw5o7969unTpkjZs2KAvvvhCQ4cODcShAACAIBTmud4Hjn6E3n//ff3Lv/yLmjZtWm/u7bffVlVVlZ566imVl5erdevWevjhh3X//fd7t9m5c6eWLVumiooKderUSbNnz1b37t2vqyan89x1vd4KNlu44uOby%2BWq4rKyn9Bj//JXf%2B9Zsd/YvgArbS/qFegSAq5NmxYBWTckA9aPEQELEj32NwIW4IuAFbiAFZK3CAEAAAKJgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAw2yBLgDAjeeeFfsDXQIA%2BBVXsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwfosQAIAQFUy/sbu9qFegSzCKK1gAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGG2QBcAwIx7VuwPdAkAgP%2BPK1gAAACGEbAAAAAMI2ABAAAYRsC6ioqKCj3yyCPq2bOn%2BvXrpyVLlqiuri7QZQEAgCDBQ%2B5XMXnyZKWlpWnXrl06deqUJkyYoNatW%2BvBBx8MdGkAACAIcAXre%2Bx2uz755BNNnz5dLVq0UIcOHTRu3DiVlpYGujQAABAkuIL1PQ6HQ0lJSYqLi/OOpaWl6fjx4zp//rxiYmKuuY/Kyko5nU6fMZstWomJicbrNSkiItznK8yjxwBwdTZbaL0vErC%2Bx%2B12KzY21mfsb2HL5XI1KGCVlpZq1apVPmOPPfaYJk%2BebK5QP6isrNTLL7%2BgvLy8H30YDFb%2B7PH7/z7Q6P6CUWVlpUpLSzmH/Yge%2Bxf9DR2hFRcN8Xg81/X6vLw8bdy40eefvLw8Q9X5j9Pp1KpVq%2BpdfYM59Ni/6K//0WP/or%2BhgytY35OQkCC32%2B0z5na7FRYWpoSEhAbtIzExkf/yAADgBsYVrO9JT0/XiRMndPr0ae%2BY3W5Xp06d1Lx58wBWBgAAggUB63tSU1OVkZGhZcuW6fz58zp69KhefPFF5efnB7o0AAAQJCLmzZs3L9BF/NjcddddKisr029%2B8xu99dZbys3N1UMPPaSwsLBAl%2BZ3zZs3V48ePbha50f02L/or//RY/%2Biv6EhzHO9T3QDAADAB7cIAQAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYN3g7Ha77r77bo0ePfofbrdy5UqlpKQoIyPD559vv/3WokqDV0N7LEklJSXKyclRt27dlJ%2Bfr/LycgsqDF5ut1tFRUXKzMxU7969NXv2bF28ePGq227cuFG33357vXP4448/trjqH7%2BKigo98sgj6tmzp/r166clS5aorq7uqttyzjZeQ/vL%2B25wswW6AATOli1b9Lvf/U6dOnXS2bNnr7n9sGHDtHjxYgsqCx2N6fHu3bu1cuVKvfDCC0pOTlZJSYkmTpyonTt3Kjo62qKKg8vcuXN1%2BfJllZWV6cqVK3r88ce1dOlSzZkz56rbd%2B/eXa%2B88orFVQafyZMnKy0tTbt27dKpU6c0YcIEtW7dWg8%2B%2BKDPdpyzP0xD%2ByvxvhvMuIJ1A7t06ZJKS0vVpUuXQJcSshrT49LSUo0YMUJdunRRVFSUxo8fL0nas2ePv8sMSt9%2B%2B6127dqlKVOmKCEhQW3bttWkSZP05ptv6sqVK4EuL2jZ7XZ98sknmj59ulq0aKEOHTpo3LhxKi0trbct52zjNaa/CG4ErBvYqFGj1LZt2wZv/%2Bmnn2rMmDHq1q2bBg8erPfee8%2BP1YWGxvTY4XAoNTXV%2B314eLhSUlJkt9v9VV5QO3LkiCIiIpScnOwdS0tL04ULF3Ts2LGrvubEiRN68MEH1b17d/Xv31%2BbN2%2B2qtyg4XA4lJSUpLi4OO9YWlqajh8/rvPnz9fblnO2cRrTX4n33WBGwEKDtGvXTu3bt9dvf/tb7d%2B/X6NGjdLEiRP/7r/I0Hhut9vnTVeS4uLi5HK5AlTRj5vb7VZMTIzCwsK8Y3/r39V6lpCQoA4dOuiJJ57Q/v37NXXqVM2aNUsHDx60rOZg4Ha7FRsb6zP29/rKOdt4jekv77vBjYAVwjZv3qzk5OSr/rNx48ZG7WvUqFF69tlndeutt6pZs2YaN26cUlJStGXLFj9VHxxM9liSPB6PH6oMXv%2BovxUVFY3qV9%2B%2BffXCCy8oNTVVTZs21eDBg3X33Xf/oP%2BfQl1j%2Bso523gN7Rnvu8GNh9xD2LBhwzRs2DC/7T8pKUmVlZV%2B238wMNnj%2BPh4ud1unzG3263OnTsb2X8w%2Bkf93b9/v86fP6/a2lpFRERIkrd/rVq1atD%2Bk5KS%2BK2370lISLjqeRgWFqaEhASfcc7ZxmtMf6%2BG993gwRUsNMh//ud/1ruVcvToUbVv3z5AFYWe9PR0ORwO7/e1tbU6fPgwv4Twd6SkpMjj8eiTTz7xjtntdsXGxqpjx471tn/99de1bds2nzHO4frS09N14sQJnT592jtmt9vVqVMnNW/evN62nLON05j%2B8r4b3AhY%2BLsGDhyo999/X9J3/4U1f/58HTt2TJcuXdK6dev05Zdfavjw4QGuMrj93x7n5%2Bdr06ZNOnTokKqrq7VmzRo1bdpUffv2DWyRP1IJCQnKycnRihUrdPr0aZ08eVKrV69Wbm6ubLbvLs4/8MAD3lB1%2BfJl/eY3v5HdbteVK1dUVlamd955R2PGjAnkYfzopKamKiMjQ8uWLdP58%2Bd19OhRvfjii8rPz5fEOXu9GtNf3neDG7cIb2A5OTn6n//5H9XW1qqurk4ZGRmSpLfffltJSUk6fvy4Lly4IEmaNm2aJGncuHFyu93q1KmTXnrpJbVr1y5g9QeDxvS4T58%2Bmjp1qoqKinTq1CllZGSouLhYUVFRgTyEH7Wnn35aTz31lPr3768mTZro3nvv1ZQpU7zzX331lc6cOSNJGjt2rKqqqvT444/L6XTq5ptv1urVq5Wenh6o8n%2B0nn32Wc2dO1e9evVSTEyMxowZo/vvv1%2BSOGcNaGh/ed8NbmEenlAEAAAwiluEAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGDY/wP0lU9xpHpweQAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-609837850269236373”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-0.2319788970337091</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.29466099351100317</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.026741720927772383</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.5360765660553791</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.08475386705376708</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.3457474230037112</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.1605192270115351</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.06284676825651125</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.19071844546134248</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.26852081390561167</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (41)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-609837850269236373”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-1.6150463789400007</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.7475382495502649</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:28%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.5360765660553791</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.3770388194174602</td> <td class=”number”>29</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:24%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.2817160725294582</td> <td class=”number”>55</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:46%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.46221921943258293</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:93%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.4771634313341099</td> <td class=”number”>16</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:23%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.8040334711354716</td> <td class=”number”>14</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:20%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.8166099613606475</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.8492897607778729</td> <td class=”number”>72</td> <td class=”number”>2.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_SLHOUSE_07_12”>PCH_SLHOUSE_07_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>32</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>7.6%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>243</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>-5.4926</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>300</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>76.8%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-6426063118142564296”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-6426063118142564296,#minihistogram-6426063118142564296”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-6426063118142564296”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-6426063118142564296”

aria-controls=”quantiles-6426063118142564296” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-6426063118142564296” aria-controls=”histogram-6426063118142564296”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-6426063118142564296” aria-controls=”common-6426063118142564296”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-6426063118142564296” aria-controls=”extreme-6426063118142564296”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-6426063118142564296”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-100</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>0</td>

</tr> <tr>

<th>95-th percentile</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>300</td>

</tr> <tr>

<th>Range</th> <td>400</td>

</tr> <tr>

<th>Interquartile range</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>35.689</td>

</tr> <tr>

<th>Coef of variation</th> <td>-6.4976</td>

</tr> <tr>

<th>Kurtosis</th> <td>9.2094</td>

</tr> <tr>

<th>Mean</th> <td>-5.4926</td>

</tr> <tr>

<th>MAD</th> <td>17.7</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.31452</td>

</tr> <tr>

<th>Sum</th> <td>-16297</td>

</tr> <tr>

<th>Variance</th> <td>1273.7</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-6426063118142564296”>

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qNHj3ru73a7NWvWLKWnp2vQoEF69NFHVVtbay48AACwNb8VrNOnT2vTpk367W9/q7y8PHXq1EkPP/ywevfurezsbG3YsOGc93/hhRc0f/58de3atdH5AQMGyOVyeX2kpqZKklatWqUNGzaosLBQ27dvV0JCgnJycmRZliRp3rx5qqmp0caNG/X666%2BrpKREBQUFZj8BAADAtkJ9vcOSkhKtWbNGb7zxhqqrqzVy5Ej96U9/Uv/%2B/T23GTBggH7/%2B99rzJgxZ91OWFiY1qxZo6efflqnTp06rzU4nU5lZ2ere/fukqTc3FylpaVpz5496ty5s95%2B%2B2397W9/U2xsrCTpnnvu0f3336%2B8vDy1adPmF6QGAAD/l/i8YI0ePVrdunXTtGnTdPPNNys6OrrBbTIyMnTixIlzbueOO%2B445/yxY8d01113qaioSJGRkZo5c6bGjRun2tpaHThwQElJSZ7bRkREqGvXrnK5XDp58qRCQkKUmJjomU9OTtb333%2BvgwcPeo0DAAA0xucFa%2BXKlRo4cGCTt9uzZ88v3kdsbKwSEhL0wAMPqEePHvr73/%2Buhx56SHFxcfrNb34jy7IUFRXldZ%2BoqChVVFQoOjpaERERCgoK8pqTpIqKimbtv6ysTOXl5V5joaHtFBcX94szNSYkJNjrX7uwa66WFhrqv8%2BXnY8Z2QKPXXNJ9s1mx1w%2BL1iJiYmaPn26JkyYoOuvv16S9N///d/64IMPtHDhwkZf0TpfgwcP1uDBgz3/P3r0aP3973/X2rVrNWfOHEnyXG/VmHPNNYfT6dTSpUu9xnJycjRz5swL2u7ZREZe3CLb9Te75mopMTHh/l6CrY8Z2QKPXXNJ9s1mp1w%2BL1j5%2Bfk6efKkevTo4RkbPHiw3nvvPS1YsEALFixokf3Gx8erqKhI0dHRCg4Oltvt9pp3u93q0KGDYmNjVVVVpbq6OoWEhHjmJKlDhw7N2ldmZqaGDh3qNRYa2k4VFdUGkvyvkJBgRUZerMrKGtXV1Rvdtj/ZNVdLM/34Oh92PmZkCzx2zSXZN1tL5vLXD58%2BL1jvv/%2B%2BNmzYoJiYGM9YQkKCCgoKdNNNNxnZx1/%2B8hdFRUVp1KhRnrGSkhJ16dJFYWFh6tmzp4qLiz2nKisrK3XkyBGlpqYqPj5elmVp//79Sk5OliS5XC5FRkaqW7duzdp/XFxcg9OB5eUndeZMy3wx1NXVt9i2/cmuuVpKa/hc2fmYkS3w2DWXZN9sdsrl85OdtbW1CgsLa7iQ4GDV1NQY2ccPP/ygp556Si6XS6dPn9bGjRv17rvvavLkyZKkrKwsrVy5UiUlJaqqqlJBQYF69%2B6tlJQUxcbGauTIkXr22Wd14sQJHT9%2BXMuWLdOECRMUGurzPgoAAAKQzxvDgAEDtGDBAs2ePdtz8fjXX3%2BtZ555xuutGpqSkpIiSZ4/s/P2229L%2BvHVpjvuuEPV1dW6//77VV5ers6dO2vZsmVyOBySpMmTJ6u8vFy33367qqurlZaW5nXN1JNPPqnHH39cw4YNU5s2bXTTTTc1eDNTAACAswmyLvSK7vN09OhR/cd//IdKS0sVERGh%2Bvp6VVdXq0uXLvrzn/%2BsTp06%2BXI5PlNeftL4NkNDgxUTE66KimrbvKQqtZ5cNz77gd/2/UtsnnWN3/bdWo5ZSyBb4LFrLsm%2B2VoyV8eO7Y1ur7l8/gpWly5d9Oabb%2Brdd9/VkSNHFBwcrG7dumnQoEGei8oBAAACmV8uKmrbtq3nLRoAAADsxucF6%2BjRo1q0aJG%2B/PLLRv%2BA8jvvvOPrJQEAABjl84L1yCOPqKysTIMGDVK7du18vXsAAIAW5/OCVVRUpHfeecfzh5QBAADsxufvg9WhQwdeuQIAALbm84I1bdo0LV269IL/3h8AAEBr5fNThO%2B%2B%2B64%2B%2B%2BwzrV27Vp07d1ZwsHfHe%2B2113y9JAAAAKN8XrAiIiJ03XXX%2BXq3AAAAPuPzgpWfn%2B/rXQIAAPiUz6/BkqSDBw/queee08MPP%2BwZ%2B9e//uWPpQAAABjn84K1a9cujR07Vlu3btXGjRsl/fjmo3fccQdvMgoAAGzB5wVr8eLFevDBB7VhwwYFBQVJ%2BvHvEy5YsEDLli3z9XIAAACM83nB%2Bp//%2BR9lZWVJkqdgSdINN9ygkpISXy8HAADAOJ8XrPbt2zf6NwjLysrUtm1bXy8HAADAOJ8XrH79%2BukPf/iDqqqqPGOHDh1SXl6err76al8vBwAAwDifv03Dww8/rDvvvFNpaWmqq6tTv379VFNTo549e2rBggW%2BXg4AAIBxPi9Yl156qTZu3KidO3fq0KFDuuiii9StWzddc801XtdkAQAABCqfFyxJatOmja6//np/7BoAAKDF%2BbxgDR069JyvVPFeWAAAIND5vGCNGjXKq2DV1dXp0KFDcrlcuvPOO329HAAAAON8XrDmzJnT6Phbb72lf/zjHz5eDQAAgHl%2B%2BVuEjbn%2B%2Buv15ptv%2BnsZAAAAF6zVFKy9e/fKsix/LwMAAOCC%2BfwU4eTJkxuM1dTUqKSkRCNGjPD1cgAAAIzzecFKSEho8FuEYWFhmjBhgiZOnOjr5QAAABjn84LFu7UDAAC783nBeuONN5p925tvvrkFVwIAANAyfF6wHn30UdXX1ze4oD0oKMhrLCgoiIIFAAACks8L1osvvqiXX35Z06dPV2JioizL0hdffKEXXnhBU6ZMUVpamq%2BXBAAAYJRfrsEqLCxUp06dPGNXXnmlunTpoqlTp2rjxo2%2BXhIAAIBRPn8frMOHDysqKqrBeGRkpEpLS329HAAAAON8XrDi4%2BO1YMECVVRUeMYqKyu1aNEiXXbZZb5eDgAAgHE%2BP0X4yCOPaPbs2XI6nQoPD1dwcLCqqqp00UUXadmyZb5eDgAAgHE%2BL1iDBg3Sjh07tHPnTh0/flyWZalTp0669tpr1b59e18vBwAAwDifFyxJuvjiizVs2DAdP35cXbp08ccSAAAAWozPr8Gqra1VXl6e%2BvbtqxtvvFHSj9dg3X333aqsrPT1cgAAAIzzecFauHCh9u3bp4KCAgUH/%2B/u6%2BrqVFBQ4OvlAAAAGOfzgvXWW2/pj3/8o2644QbPH32OjIxUfn6%2Btm7d6uvlAAAAGOfzglVdXa2EhIQG47Gxsfr%2B%2B%2B99vRwAAADjfF6wLrvsMv3jH/%2BQJK%2B/Pbhlyxb9%2Bte/9vVyAAAAjPP5bxHedtttuu%2B%2B%2B3Trrbeqvr5er7zyioqKivTWW2/p0Ucf9fVyAAAAjPN5wcrMzFRoaKheffVVhYSE6Pnnn1e3bt1UUFCgG264wdfLAQAAMM7nBevEiRO69dZbdeutt/p61wAAAD7h82uwhg0b5nXtFQAAgN34vGClpaVp8%2BbNvt4tAACAz/j8FOGvfvUrPf300yosLNRll12mNm3aeM0vWrTI10sCAAAwyucF68CBA/rNb34jSaqoqPD17gEAAFqczwpWbm6uFi9erD//%2Bc%2BesWXLliknJ8dXSwAAAPAJn12DtW3btgZjhYWFvto9AACAz/isYDX2m4P8NiEAALAjnxWsn/6wc1Nj5%2BO9995Tenq6cnNzG8xt2rRJY8aMUd%2B%2BfTV%2B/Hi9//77nrn6%2BnotXrxYw4YN04ABAzR16lQdPXrUM%2B92uzVr1iylp6dr0KBBevTRR1VbW3tBawUAAP93%2BPxtGkx54YUXNH/%2BfHXt2rXB3L59%2B5SXl6c5c%2Bboo48%2BUnZ2tu69914dP35ckrRq1Spt2LBBhYWF2r59uxISEpSTk%2BN5RW3evHmqqanRxo0b9frrr6ukpEQFBQU%2BzQcAAAJXwBassLAwrVmzptGCtXr1amVkZCgjI0NhYWEaO3asevXqpfXr10uSnE6nsrOz1b17d0VERCg3N1clJSXas2ePvvnmG7399tvKzc1VbGysOnXqpHvuuUevv/66Tp8%2B7euYAAAgAPnstwhPnz6t2bNnNznW3PfBuuOOO846V1xcrIyMDK%2BxpKQkuVwu1dbW6sCBA0pKSvLMRUREqGvXrnK5XDp58qRCQkKUmJjomU9OTtb333%2BvgwcPeo0DAAA0xmcFq3///iorK2tyzAS3262oqCivsaioKB04cEDfffedLMtqdL6iokLR0dGKiIjwuj7sp9s29327ysrKVF5e7jUWGtpOcXFxvyTOWYWEBHv9axd2zdXSQkP99/my8zEjW%2BCxay7JvtnsmMtnBevf3//KF5r6DcVzzV/obzc6nU4tXbrUaywnJ0czZ868oO2eTWTkxS2yXX%2Bza66WEhMT7u8l2PqYkS3w2DWXZN9sdsrl83dy94WYmBi53W6vMbfbrdjYWEVHRys4OLjR%2BQ4dOig2NlZVVVWqq6tTSEiIZ06SOnTo0Kz9Z2ZmaujQoV5joaHtVFFR/UsjNSokJFiRkRersrJGdXX1RrftT3bN1dJMP77Oh52PGdkCj11zSfbN1pK5/PXDpy0LlsPhUFFRkdeYy%2BXS6NGjFRYWpp49e6q4uFgDBw6UJFVWVurIkSNKTU1VfHy8LMvS/v37lZyc7LlvZGSkunXr1qz9x8XFNTgdWF5%2BUmfOtMwXQ11dfYtt25/smqultIbPlZ2PGdkCj11zSfbNZqdc9jnZ%2BW8mTZqkDz/8UDt27NCpU6e0Zs0aHT58WGPHjpUkZWVlaeXKlSopKVFVVZUKCgrUu3dvpaSkKDY2ViNHjtSzzz6rEydO6Pjx41q2bJkmTJig0FBb9lEAAGBYwDaGlJQUSdKZM2ckSW%2B//bakH19t6tWrlwoKCpSfn6/S0lL16NFDK1asUMeOHSVJkydPVnl5uW6//XZVV1crLS3N65qpJ598Uo8//riGDRumNm3a6Kabbmr0zUwBAAAaE2Tx92p8orz8pPFthoYGKyYmXBUV1bZ5SVVqPblufPYDv%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%2Bmn06NFav369131XrlypkSNHql%2B/fsrKylJRUZE/IgAAgAAV6u8FtKQtW7aoc%2BfOXmNlZWW655579Oijj2rMmDH69NNPNWPGDHXr1k0pKSnatm2bnnvuOb344otKTEzUypUrNX36dG3dulXt2rXzUxIAABBIbPsK1tls2LBBCQkJmjBhgsLCwpSenq6hQ4dq9erVkiSn06nx48erT58%2Buuiii3T33XdLkrZv3%2B7PZQMAgABi61ewFi1apH/961%2BqqqrSjTfeqLlz56q4uFhJSUlet0tKStLmzZslScXFxRo1apRnLjg4WL1795bL5dLo0aObtd%2BysjKVl5d7jYWGtlNcXNwFJvIWEhLs9a9d2DVXSwsN9d/ny87HjGyBx665JPtms2Mu2xasK664Qunp6XrmmWd09OhRzZo1S0888YTcbrc6derkddvo6GhVVFRIktxut6Kiorzmo6KiPPPN4XQ6tXTpUq%2BxnJwczZw58xemObfIyItbZLv%2BZtdcLSUmJtzfS7D1MSNb4LFrLsm%2B2eyUy7YFy%2Bl0ev67e/fumjNnjmbMmKH%2B/fs3eV/Lsi5o35mZmRo6dKjXWGhoO1VUVF/Qdn8uJCRYkZEXq7KyRnV19Ua37U92zdXSTD%2B%2BzoedjxnZAo9dc0n2zdaSufz1w6dtC9bPde7cWXV1dQoODpbb7faaq6ioUGxsrCQpJiamwbzb7VbPnj2bva%2B4uLgGpwPLy0/qzJmW%2BWKoq6tvsW37k11ztZTW8Lmy8zEjW%2BCxay7JvtnslMs%2BJzv/zd69e7VgwQKvsZKSErVt21YZGRkN3nahqKhIffr0kSQ5HA4VFxd75urq6rR3714faJlpAAAPvUlEQVTPPAAAQFNsWbA6dOggp9OpwsJC/fDDDzp06JCWLFmizMxMjRs3TqWlpVq9erVOnTqlnTt3aufOnZo0aZIkKSsrS2%2B88YZ2796tmpoaLV%2B%2BXG3bttXgwYP9GwoAAAQMW54i7NSpkwoLC7Vo0SJPQbrllluUm5ursLAwrVixQvPnz9cTTzyh%2BPh4LVy4UJdffrkk6brrrtMDDzygWbNm6dtvv1VKSooKCwt10UUX%2BTkVAAAIFLYsWJI0YMAAvfbaa2edW7du3Vnve9ttt%2Bm2225rqaUBAACbs%2BUpQgAAAH%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%2Bvp6fy8LAAAECF7BasR9992n5ORkvf322/r22281bdo0XXLJJbrrrrv8vTQAABAAKFg/43K5tH//fr3yyitq37692rdvr%2BzsbP3pT3%2BiYAGAYYF0alvi9Daaj4L1M8XFxYqPj1dUVJRnLDk5WYcOHVJVVZUiIiKa3EZZWZnKy8u9xkJD2ykuLs7oWkNCOMOLwBRo31QDTWho854bfnoO4bmk%2BZr7uW0pdj1mdsxFwfoZt9utyMhIr7GfylZFRUWzCpbT6dTSpUu9xu69917dd9995haqH4vcnZd%2BqczMTOPlzZ/KysrkdDptl0uybza75pLsn%2B1Pf3rRr9k%2BefoG49vkmAUeO%2BayT1U0yLKsC7p/Zmam1q5d6/WRmZlpaHX/q7y8XEuXLm3walmgs2suyb7Z7JpLIlsgsmsuyb7Z7JiLV7B%2BJjY2Vm6322vM7XYrKChIsbGxzdpGXFycbRo4AAA4f7yC9TMOh0PHjh3TiRMnPGMul0s9evRQeHi4H1cGAAACBQXrZ5KSkpSSkqJFixapqqpKJSUleuWVV5SVleXvpQEAgAAR8vvf//73/l5Ea3Pttddq48aNeuqpp/Tmm29qwoQJmjp1qoKCgvy9tAbCw8M1cOBA2726Ztdckn2z2TWXRLZAZNdckn2z2S1XkHWhV3QDAADAC6cIAQAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAUIl8ul4cOHa9KkSQ3mdu3apQkTJqhfv34aPXq01q9f7zW/cuVKjRw5Uv369VNWVpaKiop8tezzMnToUDkcDqWkpHg%2Bpk%2Bf7pnft2%2BfpkyZov79%2B2vEiBF6%2BeWX/bja81daWqrf/e53SktL05AhQ7Rw4ULV19f7e1nnLTExscFxeuqppyQ1/Vhsbd577z2lp6crNze3wdymTZs0ZswY9e3bV%2BPHj9f777/vmauvr9fixYs1bNgwDRgwQFOnTtXRo0d9ufQmnS3b2rVrdfnll3sdv5SUFH3%2B%2BeeSWn%2B20tJS5eTkKC0tTenp6Zo7d64qKyslNf0cca5j2hqcLdtXX32lxMTEBsfspZde8ty3NWfbv3%2B/7rzzTvXv31/p6emaNWuWysvLJdnn%2B1ejLLR669atszIyMqypU6daEydO9Jr7%2BuuvrSuuuMJavXq1VVtba33wwQdWamqq9fnnn1uWZVnvvPOOdeWVV1q7d%2B%2B2ampqrBUrVljXXHONVV1d7Y8o5zRkyBDro48%2BanSupqbGuvbaa63nnnvOqq6utoqKiqyBAwdab731lo9X%2Bcvdcsst1mOPPWZVVlZahw4dskaMGGG9/PLL/l7WeevVq5d19OjRBuNNPRZbm8LCQmvEiBHW5MmTrVmzZnnN7d2713I4HNaOHTus2tpaa926dVafPn2sY8eOWZZlWStXrrSGDBliHThwwDp58qT15JNPWmPGjLHq6%2Bv9EaWBc2V7/fXXrSlTppz1vq0920033WTNnTvXqqqqso4dO2aNHz/eeuSRR5p8jmjqmLYGZ8t29OhRq1evXme9X2vOdurUKevqq6%2B2li5dap06dcr69ttvrSlTplj33HOPrb5/NYZXsALAqVOn5HQ61adPnwZzGzZsUEJCgiZMmKCwsDClp6dr6NChWr16tSTJ6XRq/Pjx6tOnjy666CLdfffdkqTt27f7NMOF2rFjh06fPq0ZM2aoXbt2Sk5O1sSJE%2BV0Ov29tGZxuVzav3%2B/5syZo/bt2yshIUHZ2dkBs/7maOqx2NqEhYVpzZo16tq1a4O51atXKyMjQxkZGQoLC9PYsWPVq1cvz0/XTqdT2dnZ6t69uyIiIpSbm6uSkhLt2bPH1zEada5sTWnN2SorK%2BVwODR79myFh4fr0ksv1S233KJPPvmkyeeIpo6pv50rW1Nac7aamhrl5uZq2rRpatu2rWJjYzV8%2BHB9%2BeWXtv/%2BRcEKABMnTlSnTp0anSsuLlZSUpLXWFJSkudl1J/PBwcHq3fv3nK5XC234AuwcuVKXX/99erbt69mzpypb7/9VtKPORITExUSEuK57b/nbO2Ki4sVHx%2BvqKgoz1hycrIOHTqkqqoqP67sl1m0aJEGDx6sK6%2B8UvPmzVN1dXWTj8XW5o477lD79u0bnTtbFpfLpdraWh04cMBrPiIiQl27dm01X1fnyiZJx44d01133aUBAwZo2LBhWrdunSS1%2BmyRkZHKz8/XJZdc4hk7duyY4uLimnyOONcxbQ3Ole0nDz30kAYNGqSrrrpKixYt0unTpyW17mxRUVGaOHGiQkNDJUkHDx7U3/72N9144422%2B/71cxSsAOd2uxUZGek1Fh0drYqKCs/8v39Tl358wP8035r07t1bqampWrdunTZt2iS32637779f0tlzut3ugLiOqbH1/3RcWuOxOJcrrrhC6enp2rp1q5xOp3bv3q0nnniiycdiIDnX1813330ny7IC5uvq52JjY5WQkKAHH3xQH3zwgR544AE98sgj2rVrV8Blc7lcevXVVzVjxowmnyMC6blQ8s7Wtm1b9e3bV8OHD9f27dtVWFio9evX67/%2B678kBcbzfGlpqRwOh0aNGqWUlBTNnDnTVt%2B/GkPBagXWrVunxMTERj/Wrl17wdu3LMvAKi9cUzmXLVumadOmKTw8XL/61a/0%2BOOP65///KeOHDly1m0GBQX5MMGFaS3H4UI5nU5NnDhRbdu2Vffu3TVnzhxt3LjR89O0XTR1vAL1eA4ePFgvvviikpKS1LZtW40ePVrDhw/3eq4JhGyffvqppk6dqtmzZys9Pf2st/v354hAyCU1zBYXF6fXXntNw4cPV5s2bZSamqpp06YF1DGLj4%2BXy%2BXSli1bdPjwYT300EPNul9rz3Uuof5eAKRx48Zp3Lhxv%2Bi%2BMTExcrvdXmMVFRWKjY0967zb7VbPnj1/2WIvwPnmjI%2BPlySVlZUpNjZWhw8f9pp3u92Kjo5WcHDr/zkhNja20eMQFBTkOVaBqnPnzqqrq1NwcPA5H4uB5GxfN7GxsZ7HXGPzHTp08OUyjYmPj1dRUVHAZNu2bZsefPBBzZs3TzfffLMkNfkcca5j2po0lq0x8fHx%2Buabb2RZVsBkCwoKUkJCgnJzczV58mRlZGQEzPevX6L1f2fCOaWkpDS4xqWoqMhzQbzD4VBxcbFnrq6uTnv37m30gnl/Ki0t1eOPP64ffvjBM1ZSUiJJ6tKlixwOh7744gudOXPGM%2B9yuVpdjrNxOBw6duyYTpw44RlzuVzq0aOHwsPD/biy87N3714tWLDAa6ykpERt27ZVRkbGOR%2BLgcThcDTI8tPjLSwsTD179vT6uqqsrNSRI0eUmprq66Wet7/85S/atGmT11hJSYm6dOkSENk%2B%2B%2Bwz5eXlacmSJV4FpKnniHMd09bibNl27dql5cuXe9324MGDio%2BPV1BQUKvOtmvXLo0cOdLrUo6ffihOTU21xfevs6FgBbgxY8aotLRUq1ev1qlTp7Rz507t3LnT835ZWVlZeuONN7R7927V1NRo%2BfLlatu2rQYPHuzfhf9Mhw4dtG3bNi1YsEDff/%2B9vv76a%2BXn52vIkCHq1KmTMjIyFBERoeXLl6umpkZ79uzRmjVrlJWV5e%2BlN0tSUpJSUlK0aNEiVVVVqaSkRK%2B88krArP8nHTp0kNPpVGFhoX744QcdOnRIS5YsUWZmpsaNG3fOx2IgmTRpkj788EPt2LFDp06d0po1a3T48GGNHTtW0o9fVytXrlRJSYmqqqpUUFCg3r17KyUlxc8rb9oPP/ygp556Si6XS6dPn9bGjRv17rvvavLkyZJad7YzZ87oscce05w5czRo0CCvuaaeI5o6pv52rmzt27fXsmXLtG7dOp0%2BfVoul0svvfRSQGRzOByqqqrSwoULVVNToxMnTui5557TlVdeqaysLFt8/zorP709BM7DiBEjLIfDYfXu3dtKTEy0HA6H5XA4rK%2B%2B%2BsqyLMv6%2BOOPrbFjx1rJycnWiBEjGrw31KpVq6yMjAzL4XBYWVlZ1hdffOGPGE3av3%2B/lZ2dbfXv39/q37%2B/NXfuXOu7777zzH/xxRfW5MmTLYfDYQ0ePNhatWqVH1d7/o4dO2bdfffdVmpqqpWenm798Y9/bDXvLXQ%2BPv74YyszM9O64oorrIEDB1r5%2BflWbW2tZ%2B5cj8XW5Kevo8svv9y6/PLLPf//k7feessaMWKElZycbI0bN876%2BOOPPXP19fXWkiVLrKuvvtpKTU21fvvb37aK9xz6ybmy1dfXW8uWLbOGDBliORwO64YbbrC2bdvmuW9rzvbPf/7T6tWrlyfPv3989dVXTT5HnOuY%2BltT2bZu3WqNHTvWSk1Nta655hrr%2Beeft%2Brq6jz3b83Z9u/fb02ZMsVKTU21rrrqKmvWrFnW8ePHLcuyz/evxgRZVgBfQQYAANAKcYoQAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAz7fzWJ1viW3VhLAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-6426063118142564296”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2464</td> <td class=”number”>76.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>209</td> <td class=”number”>6.5%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>88</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>68</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.33333333333333</td> <td class=”number”>31</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-66.66666666666666</td> <td class=”number”>15</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>13</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.33333333333333</td> <td class=”number”>11</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-25.0</td> <td class=”number”>9</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (21)</td> <td class=”number”>35</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>243</td> <td class=”number”>7.6%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-6426063118142564296”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>209</td> <td class=”number”>6.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-75.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-71.42857142857143</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-66.66666666666666</td> <td class=”number”>15</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-60.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>75.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>68</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>150.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>13</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>300.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_SNAPSPTH_12_16”><s>PCH_SNAPSPTH_12_16</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_SNAPS_12_16”><code>PCH_SNAPS_12_16</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.98953</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_SNAPS_12_16”>PCH_SNAPS_12_16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2655</td>

</tr> <tr>

<th>Unique (%)</th> <td>82.7%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.3%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>107</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>9.5041</td>

</tr> <tr>

<th>Minimum</th> <td>-76.471</td>

</tr> <tr>

<th>Maximum</th> <td>1100</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>3.7%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram2530593834342512216”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives2530593834342512216,#minihistogram2530593834342512216”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives2530593834342512216”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles2530593834342512216”

aria-controls=”quantiles2530593834342512216” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram2530593834342512216” aria-controls=”histogram2530593834342512216”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common2530593834342512216” aria-controls=”common2530593834342512216”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme2530593834342512216” aria-controls=”extreme2530593834342512216”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles2530593834342512216”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-76.471</td>

</tr> <tr>

<th>5-th percentile</th> <td>-18.004</td>

</tr> <tr>

<th>Q1</th> <td>-1.6829</td>

</tr> <tr>

<th>Median</th> <td>6.3075</td>

</tr> <tr>

<th>Q3</th> <td>17.157</td>

</tr> <tr>

<th>95-th percentile</th> <td>41.954</td>

</tr> <tr>

<th>Maximum</th> <td>1100</td>

</tr> <tr>

<th>Range</th> <td>1176.5</td>

</tr> <tr>

<th>Interquartile range</th> <td>18.84</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>29.747</td>

</tr> <tr>

<th>Coef of variation</th> <td>3.13</td>

</tr> <tr>

<th>Kurtosis</th> <td>588.69</td>

</tr> <tr>

<th>Mean</th> <td>9.5041</td>

</tr> <tr>

<th>MAD</th> <td>14.772</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>17.101</td>

</tr> <tr>

<th>Sum</th> <td>29491</td>

</tr> <tr>

<th>Variance</th> <td>884.91</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram2530593834342512216”>

<img 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qOTlZycnJCg0N1bBhw9S%2BfXutXr1akpSTk6OxY8fqoosuUlhYmDIyMpSfn68vv/zSn0sGAAABJCADVnh4uObMmaPzzjvPO1ZYWKiYmBjl5eWpQ4cOCg4O9s7FxcUpNzdXkpSXl6e4uDif/cXFxcntdquyslI7d%2B70mQ8LC1OrVq3kdrtreVUAAKCuCPF3AXZwu91avny5lixZovXr1ys8PNxnPjIyUh6PRzU1NfJ4PIqIiPCZj4iI0M6dO3Xw4EEZY044X1JSYrmeoqIiFRcX%2B4yFhDRSTEzMb1xZ3RISEpB5/jcLDnb5fMfJ0Str6JM19Mka%2BuSMgA9Yn332mSZOnKgpU6YoKSlJ69evP%2BF2QUFB3v8%2B3f1UZ3q/VU5OjhYtWuQzlpaWpsmTJ5/RfgNdVFRjf5fgqPDwc/1dQsCgV9bQJ2vokzX0qXYFdMDauHGj7r33Xs2YMUPDhw%2BXJEVHR2v37t0%2B23k8HkVGRsrlcikqKkoej%2Be4%2BejoaO82J5pv2rSp5bpSUlLUv39/n7GQkEYqKSn/Daure%2BrL%2BoODXQoPP1elpRWqrq7xdzlnNXplDX2yhj5ZU9/65K9f7gM2YH3%2B%2BefKzMzUU089pT59%2BnjH4%2BPj9de//lVVVVUKCflpeW63W126dPHO/3w/1s/cbreGDBmi0NBQtWvXTnl5eerVq5ekn26o37NnjxISEizXFhMTc9zlwOLiQ6qqqvs/yKdS39ZfXV1T79b8e9Era%2BiTNfTJGvpUuwLyAmxVVZUefPBBTZ061SdcSVJycrLCwsK0ZMkSVVRU6Msvv9SqVauUmpoqSRo1apQ%2B%2Bugjbd68WUeOHNGqVau0e/duDRs2TJKUmpqqZcuWKT8/X2VlZcrKylJsbKw6d%2B7s%2BDoBAEBgCsgzWNu2bVN%2Bfr5mz56t2bNn%2B8y9%2BeabeuaZZ/Twww8rOztb5513njIyMtS3b19JUvv27ZWVlaU5c%2BaooKBAbdu21dKlS9WsWTNJ0ujRo1VcXKybbrpJ5eXlSkxMPO5%2BKgAAgFMJMjxB0xHFxYdqZb%2BDn/ywVvZbG9an9/Z3CY4ICXEpKqqxSkrKOf1%2BGvTKGvpkDX2ypr71qVmzJn45bkBeIgQAADibEbAAAABsRsACAACwGQELAADAZgQsAAAAmxGwAAAAbEbAAgAAsBkBCwAAwGaOB6z%2B/ftr0aJFKiwsdPrQAAAAjnA8YF1//fVat26drrjiCo0fP15vvfWWqqqqnC4DAACg1jgesNLS0rRu3Tq98sorateunR5//HElJydr3rx52rVrl9PlAAAA2M5v92B16tRJmZmZ2rRpk6ZPn65XXnlFV199tcaNG6evvvrKX2UBAACcMb8FrGPHjmndunW67bbblJmZqebNm%2Bv%2B%2B%2B9XbGysxo4dqzVr1virNAAAgDMS4vQB8/PztWrVKr322msqLy/XoEGD9MILL6h79%2B7ebXr27KmZM2dq6NChTpcHAABwxhwPWEOGDFGbNm10xx13aPjw4YqMjDxum%2BTkZB04cMDp0gAAAGzheMBatmyZevXqddrtvvzySweqAQAAsJ/j92B16NBBEyZM0DvvvOMd%2B%2B///m/ddttt8ng8TpcDAABgO8cD1pw5c3To0CG1bdvWO9a3b1/V1NRo7ty5TpcDAABgO8cvEX7wwQdas2aNoqKivGOtW7dWVlaWrrnmGqfLAQAAsJ3jZ7AqKysVGhp6fCEulyoqKpwuBwAAwHaOB6yePXtq7ty5OnjwoHfshx9%2B0KxZs3we1QAAABCoHL9EOH36dN1666269NJLFRYWppqaGpWXl6tly5Z68cUXnS4HAADAdo4HrJYtW%2BqNN97Qe%2B%2B9pz179sjlcqlNmzbq06ePgoODnS4HAADAdo4HLElq0KCBrrjiCn8cGgAAoNY5HrD27t2r%2BfPn69tvv1VlZeVx8%2B%2B%2B%2B67TJQEAANjKL/dgFRUVqU%2BfPmrUqJHThwcAAKh1jges3Nxcvfvuu4qOjnb60AAAAI5w/DENTZs25cwVAACo0xwPWHfccYcWLVokY4zThwYAAHCE45cI33vvPX3%2B%2Bed69dVXdcEFF8jl8s14L7/8stMlAQAA2MrxgBUWFqbLL7/c6cMCAAA4xvGANWfOHKcPCQAA4CjH78GSpO%2B%2B%2B04LFy7U/fff7x374osv/FEKAACA7RwPWFu3btWwYcP01ltvae3atZJ%2BevjomDFjeMgoAACoExwPWAsWLNC9996rNWvWKCgoSNJPf59w7ty5Wrx4sdPlAAAA2M7xgPXPf/5TqampkuQNWJJ01VVXKT8/3%2BlyAAAAbOd4wGrSpMkJ/wZhUVGRGjRo4HQ5AAAAtnM8YHXr1k2PP/64ysrKvGO7du1SZmamLr30UqfLAQAAsJ3jj2m4//77dfPNNysxMVHV1dXq1q2bKioq1K5dO82dO9fpcgAAAGzneMA6//zztXbtWm3ZskW7du1Sw4YN1aZNG/Xu3dvnniwAAIBA5XjAkqRzzjlHV1xxhT8ODQAAUOscD1j9%2B/c/5Zmq3/IsrPfff1%2BZmZlKTEzUggULvOOvvvqqpk%2BfrnPOOcdn%2BxUrVighIUE1NTV66qmntHbtWpWWliohIUEzZ85Uy5YtJUkej0czZ87UJ598IpfLpeTkZM2YMUMNGzb8jasFAAD1keMB6%2Bqrr/YJWNXV1dq1a5fcbrduvvlmy/t59tlntWrVKrVq1eqE8z179tSLL754wrkVK1ZozZo1evbZZ9W8eXMtWLBAaWlpev311xUUFKQZM2bo6NGjWrt2rY4dO6a7775bWVlZevDBB3/bYgEAQL3keMCaOnXqCcc3bNigf/zjH5b3ExoaqlWrVumxxx7TkSNHflMNOTk5Gjt2rC666CJJUkZGhhITE/Xll1/qggsu0DvvvKO///3vio6OliTdeeeduvvuu5WZmXncWTEAAIBf88vfIjyRK664Qm%2B88Ybl7ceMGaMmTZqcdL6wsFC33HKLevbsqQEDBuj111%2BXJFVWVmrnzp2Ki4vzbhsWFqZWrVrJ7XZrx44dCg4OVocOHbzznTp10uHDh/Xdd9/9jpUBAID6xi83uZ/I9u3bZYyxZV/R0dFq3bq17rnnHrVt21Zvv/227rvvPsXExOiPf/yjjDGKiIjweU1ERIRKSkoUGRmpsLAwn8uYP29bUlJi6fhFRUUqLi72GQsJaaSYmJgzXFlgCwk5a/J8rQoOdvl8x8nRK2vokzX0yRr65AzHA9bo0aOPG6uoqFB%2Bfr4GDhxoyzH69u2rvn37ev89ZMgQvf3223r11Ve9lyhPFebONOjl5ORo0aJFPmNpaWmaPHnyGe030EVFNfZ3CY4KDz/X3yUEDHplDX2yhj5ZQ59ql%2BMBq3Xr1sd9ijA0NFQjR47UDTfcUGvHbdGihXJzcxUZGSmXyyWPx%2BMz7/F41LRpU0VHR6usrEzV1dUKDg72zklS06ZNLR0rJSVF/fv39xkLCWmkkpJyG1YSuOrL%2BoODXQoPP1elpRWqrq7xdzlnNXplDX2yhj5ZU9/65K9f7h0PWE48rf2vf/2rIiIidPXVV3vH8vPz1bJlS4WGhqpdu3bKy8tTr169JEmlpaXas2ePEhIS1KJFCxlj9PXXX6tTp06SJLfbrfDwcLVp08bS8WNiYo67HFhcfEhVVXX/B/lU6tv6q6tr6t2afy96ZQ19soY%2BWUOfapfjAeu1116zvO3w4cN/1zGOHj2qRx99VC1btlTHjh21YcMGvffee3rllVckSampqcrOztbll1%2Bu5s2bKysrS7GxsercubMkadCgQXryySf1xBNP6OjRo1q8eLFGjhypkJCz5pY1AABwFnM8MTzwwAOqqak57j6noKAgn7GgoKBTBqyfw1BVVZUk6Z133pH009mmMWPGqLy8XHfffbeKi4t1wQUXaPHixYqPj5f0031gxcXFuummm1ReXq7ExESfe6YeeeQRPfzwwxowYIDOOeccXXPNNcrIyLCnAQAAoM4LMnZ9dM%2BirVu36rnnntOECRPUoUMHGWP0zTff6Nlnn9WNN96oxMRE77YNGjRwsrRaVVx8qFb2O/jJD2tlv7VhfXpvf5fgiJAQl6KiGqukpJzT76dBr6yhT9bQJ2vqW5%2BaNTv5I51qk1/uwcrOzlbz5s29Yz169FDLli01btw4rV271umSAAAAbOX4QzB279593DOoJCk8PFwFBQVOlwMAAGA7xwNWixYtNHfuXJ%2BHdpaWlmr%2B/Pm68MILnS4HAADAdo5fIpw%2BfbqmTJminJwcNW7cWC6XS2VlZWrYsKEWL17sdDkAAAC2czxg9enTR5s3b9aWLVu0b98%2BGWPUvHlzXXbZZaf824IAAACBwi8Pdjr33HM1YMAA7du3Ty1btvRHCQAAALXG8XuwKisrlZmZqa5du2rw4MGSfroHa/z48SotLXW6HAAAANs5HrDmzZunHTt2KCsrSy7X/x2%2BurpaWVlZTpcDAABgO8cD1oYNG/T000/rqquu8v7R5/DwcM2ZM0dvvfWW0%2BUAAADYzvGAVV5ertatWx83Hh0drcOHDztdDgAAgO0cD1gXXnih/vGPf0iSz98efPPNN/Vv//ZvTpcDAABgO8c/Rfjv//7vmjRpkq6//nrV1NTo%2BeefV25urjZs2KAHHnjA6XIAAABs53jASklJUUhIiJYvX67g4GA988wzatOmjbKysnTVVVc5XQ4AAIDtHA9YBw4c0PXXX6/rr7/e6UMDAAA4wvF7sAYMGOBz7xUAAEBd43jASkxM1Pr1650%2BLAAAgGMcv0T4hz/8QY899piys7N14YUX6pxzzvGZnz9/vtMlAQAA2MrxgLVz50798Y9/lCSVlJQ4fXgAAIBa51jAysjI0IIFC/Tiiy96xxYvXqy0tDSnSgAAAHCEY/dgbdy48bix7Oxspw4PAADgGMcC1ok%2BOcinCQEAQF3kWMD6%2BQ87n24MAAAg0Dn%2BmAYAAIC6joAFAABgM8c%2BRXjs2DFNmTLltGM8BwsAAAQ6xwJW9%2B7dVVRUdNoxAACAQOdYwPrl868AAADqMu7BAgAAsBkBCwAAwGYELAAAAJsRsAAAAGxGwAIAALAZAQsAAMBmBCwAAACbEbAAAABsRsACAACwGQELAADAZgQsAAAAmxGwAAAAbEbAAgAAsBkBCwAAwGYBHbDef/99JSUlKSMj47i5devWaejQoeratatGjBihDz74wDtXU1OjBQsWaMCAAerZs6fGjRunvXv3euc9Ho/S09OVlJSkPn366IEHHlBlZaUjawIAAIEvYAPWs88%2Bq9mzZ6tVq1bHze3YsUOZmZmaOnWqPv74Y40dO1Z33XWX9u3bJ0lasWKF1qxZo%2BzsbG3atEmtW7dWWlqajDGSpBkzZqiiokJr167V3/72N%2BXn5ysrK8vR9QEAgMAVsAErNDRUq1atOmHAWrlypZKTk5WcnKzQ0FANGzZM7du31%2BrVqyVJOTk5Gjt2rC666CKFhYUpIyND%2Bfn5%2BvLLL/Xjjz/qnXfeUUZGhqKjo9W8eXPdeeed%2Btvf/qZjx445vUwAABCAQvxdwO81ZsyYk87l5eUpOTnZZywuLk5ut1uVlZXauXOn4uLivHNhYWFq1aqV3G63Dh06pODgYHXo0ME736lTJx0%2BfFjfffedz/jJFBUVqbi42GcsJKSRYmJirC6vTgoJCdg8/5sEB7t8vuPk6JU19Mka%2BmQNfXJGwAasU/F4PIqIiPAZi4iI0M6dO3Xw4EEZY044X1JSosjISIWFhSkoKMhnTpJKSkosHT8nJ0eLFi3yGUtLS9PkyZN/z3LqjKioxv4uwVHh4ef6u4SAQa%2BsoU/W0Cdr6FPtqpMBS5L3fqrfM3%2B6155OSkqK%2Bvfv7zMWEtJIJSXlZ7TfQFdf1h8c7FJ4%2BLkqLa1QdXWNv8s5q9Era%2BiTNfTJmvrWJ3/9cl8nA1ZUVJQ8Ho/PmMfjUXR0tCIjI%2BVyuU4437RpU0VHR6usrEzV1dUKDg72zklS06ZNLR0/JibmuMuBxcWHVFVV93%2BQT6W%2Brb%2B6uqberfn3olfW0Cdr6JM19Kl21ckLsPHx8crNzfUZc7vd6tKli0JDQ9WuXTvl5eV550pLS7Vnzx4lJCQoNjZWxhh9/fXXPq8NDw9XmzZtHFsDAAAIXHUyYI0aNUofffSRNm/erCNHjmjVqlXavXu3hg0bJklKTU3VsmXLlJ%2Bfr7KyMmVlZSk2NladO3dWdHS0Bg0apCeffFIHDhzQvn37tHjxYo0cOVIhIXXyhB8AALBZwCaGzp07S5KqqqokSe%2B8846kn842tW/fXllZWZr/FSBDAAARq0lEQVQzZ44KCgrUtm1bLV26VM2aNZMkjR49WsXFxbrppptUXl6uxMREn5vSH3nkET388MMaMGCAzjnnHF1zzTUnfJgpAADAiQSZM72jG5YUFx%2Bqlf0OfvLDWtlvbVif3tvfJTgiJMSlqKjGKikp5/6G06BX1tAna%2BiTNfWtT82aNfHLcevkJUIAAAB/ImABAADYjIAFAABgMwIWAACAzQhYAAAANiNgAQAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADYjIAFAABgMwIWAACAzQhYAAAANiNgAQAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADYjIAFAABgMwIWAACAzQhYAAAANiNgAQAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADYjIAFAABgMwIWAACAzQhYAAAANquzAatDhw6Kj49X586dvV%2BPPvqoJGnr1q0aOXKkunXrpiFDhmj16tU%2Br122bJkGDRqkbt26KTU1Vbm5uf5YAgAACFAh/i6gNr355pu64IILfMaKiop055136oEHHtDQoUP12WefaeLEiWrTpo06d%2B6sjRs3auHChfrzn/%2BsDh06aNmyZZowYYLeeustNWrUyE8rAQAAgaTOnsE6mTVr1qh169YaOXKkQkNDlZSUpP79%2B2vlypWSpJycHI0YMUJdunRRw4YNNX78eEnSpk2b/Fk2AAAIIHX6DNb8%2BfP1xRdfqKysTIMHD9a0adOUl5enuLg4n%2B3i4uK0fv16SVJeXp6uvvpq75zL5VJsbKzcbreGDBli6bhFRUUqLi72GQsJaaSYmJgzXFFgCwmpH3k%2BONjl8x0nR6%2BsoU/W0Cdr6JMz6mzAuvjii5WUlKQnnnhCe/fuVXp6umbNmiWPx6PmzZv7bBsZGamSkhJJksfjUUREhM98RESEd96KnJwcLVq0yGcsLS1NkydP/p2rqRuiohr7uwRHhYef6%2B8SAga9soY%2BWUOfrKFPtavOBqycnBzvf1900UWaOnWqJk6cqO7du5/2tcaYMzp2SkqK%2Bvfv7zMWEtJIJSXlZ7TfQFdf1h8c7FJ4%2BLkqLa1QdXWNv8s5q9Era%2BiTNfTJmvrWJ3/9cl9nA9avXXDBBaqurpbL5ZLH4/GZKykpUXR0tCQpKirquHmPx6N27dpZPlZMTMxxlwOLiw%2Bpqqru/yCfSn1bf3V1Tb1b8%2B9Fr6yhT9bQJ2voU%2B2qkxdgt2/frrlz5/qM5efnq0GDBkpOTj7usQu5ubnq0qWLJCk%2BPl55eXneuerqam3fvt07DwAAcDp1MmA1bdpUOTk5ys7O1tGjR7Vr1y499dRTSklJ0bXXXquCggKtXLlSR44c0ZYtW7RlyxaNGjVKkpSamqrXXntN27ZtU0VFhZYsWaIGDRqob9%2B%2B/l0UAAAIGHXyEmHz5s2VnZ2t%2BfPnewPSddddp4yMDIWGhmrp0qWaPXu2Zs2apRYtWmjevHnq2LGjJOnyyy/XPffco/T0dO3fv1%2BdO3dWdna2GjZs6OdVAQCAQBFkzvSOblhSXHyoVvY7%2BMkPa2W/tWF9em9/l%2BCIkBCXoqIaq6SknPsbToNeWUOfrKFP1tS3PjVr1sQvx62TlwgBAAD8iYAFAABgMwIWAACAzQhYAAAANiNgAQAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADYjIAFAABgMwIWAACAzQhYAAAANiNgAQAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADYjIAFAABgMwIWAACAzQhYAAAANiNgAQAA2CzE3wWg/hj85If%2BLuE3WZ/e298lAAACFGewAAAAbEbAAgAAsBkBCwAAwGYELAAAAJsRsAAAAGxGwAIAALAZAQsAAMBmBCwAAACbEbAAAABsRsACAACwGQELAADAZgSsEygoKNDtt9%2BuxMRE9evXT/PmzVNNTY2/ywIAAAGCP/Z8ApMmTVKnTp30zjvvaP/%2B/brjjjt03nnn6ZZbbvF3aQAAIABwButX3G63vv76a02dOlVNmjRR69atNXbsWOXk5Pi7NAAAECA4g/UreXl5atGihSIiIrxjnTp10q5du1RWVqawsLDT7qOoqEjFxcU%2BYyEhjRQTE2N7vag9g5/80N8l/CZvT73M3yX8ZsHBLp/vODH6ZA19soY%2BOYOA9Ssej0fh4eE%2BYz%2BHrZKSEksBKycnR4sWLfIZu%2BuuuzRp0iT7Cv2XTx%2B7yvZ9nkxRUZFycnKUkpJCWDwF%2BmRdUVGRXnjhz/TqNOiTNfTJGvrkDOLrCRhjzuj1KSkpevXVV32%2BUlJSbKrOf4qLi7Vo0aLjzs7BF32yjl5ZQ5%2BsoU/W0CdncAbrV6Kjo%2BXxeHzGPB6PgoKCFB0dbWkfMTEx/FYAAEA9xhmsX4mPj1dhYaEOHDjgHXO73Wrbtq0aN27sx8oAAECgIGD9SlxcnDp37qz58%2BerrKxM%2Bfn5ev7555Wamurv0gAAQIAInjlz5kx/F3G2ueyyy7R27Vo9%2BuijeuONNzRy5EiNGzdOQUFB/i7N7xo3bqxevXpxNu806JN19Moa%2BmQNfbKGPtW%2BIHOmd3QDAADAB5cIAQAAbEbAAgAAsBkBCwAAwGYELAAAAJsRsAAAAGxGwAIAALAZAQsAAMBmBCwAAACbEbAAAABsRsCCJQUFBbr99tuVmJiofv36ad68eaqpqfF3WY4rKChQWlqaEhMTlZSUpGnTpqm0tFSStGPHDt14443q3r27Bg4cqOeee87ntevWrdPQoUPVtWtXjRgxQh988IE/luC4xx9/XB06dPD%2Be%2BvWrRo5cqS6deumIUOGaPXq1T7bL1u2TIMGDVK3bt2Umpqq3Nxcp0t23JIlS9SnTx9dfPHFGjt2rL7//ntJ9OqXtm/frjFjxqhHjx7q3bu3pk6dqgMHDkiiT%2B%2B//76SkpKUkZFx3Nyp3ndqamq0YMECDRgwQD179tS4ceO0d%2B9e77zH41F6erqSkpLUp08fPfDAA6qsrHRkTXWCASy47rrrzIMPPmhKS0vNrl27zMCBA81zzz3n77Icd80115hp06aZsrIyU1hYaEaMGGGmT59uKioqzGWXXWYWLlxoysvLTW5urunVq5fZsGGDMcaY7du3m/j4eLN582ZTWVlpXn/9ddOlSxdTWFjo5xXVru3bt5tevXqZ9u3bG2OM%2BeGHH8zFF19sVq5caSorK82HH35oEhISzFdffWWMMebdd981PXr0MNu2bTMVFRVm6dKlpnfv3qa8vNyfy6hVy5cvN1dddZXJz883hw4dMo8%2B%2Bqh59NFH6dUvHDt2zPTu3dvMnz/fHDlyxBw4cMDccsstZtKkSfW%2BT9nZ2WbgwIFm9OjRJj093WfudO87y5YtM/369TM7d%2B40hw4dMo888ogZOnSoqampMcYYc9ddd5nbb7/d7N%2B/3%2Bzbt8%2BkpKSYRx991PE1BioCFk7rq6%2B%2BMrGxscbj8XjHXnrpJTNo0CA/VuW8gwcPmmnTppni4mLv2IsvvmgGDhxo1q9fby655BJTVVXlnZs3b5659dZbjTHGzJo1y6Slpfns74YbbjBLly51png/qK6uNjfccIP5r//6L2/A%2BvOf/2yGDx/us116erqZMWOGMcaY22%2B/3Tz%2B%2BOM%2B%2B%2Bjdu7dZu3atc4U7rH///t4g/kv06v/8v//3/0z79u3Nzp07vWMvvfSSueKKK%2Bp9n1544QVTWlpqMjMzjwtYp3vfGTJkiHnhhRe8c4cOHTJxcXHmiy%2B%2BMMXFxaZjx45mx44d3vktW7aYiy%2B%2B2Bw9erQWV1R3cIkQp5WXl6cWLVooIiLCO9apUyft2rVLZWVlfqzMWeHh4ZozZ47OO%2B8871hhYaFiYmKUl5enDh06KDg42DsXFxfnvRSRl5enuLg4n/3FxcXJ7XY7U7wfvPzyywoNDdXQoUO9Yyfrw8n65HK5FBsbW2f79MMPP%2Bj777/XwYMHdfXVVysxMVGTJ0/WgQMH6NUvNG/eXLGxscrJyVF5ebn279%2Bvt956S3379q33fRozZoyaNGlywrlTve9UVlZq586dPvNhYWFq1aqV3G63duzYoeDgYJ/L%2B506ddLhw4f13Xff1c5i6hgCFk7L4/EoPDzcZ%2BznsFVSUuKPks4Kbrdby5cv18SJE0/Yo8jISHk8HtXU1Mjj8fgEVOmnHtbV/v34449auHChHn74YZ/xk/Xp5z7Utz7t27dPkvTmm2/q%2Beef1%2Buvv659%2B/bpwQcfpFe/4HK5tHDhQr377rvq1q2bkpKSVFVVpSlTptCnUzjV2g8ePChjzEnnPR6PwsLCFBQU5DMn1e/3/d%2BCgAVLjDH%2BLuGs8tlnn2ncuHGaMmWKkpKSTrrdL9%2Bc6lMP58yZoxEjRqht27a/%2BbX1qU8/r3X8%2BPFq3ry5zj//fE2aNEkbN278Ta%2Bv644ePaoJEyboqquu0qeffqr33ntPTZo00dSpUy29vr706UROt/ZTzdfnvtmBgIXTio6Olsfj8RnzeDwKCgpSdHS0n6ryn40bN%2Br222/X9OnTNWbMGEk/9ejXv9V5PB5FRkbK5XIpKirqhD2si/3bunWrvvjiC6WlpR03d6I%2BlJSUePtQn/okyXu5%2BZdnYFq0aCFjjI4dO0av/mXr1q36/vvvdc8996hJkyZq3ry5Jk%2BerLffflsul4s%2BncSp1v7ze9OJ5ps2baro6GiVlZWpurraZ06SmjZtWvvF1wEELJxWfHy8CgsLvR%2BJln66PNa2bVs1btzYj5U57/PPP1dmZqaeeuopDR8%2B3DseHx%2Bvb775RlVVVd4xt9utLl26eOd//dHwX87XJatXr9b%2B/fvVr18/JSYmasSIEZKkxMREtW/f/rg%2B5Obm%2BvQpLy/PO1ddXa3t27fXyT5J0vnnn6%2BwsDDt2LHDO1ZQUKBzzjlHycnJ9OpfqqurVVNT43NG5ejRo5KkpKQk%2BnQSp3rfCQ0NVbt27Xx6U1paqj179ighIUGxsbEyxujrr7/2eW14eLjatGnj2BoCml9urUfAueGGG8z06dPNoUOHzM6dO03//v3N8uXL/V2Wo44dO2YGDx5sXn755ePmjhw5Yvr162eefvppc/jwYbNt2zbTo0cPs2nTJmOMMd98843p3Lmz2bRpk6msrDQrV640Xbt2NUVFRQ6vovZ5PB5TWFjo/friiy9M%2B/btTWFhoSkoKDBdu3Y1r7zyiqmsrDSbN282CQkJ3k8qbdmyxXTv3t188cUX5vDhw2bhwoUmOTnZVFRU%2BHlVtefxxx83AwYMMLt37zY//vijSUlJMdOmTTM//vgjvfqXAwcOmF69epk//elP5vDhw%2BbAgQNmwoQJ5j/%2B4z/o07%2Bc6FOEp3vfeemll0zfvn29j2mYMWOGuf76672vT09PN%2BPHjzf79%2B83hYWF5vrrrzdz5851dF2BjIAFSwoLC8348eNNQkKCSUpKMk8//bT3WSn1xf/8z/%2BY9u3bm/j4%2BOO%2Bvv/%2Be/PNN9%2BY0aNHm/j4eNO3b1%2BzYsUKn9dv2LDBDBw40HTq1Mlce%2B215pNPPvHTSpy1d%2B9e72MajDHmk08%2BMcOGDTOdOnUyAwcOPO4RBStWrDDJyckmPj7epKammm%2B%2B%2Bcbpkh115MgRM3PmTNOzZ09z8cUXm8zMTFNWVmaMoVe/5Ha7zY033mh69OhhkpKSTHp6utm3b58xpn736ef3oI4dO5qOHTt6//2zU73v1NTUmKeeespceumlJiEhwdx2220%2Bz%2BYrLS01GRkZ5uKLLzY9e/Y0s2bNMkeOHHF0fYEsyBjuYgMAALAT92ABAADYjIAFAABgMwIWAACAzQhYAAAANiNgAQAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADY7P8D6k2FqArw%2Br0AAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common2530593834342512216”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.3333333333333</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>16</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>15</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>15</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-14.2857142857143</td> <td class=”number”>11</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.0</td> <td class=”number”>11</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16.6666666666667</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-20.0</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.3333333333333</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2644)</td> <td class=”number”>2873</td> <td class=”number”>89.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>107</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme2530593834342512216”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-76.4705882352941</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-75.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-72.093023255814</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-68.75</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-66.6666666666667</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>176.923076923077</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>241.666666666667</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>300.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1100.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_SNAP_12_16”>PCH_SNAP_12_16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>52</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>-1.8106</td>

</tr> <tr>

<th>Minimum</th> <td>-4.8657</td>

</tr> <tr>

<th>Maximum</th> <td>2.0182</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-5987419617518697065”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-5987419617518697065”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-5987419617518697065”

aria-controls=”quantiles-5987419617518697065” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-5987419617518697065” aria-controls=”histogram-5987419617518697065”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-5987419617518697065” aria-controls=”common-5987419617518697065”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-5987419617518697065” aria-controls=”extreme-5987419617518697065”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-5987419617518697065”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-4.8657</td>

</tr> <tr>

<th>5-th percentile</th> <td>-4.3623</td>

</tr> <tr>

<th>Q1</th> <td>-2.9504</td>

</tr> <tr>

<th>Median</th> <td>-1.7087</td>

</tr> <tr>

<th>Q3</th> <td>-1.1284</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.4874</td>

</tr> <tr>

<th>Maximum</th> <td>2.0182</td>

</tr> <tr>

<th>Range</th> <td>6.8838</td>

</tr> <tr>

<th>Interquartile range</th> <td>1.822</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>1.4481</td>

</tr> <tr>

<th>Coef of variation</th> <td>-0.79978</td>

</tr> <tr>

<th>Kurtosis</th> <td>-0.19106</td>

</tr> <tr>

<th>Mean</th> <td>-1.8106</td>

</tr> <tr>

<th>MAD</th> <td>1.1248</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.0013237</td>

</tr> <tr>

<th>Sum</th> <td>-5670.9</td>

</tr> <tr>

<th>Variance</th> <td>2.097</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-5987419617518697065”>

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FKi4u1tixYyVJWVlZ2rx5s/bv36%2BamhqtXbtWAQEBSk1NNbcpAADQZvjkJUJJmjNnjs6dO6cxY8bou%2B%2B%2B09ChQ7Vw4UJ17NhR69at07Jly7R06VJ1795dubm56tOnjyRp8ODBmj17tmbOnKnjx48rPj5e%2Bfn5CgoKMrkjAADQVvhswAoICNDixYu1ePHiJnP9%2B/fXli1bLrju%2BPHjNX78%2BJYsDwAA%2BDCfvEQIAABgJgIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAbzuoCVlpamvLw8HT161OxSAAAALonXBax7771XO3bs0G233aZJkyZp9%2B7dqq2tNbssAAAAj3ldwMrOztaOHTv0%2B9//Xr169dLy5cuVkpKi3NxcHT582OzyAAAALsrrAtZ5119/vebNm6c9e/bo8ccf1%2B9//3sNGzZMEydO1Icffmh2eQAAABfktQHru%2B%2B%2B044dOzR58mTNmzdPUVFRmj9/vvr27asJEyZo27ZtZpcIAADwo%2BxmF/BDhw4d0qZNm7R582adPn1aQ4cO1csvv6x%2B/fo1LtO/f38tWbJEI0aMMLFSAACAH%2Bd1AWv48OHq0aOHpkyZonvuuUdhYWFNlklJSdGJEyf%2B5XZiYmLUrl07%2Bfn5NY6NHTtWixYtUklJiVavXq3PPvtMP/nJTzRlyhSNHDmycbmCggJt3LhRlZWViomJ0YIFCxQXF2dckwAAwKd5XcAqKCjQgAEDLrrcBx98cNFlXn/9dV155ZVuYxUVFZo%2BfboWLFigESNGaN%2B%2BfZo2bZp69Oih%2BPh4FRUVac2aNXrhhRcUExOjgoICTZ06Vbt371aHDh0uuS8AAGAdXncPVkxMjKZOnao33nijcey3v/2tJk%2BeLJfLddnb37Ztm6Kjo5WRkaHAwEAlJycrLS1NhYWFkiSHw6H09HQlJCQoKChIkyZNkiTt2bPnsvcNAACswesC1ooVK3Ty5En17NmzcSw1NVX19fVauXJls7a1evVqpaam6uabb9aiRYt0%2BvRplZWVKTY21m252NhYlZaWSlKTeZvNpr59%2B8rpdF5GVwAAwEq87hLhO%2B%2B8o23btik8PLxxLDo6WqtWrdLdd9/t8XZuvPFGJScn64knntBXX32lmTNnaunSpXK5XIqKinJbNiwsTFVVVZIkl8ul0NBQt/nQ0NDGeU9UVFSosrLSbcxu76DIyEiPt%2BHvb3P7bjVW7/9S2O08Vi2ltR5bKx/3Vu5dsnb/vtq71wWsM2fOKDAwsMm4zWZTTU2Nx9txOByN/33ddddp7ty5mjZtmttfI15IQ0ODx/u50L7z8vLcxrKzs5WTk9PsbYWEtL%2BsWto6q/ffHOHhHc0uwWe19mNr5ePeyr1L1u7f13r3uoDVv39/rVy5UnPmzGk8k/T111/riSee8CgcXciVV16puro62Wy2JvdyVVVVKSIiQpIUHh7eZN7lcqlXr14e7yszM1NpaWluY3Z7B1VVnfZ4G/7%2BNoWEtFd1dY3q6uo9Xs9XWL3/S9Gc4wvN01qPrZWPeyv3Llm7/5bu3axfPr0uYD3%2B%2BON64IEH9LOf/UzBwcGqr6/X6dOnddVVV%2BmVV17xaBsHDhzQ1q1b9dhjjzWOHTp0SAEBAUpJSdEf/vAHt%2BVLS0uVkJAgSYqLi1NZWZlGjx4tSaqrq9OBAweUkZHhcQ%2BRkZFNLgdWVp5UbW3zD5y6uvpLWs9XWL3/5uBxajmt/dha%2Bbi3cu%2BStfv3td69LmBdddVVeu211/THP/5RX375pWw2m3r06KFBgwbJ39/fo2107txZDodDERERmjBhgsrLy/XMM88oMzNTo0aNUl5engoLCzVy5Ei9%2B%2B67Ki4ubrykmJWVpdmzZ%2Bvuu%2B9WTEyMXnzxRQUEBCg1NbUFuwYAAL7E6wKWJAUEBOi222675PWjoqKUn5%2Bv1atXa%2B3atQoICNDo0aM1a9YsBQYGat26dVq2bJmWLl2q7t27Kzc3V3369JEkDR48WLNnz9bMmTN1/PhxxcfHKz8/X0FBQUa1BwAAfJzXBayvvvpKq1ev1ieffKIzZ840mX/zzTc92k7//v31u9/97oJzW7ZsueC648eP1/jx4z0rGAAA4Ae8LmA9/vjjqqio0KBBg3jndAAA0CZ5XcAqLS3Vm2%2B%2B2fhXfQAAAG2N172rV%2BfOnTlzBQAA2jSvC1hTpkxRXl7eZb/ZJwAAgFm87hLhH//4R73//vt69dVXdeWVV8pmc8%2BAF7pxHQAAwFt4XcAKDg7W4MGDzS4DAADgknldwFqxYoXZJQAAAFwWr7sHS5I%2B%2B%2BwzrVmzRvPnz28c%2B%2Btf/2piRQAAAJ7zuoBVUlKikSNHavfu3dq%2Bfbuk79989L777vP4TUYBAADM5HUB66mnntIjjzyibdu2yc/PT9L3n0%2B4cuVKPfvssyZXBwAAcHFeF7D%2B7//%2BT1lZWZLUGLAk6c4779ShQ4fMKgsAAMBjXhewOnXq9KOfQVhRUaGAgAATKgIAAGger/srwsTERC1fvlwLFy5sHDt8%2BLAWL16sn/3sZyZWBsCq7nr6T2aX0Cw7Zw40uwTA8rwuYM2fP1/333%2B/kpKSVFdXp8TERNXU1KhXr15auXKl2eUBAABclNcFrG7dumn79u0qLi7W4cOHFRQUpB49emjgwIFu92QBAAB4K68LWJLUrl073XbbbWaXAQAAcEm8LmClpaX9yzNVvBcWAADwdl4XsIYNG%2BYWsOrq6nT48GE5nU7df//9JlYGAADgGa8LWHPnzv3R8V27dunPf/5zK1cDAADQfF73PlgXctttt%2Bm1114zuwwAAICLajMB68CBA2poaDC7DAAAgIvyukuE48aNazJWU1OjQ4cO6Y477jChIgAAgObxuoAVHR3d5K8IAwMDlZGRoTFjxphUFQAAgOe8LmDxbu0AAKCt87qAtXnzZo%2BXveeee1qwEgAAgEvjdQFrwYIFqq%2Bvb3JDu5%2Bfn9uYn58fAQsAAHglr/srwhdeeEGDBg3Sxo0b9d577%2Bkvf/mLNmzYoMGDB%2Bv555/Xhx9%2BqA8//FAffPCBx9tcvny5YmJiGv9dUlKijIwMJSYmavjw4dq6davb8gUFBRo6dKgSExOVlZWl0tJSw/oDAAC%2Bz%2BvOYK1cuVL5%2BfmKiopqHLv55pt11VVXaeLEidq%2BfXuztnfw4EFt2bKl8d8VFRWaPn26FixYoBEjRmjfvn2aNm2aevToofj4eBUVFWnNmjV64YUXFBMTo4KCAk2dOlW7d%2B9Whw4dDOsTAAD4Lq87g/X5558rNDS0yXhISIjKy8ubta36%2BnotXrxYEyZMaBzbtm2boqOjlZGRocDAQCUnJystLU2FhYWSJIfDofT0dCUkJCgoKEiTJk2SJO3Zs%2BfSmwIAAJbidWewunfvrpUrV%2Brhhx9WeHi4JKm6ulq/%2Bc1vdPXVVzdrW7/73e8UGBioESNG6Omnn5YklZWVKTY21m252NhY7dy5s3F%2B2LBhjXM2m019%2B/aV0%2BnU8OHDPdpvRUWFKisr3cbs9g6KjIz0uHZ/f5vbd6uxev%2BXwm7nscL32uKxYPWfeSv376u9e13AevzxxzVnzhw5HA517NhRNptNp06dUlBQkJ599lmPt/PNN99ozZo1euWVV9zGXS6X2%2BVHSQoLC1NVVVXj/A/PoIWGhjbOe8LhcCgvL89tLDs7Wzk5OR5v47yQkPbNXseXWL3/5ggP72h2CfASbflYsPrPvJX797XevS5gDRo0SG%2B99ZaKi4t17NgxNTQ0KCoqSrfccos6derk8XZWrFih9PR09ezZU0eOHGlWDZf7kTyZmZlKS0tzG7PbO6iq6rTH2/D3tykkpL2qq2tUV1d/WfW0RVbv/1I05/iCb2uLx4LVf%2Bat3H9L927WLxxeF7AkqX379rr11lt17NgxXXXVVc1ev6SkRH/9619/9Ib48PBwuVwut7GqqipFRERccN7lcqlXr14e7z8yMrLJ5cDKypOqrW3%2BgVNXV39J6/kKq/ffHDxOOK8tHwtW/5m3cv%2B%2B1rvXXfA8c%2BaM5s2bp5tuukl33XWXpO/vwZo0aZKqq6s92sbWrVt1/PhxDRkyRElJSUpPT5ckJSUlqXfv3k3edqG0tFQJCQmSpLi4OJWVlTXO1dXV6cCBA43zAAAAF%2BN1ASs3N1cHDx7UqlWrZLP9/%2BXV1dVp1apVHm3jscce065du7RlyxZt2bJF%2Bfn5kqQtW7ZoxIgRKi8vV2Fhoc6ePavi4mIVFxdr7NixkqSsrCxt3rxZ%2B/fvV01NjdauXauAgAClpqYa3isAAPBNXhewdu3apd/85je68847Gz/0OSQkRCtWrNDu3bs92kZoaKi6devW%2BNWlSxdJUrdu3XTFFVdo3bp12rBhg/r166fly5crNzdXffr0kSQNHjxYs2fP1syZMzVgwADt3btX%2Bfn5CgoKapmGAQCAz/G6e7BOnz6t6OjoJuMRERH69ttvL2mbV155pT7%2B%2BOPGf/fv39/tzUd/aPz48Ro/fvwl7QsAAMDrzmBdffXV%2BvOf/yzJ/a/5Xn/9dV1xxRVmlQUAAOAxrzuDNX78eM2YMUP33nuv6uvrtX79epWWlmrXrl1asGCB2eUBAABclNcFrMzMTNntdm3YsEH%2B/v567rnn1KNHD61atUp33nmn2eUBAABclNcFrBMnTujee%2B/Vvffea3YpAAAAl8Tr7sG69dZbL/ud1AEAAMzkdQErKSmp8YOXAQAA2iKvu0T4k5/8RL/%2B9a%2BVn5%2Bvq6%2B%2BWu3atXObX716tUmVAQAAeMbrAtann36qa6%2B9VtL3nxEIAADQ1nhNwJo1a5aeeuopvfLKK41jzz77rLKzs02sCgAAoPm85h6soqKiJmPnP0MQAACgLfGagPVjfznIXxMCAIC2yGsC1vkPdr7YGAAAgLfzmoAFAADgKwhYAAAABvOavyL87rvvNGfOnIuO8T5YAADA23lNwOrXr58qKiouOgYAAODtvCZg/fP7XwEArOGup/9kdgnNsnPmQLNLQBvBPVgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMF8NmB99NFHuv/%2B%2B9WvXz8lJydr5syZqqyslCSVlJQoIyNDiYmJGj58uLZu3eq2bkFBgYYOHarExERlZWWptLTUjBYAAEAb5ZMB69y5c3rggQc0YMAAlZSUaPv27Tp%2B/LiWLFmiiooKTZ8%2BXePGjVNJSYkWLFigRYsWyel0SpKKioq0Zs0aPfnkk9q7d6%2BGDBmiqVOn6ttvvzW5KwAA0Fb4ZMCqqanRrFmzNGXKFAUEBCgiIkK33367PvnkE23btk3R0dHKyMhQYGCgkpOTlZaWpsLCQkmSw%2BFQenq6EhISFBQUpEmTJkmS9uzZY2ZLAACgDfHJgBUaGqoxY8bIbv/%2BoxY/%2B%2Bwz/eEPf9Bdd92lsrIyxcbGui0fGxvbeBnwh/M2m019%2B/ZtPMMFAABwMV7zYc8toby8XEOHDlVtba3Gjh2rnJwcTZ48WVFRUW7LhYWFqaqqSpLkcrkUGhrqNh8aGto474mKiorG%2B73Os9s7KDIy0uNt%2BPvb3L5bjdX7vxR2O48Vvsex0HJa6rG18muer/bu0wGre/fucjqd%2BuKLL/SLX/xCjz76qEfrNTQ0XNZ%2BHQ6H8vLy3Mays7OVk5PT7G2FhLS/rFraOqv33xzh4R3NLgFegmOh5bT0Y2vl1zxf692nA5Yk%2Bfn5KTo6WrNmzdK4ceOUkpIil8vltkxVVZUiIiIkSeHh4U3mXS6XevXq5fE%2BMzMzlZaW5jZmt3dQVdVpj7fh729TSEh7VVfXqK6u3uP1fIXV%2B78UzTm%2B4Ns4FlpOSz22Vn7Na%2BnezfqFwycDVklJiZYsWaKdO3fKZvv%2BlOP57zfccIN27drltnxpaakSEhIkSXFxcSorK9Po0aMlSXV1dTpw4IAyMjI83n9kZGSTy4GVlSdVW9v8A6eurv6S1vMVVu%2B/OXiccB7HQstp6cfWyq95vta7b13w/Ie4uDidOnVKubm5qqmp0YkTJ7RmzRrdfPPNysrKUnl5uQoLC3X27FkVFxeruLhYY8eOlSRlZWVp8%2BbN2r9/v2pqarR27VoFBAQoNTXV3KYAAECb4ZMBq1OnTnrppZdUWlqqn/4h6DpTAAARgklEQVT0pxo%2BfLg6deqk//zP/1Tnzp21bt06bdiwQf369dPy5cuVm5urPn36SJIGDx6s2bNna%2BbMmRowYID27t2r/Px8BQUFmdwVAABoK3zyEqEkxcTE6JVXXvnRuf79%2B2vLli0XXHf8%2BPEaP358S5UGAAB8nE%2BewQIAADATAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYD77Ng0AYFV3Pf0ns0sALI8zWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMH4sOc2ri19qOvOmQPNLgEAgFbBGSwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADOazAau8vFzZ2dlKSkpScnKyHnvsMVVXV0uSDh48qJ///Ofq16%2Bf7rjjDr300ktu6%2B7YsUMjRozQTTfdpPT0dL3zzjtmtAAAANoonw1YU6dOVUhIiIqKivTqq6/qk08%2B0RNPPKEzZ85oypQp%2BulPf6q3335bTz31lNatW6fdu3dL%2Bj58zZs3T3PnztW7776rCRMm6KGHHtKxY8dM7ggAALQVPhmwqqurFRcXpzlz5qhjx47q1q2bRo8erffee09vvfWWvvvuO02bNk0dOnTQ9ddfrzFjxsjhcEiSCgsLlZKSopSUFAUGBmrkyJHq3bu3tm7danJXAACgrfDJd3IPCQnRihUr3MaOHj2qyMhIlZWVKSYmRv7%2B/o1zsbGxKiwslCSVlZUpJSXFbd3Y2Fg5nU6P919RUaHKykq3Mbu9gyIjIz3ehr%2B/ze27L7DbPe/FF/tvac15fAFcmpb6ObPya56v9u6TAeuHnE6nNmzYoLVr12rnzp0KCQlxmw8LC5PL5VJ9fb1cLpdCQ0Pd5kNDQ/Xpp596vD%2BHw6G8vDy3sezsbOXk5DS79pCQ9s1ex1uFh3ds9jq%2B1H9Lu5THF0DztPTPmZVf83ytd58PWPv27dO0adM0Z84cJScna%2BfOnT%2B6nJ%2BfX%2BN/NzQ0XNY%2BMzMzlZaW5jZmt3dQVdVpj7fh729TSEh7VVfXqK6u/rLq8RZW77%2BlNefxBXBpWurnzMqveS3du1m/fPp0wCoqKtIjjzyiRYsW6Z577pEkRURE6PPPP3dbzuVyKSwsTDabTeHh4XK5XE3mIyIiPN5vZGRkk8uBlZUnVVvb/AOnrq7%2BktbzRlbvv6XxOAEtr6V/zqz8mudrvfvWBc9/8v7772vevHl65plnGsOVJMXFxenjjz9WbW1t45jT6VRCQkLjfGlpqdu2/nkeAADgYnwyYNXW1mrhwoWaO3euBg0a5DaXkpKi4OBgrV27VjU1Nfrggw%2B0adMmZWVlSZLGjh2rvXv36q233tLZs2e1adMmff755xo5cqQZrQAAgDbIJy8R7t%2B/X4cOHdKyZcu0bNkyt7nXX39dzz33nBYvXqz8/Hx16dJFs2bNUmpqqiSpd%2B/eWrVqlVasWKHy8nL17NlT69atU9euXU3oBAAAtEU%2BGbBuvvlmffzxx/9ymf/%2B7/%2B%2B4Nwdd9yhO%2B64w%2BiyAACARfjkJUIAAAAzEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwmN3sAgAY466n/2R2CQCAf%2BAMFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwbnJHq%2BEmbACAVXAGCwAAwGAELAAAAIMRsAAAAAxGwAIAADCYzwast99%2BW8nJyZo1a1aTuR07dmjEiBG66aablJ6ernfeeadxrr6%2BXk899ZRuvfVW9e/fXxMnTtRXX33VmqUDAIA2zicD1vPPP69ly5bpmmuuaTJ38OBBzZs3T3PnztW7776rCRMm6KGHHtKxY8ckSRs3btS2bduUn5%2BvPXv2KDo6WtnZ2WpoaGjtNgAAQBvlkwErMDBQmzZt%2BtGAVVhYqJSUFKWkpCgwMFAjR45U7969tXXrVkmSw%2BHQhAkTdN111yk4OFizZs3SoUOH9MEHH7R2GwAAoI3yyYB13333qVOnTj86V1ZWptjYWLex2NhYOZ1OnTlzRp9%2B%2BqnbfHBwsK655ho5nc4WrRkAAPgOy73RqMvlUmhoqNtYaGioPv30U/39739XQ0PDj85XVVV5vI%2BKigpVVla6jdntHRQZGenxNvz9bW7fAQDms9tb5jXZyq/5vtq75QKWpIveT3W591s5HA7l5eW5jWVnZysnJ6fZ2woJaX9ZtQAAjBMe3rFFt2/l13xf691yASs8PFwul8ttzOVyKSIiQmFhYbLZbD8637lzZ4/3kZmZqbS0NLcxu72DqqpOe7wNf3%2BbQkLaq7q6RnV19R6vBwBoOc15HW8OK7/mt3TvLR2KL8RyASsuLk6lpaVuY06nU8OHD1dgYKB69eqlsrIyDRgwQJJUXV2tL7/8UjfccIPH%2B4iMjGxyObCy8qRqa5t/4NTV1V/SegAA47X067GVX/N9rXffuuDpgbFjx2rv3r166623dPbsWW3atEmff/65Ro4cKUnKyspSQUGBDh06pFOnTmnVqlXq27ev4uPjTa4cAAC0FT55But8GKqtrZUkvfHGG5K%2BP1PVu3dvrVq1SitWrFB5ebl69uypdevWqWvXrpKkcePGqbKyUv/%2B7/%2Bu06dPKykpqcn9VAAAAP%2BKXwPvoNkqKitPNmt5u92m8PCOqqo6/S9Pmd719J8utzQAgId2zhzYItv19DXfF7V07127/vjbNrU0y10iBAAAaGkELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAM5pNvNAoAQEtoa%2B892FLv24WL4wwWAACAwQhYAAAABiNgAQAAGIyABQAAYDBucgcAwEe1pZvy/7%2B5t5hdgqE4gwUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAtaPKC8v14MPPqikpCQNGTJEubm5qq%2BvN7ssAADQRtjNLsAbzZgxQ9dff73eeOMNHT9%2BXFOmTFGXLl30H//xH2aXBgAA2gDOYP2A0%2BnURx99pLlz56pTp06Kjo7WhAkT5HA4zC4NAAC0EZzB%2BoGysjJ1795doaGhjWPXX3%2B9Dh8%2BrFOnTik4OPii26ioqFBlZaXbmN3eQZGRkR7X4e9vc/sOAIAv87X/3xGwfsDlcikkJMRt7HzYqqqq8ihgORwO5eXluY099NBDmjFjhsd1VFRU6OWXX1BmZua/DGbv/fpOj7fZllRUVMjhcFy0f19E79bsXbJ2/1buXbJ2/%2Bd7P3Mm0ad69624aJCGhobLWj8zM1Ovvvqq21dmZmaztlFZWam8vLwmZ8Kswsr907s1e5es3b%2BVe5es3b%2Bv9s4ZrB%2BIiIiQy%2BVyG3O5XPLz81NERIRH24iMjPSpFA4AAJqHM1g/EBcXp6NHj%2BrEiRONY06nUz179lTHjh1NrAwAALQVBKwfiI2NVXx8vFavXq1Tp07p0KFDWr9%2BvbKysswuDQAAtBH%2BS5YsWWJ2Ed7mlltu0fbt2/WrX/1Kr732mjIyMjRx4kT5%2Bfm1ah0dO3bUgAEDLHvmzMr907s1e5es3b%2BVe5es3b8v9u7XcLl3dAMAAMANlwgBAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwPJyaWlpiouLU3x8fOPX1KlTzS6r1b388suKiYnRkSNHzC6l1Rw5ckTTp0/XgAEDlJSUpMmTJ%2Bvw4cNml9UqqqqqNG/ePA0cOFBJSUl66KGHdPToUbPLalVOp1O33367xo4da3YpLa68vFwPPvigkpKSNGTIEOXm5qq%2Bvt7sslrN22%2B/reTkZM2aNcvsUlpdeXm5srOzlZSUpOTkZD322GOqrq42uyxDELDagBdffFFOp7Px67nnnjO7pFb19ddf66WXXjK7jFaXnZ2tLl26aM%2BePXrzzTcVHBxsmRfg%2BfPn65tvvtG2bdu0a9cufffdd5o/f77ZZbWarVu3asaMGbrmmmvMLqVVzJgxQ1FRUXrjjTe0fv16vfHGG3r55ZfNLqtVPP/881q2bJllnusfmjp1qkJCQlRUVKRXX31Vn3zyiZ544gmzyzIEAQte79e//rXGjRtndhmt6ty5c/r5z3%2BuOXPmqGPHjgoODtbdd9%2BtTz/9VL7%2B8aENDQ2KiorSvHnzFBERobCwMI0bN0779u3z%2Bd7PO3v2rBwOhxISEswupcU5nU599NFHmjt3rjp16qTo6GhNmDBBDofD7NJaRWBgoDZt2mTJgFVdXa24uLjG17lu3bpp9OjReu%2B998wuzRAErDagoKBAt912m2666Sbl5OTo%2BPHjZpfUaoqLi/Xxxx9r4sSJZpfSqgICAjRmzBiFhoZKko4ePar/%2Bq//0p133ik/Pz%2BTq2tZfn5%2BWrp0qXr37t04dvToUXXt2tXnez9vzJgxioqKMruMVlFWVqbu3bs3HuuSdP311%2Bvw4cM6deqUiZW1jvvuu0%2BdOnUyuwxThISEaMWKFerSpUvj2NGjRxUZGWliVcYhYHm5vn376oYbbtCWLVu0Y8cOuVwuPfzww2aX1SrOnDmjX/3qV/rFL36hgIAAs8sxTVxcnFJTU9W%2BfXv98pe/NLucVnfkyBE988wzmjZtmtmloAW4XC6FhIS4jZ0PW1VVVWaUBJM4nU5t2LDBZ37W7WYXYHVbtmzRo48%2B%2BqNzK1as0LPPPtv4744dO2rx4sUaNmyYvvzyS1199dWtVWaLuFjvX3zxheLi4jRw4MBWrqx1XKz/9PR0SVJpaamOHTumJ554QhMnTtTGjRtls7Xt34087f3QoUOaOHGiRo8erTFjxrRmiS3K0/6twiqXfnFh%2B/bt07Rp0zRnzhwlJyebXY4hCFgmGzVqlEaNGuXx8t27d5ckVVRUtPmA9a96P3TokHJzc7V58%2BZWrqr1NOe579atm%2BbPn69bbrlFZWVlio%2BPb%2BHqWpYnvX/44YeaPHmyHnjgAU2ZMqWVKmsdzf2592URERFyuVxuYy6XS35%2BfoqIiDCpKrSmoqIiPfLII1q0aJHuueces8sxTNv%2BNdjHlZeXa/HixTp37lzj2KFDhyRJV111lVlltYqdO3fq5MmTGjlypJKSkpSUlCRJSk9P1/PPP29ydS3vs88%2BU0pKitslkvNnrdq1a2dWWa3m888/14MPPqh58%2Bb5XLiCu7i4OB09elQnTpxoHHM6nerZs6c6duxoYmVoDe%2B//77mzZunZ555xqfClcQZLK/WuXNnFRUVyd/fX3PnztXJkye1YsUKDRkyxOdvgJ0wYYIyMjLcxlJSUpSfn6%2BePXuaVFXrueaaa9SpUyctW7ZMixcvls1m0%2BrVq3X11Vfr2muvNbu8FvfLX/5SY8eOtdylMiuKjY1VfHy8Vq9erfnz5%2Bvrr7/W%2BvXr9cADD5hdGlpYbW2tFi5cqLlz52rQoEFml2M4vwYufnu1jz/%2BWCtXrpTT6ZQk3X777Zo/f36Tm0KtICYmRm%2B%2B%2BaauvPJKs0tpFeXl5Vq2bJneffddBQQE6IYbbtBjjz2m6667zuzSWtTRo0eVmpqqdu3aNfmrwZdeekn9%2B/c3qbLWM3ToUP3tb39TXV2d6uvrG89avv766423CfiSY8eOadGiRfrf//1fBQcHa9y4cXrooYcs8Vej5y/319bWSpLs9u/Pe5x/zfdl7733nv7t3/7tR/%2BIyReOdQIWAACAwbgHCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGD/D4OKhojbKlYwAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-5987419617518697065”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-1.708690387971247</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-2.9803494601194487</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.4358604025986246</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-4.8656865219835925</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-2.8051481646864413</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.947113431694829</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.03446033376004998</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.5205929978670714</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.2745595423513532</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-4.362260747189428</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (41)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-5987419617518697065”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-4.8656865219835925</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-4.819767060392673</td> <td class=”number”>16</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-4.362260747189428</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:79%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-3.699569351713766</td> <td class=”number”>44</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:37%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-3.612576324823852</td> <td class=”number”>83</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:69%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.48739945519058203</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.8099985931559885</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0344030631389174</td> <td class=”number”>55</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:94%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.6980611560603478</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:57%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0181617256231856</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:30%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_SPECSPTH_09_14”><s>PCH_SPECSPTH_09_14</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_SPECS_09_14”><code>PCH_SPECS_09_14</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99719</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_SPECS_09_14”>PCH_SPECS_09_14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>288</td>

</tr> <tr>

<th>Unique (%)</th> <td>9.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>8.3%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>266</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>-5.6471</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>400</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>44.7%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-7173855829487345455”>

</div> <div class=”col-md-12 text-right”>

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<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-7173855829487345455”

aria-controls=”quantiles-7173855829487345455” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-7173855829487345455” aria-controls=”histogram-7173855829487345455”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-7173855829487345455” aria-controls=”common-7173855829487345455”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-7173855829487345455” aria-controls=”extreme-7173855829487345455”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-7173855829487345455”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-100</td>

</tr> <tr>

<th>Q1</th> <td>-25</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>0</td>

</tr> <tr>

<th>95-th percentile</th> <td>100</td>

</tr> <tr>

<th>Maximum</th> <td>400</td>

</tr> <tr>

<th>Range</th> <td>500</td>

</tr> <tr>

<th>Interquartile range</th> <td>25</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>52.646</td>

</tr> <tr>

<th>Coef of variation</th> <td>-9.3227</td>

</tr> <tr>

<th>Kurtosis</th> <td>8.2296</td>

</tr> <tr>

<th>Mean</th> <td>-5.6471</td>

</tr> <tr>

<th>MAD</th> <td>31.307</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.5582</td>

</tr> <tr>

<th>Sum</th> <td>-16625</td>

</tr> <tr>

<th>Variance</th> <td>2771.6</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-7173855829487345455”>

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re3k9td26pxvys83CaHo61qaurU2Nhk6tiXu2D8FGX2%2BrIK69Za1Nc61NY6VtY2UB8%2BQzJgNTU16aGHHtJHH32kP/3pT7r66qu9fbGxsd4zVWd5PB717NlTcXFx3u/bt/%2B//0O%2B/vprdezYscX7j4%2BPb3Y50OU6oYYGa34gGxubLBsbwSPY1gDr1lrU1zrU1jqhVNvg%2B5jeAk888YQ%2B%2BeSTZuFKklJSUlRZWen9vrGxUXv37lVqaqquvvpqRUdH%2B/T/85//1DfffKOUlBS/zR8AAAS3kAtY77//vtatW6fS0lLFxMQ068/JydHrr7%2Bu3bt3q66uTsuXL1ebNm00dOhQhYeHa%2BLEifrd736nI0eOyO1267e//a1GjBihK6%2B8MgBHAwAAglFQXiLs06ePpG9/Q1CStmzZIklyOp1as2aNTpw4oWHDhvm8ZuDAgXrhhRc0ZMgQPfDAAyooKNCxY8fUp08flZaWKjIyUpKUn5%2Bv2tpajR8/Xg0NDRo2bJgeffRR/x0cAAAIemFGa%2B/oRou4XCdMH9Nutyk2tr3c7tqQuWZ9qbDbbRpRtDPQ07goGwtuDPQUWoR1ay3qax1qax0ra9upUwdTx2upkLtECAAAEGgELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACT%2BT1gZWZmqqSkREeOHPH3rgEAAPzC7wHrjjvu0Jtvvqmbb75Z06dP1%2BbNm9XQ0ODvaQAAAFjG7wErLy9Pb775pl599VX17NlTTzzxhDIyMrR48WIdOnTI39MBAAAwXcDuwUpOTtacOXO0bds2Pfzww3r11Vc1evRoTZs2TR999FGgpgUAANBqAQtYZ86c0Ztvvqm7775bc%2BbMUUJCgh566CElJiYqNzdX69evD9TUAAAAWsXu7x0ePHhQq1ev1uuvv67a2lqNGjVKf/zjHzVgwADvNgMHDtSjjz6qsWPH%2Bnt6AAAAreb3gDVmzBh169ZN99xzj2677TbFxMQ02yYjI0PHjx/399QAAABM4fdLhCtWrNDGjRuVm5t7znB11p49ey441s6dO5Wenq7CwsJmfW%2B%2B%2BabGjh2rfv36KSsrS2%2B//ba3r6mpSUuXLtXw4cM1cOBATZs2TYcPH/b2ezweFRQUKD09XYMHD9bcuXNVX19/kUcKAAAuV34PWL1799aMGTO0ZcsWb9sf/vAH3X333fJ4PC0e5/nnn9eCBQvUpUuXZn379u3TnDlzNHv2bP39739Xbm6u7rvvPh09elSStHLlSq1fv16lpaXatm2bunbtqry8PBmGIUmaN2%2Be6urqtGHDBq1Zs0YHDx5UUVFRK48cAABcLvwesBYuXKgTJ06oR48e3rahQ4eqqalJixYtavE4ERERWr169TkD1qpVq5SRkaGMjAxFRERo3Lhx6tWrl9atWydJKisrU25urrp3766oqCgVFhbq4MGD2rNnj7766itt2bJFhYWFiouLU0JCgmbOnKk1a9bozJkzrS8AAAAIeX6/B%2Bvtt9/W%2BvXrFRsb623r2rWrioqKdOutt7Z4nKlTp563r7KyUhkZGT5tSUlJcjqdqq%2Bv14EDB5SUlOTti4qKUpcuXeR0OnXixAmFh4erd%2B/e3v7k5GSdOnVKn376qU/7%2BVRXV8vlcvm02e3tFB8f39LDa5HwcJvPV5gnGGtqtwfHnFm31qK%2B1qG21gnF2vo9YNXX1ysiIqJZu81mU11dnSn78Hg8io6O9mmLjo7WgQMH9PXXX8swjHP2u91uxcTEKCoqSmFhYT59kuR2u1u0/7KyMpWUlPi05eXlKT8//4cczgU5HG0tGRfBJTa2faCncFFYt9aivtahttYJpdr6PWANHDhQixYt0qxZs7zB5csvv9STTz7p86iG1jp7P9UP6b/Qay9k0qRJyszM9Gmz29vJ7a5t1bjfFR5uk8PRVjU1dWpsbDJ17MtdMH6KMnt9WYV1ay3qax1qax0raxuoD59%2BD1gPP/ywfvnLX%2BqGG25QVFSUmpqaVFtbq6uvvlovvfSSKfuIjY1tdsO8x%2BNRXFycYmJiZLPZztnfsWNHxcXF6eTJk2psbFR4eLi3T5I6duzYov3Hx8c3uxzocp1QQ4M1P5CNjU2WjY3gEWxrgHVrLeprHWprnVCqrd8D1tVXX6033nhDf/3rX/X555/LZrOpW7duGjx4sDfQtFZKSooqKip82pxOp8aMGaOIiAj17NlTlZWVGjRokCSppqZGn3/%2Bufr27avOnTvLMAzt379fycnJ3tc6HA5169bNlPkBAIDQ5veAJUlt2rTRzTffbNn4EydOVHZ2trZv364bbrhB69ev12effaZx48ZJknJyclRaWqohQ4YoISFBRUVFSkxMVJ8%2BfSRJo0aN0tNPP60nn3xS33zzjZYtW6bs7GzZ7QEpFwAACDJ%2BTwyHDx/WkiVL9Mknn5zz4Z1/%2BctfWjTO2TDU0NAgSd7najmdTvXq1UtFRUVauHChqqqq1KNHDz333HPq1KmTJGny5MlyuVy66667VFtbq7S0NJ%2Bb0h9//HHNnz9fw4cP1xVXXKFbb731nA8zBQAAOJcwo7V3dF%2Bku%2B66S9XV1Ro8eLDatWvXrH/WrFn%2BnI7fuFwnTB/TbrcpNra93O7akLlmfamw220aUbQz0NO4KBsLbgz0FFqEdWst6msdamsdK2vbqVMHU8drKb%2BfwaqoqNBf/vIXxcXF%2BXvXAAAAfuH330Xv2LHjOc9cAQAAhAq/B6x77rlHJSUlrX7WFAAAwKXK75cI//rXv%2BqDDz7Qa6%2B9ph//%2BMey2Xwz3iuvvOLvKQEAAJjK7wErKipKQ4YM8fduAQAA/MbvAWvhwoX%2B3iUAAIBfBeQPrn366acqLi7WQw895G378MMPAzEVAAAA0/k9YJWXl2vcuHHavHmzNmzYIOnbh49OnTq1xQ8ZBQAAuJT5PWAtXbpUv/71r7V%2B/XqFhYVJ%2BvbvEy5atEjLli3z93QAAABM5/eA9c9//lM5OTmS5A1YknTLLbfo4MGD/p4OAACA6fwesDp06HDOv0FYXV2tNm3a%2BHs6AAAApvN7wOrfv7%2BeeOIJnTx50tt26NAhzZkzRzfccIO/pwMAAGA6vz%2Bm4aGHHtIvfvELpaWlqbGxUf3791ddXZ169uypRYsW%2BXs6AAAApvN7wLrqqqu0YcMG7dixQ4cOHVJkZKS6deumG2%2B80eeeLAAAgGDl94AlSVdccYVuvvnmQOwaAADAcn4PWJmZmd97popnYQEAgGDn94A1evRon4DV2NioQ4cOyel06he/%2BIW/pwMAAGA6vwes2bNnn7N906ZNeuedd/w8GwAAAPMF5G8RnsvNN9%2BsN954I9DTAAAAaLVLJmDt3btXhmEEehoAAACt5vdLhJMnT27WVldXp4MHD2rkyJH%2Bng4AAIDp/B6wunbt2uy3CCMiIpSdna0JEyb4ezoAAACm83vA4mntAAAg1Pk9YL3%2B%2Bust3va2226zcCYAAADW8HvAmjt3rpqamprd0B4WFubTFhYWRsACAABBye%2B/Rfj73/9egwcP1sqVK/Xee%2B/p3Xff1csvv6whQ4bo%2Beef10cffaSPPvpIe/bsadV%2B9u7dq6lTp%2Br666/XjTfeqNmzZ%2Bv48eOSpPLycmVnZ6t///4aM2aM1q1b5/PaFStWaNSoUerfv79ycnJUUVHRqrkAAIDLi98D1qJFi7RgwQINGDBAUVFR6tChg66//no9/vjjevLJJ9WmTRvvvx%2BqoaFBv/rVr3Tddddp165d2rBhg44fP65HH31U1dXVmjlzpiZPnqzy8nLNnTtX8%2BbNk9PplCRt3bpVxcXFeuqpp7Rr1y4NGzZMM2bM0KlTp8wqAQAACHF%2BD1ifffaZoqOjm7U7HA5VVVWZsg%2BXyyWXy6Xx48erTZs2io2N1YgRI7Rv3z6tX79eXbt2VXZ2tiIiIpSenq7MzEytWrVKklRWVqasrCylpqYqMjJS06dPlyRt27bNlLkBAIDQ5/eA1blzZy1atEhut9vbVlNToyVLluiaa64xZR8JCQlKTExUWVmZamtrdezYMW3evFlDhw5VZWWlkpKSfLZPSkryXgb8br/NZlNiYqL3DBcAAMCF%2BP0m94cfflizZs1SWVmZ2rdvL5vNppMnTyoyMlLLli0zZR82m03FxcXKzc3VH//4R0nSoEGDNGvWLM2cOVMJCQk%2B28fExHgDn8fjaXaGLTo62icQXkh1dbVcLpdPm93eTvHx8T/kcM4rPNzm8xXmCcaa2u3BMWfWrbWor3WorXVCsbZ%2BD1iDBw/W9u3btWPHDh09elSGYSghIUE33XSTOnToYMo%2BvvnmG82YMUO33HKL9/6pxx577Lx/aPq7Wvsne8rKylRSUuLTlpeXp/z8/FaNez4OR1tLxkVwiY1tH%2BgpXBTWrbWor3WorXVCqbZ%2BD1iS1LZtWw0fPlxHjx7V1Vdfbfr45eXl%2BuKLL/TAAw8oPDxcHTp0UH5%2BvsaPH6%2BbbrpJHo/HZ3u32624uDhJUmxsbLN%2Bj8ejnj17tnj/kyZNUmZmpk%2Bb3d5ObnftDzyicwsPt8nhaKuamjo1NjaZOvblLhg/RZm9vqzCurUW9bUOtbWOlbUN1IdPvwes%2Bvp6zZ8/X2%2B88YYkqaKiQjU1NXrggQf029/%2BVg6Ho9X7aGxsbPasrW%2B%2B%2BUaSlJ6erj//%2Bc8%2B21dUVCg1NVWSlJKSosrKSt1%2B%2B%2B3esfbu3avs7OwW7z8%2BPr7Z5UCX64QaGqz5gWxsbLJsbASPYFsDrFtrUV/rUFvrhFJt/f4xffHixdq3b5%2BKiopks/3f7hsbG1VUVGTKPvr166d27dqpuLhYdXV1crvdWr58uQYOHKjx48erqqpKq1at0unTp7Vjxw7t2LFDEydOlCTl5OTo9ddf1%2B7du1VXV6fly5erTZs2Gjp0qClzAwAAoc/vAWvTpk169tlndcstt3j/6LPD4dDChQu1efNmU/YRGxur//7v/9YHH3ygIUOG6NZbb1VkZKSWLFmijh076rnnntPLL7%2BsAQMG6IknntDixYt17bXXSpKGDBmiBx54QAUFBRo0aJB27dql0tJSRUZGmjI3AAAQ%2Bvx%2BibC2tlZdu3Zt1h4XF2fqwzxTUlL00ksvnbNv4MCBWrt27XlfO2XKFE2ZMsW0uQAAgMuL389gXXPNNXrnnXck%2Bf623ltvvaV/%2B7d/8/d0AAAATOf3M1hTpkzR/fffrzvuuENNTU168cUXVVFRoU2bNmnu3Ln%2Bng4AAIDp/B6wJk2aJLvdrpdfflnh4eH63e9%2Bp27duqmoqEi33HKLv6cDAABgOr8HrOPHj%2BuOO%2B7QHXfc4e9dAwAA%2BIXf78EaPnx4q5%2BUDgAAcCnze8BKS0vTxo0b/b1bAAAAv/H7JcIf/ehH%2Bq//%2Bi%2BVlpbqmmuu0RVXXOHTv2TJEn9PCQAAwFR%2BD1gHDhzQT37yE0nf/g1AAACAUOO3gFVYWKilS5f6PPxz2bJlysvL89cUAAAA/MJv92Bt3bq1WVtpaam/dg8AAOA3fgtY5/rNQX6bEAAAhCK/Bayzf9j5Qm0AAADBzu%2BPaQAAAAh1BCwAAACT%2Be23CM%2BcOaNZs2ZdsI3nYAEAgGDnt4A1YMAAVVdXX7ANAAAg2PktYP3r868AAABCGfdgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAm89uDRoGfPf23QE8BAAC/COkzWMuXL9fgwYN13XXXKTc3V1988YUkqby8XNnZ2erfv7/GjBmjdevW%2BbxuxYoVGjVqlPr376%2BcnBxVVFQEYvoAACBIhWzAWrlypdatW6cVK1bo7bffVo8ePfSHP/xB1dXVmjlzpiZPnqzy8nLNnTtX8%2BbNk9PplCRt3bpVxcXFeuqpp7Rr1y4NGzZMM2bM0KlTpwJ8RAAAIFiEbMB64YUXVFhYqJ/85CeKiorSI488okceeUTr169X165dlZ2drYiICKWnpyszM1OrVq2SJJWVlSkrK0upqamKjIzU9OnTJUnbtm0L5OEAAIAgEpL3YH355Zf64osv9PXXX2v06NE6duyY0tLS9Oijj6qyslJJSUk%2B2yclJWnjxo2SpMrKSo0ePdrbZ7PZlJiYKKfTqTFjxrRo/9XV1XK5XD5tdns7xcfHt/LIfIWH23y%2B4vJmtwfHOmDdWouluHL/AAAV6klEQVT6WofaWicUaxuSAevo0aOSpLfeeksvvviiDMNQfn6%2BHnnkEdXX1yshIcFn%2B5iYGLndbkmSx%2BNRdHS0T390dLS3vyXKyspUUlLi05aXl6f8/PwfcjgX5HC0tWRcBJfY2PaBnsJFYd1ai/pah9paJ5RqG5IByzAMSdL06dO9Yer%2B%2B%2B/X3XffrfT09Ba//oeaNGmSMjMzfdrs9nZyu2tbNe53hYfb5HC0VU1NnRobm0wdG8HH7PVlFdattaivdaitdaysbaA%2BfIZkwLryyislSQ6Hw9vWuXNnGYahM2fOyOPx%2BGzvdrsVFxcnSYqNjW3W7/F41LNnzxbvPz4%2BvtnlQJfrhBoarPmBbGxssmxsBI9gWwOsW2tRX%2BtQW%2BuEUm1D52Lnv7jqqqsUFRWlffv2eduqqqp0xRVXKCMjo9ljFyoqKpSamipJSklJUWVlpbevsbFRe/fu9fYDAABcSEgGLLvdruzsbP3ud7/T//7v/%2BrYsWNatmyZxo4dq9tvv11VVVVatWqVTp8%2BrR07dmjHjh2aOHGiJCknJ0evv/66du/erbq6Oi1fvlxt2rTR0KFDA3tQAAAgaITkJUJJmjVrlr755htNmDBBZ86c0ahRo/TII4%2Boffv2eu6557RgwQI99thj6ty5sxYvXqxrr71WkjRkyBA98MADKigo0LFjx9SnTx%2BVlpYqMjIywEcEAACCRZjR2ju60SIu1wnTx7TbbYqNbS%2B3uzYorlnzp3KstbHgxkBPoUWCbd0GG%2BprHWprHStr26lTB1PHa6mQvEQIAAAQSAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZJdFwHriiSfUu3dv7/fl5eXKzs5W//79NWbMGK1bt85n%2BxUrVmjUqFHq37%2B/cnJyVFFR4e8pAwCAIBbyAWvfvn1au3at9/vq6mrNnDlTkydPVnl5uebOnat58%2BbJ6XRKkrZu3ari4mI99dRT2rVrl4YNG6YZM2bo1KlTgToEAAAQZEI6YDU1NWn%2B/PnKzc31tq1fv15du3ZVdna2IiIilJ6erszMTK1atUqSVFZWpqysLKWmpioyMlLTp0%2BXJG3bti0QhwAAAIKQPdATsNIrr7yiiIgIjR07Vk8//bQkqbKyUklJST7bJSUlaePGjd7%2B0aNHe/tsNpsSExPldDo1ZsyYFu23urpaLpfLp81ub6f4%2BPjWHE4z4eE2n6%2B4vNntwbEOWLfWor7WobbWCcXahmzA%2Buqrr1RcXKyXXnrJp93j8SghIcGnLSYmRm6329sfHR3t0x8dHe3tb4mysjKVlJT4tOXl5Sk/P/9iDqHFHI62loyL4BIb2z7QU7gorFtrUV/rUFvrhFJtQzZgLVy4UFlZWerRo4e%2B%2BOKLi3qtYRit2vekSZOUmZnp02a3t5PbXduqcb8rPNwmh6Otamrq1NjYZOrYCD5mry%2BrsG6tRX2tQ22tY2VtA/XhMyQDVnl5uT788ENt2LChWV9sbKw8Ho9Pm9vtVlxc3Hn7PR6Pevbs2eL9x8fHN7sc6HKdUEODNT%2BQjY1Nlo2N4BFsa4B1ay3qax1qa51Qqm3oXOz8F%2BvWrdOxY8c0bNgwpaWlKSsrS5KUlpamXr16NXvsQkVFhVJTUyVJKSkpqqys9PY1NjZq79693n4AAIALCcmA9eCDD2rTpk1au3at1q5dq9LSUknS2rVrNXbsWFVVVWnVqlU6ffq0duzYoR07dmjixImSpJycHL3%2B%2BuvavXu36urqtHz5crVp00ZDhw4N4BEBAIBgEpKXCKOjo31uVG9oaJAkXXXVVZKk5557TgsWLNBjjz2mzp07a/Hixbr22mslSUOGDNEDDzyggoICHTt2TH369FFpaakiIyP9fyAAACAohWTA%2Bq4f//jH%2Bvjjj73fDxw40Ofho981ZcoUTZkyxR9TAwAAISgkLxECAAAE0mVxBiuUXT/3rUBPAQAAfAdnsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQhG7CqqqqUl5entLQ0paen68EHH1RNTY0kad%2B%2Bfbrzzjs1YMAAjRw5Ui%2B88ILPa998802NHTtW/fr1U1ZWlt5%2B%2B%2B1AHAIAAAhSIRuwZsyYIYfDoa1bt%2Bq1117TJ598oieffFL19fW655579NOf/lQ7d%2B7U0qVL9dxzz2nz5s2Svg1fc%2BbM0ezZs/X3v/9dubm5uu%2B%2B%2B3T06NEAHxEAAAgWIRmwampqlJKSolmzZql9%2B/a66qqrdPvtt%2Bu9997T9u3bdebMGd17771q166dkpOTNWHCBJWVlUmSVq1apYyMDGVkZCgiIkLjxo1Tr169tG7dugAfFQAACBYhGbAcDocWLlyoK6%2B80tt25MgRxcfHq7KyUr1791Z4eLi3LykpSRUVFZKkyspKJSUl%2BYyXlJQkp9Ppn8kDAICgZw/0BPzB6XTq5Zdf1vLly7Vx40Y5HA6f/piYGHk8HjU1Ncnj8Sg6OtqnPzo6WgcOHGjx/qqrq%2BVyuXza7PZ2io%2BP/%2BEHcQ7h4SGZj/ED2e3BsR7OrlvWrzWor3WorXVCsbYhH7Def/993XvvvZo1a5bS09O1cePGc24XFhbm/d%2BGYbRqn2VlZSopKfFpy8vLU35%2BfqvGBb5PbGz7QE/hojgcbQM9hZBGfa1Dba0TSrUN6YC1detW/frXv9a8efN02223SZLi4uL02Wef%2BWzn8XgUExMjm82m2NhYeTyeZv1xcXEt3u%2BkSZOUmZnp02a3t5PbXfvDDuQ8Qinpo/XMXl9WCQ%2B3yeFoq5qaOjU2NgV6OiGH%2BlqH2lrHytoG6sNnyAasDz74QHPmzNEzzzyjwYMHe9tTUlL0pz/9SQ0NDbLbvz18p9Op1NRUb//Z%2B7HOcjqdGjNmTIv3HR8f3%2BxyoMt1Qg0N/EDCOsG2vhobm4JuzsGE%2BlqH2lonlGobkqdAGhoa9Mgjj2j27Nk%2B4UqSMjIyFBUVpeXLl6uurk579uzR6tWrlZOTI0maOHGidu3ape3bt%2Bv06dNavXq1PvvsM40bNy4QhwIAAIJQmNHaG44uQe%2B9955%2B/vOfq02bNs363nrrLdXW1mr%2B/PmqqKjQlVdeqbvvvltTpkzxbrN582YtWbJEVVVV6tGjh%2BbOnauBAwe2ak4u14lWvf5c7HabRhTtNH1cBKeNBTcGegotYrfbFBvbXm53bch8Ur2UUF/rUFvrWFnbTp06mDpeS4XkJcLrr79eH3/88fdu86c//em8fSNHjtTIkSPNnhYAALhMhOQlQgAAgEAiYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyULyQaMALm3Xz30r0FO4KMHylHwAlw7OYAEAAJiMgAUAAGAyLhECIeJnT/8t0FMAAPx/nMECAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwmT3QEwAAmOv6uW8FegottrHgxkBPAbAEZ7DOoaqqSr/61a%2BUlpamYcOGafHixWpqagr0tAAAQJDgDNY53H///UpOTtaWLVt07Ngx3XPPPbryyiv17//%2B74GeGgAACAIErO9wOp3av3%2B/XnzxRXXo0EEdOnRQbm6u/vjHPxKwgMvUz57%2BW6CngEtEMF1%2BlbgEG0gErO%2BorKxU586dFR0d7W1LTk7WoUOHdPLkSUVFRV1wjOrqarlcLp82u72d4uPjTZ1reDhXeAEEN7s9eN7HgvE9N5g%2BHLz3X7cEZY3Ph4D1HR6PRw6Hw6ftbNhyu90tClhlZWUqKSnxabvvvvt0//33mzdRfRvkfnHVJ5o0aZLp4e1yV11drbKyMmprAWprLeprHd5zrVNdXa3i4uKQqm3oREUTGYbRqtdPmjRJr732ms%2B/SZMmmTS7/%2BNyuVRSUtLsbBlaj9pah9pai/pah9paJxRryxms74iLi5PH4/Fp83g8CgsLU1xcXIvGiI%2BPD5kEDgAALh5nsL4jJSVFR44c0fHjx71tTqdTPXr0UPv27QM4MwAAECwIWN%2BRlJSkPn36aMmSJTp58qQOHjyoF198UTk5OYGeGgAACBLhjz766KOBnsSl5qabbtKGDRv0n//5n3rjjTeUnZ2tadOmKSwsLNBTa6Z9%2B/YaNGgQZ9csQG2tQ22tRX2tQ22tE2q1DTNae0c3AAAAfHCJEAAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZASsIOF0OjVixAhNnDixWV95ebmys7PVv39/jRkzRuvWrfPpX7FihUaNGqX%2B/fsrJydHFRUV/pp2UKqqqtKvfvUrpaWladiwYVq8eLGampoCPa2gsXPnTqWnp6uwsLBZ35tvvqmxY8eqX79%2BysrK0ttvv%2B3ta2pq0tKlSzV8%2BHANHDhQ06ZN0%2BHDh/059UteVVWV8vLylJaWpvT0dD344IOqqamRJO3bt0933nmnBgwYoJEjR%2BqFF17wee331R7S/v379Ytf/EIDBgxQenq6CgoK5HK5JPEea6YnnnhCvXv39n4f0rU1cMlbu3atkZGRYUybNs2YMGGCT9%2BXX35pXHfddcaqVauM%2Bvp6429/%2B5vRt29f46OPPjIMwzD%2B8pe/GNdff72xe/duo66uznjuueeMG2%2B80aitrQ3EoQSF22%2B/3XjkkUeMmpoa49ChQ8bIkSONF154IdDTCgqlpaXGyJEjjcmTJxsFBQU%2BfXv37jVSUlKM7du3G/X19cbatWuN1NRU48iRI4ZhGMaKFSuMYcOGGQcOHDBOnDhhPP7448bYsWONpqamQBzKJenWW281HnzwQePkyZPGkSNHjKysLOPhhx826urqjJtuuskoLi42amtrjYqKCmPQoEHGpk2bDMO4cO0vd6dPnzZuuOEGo6SkxDh9%2BrRx7Ngx48477zRmzpzJe6yJ9u7dawwaNMjo1auXYRih/98vzmAFgdOnT6usrEypqanN%2BtavX6%2BuXbsqOztbERERSk9PV2ZmplatWiVJKisrU1ZWllJTUxUZGanp06dLkrZt2%2BbXYwgWTqdT%2B/fv1%2BzZs9WhQwd17dpVubm5KisrC/TUgkJERIRWr16tLl26NOtbtWqVMjIylJGRoYiICI0bN069evXyfmItKytTbm6uunfvrqioKBUWFurgwYPas2ePvw/jklRTU6OUlBTNmjVL7du311VXXaXbb79d7733nrZv364zZ87o3nvvVbt27ZScnKwJEyZ41%2B2Fan%2B5q6urU2Fhoe655x61adNGcXFxGjFihD755BPeY03S1NSk%2BfPnKzc319sW6rUlYAWBCRMmKCEh4Zx9lZWVSkpK8mlLSkrynkb9br/NZlNiYqKcTqd1Ew5ilZWV6ty5s6Kjo71tycnJOnTokE6ePBnAmQWHqVOnqkOHDufsO99adTqdqq%2Bv14EDB3z6o6Ki1KVLF9bq/%2BdwOLRw4UJdeeWV3rYjR44oPj5elZWV6t27t8LDw7193/c%2BcLaf2n4rOjpaEyZMkN1ulyR9%2Bumn%2BvOf/6yf/exnvMea5JVXXlFERITGjh3rbQv12hKwgpzH45HD4fBpi4mJkdvt9vb/a1iQvn0zOdsPX%2Beq59n6UbPW%2Bb61%2BPXXX8swDNbqRXA6nXr55Zd17733nvd9wOPxqKmpifeBFqqqqlJKSopGjx6tPn36KD8/n/dYE3z11VcqLi7W/PnzfdpDvbYErEvA2rVr1bt373P%2Be%2B2111o9vmEYJszy8kG9rHOh2lL7lnn//fc1bdo0zZo1S%2Bnp6efdLiwszPu/qe2Fde7cWU6nU2%2B99ZY%2B%2B%2Bwz/eY3v2nR66jt91u4cKGysrLUo0ePi35tMNfWHugJQBo/frzGjx//g14bGxsrj8fj0%2BZ2uxUXF3fefo/Ho549e/6wyYa4uLi4c9YrLCzMW1P8MOdbi3FxcYqJiZHNZjtnf8eOHf05zUve1q1b9etf/1rz5s3TbbfdJunbdfvZZ5/5bOfxeLx1/b7aw1dYWJi6du2qwsJCTZ48WRkZGbzHtkJ5ebk%2B/PBDbdiwoVlfqP/3izNYQa5Pnz7Nfm21oqLCe0N8SkqKKisrvX2NjY3au3fvOW%2BYx7f1OnLkiI4fP%2B5tczqd6tGjh9q3bx/AmQW/lJSUZmvV6XQqNTVVERER6tmzp89aramp0eeff66%2Bffv6e6qXrA8%2B%2BEBz5szRM8884w1X0re1/fjjj9XQ0OBtO1vbs/3nqz2%2BDQGjRo3yeRyLzfbtfx779u3Le2wrrFu3TseOHdOwYcOUlpamrKwsSVJaWpp69eoV2rUN3C8w4mI9%2B%2ByzzR7T8NVXXxn9%2BvUzXn31VaO%2Bvt7Yvn270bdvX2Pfvn2GYRjGjh07jAEDBhgffvihcerUKaO4uNjIyMgw6urqAnEIQWHChAnGww8/bJw4ccI4cOCAkZmZabz88suBnlZQmTNnTrPHNHz88cdGnz59jG3bthn19fXGqlWrjH79%2BhnV1dWGYRjG//zP/xhDhw71PqZh3rx5xh133BGI6V%2BSzpw5Y/zsZz8zXnnllWZ9p0%2BfNoYNG2Y8%2B%2ByzxqlTp4zdu3cb119/vbFt2zbDMC5c%2B8tdTU2NkZ6ebixatMg4deqUcezYMWPatGnGlClTeI9tJY/HYxw5csT778MPPzR69eplHDlyxKiqqgrp2hKwgsDIkSONlJQUIzEx0ejdu7eRkpJipKSkGF988YVhGIbxj3/8wxg3bpyRnJxsjBw50vvsm7NWrlxpZGRkGCkpKUZOTo7x8ccfB%2BIwgsaRI0eM6dOnG3379jXS09ONZ599lmcxtdDZtXnttdca1157rff7szZt2mSMHDnSSE5ONsaPH2/84x//8PY1NTUZzzzzjHHDDTcYffv2Ne6%2B%2B26e0/Qv3n33XaNXr17emv7rvy%2B%2B%2BML4%2BOOPjcmTJxspKSnG0KFDjZUrV/q8/vtqD8PYv3%2B/ceeddxp9%2B/Y1fvrTnxoFBQXG0aNHDcPgPdZMhw8f9j4HyzBCu7ZhhhHEd5ABAABcgrgHCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGT/D1llrOmjKMMdAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-7173855829487345455”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1435</td> <td class=”number”>44.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>264</td> <td class=”number”>8.2%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>153</td> <td class=”number”>4.8%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>116</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.333333333</td> <td class=”number”>62</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>57</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-66.666666667</td> <td class=”number”>50</td> <td class=”number”>1.6%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.333333333</td> <td class=”number”>45</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-25.0</td> <td class=”number”>43</td> <td class=”number”>1.3%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-20.0</td> <td class=”number”>34</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (277)</td> <td class=”number”>685</td> <td class=”number”>21.3%</td> <td>

<div class=”bar” style=”width:48%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>266</td> <td class=”number”>8.3%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-7173855829487345455”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>264</td> <td class=”number”>8.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-83.333333333</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-80.0</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-75.0</td> <td class=”number”>13</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-71.428571429</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>225.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>250.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>300.0</td> <td class=”number”>9</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>350.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>400.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:23%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_SUPERCPTH_09_14”><s>PCH_SUPERCPTH_09_14</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_SUPERC_09_14”><code>PCH_SUPERC_09_14</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99027</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_SUPERC_09_14”>PCH_SUPERC_09_14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>72</td>

</tr> <tr>

<th>Unique (%)</th> <td>2.2%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>6.7%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>216</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>6.7204</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>300</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>80.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram806858273980192695”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives806858273980192695,#minihistogram806858273980192695”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives806858273980192695”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles806858273980192695”

aria-controls=”quantiles806858273980192695” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram806858273980192695” aria-controls=”histogram806858273980192695”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common806858273980192695” aria-controls=”common806858273980192695”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme806858273980192695” aria-controls=”extreme806858273980192695”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles806858273980192695”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>0</td>

</tr> <tr>

<th>95-th percentile</th> <td>50</td>

</tr> <tr>

<th>Maximum</th> <td>300</td>

</tr> <tr>

<th>Range</th> <td>400</td>

</tr> <tr>

<th>Interquartile range</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>26.425</td>

</tr> <tr>

<th>Coef of variation</th> <td>3.932</td>

</tr> <tr>

<th>Kurtosis</th> <td>30.95</td>

</tr> <tr>

<th>Mean</th> <td>6.7204</td>

</tr> <tr>

<th>MAD</th> <td>12.605</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>4.7892</td>

</tr> <tr>

<th>Sum</th> <td>20121</td>

</tr> <tr>

<th>Variance</th> <td>698.26</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram806858273980192695”>

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PPZo9e7Y6deqkP/zhD%2BrVq5dycnK0ceNGfy0NAACgRUJ9vcOSkhKtXbtWb7zxhqqrqzVixAj95S9/Ub9%2B/dy36d%2B/vx5//HGNGjXK18sDAABoMZ8XrJEjR6pr166aMmWKbrnlFrVv3/6M22RmZurEiRO%2BXhoAAIARPi9Yq1at0oABA5q93Z49e3ywGgAAAPN8fg5WUlKSpk6dqq1bt7rH/uu//kv33HOPXC6Xr5cDAABgnM8L1oIFC/T999%2Bre/fu7rFBgwapsbFRCxcu9PVyAAAAjPP5W4QffPCBNm7cqNjYWPdYYmKi8vPzddNNN/l6OQAAAMb5/BWs2tpahYeHn7mQ4GDV1NT4ejkAAADG%2Bbxg9e/fXwsXLtTJkyfdY998842eeOIJj0s1AAAABCqfv0U4d%2B5c/fu//7uuvvpqRUZGqrGxUdXV1ercubP%2B%2Bte/%2Bno5AAAAxvm8YHXu3Flvvvmm3nvvPR05ckTBwcHq2rWrBg4cqJCQEF8vBwAAwDifFyxJCgsL03XXXeePXQMAAHidzwvW0aNHtXjxYn311Veqra09Y/7dd9/19ZIAAACM8ss5WGVlZRo4cKDatWvXom29//77mj17ttLT07VkyRL3%2BLp16zR37ly1adPG4/arV69WWlqaGhsbtXTpUm3atEmVlZVKS0vT448/rs6dO0uSXC6XHn/8cX3yyScKDg5WZmam5s2bp7Zt27ZovQAA4P8GnxesoqIivfvuu4qLi2vRdp5//nmtXbtWXbp0aXK%2Bf//%2BZz1pfvXq1dq4caOef/55derUSUuWLFFubq7Wr1%2BvoKAgzZs3T6dPn9amTZtUV1enBx54QPn5%2BXr00UdbtGYAAPB/g88v09ChQ4cWv3IlSeHh4ecsWOdSWFionJwcdevWTZGRkcrLy1NJSYn27Nmjb7/9Vlu3blVeXp7i4uLUqVMn3XvvvXr99ddVV1fX4nUDAAD783nBmjJlipYtWybLslq0nTvuuENRUVFnnT927Jjuuusu9e/fX0OHDtX69esl/Xih0wMHDig5Odl928jISHXp0kVOp1P79u1TSEiIkpKS3PMpKSn64YcfdPDgwRatGQAA/N/g87cI33vvPX3%2B%2Bedat26dLr30UgUHe3a81157rcX7iIuLU2Jioh588EF1795d//jHP/Twww8rPj5ev/3tb2VZlmJiYjzuExMTo4qKCrVv316RkZEKCgrymJOkioqK89p/WVmZysvLPcZCQ9spPj6%2Bhck8hYQEe/xpF3bN5W2hof77fNn5mJEt8Ng1l2TfbHbM5fOCFRkZqd/97nde3cegQYM0aNAg979Hjhypf/zjH1q3bp1mzZolSed8Ba2lr64VFhZq2bJlHmO5ubmaPn16i7Z7NtHRF3llu/5m11zeEhsb4e8l2PqYkS3w2DWXZN9sdsrl84K1YMECX%2B9SkpSQkKCioiK1b99ewcHBcrlcHvMul0sdOnRQXFycqqqq1NDQ4L7w6U%2B37dChw3ntKysrS0OGDPEYCw1tp4qKagNJ/ldISLCioy9SZWWNGhoajW7bn%2Byay9tMP74uhJ2PGdkCj11zSfbN5s1c/vrh0y8XGj148KDefPNN/c///I%2B7cP33f/%2B3%2BvTpY2T7r776qmJiYnTjjTe6x0pKStS5c2eFh4erR48eKi4u1oABAyRJlZWVOnLkiNLS0pSQkCDLsrR//36lpKRIkpxOp6Kjo9W1a9fz2n98fPwZbweWl3%2Bv%2BnrvfDE0NDR6bdv%2BZNdc3tIaPld2PmZkCzx2zSXZN5udcvn8zc5du3Zp9OjR2rJlizZt2iTpx4uP3nHHHcYuMnr69Gk99dRTcjqdqqur06ZNm/Tee%2B9p4sSJkqTs7GytWrVKJSUlqqqqUn5%2Bvnr16qXU1FTFxcVpxIgReuaZZ3TixAkdP35cy5cv17hx4xQa6pc%2BCgAAAozPG8OSJUv00EMP6c4771RaWpqkH38/4cKFC7V8%2BXINHTr0vLaTmpoqSaqvr5ckbd26VdKPrzbdcccdqq6u1gMPPKDy8nJdeumlWr58uRwOhyRp4sSJKi8v1%2B23367q6mqlp6d7nDP15JNP6rHHHtPQoUPVpk0b3XTTTcrLyzP2OQAAAPYWZLX0jO4LdMUVV%2BiTTz5RWFiYevfurT179kiSGhoa1LdvX/e/7aa8/Hvj2wwNDVZsbIQqKqpt85Kq1Hpy3fDMh37b96/x1oxr/Lbv1nLMvIFsgceuuST7ZvNmro4dz35JJ2/y%2BVuEUVFRTf4OwrKyMoWFhfl6OQAAAMb5vGD17dtXf/rTn1RVVeUeO3TokGbPnq2rr77a18sBAAAwzufnYP3hD3/QnXfeqfT0dPfbgjU1NerRo4cWLlzo6%2BUAAAAY5/OCdckll2jTpk3auXOnDh06pLZt26pr16665pprPK6eDgAAEKj8ct2BNm3a6LrrrvPHrgEAALzO5wVryJAh53ylytS1sAAAAPzF5wXrxhtv9ChYDQ0NOnTokJxOp%2B68805fLwcAAMA4nxesn37Z8i%2B98847%2Buc//%2Bnj1QAAAJjn88s0nM11112nN99809/LAAAAaLFWU7D27t0rH19UHgAAwCt8/hbhT79w%2BedqampUUlKi4cOH%2B3o5AAAAxvm8YCUmJp7xvwjDw8M1btw4jR8/3tfLAQAAMM7nBYurtQMAALvzecF64403zvu2t9xyixdXAgAA4B0%2BL1iPPPKIGhsbzzihPSgoyGMsKCiIggUAAAKSzwvWCy%2B8oJdeeklTp05VUlKSLMvSl19%2Bqeeff16TJk1Senq6r5cEAABglF/OwSooKFCnTp3cY1deeaU6d%2B6syZMna9OmTb5eEgAAgFE%2Bvw7W4cOHFRMTc8Z4dHS0SktLfb0cAAAA43xesBISErRw4UJVVFS4xyorK7V48WJddtllvl4OAACAcT5/i3Du3LmaOXOmCgsLFRERoeDgYFVVValt27Zavny5r5cDAABgnM8L1sCBA7Vjxw7t3LlTx48fl2VZ6tSpk6699lpFRUX5ejkAAADG%2BbxgSdJFF12koUOH6vjx4%2BrcubM/lgAAAOA1Pj8Hq7a2VrNnz1afPn10ww03SPrxHKy7775blZWVvl4OAACAcT4vWIsWLdK%2BffuUn5%2Bv4OD/3X1DQ4Py8/N9vRwAAADjfF6w3nnnHf35z3/W9ddf7/6lz9HR0VqwYIG2bNni6%2BUAAAAY5/OCVV1drcTExDPG4%2BLi9MMPP/h6OQAAAMb5vGBddtll%2Buc//ylJHr978O2339a//du/%2BXo5AAAAxvn8fxHedtttuv/%2B%2B3XrrbeqsbFRL7/8soqKivTOO%2B/okUce8fVyAAAAjPN5wcrKylJoaKheeeUVhYSE6LnnnlPXrl2Vn5%2Bv66%2B/3tfLAQAAMM7nBevEiRO69dZbdeutt/p61wAAAD7h83Owhg4d6nHuFQAAgN34vGClp6frrbfe8vVuAQAAfMbnbxH%2B5je/0R//%2BEcVFBTosssuU5s2bTzmFy9e7OslAQAAGOXzgnXgwAH99re/lSRVVFT4evcAAABe57OClZeXpyVLluivf/2re2z58uXKzc311RIAAAB8wmfnYG3btu2MsYKCAl/tHgAAwGd8VrCa%2Bp%2BD/G9CAABgRz4rWD/9YufmxgAAAAKdzy/TAAAAYHcULAAAAMN89r8I6%2BrqNHPmzGbHuA4WAAAIdD4rWP369VNZWVmzYwAAAIHOZwXr59e/AgAAsDPOwQIAADCMggUAAGAYBQsAAMCwgC5Y77//vjIyMpSXl3fG3ObNmzVq1Cj16dNHY8eO1QcffOCea2xs1JIlSzR06FD1799fkydP1tGjR93zLpdLM2bMUEZGhgYOHKhHHnlEtbW1PskEAAACX8AWrOeff17z589Xly5dzpjbt2%2BfZs%2BerVmzZunjjz9WTk6O7rvvPh0/flyStHr1am3cuFEFBQXavn27EhMTlZub6/7VPfPmzVNNTY02bdqk119/XSUlJcrPz/dpPgAAELgCtmCFh4dr7dq1TRasNWvWKDMzU5mZmQoPD9fo0aPVs2dPbdiwQZJUWFionJwcdevWTZGRkcrLy1NJSYn27Nmjb7/9Vlu3blVeXp7i4uLUqVMn3XvvvXr99ddVV1fn65gAACAABWzBuuOOOxQVFdXkXHFxsZKTkz3GkpOT5XQ6VVtbqwMHDnjMR0ZGqkuXLnI6ndq3b59CQkKUlJTknk9JSdEPP/yggwcPeicMAACwFZ9dB8uXXC6XYmJiPMZiYmJ04MABnTx5UpZlNTlfUVGh9u3bKzIy0uMXUf9024qKivPaf1lZmcrLyz3GQkPbKT4%2B/tfEOauQkGCPP%2B3Crrm8LTTUf58vOx8zsgUeu%2BaS7JvNjrlsWbAkuc%2Bn%2BjXzzd23OYWFhVq2bJnHWG5urqZPn96i7Z5NdPRFXtmuv9k1l7fExkb4ewm2PmZkCzx2zSXZN5udctmyYMXGxsrlcnmMuVwuxcXFqX379goODm5yvkOHDoqLi1NVVZUaGhoUEhLinpOkDh06nNf%2Bs7KyNGTIEI%2Bx0NB2qqio/rWRmhQSEqzo6ItUWVmjhoZGo9v2J7vm8jbTj68LYedjRrbAY9dckn2zeTOXv374tGXBcjgcKioq8hhzOp0aOXKkwsPD1aNHDxUXF2vAgAGSpMrKSh05ckRpaWlKSEiQZVnav3%2B/UlJS3PeNjo5W165dz2v/8fHxZ7wdWF7%2BverrvfPF0NDQ6LVt%2B5Ndc3lLa/hc2fmYkS3w2DWXZN9sdsplnzc7f2bChAn66KOPtGPHDp06dUpr167V4cOHNXr0aElSdna2Vq1apZKSElVVVSk/P1%2B9evVSamqq4uLiNGLECD3zzDM6ceKEjh8/ruXLl2vcuHEKDbVlHwUAAIYFbGNITU2VJNXX10uStm7dKunHV5t69uyp/Px8LViwQKWlperevbtWrlypjh07SpImTpyo8vJy3X777aqurlZ6errHOVNPPvmkHnvsMQ0dOlRt2rTRTTfd1OTFTAEAAJoSZLX0jG6cl/Ly741vMzQ0WLGxEaqoqLbNS6pS68l1wzMf%2Bm3fv8ZbM67x275byzHzBrIFHrvmkuybzZu5OnZs%2BpJO3mbLtwgBAAD8iYIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACG2bZgJSUlyeFwKDU11f3x1FNPSZJ27dqlcePGqW/fvho5cqQ2bNjgcd9Vq1ZpxIgR6tu3r7Kzs1VUVOSPCAAAIECF%2BnsB3vT222/r0ksv9RgrKyvTvffeq0ceeUSjRo3SZ599pmnTpqlr165KTU3Vtm3b9Oyzz%2BqFF15QUlKSVq1apalTp2rLli1q166dn5IAAIBAYttXsM5m48aNSkxM1Lhx4xQeHq6MjAwNGTJEa9askSQVFhZq7Nix6t27t9q2bau7775bkrR9%2B3Z/LhsAAAQQWxesxYsXa9CgQbryyis1b948VVdXq7i4WMnJyR63S05Odr8N%2BMv54OBg9erVS06n06drBwAAgcu2bxFeccUVysjI0NNPP62jR49qxowZeuKJJ%2BRyudSpUyeP27Zv314VFRWSJJfLpZiYGI/5mJgY9/z5KCsrU3l5ucdYaGg7xcfH/8o0TQsJCfb40y7smsvbQkP99/my8zEjW%2BCxay7JvtnsmMu2BauwsND9927dumnWrFmaNm2a%2BvXr1%2Bx9Lctq8b6XLVvmMZabm6vp06e3aLtnEx19kVe26292zeUtsbER/l6CrY8Z2QKPXXNJ9s1mp1y2LVi/dOmll6qhoUHBwcFyuVwecxUVFYqLi5MkxcbGnjHvcrnUo0eP895XVlaWhgwZ4jEWGtpOFRXVv3L1TQsJCVZ09EWqrKxRQ0Oj0W37k11zeZvpx9eFsPMxI1vgsWsuyb7ZvJnLXz982rJg7d27Vxs2bNCcOXPcYyUlJQoLC1Pdy2rFAAAP4UlEQVRmZqb%2B/ve/e9y%2BqKhIvXv3liQ5HA4VFxdrzJgxkqSGhgbt3btX48aNO%2B/9x8fHn/F2YHn596qv984XQ0NDo9e27U92zeUtreFzZedjRrbAY9dckn2z2SmXfd7s/JkOHTqosLBQBQUFOn36tA4dOqSlS5cqKytLN998s0pLS7VmzRqdOnVKO3fu1M6dOzVhwgRJUnZ2tt544w3t3r1bNTU1WrFihcLCwjRo0CD/hgIAAAHDlq9gderUSQUFBVq8eLG7II0ZM0Z5eXkKDw/XypUrNX/%2BfD3xxBNKSEjQokWLdPnll0uSfve73%2BnBBx/UjBkz9N133yk1NVUFBQVq27atn1MBAIBAYcuCJUn9%2B/fXa6%2B9dta59evXn/W%2Bt912m2677TZvLQ0AANicLd8iBAAA8CcKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADAs1N8LAGDGDc986O8lnLe3Zlzj7yUAgFfxChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhvG7CAGgGVc%2B8ra/l3BB%2BF2PgP/xChYAAIBhFCwAAADDKFgAAACGUbCaUFpaqt///vdKT0/X4MGDtWjRIjU2Nvp7WQAAIEBwknsT7r//fqWkpGjr1q367rvvNGXKFF188cW66667/L00wBZueOZDfy8BALyKgvULTqdT%2B/fv18svv6yoqChFRUUpJydHf/nLXyhYLcQ3VQC/FGjPC/wPTZwvCtYvFBcXKyEhQTExMe6xlJQUHTp0SFVVVYqMjGx2G2VlZSovL/cYCw1tp/j4eKNrDQkJ9vgTAKTAKy2BJDTUv8%2B3dn3et2MuCtYvuFwuRUdHe4z9VLYqKirOq2AVFhZq2bJlHmP33Xef7r//fnML1Y9F7i9/eUFZWVnGy5s3fPrH68/rdmVlZSosLAyYXBfCrtnsmksiWyCyay4p8J73z5cdc9mnKhpkWVaL7p%2BVlaV169Z5fGRlZRla3f8qLy/XsmXLzni1LNDZNZdk32x2zSWRLRDZNZdk32x2zMUrWL8QFxcnl8vlMeZyuRQUFKS4uLjz2kZ8fLxtGjgAALhwvIL1Cw6HQ8eOHdOJEyfcY06nU927d1dERIQfVwYAAAIFBesXkpOTlZqaqsWLF6uqqkolJSV6%2BeWXlZ2d7e%2BlAQCAABHy%2BOOPP%2B7vRbQ21157rTZt2qSnnnpKb775psaNG6fJkycrKCjI30s7Q0REhAYMGGC7V9fsmkuybza75pLIFojsmkuybza75QqyWnpGNwAAADzwFiEAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRSsAOF0OjVs2DBNmDDhjLldu3Zp3Lhx6tu3r0aOHKkNGzZ4zK9atUojRoxQ3759lZ2draKiIl8t%2B4IMGTJEDodDqamp7o%2BpU6e65/ft26dJkyapX79%2BGj58uF566SU/rvbClZaW6ve//73S09M1ePBgLVq0SI2Njf5e1gVLSko64zg99dRTkpp/LLY277//vjIyMpSXl3fG3ObNmzVq1Cj16dNHY8eO1QcffOCea2xs1JIlSzR06FD1799fkydP1tGjR3259GadLdu6det0%2BeWXexy/1NRUffHFF5Jaf7bS0lLl5uYqPT1dGRkZmjNnjiorKyU1/xxxrmPaGpwt29dff62kpKQzjtmLL77ovm9rzrZ//37deeed6tevnzIyMjRjxgyVl5dLss/3ryZZaPXWr19vZWZmWpMnT7bGjx/vMffNN99YV1xxhbVmzRqrtrbW%2BvDDD620tDTriy%2B%2BsCzLst59913ryiuvtHbv3m3V1NRYK1eutK655hqrurraH1HOafDgwdbHH3/c5FxNTY117bXXWs8%2B%2B6xVXV1tFRUVWQMGDLDeeecdH6/y1xszZoz16KOPWpWVldahQ4es4cOHWy%2B99JK/l3XBevbsaR09evSM8eYei61NQUGBNXz4cGvixInWjBkzPOb27t1rORwOa8eOHVZtba21fv16q3fv3taxY8csy7KsVatWWYMHD7YOHDhgff/999aTTz5pjRo1ympsbPRHlDOcK9vrr79uTZo06az3be3ZbrrpJmvOnDlWVVWVdezYMWvs2LHW3Llzm32OaO6YtgZny3b06FGrZ8%2BeZ71fa8526tQp6%2Bqrr7aWLVtmnTp1yvruu%2B%2BsSZMmWffee6%2Btvn81hVewAsCpU6dUWFio3r17nzG3ceNGJSYmaty4cQoPD1dGRoaGDBmiNWvWSJIKCws1duxY9e7dW23bttXdd98tSdq%2BfbtPM7TUjh07VFdXp2nTpqldu3ZKSUnR%2BPHjVVhY6O%2BlnRen06n9%2B/dr1qxZioqKUmJionJycgJm/eejucdiaxMeHq61a9eqS5cuZ8ytWbNGmZmZyszMVHh4uEaPHq2ePXu6f7ouLCxUTk6OunXrpsjISOXl5amkpER79uzxdYwmnStbc1pztsrKSjkcDs2cOVMRERG65JJLNGbMGH366afNPkc0d0z97VzZmtOas9XU1CgvL09TpkxRWFiY4uLiNGzYMH311Ve2//5FwQoA48ePV6dOnZqcKy4uVnJyssdYcnKy%2B2XUX84HBwerV69ecjqd3ltwC6xatUrXXXed%2BvTpo%2BnTp%2Bu7776T9GOOpKQkhYSEuG/785ytXXFxsRISEhQTE%2BMeS0lJ0aFDh1RVVeXHlf06ixcv1qBBg3TllVdq3rx5qq6ubvax2NrccccdioqKanLubFmcTqdqa2t14MABj/nIyEh16dKl1XxdnSubJB07dkx33XWX%2Bvfvr6FDh2r9%2BvWS1OqzRUdHa8GCBbr44ovdY8eOHVN8fHyzzxHnOqatwbmy/eThhx/WwIEDddVVV2nx4sWqq6uT1LqzxcTEaPz48QoNDZUkHTx4UH//%2B991ww032O771y9RsAKcy%2BVSdHS0x1j79u1VUVHhnv/5N3Xpxwf8T/OtSa9evZSWlqb169dr8%2BbNcrlceuCBBySdPafL5QqI85iaWv9Px6U1HotzueKKK5SRkaEtW7aosLBQu3fv1hNPPNHsYzGQnOvr5uTJk7IsK2C%2Brn4pLi5OiYmJeuihh/Thhx/qwQcf1Ny5c7Vr166Ay%2BZ0OvXKK69o2rRpzT5HBNJzoeSZLSwsTH369NGwYcO0fft2FRQUaMOGDfrP//xPSYHxPF9aWiqHw6Ebb7xRqampmj59uq2%2BfzWFgtUKrF%2B/XklJSU1%2BrFu3rsXbtyzLwCpbrrmcy5cv15QpUxQREaHf/OY3euyxx/Svf/1LR44cOes2g4KCfJigZVrLcWipwsJCjR8/XmFhYerWrZtmzZqlTZs2uX%2BatovmjlegHs9BgwbphRdeUHJyssLCwjRy5EgNGzbM47kmELJ99tlnmjx5smbOnKmMjIyz3u7nzxGBkEs6M1t8fLxee%2B01DRs2TG3atFFaWpqmTJkSUMcsISFBTqdTb7/9tg4fPqyHH374vO7X2nOdS6i/FwDp5ptv1s033/yr7hsbGyuXy%2BUxVlFRobi4uLPOu1wu9ejR49cttgUuNGdCQoIkqaysTHFxcTp8%2BLDHvMvlUvv27RUc3Pp/ToiLi2vyOAQFBbmPVaC69NJL1dDQoODg4HM%2BFgPJ2b5u4uLi3I%2B5puY7dOjgy2Uak5CQoKKiooDJtm3bNj300EOaN2%2BebrnlFklq9jniXMe0NWkqW1MSEhL07bffyrKsgMkWFBSkxMRE5eXlaeLEicrMzAyY71%2B/Ruv/zoRzSk1NPeMcl6KiIvcJ8Q6HQ8XFxe65hoYG7d27t8kT5v2ptLRUjz32mE6fPu0eKykpkSR17txZDodDX375perr693zTqez1eU4G4fDoWPHjunEiRPuMafTqe7duysiIsKPK7swe/fu1cKFCz3GSkpKFBYWpszMzHM%2BFgOJw%2BE4I8tPj7fw8HD16NHD4%2BuqsrJSR44cUVpamq%2BXesFeffVVbd682WOspKREnTt3Dohsn3/%2BuWbPnq2lS5d6FJDmniPOdUxbi7Nl27Vrl1asWOFx24MHDyohIUFBQUGtOtuuXbs0YsQIj1M5fvqhOC0tzRbfv86GghXgRo0apdLSUq1Zs0anTp3Szp07tXPnTvf1srKzs/XGG29o9%2B7dqqmp0YoVKxQWFqZBgwb5d%2BG/0KFDB23btk0LFy7UDz/8oG%2B%2B%2BUYLFizQ4MGD1alTJ2VmZioyMlIrVqxQTU2N9uzZo7Vr1yo7O9vfSz8vycnJSk1N1eLFi1VVVaWSkhK9/PLLAbP%2Bn3To0EGFhYUqKCjQ6dOndejQIS1dulRZWVm6%2Beabz/lYDCQTJkzQRx99pB07dujUqVNau3atDh8%2BrNGjR0v68etq1apVKikpUVVVlfLz89WrVy%2Blpqb6eeXNO336tJ566ik5nU7V1dVp06ZNeu%2B99zRx4kRJrTtbfX29Hn30Uc2aNUsDBw70mGvuOaK5Y%2Bpv58oWFRWl5cuXa/369aqrq5PT6dSLL74YENkcDoeqqqq0aNEi1dTU6MSJE3r22Wd15ZVXKjs72xbfv87KT5eHwAUYPny45XA4rF69ellJSUmWw%2BGwHA6H9fXXX1uWZVmffPKJNXr0aCslJcUaPnz4GdeGWr16tZWZmWk5HA4rOzvb%2BvLLL/0Ro1n79%2B%2B3cnJyrH79%2Bln9%2BvWz5syZY508edI9/%2BWXX1oTJ060HA6HNWjQIGv16tV%2BXO2FO3bsmHX33XdbaWlpVkZGhvXnP/%2B51Vxb6EJ88sknVlZWlnXFFVdYAwYMsBYsWGDV1ta65871WGxNfvo6uvzyy63LL7/c/e%2BfvPPOO9bw4cOtlJQU6%2Babb7Y%2B%2BeQT91xjY6O1dOlS6%2Bqrr7bS0tKse%2B65p1Vcc%2Bgn58rW2NhoLV%2B%2B3Bo8eLDlcDis66%2B/3tq2bZv7vq0527/%2B9S%2BrZ8%2Be7jw///j666%2BbfY441zH1t%2BaybdmyxRo9erSVlpZmXXPNNdZzzz1nNTQ0uO/fmrPt37/fmjRpkpWWlmZdddVV1owZM6zjx49blmWf719NCbKsAD6DDAAAoBXiLUIAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMOz/AVay2wav6%2Bn1AAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common806858273980192695”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2596</td> <td class=”number”>80.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>69</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.333333333</td> <td class=”number”>35</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.0</td> <td class=”number”>20</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>18</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16.666666667</td> <td class=”number”>15</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>66.666666667</td> <td class=”number”>13</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14.285714286</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (61)</td> <td class=”number”>136</td> <td class=”number”>4.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>216</td> <td class=”number”>6.7%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme806858273980192695”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:67%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.333333333</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:83%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-25.0</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-20.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:67%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>125.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>133.333333333</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>150.0</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:28%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>18</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>300.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_VEG_ACRESPTH_07_12”>PCH_VEG_ACRESPTH_07_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2243</td>

</tr> <tr>

<th>Unique (%)</th> <td>69.9%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>30.2%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>968</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>-1.4568</td>

</tr> <tr>

<th>Minimum</th> <td>-43.114</td>

</tr> <tr>

<th>Maximum</th> <td>13.396</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram2999238983429112587”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives2999238983429112587”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles2999238983429112587”

aria-controls=”quantiles2999238983429112587” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram2999238983429112587” aria-controls=”histogram2999238983429112587”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common2999238983429112587” aria-controls=”common2999238983429112587”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme2999238983429112587” aria-controls=”extreme2999238983429112587”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles2999238983429112587”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-43.114</td>

</tr> <tr>

<th>5-th percentile</th> <td>-9.0041</td>

</tr> <tr>

<th>Q1</th> <td>-3.5725</td>

</tr> <tr>

<th>Median</th> <td>-0.87767</td>

</tr> <tr>

<th>Q3</th> <td>1.2833</td>

</tr> <tr>

<th>95-th percentile</th> <td>4.2241</td>

</tr> <tr>

<th>Maximum</th> <td>13.396</td>

</tr> <tr>

<th>Range</th> <td>56.509</td>

</tr> <tr>

<th>Interquartile range</th> <td>4.8558</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>4.2378</td>

</tr> <tr>

<th>Coef of variation</th> <td>-2.9089</td>

</tr> <tr>

<th>Kurtosis</th> <td>5.7643</td>

</tr> <tr>

<th>Mean</th> <td>-1.4568</td>

</tr> <tr>

<th>MAD</th> <td>3.1555</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-1.1795</td>

</tr> <tr>

<th>Sum</th> <td>-3266.2</td>

</tr> <tr>

<th>Variance</th> <td>17.959</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram2999238983429112587”>

<img 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/VefOndP27ds1YMAAVVZWKjU11ef1qamp2rp1qySpsrJSQ4YM8c6FhoaqZ8%2BecjqdGjp0aLPWr6mpUW1trc%2BYwxGlpKSkFh2PXcLCQn2%2B4tLomf/oWcvQN3s4HG2r3%2ByzlrM8YFVVVWn9%2BvXasGGDzp49q8GDB%2BvFF1/UTTfd5H1N3759NX/%2B/BYHrNDQUC1fvlwTJkzQiy%2B%2BKEm65ZZbNGvWLE2dOlWdO3f2eX18fLxcLpckye12Ky4uzmc%2BLi7OO98cpaWlWrFihc9YQUGBCgsLW3I4touNbW93CQGHnvmPnrUMfbNWQkK03SXYgn3mP8sD1tChQ3Xddddp8uTJuvfeexUfH9/kNdnZ2d4b0lvi66%2B/1pQpU3TXXXd5759asGCBZs%2Be3az3ezyeFq8tSaNHj1ZOTo7PmMMRJZfrbKs%2B12phYaGKjW2vU6fq1NDQaHc5AYGe%2BY%2BetQx9s0eg/RxvrWDYZ3aFYssD1rp163TLLbdc8nUffPBBi9fYt2%2Bfjh49qkceeURhYWHq0KGDCgsLNWLECN12221yu90%2Br3e5XEpMTJQkJSQkNJl3u93q3r17s9dPSkpqcjmwtva06usDc3M2NDQGbO12oWf%2Bo2ctQ9%2Bs1VZ7zT7zn%2BUXVVNSUjRlyhTt2LHDO/a73/1ODz74YJNg01INDQ1qbGz0ORP19ddfS5KysrKaPHahoqJCGRkZkqS0tDRVVlb6fNaBAwe88wAAAJdiecBauHChTp8%2BrW7dunnHBgwYoMbGRj3zzDNG1ujdu7eioqK0fPly1dXVyeVyadWqVerbt69GjBih6upqlZWV6fz58yovL1d5eblGjRolScrPz9eGDRv0/vvvq66uTqtWrVJ4eLgGDBhgpDYAABD8LL9E%2BPbbb2vz5s1KSEjwjiUnJ2vx4sW65557jKyRkJCg//iP/9Czzz6r/v37Kzw8XLfccovmz5%2BvK664QqtXr1ZxcbEWLFigLl26aNGiRbrxxhslSf3799cjjzyiGTNm6MSJE0pPT1dJSYkiIyON1AYAAIKf5QHr3LlzioiIaDIeGhqquro6Y%2BukpaXppZdeuuhc3759tXHjxu9979ixYzV27FhjtQAAgLbF8kuEffv21TPPPKMvv/zSO/b5559rwYIFPo9qAAAACFSWn8F67LHH9Itf/EI///nPFRMTo8bGRp09e1Zdu3b93jNOAAAAgcTygNW1a1e9/vrr%2BtOf/qS///3vCg0N1XXXXadbb71VYWFhVpcDAABgnC1/Kic8PFx33HGHHUsDAAD86CwPWP/4xz%2B0ZMkSffLJJzp37lyT%2BbfeesvqkgAAAIyy5R6smpoa3XrrrYqKirJ6eQAAgB%2Bd5QGroqJCb731lvdP0wAAAAQbyx/TcMUVV3DmCgAABDXLA9bkyZO1YsUKn78TCAAAEEwsv0T4pz/9Se%2B9955ee%2B01XX311QoN9c14f/zjH60uCQAAwCjLA1ZMTIz69%2B9v9bIAAACWsTxgLVy40OolAQAALGX5PViS9Nlnn2n58uWaM2eOd%2Bxvf/ubHaUAAAAYZ3nA2rdvn4YPH67t27dry5Ytkr55%2BOj48eN5yCgAAAgKlgespUuX6pe//KU2b96skJAQSd/8fcJnnnlGK1eutLocAAAA4ywPWB9//LHy8/MlyRuwJOmuu%2B5SVVWV1eUAAAAYZ3nA6tChw0X/BmFNTY3Cw8OtLgcAAMA4ywNWnz599PTTT%2BvMmTPescOHD6uoqEg///nPrS4HAADAOMsf0zBnzhw98MADyszMVENDg/r06aO6ujp1795dzzzzjNXlAAAAGGd5wLryyiu1ZcsWlZeX6/Dhw4qMjNR1112nfv36%2BdyTBQAAEKgsD1iS1K5dO91xxx12LA0AAPCjszxg5eTk/OCZKp6FBQAAAp3lAWvIkCE%2BAauhoUGHDx%2BW0%2BnUAw88YHU5AAAAxlkesGbPnn3R8W3btunPf/6zxdUAAACYZ8vfIryYO%2B64Q6%2B//rrdZQAAALTaZROwDhw4II/HY3cZAAAArWb5JcIxY8Y0Gaurq1NVVZUGDRpkdTkAAADGWR6wkpOTm/wWYUREhPLy8jRy5EirywEAADDO8oDF09oBAECwszxgbdiwodmvvffee3/ESgAAAH4clgesuXPnqrGxsckN7SEhIT5jISEhBCwAABCQLA9Yv/3tb7V27VpNmTJFKSkp8ng8OnTokNasWaNx48YpMzPT6pIAAACMsuUerJKSEnXu3Nk7dvPNN6tr166aOHGitmzZYnVJAAAARln%2BHKwjR44oLi6uyXhsbKyqq6utLgcAAMA4ywNWly5d9Mwzz8jlcnnHTp06pSVLluiaa66xuhwAAADjLL9E%2BNhjj2nWrFkqLS1VdHS0QkNDdebMGUVGRmrlypVWlwMAAGCc5QHr1ltv1e7du1VeXq7jx4/L4/Goc%2BfOuu2229ShQweja61atUovv/yyzpw5o5/%2B9KcqLi7W1VdfrX379mnJkiX67LPPdNVVV2ny5MkaPny4933r1q3Tyy%2B/rNraWqWkpGju3LlKS0szWhsAAAhelgcsSWrfvr1uv/12HT9%2BXF27dv1R1nj55Ze1adMmrVu3TklJSVq2bJl%2B97vf6aGHHtLUqVM1d%2B5cDRs2TO%2B%2B%2B64efvhhXXfddUpPT9fOnTu1fPly/fa3v1VKSorWrVunKVOmaPv27YqKivpRagUAAMHF8nuwzp07p6KiIvXu3Vt33323pG/uwZo0aZJOnTplbJ21a9dq5syZuv766xUTE6PHH39cjz/%2BuDZv3qzk5GTl5eUpIiJCWVlZysnJUVlZmSSptLRUubm5ysjIUGRkpCZNmiRJ2rVrl7HaAABAcLP8DNaiRYt08OBBLV68WP/2b//mHW9oaNDixYv15JNPtnqNzz//XEePHtWXX36pIUOG6MSJE8rMzNT8%2BfNVWVmp1NRUn9enpqZq69atkqTKykoNGTLEOxcaGqqePXvK6XRq6NChzVq/pqZGtbW1PmMOR5SSkpJaeWTWCgsL9fmKS6Nn/qNnLUPf7OFwtK1%2Bs89azvKAtW3bNv3%2B979XcnKyioqKJH3ziIaFCxfq3nvvNRKwjh8/Lkl688039cILL8jj8aiwsFCPP/64zp075/MMLkmKj4/3/laj2%2B1u8hiJuLg4n996vJTS0lKtWLHCZ6ygoECFhYUtORzbxca2t7uEgEPP/EfPWoa%2BWSshIdruEmzBPvOf5QHr7NmzSk5ObjKemJior776ysga3/7JnUmTJnnD1PTp0/Xggw8qKyur2e9vqdGjRysnJ8dnzOGIkst1tlWfa7WwsFDFxrbXqVN1amhotLucgEDP/EfPWoa%2B2SPQfo63VjDsM7tCseUB65prrtGf//xnZWZm%2BgSZN998U//0T/9kZI2OHTtK%2BubM2Le6dOkij8ejCxcuyO12%2B7ze5XIpMTFRkpSQkNBk3u12q3v37s1ePykpqcnlwNra06qvD8zN2dDQGLC124We%2BY%2BetQx9s1Zb7TX7zH%2BWX1QdO3aspk%2BfrmeffVaNjY164YUXNGvWLD322GN64IEHjKxx5ZVXKiYmRgcPHvSOVVdXq127dsrOzlZFRYXP6ysqKpSRkSFJSktLU2VlpXeuoaFBBw4c8M4DAABciuUBa/To0SoqKtL%2B/fsVFham559/XtXV1Vq8eLHy8/ONrOFwOJSXl6fnn39e//M//6MTJ05o5cqVGjZsmO677z5VV1errKxM58%2BfV3l5ucrLyzVq1ChJUn5%2BvjZs2KD3339fdXV1WrVqlcLDwzVgwAAjtQEAgOBn%2BSXCkydP6v7779f999//o64za9Ysff311xo5cqQuXLigwYMH6/HHH1d0dLRWr16t4uJiLViwQF26dNGiRYt04403SpL69%2B%2BvRx55RDNmzNCJEyeUnp6ukpISRUZG/qj1AgCA4BHiae0d3X7q3bu33nvvPYWEhFi5rO1qa0/bXYLfHI5QJSREy%2BU6y7X3ZqJn/qNnLRMsfbt72Tt2l%2BCXrTP62V2CpYJhn3XqZPavxDSX5ZcIMzMzvc%2BcAgAACEaWXyK86qqr9NRTT6mkpETXXHON2rVr5zO/ZMkSq0sCAAAwyvKA9emnn%2Br666%2BXJL8e3gkAABAoLAtYM2fO1NKlS/XSSy95x1auXKmCggKrSgAAALCEZfdg7dy5s8lYSUmJVcsDAABYxrKAdbFfVrT4FxgBAAAsYVnAuthjGdraoxoAAEDbYPljGgAAAIIdAQsAAMAwy36L8MKFC5o1a9Ylx3gOFgAACHSWBaybbrpJNTU1lxwDAAAIdJYFrP/7/CsAAIBgxj1YAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwh90FAADMunvZO3aXALR5nMECAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMKxNBKynn35aKSkp3u/37dunvLw89enTR0OHDtWmTZt8Xr9u3ToNHjxYffr0UX5%2BvioqKqwuGQAABLCgD1gHDx7Uxo0bvd/X1NRo6tSpGjNmjPbt26e5c%2Bdq3rx5cjqdkqSdO3dq%2BfLleu6557R3714NHDhQU6ZM0VdffWXXIQAAgAAT1AGrsbFRTzzxhCZMmOAd27x5s5KTk5WXl6eIiAhlZWUpJydHZWVlkqTS0lLl5uYqIyNDkZGRmjRpkiRp165ddhwCAAAIQEH9x57/%2BMc/KiIiQsOGDdOyZcskSZWVlUpNTfV5XWpqqrZu3eqdHzJkiHcuNDRUPXv2lNPp1NChQ5u1bk1NjWpra33GHI4oJSUlteZwLBcWFurzFZdGz/xHz1qGvtnD4Whb/WaftVzQBqwvvvhCy5cv10svveQz7na71blzZ5%2Bx%2BPh4uVwu73xcXJzPfFxcnHe%2BOUpLS7VixQqfsYKCAhUWFvpzCJeN2Nj2dpcQcOiZ/%2BhZy9A3ayUkRNtdgi3YZ/4L2oC1cOFC5ebmqlu3bjp69Khf7/V4PK1ae/To0crJyfEZczii5HKdbdXnWi0sLFSxse116lSdGhoa7S4nINAz/9GzlqFv9gi0n%2BOtFQz7zK5QHJQBa9%2B%2Bffrb3/6mLVu2NJlLSEiQ2%2B32GXO5XEpMTPzeebfbre7duzd7/aSkpCaXA2trT6u%2BPjA3Z0NDY8DWbhd65j961jL0zVpttdfsM/8F5UXVTZs26cSJExo4cKAyMzOVm5srScrMzFSPHj2aPHahoqJCGRkZkqS0tDRVVlZ65xoaGnTgwAHvPAAAwKUEZcB69NFHtW3bNm3cuFEbN25USUmJJGnjxo0aNmyYqqurVVZWpvPnz6u8vFzl5eUaNWqUJCk/P18bNmzQ%2B%2B%2B/r7q6Oq1atUrh4eEaMGCAjUcEAAACSVBeIoyLi/O5Ub2%2Bvl6SdOWVV0qSVq9ereLiYi1YsEBdunTRokWLdOONN0qS%2Bvfvr0ceeUQzZszQiRMnlJ6erpKSEkVGRlp/IAAAICAFZcD6rquvvlqHDh3yft%2B3b1%2Bfh49%2B19ixYzV27FgrSgMAAEEoKC8RAgAA2KlNnMECAMCEu5e9Y3cJftk6o5/dJbRZnMECAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYFjQBqzq6moVFBQoMzNTWVlZevTRR3Xq1ClJ0sGDBzVu3DjddNNNGjRokNauXevz3jfeeEPDhg1T7969lZubq7ffftuOQwAAAAEqaAPWlClTFBsbq507d%2Bq1117TJ598omeffVbnzp3T5MmT9bOf/Ux79uzR0qVLtXr1am3fvl3SN%2BGrqKhIs2fP1v79%2BzVhwgRNmzZNx48ft/mIAABAoAjKgHXq1CmlpaVp1qxZio6O1pVXXqn77rtPf/3rX7V7925duHBBDz/8sKKiotSrVy%2BNHDlSpaWlkqSysjJlZ2crOztbERERGj58uHr06KFNmzbZfFQAACBQOOwu4McQGxurhQsX%2BoxhzaaQAAAMTUlEQVQdO3ZMSUlJqqysVEpKisLCwrxzqampKisrkyRVVlYqOzvb572pqalyOp3NXr%2Bmpka1tbU%2BYw5HlJKSkvw9FFuFhYX6fMWl0TP/0bOWoW9oDoejdfuDfdZyQRmwvsvpdOr3v/%2B9Vq1apa1btyo2NtZnPj4%2BXm63W42NjXK73YqLi/OZj4uL06efftrs9UpLS7VixQqfsYKCAhUWFrb8IGwUG9ve7hICDj3zHz1rGfqGH5KQEG3kc9hn/gv6gPXuu%2B/q4Ycf1qxZs5SVlaWtW7de9HUhISHef/d4PK1ac/To0crJyfEZczii5HKdbdXnWi0sLFSxse116lSdGhoa7S4nINAz/9GzlqFvaI7W/ncnGPaZqZDpr6AOWDt37tQvf/lLzZs3T/fee68kKTExUUeOHPF5ndvtVnx8vEJDQ5WQkCC3291kPjExsdnrJiUlNbkcWFt7WvX1gbk5GxoaA7Z2u9Az/9GzlqFv%2BCGm9gb7zH9Be1H1vffeU1FRkX7zm994w5UkpaWl6dChQ6qvr/eOOZ1OZWRkeOcrKip8Puv/zgMAAFxKUAas%2Bvp6Pf7445o9e7ZuvfVWn7ns7GzFxMRo1apVqqur0wcffKD169crPz9fkjRq1Cjt3btXu3fv1vnz57V%2B/XodOXJEw4cPt%2BNQAABAAArKS4Tvv/%2B%2BqqqqVFxcrOLiYp%2B5N998U88//7yeeOIJlZSUqGPHjpo5c6YGDBggSerRo4cWL16shQsXqrq6Wt26ddPq1avVqVMnG44EAAAEoqAMWDfffLMOHTr0g6/5wx/%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%2BPBhnTlzRjExMZf8jJqaGtXW1vqMORxRSkpKMl7vjyksLNTnKy6NngFAyzgcwfVzk4D1HW63W7GxsT5j34Ytl8vVrIBVWlqqFStW%2BIxNmzZN06dPN1eoBWpqavTii7/V6NGjAy4c2oWeNc9fn7rL%2B%2B81NTUqLS2lZ36ib/6jZ/6jZy0XXHHREI/H06r3jx49Wq%2B99prPP6NHjzZUnXVqa2u1YsWKJmfj8P3omf/oWcvQN//RM//Rs5bjDNZ3JCYmyu12%2B4y53W6FhIQoMTGxWZ%2BRlJRE0gcAoA3jDNZ3pKWl6dixYzp58qR3zOl0qlu3boqOjraxMgAAECgIWN%2BRmpqq9PR0LVmyRGfOnFFVVZVeeOEF5efn210aAAAIEGHz58%2Bfb3cRl5vbbrtNW7Zs0a9//Wu9/vrrysvL08SJExUSEmJ3aZaLjo7WLbfcwtk7P9Az/9GzlqFv/qNn/qNnLRPiae0d3QAAAPDBJUIAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhY%2BEEvvviiUlJSdPToUe/YwYMHNW7cON10000aNGiQ1q5da2OFl4ejR49q6tSpuuWWW5SZmakHH3xQhw8f9s7Ts6ZcLpeKiorUr18/ZWZmatq0aTp27Jh3vrq6Wg899JAyMzM1cOBALVq0SI2NjTZWfHlwOp268847NWrUqCZz%2B/btU15envr06aOhQ4dq06ZNNlR4%2BWEvNc%2BePXuUlZWlmTNnNpl74403NGzYMPXu3Vu5ubl6%2B%2B23bagwsBCw8L0%2B//zzJkHg3Llzmjx5sn72s59pz549Wrp0qVavXq3t27fbVOXloaCgQB07dtSuXbv01ltvKSYmxvtDip5d3Jw5c/TFF19o8%2BbN2rZtmy5cuKA5c%2BZ456dPn67OnTtrx44deuGFF7Rjxw69%2BOKLNlZsv02bNmn69Om69tprm8zV1NRo6tSpGjNmjPbt26e5c%2Bdq3rx5cjqdNlR6eWEvXdqaNWtUXFx80b118OBBFRUVafbs2dq/f78mTJigadOm6fjx4zZUGjgIWPheTz31lMaMGeMztnv3bl24cEEPP/ywoqKi1KtXL40cOVKlpaU2VWm/r7/%2BWuPGjdOsWbMUHR2tmJgY3XPPPfr000/l8Xjo2UV4PB517txZRUVFSkxMVHx8vMaMGaN3331XHo9HTqdTH330kWbPnq0OHTooOTlZEyZMaNM9k6Tz58%2BrtLRUGRkZTeY2b96s5ORk5eXlKSIiQllZWcrJyVFZWZkNlV4%2B2EvNExERofXr1180YJWVlSk7O1vZ2dmKiIjQ8OHD1aNHD86QXgIBCxdVXl6uQ4cOaeLEiT7jlZWVSklJUVhYmHcsNTVVFRUVVpd42QgPD9fIkSMVFxcnSTp27Jj%2B8z//U3fddZdCQkLo2UWEhIRowYIF6tGjh3fs2LFj6tSpk7dnXbp08fZUknr16qXDhw/rzJkzdpR8WRg5cqQ6d%2B580bnKykqlpqb6jLX1fSaJvdRM48ePV4cOHS469317i7OjP4yAhSbOnTunX//61/rVr36l8PBwnzm3263Y2Fifsfj4eLndbu5pkJSWlqYBAwaoffv2evLJJyXRs%2BY4evSofvOb3%2Bjhhx%2BWdPGeffsfSJfLZXl9geD79llb7xd7qfXcbrdPQJW%2B6SH9%2B2EErDZo48aNSklJueg/r732mlatWqW0tDT169ev2Z8ZEhLyI1Zsv0v17FsVFRUqLy9Xu3btNHHixB8MUPTsG1VVVRo3bpzuu%2B8%2BjRw50jvu8XjsKNtWze0Z/NMW95Jp9NB/DrsLgPVGjBihESNGXHSuqqpKixYt0oYNGy46n5iYqCNHjviMud1uxcfHKzQ0ePP6D/Xsu6688krNmTNHt912myorK%2BnZD/jwww/14IMP6he/%2BIUmT57sHU9MTJTb7fZ5rdvtVkhIiBITE3%2BUei8H/uyz70pISGjSM5fLFdT9ao62updMutjecrvd9O8SgvenO1pk69atOn36tIYPH67MzExlZmZKknJzc7VmzRqlpaXp0KFDqq%2Bv977H6XRe9KbbtuKzzz5Tdna2z%2Bnyb4NTu3bt6Nn3OHLkiB566CEVFRX5hCvpm0utx44d08mTJ71jTqdT3bp1U3R0tNWlBoT09PQm91tVVFS0%2BX3GXmq9tLS0JnuLn2GXRsCCjwkTJmjHjh3auHGj9x9JKikpUX5%2BvrKzsxUTE6NVq1aprq5OH3zwgdavX6/8/HybK7fPtddeqw4dOqi4uFinTp3SmTNntGTJEl1zzTW6/vrr6dn3ePLJJzVq1Cjl5uY2mUtNTVV6erqWLFmiM2fOqKqqSi%2B88EKb79kPGTZsmKqrq1VWVqbz58%2BrvLxc5eXlF31eVlvCXmq9UaNGae/evdq9e7fOnz%2Bv9evX68iRIxo%2BfLjdpV3WQjxcWMUlpKSk6K233tLVV18tSfr444/1xBNPqKKiQh07dtSDDz6osWPH2lylvaqrq1VcXKz9%2B/crPDxcP/nJT/Too4/qhhtukETPvuvYsWMaMGCA2rVr1%2BRetLVr16pv3746fvy45s2bp//%2B7/9WTEyMxowZo2nTpgX9vWs/ZPDgwfrf//1fNTQ0qLGxUe3atZMkvfnmm%2BrSpYv%2B8pe/qLi4WFVVVerSpYtmzZqlQYMG2Vy1/dhLl5aeni5J3jPtDsc3dxB9%2B5uC27dv15IlS1RdXa1u3bpp7ty56tu3rz3FBggCFgAAgGFcIgQAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAw/4fxIJkRcETGcEAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common2999238983429112587”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>2.1156263091746883</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.548062973510286</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.6563057153429522</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.780922176140511</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-4.73644935893087</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-7.021709449426987</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-3.2539974091761596</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.4870623967317869</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.7429535997487655</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.09903487709397067</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2232)</td> <td class=”number”>2232</td> <td class=”number”>69.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>968</td> <td class=”number”>30.2%</td> <td>

<div class=”bar” style=”width:43%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme2999238983429112587”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-43.113541641571715</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-26.345885267068947</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-21.258723334847897</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-20.120450277775085</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-19.92289766679134</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>10.44776119402985</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.892116182572611</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.847672778561352</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.188528443817589</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.39568562377434</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_VEG_ACRES_07_12”>PCH_VEG_ACRES_07_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>1718</td>

</tr> <tr>

<th>Unique (%)</th> <td>53.5%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>35.9%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>1153</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>59.285</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>22109</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.5%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-8584789892322565798”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-8584789892322565798,#minihistogram-8584789892322565798”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-8584789892322565798”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-8584789892322565798”

aria-controls=”quantiles-8584789892322565798” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-8584789892322565798” aria-controls=”histogram-8584789892322565798”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-8584789892322565798” aria-controls=”common-8584789892322565798”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-8584789892322565798” aria-controls=”extreme-8584789892322565798”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-8584789892322565798”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-76.544</td>

</tr> <tr>

<th>Q1</th> <td>-29.93</td>

</tr> <tr>

<th>Median</th> <td>6.6667</td>

</tr> <tr>

<th>Q3</th> <td>60.615</td>

</tr> <tr>

<th>95-th percentile</th> <td>275.47</td>

</tr> <tr>

<th>Maximum</th> <td>22109</td>

</tr> <tr>

<th>Range</th> <td>22209</td>

</tr> <tr>

<th>Interquartile range</th> <td>90.545</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>521.24</td>

</tr> <tr>

<th>Coef of variation</th> <td>8.7922</td>

</tr> <tr>

<th>Kurtosis</th> <td>1560.2</td>

</tr> <tr>

<th>Mean</th> <td>59.285</td>

</tr> <tr>

<th>MAD</th> <td>108.21</td>

</tr> <tr class=”alert”>

<th>Skewness</th> <td>37.151</td>

</tr> <tr>

<th>Sum</th> <td>121950</td>

</tr> <tr>

<th>Variance</th> <td>271690</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-8584789892322565798”>

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9bFREdH6%2BDBg2rRooWCg4PldDrd5p1Op1q2bKnIyEiVlpaqqqpKISEhrjlJatmyZa2OFRUVVeNyoN1eosrKhveF/V0NZf1VVdUNZq2%2BjD74DnrhG%2BiDZwXkBdmXX35ZW7dudRsrKChQ%2B/btFRoaqq5duyo/P981V1xcrJMnT6pHjx7q3r27DMPQJ5984pq32WyyWq3q1KmTx9YAAAD8V0AGrAsXLmjhwoWy2WyqqKhQXl6e3n77bY0dO1aSlJaWpnXr1qmgoEClpaXKyspS9%2B7dlZCQoMjISA0dOlQrV67U2bNndebMGa1evVopKSmyWBrMCT8AAFAHfpsYEhISJEmVlZWSpJ07d0r65mzTuHHjVFZWpqlTp8put6tdu3ZavXq14uPjJUljx46V3W7Xfffdp7KyMiUmJrrdlP7EE0/o8ccf1%2BDBg9WoUSPdeeedNR5mCgAAcClBRl3v6Eat2O0l9bLf21fuq5f91odt0/p5u4R6ZbEEKyKimRyOMu5z8CL64DvohW9o6H1o3bq5V44bkJcIAQAAvImABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyTwesAYNGqTs7GwVFhZ6%2BtAAAAAe4fGAdffdd2vr1q265ZZb9MADD2jHjh2qrKz0dBkAAAD1xuMBKz09XVu3btVf/vIXde3aVU899ZSSk5O1bNkyHT9%2B3NPlAAAAmM5r92DFxcUpMzNTb731lmbPnq2//OUvuuOOOzRhwgQdOHDgiq/fu3evkpKSlJGRUWNux44dGj58uHr37q2hQ4fqL3/5i2vu6aefVvfu3ZWQkOD28cUXX0iSzp8/r9/85jcaMGCAEhMTNWXKFDkcDvMWDgAAAp7XAlZFRYW2bt2qX/3qV8rMzFSbNm302GOPqXv37ho/fry2bNlyydc%2B%2B%2ByzWrRokTp06FBj7sCBA5o5c6amTJmi999/X7Nnz9YTTzyhDz74wLXNiBEjZLPZ3D5atWolSVqxYoXy8/OVm5ur7du3yzAMPfbYY%2Ba/AQAAIGBZPH3AgoICbdy4Ua%2B%2B%2BqrKyso0dOhQvfjii7ruuutc2/Tt21fz58/XXXfdddF9hIaGauPGjXryySd1/vx5tzmn06mJEyfqlltukSQlJyerW7du%2BuCDD9SnT5/L1lZZWamNGzdq6dKluvrqqyVJ06ZN07Bhw/T555%2BrTZs2dVk6AABoIDwesIYNG6ZOnTpp4sSJGjlypFq0aFFjm%2BTkZJ09e/aS%2Bxg3btwl5wYMGKABAwa4/l1ZWSm73e4Wjo4cOaKxY8fq008/1dVXX63HHntM/fv318mTJ1VSUqK4uDjXtp07d1aTJk2Un59PwAIAALXi8YC1bt06XX/99Vfc7uOPPzbleFlZWWratKnuuOMOSVLbtm3Vvn17zZgxQ1FRUcrNzdWkSZO0efNmOZ1OSZLVanXbh9Vq/UH3YRUVFclut7uNWSxNFRUVVcfV%2BDeLJbAfuxYSEuz2Gd5BH3wHvfAN9ME7PB6wYmJiNGnSJKWkpLgu4/3hD3/Qvn37tGzZsoue0foxDMNQVlaW8vLytG7dOoWGhkqS7rnnHt1zzz2u7caPH6/XX39dmzdvdp35MgyjTsfOzc1Vdna221h6erqmTJlSp/36u4iIZt4uwSOs1qu8XQJEH3wJvfAN9MGzPB6wFi9erJKSEnXp0sU1NnDgQO3du1dLlizRkiVL6nyM6upqPfbYYzpw4IBefvlltW/f/rLbR0dHq6ioSJGRkZK%2BuY%2BrWbP/CwNfffWVWrZsWevjp6amatCgQW5jFktTORxlP2AVgSfQ1x8SEiyr9SoVF5erqqra2%2BU0WPTBd9AL39DQ%2B%2BCtH%2B49HrDeeecdbdmyRREREa6xjh07KisrS3feeacpx3jqqaf073//Wy%2B//HKNM2L/8z//o969e%2BvGG290jRUUFOiOO%2B5Q%2B/btFR4ervz8fEVHR0uSPv30U124cEHx8fG1Pn5UVFSNy4F2e4kqKxveF/Z3NZT1V1VVN5i1%2BjL64DvohW%2BgD57l8Quy586dc12ucyskOFjl5eV13v%2BHH36ozZs3Kycn56KXG51OpxYsWKBjx47p/Pnzev7553Xy5EmNGjVKISEhGjNmjJ555hkVFhbK4XDot7/9rW699VbXYxwAAACuxONnsPr27aslS5ZoxowZCg8PlyR9/vnnWrp0qdujGi4nISFBklx/Ymfnzp2SJJvNpldeeUUlJSW6%2Beabaxz3%2Beef14wZMyR9c%2B%2BV0%2BlUly5d9Ic//EFt27aVJE2ZMkVlZWUaMWKEKisrdfPNN2v%2B/Pl1XjcAAGg4goy63tH9A506dUr333%2B/Tp8%2BrbCwMFVXV6usrEzt27fXH//4x4B9FILdXlIv%2B7195b562W992Datn7dLqFcWS7AiIprJ4SjjNLwX0QffQS98Q0PvQ%2BvWzb1yXI%2BfwWrfvr1ef/11vf322zp58qSCg4PVqVMn9e/fXyEhIZ4uBwAAwHQeD1iS1LhxY9cjGgAAAAKNxwPWqVOntHz5cv373//WuXPnasy/%2Beabni4JAADAVB4PWLNnz1ZRUZH69%2B%2Bvpk2bevrwAAAA9c7jAevgwYN68803XQ/1BAAACDQefw5Wy5YtOXMFAAACmscD1sSJE5WdnV3nv/cHAADgqzx%2BifDtt9/WRx99pE2bNqldu3YKDnbPeH/%2B8589XRIAAICpPB6wwsLCNGDAAE8fFgAAwGM8HrAWL17s6UMCAAB4lMfvwZKkY8eO6emnn9Zjjz3mGvvXv/7ljVIAAABM5/GA9e6772r48OHasWOH8vLyJH3z8NFx48bxkFEAABAQPB6wVqxYoUcffVRbtmxRUFCQpG/%2BPuGSJUu0evVqT5cDAABgOo8HrE8//VRpaWmS5ApYknTbbbepoKDA0%2BUAAACYzuMBq3nz5hf9G4RFRUVq3Lixp8sBAAAwnccD1rXXXqunnnpKpaWlrrHjx48rMzNTN954o6fLAQAAMJ3HH9Pw2GOP6Re/%2BIUSExNVVVWla6%2B9VuXl5eratauWLFni6XIAAABM5/GA1bZtW%2BXl5WnPnj06fvy4mjRpok6dOqlfv35u92QBAAD4K48HLElq1KiRbrnlFm8cGgAAoN55PGANGjTosmeqeBYWAADwdx4PWHfccYdbwKqqqtLx48dls9n0i1/8wtPlAAAAmM7jAWvmzJkXHd%2B%2Bfbv%2B%2Bc9/ergaAAAA83nlbxFezC233KLXX3/d22UAAADUmc8ErEOHDskwDG%2BXAQAAUGcev0Q4duzYGmPl5eUqKCjQkCFDPF0OAACA6TwesDp27FjjtwhDQ0OVkpKie%2B65x9PlAAAAmM7jAcvMp7Xv3btXmZmZSkxM1IoVK9zmtm7dqjVr1uizzz5Tp06dNH36dPXv31%2BSVF1drVWrVikvL0/FxcXq0aOH5s%2Bfr/bt20uSnE6n5s%2Bfr/fee0/BwcFKTk7WvHnz1KRJE9NqBwAAgcvjAevVV1%2Bt9bYjR4685Nyzzz6rjRs3qkOHDjXmDh8%2BrMzMTGVnZ%2BuGG27Q9u3b9fDDD%2BuNN95Q27ZttX79em3ZskXPPvus2rRpoxUrVig9PV2vvfaagoKCNG/ePF24cEF5eXmqqKjQ1KlTlZWVpblz5/6oNQMAgIbF4wFrzpw5qq6urnFDe1BQkNtYUFDQZQNWaGioNm7cqCeffFLnz593m9uwYYOSk5OVnJwsSRo%2BfLheeuklbd68WQ8%2B%2BKByc3M1fvx4de7cWZKUkZGhxMREffzxx2rXrp127typv/71r4qMjJQkPfTQQ5o6daoyMzPVqFEjU94HAAAQuDwesJ577jk9//zzmjRpkmJiYmQYho4cOaJnn31W9957rxITE2u1n3Hjxl1yLj8/3xWuvhUbGyubzaZz587p6NGjio2Ndc2FhYWpQ4cOstlsKikpUUhIiGJiYlzzcXFx%2Bvrrr3Xs2DG3cQAAgIvxyj1YOTk5atOmjWusT58%2Bat%2B%2BvSZMmKC8vLw6H8PpdCo8PNxtLDw8XEePHtVXX30lwzAuOu9wONSiRQuFhYW53Yj/7bYOh6NWxy8qKpLdbncbs1iaKioq6scsJ2BYLD7zVJB6ERIS7PYZ3kEffAe98A30wTs8HrBOnDhRI9xIktVq1enTp007zpWeqXW5%2Bbo%2Bjys3N1fZ2dluY%2Bnp6ZoyZUqd9uvvIiKaebsEj7Bar/J2CRB98CX0wjfQB8/yeMCKjo7WkiVLNHXqVEVEREiSiouL9bvf/U4//elPTTlGRESEnE6n25jT6VRkZKRatGih4ODgi863bNlSkZGRKi0tVVVVlUJCQlxzktSyZctaHT81NVWDBg1yG7NYmsrhKPuxSwoIgb7%2BkJBgWa1Xqbi4XFVV1d4up8GiD76DXviGht4Hb/1w7/GANXv2bM2YMUO5ublq1qyZgoODVVpaqiZNmmj16tWmHCM%2BPl4HDx50G7PZbBo2bJhCQ0PVtWtX5efn6/rrr5f0TcA7efKkevTooejoaBmGoU8%2B%2BURxcXGu11qtVnXq1KlWx4%2BKiqpxOdBuL1FlZcP7wv6uhrL%2BqqrqBrNWX0YffAe98A30wbM8HrD69%2B%2Bv3bt3a8%2BePTpz5owMw1CbNm100003qXnz5qYcY8yYMUpJSdHu3bt14403asuWLTpx4oSGDx8uSUpLS1NOTo4GDBigNm3aKCsrS927d1dCQoIkaejQoVq5cqWWLl2qCxcuaPXq1UpJSZHF4vG3CwAA%2BCGvJIarrrpKgwcP1pkzZ1wP9/yhvg1DlZWVkqSdO3dK%2BuZsU7du3ZSVlaXFixfr9OnT6tKli9auXavWrVtL%2BubP9djtdt13330qKytTYmKi2z1TTzzxhB5//HENHjxYjRo10p133qmMjIy6LBkAADQgQYaH/8LyuXPn9Pjjj%2Bv111%2BXJB08eFDFxcWaPn26fvvb38pqtXqyHI%2Bx20vqZb%2B3r9xXL/utD9um9fN2CfXKYglWREQzORxlnIb3IvrgO%2BiFb2jofWjd2pyrYz%2BUx39nc9myZTp8%2BLCysrIUHPx/h6%2BqqlJWVpanywEAADCdxwPW9u3b9bvf/U633Xab61lTVqtVixcv1o4dOzxdDgAAgOk8HrDKysrUsWPHGuORkZH6%2BuuvPV0OAACA6TwesH7605/qn//8pyT3B3q%2B8cYb%2BslPfuLpcgAAAEzn8d8i/PnPf65HHnlEd999t6qrq/XCCy/o4MGD2r59u%2BbMmePpcgAAAEzn8YCVmpoqi8Wil156SSEhIXrmmWfUqVMnZWVl6bbbbvN0OQAAAKbzeMA6e/as7r77bt19992ePjQAAIBHePwerMGDB9f5jykDAAD4Mo8HrMTERG3bts3ThwUAAPAYj18ivPrqq/Xkk08qJydHP/3pT9WoUSO3%2BeXLl3u6JAAAAFN5PGAdPXpUP/vZzyRJDofD04cHAACodx4LWBkZGVqxYoX%2B%2BMc/usZWr16t9PR0T5UAAADgER67B2vXrl01xnJycjx1eAAAAI/xWMC62G8O8tuEAAAgEHksYH37h52vNAYAAODvPP6YBgAAgEBHwAIAADCZx36LsKKiQjNmzLjiGM/BAgAA/s5jAeu6665TUVHRFccAAAD8nccC1neffwUAABDIuAcLAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJN57DENnvT%2B%2B%2B/r/vvvdxszDEMVFRVat26dxo0bp8aNG7vN//d//7duv/12SdK6deu0fv162e12xcTEaM6cOYqPj/dY/QAAwL8FZMDq27evbDab29gzzzyjTz75RJIUHR2tXbt2XfS1u3bt0tNPP63nnntOMTExWrdunSZNmqQdO3aoadOm9V47AADwfw3iEuF//vMfvfDCC/r1r399xW1zc3M1evRo9ezZU02aNNEDDzwgSXrrrbfqu0wAABAgGkTAWrVqle6%2B%2B2795Cc/kSSVlZUpPT1diYmJuummm/TCCy/IMAxJUn5%2BvmJjY12vDQ4OVvfu3WucEQMAALiUgLxE%2BF2fffaZduzYoR07dkiSwsLC1K1bN/3iF7/QihUr9N5772nq1Klq3ry5UlJS5HQ6FR4e7raP8PBwORyOWh%2BzqKhIdrvdbcxiaaqoqKi6L8iPWSyBnedDQoLdPsM76IPvoBdCDlsnAAAWOklEQVS%2BgT54R8AHrPXr12vIkCFq3bq1JCkuLs7t7yL2799fY8eO1aZNm5SSkiJJrrNZP1Zubq6ys7PdxtLT0zVlypQ67dffRUQ083YJHmG1XuXtEiD64EvohW%2BgD54V8AFr%2B/btyszMvOw20dHR2r59uyQpIiJCTqfTbd7pdKpr1661PmZqaqoGDRrkNmaxNJXDUVbrfQSiQF9/SEiwrNarVFxcrqqqam%2BX02DRB99BL3xDQ%2B%2BDt364D%2BiAdfjwYZ0%2BfVr9%2BvVzjW3btk0Oh0M///nPXWPHjh1T%2B/btJUnx8fHKz8/XqFGjJElVVVU6dOiQ6%2BxWbURFRdW4HGi3l6iysuF9YX9XQ1l/VVV1g1mrL6MPvoNe%2BAb64FkBfUH20KFDatGihcLCwlxjjRo10tKlS/XOO%2B%2BooqJC%2B/bt0yuvvKK0tDRJUlpaml599VXt379f5eXlWrNmjRo3bqyBAwd6aRUAAMDfBPQZrC%2B%2B%2BMJ179W3brnlFs2ePVsLFy5UYWGhWrVqpdmzZ2vIkCGSpAEDBmj69OmaNm2avvzySyUkJCgnJ0dNmjTxxhIAAIAfCjLqekc3asVuL6mX/d6%2Bcl%2B97Lc%2BbJvW78ob%2BTGLJVgREc3kcJRxGt6L6IPvoBe%2BoaH3oXXr5l45bkBfIgQAAPAGAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJAjZgxcTEKD4%2BXgkJCa6PhQsXSpLeffddpaSk6Nprr9WwYcO0efNmt9euW7dOQ4cO1bXXXqu0tDQdPHjQG0sAAAB%2ByuLtAurTG2%2B8oXbt2rmNFRUV6aGHHtKcOXN011136cMPP9TkyZPVqVMnJSQkaNeuXXr66af13HPPKSYmRuvWrdOkSZO0Y8cONW3a1EsrAQAA/iRgz2BdypYtW9SxY0elpKQoNDRUSUlJGjRokDZs2CBJys3N1ejRo9WzZ081adJEDzzwgCTprbfe8mbZAADAjwR0wFq%2BfLkGDhyoPn36aN68eSorK1N%2Bfr5iY2PdtouNjXVdBvz%2BfHBwsLp37y6bzebR2gEAgP8K2EuEvXr1UlJSkpYuXapTp05p2rRpWrBggZxOp9q0aeO2bYsWLeRwOCRJTqdT4eHhbvPh4eGu%2BdooKiqS3W53G7NYmioqKupHriYwWCwBnecVEhLs9hneQR98B73wDfTBOwI2YOXm5rr%2Bu3Pnzpo5c6YmT56s66677oqvNQyjzsfOzs52G0tPT9eUKVPqtF9/FxHRzNsleITVepW3S4Dogy%2BhF76BPnhWwAas72vXrp2qqqoUHBwsp9PpNudwOBQZGSlJioiIqDHvdDrVtWvXWh8rNTVVgwYNchuzWJrK4Sj7kdUHhkBff0hIsKzWq1RcXK6qqmpvl9Ng0QffQS98Q0Pvg7d%2BuA/IgHXo0CFt3rxZs2bNco0VFBSocePGSk5O1l//%2Ble37Q8ePKiePXtKkuLj45Wfn69Ro0ZJkqqqqnTo0CGlpKTU%2BvhRUVE1Lgfa7SWqrGx4X9jf1VDWX1VV3WDW6svog%2B%2BgF76BPnhWQF6QbdmypXJzc5WTk6MLFy7o%2BPHjWrVqlVJTUzVixAidPn1aGzZs0Pnz57Vnzx7t2bNHY8aMkSSlpaXp1Vdf1f79%2B1VeXq41a9aocePGGjhwoHcXBQAA/EZAnsFq06aNcnJytHz5cldAGjVqlDIyMhQaGqq1a9dq0aJFWrBggaKjo7Vs2TJdc801kqQBAwZo%2BvTpmjZtmr788kslJCQoJydHTZo08fKqAACAvwgy6npHN2rFbi%2Bpl/3evnJfvey3Pmyb1s/bJdQriyVYERHN5HCUcRrei%2BiD76AXvqGh96F16%2BZeOW5AXiIEAADwJgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQI2YJ0%2BfVrp6elKTExUUlKSZs2apeLiYn322WeKiYlRQkKC28fvf/9712u3bt2qu%2B66S71799bo0aP1zjvveHElAADA31i8XUB9mTRpkuLj47Vr1y6VlJQoPT1dS5cu1eTJkyVJNpvtoq87fPiwMjMzlZ2drRtuuEHbt2/Xww8/rDfeeENt27b15BIAAICfCsgzWMXFxYqPj9eMGTPUrFkztW3bVqNGjdIHH3xwxddu2LBBycnJSk5OVmhoqIYPH65u3bpp8%2BbNHqgcAAAEgoAMWFarVYsXL1arVq1cY4WFhYqKinL9%2B9e//rX69%2B%2BvG264QcuXL1dFRYUkKT8/X7GxsW77i42NveQZLwAAgO8L2EuE32Wz2fTSSy9pzZo1aty4sXr37q1bb71VTz75pA4fPqxHHnlEFotFU6dOldPpVHh4uNvrw8PDdfTo0Vofr6ioSHa73W3MYmnqFvAaIoslIPO8S0hIsNtneAd98B30wjfQB%2B8I%2BID14YcfavLkyZoxY4aSkpIkSX/%2B859d8z169NDEiRO1du1aTZ06VZJkGEadjpmbm6vs7Gy3sfT0dE2ZMqVO%2B/V3ERHNvF2CR1itV3m7BIg%2B%2BBJ64Rvog2cFdMDatWuXHn30Uc2bN08jR4685HbR0dH64osvZBiGIiIi5HQ63eadTqciIyNrfdzU1FQNGjTIbcxiaSqHo%2ByHLSDABPr6Q0KCZbVepeLiclVVVXu7nAaLPvgOeuEbGnofvPXDfcAGrI8%2B%2BkiZmZlatWqV%2Bvfv7xp/9913tX//ftdvE0rSsWPHFB0draCgIMXHx%2BvgwYNu%2B7LZbBo2bFitjx0VFVXjcqDdXqLKyob3hf1dDWX9VVXVDWatvow%2B%2BA564Rvog2cF5AXZyspKzZ07VzNnznQLV5LUvHlzrV69Wq%2B99poqKipks9n0%2B9//XmlpaZKkMWPG6O9//7t2796t8%2BfPa%2BPGjTpx4oSGDx/ujaUAAAA/FGTU9YYjH/TBBx/ov/7rv9S4ceMac2%2B88YYOHTqk7OxsnThxQs2bN9d9992nX/3qVwoO/iZv7tixQ8uXL9fp06fVpUsXzZkzR3379q1TTXZ7SZ1efym3r9xXL/utD9um9fN2CfXKYglWREQzORxl/JToRfTBd9AL39DQ%2B9C6dXOvHDcgLxH26dNHR44cueR8dHS0br311kvODxkyREOGDKmP0gAAQAMQkJcIAQAAvImABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWBdx%2BvRpPfjgg0pMTNTNN9%2BsZcuWqbq62ttlAQAAP2HxdgG%2B6JFHHlFcXJx27typL7/8UhMnTlSrVq30y1/%2B0tulAQAAP8AZrO%2Bx2Wz65JNPNHPmTDVv3lwdO3bU%2BPHjlZub6%2B3SAACAn%2BAM1vfk5%2BcrOjpa4eHhrrG4uDgdP35cpaWlCgsLu%2BI%2BioqKZLfb3cYslqaKiooyvV5/YrEEdp4PCQl2%2BwzvoA%2B%2Bg174BvrgHQSs73E6nbJarW5j34Yth8NRq4CVm5ur7Oxst7GHH35YjzzyiHmF/n8fPHmbioqKlJubq9TU1AYf4rypqKhIL774HH3wMvrgO%2BiFb6AP3kGcvQjDMOr0%2BtTUVG3atMntIzU11aTqarLb7crOzq5x1gyeRR98A33wHfTCN9AH7%2BAM1vdERkbK6XS6jTmdTgUFBSkyMrJW%2B4iKiuKnBAAAGjDOYH1PfHy8CgsLdfbsWdeYzWZTly5d1KxZMy9WBgAA/AUB63tiY2OVkJCg5cuXq7S0VAUFBXrhhReUlpbm7dIAAICfCJk/f/58bxfha2666Sbl5eVp4cKFev3115WSkqIJEyYoKCjI26VdUrNmzXT99ddzls3L6INvoA%2B%2Bg174BvrgeUFGXe/oBgAAgBsuEQIAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyA5cdOnz6tBx98UImJibr55pu1bNkyVVdXe7usgBATE6P4%2BHglJCS4PhYuXChJevfdd5WSkqJrr71Ww4YN0%2BbNm91eu27dOg0dOlTXXnut0tLSdPDgQdfc%2BfPn9Zvf/EYDBgxQYmKipkyZIofD4dG1%2Bbq9e/cqKSlJGRkZNea2bt2qu%2B66S71799bo0aP1zjvvuOaqq6u1YsUKDR48WH379tWECRN06tQp17zT6dS0adOUlJSk/v37a86cOTp37pxr/vDhw7r33nt13XXXaciQIXr%2B%2Befrd6E%2B7lJ92LRpk6655hq3742EhAQdOHBAEn0w2%2BnTp5Wenq7ExEQlJSVp1qxZKi4ulnTl96o%2Bv19QCwb81qhRo4y5c%2BcaxcXFxvHjx40hQ4YYzz//vLfLCgjdunUzTp06VWP8888/N3r16mVs2LDBOHfunLFv3z6jR48exoEDBwzDMIw333zT6NOnj7F//36jvLzcWLt2rdGvXz%2BjrKzMMAzDWLx4sTF69GjjP//5j%2BFwOIyHH37YmDhxokfX5stycnKMIUOGGGPHjjWmTZvmNnfo0CEjPj7e2L17t3Hu3DnjtddeM3r27GkUFhYahmEY69atM26%2B%2BWbj6NGjRklJifHEE08Yd911l1FdXW0YhmE8/PDDxoMPPmh8%2BeWXxpkzZ4zU1FRj4cKFhmEYRnl5uXHTTTcZTz/9tFFWVmYcPHjQuP76643t27d79g3wEZfrwyuvvGLce%2B%2B9l3wtfTDXnXfeacyaNcsoLS01CgsLjdGjRxuzZ8%2B%2B4ntVn98vqB0Clp86cOCA0b17d8PpdLrG/vSnPxlDhw71YlWB41IB67nnnjNGjhzpNjZt2jRj3rx5hmEYxoMPPmg89dRTrrmqqiqjX79%2BRl5enlFRUWFcd911xs6dO13zR48eNWJiYowzZ87U00r8y4svvmgUFxcbmZmZNf7HvmDBAiM9Pd1t7J577jHWrl1rGIZhDBs2zHjxxRddcyUlJUZsbKzxr3/9y7Db7cY111xjHD582DW/Z88eo1evXsaFCxeMbdu2GTfccINRWVnpml%2B2bJlx//3318cyfd7l%2BnClgEUfzPPVV18Zs2bNMux2u2vsj3/8ozFkyJArvlf1%2Bf2C2uESoZ/Kz89XdHS0wsPDXWNxcXE6fvy4SktLvVhZ4Fi%2BfLkGDhyoPn36aN68eSorK1N%2Bfr5iY2PdtouNjXVdBvz%2BfHBwsLp37y6bzaaTJ0%2BqpKREcXFxrvnOnTurSZMmys/P98yifNy4cePUvHnzi85d6r232Ww6d%2B6cjh496jYfFhamDh06yGaz6fDhwwoJCVFMTIxrPi4uTl9//bWOHTum/Px8xcTEKCQkxG3f372825Bcrg%2BSVFhYqF/%2B8pfq27evBg8erNdee02S6IPJrFarFi9erFatWrnGCgsLFRUVdcX3qj6/X1A7BCw/5XQ6ZbVa3ca%2BDVvc01N3vXr1UlJSknbs2KHc3Fzt379fCxYsuOj73qJFC9d77nQ63UKv9E1fHA6HnE6nJNV4vdVqpWe1cLn39quvvpJhGJd978PCwhQUFOQ2J8k1f7G%2BOp1O7mv8nsjISHXs2FGPPvqo9u3bp%2BnTp2v27Nl699136UM9s9lseumllzR58uQrvlf1%2Bf2C2iFg%2BTHDMLxdQsDKzc3VPffco8aNG6tz586aOXOm8vLyVFFRccXXXqkv9O3Hq8t7%2B2Pe9%2B/%2BDwbfGDhwoJ577jnFxsaqcePGGjZsmG699VZt2rTJtQ19MN%2BHH36oCRMmaMaMGUpKSrrkdt99rzz9/QJ3BCw/FRkZ6Toj8i2n06mgoCBFRkZ6qarA1a5dO1VVVSk4OLjG%2B%2B5wOFzveURExEX7EhkZ6drm%2B/NfffWVWrZsWY/VB4bLvbctWrS4aG%2BcTqdatmypyMhIlZaWqqqqym1Okmv%2B%2Bz%2BZO51O135xedHR0SoqKqIP9WTXrl168MEHNXv2bI0bN06Srvhe1ef3C2qn4X7F%2Brn4%2BHgVFhbq7NmzrjGbzaYuXbqoWbNmXqzM/x06dEhLlixxGysoKFDjxo2VnJxc436QgwcPqmfPnpK%2B6ct376eqqqrSoUOH1LNnT7Vv317h4eFu859%2B%2BqkuXLig%2BPj4elxRYIiPj6/x3ttsNvXs2VOhoaHq2rWr23tbXFyskydPqkePHurevbsMw9Ann3zi9lqr1apOnTopPj5eR44cUWVlZY19w93LL7%2BsrVu3uo0VFBSoffv29KEefPTRR8rMzNSqVas0cuRI1/iV3qv6/H5B7RCw/FRsbKwSEhK0fPlylZaWqqCgQC%2B88ILS0tK8XZrfa9mypXJzc5WTk6MLFy7o%2BPHjWrVqlVJTUzVixAidPn1aGzZs0Pnz57Vnzx7t2bNHY8aMkSSlpaXp1Vdf1f79%2B1VeXq41a9aocePGGjhwoEJCQjRmzBg988wzKiwslMPh0G9/%2B1vdeuutbjex4uLGjBmjv//979q9e7fOnz%2BvjRs36sSJExo%2BfLikb977devWqaCgQKWlpcrKylL37t2VkJCgyMhIDR06VCtXrtTZs2d15swZrV69WikpKbJYLEpOTlZYWJjWrFmj8vJyffzxx9q4cSPfTxdx4cIFLVy4UDabTRUVFcrLy9Pbb7%2BtsWPHSqIPZqqsrNTcuXM1c%2BZM9e/f323uSu9VfX6/oJY8/nuLME1hYaHxwAMPGD169DCSkpKM3/3ud65nmKBu3nvvPSM1NdXo1auXcf311xuLFy82zp0755obPny4ERcXZwwZMqTGM3rWr19vJCcnG/Hx8UZaWppx5MgR19z58%2BeN%2BfPnG3379jV69%2B5tTJ8%2B3SguLvbo2nxZfHy8ER8fb1xzzTXGNddc4/r3t7Zv324MGTLEiIuLM0aMGGG89957rrnq6mpj1apVxo033mj06NHD%2BNWvfuV65o9hGEZxcbGRkZFh9OrVy%2Bjbt6%2BxYMEC4/z58675I0eOGGPHjjXi4%2BONgQMHGuvXr/fMon3Q5fpQXV1trF692rj55puN%2BPh447bbbjN27drlei19MM/7779vdOvWzfX%2Bf/fjs88%2Bu%2BJ7VZ/fL7iyIMPgTjYAAAAzcYkQAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZP8P94IcKxnEynQAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-8584789892322565798”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>43</td> <td class=”number”>1.3%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>16</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>15</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>13</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>12</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.33333333333333</td> <td class=”number”>12</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-20.0</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.33333333333333</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>150.0</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-66.66666666666666</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (1707)</td> <td class=”number”>1918</td> <td class=”number”>59.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>1153</td> <td class=”number”>35.9%</td> <td>

<div class=”bar” style=”width:60%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-8584789892322565798”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>43</td> <td class=”number”>1.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-97.5</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-95.34883720930233</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-95.08196721311475</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-94.9685534591195</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1600.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1988.888888888889</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2470.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3066.666666666667</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>22109.090909090908</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_VEG_FARMS_07_12”>PCH_VEG_FARMS_07_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>836</td>

</tr> <tr>

<th>Unique (%)</th> <td>26.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>13.1%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>420</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>22.921</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>1800</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>7.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-4172138952077250409”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-4172138952077250409,#minihistogram-4172138952077250409”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-4172138952077250409”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-4172138952077250409”

aria-controls=”quantiles-4172138952077250409” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-4172138952077250409” aria-controls=”histogram-4172138952077250409”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-4172138952077250409” aria-controls=”common-4172138952077250409”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-4172138952077250409” aria-controls=”extreme-4172138952077250409”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-4172138952077250409”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-80</td>

</tr> <tr>

<th>Q1</th> <td>-31.25</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>40</td>

</tr> <tr>

<th>95-th percentile</th> <td>200</td>

</tr> <tr>

<th>Maximum</th> <td>1800</td>

</tr> <tr>

<th>Range</th> <td>1900</td>

</tr> <tr>

<th>Interquartile range</th> <td>71.25</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>112.41</td>

</tr> <tr>

<th>Coef of variation</th> <td>4.9041</td>

</tr> <tr>

<th>Kurtosis</th> <td>40.469</td>

</tr> <tr>

<th>Mean</th> <td>22.921</td>

</tr> <tr>

<th>MAD</th> <td>65.45</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>4.6547</td>

</tr> <tr>

<th>Sum</th> <td>63951</td>

</tr> <tr>

<th>Variance</th> <td>12636</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-4172138952077250409”>

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1ysrKUlRUlJo1a6bHH39cS5YsUXV19XnNDAAAfEOgq5/w3nvv1QcffKA5c%2Bboxhtv1KBBg9SjRw8FBp7dKA8%2B%2BOAp9/fs2aOHH35YmzdvVlhYmEaOHKl77rlHVVVV2rZtm%2BLi4hy3DQkJUcuWLWWz2XTw4EEFBASoQ4cOjv34%2BHj98ccf2r59u9P6yZSUlKi0tNRpLTCwsaKjo88qo7cJDGxYB0wDAvyd/vRWvpCTjN7DF3L6QkbJd3KejMsLVmZmpjIzM1VYWKgVK1bohRde0NSpU9WvXz%2BlpaWpdevW5/0cUVFRatWqlZ566im1bdtWH3/8scaNG6fo6GhdeeWVsixL4eHhTvcJDw9XWVmZIiIiFBISIj8/P6c9SSorKzuj5y8oKFBeXp7TWmZmpkaOHHmeyTxbZGQTd49wQmFhF7t7BJfwhZxk9B6%2BkNMXMkq%2Bk/N4Li9Yx8THxys%2BPl7jxo3Thx9%2BqClTpmjevHlKSUnRk08%2B6bhe6lx069ZN3bp1c/x779699fHHH2vp0qUaM2aMJDmutzqRU%2B2difT0dPXo0cNpLTCwscrKKs/rcT1dQ8sfEOCvsLCLVV5%2BSLW1de4e54LxhZxk9B6%2BkNMXMkoNJ6e7vrl3W8Gqrq52lJ6vvvpKrVq10hNPPKGSkhINGTJEU6dOVZ8%2BfYw9X0xMjDZv3qyIiAj5%2B/vLbrc77dvtdjVt2lRRUVGqqKhQbW2tAgICHHuS1LRp0zN6rujo6HqnA0tLD6qmxnu/kM5EQ81fW1vXYGczyRdyktF7%2BEJOX8go%2BU7O47m8YBUVFWnx4sV67733VFlZqV69eumtt97Stdde67hNUlKSpkyZcs4F65133lF4eLjuuusup%2BeNjY1VcHCw2rVrp8LCQl1//fWSpPLycu3atUudOnVSTEyMLMvSTz/9pPj4eEmSzWZTWFiYkdOXAADA%2B7m8YPXu3VutW7fWsGHD1K9fP0VERNS7TWpqqvbv33/Oz3HkyBFNmzZNsbGxuuqqq/TRRx/ps88%2B0z/%2B8Q9JUkZGhvLz83XLLbeoWbNmysnJUceOHZWYmChJ6tWrl1588UXNmDFDR44c0ezZs5WWlnbWF%2BIDAADf5PLGMH/%2BfMeRo1PZtGnTKfePlaGamhpJ0po1ayQdPdr04IMPqrKyUk8%2B%2BaRKS0vVokULzZ49WwkJCZKkwYMHq7S0VA888IAqKyuVnJzsdFH6c889p2effVY9e/ZUo0aNdPfdd9d7M1MAAICT8bPO94rus3TgwAGNHz9eaWlpuvXWWyVJ//3f/60NGzZo5syZJzyi5Q1KSw9ekMe988UNF%2BRxL4SVo25y9whOAgP9FRnZRGVllV59fYAv5CSj9/CFnL6QUWo4OS%2B9NNQtz%2BvyN6fIzs7WwYMH1bZtW8dat27dVFdXp%2BnTp7t6HAAAAONcforwiy%2B%2B0PLlyxUZGelYa9WqlXJycnT33Xe7ehwAAADjXH4Eq6qqSsHBwfUH8ffXoUOHXD0OAACAcS4vWElJSZo%2BfboOHDjgWPvtt980depUp7dqAAAA8FQuP0U4ceJE/fnPf9aNN96okJAQ1dXVqbKyUrGxsXr77bddPQ4AAIBxLi9YsbGx%2BuCDD/TZZ59p165d8vf3V%2BvWrdW1a1fHO6cDAAB4Mre8c2ZQUJDjLRoAAAC8jcsL1u7du5Wbm6tffvlFVVVV9fY/%2BeQTV48EAABglFuuwSopKVHXrl3VuHFjVz89AADABefygrV582Z98sknioqKcvVTAwAAuITL36ahadOmHLkCAABezeUFa9iwYcrLy5OLfwUiAACAy7j8FOFnn32m77//XkuXLlWLFi3k7%2B/c8d59911XjwQAAGCUywtWSEiIbrnlFlc/LQAAgMu4vGBlZ2e7%2BikBAABcyuXXYEnS9u3b9corr%2Bjpp592rP3zn/90xygAAADGubxgbdy4UX379tXq1au1YsUKSUfffPTBBx/kTUYBAIBXcHnBmjVrlsaOHavly5fLz89P0tHfTzh9%2BnTNnj3b1eMAAAAY5/KC9a9//UsZGRmS5ChYknTHHXeoqKjI1eMAAAAY5/KCFRoaesLfQVhSUqKgoCBXjwMAAGCcywvWNddcoxdeeEEVFRWOtR07dmj8%2BPG68cYbXT0OAACAcS5/m4ann35aDz30kJKTk1VbW6trrrlGhw4dUrt27TR9%2BnRXjwMAAGCcywtW8%2BbNtWLFCn366afasWOHLrroIrVu3Vo33XST0zVZAAAAnsrlBUuSGjVqpFtvvdUdTw0AAHDBubxg9ejR45RHqngvLAAA4OlcXrDuuusup4JVW1urHTt2yGaz6aGHHnL1OAAAAMa5vGCNGTPmhOsfffSRvv76axdPAwAAYJ5bfhfhidx666364IMP3D0GAADAeWswBWvLli2yLMvdYwAAAJw3l58iHDx4cL21Q4cOqaioSLfffrurxwEAADDO5QWrVatW9X6KMDg4WGlpaRo4cKCrxwEAADDO5QWLd2sHAADezuUF67333jvj2/br1%2B8CTgIAAHBhuLxgTZo0SXV1dfUuaPfz83Na8/Pzo2ABAACP5PKC9frrr2vevHl67LHH1KFDB1mWpZ9//lmvvfaa7r//fiUnJ7t6JAAAAKPccg1Wfn6%2BmjVr5li77rrrFBsbq6FDh2rFihWuHgkAAMAol78P1s6dOxUeHl5vPSwsTMXFxa4eBwAAwDiXF6yYmBhNnz5dZWVljrXy8nLl5ubqiiuucPU4AAAAxrn8FOHEiRM1evRoFRQUqEmTJvL391dFRYUuuugizZ4929XjAAAAGOfygtW1a1etX79en376qfbu3SvLstSsWTPdfPPNCg0NdfU4AAAAxrm8YEnSxRdfrJ49e2rv3r2KjY11xwgAAAAXjMuvwaqqqtL48ePVpUsX3XnnnZKOXoP1yCOPqLy83NXjAAAAGOfygjVz5kxt3bpVOTk58vf//5%2B%2BtrZWOTk5rh4HAADAOJcXrI8%2B%2Bkgvv/yy7rjjDscvfQ4LC1N2drZWr17t6nEAAACMc3nBqqysVKtWreqtR0VF6Y8//nD1OAAAAMa5vGBdccUV%2BvrrryXJ6XcPrlq1SpdffrmrxwEAADDO5T9FeN999%2BmJJ57Qvffeq7q6Or355pvavHmzPvroI02aNMnV4wAAABjn8oKVnp6uwMBALViwQAEBAXr11VfVunVr5eTk6I477nD1OAAAAMa5vGDt379f9957r%2B69915XPzUAAIBLuPwarJ49ezpdewUAAOBtXF6wkpOTtXLlSlc/LQAAgMu4/BThZZddpr/%2B9a/Kz8/XFVdcoUaNGjnt5%2BbmunokAAAAo1x%2BBGvbtm268sorFRoaqrKyMpWUlDh9nI3PP/9cKSkpysrKqrf34Ycfqk%2BfPurSpYsGDBigL774wrFXV1enWbNmqWfPnkpKStLQoUO1e/dux77dbteoUaOUkpKirl27atKkSaqqqjr30AAAwKe47AhWVlaWZs2apbffftuxNnv2bGVmZp7T47322mtavHixWrZsWW9v69atGj9%2BvPLy8nTDDTfoo48%2B0ogRI7Rq1So1b95cCxcu1PLly/Xaa6%2BpWbNmmjVrljIzM/X%2B%2B%2B/Lz89PkydP1pEjR7RixQpVV1frySefVE5Ojp555plzzg8AAHyHy45grV27tt5afn7%2BOT9ecHDwSQvWokWLlJqaqtTUVAUHB6tv375q3769li1bJkkqKCjQkCFD1KZNG4WEhCgrK0tFRUXatGmT9u3bpzVr1igrK0tRUVFq1qyZHn/8cS1ZskTV1dXnPC8AAPAdLitYJ/rJwfP5acIHH3xQoaGhJ9wrLCxUXFyc01pcXJxsNpuqqqq0bds2p/2QkBC1bNlSNptNW7duVUBAgDp06ODYj4%2BP1x9//KHt27ef87wAAMB3uOwU4bFf7Hy6NRPsdrvCw8Od1sLDw7Vt2zYdOHBAlmWdcL%2BsrEwREREKCQlxmu3YbcvKys7o%2BUtKSlRaWuq0FhjYWNHR0ecSx2sEBrr8kr9TCgjwd/rTW/lCTjJ6D1/I6QsZJd/JeTIu/ylCVznd0bFT7Z/v%2B3QVFBQoLy/PaS0zM1MjR448r8f1dJGRTdw9wgmFhV3s7hFcwhdyktF7%2BEJOX8go%2BU7O43llwYqMjJTdbndas9vtioqKUkREhPz9/U%2B437RpU0VFRamiokK1tbUKCAhw7ElS06ZNz%2Bj509PT1aNHD6e1wMDGKiurPNdIXqGh5Q8I8FdY2MUqLz%2Bk2to6d49zwfhCTjJ6D1/I6QsZpYaT013f3LusYFVXV2v06NGnXTPxPlgJCQnavHmz05rNZlPv3r0VHBysdu3aqbCwUNdff70kqby8XLt27VKnTp0UExMjy7L0008/KT4%2B3nHfsLAwtW7d%2BoyePzo6ut7pwNLSg6qp8d4vpDPRUPPX1tY12NlM8oWcZPQevpDTFzJKvpPzeC47MXrttdfWe8%2BrE62ZMGjQIH355Zdav369Dh8%2BrMWLF2vnzp3q27evJCkjI0Pz589XUVGRKioqlJOTo44dOyoxMVFRUVHq1auXXnzxRe3fv1979%2B7V7NmzlZaWpsBArzzgBwAADHNZY/jP978yITExUZJUU1MjSVqzZo2ko0eb2rdvr5ycHGVnZ6u4uFht27bV3Llzdemll0qSBg8erNLSUj3wwAOqrKxUcnKy0zVTzz33nJ599ln17NlTjRo10t13333CNzMFAAA4ET%2BL37zsEqWlBy/I49754oYL8rgXwspRN7l7BCeBgf6KjGyisrJKrz587Qs5yeg9fCGnL2SUGk7OSy898Vs6XWi%2B%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%2B8otdff10dOnTQ/Pnz9dhjj2n16tVq3Lixm5IAAABP4rVHsE5m%2BfLlatWqldLS0hQcHKyUlBT16NFDixYtkiQVFBRowIAB6ty5sy666CI98sgjkqR169a5c2wAAOBBvPoIVm5urv75z3%2BqoqJCd955pyZMmKDCwkLFxcU53S4uLk4rV66UJBUWFuquu%2B5y7Pn7%2B6tjx46y2Wzq3bv3GT1vSUmJSktLndYCAxsrOjr6PBN5tsDAhtXnAwL8nf70Vr6Qk4zewxdy%2BkJGyXdynozXFqyrr75aKSkpmjFjhnbv3q1Ro0Zp6tSpstvtatasmdNtIyIiVFZWJkmy2%2B0KDw932g8PD3fsn4mCggLl5eU5rWVmZmrkyJHnmMY7REY2cfcIJxQWdrG7R3AJX8hJRu/hCzl9IaPkOzmP57UFq6CgwPHPbdq00ZgxYzR8%2BHBde%2B21p72vZVnn9dzp6enq0aOH01pgYGOVlVWe1%2BN6uoaWPyDAX2FhF6u8/JBqa%2BvcPc4F4ws5yeg9fCGnL2SUGk5Od31z77UF63gtWrRQbW2t/P39ZbfbnfbKysoUFRUlSYqMjKy3b7fb1a5duzN%2Brujo6HqnA0tLD6qmxnu/kM5EQ81fW1vXYGczyRdyktF7%2BEJOX8go%2BU7O43nlidEtW7Zo%2BvTpTmtFRUUKCgpSampqvbdd2Lx5szp37ixJSkhIUGFhoWOvtrZWW7ZscewDAACcjlcWrKZNm6qgoED5%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%2BD8cwQIAADCMggUAAGAYBesEiouL9eijjyo5OVndu3fXzJkzVVdX5%2B6xAACAh%2BAarBN44oknFB8frzVr1uj333/XsGHDdMkll%2Bjhhx9292gAAMADULCOY7PZ9NNPP%2BnNN99UaGioQkNDNWTIEL311lsULDRonnRRPhfkA/B2FKzjFBYWKiYmRuHh4Y61%2BPh47dixQxUVFQoJCTntY5SUlKi0tNRpLTCwsaKjo43PC3giTyqDkvTxmJvdPYICAvyd/vRWvpDTFzJKvpPzZChYx7Hb7QoLC3NaO1a2ysrKzqhgFRQUKC8vz2ltxIgReuKJJ8wN%2Bn%2B%2B/esdxh9TOloSCwoKlJ6e7rXF0BcySr6R01cyvvXW616dUfKNnL6QUfKdnCfjm7XyNCzLOq/7p6ena%2BnSpU4f6enphqZzjdLSUuXl5dU7EudNfCGj5Bs5yeg9fCGnL2SUfCfnyXAE6zhRUVGy2%2B1Oa3a7XX5%2BfoqKijqjx4iOjvbJtg4AAI7iCNZxEhIStGfPHu3fv9%2BxZrPZ1LZtWzVp0sSNkwEAAE9BwTpOXFycEhMTlZubq4qKChUVFenNN99URkaGu0cDAAAeImDKlClT3D1EQ3PzzTdrxYoVmjZtmj744AOlpaVp6NCh8vPzc/doLtWkSRNdf/31Xn3kzhcySr6Rk4zewxdy%2BkJGyXdynoifdb5XdAMAAMAJpwgBAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgwUlxcbEeffRRJScnq3v37po5c6bq6urcPdZZKy4uVmZmppKTk5WSkqIJEyaovLxcv/76qzp06KDExESnjzfeeMNx3w8//FB9%2BvRRly5dNGDAAH3xxRduTHJqHTp0UEJCglOWadOmSZI2btyotLQ0XXPNNerdu7eWLVvmdN/58%2BerV69euuaaa5SRkaHNmze7I8IpffPNN/Veq4SEBHXo0EFff/31CV/LlStXOu7fkDN%2B/vnnSklJUVZWVr29U30O1tXVadasWerZs6eSkpI0dOhQ7d6927Fvt9s1atQopaSkqGvXrpo0aZKqqqpckul4p8q4evVq9e3bV126dFGvXr30j3/8w7H3yiuvqGPHjvVe23379kmSDh8%2BrP/3//6fbrnlFiUnJ2vkyJEqKytzWa7jnSzn0qVLddVVV9XL8eOPP0ryjtfymWeeqZcvLi5OTz/9tCRpwoQJjt/xe%2Bzjuuuuc9y/IWU0zgL%2BQ//%2B/a1nnnnGKi8vt3bs2GHdfvvt1rx589w91lm7%2B%2B67rQkTJlgVFRXWnj17rAEDBlgTJ060du/ebbVv3/6k99uyZYuVkJBgrV%2B/3qqqqrLef/99q3PnztaePXtcOP2Za9%2B%2BvbV79%2B5667/99pt19dVXW4sWLbKqqqqsDRs2WJ06dbJ%2B/PFHy7Is65NPPrGuu%2B4664cffrAOHTpkzZ0717rpppusyspKV0c4a3PmzLGefPJJ66uvvrK6d%2B9%2B0ts15Iz5%2BfnW7bffbg0ePNgaNWqU097pPgfnz59vde/e3dq2bZt18OBB67nnnrP69Olj1dXVWZZlWSNGjLAeffRR6/fff7f27t1rpaenW9OmTWtQGTdt2mQlJiZaH3/8sVVdXW2tX7/eio%2BPt7755hvLsizr5ZdftsaPH3/Sx87OzrYGDBhg/fvf/7bKysqsESNGWMOGDbugeU7mVDmXLFli3X///Se9rze8lserrq62evfuba1fv96yLMsaP3689fLLL5/09g0l44XAESw42Gw2/fTTTxozZoxCQ0PVqlUrDRkyRAUFBe4e7ayUl5crISFBo0ePVpMmTdS8eXP1799f33777Wnvu2jRIqWmpio1NVXBwcHq27ev2rdvX%2B/oT0O3fPlytWrVSmlpaQoODlZKSop69OihRYsWSZIKCgo0YMAAde7cWRdddJEeeeQRSdK6devcOfZp/fvf/9abb76pcePGnfa2DTljcHCwFi9erJYtW9bbO93nYEFBgYYMGaI2bdooJCREWVlZKioq0qZNm7Rv3z6tWbNGWVlZioqKUrNmzfT4449ryZIlqq6ubjAZ7Xa7hg0bpltvvVWBgYFKTU1V%2B/btz%2BhrtKamRosXL9bjjz%2Buyy67TBERERo1apTWr1%2Bv33777UJEOaVT5Twdb3gtj/fWW2/p8ssvV2pq6mlv25AyXggULDgUFhYqJiZG4eHhjrX4%2BHjt2LFDFRUVbpzs7ISFhSk7O1uXXHKJY23Pnj2Kjo52/Pu4cePUtWtX3XDDDcrNzXV8MRcWFiouLs7p8eLi4mSz2Vwz/DnIzc1Vt27ddN1112ny5MmqrKw8aY5jp8iO3/f391fHjh0bdE5Jeumll3Tvvffq8ssvlyRVVlY6TgXffPPNevPNN2X93%2B%2Bvb8gZH3zwQYWGhp5w71Sfg1VVVdq2bZvTfkhIiFq2bCmbzaatW7cqICBAHTp0cOzHx8frjz/%2B0Pbt2y9MmJM4VcZbbrlFmZmZjn%2BvqalRaWmpmjVr5lj7%2BeefNXjwYMcp7mOnSXft2qWDBw8qPj7ecds2bdrooosuUmFh4QVKc3Knyikd/W/Pww8/rKSkJPXs2VPvv/%2B%2BJHnNa/mfysvL9eqrr2rs2LFO61999ZX69eunLl26KC0tzfHfoYaU8UKgYMHBbrcrLCzMae1Y2XLn9Q3ny2azacGCBRo%2BfLiCgoLUpUsX3XbbbVq3bp3y8/O1bNky/e1vf5N09O/gPwumdPTvoKHmv/rqq5WSkqLVq1eroKBAP/zwg6ZOnXrC1zIiIsKRw9NyStKvv/6q1atX6%2BGHH5Z09H9G7du310MPPaTPP/9c2dnZysvL05IlSyR5Zkbp1HMfOHBAlmWddN9utyskJER%2Bfn5Oe1LD/hrOyclR48aNddddd0mSmjdvrtjYWM2YMUMbNmzQwIED9dhjj2n79u2y2%2B2SVO/zOywsrMFljIqKUqtWrTR27Fht2LBBTz31lCZOnKiNGzd65Wu5YMECJSUlqV27do612NhYtWzZUnPnztXnn3%2Bu6667Tn/%2B8589NuPZoGDBybHv/r3Fd999p6FDh2r06NFKSUlRdHS03n33Xd12221q1KiROnXqpGHDhmnp0qWO%2B3jS30FBQYEGDhyooKAgtWnTRmPGjNGKFSvO6PC6J%2BWUpIULF%2Br222/XpZdeKunod7pvv/22rr/%2BegUFBalr164aPHiwx76W/%2Bl0c59q35MyW5almTNnasWKFZozZ46Cg4MlSQMHDtTLL7%2Bsli1b6uKLL9aQIUPUsWNHp1P1npCzW7duev311xUXF6egoCD17t1bt9122xl/jnpCxmNqa2u1cOFCPfjgg07rmZmZeuGFF9SsWTOFhIRo7NixCgoK0po1ayR5VsazRcGCQ1RUlOO7w2Psdrv8/PwUFRXlpqnO3dq1a/Xoo49q4sSJ9b7o/1NMTIz27dsny7IUGRl5wr8DT8nfokUL1dbWyt/fv16OsrIyRw5PzPnRRx%2BpR48ep7xNTEyMSkpKJHlmRunUc0dERJzwtbXb7WratKmioqJUUVGh2tpapz1Jatq06YUf/izU1dVpwoQJWrt2rd555x1deeWVp7z9sdf22Ot3/N/BgQMHGlzGEzmWw5teS%2BnoT/weOXLE6ScETyQgIECXXXaZ47X0pIxni4IFh4SEBO3Zs0f79%2B93rNlsNrVt21ZNmjRx42Rn7/vvv9f48eP10ksvqV%2B/fo71jRs3as6cOU633b59u2JiYuTn56eEhIR6P8pvs9nUuXNnl8x9NrZs2aLp06c7rRUVFSkoKEipqan1cmzevNmRIyEhwel6ldraWm3ZsqVB5pSOXqtRXFysm266ybG2cuVK/f3vf3e63fbt2xUbGyvJ8zIec6rPweDgYLVr184pV3l5uXbt2qVOnTqpY8eOsixLP/30k9N9w8LC1Lp1a5dlOBMvvPCCfvnlF73zzjuO1%2ByYv/3tb9q4caPTWlFRkWJjYxUbG6vw8HCnv4N//etfOnLkiBISElwy%2B5l655139OGHHzqtHcvhTa%2BlJH3yySe64YYbFBgY6FizLEvZ2dlOGY4cOaJdu3YpNjbW4zKeLQoWHI69V0lubq4qKipUVFSkN998UxkZGe4e7azU1NTomWee0ZgxY9S1a1envdDQUM2ePVvvv/%2B%2BqqurZbPZ9MYbbzgyDho0SF9%2B%2BaXWr1%2Bvw4cPa/Hixdq5c6f69u3rjiin1LRpUxUUFCg/P19HjhzRjh079NJLLyk9PV333HOPiouLtWjRIh0%2BfFiffvqpPv30Uw0aNEiSlJGRoffee08//PCDDh06pDlz5igoKEjdunVzb6iT2LJliyIiIhQSEuJYa9SokWbMmKEvvvhC1dXV2rBhg5YsWeJ4LT0t4zGn%2BxzMyMjQ/PnzVVRUpIqKCuXk5DjeMyoqKkq9evXSiy%2B%2BqP3792vv3r2aPXu20tLSnP7H527fffedli1bpvz8fEVERNTbt9vtmjp1qrZv367Dhw9r3rx52rVrl/r376%2BAgAANGjRIr776qvbs2aOysjL913/9l2677TanH2xpCI4cOaJp06bJZrOpurpaK1as0GeffabBgwdL8o7X8pitW7eqRYsWTmt%2Bfn769ddfNXXqVP3222%2BqrKxUTk6OGjVqpFtvvdXjMp4tP8ubT4DirO3du1eTJ0/W//7v/yokJESDBw/WiBEjnC5CbOi%2B/fZb/elPf1JQUFC9vVWrVmnLli3Ky8vTzp07FRoaqgceeEB/%2Bctf5O9/9PuN1atXKzc3V8XFxWrbtq0mTZqkpKQkV8c4I998841yc3P1888/KygoSP3791dWVpaCg4P1zTff6Pnnn1dRUZFiYmI0evRo3X777Y77/v3vf1d%2Bfr5%2B//13JSYmasqUKWrfvr0b05zc3LlztXz5cq1YscJpvaCgQPPmzdOePXt0ySWXaPjw4Ro4cKBjv6FmTExMlHT0mwFJjv%2BZHPsJx1N9DlqWpVdeeUXvvvuuKisrlZycrOeee07NmzeXJB08eFDPPvus1q1bp0aNGunuu%2B/WhAkTTvj14K6MEydO1P/8z//U%2B59oUlKS5s2bp8OHDys3N1erVq2S3W5X27ZtNXnyZHXp0kXS0eKSnZ2tDz74QDU1NerevbumTJlyRj/pZtqpclqWpTlz5mjx4sUqLS1VixYtNG7cOHXv3l2Sd7yWx/Tq1UuDBg3S0KFDne5rt9s1Y8YMffbZZ6qoqFCnTp00ZcoUtWnTRlLDyXghULAAAAAM4xQhAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABj2/wF7Erkf5kf6MAAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-4172138952077250409”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>230</td> <td class=”number”>7.2%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>112</td> <td class=”number”>3.5%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>75</td> <td class=”number”>2.3%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>73</td> <td class=”number”>2.3%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.33333333333333</td> <td class=”number”>60</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-25.0</td> <td class=”number”>44</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>44</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.33333333333333</td> <td class=”number”>44</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>41</td> <td class=”number”>1.3%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-66.66666666666666</td> <td class=”number”>37</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (825)</td> <td class=”number”>2030</td> <td class=”number”>63.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>420</td> <td class=”number”>13.1%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-4172138952077250409”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>112</td> <td class=”number”>3.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-93.33333333333333</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-90.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-88.88888888888889</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-87.5</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>800.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>900.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1000.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1100.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1800.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_WICSPTH_08_12”><s>PCH_WICSPTH_08_12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_WICS_08_12”><code>PCH_WICS_08_12</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.98279</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_WICS_08_12”>PCH_WICS_08_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>353</td>

</tr> <tr>

<th>Unique (%)</th> <td>11.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>97</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>-1.9004</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>300</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>41.3%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram6393801926614804818”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives6393801926614804818”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles6393801926614804818”

aria-controls=”quantiles6393801926614804818” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram6393801926614804818” aria-controls=”histogram6393801926614804818”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common6393801926614804818” aria-controls=”common6393801926614804818”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme6393801926614804818” aria-controls=”extreme6393801926614804818”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles6393801926614804818”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-41.842</td>

</tr> <tr>

<th>Q1</th> <td>-12.5</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>0</td>

</tr> <tr>

<th>95-th percentile</th> <td>39.3</td>

</tr> <tr>

<th>Maximum</th> <td>300</td>

</tr> <tr>

<th>Range</th> <td>400</td>

</tr> <tr>

<th>Interquartile range</th> <td>12.5</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>25.823</td>

</tr> <tr>

<th>Coef of variation</th> <td>-13.589</td>

</tr> <tr>

<th>Kurtosis</th> <td>12.303</td>

</tr> <tr>

<th>Mean</th> <td>-1.9004</td>

</tr> <tr>

<th>MAD</th> <td>15.686</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.0166</td>

</tr> <tr>

<th>Sum</th> <td>-5915.9</td>

</tr> <tr>

<th>Variance</th> <td>666.85</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram6393801926614804818”>

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vf/1rjRkzxu7lAQAAXDbbC9bo0aPVuXNnTZs2TbfffrvatGlT6zbp6ek6ceKE3UsDAAAwwvaCtXr1al133XX13m7v3r02rAYAAMA826/BSkxM1IMPPqjt27f7x/77v/9bDzzwgHw%2Bn93LAQAAMM72grVw4UKdOnVKXbt29Y9lZGSopqZGixYtsns5AAAAxtn%2BFuGHH36ojRs3qm3btv6xhIQE5ebm6tZbb7V7OQAAAMbZfgarqqpKUVFRtRcSHq7Kykq7lwMAAGCc7QWrf//%2BWrRokU6ePOkf%2B/bbb/XMM88EfFQDAABAqLL9LcInn3xS//7v/66BAwcqOjpaNTU1qqioUMeOHfWHP/zB7uUAAAAYZ3vB6tixo9555x29//77OnLkiMLDw9W5c2cNGjRIERERdi8HAADAONsLliQ1b95cw4YNC8auAQAAGp3tBevo0aNasmSJvvrqK1VVVdWaf%2B%2B99%2BxeEgAAgFFBuQaruLhYgwYNUsuWLe3ePQAAQKOzvWDl5%2Bfrvffek8vlsnvXAAAAtrD9YxratWvHmSsAAOBothesadOmafny5bIsy%2B5dAwAA2ML2twjff/99ffbZZ1q3bp06dOig8PDAjvfmm2/avSQAAACjbC9Y0dHRuummm%2BzeLQAAgG1sL1gLFy60e5cAAAC2sv0aLEn6%2Buuv9eKLL%2BqJJ57wj/3P//xPMJYCAABgnO0Fa8%2BePRo7dqy2bdumTZs2Sfrxw0cnT57Mh4wCAABHsL1gLV26VI899pg2btyosLAwST/%2BfcJFixZpxYoVdi8HAADAONsL1v/%2B7/8qKytLkvwFS5JuvvlmFRYW2r0cAAAA42wvWK1bt67zbxAWFxerefPmdi8HAADAONsLVp8%2BffSb3/xG5eXl/rFDhw5pzpw5GjhwoN3LAQAAMM72j2l44okndO%2B992rAgAGqrq5Wnz59VFlZqW7dumnRokV2LwcAAMA42wvW1VdfrU2bNmn37t06dOiQWrRooc6dO%2BuGG24IuCYLAAAgVNlesCSpWbNmGjZsWDB2DQAA0OhsL1hDhgy56JkqPgsLAACEOtsL1qhRowIKVnV1tQ4dOiSv16t7773X7uUAAAAYZ3vBmj17dp3jW7du1d/%2B9jebVwMAAGBeUP4WYV2GDRumd955J9jLAAAAuGxNpmDt27dPlmUFexkAAACXzfa3CCdOnFhrrLKyUoWFhRoxYoTdywEAADDO9oKVkJBQ6/8ijIqK0vjx4zVhwgS7lwMAAGCc7QWLT2sHAABOZ3vBevvttxt829tvv70RVwIAANA4bC9Y8%2BbNU01NTa0L2sPCwgLGwsLCKFgAACAk2V6wXnnlFb366qt68MEHlZiYKMuydODAAb388suaNGmSBgwYYPeSAAAAjArKNVh5eXlq3769f6xfv37q2LGjpk6dqk2bNtm9JAAAAKNs/xysw4cPKzY2ttZ4TEyMioqK7F4OAACAcbYXrPj4eC1atEilpaX%2BsbKyMi1ZskTXXHON3csBAAAwzva3CJ988knNmjVLHo9HrVq1Unh4uMrLy9WiRQutWLHC7uUAAAAYZ3vBGjRokHbt2qXdu3fr%2BPHjsixL7du314033qjWrVvbvRwAAADjbC9YknTFFVdo6NChOn78uDp27BiMJQAAADQa26/Bqqqq0pw5c9S7d2/dcsstkn68Buv%2B%2B%2B9XWVmZ3csBAAAwzvaCtXjxYn355ZfKzc1VePj/7b66ulq5ubl2LwcAAMA42wvW1q1b9dvf/lY333yz/48%2Bx8TEaOHChdq2bdslbeuDDz5QWlqacnJyas1t3rxZY8aMUe/evTVu3Dh9%2BOGH/rmamhotXbpUQ4cOVf/%2B/TV16lQdPXrUP%2B/z%2BTRz5kylpaVp0KBBmjdvnqqqqn5mYgAA8K/G9oJVUVGhhISEWuMul0s//PBDg7fz8ssva8GCBerUqVOtuS%2B//FJz5szR7Nmz9fHHH2vKlCl6%2BOGHdfz4cUnSG2%2B8oY0bNyovL087d%2B5UQkKCsrOz/X%2BqZ/78%2BaqsrNSmTZv01ltvqbCwkLNrAACgwWwvWNdcc43%2B9re/SVLA3x5899139W//9m8N3k5UVJTWrl1bZ8Fas2aN0tPTlZ6erqioKI0dO1bdu3fXhg0bJEkej0dTpkxRly5dFB0drZycHBUWFmrv3r367rvvtH37duXk5Mjlcql9%2B/Z66KGH9NZbb%2Bns2bOXmR4AAPwrsP3/Irz77rv1yCOP6M4771RNTY1ee%2B015efna%2BvWrZo3b16DtzN58uQLzhUUFCg9PT1gLCkpSV6vV1VVVTp48KCSkpL8c9HR0erUqZO8Xq9OnTqliIgIJSYm%2BueTk5P1ww8/6Ouvvw4Yv5Di4mKVlJQEjEVGtlRcXFxD4zVIRER4wD%2Bdwqm5GltkZPC%2BX04%2BZmQLPU7NJTk3mxNz2V6wMjMzFRkZqddff10RERF66aWX1LlzZ%2BXm5urmm282sg%2Bfz1frz/HExsbq4MGDOnnypCzLqnO%2BtLRUbdq0UXR0tP/6sJ/mJAV8%2BvzFeDweLV%2B%2BPGAsOztbM2bM%2BDlx6hUTc0WjbDfYnJqrsbRt2yrYS3D0MSNb6HFqLsm52ZyUy/aCdeLECd1555268847G3U///z246XO13ff%2BmRmZmrIkCEBY5GRLVVaWnFZ2z1fRES4YmKuUFlZpaqra4xuO5icmquxmX58XQonHzOyhR6n5pKcm60xcwXrl0/bC9bQoUP12WefBZwhMq1t27by%2BXwBYz6fTy6XS23atFF4eHid8%2B3atZPL5VJ5ebmqq6sVERHhn5Okdu3aNWj/cXFxtd4OLCk5pXPnGufJUF1d02jbDian5mosTeF75eRjRrbQ49RcknOzOSmX7W92DhgwQFu2bGnUfbjdbuXn5weMeb1e9ezZU1FRUerWrZsKCgr8c2VlZTpy5IhSU1PVo0cPWZal/fv3B9w3JiZGnTt3btR1AwAAZ7D9DNYvfvEL/ed//qfy8vJ0zTXXqFmzZgHzS5Ysuex93HXXXRo/frx27dqlgQMHauPGjTp8%2BLDGjh0rScrKylJeXp5uuukmtW/fXrm5uerRo4dSUlIkSSNHjtQLL7yg559/XmfOnNGKFSs0fvx4RUYG5S8LAQCAEGN7Yzh48KB%2B%2BctfSmr4ReN1%2BakMnTt3TpK0fft2ST%2Beberevbtyc3O1cOFCFRUVqWvXrlq1apWuuuoqSdLEiRNVUlKie%2B65RxUVFRowYEDARenPPvusnn76aQ0dOlTNmjXTrbfeWueHmQIAANQlzLrcK7obKCcnR0uXLg0YW7FihbKzs%2B3YfdCVlJwyvs3IyHC1bdtKpaUVjnnPWmo6uW554aOg7fvn2DLzhqDtu6kcs8ZAttDj1FySc7M1Zq6rrmptdHsNZds1WDt27Kg1lpeXZ9fuAQAAbGNbwarrRJlNJ88AAABsZVvBqutjGRrzoxoAAACCxTmfSQ8AANBEULAAAAAMs%2B1jGs6ePatZs2bVO2bic7AAAACCybaC1bdvXxUXF9c7BgAAEOpsK1h/%2BMMf7NoVAABAUHENFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGGOLViJiYlyu91KSUnxfz333HOSpD179mj8%2BPHq06ePRo8erQ0bNgTcd/Xq1Ro5cqT69OmjrKws5efnByMCAAAIUZHBXkBjevfdd9WhQ4eAseLiYj300EOaN2%2BexowZo08//VTTp09X586dlZKSoh07dujFF1/UK6%2B8osTERK1evVoPPvigtm3bppYtWwYpCQAACCWOPYN1IRs3blRCQoLGjx%2BvqKgopaWlaciQIVqzZo0kyePxaNy4cerZs6datGih%2B%2B%2B/X5K0c%2BfOYC4bAACEEEcXrCVLligjI0P9%2BvXT/PnzVVFRoYKCAiUlJQXcLikpyf824Pnz4eHh6tGjh7xer61rBwAAocuxbxH26tVLaWlpev7553X06FHNnDlTzzzzjHw%2Bn9q3bx9w2zZt2qi0tFSS5PP5FBsbGzAfGxvrn2%2BI4uJilZSUBIxFRrZUXFzcz0xTt4iI8IB/OoVTczW2yMjgfb%2BcfMzIFnqcmktybjYn5nJswfJ4PP5/79Kli2bPnq3p06erb9%2B%2B9d7XsqzL3vfy5csDxrKzszVjxozL2u6FxMRc0SjbDTan5mosbdu2CvYSHH3MyBZ6nJpLcm42J%2BVybME6X4cOHVRdXa3w8HD5fL6AudLSUrlcLklS27Zta837fD5169atwfvKzMzUkCFDAsYiI1uqtLTiZ66%2BbhER4YqJuUJlZZWqrq4xuu1gcmquxmb68XUpnHzMyBZ6nJpLcm62xswVrF8%2BHVmw9u3bpw0bNmju3Ln%2BscLCQjVv3lzp6en685//HHD7/Px89ezZU5LkdrtVUFCgO%2B64Q5JUXV2tffv2afz48Q3ef1xcXK23A0tKTuncucZ5MlRX1zTatoPJqbkaS1P4Xjn5mJEt9Dg1l%2BTcbE7K5Zw3O/9Ju3bt5PF4lJeXpzNnzujQoUNatmyZMjMzddttt6moqEhr1qzR6dOntXv3bu3evVt33XWXJCkrK0tvv/22Pv/8c1VWVmrlypVq3ry5MjIyghsKAACEDEeewWrfvr3y8vK0ZMkSf0G64447lJOTo6ioKK1atUoLFizQM888o/j4eC1evFjXXnutJOmmm27So48%2BqpkzZ%2Br7779XSkqK8vLy1KJFiyCnAgAAocKRBUuS%2BvfvrzfffPOCc%2BvXr7/gfe%2B%2B%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%2BaiYJ3H5/MpJiYmYOynslVaWtqgguXxeLR8%2BfKAsYcffliPPPKIuYXqxyJ379VfKTMz03h5C6bi4mJ5PB7H5ZKcm82puSTnZ/v9719xXDan5pKcm82JuZxTFQ2yLOuy7p%2BZmal169YFfGVmZhpa3f8pKSnR8uXLa50tC3VOzSU5N5tTc0lkC0VOzSU5N5sTc3EG6zwul0s%2Bny9gzOfzKSwsTC6Xq0HbiIuLc0wDBwAAl44zWOdxu906duyYTpw44R/zer3q2rWrWrVqFcSVAQCAUEHBOk9SUpJSUlK0ZMkSlZeXq7CwUK%2B99pqysrKCvTQAABAiIn7961//OtiLaGpuvPFGbdq0Sc8995zeeef9oPf2AAALoklEQVQdjR8/XlOnTlVYWFiwl1ZLq1atdN111znu7JpTc0nOzebUXBLZQpFTc0nOzea0XGHW5V7RDQAAgAC8RQgAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAUrRHi9Xg0fPlx33XVXrbk9e/Zo/Pjx6tOnj0aPHq0NGzYEzK9evVojR45Unz59lJWVpfz8fLuWfUmGDBkit9utlJQU/9eDDz7on//yyy81adIk9e3bVyNGjNCrr74axNVeuqKiIv3Hf/yHBgwYoMGDB2vx4sWqqakJ9rIuWWJiYq3j9Nxzz0mq/7HY1HzwwQdKS0tTTk5OrbnNmzdrzJgx6t27t8aNG6cPP/zQP1dTU6OlS5dq6NCh6t%2B/v6ZOnaqjR4/aufR6XSjbunXrdO211wYcv5SUFH3xxReSmn62oqIiZWdna8CAAUpLS9PcuXNVVlYmqf7XiIsd06bgQtm%2B%2BeYbJSYm1jpmv/vd7/z3bcrZ9u/fr3vvvVd9%2B/ZVWlqaZs6cqZKSEknO%2BflVJwtN3vr166309HRr6tSp1oQJEwLmvv32W6tXr17WmjVrrKqqKuujjz6yUlNTrS%2B%2B%2BMKyLMt67733rH79%2Blmff/65VVlZaa1atcq64YYbrIqKimBEuajBgwdbH3/8cZ1zlZWV1o033mi9%2BOKLVkVFhZWfn29dd9111tatW21e5c93xx13WE899ZRVVlZmHTp0yBoxYoT16quvBntZl6x79%2B7W0aNHa43X91hsavLy8qwRI0ZYEydOtGbOnBkwt2/fPsvtdlu7du2yqqqqrPXr11s9e/a0jh07ZlmWZa1evdoaPHiwdfDgQevUqVPWs88%2Ba40ZM8aqqakJRpRaLpbtrbfesiZNmnTB%2Bzb1bLfeeqs1d%2B5cq7y83Dp27Jg1btw468knn6z3NaK%2BY9oUXCjb0aNHre7du1/wfk052%2BnTp62BAwday5cvt06fPm19//331qRJk6yHHnrIUT%2B/6sIZrBBw%2BvRpeTwe9ezZs9bcxo0blZCQoPHjxysqKkppaWkaMmSI1qxZI0nyeDwaN26cevbsqRYtWuj%2B%2B%2B%2BXJO3cudPWDJdr165dOnv2rKZPn66WLVsqOTlZEyZMkMfjCfbSGsTr9Wr//v2aPXu2WrdurYSEBE2ZMiVk1t8Q9T0Wm5qoqCitXbtWnTp1qjW3Zs0apaenKz09XVFRURo7dqy6d%2B/u/%2B3a4/FoypQp6tKli6Kjo5WTk6PCwkLt3bvX7hh1uli2%2BjTlbGVlZXK73Zo1a5ZatWqlq6%2B%2BWnfccYf%2B8Y9/1PsaUd8xDbaLZatPU85WWVmpnJwcTZs2Tc2bN5fL5dLw4cP11VdfOf7nFwUrBEyYMEHt27evc66goEBJSUkBY0lJSf7TqOfPh4eHq0ePHvJ6vY234MuwevVqDRs2TL1799aMGTP0/fffS/oxR2JioiIiIvy3/eecTV1BQYHi4%2BMVGxvrH0tOTtahQ4dUXl4exJX9PEuWLFFGRob69eun%2BfPnq6Kiot7HYlMzefJktW7dus65C2Xxer2qqqrSwYMHA%2Bajo6PVqVOnJvO8ulg2STp27Jjuu%2B8%2B9e/fX0OHDtX69eslqclni4mJ0cKFC3XllVf6x44dO6a4uLh6XyMudkybgotl%2B8njjz%2BuQYMG6frrr9eSJUt09uxZSU07W2xsrCZMmKDIyEhJ0tdff60///nPuuWWWxz38%2Bt8FKwQ5/P5FBMTEzDWpk0blZaW%2Buf/%2BYe69OMD/qf5pqRHjx5KTU3V%2BvXrtXnzZvl8Pv3qV7%2BSdOGcPp8vJK5jqmv9Px2XpngsLqZXr15KS0vTtm3b5PF49Pnnn%2BuZZ56p97EYSi72vDl58qQsywqZ59X5XC6XEhIS9Nhjj%2Bmjjz7So48%2BqieffFJ79uwJuWxer1evv/66pk%2BfXu9rRCi9FkqB2Zo3b67evXtr%2BPDh2rlzp/Ly8rRhwwb913/9l6TQeJ0vKiqS2%2B3WqFGjlJKSohkzZjjq51ddKFhNwPr165WYmFjn17p16y57%2B5ZlGVjl5asv54oVKzRt2jS1atVKv/jFL/T000/r73//u44cOXLBbYaFhdmY4PI0leNwuTwejyZMmKDmzZurS5cumj17tjZt2uT/bdop6jteoXo8MzIy9MorrygpKUnNmzfX6NGjNXz48IDXmlDI9umnn2rq1KmaNWuW0tLSLni7f36NCIVcUu1scXFxevPNNzV8%2BHA1a9ZMqampmjZtWkgds/j4eHm9Xr377rs6fPiwHn/88Qbdr6nnupjIYC8A0m233abbbrvtZ923bdu28vl8AWOlpaVyuVwXnPf5fOrWrdvPW%2BxluNSc8fHxkqTi4mK5XC4dPnw4YN7n86lNmzYKD2/6vye4XK46j0NYWJj/WIWqDh06qLq6WuHh4Rd9LIaSCz1vXC6X/zFX13y7du3sXKYx8fHxys/PD5lsO3bs0GOPPab58%2Bfr9ttvl6R6XyMudkybkrqy1SU%2BPl7fffedLMsKmWxhYWFKSEhQTk6OJk6cqPT09JD5%2BfVzNP2fTLiolJSUWte45Ofn%2By%2BId7vdKigo8M9VV1dr3759dV4wH0xFRUV6%2BumndebMGf9YYWGhJKljx45yu906cOCAzp0755/3er1NLseFuN1uHTt2TCdOnPCPeb1ede3aVa1atQriyi7Nvn37tGjRooCxwsJCNW/eXOnp6Rd9LIYSt9tdK8tPj7eoqCh169Yt4HlVVlamI0eOKDU11e6lXrI//vGP2rx5c8BYYWGhOnbsGBLZPvvsM82ZM0fLli0LKCD1vUZc7Jg2FRfKtmfPHq1cuTLgtl9//bXi4%2BMVFhbWpLPt2bNHI0eODLiU46dfilNTUx3x8%2BtCKFghbsyYMSoqKtKaNWt0%2BvRp7d69W7t37/Z/XlZWVpbefvttff7556qsrNTKlSvVvHlzZWRkBHfh52nXrp127NihRYsW6YcfftC3336rhQsXavDgwWrfvr3S09MVHR2tlStXqrKyUnv37tXatWuVlZUV7KU3SFJSklJSUrRkyRKVl5ersLBQr732Wsis/yft2rWTx%2BNRXl6ezpw5o0OHDmnZsmXKzMzUbbfddtHHYii566679Ne//lW7du3S6dOntXbtWh0%2BfFhjx46V9OPzavXq1SosLFR5eblyc3PVo0cPpaSkBHnl9Ttz5oyee%2B45eb1enT17Vps2bdL777%2BviRMnSmra2c6dO6ennnpKs2fP1qBBgwLm6nuNqO%2BYBtvFsrVu3VorVqzQ%2BvXrdfbsWXm9Xv3ud78LiWxut1vl5eVavHixKisrdeLECb344ovq16%2BfsrKyHPHz64KC9PEQuAQjRoyw3G631aNHDysxMdFyu92W2%2B22vvnmG8uyLOuTTz6xxo4dayUnJ1sjRoyo9dlQb7zxhpWenm653W4rKyvLOnDgQDBi1Gv//v3WlClTrL59%2B1p9%2B/a15s6da508edI/f%2BDAAWvixImW2%2B22MjIyrDfeeCOIq710x44ds%2B6//34rNTXVSktLs3772982mc8WuhSffPKJlZmZafXq1cu67rrrrIULF1pVVVX%2BuYs9FpuSn55H1157rXXttdf6//snW7dutUaMGGElJydbt912m/XJJ5/452pqaqxly5ZZAwcOtFJTU60HHnigSXzm0E8ulq2mpsZasWKFNXjwYMvtdls333yztWPHDv99m3K2v//971b37t39ef7565tvvqn3NeJixzTY6su2bds2a%2BzYsVZqaqp1ww03WC%2B99JJVXV3tv39TzrZ//35r0qRJVmpqqnX99ddbM2fOtI4fP25ZlnN%2BftUlzLJC%2BAoyAACAJoi3CAEAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAsP8HQ23PXfz8/M8AAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common6393801926614804818”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1325</td> <td class=”number”>41.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-25.0</td> <td class=”number”>119</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-20.0</td> <td class=”number”>112</td> <td class=”number”>3.5%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.33333</td> <td class=”number”>98</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>86</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-16.66667</td> <td class=”number”>74</td> <td class=”number”>2.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>62</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.33333</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16.66667</td> <td class=”number”>42</td> <td class=”number”>1.3%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>42</td> <td class=”number”>1.3%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (342)</td> <td class=”number”>1100</td> <td class=”number”>34.3%</td> <td>

<div class=”bar” style=”width:83%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme6393801926614804818”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>26</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-75.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-73.33334</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-66.66666</td> <td class=”number”>20</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:77%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-60.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>28</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>120.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>125.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>250.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>300.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_WIC_09_15”>PCH_WIC_09_15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>52</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>-0.59653</td>

</tr> <tr>

<th>Minimum</th> <td>-2.4915</td>

</tr> <tr>

<th>Maximum</th> <td>0.027258</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-6365623052592114504”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-6365623052592114504,#minihistogram-6365623052592114504”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-6365623052592114504”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-6365623052592114504”

aria-controls=”quantiles-6365623052592114504” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-6365623052592114504” aria-controls=”histogram-6365623052592114504”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-6365623052592114504” aria-controls=”common-6365623052592114504”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-6365623052592114504” aria-controls=”extreme-6365623052592114504”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-6365623052592114504”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-2.4915</td>

</tr> <tr>

<th>5-th percentile</th> <td>-2.4915</td>

</tr> <tr>

<th>Q1</th> <td>-0.65445</td>

</tr> <tr>

<th>Median</th> <td>-0.51373</td>

</tr> <tr>

<th>Q3</th> <td>-0.36604</td>

</tr> <tr>

<th>95-th percentile</th> <td>-0.14835</td>

</tr> <tr>

<th>Maximum</th> <td>0.027258</td>

</tr> <tr>

<th>Range</th> <td>2.5187</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.28841</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.47735</td>

</tr> <tr>

<th>Coef of variation</th> <td>-0.80021</td>

</tr> <tr>

<th>Kurtosis</th> <td>9.783</td>

</tr> <tr>

<th>Mean</th> <td>-0.59653</td>

</tr> <tr>

<th>MAD</th> <td>0.25839</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-3.0479</td>

</tr> <tr>

<th>Sum</th> <td>-1868.3</td>

</tr> <tr>

<th>Variance</th> <td>0.22786</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-6365623052592114504”>

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bMRERHq0KGDx84BAAD4Lr8MWCNGjNCuXbv07rvv6vTp01q1apW%2B/vprDR48WJKUnZ2tFStWqLy8XCdOnFB%2Bfr6SkpKUmpqqmJgY9e/fX88%2B%2B6yOHj2qQ4cOafHixcrMzJTF4pcX/AAAgGE%2BmxhSU1MlSdXV1ZKkLVu2SDp3temaa65Rfn6%2B5syZowMHDqhjx45aunSp2rRpI0nKysqSzWbTH//4R1VWViotLc3lofQnn3xSTzzxhPr06aNmzZpp4MCBDb7MFAAAoCEBdU19ohuNYrMdN7YviyVQ0dFhstsr/fr%2BtTfQW/eht%2B7h6331tV%2Be7GsuxV/27Ok126ZNS7cfoyF%2BeYsQAADAmwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGObxgJWRkaGCggIdPHjQ04cGAADwCI8HrLvuuksbNmxQ3759NWrUKG3evFnV1dWeLgMAAMBtPB6wcnNztWHDBr355pvq1KmTnn76afXs2VPz58/X/v37PV0OAACAcV57Bqtz586aMmWKtm3bpmnTpunNN9/UHXfcoQceeECff/65t8oCAABoMq8FrLNnz2rDhg168MEHNWXKFMXFxWnq1KlKSkpSTk6O1q9f763SAAAAmsTi6QOWl5dr1apVeuutt1RZWan%2B/fvrlVde0Q033ODcplu3bpo5c6YGDRrk6fIAAACazOMBa8CAAerQoYNGjx6tO%2B%2B8U1FRUfW26dmzp44ePdqk4%2BzevVtz587V7t27FRISoptuuknTpk1TTEyMiouLtWDBAu3bt09XXHGFRo8ercGDBzs/u2LFCr3%2B%2Buuy2WxKTEzU9OnTlZKS0qR6AADA5cPjtwhXrFihjRs3Kicnp8Fw9aPPPvvsNx%2BjurpaDz30kH7/%2B99r165dKioq0tGjRzVz5kxVVFRo7NixysrKUnFxsaZPn64ZM2aopKREkrR161YtWrRIzzzzjHbt2qXevXtrzJgxOnny5G%2BuBwAAXF48HrASExM1ZswYbdmyxTn28ssv68EHH5TD4TByDJvNJpvNpiFDhig4OFjR0dG69dZbtWfPHq1fv14JCQnKzMxUSEiI0tPTlZGRoZUrV0qSrFarhg0bpi5duig0NFSjRo2SJG3bts1IbQAAwP95PGDNmTNHx48fV8eOHZ1jvXr1Um1trebOnWvkGHFxcUpKSpLValVlZaWOHDmizZs3q1evXiorK1NycrLL9snJySotLZWkevOBgYFKSkpyXuECAAD4NR5/BmvHjh1av369oqOjnWMJCQnKz8/XwIEDjRwjMDBQixYtUk5Ojl555RVJ0o033qhJkyZp7NixiouLc9k%2BKipKdrtdkuRwOBQZGekyHxkZ6ZxvjIqKCtlsNpcxi6WFYmNjf8vp1BMUFOjyN8yht%2B5Db92DvuJCLJZLb11cLmvW4wHr1KlTCgkJqTceGBioqqoqI8c4c%2BaMxowZo9tuu835/NSsWbM0efLkRn2%2Brq6uSce3Wq0qKChwGcvNzVVeXl6T9vtLERHNje4PP6G37kNv3YO%2BoiHR0WHeLuG8/H3NejxgdevWTXPnztWkSZOcV4q%2B//57zZs3z%2BVVDU1RXFys7777ThMnTlRQUJBatmypvLw8DRkyRLfccku9Z73sdrtiYmIkSdHR0fXmHQ6HOnXq1Ojjjxw5UhkZGS5jFksL2e2Vv/GMXAUFBSoiormOHatSTU2tkX3iHHrrPvTWPegrLsTU9x2TPL1mvRUyPR6wpk2bpvvvv1833XSTwsPDVVtbq8rKSrVr106vvvqqkWPU1NSotrbW5UrUmTNnJEnp6en6n//5H5ftS0tL1aVLF0lSSkqKysrKNHToUOe%2Bdu/erczMzEYfPzY2tt7tQJvtuKqrzS6kmppa4/vEOfTWfeite9BXNORSXhP%2BvmY9HrDatWunt99%2BW%2B%2B9956%2B%2BeYbBQYGqkOHDurevbuCgoKMHOP6669XixYttGjRIo0ZM0anTp3SkiVL1K1bNw0ZMkQFBQVauXKlBg8erA8%2B%2BEDbt2%2BX1WqVJGVnZ2vixIkaOHCgEhMT9dJLLyk4OFi9evUyUhsAAPB/Hg9YkhQcHKy%2Bffu6bf/R0dF66aWXNG/ePPXo0UPBwcG68cYbNXPmTLVq1UpLly7V7NmzNWvWLMXHx2v%2B/Pm69tprJUk9evTQxIkTNX78eB05ckSpqakqLCxUaGio2%2BoFAAD%2BJaCuqU90X6Rvv/1WCxYs0JdffqlTp07Vm//b3/7myXI8xmY7bmxfFkugoqPDZLdX%2BvXlVW%2Bgt%2B5Db93D1/t6%2B7M7vV2CX9s4/mZvl1CPp9dsmzYt3X6MhnjlGayKigp1795dLVq08PThAQAA3M7jAau0tFR/%2B9vfnD%2B1BwAA4G88/pavVq1aceUKAAD4NY8HrNGjR6ugoKDJL/MEAAC4VHn8FuF7772nTz75RGvWrNGVV16pwEDXjPfXv/7V0yUBAAAY5fGAFR4erh49enj6sAAAAB7j8YA1Z84cTx8SAADAo7zyq6z37dunRYsWaerUqc6xv//9794oBQAAwDiPB6zi4mINHjxYmzdvVlFRkaRzLx%2B99957/fYlowAA4PLi8YC1cOFCPfroo1q/fr0CAgIknfv9hHPnztXixYs9XQ4AAIBxHg9Y//jHP5SdnS1JzoAlSbfddpvKy8s9XQ4AAIBxHg9YLVu2bPB3EFZUVCg4ONjT5QAAABjn8YDVtWtXPf300zpx4oRzbP/%2B/ZoyZYpuuukmT5cDAABgnMdf0zB16lTdd999SktLU01Njbp27aqqqip16tRJc%2BfO9XQ5AAAAxnk8YLVt21ZFRUXavn279u/fr9DQUHXo0EE333yzyzNZAAAAvsrjAUuSmjVrpr59%2B3rj0AAAAG7n8YCVkZFxwStVvAsLAAD4Oo8HrDvuuMMlYNXU1Gj//v0qKSnRfffd5%2BlyAAAAjPN4wJo8eXKD45s2bdKHH37o4WoAAADM88rvImxI37599fbbb3u7DAAAgCa7ZALW7t27VVdX5%2B0yAAAAmszjtwizsrLqjVVVVam8vFz9%2BvXzdDkAAADGeTxgJSQk1PspwpCQEGVmZmr48OGeLgcAAMA4jwcs3tYOAAD8nccD1ltvvdXobe%2B88043VgIAAOAeHg9Y06dPV21tbb0H2gMCAlzGAgICCFgAAMAneTxg/eUvf9GyZcs0ZswYJSYmqq6uTl988YVefPFF3XPPPUpLS/N0SQAAAEZ55RmswsJCxcXFOcf%2B8Ic/qF27dnrggQdUVFTk6ZIAAACM8vh7sL7%2B%2BmtFRkbWG4%2BIiNCBAwc8XQ4AAIBxHg9Y8fHxmjt3rux2u3Ps2LFjWrBgga666ipPlwMAAGCcx28RTps2TZMmTZLValVYWJgCAwN14sQJhYaGavHixZ4uBwAAwDiPB6zu3bvr3Xff1fbt23Xo0CHV1dUpLi5Ot9xyi1q2bOnpcgAAAIzzeMCSpObNm6tPnz46dOiQ2rVr540SAAAA3Mbjz2CdOnVKU6ZM0fXXX6/bb79d0rlnsEaNGqVjx455uhwAAADjPB6w5s%2Bfrz179ig/P1%2BBgT8dvqamRvn5%2BZ4uBwAAwDiPB6xNmzbp%2Beef12233eb8pc8RERGaM2eONm/ebPRYS5YsUffu3fX73/9eOTk5%2Bu677yRJxcXFyszMVNeuXTVgwACtW7fO5XMrVqxQ//791bVrV2VnZ6u0tNRoXQAAwL95PGBVVlYqISGh3nhMTIxOnjxp7Divv/661q1bpxUrVmjHjh3q2LGjXn75ZVVUVGjs2LHKyspScXGxpk%2BfrhkzZqikpESStHXrVi1atEjPPPOMdu3apd69e2vMmDFGawMAAP7N4wHrqquu0ocffihJLr978J133tG//Mu/GDvOsmXLNGHCBP3ud79TeHi4Hn/8cT3%2B%2BONav369EhISlJmZqZCQEKWnpysjI0MrV66UJFmtVg0bNkxdunRRaGioRo0aJUnatm2bsdoAAIB/8/hPEd59990aN26c7rrrLtXW1mr58uUqLS3Vpk2bNH36dCPH%2BP777/Xdd9/phx9%2B0B133KEjR44oLS1NM2fOVFlZmZKTk122T05O1saNGyVJZWVluuOOO5xzgYGBSkpKUklJiQYMGNCo41dUVMhms7mMWSwtFBsb28QzOycoKNDlb5hDb92H3roHfcWFWCyX3rq4XNasxwPWyJEjZbFY9NprrykoKEgvvPCCOnTooPz8fN12221GjnHo0CFJ566KLV%2B%2BXHV1dcrLy9Pjjz%2BuU6dOufweREmKiopyvlne4XDU%2B1U%2BkZGRLm%2Be/zVWq1UFBQUuY7m5ucrLy/stp3NeERHNje4PP6G37kNv3YO%2BoiHR0WHeLuG8/H3NejxgHT16VHfddZfuuusutx3jx1uPo0aNcoapcePG6cEHH1R6enqjP/9bjRw5UhkZGS5jFksL2e2VTdrvj4KCAhUR0VzHjlWppqbWyD5xDr11H3rrHvQVF2Lq%2B45Jnl6z3gqZHg9Yffr00SeffOL8CUJ3aN26taRzP534o/j4eNXV1ens2bNyOBwu29vtdsXExEiSoqOj6807HA516tSp0cePjY2tdzvQZjuu6mqzC6mmptb4PnEOvXUfeuse9BUNuZTXhL%2BvWY/fAE1LS3M%2B7%2BQubdu2VXh4uPbs2eMcO3DggJo1a6aePXvWe%2B1CaWmpunTpIklKSUlRWVmZc66mpka7d%2B92zgMAAPwaj1/BuuKKK/Sf//mfKiws1FVXXaVmzZq5zC9YsKDJx7BYLMrMzNQLL7ygbt26KTw8XIsXL9agQYM0dOhQ/fnPf9bKlSs1ePBgffDBB9q%2BfbusVqskKTs7WxMnTtTAgQOVmJiol156ScHBwerVq1eT6wIAAJcHjwesr776Sr/73e8k6aIeHL9YkyZN0pkzZzR8%2BHCdPXtW/fv31%2BOPP66wsDAtXbpUs2fP1qxZsxQfH6/58%2Bfr2muvlST16NFDEydO1Pjx43XkyBGlpqaqsLBQoaGhbqsVAAD4l4C6pj7R3UgTJkzQwoULXcYWL16s3NxcTxze62y248b2ZbEEKjo6THZ7pV/fv/YGeus%2B9NY9fL2vtz%2B709sl%2BLWN42/2dgn1eHrNtmnT0u3HaIjHnsHaunVrvbHCwkJPHR4AAMBjPBawGrpQ5qGLZwAAAB7lsYDV0GsZ3PmqBgAAAG/x7/fUAwAAeAEBCwAAwDCPvabh7NmzmjRp0q%2BOmXgPFgAAgDd5LGDdcMMNqqio%2BNUxAAAAX%2BexgPXqq6966lAAAABexTNYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADLssAtbTTz%2BtxMRE59fFxcXKzMxU165dNWDAAK1bt85l%2BxUrVqh///7q2rWrsrOzVVpa6umSAQCAD/P7gLVnzx6tXbvW%2BXVFRYXGjh2rrKwsFRcXa/r06ZoxY4ZKSkokSVu3btWiRYv0zDPPaNeuXerdu7fGjBmjkydPeusUAACAj/HrgFVbW6snnnhCOTk5zrH169crISFBmZmZCgkJUXp6ujIyMrRy5UpJktVq1bBhw9SlSxeFhoZq1KhRkqRt27Z54xQAAIAP8uuA9de//lUhISEaNGiQc6ysrEzJycku2yUnJztvA/5yPjAwUElJSc4rXAAAAL/G4u0C3OXw4cNatGiRXn31VZdxh8OhuLg4l7GoqCjZ7XbnfGRkpMt8ZGSkc74xKioqZLPZXMYslhaKjY29mFM4r6CgQJe/YQ69dR966x70FRdisVx66%2BJyWbN%2BG7DmzJmjYcOGqWPHjvruu%2B8u6rN1dXVNOrbValVBQYHLWG5urvLy8pq031%2BKiGhudH/4Cb11H3rrHvQVDYmODvN2Cefl72vWLwNWcXGx/v73v6uoqKjeXHR0tBwOh8uY3W5XTEzMeecdDoc6derU6OOPHDlSGRkZLmMWSwvZ7ZUlmKQ6AAAQzElEQVSN3seFBAUFKiKiuY4dq1JNTa2RfeIceus%2B9NY96CsuxNT3HZM8vWa9FTL9MmCtW7dOR44cUe/evSX9dEUqLS1N999/f73gVVpaqi5dukiSUlJSVFZWpqFDh0qSampqtHv3bmVmZjb6%2BLGxsfVuB9psx1VdbXYh1dTUGt8nzqG37kNv3YO%2BoiGX8prw9zXrlzdAH3vsMW3atElr167V2rVrVVhYKElau3atBg0apAMHDmjlypU6ffq0tm/fru3bt2vEiBGSpOzsbL311lv69NNPVVVVpSVLlig4OFi9evXy4hkBAABf4pdXsCIjI10eVK%2BurpYktW3bVpK0dOlSzZ49W7NmzVJ8fLzmz5%2Bva6%2B9VpLUo0cPTZw4UePHj9eRI0eUmpqqwsJChYaGev5EAACAT/LLgPVLV155pb744gvn1926dXN5%2Begv3X333br77rs9URoAAPBDfnmLEAAAwJsuiytYAHA5uf3Znd4uAbjscQULAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDC/DVgHDhxQbm6u0tLSlJ6erscee0zHjh2TJO3Zs0f33HOPbrjhBvXr10/Lli1z%2BeyGDRs0aNAgXX/99Ro2bJh27NjhjVMAAAA%2Bym8D1pgxYxQREaGtW7dqzZo1%2BvLLLzVv3jydOnVKo0eP1r/%2B67/q/fff18KFC7V06VJt3rxZ0rnwNWXKFE2ePFkffPCBcnJy9Mgjj%2BjQoUNePiMAAOArLN4uwB2OHTumlJQUTZo0SWFhYQoLC9PQoUP16quv6t1339XZs2f18MMPKygoSJ07d9bw4cNltVrVr18/rVy5Uj179lTPnj0lSYMHD9Zrr72mdevW6aGHHvLymQEA0Hi3P7vT2yU02sbxN3u7BKP8MmBFRERozpw5LmMHDx5UbGysysrKlJiYqKCgIOdccnKyVq5cKUkqKytzhqufz5eUlDT6%2BBUVFbLZbC5jFksLxcbGXuypNCgoKNDlb5hDb92H3roHfYW/sFj8aw37ZcD6pZKSEr322mtasmSJNm7cqIiICJf5qKgoORwO1dbWyuFwKDIy0mU%2BMjJSX331VaOPZ7VaVVBQ4DKWm5urvLy8334SDYiIaG50f/gJvXUfeuse9BW%2BLjo6zNslGOX3Aevjjz/Www8/rEmTJik9PV0bN25scLuAgADn/66rq2vSMUeOHKmMjAyXMYulhez2yibt90dBQYGKiGiuY8eqVFNTa2SfOIfeug%2B9dQ/6Cn9h6nvkL3kruPl1wNq6daseffRRzZgxQ3feeackKSYmRl9//bXLdg6HQ1FRUQoMDFR0dLQcDke9%2BZiYmEYfNzY2tt7tQJvtuKqrzf7jV1NTa3yfOIfeug%2B9dQ/6Cl/nb%2BvXv254/swnn3yiKVOm6LnnnnOGK0lKSUnRF198oerqaudYSUmJunTp4pwvLS112dfP5wEAAH6NXwas6upqPf7445o8ebK6d%2B/uMtezZ0%2BFh4dryZIlqqqq0meffaZVq1YpOztbkjRixAjt2rVL7777rk6fPq1Vq1bp66%2B/1uDBg71xKgAAwAcF1DX1gaNL0EcffaR/%2B7d/U3BwcL25d955R5WVlXriiSdUWlqq1q1b68EHH9Tdd9/t3Gbz5s1asGCBDhw4oI4dO2r69Onq1q1bk2qy2Y436fM/Z7EEKjo6THZ7pd9dUvU2eus%2B9NY9GuqrL/1oPvAjd72moU2blm7Z76/xy2ew/vCHP%2BiLL7644DZvvPHGeef69eunfv36mS4LAABcJvzyFiEAAIA3EbAAAAAMI2ABAAAY5pfPYF1OfOlhVn/7PVMAAJwPV7AAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYRZvFwAAl7rbn93p7RIA%2BBiuYDXgwIEDeuihh5SWlqbevXtr/vz5qq2t9XZZAADAR3AFqwHjxo1T586dtWXLFh05ckSjR49W69at9ac//cnbpQEAAB/AFaxfKCkp0d69ezV58mS1bNlSCQkJysnJkdVq9XZpAADAR3AF6xfKysoUHx%2BvyMhI51jnzp21f/9%2BnThxQuHh4b%2B6j4qKCtlsNpcxi6WFYmNjjdQYFBTo8revsFgu/Xp9tbe%2BgN4CuBBf%2BB5xMQhYv%2BBwOBQREeEy9mPYstvtjQpYVqtVBQUFLmOPPPKIxo0bZ6TGiooKvfLKXzRy5Eh99J%2B3Gdknzvl5b00FYpzjy729lP87q6iokNVq9cm%2BXurorXtcLn31r7hoSF1dXZM%2BP3LkSK1Zs8blz8iRIw1VJ9lsNhUUFNS7Soamo7fuQ2/dg766D711j8ulr1zB%2BoWYmBg5HA6XMYfDoYCAAMXExDRqH7GxsX6dygEAwIVxBesXUlJSdPDgQR09etQ5VlJSoo4dOyosLMyLlQEAAF9BwPqF5ORkpaamasGCBTpx4oTKy8u1fPlyZWdne7s0AADgI4Jmzpw509tFXGpuueUWFRUV6amnntLbb7%2BtzMxMPfDAAwoICPB2aU5hYWG68cYbuarmBvTWfeite9BX96G37nE59DWgrqlPdAMAAMAFtwgBAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNg%2BQC73a4pU6bo5ptvVlpamh555BEdPHiwwW0//PBDJSYmKjU11eXPxo0bPVy1b7iY3kpScXGxMjMz1bVrVw0YMEDr1q3zYLW%2Bp6SkRLfeeqtGjBhxwe0WLVqkpKSkeuv28OHDHqrUtzS2r5K0YsUK9e/fX127dlV2drZKS0s9UKFvcjgcGj9%2BvNLT09W9e3dNnz5dp06danDbNWvW6Nprr623Zj///HMPV31pOnDggB566CGlpaWpd%2B/emj9/vmpraxvc1l/XKAHLB0ydOlWHDx/W%2BvXrtWnTJp09e1ZTp0497/bx8fEqKSlx%2BXP77bd7sGLfcTG9raio0NixY5WVlaXi4mJNnz5dM2bMUElJiYer9g3r1q3TuHHj1L59%2B0ZtP2TIkHrrtnXr1m6u0vdcTF%2B3bt2qRYsW6ZlnntGuXbvUu3dvjRkzRidPnvRApb5nxowZqqqqUlFRkVavXq3y8nLl5%2Befd/tu3brVW7PXXXedByu%2BdI0bN05xcXHasmWLli9fri1btuiVV16pt50/r1EC1iWurq5OcXFxmjJlimJiYhQVFaWsrCx9/PHH4tdINs3F9nb9%2BvVKSEhQZmamQkJClJ6eroyMDK1cudIL1V/6Tp8%2BLavVqi5duni7FL9yMX21Wq0aNmyYunTpotDQUI0aNUqStG3bNneX6XMOHz6sLVu2aMKECYqJiVFcXJzGjh2r1atX6%2BzZs94uz6eUlJRo7969mjx5slq2bKmEhATl5OTIarXW29af1ygB6xIXEBCgWbNm6ZprrnGOHTx4UG3atFFAQECDn6msrFRubq7S0tJ0yy23aPny5YSxBlxsb8vKypScnOwylpyc7DeXs00bPny44uLiGr39F198oaysLOft1x07drixOt91MX395ZoNDAxUUlISV10bsGfPHgUFBSkxMdE51rlzZ508eVL79u1r8DMHDx7Un/70J3Xr1k19%2BvTR2rVrPVXuJa2srEzx8fGKjIx0jnXu3Fn79%2B/XiRMn6m3rr2uUgOVjvvvuOz333HN6%2BOGHG5wPDw/XNddco/vuu0/vv/%2B%2B5syZo4KCAq1evdrDlfqeX%2Butw%2BFQRESEy1hUVJTsdrsnyvNrbdu2Vbt27TRv3jzt3LlTw4cP15gxY877jQ2N43A4XL7JSVJkZCRrtgEOh0Ph4eEu/%2Bfqx9411K%2BYmBglJCTo0Ucf1c6dOzVx4kRNmzZNxcXFHqv5UtXQv5Xn66U/r1EC1iVg7dq1SkxMbPDPmjVrnNuVl5frnnvu0dChQzV8%2BPAG99W5c2e9%2BuqruvHGGxUcHKzu3bsrKyvLZT%2BXE5O9havG9rYxhg8frueff17t27dX8%2BbNlZOTo6SkpMvyhwhM9lUSV69/5kK9PXDgwEX1qlevXvrLX/6i5ORkBQcHa8CAAbr11lsv239rf%2Blieumva9Ti7QJw7uHeIUOGXHCbzz//XA8%2B%2BKDuv/9%2BjR49%2BqL2Hx8fr02bNjWlRJ9lsrfR0dFyOBwuY3a7XTExMUZq9TWN6W1TxMfHq6Kiwm37v1SZ7GtDa9bhcKhTp05G9u9rLtTbnTt36sSJE6qpqVFQUJAkOXvXqlWrRu0/Pj6eRwZ07upeQ%2BsuICCg3r%2BX/rxGuYLlA77%2B%2Bms99NBDmjJlyq%2BGq40bN%2Bq///u/Xcb27dundu3aubNEn3UxvU1NTa33j2dpaSkPcRvw5z//ud6tlfLyctZtE6WkpKisrMz5dU1NjXbv3s2abUBSUpLq6uq0d%2B9e51hJSYkiIiLUoUOHetu/8cYb2rBhg8sYa/aclJQUHTx4UEePHnWOlZSUqGPHjgoLC6u3rb%2BuUQKWD3jyySc1YsQIDRs2rMH5//iP/9Dy5cslSc2aNdO8efO0Y8cOnT17Vjt37tTq1auVnZ3tyZJ9xsX0dtCgQTpw4IBWrlyp06dPa/v27dq%2BfXuj3kWE%2Bm677TZ99NFHks79P9ZZs2Zp3759On36tJYtW6ZvvvlGQ4cO9XKVvufnfc3OztZbb72lTz/9VFVVVVqyZImCg4PVq1cv7xZ5CYqJiVH//v317LPP6ujRozp06JAWL16szMxMWSznbvbcd999zlB15swZPfXUUyopKdHZs2dVVFSk9957T1lZWd48jUtCcnKyUlNTtWDBAp04cULl5eVavny58/vQ5bJGuUV4iTt48KB27typ//3f/3V%2Bo//RsmXL1K1bNx08eFCxsbGSpL59%2B2ratGl66qmndPDgQbVu3VrTpk1Tv379vFH%2BJe1ie9uqVSstXbpUs2fP1qxZsxQfH6/58%2Bfr2muv9Ub5l7z%2B/fvrn//8p2pqalRbW6vU1FRJ0jvvvKP4%2BHjt37/f%2Ba6bSZMmSZJycnLkcDjUsWNHvfzyy2rbtq3X6r9UXUxfe/TooYkTJ2r8%2BPE6cuSIUlNTVVhYqNDQUG%2BewiXrySef1BNPPKE%2BffqoWbNmGjhwoCZMmOCc//bbb/XDDz9Iku69915VVlbq3//932Wz2XTllVdq8eLFSklJ8Vb5l5Tnn39eM2bM0M0336zw8HBlZWXp7rvvlqTLZo0G1Pnr02UAAABewi1CAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADDs/wO8vK2BixHU4gAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-6365623052592114504”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-0.7777581929748312</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-2.4914751705058906</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.3660385278387144</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.6606871436162249</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.29216362776089877</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.5727577363531964</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.47485297220779166</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.4539179868851524</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.48279832271787315</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.5023841583305733</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (41)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-6365623052592114504”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-2.4914751705058906</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:62%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.0306852483372513</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.8420947050758385</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.7870816411765578</td> <td class=”number”>82</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:32%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.7777581929748312</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-0.19070303995662163</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:29%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.14834674182593544</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:80%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.13700748954855402</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:26%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.03610139992422701</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.027258200990513792</td> <td class=”number”>83</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_18YOUNGER10”>PCT_18YOUNGER10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3132</td>

</tr> <tr>

<th>Unique (%)</th> <td>97.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>23.417</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>40.127</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-7263381783482061869”>

</div> <div class=”col-md-12 text-right”>

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<li role=”presentation” class=”active”><a href=”#quantiles-7263381783482061869”

aria-controls=”quantiles-7263381783482061869” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-7263381783482061869” aria-controls=”histogram-7263381783482061869”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-7263381783482061869” aria-controls=”common-7263381783482061869”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-7263381783482061869” aria-controls=”extreme-7263381783482061869”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-7263381783482061869”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>18.275</td>

</tr> <tr>

<th>Q1</th> <td>21.435</td>

</tr> <tr>

<th>Median</th> <td>23.333</td>

</tr> <tr>

<th>Q3</th> <td>25.102</td>

</tr> <tr>

<th>95-th percentile</th> <td>29.155</td>

</tr> <tr>

<th>Maximum</th> <td>40.127</td>

</tr> <tr>

<th>Range</th> <td>40.127</td>

</tr> <tr>

<th>Interquartile range</th> <td>3.6667</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>3.346</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.14288</td>

</tr> <tr>

<th>Kurtosis</th> <td>2.5178</td>

</tr> <tr>

<th>Mean</th> <td>23.417</td>

</tr> <tr>

<th>MAD</th> <td>2.4612</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.33491</td>

</tr> <tr>

<th>Sum</th> <td>73343</td>

</tr> <tr>

<th>Variance</th> <td>11.196</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-7263381783482061869”>

<img 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2q%2BYKNrGxrZv8WKueM3IFH6tls2TBkuS9n%2BrXbD/T557JmDFjlJ6e7rNms7VSRUX1Oe33l8LCQmW3t1RlZY3q6xuM7juQrJpLsm42q%2BcKNk35XmP1c0au4NHc2c7mBw6TLFmwYmNj5Xa7fdbcbrfi4uIUExOj0NDQk25v06aN4uLiVFVVpfr6eoWFhXm3SVKbNm2adPz4%2BPhGlwNdriOqq2ueJ0V9fUOz7TuQrJpLsm42q%2BYKNmdzDqx6zsgVfKyWzVoXPP8uKSlJxcXFPmtOp1Pdu3dXRESEOnXqpJKSEu%2B2yspKffPNN7riiivUtWtXeTweffnllz6fa7fbdemll/otAwAACF6WLFijR4/Wzp07tW3bNh07dkyrVq3S/v37NWLECElSVlaWVqxYodLSUlVVVSkvL09du3ZVcnKy4uLiNGTIED311FM6fPiwDh48qKVLlyozM1M2myVf8AMAAIYFbWNITk6WJNXV1UmS3n77bUk/vdrUuXNn5eXlacGCBSorK1PHjh317LPP6vzzz5ckjR07Vi6XS7fffruqq6uVkpLic1P6I488orlz52rgwIE677zzdOONN570zUwBAABOJsRzrnd0o0lcriPG92mzhSo2trUqKqotdd3aqrkk62azeq5eszcGehTL2jDlWqP7s/rXotVySc2f7fzzo4zvsykseYkQAAAgkChYAAAAhvm9YKWnpys/P18HDhzw96EBAAD8wu8F65ZbbtH69et1/fXXa8KECdq8ebP3RnUAAAAr8HvBys7O1vr16/X666%2BrU6dOeuyxx5SWlqZFixZp3759/h4HAADAuIDdg9WtWzfNmDFDW7du1axZs/T6669r6NChuvPOO/XFF18EaiwAAIBzFrCCdeLECa1fv1533XWXZsyYoXbt2unBBx9U165dNX78eK1bty5QowEAAJwTv7/RaGlpqVatWqU333xT1dXVGjJkiP785z/rqquu8j6md%2B/emjdvnoYPH%2B7v8QAAAM6Z3wvWsGHDdOmll2rixIm6%2BeabFRMT0%2BgxaWlpOnz4sL9HAwAAMMLvBWvFihXq06fPGR/3%2Beef%2B2EaAAAA8/x%2BD1aXLl00adIk798OlKQXX3xRd911l9xut7/HAQAAMM7vBWvBggU6cuSIOnbs6F3r37%2B/GhoatHDhQn%2BPAwAAYJzfLxG%2B%2B%2B67WrdunWJjY71rCQkJysvL04033ujvcQAAAIzz%2BytYtbW1ioiIaDxIaKhqamr8PQ4AAIBxfi9YvXv31sKFC/Xjjz96177//ns9/PDDPm/VAAAAEKz8folw1qxZ%2Bvd//3ddc801ioyMVENDg6qrq9WhQwe99NJL/h4HAADAOL8XrA4dOuitt97SO%2B%2B8o2%2B%2B%2BUahoaG69NJL1bdvX4WFhfl7HAAAAOP8XrAkKTw8XNdff30gDg0AANDs/F6wvv32Wy1evFhfffWVamtrG23/61//6u%2BRAAAAjArIPVjl5eXq27evWrVq5e/DAwAANDu/F6zi4mL99a9/VVxcnL8PDQAA4Bd%2Bf5uGNm3a8MoVAACwNL8XrIkTJyo/P18ej8ffhwYAAPALv18ifOedd/TJJ5/ojTfe0EUXXaTQUN%2BO99prr/l7JAAAAKP8XrAiIyPVr18/fx8WAADAb/xesBYsWODvQwIAAPiV3%2B/BkqS9e/dqyZIlevDBB71rn376aSBGAQAAMM7vBauoqEgjRozQ5s2bVVhYKOmnNx8dN24cbzIKAAAswe8F68knn9T999%2BvdevWKSQkRNJPf59w4cKFWrp0qb/HAQAAMM7vBetvf/ubsrKyJMlbsCTphhtuUGlpqbHj7Nq1S%2BPGjVOvXr107bXXavr06Tp8%2BLCkn15Fy8zMVM%2BePTVs2DCtXbvW53NXrFihIUOGqGfPnsrKylJxcbGxuQAAgPX5vWBFRUWd9G8QlpeXKzw83Mgx6urqdPfdd%2BvKK6/Uzp07VVhYqMOHD2vevHkqLy/X5MmTNXbsWBUVFWn27NmaM2eOnE6nJGnLli1asmSJnnjiCe3cuVMDBgzQpEmTdPToUSOzAQAA6/N7werZs6cee%2BwxVVVVedf27dunGTNm6JprrjFyDJfLJZfLpZtuuknh4eGKjY3VoEGDtHv3bq1bt04JCQnKzMxURESEUlNTlZ6erpUrV0qSHA6HMjIy1L17d7Vo0UITJkyQJG3dutXIbAAAwPr8XrAefPBBffrpp0pJSdGxY8fUs2dPDR06VG63WzNnzjRyjHbt2qlr165yOByqrq7WoUOHtHnzZvXv318lJSVKTEz0eXxiYqL3MuAvt4eGhqpr167eV7gAAADOxO/vg3XBBReosLBQ27dv1759%2B9SiRQtdeumluvbaa33uyToXoaGhWrJkicaPH68///nPkqQ%2Bffpo2rRpmjx5stq1a%2Bfz%2BJiYGFVUVEiS3G63oqOjfbZHR0d7tzdFeXm5XC6Xz5rN1krx8fG/Js4phYWF%2BvxrFVbNJVk3m9VzofnYbGb/G1v9a9FquSTrZvN7wZKk8847T9dff32z7f/48eOaNGmSbrjhBu/9Uw8//LCmT5/epM8/17%2BT6HA4lJ%2Bf77OWnZ2tnJycc9rvqdjtLZtlv4Fm1VySdbNZNReaT2xs62bZr1W/Fq2aS7JeNr8XrPT09NO%2BUmXivbCKior03XffaerUqQoLC1NUVJRycnJ000036brrrpPb7fZ5fEVFheLi4iRJsbGxjba73W516tSpyccfM2aM0tPTfdZstlaqqKj%2BlYlOLiwsVHZ7S1VW1qi%2BvsHovgPJqrkk62azei40H74vNo1Vc0nNn625SvyZ%2BL1gDR061Kdg1dfXa9%2B%2BfXI6nbrjjjuMHKO%2Bvl4NDQ0%2Br0QdP35ckpSamqq//OUvPo8vLi5W9%2B7dJUlJSUkqKSnRyJEjvfvatWuXMjMzm3z8%2BPj4RpcDXa4jqqtrnidFfX1Ds%2B07kKyaS7JuNqvmQvPh%2B%2BLZsWouyXrZ/F6wTnWZbtOmTfrggw%2BMHKNHjx5q1aqVlixZokmTJqm2tlbLli1T7969ddNNNyk/P18rV67UiBEj9P7772v79u1yOBySpKysLE2dOlU33nijunTpoueff17h4eHq37%2B/kdkAAID1/WbuKLv%2B%2Buv11ltvGdlXbGysnn/%2BeX3yySfq16%2BfbrzxRrVo0UKLFy9WmzZt9Oyzz%2Brll1/WVVddpccee0yLFi3S5ZdfLknq16%2Bfpk6dqilTpqhPnz7auXOnCgoK1KJFCyOzAQAA6wvITe4ns2vXrnO%2BufwfJSUl6aWXXjrptt69e2vNmjWn/Nxbb71Vt956q7FZAADAPxe/F6yxY8c2WqupqVFpaakGDx7s73EAAACM83vBSkhIaPRbhBEREcrMzNSoUaP8PQ4AAIBxfi9YCxcu9PchAQAA/MrvBevNN99s8mNvvvnmZpwEAACgefi9YM2ePbvRe1RJUkhIiM9aSEgIBQsAAAQlvxesP/3pT1q%2BfLkmTZqkLl26yOPxaM%2BePXruued02223KSUlxd8jAQAAGBWQe7AKCgp8/uByr1691KFDB915550qLCz090gAAABG%2Bf2NRvfv36/o6OhG63a7XWVlZf4eBwAAwDi/F6z27dtr4cKFqqio8K5VVlZq8eLFuvjii/09DgAAgHF%2Bv0Q4a9YsTZs2TQ6HQ61bt1ZoaKiqqqrUokULLV261N/jAAAAGOf3gtW3b19t27ZN27dv18GDB%2BXxeNSuXTtdd911ioqK8vc4AAAAxgXkbxG2bNlSAwcO1MGDB9WhQ4dAjAAAANBs/H4PVm1trWbMmKEePXrod7/7naSf7sGaMGGCKisr/T0OAACAcX4vWIsWLdLu3buVl5en0ND/O3x9fb3y8vL8PQ4AAIBxfi9YmzZt0n/%2B53/qhhtu8P7RZ7vdrgULFmjz5s3%2BHgcAAMA4vxes6upqJSQkNFqPi4vT0aNH/T0OAACAcX4vWBdffLE%2B%2BOADSfL524MbN27Uv/zLv/h7HAAAAOP8/luEt956q%2B677z7dcsstamho0AsvvKDi4mJt2rRJs2fP9vc4AAAAxvm9YI0ZM0Y2m00vv/yywsLC9Mwzz%2BjSSy9VXl6ebrjhBn%2BPAwAAYJzfC9bhw4d1yy236JZbbvH3oQEAAPzC7/dgDRw40OfeKwAAAKvxe8FKSUnRhg0b/H1YAAAAv/H7JcILL7xQf/jDH1RQUKCLL75Y5513ns/2xYsX%2B3skAAAAo/xesL7%2B%2Bmv967/%2BqySpoqLC34cHAABodn4rWLm5uXryySf10ksvedeWLl2q7Oxsf40AAADgF367B2vLli2N1goKCvx1eAAAAL/xW8E62W8O8tuEAADAivxWsH7%2Bw85nWgMAAAh2fn%2BbBgAAAKujYAEAABjmt98iPHHihKZNm3bGNZPvg7Vs2TK98sorqqqq0pVXXqn58%2BfroosuUlFRkRYvXqy9e/fqwgsv1MSJEzVixAjv561YsUKvvPKKXC6XunTpotmzZyspKcnYXAAAwNr89grWVVddpfLycp//nWzNlFdeeUVr167VihUr9O6776pjx4568cUXVV5ersmTJ2vs2LEqKirS7NmzNWfOHDmdTkk//bbjkiVL9MQTT2jnzp0aMGCAJk2apKNHjxqbDQAAWJvfXsH6x/e/8ofly5drxowZ3jc1feihhyRJzz//vBISEpSZmSlJSk1NVXp6ulauXKnk5GQ5HA5lZGSoe/fukqQJEyZoxYoV2rp1q4YNG%2BbXDAAAIDj5/Z3c/eH777/Xd999px9//FFDhw7VoUOHlJKSonnz5qmkpESJiYk%2Bj09MTPT%2BfcSSkhINHTrUuy00NFRdu3aV0%2BlscsEqLy%2BXy%2BXyWbPZWik%2BPv4ck/kKCwv1%2BdcqrJpLsm42q%2BdC87HZzP43tvrXotVySdbNZsmCdfDgQUnSxo0b9cILL8jj8SgnJ0cPPfSQamtr1a5dO5/Hx8TEeP9sj9vtVnR0tM/26Ojos/qzPg6HQ/n5%2BT5r2dnZysnJ%2BTVxzshub9ks%2Bw00q%2BaSrJvNqrnQfGJjWzfLfq36tWjVXJL1slmyYP38BqYTJkzwlqn77rtPd911l1JTU5v8%2Bb/WmDFjlJ6e7rNms7VSRUX1Oe33l8LCQmW3t1RlZY3q6xuM7juQrJpLsm42q%2BdC8%2BH7YtNYNZfU/Nmaq8SfiSULVtu2bSVJdrvdu9a%2BfXt5PB6dOHFCbrfb5/EVFRWKi4uTJMXGxjba7na71alTpyYfPz4%2BvtHlQJfriOrqmudJUV/f0Gz7DiSr5pKsm82qudB8%2BL54dqyaS7JeNmtd8Py7Cy64QJGRkdq9e7d3raysTOedd57S0tJUXFzs8/ji4mLvTe1JSUkqKSnxbquvr9euXbu82wEAAM7EkgXLZrMpMzNTzzzzjP73f/9Xhw4d0tKlSzV8%2BHCNHDlSZWVlWrlypY4dO6bt27dr%2B/btGj16tCQpKytLb775pj777DPV1NRo2bJlCg8PV//%2B/QMbCgAABA1LXiKUpGnTpun48eMaNWqUTpw4oSFDhuihhx5S69at9eyzz2r%2B/Pl6%2BOGH1b59ey1atEiXX365JKlfv36aOnWqpkyZokOHDik5OVkFBQVq0aJFgBMBAIBgEeI51zu60SQu1xHj%2B7TZQhUb21oVFdWWum5t1VySdbNZPVev2RsDPYplbZhyrdH9Wf1r0Wq5pObPdv75Ucb32RSWvEQIAAAQSBQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGPZPUbAee%2BwxdenSxftxUVGRMjMz1bNnTw0bNkxr1671efyKFSs0ZMgQ9ezZU1lZWSouLvb3yAAAIIhZvmDt3r1ba9as8X5cXl6uyZMna%2BzYsSoqKtLs2bM1Z84cOZ1OSdKWLVu0ZMkSPfHEE9q5c6cGDBigSZMm6ejRo4GKAAAAgoylC1ZDQ4Pmzp2r8ePHe9fWrVunhIQEZWZmKiIiQqmpqUpPT9fKlSslSQ6HQxkZGerevbtatGihCRMmSJK2bt0aiAgAACAI2QI9QHN67bXXFBERoeHDh%2Bupp56SJJWUlCgxMdHncYmJidqwYYN3%2B9ChQ73bQkND1bVrVzmdTg3ZyOhCAAATWklEQVQbNqxJxy0vL5fL5fJZs9laKT4%2B/lziNBIWFurzr1VYNZdk3WxWz4XmY7OZ/W9s9a9Fq%2BWSrJvNsgXrhx9%2B0JIlS/TSSy/5rLvdbrVr185nLSYmRhUVFd7t0dHRPtujo6O925vC4XAoPz/fZy07O1s5OTlnE6HJ7PaWzbLfQLNqLsm62ayaC80nNrZ1s%2BzXql%2BLVs0lWS%2BbZQvWggULlJGRoY4dO%2Bq77747q8/1eDzndOwxY8YoPT3dZ81ma6WKiupz2u8vhYWFym5vqcrKGtXXNxjddyBZNZdk3WxWz4Xmw/fFprFqLqn5szVXiT8TSxasoqIiffrppyosLGy0LTY2Vm6322etoqJCcXFxp9zudrvVqVOnJh8/Pj6%2B0eVAl%2BuI6uqa50lRX9/QbPsOJKvmkqybzaq50Hz4vnh2rJpLsl42a13w/Lu1a9fq0KFDGjBggFJSUpSRkSFJSklJUefOnRu97UJxcbG6d%2B8uSUpKSlJJSYl3W319vXbt2uXdDgAAcCaWLFgzZ87Upk2btGbNGq1Zs0YFBQWSpDVr1mj48OEqKyvTypUrdezYMW3fvl3bt2/X6NGjJUlZWVl688039dlnn6mmpkbLli1TeHi4%2BvfvH8BEAAAgmFjyEmF0dLTPjep1dXWSpAsuuECS9Oyzz2r%2B/Pl6%2BOGH1b59ey1atEiXX365JKlfv36aOnWqpkyZokOHDik5OVkFBQVq0aKF/4MAAICgZMmC9UsXXXSR9uzZ4/24d%2B/ePm8%2B%2Bku33nqrbr31Vn%2BMBgAALMiSlwgBAAAC6Z/iFSwAvy2/e%2Bq9QI8AAM2KV7AAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwzLIFq6ysTNnZ2UpJSVFqaqpmzpypyspKSdLu3bt122236aqrrtLgwYO1fPlyn89dv369hg8frh49eigjI0PvvvtuICIAAIAgZdmCNWnSJNntdm3ZskVvvPGGvvrqKz3%2B%2BOOqra3VxIkTdfXVV2vHjh168skn9eyzz2rz5s2SfipfM2bM0PTp0/X%2B%2B%2B9r/Pjxuvfee3Xw4MEAJwIAAMHCkgWrsrJSSUlJmjZtmlq3bq0LLrhAI0eO1EcffaRt27bpxIkTuueee9SqVSt169ZNo0aNksPhkCStXLlSaWlpSktLU0REhEaMGKHOnTtr7dq1AU4FAACChSULlt1u14IFC9S2bVvv2oEDBxQfH6%2BSkhJ16dJFYWFh3m2JiYkqLi6WJJWUlCgxMdFnf4mJiXI6nf4ZHgAABD1boAfwB6fTqZdfflnLli3Thg0bZLfbfbbHxMTI7XaroaFBbrdb0dHRPtujo6P19ddfN/l45eXlcrlcPms2WyvFx8f/%2BhAnERYW6vOvVVg1l2TdbFbNheZns5n9mrHq16JVc0nWzWb5gvXxxx/rnnvu0bRp05SamqoNGzac9HEhISHe/%2B/xeM7pmA6HQ/n5%2BT5r2dnZysnJOaf9nord3rJZ9htoVs0lWTebVXOh%2BcTGtm6W/Vr1a9GquSTrZbN0wdqyZYvuv/9%2BzZkzRzfffLMkKS4uTvv37/d5nNvtVkxMjEJDQxUbGyu3291oe1xcXJOPO2bMGKWnp/us2WytVFFR/euCnEJYWKjs9paqrKxRfX2D0X0HklVzSdbNZtVcaH69Zm8M9Ahn5f9Nvy4gx7Xyc6y5szVXiT8TyxasTz75RDNmzNDTTz%2Btvn37eteTkpL06quvqq6uTjbbT/GdTqe6d%2B/u3f7z/Vg/czqdGjZsWJOPHR8f3%2BhyoMt1RHV1zfOkqK9vaLZ9B5JVc0nWzWbVXMDPAv31beXnmNWyWeuC59/V1dXpoYce0vTp033KlSSlpaUpMjJSy5YtU01NjT7//HOtWrVKWVlZkqTRo0dr586d2rZtm44dO6ZVq1Zp//79GjFiRCCiAACAIGTJV7A%2B%2B%2BwzlZaWav78%2BZo/f77Pto0bN%2BqZZ57R3LlzVVBQoLZt2yo3N1f9%2B/eXJHXu3Fl5eXlasGCBysrK1LFjRz377LM6//zzA5AEAAAEI0sWrF69emnPnj2nfcyrr756ym2DBw/W4MGDTY8FAAD%2BSVjyEiEAAEAgUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACG2QI9AAAAweJ3T70X6BHOyoYp1wZ6hH9avIIFAABgGK9gARYRbD9ZA4CV8QoWAACAYRQsAAAAwyhYJ1FWVqa7775bKSkpGjBggBYtWqSGhoZAjwUAAIIE92CdxH333adu3brp7bff1qFDhzRx4kS1bdtWv//97wM9GgAACAK8gvULTqdTX375paZPn66oqCglJCRo/PjxcjgcgR4NAAAECV7B%2BoWSkhK1b99e0dHR3rVu3bpp3759qqqqUmRk5Bn3UV5eLpfL5bNms7VSfHy80VnDwkJ9/rWK30quQXk7Anp8ADhXwfTbxVtmpAV6BKMoWL/gdrtlt9t91n4uWxUVFU0qWA6HQ/n5%2BT5r9957r%2B677z5zg%2BqnIvfnP/9JY8aMMV7eAum3kuujP9xgfJ/l5eVyOBwBz2YauYKPVbORK/j8nK22tqelslnrpQ9DPB7POX3%2BmDFj9MYbb/j8b8yYMYam%2Bz8ul0v5%2BfmNXi0LdlbNJVk3G7mCj1WzkSv4WDUbr2D9QlxcnNxut8%2Ba2%2B1WSEiI4uLimrSP%2BPh4S7VwAABwdngF6xeSkpJ04MABHT582LvmdDrVsWNHtW7dOoCTAQCAYEHB%2BoXExEQlJydr8eLFqqqqUmlpqV544QVlZWUFejQAABAkwubNmzcv0EP81lx33XUqLCzUo48%2BqrfeekuZmZm68847FRISEujRGmndurX69OljuVfXrJpLsm42cgUfq2YjV/CxYrYQz7ne0Q0AAAAfXCIEAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCFYTKysp09913KyUlRQMGDNCiRYvU0NAQ6LGM6NKli5KSkpScnOz936OPPhrosX6VHTt2KDU1Vbm5uY22rV%2B/XsOHD1ePHj2UkZGhd999NwAT/jqnyvXGG2/o8ssv9zl3ycnJ%2BuKLLwI06dkpKytTdna2UlJSlJqaqpkzZ6qyslKStHv3bt1222266qqrNHjwYC1fvjzA056dU2X77rvv1KVLl0bn7Pnnnw/0yE3y5Zdf6o477tBVV12l1NRUTZkyRS6XS5JUVFSkzMxM9ezZU8OGDdPatWsDPG3TnSrXBx98cNLztWHDhkCPfNYee%2BwxdenSxftxMJ%2BvU/Ig6IwcOdLz0EMPeSorKz379u3zDB482LN8%2BfJAj2VE586dPd9%2B%2B22gxzhnBQUFnsGDB3vGjh3rmTJlis%2B2Xbt2eZKSkjzbtm3z1NbWetasWePp3r2758CBAwGatulOl2v16tWe2267LUCTnbsbb7zRM3PmTE9VVZXnwIEDnoyMDM%2BsWbM8NTU1nuuuu86zZMkST3V1tae4uNjTp08fz6ZNmwI9cpOdKtu3337r6dy5c6DH%2B1WOHTvmueaaazz5%2BfmeY8eOeQ4dOuS57bbbPJMnT/Z8//33niuvvNKzcuVKT21tree9997zXHHFFZ4vvvgi0GOf0elyvf/%2B%2B54BAwYEesRztmvXLk%2BfPn28X3vBfL5Oh1ewgozT6dSXX36p6dOnKyoqSgkJCRo/frwcDkegR8M/iIiI0KpVq3TJJZc02rZy5UqlpaUpLS1NERERGjFihDp37hwUP7GdLlcwq6ysVFJSkqZNm6bWrVvrggsu0MiRI/XRRx9p27ZtOnHihO655x61atVK3bp106hRo4LmOXe6bMGspqZGubm5mjhxosLDwxUXF6dBgwbpq6%2B%2B0rp165SQkKDMzExFREQoNTVV6enpWrlyZaDHPqPT5bKChoYGzZ07V%2BPHj/euBfP5Oh0KVpApKSlR%2B/btFR0d7V3r1q2b9u3bp6qqqgBOZs7ixYvVv39/9erVS3PmzFF1dXWgRzpr48aNU1RU1Em3lZSUKDEx0WctMTFRTqfTH6Odk9PlkqQDBw7o97//vXr37q2BAwdqzZo1fpzu17Pb7VqwYIHatm3rXTtw4IDi4%2BNVUlKiLl26KCwszLstMTFRxcXFgRj1rJ0u288eeOAB9e3bV1dffbUWL16sEydOBGLUsxIdHa1Ro0bJZrNJkvbu3au//OUv%2Bt3vfnfK51gwnLPT5ZKk6upq7%2BXe6667Ti%2B88II8Hk8gRz4rr732miIiIjR8%2BHDvWjCfr9OhYAUZt9stu93us/Zz2aqoqAjESEZdeeWVSk1N1ebNm%2BVwOPTZZ5/p4YcfDvRYRrndbp%2BCLP10DoP9/MXFxSkhIUH333%2B/3nvvPU2dOlWzZs1SUVFRoEc7a06nUy%2B//LLuueeekz7nYmJi5Ha7g/Lex3/MFh4erh49emjQoEHaunWrCgoKtHbtWv3Xf/1XoMdssrKyMiUlJWno0KFKTk5WTk7OKc9ZMD3HTpYrMjJSnTt31h133KEdO3ZowYIFys/P1%2BrVqwM9bpP88MMPWrJkiebOneuzboXzdTIUrCAUTD%2BtnC2Hw6FRo0YpPDxcl112maZPn67CwkIdP3480KMZZcVz2L9/f/3pT39SYmKiwsPDNWzYMA0aNEhvvPFGoEc7Kx9//LHuvPNOTZs2Tampqad8XEhIiB%2BnMuOX2eLj4/Xaa69p0KBBOu%2B883TFFVdo4sSJQXXO2rdvL6fTqY0bN2r//v164IEHAj2SESfL1a1bN7300kvq06ePwsPD1bdvX40dOzZozteCBQuUkZGhjh07BnoUv6BgBZm4uDi53W6fNbfbrZCQEMXFxQVoquZz0UUXqb6%2BXocOHQr0KMbExsae9Bxa8fy1b99e5eXlgR6jybZs2aK7775bs2bN0rhx4yT99Jz75U/SbrdbMTExCg0Nnm%2BhJ8t2Mu3bt9cPP/wQVD8EhISEKCEhQbm5uSosLJTNZmv0HKuoqAi659gvcx0%2BfLjRY4LlOVZUVKRPP/1U2dnZjbad7HtiMJ6vXwqe7w6QJCUlJenAgQM%2BTzSn06mOHTuqdevWAZzs3O3atUsLFy70WSstLVV4eLjP/SLBLikpqdG9BU6nU927dw/QRGa8%2BuqrWr9%2Bvc9aaWmpOnToEKCJzs4nn3yiGTNm6Omnn9bNN9/sXU9KStKePXtUV1fnXQu283WqbEVFRVq2bJnPY/fu3av27dv/5l%2BhKyoq0pAhQ3wu0/5ceK%2B44opGz7Hi4uKgOGeny7V9%2B3b993//t8/j9%2B7dGxTPsbVr1%2BrQoUMaMGCAUlJSlJGRIUlKSUlR586dg/Z8nQ4FK8gkJiYqOTlZixcvVlVVlUpLS/XCCy8oKysr0KOdszZt2sjhcKigoEDHjx/Xvn379PTTT2vMmDE%2BNxgHu9GjR2vnzp3atm2bjh07plWrVmn//v0aMWJEoEc7J8ePH9ejjz4qp9OpEydOqLCwUO%2B8847Gjh0b6NHOqK6uTg899JCmT5%2Buvn37%2BmxLS0tTZGSkli1bppqaGn3%2B%2BedatWpV0DznTpctKipKS5cu1Zo1a3TixAk5nU49//zzQZEtKSlJVVVVWrRokWpqanT48GEtWbJEvXr1UlZWlsrKyrRy5UodO3ZM27dv1/bt2zV69OhAj31Gp8sVFRWlxx9/XO%2B%2B%2B65OnDih9957T6tXrw6K8zVz5kxt2rRJa9as0Zo1a1RQUCBJWrNmjYYPHx605%2Bt0QjzB9DowJEkHDx7UnDlz9D//8z%2BKjIzU2LFjde%2B99/7mf%2BJsig8//FCLFy/Wnj17FB4erpEjRyo3N1cRERGBHu2sJCcnS5L3VY%2BffyPo598U3Lx5sxYvXqyysjJ17NhRs2fPVu/evQMz7Fk4XS6Px6Nly5Zp1apVcrlcuuiii/TAAw9owIABAZu3qT766CP927/9m8LDwxtt27hxo6qrqzV37lwVFxerbdu2uuuuu3TrrbcGYNKzd6Zsu3btUn5%2Bvvbv36%2BoqCjdfvvtuuuuu4Li8ueePXs0f/58ffHFF2rVqpWuvvpqzZw5U%2B3atdOHH36o%2BfPnq7S0VO3bt9e0adM0ePDgQI/cJKfL5XA4tHz5ch04cEBt27bVPffco1GjRgV65LP23XffaeDAgdqzZ48kBfX5OhUKFgAAgGG//R9RAAAAggwFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACG/X/UupLJpBN9GwAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-7263381783482061869”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>30.077120822622106</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>22.337390642829973</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>22.804739490342477</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>22.57856567284448</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.839405421160013</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>30.565349168058393</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>15.713755918064606</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>26.79842744817727</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>23.60522242412006</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.036039584461626</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3121)</td> <td class=”number”>3121</td> <td class=”number”>97.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-7263381783482061869”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.11153928494969</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.980096673301109</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.139165009940358</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.975609756097562</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>36.54852171868571</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>37.44876049189928</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>39.09318100678329</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>39.22594142259414</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>40.12692467748648</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_65OLDER10”>PCT_65OLDER10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3131</td>

</tr> <tr>

<th>Unique (%)</th> <td>97.5%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>15.894</td>

</tr> <tr>

<th>Minimum</th> <td>3.4706</td>

</tr> <tr>

<th>Maximum</th> <td>43.385</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram8426888410243600230”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives8426888410243600230,#minihistogram8426888410243600230”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives8426888410243600230”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles8426888410243600230”

aria-controls=”quantiles8426888410243600230” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram8426888410243600230” aria-controls=”histogram8426888410243600230”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common8426888410243600230” aria-controls=”common8426888410243600230”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme8426888410243600230” aria-controls=”extreme8426888410243600230”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles8426888410243600230”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>3.4706</td>

</tr> <tr>

<th>5-th percentile</th> <td>9.7045</td>

</tr> <tr>

<th>Q1</th> <td>13.128</td>

</tr> <tr>

<th>Median</th> <td>15.57</td>

</tr> <tr>

<th>Q3</th> <td>18.207</td>

</tr> <tr>

<th>95-th percentile</th> <td>23.44</td>

</tr> <tr>

<th>Maximum</th> <td>43.385</td>

</tr> <tr>

<th>Range</th> <td>39.914</td>

</tr> <tr>

<th>Interquartile range</th> <td>5.0793</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>4.1837</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.26322</td>

</tr> <tr>

<th>Kurtosis</th> <td>1.2147</td>

</tr> <tr>

<th>Mean</th> <td>15.894</td>

</tr> <tr>

<th>MAD</th> <td>3.2221</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.60325</td>

</tr> <tr>

<th>Sum</th> <td>49780</td>

</tr> <tr>

<th>Variance</th> <td>17.503</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram8426888410243600230”>

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CwmoOEhyssrIyu8cBAAAwzvaC1a1bNy1atEjff/999dp3332nefPm%2BX1UAwAAQKCy/RLhzJkz9a//%2Bq%2B64447FB4erqqqKpWWlurGG2/U2rVr7R4HAADAONsL1o033qh3331Xf/nLX/T3v/9dwcHBuuWWW9SzZ0%2BFhITYPQ4AAIBxthcsSQoNDdXdd9/txK4BAADqne0F69tvv9XSpUv19ddfq7y8vMb2P//5z3aPBAAAYJQj92AVFhaqZ8%2Beaty4sd27BwAAqHe2F6zc3Fz9%2Bc9/VnR0tN27BgAAsIXtH9Nw3XXXceYKAAC4mu0Fa/z48crIyJBlWXbvGgAAwBa2XyL8y1/%2Bos8%2B%2B0zvvPOObrjhBgUH%2B3e8N9980%2B6RAAAAjLK9YIWHh%2Buuu%2B6ye7cAAAC2sb1gLVy40O5dAgAA2Mr2e7Ak6eDBg1q2bJmeffbZ6rXPP//ciVEAAACMs71g5eTkaOjQodq2bZs2b94s6ccPH33kkUeMf8joihUr1LNnT3Xq1EmjR4/WkSNHqmdIS0tTly5dNGjQIG3cuNHv69asWaP%2B/furS5cuGjlypHJzc43OBQAA3M32gvXiiy/qqaee0qZNmxQUFCTpx99PuGjRIi1fvtzYfv70pz9p48aNWrNmjXbv3q3WrVvr1VdfVWFhoSZOnKgRI0YoJydHs2bN0pw5c%2BT1eiVJ27dv17Jly/TCCy9oz5496tOnjyZMmKDTp08bmw0AALib7QXrf//3fzVy5EhJqi5YknTvvfcqPz/f2H5WrVqlKVOm6Be/%2BIXCw8M1e/ZszZ49W5s2bVKrVq2UlpamsLAwJScnKyUlRdnZ2ZKkrKwspaamqmPHjmrUqJHGjh0rSdqxY4ex2QAAgLvZXrCaNm1a6%2B8gLCwsVGhoqJF9fPfddzpy5Ii%2B//57DRw4UElJSZo0aZJOnjypvLw8xcfH%2Bz0%2BPj6%2B%2BjLg%2BduDg4PVrl276jNcAAAAl2L7TxF26dJFCxYs0OzZs6vXDh06pLlz5%2BqOO%2B4wso9jx45Jkt577z2tXr1almVp0qRJmj17tsrLy9WiRQu/xzdr1kzFxcWSJJ/Pp8jISL/tkZGR1dvrorCwUEVFRX5rHk9jxcTEXEmcehUSEuz3p1u4NZdbeDy1Hxe3Hje35pLIFojcmktqWNlsL1jPPvusHn30USUlJamyslJdunRRWVmZ2rRpo0WLFhnZx0%2BfEj927NjqMvXkk09q3LhxSk5OrvPXX6msrCxlZGT4raWnp2vSpElX9bz1KSLiWqdHqBduzRXooqKaXHS7W4%2BbW3NJZAtEbs0lNYxsthesli1bavPmzdq1a5cOHTqkRo0a6ZZbblGPHj387sm6Gs2bN5ckRUREVK/FxsbKsiydO3dOPp/P7/HFxcXVv3w6Kiqqxnafz6c2bdrUef/Dhw9XSkqK35rH01jFxaWXlcMOISHBioi4ViUlZaqsrHJ6HGPcmsstLvRecOtxc2suiWyByK25pNqzXeobuvpie8GSpGuuuUZ33313vT1/y5YtFR4erv3796t9%2B/aSpIKCAl1zzTXq1auXNmzY4Pf43NxcdezYUZKUkJCgvLw8PfDAA5KkyspK7du3T2lpaXXef0xMTI3LgUVFP6iiouG%2BkCsrqxr0fFfKrbkC3aWOiVuPm1tzSWQLRG7NJTWMbLYXrJSUlIueqTLxWVgej0dpaWl6%2BeWX1a1bN4WHh2v58uUaMmSIHnjgAf3%2B979Xdna2hg4dqo8//li7du1SVlaWJGnkyJGaOnWqBg8erLZt2%2BqVV15RaGioevfufdVzAQCAnwfbC9bAgQP9ClZlZaUOHTokr9erRx991Nh%2Bpk2bprNnz2rYsGE6d%2B6c%2Bvfvr9mzZ6tJkyZauXKl5s%2Bfr3nz5ik2NlaLFy/WrbfeKkm66667NHXqVE2ePFknTpxQYmKiMjMz1ahRI2OzAQAAdwuyrvaObkPef/99ffLJJ/r3f/93p0epF0VFPzg9Qq08nmBFRTVRcXGp46dTTTKRa8BLHxmeCj/ZOrlHreu8HgMP2QKPW3NJtWe7/vqmjszi/M8x/p%2B7775b7777rtNjAAAAXLUGU7D27dt31R%2BPAAAA0BDYfg/WiBEjaqyVlZUpPz9f/fr1s3scAAAA42wvWK1atarxU4RhYWFKS0vTsGHD7B4HAADAONsLlqlPawcAAGiobC9Y69evr/Nj77///nqcBAAAoH7YXrBmzZqlqqqqGje0BwUF%2Ba0FBQVRsAAAQECyvWD98Y9/1KpVqzRhwgS1bdtWlmXpwIED%2BsMf/qCHH35YSUlJdo8EAABglCP3YGVmZqpFixbVa7fddptuvPFGjRkzRps3b7Z7JAAAAKNs/xysw4cPKzIyssZ6RESECgoK7B4HAADAONsLVmxsrBYtWqTi4uLqtZKSEi1dulQ33XST3eMAAAAYZ/slwpkzZ2ratGnKyspSkyZNFBwcrFOnTqlRo0Zavny53eMAAAAYZ3vB6tmzp3bu3Kldu3bp2LFjsixLLVq00J133qmmTZ35hYwAAAAm2V6wJOnaa69V3759dezYMd14441OjAAAAFBvbL8Hq7y8XDNmzFDnzp01YMAAST/egzV27FiVlJTYPQ4AAIBxthesxYsXa//%2B/VqyZImCg///7isrK7VkyRK7xwEAADDO9oL1/vvv63e/%2B53uvffe6l/6HBERoYULF2rbtm12jwMAAGCc7QWrtLRUrVq1qrEeHR2t06dP2z0OAACAcbYXrJtuukmffPKJJPn97sH33ntP//zP/2z3OAAAAMbZ/lOEo0aN0pNPPqkHH3xQVVVVWr16tXJzc/X%2B%2B%2B9r1qxZdo8DAABgnO0Fa/jw4fJ4PHr99dcVEhKil19%2BWbfccouWLFmie%2B%2B91%2B5xAAAAjLO9YJ08eVIPPvigHnzwQbt3DQAAYAvb78Hq27ev371XAAAAbmN7wUpKStLWrVvt3i0AAIBtbL9E%2BE//9E/6zW9%2Bo8zMTN1000265ppr/LYvXbrU7pEAAACMsr1gffPNN/rFL34hSSouLrZ79wAAAPXOtoI1ZcoUvfjii1q7dm312vLly5Wenm7XCAAAALaw7R6s7du311jLzMy0a/cAAAC2sa1g1faTg/w0IQAAcCPbCtZPv9j5UmsAAACBzvaPaQAAAHA7ChYAAIBhtv0U4blz5zRt2rRLrvE5WAAAINDZVrC6du2qwsLCS64BAAAEOtsK1j9%2B/hUAAICbcQ8WAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGE/i4K1YMECtW3btvrvOTk5SktLU5cuXTRo0CBt3LjR7/Fr1qxR//791aVLF40cOVK5ubl2jwwAAAKY6wvW/v37tWHDhuq/FxYWauLEiRoxYoRycnI0a9YszZkzR16vV5K0fft2LVu2TC%2B88IL27NmjPn36aMKECTp9%2BrRTEQAAQICx7XcROqGqqkpz587V6NGj9dJLL0mSNm3apFatWiktLU2SlJycrJSUFGVnZysxMVFZWVlKTU1Vx44dJUljx47VmjVrtGPHDg0aNMixLICbDHjpI6dHuCxbJ/dwegQAAcbVBevNN99UWFiYhgwZUl2w8vLyFB8f7/e4%2BPh4bd26tXr7wIEDq7cFBwerXbt28nq9dS5YhYWFKioq8lvzeBorJibmauLUi5CQYL8/3cKtueAMj%2BfqXkdufj2SLfC4NZfUsLK5tmAdP35cy5Yt09q1a/3WfT6fWrRo4bfWrFkzFRcXV2%2BPjIz02x4ZGVm9vS6ysrKUkZHht5aenq5JkyZdTgRbRURc6/QI9cKtuWCvqKgmRp7Hza9HsgUet%2BaSGkY21xashQsXKjU1Va1bt9aRI0cu62sty7qqfQ8fPlwpKSl%2Bax5PYxUXl17V89aHkJBgRURcq5KSMlVWVjk9jjFuzQVnXO17182vR7IFHrfmkmrPZuobpMvlyoKVk5Ojzz//XJs3b66xLSoqSj6fz2%2BtuLhY0dHRF9zu8/nUpk2bOu8/JiamxuXAoqIfVFHRcF/IlZVVDXq%2BK%2BXWXLCXqdeQm1%2BPZAs8bs0lNYxszl%2BkrAcbN27UiRMn1KdPHyUlJSk1NVWSlJSUpLi4uBofu5Cbm1t9U3tCQoLy8vKqt1VWVmrfvn3V2wEAAC7FlQXrmWee0fvvv68NGzZow4YNyszMlCRt2LBBQ4YMUUFBgbKzs3XmzBnt2rVLu3bt0kMPPSRJGjlypNavX68vvvhCZWVlWrFihUJDQ9W7d28HEwEAgEDiykuEkZGRfjeqV1RUSJJatmwpSVq5cqXmz5%2BvefPmKTY2VosXL9att94qSbrrrrs0depUTZ48WSdOnFBiYqIyMzPVqFEj%2B4MAAICA5MqCdb4bbrhBBw4cqP57t27d/D589HyjRo3SqFGj7BgNAAC4kCsvEQIAADjpZ3EGCw1DoH16NwAAV4ozWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhrm2YBUUFCg9PV1JSUlKTk7WM888o5KSEknS/v379fDDD6tr167q16%2BfVq1a5fe1W7Zs0ZAhQ9S5c2elpqZq9%2B7dTkQAAAAByrUFa8KECYqIiND27dv1zjvv6Ouvv9Z//ud/qry8XOPHj9ftt9%2BuDz/8UC%2B%2B%2BKJWrlypbdu2SfqxfM2YMUPTp0/Xxx9/rNGjR%2BuJJ57QsWPHHE4EAAAChSsLVklJiRISEjRt2jQ1adJELVu21AMPPKBPP/1UO3fu1Llz5/T444%2BrcePGat%2B%2BvYYNG6asrCxJUnZ2tnr16qVevXopLCxMQ4cOVVxcnDZu3OhwKgAAEChcWbAiIiK0cOFCNW/evHrt6NGjiomJUV5entq2bauQkJDqbfHx8crNzZUk5eXlKT4%2B3u/54uPj5fV67RkeAAAEPI/TA9jB6/Xq9ddf14oVK7R161ZFRET4bW/WrJl8Pp%2Bqqqrk8/kUGRnptz0yMlLffPNNnfdXWFiooqIivzWPp7FiYmKuPEQ9CQkJ9vsTQE0ez9W9P9z8PiNb4HFrLqlhZXN9wfrb3/6mxx9/XNOmTVNycrK2bt1a6%2BOCgoKq/9%2ByrKvaZ1ZWljIyMvzW0tPTNWnSpKt63voUEXGt0yMADVZUVBMjz%2BPm9xnZAo9bc0kNI5urC9b27dv11FNPac6cObr//vslSdHR0Tp8%2BLDf43w%2Bn5o1a6bg4GBFRUXJ5/PV2B4dHV3n/Q4fPlwpKSl%2Bax5PYxUXl15ZkHoUEhKsiIhrVVJSpsrKKqfHARqkq33vuvl9RrbA49ZcUu3ZTH2DdLlcW7A%2B%2B%2BwzzZgxQ7/97W/Vs2fP6vWEhAS98cYbqqiokMfzY3yv16uOHTtWb//pfqyfeL1eDRo0qM77jomJqXE5sKjoB1VUNNwXcmVlVYOeD3CSqfeGm99nZAs8bs0lNYxszl%2BkrAcVFRWaPXu2pk%2Bf7leuJKlXr14KDw/XihUrVFZWpi%2B//FLr1q3TyJEjJUkPPfSQ9uzZo507d%2BrMmTNat26dDh8%2BrKFDhzoRBQAABCBXnsH64osvlJ%2Bfr/nz52v%2B/Pl%2B29577z29/PLLmjt3rjIzM9W8eXNNmTJFvXv3liTFxcVpyZIlWrhwoQoKCtS6dWutXLlS119/vQNJAABAIHJlwbrtttt04MCBiz7mjTfeuOC2fv36qV%2B/fqbHAgAAPxOuvEQIAADgJAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIa58pc9A4BJA176yOkRLsvWyT2cHgH42eMMFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAABW%2BnQTAAAL4klEQVTAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDCP0wPg6gx46SOnRwAAAOfhDBYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYxk8RAoDLBNJPF2%2Bd3MPpEYB6wRmsWhQUFOixxx5TUlKS%2BvTpo8WLF6uqqsrpsQAAQIDgDFYtnnzySbVv314ffPCBTpw4ofHjx6t58%2Bb69a9/7fRoAAAgAFCwzuP1evXVV19p9erVatq0qZo2barRo0frtddeo2ABgGGBdDlT4pIm6o6CdZ68vDzFxsYqMjKyeq19%2B/Y6dOiQTp06pfDw8Es%2BR2FhoYqKivzWPJ7GiomJMT4vAMA%2BHk/g31kTEhLs96ebNKRsFKzz%2BHw%2BRURE%2BK39VLaKi4vrVLCysrKUkZHht/bEE0/oySefNDfo//n0N/de1dcXFhYqKytLw4cPd1UBdGsuiWyByK25JLIFosLCQr322h9dl0tqWNmcr3gNkGVZV/X1w4cP1zvvvOP33/Dhww1NZ1ZRUZEyMjJqnHELdG7NJZEtELk1l0S2QOTWXFLDysYZrPNER0fL5/P5rfl8PgUFBSk6OrpOzxETE%2BN4cwYAAM7hDNZ5EhISdPToUZ08ebJ6zev1qnXr1mrSpImDkwEAgEBBwTpPfHy8EhMTtXTpUp06dUr5%2BflavXq1Ro4c6fRoAAAgQIQ899xzzzk9RENz5513avPmzXr%2B%2Bef17rvvKi0tTWPGjFFQUJDTo9WLJk2aqHv37q47Q%2BfWXBLZApFbc0lkC0RuzSU1nGxB1tXe0Q0AAAA/XCIEAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyC9TPVtm1bJSQkKDExsfq/559/3umxrtiHH36o5ORkTZkypca2LVu2aMiQIercubNSU1O1e/duBya8MhfK9c477%2BjWW2/1O36JiYnau3evQ5NenoKCAqWnpyspKUnJycl65plnVFJSIknav3%2B/Hn74YXXt2lX9%2BvXTqlWrHJ728lwo25EjR9S2bdsax%2ByVV15xeuQ6%2B%2Bqrr/Too4%2Bqa9euSk5O1uTJk1VUVCRJysnJUVpamrp06aJBgwZp48aNDk9bdxfK9cknn9R6zLZu3er0yFdkwYIFatu2bfXfA/mY/aN/zNWgjpmFn6W4uDjr22%2B/dXoMIzIzM61%2B/fpZI0aMsCZPnuy3bd%2B%2BfVZCQoK1c%2BdOq7y83NqwYYPVsWNH6%2BjRow5NW3cXy/X2229bDz/8sEOTXb3BgwdbzzzzjHXq1Cnr6NGjVmpqqjVz5kyrrKzMuvPOO61ly5ZZpaWlVm5urtW9e3fr/fffd3rkOrtQtm%2B//daKi4tzerwrdubMGeuOO%2B6wMjIyrDNnzlgnTpywHn74YWvixInWd999Z3Xq1MnKzs62ysvLrY8%2B%2Bsjq0KGDtXfvXqfHvqSL5fr444%2BtPn36OD2iEfv27bO6d%2B9e/RoM5GP2j87P1ZCOGWewEPDCwsK0bt063XzzzTW2ZWdnq1evXurVq5fCwsI0dOhQxcXFBcR3ahfLFchKSkqUkJCgadOmqUmTJmrZsqUeeOABffrpp9q5c6fOnTunxx9/XI0bN1b79u01bNgwZWVlOT12nVwsW6ArKyvTlClTNH78eIWGhio6Olr33HOPvv76a23atEmtWrVSWlqawsLClJycrJSUFGVnZzs99iVdLJdbVFVVae7cuRo9enT1WiAfs5/UlqshoWD9jC1dulS9e/fWbbfdpjlz5qi0tNTpka7II488oqZNm9a6LS8vT/Hx8X5r8fHx8nq9dox2VS6WS5KOHj2qX//61%2BrWrZv69u2rDRs22DjdlYuIiNDChQvVvHnz6rWjR48qJiZGeXl5atu2rUJCQqq3xcfHKzc314lRL9vFsv3k6aefVs%2BePXX77bdr6dKlOnfunBOjXrbIyEgNGzZMHo9HknTw4EH993//twYMGHDB91kgHLeL5ZKk0tLS6ku%2Bd955p1avXi3Lspwc%2BbK9%2BeabCgsL05AhQ6rXAvmY/aS2XFLDOWYUrJ%2BpTp06KTk5Wdu2bVNWVpa%2B%2BOILzZs3z%2BmxjPP5fIqMjPRbi4yMVHFxsUMTmREdHa1WrVrpqaee0kcffaSpU6dq5syZysnJcXq0y%2Bb1evX666/r8ccfl8/nU0REhN/2Zs2ayefzqaqqyqEJr9w/ZgsNDVXnzp11zz33aMeOHcrMzNTGjRv1%2B9//3ukxL0tBQYESEhI0cOBAJSYmatKkSRc8boH0PqstV3h4uOLi4vToo4/qww8/1MKFC5WRkaG3337b6XHr7Pjx41q2bJnmzp3rtx7ox%2BxCuRrSMaNg/UxlZWVp2LBhCg0N1S9/%2BUtNnz5dmzdv1tmzZ50ezbhA%2B26zLnr37q0//vGPio%2BPV2hoqAYNGqR77rlH77zzjtOjXZa//e1vGjNmjKZNm6bk5OQLPi4oKMjGqcw4P1tMTIzefPNN3XPPPbrmmmvUoUMHjR8/PuCOWWxsrLxer9577z0dPnxYTz/9tNMjGVFbrvbt22vt2rXq3r27QkND1bNnT40YMSKgjtnChQuVmpqq1q1bOz2KURfK1ZCOGQULkqQbbrhBlZWVOnHihNOjGBUVFSWfz%2Be35vP5FB0d7dBE9Sc2NlaFhYVOj1Fn27dv12OPPaaZM2fqkUcekfTjmbnzv4P2%2BXxq1qyZgoMD55%2Br2rLVJjY2VsePHw%2B4bwKCgoLUqlUrTZkyRZs3b5bH46nxPisuLg6499n5uU6ePFnjMYH0PsvJydHnn3%2Bu9PT0Gttq%2B7cxUI7ZxXLVxqljFjj/YsGYffv2adGiRX5r%2Bfn5Cg0N9btXxA0SEhJq3FPg9XrVsWNHhyYy44033tCWLVv81vLz83XjjTc6NNHl%2BeyzzzRjxgz99re/1f3331%2B9npCQoAMHDqiioqJ6LdCO14Wy5eTkaMWKFX6PPXjwoGJjYwPiDF1OTo769%2B/vd6n2p9LboUOHGu%2Bz3NzcgDhuF8u1a9cu/dd//Zff4w8ePBgw77ONGzfqxIkT6tOnj5KSkpSamipJSkpKUlxcXMAes4vlWr9%2BfcM5Zs7%2BECOccOzYMatTp07WypUrrTNnzlgHDx60Bg4caD3//PNOj3ZVZsyYUePjDA4cOGAlJiZaO3bssMrLy63s7Gyrc%2BfOVmFhoUNTXr7acr366qvW7bffbu3du9c6e/astWnTJqtdu3aW1%2Bt1aMq6O3funDVgwADrzTffrLHtzJkzVp8%2Bfazf/e531unTp60vvvjCuu2226wdO3bYP%2BgVuFg2r9drtW/f3lq/fr119uxZa%2B/evVaPHj2sVatWOTDp5SspKbGSk5OtRYsWWadPn7ZOnDhhjRkzxho1apR1/Phxq3PnztZbb71llZeXWzt37rQ6dOhg7d%2B/3%2BmxL%2Bliuf7nf/7H6tChg/Xhhx9aZ8%2BetXbv3m116tQpYD42xOfzWUePHq3%2B7/PPP7fi4uKso0ePWgUFBQF7zC6WqyEdsyDLCrBz0zDir3/9q5YuXaoDBw4oNDRUDzzwgKZMmaKwsDCnR7tsiYmJklR91uOnnwb66ScFt23bpqVLl6qgoECtW7fWrFmz1K1bN2eGvQwXy2VZllasWKF169apqKhIN9xwg55%2B%2Bmn16dPHsXnr6tNPP9W//Mu/KDQ0tMa29957T6WlpZo7d65yc3PVvHlzjRs3TqNGjXJg0st3qWz79u1TRkaGDh8%2BrKZNm%2BpXv/qVxo0bFzCXPw8cOKD58%2Bdr7969aty4sW6//XY988wzatGihf76179q/vz5ys/PV2xsrKZNm6Z%2B/fo5PXKdXCxXVlaWVq1apaNHj6p58%2BZ6/PHHNWzYMKdHviJHjhxR3759deDAAUkK6GP2j87P1VCOGQULAADAsMD4tgkAACCAULAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYNj/AxGzx3urq%2BmyAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common8426888410243600230”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>17.845484221980414</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16.601101494885917</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17.93617358610224</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.239792984473835</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>18.397019947128094</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17.870607751470367</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>15.831190185083688</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>34.12906774681519</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.704046932164328</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17.21773905591659</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3120)</td> <td class=”number”>3120</td> <td class=”number”>97.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme8426888410243600230”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>3.4705988131631003</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.7277003638945594</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.262990455991517</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.934734161095192</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.573316283034953</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>32.213066628874536</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>32.61750207428376</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.9622641509434</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>34.12906774681519</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>43.38471419396275</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_CACFP09”>PCT_CACFP09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>52</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1.2225</td>

</tr> <tr>

<th>Minimum</th> <td>0.38792</td>

</tr> <tr>

<th>Maximum</th> <td>2.5525</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-5128205962103775460”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-5128205962103775460,#minihistogram-5128205962103775460”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-5128205962103775460”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-5128205962103775460”

aria-controls=”quantiles-5128205962103775460” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-5128205962103775460” aria-controls=”histogram-5128205962103775460”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-5128205962103775460” aria-controls=”common-5128205962103775460”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-5128205962103775460” aria-controls=”extreme-5128205962103775460”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-5128205962103775460”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0.38792</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.69396</td>

</tr> <tr>

<th>Q1</th> <td>0.95401</td>

</tr> <tr>

<th>Median</th> <td>1.2247</td>

</tr> <tr>

<th>Q3</th> <td>1.4249</td>

</tr> <tr>

<th>95-th percentile</th> <td>2.0079</td>

</tr> <tr>

<th>Maximum</th> <td>2.5525</td>

</tr> <tr>

<th>Range</th> <td>2.1646</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.47085</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.39711</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.32484</td>

</tr> <tr>

<th>Kurtosis</th> <td>1.5928</td>

</tr> <tr>

<th>Mean</th> <td>1.2225</td>

</tr> <tr>

<th>MAD</th> <td>0.3003</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.0943</td>

</tr> <tr>

<th>Sum</th> <td>3828.8</td>

</tr> <tr>

<th>Variance</th> <td>0.1577</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-5128205962103775460”>

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hBzkcDl100UXq3bu3goODrR4PAACgQbYLLEkKCQnRgAEDrB4DAADgtNgusH788UctXLhQ33zzjY4dO1Zv/7vvvmvBVAAAAL6zXWBNnTpVpaWl6t27t1q2bGn1OAAAAI1mu8AqLCzUu%2B%2B%2Bq5iYGKtHAQAAOC22%2B5iGc889lztXAACgWbNdYI0fP155eXmqq6uzehQAAIDTYruXCN9//33t2rVLa9eu1fnnny%2BHw7sBX3vtNYsmAwAA8I3tAisiIkJ9%2BvSxegwAAIDTZrvAmjt3rtUjAAAAnBHbvQdLkr777jstWbJEDz/8sGfbZ599ZuFEAAAAvrNdYBUUFGjo0KHavHmzNmzYIOnXDx8dPXo0HzIKAACaBdsF1qJFi/Tggw9q/fr1CgoKkvTrzyecN2%2Beli5davF0AAAADbNdYP3nP/9RRkaGJHkCS5IGDRqk4uJiq8YCAADwme0Cq1WrVqf8GYSlpaUKCQmxYCIAAIDGsV1gJSYmas6cOTp69Khn2969ezVlyhRdc801Fk4GAADgG9t9TMPDDz%2BsO%2B64Q0lJSaqpqVFiYqKqqqrUuXNnzZs3z%2BrxAAAAGmS7wGrfvr02bNig7du3a%2B/evQoLC9NFF12kXr16eb0nCwAAwK5sF1iSdM4552jAgAFWjwEAAHBabBdYKSkpf3mnis/CAgAAdme7wBo8eLBXYNXU1Gjv3r1yuVy64447LJwMAADAN7YLrNzc3FNuf%2Bedd/Txxx838TQAAACNZ7uPafgzAwYM0FtvvWX1GAAAAA1qNoG1e/du1dXVWT0GAABAg2z3EuGoUaPqbauqqlJxcbFuuOEGCyYCAABoHNsFVlxcXL1/RRgaGqq0tDSNHDnSoqkAAAB8Z7vA4tPaAQBAc2e7wHrjjTd8PnbYsGFncRIAAIDTY7vAmjZtmmpra%2Bu9oT0oKMhrW1BQEIEFAABsyXaB9eyzz%2Br5559XVlaWunTporq6On399dd65plndPvttyspKcnqEQEAAP6S7QJr3rx5WrFihdq1a%2BfZdtVVV6ljx44aM2aMNmzYYOF0AAAADbPd52B9//33ioqKqrc9MjJSJSUlFkwEAADQOLYLrA4dOmjevHkqLy/3bKuoqNDChQt1wQUXWDgZAACAb2z3EuHUqVOVk5Oj/Px8hYeHy%2BFw6OjRowoLC9PSpUutHg8AAKBBtgus3r1767333tP27dt14MAB1dXVqV27drr22mvVqlUrq8cDAABokO0CS5JatGih6667TgcOHFDHjh2tHgcAAKBRbPcerGPHjmnKlCnq0aOHbrzxRkm/vgdr7NixqqiosHg6AACAhtkusObPn689e/ZowYIFcjj%2Bf7yamhotWLDAwskAAAB8Y7vAeuedd/TUU09p0KBBnh/6HBkZqblz52rz5s2n9Zhz5sxRly5dPP9dUFCgtLQ0JSYmKjU1VevWrfM6ftWqVRo4cKASExOVkZGhwsLC0z8hAAAQcGwXWJWVlYqLi6u3PSYmRr/88kujH2/Pnj168803Pf9dWlqqe%2B%2B9V6NGjVJBQYGmTZumGTNmyOVySZK2bt2qJUuW6IknntDOnTvVv39/ZWVlndZzAwCAwGS7wLrgggv08ccfS5LXzx58%2B%2B239be//a1Rj1VbW6tHHnlEmZmZnm3r169XXFyc0tLSFBoaquTkZKWkpGj16tWSpPz8fI0YMULdu3dXWFiYxo4dK0natm3bGZ4ZAAAIFLb7V4S33XabJkyYoFtuuUW1tbVauXKlCgsL9c4772jatGmNeqzXXntNoaGhGjJkiBYvXixJKioqUnx8vNdx8fHx2rRpk2f/4MGDPfscDoe6du0ql8ul1NRUn563tLRUZWVlXtuczpaKjY1t1PxWCQ52eP0OBDqns/61wHViP6yJ/QTymtgusNLT0%2BV0OvXyyy8rODhYTz/9tC666CItWLBAgwYN8vlxfv75Zy1ZskQvvfSS13a32%2B31cw4lqXXr1p5Pjne73fV%2BVE9UVJTXJ8s3JD8/X3l5eV7bsrOzNXHiRJ8fww4iI1tYPQJgC9HR4X%2B6j%2BvEflgT%2BwnENbFdYB06dEi33HKLbrnlljN6nLlz52rEiBHq1KmT9u3b16iv/f1Lk6cjPT1dKSkpXtuczpYqL688o8dtKsHBDkVGtlBFRZVqamqtHgew3KmuXa4T%2B2FN7McOa/JXf0E6m2wXWNddd5127drl%2BReEp6OgoECfffaZNmzYUG9fdHS03G6317by8nLFxMT86X63263OnTv7/PyxsbH1Xg4sKzui6urmdcHX1NQ2u5mBs%2BGvrgOuE/thTewnENfEdi%2BKJiUled4PdbrWrVungwcPqn///kpKStKIESM8j33JJZfU%2B9iFwsJCde/eXZKUkJCgoqIiz76amhrt3r3bsx8AAKAhtruDdd555%2Bmxxx7TihUrdMEFF%2Bicc87x2r9w4cIGH%2BOhhx7S/fff7/nvAwcOKD09XW%2B%2B%2BaZqa2u1fPlyrV69WkOHDtVHH32k7du3Kz8/X5KUkZGhyZMn66abblKXLl303HPPKSQkRP369TN6ngAAwH/ZLrC%2B/fZbXXzxxZLUqDeW/15UVJTXG9Wrq6slSe3bt5ckLV%2B%2BXLNnz9asWbPUoUMHzZ8/X5deeqkkqU%2BfPpo8ebImTZqkgwcP6rLLLtOKFSsUFhZ2JqcFAAACSFDdmb6j25AHHnhAixYt8tq2dOlSZWdnWzSRWWVlR6wewWdOp0PR0eEqL68MuNfMf%2B/GxTusHgE2sWlSr3rbuE7shzWxHzusSdu2rSx5Xtu8B2vr1q31tq1YscKCSQAAAM6MbQLrVDfSbHJzDQAAoFFsE1in%2BliGM/moBgAAAKvYJrAAAAD8BYEFAABgmG0%2BpuHkyZPKyclpcJsvn4MFAABgJdsE1pVXXqnS0tIGtwEAANidbQLrpZdesnoEAAAAI3gPFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGG2%2BWHPAGBXNy7eYfUIjbJpUi%2BrRwACHnewAAAADCOwAAAADCOwAAAADCOwAAAADONN7mgyze2NwgAAnC7uYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABjmt4FVUlKi7OxsJSUlKTk5WQ899JAqKiokSXv27NHtt9%2BuK6%2B8UjfccIOef/55r6/duHGjhgwZoh49emjEiBH68MMPrTgFAADQTPltYGVlZSkyMlJbt27V2rVr9c033%2Bjxxx/XsWPHNH78eP3P//yPPvjgAy1atEjLly/X5s2bJf0aX1OmTFFubq4%2B%2BugjZWZm6r777tOBAwcsPiMAANBc%2BGVgVVRUKCEhQTk5OQoPD1f79u01fPhwffLJJ3rvvfd08uRJ3XPPPWrZsqW6deumkSNHKj8/X5K0evVq9e3bV3379lVoaKiGDh2qSy65ROvWrbP4rAAAQHPhl4EVGRmpuXPnqk2bNp5t%2B/fvV2xsrIqKitSlSxcFBwd79sXHx6uwsFCSVFRUpPj4eK/Hi4%2BPl8vlaprhAQBAs%2Be0eoCm4HK59PLLL2vZsmXatGmTIiMjvfa3bt1abrdbtbW1crvdioqK8tofFRWlb7/91ufnKy0tVVlZmdc2p7OlYmNjT/8kmlBwsMPrdwDNi9MZmNcu37vsJ5DXxO8D69NPP9U999yjnJwcJScna9OmTac8LigoyPO/6%2Brqzug58/PzlZeX57UtOztbEydOPKPHbWqRkS2sHgHAaYiODrd6BEvxvct%2BAnFN/Dqwtm7dqgcffFAzZszQsGHDJEkxMTH6/vvvvY5zu91q3bq1HA6HoqOj5Xa76%2B2PiYnx%2BXnT09OVkpLitc3pbKny8srTO5EmFhzsUGRkC1VUVKmmptbqcQA0UnP5XmMa37vsxw5rYtVfOPw2sHbt2qUpU6boySefVO/evT3bExIS9Oqrr6q6ulpO56%2Bn73K51L17d8/%2B396P9RuXy6XU1FSfnzs2Nrbey4FlZUdUXd28LviamtpmNzMABfx1y/cu%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%2B2zSpl9UjAGcFd7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAM42MaAADwUXP6CAyJj8GwEnewAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADHNaPQAAADg7bly8w%2BoRfLZpUi%2BrRzCKO1gAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACG8a8Im7nm9C9EAAAIFAQWAMAy/CUR/oqXCAEAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsE6hpKRE48aNU1JSkvr376/58%2BertrbW6rEAAEAzwedgncKECRPUrVs3bdmyRQcPHtT48ePVpk0b3XnnnVaPBgAAmgHuYP2By%2BXSV199pdzcXLVq1UpxcXHKzMxUfn6%2B1aMBAIBmgjtYf1BUVKQOHTooKirKs61bt27au3evjh49qoiIiAYfo7S0VGVlZV7bnM6Wio2NNT4vAAD%2BwOn0r3s%2BBNYfuN1uRUZGem37LbbKy8t9Cqz8/Hzl5eV5bbvvvvs0YcIEc4P%2Bn08eG2T8MUtLS5Wfn6/09HSi0CZYE/thTeyHNbGfQF4T/8pFQ%2Brq6s7o69PT07V27VqvX%2Bnp6YamO/vKysqUl5dX7y4crMOa2A9rYj%2Bsif0E8ppwB%2BsPYmJi5Ha7vba53W4FBQUpJibGp8eIjY0NuFIHAAD/jztYf5CQkKD9%2B/fr0KFDnm0ul0udOnVSeHi4hZMBAIDmgsD6g/j4eF122WVauHChjh49quLiYq1cuVIZGRlWjwYAAJqJ4JkzZ860egi7ufbaa7VhwwY9%2Buijeuutt5SWlqYxY8YoKCjI6tGaTHh4uK6%2B%2Bmru2tkIa2I/rIn9sCb2E6hrElR3pu/oBgAAgBdeIgQAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwApQJSUlGjdunJKSktS/f3/Nnz9ftbW19Y5bsmSJunbtqssuu8zr188//2zB1P7vgw8%2BUHJysh544IG/PK62tlaLFi3Sddddp549e2rMmDH68ccfm2jKwOHrejz00EOen2P626%2BrrrqqiaYMLCUlJcrOzlZSUpKSk5P10EMPqaKi4pTHbty4UUOGDFGPHj00YsQIffjhh008bWDwdU3Wrl2rSy%2B9tN6fJ19%2B%2BaUFU599BFaAmjBhgtq1a6ctW7Zo5cqV2rJli1588cVTHnvzzTfL5XJ5/WrTpk0TT%2Bz/nnnmGc2ePVsXXnhhg8e%2B8sorWr9%2BvVasWKFt27YpLi5O2dnZ4idfmdOY9ZCke%2B65x%2Bsa%2BeSTT87yhIEpKytLkZGR2rp1q9auXatvvvlGjz/%2BeL3j9uzZoylTpig3N1cfffSRMjMzdd999%2BnAgQMWTO3ffF0TSerZs2e9P08uv/zyJp64aRBYAcjlcumrr75Sbm7x28k7AAAFUElEQVSuWrVqpbi4OGVmZio/P9/q0QJaaGio1qxZ49Mf6Pn5%2BcrMzNTf//53RURE6IEHHlBxcbG%2B%2BOKLJpg0MDRmPdA0KioqlJCQoJycHIWHh6t9%2B/YaPnz4KWN29erV6tu3r/r27avQ0FANHTpUl1xyidatW2fB5P6rMWsSaAisAFRUVKQOHTooKirKs61bt27au3evjh49Wu/4r7/%2BWqNGjVJiYqJSU1O5zX6WjB49Wq1atWrwuGPHjunbb79VfHy8Z1tERIQuvPBCuVyuszliQPF1PX7z0UcfadiwYerRo4fS0tJUWFh4FqcLTJGRkZo7d67XHfT9%2B/crNja23rFFRUVe14gkxcfHc40Y1pg1%2BW3fnXfeqZ49e%2Bq6667Tm2%2B%2B2VSjNjkCKwC53W5FRkZ6bfsttsrLy722t2/fXh07dtTjjz%2BuHTt2aOTIkcrKytJ3333XZPPC2%2BHDh1VXV%2BcVyNKva/jH9UPT6Nixoy688EItX75cH3zwga666irdddddrMdZ5nK59PLLL%2Buee%2B6pt8/tdnONWOCv1iQmJkZxcXF68MEHtWPHDk2ePFlTp05VQUGBBZOefQRWgPL1vTojR47UU089pQsvvFAtWrRQZmamunbtym12G%2BD9VvaRnZ2tOXPmqF27doqIiNCDDz6okJAQbdmyxerR/Nann36qMWPGKCcnR8nJyac8hmukaTW0Jv369dOzzz6r%2BPh4hYSEKDU1Vddff73Wrl1rwbRnH4EVgGJiYuR2u722ud1uBQUFKSYmpsGv79Chg0pLS8/WeGhA69at5XA4TrmG5557rkVT4feCg4N13nnncZ2cJVu3btW4ceM0depUjR49%2BpTHREdHn/Ia8eV7HBrPlzU5FX/%2B84TACkAJCQnav3%2B/Dh065NnmcrnUqVMnhYeHex37r3/9q97t2%2BLiYnXs2LFJZkV9oaGh6ty5s4qKijzbKioq9MMPP/jtv8axs7q6Os2dO1dfffWVZ9uJEyf0ww8/cJ2cBbt27dKUKVP05JNPatiwYX96XEJCQr33wblcLnXv3v1sjxhwfF2TV199VRs3bvTa5s9/nhBYAei3z%2BtZuHChjh49quLiYq1cuVIZGRmSpEGDBnn%2BBYjb7dasWbP03Xff6fjx43r%2B%2Bef1ww8/aPjw4VaeQsD56aefNGjQIM9nXWVkZGjVqlUqLi7W0aNHtWDBAs/nleHs%2B/16BAUFad%2B%2BfZo1a5Z%2B%2BuknVVZWasGCBTrnnHM0YMAAq0f1K9XV1Zo%2Bfbpyc3PVu3fvevvvuOMOzx/gt956q3bu3Kn33ntPx48f15o1a/T9999r6NChTT22X2vMmpw4cUKPPvqoXC6XTp48qQ0bNuj999/XqFGjmnrsJuG0egBY46mnntKMGTPUq1cvRUREaNSoUbrtttskSXv37tUvv/wiScrJyZEkZWZmyu12q1OnTnrhhRfUvn17y2b3V7/FUXV1tSR53r/z2zejvXv36sSJE5KkUaNGqaysTP/85z9VWVmppKQk5eXlWTO4n2rMejz22GN6/PHHNWLECB09elSXX365XnzxRbVs2dKa4f3U559/ruLiYs2ePVuzZ8/22vf222/rxx9/1OHDhyVJl1xyiRYsWKC5c%2BeqpKREnTp10vLly9W2bVsrRvdbjVmT0aNHq7KyUvfff7/Kysp0/vnna%2BnSpUpISLBi9LMuqI53AQIAABjFS4QAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACG/S9gSy%2BObkcYBAAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-5128205962103775460”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.2247449813177163</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.424865129052576</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.7474066265022029</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0607742541746126</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0504410797016492</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.6784053340012424</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9615961818095771</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.358123605408616</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.2477990967652708</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9540117028315566</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (41)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-5128205962103775460”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.3879179065372472</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:27%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5308832022847476</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5354170205537625</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6260184849149406</td> <td class=”number”>44</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:69%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6862035668256398</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.773987916176593</td> <td class=”number”>87</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:93%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.7820142308066007</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.007890843824686</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:36%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.2861831028170134</td> <td class=”number”>93</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.5525474457519897</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:57%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_CACFP15”>PCT_CACFP15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>52</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1.3782</td>

</tr> <tr>

<th>Minimum</th> <td>0.56811</td>

</tr> <tr>

<th>Maximum</th> <td>2.3593</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram6395216800566172397”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives6395216800566172397,#minihistogram6395216800566172397”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives6395216800566172397”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles6395216800566172397”

aria-controls=”quantiles6395216800566172397” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram6395216800566172397” aria-controls=”histogram6395216800566172397”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common6395216800566172397” aria-controls=”common6395216800566172397”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme6395216800566172397” aria-controls=”extreme6395216800566172397”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles6395216800566172397”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0.56811</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.7939</td>

</tr> <tr>

<th>Q1</th> <td>1.0991</td>

</tr> <tr>

<th>Median</th> <td>1.3297</td>

</tr> <tr>

<th>Q3</th> <td>1.7046</td>

</tr> <tr>

<th>95-th percentile</th> <td>2.2827</td>

</tr> <tr>

<th>Maximum</th> <td>2.3593</td>

</tr> <tr>

<th>Range</th> <td>1.7912</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.60555</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.42491</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.30831</td>

</tr> <tr>

<th>Kurtosis</th> <td>-0.0087477</td>

</tr> <tr>

<th>Mean</th> <td>1.3782</td>

</tr> <tr>

<th>MAD</th> <td>0.33115</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.68721</td>

</tr> <tr>

<th>Sum</th> <td>4316.5</td>

</tr> <tr>

<th>Variance</th> <td>0.18055</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram6395216800566172397”>

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UVHRysrK0sBAQFOrgoAALgLjwxYktSlSxe9//77V%2BxbtWrVFV87cuRIjRw58npNDQAAeDiPvEUIAADgTAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIO5XMBKSEhQZmamjh496uypAAAAXBOXC1hDhw7VunXr1KdPH40ZM0YbN25URUWFs6cFAADgMJcLWKmpqVq3bp0%2B%2BOADtWnTRnPmzFF8fLzmz5%2BvQ4cOOXt6AAAANXK5gHVRhw4dNHXqVG3ZskXPPfecPvjgAw0YMECPP/64/ud//sfZ0wMAALgilw1YFy5c0Lp16/TEE09o6tSpatq0qX7/%2B9%2Brffv2GjVqlNasWePsKQIAAFyWydkTuFRBQYFWrFihlStX6syZM%2BrXr5/efvttde7c2TamS5cumjlzpgYOHOjEmQIAAFyeywWsxMREtWrVSmPHjtVDDz2kkJCQamPi4%2BN18uRJJ8wOAACgZi4XsLKzs9W1a9cax%2B3Zs%2BcGzAYAAKD2XO4ZrIiICI0bN06bNm2ytb311lt64oknZLFYnDgzAAAAx7hcwJo7d65Onz6t1q1b29p69uypqqoqzZs3z4kzAwAAcIzL3SL8/PPPtWbNGoWGhtraWrZsqYyMDD344INOnBkAAIBjXO4K1tmzZ%2BXv71%2Bt3dfXV%2BXl5U6YEQAAQO24XMDq0qWL5s2bp1OnTtnajh8/rlmzZtl9VAMAAICrcrlbhM8995xGjx6t%2B%2B67T4GBgaqqqtKZM2fUokUL/e1vf3P29AAAAGrkcgGrRYsW%2Bvjjj/WPf/xDhw8flq%2Bvr1q1aqXu3bvLz8/vmtY5Z84cvf322/r6668lSTt27NCCBQt08OBB3XLLLRo7dqwGDRpkG5%2Bdna13331XxcXFioiI0PTp0xUVFWVIfQAAwPO5XMCSpPr166tPnz6GrGvfvn1atWqVbbmoqEgTJkzQ9OnTNXDgQO3atUvjx49Xq1atFB0drc2bN2vRokV6/fXXFRERoezsbI0bN04bN25UgwYNDJkTAADwbC73DNaRI0eUnp6uxMRE9e7du9qf2qiqqtILL7ygUaNG2drWrFmjli1bKikpSf7%2B/oqLi1NCQoJyc3MlSTk5ORoyZIhiYmIUEBCgMWPGSJK2bNliWI0AAMCzudwVrOeee05FRUXq3r17na8Yvf/%2B%2B/L399fAgQP16quvSpLy8/MVGRlpNy4yMlLr16%2B39Q8YMMDW5%2Bvrq/bt28tsNisxMbFO8wEAAN7B5QJWXl6ePv30U4WFhdVpPT/%2B%2BKMWLVpU7cF4i8Wipk2b2rWFhISopKTE1h8cHGzXHxwcbOt3RFFRkYqLi%2B3aTKYGCg8Pty37%2BfnaffUG3lgzPIPJdPVjlmP72tX03roSb9zP3lizUVwuYDVu3NiQZ53mzp2rIUOGqHXr1vr%2B%2B%2B9r9Vqr1Vqnbefk5CgzM9OuLTU1VWlpadXGBgXdVKdtuSNvrBnuLTS0oUPjOLZrz9H31pV44372xprryuUC1tixY5WZmanJkyfLx8fnmtaxY8cOffXVV1q7dm21vtDQ0Gq/07CkpMR2xexy/RaLRW3atHF4%2B8nJyUpISLBrM5kaqKTkjG3Zz89XQUE3qbS0XJWVVQ6v2515Y83wDL88dy%2BHY/va1fTeuhJv3M%2BeULOzQrzLBax//OMf%2BvLLL/XRRx/p1ltvla%2Bv/WXJ999/v8Z1rF69WidOnFCvXr0k/f8VqdjYWI0ePbpa8MrLy1NMTIwkKSoqSvn5%2BRo8eLAkqbKyUnv37lVSUpLDNYSHh9vdDpSk4uLTqqiofnBWVlZdtt2TeWPNcG%2BOHq8c27Xnju%2BXN%2B5nb6y5rlwuYAUGBqpHjx51Wse0adP0u9/9zrZ87NgxJScna9WqVaqqqtKyZcuUm5urQYMGaefOndq2bZtycnIkSSkpKZo0aZIefPBBRURE6I033lD9%2BvXVs2fPOs0JAAB4D5cLWHPnzq3zOoKDg%2B0eVK%2BoqJAkNWvWTJK0bNkyzZ49W7NmzVLz5s01f/58tWvXTpLUo0cPTZo0Senp6Tpx4oSio6OVlZWlgICAOs8LAAB4B5cLWJJ08OBBffzxx/rhhx9sgeurr75Sx44dr2l9t956q%2B1T3KWff9/hLz989FIjR47UyJEjr2lbAAAALvdzlzt27NCgQYO0ceNG27NSR44c0aOPPqpPP/3UybMDAAComcsFrFdeeUXPPPOM1qxZY/spwhYtWmjevHlavHixk2cHAABQM5cLWN98841SUlIkye5jGh544AEVFBQ4a1oAAAAOc7mA1ahRI509e7Zae1FRkerXr%2B%2BEGQEAANSOywWsTp06ac6cOSorK7O1HTp0SFOnTtV9993nxJkBAAA4xuV%2BivD3v/%2B9HnvsMcXGxqqyslKdOnVSeXm52rRpo3nz5jl7egAAADVyuYDVrFkzrV27Vtu2bdOhQ4cUEBCgVq1aqVu3btf8q3MAAABuJJcLWJJUr1499enTx9nTAAAAuCYuF7ASEhKueqWKz8ICAACuzuUC1oABA%2BwCVmVlpQ4dOiSz2azHHnvMiTMDAABwjMsFrClTply2/ZNPPtE///nPGzwbAACA2nO5j2m4kj59%2Bujjjz929jQAAABq5DYBa%2B/evbJarc6eBgAAQI1c7hbhiBEjqrWVl5eroKBAffv2dcKMAAAAasflAlbLli2r/RShv7%2B/kpKSNGzYMCfNCgAAwHEuF7D4tHYAAODuXC5grVy50uGxDz300HWcCQAAwLVxuYA1ffp0VVVVVXug3cfHx67Nx8eHgAUAAFySywWs119/XcuXL9e4ceMUEREhq9Wqr7/%2BWn/961/18MMPKzY21tlTBAAAuCqXC1jz5s1TVlaWmjZtamu755571KJFCz3%2B%2BONau3atE2cHAABQM5f7HKxvv/1WwcHB1dqDgoJUWFjohBkBAADUjssFrObNm2vevHkqKSmxtZWWlmrBggW67bbbnDgzAAAAx7jcLcLnnntOkydPVk5Ojho2bChfX1%2BVlZUpICBAixcvdvb0AAAAauRyAat79%2B7aunWrtm3bpmPHjslqtapp06b69a9/rUaNGjl7egAAADVyuYAlSTfddJN69%2B6tY8eOqUWLFs6eDgAAQK243DNYZ8%2Be1dSpU9WxY0f1799f0s/PYI0ZM0alpaVOnh0AAEDNXC5gzZ8/X/v27VNGRoZ8ff9/epWVlcrIyHDizAAAABzjcgHrk08%2B0cKFC/XAAw/YfulzUFCQ5s6dq40bNzp5dgAAADVzuYB15swZtWzZslp7WFiYfvrppxs/IQAAgFpyuYB122236Z///Kck2f3uwQ0bNuhXv/qVs6YFAADgMJf7KcKRI0fq6aef1tChQ1VVVaU333xTeXl5%2BuSTTzR9%2BnRnTw8AAKBGLhewkpOTZTKZ9M4778jPz09Lly5Vq1atlJGRoQceeMDZ0wMAAKiRywWskydPaujQoRo6dKizpwIAAHBNXO4ZrN69e9s9e3Wt9u/fr8cee0ydO3dWXFyc0tPTVVxcLEnasWOHkpKS1KlTJyUmJmr16tV2r83Ozla/fv3UqVMnpaSkKC8vr87zAQAA3sPlAlZsbKzWr19fp3WcP39eo0ePVteuXbVjxw6tXbtWJ06c0MyZM1VUVKQJEyZoxIgR2rFjh6ZPn67nn39eZrNZkrR582YtWrRIf/rTn7R9%2B3b16tVL48aN4ycYAQCAw1zuFuEtt9yil156SVlZWbrttttUr149u/4FCxbUuI7y8nJNnDhRgwcPlslkUlhYmO6//3698847WrNmjVq2bKmkpCRJUlxcnBISEpSbm6vo6Gjl5ORoyJAhiomJkSSNGTNG2dnZ2rJlixITE40vGAAAeByXC1gHDhzQHXfcIUkqKSm5pnUEBwdr2LBhtuWDBw/q73//u/r376/8/HxFRkbajY%2BMjLRdNcvPz9eAAQNsfb6%2Bvmrfvr3MZrPDAauoqMh2O/Iik6mBwsPDbct%2Bfr52X72BN9YMz2AyXf2Y5di%2BdjW9t67EG/ezN9ZsFJcJWBMnTtQrr7yiv/3tb7a2xYsXKzU19ZrXWVhYqH79%2BqmiokLDhw9XWlqannjiCTVt2tRuXEhIiC3MWSwWBQcH2/UHBwfXKuzl5OQoMzPTri01NVVpaWnVxgYF3eTwej2FN9YM9xYa2tChcRzbtefoe%2BtKvHE/e2PNdeUyAWvz5s3V2rKysuoUsJo3by6z2azvvvtOf/jDH/Tss8869Lq6PmSfnJyshIQEuzaTqYFKSs7Ylv38fBUUdJNKS8tVWVlVp%2B25C2%2BsGZ7hl%2Bfu5XBsX7ua3ltX4o372RNqdlaId5mAdblQY8RPE/r4%2BKhly5aaOHGiRowYofj4eFksFrsxJSUlCgsLkySFhoZW67dYLGrTpo3D2wwPD7e7HShJxcWnVVFR/eCsrKy6bLsn88aa4d4cPV45tmvPHd8vb9zP3lhzXbnMTdWLv9i5pjZH7NixQ/369VNV1f8fDL6%2BP5d61113VfvYhby8PNtD7VFRUcrPz7f1VVZWau/evbZ%2BAACAmrhMwDJSVFSUysrKNH/%2BfJWXl%2BvkyZNatGiR7rnnHqWkpKiwsFC5ubk6d%2B6ctm3bpm3btmn48OGSpJSUFK1cuVK7d%2B9WeXm5lixZovr166tnz57OLQoAALgNjwxYjRo10vLly5WXl6d7771XiYmJatSokf785z%2BrcePGWrZsmd555x117txZc%2BbM0fz589WuXTtJUo8ePTRp0iSlp6era9eu2r59u7KyshQQEODkqgAAgLtwmWewLly4oMmTJ9fY5sjnYElSRESE3U8k/lKXLl20atWqK7525MiRGjlypEPbAQAAuJTLBKzOnTurqKioxjYAAOCY/q9%2B4ewpOGx9ejdnT8FQLhOwrnS1CQAAwN145DNYAAAAzkTAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADCYy3xMAzzf/RmfOXsKAADcEFzBAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAg3lswCosLFRqaqpiY2MVFxenadOmqbS0VJK0b98%2BPfzww%2BrcubP69u2r5cuX27123bp1GjhwoDp27KghQ4bo888/d0YJAADATXlswBo3bpyCgoK0efNmffTRR/rPf/6jl19%2BWWfPntXYsWN177336rPPPtMrr7yiZcuWaePGjZJ%2BDl9Tp07VlClTtHPnTo0aNUpPPfWUjh075uSKAACAu/DIgFVaWqqoqChNnjxZDRs2VLNmzTR48GD9%2B9//1tatW3XhwgWNHz9eDRo0UIcOHTRs2DDl5ORIknJzcxUfH6/4%2BHj5%2B/tr0KBBatu2rVavXu3kqgAAgLvwyIAVFBSkuXPn6uabb7a1HT16VOHh4crPz1dERIT8/PxsfZGRkcrLy5Mk5efnKzIy0m59kZGRMpvNN2byAADA7ZmcPYEbwWw265133tGSJUu0fv16BQUF2fWHhITIYrGoqqpKFotFwcHBdv3BwcE6cOCAw9srKipScXGxXZvJ1EDh4eG2ZT8/X7uvAFyXyXT185Tz%2BdrV9N66Evbz9eVOx4IjPD5g7dq1S%2BPHj9fkyZMVFxen9evXX3acj4%2BP7e9Wq7VO28zJyVFmZqZdW2pqqtLS0qqNDQq6qU7bAnD9hYY2dGgc53PtOfreuhL28/XhjsfC1Xh0wNq8ebOeeeYZPf/883rooYckSWFhYfr222/txlksFoWEhMjX11ehoaGyWCzV%2BsPCwhzebnJyshISEuzaTKYGKik5Y1v28/NVUNBNKi0tV2VlVS0rA3Aj/fLcvRzO52tX03vrStjP19f1OhacFdw8NmB9%2BeWXmjp1ql577TV1797d1h4VFaX33ntPFRUVMpl%2BLt9sNismJsbWf/F5rIvMZrMSExMd3nZ4eLjd7UBJKi4%2BrYqK6idkZWXVZdsBuA5Hz1HO59pzx/eL/Xx9eNp76lk3PP9PRUWFZsyYoSlTptiFK0mKj49XYGCglixX3r%2BMAAAT6klEQVRZovLycu3Zs0crVqxQSkqKJGn48OHavn27tm7dqnPnzmnFihX69ttvNWjQIGeUAgAA3JBHXsHavXu3CgoKNHv2bM2ePduub8OGDVq6dKleeOEFZWVl6eabb9bEiRPVs2dPSVLbtm2VkZGhuXPnqrCwUK1bt9ayZcvUpEkTJ1QCAADckUcGrHvuuUdff/31Vce89957V%2Bzr27ev%2Bvbta/S0AACAl/DIW4QAAADORMACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAzmsQHrs88%2BU1xcnCZOnFitb926dRo4cKA6duyoIUOG6PPPP7f1VVVV6ZVXXlHv3r3VpUsXPf744zpy5MiNnDoAAHBzHhmw/vrXv2r27Nm6/fbbq/Xt27dPU6dO1ZQpU7Rz506NGjVKTz31lI4dOyZJevfdd7VmzRplZWVpy5YtatmypVJTU2W1Wm90GQAAwE15ZMDy9/fXihUrLhuwcnNzFR8fr/j4ePn7%2B2vQoEFq27atVq9eLUnKycnRqFGjdOeddyowMFATJ05UQUGB9uzZc6PLAAAAbsrk7AlcD48%2B%2BugV%2B/Lz8xUfH2/XFhkZKbPZrLNnz%2BrAgQOKjIy09QUGBur222%2BX2WzW3Xff7dD2i4qKVFxcbNdmMjVQeHi4bdnPz9fuKwDXZTJd/TzlfL52Nb23roT9fH2507HgCI8MWFdjsVgUHBxs1xYcHKwDBw7o1KlTslqtl%2B0vKSlxeBs5OTnKzMy0a0tNTVVaWlq1sUFBN9Vi9gCcITS0oUPjOJ9rz9H31pWwn68PdzwWrsbrApakGp%2BnquvzVsnJyUpISLBrM5kaqKTkjG3Zz89XQUE3qbS0XJWVVXXaHoDr65fn7uVwPl%2B7mt5bV8J%2Bvr6u17HgrODmdQErNDRUFovFrs1isSgsLEwhISHy9fW9bH/jxo0d3kZ4eLjd7UBJKi4%2BrYqK6idkZWXVZdsBuA5Hz1HO59pzx/eL/Xx9eNp76nUBKyoqSnl5eXZtZrNZiYmJ8vf3V5s2bZSfn6%2BuXbtKkkpLS3X48GHdddddzphujfq/%2BoWzpwAAAC7hWU%2BUOWD48OHavn27tm7dqnPnzmnFihX69ttvNWjQIElSSkqKsrOzVVBQoLKyMmVkZKh9%2B/aKjo528swBAIC78MgrWBfDUEVFhSRp06ZNkn6%2BUtW2bVtlZGRo7ty5KiwsVOvWrbVs2TI1adJEkjRixAgVFxfrkUce0ZkzZxQbG1vtgXUAAICr8ciAZTabr9rft29f9e3b97J9Pj4%2BSktLu%2BxP/AEAADjC624RAgAAXG8ELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAM5pGfgwUAwPVwz/QNzp4C3ARXsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYCZnTwAAXF3/V79w9hQAuBmuYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIB1GYWFhXryyScVGxurXr16af78%2BaqqqnL2tAAAgJvgYxou4%2Bmnn1aHDh20adMmnThxQmPHjtXNN9%2Bs3/72t86eGgAAcANcwbqE2WzW/v37NWXKFDVq1EgtW7bUqFGjlJOT4%2BypAQAAN8EVrEvk5%2BerefPmCg4OtrV16NBBhw4dUllZmQIDA2tcR1FRkYqLi%2B3aTKYGCg8Pty37%2BfnafQUAb2Qyuc%2B/gfx7fX2507HgCALWJSwWi4KCguzaLoatkpIShwJWTk6OMjMz7dqeeuopPf3007bloqIivf3260pOTrYLXrX175ceuObX3mhFRUXKycmpc83uhJqp2VN5a82PNfuP19XsbfvZKJ4VFw1itVrr9Prk5GR99NFHdn%2BSk5PtxhQXFyszM7PalS5PRs3egZq9AzV7B2%2Bs2ShcwbpEWFiYLBaLXZvFYpGPj4/CwsIcWkd4eDhJHwAAL8YVrEtERUXp6NGjOnnypK3NbDardevWatiwoRNnBgAA3AUB6xKRkZGKjo7WggULVFZWpoKCAr355ptKSUlx9tQAAICb8Js5c%2BZMZ0/C1fz617/W2rVr9eKLL%2Brjjz9WUlKSHn/8cfn4%2BBi6nYYNG6pr165edWWMmr0DNXsHavYO3lizEXysdX2iGwAAAHa4RQgAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACloEKCwv15JNPKjY2Vr169dL8%2BfNVVVVVbdyiRYvUvn17RUdH2/358ccfJUnnzp3TH/7wB/Xo0UOxsbFKS0tTSUnJjS7HIY7WPHr06Gr1tm/fXpmZmZKkRx55RB06dLDrHzRo0I0ux2GfffaZ4uLiNHHixKuOq6qq0iuvvKLevXurS5cuevzxx3XkyBFbv8ViUXp6uuLi4tS9e3dNnz5dZ8%2Bevd7Tr7Xa1JuZmamEhAR17NhRycnJ%2Bve//23rd6f97GjN06ZNs/0O04t/7rnnHlu/u%2BxjyfGa%2B/XrV%2B18bteunf7%2B979LkhISEhQVFWXXP27cuBtRQq0VFhYqNTVVsbGxiouL07Rp01RaWnrZsevWrdPAgQPVsWNHDRkyRJ9//rmtr6Zz3ZXUpuaNGzdq0KBB6tixo/r166cPPvjA1lfT9zKvZ4VhBg8ebJ0xY4a1tLTUeujQIWvfvn2ty5cvrzZu4cKF1qlTp15xPXPnzrUOGTLE%2BsMPP1hLSkqsTz31lHXs2LHXc%2BrXzNGaL3Xq1Clrt27drPv377darVbrww8/bP3www%2Bv93QNkZWVZe3bt691xIgR1vT09KuOzc7Otvbq1ct64MAB6%2BnTp61//OMfrQMHDrRWVVVZrVar9amnnrI%2B%2BeST1hMnTliPHTtmTU5Otr744os3ogyH1abeN954w9qzZ0/rN998Yz137px14cKF1q5du1pPnz5ttVrdZz/XpuapU6daFy5ceMV%2Bd9jHVmvtar7U4cOHrffdd5%2B1uLjYarVarb169bLu3LnzekzTcA8%2B%2BKB12rRp1rKyMuvRo0etQ4YMsT733HPVxu3du9caFRVl3bp1q/Xs2bPWVatWWWNiYqxHjx61Wq01n%2BuuxNGa9%2BzZY42Ojrb%2B93//t/XChQvWrVu3Wjt06GD917/%2BZbVaa/5e5u24gmUQs9ms/fv3a8qUKWrUqJFatmypUaNGKScnp1brqaio0IoVKzRhwgTdcsstCgkJUXp6urZu3arjx49fp9lfm7rU/Oqrr%2Br%2B%2B%2B9XRETEDZipsfz9/bVixQrdfvvtNY7NycnRqFGjdOeddyowMFATJ05UQUGB9uzZox9//FGbNm3SxIkTFRYWpqZNm2rChAn68MMPdeHChRtQiWNqU6%2Bvr6%2BeffZZtWnTRvXr19fo0aNlsVj0zTff3ICZGqc2NV%2BNu%2BxjqW41v/TSSxo9erRuvvnm6zCz66e0tFRRUVGaPHmyGjZsqGbNmmnw4MF2V10vys3NVXx8vOLj4%2BXv769Bgwapbdu2Wr16taSrn%2BuupDY1WywWjR07Vn369JHJZFJ8fLzatm172bGojoBlkPz8fDVv3lzBwcG2tg4dOujQoUMqKyurNv7rr7/WiBEj1KlTJyUmJtouNR8%2BfFinT59Whw4dbGPvvPNOBQQEKD8///oXUgu1rfmi7777TitXrtTTTz9t175u3ToNGDBAHTt21KhRo3T48OHrNve6ePTRR9WoUaMax509e1YHDhxQZGSkrS0wMFC33367zGaz9u3bJz8/P7uQ2aFDB/300086ePDgdZn7tXC0XkkaNWqU%2Bvfvb1s%2BduyYJCk8PNzW5g77uTY1S9LOnTv10EMPqWPHjkpKSlJeXp4kuc0%2Blmpf80U7d%2B7Uvn379Oijj9q1Z2dnq0%2BfPurYsaPS0tJ04sQJo6ZqmKCgIM2dO9cuGB49etTueL0oPz/f7lyWpMjISJnN5hrPdVdSm5p79Oih1NRU23JFRYWKi4vVtGlTW9uVvpeBgGUYi8WioKAgu7aLwePS56eaNWumFi1a6OWXX9YXX3yhYcOGady4cTp48KAsFoskVVtXUFCQyz2HVZuafykrK0tDhw5VWFiYre3OO%2B9UmzZt9F//9V/69NNPFRYWpjFjxuj8%2BfPXZ/I3wKlTp2S1Wu0CqPTze1RSUiKLxaLAwED5%2BPjY9UlXf//cxfnz5zV9%2BnQNGjRIt956qyTP3M8tWrTQ7bffrmXLlumzzz7TPffco9GjR3vFPpakpUuX6re//a3q169va2vfvr3uuusurVq1SuvWrZPFYtHvfvc7J87SMWazWe%2B8847Gjx9frc9isVzxXK7pXHdlV6v5UhkZGWrQoIEGDBgg6erfyyCZnD0BT2K1Wh0aN2zYMA0bNsy2PGrUKH388cdavXq1evToUat1OVtt52mxWLRq1SqtX7/ern3mzJl2y3/84x8VGxurXbt26b777qvrNJ3qau%2BRu%2Bzn2iorK1Nqaqr8/Pw0a9YsW7sn7udf/g9fkp555hmtXbtWmzZtUkBAgMfuY0n65ptvtHv3bv3lL3%2Bxa1%2B8eLHt7w0bNtQLL7ygAQMG6PDhw7rttttu9DQdsmvXLo0fP16TJ09WXFzcZcfUtC/dbV87UrP0c10ZGRlau3atsrOz5e/vL%2Bnq38vS09Ov%2B/xdHVewDBIWFma7%2BnSRxWKRj4%2BP3ZWaK2nevLmKiopsYy9d16lTp9S4cWPjJmyAa6n5008/VatWrdSiRYurrjswMFDBwcEu99xZbYSEhMjX1/ey71Hjxo0VFhamsrIyVVZW2vVJcrl9XRsnT57Uww8/rEaNGumNN95QgwYNrjjWE/bzpfz8/HTLLbfYzmdP3McXbdiwQffee%2B9V97H0879vklRUVHQjplVrmzdv1pNPPqnnnnuu2q3Oi0JDQy97LoeFhdV4rrsiR2qWfv7pyGnTpmnz5s167733dMcdd1x1vRe/l4GAZZioqCgdPXpUJ0%2BetLWZzWa1bt1aDRs2tBv7l7/8RTt27LBrKygoUIsWLdSiRQsFBwfbPW/1zTff6Pz584qKirq%2BRdRSbWq%2B6NNPP1W3bt3s2srKyjRz5ky7b7InT57UyZMnawxirszf319t2rSx25elpaU6fPiw7rrrLrVv315Wq1X79%2B%2B39ZvNZgUFBalVq1bOmHKdnTt3TmPHjlWHDh20cOFCBQQE2Po8cT9brVbNnTvXbh%2BeP39ehw8fVosWLTxyH//S5c7nwsJCvfDCC3a3fQsKCiTJJffzl19%2BqalTp%2Bq1117TQw89dMVxUVFRtmfrLjKbzYqJianxXHc1jtYsSXPmzNF//vMfvffee9X239W%2Bl4GAZZiLn4OzYMEClZWVqaCgQG%2B%2B%2BaZSUlIkSQ888IDtJy8sFotmzZqlgwcP6ty5c1q%2BfLkOHz6swYMHy8/PT8OHD9fSpUt19OhRlZSU6M9//rPuv/9%2Bl/sJndrUfNG%2Bfftsz%2BNcFBgYqD179mj27NmyWCw6deqUZs2apYiICHXs2PGG1WOE48eP64EHHrB9/k1KSoqys7NVUFCgsrIyZWRk2D43JiwsTP369dOrr76qkydP6tixY1q8eLGSkpJkMrnH3ftL612%2BfLnq1aunF198Ub6%2B9v%2B8eMp%2B/mXNPj4%2B%2Bv777zVr1iwdP35cZ86cUUZGhurVq6c%2Bffp4xD6Wqu9n6ecgeeDAgWrnc%2BPGjbV582bNmzdPP/30k44fP665c%2BeqV69edg9Hu4KKigrNmDFDU6ZMUffu3av1P/bYY1q3bp0kafjw4dq%2Bfbu2bt2qc%2BfOacWKFfr2229tn%2BN2tXPdldSm5l27dmn16tXKyspSSEhItbFX%2B14GnsEy1MKFC/X888%2BrW7duCgwM1IgRIzRy5EhJ0qFDh/TTTz9JkiZPnizp5/vVFotFrVu31ltvvaVmzZpJktLS0nTmzBn95je/UUVFhXr16lXt2RVX4WjNFxUXF182KC5evFhz5sxRv379dP78ed13333Kysqq9k3aFVz8B7OiokKStGnTJkk//2/2woULOnTokO1/7yNGjFBxcbEeeeQRnTlzRrGxsbYPV5V%2BfgbphRdeUO/evVWvXj09%2BOCDNX7I441Wm3o//PBDHT16VDExMXbrGD9%2BvCZMmOA2%2B7k2Nb/00kt6%2BeWXNWTIEJWVlemuu%2B7S22%2B/bbtt5g77WKpdzdLP31wrKiqqnc8BAQF6/fXXNW/ePNszpffff79%2B//vf34gyamX37t0qKCjQ7NmzNXv2bLu%2BDRs26MiRIzp16pQkqW3btsrIyNDcuXNVWFio1q1ba9myZWrSpImkms91V1Gbmj/88EOdPn1avXr1shvXpUsXLV%2B%2BvMbvZd7Ox%2BpuT%2BUBAAC4ONf6byMAAIAHIGABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAG%2B1/OXM9AUrCy8QAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common6395216800566172397”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.7289336306951872</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.3873636055707077</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.8171991793384535</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.2845439145672</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0990853879038844</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.7137243224697</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0999110030758177</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.2387354644649968</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.412745738578616</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.1208052847302827</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (41)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme6395216800566172397”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.5681124377128487</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:39%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6091323979652933</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6232658886522023</td> <td class=”number”>15</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6841797133340548</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:23%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.7053017348165783</td> <td class=”number”>44</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>2.1129116830561117</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.238598341499091</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:69%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.2827348294475462</td> <td class=”number”>75</td> <td class=”number”>2.3%</td> <td>

<div class=”bar” style=”width:80%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.344248520663155</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:57%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.3593099847589114</td> <td class=”number”>93</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_DIABETES_ADULTS08”>PCT_DIABETES_ADULTS08<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>127</td>

</tr> <tr>

<th>Unique (%)</th> <td>4.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>9.9147</td>

</tr> <tr>

<th>Minimum</th> <td>3</td>

</tr> <tr>

<th>Maximum</th> <td>18.2</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram8369817400398454584”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives8369817400398454584”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles8369817400398454584”

aria-controls=”quantiles8369817400398454584” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram8369817400398454584” aria-controls=”histogram8369817400398454584”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common8369817400398454584” aria-controls=”common8369817400398454584”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme8369817400398454584” aria-controls=”extreme8369817400398454584”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles8369817400398454584”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>3</td>

</tr> <tr>

<th>5-th percentile</th> <td>6.7</td>

</tr> <tr>

<th>Q1</th> <td>8.5</td>

</tr> <tr>

<th>Median</th> <td>9.8</td>

</tr> <tr>

<th>Q3</th> <td>11.3</td>

</tr> <tr>

<th>95-th percentile</th> <td>13.445</td>

</tr> <tr>

<th>Maximum</th> <td>18.2</td>

</tr> <tr>

<th>Range</th> <td>15.2</td>

</tr> <tr>

<th>Interquartile range</th> <td>2.8</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>2.0578</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.20755</td>

</tr> <tr>

<th>Kurtosis</th> <td>0.21918</td>

</tr> <tr>

<th>Mean</th> <td>9.9147</td>

</tr> <tr>

<th>MAD</th> <td>1.6329</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.18334</td>

</tr> <tr>

<th>Sum</th> <td>31053</td>

</tr> <tr>

<th>Variance</th> <td>4.2345</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram8369817400398454584”>

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TR8%2BHCrxwAAALgiPhdY3377rbKysvTVV1/p9OnTF13%2BzjvvWDAVAABA8/lcYM2fP1%2BVlZUaPHiwOnToYPU4AAAALeZzgVVaWqp33nlHkZGRVo8CAABwRXzubRquv/56zlwBAIA2zecCa%2BrUqVq7dq08Ho/VowAAAFwRn3uK8N1339XHH3%2Bs1157Tf/0T/8kh8O7AV955RWLJgMAAGgenwuskJAQDRkyxOoxAAAArpjPBdby5cutHgEAAOCq%2BNxrsCTpv//7v5Wdna1nnnmm8dgnn3xi4UQAAADN53OBVVxcrPHjx2vXrl0qLCyU9NObjz788MO8ySgAAGgTfC6w1qxZo1/%2B8pfasWOHAgICJP30%2BYQrVqzQunXrLJ4OAACgaT4XWH/%2B85%2BVlpYmSY2BJUmjR4/W4cOHrRoLAACg2XwusEJDQy/5GYSVlZVq166dBRMBAAC0jM8FVnx8vJYtW6ZTp041Hjty5Ijmzp2rO%2B64w8LJAAAAmsfn3qbhmWee0SOPPKKEhAQ1NDQoPj5edXV16tatm1asWGH1eAAAAE3yucDq3LmzCgsLtW/fPh05ckTBwcG65ZZbNGjQIK/XZAEAAPgqnwssSbruuus0fPhwq8cAAAC4Ij4XWMnJyZc9U8V7YQEAAF/nc4E1ZswYr8BqaGjQkSNHVFJSokceecTCyQAAAJrH5wIrMzPzksffeustffjhh9d4GgAAgJbzubdp%2BHuGDx%2BuN954w%2BoxAAAAmtRmAuvQoUPyeDxWjwEAANAkn3uKcPLkyRcdq6ur0%2BHDhzVy5EgLJgIAAGgZnwusLl26XPRThEFBQUpJSdEDDzxg0VQAAADN53OBxbu1AwCAts7nAuv1119v9nXvu%2B%2B%2BVpwEAADgyvhcYC1YsEBut/uiF7QHBAR4HQsICCCwAACAT/K5wPr973%2BvTZs2adq0aerRo4c8Ho%2B%2B/PJL/e53v9NDDz2khIQEq0cEAAC4LJ8LrBUrVmjjxo2Kjo5uPNa/f3/ddNNNeuyxx1RYWGjhdAAAAE3zuffB%2BuabbxQeHn7R8bCwMFVUVFgwEQAAQMv4XGDFxMRoxYoVqqmpaTx28uRJZWVl6eabb7ZwMgAAgObxuacI58%2Bfrzlz5ig3N1cdO3aUw%2BHQqVOnFBwcrHXr1lk9HgAAQJN8LrAGDx6svXv3at%2B%2BfTp27Jg8Ho%2Bio6N15513KjQ01OrxAAAAmuRzgSVJ7du311133aVjx47ppptusnocAACAFvG512CdPn1ac%2BfOVb9%2B/XT33XdL%2Buk1WFOmTNHJkyctng4AAKBpPhdYq1atUnl5uVavXi2H4//Ha2ho0OrVqy2cDAAAoHl8LrDeeust/cd//IdGjx7d%2BKHPYWFhWr58uXbt2mXxdAAAAE3zucCqra1Vly5dLjoeGRmpH3/88Ypuc9myZerRo0fj74uLi5WSkqL4%2BHiNHTtWBQUFXtffunWrRo0apfj4eKWlpam0tPSK7hcAAPgnnwusm2%2B%2BWR9%2B%2BKEkeX324Jtvvql//Md/bPHtlZeXKz8/v/H3lZWVeuqppzR58mQVFxdrwYIFWrRokUpKSiRJu3fvVnZ2tlauXKkDBw5o2LBhmjZt2hXHHQAA8D8%2BF1gPPvigZsyYoeeee05ut1ubN2/WnDlzNH/%2BfD3yyCMtui23263FixcrPT298diOHTvUpUsXpaSkKCgoSImJiUpOTlZeXp4kKTc3VxMnTlRcXJyCg4M1ZcoUSdKePXuM7QgAAOzN596mITU1VU6nUy%2B%2B%2BKICAwO1fv163XLLLVq9erVGjx7dott65ZVXFBQUpHHjxun555%2BXJJWVlSk2NtbrerGxsSoqKmq8fMyYMY2XORwO9ezZUyUlJRo7dmyz7reyslJVVVVex5zODoqKimrR/L4oMNDh9asd%2BcOOsDen035/d/3hccmO9uJzgXXixAndf//9uv/%2B%2B6/qdv76178qOztb27Zt8zrucrm8Pkhakjp16tT40Twul%2Buiz0IMDw/3%2BuiepuTm5mrt2rVex6ZPn66MjIyWrODTwsLaWz1Cq/OHHWFPEREdrR6h1fjD45Id7cHnAuuuu%2B7Sxx9/3PgThFdq%2BfLlmjhxorp27aqjR4%2B26Gv/9rVfVyI1NVXJyclex5zODqqpqb2q2/UFgYEOhYW118mTdWpocFs9Tqvwhx1hb3b4XnMhf3hcsmPrsOofHD4XWAkJCSoqKvJ6mq6liouL9cknn6iwsPCiyyIiIuRyubyO1dTUKDIy8u9e7nK51K1bt2bff1RU1EVPB1ZV/aD6evs8YBoa3Lba51L8YUfYk53/3vrD45Id7cHnAusf/uEf9Jvf/EYbN27UzTffrOuuu87r8qysrCZvo6CgQNXV1Ro2bJik/z8jlZCQoEcfffSi8CotLVVcXJwkqXfv3iorK9OECRMk/fQGp4cOHVJKSspV7wYAAPyDzwXW119/rVtvvVWSWvS6p781b948/eIXv2j8/bFjx5Samqr8/Hy53W5t2LBBeXl5Gj9%2BvD744APt27dPubm5kqS0tDTNnj1b99xzj3r06KE//OEPateunYYOHXrVuwEAAP/gM4E1a9YsrVmzxutF6evWrdP06dNbfFvh4eFeL1Svr6%2BXJHXu3FmStGHDBi1dulTPPvusYmJitGrVKt12222SpCFDhmj27NmaOXOmqqur1adPH23cuFHBwcFXsx4AAPAjAZ6rfUW3IXFxcfrss8%2BaPNZWVVX9YPUIRjidDkVEdFRNTa1tnz9vqzve/fz7Vo8AH1E0c5DVIxjXVh%2BXLcGOrePGG0Ovyf1cyGfeiOJSnecj7QcAANAiPhNYl3pbhqt9qwYAAAAr%2BExgAQAA2AWBBQAAYJjP/BThuXPnNGfOnCaPNed9sAAAAKzkM4F1%2B%2B23q7KyssljAAAAvs5nAuvCD2UGAABoq3gNFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGE%2B81mEAAAz7n7%2BfatHaLaimYOsHgFoFZzBAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMMxp9QCArxqx%2Bj2rRwAAtFGcwQIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADDMtoFVUVGh6dOnKyEhQYmJiZo3b55OnjwpSSovL9dDDz2k22%2B/XSNHjtSmTZu8vnbnzp0aN26c%2BvXrp4kTJ2r//v1WrAAAANoo2wbWtGnTFBYWpt27d%2Bu1117TV199peeee06nT5/W1KlT9S//8i967733tGbNGm3YsEG7du2S9FN8zZ07V5mZmfrggw%2BUnp6up59%2BWseOHbN4IwAA0FbYMrBOnjyp3r17a86cOerYsaM6d%2B6sCRMm6ODBg9q7d6/OnTunJ598Uh06dFCvXr30wAMPKDc3V5KUl5enpKQkJSUlKSgoSOPHj1f37t1VUFBg8VYAAKCtcFo9QGsICwvT8uXLvY599913ioqKUllZmXr06KHAwMDGy2JjY5WXlydJKisrU1JSktfXxsbGqqSkpNn3X1lZqaqqKq9jTmcHRUVFtXQVnxMY6PD61Y7svBvga5zO5j3e/Ol7Dzvagy0D60IlJSV68cUXlZOTo6KiIoWFhXld3qlTJ7lcLrndbrlcLoWHh3tdHh4erq%2B//rrZ95ebm6u1a9d6HZs%2BfboyMjKufAkfExbW3uoRANhARETHFl3fH773sKM92D6wPvroIz355JOaM2eOEhMTVVRUdMnrBQQENP53j8dzVfeZmpqq5ORkr2NOZwfV1NRe1e36gsBAh8LC2uvkyTo1NLitHqdVnN8RQOtr7vdFf/rew45mtTTiTbF1YO3evVu//OUvtWjRIt13332SpMjISH3zzTde13O5XOrUqZMcDociIiLkcrkuujwyMrLZ9xsVFXXR04FVVT%2Bovt4%2BD5iGBret9gFgjZZ%2BH/GH7z3saA%2B2fRL0448/1ty5c/Xb3/62Ma4kqXfv3vryyy9VX1/feKykpERxcXGNl5eWlnrd1t9eDgAA0BRbBlZ9fb0WLlyozMxMDR482OuypKQkhYSEKCcnR3V1dfrss8%2B0fft2paWlSZImTZqkAwcOaO/evTpz5oy2b9%2Bub775RuPHj7diFQAA0AbZ8inCTz/9VIcPH9bSpUu1dOlSr8vefPNNrV%2B/XosXL9bGjRt1ww03aNasWRo6dKgkqXv37lq9erWWL1%2BuiooKde3aVRs2bNCNN95owSYAAKAtsmVg9e/fX19%2B%2BeVlr/Pyyy//3ctGjhypkSNHmh4LAAD4CVs%2BRQgAAGAlAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwp9UDAAD8193Pv2/1CC1SNHOQ1SOgjeAMFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGG8kzuumbb2js0AAFwpzmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYxoc9AwDQTG3tQ%2BuLZg6yegS/xRksAAAAwziD1ca1tX9NAQDgDziDdQkVFRV64oknlJCQoGHDhmnVqlVyu91WjwUAANoIzmBdwowZM9SrVy%2B9/fbbqq6u1tSpU3XDDTfoX//1X60eDQAAtAEE1gVKSkr0xRdfaPPmzQoNDVVoaKjS09O1ZcsWAgsA0Ka0pZeR2O0F%2BQTWBcrKyhQTE6Pw8PDGY7169dKRI0d06tQphYSENHkblZWVqqqq8jrmdHZQVFSU8XkBALADp9Ner1oisC7gcrkUFhbmdex8bNXU1DQrsHJzc7V27VqvY08//bRmzJhhbtD/c/A3o43f5uVUVlYqNzdXqamptg1GdrQPf9jTH3aU/GNPdrQXe%2BWiIR6P56q%2BPjU1Va%2B99prXf1JTUw1NZ62qqiqtXbv2ojN0dsKO9uEPe/rDjpJ/7MmO9sIZrAtERkbK5XJ5HXO5XAoICFBkZGSzbiMqKsr2ZQ4AAP4%2BzmBdoHfv3vruu%2B904sSJxmMlJSXq2rWrOnbsaOFkAACgrSCwLhAbG6s%2BffooKytLp06d0uHDh7V582alpaVZPRoAAGgjApcsWbLE6iF8zZ133qnCwkL9%2B7//u9544w2lpKToscceU0BAgNWj%2BYSOHTtq4MCBtj6jx4724Q97%2BsOOkn/syY72EeC52ld0AwAAwAtPEQIAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYKHFli1bph49elg9RqvJycnR4MGD1bdvX6Wnp%2Bvo0aNWj2TUoUOH9PDDD6t///4aNGiQMjMzvT7cvK167733lJiYqFmzZl102c6dOzVu3Dj169dPEydO1P79%2By2Y8Opdbsddu3Zp/Pjx6tevn0aNGqVXX33VggnNuNye59XW1mro0KGaN2/eNZzMnMvtePz4cT355JPq27evEhMTlZWVJbfbbcGUV%2B9ye7700ksaNWpU49/Zbdu2WTBh6yGw0CLl5eXKz8%2B3eoxW89JLL6mgoEBbt27V/v371bVrV73wwgtWj2VMfX29nnjiCfXt21cHDhxQYWGhTpw4obb%2BkaS/%2B93vtHTpUv3zP//zRZeVl5dr7ty5yszM1AcffKD09HQ9/fTTOnbsmAWTXrnL7fj5558rMzNTGRkZ%2BtOf/qT58%2Bfr17/%2BtQ4ePGjBpFfncnv%2BrezsbJ06deoaTWXW5Xb0eDx6%2BumnFRMTo/3792vbtm0qLi7Whx9%2BaMGkV%2Bdye%2B7bt0%2BrVq35715xAAAGlUlEQVTSypUr9dFHH2nlypXKysrS3r17r/2grYTAQrO53W4tXrxY6enpVo/SajZt2qRZs2bp1ltvVUhIiBYuXKiFCxdaPZYxVVVVqqqq0r333qt27dopIiJCI0aMUHl5udWjXZWgoCBt3779kt/I8/LylJSUpKSkJAUFBWn8%2BPHq3r27CgoKLJj0yl1uR5fLpalTp2r48OFyOp1KSkpS9%2B7d22RgXW7P87744gsVFhZqwoQJ13Aycy6345/%2B9Cd9%2B%2B23%2Brd/%2BzeFhIToZz/7mbZv36477rjDgkmvzuX2LC0tVbdu3RQXFyeHw6G4uDh1795dhw4dsmDS1kFgodleeeUVBQUFady4cVaP0iqOHz%2Buo0eP6vvvv9eYMWOUkJCgjIwMWzx9dl50dLR69uyp3Nxc1dbWqrq6Wrt27dLQoUOtHu2qPPzwwwoNDb3kZWVlZYqNjfU6Fhsbq5KSkmsxmjGX23HIkCGaPn164%2B/r6%2BtVVVWl6OjoazWeMZfbU/rpDM%2BSJUs0a9YshYWFXcPJzLncjh999JG6d%2B%2BuNWvWKCEhQXfddZc2bdp0jSc043J73nnnnfr666/14Ycf6uzZs/rkk090%2BPBhDR48%2BBpP2XoILDTLX//6V2VnZ2vx4sVWj9Jqzj9l9Oabb2rz5s3Kz8/XsWPHbHUGy%2BFwKDs7W%2B%2B8847i4%2BOVmJio%2Bvp6zZkzx%2BrRWo3L5VJ4eLjXsfDwcNXU1Fg0UetbvXq1OnTooDFjxlg9inG5ubkKCAjQxIkTrR6lVRw7dkyffvqprr/%2Beu3du1e/%2BtWvtGbNGr399ttWj2bUz3/%2Bcz3zzDN69NFH1adPHz300EOaOXOmfv7zn1s9mjEEFppl%2BfLlmjhxorp27Wr1KK3G4/FIkqZMmaLo6Gh17txZM2bM0O7du3XmzBmLpzPj7NmzmjZtmkaPHq2DBw/q3XffVWhoqDIzM60erVWd/7O1O4/Ho1WrVqmwsFA5OTkKCgqyeiSjqqur9dvf/lZLlixRQECA1eO0Co/Ho8jISE2ZMkXt27dXUlKSRowYoaKiIqtHM%2BqDDz5QVlaWfv/73%2Bvzzz/Xli1btH79eluFJIGFJhUXF%2BuTTz7xegrCjm644QZJ8nraISYmRh6PR9XV1VaNZVRxcbGOHj2q2bNnKzQ0VNHR0crIyNB//dd/yeVyWT1eq4iIiLhoN5fLpcjISIsmah1ut1vz5s3T7t279fLLL%2BvWW2%2B1eiTjVqxYofvuu8/WP8V84403XvS0WkxMjKqqqiyaqHW8/PLLGjlypO644w4FBQWpf//%2BGjt2rLZv3271aMY4rR4Avq%2BgoEDV1dUaNmyYpP8/G5CQkKBf/epXGjt2rJXjGdO5c2eFhISovLxcvXr1kiRVVFTouuuuU1RUlMXTmdHQ0CC32%2B11Rufs2bMWTtT6evfurdLSUq9jJSUltvl7e96yZcv01Vdf6eWXX1anTp2sHqdVFBQUKCwsTK%2B99pok6fTp03K73dqzZ0%2Bb/Cm7S/nZz36mb7/9VrW1terYsaOkn74PxcTEWDyZWW63Ww0NDV7H7Pa9iDNYaNK8efP01ltvKT8/X/n5%2Bdq4caMkKT8/X8nJyRZPZ47T6VRKSorWr1%2Bvv/zlL6qurta6des0btw4OZ32%2BLdIv3791KFDB2VnZ6uurk41NTXKycnRgAEDbPt/ypMmTdKBAwe0d%2B9enTlzRtu3b9c333yj8ePHWz2aMR999JEKCgq0ceNG2/45Sj/9aP%2BOHTsavxdNnjxZycnJtnrrmOTkZIWFhWnlypX68ccfVVxcrLffftt2rzlLTk7WW2%2B9pYMHD6q%2Bvl6ff/65ioqKNGLECKtHMybA4y8vToAxR48e1V133aUvv/zS6lGMO3v2rJYvX6433nhD586d06hRo7Ro0aLGf0naQWlpqZ577jl98cUXateunQYOHKh58%2Ba1yZ84O69Pnz6SfvrpOUmNQXz%2BJwV37dqlrKwsVVRUqGvXrlqwYIEGDBhgzbBX6HI7zp8/X3/84x8v%2BofAgAED2txPoDX1Z/m3srOzVVFRoRUrVly7AQ1oasc///nPWrx4scrKyhQZGalf/OIXbfItKZrac8uWLfrP//xPHT9%2BXNHR0Zo0aZIeffRR27y%2BjsACAAAwjKcIAQAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADPtfF3ikyeuoT6UAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common8369817400398454584”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>9.5</td> <td class=”number”>84</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.7</td> <td class=”number”>72</td> <td class=”number”>2.2%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.4</td> <td class=”number”>69</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.9</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.2</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.1</td> <td class=”number”>66</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.6</td> <td class=”number”>65</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>65</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.4</td> <td class=”number”>63</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.1</td> <td class=”number”>63</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (116)</td> <td class=”number”>2451</td> <td class=”number”>76.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme8369817400398454584”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.4</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.5</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.6</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.9</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>16.2</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:67%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16.8</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17.1</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17.5</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>18.2</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_DIABETES_ADULTS13”>PCT_DIABETES_ADULTS13<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>151</td>

</tr> <tr>

<th>Unique (%)</th> <td>4.7%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>11.246</td>

</tr> <tr>

<th>Minimum</th> <td>3.3</td>

</tr> <tr>

<th>Maximum</th> <td>23.5</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram993203550815937198”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives993203550815937198,#minihistogram993203550815937198”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives993203550815937198”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles993203550815937198”

aria-controls=”quantiles993203550815937198” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram993203550815937198” aria-controls=”histogram993203550815937198”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common993203550815937198” aria-controls=”common993203550815937198”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme993203550815937198” aria-controls=”extreme993203550815937198”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles993203550815937198”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>3.3</td>

</tr> <tr>

<th>5-th percentile</th> <td>7.5</td>

</tr> <tr>

<th>Q1</th> <td>9.5</td>

</tr> <tr>

<th>Median</th> <td>11.1</td>

</tr> <tr>

<th>Q3</th> <td>12.9</td>

</tr> <tr>

<th>95-th percentile</th> <td>15.5</td>

</tr> <tr>

<th>Maximum</th> <td>23.5</td>

</tr> <tr>

<th>Range</th> <td>20.2</td>

</tr> <tr>

<th>Interquartile range</th> <td>3.4</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>2.4784</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.22039</td>

</tr> <tr>

<th>Kurtosis</th> <td>0.3569</td>

</tr> <tr>

<th>Mean</th> <td>11.246</td>

</tr> <tr>

<th>MAD</th> <td>1.9646</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.30933</td>

</tr> <tr>

<th>Sum</th> <td>35222</td>

</tr> <tr>

<th>Variance</th> <td>6.1425</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram993203550815937198”>

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Stm3btHz5cj377LPatWuX%2BvfvrwkTJujEiRPGagMAAN7N8oD10UcfKSMjQ%2B3bt3eOtW/fXunp6frwww%2BNrWfVqlWaOnWqrrrqKgUFBWnOnDmaM2eONmzYoPbt2yspKUkBAQGKj49XYmKiVq9eLUmy2WwaNWqUunXrpsDAQI0bN06StH37dmO1AQAA72Z5wDp58qQCAgJqF%2BLrq4qKCiPr%2BOGHH3To0CH99NNPGjp0qOLi4jR58mQdO3ZM%2Bfn5iomJcVk%2BJibGeRrwt/O%2Bvr6Kjo52HuECAAA4G8svcu/Vq5cWL16s6dOnKyQkRNLPgeiZZ55xuVXD%2BThy5Igk6b333tPLL78sh8OhyZMna86cOTp58qQiIyNdlg8NDVVJSYkkyW63O%2Bv6RUhIiHO%2BIYqKilRcXOwy5u/fXBEREc7Hfn6%2BLv96C/qCN/L3bxrb3Vvfh/TlWby1L9MsD1izZs3S/fffrxtvvFFBQUGqqalReXm52rVrp9dee83IOhwOhyRp3LhxzjD16KOP6sEHH1R8fHyDn/972Ww2ZWRkuIxNmjRJkydPrrVscPDF57Wupoq%2B4E3Cwlq4uwQX3vo%2BpC/P4q19mWJ5wGrXrp3effddffDBB/ruu%2B/k6%2BurDh06qE%2BfPvLz8zOyjtatW0uSgoODnWNt27aVw%2BFQZWVlrW8rlpSUKDw8XJIUFhZWa95ut6tTp04NXn9ycrISExNdxvz9m6ukpNz52M/PV8HBF6u0tELV1TUNfu2mjr7gjX792XUnb30f0pdn8bS%2B3PULkuUBS5KaNWumgQMHNtrrt2nTRkFBQdq/f7%2B6dOkiSSosLNRFF12khIQErVu3zmX5vLw8devWTZIUGxur/Px8jRw5UpJUXV2tffv2KSkpqcHrj4iIcDkdKEnFxcdVVVX7jVhdXVPnuKejL3iTprbNvfV9SF%2BexVv7MsXygPX9999r6dKl%2Bvrrr3Xy5Mla8%2B%2B///55r8Pf319JSUl68cUX1atXLwUFBWnFihUaPny4Ro4cqT/96U9avXq1RowYoU8%2B%2BUQ7d%2B6UzWaTJKWkpGjatGm69dZbFRUVpb/85S9q1qyZ%2BvXrd951AQCAC4NbrsEqKipSnz591Lx580Zbz/Tp03X69GmNHj1alZWVGjx4sObMmaMWLVpo5cqVWrhwoRYsWKC2bdtqyZIluuaaayRJffv21bRp0zRlyhQdPXpUXbt2VVZWlgIDAxutVgAA4F18HOd7Rfc56t69u95//33nNU8XiuLi4y6P/f19FRbWQiUl5V51iJW%2B3OeW5z52dwlea9OU3u4uQZJnvA9/D/ryLJ7W1yWXtHTLei3/jmWrVq0a9cgVAACAu1kesMaPH6%2BMjIzzvhUCAABAU2X5NVgffPCBPv/8c7399tu6/PLL5evrmvH%2B%2Bte/Wl0SAACAUZYHrKCgIPXt29fq1QIAAFjG8oCVlpZm9SoBAAAs5ZYbjX7zzTd699139a9//csZuP75z3%2Bqe/fu7igHqBPfygMA/F6WX%2BS%2Be/dujRgxQlu2bFFOTo6kn28%2Bes899xi5ySgAAIC7WR6wli1bpscff1wbNmyQj4%2BPpJ//PuHixYu1YsUKq8sBAAAwzvKA9b//%2B79KSUmRJGfAkqQhQ4aooKDA6nIAAACMszxgtWzZss6/QVhUVKRmzZpZXQ4AAIBxlgesHj16aNGiRSorK3OOHTx4UDNnztSNN95odTkAAADGWf4twieffFL33nuv4uLiVF1drR49eqiiokKdOnXS4sWLrS4HAADAOMsDVps2bZSTk6OdO3fq4MGDCgwMVIcOHdS7d2%2BXa7IAAAA8lVvug3XRRRdp4MCB7lg1AABAo7M8YCUmJtZ7pIp7YQEAAE9necAaOnSoS8Cqrq7WwYMHlZubq3vvvdfqcgAAAIyzPGDNmDGjzvHNmzfr73//u8XVAAAAmGf5bRrOZODAgXr33XfdXQYAAMB5azIBa9%2B%2BfXI4HO4uAwAA4LxZfopwzJgxtcYqKipUUFCgQYMGWV0OAACAcZYHrPbt29f6FmFAQICSkpI0evRoq8sBAAAwzvKAxd3aAQCAt7M8YL3zzjsNXvb2229vxEoAAAAah%2BUBa/bs2aqpqal1QbuPj4/LmI%2BPDwELAAB4JMsD1ksvvaRVq1ZpwoQJioqKksPh0FdffaU///nPuuuuuxQXF2d1SQAAAEa55RqsrKwsRUZGOsd69uypdu3a6YEHHlBOTo7VJQEAABhl%2BX2wvv32W4WEhNQaDw4OVmFhodXlAAAAGGd5wGrbtq0WL16skpIS51hpaamWLl2qK664wupyAAAAjLP8FOGsWbM0ffp02Ww2tWjRQr6%2BviorK1NgYKBWrFhhdTkAAADGWR6w%2BvTpox07dmjnzp06cuSIHA6HIiMjddNNN6lly5ZWlwMAAGCc5QFLki6%2B%2BGINGDBAR44cUbt27dxRAgAAQKOx/BqskydPaubMmerevbtuueUWST9fgzVu3DiVlpZaXQ4AAIBxlgesJUuWaP/%2B/UpPT5ev779XX11drfT0dKvLAQAAMM7ygLV582a98MILGjJkiPOPPgcHBystLU1btmyxuhwAAADjLA9Y5eXlat%2B%2Bfa3x8PBwnThxwupyAAAAjLM8YF1xxRX6%2B9//Lkkuf3vwvffe02WXXWZ1OQAAAMZZ/i3CsWPH6tFHH9Udd9yhmpoavfzyy8rLy9PmzZs1e/Zsq8sBAAAwzvKAlZycLH9/f73%2B%2Buvy8/PTiy%2B%2BqA4dOig9PV1DhgyxuhwAAADjLA9Yx44d0x133KE77rjD6lUDwO9yy3Mfu7uEc7JpSm93lwBc8Cy/BmvAgAEu114BAAB4G8sDVlxcnDZt2mT1agEAACxj%2BSnCSy%2B9VH/84x%2BVlZWlK664QhdddJHL/NKlS60uCQAAwCjLA9aBAwd01VVXSZJKSkqsXj0AAECjsyxgTZ06VcuWLdNrr73mHFuxYoUmTZpkVQkAAACWsOwarG3bttUay8rKsmr1AAAAlrEsYNX1zUG%2BTQgAALyRZQHrlz/sfLYxAAAAT2f5bRoAAAC8HQELAADAMMu%2BRVhZWanp06efdawx7oO1aNEivfrqq/rqq68kSbt379bSpUv1zTff6NJLL9X48eM1YsQI5/LZ2dl64403VFxcrKioKM2ePVuxsbHG6wIAAN7JsoB1/fXXq6io6Kxjpu3fv1/r1q1zPi4qKtLEiRM1e/ZsDR8%2BXJ999pkefvhhdejQQV27dtW2bdu0fPlyvfTSS4qKilJ2drYmTJigLVu2qHnz5o1aKwAA8A6WBaxf3//KKjU1NZo3b55SU1P13HPPSZI2bNig9u3bKykpSZIUHx%2BvxMRErV69Wl27dpXNZtOoUaPUrVs3SdK4ceOUnZ2t7du3a9iwYZb3AAAAPI9XX4P117/%2BVQEBARo%2BfLhzLD8/XzExMS7LxcTEKC8vr855X19fRUdHKzc315qiAQCAx7P8T%2BVY5ccff9Ty5ctrHTmz2%2B2KjIx0GQsNDXX%2B2R673a6QkBCX%2BZCQkHP6sz5FRUUqLi52GfP3b66IiAjnYz8/X5d/vYW39gV4En9/z/r8eet%2Bg74ubF4bsNLS0jRq1Ch17NhRhw4dOqfnnu8NUG02mzIyMlzGJk2apMmTJ9daNjj44vNaV1PlrX0BniAsrIW7S/hdvHW/QV8XJq8MWLt379Y///lP5eTk1JoLCwuT3W53GSspKVF4ePgZ5%2B12uzp16tTg9ScnJysxMdFlzN%2B/uUpKyp2P/fx8FRx8sUpLK1RdXdPg127qvLUvwJP8el/jCbx1v0FfTYO7fuHwyoC1fv16HT16VP3795f07yNScXFxuv/%2B%2B2sFr7y8POdF7bGxscrPz9fIkSMlSdXV1dq3b5/zoviGiIiIcDkdKEnFxcdVVVX7jVhdXVPnuKfz1r4AT%2BCpnz1v3W/Q14XJK0%2BgPvHEE9q8ebPWrVundevWOf%2Bo9Lp16zR8%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%2BvJ554QqWlpZKk/fv366677tL111%2BvQYMGadWqVS7P3bhxo4YPH67u3btr1KhR%2Buijj9zRAgAA8FBeG7AmTJig4OBgbdu2TW%2B//ba%2B/vprPfPMMzp58qTGjx%2BvP/zhD/rwww%2B1bNkyrVy5Ulu2bJH0c/iaOXOmZsyYoU8%2B%2BUSpqal65JFHdOTIETd3BAAAPIVXBqzS0lLFxsZq%2BvTpatGihdq0aaORI0fqH//4h3bs2KHKyko9/PDDat68ubp06aLRo0fLZrNJklavXq2EhAQlJCQoICBAI0aMUOfOnbV%2B/Xo3dwUAADyFVwas4OBgpaWlqXXr1s6xw4cPKyIiQvn5%2BYqKipKfn59zLiYmRnl5eZKk/Px8xcTEuLxeTEyMcnNzrSkeAAB4PH93F2CF3Nxcvf7668rMzNSmTZsUHBzsMh8aGiq73a6amhrZ7XaFhIS4zIeEhOjAgQMNXl9RUZGKi4tdxvz9mysiIsL52M/P1%2BVfb%2BGtfQGexN/fsz5/3rrfoK8Lm9cHrM8%2B%2B0wPP/ywpk%2Bfrvj4eG3atKnO5Xx8fJz/3%2BFwnNc6bTabMjIyXMYmTZqkyZMn11o2OPji81pXU%2BWtfQGeICyshbtL%2BF28db9BXxcmrw5Y27Zt0%2BOPP665c%2Bfq9ttvlySFh4fr22%2B/dVnObrcrNDRUvr6%2BCgsLk91urzUfHh7e4PUmJycrMTHRZczfv7lKSsqdj/38fBUcfLFKSytUXV1zjp01Xd7aF%2BBJfr2v8QTeut%2Bgr6bBXb9weG3A%2BvzzzzVz5kw9//zz6tOnj3M8NjZWb775pqqqquTv/3P7ubm56tatm3P%2Bl%2BuxfpGbm6thw4Y1eN0REREupwMlqbj4uKqqar8Rq6tr6hz3dN7aF%2BAJPPWz5637Dfq6MHnlCdSqqirNmTNHM2bMcAlXkpSQkKCgoCBlZmaqoqJCe/fu1Zo1a5SSkiJJuvPOO7Vr1y7t2LFDp06d0po1a/Ttt99qxIgR7mgFAAB4IK88grVnzx4VFBRo4cKFWrhwocvce%2B%2B9pxdffFHz5s1TVlaWWrduralTp6pfv36SpM6dOys9PV1paWkqLCxUx44dtXLlSl1yySVu6AQAAHgirwxYPXv21FdffVXvMm%2B%2B%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%2B/btlZqaKpvN5u7SAACAh%2BAI1m/k5%2Berbdu2CgkJcY516dJFBw8eVFlZmYKCgs76GkVFRSouLnYZ8/dvroiICOdjPz9fl38BAE0f1702nr/NuMndJRhFwPoNu92u4OBgl7FfwlZJSUmDApbNZlNGRobL2COPPKJHH33U%2BbioqEivvvqSkpOTXYLXufrHH4f87uc2hqKiItlstvPuq6mhL89CX56FvjyLt/ZlGodP6uBwOM7r%2BcnJyXr77bdd/pecnOyyTHFxsTIyMmod6fJ09OVZ6Muz0Jdnoa8LG0ewfiM8PFx2u91lzG63y8fHR%2BHh4Q16jYiICFI9AAAXMI5g/UZsbKwOHz6sY8eOOcdyc3PVsWNHtWjRwo2VAQAAT0HA%2Bo2YmBh17dpVS5cuVVlZmQoKCvTyyy8rJSXF3aUBAAAP4Td//vz57i6iqbnpppuUk5Ojp59%2BWu%2B%2B%2B66SkpL0wAMPyMfHx%2Bh6WrRooRtuuMHrjozRl2ehL89CX56Fvi5cPo7zvaIbAAAALjhFCAAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAasRFRYWatKkSYqLi1N8fLyeeOIJlZaW1lru7bff1jXXXKOuXbu6/O%2BLL75wQ9X1i4qKUmxsrEudTz/9dJ3LZmdna/DgwerRo4dSUlKUl5dncbUN9%2Bmnn9b67x8bG6uoqKhayy5fvlzR0dG1lv/xxx/dUHltH374oeLj4zV16tRacxs3btTw4cPVvXt3jRo1Sh999NEZX8dut2vKlCmKj49Xnz59NHv2bJ08ebIxS69XfX1t2bJFI0aMUPfu3TV48GC99dZbZ3ydu%2B%2B%2BW126dHHZdiNGjGjM0ut1pr7Odb/gKdtrzpw5tXqKiYnRk08%2BWefrJCYm1trnTJgwwYoW6lTffn3//v266667dP3112vQoEFatWrVGV%2BnpqZGy5Yt04ABA9SrVy898MAD%2Bv77761qo5b6%2Bvryyy%2BVmpqqnj17qm/fvvrjH/%2Bo06dP1/k6TX3/aCkHGs2tt97qeOKJJxxlZWWOw4cPO0aNGuWYNWtWreXWrl3ruOuuu9xQ4bnr3Lmz4/vvvz/rcu%2B//76jZ8%2Bejj179jgqKiocK1eudPTu3dtRXl5uQZVmZGZmOh577LFa4y%2B88IJj5syZbqjo7LKyshyDBg1yjBkzxjFlyhSXuX379jliY2MdO3bscJw8edKxbt06R7du3RyHDx%2Bu87UeeeQRx0MPPeQ4evSo48iRI47k5GTH008/bUUbtdTX1969ex1du3Z1/O1vf3NUVlY6duzY4ejSpYvj008/rfO17rrrLsfatWutKPus6uvrXPcLnrK9fquystIxbNgwx44dO%2Bqc79%2B/v%2BOTTz5pjDJ/lzPt1ysqKhw33XSTY/ny5Y7y8nJHXl6e44YbbnBs3ry5ztfJzs529O/f33HgwAHH8ePHHU899ZRj%2BPDhjpqaGos7%2BtmZ%2BiorK3P07t3b8Z//%2BZ%2BOU6dOOQ4cOODo37%2B/Y8WKFXW%2BTlPeP1qNI1iNpLS0VLGxsZo%2BfbpatGihNm3aaOTIkfrHP/7h7tIsYbPZNGrUKHXr1k2BgYEaN26cJGn79u1urqxh/vWvf%2Bnll1/Wf/zHf7i7lHMSEBCgNWvW6Morr6w1t3r1aiUkJCghIUEBAQEaMWKEOnfurPXr19da9scff9TWrVs1depUhYeHKzIyUhMnTtTatWtVWVlpRSsu6uvLbrdr/PjxGjhwoPz9/ZWQkKDOnTt7xGetvr7OhSdtr9969dVXddlllykhIcGCys5Pffv1HTt2qLKyUg8//LCaN2%2BuLl26aPTo0bLZbHW%2Bls1mU2pqqq6%2B%2BmoFBQVp6tSpKigo0N69ey3uqv6%2Bjh49qptuukmPPvqomjVrpquvvlqDBw/2iM%2BXuxGwGklwcLDS0tLUunVr59jhw4cVERFR5/KHDx/Wfffdp169emnAgAFat26dVaWes6VLl6pfv37q2bOn5s6dq/Ly8lrL5OfnKyYmxvnY19dX0dHRys3NtbLU3%2B3555/XHXfcocsuu6zO%2Ba%2B%2B%2BkpjxoxRjx49NGzYsHpPtVnpnnvuUcuWLeuc%2B%2B02kaSYmJg6t8n%2B/fvl5%2Bfncoq0S5cuOnHihL755huzRTdAfX317dtXkyZNcj6uqqpScXGxIiMjz/h6Gzdu1NChQ9W9e3elpqbqu%2B%2B%2BM15zQ9TXl9Tw/YInba9fKy0t1YsvvqjHH3%2B83uWys7M1cOBAde/eXZMnT9bRo0dNlXpO6tuv5%2BfnKyoqSn5%2Bfs65mJiYOi%2BNOHnypA4cOODyeQwKCtKVV17pln1kfX1dccUVSktLk7%2B/v8tcfZ%2Bvprp/tBoByyK5ubl6/fXX9fDDD9eaCw8PV/v27fX444/r448/1rRp0zRr1izt3r3bDZXW77rrrlN8fLy2bNkim82mPXv2aMGCBbWWs9vtCgkJcRkLCQlRSUmJVaX%2BbocOHdKWLVt033331Tnfpk0btWvXTs8884w%2B/vhjjR49WhMmTHDLD7JzcS7bxG63KygoSD4%2BPi7LSmry2zA9PV3NmzfX0KFD65y/%2Buqr1alTJ/3Xf/2X3n//fYWHh2vcuHFnvKbEXc5lv%2BCp2%2Bv1119Xr1691KlTpzMuEx0drWuvvVbr1q3Txo0bZbfb9dhjj1lY5Zn9er9ut9sVHBzsMh8aGiq73a6amhqX8Z9%2B%2BkkOh6PJ7iPr%2B3n1/vvva/v27br//vvrfK6n7h8bAwHLAp999pkeeOABTZ8%2BXfHx8bXm%2B/Xrp5deekkxMTFq1qyZhg0bpptvvllvv/22G6qtn81m0%2BjRo52HimfMmKGcnJw6fzg5HA43VHj%2B3njjDQ0aNEiXXHJJnfOjR4/WCy%2B8oCuvvFIXX3yxUlNTFR0dXeeptqbmXLaJp20/h8OhJUuWKCcnR5mZmQoICKhzufnz52vmzJkKDQ1VeHi4nnrqKRUWFuqzzz6zuOL6net%2BwdO2V3V1td544w3dc8899S63YsUKjR8/Xi1atNCll16qefPm6dNPP3XbUcdfnG2//otfh97faorbrL6%2BtmzZohkzZujZZ589Yyj25P2jaQSsRrZt2zY99NBDmjVr1ll3JL/Wtm1bFRUVNWJlZlx%2B%2BeWqrq6udcg%2BLCxMdrvdZcxutys8PNzK8n6XzZs3KzEx8Zye4wnb61y2SXh4uMrKylRdXe2yrCS1atWqcQv9HWpqavTEE09o27ZtevPNN3XVVVc1%2BLlBQUEKCQnRDz/80IgVmnGm95mnbS/p52/unj59Wj179jyn57Vt21aS3Pp5q2u/Hh4eXuvok91uV2hoqHx9XX/U/jJW1%2BfRndurvp9XNptNs2fP1vLlyzV48OBzel1P2D82BgJWI/r88881c%2BZMPf/887r99tvPuNybb76pjRs3uowVFBSoXbt2jV3iOdm3b58WL17sMlZQUKBmzZrVurYsNjZW%2Bfn5zsfV1dXat2%2BfunXrZkmtv9f%2B/ftVWFio3r17n3GZP/3pT7VO0zTF7fVbsbGxta4Hyc3NrXObREdHy%2BFw6Msvv3RZNjg4WB06dGj0Ws/VokWL9PXXX%2BvNN9%2BsdzuUlZVp/vz5LmHq2LFjOnbsWJPbfueyX/C07SX9fKrpD3/4g8u1Pb9VWFioefPmuRwhLygokCS3ba8z7ddjY2P11Vdfqaqqyjl2ps9XQECAOnXq5LKPLC0t1Xfffadrr722cRs4g/p%2BXr333ntatmyZsrOz1adPn3pfx1P3j42BgNVIqqqqNGfOHM2YMaPON%2BS9997r3HmePn1aTz/9tHJzc1VZWamcnBx98MEHGjNmjNVl16tVq1ay2WzKysrS6dOndfDgQT3//PNKTk6Wn5%2BfhgwZ4vxmSUpKit555x3t2bNHFRUVyszMVLNmzdSvXz/3NnEW%2B/btU2hoqIKCglzGf92b3W7XggUL9M033%2BjUqVNatWqVvvvuO40cOdIdJTfYnXfeqV27dmnHjh06deqU1qxZo2%2B//dZ5D6i//e1vGjt2rKSffxsfPHiwnnvuOR07dkxHjhzRihUrlJSUVO8PRHf47LPPtH79emVlZSk0NLTW/BdffKEhQ4bo9OnTCgoK0t69e7Vw4ULZ7Xb99NNPWrBggaKiotS9e3c3VH9mZ9sveOr2%2BsX%2B/ft1%2BeWX1xr/dV%2BtWrXStm3btHjxYp04cUI//PCD0tLS1L9//3ovsm4s9e3XExISFBQUpMzMTFVUVGjv3r1as2aNUlJSJEk//PCDhgwZ4rzXVUpKirKzs1VQUKCysjKlp6c77x/VlPo6fvy45s%2BfryVLlig6OrrO53vD/rExNM1PnhfYs2ePCgoKtHDhQi1cuNBl7r333tP333%2Bvn376SdLP37gpLy/XY489puLiYl1%2B%2BeVasWKFYmNj3VH6GUVGRiorK0tLly51BqaRI0c6byR48OBBnThxQtLP3%2ByaNm2apkyZoqNHj6pr167KyspSYGCgO1s4qx9//LHOa69%2B3dv06dMlSampqbLb7erYsaNeeeUVtWnTxtJa6/LLzvmX36K3bt0q6effpDt37qz09HSlpaWpsLBQHTt21MqVK539Hj9%2BXP/3f//nfK2nnnpK8%2BbN04ABA3TRRRfp1ltvrfMmn1aor6%2B1a9fq%2BPHj6t%2B/v8tzevXqpVWrVqmiokIHDx50Xu%2ByYsUKLVq0SIMHD9bp06d14403Kisrq9ZpHCvU19fZ9gueur1%2BUVxc7PKttV/8uq/AwEC99NJLWrx4sfr27StJuvnmm894U9LGdrb9%2Bosvvqh58%2BYpKytLrVu31tSpU52/VFZWVurgwYPOo3FjxoxRcXGx7r77bpWXlysuLk4ZGRlWtySp/r6eeuoplZSUaOLEibWe98v29JT9o9V8HE3xKjsAAAAPxilCAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADDs/wGUEPC0PyfHjAAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common993203550815937198”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>9.5</td> <td class=”number”>61</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.5</td> <td class=”number”>61</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.2</td> <td class=”number”>60</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.1</td> <td class=”number”>59</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.0</td> <td class=”number”>59</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.9</td> <td class=”number”>55</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.8</td> <td class=”number”>54</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.4</td> <td class=”number”>54</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.6</td> <td class=”number”>54</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.6</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (140)</td> <td class=”number”>2562</td> <td class=”number”>79.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme993203550815937198”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>3.3</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.1</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.2</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:67%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.3</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.6</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>20.1</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.2</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>21.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>21.7</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>23.5</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_FMRKT_ANMLPROD16”>PCT_FMRKT_ANMLPROD16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>109</td>

</tr> <tr>

<th>Unique (%)</th> <td>3.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>30.1%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>967</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>53.554</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>19.8%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram1542504154746675284”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives1542504154746675284,#minihistogram1542504154746675284”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives1542504154746675284”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles1542504154746675284”

aria-controls=”quantiles1542504154746675284” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram1542504154746675284” aria-controls=”histogram1542504154746675284”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common1542504154746675284” aria-controls=”common1542504154746675284”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme1542504154746675284” aria-controls=”extreme1542504154746675284”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles1542504154746675284”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>50</td>

</tr> <tr>

<th>Q3</th> <td>100</td>

</tr> <tr>

<th>95-th percentile</th> <td>100</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr>

<th>Range</th> <td>100</td>

</tr> <tr>

<th>Interquartile range</th> <td>100</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>40.441</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.75515</td>

</tr> <tr>

<th>Kurtosis</th> <td>-1.5251</td>

</tr> <tr>

<th>Mean</th> <td>53.554</td>

</tr> <tr>

<th>MAD</th> <td>35.75</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.16655</td>

</tr> <tr>

<th>Sum</th> <td>120120</td>

</tr> <tr>

<th>Variance</th> <td>1635.5</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram1542504154746675284”>

<img 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ZXp1KlTKi8vV3l5ucaNGydJys/P14YNG/TJJ5%2BosbFRq1atUqdOnTRs2DBzFwUAAPxGQF4ijIuLU0lJiVasWOEOSGPHjtWMGTMUGhqq1atXa/HixVq0aJF69eqlZcuW6YYbbpAkDR06VDNnztT06dNVV1en5ORklZSUqHPnziavCgAA%2BIuADFiSNGjQIL322ms/Ordx48Yffe2ECRM0YcIEb5UGAAACXEBeIgQAADATAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwmM8FrMzMTK1cuVJHjhwxuxQAAICL4nMB695779WWLVt0xx13aPLkydqxY4eamprMLgsAAKDNfC5gFRQUaMuWLfrTn/6kvn376umnn1ZGRoaWLVumQ4cOmV0eAADABflcwDprwIABmjNnjnbt2qV58%2BbpT3/6k0aPHq1Jkybpr3/9q9nlAQAA/CifDVhnzpzRli1b9NBDD2nOnDmKi4vT448/rv79%2B2vixInavHmz2SUCAACcl8/9qZyqqiqtX79eGzZsUENDg0aOHKl///d/18CBA93bDBo0SE888YSys7NNrBQAAOD8fC5gZWVlqXfv3nrkkUd0zz33KCoqqtU2GRkZOnbsmAnVAQAAXJjPBazS0lINHjz4gtt9%2Bumnl6AaAACA9vO5e7ASEhI0ZcoUvfXWW%2B6xP/7xj3rooYfkdDpNrAwAAKBtfC5gLVmyRMePH1efPn3cY8OGDVNLS4uWLl1qYmUAAABt43OXCN977z1t3rxZ0dHR7rH4%2BHgtX75cd911l4mVAQAAtI3PncE6efKkQkNDW40HBwersbHRhIoAAADax%2BcC1qBBg7R06VJ999137rFvvvlGixYt8nhUAwAAgK/yuUuE8%2BbN04MPPqhf/vKXCg8PV0tLixoaGnT11VfrlVdeMbs8AACAC/K5gHX11VfrzTff1DvvvKOvvvpKwcHB6t27t4YMGaKQkBCzywMAALggnwtYktSpUyfdcccdZpcBAABwUXwuYH399ddasWKF/va3v%2BnkyZOt5t9%2B%2B20TqgIAAGg7nwtY8%2BbNU01NjYYMGaKuXbuaXQ4AAEC7%2BVzAqqys1Ntvv62YmBizSwEAALgoPveYhu7du3PmCgAA%2BDWfC1iPPPKIVq5cKZfLZXYpAAAAF8XnLhG%2B8847%2Buijj/T666/rqquuUnCwZwZ87bXXTKoMAACgbXwuYIWHh2vo0KFmlwEAAHDRfC5gLVmyxOwSAAAAOsTn7sGSpM8//1zFxcV6/PHH3WMff/yxiRUBAAC0nc8FrIqKCo0ZM0Y7duzQG2%2B8IemHh48%2B8MADF/2Q0aeffloJCQkex8jNzVVqaqqysrK0adMmj%2B1LS0s1cuRIpaamKj8/X5WVlRe/IAAAcNnxuYD13HPP6be//a02b96soKAgST/8fcKlS5fqxRdfbPf%2B9u/fr40bN7o/rqmp0aOPPqrx48eroqJCRUVFWrBggWw2myRp586dKi4u1rPPPqs9e/Zo%2BPDhmjJlir7//ntjFggAAAKezwWs//mf/1F%2Bfr4kuQOWJI0aNUpVVVXt2ldLS4sWLlyoiRMnusc2b96s%2BPh45ebmKjQ0VOnp6crMzFRZWZkkyWq1KicnRykpKercubMmT54sSdq1a1cHVwYAAC4XPneTe7du3XTy5El16tTJY7ympqbV2IW89tprCg0NVXZ2tp5//nlJkt1uV2Jiosd2iYmJ2rp1q3t%2B9OjR7rng4GD1799fNptNWVlZbTpuTU2NamtrPcYslq6KjY1tV/0XEhLic/n4J1ks/lPv2d76W4/9Ab31LvrrPfTW%2BwKptz4XsFJTU/X0009r/vz57rFDhw5p4cKF%2BuUvf9nm/Xz77bcqLi7WK6%2B84jHudDoVFxfnMRYVFSWHw%2BGej4yM9JiPjIx0z7eF1WrVypUrPcYKCgpUWFjY5n0EoujoMLNLaLeIiC5mlxCw6K130V/vobfeE0i99bmA9fjjj%2BvXv/610tLS1NzcrNTUVDU2Nqpv375aunRpm/ezZMkS5eTkqE%2BfPjp8%2BHC7aujoU%2BTz8vKUmZnpMWaxdJXD0dCh/Z7L35K%2B0ev3ppCQYEVEdFF9faOam1vMLieg0Fvvor/eQ2%2B9zxu9NeuXe58LWD179tQbb7yh8vJyHTp0SJ07d1bv3r116623etyT9VMqKir08ccfu9%2BF%2BPeio6PldDo9xhwOh/uPS59v3ul0qm/fvm1eQ2xsbKvLgbW1x9XUdHl/Qfrj%2BpubW/yybn9Ab72L/noPvfWeQOqtzwUsSbriiit0xx13XPTrN23apLq6Og0fPlzS/5%2BRSktL04MPPtgqeFVWViolJUWSlJSUJLvdrrFjx0qSmpubtW/fPuXm5l50PQAA4PLicwErMzPzJ89UteVZWHPnztU//dM/uT8%2BevSo8vLytHHjRrW0tGj16tUqKyvTmDFj9MEHH6i8vFxWq1WSlJ%2Bfr5kzZ%2Bquu%2B5SQkKC/u3f/k2dOnXSsGHDOrw2AABwefC5gDV69GiPgNXc3KxDhw7JZrPp17/%2BdZv2ERkZ6XGjelNTk6QfLj9K0urVq7V48WItWrRIvXr10rJly3TDDTdIkoYOHaqZM2dq%2BvTpqqurU3JyskpKStS5c2ejlggAAAKczwWs2bNnn3d8%2B/bt%2BvOf/3xR%2B7zqqqt04MAB98eDBg3yePjouSZMmKAJEyZc1LEAAAD85m1od9xxh958802zywAAALggvwlY%2B/bt6/DjEwAAAC4Fn7tEOH78%2BFZjjY2Nqqqq0ogRI0yoCAAAoH18LmDFx8e3ehdhaGiocnNzdd9995lUFQAAQNv5XMBqz9PaAQAAfJHPBawNGza0edt77rnHi5UAAABcHJ8LWEVFRWppaWl1Q3tQUJDHWFBQEAELAAD4JJ8LWC%2B//LLWrFmjKVOmKCEhQS6XSwcOHNAf/vAH3X///UpLSzO7RAAAgJ/kcwFr6dKlKikpUVxcnHvslltu0dVXX61Jkyad9w84AwAA%2BBKfew7WF1984fFnbs6KiIhQdXW1CRUBAAC0j88FrF69emnp0qVyOBzusfr6eq1YsULXXHONiZUBAAC0jc9dIpw3b55mzZolq9WqsLAwBQcH68SJE%2BrcubNefPFFs8sDAAC4IJ8LWEOGDNHu3btVXl6uo0ePyuVyKS4uTrfddpu6detmdnkAAAAX5HMBS5K6dOmi22%2B/XUePHtXVV19tdjkAAADt4nP3YJ08eVJz5szRzTffrDvvvFPSD/dgTZ48WfX19SZXBwAAcGE%2BF7CWLVum/fv3a/ny5QoO/v/ympubtXz5chMrAwAAaBufC1jbt2/Xv/7rv2rUqFHuP/ocERGhJUuWaMeOHSZXBwAAcGE%2BF7AaGhoUHx/fajwmJkbff//9pS8IAACgnXwuYF1zzTX685//LEkef3tw27Zt%2BvnPf25WWQAAAG3mc%2B8inDBhgqZNm6Z7771XLS0tWrt2rSorK7V9%2B3YVFRWZXR4AAMAF%2BVzAysvLk8Vi0bp16xQSEqKXXnpJvXv31vLlyzVq1CizywMAALggnwtYx44d07333qt7773X7FIAAAAuis/dg3X77bd73HsFAADgb3wuYKWlpWnr1q1mlwEAAHDRfO4S4c9%2B9jM99dRTKikp0TXXXKMrrrjCY37FihUmVQYAANA2PhewDh48qOuuu06S5HA4TK4GAACg/XwmYM2YMUPPPfecXnnlFffYiy%2B%2BqIKCAhOrAgAAaD%2BfuQdr586drcZKSkpMqAQAAKBjfCZgne%2Bdg7ybEAAA%2BCOfCVhn/7DzhcYAAAB8nc/cg2W0zz77TEuWLFFlZaVCQ0M1ePBgFRUVqUePHqqoqNCKFSv0%2Beef62c/%2B5keeeQRjRkzxv3a0tJSvfrqq6qtrVVCQoKKioqUlJRk4moAmOnO5983u4R22fsUf/UCMJvPnMEy0unTp/Xggw9q8ODBqqio0BtvvKG6ujo98cQTqqmp0aOPPqrx48eroqJCRUVFWrBggWw2m6Qf7gUrLi7Ws88%2Bqz179mj48OGaMmWKvv/%2Be5NXBQAA/IXPnME6c%2BaMZs2adcGxtjwHq7GxUTNmzNDYsWNlsVgUExOjX/3qV1q3bp02b96s%2BPh45ebmSpLS09OVmZmpsrIyJScny2q1KicnRykpKZKkyZMnq7S0VLt27VJWVpZBqwUAAIHMZwLWwIEDVVNTc8GxtoiMjNR9993n/vjzzz/Xf/7nf%2BrOO%2B%2BU3W5XYmKix/aJiYnup8fb7XaNHj3aPRccHKz%2B/fvLZrO1OWDV1NSotrbWY8xi6arY2Nh2r%2BWnhIT41wlIi8V/6j3bW3/rsT%2Bgt5cG/TUen7veF0i99ZmA9ffPvzJKdXW1Ro4cqaamJo0bN06FhYV66KGHFBcX57FdVFSU%2B6GmTqdTkZGRHvORkZHteuip1WrVypUrPcYKCgpUWFh4kSsJDNHRYWaX0G4REV3MLiFg0Vvvor/eQ2%2B9J5B66zMByxt69eolm82mL7/8Uv/8z/%2Bs3/3ud216XUcfD5GXl6fMzEyPMYulqxyOhg7t91z%2BlvSNXr83hYQEKyKii%2BrrG9Xc3GJ2OQGF3l4a9Nd4fO56nzd6a9Yv9wEdsKQfHvUQHx%2BvGTNmaPz48crIyJDT6fTYxuFwKCYmRpIUHR3dat7pdKpv375tPmZsbGyry4G1tcfV1HR5f0H64/qbm1v8sm5/QG%2B9i/56D731nkDqrX%2BdAmmjiooKjRw5Ui0t//9/UnDwD0u98cYbVVlZ6bF9ZWWl%2B6b2pKQk2e1291xzc7P27dvnngcAALiQgAxYSUlJOnHihJYtW6bGxkYdO3ZMxcXFuuWWW5Sfn6/q6mqVlZXp1KlTKi8vV3l5ucaNGydJys/P14YNG/TJJ5%2BosbFRq1atUqdOnTRs2DBzFwUAAPxGQAasbt26ac2aNaqsrNQvfvELZWVlqVu3bvqXf/kXde/eXatXr9a6des0cOBAPf3001q2bJluuOEGSdLQoUM1c%2BZMTZ8%2BXYMHD9aePXtUUlKizp07m7wqAADgLwL2HqyEhIQffWfioEGDtHHjxh997YQJEzRhwgRvlQYAAAJcQJ7BAgAAMBMBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgARuwqqurVVBQoLS0NKWnp2vu3Lmqr6%2BXJO3fv1/333%2B/Bg4cqBEjRmjNmjUer92yZYuys7N18803KycnR%2B%2B9954ZSwAAAH4qYAPWlClTFBERoZ07d%2Br111/X3/72Nz3zzDM6efKkHnnkEf3iF7/Qu%2B%2B%2Bq%2Beee06rV6/Wjh07JP0QvubMmaPZs2frgw8%2B0MSJE/XYY4/p6NGjJq8IAAD4i4AMWPX19UpKStKsWbMUFhamnj17auzYsdq7d692796tM2fOaOrUqeratasGDBig%2B%2B67T1arVZJUVlamjIwMZWRkKDQ0VGPGjFG/fv20adMmk1cFAAD8hcXsArwhIiJCS5Ys8Rg7cuSIYmNjZbfblZCQoJCQEPdcYmKiysrKJEl2u10ZGRker01MTJTNZmvz8WtqalRbW%2BsxZrF0VWxsbHuX8pNCQvwrH1ss/lPv2d76W4/9Ab29NOiv8fjc9b5A6m1ABqxz2Ww2rVu3TqtWrdLWrVsVERHhMR8VFSWn06mWlhY5nU5FRkZ6zEdGRurgwYNtPp7VatXKlSs9xgoKClRYWHjxiwgA0dFhZpfQbhERXcwuIWDRW%2B%2Biv95Db70nkHob8AHrww8/1NSpUzVr1iylp6dr69at590uKCjI/d8ul6tDx8zLy1NmZqbHmMXSVQ5HQ4f2ey5/S/pGr9%2BbQkKCFRHRRfX1jWpubjG7nIBCby8N%2Bms8Pne9zxu9NeuX%2B4AOWDt37tRvf/tbLViwQPfcc48kKSYmRl988YXHdk6nU1FRUQoODlZ0dLScTmer%2BZiYmDYfNzY2ttXlwNra42pqury/IP1x/c3NLX5Ztz%2Bgt95Ff72H3npPIPXWv06BtMNHH32kOXPm6IUXXnCHK0lKSkrSgQMH1NTU5B6z2WxKSUlxz1dWVnrs6%2B/nAQAALiQgA1ZTU5Pmz5%2Bv2bNna8iQIR5zGRnzL6G/AAAPt0lEQVQZCg8P16pVq9TY2KhPP/1U69evV35%2BviRp3Lhx2rNnj3bv3q1Tp05p/fr1%2BuKLLzRmzBgzlgIAAPxQQF4i/OSTT1RVVaXFixdr8eLFHnPbtm3TSy%2B9pIULF6qkpERXXnmlZsyYoWHDhkmS%2BvXrp%2BXLl2vJkiWqrq5Wnz59tHr1avXo0cOElQAAAH8UkAHrlltu0YEDB35ym//4j//40bkRI0ZoxIgRRpcFAAAuEwF5iRAAAMBMAXkGCwAuZ7cUbTO7hDbbOv1Ws0sAvIIzWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBAjZgvfvuu0pPT9eMGTNazW3ZskXZ2dm6%2BeablZOTo/fee88919LSoueee0633367Bg0apEmTJunrr7%2B%2BlKUDAAA/F5AB6w9/%2BIMWL16sa6%2B9ttXc/v37NWfOHM2ePVsffPCBJk6cqMcee0xHjx6VJL366qvavHmzSkpKtGvXLsXHx6ugoEAul%2BtSLwMAAPipgAxYoaGhWr9%2B/XkDVllZmTIyMpSRkaHQ0FCNGTNG/fr106ZNmyRJVqtVEydO1PXXX6/w8HDNmDFDVVVV%2BvTTTy/1MgAAgJ%2BymF2ANzzwwAM/Ome325WRkeExlpiYKJvNppMnT%2BrgwYNKTEx0z4WHh%2Bvaa6%2BVzWbTTTfd1Kbj19TUqLa21mPMYumq2NjYdqziwkJC/CsfWyz%2BU%2B/Z3vpbj/0BvcXf4/sC/l4g9TYgA9ZPcTqdioyM9BiLjIzUwYMH9d1338nlcp133uFwtPkYVqtVK1eu9BgrKChQYWHhxRceAKKjw8wuod0iIrqYXULAoreQ%2BL4AT4HU28suYEm64P1UHb3fKi8vT5mZmR5jFktXORwNHdrvufwt6Ru9fm8KCQlWREQX1dc3qrm5xexy2uRXy981u4Q22/vUKL/qLbyH7wv4e97orVkh/rILWNHR0XI6nR5jTqdTMTExioqKUnBw8Hnnu3fv3uZjxMbGtrocWFt7XE1Nl/cXpD%2Buv7m5xS/r9gf0FhLfF%2BApkHrrX6dADJCUlKTKykqPMZvNppSUFIWGhqpv376y2%2B3uufr6en311Ve68cYbL3WpAADAT112AWvcuHHas2ePdu/erVOnTmn9%2BvX64osvNGbMGElSfn6%2BSktLVVVVpRMnTmj58uXq37%2B/kpOTTa4cAAD4i4C8RHg2DDU1NUmS3nrrLUk/nKnq16%2Bfli9friVLlqi6ulp9%2BvTR6tWr1aNHD0nS%2BPHjVVtbq3/8x39UQ0OD0tLSWt2wDgAA8FMCMmDZbLafnB8xYoRGjBhx3rmgoCAVFhZe9u/4AwAAFy8gAxYAwD/c%2Bfz7ZpfQLnufGmV2CfATl909WAAAAN5GwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGI9pAACgjW4p2mZ2CfATnMECAAAwGGewgB/Bb6oAgIvFGSwAAACDEbAAAAAMRsACAAAwGAELAADAYNzkDuCS4w0EAAIdZ7AAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACD8ceeccnc%2Bfz7ZpcAAMAlwRksAAAAgxGwAAAADEbAOo/q6mo9/PDDSktL0/Dhw7Vs2TK1tLSYXRYAAPAT3IN1HtOmTdOAAQP01ltvqa6uTo888oiuvPJK/eY3vzG7NAAA4Ac4g3UOm82mzz77TLNnz1a3bt0UHx%2BviRMnymq1ml0aAADwE5zBOofdblevXr0UGRnpHhswYIAOHTqkEydOKDw8/IL7qKmpUW1trceYxdJVsbGxhtYaEkI%2BBgAEjkD6uUbAOofT6VRERITH2Nmw5XA42hSwrFarVq5c6TH22GOPadq0acYVqh%2BC3K97/k15eXmGh7fLXU1NjaxWK731AnrrXfTXe%2Bit99TU1Ki4uDigehs4UdFALperQ6/Py8vT66%2B/7vEvLy/PoOr%2BX21trVauXNnqbBk6jt56D731LvrrPfTWewKxt5zBOkdMTIycTqfHmNPpVFBQkGJiYtq0j9jY2IBJ4AAAoP04g3WOpKQkHTlyRMeOHXOP2Ww29enTR2FhYSZWBgAA/AUB6xyJiYlKTk7WihUrdOLECVVVVWnt2rXKz883uzQAAOAnQp544oknzC7C19x2221644039OSTT%2BrNN99Ubm6uJk2apKCgILNLayUsLEyDBw/m7JoX0FvvobfeRX%2B9h956T6D1NsjV0Tu6AQAA4IFLhAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNg%2BaHq6mo9/PDDSktL0/Dhw7Vs2TK1tLSYXZbfqq6uVkFBgdLS0pSenq65c%2Beqvr5ekrR//37df//9GjhwoEaMGKE1a9aYXK3/evrpp5WQkOD%2BuKKiQrm5uUpNTVVWVpY2bdpkYnX%2Ba9WqVRoyZIhuuukmTZw4UYcPH5ZEfztq3759euCBB3TLLbfo1ltv1ezZs3Xs2DFJ9PZivPvuu0pPT9eMGTNazW3ZskXZ2dm6%2BeablZOTo/fee88919LSoueee0633367Bg0apEmTJunrr7%2B%2BlKVfPBf8ztixY13z58931dfXuw4dOuQaMWKEa82aNWaX5bfuuusu19y5c10nTpxwHTlyxJWTk%2BOaN2%2Beq7Gx0XXbbbe5iouLXQ0NDa7KykrX4MGDXdu3bze7ZL%2Bzb98%2B1%2BDBg139%2BvVzuVwu1zfffOO66aabXGVlZa6TJ0%2B63n//fdeNN97o%2Butf/2pypf5l3bp1rlGjRrmqqqpcx48fdz355JOuJ598kv520JkzZ1y33nqra8WKFa5Tp065jh075vrNb37jmjZtGr29CCUlJa4RI0a4xo8f75o%2BfbrH3L59%2B1xJSUmu3bt3u06ePOnauHGjKyUlxXXkyBGXy%2BVylZaWuoYPH%2B46ePCg6/jx467f//73ruzsbFdLS4sZS2kXzmD5GZvNps8%2B%2B0yzZ89Wt27dFB8fr4kTJ8pqtZpdml%2Bqr69XUlKSZs2apbCwMPXs2VNjx47V3r17tXv3bp05c0ZTp05V165dNWDAAN133330up1aWlq0cOFCTZw40T22efNmxcfHKzc3V6GhoUpPT1dmZqbKysrMK9QPrVmzRjNmzNB1112n8PBwzZ8/X/Pnz6e/HVRbW6va2lrdfffd6tSpk6Kjo/WrX/1K%2B/fvp7cXITQ0VOvXr9e1117baq6srEwZGRnKyMhQaGioxowZo379%2BrnPClqtVk2cOFHXX3%2B9wsPDNWPGDFVVVenTTz%2B91MtoNwKWn7Hb7erVq5ciIyPdYwMGDNChQ4d04sQJEyvzTxEREVqyZImuvPJK99iRI0cUGxsru92uhIQEhYSEuOcSExNVWVlpRql%2B67XXXlNoaKiys7PdY3a7XYmJiR7b0dv2%2Beabb3T48GF99913Gj16tNLS0lRYWKhjx47R3w6Ki4tT//79ZbVa1dDQoLq6Ou3YsUPDhg2jtxfhgQceULdu3c4792P9tNlsOnnypA4ePOgxHx4ermuvvVY2m82rNRuBgOVnnE6nIiIiPMbOhi2Hw2FGSQHFZrNp3bp1mjp16nl7HRUVJafTyT1vbfTtt9%2BquLhYCxcu9Bj/sd7yOdx2R48elSRt27ZNa9eu1caNG3X06FHNnz%2Bf/nZQcHCwiouL9fbbbys1NVXp6elqamrSrFmz6K3BnE6nxwkD6YefaQ6HQ999951cLtePzvs6ApYfcrlcZpcQkD788ENNmjRJs2bNUnp6%2Bo9uFxQUdAmr8m9LlixRTk6O%2BvTpY3YpAefs94HJkycrLi5OPXv21LRp07Rz506TK/N/p0%2Bf1pQpUzRq1Cjt3btX77zzjrp166bZs2ebXVpAutDPNH/9mUfA8jMxMTFyOp0eY06nU0FBQYqJiTGpKv%2B3c%2BdOPfzww5o3b54eeOABST/0%2BtzfkpxOp6KiohQczJfOhVRUVOjjjz9WQUFBq7no6OhWn8cOh4PP4XY4e1n778%2Bm9OrVSy6XS2fOnKG/HVBRUaHDhw9r5syZ6tatm%2BLi4lRYWKj/%2Bq//UnBwML010Pm%2BFzidTsXExLi/155vvnv37peyzIvCTwk/k5SUpCNHjrjfLiz9cFmrT58%2BCgsLM7Ey//XRRx9pzpw5euGFF3TPPfe4x5OSknTgwAE1NTW5x2w2m1JSUswo0%2B9s2rRJdXV1Gj58uNLS0pSTkyNJSktLU79%2B/Vrds1JZWUlv26Fnz54KDw/X/v373WPV1dW64oorlJGRQX87oLm5WS0tLR5nTk6fPi1JSk9Pp7cGSkpKatXPs99nQ0ND1bdvX9ntdvdcfX29vvrqK914442XutR2I2D5mcTERCUnJ2vFihU6ceKEqqqqtHbtWuXn55tdml9qamrS/PnzNXv2bA0ZMsRjLiMjQ%2BHh4Vq1apUaGxv16aefav369fS6jebOnavt27dr48aN2rhxo0pKSiRJGzduVHZ2tqqrq1VWVqZTp06pvLxc5eXlGjdunMlV%2Bw%2BLxaLc3Fy99NJL%2BvLLL1VXV6cXX3xR2dnZGjt2LP3tgJtvvlldu3ZVcXGxGhsb5XA4tGrVKg0aNEh33303vTXQuHHjtGfPHu3evVunTp3S%2BvXr9cUXX2jMmDGSpPz8fJWWlqqqqkonTpzQ8uXL1b9/fyUnJ5tc%2BYUFufz14uZl7OjRo1qwYIH%2B%2B7//W%2BHh4Ro/frwee%2Bwx7g26CHv37tU//MM/qFOnTq3mtm3bpoaGBi1cuFCVlZW68sor9dBDD2nChAkmVOr/Dh8%2BrNtvv10HDhyQJP3lL3/R4sWLVVVVpV69emnWrFkaMWKEyVX6l9OnT2vJkiV68803debMGY0cOVILFixQWFgY/e2gyspKPfPMM/rss8/UqVMnDR48WHPnzlVcXBy9baezYejs1QCLxSJJ7ncC7tixQytWrFB1dbX69OmjoqIiDRo0SNIP918VFxfrtddeU0NDg9LS0vT73/9ePXv2NGEl7UPAAgAAMBiXCAEAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAz2f5i/1A%2BXdSsxAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common1542504154746675284”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>732</td> <td class=”number”>22.8%</td> <td>

<div class=”bar” style=”width:75%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>636</td> <td class=”number”>19.8%</td> <td>

<div class=”bar” style=”width:66%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>277</td> <td class=”number”>8.6%</td> <td>

<div class=”bar” style=”width:29%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>66.6666666666667</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.3333333333333</td> <td class=”number”>82</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>75.0</td> <td class=”number”>50</td> <td class=”number”>1.6%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>60.0</td> <td class=”number”>38</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>37</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>80.0</td> <td class=”number”>31</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>40.0</td> <td class=”number”>25</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (98)</td> <td class=”number”>235</td> <td class=”number”>7.3%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>967</td> <td class=”number”>30.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme1542504154746675284”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>636</td> <td class=”number”>19.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.14285714285714</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.69230769230769</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.33333333333333</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>91.6666666666667</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>92.3076923076923</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>92.8571428571429</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>95.6521739130435</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>732</td> <td class=”number”>22.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_FMRKT_BAKED16”>PCT_FMRKT_BAKED16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>108</td>

</tr> <tr>

<th>Unique (%)</th> <td>3.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>30.1%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>967</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>57.31</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>17.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-5370483874379831134”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-5370483874379831134”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-5370483874379831134”

aria-controls=”quantiles-5370483874379831134” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-5370483874379831134” aria-controls=”histogram-5370483874379831134”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-5370483874379831134” aria-controls=”common-5370483874379831134”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-5370483874379831134” aria-controls=”extreme-5370483874379831134”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-5370483874379831134”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>64.286</td>

</tr> <tr>

<th>Q3</th> <td>100</td>

</tr> <tr>

<th>95-th percentile</th> <td>100</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr>

<th>Range</th> <td>100</td>

</tr> <tr>

<th>Interquartile range</th> <td>100</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>40.24</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.70215</td>

</tr> <tr>

<th>Kurtosis</th> <td>-1.4492</td>

</tr> <tr>

<th>Mean</th> <td>57.31</td>

</tr> <tr>

<th>MAD</th> <td>35.654</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.3264</td>

</tr> <tr>

<th>Sum</th> <td>128550</td>

</tr> <tr>

<th>Variance</th> <td>1619.3</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-5370483874379831134”>

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KKYmI6y2Y76zSlVb0FvzUNvzUV/zeOLvb39d%2B97ugS37Xx6mCm9veKKCEPXc5dfXiIEAADwJAIWAACAwfz2HqxLRd85mz1dgts2Tb3J0yUAAHBRcAYLAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgXhewsrKyVFJSov3793u6FAAAgPPidQHrrrvu0saNG3Xrrbdq/Pjx2rJli1paWjxdFgAAgNu8LmAVFhZq48aN%2Bvd//3d1795dCxcuVGZmppYsWaK9e/e6vU5iYqKSk5OVkpLi/PXUU09JkiorK5WXl6e0tDRlZ2dr/fr1Lo8tKyvT0KFDlZaWpoKCAlVXVxu6RwAA4N%2BCPF3AmfTu3Vu9e/fWo48%2Bqo0bN%2BrJJ5/UypUrlZGRoUceeUTXX3/9WdfYvHmzrrzySpexhoYGTZ48WXPmzFFOTo4%2B/PBDTZo0SV27dlVKSoq2bt2q4uJivfTSS0pMTFRZWZkmTpyoLVu2KCwszKztAgAAP%2BJ1Z7BOOXnypDZu3KgHH3xQs2bNUnx8vB577DH16tVL48aN04YNG85r3Q0bNighIUF5eXkKCQlRRkaGsrKyVF5eLkmyWq3Kzc1VamqqQkNDNX78eEnStm3bDNsbAADwb153Bquurk5r1qzR2rVrdfToUQ0dOlQvv/yy%2BvTp4zymX79%2BevLJJ5WTk/NP11q2bJk%2B/vhjHTlyRLfffrtmz56tmpoaJSUluRyXlJSkTZs2SZJqamo0fPhw55zFYlGvXr1UVVWl7OxsA3cKAAD8ldcFrOzsbHXt2lUTJkzQnXfeqejo6HbHZGZm6tChQ/90nRtuuEEZGRl65pln9M0332jq1KmaP3%2B%2B7Ha74uPjXY6Njo6WzWaTJNntdkVFRbnMR0VFOefd0dDQoMbGRpexoKAwxcXFub2GOwIDvfYE5GkFBflOvad662s99gX01lz01zz01nz%2B1FuvC1hlZWXq37//WY/75JNP/um81Wp1/vd1112nmTNnatKkSS5nws7E4XCcvdCzPHdJSYnLWGFhoYqKii5oXV8XE9PR0yWcs8jIDp4uwW/RW3PRX/PQW/P4U2%2B9LmAlJiZq4sSJysvL06233ipJ%2BtOf/qT3339fS5YsOe0ZLXdceeWVam1tlcVikd1ud5mz2WyKjY2VJMXExLSbt9vt6t69u9vPlZ%2Bfr6ysLJexoKAw2WxHz6v2M/G1pG/0/s0UGGhRZGQHNTU1q7W1zdPl%2BBV6ay76ax56az4zeuupb%2B69LmAtWrRIhw8fVrdu3ZxjgwYN0rvvvqvFixdr8eLFZ12jtrZW69ev1%2BzZs51jdXV1Cg4OVmZmpv7yl7%2B4HF9dXa3U1FRJUnJysmpqajRq1ChJUmtrq2pra5WXl%2Bf2HuLi4tpdDmxsPKyWlkv7C9IX99/a2uaTdfsCemsu%2Bmseemsef%2Bqt150Cee%2B991RSUqKEhATnWEJCgpYuXap3333XrTU6deokq9Wq0tJSnThxQnv37tXzzz%2Bv/Px8jRw5UvX19SovL9fx48dVUVGhiooKjR49WpJUUFCgtWvXateuXWpubtby5csVHBysQYMGmbBbAADgj7zuDNaxY8cUEhLSbtxisai5udmtNeLj41VaWqply5Y5A9KoUaM0bdo0hYSEaMWKFVqwYIHmz5%2BvLl26aMmSJerZs6ckaeDAgZo%2BfbqmTp2qgwcPKiUlRaWlpQoNDTV0nwAAwH95XcDq16%2BfFi9erBkzZjjfzfftt9/qmWeecesG9X9c57XXXjvj3Lp168742LFjx2rs2LHnVjgAAMD/53UB6/HHH9f999%2BvX/7ylwoPD1dbW5uOHj2qq666Sq%2B88oqnywMAADgrrwtYV111ld5880298847%2Bvrrr2WxWNS1a1cNGDBAgYGBni4PAADgrLwuYElScHCw8yMaAAAAfI3XBaxvvvlGy5Yt0%2Beff65jx461m3/77bc9UBUAAID7vC5gPf7442poaNCAAQMUFhbm6XIAAADOmdcFrOrqar399tvOT1YHAADwNV73QaOdOnXizBUAAPBpXhewJkyYoJKSkgv%2BgcsAAACe4nWXCN955x199NFHev3113XllVfKYnHNgGf68FAAAABv4XUBKzw8XAMHDvR0GQAAAOfN6wLWokWLPF0CAADABfG6e7Ak6YsvvlBxcbEee%2Bwx59jHH3/swYoAAADc53UBq7KyUiNGjNCWLVv0xhtvSPrxw0fvvfdePmQUAAD4BK8LWM8995x%2B/etfa8OGDQoICJD0488nXLx4sV544QUPVwcAAHB2Xhew/ud//kcFBQWS5AxYkjRs2DDV1dV5qiwAAAC3eV3AioiIOO3PIGxoaFBwcLAHKgIAADg3Xhew0tLStHDhQh05csQ5tnfvXs2aNUu//OUvPVgZAACAe7zuYxoee%2Bwx3XfffUpPT1dra6vS0tLU3Nys7t27a/HixZ4uDwAA4Ky8LmB17txZb7zxhioqKrR3716Fhoaqa9euuummm1zuyQIAAPBWXhewJOmyyy7Trbfe6ukyAAAAzovXBaysrKx/eqaKz8ICAADezusC1vDhw10CVmtrq/bu3auqqirdd999HqwMAADAPV4XsGbOnHna8bfeekt/%2B9vfLnI1AAAA587rPqbhTG699Va9%2Beabni4DAADgrHwmYNXW1srhcHi6DAAAgLPyukuEY8aMaTfW3Nysuro6DRkyxAMVAQAAnBuvC1gJCQnt3kUYEhKivLw83X333R6qCgAAwH1eF7DM%2BLT2hQsX6uWXX9Znn30mSaqsrNSyZcv0xRdf6Gc/%2B5kmTJigESNGOI8vKyvTq6%2B%2BqsbGRiUmJmrOnDlKTk42vC4AAOCfvC5grV271u1j77zzzrMes3v3bq1bt87554aGBk2ePFlz5sxRTk6OPvzwQ02aNEldu3ZVSkqKtm7dquLiYr300ktKTExUWVmZJk6cqC1btigsLOy89gQAAC4tXhew5syZo7a2tnY3tAcEBLiMBQQEnDVgtbW1ad68eRo3bpx%2B97vfSZI2bNighIQE5eXlSZIyMjKUlZWl8vJypaSkyGq1Kjc3V6mpqZKk8ePHq6ysTNu2bVN2draRWwUAAH7K6wLWSy%2B9pJUrV2rixIlKTEyUw%2BHQZ599pj/84Q%2B65557lJ6e7vZar732mkJCQpSTk%2BMMWDU1NUpKSnI5LikpSZs2bXLODx8%2B3DlnsVjUq1cvVVVVuR2wGhoa1NjY6DIWFBSmuLg4t2t3R2Cgz7wJVJIUFOQ79Z7qra/12BfQW3PRX/PQW/P5U2%2B9LmAtXrxYpaWlio%2BPd4717dtXV111lR544AG98cYbbq3z3Xffqbi4WK%2B88orLuN1ud1lbkqKjo2Wz2ZzzUVFRLvNRUVHOeXdYrVaVlJS4jBUWFqqoqMjtNfxRTExHT5dwziIjO3i6BL9Fb81Ff81Db83jT731uoD15Zdftgs4khQZGan6%2Bnq311m0aJFyc3PVrVs37du375xquNDP28rPz1dWVpbLWFBQmGy2oxe07k/5WtI3ev9mCgy0KDKyg5qamtXa2ubpcvwKvTUX/TUPvTWfGb311Df3XhewunTposWLF%2BuRRx5RTEyMJKmpqUm///3vdfXVV7u1RmVlpT7%2B%2BOPTnu2KiYmR3W53GbPZbIqNjT3jvN1uV/fu3d3eQ1xcXLvLgY2Nh9XScml/Qfri/ltb23yybl9Ab81Ff81Db83jT731uoD1%2BOOPa8aMGbJarerYsaMsFouOHDmi0NBQvfDCC26tsX79eh08eFCDBw%2BW9H9npNLT03X//fe3C17V1dXOm9qTk5NVU1OjUaNGSfrxh03X1tY6b4oHAAA4G68LWAMGDND27dtVUVGhAwcOyOFwKD4%2BXjfffLMiIiLcWmP27Nl65JFHnH8%2BcOCA8vPztW7dOrW1tWnFihUqLy/XiBEj9MEHH6iiokJWq1WSVFBQoOnTp%2BuOO%2B5QYmKi/vjHPyo4OFiDBg0yY7sAAMAPeV3AkqQOHTrolltu0YEDB3TVVVed8%2BOjoqJc7uNqaWmRJHXu3FmStGLFCi1YsEDz589Xly5dtGTJEvXs2VOSNHDgQE2fPl1Tp07VwYMHlZKSotLSUoWGhhqwMwAAcCnwuoB17NgxzZs3T2%2B%2B%2BaakHy/fNTU1afr06frtb3%2BryMjIc17zyiuvdH6KuyT169fP5cNHf2rs2LEaO3bsuRcPAAAgyevehrZkyRLt3r1bS5culcXyf%2BW1trZq6dKlHqwMAADAPV4XsN566y39/ve/17Bhw5w/9DkyMlKLFi3Sli1bPFwdAADA2XldwDp69KgSEhLajcfGxuqHH364%2BAUBAACcI68LWFdffbX%2B9re/SXL9wM/Nmzfr5z//uafKAgAAcJvX3eQ%2BduxYTZkyRXfddZfa2tq0atUqVVdX66233tKcOXM8XR4AAMBZeV3Ays/PV1BQkFavXq3AwEC9%2BOKL6tq1q5YuXaphw4Z5ujwAAICz8rqAdejQId1111266667PF0KAADAefG6e7BuueWWC/5hywAAAJ7kdQErPT1dmzZt8nQZAAAA583rLhH%2B7Gc/09NPP63S0lJdffXVuuyyy1zmly1b5qHKAAAA3ON1AWvPnj269tprJUk2m83D1QAAAJw7rwlY06ZN03PPPadXXnnFOfbCCy%2BosLDQg1UBAACcO6%2B5B2vr1q3txkpLSz1QCQAAwIXxmoB1uncO8m5CAADgi7wmYJ36wc5nGwMAAPB2XhOwAAAA/AUBCwAAwGBe8y7CkydPasaMGWcd43OwAACAt/OagNWnTx81NDScdQwAAMDbeU3A%2BsfPvwIAAPBl3IMFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMH8NmB9%2Bumnuu%2B%2B%2B9SnTx9lZGRo6tSpamxslCRVVlYqLy9PaWlpys7O1vr1610eW1ZWpqFDhyotLU0FBQWqrq72xBYAAICP8suAdeLECd1///3q37%2B/Kisr9cYbb%2BjgwYN68skn1dDQoMmTJ2vMmDGqrKzUnDlz9MQTT6iqqkqStHXrVhUXF%2BvZZ5/Vjh07NHjwYE2cOFE//PCDh3cFAAB8hV8GrObmZk2bNk0TJkxQcHCwYmNjddttt%2Bnzzz/Xhg0blJCQoLy8PIWEhCgjI0NZWVkqLy%2BXJFmtVuXm5io1NVWhoaEaP368JGnbtm2e3BIAAPAhXvNJ7kaKiorS3Xff7fzzF198ob/85S%2B6/fbbVVNTo6SkJJfjk5KStGnTJklSTU2Nhg8f7pyzWCzq1auXqqqqlJ2d7dbzNzQ0OC9HnhIUFKa4uLjz3dJpBQb6Vj4OCvKdek/11td67At8sbe3LX3X0yWck51PD/Op/voKX3zt%2Bhp/6q1fBqxT6uvrNXToULW0tGj06NEqKirSgw8%2BqPj4eJfjoqOjZbPZJEl2u11RUVEu81FRUc55d1itVpWUlLiMFRYWqqio6Dx34h9iYjp6uoRzFhnZwdMl%2BC16ay76ax56ax5/6q1fB6wuXbqoqqpKX331lf7t3/5Njz76qFuPczgcF/S8%2Bfn5ysrKchkLCgqTzXb0gtb9KV9L%2Bkbv30yBgRZFRnZQU1OzWlvbPF2OX6G3Fwf9NR6vXfOZ0VtPfXPv1wFLkgICApSQkKBp06ZpzJgxyszMlN1udznGZrMpNjZWkhQTE9Nu3m63q3v37m4/Z1xcXLvLgY2Nh9XScml/Qfri/ltb23yybl9Ab81Ff81Db83jT731rVMgbqqsrNTQoUPV1vZ//5Mslh%2B3ev3117f72IXq6mqlpqZKkpKTk1VTU%2BOca21tVW1trXMeAADgbPwyYCUnJ%2BvIkSNasmSJmpubdejQIRUXF6tv374qKChQfX29ysvLdfz4cVVUVKiiokKjR4%2BWJBUUFGjt2rXatWuXmpubtXz5cgUHB2vQoEGe3RQAAPAZfhmwIiIitHLlSlVXV%2BsXv/iFsrOzFRERod/%2B9rfq1KmTVqxYodWrV6tPnz5auHChlixZop49e0qSBg4cqOnTp2vq1Knq37%2B/duzYodLSUoWGhnp4VwAAwFf47T1YiYmJeuWVV047169fP61bt%2B6Mjx07dqzGjh1rVmkAAMDP%2BeUZLAB8gjksAAATB0lEQVQAAE8iYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABjMbwNWfX29CgsLlZ6eroyMDM2ePVtNTU2SpN27d%2Buee%2B5Rnz59NGTIEK1cudLlsRs3blROTo5uvPFG5ebm6r333vPEFgAAgI/y24A1ceJERUZGauvWrXr99df1%2Beef65lnntGxY8c0YcIE/eIXv9C7776r5557TitWrNCWLVsk/Ri%2BZs2apZkzZ%2BqDDz7QuHHj9PDDD%2BvAgQMe3hEAAPAVfhmwmpqalJycrBkzZqhjx47q3LmzRo0apZ07d2r79u06efKkJk2apLCwMPXu3Vt33323rFarJKm8vFyZmZnKzMxUSEiIRowYoR49emj9%2BvUe3hUAAPAVfhmwIiMjtWjRIl1%2B%2BeXOsf379ysuLk41NTVKTExUYGCgcy4pKUnV1dWSpJqaGiUlJbmsl5SUpKqqqotTPAAA8HlBni7gYqiqqtLq1au1fPlybdq0SZGRkS7z0dHRstvtamtrk91uV1RUlMt8VFSU9uzZ4/bzNTQ0qLGx0WUsKChMcXFx57%2BJ0wgM9K18HBTkO/We6q2v9dgX0NuLg/4aj9eu%2Bfypt34fsD788ENNmjRJM2bMUEZGhjZt2nTa4wICApz/7XA4Lug5rVarSkpKXMYKCwtVVFR0Qev6upiYjp4u4ZxFRnbwdAl%2Bi96ai/6ah96ax59669cBa%2BvWrfr1r3%2BtJ554QnfeeackKTY2Vl9%2B%2BaXLcXa7XdHR0bJYLIqJiZHdbm83Hxsb6/bz5ufnKysry2UsKChMNtvR89vIGfha0jd6/2YKDLQoMrKDmpqa1dra5uly/Aq9vTjor/F47ZrPjN566pt7vw1YH330kWbNmqXnn39eAwYMcI4nJyfrz3/%2Bs1paWhQU9OP2q6qqlJqa6pw/dT/WKVVVVcrOznb7uePi4tpdDmxsPKyWlkv7C9IX99/a2uaTdfsCemsu%2Bmseemsef%2Bqtb50CcVNLS4vmzp2rmTNnuoQrScrMzFR4eLiWL1%2Bu5uZmffLJJ1qzZo0KCgokSaNHj9aOHTu0fft2HT9%2BXGvWrNGXX36pESNGeGIrAADAB/nlGaxdu3aprq5OCxYs0IIFC1zmNm/erBdffFHz5s1TaWmpLr/8ck2bNk2DBg2SJPXo0UNLly7VokWLVF9fr27dumnFihW64oorPLATAADgi/wyYPXt21efffbZPz3mz3/%2B8xnnhgwZoiFDhhhdFgAAuET45SVCAAAATyJgAQAAGIyABQAAYDACFgAAgMEIWAAAAAbzy3cRAsClrO%2BczZ4uwW2bpt7k6RIAU3AGCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwmN8GrHfffVcZGRmaNm1au7mNGzcqJydHN954o3Jzc/Xee%2B8559ra2vTcc8/plltuUb9%2B/fTAAw/om2%2B%2BuZilAwAAH%2BeXAesPf/iDFixYoGuuuabd3O7duzVr1izNnDlTH3zwgcaNG6eHH35YBw4ckCS9%2Buqr2rBhg0pLS7Vt2zYlJCSosLBQDofjYm8DAAD4KL8MWCEhIVqzZs1pA1Z5ebkyMzOVmZmpkJAQjRgxQj169ND69eslSVarVePGjdN1112n8PBwTZs2TXV1dfrkk08u9jYAAICP8suAde%2B99yoiIuK0czU1NUpKSnIZS0pKUlVVlY4dO6Y9e/a4zIeHh%2Buaa65RVVWVqTUDAAD/EeTpAi42u92uqKgol7GoqCjt2bNH33//vRwOx2nnbTab28/R0NCgxsZGl7GgoDDFxcWdf%2BGnERjoW/k4KMh36j3VW1/rsS%2Bgt/hHt//ufU%2BXcE52Pj2M166J/Km3l1zAknTW%2B6ku9H4rq9WqkpISl7HCwkIVFRVd0Lq%2BLiamo6dLOGeRkR08XYLforfwVbx2zeNPvb3kAlZMTIzsdrvLmN1uV2xsrKKjo2WxWE4736lTJ7efIz8/X1lZWS5jQUFhstmOnn/hp%2BFrSd/o/ZspMNCiyMgOampqVmtrm6fL8Sv0Fr6O1655zOitp765v%2BQCVnJysqqrq13GqqqqlJ2drZCQEHXv3l01NTXq37%2B/JKmpqUlff/21rr/%2BerefIy4urt3lwMbGw2ppubS/IH1x/62tbT5Zty%2Bgt/BVvHbN40%2B99a1TIAYYPXq0duzYoe3bt%2Bv48eNas2aNvvzyS40YMUKSVFBQoLKyMtXV1enIkSNaunSpevXqpZSUFA9XDgAAfIVfnsE6FYZaWlokSX/9618l/XimqkePHlq6dKkWLVqk%2Bvp6devWTStWrNAVV1whSRozZowaGxv1r//6rzp69KjS09Pb3U8FAADwz/hlwDrbRyoMGTJEQ4YMOe1cQECAioqKLvkb0gEAwPnzy4AFXIp86e3uO58e5ukSgPPSd85mT5cAH3HJ3YMFAABgNgIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDB%2BFE5wBnwIzEAAOeLM1gAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABuNdhAAuOt6hCcDfcQYLAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYHzSKi%2Bb2373v6RIAALgoOIN1GvX19XrooYeUnp6uwYMHa8mSJWpra/N0WQAAwEdwBus0pkyZot69e%2Buvf/2rDh48qAkTJujyyy/Xr371K0%2BXBgAAfABnsH6iqqpKn376qWbOnKmIiAglJCRo3Lhxslqtni4NAAD4CM5g/URNTY26dOmiqKgo51jv3r21d%2B9eHTlyROHh4Wddo6GhQY2NjS5jQUFhiouLM7TWwEDyMQDAf/jTv2sErJ%2Bw2%2B2KjIx0GTsVtmw2m1sBy2q1qqSkxGXs4Ycf1pQpU4wrVD8Gufs6f678/HzDw9ulrqGhQVarld6agN6ai/6ah96ap6GhQcXFxX7VW/%2BJigZyOBwX9Pj8/Hy9/vrrLr/y8/MNqu7/NDY2qqSkpN3ZMlw4emseemsu%2Bmseemsef%2BwtZ7B%2BIjY2Vna73WXMbrcrICBAsbGxbq0RFxfnNwkcAACcO85g/URycrL279%2BvQ4cOOceqqqrUrVs3dezY0YOVAQAAX0HA%2BomkpCSlpKRo2bJlOnLkiOrq6rRq1SoVFBR4ujQAAOAjAp988sknPV2Et7n55pv1xhtv6KmnntKbb76pvLw8PfDAAwoICPB0ae107NhR/fv35%2ByaCeiteeitueiveeitefyttwGOC72jGwAAAC64RAgAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDAClg%2Bqr6/XQw89pPT0dA0ePFhLlixRW1ubp8vyWfX19SosLFR6eroyMjI0e/ZsNTU1SZJ2796te%2B65R3369NGQIUO0cuVKD1fruxYuXKjExETnnysrK5WXl6e0tDRlZ2dr/fr1HqzOdy1fvlwDBgzQDTfcoHHjxmnfvn2S6O%2BFqq2t1b333qu%2Bffvqpptu0syZM3Xo0CFJ9PZ8vPvuu8rIyNC0adPazW3cuFE5OTm68cYblZubq/fee88519bWpueee0633HKL%2BvXrpwceeEDffPPNxSz9/Dngc0aNGuWYO3euo6mpybF3717HkCFDHCtXrvR0WT7rjjvucMyePdtx5MgRx/79%2Bx25ubmOxx9/3NHc3Oy4%2BeabHcXFxY6jR486qqurHf3793e89dZbni7Z59TW1jr69%2B/v6NGjh8PhcDi%2B/fZbxw033OAoLy93HDt2zPH%2B%2B%2B87rr/%2Besd///d/e7hS37J69WrHsGHDHHV1dY7Dhw87nnrqKcdTTz1Ffy/QyZMnHTfddJNj2bJljuPHjzsOHTrk%2BNWvfuWYMmUKvT0PpaWljiFDhjjGjBnjmDp1qstcbW2tIzk52bF9%2B3bHsWPHHOvWrXOkpqY69u/f73A4HI6ysjLH4MGDHXv27HEcPnzY8Zvf/MaRk5PjaGtr88RWzglnsHxMVVWVPv30U82cOVMRERFKSEjQuHHjZLVaPV2aT2pqalJycrJmzJihjh07qnPnzho1apR27typ7du36%2BTJk5o0aZLCwsLUu3dv3X333fT6HLW1tWnevHkaN26cc2zDhg1KSEhQXl6eQkJClJGRoaysLJWXl3uuUB%2B0cuVKTZs2Tddee63Cw8M1d%2B5czZ07l/5eoMbGRjU2NmrkyJEKDg5WTEyMbrvtNu3evZvenoeQkBCtWbNG11xzTbu58vJyZWZmKjMzUyEhIRoxYoR69OjhPCtotVo1btw4XXfddQoPD9e0adNUV1enTz755GJv45wRsHxMTU2NunTpoqioKOdY7969tXfvXh05csSDlfmmyMhILVq0SJdffrlzbP/%2B/YqLi1NNTY0SExMVGBjonEtKSlJ1dbUnSvVZr732mkJCQpSTk%2BMcq6mpUVJSkstx9PbcfPvtt9q3b5%2B%2B//57DR8%2BXOnp6SoqKtKhQ4fo7wWKj49Xr169ZLVadfToUR08eFBbtmzRoEGD6O15uPfeexUREXHauTP1s6qqSseOHdOePXtc5sPDw3XNNdeoqqrK1JqNQMDyMXa7XZGRkS5jp8KWzWbzREl%2BpaqqSqtXr9akSZNO2%2Bvo6GjZ7XbueXPTd999p%2BLiYs2bN89l/Ey95TXsvgMHDkiSNm/erFWrVmndunU6cOCA5s6dS38vkMViUXFxsd5%2B%2B22lpaUpIyNDLS0tmjFjBr01mN1udzlhIP34b5rNZtP3338vh8NxxnlvR8DyQQ6Hw9Ml%2BKUPP/xQDzzwgGbMmKGMjIwzHhcQEHARq/JtixYtUm5urrp16%2BbpUvzOqb8Hxo8fr/j4eHXu3FlTpkzR1q1bPVyZ7ztx4oQmTpyoYcOGaefOnXrnnXcUERGhmTNnero0v3S2f9N89d88ApaPiY2Nld1udxmz2%2B0KCAhQbGysh6ryfVu3btVDDz2kxx9/XPfee6%2BkH3v90%2B%2BS7Ha7oqOjZbHwpXM2lZWV%2Bvjjj1VYWNhuLiYmpt3r2Gaz8Ro%2BB6cua//j2ZQuXbrI4XDo5MmT9PcCVFZWat%2B%2BfZo%2BfboiIiIUHx%2BvoqIi/ed//qcsFgu9NdDp/i6w2%2B2KjY11/l17uvlOnTpdzDLPC/9K%2BJjk5GTt37/f%2BXZh6cfLWt26dVPHjh09WJnv%2BuijjzRr1iw9//zzuvPOO53jycnJ%2Buyzz9TS0uIcq6qqUmpqqifK9Dnr16/XwYMHNXjwYKWnpys3N1eSlJ6erh49erS7Z6W6uprenoPOnTsrPDxcu3fvdo7V19frsssuU2ZmJv29AK2trWpra3M5c3LixAlJUkZGBr01UHJycrt%2Bnvp7NiQkRN27d1dNTY1zrqmpSV9//bWuv/76i13qOSNg%2BZikpCSlpKRo2bJlOnLkiOrq6rRq1SoVFBR4ujSf1NLSorlz52rmzJkaMGCAy1xmZqbCw8O1fPlyNTc365NPPtGaNWvotZtmz56tt956S%2BvWrdO6detUWloqSVq3bp1ycnJUX1%2Bv8vJyHT9%2BXBUVFaqoqNDo0aM9XLXvCAoKUl5enl588UV99dVXOnjwoF544QXl5ORo1KhR9PcC3HjjjQoLC1NxcbGam5tls9m0fPly9evXTyNHjqS3Bho9erR27Nih7du36/jx41qzZo2%2B/PJLjRgxQpJUUFCgsrIy1dXV6ciRI1q6dKl69eqllJQUD1d%2BdgEOX724eQk7cOCAnnjiCf3Xf/2XwsPDNWbMGD388MPcG3Qedu7cqX/5l39RcHBwu7nNmzfr6NGjmjdvnqqrq3X55ZfrwQcf1NixYz1Qqe/bt2%2BfbrnlFn322WeSpL///e9asGCB6urq1KVLF82YMUNDhgzxcJW%2B5cSJE1q0aJHefPNNnTx5UkOHDtUTTzyhjh070t8LVF1drWeeeUaffvqpgoOD1b9/f82ePVvx8fH09hydCkOnrgYEBQVJkvOdgFu2bNGyZctUX1%2Bvbt26ac6cOerXr5%2BkH%2B%2B/Ki4u1muvvaajR48qPT1dv/nNb9S5c2cP7OTcELAAAAAMxiVCAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAg/0/bjxmoNQe45MAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-5370483874379831134”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>810</td> <td class=”number”>25.2%</td> <td>

<div class=”bar” style=”width:83%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>576</td> <td class=”number”>17.9%</td> <td>

<div class=”bar” style=”width:59%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>266</td> <td class=”number”>8.3%</td> <td>

<div class=”bar” style=”width:28%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>66.6666666666667</td> <td class=”number”>106</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.3333333333333</td> <td class=”number”>72</td> <td class=”number”>2.2%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>75.0</td> <td class=”number”>62</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>80.0</td> <td class=”number”>39</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>60.0</td> <td class=”number”>35</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>29</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>40.0</td> <td class=”number”>19</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (97)</td> <td class=”number”>229</td> <td class=”number”>7.1%</td> <td>

<div class=”bar” style=”width:24%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>967</td> <td class=”number”>30.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-5370483874379831134”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>576</td> <td class=”number”>17.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.14285714285714</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.69230769230769</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.5</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>92.3076923076923</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>92.8571428571429</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>93.75</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>95.6521739130435</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>810</td> <td class=”number”>25.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_FMRKT_CREDIT16”>PCT_FMRKT_CREDIT16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>105</td>

</tr> <tr>

<th>Unique (%)</th> <td>3.3%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>30.1%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>967</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>49.269</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>22.6%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram6452791056056222707”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives6452791056056222707,#minihistogram6452791056056222707”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives6452791056056222707”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles6452791056056222707”

aria-controls=”quantiles6452791056056222707” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram6452791056056222707” aria-controls=”histogram6452791056056222707”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common6452791056056222707” aria-controls=”common6452791056056222707”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme6452791056056222707” aria-controls=”extreme6452791056056222707”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles6452791056056222707”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>50</td>

</tr> <tr>

<th>Q3</th> <td>100</td>

</tr> <tr>

<th>95-th percentile</th> <td>100</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr>

<th>Range</th> <td>100</td>

</tr> <tr>

<th>Interquartile range</th> <td>100</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>40.55</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.82304</td>

</tr> <tr>

<th>Kurtosis</th> <td>-1.5614</td>

</tr> <tr>

<th>Mean</th> <td>49.269</td>

</tr> <tr>

<th>MAD</th> <td>35.84</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.0057969</td>

</tr> <tr>

<th>Sum</th> <td>110510</td>

</tr> <tr>

<th>Variance</th> <td>1644.3</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram6452791056056222707”>

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2OjVq5cqdDQUA0aNMjaRQEAAL8RkDtYcXFxKioq0vLly10BadSoUZo%2BfbrCwsK0atUqLVq0SAsXLlTXrl21dOlS3XrrrZKkgQMHasaMGZo2bZqOHj2q5ORkFRUVqV27dhavCgAA%2BIuADFiS1K9fP73xxhsXnCsrK7vgY8eOHauxY8d6qzQAABDgAvISIQAAgJUIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACG%2BVzAyszM1IoVK3T48GGrSwEAALgkPhewHnzwQW3atEl33323JkyYoK1bt6qpqcnqsgAAADzmcwErPz9fmzZt0h/%2B8Af16NFDzz//vDIyMrR06VIdOHDA6vIAAAAuyucC1lm9e/fW7NmztX37ds2dO1d/%2BMMfdN9992n8%2BPH629/%2BZnV5AAAAF%2BSzAevMmTPatGmTJk6cqNmzZysuLk5PPfWUevXqpXHjxmnjxo1WlwgAAHBeNqsLONf%2B/fu1bt06rV%2B/Xg0NDRo2bJh%2B97vfqU%2BfPq5j%2BvXrp2eeeUZZWVkWVgoAAHB%2BPhewRowYoW7dumnSpEl64IEHFBUV1eqYjIwMHTt2zILqAAAALs7nAlZxcbH69%2B9/0eM%2B%2B%2ByzK1ANAABA2/ncPVgJCQmaPHmy3n33XdfYb3/7W02cOFEOh8PCygAAADzjcwFr8eLFOn78uLp37%2B4aGzRokFpaWrRkyRILKwMAAPCMz10i/PDDD7Vx40ZFR0e7xuLj47Vs2TL97Gc/s7AyAAAAz/jcDtbJkycVFhbWajw4OFiNjY0WVAQAANA2Phew%2BvXrpyVLluj77793jX377bdauHCh21s1AAAA%2BCqfu0Q4d%2B5cPfbYY/rpT3%2Bq8PBwtbS0qKGhQTfccIN%2B//vfW10eAADARflcwLrhhhv09ttv6/3339fXX3%2Bt4OBgdevWTQMGDFBISIjV5QEAAFyUzwUsSQoNDdXdd99tdRkAAACXxOcC1jfffKPly5friy%2B%2B0MmTJ1vNv/feexZUBQAA4DmfC1hz585VbW2tBgwYoA4dOlhdDgAAQJv5XMCqqqrSe%2B%2B9p5iYGKtLAQAAuCQ%2B9zYNnTt3ZucKAAD4NZ8LWJMmTdKKFSvkdDqtLgUAAOCS%2BNwlwvfff1%2BffPKJ3nzzTV1//fUKDnbPgG%2B88Uabz/n888/rd7/7nfbu3StJqqio0PLly/Xll1/qRz/6kSZNmqSRI0e6ji8uLtbrr7%2Buuro6JSQkaN68eUpKSrq8hQEAgKuGzwWs8PBwDRw40Nj59uzZo7KyMtfHtbW1euKJJzRv3jxlZWXp448/1pQpU9StWzclJydr27ZtKiws1GuvvaaEhAQVFxdr8uTJ2rp1K5cuAQCAR3wuYC1evNjYuVpaWvT0009r3LhxeumllyRJGzduVHx8vHJyciRJ6enpyszMVGlpqZKTk1VSUqLs7GylpKRIkiZMmKDi4mJt375dI0aMMFYbAAAIXD53D5YkffnllyosLNRTTz3lGvv000/bfJ433nhDYWFhysrKco1VV1crMTHR7bjExERVVVWddz44OFi9evVSZWVlm58fAABcnXxuB6uiokITJ05Ut27ddPDgQS1evFjffPONHnnkEb300ksaMmSIR%2Bf57rvvVFhY2OrvFzocDsXFxbmNRUVFyW63u%2BYjIyPd5iMjI13znqitrVVdXZ3bmM3WQbGxsR6fwxMhIT6Zjy/IZvOfes/21t967A/orXfRX%2B%2Bht94XSL31uYD14osv6he/%2BIUeffRR3XbbbZJ%2B%2BPuES5Ys0SuvvOJxwFq8eLGys7PVvXt3HTp0qE01XO4rGEtKSrRixQq3sfz8fBUUFFzWef1ddHRHq0tos4iI9laXELDorXfRX%2B%2Bht94TSL31uYD13//931q7dq0kKSgoyDU%2BfPhwzZ0716NzVFRU6NNPP9Vbb73Vai46OloOh8NtzG63u97Y9HzzDodDPXr08HgNubm5yszMdBuz2TrIbm/w%2BBye8Lekb3r93hQSEqyIiPaqr29Uc3OL1eUEFHrrXfTXe%2Bit93mjt1b9cu9zAatTp046efKkQkND3cZra2tbjV3Ihg0bdPToUQ0ePFjS/%2B1IpaWl6bHHHmsVvKqqqlw3tSclJam6ulqjRo2SJDU3N2v37t2um%2BI9ERsb2%2BpyYF3dcTU1Xd1fkP64/ubmFr%2Bs2x/QW%2B%2Biv95Db70nkHrrc1sgqampev7553XixAnX2IEDBzR79mz99Kc/9egcc%2BbM0ZYtW1RWVqaysjIVFRVJksrKypSVlaWamhqVlpbq1KlTKi8vV3l5uUaPHi1JysvL0/r167Vr1y41NjZq5cqVCg0N1aBBg4yvFQAABCaf28F66qmn9OijjyotLU3Nzc1KTU1VY2OjevTooSVLlnh0jsjISLcb1ZuamiRJXbp0kSStWrVKixYt0sKFC9W1a1ctXbpUt956qyRp4MCBmjFjhqZNm6ajR48qOTlZRUVFateuneGVAgCAQOVzAatLly566623VF5ergMHDqhdu3bq1q2b7rzzTrd7stri%2Buuvd72LuyT169fP7c1HzzV27FiNHTv2kp4LAADA5wKWJF1zzTW6%2B%2B67rS4DAADgkvhcwMrMzPyHO1XvvffeFawGAACg7XwuYN13331uAau5uVkHDhxQZWWlHn30UQsrAwAA8IzPBaxZs2add3zLli36y1/%2BcoWrAQAAaDufe5uGC7n77rv19ttvW10GAADARflNwNq9e/dl/wkbAACAK8HnLhGOGTOm1VhjY6P279%2BvoUOHWlARAABA2/hcwIqPj2/1KsKwsDDl5OTooYcesqgqAAAAz/lcwPL03doBAAB8lc8FrPXr13t87AMPPODFSgAAAC6NzwWsefPmqaWlpdUN7UFBQW5jQUFBBCwAAOCTfC5gvfbaa1q9erUmT56shIQEOZ1O7d27V7/5zW/08MMPKy0tzeoSAQAA/iGfC1hLlixRUVGR4uLiXGN9%2B/bVDTfcoPHjx%2Butt96ysDoAAICL87n3wTp48KAiIyNbjUdERKimpsaCigAAANrG5wJW165dtWTJEtntdtdYfX29li9frhtvvNHCygAAADzjc5cI586dq5kzZ6qkpEQdO3ZUcHCwTpw4oXbt2umVV16xujwAAICL8rmANWDAAO3YsUPl5eU6cuSInE6n4uLidNddd6lTp05WlwcAAHBRPhewJKl9%2B/YaMmSIjhw5ohtuuMHqcgAAANrE5%2B7BOnnypGbPnq077rhD9957r6Qf7sGaMGGC6uvrLa4OAADg4nwuYC1dulR79uzRsmXLFBz8f%2BU1Nzdr2bJlFlYGAADgGZ8LWFu2bNG//du/afjw4a4/%2BhwREaHFixdr69atFlcHAABwcT4XsBoaGhQfH99qPCYmRn//%2B9%2BvfEEAAABt5HMB68Ybb9Rf/vIXSXL724ObN2/Wj3/8Y6vKAgAA8JjPvYpw7Nixmjp1qh588EG1tLRozZo1qqqq0pYtWzRv3jyrywMAALgonwtYubm5stlsWrt2rUJCQvTqq6%2BqW7duWrZsmYYPH251eQAAABflcwHr2LFjevDBB/Xggw9aXQoAAMAl8bl7sIYMGeJ27xUAAIC/8bmAlZaWpnfeecfqMgAAAC6Zz10i/NGPfqTnnntORUVFuvHGG3XNNde4zS9fvtyiygAAADzjcwFr3759uvnmmyVJdrvd4moAAADazmcC1vTp0/Xiiy/q97//vWvslVdeUX5%2BvoVVAQAAtJ3P3IO1bdu2VmNFRUWXfL7PP/9cjz76qPr06aP09HRNmzZNdXV1kqSKigrl5OQoNTVVI0aM0IYNG9weW1xcrGHDhik1NVV5eXmqqqq65DoAAMDVx2cC1vleOXipryY8ffq0HnvsMfXv318VFRV66623dPToUT3zzDOqra3VE088oTFjxqiiokLz5s3TggULVFlZKemHoFdYWKhf//rX2rlzpwYPHqzJkyfzZ3oAAIDHfCZgnf3Dzhcb80RjY6OmT5%2BuSZMmKTQ0VDExMbrnnnv0xRdfaOPGjYqPj1dOTo7CwsKUnp6uzMxMlZaWSpJKSkqUnZ2tlJQUtWvXThMmTJAkbd%2B%2B/dIXBwAArio%2Bcw%2BWSZGRkXrooYdcH3/55Zf64x//qHvvvVfV1dVKTEx0Oz4xMdH11hDV1dW67777XHPBwcHq1auXKisrNWLECI%2Bev7a21nU58iybrYNiY2MvdUnnFRLiM/nYIzab/9R7trf%2B1mN/QG%2B9i/56D731vkDqbUAGrLNqamo0bNgwNTU1afTo0SooKNDEiRMVFxfndlxUVJTrFYsOh0ORkZFu85GRkW16RWNJSYlWrFjhNpafn6%2BCgoJLXElgiI7uaHUJbRYR0d7qEgIWvfUu%2Bus99NZ7Aqm3PhOwzpw5o5kzZ150rC3vg9W1a1dVVlbqq6%2B%2B0r/8y7/ol7/8pUePu9x3ks/NzVVmZqbbmM3WQXZ7w2Wd91z%2BlvRNr9%2BbQkKCFRHRXvX1jWpubrG6nIBCb72L/noPvfU%2Bb/TWql/ufSZg9enTR7W1tRcda6ugoCDFx8dr%2BvTpGjNmjDIyMuRwONyOsdvtiomJkSRFR0e3mnc4HOrRo4fHzxkbG9vqcmBd3XE1NV3dX5D%2BuP7m5ha/rNsf0Fvvor/eQ2%2B9J5B66zMB6/%2B//9Xlqqio0DPPPKN33nlHwcE/7PKc/e9tt92mLVu2uB1fVVWllJQUSVJSUpKqq6s1atQoSVJzc7N2796tnJwcY/UB8C/3vvQnq0tok4%2BeG251CcBVz7%2BuMXkoKSlJJ06c0NKlS9XY2Khjx46psLBQffv2VV5enmpqalRaWqpTp06pvLxc5eXlGj16tCQpLy9P69ev165du9TY2KiVK1cqNDRUgwYNsnZRAADAbwRkwOrUqZNWr16tqqoq/eQnP9GIESPUqVMn/eu//qs6d%2B6sVatWae3aterTp4%2Bef/55LV26VLfeeqskaeDAgZoxY4amTZum/v37a%2BfOnSoqKlK7du0sXhUAAPAXPnOJ0LSEhIQLXnbs16%2BfysrKLvjYsWPHauzYsd4qDQAABLiA3MECAACwEgELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGBawAaumpkb5%2BflKS0tTenq65syZo/r6eknSnj179PDDD6tPnz4aOnSoVq9e7fbYTZs2KSsrS3fccYeys7P14YcfWrEEAADgpwI2YE2ePFkRERHatm2b3nzzTX3xxRd64YUXdPLkSU2aNEk/%2BclP9MEHH%2BjFF1/UqlWrtHXrVkk/hK/Zs2dr1qxZ%2BvOf/6xx48bpySef1JEjRyxeEQAA8BcBGbDq6%2BuVlJSkmTNnqmPHjurSpYtGjRqljz76SDt27NCZM2c0ZcoUdejQQb1799ZDDz2kkpISSVJpaakyMjKUkZGhsLAwjRw5Uj179tSGDRssXhUAAPAXNqsL8IaIiAgtXrzYbezw4cOKjY1VdXW1EhISFBIS4ppLTExUaWmpJKm6uloZGRluj01MTFRlZaXHz19bW6u6ujq3MZutg2JjY9u6lH8oJMS/8rHN5j/1nu2tv/XYH9DbK4P%2BmsfnrvcFUm8DMmCdq7KyUmvXrtXKlSv1zjvvKCIiwm0%2BKipKDodDLS0tcjgcioyMdJuPjIzUvn37PH6%2BkpISrVixwm0sPz9fBQUFl76IABAd3dHqEtosIqK91SUELHrrXfTXe%2Bit9wRSbwM%2BYH388ceaMmWKZs6cqfT0dL3zzjvnPS4oKMj1/06n87KeMzc3V5mZmW5jNlsH2e0Nl3Xec/lb0je9fm8KCQlWRER71dc3qrm5xepyAgq9vTLor3l87nqfN3pr1S/3AR2wtm3bpl/84hdasGCBHnjgAUlSTEyMDh486Hacw%2BFQVFSUgoODFR0dLYfD0Wo%2BJibG4%2BeNjY1tdTmwru64mpqu7i9If1x/c3OLX9btD%2Bitd9Ff76G33hNIvfWvLZA2%2BOSTTzR79my9/PLLrnAlSUlJSdq7d6%2BamppcY5WVlUpJSXHNV1VVuZ3r/88DAABcTEAGrKbhAnRLAAAP2UlEQVSmJs2fP1%2BzZs3SgAED3OYyMjIUHh6ulStXqrGxUZ999pnWrVunvLw8SdLo0aO1c%2BdO7dixQ6dOndK6det08OBBjRw50oqlAAAAPxSQlwh37dql/fv3a9GiRVq0aJHb3ObNm/Xqq6/q6aefVlFRka699lpNnz5dgwYNkiT17NlTy5Yt0%2BLFi1VTU6Pu3btr1apVuu666yxYCQAA8EcBGbD69u2rvXv3/sNj/v3f//2Cc0OHDtXQoUNNlwUAAK4SAXmJEAAAwEoELAAAAMMC8hIhAFzN%2Bs7bbHUJHntn2p1WlwB4BTtYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMCxgA9YHH3yg9PR0TZ8%2BvdXcpk2blJWVpTvuuEPZ2dn68MMPXXMtLS168cUXNWTIEPXr10/jx4/XN998cyVLBwAAfi4gA9ZvfvMbLVq0SDfddFOruT179mj27NmaNWuW/vznP2vcuHF68skndeTIEUnS66%2B/ro0bN6qoqEjbt29XfHy88vPz5XQ6r/QyAACAn7JZXYA3hIWFad26dXruued06tQpt7nS0lJlZGQoIyNDkjRy5EitXbtWGzZs0OOPP66SkhKNGzdOt9xyiyRp%2BvTpSktL02effabbb7/9iq8F8NS9L/3J6hI89tFzw60uAQC8KiAD1iOPPHLBuerqale4OisxMVGVlZU6efKk9u3bp8TERNdceHi4brrpJlVWVnocsGpra1VXV%2Bc2ZrN1UGxsbBtWcXEhIf61AWmz%2BU%2B9Z3vrbz32J/QWEt8X4C6QehuQAesfcTgcioyMdBuLjIzUvn379P3338vpdJ533m63e/wcJSUlWrFihdtYfn6%2BCgoKLr3wABAd3dHqEtosIqK91SUELHoLSbpn2QdWl9AmHz03nM9dLwqk3l51AUvSRe%2Bnutz7rXJzc5WZmek2ZrN1kN3ecFnnPZe/JX3T6/emkJBgRUS0V319o5qbW6wuJyDRW/grPne9xxu9teqX%2B6suYEVHR8vhcLiNORwOxcTEKCoqSsHBweed79y5s8fPERsb2%2BpyYF3dcTU1Xd1fkP64/ubmFr%2Bs2x/QW/grPne9J5B6619bIAYkJSWpqqrKbayyslIpKSkKCwtTjx49VF1d7Zqrr6/X119/rdtuu%2B1KlwoAAPzUVRewRo8erZ07d2rHjh06deqU1q1bp4MHD2rkyJGSpLy8PBUXF2v//v06ceKEli1bpl69eik5OdniygEAgL8IyEuEZ8NQU1OTJOndd9%2BV9MNOVc%2BePbVs2TItXrxYNTU16t69u1atWqXrrrtOkjRmzBjV1dXpn//5n9XQ0KC0tLRWN6wDAAD8IwEZsCorK//h/NChQzV06NDzzgUFBamgoOCqf8UfAAC4dFfdJUIAAABvI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQH5Ng2ACX3nbba6BACAn2IHCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCM98ECAMBDvD8ePEXAAnDF8UMKQKDjEiEAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADOOPPeOKufelP1ldAgAAVwQ7WAAAAIYRsAAAAAwjYJ1HTU2NHn/8caWlpWnw4MFaunSpWlparC4LAAD4Ce7BOo%2BpU6eqd%2B/eevfdd3X06FFNmjRJ1157rX7%2B859bXRoAAPAD7GCdo7KyUp9//rlmzZqlTp06KT4%2BXuPGjVNJSYnVpQEAAD/BDtY5qqur1bVrV0VGRrrGevfurQMHDujEiRMKDw%2B/6Dlqa2tVV1fnNmazdVBsbKzRWkNCyMcAgMARSD/XCFjncDgcioiIcBs7G7bsdrtHAaukpEQrVqxwG3vyySc1depUc4XqhyD3aJcvlJubazy8Xe1qa2tVUlJCb72A3noX/fUeeus9tbW1KiwsDKjeBk5UNMjpdF7W43Nzc/Xmm2%2B6/cvNzTVU3f%2Bpq6vTihUrWu2W4fLRW%2B%2Bht95Ff72H3npPIPaWHaxzxMTEyOFwuI05HA4FBQUpJibGo3PExsYGTAIHAABtxw7WOZKSknT48GEdO3bMNVZZWanu3burY8eOFlYGAAD8BQHrHImJiUpOTtby5ct14sQJ7d%2B/X2vWrFFeXp7VpQEAAD8R8swzzzxjdRG%2B5q677tJbb72lZ599Vm%2B//bZycnI0fvx4BQUFWV1aKx07dlT//v3ZXfMCeus99Na76K/30FvvCbTeBjkv945uAAAAuOESIQAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCw/VFNTo8cff1xpaWkaPHiwli5dqpaWFqvL8ls1NTXKz89XWlqa0tPTNWfOHNXX10uS9uzZo4cfflh9%2BvTR0KFDtXr1aour9V/PP/%2B8EhISXB9XVFQoJydHqampGjFihDZs2GBhdf5r5cqVGjBggG6//XaNGzdOhw4dkkR/L9fu3bv1yCOPqG/fvrrzzjs1a9YsHTt2TBK9vRQffPCB0tPTNX369FZzmzZtUlZWlu644w5lZ2frww8/dM21tLToxRdf1JAhQ9SvXz%2BNHz9e33zzzZUs/dI54XdGjRrlnD9/vrO%2Bvt554MAB59ChQ52rV6%2B2uiy/9bOf/cw5Z84c54kTJ5yHDx92ZmdnO%2BfOnetsbGx03nXXXc7CwkJnQ0ODs6qqytm/f3/nli1brC7Z7%2BzevdvZv39/Z8%2BePZ1Op9P57bffOm%2B//XZnaWmp8%2BTJk84//elPzttuu835t7/9zeJK/cvatWudw4cPd%2B7fv995/Phx57PPPut89tln6e9lOnPmjPPOO%2B90Ll%2B%2B3Hnq1CnnsWPHnD//%2Bc%2BdU6dOpbeXoKioyDl06FDnmDFjnNOmTXOb2717tzMpKcm5Y8cO58mTJ51lZWXOlJQU5%2BHDh51Op9NZXFzsHDx4sHPfvn3O48ePO3/1q185s7KynC0tLVYspU3YwfIzlZWV%2BvzzzzVr1ix16tRJ8fHxGjdunEpKSqwuzS/V19crKSlJM2fOVMeOHdWlSxeNGjVKH330kXbs2KEzZ85oypQp6tChg3r37q2HHnqIXrdRS0uLnn76aY0bN841tnHjRsXHxysnJ0dhYWFKT09XZmamSktLrSvUD61evVrTp0/XzTffrPDwcM2fP1/z58%2Bnv5eprq5OdXV1uv/%2B%2BxUaGqro6Gjdc8892rNnD729BGFhYVq3bp1uuummVnOlpaXKyMhQRkaGwsLCNHLkSPXs2dO1K1hSUqJx48bplltuUXh4uKZPn679%2B/frs88%2Bu9LLaDMClp%2Bprq5W165dFRkZ6Rrr3bu3Dhw4oBMnTlhYmX%2BKiIjQ4sWLde2117rGDh8%2BrNjYWFVXVyshIUEhISGuucTERFVVVVlRqt964403FBYWpqysLNdYdXW1EhMT3Y6jt23z7bff6tChQ/r%2B%2B%2B913333KS0tTQUFBTp27Bj9vUxxcXHq1auXSkpK1NDQoKNHj2rr1q0aNGgQvb0EjzzyiDp16nTeuQv1s7KyUidPntS%2Bffvc5sPDw3XTTTepsrLSqzWbQMDyMw6HQxEREW5jZ8OW3W63oqSAUllZqbVr12rKlCnn7XVUVJQcDgf3vHnou%2B%2B%2BU2FhoZ5%2B%2Bmm38Qv1ls9hzx05ckSStHnzZq1Zs0ZlZWU6cuSI5s%2BfT38vU3BwsAoLC/Xee%2B8pNTVV6enpampq0syZM%2BmtYQ6Hw23DQPrhZ5rdbtf3338vp9N5wXlfR8DyQ06n0%2BoSAtLHH3%2Bs8ePHa%2BbMmUpPT7/gcUFBQVewKv%2B2ePFiZWdnq3v37laXEnDOfh%2BYMGGC4uLi1KVLF02dOlXbtm2zuDL/d/r0aU2ePFnDhw/XRx99pPfff1%2BdOnXSrFmzrC4tIF3sZ5q//swjYPmZmJgYORwOtzGHw6GgoCDFxMRYVJX/27Ztmx5//HHNnTtXjzzyiKQfen3ub0kOh0NRUVEKDuZL52IqKir06aefKj8/v9VcdHR0q89ju93O53AbnL2s/f93U7p27Sqn06kzZ87Q38tQUVGhQ4cOacaMGerUqZPi4uJUUFCg//zP/1RwcDC9Neh83wscDodiYmJc32vPN9%2B5c%2BcrWeYl4aeEn0lKStLhw4ddLxeWfris1b17d3Xs2NHCyvzXJ598otmzZ%2Bvll1/WAw884BpPSkrS3r171dTU5BqrrKxUSkqKFWX6nQ0bNujo0aMaPHiw0tLSlJ2dLUlKS0tTz549W92zUlVVRW/boEuXLgoPD9eePXtcYzU1NbrmmmuUkZFBfy9Dc3OzWlpa3HZOTp8%2BLUlKT0%2BntwYlJSW16ufZ77NhYWHq0aOHqqurXXP19fX6%2Buuvddttt13pUtuMgOVnEhMTlZycrOXLl%2BvEiRPav3%2B/1qxZo7y8PKtL80tNTU2aP3%2B%2BZs2apQEDBrjNZWRkKDw8XCtXrlRjY6M%2B%2B%2BwzrVu3jl57aM6cOdqyZYvKyspUVlamoqIiSVJZWZmysrJUU1Oj0tJSnTp1SuXl5SovL9fo0aMtrtp/2Gw25eTk6NVXX9VXX32lo0eP6pVXXlFWVpZGjRpFfy/DHXfcoQ4dOqiwsFCNjY2y2%2B1auXKl%2BvXrp/vvv5/eGjR69Gjt3LlTO3bs0KlTp7Ru3TodPHhQI0eOlCTl5eWpuLhY%2B/fv14kTJ7Rs2TL16tVLycnJFld%2BcUFOf724eRU7cuSIFixYoP/6r/9SeHi4xowZoyeffJJ7gy7BRx99pH/6p39SaGhoq7nNmzeroaFBTz/9tKqqqnTttddq4sSJGjt2rAWV%2Br9Dhw5pyJAh2rt3ryTpr3/9qxYtWqT9%2B/era9eumjlzpoYOHWpxlf7l9OnTWrx4sd5%2B%2B22dOXNGw4YN04IFC9SxY0f6e5mqqqr0wgsv6PPPP1doaKj69%2B%2BvOXPmKC4ujt620dkwdPZqgM1mkyTXKwG3bt2q5cuXq6amRt27d9e8efPUr18/ST/cf1VYWKg33nhDDQ0NSktL069%2B9St16dLFgpW0DQELAADAMC4RAgAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBh/wuLudOATEFqpgAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common6452791056056222707”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>726</td> <td class=”number”>22.6%</td> <td>

<div class=”bar” style=”width:75%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>636</td> <td class=”number”>19.8%</td> <td>

<div class=”bar” style=”width:66%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>268</td> <td class=”number”>8.3%</td> <td>

<div class=”bar” style=”width:28%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>66.6666666666667</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.3333333333333</td> <td class=”number”>84</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>75.0</td> <td class=”number”>60</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>46</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>60.0</td> <td class=”number”>37</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>40.0</td> <td class=”number”>29</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>80.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (94)</td> <td class=”number”>235</td> <td class=”number”>7.3%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>967</td> <td class=”number”>30.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme6452791056056222707”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>726</td> <td class=”number”>22.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.69230769230769</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.5</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14.2857142857143</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>93.0232558139535</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>93.3333333333333</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>94.8717948717949</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>95.6521739130435</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>636</td> <td class=”number”>19.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_FMRKT_FRVEG16”>PCT_FMRKT_FRVEG16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>111</td>

</tr> <tr>

<th>Unique (%)</th> <td>3.5%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>30.1%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>967</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>62.482</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>15.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram7048992338828648807”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives7048992338828648807”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles7048992338828648807”

aria-controls=”quantiles7048992338828648807” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram7048992338828648807” aria-controls=”histogram7048992338828648807”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common7048992338828648807” aria-controls=”common7048992338828648807”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme7048992338828648807” aria-controls=”extreme7048992338828648807”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles7048992338828648807”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>33.333</td>

</tr> <tr>

<th>Median</th> <td>75</td>

</tr> <tr>

<th>Q3</th> <td>100</td>

</tr> <tr>

<th>95-th percentile</th> <td>100</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr>

<th>Range</th> <td>100</td>

</tr> <tr>

<th>Interquartile range</th> <td>66.667</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>39.538</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.63279</td>

</tr> <tr>

<th>Kurtosis</th> <td>-1.255</td>

</tr> <tr>

<th>Mean</th> <td>62.482</td>

</tr> <tr>

<th>MAD</th> <td>34.897</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.54443</td>

</tr> <tr>

<th>Sum</th> <td>140150</td>

</tr> <tr>

<th>Variance</th> <td>1563.3</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram7048992338828648807”>

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Xfq4MGDevPNN/W3v/1Nvr6%2BGjBggMaPHy8/Pz%2B7xwMAAOiU2wWWJPXq1UsPPvig3WMAAABcF7cLrL///e/Ky8vTRx99pKampg7nX3/9dRumAgAA6Dq3C6w1a9aourpa48ePV1BQkN3jAAAAdJvbBVZ5eblef/11RURE2D0KAADAdXG7j2m47bbbuHMFAAA8mtsF1uLFi7Vt2zZZlmX3KAAAANfF7V4ifPPNN/Xee%2B/p1Vdf1be%2B9S35%2Bjo34CuvvGLTZAAAAF3jdoEVEhKiCRMm2D0GAADAdXO7wMrNzbV7BAAAgB5xu/dgSdJf/vIX5efn60c/%2BlH7sffff9/GiQAAALrO7QKrtLRUM2fO1NGjR3XgwAFJX3z46IIFC/iQUQAA4BHcLrCef/55/fCHP9T%2B/fvl4%2BMj6Yu/n3Dz5s168cUXbZ4OAACgc24XWP/zP/%2BjtLQ0SWoPLEmaNm2aKisr7RoLAACgy9wusHr37v21fwdhdXW1evXqZcNEAAAA3eN2gTVy5Eht2rRJV65caT927tw5rVq1Svfdd5%2BNkwEAAHSN231Mw49%2B9CM9/vjjio%2BPV2trq0aOHKnGxkYNGjRImzdvtns8AACATrldYPXr108HDhxQSUmJzp07p8DAQA0YMEDjxo1zek8WAACAu3K7wJKkW265RQ8%2B%2BKDdYwAAAFwXtwusxMTEf3qnis/CAgAA7s7tAmv69OlOgdXa2qpz586prKxMjz/%2BuI2TAQAAdI3bBVZ2dvbXHj9y5Ij%2B9Kc/3eBp3N/otYftHqHLDi0fZ/cIAADcEG73MQ3f5MEHH9TBgwftHgMAAKBTHhNYp0%2BflmVZdo8BAADQKbd7iXD%2B/PkdjjU2NqqyslJTpkyxYSIAAIDucbvAio6O7vCnCAMCApSSkqJHHnnEpqkAAAC6zu0Ci09rBwAAns7tAuu1117r8mNnz57twkkAAACuj9sF1tq1a9XW1tbhDe0%2BPj5Ox3x8fAgsAADgltwusH7xi19ox44dWrJkiYYMGSLLsnT27Fn9x3/8hx577DHFx8fbPSIAAMA/5XaBtXnzZhUWFioqKqr92OjRo3XnnXdq4cKFOnDggI3TAQAAdM7tPgfrk08%2BUVhYWIfjoaGhqqqqsmEiAACA7nG7wOrfv782b96surq69mP19fXKy8vTXXfdZeNkAAAAXeN2LxGuWbNGWVlZKioqUnBwsHx9fXXlyhUFBgbqxRdftHs8AACATrldYI0fP17Hjx9XSUmJLl68KMuyFBUVpfvvv1%2B9e/e2ezwAAIBOuV1gSdKtt96qBx54QBcvXtSdd95p9zgAAADd4nbvwWpqatKqVas0YsQIPfTQQ5K%2BeA/WokWLVF9fb/N0AAAAnXO7wNqyZYvOnDmjrVu3ytf3/8ZrbW3V1q1bbZwMAACga9wusI4cOaKf//znmjZtWvtf%2BhwaGqrc3FwdPXrU5ukAAAA653aB1dDQoOjo6A7HIyIi9Pnnn9/4gQAAALrJ7QLrrrvu0p/%2B9CdJcvq7Bw8fPqx/%2BZd/sWssAACALnO7P0X46KOPatmyZZo7d67a2tq0c%2BdOlZeX68iRI1q7dq3d4wEAAHTK7QIrNTVV/v7%2B2r17t/z8/PTSSy9pwIAB2rp1q6ZNm2b3eAAAAJ1yu8Cqra3V3LlzNXfuXLtHAQAAuC5u9x6sBx54wOm9VwAAAJ7G7QIrPj5ehw4dsnsMAACA6%2BZ2LxHecccdeu6551RYWKi77rpLt9xyi9P5vLy8bj/npk2b9Otf/1pnz56VJJWWliovL09/%2BctfdMcdd2jx4sWaOXNm%2B%2BN37dql3/zmN6qpqdGQIUO0du1axcbG9uzCAADATcPtAuvjjz/W3XffLUmqq6vr8fOdOXNGe/fubf/36upqPfXUU1q7dq2SkpL07rvvaunSpRowYICGDx%2BuY8eOKT8/X7/4xS80ZMgQ7dq1S0uWLNHRo0cVFBTU43kAAID3c5uXCFesWCFJevnll9v/%2Be53v%2Bv07y%2B//HK3nrOtrU3r169Xenp6%2B7H9%2B/crOjpaKSkpCggIUEJCghITE1VcXCxJKioqUnJysuLi4hQYGKhFixZJkt544w0zFwoAALye29zBOnbsWIdjhYWFysjIuO7nfOWVVxQQEKCkpCS98MILkqSKigrFxMQ4PS4mJqb9fV8VFRWaPn16%2BzlfX18NHTpUZWVlmjFjRpe%2Bb3V1tWpqapyO%2BfsHKTIy8rqv5ev4%2BblNH3eJv7/nzPvlbj1tx56A3boW%2B3Uddut63rRbtwmsr/uTgz3504T/%2BMc/lJ%2Bf3%2BGul8PhUFRUlNOxPn36tL8c6XA4FBYW5nQ%2BLCysWy9XFhUVadu2bU7HMjIylJmZ2Z1L8Drh4cF2j9BtoaG32j2C12K3rsV%2BXYfduo437dZtAuvLv9i5s2NdlZubq%2BTkZA0cOFDnz5/v1tf29GMiUlNTlZiY6HTM3z9IdXUNPXrer/K00jd9/a7k5%2Ber0NBbVV/fqNbWNrvH8Srs1rXYr%2BuwW9dzxW7t%2Bo97twksk0pLS/X%2B%2B%2B/rwIEDHc6Fh4fL4XA4Haurq1NERMQ3nnc4HBo0aFCXv39kZGSHlwNrai6rpeXm/gnpidff2trmkXN7AnbrWuzXddit63jTbj3rFkgX7du3T5cuXdLkyZMVHx%2Bv5ORkSV98xtbgwYNVXl7u9Pjy8nLFxcVJkmJjY1VRUdF%2BrrW1VadPn24/DwAA0Bm3uYPV3NysrKysTo915XOwVq9erR/84Aft/37x4kWlpqZq7969amtr0/bt21VcXKyZM2fqnXfeUUlJiYqKiiRJaWlpWrlypR5%2B%2BGENGTJEv/zlL9WrVy9NmjSp5xcJAABuCm4TWKNGjVJ1dXWnx7oiLCzM6Y3qLS0tkqR%2B/fpJkrZv366NGzdqw4YN6t%2B/v7Zs2aJ77rlHkjRhwgStXLlSy5cv16VLlzR8%2BHAVFhYqMDDwei8NAADcZNwmsLr7GVfd8a1vfav9U9wlacyYMU4fPvpVjz76qB599FGXzQMAALybV74HCwAAwE4EFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGH%2Bdg8AAO7uoRfetnuEbjn53DS7RwBuetzBAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMMxrA6uqqkoZGRmKj49XQkKCVq9erfr6eknSmTNn9Nhjj2nUqFGaMmWKduzY4fS1v/vd75SUlKQRI0YoOTlZb731lh2XAAAAPJTXBtaSJUsUGhqqY8eO6dVXX9VHH32kn/zkJ2pqatLixYv13e9%2BV3/84x/1/PPPa/v27Tp69KikL%2BJr1apVys7O1jvvvKP09HQ9/fTTunjxos1XBAAAPIVXBlZ9fb1iY2OVlZWl4OBg9evXT3PmzNHJkyd1/PhxNTc3a%2BnSpQoKCtKwYcP0yCOPqKioSJJUXFysiRMnauLEiQoICNDMmTM1ePBg7du3z%2BarAgAAnsIrAys0NFS5ubnq27dv%2B7ELFy4oMjJSFRUVGjJkiPz8/NrPxcTEqLy8XJJUUVGhmJgYp%2BeLiYlRWVnZjRkeAAB4PH%2B7B7gRysrKtHv3bhUUFOjQoUMKDQ11Ot%2BnTx85HA61tbXJ4XAoLCzM6XxYWJg%2B/vjjLn%2B/6upq1dTUOB3z9w9SZGTk9V/E1/Dz86w%2B9vf3nHm/3K2n7dgTsNsbg/2ax49d1/Om3Xp9YL377rtaunSpsrKylJCQoEOHDn3t43x8fNr/v2VZPfqeRUVF2rZtm9OxjIwMZWZm9uh5PV14eLDdI3RbaOitdo/gtdita7Ff12G3ruNNu/XqwDp27Jh%2B%2BMMfat26dZo9e7YkKSIiQp988onT4xwOh/r06SNfX1%2BFh4fL4XB0OB8REdHl75uamqrExESnY/7%2BQaqra7i%2BC/kGnlb6pq/flfz8fBUaeqvq6xvV2tpm9zhehd3eGOzXPH7sup4rdmvXf9x7bWC99957WrVqlX72s59p/Pjx7cdjY2P129/%2BVi0tLfL3/%2BLyy8rKFBcX137%2By/djfamsrEwzZszo8veOjIzs8HJgTc1ltbTc3D8hPfH6W1vbPHJuT8BuXYv9ug67dR1v2q1n3QLpopaWFuXk5Cg7O9spriRp4sSJCgkJUUFBgRobG3Xq1Cnt2bNHaWlpkqR58%2BbpxIkTOn78uK5evao9e/bok08%2B0cyZM%2B24FAAA4IG88g7WBx98oMrKSm3cuFEbN250Onf48GG99NJLWr9%2BvQoLC9W3b1%2BtWLFCkyZNkiQNHjxYW7duVW5urqqqqjRw4EBt375dt99%2Buw1XAgAAPJFXBtbo0aN19uzZf/qY3/72t994bsqUKZoyZYrpsQAAwE3CK18iBAAAsBOBBQAAYBiBBQAAYBiBBQAAYBiBBQAAYBiBBQAAYBiBBQAAYBiBBQAAYBiBBQAAYJhXfpI7ANzMRq89bPcIXXZo%2BTi7RwBcgjtYAAAAhhFYAAAAhhFYAAAAhhFYAAAAhhFYAAAAhhFYAAAAhhFYAAAAhhFYAAAAhhFYAAAAhhFYAAAAhhFYAAAAhhFYAAAAhhFYAAAAhvnbPQAA4Ob10Atv2z1Ct5x8bprdI8BDcAcLAADAMAILAADAMF4iBACgi0avPWz3CPAQ3MECAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjA8aBbyEJ/2dbvx9bgC8HXewAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADONjGoBvMHrtYbtHAAB4KO5gAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGMafIgRww/EnNAF4O%2B5gAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGMafIvwaVVVV2rBhg06dOqWgoCBNnz5dWVlZ8vWlR3vioRfetnsEAABuCALrayxbtkzDhg3TH/7wB126dEmLFy9W37599f3vf9/u0QAAgAfglsxXlJWV6cMPP1R2drZ69%2B6t6Ohopaenq6ioyO7RAACAh%2BAO1ldUVFSof//%2BCgsLaz82bNgwnTt3TleuXFFISEinz1FdXa2amhqnY/7%2BQYqMjDQ6q58ffQwA8B7e9PsagfUVDodDoaGhTse%2BjK26urouBVZRUZG2bdvmdOzpp5/WsmXLzA2qL0Lu8X4fKTU11Xi83eyqq6tVVFTEbl2A3boW%2B3Uddus61dXVys/P96rdek8qGmRZVo%2B%2BPjU1Va%2B%2B%2BqrTP6mpqYam%2Bz81NTXatm1bh7tl6Dl26zrs1rXYr%2BuwW9fxxt1yB%2BsrIiIi5HA4nI45HA75%2BPgoIiKiS88RGRnpNQUOAAC6jztYXxEbG6sLFy6otra2/VhZWZkGDhyo4OBgGycDAACegsD6ipiYGA0fPlx5eXm6cuWKKisrtXPnTqWlpdk9GgAA8BB%2BzzzzzDN2D%2BFu7r//fh04cEDPPvusDh48qJSUFC1cuFA%2BPj52j9ZBcHCwxo4dy901F2C3rsNuXYv9ug67dR1v262P1dN3dAMAAMAJLxECAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmB5oKqqKj355JOKj4/X5MmTtWXLFrW1tdk9lseqqqpSRkaG4uPjlZCQoNWrV6u%2Bvl6SdObMGT322GMaNWqUpkyZoh07dtg8refatGmThgwZ0v7vpaWlSklJ0ciRIzVjxgzt27fPxuk8V0FBgcaPH697771X6enpOn/%2BvCT221OnT5/WggULNHr0aI0bN07Z2dmqra2VxG6vxx//%2BEclJCRoxYoVHc797ne/U1JSkkaMGKHk5GS99dZb7efa2tr0/PPP64EHHtCYMWO0cOFC/f3vf7%2BRo18/Cx5nzpw5Vk5OjlVfX2%2BdO3fOmjJlirVjxw67x/JYDz/8sLV69WrrypUr1oULF6zk5GRrzZo1VmNjo3X//fdb%2Bfn5VkNDg1VeXm6NHTvWOnLkiN0%2BVyc8AAAHFklEQVQje5zTp09bY8eOtQYPHmxZlmV9%2Bumn1r333msVFxdbTU1N1ttvv2195zvfsf785z/bPKln2b17tzVt2jSrsrLSunz5svXss89azz77LPvtoebmZmvcuHFWXl6edfXqVau2ttb6/ve/by1btozdXofCwkJrypQp1vz5863ly5c7nTt9%2BrQVGxtrHT9%2B3GpqarL27t1rxcXFWRcuXLAsy7J27dplTZ482fr444%2Bty5cvWz/%2B8Y%2BtpKQkq62tzY5L6RbuYHmYsrIyffjhh8rOzlbv3r0VHR2t9PR0FRUV2T2aR6qvr1dsbKyysrIUHBysfv36ac6cOTp58qSOHz%2Bu5uZmLV26VEFBQRo2bJgeeeQRdt1NbW1tWr9%2BvdLT09uP7d%2B/X9HR0UpJSVFAQIASEhKUmJio4uJi%2Bwb1QDt27NCKFSt09913KyQkRDk5OcrJyWG/PVRTU6OamhrNmjVLvXr1Unh4uL73ve/pzJkz7PY6BAQEaM%2BePfr2t7/d4VxxcbEmTpyoiRMnKiAgQDNnztTgwYPb7woWFRUpPT1d//qv/6qQkBCtWLFClZWVOnXq1I2%2BjG4jsDxMRUWF%2Bvfvr7CwsPZjw4YN07lz53TlyhUbJ/NMoaGhys3NVd%2B%2BfduPXbhwQZGRkaqoqNCQIUPk5%2BfXfi4mJkbl5eV2jOqxXnnlFQUEBCgpKan9WEVFhWJiYpwex26759NPP9X58%2Bf12Wefafr06YqPj1dmZqZqa2vZbw9FRUVp6NChKioqUkNDgy5duqSjR49q0qRJ7PY6LFiwQL179/7ac9%2B0z7KyMjU1Nenjjz92Oh8SEqJvf/vbKisrc%2BnMJhBYHsbhcCg0NNTp2JexVVdXZ8dIXqWsrEy7d%2B/W0qVLv3bXffr0kcPh4D1vXfSPf/xD%2Bfn5Wr9%2BvdPxb9otP4a77uLFi5Kkw4cPa%2BfOndq7d68uXryonJwc9ttDvr6%2Bys/P1%2Buvv66RI0cqISFBLS0tysrKYreGORwOpxsG0he/p9XV1emzzz6TZVnfeN7dEVgeyLIsu0fwSu%2B%2B%2B64WLlyorKwsJSQkfOPjfHx8buBUni03N1fJyckaOHCg3aN4nS9/HVi0aJGioqLUr18/LVu2TMeOHbN5Ms937do1LVmyRNOmTdPJkyf15ptvqnfv3srOzrZ7NK/U2e9pnvp7HoHlYSIiIuRwOJyOORwO%2Bfj4KCIiwqapPN%2BxY8f05JNPas2aNVqwYIGkL3b91f9Kcjgc6tOnj3x9%2BanTmdLSUr3//vvKyMjocC48PLzDj%2BO6ujp%2BDHfDly9r//%2B7Kf3795dlWWpubma/PVBaWqrz589r5cqV6t27t6KiopSZmanf//738vX1ZbcGfd2vBQ6HQxEREe2/1n7d%2Bdtuu%2B1Gjnld%2BF3Cw8TGxurChQvtf1xY%2BuJlrYEDByo4ONjGyTzXe%2B%2B9p1WrVulnP/uZZs%2Be3X48NjZWZ8%2BeVUtLS/uxsrIyxcXF2TGmx9m3b58uXbqkyZMnKz4%2BXsnJyZKk%2BPh4DR48uMN7VsrLy9ltN/Tr108hISE6c%2BZM%2B7GqqirdcsstmjhxIvvtgdbWVrW1tTndObl27ZokKSEhgd0aFBsb22GfX/46GxAQoEGDBqmioqL9XH19vf72t7/pO9/5zo0etdsILA8TExOj4cOHKy8vT1euXFFlZaV27typtLQ0u0fzSC0tLcrJyVF2drbGjx/vdG7ixIkKCQlRQUGBGhsbderUKe3Zs4ddd9Hq1at15MgR7d27V3v37lVhYaEkae/evUpKSlJVVZWKi4t19epVlZSUqKSkRPPmzbN5as/h7%2B%2BvlJQUvfTSS/rrX/%2BqS5cu6cUXX1RSUpLmzJnDfntgxIgRCgoKUn5%2BvhobG1VXV6eCggKNGTNGs2bNYrcGzZs3TydOnNDx48d19epV7dmzR5988olmzpwpSUpLS9OuXbtUWVmpK1euaOvWrRo6dKiGDx9u8%2BSd87E89cXNm9jFixe1bt06/dd//ZdCQkI0f/58Pf3007w36DqcPHlS//Zv/6ZevXp1OHf48GE1NDRo/fr1Ki8vV9%2B%2BffXEE0/o0UcftWFSz3f%2B/Hk98MADOnv2rCTpv//7v7Vx40ZVVlaqf//%2BysrK0pQpU2ye0rNcu3ZNubm5OnjwoJqbmzV16lStW7dOwcHB7LeHysvL9ZOf/EQffvihevXqpbFjx2r16tWKiopit930ZQx9%2BWqAv7%2B/JLX/ScCjR48qLy9PVVVVGjhwoNauXasxY8ZI%2BuL9V/n5%2BXrllVfU0NCg%2BPh4/fjHP1a/fv1suJLuIbAAAAAM4yVCAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAw/4XT2aMHlJ93psAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common7048992338828648807”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>927</td> <td class=”number”>28.9%</td> <td>

<div class=”bar” style=”width:95%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>488</td> <td class=”number”>15.2%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>246</td> <td class=”number”>7.7%</td> <td>

<div class=”bar” style=”width:26%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>66.6666666666667</td> <td class=”number”>104</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.3333333333333</td> <td class=”number”>65</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>75.0</td> <td class=”number”>63</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>80.0</td> <td class=”number”>44</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>28</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>60.0</td> <td class=”number”>26</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>40.0</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (100)</td> <td class=”number”>235</td> <td class=”number”>7.3%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>967</td> <td class=”number”>30.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme7048992338828648807”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>488</td> <td class=”number”>15.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.14285714285714</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.5</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14.2857142857143</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>93.75</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>94.7368421052632</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>95.3488372093023</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>95.6521739130435</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>927</td> <td class=”number”>28.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_FMRKT_OTHERFOOD16”><s>PCT_FMRKT_OTHERFOOD16</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCT_FMRKT_FRVEG16”><code>PCT_FMRKT_FRVEG16</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.90806</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_FMRKT_SFMNP16”>PCT_FMRKT_SFMNP16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>102</td>

</tr> <tr>

<th>Unique (%)</th> <td>3.2%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>30.1%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>967</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>25.93</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>41.4%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram3612558746946165060”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives3612558746946165060,#minihistogram3612558746946165060”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives3612558746946165060”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles3612558746946165060”

aria-controls=”quantiles3612558746946165060” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram3612558746946165060” aria-controls=”histogram3612558746946165060”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common3612558746946165060” aria-controls=”common3612558746946165060”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme3612558746946165060” aria-controls=”extreme3612558746946165060”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles3612558746946165060”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>50</td>

</tr> <tr>

<th>95-th percentile</th> <td>100</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr>

<th>Range</th> <td>100</td>

</tr> <tr>

<th>Interquartile range</th> <td>50</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>36.796</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.419</td>

</tr> <tr>

<th>Kurtosis</th> <td>-0.38095</td>

</tr> <tr>

<th>Mean</th> <td>25.93</td>

</tr> <tr>

<th>MAD</th> <td>31.569</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.0779</td>

</tr> <tr>

<th>Sum</th> <td>58161</td>

</tr> <tr>

<th>Variance</th> <td>1353.9</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram3612558746946165060”>

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9WpKUlZWldevWqaKiQqdPn1ZBQYF69uyp3r17Kz4%2BXsOGDdOTTz6pEydO6Pjx41q1apUyMzNlswXlCT8AAGBYwCaG3r17S5Lq6%2BslSdu2bZP0w9mm7t27q6CgQEuWLNHRo0fVtWtXPfPMM7r88sslSePHj5fD4dDvfvc71dTUKDU11eum9EceeUQLFy7UkCFD1KpVK40cOfKCDzMFAAC4kBB3S%2B/oRrM4HKeMH9NmC9VtBbuNH9cqb02/2d8lNJvNFqq4uEg5nTVBcz/ALwW9tRb9tQ69tY6Vvb388nZGj9dcQXmJEAAAwJ8IWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABjm84CVkZGhlStX6tixY75eGgAAwCd8HrDuuusuvfnmm7r11ls1adIkbd26VfX19b4uAwAAwDI%2BD1g5OTl688039corr6hbt2567LHHlJ6ermXLlunw4cO%2BLgcAAMA4v92D1atXL82aNUs7duzQ3Llz9corr2jEiBGaOHGiPvvsM3%2BVBQAA0GJ%2BC1jnzp3Tm2%2B%2Bqfvvv1%2BzZs1SYmKi5syZo549eyo7O1ubNm3yV2kAAAAtYvP1ghUVFVq/fr1ef/111dTUaNiwYfrLX/6iG264wfOafv36adGiRRo1apSvywMAAGgxnwes22%2B/XV26dNHkyZN15513KjY2tslr0tPTdeLEiRats2/fPi1dulT79u1TeHi4brrpJs2dO1fx8fEqKSnR8uXL9eWXX%2BqKK67Q5MmTNXr0aM97161bp5deekkOh0M9evTQvHnzlJyc3KJ6AADAr4fPLxGuW7dOb731lrKzsy8Yrs7bu3fvJa9RX1%2BvBx54QNdff7327NmjzZs368SJE1q0aJEqKys1depUjR8/XiUlJZo3b54WLFig0tJSSdL27dtVWFioJ554Qnv27NHgwYM1ZcoUnTlz5pLrAQAAvy4%2BD1g9evTQlClTtG3bNs/Y888/r/vvv18ul8vIGg6HQw6HQ3fccYdat26tuLg43Xbbbdq/f782bdqkzp07KzMzU%2BHh4UpLS1NGRoaKi4slSXa7XWPHjlVKSoratGmjSZMmSZJ27NhhpDYAABD8fB6wlixZolOnTqlr166esUGDBqmxsVFLly41skZiYqJ69uwpu92umpoaVVVVaevWrRo0aJDKy8uVlJTk9fqkpCSVlZVJUpP50NBQ9ezZ03OGCwAA4Of4/B6s9957T5s2bVJcXJxnrHPnziooKNDIkSONrBEaGqrCwkJlZ2frL3/5iySpf//%2Bys/P19SpU5WYmOj1%2BtjYWDmdTkmSy%2BVSTEyM13xMTIxnvjkqKyvlcDi8xmy2CCUkJFzKdn5SWFhg/aYjmy1w6j3f20DrcSCgt9aiv9aht9YJxt76PGDV1dUpPDy8yXhoaKhqa2uNrHH27FlNmTJFw4cP99w/9fDDD2vmzJnNer/b7W7R%2Bna7XStXrvQay8nJUW5ubouOG%2Bji4iL9XcJFi45u6%2B8Sgha9tRb9tQ69tU4w9dbnAatfv35aunSp8vPzPWeKvv32Wz3%2B%2BONej2poiZKSEh05ckQzZsxQWFiY2rVrp9zcXN1xxx265ZZbmtzr5XQ6FR8fL0mKi4trMu9yudStW7dmrz9u3DhlZGR4jdlsEXI6ay5xRxcWaEnf9P6tFBYWqujotqqurlVDQ6O/ywkq9NZa9Nc69NY6VvbWX9/c%2BzxgzZ07V3/84x910003KSoqSo2NjaqpqdGVV16pF154wcgaDQ0Namxs9DoTdfbsWUlSWlqa/vM//9Pr9WVlZUpJSZEkJScnq7y8XGPGjPEca9%2B%2BfcrMzGz2%2BgkJCU0uBzocp1Rf/%2Bv%2BhAzE/Tc0NAZk3YGA3lqL/lqH3lonmHrr84B15ZVX6o033tC7776rr7/%2BWqGhoerSpYsGDBigsLAwI2v06dNHERERKiws1JQpU1RXV6fVq1erX79%2BuuOOO7Ry5UoVFxdr9OjRev/997Vr1y7Z7XZJUlZWlmbMmKGRI0eqR48eeu6559S6dWsNGjTISG0AACD4%2BTxgSVLr1q116623Wnb8uLg4Pffcc3r88cc1cOBAtW7dWv3799eiRYvUvn17PfPMM1q8eLEefvhhdezYUcuWLdO1114rSRo4cKBmzJih6dOnq6qqSr1791ZRUZHatGljWb0AACC4hLhbekf3Rfrmm2%2B0fPlyffHFF6qrq2sy/8477/iyHJ9xOE4ZP6bNFqrbCnYbP65V3pp%2Bs79LaDabLVRxcZFyOmuC5nT1LwW9tRb9tQ69tY6Vvb388nZGj9dcfrkHq7KyUgMGDFBERISvlwcAALCczwNWWVmZ3nnnHc9P7QEAAAQbn/%2Bcf/v27TlzBQAAgprPA9bkyZO1cuXKFj/MEwAA4JfK55cI3333XX388cd67bXX9Jvf/Eahod4Z7%2BWXX/Z1SQAAAEb5PGBFRUVp4MCBvl4WAADAZ3wesJYsWeLrJQEAAHzKL7/M7ssvv1RhYaHmzJnjGfvkk0/8UQoAAIBxPg9YJSUlGj16tLZu3arNmzdL%2BuHhoxMmTAjah4wCAIBfF58HrBUrVuhPf/qTNm3apJCQEEk//H7CpUuXatWqVb4uBwAAwDifB6zPP/9cWVlZkuQJWJI0fPhwVVRU%2BLocAAAA43wesNq1a3fB30FYWVmp1q1b%2B7ocAAAA43wesPr27avHHntMp0%2Bf9owdPnxYs2bN0k033eTrcgAAAIzz%2BWMa5syZo9///vdKTU1VQ0OD%2Bvbtq9raWnXr1k1Lly71dTkAAADG%2BTxgdejQQZs3b9auXbt0%2BPBhtWnTRl26dNHNN9/sdU8WAABAoPJ5wJKkVq1a6dZbb/XH0gAAAJbzecDKyMj4h2eqeBYWAAAIdD4PWCNGjPAKWA0NDTp8%2BLBKS0v1%2B9//3tflAAAAGOfzgDVz5swLjm/ZskUffPCBj6sBAAAwzy%2B/i/BCbr31Vr3xxhv%2BLgMAAKDFfjEBa9%2B%2BfXK73f4uAwAAoMV8folw/PjxTcZqa2tVUVGhoUOH%2BrocAAAA43wesDp37tzkpwjDw8OVmZmpu%2B%2B%2B29flAAAAGOfzgMXT2gEAQLDzecB6/fXXm/3aO%2B%2B808JKAAAArOHzgDVv3jw1NjY2uaE9JCTEaywkJISABQAAApLPA9Z//Md/aM2aNZoyZYp69Oght9utgwcP6tlnn9V9992n1NRUX5cEAABglF/uwSoqKlJiYqJn7MYbb9SVV16piRMnavPmzb4uCQAAwCifPwfrq6%2B%2BUkxMTJPx6OhoHT161NflAAAAGOfzgNWxY0ctXbpUTqfTM1ZdXa3ly5frqquu8nU5AAAAxvn8EuHcuXOVn58vu92uyMhIhYaG6vTp02rTpo1WrVrl63IAAACM83nAGjBggHbu3Kldu3bp%2BPHjcrvdSkxM1C233KJ27dr5uhwAAADjfB6wJKlt27YaMmSIjh8/riuvvNIfJQAAAFjG5/dg1dXVadasWerTp49%2B%2B9vfSvrhHqxJkyapurra1%2BUAAAAY5/OAtWzZMu3fv18FBQUKDf3f5RsaGlRQUODrcgAAAIzzecDasmWL/u3f/k3Dhw/3/NLn6OhoLVmyRFu3bjW61urVqzVgwABdf/31ys7O1pEjRyRJJSUlyszMVN%2B%2BfXX77bdr48aNXu9bt26dhg0bpr59%2ByorK0tlZWVG6wIAAMHN5wGrpqZGnTt3bjIeHx%2BvM2fOGFvnpZde0saNG7Vu3Tq999576tq1q55//nlVVlZq6tSpGj9%2BvEpKSjRv3jwtWLBApaWlkqTt27ersLBQTzzxhPbs2aPBgwdrypQpRmsDAADBzecB66qrrtIHH3wgSV6/e/Dtt9/WP/3TPxlbZ82aNcrLy9PVV1%2BtqKgozZ8/X/Pnz9emTZvUuXNnZWZmKjw8XGlpacrIyFBxcbEkyW63a%2BzYsUpJSVGbNm00adIkSdKOHTuM1QYAAIKbz3%2BK8N5779W0adN01113qbGxUWvXrlVZWZm2bNmiefPmGVnj22%2B/1ZEjR3Ty5EmNGDFCVVVVSk1N1aJFi1ReXq6kpCSv1yclJemtt96SJJWXl2vEiBGeudDQUPXs2VOlpaW6/fbbm7V%2BZWWlHA6H15jNFqGEhIQW7sxbWJjP83GL2GyBU%2B/53gZajwMBvbUW/bUOvbVOMPbW5wFr3LhxstlsevHFFxUWFqann35aXbp0UUFBgYYPH25kjePHj0v64azY2rVr5Xa7lZubq/nz56uurs7r9yBKUmxsrOfJ8i6Xq8mv8omJifF68vzPsdvtWrlypddYTk6OcnNzL2U7QSMuLtLfJVy06Oi2/i4haNFba9Ff69Bb6wRTb30esE6cOKG77rpLd911l2VrnL/0OGnSJE%2BYmjZtmu6//36lpaU1%2B/2Xaty4ccrIyPAas9ki5HTWtOi4PxZoSd/0/q0UFhaq6Oi2qq6uVUNDo7/LCSr01lr01zr01jpW9tZf39z7PGANGTJEH3/8secnCK1w2WWXSfrhpxPP69ixo9xut86dOyeXy%2BX1eqfTqfj4eElSXFxck3mXy6Vu3bo1e/2EhIQmlwMdjlOqr/91f0IG4v4bGhoDsu5AQG%2BtRX%2BtQ2%2BtE0y99fkpkNTUVM/9Tlbp0KGDoqKitH//fs/Y0aNH1apVK6Wnpzd57EJZWZlSUlIkScnJySovL/fMNTQ0aN%2B%2BfZ55AACAn%2BPzM1hXXHGF/vznP6uoqEhXXXWVWrVq5TW/fPnyFq9hs9mUmZmpp59%2BWv369VNUVJRWrVqlUaNGacyYMfr3f/93FRcXa/To0Xr//fe1a9cu2e12SVJWVpZmzJihkSNHqkePHnruuefUunVrDRo0qMV1AQCAXwefB6xDhw7p6quvlqSLunH8YuXn5%2Bvs2bO6%2B%2B67de7cOQ0bNkzz589XZGSknnnmGS1evFgPP/ywOnbsqGXLlunaa6%2BVJA0cOFAzZszQ9OnTVVVVpd69e6uoqEht2rSxrFYAABBcQtwtvaO7mfLy8rRixQqvsVWrViknJ8cXy/udw3HK%2BDFttlDdVrDb%2BHGt8tb0m/1dQrPZbKGKi4uU01kTNPcD/FLQW2vRX%2BvQW%2BtY2dvLL29n9HjN5bN7sLZv395krKioyFfLAwAA%2BIzPAtaFTpT56OQZAACAT/ksYF3osQxWPqoBAADAXwLrSZUAAAABgIAFAABgmM8e03Du3Dnl5%2Bf/7JiJ52ABAAD4k88C1g033KDKysqfHQMAAAh0PgtYL7zwgq%2BWAgAA8CvuwQIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGDYryJgPfbYY%2BrRo4fn45KSEmVmZqpv3766/fbbtXHjRq/Xr1u3TsOGDVPfvn2VlZWlsrIyX5cMAAACWNAHrP3792vDhg2ejysrKzV16lSNHz9eJSUlmjdvnhYsWKDS0lJJ0vbt21VYWKgnnnhCe/bs0eDBgzVlyhSdOXPGX1sAAAABJqgDVmNjoxYuXKjs7GzP2KZNm9S5c2dlZmYqPDxcaWlpysjIUHFxsSTJbrdr7NixSklJUZs2bTRp0iRJ0o4dO/yxBQAAEICCOmC9/PLLCg8P16hRozxj5eXlSkpK8npdUlKS5zLgj%2BdDQ0PVs2dPzxkuAACAn2PzdwFW%2Be6771RYWKgXXnjBa9zlcikxMdFrLDY2Vk6n0zMfExPjNR8TE%2BOZb47Kyko5HA6vMZstQgkJCRezhZ8VFhZY%2BdhmC5x6z/c20HocCOitteivdeitdYKxt0EbsJYsWaKxY8eqa9euOnLkyEW91%2B12t2htu92ulStXeo3l5OQoNze3RccNdHFxkf4u4aJFR7f1dwlBi95ai/5ah95aJ5h6G5QBq6SkRJ988ok2b97cZC4uLk4ul8trzOl0Kj4%2B/ifnXS6XunXr1uz1x40bp4yMDK8xmy08DZTpAAARSklEQVRCTmdNs4/RHIGW9E3v30phYaGKjm6r6upaNTQ0%2BrucoEJvrUV/rUNvrWNlb/31zX1QBqyNGzeqqqpKgwcPlvS/Z6RSU1P1xz/%2BsUnwKisrU0pKiiQpOTlZ5eXlGjNmjCSpoaFB%2B/btU2ZmZrPXT0hIaHI50OE4pfr6X/cnZCDuv6GhMSDrDgT01lr01zr01jrB1NvAOgXSTLNnz9aWLVu0YcMGbdiwQUVFRZKkDRs2aNSoUTp69KiKi4v1/fffa9euXdq1a5fuueceSVJWVpZef/11ffrpp6qtrdXq1avVunVrDRo0yI87AgAAgSQoz2DFxMR43aheX18vSerQoYMk6ZlnntHixYv18MMPq2PHjlq2bJmuvfZaSdLAgQM1Y8YMTZ8%2BXVVVVerdu7eKiorUpk0b328EAAAEpKAMWD/2m9/8RgcPHvR83K9fP6%2BHj/7Yvffeq3vvvdcXpQEAgCAUlJcIAQAA/ImABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDbP4uAAAAWOO3T/7N3yU024d/Hu7vEoziDBYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABgWtAHr6NGjysnJUWpqqtLS0jR79mxVV1dLkvbv36/77rtPN9xwg4YOHao1a9Z4vffNN9/UqFGj1KdPH40dO1bvvfeeP7YAAAACVNAGrClTpig6Olrbt2/Xa6%2B9pi%2B%2B%2BEKPP/646urqNHnyZP3zP/%2Bzdu/erRUrVuiZZ57R1q1bJf0QvmbNmqWZM2fq/fffV3Z2th566CEdP37czzsCAACBIigDVnV1tZKTk5Wfn6/IyEh16NBBY8aM0YcffqidO3fq3LlzevDBBxUREaFevXrp7rvvlt1ulyQVFxcrPT1d6enpCg8P1%2BjRo9W9e3dt3LjRz7sCAACBwubvAqwQHR2tJUuWeI0dO3ZMCQkJKi8vV48ePRQWFuaZS0pKUnFxsSSpvLxc6enpXu9NSkpSaWlps9evrKyUw%2BHwGrPZIpSQkHCxW/mHwsICKx/bbIFT7/neBlqPAwG9tRb9tQ69tV4w9TYoA9aPlZaW6sUXX9Tq1av11ltvKTo62ms%2BNjZWLpdLjY2NcrlciomJ8ZqPiYnRoUOHmr2e3W7XypUrvcZycnKUm5t76ZsIAnFxkf4u4aJFR7f1dwlBi95ai/5ah95aJ5h6G/QB66OPPtKDDz6o/Px8paWl6a233rrg60JCQjz/73a7W7TmuHHjlJGR4TVms0XI6axp0XF/LNCSvun9WyksLFTR0W1VXV2rhoZGf5cTVOitteivdeit9azorb%2B%2BuQ/qgLV9%2B3b96U9/0oIFC3TnnXdKkuLj4/XVV195vc7lcik2NlahoaGKi4uTy%2BVqMh8fH9/sdRMSEppcDnQ4Tqm%2B/tf9CRmI%2B29oaAzIugMBvbUW/bUOvbVOMPU2sE6BXISPP/5Ys2bN0lNPPeUJV5KUnJysgwcPqr6%2B3jNWWlqqlJQUz3xZWZnXsf7vPAAAwM8JyjNY9fX1mj9/vmbOnKkBAwZ4zaWnpysqKkqrV6/WpEmT9Pnnn2v9%2BvVatmyZJOmee%2B5RZmamdu7cqZtuukmbNm3SV199pdGjR/tjKwB%2BAX775N/8XcJF%2BfDPw/1dAvCrF5QB69NPP1VFRYUWL16sxYsXe829/fbbevrpp7Vw4UIVFRXpsssuU15engYNGiRJ6t69uwoKCrRkyRIdPXpUXbt21TPPPKPLL7/cDzsBAACBKCgD1o033qiDBw/%2Bw9f89a9//cm5oUOHaujQoabLAgAAvxJBew8WAACAvxCwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMs/m7AABm/PbJv/m7hGb78M/D/V0CAFiKM1gAAACGcQYLAILMjfPe9ncJzfbW9Jv9XQJgCQIW8BMC6YsUAOCXhUuEAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyb3OEzgfScJgAAWoIzWAAAAIYRsAAAAAwjYAEAABjGPVgAfI6HuOK8QLs3k9%2BjiebiDBYAAIBhBCwAAADDCFgAAACGEbAAAAAM4yb3Czh69Kgefvhh7d27VxERERoxYoTy8/MVGkoeBYBfM35AA81FwLqAadOmqVevXtq2bZuqqqo0efJkXXbZZfrDH/7g79IAAEAA4JTMj5SWlurAgQOaOXOm2rVrp86dOys7O1t2u93fpQEAgADBGawfKS8vV8eOHRUTE%2BMZ69Wrlw4fPqzTp08rKirqZ49RWVkph8PhNWazRSghIcForWFh5GMAQPAIpq9rBKwfcblcio6O9ho7H7acTmezApbdbtfKlSu9xh566CFNmzbNXKH6Icj9vsMXGjdunPHw9mtXWVkpu91Oby1Ab61Ff61Db61TWVmpwsLCoOpt8ERFg9xud4veP27cOL322mte/40bN85Qdf/L4XBo5cqVTc6WoeXorXXorbXor3XorXWCsbecwfqR%2BPh4uVwurzGXy6WQkBDFx8c36xgJCQlBk8ABAMDF4wzWjyQnJ%2BvYsWM6ceKEZ6y0tFRdu3ZVZGSkHysDAACBgoD1I0lJSerdu7eWL1%2Bu06dPq6KiQmvXrlVWVpa/SwMAAAEibNGiRYv8XcQvzS233KLNmzfr0Ucf1RtvvKHMzExNnDhRISEh/i6ticjISPXv35%2Bzaxagt9aht9aiv9aht9YJtt6GuFt6RzcAAAC8cIkQAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACVgA6evSoHnjgAaWmpmrw4MFatmyZGhsb/V1WwDp69KhycnKUmpqqtLQ0zZ49W9XV1ZKk/fv367777tMNN9ygoUOHas2aNX6uNnA99thj6tGjh%2BfjkpISZWZmqm/fvrr99tu1ceNGP1YXuFavXq0BAwbo%2BuuvV3Z2to4cOSKJ/rbUvn37NGHCBN144426%2BeabNXPmTJ04cUISvb0Uu3fvVlpamvLy8prMvfnmmxo1apT69OmjsWPH6r333vPMNTY2asWKFRoyZIj69euniRMn6ptvvvFl6ZfOjYAzZswY9/z5893V1dXuw4cPu4cOHepes2aNv8sKWCNHjnTPnj3bffr0afexY8fcY8eOdc%2BdO9ddW1vrvuWWW9yFhYXumpoad1lZmbt///7uLVu2%2BLvkgLNv3z53//793d27d3e73W73t99%2B677%2B%2BuvdxcXF7rq6Ovff/vY393XXXef%2B7LPP/FxpYHnxxRfdw4cPd1dUVLhPnTrlfvTRR92PPvoo/W2hc%2BfOuW%2B%2B%2BWb38uXL3d9//737xIkT7j/84Q/uadOm0dtLUFRU5B46dKh7/Pjx7unTp3vN7du3z52cnOzeuXOnu66uzr1hwwZ3SkqK%2B9ixY2632%2B1et26de/Dgwe5Dhw65T5065X7kkUfco0aNcjc2NvpjKxeFM1gBprS0VAcOHNDMmTPVrl07de7cWdnZ2bLb7f4uLSBVV1crOTlZ%2Bfn5ioyMVIcOHTRmzBh9%2BOGH2rlzp86dO6cHH3xQERER6tWrl%2B6%2B%2B256fZEaGxu1cOFCZWdne8Y2bdqkzp07KzMzU%2BHh4UpLS1NGRoaKi4v9V2gAWrNmjfLy8nT11VcrKipK8%2BfP1/z58%2BlvCzkcDjkcDt1xxx1q3bq14uLidNttt2n//v309hKEh4dr/fr16tSpU5O54uJipaenKz09XeHh4Ro9erS6d%2B/uOStot9uVnZ2ta665RlFRUcrLy1NFRYX27t3r621cNAJWgCkvL1fHjh0VExPjGevVq5cOHz6s06dP%2B7GywBQdHa0lS5bosssu84wdO3ZMCQkJKi8vV48ePRQWFuaZS0pKUllZmT9KDVgvv/yywsPDNWrUKM9YeXm5kpKSvF5Hby/Ot99%2BqyNHjujkyZMaMWKEUlNTlZubqxMnTtDfFkpMTFTPnj1lt9tVU1Ojqqoqbd26VYMGDaK3l2DChAlq167dBed%2Bqp%2BlpaWqq6vToUOHvOajoqLUqVMnlZaWWlqzCQSsAONyuRQdHe01dj5sOZ1Of5QUVEpLS/Xiiy/qwQcfvGCvY2Nj5XK5uOetmb777jsVFhZq4cKFXuM/1Vv%2BDjff8ePHJUlvv/221q5dqw0bNuj48eOaP38%2B/W2h0NBQFRYW6p133lHfvn2Vlpam%2Bvp65efn01vDXC6X1wkD6YevaU6nUydPnpTb7f7J%2BV86AlYAcrvd/i4hKH300UeaOHGi8vPzlZaW9pOvCwkJ8WFVgW3JkiUaO3asunbt6u9Sgs75fwcmTZqkxMREdejQQdOmTdP27dv9XFngO3v2rKZMmaLhw4frww8/1Lvvvqt27dpp5syZ/i4tKP3c17RA/ZpHwAow8fHxcrlcXmMul0shISGKj4/3U1WBb/v27XrggQc0d%2B5cTZgwQdIPvf7xd0kul0uxsbEKDeVT5%2BeUlJTok08%2BUU5OTpO5uLi4Jn%2BPnU4nf4cvwvnL2v/3bErHjh3ldrt17tw5%2BtsCJSUlOnLkiGbMmKF27dopMTFRubm5%2Bq//%2Bi%2BFhobSW4Mu9G%2BBy%2BVSfHy859/aC823b9/el2VeEr5KBJjk5GQdO3bM8%2BPC0g%2BXtbp27arIyEg/Vha4Pv74Y82aNUtPPfWU7rzzTs94cnKyDh48qPr6es9YaWmpUlJS/FFmwNm4caOqqqo0ePBgpaamauzYsZKk1NRUde/evck9K2VlZfT2InTo0EFRUVHav3%2B/Z%2Bzo0aNq1aqV0tPT6W8LNDQ0qLGx0evMydmzZyVJaWlp9Nag5OTkJv08/%2B9seHi4unXrpvLycs9cdXW1vv76a1133XW%2BLvWiEbACTFJSknr37q3ly5fr9OnTqqio0Nq1a5WVleXv0gJSfX295s%2Bfr5kzZ2rAgAFec%2Bnp6YqKitLq1atVW1urvXv3av369fS6mWbPnq0tW7Zow4YN2rBhg4qKiiRJGzZs0KhRo3T06FEVFxfr%2B%2B%2B/165du7Rr1y7dc889fq46cNhsNmVmZurpp5/W//zP/6iqqkqrVq3SqFGjNGbMGPrbAn369FFERIQKCwtVW1srp9Op1atXq1%2B/frrjjjvorUH33HOP9uzZo507d%2Br777/X%2BvXr9dVXX2n06NGSpKysLK1bt04VFRU6ffq0CgoK1LNnT/Xu3dvPlf%2B8EHegXtz8FTt%2B/LgWLFig//7v/1ZUVJTGjx%2Bvhx56iHuDLsGHH36of/mXf1Hr1q2bzL399tuqqanRwoULVVZWpssuu0z333%2B/7r33Xj9UGviOHDmiIUOG6ODBg5Kkv//971q8eLEqKirUsWNH5efna%2BjQoX6uMrCcPXtWS5Ys0RtvvKFz585p2LBhWrBggSIjI%2BlvC5WVlenxxx/XgQMH1Lp1a/Xv31%2BzZ89WYmIivb1I58PQ%2BasBNptNkjw/Cbh161YtX75cR48eVdeuXTVv3jz169dP0g/3XxUWFurll19WTU2NUlNT9cgjj6hDhw5%2B2MnFIWABAAAYxiVCAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADDs/wP31PQkvzncTwAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common3612558746946165060”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1329</td> <td class=”number”>41.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>315</td> <td class=”number”>9.8%</td> <td>

<div class=”bar” style=”width:24%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>160</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.3333333333333</td> <td class=”number”>69</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>66.6666666666667</td> <td class=”number”>48</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>36</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>75.0</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.0</td> <td class=”number”>20</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>60.0</td> <td class=”number”>19</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16.6666666666667</td> <td class=”number”>18</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (91)</td> <td class=”number”>208</td> <td class=”number”>6.5%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>967</td> <td class=”number”>30.1%</td> <td>

<div class=”bar” style=”width:73%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme3612558746946165060”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1329</td> <td class=”number”>41.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.26315789473684</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.88235294117647</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.66666666666667</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.33333333333333</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>85.7142857142857</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>87.5</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>90.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>90.6976744186046</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>315</td> <td class=”number”>9.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_FMRKT_SNAP16”>PCT_FMRKT_SNAP16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>108</td>

</tr> <tr>

<th>Unique (%)</th> <td>3.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>30.1%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>967</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>24.174</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>40.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-2913189243800910705”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-2913189243800910705”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-2913189243800910705”

aria-controls=”quantiles-2913189243800910705” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-2913189243800910705” aria-controls=”histogram-2913189243800910705”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-2913189243800910705” aria-controls=”common-2913189243800910705”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-2913189243800910705” aria-controls=”extreme-2913189243800910705”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-2913189243800910705”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>50</td>

</tr> <tr>

<th>95-th percentile</th> <td>100</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr>

<th>Range</th> <td>100</td>

</tr> <tr>

<th>Interquartile range</th> <td>50</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>34.372</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.4218</td>

</tr> <tr>

<th>Kurtosis</th> <td>0.046684</td>

</tr> <tr>

<th>Mean</th> <td>24.174</td>

</tr> <tr>

<th>MAD</th> <td>28.736</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.1933</td>

</tr> <tr>

<th>Sum</th> <td>54223</td>

</tr> <tr>

<th>Variance</th> <td>1181.4</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-2913189243800910705”>

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lZGSorKxMkmS325WVlaUBAwYoLCxM06dPlyTt3r3byLoBAEDgs3n7hNXV1dqwYYNef/11NTQ0aPTo0frDH/6gW2%2B91f2aQYMG6fHHH1dmZuYVnSMhIUF9%2B/aV3W7Xv/zLv6ipqUk7d%2B7U8OHDVVVVpaSkJI/XJyUladu2bZKkqqoqjR071j0XHBysvn37qqKiQuPGjWvX%2BWtra1VXV%2BcxZrOFKz4%2B/orW80NCQvzrl0BtNv%2Bp97ve%2BluP/QG9tRb9tQ69tU4g9tbrAWvcuHG6/vrr9fDDD%2Bvuu%2B9WTExMm9ekp6e775e6EsHBwSouLlZubq7%2B8Ic/SJIGDx6sgoICzZw5UwkJCR6vj4mJkcPhkCQ5nU5FR0d7zEdHR7vn28Nut6ukpMRjLC8vT/n5%2BVeynIARGxvh6xIuW1TU1b4uIWDRW2vRX%2BvQW%2BsEUm%2B9HrDWr1%2BvwYMHX/J1Bw8evOJznD9/XjNmzNCYMWPc90898cQTKiwsbNf7XS7XFZ9bkiZPnqyMjAyPMZstXA5HQ4eO%2B33%2BlvRNr99KISHBioq6WvX1jWppafV1OQGF3lqL/lqH3lrHyt766od7rwesPn36aMaMGcrOztYdd9whSfr973%2Bvd999VytWrLjojtblKi8v1/HjxzV37lyFhISoc%2BfOys/P14QJE3T77bfL6XR6vN7hcCguLk6SFBsb22be6XSqV69e7T5/fHx8m8uBdXVn1Nz84/6G9Mf1t7S0%2BmXd/oDeWov%2BWofeWieQeuv1LZDly5frzJkz6tmzp3ts%2BPDham1t1dNPP23kHC0tLWptbfXYiTp//rwkKS0trc1jFyorKzVgwABJUnJysqqqqjyOdejQIfc8AADApXg9YL3zzjsqKSlRYmKieywxMVFFRUXat2%2BfkXMMHDhQ4eHhKi4uVmNjoxwOh9asWaNBgwZpwoQJqqmpUVlZmc6dO6e9e/dq7969mjRpkiQpJydHr7/%2Buj766CM1NjZqzZo16tSpk4YPH26kNgAAEPi8HrCampoUGhratpDgYDU2Nho5R2xsrH73u9/pgw8%2B0LBhw3TXXXcpLCxMK1euVJcuXbR27Vq99NJLuvXWW7Vs2TKtWLFCN910kyRp2LBhmjt3rmbPnq3Bgwdr//79Ki0tVVhYmJHaAABA4AtydfSO7sv0yCOPqFu3biooKHD/tt6XX36pZ555RmfOnNHzzz/vzXK8pq7ujPFj2mzBurPIzK6fN2ybPcTXJbSbzRas2NgIORwNAXM/wN8Lemst%2BmsdemsdK3vbrVtno8drL6/f5L5gwQL98pe/1M9%2B9jNFRkaqtbVVDQ0Nuuaaa/Tiiy96uxwAAADjvB6wrrnmGr3xxht6%2B%2B239fnnnys4OFjXX3%2B9hg4dqpCQEG%2BXAwAAYJzXA5YkderUyf2IBgAAgEDj9YD1xRdfaOXKlfr000/V1NTUZv6tt97ydkkAAABG%2BeQerNraWg0dOlTh4eHePj0AAIDlvB6wKisr9dZbb7mfnA4AABBovP4crC5durBzBQAAAprXA9bDDz%2BskpKSDn%2BgMgAAwN8rr18ifPvtt/XBBx/otdde009/%2BlMFB3tmvJdfftnbJQEAABjl9YAVGRmpYcOGefu0AAAAXuP1gLV8%2BXJvnxIAAMCrvH4PliT97W9/U3FxsR577DH32IcffuiLUgAAAIzzesAqLy/X%2BPHjtXPnTm3ZskXStw8fnTp1Kg8ZBQAAAcHrAWvVqlX61a9%2Bpc2bNysoKEjSt59P%2BPTTT2v16tXeLgcAAMA4rwesv/71r8rJyZEkd8CSpDFjxqi6utrb5QAAABjn9YDVuXPni34GYW1trTp16uTtcgAAAIzzesBKSUnRsmXLdPbsWffYsWPHNG/ePP3sZz/zdjkAAADGef0xDY899ph%2B8YtfKDU1VS0tLUpJSVFjY6N69eqlp59%2B2tvlAAAAGOf1gNW9e3dt2bJFe/fu1bFjxxQWFqbrr79eQ4YM8bgnCwAAwF95PWBJ0lVXXaU77rjDF6cGAACwnNcDVkZGxv%2B5U8WzsAAAgL/zesAaO3asR8BqaWnRsWPHVFFRoV/84hfeLgcAAMA4rweswsLCi47v2LFDf/7zn71cDQAAgHk%2B%2BSzCi7njjjv0xhtv%2BLoMAACADvu7CViHDh2Sy%2BXydRkAAAAd5vVLhFOmTGkz1tjYqOrqao0aNcrb5QAAABjn9YCVmJjY5rcIQ0NDlZ2drXvvvdfb5QAAABjn9YDF09oBAECg83rAev3119v92rvvvtvCSgAAAKzh9YC1cOFCtba2trmhPSgoyGMsKCiIgAUAAPyS1wPWf/zHf2jdunWaMWOG%2BvTpI5fLpSNHjuj555/X/fffr9TUVG%2BXBAAAYJRP7sEqLS1VQkKCe%2By2227TNddco2nTpmnLli3eLgkAAMAorz8H67PPPlN0dHSb8aioKNXU1Hi7HAAAAOO8HrB69Oihp59%2BWg6Hwz1WX1%2BvlStX6tprr/V2OQAAAMZ5/RLhggULVFBQILvdroiICAUHB%2Bvs2bMKCwvT6tWrvV0OAACAcV4PWEOHDtWePXu0d%2B9enTx5Ui6XSwkJCbr99tvVuXNnb5cDAABgnE8%2Bi/Dqq6/WyJEjNXLkSD3wwAMaO3asJeFqzZo1Gjp0qG655Rbl5ubq%2BPHjkqTy8nJlZ2crJSVF48aN06ZNmzzet379eo0ePVopKSnKyclRZWWl8doAAEDg8nrAampq0rx58zRw4ED9/Oc/l/TtPVjTp09XfX29sfP88Y9/1KZNm7R%2B/Xq988476tmzp37/%2B9%2BrtrZWM2fO1JQpU1ReXq6FCxdq8eLFqqiokCTt2rVLxcXFevbZZ7V//36NGDFCM2bM0DfffGOsNgAAENi8HrBWrFihw4cPq6ioSMHB/3P6lpYWFRUVGTvPunXrNGfOHN1www2KjIzUokWLtGjRIm3evFmJiYnKzs5WaGio0tLSlJGRobKyMkmS3W5XVlaWBgwYoLCwME2fPl2StHv3bmO1AQCAwOb1e7B27Nihl156SYmJiZo3b56kbx/RsHz5ct1999168sknO3yOL7/8UsePH9fXX3%2BtsWPH6tSpU0pNTdXjjz%2BuqqoqJSUlebw%2BKSlJ27ZtkyRVVVVp7Nix7rng4GD17dtXFRUVGjduXLvOX1tbq7q6Oo8xmy1c8fHxHVyZp5AQn1zhvWI2m//U%2B11v/a3H/oDeWov%2BWofeWicQe%2Bv1gNXQ0KDExMQ243FxccYuw508eVKStH37dr3wwgtyuVzKz8/XokWL1NTU5PGQU0mKiYlxPzbC6XS2eU5XdHS0x2MlLsVut6ukpMRjLC8vT/n5%2BVeynIARGxvh6xIuW1TU1b4uIWDRW2vRX%2BvQW%2BsEUm%2B9HrCuvfZa/fnPf1ZqaqrHZw9u375d//AP/2DkHN8dd/r06e4wNWvWLD344INKS0tr9/uv1OTJk5WRkeExZrOFy%2BFo6NBxv8/fkr7p9VspJCRYUVFXq76%2BUS0trb4uJ6DQW2vRX%2BvQW%2BtY2Vtf/XDv9YB13333adasWbrnnnvU2tqqF154QZWVldqxY4cWLlxo5Bxdu3aV9O2lx%2B/06NFDLpdLFy5ckNPp9Hi9w%2BFQXFycJCk2NrbNvNPpVK9evdp9/vj4%2BDaXA%2Bvqzqi5%2Bcf9DemP629pafXLuv0BvbUW/bUOvbVOIPXW61sgkydP1rx58/Tee%2B8pJCREzz33nGpqalRUVKScnBwj5%2BjevbsiIyN1%2BPBh91hNTY2uuuoqpaent3nsQmVlpQYMGCBJSk5OVlVVlXuupaVFhw4dcs8DAABcitd3sE6fPq177rlH99xzj2XnsNlsys7O1nPPPadBgwYpMjJSq1evVmZmpiZOnKh///d/V1lZmcaPH6/33ntPe/fuld1ulyTl5ORo7ty5uuuuu9SnTx/97ne/U6dOnTR8%2BHDL6gUAAIHF6wFr5MiR%2BuCDDxQUFGTpeQoKCnT%2B/Hnde%2B%2B9unDhgkaPHq1FixYpIiJCa9eu1dKlS/XEE0%2BoR48eWrFihW666SZJ0rBhwzR37lzNnj1bp06dUv/%2B/VVaWqqwsDBL6wUAAIEjyNXRO7ov04wZMzR%2B/HiPRyH8GNTVnTF%2BTJstWHcW7TN%2BXKtsmz3E1yW0m80WrNjYCDkcDQFzP8DfC3prLfprHXprHSt7262bbz6Gz%2Bs7WD/5yU/061//WqWlpbr22mt11VVXecyvXLnS2yUBAAAY5fWAdfToUd1www2SdFnPlgIAAPAXXgtYc%2BbM0apVq/Tiiy%2B6x1avXq28vDxvlQAAAOAVXntMw65du9qMlZaWeuv0AAAAXuO1gHWxe%2Bm9fH89AACAV3gtYF3ssQxWP6oBAADAF/zrw%2BwAAAD8AAELAADAMK/9FuGFCxdUUFBwyTGegwUAAPyd1wLWrbfeqtra2kuOAQAA%2BDuvBaz//fwrAACAQMY9WAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMN%2BFAFr2bJl6tOnj/vr8vJyZWdnKyUlRePGjdOmTZs8Xr9%2B/XqNHj1aKSkpysnJUWVlpbdLBgAAfizgA9bhw4e1ceNG99e1tbWaOXOmpkyZovLyci1cuFCLFy9WRUWFJGnXrl0qLi7Ws88%2Bq/3792vEiBGaMWOGvvnmG18tAQAA%2BJmADlitra1asmSJcnNz3WObN29WYmKisrOzFRoaqrS0NGVkZKisrEySZLfblZWVpQEDBigsLEzTp0%2BXJO3evdsXSwAAAH4ooAPWyy%2B/rNDQUGVmZrrHqqqqlJSU5PG6pKQk92XA788HBwerb9%2B%2B7h0uAACAS7H5ugCrfPXVVyouLtaLL77oMe50OpWQkOAxFhMTI4fD4Z6Pjo72mI%2BOjnbPt0dtba3q6uo8xmy2cMXHx1/OEi4pJMS/8rHN5j/1ftdbf%2BuxP6C31qK/1qG31gnE3gZswFq%2BfLmysrLUs2dPHT9%2B/LLe63K5OnRuu92ukpISj7G8vDzl5%2Bd36Lj%2BLjY2wtclXLaoqKt9XULAorfWor/WobfWCaTeBmTAKi8v14cffqgtW7a0mYuNjZXT6fQYczgciouL%2B8F5p9OpXr16tfv8kydPVkZGhseYzRYuh6Oh3cdoD39L%2BqbXb6WQkGBFRV2t%2BvpGtbS0%2BrqcgEJvrUV/rUNvrWNlb331w31ABqxNmzbp1KlTGjFihKT/2ZFKTU3VL3/5yzbBq7KyUgMGDJAkJScnq6qqShMnTpQktbS06NChQ8rOzm73%2BePj49tcDqyrO6Pm5h/3N6Q/rr%2BlpdUv6/YH9NZa9Nc69NY6gdRb/9oCaaf58%2Bdrx44d2rhxozZu3KjS0lJJ0saNG5WZmamamhqVlZXp3Llz2rt3r/bu3atJkyZJknJycvT666/ro48%2BUmNjo9asWaNOnTpp%2BPDhPlwRAADwJwG5gxUdHe1xo3pzc7MkqXv37pKktWvXaunSpXriiSfUo0cPrVixQjfddJMkadiwYZo7d65mz56tU6dOqX///iotLVVYWJj3FwIAAPxSQAas7/vpT3%2BqI0eOuL8eNGiQx8NHv%2B%2B%2B%2B%2B7Tfffd543SAABAAArIS4QAAAC%2BRMACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMJuvCwAAANb4%2BW/e9XUJ7Xbg12N8XYJR7GABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhgVswKqpqVFeXp5SU1OVlpam%2BfPnq76%2BXpJ0%2BPBh3X///br11ls1atQorVu3zuO9W7duVWZmpgYOHKisrCy98847vlgCAADwUwEbsGbMmKGoqCjt2rVLr732mj799FM988wzampq0sMPP6x//Md/1L59%2B7Rq1SqtXbtWO3fulPRt%2BJo3b54KCwv13nvvKTc3V48%2B%2BqhOnjzp4xUBAAB/EZABq76%2BXsnJySooKFBERIS6d%2B%2BuiRMn6sCBA9qzZ48uXLigRx55ROHh4erQ7VbAAAAONklEQVTXr5/uvfde2e12SVJZWZnS09OVnp6u0NBQjR8/Xr1799amTZt8vCoAAOAvAvKjcqKiorR8%2BXKPsRMnTig%2BPl5VVVXq06ePQkJC3HNJSUkqKyuTJFVVVSk9Pd3jvUlJSaqoqGj3%2BWtra1VXV%2BcxZrOFKz4%2B/nKX8n8KCfGvfGyz%2BU%2B93/XW33rsD%2Bitteivdeit9QKptwEZsL6voqJCL730ktasWaNt27YpKirKYz4mJkZOp1Otra1yOp2Kjo72mI%2BOjtbRo0fbfT673a6SkhKPsby8POXn51/5IgJAbGyEr0u4bFFRV/u6hIBFb61Ff61Db60TSL0N%2BID1/vvv65FHHlFBQYHS0tK0bdu2i74uKCjI/b9dLleHzjl58mRlZGR4jNls4XI4Gjp03O/zt6Rvev1WCgkJVlTU1aqvb1RLS6uvywko9NZa9Nc69NZ6VvTWVz/cB3TA2rVrl371q19p8eLFuvvuuyVJcXFx%2Buyzzzxe53Q6FRMTo%2BDgYMXGxsrpdLaZj4uLa/d54%2BPj21wOrKs7o%2BbmH/c3pD%2Buv6Wl1S/r9gf%2B1Nuf/%2BZdX5dwWQ78eoxf9dff0FvrBFJv/WsL5DJ88MEHmjdvnn7729%2B6w5UkJScn68iRI2pubnaPVVRUaMCAAe75yspKj2P973kAAIBLCciA1dzcrEWLFqmwsFBDhw71mEtPT1dkZKTWrFmjxsZGHTx4UBs2bFBOTo4kadKkSdq/f7/27Nmjc%2BfOacOGDfrss880fvx4XywFAAD4oYC8RPjRRx%2BpurpaS5cu1dKlSz3mtm/frueee05LlixRaWmpunbtqjlz5mj48OGSpN69e6uoqEjLly9XTU2NevbsqbVr16pbt24%2BWAkAAPBHARmwbrvtNh05cuT/fM2f/vSnH5wbNWqURo0aZbosAADwIxGQlwgBAAB8iYAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwLCA/CxCwITbFm73dQmXZdvsIb4uAQDw/7GDBQAAYBg7WECA%2BPlv3vV1Ce124NdjfF0CAFiKHSwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYTxoFAACjD99zBMf8YRAxQ4WAACAYexgwWv86aNcAADoCHawAAAADCNgAQAAGEbAAgAAMIx7sAB4nT/9lhsAXAl2sAAAAAwjYAEAABhGwAIAADCMe7AAAD7jb8/HO/DrMb4uAX6CHSwAAADDCFgAAACGEbAAAAAM4x6si6ipqdETTzyhgwcPKjw8XGPHjlVBQYGCg8mjAPBjxjPc0F4ErIuYNWuW%2BvXrpzfffFOnTp3Sww8/rK5du%2BqBBx7wdWkAAMAPsCXzPRUVFfrkk09UWFiozp07KzExUbm5ubLb7b4uDQAA%2BAl2sL6nqqpKPXr0UHR0tHusX79%2BOnbsmM6ePavIyMhLHqO2tlZ1dXUeYzZbuOLj443WGhJCPgYABI5A%2BneNgPU9TqdTUVFRHmPfhS2Hw9GugGW321VSUuIx9uijj2rWrFnmCtW3Qe4X3T/V5MmTjYe3H7va2lrZ7XZ6awF6ay36ax16a53a2loVFxcHVG8DJyoa5HK5OvT%2ByZMn67XXXvP4b/LkyYaq%2Bx91dXUqKSlps1uGjqO31qG31qK/1qG31gnE3rKD9T1xcXFyOp0eY06nU0FBQYqLi2vXMeLj4wMmgQMAgMvHDtb3JCcn68SJEzp9%2BrR7rKKiQj179lRERIQPKwMAAP6CgPU9SUlJ6t%2B/v1auXKmzZ8%2BqurpaL7zwgnJycnxdGgAA8BMhjz/%2B%2BOO%2BLuLvze23364tW7boqaee0htvvKHs7GxNmzZNQUFBvi6tjYiICA0ePJjdNQvQW%2BvQW2vRX%2BvQW%2BsEWm%2BDXB29oxsAAAAeuEQIAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgByw/V1NTooYceUmpqqkaMGKEVK1aotbXV12X5rZqaGuXl5Sk1NVVpaWmaP3%2B%2B6uvrJUmHDx/W/fffr1tvvVWjRo3SunXrfFyt/1q2bJn69Onj/rq8vFzZ2dlKSUnRuHHjtGnTJh9W57/WrFmjoUOH6pZbblFubq6OHz8uif521KFDhzR16lTddtttGjJkiAoLC3X69GlJ9PZK7Nu3T2lpaZozZ06bua1btyozM1MDBw5UVlaW3nnnHfdca2urVq1apZEjR2rQoEGaNm2avvjiC2%2BWfuVc8DsTJ050LVq0yFVfX%2B86duyYa9SoUa5169b5uiy/ddddd7nmz5/vOnv2rOvEiROurKws14IFC1yNjY2u22%2B/3VVcXOxqaGhwVVZWugYPHuzasWOHr0v2O4cOHXINHjzY1bt3b5fL5XJ9%2BeWXrltuucVVVlbmampqcr377ruum2%2B%2B2fXxxx/7uFL/8tJLL7nGjBnjqq6udp05c8b11FNPuZ566in620EXLlxwDRkyxLVy5UrXuXPnXKdPn3Y98MADrlmzZtHbK1BaWuoaNWqUa8qUKa7Zs2d7zB06dMiVnJzs2rNnj6upqcm1ceNG14ABA1wnTpxwuVwu1/r1610jRoxwHT161HXmzBnXk08%2B6crMzHS1trb6YimXhR0sP1NRUaFPPvlEhYWF6ty5sxITE5Wbmyu73e7r0vxSfX29kpOTVVBQoIiICHXv3l0TJ07UgQMHtGfPHl24cEGPPPKIwsPD1a9fP9177730%2BjK1trZqyZIlys3NdY9t3rxZiYmJys7OVmhoqNLS0pSRkaGysjLfFeqH1q1bpzlz5uiGG25QZGSkFi1apEWLFtHfDqqrq1NdXZ0mTJigTp06KTY2VnfeeacOHz5Mb69AaGioNmzYoOuuu67NXFlZmdLT05Wenq7Q0FCNHz9evXv3du8K2u125ebm6sYbb1RkZKTmzJmj6upqHTx40NvLuGwELD9TVVWlHj16KDo62j3Wr18/HTt2TGfPnvVhZf4pKipKy5cvV9euXd1jJ06cUHx8vKqqqtSnTx%2BFhIS455KSklRZWemLUv3Wyy%2B/rNDQUGVmZrrHqqqqlJSU5PE6ent5vvzySx0/flxff/21xo4dq9TUVOXn5%2Bv06dP0t4MSEhLUt29f2e12NTQ06NSpU9q5c6eGDx9Ob6/A1KlT1blz54vO/VA/Kyoq1NTUpKNHj3rMR0ZG6rrrrlNFRYWlNZtAwPIzTqdTUVFRHmPfhS2Hw%2BGLkgJKRUWFXnrpJT3yyCMX7XVMTIycTif3vLXTV199peLiYi1ZssRj/Id6y5/h9jt58qQkafv27XrhhRe0ceNGnTx5UosWLaK/HRQcHKzi4mK99dZbSklJUVpampqbm1VQUEBvDXM6nR4bBtK3/6Y5HA59/fXXcrlcPzj/946A5YdcLpevSwhI77//vqZNm6aCggKlpaX94OuCgoK8WJV/W758ubKystSzZ09flxJwvvt7YPr06UpISFD37t01a9Ys7dq1y8eV%2Bb/z589rxowZGjNmjA4cOKC3335bnTt3VmFhoa9LC0iX%2BjfNX//NI2D5mbi4ODmdTo8xp9OpoKAgxcXF%2Bagq/7dr1y499NBDWrBggaZOnSrp215//6ckp9OpmJgYBQfzrXMp5eXl%2BvDDD5WXl9dmLjY2ts2fY4fDwZ/hy/DdZe3/vZvSo0cPuVwuXbhwgf52QHl5uY4fP665c%2Beqc%2BfOSkhIUH5%2Bvv7zP/9TwcHB9Nagi/1d4HQ6FRcX5/679mLzXbp08WaZV4R/JfxMcnKyTpw44f51Yenby1o9e/ZURESEDyvzXx988IHmzZun3/72t7r77rvd48nJyTpy5Iiam5vdYxUVFRowYIAvyvQ7mzZt0qlTpzRixAilpqYqKytLkpSamqrevXu3uWelsrKS3l6G7t27KzIyUocPH3aP1dTU6KqrrlJ6ejr97YCWlha1trZ67JycP39ekpSWlkZvDUpOTm7Tz%2B/%2Bng0NDVWvXr1UVVXlnquvr9fnn3%2Bum2%2B%2B2dulXjYClp9JSkpS//79tXLlSp09e1bV1dV64YUXlJOT4%2BvS/FJzc7MWLVqkwsJCDR061GMuPT1dkZGRWrNmjRobG3Xw4EFt2LCBXrfT/PnztWPHDm3cuFEbN25UaWmpJGnjxo3KzMxUTU2NysrKdO7cOe3du1d79%2B7VpEmTfFy1/7DZbMrOztZzzz2n//7v/9apU6e0evVqZWZmauLEifS3AwYOHKjw8HAVFxersbFRDodDa9as0aBBgzRhwgR6a9CkSZO0f/9%2B7dmzR%2BfOndOGDRv02Wefafz48ZKknJwcrV%2B/XtXV1Tp79qyKiorUt29f9e/f38eVX1qQy18vbv6InTx5UosXL9Z//dd/KTIyUlOmTNGjjz7KvUFX4MCBA/qnf/onderUqc3c9u3b1dDQoCVLlqiyslJdu3bVgw8%2BqPvuu88Hlfq/48ePa%2BTIkTpy5Igk6S9/%2BYuWLl2q6upq9ejRQwUFBRo1apSPq/Qv58%2Bf1/Lly/XGG2/owoULGj16tBYvXqyIiAj620GVlZV65pln9Mknn6hTp04aPHiw5s%2Bfr4SEBHp7mb4LQ99dDbDZbJLk/k3AnTt3auXKlaqpqVHPnj21cOFCDRo0SNK3918VFxfr5ZdfVkNDg1JTU/Xkk0%2Bqe/fuPljJ5SFgAQAAGMYlQgAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAw7P8B4ImGlh%2BQxf8AAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-2913189243800910705”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1289</td> <td class=”number”>40.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>253</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:20%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>180</td> <td class=”number”>5.6%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.3333333333333</td> <td class=”number”>87</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>66.6666666666667</td> <td class=”number”>43</td> <td class=”number”>1.3%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.0</td> <td class=”number”>26</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16.6666666666667</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>40.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>60.0</td> <td class=”number”>19</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (97)</td> <td class=”number”>234</td> <td class=”number”>7.3%</td> <td>

<div class=”bar” style=”width:18%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>967</td> <td class=”number”>30.1%</td> <td>

<div class=”bar” style=”width:75%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-2913189243800910705”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1289</td> <td class=”number”>40.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.55555555555556</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.88235294117647</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.25</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>83.3333333333333</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>85.7142857142857</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>87.5</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>88.8888888888889</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>253</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_FMRKT_WIC16”>PCT_FMRKT_WIC16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>105</td>

</tr> <tr>

<th>Unique (%)</th> <td>3.3%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>30.1%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>967</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>21.917</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>44.1%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram1499072752388396222”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives1499072752388396222,#minihistogram1499072752388396222”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives1499072752388396222”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles1499072752388396222”

aria-controls=”quantiles1499072752388396222” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram1499072752388396222” aria-controls=”histogram1499072752388396222”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common1499072752388396222” aria-controls=”common1499072752388396222”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme1499072752388396222” aria-controls=”extreme1499072752388396222”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles1499072752388396222”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>42.857</td>

</tr> <tr>

<th>95-th percentile</th> <td>100</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr>

<th>Range</th> <td>100</td>

</tr> <tr>

<th>Interquartile range</th> <td>42.857</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>33.963</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.5496</td>

</tr> <tr>

<th>Kurtosis</th> <td>0.26311</td>

</tr> <tr>

<th>Mean</th> <td>21.917</td>

</tr> <tr>

<th>MAD</th> <td>28.291</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.305</td>

</tr> <tr>

<th>Sum</th> <td>49160</td>

</tr> <tr>

<th>Variance</th> <td>1153.5</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram1499072752388396222”>

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mFhoYqObq3KyhrV1zcEupygQm%2BtRX%2BtQ2%2BtY2VvA/XDfVAGrLi4OLndbp8xt9ut%2BPh4xcbGKjQ09KLzbdu2VXx8vM6cOaP6%2BnqFhYV55ySpbdu2Tbp%2BQkJCo9uBTmeV6up%2B2t%2BQdlx/fX2DLeu2A3prLfprHXprnWDqrb22QJooOTlZJSUlPmPFxcXq3bu3wsPD1bVrV5WWlnrnKisr9eWXX%2Bq6665Tjx495PF49Nlnn/m8Njo6Wp07d/bbGgAAgH0FZcAaP3689u7dq127duns2bPasGGDjh49qtGjR0uSMjMztX79epWVlenMmTPKy8tTjx491KtXL8XHx2vYsGF66qmndOrUKZ04cUKrVq1SRkaGHI6g3PADAACG2TYx9OrVS5JUV1cnSdq%2Bfbuk73abunXrpry8PC1dulTl5eXq0qWL1qxZoyuuuEKSNHHiRDmdTv3yl79UdXW1UlJSfB5Kf/TRR7Vw4UINGTJELVq00O23337RDzMFAAC4mBBPc5/oRpM4nVXGz%2BlwhOq2vD3Gz2uVt2bcHOgSmszhCFVcXKRcruqgeR7gnwW9tRb9tQ69tY6Vvb3iijZGz9dUQXmLEAAAIJAIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwzO8BKz09XStXrtTx48f9fWkAAAC/8HvAuvPOO/Xmm2/q1ltv1ZQpU7Rt2zbV1dX5uwwAAADL%2BD1gZWdn680339Qrr7yirl27asmSJUpLS9OTTz6pI0eO%2BLscAAAA4wL2DFbPnj01e/Zs7dy5U3PnztUrr7yiESNGaPLkyfr0008DVRYAAECzBSxgnT9/Xm%2B%2B%2BabuvfdezZ49W4mJiXr44YfVo0cPZWVlafPmzYEqDQAAoFkc/r5gWVmZNmzYoNdff13V1dUaNmyY/vCHP%2BiGG27wHtOvXz8tWrRIo0aN8nd5AAAAzeb3gDVy5Eh17txZ999/v%2B644w7FxsY2OiYtLU2nTp3yd2kAAABG%2BD1grV%2B/Xv379//R4/bv3%2B%2BHagAAAMzz%2BzNY3bt319SpU7V9%2B3bv2HPPPad7771Xbrfb3%2BUAAAAY5/eAtXTpUlVVValLly7esUGDBqmhoUHLli3zdzkAAADG%2Bf0W4XvvvafNmzcrLi7OO9apUyfl5eXp9ttv93c5AAAAxvl9B6u2tlbh4eGNCwkNVU1Njb/LAQAAMM7vAatfv35atmyZTp8%2B7R37%2Buuv9cgjj/h8VAMAAIBd%2Bf0W4dy5c/Wb3/xGN910k6KiotTQ0KDq6mpdddVVev755/1dDgAAgHF%2BD1hXXXWV3njjDb377rv68ssvFRoaqs6dO2vAgAEKCwvzdzkAAADG%2BT1gSVLLli116623BuLSAAAAlvN7wPrqq6%2B0fPlyff7556qtrW00/8477/i7JAAAAKMC8gxWRUWFBgwYoIiICH9fHgAAwHJ%2BD1glJSV65513FB8fb%2Bl1Dhw4oGXLlunAgQMKDw/XTTfdpLlz5yo%2BPl779u3T8uXL9cUXX%2BjKK6/U/fffr9GjR3tfu379er344otyOp3q3r275s2bp%2BTkZEvrBQAAwcPvH9PQtm1by3eu6urqdN999%2Bn666/X3r17tWXLFp06dUqLFi1SRUWFpk2bpokTJ2rfvn2aN2%2BeFixYoOLiYknSjh07lJ%2BfryeeeEJ79%2B7V4MGDNXXqVH377beW1gwAAIKH3wPW/fffr5UrV8rj8Vh2DafTKafTqTFjxqhly5aKi4vTbbfdpoMHD2rz5s3q1KmTMjIyFB4ertTUVKWnp6uoqEiSVFhYqHHjxql3795q1aqVpkyZIknauXOnZfUCAIDg4vdbhO%2B%2B%2B64%2B%2Bugjvfbaa/rZz36m0FDfjPfyyy83%2BxqJiYnq0aOHCgsL9e///u%2Bqra3Vtm3bNGjQIJWWliopKcnn%2BKSkJL311luSpNLSUo0YMcI7Fxoaqh49eqi4uFgjR45sdm0AACD4%2BT1gRUVFaeDAgZZeIzQ0VPn5%2BcrKytIf/vAHSVL//v01c%2BZMTZs2TYmJiT7Hx8bGyuVySZLcbrdiYmJ85mNiYrzzTVFRUSGn0%2Bkz5nBEKCEh4XKW84PCwvy%2BAdksDod96r3QW7v12A7orbXor3XorXWCsbd%2BD1hLly61/Brnzp3T1KlTNXz4cO/zU4888ohmzZrVpNc39/ZlYWGhVq5c6TOWnZ2tnJycZp3X7uLiIgNdwiWLjm4d6BKCFr21Fv21Dr21TjD1NiAfNPrFF1/ojTfe0N/%2B9jdv4Pr444/Vp08fI%2Bfft2%2Bfjh07poceekhhYWFq06aNcnJyNGbMGN1yyy1yu90%2Bx7tcLu%2B7GuPi4hrNu91ude3atcnXnzBhgtLT033GHI4IuVzVl7mii7Nb0je9fiuFhYUqOrq1KitrVF/fEOhyggq9tRb9tQ69tY6VvQ3UD/d%2BD1j79u3Tvffeq86dO%2Bvo0aNaunSpvvrqK02aNElPPfWUhgwZ0uxr1NfXq6GhwWcn6ty5c5Kk1NRU/fd//7fP8SUlJerdu7ckKTk5WaWlpRo7dqz3XAcOHFBGRkaTr5%2BQkNDodqDTWaW6up/2N6Qd119f32DLuu2A3lqL/lqH3lonmHrr9y2QFStW6Le//a02b96skJAQSd/9fsJly5Zp1apVRq7Rp08fRUREKD8/XzU1NXK5XFq9erX69eunMWPGqLy8XEVFRTp79qx2796t3bt3a/z48ZKkzMxMvf766/rkk09UU1Oj1atXq2XLlho0aJCR2gAAQPDze8D6y1/%2BoszMTEnyBixJGj58uMrKyoxcIy4uTv/1X/%2Bljz76SAMHDtTtt9%2BuVq1aafny5Wrbtq3WrFmjF154QTfccIOWLFmiJ598Utdee60kaeDAgXrooYc0Y8YM9e/fX3v37lVBQYFatWplpDYAABD8/H6LsE2bNqqtrVXLli19xisqKhqNNUdycrKef/75i87169dPGzdu/MHX3n333br77ruN1QIAAH5a/L6D1bdvXy1ZskRnzpzxjh05ckSzZ8/WTTfd5O9yAAAAjPP7DtbDDz%2BsX/3qV0pJSVF9fb369u2rmpoade3aVcuWLfN3OQAAAMb5PWC1b99eW7Zs0e7du3XkyBG1atVKnTt31s033%2BzzTBYAAIBdBeRzsFq0aKFbb701EJcGAACwnN8DVnp6%2Bj/cqXrnnXf8WA0AAIB5fg9YI0aM8AlY9fX1OnLkiIqLi/WrX/3K3%2BUAAAAY5/eA9UO/D3Dr1q16//33/VwNAACAef80v8zu1ltv1RtvvBHoMgAAAJrtnyZgHThwwOd3BwIAANiV328RTpw4sdFYTU2NysrKNHToUH%2BXAwAAYJzfA1anTp0avYswPDxcGRkZuuuuu/xdDgAAgHF%2BD1h8WjsAAAh2fg9Yr7/%2BepOPveOOOyysBAAAwBp%2BD1jz5s1TQ0NDowfaQ0JCfMZCQkIIWAAAwJb8HrB%2B//vfa%2B3atZo6daq6d%2B8uj8ejQ4cO6dlnn9U999yjlJQUf5cEAABgVECewSooKFBiYqJ37MYbb9RVV12lyZMna8uWLf4uCQAAwCi/fw7W0aNHFRMT02g8Ojpa5eXl/i4HAADAOL8HrA4dOmjZsmVyuVzescrKSi1fvlw///nP/V0OAACAcX6/RTh37lzNnDlThYWFioyMVGhoqM6cOaNWrVpp1apV/i4HAADAOL8HrAEDBmjXrl3avXu3Tpw4IY/Ho8TERN1yyy1q06aNv8sBAAAwzu8BS5Jat26tIUOG6MSJE7rqqqsCUQIAAIBl/P4MVm1trWbPnq0%2BffroF7/4haTvnsGaMmWKKisr/V0OAACAcX4PWE8%2B%2BaQOHjyovLw8hYb%2B3%2BXr6%2BuVl5fn73IAAACM83vA2rp1q/7zP/9Tw4cP9/7S5%2BjoaC1dulTbtm3zdzkAAADG%2BT1gVVdXq1OnTo3G4%2BPj9e233/q7HAAAAOP8HrB%2B/vOf6/3335ckn989%2BPbbb%2Btf/uVf/F0OAACAcX5/F%2BHdd9%2Bt6dOn684771RDQ4PWrVunkpISbd26VfPmzfN3OQAAAMb5PWBNmDBBDodDL7zwgsLCwvTMM8%2Boc%2BfOysvL0/Dhw/1dDgAAgHF%2BD1inTp3SnXfeqTvvvNPflwYAAPALvz%2BDNWTIEJ9nrwAAAIKN3wNWSkqK3nrrLX9fFgAAwG/8fovwyiuv1O9%2B9zsVFBTo5z//uVq0aOEzv3z5cn%2BXBAAAYJTfd7AOHz6sq6%2B%2BWm3atJHL5VJFRYXPfyatXr1aAwYM0PXXX6%2BsrCwdO3ZMkrRv3z5lZGSob9%2B%2BGjlypDZt2uTzuvXr12vYsGHq27evMjMzVVJSYrQuAAAQ3Py2g5Wbm6sVK1bo%2Beef946tWrVK2dnZllzvxRdf1KZNm7R%2B/XolJCToqaee0nPPPaf77rtP06ZN07x58zRq1Ch9%2BOGHeuCBB9S5c2f16tVLO3bsUH5%2Bvn7/%2B9%2Bre/fuWr9%2BvaZOnapt27YpIiLCkloBAEBw8dsO1o4dOxqNFRQUWHa9tWvXKjc3V1dffbWioqI0f/58zZ8/X5s3b1anTp2UkZGh8PBwpaamKj09XUVFRZKkwsJCjRs3Tr1791arVq00ZcoUSdLOnTstqxUAAAQXv%2B1gXeydg1a9m/Drr7/WsWPHdPr0aY0YMUInT55USkqKFi1apNLSUiUlJfkcn5SU5H3wvrS0VCNGjPDOhYaGqkePHiouLtbIkSObdP2Kigo5nU6fMYcjQgkJCc1cma%2BwML/f4W0Wh8M%2B9V7ord16bAf01lr01zr01jrB2Fu/BawLv9j5x8ZMOHHihKTvfv3OunXr5PF4lJOTo/nz56u2tlaJiYk%2Bx8fGxsrlckmS3G63YmJifOZjYmK8801RWFiolStX%2BoxlZ2crJyfncpYTNOLiIgNdwiWLjm4d6BKCFr21Fv21Dr21TjD11u/vIvSHCztjU6ZM8Yap6dOn695771VqamqTX3%2B5JkyYoPT0dJ8xhyNCLld1s877fXZL%2BqbXb6WwsFBFR7dWZWWN6usbAl1OUKG31qK/1qG31rGyt4H64T4oA1a7du0kSdHR0d6xDh06yOPx6Pz583K73T7Hu1wuxcfHS5Li4uIazbvdbnXt2rXJ109ISGh0O9DprFJd3U/7G9KO66%2Bvb7Bl3XZAb61Ff61Db60TTL31W8A6f/68Zs6c%2BaNjJj4Hq3379oqKitLBgwfVs2dPSVJ5eblatGihtLQ0bdy40ef4kpIS9e7dW5KUnJys0tJSjR07VpJUX1%2BvAwcOKCMjo9l1AQCAnwa/3WO64YYbGn3m1cXGTHA4HMrIyNAzzzyjv/71rzp58qRWrVqlUaNGaezYsSovL1dRUZHOnj2r3bt3a/fu3Ro/frwkKTMzU6%2B//ro%2B%2BeQT1dTUaPXq1WrZsqUGDRpkpDYAABD8/LaD9feff%2BUPM2fO1Llz53TXXXfp/PnzGjZsmObPn6/IyEitWbNGixcv1iOPPKIOHTroySef1LXXXitJGjhwoB566CHNmDFDJ0%2BeVK9evVRQUKBWrVr5tX4AAGBfIR5%2B87JfOJ1Vxs/pcITqtrw9xs9rlbdm3BzoEprM4QhVXFykXK7qoHke4J8FvbUW/bUOvbWOlb294oo2Rs/XVPZ6GxoAAIANELAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYT%2BJgLVkyRJ1797d%2B/W%2BffuUkZGhvn37auTIkdq0aZPP8evXr9ewYcPUt29fZWZmqqSkxN8lAwAAGwv6gHXw4EFt3LjR%2B3VFRYWmTZumiRMnat%2B%2BfZo3b54WLFig4uJiSdKOHTuUn5%2BvJ554Qnv37tXgwYM1depUffvtt4FaAgAAsJmgDlgNDQ1auHChsrKyvGObN29Wp06dlJGRofDwcKWmpio9PV1FRUWSpMLCQo0bN069e/dWq1atNGXKFEnSzp07A7EEAABgQ0EdsF5%2B%2BWWFh4dr1KhR3rHS0lIlJSX5HJeUlOS9Dfj9%2BdDQUPXo0cO7wwUAAPBjHIEuwCrffPON8vPz9fzzz/uMu91uJSYm%2BozFxsaYTnOCAAAR0UlEQVTK5XJ552NiYnzmY2JivPNNUVFRIafT6TPmcEQoISHhUpbwo8LC7JWPHQ771Huht3brsR3QW2vRX%2BvQW%2BsEY2%2BDNmAtXbpU48aNU5cuXXTs2LFLeq3H42nWtQsLC7Vy5UqfsezsbOXk5DTrvHYXFxcZ6BIuWXR060CXELTorbXor3XorXWCqbdBGbD27dunjz/%2BWFu2bGk0FxcXJ7fb7TPmcrkUHx//g/Nut1tdu3Zt8vUnTJig9PR0nzGHI0IuV3WTz9EUdkv6ptdvpbCwUEVHt1ZlZY3q6xsCXU5QobfWor/WobfWsbK3gfrhPigD1qZNm3Ty5EkNHjxY0v/tSKWkpOg3v/lNo%2BBVUlKi3r17S5KSk5NVWlqqsWPHSpLq6%2Bt14MABZWRkNPn6CQkJjW4HOp1Vqqv7aX9D2nH99fUNtqzbDuitteivdeitdYKpt/baAmmiOXPmaOvWrdq4caM2btyogoICSdLGjRs1atQolZeXq6ioSGfPntXu3bu1e/dujR8/XpKUmZmp119/XZ988olqamq0evVqtWzZUoMGDQrgigAAgJ0E5Q5WTEyMz4PqdXV1kqT27dtLktasWaPFixfrkUceUYcOHfTkk0/q2muvlSQNHDhQDz30kGbMmKGTJ0%2BqV69eKigoUKtWrfy/EAAAYEtBGbC%2B72c/%2B5kOHTrk/bpfv34%2BHz76fXfffbfuvvtuf5QGAACCUFDeIgQAAAgkAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwzBHoAgAAgDV%2B8dQfA11Ck33wu%2BGBLsGooN3BKi8vV3Z2tlJSUpSamqo5c%2BaosrJSknTw4EHdc889uuGGGzR06FCtXbvW57VvvvmmRo0apT59%2BmjcuHF67733ArEEAABgU0EbsKZOnaro6Gjt2LFDr732mj7//HM9/vjjqq2t1f33369//dd/1Z49e7RixQqtWbNG27Ztk/Rd%2BJo9e7ZmzZqlP/3pT8rKytKDDz6oEydOBHhFAADALoIyYFVWVio5OVkzZ85UZGSk2rdvr7Fjx%2BqDDz7Qrl27dP78eT3wwAOKiIhQz549ddddd6mwsFCSVFRUpLS0NKWlpSk8PFyjR49Wt27dtGnTpgCvCgAA2EVQPoMVHR2tpUuX%2BowdP35cCQkJKi0tVffu3RUWFuadS0pKUlFRkSSptLRUaWlpPq9NSkpScXFxk69fUVEhp9PpM%2BZwRCghIeFSl/IPhYXZKx87HPap90Jv7dZjO6C31qK/1qG31gum3gZlwPq%2B4uJivfDCC1q9erXeeustRUdH%2B8zHxsbK7XaroaFBbrdbMTExPvMxMTE6fPhwk69XWFiolStX%2BoxlZ2crJyfn8hcRBOLiIgNdwiWLjm4d6BKCFr21Fv21Dr21TjD1NugD1ocffqgHHnhAM2fOVGpqqt56662LHhcSEuL9f4/H06xrTpgwQenp6T5jDkeEXK7qZp33%2B%2ByW9E2v30phYaGKjm6tysoa1dc3BLqcoEJvrUV/rUNvrWdFbwP1w31QB6wdO3bot7/9rRYsWKA77rhDkhQfH6%2BjR4/6HOd2uxUbG6vQ0FDFxcXJ7XY3mo%2BPj2/ydRMSEhrdDnQ6q1RX99P%2BhrTj%2BuvrG2xZtx3QW2vRX%2BvQW%2BsEU2/ttQVyCT766CPNnj1bTz/9tDdcSVJycrIOHTqkuro671hxcbF69%2B7tnS8pKfE519/PAwAA/JigDFh1dXWaP3%2B%2BZs2apQEDBvjMpaWlKSoqSqtXr1ZNTY3279%2BvDRs2KDMzU5I0fvx47d27V7t27dLZs2e1YcMGHT16VKNHjw7EUgAAgA0F5S3CTz75RGVlZVq8eLEWL17sM/f222/rmWee0cKFC1VQUKB27dopNzdXgwYNkiR169ZNeXl5Wrp0qcrLy9WlSxetWbNGV1xxRQBWAgAA7CgoA9aNN96oQ4cO/cNjXnrppR%2BcGzp0qIYOHWq6LAAA8BMRlLcIAQAAAikod7AAwCQ7/cJcKfh%2BaS5gR%2BxgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGOQJdAAAzfvHUHwNdQpN98LvhgS4BACxFwAJ%2BwI3z3g50CcBlsdOf3bdm3BzoEgBLcIsQAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADONdhPAbO32MAAAAzcEOFgAAgGHsYAEAAsZuO9t8SC6aih0sAAAAw9jBAuB3dvqkcQC4HOxgAQAAGEbAAgAAMIyABQAAYBjPYF1EeXm5HnnkEe3fv18REREaMWKEZs6cqdBQ8igA/JTx/CCaioB1EdOnT1fPnj21fft2nTx5Uvfff7/atWunX//614EuDQAA2ABbMt9TXFyszz77TLNmzVKbNm3UqVMnZWVlqbCwMNClAQAAm2AH63tKS0vVoUMHxcTEeMd69uypI0eO6MyZM4qKivrRc1RUVMjpdPqMORwRSkhIMFprWBj5GAAQPILp3zUC1ve43W5FR0f7jF0IWy6Xq0kBq7CwUCtXrvQZe/DBBzV9%2BnRzheq7IPer9p9rwoQJxsPbT11FRYUKCwvprQXorbXor3XorXUqKiqUn58fVL0NnqhokMfjadbrJ0yYoNdee83nvwkTJhiq7v84nU6tXLmy0W4Zmo/eWofeWov%2BWofeWicYe8sO1vfEx8fL7Xb7jLndboWEhCg%2BPr5J50hISAiaBA4AAC4dO1jfk5ycrOPHj%2BvUqVPeseLiYnXp0kWRkZEBrAwAANgFAet7kpKS1KtXLy1fvlxnzpxRWVmZ1q1bp8zMzECXBgAAbCJs0aJFiwJdxD%2BbW265RVu2bNFjjz2mN954QxkZGZo8ebJCQkICXVojkZGR6t%2B/P7trFqC31qG31qK/1qG31gm23oZ4mvtENwAAAHxwixAAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAKWDZWXl%2Bu%2B%2B%2B5TSkqKBg8erCeffFINDQ2BLsu2ysvLlZ2drZSUFKWmpmrOnDmqrKyUJB08eFD33HOPbrjhBg0dOlRr164NcLX2tWTJEnXv3t379b59%2B5SRkaG%2Bfftq5MiR2rRpUwCrs6/Vq1drwIABuv7665WVlaVjx45Jor/NdeDAAU2aNEk33nijbr75Zs2aNUunTp2SRG8vx549e5Samqrc3NxGc2%2B%2B%2BaZGjRqlPn36aNy4cXrvvfe8cw0NDVqxYoWGDBmifv36afLkyfrqq6/8Wfrl88B2xo4d65k/f76nsrLSc%2BTIEc/QoUM9a9euDXRZtnX77bd75syZ4zlz5ozn%2BPHjnnHjxnnmzp3rqamp8dxyyy2e/Px8T3V1taekpMTTv39/z9atWwNdsu0cOHDA079/f0%2B3bt08Ho/H8/XXX3uuv/56T1FRkae2ttbzxz/%2B0XPdddd5Pv300wBXai8vvPCCZ/jw4Z6ysjJPVVWV57HHHvM89thj9LeZzp8/77n55ps9y5cv95w9e9Zz6tQpz69//WvP9OnT6e1lKCgo8AwdOtQzceJEz4wZM3zmDhw44ElOTvbs2rXLU1tb69m4caOnd%2B/enuPHj3s8Ho9n/fr1nsGDB3sOHz7sqaqq8jz66KOeUaNGeRoaGgKxlEvCDpbNFBcX67PPPtOsWbPUpk0bderUSVlZWSosLAx0abZUWVmp5ORkzZw5U5GRkWrfvr3Gjh2rDz74QLt27dL58%2Bf1wAMPKCIiQj179tRdd91Fry9RQ0ODFi5cqKysLO/Y5s2b1alTJ2VkZCg8PFypqalKT09XUVFR4Aq1obVr1yo3N1dXX321oqKiNH/%2BfM2fP5/%2BNpPT6ZTT6dSYMWPUsmVLxcXF6bbbbtPBgwfp7WUIDw/Xhg0b1LFjx0ZzRUVFSktLU1pamsLDwzV69Gh169bNuytYWFiorKwsXXPNNYqKilJubq7Kysq0f/9%2Bfy/jkhGwbKa0tFQdOnRQTEyMd6xnz546cuSIzpw5E8DK7Ck6OlpLly5Vu3btvGPHjx9XQkKCSktL1b17d4WFhXnnkpKSVFJSEohSbevll19WeHi4Ro0a5R0rLS1VUlKSz3H09tJ8/fXXOnbsmE6fPq0RI0YoJSVFOTk5OnXqFP1tpsTERPXo0UOFhYWqrq7WyZMntW3bNg0aNIjeXoZJkyapTZs2F537oX4WFxertrZWhw8f9pmPiopSx44dVVxcbGnNJhCwbMbtdis6Otpn7ELYcrlcgSgpqBQXF%2BuFF17QAw88cNFex8bGyu1288xbE33zzTfKz8/XwoULfcZ/qLf8GW66EydOSJLefvttrVu3Ths3btSJEyc0f/58%2BttMoaGhys/P1zvvvKO%2BffsqNTVVdXV1mjlzJr01zO12%2B2wYSN/9m%2BZyuXT69Gl5PJ4fnP9nR8CyIY/HE%2BgSgtKHH36oyZMna%2BbMmUpNTf3B40JCQvxYlb0tXbpU48aNU5cuXQJdStC58PfAlClTlJiYqPbt22v69OnasWNHgCuzv3Pnzmnq1KkaPny4PvjgA7377rtq06aNZs2aFejSgtKP/Ztm13/zCFg2Ex8fL7fb7TPmdrsVEhKi%2BPj4AFVlfzt27NB9992nuXPnatKkSZK%2B6/X3f0pyu92KjY1VaCjfOj9m3759%2Bvjjj5Wdnd1oLi4urtGfY5fLxZ/hS3Dhtvbf76Z06NBBHo9H58%2Bfp7/NsG/fPh07dkwPPfSQ2rRpo8TEROXk5Oh//ud/FBoaSm8NutjfBW63W/Hx8d6/ay8237ZtW3%2BWeVn4V8JmkpOTdfz4ce/bhaXvbmt16dJFkZGRAazMvj766CPNnj1bTz/9tO644w7veHJysg4dOqS6ujrvWHFxsXr37h2IMm1n06ZNOnnypAYPHqyUlBSNGzdOkpSSkqJu3bo1emalpKSE3l6C9u3bKyoqSgcPHvSOlZeXq0WLFkpLS6O/zVBfX6%2BGhgafnZNz585JklJTU%2BmtQcnJyY36eeHv2fDwcHXt2lWlpaXeucrKSn355Ze67rrr/F3qJSNg2UxSUpJ69eql5cuX68yZMyorK9O6deuUmZkZ6NJsqa6uTvPnz9esWbM0YMAAn7m0tDRFRUVp9erVqqmp0f79%2B7VhwwZ63URz5szR1q1btXHjRm3cuFEFBQWSpI0bN2rUqFEqLy9XUVGRzp49q927d2v37t0aP358gKu2D4fDoYyMDD3zzDP661//qpMnT2rVqlUaNWqUxo4dS3%2BboU%2BfPoqIiFB%2Bfr5qamrkcrm0evVq9evXT2PGjKG3Bo0fP1579%2B7Vrl27dPbsWW3YsEFHjx7V6NGjJUmZmZlav369ysrKdObMGeXl5alHjx7q1atXgCv/cSEeu97c/Ak7ceKEFixYoP/93/9VVFSUJk6cqAcffJBngy7DBx98oH/7t39Ty5YtG829/fbbqq6u1sKFC1VSUqJ27drp3nvv1d133x2ASu3v2LFjGjJkiA4dOiRJ%2BvOf/6zFixerrKxMHTp00MyZMzV06NAAV2kv586d09KlS/XGG2/o/PnzGjZsmBYsWKDIyEj620wlJSV6/PHH9dlnn6lly5bq37%2B/5syZo8TERHp7iS6EoQt3AxwOhyR53wm4bds2LV%2B%2BXOXl5erSpYvmzZunfv36Sfru%2Bav8/Hy9/PLLqq6uVkpKih599FG1b98%2BACu5NAQsAAAAw7hFCAAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACG/X9h6A3W%2BDHYuQAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common1499072752388396222”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1416</td> <td class=”number”>44.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>222</td> <td class=”number”>6.9%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>146</td> <td class=”number”>4.5%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.3333333333333</td> <td class=”number”>74</td> <td class=”number”>2.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>39</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>66.6666666666667</td> <td class=”number”>38</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>75.0</td> <td class=”number”>29</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.0</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>60.0</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>40.0</td> <td class=”number”>18</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (94)</td> <td class=”number”>216</td> <td class=”number”>6.7%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>967</td> <td class=”number”>30.1%</td> <td>

<div class=”bar” style=”width:68%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme1499072752388396222”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1416</td> <td class=”number”>44.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.34782608695652</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.88235294117647</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.25</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.66666666666667</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>85.7142857142857</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>87.5</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>90.6976744186046</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>92.8571428571429</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>222</td> <td class=”number”>6.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_FMRKT_WICCASH16”>PCT_FMRKT_WICCASH16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>84</td>

</tr> <tr>

<th>Unique (%)</th> <td>2.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>30.1%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>967</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>10.538</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>53.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-5172794290746011392”>

</div> <div class=”col-md-12 text-right”>

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<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-5172794290746011392”

aria-controls=”quantiles-5172794290746011392” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-5172794290746011392” aria-controls=”histogram-5172794290746011392”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-5172794290746011392” aria-controls=”common-5172794290746011392”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-5172794290746011392” aria-controls=”extreme-5172794290746011392”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

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<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-5172794290746011392”>
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<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>0</td>

</tr> <tr>

<th>95-th percentile</th> <td>66.667</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr>

<th>Range</th> <td>100</td>

</tr> <tr>

<th>Interquartile range</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>24.286</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.3045</td>

</tr> <tr>

<th>Kurtosis</th> <td>6.0387</td>

</tr> <tr>

<th>Mean</th> <td>10.538</td>

</tr> <tr>

<th>MAD</th> <td>16.352</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.592</td>

</tr> <tr>

<th>Sum</th> <td>23638</td>

</tr> <tr>

<th>Variance</th> <td>589.81</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

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ccXFnqbz8sGpr64JdTkiht9aiv9aht9axsrfB%2BuE%2BJAPWI488on/84x966aWX1LRpU7%2B5//mf/1GXLl10xRVX%2BMZKS0s1YMAApaamKj4%2BXiUlJUpJSZEk/f3vf9exY8eUnp5e7%2BsnJSWdcDvQ5apQTc2v%2BwvSjuuvra2zZd12QG%2BtRX%2BtQ2%2BtE0q9tdcWSD188sknWrlypZYsWXJCuJJ%2B2J2aNWuWdu7cqaNHj%2BrZZ5/V7t27NXToUEVEROjGG2/Uk08%2Bqf3798vtdusPf/iDrrnmGt/bOAAAAJyOLXewMjIyJP3wG4KStGHDBkmS0%2BnUq6%2B%2BqoqKCl199dV%2Br%2BnWrZueffZZTZw4UdIPz155PB61bdtWf/rTn9SyZUtJUl5enqqqqjRkyBDV1NTo6quv1syZMwO0MgAAEArCvA19ohv14nJVGD%2BnwxGuawo2Gz%2BvVd6c0D3YJdSbwxGuhIRoud1VIbNd/UtBb61Ff61Db61jZW9btDj52zpZLeRuEQIAAAQbAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYFjAA1avXr1UWFio/fv3B/rSAAAAARHwgHX99ddrzZo16tOnj26//XatX79eNTU1gS4DAADAMgEPWLm5uVqzZo1eeeUVtWvXTo888oiys7P12GOPadeuXYEuBwAAwLigPYPVqVMnTZ48We%2B8846mTp2qV155RQMGDNBtt92mL7/8MlhlAQAANFjQAlZ1dbXWrFmjO%2B64Q5MnT1ZycrIeeOABdejQQaNHj9aqVauCVRoAAECDOAJ9wdLSUi1fvlyvv/66qqqq1K9fPz3//PO65JJLfMd069ZNM2fO1KBBgwJdHgAAQIMFPGANHDhQbdq00V133aXrrrtOTZs2PeGY7OxsHTp0KNClAQAAGBHwgLV06VJddtllpz3uiy%2B%2BCEA1AAAA5gX8Gay0tDSNGTNGGzZs8I396U9/0h133CGPxxPocgAAAIwLeMCaO3euKioq1LZtW9/YVVddpbq6Os2bNy/Q5QAAABgX8FuE77//vlatWqWEhATfWOvWrVVQUKBrr7020OUAAAAYF/AdrCNHjigyMvLEQsLDdfjw4UCXAwAAYFzAA1a3bt00b948fffdd76xb775RrNmzfJ7qwYAAAC7CvgtwqlTp%2BrWW2/VFVdcoZiYGNXV1amqqkqpqan685//HOhyAAAAjAt4wEpNTdUbb7yh9957T7t371Z4eLjatGmjHj16KCIiItDlAAAAGBfwgCVJjRs3Vp8%2BfRp8ns2bN2vy5MnKzMzUggUL/ObWrFmjxYsXa%2B/evWrTpo3uvfde9ejRQ5JUV1enJ554QqtXr1Z5ebk6d%2B6smTNnKjU1VZLk8Xg0c%2BZMffTRRwoPD1d2dramT5%2BuJk2aNLhmAAAQ%2BgIesPbs2aP58%2BfrH//4h44cOXLC/Ntvv12v8zz99NNavny5WrVqdcLctm3bNHnyZBUWFuryyy/XunXrdM8992jt2rVq2bKlXnzxRa1atUpPP/20kpOTtWDBAuXm5mrFihUKCwvT9OnTdezYMa1evVrV1dUaP368CgoKNG3atAavHwAAhL6gPINVVlamHj16KCoq6mefJzIyUsuXL9fDDz%2Bso0eP%2Bs0tW7ZM2dnZys7OliQNHjxYL7zwglauXKk777xTRUVFGj16tC644AJJUn5%2BvjIzM/XFF1/o3HPP1YYNG/S///u/SkxMlCTdfffdGj9%2BvCZPnqxGjRr97JoBAMCvQ8ADVnFxsd5%2B%2B21fePm5Ro0adcq5kpISX7g6rmPHjnI6nTpy5Ih27Nihjh07%2BuZiYmLUqlUrOZ1OVVRUKCIiQmlpab75Tp066fvvv9fOnTv9xgEAAE4m4AGrWbNmDdq5qg%2BPx6P4%2BHi/sfj4eO3YsUPfffedvF7vSefdbreaNm2qmJgYhYWF%2Bc1Jktvtrtf1y8rK5HK5/MYcjiglJSX9nOWcUkREwN9lo0EcDvvUe7y3duuxHdBba9Ff69Bb64RibwMesO666y4VFhZq4sSJfiHGNK/X%2B7PnT/fa0ykqKlJhYaHfWG5urvLy8hp0XrtLSIgOdglnLC7urGCXELLorbXor3XorXVCqbcBD1jvvfeePv30U7322ms699xzFR7un1ZffvnlBl8jISHhhD8c7fF4lJiYqKZNmyo8PPyk882aNVNiYqIqKytVW1vre9uI48c2a9asXtcfPny4evXq5TfmcETJ7a76uUs6KbslfdPrt1JERLji4s5Seflh1dbWBbuckEJvrUV/rUNvrWNlb4P1w33AA1ZMTIx69uxp6TXS09NVXFzsN%2BZ0OjVw4EBFRkaqXbt2Kikp0WWXXSZJKi8v1%2B7du9W5c2elpKTI6/Vq%2B/bt6tSpk%2B%2B1cXFxatOmTb2un5SUdMLtQJerQjU1v%2B4vSDuuv7a2zpZ12wG9tRb9tQ69tU4o9TbgAWvu3LmWX%2BPGG29UTk6ONm3apCuuuEKrVq3S119/rcGDB0uSRo4cqSVLlqhnz55KTk5WQUGBOnTooIyMDElSv3799Pjjj%2BvRRx/VsWPHtGjRIuXk5MjhCMrbhgEAAJsJSmLYuXOn3njjDf3rX//yBa7PPvtMXbp0qfc5joehmpoaSdKGDRsk/bDb1L59exUUFGju3Lnat2%2Bf2rZtq6eeekotWrSQJI0YMUIul0s33XSTqqqqlJmZ6ffM1OzZszVjxgz17t1bjRo10rXXXqv8/HwjawcAAKEvzNvQJ7rP0AcffKA77rhDbdq00ddffy2n06k9e/ZowIABevzxx9W7d%2B9AlhMwLleF8XM6HOG6pmCz8fNa5c0J3YNdQr05HOFKSIiW210VMtvVvxT01lr01zr01jpW9rZFi1ij56uvgD8lvWDBAt13331atWqV77cIU1NTNW/ePC1atCjQ5QAAABgX8ID197//XSNHjpQkv7dp6N%2B/v0pLSwNdDgAAgHEBD1ixsbEn/RuEZWVlaty4caDLAQAAMC7gAatr16565JFHVFlZ6RvbtWuXJk%2BerCuuuCLQ5QAAABgX8N8ifOCBB3TzzTcrMzNTtbW16tq1qw4fPqx27dpp3rx5gS4HAADAuIAHrJYtW2r16tV69913tWvXLjVp0kRt2rRR9%2B7dLf3TOQAAAIESlPfBatSokfr06ROMSwMAAFgu4AGrV69eP7lT9fbbbwewGgAAAPMCHrAGDBjgF7Bqa2u1a9cuOZ1O3XzzzYEuBwAAwLiAB6xJkyaddHzdunX68MMPA1wNAACAeQF/m4ZT6dOnj954441glwEAANBgv5iAtXXrVgX4zyICAABYIuC3CEeMGHHC2OHDh1VaWqq%2BffsGuhwAAADjAh6wWrdufcJvEUZGRionJ0c33HBDoMsBAAAwLuABi3drBwAAoS7gAev111%2Bv97HXXXedhZUAAABYI%2BAB68EHH1RdXd0JD7SHhYX5jYWFhRGwAACALQU8YD3zzDN69tlnNWbMGKWlpcnr9eqrr77S008/rd/97nfKzMwMdEkAAABGBeUZrCVLlig5Odk3dumllyo1NVW33XabVq9eHeiSAAAAjAr4%2B2B9/fXXio%2BPP2E8Li5O%2B/btC3Q5AAAAxgU8YKWkpGjevHlyu92%2BsfLycs2fP1/nnXdeoMsBAAAwLuC3CKdOnaqJEyeqqKhI0dHRCg8PV2VlpZo0aaJFixYFuhwAAADjAh6wevTooU2bNundd9/VgQMH5PV6lZycrCuvvFKxsbGBLgcAAMC4gAcsSTrrrLPUu3dvHThwQKmpqcEoAQAAwDIBfwbryJEjmjx5srp06aLf/OY3kn54Buv2229XeXl5oMsBAAAwLuAB67HHHtO2bdtUUFCg8PB/X762tlYFBQWBLgcAAMC4gAesdevW6b//%2B7/Vv39/3x99jouL09y5c7V%2B/fpAlwMAAGBcwANWVVWVWrdufcJ4YmKivv/%2B%2B0CXAwAAYFzAA9Z5552nDz/8UJL8/vbg2rVrdc455wS6HAAAAOMC/luEv/3tbzVu3Dhdf/31qqur03PPPafi4mKtW7dODz74YKDLAQAAMC7gAWv48OFyOBx64YUXFBERoSeffFJt2rRRQUGB%2BvfvH%2BhyAAAAjAt4wDp06JCuv/56XX/99YG%2BNAAAQEAE/Bms3r17%2Bz17BQAAEGoCHrAyMzP15ptvWnqNjz/%2BWBkZGX4f6enpSktL04cffqi0tLQT5v%2BzpqVLl6pfv37q2rWrRo4cqeLiYkvrBQAAoSXgtwjPPvtsPfzww1qyZInOO%2B88NWrUyG9%2B/vz5Db5Gt27d5HQ6/caefPJJbd%2B%2BXZKUkpKijRs3nvS1Gzdu1MKFC/XMM88oLS1NS5cu1ZgxY7R%2B/XpFRUU1uDYAABD6Ar6DtWPHDp1//vmKjY2V2%2B1WWVmZ34cV/vWvf%2Bm5557T/ffff9pji4qKNGzYMF100UVq0qSJbr/9dknSO%2B%2B8Y0ltAAAg9ARsBys/P18LFizQn//8Z9/YokWLlJuba/m1n3jiCV1//fU655xztGfPHlVVVSk3N1dbtmxR48aNdeutt2r06NEKCwtTSUmJBgwY4HtteHi4OnToIKfTqYEDB9bremVlZXK5XH5jDkeUkpKSjK4rIiLg%2BbhBHA771Hu8t3brsR3QW2vRX%2BvQW%2BuEYm8DFrBOdktuyZIllgesvXv3av369b4/wxMTE6P27dvr5ptv1oIFC/TRRx9p/Pjxio2NVU5Ojjwej%2BLj4/3OER8fL7fbXe9rFhUVqbCw0G8sNzdXeXl5DV%2BQjSUkRAe7hDMWF3dWsEsIWfTWWvTXOvTWOqHU24AFrJP95mAgfpvwxRdfVN%2B%2BfdWiRQtJUqdOnfx20Xr06KERI0botddeU05OjpG6hg8frl69evmNORxRcrurGnTeH7Nb0je9fitFRIQrLu4slZcfVm1tXbDLCSn01lr01zr01jpW9jZYP9wHLGAd/8POpxszbd26dZo8efJPHpOSkqJ169ZJkhISEuTxePzmPR6P2rVrV%2B9rJiUlnXA70OWqUE3Nr/sL0o7rr62ts2XddkBvrUV/rUNvrRNKvbXXFsgZ2rZtm/bt26fu3bv7xt5880395S9/8Ttu586dSk1NlSSlp6erpKTEN1dbW6utW7fqoosuCkzRAADA9kI6YG3dulVNmzZVTEyMb6xRo0Z69NFH9f7776u6ulp//etf9eqrr2rkyJGSpJEjR%2Br111/X559/rsOHD2vx4sVq3LixrrrqqiCtAgAA2E3AbhFWV1dr4sSJpx0z8T5Yxx08eND37NVxffr00dSpU/XQQw9p//79at68uaZOnaq%2BfftKknr27Kl7771XEyZM0LfffquMjAwtWbJETZo0MVYXAAAIbWHeAP3dmptuuqlex/3nA%2BihxOWqMH5OhyNc1xRsNn5eq7w5ofvpD/qFcDjClZAQLbe7KmSeB/iloLfWor/WobfWsbK3LVrEGj1ffQVsBytUgxMAAMCPhfQzWAAAAMFAwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGhWzASktLU3p6ujIyMnwfDz30kCTpgw8%2BUE5Ojrp27aqBAwdq5cqVfq9dunSp%2BvXrp65du2rkyJEqLi4OxhIAAIBNOYJdgJXWrl2rc88912%2BsrKxMd999tx588EENGjRIn3zyicaOHas2bdooIyNDGzdu1MKFC/XMM88oLS1NS5cu1ZgxY7R%2B/XpFRUUFaSUAAMBOQnYH61RWrVql1q1bKycnR5GRkcrKylKvXr20bNkySVJRUZGGDRumiy66SE2aNNHtt98uSXrnnXeCWTYAALCRkN7Bmj9/vj777DNVVlbqN7/5jaZMmaKSkhJ17NjR77iOHTvqzTfflCSVlJRowIABvrnw8HB16NBBTqdTAwcOrNd1y8rK5HK5/MYcjiglJSU1cEX%2BIiLslY8dDvvUe7y3duuxHdBba9Ff69Bb64Rib0M2YF188cXKysrSo48%2Bqj179mjChAmaNWuWPB6PkpOT/Y5t2rSSj0vYAAAR2ElEQVSp3G63JMnj8Sg%2BPt5vPj4%2B3jdfH0VFRSosLPQby83NVV5e3s9cTWhISIgOdglnLC7urGCXELLorbXor3XorXVCqbchG7CKiop8/33BBRdo0qRJGjt2rC655JLTvtbr9Tbo2sOHD1evXr38xhyOKLndVQ0674/ZLembXr%2BVIiLCFRd3lsrLD6u2ti7Y5YQUemst%2BmsdemsdK3sbrB/uQzZg/di5556r2tpahYeHy%2BPx%2BM253W4lJiZKkhISEk6Y93g8ateuXb2vlZSUdMLtQJerQjU1v%2B4vSDuuv7a2zpZ12wG9tRb9tQ69tU4o9dZeWyD1tHXrVs2bN89vrLS0VI0bN1Z2dvYJb7tQXFysiy66SJKUnp6ukpIS31xtba22bt3qmwcAADidkAxYzZo1U1FRkZYsWaJjx45p165deuKJJzR8%2BHANGTJE%2B/bt07Jly3T06FG9%2B%2B67evfdd3XjjTdKkkaOHKnXX39dn3/%2BuQ4fPqzFixercePGuuqqq4K7KAAAYBsheYswOTlZS5Ys0fz5830BaejQocrPz1dkZKSeeuopzZkzR7NmzVJKSooee%2BwxXXjhhZKknj176t5779WECRP07bffKiMjQ0uWLFGTJk2CvCoAAGAXYd6GPtGNenG5Koyf0%2BEI1zUFm42f1ypvTuge7BLqzeEIV0JCtNzuqpB5HuCXgt5ai/5ah95ax8retmgRa/R89RWStwgBAACCiYAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGBayAWvfvn3Kzc1VZmamsrKyNGXKFJWXl2vv3r1KS0tTRkaG38cf//hH32vXrFmjQYMGqUuXLho2bJjef//9IK4EAADYjSPYBVhlzJgxSk9P18aNG1VRUaHc3Fw9%2BuijGjt2rCTJ6XSe9HXbtm3T5MmTVVhYqMsvv1zr1q3TPffco7Vr16ply5aBXAIAALCpkNzBKi8vV3p6uiZOnKjo6Gi1bNlSQ4cO1ZYtW0772mXLlik7O1vZ2dmKjIzU4MGD1b59e61cuTIAlQMAgFAQkgErLi5Oc%2BfOVfPmzX1j%2B/fvV1JSku/z%2B%2B%2B/Xz169NDll1%2Bu%2BfPnq7q6WpJUUlKijh07%2Bp2vY8eOp9zxAgAA%2BLGQvUX4n5xOp1544QUtXrxYjRs3VpcuXXTNNdfo4Ycf1rZt2zRu3Dg5HA6NHz9eHo9H8fHxfq%2BPj4/Xjh076n29srIyuVwuvzGHI8ov4JkQEWGvfOxw2Kfe4721W4/tgN5ai/5ah95aJxR7G/IB65NPPtHYsWM1ceJEZWVlSZJefvll33znzp1111136amnntL48eMlSV6vt0HXLCoqUmFhod9Ybm6u8vLyGnReu0tIiA52CWcsLu6sYJcQsuitteivdeitdUKptyEdsDZu3Kj77rtP06dP13XXXXfK41JSUnTw4EF5vV4lJCTI4/H4zXs8HiUmJtb7usOHD1evXr38xhyOKLndVWe2gNOwW9I3vX4rRUSEKy7uLJWXH1ZtbV2wywkp9NZa9Nc69NY6VvY2WD/ch2zA%2BvTTTzV58mQ98cQT6tGjh2/8gw8%2B0Oeff%2B77bUJJ2rlzp1JSUhQWFqb09HQVFxf7ncvpdGrgwIH1vnZSUtIJtwNdrgrV1Py6vyDtuP7a2jpb1m0H9NZa9Nc69NY6odRbe22B1FNNTY2mTZumSZMm%2BYUrSYqNjdWiRYu0YsUKVVdXy%2Bl06o9//KNGjhwpSbrxxhv1t7/9TZs2bdLRo0e1fPlyff311xo8eHAwlgIAAGwoJHewPv/8c5WWlmrOnDmaM2eO39zatWu1YMECFRYW6ve//71iY2N100036eabb5YktW/fXgUFBZo7d6727duntm3b6qmnnlKLFi2CsRQAAGBDIRmwLr30Un311VennE9JSdE111xzyvm%2Bffuqb9%2B%2BVpQGAAB%2BBULyFiEAAEAwEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAxzBLsA4Jfq0gfXBruEM/LmhO7BLgEA8P%2BxgwUAAGAYO1gAAISo3zz%2B12CXUG9bHu4f7BKMImABwGnY6ZuUFHrfqAA74hYhAACAYQQsAAAAwwhYAAAAhhGwAAAADOMhdwSM3R4Uths79ZeHsAGEOnawAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGG8TcNJ7Nu3T7NmzdIXX3yhqKgoDRgwQBMnTlR4OHkUwC/fpQ%2BuDXYJ9fbmhO7BLgGwBAHrJMaNG6dOnTppw4YN%2Bvbbb3XXXXepefPmuuWWW4JdGhAS7BQAAODnIGD9iNPp1Pbt2/Xcc88pNjZWsbGxGj16tJ5//nkCFgAYZqc3yJV4k1zUHwHrR0pKSpSSkqL4%2BHjfWKdOnbRr1y5VVlYqJibmtOcoKyuTy%2BXyG3M4opSUlGS01ogIblkCQKDxb691Qqm3BKwf8Xg8iouL8xs7Hrbcbne9AlZRUZEKCwv9xu655x6NGzfOXKH6Icjd3PIfGj58uPHw9mtXVlamoqIiemsBemst%2BmudsrIyLVy40Fa9tcuOmx17ezqhExUN8nq9DXr98OHD9dprr/l9DB8%2B3FB1/%2BZyuVRYWHjCbhkajt5ah95ai/5ah95aJxR7yw7WjyQmJsrj8fiNeTwehYWFKTExsV7nSEpKCpkEDgAAzhw7WD%2BSnp6u/fv369ChQ74xp9Optm3bKjo6OoiVAQAAuyBg/UjHjh2VkZGh%2BfPnq7KyUqWlpXruuec0cuTIYJcGAABsImLmzJkzg13EL82VV16p1atX66GHHtIbb7yhnJwc3XbbbQoLCwt2aSeIjo7WZZddxu6aBeitdeitteivdeitdUKtt2Hehj7RDQAAAD/cIgQAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIBlQ/v27dOdd96pzMxMXX311XrsscdUV1cX7LJsa9%2B%2BfcrNzVVmZqaysrI0ZcoUlZeXS5K2bdum3/3ud7rkkkvUt29fPfvss0Gu1r4eeeQRpaWl%2BT7/4IMPlJOTo65du2rgwIFauXJlEKuzr8WLF6tHjx66%2BOKLNXr0aO3du1cS/W2orVu3atSoUbr00kvVvXt3TZo0SYcOHZJEb3%2BOzZs3KysrS/n5%2BSfMrVmzRoMGDVKXLl00bNgwvf/%2B%2B765uro6LViwQL1791a3bt102223ac%2BePYEs/efzwnaGDh3qnTZtmre8vNy7a9cub9%2B%2Bfb3PPvtssMuyrWuvvdY7ZcoUb2VlpXf//v3eYcOGeadOneo9fPiw98orr/QuXLjQW1VV5S0uLvZedtll3nXr1gW7ZNvZunWr97LLLvO2b9/e6/V6vd9884334osv9i5btsx75MgR71//%2Bldv586dvV9%2B%2BWWQK7WXF154wdu/f39vaWmpt6KiwvvQQw95H3roIfrbQNXV1d7u3bt758%2Bf7z169Kj30KFD3ltuucU7btw4evszLFmyxNu3b1/viBEjvBMmTPCb27p1qzc9Pd27adMm75EjR7wrVqzwXnTRRd79%2B/d7vV6vd%2BnSpd6rr77au2PHDm9FRYV39uzZ3kGDBnnr6uqCsZQzwg6WzTidTm3fvl2TJk1SbGysWrdurdGjR6uoqCjYpdlSeXm50tPTNXHiREVHR6tly5YaOnSotmzZok2bNqm6ulpjx45VVFSUOnXqpBtuuIFen6G6ujrNmDFDo0eP9o2tWrVKrVu3Vk5OjiIjI5WVlaVevXpp2bJlwSvUhp599lnl5%2Bfr/PPPV0xMjKZNm6Zp06bR3wZyuVxyuVwaMmSIGjdurISEBF1zzTXatm0bvf0ZIiMjtXz5crVq1eqEuWXLlik7O1vZ2dmKjIzU4MGD1b59e9%2BuYFFRkUaPHq0LLrhAMTExys/PV2lpqb744otAL%2BOMEbBspqSkRCkpKYqPj/eNderUSbt27VJlZWUQK7OnuLg4zZ07V82bN/eN7d%2B/X0lJSSopKVFaWpoiIiJ8cx07dlRxcXEwSrWtl19%2BWZGRkRo0aJBvrKSkRB07dvQ7jt6emW%2B%2B%2BUZ79%2B7Vd999pwEDBigzM1N5eXk6dOgQ/W2g5ORkdejQQUVFRaqqqtK3336r9evX66qrrqK3P8OoUaMUGxt70rlT9dPpdOrIkSPasWOH33xMTIxatWolp9Npac0mELBsxuPxKC4uzm/seNhyu93BKCmkOJ1OvfDCCxo7duxJe920aVN5PB6eeaungwcPauHChZoxY4bf%2BKl6y//D9XfgwAFJ0tq1a/Xcc89pxYoVOnDggKZNm0Z/Gyg8PFwLFy7U22%2B/ra5duyorK0s1NTWaOHEivTXM4/H4bRhIP3xPc7vd%2Bu677%2BT1ek85/0tHwLIhr9cb7BJC0ieffKLbbrtNEydOVFZW1imPCwsLC2BV9jZ37lwNGzZMbdu2DXYpIef4vwO33367kpOT1bJlS40bN04bN24McmX2d%2BzYMY0ZM0b9%2B/fXli1b9N577yk2NlaTJk0Kdmkh6XTf0%2Bz6PY%2BAZTOJiYnyeDx%2BYx6PR2FhYUpMTAxSVfa3ceNG3XnnnZo6dapGjRol6Yde//inJI/Ho6ZNmyo8nC%2Bd0/nggw/02WefKTc394S5hISEE/4/drvd/D98Bo7f1v7P3ZSUlBR5vV5VV1fT3wb44IMPtHfvXt17772KjY1VcnKy8vLy9NZbbyk8PJzeGnSyfws8Ho8SExN9/9aebL5Zs2aBLPNn4buEzaSnp2v//v2%2BXxeWfrit1bZtW0VHRwexMvv69NNPNXnyZD3xxBO67rrrfOPp6en66quvVFNT4xtzOp266KKLglGm7axcuVLffvutrr76amVmZmrYsGGSpMzMTLVv3/6EZ1aKi4vp7Rlo2bKlYmJitG3bNt/Yvn371KhRI2VnZ9PfBqitrVVdXZ3fzsmxY8ckSVlZWfTWoPT09BP6efzf2cjISLVr104lJSW%2BufLycu3evVudO3cOdKlnjIBlMx07dlRGRobmz5%2BvyspKlZaW6rnnntPIkSODXZot1dTUaNq0aZo0aZJ69OjhN5edna2YmBgtXrxYhw8f1hdffKHly5fT63qaMmWK1q1bpxUrVmjFihVasmSJJGnFihUaNGiQ9u3bp2XLluno0aN699139e677%2BrGG28MctX24XA4lJOToyeffFL//Oc/9e2332rRokUaNGiQhg4dSn8boEuXLoqKitLChQt1%2BPBhud1uLV68WN26ddOQIUPorUE33nij/va3v2nTpk06evSoli9frq%2B//lqDBw%2BWJI0cOVJLly5VaWmpKisrVVBQoA4dOigjIyPIlZ9emNeuNzd/xQ4cOKDp06fro48%2BUkxMjEaMGKF77rmHZ4N%2Bhi1btui//uu/1Lhx4xPm1q5dq6qqKs2YMUPFxcVq3ry57rjjDv32t78NQqX2t3fvXvXu3VtfffWVJOnjjz/WnDlzVFpaqpSUFE2cOFF9%2B/YNcpX2cuzYMc2dO1dvvPGGqqur1a9fP02fPl3R0dH0t4GKi4v16KOPavv27WrcuLEuu%2BwyTZkyRcnJyfT2DB0PQ8fvBjgcDkny/Sbg%2BvXrNX/%2BfO3bt09t27bVgw8%2BqG7dukn64fmrhQsX6uWXX1ZVVZUyMzM1e/ZstWzZMggrOTMELAAAAMO4RQgAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhv0/kt/RhasJiJsAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-5172794290746011392”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1730</td> <td class=”number”>53.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>98</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>90</td> <td class=”number”>2.8%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.3333333333333</td> <td class=”number”>50</td> <td class=”number”>1.6%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>48</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.0</td> <td class=”number”>29</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16.6666666666667</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>66.6666666666667</td> <td class=”number”>15</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14.2857142857143</td> <td class=”number”>13</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.5</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (73)</td> <td class=”number”>143</td> <td class=”number”>4.5%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>967</td> <td class=”number”>30.1%</td> <td>

<div class=”bar” style=”width:56%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-5172794290746011392”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1730</td> <td class=”number”>53.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.2258064516129</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.57142857142857</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.63636363636364</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>73.9130434782609</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>75.0</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>80.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>92.8571428571429</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>98</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_FREE_LUNCH09”>PCT_FREE_LUNCH09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3044</td>

</tr> <tr>

<th>Unique (%)</th> <td>94.8%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>4.8%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>153</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>41.304</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>99.478</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.1%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-1224701385723192023”>

</div> <div class=”col-md-12 text-right”>

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<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-1224701385723192023”

aria-controls=”quantiles-1224701385723192023” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-1224701385723192023” aria-controls=”histogram-1224701385723192023”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-1224701385723192023” aria-controls=”common-1224701385723192023”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-1224701385723192023” aria-controls=”extreme-1224701385723192023”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

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<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-1224701385723192023”>
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<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>17.981</td>

</tr> <tr>

<th>Q1</th> <td>29.525</td>

</tr> <tr>

<th>Median</th> <td>40.092</td>

</tr> <tr>

<th>Q3</th> <td>50.833</td>

</tr> <tr>

<th>95-th percentile</th> <td>69.829</td>

</tr> <tr>

<th>Maximum</th> <td>99.478</td>

</tr> <tr>

<th>Range</th> <td>99.478</td>

</tr> <tr>

<th>Interquartile range</th> <td>21.308</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>16.219</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.39268</td>

</tr> <tr>

<th>Kurtosis</th> <td>0.626</td>

</tr> <tr>

<th>Mean</th> <td>41.304</td>

</tr> <tr>

<th>MAD</th> <td>12.783</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.62192</td>

</tr> <tr>

<th>Sum</th> <td>126270</td>

</tr> <tr>

<th>Variance</th> <td>263.07</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

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VotXbpUS5YsUffu3bVixQrdcMMNkqRbb71Vs2bN0owZM3Ts2DElJyersLBQISEhFu8VAADwFQGuS72jG21SW3vC%2BJp2u03R0aFyOOp9/lSqN7rz6betLgFeYtuMwVaX0C4cGzyL/nqOJ3rbtWu4kXXayy8vEQIAAFiJgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYV4XsDIyMrR69WodOXLE6lIAAAA6xOsC1r333qutW7fqtttu06RJk7Rz5041NjZaXRYAAECbeV3AysvL09atW/X73/9evXr10hNPPKH09HStWLFCBw8etLo8AACAi/K6gHVO3759NXfuXO3evVvz58/X73//e40aNUoTJ07UX/7yF6vLAwAAuCCvDVhnz57V1q1bNXnyZM2dO1dxcXH613/9V/Xp00cTJkzQli1brC4RAADgvOxWF/BdVVVV2rBhgzZu3Kj6%2BnqNHDlSv/3tbzVgwICWbQYOHKjHHntMmZmZFlYKAABwfl4XsEaPHq0ePXpoypQpuueeexQVFdVqm/T0dB0/ftyC6gAAAC7O6wJWUVGRBg0adNHtPvroo8tQDQAAQPt53T1YCQkJmjp1ql577bWWsd/85jeaPHmynE6nhZUBAAC0jdcFrGXLlunEiRPq2bNny9jQoUPV3Nys5cuXW1gZAABA23jdJcK33npLW7ZsUXR0dMtYfHy8Vq5cqbvuusvCygAAANrG6wLWqVOnFBwc3GrcZrOpoaHBgooAfN/d%2BfTbVpfQLn%2Bcc4vVJQDfe153iXDgwIFavny5vvnmm5axr776SkuWLHH7qAYAAABv5XVnsObPn6%2Bf/exn%2BvGPf6ywsDA1Nzervr5e11xzjX73u99ZXR4AAMBFeV3Auuaaa/Tqq6/qjTfe0BdffCGbzaYePXpoyJAhCgwMtLo8AACAi/K6gCVJQUFBuu2226wuAwAAoEO8LmB9%2BeWXWrVqlT799FOdOnWq1fzrr79uQVUAAABt53UBa/78%2BaqpqdGQIUPUqVMnq8sBAABoN68LWBUVFXr99dcVExNjdSkAAAAd4nUf09C5c2fOXAEAAJ/mdQFrypQpWr16tVwul7E1n3jiCSUkJLT8vaysTNnZ2erfv79Gjx6tzZs3u21fVFSkkSNHqn///srNzVVFRYWxWgAAgP/zukuEb7zxht5//329/PLLuvrqq2WzuWfAl156qV3r7d%2B/X5s2bWr5e01NjR555BEtWLBAmZmZeu%2B99zRt2jT16NFDycnJ2rVrlwoKCvT8888rISFBRUVFmjp1qnbu3MmZNQAA0CZedwYrLCxMt956q9LT03X99derR48ebn/ao7m5WYsXL9aECRNaxrZs2aL4%2BHhlZ2crODhYaWlpysjIUElJiSSpuLhYWVlZSklJUUhIiCZNmiRJ2r17t7F9BAAA/s3rzmAtW7bM2FovvfSSgoODlZmZqaefflqSVFlZqcTERLftEhMTtW3btpb5UaNGtczZbDb16dNH5eXlGj16dJuet6amRrW1tW5jdnsnxcbGXsrutBIYaHP7CgASxwZPo7%2Be40%2B99bqAJUmfffaZXn31Vf31r39tCVwffPCB%2BvXr1%2BY1vv76axUUFLT69TpOp1NxcXFuY1FRUXI4HC3zkZGRbvORkZEt821RXFys1atXu43l5eUpPz%2B/zWu0R0TElR5ZF4BvOndM4NjgWfTXc/yht14XsMrKyjR58mT16NFDhw4d0rJly/Tll1/qwQcf1NNPP63hw4e3aZ1ly5YpKytLPXv21OHDh9tVw6XeYJ%2BTk6OMjAy3Mbu9kxyO%2Bkta97sCA22KiLhSdXUNampqNro2AN9VV9fAscGDOPZ6jid6Gx0damSd9vK6gPXUU0/p5z//uR566CHdeOONkr79/YTLly/Xs88%2B26aAVVZWpg8%2B%2BECvvPJKq7no6Gg5nU63MYfD0fK5W%2Bebdzqd6tWrV5v3ITY2ttXlwNraE2ps9Mwbsamp2WNrA/A9574xcWzwLPrrOf7QW6%2B7yPl///d/ys3NlSQFBAS0jN9xxx2qqqpq0xqbN2/WsWPHNGzYMKWmpiorK0uSlJqaqt69e7f62IWKigqlpKRIkpKSklRZWdky19TUpH379rXMAwAAXIzXBazw8PDz/g7CmpoaBQUFtWmNefPmaceOHdq0aZM2bdqkwsJCSdKmTZuUmZmp6upqlZSU6PTp0yotLVVpaanGjRsnScrNzdXGjRv14YcfqqGhQWvWrFFQUJCGDh1qbB8BAIB/87pLhP3799cTTzyhhQsXtowdPHhQixcv1o9//OM2rREZGel2o3pjY6MkqVu3bpKktWvXaunSpVqyZIm6d%2B%2BuFStW6IYbbpAk3XrrrZo1a5ZmzJihY8eOKTk5WYWFhQoJCTG1iwAAwM8FuEx%2BZLoBR48e1UMPPaTDhw%2BrqalJnTp1UkNDg3r16qXnnntOV111ldUldkht7Qnja9rtNkVHh8rhqPf5a9Xe6M6n37a6BKBD/jjnFo4NHsSx13M80duuXcONrNNeXncGq1u3bnrllVdUWlqqgwcPKiQkRD169NDgwYPd7skCAADwVl4XsCTpiiuu0G233WZ1GQAAAB3idQErIyPjn56pev311y9jNQAAAO3ndQFr1KhRbgGrqalJBw8eVHl5uR566CELKwMAAGgbrwtYc%2BbMOe/4jh079Kc//ekyVwMAANB%2BXvc5WBdy22236dVXX7W6DAAAgIvymYC1b9%2B%2BS/4dgQAAAJeD110iHD9%2BfKuxhoYGVVVVacSIERZUBAAA0D5eF7Di4%2BNb/RRhcHCwsrOzdd9991lUFQAAQNt5XcBavny51SUAAABcEq8LWBs3bmzztvfcc48HKwEAAOgYrwtYCxYsUHNzc6sb2gMCAtzGAgICCFgAAMAreV3Aev7557Vu3TpNnTpVCQkJcrlc%2BuSTT/TrX/9aDzzwgFJTU60uEQAA4J/yuoC1fPlyFRYWKi4urmXs5ptv1jXXXKOJEyfqlVdesbA6AACAi/O6z8E6dOiQIiMjW41HRESourragooAAADax%2BsCVvfu3bV8%2BXI5HI6Wsbq6Oq1atUo//OEPLawMAACgbbzuEuH8%2BfM1e/ZsFRcXKzQ0VDabTSdPnlRISIieffZZq8sDAAC4KK8LWEOGDNGePXtUWlqqo0ePyuVyKS4uTrfccovCw8OtLg8AAOCivC5gSdKVV16p4cOH6%2BjRo7rmmmusLgcAAKBdvO4erFOnTmnu3Lnq16%2Bf7rzzTknf3oM1adIk1dXVWVwdAADAxXldwFqxYoX279%2BvlStXymb7e3lNTU1auXKlhZUBAAC0jdcFrB07dug///M/dccdd7T80ueIiAgtW7ZMO3futLg6AACAi/O6gFVfX6/4%2BPhW4zExMfrb3/52%2BQsCAABoJ68LWD/84Q/1pz/9SZLcfvfg9u3bddVVV1lVFgAAQJt53U8R3n///Zo%2BfbruvfdeNTc3a/369aqoqNCOHTu0YMECq8sDAAC4KK8LWDk5ObLb7XrhhRcUGBio5557Tj169NDKlSt1xx13WF0eAADARXldwDp%2B/Ljuvfde3XvvvVaXAgAA0CFedw/W8OHD3e69AgAA8DVeF7BSU1O1bds2q8sAAADoMK%2B7RPiDH/xA//7v/67CwkL98Ic/1BVXXOE2v2rVKosqAwAAaBuvC1gHDhzQddddJ0lyOBwWVwMAANB%2BXhOwZs6cqaeeekq/%2B93vWsaeffZZ5eXldWi9jz/%2BWMuWLVNFRYWCg4M1aNAgLViwQF27dlVZWZlWrVqlzz77TD/4wQ80ZcoUjRkzpuWxRUVFevHFF1VbW6uEhAQtWLBASUlJl7yPAADg%2B8Fr7sHatWtXq7HCwsIOrXXmzBn97Gc/06BBg1RWVqZXXnlFx44d02OPPaaamho98sgjGj9%2BvMrKyrRgwQItWrRI5eXlLXUUFBToV7/6lfbu3athw4Zp6tSpfIo8AABoM68JWOf7ycGO/jRhQ0ODZs6cqSlTpigoKEgxMTG6/fbb9emnn2rLli2Kj49Xdna2goODlZaWpoyMDJWUlEiSiouLlZWVpZSUFIWEhGjSpEmSpN27d3d85wAAwPeK11wiPPeLnS821haRkZG67777Wv7%2B2Wef6Q9/%2BIPuvPNOVVZWKjEx0W37xMTElp9crKys1KhRo1rmbDab%2BvTpo/Lyco0ePbpNz19TU6Pa2lq3Mbu9k2JjYzu0PxcSGGhz%2BwoAEscGT6O/nuNPvfWagOUJ1dXVGjlypBobGzVu3Djl5%2Bdr8uTJiouLc9suKiqq5YZ6p9OpyMhIt/nIyMh23XBfXFys1atXu43l5eUpPz%2B/g3vyz0VEXOmRdQH4pnPHBI4NnkV/PccfeuvXAat79%2B4qLy/X559/rn/7t3/TL37xizY97lI/6DQnJ0cZGRluY3Z7Jzkc9Ze07ncFBtoUEXGl6uoa1NTUbHRtAL6rrq6BY4MHcez1HE/0Njo61Mg67eU1Aevs2bOaPXv2Rcfa%2BzlYAQEBio%2BP18yZMzV%2B/Hilp6fL6XS6beNwOBQTEyNJio6ObjXvdDrVq1evNj9nbGxsq8uBtbUn1NjomTdiU1Ozx9YG4HvOfWPi2OBZ9Ndz/KG3XnORc8CAAaqpqXH7c76xtigrK9PIkSPV3Pz3/zk227e7euONN6qiosJt%2B4qKCqWkpEiSkpKSVFlZ2TLX1NSkffv2tcwDAABcjNecwfrHz7%2B6VElJSTp58qRWrFih/Px8NTQ0qKCgQDfffLNyc3O1bt06lZSUaMyYMXrnnXdUWlqq4uJiSVJubq5mzZqlu%2B66SwkJCfqv//ovBQUFaejQocbqAwAA/s1rzmCZFB4ernXr1qmiokI/%2BtGPNHr0aIWHh%2Bs//uM/1LlzZ61du1YvvPCCBgwYoCeeeEIrVqzQDTfcIEm69dZbNWvWLM2YMUODBg3S3r17VVhYqJCQEIv3CgAA%2BIoA16Xe0Y02qa09YXxNu92m6OhQORz1Pn%2Bt2hvd%2BfTbVpcAdMgf59zCscGDOPZ6jid627VruJF12ssvz2ABAABYiYAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADD7FYXAAAw6/aVb1pdQpttmzHY6hIAj%2BAMFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhfhuwqqurlZeXp9TUVKWlpWnevHmqq6uTJO3fv18PPPCABgwYoBEjRmjdunVuj926dasyMzPVr18/ZWVl6a233rJiFwAAgI/y24A1depURUREaNeuXXr55Zf16aef6sknn9SpU6c0ZcoU/ehHP9Kbb76pp556SmvXrtXOnTslfRu%2B5s6dqzlz5uidd97RhAkT9Oijj%2Bro0aMW7xEAAPAVfhmw6urqlJSUpNmzZys0NFTdunXT2LFj9e6772rPnj06e/aspk2bpk6dOqlv37667777VFxcLEkqKSlRenq60tPTFRwcrDFjxqh3797avHmzxXsFAAB8hV8GrIiICC1btkxdunRpGTty5IhiY2NVWVmphIQEBQYGtswlJiaqoqJCklRZWanExES39RITE1VeXn55igcAAD7PbnUBl0N5ebleeOEFrVmzRtu2bVNERITbfFRUlJxOp5qbm%2BV0OhUZGek2HxkZqQMHDrT5%2BWpqalRbW%2Bs2Zrd3UmxsbMd34jwCA21uXwHA19jtvnf84tjrOf7UW78PWO%2B9956mTZum2bNnKy0tTdu2bTvvdgEBAS3/7XK5Luk5i4uLtXr1arexvLw85efnX9K6FxIRcaVH1gUAT4uODrW6hA7j2Os5/tBbvw5Yu3bt0s9//nMtWrRI99xzjyQpJiZGhw4dctvO6XQqKipKNptN0dHRcjqdreZjYmLa/Lw5OTnKyMhwG7PbO8nhqO/YjlxAYKBNERFXqq6uQU1NzUbX9oTbV75pdQkAvIzp4%2BLl4GvHXl/iid5aFeL9NmC9//77mjt3rp555hkNGTKkZTwpKUn//d//rcbGRtnt3%2B5%2BeXm5UlJSWubP3Y91Tnl5uUaPHt3m546NjW11ObC29oQaGz3zRmxqavbY2gDgSb587OLIFWlLAAAP4klEQVTY6zn%2B0Fvfv8h5Ho2NjVq4cKHmzJnjFq4kKT09XWFhYVqzZo0aGhr00UcfacOGDcrNzZUkjRs3Tnv37tWePXt0%2BvRpbdiwQYcOHdKYMWOs2BUAAOCDAlyXesORF3r33Xf1k5/8REFBQa3mtm/frvr6ei1evFgVFRXq0qWLJk%2BerPvvv79lm507d2rVqlWqrq5Wz549tWDBAg0cOPCSaqqtPXFJjz8fu92m6OhQORz1PpH073z6batLAOBlts0YbHUJ7eZrx15f4onedu0abmSd9vLLgOWNCFgELACtEbDwj/wpYPnlJUIAAAArEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYX4bsN58802lpaVp5syZrea2bt2qzMxM9evXT1lZWXrrrbda5pqbm/XUU09p%2BPDhGjhwoCZOnKgvv/zycpYOAAB8nF8GrF//%2BtdaunSprr322lZz%2B/fv19y5czVnzhy98847mjBhgh599FEdPXpUkvTiiy9qy5YtKiws1O7duxUfH6%2B8vDy5XK7LvRsAAMBH%2BWXACg4O1oYNG84bsEpKSpSenq709HQFBwdrzJgx6t27tzZv3ixJKi4u1oQJE3T99dcrLCxMM2fOVFVVlT766KPLvRsAAMBH%2BWXAevDBBxUeHn7eucrKSiUmJrqNJSYmqry8XKdOndKBAwfc5sPCwnTttdeqvLzcozUDAAD/Ybe6gMvN6XQqMjLSbSwyMlIHDhzQN998I5fLdd55h8PR5ueoqalRbW2t25jd3kmxsbEdL/w8AgNtbl8BwNfY7b53/OLY6zn%2B1NvvXcCSdNH7qS71fqvi4mKtXr3abSwvL0/5%2BfmXtO6FRERc6ZF1AcDToqNDrS6hwzj2eo4/9PZ7F7Cio6PldDrdxpxOp2JiYhQVFSWbzXbe%2Bc6dO7f5OXJycpSRkeE2Zrd3ksNR3/HCzyMw0KaIiCtVV9egpqZmo2sDwOVg%2Brh4OXDs9RxP9NaqEP%2B9C1hJSUmqqKhwGysvL9fo0aMVHBysXr16qbKyUoMGDZIk1dXV6YsvvtCNN97Y5ueIjY1tdTmwtvaEGhs980Zsamr22NoA4Em%2BfOzi2Os5/tBb37/I2U7jxo3T3r17tWfPHp0%2BfVobNmzQoUOHNGbMGElSbm6uioqKVFVVpZMnT2rlypXq06ePkpOTLa4cAAD4Cr88g3UuDDU2NkqSXnvtNUnfnqnq3bu3Vq5cqWXLlqm6ulo9e/bU2rVr1bVrV0nS%2BPHjVVtbq3/5l39RfX29UlNTW91PBQAw486n37a6hHbZNmOw1SXARwS4%2BATNy6K29oTxNe12m6KjQ%2BVw1PvEqVRfO5ACwHdtmzHY5469vsQTve3a9fwf2%2BRp37tLhAAAAJ5GwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGF2qwsAAACecefTb1tdQpttmzHY6hKM4gwWAACAYZzBAgCgjXzpjBCsxRksAAAAwwhYAAAAhhGwAAAADCNgAQAAGMZN7j7u5gXbrS4BAAB8B2ewAAAADCNgAQAAGEbAAgAAMIyAdR7V1dV6%2BOGHlZqaqmHDhmnFihVqbm62uiwAAOAjuMn9PKZPn66%2Bffvqtdde07FjxzRlyhR16dJFP/3pT60uDQAA%2BADOYH1HeXm5Pv74Y82ZM0fh4eGKj4/XhAkTVFxcbHVpAADAR3AG6zsqKyvVvXt3RUZGtoz17dtXBw8e1MmTJxUWFnbRNWpqalRbW%2Bs2Zrd3UmxsrNFaAwPJxwAA/2C321q%2Br/nD9zcC1nc4nU5FRES4jZ0LWw6Ho00Bq7i4WKtXr3Ybe/TRRzV9%2BnRzherbIPdQt0%2BVk5NjPLzh2/4WFxfTXw%2Bgt55Ffz2L/npOTU2Nfvvb5/2it74fET3A5XJd0uNzcnL08ssvu/3JyckxVN3f1dbWavXq1a3OlsEM%2Bus59Naz6K9n0V/P8afecgbrO2JiYuR0Ot3GnE6nAgICFBMT06Y1YmNjfT55AwCAjuMM1nckJSXpyJEjOn78eMtYeXm5evbsqdDQUAsrAwAAvoKA9R2JiYlKTk7WqlWrdPLkSVVVVWn9%2BvXKzc21ujQAAOAjAh977LHHrC7C29xyyy165ZVX9Pjjj%2BvVV19Vdna2Jk6cqICAAKtLayU0NFSDBg3i7JqH0F/PobeeRX89i/56jr/0NsB1qXd0AwAAwA2XCAEAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2D5oOrqaj388MNKTU3VsGHDtGLFCjU3N1tdls%2Bqrq5WXl6eUlNTlZaWpnnz5qmurk6StH//fj3wwAMaMGCARowYoXXr1llcrW974oknlJCQ0PL3srIyZWdnq3///ho9erQ2b95sYXW%2Ba82aNRoyZIhuuukmTZgwQYcPH5ZEf03Yt2%2BfHnzwQd18880aPHiw5syZo%2BPHj0uivx3x5ptvKi0tTTNnzmw1t3XrVmVmZqpfv37KysrSW2%2B91TLX3Nysp556SsOHD9fAgQM1ceJEffnll5ez9PZzweeMHTvWtXDhQlddXZ3r4MGDrhEjRrjWrVtndVk%2B66677nLNmzfPdfLkSdeRI0dcWVlZrvnz57saGhpct9xyi6ugoMBVX1/vqqiocA0aNMi1Y8cOq0v2Sfv27XMNGjTI1bt3b5fL5XJ99dVXrptuuslVUlLiOnXqlOvtt9923Xjjja6//OUvFlfqW1544QXXHXfc4aqqqnKdOHHC9fjjj7sef/xx%2BmvA2bNnXYMHD3atWrXKdfr0adfx48ddP/3pT13Tp0%2Bnvx1QWFjoGjFihGv8%2BPGuGTNmuM3t27fPlZSU5NqzZ4/r1KlTrk2bNrlSUlJcR44ccblcLldRUZFr2LBhrgMHDrhOnDjh%2BuUvf%2BnKzMx0NTc3W7ErbcIZLB9TXl6ujz/%2BWHPmzFF4eLji4%2BM1YcIEFRcXW12aT6qrq1NSUpJmz56t0NBQdevWTWPHjtW7776rPXv26OzZs5o2bZo6deqkvn376r777qPXHdDc3KzFixdrwoQJLWNbtmxRfHy8srOzFRwcrLS0NGVkZKikpMS6Qn3QunXrNHPmTF133XUKCwvTwoULtXDhQvprQG1trWpra3X33XcrKChI0dHRuv3227V//3762wHBwcHasGGDrr322lZzJSUlSk9PV3p6uoKDgzVmzBj17t275axgcXGxJkyYoOuvv15hYWGaOXOmqqqq9NFHH13u3WgzApaPqaysVPfu3RUZGdky1rdvXx08eFAnT560sDLfFBERoWXLlqlLly4tY0eOHFFsbKwqKyuVkJCgwMDAlrnExERVVFRYUapPe%2BmllxQcHKzMzMyWscrKSiUmJrptR3/b56uvvtLhw4f1zTffaNSoUUpNTVV%2Bfr6OHz9Ofw2Ii4tTnz59VFxcrPr6eh07dkw7d%2B7U0KFD6W8HPPjggwoPDz/v3IX6WV5erlOnTunAgQNu82FhYbr22mtVXl7u0ZovBQHLxzidTkVERLiNnQtbDofDipL8Snl5uV544QVNmzbtvL2OioqS0%2Bnknrd2%2BPrrr1VQUKDFixe7jV%2Bov7yO2%2B7o0aOSpO3bt2v9%2BvXatGmTjh49qoULF9JfA2w2mwoKCvT666%2Brf//%2BSktLU2Njo2bPnk1/DXM6nW4nDqRvv7c5HA598803crlcF5z3VgQsH%2BRyuawuwS%2B99957mjhxombPnq20tLQLbhcQEHAZq/J9y5YtU1ZWlnr27Gl1KX7n3LFg0qRJiouLU7du3TR9%2BnTt2rXL4sr8w5kzZzR16lTdcccdevfdd/XGG28oPDxcc%2BbMsbo0v3Sx722%2B9r2PgOVjYmJi5HQ63cacTqcCAgIUExNjUVW%2Bb9euXXr44Yc1f/58Pfjgg5K%2B7fV3/3XkdDoVFRUlm423TluUlZXpgw8%2BUF5eXqu56OjoVq9lh8PB67gdzl3a/sczKd27d5fL5dLZs2fp7yUqKyvT4cOHNWvWLIWHhysuLk75%2Bfn64x//KJvNRn8NOt/xwOl0KiYmpuWYe775zp07X84y24XvEj4mKSlJR44cafkxYenby1o9e/ZUaGiohZX5rvfff19z587VM888o3vuuadlPCkpSZ988okaGxtbxsrLy5WSkmJFmT5p8%2BbNOnbsmIYNG6bU1FRlZWVJklJTU9W7d%2B9W96tUVFTQ33bo1q2bwsLCtH///pax6upqXXHFFUpPT6e/l6ipqUnNzc1uZ07OnDkjSUpLS6O/BiUlJbXq57njbXBwsHr16qXKysqWubq6On3xxRe68cYbL3epbUbA8jGJiYlKTk7WqlWrdPLkSVVVVWn9%2BvXKzc21ujSf1NjYqIULF2rOnDkaMmSI21x6errCwsK0Zs0aNTQ06KOPPtKGDRvodTvMmzdPO3bs0KZNm7Rp0yYVFhZKkjZt2qTMzExVV1erpKREp0%2BfVmlpqUpLSzVu3DiLq/Yddrtd2dnZeu655/T555/r2LFjevbZZ5WZmamxY8fS30vUr18/derUSQUFBWpoaJDD4dCaNWs0cOBA3X333fTXoHHjxmnv3r3as2ePTp8%2BrQ0bNujQoUMaM2aMJCk3N1dFRUWqqqrSyZMntXLlSvXp00fJyckWV35hAS5fu6gJHT16VIsWLdL//u//KiwsTOPHj9ejjz7KvUEd8O677%2BonP/mJgoKCWs1t375d9fX1Wrx4sSoqKtSlSxdNnjxZ999/vwWV%2BofDhw9r%2BPDh%2BuSTTyRJf/7zn7V06VJVVVWpe/fumj17tkaMGGFxlb7lzJkzWrZsmV599VWdPXtWI0eO1KJFixQaGkp/DaioqNCTTz6pjz/%2BWEFBQRo0aJDmzZunuLg4%2BttO58LQuasCdrtdklp%2BEnDnzp1atWqVqqur1bNnTy1YsEADBw6U9O39VwUFBXrppZdUX1%2Bv1NRU/fKXv1S3bt0s2JO2IWABAAAYxiVCAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADDs/wE85R2lUY4keAAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-1224701385723192023”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>45.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>23.52941176470588</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>34.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>38.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>30.069930069930066</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>29.807692307692307</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>27.835051546391753</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.33333333333333</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>39.60923623445826</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3033)</td> <td class=”number”>3034</td> <td class=”number”>94.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>153</td> <td class=”number”>4.8%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-1224701385723192023”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.45045045045045046</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.7882182119892139</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.2222222222222223</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.7227999650441315</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>99.29865575686733</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>99.3050193050193</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>99.31714719271623</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>99.3908067195865</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>99.47765525246662</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_FREE_LUNCH14”>PCT_FREE_LUNCH14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2864</td>

</tr> <tr>

<th>Unique (%)</th> <td>89.2%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>9.8%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>314</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>45.969</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram5975568598349211504”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives5975568598349211504”>

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<li role=”presentation” class=”active”><a href=”#quantiles5975568598349211504”

aria-controls=”quantiles5975568598349211504” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram5975568598349211504” aria-controls=”histogram5975568598349211504”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common5975568598349211504” aria-controls=”common5975568598349211504”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme5975568598349211504” aria-controls=”extreme5975568598349211504”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles5975568598349211504”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>20.581</td>

</tr> <tr>

<th>Q1</th> <td>33.332</td>

</tr> <tr>

<th>Median</th> <td>44.134</td>

</tr> <tr>

<th>Q3</th> <td>55.676</td>

</tr> <tr>

<th>95-th percentile</th> <td>81.454</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr>

<th>Range</th> <td>100</td>

</tr> <tr>

<th>Interquartile range</th> <td>22.344</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>18.046</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.39257</td>

</tr> <tr>

<th>Kurtosis</th> <td>0.60302</td>

</tr> <tr>

<th>Mean</th> <td>45.969</td>

</tr> <tr>

<th>MAD</th> <td>14.006</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.68882</td>

</tr> <tr>

<th>Sum</th> <td>133130</td>

</tr> <tr>

<th>Variance</th> <td>325.66</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram5975568598349211504”>

<img 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dsePSKNrucqvzxFCAAA4EkELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIZ5XcDKzs5WSUmJjhw54ulSAAAALorXBay7775bW7Zs0W233abJkydrx44damtr83RZAAAALvO6gFVUVKQtW7boj3/8o/r06aOnn35aWVlZWrFihQ4dOuTp8gAAAM7L6wLW9wYMGKB58%2BZp165dWrBggf74xz9qzJgxmjRpkv72t795ujwAAIAf5bUB68yZM9qyZYumTJmiefPmKSEhQY899pj69%2B%2BvwsJCbd682dMlAgAAnFOQpws4W11dnTZs2KCNGzeqpaVFo0aN0n/%2B539q4MCBjscMHjxYTzzxhHJycjxYKQAAwLl5XcAaO3asevXqpYcfflh33XWXYmJiOj0mKytLx44d80B1uJQMWrjN0yUAAHyU150iXL9%2BvbZu3arCwsJzhqvv7du3z%2BU1n376afXr18/xeVVVlfLy8pSenq6xY8eqoqKiUw2jRo1Senq6CgoKVFNTc%2BEbAgAALlleF7D69eunqVOn6s0333SM/f73v9eUKVNks9kueL0DBw5o06ZNjs8bGhr0yCOPaOLEiaqqqtLChQu1ePFiVVdXS5J27typ4uJiPfvss9qzZ49GjBihqVOn6ttvv%2B36xgEAgEuC1wWsZcuW6fjx4%2Brdu7djbPjw4ero6NDy5csvaK2Ojg49/vjjKiwsdIxt3rxZSUlJysvLU0hIiDIzM5Wdna3y8nJJUllZmXJzc5WWlqbQ0FBNnjxZkrRr166ubxwAALgkeF3Aevfdd1VSUqKkpCTHWFJSklauXKl33nnngtZ69dVXFRIS4nQxfG1trZKTk50el5yc7DgNePa8xWJR//79HUe4AAAAzsfrLnI/efKkQkJCOo1bLBa1tra6vM7XX3%2Bt4uJi/eEPf3Aat9lsSkhIcBqLiYmR1Wp1zEdHRzvNR0dHO%2BZd0dDQoMbGRqexoKAwxcfHu7yGKwIDLU4fYQ49ha/jPWwe%2B1z38cfeel3AGjx4sJYvX645c%2BY4gs5XX32lZ555xulWDeezbNky5ebmqnfv3jp8%2BPAF1WC32y/o8WcrKytTSUmJ01hRUZFmzJjRpXV/TFRUN7esC8B3sV9wH3rrPv7UW68LWAsWLNCDDz6on//854qIiFBHR4daWlp05ZVXdjoa9WOqqqr04Ycf6vXXX%2B80Fxsb2%2BlieavVqri4uB%2Bdt9ls6tOnj8vbkJ%2Bfr%2BzsbKexoKAwWa0tLq/hisBAi6Kiuqm5uVXt7R1G177U%2BdNPUbg0sV8wj32u%2B7izt7Gx4UbXc5XXBawrr7xSb7zxht5%2B%2B2198cUXslgs6tWrl4YOHarAwECX1qioqFBTU5NGjBgh6f%2BOSGVkZOjBBx/sFLxqamqUlpYmSUpJSVFtba3Gjx8vSWpvb9f%2B/fuVl5fn8jbEx8d3Oh3Y2HhcbW3u%2BYJsb%2B9w29oAfBP7Bfeht%2B7jT731uoAlScHBwbrtttsu%2Bvnz58/Xv/zLvzg%2BP3r0qPLz87Vp0yZ1dHRozZo1Ki8v17hx4/Tee%2B%2BpsrJSZWVlkqSCggLNnj1bv/jFL9SvXz/9x3/8h4KDgzV8%2BPCubhYAALhEeF3A%2BvLLL7Vq1Sp9%2BumnOnnyZKf5t95667xrREdHO12o3tbWJknq2bOnJGnNmjVaunSplixZosTERK1YsULXXXedJGnYsGGaPXu2Zs6cqaamJqWmpqq0tFShoaEmNg8AAFwCvC5gLViwQA0NDRo6dKjCwsKMrHnFFVfok08%2BcXw%2BePBgp5uPnu3ee%2B/Vvffea%2BS1AQDApcfrAlZNTY3eeustx0XnAAAAvsbrflWqe/fuxo5cAQAAeILXBayHH35YJSUlXb4XFQAAgKd43SnCt99%2BWx988IFee%2B01XXHFFbJYnDPgq6%2B%2B6qHKAAAAXON1ASsiIkLDhg3zdBkAAAAXzesC1rJlyzxdAgAAQJd43TVYkvT3v/9dxcXFeuyxxxxjH374oQcrAgAAcJ3XBayqqiqNGzdOO3bscPxJmy%2B//FL333%2B/SzcZBQAA8DSvC1jPPfecfv3rX2vz5s0KCAiQ9N3fJ1y%2BfLleeOEFD1cHAABwfl4XsP7nf/5HBQUFkuQIWJI0evRo1dXVeaosAAAAl3ldwIqMjDzn3yBsaGhQcHCwByoCAAC4MF4XsNLT0/X000/rxIkTjrFDhw5p3rx5%2BvnPf%2B7BygAAAFzjdbdpeOyxx/TAAw8oIyND7e3tSk9PV2trq/r06aPly5d7ujwAAIDz8rqA1bNnT73%2B%2BuuqrKzUoUOHFBoaql69eunmm292uiYLAADAW3ldwJKkyy67TLfddpunywAAALgoXhewsrOz/%2BGRKu6FBQAAvJ3XBawxY8Y4Baz29nYdOnRI1dXVeuCBBzxYGQAAgGu8LmDNnTv3nOPbt2/XX/7yl5%2B4GgAAgAvndbdp%2BDG33Xab3njjDU%2BXAQAAcF4%2BE7D2798vu93u6TIAAADOy%2BtOEU6cOLHTWGtrq%2Brq6jRy5EgPVAQAAHBhvC5gJSUldfotwpCQEOXl5emee%2B7xUFUAAACu87qAxd3aAQCAr/O6gLVx40aXH3vXXXe5sRIAAICL43UBa%2BHChero6Oh0QXtAQIDTWEBAAAELAAB4Ja8LWC%2B99JLWrl2rqVOnql%2B/frLb7frkk0/0u9/9Tvfdd58yMjI8XSIAAMA/5HUBa/ny5SotLVVCQoJjbNCgQbryyis1adIkvf766x6sDgAA4Py87j5Yn332maKjozuNR0VFqb6%2B3gMVAQAAXBivC1iJiYlavny5rFarY6y5uVmrVq3SVVdd5cHKAAAAXON1pwgXLFigOXPmqKysTOHh4bJYLDpx4oRCQ0P1wgsveLo8AACA8/K6gDV06FDt3r1blZWVOnr0qOx2uxISEnTLLbcoMjLS0%2BUBAACcl9cFLEnq1q2bbr31Vh09elRXXnmlp8sBAAC4IF53DdbJkyc1b9483XjjjbrjjjskfXcN1uTJk9Xc3Ozh6gAAAM7P6wLWihUrdODAAa1cuVIWy/%2BV197erpUrV3qwMgAAANd4XcDavn27/v3f/12jR492/NHnqKgoLVu2TDt27PBwdQAAAOfndQGrpaVFSUlJncbj4uL07bffurzOxx9/rAceeEADBw5UZmamZs6cqcbGRklSVVWV8vLylJ6errFjx6qiosLpuevXr9eoUaOUnp6ugoIC1dTUdGmbAADApcXrAtZVV12lv/zlL5Lk9LcHt23bpp/97GcurXH69Gk9%2BOCDGjJkiKqqqvT666%2BrqalJTzzxhBoaGvTII49o4sSJqqqq0sKFC7V48WJVV1dLknbu3Kni4mI9%2B%2Byz2rNnj0aMGKGpU6deULgDAACXNq8LWPfee6%2BmT5%2BuZ555Rh0dHVq3bp3mzJmjBQsW6IEHHnBpjdbWVs2aNUsPP/ywgoODFRcXp9tvv12ffvqpNm/erKSkJOXl5SkkJESZmZnKzs5WeXm5JKmsrEy5ublKS0tTaGioJk%2BeLEnatWuX27YZAAD4F6%2B7TUN%2Bfr6CgoL08ssvKzAwUC%2B%2B%2BKJ69eqllStXavTo0S6tER0drXvuucfx%2Bd///nf96U9/0h133KHa2lolJyc7PT45OVlbt26VJNXW1mrMmDGOOYvFov79%2B6u6ulpjx4516fUbGhocpyO/FxQUpvj4eJee76rAQIvTR5hDT%2BHreA%2Bbxz7Xffyxt14XsI4dO6a7775bd999d5fXqq%2Bv16hRo9TW1qYJEyZoxowZmjJlitMfkpakmJgYx5/msdlsnf4WYnR0tNOf7jmfsrIylZSUOI0VFRVpxowZF7kl/1hUVDe3rAvAd7FfcB966z7%2B1FuvC1i33nqrPvjgA8dvEHZFYmKiqqur9fnnn%2Btf//Vf9Zvf/Mal5/3w2q%2BLkZ%2Bfr%2BzsbKexoKAwWa0tXVr3bIGBFkVFdVNzc6va2zuMrn2p86efonBpYr9gHvtc93Fnb2Njw42u5yqvC1gZGRnaunWr02m6rggICFBSUpJmzZqliRMnKisrSzabzekxVqtVcXFxkqTY2NhO8zabTX369HH5NePj4zudDmxsPK62Nvd8Qba3d7htbQC%2Bif2C%2B9Bb9/Gn3npdwPqnf/onPfXUUyotLdVVV12lyy67zGl%2B1apV512jqqpKTzzxhLZu3eq4Wen3H6%2B//npt377d6fE1NTVKS0uTJKWkpKi2tlbjx4%2BX9N0NTvfv36%2B8vLwubxsAALg0eN15kIMHD%2Bqaa65RZGSkrFarGhoanP65IiUlRSdOnNCKFSvU2tqqY8eOqbi4WIMGDVJBQYHq6%2BtVXl6uU6dOqbKyUpWVlZowYYIkqaCgQBs3btRHH32k1tZWrV69WsHBwRo%2BfLgbtxoAAPgTrzmCNWvWLD333HP6wx/%2B4Bh74YUXVFRUdMFrRUZGau3atVq6dKluuukmhYWF6aabbtJTTz2l7t27a82aNVq6dKmWLFmixMRErVixQtddd50kadiwYZo9e7ZmzpyppqYmpaamqrS0VKGhoca2FQAA%2BLcAe1ev6DYkLS1N%2B/btO%2B%2BYr2psPG58zaAgi2Jjw2W1tvjNOWtvERRk0e0r3/F0GcBF2fvUaPYLbsA%2B133c2dsePSKNrucqrzlFeK6c5yXZDwAA4IJ4TcA6120ZTNyqAQAA4KfmNQELAADAXxCwAAAADPOa3yI8c%2BaM5syZc94xV%2B6DBQAA4EleE7AGDhzY6T5X5xoDAADwdl4TsH54/ysAAABfxjVYAAAAhhGwAAAADPOaU4QAADMGLdzm6RJctnXmzZ4uAXALjmABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAM89uAVV9fr6KiImVkZCgzM1Pz589Xc3OzJOnAgQO67777NHDgQI0cOVJr1651eu6WLVuUk5OjG2%2B8Ubm5uXr33Xc9sQkAAMBH%2BW3Amjp1qqKiorRz50699tpr%2BvTTT/XMM8/o5MmTevjhh3XTTTfpnXfe0XPPPac1a9Zox44dkr4LX/PmzdPcuXP13nvvqbCwUI8%2B%2BqiOHj3q4S0CAAC%2Bwi8DVnNzs1JSUjRnzhyFh4erZ8%2BeGj9%2BvPbu3avdu3frzJkzmjZtmsLCwjRgwADdc889KisrkySVl5crKytLWVlZCgkJ0bhx49S3b19VVFR4eKsAAICvCPJ0Ae4QFRWlZcuWOY0dOXJE8fHxqq2tVb9%2B/RQYGOiYS05OVnl5uSSptrZWWVlZTs9NTk5WdXW1y6/f0NCgxsZGp7GgoDDFx8df6Kb8Q4GBFqePMIeeAj%2BNoCDf%2BVpjn%2Bs%2B/thbvwxYZ6uurtbLL7%2Bs1atXa%2BvWrYqKinKaj4mJkc1mU0dHh2w2m6Kjo53mo6OjdfDgQZdfr6ysTCUlJU5jRUVFmjFjxsVvxD8QFdXNLesCgLvFxoZ7uoQLxj7Xffypt34fsN5//31NmzZNc%2BbMUWZmprZu3XrOxwUEBDj%2Bb7fbu/Sa%2Bfn5ys7OdhoLCgqT1drSpXXPFhhoUVRUNzU3t6q9vcPo2pc6f/opCvBmpveL7sQ%2B133c2VtPhXi/Dlg7d%2B7Ur3/9ay1evFh33XWXJCkuLk6fffaZ0%2BNsNptiYmJksVgUGxsrm83WaT4uLs7l142Pj%2B90OrCx8bja2tzzBdne3uG2tQHAnXxx38U%2B1338qbd%2B%2B2P6Bx98oHnz5un55593hCtJSklJ0SeffKK2tjbHWHV1tdLS0hzzNTU1Tmv9cB4AAOB8/DJgtbW1adGiRZo7d66GDh3qNJeVlaWIiAitXr1ara2t2rdvnzZs2KCCggJJ0oQJE7Rnzx7t3r1bp06d0oYNG/TZZ59p3LhxntgUAADggwLsXb3gyAvt3btXv/zlLxUcHNxpbtu2bWppadHjjz%2BumpoaXX755ZoyZYruvfdex2N27NihVatWqb6%2BXr1799bChQs1ePDgLtXU2Hi8S88/l6Agi2IJCLHkAAAPWklEQVRjw2W1tvjNIVVvERRk0e0r3/F0GYDf2zrzZk%2BX4DL2ue7jzt726BFpdD1X%2BWXA8kYELN9CwAJ%2BGgQsSP4ZsPzyFCEAAIAnEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYUGeLgAAcOm649/%2B7OkSLsjep0Z7ugT4CAIWfjK%2BtiMFAOBicYoQAADAML8NWO%2B8844yMzM1a9asTnNbtmxRTk6ObrzxRuXm5urdd991zHV0dOi5557TrbfeqsGDB2vSpEn68ssvf8rSAQCAj/PLgPW73/1OS5cu1dVXX91p7sCBA5o3b57mzp2r9957T4WFhXr00Ud19OhRSdIrr7yizZs3q7S0VLt27VJSUpKKiopkt9t/6s0AAAA%2Byi8DVkhIiDZs2HDOgFVeXq6srCxlZWUpJCRE48aNU9%2B%2BfVVRUSFJKisrU2Fhoa699lpFRERo1qxZqqur0759%2B37qzQAAAD7KLwPW/fffr8jIyHPO1dbWKjk52WksOTlZ1dXVOnnypA4ePOg0HxERoauvvlrV1dVurRkAAPiPS%2B63CG02m6Kjo53GoqOjdfDgQX3zzTey2%2B3nnLdarS6/RkNDgxobG53GgoLCFB8ff/GFn0NgoMXpIwDA/djnmueP388uuYAl6bzXU3X1equysjKVlJQ4jRUVFWnGjBldWvfHREV1c8u6AIDO2Oe6jz/19pILWLGxsbLZbE5jNptNcXFxiomJkcViOed89%2B7dXX6N/Px8ZWdnO40FBYXJam25%2BMLPITDQoqiobmpublV7e4fRtQEA58Y%2B1zx3fj%2BLjQ03up6rLrmAlZKSopqaGqex6upqjR07ViEhIerTp49qa2s1ZMgQSVJzc7O%2B%2BOILXX/99S6/Rnx8fKfTgY2Nx9XW5p4vyPb2DretDQBwxj7Xffypt/5zstNFEyZM0J49e7R7926dOnVKGzZs0GeffaZx48ZJkgoKCrR%2B/XrV1dXpxIkTWrlypfr376/U1FQPVw4AAHyFXx7B%2Bj4MtbW1SZLefPNNSd8dqerbt69WrlypZcuWqb6%2BXr1799aaNWvUo0cPSdLEiRPV2Niof/7nf1ZLS4syMjI6XU8FAADwjwTYuYPmT6Kx8bjxNYOCLIqNDZfV2uITh1T5W4QAfN3ep0b7zD7Xl7jz%2B1mPHue%2BbZO7XXKnCAEAANzNL08RAgAA3zpzsPep0Z4uwSiOYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwLnIHAMBFgxZu83QJ8BEcwQIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADAsyNMFoGsGLdzm6RIAAMBZOIIFAABgGAELAADAMALWOdTX1%2Buhhx5SRkaGRowYoRUrVqijo8PTZQEAAB/BNVjnMH36dA0YMEBvvvmmmpqa9PDDD%2Bvyyy/Xr371K0%2BXBgAAfABHsM5SXV2tjz/%2BWHPnzlVkZKSSkpJUWFiosrIyT5cGAAB8BEewzlJbW6vExERFR0c7xgYMGKBDhw7pxIkTioiIOO8aDQ0NamxsdBoLCgpTfHy80VoDA8nHAAD/4U/f1whYZ7HZbIqKinIa%2Bz5sWa1WlwJWWVmZSkpKnMYeffRRTZ8%2B3Vyh%2Bi7IPdDzU%2BXn5xsPb5e6hoYGlZWV0Vs3oLfuRX/dh966T0NDg4qLi/2qt/4TFQ2y2%2B1den5%2Bfr5ee%2B01p3/5%2BfmGqvs/jY2NKikp6XS0DF1Hb92H3roX/XUfeus%2B/thbjmCdJS4uTjabzWnMZrMpICBAcXFxLq0RHx/vNwkcAABcOI5gnSUlJUVHjhzRsWPHHGPV1dXq3bu3wsPDPVgZAADwFQSssyQnJys1NVWrVq3SiRMnVFdXp3Xr1qmgoMDTpQEAAB8R%2BMQTTzzh6SK8zS233KLXX39dTz75pN544w3l5eVp0qRJCggI8HRpnYSHh2vIkCEcXXMDeus%2B9Na96K/70Fv38bfeBti7ekU3AAAAnHCKEAAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwApYPqq%2Bv10MPPaSMjAyNGDFCK1asUEdHh6fL8ln19fUqKipSRkaGMjMzNX/%2BfDU3N0uSDhw4oPvuu08DBw7UyJEjtXbtWg9X67uefvpp9evXz/F5VVWV8vLylJ6errFjx6qiosKD1fmu1atXa%2BjQobrhhhtUWFiow4cPS6K/XbV//37df//9GjRokG6%2B%2BWbNnTtXx44dk0RvL8Y777yjzMxMzZo1q9Pcli1blJOToxtvvFG5ubl69913HXMdHR167rnndOutt2rw4MGaNGmSvvzyy5%2By9Itnh88ZP368fdGiRfbm5mb7oUOH7CNHjrSvXbvW02X5rF/84hf2%2BfPn20%2BcOGE/cuSIPTc3175gwQJ7a2ur/ZZbbrEXFxfbW1pa7DU1NfYhQ4bYt2/f7umSfc7%2B/fvtQ4YMsfft29dut9vtX331lf2GG26wl5eX20%2BePGn/85//bL/%2B%2Buvtf/vb3zxcqW95%2BeWX7aNHj7bX1dXZjx8/bn/yySftTz75JP3tojNnzthvvvlm%2B6pVq%2BynTp2yHzt2zP6rX/3KPn36dHp7EUpLS%2B0jR460T5w40T5z5kynuf3799tTUlLsu3fvtp88edK%2BadMme1pamv3IkSN2u91uX79%2BvX3EiBH2gwcP2o8fP27/7W9/a8/JybF3dHR4YlMuCEewfEx1dbU%2B/vhjzZ07V5GRkUpKSlJhYaHKyso8XZpPam5uVkpKiubMmaPw8HD17NlT48eP1969e7V7926dOXNG06ZNU1hYmAYMGKB77rmHXl%2Bgjo4OPf744yosLHSMbd68WUlJScrLy1NISIgyMzOVnZ2t8vJyzxXqg9auXatZs2bpmmuuUUREhBYtWqRFixbR3y5qbGxUY2Oj7rzzTgUHBys2Nla33367Dhw4QG8vQkhIiDZs2KCrr76601x5ebmysrKUlZWlkJAQjRs3Tn379nUcFSwrK1NhYaGuvfZaRUREaNasWaqrq9O%2Bfft%2B6s24YAQsH1NbW6vExERFR0c7xgYMGKBDhw7pxIkTHqzMN0VFRWnZsmW6/PLLHWNHjhxRfHy8amtr1a9fPwUGBjrmkpOTVVNT44lSfdarr76qkJAQ5eTkOMZqa2uVnJzs9Dh6e2G%2B%2BuorHT58WN98843GjBmjjIwMzZgxQ8eOHaO/XZSQkKD%2B/furrKxMLS0tampq0o4dOzR8%2BHB6exHuv/9%2BRUZGnnPux/pZXV2tkydP6uDBg07zERERuvrqq1VdXe3Wmk0gYPkYm82mqKgop7Hvw5bVavVESX6lurpaL7/8sqZNm3bOXsfExMhms3HNm4u%2B/vprFRcX6/HHH3ca/7He8h523dGjRyVJ27Zt07p167Rp0yYdPXpUixYtor9dZLFYVFxcrLfeekvp6enKzMxUW1ub5syZQ28Ns9lsTgcMpO%2B%2Bp1mtVn3zzTey2%2B0/Ou/tCFg%2ByG63e7oEv/T%2B%2B%2B9r0qRJmjNnjjIzM3/0cQEBAT9hVb5t2bJlys3NVe/evT1dit/5fj8wefJkJSQkqGfPnpo%2Bfbp27tzp4cp83%2BnTpzV16lSNHj1ae/fu1dtvv63IyEjNnTvX06X5pfN9T/PV73kELB8TFxcnm83mNGaz2RQQEKC4uDgPVeX7du7cqYceekgLFizQ/fffL%2Bm7Xp/9U5LNZlNMTIwsFr50zqeqqkoffvihioqKOs3FxsZ2eh9brVbewxfg%2B9PaPzyakpiYKLvdrjNnztDfLqiqqtLhw4c1e/ZsRUZGKiEhQTNmzNB//dd/yWKx0FuDzrUvsNlsiouLc%2BxrzzXfvXv3n7LMi8J3CR%2BTkpKiI0eOOH5dWPrutFbv3r0VHh7uwcp81wcffKB58%2Bbp%2Beef11133eUYT0lJ0SeffKK2tjbHWHV1tdLS0jxRps%2BpqKhQU1OTRowYoYyMDOXm5kqSMjIy1Ldv307XrNTU1NDbC9CzZ09FRETowIEDjrH6%2BnpddtllysrKor9d0N7ero6ODqcjJ6dPn5YkZWZm0luDUlJSOvXz%2B/1sSEiI%2BvTpo9raWsdcc3OzvvjiC11//fU/dakXjIDlY5KTk5WamqpVq1bpxIkTqqur07p161RQUODp0nxSW1ubFi1apLlz52ro0KFOc1lZWYqIiNDq1avV2tqqffv2acOGDfTaRfPnz9f27du1adMmbdq0SaWlpZKkTZs2KScnR/X19SovL9epU6dUWVmpyspKTZgwwcNV%2B46goCDl5eXpxRdf1Oeff66mpia98MILysnJ0fjx4%2BlvF9x4440KCwtTcXGxWltbZbVatXr1ag0ePFh33nknvTVowoQJ2rNnj3bv3q1Tp05pw4YN%2BuyzzzRu3DhJUkFBgdavX6%2B6ujqdOHFCK1euVP/%2B/ZWamurhys8vwO6rJzcvYUePHtXixYv13//934qIiNDEiRP16KOPcm3QRdi7d69%2B%2BctfKjg4uNPctm3b1NLSoscff1w1NTW6/PLLNWXKFN17770eqNT3HT58WLfeeqs%2B%2BeQTSdJf//pXLV26VHV1dUpMTNScOXM0cuRID1fpW06fPq1ly5bpjTfe0JkzZzRq1CgtXrxY4eHh9LeLampq9Mwzz%2Bjjjz9WcHCwhgwZovnz5yshIYHeXqDvw9D3ZwOCgoIkyfGbgDt27NCqVatUX1%2Bv3r17a%2BHChRo8eLCk766/Ki4u1quvvqqWlhZlZGTot7/9rXr27OmBLbkwBCwAAADDOEUIAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIb9P1ph%2BJtY6gBJAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common5975568598349211504”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>40.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>37.93103448275862</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>30.76923076923077</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>35.064935064935064</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>44.44444444444444</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>35.714285714285715</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>35.97093257973355</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2853)</td> <td class=”number”>2853</td> <td class=”number”>88.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>314</td> <td class=”number”>9.8%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme5975568598349211504”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.6753720311422096</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.821683309557775</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.583850931677018</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.909444039815489</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>99.89432898908066</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>99.91009889121966</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>99.91431019708654</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>99.96734800496311</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_HISP10”>PCT_HISP10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3132</td>

</tr> <tr>

<th>Unique (%)</th> <td>97.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>8.3036</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>95.745</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram4395602454844545448”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives4395602454844545448”>

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<li role=”presentation” class=”active”><a href=”#quantiles4395602454844545448”

aria-controls=”quantiles4395602454844545448” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram4395602454844545448” aria-controls=”histogram4395602454844545448”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common4395602454844545448” aria-controls=”common4395602454844545448”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme4395602454844545448” aria-controls=”extreme4395602454844545448”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles4395602454844545448”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.79737</td>

</tr> <tr>

<th>Q1</th> <td>1.5928</td>

</tr> <tr>

<th>Median</th> <td>3.2855</td>

</tr> <tr>

<th>Q3</th> <td>8.2574</td>

</tr> <tr>

<th>95-th percentile</th> <td>36.963</td>

</tr> <tr>

<th>Maximum</th> <td>95.745</td>

</tr> <tr>

<th>Range</th> <td>95.745</td>

</tr> <tr>

<th>Interquartile range</th> <td>6.6646</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>13.21</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.5908</td>

</tr> <tr>

<th>Kurtosis</th> <td>12.524</td>

</tr> <tr>

<th>Mean</th> <td>8.3036</td>

</tr> <tr>

<th>MAD</th> <td>8.1762</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>3.2645</td>

</tr> <tr>

<th>Sum</th> <td>26007</td>

</tr> <tr>

<th>Variance</th> <td>174.49</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram4395602454844545448”>

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Xjh07lJCQoOzsbFmWJUmaMWOGqqqqtGHDBr355pvyer3Ky8u7%2BsUDAIBrQtCuwerataumTJmiHTt26Nlnn9Vf/vIX3XvvvRozZoz/SNP3iYiI0OrVq78zYH2fgoICZWVlqX379nK5XMrJyZHX69WBAwf05Zdfatu2bcrJyVFcXJxatWqlJ554Qm%2B%2B%2BaZqamquZKkAAOAa4wzWjmtqavTOO%2B9ozZo1ev/995WQkKAJEyaopKREWVlZeu655zR48ODvfP/o0aO/d/snTpzQI488osLCQrndbk2cOFH333%2B/qqurdfjwYSUmJvpf63K51LZtW3k8Hp05c0bh4eHq3Lmzf75r1676%2Buuv9dlnnwWMf5eSkhKVlpYGjDmdLRQfH3/J9/4Q4eGhdY%2BC0xla9V7ofK9DreehiF7bh17bh17b68fQb9sDltfr1erVq/XWW2%2BpsrJSd999t1577TXddttt/tekpKTod7/73fcGrO8TFxenhIQEPf300%2BrQoYPeeecd/eY3v1F8fLxuueUWWZal6OjogPdER0errKxMMTExcrlcCgsLC5iTpLKyssvaf0FBgRYtWhQwlp2drYkTJ17RepqK2NjIYJdw1dzu64NdwjWDXtuHXtuHXtsrmP22PWANGjRI7dq109ixYzV06FDFxMQ0eE16erpOnTp1xfvo27ev%2BvbtG7DP80fLcnNzJcl/vdXFfN/c5cjIyFC/fv0CxpzOFiorq7yq7X5bqP0mZHr9dgoPd8jtvl7l5VWqq6sPdjlNGr22D722D72214X9DlbIsj1grVixQr169brk6w4cOGB0v23atFFhYaFiYmLkcDjk8/kC5n0%2Bn1q2bKm4uDhVVFSorq5O4eHh/jlJatmy5WXtKz4%2BvsHpwNLSM6qtvba/qJrC%2Buvq6pvEOkIBvbYPvbYPvbZXMMOs7YdAOnfurHHjxmnbtm3%2Bsf/4j//QY4891iD0XKk///nP2rhxY8CY1%2BvVTTfdpIiICHXs2FFFRUX%2BufLych07dkzdunVTly5dZFmWPv74Y/%2B8x%2BOR2%2B1Wu3btjNQHAACaNtsD1ty5c3XmzBl16NDBP9a3b1/V19dr3rx5RvZx7tw5zZ49Wx6PRzU1NdqwYYPee%2B89jRo1SpKUmZmpFStWyOv1qqKiQnl5eerSpYuSk5MVFxenu%2B%2B%2BWy%2B99JJOnTqlkydPavHixRoxYoSczqDdEwAAAEKI7Ylhz549Wr9%2BvWJjY/1jCQkJysvL03333XfZ20lOTpYk/0NKzx8R83g8Gj16tCorK/XUU0%2BptLRUN954oxYvXqykpCRJ0qhRo1RaWqqHH35YlZWVSk1NDbgofdasWZo5c6b69%2B%2BvZs2a6b777mvwMFMAAIDvYnvAqq6uVkRERINxh8Ohqqqqy96Ox%2BP5zrmwsDA98cQTeuKJJ75zfuLEid95V19UVJT%2B7d/%2B7bJrAQAAuJDtpwhTUlI0b948nT592j/2xRdf6Lnnngt4VAMAAECosv0I1rPPPqt//dd/1c9//nO5XC7V19ersrJSN910k/70pz/ZXQ4AAIBxtgesm266SW%2B//bbee%2B89HTt2TA6HQ%2B3atVOfPn38j0UAAAAIZUG5La558%2Ba68847g7FrAACARmd7wDp%2B/Ljmz5%2BvTz/9VNXV1Q3m3333XbtLAgAAMCoo12CVlJSoT58%2BatGihd27BwAAaHS2B6zCwkK9%2B%2B67iouLs3vXAAAAtrD9MQ0tW7bkyBUAAGjSbA9YY8eO1aJFi2RZlt27BgAAsIXtpwjfe%2B89ffjhh1qzZo1uvPFGORyBGe%2BNN96wuyQAAACjbA9YLpdLv/jFL%2BzeLQAAgG1sD1hz5861e5cAAAC2sv0aLEn67LPPtHDhQv32t7/1j3300UfBKAUAAMA42wPWvn37NGTIEG3dulUbNmyQ9M3DR0ePHs1DRgEAQJNge8BasGCBnnnmGa1fv15hYWGSvvn7hPPmzdPixYvtLgcAAMA42wPWf//3fyszM1OS/AFLkgYOHCiv12t3OQAAAMbZHrCioqIu%2BjcIS0pK1Lx5c7vLAQAAMM72gNWzZ089//zzqqio8I8dOXJEU6ZM0c9//nO7ywEAADDO9sc0/Pa3v9Wvf/1rpaamqq6uTj179lRVVZU6duyoefPm2V0OAACAcbYHrNatW2vDhg3atWuXjhw5ouuuu07t2rVT7969A67JAgAACFW2ByxJatasme68885g7BoAAKDR2R6w%2BvXr971HqngWFgAACHW2B6x77703IGDV1dXpyJEj8ng8%2BvWvf213OQAAAMbZHrByc3MvOr5lyxb97W9/s7kaAAAA84Lytwgv5s4779Tbb78d7DIAAACu2o8mYB08eFCWZQW7DAAAgKtm%2BynCUaNGNRirqqqS1%2BvVgAED7C4HAADAONsDVkJCQoO7CCMiIjRixAg9%2BOCDdpcDAABgnO0Bi6e1AwCAps72gPXWW29d9muHDh3aiJUAAAA0DtsD1rRp01RfX9/ggvawsLCAsbCwMAIWAAAISbYHrD/84Q9atmyZxo0bp86dO8uyLH3yySd69dVX9dBDDyk1NdXukgAAAIwKyjVY%2Bfn5atWqlX/sZz/7mW666SaNGTNGGzZssLskAAAAo2x/DtbRo0cVHR3dYNztdqu4uNjucgAAAIyzPWC1adNG8%2BbNU1lZmX%2BsvLxc8%2BfP180332x3OQAAAMbZforw2Wef1eTJk1VQUKDIyEg5HA5VVFTouuuu0%2BLFi%2B0uBwAAwDjbA1afPn20c%2BdO7dq1SydPnpRlWWrVqpVuv/12RUVF2V0OAACAcbYHLEm6/vrr1b9/f508eVI33XRTMEoAAABoNLZfg1VdXa0pU6aoR48euueeeyR9cw3Wo48%2BqvLycrvLAQAAMM72gPXiiy/q0KFDysvLk8Px/7uvq6tTXl6e3eUAAAAYZ3vA2rJli/793/9dAwcO9P/RZ7fbrblz52rr1q12lwMAAGCc7QGrsrJSCQkJDcbj4uL09ddf210OAACAcbYHrJtvvll/%2B9vfJCngbw9u3rxZ//zP/2x3OQAAAMbZfhfhL3/5S02YMEEPPPCA6uvrtXz5chUWFmrLli2aNm2a3eUAAAAYZ3vAysjIkNPp1MqVKxUeHq5XXnlF7dq1U15engYOHGh3OQAAAMbZHrBOnTqlBx54QA888IDduwYAALCF7ddg9e/fP%2BDaKwAAgKbG9oCVmpqqTZs22b1bAAAA29h%2BivCf/umf9Pvf/175%2Bfm6%2Beab1axZs4D5%2BfPn210SAACAUbYHrMOHD%2BuWW26RJJWVldm9ewAAgEZnW8DKycnRggUL9Kc//ck/tnjxYmVnZ9tVAgAAgC1suwZr%2B/btDcby8/Ovapu7d%2B9WWlqacnJyGsxt3LhRgwcPVo8ePTR8%2BHDt2bPHP1dfX68FCxaof//%2BSklJ0ZgxY3T8%2BHH/vM/n06RJk5SWlqY%2Bffpo2rRpqq6uvqpaAQDAtcO2gHWxOwev5m7CV199VXPmzFHbtm0bzB06dEhTpkxRbm6u3n//fWVlZenJJ5/UyZMnJUmvv/661q9fr/z8fO3YsUMJCQnKzs721zNjxgxVVVVpw4YNevPNN%2BX1evlD1AAA4LLZFrDO/2HnS41droiICK1evfqiAWvVqlVKT09Xenq6IiIiNGTIEHXq1Enr1q2TJBUUFCgrK0vt27eXy%2BVSTk6OvF6vDhw4oC%2B//FLbtm1TTk6O4uLi1KpVKz3xxBN68803VVNTc8X1AgCAa4ftj2kwZfTo0YqKirroXFFRkRITEwPGEhMT5fF4VF1drcOHDwfMu1wutW3bVh6PR4cOHVJ4eLg6d%2B7sn%2B/atau%2B/vprffbZZ42zGAAA0KTYfhehHXw%2Bn6KjowPGoqOjdfjwYZ0%2BfVqWZV10vqysTDExMXK5XAFH186/9nLveiwpKVFpaWnAmNPZQvHx8VeynO8UHh5a%2BdjpDK16L3S%2B16HW81BEr%2B1Dr%2B1Dr%2B31Y%2Bi3bQGrpqZGkydPvuSYqedgXer6ru%2Bbv9onzRcUFGjRokUBY9nZ2Zo4ceJVbTfUxcZGBruEq%2BZ2Xx/sEq4Z9No%2B9No%2B9Npewey3bQHrtttuU0lJySXHTIiNjZXP5wsY8/l8iouLU0xMjBwOx0XnW7Zsqbi4OFVUVKiurk7h4eH%2BOUlq2bLlZe0/IyND/fr1CxhzOluorKzySpd0UaH2m5Dp9dspPNwht/t6lZdXqa6uPtjlNGn02j702j702l4X9jtYIcu2gHXh868aW1JSkgoLCwPGPB6PBg0apIiICHXs2FFFRUXq1auXJKm8vFzHjh1Tt27d1KZNG1mWpY8//lhdu3b1v9ftdqtdu3aXtf/4%2BPgGpwNLS8%2Botvba/qJqCuuvq6tvEusIBfTaPvTaPvTaXsEMs6F1COQyjRw5Unv37tXOnTt19uxZrV69WkePHtWQIUMkSZmZmVqxYoW8Xq8qKiqUl5enLl26KDk5WXFxcbr77rv10ksv6dSpUzp58qQWL16sESNGyOlskpesAQAAw0I2MSQnJ0uSamtrJUnbtm2T9M3Rpk6dOikvL09z585VcXGxOnTooKVLl%2BonP/mJJGnUqFEqLS3Vww8/rMrKSqWmpgZcMzVr1izNnDlT/fv3V7NmzXTfffdd9GGmAAAAFxNmXe0V3bgspaVnjG/T6XTorrzdxrfbWDZN6h3sEq6Y0%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%2B%2BEDjx49Xu3btlJycrO3bt2vhwoX6wx/%2BoM6dO2vFihUaN26ctm7dqhYtWgRpJQAAIJQ02SNY32X9%2BvVKSEjQiBEjFBERobS0NPXr10%2BrVq2SJBUUFGj48OHq3r27rrvuOj366KOSpB07dgSzbAAAEEKa9BGs%2BfPn66OPPlJFRYXuueceTZ06VUVFRUpMTAx4XWJiojZt2iRJKioq0r333uufczgc6tKlizwejwYNGnRZ%2By0pKVFpaWnAmNPZQvHx8Ve5okDh4aGVj53O0Kr3Qud7HWo9D0X02j702j702l4/hn432YB16623Ki0tTS%2B88IKOHz%2BuSZMm6bnnnpPP51OrVq0CXhsTE6OysjJJks/nU3R0dMB8dHS0f/5yFBQUaNGiRQFj2dnZmjhx4hWupmmIjY0MdglXze2%2BPtglXDPotX3otX3otb2C2e8mG7AKCgr8/27fvr1yc3M1fvx43XbbbZd8r2VZV7XvjIwM9evXL2DM6WyhsrLKq9rut4Xab0Km12%2Bn8HCH3O7rVV5epbq6%2BmCX06TRa/vQa/vQa3td2O9ghawmG7C%2B7cYbb1RdXZ0cDod8Pl/AXFlZmeLi4iRJsbGxDeZ9Pp86dux42fuKj49vcDqwtPSMamuv7S%2BqprD%2Burr6JrGOUECv7UOv7UOv7RXMMBtah0Au08GDBzVv3ryAMa/Xq%2BbNmys9Pb3BYxcKCwvVvXt3SVJSUpKKior8c3V1dTp48KB/HgAA4FKaZMBq2bKlCgoKlJ%2Bfr3PnzunIkSN6%2BeWXlZGRofvvv1/FxcVatWqVzp49q127dmnXrl0aOXKkJCkzM1NvvfWW/v73v6uqqkpLlixR8%2BbN1bdv3%2BAuCgAAhIwmeYqwVatWys/P1/z58/0BadiwYcrJyVFERISWLl2qOXPm6LnnnlObNm304osv6qc//akk6Re/%2BIWefvppTZo0SV999ZWSk5OVn5%2Bv6667LsirAgAAoSLMutorunFZSkvPGN97iPLlAAAMDElEQVSm0%2BnQXXm7jW%2B3sWya1DvYJVwxp9Oh2NhIlZVVcv1EI6PX9qHX9qHX9rqw38G6g71JniIEAAAIJgIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABjmDHYBuHbc89Jfg13CD7JpUu9glwAACFEcwQIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABjmDHYBwI/VPS/9Ndgl/CCbJvUOdgkAgP/DESwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGI9pAJqIUHqsBI%2BUANDUcQQLAADAMAIWAACAYZwiBGC7UDqdKXFKE8APR8ACgEsgEAL4oThFCAAAYBgB6yKKi4v1%2BOOPKzU1VXfccYdefPFF1dfXB7ssAAAQIjhFeBETJkxQ165dtW3bNn311VcaO3asbrjhBj3yyCPBLg0AAIQAAta3eDweffzxx1q%2BfLmioqIUFRWlrKwsvfbaawQsACEhlK4ZC7XrxUKpt6Em1D4XLoWA9S1FRUVq06aNoqOj/WNdu3bVkSNHVFFRIZfLdcltlJSUqLS0NGDM6Wyh%2BPh4o7WGh3OGF0BoI7DgPKfT3M%2B08z8fg/lzkoD1LT6fT263O2DsfNgqKyu7rIBVUFCgRYsWBYw9%2BeSTmjBhgrlC9U2Q%2B3XrT5WRkWE8vCFQSUmJCgoK6LUN6LV96LV96LW9SkpK9Nprf1BGRobc7uuDUgOHQC7Csqyren9GRobWrFkT8JGRkWGouv9XWlqqRYsWNThaBvPotX3otX3otX3otb1%2BDP3mCNa3xMXFyefzBYz5fD6FhYUpLi7usrYRHx/PbygAAFzDOIL1LUlJSTpx4oROnTrlH/N4POrQoYMiIyODWBkAAAgVBKxvSUxMVHJysubPn6%2BKigp5vV4tX75cmZmZwS4NAACEiPDf/e53vwt2ET82t99%2BuzZs2KDZs2fr7bff1ogRIzRmzBiFhYUFu7QGIiMj1atXL46u2YBe24de24de24de2yvY/Q6zrvaKbgAAAATgFCEAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQSsEFRcXKzHH39cqampuuOOO/Tiiy%2Bqvr4%2B2GU1GcXFxcrOzlZqaqrS0tI0depUlZeXS5IOHTqkhx56SLfddpsGDBigZcuWBbnapuP5559X586d/f%2B/b98%2BjRgxQj179tSgQYO0bt26IFbXNCxZskR9%2BvTRrbfeqqysLH3%2B%2BeeS6LVpBw8e1OjRo/Wzn/1MvXv3Vm5urk6dOiWJXpuwe/dupaWlKScnp8Hcxo0bNXjwYPXo0UPDhw/Xnj17/HP19fVasGCB%2Bvfvr5SUFI0ZM0bHjx9vvEIthJxhw4ZZ06dPt8rLy60jR45YAwYMsJYtWxbsspqM%2B%2B67z5o6dapVUVFhnThxwho%2BfLj17LPPWlVVVdbtt99uLVy40KqsrLQKCwutXr16WVu2bAl2ySHv4MGDVq9evaxOnTpZlmVZX3zxhXXrrbdaq1atsqqrq62//vWvVrdu3ax//OMfQa40dK1cudIaOHCg5fV6rTNnzlizZ8%2B2Zs%2BeTa8Nq6mpsXr37m3Nnz/fOnv2rHXq1CnrkUcesSZMmECvDcjPz7cGDBhgjRo1ypo0aVLA3MGDB62kpCRr586dVnV1tbV27Vqre/fu1okTJyzLsqwVK1ZYd9xxh3X48GHrzJkz1qxZs6zBgwdb9fX1jVIrR7BCjMfj0ccff6zc3FxFRUUpISFBWVlZKigoCHZpTUJ5ebmSkpI0efJkRUZGqnXr1ho2bJj279%2BvnTt3qqamRuPHj1eLFi3UtWtXPfjgg/T%2BKtXX12vmzJnKysryj61fv14JCQkaMWKEIiIilJaWpn79%2BmnVqlXBKzTELVu2TDk5Obrlllvkcrk0ffp0TZ8%2BnV4bVlpaqtLSUt1///1q3ry5YmNjddddd%2BnQoUP02oCIiAitXr1abdu2bTC3atUqpaenKz09XRERERoyZIg6derkP0pYUFCgrKwstW/fXi6XSzk5OfJ6vTpw4ECj1ErACjFFRUVq06aNoqOj/WNdu3bVkSNHVFFREcTKmga32625c%2Bfqhhtu8I%2BdOHFC8fHxKioqUufOnRUeHu6fS0xMVGFhYTBKbTLeeOMNRUREaPDgwf6xoqIiJSYmBryOXl%2B5L774Qp9//rlOnz6te%2B%2B9V6mpqZo4caJOnTpFrw1r1aqVunTpooKCAlVWVuqrr77S1q1b1bdvX3ptwOjRoxUVFXXRue/qr8fjUXV1tQ4fPhww73K51LZtW3k8nkaplYAVYnw%2Bn9xud8DY%2BbBVVlYWjJKaNI/Ho5UrV2r8%2BPEX7X1MTIx8Ph/XwF2hL7/8UgsXLtTMmTMDxr%2Br13yOX5mTJ09KkjZv3qzly5dr7dq1OnnypKZPn06vDXM4HFq4cKHeffdd9ezZU2lpaaqtrdXkyZPpdSPz%2BXwBBx%2Bkb34%2BlpWV6fTp07Is6zvnGwMBKwRZlhXsEq4JH3zwgcaMGaPJkycrLS3tO18XFhZmY1VNy9y5czV8%2BHB16NAh2KU0aee/Zzz66KNq1aqVWrdurQkTJmj79u1BrqzpOXfunMaNG6eBAwdq//79eu%2B99xQVFaXc3Nxgl3ZNuNTPRzt/fhKwQkxcXJx8Pl/AmM/nU1hYmOLi4oJUVdOzfft2Pf7443r22Wc1evRoSd/0/tu/6fh8PsXExMjh4Evph9q3b58%2B%2BugjZWdnN5iLjY1t8HleVlbG5/gVOn/K%2B8KjJ23atJFlWaqpqaHXBu3bt0%2Bff/65nn76aUVFRalVq1aaOHGi3nnnHTkcDnrdiC72fcPn8ykuLs7/ffpi8y1btmyUevipEGKSkpJ04sQJ/y2/0jensTp06KDIyMggVtZ0fPjhh5oyZYpefvllDR061D%2BelJSkTz75RLW1tf4xj8ej7t27B6PMkLdu3Tp99dVXuuOOO5Samqrhw4dLklJTU9WpU6cG16UUFhbS6yvUunVruVwuHTp0yD9WXFysZs2aKT09nV4bVFdXp/r6%2BoAjJefOnZMkpaWl0etGlJSU1KC/579HR0REqGPHjioqKvLPlZeX69ixY%2BrWrVuj1EPACjGJiYlKTk7W/PnzVVFRIa/Xq%2BXLlyszMzPYpTUJtbW1mj59unJzc9WnT5%2BAufT0dLlcLi1ZskRVVVU6cOCAVq9eTe%2Bv0NSpU7VlyxatXbtWa9euVX5%2BviRp7dq1Gjx4sIqLi7Vq1SqdPXtWu3bt0q5duzRy5MggVx2anE6nRowYoVdeeUX/8z//o6%2B%2B%2BkqLFy/W4MGDNWzYMHptUI8ePdSiRQstXLhQVVVVKisr05IlS5SSkqL777%2BfXjeikSNHau/evdq5c6fOnj2r1atX6%2BjRoxoyZIgkKTMzUytWrJDX61VFRYXy8vLUpUsXJScnN0o9YRYX9ISckydPasaMGfrP//xPuVwujRo1Sk8%2B%2BSTXAhmwf/9%2B/epXv1Lz5s0bzG3evFmVlZWaOXOmCgsLdcMNN%2Bixxx7TL3/5yyBU2vR8/vnn6t%2B/vz755BNJ0n/9139pzpw58nq9atOmjSZPnqwBAwYEucrQde7cOc2dO1dvv/22ampqdPfdd2vGjBmKjIyk14YVFhbqhRde0Mcff6zmzZurV69emjp1qlq1akWvr9L5MHT%2BTILT6ZQk/52AW7du1fz581VcXKwOHTpo2rRpSklJkfTN9VcLFy7UG2%2B8ocrKSqWmpmrWrFlq3bp1o9RKwAIAADCMU4QAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYNj/AuAZ5ln05GA2AAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common4395602454844545448”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.9012256669069935</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>43.2923570283271</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.2209125919010386</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14.235764235764236</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.250355618776672</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.3161869110623465</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>37.04456672239668</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.570057581573896</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>19.672131147540984</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.419080843318671</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3121)</td> <td class=”number”>3121</td> <td class=”number”>97.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme4395602454844545448”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.1310615989515072</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.19064378941193721</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.24875621890547264</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.29239766081871343</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>93.33713796547296</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>93.86828808769376</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>95.67989973828745</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>95.6846214407558</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>95.74477435438507</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_HSPA15”>PCT_HSPA15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>31</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>37.3%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>1196</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>25.547</td>

</tr> <tr>

<th>Minimum</th> <td>19.5</td>

</tr> <tr>

<th>Maximum</th> <td>32.2</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram4929271648784950793”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives4929271648784950793,#minihistogram4929271648784950793”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives4929271648784950793”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles4929271648784950793”

aria-controls=”quantiles4929271648784950793” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram4929271648784950793” aria-controls=”histogram4929271648784950793”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common4929271648784950793” aria-controls=”common4929271648784950793”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme4929271648784950793” aria-controls=”extreme4929271648784950793”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles4929271648784950793”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>19.5</td>

</tr> <tr>

<th>5-th percentile</th> <td>20.2</td>

</tr> <tr>

<th>Q1</th> <td>24.3</td>

</tr> <tr>

<th>Median</th> <td>25.3</td>

</tr> <tr>

<th>Q3</th> <td>26.8</td>

</tr> <tr>

<th>95-th percentile</th> <td>30.9</td>

</tr> <tr>

<th>Maximum</th> <td>32.2</td>

</tr> <tr>

<th>Range</th> <td>12.7</td>

</tr> <tr>

<th>Interquartile range</th> <td>2.5</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>2.8892</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.11309</td>

</tr> <tr>

<th>Kurtosis</th> <td>0.0011864</td>

</tr> <tr>

<th>Mean</th> <td>25.547</td>

</tr> <tr>

<th>MAD</th> <td>2.1316</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.11987</td>

</tr> <tr>

<th>Sum</th> <td>51452</td>

</tr> <tr>

<th>Variance</th> <td>8.3475</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram4929271648784950793”>

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isrtXv37urtAAAAZ%2BNzBSs5OVmZmZk6cODAed/H7NmztXnzZq1fv17r169XVlaWJGn9%2BvUaOnSoioqKtHbtWh0/flx5eXnKy8vT6NGjJUlpaWlat26ddu3apfLycq1cuVKNGzdW3759TeweAAAIAD53ivCmm27SK6%2B8opUrV6pnz54aPXq0kpOT5XLVftSIiAjbheoVFRWSpEsuuUSStGrVKi1YsEDz589X69attXjxYl111VWSpN69e%2BvOO%2B/UtGnTdPDgQXXu3FlZWVlq0qSJwb0EAAD%2BLMi60Cu660hhYaE2btyo3NxcnTx5UsOHD1dKSoratm3r9GjnpaTkB6dHOC2XK1hRUc1VWnokIM6J1waZ2NVFHoOWv2XkfupL7rTrbX/mOeKNTLyRiZ1TeVx8cYt6e6yf87lThD%2B5%2BuqrNWvWLG3btk333nuvXnjhBQ0ePFjjx4/Xxx9/7PR4AAAANfLZgnXy5Em9%2BuqrmjhxombNmqVWrVrpnnvuUadOnTRu3Dht2LDB6REBAABOy%2Beuwdq3b59efPFFrVu3TkeOHNHAgQP1P//zP%2BratWv1bbp376558%2BZp6NChDk4KAABwej5XsIYMGaK2bdtq0qRJGj58uCIjI71u06dPHx06dMiB6QAAAM7O5wrW6tWr1aNHj7Pe7qOPPqqHaQAAAM6dz12D1bFjR02ePFmvvfZa9dpf/vIXTZw40esd1gEAAHyRzxWshQsX6ocfflC7du2q1/r27auqqiotWrTIwckAAABqx%2BdOEb755pvasGGDoqKiqtfatGmjJUuW6D//8z8dnAwAAKB2fO4I1rFjxxQaGuq1HhwcrPLycgcmAgAAODc%2BV7C6d%2B%2BuRYsW6fvvv69e%2B%2B677zR//nzbWzUAAAD4Kp87RXjvvffqtttuU8%2BePRUWFqaqqiodOXJEl19%2BuZ599lmnxwMAADgrnytYl19%2BuV555RW98cYb%2BuqrrxQcHKy2bduqV69eCgkJcXo8AACAs/K5giVJjRs31g033OD0GAAAAOfF5wrW119/raVLl2rv3r06duyY1/bXX3/dgakAAABqz%2BcK1r333qvi4mL16tVLzZo1c3ocAACAc%2BZzBaugoECvv/66oqOjnR4FAADgvPjc2zRcdNFFHLkCAAANms8VrEmTJikzM1OWZTk9CgAAwHnxuVOEb7zxhj744AO99NJLuuyyyxQcbO%2BAzz//vEOTAQhUg5a/5fQI5yR32vVOjwAEPJ8rWGFhYerdu7fTYwAAAJw3nytYCxcudHoEAACAC%2BJz12BJ0hdffKEVK1bonnvuqV778MMPHZwIAACg9nyuYOXn52vYsGHasmWLNm7cKOnHNx/93e9%2Bx5uMAgCABsHnCtayZct01113acOGDQoKCpL04%2B8nXLRokR5//HGHpwMAADg7nytYn332mdLS0iSpumBJ0o033qh9%2B/Y5NRYAAECt%2BVzBatGixWl/B2FxcbEaN27swEQAAADnxucKVkJCgh588EEdPny4eu3LL7/UrFmz1LNnTwcnAwAAqB2fe5uGe%2B65R7feeqsSExNVWVmphIQElZeXq3379lq0aJHT4wEAAJyVzxWsSy65RBs3blReXp6%2B/PJLNWnSRG3bttX1119vuyYLAADAV/lcwZKkRo0a6YYbbnB6DAAAgPPicwUrOTn5jEeqeC8sAADg63yuYA0ePNhWsCorK/Xll1/K7Xbr1ltvdXAyAACA2vG5gjVz5szTrm/evFnvvvtuPU8DAABw7nzubRpqcsMNN%2BiVV15xegwAAICzajAFa/fu3bIsy%2BkxAAAAzsrnThGOGTPGa628vFz79u3TgAEDHJgIAADg3PhcwWrTpo3XTxGGhoYqJSVFo0aNcmgqAACA2vO5gsW7tQMAgIbO5wrWunXran3b4cOH17jtk08%2B0cKFC1VQUKDQ0FD16NFDc%2BbM0cUXX6z8/HwtXbpUX3zxhS699FJNmjRJw4YNq/7c1atX67nnnlNJSYk6duyoOXPmKC4u7oL2CwAABA6fK1hz5sxRVVWV1wXtQUFBtrWgoKAaC9aJEyd022236eabb9aTTz6pw4cP6w9/%2BIPmzZun%2B%2B%2B/X1OnTtWcOXM0dOhQ7dy5U1OmTFHbtm3VuXNnbd26VStWrNBTTz2ljh07avXq1Zo8ebK2bNmiZs2a1em%2BAwAA/%2BBzP0X41FNPqVevXnruuef0/vvv6%2B9//7vWrFmj3r1768knn9THH3%2Bsjz/%2BWB999FGN91FeXq7p06dr0qRJaty4saKjo/Uf//Ef2rt3rzZs2KA2bdooJSVFoaGhSkpKUnJystauXStJys7O1siRIxUfH68mTZpowoQJkqRt27bVy/4DAICGz%2BeOYC1atEhZWVlq1apV9Vq3bt10%2BeWXa/z48dq4ceNZ7yMiIsJ2QfwXX3yhl19%2BWYMGDVJhYaFiY2Ntt4%2BNjVVubq4kqbCwUIMHD67eFhwcrE6dOsntdmvIkCG12ofi4mKVlJTY1lyuZoqJianV59enkJBg20eQyanIo%2BFxuer/74rniTcysQu0PHyuYO3fv18RERFe6%2BHh4SoqKjqn%2ByoqKtLAgQNVUVGh0aNHKyMjQxMnTrSVN0mKjIxUaWmpJMnj8Xg9fkRERPX22sjOzlZmZqZtLT09XRkZGec0f30KD2/q9Ag%2Bh0zsyKPhiIpq7thj8zzxRiZ2gZKHzxWs1q1ba9GiRfrDH/6gqKgoSVJZWZkee%2BwxXXHFFed8X263W//4xz/03//937r77rtr9XkX%2BoamqampSk5Otq25XM1UWnrkgu63LoSEBCs8vKnKyspVWVnl9Dg%2BgUzsyKPhceJrDc8Tb2Ri51QeTn3D4XMF695779WMGTOUnZ2t5s2bKzg4WIcPH1aTJk30%2BOOPn/P9BQUFqU2bNpo%2BfbrGjBmjPn36yOPx2G5TWlqq6OhoSVJUVJTXdo/Ho/bt29f6MWNiYrxOB5aU/KCKCt99gVVWVvn0fE4gEzvyaDic/HvieeKNTOwCJQ%2BfK1i9evXS9u3blZeXp2%2B//VaWZalVq1b69a9/rRYtWtTqPvLz8zVv3jzl5uYqOPjHc70/fezSpYs2b95su31BQYHi4%2BMlSXFxcSosLNSIESMkSZWVldq9e7dSUlJM7SIAAPBzPnmlWdOmTdW/f3/1799fv//97zV48OBalyvpx5J0%2BPBhLV68WOXl5Tp06JBWrFihbt26KS0tTUVFRVq7dq2OHz%2BuvLw85eXlafTo0ZKktLQ0rVu3Trt27VJ5eblWrlypxo0bq2/fvnW0twAAwN/4XME6duyYZs2apWuvvVaDBg2S9OM1WBMmTFBZWVmt7qNFixZ6%2BumnVVBQoOuuu05DhgxRixYt9Mgjj%2Biiiy7SqlWrtGbNGnXt2lUPPvigFi9erKuuukqS1Lt3b915552aNm2aevToobfffltZWVlq0qRJne0zAADwLz53inDx4sXas2ePlixZYrsovbKyUkuWLNEDDzxQq/vp2LGjnn322dNu6969u9avX1/j544dO1Zjx449t8EBAAD%2BzeeOYG3evFmPPfaYbrzxxupf%2BhweHq6FCxdqy5YtDk8HAABwdj5XsI4cOaI2bdp4rUdHR%2Bvo0aP1PxAAAMA58rmCdcUVV%2Bjdd9%2BVZH8/qk2bNukXv/iFU2MBAADUms9dgzV27Fjdcccduummm1RVVaVnnnlGBQUF2rx5s%2BbMmeP0eAAAAGflcwUrNTVVLpdLa9asUUhIiJ544gm1bdtWS5Ys0Y033uj0eAAAAGflcwXr0KFDuummm3TTTTc5PQoAAMB58blrsPr373/BvwsQAADAST5XsBITE5Wbm%2Bv0GAAAAOfN504RXnrppfrTn/6krKwsXXHFFWrUqJFt%2B9KlSx2aDAAAoHZ8rmB9/vnn%2BtWvfiVJKi0tdXgaAACAc%2BczBWv69OlatmyZ7dfbPP7440pPT3dwKgAAgHPnM9dgbd261WstKyvLgUkAAAAujM8UrNP95CA/TQgAABoinylYP/1i57OtAQAA%2BDqfKVgAAAD%2BgoIFAABgmM/8FOHJkyc1Y8aMs67xPlgAAMDX%2BUzB6tq1q4qLi8%2B6BgAA4Ot8pmD9/P2vAAAAGjKuwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYT7zy54BXJhBy99yegQAwL9xBAsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAY5rcFq6ioSOnp6UpMTFRSUpJmz56tsrIySdKePXt0yy23qGvXrhowYICefvpp2%2Be%2B%2BuqrGjp0qK699lqNHDlSb775phO7AAAAGii/LViTJ09WeHi4tm7dqpdeekl79%2B7VQw89pGPHjmnSpEm67rrrtGPHDi1btkyrVq3Sli1bJP1YvmbNmqWZM2fqnXfe0bhx43T77bfr22%2B/dXiPAABAQ%2BGXBausrExxcXGaMWOGmjdvrksuuUQjRozQ%2B%2B%2B/r%2B3bt%2BvkyZOaMmWKmjVrpquvvlqjRo1Sdna2JGnt2rXq06eP%2BvTpo9DQUA0bNkwdOnRQTk6Ow3sFAAAaCr8sWOHh4Vq4cKFatmxZvXbgwAHFxMSosLBQHTt2VEhISPW22NhYFRQUSJIKCwsVGxtru7/Y2Fi53e76GR4AADR4AfGrctxut9asWaOVK1cqNzdX4eHhtu2RkZHyeDyqqqqSx%2BNRRESEbXtERIQ%2B//zzWj9ecXGxSkpKbGsuVzPFxMSc/07UkZCQYNtHkAkaPper/p%2B7vG68kYldoOXh9wVr586dmjJlimbMmKGkpCTl5uae9nZBQUHV/29Z1gU9ZnZ2tjIzM21r6enpysjIuKD7rUvh4U2dHsHnkAkaqqio5o49Nq8bb2RiFyh5%2BHXB2rp1q%2B666y7dd999Gj58uCQpOjpa%2B/fvt93O4/EoMjJSwcHBioqKksfj8doeHR1d68dNTU1VcnKybc3laqbS0iPntyN1KCQkWOHhTVVWVq7Kyiqnx/EJZIKGzomvNbxuvJGJnVN5OPUNh98WrA8%2B%2BECzZs3So48%2Bql69elWvx8XF6a9//asqKirkcv24%2B263W/Hx8dXbf7oe6ydut1tDhgyp9WPHxMR4nQ4sKflBFRW%2B%2BwKrrKzy6fmcQCZoqJx83vK68UYmdoGSh18WrIqKCs2dO1czZ860lStJ6tOnj8LCwrRy5UpNmDBBn332mV588UUtXrxYkjR69GilpKRo%2B/bt6tmzpzZs2KD9%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%2Beab1duqqqq0bNky9e/fX927d9f48eP19ddf1%2BfoAACggfPLgvXkk09qwYIF%2BuUvf%2Bm1bc%2BePZo1a5Zmzpypd955R%2BPGjdPtt9%2Bub7/9VpL03HPPacOGDcrKytK2bdvUpk0bpaeny7Ks%2Bt4NAADQQPllwQoNDdWLL7542oK1du1a9enTR3369FFoaKiGDRumDh06KCcnR5KUnZ2tcePG6corr1RYWJimT5%2Buffv26aOPPqrv3QAAAA2UXxas3/3ud2rRosVptxUWFio2Nta2FhsbK7fbrWPHjunzzz%2B3bQ8LC9Mvf/lLud3uOp0ZAAD4j4B7J3ePx6OIiAjbWkREhD7//HN9//33sizrtNtLS0tr/RjFxcUqKSmxrblczRThy6b5AAANu0lEQVQTE3P%2Bg6PehYT45fcfCAAuV/0/d396vfC68S1OPBdqEmjPkYArWJLOej3VhV5vlZ2drczMTNtaenq6MjIyLuh%2BUb/Cw5s6PQJwXqKimjv22LxufIuTz4WaBMpzJOAKVlRUlDwej23N4/EoOjpakZGRCg4OPu32iy66qNaPkZqaquTkZNuay9VMpaVHzn9w1LuysnJVVlY5PQZwzpz4WhMSEqzw8Ka8bnyML/2749RzxKmSGXAFKy4uTgUFBbY1t9utIUOGKDQ0VO3bt1dhYaF69OghSSorK9NXX32lLl261PoxYmJivE4HlpT8oIoKvug0JJWVVfydoUFy8nnL68a3%2BOLfRaA8RwLjROjPjB49Wm%2B//ba2b9%2Bu48eP68UXX9T%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%2BfFi9erKqqKqfHAgAADYTL6QF80R133KGrr75ar732mg4ePKhJkyapZcuW%2Bv3vf%2B/0aAAAoAHgCNYp3G63PvnkE82cOVMtWrRQmzZtNG7cOGVnZzs9GgAAaCA4gnWKwsJCtW7dWhEREdVrV199tb788ksdPnxYYWFhZ72P4uJilZSU2NZcrmaKiYkxPi8AAP7A5fKvYz4UrFN4PB6Fh4fb1n4qW6WlpbUqWNnZ2crMzLSt3X777brjjjvMDfpv7//pxgv6/OLiYmVnZys1NZUC%2BG9kYkce3sjEG5l4IxO7QMvDv%2BqiIZZlXdDnp6am6qWXXrL9l5qaamg6s0pKSpSZmel1xC2QkYkdeXgjE29k4o1M7AItD45gnSI6Oloej8e25vF4FBQUpOjo6FrdR0xMTEC0cwAAcHocwTpFXFycDhw4oEOHDlWvud1utWvXTs2bN3dwMgAA0FBQsE4RGxurzp07a%2BnSpTp8%2BLD27dunZ555RmlpaU6PBgAAGoiQefPmzXN6CF/z61//Whs3btQf//hHvfLKK0pJSdH48eMVFBTk9Gh1onnz5urRowdH6H6GTOzIwxuZeCMTb2RiF0h5BFkXekU3AAAAbDhFCAAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBSuAFBUVKT09XYmJiUpKStLs2bNVVlYmSdqzZ49uueUWde3aVQMGDNDTTz/t8LT140yZfPLJJxo3bpy6deum3r17609/%2BpNOnDjh8MR160x5/Fx6erqSk5MdmLD%2BnSmTkydP6sEHH1RiYqISEhKUkZEhj8fj8MR170yZ5Ofna9SoUUpISFDv3r01f/58lZeXOzxx3frkk0906623qmvXrkpKStK0adNUUlIi6cc8UlJSlJCQoCFDhignJ8fhaevHmTJ57733lJqaqoSEBCUnJ%2BvPf/6zw9PWDQpWAJk8ebLCw8O1detWvfTSS9q7d68eeughHTt2TJMmTdJ1112nHTt2aNmyZVq1apW2bNni9Mh1rqZMjhw5ogkTJig%2BPl5vv/22nnnmGb3%2B%2But66qmnnB65TtWUx89t27ZN7777rkMT1r8zZfLII4%2BooKBAOTk5eu211xQSEqIXXnjB4YnrXk2ZHDp0SFOnTtWIESP03nvv6YUXXtDOnTv12GOPOT1ynTlx4oRuu%2B029ejRQ/n5%2Bdq4caMOHjyoefPmqbi4WFOnTtWYMWOUn5%2BvOXPm6L777pPb7XZ67Dp1pkz%2B%2Bc9/atKkSRo%2BfLjeffddLV%2B%2BXE8//bTWr1/v9NjG8cueA0RZWZk%2B%2B%2BwzzZgxQ5GRkQoLC9PJkye1adMmXXrppdq6datWrlyp0NBQxcTE6Pvvv9f27dv1m9/8xunR68yZMrnhhhtUXFys2bNnq1GjRoqOjta3334rt9vtt5mcKY/f/va3kqTy8nJNmjRJN998s3bv3q1bb73V4anr1pkyGTVqlKZNm6bly5erbdu2atq0qW688UZ17drV6bHr1JkySUhIUHZ2th577DE1btxYYWFhKioq0u7duzV8%2BHCnR68Thw8fVkxMjG655RY1atRITZs21eHDh7V9%2B3Y1atRIxcXFeuCBB%2BRyuXT55Zfrs88%2B0%2Beff65%2B/fo5PXqdOVMmvXr1ksvl0tSpUxUSEqJWrVqpoKBAhw4d8ruj4hzBChDh4eFauHChWrZsWb124MABxcTEqLCwUB07dlRISEj1ttjYWBUUFDgxar05UyZXXHGFFi5cKJfLZdvWqlUrJ0atF2fK4yeZmZnq3r2735eIn5ztdVNRUaG9e/eqf//%2B6tmzp%2BbOnaujR486OHHdO1MmnTp1UkxMjP73f/9Xx48f1zfffKO8vDz17dvXuYHrWEREhEaNGlX9teKLL77Qyy%2B/rEGDBqmwsFCxsbG22wfC19YzZdKlSxfNmTPHdnt//dpKwQpQbrdba9as0ZQpU%2BTxeBQeHm7bHhkZKY/Ho6qqKocmrH8/z%2BRUr7/%2BurZt26bbbrvNgcmccWoen332mV5%2B%2BWXdfffdDk/mnJ9n8t1330mSduzYof/7v//TmjVr9N5772nZsmUOT1m/fp5J8%2BbN9fjjjysrK0tdunRR//791b59e78/0in9eF1aXFycBg8erM6dO1dfj3e6r62lpaUOTVm/TpfJqZ599ll99dVXGjNmjAMT1i0KVgDauXOnxo8frxkzZigpKanG2wUFBdXjVM46UyZbtmzRzJkz9fDDD6t9%2B/YOTVi/Ts3DsizNmzdPt99%2Buy666CKnx3PE6TI5efKkpk2bpsjISF155ZW67bbblJub6/So9ebUTH66Bmvq1Kn68MMP9be//U3//Oc/tWjRIqdHrXOtW7eW2%2B3Wpk2btH///oD%2BRuQnZ8tkzZo1evTRR/XnP//ZdkTUb1gIKK%2B//rqVkJBgvfzyy9VrjzzyiHXLLbfYbvfKK69YiYmJ9T2eI06XyU%2Bef/55q1u3btaOHTscmMwZp8vjhRdesEaNGmVVVlZalmVZ77zzjtWvXz%2BnRqx3p8vknXfesTp06GCVlpZWr73xxhtWp06drKqqKifGrFeny2TNmjXWgAEDbLf729/%2BZl177bX1PZ6jPvjgA6tDhw7WxIkTrdmzZ9u2rVq1yho5cqRDkznnp0wOHjxoWdaP/%2B5cf/31VmFhocOT1R2OYAWQDz74QLNmzdKjjz5qu%2BA0Li5On376qSoqKqrX3G634uPjnRizXtWUiSRt2rRJy5Yt0%2BrVq9WrVy%2BHJqxfNeWRk5OjvXv3qmfPnkpMTNTUqVN14MABJSYmaufOnQ5OXPdqyuTKK69UUFCQ9uzZU71WVFSkSy65xO%2BP/taUSVVVlddlBSdOnPDrPPLz8zVw4EDbfgcH//hPa5cuXbyutyooKPD7r61nyqRRo0Z65plntHHjRmVnZ3tdo%2BZXnG54qB8nT560Bg0aZD3//PNe244fP27169fPeuyxx6yjR49au3btsrp162Zt27at/getR2fKpKyszEpMTLTeeOMNByZzxpnyOHjwoHXgwIHq/1599VWrd%2B/e1oEDB6zjx487MG39OFMmlmVZ6enpVkpKilVcXGx99dVX1oABA6wVK1bU85T160yZfPHFF1ZcXJz13HPPWcePH7cOHDhgjR492rr77rsdmLR%2BlJWVWUlJSdaiRYuso0ePWgcPHrTGjx9vjR071vrXv/5lXXvttdYLL7xgHTt2zNq%2BfbvVpUsXa8%2BePU6PXafOlMlXX31lXXPNNdann37q9Jh1LsiyLMvpkoe69/777%2Bvmm29W48aNvbZt2rRJR44c0f3336%2BCggK1bNlSEydO1NixYx2YtP6cKZMHHnhAs2fPPu02f30Pm7M9R1q3bl3953fffVf33HOPtm7dWp8j1ruzZdKiRQvNmzdP27dvV0hIiFJSUnTnnXeqUaNGDkxbP86Wyf79%2B7V8%2BXLt27dPYWFh6tu3r%2B666y61aNHCgWnrx6effqoFCxbo448/VrNmzXTddddp9uzZatWqlf7%2B979rwYIF2rdvn1q3bq0ZM2ZowIABTo9c52rK5MUXX9SKFSu8XiO/%2BMUvtHnzZoemrRsULAAAAMO4BgsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADPt/HBO0/dwCXSkAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common4929271648784950793”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>25.299999999999997</td> <td class=”number”>158</td> <td class=”number”>4.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.099999999999994</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>26.0</td> <td class=”number”>130</td> <td class=”number”>4.0%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.200000000000003</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.400000000000006</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>26.799999999999997</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>24.299999999999997</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.900000000000006</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>29.700000000000003</td> <td class=”number”>93</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>28.599999999999994</td> <td class=”number”>92</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (20)</td> <td class=”number”>871</td> <td class=”number”>27.1%</td> <td>

<div class=”bar” style=”width:73%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>1196</td> <td class=”number”>37.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme4929271648784950793”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>19.5</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:20%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.200000000000003</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.299999999999997</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.900000000000006</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>21.200000000000003</td> <td class=”number”>82</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:68%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>28.700000000000003</td> <td class=”number”>56</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:60%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>29.599999999999994</td> <td class=”number”>44</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:47%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>29.700000000000003</td> <td class=”number”>93</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>30.900000000000006</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:36%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>32.2</td> <td class=”number”>77</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:82%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_BLACK15”>PCT_LACCESS_BLACK15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2926</td>

</tr> <tr>

<th>Unique (%)</th> <td>91.2%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>97</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1.8853</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>50.136</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>5.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram7156839023896317241”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives7156839023896317241,#minihistogram7156839023896317241”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives7156839023896317241”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles7156839023896317241”

aria-controls=”quantiles7156839023896317241” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram7156839023896317241” aria-controls=”histogram7156839023896317241”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common7156839023896317241” aria-controls=”common7156839023896317241”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme7156839023896317241” aria-controls=”extreme7156839023896317241”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles7156839023896317241”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0.057349</td>

</tr> <tr>

<th>Median</th> <td>0.25723</td>

</tr> <tr>

<th>Q3</th> <td>1.6926</td>

</tr> <tr>

<th>95-th percentile</th> <td>9.4192</td>

</tr> <tr>

<th>Maximum</th> <td>50.136</td>

</tr> <tr>

<th>Range</th> <td>50.136</td>

</tr> <tr>

<th>Interquartile range</th> <td>1.6352</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>4.1454</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.1989</td>

</tr> <tr>

<th>Kurtosis</th> <td>27.426</td>

</tr> <tr>

<th>Mean</th> <td>1.8853</td>

</tr> <tr>

<th>MAD</th> <td>2.3677</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>4.4807</td>

</tr> <tr>

<th>Sum</th> <td>5868.8</td>

</tr> <tr>

<th>Variance</th> <td>17.184</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram7156839023896317241”>

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x%2Bu3/3ud%2Brfv7//MQMGDNBTTz2lkSNH2j0eAADAJbO9YI0YMUJdu3bV5MmTdffddysuLq7ZYzIzM3X69Gm7RwMAADDC9oK1atUqDRw48Hsft3//fhumAQAAMM/2e7CSk5M1ZcoUbd%2B%2B3b/2X//1X3rwwQfl9XrtHgcAAMA42wvWwoULdebMGXXv3t2/NnjwYDU1NWnRokV2jwMAAGCc7W8R7t27Vxs2bFB8fLx/LSkpSQUFBbrzzjvtHgcAAMA4269g1dfXKzIysvkg4eGqq6uzexwAAADjbC9YAwYM0KJFi/Tll1/61/7xj3/o6aefDvioBgAAAKey/S3COXPm6N///d91/fXXKzo6Wk1NTaqtrVXnzp31%2B9//3u5xAAAAjLO9YHXu3Flvv/223nnnHX322WcKDw9X165dNWjQIEVERNg9DgAAgHG2FyxJat26tW655ZZgvDQAAIDlbC9YJ06cUGFhoT799FPV19c32//Tn/5k90gAAABGBeUerMrKSg0aNEht27a1%2B%2BUBAAAsZ3vBKi0t1Z/%2B9CclJCTY/dIAAAC2sP1jGtq3b8%2BVKwAAENJsL1iTJ09WUVGRfD6f3S8NAABgC9vfInznnXf04Ycfat26dbryyisVHh7Y8V577TW7RwIAADDK9oIVHR2tm266ye6XBQAAsI3tBWvhwoXGjrVnzx7NmjVL6enpWrJkiX993bp1mjNnjlq1ahXw%2BFdffVV9%2BvRRU1OTli5dqo0bN6q6ulp9%2BvTRU089pc6dO0uSvF6vnnrqKf31r39VeHi4MjMzNW/ePLVp08bY7AAAIHTZfg%2BWJB09elTLli3TE0884V/76KOPLuoYL774ohYsWKAuXbpccH/AgAFyu90BX3369JH0TdHasGGDiouLtXPnTiUlJSk3N9d/X9i8efNUV1enjRs36o033lB5ebkKCgp%2B4NkCAICfGtsL1r59%2BzRq1Cht27ZNGzdulPTNh49OnDjxoj5kNDIyUmvXrv3OgvXPlJSUKCcnR926dVN0dLTy8vJUXl6u/fv36/PPP9f27duVl5enhIQEdezYUQ8//LDeeOMNnTt37qJfCwAA/PTY/hbhkiVL9Nhjj%2Bn%2B%2B%2B/3X1Hq3LmzFi1apOXLl2vo0KEtOs7EiRP/6f7Jkyf1y1/%2BUqWlpYqJidH06dN11113qb6%2BXkeOHFFKSor/sdHR0erSpYvcbrfOnDmjiIgIJScn%2B/d79%2B6tr776SkePHg1Y/y6VlZXyeDwBay5XWyUmJrbo3FoqIiIoFyB/MJfLOfOez9ZpGTsB2VqHbK1DttYJ1WxtL1j/8z//o9WrV0uSwsLC/Ou33Xab5syZY%2BQ1EhISlJSUpF/96lfq3r27/vjHP%2Brxxx9XYmKifvazn8nn8yk2NjbgObGxsaqqqlJcXJyio6MDZjv/2Kqqqha9fklJiYqKigLWcnNzNX369Es8M2eLj48K9ggXLSbmsmCPELLI1jpkax2ytU6oZWt7wWrXrp3q6%2BvVunXrgPXKyspmaz/U4MGDNXjwYP//jxgxQn/84x%2B1bt065efnS9I//RyuS/2MrqysLA0ZMiRgzeVqq6qq2ks67rc5re2bPn8rRUSEKybmMlVX16mxsSnY44QUsrUO2VqHbK1jdbbB%2BuHe9oLVr18/Pffcc3ryySf9a8eOHdP8%2BfN1/fXXW/a6nTp1UmlpqeLi4hQeHi6v1xuw7/V61b59eyUkJKimpkaNjY2KiIjw70nffAp9SyQmJjZ7O9DjOaOGhp/2H0onnn9jY5Mj53YCsrUO2VqHbK0TatnafgnkiSee0EcffaT09HSdPXtW/fr10x133CGv16vZs2cbeY3//u//1qZNmwLWysvL1blzZ0VGRqpHjx4qKyvz71VXV%2Buzzz5Tnz591KtXL/l8Ph06dMi/73a7FRMTo65duxqZDwAAhDbbr2BdccUV2rhxo3bv3q1jx46pTZs26tq1q2644YaA%2B54uxddff61nnnlGnTt31s9//nNt3bpV77zzjl5//XVJUnZ2toqLi3XTTTepY8eOKigoUK9evZSWliZJGj58uF544QU9//zz%2Bvrrr7V8%2BXKNHTtWLpftcQEAAAcKSmNo1aqVbrnllks6xvky1NDQIEnavn27pG%2BuNk2cOFG1tbV69NFH5fF4dOWVV2r58uVKTU2VJI0fP14ej0cTJkxQbW2t0tPTA25K//Wvf6358%2Bdr6NChatWqle68807l5eVd0rwAAOCnI8xn87%2B6PGTIkH96pepiPgvLSTyeM8aP6XKF69aCPcaPa5XNM24I9ggt5nKFKz4%2BSlVVtSF1T8CPAdlah2ytQ7bWsTrbDh3aGT9mS9h%2BBeuOO%2B4IKFiNjY06duyY3G637r//frvHAQAAMM72gnX%2BYxK%2BbevWrfrLX/5i8zQAAADm/Wg%2BSOmWW27R22%2B/HewxAAAALtmPpmAdOHDgkj/gEwAA4MfA9rcIx48f32ytrq5O5eXlGjZsmN3jAAAAGGd7wUpKSmr2W4SRkZEaO3as7r33XrvHAQAAMM72grVo0SK7XxIAAMBWthesN998s8WPvfvuuy2cBAAAwBq2F6y5c%2Beqqamp2Q3tYWFhAWthYWEULAAA4Ei2F6yXXnpJL7/8sqZMmaLk5GT5fD4dPnxYL774ou677z6lp6fbPRIAAIBRQbkHq7i4WB07dvSvXXvttercubMmTZqkjRs32j0SAACAUbZ/Dtbx48cVGxvbbD0mJkYVFRV2jwMAAGCc7QWrU6dOWrRokaqqqvxr1dXVKiws1FVXXWX3OAAAAMbZ/hbhnDlzNHPmTJWUlCgqKkrh4eGqqalRmzZttHz5crvHAQAAMM72gjVo0CDt2rVLu3fv1qlTp%2BTz%2BdSxY0fdeOONateund3jAAAAGGd7wZKkyy67TEOHDtWpU6fUuXPnYIwAAABgGdvvwaqvr9esWbPUt29f3X777ZK%2BuQfrgQceUHV1td3jAAAAGGd7wVq8eLEOHjyogoIChYf/78s3NjaqoKDA7nEAAACMs71gbd26Vf/5n/%2Bp2267zf%2BPPsfExGjhwoXatm2b3eMAAAAYZ3vBqq2tVVJSUrP1hIQEffXVV3aPAwAAYJztBeuqq67SX/7yF0kK%2BLcHt2zZon/913%2B1exwAAADjbP8twl/84hd65JFHdM8996ipqUmvvPKKSktLtXXrVs2dO9fucQAAAIyzvWBlZWXJ5XJp9erVioiI0G9%2B8xt17dpVBQUFuu222%2BweBwAAwDjbC9bp06d1zz336J577rH7pQEAAGxh%2Bz1YQ4cODbj3CgAAINTYXrDS09O1efNmu18WAADANra/Rfgv//IvevbZZ1VcXKyrrrpKrVq1CtgvLCy0eyQAAACjbC9YR44c0c9%2B9jNJUlVVld0vDwAAYDnbClZeXp6WLFmi3//%2B9/615cuXKzc3164RAAAAbGHbPVg7duxotlZcXGzXywMAANjGtoJ1od8c5LcJAQBAKLKtYJ3/h52/bw0AAMDpbP%2BYBgAAgFBHwQIAADDMtt8iPHfunGbOnPm9a3wOFgAAcDrbClb//v1VWVn5vWsAAABOZ1vB%2Br%2BffwUAABDKuAcLAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMMzRBWvPnj3KyMhQXl5es71NmzZp5MiR6tu3r8aMGaO9e/f695qamrRkyRINHTpUAwYM0KRJk3TixAn/vtfr1YwZM5SRkaFBgwZp7ty5qq%2Bvt%2BWcAACA8zm2YL344otasGCBunTp0mzv4MGDmjVrlvLz8/Xee%2B8pJydH06ZN06lTpyRJr776qjZs2KDi4mLt3LlTSUlJys3Nlc/nkyTNmzdPdXV12rhxo9544w2Vl5eroKDA1vMDAADO5diCFRkZqbVr116wYK1Zs0aZmZnKzMxUZGSkRo0apZ49e2r9%2BvWSpJKSEuXk5Khbt26Kjo5WXl6eysvLtX//fn3%2B%2Befavn278vLylJCQoI4dO%2Brhhx/WG2%2B8oXPnztl9mgAAwIEcW7AmTpyodu3aXXCvrKxMKSkpAWspKSlyu92qr6/XkSNHAvajo6PVpUsXud1uHTx4UBEREUpOTvbv9%2B7dW1999ZWOHj1qzckAAICQYtu/RWgnr9er2NjYgLXY2FgdOXJEX375pXw%2B3wX3q6qqFBcXp%2BjoaIWFhQXsSVJVVVWLXr%2ByslIejydgzeVqq8TExB9yOt8pIsJZ/djlcs6857N1WsZOQLbWIVvrkK11QjXbkCxYkvz3U/2Q/e977vcpKSlRUVFRwFpubq6mT59%2BScd1uvj4qGCPcNFiYi4L9gghi2ytQ7bWIVvrhFq2IVmw4uPj5fV6A9a8Xq8SEhIUFxen8PDwC%2B63b99eCQkJqqmpUWNjoyIiIvx7ktS%2BffsWvX5WVpaGDBkSsOZytVVVVe0PPaULclrbN33%2BVoqICFdMzGWqrq5TY2NTsMcJKWRrHbK1Dtlax%2Bpsg/XDfUgWrNTUVJWWlgasud1ujRgxQpGRkerRo4fKyso0cOBASVJ1dbU%2B%2B%2Bwz9enTR506dZLP59OhQ4fUu3dv/3NjYmLUtWvXFr1%2BYmJis7cDPZ4zamj4af%2BhdOL5NzY2OXJuJyBb65CtdcjWOqGWrbMugbTQuHHj9O6772rXrl06e/as1q5dq%2BPHj2vUqFGSpOzsbK1atUrl5eWqqalRQUGBevXqpbS0NCUkJGj48OF64YUXdPr0aZ06dUrLly/X2LFj5XKFZB8FAACGObYxpKWlSZIaGhokSdu3b5f0zdWmnj17qqCgQAsXLlRFRYW6d%2B%2BulStXqkOHDpKk8ePHy%2BPxaMKECaqtrVV6enrAPVO//vWvNX/%2BfA0dOlStWrXSnXfeecEPMwUAALiQMN%2Bl3tGNFvF4zhg/pssVrlsL9hg/rlU2z7gh2CO0mMsVrvj4KFVV1YbUJesfA7K1Dtlah2ytY3W2HTpc%2BCOdrBaSbxECAAAEEwULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDQrZgJScnKzU1VWlpaf6vZ555RpK0b98%2BjR07Vv369dOIESO0fv36gOeuWrVKw4cPV79%2B/ZSdna3S0tJgnAIAAHAoV7AHsNKWLVt05ZVXBqxVVlbq4Ycf1ty5czVy5Eh98MEHmjp1qrp27aq0tDTt2LFDy5Yt00svvaTk5GStWrVKU6ZM0bZt29S2bdsgnQkAAHCSkL2C9V02bNigpKQkjR07VpGRkcrIyNCQIUO0Zs0aSVJJSYnGjBmjq6%2B%2BWm3atNEDDzwgSdq5c2cwxwYAAA4S0gWrsLBQgwcP1rXXXqt58%2BaptrZWZWVlSklJCXhcSkqK/23Ab%2B%2BHh4erV69ecrvdts4OAACcK2TfIrzmmmuUkZGh559/XidOnNCMGTP09NNPy%2Bv1qmPHjgGPjYuLU1VVlSTJ6/UqNjY2YD82Nta/3xKVlZXyeDwBay5XWyUmJv7As7mwiAhn9WOXyznzns/WaRk7Adlah2ytQ7bWCdVsQ7ZglZSU%2BP%2B7W7duys/P19SpU9W/f//vfa7P57vk1y4qKgpYy83N1fTp0y/puE4XHx8V7BEuWkzMZcEeIWSRrXXI1jpka51QyzZkC9a3XXnllWpsbFR4eLi8Xm/AXlVVlRISEiRJ8fHxzfa9Xq969OjR4tfKysrSkCFDAtZcrraqqqr9gdNfmNPavunzt1JERLhiYi5TdXWdGhubgj1OSCFb65CtdcjWOlZnG6wf7kOyYB04cEDr16/X7Nmz/Wvl5eVq3bq1MjMz9Yc//CHg8aWlpbr66qslSampqSorK9Po0aMlSY2NjTpw4IDGjh3b4tdPTExs9nagx3NGDQ0/7T%2BUTjz/xsYmR87tBGRrHbK1DtlaJ9SyddYlkBZq3769SkpKVFxcrK%2B//lrHjh3T0qVLlZWVpbvuuksVFRVas2aNzp49q927d2v37t2QH48rAAALT0lEQVQaN26cJCk7O1tvvvmmPv74Y9XV1WnFihVq3bq1Bg8eHNyTAgAAjhGSV7A6duyo4uJiFRYW%2BgvS6NGjlZeXp8jISK1cuVILFizQ008/rU6dOmnx4sX6%2Bc9/Lkm66aab9Ktf/UozZszQF198obS0NBUXF6tNmzZBPisAAOAUYb5LvaMbLeLxnDF%2BTJcrXLcW7DF%2BXKtsnnFDsEdoMZcrXPHxUaqqqg2pS9Y/BmRrHbK1Dtlax%2BpsO3RoZ/yYLRGSbxECAAAEEwULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMFewB8BPx%2B0v/DnYI1yU95%2B9LdgjAAAciitYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGCYK9gDAD9W187dEuwRLsrmGTcEewQAwP/HFSwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsG6gIqKCj300ENKT0/XzTffrMWLF6upqSnYYwEAAIfgtwgv4JFHHlHv3r21fft2ffHFF5o8ebIuv/xy/fKXvwz2aMB3uv2FPwd7hBZ7/9nbgj0CAFiKK1jf4na7dejQIeXn56tdu3ZKSkpSTk6OSkpKgj0aAABwCK5gfUtZWZk6deqk2NhY/1rv3r117Ngx1dTUKDo6%2BnuPUVlZKY/HE7DmcrVVYmKi0VkjIujHcCanfcYYrPPH/BuDPUKLnP/7lr93zQvVbClY3%2BL1ehUTExOwdr5sVVVVtahglZSUqKioKGBt2rRpeuSRR8wNqm%2BK3P1XfKqsrCzj5e2nrrKyUiUlJWRrAbK1Dtlap7KyUr/73Utka4FQzTa06qIhPp/vkp6flZWldevWBXxlZWUZmu5/eTweFRUVNbtahktHttYhW%2BuQrXXI1jqhmi1XsL4lISFBXq83YM3r9SosLEwJCQktOkZiYmJItXAAAHBxuIL1LampqTp58qROnz7tX3O73erevbuioqKCOBkAAHAKCta3pKSkKC0tTYWFhaqpqVF5ebleeeUVZWdnB3s0AADgEBFPPfXUU8Ee4sfmxhtv1MaNG/XMM8/o7bff1tixYzVp0iSFhYUFe7RmoqKiNHDgQK6uWYBsrUO21iFb65CtdUIx2zDfpd7RDQAAgAC8RQgAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAXLgSoqKvTQQw8pPT1dN998sxYvXqympqZgj%2BVYe/bsUUZGhvLy8prtbdq0SSNHjlTfvn01ZswY7d27NwgTOldFRYVyc3OVnp6ujIwMzZ49W9XV1ZKkgwcP6r777lP//v01bNgwvfzyy0Ge1lkOHTqk%2B%2B%2B/X/3791dGRoZmzJghj8cjSdq3b5/Gjh2rfv36acSIEVq/fn2Qp3Wu5557TsnJyf7/J9tLk5ycrNTUVKWlpfm/nnnmGUkhmK0PjjN69Gjfk08%2B6auurvYdO3bMN2zYMN/LL78c7LEcqbi42Dds2DDf%2BPHjfTNmzAjYO3DggC81NdW3a9cuX319ve%2Btt97yXX311b6TJ08GaVrnufPOO32zZ8/21dTU%2BE6ePOkbM2aMb86cOb66ujrfjTfe6Fu2bJmvtrbWV1pa6hs4cKBv69atwR7ZEc6ePeu7/vrrfUVFRb6zZ8/6vvjiC999993ne/jhh33/%2BMc/fNdcc41vzZo1vvr6et%2Bf//xnX58%2BfXyffPJJsMd2nAMHDvgGDhzo69mzp8/n85GtAT179vSdOHGi2XooZssVLIdxu906dOiQ8vPz1a5dOyUlJSknJ0clJSXBHs2RIiMjtXbtWnXp0qXZ3po1a5SZmanMzExFRkZq1KhR6tmzp/N/qrJJdXW1UlNTNXPmTEVFRemKK67Q6NGj9f7772vXrl06d%2B6cpk6dqrZt26p379669957%2BT5uobq6OuXl5Wny5Mlq3bq1EhISdOutt%2BrTTz/Vhg0blJSUpLFjxyoyMlIZGRkaMmSI1qxZE%2ByxHaWpqUnz589XTk6Of41srROK2VKwHKasrEydOnVSbGysf6137946duyYampqgjiZM02cOFHt2rW74F5ZWZlSUlIC1lJSUuR2u%2B0YzfFiYmK0cOFCXX755f61kydPKjExUWVlZUpOTlZERIR/LyUlRaWlpcEY1XFiY2N17733yuVySZKOHj2qP/zhD7r99tu/8/uWbC/Oa6%2B9psjISI0cOdK/RrZmFBYWavDgwbr22ms1b9481dbWhmS2FCyH8Xq9iomJCVg7X7aqqqqCMVLI8nq9AUVW%2BiZrcv5h3G63Vq9eralTp17w%2BzguLk5er5f7CS9CRUWFUlNTdccddygtLU3Tp0//zmz5vm25zz//XMuWLdP8%2BfMD1sn20l1zzTXKyMjQtm3bVFJSoo8//lhPP/10SGZLwXIgn88X7BF%2BMsjajA8%2B%2BECTJk3SzJkzlZGR8Z2PCwsLs3Eq5%2BvUqZPcbre2bNmi48eP6/HHHw/2SCFh4cKFGjNmjLp37x7sUUJOSUmJ7r33XrVu3VrdunVTfn6%2BNm7cqHPnzgV7NOMoWA6TkJAgr9cbsOb1ehUWFqaEhIQgTRWa4uPjL5g1OV%2BcHTt26KGHHtKcOXM0ceJESd98H3/7J1Ov16u4uDiFh/PX0sUICwtTUlKS8vLytHHjRrlcrmbft1VVVXzfttC%2Bffv00UcfKTc3t9nehf5OINtLc%2BWVV6qxsVHh4eEhly1/kzlMamqqTp48qdOnT/vX3G63unfvrqioqCBOFnpSU1Obvf/vdrt19dVXB2ki5/nwww81a9YsLV26VHfffbd/PTU1VYcPH1ZDQ4N/jWxbbt%2B%2BfRo%2BfHjA26nni2mfPn2afd%2BWlpaSbQutX79eX3zxhW6%2B%2BWalp6drzJgxkqT09HT17NmTbC/BgQMHtGjRooC18vJytW7dWpmZmSGXLQXLYVJSUpSWlqbCwkLV1NSovLxcr7zyirKzs4M9WsgZN26c3n33Xe3atUtnz57V2rVrdfz4cY0aNSrYozlCQ0ODnnzySeXn52vQoEEBe5mZmYqOjtaKFStUV1en/fv3a%2B3atXwft1Bqaqpqamq0ePFi1dXV6fTp01q2bJmuvfZaZWdnq6KiQmvWrNHZs2e1e/du7d69W%2BPGjQv22I4we/Zsbd26VW%2B99ZbeeustFRcXS5LeeustjRw5kmwvQfv27VVSUqLi4mJ9/fXXOnbsmJYuXaqsrCzdddddIZdtmI%2BbTBzn1KlTmjdvnv76178qOjpa48eP17Rp07h/5QdIS0uTJP%2BVlPO/lXX%2BNwW3bdumwsJCVVRUqHv37po7d64GDBgQnGEd5v3339e//du/qXXr1s32tmzZotraWs2fP1%2BlpaW6/PLL9eCDD%2BoXv/hFECZ1psOHD2vBggX65JNP1LZtW1133XWaPXu2OnbsqL/97W9asGCBysvL1alTJ82cOVPDhg0L9siO9Pe//11Dhw7V4cOHJYlsL9Hf/vY3FRYW6vDhw2rdurVGjx6tvLw8RUZGhly2FCwAAADDeIsQAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAz7f4C2e4aVcCCzAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common7156839023896317241”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>189</td> <td class=”number”>5.9%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.8070571373242753</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.8017713481333834</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.07532033866983129</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.14341323313692583</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.416874787275482</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.042444821358068444</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.219054664133509</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.04007183864993543</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.17315777276231314</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2915)</td> <td class=”number”>2915</td> <td class=”number”>90.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme7156839023896317241”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>189</td> <td class=”number”>5.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0455696392644135e-10</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.4126198468572791e-06</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.738483421165433e-06</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.216050325461762e-05</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>34.37088606749391</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>37.494038484447195</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>37.56869361046067</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>40.791256943332556</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.136323722588436</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_CHILD10”><s>PCT_LACCESS_CHILD10</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCT_LACCESS_POP10”><code>PCT_LACCESS_POP10</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.96149</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_CHILD15”><s>PCT_LACCESS_CHILD15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCT_LACCESS_POP15”><code>PCT_LACCESS_POP15</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.95773</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_HHNV10”>PCT_LACCESS_HHNV10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3115</td>

</tr> <tr>

<th>Unique (%)</th> <td>97.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>3.1235</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>60.871</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.6%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram3834999604234194951”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives3834999604234194951,#minihistogram3834999604234194951”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives3834999604234194951”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles3834999604234194951”

aria-controls=”quantiles3834999604234194951” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram3834999604234194951” aria-controls=”histogram3834999604234194951”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common3834999604234194951” aria-controls=”common3834999604234194951”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme3834999604234194951” aria-controls=”extreme3834999604234194951”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles3834999604234194951”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.67693</td>

</tr> <tr>

<th>Q1</th> <td>1.6091</td>

</tr> <tr>

<th>Median</th> <td>2.5693</td>

</tr> <tr>

<th>Q3</th> <td>3.8001</td>

</tr> <tr>

<th>95-th percentile</th> <td>7.253</td>

</tr> <tr>

<th>Maximum</th> <td>60.871</td>

</tr> <tr>

<th>Range</th> <td>60.871</td>

</tr> <tr>

<th>Interquartile range</th> <td>2.191</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>2.9534</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.94555</td>

</tr> <tr>

<th>Kurtosis</th> <td>120.75</td>

</tr> <tr>

<th>Mean</th> <td>3.1235</td>

</tr> <tr>

<th>MAD</th> <td>1.6502</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>8.0895</td>

</tr> <tr>

<th>Sum</th> <td>9782.8</td>

</tr> <tr>

<th>Variance</th> <td>8.7227</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram3834999604234194951”>

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kqFOnTgoICKiZi4mJUXZ2tiQpJydHMTExbseLiYmR0%2BlUWVmZDh065DYfHBysNm3ayOl0XuWzAgAAdYXD2xswwel0avXq1Vq%2BfLk2b96skJAQt/mwsDC5XC5VV1fL5XIpNDTUbT40NFSHDh3S6dOnZVnWRecLCwsvez/5%2BfkqKChwG3M4GisyMvJnnplnAQG%2B1Y8dDnv3ez4fX8vJDmTjGfl4Rj61IxvP6lM%2BPl%2BwvvjiC02cOFFTpkxRQkKCNm/efNHH%2Bfn51fz3pe6nutL7rbKysrR06VK3sdTUVKWlpV3RcX1deHiQV9YNCbnOK%2Bv6ArLxjHw8I5/akY1n9SEfny5Y27dv1zPPPKPZs2fr/vvvlyRFRETo6NGjbo9zuVwKCwuTv7%2B/wsPD5XK5LpiPiIioeczF5ps2bXrZ%2B0pKSlL//v3dxhyOxiosLPkZZ3dpvvYdgOnzv5SAAH%2BFhFynoqJSVVVV27r2tY5sPCMfz8indmTjmTfy8dY39z5bsL788ktNmzZNr776qvr06VMzHhsbq7/85S%2BqrKyUw3Hu9JxOp7p161Yzf/5%2BrPOcTqeGDBmiwMBAdejQQTk5Oerdu7ekczfUf/vtt%2Bratetl7y0yMvKCtwMLCs6osrJ%2B/2Xz1vlXVVXX%2B%2BxrQzaekY9n5FM7svGsPuTjW5dA/ldlZaVmzZqlqVOnupUrSUpMTFRwcLCWL1%2Bu0tJS7d27V2vXrlVycrIkafTo0fr000%2B1c%2BdOlZeXa%2B3atTp69KiGDRsmSUpOTtaqVauUm5ur4uJiZWRkqHPnzoqLi7P9PAEAgG/yyStYe/bsUW5urubNm6d58%2Ba5zW3ZskWvvfaa5syZo8zMTDVr1kzp6enq27evJKljx47KyMjQ/PnzlZeXp/bt22vFihVq3ry5JGnMmDEqKCjQww8/rJKSEsXHx19wPxUAAIAnfhafoGmLgoIzxo/pcPjrroyPjR/3atk8%2BTZb13M4/BUeHqTCwpI6fyn65yIbz8jHM/KpHdl45o18mjdvYss6P%2BWTbxECAABcyyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADDM9oLVv39/LV26VMePH7d7aQAAAFvYXrAeeOABbdq0SXfeeaceffRRbd26VZWVlXZvAwAA4KqxvWClpqZq06ZNevvtt9WhQwe9/PLLSkxM1MKFC3XkyBG7twMAAGCc1%2B7B6tKli6ZNm6YdO3ZoxowZevvttzV48GCNGzdOX3/9tbe2BQAAcMW8VrAqKiq0adMmPfbYY5o2bZpatGih5557Tp07d1ZKSoo2bNjgra0BAABcEYfdC%2Bbm5mrt2rV67733VFJSokGDBulPf/qTevbsWfOYXr166YUXXtDQoUPt3h4AAMAVs71gDRkyRG3bttX48eN1//33Kyws7ILHJCYm6tSpU3ZvDQAAwAjbC9aqVavUu3fvSz5u7969NuwGAADAPNvvwerUqZMmTJigbdu21Yz913/9lx577DG5XC67twMAAGCc7QVr/vz5OnPmjNq3b18z1rdvX1VXV2vBggV2bwcAAMA4298i/OSTT7RhwwaFh4fXjEVHRysjI0P33nuv3dsBAAAwzvYrWGVlZQoMDLxwI/7%2BKi0ttXs7AAAAxtlesHr16qUFCxbo9OnTNWPff/%2B9XnzxRbePagAAAPBVtr9FOGPGDP3mN7/RrbfequDgYFVXV6ukpEStW7fWn//8Z7u3AwAAYJztBat169b64IMP9NFHH%2Bnbb7%2BVv7%2B/2rZtqz59%2BiggIMDu7QAAABhne8GSpIYNG%2BrOO%2B/0xtIAAABXne0F69ixY1q0aJG%2B%2BeYblZWVXTD/t7/9ze4tAQAAGOWVe7Dy8/PVp08fNW7c2O7lAQAArjrbC1Z2drb%2B9re/KSIiwu6lAQAAbGH7xzQ0bdqUK1cAAKBOs71gjR8/XkuXLpVlWXYvDQAAYAvb3yL86KOP9OWXX2rdunVq1aqV/P3dO95bb71l95YAAACMsr1gBQcH64477rB7WQAAANvYXrDmz59v95IAAAC2sv0eLEk6fPiwlixZoueee65m7KuvvvLGVgAAAIyzvWDt3r1bw4YN09atW7Vx40ZJ5z58dOzYsXzIKAAAqBNsL1iLFy/WM888ow0bNsjPz0/Sud9PuGDBAi1btszu7QAAABhne8H65z//qeTkZEmqKViSdPfddys3N9fu7QAAABhne8Fq0qTJRX8HYX5%2Bvho2bGj3dgAAAIyzvWD16NFDL7/8soqLi2vGjhw5omnTpunWW2/9Wcf6%2BOOPlZCQoPT0dLfxdevW6cYbb1RcXJzb19dffy1Jqq6u1uLFizVgwAD16tVL48aN07Fjx2qe73K5NHnyZCUkJKhPnz6aOXPmRUshAADAxdhesJ577jl99dVXio%2BPV3l5uXr06KHBgwfL5XJp%2BvTpl32c119/XfPmzVObNm0uOt%2BrVy85nU63r65du0qS3nzzTW3YsEGZmZnasWOHoqOjlZqaWvPp8rNnz1Zpaak2btyod955R7m5ucrIyLjykwcAAPWC7Z%2BD1bJlS23cuFG7du3SkSNH1KhRI7Vt21a33Xab2z1ZlxIYGKi1a9fqpZdeUnl5%2Bc/aQ1ZWllJSUtSuXTtJUnp6uuLj47V37161atVK27Zt07vvvlvzC6knTZqkp556StOmTVODBg1%2B1loAAKD%2Bsb1gSVKDBg105513XtExxo4d63H%2B%2BPHj%2BvWvf63s7GyFhIQoLS1N9913n8rKynTo0CHFxMTUPDY4OFht2rSR0%2BnUmTNnFBAQoE6dOtXMd%2BnSRWfPntXhw4fdxgEAAC7G9oLVv39/j1eqTHwWVkREhKKjo/X000%2Brffv2%2Butf/6pnn31WkZGRuuGGG2RZlkJDQ92eExoaqsLCQoWFhSk4ONhtj%2BcfW1hYeFnr5%2Bfnq6CgwG3M4WisyMjIKzwzdwEBXvmc2F/M4bB3v%2Bfz8bWc7EA2npGPZ%2BRTO7LxrD7lY3vBGjx4sFt5qaqq0pEjR%2BR0OvXII48YWaNv377q27dvzZ%2BHDBmiv/71r1q3bp2mTp0qSTX3W12Mp7nLkZWVpaVLl7qNpaamKi0t7YqO6%2BvCw4O8sm5IyHVeWdcXkI1n5OMZ%2BdSObDyrD/nYXrDOF5yf%2BvDDD/WPf/zjqq0bFRWl7OxshYWFyd/fXy6Xy23e5XKpadOmioiIUHFxsaqqqhQQEFAzJ0lNmza9rLWSkpLUv39/tzGHo7EKC0sMnMm/%2BNp3AKbP/1ICAvwVEnKdiopKVVVVbeva1zqy8Yx8PCOf2pGNZ97Ix1vf3HvlHqyLufPOO/X888/r%2Beefv%2BJj/eUvf1FoaKgGDx5cM5abm6vWrVsrMDBQHTp0UE5Ojnr37i1JKioq0rfffquuXbsqKipKlmXpwIED6tKliyTJ6XQqJCREbdu2vaz1IyMjL3g7sKDgjCor6/dfNm%2Bdf1VVdb3PvjZk4xn5eEY%2BtSMbz%2BpDPtfMJZB9%2B/Zd8Vtz5/3444%2BaO3eunE6nKioqtHHjRn300UcaM2aMJCk5OVmrVq1Sbm6uiouLlZGRoc6dOysuLk4REREaNGiQfve73%2BnUqVM6ceKEli1bppEjR8rhuGb6KAAAuIbZ3hjOl5z/q7S0VLm5uRo4cOBlHycuLk6SVFlZKUnatm2bpHNXm8aOHauSkhI99dRTKigoUKtWrbRs2TLFxsbW7KGgoEAPP/ywSkpKFB8f73bP1G9/%2B1vNmTNHAwYMUIMGDXTvvfde8GGmAAAAtfGzTF02ukzTp0%2B/4KcIAwMD1a5dO40aNUqNGjWyczu2KSg4Y/yYDoe/7sr42Phxr5bNk2%2BzdT2Hw1/h4UEqLCyp85eify6y8Yx8PCOf2pGNZ97Ip3nzJras81O2X8FasGCB3UsCAADYyvaC9d577132Y%2B%2B///6ruBMAAICrw/aCNXPmTFVXV19wQ7ufn5/bmJ%2BfHwULAAD4JNsL1h/%2B8AetXLlSEyZMUKdOnWRZlg4ePKjXX39dDz30kOLj4%2B3eEgAAgFFeuQcrMzNTLVq0qBm7%2Beab1bp1a40bN04bN260e0sAAABG2f45WEePHr3g9wBKUkhIiPLy8uzeDgAAgHG2F6yoqCgtWLDA7RcnFxUVadGiRbr%2B%2Buvt3g4AAIBxtr9FOGPGDE2ZMkVZWVkKCgqSv7%2B/iouL1ahRIy1btszu7QAAABhne8Hq06ePdu7cqV27dunEiROyLEstWrTQ7bffriZNvPNhYAAAACZ55ZfrXXfddRowYIBOnDih1q1be2MLAAAAV43t92CVlZVp2rRp6t69u%2B655x5J5%2B7BevTRR1VUVGT3dgAAAIyzvWAtXLhQ%2B/fvV0ZGhvz9/7V8VVWVMjIy7N4OAACAcbYXrA8//FC///3vdffdd9f80ueQkBDNnz9fW7dutXs7AAAAxtlesEpKShQdHX3BeEREhM6ePWv3dgAAAIyzvWBdf/31%2Bsc//iFJbr97cMuWLfq3f/s3u7cDAABgnO0/Rfjggw/qySef1AMPPKDq6mq98cYbys7O1ocffqiZM2favR0AAADjbC9YSUlJcjgcWr16tQICAvTaa6%2Bpbdu2ysjI0N133233dgAAAIyzvWCdOnVKDzzwgB544AG7lwYAALCF7fdgDRgwwO3eKwAAgLrG9oIVHx%2BvzZs3270sAACAbWx/i/BXv/qVXnrpJWVmZur6669XgwYN3OYXLVpk95YAAACMsr1gHTp0SDfccIMkqbCw0O7lAQAArjrbClZ6eroWL16sP//5zzVjy5YtU2pqql1bAAAAsIVt92Bt3779grHMzEy7lgcAALCNbQXrYj85yE8TAgCAusi2gnX%2BFztfagwAAMDX2f4xDQAAAHUdBQsAAMAw236KsKKiQlOmTLnkGJ%2BDBQAAfJ1tBatnz57Kz8%2B/5BgAAICvs61g/d/PvwIAAKjLuAcLAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMN8umB9/PHHSkhIUHp6%2BgVzmzZt0tChQ9W9e3eNGDFCn3zySc1cdXW1Fi9erAEDBqhXr14aN26cjh07VjPvcrk0efJkJSQkqE%2BfPpo5c6bKyspsOScAAOD7fLZgvf7665o3b57atGlzwdz%2B/fs1bdo0TZ06VZ999plSUlL0xBNP6MSJE5KkN998Uxs2bFBmZqZ27Nih6OhopaamyrIsSdLs2bNVWlqqjRs36p133lFubq4yMjJsPT8AAOC7fLZgBQYGau3atRctWGvWrFFiYqISExMVGBioYcOGqWPHjlq/fr0kKSsrSykpKWrXrp2Cg4OVnp6u3Nxc7d27Vz/88IO2bdum9PR0RUREqEWLFpo0aZLeeecdVVRU2H2aAADAB/lswRo7dqyaNGly0bmcnBzFxMS4jcXExMjpdKqsrEyHDh1ymw8ODlabNm3kdDq1f/9%2BBQQEqFOnTjXzXbp00dmzZ3X48OGrczIAAKBOcXh7A1eDy%2BVSaGio21hoaKgOHTqk06dPy7Ksi84XFhYqLCxMwcHB8vPzc5uTpMLCwstaPz8/XwUFBW5jDkdjRUZG/pLTqVVAgG/1Y4fD3v2ez8fXcrID2XhGPp6RT%2B3IxrP6lE%2BdLFiSau6n%2BiXzl3rupWRlZWnp0qVuY6mpqUpLS7ui4/q68PAgr6wbEnKdV9b1BWTjGfl4Rj61IxvP6kM%2BdbJghYeHy%2BVyuY25XC5FREQoLCxM/v7%2BF51v2rSpIiIiVFxcrKqqKgUEBNTMSVLTpk0va/2kpCT179/fbczhaKzCwpJfekoX5WvfAZg%2B/0sJCPBXSMh1KioqVVVVta1rX%2BvIxjPy8Yx8akc2nnkjH299c18nC1ZsbKyys7PdxpxOp4YMGaLAwEB16NBBOTk56t27tySpqKhI3377rbp27aqoqChZlqUDBw6oS5cuNc8NCQlR27ZtL2v9yMjIC94OLCg4o8rK%2Bv2XzVvnX1VVXe%2Bzrw3ZeEY%2BnpFP7cjGs/qQj29dArlMo0eP1qeffqqdO3eqvLxca9eu1dGjRzVs2DBJUnJyslatWqXc3FwVFxcrIyNDnTt3VlxcnCIiIjRo0CD97ne/06lTp3TixAktW7ZMI0eOlMNRJ/soAAAwzGcbQ1xcnCSpsrJSkrRt2zZJ5642dezYURkZGZo/f77y8vLUvn17rVixQs2bN5eWIzVhAAAQj0lEQVQkjRkzRgUFBXr44YdVUlKi%2BPh4t3umfvvb32rOnDkaMGCAGjRooHvvvfeiH2YKAABwMX7Wld7RjctSUHDG%2BDEdDn/dlfGx8eNeLZsn32breg6Hv8LDg1RYWFLnL0X/XGTjGfl4Rj61IxvPvJFP8%2BYX/0inq61OvkUIAADgTRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADD6mzB6tSpk2JjYxUXF1fzNXfuXEnS7t27NXLkSPXo0UNDhgzR%2BvXr3Z67atUqDRo0SD169FBycrKys7O9cQoAAMBHOby9gatpy5YtatWqldtYfn6%2BJk2apJkzZ2ro0KH64osvNHHiRLVt21ZxcXHavn27lixZoj/84Q/q1KmTVq1apQkTJmjr1q1q3Lixl84EAAD4kjp7Bas2GzZsUHR0tEaOHKnAwEAlJCSof//%2BWrNmjSQpKytLI0aMULdu3dSoUSM9%2BuijkqQdO3Z4c9sAAMCH1OmCtWjRIvXt21c333yzZs%2BerZKSEuXk5CgmJsbtcTExMTVvA/503t/fX507d5bT6bR17wAAwHfV2bcIb7rpJiUkJOiVV17RsWPHNHnyZL344otyuVxq0aKF22PDwsJUWFgoSXK5XAoNDXWbDw0NrZm/HPn5%2BSooKHAbczgaKzIy8heezcUFBPhWP3Y47N3v%2BXx8LSc7kI1n5OMZ%2BdSObDyrT/nU2YKVlZVV89/t2rXT1KlTNXHiRPXs2fOSz7Us64rXXrp0qdtYamqq0tLSrui4vi48PMgr64aEXOeVdX0B2XhGPp6RT%2B3IxrP6kE%2BdLVg/1apVK1VVVcnf318ul8ttrrCwUBEREZKk8PDwC%2BZdLpc6dOhw2WslJSWpf//%2BbmMOR2MVFpb8wt1fnK99B2D6/C8lIMBfISHXqaioVFVV1baufa0jG8/IxzPyqR3ZeOaNfLz1zX2dLFj79u3T%2BvXrNX369Jqx3NxcNWzYUImJiXr33XfdHp%2Bdna1u3bpJkmJjY5WTk6Phw4dLkqqqqrRv3z6NHDnystePjIy84O3AgoIzqqys33/ZvHX%2BVVXV9T772pCNZ%2BTjGfnUjmw8qw/5%2BNYlkMvUtGlTZWVlKTMzUz/%2B%2BKOOHDmiV199VUlJSbrvvvuUl5enNWvWqLy8XLt27dKuXbs0evRoSVJycrLee%2B897dmzR6WlpVq%2BfLkaNmyovn37evekAACAz6iTV7BatGihzMxMLVq0qKYgDR8%2BXOnp6QoMDNSKFSs0b948vfjii4qKitLChQt14403SpLuuOMOPf3005o8ebJOnjypuLg4ZWZmqlGjRl4%2BKwAA4Cv8rCu9oxuXpaDgjPFjOhz%2BuivjY%2BPHvVo2T77N1vUcDn%2BFhwepsLCkzl%2BK/rnIxjPy8Yx8akc2nnkjn%2BbNm9iyzk/VybcIAQAAvImCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhjm8vQHUH/f87u/e3sLPsnnybd7eAgDAR3EFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwLiIvL0%2BPP/644uPj1a9fPy1cuFDV1dXe3hYAAPARDm9v4Fr05JNPqkuXLtq2bZtOnjyp8ePHq1mzZvr1r3/t7a0BAAAfQMH6CafTqQMHDuiNN95QkyZN1KRJE6WkpOhPf/oTBaueued3f/f2Fn6WzZNv8/YWAAD/i4L1Ezk5OYqKilJoaGjNWJcuXXTkyBEVFxcrODj4ksfIz89XQUGB25jD0ViRkZFG9xoQwDu8%2BBeH4/JfD%2BdfO7yGLo58PCOf2pGNZ/UpHwrWT7hcLoWEhLiNnS9bhYWFl1WwsrKytHTpUrexJ554Qk8%2B%2BaS5jepckXuk5TdKSkoyXt7qgvz8fGVlZZHPReTn5%2BtPf/oD2dSCfDwjn9qRjWf1KZ%2B6XyF/Acuyruj5SUlJWrdundtXUlKSod39S0FBgZYuXXrB1TKcQz61IxvPyMcz8qkd2XhWn/LhCtZPREREyOVyuY25XC75%2BfkpIiLiso4RGRlZ55s5AACoHVewfiI2NlbHjx/XqVOnasacTqfat2%2BvoKAgL%2B4MAAD4CgrWT8TExCguLk6LFi1ScXGxcnNz9cYbbyg5OdnbWwMAAD4i4IUXXnjB25u41tx%2B%2B%2B3auHGj5s6dqw8%2B%2BEAjR47UuHHj5Ofn5%2B2tXSAoKEi9e/fm6lotyKd2ZOMZ%2BXhGPrUjG8/qSz5%2B1pXe0Q0AAAA3vEUIAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFywfl5eXp8ccfV3x8vPr166eFCxequrra29vyqo8//lgJCQlKT0%2B/YG7Tpk0aOnSounfvrhEjRuiTTz7xwg69Jy8vT6mpqYqPj1dCQoKmT5%2BuoqIiSdL%2B/fv10EMPqWfPnho4cKBWrlzp5d3a78CBA3rkkUfUs2dPJSQkaPLkySooKJAk7d69WyNHjlSPHj00ZMgQrV%2B/3su79Z6XX35ZnTp1qvkz2UidOnVSbGys4uLiar7mzp0riXzOW758ufr06aObbrpJKSkp%2Bu677yTVk3ws%2BJzhw4dbs2bNsoqKiqwjR45YAwcOtFauXOntbXlNZmamNXDgQGvMmDHW5MmT3eb27dtnxcbGWjt37rTKysqs999/3%2BrWrZt1/PhxL%2B3Wfvfee681ffp0q7i42Dp%2B/Lg1YsQIa8aMGVZpaal1%2B%2B23W0uWLLFKSkqs7Oxsq3fv3taHH37o7S3bpry83Lr11lutpUuXWuXl5dbJkyethx56yJo0aZL1/fffWzfddJO1Zs0aq6yszPr73/9ude3a1fr666%2B9vW3b7du3z%2Brdu7fVsWNHy7IssvlfHTt2tI4dO3bBOPmcs3r1auvuu%2B%2B2cnNzrTNnzlhz58615s6dW2/y4QqWj3E6nTpw4ICmTp2qJk2aKDo6WikpKcrKyvL21rwmMDBQa9euVZs2bS6YW7NmjRITE5WYmKjAwEANGzZMHTt2rJvfLV1EUVGRYmNjNWXKFAUFBally5YaPny4Pv/8c%2B3cuVMVFRWaOHGiGjdurC5dumjUqFH16rVUWlqq9PR0jR8/Xg0bNlRERITuuusuffPNN9qwYYOio6M1cuRIBQYGKiEhQf3799eaNWu8vW1bVVdXa86cOUpJSakZIxvPyOeclStXKj09XTfccIOCg4M1a9YszZo1q97kQ8HyMTk5OYqKilJoaGjNWJcuXXTkyBEVFxd7cWfeM3bsWDVp0uSiczk5OYqJiXEbi4mJkdPptGNrXhcSEqL58%2BerWbNmNWPHjx9XZGSkcnJy1KlTJwUEBNTMxcTEKDs72xtb9YrQ0FCNGjVKDodDknT48GG9%2B%2B67uueee2p97dSnfCTprbfeUmBgoIYOHVozRjb/smjRIvXt21c333yzZs%2BerZKSEvKR9P333%2Bu7777T6dOnNXjwYMXHxystLU2nTp2qN/lQsHyMy%2BVSSEiI29j5slVYWOiNLV3TXC6XWxmVzuVVX7NyOp1avXq1Jk6ceNHXUlhYmFwuV727py8vL0%2BxsbEaPHiw4uLilJaWVms%2B9em188MPP2jJkiWaM2eO2zjZnHPTTTcpISFBW7duVVZWlvbs2aMXX3yRfCSdOHFCkrRlyxa98cYbev/993XixAnNmjWr3uRDwfJBlmV5ews%2BhbzO%2BeKLLzRu3DhNmTJFCQkJtT7Oz8/Pxl1dG6KiouR0OrVlyxYdPXpUzz77rLe3dE2YP3%2B%2BRowYofbt23t7K9ekrKwsjRo1Sg0bNlS7du00depUbdy4URUVFd7emted/3f30UcfVYsWLdSyZUs9%2BeST2r59u5d3Zh8Klo%2BJiIiQy%2BVyG3O5XPLz81NERISXdnXtCg8Pv2he9S2r7du36/HHH9eMGTM0duxYSedeSz/9jtHlciksLEz%2B/vXvnwY/Pz9FR0crPT1dGzdulMPhuOC1U1hYWG9eO7t379ZXX32l1NTUC%2BYu9veqPmVTm1atWqmqqkr%2B/v71Pp/ztyX83ytVUVFRsixLFRUV9SKf%2BvevqI%2BLjY3V8ePHderUqZoxp9Op9u3bKygoyIs7uzbFxsZe8L6%2B0%2BlUt27dvLQj%2B3355ZeaNm2aXn31Vd1///0147GxsTp48KAqKytrxupbNrt379agQYPc3hI9Xy67du16wWsnOzu73uSzfv16nTx5Uv369VN8fLxGjBghSYqPj1fHjh3rdTaStG/fPi1YsMBtLDc3Vw0bNlRiYmK9z6dly5YKDg7W/v37a8by8vLUoEGD%2BpOPd3%2BIEb/EqFGjrBkzZlhnzpyxDh06ZPXv399avXq1t7flddOmTbvgYxoOHjxoxcXFWTt27LDKysqsNWvWWN27d7fy8/O9tEt7VVRUWPfcc4/11ltvXTBXXl5u9evXz/r9739vnT171tqzZ4918803Wzt27LB/o15SVFRkJSQkWAsWLLDOnj1rnTx50ho3bpz14IMPWj/88IPVvXt36%2B2337bKysqsnTt3Wl27drX279/v7W3bwuVyWcePH6/5%2Buqrr6yOHTtax48ft/Ly8up1NpZlWSdOnLBuuukma8WKFVZ5ebl1%2BPBha/DgwdbcuXPr/WvnvJdfftkaMGCAdfToUeuHH36wkpKSrOnTp9ebfPwsixtUfM2JEyc0e/Zs/fd//7eCg4M1ZswYPfHEE/Xy3hlJiouLk6SaKzHnfyLs/E8Kbt26VYsWLVJeXp7at2%2BvmTNnqlevXt7ZrM0%2B//xz/fu//7saNmx4wdyWLVtUUlKiOXPmKDs7W82aNdNjjz2mBx980As79Z6DBw9q3rx5%2Bvrrr9W4cWPdcsstmj59ulq0aKH/%2BZ//0bx585Sbm6uoqChNmTJFAwcO9PaWveK7777TgAEDdPDgQUkiG53LYNGiRTp48KAaNmyo4cOHKz09XYGBgeQj6ccff9T8%2BfP1wQcfqKKiQoMGDdLs2bMVFBRUL/KhYAEAABjGPVgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYNj/B7svt4jF29elAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common3834999604234194951”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>19</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.087808198318207</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.803064768439417</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.7370731642297117</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.776464742317004</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.4459863892231977</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.6875418151397845</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.137768995705271</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.9145233685396565</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.8917347824530615</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3104)</td> <td class=”number”>3104</td> <td class=”number”>96.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme3834999604234194951”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>19</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.003213955098514565</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.012559523477809332</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.014004634039482773</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.032745074701054845</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>43.67099663318213</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>43.80599000365968</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.17823056001488</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>53.54214106795225</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>60.871017417782205</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_HHNV15”>PCT_LACCESS_HHNV15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3123</td>

</tr> <tr>

<th>Unique (%)</th> <td>97.3%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.5%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>80</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>3.2582</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>53.515</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.3%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram5768823793127964320”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives5768823793127964320”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles5768823793127964320”

aria-controls=”quantiles5768823793127964320” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram5768823793127964320” aria-controls=”histogram5768823793127964320”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common5768823793127964320” aria-controls=”common5768823793127964320”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme5768823793127964320” aria-controls=”extreme5768823793127964320”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles5768823793127964320”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.76964</td>

</tr> <tr>

<th>Q1</th> <td>1.683</td>

</tr> <tr>

<th>Median</th> <td>2.6716</td>

</tr> <tr>

<th>Q3</th> <td>4.0342</td>

</tr> <tr>

<th>95-th percentile</th> <td>7.4171</td>

</tr> <tr>

<th>Maximum</th> <td>53.515</td>

</tr> <tr>

<th>Range</th> <td>53.515</td>

</tr> <tr>

<th>Interquartile range</th> <td>2.3512</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>3.0043</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.92209</td>

</tr> <tr>

<th>Kurtosis</th> <td>93.439</td>

</tr> <tr>

<th>Mean</th> <td>3.2582</td>

</tr> <tr>

<th>MAD</th> <td>1.6933</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>7.2297</td>

</tr> <tr>

<th>Sum</th> <td>10198</td>

</tr> <tr>

<th>Variance</th> <td>9.026</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram5768823793127964320”>

<img 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169e/dWRkaGtm7d6q3RAAAAPGKz%2BoAlJSXauHGj3nnnHVVVVWn48OH6zW9%2BowEDBjQ%2BZuDAgXrhhRc0atQoq8cDAADwmOUFa%2BTIkerWrZumTZumMWPGKDw8vMljUlJSdOnSJatHAwAAMMLygrV27VoNGjToex939OhRC6YBAAAwz/J7sGJjYzV9%2BnTt2bOnce2///u/9cQTT8jpdFo9DgAAgHGWF6ycnBxdvnxZPXr0aFwbMmSIGhoatHTpUqvHAQAAMM7ytwgPHTqkrVu3KiIionEtJiZGubm5evjhh60eBwAAwDjLr2DV1NQoKCio6SD%2B/qqurrZ6HAAAAOMsL1gDBw7U0qVL9eWXXzau/f3vf9eLL77o9lENAAAAvsrytwjnz5%2Bv//iP/9Ddd9%2BtkJAQNTQ0qKqqSl26dNFvf/tbq8cBAAAwzvKC1aVLF7377rt67733dO7cOfn7%2B6tbt24aPHiwAgICrB4HAADAOMsLliQFBgbq/vvv98ahAQAAbjrLC9b58%2BeVl5enzz77TDU1NU32//jHP1o9EgAAgFFeuQerrKxMgwcPVtu2ba0%2BPAAAwE1necEqKirSH//4R0VGRlp9aAAAAEtY/jEN7du358oVAABo1SwvWNOmTVN%2Bfr5cLpfVhwYAALCE5W8Rvvfee/r444%2B1adMmde7cWf7%2B7h3vrbfesnokAAAAoywvWCEhIfrxj39s5LkOHjyouXPnKikpScuXL29c37Rpk%2BbPn69bbrnF7fHr169XYmKiGhoatGLFCm3btk0VFRVKTEzUCy%2B8oC5dukiSnE6nXnjhBf3lL3%2BRv7%2B/UlJStGjRIrVp08bI3AAAoHWzvGDl5OQYeZ7XXntNGzduVNeuXa%2B5P3DgwOt%2BMvz69eu1detWvfbaa%2BrUqZOWL1%2BuzMxMbd68WX5%2Bflq0aJG%2B/vprbdu2TbW1tXrmmWeUm5urhQsXGpkdAAC0bpbfgyVJp0%2Bf1sqVK/X88883rn3yySc/6DmCgoK%2Bs2B9l8LCQmVkZKh79%2B4KCQlRVlaWSkpKdPToUX3%2B%2Befas2ePsrKyFBkZqU6dOumpp57S22%2B/rdra2h98LAAA8M/H8oJ1%2BPBhjR49Wrt379a2bdskffPho1OmTPlBHzI6ZcoUtWvX7rr7Fy5c0M9%2B9jMNHDhQqamp2rx5sySppqZGp06dUlxcXONjQ0JC1LVrV9ntdh0/flwBAQGKjY1t3O/Tp4%2B%2B%2BuornT59%2BoeeLgAA%2BCdk%2BVuEy5cv17PPPqvHHntMiYmJkr75/YRLly7VqlWrlJqa6vExIiMjFRMTo5///Ofq0aOH/vCHP%2Bi5555TVFSUfvSjH8nlciksLMzta8LCwlReXq7w8HCFhITIz8/PbU%2BSysvLm3X8srIyORwOtzWbra2ioqI8PDN3AQFeuQB5w2y2ljfvtxn6WpYtDTl6jgzNIEczyNFzlhes//3f/9W6deskya3EPPjgg5o/f76RYwwZMkRDhgxp/PvIkSP1hz/8QZs2bVJ2drYkfefHRHj6ERKFhYXKz893W8vMzNSsWbM8el5fFxER7O0Rris09FZvj9AqkKPnyNAMcjSDHG%2Bc5QWrXbt2qqmpUWBgoNt6WVlZkzWToqOjVVRUpPDwcPn7%2B8vpdLrtO51OtW/fXpGRkaqsrFR9fb0CAgIa96RvPiS1OdLS0jR06FC3NZutrcrLqwycyT/42ncWps/fhIAAf4WG3qqKimrV1zd4exyfRY6eI0MzyNGM1pSjt765t7xg9e/fXy%2B//LLbT%2BSdOXNGixcv1t13323kGP/zP/%2BjsLAwjRgxonGtpKREXbp0UVBQkHr27Kni4mINGjRIklRRUaFz584pMTFR0dHRcrlcOnHihPr06SNJstvtCg0NVbdu3Zp1/KioqCZvBzocl1VX59svUk%2B15POvr29o0fP5CnL0HBmaQY5mkOONs/wSyPPPP69PPvlESUlJunLlivr3768RI0bI6XRq3rx5Ro7x9ddf66WXXpLdbldtba22bdum9957T5MmTZIkpaena%2B3atSopKVFlZaVyc3PVu3dvJSQkKDIyUsOHD9err76qS5cu6eLFi1q1apXGjx8vm83yPgoAAHyQ5Y3htttu07Zt23TgwAGdOXNGbdq0Ubdu3XTPPfe43ZP1fRISEiRJdXV1kqQ9e/ZI%2BuZq05QpU1RVVaVnnnlGDodDnTt31qpVqxQfHy9JmjRpkhwOhyZPnqyqqiolJSW53TP1i1/8QosXL1ZqaqpuueUWPfzww8rKyjIVAQAAaOX8XPxSQEs4HJeNP6fN5q8Hcg8af96bZcfse7w9QhM2m78iIoJVXl7FZXAPkKPnyNAMcjSjNeXYseP1P9LpZrL8CtbQoUO/80rVD/ksLAAAgJbI8oI1YsQIt4JVX1%2BvM2fOyG6367HHHrN6HAAAAOMsL1jffg7V1Xbt2qU///nPFk8DAABgXov5IKX7779f7777rrfHAAAA8FiLKVjHjh3z%2BBPUAQAAWgLL3yL89rOo/q/q6mqVlJRo2LBhVo8DAABgnOUFKyYmpslPEQYFBWn8%2BPGaMGGC1eMAAAAYZ3nBWrp0qdWHBAAAsJTlBeudd95p9mPHjBlzEycBAAC4OSwvWAsWLFBDQ0OTG9r9/Pzc1vz8/ChYAADAJ1lesF5//XW98cYbmj59umJjY%2BVyuXTy5Em99tprevTRR5WUlGT1SAAAAEZ55R6sgoICderUqXHtzjvvVJcuXTR16lRt27bN6pEAAACMsvxzsM6ePauwsLAm66GhoSotLbV6HAAAAOMsL1jR0dFaunSpysvLG9cqKiqUl5en22%2B/3epxAAAAjLP8LcL58%2Bdrzpw5KiwsVHBwsPz9/VVZWak2bdpo1apVVo8DAABgnOUFa/Dgwdq/f78OHDigixcvyuVyqVOnTrr33nvVrl07q8cBAAAwzvKCJUm33nqrUlNTdfHiRXXp0sUbIwAAANw0lt%2BDVVNTo7lz56pfv3566KGHJH1zD9bjjz%2BuiooKq8cBAAAwzvKCtWzZMh0/fly5ubny9//H4evr65Wbm2v1OAAAAMZZXrB27dql//qv/9KDDz7Y%2BEufQ0NDlZOTo927d1s9DgAAgHGWF6yqqirFxMQ0WY%2BMjNRXX31l9TgAAADGWV6wbr/9dv35z3%2BWJLffPbhz507967/%2Bq9XjAAAAGGf5TxH%2B9Kc/1dNPP61HHnlEDQ0NevPNN1VUVKRdu3ZpwYIFVo8DAABgnOUFKy0tTTabTevWrVNAQIB%2B9atfqVu3bsrNzdWDDz5o9TgAAADGWV6wLl26pEceeUSPPPKI1YcGAACwhOX3YKWmprrdewUAANDaWF6wkpKStGPHDqsPCwAAYBnL3yL8l3/5F/3yl79UQUGBbr/9dt1yyy1u%2B3l5eVaPBAAAYJTlBevUqVP60Y9%2BJEkqLy%2B3%2BvAAAAA3nWUFKysrS8uXL9dvf/vbxrVVq1YpMzPTqhEAAAAsYdk9WHv37m2yVlBQYNXhAQAALGNZwbrWTw7y04QAAKA1sqxgffuLnb9vDQAAwNdZ/jENAAAArR0FCwAAwDDLfoqwtrZWc%2BbM%2Bd41PgcLAAD4OssK1oABA1RWVva9awAAAL7OsoL1fz//CgAAoDXjHiwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMJ8uWAcPHlRycrKysrKa7G3fvl2jRo1Sv379NG7cOB06dKhxr6GhQcuXL1dqaqoGDhyoqVOn6vz58437TqdTs2fPVnJysgYPHqwFCxaopqbGknMCAAC%2Bz2cL1muvvaYlS5aoa9euTfaOHz%2BuuXPnKjs7Wx988IEyMjI0c%2BZMXbx4UZK0fv16bd26VQUFBdq3b59iYmKUmZkpl8slSVq0aJGqq6u1bds2vf322yopKVFubq6l5wcAAHyXzxasoKAgbdy48ZoFa8OGDUpJSVFKSoqCgoI0evRo9erVS1u2bJEkFRYWKiMjQ927d1dISIiysrJUUlKio0eP6vPPP9eePXuUlZWlyMhIderUSU899ZTefvtt1dbWWn2aAADAB1n2q3JMmzJlynX3iouLlZKS4rYWFxcnu92umpoanTp1SnFxcY17ISEh6tq1q%2Bx2uy5fvqyAgADFxsY27vfp00dfffWVTp8%2B7bZ%2BPWVlZXI4HG5rNltbRUVFNff0miUgwLf6sc3W8ub9NkNfy7KlIUfPkaEZ5GgGOXrOZwvWd3E6nQoLC3NbCwsL06lTp/Tll1/K5XJdc7%2B8vFzh4eEKCQmRn5%2Bf254klZeXN%2Bv4hYWFys/Pd1vLzMzUrFmzbuR0Wo2IiGBvj3BdoaG3enuEVoEcPUeGZpCjGeR441plwZLUeD/Vjex/39d%2Bn7S0NA0dOtRtzWZrq/LyKo%2Be92q%2B9p2F6fM3ISDAX6Ght6qiolr19Q3eHsdnkaPnyNAMcjSjNeXorW/uW2XBioiIkNPpdFtzOp2KjIxUeHi4/P39r7nfvn17RUZGqrKyUvX19QoICGjck6T27ds36/hRUVFN3g50OC6rrs63X6SeasnnX1/f0KLn8xXk6DkyNIMczSDHG%2Bdbl0CaKT4%2BXkVFRW5rdrtdffv2VVBQkHr27Kni4uLGvYqKCp07d06JiYnq3bu3XC6XTpw44fa1oaGh6tatm2XnAAAAfFerLFgTJ07U%2B%2B%2B/r/379%2BvKlSvauHGjzp49q9GjR0uS0tPTtXbtWpWUlKiyslK5ubnq3bu3EhISFBkZqeHDh%2BvVV1/VpUuXdPHiRa1atUrjx4%2BXzdYqL/gBAADDfLYxJCQkSJLq6uokSXv27JH0zdWmXr16KTc3Vzk5OSotLVWPHj20Zs0adezYUZI0adIkORwOTZ48WVVVVUpKSnK7Kf0Xv/iFFi9erNTUVN1yyy16%2BOGHr/lhpgAAANfi5/L0jm40i8Nx2fhz2mz%2BeiD3oPHnvVl2zL7H2yM0YbP5KyIiWOXlVdxn4AFy9BwZmkGOZrSmHDt2bOeV47bKtwgBAAC8iYIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhrbZgxcbGKj4%2BXgkJCY1/XnrpJUnS4cOHNX78ePXv318jR47Uli1b3L527dq1Gj58uPr376/09HQVFRV54xQAAICPsnl7gJtp586d6ty5s9taWVmZnnrqKS1YsECjRo3SRx99pBkzZqhbt25KSEjQ3r17tXLlSr3%2B%2BuuKjY3V2rVrNX36dO3evVtt27b10pkAAABf0mqvYF3P1q1bFRMTo/HjxysoKEjJyckaOnSoNmzYIEkqLCzUuHHj1LdvX7Vp00aPP/64JGnfvn3eHBsAAPiQVl2w8vLyNGTIEN15551atGiRqqqqVFxcrLi4OLfHxcXFNb4NePW%2Bv7%2B/evfuLbvdbunsAADAd7XatwjvuOMOJScn65VXXtH58%2Bc1e/Zsvfjii3I6nerUqZPbY8PDw1VeXi5JcjqdCgsLc9sPCwtr3G%2BOsrIyORwOtzWbra2ioqJu8GyuLSDAt/qxzdby5v02Q1/LsqUhR8%2BRoRnkaAY5eq7VFqzCwsLG/%2B7evbuys7M1Y8YMDRgw4Hu/1uVyeXzs/Px8t7XMzEzNmjXLo%2Bf1dRERwd4e4bpCQ2/19gitAjl6jgzNIEczyPHGtdqCdbXOnTurvr5e/v7%2Bcjqdbnvl5eWKjIyUJEVERDTZdzqd6tmzZ7OPlZaWpqFDh7qt2WxtVV5edYPTX5uvfWdh%2BvxNCAjwV2joraqoqFZ9fYO3x/FZ5Og5MjSDHM1oTTl665v7Vlmwjh07pi1btmjevHmNayUlJQoMDFRKSop%2B//vfuz2%2BqKhIffv2lSTFx8eruLhYY8eOlSTV19fr2LFjGj9%2BfLOPHxUV1eTtQIfjsurqfPtF6qmWfP719Q0tej5fQY6eI0MzyNEMcrxxvnUJpJnat2%2BvwsJCFRQU6Ouvv9aZM2e0YsUKpaWl6Sc/%2BYlKS0u1YcMGXblyRQcOHNCBAwc0ceJESVJ6erreeeepyuQHAAALJklEQVQdHTlyRNXV1Vq9erUCAwM1ZMgQ754UAADwGa3yClanTp1UUFCgvLy8xoI0duxYZWVlKSgoSGvWrNGSJUv04osvKjo6WsuWLdO//du/SZJ%2B/OMf6%2Bc//7lmz56tL774QgkJCSooKFCbNm28fFYAAMBX%2BLk8vaMbzeJwXDb%2BnDabvx7IPWj8eW%2BWHbPv8fYITdhs/oqICFZ5eRWXwT1Ajp4jQzPI0YzWlGPHju28ctxW%2BRYhAACAN1GwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABjWKn/ZM1qmh179k7dH%2BEFa4u9OBAD4Bq5gAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwbqG0tJSPfnkk0pKStJ9992nZcuWqaGhwdtjAQAAH2Hz9gAt0dNPP60%2Bffpoz549%2BuKLLzRt2jR16NBBP/vZz7w9Giz00Kt/8vYIP8iO2fd4ewQAwP/HFayr2O12nThxQtnZ2WrXrp1iYmKUkZGhwsJCb48GAAB8BFewrlJcXKzo6GiFhYU1rvXp00dnzpxRZWWlQkJCvvc5ysrK5HA43NZstraKiooyOmtAAP0Y/%2BBLV9z%2BkH2vt0docb7998y/a8%2BQoxnk6DkK1lWcTqdCQ0Pd1r4tW%2BXl5c0qWIWFhcrPz3dbmzlzpp5%2B%2Bmlzg%2BqbIvfYbZ8pLS3NeHn7Z1FWVqbCwkIy9BA5eq6srEy/%2Bc3rZOghcjSDHD1HNb0Gl8vl0denpaVp06ZNbn/S0tIMTfcPDodD%2Bfn5Ta6WofnI0Axy9BwZmkGOZpCj57iCdZXIyEg5nU63NafTKT8/P0VGRjbrOaKiomj8AAD8E%2BMK1lXi4%2BN14cIFXbp0qXHNbrerR48eCg4O9uJkAADAV1CwrhIXF6eEhATl5eWpsrJSJSUlevPNN5Wenu7t0QAAgI8IeOGFF17w9hAtzb333qtt27bppZde0rvvvqvx48dr6tSp8vPz8/ZoTQQHB2vQoEFcXfMAGZpBjp4jQzPI0Qxy9Iyfy9M7ugEAAOCGtwgBAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNg%2BaDS0lI9%2BeSTSkpK0n333adly5apoaHB22P5hIMHDyo5OVlZWVlN9rZv365Ro0apX79%2BGjdunA4dOuSFCVu%2B0tJSZWZmKikpScnJyZo3b54qKiokScePH9ejjz6qAQMGaNiwYXrjjTe8PG3LdOLECT322GMaMGCAkpOTNXv2bDkcDknS4cOHNX78ePXv318jR47Uli1bvDytb3j55ZcVGxvb%2BHdybJ7Y2FjFx8crISGh8c9LL70kiQw95oLPGTt2rGvhwoWuiooK15kzZ1zDhg1zvfHGG94eq8UrKChwDRs2zDVp0iTX7Nmz3faOHTvmio%2BPd%2B3fv99VU1Pj2rx5s6tv376uCxcueGnaluvhhx92zZs3z1VZWem6cOGCa9y4ca758%2Be7qqurXffee69r5cqVrqqqKldRUZFr0KBBrl27dnl75BblypUrrrvvvtuVn5/vunLliuuLL75wPfroo66nnnrK9fe//911xx13uDZs2OCqqalx/elPf3IlJia6Pv30U2%2BP3aIdO3bMNWjQIFevXr1cLpeLHH%2BAXr16uc6fP99knQw9xxUsH2O323XixAllZ2erXbt2iomJUUZGhgoLC709WosXFBSkjRs3qmvXrk32NmzYoJSUFKWkpCgoKEijR49Wr169%2BI7tKhUVFYqPj9ecOXMUHBys2267TWPHjtWHH36o/fv3q7a2VjNmzFDbtm3Vp08fTZgwgdfmVaqrq5WVlaVp06YpMDBQkZGReuCBB/TZZ59p69atiomJ0fjx4xUUFKTk5GQNHTpUGzZs8PbYLVZDQ4MWL16sjIyMxjVy9BwZeo6C5WOKi4sVHR2tsLCwxrU%2BffrozJkzqqys9OJkLd%2BUKVPUrl27a%2B4VFxcrLi7ObS0uLk52u92K0XxGaGiocnJy1KFDh8a1CxcuKCoqSsXFxYqNjVVAQEDjXlxcnIqKirwxaosVFhamCRMmyGazSZJOnz6t3//%2B93rooYeu%2Bzokw%2Bt76623FBQUpFGjRjWukeMPk5eXpyFDhujOO%2B/UokWLVFVVRYYGULB8jNPpVGhoqNvat2WrvLzcGyO1Ck6n0620St/kSqbfzW63a926dZoxY8Y1X5vh4eFyOp3cI3gNpaWlio%2BP14gRI5SQkKBZs2ZdN0Neh9f2%2Beefa%2BXKlVq8eLHbOjk23x133KHk5GTt3r1bhYWFOnLkiF588UUyNICC5YNcLpe3R2iVyPWH%2BeijjzR16lTNmTNHycnJ132cn5%2BfhVP5jujoaNntdu3cuVNnz57Vc8895%2B2RfE5OTo7GjRunHj16eHsUn1VYWKgJEyYoMDBQ3bt3V3Z2trZt26ba2lpvj%2BbzKFg%2BJjIyUk6n023N6XTKz89PkZGRXprK90VERFwzVzK9tr179%2BrJJ5/U/PnzNWXKFEnfvDav/u7W6XQqPDxc/v78r%2BZa/Pz8FBMTo6ysLG3btk02m63J67C8vJzX4TUcPnxYn3zyiTIzM5vsXevfMzk2T%2BfOnVVfXy9/f38y9BD/1/Mx8fHxunDhgi5dutS4Zrfb1aNHDwUHB3txMt8WHx/f5N4Cu92uvn37emmiluvjjz/W3LlztWLFCo0ZM6ZxPT4%2BXidPnlRdXV3jGhk2dfjwYQ0fPtztbdNvC2hiYmKT12FRUREZXsOWLVv0xRdf6L777lNSUpLGjRsnSUpKSlKvXr3IsRmOHTumpUuXuq2VlJQoMDBQKSkpZOghCpaPiYuLU0JCgvLy8lRZWamSkhK9%2BeabSk9P9/ZoPm3ixIl6//33tX//fl25ckUbN27U2bNnNXr0aG%2BP1qLU1dVp4cKFys7O1uDBg932UlJSFBISotWrV6u6ulpHjx7Vxo0beW1eJT4%2BXpWVlVq2bJmqq6t16dIlrVy5UnfeeafS09NVWlqqDRs26MqVKzpw4IAOHDigiRMnenvsFmfevHnatWuXNm/erM2bN6ugoECStHnzZo0aNYocm6F9%2B/YqLCxUQUGBvv76a505c0YrVqxQWlqafvKTn5Chh/xc3Hjicy5evKhFixbpL3/5i0JCQjRp0iTNnDmTe12%2BR0JCgiQ1XmH59qe4vv1Jwd27dysvL0%2BlpaXq0aOHFixYoIEDB3pn2Bbqww8/1L//%2B78rMDCwyd7OnTtVVVWlxYsXq6ioSB06dNATTzyhn/70p16YtGU7efKklixZok8//VRt27bVXXfdpXnz5qlTp07661//qiVLlqikpETR0dGaM2eOhg0b5u2RW7y//e1vSk1N1cmTJyWJHJvpr3/9q/Ly8nTy5EkFBgZq7NixysrKUlBQEBl6iIIFAABgGG8RAgAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBh/w/5yHXgEtPquQAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common5768823793127964320”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>9</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9040073130323529</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0807507257585356</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0354787039594395</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.172217619557041</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.125516036726128</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.35239984632855</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.888540455691249</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.8237595827618455</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0038830556794247</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3112)</td> <td class=”number”>3112</td> <td class=”number”>96.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>80</td> <td class=”number”>2.5%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme5768823793127964320”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>9</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0018013317443853999</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.005858824349547977</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.020729707271763757</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.02077190988971637</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>43.24343009503053</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>45.955790874295914</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>49.19482970211735</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.38088680450114</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>53.51506658816814</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_HISP15”>PCT_LACCESS_HISP15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3062</td>

</tr> <tr>

<th>Unique (%)</th> <td>95.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>97</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>2.0253</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>79.324</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>1.7%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-1764657150316893501”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-1764657150316893501,#minihistogram-1764657150316893501”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-1764657150316893501”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-1764657150316893501”

aria-controls=”quantiles-1764657150316893501” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-1764657150316893501” aria-controls=”histogram-1764657150316893501”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-1764657150316893501” aria-controls=”common-1764657150316893501”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-1764657150316893501” aria-controls=”extreme-1764657150316893501”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-1764657150316893501”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.020982</td>

</tr> <tr>

<th>Q1</th> <td>0.19757</td>

</tr> <tr>

<th>Median</th> <td>0.58063</td>

</tr> <tr>

<th>Q3</th> <td>1.6804</td>

</tr> <tr>

<th>95-th percentile</th> <td>8.0931</td>

</tr> <tr>

<th>Maximum</th> <td>79.324</td>

</tr> <tr>

<th>Range</th> <td>79.324</td>

</tr> <tr>

<th>Interquartile range</th> <td>1.4828</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>5.0385</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.4877</td>

</tr> <tr>

<th>Kurtosis</th> <td>67.234</td>

</tr> <tr>

<th>Mean</th> <td>2.0253</td>

</tr> <tr>

<th>MAD</th> <td>2.3136</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>6.8912</td>

</tr> <tr>

<th>Sum</th> <td>6304.9</td>

</tr> <tr>

<th>Variance</th> <td>25.387</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-1764657150316893501”>

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UGBio4cOHq0uXLlq3bp0kKS8vTxMmTFDHjh0VFBSkrKwsFRYW6osvvvBlZAAAYCO2LFghISGaN2%2BeLr30UvdacXGxIiMjVVBQoK5duyogIMC9Fxsbq/z8fElSQUGBYmNjPY4XGxsrp9Op6upq7d%2B/32M/KChI7du3l9PpbORUAACgqXD4egATnE6nXnnlFS1btkybNm1SSEiIx35YWJhcLpfq6urkcrkUGhrqsR8aGqr9%2B/fr%2B%2B%2B/l2VZ59wvKyur9zwlJSUqLS31WHM4WioyMrKByX5eQIC9%2BrHDUf95z2SzW8b6IJs9kc2eyGZPTSGb7QvWJ598oilTpmjatGlKTk7Wpk2bzvk4Pz8/9///0v1UF3q/VV5enpYsWeKxlpGRoczMzAs6rt2Fh7dq8HNCQi5phEkuDmSzJ7LZE9nsyc7ZbF2wtm/frocfflizZs3SLbfcIkmKiIjQoUOHPB7ncrkUFhYmf39/hYeHy%2BVynbUfERHhfsy59lu3bl3vudLS0pSamuqx5nC0VFlZZQPS/TK7NfuG5A8I8FdIyCUqL69SbW1dI07lfWSzJ7LZE9nsyWS28/nm3gTbFqxPP/1U2dnZeu6559SvXz/3enx8vP7yl7%2BopqZGDscP8ZxOp7p37%2B7eP3M/1hlOp1NDhw5VYGCgOnfurIKCAvXt21fSDzfUf/3110pMTKz3bJGRkWddDiwtPa6amqb1BdBQ55O/trauyf6%2Bkc2eyGZPZLMnO2ez1ymQ/6%2BmpkYzZ87U9OnTPcqVJKWkpCgoKEjLli1TVVWVvvjiC61Zs0bp6emSpDFjxuiDDz7Qzp07dfLkSa1Zs0aHDh3S8OHDJUnp6elauXKlCgsLVVFRoZycHHXr1k0JCQlezwkAAOzJlmewPv/8cxUWFmru3LmaO3eux97mzZv1wgsv6Mknn1Rubq4uvfRSZWVlqX///pKkLl26KCcnR/PmzVNRUZE6deqk5cuXq02bNpKksWPHqrS0VHfccYcqKyuVlJR01v1UAAAAP8fP4h00vaK09LjxYzoc/hqY857x4zaWTVOvqfdjHQ5/hYe3UllZpW1PD/8UstkT2eyJbPZkMlubNsGGpmoYW14iBAAAuJhRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhXi9YqampWrJkiYqLi7390gAAAF7h9YJ12223aePGjbrhhht09913a%2BvWraqpqfH2GAAAAI3G6wUrIyNDGzdu1GuvvabOnTvrmWeeUUpKihYsWKCDBw96exwAAADjfHYPVlxcnLKzs7Vjxw7NmDFDr732moYMGaKJEyfqH//4h6/GAgAAuGA%2BK1inT5/Wxo0bdc899yg7O1tRUVF67LHH1K1bN02YMEHr16/31WgAAAAXxOHtFywsLNSaNWv0xhtvqLKyUoMHD9af//xn9e7d2/2YPn36aPbs2Ro2bJi3xwMAALhgXi9YQ4cOVYcOHTRp0iTdcsstCgsLO%2BsxKSkpOnbsmLdHAwAAMMLrBWvlypXq27fvLz7uiy%2B%2B8MI0AAAA5nn9HqyuXbtq8uTJ2rZtm3vtv/7rv3TPPffI5XJ5exwAAADjvF6w5s2bp%2BPHj6tTp07utf79%2B6uurk7z58/39jgAAADGef0S4fvvv6/169crPDzcvRYTE6OcnBzdfPPN3h4HAADAOK%2BfwaqurlZgYODZg/j7q6qqytvjAAAAGOf1gtWnTx/Nnz9f33//vXvt22%2B/1VNPPeXxVg0AAAB25fVLhDNmzNBvf/tbXX311QoKClJdXZ0qKyvVrl07vfzyy94eBwAAwDivF6x27drprbfe0rvvvquvv/5a/v7%2B6tChg/r166eAgABvjwMAAGCc1wuWJDVv3lw33HCDL14aAACg0Xm9YB0%2BfFgLFy7UV199perq6rP233nnHW%2BPBAAAYJRP7sEqKSlRv3791LJlS2%2B/PAAAQKPzesHKz8/XO%2B%2B8o4iICG%2B/NAAAgFd4/W0aWrduzZkrAADQpHm9YE2aNElLliyRZVnefmkAAACv8PolwnfffVeffvqp1q5dq8suu0z%2B/p4d79VXX/X2SAAAAEZ5vWAFBQXpuuuu8/bLAgAAeI3XC9a8efO8/ZIAAABe5fV7sCTpwIEDWrx4sR577DH32meffeaLUQAAAIzzesHavXu3hg8frq1bt2rDhg2Sfnjz0fHjx/MmowAAoEnwesFatGiRHn74Ya1fv15%2Bfn6Sfvj3CefPn6%2BlS5d6exwAAADjvF6w/vnPfyo9PV2S3AVLkm688UYVFhZ6exwAAADjvF6wgoODz/lvEJaUlKh58%2BbeHgcAAMA4rxesXr166ZlnnlFFRYV77eDBg8rOztbVV1/t7XEAAACM8/rbNDz22GO68847lZSUpNraWvXq1UtVVVXq3Lmz5s%2Bf7%2B1xAAAAjPN6wWrbtq02bNigXbt26eDBg2rRooU6dOiga665xuOeLAAAALvyesGSpGbNmumGG27wxUsDAAA0Oq8XrNTU1J89U8V7YQEAALvzesEaMmSIR8Gqra3VwYMH5XQ6deeddzboWO%2B9956ys7OVlJSkRYsWudfXrl2rGTNmqFmzZh6PX7VqlRITE1VXV6fnnntOGzZsUHl5uRITEzV79my1a9dOkuRyuTR79mx99NFH8vf3V0pKimbNmqUWLVpcQHIAAPDvwusFa/r06edc37Jli/72t7/V%2Bzgvvvii1qxZo/bt259zv0%2BfPnr55ZfPubdq1SqtX79eL774oqKiorRo0SJlZGTozTfflJ%2Bfn2bNmqVTp05pw4YNOn36tB588EHl5ORo5syZ9Z4PAAD8%2B/LJv0V4LjfccIPeeuutej8%2BMDDwZwvWz8nLy9OECRPUsWNHBQUFKSsrS4WFhfriiy/03Xffadu2bcrKylJERISioqJ033336fXXX9fp06cb/FoAAODfj09ucj%2BXPXv2yLKsej9%2B/PjxP7tfXFysu%2B66S/n5%2BQoJCVFmZqZGjBih6upq7d%2B/X7Gxse7HBgUFqX379nI6nTp%2B/LgCAgLUtWtX935cXJxOnDihAwcOeKz/lJKSEpWWlnqsORwtFRkZWe989REQcNH043pxOOo/75lsdstYH2SzJ7LZE9nsqSlk83rBGjt27FlrVVVVKiws1KBBg4y8RkREhGJiYvTQQw%2BpU6dOevvtt/XII48oMjJSl19%2BuSzLUmhoqMdzQkNDVVZWprCwMAUFBXncJ3bmsWVlZfV6/by8PC1ZssRjLSMjQ5mZmReYzN7Cw1s1%2BDkhIZc0wiQXB7LZE9nsiWz2ZOdsXi9YMTExZ/0UYWBgoEaNGqXRo0cbeY3%2B/furf//%2B7l8PHTpUb7/9ttauXeu%2BB%2BznzpY15EzauaSlpSk1NdVjzeFoqbKyygs67o/Zrdk3JH9AgL9CQi5ReXmVamvrGnEq7yObPZHNnshmTyaznc839yZ4vWD56t3ao6OjlZ%2Bfr7CwMPn7%2B8vlcnnsu1wutW7dWhEREaqoqFBtba0CAgLce5LUunXrer1WZGTkWZcDS0uPq6amaX0BNNT55K%2BtrWuyv29ksyey2RPZ7MnO2bxesN544416P/aWW245r9f4y1/%2BotDQUA0ZMsS9VlhYqHbt2ikwMFCdO3dWQUGB%2BvbtK0kqLy/X119/rcTEREVHR8uyLO3bt09xcXGSJKfTqZCQEHXo0OG85gEAAP9evF6wHn/8cdXV1Z11Gc7Pz89jzc/P77wL1qlTpzRnzhy1a9dOV1xxhbZs2aJ3331Xr732miQpPT1dubm5uu666xQVFaWcnBx169ZNCQkJkqTBgwfrD3/4g5599lmdOnVKS5cu1ahRo%2BRwXDQ/EwAAAC5iXm8Mf/zjH7VixQpNnjxZXbt2lWVZ%2BvLLL/Xiiy9q3LhxSkpKqtdxzpShmpoaSdK2bdsk/XC2afz48aqsrNSDDz6o0tJSXXbZZVq6dKni4%2BMl/XCjfWlpqe644w5VVlYqKSnJ46b03/3ud3ryySc1YMAANWvWTDfffLOysrJM/jYAAIAmzM%2B60Du6G2jEiBHKzc1VVFSUx/q3336riRMnasOGDd4cx2tKS48bP6bD4a%2BBOe8ZP25j2TT1mno/1uHwV3h4K5WVVdr2%2BvtPIZs9kc2eyGZPJrO1aRNsaKqG8fqPoR06dOist0iQpJCQEBUVFXl7HAAAAOO8XrCio6M1f/58j/eUKi8v18KFC/Wb3/zG2%2BMAAAAY5/V7sGbMmKFp06YpLy9PrVq1kr%2B/vyoqKtSiRQstXbrU2%2BMAAAAY5/WC1a9fP%2B3cuVO7du3SkSNHZFmWoqKidO211yo42DfXSQEAAEzyyfsOXHLJJRowYICOHDmidu3a%2BWIEAACARuP1e7Cqq6uVnZ2tnj176qabbpL0wz1Yd999t8rLy709DgAAgHFeL1gLFizQ3r17lZOTI3///3v52tpa5eTkeHscAAAA47xesLZs2aLnn39eN954o/sffQ4JCdG8efO0detWb48DAABgnNcLVmVlpWJiYs5aj4iI0IkTJ7w9DgAAgHFeL1i/%2Bc1v9Le//U2SPP7twc2bN%2BvXv/61t8cBAAAwzus/RXj77bfrgQce0G233aa6ujq99NJLys/P15YtW/T44497exwAAADjvF6w0tLS5HA49MorryggIEAvvPCCOnTooJycHN14443eHgcAAMA4rxesY8eO6bbbbtNtt93m7ZcGAADwCq/fgzVgwACPe68AAACaGq8XrKSkJG3atMnbLwsAAOA1Xr9E%2BKtf/UpPP/20cnNz9Zvf/EbNmjXz2F%2B4cKG3RwIAADDK6wVr//79uvzyyyVJZWVl3n55AACARue1gpWVlaVFixbp5Zdfdq8tXbpUGRkZ3hoBAADAK7x2D9b27dvPWsvNzfXWywMAAHiN1wrWuX5ykJ8mBAAATZHXCtaZf9j5l9YAAADszutv0wAAANDUUbAAAAAM89pPEZ4%2BfVrTpk37xTXeBwsAANid1wpW7969VVJS8otrAAAAdue1gvWv738FAADQlHEPFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwzNYF67333lNycrKysrLO2tu4caOGDRumnj17auTIkXr//ffde3V1dVq0aJEGDBigPn36aOLEiTp8%2BLB73%2BVyaerUqUpOTla/fv30%2BOOPq7q62iuZAACA/dm2YL344ouaO3eu2rdvf9be3r17lZ2drenTp%2BvDDz/UhAkTdP/99%2BvIkSOSpFWrVmn9%2BvXKzc3Vjh07FBMTo4yMDFmWJUmaNWuWqqqqtGHDBr3%2B%2BusqLCxUTk6OV/MBAAD7sm3BCgwM1Jo1a85ZsFavXq2UlBSlpKQoMDBQw4cPV5cuXbRu3TpJUl5eniZMmKCOHTsqKChIWVlZKiws1BdffKHvvvtO27ZtU1ZWliIiIhQVFaX77rtPr7/%2Buk6fPu3tmAAAwIZsW7DGjx%2Bv4ODgc%2B4VFBQoNjbWYy02NlZOp1PV1dXav3%2B/x35QUJDat28vp9OpvXv3KiAgQF27dnXvx8XF6cSJEzpw4EDjhAEAAE2Kw9cDNAaXy6XQ0FCPtdDQUO3fv1/ff/%2B9LMs6535ZWZnCwsIUFBQkPz8/jz1JKisrq9frl5SUqLS01GPN4WipyMjI84nzkwIC7NWPHY76z3smm90y1gfZ7Ils9kQ2e2oK2ZpkwZLkvp/qfPZ/6bm/JC8vT0uWLPFYy8jIUGZm5gUd1%2B7Cw1s1%2BDkhIZc0wiQXB7LZE9nsiWz2ZOdsTbJghYeHy%2BVyeay5XC5FREQoLCxM/v7%2B59xv3bq1IiIiVFFRodraWgUEBLj3JKl169b1ev20tDSlpqZ6rDkcLVVWVnm%2Bkc7Jbs2%2BIfkDAvwVEnKJysurVFtb14hTeR/Z7Ils9kQ2ezKZ7Xy%2BuTehSRas%2BPh45efne6w5nU4NHTpUgYGB6ty5swoKCtS3b19JUnl5ub7%2B%2BmslJiYqOjpalmVp3759iouLcz83JCREHTp0qNfrR0ZGnnU5sLT0uGpqmtYXQEOdT/7a2rom%2B/tGNnsimz2RzZ7snM1ep0DqacyYMfrggw%2B0c%2BdOnTx5UmvWrNGhQ4c0fPhwSVJ6erpWrlypwsJCVVRUKCcnR926dVNCQoIiIiI0ePBg/eEPf9CxY8d05MgRLV26VKNGjZLD0ST7KAAAMMy2jSEhIUGSVFNTI0natm2bpB/ONnXp0kU5OTmaN2%2BeioqK1KlTJy1fvlxt2rS360q5AAAR1UlEQVSRJI0dO1alpaW64447VFlZqaSkJI97pn73u9/pySef1IABA9SsWTPdfPPN53wzUwAAgHPxsy70jm7US2npcePHdDj8NTDnPePHbSybpl5T78c6HP4KD2%2BlsrJK254e/ilksyey2RPZ7MlktjZtzv2WTo2tSV4iBAAA8CUKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAsCZbsLp27ar4%2BHglJCS4P%2BbMmSNJ2r17t0aNGqVevXpp6NChWrduncdzV65cqcGDB6tXr15KT09Xfn6%2BLyIAAACbcvh6gMa0efNmXXbZZR5rJSUluu%2B%2B%2B/T4449r2LBh%2BuSTTzRlyhR16NBBCQkJ2r59uxYvXqw//vGP6tq1q1auXKnJkydr69atatmypY%2BSAAAAO2myZ7B%2Byvr16xUTE6NRo0YpMDBQycnJSk1N1erVqyVJeXl5GjlypLp3764WLVro7rvvliTt2LHDl2MDAAAbadJnsBYuXKjPPvtMFRUVuummm/Too4%2BqoKBAsbGxHo%2BLjY3Vpk2bJEkFBQUaMmSIe8/f31/dunWT0%2BnU0KFD6/W6JSUlKi0t9VhzOFoqMjLyAhN5CgiwVz92OOo/75lsdstYH2SzJ7LZE9nsqSlka7IFq0ePHkpOTtazzz6rw4cPa%2BrUqXrqqafkcrkUFRXl8diwsDCVlZVJklwul0JDQz32Q0ND3fv1kZeXpyVLlnisZWRkKDMz8zzTNA3h4a0a/JyQkEsaYZKLA9nsiWz2RDZ7snO2Jluw8vLy3P/fsWNHTZ8%2BXVOmTFHv3r1/8bmWZV3Qa6elpSk1NdVjzeFoqbKyygs67o/Zrdk3JH9AgL9CQi5ReXmVamvrGnEq7yObPZHNnshmTyaznc839yY02YL1Y5dddplqa2vl7%2B8vl8vlsVdWVqaIiAhJUnh4%2BFn7LpdLnTt3rvdrRUZGnnU5sLT0uGpqmtYXQEOdT/7a2rom%2B/tGNnsimz2RzZ7snM1ep0Dqac%2BePZo/f77HWmFhoZo3b66UlJSz3nYhPz9f3bt3lyTFx8eroKDAvVdbW6s9e/a49wEAAH5JkyxYrVu3Vl5ennJzc3Xq1CkdPHhQzz33nNLS0jRixAgVFRVp9erVOnnypHbt2qVdu3ZpzJgxkqT09HS98cYb%2Bvzzz1VVVaVly5apefPm6t%2B/v29DAQAA22iSlwijoqKUm5urhQsXugvSrbfeqqysLAUGBmr58uWaO3eunnrqKUVHR2vBggW64oorJEnXXXedHnroIU2dOlVHjx5VQkKCcnNz1aJFCx%2BnAgAAdtEkC5Yk9enTR6%2B%2B%2BupP7r355ps/%2Bdzbb79dt99%2Be2ONBgAAmrgmeYkQAADAlyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAxz%2BHoA/Pu46Q//7esRGmTT1Gt8PQIAwKY4gwUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbDOoaioSPfee6%2BSkpJ0/fXXa8GCBaqrq/P1WAAAwCZ4m4ZzeOCBBxQXF6dt27bp6NGjmjRpki699FLdddddvh4NXsTbSgAAzhcF60ecTqf27dunl156ScHBwQoODtaECRP05z//mYKFi5qdCiFlEEBTR8H6kYKCAkVHRys0NNS9FhcXp4MHD6qiokJBQUG/eIySkhKVlpZ6rDkcLRUZGWl01oAArvDCnuxUBu3o7enXNujxZ/4uaYp/p5DNnppCNgrWj7hcLoWEhHisnSlbZWVl9SpYeXl5WrJkicfa/fffrwceeMDcoPqhyN3Z9iulpaUZL2%2B%2BVlJSory8PLLZDNnsqaSkRH/%2B8x/JZjNku7jZtxo2IsuyLuj5aWlpWrt2rcdHWlqaoen%2BT2lpqZYsWXLW2bKmgGz2RDZ7Ips9ke3ixhmsH4mIiJDL5fJYc7lc8vPzU0RERL2OERkZadvGDQAALhxnsH4kPj5excXFOnbsmHvN6XSqU6dOatWqlQ8nAwAAdkHB%2BpHY2FglJCRo4cKFqqioUGFhoV566SWlp6f7ejQAAGATAbNnz57t6yEuNtdee602bNigOXPm6K233tKoUaM0ceJE%2Bfn5%2BXq0s7Rq1Up9%2B/ZtkmfXyGZPZLMnstkT2S5eftaF3tENAAAAD1wiBAAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMgmVDRUVFuvfee5WUlKTrr79eCxYsUF1dna/HOm/vvfeekpOTlZWVddbexo0bNWzYMPXs2VMjR47U%2B%2B%2B/74MJz19RUZEyMjKUlJSk5ORkPfrooyovL5ck7d27V%2BPGjVPv3r01aNAgrVixwsfTNsy%2Bfft05513qnfv3kpOTtbUqVNVWloqSdq9e7dGjRqlXr16aejQoVq3bp2Ppz0/zzzzjLp27er%2BdVPI1bVrV8XHxyshIcH9MWfOHElNI9%2ByZcvUr18/9ejRQxMmTNA333wjyd7Z/v73v3t8vhISEhQfH%2B/%2Bs2nnbJK0Z88ejR8/XldeeaWuueYaTZ8%2BXceOHZNk82wWbOfWW2%2B1Zs6caZWXl1sHDx60Bg0aZK1YscLXY52X3Nxca9CgQdbYsWOtqVOneuzt2bPHio%2BPt3bu3GlVV1dbb775ptW9e3eruLjYR9M23M0332w9%2BuijVkVFhVVcXGyNHDnSmjFjhlVVVWVde%2B211uLFi63KykorPz/f6tu3r7VlyxZfj1wvJ0%2BetK6%2B%2BmpryZIl1smTJ62jR49a48aNs%2B677z7r22%2B/tXr06GGtXr3aqq6utv77v//bSkxMtP7xj3/4euwG2bNnj9W3b1%2BrS5culmVZTSZXly5drMOHD5%2B13hTyvfLKK9aNN95oFRYWWsePH7fmzJljzZkzp0lk%2B7Fly5ZZDz74oO2znT592rrmmmushQsXWidPnrSOHTtm3XXXXdYDDzxg%2B2ycwbIZp9Opffv2afr06QoODlZMTIwmTJigvLw8X492XgIDA7VmzRq1b9/%2BrL3Vq1crJSVFKSkpCgwM1PDhw9WlSxfbfAdTXl6u%2BPh4TZs2Ta1atVLbtm1166236uOPP9bOnTt1%2BvRpTZkyRS1btlRcXJxGjx5tm89jVVWVsrKyNGnSJDVv3lwREREaOHCgvvrqK61fv14xMTEaNWqUAgMDlZycrNTUVK1evdrXY9dbXV2dnnzySU2YMMG91hRy/ZymkG/FihXKysrS5ZdfrqCgIM2cOVMzZ85sEtn%2B1f/%2B7//qpZde0iOPPGL7bKWlpSotLdWIESPUvHlzhYeHa%2BDAgdq7d6/ts1GwbKagoEDR0dEKDQ11r8XFxengwYOqqKjw4WTnZ/z48QoODj7nXkFBgWJjYz3WYmNj5XQ6vTHaBQsJCdG8efN06aWXuteKi4sVGRmpgoICde3aVQEBAe692NhY5efn%2B2LUBgsNDdXo0aPlcDgkSQcOHNBf//pX3XTTTT/5ebNLNkl69dVXFRgYqGHDhrnXmkKuMxYuXKj%2B/fvryiuv1KxZs1RZWWn7fN9%2B%2B62%2B%2BeYbff/99xoyZIiSkpKUmZmpY8eO2T7bjz333HO67bbb9Otf/9r22aKiotStWzfl5eWpsrJSR48e1datW9W/f3/bZ6Ng2YzL5VJISIjH2pmyVVZW5ouRGo3L5fIoktIPWe2a0%2Bl06pVXXtGUKVPO%2BXkMCwuTy%2BWy1f10RUVFio%2BP15AhQ5SQkKDMzMyfzGaXz9t3332nxYsX68knn/RYt3uuM3r06KHk5GRt3bpVeXl5%2Bvzzz/XUU0/ZPt%2BRI0ckSZs3b9ZLL72kN998U0eOHNHMmTNtn%2B1fffPNN9q6davuuusuSfb/c%2Bnv76/FixfrnXfeUa9evZScnKyamhpNmzbN/tl8PQAazrIsX4/gNU0l6yeffKKJEydq2rRpSk5O/snH%2Bfn5eXGqCxcdHS2n06nNmzfr0KFDeuSRR3w90gWbN2%2BeRo4cqU6dOvl6lEaRl5en0aNHq3nz5urYsaOmT5%2BuDRs26PTp074e7YKc%2Bbvi7rvvVlRUlNq2basHHnhA27dv9/FkZq1atUqDBg1SmzZtfD2KEadOndLkyZN144036uOPP9a7776r4OBgTZ8%2B3dejXTAKls1ERETI5XJ5rLlcLvn5%2BSkiIsJHUzWO8PDwc2a1W87t27fr3nvv1YwZMzR%2B/HhJP3wef/xdmMvlUlhYmPz97fVl6efnp5iYGGVlZWnDhg1yOBxnfd7Kysps8XnbvXu3PvvsM2VkZJy1d64/j3bJ9XMuu%2Bwy1dbWyt/f39b5zlyK/9czHtHR0bIsS6dPn7Z1tn%2B1ZcsWpaamun9t9z%2BXu3fv1jfffKOHHnpIwcHBioqKUmZmpt5%2B%2B23b/5m019/kUHx8vIqLi90/wir9cOmpU6dOatWqlQ8nMy8%2BPv6sa%2B1Op1Pdu3f30UQN9%2Bmnnyo7O1vPPfecbrnlFvd6fHy8vvzyS9XU1LjX7JRt9%2B7dGjx4sMflzDPFMDEx8azPW35%2Bvi2yrVu3TkePHtX111%2BvpKQkjRw5UpKUlJSkLl262DbXGXv27NH8%2BfM91goLC9W8eXOlpKTYOl/btm0VFBSkvXv3uteKiorUrFkz22c7Y%2B/evSoqKtI111zjXktISLB1ttraWtXV1XlcrTh16pQkKTk52dbZeJsGGxo9erQ1Y8YM6/jx49b%2B/fut1NRU65VXXvH1WBckOzv7rLdp%2BPLLL62EhARrx44dVnV1tbV69WqrZ8%2BeVklJiY%2BmbJjTp09bN910k/Xqq6%2BetXfy5Enr%2Buuvt55//nnrxIkT1ueff25deeWV1o4dO7w/6HkoLy%2B3kpOTrfnz51snTpywjh49ak2cONG6/fbbre%2B%2B%2B87q2bOn9dprr1nV1dXWzp07rcTERGvv3r2%2BHvsXuVwuq7i42P3x2WefWV26dLGKi4utoqIi2%2BY648iRI1aPHj2s5cuXWydPnrQOHDhgDRkyxJozZ46tP29nPPPMM9aAAQOsQ4cOWd99952VlpZmPfroo00im2VZ1po1a6y%2Bfft6rNk927Fjx6y%2Bfftav//9760TJ05Yx44dsyZPnmz9x3/8h%2B2zUbBsqLi42Lr77rutxMREKzk52Xr%2B%2Beeturo6X491XuLj4634%2BHjriiuusK644gr3r8/YsmWLNWjQICsuLs4aMWKE9dFHH/lw2ob5%2B9//bnXp0sWd6V8/vvnmG%2BvLL7%2B0xo4da8XHx1v9%2B/e3Vq1a5euRG2Tfvn3WuHHjrMTEROuqq66ypk6dah05csSyLMv66KOPrOHDh1txcXHWoEGDbPP%2BXj92%2BPBh9/tgWVbTyPXRRx9ZaWlpVo8ePay%2Bffta8%2BbNs6qrq917ds538uRJa/bs2VafPn2sHj16WNnZ2VZFRYVlWfbPZlmW9cILL1hDhw49a93u2ZxOpzVu3DjryiuvtJKTk5vM3yV%2BltVE7iIGAAC4SHAPFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAY9v8AyE5VYC7HJoEAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-1764657150316893501”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.121867826880777</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.30216693920349025</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5188319908750236</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.5198711385818262</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6911343106393006</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.08480165721152727</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16.186520475918503</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.7724189823898375</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.4024793759503564</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3051)</td> <td class=”number”>3051</td> <td class=”number”>95.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-1764657150316893501”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.738483421165433e-06</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.424255111408063e-06</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.748209735538758e-05</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.28321450127231e-05</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>52.522198670029475</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>62.69058885809977</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>66.0256808684387</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>75.81178871697067</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>79.32396806273532</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_LOWI10”><s>PCT_LACCESS_LOWI10</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCT_LACCESS_POP10”><code>PCT_LACCESS_POP10</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.90295</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_LOWI15”><s>PCT_LACCESS_LOWI15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCT_LACCESS_POP15”><code>PCT_LACCESS_POP15</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.90394</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_MULTIR15”>PCT_LACCESS_MULTIR15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3077</td>

</tr> <tr>

<th>Unique (%)</th> <td>95.9%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>97</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1.1525</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>27.121</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>1.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram7453031502557866489”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives7453031502557866489,#minihistogram7453031502557866489”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives7453031502557866489”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles7453031502557866489”

aria-controls=”quantiles7453031502557866489” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram7453031502557866489” aria-controls=”histogram7453031502557866489”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common7453031502557866489” aria-controls=”common7453031502557866489”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme7453031502557866489” aria-controls=”extreme7453031502557866489”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles7453031502557866489”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.029079</td>

</tr> <tr>

<th>Q1</th> <td>0.22101</td>

</tr> <tr>

<th>Median</th> <td>0.57244</td>

</tr> <tr>

<th>Q3</th> <td>1.318</td>

</tr> <tr>

<th>95-th percentile</th> <td>4.1694</td>

</tr> <tr>

<th>Maximum</th> <td>27.121</td>

</tr> <tr>

<th>Range</th> <td>27.121</td>

</tr> <tr>

<th>Interquartile range</th> <td>1.097</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>1.8599</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.6138</td>

</tr> <tr>

<th>Kurtosis</th> <td>41.427</td>

</tr> <tr>

<th>Mean</th> <td>1.1525</td>

</tr> <tr>

<th>MAD</th> <td>1.0507</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>5.1297</td>

</tr> <tr>

<th>Sum</th> <td>3587.7</td>

</tr> <tr>

<th>Variance</th> <td>3.4592</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram7453031502557866489”>

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B7Qyy%2B/rH79%2BjU%2Bp3///vr1r3%2Bt4cOH210eAACAZbYHrGHDhqlbt26aPHmyRo4cqZiYmIuek5mZqZMnT9pdGgAAgBG2B6zVq1drwIABP/i8ffv22VANAACAebZfg5WUlKQpU6Zo27ZtjWP/9m//poceekher9fucgAAAIyzPWAtWrRIp06dUo8ePRrHBg0apIaGBi1evNjucgAAAIyz/RTh7t27tX79esXGxjaOJSYmKj8/X3fffbfd5QAAABhn%2BxGss2fPKiws7OJCgoN15swZu8sBAAAwzvaA1b9/fy1evFhffvll49j//M//6JlnnvG7VQMAAIBT2X6KcM6cOfrVr36ln/70p4qIiFBDQ4NqamrUpUsXvfLKK3aXAwAAYJztAatLly7605/%2BpHfffVdHjx5VcHCwunXrpoEDByokJMTucgAAAIyzPWBJUmhoqG677bZA7BoAAOCysz1gHTt2TAUFBfrss8909uzZi%2Bb/8pe/2F0SAACAUQG5BquyslIDBw5U27Zt7d49AADAZWd7wCotLdVf/vIXxcXF2b1rAAAAW9h%2Bm4Z27dpx5AoAALRqtgesyZMnq7CwUD6fz%2B5dAwAA2ML2U4TvvvuuPvroI61du1adO3dWcLB/xnv99dftLgkAAMAo2wNWRESEbrnlFrt3CwAAYBvbA9aiRYvs3iUAAICtbL8GS5IOHTqk3//%2B93rqqacax/7rv/4rEKUAAAAYZ3vA2rNnj0aMGKGtW7dqw4YNkr6%2B%2BeiECRO4ySgAAGgVbA9Yy5Yt0%2BOPP67169crKChI0te/T7h48WKtWLHiR21r165dysjIUG5urt/42rVrde211yotLc3v8fHHH0uSGhoatGzZMg0ZMkT9%2B/fXpEmTdOzYscbXe71ezZw5UxkZGRo4cKDmzp3b5F3nAQAAmmJ7wPrv//5vZWdnS1JjwJKkO%2B%2B8U%2BXl5c3eznPPPaeFCxeqa9euTc73799fbrfb79GnTx9J0muvvab169erqKhIO3bsUGJionJychpvHTFv3jydOXNGGzZs0Jtvvqny8nLl5%2Bdf6pIBAMAVxvaAFRkZ2eTRoMrKSoWGhjZ7O2FhYSopKfnOgPV9iouLNXHiRHXv3l0RERHKzc1VeXm59u3bp88//1zbtm1Tbm6u4uLi1LFjRz3yyCN68803VVtb%2B6P3BQAArjy2f4vwhhtu0O9%2B9zs9/fTTjWOHDx/W/Pnz9dOf/rTZ25kwYcL3zh8/flwPPPCASktLFRUVpRkzZugXv/iFzp49q4MHDyo5ObnxuREREeratavcbrdOnTqlkJAQJSUlNc6npKToq6%2B%2B0qFDh/zGv0tlZaU8Ho/fmMvVVvHx8c1eX3OEhATkOwqXzOVqWfVe6J/T%2BtiS0ENr6J919NAa%2Bnf52B6wnnrqKd1///1KT09XfX29brjhBp05c0Y9e/bU4sWLjewjLi5OiYmJeuyxx9SjRw/9%2Bc9/1hNPPKH4%2BHj95Cc/kc/nU3R0tN9roqOjVVVVpZiYGEVERPidvrzw3Kqqqmbtv7i4WIWFhX5jOTk5mjFjhsWVOVtsbHigS2hSVNTVgS7B8eihNfTPOnpoDf0zz/aA1alTJ23YsEHvvPOODh8%2BrKuuukrdunXTzTff7BdqrBg0aJAGDRrU%2BO9hw4bpz3/%2Bs9auXau8vDxJ%2Bt6f6rH6Mz5ZWVkaPHiw35jL1VZVVTWWtvttTvvEYXr9VoWEBCsq6mpVV59RfX1DoMtxJHpoDf2zjh5acyX0L1Af7m0PWJLUpk0b3XbbbbbuMyEhQaWlpYqJiVFwcLC8Xq/fvNfrVbt27RQXF6fTp0%2Brvr5eISEhjXPS1z9U3Rzx8fEXnQ70eE6prq51vnmbq6Wuv76%2BocXW5hT00Br6Zx09tIb%2BmWd7wBo8ePD3HqkycS%2BsP/7xj4qOjtZdd93VOFZeXq4uXbooLCxMPXv2VFlZmQYMGCBJqq6u1tGjR9WnTx8lJCTI5/Ppk08%2BUUpKiiTJ7XYrKipK3bp1s1wbAABo/WwPWHfddZdfwKqvr9fhw4fldrt1//33G9nH%2BfPntWDBAnXp0kXXXnuttmzZonfffVdvvPGGJCk7O1tFRUW65ZZb1LFjR%2BXn56t3795KS0uTJN1xxx169tlntWTJEp0/f14rVqzQmDFj5HIF5IAfAABwGNsTw4VroL5ty5Yt%2Btvf/tbs7VwIQ3V1dZKkbdu2Sfr6aNOECRNUU1OjRx99VB6PR507d9aKFSuUmpoqSRo3bpw8Ho/Gjx%2Bvmpoapaen%2B12U/pvf/Ebz58/XkCFD1KZNG919990X3cwUAADguwT5rF7RbUh9fb0yMjJ%2BVMhyEo/nlPFtulzBGpq/y/h2L5dNM28OdAl%2BXK5gxcaGq6qqhmsPLhE9tIb%2BWUcPrbkS%2BtehQ2RA9ttivoa2f/9%2By9/eAwAAaAlsP0U4bty4i8bOnDmj8vJy3X777XaXAwAAYJztASsxMfGibxGGhYVpzJgxuvfee%2B0uBwAAwDjbA5apu7UDAAC0VLYHrLfeeqvZzx05cuRlrAQAAODysD1gzZ07Vw0NDRdd0B4UFOQ3FhQURMACAACOZHvAev755/Xiiy9qypQpSkpKks/n06effqrnnntO9913n9LT0%2B0uCQAAwKiAXINVVFSkjh07No7deOON6tKliyZNmqQNGzbYXRIAAIBRtt8H68iRI4qOjr5oPCoqShUVFXaXAwAAYJztASshIUGLFy9WVVVV41h1dbUKCgp0zTXX2F0OAACAcbafIpwzZ45mzZql4uJihYeHKzg4WKdPn9ZVV12lFStW2F0OAACAcbYHrIEDB2rnzp165513dOLECfl8PnXs2FE/%2B9nPFBkZmN8LAgAAMMn2gCVJV199tYYMGaITJ06oS5cugSgBAADgsrH9GqyzZ89q9uzZ6tu3r37%2B859L%2BvoarAcffFDV1dV2lwMAAGCc7QFr6dKlOnDggPLz8xUc/P%2B7r6%2BvV35%2Bvt3lAAAAGGd7wNqyZYv%2B5V/%2BRXfeeWfjjz5HRUVp0aJF2rp1q93lAAAAGGd7wKqpqVFiYuJF43Fxcfrqq6/sLgcAAMA42wPWNddco7/97W%2BS5Pfbg5s3b9Y//uM/2l0OAACAcbZ/i/CXv/ylpk%2BfrnvuuUcNDQ166aWXVFpaqi1btmju3Ll2lwMAAGCc7QErKytLLpdLr776qkJCQvSHP/xB3bp1U35%2Bvu688067ywEAADDO9oB18uRJ3XPPPbrnnnvs3jUAAIAtbL8Ga8iQIX7XXgEAALQ2tges9PR0bdq0ye7dAgAA2Mb2U4T/8A//oN/%2B9rcqKirSNddcozZt2vjNFxQU2F0SAACAUbYHrIMHD%2BonP/mJJKmqqsru3QMAAFx2tgWs3NxcLVu2TK%2B88krj2IoVK5STk2NXCQAAALaw7Rqs7du3XzRWVFRk1%2B4BAABsY1vAauqbg3ybEAAAtEa2BawLP%2Bz8Q2MAAABOZ/ttGgAAAFo7AhYAAIBhtn2LsLa2VrNmzfrBMe6DBQAAnM62gNWvXz9VVlb%2B4BgAAIDT2Rawvnn/KwAAgNaMa7AAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADHN0wNq1a5cyMjKUm5t70dzGjRs1fPhw9e3bV6NHj9bu3bsb5xoaGrRs2TINGTJE/fv316RJk3Ts2LHGea/Xq5kzZyojI0MDBw7U3LlzdfbsWVvWBAAAnM%2BxAeu5557TwoUL1bVr14vmDhw4oNmzZysvL0/vv/%2B%2BJk6cqGnTpunEiROSpNdee03r169XUVGRduzYocTEROXk5Mjn80mS5s2bpzNnzmjDhg168803VV5ervz8fFvXBwAAnMuxASssLEwlJSVNBqw1a9YoMzNTmZmZCgsL04gRI9SrVy%2BtW7dOklRcXKyJEyeqe/fuioiIUG5ursrLy7Vv3z59/vnn2rZtm3JzcxUXF6eOHTvqkUce0Ztvvqna2lq7lwkAABzIsQFrwoQJioyMbHKurKxMycnJfmPJyclyu906e/asDh486DcfERGhrl27yu1268CBAwoJCVFSUlLjfEpKir766isdOnTo8iwGAAC0Kq5AF3A5eL1eRUdH%2B41FR0fr4MGD%2BvLLL%2BXz%2BZqcr6qqUkxMjCIiIhQUFOQ3J0lVVVXN2n9lZaU8Ho/fmMvVVvHx8ZeynO8UEuKsfOxytax6L/TPaX1sSeihNfTPOnpoDf27fFplwJLUeD3Vpcz/0Gt/SHFxsQoLC/3GcnJyNGPGDEvbdbrY2PBAl9CkqKirA12C49FDa%2BifdfTQGvpnXqsMWLGxsfJ6vX5jXq9XcXFxiomJUXBwcJPz7dq1U1xcnE6fPq36%2BnqFhIQ0zklSu3btmrX/rKwsDR482G/M5WqrqqqaS11Sk5z2icP0%2Bq0KCQlWVNTVqq4%2Bo/r6hkCX40j00Br6Zx09tOZK6F%2BgPty3yoCVmpqq0tJSvzG3261hw4YpLCxMPXv2VFlZmQYMGCBJqq6u1tGjR9WnTx8lJCTI5/Ppk08%2BUUpKSuNro6Ki1K1bt2btPz4%2B/qLTgR7PKdXVtc43b3O11PXX1ze02Nqcgh5aQ/%2Bso4fW0D/znHUIpJnGjh2r9957Tzt37tS5c%2BdUUlKiI0eOaMSIEZKk7OxsrV69WuXl5Tp9%2BrTy8/PVu3dvpaWlKS4uTnfccYeeffZZnTx5UidOnNCKFSs0ZswYuVytMo8CAADDHJsY0tLSJEl1dXWSpG3btkn6%2BmhTr169lJ%2Bfr0WLFqmiokI9evTQqlWr1KFDB0nSuHHj5PF4NH78eNXU1Cg9Pd3vmqnf/OY3mj9/voYMGaI2bdro7rvvbvJmpgAAAE0J8lm9ohvN4vGcMr5NlytYQ/N3Gd/u5bJp5s2BLsGPyxWs2NhwVVXVcGj8EtFDa%2BifdfTQmiuhfx06NH1Lp8utVZ4iBAAACCQCFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAw1ptwEpKSlJqaqrS0tIaHwsWLJAk7dmzR2PGjNENN9ygYcOGad26dX6vXb16te644w7dcMMNys7OVmlpaSCWAAAAHMoV6AIup82bN6tz585%2BY5WVlXrkkUc0d%2B5cDR8%2BXB9%2B%2BKGmTp2qbt26KS0tTdu3b9fvf/97Pf/880pKStLq1as1ZcoUbd26VW3btg3QSgAAgJO02iNY32X9%2BvVKTEzUmDFjFBYWpoyMDA0ePFhr1qyRJBUXF2v06NG67rrrdNVVV%2BnBBx%2BUJO3YsSOQZQMAAAdp1QGroKBAgwYN0o033qh58%2BappqZGZWVlSk5O9ntecnJy42nAb88HBwerd%2B/ecrvdttYOAACcq9WeIrz%2B%2BuuVkZGhJUuW6NixY5o5c6aeeeYZeb1edezY0e%2B5MTExqqqqkiR5vV5FR0f7zUdHRzfON0dlZaU8Ho/fmMvVVvHx8Ze4mqaFhDgrH7tcLaveC/1zWh9bEnpoDf2zjh5aQ/8un1YbsIqLixv/u3v37srLy9PUqVPVr1%2B/H3ytz%2BezvO/CwkK/sZycHM2YMcPSdp0uNjY80CU0KSrq6kCX4Hj00Br6Zx09tIb%2BmddqA9a3de7cWfX19QoODpbX6/Wbq6qqUlxcnCQpNjb2onmv16uePXs2e19ZWVkaPHiw35jL1VZVVTWXWH3TnPaJw/T6rQoJCVZU1NWqrj6j%2BvqGQJfjSPTQGvpnHT205kroX6A%2B3LfKgLV//36tW7dOTz75ZONYeXm5QkNDlZmZqX//93/3e35paamuu%2B46SVJqaqrKyso0atQoSVJ9fb3279%2BvMWPGNHv/8fHxF50O9HhOqa6udb55m6ulrr%2B%2BvqHF1uYU9NAa%2BmcdPbSG/pnnrEMgzdSuXTsVFxerqKhI58%2Bf1%2BHDh7V8%2BXJlZWXpF7/4hSoqKrRmzRqdO3dO77zzjt555x2NHTtWkpSdna233no68A12AAAKm0lEQVRLe/fu1ZkzZ7Ry5UqFhoZq0KBBgV0UAABwjFZ5BKtjx44qKipSQUFBY0AaNWqUcnNzFRYWplWrVmnhwoV65plnlJCQoKVLl%2Braa6%2BVJN1yyy167LHHNHPmTH3xxRdKS0tTUVGRrrrqqgCvCgAAOEWQz%2BoV3WgWj%2BeU8W26XMEamr/L%2BHYvl00zbw50CX5crmDFxoarqqqGQ%2BOXiB5aQ/%2Bso4fWXAn969AhMiD7bZWnCAEAAAKJgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADDMFegCcOX4%2BbN/DXQJP8qmmTcHugQAgENxBAsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDBXoAsAWqqfP/vXQJfwo2yaeXOgSwAA/B%2BOYDWhoqJCDz/8sNLT03Xrrbdq6dKlamhoCHRZAADAITiC1YTp06crJSVF27Zt0xdffKHJkyerffv2euCBBwJdGgAAcAAC1re43W598skneumllxQZGanIyEhNnDhRL7/8MgELLZqTTmlyOhNAa0fA%2BpaysjIlJCQoOjq6cSwlJUWHDx/W6dOnFRER8YPbqKyslMfj8RtzudoqPj7eaK0hIZzhhTM5KQw60Z/zfhboEmxz4e8gfw8vDf27fAhY3%2BL1ehUVFeU3diFsVVVVNStgFRcXq7Cw0G9s2rRpmj59urlC9XWQu7/TZ8rKyjIe3q4ElZWVKi4upn8W0ENr6J91lZWVevnl5%2BnhJaJ/lw%2BRtQk%2Bn8/S67OysrR27Vq/R1ZWlqHq/p/H41FhYeFFR8vQPPTPOnpoDf2zjh5aQ/8uH45gfUtcXJy8Xq/fmNfrVVBQkOLi4pq1jfj4eD4JAABwBeMI1rekpqbq%2BPHjOnnyZOOY2%2B1Wjx49FB4eHsDKAACAUxCwviU5OVlpaWkqKCjQ6dOnVV5erpdeeknZ2dmBLg0AADhEyK9//etfB7qIluZnP/uZNmzYoAULFuhPf/qTxowZo0mTJikoKCjQpV0kPDxcAwYM4OjaJaJ/1tFDa%2BifdfTQGvp3eQT5rF7RDQAAAD%2BcIgQAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIDlQBUVFXr44YeVnp6uW2%2B9VUuXLlVDQ0Ogy3KUpKQkpaamKi0trfGxYMGCQJfVou3atUsZGRnKzc29aG7jxo0aPny4%2Bvbtq9GjR2v37t0BqLBl%2B67%2BrV27Vtdee63fezEtLU0ff/xxgCptuSoqKpSTk6P09HRlZGToySefVHV1tSTpwIEDuu%2B%2B%2B9SvXz/dfvvtevHFFwNcbcvzXf37%2B9//rqSkpIvegy%2B88EKgS3Y0V6ALwI83ffp0paSkaNu2bfriiy80efJktW/fXg888ECgS3OUzZs3q3PnzoEuwxGee%2B45lZSUqGvXrhfNHThwQLNnz1ZhYaFuuukmbdmyRdOmTdPmzZvVqVOnAFTb8nxf/ySpf//%2BeuWVV2yuynmmTJmi1NRUbd%2B%2BXadOnVJOTo6WLFmiefPmafLkyRo7dqyKiop0%2BPBh/epXv1Lnzp11%2B%2B23B7rsFuO7%2Bjd16lRJktvtDnCFrQtHsBzG7Xbrk08%2BUV5eniIjI5WYmKiJEyequLg40KWhFQsLC/vOgLBmzRplZmYqMzNTYWFhGjFihHr16qV169YFoNKW6fv6h%2Baprq5WamqqZs2apfDwcHXq1EmjRo3SBx98oJ07d6q2tlZTp05V27ZtlZKSonvvvZe/i9/wff3D5UHAcpiysjIlJCQoOjq6cSwlJUWHDx/W6dOnA1iZ8xQUFGjQoEG68cYbNW/ePNXU1AS6pBZrwoQJioyMbHKurKxMycnJfmPJycl8Gv6G7%2BufJB0/flwPPPCA%2BvfvryFDhujtt9%2B2sTpniIqK0qJFi9S%2BffvGsePHjys%2BPl5lZWVKSkpSSEhI41xycrJKS0sDUWqL9H39u%2BCJJ57QwIEDddNNN6mgoEC1tbWBKLXVIGA5jNfrVVRUlN/YhbBVVVUViJIc6frrr1dGRoa2bt2q4uJi7d27V88880ygy3Ikr9frF/ilr9%2BTvB%2BbJy4uTomJiXr88cf117/%2BVY899pjmzJmjPXv2BLq0Fs3tduvVV1/V1KlTm/y7GBMTI6/Xy/Wp3%2BGb/QsNDVXfvn01dOhQ7dixQ0VFRVq3bp3%2B9V//NdBlOhoBy4F8Pl%2BgS3C84uJi3XvvvQoNDVX37t2Vl5enDRs26Pz584EuzZF4T166QYMG6fnnn1dycrJCQ0M1bNgwDR06VGvXrg10aS3Whx9%2BqEmTJmnWrFnKyMj4zucFBQXZWJVzfLt/8fHxev311zV06FC1adNGffr00eTJk3kPWkTAcpi4uDh5vV6/Ma/Xq6CgIMXFxQWoKufr3Lmz6uvr9cUXXwS6FMeJjY1t8j3J%2B/HSJSQkqLKyMtBltEjbt2/Xww8/rDlz5mjChAmSvv67%2BO0jpl6vVzExMQoO5n9z39RU/5qSkJCgzz//nA9PFvDOc5jU1FQdP35cJ0%2BebBxzu93q0aOHwsPDA1iZc%2Bzfv1%2BLFy/2GysvL1doaKjf9QhontTU1IuudXG73bruuusCVJGz/PGPf9TGjRv9xsrLy9WlS5cAVdRyffTRR5o9e7aWL1%2BukSNHNo6npqbq008/VV1dXeMY78GLfVf/9uzZo5UrV/o999ChQ0pISOAooAUELIdJTk5WWlqaCgoKdPr0aZWXl%2Bull15SdnZ2oEtzjHbt2qm4uFhFRUU6f/68Dh8%2BrOXLlysrK8vvIlk0z9ixY/Xee%2B9p586dOnfunEpKSnTkyBGNGDEi0KU5wvnz57VgwQK53W7V1tZqw4YNevfddzVu3LhAl9ai1NXV6emnn1ZeXp4GDhzoN5eZmamIiAitXLlSZ86c0b59%2B1RSUsLfxW/4vv5FRkZqxYoVevvtt1VbWyu3260XXniB/lkU5OP4n%2BOcOHFC8%2BbN03/8x38oIiJC48aN07Rp0/ik8SP853/%2BpwoKCvTpp58qNDRUo0aNUm5ursLCwgJdWouUlpYmSY1HCFyur2%2Bhd%2BGbglu3blVBQYEqKirUo0cPzZ07V/379w9MsS3Q9/XP5/Np5cqVKikpkcfjUefOnfXEE0/o1ltvDVi9LdEHH3ygf/qnf1JoaOhFc5s3b1ZNTY3mz5%2Bv0tJStW/fXg899JB%2B%2BctfBqDSlumH%2Brd//34VFhbqyJEjioyM1Pjx4/XQQw9xitUCAhYAAIBhRFMAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMOx/AfkpVEXoiRoSAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common7453031502557866489”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>38</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.8043776284454447</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.4736572024798587</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.16187731751297074</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.12845496497855796</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.36235922873307247</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.140702061098989</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.8768511999938557</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.3120137344415888</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.8693333683503464</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3066)</td> <td class=”number”>3066</td> <td class=”number”>95.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme7453031502557866489”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>38</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.394831152199691e-07</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.1873108393812605e-07</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.161115441987928e-06</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0793447726680707e-05</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>17.91188201497283</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>18.234372136300316</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>18.77323422389639</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.356415968257945</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>27.12065186150593</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_NHASIAN15”>PCT_LACCESS_NHASIAN15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2885</td>

</tr> <tr>

<th>Unique (%)</th> <td>89.9%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>97</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.24886</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>25.088</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>7.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram6195016787616571983”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives6195016787616571983,#minihistogram6195016787616571983”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives6195016787616571983”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles6195016787616571983”

aria-controls=”quantiles6195016787616571983” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram6195016787616571983” aria-controls=”histogram6195016787616571983”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common6195016787616571983” aria-controls=”common6195016787616571983”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme6195016787616571983” aria-controls=”extreme6195016787616571983”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles6195016787616571983”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0.026974</td>

</tr> <tr>

<th>Median</th> <td>0.0855</td>

</tr> <tr>

<th>Q3</th> <td>0.24907</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.96994</td>

</tr> <tr>

<th>Maximum</th> <td>25.088</td>

</tr> <tr>

<th>Range</th> <td>25.088</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.22209</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.69393</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.7885</td>

</tr> <tr>

<th>Kurtosis</th> <td>578</td>

</tr> <tr>

<th>Mean</th> <td>0.24886</td>

</tr> <tr>

<th>MAD</th> <td>0.2679</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>18.998</td>

</tr> <tr>

<th>Sum</th> <td>774.69</td>

</tr> <tr>

<th>Variance</th> <td>0.48154</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram6195016787616571983”>

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nCwAAbMSWBauiokIpKSmaMWOGIiMjdeWVV2r48OH64IMPtGvXLtXU1GjSpElq1qyZunbtqjvvvFMFBQWSpLVr1yojI0MZGRmKiIjQsGHD1KlTJ61fv16SVFBQoHHjxql9%2B/aKiopSTk6OSkpKtH///kCeMgAAsBFbFiyn06lFixapRYsWDWvHjh1TQkKCiouL1blzZ4WFhTXsJScnq6ioSJJUXFys5ORkn%2BMlJyfL5XLp9OnTOnTokM9%2BVFSU2rZtK5fLZfFZAQCAYOEI9AAmuFwuvfzyy1q5cqU2b94sp9Ppsx8bGyuPx6P6%2Bnp5PB7FxMT47MfExOjQoUP68ssv5fV6L7jvdrsbPU9ZWZnKy8t91hyOZkpISLjEM7u4sDB79WOHwz7znsvWbhnbAdlah2ytQ7bWCdZsbV%2BwPvzwQ02aNEkzZsxQenq6Nm/efMHHhYSENPzzd91Pdbn3WxUUFCgvL89nLTs7W1OnTr2s49pdXFxkoEe4ZE7nFYEeIWiRrXXI1jpka51gy9bWBWvHjh168MEHNW/ePN1%2B%2B%2B2SpPj4eB09etTncR6PR7GxsQoNDVVcXJw8Hs95%2B/Hx8Q2PudB%2B8%2BbNGz1XZmamBgwY4LPmcDST2111CWf33ezW9k2fv5XCwkLldF6hiopq1dXVB3qcoEK21iFb65CtdazONlDf3Nu2YH300UeaPXu2li9frr59%2Bzasp6Sk6JVXXlFtba0cjq9Pz%2BVyqXv37g375%2B7HOsflcmnIkCGKiIhQx44dVVxcrGuvvVbS1zfUf/bZZ%2BrWrVujZ0tISDjv5cDy8lOqrf1xf1Ha8fzr6uptObcdkK11yNY6ZGudYMvWXpdA/ldtba0eeeQRzZw506dcSVJGRoaioqK0cuVKVVdXa//%2B/SosLFRWVpYkadSoUXr33Xe1a9cunTlzRoWFhTp69KiGDRsmScrKytLq1atVUlKiyspK5ebmqkuXLkpNTfX7eQIAAHvy%2BxWsAQMGaMSIEbrjjjt01VVXfa9j7Nu3TyUlJVq4cKEWLlzos7dlyxY988wzeuyxx5Sfn68WLVooJydH/fv3lyR16tRJubm5WrRokUpLS9WhQwetWrVKLVu2lCSNHj1a5eXlGjNmjKqqqpSWlnbe/VQAAAAXE%2BL18ztorlixQm%2B99Zb%2B8Y9/6LrrrtOoUaM0YMCAhpfzglV5%2BSnjx3Q4QjUod4/x41pl8/TrAz1CozkcoYqLi5TbXRVUl6x/CMjWOmRrHbK1jtXZtmwZbfyYjeH3lwizs7O1adMmvfbaa%2BrYsaN%2B85vfKCMjQ0uWLNGRI0f8PQ4AAIBxAbsHq2vXrpo9e7Z27typOXPm6LXXXtMtt9yi8ePH6%2BOPPw7UWAAAAJctYAWrpqZGmzZt0r333qvZs2erVatWevjhh9WlSxeNGzdOGzZsCNRoAAAAl8XvNz6VlJSosLBQb7zxhqqqqjR48GD94Q9/UK9evRoe07t3b82fP19Dhw7193gAAACXze8Fa8iQIWrXrp0mTJig22%2B/XbGxsec9JiMjQydPnvT3aAAAAEb4vWCtXr264U08L2b//v1%2BmAYAAMA8v9%2BD1blzZ02cOFHbt29vWPuv//ov3Xvvvef9ihoAAAA78nvBWrRokU6dOqUOHTo0rPXv31/19fVavHixv8cBAAAwzu8vEb7zzjvasGGD4uLiGtaSkpKUm5urW2%2B91d/jAAAAGOf3K1inT59WRETE%2BYOEhqq6utrf4wAAABjn94LVu3dvLV68WF9%2B%2BWXD2r/%2B9S89/vjjPm/VAAAAYFd%2Bf4lwzpw5uueee3TdddcpKipK9fX1qqqqUps2bfTSSy/5exwAAADj/F6w2rRpo7feektvv/22PvvsM4WGhqpdu3bq27evwsLC/D0OAACAcX4vWJIUHh6uG2%2B8MRBPDQAAYDm/F6zPP/9cS5cu1aeffqrTp0%2Bft//nP//Z3yMBAAAYFZB7sMrKytS3b181a9bM308PAABgOb8XrKKiIv35z39WfHy8v58aAADAL/z%2BNg3NmzfnyhUAAAhqfi9YEyZMUF5enrxer7%2BfGgAAwC/8/hLh22%2B/rY8%2B%2Bkjr1q1T69atFRrq2/FeffVVf48EAABglN8LVlRUlPr16%2BfvpwUAAPAbvxesRYsW%2BfspAQAA/Mrv92BJ0uHDh/X000/r4Ycfblj7n//5n0CMAgAAYJzfC9bevXs1bNgwbdu2TRs3bpT09ZuPjh07ljcZBQAAQcHvBWvZsmV68MEHtWHDBoWEhEj6%2BvcTLl68WCtWrPD3OAAAAMb5vWD9/e9/V1ZWliQ1FCxJuvnmm1VSUuLvcQAAAIzze8GKjo6%2B4O8gLCsrU3h4uL/HAQAAMM7vBatnz576zW9%2Bo8rKyoa1I0eOaPbs2bruuuv8PQ4AAIBxfn%2Bbhocfflh33XWX0tLSVFdXp549e6q6ulodO3bU4sWL/T0OAACAcX4vWFdeeaU2btyo3bt368iRI2ratKnatWun66%2B/3ueeLAAAALvye8GSpCZNmujGG28MxFMDAABYzu8Fa8CAARe9UsV7YQEAALvze8G65ZZbfApWXV2djhw5IpfLpbvuusvf4wAAABjn94I1c%2BbMC65v3bpVf/nLX/w8DQAAgHkB%2BV2EF3LjjTfqrbfeCvQYAAAAl%2B0HU7AOHDggr9cb6DEAAAAum99fIhw9evR5a9XV1SopKdFNN93k73EAAACM83vBSkpKOu%2BnCCMiIjRy5Ejdeeed/h4HAADAOL8XLN6tHQAABDu/F6w33nij0Y%2B9/fbbL7q/Z88ezZ49W2lpaVq2bFnD%2Brp16zRnzhw1adLE5/Fr1qxRt27dVF9fr%2BXLl2vjxo2qqKhQt27dNH/%2BfLVp00aS5PF4NH/%2BfP31r39VaGioMjIyNG/ePDVt2vQSzhQAAPxY%2Bb1gzZ07V/X19efd0B4SEuKzFhISctGC9eyzz6qwsFBt27a94H7v3r310ksvXXBvzZo12rBhg5599lm1atVKy5YtU3Z2tt58802FhIRo3rx5Onv2rDZu3KiamhpNmzZNubm5euSRR77HGQMAgB8bv/8U4XPPPae%2BfftqzZo1%2BuCDD/T%2B%2B%2B/r5ZdfVr9%2B/fTss8/q448/1scff6z9%2B/df9DgREREXLVgXU1BQoHHjxql9%2B/aKiopSTk6OSkpKtH//fn3xxRfavn27cnJyFB8fr1atWmny5Ml6/fXXVVNT831PGwAA/IgE5B6s/Px8tWrVqmHtmmuuUZs2bTR%2B/Hht3LixUccZO3bsRfePHTumu%2B%2B%2BW0VFRXI6nZo6dapuu%2B02nT59WocOHVJycnLDY6OiotS2bVu5XC6dOnVKYWFh6ty5c8N%2B165d9dVXX%2Bnw4cM%2B69%2BmrKxM5eXlPmsORzMlJCQ06twaKyzsB/MuG43icNhn3nPZ2i1jOyBb65CtdcjWOsGard8L1tGjRxUTE3PeutPpVGlpqZHniI%2BPV1JSkh544AF16NBBf/rTnzRr1iwlJCTopz/9qbxe73kzxMTEyO12KzY2VlFRUT4/6XjusW63u1HPX1BQoLy8PJ%2B17OxsTZ069TLPzN7i4iIDPcIlczqvCPQIQYtsrUO21iFb6wRbtn4vWImJiVq8eLGmTZumuLg4SVJFRYV%2B//vf6yc/%2BYmR5%2Bjfv7/69%2B/f8O9DhgzRn/70J61bt67hV/Vc7E1NL/cNTzMzMzVgwACfNYejmdzuqss67jfZre2bPn8rhYWFyum8QhUV1aqrqw/0OEGFbK1DttYhW%2BtYnW2gvrn3e8GaM2eOZsyYoYKCAkVGRio0NFSVlZVq2rSpVqxYYdnzJiYmqqioSLGxsQoNDZXH4/HZ93g8at68ueLj41VZWam6ujqFhYU17ElS8%2BbNG/VcCQkJ570cWF5%2BSrW1P%2B4vSjuef11dvS3ntgOytQ7ZWodsrRNs2fq9YPXt21e7du3S7t27dfz4cXm9XrVq1Uo///nPFR0dbeQ5XnnlFcXExOiWW25pWCspKVGbNm0UERGhjh07qri4WNdee62kr6%2BgffbZZ%2BrWrZsSExPl9Xr1ySefqGvXrpIkl8slp9Opdu3aGZkPAAAEN78XLEm64oorNHDgQB0/frzhvadMOnv2rBYsWKA2bdro6quv1tatW/X222/rtddekyRlZWUpPz9f/fr1U6tWrZSbm6suXbooNTVVkjR48GD97ne/01NPPaWzZ89qxYoVGjlypByOgMQFAABsxu%2BN4fTp03rsscf01ltvSZKKiopUUVGhBx54QL/97W/ldDobdZxzZai2tlaStH37dklfX20aO3asqqqqNG3aNJWXl6t169ZasWKFUlJSJH39%2BxDLy8s1ZswYVVVVKS0tzeem9CeeeEKPPfaYBg4cqCZNmujWW29VTk6OsQwAAEBwC/Fe7h3dl2jBggV6//33NXnyZM2aNUsff/yxKioqNG3aNLVp00ZPPPGEP8fxm/LyU8aP6XCEalDuHuPHtcrm6dcHeoRGczhCFRcXKbe7KqjuCfghIFvrkK11yNY6VmfbsqWZ248uld9/DG3r1q36/e9/r5tvvrnhrRCcTqcWLVqkbdu2%2BXscAAAA4/xesKqqqpSUlHTeenx8vL766it/jwMAAGCc3wvWT37yE/3lL3%2BR5Pt%2BU1u2bNG//du/%2BXscAAAA4/x%2Bk/svf/lL3X///brjjjtUX1%2BvF198UUVFRdq6davmzp3r73EAAACM83vByszMlMPh0Msvv6ywsDA988wzateunXJzc3XzzTf7exwAAADj/F6wTp48qTvuuEN33HGHv58aAADAL/x%2BD9bAgQMv%2B3f9AQAA/JD5vWClpaVp8%2BbN/n5aAAAAv/H7S4RXXXWVnnzySeXn5%2BsnP/mJmjRp4rO/dOlSf48EAABglN8L1qFDh/TTn/5UkuR2u/399AAAAJbzW8HKycnRsmXL9NJLLzWsrVixQtnZ2f4aAQAAwC/8dg/Wjh07zlvLz8/319MDAAD4jd8K1oV%2BcpCfJgQAAMHIbwXr3C92/q41AAAAu/P72zQAAAAEOwoWAACAYX77KcKamhrNmDHjO9d4HywAAGB3fitYvXr1UllZ2XeuAQAA2J3fCtb///5XAAAAwYx7sAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMMzWBWvPnj1KT09XTk7OeXubNm3S0KFD1aNHD40YMULvvPNOw159fb2WLVumgQMHqnfv3ho/frw%2B//zzhn2Px6Pp06crPT1dffv21dy5c3X69Gm/nBMAALA/2xasZ599VgsXLlTbtm3P2zt48KBmz56tmTNn6r333tO4ceM0ZcoUHT9%2BXJK0Zs0abdiwQfn5%2Bdq5c6eSkpKUnZ0tr9crSZo3b56qq6u1ceNGvf766yopKVFubq5fzw8AANiXbQtWRESECgsLL1iw1q5dq4yMDGVkZCgiIkLDhg1Tp06dtH79eklSQUGBxo0bp/bt2ysqKko5OTkqKSnR/v379cUXX2j79u3KyclRfHy8WrVqpcmTJ%2Bv1119XTU2Nv08TAADYkCPQA3xfY8eO/da94uJiZWRk%2BKwlJyfL5XLp9OnTOnTokJKTkxv2oqKi1LZtW7lcLp06dUphYWHq3Llzw37Xrl311Vdf6fDhwz7r36asrEzl5eU%2Baw5HMyUkJDT29BolLMxe/djhsM%2B857K1W8Z2QLbWIVvrkK11gjVb2xasi/F4PIqJifFZi4mJ0aFDh/Tll1/K6/VecN/tdis2NlZRUVEKCQnx2ZMkt9vdqOcvKChQXl6ez1p2dramTp36fU4naMTFRQZ6hEvmdF4R6BGCFtlah2ytQ7bWCbZsg7JgSWq4n%2Br77H/Xx36XzMxMDRgwwGfN4Wgmt7vqso77TXZr%2B6bP30phYaFyOq9QRUW16urqAz1OUCFb65CtdcjWOlZnG6hv7oOyYMXFxcnj8fiseTwexcfHKzY2VqGhoRfcb968ueLj41VZWam6ujqFhYU17ElS8%2BbNG/X8CQkJ570cWF5%2BSrW1P%2B4vSjuef11dvS3ntgOytQ7ZWodsrRNs2drrEkgjpaSkqKioyGfN5XKpe/fuioiIUMeOHVVcXNywV1FRoc8%2B%2B0zdunVTly5d5PV69cknn/h8rNPpVLt27fx2DgAAwL6CsmCNGjVK7777rnbt2qUzZ86osLBQR4/QOAinAAAO%2B0lEQVQe1bBhwyRJWVlZWr16tUpKSlRZWanc3Fx16dJFqampio%2BP1%2BDBg/W73/1OJ0%2Be1PHjx7VixQqNHDlSDkdQXvADAACG2bYxpKamSpJqa2slSdu3b5f09dWmTp06KTc3V4sWLVJpaak6dOigVatWqWXLlpKk0aNHq7y8XGPGjFFVVZXS0tJ8bkp/4okn9Nhjj2ngwIFq0qSJbr311gu%2BmSkAAMCFhHgv945uNEp5%2BSnjx3Q4QjUod4/x41pl8/TrAz1CozkcoYqLi5TbXRVU9wT8EJCtdcjWOmRrHauzbdky2vgxGyMoXyIEAAAIJAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABgWtAWrc%2BfOSklJUWpqasOfBQsWSJL27t2rkSNHqmfPnhoyZIjWr1/v87GrV6/W4MGD1bNnT2VlZamoqCgQpwAAAGzKEegBrLRlyxa1bt3aZ62srEyTJ0/W3LlzNXToUH344YeaNGmS2rVrp9TUVO3YsUNPP/20nnvuOXXu3FmrV6/WxIkTtW3bNjVr1ixAZwIAAOwkaK9gfZsNGzYoKSlJI0eOVEREhNLT0zVgwACtXbtWklRQUKARI0aoe/fuatq0qX79619Lknbu3BnIsQEAgI0EdcFaunSp%2Bvfvr2uuuUbz5s1TVVWViouLlZyc7PO45OTkhpcBv7kfGhqqLl26yOVy%2BXV2AABgX0H7EuHPfvYzpaen66mnntLnn3%2Bu6dOn6/HHH5fH41GrVq18HhsbGyu32y1J8ng8iomJ8dmPiYlp2G%2BMsrIylZeX%2B6w5HM2UkJDwPc/mwsLC7NWPHQ77zHsuW7tlbAdkax2ytQ7ZWidYsw3aglVQUNDwz%2B3bt9fMmTM1adIk9erV6zs/1uv1XvZz5%2BXl%2BaxlZ2dr6tSpl3Vcu4uLiwz0CJfM6bwi0CMELbK1Dtlah2ytE2zZBm3B%2BqbWrVurrq5OoaGh8ng8Pntut1vx8fGSpLi4uPP2PR6POnbs2OjnyszM1IABA3zWHI5mcrurvuf0F2a3tm/6/K0UFhYqp/MKVVRUq66uPtDjBBWytQ7ZWodsrWN1toH65j4oC9aBAwe0fv16PfTQQw1rJSUlCg8PV0ZGhv74xz/6PL6oqEjdu3eXJKWkpKi4uFjDhw%2BXJNXV1enAgQMaOXJko58/ISHhvJcDy8tPqbb2x/1Facfzr6urt%2BXcdkC21iFb65CtdYItW3tdAmmk5s2bq6CgQPn5%2BTp79qyOHDmi5cuXKzMzU7fddptKS0u1du1anTlzRrt379bu3bs1atQoSVJWVpbeeOMN7du3T9XV1Vq5cqXCw8PVv3//wJ4UAACwjaC8gtWqVSvl5%2Bdr6dKlDQVp%2BPDhysnJUUREhFatWqWFCxfq8ccfV2JiopYsWaKrr75aktSvXz898MADmj59uk6cOKHU1FTl5%2BeradOmAT4rAABgFyHey72jG41SXn7K%2BDEdjlANyt1j/LhW2Tz9%2BkCP0GgOR6ji4iLldlcF1SXrHwKytQ7ZWodsrWN1ti1bRhs/ZmME5UuEAAAAgUTBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAuoLS0VPfdd5/S0tJ0ww03aMmSJaqvrw/0WAAAwCYcgR7gh%2Bj%2B%2B%2B9X165dtX37dp04cUITJkxQixYtdPfddwd6NAAAYANcwfoGl8ulTz75RDNnzlR0dLSSkpI0btw4FRQUBHo0AABgE1zB%2Bobi4mIlJiYqJiamYa1r1646cuSIKisrFRUV9Z3HKCsrU3l5uc%2Baw9FMCQkJRmcNC7NXP/7F7/470CNckg%2BevNl2GdvBuUzJ1jyytQ7ZWidYs6VgfYPH45HT6fRZO1e23G53owpWQUGB8vLyfNamTJmi%2B%2B%2B/39yg%2BrrI3XXlp8rMzDRe3n7sysrK9PTTT5OtBcrKyvSHPzxHthYgW%2BuQrXWCNdvgqouGeL3ey/r4zMxMrVu3zudPZmamoen%2BT3l5ufLy8s67WobLR7bWIVvrkK11yNY6wZotV7C%2BIT4%2BXh6Px2fN4/EoJCRE8fHxjTpGQkJCULVwAABwabiC9Q0pKSk6duyYTp482bDmcrnUoUMHRUZGBnAyAABgFxSsb0hOTlZqaqqWLl2qyspKlZSU6MUXX1RWVlagRwMAADYRNn/%2B/PmBHuKH5uc//7k2btyoBQsW6K233tLIkSM1fvx4hYSEBHq080RGRuraa6/l6poFyNY6ZGsdsrUO2VonGLMN8V7uHd0AAADwwUuEAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsGyotLRU9913n9LS0nTDDTdoyZIlqq%2BvD/RYQaFz585KSUlRampqw58FCxYEeizb2rNnj9LT05WTk3Pe3qZNmzR06FD16NFDI0aM0DvvvBOACe3r27Jdt26drr76ap/P4dTUVH388ccBmtR%2BSktLlZ2drbS0NKWnp%2Buhhx5SRUWFJOngwYP61a9%2BpV69eummm27SCy%2B8EOBp7eXbsv3nP/%2Bpzp07n/d5%2B/zzzwd65O/NEegBcOnuv/9%2Bde3aVdu3b9eJEyc0YcIEtWjRQnfffXegRwsKW7ZsUevWrQM9hu09%2B%2ByzKiwsVNu2bc/bO3jwoGbPnq28vDz16dNHW7du1ZQpU7RlyxZdeeWVAZjWXi6WrST17t1bL730kp%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%2BOR0%2Bn0WTtXttxudyBGCio/%2B9nPlJ6erm3btqmgoED79u3T448/Huixgo7H4/H5JkH6%2BvOYz%2BHLFx8fr6SkJD344IP67//%2Bbz3wwAOaM2eO9u7dG%2BjRbMnlcunll1/WpEmTLvj3b2xsrDweD/fBfg//f7bh4eHq0aOHBg0apJ07dyo/P1/r16/Xf/7nfwZ6zO%2BNgmVDXq830CMErYKCAt15550KDw9X%2B/btNXPmTG3cuFFnz54N9GhBh89ja/Tv31/PPfeckpOTFR4eriFDhmjQoEFat25doEeznQ8//FDjx4/XjBkzlJ6e/q2PCwkJ8eNUweGb2SYkJOjVV1/VoEGD1KRJE3Xr1k0TJkyw9ectBctm4uPj5fF4fNY8Ho9CQkIUHx8foKmCV%2BvWrVVXV6cTJ04EepSgEhcXd8HPYz6HrZGYmKiysrJAj2ErO3bs0H333ac5c%2BZo7Nixkr7%2B%2B/ebV1k9Ho9iY2MVGsr/ThvrQtleSGJior744gvbfjPGZ4TNpKSk6NixYzp58mTDmsvlUocOHRQZGRnAyezvwIEDWrx4sc9aSUmJwsPDfe4RwOVLSUk5774Vl8ul7t27B2ii4PHKK69o06ZNPmslJSVq06ZNgCayn48%2B%2BkizZ8/W8uXLdfvttzesp6Sk6G9/%2B5tqa2sb1vi8vTTflu3evXu1cuVKn8cePnxYiYmJtr1CSMGymeTkZKWmpmrp0qWqrKxUSUmJXnzxRWVlZQV6NNtr3ry5CgoKlJ%2Bfr7Nnz%2BrIkSNavny5MjMzfW5qxeUbNWqU3n33Xe3atUtnzpxRYWGhjh49qmHDhgV6NNs7e/asFixYIJfLpZqaGm3cuFFvv/22Ro8eHejRbKG2tlaPPPKIZs6cqb59%2B/rsZWRkKCoqSitXrlR1dbX279%2BvwsJC/v5tpItlGx0drRUrVujNN99UTU2NXC6Xnn/%2BeVtnG%2BK167W3H7Hjx49r3rx5%2Butf/6qoqCiNHj1aU6ZMsW3L/yF5//33tXTpUv3tb39TeHi4hg8frpycHEVERAR6NNtJTU2VpIbv9h2Or99279xPCm7btk1Lly5VaWmpOnTooLlz56p3796BGdZmLpat1%2BvVypUrVVhYqPLycrVu3VqzZs3SDTfcELB57eSDDz7Qv//7vys8PPy8vS1btqiqqkqPPfaYioqK1KJFC91777365S9/GYBJ7ee7sj1w4IDy8vJ09OhRRUdHa8yYMbr33ntt%2B/IrBQsAAMAwe9ZCAACAHzAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAM%2B3/PbAu7MtTcvgAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common6195016787616571983”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>230</td> <td class=”number”>7.2%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00488471482991773</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.2726639462994582</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0837245293810234</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.03153662117278969</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.03212077351235999</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.03427592152288209</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.20402215352700614</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.13850379492086456</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5651772464780063</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2874)</td> <td class=”number”>2874</td> <td class=”number”>89.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme6195016787616571983”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>230</td> <td class=”number”>7.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.546316229191107e-06</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.235394888818366e-06</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.1382045863177702e-05</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.8029219907524265e-05</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>6.971474256436248</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.839510182141182</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.623275578940993</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.223929168093157</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.087551491157946</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_NHNA15”>PCT_LACCESS_NHNA15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2945</td>

</tr> <tr>

<th>Unique (%)</th> <td>91.7%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>97</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.748</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>83.844</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>5.3%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-2000633535235731057”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-2000633535235731057,#minihistogram-2000633535235731057”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-2000633535235731057”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-2000633535235731057”

aria-controls=”quantiles-2000633535235731057” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-2000633535235731057” aria-controls=”histogram-2000633535235731057”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-2000633535235731057” aria-controls=”common-2000633535235731057”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-2000633535235731057” aria-controls=”extreme-2000633535235731057”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-2000633535235731057”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0.027008</td>

</tr> <tr>

<th>Median</th> <td>0.071824</td>

</tr> <tr>

<th>Q3</th> <td>0.18568</td>

</tr> <tr>

<th>95-th percentile</th> <td>1.7475</td>

</tr> <tr>

<th>Maximum</th> <td>83.844</td>

</tr> <tr>

<th>Range</th> <td>83.844</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.15867</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>4.4721</td>

</tr> <tr>

<th>Coef of variation</th> <td>5.9788</td>

</tr> <tr>

<th>Kurtosis</th> <td>142.75</td>

</tr> <tr>

<th>Mean</th> <td>0.748</td>

</tr> <tr>

<th>MAD</th> <td>1.1582</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>11.135</td>

</tr> <tr>

<th>Sum</th> <td>2328.5</td>

</tr> <tr>

<th>Variance</th> <td>20</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-2000633535235731057”>

<img 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VlZqdTUVE2aNEmRkZFq1aqVhg4dqk8%2B%2BUTbtm3TyZMnNW7cODVv3lxdunTRvffeq%2BLiYknS8uXLlZmZqczMTEVERGjIkCHq2LGjVq9eLUkqLi5WTk6O2rVrp6ioKOXn56usrEw7d%2B4M5JIBAEAIsQe6gH%2BFw%2BHQ7Nmz/cYOHDighIQElZaWqlOnTgoPD/fNpaSkaPny5ZKk0tJSZWZm%2Br03JSVFTqdTx44d0969e5WSkuKbi4qKUps2beR0OnXttddeUH0VFRVyuVx%2BY3Z7cyUkJPxT6zyf8PDQysd2e2jV21A/9yfU%2BtRY0J/gRn%2BCG/05v5AMWKdyOp16/fXXtXjxYq1fv14Oh8NvPjY2Vh6PR/X19fJ4PIqJifGbj4mJ0d69e3XkyBF5vd4zzrvd7guup7i4WAsXLvQby83NVV5e3j%2B5sktLXFxkoEsICIfjskCXgHOgP8GN/gQ3%2BnN2IR%2BwPv30U40bN06TJk1SRkaG1q9ff8bXhYWF%2Bf79fPupGrrfasSIEerXr5/fmN3eXG53dYOOe6pQ%2B%2BRgev3BLjzcJofjMlVW1qiurj7Q5eAU9Ce40Z/gFkr9CdSH%2B5AOWFu2bNETTzyh6dOn6%2B6775YkxcfH65tvvvF7ncfjUWxsrGw2m%2BLi4uTxeE6bj4%2BP973mTPMtWrS44LoSEhJOux3och1VbW1wfxFebI11/XV19Y127aGA/gQ3%2BhPc6M/ZhdYlkP/js88%2BU0FBgV566SVfuJKk1NRUffnll6qtrfWNOZ1OdevWzTdfUlLid6yf5yMiItShQweVlpb65iorK7V//3517dr1Iq8IAABcKkIyYNXW1mratGmaPHmy%2BvTp4zeXmZmpqKgoLV68WDU1Ndq5c6dWrFih7OxsSdLw4cP10Ucfadu2bTp%2B/LhWrFihb775RkOGDJEkZWdna9myZSorK1NVVZUKCwvVuXNnpaWlWb5OAAAQmkLyFuEXX3yhsrIyzZo1S7NmzfKb27Bhg15%2B%2BWXNmDFDRUVFuvzyy5Wfn6%2B%2BfftKkjp27KjCwkLNnj1b5eXlat%2B%2BvZYsWaKWLVtKkkaOHCmXy6X7779f1dXVSk9PP23DOgAAwLmEeS1%2Bgma/fv00bNgw3XPPPbriiiusPHVAuVxHjR/TbrdpQOEHxo97sayfeEOgS7CU3W5TXFyk3O5q9igEIfoT3OhPcAul/rRsGR2Q81p%2Bi/Cee%2B7RunXrdMstt%2Bihhx7Spk2b/PZLAQAAhDrLA1Zubq7WrVunt956Sx06dNALL7ygzMxMzZ07V/v27bO6HAAAAOMCtsm9S5cuKigo0NatWzV16lS99dZbuuOOOzR69Gj99a9/DVRZAAAADRawgHXy5EmtW7dODz/8sAoKCpSYmKinnnpKnTt3Vk5OjtasWROo0gAAABrE8p8iLCsr04oVK/T222%2BrurpaAwcO1B/%2B8Af17NnT95pevXpp5syZGjx4sNXlAQAANJjlAWvQoEFq27atxowZo7vvvluxsbGnvSYzM1OHDx%2B2ujQAAAAjLA9Yy5YtU%2B/evc/7up07d1pQDQAAgHmW78Hq1KmTxo4dq82bN/vG/uu//ksPP/zwab8DEAAAIBRZHrBmz56to0ePqn379r6xvn37qr6%2BXnPmzLG6HAAAAOMsv0X44Ycfas2aNYqLi/ONJScnq7CwUHfeeafV5QAAABhn%2BRWsY8eOKSIi4vRCbDbV1NRYXQ4AAIBxlgesXr16ac6cOTpy5Ihv7Pvvv9ezzz7r96gGAACAUGX5LcKpU6fqV7/6lX7xi18oKipK9fX1qq6uVuvWrfXaa69ZXQ4AAIBxlges1q1b65133tH777%2Bv/fv3y2azqW3bturTp4/Cw8OtLgcAAMA4ywOWJDVt2lS33HJLIE4NAABw0VkesL799lvNmzdPX331lY4dO3ba/HvvvWd1SQAAAEYFZA9WRUWF%2BvTpo%2BbNm1t9egAAgIvO8oBVUlKi9957T/Hx8VafGgAAwBKWP6ahRYsWXLkCAACXNMsD1pgxY7Rw4UJ5vV6rTw0AAGAJy28Rvv/%2B%2B/rss8%2B0cuVKXXnllbLZ/DPem2%2B%2BaXVJAAAARlkesKKionTTTTdZfVoAAADLWB6wZs%2BebfUpAQAALGX5HixJ%2Bvrrr7VgwQI99dRTvrHPP/88EKUAAAAYZ3nA2rFjh4YMGaJNmzZp7dq1kn56%2BOioUaN4yCgAALgkWB6w5s%2BfryeeeEJr1qxRWFiYpJ9%2BP%2BGcOXO0aNEiq8sBAAAwzvKA9be//U3Z2dmS5AtYknTbbbeprKzM6nIAAACMszxgRUdHn/F3EFZUVKhp06ZWlwMAAGCc5QGrR48eeuGFF1RVVeUb27dvnwoKCvSLX/zC6nIAAACMs/wxDU899ZQeeOABpaenq66uTj169FBNTY06dOigOXPmWF0OAACAcZYHrFatWmnt2rXavn279u3bp2bNmqlt27a64YYb/PZkAQAAhCrLA5YkNWnSRLfccksgTg0AAHDRWR6w%2BvXrd84rVTwLCwAAhDrLA9Ydd9zhF7Dq6uq0b98%2BOZ1OPfDAA1aXAwAAYJzlAWvy5MlnHN%2B4caP%2B/Oc/W1wNAACAeQH5XYRncsstt%2Bidd94JdBkAAAANFjQBa9euXfJ6vYEuAwAAoMEsv0U4cuTI08ZqampUVlamW2%2B91epyAAAAjLM8YCUnJ5/2U4QRERHKysrSvffe%2B08d64MPPlBBQYHS09M1f/583/jKlSs1depUNWnSxO/1b7zxhrp27ar6%2Bnq99NJLWrt2rSorK9W1a1fNnDlTrVu3liR5PB7NnDlTf/nLX2Sz2ZSZmanp06erWbNm/%2BKqAQBAY2J5wDL1tPZXXnlFK1asUJs2bc4436tXL7322mtnnHvjjTe0Zs0avfLKK0pMTNT8%2BfOVm5urVatWKSwsTNOnT9eJEye0du1anTx5UhMmTFBhYaGmTZtmpHYAAHBpszxgvf322xf82rvvvvuscxEREVqxYoWef/55HT9%2B/J%2Bqobi4WDk5OWrXrp0kKT8/X%2Bnp6dq5c6euvPJKbd68WX/6058UHx8vSXr00Uc1YcIEFRQUnHZVDAAA4FSWB6ynn35a9fX1p21oDwsL8xsLCws7Z8AaNWrUOc9z4MABPfjggyopKZHD4VBeXp7uuusuHTt2THv37lVKSorvtVFRUWrTpo2cTqeOHj2q8PBwderUyTffpUsX/fjjj/r666/9xgEAAM7E8oD1u9/9TkuXLtXYsWPVqVMneb1effnll3rllVd03333KT09vcHniI%2BPV3Jysh5//HG1b99e7777rp588kklJCTo6quvltfrVUxMjN97YmJi5Ha7FRsbq6ioKL99Yj%2B/1u12X9D5Kyoq5HK5/Mbs9uZKSEho4Mr8hYcHzQ%2BBXhC7PbTqbaif%2BxNqfWos6E9woz/Bjf6cX0D2YBUVFSkxMdE3dt1116l169YaPXq01q5d2%2BBz9O3bV3379vX9fdCgQXr33Xe1cuVK34NOz/VIiIY%2BLqK4uFgLFy70G8vNzVVeXl6Djhvq4uIiA11CQDgclwW6BJwD/Qlu9Ce40Z%2BzszxgffPNN6ddPZIkh8Oh8vLyi3bepKQklZSUKDY2VjabTR6Px2/e4/GoRYsWio%2BPV1VVlerq6hQeHu6bk6QWLVpc0LlGjBihfv36%2BY3Z7c3ldlcbWMk/hNonB9PrD3bh4TY5HJepsrJGdXX1gS4Hp6A/wY3%2BBLdQ6k%2BgPtxbHrCSkpI0Z84cTZgwQXFxcZKkyspK/fa3v9VVV11l5Bz//d//rZiYGN1xxx2%2BsbKyMrVu3VoRERHq0KGDSktL1bt3b9/59%2B/fr65duyopKUler1d79uxRly5dJElOp1MOh0Nt27a9oPMnJCScdjvQ5Tqq2trg/iK82Brr%2Buvq6hvt2kMB/Qlu9Ce40Z%2BzszxgTZ06VZMmTVJxcbEiIyNls9lUVVWlZs2aadGiRUbOceLECT333HNq3bq1rrnmGm3cuFHvv/%2B%2B3nrrLUlSdna2ioqKdNNNNykxMVGFhYXq3Lmz0tLSJEkDBw7Ub37zG7344os6ceKEFi1apKysLNntlv/nAgAAIcjyxNCnTx9t27ZN27dv18GDB%2BX1epWYmKgbb7xR0dHRF3ycn8NQbW2tJGnz5s2SfrraNGrUKFVXV2vChAlyuVy68sortWjRIqWmpkr66WnyLpdL999/v6qrq5Wenu63Z%2BrXv/61ZsyYof79%2B6tJkya68847lZ%2Bfb%2Bo/AQAAuMSFeQP0CwBPnjypgwcP%2Bp6efqlzuY4aP6bdbtOAwg%2BMH/diWT/xhkCXYCm73aa4uEi53dVcQg9C9Ce40Z/gFkr9adnywi/emGT5Luljx46poKBA3bt31%2B233y7ppz1QDz30kCorK60uBwAAwDjLA9bcuXO1e/duFRYWymb7x%2Bnr6upUWFhodTkAAADGWR6wNm7cqN/%2B9re67bbbfA/zdDgcmj17tjZt2mR1OQAAAMZZHrCqq6uVnJx82nh8fLx%2B/PFHq8sBAAAwzvKAddVVV%2BnPf/6zJP8npm/YsEH/9m//ZnU5AAAAxln%2BmIZf/vKXGj9%2BvO655x7V19fr1VdfVUlJiTZu3Kinn37a6nIAAACMszxgjRgxQna7Xa%2B//rrCw8P18ssvq23btiosLNRtt91mdTkAAADGWR6wDh8%2BrHvuuUf33HOP1acGAACwhOV7sPr3768APdsUAADAEpYHrPT0dK1fv97q0wIAAFjG8luEV1xxhZ5//nkVFRXpqquuUpMmTfzm582bZ3VJAAAARlkesPbu3aurr75akuR2u60%2BPQAAwEVnWcDKz8/X/Pnz9dprr/nGFi1apNzcXKtKAAAAsIRle7C2bNly2lhRUZFVpwcAALCMZQHrTD85yE8TAgCAS5FlAevnX%2Bx8vjEAAIBQZ/ljGgAAAC51BCwAAADDLPspwpMnT2rSpEnnHeM5WAAAINRZFrB69uypioqK844BAACEOssC1v99/hUAAMCljD1YAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMC%2BmA9cEHHygjI0P5%2Bfmnza1bt06DBw9W9%2B7dNWzYMH344Ye%2Bufr6es2fP1/9%2B/dXr169NHr0aH377be%2BeY/Ho4kTJyojI0N9%2BvTR008/rWPHjlmyJgAAEPpCNmC98sormjVrltq0aXPa3O7du1VQUKDJkyfr448/Vk5Ojh577DEdPHhQkvTGG29ozZo1Kioq0tatW5WcnKzc3Fx5vV5J0vTp01VTU6O1a9fqj3/8o8rKylRYWGjp%2BgAAQOgK2YAVERGhFStWnDFgLV%2B%2BXJmZmcrMzFRERISGDBmijh07avXq1ZKk4uJi5eTkqF27doqKilJ%2Bfr7Kysq0c%2BdO/fDDD9q8ebPy8/MVHx%2BvxMREPfroo/rjH/%2BokydPWr1MAAAQgkI2YI0aNUrR0dFnnCstLVVKSorfWEpKipxOp44dO6a9e/f6zUdFRalNmzZyOp3avXu3wsPD1alTJ998ly5d9OOPP%2Brrr7%2B%2BOIsBAACXFHugC7gYPB6PYmJi/MZiYmK0d%2B9eHTlyRF6v94zzbrdbsbGxioqKUlhYmN%2BcJLnd7gs6f0VFhVwul9%2BY3d5cCQkJ/8pyzio8PLTysd0eWvU21M/9CbU%2BNRb0J7jRn%2BBGf87vkgxYknz7qf6V%2BfO993yKi4u1cOFCv7Hc3Fzl5eU16LihLi4uMtAlBITDcVmgS8A50J/gRn%2BCG/05u0syYMXFxcnj8fiNeTwexcfHKzY2Vjab7YzzLVq0UHx8vKqqqlRXV6fw8HDfnCS1aNHigs4/YsQI9evXz2/Mbm8ut7v6X13SGYXaJwfT6w924eE2ORyXqbKyRnV19YEuB6egP8GN/gS3UOpPoD7cX5IBKzU1VSUlJX5jTqdTgwYNUkREhDp06KDS0lL17t1bklRZWan9%2B/era9euSkpKktfr1Z49e9SlSxffex0Oh9q2bXtB509ISDjtdqDLdVS1tcH9RXixNdb119XVN9q1hwL6E9zoT3CjP2cXWpdALtDw4cP10Ucfadu2bTp%2B/LhWrFihb775RkOGDJEkZWdna9myZSorK1NVVZUKCwvVuXNnpaWlKT4%2BXgMHDtRvfvMbHT58WAcPHtSiRYuUlZUlu/2SzKMAAMCwkE0MaWlpkqTa2lpJ0ubNmyX9dLWpY8eOKixpNS%2B3AAAOiklEQVQs1OzZs1VeXq727dtryZIlatmypSRp5MiRcrlcuv/%2B%2B1VdXa309HS/PVO//vWvNWPGDPXv319NmjTRnXfeecaHmQIAAJxJmLehO7pxQVyuo8aPabfbNKDwA%2BPHvVjWT7wh0CVYym63KS4uUm53NZfQgxD9CW70J7iFUn9atjzzI50utkvyFiEAAEAgEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGGXbMDq1KmTUlNTlZaW5vvz3HPPSZJ27NihrKws9ejRQ4MGDdLq1av93rts2TINHDhQPXr0UHZ2tkpKSgKxBAAAEKLsgS7gYtqwYYOuvPJKv7GKigo9%2BuijevrppzV48GB9%2BumnGjdunNq2bau0tDRt2bJFCxYs0O9%2B9zt16tRJy5Yt09ixY7Vp0yY1b948QCsBAACh5JK9gnU2a9asUXJysrKyshQREaGMjAz169dPy5cvlyQVFxdr2LBh6tatm5o1a6aHHnpIkrR169ZAlg0AAELIJX0Fa968efr8889VVVWl22%2B/XVOmTFFpaalSUlL8XpeSkqL169dLkkpLS3XHHXf45mw2mzp37iyn06lBgwZd0HkrKirkcrn8xuz25kpISGjgivyFh4dWPrbbQ6vehvq5P6HWp8aC/gQ3%2BhPc6M/5XbIB69prr1VGRoZefPFFffvtt5o4caKeffZZeTweJSYm%2Br02NjZWbrdbkuTxeBQTE%2BM3HxMT45u/EMXFxVq4cKHfWG5urvLy8v7F1Vwa4uIiA11CQDgclwW6BJwD/Qlu9Ce40Z%2Bzu2QDVnFxse/f27Vrp8mTJ2vcuHHq2bPned/r9XobdO4RI0aoX79%2BfmN2e3O53dUNOu6pQu2Tg%2Bn1B7vwcJscjstUWVmjurr6QJeDU9Cf4EZ/glso9SdQH%2B4v2YB1qiuvvFJ1dXWy2WzyeDx%2Bc263W/Hx8ZKkuLi40%2BY9Ho86dOhwwedKSEg47Xagy3VUtbXB/UV4sTXW9dfV1TfatYcC%2BhPc6E9woz9nF1qXQC7Qrl27NGfOHL%2BxsrIyNW3aVJmZmac9dqGkpETdunWTJKWmpqq0tNQ3V1dXp127dvnmAQAAzueSDFgtWrRQcXGxioqKdOLECe3bt08vvfSSRowYobvuukvl5eVavny5jh8/ru3bt2v79u0aPny4JCk7O1tvv/22vvjiC9XU1Gjx4sVq2rSp%2BvbtG9hFAQCAkHFJ3iJMTExUUVGR5s2b5wtIQ4cOVX5%2BviIiIrRkyRLNmjVLzz77rJKSkjR37lxdc801kqSbbrpJjz/%2BuCZOnKhDhw4pLS1NRUVFatasWYBXBQAAQkWYt6E7unFBXK6jxo9pt9s0oPAD48e9WNZPvCHQJVjKbrcpLi5Sbnc1exSCEP0JbvQnuIVSf1q2jA7IeS/JW4QAAACBRMACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsM6gvLxcjzzyiNLT03XzzTdr7ty5qq%2BvD3RZAAAgRNgDXUAwGj9%2BvLp06aLNmzfr0KFDGjNmjC6//HI9%2BOCDgS4NAACEAALWKZxOp/bs2aNXX31V0dHRio6OVk5Ojv7whz8QsBro9t/8T6BL%2BKesn3hDoEsAAIQoAtYpSktLlZSUpJiYGN9Yly5dtG/fPlVVVSkqKuq8x6ioqJDL5fIbs9ubKyEhwWit4eHc4b2YQi0QhpJ3J98Y6BJ83z98HwUn%2BhPc6M/5EbBO4fF45HA4/MZ%2BDltut/uCAlZxcbEWLlzoN/bYY49p/Pjx5grVT0HugVZfacSIEcbDGxquoqJCxcXF9CdIVVRU6A9/%2BB39CVL0J7jRn/Mjep6B1%2Btt0PtHjBihlStX%2Bv0ZMWKEoer%2BweVyaeHChaddLUNwoD/Bjf4EN/oT3OjP%2BXEF6xTx8fHyeDx%2BYx6PR2FhYYqPj7%2BgYyQkJJDoAQBoxLiCdYrU1FQdOHBAhw8f9o05nU61b99ekZGRAawMAACECgLWKVJSUpSWlqZ58%2BapqqpKZWVlevXVV5WdnR3o0gAAQIgInzlz5sxAFxFsbrzxRq1du1bPPfec3nnnHWVlZWn06NEKCwsLdGmniYyMVO/evbm6FqToT3CjP8GN/gQ3%2BnNuYd6G7ugGAACAH24RAgAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwApB5eXleuSRR5Senq6bb75Zc%2BfOVX19faDLatTKy8uVm5ur9PR0ZWRkaMqUKaqsrJQk7d69W/fdd5969uypW2%2B9VUuXLg1wtY3bCy%2B8oE6dOvn%2BvmPHDmVlZalHjx4aNGiQVq9eHcDqGq/FixerT58%2Buvbaa5WTk6PvvvtOEv0JBrt27dKoUaN03XXX6YYbbtDkyZN1%2BPBhSfTnnLwIOUOHDvVOmzbNW1lZ6d23b5/31ltv9S5dujTQZTVqd955p3fKlCneqqoq74EDB7zDhg3zTp061VtTU%2BO98cYbvQsWLPBWV1d7S0pKvL179/Zu3Lgx0CU3Srt27fL27t3b27FjR6/X6/V%2B//333muvvda7fPly77Fjx7z/8z//4%2B3atav3r3/9a4ArbVxef/1172233eYtKyvzHj161Pvcc895n3vuOfoTBE6ePOm94YYbvPPmzfMeP37ce/jwYe%2BDDz7oHT9%2BPP05D65ghRin06k9e/Zo8uTJio6OVnJysnJyclRcXBzo0hqtyspKpaamatKkSYqMjFSrVq00dOhQffLJJ9q2bZtOnjypcePGqXnz5urSpYvuvfde%2BhUA9fX1mjFjhnJycnxja9asUXJysrKyshQREaGMjAz169dPy5cvD1yhjdDSpUuVn5%2Bvq6%2B%2BWlFRUZo2bZqmTZtGf4KAy%2BWSy%2BXSXXfdpaZNmyouLk4DBgzQ7t276c95ELBCTGlpqZKSkhQTE%2BMb69Kli/bt26eqqqoAVtZ4ORwOzZ49W5dffrlv7MCBA0pISFBpaak6deqk8PBw31xKSopKSkoCUWqj9uabbyoiIkKDBw/2jZWWliolJcXvdfTHWt9//72%2B%2B%2B47HTlyRHfccYfS09OVl5enw4cP058gkJiYqM6dO6u4uFjV1dU6dOiQNm3apL59%2B9Kf8yBghRiPxyOHw%2BE39nPYcrvdgSgJp3A6nXr99dc1bty4M/YrNjZWHo%2BHfXMW%2BuGHH7RgwQLNmDHDb/xs/eF7yToHDx6UJG3YsEGvvvqqVq1apYMHD2ratGn0JwjYbDYtWLBA7733nnr06KGMjAzV1tZq0qRJ9Oc8CFghyOv1BroEnMWnn36q0aNHa9KkScrIyDjr68LCwiysCrNnz9awYcPUvn37QJeCU/z8/7OHHnpIiYmJatWqlcaPH68tW7YEuDJI0okTJzR27Fjddttt%2BuSTT/T%2B%2B%2B8rOjpakydPDnRpQY%2BAFWLi4%2BPl8Xj8xjwej8LCwhQfHx%2BgqiBJW7Zs0SOPPKKpU6dq1KhRkn7q16mf5jwej2JjY2Wz8e1nhR07dujzzz9Xbm7uaXNxcXGnfT%2B53W6%2Blyz08631/3slJCkpSV6vVydPnqQ/AbZjxw599913evzxxxUdHa3ExETl5eXp3Xfflc1moz/nwP/hQ0xqaqoOHDjg%2BxFZ6adbUu3bt1dkZGQAK2vcPvvsMxUUFOill17S3Xff7RtPTU3Vl19%2BqdraWt%2BY0%2BlUt27dAlFmo7R69WodOnRIN998s9LT0zVs2DBJUnp6ujp27HjafpGSkhL6Y6FWrVopKipKu3fv9o2Vl5erSZMmyszMpD8BVldXp/r6er87JydOnJAkZWRk0J9zIGCFmJSUFKWlpWnevHmqqqpSWVmZXn31VWVnZwe6tEartrZW06ZN0%2BTJk9WnTx%2B/uczMTEVFRWnx4sWqqanRzp07tWLFCvploSlTpmjjxo1atWqVVq1apaKiIknSqlWrNHjwYJWXl2v58uU6fvy4tm/fru3bt2v48OEBrrrxsNvtysrK0ssvv6y///3vOnTokBYtWqTBgwdr6NCh9CfAunfvrubNm2vBggWqqamR2%2B3W4sWL1atXL91111305xzCvGzoCTkHDx7U9OnT9Ze//EVRUVEaOXKkHnvsMfb1BMgnn3yif//3f1fTpk1Pm9uwYYOqq6s1Y8YMlZSU6PLLL9fDDz%2BsX/7ylwGoFJL03XffqX///vryyy8lSf/7v/%2BrWbNmqaysTElJSZo0aZJuvfXWAFfZuJw4cUKzZ8/WO%2B%2B8o5MnT2rgwIGaPn26IiMj6U8QKCkp0Ysvvqg9e/aoadOm6t27t6ZMmaLExET6cw4ELAAAAMO4RQgAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhv0/KRGVLCkD9WgAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-2000633535235731057”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>170</td> <td class=”number”>5.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0782175353377071</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.03119727129711379</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.12931822163007417</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>37.303971750687346</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0065836701937614</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.056038890236844795</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0700982012084883</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.09722336493813279</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0012974468757656987</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2934)</td> <td class=”number”>2934</td> <td class=”number”>91.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-2000633535235731057”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>170</td> <td class=”number”>5.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.306834924921786e-05</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.4952758075379475e-05</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.893685272581147e-05</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.4010577934910866e-05</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>58.44887942118595</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>60.86846305401751</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>65.05211511345065</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>71.4387974379141</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>83.84421028494457</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_NHPI15”>PCT_LACCESS_NHPI15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>1940</td>

</tr> <tr>

<th>Unique (%)</th> <td>60.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>97</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.019193</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>4.4695</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>36.6%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram9118821459294534175”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives9118821459294534175,#minihistogram9118821459294534175”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives9118821459294534175”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles9118821459294534175”

aria-controls=”quantiles9118821459294534175” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram9118821459294534175” aria-controls=”histogram9118821459294534175”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common9118821459294534175” aria-controls=”common9118821459294534175”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme9118821459294534175” aria-controls=”extreme9118821459294534175”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles9118821459294534175”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0.0032411</td>

</tr> <tr>

<th>Q3</th> <td>0.013016</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.059681</td>

</tr> <tr>

<th>Maximum</th> <td>4.4695</td>

</tr> <tr>

<th>Range</th> <td>4.4695</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.013016</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.1351</td>

</tr> <tr>

<th>Coef of variation</th> <td>7.0392</td>

</tr> <tr>

<th>Kurtosis</th> <td>725.15</td>

</tr> <tr>

<th>Mean</th> <td>0.019193</td>

</tr> <tr>

<th>MAD</th> <td>0.025167</td>

</tr> <tr class=”alert”>

<th>Skewness</th> <td>25.235</td>

</tr> <tr>

<th>Sum</th> <td>59.748</td>

</tr> <tr>

<th>Variance</th> <td>0.018253</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram9118821459294534175”>

<img 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1S9HR0erSpYtuv/127d27Vzt37tS5c%2Bd03333qWPHjurbt6/uvPNOlZaWSpLWrFmjrKwsZWVlKSoqSmPGjNFVV12lDRs2SJJKS0uVl5enHj16KCYmRoWFhaqsrNS%2BffusXDIAAAgjYRmwHA6HFi1apMsvv9w/duzYMSUmJqqiokK9e/dWZGSkfy41NVXl5eWSpIqKCqWmpgbsLzU1VS6XS6dPn9ahQ4cC5mNiYtStWze5XK5LvCoAANBa2K0uwASXy6WXX35ZK1as0ObNm%2BVwOALm4%2BPj5fV61dTUJK/Xq7i4uID5uLg4HTp0SJ999pl8Pt8F5z0eT4vrqa6ultvtDhiz2zsqMTHxG67sq0VGhlc%2BttvDq96WON%2BDcOtFa0MfQgN9sB49CB1hH7Dee%2B893XfffZo1a5YyMzO1efPmC24XERHh/%2B%2Bvu5/qYu%2B3Ki0tVXFxccBYfn6%2BCgoKLmq/4S4hIdrqEi4Zh%2BMyq0uA6EOooA/WowfWC%2BuAtX37dj300EOaP3%2B%2Bxo4dK0lyOp06evRowHZer1fx8fGy2WxKSEiQ1%2BttNu90Ov3bXGi%2BU6dOLa4rJydH2dnZAWN2e0d5PHXfYHVfL9zeoZhefyiIjLTJ4bhMNTX1amxssrqcNos%2BhAb6YD160JxVb%2B7DNmC9//77mjNnjp5//nkNGTLEP56Wlqbf//73amhokN3%2BxfJcLpf69%2B/vnz9/P9Z5LpdLo0aNUlRUlHr16qWKigoNHjxY0hc31H/88cfq169fi2tLTExsdjnQ7T6lhoa2/cvemtff2NjUqtcXLuhDaKAP1qMH1gv6KZDs7GwVFxfr2LFj33ofDQ0NeuyxxzR79uyAcCVJWVlZiomJ0YoVK1RfX699%2B/Zp7dq1ys3NlSRNmDBB77zzjnbu3KkzZ85o7dq1Onr0qMaMGSNJys3N1apVq1RZWana2loVFRWpT58%2BSk9P//aLBgAAbUqEL8gPeFq%2BfLk2bdqkf/7zn7r%2B%2Bus1YcIEZWdn%2B882tcTevXv14x//WO3bt282t2XLFtXV1emJJ55QeXm5Lr/8ct1zzz360Y9%2B5N/mzTff1JIlS1RVVaWePXtq3rx5GjRokKQv7r9atmyZXnnlFdXV1SkjI0NPPfWUunTpclHrdrtPXdTrL8Rut2lE0W7j%2B71UNs%2B8weoSjLPbbUpIiJbHU8e7RQvRh9BAH6xHD5rr3DnWkuMGPWCdV1FRobKyMm3evFnnzp3T2LFjNX78eHXv3t2Kci45AhYBC5cOfQgN9MF69KA5qwKWZXdJ9%2B3bV3PmzNGOHTv06KOP6g9/%2BINuueUWTZkyRR9%2B%2BKFVZQEAAFw0ywLWuXPn9MYbb%2Biee%2B7RnDlzlJSUpEceeUR9%2BvRRXl6eNm7caFVpAAAAFyXonyKsrKzU2rVr9frrr6uurk4jR47U7373Ow0cONC/zaBBg7RgwQKNHj062OUBAABctKAHrFGjRql79%2B6aOnWqxo4dq/j4%2BGbbZGVl6eTJk8EuDQAAwIigB6xVq1b5nzH1VfhyZQAAEK6Cfg9W7969NW3aNG3bts0/9t///d%2B65557mj1BHQAAIBwFPWAtWrRIp06dUs%2BePf1jw4YNU1NTk5599tlglwMAAGBc0C8Rvv3229q4caMSEhL8YykpKSoqKtKtt94a7HIAAACMC/oZrNOnTysqKqp5ITab6uvrg10OAACAcUEPWIMGDdKzzz6rzz77zD/273//W08%2B%2BWTAoxoAAADCVdAvET766KO6%2B%2B67df311ysmJkZNTU2qq6tT165d9dJLLwW7HAAAAOOCHrC6du2qTZs26a233tLHH38sm82m7t27a8iQIYqMjAx2OQAAAMYFPWBJUvv27XXTTTdZcWgAAIBLLugB65NPPtGSJUt08OBBnT59utn8n/70p2CXBAAAYJQl92BVV1dryJAh6tixY7APDwAAcMkFPWCVl5frT3/6k5xOZ7APDQAAEBRBf0xDp06dOHMFAABataAHrKlTp6q4uFg%2Bny/YhwYAAAiKoF8ifOutt/T%2B%2B%2B9r3bp1uuKKK2SzBWa8V155JdglAQAAGBX0gBUTE6OhQ4cG%2B7AAAABBE/SAtWjRomAfEgAAIKiCfg%2BWJB0%2BfFjLli3TI4884h/729/%2BZkUpAAAAxgU9YO3Zs0djxozRm2%2B%2BqbKyMklfPHx00qRJPGQUAAC0CkEPWEuXLtVDDz2kjRs3KiIiQtIX30/47LPPavny5cEuBwAAwLigB6x//OMfys3NlSR/wJKkm2%2B%2BWZWVlcEuBwAAwLigB6zY2NgLfgdhdXW12rdvH%2BxyAAAAjAt6wBowYIB%2B9rOfqba21j925MgRzZkzR9dff32wywEAADAu6I9peOSRRzR58mRlZGSosbFRAwYMUH19vXr16qVnn3022OUAAAAYF/SA1aVLF5WVlWnXrl06cuSIOnTooO7du%2BuGG24IuCcLAAAgXAU9YElSu3btdNNNN1lxaAAAgEsu6AErOzv7K89U8SwsAAAQ7oIesG655ZaAgNXY2KgjR47I5XJp8uTJwS4HAADAuKAHrNmzZ19wfOvWrfrzn/8c5GoAAADMs%2BS7CC/kpptu0qZNm6wuAwAA4KKFTMD66KOP5PP5rC4DAADgogX9EuHEiRObjdXX16uyslI/%2BMEPgl0OAACAcUEPWCkpKc0%2BRRgVFaXx48frzjvvDHY5AAAAxgU9YPG0dgAA0NoFPWC9/vrrLd527NixXzm/e/duzZkzRxkZGVq6dKl/fN26dXr00UfVrl27gO1Xr16tfv36qampSc8//7zKyspUU1Ojfv36acGCBerataskyev1asGCBfrLX/4im82mrKwszZ8/Xx06dPgGKwUAAG1V0APWvHnz1NTU1OyG9oiIiICxiIiIrwxYL7zwgtauXatu3bpdcH7QoEF66aWXLji3evVqbdy4US%2B88IKSkpK0dOlS5efna/369YqIiND8%2BfN19uxZlZWV6dy5c5oxY4aKior02GOPfYsVAwCAtibonyL89a9/rSFDhmj16tXau3ev/vrXv%2Brll1/W0KFD9cILL%2BjDDz/Uhx9%2BqH379n3lfqKior4yYH2V0tJS5eXlqUePHoqJiVFhYaEqKyu1b98%2Bffrpp9q2bZsKCwvldDqVlJSk6dOn69VXX9W5c%2Be%2B7bIBAEAbYsk9WCUlJUpKSvKPXXvtterataumTJmisrKyFu1n0qRJXzl/7Ngx/eQnP1F5ebkcDocKCgp022236fTp0zp06JBSU1P928bExKhbt25yuVw6deqUIiMj1bt3b/9837599fnnn%2Bvw4cMB4wAAABcS9IB19OhRxcXFNRt3OByqqqoycgyn06mUlBQ9%2BOCD6tmzp/74xz/q4YcfVmJior773e/K5/M1qyEuLk4ej0fx8fGKiYkJ%2BKTj%2BW09Hk%2BLjl9dXS232x0wZrd3VGJi4kWuLFBkZMg8xqxF7Pbwqrclzvcg3HrR2tCH0EAfrEcPQkfQA1ZycrKeffZZzZgxQwkJCZKkmpoa/fKXv9SVV15p5BjDhg3TsGHD/H8eNWqU/vjHP2rdunX%2Br%2Br5qoeaXuwDT0tLS1VcXBwwlp%2Bfr4KCgovab7hLSIi2uoRLxuG4zOoSIPoQKuiD9eiB9YIesB599FHNmjVLpaWlio6Ols1mU21trTp06KDly5dfsuMmJyervLxc8fHxstls8nq9AfNer1edOnWS0%2BlUbW2tGhsbFRkZ6Z%2BTpE6dOrXoWDk5OcrOzg4Ys9s7yuOpM7CS/xFu71BMrz8UREba5HBcppqaejU2NlldTptFH0IDfbAePWjOqjf3QQ9YQ4YM0c6dO7Vr1y4dP35cPp9PSUlJ%2Bv73v6/Y2Fgjx/j973%2BvuLg43XLLLf6xyspKde3aVVFRUerVq5cqKio0ePBgSV%2BcQfv444/Vr18/JScny%2Bfz6cCBA%2Brbt68kyeVyyeFwqHv37i06fmJiYrPLgW73KTU0tO1f9ta8/sbGpla9vnBBH0IDfbAePbBe0AOWJF122WUaPny4jh8/7n/2lElnz57V008/ra5du%2Brqq6/W1q1b9dZbb%2BkPf/iDJCk3N1clJSUaOnSokpKSVFRUpD59%2Big9PV2SNHLkSP3iF7/Qc889p7Nnz2r58uUaP3687HZL/roAAECYCXpiOH36tJ544glt2rRJklReXq6amho9%2BOCD%2BvnPfy6Hw9Gi/ZwPQw0NDZKkbdu2SfribNOkSZNUV1enGTNmyO1264orrtDy5cuVlpYm6YvvQ3S73brrrrtUV1enjIyMgHumnnrqKT3xxBMaPny42rVrp1tvvVWFhYXG/g4AAEDrFuG72Du6v6Gnn35af/3rXzV9%2BnQ9/PDD%2BvDDD1VTU6MZM2aoa9eueuqpp4JZTtC43aeM79Nut2lE0W7j%2B71UNs%2B8weoSjLPbbUpIiJbHU8fpeAvRh9BAH6xHD5rr3NnM7UffVNDvkt66dat%2B%2Bctf6uabb/Y/CsHhcGjRokV68803g10OAACAcUEPWHV1dUpJSWk27nQ69fnnnwe7HAAAAOOCHrCuvPJK/fnPf5YU%2BLypLVu26D/%2B4z%2BCXQ4AAIBxQb/J/Uc/%2BpEeeOAB3XHHHWpqatKLL76o8vJybd26VfPmzQt2OQAAAMYFPWDl5OTIbrfr5ZdfVmRkpH71q1%2Bpe/fuKioq0s033xzscgAAAIwLesA6efKk7rjjDt1xxx3BPjQAAEBQBP0erOHDh1/0d/0BAACEsqAHrIyMDG3evDnYhwUAAAiaoF8i/M53vqNnnnlGJSUluvLKK9WuXbuA%2BSVLlgS7JAAAAKOCHrAOHTqk7373u5Ikj8cT7MMDAABcckELWIWFhVq6dKleeukl/9jy5cuVn58frBIAAACCImj3YG3fvr3ZWElJSbAODwAAEDRBC1gX%2BuQgnyYEAACtUdAC1vkvdv66MQAAgHAX9Mc0AAAAtHYELAAAAMOC9inCc%2BfOadasWV87xnOwAABAuAtawBo4cKCqq6u/dgwAACDcBS1g/e/nXwEAALRm3IMFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhYR2wdu/erczMTBUWFjabe%2BONNzR69Ghdc801GjdunN5%2B%2B23/XFNTk5YuXarhw4dr0KBBmjJlij755BP/vNfr1cyZM5WZmakhQ4Zo3rx5On36dFDWBAAAwl/YBqwXXnhBCxcuVLdu3ZrN7d%2B/X3PmzNHs2bP17rvvKi8vT/fff7%2BOHz8uSVq9erU2btyokpIS7dixQykpKcrPz5fP55MkzZ8/X/X19SorK9Orr76qyspKFRUVBXV9AAAgfIVtwIqKitLatWsvGLDWrFmjrKwsZWVlKSoqSmPGjNFVV12lDRs2SJJKS0uVl5enHj16KCYmRoWFhaqsrNS%2Bffv06aefatu2bSosLJTT6VRSUpKmT5%2BuV199VefOnQv2MgEAQBgK24A1adIkxcbGXnCuoqJCqampAWOpqalyuVw6ffq0Dh06FDAfExOjbt26yeVyaf/%2B/YqMjFTv3r3983379tXnn3%2Buw4cPX5rFAACAVsVudQGXgtfrVVxcXMBYXFycDh06pM8%2B%2B0w%2Bn%2B%2BC8x6PR/Hx8YqJiVFERETAnCR5PJ4WHb%2B6ulputztgzG7vqMTExG%2BznP9TZGR45WO7PbzqbYnzPQi3XrQ29CE00Afr0YPQ0SoDliT//VTfZv7rXvt1SktLVVxcHDCWn5%2BvgoKCi9pvuEtIiLa6hEvG4bjM6hIg%2BhAq6IP16IH1WmXASkhIkNfrDRjzer1yOp2Kj4%2BXzWa74HynTp3kdDpVW1urxsZGRUZG%2BuckqVOnTi06fk5OjrKzswPG7PaO8njqvu2SLijc3qGYXn8oiIy0yeG4TDU19WpsbLK6nDaLPoQG%2BmA9etCcVW/uW2XASktLU3l5ecCYy%2BXSqFGjFBUVpV69eqmiokKDBw%2BWJNXU1Ojjjz9Wv379lJycLJ/PpwMHDqhv377%2B1zocDnXv3r1Fx09MTGx2OdDtPqWGhrb9y96a19/Y2NSq1xcu6ENooA/WowfWC69TIC00YcIEvfPOO9q5c6fOnDmjtWvX6ujRoxozZowkKTc3V6tWrVJlZaVqa2tVVFSkPn36KD09XU6nUyNHjtQvfvELnTx5UsePH9fy5cs1fvx42e2tMo8CAADDwjZM7UdsAAAMP0lEQVQxpKenS5IaGhokSdu2bZP0xdmmq666SkVFRVq0aJGqqqrUs2dPrVy5Up07d5YkTZw4UW63W3fddZfq6uqUkZERcM/UU089pSeeeELDhw9Xu3btdOutt17wYaYAAAAXEuG72Du60SJu9ynj%2B7TbbRpRtNv4fi%2BVzTNvsLoE4%2Bx2mxISouXx1HE63kL0ITTQB%2BvRg%2BY6d77wI50utVZ5iRAAAMBKBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMKzVBqzevXsrLS1N6enp/p%2Bnn35akrRnzx6NHz9eAwYM0KhRo7Rhw4aA165atUojR47UgAEDlJubq/LyciuWAAAAwpTd6gIupS1btuiKK64IGKuurtb06dM1b948jR49Wu%2B9957uu%2B8%2Bde/eXenp6dq%2BfbuWLVumX//61%2Brdu7dWrVqladOm6c0331THjh0tWgkAAAgnrfYM1v9l48aNSklJ0fjx4xUVFaXMzExlZ2drzZo1kqTS0lKNGzdO/fv3V4cOHfTTn/5UkrRjxw4rywYAAGGkVQesJUuWaNiwYbr22ms1f/581dXVqaKiQqmpqQHbpaam%2Bi8DfnneZrOpT58%2BcrlcQa0dAACEr1Z7ifB73/ueMjMz9dxzz%2BmTTz7RzJkz9eSTT8rr9SopKSlg2/j4eHk8HkmS1%2BtVXFxcwHxcXJx/viWqq6vldrsDxuz2jkpMTPyWq7mwyMjwysd2e3jV2xLnexBuvWht6ENooA/Woweho9UGrNLSUv9/9%2BjRQ7Nnz9Z9992ngQMHfu1rfT7fRR%2B7uLg4YCw/P18FBQUXtd9wl5AQbXUJl4zDcZnVJUD0IVTQB%2BvRA%2Bu12oD1ZVdccYUaGxtls9nk9XoD5jwej5xOpyQpISGh2bzX61WvXr1afKycnBxlZ2cHjNntHeXx1H3L6i8s3N6hmF5/KIiMtMnhuEw1NfVqbGyyupw2iz6EBvpgPXrQnFVv7ltlwProo4%2B0YcMGzZ071z9WWVmp9u3bKysrS6%2B99lrA9uXl5erfv78kKS0tTRUVFbr99tslSY2Njfroo480fvz4Fh8/MTGx2eVAt/uUGhra9i97a15/Y2NTq15fuKAPoYE%2BWI8eWC%2B8ToG0UKdOnVRaWqqSkhKdPXtWR44c0fPPP6%2BcnBzddtttqqqq0po1a3TmzBnt2rVLu3bt0oQJEyRJubm5ev311/XBBx%2Bovr5eK1asUPv27TVs2DBrFwUAAMJGqzyDlZSUpJKSEi1ZssQfkG6//XYVFhYqKipKK1eu1MKFC/Xkk08qOTlZixcv1tVXXy1JGjp0qB588EHNnDlTJ06cUHp6ukpKStShQweLVwUAAMJFhO9i7%2BhGi7jdp4zv0263aUTRbuP7vVQ2z7zB6hKMs9ttSkiIlsdTx%2Bl4C9GH0EAfrEcPmuvcOdaS47bKS4QAAABWImABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWBdQVVWle%2B%2B9VxkZGbrxxhu1ePFiNTU1WV0WAAAIE3arCwhFDzzwgPr27att27bpxIkTmjp1qi6//HL95Cc/sbo0AAAQBjiD9SUul0sHDhzQ7NmzFRsbq5SUFOXl5am0tNTq0gAAQJjgDNaXVFRUKDk5WXFxcf6xvn376siRI6qtrVVMTMzX7qO6ulputztgzG7vqMTERKO1RkaGVz6228Or3pY434Nw60VrQx9CA32wHj0IHQSsL/F6vXI4HAFj58OWx%2BNpUcAqLS1VcXFxwNj999%2BvBx54wFyh%2BiLITe5yUDk5OcbDG1qmurpav/vdr%2BmBxehDaKAP1qMHoYOIewE%2Bn%2B%2BiXp%2BTk6N169YF/OTk5Biq7n%2B43W4VFxc3O1uG4KEHoYE%2BhAb6YD16EDo4g/UlTqdTXq83YMzr9SoiIkJOp7NF%2B0hMTOSdAwAAbRhnsL4kLS1Nx44d08mTJ/1jLpdLPXv2VHR0tIWVAQCAcEHA%2BpLU1FSlp6dryZIlqq2tVWVlpV588UXl5uZaXRoAAAgTkQsWLFhgdRGh5vvf/77Kysr09NNPa9OmTRo/frymTJmiiIgIq0trJjo6WoMHD%2BbsmoXoQWigD6GBPliPHoSGCN/F3tENAACAAFwiBAAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgBWGqqqqdO%2B99yojI0M33nijFi9erKamJqvLapN2796tzMxMFRYWWl1Km1VVVaX8/HxlZGQoMzNTc%2BfOVU1NjdVltSkHDhzQ5MmTNXDgQGVmZmrmzJlyu91Wl9Wm/exnP1Pv3r2tLqNNI2CFoQceeEBJSUnatm2bXnzxRW3btk2/%2B93vrC6rzXnhhRe0cOFCdevWzepS2rRp06bJ4XBo%2B/btWrdunQ4ePKjnnnvO6rLajLNnz%2Bruu%2B/W4MGDtWfPHpWVlenEiRPia26ts3//fq1fv97qMto8AlaYcblcOnDggGbPnq3Y2FilpKQoLy9PpaWlVpfW5kRFRWnt2rUELAvV1NQoLS1Ns2bNUnR0tLp06aLbb79de/futbq0NqO%2Bvl6FhYWaOnWq2rdvL6fTqREjRujgwYNWl9YmNTU16YknnlBeXp7VpbR5BKwwU1FRoeTkZMXFxfnH%2BvbtqyNHjqi2ttbCytqeSZMmKTY21uoy2jSHw6FFixbp8ssv948dO3ZMiYmJFlbVtsTFxenOO%2B%2BU3W6XJB0%2BfFivvfaafvjDH1pcWdv0yiuvKCoqSqNHj7a6lDbPbnUB%2BGa8Xq8cDkfA2Pmw5fF4FBMTY0VZQEhwuVx6%2BeWXtWLFCqtLaXOqqqo0cuRINTQ0aMKECSooKLC6pDbn008/1bJly/TSSy9ZXQrEGayw5PP5rC4BCDnvvfeepkyZolmzZikzM9Pqctqc5ORkuVwubdmyRUePHtXDDz9sdUltzqJFizRu3Dj17NnT6lIgAlbYcTqd8nq9AWNer1cRERFyOp0WVQVYa/v27br33nv16KOPatKkSVaX02ZFREQoJSVFhYWFKisr08mTJ60uqc3Ys2eP/va3vyk/P9/qUvD/EbDCTFpamo4dOxbwPy6Xy6WePXsqOjrawsoAa7z//vuaM2eOnn/%2BeY0dO9bqctqcPXv2aOTIkQGPirHZvvinpV27dlaV1eZs2LBBJ06c0I033qiMjAyNGzdOkpSRkaFNmzZZXF3bRMAKM6mpqUpPT9eSJUtUW1uryspKvfjii8rNzbW6NCDoGhoa9Nhjj2n27NkaMmSI1eW0SWlpaaqtrdXixYtVX1%2BvkydPatmyZbr22mv5EEgQzZ07V1u3btX69eu1fv16lZSUSJLWr1%2Bv7Oxsi6trmyJ83NATdo4fP6758%2BfrL3/5i2JiYjRx4kTdf//9ioiIsLq0NiU9PV3SF//IS/J/isrlcllWU1uzd%2B9e/fjHP1b79u2bzW3ZskXJyckWVNX2/P3vf9fChQv14YcfqmPHjrruuus0d%2B5cJSUlWV1am/Wvf/1Lw4cP19///nerS2mzCFgAAACGcYkQAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAz7f1sDgn8z7DxMAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common9118821459294534175”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1175</td> <td class=”number”>36.6%</td> <td>

<div class=”bar” style=”width:61%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.01194681129579588</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.033801395162232704</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.04615207326629525</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.021842555455531307</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.004196810313842147</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.000855791228875384</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.009380600387265877</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.005490639325721949</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.874654190620268e-05</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (1929)</td> <td class=”number”>1929</td> <td class=”number”>60.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme9118821459294534175”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1175</td> <td class=”number”>36.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.3248328670178477e-07</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.585739759080649e-07</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.580018192056047e-06</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.69923180266681e-06</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.8126888317341692</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.24369555207724</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.775812478674561</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.065210356214455</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.469546279819035</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_POP10”>PCT_LACCESS_POP10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3106</td>

</tr> <tr>

<th>Unique (%)</th> <td>96.8%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>23.538</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-6235403665571426030”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-6235403665571426030,#minihistogram-6235403665571426030”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-6235403665571426030”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-6235403665571426030”

aria-controls=”quantiles-6235403665571426030” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-6235403665571426030” aria-controls=”histogram-6235403665571426030”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-6235403665571426030” aria-controls=”common-6235403665571426030”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-6235403665571426030” aria-controls=”extreme-6235403665571426030”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-6235403665571426030”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>1.9934</td>

</tr> <tr>

<th>Q1</th> <td>10.852</td>

</tr> <tr>

<th>Median</th> <td>19.671</td>

</tr> <tr>

<th>Q3</th> <td>29.578</td>

</tr> <tr>

<th>95-th percentile</th> <td>63.96</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr>

<th>Range</th> <td>100</td>

</tr> <tr>

<th>Interquartile range</th> <td>18.727</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>20.233</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.85959</td>

</tr> <tr>

<th>Kurtosis</th> <td>5.3486</td>

</tr> <tr>

<th>Mean</th> <td>23.538</td>

</tr> <tr>

<th>MAD</th> <td>13.626</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.1205</td>

</tr> <tr>

<th>Sum</th> <td>73720</td>

</tr> <tr>

<th>Variance</th> <td>409.36</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-6235403665571426030”>

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Ztvvql3331Xf/rTn%2BRwOHTbbbdp7NixCgwMtHs8AACAa/K6wJKkoKAg3XvvvXaPAQAA0CNeF1h//vOfVVhYqE8%2B%2BUQXL17sdPvbb79tw1QAAABd53WBtXz5ctXW1mrs2LEKCQmxexwAAIBu87rAqqys1Ntvv63o6Gi7RwEAAOgRr/uYhptuuokrVwAAwKd5XWA98cQT2rhxoyzLsnsUAACAHvG6lwjfffddHTt2TK%2B//rr%2B4R/%2BQQ6HewO%2B9tprNk0GAADQNV4XWGFhYRo3bpzdYwAAAPSY1wXW2rVr7R4BAACgV7zuPViS9L//%2B78qKirSM88803Hsww8/tHEiAACArvO6wCovL9f06dN18OBB7d27V9JXHz46Z84cPmQUAAD4BK8LrJdfflk/%2BtGPtGfPHgUEBEj66ucTrlu3Tps2bbJ5OgAAgGvzusD6n//5H2VlZUlSR2BJ0uTJk1VdXW3XWAAAAF3mdYEVHh5%2B1Z9BWFtbq6CgIBsmAgAA6B6vC6ykpCStWbNGFy5c6Dh26tQpLV26VN/73vdsnAwAAKBrvO5jGp555hk99thjSk5OVltbm5KSktTc3KxBgwZp3bp1do8HAABwTV4XWP3799fevXtVVlamU6dOqU%2BfPrrttts0ZswYt/dkAQAAeCuvCyxJuuGGG3TvvffaPQYAAECPeF1gpaWl/c0rVXwWFgAA8HZeF1hTpkxxC6y2tjadOnVKFRUVeuyxx2ycDAAAoGu8LrDy8/Ovevytt97Sb3/72295GgAAgO7zuo9p%2BCb33nuv3nzzTbvHAAAAuCafCawTJ07Isiy7xwAAALgmr3uJcPbs2Z2ONTc3q7q6WhMnTrRhIgAAgO7xusCKi4vr9K8Ig4ODlZGRoYcfftimqQAAALrO6wKLT2sHAAC%2BzusC64033ujyfR988EEPTgIAANAzXhdYK1asUHt7e6c3tAcEBLgdCwgIILAAAIBX8rrA%2Brd/%2Bzdt3bpVCxYs0ODBg2VZlj7%2B%2BGP967/%2Bqx599FElJyfbPSIAAMDf5HWBtW7dOm3ZskWxsbEdx0aOHKlbbrlFc%2BfO1d69e22cDgAA4Nq87nOwPvvsM0VGRnY6HhERoZqaGhsmAgAA6B6vC6wBAwZo3bp1amho6DjW2NiowsJC3XrrrTZOBgAA0DVe9xLh8uXLlZeXp5KSEoWGhsrhcOjChQvq06ePNm3aZPd4AAAA1%2BR1gTV27FgdPnxYZWVlOnv2rCzLUmxsrO6%2B%2B26Fh4fbPR4AAMA1eV1gSdKNN96oe%2B65R2fPntUtt9xi9zgAAADd4nXvwbp48aKWLl2q4cOH6/7775f01Xuw5s2bp8bGRpunAwAAuDavC6z169fr5MmT2rBhgxyO/xuvra1NGzZssHEyAACArvG6wHrrrbf0L//yL5o8eXLHD32OiIjQ2rVrdfDgwR6dc82aNRo8eHDHf5eXlysjI0NJSUmaOnWqdu/e7Xb/7du3a9KkSUpKSlJWVpYqKyt7/oAAAMB1x%2BsCq6mpSXFxcZ2OR0dH68svv%2Bz2%2BU6ePKldu3Z1/Hdtba2eeuopzZ49W%2BXl5VqxYoVWrlypiooKSdKhQ4dUVFSkl156SUeOHNGECRO0YMGCHn1vAABwffK6wLr11lv129/%2BVpLcfvbggQMH9Pd///fdOld7e7tWrVql7OzsjmN79uxRXFycMjIyFBwcrJSUFKWlpam0tFSSVFJSovT0dCUmJqpPnz6aN2%2BeJOmdd97p5SMDAADXC68LrEceeUQLFy7Uiy%2B%2BqPb2dm3btk15eXlavny5HnvssW6d67XXXlNwcLCmTZvWcayqqkrx8fFu94uPj%2B94GfDrtzscDg0ZMqTjChcAAMC1eN3HNGRmZsrpdGrHjh0KDAzUK6%2B8ottuu00bNmzQ5MmTu3yev/zlLyoqKtKrr77qdtzlcrn9nENJ6tu3b8cnx7tcrk4/qicyMtLtk%2BWvpba2VnV1dW7HnM4QxcTEdPkcXREY6HV9DBs5nb7xfLjyvOX56xns13PYref44269LrDq6%2Bv10EMP6aGHHurVedauXav09HQNHDhQp0%2Bf7tbX/vVLkz1RUlKijRs3uh3LyclRbm5ur84L/C1RUaF2j9AtERE32j2CX2O/nsNuPcefdut1gXXPPffo2LFjHf%2BCsCfKy8v14Ycfau/evZ1ui4qKksvlcjvW0NCg6Ojob7zd5XJp0KBBXf7%2BmZmZSktLczvmdIaooaGpy%2BfoCn8qffSe6eeXpwQGOhQRcaMaG5vV1tZu9zh%2Bh/16Drv1HE/u1q6/fHpdYCUnJ2v//v2aMmVKj8%2Bxe/dunTt3ThMmTJD0f1ekkpOT9cMf/rBTeFVWVioxMVGSlJCQoKqqKs2cOVPSV5%2B/deLECWVkZHT5%2B8fExHR6ObCu7rxaW/kFCc/xtedXW1u7z83sS9iv57Bbz/Gn3XpdYP3d3/2dXnjhBW3ZskW33nqrbrjhBrfbCwsLr3mOZcuW6Z//%2BZ87/vvs2bPKzMzUrl271N7ers2bN6u0tFTTp0/Xb37zG5WVlamkpESSlJWVpSVLluiBBx7Q4MGD9bOf/UxBQUEaP3680ccJAAD8l9cF1qeffqrbb79dkrr1xvK/FhkZ6fZG9dbWVklS//79JUmbN2/W888/r9WrV2vAgAFav3697rzzTknSuHHjtGTJEi1atEjnzp3TsGHDtGXLFvXp06c3DwvwuPt/8oHdI3TZ0Re6/g9WAMAXBVi9fUe3IYsXL9bLL7/sdmzTpk3KycmxaSKz6urOGz%2Bn0%2BnQfRveM35ewNOOvjBZDQ1NfvNSgDdxOh2Kigplvx7Abj3Hk7u9%2BeZwo%2BfrKq95l/ShQ4c6HduyZYsNkwAAAPSO1wTW1S6kecnFNQAAgG7xmsC62scy9OajGgAAAOziNYEFAADgLwgsAAAAw7zmYxpaWlqUl5d3zWNd%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%2B%2Buv65JNP9OKLL%2BrixYt64okn9E//9E9677339PLLL2vz5s06ePCgpK/ia%2BnSpcrPz9dvfvMbZWdn6%2Bmnn9bZs2dtfkQAAMBX%2BGVgNTY2KiEhQXl5eQoNDVX//v01c%2BZMHT16VIcPH1ZLS4uefPJJhYSEaOjQoXr44YdVUlIiSSotLVVqaqpSU1MVHBys6dOn64477tDu3bttflQAAMBX%2BGVgRUREaO3aterXr1/HsTNnzigmJkZVVVUaPHiwAgMDO26Lj49XZWWlJKmqqkrx8fFu54uPj1dFRcW3MzwAAPB5TrsH%2BDZUVFRox44dKi4u1v79%2BxUREeF2e9%2B%2BfeVyudTe3i6Xy6XIyEi32yMjI/Xpp592%2BfvV1taqrq7O7ZjTGaKYmJieP4irCAz0yz7GdYLnr2dc2Sv7NY/deo4/7tbvA%2Bt3v/udnnzySeXl5SklJUX79%2B%2B/6v0CAgI6/rdlWb36niUlJdq4caPbsZycHOXm5vbqvIA/iYi40e4R/Br79Rx26zn%2BtFu/DqxDhw7pRz/6kVauXKkHH3xQkhQdHa3PPvvM7X4ul0t9%2B/aVw%2BFQVFSUXC5Xp9ujo6O7/H0zMzOVlpbmdszpDFFDQ1PPHsg38KfSx/WnsbFZbW3tdo/hdwIDHYqIuJH9egC79RxP7jYqKtTo%2BbrKbwPr2LFjWrp0qX76059q7NixHccTEhL0y1/%2BUq2trXI6v3r4FRUVSkxM7Lj9yvuxrqioqNDUqVO7/L1jYmI6vRxYV3dera38ggSuaGtr59eEB7Ffz2G3nuNPu/XLSyCtra0qKChQfn6%2BW1xJUmpqqsLCwlRcXKzm5mYdP35cO3fuVFZWliRp1qxZOnLkiA4fPqxLly5p586d%2BuyzzzR9%2BnQ7HgoAAPBBAVZv33DkhY4eParvf//7CgoK6nTbgQMH1NTUpFWrVqmyslL9%2BvXT/Pnz9cgjj3Tc5%2BDBgyosLFRNTY0GDhyoFStWaNSoUb2aqa7ufK%2B%2B/mqcTofu2/Ce8fMCnnb0hclqaGjym7%2BpehOn06GoqFD26wHs1nM8udubbw43er6u8suXCEeOHKmPP/74b97nl7/85TfeNnHiRE2cONH0WAAA4Drhly8RAgAA2InAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMMwvf9gzAO82csUBu0folv2Lxtg9AgAfwxUsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAw/igUQAA/NT9P/nA7hG67OgLk%2B0ewSiuYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABjmtHsAAPB29//kA7tH6JajL0y2ewTguscVLAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMP4UTkA4GdGrjhg9whdtn/RGLtHADyCwAIA2Iaf8wh/RWBdRU1NjVavXq3jx48rJCREU6ZMUV5enhwOXlEFgOuZL10dhL0IrKtYuHChhg4dql//%2Btc6d%2B6cnnjiCfXr108/%2BMEP7B4NAAD4AC7JfE1FRYU%2B%2Bugj5efnKzw8XHFxccrOzlZJSYndowEAAB/BFayvqaqq0oABAxQZGdlxbOjQoTp16pQuXLigsLCwa56jtrZWdXV1bseczhDFxMQYnTUwkD4GAPgPf/pzjcD6GpfLpYiICLdjV2KroaGhS4FVUlKijRs3uh17%2BumntXDhQnOD6quQe6z/J8rMzDQeb9e72tpalZSUsFsPYLeexX49h916Tm1trYqKivxqt/6TigZZltWrr8/MzNTrr7/u9n%2BZmZmGpvs/dXV12rhxY6erZeg9dus57Naz2K/nsFvP8cfdcgXra6Kjo%2BVyudyOuVwuBQQEKDo6ukvniImJ8ZsCBwAA3ccVrK9JSEjQmTNnVF9f33GsoqJCAwcOVGhoqI2TAQAAX0FgfU18fLyGDRumwsJCXbhwQdXV1dq2bZuysrLsHg0AAPiIwGefffZZu4fwNnfffbf27t2r5557Tm%2B%2B%2BaYyMjI0d%2B5cBQQE2D1aJ6GhoRo9ejRX1zyA3XoOu/Us9us57NZz/G23AVZv39ENAAAAN7xECAAAYBiBBQAAYBiBBQAAYBiBBQAAYBiBBQAAYBiBBQAAYBiBBQAAYBiBBQAAYBiBBQAAYBiB5YNqamr0%2BOOPKzk5WRMmTND69evV3t5u91g%2Bq6amRjk5OUpOTlZKSoqWLVumxsZGSdLJkyf16KOPasSIEZo4caK2bt1q87S%2Ba82aNRo8eHDHf5eXlysjI0NJSUmaOnWqdu/ebeN0vqu4uFhjx47VXXfdpezsbJ0%2BfVoS%2B%2B2tEydOaM6cORo5cqTGjBmj/Px81dfXS2K3PfHee%2B8pJSVFixcv7nTbvn37NG3aNA0fPlzp6el6//33O25rb2/Xyy%2B/rHvuuUejRo3S3Llz9ec///nbHL3nLPicmTNnWgUFBVZjY6N16tQpa%2BLEidbWrVvtHstnPfDAA9ayZcusCxcuWGfOnLHS09Ot5cuXW83Nzdbdd99tFRUVWU1NTVZq1TAAAAAHI0lEQVRlZaU1evRo66233rJ7ZJ9z4sQJa/To0dYdd9xhWZZlff7559Zdd91llZaWWhcvXrQ%2B%2BOAD67vf/a71hz/8weZJfcuOHTusyZMnW9XV1db58%2Bet5557znruuefYby%2B1tLRYY8aMsQoLC61Lly5Z9fX11g9%2B8ANr4cKF7LYHtmzZYk2cONGaPXu2tWjRIrfbTpw4YSUkJFiHDx%2B2Ll68aO3atctKTEy0zpw5Y1mWZW3fvt2aMGGC9emnn1rnz5%2B3fvzjH1vTpk2z2tvb7Xgo3cIVLB9TUVGhjz76SPn5%2BQoPD1dcXJyys7NVUlJi92g%2BqbGxUQkJCcrLy1NoaKj69%2B%2BvmTNn6ujRozp8%2BLBaWlr05JNPKiQkREOHDtXDDz/Mrrupvb1dq1atUnZ2dsexPXv2KC4uThkZGQoODlZKSorS0tJUWlpq36A%2BaOvWrVq8eLFuv/12hYWFqaCgQAUFBey3l%2Brq6lRXV6cZM2YoKChIUVFRuu%2B%2B%2B3Ty5El22wPBwcHauXOnvvOd73S6rbS0VKmpqUpNTVVwcLCmT5%2BuO%2B64o%2BOqYElJibKzs/WP//iPCgsL0%2BLFi1VdXa3jx49/2w%2Bj2wgsH1NVVaUBAwYoMjKy49jQoUN16tQpXbhwwcbJfFNERITWrl2rfv36dRw7c%2BaMYmJiVFVVpcGDByswMLDjtvj4eFVWVtoxqs967bXXFBwcrGnTpnUcq6qqUnx8vNv92G33fP755zp9%2BrS%2B%2BOILTZkyRcnJycrNzVV9fT377aXY2FgNGTJEJSUlampq0rlz53Tw4EGNHz%2Be3fbAnDlzFB4eftXbvmmfFRUVunjxoj799FO328PCwvSd73xHFRUVHp3ZBALLx7hcLkVERLgduxJbDQ0NdozkVyoqKrRjxw49%2BeSTV91137595XK5eM9bF/3lL39RUVGRVq1a5Xb8m3bLc7jrzp49K0k6cOCAtm3bpl27duns2bMqKChgv73kcDhUVFSkt99%2BW0lJSUpJSVFra6vy8vLYrWEul8vtgoH01Z9pDQ0N%2BuKLL2RZ1jfe7u0ILB9kWZbdI/il3/3ud5o7d67y8vKUkpLyjfcLCAj4FqfybWvXrlV6eroGDhxo9yh%2B58rvA/PmzVNsbKz69%2B%2BvhQsX6tChQzZP5vsuX76sBQsWaPLkyTp69KjeffddhYeHKz8/3%2B7R/NK1/kzz1T/zCCwfEx0dLZfL5XbM5XIpICBA0dHRNk3l%2Bw4dOqTHH39cy5cv15w5cyR9teuv/y3J5XKpb9%2B%2Bcjj4pXMt5eXl%2BvDDD5WTk9PptqioqE7P44aGBp7D3XDlZe2/vpoyYMAAWZallpYW9tsL5eXlOn36tJYsWaLw8HDFxsYqNzdX//mf/ymHw8FuDbra7wUul0vR0dEdv9de7fabbrrp2xyzR/hTwsckJCTozJkzHf9cWPrqZa2BAwcqNDTUxsl817Fjx7R06VL99Kc/1YMPPthxPCEhQR9//LFaW1s7jlVUVCgxMdGOMX3O7t27de7cOU2YMEHJyclKT0%2BXJCUnJ%2BuOO%2B7o9J6VyspKdtsN/fv3V1hYmE6ePNlxrKamRjfccINSU1PZby%2B0tbWpvb3d7crJ5cuXJUkpKSns1qCEhIRO%2B7zy%2B2xwcLAGDRqkqqqqjtsaGxv1pz/9Sd/97ne/7VG7jcDyMfHx8Ro2bJgKCwt14cIFVVdXa9u2bcrKyrJ7NJ/U2tqqgoIC5efna%2BzYsW63paamKiwsTMXFxWpubtbx48e1c%2BdOdt1Fy5Yt01tvvaVdu3Zp165d2rJliyRp165dmjZtmmpqalRaWqpLly6prKxMZWVlmjVrls1T%2Bw6n06mMjAy98sor%2BuMf/6hz585p06ZNmjZtmmbOnMl%2Be2H48OEKCQlRUVGRmpub1dDQoOLiYo0aNUozZsxgtwbNmjVLR44c0eHDh3Xp0iXt3LlTn332maZPny5JysrK0vbt21VdXa0LFy5ow4YNGjJkiIYNG2bz5NcWYPnqi5vXsbNnz2rlypX6r//6L4WFhWn27Nl6%2BumneW9QDxw9elTf//73FRQU1Om2AwcOqKmpSatWrVJlZaX69eun%2BfPn65FHHrFhUt93%2BvRp3XPPPfr4448lSf/93/%2Bt559/XtXV1RowYIDy8vI0ceJEm6f0LZcvX9batWv15ptvqqWlRZMmTdLKlSsVGhrKfnupsrJSL774oj766CMFBQVp9OjRWrZsmWJjY9ltN12JoSuvBjidTknq%2BJeABw8eVGFhoWpqajRw4ECtWLFCo0aNkvTV%2B6%2BKior02muvqampScnJyfrxj3%2Bs/v372/BIuofAAgAAMIyXCAEAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAz7f/7dfDWY1/64AAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-6235403665571426030”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>28</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.633605132861428</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>47.00526201334661</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0725893951893353</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.46845778160539</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>36.696235585931966</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>32.1064742023219</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>32.95946099612089</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>21.74880484541413</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>24.615616591206695</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3095)</td> <td class=”number”>3095</td> <td class=”number”>96.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-6235403665571426030”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>28</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.944829763351498e-05</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00012611350341420783</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00023948474105398398</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0005359863967977933</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>100.00000059283695</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.00000074171741</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.00000094378623</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.00000095859588</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.00000097149982</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_POP15”>PCT_LACCESS_POP15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3106</td>

</tr> <tr>

<th>Unique (%)</th> <td>96.8%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>97</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>23.042</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.3%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-2921851244024598451”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-2921851244024598451,#minihistogram-2921851244024598451”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-2921851244024598451”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-2921851244024598451”

aria-controls=”quantiles-2921851244024598451” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-2921851244024598451” aria-controls=”histogram-2921851244024598451”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-2921851244024598451” aria-controls=”common-2921851244024598451”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-2921851244024598451” aria-controls=”extreme-2921851244024598451”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-2921851244024598451”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>2.2369</td>

</tr> <tr>

<th>Q1</th> <td>10.93</td>

</tr> <tr>

<th>Median</th> <td>19.177</td>

</tr> <tr>

<th>Q3</th> <td>28.823</td>

</tr> <tr>

<th>95-th percentile</th> <td>58.107</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr>

<th>Range</th> <td>100</td>

</tr> <tr>

<th>Interquartile range</th> <td>17.893</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>19.539</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.84799</td>

</tr> <tr>

<th>Kurtosis</th> <td>5.8339</td>

</tr> <tr>

<th>Mean</th> <td>23.042</td>

</tr> <tr>

<th>MAD</th> <td>13.119</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.1814</td>

</tr> <tr>

<th>Sum</th> <td>71730</td>

</tr> <tr>

<th>Variance</th> <td>381.79</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-2921851244024598451”>

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ttts0YcIE%2Bfn52T0eAADAVbldYElSQECA7r33XrvHAAAAuCZuF1h/%2B9vfVFBQoE8%2B%2BUTnz5/vcf6NN96wYSoAAIDec7vAWrNmjerr6zVhwgQFBwfbPQ4AAECfuV1gVVdX64033lBUVJTdowAAAFwTt/s2DTfddBNXrgAAgEdzu8B6/PHHtXXrVlmWZfcoAAAA18TtbhG%2B9dZbev/99/Xqq6/q29/%2Btnx9nRvwlVdesWkyAACA3nG7wAoNDdXEiRPtHgMAAOCauV1gbdy40e4RAAAA%2BsXtPoMlSf/zP/%2BjwsJCPfXUU93HPvjgAxsnAgAA6D23C6zKykrNmDFD5eXl2rdvn6SvvvnoggUL%2BCajAADAI7hdYL3wwgv60Y9%2BpL1798rHx0fSVz%2BfcNOmTdq2bZvN0wEAAFyd2wXWf//3fyszM1OSugNLkqZNm6ba2lq7xgIAAOg1twussLCwK/4Mwvr6egUEBNgwEQAAQN%2B4XWAlJiZqw4YNOnfuXPexkydPatWqVfre975n42QAAAC943bfpuGpp57So48%2BqqSkJHV2dioxMVFtbW0aOnSoNm3aZPd4AAAAV%2BV2gTVo0CDt27dPFRUVOnnypIKCgnTbbbdp/PjxTp/JAgAAcFduF1iSdMMNN%2Bjee%2B%2B1ewwAAIBr4naBlZqa%2Bg%2BvVPG9sAAAgLtzu8B64IEHnAKrs7NTJ0%2BeVFVVlR599FEbJwMAAOgdtwusvLy8Kx4/ePCg/vSnP33D0wAAAPSd232bhq9z77336rXXXrN7DAAAgKvymMA6duyYLMuyewwAAICrcrtbhPPmzetxrK2tTbW1tZoyZYoNEwEAAPSN2wVWbGxsj79FGBgYqIyMDD388MM2TQUAANB7bhdYfLd2AADg6dwusHbv3t3rxz700EMunAQAAODauF1grV27Vl1dXT0%2B0O7j4%2BN0zMfHh8ACAABuye0C65e//KW2b9%2BuJUuWaNiwYbIsSydOnNAvfvELPfLII0pKSrJ7RAAAgH/I7QJr06ZNKi4uVkxMTPexMWPG6JZbbtHChQu1b98%2BG6cDAAC4Orf7PlifffaZIiIiehwPDw9XXV2dDRMBAAD0jdsF1uDBg7Vp0yY1Nzd3H2tpaVFBQYFuvfVWGycDAADoHbe7RbhmzRrl5uaqpKREISEh8vX11blz5xQUFKRt27bZPR4AAMBVuV1gTZgwQYcPH1ZFRYXOnDkjy7IUExOju%2B%2B%2BW2FhYXaPBwAAcFVuF1iSdOONN%2Bqee%2B7RmTNndMstt9g9DgAAQJ%2B43Wewzp8/r1WrVmnUqFG6//77JX31GaxFixappaXF5ukAAACuzu0Ca/PmzTp%2B/Li2bNkiX9//G6%2Bzs1NbtmyxcTIAAIDecbvAOnjwoP71X/9V06ZN6/6hz%2BHh4dq4caPKy8ttng4AAODq3C6wWltbFRsb2%2BN4VFSUvvzyy29%2BIAAAgD5yu8C69dZb9ac//UmSnH724IEDB/RP//RPdo0FAADQa273twjnz5%2BvZcuWafbs2erq6tKOHTtUXV2tgwcPau3atXaPBwAAcFVudwVr7ty5WrVqlf74xz/Kz89PL774ourq6rRlyxZlZmZe03Nu2LBBw4YN6/7nyspKZWRkKDExUdOnT9eePXucHr9z505NnTpViYmJyszMVHV1db9eEwAAuL643RWspqYmzZ49W7NnzzbyfMePH1dZWVn3P9fX1%2BuJJ57Q2rVrlZaWpvfee09Lly7VbbfdppEjR%2BrQoUMqLCzUL3/5Sw0bNkw7d%2B7UkiVLVF5eruDgYCMzAQAA7%2BZ2V7Duuecep89e9UdXV5fWr1%2BvrKys7mN79%2B5VbGysMjIyFBgYqOTkZKWmpqq0tFSSVFJSovT0dCUkJCgoKEiLFi2SJL355ptGZgIAAN7P7QIrKSlJr7/%2BupHneuWVVxQYGKi0tLTuYzU1NYqLi3N6XFxcXPdtwMvP%2B/r6avjw4aqqqjIyEwAA8H5ud4vwW9/6lp5//nkVFxfr1ltv1Q033OB0vqCgoFfP8/e//12FhYX67W9/63Tc4XAoJibG6diAAQPU3NzcfT4iIsLpfERERPf53qivr1dDQ4PTMX//YEVHR/f6OXrDz8/t%2Bhg28vf3jPfDpfct71/XYL%2Buw25dxxt363aB9emnn%2Br222%2BXpD5FzeU2btyo9PR0DRkyRKdOnerTr%2B3vLcqSkhJt3brV6Vh2drZycnL69bzAPxIZGWL3CH0SHn6j3SN4NfbrOuzWdbxpt24TWCtWrNALL7zgdMVp27Ztys7O7vNzVVZW6oMPPtC%2Bfft6nIuMjJTD4XA61tzcrKioqK8973A4NHTo0F5//blz5yo1NdXpmL9/sJqbW3v9HL3hTaWP/jP9/nIVPz9fhYffqJaWNnV2dtk9jtdhv67Dbl3Hlbu16z8%2B3SawDh061ONYcXHxNQXWnj171NjYqMmTJ0v6vytSSUlJ%2BuEPf9gjvKqrq5WQkCBJio%2BPV01NjWbNmiXpq5%2BBeOzYMWVkZPT660dHR/e4HdjQcFYdHfyGhOt42vurs7PL42b2JOzXddit63jTbt3mEsiVbstd66261atX6%2BDBgyorK1NZWZmKi4slSWVlZUpLS1NdXZ1KS0t14cIFVVRUqKKiQnPmzJEkZWZmavfu3frwww/V1tamoqIiBQQEaNKkSdf82gAAwPXFba5gXfrBzlc71hsRERFOH1Tv6OiQJA0aNEiS9NJLL%2Bm5557TM888o8GDB2vz5s268847JUkTJ07UypUrtXz5cjU2NmrkyJEqLi5WUFDQNc0CAACuP24TWK707W9/WydOnOj%2B57Fjxzp989HLzZ8/X/Pnz/8mRgMAAF7IbW4RAgAAeAu3uYLV3t6u3Nzcqx7r7ffBAgAAsIvbBNbo0aNVX19/1WMAAADuzm0C6/LvuA4AAOCp%2BAwWAACAYQQWAACAYQQWAACAYW7zGSwA/XP/z961e4ReO/r8NLtHAACX4goWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYV4bWHV1dcrOzlZSUpKSk5O1evVqtbS0SJKOHz%2BuRx55RKNHj9aUKVO0fft2p1%2B7f/9%2BpaWladSoUUpPT9c777xjx0sAAAAeymsDa8mSJQoPD9ehQ4f06quv6pNPPtFPfvITnT9/Xo8//rj%2B5V/%2BRW%2B//bZeeOEFvfTSSyovL5f0VXytWrVKeXl5%2BuMf/6isrCw9%2BeSTOnPmjM2vCAAAeAqvDKyWlhbFx8crNzdXISEhGjRokGbNmqWjR4/q8OHDam9v19KlSxUcHKwRI0bo4YcfVklJiSSptLRUKSkpSklJUWBgoGbMmKE77rhDe/bssflVAQAAT%2BGVgRUeHq6NGzdq4MCB3cdOnz6t6Oho1dTUaNiwYfLz8%2Bs%2BFxcXp%2BrqaklSTU2N4uLinJ4vLi5OVVVV38zwAADA4/nbPcA3oaqqSi%2B//LKKior0%2BuuvKzw83On8gAED5HA41NXVJYfDoYiICKfzERER%2BvTTT3v99err69XQ0OB0zN8/WNHR0df%2BIq7Az88r%2BxjXCd6/rnFpr%2BzXPHbrOt64W68PrPfee09Lly5Vbm6ukpOT9frrr1/xcT4%2BPt3/27Ksfn3NkpISbd261elYdna2cnJy%2BvW8gDcJD7/R7hG8Gvt1HXbrOt60W68OrEOHDulHP/qR1q1bp4ceekiSFBUVpc8%2B%2B8zpcQ6HQwMGDJCvr68iIyPlcDh6nI%2BKiur11507d65SU1Odjvn7B6u5ufXaXsjX8KbSx/WnpaVNnZ1ddo/hdfz8fBUefiP7dQF26zqu3G1kZIjR5%2Bstrw2s999/X6tWrdLPf/5zTZgwoft4fHy8fve736mjo0P%2B/l%2B9/KqqKiUkJHSfv/R5rEuqqqo0ffr0Xn/t6OjoHrcDGxrOqqOD35DAJZ2dXfyecCH26zrs1nW8abdeeQmko6ND%2Bfn5ysvLc4orSUpJSVFoaKiKiorU1tamjz76SLt27VJmZqYkac6cOTpy5IgOHz6sCxcuaNeuXfrss880Y8YMO14KAADwQD5Wfz9w5IaOHj2q73//%2BwoICOhx7sCBA2ptbdX69etVXV2tgQMHavHixZo/f373Y8rLy1VQUKC6ujoNGTJEa9eu1dixY/s1U0PD2X79%2Bivx9/fVfVveNv68gKsdfX6amptbvea/VN2Jv7%2BvIiND2K8LsFvXceVub745zOjz9ZZX3iIcM2aMTpw48Q8f87vf/e5rz02ZMkVTpkwxPRYAALhOeOUtQgAAADsRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIb52z0AgOvPmLUH7B6hT15fPt7uEQB4GK5gAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGMY3GgUAwEvd/7N37R6h144%2BP83uEYziChYAAIBhBBYAAIBhBBYAAIBhBBYAAIBhBBYAAIBhBBYAAIBhBBYAAIBhBBYAAIBhBBYAAIBhBBYAAIBhBBYAAIBhBBYAAIBhBBYAAIBhBBYAAIBhBBYAAIBhBBYAAIBh/nYPAADu7v6fvWv3CH1y9Plpdo8AXPe4ggUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYPyoHALzMmLUH7B6h115fPt7uEQCXILAAALbxtJ/zCPQWtwivoK6uTo899piSkpI0efJkbd68WV1dXXaPBQAAPARXsK5g2bJlGjFihP7whz%2BosbFRjz/%2BuAYOHKgf/OAHdo8GAAA8AFewLlNVVaWPP/5YeXl5CgsLU2xsrLKyslRSUmL3aAAAwENwBesyNTU1Gjx4sCIiIrqPjRgxQidPntS5c%2BcUGhp61eeor69XQ0OD0zF//2BFR0cbndXPjz4GAHgPb/pzjcC6jMPhUHh4uNOxS7HV3Nzcq8AqKSnR1q1bnY49%2BeSTWrZsmblB9VXIPTroE82dO9d4vF3v6uvrVVJSwm5dgN26Fvt1HXbrOvX19SosLPSq3XpPKhpkWVa/fv3cuXP16quvOv3f3LlzDU33fxoaGrR169YeV8vQf%2BzWddita7Ff12G3ruONu%2BUK1mWioqLkcDicjjkcDvn4%2BCgqKqpXzxEdHe01BQ4AAPqOK1iXiY%2BP1%2BnTp9XU1NR9rKqqSkOGDFFISIiNkwEAAE9BYF0mLi5OI0eOVEFBgc6dO6fa2lrt2LFDmZmZdo8GAAA8hN/TTz/9tN1DuJu7775b%2B/bt07PPPqvXXntNGRkZWrhwoXx8fOwerYeQkBCNGzeOq2suwG5dh926Fvt1HXbrOt62Wx%2Brv5/oBgAAgBNuEQIAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYHmguro6PfbYY0pKStLkyZO1efNmdXV12T2Wx6qrq1N2draSkpKUnJys1atXq6WlRZJ0/PhxPfLIIxo9erSmTJmi7du32zyt59qwYYOGDRvW/c%2BVlZXKyMhQYmKipk%2Bfrj179tg4necqKirShAkTdNdddykrK0unTp2SxH7769ixY1qwYIHGjBmj8ePHKy8vT01NTZLY7bV4%2B%2B23lZycrBUrVvQ4t3//fqWlpWnUqFFKT0/XO%2B%2B8032uq6tLL7zwgu655x6NHTtWCxcu1N/%2B9rdvcvRrZ8HjzJo1y8rPz7daWlqskydPWlOmTLG2b99u91ge68EHH7RWr15tnTt3zjp9%2BrSVnp5urVmzxmpra7Puvvtuq7Cw0GptbbWqq6utcePGWQcPHrR7ZI9z7Ngxa9y4cdYdd9y1pJkMAAAHA0lEQVRhWZZlff7559Zdd91llZaWWufPn7feffdd67vf/a715z//2eZJPcvLL79sTZs2zaqtrbXOnj1rPfvss9azzz7Lfvupvb3dGj9%2BvFVQUGBduHDBampqsn7wgx9Yy5YtY7fXoLi42JoyZYo1b948a/ny5U7njh07ZsXHx1uHDx%2B2zp8/b5WVlVkJCQnW6dOnLcuyrJ07d1qTJ0%2B2Pv30U%2Bvs2bPWj3/8YystLc3q6uqy46X0CVewPExVVZU%2B/vhj5eXlKSwsTLGxscrKylJJSYndo3mklpYWxcfHKzc3VyEhIRo0aJBmzZqlo0eP6vDhw2pvb9fSpUsVHBysESNG6OGHH2bXfdTV1aX169crKyur%2B9jevXsVGxurjIwMBQYGKjk5WampqSotLbVvUA%2B0fft2rVixQrfffrtCQ0OVn5%2Bv/Px89ttPDQ0Namho0MyZMxUQEKDIyEjdd999On78OLu9BoGBgdq1a5e%2B853v9DhXWlqqlJQUpaSkKDAwUDNmzNAdd9zRfVWwpKREWVlZ%2Bud//meFhoZqxYoVqq2t1UcfffRNv4w%2BI7A8TE1NjQYPHqyIiIjuYyNGjNDJkyd17tw5GyfzTOHh4dq4caMGDhzYfez06dOKjo5WTU2Nhg0bJj8/v%2B5zcXFxqq6utmNUj/XKK68oMDBQaWlp3cdqamoUFxfn9Dh22zeff/65Tp06pS%2B%2B%2BEIPPPCAkpKSlJOTo6amJvbbTzExMRo%2BfLhKSkrU2tqqxsZGlZeXa9KkSez2GixYsEBhYWFXPPd1%2B6yqqtL58%2Bf16aefOp0PDQ3Vd77zHVVVVbl0ZhMILA/jcDgUHh7udOxSbDU3N9sxklepqqrSyy%2B/rKVLl15x1wMGDJDD4eAzb73097//XYWFhVq/fr3T8a/bLe/h3jtz5owk6cCBA9qxY4fKysp05swZ5efns99%2B8vX1VWFhod544w0lJiYqOTlZHR0dys3NZbeGORwOpwsG0ld/pjU3N%2BuLL76QZVlfe97dEVgeyLIsu0fwSu%2B9954WLlyo3NxcJScnf%2B3jfHx8vsGpPNvGjRuVnp6uIUOG2D2K17n074FFixYpJiZGgwYN0rJly3To0CGbJ/N8Fy9e1JIlSzRt2jQdPXpUb731lsLCwpSXl2f3aF7pan%2BmeeqfeQSWh4mKipLD4XA65nA45OPjo6ioKJum8nyHDh3SY489pjVr1mjBggWSvtr15f%2BV5HA4NGDAAPn68lvnaiorK/XBBx8oOzu7x7nIyMge7%2BPm5mbew31w6bb2/7%2BaMnjwYFmWpfb2dvbbD5WVlTp16pRWrlypsLAwxcTEKCcnR//xH/8hX19fdmvQlf5d4HA4FBUV1f3v2iudv%2Bmmm77JMa8Jf0p4mPj4eJ0%2Bfbr7rwtLX93WGjJkiEJCQmyczHO9//77WrVqlX7%2B85/roYce6j4eHx%2BvEydOqKOjo/tYVVWVEhIS7BjT4%2BzZs0eNjY2aPHmykpKSlJ6eLklKSkrSHXfc0eMzK9XV1ey2DwYNGqTQ0FAdP368%2B1hdXZ1uuOEGpaSksN9%2B6OzsVFdXl9OVk4sXL0qSkpOT2a1B8fHxPfZ56d%2BzgYGBGjp0qGpqarrPtbS06K9//au%2B%2B93vftOj9hmB5WHi4uI0cuRIFRQU6Ny5c6qtrdWOHTuUmZlp92geqaOjQ/n5%2BcrLy9OECROczqWkpCg0NFRFRUVqa2vTRx99pF27drHrXlq9erUOHjyosrIylZWVqbi4WJJUVlamtLQ01dXVqbS0VBcuXFBFRYUqKio0Z84cm6f2HP7%2B/srIyNCLL76ov/zlL2psbNS2bduUlpamWbNmsd9%2BGDVqlIKDg1VYWKi2tjY1NzerqKhIY8eO1cyZM9mtQXPmzNGRI0d0%2BPBhXbhwQbt27dJnn32mGTNmSJIyMzO1c%2BdO1dbW6ty5c9qyZYuGDx%2BukSNH2jz51flYnnpz8zp25swZrVu3Tv/5n/%2Bp0NBQzZs3T08%2B%2BSSfDboGR48e1fe//30FBAT0OHfgwAG1trZq/fr1qq6u1sCBA7V48WLNnz/fhkk936lTp3TPPffoxIkTkqT/%2Bq//0nPPPafa2loNHjxYubm5mjJlis1TepaLFy9q48aNeu2119Te3q6pU6dq3bp1CgkJYb/9VF1drZ/85Cf6%2BOOPFRAQoHHjxmn16tWKiYlht310KYYu3Q3w9/eXpO6/CVheXq6CggLV1dVpyJAhWrt2rcaOHSvpq89fFRYW6pVXXlFra6uSkpL04x//WIMGDbLhlfQNgQUAAGAYtwgBAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAM%2B19HOl7j63J0ugAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-2921851244024598451”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>9</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14.158687217087312</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.905375536615939</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.69408236774551</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>18.900998542829868</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17.8781375304654</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>44.82067368329241</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.40711440880959</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.4425962973110096</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>23.992752803469884</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3095)</td> <td class=”number”>3095</td> <td class=”number”>96.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-2921851244024598451”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>9</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00012611350341420783</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.003080740593036945</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.010001508740149247</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0135249805431423</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>100.00000057203329</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.00000059283695</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.00000074171741</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.00000095859588</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.00000097149982</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_SENIORS10”><s>PCT_LACCESS_SENIORS10</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCT_LACCESS_POP10”><code>PCT_LACCESS_POP10</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.92085</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_SENIORS15”><s>PCT_LACCESS_SENIORS15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCT_LACCESS_POP15”><code>PCT_LACCESS_POP15</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.91044</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_SNAP15”>PCT_LACCESS_SNAP15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3101</td>

</tr> <tr>

<th>Unique (%)</th> <td>96.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>97</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>2.8931</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>37.254</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.4%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram5437687410286935982”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives5437687410286935982,#minihistogram5437687410286935982”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives5437687410286935982”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles5437687410286935982”

aria-controls=”quantiles5437687410286935982” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram5437687410286935982” aria-controls=”histogram5437687410286935982”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common5437687410286935982” aria-controls=”common5437687410286935982”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme5437687410286935982” aria-controls=”extreme5437687410286935982”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles5437687410286935982”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.24561</td>

</tr> <tr>

<th>Q1</th> <td>1.1318</td>

</tr> <tr>

<th>Median</th> <td>2.1231</td>

</tr> <tr>

<th>Q3</th> <td>3.6644</td>

</tr> <tr>

<th>95-th percentile</th> <td>7.7156</td>

</tr> <tr>

<th>Maximum</th> <td>37.254</td>

</tr> <tr>

<th>Range</th> <td>37.254</td>

</tr> <tr>

<th>Interquartile range</th> <td>2.5326</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>3.0461</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.0529</td>

</tr> <tr>

<th>Kurtosis</th> <td>25.315</td>

</tr> <tr>

<th>Mean</th> <td>2.8931</td>

</tr> <tr>

<th>MAD</th> <td>1.9113</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>3.8829</td>

</tr> <tr>

<th>Sum</th> <td>9006.2</td>

</tr> <tr>

<th>Variance</th> <td>9.2789</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram5437687410286935982”>

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io6Ob3SY9PV1nzpyxejQAAAAjLC9Y69at06BBg773dgcPHrRgGgAAAPMsv8g9ISFBjz32WNM1U5L0H//xH3rkkUeafXQCAACAL7K8YC1btkxnz55Vjx49mtaGDBmixsZGLV%2B%2B3OpxAAAAjLP8LcL9%2B/dr8%2BbNiomJaVqLj49Xbm6uHnjgAavHAQAAMM7yM1h1dXUKCwtrPkhwsGpra60eBwAAwDjLC9bAgQO1fPlyff31101rf//737V48WKPj2oAAADwVZa/RbhgwQL9y7/8i%2B644w6Fh4ersbFRNTU16tKli1577TWrxwEAADDO8oLVpUsXvf3223rnnXf0xRdfKDg4WN26ddPgwYObfrkyAACAL/PKr8oJDQ3VPffc441DAwAAXHOWF6yTJ08qLy9Pn3/%2Buerq6prt/%2BUvf7F6JAAAAKO8cg1WeXm5Bg8erLZt21p9eAAAgGvO8oJVXFysv/zlL7Lb7VYfGgAAwBKWf0xD%2B/btOXMFAAD8muUFa8aMGcrPz5fb7bb60AAAAJaw/C3Cd955Rx9//LE2btyozp07KzjYs%2BP98Y9/tHokAAAAoywvWOHh4frJT35i9WEBAAAsY3nBWrZsmdWHBAAAsJTl12BJ0rFjx/TSSy/pl7/8ZdPa3/72N2%2BMAgAAYJzlBevAgQMaM2aMduzYoaKiIknffvjo1KlT%2BZBRAADgFywvWCtXrtTTTz%2BtzZs3KygoSNK3v59w%2BfLlWrVqldXjAAAAGGd5wfqf//kfZWVlSVJTwZKke%2B%2B9V6WlpVaPAwAAYJzlBSsiIuKSv4OwvLxcoaGhVo8DAABgnOUFq3///vrNb36j6urqprXjx49r3rx5uuOOO6weBwAAwDjLP6bhl7/8pR566CGlpqaqoaFB/fv3V21trXr27Knly5dbPQ4AAIBxlhesm266SUVFRdq7d6%2BOHz%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%2B2ehwAAADjLH%2BLcMGCBZozZ44cDofatWun4OBgVVdX64YbbtCqVausHgcAAMA4ywvW4MGDtWfPHu3du1enT5%2BW2%2B1Wp06ddNdddykiIsLqcQAAAIyzvGBJ0o033qhhw4bp9OnT6tKlizdGAAAAuGYsvwarrq5O8%2BbNU79%2B/XTfffdJ%2BvYarJ///OeqqqqyehwAAADjLC9YK1as0Geffabc3FwFB//f4RsaGpSbm2v1OAAAAMZZXrC2b9%2Buf/u3f9O9997b9EufIyMjtWzZMu3YscPqcQAAAIyzvGDV1NQoPj6%2B2brdbtc333xj9TgAAADGWV6wbr75Zn3wwQeS5PG7B7dt26Z/%2Bqd/snocAAAA4yz/KcKf/exnevLJJzV%2B/Hg1NjZq7dq1Ki4u1vbt27Vw4UKrxwEAADDO8oKVkZEhm82m119/XSEhIfrtb3%2Brbt26KTc3V/fee6/V4wAAABhnecE6c%2BaMxo8fr/Hjx1t9aAAAAEtYfg3WsGHDPK69AgAA8DeWF6zU1FRt3brVyGPt27dPaWlpysnJaba3ZcsWjR49Wv369dO4ceO0f//%2Bpr3GxkatXLlSw4YN08CBAzV9%2BnSdPHmyad/lcmn27NlKS0vT4MGDtXDhQtXV1RmZGQAA%2BD/L3yL80Y9%2BpH/9139VQUGBbr75ZrVp08ZjPy8v76oe55VXXlFhYaG6du3abO%2Bzzz7TvHnzlJ%2Bfr9tvv13bt2/XE088oW3btummm27SG2%2B8oc2bN%2BuVV15Rp06dtHLlSmVnZ%2Butt95SUFCQFi1apPPnz6uoqEj19fX6xS9%2BodzcXD377LNGvgYAAMC/WX4G6%2BjRo/rxj3%2BsiIgIVVZWqry83OPP1QoLC7tswVq/fr3S09OVnp6usLAwjRkzRr169dKmTZskSQ6HQ9OmTVP37t0VHh6unJwclZaW6uDBg/ryyy%2B1c%2BdO5eTkyG63q1OnTnr88ce1YcMG1dfXG/s6AAAA/2XZGaycnBytXLlSr732WtPaqlWrlJ2d3aLHmzp16mX3SkpKlJ6e7rGWmJgop9Opuro6HT16VImJiU174eHh6tq1q5xOp86ePauQkBAlJCQ07SclJembb77RsWPHPNYBAAAuxbKCtWvXrmZrBQUFLS5YV%2BJyuRQVFeWxFhUVpaNHj%2Brrr7%2BW2%2B2%2B5H5lZaWio6MVHh7e9Gt8vtuTpMrKyqs6fnl5uSoqKjzWbLa2io2NbUmcywoJsfwEZKvYbC2b97ucvpa3JcjqfwIlpxQ4WQMlpxRYWU2zrGBd6icHr%2BVPE37fY19pv7VzORwO5efne6xlZ2dr1qxZrXpcXxcT065V94%2BMvNHQJNc/svqfQMkpBU7WQMkpBVZWUywrWP94RuhKaybExMTI5XJ5rLlcLtntdkVHRys4OPiS%2B%2B3bt5fdbld1dbUaGhoUEhLStCdJ7du3v6rjZ2RkaOjQoR5rNltbVVbWtDTSJfnaK4qW5g8JCVZk5I2qqqpVQ0Oj4amuL2T1P4GSUwqcrIGSU/KPrK19cd9Slv8UoRWSk5NVXFzsseZ0OjVq1CiFhYWpZ8%2BeKikp0aBBgyRJVVVV%2BuKLL9SnTx/FxcXJ7Xbr8OHDSkpKarpvZGSkunXrdlXHj42NbfZ2YEXFWV244Jt/OU1pbf6GhsaA%2BRqS1f8ESk4pcLIGSk4psLKa4lunQK7SpEmT9N5772nPnj06d%2B6cCgsLdeLECY0ZM0aSlJWVpXXr1qm0tFTV1dXKzc1V7969lZKSIrvdrpEjR%2BqFF17QmTNndPr0aa1atUoTJkyQzeaXfRQAABhmWWOor6/XnDlzvnftaj8HKyUlRZJ04cIFSdLOnTslfXu2qVevXsrNzdWyZctUVlamHj166OWXX1bHjh0lSZmZmaqoqNCUKVNUU1Oj1NRUj2umfv3rX%2Bu5557TsGHD1KZNGz3wwAOX/DBTAACASwlyW/R7a6ZMmXJVt/vHj3HwJxUVZ40/ps0WrOG5%2B4w/7rWydfadLbqfzRasmJh2qqys8ftT1GT1P4GSUwqcrIGSU/KPrB07RnjluJadwfLX4gQAAHAxv7wGCwAAwJsoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYJjN2wMgcNz3wrveHuEH2Tr7Tm%2BPAADwUZzBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIb5bcFKSEhQcnKyUlJSmv4sWbJEknTgwAFNmDBB/fv316hRo7Rp0yaP%2B65bt04jR45U//79lZWVpeLiYm9EAAAAPsqvPwdr27Zt6ty5s8daeXm5Hn/8cS1cuFCjR4/WRx99pJkzZ6pbt25KSUnRrl279NJLL%2BnVV19VQkKC1q1bp8cee0w7duxQ27ZtvZQEAAD4Er89g3U5mzdvVnx8vCZMmKCwsDClpaVp6NChWr9%2BvSTJ4XBo3Lhx6tu3r2644Qb9/Oc/lyTt3r3bm2MDAAAf4tdnsPLy8vS3v/1N1dXVuu%2B%2B%2BzR//nyVlJQoMTHR43aJiYnaunWrJKmkpET3339/015wcLB69%2B4tp9OpUaNGXdVxy8vLVVFR4bFms7VVbGxsKxN5CgkJuH5sKZvN%2Bq/vd89pIDy3gZI1UHJKgZM1UHJKgZXVNL8tWLfeeqvS0tL0/PPP6%2BTJk5o9e7YWL14sl8ulTp06edw2OjpalZWVkiSXy6WoqCiP/aioqKb9q%2BFwOJSfn%2B%2Bxlp2drVmzZrUwDbwhJqad144dGXmj145ttUDJGig5pcDJGig5pcDKaorfFiyHw9H0z927d9fcuXM1c%2BZMDRgw4Hvv63a7W3XsjIwMDR061GPNZmurysqaVj3uxXhFcW2Zfr6uRkhIsCIjb1RVVa0aGhotP76VAiVroOSUAidroOSU/COrt14s%2B23Buljnzp3V0NCg4OBguVwuj73KykrZ7XZJUkxMTLN9l8ulnj17XvWxYmNjm70dWFFxVhcu%2BOZfzkDlzeeroaExYP6%2BBErWQMkpBU7WQMkpBVZWU/zyFMihQ4e0fPlyj7XS0lKFhoYqPT292ccuFBcXq2/fvpKk5ORklZSUNO01NDTo0KFDTfsAAADfxy8LVvv27eVwOFRQUKDz58/r%2BPHjevHFF5WRkaGf/vSnKisr0/r163Xu3Dnt3btXe/fu1aRJkyRJWVlZevPNN/XJJ5%2BotrZWq1evVmhoqIYMGeLdUAAAwGf45VuEnTp1UkFBgfLy8poK0tixY5WTk6OwsDC9/PLLWrp0qRYvXqy4uDitWLFCt9xyiyTpJz/5iZ566inNnj1bX331lVJSUlRQUKAbbrjBy6kAAICvCHK39opuXJWKirPGH9NmC9bw3H3GHxff2jr7TsuPabMFKyamnSora/z%2BeodAyRooOaXAyRooOSX/yNqxY4RXjuuXbxECAAB4EwULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACG2bw9AHC9uu%2BFd709wg%2Bydfad3h4BAPD/cQYLAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIzPwQL8hC99bhef2QXA33EG6xLKysr06KOPKjU1VXfffbdWrFihxsZGb48FAAB8BGewLuHJJ59UUlKSdu7cqa%2B%2B%2BkozZsxQhw4d9PDDD3t7NAAA4AMoWBdxOp06fPiw1q5dq4iICEVERGjatGn6/e9/T8ECDPGltzMl3tIE8MNRsC5SUlKiuLg4RUVFNa0lJSXp%2BPHjqq6uVnh4%2BPc%2BRnl5uSoqKjzWbLa2io2NNTprSAjv8AJW8LVC6Ev%2BPPcub4/wgwzP3eftEX6Q1n59v/v/DP%2B/%2BeEoWBdxuVyKjIz0WPuubFVWVl5VwXI4HMrPz/dYe%2BKJJ/Tkk0%2BaG1TfFrmHbvpcGRkZxsvb9aS8vFwOh8Pvc0pk9UeBklMKjKwf/uu9AZHzO%2BXl5fr9718NiKymUUkvwe12t%2Br%2BGRkZ2rhxo8efjIwMQ9P9n4qKCuXn5zc7W%2BZvAiWnRFZ/FCg5pcDJGig5pcDKahpnsC5it9vlcrk81lwul4KCgmS326/qMWJjY2n6AAAEMM5gXSQ5OVmnTp3SmTNnmtacTqd69Oihdu3aeXEyAADgKyhYF0lMTFRKSory8vJUXV2t0tJSrV27VllZWd4eDQAA%2BIiQX/3qV7/y9hDXm7vuuktRDyYvAAAJZ0lEQVRFRUVasmSJ3n77bU2YMEHTp09XUFCQt0drpl27dho0aJDfn10LlJwSWf1RoOSUAidroOSUAiurSUHu1l7RDQAAAA%2B8RQgAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAXLB5WVlenRRx9Vamqq7r77bq1YsUKNjY3eHuuaSEhIUHJyslJSUpr%2BLFmyxNtjGbFv3z6lpaUpJyen2d6WLVs0evRo9evXT%2BPGjdP%2B/fu9MKE5l8u6ceNG3XLLLR7Pb0pKij799FMvTdo6ZWVlys7OVmpqqtLS0jR//nxVVVVJkj777DNNnjxZAwYM0IgRI7RmzRovT9s6l8v6v//7v0pISGj2nP7ud7/z9sgtcvjwYT300EMaMGCA0tLSNHv2bFVUVEiSDhw4oAkTJqh///4aNWqUNm3a5OVpW%2BdyWT/44INLPqdbt2719sjXNzd8ztixY93PPvusu6qqyn38%2BHH3iBEj3GvWrPH2WNdEr1693CdPnvT2GMYVFBS4R4wY4c7MzHTPnj3bY%2B/QoUPu5ORk9549e9x1dXXut956y923b1/3qVOnvDRt61wp64YNG9yTJ0/20mTmPfDAA%2B758%2Be7q6ur3adOnXKPGzfOvWDBAndtba37rrvucr/00kvumpoad3FxsXvQoEHu7du3e3vkFrtc1pMnT7p79erl7fGMOHfunPuOO%2B5w5%2Bfnu8%2BdO%2Bf%2B6quv3JMnT3Y//vjj7r///e/uW2%2B91b1%2B/Xp3XV2d%2B91333X36dPH/emnn3p77Ba5Utb333/ffffdd3t7RJ/DGSwf43Q6dfjwYc2dO1cRERGKj4/XtGnT5HA4vD0afoCwsDAVFhaqa9euzfbWr1%2Bv9PR0paenKywsTGPGjFGvXr189tXxlbL6k6qqKiUnJ2vOnDlq166dbrrpJo0dO1Yffvih9uzZo/r6es2cOVNt27ZVUlKSJk6c6LPft1fK6k9qa2uVk5OjGTNmKDQ0VHa7XcOHD9fnn3%2BuzZs3Kz4%2BXhMmTFBYWJjS0tI0dOhQrV%2B/3ttjt8iVsqJlKFg%2BpqSkRHFxcYqKimpaS0pK0vHjx1VdXe3Fya6dvLw8DRkyRLfddpsWLVqkmpoab4/UalOnTlVERMQl90pKSpSYmOixlpiYKKfTacVoxl0pqySdOnVKDz/8sAYOHKhhw4bprbfesnA6cyIjI7Vs2TJ16NChae3UqVOKjY1VSUmJEhISFBIS0rSXmJio4uJib4zaalfK%2Bp1nnnlGgwcP1u233668vDzV19d7Y9RWiYqK0sSJE2Wz2SRJx44d03/913/pvvvuu%2Bz3qa8%2Bp1fKKkk1NTVNbwnfddddWrt2rdxutzdHvu5RsHyMy%2BVSZGSkx9p3ZauystIbI11Tt956q9LS0rRjxw45HA598sknWrx4sbfHuqZcLpdHgZa%2BfY798fm12%2B2Kj4/X008/rXfffVdPPfWUFixYoAMHDnh7tFZzOp16/fXXNXPmzEt%2B30ZHR8vlcvnF9ZP/mDU0NFT9%2BvXT8OHDtXv3bhUUFGjTpk3693//d2%2BP2WJlZWVKTk7W/fffr5SUFM2aNeuyz6mvf59eKmt4eLh69eqlhx56SPv27dOyZcuUn5%2BvDRs2eHvc6xoFywcF0qsGh8OhiRMnKjQ0VN27d9fcuXNVVFSk8%2BfPe3u0aypQnuMhQ4bo1VdfVWJiokJDQzVq1CgNHz5cGzdu9PZorfLRRx9p%2BvTpmjNnjtLS0i57u6CgIAunujYuzhobG6s//vGPGj58uNq0aaM%2BffpoxowZPv2cxsXFyel0atu2bTpx4oSeeeYZb490zVwqa1JSkl577TUNGjRIoaGhGjx4sDIzM336ObUCBcvH2O12uVwujzWXy6WgoCDZ7XYvTWWdzp07q6GhQV999ZW3R7lmYmJiLvkcB8LzK337H/jy8nJvj9Fiu3bt0qOPPqoFCxZo6tSpkr79vr34zIbL5VJ0dLSCg333P8OXynopcXFx%2BvLLL336hUNQUJDi4%2BOVk5OjoqIi2Wy2Zt%2BnlZWVfvF9enHWM2fONLuNr3%2BfWsF3v7MDVHJysk6dOuXxF97pdKpHjx5q166dFycz79ChQ1q%2BfLnHWmlpqUJDQz2u9fA3ycnJza7jcDqd6tu3r5cmunb%2B8Ic/aMuWLR5rpaWl6tKli5cmap2PP/5Y8%2BbN04svvqgHH3ywaT05OVlHjhzRhQsXmtZ8/Tm9XNYDBw5o9erVHrc9duyY4uLifO6M3YEDBzRy5EiPt3G/K8R9%2BvRp9n1aXFzss8/plbLu3btX//mf/%2Blx%2B2PHjvns96lVKFg%2BJjExUSkpKcrLy1N1dbVKS0u1du1aZWVleXs049q3by%2BHw6GCggKdP39ex48f14svvqiMjAyPi4X9zaRJk/Tee%2B9pz549OnfunAoLC3XixAmNGTPG26MZd/78eS1ZskROp1P19fUqKirSO%2B%2B8o8zMTG%2BP9oNduHBBzz77rObOnavBgwd77KWnpys8PFyrV69WbW2tDh48qMLCQp/9vr1S1oiICK1atUpvvfWW6uvr5XQ69bvf/c4nsyYnJ6u6ulorVqxQbW2tzpw5o5deekm33XabsrKyVFZWpvXr1%2BvcuXPau3ev9u7dq0mTJnl77Ba5UtaIiAg9//zz2r9/v%2Brr6/Xuu%2B9qw4YNPvmcWinI7cvnbAPU6dOntWjRIv33f/%2B3wsPDlZmZqSeeeMLnXh1ejb/%2B9a/Ky8vTkSNHFBoaqrFjxyonJ0dhYWHeHq1VUlJSJKnpjMZ3P7nz3U8K7tixQ3l5eSorK1OPHj20cOFCDRw40DvDttKVsrrdbq1evVqFhYWqqKhQ586d9cwzz%2Bjuu%2B/22rwt9eGHH%2Bqf//mfFRoa2mxv27Ztqqmp0XPPPafi4mJ16NBBjzzyiH72s595YdLW%2B76shw4dUn5%2Bvk6cOKGIiAhNmTJFjzzyiE%2B%2BHXrkyBEtXbpUn376qdq2bavbb79d8%2BfPV6dOnfTXv/5VS5cuVWlpqeLi4jRnzhyNGDHC2yO32JWyOhwOrVmzRqdOnVKHDh00c%2BZMTZw40dsjX9coWAAAAIb53ssJAACA6xwFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACG/T88LSVXNyAUoAAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common5437687410286935982”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>14</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.5768005767045246</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.7390108700576679</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.6982309627506187</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.090028092868416</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.898523723161081</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0690733021956598</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.3535797992116514</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.151558099261077</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.2236079065374443</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3090)</td> <td class=”number”>3090</td> <td class=”number”>96.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme5437687410286935982”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>14</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0004437389204020983</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0008080882371859443</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0011150201499580523</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0011809836054727893</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>30.334506176606347</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>31.167352189637803</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>31.450721209209693</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>32.40852845002016</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>37.25448848259154</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_WHITE15”><s>PCT_LACCESS_WHITE15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCT_LACCESS_SENIORS15”><code>PCT_LACCESS_SENIORS15</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.93066</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LOCLFARM07”>PCT_LOCLFARM07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2679</td>

</tr> <tr>

<th>Unique (%)</th> <td>83.5%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>4.3%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>139</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>6.3292</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>1.7%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-2072843032676419397”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-2072843032676419397,#minihistogram-2072843032676419397”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-2072843032676419397”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-2072843032676419397”

aria-controls=”quantiles-2072843032676419397” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-2072843032676419397” aria-controls=”histogram-2072843032676419397”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-2072843032676419397” aria-controls=”common-2072843032676419397”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-2072843032676419397” aria-controls=”extreme-2072843032676419397”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-2072843032676419397”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.98308</td>

</tr> <tr>

<th>Q1</th> <td>2.6892</td>

</tr> <tr>

<th>Median</th> <td>4.5757</td>

</tr> <tr>

<th>Q3</th> <td>7.9447</td>

</tr> <tr>

<th>95-th percentile</th> <td>17.87</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr>

<th>Range</th> <td>100</td>

</tr> <tr>

<th>Interquartile range</th> <td>5.2555</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>5.9832</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.94533</td>

</tr> <tr>

<th>Kurtosis</th> <td>43.622</td>

</tr> <tr>

<th>Mean</th> <td>6.3292</td>

</tr> <tr>

<th>MAD</th> <td>4.0131</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>4.148</td>

</tr> <tr>

<th>Sum</th> <td>19437</td>

</tr> <tr>

<th>Variance</th> <td>35.799</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-2072843032676419397”>

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YX3MUOGDNGjjz6qsWPH%2Bns8AACAdvN7wRozZox69Oihe%2B%2B9V7fccovi4%2BNbPSY7O1snT57092gAAABG%2BP0lwjVr1mjbtm3Kzc09Z7k6a//%2B/T96rL179yorK0v5%2Bfk%2B6xs2bNBll12mjIwMn4%2Bz93a1tLRo%2BfLlGjlypIYMGaLp06fr2LFj3s/3eDyaM2eOsrKyNGzYMM2fP18NDQ0/8YwBAMAvjd8LVt%2B%2BfTVjxgy98cYb3rX/%2BI//0N133y2Px9Pm4zz77LNavHixunfvfs79IUOGyOl0%2BnwMGDBAkvTiiy9q8%2BbNKi0t1VtvvaXU1FTl5eXJsixJ0sKFC1VfX68tW7bolVdeUWVlpYqLi9tx1gAA4JfE7wWrqKhIp06dUq9evbxrw4cPV0tLi5YsWdLm40RGRmr9%2BvXfW7B%2BSFlZmXJzc9WzZ09FR0crPz9flZWV2r9/v7788ku98cYbys/PV2JiopKTk3XffffplVdeUWNj43k/FwAA%2BOXx%2Bz1Y77zzjjZv3qyEhATvWmpqqoqLi3XTTTe1%2BTjTpk37wf3jx4/rzjvvVHl5uWJjYzV79mzdfPPNamho0KFDh5SWluZ9bHR0tLp37y6n06lTp04pIiJCffv29e73799f33zzjT777DOf9e9TXV0tl8vls%2BZwdFJSUlKbz68tIiKC6zcdORzBM%2B/ZbIMt42BAtvYiX/uQrX1CMVu/F6yGhgZFRka2Wg8PD1d9fb2R50hMTFRqaqoefPBB9erVS6%2B//rp%2B97vfKSkpSZdeeqksy1JcXJzP58TFxcntdis%2BPl7R0dEKCwvz2ZMkt9vdpucvKytTSUmJz1peXp5mz57dzjMLbgkJUYEe4bzFxl4Y6BFCFtnai3ztQ7b2CaVs/V6whgwZoiVLlqigoMBbXL744gstXbrU560a2mP48OEaPny49//HjBmj119/XRs2bFBhYaEkee%2B3Opcf2muLyZMna8SIET5rDkcnud117TrudwVb0zd9/naKiAhXbOyFqqmpV3NzS6DHCSlkay/ytQ/Z2sfObAP1zb3fC9Yjjzyif/3Xf9Wvf/1rRUdHq6WlRXV1derWrZteeOEF2543JSVF5eXlio%2BPV3h4eKsb6j0ejzp37qzExETV1taqublZERER3j1J6ty5c5ueKykpqdXLgS7XKTU1/bK/IIPx/JubW4Jy7mBAtvYiX/uQrX1CKVu/F6xu3brptdde09tvv62jR48qPDxcPXr00LBhw7yFpr3%2B8pe/KC4uTjfeeKN3rbKyUt26dVNkZKR69%2B6tiooKDR06VJJUU1Ojo0ePasCAAUpJSZFlWfr444/Vv39/SZLT6VRsbKx69OhhZD4AABDa/F6wJKljx4667rrrbDv%2BmTNn9Pjjj6tbt2667LLLtGPHDr399tt6%2BeWXJUk5OTkqLS3VNddco%2BTkZBUXF6tfv37KyMiQJN1www36wx/%2BoKVLl%2BrMmTNauXKlJk6cKIcjIHEBAIAg4/fGcOzYMS1btkyffvrpOd%2B8880332zTcc6WoaamJknyvq%2BW0%2BnUtGnTVFdXpwceeEAul0sXX3yxVq5cqfT0dEnSlClT5HK5NHXqVNXV1SkzM9PnpvTHHntMixYt0siRI9WhQwfddNNNrd7MFAAA4PuEWe29o/s8TZ06VdXV1Ro2bJg6derUar%2BgoMCf4/iNy3XK%2BDEdjnBdX7zX%2BHHtsm3OVYEeoc0cjnAlJETJ7a4LmfsBfi7I1l7kax%2BytY%2Bd2XbpEmP0eG3l9ytY5eXlevPNN5WYmOjvpwYAAPALv/%2Bcf%2BfOnc955QoAACBU%2BL1g3XvvvSopKWn3e00BAAD8XPn9JcK3335bH3zwgTZs2KCLL75Y4eG%2BHe%2Bll17y90gAAABG%2Bb1gRUdH65prrvH30wIAAPiN3wtWUVGRv58SAADArwLyy%2Bw%2B%2B%2BwzrVixQg8//LB37cMPPwzEKAAAAMb5vWDt27dP48aN086dO7VlyxZJ37756LRp09r8JqMAAAA/Z34vWMuXL9dDDz2kzZs3KywsTNK3v59wyZIlWrlypb/HAQAAMM7vBeu///u/lZOTI0negiVJo0ePVmVlpb/HAQAAMM7vBSsmJuacv4OwurpaHTt29Pc4AAAAxvm9YA0ePFhPPvmkamtrvWuHDx/W3Llz9etf/9rf4wAAABjn97dpePjhh3XHHXcoMzNTzc3NGjx4sOrr69W7d28tWbLE3%2BMAAAAY5/eC1bVrV23ZskV79uzR4cOHdcEFF6hHjx666qqrfO7JAgAACFZ%2BL1iS1KFDB1133XWBeGoAAADb%2Bb1gjRgx4gevVPFeWAAAINj5vWDdeOONPgWrublZhw8fltPp1B133OHvcQAAAIzze8EqLCw85/qOHTv097//3c/TAAAAmBeQ30V4Ltddd51ee%2B21QI8BAADQbj%2BbgnXgwAFZlhXoMQAAANrN7y8RTpkypdVafX29KisrNWrUKH%2BPAwAAYJzfC1ZqamqrnyKMjIzUxIkTddttt/l7HAAAAOP8XrB4t3YAABDq/F6wXn311TY/9pZbbrFxEgAAAHv4vWDNnz9fLS0trW5oDwsL81kLCwujYAEAgKDk94L1xz/%2BUc8995xmzJihvn37yrIsffLJJ3r22Wd1%2B%2B23KzMz098jAQAAGBWQe7BKS0uVnJzsXbvyyivVrVs3TZ8%2BXVu2bPH3SAAAAEb5/X2wjhw5ori4uFbrsbGxqqqq8vc4AAAAxvm9YKWkpGjJkiVyu93etZqaGi1btkyXXHKJv8cBAAAwzu8vET7yyCMqKChQWVmZoqKiFB4ertraWl1wwQVauXKlv8cBAAAwzu8Fa9iwYdq9e7f27NmjEydOyLIsJScn6%2Bqrr1ZMTIy/xwEAADDO7wVLki688EKNHDlSJ06cULdu3QIxAgAAgG38fg9WQ0OD5s6dq0GDBuk3v/mNpG/vwbrrrrtUU1Pj73EAAACM83vBeuqpp3Tw4EEVFxcrPPx/n765uVnFxcX%2BHgcAAMA4vxesHTt26N///d81evRo7y99jo2NVVFRkXbu3OnvcQAAAIzze8Gqq6tTampqq/XExER98803/h4HAADAOL8XrEsuuUR///vfJcnndw9u375d//zP/%2BzvcQAAAIzz%2B08R/va3v9WsWbN06623qqWlRc8//7zKy8u1Y8cOzZ8/39/jAAAAGOf3gjV58mQ5HA6tXbtWEREReuaZZ9SjRw8VFxdr9OjR/h4HAADAOL8XrJMnT%2BrWW2/Vrbfe6u%2BnBgAA8Au/34M1cuRIn3uvAAAAQo3fC1ZmZqa2bdvm76cFAADwG7%2B/RPhP//RPeuKJJ1RaWqpLLrlEHTp08NlftmyZv0cCAAAwyu8F69ChQ7r00kslSW63299PDwAAYDu/Faz8/HwtX75cL7zwgndt5cqVysvL89cIAAAAfuG3e7B27drVaq20tNRfTw8AAOA3fitY5/rJQX6aEAAAhCK/Fayzv9j5x9YAAACCnd/fpgEAACDUBXXB2rt3r7KyspSfn99qb%2BvWrRo7dqwGDRqkCRMm6J133vHutbS0aPny5Ro5cqSGDBmi6dOn69ixY959j8ejOXPmKCsrS8OGDdP8%2BfPV0NDgl3MCAADBz28/RdjY2KiCgoIfXWvr%2B2A9%2B%2ByzWr9%2Bvbp3795q7%2BDBg5o7d65KSkr0q1/9Sjt27ND999%2Bv7du3q2vXrnrxxRe1efNmPfvss0pOTtby5cuVl5enjRs3KiwsTAsXLtSZM2e0ZcsWNTY26oEHHlBxcbEWLFjw0wMAAAC/GH67gnXFFVeourra5%2BNca20VGRn5vQVr3bp1ys7OVnZ2tiIjIzVu3Dj16dNHmzZtkiSVlZUpNzdXPXv2VHR0tPLz81VZWan9%2B/fryy%2B/1BtvvKH8/HwlJiYqOTlZ9913n1555RU1NjYaywMAAIQuv13B%2Br/vf2XCtGnTvnevoqJC2dnZPmtpaWlyOp1qaGjQoUOHlJaW5t2Ljo5W9%2B7d5XQ6derUKUVERKhv377e/f79%2B%2Bubb77RZ5995rMOAABwLn5/J3d/8Hg8iouL81mLi4vToUOH9PXXX8uyrHPuu91uxcfHKzo62ucnHM8%2Btq3vPF9dXS2Xy%2BWz5nB0UlJS0k85ne8VERFct9A5HMEz79lsgy3jYEC29iJf%2B5CtfUIx25AsWNKPv8fWD%2B239/25ysrKVFJS4rOWl5en2bNnt%2Bu4wS4hISrQI5y32NgLAz1CyCJbe5GvfcjWPqGUbUgWrISEBHk8Hp81j8ejxMRExcfHKzw8/Jz7nTt3VmJiompra9Xc3KyIiAjvniR17ty5Tc8/efJkjRgxwmfN4egkt7vup57SOQVb0zd9/naKiAhXbOyFqqmpV3NzS6DHCSlkay/ytQ/Z2sfObAP1zX1IFqz09HSVl5f7rDmdTo0ZM0aRkZHq3bu3KioqNHToUElSTU2Njh49qgEDBiglJUWWZenjjz9W//79vZ8bGxurHj16tOn5k5KSWr0c6HKdUlPTL/sLMhjPv7m5JSjnDgZkay/ytQ/Z2ieUsg2uSyBtNGnSJL377rvavXu3Tp8%2BrfXr1%2BvIkSMaN26cJCknJ0dr1qxRZWWlamtrVVxcrH79%2BikjI0OJiYm64YYb9Ic//EEnT57UiRMntHLlSk2cOFEOR0j2UQAAYFjQNoaMjAxJUlNTkyTpjTfekPTt1aY%2BffqouLhYRUVFqqqqUq9evbR69Wp16dJFkjRlyhS5XC5NnTpVdXV1yszM9Lln6rHHHtOiRYs0cuRIdejQQTfddNM538wUAADgXMIsfuOyX7hcp4wf0%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%2B/bt08SJEzV48GCNGTNGmzZt8vncNWvW6IYbbtDgwYOVk5Oj8vLyQJwCAAAIUo5AD2Cn7du36%2BKLL/ZZq66u1n333af58%2Bdr7Nixev/99zVz5kz16NFDGRkZ2rVrl1asWKE//vGP6tu3r9asWaMZM2Zo586d6tSpU4DOBAAABJOQvYL1fTZv3qzU1FRNnDhRkZGRysrK0ogRI7Ru3TpJUllZmSZMmKCBAwfqggsu0F133SVJeuuttwI5NgAACCIhfQVr2bJl%2BvDDD1VbW6vf/OY3mjdvnioqKpSWlubzuLS0NG3btk2SVFFRoRtvvNG7Fx4ern79%2BsnpdGrMmDFtet7q6mq5XC6fNYejk5KSktp5Rr4iIoKrHzscwTPv2WyDLeNgQLb2Il/7kK19QjHbkC1Yl19%2BubKysrR06VIdO3ZMc%2BbM0e9//3t5PB4lJyf7PDY%2BPl5ut1uS5PF4FBcX57MfFxfn3W%2BLsrIylZSU%2BKzl5eVp9uzZP/FsQkNCQlSgRzhvsbEXBnqEkEW29iJf%2B5CtfUIp25AtWGVlZd7/7tmzpwoLCzVz5kxdccUVP/q5lmW167knT56sESNG%2BKw5HJ3kdte167jfFWxN3/T52ykiIlyxsReqpqZezc0tgR4npJCtvcjXPmRrHzuzDdQ39yFbsL7r4osvVnNzs8LDw%2BXxeHz23G63EhMTJUkJCQmt9j0ej3r37t3m50pKSmr1cqDLdUpNTb/sL8hgPP/m5pagnDsYkK29yNc%2BZGufUMo2uC6BtNGBAwe0ZMkSn7XKykp17NhR2dnZrd52oby8XAMHDpQkpaenq6KiwrvX3NysAwcOePcBAAB%2BTEgWrM6dO6usrEylpaU6c%2BaMDh8%2BrKefflqTJ0/WzTffrKqqKq1bt06nT5/Wnj17tGfPHk2aNEmSlJOTo1dffVUfffSR6uvrtWrVKnUFisa0AAAMWklEQVTs2FHDhw8P7EkBAICgEZIvESYnJ6u0tFTLli3zFqTx48crPz9fkZGRWr16tRYvXqzf//73SklJ0VNPPaXLLrtMknTNNdfowQcf1Jw5c/TVV18pIyNDpaWluuCCCwJ8VgAAIFiEWe29oxtt4nKdMn5MhyNc1xfvNX5cfOu9J0bL7a4LmfsBfi4cjnAlJESRrU3I1z5kax87s%2B3SJcbo8doqJF8iBAAACCQKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwxyBHgD4ubpy/vZAj3Bets25KtAjAAD%2BP65gAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWOdQVVWle%2B65R5mZmbr22mv11FNPqaWlJdBjAQCAIMH7YJ3DrFmz1L9/f73xxhv66quvdO%2B99%2Bqiiy7SnXfeGejRAABAEOAK1nc4nU59/PHHKiwsVExMjFJTU5Wbm6uysrJAjwYAAIIEV7C%2Bo6KiQikpKYqLi/Ou9e/fX4cPH1Ztba2io6N/9BjV1dVyuVw%2Baw5HJyUlJRmdNSKCfoz/9Zs//DXQI7TZe0%2BM5s%2BvTc7mSr7mka19QjFbCtZ3eDwexcbG%2BqydLVtut7tNBausrEwlJSU%2Ba/fff79mzZplblB9W%2BTu6PqpJk%2BebLy8/dJVV1errKyMbG1QXV2tFStWkK1Nqqur9ec//5F8bUC29gnFbEOnKhpkWVa7Pn/y5MnasGGDz8fkyZMNTfe/XC6XSkpKWl0tQ/uRrX3I1l7kax%2BytU8oZssVrO9ITEyUx%2BPxWfN4PAoLC1NiYmKbjpGUlBQyDRwAAJw/rmB9R3p6uo4fP66TJ09615xOp3r16qWoqKgATgYAAIIFBes70tLSlJGRoWXLlqm2tlaVlZV6/vnnlZOTE%2BjRAABAkIh49NFHHw30ED83V199tbZs2aLHH39cr732miZOnKjp06crLCws0KO1EhUVpaFDh3J1zQZkax%2BytRf52ods7RNq2YZZ7b2jGwAAAD54iRAAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMApWEKqqqtI999yjzMxMXXvttXrqqafU0tIS6LGCVlVVlfLy8pSZmamsrCzNmzdPNTU1kqSDBw/q9ttv1xVXXKFRo0bpueeeC/C0wevJJ59U3759vf%2B/b98%2BTZw4UYMHD9aYMWO0adOmAE4XvFatWqVhw4bp8ssvV25urj7//HNJ5NteBw4c0LRp03TllVfqqquuUmFhoU6ePCmJbH%2BKvXv3KisrS/n5%2Ba32tm7dqrFjx2rQoEGaMGGC3nnnHe9eS0uLli9frpEjR2rIkCGaPn26jh075s/RfzoLQWf8%2BPHWggULrJqaGuvw4cPWqFGjrOeeey7QYwWtm266yZo3b55VW1trHT9%2B3JowYYL1yCOPWPX19dbVV19trVixwqqrq7PKy8utoUOHWjt27Aj0yEHnwIED1tChQ60%2BffpYlmVZX3zxhXX55Zdb69atsxoaGqy//vWv1oABA6x//OMfAZ40uKxdu9YaPXq0VVlZaZ06dcp6/PHHrccff5x826mxsdG66qqrrGXLllmnT5%2B2Tp48ad15553WrFmzyPYnKC0ttUaNGmVNmTLFmjNnjs/egQMHrPT0dGv37t1WQ0ODtXHjRmvgwIHW8ePHLcuyrDVr1ljXXnutdejQIevUqVPWY489Zo0dO9ZqaWkJxKmcF65gBRmn06mPP/5YhYWFiomJUWpqqnJzc1VWVhbo0YJSTU2N0tPTVVBQoKioKHXt2lXjx4/Xe%2B%2B9p927d6uxsVEzZ85Up06d1L9/f912221kfZ5aWlq0aNEi5ebmetc2b96s1NRUTZw4UZGRkcrKytKIESO0bt26wA0ahJ577jnl5%2Bfr0ksvVXR0tBYsWKAFCxaQbzu5XC65XC7dfPPN6tixoxISEnT99dfr4MGDZPsTREZGav369erevXurvXXr1ik7O1vZ2dmKjIzUuHHj1KdPH%2B9VwbKyMuXm5qpnz56Kjo5Wfn6%2BKisrtX//fn%2BfxnmjYAWZiooKpaSkKC4uzrvWv39/HT58WLW1tQGcLDjFxsaqqKhIF110kXft%2BPHjSkpKUkVFhfr27auIiAjvXlpamsrLywMxatB66aWXFBkZqbFjx3rXKioqlJaW5vM4sj0/X3zxhT7//HN9/fXXuvHGG5WZmanZs2fr5MmT5NtOycnJ6tevn8rKylRXV6evvvpKO3fu1PDhw8n2J5g2bZpiYmLOufd9eTqdTjU0NOjQoUM%2B%2B9HR0erevbucTqetM5tAwQoyHo9HsbGxPmtny5bb7Q7ESCHF6XRq7dq1mjlz5jmzjo%2BPl8fj4Z63Nvryyy%2B1YsUKLVq0yGf9%2B7Llz3DbnThxQpK0fft2Pf/889q4caNOnDihBQsWkG87hYeHa8WKFXrzzTc1ePBgZWVlqampSQUFBWRrmMfj8blgIH37b5rb7dbXX38ty7K%2Bd//njoIVhCzLCvQIIen999/X9OnTVVBQoKysrO99XFhYmB%2BnCm5FRUWaMGGCevXqFehRQs7ZvwfuuusuJScnq2vXrpo1a5Z27doV4MmC35kzZzRjxgyNHj1a7733nt5%2B%2B23FxMSosLAw0KOFpB/7Ny1Y/82jYAWZxMREeTwenzWPx6OwsDAlJiYGaKrgt2vXLt1zzz165JFHNG3aNEnfZv3d75I8Ho/i4%2BMVHs6Xzo/Zt2%2BfPvzwQ%2BXl5bXaS0hIaPXn2O1282f4PJx9Wfv/Xk1JSUmRZVlqbGwk33bYt2%2BfPv/8cz344IOKiYlRcnKyZs%2Berddff13h4eFka9C5/i7weDxKTEz0/l17rv3OnTv7c8yfhH8lgkx6erqOHz/u/XFh6duXtXr16qWoqKgATha8PvjgA82dO1dPP/20brnlFu96enq6PvnkEzU1NXnXnE6nBg4cGIgxg86mTZv01Vdf6dprr1VmZqYmTJggScrMzFSfPn1a3bNSXl5Otueha9euio6O1sGDB71rVVVV6tChg7Kzs8m3HZqbm9XS0uJz5eTMmTOSpKysLLI1KD09vVWeZ/%2BejYyMVO/evVVRUeHdq6mp0dGjRzVgwAB/j3reKFhBJi0tTRkZGVq2bJlqa2tVWVmp559/Xjk5OYEeLSg1NTVpwYIFKiws1LBhw3z2srOzFR0drVWrVqm%2Bvl779%2B/X%2BvXrybqN5s2bpx07dmjjxo3auHGjSktLJUkbN27U2LFjVVVVpXXr1un06dPas2eP9uzZo0mTJgV46uDhcDg0ceJEPfPMM/qf//kfffXVV1q5cqXGjh2r8ePHk287DBo0SJ06ddKKFStUX18vt9utVatWaciQIbr55pvJ1qBJkybp3Xff1e7du3X69GmtX79eR44c0bhx4yRJOTk5WrNmjSorK1VbW6vi4mL169dPGRkZAZ78x4VZwfri5i/YiRMntHDhQv3nf/6noqOjNWXKFN1///3cG/QTvPfee/qXf/kXdezYsdXe9u3bVVdXp0WLFqm8vFwXXXSR7r77bv32t78NwKTB7/PPP9fIkSP1ySefSJL%2B67/%2BS4sXL1ZlZaVSUlJUUFCgUaNGBXjK4HLmzBkVFRXptddeU2Njo2644QYtXLhQUVFR5NtO5eXlWrp0qT7%2B%2BGN17NhRQ4cO1bx585ScnEy25%2BlsGTr7aoDD4ZAk708C7ty5U8uWLVNVVZV69eql%2BfPna8iQIZK%2Bvf9qxYoVeumll1RXV6fMzEw99thj6tq1awDO5PxQsAAAAAzjJUIAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMOz/AeeV9PUF8TViAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-2072843032676419397”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>55</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14.285714285714285</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.166666666666666</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.125</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.197802197802198</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.761904761904762</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.405405405405405</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.894736842105263</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2668)</td> <td class=”number”>2970</td> <td class=”number”>92.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>139</td> <td class=”number”>4.3%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-2072843032676419397”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>55</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.1949317738791423</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.19880715705765406</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.23201856148491878</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.24390243902439024</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>38.46153846153847</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>41.17647058823529</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>53.333333333333336</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LOCLFARM12”>PCT_LOCLFARM12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2692</td>

</tr> <tr>

<th>Unique (%)</th> <td>83.9%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>4.3%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>138</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>7.1398</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>80</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>2.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-1737558055904162978”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-1737558055904162978,#minihistogram-1737558055904162978”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-1737558055904162978”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-1737558055904162978”

aria-controls=”quantiles-1737558055904162978” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-1737558055904162978” aria-controls=”histogram-1737558055904162978”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-1737558055904162978” aria-controls=”common-1737558055904162978”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-1737558055904162978” aria-controls=”extreme-1737558055904162978”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-1737558055904162978”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.82297</td>

</tr> <tr>

<th>Q1</th> <td>2.7808</td>

</tr> <tr>

<th>Median</th> <td>4.9773</td>

</tr> <tr>

<th>Q3</th> <td>9.2813</td>

</tr> <tr>

<th>95-th percentile</th> <td>21.017</td>

</tr> <tr>

<th>Maximum</th> <td>80</td>

</tr> <tr>

<th>Range</th> <td>80</td>

</tr> <tr>

<th>Interquartile range</th> <td>6.5005</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>6.8079</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.95352</td>

</tr> <tr>

<th>Kurtosis</th> <td>9.1866</td>

</tr> <tr>

<th>Mean</th> <td>7.1398</td>

</tr> <tr>

<th>MAD</th> <td>4.8256</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.3494</td>

</tr> <tr>

<th>Sum</th> <td>21933</td>

</tr> <tr>

<th>Variance</th> <td>46.348</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-1737558055904162978”>

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48/rpEjR9o9HgAAwAWzvWCNGDFCXbp00ZQpU3TrrbcqMjKyyWPS09NVUVFh92gAAABG2F6wVqxYoQEDBvzs4z799FMbpgEAADDP9muwEhISNHXqVG3durVx7T//8z/129/%2BVj6fz%2B5xAAAAjLO9YM2fP19Hjx5Vt27dGtcGDx6shoYGLViwwO5xAAAAjLP9LcL33ntP69atU1RUVONaXFyc8vLydPPNN9s9DgAAgHG2n8Gqra1VaGho00GCg3X8%2BHG7xwEAADDO9oLVv39/LViwQN99913j2jfffKMnnnjC71YNAAAATmX7W4SzZ8/Wf/zHf%2Biaa65RWFiYGhoaVFNTo8suu0x/%2Bctf7B4HAADAONsL1mWXXaY333xT7777rr766isFBwerS5cuGjRokEJCQuweBwAAwDjbC5YktW7dWtdff30gnhoAAKDF2V6wDh06pEWLFumLL75QbW1tk/23337b7pEAAACMCsg1WGVlZRo0aJDatGlj99MDAAC0ONsL1u7du/X2228rOjra7qcGAACwhe23aWjfvj1nrgAAgKvZXrCmTJmi/Px8WZZl91MDAADYwva3CN9991199NFHWrNmjS699FIFB/t3vFdeecXukQAAAIyyvWCFhYXp17/%2Btd1PCwAAYBvbC9b8%2BfPtfkoAAABb2X4NliR9%2BeWXWrJkiR555JHGtY8//jgQowAAABhne8HatWuXRo0apS1btmj9%2BvWSTt98dOLEidxkFAAAuILtBWvx4sX63e9%2Bp3Xr1ikoKEjS6Z9PuGDBAi1dutTucQAAAIyzvWD9z//8j8aPHy9JjQVLkm688UYVFxfbPQ4AAIBxthesdu3anfVnEJaVlal169Z2jwMAAGCc7QWrT58%2Bevrpp1VdXd24duDAAeXm5uqaa66xexwAAADjbL9NwyOPPKK77rpLqampqq%2BvV58%2BfXT8%2BHF1795dCxYssHscAAAA42wvWB07dtT69ev1zjvv6MCBA7rooovUpUsXDRw40O%2BaLAAAAKeyvWBJUqtWrXT99dcH4qkBAABanO0FKyMj4yfPVHEvLAAA4HS2F6zhw4f7Faz6%2BnodOHBAXq9Xd911l93jAAAAGGd7wcrJyTnr%2BubNm/WPf/zD5mkAAADMC8jPIjyb66%2B/Xm%2B%2B%2BWagxwAAALhgv5iCtWfPHlmWFegxAAAALpjtbxGOGzeuydrx48dVXFysoUOH2j0OAACAcbYXrLi4uCbfRRgaGqoxY8Zo7Nixdo8DAABgnO0Fi7u1AwAAt7O9YL3%2B%2BuvNfuytt97agpMAAAC0DNsL1qOPPqqGhoYmF7QHBQX5rQUFBVGwAACAI9lesP70pz9p%2BfLlmjp1qhISEmRZlj7//HO9%2BOKLmjBhglJTU%2B0eCQAAwKiAXINVUFCg2NjYxrV%2B/frpsssu0%2BTJk7V%2B/Xq7RwIAADDK9vtgHTx4UBEREU3Ww8PDVVJSYvc4AAAAxtlesDp16qQFCxaosrKyca2qqkqLFi3S5Zdfbvc4AAAAxtlesGbPnq2NGzcqLS1N/fr104ABA3T11VdrzZo1mjVr1jkda%2BfOnUpLS1N2dnaTvQ0bNmjkyJHq3bu3Ro8erffee69xr6GhQYsXL9aQIUPUv39/TZ48WYcOHWrc9/l8mjFjhtLS0jRo0CA9%2Buijqq2tPf/QAADgX4rt12ANGjRIO3bs0DvvvKPDhw/LsizFxsbq2muvVbt27Zp9nBdffFGrV69W586dm%2Bzt3btXubm5ys/P19VXX63Nmzfr/vvv16ZNm9SxY0e9/PLLWrdunV588UXFxsZq8eLFysrK0htvvKGgoCA99thjOnnypNavX6%2B6ujo9%2BOCDysvL05w5c0z%2BUQAAAJcKyM8ivPjiizVkyBANGTJEd999t4YPH35O5Uo6fff3HytYq1atUnp6utLT0xUaGqpRo0YpPj5ea9eulSQVFhZq0qRJ6tq1q8LCwpSdna3i4mJ9%2Bumn%2Bvbbb7V161ZlZ2crOjpasbGxuu%2B%2B%2B/Taa6%2Bprq7OSH4AAOButhes2tpa5ebmqnfv3rrpppsknb4G65577lFVVVWzjzNx4sQfLWVFRUVKTEz0W0tMTJTX61Vtba3279/vtx8WFqbOnTvL6/Vq7969CgkJUUJCQuN%2BUlKSjh07pi%2B//PJcogIAgH9Rtr9FuHDhQu3du1d5eXl6%2BOGHG9fr6%2BuVl5enJ5988oKfw%2BfzNflOxYiICO3fv1/fffedLMs6635lZaUiIyMVFhbm9/MSzzz2%2Bxfm/5SysjKVl5f7rXk8bRQTE3M%2BcX5USEhATkCeN4%2BnefOeyeW0fM3h1mxuzSWRzYncmktybzY35rK9YG3evFkrV65UXFyccnNzJZ2%2BRcP8%2BfN16623GilYkprcKf5c9n/uc39OYWGh8vPz/daysrI0ffr0Czqu00VFtT2nx4eHX9xCkwSeW7O5NZdENidyay7JvdnclMv2glVTU6O4uLgm69HR0Tp27JiR54iKipLP5/Nb8/l8io6OVmRkpIKDg8%2B63759e0VHR6u6ulr19fUKCQlp3JOk9u3bN%2Bv5MzMzlZGR4bfm8bRRZWXN%2BUY6K6c1/ebmDwkJVnj4xaqqOq76%2BoYWnspebs3m1lwS2ZzIrbkk92ZryVzn%2BsW9KbYXrMsvv1z/%2BMc/lJqa6nemaNOmTfrVr35l5DmSk5O1e/duvzWv16sRI0YoNDRU3bt3V1FRkQYMGCDp9DVgX331lXr27KlOnTrJsizt27dPSUlJjZ8bHh6uLl26NOv5Y2JimrwdWF5%2BVKdOuecfw/k41/z19Q2u/TNzaza35pLI5kRuzSW5N5ubctl%2BCuSOO%2B7QAw88oGeeeUYNDQ166aWXNHPmTM2ePVt33XWXkee4/fbb9fe//107duzQiRMntHr1ah08eFCjRo2SJI0fP14rVqxQcXGxqqurlZeXpx49eiglJUXR0dEaNmyYnn32WVVUVOjw4cNaunSpxowZI4/H9j4KAAAcyPbGkJmZKY/Ho5UrVyokJEQvvPCCunTpory8PN14443NPk5KSook6dSpU5KkrVu3Sjp9tik%2BPl55eXmaP3%2B%2BSkpK1K1bNy1btkwdOnSQJI0bN07l5eW68847VVNTo9TUVL9rpp588knNnTtXQ4YMUatWrXTzzTef9WamAAAAZxNkXegV3eeooqJC0dHRdj7lL0J5%2BVHjx/R4gnVD3k7jx20pG2cMbNbjPJ5gRUW1VWVljWtOFZ/h1mxuzSWRzYncmktyb7aWzNWhw7ndZ9MU298iHDJkyAV/lx4AAMAvme0FKzU1VRs3brT7aQEAAGxj%2BzVY//Zv/6bf//73Kigo0OWXX65WrVr57S9atMjukQAAAIyyvWDt379fV1xxhaTm3xkdAADASWwrWNnZ2Vq8eLH%2B8pe/NK4tXbpUWVlZdo0AAABgC9uuwdq2bVuTtYKCArueHgAAwDa2Fayzfecg300IAADcyLaCFRQU1Kw1AAAAp3PWTwsGAABwAAoWAACAYbZ9F2FdXZ1mzpz5s2vcBwsAADidbQWrb9%2B%2BKisr%2B9k1AAAAp7OtYH3//lcAAABuxjVYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABjmCfQA%2BNdx07N/C/QI52TjjIGBHgEA4FCcwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMNcWrISEBCUnJyslJaXxY968eZKkXbt2acyYMerTp49GjBihtWvX%2Bn3uihUrNGzYMPXp00fjx4/X7t27AxEBAAA4lKt/2POmTZt06aWX%2Bq2VlZXpvvvu06OPPqqRI0fqww8/1LRp09SlSxelpKRo27ZtWrJkif70pz8pISFBK1as0NSpU7Vlyxa1adMmQEkAAICTuPYM1o9Zt26d4uLiNGbMGIWGhiotLU0ZGRlatWqVJKmwsFCjR4/WVVddpYsuukj33HOPJGn79u2BHBsAADiIq89gLVq0SB9//LGqq6t10003adasWSoqKlJiYqLf4xITE7Vx40ZJUlFRkYYPH964FxwcrB49esjr9WrEiBHNet6ysjKVl5f7rXk8bRQTE3OBifyFhPzL9WNbeTzm/3zPvGZue%2B3cmksimxO5NZfk3mxuzOXagtWrVy%2BlpaXpmWee0aFDhzRjxgw98cQT8vl8io2N9XtsZGSkKisrJUk%2Bn08RERF%2B%2BxEREY37zVFYWKj8/Hy/taysLE2fPv080yAQoqLattixw8MvbrFjB5Jbc0lkcyK35pLcm81NuVxbsAoLCxt/3bVrV%2BXk5GjatGnq27fvz36uZVkX9NyZmZnKyMjwW/N42qiysuaCjvtDbmr6v0SmXy/p9GsWHn6xqqqOq76%2BwfjxA8WtuSSyOZFbc0nuzdaSuVryi%2BWf4tqC9UOXXnqp6uvrFRwcLJ/P57dXWVmp6OhoSVJUVFSTfZ/Pp%2B7duzf7uWJiYpq8HVheflSnTrnnH8O/gpZ8verrG1z598GtuSSyOZFbc0nuzeamXK48BbJnzx4tWLDAb624uFitW7dWenp6k9su7N69W1dddZUkKTk5WUVFRY179fX12rNnT%2BM%2BAADAz3FlwWrfvr0KCwtVUFCgkydP6sCBA3ruueeUmZmpW265RSUlJVq1apVOnDihd955R%2B%2B8845uv/12SdL48eP1%2Buuv65NPPtHx48f1/PPPq3Xr1ho8eHBgQwEAAMdw5VuEsbGxKigo0KJFixoL0m233abs7GyFhoZq2bJleuqpp/TEE0%2BoU6dOWrhwoa688kpJ0q9//Ws99NBDmjFjho4cOaKUlBQVFBTooosuCnAqAADgFK4sWJLUv39/vfLKKz%2B698Ybb/zo595xxx264447Wmo0AADgcq58ixAAACCQKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGOYJ9ADAL9VNz/4t0COck40zBgZ6BADA/%2BEMFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYZ5ADwDAjJue/VugR2i2jTMGBnoEAGhRnMECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYZ1FSUqJ7771Xqampuu6667Rw4UI1NDQEeiwAAOAQ3KbhLB544AElJSVp69atOnLkiKZMmaJLLrlEd999d6BHA1zBSbeUkKQPfn9joEcA4DCcwfoBr9erffv2KScnR%2B3atVNcXJwmTZqkwsLCQI8GAAAcgjNYP1BUVKROnTopIiKicS0pKUkHDhxQdXW1wsLCfvYYZWVlKi8v91vzeNooJibG6KwhIfRjwA79Ht0U6BHOyVs51zbrcWf%2BD3Hb/yVuzSW5N5sbc1GwfsDn8yk8PNxv7UzZqqysbFbBKiwsVH5%2Bvt/a/fffrwceeMDcoDpd5O7q%2BIUyMzONl7dAKisrU2FhoetySe7N5tZckvuz/fnPf3JdNrfmktybzY253FMVDbIs64I%2BPzMzU2vWrPH7yMzMNDTd/1deXq78/PwmZ8uczq25JPdmc2suiWxO5NZcknuzuTEXZ7B%2BIDo6Wj6fz2/N5/MpKChI0dHRzTpGTEyMaxo4AAA4d5zB%2BoHk5GSVlpaqoqKicc3r9apbt25q27ZtACcDAABOQcH6gcTERKWkpGjRokWqrq5WcXGxXnrpJY0fPz7QowEG2L2oAAAKa0lEQVQAAIcIefzxxx8P9BC/NNdee63Wr1%2BvefPm6c0339SYMWM0efJkBQUFBXq0Jtq2basBAwa47uyaW3NJ7s3m1lwS2ZzIrbkk92ZzW64g60Kv6AYAAIAf3iIEAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyC5UAlJSW69957lZqaquuuu04LFy5UQ0NDoMc6bzt37lRaWpqys7Ob7G3YsEEjR45U7969NXr0aL333nsBmPD8lJSUKCsrS6mpqUpLS9OsWbNUVVUlSdq7d68mTJigvn37aujQoVq%2BfHmAp22%2Bffv26a677lLfvn2VlpamGTNmqLy8XJK0a9cujRkzRn369NGIESO0du3aAE97/p5%2B%2BmklJCQ0/t7J2RISEpScnKyUlJTGj3nz5klydq4znn/%2BeQ0aNEi9evXSpEmT9PXXX0tydrZ//vOffq9XSkqKkpOTG/9OOjnbnj17NHHiRPXr108DBw5UTk6OKioqJDk7VxMWHOe2226z5syZY1VVVVkHDhywhg4dai1fvjzQY52XgoICa%2BjQoda4ceOsGTNm%2BO3t2bPHSk5Otnbs2GHV1tZab7zxhnXVVVdZpaWlAZr23Nx8883WrFmzrOrqaqu0tNQaPXq0NXv2bOv48ePWtddeay1ZssSqqamxdu/ebQ0YMMDavHlzoEf%2BWSdOnLCuueYaKz8/3zpx4oR15MgRa8KECdZ9991nffPNN1avXr2sVatWWbW1tdbf/vY3q2fPntZnn30W6LHP2Z49e6wBAwZY8fHxlmVZjs8WHx9vHTp0qMm603NZlmWtXLnSuvHGG63i4mLr6NGj1rx586x58%2Ba5ItsPPf/889aDDz7o6Gx1dXXWwIEDrUWLFlknTpywKioqrLvvvtt64IEHHJ3rbDiD5TBer1f79u1TTk6O2rVrp7i4OE2aNEmFhYWBHu28hIaGavXq1ercuXOTvVWrVik9PV3p6ekKDQ3VqFGjFB8f74ivaKqqqpScnKyZM2eqbdu26tixo2677TZ98MEH2rFjh%2Brq6jRt2jS1adNGSUlJGjt2rCNew%2BPHjys7O1tTpkxR69atFR0drRtuuEFffPGF1q1bp7i4OI0ZM0ahoaFKS0tTRkaGVq1aFeixz0lDQ4Pmzp2rSZMmNa65JdsPuSHX8uXLlZ2drSuuuEJhYWGaM2eO5syZ44ps3/e///u/eumll/Twww87Olt5ebnKy8t1yy23qHXr1oqKitINN9ygvXv3OjrX2VCwHKaoqEidOnVSRERE41pSUpIOHDig6urqAE52fiZOnKh27dqdda%2BoqEiJiYl%2Ba4mJifJ6vXaMdkHCw8M1f/58XXLJJY1rpaWliomJUVFRkRISEhQSEtK4l5iYqN27dwdi1HMSERGhsWPHyuPxSJK%2B/PJL/dd//ZduuummH329nJDr%2B1555RWFhoZq5MiRjWtuyLZo0SINHjxY/fr102OPPaaamhrH5/rmm2/09ddf67vvvtPw4cOVmpqq6dOnq6KiwvHZfui5557Tb37zG/3qV79ydLbY2Fj16NFDhYWFqqmp0ZEjR7RlyxYNHjzY0bnOhoLlMD6fT%2BHh4X5rZ8pWZWVlIEZqMT6fz69ISqezOjGn1%2BvVypUrNW3atLO%2BhpGRkfL5fI65lq6kpETJyckaPny4UlJSNH369B/N5aTX69tvv9WSJUs0d%2B5cv3WnZ%2BvVq5fS0tK0ZcsWFRYW6pNPPtETTzzh%2BFyHDx%2BWJG3atEkvvfSS3njjDR0%2BfFhz5sxxfLbv%2B/rrr7Vlyxbdfffdkpz99zE4OFhLlizR22%2B/rT59%2BigtLU2nTp3SzJkzHZ3rbChYDmRZVqBHsI0bsn744YeaPHmyZs6cqbS0tB99XFBQkI1TXZhOnTrJ6/Vq06ZNOnjwoB5%2B%2BOFAj2TE/PnzNXr0aHXr1i3QoxhVWFiosWPHqnXr1uratatycnK0fv161dXVBXq0C3Lm/4d77rlHsbGx6tixox544AFt27YtwJOZ9fLLL2vo0KHq0KFDoEe5YCdPntTUqVN144036oMPPtC7776rdu3aKScnJ9CjGUfBcpjo6Gj5fD6/NZ/Pp6CgIEVHRwdoqpYRFRV11qxOyrlt2zbde%2B%2B9mj17tiZOnCjp9Gv4w6/IfD6fIiMjFRzsnH%2BSQUFBiouLU3Z2ttavXy%2BPx9Pk9aqsrHTM67Vr1y59/PHHysrKarJ3tr%2BLTsr2Q5deeqnq6%2BsVHBzs6Fxn3oL//lmPTp06ybIs1dXVOTrb923evFkZGRmNv3fy38ddu3bp66%2B/1kMPPaR27dopNjZW06dP11tvveX4v48/5Jz/zSFJSk5OVmlpaeO3tEqn337q1q2b2rZtG8DJzEtOTm7y3rvX69VVV10VoInOzUcffaTc3Fw999xzuvXWWxvXk5OT9fnnn%2BvUqVONa07JtWvXLg0bNszvrcwzpbBnz55NXq/du3c7IpckrV27VkeOHNF1112n1NRUjR49WpKUmpqq%2BPh4x2bbs2ePFixY4LdWXFys1q1bKz093bG5JKljx44KCwvT3r17G9dKSkrUqlUrx2c7Y%2B/evSopKdHAgQMb11JSUhybrb6%2BXg0NDX7vTpw8eVKSlJaW5thcZxXQ72HEeRk7dqw1e/Zs6%2BjRo9b%2B/futjIwMa%2BXKlYEe64Lk5uY2uU3D559/bqWkpFjbt2%2B3amtrrVWrVlm9e/e2ysrKAjRl89XV1Vk33XST9corrzTZO3HihHXddddZf/zjH61jx45Zn3zyidWvXz9r%2B/bt9g96jqqqqqy0tDRrwYIF1rFjx6wjR45YkydPtu644w7r22%2B/tXr37m29%2BuqrVm1trbVjxw6rZ8%2Be1t69ewM9drP4fD6rtLS08ePjjz%2B24uPjrdLSUqukpMSx2Q4fPmz16tXLWrZsmXXixAnryy%2B/tIYPH27NmzfP8a%2BZZVnW008/bQ0ZMsQ6ePCg9e2331qZmZnWrFmzXJHNsixr9erV1oABA/zWnJytoqLCGjBggPWHP/zBOnbsmFVRUWFNnTrV%2Bvd//3dH5zobCpYDlZaWWvfcc4/Vs2dPKy0tzfrjH/9oNTQ0BHqs85KcnGwlJydbV155pXXllVc2/v6MzZs3W0OHDrWSkpKsW265xXr//fcDOG3z/fOf/7Ti4%2BMb83z/4%2Buvv7Y%2B//xza9y4cVZycrI1ePBg6%2BWXXw70yM22b98%2Ba8KECVbPnj2tq6%2B%2B2poxY4Z1%2BPBhy7Is6/3337dGjRplJSUlWUOHDnXEvb1%2BzKFDhxrvg2VZzs72/vvvW5mZmVavXr2sAQMGWPPnz7dqa2sb95yay7JOf8Hy%2BOOPW/3797d69epl5ebmWtXV1ZZlOT%2BbZVnWCy%2B8YI0YMaLJupOzeb1ea8KECVa/fv2stLQ01/4fEmRZLriKGAAA4BeEa7AAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwLD/Bx1HZgyfx8RCAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-1737558055904162978”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>72</td> <td class=”number”>2.2%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.0</td> <td class=”number”>9</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.952380952380952</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.084745762711865</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.88235294117647</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.225806451612903</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.555555555555555</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.761904761904762</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2681)</td> <td class=”number”>2950</td> <td class=”number”>91.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>138</td> <td class=”number”>4.3%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-1737558055904162978”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>72</td> <td class=”number”>2.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.16420361247947454</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.16778523489932887</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.1697792869269949</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.18796992481203006</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>43.984962406015036</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>44.39252336448598</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>45.45454545454545</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>80.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LOCLSALE07”>PCT_LOCLSALE07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2747</td>

</tr> <tr>

<th>Unique (%)</th> <td>85.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>13.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>416</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.98733</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>33.333</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>1.5%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-2851255445431252041”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-2851255445431252041,#minihistogram-2851255445431252041”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-2851255445431252041”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-2851255445431252041”

aria-controls=”quantiles-2851255445431252041” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-2851255445431252041” aria-controls=”histogram-2851255445431252041”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-2851255445431252041” aria-controls=”common-2851255445431252041”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-2851255445431252041” aria-controls=”extreme-2851255445431252041”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-2851255445431252041”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.011757</td>

</tr> <tr>

<th>Q1</th> <td>0.080359</td>

</tr> <tr>

<th>Median</th> <td>0.29833</td>

</tr> <tr>

<th>Q3</th> <td>0.91001</td>

</tr> <tr>

<th>95-th percentile</th> <td>4.1991</td>

</tr> <tr>

<th>Maximum</th> <td>33.333</td>

</tr> <tr>

<th>Range</th> <td>33.333</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.82965</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>2.2085</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.2368</td>

</tr> <tr>

<th>Kurtosis</th> <td>53.84</td>

</tr> <tr>

<th>Mean</th> <td>0.98733</td>

</tr> <tr>

<th>MAD</th> <td>1.111</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>6.1046</td>

</tr> <tr>

<th>Sum</th> <td>2758.6</td>

</tr> <tr>

<th>Variance</th> <td>4.8773</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-2851255445431252041”>

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BvPUxI0aM0M9//nNNmDDB6vEAAADOm%2BUFa/z48erfv7/mzp2rW2%2B9VdHR0W0ek5mZqRMnTlg9GgAAgBGWF6x169Zp5MiRP/q4/fv3WzANAACAeZbfg5WYmKh77rlHb7/9duvaf/3Xf%2Bmuu%2B6Sx%2BOxehwAAADjLC9Yy5cv18mTJzVw4MDWtVGjRqmlpUUrVqywehwAAADjLH%2BLcO/evdq0aZNiYmJa1xISElRQUKBbbrnF6nEAAACMs/wKVkNDg8LCwtoOEhys%2Bvp6q8cBAAAwzvKCNWLECK1YsULffPNN69pf//pXPf74434f1dAee/bsUXp6unJzc/3WN2zYoEsvvVSpqal%2B/3wfYNrS0qJVq1ZpzJgxGjFihObMmaNjx461fr3H49GCBQuUnp6ujIwMLVmyRA0NDedx1gAAoCux/C3CxYsX69/%2B7d909dVXKzw8XC0tLaqrq1Pfvn310ksvtfs4zz77rEpKStSvX7%2Bz7o8YMeIHj/fKK69o06ZNevbZZ9W7d2%2BtWrVKOTk5euONNxQUFKSlS5fq9OnT2rx5sxobG/XAAw%2BooKBAjz766N91zgAAoGuxvGD17dtXb775pt555x19%2BeWXCg4OVv/%2B/ZWRkaGQkJB2HycsLEwlJSV68sknderUqb9pBqfTqdmzZ2vAgAGSpNzcXKWlpWn//v3q06eP3n77bf3%2B979XbGysJOnee%2B/VAw88oPz8fHXr1u1vei4AAND1WF6wJCk0NFTXX3/9eR1j5syZ59yvrKzUHXfcodLSUkVGRmr%2B/Pn66U9/qoaGBh06dEhJSUmtjw0PD1e/fv3kcrl08uRJhYSEKDExsXU/OTlZ3377rQ4fPuy3DgAAcDaWF6xjx46psLBQX3zxxVnva/rjH/943s8RGxurhIQEPfjggxo4cKD%2B8Ic/6OGHH1ZcXJx%2B8pOfyOv1Kioqyu9roqKiVF1drejoaIWHhysoKMhvT5Kqq6vb9fxVVVVyu91%2Baw5Hd8XFxZ3nmfkLCbHtT0n%2BXRwO8/P6Mgi0LEwjBzLwIQcy8CEHe8/dlnuwqqqqlJGRoe7du3fIc4waNUqjRo1q/e/x48frD3/4gzZs2KC8vDxJktfr/cGvP9deezidThUVFfmt5eTkaP78%2Bed13EAXE9Ojw44dGXlhhx07kJADGfiQAxn4kIM9LC9YpaWl%2BuMf/9h6f5NV4uPjVVpaqujoaAUHB7f51HiPx6OePXsqNjZWtbW1am5ubr0nzPfYnj17tuu5srKyNHr0aL81h6O7qqvrDJzJdwLtpxLT5y%2BdySAy8kLV1NSrubnF%2BPEDBTmQgQ85kIEPOXyXgR0sL1g9e/bssCtXPr/97W8VFRWlm2%2B%2BuXWtvLxcffv2VVhYmAYNGqSysrLWv4lYU1OjL7/8UkOHDlV8fLy8Xq8%2B%2B%2BwzJScnS5JcLpciIyPVv3//dj1/XFxcm7cD3e6Tamrqmi9wn448/%2Bbmli6fr0QOEhn4kAMZ%2BJCDPSy/BDJ37lwVFRWd99tw53L69Gk98cQTcrlcamxs1ObNm/XOO%2B9o%2BvTpkqTs7GytW7dO5eXlqq2tVUFBgYYMGaLU1FTFxsbqhhtu0K9%2B9SudOHFCx48f1%2BrVqzVlyhQ5HLb8TgAAAAgwljeGd955Rx9//LE2bNigPn36KDjYv%2BO9%2Buqr7TpOamqqJKmpqUmSWv94tMvl0syZM1VXV6cHHnhAbrdbffr00erVq5WSkiJJmj59utxut2bMmKG6ujqlpaX53TP1i1/8Qo899pjGjBmjbt266ZZbbmnzYaYAAAA/JMjbkZeSzuKRRx455/7y5cstmsRabvdJ48d0OII1tmCP8eN2lK0LrjF%2BTIcjWDExPVRdXdelL4GTAxn4kAMZ%2BJDDdxnY8txWP2FnLVAAAAA%2Btvwa2uHDh/XMM8/4Xc3685//bMcoAAAAxllesPbt26eJEydq%2B/bt2rx5s6QzHz46c%2BZMIx8yCgAAYDfLC9aqVav00EMPadOmTa2flt63b1%2BtWLFCq1evtnocAAAA4ywvWP/7v/%2Br7OxsSfL7czQ33nijysvLrR4HAADAOMsLVkRExFn/BmFVVZVCQ0OtHgcAAMA4ywvWFVdcoV/%2B8peqra1tXTty5Ijy8/N19dVXWz0OAACAcZZ/TMMjjzyiWbNmKS0tTc3NzbriiitUX1%2BvQYMGacWKFVaPAwAAYJzlBeviiy/W5s2btXv3bh05ckQXXHCB%2Bvfvr2uuucbvniwAAIBAZcsf1%2BvWrZuuv/56O54aAACgw1lesEaPHn3OK1V8FhYAAAh0lhesm2%2B%2B2a9gNTc368iRI3K5XJo1a5bV4wAAABhnecHKy8s76/pbb72lDz74wOJpAAAAzLPlbxGezfXXX68333zT7jEAAADO2z9MwTpw4IC8Xq/dYwAAAJw3y98inD59epu1%2Bvp6lZeXa9y4cVaPAwAAYJzlBSshIaHNbxGGhYVpypQpmjp1qtXjAAAAGGd5weLT2gEAQGdnecF6/fXX2/3YW2%2B9tQMnAQAA6BiWF6wlS5aopaWlzQ3tQUFBfmtBQUEULAAAEJAsL1jPPfecXnjhBd1zzz1KTEyU1%2BvV559/rmeffVa333670tLSrB4JAADAKFvuwSouLlbv3r1b16688kr17dtXc%2BbM0ebNm60eCQAAwCjLPwfr6NGjioqKarMeGRmpiooKq8cBAAAwzvKCFR8frxUrVqi6urp1raamRoWFhbrkkkusHgcAAMA4y98iXLx4sRYuXCin06kePXooODhYtbW1uuCCC7R69WqrxwEAADDO8oKVkZGhXbt2affu3Tp%2B/Li8Xq969%2B6ta6%2B9VhEREVaPAwAAYJzlBUuSLrzwQo0ZM0bHjx9X37597RgBAACgw1h%2BD1ZDQ4Py8/M1bNgw3XTTTZLO3IN15513qqamxupxAAAAjLO8YK1cuVIHDx5UQUGBgoO/e/rm5mYVFBRYPQ4AAIBxlhest956S//xH/%2BhG2%2B8sfWPPkdGRmr58uXavn271eMAAAAYZ3nBqqurU0JCQpv12NhYffvtt1aPAwAAYJzlBeuSSy7RBx98IEl%2Bf3tw27Zt%2Bud//merxwEAADDO8t8i/NnPfqb7779ft912m1paWvTiiy%2BqtLRUb731lpYsWWL1OAAAAMZZXrCysrLkcDj08ssvKyQkRL/%2B9a/Vv39/FRQU6MYbb7R6HAAAAOMsL1gnTpzQbbfdpttuu83qpwYAALCE5fdgjRkzxu/eKwAAgM7G8oKVlpamrVu3Wv20AAAAlrH8LcJ/%2Bqd/0pNPPqni4mJdcskl6tatm99%2BYWGh1SMBAAAYZXnBOnTokH7yk59Ikqqrq61%2BegAAgA5nWcHKzc3VqlWr9NJLL7WurV69Wjk5OVaNAAAAYAnL7sHasWNHm7Xi4mKrnh4AAMAylhWss/3mIL9NCAAAOiPLCpbvDzv/2BoAAECgs/xjGgAAADo7ChYAAIBhlv0WYWNjoxYuXPija3wOFgAACHSWFazhw4erqqrqR9cAAAACnWUF6/9%2B/hUAAEBnFtD3YO3Zs0fp6enKzc1ts7dlyxZNmDBBw4YN0%2BTJk7V3797WvZaWFq1atUpjxozRiBEjNGfOHB07dqx13%2BPxaMGCBUpPT1dGRoaWLFmihoYGS84JAAAEvoAtWM8%2B%2B6yWLVumfv36tdk7ePCg8vPzlZeXp/fff1%2BzZ8/Wfffdp%2BPHj0uSXnnlFW3atEnFxcXauXOnEhISlJOT0/q5XEuXLlV9fb02b96s1157TeXl5SooKLD0/AAAQOAK2IIVFhamkpKSsxas9evXKzMzU5mZmQoLC9PEiRM1ePBgbdy4UZLkdDo1e/ZsDRgwQOHh4crNzVV5ebn279%2Bvr776Sm%2B//bZyc3MVGxur3r17695779Vrr72mxsZGq08TAAAEoIAtWDNnzlRERMRZ98rKypSUlOS3lpSUJJfLpYaGBh06dMhvPzw8XP369ZPL5dLBgwcVEhKixMTE1v3k5GR9%2B%2B23Onz4cMecDAAA6FQsu8ndSh6PR1FRUX5rUVFROnTokL755ht5vd6z7ldXVys6Olrh4eF%2BnzLve2x1dXW7nr%2Bqqkput9tvzeHorri4uL/ndH5QSEhg9WOHw/y8vgwCLQvTyIEMfMiBDHzIwd5z75QFS/rxv3N4rv3z/RuJTqdTRUVFfms5OTmaP3/%2BeR030MXE9OiwY0dGXthhxw4k5EAGPuRABj7kYI9OWbBiYmLk8Xj81jwej2JjYxUdHa3g4OCz7vfs2VOxsbGqra1Vc3OzQkJCWvckqWfPnu16/qysLI0ePdpvzeHorurqur/3lM4q0H4qMX3%2B0pkMIiMvVE1NvZqbW4wfP1CQAxn4kAMZ%2BJDDdxnYoVMWrJSUFJWWlvqtuVwujR8/XmFhYRo0aJDKyso0cuRISVJNTY2%2B/PJLDR06VPHx8fJ6vfrss8%2BUnJzc%2BrWRkZHq379/u54/Li6uzduBbvdJNTV1zRe4T0eef3NzS5fPVyIHiQx8yIEMfMjBHoF1CaSdpk2bpvfee0%2B7du3SqVOnVFJSoqNHj2rixImSpOzsbK1bt07l5eWqra1VQUGBhgwZotTUVMXGxuqGG27Qr371K504cULHjx/X6tWrNWXKFDkcnbKPAgAAwwK2MaSmpkqSmpqaJElvv/22pDNXmwYPHqyCggItX75cFRUVGjhwoNauXauLLrpIkjR9%2BnS53W7NmDFDdXV1SktL87tn6he/%2BIUee%2BwxjRkzRt26ddMtt9xy1g8zBQAAOJsg7/ne0Y12cbtPGj%2BmwxGssQV7jB%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%2BWLFmiCRMm6KOPPtK8efPUv39/paamaseOHXrmmWf03HPPKTExUevWrdM999yj7du3q3v37jadCQAACCSd9grWD9m0aZMSEhI0ZcoUhYWFKT09XaNHj9b69eslSU6nU5MnT9Zll12mCy64QHfeeackaefOnXaODQAAAkinvoJVWFioP//5z6qtrdVNN92kRYsWqaysTElJSX6PS0pK0tatWyVJZWVluvnmm1v3goODNWTIELlcLo0fP75dz1tVVSW32%2B235nB0V1xc3Hmekb%2BQkMDqxw6H%2BXl9GQRaFqaRAxn4kAMZ%2BJCDvefeaQvW5ZdfrvT0dD311FM6duyYFixYoMcff1wej0e9e/f2e2x0dLSqq6slSR6PR1FRUX77UVFRrfvt4XQ6VVRU5LeWk5Oj%2BfPn/51n0znExPTosGNHRl7YYccOJORABj7kQAY%2B5GCPTluwnE5n6/8eMGCA8vLyNG/ePA0fPvxHv9br9Z7Xc2dlZWn06NF%2Baw5Hd1VX153Xcb8v0H4qMX3%2B0pkMIiMvVE1NvZqbW4wfP1CQAxn4kAMZ%2BJDDdxnYodMWrO/r06ePmpubFRwcLI/H47dXXV2t2NhYSVJMTEybfY/Ho0GDBrX7ueLi4tq8Heh2n1RTU9d8gft05Pk3N7d0%2BXwlcpDIwIccyMCHHOwRWJdA2unAgQNasWKF31p5eblCQ0OVmZnZ5mMXSktLddlll0mSUlJSVFZW1rrX3NysAwcOtO4DAAD8mE5ZsHr27Cmn06ni4mKdPn1aR44c0dNPP62srCz99Kc/VUVFhdavX69Tp05p9%2B7d2r17t6ZNmyZJys7O1uuvv65PPvlE9fX1WrNmjUJDQzVq1Ch7TwoAAASMTvkWYe/evVVcXKzCwsLWgjRp0iTl5uYqLCxMa9eu1bJly/T4448rPj7QLxGDAAAMx0lEQVReK1eu1KWXXipJ%2Bpd/%2BRc9%2BOCDWrBggb7%2B%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%2B%2B239fXXX2vu3Lnq1auX7rjjDrtHAwAAAYCC9T0ul0ufffaZXnzxRUVERCgiIkKzZ8/Wb37zGwoW/qFduWSb3SO0G39IG0BnR8H6nrKyMsXHxysqKqp1LTk5WUeOHFFtba3Cw8N/9BhVVVVyu91%2Baw5Hd8XFxRmdNSSEd3gRmALt/rY/5F1r9wjt5vu%2B0JW/P5DBGeRg77lTsL7H4/EoMjLSb81Xtqqrq9tVsJxOp4qKivzW7rvvPt1///3mBtWZIjfr4i%2BUlZVlvLwFiqqqKjmdzi6dgUQOEhn4VFVV6Te/ea5L50AGZ5CDvRl03Vp7Dl6v97y%2BPisrSxs2bPD7l5WVZWi677jdbhUVFbW5WtaVkMEZ5EAGPuRABj7kYG8GXMH6ntjYWHk8Hr81j8ejoKAgxcbGtusYcXFxXfanBQAAwBWsNlJSUlRZWakTJ060rrlcLg0cOFA9evSwcTIAABAoKFjfk5SUpNTUVBUWFqq2tlbl5eV68cUXlZ2dbfdoAAAgQIT8/Oc//7ndQ/yjufbaa7V582Y98cQTevPNNzVlyhTNmTNHQUFBdo/WRo8ePTRy5MgufXWNDM4gBzLwIQcy8CEH%2BzII8p7vHd0AAADww1uEAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAJQRUWF7r77bqWlpem6667TypUr1dLSYvdYlktMTFRKSopSU1Nb/z3xxBN2j9Xh9uzZo/T0dOXm5rbZ27JliyZMmKBhw4Zp8uTJ2rt3rw0TdrwfymDDhg269NJL/V4Tqamp%2BvTTT22atGNVVFQoJydHaWlpSk9P16JFi1RTUyNJOnjwoG6//XYNHz5c48aN0wsvvGDztB3jhzL4y1/%2BosTExDavheeff97ukY377LPPNGvWLA0fPlzp6elasGCB3G63JGnfvn2aMmWKrrjiCo0fP14bN260edqO80M5fPDBB2d9LWzdurVjB/Ii4EyaNMn76KOPemtqarxHjhzxjhs3zvvCCy/YPZblBg8e7D127JjdY1iquLjYO27cOO/06dO9CxYs8Ns7cOCANyUlxbtr1y5vQ0OD94033vBedtll3srKSpum7RjnyuC1117z3n777TZNZr1bbrnFu2jRIm9tba23srLSO3nyZO/ixYu99fX13muvvdb7zDPPeOvq6rylpaXekSNHet966y27RzbuhzI4duyYd/DgwXaP1%2BFOnTrlvfrqq71FRUXeU6dOeb/%2B%2Bmvv7bff7r333nu9f/3rX72XX365d/369d6Ghgbvu%2B%2B%2B6x06dKj3008/tXts486Vw/vvv%2B%2B97rrrLJ%2BJK1gBxuVy6bPPPlNeXp4iIiKUkJCg2bNny%2Bl02j0aLBAWFqaSkhL169evzd769euVmZmpzMxMhYWFaeLEiRo8eHCn%2B4n1XBl0JTU1NUpJSdHChQvVo0cPXXzxxZo0aZI%2B/PBD7dq1S42NjZo3b566d%2B%2Bu5ORkTZ06tdN9nzhXBl1FfX29cnNzNXfuXIWGhio2NlZjx47VF198oU2bNikhIUFTpkxRWFiY0tPTNXr0aK1fv97usY07Vw52oWAFmLKyMsXHxysqKqp1LTk5WUeOHFFtba2Nk9mjsLBQo0aN0pVXXqmlS5eqrq7O7pE61MyZMxUREXHWvbKyMiUlJfmtJSUlyeVyWTGaZc6VgSRVVlbqjjvu0IgRIzRmzBi98cYbFk5nncjISC1fvly9evVqXausrFRcXJzKysqUmJiokJCQ1r2kpCSVlpbaMWqHOVcGPg8//LAyMjJ01VVXqbCwUI2NjXaM2mGioqI0depUORwOSdLhw4f1%2B9//XjfddNMPfk/obK8D6dw5SFJdXV3rW8nXXnutXnzxRXm93g6diYIVYDwejyIjI/3WfGWrurrajpFsc/nllys9PV3bt2%2BX0%2BnUJ598oscff9zusWzj8Xj8ird05rXRlV4XsbGxSkhI0EMPPaR3331XDz74oBYvXqx9%2B/bZPVqHc7lcevnllzVv3ryzfp%2BIjo6Wx%2BPp1Pdr/t8MQkNDNWzYMI0dO1Y7d%2B5UcXGxNm7cqP/8z/%2B0e8wOUVFRoZSUFN18881KTU3V/Pnzf/B10Jm/J5wth/DwcA0ePFizZs3Snj17tHz5chUVFem1117r0FkoWAGoo1t3oHA6nZo6dapCQ0M1YMAA5eXlafPmzTp9%2BrTdo9mmq782Ro0apeeee05JSUkKDQ3V%2BPHjNXbsWG3YsMHu0TrURx99pDlz5mjhwoVKT0//wccFBQVZOJW1vp9BXFycXn31VY0dO1bdunXT0KFDNXfu3E77WoiPj5fL5dK2bdt09OhRPfzww3aPZIuz5ZCcnKyXXnpJI0eOVGhoqDIyMjR9%2BvQOfy1QsAJMbGysPB6P35rH41FQUJBiY2NtmuofQ58%2BfdTc3Kyvv/7a7lFsERMTc9bXRld/XcTHx6uqqsruMTrMjh07dPfdd2vx4sWaOXOmpDPfJ75/lcLj8Sg6OlrBwZ3v2/7ZMjib%2BPh4ffXVV532B5GgoCAlJCQoNzdXmzdvlsPhaPM9obq6utN/T/h%2BDidOnGjzGCu%2BL3S%2B/6d1cikpKaqsrPR7wbhcLg0cOFA9evSwcTJrHThwQCtWrPBbKy8vV2hoqN/9F11JSkpKm3srXC6XLrvsMpsmst5vf/tbbdmyxW%2BtvLxcffv2tWmijvXxxx8rPz9fTz/9tG699dbW9ZSUFH3%2B%2BedqampqXeusr4UfymDfvn1as2aN32MPHz6s%2BPj4TnUlb9%2B%2Bfbrhhhv83vr1leihQ4e2%2BZ5QWlraKV8H58ph9%2B7d%2Bu///m%2B/xx8%2BfLjDvy9QsAJMUlKSUlNTVVhYqNraWpWXl%2BvFF19Udna23aNZqmfPnnI6nSouLtbp06d15MgRPf3008rKyvK7sbcrmTZtmt577z3t2rVLp06dUklJiY4ePaqJEyfaPZplTp8%2BrSeeeEIul0uNjY3avHmz3nnnHU2fPt3u0YxramrSo48%2Bqry8PGVkZPjtZWZmKjw8XGvWrFF9fb3279%2BvkpKSTvd94lwZREREaPXq1XrjjTfU2Ngol8ul559/vtNlkJKSotraWq1cuVL19fU6ceKEnnnmGV155ZXKzs5WRUWF1q9fr1OnTmn37t3avXu3pk2bZvfYxp0rh4iICD311FPau3evGhsb9e677%2Bq1117r8NdCkLezXivtxI4fP66lS5fqf/7nfxQeHq7p06frvvvu61Q/lbXHn/70JxUWFurzzz9XaGioJk2apNzcXIWFhdk9WodJTU2VpNYrE77fmPH9puD27dtVWFioiooKDRw4UEuWLNGIESPsGbaDnCsDr9erNWvWqKSkRG63W3369NHDDz%2Bs6667zrZ5O8qHH36of/3Xf1VoaGibvW3btqmurk6PPfaYSktL1atXL91111362c9%2BZsOkHefHMjhw4ICKiop09OhRRUREaMaMGbrrrrs63dukn3/%2BuZYtW6ZPP/1U3bt311VXXaVFixapd%2B/e%2BtOf/qRly5apvLxc8fHxWrhwocaNG2f3yB3iXDk4nU698MILqqysVK9evTRv3jxNnTq1Q%2BehYAEAABjWuWo8AADAPwAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAM%2B3/pv52h%2B5WO8QAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-2851255445431252041”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>48</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.4743323130125944</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.013085722386399573</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.5539622184681585</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.3481856264621076</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.957031869351514</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.09557059546332235</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.9538934199765707</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.3474178403755865</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.3333449684107648</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2736)</td> <td class=”number”>2736</td> <td class=”number”>85.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>416</td> <td class=”number”>13.0%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-2851255445431252041”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>48</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0006504403481156743</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.000989481808376953</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0012369807773187204</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0013323873383231238</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>21.695859872611464</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>22.666666666666664</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>26.69404517453799</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>28.382502543234995</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.33333333333333</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LOCLSALE12”>PCT_LOCLSALE12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2789</td>

</tr> <tr>

<th>Unique (%)</th> <td>86.9%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>11.1%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>357</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1.0457</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>39.312</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>2.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-704858508242905167”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-704858508242905167,#minihistogram-704858508242905167”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-704858508242905167”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-704858508242905167”

aria-controls=”quantiles-704858508242905167” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-704858508242905167” aria-controls=”histogram-704858508242905167”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-704858508242905167” aria-controls=”common-704858508242905167”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-704858508242905167” aria-controls=”extreme-704858508242905167”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-704858508242905167”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.0059177</td>

</tr> <tr>

<th>Q1</th> <td>0.057424</td>

</tr> <tr>

<th>Median</th> <td>0.22488</td>

</tr> <tr>

<th>Q3</th> <td>0.79798</td>

</tr> <tr>

<th>95-th percentile</th> <td>4.914</td>

</tr> <tr>

<th>Maximum</th> <td>39.312</td>

</tr> <tr>

<th>Range</th> <td>39.312</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.74056</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>2.6727</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.5558</td>

</tr> <tr>

<th>Kurtosis</th> <td>56.312</td>

</tr> <tr>

<th>Mean</th> <td>1.0457</td>

</tr> <tr>

<th>MAD</th> <td>1.2802</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>6.3992</td>

</tr> <tr>

<th>Sum</th> <td>2983.5</td>

</tr> <tr>

<th>Variance</th> <td>7.1435</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-704858508242905167”>

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D311FM6fPiwt8cBAAAwzmfXYCUkJCgvL087duzQggUL9Ic//EE33XSTZs6cqY8//thXYwEAAHSYzwpWY2OjNm3apLvuukt5eXnq1auXfvWrX2nQoEHKysrSxo0bfTUaAABAhzi8/YAVFRVas2aNXnvtNdXX1%2BvGG2/U73//ew0dOrT1NsOGDdOjjz6qcePGeXs8AACADvN6wRo7dqz69eunWbNm6ZZbblF4ePhZt0lLS9OJEye8PRoAAIARXi9Yq1at0vDhw3/wdvv27fPCNAAAAOZ5/RqsuLg43X333dq2bVvr2v/8z//orrvuksvl8vY4AAAAxnm9YOXn5%2Bubb77RgAEDWtdGjhyplpYWLVmyxNvjAAAAGOf1lwj37NmjjRs3KiIionUtJiZGBQUFuvnmm709DgAAgHFeP4N18uRJBQUFnT2Iv78aGhq8PQ4AAIBxXi9Yw4YN05IlS/T111%2B3rv3tb3/TY4895vFWDQAAAFbl9ZcIFyxYoP/4j//QNddco%2BDgYLW0tKi%2Bvl59%2BvTRiy%2B%2B6O1xAAAAjPN6werTp49ef/11vfnmm/r888/l7%2B%2Bvfv36acSIEQoICPD2OAAAAMZ5vWBJUmBgoG644QZfPDQAAECn83rBOnr0qAoLC/XZZ5/p5MmTZ%2B3/%2Bc9/9vZIAAAARvnkGqyqqiqNGDFCXbt27dCxdu/erby8PKWkpGjp0qWt6%2BvWrdOCBQvUpUsXj9u//PLLSk5OVktLi5YtW6bS0lLV1tYqOTlZjz76qPr06SNJcrlcevTRR/Xee%2B/J399faWlpWrhwoS655JIOzQsAAP45eL1glZWV6c9//rMiIyM7dJxnnnlGa9asUd%2B%2Bfc%2B5P2zYsPNeNP/yyy9r48aNeuaZZ9SrVy8tXbpU2dnZWr9%2Bvfz8/LRw4UKdPn1apaWlamxs1P3336%2BCggI98sgjHZoZAAD8c/D62zR07969w2euJCkoKOiCBetCSkpKlJWVpf79%2Bys4OFg5OTmqqKjQvn379OWXX2rbtm3KyclRZGSkevXqpXvuuUdr165VY2Njh%2BcGAAD25/WCNWvWLBUVFcntdnfoONOnT1dISMh5948dO6Y77rhDw4YNU3p6utavXy/puzc6PXjwoOLj41tvGxwcrL59%2B8rpdOrAgQMKCAhQXFxc635CQoK%2B/fZbHTp0qEMzAwCAfw5ef4nwzTff1Icffqh169apd%2B/e8vf37Hivvvpqhx8jMjJSMTExeuCBBzRgwAD96U9/0kMPPaSoqCj95Cc/kdvtVlhYmMd9wsLCVFNTo/DwcAUHB8vPz89jT5Jqamra9PhVVVWqrq72WHM4uioqKqqDyTwFBHi9H3eIw9H2ec9ks1rGtrJzPjtnk%2Bydz87ZJHvns3M2yZr5vF6wgoOD9bOf/axTH2PkyJEaOXJk69/Hjh2rP/3pT1q3bp1yc3Ml6YJn0Dp6dq2kpERFRUUea9nZ2ZozZ06Hjmt1ERHd2n2f0NBLO2GSHw8757NzNsne%2BeycTbJ3Pjtnk6yVz%2BsFKz8/39sPKUmKjo5WWVmZwsPD5e/vL5fL5bHvcrnUvXt3RUZGqq6uTs3Nza1vfHrmtt27d2/TY2VkZGjUqFEeaw5HV9XU1BtI8ndWavKS2pU/IMBfoaGXqra2Qc3NLZ04lW/YOZ%2Bds0n2zmfnbJK989k5m9SxfBfzzb0JPnmj0UOHDun111/X//3f/7UWrr/%2B9a8aMmSIkeO/8sorCgsL00033dS6VlFRoT59%2BigoKEgDBw5UeXm5hg8fLkmqra3V559/ruTkZEVHR8vtduuTTz5RQkKCJMnpdCo0NFT9%2BvVr0%2BNHRUWd9XJgdfU3amqy3yd9e1xM/ubmFlt/3Oycz87ZJHvns3M2yd757JxNslY%2Br58C2bt3r8aPH6%2BtW7eqtLRU0ndvPjp9%2BnRjbzJ6%2BvRpPf7443I6nWpsbFRpaanefPNNTZ06VZKUmZmpVatWqaKiQnV1dSooKNCgQYOUlJSkyMhI3Xjjjfrtb3%2BrEydO6Pjx41q%2BfLkmTZokh8MnfRQAAFiM1xvD0qVL9eCDD2rGjBlKTk6W9N3vJ1yyZImWL1%2Bu9PT0Nh0nKSlJktTU1CRJ2rZtm6TvzjZNnz5d9fX1uv/%2B%2B1VdXa3evXtr%2BfLlSkxMlCRNnTpV1dXVmjZtmurr65WSkuJxzdSvf/1rLVq0SOnp6erSpYtuvvlm5eTkGPsYAAAAe/Nzd/SK7na64oor9N577ykwMFCDBw/Wvn37JEnNzc268sorW/9uN9XV3xg/psPhr9EFu40ft7Nsnnttm2/rcPgrIqKbamrqLXM6uD3snM/O2SR757NzNsne%2BeycTepYvp49z/%2BWTp3J6y8RhoSEnPN3EFZVVSkwMNDb4wAAABjn9YJ15ZVX6oknnlBdXV3r2uHDh5WXl6drrrnG2%2BMAAAAY5/VrsH71q19pxowZSklJaX1ZsKGhQQMHDtSSJUu8PQ4AAIBxXi9Yl112mUpLS7Vr1y4dPnxYl1xyifr166drr73W493TAQAArMon7zvQpUsX3XDDDb54aAAAgE7n9YI1atSoC56pMvVeWAAAAL7i9YJ10003eRSs5uZmHT58WE6nUzNmzPD2OAAAAMZ5vWCd%2BWXL3/fGG2/o3Xff9fI0AAAA5v1oflvwDTfcoNdff93XYwAAAHTYj6Zg7d%2B/X15%2BU3kAAIBO4fWXCM/8wuV/1NDQoIqKCo0ZM8bb4wAAABjn9YIVExNz1k8RBgUFadKkSZo8ebK3xwEAADDO6wWLd2sHAAB25/WC9dprr7X5trfccksnTgIAANA5vF6wHn74YbW0tJx1Qbufn5/Hmp%2BfHwULAABYktcL1rPPPqvnn39ed999t%2BLi4uR2u/Xpp5/qmWee0e23366UlBRvjwQAAGCUT67BKi4uVq9evVrXrrrqKvXp00czZ85UaWmpt0cCAAAwyuvvg3XkyBGFhYWdtR4aGqrKykpvjwMAAGCc1wtWdHS0lixZopqamta12tpaFRYW6vLLL/f2OAAAAMZ5/SXCBQsWaN68eSopKVG3bt3k7%2B%2Bvuro6XXLJJVq%2BfLm3xwEAADDO6wVrxIgR2rlzp3bt2qXjx4/L7XarV69euu666xQSEuLtcQAAAIzzesGSpEsvvVTp6ek6fvy4%2BvTp44sRAAAAOo3Xr8E6efKk8vLyNGTIEP3iF7%2BQ9N01WHfeeadqa2u9PQ4AAIBxXi9YTz31lA4cOKCCggL5%2B//94Zubm1VQUODtcQAAAIzzesF644039F//9V/6%2Bc9/3vpLn0NDQ5Wfn6%2BtW7d6exwAAADjvF6w6uvrFRMTc9Z6ZGSkvv32W2%2BPAwAAYJzXC9bll1%2Bud999V5I8fvfgli1b9K//%2Bq/eHgcAAMA4r/8U4W233ab77rtPt956q1paWvTCCy%2BorKxMb7zxhh5%2B%2BGFvjwMAAGCc1wtWRkaGHA6HXnrpJQUEBOh3v/ud%2BvXrp4KCAv385z/39jgAAADGeb1gnThxQrfeeqtuvfVWbz80AACAV3j9Gqz09HSPa68AAADsxusFKyUlRZs3b/b2wwIAAHiN118i/Jd/%2BRf95je/UXFxsS6//HJ16dLFY7%2BwsNDbIwEAABjl9YJ18OBB/eQnP5Ek1dTUePvhAQAAOp3XClZOTo6WLl2qF198sXVt%2BfLlys7O9tYIAAAAXuG1a7C2b99%2B1lpxcbG3Hh4AAMBrvFawzvWTg/w0IQAAsCOvFawzv9j5h9YAAACszutv0wAAAGB3FCwAAADDvPZThI2NjZo3b94PrvE%2BWAAAwOq8VrCGDh2qqqqqH1wDAACwOq8VrH98/ysAAAA74xosAAAAwyhYAAAAhlGwAAAADLN0wdq9e7dSU1OVk5Nz1t6mTZs0btw4DRkyRBMnTtSePXta91paWrR06VKlp6dr2LBhmjlzpo4ePdq673K5NHfuXKWmpmrEiBF6%2BOGHdfLkSa9kAgAA1mfZgvXMM89o8eLF6tu371l7Bw4cUF5ennJzc/XOO%2B8oKytL9957r44fPy5Jevnll7Vx40YVFxdrx44diomJUXZ2duuv7lm4cKEaGhpUWlqqtWvXqqKiQgUFBV7NBwAArMuyBSsoKEhr1qw5Z8FavXq10tLSlJaWpqCgII0fP16xsbHasGGDJKmkpERZWVnq37%2B/goODlZOTo4qKCu3bt09ffvmltm3bppycHEVGRqpXr1665557tHbtWjU2Nno7JgAAsCDLFqzp06crJCTknHvl5eWKj4/3WIuPj5fT6dTJkyd18OBBj/3g4GD17dtXTqdTBw4cUEBAgOLi4lr3ExIS9O233%2BrQoUOdEwYAANiK194Hy5tcLpfCwsI81sLCwnTw4EF9/fXXcrvd59yvqalReHi4goODPX4R9Znb1tTUtOnxq6qqVF1d7bHmcHRVVFTUxcQ5r4AAa/Vjh6Pt857JZrWMbWXnfHbOJtk7n52zSfbOZ%2BdskjXz2bJgSWq9nupi9n/ovj%2BkpKRERUVFHmvZ2dmaM2dOh45rdRER3dp9n9DQSzthkh8PO%2BezczbJ3vnsnE2ydz47Z5Oslc%2BWBSsiIkIul8tjzeVyKTIyUuHh4fL39z/nfvfu3RUZGam6ujo1NzcrICCgdU%2BSunfv3qbHz8jI0KhRozzWHI6uqqmpv9hI52SlJi%2BpXfkDAvwVGnqpamsb1Nzc0olT%2BYad89k5m2TvfHbOJtk7n52zSR3LdzHf3Jtgy4KVmJiosrIyjzWn06mxY8cqKChIAwcOVHl5uYYPHy5Jqq2t1eeff67k5GRFR0fL7Xbrk08%2BUUJCQut9Q0ND1a9fvzY9flRU1FkvB1ZXf6OmJvt90rfHxeRvbm6x9cfNzvnsnE2ydz47Z5Psnc/O2SRr5bPWKZA2mjJlit5%2B%2B23t3LlTp06d0po1a3TkyBGNHz9ekpSZmalVq1apoqJCdXV1Kigo0KBBg5SUlKTIyEjdeOON%2Bu1vf6sTJ07o%2BPHjWr58uSZNmiSHw5Z9FAAAGGbZxpCUlCRJampqkiRt27ZN0ndnm2JjY1VQUKD8/HxVVlZqwIABWrlypXr27ClJmjp1qqqrqzVt2jTV19crJSXF45qpX//611q0aJHS09PVpUsX3Xzzzed8M1MAAIBz8XN39IputEl19TfGj%2Blw%2BGt0wW7jx%2B0sm%2Bde2%2BbbOhz%2BiojoppqaesucDm4PO%2BezczbJ3vnsnE2ydz47Z5M6lq9nz3O/pVNns%2BVLhAAAAL5EwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMNsW7Di4uKUmJiopKSk1j%2BPP/64JGnv3r2aNGmSrrzySo0dO1YbNmzwuO%2BqVat044036sorr1RmZqbKysp8EQEAAFiUw9cDdKYtW7aod%2B/eHmtVVVW655579PDDD2vcuHH64IMPNHv2bPXr109JSUnavn27nn76aT377LOKi4vTqlWrdPfdd2vr1q3q2rWrj5IAAAArse0ZrPPZuHGjYmJiNGnSJAUFBSk1NVWjRo3S6tWrJUklJSWaOHGiBg8erEsuuUR33nmnJGnHjh2%2BHBsAAFiIrQtWYWGhRo4cqauuukoLFy5UfX29ysvLFR8f73G7%2BPj41pcBv7/v7%2B%2BvQYMGyel0enV2AABgXbZ9ifCKK65QamqqnnzySR09elRz587VY489JpfLpV69enncNjw8XDU1NZIkl8ulsLAwj/2wsLDW/baoqqpSdXW1x5rD0VVRUVEXmebcAgKs1Y8djrbPeyab1TK2lZ3z2TmbZO98ds4m2TufnbNJ1sxn24JVUlLS%2Bt/9%2B/dXbm6uZs%2BeraFDh/7gfd1ud4cfu6ioyGMtOztbc%2BbM6dBxrS4iolu77xMaemknTPLjYed8ds4m2TufnbNJ9s5n52yStfLZtmB9X%2B/evdXc3Cx/f3%2B5XC6PvZqaGkVGRkqSIiIiztp3uVwaOHBgmx8rIyNDo0aN8lhzOLqqpqb%2BIqcNTfJlAAAOlElEQVQ/Nys1eUntyh8Q4K/Q0EtVW9ug5uaWTpzKN%2Bycz87ZJHvns3M2yd757JxN6li%2Bi/nm3gRbFqz9%2B/drw4YNmj9/futaRUWFAgMDlZaWpj/%2B8Y8ety8rK9PgwYMlSYmJiSovL9eECRMkSc3Nzdq/f78mTZrU5sePioo66%2BXA6upv1NRkv0/69riY/M3NLbb%2BuNk5n52zSfbOZ%2Bdskr3z2TmbZK181joF0kbdu3dXSUmJiouLdfr0aR0%2BfFjLli1TRkaG/u3f/k2VlZVavXq1Tp06pV27dmnXrl2aMmWKJCkzM1OvvfaaPvroIzU0NGjFihUKDAzUyJEjfRsKAABYhi3PYPXq1UvFxcUqLCxsLUgTJkxQTk6OgoKCtHLlSi1evFiPPfaYoqOj9dRTT%2BmnP/2pJOlnP/uZHnjgAc2dO1dfffWVkpKSVFxcrEsuucTHqQAAgFXYsmBJ0rBhw/Tqq6%2Bed2/9%2BvXnve9tt92m2267rbNGAwAANmfLlwgBAAB8iYIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIY5fD0A/nn84rdv%2BXqEdtk891pfjwAAsCjOYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAM441Gz6GyslKPPfaY9u3bp65du%2Bqmm27SvHnz5O9PH/1nwhujAgAuFgXrHO677z4lJCRo27Zt%2BuqrrzRr1iz16NFDd9xxh69HAwAAFsApme9xOp365JNPlJubq5CQEMXExCgrK0slJSW%2BHg0AAFgEZ7C%2Bp7y8XNHR0QoLC2tdS0hI0OHDh1VXV6fg4OAfPEZVVZWqq6s91hyOroqKijI6a0AA/Rh/Z7WXNNF5/pR73UXd78y/KXb9t8XO%2BeycTbJmPgrW97hcLoWGhnqsnSlbNTU1bSpYJSUlKioq8li79957dd9995kbVN8VuRmXfaaMjAzj5c3XqqqqVFJSYstskr3z2TmbZO98VVVV%2Bv3vn7VlNsne%2BeycTbJmPutUQS9yu90dun9GRobWrVvn8ScjI8PQdH9XXV2toqKis86W2YGds0n2zmfnbJK989k5m2TvfHbOJlkzH2ewvicyMlIul8tjzeVyyc/PT5GRkW06RlRUlGUaNgAAMI8zWN%2BTmJioY8eO6cSJE61rTqdTAwYMULdu3Xw4GQAAsAoK1vfEx8crKSlJhYWFqqurU0VFhV544QVlZmb6ejQAAGARAY8%2B%2Buijvh7ix%2Ba6665TaWmpHn/8cb3%2B%2BuuaNGmSZs6cKT8/P1%2BPdpZu3bpp%2BPDhtjy7Zudskr3z2TmbZO98ds4m2TufnbNJ1svn5%2B7oFd0AAADwwEuEAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsCyosrJSv/zlL5WSkqLrr79eTz31lFpaWnw9ljFxcXFKTExUUlJS65/HH3/c12NdtN27dys1NVU5OTln7W3atEnjxo3TkCFDNHHiRO3Zs8cHE3bM%2BfKtW7dOP/3pTz2ex6SkJH388cc%2BmrT9KisrlZ2drZSUFKWmpmr%2B/Pmqra2VJB04cEC33367hg4dqjFjxuj555/38bTtc75sX3zxheLi4s563p577jlfj9wun3zyiWbMmKGhQ4cqNTVVc%2BfOVXV1tSRp7969mjRpkq688kqNHTtWGzZs8PG07XO%2BbO%2B%2B%2B%2B45n7vNmzf7euSL8sQTTyguLq7175Z73tywnAkTJrgfeeQRd21trfvw4cPuMWPGuJ9//nlfj2VMbGys%2B%2BjRo74ew4ji4mL3mDFj3FOnTnXPnTvXY2///v3uxMRE986dO90nT550r1%2B/3j148GD3sWPHfDRt%2B10o39q1a9233367jyYz4%2Babb3bPnz/fXVdX5z527Jh74sSJ7gULFrgbGhrc1113nfvpp59219fXu8vKytzDhw93v/HGG74euc3Ol%2B3o0aPu2NhYX4/XIadOnXJfc8017qKiIvepU6fcX331lfv2229333PPPe6//e1v7iuuuMK9evVq98mTJ91vvfWWOzk52f3xxx/7euw2uVC2d955x3399df7ekQj9u/f7x4%2BfHjr56IVnzfOYFmM0%2BnUJ598otzcXIWEhCgmJkZZWVkqKSnx9Wg4h6CgIK1Zs0Z9%2B/Y9a2/16tVKS0tTWlqagoKCNH78eMXGxv74vyv7BxfKZ3W1tbVKTEzUvHnz1K1bN1122WWaMGGC3n//fe3cuVONjY2aPXu2unbtqoSEBE2ePNkyX4cXymYHDQ0NysnJ0axZsxQYGKjIyEiNHj1an332mTZu3KiYmBhNmjRJQUFBSk1N1ahRo7R69Wpfj90mF8pmFy0tLVq0aJGysrJa16z4vFGwLKa8vFzR0dEKCwtrXUtISNDhw4dVV1fnw8nMKiws1MiRI3XVVVdp4cKFqq%2Bv9/VIF2X69OkKCQk55155ebni4%2BM91uLj4%2BV0Or0xmhEXyidJx44d0x133KFhw4YpPT1d69ev9%2BJ0HRMaGqr8/Hz16NGjde3YsWOKiopSeXm54uLiFBAQ0LoXHx%2BvsrIyX4zabhfKdsZDDz2kESNG6Oqrr1ZhYaEaGxt9MepFCQsL0%2BTJk%2BVwOCRJhw4d0h//%2BEf94he/OO/XnVWeuwtlk6T6%2BvrWl36vu%2B46vfDCC3K73b4cud1effVVBQUFady4ca1rVnzeKFgW43K5FBoa6rF2pmzV1NT4YiTjrrjiCqWmpmrr1q0qKSnRRx99pMcee8zXYxnncrk8irL03XNpl%2BcxMjJSMTExevDBB/XWW2/pgQce0IIFC7R3715fj3ZRnE6nXnrpJc2ePfucX4fh4eFyuVyWvB7yH7MFBgZqyJAhGj16tHbs2KHi4mJt2LBB//3f/%2B3rMdutsrJSiYmJuummm5SUlKQ5c%2Bac97mz2tfdubIFBwcrNjZWM2bM0O7du5Wfn6%2BioiKtXbvW1%2BO22Zdffqmnn35aixYt8li34vNGwbIgq3030l4lJSWaPHmyAgMD1b9/f%2BXm5qq0tFSnT5/29WjG2fm5HDlypJ599lnFx8crMDBQY8eO1ejRo7Vu3Tpfj9ZuH3zwgWbOnKl58%2BYpNTX1vLfz8/Pz4lRmfD9bVFSUXn31VY0ePVpdunRRcnKyZs2aZcnnLTo6Wk6nU1u2bNGRI0f00EMP%2BXokY86VLSEhQS%2B%2B%2BKKGDx%2BuwMBAjRgxQlOnTrXUc5efn6%2BJEydqwIABvh6lwyhYFhMZGSmXy%2BWx5nK55Ofnp8jISB9N1bl69%2B6t5uZmffXVV74exaiIiIhzPpd2fR6l7/6nUFVV5esx2mX79u365S9/qQULFmj69OmSvvs6/P53zi6XS%2BHh4fL3t84/q%2BfKdi7R0dH68ssvLfkNgZ%2Bfn2JiYpSTk6PS0lI5HI6zvu5qamos%2BXX3/WwnTpw46zZW%2Bprbu3ev/vrXvyo7O/usvXP9e/ljf96s8y8BJEmJiYk6duyYxxeS0%2BnUgAED1K1bNx9OZsb%2B/fu1ZMkSj7WKigoFBgZ6XB9iB4mJiWddP%2BB0OjV48GAfTWTWK6%2B8ok2bNnmsVVRUqE%2BfPj6aqP0%2B/PBD5eXladmyZbrlllta1xMTE/Xpp5%2Bqqampdc1qz935su3du1crVqzwuO2hQ4cUHR1tmTN0e/fu1Y033ujxcu2Z4pucnHzW111ZWZllnrsLZdu1a5f%2B93//1%2BP2hw4dsszX3IYNG/TVV1/p%2BuuvV0pKiiZOnChJSklJUWxsrOWeNwqWxcTHxyspKUmFhYWqq6tTRUWFXnjhBWVmZvp6NCO6d%2B%2BukpISFRcX6/Tp0zp8%2BLCWLVumjIwMjwuK7WDKlCl6%2B%2B23tXPnTp06dUpr1qzRkSNHNH78eF%2BPZsTp06f1%2BOOPy%2Bl0qrGxUaWlpXrzzTc1depUX4/WJk1NTXrkkUeUm5urESNGeOylpaUpODhYK1asUENDg/bt26c1a9ZY5uvwQtlCQkK0fPlyrV%2B/Xo2NjXI6nXruuecsk036rgDX1dXpqaeeUkNDg06cOKGnn35aV111lTIzM1VZWanVq1fr1KlT2rVrl3bt2qUpU6b4euw2uVC2kJAQPfnkk9qzZ48aGxv11ltvae3atZZ57ubPn6833nhD69ev1/r161VcXCxJWr9%2BvcaNG2e5583PbcVzvv/kjh8/roULF%2Bq9995TcHCwpk6dqnvvvdcy313%2BkL/85S8qLCzUp59%2BqsDAQE2YMEE5OTkKCgry9WjtlpSUJEmtZzrO/OTPmZ8U3Lp1qwoLC1VZWakBAwbo4Ycf1rBhw3wz7EW4UD63260VK1ZozZo1qq6uVu/evfXQQw/p%2Buuv99m87fH%2B%2B%2B/r3//93xUYGHjW3pYtW1RfX69FixaprKxMPXr00F133aXbbrvNB5O23w9l279/v4qKinTkyBGFhIRo2rRpuuuuuyz18uenn36qxYsX6%2BOPP1bXrl119dVXa/78%2BerVq5f%2B8pe/aPHixaqoqFB0dLTmzZunMWPG%2BHrkNrtQtpKSEj3//PM6duyYevToodmzZ2vy5Mm%2BHvmifPHFF0pPT9enn34qSZZ73ihYAAAAhlnn2xEAAACLoGABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwLD/B5qfoZsW3DUVAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-704858508242905167”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>65</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.22002200220022</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.02728200795578555</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.15152180596424963</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.10075097576050054</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.08313287109098627</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6336501584125396</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0947472688381975</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9248960955335714</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.3565166569257744</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2778)</td> <td class=”number”>2778</td> <td class=”number”>86.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>357</td> <td class=”number”>11.1%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-704858508242905167”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>65</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00024394408801502694</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0006058219489292097</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0007219955958268654</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0007621325145207414</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>27.56183745583039</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>28.10693641618497</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>28.143021914648216</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>35.80246913580247</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>39.311594202898554</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_NHASIAN10”>PCT_NHASIAN10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3105</td>

</tr> <tr>

<th>Unique (%)</th> <td>96.7%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1.1365</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>43.015</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.7%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-3921542984314459742”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-3921542984314459742,#minihistogram-3921542984314459742”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-3921542984314459742”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-3921542984314459742”

aria-controls=”quantiles-3921542984314459742” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-3921542984314459742” aria-controls=”histogram-3921542984314459742”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-3921542984314459742” aria-controls=”common-3921542984314459742”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-3921542984314459742” aria-controls=”extreme-3921542984314459742”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-3921542984314459742”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.11225</td>

</tr> <tr>

<th>Q1</th> <td>0.26951</td>

</tr> <tr>

<th>Median</th> <td>0.46308</td>

</tr> <tr>

<th>Q3</th> <td>0.97933</td>

</tr> <tr>

<th>95-th percentile</th> <td>4.1366</td>

</tr> <tr>

<th>Maximum</th> <td>43.015</td>

</tr> <tr>

<th>Range</th> <td>43.015</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.70982</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>2.4731</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.1761</td>

</tr> <tr>

<th>Kurtosis</th> <td>84.991</td>

</tr> <tr>

<th>Mean</th> <td>1.1365</td>

</tr> <tr>

<th>MAD</th> <td>1.1162</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>7.7865</td>

</tr> <tr>

<th>Sum</th> <td>3559.5</td>

</tr> <tr>

<th>Variance</th> <td>6.1162</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-3921542984314459742”>

<img 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2B/ZKSkrS%2BPHjVVxcLEkqKSlRRkaGMjIyFBoaqjFjxqhXr15au3atJKm4uFiTJ09W9%2B7dFR4erry8PJWXl2vv3r2%2BXDIAAAggARmwIiMjtWjRInXs2NEzdvToUcXFxamsrEy9e/dWSEiIZy4xMVGlpaWSpLKyMiUmJnrtLzExUXa7XadOndLBgwe95sPDw9W1a1fZ7fZmXhUAAGgpbL4uwAS73a6XXnpJK1as0MaNGxUZGek1Hx0dLZfLpYaGBrlcLkVFRXnNR0VF6eDBg/rqq6/kdrsbnXc6nU2up7KyUg6Hw2vMZmuvuLi4H7my7xcSElj52GYLrHrPx9meBFpvWjr64n/oiX%2BiL%2BYEfMB6//33NW3aNM2cOVPp6enauHFjo9sFBQV5/v%2BH7qe60PutiouLVVhY6DWWk5Oj3NzcC9pvoIuJCfN1CZaJjLzI1yWgEfTF/9AT/0RfLlxAB6xt27bpwQcf1Pz583XLLbdIkmJjY3XkyBGv7Vwul6KjoxUcHKyYmBi5XK5z5mNjYz3bNDbfoUOHJteVlZWloUOHeo3ZbO3ldNb8iNX9sEB7h2F6/f4oJCRYkZEXqaqqVvX1Db4uB/9AX/wPPfFPLbEvvnpzH7AB64MPPtDs2bP1zDPPaPDgwZ7x5ORk/f73v1ddXZ1stm%2BXZ7fb1bdvX8/82fuxzrLb7Ro1apRCQ0PVs2dPlZWVadCgQZK%2BvaH%2Bs88%2BU2pqapNri4uLO%2BdyoMNxUnV1LeOb9Xy1pvXX1ze0qvUGCvrif%2BiJf6IvFy6wToH8Q11dnR555BHNmjXLK1xJUkZGhsLDw7VixQrV1tZq7969Wr16tbKzsyVJEyZM0O7du7Vjxw6dPn1aq1ev1pEjRzRmzBhJUnZ2tlatWqXy8nJVV1eroKBAffr0UUpKiuXrBAAAgSkgz2B99NFHKi8vV35%2BvvLz873mNm3apGeffVYLFixQUVGROnbsqLy8PA0ZMkSS1KtXLxUUFGjRokWqqKhQjx49tHLlSnXq1EmSNHHiRDkcDt1%2B%2B%2B2qqalRWlraOfdTAQAAfJ8gN0/QtITDcdL4Pm22YA0v2GV8v81l44yrfF1Cs7PZghUTEyans4bT636EvvgfeuKfWmJfOnWK8MlxA/ISIQAAgD8jYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwywPW0KFDVVhYqKNHj1p9aAAAAEtYHrBuvfVWbdiwQdddd53uvPNObdmyRXV1dVaXAQAA0GwsD1g5OTnasGGD/vCHP6hnz5568sknlZGRocWLF%2Bvw4cNWlwMAAGCcz%2B7BSkpK0uzZs7V9%2B3bNnTtXf/jDH3TjjTdqypQp%2Bvjjj31VFgAAwAXzWcA6c%2BaMNmzYoLvuukuzZ89W586d9fDDD6tPnz6aPHmy1q1b56vSAAAALojln0VYXl6u1atX6/XXX1dNTY1GjhypF198UQMGDPBsM3DgQC1cuFCjR4%2B2ujwAAIALZnnAGjVqlLp166apU6fqlltuUXR09DnbZGRk6MSJE1aXBgAAYITlAWvVqlUaNGjQD263d%2B9eC6oBAAAwz/J7sHr37q177rlHW7du9Yz97//%2Br%2B666y65XC6rywEAADDO8oC1aNEinTx5Uj169PCMDRkyRA0NDXrqqaesLgcAAMA4yy8Rvv3221q3bp1iYmI8YwkJCSooKNBNN91kdTkAAADGWX4G69SpUwoNDT23kOBg1dbWWl0OAACAcZYHrIEDB%2Bqpp57SV1995Rn74osv9Nhjj3k9qgEAACBQWX6JcO7cufrlL3%2BpK6%2B8UuHh4WpoaFBNTY26dOmi3/3ud1aXAwAAYJzlAatLly5688039dZbb%2Bmzzz5TcHCwunXrpsGDByskJMTqcgAAAIyzPGBJUtu2bXXdddf54tAAAADNzvKA9fnnn2vJkiX69NNPderUqXPm//SnP1ldEgAAgFE%2BuQersrJSgwcPVvv27a0%2BPAAAQLOzPGCVlpbqT3/6k2JjY60%2BNAAAgCUsf0xDhw4dOHMFAABaNMsD1tSpU1VYWCi32231oQEAACxh%2BSXCt956Sx988IHWrFmjSy65RMHB3hnvlVdesbokAAAAoywPWOHh4brmmmusPiwAAIBlLA9YixYtsvqQAAAAlrL8HixJOnTokJYtW6aHH37YM/bhhx/6ohQAAADjLA9Ye/bs0ZgxY7RlyxatX79e0rcPH500aRIPGQUAAC2C5QFr6dKlevDBB7Vu3ToFBQVJ%2BvbzCZ966iktX77c6nIAAACMszxg/e1vf1N2drYkeQKWJF1//fUqLy%2B3uhwAAADjLA9YERERjX4GYWVlpdq2bWt1OQAAAMZZHrD69%2B%2BvJ598UtXV1Z6xw4cPa/bs2bryyiutLgcAAMA4yx/T8PDDD%2BuOO%2B5QWlqa6uvr1b9/f9XW1qpnz5566qmnrC4HAADAOMsD1sUXX6z169dr586dOnz4sNq1a6du3brpqquu8ronCwAAIFBZHrAkqU2bNrruuut8cWgAAIBmZ3nAGjp06PeeqeJZWAAAINBZHrBuvPFGr4BVX1%2Bvw4cPy26364477rC6HAAAAOMsD1izZs1qdHzz5s169913f9S%2Bdu3apdmzZystLU1Lly71jK9Zs0Zz585VmzZtvLZ/%2BeWXlZqaqoaGBj3zzDNav369qqqqlJqaqoULF6pLly6SJJfLpYULF%2Bovf/mLgoODlZGRofnz56tdu3Y/crUAAKA18slnETbmuuuu05tvvtnk7Z977jnl5%2Bera9eujc4PHDhQdrvd6ys1NVXSt0Fr3bp1Kioq0vbt25WQkKCcnBy53W5J0vz581VbW6v169fr1VdfVXl5uQoKCi58kQAAoFXwm4C1b98%2BT8BpitDQUK1evfrfBqzvU1xcrMmTJ6t79%2B4KDw9XXl6eysvLtXfvXn355ZfaunWr8vLyFBsbq86dO2v69Ol69dVXdebMmR99LAAA0PpYfolw4sSJ54zV1taqvLxcI0aMaPJ%2BJk2a9L3zR48e1S9%2B8QuVlpYqMjJSubm5uvnmm3Xq1CkdPHhQiYmJnm3Dw8PVtWtX2e12nTx5UiEhIerdu7dnPikpSV9//bUOHTrkNQ4AANAYywNWQkLCOX9FGBoaqszMTI0fP97IMWJjY5WQkKAHHnhAPXr00B//%2BEc99NBDiouL009/%2BlO53W5FRUV5vSYqKkpOp1PR0dEKDw/3qvHstk6ns0nHr6yslMPh8Bqz2dorLi7uAlfmLSTEb05ANonNFlj1no%2BzPQm03rR09MX/0BP/RF/MsTxgWfG09iFDhmjIkCGef48aNUp//OMftWbNGs9N9t93OfLHXKpsTHFxsQoLC73GcnJylJube0H7DXQxMWG%2BLsEykZEX%2BboENIK%2B%2BB964p/oy4WzPGC9/vrrTd72lltuMXbc%2BPh4lZaWKjo6WsHBwXK5XF7zLpdLHTp0UGxsrKqrq1VfX6%2BQkBDPnCR16NChScfKysrS0KFDvcZstvZyOmsMrOSfAu0dhun1%2B6OQkGBFRl6kqqpa1dc3%2BLoc/AN98T/0xD%2B1xL746s295QFr3rx5amhoOOcsUVBQkNdYUFDQeQes3//%2B94qKitKNN97oGSsvL1eXLl0UGhqqnj17qqysTIMGDZIkVVVV6bPPPlNqaqri4%2BPldrt14MABJSUlSZLsdrsiIyPVrVu3Jh0/Li7unMuBDsdJ1dW1jG/W89Wa1l9f39Cq1hso6Iv/oSf%2Bib5cOMtPgfzmN7/R4MGD9fLLL%2Bu9997TX//6V7300ku65ppr9Nxzz%2Bnjjz/Wxx9/rL179573Mb755hs9/vjjstvtOnPmjNavX6%2B33nrLc4N9dna2Vq1apfLyclVXV6ugoEB9%2BvRRSkqKYmNjNXLkSP3617/WiRMndOzYMS1fvlyZmZmy2XzyyUIAACDA%2BOQerKKiInXu3Nkzdvnll6tLly6aMmWK1q9f36T9pKSkSJLq6uokSVu3bpX07dmmSZMmqaamRvfff78cDocuueQSLV%2B%2BXMnJyZK%2B/UtGh8Oh22%2B/XTU1NUpLS/O6Z%2BpXv/qVFixYoGHDhqlNmza66aablJeXZ2T9AACg5QtyX%2Bgd3T9S37599e67757zVPTa2lqlp6frww8/tLIcyzgcJ43v02YL1vCCXcb321w2zrjK1yU0O5stWDExYXI6azi97kfoi/%2BhJ/6pJfalU6cInxzX8kuE8fHxeuqpp7weeVBVVaUlS5bo0ksvtbocAAAA4yy/RDh37lzNnDlTxcXFCgsLU3BwsKqrq9WuXTstX77c6nIAAACMszxgDR48WDt27NDOnTt17Ngxud1ude7cWVdffbUiInxzGg8AAMAkn/xZ3EUXXaRhw4bp2LFj6tKliy9KAAAAaDaW34N16tQpzZ49W/369dMNN9wg6dt7sO68805VVVVZXQ4AAIBxlgesxYsXa//%2B/SooKFBw8D8PX19fr4KCAqvLAQAAMM7ygLV582b9z//8j66//nrPBypHRkZq0aJF2rJli9XlAAAAGGd5wKqpqVFCQsI547Gxsfr666%2BtLgcAAMA4ywPWpZdeqnfffVeSvD57cNOmTfqP//gPq8sBAAAwzvK/Ivz5z3%2Bu%2B%2B67T7feeqsaGhr0wgsvqLS0VJs3b9a8efOsLgcAAMA4ywNWVlaWbDabXnrpJYWEhOjZZ59Vt27dVFBQoOuvv97qcgAAAIyzPGCdOHFCt956q2699VarDw0AAGAJy%2B/BGjZsmCz%2BfGkAAABLWR6w0tLStHHjRqsPCwAAYBnLLxH%2B5Cc/0RNPPKGioiJdeumlatOmjdf8kiVLrC4JAADAKMsD1sGDB/XTn/5UkuR0Oq0%2BPAAAQLOzLGDl5eVp6dKl%2Bt3vfucZW758uXJycqwqAQAAwBKW3YO1bdu2c8aKioqsOjwAAIBlLAtYjf3lIH9NCAAAWiLLAtbZD3b%2BoTEAAIBAZ/ljGgAAAFo6AhYAAIBhlv0V4ZkzZzRz5swfHOM5WAAAINBZFrAGDBigysrKHxwDAAAIdJYFrH99/hUAAEBLxj1YAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhAR2wdu3apfT0dOXl5Z0zt2HDBo0ePVr9%2BvXTuHHj9Pbbb3vmGhoatHTpUg0bNkwDBw7UlClT9Pnnn3vmXS6XZsyYofT0dA0ePFjz5s3TqVOnLFkTAAAIfAEbsJ577jnl5%2Bera9eu58zt379fs2fP1qxZs/TOO%2B9o8uTJuvfee3Xs2DFJ0ssvv6x169apqKhI27dvV0JCgnJycuR2uyVJ8%2BfPV21trdavX69XX31V5eXlKigosHR9AAAgcAVswAoNDdXq1asbDVglJSXKyMhQRkaGQkNDNWbMGPXq1Utr166VJBUXF2vy5Mnq3r27wsPDlZeXp/Lycu3du1dffvmltm7dqry8PMXGxqpz586aPn26Xn31VZ05c8bqZQIAgAAUsAFr0qRJioiIaHSurKxMiYmJXmOJiYmy2%2B06deqUDh486DUfHh6url27ym63a//%2B/QoJCVHv3r0980lJSfr666916NCh5lkMAABoUWy%2BLqA5uFwuRUVFeY1FRUXp4MGD%2Buqrr%2BR2uxuddzqdio6OVnh4uIKCgrzmJMnpdDbp%2BJWVlXI4HF5jNlt7xcXFnc9y/q2QkMDKxzZbYNV7Ps72JNB609LRF/9DT/wTfTGnRQYsSZ77qc5n/ode%2B0OKi4tVWFjoNZaTk6Pc3NwL2m%2Bgi4kJ83UJlomMvMjXJaAR9MX/0BP/RF8uXIsMWDExMXK5XF5jLpdLsbGxio6OVnBwcKPzHTp0UGxsrKqrq1VfX6%2BQkBDPnCR16NChScfPysrS0KFDvcZstvZyOmvOd0mNCrR3GKbX749CQoIVGXmRqqpqVV/f4Oty8A/0xf/QE//UEvviqzf3LTJgJScnq7S01GvMbrdr1KhRCg0NVc%2BePVVWVqZBgwZJkqqqqvTZZ58pNTVV8fHxcrvdOnDggJKSkjyvjYyMVLdu3Zp0/Li4uHMuBzocJ1VX1zK%2BWc9Xa1p/fX1Dq1pvoKAv/oee%2BCf6cuEC6xRIE02YMEG7d%2B/Wjh07dPr0aa1evVpHjhzRmDFjJEnZ2dlatWqVysvLVV1drYKCAvXp00cpKSmKjY3VyJEj9etf/1onTpzQsWPHtHz5cmVmZspma5F5FAAAGBawiSElJUWSVFdXJ0naunWrpG/PNvXq1UsFBQVatGiRKioq1KNHD61cuVKdOnWSJE3QBD9FAAAOXElEQVScOFEOh0O33367ampqlJaW5nXP1K9%2B9SstWLBAw4YNU5s2bXTTTTc1%2BjBTAACAxgS5L/SObjSJw3HS%2BD5ttmANL9hlfL/NZeOMq3xdQrOz2YIVExMmp7OG0%2Bt%2BhL74H3rin1piXzp1avyRTs2tRV4iBAAA8CUCFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgWIsNWL1791ZycrJSUlI8X48//rgkac%2BePcrMzFT//v01atQorV271uu1q1at0siRI9W/f39lZ2ertLTUF0sAAAAByubrAprTpk2bdMkll3iNVVZWavr06Zo3b55Gjx6t999/X9OmTVO3bt2UkpKibdu2admyZfrNb36j3r17a9WqVbrnnnu0ZcsWtW/f3kcrAQAAgaTFnsH6d9atW6eEhARlZmYqNDRU6enpGjp0qEpKSiRJxcXFGjdunPr27at27drpzjvvlCRt377dl2UDAIAA0qID1pIlSzRkyBBdfvnlmj9/vmpqalRWVqbExESv7RITEz2XAb87HxwcrD59%2Bshut1taOwAACFwt9hLhZZddpvT0dD399NP6/PPPNWPGDD322GNyuVzq3Lmz17bR0dFyOp2SJJfLpaioKK/5qKgoz3xTVFZWyuFweI3ZbO0VFxd3nqtpXEhIYOVjmy2w6j0fZ3sSaL1p6eiL/6En/om%2BmNNiA1ZxcbHn/7t3765Zs2Zp2rRpGjBgwA%2B%2B1u12X/CxCwsLvcZycnKUm5t7QfsNdDExYb4uwTKRkRf5ugQ0gr74H3rin%2BjLhWuxAeu7LrnkEtXX1ys4OFgul8trzul0KjY2VpIUExNzzrzL5VLPnj2bfKysrCwNHTrUa8xmay%2Bns%2BY8q29coL3DML1%2BfxQSEqzIyItUVVWr%2BvoGX5eDf6Av/oee%2BKeW2BdfvblvkQFr3759Wrt2rebMmeMZKy8vV9u2bZWRkaHXXnvNa/vS0lL17dtXkpScnKyysjKNHTtWklRfX699%2B/YpMzOzycePi4s753Kgw3FSdXUt45v1fLWm9dfXN7Sq9QYK%2BuJ/6Il/oi8XLrBOgTRRhw4dVFxcrKKiIn3zzTc6fPiwnnnmGWVlZenmm29WRUWFSkpKdPr0ae3cuVM7d%2B7UhAkTJEnZ2dl6/fXX9dFHH6m2tlYrVqxQ27ZtNWTIEN8uCgAABIwWeQarc%2BfOKioq0pIlSzwBaezYscrLy1NoaKhWrlyp/Px8PfbYY4qPj9fixYv1s5/9TJJ0zTXX6IEHHtCMGTN0/PhxpaSkqKioSO3atfPxqgAAQKAIcl/oHd1oEofjpPF92mzBGl6wy/h%2Bm8vGGVf5uoRmZ7MFKyYmTE5nDafX/Qh98T/0xD%2B1xL506hThk%2BO2yEuEAAAAvkTAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADDM5usC0Hrc8Os/%2B7qEH2XjjKt8XQIAIEBxBgsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsBqREVFhe6%2B%2B26lpaXp2muv1eLFi9XQ0ODrsgAAQIDgOViNuO%2B%2B%2B5SUlKStW7fq%2BPHjmjp1qjp27Khf/OIXvi4NAAAEAALWd9jtdh04cEAvvPCCIiIiFBERocmTJ%2BvFF18kYLUygfZg1EDCQ1wBtHQErO8oKytTfHy8oqKiPGNJSUk6fPiwqqurFR4e/oP7qKyslMPh8Bqz2dorLi7OaK0hIVzhRWAKtPD6x1lX%2B7qEFufs7y9%2Bj/kX%2BmIOAes7XC6XIiMjvcbOhi2n09mkgFVcXKzCwkKvsXvvvVf33XefuUL1bZC74%2BJPlZWVZTy84fxUVlaquLiYnvgZ%2BuJ/Kisr9eKLv6Enfoa%2BmENEbYTb7b6g12dlZWnNmjVeX1lZWYaq%2ByeHw6HCwsJzzpbBd%2BiJf6Iv/oee%2BCf6Yg5nsL4jNjZWLpfLa8zlcikoKEixsbFN2kdcXBzJHwCAVowzWN%2BRnJyso0eP6sSJE54xu92uHj16KCwszIeVAQCAQEHA%2Bo7ExESlpKRoyZIlqq6uVnl5uV544QVlZ2f7ujQAABAgQhYuXLjQ10X4m6uvvlrr16/X448/rjfffFOZmZmaMmWKgoKCfF3aOcLCwjRo0CDOrvkReuKf6Iv/oSf%2Bib6YEeS%2B0Du6AQAA4IVLhAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbACUEVFhe6%2B%2B26lpaXp2muv1eLFi9XQ0ODrslqlXbt2KT09XXl5eefMbdiwQaNHj1a/fv00btw4vf322z6osPWpqKhQTk6O0tLSlJ6erjlz5qiqqkqStH//ft12220aMGCARowYoeeff97H1bYOBw4c0B133KEBAwYoPT1dM2bMkMPhkCTt2bNHmZmZ6t%2B/v0aNGqW1a9f6uNrW58knn1Tv3r09/6YnhrgRcMaOHet%2B5JFH3FVVVe7Dhw%2B7R4wY4X7%2B%2Bed9XVarU1RU5B4xYoR74sSJ7hkzZnjN7du3z52cnOzesWOH%2B9SpU%2B433njD3bdvX/fRo0d9VG3rcdNNN7nnzJnjrq6udh89etQ9btw499y5c921tbXuq6%2B%2B2r1s2TJ3TU2Nu7S01D1o0CD35s2bfV1yi3b69Gn3lVde6S4sLHSfPn3affz4cfdtt93mnj59uvuLL75wX3bZZe6SkhL3qVOn3H/%2B85/dqamp7o8//tjXZbca%2B/btcw8aNMjdq1cvt9vtpicGcQYrwNjtdh04cECzZs1SRESEEhISNHnyZBUXF/u6tFYnNDRUq1evVteuXc%2BZKykpUUZGhjIyMhQaGqoxY8aoV69evBNsZlVVVUpOTtbMmTMVFhamiy%2B%2BWGPHjtV7772nHTt26MyZM5o2bZrat2%2BvpKQkjR8/np%2BdZlZbW6u8vDxNnTpVbdu2VWxsrIYPH65PP/1U69atU0JCgjIzMxUaGqr09HQNHTpUJSUlvi67VWhoaNCCBQs0efJkzxg9MYeAFWDKysoUHx%2BvqKgoz1hSUpIOHz6s6upqH1bW%2BkyaNEkRERGNzpWVlSkxMdFrLDExUXa73YrSWq3IyEgtWrRIHTt29IwdPXpUcXFxKisrU%2B/evRUSEuKZS0xMVGlpqS9KbTWioqI0fvx42Ww2SdKhQ4f02muv6YYbbvi3Pyf0xBqvvPKKQkNDNXr0aM8YPTGHgBVgXC6XIiMjvcbOhi2n0%2BmLktAIl8vlFYKlb/tEj6xlt9v10ksvadq0aY3%2B7ERHR8vlcnEPowUqKiqUnJysG2%2B8USkpKcrNzf23PeHnpPl9%2BeWXWrZsmRYsWOA1Tk/MIWAFILfb7esS0AT0ybfef/99TZkyRTNnzlR6evq/3S4oKMjCqlqv%2BPh42e12bdq0SUeOHNFDDz3k65JatUWLFmncuHHq0aOHr0tpsQhYASY2NlYul8trzOVyKSgoSLGxsT6qCt8VExPTaJ/okTW2bdumu%2B%2B%2BW3PnztWkSZMkffuz89134S6XS9HR0QoO5lehFYKCgpSQkKC8vDytX79eNpvtnJ8Tp9PJz0kz27Nnjz788EPl5OScM9fY7y56cn74rRJgkpOTdfToUZ04ccIzZrfb1aNHD4WFhfmwMvyr5OTkc%2B5ZsNvt6tu3r48qaj0%2B%2BOADzZ49W88884xuueUWz3hycrI%2B%2BeQT1dXVecboSfPbs2ePRo4c6XUZ9mygTU1NPefnpLS0lJ40s7Vr1%2Br48eO69tprlZaWpnHjxkmS0tLS1KtXL3piCAErwCQmJiolJUVLlixRdXW1ysvL9cILLyg7O9vXpeFfTJgwQbt379aOHTt0%2BvRprV69WkeOHNGYMWN8XVqLVldXp0ceeUSzZs3S4MGDveYyMjIUHh6uFStWqLa2Vnv37tXq1av52WlmycnJqq6u1uLFi1VbW6sTJ05o2bJluvzyy5Wdna2KigqVlJTo9OnT2rlzp3bu3KkJEyb4uuwWbc6cOdq8ebPeeOMNvfHGGyoqKpIkvfHGGxo9ejQ9MSTIzY0iAefYsWOaP3%2B%2B/vKXvyg8PFwTJ07Uvffey70kFktJSZEkzxmRs38ldfYvBbds2aIlS5aooqJCPXr00Lx58zRw4EDfFNtKvPfee/rP//xPtW3b9py5TZs2qaamRgsWLFBpaak6duyou%2B66Sz//%2Bc99UGnr8sknnyg/P18ff/yx2rdvryuuuEJz5sxR586d9de//lX5%2BfkqLy9XfHy8Zs6cqREjRvi65Fbl73//u4YNG6ZPPvlEkuiJIQQsAAAAw7hECAAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACG/T%2BHiFyQ4QZNGgAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-3921542984314459742”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.1404494382022472</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.4316546762589928</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.19880715705765406</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.1342281879194631</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.21645021645021645</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5454545454545455</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.060441220912662436</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.15552099533437014</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.122888771420214</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3094)</td> <td class=”number”>3094</td> <td class=”number”>96.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-3921542984314459742”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.010123506782749543</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.023629489603024575</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.025691530358825036</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.025886616619207874</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>30.251449523781133</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>31.738474957370784</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>32.99657863853409</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>35.43457497612225</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>43.014686211914096</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_NHBLACK10”>PCT_NHBLACK10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3094</td>

</tr> <tr>

<th>Unique (%)</th> <td>96.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>8.7707</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>85.439</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>1.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram8618050182013283984”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives8618050182013283984,#minihistogram8618050182013283984”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives8618050182013283984”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles8618050182013283984”

aria-controls=”quantiles8618050182013283984” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram8618050182013283984” aria-controls=”histogram8618050182013283984”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common8618050182013283984” aria-controls=”common8618050182013283984”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme8618050182013283984” aria-controls=”extreme8618050182013283984”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles8618050182013283984”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.12959</td>

</tr> <tr>

<th>Q1</th> <td>0.40259</td>

</tr> <tr>

<th>Median</th> <td>1.9089</td>

</tr> <tr>

<th>Q3</th> <td>9.7899</td>

</tr> <tr>

<th>95-th percentile</th> <td>41.825</td>

</tr> <tr>

<th>Maximum</th> <td>85.439</td>

</tr> <tr>

<th>Range</th> <td>85.439</td>

</tr> <tr>

<th>Interquartile range</th> <td>9.3873</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>14.438</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.6462</td>

</tr> <tr>

<th>Kurtosis</th> <td>5.207</td>

</tr> <tr>

<th>Mean</th> <td>8.7707</td>

</tr> <tr>

<th>MAD</th> <td>10.204</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.2916</td>

</tr> <tr>

<th>Sum</th> <td>27470</td>

</tr> <tr>

<th>Variance</th> <td>208.46</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram8618050182013283984”>

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nuwkpKSNGvWLN%2BvtpGk//iP/9C9994rj8dj9TgAAADGWR5YxcXFOn36tAYMGOBbGz16tFpbW7Vs2TKrxwEAADDO8rcI33rrLW3ZskVxcXG%2BtcTERJWUlOjmm2%2B2ehwAAADjLL%2BC1djYqIiIiLaD2GxqaGiwehwAAADjLA%2Bs4cOHa9myZfrqq698a1988YUee%2Bwxv49qAAAACFWWv0W4YMEC/dM//ZN%2B%2BctfKioqSq2traqvr1ffvn31hz/8wepxAAAAjLM8sPr27atXX31Vb775pj777DPZbDb1799fo0aNUnh4uNXjAAAAGGd5YElS165ddcMNNwTipQEAAC46ywPr%2BPHjWrFihT7%2B%2BGM1Nja2Of7GG29YPRIAAIBRAbkHq7q6WqNGjVK3bt2sfnkAAICLzvLAKi8v1xtvvCGHw2H1SwMAAFjC8o9p6NGjB1euAABAh2Z5YN1///1avXq1vF6v1S8NAABgCcvfInzzzTf1/vvva9OmTbrssstks/k33osvvmj1SAAAAEZZHlhRUVH61a9%2BZfXLAgAAWMbywCouLrb6JQEAACxl%2BT1YkvTJJ59o1apVevjhh31rH3zwQSBGAQAAMM7ywDpw4ICmTJminTt3auvWrZK%2B%2BfDRGTNm8CGjAACgQ7A8sFauXKnf/va32rJli8LCwiR98/sJly1bpjVr1lg9DgAAgHGWB9b//u//KicnR5J8gSVJEyZMkMvlsnocAAAA4ywPrO7du5/zdxBWV1era9euVo8DAABgnOWBNXToUD3%2B%2BOOqq6vzrR07dkxFRUX65S9/afU4AAAAxln%2BMQ0PP/yw7rrrLqWnp6ulpUVDhw5VQ0ODBg4cqGXLllk9DgAAgHGWB1bv3r21detW7du3T8eOHdMll1yi/v37a%2BTIkX73ZAEAAIQqywNLkrp06aIbbrghEC8NAABw0VkeWGPGjPnRK1V8FhYAAAh1lgfWxIkT/QKrpaVFx44dk9Pp1F133WX1OAAAAMZZHliFhYXnXH/ttdf0zjvvWDwNAACAeQH5XYTncsMNN%2BjVV18N9BgAAADtFjSBdejQIXm93kCPAQAA0G6Wv0U4ffr0NmsNDQ1yuVwaN26c1eMAAAAYZ3lgJSYmtvkpwoiICGVlZem2226zehwAAADjLA8sPq0dAAB0dJYH1ssvv3zej7311lsv4iQAAAAXh%2BWB9cgjj6i1tbXNDe1hYWF%2Ba2FhYQQWAAAISZYH1u9//3utW7dOs2bNUlJSkrxerz766CM9%2B%2ByzuuOOO5Senm71SAAAAEYF5B6s0tJSJSQk%2BNauvfZa9e3bVzNnztTWrVutHgkAAMAoyz8H69NPP1VMTEyb9ejoaFVWVlo9DgAAgHGWB1afPn20bNky1dTU%2BNZqa2u1YsUKXX755VaPAwAAYJzlbxEuWLBABQUFKisrU2RkpGw2m%2Brq6nTJJZdozZo1Vo8DAABgnOWBNWrUKO3du1f79u1TVVWVvF6vEhISdN1116l79%2B5WjwMAAGCc5YElSZdeeqnGjh2rqqoq9e3bNxAjAAAAXDSW34PV2NiooqIiXXPNNbrpppskfXMP1j333KPa2lqrxwEAADDO8sBavny5Dh8%2BrJKSEtls///lW1paVFJSYvU4AAAAxlkeWK%2B99pr%2B/d//XRMmTPD90ufo6GgVFxdr586dVo8DAABgnOWBVV9fr8TExDbrDodDX3/9tdXjAAAAGGd5YF1%2B%2BeV65513JMnvdw/u2LFDf//3f2/1OAAAAMZZHli/%2Bc1vNGfOHD3xxBNqbW3Vc889p4KCAi1YsEB33XXXz3qu/fv3KyMjQ/n5%2BW2Obdu2TZMnT9Y111yjadOm6a233vIda21t1cqVKzV27FgNHz5cM2fO1PHjx33HPR6P5s2bp4yMDI0aNUqPPPKIGhsbL/ykAQBAp2J5YGVnZ6uoqEhvv/22wsPD9fTTT6uyslIlJSXKyck57%2Bd59tlntXTpUvXr16/NscOHD6uoqEiFhYV6%2B%2B23lZubqwcffFBVVVWSpBdeeEFbtmxRaWmp9uzZo8TEROXl5fmuqC1atEgNDQ3aunWrXnrpJblcLm7ABwAA583ywDp16pR%2B/etf67/%2B67908OBBvfPOO3rxxRc1YcKEn/U8ERER2rhx4zkDa8OGDcrMzFRmZqYiIiI0ZcoUDRo0SJs3b5YklZWVKTc3V1deeaWioqKUn58vl8ulgwcP6ssvv9SuXbuUn58vh8OhhIQEPfDAA3rppZfU1NRk5P8DAADQsVn%2BQaNjx47V%2B%2B%2B/7/sJwgs1Y8aMHzxWUVGhzMxMv7Xk5GQ5nU41Njbq6NGjSk5O9h2LiopSv3795HQ6dfr0aYWHhyspKcl3PCUlRV9//bU%2B%2BeQTv/UfUl1dLbfb7bdmt3dTfHz8%2BZ7eeQkPt7yP28VuD615L9R3%2BxJq%2B9PRsS/BiX0JTuxL%2B1keWOnp6dq%2BfbsmTpx40V7D4/EoJibGby0mJkZHjx7VV199Ja/Xe87jNTU1io2NVVRUlF8AfvfYv/0F1T%2BmrKxMq1ev9lvLy8vT3LlzL%2BR0Ooy4uMhAj2Cp6OhLAz0CzoF9CU7sS3BiXy6c5YH1d3/3d/rXf/1XlZaW6vLLL1eXLl38jq9YscLI6/ztTyj%2B3OM/9bU/JTs7W2PGjPFbs9u7qaamvl3P%2B32h9p2F6fMPVuHhNkVHX6ra2ga1tLQGehx8i30JTuxLcOpI%2BxKob%2B4tD6yjR4/qiiuukHT%2BV4R%2Brri4OHk8Hr81j8cjh8Oh2NhY2Wy2cx7v0aOHHA6H6urq1NLSovDwcN8xSerRo8d5vX58fHybtwPd7tNqbg7tf0nbq7Odf0tLa6c751DAvgQn9iU4sS8XzrLAys/P18qVK/WHP/zBt7ZmzRrl5eUZf63U1FSVl5f7rTmdTk2aNEkREREaOHCgKioqNGLECEnf/C7Ezz77TEOGDFGfPn3k9Xp15MgRpaSk%2BL42Ojpa/fv3Nz4rAADoeCx7j2n37t1t1kpLSy/Ka91%2B%2B%2B36y1/%2Bor179%2BrMmTPauHGjPv30U02ZMkWSlJOTo/Xr18vlcqmurk4lJSUaPHiw0tLS5HA4NH78eD355JM6deqUqqqqtGbNGmVlZclut/yCHwAACEGWFcO57mtqz71OaWlpkqTm5mZJ0q5duyR9c7Vp0KBBKikpUXFxsSorKzVgwAA988wz6tWrlyRp%2BvTpcrvduvPOO1VfX6/09HS/m9J/97vfafHixRo7dqy6dOmim2%2B%2B%2BZwfZgoAAHAulgXWuT6WoT0f1eB0On/0%2BLhx4zRu3LgfnGXu3Lk/%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%2BerPfee0%2BzZ89W//79lZaWpt27d2vVqlX6/e9/r6SkJK1fv16zZs3Szp071a1btwCdCQAACCUd9grWD9myZYsSExOVlZWliIgIZWRkaMyYMdqwYYMkqaysTNOmTdNVV12lSy65RPfcc48kac%2BePYEcGwAAhJAOfQVrxYoV%2BuCDD1RXV6ebbrpJ8%2BfPV0VFhZKTk/0el5ycrO3bt0uSKioqNHHiRN8xm82mwYMHy%2Bl0atKkSef1utXV1XK73X5rdns3xcfHt/OM/IWHh1Yf2%2B2hNe%2BF%2Bm5fQm1/Ojr2JTixL8GJfWm/DhtYV199tTIyMvTEE0/o%2BPHjmjdvnh577DF5PB4lJCT4PTY2NlY1NTWSJI/Ho5iYGL/jMTExvuPno6ysTKtXr/Zby8vL09y5cy/wbDqGuLjIQI9gqejoSwM9As6BfQlO7EtwYl8uXIcNrLKyMt//vvLKK1VYWKjZs2dr2LBhP/m1Xq%2B3Xa%2BdnZ2tMWPG%2BK3Z7d1UU1Pfruf9vlD7zsL0%2BQer8HCboqMvVW1tg1paWgM9Dr7FvgQn9iU4daR9CdQ39x02sL7vsssuU0tLi2w2mzwej9%2BxmpoaORwOSVJcXFyb4x6PRwMHDjzv14qPj2/zdqDbfVrNzaH9L2l7dbbzb2lp7XTnHArYl%2BDEvgQn9uXChdYlkPN06NAhLVu2zG/N5XKpa9euyszMbPOxC%2BXl5brqqqskSampqaqoqPAda2lp0aFDh3zHAQAAfkqHDKwePXqorKxMpaWlOnv2rI4dO6annnpK2dnZuuWWW1RZWakNGzbozJkz2rdvn/bt26fbb79dkpSTk6OXX35Zf/3rX9XQ0KC1a9eqa9euGj16dGBPCgAAhIwO%2BRZhQkKCSktLtWLFCl8gTZ06Vfn5%2BYqIiNAzzzyjpUuX6rHHHlOfPn20fPly/eIXv5Ak/epXv9JDDz2kefPm6eTJk0pLS1NpaakuueSSAJ8VAAAIFWHe9t7RjfPidp82/px2u003luw3/rwXy/Z5IwM9giXsdpvi4iJVU1PPvQtBhH0JTuxLcOpI%2B9KrV/eAvG6HfIsQAAAgkAgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAw%2ByBHgCdx01P/jnQI/ws2%2BeNDPQIAIAQxRUsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAw%2ByBHgAIVjc9%2BedAj/CzbJ83MtAjAAC%2BxRUsAAAAwwgsAAAAwwgsAAAAwwgsAAAAwwgsAAAAw/gpQqCDCKWfeuQnHgF0dFzBAgAAMIzAAgAAMIzAAgAAMIx7sABYLpTuF5O4ZwzAz8cVLAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMP4KUIA%2BAn81COAn4srWAAAAIYRWAAAAIbxFiEAdDCh9Jbm64XXBXoE4KLgCtY5VFZW6r777lN6erquv/56LV%2B%2BXK2trYEeCwAAhAiuYJ3DnDlzlJKSol27dunkyZO6//771bNnT919992BHg0AOpQbS/YHeoQOjR94CByuYH2P0%2BnUkSNHVFhYqO7duysxMVG5ubkqKysL9GgAACBEcAXreyoqKtSnTx/FxMT41lJSUnTs2DHV1dUpKirqJ5%2Bjurpabrfbb81u76b4%2BHijs4aH08cAgB/G/XiBQ2B9j8fjUXR0tN/ad7FVU1NzXoFVVlam1atX%2B609%2BOCDmjNnjrlB9U3I3dX7Y2VnZxuPN1y46upqlZWVsS9Bhn0JTuxLcGJf2o9LIOfg9Xrb9fXZ2dnatGmT35/s7GxD0/1/brdbq1evbnO1DIHFvgQn9iU4sS/BiX1pP65gfY/D4ZDH4/Fb83g8CgsLk8PhOK/niI%2BPp/gBAOjEuIL1PampqTpx4oROnTrlW3M6nRowYIAiIyMDOBkAAAgVBNb3JCcnKy0tTStWrFBdXZ1cLpeee%2B455eTkBHo0AAAQIsIfffTRRwM9RLC57rrrtHXrVi1ZskSvvvqqsrKyNHPmTIWFhQV6tDYiIyM1YsQIrq4FGfYlOLEvwYl9CU7sS/uEedt7RzcAAAD88BYhAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQRWCKqsrNR9992n9PR0XX/99Vq%2BfLlaW1tYHAImAAAGZ0lEQVQDPVanVFlZqby8PKWnpysjI0Pz589XbW2tJOnw4cO64447NGzYMI0bN07r1q0L8LSd0%2BOPP66kpCTfPx84cEBZWVkaOnSoJk2apM2bNwdwus5n7dq1GjVqlK6%2B%2Bmrl5ubq888/l8S%2BBNKhQ4c0Y8YMXXvttRo5cqQKCwt16tQpSexLu3gRcqZOnepduHCht7a21nvs2DHvuHHjvOvWrQv0WJ3SzTff7J0/f763rq7Oe%2BLECe%2B0adO8CxYs8DY0NHivu%2B4676pVq7z19fXe8vJy74gRI7yvvfZaoEfuVA4dOuQdMWKEd9CgQV6v1%2Bv94osvvFdffbV3w4YN3sbGRu%2Bf//xn75AhQ7wffvhhgCftHJ5//nnvhAkTvC6Xy3v69GnvkiVLvEuWLGFfAqipqck7cuRI74oVK7xnzpzxnjp1ynv33Xd758yZw760E1ewQozT6dSRI0dUWFio7t27KzExUbm5uSorKwv0aJ1ObW2tUlNTVVBQoMjISPXu3VtTp07Vu%2B%2B%2Bq71796qpqUmzZ89Wt27dlJKSottuu419slBra6sWL16s3Nxc39qWLVuUmJiorKwsRUREKCMjQ2PGjNGGDRsCN2gnsm7dOuXn5%2BuKK65QVFSUFi5cqIULF7IvAeR2u%2BV2u3XLLbeoa9euiouL04033qjDhw%2BzL%2B1EYIWYiooK9enTRzExMb61lJQUHTt2THV1dQGcrPOJjo5WcXGxevbs6Vs7ceKE4uPjVVFRoaSkJIWHh/uOJScnq7y8PBCjdkovvviiIiIiNHnyZN9aRUWFkpOT/R7Hvljjiy%2B%2B0Oeff66vvvpKEydOVHp6uubOnatTp06xLwGUkJCgwYMHq6ysTPX19Tp58qR27typ0aNHsy/tRGCFGI/Ho%2BjoaL%2B172KrpqYmECPhW06nU88//7xmz559zn2KjY2Vx%2BPhfjkLfPnll1q1apUWL17st/5D%2B8LfnYuvqqpKkrRjxw4999xzeuWVV1RVVaWFCxeyLwFks9m0atUqvfHGGxo6dKgyMjLU3NysgoIC9qWdCKwQ5PV6Az0Cvue9997TzJkzVVBQoIyMjB98XFhYmIVTdV7FxcWaNm2aBgwYEOhR8K3v/rt1zz33KCEhQb1799acOXO0e/fuAE/WuZ09e1azZs3ShAkT9O677%2BrNN99U9%2B7dVVhYGOjRQh6BFWIcDoc8Ho/fmsfjUVhYmBwOR4Cm6tx2796t%2B%2B67TwsWLNCMGTMkfbNP3/8uz%2BPxKDY2VjYbf%2B0upgMHDuiDDz5QXl5em2NxcXFt/v7U1NTwd8cC372V/rdXRPr06SOv16umpib2JUAOHDigzz//XA899JC6d%2B%2BuhIQEzZ07V6%2B//rpsNhv70g78lz7EpKam6sSJE74foZW%2BeWtqwIABioyMDOBkndP777%2BvoqIiPfXUU7r11lt966mpqfroo4/U3NzsW3M6nbrqqqsCMWansnnzZp08eVLXX3%2B90tPTNW3aNElSenq6Bg0a1Ob%2BkfLycvbFAr1791ZUVJQOHz7sW6usrFSXLl2UmZnJvgRIS0uLWltb/d4ZOXv2rCQpIyODfWkHAivEJCcnKy0tTStWrFBdXZ1cLpeee%2B455eTkBHq0Tqe5uVkLFy5UYWGhRo0a5XcsMzNTUVFRWrt2rRoaGnTw4EFt3LiRfbLA/Pnz9dprr%2BmVV17RK6%2B8otLSUknSK6%2B8osmTJ6uyslIbNmzQmTNntG/fPu3bt0%2B33357gKfu%2BOx2u7KysvT000/r//7v/3Ty5EmtWbNGkydP1tSpU9mXALnmmmvUrVs3rVq1Sg0NDaqpqdHatWs1fPhw3XLLLexLO4R5uaEn5FRVVWnRokX67//%2Bb0VFRWn69Ol68MEHub/HYu%2B%2B%2B67%2B8R//UV27dm1zbMeOHaqvr9fixYtVXl6unj176t5779VvfvObAEzauX3%2B%2BecaO3asPvroI0nS//zP/2jp0qVyuVzq06ePCgoKNG7cuABP2TmcPXtWxcXFevXVV9XU1KTx48dr0aJFioyMZF8CqLy8XE888YSOHDmirl27asSIEZo/f74SEhLYl3YgsAAAAAzjLUIAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADD/h%2BRJU9O4/cmigAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common8618050182013283984”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.14534883720930233</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.09633911368015415</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.17905102954341987</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.07042253521126761</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.08743806470416789</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.20920502092050208</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>41.09359530881121</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.26232741617357</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.3512805773775308</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3083)</td> <td class=”number”>3083</td> <td class=”number”>96.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme8618050182013283984”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.015015015015015015</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.01720282126268708</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.022568269013766643</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.023512814483893724</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>81.24930901050304</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>82.18814096587731</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>82.95134909886447</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>84.04831320283215</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>85.43877815169557</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_NHNA10”>PCT_NHNA10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3117</td>

</tr> <tr>

<th>Unique (%)</th> <td>97.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1.7811</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>86.319</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram3101579661894575355”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives3101579661894575355,#minihistogram3101579661894575355”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives3101579661894575355”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles3101579661894575355”

aria-controls=”quantiles3101579661894575355” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram3101579661894575355” aria-controls=”histogram3101579661894575355”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common3101579661894575355” aria-controls=”common3101579661894575355”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme3101579661894575355” aria-controls=”extreme3101579661894575355”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles3101579661894575355”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.1092</td>

</tr> <tr>

<th>Q1</th> <td>0.19496</td>

</tr> <tr>

<th>Median</th> <td>0.30497</td>

</tr> <tr>

<th>Q3</th> <td>0.62471</td>

</tr> <tr>

<th>95-th percentile</th> <td>6.8275</td>

</tr> <tr>

<th>Maximum</th> <td>86.319</td>

</tr> <tr>

<th>Range</th> <td>86.319</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.42975</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>7.1799</td>

</tr> <tr>

<th>Coef of variation</th> <td>4.0313</td>

</tr> <tr>

<th>Kurtosis</th> <td>72.445</td>

</tr> <tr>

<th>Mean</th> <td>1.7811</td>

</tr> <tr>

<th>MAD</th> <td>2.4846</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>7.9989</td>

</tr> <tr>

<th>Sum</th> <td>5578.3</td>

</tr> <tr>

<th>Variance</th> <td>51.551</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram3101579661894575355”>

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%2BfLkyMjKUkZGhiIgIDRs2TB07dtTq1aslSYWFhcrKylK7du0UFRWl3NxclZSUaNeuXYFcMgAACCEhGbAcDofmzJmj5s2b%2B8YOHjyohIQEFRcXq1OnTgoPD/fNJScnq6ioSJJUXFys5ORkv%2BMlJyfL6XTq%2BPHj2rdvn998VFSU2rRpI6fTeYlXBQAA6gt7oAswwel06pVXXtHixYu1fv16ORwOv/nY2Fh5PB7V1tbK4/EoJibGbz4mJkb79u3T0aNH5fV6zznvdrvrXE9ZWZlcLpffmN3eVAkJCee5sh8XHh5a%2BdhuD616L8TpnoRab%2BozehJ86EnwoSfmhXzA%2BuijjzRx4kRNmTJF6enpWr9%2B/TlfFxYW5vv3n9pPdbH7rQoLC7Vw4UK/sezsbOXk5FzUcUNdXFxkoEuwjMNxWaBLwBnoSfChJ8GHnpgT0gFry5YteuSRRzRz5kzddtttkqT4%2BHh99dVXfq/zeDyKjY2VzWZTXFycPB7PWfPx8fG%2B15xrvlmzZnWua/To0erXr5/fmN3eVG535Xms7qeF2icN0%2BsPRuHhNjkcl6m8vEo1NbWBLgeiJ8GIngSf%2BtyTQH24D9mA9fHHH2vq1Kl64YUX1KdPH994SkqK/vSnP6m6ulp2%2Bw/Lczqd6tatm2/%2B9H6s05xOp4YMGaKIiAh16NBBxcXF6t27t6QfNtR//fXX6tq1a51rS0hIOOt2oMt1TNXV9eub9nw1pPXX1NQ2qPWGAnoSfOhJ8KEn5oTWJZD/V11drRkzZigvL88vXElSRkaGoqKitHjxYlVVVWnXrl1asWKFMjMzJUmjRo3SBx98oG3btunEiRNasWKFvvrqKw0bNkySlJmZqWXLlqmkpEQVFRXKz89X586dlZqaavk6AQBAaArJK1iffvqpSkpKNHv2bM2ePdtvbsOGDXrxxRc1a9YsFRQUqHnz5srNzVXfvn0lSR07dlR%2Bfr7mzJmj0tJStW/fXkuWLFGLFi0kSWPGjJHL5dJdd92lyspKpaWlnbWfCgAA4MeEeXmCpiVcrmPGj2m32zQg/z3jx71U1k%2B%2BNtAlXHJ2u01xcZFyuyu5zB4k6EnwoSfBpz73pEWL6ICcNyRvEQIAAAQzAhYAAIBhBCwAAADDCFgAAACGWR6w%2BvXrp4ULF%2BrgwYNWnxoAAMASlges22%2B/XevWrdONN96oe%2B%2B9V5s2bVJ1dbXVZQAAAFwylges7OxsrVu3Tq%2B//ro6dOigZ599VhkZGZo7d672799vdTkAAADGBWwPVpcuXTR16lRt3bpVjz/%2BuF5//XUNHjxY48aN02effRaosgAAAC5awALWqVOntG7dOt13332aOnWqEhMT9dhjj6lz587KysrSmjVrAlUaAADARbH8V%2BWUlJRoxYoVevPNN1VZWamBAwfqD3/4g3r27Ol7Ta9evfTkk09q6NChVpcHAABw0SwPWEOGDFHbtm01fvx43XbbbYqNjT3rNRkZGTpy5IjVpQEAABhhecBatmyZevfu/ZOv27VrlwXVAAAAmGf5HqxOnTppwoQJ2rx5s2/sf/7nf3TffffJ4/FYXQ4AAIBxlgesOXPm6NixY2rfvr1vrG/fvqqtrdVzzz1ndTkAAADGWX6L8P3339eaNWsUFxfnG0tKSlJ%2Bfr5uueUWq8sBAAAwzvIrWMePH1dERMTZhdhsqqqqsrocAAAA4ywPWL169dJzzz2no0eP%2Bsa%2B/fZbPfXUU36PagAAAAhVlt8ifPzxx/WrX/1Kv/jFLxQVFaXa2lpVVlaqdevW%2BuMf/2h1OQAAAMZZHrBat26tt956S%2B%2B%2B%2B66%2B/vpr2Ww2tW3bVn369FF4eLjV5QAAABhnecCSpMaNG%2BvGG28MxKkBAAAuOcsD1oEDBzRv3jx98cUXOn78%2BFnz77zzjtUlAQAAGBWQPVhlZWXq06ePmjZtavXpAQAALjnLA1ZRUZHeeecdxcfHW31qAAAAS1j%2BmIZmzZpx5QoAANRrlges8ePHa%2BHChfJ6vVafGgAAwBKW3yJ899139fHHH2vlypW6/PLLZbP5Z7zXXnvN6pIAAACMsjxgRUVF6frrr7f6tAAAAJaxPGDNmTPH6lMCAABYyvI9WJL05ZdfasGCBXrsscd8Y5988kkgSgEAADDO8oC1Y8cODRs2TJs2bdLatWsl/fDw0bFjx/KQUQAAUC9YHrDmz5%2BvRx55RGvWrFFYWJikH34/4XPPPadFixZZXQ4AAIBxlgesv//978rMzJQkX8CSpEGDBqmkpMTqcgAAAIyzPGBFR0ef83cQlpWVqXHjxlaXAwAAYJzlAatHjx569tlnVVFR4Rvbv3%2B/pk6dql/84hdWlwMAAGCc5Y9peOyxx3T33XcrLS1NNTU16tGjh6qqqtShQwc999xzVpcDAABgnOUBq2XLllq7dq22b9%2Bu/fv3q0mTJmrbtq2uvfZavz1ZAAAAocrygCVJjRo10o033hiIUwMAAFxylgesfv36/eiVKp6FBQAAQp3lAWvw4MF%2BAaumpkb79%2B%2BX0%2BnU3XffbXU5AAAAxlkesPLy8s45vnHjRv31r3%2B1uBoAAADzAvK7CM/lxhtv1FtvvXVe73nvvfeUnp6u3Nxcv/GVK1fqyiuvVGpqqt/XZ599Jkmqra3V/Pnz1b9/f/Xq1Uvjxo3TgQMHfO/3eDyaPHmy0tPT1adPH02fPv2cz%2B4CAAA4l6AJWLt375bX663z61966SXNnj1bbdq0Oed8r1695HQ6/b66du0qSXr11Ve1Zs0aFRQUaOvWrUpKSlJ2drbv/DNnzlRVVZXWrl2rN954QyUlJcrPz7/4RQIAgAbB8luEY8aMOWusqqpKJSUluummm%2Bp8nIiICK1YsULPPPOMTpw4cV41FBYWKisrS%2B3atZMk5ebmKi0tTbt27dLll1%2BuzZs3689//rPi4%2BMlSQ888IAeeughTZ06VY0aNTqvcwEAgIbH8oCVlJR01k8RRkREaOTIkbrjjjvqfJyxY8f%2B6PzBgwd1zz33qKioSA6HQzk5Obr11lt1/Phx7du3T8nJyb7XRkVFqU2bNnI6nTp27JjCw8PVqVMn33yXLl30/fff68svv/QbBwAAOBfLA5YVT2uPj49XUlKSHn74YbVv315vv/22Hn30USUkJOiKK66Q1%2BtVTEyM33tiYmLkdrsVGxurqKgovxB4%2BrVut7tO5y8rK5PL5fIbs9ubKiEh4SJX5i88PGju8NaJ3R5a9V6I0z0Jtd7UZ/Qk%2BNCT4ENPzLM8YL355pt1fu1tt912Qefo27ev%2Bvbt6/vzkCFD9Pbbb2vlypW%2Bn2L8sf1e57MX7FwKCwu1cOFCv7Hs7Gzl5ORc1HFDXVxcZKBLsIzDcVmgS8AZ6EnwoSfBh56YY3nAmj59umpra88KMWFhYX5jYWFhFxywzqVVq1YqKipSbGysbDabPB6P37zH41GzZs0UHx%2BviooK1dTUKDw83DcnSc2aNavTuUaPHq1%2B/fr5jdntTeV2VxpYyT%2BF2icN0%2BsPRuHhNjkcl6m8vEo1NbWBLgeiJ8GIngSf%2BtyTQH24tzxg/e53v9PSpUs1YcIEderUSV6vV59//rleeukl3XnnnUpLS7voc/zpT39STEyMBg8e7BsrKSlR69atFRERoQ4dOqi4uFi9e/eWJJWXl%2Bvrr79W165d1apVK3m9Xu3du1ddunSRJDmdTjkcDrVt27ZO509ISDjrdqDLdUzV1fXrm/Z8NaT119TUNqj1hgJ6EnzoSfChJ%2BYEZA9WQUGBEhMTfWNXX321WrdurXHjxmnt2rUXfY6TJ0/q6aefVuvWrXXllVdq48aNevfdd/X6669LkjIzM1VQUKDrr79eiYmJys/PV%2BfOnZWamipJGjhwoH7zm9/o%2Beef18mTJ7Vo0SKNHDlSdntAfnUjAAAIMZYnhq%2B%2B%2BuqsDeaS5HA4VFpaWufjnA5D1dXVkqTNmzdL%2BuFq09ixY1VZWamHHnpILpdLl19%2BuRYtWqSUlBRJPzwqwuVy6a677lJlZaXS0tL89kz9%2Bte/1qxZs9S/f381atRIt9xyy1kPMwUAAPh3wrwXu6P7PA0ePFi9e/fWQw89pLi4OEk/3KL77W9/q7/97W9atWqVleVYxuU6ZvyYdrtNA/LfM37cS2X95GsDXcIlZ7fbFBcXKbe7ksvsQYKeBB96Enzqc09atIgOyHktv4L1%2BOOPa8qUKSosLFRkZKRsNpsqKirUpEkTLVq0yOpyAAAAjLM8YPXp00fbtm3T9u3bdejQIXm9XiUmJuq6665TdHRgUiYAAIBJAdm1fdlll6l///46dOiQWrduHYgSAAAALhnLH6R0/PhxTZ06Vd27d9fNN98s6Yc9WPfee6/Ky8utLgcAAMA4ywPW3LlztWfPHuXn58tm%2B%2Bfpa2pqlJ%2Bfb3U5AAAAxlkesDZu3Kjf/va3GjRokO/3/TkcDs2ZM0ebNm2yuhwAAADjLA9YlZWVSkpKOms8Pj5e33//vdXlAAAAGGd5wPr5z3%2Buv/71r5L8f6nyhg0b9B//8R9WlwMAAGCc5T9F%2BMtf/lKTJk3S7bffrtraWr388ssqKirSxo0bNX36dKvLAQAAMM7ygDV69GjZ7Xa98sorCg8P14svvqi2bdsqPz9fgwYNsrocAAAA4ywPWEeOHNHtt9%2Bu22%2B/3epTAwAAWMLyPVj9%2B/eXxb/%2BEAAAwFKWB6y0tDStX7/e6tMCAABYxvJbhD/72c/0zDPPqKCgQD//%2Bc/VqFEjv/l58%2BZZXRIAAIBRlgesffv26YorrpAkud1uq08PAABwyVkWsHJzczV//nz98Y9/9I0tWrRI2dnZVpUAAABgCcv2YG3ZsuWssYKCAqtODwAAYBnLAta5fnKQnyYEAAD1kWUB6/Qvdv6pMQAAgFBn%2BWMaAAAA6jsCFgAAgGGW/RThqVOnNGXKlJ8c4zlYAAAg1FkWsHr27KmysrKfHAMAAAh1lgWsf33%2BFQAAQH3GHiwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEhHbDee%2B89paenKzc396y5devWaejQoerevbtGjBih999/3zdXW1ur%2BfPnq3///urVq5fGjRunAwcO%2BOY9Ho8mT56s9PR09enTR9OnT9fx48ctWRMAAAh9IRuwXnrpJc2ePVtt2rQ5a27Pnj2aOnWq8vLy9OGHHyorK0sPPvigDh06JEl69dVXtWbNGhUUFGjr1q1KSkpSdna2vF6vJGnmzJmqqqrS2rVr9cYbb6ikpET5%2BfmWrg8AAISukA1YERERWrFixTkD1vLly5WRkaGMjAxFRERo2LBh6tixo1avXi1JKiwsVFZWltq1a6eoqCjl5uaqpKREu3bt0nfffafNmzcrNzdX8fHxSkxM1AMPPKA33nhDp06dsnqZAAAgBNkDXcCFGjt27L%2BdKy4uVkZGht9YcnKynE6njh8/rn379ik5Odk3FxUVpTZt2sjpdOrYsWMKDw9Xp06dfPNdunTR999/ry%2B//NJv/N8pKyuTy%2BXyG7PbmyohIaGuy6uT8PDQysd2e2jVeyFO9yTUelOf0ZPgQ0%2BCDz0xL2QD1o/xeDyKiYnxG4uJidG%2Bfft09OhReb3ec8673W7FxsYqKipKYWFhfnOS5Ha763T%2BwsJCLVy40G8sOztbOTk5F7KceiMuLjLQJVjG4bgs0CXgDPQk%2BNCT4ENPzKmXAUuSbz/Vhcz/1Ht/yujRo9WvXz%2B/Mbu9qdzuyos67plC7ZOG6fUHo/BwmxyOy1ReXqWamtpAlwPRk2BET4JPfe5JoD7c18uAFRcXJ4/H4zfm8XgUHx%2Bv2NhY2Wy2c843a9ZM8fHxqqioUE1NjcLDw31zktSsWbM6nT8hIeGs24Eu1zFVV9evb9rz1ZDWX1NT26DWGwroSfChJ8GHnpgTWpdA6iglJUVFRUV%2BY06nU926dVNERIQ6dOig4uJi31x5ebm%2B/vprde3aVZ07d5bX69XevXv93utwONS2bVvL1gAAAEJXvQxYo0aN0gcffKBt27bpxIkTWrFihb766isNGzZMkpSZmally5appKREFRUVys/PV%2BfOnZWamqr4%2BHgNHDhQv/nNb3TkyBEdOnRIixYt0siRI2W318sLfgAAwLCQTQypqamSpOrqaknS5s2bJf1wtaljx47Kz8/XnDlzVFpaqvbt22vJkiVq0aKFJGnMmDFyuVy66667VFlZqbTseLelAAAOhUlEQVS0NL9N6b/%2B9a81a9Ys9e/fX40aNdItt9xyzoeZAgAAnEuY92J3dKNOXK5jxo9pt9s0IP8948e9VNZPvjbQJVxydrtNcXGRcrsr2ccQJOhJ8KEnwac%2B96RFi%2BiAnLde3iIEAAAIJAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGBYvQ1YnTp1UkpKilJTU31fTz/9tCRpx44dGjlypHr06KEhQ4Zo9erVfu9dtmyZBg4cqB49eigzM1NFRUWBWAIAAAhR9kAXcClt2LBBl19%2Bud9YWVmZHnjgAU2fPl1Dhw7VRx99pIkTJ6pt27ZKTU3Vli1btGDBAv3ud79Tp06dtGzZMk2YMEGbNm1S06ZNA7QSAAAQSurtFax/Z82aNUpKStLIkSMVERGh9PR09evXT8uXL5ckFRYWasSIEerWrZuaNGmie%2B%2B9V5K0devWQJYNAABCSL2%2BgjVv3jx98sknqqio0M0336xp06apuLhYycnJfq9LTk7W%2BvXrJUnFxcUaPHiwb85ms6lz585yOp0aMmRInc5bVlYml8vlN2a3N1VCQsJFrshfeHho5WO7PbTqvRCnexJqvanP6EnwoSfBh56YV28D1lVXXaX09HQ9//zzOnDggCZPnqynnnpKHo9HiYmJfq%2BNjY2V2%2B2WJHk8HsXExPjNx8TE%2BObrorCwUAsXLvQby87OVk5OzgWupn6Ii4sMdAmWcTguC3QJOAM9CT70JPjQE3PqbcAqLCz0/Xu7du2Ul5eniRMnqmfPnj/5Xq/Xe1HnHj16tPr16%2Bc3Zrc3ldtdeVHHPVOofdIwvf5gFB5uk8NxmcrLq1RTUxvociB6EozoSfCpzz0J1If7ehuwznT55ZerpqZGNptNHo/Hb87tdis%2BPl6SFBcXd9a8x%2BNRhw4d6nyuhISEs24HulzHVF1dv75pz1dDWn9NTW2DWm8ooCfBh54EH3piTmhdAqmj3bt367nnnvMbKykpUePGjZWRkXHWYxeKiorUrVs3SVJKSoqKi4t9czU1Ndq9e7dvHgAA4KfUy4DVrFkzFRYWqqCgQCdPntT%2B/fv1wgsvaPTo0br11ltVWlqq5cuX68SJE9q%2Bfbu2b9%2BuUaNGSZIyMzP15ptv6tNPP1VVVZUWL16sxo0bq2/fvoFdFAAACBn18hZhYmKiCgoKNG/ePF9AGj58uHJzcxUREaElS5Zo9uzZeuqpp9SqVSvNnTtXV155pSTp%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%2BPBhjR8/Xs2bN9c999wT6NIAAEAIIGCdwel0au/evXr55ZcVHR2t6OhoZWVl6Q9/%2BAMBq4FhUz4A4EIRsM5QXFysVq1aKSYmxjfWpUsX7d%2B/XxUVFYqKivrJY5SVlcnlcvmN2e1NlZCQYLTW8HDu8OKfQi0QhpK3864LdAnnZUD%2Be4Euoc74b4vTQu174acQsM7g8XjkcDj8xk6HLbfbXaeAVVhYqIULF/qNPfjgg5o0aZK5QvVDkLu75RcaPXq08fCGC1NWVqbCwkJ6EkQaYk92PjMo0CX8qH/tSVxcZKDLOS/B/t/2QjXEvyeXGpdAzsHr9V7U%2B0ePHq2VK1f6fY0ePdpQdf/kcrm0cOHCs66WIXDoSfChJ8GHngQfemIeV7DOEB8fL4/H4zfm8XgUFham%2BPj4Oh0jISGBTwAAADRgXME6Q0pKig4ePKgjR474xpxOp9q3b6/IyNC6lA0AAAKDgHWG5ORkpaamat68eaqoqFBJSYlefvllZWZmBro0AAAQIsKffPLJJwNdRLC57rrrtHbtWj399NN66623NHLkSI0bN05hYWGBLu0skZGR6t27N1fXggg9CT70JPjQk%2BBDT8wK817sjm4AAAD44RYhAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEErBBUWlqq%2B%2B%2B/X2lpabrhhhs0d%2B5c1dbWBrqsBqe0tFTZ2dlKS0tTenq6pk2bpvLycknSnj17dOedd6pnz5666aabtHTp0gBX2/A8%2B%2Byz6tSpk%2B/PO3bs0MiRI9WjRw8NGTJEq1evDmB1DcvixYvVp08fXXXVVcrKytI333wjiZ4Eyu7duzV27FhdffXVuvbaa5WXl6cjR45IoidGeRFyhg8f7p0xY4a3vLzcu3//fu9NN93kXbp0aaDLanBuueUW77Rp07wVFRXegwcPekeMGOF9/PHHvVVVVd7rrrvOu2DBAm9lZaW3qKjI27t3b%2B/GjRsDXXKDsXv3bm/v3r29HTt29Hq9Xu%2B3337rveqqq7zLly/3Hj9%2B3PuXv/zF27VrV%2B9nn30W4Errv1deecU7aNAgb0lJiffYsWPep59%2B2vv000/TkwA5deqU99prr/XOmzfPe%2BLECe%2BRI0e899xzj3fSpEn0xDCuYIUYp9OpvXv3Ki8vT9HR0UpKSlJWVpYKCwsDXVqDUl5erpSUFE2ZMkWRkZFq2bKlhg8frp07d2rbtm06deqUJk6cqKZNm6pLly6644476JFFamtrNWvWLGVlZfnG1qxZo6SkJI0cOVIRERFKT09Xv379tHz58sAV2kAsXbpUubm5uuKKKxQVFaUZM2ZoxowZ9CRAXC6XXC6Xbr31VjVu3FhxcXEaMGCA9uzZQ08MI2CFmOLiYrVq1UoxMTG%2BsS5dumj//v2qqKgIYGUNi8Ph0Jw5c9S8eXPf2MGDB5WQkKDi4mJ16tRJ4eHhvrnk5GQVFRUFotQG57XXXlNERISGDh3qGysuLlZycrLf6%2BjJpfftt9/qm2%2B%2B0dGjRzV48GClpaUpJydHR44coScBkpiYqM6dO6uwsFCVlZU6fPiwNm3apL59%2B9ITwwhYIcbj8cjhcPiNnQ5bbrc7ECVBP1xZfOWVVzRx4sRz9ig2NlYej4e9cpfYd999pwULFmjWrFl%2B4/%2BuJ/ydubQOHTokSdqwYYNefvllrVq1SocOHdKMGTPoSYDYbDYtWLBA77zzjnr06KH09HRVV1drypQp9MQwAlYI8nq9gS4B/%2BKjjz7SuHHjNGXKFKWnp//b14WFhVlYVcM0Z84cjRgxQu3btw90KdA//1917733KjExUS1bttSkSZO0ZcuWAFfWcJ08eVITJkzQoEGDtHPnTr377ruKjo5WXl5eoEurdwhYISY%2BPl4ej8dvzOPxKCwsTPHx8QGqquHasmWL7r//fj3%2B%2BOMaO3aspB96dOYnPo/Ho9jYWNls/JW7VHbs2KFPPvlE2dnZZ83FxcWd9ffG7Xbzd%2BYSO30L/V%2BvirRq1Uper1enTp2iJwGwY8cOffPNN3r44YcVHR2txMRE5eTk6O2335bNZqMnBvF/%2BxCTkpKigwcP%2Bn6kVvrh9lT79u0VGRkZwMoano8//lhTp07VCy%2B8oNtuu803npKSos8//1zV1dW%2BMafTqW7dugWizAZj9erVOnz4sG644QalpaVpxIgRkqS0tDR17NjxrH0kRUVF9OQSa9mypaKiorRnzx7fWGlpqRo1aqSMjAx6EgA1NTWqra31uxNy8uRJSVJ6ejo9MYiAFWKSk5OVmpqqefPmqaKiQiUlJXr55ZeVmZkZ6NIalOrqas2YMUN5eXnq06eP31xGRoaioqK0ePFiVVVVadeuXVqxYgU9usSmTZumjRs3atWqVVq1apUKCgokSatWrdLQoUNVWlqq5cuX68SJE9q%2Bfbu2b9%2BuUaNGBbjq%2Bs1ut2vkyJF68cUX9Y9//EOHDx/WokWLNHToUA0fPpyeBED37t3VtGlTLViwQFVVVXK73Vq8eLF69eqlW2%2B9lZ4YFOZlQ0/IOXTokGbOnKn//d//VVRUlMaMGaMHH3yQPT4W2rlzp/7zP/9TjRs3Pmtuw4YNqqys1KxZs1RUVKTmzZvrvvvu0y9/%2BcsAVNpwffPNN%2Brfv78%2B//xzSdLf/vY3zZ49WyUlJWrVqpWmTJmim266KcBV1n8nT57UnDlz9NZbb%2BnUqVMaOHCgZs6cqcjISHoSIEVFRXr%2B%2Bee1d%2B9eNW7cWL1799a0adOUmJhITwwiYAEAABjGLUIAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMOz/AC3uqHHJ5KbhAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common3101579661894575355”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5952380952380952</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5189990732159407</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.24096385542168677</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.3019627579265224</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.2098635886673662</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.28776978417266186</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.21337126600284498</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.3770589402659258</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.41265474552957354</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3106)</td> <td class=”number”>3107</td> <td class=”number”>96.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme3101579661894575355”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.014654161781946071</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.020968756552736424</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.027883479858286313</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.028893383415197923</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>82.3744769874477</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>82.61917357314994</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>82.66313508307248</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>83.60113421550095</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>86.31918435289222</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_NHPI10”>PCT_NHPI10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2635</td>

</tr> <tr>

<th>Unique (%)</th> <td>82.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.080475</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>48.889</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>15.1%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-1931205345605329792”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-1931205345605329792,#minihistogram-1931205345605329792”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-1931205345605329792”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-1931205345605329792”

aria-controls=”quantiles-1931205345605329792” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-1931205345605329792” aria-controls=”histogram-1931205345605329792”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-1931205345605329792” aria-controls=”common-1931205345605329792”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-1931205345605329792” aria-controls=”extreme-1931205345605329792”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-1931205345605329792”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0.0099493</td>

</tr> <tr>

<th>Median</th> <td>0.022894</td>

</tr> <tr>

<th>Q3</th> <td>0.046435</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.18141</td>

</tr> <tr>

<th>Maximum</th> <td>48.889</td>

</tr> <tr>

<th>Range</th> <td>48.889</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.036486</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.94721</td>

</tr> <tr>

<th>Coef of variation</th> <td>11.77</td>

</tr> <tr>

<th>Kurtosis</th> <td>2267.6</td>

</tr> <tr>

<th>Mean</th> <td>0.080475</td>

</tr> <tr>

<th>MAD</th> <td>0.099499</td>

</tr> <tr class=”alert”>

<th>Skewness</th> <td>45.127</td>

</tr> <tr>

<th>Sum</th> <td>252.05</td>

</tr> <tr>

<th>Variance</th> <td>0.89722</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-1931205345605329792”>

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k6fXXX9fatWuVn5%2Bvd999V8nJycrJyZHX6w3m6QIAABuxZcEqLy9XamqqJk%2BerMjISF111VUaPHiwPvzwQ23btk3nzp3TuHHj1KRJE3Xo0EHDhg2T0%2BmUJBUUFCgzM1OZmZmKiIjQwIED1bZtW61Zs0aS5HQ6NWrUKLVq1UpRUVHKzc1VcXGxdu/eHcxTBgAANuII9gAXIyYmRnPnzvVbO3bsmBITE1VUVKR27dopLCzMt5eSkqKCggJJUlFRkTIzM/2em5KSIpfLpdOnT%2BvAgQNKSUnx7UVFRalFixZyuVy6/vrr6zVfaWmpysrK/NYcjiZKTEy8oPP8MWFh9urHDoe95pW%2BzdhuWdsJGVuDnAOPjAPPThnbsmB9n8vl0muvvaZly5Zpw4YNiomJ8duPi4uTx%2BNRbW2tPB6PYmNj/fZjY2N14MABffnll/J6vefdd7vd9Z7H6XRqyZIlfms5OTmaMGHCBZ5ZwxIfHxnsES5aTMwVwR6hwSNja5Bz4JFx4NkhY9sXrI8%2B%2Bkjjxo3T5MmTlZGRoQ0bNpz3cSEhIb5//7H7qS71fqusrCz16tXLb83haCK3u/KSjvt9dmjw32X6/K0QFhaqmJgrVF5epZqa2mCP0yCRsTXIOfDIOPAuJuNgfXNvecHq1auXhgwZonvuuUdXX331JR1r69atevzxxzVjxgwNGjRIkpSQkKDDhw/7Pc7j8SguLk6hoaGKj4%2BXx%2BOps5%2BQkOB7zPn2mzZtWu%2B5EhMT67wdWFZ2StXVP%2B2/cHY%2B/5qaWlvPbwdkbA1yDjwyDjw7ZGz5JZB77rlH69ev1%2B23364HH3xQmzdvVnV19QUf5%2BOPP9bUqVO1aNEiX7mSpNTUVO3fv9/vmC6XS506dfLtFxYW%2Bh3rm/2IiAi1adNGRUVFvr3y8nIdOXJEHTt2vOAZAQDAT5PlBSsnJ0fr16/X//zP/6hNmzZ64YUXlJmZqfnz5%2BvQoUP1OkZ1dbWefvppTZkyRT169PDby8zMVFRUlJYtW6aqqirt3r1bK1euVHZ2tiRp%2BPDh2rlzp7Zt26YzZ85o5cqVOnz4sAYOHChJys7O1ooVK1RcXKyKigrl5eWpffv2SktLMxsEAABosEK8Qf6AJ6/Xq/Xr12vWrFmqqKhQRkaGJk6c%2BINXjD788EP98pe/VHh4eJ29jRs3qrKyUjNnzlRhYaGuvPJKPfTQQ7r33nt9j9m8ebMWLFigkpIStW7dWtOnT1e3bt188yxevFhvvPGGKisrlZ6ermeffVZXXXXVJZ1nWdmpS3r%2B%2BTgcoeqTt8P4cQNlw6Sbgz3CBXM4QhUfHym3u/KyvxxtV2RsDXIOPDIOvIvJuFmz6ABPdX5BK1jnzp3TO%2B%2B8o1WrVumDDz5QcnKyhg8frtLSUv3Xf/2XZs%2BerQEDBgRjtICgYFGwcH5kbA1yDjwyDjw7FSzLb3IvLi7WypUr9dZbb6myslL9%2BvXTH//4R3Xt2tX3mG7dumnWrFkNqmABAICfDssLVv/%2B/dWyZUuNGTNGgwYNUlxcXJ3HZGZm6uTJk1aPBgAAYITlBWvFihXq3r37jz6OX00DAADsyvKfImzXrp3Gjh2rLVu2%2BNb%2B8z//Uw899FCdz58CAACwI8sL1ty5c3Xq1Cm1bt3at9azZ0/V1tZq3rx5Vo8DAABgnOVvEb7//vtau3at4uPjfWvJycnKy8vTXXfdZfU4AAAAxll%2BBev06dOKiIioO0hoqKqqqqweBwAAwDjLC1a3bt00b948ffnll761zz//XLNnz/b7qAYAAAC7svwtwqeeekq//vWvddNNNykqKkq1tbWqrKzUtddeq1dffdXqcQAAAIyzvGBde%2B21evvtt/Xee%2B/pyJEjCg0NVcuWLdWjRw%2BFhYVZPQ4AAIBxlhcsSQoPD9ftt98ejJcGAAAIOMsL1tGjR7VgwQJ99tlnOn36dJ39P//5z1aPBAAAYFRQ7sEqLS1Vjx491KRJE6tfHgAAIOAsL1iFhYX685//rISEBKtfGgAAwBKWf0xD06ZNuXIFAAAaNMsL1pgxY7RkyRJ5vV6rXxoAAMASlr9F%2BN577%2Bnjjz/WqlWrdM011yg01L/jvfHGG1aPBAAAYJTlBSsqKkq33nqr1S8LAABgGcsL1ty5c61%2BSQAAAEtZfg%2BWJB08eFCLFy/Wk08%2B6Vv75JNPgjEKAACAcZYXrF27dmngwIHavHmz1q1bJ%2BnrDx8dOXIkHzIKAAAaBMsL1sKFC/X4449r7dq1CgkJkfT17yecN2%2Beli5davU4AAAAxllesP7xj38oOztbknwFS5J%2B8YtfqLi42OpxAAAAjLO8YEVHR5/3dxCWlpYqPDzc6nEAAACMs7xgdenSRS%2B88IIqKip8a4cOHdLUqVN10003WT0OAACAcZZ/TMOTTz6pBx54QOnp6aqpqVGXLl1UVVWlNm3aaN68eVaPAwAAYJzlBeuqq67SunXrtH37dh06dEiNGzdWy5YtdfPNN/vdkwUAAGBXlhcsSWrUqJFuv/32YLw0AABAwFlesHr16vWDV6r4LCwAAGB3lhesO%2B%2B8069g1dTU6NChQ3K5XHrggQesHgcAAMA4ywvWlClTzru%2BadMm/eUvf7F4GgAAAPOC8rsIz%2Bf222/X22%2B/HewxAAAALtllU7D27Nkjr9cb7DEAAAAumeVvEY4YMaLOWlVVlYqLi9W3b1%2BrxwEAADDO8oKVnJxc56cIIyIiNHToUA0bNszqcQAAAIyzvGDxae0AAKChs7xgvfXWW/V%2B7KBBgwI4CQAAQGBYXrCmT5%2Bu2traOje0h4SE%2BK2FhIT8aMHasWOHpk6dqvT0dC1cuNC3vmrVKj311FNq1KiR3%2BNff/11dezYUbW1tVq0aJHWrVun8vJydezYUbNmzdK1114rSfJ4PJo1a5b%2B%2Bte/KjQ0VJmZmZoxY4YaN258qacPAAB%2BAiwvWL///e/18ssva%2BzYsWrXrp28Xq/279%2Bvl156Sffdd5/S09PrdZyXXnpJK1euVIsWLc67361bN7366qvn3Xv99de1du1avfTSS2revLkWLlyonJwcrV69WiEhIZoxY4bOnj2rdevW6dy5c5o4caLy8vL09NNPX/R5AwCAnw7LP6Zh3rx5mjNnjrp27aqoqChFR0frhhtu0LPPPqsXX3xR4eHhvj8/JCIi4gcL1g9xOp0aNWqUWrVqpaioKOXm5qq4uFi7d%2B/WF198oS1btig3N1cJCQlq3ry5xo8frzfffFPnzp272NMGAAA/IZYXrMOHDys2NrbOekxMjEpKSup9nJEjRyo6Ovpf7h87dky/%2BtWv1K1bN/Xu3VurV6%2BWJJ0%2BfVoHDhxQSkqK77FRUVFq0aKFXC6X9u7dq7CwMLVr186336FDB3311Vc6ePBgvecDAAA/XZa/RZiUlKR58%2BZp4sSJio%2BPlySVl5frP/7jP3TdddcZeY2EhAQlJyfrscceU%2BvWrfXOO%2B/oiSeeUGJion72s5/J6/XWKXmxsbFyu92Ki4tTVFSU30dJfPNYt9tdr9cvLS1VWVmZ35rD0USJiYmXeGb%2BwsIum8%2BJrReHw17zSt9mbLes7YSMrUHOgUfGgWenjC0vWE899ZQmT54sp9OpyMhIhYaGqqKiQo0bN9bSpUuNvEbPnj3Vs2dP33/3799f77zzjlatWuX7XYg/9Knxl/qJ8k6nU0uWLPFby8nJ0YQJEy7puHYXHx8Z7BEuWkzMFcEeocEjY2uQc%2BCRceDZIWPLC1aPHj20bds2bd%2B%2BXcePH5fX61Xz5s11yy23/OBbfpcqKSlJhYWFiouLU2hoqDwej9%2B%2Bx%2BNR06ZNlZCQoIqKCtXU1CgsLMy3J0lNmzat12tlZWWpV69efmsORxO53ZUGzuRbdmjw32X6/K0QFhaqmJgrVF5epZqa2mCP0yCRsTXIOfDIOPAuJuNgfXNvecGSpCuuuEK9e/fW8ePHfR%2BNYNJ///d/KzY2Vnfeeadvrbi4WNdee60iIiLUpk0bFRUVqXv37pK%2BfovyyJEj6tixo5KSkuT1erVv3z516NBBkuRyuRQTE6OWLVvW6/UTExPrvB1YVnZK1dU/7b9wdj7/mppaW89vB2RsDXIOPDIOPDtkbPklkNOnT2vq1Knq3Lmz7rjjDklfF5wHH3xQ5eXlRl7j7Nmzeu655%2BRyuXTu3DmtW7dO7733nu/3IGZnZ2vFihUqLi5WRUWF8vLy1L59e6WlpSkhIUH9%2BvXTb3/7W508eVLHjx/X0qVLNXToUDkcQemjAADAZixvDPPnz9fevXuVl5enJ554wrdeU1OjvLw8Pfvss/U6TlpamiSpurpakrRlyxZJX19tGjlypCorKzVx4kSVlZXpmmuu0dKlS5Wamirp6184XVZWpvvvv1%2BVlZVKT0/3u2fq2Wef1cyZM9W7d281atRId911l3Jzc42cPwAAaPhCvJd6R/cF6tGjh1577TUlJyerU6dO2r17tyTp%2BPHjGjRokD744AMrx7FMWdkp48d0OELVJ2%2BH8eMGyoZJNwd7hAvmcIQqPj5SbnflZX852q7I2BrkHHhkHHgXk3GzZoG7v/uHWP4WYWVlpZKTk%2BusJyQk6KuvvrJ6HAAAAOMsL1jXXXed/vKXv0jy/ziEjRs36t/%2B7d%2BsHgcAAMA4y%2B/Buvfee/Xoo4/qnnvuUW1trV555RUVFhZq06ZNmj59utXjAAAAGGd5wcrKypLD4dBrr72msLAw/e53v1PLli2Vl5enX/ziF1aPAwAAYJzlBevkyZO65557dM8991j90gAAAJaw/B6s3r17X/KvogEAALicWV6w0tPTtWHDBqtfFgAAwDKWv0V49dVX6/nnn1d%2Bfr6uu%2B46NWrUyG9/wYIFVo8EAABglOUF68CBA/rZz34mSXK73Va/PAAAQMBZVrByc3O1cOFCvfrqq761pUuXKicnx6oRAAAALGHZPVhbt26ts5afn2/VywMAAFjGsoJ1vp8c5KcJAQBAQ2RZwQoJCanXGgAAgN1Z/jENAAAADR0FCwAAwDDLforw3Llzmjx58o%2Bu8TlYAADA7iwrWF27dlVpaemPrgEAANidZQXru59/BQAA0JBxDxYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyzdcHasWOHMjIylJubW2dv/fr1GjBggDp37qwhQ4bo/fff9%2B3V1tZq4cKF6t27t7p166bRo0fr6NGjvn2Px6NJkyYpIyNDPXr00PTp03X69GlLzgkAANifbQvWSy%2B9pDlz5qhFixZ19vbu3aupU6dqypQp%2BuCDDzRq1Cg98sgjOn78uCTp9ddf19q1a5Wfn693331XycnJysnJkdfrlSTNmDFDVVVVWrdund58800VFxcrLy/P0vMDAAD2ZduCFRERoZUrV563YBUUFCgzM1OZmZmKiIjQwIED1bZtW61Zs0aS5HQ6NWrUKLVq1UpRUVHKzc1VcXGxdu/erS%2B%2B%2BEJbtmxRbm6uEhIS1Lx5c40fP15vvvmmzp07Z/VpAgAAG7JtwRo5cqSio6PPu1dUVKSUlBS/tZSUFLlcLp0%2BfVoHDhzw24%2BKilKLFi3kcrm0d%2B9ehYWFqV27dr79Dh066KuvvtLBgwcDczIAAKBBcQR7gEDweDyKjY31W4uNjdWBAwf05Zdfyuv1nnff7XYrLi5OUVFRCgkJ8duTJLfbXa/XLy0tVVlZmd%2Baw9FEiYmJF3M6/1JYmL36scNhr3mlbzO2W9Z2QsbWIOfAI%2BPAs1PGDbJgSfLdT3Ux%2Bz/23B/jdDq1ZMkSv7WcnBxNmDDhko5rd/HxkcEe4aLFxFwR7BEaPDK2BjkHHhkHnh0ybpAFKz4%2BXh6Px2/N4/EoISFBcXFxCg0NPe9%2B06ZNlZCQoIqKCtXU1CgsLMy3J0lNmzat1%2BtnZWWpV69efmsORxO53ZUXe0rnZYcG/12mz98KYWGhiom5QuXlVaqpqQ32OA0SGVuDnAOPjAPvYjIO1jf3DbJgpaamqrCw0G/N5XKpf//%2BioiIUJs2bVRUVKTu3btLksrLy3XkyBF17NhRSUlJ8nq92rdvnzp06OB7bkxMjFq2bFmv109MTKzzdmBZ2SlVV/%2B0/8LZ%2BfxramptPb8dkLE1yDnwyDjw7JCxvS6B1NPw4cO1c%2BdObdu2TWfOnNHF6w3FAAAPfElEQVTKlSt1%2BPBhDRw4UJKUnZ2tFStWqLi4WBUVFcrLy1P79u2VlpamhIQE9evXT7/97W918uRJHT9%2BXEuXLtXQoUPlcDTIPgoAAAyzbWNIS0uTJFVXV0uStmzZIunrq01t27ZVXl6e5s6dq5KSErVu3VrLly9Xs2bNJEkjRoxQWVmZ7r//flVWVio9Pd3vnqlnn31WM2fOVO/evdWoUSPddddd5/0wUwAAgPMJ8V7qHd2ol7KyU8aP6XCEqk/eDuPHDZQNk24O9ggXzOEIVXx8pNzuysv%2BcrRdkbE1yDnwyDjwLibjZs3O/5FOgdYg3yIEAAAIJgoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABjWYAtWu3btlJqaqrS0NN%2Bf5557TpK0a9cuDR06VF26dFH//v21Zs0av%2BeuWLFC/fr1U5cuXZSdna3CwsJgnAIAALApR7AHCKSNGzfqmmuu8VsrLS3V%2BPHjNX36dA0YMEAfffSRxo0bp5YtWyotLU1bt27V4sWL9fvf/17t2rXTihUrNHbsWG3evFlNmjQJ0pkAAAA7abBXsP6VtWvXKjk5WUOHDlVERIQyMjLUq1cvFRQUSJKcTqeGDBmiTp06qXHjxnrwwQclSe%2B%2B%2B24wxwYAADbSoK9gLViwQJ988okqKip0xx13aNq0aSoqKlJKSorf41JSUrRhwwZJUlFRke68807fXmhoqNq3by%2BXy6X%2B/fvX63VLS0tVVlbmt%2BZwNFFiYuIlnpG/sDB79WOHw17zSt9mbLes7YSMrUHOgUfGgWenjBtswbr%2B%2BuuVkZGhF198UUePHtWkSZM0e/ZseTweNW/e3O%2BxcXFxcrvdkiSPx6PY2Fi//djYWN9%2BfTidTi1ZssRvLScnRxMmTLjIs2kY4uMjgz3CRYuJuSLYIzR4ZGwNcg48Mg48O2TcYAuW0%2Bn0/XurVq00ZcoUjRs3Tl27dv3R53q93kt67aysLPXq1ctvzeFoIre78pKO%2B312aPDfZfr8rRAWFqqYmCtUXl6lmpraYI/TIJGxNcg58Mg48C4m42B9c99gC9b3XXPNNaqpqVFoaKg8Ho/fntvtVkJCgiQpPj6%2Bzr7H41GbNm3q/VqJiYl13g4sKzul6uqf9l84O59/TU2tree3AzK2BjkHHhkHnh0yttclkHras2eP5s2b57dWXFys8PBwZWZm1vnYhcLCQnXq1EmSlJqaqqKiIt9eTU2N9uzZ49sHAAD4MQ2yYDVt2lROp1P5%2Bfk6e/asDh06pEWLFikrK0t33323SkpKVFBQoDNnzmj79u3avn27hg8fLknKzs7WW2%2B9pU8//VRVVVVatmyZwsPD1bNnz%2BCeFAAAsI0G%2BRZh8%2BbNlZ%2BfrwULFvgK0uDBg5Wbm6uIiAgtX75cc%2BbM0ezZs5WUlKT58%2Bfr5z//uSTp1ltv1WOPPaZJkybpxIkTSktLU35%2Bvho3bhzkswIAAHYR4r3UO7pRL2Vlp4wf0%2BEIVZ%2B8HcaPGygbJt0c7BEumMMRqvj4SLndlZf9%2B/12RcbWIOfAI%2BPAu5iMmzWLDvBU59cg3yIEAAAIJgoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwTqPkpISPfzww0pPT9dtt92m%2BfPnq7a2NthjAQAAm3AEe4DL0aOPPqoOHTpoy5YtOnHihMaMGaMrr7xSv/rVr4I9GgAAsAGuYH2Py%2BXSvn37NGXKFEVHRys5OVmjRo2S0%2BkM9mgAAMAmuIL1PUVFRUpKSlJsbKxvrUOHDjp06JAqKioUFRX1o8coLS1VWVmZ35rD0USJiYlGZw0Ls1c/djjsNa/0bcZ2y9pOyNga5Bx4ZBx4dsqYgvU9Ho9HMTExfmvflC23212vguV0OrVkyRK/tUceeUSPPvqouUH1dZF74KrPlJWVZby84WulpaX64x9/T8YBRMbWIOfAI%2BPAs1PGl38FDAKv13tJz8/KytKqVav8/mRlZRma7ltlZWVasmRJnatlMIeMA4%2BMrUHOgUfGgWenjLmC9T0JCQnyeDx%2Bax6PRyEhIUpISKjXMRITEy/7Zg0AAAKHK1jfk5qaqmPHjunkyZO%2BNZfLpdatWysyMjKIkwEAALugYH1PSkqK0tLStGDBAlVUVKi4uFivvPKKsrOzgz0aAACwibBZs2bNCvYQl5tbbrlF69at03PPPae3335bQ4cO1ejRoxUSEhLs0eqIjIxU9%2B7duboWQGQceGRsDXIOPDIOPLtkHOK91Du6AQAA4Ie3CAEAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2DZUElJiR5%2B%2BGGlp6frtttu0/z581VbWxvssWxvx44dysjIUG5ubp299evXa8CAAercubOGDBmi999/PwgT2l9JSYlycnKUnp6ujIwMTZs2TeXl5ZKkvXv36r777lPXrl3Vt29fvfzyy0Ge1p727dunBx54QF27dlVGRoYmTZqksrIySdKuXbs0dOhQdenSRf3799eaNWuCPK39vfDCC2rXrp3vv8nYnHbt2ik1NVVpaWm%2BP88995wkm%2BTshe0MHjzY%2B/TTT3vLy8u9hw4d8vbt29f78ssvB3ssW8vPz/f27dvXO2LECO%2BkSZP89vbs2eNNTU31btu2zXv69Gnv6tWrvZ06dfIeO3YsSNPa11133eWdNm2at6Kiwnvs2DHvkCFDvE899ZS3qqrKe8stt3gXL17srays9BYWFnq7d%2B/u3bRpU7BHtpUzZ854b7rpJu%2BSJUu8Z86c8Z44ccJ73333ecePH%2B/9/PPPvddff723oKDAe/r0ae///u//ejt27Oj9%2B9//HuyxbWvPnj3e7t27e9u2bev1er1kbFjbtm29R48erbNul5y5gmUzLpdL%2B/bt05QpUxQdHa3k5GSNGjVKTqcz2KPZWkREhFauXKkWLVrU2SsoKFBmZqYyMzMVERGhgQMHqm3btpfnd0yXsfLycqWmpmry5MmKjIzUVVddpcGDB%2BvDDz/Utm3bdO7cOY0bN05NmjRRhw4dNGzYML6uL1BVVZVyc3M1ZswYhYeHKyEhQX369NFnn32mtWvXKjk5WUOHDlVERIQyMjLUq1cvFRQUBHtsW6qtrdXMmTM1atQo3xoZW8MuOVOwbKaoqEhJSUmKjY31rXXo0EGHDh1SRUVFECezt5EjRyo6Ovq8e0VFRUpJSfFbS0lJkcvlsmK0BiMmJkZz587VlVde6Vs7duyYEhMTVVRUpHbt2iksLMy3l5KSosLCwmCMaluxsbEaNmyYHA6HJOngwYP605/%2BpDvuuONffh2T8cV54403FBERoQEDBvjWyNi8BQsWqGfPnrrhhhs0Y8YMVVZW2iZnCpbNeDwexcTE%2BK19U7bcbncwRmrwPB6PX6GVvs6cvC%2BNy%2BXSa6%2B9pnHjxp336zouLk4ej4f7Cy9CSUmJUlNTdeeddyotLU0TJkz4lxnzdXzhvvjiCy1evFgzZ870Wydjs66//nplZGRo8%2BbNcjqd%2BvTTTzV79mzb5EzBsiGv1xvsEX5yyNysjz76SKNHj9bkyZOVkZHxLx8XEhJi4VQNR1JSklwulzZu3KjDhw/riSeeCPZIDcrcuXM1ZMgQtW7dOtijNGhOp1PDhg1TeHi4WrVqpSlTpmjdunU6d%2B5csEerFwqWzSQkJMjj8fiteTwehYSEKCEhIUhTNWzx8fHnzZy8L87WrVv18MMP66mnntLIkSMlff11/f3vPj0ej%2BLi4hQayv%2BmLkZISIiSk5OVm5urdevWyeFw1Pk6drvdfB1foF27dumTTz5RTk5Onb3z/b%2BCjM255pprVFNTo9DQUFvkzP%2B5bCY1NVXHjh3TyZMnfWsul0utW7dWZGRkECdruFJTU%2Bu8t%2B9yudSpU6cgTWRfH3/8saZOnapFixZp0KBBvvXU1FTt379f1dXVvjUyvnC7du1Sv379/N5W/aagduzYsc7XcWFhIRlfoDVr1ujEiRO67bbblJ6eriFDhkiS0tPT1bZtWzI2ZM%2BePZo3b57fWnFxscLDw5WZmWmLnClYNpOSkqK0tDQtWLBAFRUVKi4u1iuvvKLs7Oxgj9ZgDR8%2BXDt37tS2bdt05swZrVy5UocPH9bAgQODPZqtVFdX6%2Bmnn9aUKVPUo0cPv73MzExFRUVp2bJlqqqq0u7du7Vy5Uq%2Bri9QamqqKioqNH/%2BfFVVVenkyZNavHixbrjhBmVnZ6ukpEQFBQU6c%2BaMtm/fru3bt2v48OHBHttWpk2bpk2bNmn16tVavXq18vPzJUmrV6/WgAEDyNiQpk2byul0Kj8/X2fPntWhQ4e0aNEiZWVl6e6777ZFziFebi6xnePHj2vGjBn661//qqioKI0YMUKPPPII96tcgrS0NEnyXUH55qewvvlJwc2bN2vBggUqKSlR69atNX36dHXr1i04w9rUhx9%2BqF/%2B8pcKDw%2Bvs7dx40ZVVlZq5syZKiws1JVXXqmHHnpI9957bxAmtbf9%2B/drzpw5%2Bvvf/64mTZroxhtv1LRp09S8eXP97W9/05w5c1RcXKykpCRNnjxZffv2DfbItvbPf/5TvXv31v79%2ByWJjA3629/%2BpgULFmj//v0KDw/X4MGDlZubq4iICFvkTMECAAAwjLcIAQAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMCw/wfb7mFKar69AQAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-1931205345605329792”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>484</td> <td class=”number”>15.1%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.018474043968224645</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0121921482565228</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.004950004950004951</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.03575898444484177</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.02469135802469136</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.025106703489831784</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.011473811026332397</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.02663115845539281</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.02254791431792559</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2624)</td> <td class=”number”>2630</td> <td class=”number”>81.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-1931205345605329792”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>484</td> <td class=”number”>15.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.002151462994836489</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.002294156782674528</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.002523468254769355</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0025673940949935818</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>8.519771653425943</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.046828233531647</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.853778885774442</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.330296792180636</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>48.888888888888886</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_NHWHITE10”>PCT_NHWHITE10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3133</td>

</tr> <tr>

<th>Unique (%)</th> <td>97.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>78.354</td>

</tr> <tr>

<th>Minimum</th> <td>2.8604</td>

</tr> <tr>

<th>Maximum</th> <td>99.163</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram1578382382665238297”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives1578382382665238297,#minihistogram1578382382665238297”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives1578382382665238297”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles1578382382665238297”

aria-controls=”quantiles1578382382665238297” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram1578382382665238297” aria-controls=”histogram1578382382665238297”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common1578382382665238297” aria-controls=”common1578382382665238297”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme1578382382665238297” aria-controls=”extreme1578382382665238297”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles1578382382665238297”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>2.8604</td>

</tr> <tr>

<th>5-th percentile</th> <td>37.787</td>

</tr> <tr>

<th>Q1</th> <td>66.995</td>

</tr> <tr>

<th>Median</th> <td>85.772</td>

</tr> <tr>

<th>Q3</th> <td>94.203</td>

</tr> <tr>

<th>95-th percentile</th> <td>97.334</td>

</tr> <tr>

<th>Maximum</th> <td>99.163</td>

</tr> <tr>

<th>Range</th> <td>96.303</td>

</tr> <tr>

<th>Interquartile range</th> <td>27.208</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>19.811</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.25284</td>

</tr> <tr>

<th>Kurtosis</th> <td>1.0049</td>

</tr> <tr>

<th>Mean</th> <td>78.354</td>

</tr> <tr>

<th>MAD</th> <td>15.983</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-1.2504</td>

</tr> <tr>

<th>Sum</th> <td>245400</td>

</tr> <tr>

<th>Variance</th> <td>392.47</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram1578382382665238297”>

<img 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X5zFms3VTbGzsOfcJCgr0%2BIquQZ%2BtQ6%2BtQ6%2BtQZ%2B7ls32TV/9odeWB6yvJSYmKjExUb/5zW/0wgsvaNmyZdq4caPS0tL0q1/9SldeeeUF/%2BzAwEAVFRUpLy9Pf/rTnyRJw4YN0/z58zVr1izFxcV5bB8VFSWHwyFJcjqdioyM9JiPjIx0z3dEaWmp1q9f7zGWn5%2BvgoKC8%2B4bEXFxh9fBhaPP1qHX1qHX1qDPXSM6OqzNmC/32msB6%2BzZs/rLX/6iZ599Vm%2B%2B%2Babi4%2BM1e/Zs1dbWKi8vT8uXL1dWVtYF/ewzZ85o5syZuuGGG9z3Ty1fvlyFhYUd2t/lcl3Qul%2BbOnWqMjMzPcZstm5yOBrOuU9QUKAiIi5WfX2jWlpaO7U%2Bzo0%2BW4deW4deW4M%2Bd61//Iw02ev2gpsVLA9Y1dXV2rx5s5577jk1NDRo7Nix%2BtOf/qSrr77avU1KSoqWLVt2wQGroqJChw8f1rx58xQUFKTu3buroKBAEydO1LXXXiun0%2BmxvcPhUExMjCQpOjq6zbzT6VT//v07vH5sbGyby4F1dSfV3Hz%2BF0lLS2uHtkPn0Gfr0Gvr0Gtr0Oeu0V5PfbnXlges8ePHq2/fvrrzzjs1adIkRUVFtdkmPT3d/UiFC9HS0qLW1laPM1FnzpyRJKWlpel//ud/PLavrKzUkCFDJElJSUmqqqrS5MmT3T9r//79ysnJueB6AADAD4vld4%2BVlJRo%2B/btysvLazdcfe2999674DWGDh2qbt26qaioSI2NjXI4HNqwYYNSUlI0ceJE1dTUqKysTKdPn9aePXu0Z88eTZkyRZKUm5ur5557Tu%2B%2B%2B64aGxu1YcMGBQcHa9SoURdcDwAA%2BGGxPGANHDhQM2fO1EsvveQe%2B%2BMf/6jbb7%2B9zaW5CxUdHa3HH39cb7/9tkaOHKkbb7xRoaGhWrt2rXr06KFHH31UmzZt0tVXX60VK1Zo9erVuuKKKyRJI0eO1Lx58zRnzhwNGzZMe/fuVXFxsUJDQ43UBgAA/J/llwhXrlypkydPql%2B/fu6xUaNG6bXXXtOqVau0atUqI%2BskJSXpySefbHcuJSVF5eXl59z3lltu0S233GKkDgAA8MNjecB6/fXXtXXrVkVHR7vH4uPjtWbNGt14441WlwMAAGCc5ZcIm5qaFBIS0raQwEA1NjZaXQ4AAIBxlgeslJQUrVq1SidOnHCPff7551q%2BfLnHoxoAAAB8leWXCO%2B99179%2B7//u376058qPDxcra2tamhoUJ8%2Bfc55zxQAAIAvsTxg9enTR88//7xeffVVffrppwoMDFTfvn01YsQIBQUFWV0OAACAcV75UznBwcG67rrrvLE0AABAl7M8YH322Wdau3atPvroIzU1NbWZf/nll60uCQAAwCiv3INVW1urESNGqFu3blYvDwAA0OUsD1iVlZV6%2BeWX3X9cGQAAwN9Y/piGHj16cOYKAAD4NcsD1p133qn169fL5XJZvTQAAIAlLL9E%2BOqrr%2Brtt9/Ws88%2Bq3/9139VYKBnxnv66aetLgkAAMAoywNWeHi4Ro4cafWyAAAAlrE8YK1cudLqJQEAACxl%2BT1YkvTxxx%2BrqKhI99xzj3vsnXfe8UYpAAAAxlkesCoqKjRhwgTt3LlT27Ztk/TVw0enT5/OQ0YBAIBfsDxgrVu3Tr/%2B9a%2B1detWBQQESPrq7xOuWrVKDz/8sNXlAAAAGGd5wPr73/%2Bu3NxcSXIHLEm64YYbVF1dbXU5AAAAxlkesLp3797u3yCsra1VcHCw1eUAAAAYZ3nASk5O1ooVK3Tq1Cn32KFDh7RgwQL99Kc/tbocAAAA4yx/TMM999yjX/ziF0pNTVVLS4uSk5PV2Nio/v37a9WqVVaXAwAAYJzlAatXr17atm2b9uzZo0OHDik0NFR9%2B/bV8OHDPe7JAgAA8FWWByxJuuiii3Tdddd5Y2kAAIAuZ3nAyszM/NYzVTwLCwAA%2BDrLA9a4ceM8AlZLS4sOHToku92uX/ziF1aXAwAAYJzlAauwsLDd8R07duivf/2rxdUAAACY55W/Rdie6667Ts8//7y3ywAAAOi0703A2r9/v1wul7fLAAAA6DTLLxFOmzatzVhjY6Oqq6s1ZswYq8sBAAAwzvKAFR8f3%2Ba3CENCQpSTk6Obb77Z6nIAAACMszxg8bR2AADg7ywPWM8991yHt500aVIXVgIAANA1LA9YixYtUmtra5sb2gMCAjzGAgICCFgAAMAnWR6w/uu//ksbN27UzJkzNXDgQLlcLn344Yd67LHHdOuttyo1NdXqkgAAAIzyyj1YxcXFiouLc49dc8016tOnj2677TZt27bN6pIAAACMsvw5WJ988okiIyPbjEdERKimpsbqcgAAAIyzPGD17t1bq1atksPhcI/V19dr7dq1uvTSS42utWHDBo0YMUJXXXWV8vLydPjwYUlSRUWFcnJylJycrPHjx2vLli0e%2B5WUlGjs2LFKTk5Wbm6uKisrjdYFAAD8m%2BUB695779X27duVlpama665RsOGDdNPfvITPfvss1q4cKGxdf785z9ry5YtKikp0euvv65%2B/frpj3/8o2prazVr1ixNmzZNFRUVWrRokZYsWSK73S5J2rVrl4qKivTggw9q7969ysjI0MyZM/Xll18aqw0AAPg3y%2B/BGjFihHbv3q09e/bo6NGjcrlciouL07XXXqvu3bsbW2fjxo1asGCBfvzjH0uSFi9eLEl6/PHHFR8fr5ycHElSWlqaMjMzVVZWpsGDB6u0tFTZ2dkaMmSIJGnGjBkqKSnRK6%2B8ovHjxxurDwAA%2BC/LA5YkXXzxxRo9erSOHj2qPn36GP/5n3/%2BuQ4fPqwTJ05o3LhxOnbsmFJTU7Vs2TJVVVUpISHBY/uEhARt375dklRVVaVx48a55wIDAzVo0CDZ7XYCFgAA6BDLA1ZTU5OWLl2q559/XpJUWVmp%2Bvp6zZs3T7///e8VERHR6TWOHj0qSXrxxRf1xBNPyOVyqaCgQIsXL1ZTU5PHbzBKUlRUlPueMKfT2eYm/MjISI97xs6ntrZWdXV1HmM2WzfFxsaec5%2BgoECPr%2Bga9Nk69No69Noa9Llr2Wzf9NUfem15wFq9erUOHDigNWvW6De/%2BY17vKWlRWvWrNF9993X6TW%2BfmDpjBkz3GFq9uzZuv3225WWltbh/S9UaWmp1q9f7zGWn5%2BvgoKC8%2B4bEXFxp9ZGx9Bn69Br69Bra9DnrhEdHdZmzJd7bXnA2rFjhzZt2qT4%2BHgtWLBA0lePaFi5cqUmTZpkJGD17NnT/XO/1rt3b7lcLp09e1ZOp9Nje4fDoZiYGElSdHR0m3mn06n%2B/ft3eP2pU6cqMzPTY8xm6yaHo%2BGc%2BwQFBSoi4mLV1zeqpaW1w2vhu6HP1qHX1qHX1qDPXesfPyNN9rq94GYFywNWQ0OD4uPj24zHxMQY%2B029Xr16KTw8XAcOHFBiYqIkqaamRhdddJHS09NVXl7usX1lZaX7pvakpCRVVVVp8uTJkr46s7Z//373TfEdERsb2%2BZyYF3dSTU3n/9F0tLS2qHt0Dn02Tr02jr02hr0uWu011Nf7rXlAevSSy/VX//6V6WmpnpcinvxxRf1L//yL0bWsNlsysnJ0SOPPKKUlBSFh4fr4YcfVlZWliZPnqz//M//VFlZmSZMmKA333xTe/bsUWlpqSQpNzdX8%2BbN04033qiBAwfq8ccfV3BwsEaNGmWkNgCA77pm0YveLgE%2BwvKAdcstt2j27Nm66aab1NraqieeeEKVlZXasWOHFi1aZGyd%2BfPn68yZM7r55pt19uxZjR07VosXL1ZYWJgeffRRPfDAA1q%2BfLl69%2B6t1atX64orrpAkjRw5UvPmzdOcOXN07NgxDR48WMXFxQoNDTVWGwAA8G8Brs7e0X0BnnnmGW3atEkff/yxQkND1bdvX%2BXl5emGG26wuhTL1NWd/NZ5my1Q0dFhcjgafPZ0qC%2Bgz9ah19ah19aw2QJ1/ZrXvF2G39o%2BZ7j7e5Ov6UsuMfeMze/C8jNYx48f10033aSbbrrJ6qUBAAAsYfkDJkaPHt3pxyAAAAB8n1kesFJTU91PTQcAAPBHll8i/NGPfqTf/va3Ki4u1qWXXqqLLrrIY37t2rVWlwQAAGCU5QHr4MGD7j/A/F3%2B/AwAAICvsCxgzZ07V%2BvWrdOTTz7pHnv44YeVn59vVQkAAACWsOwerF27drUZKy4utmp5AAAAy1gWsNr7zUF%2BmxAAAPgjywJWQEBAh8YAAAB8neWPaQAAAPB3BCwAAADDLPstwrNnz2r%2B/PnnHeM5WAAAwNdZFrCuvvpq1dbWnncMAADA11kWsP7x%2BVcAAAD%2BjHuwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAw34QAWvFihUaOHCg%2B98VFRXKyclRcnKyxo8fry1btnhsX1JSorFjxyo5OVm5ubmqrKy0umQAAODD/D5gHThwQOXl5e5/19bWatasWZo2bZoqKiq0aNEiLVmyRHa7XZK0a9cuFRUV6cEHH9TevXuVkZGhmTNn6ssvv/TWIQAAAB/j1wGrtbVVS5cuVV5ennts69atio%2BPV05OjkJCQpSWlqbMzEyVlZVJkkpLS5Wdna0hQ4YoNDRUM2bMkCS98sor3jgEAADgg2zeLqArPf300woJCVFWVpYeeughSVJVVZUSEhI8tktISND27dvd8%2BPGjXPPBQYGatCgQbLb7Ro/fnyH1q2trVVdXZ3HmM3WTbGxsefcJygo0OMrugZ9tg69tg69tgb97Vo22zf99YfXtN8GrC%2B%2B%2BEJFRUV68sknPcadTqfi4uI8xqKiouRwONzzkZGRHvORkZHu%2BY4oLS3V%2BvXrPcby8/NVUFBw3n0jIi7u8Dq4cPTZOvTaOvQaviw6OqzNmC%2B/pv02YK1cuVLZ2dnq16%2BfDh8%2B/J32dblcnVp76tSpyszM9Biz2brJ4Wg45z5BQYGKiLhY9fWNamlp7dT6ODf6bB16bR16bQ1fPpviC/7xM9Lka7q94GYFvwxYFRUVeuedd7Rt27Y2c9HR0XI6nR5jDodDMTEx55x3Op3q379/h9ePjY1tczmwru6kmpvP/yJpaWnt0HboHPpsHXptHXoNX9bea9eXX9N%2BGce3bNmiY8eOKSMjQ6mpqcrOzpYkpaamasCAAW0eu1BZWakhQ4ZIkpKSklRVVeWea2lp0f79%2B93zAAAA5%2BOXAWvhwoXasWOHysvLVV5eruLiYklSeXm5srKyVFNTo7KyMp0%2BfVp79uzRnj17NGXKFElSbm6unnvuOb377rtqbGzUhg0bFBwcrFGjRnnxiAAAgC/xy0uEkZGRHjeqNzc3S5J69eolSXr00Uf1wAMPaPny5erdu7dWr16tK664QpI0cuRIzZs3T3PmzNGxY8c0ePBgFRcXKzQ01PoDAQAAPinA1dk7utEhdXUnv3XeZgtUdHSYHI4Gn73e7Avos3XotXV8udc/e%2BgNb5eA74ntc4a7vzf5mr7kku6dLe2C%2BOUlQgAAAG8iYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAw2zeLgAAYNbPHnrD2yUAP3icwQIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYJjN2wUAwPfdzx56w9slAPAxnMECAAAwzG8DVk1NjfLz85Wamqq0tDQtXLhQ9fX1kqQDBw7o1ltv1dVXX60xY8Zo48aNHvu%2B8MILysrK0tChQ5Wdna3XX3/dG4cAAAB8lN8GrJkzZyoiIkK7du3Ss88%2Bq48%2B%2Bki/%2B93v1NTUpDvvvFM/%2BclP9Nprr2ndunV69NFHtXPnTklfha8FCxaosLBQb775pvLy8nT33Xfr6NGjXj4iAADgK/wyYNXX1yspKUnz589XWFiYevXqpcmTJ2vfvn3avXu3zp49q7vuukvdunVTYmKibr75ZpWWlkqSysrKlJ4bjktUAAAOHElEQVServT0dIWEhGjChAkaMGCAtmzZ4uWjAgAAvsIvA1ZERIRWrlypnj17useOHDmi2NhYVVVVaeDAgQoKCnLPJSQkqLKyUpJUVVWlhIQEj5%2BXkJAgu91uTfEAAMDn/SB%2Bi9But2vTpk3asGGDtm/froiICI/5qKgoOZ1Otba2yul0KjIy0mM%2BMjJSBw8e7PB6tbW1qqur8xiz2bopNjb2nPsEBQV6fEXXoM/WodcAvgub7Zv3Cn94//D7gPXWW2/prrvu0vz585WWlqbt27e3u11AQID7e5fL1ak1S0tLtX79eo%2Bx/Px8FRQUnHffiIiLO7U2OoY%2BW4deA%2BiI6OiwNmO%2B/P7h1wFr165d%2BvWvf60lS5Zo0qRJkqSYmBh98sknHts5nU5FRUUpMDBQ0dHRcjqdbeZjYmI6vO7UqVOVmZnpMWazdZPD0XDOfYKCAhURcbHq6xvV0tLa4bXw3dBn69BrAN/FP35Gmnz/aC%2B4WcFvA9bbb7%2BtBQsW6A9/%2BINGjBjhHk9KStJTTz2l5uZm2WxfHb7dbteQIUPc81/fj/U1u92u8ePHd3jt2NjYNpcD6%2BpOqrn5/C%2BSlpbWDm2HzqHP1qHXADqivfcJX37/8N2Lm9%2BiublZixcvVmFhoUe4kqT09HSFh4drw4YNamxs1HvvvafNmzcrNzdXkjRlyhTt3btXu3fv1unTp7V582Z98sknmjBhgjcOBQAA%2BKAAV2dvOPoe2rdvn37%2B858rODi4zdyLL76ohoYGLV26VJWVlerZs6duv/123XLLLe5tdu7cqbVr16qmpkb9%2BvXTokWLlJKS0qma6upOfuu8zRao6OgwORwNPpvWfQF9to4/9Zo/lQN0ve1zhru/N/n%2Bcckl3Ttb2gXxy0uE11xzjT788MNv3eapp54659yYMWM0ZswY02UBAIAfCL%2B8RAgAAOBNBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwzC8f0wDg%2B43nSgHwd5zBAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMJ7kDfoKnowPA9wdnsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMJu3CwC%2Br3720BveLgEA4KM4gwUAAGAYZ7BgGc4IAQB%2BKDiDBQAAYBgBCwAAwDACFgAAgGHcg%2BXjuK8JAIDvH85gtaOmpkZ33HGHUlNTlZGRodWrV6u1tdXbZQEAAB/BGax2zJ49W4mJiXrppZd07Ngx3XnnnerZs6d%2B%2Bctfers0AADgAziD9U/sdrs%2B%2BOADFRYWqnv37oqPj1deXp5KS0u9XRoAAPARnMH6J1VVVerdu7ciIyPdY4mJiTp06JBOnTql8PDw8/6M2tpa1dXVeYzZbN0UGxt7zn2CggI9vgIA8ENis33z%2BecPn4kErH/idDoVERHhMfZ12HI4HB0KWKWlpVq/fr3H2N13363Zs2efc5/a2lr96U//palTp35rEPtn%2B357Q4e3xVd9Li0t/c59xndHr61Dr61Bn61zoZ%2BJ3ye%2BGw27kMvl6tT%2BU6dO1bPPPuvxn6lTp37rPnV1dVq/fn2bM18wiz5bh15bh15bgz5bxx96zRmsfxITEyOn0%2Bkx5nQ6FRAQoJiYmA79jNjYWJ9N3AAAoPM4g/VPkpKSdOTIER0/ftw9Zrfb1a9fP4WFhXmxMgAA4CsIWP8kISFBgwcP1tq1a3Xq1ClVV1friSeeUG5urrdLAwAAPiJo2bJly7xdxPfNtddeq23btun%2B%2B%2B/X888/r5ycHN12220KCAjo0nXDwsI0bNgwzpR1MfpsHXptHXptDfpsHV/vdYCrs3d0AwAAwAOXCAEAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2B5WU1Nje644w6lpqYqIyNDq1evVmtrq7fL8gs1NTXKz89Xamqq0tLStHDhQtXX10uSDhw4oFtvvVVXX321xowZo40bN3q5Wv%2BxYsUKDRw40P3viooK5eTkKDk5WePHj9eWLVu8WJ1/2LBhg0aMGKGrrrpKeXl5Onz4sCR6bdL%2B/fs1ffp0XXPNNRo%2BfLgKCwt1/PhxSfS5s1577TWlpaVp7ty5beZeeOEFZWVlaejQocrOztbrr7/unmttbdW6des0evRopaSk6LbbbtNnn31mZenfjQteNXnyZNfixYtd9fX1rkOHDrnGjBnj2rhxo7fL8gs33nija%2BHCha5Tp065jhw54srOznbde%2B%2B9rsbGRte1117rKioqcjU0NLgqKytdw4YNc%2B3YscPbJfu8/fv3u4YNG%2BYaMGCAy%2BVyuT7//HPXVVdd5SorK3M1NTW53njjDdeVV17pev/9971cqe/atGmT64YbbnBVV1e7Tp486br//vtd999/P7026OzZs67hw4e71q5d6zp9%2BrTr%2BPHjrl/%2B8peu2bNn0%2BdOKi4udo0ZM8Y1bdo015w5czzm9u/f70pKSnLt3r3b1dTU5CovL3cNGTLEdeTIEZfL5XKVlJS4MjIyXAcPHnSdPHnSdd9997mysrJcra2t3jiU8%2BIMlhfZ7XZ98MEHKiwsVPfu3RUfH6%2B8vDyVlpZ6uzSfV19fr6SkJM2fP19hYWHq1auXJk%2BerH379mn37t06e/as7rrrLnXr1k2JiYm6%2Beab6Xsntba2aunSpcrLy3OPbd26VfHx8crJyVFISIjS0tKUmZmpsrIy7xXq4zZu3Ki5c%2Bfqxz/%2BscLDw7V48WItXryYXhtUV1enuro6TZw4UcHBwYqOjtb111%2BvAwcO0OdOCgkJ0ebNm3XZZZe1mSsrK1N6errS09MVEhKiCRMmaMCAAe4zhKWlpcrLy9Pll1%2Bu8PBwzZ07V9XV1XrvvfesPowOIWB5UVVVlXr37q3IyEj3WGJiog4dOqRTp055sTLfFxERoZUrV6pnz57usSNHjig2NlZVVVUaOHCggoKC3HMJCQmqrKz0Rql%2B4%2Bmnn1ZISIiysrLcY1VVVUpISPDYjl5fuM8//1yHDx/WiRMnNG7cOKWmpqqgoEDHjx%2Bn1wbFxcVp0KBBKi0tVUNDg44dO6adO3dq1KhR9LmTpk%2Bfru7du7c7d67e2u12NTU16eDBgx7z4eHhuuyyy2S327u05gtFwPIip9OpiIgIj7Gvw5bD4fBGSX7Lbrdr06ZNuuuuu9rte1RUlJxOJ/e/XaAvvvhCRUVFWrp0qcf4uXrN6/vCHD16VJL04osv6oknnlB5ebmOHj2qxYsX02uDAgMDVVRUpJdfflnJyclKS0tTc3Oz5s%2BfT5%2B7kNPp9DjhIH31mehwOHTixAm5XK5zzn8fEbC8zOVyebsEv/fWW2/ptttu0/z585WWlnbO7QICAiysyr%2BsXLlS2dnZ6tevn7dL8Wtfv1/MmDFDcXFx6tWrl2bPnq1du3Z5uTL/cubMGc2cOVM33HCD9u3bp1dffVXdu3dXYWGht0vze%2Bf7TPSlz0wClhfFxMTI6XR6jDmdTgUEBCgmJsZLVfmXXbt26Y477tC9996r6dOnS/qq7//8/3icTqeioqIUGMj/JL6riooKvfPOO8rPz28zFx0d3eY17nA4eH1foK8vef/jGZTevXvL5XLp7Nmz9NqQiooKHT58WPPmzVP37t0VFxengoIC/eUvf1FgYCB97iLtvV84nU7FxMS435/bm%2B/Ro4eVZXYYnyZelJSUpCNHjrh/9Vf66lJWv379FBYW5sXK/MPbb7%2BtBQsW6A9/%2BIMmTZrkHk9KStKHH36o5uZm95jdbteQIUO8UabP27Jli44dO6aMjAylpqYqOztbkpSamqoBAwa0uTelsrKSXl%2BgXr16KTw8XAcOHHCP1dTU6KKLLlJ6ejq9NqSlpUWtra0eZ0vOnDkjSUpLS6PPXSQpKalNb79%2Bbw4JCVH//v1VVVXlnquvr9enn36qK6%2B80upSO4SA5UUJCQkaPHiw1q5dq1OnTqm6ulpPPPGEcnNzvV2az2tubtbixYtVWFioESNGeMylp6crPDxcGzZsUGNjo9577z1t3ryZvl%2BghQsXaseOHSovL1d5ebmKi4slSeXl5crKylJNTY3Kysp0%2BvRp7dmzR3v27NGUKVO8XLVvstlsysnJ0SOPPKL/%2B7//07Fjx/Twww8rKytLkydPpteGDB06VN26dVNRUZEaGxvlcDi0YcMGpaSkaOLEifS5i0yZMkV79%2B7V7t27dfr0aW3evFmffPKJJkyYIEnKzc1VSUmJqqurderUKa1Zs0aDBg3S4MGDvVx5%2BwJcvnRB0w8dPXpUS5Ys0f/%2B7/8qPDxc06ZN09133839QJ20b98%2B/fznP1dwcHCbuRdffFENDQ1aunSpKisr1bNnT91%2B%2B%2B265ZZbvFCp/zl8%2BLBGjx6tDz/8UJL0t7/9TQ888ICqq6vVu3dvzZ8/X2PGjPFylb7rzJkzWrlypZ5//nmdPXtWY8eO1ZIlSxQWFkavDaqsrNTvfvc7ffDBBwoODtawYcO0cOFCxcXF0edO%2BDoMfX0FwWazSZL7NwF37typtWvXqqamRv369dOiRYuUkpIi6av7r4qKivT000%2BroaFBqampuu%2B%2B%2B9SrVy8vHMn5EbAAAAAM4xIhAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABj2/0MC2QuChLaQAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common1578382382665238297”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>57.856774858320456</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>85.75822673567033</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>75.24600756946369</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>93.54163552100462</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>95.89225589225589</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>88.80296658074435</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>86.5911582024114</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>41.37719656722517</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>92.44110860359561</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>64.25179814528849</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3122)</td> <td class=”number”>3122</td> <td class=”number”>97.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme1578382382665238297”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>2.8604076818165063</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.3339459217591414</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.016861304290775</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.540806714053267</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.14210301041518</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>98.63636363636363</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>98.67863720073665</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>98.68228404099561</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>98.90776699029125</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>99.16317991631799</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_NSLP09”>PCT_NSLP09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>52</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>11.13</td>

</tr> <tr>

<th>Minimum</th> <td>6.9543</td>

</tr> <tr>

<th>Maximum</th> <td>13.744</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram3831597233006397471”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives3831597233006397471,#minihistogram3831597233006397471”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives3831597233006397471”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles3831597233006397471”

aria-controls=”quantiles3831597233006397471” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram3831597233006397471” aria-controls=”histogram3831597233006397471”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common3831597233006397471” aria-controls=”common3831597233006397471”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme3831597233006397471” aria-controls=”extreme3831597233006397471”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles3831597233006397471”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>6.9543</td>

</tr> <tr>

<th>5-th percentile</th> <td>7.9909</td>

</tr> <tr>

<th>Q1</th> <td>9.2751</td>

</tr> <tr>

<th>Median</th> <td>11.417</td>

</tr> <tr>

<th>Q3</th> <td>13.112</td>

</tr> <tr>

<th>95-th percentile</th> <td>13.551</td>

</tr> <tr>

<th>Maximum</th> <td>13.744</td>

</tr> <tr>

<th>Range</th> <td>6.7895</td>

</tr> <tr>

<th>Interquartile range</th> <td>3.8369</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>1.8825</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.16915</td>

</tr> <tr>

<th>Kurtosis</th> <td>-1.2579</td>

</tr> <tr>

<th>Mean</th> <td>11.13</td>

</tr> <tr>

<th>MAD</th> <td>1.6675</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.32225</td>

</tr> <tr>

<th>Sum</th> <td>34858</td>

</tr> <tr>

<th>Variance</th> <td>3.5439</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram3831597233006397471”>

<img 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t2za98847%2Buc//ymHw6EePXpo6NCh8vX1tbo8AACABtkuYEmSn5%2BfRo4caXUZAAAAF8R2Aetf//qX1q1bp88//1wnTpyot/3NN9%2B0oCoAAIDGs13AWrZsmUpLSzV06FB17NjR6nIAAACazHYBq7CwUG%2B%2B%2BaZCQkKsLgUAAOCC2O42DRdddBErVwAAoFWzXcCaO3euUlNTVVtba3UpAAAAF8R2pwjfeecd7du3T6%2B99pp%2B8pOfyOFwz4B//OMfLaoMAACgcWwXsDp37qzhw4dbXQYAAMAFs13AWr16tdUlAAAANIvtrsGSpL///e9KSUnRgw8%2BWDf28ccfW1gRAABA49kuYOXn52vChAnKzc1VTk6OpO9vPjpr1ixuMgoAAFoF2wWsJ554Qvfff7%2B2bt0qHx8fSd9/PmFycrLWr19vcXUAAAANs13A%2Btvf/qaEhARJqgtYkjR27FgdPHjQqrIAAAAazXYBq0uXLmf9DMLS0lL5%2BflZUBEAAEDT2C5gRUZGatWqVTp27Fjd2JdffqmlS5fqZz/7mYWVAQAANI7tbtPw4IMP6tZbb9XgwYNVXV2tyMhIVVZWqlevXkpOTra6PAAAgAbZLmB17dpVOTk5ysvL05dffqn27durR48eGjJkiNs1WQAAAHZlu4AlSe3atdPIkSOtLgMAAOCC2C5gxcXFnXelinthAQAAu7NdwLr55pvdAlZ1dbW%2B/PJLFRQU6NZbb7WwMgAAgMaxXcBKTEw86/iuXbv0wQcfeLgaAACAprPdbRrOZeTIkdq2bZvVZQAAADSo1QSsAwcOqLa21uoyAAAAGmS7U4QzZsyoN1ZZWamDBw9q9OjRFlQEAADQNLYLWN27d6/3LkJ/f3/Fx8frlltusagqAACAxrNdwOJu7QAAoLWzXcDasmVLox87adKkFqwEAADgwtguYC1fvlw1NTX1Lmj38fFxG/Px8SFgAQAAW7JdwHruuef0/PPPa968eerTp49qa2v12Wef6dlnn9UvfvELDR482OoSAQAAzst2ASs5OVnp6ekKCwurGxs4cKAuv/xy3XHHHcrJybGwOgAAgIbZ7j5YX331lQIDA%2BuNBwQEqKSkxIKKAAAAmsZ2Aatbt25KTk5WeXl53djRo0e1bt06XXHFFRZWBgAA0Di2O0W4bNkyLVmyRBkZGerUqZMcDoeOHTum9u3ba/369VaXBwAA0CDbBayhQ4fq7bffVl5enr7%2B%2BmvV1tYqLCxMw4YNU5cuXawuDwAAoEG2C1iS1KFDB9144436%2Buuvdfnll1tdDgAAQJPY7hqsEydOaOnSpRowYIBuuukmSd9fgzVnzhwdPXrU4uoAAAAaZruAtWbNGhUXF2vt2rVyOP5XXnV1tdauXWthZQAAAI1ju4C1a9cuPf300xo7dmzdhz4HBARo9erVys3Ntbg6AACAhtkuYFVUVKh79%2B71xkNCQnT8%2BHHPFwQAANBEtgtYV1xxhT744ANJcvvswZ07d%2Bqyyy6zqiwAAIBGs927CGfOnKl7771XU6dOVU1NjV544QUVFhZq165dWr58udXlAQAANMh2AWv69OlyOp16%2BeWX5evrq2eeeUY9evTQ2rVrNXbsWKvLAwAAaJDtAtaRI0c0depUTZ061epSAAAALojtrsG68cYb3a69MmHVqlXq06dP3ff5%2BfmKj49XZGSkxo0bp%2BzsbLfHb9y4UWPGjFFkZKQSEhJUWFhotB4AAODdbBewBg8erB07dhjbX3FxsbKysuq%2BLy0t1fz58zVjxgzl5%2Bdr%2BfLlWrFihQoKCiRJu3fvVkpKih5//HHt2bNHI0aM0Lx583gHIwAAaDTbnSK89NJL9eijjyo9PV1XXHGF2rVr57Z93bp1jd5XTU2NHn74Yc2ePVtPPvmkJGnr1q3q3r274uPjJUnR0dGKi4tTZmam%2BvXrp4yMDE2ZMkX9%2B/eXJM2ZM0cbN27UW2%2B9pXHjxhnqEgAAeDPbrWB98cUXuuqqq9SlSxeVl5ertLTU7VdT/PGPf5S/v7/Gjx9fN1ZUVKTw8HC3x4WHh9edBjxzu8PhUN%2B%2BfetWuAAAABpimxWsRYsW6YknntBLL71UN7Z%2B/XotWLDggvb3zTffKCUlxW1/kuRyuRQWFuY2FhQUpPLy8rrtgYGBbtsDAwPrtjdGaWmpysrK3Maczo4KDQ1tSgtN4uvrcPva1tB/2%2B5fYg7on/5//LU1cjqbV7vd5sA2AWv37t31xtLT0y84YK1evVpTpkxRz549dejQoSY9t7kX2WdkZCg1NdVtbMGCBVq4cGGz9tsYAQEdWvwYdkb/bbt/iTmgf/pvrYKDOxnZj13mwDYB62yh5kKDTn5%2Bvj7%2B%2BGPl5OTU2xYcHCyXy%2BU2Vl5erpCQkHNud7lc6tWrV6OPP336dMXFxbmNOZ0dVV5e0eh9NJWvr0MBAR109GilqqtrWuw4dkX/bbt/qWXnYNTad43ur6X9OXGY1SV4XFv/N%2BAN/Tf3NfJcc2AquDWVbQLWDx/s3NBYY2RnZ%2Bvw4cMaMWKEpP8FtcGDB%2Bv222%2BvF7wKCwvrLmqPiIhQUVGRJk%2BeLEmqrq7WgQMH6i6Kb4zQ0NB6pwPLyr5TVVXL/6Wvrq7xyHHsiv7bdv8ScyCpTfff1v/8W3P/puq2yxzY40SlYQ888IB27dqlrKwsZWVlKT09XZKUlZWl8ePHq6SkRJmZmTp58qTy8vKUl5enadOmSZISEhK0ZcsW7d%2B/X5WVlUpLS5Ofn59iY2Mt7AgAALQmtlnBMikwMNDtQvWqqipJUteuXSVJGzZs0MqVK/XII4%2BoW7duWrNmja6%2B%2BmpJ0vDhw7V48WLdd999Onz4sPr166f09HS1b9/e840AAIBWyTYB6/Tp01qyZEmDY025D9YPfvKTn%2Bizzz6r%2Bz4qKsrt5qNnmjlzpmbOnNnk4wAAAEg2CljXX399vftcnW0MAADA7mwTsM68XxUAAEBr5ZUXuQMAAFiJgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgmG0%2BKgdA8wxcvtPqEhptx31DrC4BAFoUAQsAYJmbnnzf6hKahP8coLE4RQgAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYxn2wgHNobffnAQDYBytYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMOcVhfQUkpKSrRq1Srt3btXvr6%2BGj58uJYtW6aAgAAVFxfr0UcfVXFxsS666CLNmDFDt99%2Be91zt2/frrS0NB06dEg9evTQ4sWLNXToUAu78Q43Pfm%2B1SUAAOARXruCNW/ePAUEBGj37t167bXX9Pnnn%2Buxxx7TiRMnNHfuXN1www1699139cQTT2jDhg3Kzc2VJBUXF2vp0qVKTEzUX/7yF82ePVv33HOPvv76a4s7AgAArYVXBqyjR48qIiJCS5YsUadOndS1a1dNnjxZe/fu1dtvv63Tp0/r7rvvVseOHXXNNdfolltuUUZGhiQpMzNTMTExiomJkb%2B/vyZMmKDevXsrOzvb4q4AAEBr4ZWnCAMCArR69Wq3sf/85z8KDQ1VUVGR%2BvTpI19f37pt4eHhyszMlCQVFRUpJibG7bnh4eEqKCho%2BcKBNoLTxQC8nVcGrDMVFBTo5ZdfVlpamnbs2KGAgAC37UFBQXK5XKqpqZHL5VJgYKDb9sDAQH3xxReNPl5paanKysrcxpzOjgoNDb3wJhrg6%2Btw%2Bwqg7XI6%2BTnQUuw6t97wGtDcubXbHHh9wProo4909913a8mSJYqOjtaOHTvO%2BjgfH5%2B639fW1jbrmBkZGUpNTXUbW7BggRYuXNis/TZGQECHFj8GAHsLDu5kdQley%2B5z25pfA0zNrV3mwKsD1u7du3X//fdrxYoVmjRpkiQpJCREX331ldvjXC6XgoKC5HA4FBwcLJfLVW97SEhIo487ffp0xcXFuY05nR1VXl5xYY00gq%2BvQwEBHXT0aKWqq2ta7DgA7K8lf9a0dXadW294DWju3J5rDqwKxV4bsPbt26elS5fqqaeecrvFQkREhDZt2qSqqio5nd%2B3X1BQoP79%2B9dtLywsdNtXQUGBxo0b1%2Bhjh4aG1jsdWFb2naqqWv4vfXV1jUeOA8C%2B%2BBnQcuw%2Bt635NcBU3XaZA3ucqDSsqqpKDz30kBITE%2BvdvyomJkadO3dWWlqaKisr9cknn2jz5s1KSEiQJE2bNk179uzR22%2B/rZMnT2rz5s366quvNGHCBCtaAQAArZBXrmDt379fBw8e1MqVK7Vy5Uq3bTt37tQzzzyjhx9%2BWOnp6br44ou1aNEixcbGSpJ69%2B6ttWvXavXq1SopKVHPnj21YcMGXXLJJRZ0AgAAWiOvDFgDBw7UZ599dt7HbNq06ZzbRo8erdGjR5suCwAAtBFeeYoQAADASgQsAAAAw7zyFCEAtGXcKR%2BwHitYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBjvIgQAoJF4hyYaixUsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAxzWl0AmuemJ9%2B3ugQAAHAGVrAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAOouSkhLdddddGjx4sEaMGKE1a9aopqbG6rIAAEArwWcRnsW9996ra665Rm%2B88YYOHz6suXPn6uKLL9Ztt91mdWkAAKAVYAXrDAUFBfr000%2BVmJioLl26qHv37po9e7YyMjKsLg0AALQSrGCdoaioSN26dVNgYGDd2DXXXKMvv/xSx44dU%2BfOnRvcR2lpqcrKytzGnM6OCg0NNV4vAADewOls3pqPr6/D7avVCFhncLlcCggIcBv7IWyVl5c3KmBlZGQoNTXVbeyee%2B7Rvffea67Q/7f30bGSvg91GRkZmj59epsMcvTftvuXmAP6p/%2B23L/0/Ry8%2BOJztpkDe8Q8m6mtrW3W86dPn67XXnvN7df06dMNVXd2ZWVlSk1Nrbdy1lbQf9vuX2IO6J/%2B23L/kv3mgBWsM4SEhMjlcrmNuVwu%2Bfj4KCQkpFH7CA0NtUV6BgAA1mAF6wwRERH6z3/%2BoyNHjtSNFRQUqGfPnurUqZOFlQEAgNaCgHWG8PBw9evXT%2BvWrdOxY8d08OBBvfDCC0pISLC6NAAA0Er4JiUlJVldhN0MGzZMOTk5%2Bs1vfqNt27YpPj5ed9xxh3x8fKwu7bw6deqkQYMGtdmVNvpv2/1LzAH9039b7l%2By1xz41Db3im4AAAC44RQhAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEErFbuww8/VL9%2B/dx%2BRUREqE%2BfPlaX5jEHDhzQrFmzNHDgQA0ZMkSJiYluH9bt7QoLCzVr1ixdf/31GjZsmH7/%2B99bXVKLe/fddxUdHa1FixbV27Z9%2B3aNHz9eAwYM0JQpU/Tee%2B9ZUGHLOl//p0%2Bf1mOPPaarr75a77zzjgXVtbzz9Z%2Bbm6sJEyZowIABGjNmjP70pz9ZUGHLO98cvPLKKxozZoyuu%2B46jRo1yit/Jpyv/x9UVFQoNjZWDzzwgAcr%2Bx%2BnJUeFMVFRUSooKHAbe%2BaZZ/Tpp59aVJFnVVVV6a677tKUKVP03HPPqaKiQkuWLFFSUpKefvppq8trcS6XS3PmzNEtt9yiDRs26NChQ5o7d64uu%2Bwy3XTTTVaX1yKeffZZbd68WVdeeWW9bcXFxVq6dKlSU1N1ww03aNeuXbrnnnu0c%2BdhXF7NAAAGJElEQVROde3a1YJqzTtf/8ePH9ett96qnj17yls/Be18/f/1r39VYmKifvvb3yo2Nlbvv/%2B%2BFixYoKuuukoDBw60oNqWcb45eOONN/T000/r2WefVUREhPbt26fbb79dV155pUaOHGlBteadr/8fS0lJ0bFjxzxUVX2sYHmZf//733rhhRf0q1/9yupSPKKsrExlZWWaOHGi/Pz8FBwcrFGjRqm4uNjq0jxi//79qqio0H333acOHTqoV69euuOOO7R582arS2sx/v7%2B5/zhmpmZqZiYGMXExMjf318TJkxQ7969lZ2dbUGlLeN8/R8/flxTp07V6tWrLajMM87Xv8vl0ty5czVy5Eg5nU7FxMSod%2B/e2rt3rwWVtpzzzUFoaKieeOIJXXvttXI4HBo4cKB%2B%2BtOf6vPPP7eg0pZxvv5/8OmnnyonJ0eTJ0/2YGXuWMHyMk899ZSmTp2qyy67zOpSPCIsLEx9%2B/ZVRkaGfvnLX%2BrEiRPKzc1VbGys1aV5jI%2BPj9v3gYGBXh0wZ82adc5tRUVFiomJcRsLDw%2Bvt8rbmp2v/4svvlgzZszwYDWed77%2Bhw8fruHDh9d9X1VVpbKyMoWFhXmiNI853xxce%2B21db8/ffq03njjDf3rX//SiBEjPFGaR5yvf0mqra1VUlKSFi1apH//%2B9/67rvvPFSZO1awvMihQ4eUm5ur2267zepSPMbhcCglJUVvvvmmIiMjFR0draqqKi1ZssTq0jxiwIAB6tChg5566ilVVlbqn//8p1599VV9%2B%2B23VpdmCZfLpcDAQLexwMBAlZeXW1QRrLR27Vp17NhRN998s9WleNzvfvc7XXvttfr1r3%2Bt5ORkXX311VaX5DEZGRny8fHRlClTLK2DgOVFXnnlFY0ePVqXXHKJ1aV4zKlTpzRv3jyNHTtWe/fu1TvvvKMuXbooMTHR6tI8IjAwUOvXr1d%2Bfr6GDBmi%2B%2B%2B/XxMnTpSvr6/VpVnGW689QuPV1tZqzZo1ysnJUVpamvz9/a0uyePmz5%2BvTz75RI8%2B%2BqiWL1%2BuvLw8q0vyiMOHD%2Bupp55SUlJSvdV9TyNgeZFdu3YpLi7O6jI8Kj8/X4cOHdLixYvVpUsXhYWFaeHChfrzn/8sl8tldXkeMXDgQGVmZmrfvn3KyMhQUFCQ150Saazg4OB6f%2B4ul0shISEWVQRPq6mp0QMPPKDdu3dr06ZNuuqqq6wuyTJ%2Bfn6Ki4vTmDFj9Oqrr1pdjkckJydr0qRJtngnPddgeYni4mKVlJRoyJAhVpfiUdXV1aqpqXFbtTh16pSFFXnWyZMntX37do0aNUqdO3eWJL3//vsaMGCAxZVZIyIiQoWFhW5jBQUFGjdunEUVwdNWrVqlzz//XJs2bVJQUJDV5XhcUlKSOnfu7LaK7%2BPjI6ezbbzcZ2dnKyAgQK%2B99pok6cSJE6qpqdFbb72lDz74wKO1sILlJQ4cOKCgoKC6F9m2YsCAAerYsaNSUlJUWVmp8vJypaWlKSoqqk38cG3Xrp1SU1OVlpamqqoqvffee8rOztatt95qdWmWmDZtmvbs2aO3335bJ0%2Be1ObNm/XVV19pwoQJVpcGD/joo4%2BUnZ2t9PT0NvHv/2wGDRqkV199VR988IGqq6u1b98%2Bbdu2zasucj%2BfvLw8bd26VVlZWcrKytKMGTMUFxenrKwsj9fiU8sFC15hw4YN2rp1q3JycqwuxeMKCwv12GOP6dNPP5Wfn58GDRqkBx54oM2cJisoKNDDDz%2BsgwcPqmvXrkpMTNSoUaOsLqvF9OvXT9L37xCTVPc/8x/eKZibm6t169appKREPXv21PLlyxUVFWVNsS3gfP1v2bJFK1askPT9Sm67du3k4%2BOjiRMnauXKldYUbNj5%2Bl%2B2bJlef/31eqs1UVFRev755z1baAtq6N/Apk2b9Oyzz%2Bqbb75R165dNW3aNM2ZM8eaYltAQ/3/WEpKikpKSpScnOy5Av8fAQsAAMAwThECAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGH/B7WWyD9wyYAzAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common3831597233006397471”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>13.131342681563643</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.134279038266653</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.54900610078667</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.229950165885779</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.77667438263873</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.647286187799049</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.898951226099808</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.250761015699586</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.112030629125861</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.990789126359896</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (41)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme3831597233006397471”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>6.954297724061087</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:71%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.434083150867912</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.502499503897138</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.590923238935214</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.6672970895081125</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:95%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>13.131342681563643</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.134279038266653</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:62%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.229950165885779</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:47%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.551342827833837</td> <td class=”number”>93</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:37%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.743785560685042</td> <td class=”number”>82</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:32%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_NSLP15”><s>PCT_NSLP15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCT_NSLP09”><code>PCT_NSLP09</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.98444</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_OBESE_ADULTS08”>PCT_OBESE_ADULTS08<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>244</td>

</tr> <tr>

<th>Unique (%)</th> <td>7.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>28.932</td>

</tr> <tr>

<th>Minimum</th> <td>11.7</td>

</tr> <tr>

<th>Maximum</th> <td>43.7</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram7514923083748395606”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives7514923083748395606,#minihistogram7514923083748395606”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives7514923083748395606”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles7514923083748395606”

aria-controls=”quantiles7514923083748395606” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram7514923083748395606” aria-controls=”histogram7514923083748395606”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common7514923083748395606” aria-controls=”common7514923083748395606”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme7514923083748395606” aria-controls=”extreme7514923083748395606”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles7514923083748395606”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>11.7</td>

</tr> <tr>

<th>5-th percentile</th> <td>22.2</td>

</tr> <tr>

<th>Q1</th> <td>27.2</td>

</tr> <tr>

<th>Median</th> <td>29.1</td>

</tr> <tr>

<th>Q3</th> <td>31</td>

</tr> <tr>

<th>95-th percentile</th> <td>34.6</td>

</tr> <tr>

<th>Maximum</th> <td>43.7</td>

</tr> <tr>

<th>Range</th> <td>32</td>

</tr> <tr>

<th>Interquartile range</th> <td>3.8</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>3.7026</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.12798</td>

</tr> <tr>

<th>Kurtosis</th> <td>2.2043</td>

</tr> <tr>

<th>Mean</th> <td>28.932</td>

</tr> <tr>

<th>MAD</th> <td>2.664</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.47329</td>

</tr> <tr>

<th>Sum</th> <td>90615</td>

</tr> <tr>

<th>Variance</th> <td>13.71</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram7514923083748395606”>

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iIsI/J0nNmjU7r/3Hx8dXuhzo8RxVWRmL/4zy8gr68Sv0w45%2BVE9d6B1rxI5%2B2AVDP5y/SFkDOnfurPz8fNuY2%2B1Wt27dFBkZqXbt2qmgoMA/V1xcrO%2B//15du3ZVx44dZVmWvvrqK9tro6KidMUVVwTsGAAAQO0VkgFr1KhR2rFjh7Zu3aqTJ09qzZo12r9/v4YNGyZJSktL06pVq7R3714dO3ZMCxcuVMeOHdWlSxfFxcVp0KBBevbZZ3XkyBEdPHhQS5cuVWpqqlyukDzhBwAADKu1iaFLly6SpLKyMknSpk2bJP1ytql9%2B/ZauHCh5s2bp8LCQrVt21bLly9XixYtJEljxoyRx%2BPR3XffrZKSEiUmJtpuSn/yySc1Z84cDRgwQPXq1dOtt95a5cNMAQAAqhJmVfeObpwXj%2Beo0yUEBZcrXLGxjeX1ljh%2BfTwY0A%2B7YO3HLc9%2B5HQJF2TjlOudLqHGBOsacQr9sKuqHy1aNHWklpC8RAgAAOAkAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABgW8ICVkpKirKwsHThwINC7BgAACIiAB6w77rhD7777rm688UZNmDBBH3zwgcrKygJdBgAAQI0JeMBKT0/Xu%2B%2B%2BqzfffFPt2rXTU089pX79%2BmnBggX67rvvAl0OAACAcY7dg9WpUydNnz5dW7Zs0axZs/Tmm29q8ODBGj9%2BvHbt2uVUWQAAANXmWMA6ffq03n33Xd17772aPn26WrZsqZkzZ6pjx44aN26c1q9f71RpAAAA1eIK9A737t2rNWvWaN26dSopKdGgQYP08ssvq2fPnv5tevXqpccff1xDhw4NdHkAAADVFvCANWTIEF1xxRWaOHGibr/9dsXExFTapl%2B/fjpy5EigSwMAADAi4AFr1apV6t279%2B9u9%2BWXXwagGgAAAPMCfg9Whw4dNGnSJG3atMk/9tJLL%2Bnee%2B%2BVz%2BcLdDkAAADGBTxgzZs3T0ePHlXbtm39Y/3791dFRYXmz58f6HIAAACMC/glwg8//FDr169XbGysf6xNmzZauHChbr311kCXAwAAYFzAz2CdOHFCkZGRlQsJD1dpaamx/ezevVtjx47VNddco%2Buvv17Tpk3z3zifl5en1NRU9ejRQ0OGDFFubq7ttatWrdKgQYPUo0cPpaWlKT8/31hdAAAg9AU8YPXq1Uvz58/Xzz//7B/76aef9MQTT9ge1VAdZWVluu%2B%2B%2B3T11Vdrx44d2rBhg44cOaLHH39cRUVFmjx5ssaMGaO8vDzNnj1bjz32mNxutyRp8%2BbNWrJkiZ555hnt2LFDycnJmjRpko4fP26kNgAAEPoCHrBmzZqlvLw8XXfdderdu7euueYa9e/fX/n5%2BZo7d66RfXg8Hnk8Ht12222qX7%2B%2BYmNjNXDgQO3Zs0fr169XmzZtlJqaqsjISCUlJSklJUWrV6%2BWJOXk5GjEiBHq1q2bGjRooAkTJkiStmzZYqQ2AAAQ%2BgJ%2BD1br1q31zjvv6L//%2B7/1/fffKzw8XFdccYX69OmjiIgII/to2bKlOnbsqJycHP3zP/%2BzTpw4oQ8%2B%2BED9%2B/dXQUGBEhISbNsnJCRo48aNkqSCggINHjzYPxceHq6OHTvK7XZryJAh57X/oqIieTwe25jL1Ujx8fHVPLLaLyIi3Pa1rqMfdvTDDJcrdPvHGrGjH3bB1I%2BAByxJql%2B/vm688cYae//w8HAtWbJE48aN08svvyxJ6t27t6ZOnarJkyerZcuWtu1jYmLk9XolST6fT9HR0bb56Oho//z5yMnJUVZWlm0sPT1dGRkZF3M4ISkqqqHTJQQV%2BmFHP6onNrax0yXUONaIHf2wC4Z%2BBDxg/fDDD1q0aJG%2B%2BeYbnThxotL8X//612rv49SpU5o0aZJuvvlm//1TTzzxhKZNm3Zer7csq1r7Hz16tFJSUmxjLlcjeb0l1XrfUBAREa6oqIYqLi5VeXmF0%2BU4jn7Y0Q8zQvlnDWvEjn7YVdUPp/7BEfCANWvWLBUVFalPnz5q1KhRjewjLy9PP/74ox566CFFRESoadOmysjI0G233aYbbrih0gNNvV6v4uLiJEmxsbGV5n0%2Bn9q1a3fe%2B4%2BPj690OdDjOaqyMhb/GeXlFfTjV%2BiHHf2onrrQO9aIHf2wC4Z%2BBDxg5efn669//as/0NSE8vJyVVRU2M5EnTp1SpKUlJSkv/zlL5Vq6tatmySpc%2BfOKigo0PDhw/3vtXv3bqWmptZYvQAAILQE/C6wZs2a1diZqzO6d%2B%2BuRo0aacmSJSotLZXX69WyZcvUq1cv3XbbbSosLNTq1at18uRJbdu2Tdu2bdOoUaMkSWlpaVq3bp127typ0tJSLVu2TPXr11f//v1rtGYAABA6Ah6wJk6cqKysrGrf53QusbGxevHFF/X555%2Brb9%2B%2BuvXWW9WgQQMtWrRIzZo10/Lly/Xqq6%2BqZ8%2Beeuqpp7RgwQJdddVVkqS%2BffvqoYce0pQpU9S7d2/t2LFD2dnZatCgQY3VCwAAQkuYVZNJpwoPPvigPv/8c1mWpUsvvVTh4faM98YbbwSynIDxeI46XUJQcLnCFRvbWF5viePXx4MB/bAL1n7c8uxHTpdwQTZOud7pEmpMsK4Rp9APu6r60aJFU2dqCfQOmzRpor59%2BwZ6twAAAAET8IA1b968QO8SAAAgoBx51Om%2Bffu0ZMkSzZw50z/2xRdfOFEKAACAcQEPWHl5eRo2bJg%2B%2BOADbdiwQdIvDx8dO3askYeMAgAAOC3gAWvx4sV6%2BOGHtX79eoWFhUn65e8Tzp8/X0uXLg10OQAAAMYFPGD9/e9/V1pamiT5A5Yk3Xzzzdq7d2%2BgywEAADAu4AGradOmVf4NwqKiItWvXz/Q5QAAABgX8IDVo0cPPfXUUzp27Jh/7LvvvtP06dN13XXXBbocAAAA4wL%2BmIaZM2fqnnvuUWJiosrLy9WjRw%2BVlpaqXbt2mj9/fqDLAQAAMC7gAeuSSy7Rhg0btG3bNn333Xdq0KCBrrjiCl1//fW2e7IAAABqq4AHLEmqV6%2BebrzxRid2DQAAUOMCHrBSUlLOeaaKZ2EBAIDaLuABa/DgwbaAVV5eru%2B%2B%2B05ut1v33HNPoMsBAAAwLuABa9q0aVWOv//%2B%2B/rkk08CXA0AAIB5jvwtwqrceOONeuedd5wuAwAAoNqCJmDt3r1blmU5XQYAAEC1BfwS4ZgxYyqNlZaWau/evbrpppsCXQ4AAIBxAQ9Ybdq0qfRbhJGRkUpNTdXIkSMDXQ4AAIBxAQ9YPK0dAACEuoAHrHXr1p33trfffnsNVgIAAFAzAh6wZs%2BerYqKiko3tIeFhdnGwsLCCFgAAKBWCnjA%2BvOf/6wVK1Zo0qRJ6tChgyzL0tdff60XXnhBd911lxITEwNdEoAAu%2BXZj5wuAQBqlCP3YGVnZ6tly5b%2BsWuuuUatW7fW%2BPHjtWHDhkCXBAAAYFTAn4O1f/9%2BRUdHVxqPiopSYWFhoMsBAAAwLuABq1WrVpo/f768Xq9/rLi4WIsWLdJll10W6HIAAACMC/glwlmzZmnq1KnKyclR48aNFR4ermPHjqlBgwZaunRpoMsBAAAwLuABq0%2BfPtq6dau2bdumgwcPyrIstWzZUjfccIOaNm0a6HIAAACMC3jAkqSGDRtqwIABOnjwoFq3bu1ECQAAADUm4PdgnThxQtOnT1f37t11yy23SPrlHqwJEyaouLg40OUAAAAYF/CAtWDBAu3Zs0cLFy5UePj/3315ebkWLlwY6HIAAACMC3jAev/99/WnP/1JN998s/%2BPPkdFRWnevHn64IMPAl0OAACAcQEPWCUlJWrTpk2l8bi4OB0/fjzQ5QAAABgX8IB12WWX6ZNPPpEk298efO%2B99/QP//APgS4HAADAuIAHrDvvvFMPPvignn76aVVUVGjlypWaOnWqZs2apXvuucfovpYtW6Y%2Bffro6quv1rhx4/Tjjz9KkvLy8pSamqoePXpoyJAhys3Ntb1u1apVGjRokHr06KG0tDTl5%2BcbrQsAAIS2gAes0aNHa/r06fr4448VERGh559/XoWFhVq4cKHS0tKM7ee1115Tbm6uVq1apQ8//FBt27bVSy%2B9pKKiIk2ePFljxoxRXl6eZs%2Berccee0xut1uStHnzZi1ZskTPPPOMduzYoeTkZE2aNInLlwAA4LwF/DlYR44c0R133KE77rijRvezYsUKTZ8%2BXf/4j/8oSXr00UclSS%2B%2B%2BKLatGmj1NRUSVJSUpJSUlK0evVqdenSRTk5ORoxYoS6desmSZowYYJWrVqlLVu2aMiQITVaMwAACA0BP4M1YMAA271XNeGnn37Sjz/%2BqJ9//lmDBw9WYmKiMjIydOTIERUUFCghIcG2fUJCgv8y4Nnz4eHh6tixo/8MFwAAwO8J%2BBmsxMREbdy4UYMHD66xfRw8eFDSLzfOr1y5UpZlKSMjQ48%2B%2BqhOnDihli1b2raPiYnx//Fpn8%2Bn6Oho23x0dLTtj1P/nqKiInk8HtuYy9VI8fHxF3M4ISUiItz2ta6jH6gJLlforic%2BM3b0wy6Y%2BhHwgPWHP/xB//Zv/6bs7Gxddtllqlevnm1%2B0aJF1d7HmTNkEyZM8IepBx98UPfee6%2BSkpLO%2B/UXKycnR1lZWbax9PR0ZWRkVOt9Q0lUVEOnSwgq9AMmxcY2drqEGsdnxo5%2B2AVDPwIesL799lv/fVEXclboQjRv3lzSLw8wPaNVq1ayLEunT5%2BWz%2Bezbe/1ehUXFydJio2NrTTv8/nUrl27897/6NGjlZKSYhtzuRrJ6y25oOMIRRER4YqKaqji4lKVl1c4XY7j6AdqwjWz33O6hPP2X9NuuKDt%2BczY0Q%2B7qvrh1D84AhawMjMztXjxYr3yyiv%2BsaVLlyo9Pd34vi655BI1adJEe/bsUadOnSRJhYWFqlevnvr166e3337btn1%2Bfr7/pvbOnTuroKBAw4cPl/TLn/DZvXu3/6b48xEfH1/pcqDHc1RlZSz%2BM8rLK%2BjHr9AP1FUXu%2B75zNjRD7tg6EfALlJu3ry50lh2dnaN7Mvlcik1NVXPP/%2B8/ud//keHDx/W0qVLNXToUA0fPlyFhYVavXq1Tp48qW3btmnbtm0aNWqUJCktLU3r1q3Tzp07VVpaqmXLlql%2B/frq379/jdQKAABCT8DOYFV1X1NN/jbh1KlTderUKY0cOVKnT5/WoEGD9Oijj6px48Zavny55s6dqyeeeEKtWrXSggULdNVVV0mS%2Bvbtq4ceekhTpkzR4cOH1aVLF2VnZ6tBgwY1VisAAAgtYVZNPzPh/3Tr1k1ffvnl746FKo/nqNMlBAWXK1yxsY3l9ZY4fvo2GNTVftzy7EdOl4AgsXHK9Re0fV39zPwW%2BmFXVT9atGjqSC3O/x4jAABAiCFgAQAAGBawe7BOnz6tqVOn/u6YiedgAQAAOClgAatnz54qKir63TEAAIDaLmAB69fPvwIAAAhl3IMFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhtWJgPXUU0%2BpQ4cO/u/z8vKUmpqqHj16aMiQIcrNzbVtv2rVKg0aNEg9evRQWlqa8vPzA10yAACoxUI%2BYO3Zs0dvv/22//uioiJNnjxZY8aMUV5enmbPnq3HHntMbrdbkrR582YtWbJEzzzzjHbs2KHk5GRNmjRJx48fd%2BoQAABALRPSAauiokJz5szRuHHj/GPr169XmzZtlJqaqsjISCUlJSklJUWrV6%2BWJOXk5GjEiBHq1q2bGjRooAkTJkiStmzZ4sQhAACAWsjldAE16Y033lBkZKSGDh2qZ599VpJUUFCghIQE23YJCQnauHGjf37w4MH%2BufDwcHXs2FFut1tDhgw5r/0WFRXJ4/HYxlyuRoqPj6/O4YSEiIhw29e6jn6grnO5Lmzt85mxox92wdSPkA1Yhw4d0pIlS/TKK6/Yxn0%2Bn1q2bGkbi4nzz1qYAAARIElEQVSJkdfr9c9HR0fb5qOjo/3z5yMnJ0dZWVm2sfT0dGVkZFzIIYS0qKiGTpcQVOgH6qrY2MYX9To%2BM3b0wy4Y%2BhGyAWvevHkaMWKE2rZtqx9//PGCXmtZVrX2PXr0aKWkpNjGXK5G8npLqvW%2BoSAiIlxRUQ1VXFyq8vIKp8txHP1AXXehPxf5zNjRD7uq%2BnGxIb66QjJg5eXl6YsvvtCGDRsqzcXGxsrn89nGvF6v4uLifnPe5/OpXbt2573/%2BPj4SpcDPZ6jKitj8Z9RXl5BP36FfqCuuth1z2fGjn7YBUM/nL9IWQNyc3N1%2BPBhJScnKzExUSNGjJAkJSYmqn379pUeu5Cfn69u3bpJkjp37qyCggL/XHl5uXbv3u2fBwAA%2BD0hGbBmzJih999/X2%2B//bbefvttZWdnS5LefvttDR06VIWFhVq9erVOnjypbdu2adu2bRo1apQkKS0tTevWrdPOnTtVWlqqZcuWqX79%2Burfv7%2BDRwQAAGqTkLxEGB0dbbtRvaysTJJ0ySWXSJKWL1%2BuuXPn6oknnlCrVq20YMECXXXVVZKkvn376qGHHtKUKVN0%2BPBhdenSRdnZ2WrQoEHgDwQAANRKIRmwznbppZfq66%2B/9n/fq1cv28NHz3bnnXfqzjvvDERpAAAgBIXkJUIAAAAnEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhoVswCosLFR6eroSExOVlJSkGTNmqLi4WJK0Z88e3XXXXerZs6duuukmrVixwvbad999V0OHDlX37t01YsQIffjhh04cAgAAqKVCNmBNmjRJUVFR2rx5s9566y198803evrpp3XixAlNnDhR1157rbZv367Fixdr%2BfLl%2BuCDDyT9Er6mT5%2BuadOm6eOPP9a4ceP0wAMP6ODBgw4fEQAAqC1CMmAVFxerc%2BfOmjp1qho3bqxLLrlEw4cP16effqqtW7fq9OnTuv/%2B%2B9WoUSN16tRJI0eOVE5OjiRp9erV6tevn/r166fIyEgNGzZM7du3V25ursNHBQAAaouQDFhRUVGaN2%2Bemjdv7h87cOCA4uPjVVBQoA4dOigiIsI/l5CQoPz8fElSQUGBEhISbO%2BXkJAgt9sdmOIBAECt53K6gEBwu9169dVXtWzZMm3cuFFRUVG2%2BZiYGPl8PlVUVMjn8yk6Oto2Hx0drW%2B//fa891dUVCSPx2Mbc7kaKT4%2B/uIPIkRERITbvtZ19AN1nct1YWufz4wd/bALpn6EfMD67LPPdP/992vq1KlKSkrSxo0bq9wuLCzM//%2BWZVVrnzk5OcrKyrKNpaenKyMjo1rvG0qioho6XUJQoR%2Boq2JjG1/U6/jM2NEPu2DoR0gHrM2bN%2Bvhhx/WY489pttvv12SFBcXp/3799u28/l8iomJUXh4uGJjY%2BXz%2BSrNx8XFnfd%2BR48erZSUFNuYy9VIXm/JxR1ICImICFdUVEMVF5eqvLzC6XIcRz9Q113oz0U%2BM3b0w66qflxsiK%2BukA1Yn3/%2BuaZPn67nnntOffr08Y937txZr7/%2BusrKyuRy/XL4brdb3bp188%2BfuR/rDLfbrSFDhpz3vuPj4ytdDvR4jqqsjMV/Rnl5Bf34FfqBuupi1z2fGTv6YRcM/XD%2BImUNKCsr06OPPqpp06bZwpUk9evXT02aNNGyZctUWlqqL7/8UmvWrFFaWpokadSoUdqxY4e2bt2qkydPas2aNdq/f7%2BGDRvmxKEAAIBaKCTPYO3cuVN79%2B7V3LlzNXfuXNvce%2B%2B9p%2Beff15z5sxRdna2mjdvrszMTPXv31%2BS1L59ey1cuFDz5s1TYWGh2rZtq%2BXLl6tFixYOHAkAAKiNwqzq3tGN8%2BLxHHW6hKDgcoUrNraxvN4Sx0/fBgOT/bjl2Y8MVQUEzsYp11/Q9vwMsaMfdlX1o0WLpo7UEpKXCAEAAJxEwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhIfmYBgBA7VDbfvv1Qn/rEXUXZ7AAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEupwsAgtUtz37kdAkAgFqKM1gAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAw3jQKAAA56m2PYB445TrnS6hzuIMFgAAgGEELAAAAMO4RIiAqW2n1gEAuFicwQIAADCMgFWFwsJC3XfffUpMTFRycrIWLFigiooKp8sCAAC1BJcIq/Dggw%2BqU6dO2rRpkw4fPqyJEyeqefPm%2BuMf/%2Bh0aQAAnLfadGtGqP3GIwHrLG63W1999ZVWrlyppk2bqmnTpho3bpxefvnloAxYtenDAwBAXUHAOktBQYFatWql6Oho/1inTp303Xff6dixY2rSpMnvvkdRUZE8Ho9tzOVqpPj4eOP1AgAQClyu6t%2B1FBERbvvqJALWWXw%2Bn6KiomxjZ8KW1%2Bs9r4CVk5OjrKws29gDDzygBx980Fyh/%2BfTf7vZ%2BHvWpKKiIuXk5Gj06NEETtGPs9EPO/pRGT2xox92RUVFevnlPwdFP5yPeEHIsqxqvX706NF66623bP%2BNHj3aUHW1m8fjUVZWVqUzfHUV/bCjH3b0ozJ6Ykc/7IKpH5zBOktcXJx8Pp9tzOfzKSwsTHFxcef1HvHx8Y4nZwAA4BzOYJ2lc%2BfOOnDggI4cOeIfc7vdatu2rRo3buxgZQAAoLYgYJ0lISFBXbp00aJFi3Ts2DHt3btXK1euVFpamtOlAQCAWiLi8ccff9zpIoLNDTfcoA0bNuhf//Vf9c477yg1NVXjx49XWFiY06WFhMaNG6t3796cEfw/9MOOftjRj8roiR39sAuWfoRZ1b2jGwAAADZcIgQAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjICFGrN9%2B3YlJSUpMzPTNv7WW2/pqquuUpcuXWz/7dq1y6FKA6OwsFDp6elKTExUUlKSZsyYoeLiYknSnj17dNddd6lnz5666aabtGLFCoerrXm/1Y8ff/xRHTp0qLQ%2BXnzxRadLrlFfffWV7rnnHvXs2VNJSUmaMmWKPB6PJCkvL0%2Bpqanq0aOHhgwZotzcXIerrXm/1Y9PPvmkyvWxceNGp0sOmKeeekodOnTwf18X18fZft2ToFkjFlADsrOzrZtuuskaM2aMNWXKFNvc2rVrrbvuusuhypxz6623WjNmzLCOHTtmHThwwBoxYoQ1a9Ysq7S01LrhhhusJUuWWCUlJVZ%2Bfr7Vu3dv6/3333e65Br1W/344YcfrPbt2ztdXkCdPHnSuu6666ysrCzr5MmT1uHDh6277rrLmjx5svXTTz9ZV199tbV69WrrxIkT1kcffWR17drV2rVrl9Nl15hz9ePjjz%2B2kpOTnS7RMbt377Z69%2B7t/4zUxfVxtrN7EixrhDNYqBGRkZFas2aNLr/8cqdLCQrFxcXq3Lmzpk6dqsaNG%2BuSSy7R8OHD9emnn2rr1q06ffq07r//fjVq1EidOnXSyJEjlZOT43TZNeZc/aiLSktLlZmZqYkTJ6p%2B/fqKi4vTwIED9c0332j9%2BvVq06aNUlNTFRkZqaSkJKWkpGj16tVOl11jztWPuqyiokJz5szRuHHj/GN1cX38WlU9CRYELNSIsWPHqmnTpr85f%2BDAAf3xj39Ur169NGDAAL399tsBrC7woqKiNG/ePDVv3tw/duDAAcXHx6ugoEAdOnRQRESEfy4hIUH5%2BflOlBoQ5%2BrHGY888oj69Omja6%2B9VosWLdLp06edKDUgoqOjNXLkSLlcLknSvn379Je//EW33HKLCgoKlJCQYNs%2B1NfHufohSSUlJf7LyzfccINWrlwpy7KcLDkg3njjDUVGRmro0KH%2Bsbq4Pn6tqp5IwbFGCFgIuLi4OLVp00YPP/ywPvroIz300EOaNWuW8vLynC4tYNxut1599VXdf//98vl8ioqKss3HxMTI5/OpoqLCoQoD69f9qF%2B/vrp3766BAwdqy5Ytys7OVm5urv7jP/7D6TJrXGFhoTp37qzBgwerS5cuysjI%2BM314fV6HaoycKrqR5MmTdS%2BfXvdc8892r59u%2BbNm6esrCytXbvW6XJr1KFDh7RkyRLNmTPHNl6X18dv9SRY1ggBCwHXv39//fnPf1ZCQoLq16%2BvIUOGaODAgXrrrbecLi0gPvvsM40fP15Tp05VUlLSb24XFhYWwKqcc3Y/4uPj9cYbb2jgwIGqV6%2BeunbtqokTJ9aJ9dGqVSu53W6999572r9/vx555BGnS3JUVf3o1KmTXnnlFfXu3Vv169dXnz59NGbMmJBfH/PmzdOIESPUtm1bp0sJGr/Vk2BZIwQsBIVWrVqpqKjI6TJq3ObNm3Xfffdp1qxZGjt2rKRfzuid/a9Nn8%2BnmJgYhYeH9ke0qn5UpVWrVjp06FCduAwUFhamNm3aKDMzUxs2bJDL5ZLP57Nt4/V6FRcX51CFgXV2P44cOVJpm1D/%2BZGXl6cvvvhC6enpleZiY2Pr5Po4V0%2Bq4sQaCe2f3ghKr7/%2But59913b2N69e9W6dWuHKgqMzz//XNOnT9dzzz2n22%2B/3T/euXNnff311yorK/OPud1udevWzYkyA%2Ba3%2BpGXl6dly5bZtt23b59atWoVsmf18vLyNGjQINsl4TPhumvXrpXup8nPzw/p9XGufmzbtk3/%2BZ//adt%2B3759If3zIzc3V4cPH1ZycrISExM1YsQISVJiYqLat29f59aHdO6erFu3LjjWiMO/xYgQN3369EqPaXjppZesa6%2B91tq1a5d16tQpa/369VbHjh0tt9vtUJU17/Tp09Ytt9xivfHGG5XmTp48aSUnJ1t/%2BtOfrOPHj1s7d%2B60rrnmGmvLli2BLzRAztUPt9ttderUyVq3bp116tQpa9euXdb1119vrVixwoFKA6O4uNhKSkqy5s%2Bfbx0/ftw6fPiwNX78eOvOO%2B%2B0Dh06ZHXv3t168803rRMnTlhbt261unbtau3Zs8fpsmvMufrxX//1X1bXrl2t7du3W6dOnbI%2B/PBD6%2Bqrrw7px5r4fD7rwIED/v%2B%2B%2BOILq3379taBAweswsLCOrc%2BLOvcPQmWNRJmWXXgnDsCrkuXLpLkPytz5reB3G63LMvSsmXLtGbNGnk8Hl166aV65JFHlJyc7Fi9Ne3TTz/VP/3TP6l%2B/fqV5t577z2VlJRozpw5ys/PV/PmzXXvvffqzjvvdKDSwPi9fuzevVtZWVnav3%2B/mjZtqrvvvlv33ntvSF8y/frrrzV37lzt2rVLjRo10rXXXqsZM2aoZcuW%2Btvf/qa5c%2Bdq7969atWqlaZOnaqbbrrJ6ZJr1Ln6kZOToxUrVujAgQNq3ry57r//fo0cOdLpkgPmxx9/1IABA/T1119LUp1cH2c7uyfBsEYIWAAAAIaF7j8HAQAAHELAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBh/w9nwUKQfFjzFQAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common7514923083748395606”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>29.8</td> <td class=”number”>55</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>29.4</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>28.6</td> <td class=”number”>51</td> <td class=”number”>1.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>29.6</td> <td class=”number”>51</td> <td class=”number”>1.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>28.4</td> <td class=”number”>50</td> <td class=”number”>1.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>30.1</td> <td class=”number”>50</td> <td class=”number”>1.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>29.7</td> <td class=”number”>49</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>29.9</td> <td class=”number”>48</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>29.3</td> <td class=”number”>48</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>28.8</td> <td class=”number”>48</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (233)</td> <td class=”number”>2629</td> <td class=”number”>81.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme7514923083748395606”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>11.7</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.9</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.2</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.7</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>41.8</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>42.1</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>42.2</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>42.7</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>43.7</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_OBESE_ADULTS13”>PCT_OBESE_ADULTS13<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>267</td>

</tr> <tr>

<th>Unique (%)</th> <td>8.3%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>31.022</td>

</tr> <tr>

<th>Minimum</th> <td>11.8</td>

</tr> <tr>

<th>Maximum</th> <td>47.6</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-7383719616159785432”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-7383719616159785432”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-7383719616159785432”

aria-controls=”quantiles-7383719616159785432” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-7383719616159785432” aria-controls=”histogram-7383719616159785432”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-7383719616159785432” aria-controls=”common-7383719616159785432”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-7383719616159785432” aria-controls=”extreme-7383719616159785432”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-7383719616159785432”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>11.8</td>

</tr> <tr>

<th>5-th percentile</th> <td>23.055</td>

</tr> <tr>

<th>Q1</th> <td>28.3</td>

</tr> <tr>

<th>Median</th> <td>31.2</td>

</tr> <tr>

<th>Q3</th> <td>33.825</td>

</tr> <tr>

<th>95-th percentile</th> <td>38</td>

</tr> <tr>

<th>Maximum</th> <td>47.6</td>

</tr> <tr>

<th>Range</th> <td>35.8</td>

</tr> <tr>

<th>Interquartile range</th> <td>5.525</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>4.5138</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.1455</td>

</tr> <tr>

<th>Kurtosis</th> <td>0.90702</td>

</tr> <tr>

<th>Mean</th> <td>31.022</td>

</tr> <tr>

<th>MAD</th> <td>3.473</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.29067</td>

</tr> <tr>

<th>Sum</th> <td>97160</td>

</tr> <tr>

<th>Variance</th> <td>20.374</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-7383719616159785432”>

<img 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88UWPWzUAAAB4K9tPES5cuFA//elP9cgjj6hhw4aqrq5WWVmZmjdvrtdff93ucgAAAIyzPWA1b95c77zzjv7whz/or3/9qwIDA9WiRQv16NFDQUFBdpcDAABgnO0BS5JCQkL06KOPOrFrAACAOmd7wPryyy%2B1Zs0a/eUvf1FFRUWt%2Bffee8/ukgAAAIxy5BqsoqIi9ejRQ/Xr17d79wAAAHXO9oCVl5en9957T9HR0XW%2Brw0bNuiNN97QpUuX9OCDD2rZsmVq1qyZcnJytGbNGp0%2BfVo/%2BMEPNHnyZA0dOrTmcZs2bdIbb7yh4uJitWnTRosWLVJiYmKd1wsAAHyD7bdpuPfee205cvXGG29ox44d2rRpk44cOaKWLVvqtddeU1FRkaZNm6ZRo0YpJydHixYt0pIlS5SbmytJOnDggNatW6eXXnpJR48eVZ8%2BfTRlyhRdvny5zmsGAAC%2BwfaANXnyZGVkZMiyrDrdz8aNGzV79mz98Ic/VMOGDbV48WItXrxYO3fuVHx8vNLT0xUaGqrk5GSlpqYqOztbkpSVlaW0tDR17NhRYWFhmjhxoiTp4MGDdVovAADwHbafIvzDH/6gjz/%2BWG%2B//baaNWumwEDPjLdly5Y73sdXX32lc%2BfO6euvv9agQYNUUlKipKQkvfDCC8rPz1dCQoLH9gkJCdqzZ48kKT8/X4MGDaqZCwwMVNu2bZWbm6vBgwffcW0AAMD32R6wGjZsqF69etXpPs6fPy9J2rt3r1599VVZlqVnn31WixcvVkVFhZo0aeKxfWRkpFwulyTJ7XYrIiLCYz4iIqJm/nYUFRWpuLjYYywmJkZhYY2%2BTzteKSgo0OOrP6Bn4LsLDr47Xzv%2B9tr2t37tYHvAWrFiRZ3v4x%2BnHydOnFgTpmbOnKlJkyYpOTn5th//fWVlZSkjI8NjbMaMGZo5c%2BYdPa83Cg%2B/x%2BkSbEfPwO2LimrgdAk35W%2BvbX/rty45cqPR06dP65133tH//d//1QSuTz75RJ06dTLy/I0bN5YkhYeH14zFxcXJsixdu3ZNbrfbY3uXy1XzqcaoqKha8263W61atbrt/Y8cOVKpqakeYzExMSotLVdVVfV36sVbBQUFKjz8Hnr2cf7YM8xyucqcLuGG/O217cv9OhXibQ9YOTk5mjRpklq0aKGzZ89qxYoV%2BvLLLzVu3DitXbtWffv2veN9NG3aVA0bNtSJEyfUrl07SVJBQYHq1aunlJQUbd%2B%2B3WP7vLw8dezYUZKUmJio/Px8DRs2TJJUVVWl48ePKz09/bb3Hxsbq9jY2FrjLleZKit964V7K1VV1fTsB/yxZ5hxt79u/O217W/91iXbT7a%2B/PLLeu6557Rz504FBARI%2BvvfJ1y5cqXWr19vZB/BwcFKT0/Xr371K/3v//6vSkpKtH79eg0ZMkTDhg1TQUGBsrOzdeXKFR0%2BfFiHDx/WiBEjJEmjR4/Wtm3b9Omnn6q8vFwbNmxQSEiIevfubaQ2AADg%2B2w/gvU///M/2rx5syTVBCxJeuyxx7Rw4UJj%2B5k7d66uXr2q4cOH69q1axowYIAWL16sBg0a6JVXXtGyZcv04osvKi4uTqtWrdIDDzwgSerVq5fmzJmjWbNmqaSkRO3bt1dmZqbCwsKM1QYAAHyb7QGrUaNGqqioUEhIiMd4UVFRrbE7ERISoqVLl2rp0qW15rp27VrrNOE3jRkzRmPGjDFWCwAA8C%2B2nyLs3Lmzli9frkuXLtWMnTlzRvPnz9cjjzxidzkAAADG2X4E62c/%2B5mefvppJSUlqaqqSp07d1Z5eblatWqllStX2l0OAACAcbYHrKZNm2rXrl06fPiwzpw5o7CwMLVo0ULdu3f3uCYLAADAWzlyH6x69erp0UcfdWLXAAAAdc72gJWamnrTI1XvvfeejdUAAACYZ3vAGjRokEfAqqqq0pkzZ5Sbm6unn37a7nIAAACMsz1gzZs374bj%2B/bt04cffmhzNQAAAObdNX82%2B9FHH9U777zjdBkAAAB37K4JWMePH5dlWU6XAQAAcMdsP0U4atSoWmPl5eU6deqU%2Bvfvb3c5AAAAxtkesOLj42t9ijA0NFTp6ekaPny43eUAAAAYZ3vA4m7tAADA19kesLZt23bb2z755JN1WAkAAEDdsD1gLVq0SNXV1bUuaA8ICPAYCwgIIGABAACvZHvA%2Bs1vfqONGzdqypQpatOmjSzL0smTJ/XrX/9aY8eOVVJSkt0lAQAAGOXINViZmZlq0qRJzdhDDz2k5s2ba8KECdq1a5fdJQEAABhl%2B32wzp49q4iIiFrj4eHhKigosLscAAAA42wPWHFxcVq5cqVcLlfNWGlpqdasWaP77rvP7nIAAACMs/0U4cKFCzV37lxlZWWpQYMGCgwM1KVLlxQWFqb169fbXQ4AAIBxtgesHj166NChQzp8%2BLDOnz8vy7LUpEkT9ezZU40aNbK7HAAAAONsD1iSdM8996hv3746f/68mjdv7kQJAAAAdcb2a7AqKio0f/58derUSQMHDpT092uwJk6cqNLSUrvLAQAAMM72gLVq1SqdOHFCq1evVmDg/7/7qqoqrV692u5yAAAAjLM9YO3bt0%2B//OUv9dhjj9X80efw8HCtWLFC%2B/fvt7scAAAA42wPWGVlZYqPj681Hh0drcuXL9tdDgAAgHG2B6z77rtPH374oSR5/O3BvXv36p/%2B6Z/sLgcAAMA42z9FOGbMGM2cOVNPPfWUqqur9eqrryovL0/79u3TokWL7C4HAADAONsD1siRIxUcHKzNmzcrKChIv/rVr9SiRQutXr1ajz32mN3lAAAAGGd7wLp48aKeeuopPfXUU3bvGgAAwBa2X4PVt29fj2uvAAAAfI3tASspKUl79uyxe7cAAAC2sf0U4Q9%2B8AP9/Oc/V2Zmpu677z7Vq1fPY37NmjV2lwQAAGCU7QHriy%2B%2B0A9/%2BENJksvlsnv3AAAAdc62gDV79my9/PLLev3112vG1q9fr%2BnTp9tVAgAAgC1suwbrwIEDtcYyMzPt2j0AAIBtbAtYN/rkIJ8mBAAAvsi2gPWPP%2Bx8qzEAAABvZ/ttGgAAAHwdAQsAAMAw2z5FeO3aNc2dO/eWY9wHCwAAeDvbAlaXLl1UVFR0yzEAAABvZ1vA%2Bub9rwAAAHwZ12ABAAAYRsACAAAwjIAFAABgGAELAADAML8IWMuXL1ebNm1qvs/JyVF6ero6d%2B6swYMHa8eOHR7bb9q0SQMGDFDnzp01evRo5eXl2V0yAADwYj4fsE6cOKHt27fXfF9UVKRp06Zp1KhRysnJ0aJFi7RkyRLl5uZK%2BvsfpV63bp1eeuklHT16VH369NGUKVN0%2BfJlp1oAAABexqcDVnV1tZYuXarx48fXjO3cuVPx8fFKT09XaGiokpOTlZqaquzsbElSVlaW0tLS1LFjR4WFhWnixImSpIMHDzrRAgAA8EK23QfLCVu2bFFoaKiGDBmitWvXSpLy8/OVkJDgsV1CQoL27NlTMz9o0KCaucDAQLVt21a5ubkaPHjwbe23qKhIxcXFHmMxMTEKC2t0J%2B14laCgQI%2Bv/oCege8uOPjufO3422vb3/q1g88GrAsXLmjdunW1bnDqdrvVpEkTj7HIyEi5XK6a%2BYiICI/5iIiImvnbkZWVpYyMDI%2BxGTNmaObMmd%2BlBZ8QHn6P0yXYjp6B2xcV1cDpEm7K317b/tZvXfLZgLVixQqlpaWpZcuWOnfu3Hd6rGVZd7TvkSNHKjU11WMsJiZGpaXlqqqqvqPn9hZBQYEKD7%2BHnn2cP/YMs1yuMqdLuCF/e237cr9OhXifDFg5OTn65JNPtGvXrlpzUVFRcrvdHmMul0vR0dHfOu92u9WqVavb3n9sbKxiY2NrjbtcZaqs9K0X7q1UVVXTsx/wx55hxt3%2BuvG317a/9VuXfPJk644dO1RSUqI%2BffooKSlJaWlpkqSkpCS1bt261m0X8vLy1LFjR0lSYmKi8vPza%2Baqqqp0/PjxmnkAAIBb8cmAtWDBAu3bt0/bt2/X9u3blZmZKUnavn27hgwZooKCAmVnZ%2BvKlSs6fPiwDh8%2BrBEjRkiSRo8erW3btunTTz9VeXm5NmzYoJCQEPXu3dvBjgAAgDfxyVOEERERHheqV1ZWSpKaNm0qSXrllVe0bNkyvfjii4qLi9OqVav0wAMPSJJ69eqlOXPmaNasWSopKVH79u2VmZmpsLAw%2BxsBAABeyScD1vWaNWumkydP1nzftWtXj5uPXm/MmDEaM2aMHaUBAAAf5JOnCAEAAJxEwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAsGCnCwBgxsC1HzhdAgDg/%2BEIFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhPhuwCgoKNH36dCUlJSk5OVkLFixQaWmpJOnEiRMaO3asunTpov79%2B2vjxo0ej929e7eGDBmiTp06KS0tTUeOHHGiBQAA4KV89k/lTJkyRYmJiTpw4ID%2B9re/afr06fq3f/s3LVmyRJMnT9aIESOUmZmpM2fO6Kc//amaNWum/v3768SJE5o/f74yMjL08MMPa9%2B%2BfZoxY4b27t2rpk2bOt0WAPgUb/sTT3tmdXe6BHgJnzyCVVpaqsTERM2dO1cNGjRQ06ZNNWzYMH300Uc6dOiQrl27pqlTp6p%2B/fpq166dhg8frqysLElSdna2UlJSlJKSotDQUA0dOlStW7fWjh07HO4KAAB4C588ghUeHq4VK1Z4jBUWFio2Nlb5%2Bflq06aNgoKCauYSEhKUnZ0tScrPz1dKSorHYxMSEpSbm3vb%2By8qKlJxcbHHWExMjMLCGn3XVrxWUFCgx1d/4I89A/4mONg339/8/DLPJwPW9XJzc7V582Zt2LBBe/bsUXh4uMd8ZGSk3G63qqur5Xa7FRER4TEfERGhL7744rb3l5WVpYyMDI%2BxGTNmaObMmd%2B/CS8VHn6P0yXYzh97BvxFVFQDp0uoU/z8MsfnA9af//xnTZ06VXPnzlVycrL27Nlzw%2B0CAgJq/tuyrDva58iRI5WamuoxFhMTo9LSclVVVd/Rc3uLoKBAhYffQ88AfIrLVeZ0CXXCl39%2BORWKfTpgHThwQM8995yWLFmiJ598UpIUHR2ts2fPemzndrsVGRmpwMBARUVFye1215qPjo6%2B7f3GxsYqNja21rjLVabKSt964d5KVVU1PQPwGb7%2B3ubnlzk%2Be7L1448/1vz58/WLX/yiJlxJUmJiok6ePKnKysqasdzcXHXs2LFmPi8vz%2BO5vjkPAABwKz4ZsCorK7V48WLNmzdPPXr08JhLSUlRw4YNtWHDBpWXl%2Buzzz7T1q1bNXr0aEnSiBEjdPToUR06dEhXrlzR1q1bdfbsWQ0dOtSJVgAAgBcKsO70gqO70EcffaQf/ehHCgkJqTW3d%2B9elZWVaenSpcrLy1Pjxo01adIkjRkzpmab/fv3a82aNSooKFDLli21aNEide3a9Y7r8qdThMHBgYqKakDPNvK2%2BwkB3shX74Pl9M%2BvuhQT48wn%2BH0yYN2tfPGF%2B218%2Bc36bZzumYAF1D0ClvdxKmD55ClCAAAAJxGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYcFOFwDcrQau/cDpEgAAXoojWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAsGCnCwAAwFsMXPuB0yV8J3tmdXe6BL/FESwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGLdpgG1sNC55AAAKkElEQVS87ePNAAB8XxzBuoGCggI988wzSkpKUp8%2BfbRq1SpVV1c7XRYAAPASHMG6gZkzZ6pdu3Z69913VVJSosmTJ6tx48b6yU9%2B4nRptXBUCADwbbzp/xG%2BdlNUjmBdJzc3V59//rnmzZunRo0aKT4%2BXuPHj1dWVpbTpQEAAC/BEazr5OfnKy4uThERETVj7dq105kzZ3Tp0iU1bNjwls9RVFSk4uJij7GYmBiFhTUyXi8AAL4gONi3jvkQsK7jdrsVHh7uMfaPsOVyuW4rYGVlZSkjI8NjrGvXrvr3f/93xcbGmitW0kc/f8zo85lSVFSkrKwsjRw50njPdyt6pmdfRc%2B%2B37O/9WsH34qLhliWdUePHzlypN5%2B%2B%2B2af6tWrdKf/vSnWke1fFlxcbEyMjLo2cfRs3%2BgZ9/nb/3agSNY14mOjpbb7fYYc7vdCggIUHR09G09R2xsLL8BAADgxziCdZ3ExEQVFhbq4sWLNWO5ublq2bKlGjRo4GBlAADAWxCwrpOQkKD27dtrzZo1unTpkk6dOqVXX31Vo0ePdro0AADgJYJeeOGFF5wu4m7Ts2dP7dq1S//6r/%2Bqd955R%2Bnp6ZowYYICAgK%2B93M2aNBA3bp186ujYPTsH%2BjZP9Cz7/O3futagHWnV3QDAADAA6cIAQAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYBn2/vvvKzk5WbNnz641t3v3bg0ZMkSdOnVSWlqajhw54kCF5n1bz2%2B//bYeeOABtW/f3uPfsWPHHKrUnIKCAk2fPl1JSUlKTk7WggULVFpaKkk6ceKExo4dqy5duqh///7auHGjw9Wa8W09nzt3Tm3atKm1zr/97W%2BdLvmOff7553r66afVpUsXJScna9asWSouLpYk5eTkKD09XZ07d9bgwYO1Y8cOh6u9c9/W74cffnjDNd6zZ4/TJRu1fPlytWnTpuZ7X1zj632zZ39ZZ9tYMCYzM9Pq37%2B/NWrUKGvWrFkec8ePH7cSExOtQ4cOWRUVFdb27dutjh07WoWFhQ5Va8bNen7rrbessWPHOlRZ3Xr88cetBQsWWJcuXbIKCwuttLQ0a%2BHChVZ5ebnVs2dPa926dVZZWZmVl5dndevWzdq3b5/TJd%2Bxb%2Bv5yy%2B/tFq3bu10ecZduXLFeuSRR6yMjAzrypUrVklJiTV27Fhr2rRp1ldffWU9%2BOCDVnZ2tlVRUWF98MEHVocOHaxjx445Xfb3drN%2B//jHP1p9%2BvRxusQ6dfz4catbt241r2VfXOPrXd%2BzP6yznTiCZVBoaKi2bt2q%2B%2B%2B/v9Zcdna2UlJSlJKSotDQUA0dOlStW7f2%2Bt%2BIbtazryotLVViYqLmzp2rBg0aqGnTpho2bJg%2B%2BugjHTp0SNeuXdPUqVNVv359tWvXTsOHD1dWVpbTZd%2BRm/Xsq8rLyzV79mxNnjxZISEhio6OVr9%2B/fSXv/xFO3fuVHx8vNLT0xUaGqrk5GSlpqYqOzvb6bK/t5v16%2Buqq6u1dOlSjR8/vmbMF9f4m27UM8wiYBk0btw4NWrU6IZz%2Bfn5SkhI8BhLSEhQbm6uHaXVmZv1LEmFhYX6yU9%2Boq5du6pv377avn27jdXVjfDwcK1YsUKNGzeuGSssLFRsbKzy8/PVpk0bBQUF1cwlJCQoLy/PiVKNuVnP//D888%2BrR48eevjhh7VmzRpdu3bNiVKNiYiI0PDhwxUcHCxJOn36tH7/%2B99r4MCB3/p%2B9uZ1vlm/klRWVlZzirhnz5569dVXZVmWkyUbs2XLFoWGhmrIkCE1Y764xt90o54l315nuxGwbOJ2uxUREeExFhERIZfL5VBFdS86Olrx8fF67rnn9MEHH2jOnDlauHChcnJynC7NqNzcXG3evFlTp06V2%2B1WeHi4x3xkZKTcbreqq6sdqtC8b/YcEhKiTp06qV%2B/fjp48KAyMzO1Y8cO/cd//IfTZRpRUFCgxMREDRo0SO3bt9ezzz77revsC%2B/nG/XbsGFDtW7dWk8//bTef/99rVixQhkZGXrrrbecLveOXbhwQevWrdPSpUs9xn15jb%2BtZ19eZycQsGzkb78F9O7dW7/5zW%2BUkJCgkJAQDR48WP369dPbb7/tdGnG/PnPf9aECRM0d%2B5cJScnf%2Bt2AQEBNlZVt67vOTY2Vlu2bFG/fv1Ur149dejQQZMnT/aZdY6Li1Nubq727t2rs2fP6vnnn3e6pDp1o37btWun119/Xd26dVNISIh69OihUaNG%2BcQar1ixQmlpaWrZsqXTpdjm23r25XV2AgHLJlFRUXK73R5jbrdb0dHRDlXkjLi4OBUVFTldhhEHDhzQM888o4ULF2rcuHGS/n7U7vrfcN1utyIjIxUY6P1vtxv1fCNxcXG6cOGCz/xSERAQoPj4eM2ePVu7du1ScHBwrfezy%2BXymffz9f1evHix1ja%2B8F7OycnRJ598ounTp9eau9HPbF9Y45v1fCO%2BsM5O8f6f%2BF4iMTGx1rn73NxcdezY0aGK6t5//dd/affu3R5jp06dUvPmzR2qyJyPP/5Y8%2BfP1y9%2B8Qs9%2BeSTNeOJiYk6efKkKisra8Z8ZZ2/reecnBxt2LDBY9vTp08rLi7Oq4/c5eTkaMCAAR6ndv8Rkjt06FDr/ZyXl%2BfV63yzfg8fPqz//M//9Nj%2B9OnTXv9e3rFjh0pKStSnTx8lJSUpLS1NkpSUlKTWrVv73BpLN%2B9527ZtPrnOjnH2Q4y%2Baf78%2BbVuWXDy5Emrffv21sGDB62KigorOzvb6tSpk1VUVORQlWbdqOfXXnvNevjhh61jx45ZV69etXbu3Gm1bdvWys3NdahKM65du2YNHDjQ2rJlS625K1euWH369LF%2B%2BctfWpcvX7Y%2B/fRT66GHHrIOHjxof6EG3azn3Nxcq127dta2bdusq1evWseOHbO6d%2B9ubdy40YFKzSktLbWSk5OtlStXWpcvX7ZKSkqsCRMmWGPGjLEuXLhgderUyXrzzTetiooK69ChQ1aHDh2sEydOOF3293azfv/7v//b6tChg/X%2B%2B%2B9bV69etY4cOWI9%2BOCDXn/7EbfbbRUWFtb8%2B%2BSTT6zWrVtbhYWFVkFBgc%2BtsWXdvGdfXWenBFiWjxzDvwu0b99ekmqOXvzj0zj/%2BKTg/v37tWbNGhUUFKhly5ZatGiRunbt6kyxhtysZ8uytGHDBm3dulXFxcVq1qyZnn/%2BefXp08exek346KOP9KMf/UghISG15vbu3auysjItXbpUeXl5aty4sSZNmqQxY8Y4UKk5t%2Br5%2BPHjysjI0NmzZ9WoUSP9%2BMc/1qRJk7z%2BtOjJkye1bNkyHTt2TPXr19fDDz%2BsBQsWqEmTJvrTn/6kZcuW6dSpU4qLi9PcuXPVv39/p0u%2BIzfrNysrSxs3blRhYaEaN26sqVOnavjw4U6XbNS5c%2BfUt29fnTx5UpJ8co2vd33P/rDOdiFgAQAAGObdv14CAADchQhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADDs/wNUiTRtGOylKgAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-7383719616159785432”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>32.6</td> <td class=”number”>40</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>30.9</td> <td class=”number”>37</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>30.1</td> <td class=”number”>36</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>30.3</td> <td class=”number”>36</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>32.8</td> <td class=”number”>34</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>31.5</td> <td class=”number”>34</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>32.1</td> <td class=”number”>34</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.0</td> <td class=”number”>32</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>31.3</td> <td class=”number”>32</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>29.1</td> <td class=”number”>32</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (256)</td> <td class=”number”>2785</td> <td class=”number”>86.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-7383719616159785432”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>11.8</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.7</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.1</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.4</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.7</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>45.1</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>45.5</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>46.1</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>46.3</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>47.6</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_REDUCED_LUNCH09”>PCT_REDUCED_LUNCH09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3000</td>

</tr> <tr>

<th>Unique (%)</th> <td>93.5%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>4.8%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>153</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>9.3546</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>45.601</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.6%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram5398648517818977324”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives5398648517818977324,#minihistogram5398648517818977324”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives5398648517818977324”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles5398648517818977324”

aria-controls=”quantiles5398648517818977324” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram5398648517818977324” aria-controls=”histogram5398648517818977324”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common5398648517818977324” aria-controls=”common5398648517818977324”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme5398648517818977324” aria-controls=”extreme5398648517818977324”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles5398648517818977324”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>4.6101</td>

</tr> <tr>

<th>Q1</th> <td>7.5314</td>

</tr> <tr>

<th>Median</th> <td>9.2415</td>

</tr> <tr>

<th>Q3</th> <td>11.065</td>

</tr> <tr>

<th>95-th percentile</th> <td>14.296</td>

</tr> <tr>

<th>Maximum</th> <td>45.601</td>

</tr> <tr>

<th>Range</th> <td>45.601</td>

</tr> <tr>

<th>Interquartile range</th> <td>3.5332</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>3.0894</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.33026</td>

</tr> <tr>

<th>Kurtosis</th> <td>7.8084</td>

</tr> <tr>

<th>Mean</th> <td>9.3546</td>

</tr> <tr>

<th>MAD</th> <td>2.2743</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.85676</td>

</tr> <tr>

<th>Sum</th> <td>28597</td>

</tr> <tr>

<th>Variance</th> <td>9.5445</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram5398648517818977324”>

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113nbp16yaPx6ODBw8qOTnZ%2B1673a6rr766VfuPj49vcTnQ6Typxsa2%2BUZoampus20jcFyOa4C1by36by36f2kLygu448eP1969e7V7926dPn1a69at05EjRzR27FhJUk5OjtauXatDhw6ptrZWhYWF6tOnj1JTUxUXF6eRI0fq6aef1okTJ3Ts2DGtWrVK2dnZstmCMo8CAADDAjYxpKamSpIaGxslSTt27JD049mmXr16qbCwUEuXLlVVVZV69Oih5557Tl26dJEkTZw4UU6nU7/97W9VV1entLQ0n3umnnjiCS1atEjDhw/XFVdcoTvuuOOsDzMFAAA4mxDPxd7RjVZxOk8a36bNFqrY2Ei5XHUBcZr49qfftbqEoLY1/yarS/CbQFv7wYb%2BW4v%2BX5guXc7%2BSKe2FpSXCAEAAKxEwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIb5PWBlZmaquLhYR48e9feuAQAA/MLvAevuu%2B/Wli1bdMstt2jq1Knavn27Ghsb/V0GAABAm/F7wMrNzdWWLVv02muvqWfPnlqyZIkyMjK0fPlyHT582N/lAAAAGGfZPVjJycmaPXu2du3apXnz5um1117TqFGjNGXKFH366adWlQUAAHDRLAtYZ86c0ZYtW/TAAw9o9uzZSkhI0Ny5c9WnTx9NnjxZmzZtsqo0AACAi2Lz9w4PHTqkdevWaf369aqrq9PIkSP1hz/8QQMGDPC%2BZuDAgXrsscc0ZswYf5cHAABw0fwesEaPHq2rr75a06ZN01133aWYmJgWr8nIyNCJEyf8XRoAAIARfg9Ya9eu1aBBg877un379vmhGgAAAPP8fg9W7969NX36dO3YscM79uKLL%2BqBBx6Q2%2B32dzkAAADG%2BT1gLV26VCdPnlSPHj28Y0OHDlVzc7OWLVvm73IAAACM8/slwnfeeUebNm1SbGysdywxMVGFhYW64447/F0OAACAcX4/g9XQ0KDw8PCWhYSGqr6%2B3th%2B9u/fr0mTJumGG27QTTfdpFmzZnlvnC8vL1d2drb69%2B%2Bv0aNHa%2BPGjT7vXbt2rUaOHKn%2B/fsrJydHFRUVxuoCAADBz%2B8Ba%2BDAgVq2bJm%2B//5779i3336rxx9/3OdRDRejsbFRDz74oK6//nrt3btXmzdv1okTJ/TYY4%2BpurpaM2bM0MSJE1VeXq758%2Bdr4cKFcjgckqSdO3eqqKhITz31lPbu3athw4Zp%2BvTpOnXqlJHaAABA8PN7wJo3b57Ky8t14403atCgQbrhhhs0dOhQVVRUaPHixUb24XQ65XQ6deedd6pdu3aKjY3VrbfeqgMHDmjTpk1KTExUdna2wsPDlZ6erszMTJWVlUmSSktLlZWVpb59%2B6p9%2B/aaOnWqJGnXrl1GagMAAMHP7/dgXXnllXrzzTf1X//1X/rqq68UGhqqq6%2B%2BWoMHD1ZYWJiRfSQkJKhPnz4qLS3VP/3TP6mhoUHbt2/X0KFDVVlZqaSkJJ/XJyUlaevWrZKkyspKjRo1yjsXGhqqPn36yOFwaPTo0a3af3V1tZxOp8%2BYzRah%2BPj4izwyX2FhoT4fcXmz2S6fdcDatxb9txb9Dwx%2BD1iS1K5dO91yyy1ttv3Q0FAVFRVp8uTJ%2BsMf/iBJGjRokGbOnKkZM2YoISHB5/UxMTFyuVySJLfbrejoaJ/56Oho73xrlJaWqri42GcsNzdXeXl5v%2BRwzstu79Am20VgiY2NtLoEv2PtW4v%2BW4v%2BX9r8HrC%2B/vprrVixQp9//rkaGhpazP/5z3%2B%2B6H388MMPmj59um677Tbv/VOPP/64Zs2a1ar3ezyei9r/hAkTlJmZ6TNms0XI5aq7qO3%2BXFhYqOz2DqqpqVdTU7PRbSPwmF5flzLWvrXov7Xo/4Wx6odPvwesefPmqbq6WoMHD1ZERESb7KO8vFzffPONHn30UYWFhaljx47Ky8vTnXfeqZtvvrnFA01dLpfi4uIkSbGxsS3m3W63evbs2er9x8fHt7gc6HSeVGNj23wjNDU1t9m2ETguxzXA2rcW/bcW/b%2B0%2BT1gVVRU6M9//rM30LSFpqYmNTc3%2B5yJ%2BuGHHyRJ6enp%2BtOf/tSipr59%2B0qSUlJSVFlZqXHjxnm3tX//fmVnZ7dZvQAAILj4/Q65Tp06tdmZq5/069dPERERKioqUn19vVwul1avXq2BAwfqzjvvVFVVlcrKynT69Gnt2bNHe/bs0fjx4yVJOTk5Wr9%2BvT755BPV19dr9erVateunYYOHdqmNQMAgODh94A1bdo0FRcXX/R9Tn9LbGysfv/73%2Bujjz7SkCFDdMcdd6h9%2B/ZasWKFOnXqpOeee04vvfSSBgwYoCVLlmj58uW69tprJUlDhgzRo48%2Bqvz8fA0aNEh79%2B5VSUmJ2rdv32b1AgCA4BLiacukcxaPPPKIPvroI3k8Hv393/%2B9QkN9M96rr77qz3L8xuk8aXybNluoYmMj5XLVBcR1%2BNufftfqEoLa1vybrC7BbwJt7Qcb%2Bm8t%2Bn9hunTpaMl%2B/X4PVlRUlIYMGeLv3QIAAPiN3wPW0qVL/b1LAAAAv7LkMbBffvmlioqKNHfuXO/Yxx9/bEUpAAAAxvk9YJWXl2vs2LHavn27Nm/eLOnHh49OmjTJyENGAQAArOb3gLVy5Ur97ne/06ZNmxQSEiLpx79PuGzZMq1atcrf5QAAABjn94D117/%2BVTk5OZLkDViSdNttt%2BnQoUP%2BLgcAAMA4vwesjh07nvVvEFZXV6tdu3b%2BLgcAAMA4vwes/v37a8mSJaqtrfWOHT58WLNnz9aNN97o73IAAACM8/tjGubOnav7779faWlpampqUv/%2B/VVfX6%2BePXtq2bJl/i4HAADAOL8HrK5du2rz5s3as2ePDh8%2BrPbt2%2Bvqq6/WTTfd5HNPFgAAQKDye8CSpCuuuEK33HKLFbsGAABoc34PWJmZmX/zTBXPwgIAAIHO7wFr1KhRPgGrqalJhw8flsPh0P333%2B/vcgAAAIzze8CaNWvWWce3bdum999/38/VAAAAmGfJ3yI8m1tuuUVvvvmm1WUAAABctEsmYO3fv18ej8fqMgAAAC6a3y8RTpw4scVYfX29Dh06pBEjRvi7HAAAAOP8HrASExNb/BZheHi4srOzdc899/i7HAAAAOP8HrB4WjsAAAh2fg9Y69evb/Vr77rrrjasBAAAoG34PWDNnz9fzc3NLW5oDwkJ8RkLCQkhYAEAgIDk94D1n//5n1qzZo2mT5%2Bu3r17y%2BPx6LPPPtPzzz%2Bv%2B%2B67T2lpaf4uCQAAwChL7sEqKSlRQkKCd%2ByGG27QlVdeqSlTpmjz5s3%2BLgkAAMAovz8H68iRI4qOjm4xbrfbVVVV5e9yAAAAjPN7wOrWrZuWLVsml8vlHaupqdGKFSt01VVX%2BbscAAAA4/x%2BiXDevHmaOXOmSktLFRkZqdDQUNXW1qp9%2B/ZatWqVv8sBAAAwzu8Ba/Dgwdq9e7f27NmjY8eOyePxKCEhQTfffLM6duzo73IAAACM83vAkqQOHTpo%2BPDhOnbsmK688korSgAAAGgzfr8Hq6GhQbNnz1a/fv10%2B%2B23S/rxHqypU6eqpqbG3%2BUAAAAY5/eAtXz5ch04cECFhYUKDf3f3Tc1NamwsNDf5QAAABjn94C1bds2/cd//Iduu%2B027x99ttvtWrp0qbZv3%2B7vcgAAAIzze8Cqq6tTYmJii/G4uDidOnXK3%2BUAAAAY5/eAddVVV%2Bn999%2BXJJ%2B/PfjWW2/p7/7u7/xdDgAAgHF%2BD1j33nuvHnnkET355JNqbm7WCy%2B8oJkzZ2revHm6//77je5r9erVGjx4sK6//npNnjxZ33zzjSSpvLxc2dnZ6t%2B/v0aPHq2NGzf6vG/t2rUaOXKk%2Bvfvr5ycHFVUVBitCwAABDe/B6wJEyZo9uzZeu%2B99xQWFqZnn31WVVVVKiwsVE5OjrH9vPzyy9q4caPWrl2rd955Rz169NCLL76o6upqzZgxQxMnTlR5ebnmz5%2BvhQsXyuFwSJJ27typoqIiPfXUU9q7d6%2BGDRum6dOnc/kSAAC0mt%2Bfg3XixAndfffduvvuu9t0P2vWrNHs2bP1D//wD5KkBQsWSJJ%2B//vfKzExUdnZ2ZKk9PR0ZWZmqqysTKmpqSotLVVWVpb69u0rSZo6darWrl2rXbt2afTo0W1aMwAACA5%2BP4M1fPhwn3uv2sK3336rb775Rt9//71GjRqltLQ05eXl6cSJE6qsrFRSUpLP65OSkryXAX8%2BHxoaqj59%2BnjPcAEAAJyP389gpaWlaevWrRo1alSb7ePYsWOSfrxx/oUXXpDH41FeXp4WLFighoYGJSQk%2BLw%2BJibG%2B8en3W63oqOjfeajo6N9/jj1%2BVRXV8vpdPqM2WwRio%2BP/yWHc05hYaE%2BH3F5s9kun3XA2rcW/bcW/Q8Mfg9Yv/rVr/Rv//ZvKikp0VVXXaUrrrjCZ37FihUXvY%2BfzpBNnTrVG6YeeeQRPfDAA0pPT2/1%2B3%2Bp0tJSFRcX%2B4zl5uYqLy/vorZ7LnZ7hzbZLgJLbGyk1SX4HWvfWvTfWvT/0ub3gPXFF19474u6kLNCF6Jz586SfnyA6U%2B6desmj8ejM2fOyO12%2B7ze5XIpLi5OkhQbG9ti3u12q2fPnq3e/4QJE5SZmekzZrNFyOWqu6DjOJ%2BwsFDZ7R1UU1OvpqZmo9tG4DG9vi5lrH1r0X9r0f8LY9UPn34LWAUFBVq5cqX%2B%2BMc/esdWrVql3Nxc4/vq2rWroqKidODAASUnJ0uSqqqqdMUVVygjI0MbNmzweX1FRYX3pvaUlBRVVlZq3Lhxkn78Ez779%2B/33hTfGvHx8S0uBzqdJ9XY2DbfCE1NzW22bQSOy3ENsPatRf%2BtRf8vbX67gLtz584WYyUlJW2yL5vNpuzsbD377LP6n//5Hx0/flyrVq3SmDFjNG7cOFVVVamsrEynT5/Wnj17tGfPHo0fP16SlJOTo/Xr1%2BuTTz5RfX29Vq9erXbt2mno0KFtUisAAAg%2BfjuDdbb7mtrytwlnzpypH374Qffcc4/OnDmjkSNHasGCBYqMjNRzzz2nxYsX6/HHH1e3bt20fPlyXXvttZKkIUOG6NFHH1V%2Bfr6OHz%2Bu1NRUlZSUqH379m1WKwAACC4hnrZ%2BZsL/07dvX%2B3bt%2B%2B8Y8HK6TxpfJs2W6hiYyPlctUFxGni259%2B1%2BoSgtrW/JusLsFvAm3tBxv6by36f2G6dOloyX75HU8AAADDCFgAAACG%2Be0erDNnzmjmzJnnHTPxHCwAAAAr%2BS1gDRgwQNXV1ecdAwAACHR%2BC1j/9/lXAAAAwYx7sAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMCwyyJgLVmyRL179/Z%2BXl5eruzsbPXv31%2BjR4/Wxo0bfV6/du1ajRw5Uv3791dOTo4qKir8XTIAAAhgQR%2BwDhw4oA0bNng/r66u1owZMzRx4kSVl5dr/vz5WrhwoRwOhyRp586dKioq0lNPPaW9e/dq2LBhmj59uk6dOmXVIQAAgAAT1AGrublZixYt0uTJk71jmzZtUmJiorKzsxUeHq709HRlZmaqrKxMklRaWqqsrCz17dtX7du319SpUyVJu3btsuIQAABAALJZXUBbevXVVxUeHq4xY8bo6aefliRVVlYqKSnJ53VJSUnaunWrd37UqFHeudDQUPXp00cOh0OjR49u1X6rq6vldDp9xmy2CMXHx1/M4bQQFhbq8xGXN5vt8lkHrH1r0X9r0f/AELQB67vvvlNRUZH%2B%2BMc/%2Boy73W4lJCT4jMXExMjlcnnno6Ojfeajo6O9861RWlqq4uJin7Hc3Fzl5eVdyCG0mt3eoU22i8ASGxtpdQl%2Bx9q3Fv23Fv2/tAVtwFq6dKmysrLUo0cPffPNNxf0Xo/Hc1H7njBhgjIzM33GbLYIuVx1F7XdnwsLC5Xd3kE1NfVqamo2um0EHtPr61LG2rcW/bcW/b8wVv3wGZTA/J8wAAAPMklEQVQBq7y8XB9//LE2b97cYi42NlZut9tnzOVyKS4u7pzzbrdbPXv2bPX%2B4%2BPjW1wOdDpPqrGxbb4Rmpqa22zbCByX4xpg7VuL/luL/l/agvIC7saNG3X8%2BHENGzZMaWlpysrKkiSlpaWpV69eLR67UFFRob59%2B0qSUlJSVFlZ6Z1ramrS/v37vfMAAADnE5QBa86cOdq2bZs2bNigDRs2qKSkRJK0YcMGjRkzRlVVVSorK9Pp06e1Z88e7dmzR%2BPHj5ck5eTkaP369frkk09UX1%2Bv1atXq127dho6dKiFRwQAAAJJUF4ijI6O9rlRvbGxUZLUtWtXSdJzzz2nxYsX6/HHH1e3bt20fPlyXXvttZKkIUOG6NFHH1V%2Bfr6OHz%2Bu1NRUlZSUqH379v4/EAAAEJBCPBd7Rzdaxek8aXybNluoYmMj5XLVBcR1%2BNufftfqEoLa1vybrC7BbwJt7Qcb%2Bm8t%2Bn9hunTpaMl%2Bg/ISIQAAgJUIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDgjZgVVVVKTc3V2lpaUpPT9ecOXNUU1MjSTpw4IDuu%2B8%2BDRgwQCNGjNCaNWt83rtlyxaNGTNG/fr1U1ZWlt555x0rDgEAAASooA1Y06dPl91u186dO/XGG2/o888/15NPPqmGhgZNmzZNv/71r/X2229r5cqVeu6557R9%2B3ZJP4av2bNna9asWXrvvfc0efJkPfzwwzp27JjFRwQAAAJFUAasmpoapaSkaObMmYqMjFTXrl01btw4ffDBB9q9e7fOnDmjhx56SBEREUpOTtY999yj0tJSSVJZWZkyMjKUkZGh8PBwjR07Vr169dLGjRstPioAABAogjJg2e12LV26VJ07d/aOHT16VPHx8aqsrFTv3r0VFhbmnUtKSlJFRYUkqbKyUklJST7bS0pKksPh8E/xAAAg4NmsLsAfHA6HXnrpJa1evVpbt26V3W73mY%2BJiZHb7VZzc7Pcbreio6N95qOjo/XFF1%2B0en/V1dVyOp0%2BYzZbhOLj43/5QZxFWFioz0dc3my2y2cdsPatRf%2BtRf8DQ9AHrA8//FAPPfSQZs6cqfT0dG3duvWsrwsJCfH%2Bv8fjuah9lpaWqri42GcsNzdXeXl5F7Xdc7HbO7TJdhFYYmMjrS7B71j71qL/1qL/l7agDlg7d%2B7U7373Oy1cuFB33XWXJCkuLk5HjhzxeZ3b7VZMTIxCQ0MVGxsrt9vdYj4uLq7V%2B50wYYIyMzN9xmy2CLlcdb/sQM4hLCxUdnsH1dTUq6mp2ei2EXhMr69LGWvfWvTfWvT/wlj1w2fQBqyPPvpIs2fP1jPPPKPBgwd7x1NSUvTKK6%2BosbFRNtuPh%2B9wONS3b1/v/E/3Y/3E4XBo9OjRrd53fHx8i8uBTudJNTa2zTdCU1Nzm20bgePWwretLqHVtubfZGQ7rH1r0X9r0f9LW1BewG1sbNSCBQs0a9Ysn3AlSRkZGYqKitLq1atVX1%2Bvffv2ad26dcrJyZEkjR8/Xnv37tXu3bt1%2BvRprVu3TkeOHNHYsWOtOBQAABCAQjwXe8PRJeiDDz7Qb37zG7Vr167F3FtvvaW6ujotWrRIFRUV6ty5sx544AHde%2B%2B93tds375dK1asUFVVlXr06KH58%2Bdr4MCBF1WT03nyot5/NjZbqGJjI%2BVy1QXETzG3P/2u1SXgEnGxZ7ACbe0HG/pvLfp/Ybp06WjJfoPyEuENN9ygzz777G%2B%2B5pVXXjnn3IgRIzRixAjTZQEAgMtEUF4iBAAAsBIBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYF5W8RXk5umP%2BW1SUAAICf4QwWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADLNZXcClqKqqSo8//rj27duniIgIjRo1SjNnzlRoKHkUMOH2p9%2B1uoQLsjX/JqtLABBgCFhn8cgjjyg5OVk7duzQ8ePHNW3aNHXu3Fn/%2BI//aHVpAAAgAHBK5mccDocOHjyoWbNmqWPHjkpMTNTkyZNVWlpqdWkAACBAcAbrZyorK9WtWzdFR0d7x5KTk3X48GHV1tYqKirqvNuorq6W0%2Bn0GbPZIhQfH2%2B01rAw8jHgD4F2SfP/m3Vzm27/p397%2BDfIGvQ/MBCwfsbtdstut/uM/RS2XC5XqwJWaWmpiouLfcYefvhhPfLII%2BYK1Y9B7v6un2vChAnGwxvOr7q6WqWlpfTfAvTeWtXV1frDH/6T/luE/gcG4u9ZeDyei3r/hAkT9MYbb/j8N2HCBEPV/S%2Bn06ni4uIWZ8vgH/TfOvTeWvTfWvQ/MHAG62fi4uLkdrt9xtxut0JCQhQXF9eqbcTHx/NTBQAAlzHOYP1MSkqKjh49qhMnTnjHHA6HevToocjISAsrAwAAgYKA9TNJSUlKTU3VihUrVFtbq0OHDumFF15QTk6O1aUBAIAAEfbYY489ZnURl5qbb75Zmzdv1r/%2B67/qzTffVHZ2tqZMmaKQkBCrS2shMjJSgwYN4uyaRei/dei9tei/tej/pS/Ec7F3dAMAAMAHlwgBAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgBaCqqio9%2BOCDSktL07Bhw7R8%2BXI1NzdbXVZQe/vtt5Wenq6CgoIWc1u2bNGYMWPUr18/ZWVl6Z133rGgwuBVVVWl3NxcpaWlKT09XXPmzFFNTY0k6cCBA7rvvvs0YMAAjRgxQmvWrLG42uBz8OBB3X///RowYIDS09OVn58vp9MpSSovL1d2drb69%2B%2Bv0aNHa%2BPGjRZXG7yWLFmi3r17ez%2Bn9wHAg4Azbtw4z4IFCzw1NTWew4cPe0aMGOFZs2aN1WUFrZKSEs%2BIESM8EydO9OTn5/vM7d%2B/35OSkuLZvXu3p6GhwbNhwwZP3759PUePHrWo2uBzxx13eObMmeOpra31HD161JOVleWZN2%2Bep76%2B3nPzzTd7ioqKPHV1dZ6KigrPoEGDPNu2bbO65KBx%2BvRpz4033ugpLi72nD592nP8%2BHHPfffd55kxY4bn22%2B/9Vx//fWesrIyT0NDg%2Bfdd9/1XHfddZ5PP/3U6rKDzv79%2Bz2DBg3y9OrVy%2BPxeOh9gOAMVoBxOBw6ePCgZs2apY4dOyoxMVGTJ09WaWmp1aUFrfDwcK1bt07du3dvMVdWVqaMjAxlZGQoPDxcY8eOVa9evfhp0pCamhqlpKRo5syZioyMVNeuXTVu3Dh98MEH2r17t86cOaOHHnpIERERSk5O1j333MP3gkH19fUqKCjQtGnT1K5dO8XFxenWW2/V559/rk2bNikxMVHZ2dkKDw9Xenq6MjMzVVZWZnXZQaW5uVmLFi3S5MmTvWP0PjAQsAJMZWWlunXrpujoaO9YcnKyDh8%2BrNraWgsrC16TJk1Sx44dzzpXWVmppKQkn7GkpCQ5HA5/lBb07Ha7li5dqs6dO3vHjh49qvj4eFVWVqp3794KCwvzziUlJamiosKKUoNSdHS07rnnHtlsNknSl19%2BqT/96U%2B6/fbbz7n26b9Zr776qsLDwzVmzBjvGL0PDASsAON2u2W3233GfgpbLpfLipIua2632yfsSj9%2BPfhatA2Hw6GXXnpJDz300Fm/F2JiYuR2u7kn0bCqqiqlpKRo1KhRSk1NVV5e3jn7z9o357vvvlNRUZEWLVrkM07vAwMBKwB5PB6rS8D/wdfDPz788ENNmTJFM2fOVHp6%2BjlfFxIS4seqLg/dunWTw%2BHQW2%2B9pSNHjuif//mfrS7psrB06VJlZWWpR48eVpeCX4CAFWDi4uLkdrt9xtxut0JCQhQXF2dRVZev2NjYs349%2BFqYtXPnTj344IOaN2%2BeJk2aJOnH74Wf/8TudrsVExOj0FD%2BaTMtJCREiYmJKigo0ObNm2Wz2VqsfZfLxdo3pLy8XB9//LFyc3NbzJ3t3x16f%2BnhX6EAk5KSoqNHj%2BrEiRPeMYfDoR49eigyMtLCyi5PKSkpLe57cDgc6tu3r0UVBZ%2BPPvpIs2fP1jPPPKO77rrLO56SkqLPPvtMjY2N3jF6b1Z5eblGjhzpc8n1p/B63XXXtVj7FRUV9N%2BQjRs36vjx4xo2bJjS0tKUlZUlSUpLS1OvXr3ofQAgYAWYpKQkpaamasWKFaqtrdWhQ4f0wgsvKCcnx%2BrSLkvjx4/X3r17tXv3bp0%2BfVrr1q3TkSNHNHbsWKtLCwqNjY1asGCBZs2apcGDB/vMZWRkKCoqSqtXr1Z9fb327dundevW8b1gUEpKimpra7V8%2BXLV19frxIkTKioq0g033KCcnBxVVVWprKxMp0%2Bf1p49e7Rnzx6NHz/e6rKDwpw5c7Rt2zZt2LBBGzZsUElJiSRpw4YNGjNmDL0PACEebiAJOMeOHdPChQv13//934qKitLEiRP18MMPc%2B9JG0lNTZUk75mSn36j6qffFNy%2BfbtWrFihqqoq9ejRQ/Pnz9fAgQOtKTbIfPDBB/rNb36jdu3atZh76623VFdXp0WLFqmiokKdO3fWAw88oHvvvdeCSoPXZ599psWLF%2BvTTz9VRESEfv3rX2vOnDlKSEjQX/7yFy1evFiHDh1St27dNHPmTI0YMcLqkoPSN998o%2BHDh%2Buzzz6TJHofAAhYAAAAhnGJEAAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAM%2B/8BEKXeRg2MyJIAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common5398648517818977324”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>20</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.11111111111111</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.333333333333334</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.090909090909092</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.987473903966595</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.179640718562874</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.846560846560847</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.377358490566039</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.643835616438356</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2989)</td> <td class=”number”>3012</td> <td class=”number”>93.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>153</td> <td class=”number”>4.8%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme5398648517818977324”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>20</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.05743825387708214</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.061087354917532075</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.1590264813662449</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.36120642947444465</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>23.776223776223777</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>23.817053886388518</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>24.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>28.82882882882883</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>45.60129620739318</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_REDUCED_LUNCH14”>PCT_REDUCED_LUNCH14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2721</td>

</tr> <tr>

<th>Unique (%)</th> <td>84.8%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>9.8%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>314</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>7.6961</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>32.051</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>4.7%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram1741441295911992359”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives1741441295911992359”>

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<li role=”presentation” class=”active”><a href=”#quantiles1741441295911992359”

aria-controls=”quantiles1741441295911992359” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram1741441295911992359” aria-controls=”histogram1741441295911992359”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common1741441295911992359” aria-controls=”common1741441295911992359”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme1741441295911992359” aria-controls=”extreme1741441295911992359”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles1741441295911992359”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>5.5174</td>

</tr> <tr>

<th>Median</th> <td>7.7375</td>

</tr> <tr>

<th>Q3</th> <td>9.91</td>

</tr> <tr>

<th>95-th percentile</th> <td>13.501</td>

</tr> <tr>

<th>Maximum</th> <td>32.051</td>

</tr> <tr>

<th>Range</th> <td>32.051</td>

</tr> <tr>

<th>Interquartile range</th> <td>4.3926</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>3.7377</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.48567</td>

</tr> <tr>

<th>Kurtosis</th> <td>1.5269</td>

</tr> <tr>

<th>Mean</th> <td>7.6961</td>

</tr> <tr>

<th>MAD</th> <td>2.837</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.27177</td>

</tr> <tr>

<th>Sum</th> <td>22288</td>

</tr> <tr>

<th>Variance</th> <td>13.971</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram1741441295911992359”>

<img 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Tp79qxCQkIaFhIYqOrqarvLAQAAMM72gJWYmKjly5fr22%2B/rR/7%2BuuvtWTJEstHNQAAAPgq228RLliwQP/2b/%2Bmu%2B66S6Ghoaqrq1NVVZU6duyo1157ze5yAAAAjLM9YHXs2FFvv/223n33Xf3jH/9QYGCgOnXqpH79%2BikoKMjucgAAAIyzPWBJUnBwsO69914nlgYAAGhytgesr776StnZ2fr888919uzZBvN//vOf7S4JAADAKEeewSorK1O/fv3UqlUru5cHAABocrYHrKKiIv35z39WZGSk3UsDAADYwvaPaWjXrh1XrgAAgF%2BzPWBNmzZNOTk58nq9di8NAABgC9tvEb777rv66KOPtHHjRt18880KDLRmvDfeeMPukgAAAIyyPWCFhoaqf//%2Bdi8LAABgG9sD1rJly%2BxeEgAAwFa2P4MlSV9%2B%2BaVWrVqlp59%2Bun7s73//u/F11qxZo379%2BumOO%2B7QpEmTdPToUUlSYWGh0tLS1KtXLw0bNkybN2%2B2vG79%2BvUaOnSoevXqpYyMDBUVFRmvDQAA%2BC/bA1ZhYaFGjhypnTt3qqCgQNJ3Hz46ceJEox8y%2Bvvf/16bN2/W%2BvXrtW/fPnXu3Fm//e1vVVZWphkzZmjcuHEqLCzUwoULtXjxYrndbknSrl27tGrVKr3wwgvav3%2B/Bg4cqOnTp%2BvMmTPGagMAAP7N9oD14osv6sknn9SWLVsUEBAg6bvfT7h8%2BXKtXr3a2Drr1q3T7Nmz9ZOf/EShoaFatGiRFi1apC1btigmJkZpaWkKCQlRcnKyUlNTlZ%2BfL0nKy8vT6NGjlZCQoJYtW2rKlCmSpN27dxurDQAA%2BDfbA9b//M//KCMjQ5LqA5Yk3XfffSopKTGyxtdff62jR4/q22%2B/1QMPPKCkpCTNnDlTJ0%2BeVHFxsWJjYy3bx8bG1t8GvHg%2BMDBQ3bt3r7/CBQAAcCW2P%2BTepk0bnT17VsHBwZbxsrKyBmPX6vjx45Kk7du369VXX5XX69XMmTO1aNEinT17Vh06dLBs37ZtW1VUVEiSPB6PwsPDLfPh4eH181ejrKxM5eXlljGXq5WioqKu5XAuKygo0PI3mjeXy3oecH5Y0Q8r%2BmFFP6zoR%2BPZHrB69eql559/XosWLaofO3z4sJ555hndddddRtb4/kNMp0yZUh%2BmnnjiCU2dOlXJyclX/fprlZeXp5ycHMtYZmamZs6c2aj9Xk5Y2A1Nsl/4loiI1pcc5/ywoh9W9MOKfljRj2tne8B6%2Bumn9cgjjygpKUm1tbXq1auXqqur1aVLFy1fvtzIGu3bt5ckhYWF1Y9FR0fL6/XqwoUL8ng8lu0rKirqfzdiREREg3mPx6MuXbpc9frp6elKTU21jLlcrVRRUfWjjuNKgoICFRZ2gyorq1VbW2d03/A9F59fnB9W9MOKfljRDyt/6sflvvlsarYHrJtuukkFBQXau3evDh8%2BrJYtW6pTp066%2B%2B67Lc9kNXaN0NBQHTx4UD169JAklZaWqkWLFkpJSdGmTZss2xcVFSkhIUGSFBcXp%2BLiYo0aNUqSVFtbqwMHDigtLe2q14%2BKimpwO7C8/JRqaprmJK2trWuyfcN3XO4c4Pywoh9W9MOKfljRj2tne8CSpBYtWujee%2B9tsv27XC6lpaXpV7/6lRITExUaGqrVq1drxIgRGjVqlH75y18qPz9fI0eO1AcffKC9e/cqLy9PkpSRkaE5c%2BZo%2BPDh6tatm1555RUFBwdrwIABTVYvYML9L73vdAlXbdusu50uAQCalO0BKzU19QevVJn6LKy5c%2Bfq/PnzGjNmjC5cuKChQ4dq0aJFat26tdauXaulS5dqyZIlio6O1ooVK3TbbbdJkvr37685c%2BZo1qxZOnHihOLj45Wbm6uWLVsaqQsAAPi/AG9jn%2Bj%2BkVauXGkJWLW1tTp8%2BLDcbrceeeQRTZ061c5ybFNefsr4Pl2uQEVEtFZFRZVPXML1pSssaFpOXMHytfdLU6MfVvTDyp/6ceONbRxZ1/YrWFlZWZcc37Fjh/7yl7/YXA0AAIB5180HXNx77716%2B%2B23nS4DAACg0a6bgHXgwIFGf/4UAADA9cD2W4Tjxo1rMFZdXa2SkhINGTLE7nIAAACMsz1gxcTENPgpwpCQEKWlpWnMmDF2lwMAAGCc7QHL1Ke1AwAAXK9sD1hvvfXWVW/70EMPNWElAAAATcP2gLVw4ULV1dU1eKA9ICDAMhYQEEDAAgAAPsn2gPWb3/xG69at0/Tp09WtWzd5vV4dOnRIv/71rzVhwgQlJSXZXRIAAIBRjjyDlZubqw4dOtSP9enTRx07dtTkyZNVUFBgd0kAAABG2f45WEeOHFF4eHiD8bCwMJWWltpdDgAAgHG2B6zo6GgtX75cFRUV9WOVlZXKzs7WLbfcYnc5AAAAxtl%2Bi3DBggWaO3eu8vLy1Lp1awUGBur06dNq2bKlVq9ebXc5AAAAxtkesPr166c9e/Zo7969On78uLxerzp06KB77rlHbdo48xuvAQAATLI9YEnSDTfcoEGDBun48ePq2LGjEyUAAAA0GdufwTp79qzmzZunnj176v7775f03TNYU6ZMUWVlpd3lAAAAGGd7wFqxYoUOHjyolStXKjDwn8vX1tZq5cqVdpcDAABgnO0Ba8eOHfqv//ov3XffffW/9DksLEzLli3Tzp077S4HAADAONsDVlVVlWJiYhqMR0ZG6syZM3aXAwAAYJztAeuWW27RX/7yF0my/O7B7du361//9V/tLgcAAMA423%2BKcPz48XriiSf08MMPq66uTq%2B%2B%2BqqKioq0Y8cOLVy40O5yAAAAjLM9YKWnp8vlcun1119XUFCQfvWrX6lTp05auXKl7rvvPrvLAQAAMM72gHXy5Ek9/PDDevjhh%2B1eGgAAwBa2P4M1aNAgy7NXAAAA/sb2gJWUlKRt27bZvSwAAIBtbL9F%2BC//8i/6j//4D%2BXm5uqWW25RixYtLPPZ2dl2lwQAAGCU7QHriy%2B%2B0E9%2B8hNJUkVFhd3LAwAANDnbAtbs2bP14osv6rXXXqsfW716tTIzM%2B0qAQAAwBa2PYO1a9euBmO5ubl2LQ8AAGAb2wLWpX5ykJ8mBAAA/si2gPX9L3a%2B0hgAAICvs/1jGgAAAPwdAQsAAMAw236K8MKFC5o7d%2B4Vx/gcLAAA4OtsC1i9e/dWWVnZFccAAAB8nW0B6/9%2B/hUAAIA/4xksAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGHNImA9//zz6tatW/2/CwsLlZaWpl69emnYsGHavHmzZfv169dr6NCh6tWrlzIyMlRUVGR3yQAAwIf5fcA6ePCgNm3aVP/vsrIyzZgxQ%2BPGjVNhYaEWLlyoxYsXy%2B12S5J27dqlVatW6YUXXtD%2B/fs1cOBATZ8%2BXWfOnHHqEAAAgI/x64BVV1enZ555RpMmTaof27Jli2JiYpSWlqaQkBAlJycrNTVV%2Bfn5kqS8vDyNHj1aCQkJatmypaZMmSJJ2r17txOHAAAAfJBtv4vQCW%2B88YZCQkI0YsQIvfTSS5Kk4uJixcbGWraLjY3Vtm3b6ucfeOCB%2BrnAwEB1795dbrdbw4YNu6p1y8rKVF5ebhlzuVopKiqqMYfTQFBQoOVvwFe4XPafs7xfrOiHFf2woh%2BN57cB65tvvtGqVasa/JJpj8ejDh06WMbatm2rioqK%2Bvnw8HDLfHh4eP381cjLy1NOTo5lLDMzUzNnzvwxh3DVwsJuaJL9Ak0lIqK1Y2vzfrGiH1b0w4p%2BXDu/DVjLli3T6NGj1blzZx09evRHvdbr9TZq7fT0dKWmplrGXK5WqqioatR%2BLxYUFKiwsBtUWVmt2to6o/sGmpLp98LV4P1iRT%2Bs6IeVP/XDqW/o/DJgFRYW6u9//7sKCgoazEVERMjj8VjGKioqFBkZedl5j8ejLl26XPX6UVFRDW4HlpefUk1N05yktbV1TbZvoCk4eb7yfrGiH1b0w4p%2BXDu/vLm6efNmnThxQgMHDlRSUpJGjx4tSUpKSlLXrl0bfOxCUVGREhISJElxcXEqLi6un6utrdWBAwfq5wEAAK7ELwPW/PnztWPHDm3atEmbNm1Sbm6uJGnTpk0aMWKESktLlZ%2Bfr3Pnzmnv3r3au3evxo4dK0nKyMjQW2%2B9pY8//ljV1dVas2aNgoODNWDAAAePCAAA%2BBK/vEUYHh5ueVC9pqZGknTTTTdJktauXaulS5dqyZIlio6O1ooVK3TbbbdJkvr37685c%2BZo1qxZOnHihOLj45Wbm6uWLVvafyAAAMAn%2BWXAutjNN9%2BsQ4cO1f87MTHR8uGjFxs/frzGjx9vR2kAAMAP%2BeUtQgAAACcRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADHM5XQAap8/C7U6XAAAALsIVLAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMP8NmCVlpYqMzNTSUlJSk5O1vz581VZWSlJOnjwoCZMmKDevXtryJAhWrduneW1W7du1YgRI9SzZ0%2BNHj1a%2B/btc%2BIQAACAj/LbgDV9%2BnSFhYVp165d2rhxoz7//HP953/%2Bp86ePatp06bpzjvv1HvvvacXX3xRa9eu1c6dOyV9F77mzZunrKwsffDBB5o0aZIef/xxHT9%2B3OEjAgAAvsIvA1ZlZaXi4uI0d%2B5ctW7dWjfddJNGjRqlDz/8UHv27NGFCxf02GOPqVWrVurRo4fGjBmjvLw8SVJ%2Bfr5SUlKUkpKikJAQjRw5Ul27dtXmzZsdPioAAOArXE4X0BTCwsK0bNkyy9ixY8cUFRWl4uJidevWTUFBQfVzsbGxys/PlyQVFxcrJSXF8trY2Fi53e6rXr%2BsrEzl5eWWMZerlaKion7sofygoCC/zMdoBlwu%2B8/d798vvG%2B%2BQz%2Bs6IcV/Wg8vwxYF3O73Xr99de1Zs0abdu2TWFhYZb5tm3byuPxqK6uTh6PR%2BHh4Zb58PBwffHFF1e9Xl5ennJycixjmZmZmjlz5rUfBOBHIiJaO7Z2WNgNjq19PaIfVvTDin5cO78PWH/729/02GOPae7cuUpOTta2bdsuuV1AQED9f3u93katmZ6ertTUVMuYy9VKFRVVjdrvxfjOAr7K9HvhagQFBSos7AZVVlartrbO9vWvN/TDin5Y%2BVM/nPqGzq8D1q5du/Tkk09q8eLFeuihhyRJkZGROnLkiGU7j8ejtm3bKjAwUBEREfJ4PA3mIyMjr3rdqKioBrcDy8tPqabGt09SwBQn3wu1tXW8F/8P%2BmFFP6zox7Xz20sgH330kebNm6eXX365PlxJUlxcnA4dOqSampr6MbfbrYSEhPr5oqIiy77%2B7zwAAMCV%2BGXAqqmp0aJFi5SVlaV%2B/fpZ5lJSUhQaGqo1a9aourpan3zyiTZs2KCMjAxJ0tixY7V//37t2bNH586d04YNG3TkyBGNHDnSiUMBAAA%2BKMDb2AeOrkMffvihfvaznyk4OLjB3Pbt21VVVaVnnnlGRUVFat%2B%2BvaZOnarx48fXb7Nz505lZ2ertLRUnTt31sKFC5WYmNiomsrLTzXq9ZficgVq8Mr3jO8XaGrbZt1t%2B5ouV6AiIlqroqKKWx6iHxejH1b%2B1I8bb2zjyLp%2B%2BQxWnz59dOjQoR/c5g9/%2BMNl54YMGaIhQ4aYLgsAADQTfnmLEAAAwEkELAAAAMMIWAAAAIYRsAAAAAzzy4fcAVzf7n/pfadL%2BFGc%2BKlHAL6NK1gAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhrmcLgAArncd%2BjG6AAAKiElEQVT3v/S%2B0yX8KNtm3e10CUCzxxUsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyAdQmlpaV69NFHlZSUpIEDB2rFihWqq6tzuiwAAOAj%2BJiGS3jiiSfUo0cPvfPOOzpx4oSmTZum9u3b6xe/%2BIXTpQHAFfnSx0rwkRLwV1zBuojb7dZnn32mrKwstWnTRjExMZo0aZLy8vKcLg0AAPgIrmBdpLi4WNHR0QoPD68f69Gjhw4fPqzTp08rNDT0ivsoKytTeXm5ZczlaqWoqCijtQYFkY8B%2BDZfutomSf8v6x6nS/hRBq98z%2BkSrpqv9fZKCFgX8Xg8CgsLs4x9H7YqKiquKmDl5eUpJyfHMvb444/riSeeMFeovgtyj9z0udLT042HN19UVlamvLw8%2BvG/6IcV/bCiH1b%2B2o8P/%2BO%2Ba3qdv/bDTlwCuQSv19uo16enp2vjxo2WP%2Bnp6Yaq%2B6fy8nLl5OQ0uFrWXNEPK/phRT%2Bs6IcV/bCiH43HFayLREZGyuPxWMY8Ho8CAgIUGRl5VfuIiooi8QMA0IxxBesicXFxOnbsmE6ePFk/5na71blzZ7Vu3drBygAAgK8gYF0kNjZW8fHxys7O1unTp1VSUqJXX31VGRkZTpcGAAB8RNCzzz77rNNFXG/uueceFRQU6LnnntPbb7%2BttLQ0TZ48WQEBAU6X1kDr1q3Vt29frq79L/phRT%2Bs6IcV/bCiH1b0o3ECvI19ohsAAAAW3CIEAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyA5YNKS0v16KOPKikpSQMHDtSKFStUV1fndFmO6tatm%2BLi4hQfH1//57nnnnO6LNu89957Sk5O1uzZsxvMbd26VSNGjFDPnj01evRo7du3z4EK7XW5fmzcuFG33Xab5TyJj4/Xp59%2B6lCl9igtLVVmZqaSkpKUnJys%2BfPnq7KyUpJ08OBBTZgwQb1799aQIUO0bt06h6ttepfrx9GjR9WtW7cG58crr7zidMlN6rPPPtMjjzyi3r17Kzk5WbNmzVJ5ebkkqbCwUGlpaerVq5eGDRumzZs3O1ytD/HC54waNcq7aNEib2Vlpffw4cPeIUOGeNetW%2Bd0WY7q2rWr96uvvnK6DEfk5uZ6hwwZ4h03bpx31qxZlrkDBw544%2BLivHv27PGePXvWu2nTJm9CQoL32LFjDlXb9H6oH2%2B%2B%2BaZ3woQJDlXmnOHDh3vnz5/vPX36tPfYsWPe0aNHexcsWOCtrq723nPPPd5Vq1Z5q6qqvEVFRd6%2Bfft6d%2BzY4XTJTepy/fjqq6%2B8Xbt2dbo8W507d8571113eXNycrznzp3znjhxwjthwgTvjBkzvF9//bX3jjvu8Obn53vPnj3rff/99723336799NPP3W6bJ/AFSwf43a79dlnnykrK0tt2rRRTEyMJk2apLy8PKdLg0NCQkK0YcMG3XrrrQ3m8vPzlZKSopSUFIWEhGjkyJHq2rWrX38X%2BkP9aI4qKysVFxenuXPnqnXr1rrppps0atQoffjhh9qzZ48uXLigxx57TK1atVKPHj00ZswYv/568kP9aI6qq6s1e/ZsTZs2TcHBwYqMjNTgwYP1%2Beefa8uWLYqJiVFaWppCQkKUnJys1NRU5efnO122TyBg%2BZji4mJFR0crPDy8fqxHjx46fPiwTp8%2B7WBlzsvOztaAAQPUp08fLV68WFVVVU6XZIuJEyeqTZs2l5wrLi5WbGysZSw2NlZut9uO0hzxQ/2QpGPHjukXv/iFEhMTNWjQIG3atMnG6uwXFhamZcuWqX379vVjx44dU1RUlIqLi9WtWzcFBQXVz8XGxqqoqMiJUm3xQ/343lNPPaV%2B/frpzjvvVHZ2ti5cuOBEqbYIDw/XmDFj5HK5JElffvml/vSnP%2Bn%2B%2B%2B%2B/7NcPfz4/TCJg%2BRiPx6OwsDDL2Pdhq6KiwomSrgt33HGHkpOTtXPnTuXl5enjjz/WkiVLnC7LcR6PxxLGpe/Ol%2BZ6rkRGRiomJkZPPvmk3n//fc2ZM0cLFixQYWGh06XZxu126/XXX9djjz12ya8nbdu2lcfjaTbPdf7ffgQHB6tnz54aPHiwdu/erdzcXG3evFm//OUvnS6zyZWWliouLk4PPPCA4uPjNXPmzMueH83168ePRcDyQV6v1%2BkSrjt5eXkaM2aMgoOD9dOf/lRZWVkqKCjQ%2BfPnnS7NcZwv/zRgwAD95je/UWxsrIKDgzVs2DANHjxYGzdudLo0W/ztb3/T5MmTNXfuXCUnJ192u4CAABurcs7F/YiKitIbb7yhwYMHq0WLFrr99ts1bdq0ZnF%2BREdHy%2B12a/v27Tpy5Iieeuopp0vyeQQsHxMZGSmPx2MZ83g8CggIUGRkpENVXX9uvvlm1dbW6sSJE06X4qiIiIhLni%2BcK/8UHR2tsrIyp8tocrt27dKjjz6qBQsWaOLEiZK%2B%2B3py8dUIj8ejtm3bKjDQv//3cKl%2BXEp0dLS%2B%2BeabZvGNSkBAgGJiYjR79mwVFBTI5XI1%2BPpRUVHB14%2Br5N/vID8UFxenY8eO6eTJk/VjbrdbnTt3VuvWrR2szDkHDhzQ8uXLLWMlJSUKDg62PFfRHMXFxTV4XsLtdishIcGhipz1hz/8QVu3brWMlZSUqGPHjg5VZI%2BPPvpI8%2BbN08svv6yHHnqofjwuLk6HDh1STU1N/VhzOD8u14/CwkKtWbPGsu2XX36p6Ohov72qV1hYqKFDh1puCX8frm%2B//fYGXz%2BKior8/vwwhYDlY2JjYxUfH6/s7GydPn1aJSUlevXVV5WRkeF0aY5p166d8vLylJubq/Pnz%2Bvw4cN6%2BeWXlZ6ebnl4tzkaO3as9u/frz179ujcuXPasGGDjhw5opEjRzpdmiPOnz%2Bv5557Tm63WxcuXFBBQYHeffddjRs3zunSmkxNTY0WLVqkrKws9evXzzKXkpKi0NBQrVmzRtXV1frkk0%2B0YcMGv/568kP9aNOmjVavXq1NmzbpwoULcrvdeuWVV/y6H3FxcTp9%2BrRWrFih6upqnTx5UqtWrVKfPn2UkZGh0tJS5efn69y5c9q7d6/27t2rsWPHOl22TwjwNofrnn7m%2BPHjWrx4sf77v/9boaGhGjdunB5//HG//Q7ravz1r39Vdna2Dh06pODgYI0aNUqzZ89WSEiI06U1ufj4eEmqvwrx/U8Dff%2BTgjt37lR2drZKS0vVuXNnLVy4UImJic4Ua4Mf6ofX69WaNWu0YcMGlZeX6%2Babb9ZTTz2lgQMHOlZvU/vwww/1s5/9TMHBwQ3mtm/frqqqKj3zzDMqKipS%2B/btNXXqVI0fP96BSu1xpX4cOHBAOTk5OnLkiNq0aaOf//znmjp1ql/fMj106JCWLl2qTz/9VK1atdKdd96p%2BfPnq0OHDvrrX/%2BqpUuXqqSkRNHR0Zo7d66GDBnidMk%2BgYAFAABgmP9GcgAAAIcQsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABg2P8H6f399tfKp94AAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common1741441295911992359”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>151</td> <td class=”number”>4.7%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.526315789473683</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.595959595959595</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.317073170731707</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.47945205479452</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.27027027027027</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.545454545454546</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.60377358490566</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.267813267813267</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.233846790034116</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2710)</td> <td class=”number”>2725</td> <td class=”number”>84.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>314</td> <td class=”number”>9.8%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme1741441295911992359”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>151</td> <td class=”number”>4.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.07354743809757293</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.09523809523809523</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.11607048644085682</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.14278914802475012</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>22.29580573951435</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>23.563218390804597</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>24.675324675324674</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>30.434782608695656</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>32.05128205128205</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_SBP09”>PCT_SBP09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>52</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>3.9531</td>

</tr> <tr>

<th>Minimum</th> <td>1.6614</td>

</tr> <tr>

<th>Maximum</th> <td>6.771</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram294343038879807941”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives294343038879807941,#minihistogram294343038879807941”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives294343038879807941”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles294343038879807941”

aria-controls=”quantiles294343038879807941” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram294343038879807941” aria-controls=”histogram294343038879807941”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common294343038879807941” aria-controls=”common294343038879807941”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme294343038879807941” aria-controls=”extreme294343038879807941”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles294343038879807941”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>1.6614</td>

</tr> <tr>

<th>5-th percentile</th> <td>2.2725</td>

</tr> <tr>

<th>Q1</th> <td>2.8289</td>

</tr> <tr>

<th>Median</th> <td>3.295</td>

</tr> <tr>

<th>Q3</th> <td>5.4277</td>

</tr> <tr>

<th>95-th percentile</th> <td>6.1692</td>

</tr> <tr>

<th>Maximum</th> <td>6.771</td>

</tr> <tr>

<th>Range</th> <td>5.1096</td>

</tr> <tr>

<th>Interquartile range</th> <td>2.5987</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>1.3965</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.35328</td>

</tr> <tr>

<th>Kurtosis</th> <td>-1.1675</td>

</tr> <tr>

<th>Mean</th> <td>3.9531</td>

</tr> <tr>

<th>MAD</th> <td>1.232</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.48837</td>

</tr> <tr>

<th>Sum</th> <td>12381</td>

</tr> <tr>

<th>Variance</th> <td>1.9503</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram294343038879807941”>

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9hrVfXiIEAACwEgELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGCY3eoCAACXrkEvfmB1Cedk06TbrS4BPoIzWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAM89uAFR8fr8TERCUlJbX8ee655yRJZWVlyszMVPfu3TVkyBCtX7/e7bHFxcUaOHCgunfvruzsbFVUVFixBAAA4KP8%2BrMIN2/erI4dO7qN1dTUaOLEicrPz1dGRoY%2B/vhjTZgwQZ06dVJSUpK2bdumgoICvfTSS4qPj1dxcbHGjx%2BvrVu3qm3bthatBAAA%2BBK/PYP1czZs2KC4uDhlZmYqODhYqampSk9PV0lJiSTJ4XBo%2BPDhSk5OVkhIiMaOHStJ2r59u5VlAwAAH%2BLXAWvJkiVKS0tTz549NXv2bB09elSVlZVKSEhw2y4hIaHlMuBP5202m7p166by8vKLWjsAAPBdfnuJ8Oabb1Zqaqp%2B%2B9vf6uuvv9akSZM0d%2B5cuVwuxcbGum0bGRmp2tpaSZLL5VJERITbfERERMt8a9TU1MjpdLqN2e1tFRMTc56r8R6BgTa3r/Aedjvfk1NxrJpHT82/zuipZ3hDX/02YDkcjpb/vv766zVt2jRNmDBBPXr0OOtjm5ubL3jfhYWFbmO5ubnKy8u7oOf1JuHhbawuAT8RFRVqdQleiWPVvEu5p556nV3KPfUkK/vqtwHrpzp27KjGxkbZbDa5XC63udraWkVHR0uSoqKiTpt3uVzq0qVLq/eVlZWl9PR0tzG7va1qa4%2BeZ/XeIzDQpvDwNqqrq1djY5PV5eAU/nB8mcSxah49Nf86o6eecWpfrQpZfhmwqqqqtH79es2YMaNlbM%2BePQoKClK/fv305ptvum1fUVGh5ORkSVJiYqIqKys1bNgwSVJjY6OqqqqUmZnZ6v3HxMScdjnQ6Tyshgb/efE0Njb51Xr8Ad%2BPM%2BNYNe9S7qmn1n0p99STrAytfnnRt3379nI4HCoqKtKJEye0b98%2BLV26VFlZWbrvvvtUXV2tkpISHT9%2BXKWlpSotLdWoUaMkSdnZ2Vq7dq127typ%2Bvp6LV%2B%2BXEFBQUpLS7N2UQAAwGd4XcBKT09XYWGhvvnmm/N%2BjtjYWBUVFWnbtm1KSUnR6NGjdccdd%2BjJJ59U%2B/bttXLlSq1evVo9evTQ/PnztWjRInXt2lWS1LdvX02ZMkWTJk1S7969tWPHDhUVFSkkJMTUEgEAgJ/zukuEI0aM0FtvvaXly5frtttu06hRo5Seni67/dxK7dWrl1577bWfnVu3bt3PPnbMmDEaM2bMOe0PAADgX7zuDFZubq7efvtt/fGPf1SXLl00f/589evXT4sWLdK%2BffusLg8AAOCsvC5g/cuNN96o6dOna/v27Zo5c6b%2B%2BMc/avDgwXr44Yf1t7/9zeryAAAAfpbXBqyTJ0/q7bff1iOPPKLp06crNjZWTz/9tLp166acnBxt2LDB6hIBAADOyOvuwdqzZ49ef/11rV27VkePHtXAgQP1yiuvuL1BaK9evTRnzhxlZGRYWCkAAMCZeV3AGjJkiDp16qRx48bp/vvvV2Rk5Gnb9OvXT4cOHbKgOgAAgLPzuoBVXFys3r17n3W7zz777CJUAwAAcO687h6s%2BPh4jR8/Xu%2B8807L2B/%2B8Ac98sgjp32EDQAAgDfyuoC1YMECHT58WJ07d24ZS0tLU1NTkxYuXGhhZQAAAK3jdZcI33//fW3YsEFRUVEtY3FxcVq8eLHuvfdeCysDAABoHa87g3Xs2DEFBwefNm6z2VRfX29BRQAAAOfG6wJWr169tHDhQn3//fctY99%2B%2B63mzp3r9lYNAAAA3srrLhHOnDlTDz30kG677TaFhYWpqalJR48e1TXXXKNXX33V6vIAAADOyusC1jXXXKO33npLf/rTn/SPf/xDNptNnTp1Up8%2BfRQYGGh1eQAAAGfldQFLkoKCgnTXXXdZXQYAAMB58bqA9fXXX2vJkiX64osvdOzYsdPm3333XQuqAgAAaD2vC1gzZ85UTU2N%2BvTpo7Zt21pdDgAAwDnzuoBVUVGhd999V9HR0VaXAgAAcF687m0a2rdvz5krAADg07wuYI0bN06FhYVqbm62uhQAAIDz4nWXCP/0pz/pk08%2B0RtvvKGOHTvKZnPPgK%2B99ppFlQEAALSO1wWssLAw9e3b1%2BoyAAAAzpvXBawFCxZYXQIAAMAF8bp7sCRp7969Kigo0NNPP90y9umnn1pYEQAAQOt5XcAqKyvT0KFDtXXrVm3cuFHSj28%2B%2BsADD/AmowAAwCd43SXCF154QU8%2B%2BaQefPBB3XTTTZJ%2B/HzChQsXatmyZbrzzjstrhDwToNe/MDqElpt06TbrS4BADzK685g/c///I%2Bys7MlSQEBAS3j99xzj/bs2WNVWQAAAK3mdQGrXbt2Z/wMwpqaGgUFBVlQEQAAwLnxukuE3bt31/z58zVr1qyWsX379unZZ5/VbbfdZmFluFC%2BdAkLAIAL4XUB6%2Bmnn9aDDz6olJQUNTY2qnv37qqvr1eXLl20cOFCq8sDAAA4K68LWFdeeaU2btyo0tJS7du3TyEhIerUqZNuv/12t3uyzsX8%2BfP1yiuvaPfu3ZJ%2B/E3FJUuWaO/evbrqqqs0btw4DR06tGX74uJirVmzRk6nU/Hx8crPz1diYqKR9QEAAP/ndQFLki677DLdddddRp5r165dWrduXcvfa2pqNHHiROXn5ysjI0Mff/yxJkyYoE6dOikpKUnbtm1TQUGBXnrpJcXHx6u4uFjjx4/X1q1b%2BRBqAADQKl4XsNLT0//tmapzeS%2BspqYmPfvss8rJydGLL74oSdqwYYPi4uKUmZkpSUpNTVV6erpKSkqUlJQkh8Oh4cOHKzk5WZI0duxYFRcXa/v27RoyZMgFrAwAAFwqvC5gDR482C1gNTY2at%2B%2BfSovL9eDDz54Ts/12muvKTg4WBkZGS0Bq7KyUgkJCW7bJSQkaNOmTS3zgwcPbpmz2Wzq1q2bysvLWx2wampq5HQ63cbs9raKiYk5p/q9UWCgze0rcD7sds8fPxyr5tFT88cuPfUMb%2Bir1wWsadOmnXF8y5Yt%2Bstf/tLq5/nuu%2B9UUFCgV1991W3c5XIpNjbWbSwyMlK1tbUt8xEREW7zERERLfOt4XA4VFhY6DaWm5urvLy8Vj%2BHtwsPb2N1CfBhUVGhF21fHKvmXco99dSxeyn31JOs7KvXBayfc9ddd%2BmZZ57RM88806rtFyxYoOHDh6tz587av3//Oe2rubn5fEpskZWVpfT0dLcxu72tamuPXtDzeoPAQJvCw9uorq5ejY1NVpcDH3UxXgscq%2BbRU/PHLj31jFP7alXI8pmAVVVV1ergU1ZWpk8//bTlswxPFRUVJZfL5TZWW1ur6Ojon513uVzq0qVLq2uNiYk57XKg03lYDQ3%2B8%2BJpbGzyq/Xg4rqYxw7HqnmXck89te5LuaeeZGVo9bqANXr06NPG6uvrtWfPHg0YMKBVz7F%2B/XodPHhQ/fv3l/T/Z6RSUlL00EMPnRa8KioqWm5qT0xMVGVlpYYNGybpx3vAqqqqWm6KBwAAOBuvC1hxcXGn/RZhcHCwMjMzNXLkyFY9x4wZM/TEE0%2B0/P3AgQPKysrSunXr1NTUpJUrV6qkpERDhw7Vhx9%2BqNLSUjkcDklSdna2pkyZonvvvVfx8fH6/e9/r6CgIKWlpRlbIwAA8G9eF7BMvFt7RESE243qDQ0Nkn58E1NJWrlypebNm6e5c%2BeqQ4cOWrRokbp27SpJ6tu3r6ZMmaJJkybp4MGDSkpKUlFRkUJCQi64LgAAcGnwuoC1du3aVm97//33t2q7jh07tryLuyT16tXL7c1Hf2rMmDEaM2ZMq%2BsAAAA4ldcFrPz8fDU1NZ12Q3tAQIDbWEBAQKsDFgAAwMXkdQHrpZde0ssvv6zx48crPj5ezc3N2r17t373u9/pV7/6lVJSUqwuEQAA4N/yuoC1cOFCFRUVub0ZaM%2BePXXNNdfo4YcfPuNbLwAAAHgTr3tv/q%2B%2B%2Buq0d1KXpPDwcFVXV1tQEQAAwLnxuoDVoUMHLVy40O2jaerq6rRkyRJde%2B21FlYGAADQOl53iXDmzJmaOnWqHA6HQkNDZbPZdOTIEYWEhGjZsmVWlwcAAHBWXhew%2BvTpo/fee0%2BlpaU6cOCAmpubFRsbqzvuuEPt2rWzujwAAICz8rqAJUlt2rTRnXfeqQMHDuiaa66xuhwAAIBz4nX3YB07dkzTp0/XLbfcokGDBkn68R6ssWPHqq6uzuLqAAAAzs7rAtaiRYu0a9cuLV68WDbb/5fX2NioxYsXW1gZAABA63hdwNqyZYv%2B67/%2BS/fcc0/Lhz6Hh4drwYIF2rp1q8XVAQAAnJ3XBayjR48qLi7utPHo6Gj98MMPF78gAACAc%2BR1Aevaa6/VX/7yF0ly%2B%2BzBzZs36%2Bqrr7aqLAAAgFbzut8iHDNmjB5//HGNGDFCTU1NWrVqlSoqKrRlyxbl5%2BdbXR4AAMBZeV3AysrKkt1u1%2BrVqxUYGKgVK1aoU6dOWrx4se655x6rywMAADgrrwtYhw4d0ogRIzRixAirSwEAADgvXncP1p133ul27xUAAICv8bqAlZKSok2bNlldBgAAwHnzukuEV111lX7zm9%2BoqKhI1157rS677DK3%2BSVLllhUGQAAQOt4XcD68ssvdd1110mSamtrLa4GAADg3HlNwJo8ebJeeOEFvfrqqy1jy5YtU25uroVVAQAAnDuvuQdr27Ztp40VFRVZUAkAAMCF8ZqAdabfHOS3CQEAgC/ymoD1rw92PtsYAACAt/OagAUAAOAvCFgAAACGec1vEZ48eVJTp0496xjvgwUAALyd1wSsHj16qKam5qxjAAAA3s5rAtap739lwueff64FCxaooqJCwcHB6t27t/Lz83XFFVeorKxMS5Ys0d69e3XVVVdp3LhxGjp0aMtji4uLtWbNGjmdTsXHxys/P1%2BJiYlG6wMAAP7LL%2B/BOnHihB566CH17t1bZWVl2rhxow4ePKg5c%2BaopqZGEydO1OjRo1VWVqb8/HzNnj1b5eXlkn58P66CggI9//zz2rFjh/r376/x48frhx9%2BsHhVAADAV/hlwKqvr9fkyZM1btw4BQUFKTo6Wnfffbe%2B%2BOILbdiwQXFxccrMzFRwcLBSU1OVnp6ukpISSZLD4dDw4cOVnJyskJAQjR07VpK0fft2K5cEAAB8iF8GrIiICI0cOVJ2%2B49XQPfu3as333xTgwYNUmVlpRISEty2T0hIUEVFhSSdNm%2Bz2dStW7eWM1wAAABn4zX3YHlCdXW1Bg4cqIaGBo0aNUp5eXl65JFHFBsb67ZdZGRkywdLu1wuRUREuM1HRESc0wdP19TUyOl0uo3Z7W0VExNznivxHoGBNrevwPmw2z1//HCsmkdPzR%2B79NQzvKGvfh2wOnTooPLycv3973/XM888o6eeeqpVj7vQj%2BhxOBwqLCx0G8vNzVVeXt4FPa83CQ9vY3UJ8GFRUaEXbV8cq%2BZdyj311LF7KffUk6zsq18HLOnHj9uJi4vT5MmTNXr0aPXr108ul8ttm9raWkVHR0uSoqKiTpt3uVzq0qVLq/eZlZWl9PR0tzG7va1qa4%2Be5yq8R2CgTeHhbVRXV6/Gxiary4GPuhivBY5V8%2Bip%2BWOXnnrGqX21KmT5ZcAqKyvTnDlztGnTJtlsP54e/NfXm266SVu2bHHbvqKiQsnJyZKkxMREVVZWatiwYZKkxsZGVVVVKTMzs9X7j4mJOe1yoNN5WA0N/vPiaWxs8qv14OK6mMcOx6p5l3JPPbXuS7mnnmRlaPXLi76JiYk6cuSIFi1apPr6eh06dEgFBQXq2bOnsrOzVV1drZKSEh0/flylpaUqLS3VqFGjJEnZ2dlau3atdu7cqfr6ei1fvlxBQUFKS0uzdlEAAMBn%2BGXAateunV5%2B%2BWVVVFTo1ltv1ZAhQ9SuXTv953/%2Bp9q3b6%2BVK1dq9erV6tGjh%2BbPn69Fixapa9eukqS%2BfftqypQpmjRpknr37q0dO3aoqKhIISEhFq8KAAD4Cr%2B8RChJ8fHxP/vu8L169dK6det%2B9rFjxozRmDFjPFUaAADwc355BgsAAMBKBCwAAADDCFgAAACGEbAAAAAMI2BSZYo0AAAQHklEQVQBAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADPPbD3sGAMC0QS9%2BYHUJ52TTpNutLuGSxRksAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADDMbnUBAC49g178wOoSzsmmSbdbXQIAH8MZLAAAAMMIWAAAAIb5bcCqrq5Wbm6uUlJSlJqaqhkzZqiurk6StGvXLv3qV79Sjx49NGDAAL388stuj3377beVkZGhW265RcOHD9f7779vxRIAAICP8tuANX78eIWHh2vbtm1644039MUXX%2Bi3v/2tjh07pnHjxunWW2/Vn//8Z73wwgtauXKltm7dKunH8DV9%2BnRNmzZNH374oXJycvTYY4/pwIEDFq8IAAD4Cr%2B8yb2urk6JiYmaOnWqQkNDFRoaqmHDhunVV1/Ve%2B%2B9p5MnT2rChAkKDAzUjTfeqJEjR8rhcGjAgAEqKSlRv3791K9fP0nS0KFDtXr1aq1fv16PPvqoxSsDYAVuygdwrvwyYIWHh2vBggVuY998841iYmJUWVmp%2BPh4BQYGtswlJCSopKREklRZWdkSrk6dLy8vb/X%2Ba2pq5HQ63cbs9raKiYk516V4ncBAm9tXAN7HbvfM65PXv%2B/x1LHg7bzhWPXLgPVT5eXlWr16tZYvX65NmzYpPDzcbT4yMlIul0tNTU1yuVyKiIhwm4%2BIiNCXX37Z6v05HA4VFha6jeXm5iovL%2B/8F%2BFlwsPbWF0CgJ8RFRXq0efn9e87PH0seDsrj1W/D1gff/yxJkyYoKlTpyo1NVWbNm0643YBAQEt/93c3HxB%2B8zKylJ6errbmN3eVrW1Ry/oeb1BYKBN4eFtVFdXr8bGJqvLAXAGnvpZw%2Bvf9/jD/3fOx6nHqlUhy68D1rZt2/Tkk09q9uzZuv/%2B%2ByVJ0dHR%2Buqrr9y2c7lcioyMlM1mU1RUlFwu12nz0dHRrd5vTEzMaZcDnc7Damjwnx9IjY1NfrUewJ94%2BrXJ6993XOrfJyv/IeC3F2c/%2BeQTTZ8%2BXUuXLm0JV5KUmJio3bt3q6GhoWWsvLxcycnJLfMVFRVuz3XqPAAAwNn4ZcBqaGjQrFmzNG3aNPXp08dtrl%2B/fgoLC9Py5ctVX1%2Bvzz77TK%2B//rqys7MlSaNGjdKOHTv03nvv6fjx43r99df11VdfaejQoVYsBQAA%2BCC/vES4c%2BdO7dmzR/PmzdO8efPc5jZv3qwVK1bo2WefVVFRkS6//HJNnjxZaWlpkqQbbrhBixcv1oIFC1RdXa3OnTtr5cqVuuKKKyxYCQAA8EV%2BGbB69uyp3bt3/9tt/vu///tn5wYMGKABAwaYLgsAAFwi/PISIQAAgJUIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGGa3ugBcmEEvfmB1CQAA4Cc4gwUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAM420aAADwU770Vj6bJt1udQlG%2Be0ZrD//%2Bc9KTU3V5MmTT5t7%2B%2B23lZGRoVtuuUXDhw/X%2B%2B%2B/3zLX1NSkF154QXfeead69eqlhx9%2BWF9//fXFLB0AAPg4vwxYv/vd7zRv3jz94he/OG1u165dmj59uqZNm6YPP/xQOTk5euyxx3TgwAFJ0po1a7RhwwYVFRVp%2B/btiouLU25urpqbmy/2MgAAgI/yy4AVHBys119//YwBq6SkRP369VO/fv0UHBysoUOH6oYbbtD69eslSQ6HQzk5Obr%2B%2BusVFhamyZMna8%2BePfrss88u9jIAAICP8suA9cADD6hdu3ZnnKusrFRCQoLbWEJCgsrLy3Xs2DF9%2BeWXbvNhYWH6xS9%2BofLyco/WDAAA/Mcld5O7y%2BVSRESE21hERIS%2B/PJLff/992pubj7jfG1tbav3UVNTI6fT6TZmt7dVTEzM%2BRcOAK1kt3vm386BgTa3r4BJJo9bbzhWL7mAJems91Nd6P1WDodDhYWFbmO5ubnKy8u7oOcFgNaIigr16POHh7fx6PPj0uSJ49bKY/WSC1hRUVFyuVxuYy6XS9HR0YqMjJTNZjvjfPv27Vu9j6ysLKWnp7uN2e1tVVt79PwLB4BW8tTPmsBAm8LD26iurl6NjU0e2QcuXSaP21OPVatC1iUXsBITE1VRUeE2Vl5eriFDhig4OFhdunRRZWWlevfuLUmqq6vTP/7xD910002t3kdMTMxplwOdzsNqaOAHEgDP8/TPmsbGJn6ewThPHFNW/kPgkruQPmrUKO3YsUPvvfeejh8/rtdff11fffWVhg4dKknKzs5WcXGx9uzZoyNHjmjx4sXq1q2bkpKSLK4cAAD4Cr88g/WvMNTQ0CBJeueddyT9eKbqhhtu0OLFi7VgwQJVV1erc%2BfOWrlypa644gpJ0ujRo%2BV0OvUf//EfOnr0qFJSUk67nwoAAODf8cuAdba3VBgwYIAGDBhwxrmAgADl5eVxQzoAADhvl9wlQgAAAE8jYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwzC8/ixAALmWDXvzA6hKASx5nsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgnUF1dbUeffRRpaSkqH///lq0aJGampqsLgsAAPgIu9UFeKPHH39cN954o9555x0dPHhQ48aN0%2BWXX65f//rXVpcGAAB8AGewfqK8vFyff/65pk2bpnbt2ikuLk45OTlyOBxWlwYAAHwEZ7B%2BorKyUh06dFBERETL2I033qh9%2B/bpyJEjCgsLO%2Btz1NTUyOl0uo3Z7W0VExNjvF4AAPyB3W7unE9goM3tqxUIWD/hcrkUHh7uNvavsFVbW9uqgOVwOFRYWOg29thjj%2Bnxxx83V%2Bj/%2Betv7jH%2BnP9OTU2NHA6HsrKyCIyG0FPPoK/m0VPz6Kln1NTU6JVXXlJWVpbCw9tYUgOXCM%2Bgubn5gh6flZWlN954w%2B1PVlaWoeqs5XQ6VVhYeNoZOpw/euoZ9NU8emoePfUMb%2BgrZ7B%2BIjo6Wi6Xy23M5XIpICBA0dHRrXqOmJgY/iUCAMAljDNYP5GYmKhvvvlGhw4dahkrLy9X586dFRoaamFlAADAVxCwfiIhIUFJSUlasmSJjhw5oj179mjVqlXKzs62ujQAAOAjAufMmTPH6iK8zR133KGNGzfqueee01tvvaXMzEw9/PDDCggIsLo0rxAaGqrevXtzRs8geuoZ9NU8emoePfUMq/sa0Hyhd3QDAADADZcIAQAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYKHVqqurlZubq5SUFKWmpmrGjBmqq6uzuiyf9vnnn%2BvBBx9Ujx49lJqaqkmTJsnpdFpdlt%2BYP3%2B%2B4uPjrS7DL8THxysxMVFJSUktf5577jmry/J5y5cvV58%2BfXTzzTcrJydH%2B/fvt7okn/bRRx%2B5HaNJSUlKTEy05OcAH5WDVsvIyFBiYqJmzZqlw4cPKzc3V127dtVvfvMbq0vzSSdOnFBaWpp%2B%2Bctf6pFHHtGRI0f0xBNPKDw8XMuWLbO6PJ%2B3a9cu5eTkyOVyaffu3VaX4/Pi4%2BP17rvvqmPHjlaX4jfWrFmj1atXa9myZYqJidGLL74oSZo1a5bFlfmXFStW6PPPP2/p78XCGSy0Sl1dnRITEzV16lSFhobqyiuv1LBhw/TXv/7V6tJ8Vn19vSZPnqxx48YpKChI0dHRuvvuu/XFF19YXZrPa2pq0rPPPqucnByrSwF%2B1ssvv6zJkyfruuuuU1hYmGbNmkW4Muyf//ynVq1apaeeeuqi75uAhVYJDw/XggULdPnll7eMffPNN4qJibGwKt8WERGhkSNHym63S5L27t2rN998U4MGDbK4Mt/32muvKTg4WBkZGVaX4leWLFmitLQ09ezZU7Nnz9bRo0etLslnffvtt9q/f7%2B%2B//57DR48WCkpKcrLy9OhQ4esLs2vLF26VCNGjNDVV1990fdNwMJ5KS8v1%2BrVqzVhwgSrS/F51dXVSkxM1ODBg5WUlKS8vDyrS/Jp3333nQoKCvTss89aXYpfufnmm5WamqqtW7fK4XBo586dmjt3rtVl%2BawDBw5IkjZv3qxVq1Zp3bp1OnDgAGewDNq/f7%2B2bt2qX//615bsn4CFc/bxxx/r4Ycf1tSpU5Wammp1OT6vQ4cOKi8v1%2BbNm/XVV19ZcirbnyxYsEDDhw9X586drS7FrzgcDo0cOVJBQUG6/vrrNW3aNG3cuFEnTpywujSf9K/bn8eOHavY2FhdeeWVevzxx7Vt2zYdP37c4ur8w5o1azRgwABdccUVluyfgIVzsm3bNj366KOaOXOmHnjgAavL8RsBAQGKi4vT5MmTtXHjRi4TnKeysjJ9%2Bumnys3NtboUv9exY0c1Njbq4MGDVpfik/51u0V4eHjLWIcOHdTc3ExPDdmyZYvS09Mt2z8BC632ySefaPr06Vq6dKnuv/9%2Bq8vxeWVlZRo4cKCamppaxmy2H1%2BSl112mVVl%2BbT169fr4MGD6t%2B/v1JSUjR8%2BHBJUkpKit566y2Lq/NdVVVVWrhwodvYnj17FBQUxH2Y5%2BnKK69UWFiYdu3a1TJWXV2tyy67jJ4asGvXLlVXV%2Bv222%2B3rAa7ZXuGT2loaNCsWbM0bdo09enTx%2Bpy/EJiYqKOHDmiRYsWKS8vT/X19SooKFDPnj3Vrl07q8vzSTNmzNATTzzR8vcDBw4oKytL69atU0REhIWV%2Bbb27dvL4XAoOjpaOTk5qq6u1tKlS5WVlaXAwECry/NJdrtdmZmZWrFihXr16qWwsDAtW7ZMGRkZLb/4gvNXVVWlyMhIhYWFWVYD74OFVvnrX/%2BqX/7ylwoKCjptbvPmzerQoYMFVfm%2B3bt3a968efrb3/6mtm3b6tZbb9WMGTMUGxtrdWl%2BYf/%2B/brzzjt5HywDPvroIy1ZskS7d%2B9WUFCQhg0bpsmTJys4ONjq0nzWiRMntGDBAr311ls6efKkBg4cqNmzZys0NNTq0nzeypUrtWHDBm3cuNGyGghYAAAAhnEPFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAY9r/cJq6IdP1z4wAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common294343038879807941”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>6.169233189071782</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.74437765147172</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.973591167370116</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.5349036986282</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.936181228476279</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.416376141597667</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.2724686723712626</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.913159996435304</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.811105318871648</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.454458158771867</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (41)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme294343038879807941”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.661363078723364</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:48%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.9586799034075664</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:38%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.9803074024152538</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.046139680874765</td> <td class=”number”>11</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:52%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.113931258359077</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:81%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>5.64811977054827</td> <td class=”number”>77</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:31%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.74437765147172</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:62%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.169233189071782</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.464341675826541</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.771011884839952</td> <td class=”number”>82</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:32%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_SBP15”><s>PCT_SBP15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCT_SBP09”><code>PCT_SBP09</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.92649</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_SFSP09”>PCT_SFSP09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>52</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.70337</td>

</tr> <tr>

<th>Minimum</th> <td>0.18804</td>

</tr> <tr>

<th>Maximum</th> <td>5.8864</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-1016541278650356988”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-1016541278650356988”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-1016541278650356988”

aria-controls=”quantiles-1016541278650356988” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-1016541278650356988” aria-controls=”histogram-1016541278650356988”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-1016541278650356988” aria-controls=”common-1016541278650356988”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-1016541278650356988” aria-controls=”extreme-1016541278650356988”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-1016541278650356988”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0.18804</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.32805</td>

</tr> <tr>

<th>Q1</th> <td>0.54888</td>

</tr> <tr>

<th>Median</th> <td>0.62553</td>

</tr> <tr>

<th>Q3</th> <td>0.7705</td>

</tr> <tr>

<th>95-th percentile</th> <td>1.2821</td>

</tr> <tr>

<th>Maximum</th> <td>5.8864</td>

</tr> <tr>

<th>Range</th> <td>5.6983</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.22162</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.37575</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.53422</td>

</tr> <tr>

<th>Kurtosis</th> <td>17.155</td>

</tr> <tr>

<th>Mean</th> <td>0.70337</td>

</tr> <tr>

<th>MAD</th> <td>0.23472</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.8958</td>

</tr> <tr>

<th>Sum</th> <td>2202.9</td>

</tr> <tr>

<th>Variance</th> <td>0.14119</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-1016541278650356988”>

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aDzzwgCZOnGh3eQAAAF1me8C66aabNGTIEM2cOVO33HKL%2Bvbt22abrKwsHTt2zO7SAAAAjLA9YK1ZsybwETWns3v3bhuqAQAAMM/2a7BSUlI0a9aswJ3XJenXv/61fvSjH7X5AGYAAIBQZHvAKi4u1vHjxzVs2LDA2NixY9Xa2qply5bZXQ4AAIBxti8Rvv7669q4caPi4%2BMDY8nJySopKdHNN99sdzkAAADG2X4Gq7m5WVFRUW0LCQ9XU1OT3eUAAAAYZ3vAGj16tJYtW6ZPPvkkMPbf//5XDz74YNCtGgAAAEKV7UuE999/v374wx/qG9/4hmJiYtTa2qrGxkYNGjRIv/nNb%2BwuBwAAwDjbA9agQYO0adMmvfrqq/rwww8VHh6uIUOGaMyYMYqIiLC7HAAAAONsD1iSFBkZqeuuu86JXQMAAJx1tgesQ4cOqbS0VO%2B//76am5vbzP/5z3%2B2uyQAAACjHLkGq6amRmPGjFHv3r3t3j0AAMBZZ3vA2rNnj/785z/L7XbbvWsAAABb2H6bhn79%2BnHmCgAA9Gi2B6yZM2eqrKxMfr/f7l0DAADYwvYlwldffVVvvfWW1q1bp4EDByo8PDjj/f73v7e7JAAAAKNsD1gxMTH61re%2BZfduAQAAbGN7wCouLrZ7lwAAALay/RosSfrggw/0%2BOOP67777guM/etf/3KiFAAAAONsD1i7du3SpEmTtH37dlVUVEj6/Oaj06dP5yajAACgR7A9YK1YsUI//elPtXHjRoWFhUn6/PMJly1bppUrV9pdDgAAgHG2B6x///vfys3NlaRAwJKkG264QVVVVXaXAwAAYJztAatPnz7tfgZhTU2NIiMj7S4HAADAONsD1hVXXKGHH35YDQ0NgbEDBw6oqKhI3/jGN%2BwuBwAAwDjbb9Nw33336c4771RGRoZaWlp0xRVXqKmpScOHD9eyZcvsLgcAAMA42wPWgAEDVFFRoVdeeUUHDhzQeeedpyFDhuiaa64JuiYLAAAgVNkesCSpV69euu6665zYNQAAwFlne8AaN27cac9UcS8sAAAQ6mwPWBMmTAgKWC0tLTpw4IC8Xq/uvPNOu8sBAAAwzvaAVVhY2O74tm3b9Pe//93magAAAMxz5LMI23Pddddp06ZNTpcBAADQZd0mYL377rvy%2B/1OlwEAANBlti8RTps2rc1YU1OTqqqqdP3119tdDgAAgHG2B6zk5OQ27yKMiorS1KlTddttt9ldDgAAgHG2Byzu1g4AAHo62wPWiy%2B%2B2Oltb7nllrNYCQAAwNlhe8BauHChWltb21zQHhYWFjQWFhZGwAIAACHJ9oD19NNP65lnntGsWbOUkpIiv9%2Bv9957T6tWrdL3v/99ZWRk2F0SAACAUY5cg1VeXq7ExMTA2JVXXqlBgwZpxowZqqiosLskAAAAo2y/D9bBgwcVFxfXZjw2NlbV1dV2lwMAAGCc7QErKSlJy5YtU11dXWCsvr5epaWluvDCC%2B0uBwAAwDjblwjvv/9%2BzZ8/Xx6PR9HR0QoPD1dDQ4POO%2B88rVy50u5yAAAAjLM9YI0ZM0Yvv/yyXnnlFR05ckR%2Bv1%2BJiYn65je/qT59%2BthdDgAAgHG2ByxJOv/885Wdna0jR45o0KBBTpQAAABw1th%2BDVZzc7OKiop0%2BeWX68Ybb5T0%2BTVYd911l%2Brr6%2B0uBwAAwDjbA9by5cu1d%2B9elZSUKDz8/%2B%2B%2BpaVFJSUldpcDAABgnO0Ba9u2bfrVr36lG264IfChz7GxsSouLtb27dvtLgcAAMA42wNWY2OjkpOT24y73W59%2BumndpcDAABgnO0B68ILL9Tf//53SQr67MGtW7fq//7v/%2BwuBwAAwDjb30X43e9%2BV3PmzNGtt96q1tZWrV69Wnv27NG2bdu0cOFCu8sBAAAwzvYzWDk5OSoqKtLf/vY3RURE6Mknn1R1dbVKSkqUm5tr6blee%2B01ZWZmqqCgoM3c5s2bNXHiRF1%2B%2BeWaMmWKXn/99cBca2urVqxYoezsbI0ePVozZszQoUOHAvM%2Bn0/z5s1TZmamxowZo4ULF6q5ufnMDxoAAJxTbD%2BDdezYMd1666269dZbu/Q8q1at0tq1azV48OA2c3v37lVRUZHKysp09dVXa9u2bbr33nu1detWDRgwQM8995w2btyoVatWKTExUStWrFB%2Bfr7Wr1%2BvsLAwLV68WCdOnFBFRYVOnjypH//4xyopKdGiRYu6VDMAADg32H4GKzs7O%2BjaqzMVFRXVYcB6/vnnlZWVpaysLEVFRWnSpEm6%2BOKLtWHDBkmSx%2BNRXl6ehg4dqpiYGBUUFKiqqkq7d%2B/Wxx9/rB07dqigoEBut1uJiYm655579MILL%2BjkyZNdrhsAAPR8tp/BysjI0JYtWzRhwoQuPc/06dM7nKusrFRWVlbQWGpqqrxer5qbm7V//36lpqYG5mJiYjR48GB5vV4dP35cERERSklJCcyPHDlSn376qT744IOg8Y7U1NSotrY2aMzl6q2EhIQ220ZEhAd978lcLjPHeC71zBR6Zg39so6eWUO/rAu1ntkesL72ta/pF7/4hcrLy3XhhReqV69eQfOlpaVd3ofP51NcXFzQWFxcnPbv369PPvlEfr%2B/3fm6ujr17dtXMTExgXt0fTEnSXV1dZ3av8fjUVlZWdBYfn6%2B5s6d2%2BFjYmPP79Rzh7L4%2BGijz3cu9Mw0emYN/bKOnllDv6wLlZ7ZHrD279%2Bviy66SFLnA8uZ%2BKplyNPNd3UJMycnR%2BPGjQsac7l6q66usc22ERHhio09X/X1TWppae3Sfru79o7/TJxLPTOFnllDv6yjZ9bQL%2BvOtGemf7nvLNsCVkFBgVasWKHf/OY3gbGVK1cqPz/f%2BL7i4%2BPl8/mCxnw%2Bn9xut/r27avw8PB25/v16ye3262Ghga1tLQoIiIiMCdJ/fr169T%2BExIS2iwH1tYe16lTHb8gWlpaTzvfE5g%2BvnOhZ6bRM2vol3X0zBr6ZV2o9My2hcydO3e2GSsvLz8r%2B0pLS9OePXuCxrxery699FJFRUVp%2BPDhqqysDMzV19frww8/1CWXXKIRI0bI7/dr3759QY%2BNjY3VkCFDzkq9AACgZ7EtYLW37Gbi3YTtuf322/XXv/5VL7/8sj777DOtXbtWBw8e1KRJkyRJubm5WrNmjaqqqtTQ0KCSkhKNGDFC6enpcrvdGj9%2BvB599FEdO3ZMR44c0cqVKzV16lS5XLavqAIAgBBkW2L434vGTzfWWenp6ZKkU6dOSZJ27Ngh6fOzTRdffLFKSkpUXFys6upqDRs2TE899ZT69%2B8vSZo2bZpqa2t1xx13qLGxURkZGUEXpf/85z/XkiVLlJ2drV69eunmm29u92amAAAA7QnZUzJer/e089dff72uv/76dufCwsI0d%2B7cDt/V16dPH/3yl7/sco0AAODcFBo3kwAAAAghtp3BOnnypObPn/%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%2BHjgQAAISSHnsGqyMbN25UcnKypk6dqqioKGVmZmrcuHF6/vnnJUkej0dTpkzRpZdeqvPOO0933XWXJOmll15ysmwAABBCenTAKi0t1dixY3XllVdq8eLFamxsVGVlpVJTU4O2S01NDSwDfnk%2BPDxcI0aMkNfrtbV2AAAQunrsEuFll12mzMxMPfLIIzp06JDmzZunBx98UD6fT4mJiUHb9u3bV3V1dZIkn8%2BnuLi4oPm4uLjAfGfU1NSotrY2aMzl6q2EhIQ220ZEhAd9R/fhcvWcvxNeZ9bQL%2BvomTX0y7pQ61mPDVgejyfw56FDh6qwsFCzZ8/WqFGjvvKxfr%2B/y/suKysLGsvPz9fcuXM7fExs7Pld2ifMi4%2BPdroE43idWUO/rKNn1tAv60KlZz02YH3ZwIED1dLSovDwcPl8vqC5uro6ud1uSVJ8fHybeZ/Pp%2BHDh3d6Xzk5ORo3blzQmMvVW3V1jW22jYgIV2zs%2Baqvb1JLS2un94Gzr72/r1DF68wa%2BmUdPbOGfll3pj1z6pflHhmw3n33XW3YsEELFiwIjFVVVSkyMlJZWVn64x//GLT9nj17dOmll0qS0tLSVFlZqcmTJ0uSWlpa9O6772rq1Kmd3n9CQkKb5cDa2uM6darjF0RLS%2Btp52G/nvj3wevMGvplHT2zhn5ZFyo9C42FTIv69esnj8ej8vJynThxQgcOHNBjjz2mnJwcfec731F1dbWef/55ffbZZ3rllVf0yiuv6Pbbb5ck5ebm6sUXX9Tbb7%2BtpqYmPfHEE4qMjNTYsWOdPSgAABAyeuQZrMTERJWXl6u0tDQQkCZPnqyCggJFRUXpqaee0tKlS/Xggw8qKSlJy5cv19e//nVJ0re%2B9S395Cc/0bx583T06FGlp6ervLxc5513nsNHBQAAQkWYv6tXdKNTamuPtzvucoUrPj5adXWNZ3TK88ZH/9LV0tCBLfOucboEY7r6OjvX0C/r6Jk19Mu6M%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%2BtjIwMXXvttVq%2BfLlaW1udLgsAAIQIlgjbMWfOHI0cOVI7duzQ0aNHNXPmTF1wwQX6wQ9%2B4HRpAAAgBHAG60u8Xq/27dunwsJC9enTR8nJycrLy5PH43G6NAAAECI4g/UllZWVSkpKUlxcXGBs5MiROnDggBoaGhQTE/OVz1FTU6Pa2tqgMZertxISEtpsGxERHvQdOBe4XN379c7PpXX0zBr6ZV2o9YyA9SU%2Bn0%2BxsbFBY1%2BErbq6uk4FLI/Ho7KysqCxe%2B%2B9V3PmzGmzbU1NjZ599mnl5OS0G8C%2Byhu/uMHyY0JdTU2NPB7PGffsXETPrOnqz%2BW5iJ5ZQ7%2BsC7WehUYMtJnf7%2B/S43NycrRu3bqgr5ycnHa3ra2tVVlZWZszXugYPbOOnllDv6yjZ9bQL%2BtCrWecwfoSt9stn88XNObz%2BRQWFia3292p50hISAiJdA0AAM4OzmB9SVpamg4fPqxjx44Fxrxer4YNG6bo6GgHKwMAAKGCgPUlqampSk9PV2lpqRoaGlRVVaXVq1crNzfX6dIAAECIiHjggQcecLqI7uab3/ymKioq9NBDD2nTpk2aOnWqZsyYobCwsLOyv%2BjoaF111VWcIbOAnllHz6yhX9bRM2uSRq/MAAAGVElEQVTol3Wh1LMwf1ev6AYAAEAQlggBAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgOai6ulp33323MjIydO2112r58uVqbW11uqxu7bXXXlNmZqYKCgqcLiUkVFdXKz8/XxkZGcrMzNSCBQtUX1/vdFnd2r59%2B3TnnXdq1KhRyszM1Lx581RbW%2Bt0WSHh4YcfVkpKitNldHspKSlKS0tTenp64Ouhhx5yuqxu74knntCYMWN02WWXKS8vTx999JHTJZ0WActBc%2BbMUWJionbs2KHVq1drx44devbZZ50uq9tatWqVli5dqsGDBztdSsiYNWuWYmNjtXPnTq1bt07vv/%2B%2BHnnkEafL6rZOnDihH/7wh7rqqqu0a9cuVVRU6OjRo%2BIjW7/a3r17tX79eqfLCBlbt26V1%2BsNfC1evNjpkrq15557Ths2bNCaNWv0%2Buuva9iwYfr1r3/tdFmnRcByiNfr1b59%2B1RYWKg%2BffooOTlZeXl58ng8TpfWbUVFRWnt2rUErE6qr69XWlqa5s%2Bfr%2BjoaA0YMECTJ0/WG2%2B84XRp3VZTU5MKCgo0c%2BZMRUZGyu1269vf/rbef/99p0vr1lpbW7VkyRLl5eU5XQp6qGeeeUYFBQW66KKLFBMTo0WLFmnRokVOl3VaBCyHVFZWKikpSXFxcYGxkSNH6sCBA2poaHCwsu5r%2BvTp6tOnj9NlhIzY2FgVFxfrggsuCIwdPnxYCQkJDlbVvcXFxem2226Ty%2BWSJH3wwQf64x//qBtvvNHhyrq33//%2B94qKitLEiROdLiVklJaWauzYsbryyiu1ePFiNTY2Ol1St/Xf//5XH330kT755BNNmDBBGRkZmjt3ro4dO%2BZ0aadFwHKIz%2BdTbGxs0NgXYauurs6JktDDeb1e/fa3v9Xs2bOdLqXbq66uVlpamiZMmKD09HTNnTvX6ZK6rY8//liPP/64lixZ4nQpIeOyyy5TZmamtm/fLo/Ho7ffflsPPvig02V1W0eOHJH0%2BbLq6tWrtX79eh05coQzWOiY3%2B93ugScI958803NmDFD8%2BfPV2ZmptPldHtJSUnyer3aunWrDh48qJ/97GdOl9RtFRcXa8qUKRo2bJjTpYQMj8ej2267TZGRkRo6dKgKCwtVUVGhEydOOF1at/TF/yvvuusuJSYmasCAAZozZ4527typzz77zOHqOkbAcojb7ZbP5wsa8/l8CgsLk9vtdqgq9EQ7d%2B7U3Xffrfvvv1/Tp093upyQERYWpuTkZBUUFKiioqLbL0c4YdeuXfrXv/6l/Px8p0sJaQMHDlRLS4uOHj3qdCnd0heXOfzvqk9SUpL8fn%2B37hkByyFpaWk6fPhw0D/aXq9Xw4YNU3R0tIOVoSd56623VFRUpMcee0y33HKL0%2BV0e7t27dL48eODbpcSHv75P5O9evVyqqxua8OGDTp69KiuvfZaZWRkaMqUKZKkjIwMbdq0yeHquqd3331Xy5YtCxqrqqpSZGQk10d2YMCAAYqJidHevXsDY9XV1erVq1e37hkByyGpqalKT09XaWmpGhoaVFVVpdWrVys3N9fp0tBDnDp1SosWLVJhYaHGjBnjdDkhIS0tTQ0NDVq%2BfLmampp07NgxPf7447ryyit5g0U7FixYoG3btmn9%2BvVav369ysvLJUnr16/XuHHjHK6ue%2BrXr588Ho/Ky8t14sQJHThwQI899phycnIUERHhdHndksvl0tSpU/Xkk0/qP//5j44ePaqVK1dq4sSJgTekdEdhfi4EcsyRI0e0ePFi/eMf/1BMTIymTZume%2B%2B9V2FhYU6X1i2lp6dL%2Bjw4SAr8YHm9Xsdq6s7eeOMNfe9731NkZGSbua1btyopKcmBqrq/9957T0uXLtU777yj3r176%2Bqrr9aCBQuUmJjodGnd3kcffaTs7Gy99957TpfSrf3zn/9UaWmp3nvvPUVGRmry5MkqKChQVFSU06V1WydOnFBxcbE2bdqkkydPavz48Vq8eHG3XvEhYAEAABjGEiEAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGPb/AFu1hYw8Qtk5AAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-1016541278650356988”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.74045179499467</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6964343323182298</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6571190433601138</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.2821175523218793</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5794995640976822</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6255261646398205</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.3902974723728737</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.565692956015659</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.3439659345394194</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.458097783221579</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (41)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-1016541278650356988”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.18804453394277368</td> <td class=”number”>15</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:24%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.21300866222614862</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:27%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.2548187491193588</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.25723950090558695</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:90%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.32805436859582837</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:82%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.2821175523218793</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.4636424740312628</td> <td class=”number”>44</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:37%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.1567211747594506</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:28%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.23432720176949</td> <td class=”number”>62</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:52%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.886365038680446</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_SFSP15”>PCT_SFSP15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>52</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.79739</td>

</tr> <tr>

<th>Minimum</th> <td>0.22708</td>

</tr> <tr>

<th>Maximum</th> <td>4.2713</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram4723362474321298721”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives4723362474321298721,#minihistogram4723362474321298721”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives4723362474321298721”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles4723362474321298721”

aria-controls=”quantiles4723362474321298721” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram4723362474321298721” aria-controls=”histogram4723362474321298721”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common4723362474321298721” aria-controls=”common4723362474321298721”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme4723362474321298721” aria-controls=”extreme4723362474321298721”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles4723362474321298721”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0.22708</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.36983</td>

</tr> <tr>

<th>Q1</th> <td>0.54866</td>

</tr> <tr>

<th>Median</th> <td>0.72662</td>

</tr> <tr>

<th>Q3</th> <td>0.97127</td>

</tr> <tr>

<th>95-th percentile</th> <td>1.4092</td>

</tr> <tr>

<th>Maximum</th> <td>4.2713</td>

</tr> <tr>

<th>Range</th> <td>4.0442</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.42261</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.35377</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.44365</td>

</tr> <tr>

<th>Kurtosis</th> <td>6.522</td>

</tr> <tr>

<th>Mean</th> <td>0.79739</td>

</tr> <tr>

<th>MAD</th> <td>0.26198</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.8328</td>

</tr> <tr>

<th>Sum</th> <td>2497.4</td>

</tr> <tr>

<th>Variance</th> <td>0.12515</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram4723362474321298721”>

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06FG5XC7953/%2Bp%2B68805dd911fugGAABYkSWPYCUmJkr64TcEJWnz5s2SJIfDoTVr1ujUqVMaNGiQz2P69OmjFStWqH///poyZYqys7N1/PhxJSYmKj8/X2FhYZKkrKwsVVZW6t5771VNTY0GDRqkp59%2B2rzmAACA5QV5rvaKbjSI03nK3yUYwmYLVlRUC7lclZd96PjuFz9upKoax6bs2/1dwlW7mv11LaMva6Evawm0vlq3vvBtnRpbwJ0iBAAA8DcCFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwS96mAf/Lar%2BZBwDAzwFHsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGCWDlg7duxQcnKycnJy6s1t3LhRI0aMUM%2BePZWamqqPPvrIO1dXV6fFixdr8ODB6tOnj8aPH68jR454591ut7Kzs5WcnKx%2B/fpp1qxZqq6uNqUnAABgfZYNWK%2B88ormzZun9u3b15vbt2%2Bfpk%2BfrmnTpukf//iHMjIy9Nhjj%2BnYsWOSpLffflsbNmxQfn6%2Btm7dqg4dOigzM1Mej0eSNHv2bFVVVamwsFBr1qxRSUmJcnNzTe0PAABYl2UDVmhoqFavXn3BgLVq1SoNGDBAAwYMUGhoqEaOHKmuXbtq/fr1kqSCggJlZGSoU6dOCg8PV05OjkpKSrR79259//332rx5s3JychQdHa02bdpo8uTJWrNmjc6dO2d2mwAAwIIsG7DGjRunli1bXnCuuLhYcXFxPmNxcXFyOByqrq7WgQMHfObDw8PVvn17ORwO7du3TyEhIerWrZt3Pj4%2BXqdPn9bBgwcbpxkAABBQbP4uoDG43W5FRET4jEVEROjAgQM6efKkPB7PBeddLpciIyMVHh6uoKAgnzlJcrlcDdp%2BWVmZnE6nz5jN1lwxMTFX0g78xGaz7M8fXiEhwT5fAwV9WQt9WUug9mW2gAxYkrzXU13J/KUeeykFBQXKy8vzGcvMzFRWVtZVPS/MFRXVwt8lGMZub%2BbvEhoFfVkLfVlLoPZlloAMWFFRUXK73T5jbrdb0dHRioyMVHBw8AXnW7VqpejoaFVUVKi2tlYhISHeOUlq1apVg7Y/ZswYpaSk%2BIzZbM3lclVeaUvwg0DYXyEhwbLbm6m8vEq1tXX%2BLscw9GUt9GUtgdaXv35YDsiAlZCQoD179viMORwODR8%2BXKGhoerSpYuKi4vVt29fSVJ5ebkOHz6sm2%2B%2BWbGxsfJ4PNq/f7/i4%2BO9j7Xb7erYsWODth8TE1PvdKDTeUo1NdZ/of6cBNL%2Bqq2tC6h%2BzqMva6EvawnUvswSkCdYR48erZ07d2rbtm06c%2BaMVq9erW%2B%2B%2BUYjR46UJKWnp2vlypUqKSlRRUWFcnNz1b17dyUmJio6OlpDhgzRiy%2B%2BqBMnTujYsWNaunSp0tLSZLMFZB4FAAAGs2xiSExMlCTV1NRIkjZv3izph6NNXbt2VW5urhYsWKDS0lJ17txZy5cvV%2BvWrSVJY8eOldPp1MMPP6zKykolJSX5XDP1zDPPaM6cORo8eLCaNGmie%2B6554I3MwUAALiQIM/VXtGNBnE6TzXK89794seN8ryQNmXf7u8SrprNFqyoqBZyuSoD6lA/fVkLfVlLoPXVuvWFb%2BnU2ALyFCEAAIA/mR6wUlJSlJeXp6NHj5q9aQAAAFOYHrDuv/9%2Bbdy4UXfccYcmTJigDz/80HsdFQAAQCAwPWBlZmZq48aN%2Bstf/qIuXbpo/vz5GjBggBYuXKhDhw6ZXQ4AAIDh/HYNVnx8vKZPn66tW7dq5syZ%2Bstf/qJhw4Zp/Pjx%2BvLLL/1VFgAAwFXzW8A6d%2B6cNm7cqN/%2B9reaPn262rRpoyeffFLdu3dXRkaGNmzY4K/SAAAArorp98EqKSnR6tWrtXbtWlVWVmrIkCF64403dMstt3jX9OnTR08//bRGjBhhdnkAAABXzfSANXz4cHXs2FETJ07Ufffdp8jIyHprBgwYoBMnTphdGgAAgCFMD1grV670fgbgxezevduEagAAAIxn%2BjVY3bp106RJk7wfbSNJr7/%2Bun7729/K7XabXQ4AAIDhTA9YCxYs0KlTp9S5c2fv2MCBA1VXV6fnnnvO7HIAAAAMZ/opwo8%2B%2BkgbNmxQVFSUd6xDhw7Kzc3VPffcY3Y5AAAAhjP9CFZ1dbVCQ0PrFxIcrKqqKrPLAQAAMJzpAatPnz567rnndPLkSe/Yd999p7lz5/rcqgEAAMCqTD9FOHPmTP3mN7/RbbfdpvDwcNXV1amyslLt2rXTm2%2B%2BaXY5AAAAhjM9YLVr107vvfee/v73v%2Bvw4cMKDg5Wx44d1a9fP4WEhJhdDgAAgOFMD1iS1LRpU91xxx3%2B2DQAAECjMz1gHTlyRIsWLdLXX3%2Bt6urqevN/%2B9vfzC4JAADAUH65BqusrEz9%2BvVT8%2BbNzd48AABAozM9YO3Zs0d/%2B9vfFB0dbfamAQAATGH6bRpatWrFkSsAABDQTA9YEydOVF5enjwej9mbBgAAMIXppwj//ve/6/PPP9e7776rG264QcHBvhnvz3/%2Bs9klAQAAGMr0gBUeHq7%2B/fubvVkAAADTmB6wFixYYPYmAQAATGX6NViSdPDgQS1ZskRPPvmkd%2ByLL77wRykAAACGMz1gFRUVaeTIkfrwww9VWFgo6Yebj44bN46bjAIAgIBgesBavHixnnjiCW3YsEFBQUGSfvh8wueee05Lly41uxwAAADDmR6w/vWvfyk9PV2SvAFLkoYOHaqSkhKzywEAADCc6QGrZcuWF/wMwrKyMjVt2tSw7ezdu1fjxo1T7969dfvtt2vatGk6ceKEpB9OU6alpalXr14aPny41q9f7/PYlStXasiQIerVq5fS09O1Z88ew%2BoCAACBz/SA1atXL82fP18VFRXesUOHDmn69Om67bbbDNlGTU2NHnnkEf3iF7/Qzp07VVhYqBMnTujpp59WWVmZJk%2BerLFjx6qoqEizZs3S7Nmz5XA4JElbtmzRkiVL9MILL2jnzp0aNGiQJk2apNOnTxtSGwAACHymB6wnn3xSX3zxhZKSknTmzBn16tVLw4YNk9vt1owZMwzZhtPplNPp1L333qumTZsqKipKd955p/bt26cNGzaoQ4cOSktLU2hoqJKTk5WSkqJVq1ZJkgoKCpSamqoePXooLCxMEyZMkCRt3brVkNoAAEDgM/0%2BWG3btlVhYaG2b9%2BuQ4cOKSwsTB07dtTtt9/uc03W1WjTpo26d%2B%2BugoIC/e53v1N1dbU%2B/PBDDRw4UMXFxYqLi/NZHxcXp02bNkmSiouLNWzYMO9ccHCwunfvLofDoeHDhxtSHwAACGymByxJatKkie64445Ge/7g4GAtWbJEGRkZeuONNyRJffv21dSpUzV58mS1adPGZ31kZKRcLpckye12KyIiwmc%2BIiLCO98QZWVlcjqdPmM2W3PFxMRcSTvwE5vNL7eJM1RISLDP10BBX9ZCX9YSqH2ZzfSAlZKSctEjVUbcC%2Bvs2bOaNGmShg4d6r1%2Bau7cuZo2bVqDHn%2B1H0RdUFCgvLw8n7HMzExlZWVd1fPCXFFRLfxdgmHs9mb%2BLqFR0Je10Je1BGpfZjE9YA0bNswnYNXW1urQoUNyOBz61a9%2BZcg2ioqK9O2332rKlCkKCQlRy5YtlZWVpXvvvVe//OUv5Xa7fda7XC5FR0dLkqKiourNu91udenSpcHbHzNmjFJSUnzGbLbmcrkqr7Aj%2BEMg7K%2BQkGDZ7c1UXl6l2to6f5djGPqyFvqylkDry18/LJsesH7qKNIHH3ygTz75xJBt1NbWqq6uzudI1NmzZyVJycnJ%2Butf/%2Bqzfs%2BePerRo4ckKSEhQcXFxRo1apT3ufbu3au0tLQGbz8mJqbe6UCn85Rqaqz/Qv05CaT9VVtbF1D9nEdf1kJf1hKofZnlmjnBescdd%2Bi9994z5Ll69uyp5s2ba8mSJaqqqpLL5dKyZcvUp08f3XvvvSotLdWqVat05swZbd%2B%2BXdu3b9fo0aMlSenp6Vq7dq127dqlqqoqLVu2TE2bNtXAgQMNqQ0AAAS%2BayZg7d2796qvfTovKipKf/rTn/T555%2Brf//%2BuueeexQWFqZFixapVatWWr58ud566y3dcsstmj9/vhYuXKibbrpJktS/f39NmTJF2dnZ6tu3r3bu3Kn8/HyFhYUZUhsAAAh8pp8iHDt2bL2xqqoqlZSU6K677jJsOwkJCXrzzTcvONenTx%2BtW7fuJx/74IMP6sEHHzSsFgAA8PNiesDq0KFDvd8iDA0NVVpamh544AGzywEAADCc6QHrueeeM3uTAAAApjI9YK1du7bBa%2B%2B7775GrAQAAKBxmB6wZs2aVe8WCpIUFBTkMxYUFETAAgAAlmR6wHr11Ve1YsUKTZo0Sd26dZPH49FXX32lV155RQ899JCSkpLMLgkAAMBQfrkGKz8/3%2BfzAHv37q127dpp/PjxKiwsNLskAAAAQ5l%2BH6xvvvmm3ocpS5LdbldpaanZ5QAAABjO9IAVGxur5557Ti6XyztWXl6uRYsW6cYbbzS7HAAAAMOZfopw5syZmjp1qgoKCtSiRQsFBweroqJCYWFhWrp0qdnlAAAAGM70gNWvXz9t27ZN27dv17Fjx%2BTxeNSmTRv98pe/VMuWLc0uBwAAwHCmByxJatasmQYPHqxjx46pXbt2/igBAACg0Zh%2BDVZ1dbWmT5%2Bunj176u6775b0wzVYEyZMUHl5udnlAAAAGM70gLVw4ULt27dPubm5Cg7%2B383X1tYqNzfX7HIAAAAMZ3rA%2BuCDD/TSSy9p6NCh3g99ttvtWrBggT788EOzywEAADCc6QGrsrJSHTp0qDceHR2t06dPm10OAACA4UwPWDfeeKM%2B%2BeQTSfL57MH3339f//Zv/2Z2OQAAAIYz/bcIH3zwQT3%2B%2BOO6//77VVdXp9dee0179uzRBx98oFmzZpldDgAAgOFMD1hjxoyRzWbTW2%2B9pZCQEL388svq2LGjcnNzNXToULPLAQAAMJzpAevEiRO6//77df/995u9aQAAAFOYfg3W4MGDfa69AgAACDSmB6ykpCRt2rTJ7M0CAACYxvRThNdff72effZZ5efn68Ybb1STJk185hctWmR2SQAAAIYyPWAdOHBA//7v/y5JcrlcZm8eAACg0ZkWsHJycrR48WK9%2Beab3rGlS5cqMzPTrBIAAABMYdo1WFu2bKk3lp%2Bfb9bmAQAATGNawLrQbw7y24QAACAQmRawzn%2Bw86XGAAAArM702zQAAAAEOgIWAACAwUz7LcJz585p6tSplxwz8j5Yy5Yt09tvv62Kigr94he/0Lx583TDDTeoqKhIixYt0sGDB3X99ddr4sSJGjlypPdxK1eu1Ntvvy2n06lu3bpp1qxZSkhIMKwuAAAQ2Ew7gnXLLbeorKzM58%2BFxozy9ttva/369Vq5cqU%2B%2Bugjde7cWa%2B//rrKyso0efJkjR07VkVFRZo1a5Zmz54th8Mh6YffdlyyZIleeOEF7dy5U4MGDdKkSZN0%2BvRpw2oDAACBzbQjWP/3/ldmWLFihaZPn%2B69qelTTz0lSfrTn/6kDh06KC0tTZKUnJyslJQUrVq1SomJiSooKFBqaqp69OghSZowYYJWrlyprVu3avjw4ab2AAAArCkgr8H67rvv9O233%2BrkyZMaNmyYkpKSlJWVpRMnTqi4uFhxcXE%2B6%2BPi4rRnzx5JqjcfHBys7t27e49wAQAAXIrpH5VjhmPHjkmS3n//fb322mvyeDzKysrSU089perqarVp08ZnfWRkpPdje9xutyIiInzmIyIiLutjfcrKyuR0On3GbLbmiomJuZJ24Cc2m/V//ggJCfb5Gijoy1roy1oCtS%2BzBWTAOn8D0wkTJnjD1OOPP67f/va3Sk5ObvDjr1RBQYHy8vJ8xjIzM5WVlXVVzwtzRUW18HcJhrHbm/m7hEZBX9ZCX9YSqH2ZJSAD1nXXXSdJstvt3rHY2Fh5PB6dO3dObrfbZ73L5VJ0dLQkKSoqqt682%2B1Wly5dGrz9MWPGKCUlxWfMZmsul6vysvqAfwXC/goJCZbd3kzl5VWqra3zdzmGoS9roS9rCbS%2B/PXDckAGrLZt2yo8PFz79u1TfHy8JKm0tFRNmjRLv2/XAAAVqklEQVTRgAEDtG7dOp/1e/bs8V7UnpCQoOLiYo0aNUqSVFtbq71793ovim%2BImJiYeqcDnc5Tqqmx/gv15ySQ9ldtbV1A9XMefVkLfVlLoPZlloA8wWqz2ZSWlqaXX35Z//M//6Pjx49r6dKlGjFihEaNGqXS0lKtWrVKZ86c0fbt27V9%2B3aNHj1akpSenq61a9dq165dqqqq0rJly9S0aVMNHDjQv00BAADLCMgjWJI0depUnT17Vg888IDOnTunIUOG6KmnnlKLFi20fPlyzZs3T3PnzlVsbKwWLlyom266SZLUv39/TZkyRdnZ2Tp%2B/LgSExOVn5%2BvsLAwP3cEAACsIshztVd0o0GczlON8rx3v/hxozwvpE3Zt/u7hKtmswUrKqqFXK7KgDrUT1/WQl/WEmh9tW7d0i/bDchThAAAAP5EwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAM9rMIWPPnz1e3bt283xcVFSktLU29evXS8OHDtX79ep/1K1eu1JAhQ9SrVy%2Blp6drz549ZpcMAAAsLOAD1r59%2B7Ru3Trv92VlZZo8ebLGjh2roqIizZo1S7Nnz5bD4ZAkbdmyRUuWLNELL7ygnTt3atCgQZo0aZJOnz7trxYAAIDFBHTAqqur05w5c5SRkeEd27Bhgzp06KC0tDSFhoYqOTlZKSkpWrVqlSSpoKBAqamp6tGjh8LCwjRhwgRJ0tatW/3RAgAAsKCADlh//vOfFRoaqhEjRnjHiouLFRcX57MuLi7Oexrwx/PBwcHq3r279wgXAADApdj8XUBj%2Bf7777VkyRK9%2BeabPuNut1tt2rTxGYuMjJTL5fLOR0RE%2BMxHRER45xuirKxMTqfTZ8xma66YmJjLaQF%2BZrNZ/%2BePkJBgn6%2BBgr6shb6sJVD7MlvABqwFCxYoNTVVnTt31rfffntZj/V4PFe17YKCAuXl5fmMZWZmKisr66qeF%2BaKimrh7xIMY7c383cJjYK%2BrIW%2BrCVQ%2BzJLQAasoqIiffHFFyosLKw3FxUVJbfb7TPmcrkUHR39k/Nut1tdunRp8PbHjBmjlJQUnzGbrblcrsoGPwf8LxD2V0hIsOz2Ziovr1JtbZ2/yzEMfVkLfVlLoPXlrx%2BWAzJgrV%2B/XsePH9egQYMk/e8RqaSkJP3mN7%2BpF7z27NmjHj16SJISEhJUXFysUaNGSZJqa2u1d%2B9epaWlNXj7MTEx9U4HOp2nVFNj/Rfqz0kg7a/a2rqA6uc8%2BrIW%2BrKWQO3LLAF5gnXGjBn64IMPtG7dOq1bt075%2BfmSpHXr1mnEiBEqLS3VqlWrdObMGW3fvl3bt2/X6NGjJUnp6elau3atdu3apaqqKi1btkxNmzbVwIED/dgRAACwkoA8ghUREeFzoXpNTY0kqW3btpKk5cuXa968eZo7d65iY2O1cOFC3XTTTZKk/v37a8qUKcrOztbx48eVmJio/Px8hYWFmd8IAACwpIAMWD92ww036KuvvvJ%2B36dPH5%2Bbj/7Ygw8%2BqAcffNCM0gAAQAAKyFOEAAAA/kTAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACD/SxuNApcibtf/NjfJVyWTdm3%2B7sEAMD/xxEsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgARuwSktLlZmZqaSkJCUnJ2vGjBkqLy%2BXJO3bt08PPfSQbrnlFt11111asWKFz2M3btyoESNGqGfPnkpNTdVHH33kjxYAAIBFBWzAmjRpkux2u7Zs2aJ3331XX3/9tZ5//nlVV1dr4sSJuvXWW7Vjxw4tXrxYy5cv14cffijph/A1ffp0TZs2Tf/4xz%2BUkZGhxx57TMeOHfNzRwAAwCoCMmCVl5crISFBU6dOVYsWLdS2bVuNGjVKn376qbZt26Zz587p0UcfVfPmzRUfH68HHnhABQUFkqRVq1ZpwIABGjBggEJDQzVy5Eh17dpV69ev93NXAADAKgIyYNntdi1YsEDXXXedd%2Bzo0aOKiYlRcXGxunXrppCQEO9cXFyc9uzZI0kqLi5WXFycz/PFxcXJ4XCYUzwAALA8m78LMIPD4dBbb72lZcuWadOmTbLb7T7zkZGRcrvdqqurk9vtVkREhM98RESEDhw40ODtlZWVyel0%2BozZbM0VExNz5U0Al2Cz1f95KSQk2OdroKAva6EvawnUvswW8AHrs88%2B06OPPqqpU6cqOTlZmzZtuuC6oKAg7397PJ6r2mZBQYHy8vJ8xjIzM5WVlXVVzwtcTFRUi5%2Bcs9ubmViJeejLWujLWgK1L7MEdMDasmWLnnjiCc2ePVv33XefJCk6OlrffPONzzq3263IyEgFBwcrKipKbre73nx0dHSDtztmzBilpKT4jNlszeVyVV5ZI0ADXOj1FRISLLu9mcrLq1RbW%2BeHqhoHfVkLfVlLoPV1sR8%2BG1PABqzPP/9c06dP1x//%2BEf169fPO56QkKB33nlHNTU1stl%2BaN/hcKhHjx7e%2BfPXY53ncDg0fPjwBm87Jiam3ulAp/OUamqs/0LFtetir6/a2rqAfP3Rl7XQl7UEal9mCcgTrDU1NXrqqac0bdo0n3AlSQMGDFB4eLiWLVumqqoq7d69W6tXr1Z6erokafTo0dq5c6e2bdumM2fOaPXq1frmm280cuRIf7QCAAAsKCCPYO3atUslJSWaN2%2Be5s2b5zP3/vvv6%2BWXX9acOXOUn5%2Bv6667Tjk5ORo4cKAkqWvXrsrNzdWCBQtUWlqqzp07a/ny5WrdurUfOgEAAFYUkAGrd%2B/e%2Buqrry665p133vnJubvuukt33XWX0WUBAICfiYA8RQgAAOBPBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMJjN3wUAMMbdL37s7xIabFP27f4uAQAaFUewAAAADMYRLACms9LRNokjbgAuH0ewAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBKwLKC0t1SOPPKKkpCQNGjRICxcuVF1dnb/LAgAAFsGNRi/g8ccfV3x8vDZv3qzjx49r4sSJuu666/TrX//a36UBAAAL4AjWjzgcDu3fv1/Tpk1Ty5Yt1aFDB2VkZKigoMDfpQEAAIvgCNaPFBcXKzY2VhEREd6x%2BPh4HTp0SBUVFQoPD7/kc5SVlcnpdPqM2WzNFRMTY3i9ABqfzWbOz6IhIcE%2BXwMFfVlLoPZlNgLWj7jdbtntdp%2Bx82HL5XI1KGAVFBQoLy/PZ%2Byxxx7T448/blyh/9%2Bnzw41/DkvpqysTAUFBRozZkxABUb6spZA7uuNN16lL4ugL1wM8fQCPB7PVT1%2BzJgxevfdd33%2BjBkzxqDq/MvpdCovL6/eETqroy9roS9roS9rCdS%2BzMYRrB%2BJjo6W2%2B32GXO73QoKClJ0dHSDniMmJobUDwDAzxhHsH4kISFBR48e1YkTJ7xjDodDnTt3VosWLfxYGQAAsAoC1o/ExcUpMTFRixYtUkVFhUpKSvTaa68pPT3d36UBAACLCHn66aef9ncR15pf/vKXKiws1B/%2B8Ae99957SktL0/jx4xUUFOTv0q4JLVq0UN%2B%2BfQPuiB59WQt9WQt9WUug9mWmIM/VXtENAAAAH5wiBAAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAEL9ZSWluqRRx5RUlKSBg0apIULF6qurq7euiVLlqh79%2B5KTEz0%2BfP999/7oeqG2bFjh5KTk5WTk3PRdXV1dVq8eLEGDx6sPn36aPz48Tpy5IhJVV6%2BhvY1Y8YM7%2Bdtnv/Tu3dvk6q8PKWlpcrMzFRSUpKSk5M1Y8YMlZeXX3Dtxo0bNWLECPXs2VOpqan66KOPTK624Rra17vvvqubbrqp3r%2BvL7/80g9VX9r%2B/fv1q1/9SrfccouSk5OVnZ0tp9N5wbUrV67UkCFD1KtXL6Wnp2vPnj0mV9twDe3Liu%2BH582fP1/dunX7yXkr7a9rigf4kVGjRnmeeuopT3l5uefQoUOeu%2B66y7NixYp661566SXP9OnT/VDhlcnPz/fcddddnrFjx3qys7MvunblypWeQYMGeQ4cOOA5deqU55lnnvGMGDHCU1dXZ1K1DXc5fU2fPt3z0ksvmVTZ1bnnnns8M2bM8FRUVHiOHj3qSU1N9cycObPeur1793oSEhI827Zt81RXV3vWrVvn6dGjh%2Bfo0aN%2BqPrSGtrXmjVrPA899JAfKrx8Z86c8dx2222evLw8z5kzZzzHjx/3PPTQQ57JkyfXW/u3v/3N07t3b8%2BuXbs8VVVVnuXLl3tuv/12T2VlpR8qv7jL6ctq74fn7d2719O3b19P165dLzhvpf11reEIFnw4HA7t379f06ZNU8uWLdWhQwdlZGSooKDA36VdtdDQUK1evVrt27e/5NqCggJlZGSoU6dOCg8PV05OjkpKSrR7924TKr08l9OXVZSXlyshIUFTp05VixYt1LZtW40aNUqffvppvbWrVq3SgAEDNGDAAIWGhmrkyJHq2rWr1q9f74fKL%2B5y%2BrKSqqoq5eTkaOLEiWratKmio6N155136uuvv663tqCgQKmpqerRo4fCwsI0YcIESdLWrVvNLvuSLqcvK6qrq9OcOXOUkZHxk2ustL%2BuNQQs%2BCguLlZsbKwiIiK8Y/Hx8Tp06JAqKirqrf/qq680duxY9erVS8OHD7%2BmT82MGzdOLVu2vOS66upqHThwQHFxcd6x8PBwtW/fXg6HozFLvCIN7eu8f/zjH7rvvvvUs2dPpaWlXZOH%2B%2B12uxYsWKDrrrvOO3b06FHFxMTUW1tcXOyzryQpLi7umtxXl9PX%2Bblf//rX6tOnjwYPHqx169aZVepliYiI0AMPPCCbzSZJOnjwoP7617/q7rvvrrf2x/srODhY3bt3vyb31%2BX0JVnr/VCS/vznPys0NFQjRoz4yTVW2l/XGgIWfLjdbtntdp%2Bx82HL5XL5jLdt21bt2rXT888/r48//lgPPPCAJk2apIMHD5pWb2M4efKkPB6PT8iUfvh7%2BPHfgdW0a9dO7du31/Lly7Vjxw717t1bv/nNb675vhwOh9566y09%2Buij9ebcbrdl99XF%2BoqOjlaHDh30xBNP6OOPP9aUKVM0c%2BZMFRUV%2BaHShiktLVVCQoKGDRumxMREZWVl1Vtjxf3VkL6s9n74/fffa8mSJZozZ85F11lxf10rCFiox%2BPxNGjdAw88oJdeeknt27dXs2bNlJGRoe7du1%2BTp2auREP/HqwkMzNT8%2BfPV5s2bRQeHq4nnnhCTZs21ebNm/1d2k/67LPPNH78eE2dOlXJyckXXGPFfXWpvgYOHKhXX31VcXFxatq0qYYPH64777xT7777rh%2BqbZjY2Fg5HA69//77%2Buabb/T73//%2Bguustr8a0pfV3g8XLFig1NRUde7c%2BZJrrba/rhUELPiIjo6W2%2B32GXO73QoKClJ0dPQlHx8bG6uysrLGKs8UkZGRCg4OvuDfQ6tWrfxUVeMICQnR9ddff83usy1btuiRRx7RzJkzNW7cuAuuiYqKuuC%2Basjr1V8a0teFWOHfV1BQkDp06KCcnBwVFhbqxIkTPvNW3F/Spfu6kGt1fxUVFemLL75QZmbmJddadX9dCwhY8JGQkKCjR4/6vHk4HA517txZLVq08Fn7X//1X/VOV5SUlKhdu3am1NpYQkND1aVLFxUXF3vHysvLdfjwYd18881%2BrOzqeDweLViwQPv37/eOnT17VocPH74m99nnn3%2Bu6dOn649//KPuu%2B%2B%2Bn1yXkJBQ7zoyh8OhHj16NHaJV6Shfb3zzjvauHGjz9i1%2Bu%2BrqKhIQ4YM8bmdS3DwD/97adKkic/ahIQEn39btbW12rt37zW5vy6nLyu9H65fv17Hjx/XoEGDlJSUpNTUVElSUlKS3nvvPZ%2B1Vtpf1xoCFnycv0fSokWLVFFRoZKSEr322mtKT0%2BXJA0dOtT7G09ut1tz587VwYMHdebMGa1YsUKHDx/WqFGj/NnCFfnuu%2B80dOhQ772u0tPTtXLlSpWUlKiiokK5ubnee9xYyf/tKygoSN9%2B%2B63mzp2r7777TpWVlcrNzVWTJk10xx13%2BLtUHzU1NXrqqac0bdo09evXr978r371K2/4GD16tHbu3Klt27bpzJkzWr16tb755huNHDnS7LIv6XL6Onv2rP7whz/I4XDo3LlzKiws1N///neNHTvW7LIvKSEhQRUVFVq4cKGqqqp04sQJLVmyRL1791bLli193jfS09O1du1a7dq1S1VVVVq2bJmaNm2qgQMH%2BreJC7icvqz0fjhjxgx98MEHWrdundatW6f8/HxJ0rp165SSkmLZ/XWtsfm7AFx7XnrpJc2ePVu33367wsPDNXbsWD344IOSpEOHDun06dOSpKlTp0qSMjIy5Ha71blzZ73%2B%2Butq27at32q/mPPhqKamRpK81x2d/x/YoUOHdPbsWUnS2LFj5XQ69fDDD6uyslJJSUnKy8vzT%2BGXcDl9Pfvss3r%2B%2BeeVmpqqiooK3XzzzXrjjTfUvHlz/xT/E3bt2qWSkhLNmzdP8%2BbN85l7//33deTIEZ08eVKS1LVrV%2BXm5mrBggUqLS1V586dtXz5crVu3dofpV/U5fQ1btw4VVZW6ne/%2B52cTqduuOEGLV26VAkJCf4o/aJatmypFStWaN68ebr11lvVvHlz3XrrrXr22Wcl%2Bb5v9O/fX1OmTFF2draOHz%2BuxMRE5efnKywszJ8tXNDl9GWl98OIiAifC9fPv3ecr9Wq%2B%2BtaE%2BTh6jUAAABDcYoQAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYP8P8TVHhK8PyNAAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common4723362474321298721”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.5084728979609608</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.991095325829233</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6838607642878862</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.7460409862665002</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6642534311514493</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9712735876435317</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5508166993844088</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6285397242721703</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5346843800007619</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.7266185971271907</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (41)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme4723362474321298721”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.22707556512709456</td> <td class=”number”>29</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:45%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.23442951992987765</td> <td class=”number”>15</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:24%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.3271212041399707</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:90%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.3698291272142557</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.4093313579253466</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.3594365854286208</td> <td class=”number”>46</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:64%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.4091829252091856</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:46%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.6425310114450082</td> <td class=”number”>72</td> <td class=”number”>2.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.217284472239579</td> <td class=”number”>62</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:86%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.271318659740445</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_SNAP12”>PCT_SNAP12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>52</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>15.157</td>

</tr> <tr>

<th>Minimum</th> <td>5.8664</td>

</tr> <tr>

<th>Maximum</th> <td>22.132</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-3777541227580257707”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-3777541227580257707,#minihistogram-3777541227580257707”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-3777541227580257707”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-3777541227580257707”

aria-controls=”quantiles-3777541227580257707” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-3777541227580257707” aria-controls=”histogram-3777541227580257707”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-3777541227580257707” aria-controls=”common-3777541227580257707”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-3777541227580257707” aria-controls=”extreme-3777541227580257707”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-3777541227580257707”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>5.8664</td>

</tr> <tr>

<th>5-th percentile</th> <td>9.4956</td>

</tr> <tr>

<th>Q1</th> <td>12.566</td>

</tr> <tr>

<th>Median</th> <td>15.223</td>

</tr> <tr>

<th>Q3</th> <td>18.353</td>

</tr> <tr>

<th>95-th percentile</th> <td>20.667</td>

</tr> <tr>

<th>Maximum</th> <td>22.132</td>

</tr> <tr>

<th>Range</th> <td>16.265</td>

</tr> <tr>

<th>Interquartile range</th> <td>5.7874</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>3.6083</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.23806</td>

</tr> <tr>

<th>Kurtosis</th> <td>-0.77222</td>

</tr> <tr>

<th>Mean</th> <td>15.157</td>

</tr> <tr>

<th>MAD</th> <td>2.9336</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.15215</td>

</tr> <tr>

<th>Sum</th> <td>47473</td>

</tr> <tr>

<th>Variance</th> <td>13.02</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-3777541227580257707”>

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2BxQVFaUZM2aorKxMkyZN0uLFi10u/f2zwsJCffjhh9q6dWuTubCwMDmdTpexyspKhYeHn3Xe6XSqV69ebteempqqpKQklzGHI1CVldVur6Ol/Px8FRzcUVVVNaqrq2%2Bz7bQ3u/bVFtry9eUOu%2B4ru/527SlMvm7t/Bqkr/Nn1S%2BfHhewDh8%2BrI0bN%2Bq1115TdXW1kpOT9eKLL2rAgAGNywwaNEiLFi06a8DKz89XRUWFbrjhBkn//4zU4MGDNXny5CbBq7i4WHFxcZKkmJgYlZSUaOzYsZKkuro6HTx4UCkpKW73EBER0eRyYHn5j6qtbft/GHV19e2ynfZm175M8pTvD/sKLdEWrxW7vgbpy7t4XMAaOXKkevTooalTp2rMmDEKDQ1tskxiYqKOHTt21nXMmzdPDzzwQOPnR48eVWpqqjZv3qz6%2BnqtXbtWeXl5Gj16tPbt26fdu3crNzdXkpSWlqbZs2frd7/7nfr06aP/%2Bq//kr%2B/v4YNG2a8VwAAYE8eF7DWr1%2Bvq6%2B%2B%2BpzLffzxx2edCwkJcblR/ed3IXbv3l2StHbtWi1ZskSLFy9WZGSksrKydMUVV0iShg4dqtmzZ2vmzJmqqKhQbGyscnJy1KFDh9a0BQAAfkM8LmD16dNH06ZNU0pKim666SZJ0gsvvKD33ntPWVlZv3pG61wuuugiffbZZ42fDxo0yOXho780ceJETZw4seXFAwAAyAOfg7V06VL9%2BOOP6tmzZ%2BPYsGHDVF9fr2XLlllYGQAAgHs87gzWnj17tGXLFoWFhTWORUVFacWKFfrd735nYWUAAADu8bgzWCdOnFBAQECTcV9fX9XU1FhQEQAAQMt4XMAaNGiQli1bph9%2B%2BKFx7Pvvv9fixYtdHtUAAADgqTzuEuH8%2BfM1efJkXXvttQoKClJ9fb2qq6t18cUXN/mzNwAAAJ7I4wLWxRdfrNdff13vvPOO/v73v8vX11c9evTQkCFD5OfnZ3V5AAAA5%2BRxAUuS/P39Gx/RAAAA4G08LmB9%2B%2B23WrlypT7//HOdOHGiyfzbb79tQVUAAADu87iANX/%2BfJWVlWnIkCEKDAy0uhwAAIAW87iAVVxcrLffflvh4eFWlwKgjdz65HtWlwAAbcrjHtNw4YUXcuYKAAB4NY8LWFOnTtXq1avV0NBgdSkAAADnxeMuEb7zzjv64IMPtGnTJl100UXy9XXNgK%2B%2B%2BqpFlQEAALjH4wJWUFCQhg4danUZAAA04W33D26beZ3VJfxmeVzAWrp0qdUlAAAAtIrH3YMlSV9%2B%2BaWys7P10EMPNY59%2BOGHFlYEAADgPo8LWIWFhRo9erR27NihrVu3Sjrz8NE777yTh4wCAACv4HEBa9WqVfrDH/6gLVu2yMfHR9KZv0%2B4bNkyPf300xZXBwAAcG4eF7D%2B9re/KS0tTZIaA5Yk3XLLLTp8%2BLBVZQEAALjN4wJW586df/VvEJaVlcnf39%2BCigAAAFrG4wJWfHy8Hn/8cR0/frxx7KuvvtLcuXN17bXXWlgZAACAezzuMQ0PPfSQ7rrrLg0ePFh1dXWKj49XTU2NevXqpWXLllldHgAAwDl5XMDq3r27tm7dqt27d%2Burr75Shw4d1KNHD1133XUu92QBAAB4Ko8LWJJ0wQUX6KabbrK6DAAAgPPicQErKSmp2TNVPAsLAAB4Oo8LWCNGjHAJWHV1dfrqq69UVFSku%2B66y8LKAAAA3ONxASszM/NXx9988029//777VwNAABAy3ncYxrO5qabbtLrr79udRkAAADn5DUB6%2BDBg2poaLC6DAAAgHPyuEuEEyZMaDJWU1Ojw4cPa/jw4RZUBAAA0DIeF7CioqKavIswICBAKSkpuv322y2qCgAAwH0eF7B4WjsAAPB2HhewXnvtNbeXHTNmTBtWAgAAcH48LmAtWLBA9fX1TW5o9/HxcRnz8fEhYAGAl7v1yfesLgFoEx4XsJ577jmtW7dO06ZNU58%2BfdTQ0KDPPvtMzz77rO644w4NHjzY6hIBAACa5XEBa9myZcrJyVG3bt0axwYOHKiLL75Yd999t7Zu3WphdQAAAOfmcc/B%2BvrrrxUSEtJkPDg4WKWlpRZUBAAA0DIeF7AiIyO1bNkyVVZWNo5VVVVp5cqVuuSSS9xez6effqq77rpLAwYMUEJCgmbOnKny8nJJUmFhoVJSUhQfH6%2BRI0cqPz/f5WvXr1%2Bv5ORkxcfHKy0tTcXFxWaaAwAAvwkeF7Dmz5%2Bvbdu2KSEhQQMHDtTVV1%2Bta665Rps2bdK8efPcWsepU6c0efJkXX311SosLNTWrVtVUVGhRYsWqaysTPfdd58mTJigwsJCLViwQA8//LCKiookSQUFBcrOztby5cu1d%2B9e3XDDDZo2bZp%2B%2BumntmwbAADYiMfdgzVkyBDt2rVLu3fv1tGjR9XQ0KBu3brp%2BuuvV%2BfOnd1aR01NjWbNmqWxY8fK4XAoPDxcN998s15%2B%2BWVt2bJFUVFRSklJkSQlJCQoKSlJeXl5io2NVW5ursaNG6e4uDhJ0pQpU7R%2B/Xrt3LlTI0eObLO%2BAQCAfXhcwJKkjh076sYbb9TRo0d18cUXt/jrQ0JCXJ76/uWXX%2BrPf/6zbr31VpWUlCg6Otpl%2BejoaG3btk2SVFJSohEjRjTO%2Bfr6qm/fvioqKnI7YJWVlTVejvyZwxGoiIiIFvfiLj8/X5ePdmHXvtqCw2Ht94h9BXgeq48LzbH7McPjAtaJEyf0yCOP6PXXX5ckFRcXq6qqSrNnz9Z//ud/Kjg42O11lZaWKjk5WbW1tRo/frwyMjJ0zz33uLxDUZJCQ0Mb7/lyOp1NbrIPCQlxuSfsXHJzc7V69WqXsfT0dGVkZLi9jvMVHNyxzbdhBbv2ZVJYWCerS5DEvgI8iaccF5pj12OGxwWsrKwsHTp0SCtWrNCDDz7YOF5XV6cVK1bo0UcfdXtdkZGRKioq0jfffKM//vGPLutrzi8fctpSqampSkpKchlzOAJVWVndqvU2x8/PV8HBHVVVVaO6uvo22057s2tfbaEtX1/uYF8Bnsfq40Jz2uuYYVXI9LiA9eabb%2Brll19WVFSU5s6dK%2BnMIxqWLl2qMWPGtChgSWee%2BB4VFaVZs2ZpwoQJSkxMlNPpdFmmsrJS4eHhkqSwsLAm806nU7169XJ7mxEREU0uB5aX/6ja2rb/oVNXV98u22lvdu3LJE/5/rCvAM/hDf8W7XrM8LgLn9XV1YqKimoyHh4e7vY7%2BQoLC5WcnKz6%2Bv%2B/w3x9z7R65ZVXNnnsQnFxceNN7TExMSopKWmcq6ur08GDBxvnAQAAzsXjAtYll1yi999/X5Lrpbrt27frX//1X91aR0xMjI4fP66srCzV1NTo2LFjys7O1sCBA5WWlqbS0lLl5eXp5MmT2r17t3bv3q3x48dLktLS0vTaa6/po48%2BUk1NjdasWSN/f38NGzbMeK8AAMCePO4S4cSJEzVjxgzddtttqq%2Bv1/PPP6/i4mK9%2BeabWrBggVvr6Ny5s9atW6clS5bommuuUWBgoK655ho99thjuvDCC7V27VotWbJEixcvVmRkpLKysnTFFVdIkoYOHarZs2dr5syZqqioUGxsrHJyctShQ4e2bBsAANiIT0Nr7%2BhuA//7v/%2Brl19%2BWV9%2B%2BaU6dOigHj16aNKkSbrlllusLu28lZf/2Kbrdzh8FRbWSZWV1ba6lm1lX7c%2B%2BV67bq%2B1ts28ztLtt2Rfedv3FvBWVh8XmtNex/euXd17hqZpHncG69ixY7rtttt02223WV0KAADAefG4e7BuvPHGVj8mAQAAwEoeF7AGDx7c%2BFR1AAAAb%2BRxlwj/5V/%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%2B9o033tCoUaN01VVXady4cdqzZ48VLQAAAC9l24A1bdo0BQcHq6CgQJs2bdLnn3%2BuJ554QidOnNDUqVN1zTXX6N1339WqVau0du1a7dixQ9KZ8DV37lxlZmZq3759mjRpku6//34dPXrU4o4AAIC3sGXAqqqqUkxMjObMmaNOnTqpe/fuGjt2rPbv369du3bp9OnTmj59ugIDA9WvXz/dfvvtys3NlSTl5eUpMTFRiYmJCggI0OjRo9W7d2/l5%2Bdb3BUAAPAWtgxYwcHBWrp0qbp06dI49t133ykiIkIlJSXq06eP/Pz8Gueio6NVXFwsSSopKVF0dLTL%2BqKjo1VUVNQ%2BxQMAAK/nsLqA9lBUVKSXX35Za9as0bZt2xQcHOwyHxoaKqfTqfr6ejmdToWEhLjMh4SE6IsvvnB7e2VlZSovL3cZczgCFRERcf5NnIOfn6/LR7uwa19tweGw9nvEvgLQGlYfw0yzfcA6cOCApk%2Bfrjlz5ighIUHbtm371eV8fHwa/7%2BhoaFV28zNzdXq1atdxtLT05WRkdGq9bojOLhjm2/DCnbty6SwsE5WlyCJfQXg/HjKMcwUWwesgoIC/eEPf9DDDz%2BsMWPGSJLCw8P19ddfuyzndDoVGhoqX19fhYWFyel0NpkPDw93e7upqalKSkpyGXM4AlVZWX1%2BjbjBz89XwcEdVVVVo7q6%2BjbbTnuza19toS1fX%2B5gXwFojbY6hlkV3GwbsD744APNnTtXTz31lIYMGdI4HhMTow0bNqi2tlYOx5n2i4qKFBcX1zj/8/1YPysqKtLIkSPd3nZERESTy4Hl5T%2Bqtrbtf%2BjU1dW3y3bam137MslTvj/sKwDnw27HDXtd8PyH2tpaLVy4UJmZmS7hSpISExMVFBSkNWvWqKamRh9//LE2btyotLQ0SdL48eO1d%2B9e7dq1SydPntTGjRv19ddfa/To0Va0AgAAvJAtz2B99NFHOnz4sJYsWaIlS5a4zG3fvl3PPPOMHnnkEeXk5KhLly6aNWuWhg0bJknq3bu3VqxYoaVLl6q0tFQ9e/bU2rVr1bVrVws6AQAA3siWAWvgwIH67LPPml1mw4YNZ50bPny4hg8fbrosAADwG2HLS4QAAABWImABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMMyWf4sQ%2BC269cn3rC4BAPAPnMECAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwzLYB691331VCQoJmzZrVZO6NN97QqFGjdNVVV2ncuHHas2dP41x9fb1WrVqlG2%2B8UYMGDdLdd9%2Btb7/9tj1LBwAAXs6WAevZZ5/VkiVLdOmllzaZO3TokObOnavMzEzt27dPkyZN0v3336%2BjR49Kkl555RVt2bJFOTk52rlzp6KiopSenq6Ghob2bgMAAHgpWwasgIAAbdy48VcDVl5enhITE5WYmKiAgACNHj1avXv3Vn5%2BviQpNzdXkyZN0uWXX66goCDNmjVLhw8f1scff9zebQAAAC/lsLqAtnDnnXeeda6kpESJiYkuY9HR0SoqKtKJEyf0xRdfKDo6unEuKChIl156qYqKitS/f3%2B3tl9WVqby8nKXMYcjUBERES3oomX8/HxdPtqFXfsCALhyOOx1nLdlwGqO0%2BlUSEiIy1hISIi%2B%2BOIL/fDDD2poaPjV%2BcrKSre3kZubq9WrV7uMpafkghgdAAAOfklEQVSnKyMj4/wLd1NwcMc234YV7NoXAOCMsLBOVpdg1G8uYEk65/1Urb3fKjU1VUlJSS5jDkegKiurW7Xe5vj5%2BSo4uKOqqmpUV1ffZttpb3btCwDgqq1%2BRloV3H5zASssLExOp9NlzOl0Kjw8XKGhofL19f3V%2BQsvvNDtbURERDS5HFhe/qNqa9s%2BINTV1bfLdtqbXfsCAJxht2O8vS54uiEmJkbFxcUuY0VFRYqLi1NAQIB69eqlkpKSxrmqqir9/e9/15VXXtnepQIAAC/1mwtY48eP1969e7Vr1y6dPHlSGzdu1Ndff63Ro0dLktLS0rR%2B/XodPnxYx48f14oVK9S3b1/FxsZaXDkAAPAWtrxE%2BHMYqq2tlSS99dZbks6cqerdu7dWrFihpUuXqrS0VD179tTatWvVtWtXSdKECRNUXl6uf//3f1d1dbUGDx7c5IZ1AACA5vg08ATNdlFe/mObrt/h8FVYWCdVVlbb6jq2lX3d%2BuR77bo9APgt2zbzujZZb9eundtkvefym7tECAAA0NYIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMs%2BUfe4Zn4m/7AQB%2BKziDBQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMIfVBaB1bn3yPatLAAAAv8AZLAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAetXlJaW6t5779XgwYN1ww03KCsrS/X19VaXBQAAvATPwfoVM2bMUL9%2B/fTWW2%2BpoqJCU6dOVZcuXfQf//EfVpcGAAC8AGewfqGoqEiffvqpMjMz1blzZ0VFRWnSpEnKzc21ujQAAOAlOIP1CyUlJYqMjFRISEjjWL9%2B/fTVV1/p%2BPHjCgoKOuc6ysrKVF5e7jLmcAQqIiLCeL0AANiBw2Gvcz4ErF9wOp0KDg52Gfs5bFVWVroVsHJzc7V69WqXsfvvv18zZswwV%2Bg/7H/sFklnQl1ubq5SU1NtFeToy3vYsSeJvryJHXuS6Mtb2SsuGtLQ0NCqr09NTdWmTZtc/ktNTTVU3a8rLy/X6tWrm5w583b05T3s2JNEX97Ejj1J9OWtOIP1C%2BHh4XI6nS5jTqdTPj4%2BCg8Pd2sdERERtkzjAADAPZzB%2BoWYmBh99913OnbsWONYUVGRevbsqU6dOllYGQAA8BYErF%2BIjo5WbGysVq5cqePHj%2Bvw4cN6/vnnlZaWZnVpAADAS/gtWrRokdVFeJrrr79eW7du1Z/%2B9Ce9/vrrSklJ0d133y0fHx%2BrS2tWp06ddPXVV9vuTBt9eQ879iTRlzexY08SfXkjn4bW3tENAAAAF1wiBAAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgGUTa9as0ZAhQ9S/f39NmjRJR44csbqkVjt48KDuvPNODRw4UNddd50yMzNd/gi3t3j33XeVkJCgWbNmNZl74403NGrUKF111VUaN26c9uzZY0GF56e5vnbs2KHRo0frqquuUnJysv7nf/7HggpbrrmeflZdXa1hw4Zp3rx57VhZ6zTX1/fff6/p06erf//%2BSkhI0MqVK1VfX29BlS3XXF%2BvvPKKkpOTG1%2BDL730kgUVtlxpaanS09M1ePBgJSQkaN68eaqqqpIkHTp0SHfccYcGDBig4cOHa926dRZX677m%2Bvr00081adIkDRw4UEOHDtVjjz2mU6dOWVxx6xGwbOCVV15Rfn6%2B1q9frz179qhnz5564YUXrC6rVWpra3Xvvfeqf//%2B2rt3r7Zu3apjx47J2/505rPPPqslS5bo0ksvbTJ36NAhzZ07V5mZmdq3b58mTZqk%2B%2B%2B/X0ePHrWg0pZprq9PPvlEmZmZysjI0F//%2BlfNnz9fjz76qPbv329Bpe5rrqd/lp2drePHj7dTVa3XXF8NDQ26//77FRkZqT179uill15SYWGh3n//fQsqbZnm%2Btq9e7eysrK0fPlyHThwQMuXL9fKlSu1a9eu9i%2B0haZNm6bg4GAVFBRo06ZN%2Bvzzz/XEE0/oxIkTmjp1qq655hq9%2B%2B67WrVqldauXasdO3ZYXbJbztZXdXW1pkyZori4OO3du1fPP/%2B83n77bT333HNWl9xqBCwbWLdunWbNmqXLLrtMQUFBWrhwoRYuXGh1Wa1SXl6u8vJy/f73v5e/v7/CwsJ0880369ChQ1aX1iIBAQHauHHjr/4QyMvLU2JiohITExUQEKDRo0erd%2B/eys/Pt6DSlmmuL6fTqalTp%2Bqmm26Sw%2BFQYmKievfu7fEBq7mefvbpp59q69atGjt2bDtW1jrN9fXXv/5V3377rR588EEFBQXp8ssv18aNG3XttddaUGnLNNdXcXGxevXqpbi4OPn6%2BiouLk69e/fWwYMHLajUfVVVVYqJidGcOXPUqVMnde/eXWPHjtX%2B/fu1a9cunT59WtOnT1dgYKD69eun22%2B/Xbm5uVaXfU7N9VVRUaHrr79eM2bMkL%2B/vy6//HIlJyd7/PHCHQQsL/f999/ryJEj%2BuGHHzRixAgNHjxYGRkZXnkp7Z9169ZNffv2VW5urqqrq1VRUaEdO3Zo2LBhVpfWInfeeac6d%2B78q3MlJSWKjo52GYuOjlZRUVF7lNYqzfU1dOhQpaenN35eW1ur8vJydevWrb3KOy/N9SSdOduzaNEizZo1S8HBwe1YWes019eBAwfUu3dvrVq1SoMHD9aNN97oNZedmuvr%2Buuv1xdffKH3339fp06d0ocffqjDhw9ryJAh7VxlywQHB2vp0qXq0qVL49h3332niIgIlZSUqE%2BfPvLz82uci46OVnFxsRWltkhzfV1yySVaunSpHA6Hy5ynHy/cQcDycj9fTtq%2Bfbuef/55bd68WUePHvX6M1i%2Bvr7Kzs7W22%2B/rfj4eCUkJKi2tlZz5syxujRjnE6nQkJCXMZCQkJUWVlpUUVtY8WKFQoMDNSIESOsLqVVcnNz5ePjo3HjxlldijFHjx7VRx99pAsvvFC7du3SH//4R61atUpvvfWW1aW1ypVXXqmHHnpIkydPVmxsrO644w7NnDlTV155pdWltUhRUZFefvllTZ8%2BXU6ns0mwDw0NldPp9Jp75n72z3390ttvv62dO3dq8uTJFlRmFgHLyzU0NEiSpkyZom7duql79%2B6aMWOGCgoKdPLkSYurO3%2BnTp3StGnTdMstt2j//v1655131LlzZ2VmZlpdmlE/7z87amhoUFZWlrZu3ao1a9YoICDA6pLOW0VFhZ566iktWrRIPj4%2BVpdjTENDg8LDwzVlyhR17NhRiYmJuvnmm7Vt2zarS2uVffv2aeXKlXruuef0ySef6MUXX9QzzzzjVcHxwIEDuvvuuzVnzhwlJCScdTlvez0219eOHTuUmZmp5cuXq1evXhZVaA4By8v9fMr1n3%2BziYyMVENDgyoqKqwqq9UKCwt15MgRzZ49W507d1a3bt2UkZGhv/zlL3I6nVaXZ0RYWFiTXpxOp8LDwy2qyJz6%2BnrNmzdPBQUF2rBhgy677DKrS2qVZcuWacyYMerTp4/VpRjVtWvXJpfZIiMjVV5eblFFZmzYsEHDhw/Xtddeq4CAAA0cOFAjR47Uxo0brS7NLQUFBbr33ns1f/583XnnnZKk8PDwJme3nU6nQkND5evrHT/Kf62vn%2BXm5mrBggXKzs5WcnKyRRWa5Tj3IvBk3bt3V1BQkA4dOqR%2B/fpJOvN22AsuuEAREREWV3f%2B6urqVF9f73KGxw5v2/1nMTExTe6fKCoq0siRIy2qyJzHH39cn3/%2BuTZs2KDQ0FCry2m1/Px8BQcHa9OmTZKkEydOqL6%2BXjt37vSKd9ydzeWXX65vv/1W1dXV6tSpk6Qzx4/IyEiLK2ud%2Bvp61dXVuYx5y/Hjgw8%2B0Ny5c/XUU0%2B53DMWExOjDRs2qLa2tvF%2BpaKiIsXFxVlVaoucrS/pzC0uq1at0vr169W3b1%2BLKjTPO2IvzsrhcCglJUXPPPOMvvnmG1VUVOjpp5/WqFGjXG4a9DZXXXWVAgMDlZ2drZqaGlVWVmrNmjUaNGiQLX5gS9L48eO1d%2B9e7dq1SydPntTGjRv19ddfa/To0VaX1ioHDhxQfn6%2BcnJybLOvdu/erS1btmjz5s3avHmzJkyYoKSkJG3evNnq0lolKSlJwcHBWr58uX766ScVFhbqrbfe8vr7zJKSkvTmm29q//79qq2t1SeffKJt27bp5ptvtrq0ZtXW1mrhwoXKzMxsEkISExMVFBSkNWvWqKamRh9//LE2btyotLQ0i6p1X3N9/fjjj1q0aJGysrJsFa4kyafBzjeB/EacOnVKS5cu1euvv67Tp08rOTlZDz/8cONvpN6quLhYTzzxhD799FP5%2B/vr6quv1rx587zq3SWxsbGSzhxgJLn85imduedg5cqVKi0tVc%2BePbVgwQINGjTImmJboLm%2B5s%2Bfrz//%2Bc9NAv6gQYM8%2Bh1q59pX/yw7O1ulpaVatmxZ%2BxV4ns7V19/%2B9jc98sgjKikpUXh4uB544AGveAzFufp68cUX9d///d/6/vvv1a1bN40fP16TJ0/26HuW9u/fr3/7t3%2BTv79/k7nt27erurpajzzyiIqLi9WlSxfdc889mjhxogWVtkxzfT366KOaN2/er855wzuqm0PAAgAAMIxLhAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABg2P8DZ%2B8J2peeoxcAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-3777541227580257707”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>15.223026955891909</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>19.3952970890651</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.167430595356233</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>19.359663723562868</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>15.67025566509675</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.544125974995477</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14.399272252077058</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16.9611639450476</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.357911883394</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.666611514886178</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (41)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-3777541227580257707”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>5.866367454814733</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.323863433830711</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:57%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.909464693629092</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.298301068286891</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:23%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.49562077236101</td> <td class=”number”>93</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>20.666611514886178</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>21.01309784781326</td> <td class=”number”>36</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:38%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>21.06563828868176</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:35%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>21.899881129769458</td> <td class=”number”>82</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:86%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>22.131516338899353</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_SNAP16”><s>PCT_SNAP16</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCT_SNAP12”><code>PCT_SNAP12</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.91642</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_WIC09”>PCT_WIC09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>52</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>2.9584</td>

</tr> <tr>

<th>Minimum</th> <td>1.3863</td>

</tr> <tr>

<th>Maximum</th> <td>5.0789</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-1654535513653926685”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-1654535513653926685,#minihistogram-1654535513653926685”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-1654535513653926685”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-1654535513653926685”

aria-controls=”quantiles-1654535513653926685” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-1654535513653926685” aria-controls=”histogram-1654535513653926685”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-1654535513653926685” aria-controls=”common-1654535513653926685”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-1654535513653926685” aria-controls=”extreme-1654535513653926685”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-1654535513653926685”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>1.3863</td>

</tr> <tr>

<th>5-th percentile</th> <td>2.0317</td>

</tr> <tr>

<th>Q1</th> <td>2.5076</td>

</tr> <tr>

<th>Median</th> <td>2.7313</td>

</tr> <tr>

<th>Q3</th> <td>3.2861</td>

</tr> <tr>

<th>95-th percentile</th> <td>5.0789</td>

</tr> <tr>

<th>Maximum</th> <td>5.0789</td>

</tr> <tr>

<th>Range</th> <td>3.6926</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.77854</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.74607</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.25218</td>

</tr> <tr>

<th>Kurtosis</th> <td>1.1355</td>

</tr> <tr>

<th>Mean</th> <td>2.9584</td>

</tr> <tr>

<th>MAD</th> <td>0.57053</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.1525</td>

</tr> <tr>

<th>Sum</th> <td>9265.8</td>

</tr> <tr>

<th>Variance</th> <td>0.55662</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-1654535513653926685”>

<img 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vvtt%2B0uBwAAwDjbA1a7du20Y8cO/fWvf9XXX38tf39/dejQQf369VNAQIDd5QAAABhne8CSpKCgIA0ZMsQThwYAAKh3tgesb775RitXrtSXX36pixcvVpv/4IMPjB1r7dq1euedd3T%2B/HndfvvtWrx4sdq2bausrCytXLlSJ0%2Be1M0336xp06Zp1KhRVY9bv3693nnnHRUWFqpLly5auHChYmJijNUFAACczSP3YBUUFKhfv35q3LhxvR3nnXfe0datW7V%2B/XpFRETopZde0ptvvqlHH31UM2bM0MKFCzVy5Eh98skneuyxx9ShQwfFxsZq3759WrVqlV599VV16dJF69ev1/Tp07Vnz556rRcAADiH7QErJydHH3zwgcLDw%2Bv1OK%2B//rpSU1P1m9/8RpL09NNPS5Jee%2B01RUZGKikpSZIUHx%2BvQYMGKTMzU7GxscrIyFBiYqK6desmSZo6darWr1%2Bv/fv3a8SIEfVaMwAAcAbbA1aLFi3q/UzQP/7xD3377bf6/vvvNXz4cBUVFSkuLk7PPvuscnNzFR0d7bY%2BOjpau3btkiTl5uZq%2BPDhVXP%2B/v6KiopSdnZ2jQNWQUGBCgsL3cYCAxsrIiKijp3VXUCAv9tHJ/PFXr1FYGDd6vXFvaVXZ6FX57M9YE2bNk2rV6/W3Llz5efnVy/HOHv2rCRp9%2B7deuONN2RZlmbOnKmnn35aFy9eVKtWrdzWN2/eXMXFxZIkl8ul0NBQt/nQ0NCq%2BZrIyMjQ6tWr3cZSUlI0c%2BbM62mnXoSENPJ0CbbxpV69RVhYEyPP40t7S6/ORK/OZXvA%2Butf/6pPP/1UmzZtUtu2beXv755o33333Tofw7IsST9e3vspTD3xxBN65JFHFB8fX%2BPHX6/x48dr0KBBbmOBgY1VXFxap%2Bc1ISDAXyEhjVRSUqaKikpPl1OvfK1Xb1LX14Kv7S29Og%2B92sfUN3S1ZXvAatq0qfr371%2Bvx7jpppskSSEhIVVjbdq0kWVZunLlilwul9v64uLiqnvCwsLCqs27XC516tSpxsePiIiodjmwsPAHlZffOC%2BiiorKG6qe%2BuRLvXoLU/vhS3tLr85Er85le8BKS0ur92O0bt1aTZs21fHjx9W1a1dJUn5%2Bvho0aKCEhARt2bLFbX1OTk7VTe0xMTHKzc3VmDFjJEkVFRU6duxY1U3xAAAA1%2BKR6wonT57UqlWr9NRTT1WNHTlyxNjzBwYGKikpSa%2B88or%2B93//V0VFRVqzZo1GjhypMWPGKD8/X5mZmbp06ZIOHjyogwcPaty4cZKk5ORkbd68WUePHlVZWZnWrl2roKAgDRgwwFh9AADA2WwPWFlZWRo1apT27Nmj7du3S/rxzUcnTZpk9E1G586dq7vuuktjx47VkCFDFBkZqaefflotWrTQunXrtGHDBvXs2VNLly7V8uXLdeutt0qS%2Bvfvrzlz5mjWrFnq06ePDh8%2BrPT0dDVs2NBYbQAAwNn8rLre0V1L48aN04gRI/TQQw/ptttu0%2Beffy5J2rFjh1577TVt2rTJznJsU1j4g6dLkPTjj8eHhTVRcXGp46%2BF%2B1qvd6845OkyamzXrL51eryv7S29Og%2B92qdly2a2H1PywBms//mf/1FycrIkub1Nwz333KO8vDy7ywEAADDO9oDVrFmzq/4OwoKCAgUFBdldDgAAgHG2B6wePXpo6dKlOn/%2BfNXYqVOnlJqaqjvvvNPucgAAAIyz/W0annrqKT300EOKi4tTRUWFevToobKyMnXq1EnLli2zuxwAAADjbA9YrVu31vbt23Xw4EGdOnVKDRs2VIcOHdS3b996%2B9U5AAAAdrI9YElSgwYNNGTIEE8cGgAAoN7ZHrAGDRr0q2eqTL4XFgAAgCfYHrCGDx/uFrAqKip06tQpZWdn66GHHrK7HAAAAONsD1jz5s276vj777%2Bvv/3tbzZXAwAAYJ5Hfhfh1QwZMkQ7duzwdBkAAAB1dsMErGPHjsnm39oDAABQL2y/RDhhwoRqY2VlZcrLy9PQoUPtLgcAAMA42wNWZGRktZ8iDA4OVlJSksaOHWt3OQAAAMbZHrB4t3YAAOB0tgeszZs313jt6NGj67ESAACA%2BmF7wFq4cKEqKyur3dDu5%2BfnNubn50fAAgAAXsn2gPXqq6/q9ddf1/Tp09WlSxdZlqUTJ07oj3/8ox588EHFxcXZXRIAAIBRHrkHKz09Xa1ataoa69Wrl9q1a6cpU6Zo%2B/btdpcEAABglO3vg3X69GmFhoZWGw8JCVF%2Bfr7d5QAAABhne8Bq06aNli1bpuLi4qqxkpISrVy5Urfccovd5QAAABhn%2ByXCBQsWaO7cucrIyFCTJk3k7%2B%2Bv8%2BfPq2HDhlqzZo3d5QAAABhne8Dq16%2BfDhw4oIMHD%2Brs2bOyLEutWrXSXXfdpWbNmtldDgAAgHG2ByxJatSokQYPHqyzZ8%2BqXbt2nigBAACg3th%2BD9bFixeVmpqq7t27695775X04z1YU6dOVUlJid3lAAAAGGd7wFq%2BfLmOHz%2BuFStWyN///w5fUVGhFStW2F0OAACAcbYHrPfff18vv/yy7rnnnqpf%2BhwSEqK0tDTt2bPH7nIAAACMsz1glZaWKjIystp4eHi4Lly4YHc5AAAAxtkesG655Rb97W9/kyS33z24e/du/cu//Ivd5QAAABhn%2B08RTpw4UU888YQeeOABVVZW6o033lBOTo7ef/99LVy40O5yAAAAjLM9YI0fP16BgYHasGGDAgIC9Morr6hDhw5asWKF7rnnHrvLAQAAMM72gHXu3Dk98MADeuCBB%2Bw%2BNAAAgC1svwdr8ODBbvdeAQAAOI3tASsuLk67du2y%2B7AAAAC2sf0S4c0336wlS5YoPT1dt9xyixo0aOA2v3LlSrtLAgAAMMr2gPXVV1/pN7/5jSSpuLjY7sMDAADUO9sC1uzZs/Xiiy/q7bffrhpbs2aNUlJS7CoBAADAFrbdg7Vv375qY%2Bnp6XYdHgAAwDa2Bayr/eQgP00IAACcyLaA9dMvdr7WGAAAgLez/W0aAAAAnI6ABQAAYJhtP0V45coVzZ0795pjvA8WAADwdrYFrJ49e6qgoOCaYwAAAN7OtoD1z%2B9/BQAA4GTcgwUAAGAYAQsAAMAwnwhYS5cuVZcuXar%2BnpWVpaSkJPXo0UMjRozQ1q1b3davX79ew4YNU48ePZScnKycnBy7SwYAAF7M8QHr%2BPHj2rJlS9XfCwoKNGPGDE2YMEFZWVlauHChFi1apOzsbEk//kqfVatW6fe//70OHz6sgQMHavr06bpw4YKnWgAAAF7G0QGrsrJSzzzzjCZPnlw1tm3bNkVGRiopKUnBwcGKj4/XoEGDlJmZKUnKyMhQYmKiunXrpoYNG2rq1KmSpP3793uiBQAA4IUcHbDeffddBQcHa%2BTIkVVjubm5io6OdlsXHR1ddRnw5/P%2B/v6KioqqOsMFAABwLba9TYPdvvvuO61atara20O4XC61atXKbax58%2BYqLi6umg8NDXWbDw0NrZqviYKCAhUWFrqNBQY2VkRERG1aqBcBAf5uH52srr3eveKQyXLwT%2B596SNPl1Ar/zXvLo8dm9esM9Gr8zk2YKWlpSkxMVEdO3bUt99%2BW6vHWpZVp2NnZGRo9erVbmMpKSmaOXNmnZ7XpJCQRp4uwTa%2B1CvqR1hYE0%2BX4FP/junVmXypV8mhASsrK0tHjhzR9u3bq82FhYXJ5XK5jRUXFys8PPwX510ulzp16lTj448fP16DBg1yGwsMbKzi4tIaP0d9CQjwV0hII5WUlKmiotLT5dQrX%2BoV9cuTr11f%2BndMr87k6V499Q2SIwPW1q1bVVRUpIEDB0r6vzNScXFxevjhh6sFr5ycHHXr1k2SFBMTo9zcXI0ZM0aSVFFRoWPHjikpKanGx4%2BIiKh2ObCw8AeVl984L6KKisobqp765Eu9on7cCP9%2BfOnfMb06ky/1Kjn0Jvf58%2Bfr/fff15YtW7Rlyxalp6dLkrZs2aKRI0cqPz9fmZmZunTpkg4ePKiDBw9q3LhxkqTk5GRt3rxZR48eVVlZmdauXaugoCANGDDAgx0BAABv4sgzWKGhoW43qpeXl0uSWrduLUlat26dFi9erOeee05t2rTR8uXLdeutt0qS%2Bvfvrzlz5mjWrFkqKipSbGys0tPT1bBhQ/sbAQAAXsmRAevn2rZtqxMnTlT9vXfv3m5vPvpzEydO1MSJE%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%2BWJMnT9bjjz%2Bus2fPergjAADgLRwZsEpKShQTE6O5c%2BeqSZMmat26tcaMGaO///3vOnDggK5cuaLHHntMjRs3VteuXTV27FhlZGRIkjIzM5WQkKCEhAQFBwdr1KhR6ty5s7Zu3erhrgAAgLcI9HQB9SEkJERpaWluY2fOnFFERIRyc3PVpUsXBQQEVM1FR0crMzNTkpSbm6uEhAS3x0ZHRys7O7vGxy8oKFBhYaHbWGBgY0VERNS2FeMCAvzdPgJwnsBAXt/1xdTn1pe%2BFvtSr//MkQHr57Kzs7VhwwatXbtWu3btUkhIiNt88%2BbN5XK5VFlZKZfLpdDQULf50NBQffXVVzU%2BXkZGhlavXu02lpKSopkzZ15/E4aFhDTydAkA6klYWBNPl%2BBYpj%2B3vvS12Jd6lXwgYH3yySd67LHHNHfuXMXHx2vXrl1XXefn51f135Zl1emY48eP16BBg9zGAgMbq7i4tE7Pa0JAgL9CQhqppKRMFRWVni4HQD24Eb7WOJWpz60vfS32dK%2Be%2BobD0QFr3759evLJJ7Vo0SKNHj1akhQeHq7Tp0%2B7rXO5XGrevLn8/f0VFhYml8tVbT48PLzGx42IiKh2ObCw8AeVl984L6KKisobqh4A5vDarj%2BmP7e%2B9LXYl3qVHHqTuyR9%2BumnSk1N1R/%2B8IeqcCVJMTExOnHihMrLy6vGsrOz1a1bt6r5nJwct%2Bf653kAAIBrcWTAKi8v19NPP6158%2BapX79%2BbnMJCQlq2rSp1q5dq7KyMn322WfauHGjkpOTJUnjxo3T4cOHdeDAAV26dEkbN27U6dOnNWrUKE%2B0AgAAvJAjLxEePXpUeXl5Wrx4sRYvXuw2t3v3br3yyit65plnlJ6erptuukmzZ8/WgAEDJEmdO3fWihUrlJaWpvz8fHXs2FHr1q1Ty5YtPdAJAADwRo4MWL169dKJEyd%2Bdc2f/vSnX5wbOnSohg4darosAADgIxx5iRAAAMCTCFgAAACGEbAAAAAMI2ABAAAY5sib3AHAl9370keeLgHweQQsAAAcypvC9q5ZfT1dglFcIgQAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGBbo6QJQN/e%2B9JGnSwAAAD/DGaybMuCeAAAK7klEQVSryM/P16OPPqq4uDgNHDhQy5cvV2VlpafLAgAAXoIzWFfxxBNPqGvXrtq7d6%2BKioo0bdo03XTTTfrd737n6dIAAIAX4AzWz2RnZ%2BuLL77QvHnz1KxZM0VGRmry5MnKyMjwdGkAAMBLcAbrZ3Jzc9WmTRuFhoZWjXXt2lWnTp3S%2BfPn1bRp02s%2BR0FBgQoLC93GAgMbKyIiwni9AAD7BAaaOS8REODv9hHmPrc3CgLWz7hcLoWEhLiN/RS2iouLaxSwMjIytHr1arexxx9/XE888YS5Qv%2B/vy%2B5p1brCwoKlJGRofHjxzs%2B8NGrc/lSv/TqTAUFBXrrrVfrvdfa/j%2BiPvjSvv4zZ8VFQyzLqtPjx48fr02bNrn9GT9%2BvKHq6qawsFCrV6%2BudobNiejVuXypX3p1Jnp1Ps5g/Ux4eLhcLpfbmMvlkp%2Bfn8LDw2v0HBERET6V0gEAgDvOYP1MTEyMzpw5o3PnzlWNZWdnq2PHjmrSpIkHKwMAAN6CgPUz0dHRio2N1cqVK3X%2B/Hnl5eXpjTfeUHJysqdLAwAAXiLg2WeffdbTRdxo7rrrLm3fvl3PP/%2B8duzYoaSkJE2ZMkV%2Bfn6eLs2IJk2aqE%2BfPj5xRo5encuX%2BqVXZ6JXZ/Oz6npHNwAAANxwiRAAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAKWQx06dEjx8fGaPXv2r66bP39%2B1e9f/OlPr169bKqy7vLz85WSkqK4uDjFx8dr/vz5KikpueranTt3auTIkerevbsSExP14Ycf2lxt3dS0102bNunWW29129PY2Fh9/vnnHqj6%2BnzxxRd66KGH1LNnT8XHx2vWrFkqLCy86tr169dr2LBh6tGjh5KTk5WTk2NztXVX035XrVqlqKioanv73XffeaDqulm6dKm6dOnyi/NO2Nd/9mv9OmFfu3TpopiYGLf6n3/%2B%2Bauuddre/iILjpOenm4NHTrUmjBhgjVr1qxfXZuammq9/PLLNlVm3n333WfNnz/fOn/%2BvHXmzBkrMTHRWrBgQbV1x44ds2JiYqwDBw5YFy9etLZs2WJ169bNOnPmjAeqvj417fW9996zHnzwQQ9UaMalS5esO%2B%2B801q9erV16dIlq6ioyHrwwQetGTNmVFv7wQcfWL169bKOHj1qlZWVWevWrbP69u1rlZaWeqDy61Obfl9%2B%2BWUrNTXVA1WadezYMatPnz5W586drzrvhH39Z9fq1wn72rlzZ%2Bubb7655jqn7e2v4QyWAwUHB2vjxo1q3769p0upVyUlJYqJidHcuXPVpEkTtW7dWmPGjNHf//73amszMzOVkJCghIQEBQcHa9SoUercubO2bt3qgcprrza9eruysjLNnj1b06ZNU1BQkMLDw3X33Xfryy%2B/rLY2IyNDiYmJ6tatmxo2bKipU6dKkvbv32932detNv06QWVlpZ555hlNnjz5F9c4YV9/UpN%2BfYmT9vZaCFgONGnSJDVr1qzG6z/%2B%2BGONHj1a3bt3V1JSktecrg0JCVFaWppuuummqrEzZ84oIiKi2trc3FxFR0e7jUVHRys7O7ve6zShNr3%2BNPe73/1OvXv31uDBg7Vlyxa7Sq2z0NBQjR07VoGBgZKkkydP6i9/%2BYvuvffeamt/vq/%2B/v6Kiorymn2VatevJJ04cUITJkxQjx49NGLECK%2B71P3uu%2B8qODhYI0eO/MU1TtjXn9SkX8n791WSVq5cqQEDBqhXr15atGiRSktLq61x0t5eCwHLx7Vr107t27fXunXrdOjQIfXq1UsPP/ywiouLPV1arWVnZ2vDhg167LHHqs25XC6Fhoa6jYWGhnpln9Kv9xoeHq7IyEg9%2BeST%2BuijjzRnzhwtWLBAWVlZHqj0%2BuXn5ysmJkbDhw9XbGysZs6cWW2Nk/a1Jv22bt1a7dq10wsvvKCPPvpIY8eO1fTp03Xy5EkPVFx73333nVatWqVnnnnmV9c5ZV9r2q%2B376sk3X777YqPj9eePXuUkZGho0eP6rnnnqu2zil7WxMELB%2BXkpKipUuXqlWrVmratKmefPJJBQUFae/evZ4urVY%2B%2BeQTTZkyRXPnzlV8fPxV11iWZXNV9eNavQ4YMECvvvqqoqOjFRQUpBEjRujuu%2B/Wpk2bPFDt9WvTpo2ys7O1e/dunT59Wv/%2B7/9%2B1XVO2dea9Dt27Fi9/PLLat%2B%2BvRo1aqTJkycrKirKay51p6WlKTExUR07drzmWifsa0379fZ9lX689Dd27FgFBQXpt7/9rebNm6ft27fr8uXL1dY6YW9rgoAFNwEBAbr55ptVUFDg6VJqbN%2B%2BfXr00Ue1YMECTZo06aprwsLC5HK53MZcLpfCw8PtKNGYmvR6NW3atPGqPf2Jn5%2BfIiMjNXv2bG3fvl3nzp1zm3fKvv7kWv1ejbfsbVZWlo4cOaKUlJRrrnXCvtam36vxln39JW3btlVFRYWKiorcxp2wtzVFwPJhlmUpLS1NX3zxRdXY5cuX9fXXX6tdu3YerKzmPv30U6WmpuoPf/iDRo8e/YvrYmJiqt1blp2drW7dutV3icbUtNc//elP2rlzp9tYXl6e1%2BxpVlaWhg0bpsrKyqoxf/8fv1Q1aNDAbW1MTIxyc3Or/l5RUaFjx4551b7Wpt///M//rHap11v2duvWrSoqKtLAgQMVFxenxMRESVJcXJx27NjhttYJ%2B1qbfr15XyXp2LFjWrZsmdtYXl6egoKCqt0n6oS9rTGP/gwj6lVqamq1t2k4e/asNWzYMOvrr7%2B2LMuyZsyYYU2YMME6e/asdf78eWvJkiVe8yOzV65cse69917r3Xffver8pEmTrB07dliWZVknTpywYmNjrf3791sXL160MjMzre7du1sFBQV2lnzdatPrm2%2B%2Bad1xxx3W559/bl2%2BfNnatm2bFRUVZWVnZ9tZ8nUrKSmx4uPjrWXLllkXLlywioqKrClTplgTJ060LMuyhg0bZv33f/%2B3ZVmWdfDgQatnz57WkSNHrAsXLlirVq2yEhISrLKyMk%2B2UCu16XfJkiXWsGHDrLy8POvixYvWa6%2B9Zt12221e8XYjLpfLOnPmTNWfI0eOWJ07d7bOnDljXbhwwXH7Wpt%2BvXlfLevH/6/cfvvt1rp166xLly5ZJ0%2BetIYPH249//zzlmU57zVbU4GeDngwLzY2VpJUXl4uSVX3U2VnZ%2BvKlSs6depU1XXxJUuW6IUXXlBiYqLOnz%2Bv2267TW%2B99ZYaN27smeJr4ejRo8rLy9PixYu1ePFit7ndu3frm2%2B%2B0ffffy9J6ty5s1asWKG0tDTl5%2BerY8eOWrdunVq2bOmJ0mutNr1OmjRJpaWl%2Brd/%2BzcVFhaqbdu2WrNmjWJiYjxReq01a9ZMr7/%2BuhYvXqw77rhDjRs31h133KElS5ZIkk6dOqULFy5Ikvr37685c%2BZo1qxZKioqUmxsrNLT09WwYUNPtlArtel37ty5kqTJkyfL5XKpY8eOevPNN9W6dWuP1V9ToaGhbjc3//T16afanbavtenXm/dVklq1aqX09HStXLlSa9euVVBQkMaMGVP1RtdO29ua8rMsH7nbDAAAwCbcgwUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhv0/f915G6QWU8wAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-1654535513653926685”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>4.004688507145139</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.078871539129641</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0316926289455624</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.2861447996378397</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.507607414013675</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.7313199801188257</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.400156338966488</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.9319091889421083</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.5149142778111715</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.7767621827200744</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (41)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-1654535513653926685”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.3862559688956837</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:48%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.7095814782644287</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:38%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.9404309065763445</td> <td class=”number”>11</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:52%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.9416980688098253</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.022527480446423</td> <td class=”number”>16</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:76%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>3.696062696768522</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.7763601305692824</td> <td class=”number”>82</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:32%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.893239222130259</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:23%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.004688507145139</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.078871539129641</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:62%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_WIC15”>PCT_WIC15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>52</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>2.3619</td>

</tr> <tr>

<th>Minimum</th> <td>1.1051</td>

</tr> <tr>

<th>Maximum</th> <td>3.2316</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram8211612595975356690”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives8211612595975356690,#minihistogram8211612595975356690”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives8211612595975356690”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles8211612595975356690”

aria-controls=”quantiles8211612595975356690” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram8211612595975356690” aria-controls=”histogram8211612595975356690”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common8211612595975356690” aria-controls=”common8211612595975356690”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme8211612595975356690” aria-controls=”extreme8211612595975356690”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles8211612595975356690”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>1.1051</td>

</tr> <tr>

<th>5-th percentile</th> <td>1.6657</td>

</tr> <tr>

<th>Q1</th> <td>2.0321</td>

</tr> <tr>

<th>Median</th> <td>2.3337</td>

</tr> <tr>

<th>Q3</th> <td>2.6255</td>

</tr> <tr>

<th>95-th percentile</th> <td>3.2269</td>

</tr> <tr>

<th>Maximum</th> <td>3.2316</td>

</tr> <tr>

<th>Range</th> <td>2.1265</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.59334</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.4515</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.19116</td>

</tr> <tr>

<th>Kurtosis</th> <td>-0.49011</td>

</tr> <tr>

<th>Mean</th> <td>2.3619</td>

</tr> <tr>

<th>MAD</th> <td>0.36302</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.26269</td>

</tr> <tr>

<th>Sum</th> <td>7397.5</td>

</tr> <tr>

<th>Variance</th> <td>0.20386</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram8211612595975356690”>

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ZGRnasmWLDh8%2BrOrqaq1bt07BwcEaOHCg6U0FAAB%2BKsBzozcc3SRFRUXasWOH8vPzdeXKFY0aNUrp6enq1KnTNa/rzJkzGjRokL744gtJ0t/%2B9jctWbJExcXF6tChg2bPnq2HH37Yu/x//ud/Kjs7W%2BfOnVNiYqIWLVqkrl273vA2cYnQfoKCHIqODqM3TWzoax9ZXQJsIn/m/VaX4Dc4njXM6bTmN/htG7C%2B5/F4tHPnTi1atEiVlZVKTk7Ws88%2Bqx49elhd2jXjm95%2BOCBZg4CF7xGwzOF41jCrApbtLhF%2B78qVK9q5c6cmTZqkuXPnqn379nr%2B%2BefVvXt3TZgwQdu3b7e6RAAAgAbZ7rcIi4uLtWnTJm3ZskVVVVUaMmSI/vjHP6p3797eZfr27atFixZpxIgRFlYKAADQMNsFrOHDh6tTp06aPHmyRo0apbZt29ZbJiUlRefPn7egOgAAgKuzXcDKyclRv379rrrckSNHmqAaAACAa2e7e7C6deumKVOm6P333/eO/eEPf9CkSZPqPQAUAADAjmwXsJYuXar/%2B7//U%2BfOnb1jAwcOVF1dnZYtW2ZhZQAAAI1ju0uEBw4c0Pbt2xUdHe0di4uL08qVK/XP//zPFlYGAADQOLY7g3Xx4kWFhITUG3c4HKqurragIgAAgGtju4DVt29fLVu2TBcuXPCOffPNN1q8eLHPoxoAAADsynaXCOfPn68nn3xSP//5zxUeHq66ujpVVVXpjjvu0Ntvv211eQAAAFdlu4B1xx136N1339UHH3ygr776Sg6HQ506ddKAAQMUGBhodXkAAABXZbuAJUnBwcEaPHiw1WUAAABcF9sFrNOnT2vVqlU6fvy4Ll68WG/%2BL3/5iwVVAQAANJ7tAtb8%2BfNVWlqqAQMGqE2bNlaXAwAAcM1sF7AKCwv1l7/8RTExMVaXAgAAcF1s95iGdu3aceYKAAA0a7YLWJMnT1ZWVpY8Ho/VpQAAAFwX210i/OCDD/Tpp59q8%2BbNuv322%2BVw%2BGbAP/3pTxZVBgAA0Di2C1jh4eF64IEHrC4DAADgutkuYC1dutTqEgAAAG6I7e7BkqQvv/xSa9as0fPPP%2B8d%2B%2ByzzyysCAAAoPFsF7AKCgo0cuRI7d69Wzt27JD03cNHx40bx0NGAQBAs2C7S4SrV6/Wc889p/Hjx6tHjx6Svvv7hMuWLdPatWs1aNAgiysE7Gnoax9ZXQIA4P9juzNY//M//6OMjAxJUkBAgHf8kUceUXFxsVVlAQAANJrtAlZERESDf4OwtLRUwcHBFlQEAABwbWwXsHr16qVXXnlFlZWV3rGTJ09q7ty5%2BvnPf25hZQAAAI1ju3uwnn/%2BeY0fP179%2B/dXbW2tevXqperqanXp0kXLli2zujwAAICrsl3AuvXWW7Vjxw7t379fJ0%2BeVGhoqDp16qT777/f554sAAAAu7JdwJKkVq1aafDgwVaXAQAAcF1sF7BSU1N/8kwVz8ICAAB2Z7uANWzYMJ%2BAVVtbq5MnT8rlcmn8%2BPEWVgYAANA4tgtYc%2BbMaXD8vffe01//%2BtcmrgYAAODa2e4xDT9m8ODBevfdd60uAwAA4KqaTcA6evSoPB6P1WUAAABcle0uEY4dO7beWHV1tYqLi/Xwww9bUBEAAMC1sV3AiouLq/dbhCEhIUpPT9fjjz9uUVUAAACNZ7uAxdPaAQAwY%2BhrH1ldQqPlz7zf6hKMsl3A2rJlS6OXHTVq1E2sBAAA4PrYLmAtWLBAdXV19W5oDwgI8BkLCAj4yYD1%2Beefa%2BnSpSosLFRISIj69eunBQsWyOl0qqCgQKtWrdKXX36p2267TZMnT9bIkSO9r83JydE777yjsrIydevWTQsWLFBCQoL5jQUAAH7JdgHrzTff1IYNGzRlyhR169ZNHo9HX3zxhd544w39%2Bte/Vv/%2B/a%2B6jsuXL%2BvJJ5/Ur371K73xxhuqrKzUs88%2Bq0WLFunFF1/U008/rQULFmjEiBH65JNPNHXqVHXq1EmJiYnas2eP1qxZozfffFPdunVTTk6OpkyZot27d6tNmzZN8BUAgJajOV3CkvzvMhZuHts9pmHZsmVasmSJevfurfDwcEVERKhPnz7693//dy1fvlzBwcHejx9TXV2tzMxMTZ48WcHBwYqJidFDDz2k48ePa/v27YqLi1N6erpCQkKUnJys1NRU5eXlSZJyc3OVlpampKQkhYaGauLEiZKkvXv3Nsn2AwCA5s92Z7BOnTqlqKioeuORkZEqKSlp1DqioqJ8fuPwyy%2B/1J///GcNHTpURUVFio%2BP91k%2BPj5e%2Bfn5kqSioiINGzbMO%2BdwONS9e3e5XC4NHz68Ue9fWlqqsrIynzGn06nQ0IhGvR5NJzDQ4fMZAH5KUJB9jxXN/Xhm56/t9bBdwOrQoYOWLVumZ599VtHR0ZKkiooK/cd//IfuvPPOa1pXSUmJhgwZopqaGo0ePVrPPPOMJk2apPbt2/ss17ZtW5WXl0uS3G53vYAXFRXlnW%2BM3NxcZWVl%2BYxNnz5dM2bMuKb60XQiI1tbXQKAZiA6OszqEq6quR7PmsPX9lrYLmDNnz9fs2fPVm5ursLCwuRwOFRZWanQ0FCtXbv2mtbVoUMHuVwu/f3vf9e//du/6be//W2jXnejT4wfM2aMUlNTfcacTqcqKqpVW1t3Q%2BuGWYGBDkVGtqY3ABqlvLzK6hJ%2BVHM/nt2sr61Vwc12AWvAgAHat2%2Bf9u/fr7Nnz8rj8ah9%2B/b6xS9%2BoYiIa7/EFhAQoLi4OGVmZmrs2LFKSUmR2%2B32Waa8vFwxMTGSpOjo6HrzbrdbXbp0afR7xsbGKjY2tt54eXmVamqa3zd9S1BbW0dvAFxVczhONNfjWXOs%2BafY8oJn69atNWjQIA0aNEhPPPGEhg0bdk3hqqCgQEOGDFFd3f/fLIfju03t0aOHCgsLfZYvLCxUUlKSJCkhIUFFRUXeudraWh09etQ7DwAAcDW2C1gXL17U3Llz1bNnTw0dOlTSd/dgTZw4URUVFY1aR0JCgiorK7VixQpVV1fr/PnzWrNmjfr06aOMjAyVlJQoLy9Ply5d0v79%2B7V//36NHj1akpSRkaEtW7bo8OHDqq6u1rp16xQcHKyBAwferE0GAAB%2BxnYBa8WKFTp27JhWrlzpPeskfXcmaeXKlY1aR0REhDZs2KDCwkLdd999Gj58uCIiIvS73/1O7dq10/r167Vx40b17t1br7zyilasWKG7775bkvTAAw9o1qxZmjlzpvr166eDBw8qOztboaGhN2V7AQCA/wnw3Ogd3YYNGDBAGzduVFxcnJKSknTkyBFJ0tmzZzVq1CgdOnTI4gqvH/dg2U9QkEPR0WF%2B0Zvm9sBGoDmy84NGGzqeNafjws362jqd1jwiyXZnsKqqqhQXF1dvPCYmRt9%2B%2B23TFwQAAHCNbBew7rzzTv31r3%2BV5Pu4hF27dumf/umfrCoLAACg0Wz3mIZf/vKXmjFjhh577DHV1dXprbfeUmFhod577z0tWLDA6vIAAACuynYBa8yYMQoKCtLGjRsVGBio3//%2B9%2BrUqZNWrlypRx55xOryAAAArsp2Aev8%2BfN67LHH9Nhjj1ldClq45nRzKADAXmx3D9agQYNu%2BE/VAAAAWMl2Aat///7Kz8%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%2BW3AWvKlCmKjIzUnj17tHnzZh0/flzLly/XxYsXNXnyZN1333368MMPtXr1aq1fv167d%2B%2BW9F34mjt3rubMmaNDhw5pwoQJmj59us6ePWvxFgEAgObCLwNWRUWFEhISNHv2bIWFhenWW2/Vo48%2Bqo8//lj79u3TlStXNHXqVLVp00b33HOPHn/8ceXm5kqS8vLylJKSopSUFIWEhGjkyJHq2rWrtm3bZvFWAQCA5iLI6gJuhsjISC1dutRn7Ouvv1ZsbKyKiorUrVs3BQYGeufi4%2BOVl5cnSSoqKlJKSorPa%2BPj4%2BVyuRr9/qWlpSorK/MZczqdCg2NuNZNwU0WGOjw%2BQwAsEZQkH8dh/0yYP2Qy%2BXSxo0btW7dOuXn5ysyMtJnvm3btnK73aqrq5Pb7VZUVJTPfFRUlE6cONHo98vNzVVWVpbP2PTp0zVjxozr3wjcVJGRra0uAQBatOjoMKtLMMrvA9Ynn3yiqVOnavbs2UpOTlZ%2Bfn6DywUEBHj/7fF4bug9x4wZo9TUVJ8xp9Opiopq1dbW3dC6YVZgoEORka3pDQBYrLy86qas16rg5tcBa8%2BePXruuef0wgsvaNSoUZKkmJgYnTp1ymc5t9uttm3byuFwKDo6Wm63u958TExMo983NjZWsbGx9cbLy6tUU8MPcTuqra2jNwBgIX87BvvXBc9/8Omnn2ru3Ll6/fXXveFKkhISEvTFF1%2BopqbGO%2BZyuZSUlOSdLyws9FnXP84DAABcjV8GrJqaGi1cuFBz5szRgAEDfOZSUlIUHh6udevWqbq6WkeOHNGmTZuUkZEhSRo9erQOHjyoffv26dKlS9q0aZNOnTqlkSNHWrEpAACgGQrw3OgNRzb08ccf61e/%2BpWCg4Prze3atUtVVVV68cUXVVhYqFtuuUWTJk3SL3/5S%2B8yu3fv1qpVq1RSUqLOnTtrwYIF6tu37w3XxSVC%2BwkKcig6OqzB3gx97SOLqgKAlid/5v03Zb1OpzW/we%2BXAcuuCFj2Q8ACAHvwt4Dll5cIAQAArETAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADD/DZgffjhh0pOTlZmZma9uZ07d2rEiBHq2bOn0tLSdODAAe9cXV2dVq9erUGDBqlv37566qmndPr06aYsHQAANHN%2BGbDeeOMNLVmyRB07dqw3d%2BzYMc2dO1dz5szRoUOHNGHCBE2fPl1nz56VJL3zzjvavn27srOztXfvXsXFxWnatGnyeDxNvRkAAKCZ8suAFRISok2bNjUYsPLy8pSSkqKUlBSFhIRo5MiR6tq1q7Zt2yZJys3N1YQJE3TXXXcpPDxcmZmZKi4u1pEjR5p6MwAAQDMVZHUBN8O4ceN%2BdK6oqEgpKSk%2BY/Hx8XK5XLp48aJOnDih%2BPh471x4eLg6duwol8ule%2B%2B9t1HvX1paqrKyMp8xp9Op0NCIa9gKNIXAQIfPZwCANYKC/Os47JcB66e43W5FRUX5jEVFRenEiRO6cOGCPB5Pg/Pl5eWNfo/c3FxlZWX5jE2fPl0zZsy4/sJxU0VGtra6BABo0aKjw6wuwagWF7AkXfV%2Bqhu932rMmDFKTU31GXM6naqoqFZtbd0NrRtmBQY6FBnZmt4AgMXKy6tuynqtCm4tLmBFR0fL7Xb7jLndbsXExKht27ZyOBwNzrdr167R7xEbG6vY2Nh64%2BXlVaqp4Ye4HdXW1tEbALCQvx2D/euCZyMkJCSosLDQZ8zlcikpKUkhISHq0qWLioqKvHMVFRX66quv1KNHj6YuFQAANFMtLmCNHj1aBw8e1L59%2B3T9dkWIAAAKjUlEQVTp0iVt2rRJp06d0siRIyVJGRkZysnJUXFxsSorK7Vy5Up1795diYmJFlcOAACaC7%2B8RPh9GKqpqZEkvf/%2B%2B5K%2BO1PVtWtXrVy5UkuXLlVJSYk6d%2B6s9evXy%2Bl0SpLGjh2rsrIy/eY3v1FVVZX69%2B9f74Z1AACAnxLg4QmaTYZ7sOwnKMih6OiwBnsz9LWPLKoKAFqe/Jn335T1Op3WPCKpxV0iBAAAuNkIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwLMjqAnBjhr72kdUlNFr%2BzPutLgEAgCZBwGpASUmJFi9erCNHjqhNmzYaNmyYZs%2BeLYeDE343ojmFQQAAbgQBqwEzZszQPffco/fff1/nzp3T5MmTdcstt%2BiJJ56wujQAANAMcErmB1wulz7//HPNmTNHERERiouL04QJE5Sbm2t1aQAAoJngDNYPFBUVqUOHDoqKivKO3XPPPTp58qQqKysVHh5%2B1XWUlpaqrKzMZ8zpdCo0NMJ4vQAA%2BIOgIP8650PA%2BgG3263IyEifse/DVnl5eaMCVm5urrKysnzG%2Bvbtq9/97neKjY01V6ykj19%2BxOj6WprS0lLl5uZqzJgxxnuD60df7Ive2Be9sRf/iouGeDyeG3r9mDFjtHnzZu/HihUr9Le//a3eWS1Yr6ysTFlZWfTGZuiLfdEb%2B6I39sIZrB%2BIiYmR2%2B32GXO73QoICFBMTEyj1hEbG8v/PQAA0IJxBusHEhIS9PXXX%2Bv8%2BfPeMZfLpc6dOyssLMzCygAAQHNBwPqB%2BPh4JSYmatWqVaqsrFRxcbHeeustZWRkWF0aAABoJgIXLVq0yOoi7OYXv/iFduzYoZdeeknvvvuu0tPT9dRTTykgIOC61xkWFqZ%2B/fpxFsyG6I090Rf7ojf2RW/sI8Bzo3d0AwAAwAeXCAEAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2AZ9OGHHyo5OVmZmZk/uVxdXZ1Wr16tQYMGqW/fvnrqqad0%2BvTpJqqy5WlsX%2BbNm%2Bf9W5Tff/Tp06eJqmyZSkpKNG3aNPXv31/JycmaN2%2BeKioqGlx2586dGjFihHr27Km0tDQdOHCgiattORrbl82bN%2Bvuu%2B/22WcSExP13//93xZU3TJ8/vnnGj9%2BvHr37q3k5GTNnDlTZWVlDS6bk5OjIUOGqFevXsrIyFBhYWETV9uyEbAMeeONN7RkyRJ17Njxqsu%2B88472r59u7Kzs7V3717FxcVp2rRp4q8WmXctfZGkqVOnyuVyeT8%2B/vjjm1xhyzZlyhRFRkZqz5492rx5s44fP67ly5fXW%2B7YsWOaO3eu5syZo0OHDmnChAmaPn26zp49a0HV/q%2BxfZGkvn37%2BuwzLpdLPXr0aOKKW4bLly/rySefVL9%2B/VRQUKAdO3bo3LlzauhPCu/Zs0dr1qzRq6%2B%2BqoMHD%2BrBBx/UlClT9O233zZ94S0UAcuQkJAQbdq0qVE/yHNzczVhwgTdddddCg8PV2ZmpoqLi3XkyJEmqLRluZa%2BoGlVVFQoISFBs2fPVlhYmG699VY9%2BuijDYbavLw8paSkKCUlRSEhIRo5cqS6du2qbdu2WVC5f7uWvqBpVVdXKzMzU5MnT1ZwcLBiYmL00EMP6fjx4/WWzc3NVVpampKSkhQaGqqJEydKkvbu3dvUZbdYBCxDxo0bp4iIiKsud/HiRZ04cULx8fHesfDwcHXs2FEul%2BtmltgiNbYv3zt06JBGjRqlnj17Kj09nVPqN1FkZKSWLl2qW265xTv29ddfKzY2tt6yRUVFPvuMJMXHx7PP3ATX0pfv55544gn17dtXgwYN0tatW5uq1BYnKipKjz/%2BuIKCgiRJX375pf785z9r6NCh9Zb94T7jcDjUvXt39pkmRMBqYhcuXJDH41FUVJTPeFRUlMrLyy2qCpJ0xx13qGPHjlq/fr0%2B/PBD9enTR08%2B%2BSR9aSIul0sbN27U1KlT68253W72GYv8VF9iYmIUFxen5557Th999JFmzZql%2BfPnq6CgwIJKW46SkhIlJCRo2LBhSkxM1DPPPFNvGfYZ6xGwLML9VvYzbdo0vfLKK2rfvr3Cw8P13HPPKTg4WO%2B//77Vpfm9Tz75RE899ZRmz56t5OTkBpdhn2l6V%2BvLwIED9eabbyo%2BPl7BwcEaPny4HnroIW3evNmCaluODh06yOVyadeuXTp16pR%2B%2B9vfNrgc%2B4y1CFhNrG3btnI4HHK73T7jbrdb7dq1s6gqNCQwMFC33XabSktLrS7Fr%2B3Zs0f/%2Bq//qvnz52vcuHENLhMdHd3gPhMTE9MUJbZIjelLQzp06MA%2B0wQCAgIUFxenzMxM7dixQ%2BfPn/eZZ5%2BxHgGriYWEhKhLly4qKiryjlVUVOirr77iN28s5PF4tHTpUn3%2B%2BefescuXL%2Burr77SHXfcYWFl/u3TTz/V3Llz9frrr2vUqFE/ulxCQkK9%2B%2BFcLpeSkpJudoktUmP78l//9V/auXOnz1hxcTH7zE1SUFCgIUOGqK6uzjvmcHz3Y7xVq1Y%2ByyYkJPj8nKmtrdXRo0fZZ5oQAasJfPPNN3rkkUe8z7rKyMhQTk6OiouLVVlZqZUrV6p79%2B5KTEy0uNKW5R/7EhAQoDNnzmjx4sX65ptvVFVVpZUrV6pVq1YaPHiw1aX6pZqaGi1cuFBz5szRgAED6s2PHz/e%2B8N79OjROnjwoPbt26dLly5p06ZNOnXqlEaOHNnUZfu9a%2BnL5cuX9dJLL8nlcunKlSvasWOHPvjgA40dO7apy24REhISVFlZqRUrVqi6ulrnz5/XmjVr1KdPH0VEROiRRx7x/rZnRkaGtmzZosOHD6u6ulrr1q1TcHCwBg4caO1GtCBBVhfgL74PRzU1NZLkvW/n%2BwPPyZMndfnyZUnS2LFjVVZWpt/85jeqqqpS//79lZWVZU3hfu5a%2BvLyyy9r%2BfLlSktLU2VlpXr06KE//vGPatOmjTXF%2B7nDhw%2BruLhYS5Ys0ZIlS3zmdu3apdOnT%2BvChQuSpK5du2rlypVaunSpSkpK1LlzZ61fv15Op9OK0v3atfRl3Lhxqqqq0rPPPquysjLdfvvtWrt2rRISEqwo3e9FRERow4YNWrJkie677z61adNG9913n15%2B%2BWVJ0smTJ73PuXrggQc0a9YszZw5U%2BfOnVNiYqKys7MVGhpq5Sa0KAEe7oIDAAAwikuEAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGDY/wNDIfX/DklgUgAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common8211612595975356690”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>3.226930314170308</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.58739636862375</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.665654101106848</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.6254576560216147</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.2154437862527763</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.1585622437656293</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.9253033667586965</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.477991202056956</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0321159550932983</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.274378024389501</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (41)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme8211612595975356690”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.1050963168716856</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.4284891444618404</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.6384970526461495</td> <td class=”number”>16</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.6401735834499231</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:40%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.665654101106848</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>2.8278648239005792</td> <td class=”number”>75</td> <td class=”number”>2.3%</td> <td>

<div class=”bar” style=”width:30%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.8862800044383787</td> <td class=”number”>77</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:31%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.9892784893927247</td> <td class=”number”>82</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:32%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.226930314170308</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.2316033504102637</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:23%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PC_DIRSALES07”>PC_DIRSALES07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2791</td>

</tr> <tr>

<th>Unique (%)</th> <td>86.9%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>11.2%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>359</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>7.8463</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>252.75</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>1.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram731340218042808453”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives731340218042808453,#minihistogram731340218042808453”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives731340218042808453”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles731340218042808453”

aria-controls=”quantiles731340218042808453” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram731340218042808453” aria-controls=”histogram731340218042808453”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common731340218042808453” aria-controls=”common731340218042808453”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme731340218042808453” aria-controls=”extreme731340218042808453”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles731340218042808453”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.23447</td>

</tr> <tr>

<th>Q1</th> <td>1.8014</td>

</tr> <tr>

<th>Median</th> <td>4.26</td>

</tr> <tr>

<th>Q3</th> <td>9.4169</td>

</tr> <tr>

<th>95-th percentile</th> <td>24.294</td>

</tr> <tr>

<th>Maximum</th> <td>252.75</td>

</tr> <tr>

<th>Range</th> <td>252.75</td>

</tr> <tr>

<th>Interquartile range</th> <td>7.6154</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>12.826</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.6346</td>

</tr> <tr>

<th>Kurtosis</th> <td>114.31</td>

</tr> <tr>

<th>Mean</th> <td>7.8463</td>

</tr> <tr>

<th>MAD</th> <td>6.7328</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>8.0222</td>

</tr> <tr>

<th>Sum</th> <td>22370</td>

</tr> <tr>

<th>Variance</th> <td>164.5</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram731340218042808453”>

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RgwQAUFBVq3bl2gSgMAADgrNqsP6HQ6VVFRoddff10NDQ269tpr9dvf/laXX365d5uhQ4fq17/%2BtcaOHWt1eQAAAGfN8oA1ZswY9e7dWzNmzNBNN92khISENtvk5OTo6NGjVpcGAABghOUBa%2BXKlRo2bNgZt9u9e7cF1QAAAJhn%2BT1YaWlpuuuuu7Rlyxbv2H/%2B53/qjjvukNvttrocAAAA4ywPWIsXL9axY8fUt29f79iIESPU2tqqJUuWWF0OAACAcZZfInz33Xe1bt06JSYmesdSU1NVUlKiG264wepyAAAAjLP8DFZjY6OioqLaFhIeruPHj1tdDgAAgHGWB6yhQ4dqyZIl%2Bvrrr71jf/3rX/XII4/4PKoBAAAgWFl%2BiXDevHn6l3/5F/385z9XTEyMWltb1dDQoJ49e%2Bp3v/ud1eUAAAAYZ3nA6tmzp95880298847OnjwoMLDw9W7d28NHz5cERERVpcDAABgnOUBS5IiIyN1zTXXBOLQAAAAfmd5wDp06JBKS0v1%2Beefq7Gxsc3822%2B/bXVJAAAARgXkHqyamhoNHz5cnTt3tvrwAAAAfmd5wKqsrNTbb7%2BtpKQkqw8NAABgCcsf09ClSxfOXAEAgJBmecCaMWOGysrK5PF4znpfO3fuVHZ2toqKinzG16xZo0suuUSZmZk%2BX5988okkqbW1VUuXLlVubq6GDh2q6dOn69ChQ97Xu91uzZ49W9nZ2Ro%2BfLjmz5/f7v1iAAAA7bH8EuE777yjjz76SGvWrFGPHj0UHu6b8V599dUO7ee5555TRUWFevXq1e780KFDT/lcrZdfflnr1q3Tc889p27dumnp0qUqLCzUG2%2B8obCwMC1YsEDffvut1q9fr6amJt13330qKSnRww8//MMWCwAAzkuWB6yYmBj94he/OOv9REVFqaKiQo8//rhOnDjxg15rt9tVUFCgPn36SJKKioqUlZWl3bt3q0ePHtqyZYv%2B8Ic/eO8Tu/vuu3Xfffdp7ty56tSp01nXDgAAQpvlAWvx4sVG9jNt2rTTzh8%2BfFi33XabKisrFRcXp1mzZunGG29UY2Oj9u3bp/T0dO%2B2MTEx6tWrlxwOh44dO6aIiAilpaV55wcOHKhvvvlGX3zxhc84AABAewLyoNEvvvhCb775pv73f//XG7j%2B%2B7//W0OGDDGy/6SkJKWmpur%2B%2B%2B9X37599cc//lEPPvigkpOT9dOf/lQej0fx8fE%2Br4mPj5fL5VJCQoJiYmIUFhbmMydJLperQ8evqalRbW2tz5jN1lnJyclnuTJfERGW30J3Vmy24Kj3ZF%2BDrb/nOvrqP/TWP%2Birf5wvfbU8YO3atUt33HGHevfurQMHDmjx4sU6dOiQpk2bpqeeekq5ublnfYwRI0ZoxIgR3v8eM2aM/vjHP2rNmjUqLi6WpNPeZH%2B2N%2BDb7XaVlZX5jBUWFmrWrFlntd9gl5gYHegSfpC4uAsDXUJIoq/%2BQ2/9g776R6j31fKAtXTpUj3wwAO69dZbNWjQIEnf/X3CJUuWaNmyZUYCVntSUlJUWVmphIQEhYeHy%2B12%2B8y73W516dJFSUlJqq%2BvV0tLi/dvI57ctkuXLh06Vl5enkaOHOkzZrN1lsvVYGAlfxds6d/0%2Bv0lIiJccXEXqq7uuFpaWgNdTsigr/5Db/2DvvqH1X0N1Id7ywPW//zP/2jVqlWS5HMZ7rrrrtO8efOMHOOVV15RfHy8rr/%2Beu%2BY0%2BlUz549FRUVpX79%2BqmqqkrDhg2TJNXV1engwYMaNGiQUlJS5PF49Omnn2rgwIGSJIfDobi4OPXu3btDx09OTm5zObC29piam8/vb9BgW39LS2vQ1RwM6Kv/0Fv/oK/%2BEep9tfwUSGxsbLvPlKqpqVFkZKSRY3z77bd67LHH5HA41NTUpPXr1%2Budd97RlClTJEn5%2BflauXKlnE6n6uvrVVJSogEDBigzM1NJSUm69tpr9dRTT%2Bno0aM6cuSIli1bpokTJ8pmC8gtawAAIMhYnhguu%2Bwy/eY3v/F5ptT%2B/fu1cOFC/fznP%2B/wfjIzMyVJzc3NkqQtW7ZI%2Bu5s07Rp09TQ0KD77rtPtbW16tGjh5YtW6aMjAxJ0pQpU1RbW6upU6eqoaFBWVlZPvdMPfroo1q4cKFyc3PVqVMn3XDDDW0eZgoAAHAqYR4Tj1T/AY4cOaJbb71VX375pVpaWtS5c2cdP35c/fr107PPPquf/OQnVpZjmdraY8b3abOFa1TJTuP79ZeNs68MdAkdYrOFKzExWi5XQ0ifvrYaffUfeusf9NU/rO5r166xfj9Geyw/g9W9e3etX79eO3bs0P79%2B3XBBReod%2B/euvLKK33uyQIAAAhWAbmpqFOnTrrmmmsCcWgAAAC/szxgjRw58rRnqt5%2B%2B20LqwEAADDP8oB1/fXX%2BwSslpYW7d%2B/Xw6HQ7feeqvV5QAAABhnecA6%2BST173vrrbf05z//2eJqAAAAzDtnHgV%2BzTXX6M033wx0GQAAAGftnAlYe/bsOeu/AQgAAHAusPwS4cmnqf%2Bj48ePy%2Bl0avTo0VaXAwAAYJzlASs1NbXNbxFGRUVp4sSJmjRpktXlAAAAGGd5wFqyZInVhwQAALCU5QHr9ddf7/C2N910kx8rAQAA8A/LA9b8%2BfPV2tra5ob2sLAwn7GwsDACFgAACEqWB6znn39eL774ou666y6lpaXJ4/Hos88%2B03PPPadbbrlFWVlZVpcEAABgVEDuwSovL1e3bt28Y1dccYV69uyp6dOna/369VaXBAAAYJTlz8E6cOCA4uPj24zHxcWpurra6nIAAACMszxgpaSkaMmSJXK5XN6xuro6lZaW6uKLL7a6HAAAAOMsv0Q4b948zZkzR3a7XdHR0QoPD1d9fb0uuOACLVu2zOpyAAAAjLM8YA0fPlzbt2/Xjh07dOTIEXk8HnXr1k1XXXWVYmNjrS4HAADAOMsDliRdeOGFys3N1ZEjR9SzZ89AlAAAAOA3lt%2BD1djYqLlz52rIkCH65S9/Kem7e7Buv/121dXVWV0OAACAcZYHrCeffFJ79%2B5VSUmJwsP/fviWlhaVlJRYXQ4AAIBxlgest956S//%2B7/%2Bu6667zvtHn%2BPi4rR48WJt3rzZ6nIAAACMszxgNTQ0KDU1tc14UlKSvvnmG6vLAQAAMM7ygHXxxRfrz3/%2BsyT5/O3BTZs26Sc/%2BYnV5QAAABhn%2BW8R/upXv9K9996rm2%2B%2BWa2trXrppZdUWVmpt956S/Pnz7e6HAAAAOMsD1h5eXmy2WxatWqVIiIi9Oyzz6p3794qKSnRddddZ3U5AAAAxlkesI4ePaqbb75ZN998s9WHBgAAsITl92Dl5ub63HsFAAAQaiwPWFlZWdq4caPVhwUAALCM5ZcI/%2Bmf/kmPP/64ysvLdfHFF6tTp04%2B86WlpVaXBAAAYJTlAWvfvn366U9/KklyuVxWHx4AAMDvLAtYRUVFWrp0qX73u995x5YtW6bCwkKrSgAAALCEZfdgbd26tc1YeXm5VYcHAACwjGUBq73fHOS3CQEAQCiyLGCd/MPOZxoDAAAIdpY/pgEAACDUEbAAAAAMs%2By3CJuamjRnzpwzjvEcLAAAEOwsC1iXX365ampqzjgGAAAQ7CwLWP/4/CsAAIBQxj1YAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYFhQB6ydO3cqOztbRUVFbeY2bNigsWPHasiQIZowYYLeffdd71xra6uWLl2q3NxcDR06VNOnT9ehQ4e88263W7Nnz1Z2draGDx%2Bu%2BfPnq7Gx0ZI1AQCA4Be0Aeu5557TokWL1KtXrzZze/fu1dy5c1VcXKz3339fBQUFuueee3TkyBFJ0ssvv6x169apvLxc27ZtU2pqqgoLC71/fHrBggU6fvy41q9fr9dee01Op1MlJSWWrg8AAASvoA1YUVFRqqioaDdgrV69Wjk5OcrJyVFUVJTGjRun/v37a%2B3atZIku92ugoIC9enTRzExMSoqKpLT6dTu3bv11VdfacuWLSoqKlJSUpK6deumu%2B%2B%2BW6%2B99pqampqsXiYAAAhCQRuwpk2bptjY2HbnqqqqlJ6e7jOWnp4uh8OhxsZG7du3z2c%2BJiZGvXr1ksPh0N69exUREaG0tDTv/MCBA/XNN9/oiy%2B%2B8M9iAABASLHsSe5Wcrvdio%2BP9xmLj4/Xvn379PXXX8vj8bQ773K5lJCQoJiYGIWFhfnMSZLL5erQ8WtqalRbW%2BszZrN1VnJy8o9ZzilFRARXPrbZgqPek30Ntv6e6%2Bir/9Bb/6Cv/nG%2B9DUkA5Yk7/1UP2b%2BTK89E7vdrrKyMp%2BxwsJCzZo166z2G%2BwSE6MDXcIPEhd3YaBLCEn01X/orX/QV/8I9b6GZMBKTEyU2%2B32GXO73UpKSlJCQoLCw8Pbne/SpYuSkpJUX1%2BvlpYWRUREeOckqUuXLh06fl5enkaOHOkzZrN1lsvV8GOX1K5gS/%2Bm1%2B8vERHhiou7UHV1x9XS0hrockIGffUfeusf9NU/rO5roD7ch2TAysjIUGVlpc%2BYw%2BHQmDFjFBUVpX79%2BqmqqkrDhg2TJNXV1engwYMaNGiQUlJS5PF49Omnn2rgwIHe18bFxal3794dOn5ycnKby4G1tcfU3Hx%2Bf4MG2/pbWlqDruZgQF/9h976B331j1Dva3CdAumgyZMn67333tP27dt14sQJVVRU6MCBAxo3bpwkKT8/XytXrpTT6VR9fb1KSko0YMAAZWZmKikpSddee62eeuopHT16VEeOHNGyZcs0ceJE2WwhmUcBAIBhQZsYMjMzJUnNzc2SpC1btkj67mxT//79VVJSosWLF6u6ulp9%2B/bVihUr1LVrV0nSlClTVFtbq6lTp6qhoUFZWVk%2B90w9%2BuijWrhwoXJzc9WpUyfdcMMN7T7MFAAAoD1hnrO9oxsdUlt7zPg%2BbbZwjSrZaXy//rJx9pWBLqFDbLZwJSZGy%2BVqCOnT11ajr/5Db/2DvvqH1X3t2rX9Rzr5W0heIgQAAAgkAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwL2YCVlpamjIwMZWZmer8ee%2BwxSdKuXbs0ceJEXXbZZRozZozWrl3r89qVK1fq2muv1WWXXab8/HxVVlYGYgkAACBI2QJdgD9t2rRJPXr08BmrqanR3Xffrfnz52vs2LH68MMPNXPmTPXu3VuZmZnaunWrnnnmGT3//PNKS0vTypUrddddd2nz5s3q3LlzgFYCAACCSciewTqVdevWKTU1VRMnTlRUVJSys7M1cuRIrV69WpJkt9s1YcIEDR48WBdccIFuv/12SdK2bdsCWTYAAAgiIR2wSktLNWLECF1xxRVasGCBGhoaVFVVpfT0dJ/t0tPTvZcBvz8fHh6uAQMGyOFwWFo7AAAIXiF7ifDSSy9Vdna2nnjiCR06dEizZ8/WI488IrfbrW7duvlsm5CQIJfLJUlyu92Kj4/3mY%2BPj/fOd0RNTY1qa2t9xmy2zkpOTv6Rq2lfRERw5WObLTjqPdnXYOvvuY6%2B%2Bg%2B99Q/66h/nS19DNmDZ7Xbvv/v06aPi4mLNnDlTl19%2B%2BRlf6/F4zvrYZWVlPmOFhYWaNWvWWe032CUmRge6hB8kLu7CQJcQkuir/9Bb/6Cv/hHqfQ3ZgPV9PXr0UEtLi8LDw%2BV2u33mXC6XkpKSJEmJiYlt5t1ut/r169fhY%2BXl5WnkyJE%2BYzZbZ7lcDT%2By%2BvYFW/o3vX5/iYgIV1zchaqrO66WltZAlxMy6Kv/0Fv/oK/%2BYXVfA/XhPiQD1p49e7R27Vo99NBD3jGn06nIyEjl5OToD3/4g8/2lZWVGjx4sCQpIyNDVVVVGj9%2BvCSppaVFe/bs0cSJEzt8/OTk5DaXA2trj6m5%2Bfz%2BBg229be0tAZdzcGAvvoPvfUP%2Buofod7X4DoF0kFdunSR3W5XeXm5vv32W%2B3fv19PP/208vLydOONN6qn6%2BgxAAAMXUlEQVS6ulqrV6/WiRMntGPHDu3YsUOTJ0%2BWJOXn5%2Bv111/Xxx9/rOPHj2v58uWKjIzUiBEjArsoAAAQNELyDFa3bt1UXl6u0tJSb0AaP368ioqKFBUVpRUrVmjRokV65JFHlJKSoieffFKXXHKJJOkXv/iF7r//fs2ePVt/%2B9vflJmZqfLycl1wwQUBXhUAAAgWYZ6zvaMbHVJbe8z4Pm22cI0q2Wl8v/6ycfaVgS6hQ2y2cCUmRsvlagjp09dWo6/%2BQ2/9g776h9V97do11u/HaE9IXiIEAAAIJAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADDMFugCcP745VN/CnQJP8gfi68KdAkAgCDFGSwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAGrHdXV1brzzjuVlZWlq6%2B%2BWk8%2B%2BaRaW1sDXRYAAAgSPAerHffee68GDhyoLVu26G9/%2B5tmzJihiy66SLfddlugS4OFRpXsDHQJP8jG2VcGugQAwP/jDNb3OBwOffrppyouLlZsbKxSU1NVUFAgu90e6NIAAECQ4AzW91RVVSklJUXx8fHesYEDB2r//v2qr69XTEzMGfdRU1Oj2tpanzGbrbOSk5ON1hoRQT7G3wXTk/J5Sr5ZJ38W8DPBLPrqH%2BdLXwlY3%2BN2uxUXF%2BczdjJsuVyuDgUsu92usrIyn7F77rlH9957r7lC9V2Qu7X758rLyzMe3s5nNTU1stvt9NWwf%2BxrYmJ0oMsJKTU1Nfrtb5/nPWsYffWP86WvoR0ffySPx3NWr8/Ly9OaNWt8vvLy8gxV93e1tbUqKytrc7YMZ4e%2B%2Bgd99R966x/01T/Ol75yBut7kpKS5Ha7fcbcbrfCwsKUlJTUoX0kJyeHdCoHAACnxxms78nIyNDhw4d19OhR75jD4VDfvn0VHc1lDQAAcGYErO9JT09XZmamSktLVV9fL6fTqZdeekn5%2BfmBLg0AAASJiF//%2Bte/DnQR55qrrrpK69ev12OPPaY333xTEydO1PTp0xUWFhbo0tqIjo7WsGHDOLtmGH31D/rqP/TWP%2Birf5wPfQ3znO0d3QAAAPDBJUIAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYQai6ulp33nmnsrKydPXVV%2BvJJ59Ua2troMsKSmlpacrIyFBmZqb367HHHpMk7dq1SxMnTtRll12mMWPGaO3atQGu9ty2c%2BdOZWdnq6ioqM3chg0bNHbsWA0ZMkQTJkzQu%2B%2B%2B651rbW3V0qVLlZubq6FDh2r69Ok6dOiQlaWf007V1zVr1uiSSy7xee9mZmbqk08%2BkURfz6S6ulqFhYXKyspSdna2HnroIdXV1UmS9u7dq1tuuUWXX365Ro8erRdffNHntad7P%2BPUvf3yyy%2BVlpbW5j37wgsveF8bUr31IOiMHz/e8/DDD3vq6uo8%2B/fv94wePdrz4osvBrqsoNS/f3/PoUOH2oz/9a9/9Vx66aWe1atXexobGz1/%2BtOfPIMGDfJ88sknAajy3FdeXu4ZPXq0Z8qUKZ7Zs2f7zO3Zs8eTkZHh2b59u6exsdHzxhtveAYPHuw5fPiwx%2BPxeFauXOm5%2BuqrPfv27fMcO3bM8%2Bijj3rGjh3raW1tDcRSzimn6%2Btrr73mueWWW075Wvp6ejfccIPnoYce8tTX13sOHz7smTBhgmfevHme48ePe6666irPM88842loaPBUVlZ6hg0b5nnrrbc8Hs%2BZ3884dW8PHTrk6d%2B//ylfF2q95QxWkHE4HPr0009VXFys2NhYpaamqqCgQHa7PdClhZR169YpNTVVEydOVFRUlLKzszVy5EitXr060KWdk6KiolRRUaFevXq1mVu9erVycnKUk5OjqKgojRs3Tv379/eeEbTb7SooKFCfPn0UExOjoqIiOZ1O7d692%2BplnHNO19czoa%2BnVldXp4yMDM2ZM0fR0dHq3r27xo8frw8%2B%2BEDbt29XU1OTZs6cqc6dO2vgwIGaNGmS92fsmd7P57vT9fZMQq23BKwgU1VVpZSUFMXHx3vHBg4cqP3796u%2Bvj6AlQWv0tJSjRgxQldccYUWLFighoYGVVVVKT093We79PR0VVZWBqjKc9u0adMUGxvb7typeulwONTY2Kh9%2B/b5zMfExKhXr15yOBx%2BrTkYnK6vknT48GHddtttGjp0qHJzc/XGG29IEn09g7i4OC1evFgXXXSRd%2Bzw4cNKTk5WVVWV0tLSFBER4Z37x%2B/9072fcfrenvTggw9q%2BPDh%2BtnPfqbS0lI1NTVJCr3eErCCjNvtVlxcnM/YybDlcrkCUVJQu/TSS5Wdna3NmzfLbrfr448/1iOPPNJunxMSEujxj%2BB2u30%2BEEjfvWddLpe%2B/vpreTyeU87j1JKSkpSamqoHHnhAf/rTn3T//fdr3rx52rVrF339gRwOh1atWqWZM2ee8nvf7XartbX1tO9ntPWPvY2MjNSQIUM0atQobdu2TeXl5Vq7dq3%2B4z/%2BQ9Lpf1YEIwJWEPJ4PIEuIWTY7XZNmjRJkZGR6tOnj4qLi7V%2B/XrvJyqYcab3LO/pH27EiBF6/vnnlZ6ersjISI0ZM0ajRo3SmjVrvNvQ1zP78MMPNX36dM2ZM0fZ2dmn3C4sLMz7b/raMd/vbXJysl599VWNGjVKnTp10qBBgzRjxoyQfc8SsIJMUlKS3G63z5jb7VZYWJiSkpICVFXo6NGjh1paWhQeHt6mzy6Xix7/CImJie2%2BZ5OSkpSQkNBur91ut7p06WJlmSEhJSVFNTU19LWDtm7dqjvvvFPz5s3TtGnTJH33M/b7Z0zcbre3p6d7P%2BPv2utte1JSUvTVV1/J4/GEXG8JWEEmIyNDhw8f1tGjR71jDodDffv2VXR0dAArCz579uzRkiVLfMacTqciIyOVk5PT5n6ryspKDR482MoSQ0JGRkabXjocDg0ePFhRUVHq16%2BfqqqqvHN1dXU6ePCgBg0aZHWpQeWVV17Rhg0bfMacTqd69uxJXzvgo48%2B0ty5c/X000/rpptu8o5nZGTos88%2BU3Nzs3fs5Pv15Pyp3s/4zql6u2vXLi1fvtxn2y%2B%2B%2BEIpKSkKCwsLud4SsIJMenq6MjMzVVpaqvr6ejmdTr300kvKz88PdGlBp0uXLrLb7SovL9e3336r/fv36%2Bmnn1ZeXp5uvPFGVVdXa/Xq1Tpx4oR27NihHTt2aPLkyYEuO%2BhMnjxZ7733nrZv364TJ06ooqJCBw4c0Lhx4yRJ%2Bfn5WrlypZxOp%2Brr61VSUqIBAwYoMzMzwJWf27799ls99thjcjgcampq0vr16/XOO%2B9oypQpkujr6TQ3N%2Bvhhx9WcXGxhg8f7jOXk5OjmJgYLV%2B%2BXMePH9fu3btVUVHh/Rl7pvfz%2Be50vY2NjdWyZcv0xhtvqKmpSQ6HQy%2B88ELI9jbME0oXPM8TR44c0YIFC/Rf//VfiomJ0ZQpU3TPPff43COAjvnLX/6i0tJSffbZZ4qMjNT48eNVVFSkqKgo/eUvf9GiRYvkdDqVkpKiOXPmaPTo0YEu%2BZx08n/aJz/122w2SfL%2B9s/mzZtVWlqq6upq9e3bV/Pnz9fQoUMlfXfPxTPPPKNXX31VDQ0NysrK0qOPPqru3bsHYCXnltP11ePxaPny5aqoqFBtba169OihBx98UFdffbUk%2Bno6H3zwgf75n/9ZkZGRbeY2bdqkhoYGLVy4UJWVlbrooot0xx136Fe/%2BpV3m9O9n893Z%2Brtnj17VFZWpgMHDig2NlZTp07VHXfcofDw7873hFJvCVgAAACGcYkQAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAz7P54SXE6eAE/dAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common731340218042808453”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>60</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.36986301369863</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.8873917228103947</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.379800853485063</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.9292730844793713</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.00744710193467</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>23.404255319148938</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.335085591050007</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.363207547169812</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>34.85265382621526</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2780)</td> <td class=”number”>2780</td> <td class=”number”>86.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>359</td> <td class=”number”>11.2%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme731340218042808453”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>60</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0065433977770769074</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.006959996616370876</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.015409745122815668</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.020357995348198064</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>110.999099909991</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>147.31876592745215</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>178.27586206896552</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>250.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>252.74725274725276</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PC_DIRSALES12”>PC_DIRSALES12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2818</td>

</tr> <tr>

<th>Unique (%)</th> <td>87.8%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>9.8%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>313</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>8.2608</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>203.91</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>2.4%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram9066453491362970376”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives9066453491362970376,#minihistogram9066453491362970376”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives9066453491362970376”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles9066453491362970376”

aria-controls=”quantiles9066453491362970376” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram9066453491362970376” aria-controls=”histogram9066453491362970376”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common9066453491362970376” aria-controls=”common9066453491362970376”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme9066453491362970376” aria-controls=”extreme9066453491362970376”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles9066453491362970376”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.22348</td>

</tr> <tr>

<th>Q1</th> <td>1.6911</td>

</tr> <tr>

<th>Median</th> <td>4.3883</td>

</tr> <tr>

<th>Q3</th> <td>9.8662</td>

</tr> <tr>

<th>95-th percentile</th> <td>27.902</td>

</tr> <tr>

<th>Maximum</th> <td>203.91</td>

</tr> <tr>

<th>Range</th> <td>203.91</td>

</tr> <tr>

<th>Interquartile range</th> <td>8.1751</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>12.897</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.5612</td>

</tr> <tr>

<th>Kurtosis</th> <td>50.556</td>

</tr> <tr>

<th>Mean</th> <td>8.2608</td>

</tr> <tr>

<th>MAD</th> <td>7.3061</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>5.4904</td>

</tr> <tr>

<th>Sum</th> <td>23932</td>

</tr> <tr>

<th>Variance</th> <td>166.32</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram9066453491362970376”>

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gULdPDgQavHAQAAMM5n52B17txZaWlp2rZtm2bMmKG33npLAwYM0Lhx4/TVV1/5aiwAAIAL5rOCVV5ervXr1%2Bv%2B%2B%2B9XWlqamjZtqscee0ydOnXS2LFjtXbtWl%2BNBgAAcEEcVt9hfn6%2BcnJy9M4776isrEz9%2B/fXX/7yF1177bWey8TFxenJJ5/UoEGDrB4PAADggllesAYOHKg2bdpo/PjxGjJkiCIiIqpdJjExUT/%2B%2BKPVowEAABhhecFavny5evTocc7L7dmzx4JpAAAAzLP8HKyOHTtqwoQJ2rJli2ftv/7rv3T//ffL5XJZPQ4AAIBxlhesuXPn6vjx42rXrp1nrVevXqqqqtK8efOsHgcAAMA4yz8i3LVrl9auXavIyEjPWuvWrZWenq7bbrvN6nEAAACMs/wI1smTJxUSElJ9kMBAnThxwupxAAAAjLO8YMXFxWnevHn66aefPGvff/%2B9Zs%2Be7fVVDQAAAP7K8o8IZ8yYoT/96U/6wx/%2BoNDQUFVVVamsrEwtW7bUa6%2B9ZvU4AAAAxllesFq2bKl3331XH3zwgQ4fPqzAwEC1adNGPXv2VFBQkNXjAAAAGGd5wZKk4OBg9e3b94JvZ%2BfOnUpLS1N8fLwWLlzoWV%2B1apVmzJihevXqeV1%2BxYoV6tq1q6qqqrRo0SKtW7dOJSUl6tq1q5588km1bNlSkuRyufTkk0/qb3/7mwIDA5WYmKhZs2bpsssuu%2BCZAQCA/VlesI4cOaKMjAx9%2B%2B23OnnyZLX9999/v06389JLLyknJ0etWrWqcT8uLu6sHzmuWLFCa9eu1UsvvaSmTZtq4cKFSk5O1urVqxUQEKBZs2bp9OnTWrduncrLyzV58mSlp6dr5syZdQ8KAAD%2BbfnkHKzCwkL17NlT9evX/823ExISopycHD3zzDM6derUeV03OztbY8eOVdu2bSVJKSkpio%2BP1549e9SiRQtt2bJFf/3rXxUVFSVJevDBBzV58mSlpaVVOyoGAADwa5YXrNzcXL3//vue8vJb3X333bXuHz16VPfee69yc3MVFhamSZMm6fbbb9fJkye1f/9%2BxcTEeC4bGhqqVq1ayel06vjx4woKClLHjh09%2B507d9bPP/%2BsAwcOeK0DAADUxPKC1ahRows6clUXUVFRat26tR555BG1a9dO7733nh599FFFR0fryiuvlNvtVnh4uNd1wsPDVVxcrIiICIWGhiogIMBrT5KKi4vrdP%2BFhYUqKiryWnM46is6OvoCk3kLCrL8WzYuiMNx7nnPZPK3bOdix1x2zCTZM5cdM0n2zGXHTJJ9c9XG8oI1fvx4ZWZmaurUqV4lxqRevXqpV69enn8fOHCg3nvvPa1atUqpqamSJLfbfdbr17ZXF9nZ2crMzPRaS05O1qRJky7odv1dZGSDOl82LOzyiziJ79gxlx0zSfbMZcdMkj1z2TGTZN9cNbG8YH3wwQf64osvtGrVKrVo0UKBgd5t9s0337wo99u8eXPl5uYqIiJCgYGB1X6xtMvlUqNGjRQVFaXS0lJVVlZ6vjbizGUbNWpUp/saNWqUevfu7bXmcNRXcXGZgST/z9/eCdQlf1BQoMLCLldJyQlVVlZZMJU17JjLjpkke%2BayYybJnrnsmEnyba7zeXNvkuUFKzQ0VDfddNNFvY833nhD4eHhGjBggGctPz9fLVu2VEhIiNq3b6%2B8vDz16NFDklRSUqLDhw%2Bra9euat68udxut77%2B%2Bmt17txZkuR0OhUWFqY2bdrU6f6jo6OrfRxYVHRcFRX2ebL8FueTv7KyypZ/X3bMZcdMkj1z2TGTZM9cdswk2TdXTSwvWHPnzr3o93H69Gk9/fTTatmypa666ipt2rRJH3zwgd566y1JUlJSkrKysnTTTTepadOmSk9PV6dOndSlSxdJUv/%2B/fXcc89p/vz5On36tBYvXqzhw4fL4fDJ14YBAAA/45PGcODAAb377rv6xz/%2B4Slcf//739W9e/c638aZMlRRUSFJ2rJli6RfjjbdfffdKisr0%2BTJk1VUVKQWLVpo8eLFio2NlSSNHj1aRUVFGjNmjMrKyhQfH%2B91ztRTTz2lJ554Qn369FG9evV02223KSUlxUh2AABgfwHuCz2j%2Bzzt3r1b999/v9q0aaNDhw7J6XTqyJEjGjBggJ577jn16dPHynEsU1R03PhtOhyBuiV9p/HbvVg2TLnhnJdxOAIVGdlAxcVltjqMbMdcdswk2TOXHTNJ9sxlx0ySb3M1adLQ0vs7w/KzpBcuXKhp06Zp7dq1np8ibNmypebNm6fFixdbPQ4AAIBxlhes//mf/1FSUpIkeX1Nw6233qr8/HyrxwEAADDO8oLVsGHDGn8HYWFhoYKDg60eBwAAwDjLC9Y111yjZ599VqWlpZ61gwcPKi0tTX/4wx%2BsHgcAAMA4y3%2BK8LHHHtM999yj%2BPh4VVZW6pprrtGJEyfUvn17zZs3z%2BpxAAAAjLO8YDVr1kzr1q3Tjh07dPDgQV122WVq06aNbrjhhov2q3MAAACs5JPvwapXr5769u3ri7sGAAC46CwvWL179671SNX7779v4TQAAADmWV6wBgwY4FWwKisrdfDgQTmdTt1zzz1WjwMAAGCc5QUrNTW1xvVNmzbpk08%2BsXgaAAAA8yz/moaz6du3r959911fjwEAAHDBLpmCtXfvXln8axEBAAAuCss/Ihw9enS1tRMnTig/P1/9%2BvWzehwAAADjLC9YrVu3rvZThCEhIRo%2BfLhGjBhh9TgAAADGWV6w%2BLZ2AABgd5YXrHfeeafOlx0yZMhFnAQAAODisLxgPf7446qqqqp2QntAQIDXWkBAAAULAAD4JcsL1ssvv6xly5ZpwoQJ6tixo9xut7755hu99NJLuuuuuxQfH2/1SAAAAEb55BysrKwsNW3a1LN23XXXqWXLlho3bpzWrVtn9UgAAABGWf49WIcOHVJ4eHi19bCwMBUUFFg9DgAAgHGWF6zmzZtr3rx5Ki4u9qyVlJQoIyNDv//9760eBwAAwDjLPyKcMWOGpk6dquzsbDVo0ECBgYEqLS3VZZddpsWLF1s9DgAAgHGWF6yePXtq%2B/bt2rFjh44dOya3262mTZvqxhtvVMOGDa0eBwAAwDjLC5YkXX755erTp4%2BOHTumli1b%2BmIEAACAi8byc7BOnjyptLQ0de/eXX/84x8l/XIO1n333aeSkhKrxwEAADDO8oK1YMEC7du3T%2Bnp6QoM/P%2B7r6ysVHp6utXjAAAAGGd5wdq0aZOef/553XrrrZ5f%2BhwWFqa5c%2Bdq8%2BbNVo8DAABgnOUFq6ysTK1bt662HhUVpZ9//tnqcQAAAIyzvGD9/ve/1yeffCJJXr97cOPGjbriiiusHgcAAMA4y3%2BK8M4779TDDz%2BsO%2B64Q1VVVXr11VeVm5urTZs26fHHH7d6HAAAAOMsL1ijRo2Sw%2BHQ66%2B/rqCgIL344otq06aN0tPTdeutt1o9DgAAgHGWF6wff/xRd9xxh%2B644w6r7xoAAMASlp%2BD1adPH69zrwAAAOzG8oIVHx%2BvDRs2WH23AAAAlrH8I8Lf/e53euaZZ5SVlaXf//73qlevntd%2BRkaG1SMBAAAYZXnB2r9/v6688kpJUnFxsdV3DwAAcNFZVrBSUlK0cOFCvfbaa561xYsXKzk52aoRAAAALGHZOVhbt26ttpaVlWXV3QMAAFjGsoJV008O8tOEAADAjiwrWGd%2BsfO51gAAAPyd5V/TAAAAYHcULAAAAMMs%2BynC8vJyTZ069ZxrfA8WAADwd5YVrGuvvVaFhYXnXAMAAPB3lhWsf/3%2BKwAAADvjHCwAAADDKFgAAACG%2BXXB2rlzpxISEpSSklJtb/369Ro0aJC6d%2B%2BuYcOGadeuXZ69qqoqLVy4UH369FFcXJzGjRunI0eOePZdLpemTJmihIQE9ezZU48//rhOnjxpSSYAAOD//LZgvfTSS5ozZ45atWpVbW/fvn1KS0tTamqqPv74Y40dO1YPPfSQjh07JklasWKF1q5dq6ysLG3btk2tW7dWcnKy55vlZ82apRMnTmjdunV6%2B%2B23lZ%2Bfr/T0dEvzAQAA/%2BW3BSskJEQ5OTk1FqyVK1cqMTFRiYmJCgkJ0eDBg9WhQwetWbNGkpSdna2xY8eqbdu2Cg0NVUpKivLz87Vnzx798MMP2rJli1JSUhQVFaWmTZvqwQcf1Ntvv63y8nKrYwIAAD9k2U8Rmnb33XefdS8vL0%2BJiYleazExMXI6nTp58qT279%2BvmJgYz15oaKhatWolp9Op48ePKygoSB07dvTsd%2B7cWT///LMOHDjgtX42hYWFKioq8lpzOOorOjq6rvHqJCjIv/qxw3Huec9k8rds52LHXHbMJNkzlx0zSfbMZcdMkn1z1cZvC1ZtXC6XwsPDvdbCw8O1f/9%2B/fTTT3K73TXuFxcXKyIiQqGhoV6/J/HMZYuLi%2Bt0/9nZ2crMzPRaS05O1qRJk35LHNuIjGxQ58uGhV1%2BESfxHTvmsmMmyZ657JhJsmcuO2aS7JurJrYsWJI851P9lv1zXfdcRo0apd69e3utORz1VVxcdkG3%2B2v%2B9k6gLvmDggIVFna5SkpOqLKyyoKprGHHXHbMJNkzlx0zSfbMZcdMkm9znc%2Bbe5NsWbAiIyPlcrm81lwul6KiohQREaHAwMAa9xs1aqSoqCiVlpaqsrJSQUFBnj1JatSoUZ3uPzo6utrHgUVFx1VRYZ8ny29xPvkrK6ts%2Bfdlx1x2zCTZM5cdM0n2zGXHTJJ9c9XEvw6B1FFsbKxyc3O91pxOp7p166aQkBC1b99eeXl5nr2SkhIdPnxYXbt2VadOneR2u/X11197XTcsLExt2rSxLAMAAPBftixYI0eO1EcffaTt27fr1KlTysnJ0aFDhzR48GBJUlJSkpYvX678/HyVlpYqPT1dnTp1UpcuXRQVFaX%2B/fvrueee048//qhjx45p8eLFGj58uBwOWx7wAwAAhvltY%2BjSpYskqaKiQpK0ZcsWSb8cberQoYPS09M1d%2B5cFRQUqF27dlq6dKmaNGkiSRo9erSKioo0ZswYlZWVKT4%2B3uuk9KeeekpPPPGE%2BvTpo3r16um2226r8ctMAQAAahLgvtAzulEnRUXHjd%2BmwxGoW9J3Gr/di2XDlBvOeRmHI1CRkQ1UXFxmq8/p7ZjLjpkke%2BayYybJnrnsmEnyba4mTRpaen9n2PIjQgAAAF%2BiYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMNsW7A6duyo2NhYdenSxfPn6aefliTt3r1bw4cP1zXXXKOBAwdqzZo1Xtddvny5%2Bvfvr2uuuUZJSUnKzc31RQQAAOCnHL4e4GLauHGjWrRo4bVWWFioBx98UI8//rgGDRqkzz//XBMnTlSbNm3UpUsXbd26VS%2B88IJefvlldezYUcuXL9eECRO0efNm1a9f30dJAACAP7HtEayzWbt2rVq3bq3hw4crJCRECQkJ6t27t1auXClJys7O1rBhw9StWzdddtlluu%2B%2B%2ByRJ27Zt8%2BXYAADAj9j6CFZGRob%2B/ve/q7S0VH/84x81ffp05eXlKSYmxutyMTEx2rBhgyQpLy9PAwYM8OwFBgaqU6dOcjqdGjhwYJ3ut7CwUEVFRV5rDkd9RUdHX2Aib0FB/tWPHY5zz3smk79lOxc75rJjJsmeueyYSbJnLjtmkuybqza2LVhXX321EhISNH/%2BfB05ckRTpkzR7Nmz5XK51LRpU6/LRkREqLi4WJLkcrkUHh7utR8eHu7Zr4vs7GxlZmZ6rSUnJ2vSpEm/MY09REY2qPNlw8Iuv4iT%2BI4dc9kxk2TPXHbMJNkzlx0zSfbNVRPbFqzs7GzPP7dt21apqamaOHGirr322nNe1%2B12X9B9jxo1Sr179/Zaczjqq7i47IJu99f87Z1AXfIHBQUqLOxylZScUGVllQVTWcNIbgEmAAARNklEQVSOueyYSbJnLjtmkuyZy46ZJN/mOp839ybZtmD9WosWLVRZWanAwEC5XC6vveLiYkVFRUmSIiMjq%2B27XC61b9%2B%2BzvcVHR1d7ePAoqLjqqiwz5Pltzif/JWVVbb8%2B7JjLjtmkuyZy46ZJHvmsmMmyb65auJfh0DqaO/evZo3b57XWn5%2BvoKDg5WYmFjtaxdyc3PVrVs3SVJsbKzy8vI8e5WVldq7d69nHwAA4FxsWbAaNWqk7OxsZWVl6fTp0zp48KAWLVqkUaNG6fbbb1dBQYFWrlypU6dOaceOHdqxY4dGjhwpSUpKStI777yjL7/8UidOnNCSJUsUHBysXr16%2BTYUAADwG7b8iLBp06bKyspSRkaGpyANHTpUKSkpCgkJ0dKlSzVnzhzNnj1bzZs314IFC3TVVVdJkm666SY98sgjmjJliv75z3%2BqS5cuysrK0mWXXebjVAAAwF/YsmBJUlxcnN58882z7q1evfqs173zzjt15513XqzRAACAzdnyI0IAAABfomABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMIevB8C/jz8%2B96GvRzgvG6bc4OsRAAB%2BiiNYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDHL4eALhU/fG5D309wnnZMOUGX48AAPg/HMGqQUFBgR544AHFx8fr5ptv1oIFC1RVVeXrsQAAgJ/gCFYNHn74YXXu3FlbtmzRP//5T40fP16NGzfWvffe6%2BvRAACAH%2BAI1q84nU59/fXXSk1NVcOGDdW6dWuNHTtW2dnZvh4NAAD4CY5g/UpeXp6aN2%2Bu8PBwz1rnzp118OBBlZaWKjQ09Jy3UVhYqKKiIq81h6O%2BoqOjjc4aFEQ/xv/zt3PG/Ml7qTf6eoQLcua1wm6vGXbMZcdMkn1z1YaC9Ssul0thYWFea2fKVnFxcZ0KVnZ2tjIzM73WHnroIT388MPmBtUvRe6eZt9q1KhRxsubrxQWFio7O9tWmSR75rJjJsmeuQoLC/WXv7xsq0ySPXPZMZNk31y1%2BfepkufB7XZf0PVHjRqlVatWef0ZNWqUoen%2BX1FRkTIzM6sdLfNndswk2TOXHTNJ9sxlx0ySPXPZMZNk31y14QjWr0RFRcnlcnmtuVwuBQQEKCoqqk63ER0d/W/T0AEAQHUcwfqV2NhYHT16VD/%2B%2BKNnzel0ql27dmrQoIEPJwMAAP6CgvUrMTEx6tKlizIyMlRaWqr8/Hy9%2BuqrSkpK8vVoAADATwQ9%2BeSTT/p6iEvNjTfeqHXr1unpp5/Wu%2B%2B%2Bq%2BHDh2vcuHEKCAjw9WjVNGjQQD169LDV0TU7ZpLsmcuOmSR75rJjJsmeueyYSbJvrrMJcF/oGd0AAADwwkeEAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsPxQQUGBHnjgAcXHx%2Bvmm2/WggULVFVV5euxzltBQYGSk5MVHx%2BvhIQETZ8%2BXSUlJfruu%2B/UsWNHdenSxevPK6%2B84uuR66Rjx46KjY31mv3pp5%2BWJO3evVvDhw/XNddco4EDB2rNmjU%2BnvbcPv3002qPRWxsrDp27KhPPvmkxsdqw4YNvh67Rjt37lRCQoJSUlKq7a1fv16DBg1S9%2B7dNWzYMO3atcuzV1VVpYULF6pPnz6Ki4vTuHHjdOTIEStHr1VtuTZv3qzBgwere/fu6t%2B/v9566y3P3gsvvKBOnTpVe/x%2B%2BOEHK8ev0dkyrVq1SldddVW1mb/66itJ/vtYzZw5s1qmmJgYPfbYY5Kk6dOne35X7pk/1113nS8iVHO213JJ2rdvn%2B666y5de%2B216tevn5YtW%2BZ13dqed37PDb8zdOhQ98yZM90lJSXugwcPuvv16%2BdetmyZr8c6b7fddpt7%2BvTp7tLSUvfRo0fdw4YNc8%2BYMcN95MgRd4cOHXw93m/WoUMH95EjR6qtf//99%2B6rr77avXLlSvfJkyfdH374obtr167ur776ygdTXpglS5a4J0%2Be7P7444/dN998s6/HqZOsrCx3v3793KNHj3ZPmTLFa2/v3r3u2NhY9/bt290nT550r1692t2tWzf30aNH3W632718%2BXL3zTff7N6/f7/7%2BPHj7qeeeso9aNAgd1VVlS%2BieKkt1549e9xdunRxv/fee%2B7y8nL39u3b3Z07d3Z/%2Bumnbrfb7X7%2B%2BefdaWlpvhi7VrVlevvtt9133XXXWa/rr4/Vr5WXl7sHDhzo3r59u9vtdrvT0tLczz//vBVjnrezvZafOHHCfeONN7pfeOEFd1lZmTs3N9fdo0cP96ZNm9xu97mfd/6OI1h%2Bxul06uuvv1ZqaqoaNmyo1q1ba%2BzYscrOzvb1aOelpKREsbGxmjp1qho0aKBmzZpp6NCh%2Buyzz3w92kWzdu1atW7dWsOHD1dISIgSEhLUu3dvrVy50tejnZd//OMfevXVV/Xoo4/6epTzEhISopycHLVq1ara3sqVK5WYmKjExESFhIRo8ODB6tChg%2BcIY3Z2tsaOHau2bdsqNDRUKSkpys/P1549e6yOUU1tuVwul8aPH6%2B%2BffvK4XAoMTFRHTp0uOSfZ7VlOhd/fax%2B7S9/%2BYuuuOIKJSYmWjDZb1fba/n27dtVXl6uiRMnqn79%2BurcubNGjBjh%2Bf/VuZ53/o6C5Wfy8vLUvHlzhYeHe9Y6d%2B6sgwcPqrS01IeTnZ%2BwsDDNnTtXjRs39qwdPXpU0dHRnn9/9NFH1bNnT11//fXKyMhQeXm5L0b9TTIyMtSrVy9dd911mjVrlsrKypSXl6eYmBivy8XExCg3N9dHU/42ixYt0h133KErrrhCklRWVub5eODGG2/Uq6%2B%2BKvcl%2BDvk7777bjVs2LDGvbM9Nk6nUydPntT%2B/fu99kNDQ9WqVSs5nc6LOnNd1JbrpptuUnJysuffKyoqVFRUpKZNm3rWvvnmG40ePdrzsfWl8BFNbZmkX14r7r33XsXFxalPnz5avXq1JPn1Y/WvSkpK9OKLL2ratGle6x9//LGGDBmi7t27a/jw4ZfEa0dtr%2BV5eXnq2LGjgoKCPHv/%2BppX2/PODihYfsblciksLMxr7UzZKi4u9sVIRjidTr3%2B%2BuuaOHGigoOD1b17d91yyy3atm2bsrKytGbNGv35z3/29Zh1cvXVVyshIUGbN29Wdna2vvzyS82ePbvGxy4iIsKvHrfvvvtOmzdv1r333ivpl/95dejQQffcc4927typuXPnKjMzU2%2B//baPJz0/LpfL602L9Mvzqri4WD/99JPcbvdZ9/1Jenq66tevrwEDBkiSmjVrppYtW2r%2B/Pn68MMPNWLECE2YMEEHDhzw8aRnFxUVpdatW2vatGn68MMP9cgjj2jGjBnavXu3bR6r119/XXFxcWrfvr1nrWXLlmrVqpWWLl2qnTt36rrrrtOf/vSnSy7Xv76Wn%2B01z%2BVyqaqqqtbnnR1QsPzQpXh04EJ8/vnnGjdunKZOnaqEhARFR0frzTff1C233KJ69eqpa9euGj9%2BvFatWuXrUeskOztbI0aMUHBwsNq2bavU1FStW7fOr47Anc2KFSvUr18/NWnSRNIvR09fe%2B019ejRQ8HBwerZs6dGjx7tN4/VvzrX88qfn3dut1sLFizQunXrtGTJEoWEhEiSRowYoeeff16tWrXS5ZdfrrFjx6pTp06X9Ec0vXr10ssvv6yYmBgFBwdr4MCBuuWWW7z%2Bm/Pnx6qyslIrVqzQ3Xff7bWenJysZ599Vk2bNlVoaKimTZum4OBgbdmyxUeTVvfr1/KzCQgI8PyzPz9W50LB8jNRUVFyuVxeay6XSwEBAYqKivLRVL/d1q1b9cADD2jGjBnVXlD%2BVfPmzfXDDz/45ZOxRYsWqqysVGBgYLXHrri42K8et02bNql37961XqZ58%2BYqLCy0aCIzIiMja3xeRUVFKSIiosbHzuVyqVGjRlaO%2BZtUVVVp%2BvTp2rp1q9544w1deeWVtV7eHx%2B/MzP7%2B2Ml/fJTu6dPnz7nTwgGBQXpd7/73SXzWNX0Wh4VFVXtaJTL5fI8TrU97%2ByAguVnYmNjdfToUf3444%2BeNafTqXbt2qlBgwY%2BnOz8ffHFF0pLS9OiRYs0ZMgQz/ru3bu1ZMkSr8seOHBAzZs393rncynau3ev5s2b57WWn5%2Bv4OBgJSYmVjtnIjc3V926dbNyxN9s3759Kigo0A033OBZ27Bhg/77v//b63IHDhxQy5YtrR7vgsTGxlZ7bJxOp7p166aQkBC1b99eeXl5nr2SkhIdPnxYXbt2tXrU8/bss8/q22%2B/1RtvvFHtcfnzn/%2Bs3bt3e63l5%2Bdf0o/fG2%2B8ofXr13utnZnZ3x8rSXr//fd1/fXXy%2BFweNbcbrfmzp2rr7/%2B2rN2%2BvRpHT58%2BJJ4rM72Wh4bG6tvvvlGFRUVnrUzz6sz%2B2d73tkBBcvPnPkelIyMDJWWlio/P1%2BvvvqqkpKSfD3aeamoqNDMmTOVmpqqnj17eu01bNhQixcv1urVq1VeXi6n06lXXnnFLzI2atRI2dnZysrK0unTp3Xw4EEtWrRIo0aN0u23366CggKtXLlSp06d0o4dO7Rjxw6NHDnS12PXyd69exUREaHQ0FDPWr169TR//nzt2rVL5eXl%2BvDDD/X222/7xWP1r0aOHKmPPvpI27dv16lTp5STk6NDhw5p8ODBkqSkpCQtX75c%2Bfn5Ki0tVXp6uuf7oy5ln3/%2BudasWaOsrCxFRERU23e5XJo9e7YOHDigU6dOadmyZTp8%2BLCGDh3qg2nr5vTp03r66afldDpVXl6udevW6YMPPtDo0aMl%2Be9jdca%2BffvUokULr7WAgAB99913mj17tr7//nuVlZUpPT1d9erVU9%2B%2BfX006S9qey1PTExUaGiolixZohMnTmjPnj3KycnxvD6c63nn7wLc/viZy7%2B5Y8eOadasWfrb3/6m0NBQjR49Wg899NAlf3TnX3322Wf6j//4DwUHB1fb27hxo/bu3avMzEwdOnRIDRs21JgxY3T//fcrMPDSf0/w6aefKiMjQ998842Cg4M1dOhQpaSkKCQkRJ9%2B%2BqnmzJmj/Px8NW/eXFOnTlW/fv18PXKdLF26VGvXrtW6deu81rOzs7Vs2TIdPXpUjRs31sSJEzVixAgfTXl2Z/4He%2Bbd9JkjBGd%2BYmnz5s3KyMhQQUGB2rVrp8cff1xxcXGSfjmC8MILL%2BjNN99UWVmZ4uPj9dRTT6lZs2Y%2BSOKttlwzZszQX//6V6%2BjIZIUFxenZcuW6dSpU8rIyNDGjRvlcrnUrl07zZo1S927d7c2xK/UlsntdmvJkiXKyclRUVGRWrRooUcffVQ333yzJP99rM7o37%2B/Ro4cqXHjxnld1%2BVyaf78%2Bfrggw9UWlqqrl276sknn1Tbtm0tmr5m53otLysr0xNPPKHc3Fw1btxY999/v%2B68807PZWp73vk7ChYAAIBhl/7hAAAAAD9DwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYf8L/smBa8yVw/wAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common9066453491362970376”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>76</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.142857142857143</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.8951358180669615</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.317789291882556</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.449838684897834</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.052701928585068</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.477966705243479</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5980642247745054</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.311077389984826</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2807)</td> <td class=”number”>2807</td> <td class=”number”>87.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>313</td> <td class=”number”>9.8%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme9066453491362970376”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>76</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.002192913206249627</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.009908347783007183</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.027526218723333974</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.030368796495440883</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>115.89698046181172</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>126.42461738847281</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>158.58310626702996</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>183.9985010305415</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>203.91061452513966</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PC_FFRSALES07”>PC_FFRSALES07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>53</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.7%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>641.71</td>

</tr> <tr>

<th>Minimum</th> <td>402.1</td>

</tr> <tr>

<th>Maximum</th> <td>1043.9</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-1419710565776076634”>

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<li role=”presentation” class=”active”><a href=”#quantiles-1419710565776076634”

aria-controls=”quantiles-1419710565776076634” role=”tab” data-toggle=”tab”>Statistics</a></li>

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role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-1419710565776076634” aria-controls=”common-1419710565776076634”

role=”tab” data-toggle=”tab”>Common Values</a></li>

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role=”tab” data-toggle=”tab”>Extreme Values</a></li>

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<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>402.1</td>

</tr> <tr>

<th>5-th percentile</th> <td>489.04</td>

</tr> <tr>

<th>Q1</th> <td>576.21</td>

</tr> <tr>

<th>Median</th> <td>632.34</td>

</tr> <tr>

<th>Q3</th> <td>721.82</td>

</tr> <tr>

<th>95-th percentile</th> <td>784.26</td>

</tr> <tr>

<th>Maximum</th> <td>1043.9</td>

</tr> <tr>

<th>Range</th> <td>641.76</td>

</tr> <tr>

<th>Interquartile range</th> <td>145.61</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>96.748</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.15077</td>

</tr> <tr>

<th>Kurtosis</th> <td>-0.12331</td>

</tr> <tr>

<th>Mean</th> <td>641.71</td>

</tr> <tr>

<th>MAD</th> <td>78.836</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.036916</td>

</tr> <tr>

<th>Sum</th> <td>2009800</td>

</tr> <tr>

<th>Variance</th> <td>9360.2</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

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B5Sly5dtH79et13331avny5evbsqdWrV6tfv36SpDFjxmjhwoUqKirS4cOHlZGRoQ0bNigmJibAKwIAAKEiwrT3iW74xe0%2BattYTqdDiYld5PE0cglX1OMMPrwTZ%2BwoGnXWPo4XK%2BphFY716N49NiD7DctbhAAAAIFEwAIAALAZAQsAAMBmBCwAAACbEbAAAABsRsACAACwGQELAADAZgQsAAAAmxGwAAAAbEbAAgAAsBkBCwAAwGYELAAAAJsRsAAAAGxGwAIAALAZAQsAAMBmBCwAAACbEbAAAABsRsACAACwGQELAADAZgQsAAAAmwVdwMrJyVFJSYkOHjwY6KkAAACcl6ALWNOnT9f27dt19dVXa/bs2SovL1dzc3OgpwUAAOC3oAtYBQUF2r59u373u9%2BpT58%2BWrFihbKzs7V69Wq98847gZ4eAADAOQVdwDpjwIABWrJkiXbt2qU777xTv/vd7zRx4kTdfPPN%2Bvvf/x7o6QEAAJxV0Aas06dPa/v27brlllu0ZMkSpaSk6Cc/%2BYn69%2B%2BvWbNmaevWrYGeIgAAwGdyBnoCn7Z//349%2BeSTeuqpp9TY2KgJEyZo48aNGjp0qG%2Bb4cOH65577tHkyZMDOFMAAIDPFnQBa9KkSerdu7fmzJmjG264QQkJCW22yc7O1pEjRz53nNTUVF1yySWKiIjwtc2cOVPLli1TZWWl1q5dq7fffltf%2BtKXNGfOHE2ZMsW3XWlpqTZv3iy3263U1FQtXbpU6enp9i0SIeHaB18O9BQAACEq6AJWaWmpRowYcc7t3njjjXNu8%2Byzz%2Bryyy%2B3tNXX12v%2B/PlaunSpJk%2BerFdffVXz5s1T7969lZGRoZ07d6q4uFiPPvqoUlNTVVpaqrlz56q8vFydO3c%2B73UBAICOI%2BiewUpNTdXcuXP1xz/%2B0df2y1/%2BUrfccou8Xm%2B7x9%2B6dat69eql3NxcRUdHKysrSzk5OSorK5MkuVwuTZs2TZmZmYqJidHs2bMlSbt27Wr3vgEAQMcQdFewVq5cqaNHj%2BqKK67wtY0dO1YvvviiVq1apVWrVvk91tq1a7Vnzx4dO3ZM1157re644w7V1NQoLS3Nsl1aWpp27NghSaqpqdHEiRN9fQ6HQ/3791dVVZUmTZrk137r6%2BvldrstbU5nZyUnJ/s9988TGemwfO3oqAdg5XSe/VjgeLGiHlbUwz5BF7Beeuklbd26VYmJib62Xr16ac2aNbruuuv8HmfQoEHKysrS/fffr/fff19FRUVavny5vF6vUlJSLNsmJCTI4/FIkrxer%2BLj4y398fHxvn5/uFwulZSUWNoKCgpUWFjo9xj%2BiIvrZOt4oY56AB9LTOxyzm04XqyohxX1aL%2BgC1gnTpxQdHR0m3aHw6Gmpia/x3G5XL7//vrXv67Fixdr3rx5lt9GPBtjjN/7%2BSx5eXnKycmxtDmdneXxNLZr3DMiIx2Ki%2BukhoYmtbS02jJmKKMegNXnnWs4Xqyoh1U41sOfHzguhKALWMOHD9eqVau0aNEi35WkDz74QPfff79f4ehsLr/8crW0tMjhcLR5lsvj8SgpKUmSlJiY2Kbf6/WqT58%2Bfu8rOTm5ze1At/uompvtfbO2tLTaPmYoox7Ax/w5DjherKiHFfVov6C7yXrnnXeqsrJS3/zmNzVixAgNGzZMY8eOVXV1te677z6/xqitrW3zrNb%2B/fsVFRWl7OxsVVdXW/qqq6uVmZkpSUpPT1dNTY2vr6WlRbW1tb5%2BAACAcwm6K1hf/vKX9cwzz%2BhPf/qT3nvvPTkcDvXu3VujR49WZGSkX2N069ZNLpdLSUlJmjVrlurq6vTQQw8pLy9P119/vUpKSlRWVqYpU6bolVdeUUVFhe%2BWYn5%2BvhYuXKjrrrtOqampeuyxxxQVFaWxY8dewFUDAIBwEnQBS5KioqJ09dVXn/frU1JStGHDBq1du1br1q1TVFSUpk6dqgULFig6Olrr16/Xfffdp%2BXLl6tnz55avXq1%2BvXrJ0kaM2aMFi5cqKKiIh0%2BfFgZGRnasGGDYmJi7FoeAAAIcxGmvU902%2Bz999/X2rVr9c9//lMnTpxo0//8888HYFbt53YftW0sp9OhxMQu8ngauUeuC1cPPskdoWpH0aiz9nH%2BsKIeVuFYj%2B7dYwOy36C7gnXnnXeqvr5eo0eP5pPTAQBASAq6gFVdXa3nn3/e91t9AAAAoSbofouwW7duXLkCAAAhLegC1pw5c1RSUtLuD/sEAAAIlKC7RfinP/1Jr732mn7/%2B9/r8ssvl8NhzYC//e1vAzQzAAAA/wRdwOratavGjBkT6GkAAACct6ALWCtXrgz0FAAAANol6J7BkqS3335bxcXF%2BslPfuJr27NnTwBnBAAA4L%2BgC1iVlZWaMmWKysvLtW3bNkkff/joTTfdFLIfMgoAADqWoAtYDzzwgH70ox9p69atioiIkPTxv0%2B4atUqPfzwwwGeHQAAwLkFXcD6xz/%2Bofz8fEnyBSxJuuaaa7R///5ATQsAAMBvQRewYmNjP/PfIKyvr1dUVFQAZgQAAPDFBF3AGjJkiFasWKFjx4752t555x0tWbJE3/zmNwM4MwAAAP8E3cc0/OQnP9H3vvc9jRw5Ui0tLRoyZIiamprUp08frVq1KtDTAwAAOKegC1g9evTQtm3bVFFRoXfeeUcxMTHq3bu3Ro0aZXkmCwAAIFgFXcCSpEsuuURXX311oKcBAABwXoIuYOXk5HzulSo%2BCwsAAAS7oAtYEydOtASslpYWvfPOO6qqqtL3vve9AM4MAADAP0EXsBYvXvyZ7c8995z%2B/Oc/X%2BTZAAAAfHFB9zENZ3P11VfrmWeeCfQ0AAAAzilkAlZtba2MMYGeBgAAwDkF3S3CG2%2B8sU1bU1OT9u/fr/HjxwdgRgAAAF9M0AWsXr16tfktwujoaOXm5mrGjBkBmhUAAID/gi5g8WntAAAg1AVdwHrqqaf83vaGG264gDMBAAA4P0EXsJYuXarW1tY2D7RHRERY2iIiIvwOWCtWrNDGjRv11ltvSZIqKyu1du1avf322/rSl76kOXPmaMqUKb7tS0tLtXnzZrndbqWmpmrp0qVKT0%2B3YXUAAKAjCLqA9eijj%2Brxxx/X3LlzlZqaKmOM3nrrLf3iF7/Qd7/7XY0cOfILjbd37149/fTTvu/r6%2Bs1f/58LV26VJMnT9arr76qefPmqXfv3srIyNDOnTtVXFysRx99VKmpqSotLdXcuXNVXl6uzp07271cAAAQhoLuYxpWrVql%2B%2B67T0OHDlXXrl0VGxurYcOG6ac//anuv/9%2BRUVF%2Bf6cS2trq%2B6%2B%2B27NmjXL17Z161b16tVLubm5io6OVlZWlnJyclRWViZJcrlcmjZtmjIzMxUTE6PZs2dLknbt2nVB1gsAAMJP0AWsd999V/Hx8W3a4%2BLiVFdX94XG%2Bu1vf6vo6GhNnjzZ11ZTU6O0tDTLdmlpaaqurv7MfofDof79%2B6uqquoL7RsAAHRcQXeLsGfPnlq1apV%2B%2BMMfKjExUZLU0NCg//7v/9ZXvvIVv8f58MMPVVxcrF/96leWdq/Xq5SUFEtbQkKCPB6Pr//TAS8%2BPt7X74/6%2Bnq53W5Lm9PZWcnJyX6P8XkiIx2Wrx0d9QCsnM6zHwscL1bUw4p62CfoAtadd96pRYsWyeVyqUuXLnI4HDp27JhiYmL08MMP%2Bz3OypUrNW3aNF1xxRU6cODAF5pDez8x3uVyqaSkxNJWUFCgwsLCdo37aXFxnWwdL9RRD%2BBjiYldzrkNx4sV9bCiHu0XdAFr9OjReuGFF1RRUaFDhw7JGKOUlBRdeeWVio2N9WuMyspK7dmzR9u2bWvTl5iYKK/Xa2nzeDxKSko6a7/X61WfPn38XkNeXp5ycnIsbU5nZ3k8jX6P8XkiIx2Ki%2BukhoYmtbS02jJmKKMegNXnnWs4Xqyoh1U41sOfHzguhKALWJLUqVMnXXXVVTp06JC%2B/OUvf%2BHXb9myRYcPH9a4ceMk/d8VqZEjR%2BoHP/hBm%2BBVXV2tzMxMSVJ6erpqamo0depUSVJLS4tqa2uVm5vr9/6Tk5Pb3A50u4%2BqudneN2tLS6vtY4Yy6gF8zJ/jgOPFinpYUY/2C7qAdeLECd1999165plnJH0cfhoaGrRw4UL9/Oc/V1xc3DnHuOOOO/TDH/7Q9/2hQ4eUl5enp59%2BWq2trVq/fr3Kyso0ZcoUvfLKK6qoqJDL5ZIk5efna%2BHChbruuuuUmpqqxx57TFFRURo7duwFWW9Hcu2DLwd6CgAAXBRB9xTb6tWrtXfvXq1Zs0YOx/9Nr6WlRWvWrPFrjPj4ePXo0cP359JLL5Uk9ejRQ5dddpnWr1%2BvTZs2aejQoVqxYoVWr16tfv36SZLGjBmjhQsXqqioSCNGjNDu3bu1YcMGxcTE2L9YAAAQliJMe5/ottno0aO1adMm9erVS5mZmXrjjTckfXwV6oYbbtArr7wS4BmeH7f7qG1jOZ0OJSZ2kcfTGFKXcLmCBVwcO4pGnbUvVM8fFwr1sArHenTv7t/z23YLuitYjY2N6tWrV5v2pKQkHT9%2B/OJPCAAA4AsKuoD1la98RX/%2B858lWT8u4dlnn9Vll10WqGkBAAD4Legecv/2t7%2Bt22%2B/XdOnT1dra6ueeOIJVVdX67nnntPSpUsDPT0AAIBzCrqAlZeXJ6fTqU2bNikyMlKPPPKIevfurTVr1uiaa64J9PQAAADOKegC1pEjRzR9%2BnRNnz490FMBAAA4L0H3DNZVV13V7n%2BqBgAAIJCCLmCNHDlSO3bsCPQ0AAAAzlvQ3SL80pe%2BpP/6r//Shg0b9JWvfEWXXHKJpX/t2rUBmhkAAIB/gi5g7du3T1/72tckffyPMAMAAISaoAlYCxYs0AMPPKBf/epXvraHH35YBQUFAZwVAADAFxc0z2Dt3LmzTduGDRsCMBMAAID2CZqA9Vm/OchvEwIAgFAUNAErIiLCrzYAAIBgFzQBCwAAIFwQsAAAAGwWNL9FePr0aS1atOicbXwOFgAACHZBE7CGDh2q%2Bvr6c7YBAAAEu6AJWJ/8/CsAAIBQxjNYAAAANiNgAQAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNwjZgvfnmm/re976noUOHKisrS0VFRXK73ZKkyspK5ebmasiQIZo0aZK2bNlieW1paakmTJigIUOGKD8/X9XV1YFYAgAACFFhGbBOnTqlH/zgBxoxYoQqKyu1bds2HT58WPfcc4/q6%2Bs1f/583XjjjaqsrNTSpUu1bNkyVVVVSZJ27typ4uJi/exnP9Pu3bs1btw4zZ07V8ePHw/wqgAAQKgIy4DV1NSkBQsWaM6cOYqKilJSUpK%2B9a1v6Z///Ke2bt2qXr16KTc3V9HR0crKylJOTo7KysokSS6XS9OmTVNmZqZiYmI0e/ZsSdKuXbsCuSQAABBCgubfIrRTfHy8ZsyY4fv%2B7bff1h/%2B8Adde%2B21qqmpUVpammX7tLQ07dixQ5JUU1OjiRMn%2BvocDof69%2B%2BvqqoqTZo0ya/919fX%2B25HnuF0dlZycvL5LskiMtJh%2BQoAn%2BR0nv3cwPnDinpYUQ/7hGXAOqOurk4TJkxQc3OzZs6cqcLCQt1yyy1KSUmxbJeQkCCPxyNJ8nq9io%2BPt/THx8f7%2Bv3hcrlUUlJiaSsoKFBhYeF5ruSzxcV1snU8AOEhMbHLObfh/GFFPayoR/uFdcDq2bOnqqqq9K9//Uv/%2BZ//qR//%2BMd%2Bvc4Y06795uXlKScnx9LmdHaWx9PYrnHPiIx0KC6ukxoamtTS0mrLmADCx%2Bedazh/WFEPq3Cshz8/cFwIYR2wJCkiIkK9evXSggULdOONNyo7O1ter9eyjcfjUVJSkiQpMTGxTb/X61WfPn383mdycnKb24Fu91E1N9v7Zm1pabV9TAChz5/zAucPK%2BphRT3aLyxvslZWVmrChAlqbf2/N4fD8fFSBw4c2OZjF6qrq5WZmSlJSk9PV01Nja%2BvpaVFtbW1vn4AAIBzCcuAlZ6ermPHjmn16tVqamrSkSNHVFxcrGHDhik/P191dXUqKyvTyZMnVVFRoYqKCs2cOVOSlJ%2Bfr6eeekqvv/66mpqatG7dOkVFRWns2LGBXRQAAAgZYRmwYmNj9fjjj6u6ulrf%2BMY3NGnSJMXGxurnP/%2B5unXrpvXr12vTpk0aOnSoVqxYodWrV6tfv36SpDEDsrmhAAAU0klEQVRjxmjhwoUqKirSiBEjtHv3bm3YsEExMTEBXhUAAAgVEaa9T3TDL273UdvGcjodSkzsIo%2BnMaTukV/74MuBngLQIewoGnXWvlA9f1wo1MMqHOvRvXtsQPYbllewAAAAAomABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADYjIAFAABgMwIWAACAzQhYAAAANiNgAQAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADYjIAFAABgMwIWAACAzQhYAAAANiNgAQAA2CxsA1ZdXZ0KCgo0cuRIZWVl6Y477lBDQ4Mkae/evfrud7%2BroUOHavz48Xr88cctr92%2BfbsmT56swYMHa9q0aXrppZcCsQQAABCiwjZgzZ07V3Fxcdq5c6d%2B//vf65///Kfuv/9%2BnThxQnPmzNE3vvENvfjii3rggQe0fv16lZeXS/o4fC1ZskSLFy/WK6%2B8olmzZum2227ToUOHArwiAAAQKsIyYDU0NCg9PV2LFi1Sly5d1KNHD02dOlV/%2B9vf9MILL%2Bj06dOaN2%2BeOnfurAEDBmjGjBlyuVySpLKyMmVnZys7O1vR0dGaMmWK%2Bvbtqy1btgR4VQAAIFSEZcCKi4vTypUrdemll/raDh48qOTkZNXU1Cg1NVWRkZG%2BvrS0NFVXV0uSampqlJaWZhkvLS1NVVVVF2fyAAAg5DkDPYGLoaqqSps2bdK6deu0Y8cOxcXFWfoTEhLk9XrV2toqr9er%2BPh4S398fLz27dvn9/7q6%2BvldrstbU5nZyUnJ5//Ij4hMtJh%2BQoAn%2BR0nv3cwPnDinpYUQ/7hH3AevXVVzVv3jwtWrRIWVlZ2rFjx2duFxER4ftvY0y79ulyuVRSUmJpKygoUGFhYbvG/bS4uE62jgcgPCQmdjnnNpw/rKiHFfVov7AOWDt37tSPfvQjLVu2TDfccIMkKSkpSe%2B%2B%2B65lO6/Xq4SEBDkcDiUmJsrr9bbpT0pK8nu/eXl5ysnJsbQ5nZ3l8TSe30I%2BJTLSobi4TmpoaFJLS6stYwIIH593ruH8YUU9rMKxHv78wHEhhG3Aeu2117RkyRI99NBDGj16tK89PT1dv/nNb9Tc3Cyn8%2BPlV1VVKTMz09d/5nmsM6qqqjRp0iS/952cnNzmdqDbfVTNzfa%2BWVtaWm0fE0Do8%2Be8wPnDinpYUY/2C8ubrM3Nzbrrrru0ePFiS7iSpOzsbHXt2lXr1q1TU1OT3njjDT355JPKz8%2BXJM2cOVO7d%2B/WCy%2B8oJMnT%2BrJJ5/Uu%2B%2B%2BqylTpgRiKQAAIARFmPY%2BcBSE/va3v%2Bk73/mOoqKi2vQ9%2B%2Byzamxs1N13363q6mpdeumluuWWW/Ttb3/bt015ebnWrl2ruro6XXHFFVq6dKmGDx/erjm53Ufb9fpPcjodSkzsIo%2BnMaR%2Bwrj2wZcDPQWgQ9hRNOqsfaF6/rhQqIdVONaje/fYgOw3LG8RDhs2TG%2B99dbnbvOb3/zmrH3jx4/X%2BPHj7Z4WAADoIMLyFiEAAEAgEbAAAABsRsACAACwGQELAADAZgQsAAAAmxGwAAAAbEbAAgAAsBkBCwAAwGYELAAAAJsRsAAAAGxGwAIAALAZAQsAAMBmBCwAAACbEbAAAABsRsACAACwGQELAADAZgQsAAAAmxGwAAAAbEbAAgAAsBkBCwAAwGYELAAAAJsRsAAAAGxGwAIAALAZAQsAAMBmBCwAAACbEbAAAABsFrYB68UXX1RWVpYWLFjQpm/79u2aPHmyBg8erGnTpumll17y9bW2tuqBBx7QVVddpeHDh%2Bvmm2/W%2B%2B%2B/fzGnDgAAQlxYBqxf/OIXuu%2B%2B%2B/TVr361Td/evXu1ZMkSLV68WK%2B88opmzZql2267TYcOHZIkbd68WVu3btWGDRu0a9cu9erVSwUFBTLGXOxlAACAEBWWASs6OlpPPvnkZwassrIyZWdnKzs7W9HR0ZoyZYr69u2rLVu2SJJcLpdmzZqlr3/96%2BratasWLFig/fv364033rjYywAAACHKGegJXAg33XTTWftqamqUnZ1taUtLS1NVVZVOnDihffv2KS0tzdfXtWtXffWrX1VVVZUGDRrk1/7r6%2BvldrstbU5nZyUnJ3%2BBVZxdZKTD8hUAPsnpPPu5gfOHFfWwoh72CcuA9Xm8Xq/i4%2BMtbfHx8dq3b58%2B%2BugjGWM%2Bs9/j8fi9D5fLpZKSEktbQUGBCgsLz3/inyEurpOt4wEID4mJXc65DecPK%2BphRT3ar8MFLEnnfJ6qvc9b5eXlKScnx9LmdHaWx9PYrnHPiIx0KC6ukxoamtTS0mrLmADCx%2Bedazh/WFEPq3Cshz8/cFwIHS5gJSYmyuv1Wtq8Xq%2BSkpKUkJAgh8Pxmf3dunXzex/Jycltbge63UfV3Gzvm7WlpdX2MQGEPn/OC5w/rKiHFfVovw53kzU9PV3V1dWWtqqqKmVmZio6Olp9%2BvRRTU2Nr6%2BhoUHvvfeeBg4ceLGnCgAAQlSHC1gzZ87U7t279cILL%2BjkyZN68skn9e6772rKlCmSpPz8fJWWlmr//v06duyY1qxZo/79%2BysjIyPAMwcAAKEiLG8RnglDzc3NkqQ//vGPkj6%2BUtW3b1%2BtWbNGK1euVF1dna644gqtX79e3bt3lyTdeOONcrvd%2Bo//%2BA81NjZq5MiRbR5YBwAA%2BDwRhk/QvCjc7qO2jeV0OpSY2EUeT2NI3SO/9sGXAz0FoEPYUTTqrH2hev64UKiHVTjWo3v32IDsNyyvYHUkhBYAAIJPh3sGCwAA4EIjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADYjIAFAABgMwIWAACAzQhYAAAANiNgAQAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADYjIAFAABgMwIWAACAzQhYAAAANiNgAQAA2IyABQAAYDNnoCcAAECouPbBlwM9hS9kR9GoQE%2Bhw%2BIKFgAAgM0IWJ%2Bhrq5Ot956q0aOHKlx48Zp9erVam1tDfS0AABAiOAW4We4/fbbNWDAAP3xj3/U4cOHNWfOHF166aX6/ve/H%2BipAQCAEEDA%2BpSqqiq9%2BeabeuKJJxQbG6vY2FjNmjVLGzduJGABAEJKKD0zFm7PixGwPqWmpkY9e/ZUfHy8r23AgAF65513dOzYMXXt2vWcY9TX18vtdlvanM7OSk5OtmWOkZEOy1cA%2BCSn8%2BznBs4fVtQjeHze%2BzYUEbA%2Bxev1Ki4uztJ2Jmx5PB6/ApbL5VJJSYml7bbbbtPtt99uyxzr6%2Bu1ceOjysvL09/%2B6xpbxgxl9fX1crlcysvLsy3EhjLqYUU9rD55/qAeX7we4X7O5XixT3jFRZsYY9r1%2Bry8PP3%2B97%2B3/MnLy7NpdpLb7VZJSUmbq2QdFfWwoh5W1MOKelhRDyvqYR%2BuYH1KUlKSvF6vpc3r9SoiIkJJSUl%2BjZGcnEzyBwCgA%2BMK1qekp6fr4MGDOnLkiK%2BtqqpKV1xxhbp06RLAmQEAgFBBwPqUtLQ0ZWRkaO3atTp27Jj279%2BvJ554Qvn5%2BYGeGgAACBGR99xzzz2BnkSwufLKK7Vt2zbde%2B%2B9euaZZ5Sbm6ubb75ZERERgZ6aT5cuXTRixAiuqv1/1MOKelhRDyvqYUU9rKiHPSJMe5/oBgAAgAW3CAEAAGxGwAIAALAZAQsAAMBmBCwAAACbEbAAAABsRsACAACwGQELAADAZgQsAAAAmxGwAAAAbEbACmIrVqxQamqq7/vKykrl5uZqyJAhmjRpkrZs2WLZvrS0VBMmTNCQIUOUn5%2Bv6urqiz1l26Wmpio9PV0ZGRm%2BP/fee6%2BkjlmPM9atW6fRo0dr0KBBmjVrlg4cOCCp49Xkr3/9q%2BW9kZGRofT0dN9x09HqUVtbq5tuuknDhg3TqFGjtHjxYt8/XN/RaiFJ1dXVuummmzR06FBdeeWVeuyxx3x927dv1%2BTJkzV48GBNmzZNL730kq%2BvtbVVDzzwgK666ioNHz5cN998s95///1ALKHdXnzxRWVlZWnBggVt%2BtpTA6/Xq6KiImVlZWn06NFaunSpTpw4cVHWFDIMglJtba0ZMWKE6du3rzHGmA8%2B%2BMAMGjTIlJWVmRMnTpiXX37ZDBw40Pz97383xhjz/PPPm2HDhpnXX3/dNDU1mfXr15tRo0aZxsbGQC6j3fr27Wvef//9Nu0dtR7GGLNp0yZzzTXXmP3795ujR4%2Bae%2B%2B919x7770duiaftG7dOvPDH/6ww9Xj9OnTZtSoUWbt2rXm5MmT5siRI%2Bb73/%2B%2Buf322ztcLYwxxuPxmJEjR5o1a9aY48ePm3/84x9m3LhxZvv27aa2ttakp6ebF154wZw4ccI8/fTTJjMz0xw8eNAYY0xpaakZN26c2bdvnzl69Kj56U9/aiZPnmxaW1sDvKovZsOGDWb8%2BPHmxhtvNEVFRZa%2B9tbgtttuM7feeqs5fPiwOXTokMnLyzP33nvvRV9jMCNgBaGWlhYzY8YM8z//8z%2B%2BgPXoo4%2BaG264wbJdUVGRWbZsmTHGmFtvvdWsWLHCMsaoUaPMtm3bLt7EL4CzBayOWg9jjMnJyTHPPfdcm/aOXJMz6urqzIgRI0xdXV2Hq8e///1v07dvX7Nv3z5f269//Wtz9dVXd7haGGPMrl27THp6umlubva1bdq0yfzgBz8wy5cvNwUFBZbtZ8yYYdavX2%2BMMWbSpElm48aNvr6jR4%2BatLQ0s2fPnoszeZts3LjRNDQ0mCVLlrQJWO2pgdvtNv369TN79%2B719VdUVJhBgwaZU6dOXcAVhRZuEQah3/72t4qOjtbkyZN9bTU1NUpLS7Nsl5aW5ruM/%2Bl%2Bh8Oh/v37q6qq6uJM%2BgJau3atxo4dq2HDhmnZsmVqbGzssPX44IMPdODAAX300UeaOHGiRo4cqcLCQh05cqTD1uSTHnroIU2fPl2XXXZZh6tHSkqK%2BvfvL5fLpcbGRh0%2BfFjl5eUaO3Zsh6vFGREREZbv4%2BPjtXfv3rPWo6qqSidOnNC%2Bffss/V27dtVXv/rVkKvHTTfdpNjY2M/sa08N9u7dq8jISMsjLAMGDNDx48f19ttvX5jFhCACVpD58MMPVVxcrLvvvtvS7vV6FRcXZ2lLSEiQx%2BPx9cfHx1v64%2BPjff2hatCgQcrKylJ5eblcLpdef/11LV%2B%2BvMPW49ChQ5KkZ599Vk888YSefvppHTp0SHfddVeHrckZBw4cUHl5ub7//e9L6njHjMPhUHFxsZ5//nkNGTJEWVlZam5u1qJFizpcLSRp8ODB6tSpkx566CE1NTXpvffe069//Wt99NFHn7vejz76SMaYsKvHp7WnBl6vV127drUE2DPbhlON2ouAFWRWrlypadOm6YorrvjCrzXGXIAZBZbL5dKMGTMUFRWlr3/961q8eLG2bdum06dPn/O14ViPM2uaPXu2UlJS1KNHD91%2B%2B%2B3auXPnF3p9ONq8ebPGjx%2Bv7t27%2B/2acKrHqVOnNHfuXF1zzTX629/%2Bpj/96U%2BKjY3V4sWL/Xp9ONVC%2Bvh/%2BA8//LAqKys1atQo/ehHP9L111%2BvyMhISedeb7jV47O0pwYdoT7tRcAKIpWVldqzZ48KCgra9CUmJsrr9VraPB6PkpKSztrv9Xp9/eHi8ssvV0tLixwOR4esx6WXXipJlqsRPXv2lDFGp0%2Bf7pA1OeO5555TTk6O7/uOdsxUVlbqwIEDWrhwoWJjY5WSkqLCwkL97//%2Bb4c9XoYNG6aysjK99tprcrlcSkhIUEpKyueuNyEh4TPr5fV61a1bt4s5/QuqPTVISkrSsWPH1NLSYumTFFY1ai8CVhDZsmWLDh8%2BrHHjxmnkyJGaNm2aJGnkyJHq27dvm1%2Bbrq6uVmZmpiQpPT1dNTU1vr6WlhbV1tb6%2BkNRbW2tVq1aZWnbv3%2B/oqKilJ2d3eHqIUk9evRQ165dtXfvXl9bXV2dLrnkkg5bE0nau3ev6urqNGrUKF9bRkZGh6pHS0uLWltbLVcWTp06JUnKysrqULWQpJMnT%2BoPf/iDjh075mt7%2BeWXNXjwYKWnp7epR1VVlTIzMxUdHa0%2BffpY6tHQ0KD33ntPAwcOvGjzv9DaU4P%2B/fvLGKM333zT8tq4uDj17t37oq0h6F385%2BpxNl6v1xw8eND3Z8%2BePaZv377m4MGDpq6uzgwePNj87ne/MydOnDAvvPCCGThwoO%2B3OCoqKszQoUPNnj17zPHjx01xcbHJzs42TU1NAV7V%2BTt06JAZNGiQWb9%2BvTl58qR5%2B%2B23zcSJE829995rPvzwww5XjzNWrFhhrrrqKvPuu%2B%2BaDz/80OTl5Zk77rijQ9fkySefNCNGjLC0dbR6HDlyxIwYMcL8/Oc/N8ePHzdHjhwxc%2BfONd/5znc6XC2M%2Bfg3IXNycszPfvYzc/r0afPiiy%2BazMxMU11dbd566y2TkZFhdu3aZU6cOGHKysrM4MGDTX19vTHm49%2B%2BHDt2rO8jCpYtW2amT58e4BWdv8/6LcL21qCoqMjMnj3bHD582Bw8eNBMnz7drFq16qKuK9gRsILY%2B%2B%2B/7/uYBmOM%2Bctf/mKmTJliBgwYYMaPH9/mV/U3b95ssrOzTXp6usnPzzdvvfXWxZ6y7f7yl7%2BYvLw8M2jQIDNixAizcuVKc%2BLECV9fR6uHMcacPHnS3HPPPWb48OFm0KBBZsmSJebYsWPGmI5bk0ceecRMmjSpTXtHq0dVVZX57ne/a4YNG2aysrJMUVGROXTokDGm49XCGGP%2B/ve/m6lTp5qBAwea8ePHm/Lycl/fc889Z8aPH28GDBhgrr/%2BevOXv/zF19fa2moeeugh881vftMMHDjQ3HLLLb7Phwol6enpJj093fTr18/069fP9/0Z7alBQ0ODWbBggRk0aJAZPny4Wb58uTl58uRFXV%2BwizCGJ9UAAADsxDNYAAAANiNgAQAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADYjIAFAABgMwIWAACAzQhYAAAANvt/6VECbrLZTygAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-1419710565776076634”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>784.256146465872</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>749.6665527793294</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>721.8231507597286</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>666.2345126601743</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>623.52054283565</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>610.8893780654645</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>629.814434686419</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>746.0213064145971</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>489.04461706906</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>702.9736436032449</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (42)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-1419710565776076634”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>402.0978044666219</td> <td class=”number”>14</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>452.12153614828173</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:68%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>487.5571873084633</td> <td class=”number”>62</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:63%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>489.04461706906</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>491.9339769542199</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:54%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>761.4471253362104</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:23%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>784.256146465872</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>939.1304521890828</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1036.4801074998122</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1043.8608605056472</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PC_FFRSALES12”>PC_FFRSALES12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>52</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>599.92</td>

</tr> <tr>

<th>Minimum</th> <td>364.11</td>

</tr> <tr>

<th>Maximum</th> <td>1035.4</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram1501659116566553094”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives1501659116566553094,#minihistogram1501659116566553094”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives1501659116566553094”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles1501659116566553094”

aria-controls=”quantiles1501659116566553094” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram1501659116566553094” aria-controls=”histogram1501659116566553094”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common1501659116566553094” aria-controls=”common1501659116566553094”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme1501659116566553094” aria-controls=”extreme1501659116566553094”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles1501659116566553094”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>364.11</td>

</tr> <tr>

<th>5-th percentile</th> <td>470.64</td>

</tr> <tr>

<th>Q1</th> <td>530.27</td>

</tr> <tr>

<th>Median</th> <td>611.29</td>

</tr> <tr>

<th>Q3</th> <td>650.72</td>

</tr> <tr>

<th>95-th percentile</th> <td>728.77</td>

</tr> <tr>

<th>Maximum</th> <td>1035.4</td>

</tr> <tr>

<th>Range</th> <td>671.28</td>

</tr> <tr>

<th>Interquartile range</th> <td>120.46</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>78.702</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.13119</td>

</tr> <tr>

<th>Kurtosis</th> <td>-0.1102</td>

</tr> <tr>

<th>Mean</th> <td>599.92</td>

</tr> <tr>

<th>MAD</th> <td>64.326</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.33739</td>

</tr> <tr>

<th>Sum</th> <td>1878900</td>

</tr> <tr>

<th>Variance</th> <td>6194</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram1501659116566553094”>

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A0Lw5HrCqq6sVHh5evxCXS1VVVdb288ILL2jr1q3atGmT3nrrLXXu3Fl/%2BMMfVFpaqunTp2vSpEnKy8vTggULtGjRIuXn50uS9uzZo9WrV%2Buxxx7TwYMHNXToUE2bNk1nz561VhsAAGjeHA9YAwcO1PLly3Xq1Cnv2JdffqnFixf73KqhsZ599lnNnj1bP/3pTxUREaGFCxdq4cKF2rZtmzp27KiMjAyFh4crNTVV6enpys3NlSTl5ORo/PjxSkpKUsuWLTV16lRJ0t69e63VBgAAmjfHA9aDDz6ovLw8XXfddUpOTtaAAQM0ZMgQFRQU6JFHHrGyjy%2B//FLHjx/XqVOnNHr0aKWkpGjmzJkqLy9XYWGhEhISfLZPSEjwvg343XmXy6UePXp4z3ABAAB8H8dv03D11Vfr1Vdf1ZtvvqnPP/9cLpdLnTp10uDBgxUaGmplHyUlJZKk1157Tc8995yMMZo5c6YWLlyo6upqxcfH%2B2wfHR0tt9stSfJ4PIqKivKZj4qK8s43RGlpqcrKynzGwsJaKy4u7ocsxy9CQ10%2BP4MRPcC3BfNxwGuBHkj04FI5HrAkqUWLFho%2BfHiTPb8xRpI0depUb5iaMWOG7rrrLqWmpjb48T9UTk6O1qxZ4zOWnZ2tmTNnNup5/SEyspW/S/A7egCJ40CiBxI9kOhBQzkesL744gutWrVKf/vb31RdXV1v/o033mj0Pq666ipJUmRkpHesQ4cOMsbowoUL9e635Xa7FRsbK0mKiYmpN%2B/xeNSlS5cG7z8zM1Pp6ek%2BY2FhreV2V17SOvwpNNSlyMhWqqioUm1tnb/L8Qt6gG8L5uOA1wI9kAK3BzExbfyyX8cD1oMPPqjS0lINHjxYrVu3bpJ9tG/fXhERESoqKlLPnj0lScXFxbriiiuUlpamLVu2%2BGxfUFCgpKQkSVJiYqIKCwt16623SpJqa2t1%2BPBhZWRkNHj/cXFx9d4OLCs7rZqawDkgv1FbWxeQddtEDyBxHEj0QKIHEj1oKMcDVkFBgd544w3vGaOmEBYWpoyMDD311FMaOHCgIiIitHbtWo0dO1a33nqrfve73yk3N1fjxo3TO%2B%2B8o/379ysnJ0eSlJWVpTlz5uimm25St27d9Mwzz6hFixYaMmRIk9ULAACaF8cD1pVXXtlkZ66%2Bbe7cuTp//rwmTpyoCxcuaOTIkVq4cKHatGmj9evX65FHHtHixYvVoUMHrVixQt27d5ck3XDDDZozZ45mzZqlkydPqlevXtqwYYNatmzZ5DUDAIDmIcQ09oruS5Sbm6v//d//1dy5cxUSEuLkrv2qrOy0v0u4JGFhLsXEtJHbXRm0p4IDrQc3PvG2v0tott57dFTAHAdNIdBeC02BHgRuD9q1a%2BuX/Tp%2BBuvNN9/U%2B%2B%2B/r5dfflk/%2BclP5HL5ftzzxRdfdLokAAAAqxwPWBEREbrhhhuc3i0AAIBjHA9Yy5Ytc3qXAAAAjvLL7Vg/%2BeQTrV69Wg888IB37NChQ/4oBQAAwDrHA1ZeXp7GjRun3bt3a/v27ZK%2Bvvno5MmTrdxkFAAAwN8cD1iPP/647rvvPm3bts37KcKrr75ay5cv19q1a50uBwAAwDrHA9Zf//pXZWVlSZLPbRpGjRqlY8eOOV0OAACAdY4HrLZt2170OwhLS0vVokULp8sBAACwzvGA1a9fPy1dulRnzpzxjn366aeaP3%2B%2BrrvuOqfLAQAAsM7x2zQ88MAD%2BtWvfqWUlBTV1taqX79%2BqqqqUpcuXbR8%2BXKnywEAALDO8YDVvn17bd%2B%2BXfv379enn36qli1bqlOnTho0aFBQfXUOAABovhwPWJJ0xRVXaPjw4f7YNQAAQJNzPGClp6f/wzNV3AsLAAAEOscD1ujRo30CVm1trT799FPl5%2BfrV7/6ldPlAAAAWOd4wJo3b95Fx3ft2qW//OUvDlcDAABgn1%2B%2Bi/Bihg8frldffdXfZQAAADTaZROwDh8%2BLGOMv8sAAABoNMffIpw0aVK9saqqKh07dkwjRoxwuhwAAADrHA9YHTt2rPcpwvDwcGVkZGjixIlOlwMAAGCd4wGLu7UDAIDmzvGA9corrzR421tuuaUJKwEAAGgajgesBQsWqK6urt4F7SEhIT5jISEhBCwAABCQHA9YTz/9tJ599llNmzZN3bp1kzFGR44c0e9//3vdfvvtSklJcbokAAAAq/xyDdaGDRsUHx/vHRswYICuvvpq3Xnnndq%2BfbvTJQEAAFjl%2BH2wPvvsM0VFRdUbj4yMVHFxsdPlAAAAWOd4wOrQoYOWL18ut9vtHauoqNCqVat0zTXXOF0OAACAdY6/Rfjggw9q7ty5ysnJUZs2beRyuXTmzBm1bNlSa9eudbocAAAA6xwPWIMHD9a%2Bffu0f/9%2BlZSUyBij%2BPh4XX/99Wrbtq3T5QAAAFjneMCSpFatWmnYsGEqKSnR1Vdf7Y8SAAAAmozj12BVV1dr/vz56tu3r2688UZJX1%2BDNXXqVFVUVDhdDgAAgHWOB6wVK1aoqKhIK1eulMv1/%2B%2B%2BtrZWK1eudLocAAAA6xwPWLt27dK///u/a9SoUd4vfY6MjNSyZcu0e/dup8sBAACwzvGAVVlZqY4dO9Ybj42N1dmzZ50uBwAAwDrHL3K/5ppr9Je//EUpKSk%2B3z342muv6cc//rHT5QB/189XHvB3CQCAAOV4wPrFL36hGTNmaMKECaqrq9Nzzz2ngoIC7dq1SwsWLHC6HAAAAOscD1iZmZkKCwvT888/r9DQUD311FPq1KmTVq5cqVGjRjldDgAAgHWOB6zy8nJNmDBBEyZMcHrXAAAAjnD8Ivdhw4b5XHsFAADQ3DgesFJSUrRz506ndwsAAOAYx98i/NGPfqRHH31UGzZs0DXXXKMrrrjCZ37VqlVOlwQAAGCV4wHr6NGj%2BulPfypJcrvdTu8eAACgyTkWsGbPnq3HH39c//Ef/%2BEdW7t2rbKzs50qAQAAwBGOXYO1Z8%2BeemMbNmxwavcAAACOcSxgXeyTg3yaEAAANEeOBaxvvtj5%2B8YAAAACneO3aQAAAGjuCFgAAACWOfYpwgsXLmju3LnfO8Z9sAAAQKBzLGD1799fpaWl3zsGAAAQ6BwLWN%2B%2B/5XTli5dqo0bN%2BrIkSOSpLy8PK1atUqffPKJfvSjH%2Bmee%2B7RuHHjvNtv2rRJL7zwgsrKytStWzctWLBAiYmJ/iofAAAEmGZ/DVZRUZG2bNni/XtpaammT5%2BuSZMmKS8vTwsWLNCiRYuUn58v6ev7da1evVqPPfaYDh48qKFDh2ratGk6e/asv5YAAAACTLMOWHV1dXrooYc0ZcoU79i2bdvUsWNHZWRkKDw8XKmpqUpPT1dubq4kKScnR%2BPHj1dSUpJatmypqVOnSpL27t3rjyUAAIAA5Ph3ETrpxRdfVHh4uMaOHasnnnhCklRYWKiEhASf7RISErRz507v/OjRo71zLpdLPXr0UH5%2BvsaMGdOg/ZaWlqqsrMxnLCysteLi4hqzHEeFhrp8fgLBLphfC/w%2BoAcSPbhUzTZgffXVV1q9enW9a788Ho/i4%2BN9xqKjo71fPO3xeBQVFeUzHxUVdUlfTJ2Tk6M1a9b4jGVnZ2vmzJmXsoTLQmRkK3%2BXAFwWeC3QA4keSPSgoZptwFq2bJnGjx%2Bvzp076/jx45f02MZ%2BhU9mZqbS09N9xsLCWsvtrmzU8zopNNSlyMhWqqioUm1tnb/LAfwumF8L/D6gB1Lg9iAmpo1f9tssA1ZeXp4OHTqk7du315uLiYmRx%2BPxGXO73YqNjf278x6PR126dGnw/uPi4uq9HVhWdlo1NYFzQH6jtrYuIOsGbOO1QA8keiDRg4Zqlm%2Bkbt26VSdPntTQoUOVkpKi8ePHS5JSUlLUtWtXFRQU%2BGxfUFCgpKQkSVJiYqIKCwu9c7W1tTp8%2BLB3HgAA4Ps0y4B1//33a9euXdqyZYu2bNmiDRs2SJK2bNmisWPHqri4WLm5uTp37pz279%2Bv/fv367bbbpMkZWVl6ZVXXtEHH3ygqqoqrVu3Ti1atNCQIUP8uCIAABBImuVbhFFRUT4XqtfU1EiS2rdvL0lav369HnnkES1evFgdOnTQihUr1L17d0nSDTfcoDlz5mjWrFk6efKkevXqpQ0bNqhly5bOLwQAAASkENPYK7rRIGVlp/1dwiUJC3MpJqaN3O7KoH2v/cYn3vZ3CbhMvPfoqKB%2BLfD7gB5IgduDdu3a%2BmW/zfItQgAAAH8iYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgWZi/C0Dw4MuTAQDBgjNYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAy8L8XQAAXO4GLHjN3yVckp2zBvm7BCDocQYLAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGXNNmAVFxcrOztbKSkpSk1N1f3336%2BKigpJUlFRkW6//Xb1799fI0aM0LPPPuvz2B07dmjs2LHq27evxo8fr7feessfSwAAAAGq2QasadOmKTIyUnv27NHLL7%2Bsv/3tb/rXf/1XVVdX65577tE//dM/6cCBA3r88ce1fv167d69W9LX4Wv%2B/PmaN2%2Be3nnnHU2ZMkX33nuvSkpK/LwiAAAQKJplwKqoqFBiYqLmzp2rNm3aqH379rr11lv13nvvad%2B%2Bfbpw4YJ%2B/etfq3Xr1urZs6cmTpyonJwcSVJubq7S0tKUlpam8PBwjRs3Tl27dtXWrVv9vCoAABAommXAioyM1LJly3TVVVd5x06cOKG4uDgVFhaqW7duCg0N9c4lJCSooKBAklRYWKiEhASf50tISFB%2Bfr4zxQMAgIAX5u8CnJCfn6/nn39e69at086dOxUZGekzHx0dLY/Ho7q6Onk8HkVFRfnMR0VF6ejRow3eX2lpqcrKynzGwsJaKy4u7ocvwmGhoS6fnwACR1iY3dctvw/ogUQPLlWzD1j/8z//o1//%2BteaO3euUlNTtXPnzotuFxIS4v1vY0yj9pmTk6M1a9b4jGVnZ2vmzJmNel5/iIxs5e8SAFyimJg2TfK8/D6gBxI9aKhmHbD27Nmj%2B%2B67T4sWLdItt9wiSYqNjdVnn33ms53H41F0dLRcLpdiYmLk8XjqzcfGxjZ4v5mZmUpPT/cZCwtrLbe78octxA9CQ12KjGyliooq1dbW%2BbscAJfA9u8afh/QAylwe9BU/%2BD4Ps02YL3//vuaP3%2B%2BnnzySQ0ePNg7npiYqD/96U%2BqqalRWNjXy8/Pz1dSUpJ3/pvrsb6Rn5%2BvMWPGNHjfcXFx9d4OLCs7rZqawDkgv1FbWxeQdQPBrKles/w%2BoAcSPWioZvlGak1NjRYuXKh58%2Bb5hCtJSktLU0REhNatW6eqqip9%2BOGH2rx5s7KysiRJt912mw4ePKh9%2B/bp3Llz2rx5sz777DONGzfOH0sBAAABKMQ09oKjy9B7772nX/7yl2rRokW9uddee02VlZV66KGHVFBQoKuuukp33XWXfvGLX3i32b17t1atWqXi4mJ17txZCxYs0MCBAxtVU1nZ6UY93mlhYS7FxLSR211p7V8qNz7xtpXnAfCP7Zw1yOrzNcXvg0BDDwK3B%2B3atfXLfpvlW4QDBgzQkSNH/uE2f/rTn/7u3IgRIzRixAjbZQEAgCDRLN8iBAAA8CcCFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwL83cBaJwbn3jb3yUAAIDv4AwWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFgOjCKdAAAL7UlEQVRGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAuori4WHfffbdSUlI0dOhQrVixQnV1df4uCwAABIgwfxdwOZoxY4Z69uyp119/XSdPntQ999yjq666SnfccYe/SwMAAAGAM1jfkZ%2Bfr48//ljz5s1T27Zt1bFjR02ZMkU5OTn%2BLg0AAAQIzmB9R2FhoTp06KCoqCjvWM%2BePfXpp5/qzJkzioiI%2BN7nKC0tVVlZmc9YWFhrxcXFWa8XAL4rLMzuv51DQ10%2BP236%2BcoD1p%2BzKe2Zn%2BbvEvymKY%2BD5oiA9R0ej0eRkZE%2BY9%2BELbfb3aCAlZOTozVr1viM3XvvvZoxY4a9Qv%2Bf9x4dZf05pa9DYk5OjjIzM4M2GNIDeiDRA%2BnrHmzc%2BHST9KCpfofZ9s1xUF3dj%2BMgiF8Ll4IYehHGmEY9PjMzUy%2B//LLPn8zMTEvVOaOsrExr1qypdyYumNADeiDRA4keSPRAogeXijNY3xEbGyuPx%2BMz5vF4FBISotjY2AY9R1xcHOkeAIAgxhms70hMTNSJEydUXl7uHcvPz1fnzp3Vpk0bP1YGAAACBQHrOxISEtSrVy%2BtWrVKZ86c0bFjx/Tcc88pKyvL36UBAIAAEfrwww8/7O8iLjfXX3%2B9tm/friVLlujVV19VRkaG7rzzToWEhPi7NEe1adNGycnJQX3mjh7QA4keSPRAogcSPbgUIaaxV3QDAADAB28RAgAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwApyS5cuVbdu3bx/z8vLU0ZGhvr166cxY8Zo69atPttv2rRJI0eOVL9%2B/ZSVlaWCggKnS7aiW7duSkxMVK9evbx/lixZIil4eiBJ69at0%2BDBg9WnTx9NmTJFx48flxQcPfjv//5vn///vXr1UmJiovf1EAw9kKTDhw9r8uTJGjBggAYNGqR58%2BZ5v%2Bw%2BWHpQUFCgyZMnq3///rr%2B%2Buv1zDPPeOd27NihsWPHqm/fvho/frzeeust71xdXZ0ef/xxDRs2TAMHDtSdd96pL774wh9L%2BEEOHDig1NRUzZ49u95cY9bt8Xg0a9YspaamavDgwVqwYIGqq6sdWdNlxSBoHT582CQnJ5uuXbsaY4z58ssvTZ8%2BfUxubq6prq42b7/9tundu7f56KOPjDHGvPHGG2bAgAHmgw8%2BMFVVVWb9%2BvVm0KBBprKy0p/L%2BEG6du1qvvjii3rjwdSD559/3owaNcocO3bMnD592ixZssQsWbIkqHrwXevWrTO/%2Bc1vgqYHFy5cMIMGDTKrVq0y586dM%2BXl5eaOO%2B4wM2bMCJoeuN1uk5KSYlauXGnOnj1r/vrXv5qhQ4eaHTt2mMOHD5vExESzb98%2BU11dbbZs2WKSkpLMiRMnjDHGbNq0yQwdOtQcPXrUnD592vz2t781Y8eONXV1dX5e1ffbsGGDGTFihJk0aZKZNWuWz1xj133vvfeau%2B%2B%2B25w8edKUlJSYzMxMs2TJEsfX6G8ErCBVW1trJk6caH73u995A9bTTz9tbrnlFp/tZs2aZRYtWmSMMebuu%2B82S5cu9XmOQYMGme3btztXuCV/L2AFUw/S09PNrl276o0HUw%2B%2Brbi42CQnJ5vi4uKg6cH//d//ma5du5qjR496x/74xz%2Ba4cOHB00P9u7daxITE01NTY137Pnnnzf/8i//YhYvXmyys7N9tp84caJZv369McaYMWPGmI0bN3rnTp8%2BbRISEsyhQ4ecKb4RNm7caCoqKsz8%2BfPrBazGrLusrMx0797dFBUVeef3799v%2BvTpY86fP9%2BEK7r88BZhkHrxxRcVHh6usWPHescKCwuVkJDgs11CQoL3tP93510ul3r06KH8/HxnirZs1apVGjJkiAYMGKBFixapsrIyaHrw5Zdf6vjx4zp16pRGjx6tlJQUzZw5U%2BXl5UHTg%2B968sknNWHCBP34xz8Omh7Ex8erR48eysnJUWVlpU6ePKndu3dryJAhQdMDSQoJCfH5e1RUlIqKiv5uD/Lz81VdXa2jR4/6zEdEROjaa68NiB5MnjxZbdu2vehcY9ZdVFSk0NBQn0tPevbsqbNnz%2BqTTz5pmsVcpghYQeirr77S6tWr9dBDD/mMezweRUZG%2BoxFR0fL7XZ756Oionzmo6KivPOBpE%2BfPkpNTdXu3buVk5OjDz74QIsXLw6aHpSUlEiSXnvtNT333HPasmWLSkpKtHDhwqDpwbcdP35cu3fv1h133CEpeF4LLpdLq1ev1htvvKF%2B/fopNTVVNTU1mjt3btD0oG/fvmrVqpWefPJJVVVV6fPPP9cf//hHnTp16h%2Bu8dSpUzLGNIsefFdj1u3xeBQREeETWr/ZNtD7cqkIWEFo2bJlGj9%2BvDp37nzJjzXGNEFFzsvJydHEiRPVokUL/exnP9O8efO0fft2Xbhw4Xsf2xx68M0apk6dqvj4eLVv314zZszQnj17LunxzcULL7ygESNGqF27dg1%2BTHPowfnz5zVt2jSNGjVK7733nt588021bdtW8%2BbNa9Djm0MPoqKitHbtWuXl5WnQoEG67777dPPNNys0NFTS96%2BxOfTgYhqz7ubak0tFwAoyeXl5OnTokLKzs%2BvNxcTEyOPx%2BIy53W7Fxsb%2B3XmPx%2BOdD2Q/%2BclPVFtbK5fLFRQ9uOqqqyTJ5wxFhw4dZIzRhQsXgqIH37Zr1y6lp6d7/x4sr4W8vDwdP35cc%2BbMUdu2bRUfH6%2BZM2fqv/7rv4LmtSBJAwYMUG5urt5//33l5OQoOjpa8fHx/3CN0dHRF%2B2Rx%2BPRlVde6WT51jVm3bGxsTpz5oxqa2t95iQFfF8uFQEryGzdulUnT57U0KFDlZKSovHjx0uSUlJS1LVr13ofsy4oKFBSUpIkKTExUYWFhd652tpaHT582DsfKA4fPqzly5f7jB07dkwtWrRQWlpaUPSgffv2ioiIUFFRkXesuLhYV1xxRdD04BtFRUUqLi7WoEGDvGO9evUKih7U1taqrq7O54zD%2BfPnJUmpqalB0YNz587pP//zP3XmzBnv2Ntvv62%2BffsqMTGxXg/y8/OVlJSk8PBwdenSxacHFRUV%2Bvzzz9W7d2/H6m8KjVl3jx49ZIzRxx9/7PPYyMhIderUybE1XBacv64e/uTxeMyJEye8fw4dOmS6du1qTpw4YYqLi03fvn3NSy%2B9ZKqrq82%2BfftM7969vZ8G2b9/v%2Bnfv785dOiQOXv2rFm9erVJS0szVVVVfl7VpSkpKTF9%2BvQx69evN%2BfOnTOffPKJGT16tFmyZIn56quvgqIHxhizdOlSM2zYMPPZZ5%2BZr776ymRmZpr7778/qHpgjDGbN282ycnJPmPB0oPy8nKTnJxs/u3f/s2cPXvWlJeXm2nTpplf/vKXQdOD2tpak56ebh577DFz4cIFc%2BDAAZOUlGQKCgrMkSNHTK9evczevXtNdXW1yc3NNX379jWlpaXGmK8/cTlkyBDv7QoWLVpkJkyY4OcVXZqLfYqwseueNWuWmTp1qjl58qQ5ceKEmTBhglm%2BfLmj67ocELCC3BdffOG9TYMxxrz77rtm3LhxpmfPnmbEiBH1Psb/wgsvmLS0NJOYmGiysrLMkSNHnC7ZinfffddkZmaaPn36mOTkZLNs2TJTXV3tnQuGHpw7d848/PDDZuDAgaZPnz5m/vz55syZM8aY4OmBMcY89dRTZsyYMfXGg6UH%2Bfn55vbbbzcDBgwwqampZtasWaakpMQYEzw9%2BOijj8ytt95qevfubUaMGGF2797tndu1a5cZMWKE6dmzp7n55pvNu%2B%2B%2B652rq6szTz75pLnuuutM7969zV133eW9V9TlLjEx0SQmJpru3bub7t27e//%2Bjcasu6KiwsyePdv06dPHDBw40CxevNicO3fO0fVdDkKM4Wo0AAAAm7gGCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAs%2B/8AjVKJppsKz0UAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common1501659116566553094”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>728.7686454396865</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>666.1460080361885</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>635.0676982024834</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>677.7319606459758</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>567.2954634409169</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>608.807173221857</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>608.7322135075386</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>644.8528183806021</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>479.4927804167687</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>665.3183915386014</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (41)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme1501659116566553094”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>364.1120020193209</td> <td class=”number”>14</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>425.6834691595228</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>436.6822439664065</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>436.7831114750767</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:32%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>447.1769309921483</td> <td class=”number”>16</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:24%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>677.7319606459758</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:47%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>694.2785858317413</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>728.7686454396865</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>836.4084339862886</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1035.3916078727304</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PC_FSRSALES07”>PC_FSRSALES07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>52</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>624.16</td>

</tr> <tr>

<th>Minimum</th> <td>371.85</td>

</tr> <tr>

<th>Maximum</th> <td>1930.2</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram346922975645130063”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives346922975645130063,#minihistogram346922975645130063”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives346922975645130063”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles346922975645130063”

aria-controls=”quantiles346922975645130063” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram346922975645130063” aria-controls=”histogram346922975645130063”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common346922975645130063” aria-controls=”common346922975645130063”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme346922975645130063” aria-controls=”extreme346922975645130063”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles346922975645130063”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>371.85</td>

</tr> <tr>

<th>5-th percentile</th> <td>444.92</td>

</tr> <tr>

<th>Q1</th> <td>534.38</td>

</tr> <tr>

<th>Median</th> <td>617.34</td>

</tr> <tr>

<th>Q3</th> <td>711.77</td>

</tr> <tr>

<th>95-th percentile</th> <td>870.87</td>

</tr> <tr>

<th>Maximum</th> <td>1930.2</td>

</tr> <tr>

<th>Range</th> <td>1558.3</td>

</tr> <tr>

<th>Interquartile range</th> <td>177.39</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>128.03</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.20513</td>

</tr> <tr>

<th>Kurtosis</th> <td>6.3488</td>

</tr> <tr>

<th>Mean</th> <td>624.16</td>

</tr> <tr>

<th>MAD</th> <td>97.739</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.2853</td>

</tr> <tr>

<th>Sum</th> <td>1954900</td>

</tr> <tr>

<th>Variance</th> <td>16393</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram346922975645130063”>

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H1XsB8/8tkb%2BeztasoXqJIVlAVr5syZ%2Buyzz/TSSy8pKirKZy4hIUGFhYUaMmSIJKmqqkq7d%2B9Wenq6mjVrpsjISBUWFqpp06aSpH/%2B8586ffq0EhISar1%2BbGxsjdOBLtdxVVYG5klcVVUdsLXt7Er5mgX78SOfvZHP3q6GfIESdCdfP/zwQ61evVp5eXk1ypUkZWRk6NVXX9WOHTtUXl6uRYsWqW7duurVq5fCwsI0dOhQLV68WIcPH5bb7dYf//hH3XbbbbrmmmsCkAYAANiRLV/BSkxMlPTNbwhK0saNGyVJBQUFevnll3X8%2BHH17t3b5z5du3bVM888o549e%2BqRRx5Rdna2jhw5osTEROXl5alevXqSpKysLJWVlenOO%2B9UZWWlevfurWnTpvkvHAAAsL0Qc6lXdKNWXK7jfl/T4QhVdHRDud1lV8RLwD978p1Ab%2BGCrMvuHtD1r7TjZzXy2Rv57O1qyheoX1gKulOEAAAAgUbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGK2LlhvvfWWkpOTlZOTU2Pu9ddf18CBA9WpUyelpaXp7bff9s5VV1dr3rx56tOnj7p27ar77rtPBw4c8M57PB5lZ2crOTlZPXr00KRJk1RRUeGXTAAAwP78XrBSU1O1YMECHT58%2BJIe5%2Bmnn9aMGTPUvHnzGnN79uzR%2BPHjNW7cOL377rsaOXKkHnroIRUXF0uSli9frjVr1igvL0%2BbN29WixYtlJmZKWOMJGnKlCkqLy/X2rVr9fLLL6uoqEi5ubmXtF8AAHD18HvBuuuuu/T666/r1ltv1ahRo7RhwwZVVlZe8OOEh4dr5cqV5yxYK1asUEpKilJSUhQeHq5BgwapTZs2Wr16tSQpPz9fI0eO1A033KCIiAjl5OSoqKhIO3fu1Ndff62NGzcqJydHMTExatKkicaMGaOXX35ZZ86cueT8AAAg%2BPm9YGVmZur111/XX/7yF7Vu3VozZ85USkqK5syZoy%2B%2B%2BKLWjzNixAg1atTonHOFhYWKi4vzGYuLi1NBQYEqKiq0b98%2Bn/mIiAg1b95cBQUF2rNnj8LCwtS2bVvvfHx8vE6ePKnPP//8AtMCAICrkSNQC8fHxys%2BPl7/9V//pddff13Tpk3TM888o%2BTkZP3mN79R%2B/btL/qxPR6PIiMjfcYiIyO1b98%2BHTt2TMaYc8673W5FRUUpIiJCISEhPnOS5Ha7a7V%2BSUmJXC6Xz5jD0UCxsbEXE%2BeihYWF%2BnzEhXE4Avt1C/bjRz57I5%2B9ke/yC1jBOnPmjP72t7/plVde0bvvvqsWLVro4YcfVklJiUaOHKnp06dr4MCBF/34Z6%2Bnupj5H7rvD8nPz9eCBQt8xjIzM5WVlXVJj3uxnM76AVnX7qKjGwZ6C5KC//iRz97IZ2/ku3z8XrCKioq0cuVKvfrqqyorK1O/fv30/PPP66abbvLepmvXrpo2bdpFF6zo6Gh5PB6fMY/Ho5iYGEVFRSk0NPSc840bN1ZMTIxOnDihqqoqhYWFeeckqXHjxrVaf9iwYUpNTfUZczgayO0uu6g8FyssLFROZ32Vlparqqrar2sHA38fr%2B8K9uNHPnsjn71dTfkCVbL8XrAGDBigli1b6oEHHtDgwYMVFRVV4zYpKSk6evToRa%2BRkJCgXbt2%2BYwVFBRowIABCg8PV%2BvWrVVYWKhu3bpJkkpLS7V//361b99eTZs2lTFGn376qeLj4733dTqdatmyZa3Wj42NrXE60OU6rsrKwDyJq6qqA7a2nV0pX7NgP37kszfy2dvVkC9Q/H5yctmyZVq3bp1Gjhx5znJ11s6dOy96jaFDh2rbtm3asmWLTp06pZUrV%2BrLL7/UoEGDJEkZGRlatmyZioqKdOLECeXm5qpdu3ZKTExUTEyM%2BvXrpyeffFJHjx5VcXGxFi5cqPT0dDkcATujCgAAbMTvjaFt27YaPXq00tPTdeutt0qSnnvuOb3zzjuaM2fOeUvXtyUmJkqS9y0eNm7cKOmbV5vatGmj3NxczZo1S4cOHVKrVq20ZMkSXXvttZKk4cOHy%2BVy6Re/%2BIXKysqUlJTkc83UY489pqlTp6pPnz6qU6eO7rjjjnO%2BmSkAAMC5%2BL1gzZo1S8ePH1erVq28Y7169dJbb72l2bNna/bs2bV6nIKCgvPO9%2B3bV3379j3nXEhIiLKysr73ovNGjRrpj3/8Y632AQAA8F1%2BL1hvv/221qxZo%2BjoaO9YixYtlJubqzvuuMPf2wEAALCc36/BqqioUHh4eM2NhIaqvLzc39sBAACwnN8LVteuXTV79mwdO3bMO/bVV19p%2BvTpPm/VAAAAYFd%2BP0X46KOP6t5779XNN9%2BsiIgIVVdXq6ysTM2aNdMLL7zg7%2B0AAABYzu8Fq1mzZnrttdf097//Xfv371doaKhatmypHj16eN/YEwAAwM4C8sZOdevW9b5FAwAAQLDxe8E6cOCA5s6dq88%2B%2B0wVFRU15t98801/bwkAAMBSAbkGq6SkRD169FCDBg38vTwAAMBl5/eCtWvXLr355puKiYnx99IAAAB%2B4fe3aWjcuDGvXAEAgKDm94L1wAMPaMGCBTLG%2BHtpAAAAv/D7KcK///3v%2Buijj/TKK6/oxz/%2BsUJDfTven//8Z39vCQAAwFJ%2BL1gRERHq2bOnv5cFAADwG78XrFmzZvl7SQAAAL/y%2BzVYkvT5559r/vz5mjhxonfs448/DsRWAAAALOf3grV9%2B3YNGjRIGzZs0Nq1ayV98%2BajI0aM4E1GAQBAUPB7wZo3b55%2B%2B9vfas2aNQoJCZH0zd8nnD17thYuXOjv7QAAAFjO7wXrn//8pzIyMiTJW7Ak6fbbb1dRUZG/twMAAGA5vxesRo0anfNvEJaUlKhu3br%2B3g4AAIDl/F6wOnfurJkzZ%2BrEiRPesS%2B%2B%2BELjx4/XzTff7O/tAAAAWM7vb9MwceJE/fKXv1RSUpKqqqrUuXNnlZeXq3Xr1po9e7a/twMAAGA5vxes6667TmvXrtXWrVv1xRdfqF69emrZsqW6d%2B/uc00WAACAXfm9YElSnTp1dOuttwZiaQAAgMvO7wUrNTX1vK9U8V5YAADA7vxesPr37%2B9TsKqqqvTFF1%2BooKBAv/zlL/29HQAAAMv5vWCNGzfunOPr16/Xe%2B%2B95%2BfdAAAAWC8gf4vwXG699Va99tprgd4GAADAJbtiCtbu3btljLH08UaMGKEuXbqoe/fuGjdunI4ePSrpm7%2BHmJ6ers6dO2vAgAFavXq1z32XLVumfv36qXPnzsrIyNCuXbss2xcAAAh%2Bfj9FOHz48Bpj5eXlKioqUt%2B%2BfS1Zo7KyUvfff7/S0tK0dOlSlZWVaezYsZo2bZomT56sMWPGaNKkSRo4cKA%2B/PBDPfjgg2rZsqUSExO1adMmzZ8/X0uXLlXbtm21bNkyjR49Whs2bFCDBg0s2R8AAAhufn8Fq0WLFmrZsqXPf506ddL48eM1c%2BZMS9ZwuVxyuVy68847VbduXUVHR%2Bu2227Tnj17tGbNGrVo0ULp6ekKDw9XcnKyUlNTtWLFCklSfn6%2B0tLS1KFDB9WrV0%2BjRo2SJG3evNmSvQEAgODn91ew/PFu7U2aNFG7du2Un5%2Bv3/zmN6qoqNCGDRvUq1cvFRYWKi4uzuf2cXFxWrdunSSpsLBQ/fv3986FhoaqXbt2Kigo0IABAy773gEAgP35vWC9%2Buqrtb7t4MGDL2qN0NBQzZ8/XyNHjtTzzz8vSerWrZvGjh2rMWPGqEmTJj63j4qKktvtliR5PB5FRkb6zEdGRnrna6OkpEQul8tnzOFooNjY2IuJc9HCwkJ9PuLCOByB/boF%2B/Ejn72Rz97Id/n5vWBNmjRJ1dXVNS5oDwkJ8RkLCQm56IJ1%2BvRpjR49WrfffrtGjx6tkydPavr06d/7FhHfdakX2%2Bfn52vBggU%2BY5mZmcrKyrqkx71YTmf9gKxrd9HRDQO9BUnBf/zIZ2/kszfyXT5%2BL1hLly7VM888o9GjR6tt27Yyxmjv3r16%2Bumndc899ygpKemS19i%2BfbsOHjyoRx55RGFhYWrUqJGysrJ055136pZbbpHH4/G5vdvtVkxMjCQpOjq6xrzH41Hr1q1rvf6wYcOUmprqM%2BZwNJDbXXaRiS5OWFionM76Ki0tV1VVtV/XDgb%2BPl7fFezHj3z2Rj57u5ryBapkBeQarLy8PJ/TdF26dFGzZs103333ae3atZe8RlVVVY1XyU6fPi1JSk5O1l//%2Blef2%2B/atUsdOnSQJCUkJKiwsFBDhgzxPtbu3buVnp5e6/VjY2NrnA50uY6rsjIwT%2BKqquqArW1nV8rXLNiPH/nsjXz2djXkCxS/n5z88ssva1zjJElOp1OHDh2yZI1OnTqpQYMGmj9/vsrLy%2BV2u7Vo0SJ17dpVd955pw4dOqQVK1bo1KlT2rp1q7Zu3aqhQ4dKkjIyMvTqq69qx44dKi8v16JFi1S3bl316tXLkr0BAIDg5/eC1bRpU82ePdvnovHS0lLNnTtX119/vSVrREdH63/%2B53/00UcfqWfPnrrjjjtUr149zZ07V40bN9aSJUv04osv6qabbtLMmTM1Z84c3XjjjZKknj176pFHHlF2dra6deumbdu2KS8vT/Xq1bNkbwAAIPiFGCvfPr0W3n77bY0dO1alpaVq2LChQkNDdeLECdWrV08LFy7UzTff7M/t%2BI3LddzvazocoYqObii3u%2ByKeAn4Z0%2B%2BE%2BgtXJB12d0Duv6VdvysRj57I5%2B9XU35AvULS36/BqtHjx7asmWLtm7dquLiYhlj1KRJE91yyy1q1KiRv7cDAABgOb8XLEmqX7%2B%2B%2BvTpo%2BLiYjVr1iwQWwAAALhs/H4NVkVFhcaPH69OnTrpZz/7maRvrsEaNWqUSktL/b0dAAAAy/m9YM2ZM0d79uxRbm6uQkP/b/mqqirl5ub6ezsAAACW83vBWr9%2Bvf70pz/p9ttvV0hIiKRv3qJh1qxZ2rBhg7%2B3AwAAYDm/F6yysjK1aNGixnhMTIxOnjzp7%2B0AAABYzu8F6/rrr9d7770nyfdv/r3xxhv6j//4D39vBwAAwHJ%2B/y3Cn//853r44Yd11113qbq6Ws8%2B%2B6x27dql9evXa9KkSf7eDgAAgOX8XrCGDRsmh8OhF198UWFhYVq8eLFatmyp3Nxc3X777f7eDgAAgOX8XrCOHj2qu%2B66S3fddZe/lwYAAPALv1%2BD1adPH/n5r/MAAAD4ld8LVlJSktatW%2BfvZQEAAPzG76cIf/SjH%2Bn3v/%2B98vLydP3116tOnTo%2B83PnzvX3loBz4o9TAwAult8L1r59%2B/STn/xEkuR2u/29PAAAwGXnt4KVk5OjefPm6YUXXvCOLVy4UJmZmf7aAgAAgF/47RqsTZs21RjLy8vz1/IAAAB%2B47eCda7fHOS3CQEAQDDyW8E6%2B4edf2gMAADA7vz%2BNg0AAADBjoIFAABgMb/9FuGZM2c0duzYHxzjfbAAAIDd%2Ba1g3XTTTSopKfnBMQAAALvzW8H69vtfAQAABDOuwQIAALCY3/9UDqxlt7%2BXBwDA1YBXsAAAACxGwQIAALBYUBesRYsWqUePHurYsaNGjhypgwcPSpK2b9%2Bu9PR0de7cWQMGDNDq1at97rds2TL169dPnTt3VkZGhnbt2hWI7QMAAJsK2oK1fPlyrV69WsuWLdPbb7%2BtVq1a6bnnnlNJSYnGjBmj4cOHa/v27Zo0aZKmTJmigoICSd/8Uer58%2BfriSee0LZt29S7d2%2BNHj1aJ0%2BeDHAiAABgF0FbsJ555hnl5OToJz/5iSIiIjR58mRNnjxZa9asUYsWLZSenq7w8HAlJycrNTVVK1askCTl5%2BcrLS1NHTp0UL169TRq1ChJ0ubNmwMZBwAA2EhQ/hbhV199pYMHD%2BrYsWPq37%2B/jhw5oqSkJE2bNk2FhYWKi4vzuX1cXJzWrVsnSSosLFT//v29c6GhoWrXrp0KCgo0YMCAWq1fUlIil8vlM%2BZwNFBsbOwlJgO%2Bn8Nhr5%2BXwsJCfT4GG/LZG/ns7UrIF5QFq7i4WJL0xhtv6Nlnn5UxRllZWZo8ebIqKirUpEkTn9tHRUXJ7XZLkjwejyI/NUODAAAZ6UlEQVQjI33mIyMjvfO1kZ%2BfrwULFviMZWZmKisr62LiALUSHd0w0Fu4KE5n/UBv4bIin72Rz94CmS8oC5YxRpI0atQob5l6%2BOGH9etf/1rJycm1vv/FGjZsmFJTU33GHI4GcrvLLulxgfOx2/MrLCxUTmd9lZaWq6qqOtDbsRz57I189vbtfIEqWUFZsK655hpJktPp9I41bdpUxhidOXNGHo/H5/Zut1sxMTGSpOjo6BrzHo9HrVu3rvX6sbGxNU4HulzHVVkZfE9iXDns%2Bvyqqqq27d5rg3z2Rj57C2R5DMqTr9ddd50iIiK0Z88e79ihQ4dUp04dpaSk1HjbhV27dqlDhw6SpISEBBUWFnrnqqqqtHv3bu88AADADwnKguVwOJSenq7FixfrX//6l44cOaKFCxdq4MCBGjJkiA4dOqQVK1bo1KlT2rp1q7Zu3aqhQ4dKkjIyMvTqq69qx44dKi8v16JFi1S3bl316tUrsKEAAIBtBOUpQkkaO3asTp8%2BrbvvvltnzpxRv379NHnyZDVs2FBLlizRjBkzNH36dDVt2lRz5szRjTfeKEnq2bOnHnnkEWVnZ%2BvIkSNKTExUXl6e6tWrF%2BBEAADALkLMpV7RjVpxuY5flsfljz3jrHXZ3QO9hQvicIQqOrqh3O6yoLwGhHz2Rj57%2B3a%2BQP2GdVCeIgQAAAgkChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWuyoK1syZM9W2bVvv59u3b1d6ero6d%2B6sAQMGaPXq1T63X7Zsmfr166fOnTsrIyNDu3bt8veWAQCAjQV9wdqzZ49WrVrl/bykpERjxozR8OHDtX37dk2aNElTpkxRQUGBJGnTpk2aP3%2B%2BnnjiCW3btk29e/fW6NGjdfLkyUBFAAAANhPUBau6ulpTp07VyJEjvWNr1qxRixYtlJ6ervDwcCUnJys1NVUrVqyQJOXn5ystLU0dOnRQvXr1NGrUKEnS5s2bAxEBAADYkCPQG7ic/vznPys8PFwDBw7Uk08%2BKUkqLCxUXFycz%2B3i4uK0bt0673z//v29c6GhoWrXrp0KCgo0YMCAWq1bUlIil8vlM%2BZwNFBsbOylxAHOy%2BGw189LYWGhPh%2BDDfnsjXz2diXkC9qC9fXXX2v%2B/Pl64YUXfMY9Ho%2BaNGniMxYVFSW32%2B2dj4yM9JmPjIz0ztdGfn6%2BFixY4DOWmZmprKysC4kAXJDo6IaB3sJFcTrrB3oLlxX57I189hbIfEFbsGbNmqW0tDS1atVKBw8evKD7GmMuae1hw4YpNTXVZ8zhaCC3u%2BySHhc4H7s9v8LCQuV01ldpabmqqqoDvR3Lkc/eyGdv384XqJIVlAVr%2B/bt%2Bvjjj7V27doac9HR0fJ4PD5jbrdbMTEx3zvv8XjUunXrWq8fGxtb43Sgy3VclZXB9yTGlcOuz6%2Bqqmrb7r02yGdv5LO3QJbHoDz5unr1ah05ckS9e/dWUlKS0tLSJElJSUlq06ZNjbdd2LVrlzp06CBJSkhIUGFhoXeuqqpKu3fv9s4DAAD8kKAsWBMmTND69eu1atUqrVq1Snl5eZKkVatWaeDAgTp06JBWrFihU6dOaevWrdq6dauGDh0qScrIyNCrr76qHTt2qLy8XIsWLVLdunXVq1evACYCAAB2EpSnCCMjI30uVK%2BsrJQkXXfddZKkJUuWaMaMGZo%2BfbqaNm2qOXPm6MYbb5Qk9ezZU4888oiys7N15MgRJSYmKi8vT/Xq1fN/EAAAYEtBWbC%2B68c//rH27t3r/bxr164%2Bbz76XT//%2Bc/185//3B9bAwAAQSgoTxECAAAEEgULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAiwVtwTp06JAyMzOVlJSk5ORkTZgwQaWlpZKkPXv26J577tFNN92kvn376plnnvG57%2Buvv66BAweqU6dOSktL09tvvx2ICAAAwKaCtmCNHj1aTqdTmzZt0iuvvKLPPvtMf/jDH1RRUaEHHnhAP/3pT/XWW29p3rx5WrJkiTZs2CDpm/I1fvx4jRs3Tu%2B%2B%2B65Gjhyphx56SMXFxQFOBAAA7CIoC1ZpaakSEhI0duxYNWzYUNddd52GDBmiDz74QFu2bNGZM2f04IMPqkGDBoqPj9fdd9%2Bt/Px8SdKKFSuUkpKilJQUhYeHa9CgQWrTpo1Wr14d4FQAAMAuHIHewOXgdDo1a9Ysn7HDhw8rNjZWhYWFatu2rcLCwrxzcXFxWrFihSSpsLBQKSkpPveNi4tTQUFBrdcvKSmRy%2BXyGXM4Gig2NvZCowC15nDY6%2BelsLBQn4/Bhnz2Rj57uxLyBWXB%2Bq6CggK9%2BOKLWrRokdatWyen0%2BkzHxUVJY/Ho%2Brqank8HkVGRvrMR0ZGat%2B%2BfbVeLz8/XwsWLPAZy8zMVFZW1sWHAH5AdHTDQG/hojid9QO9hcuKfPZGPnsLZL6gL1gffvihHnzwQY0dO1bJyclat27dOW8XEhLi/d/GmEtac9iwYUpNTfUZczgayO0uu6THBc7Hbs%2BvsLBQOZ31VVparqqq6kBvx3Lkszfy2du38wWqZAV1wdq0aZN%2B%2B9vfasqUKRo8eLAkKSYmRl9%2B%2BaXP7Twej6KiohQaGqro6Gh5PJ4a8zExMbVeNzY2tsbpQJfruCorg%2B9JjCuHXZ9fVVXVtt17bZDP3shnb4Esj8F58lXSRx99pPHjx%2Bupp57ylitJSkhI0N69e1VZWekdKygoUIcOHbzzu3bt8nmsb88DAAD8kKAsWJWVlZo8ebLGjRunHj16%2BMylpKQoIiJCixYtUnl5uXbu3KmVK1cqIyNDkjR06FBt27ZNW7Zs0alTp7Ry5Up9%2BeWXGjRoUCCiAAAAGwrKU4Q7duxQUVGRZsyYoRkzZvjMvfHGG1q8eLGmTp2qvLw8XXPNNcrJyVGvXr0kSW3atFFubq5mzZqlQ4cOqVWrVlqyZImuvfbaACQBAAB2FJQFq0uXLtq7d%2B95b/PSSy9971zfvn3Vt29fq7cFAACuEkF5ihAAACCQKFgAAAAWC8pThMDV6GdPvhPoLdTauuzugd4CAFxWvIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIJ1DocOHdL999%2BvpKQk9e7dW3PmzFF1dXWgtwUAAGzCEegNXIkefvhhxcfHa%2BPGjTpy5IgeeOABXXPNNfrVr34V6K0BCICfPflOoLdwQdZldw/0FoCrHq9gfUdBQYE%2B/fRTjRs3To0aNVKLFi00cuRI5efnB3prAADAJngF6zsKCwvVtGlTRUZGesfi4%2BP1xRdf6MSJE4qIiPjBxygpKZHL5fIZczgaKDY21vL9AnbkcIQqLOybn%2B/OfoR17PSK29/G3RLoLZxTsD8/yXf5UbC%2Bw%2BPxyOl0%2BoydLVtut7tWBSs/P18LFizwGXvooYf08MMPW7fR/%2B%2BD39/%2BvXMlJSXKz8/XsGHDgrLckc/eSkpK9PzzS22R73z/zr7P1XD8gj2fXZ6fF%2BNqyud01g/IHoKzul4iY8wl3X/YsGF65ZVXfP4bNmyYRburPZfLpQULFtR4NS1YkM/eyGdv5LM38l1%2BvIL1HTExMfJ4PD5jHo9HISEhiomJqdVjxMbGBuVPBAAAoHZ4Bes7EhISdPjwYR09etQ7VlBQoFatWqlhw4YB3BkAALALCtZ3xMXFKTExUXPnztWJEydUVFSkZ599VhkZGYHeGgAAsImwadOmTQv0Jq40t9xyi9auXavHH39cr732mtLT03XfffcpJCQk0Fu7YA0bNlS3bt2C9tU38tkb%2BeyNfPZGvssrxFzqFd0AAADwwSlCAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbBsbubMmWrbtq338%2B3btys9PV2dO3fWgAEDtHr1ap/bL1u2TP369VPnzp2VkZGhXbt2%2BXvLtbZo0SL16NFDHTt21MiRI3Xw4EFJwZFx9%2B7dGjFihLp06aLu3btr3Lhx3j8wbsd8b731lpKTk5WTk1Nj7vXXX9fAgQPVqVMnpaWl6e233/bOVVdXa968eerTp4%2B6du2q%2B%2B67TwcOHPDOezweZWdnKzk5WT169NCkSZNUUVHhl0zfdr58GzZs0KBBg9SpUyf169dPf/nLX3zmz3e8Tp06pd/97nfq2bOnkpKSlJWVJbfbfdnzfNf58p1VVlamXr16acKECd6xYDh%2BX331lR588EF17NhRycnJmjt3rqqrqyUFR77ly5erX79%2B3ufnCy%2B84J2zS75Dhw4pMzNTSUlJSk5O1oQJE1RaWipJ2rNnj%2B655x7ddNNN6tu3r5555hmf%2B17K959LZmBbu3fvNt26dTNt2rQxxhjz1VdfmY4dO5oVK1aYiooK884775j27dubTz75xBhjzJtvvmm6dOliduzYYcrLy82SJUtM9%2B7dTVlZWSBjnNOLL75obr/9dlNUVGSOHz9uHn/8cfP4448HRcYzZ86Y7t27m7lz55pTp06Zo0ePml/96lfm4YcftmW%2BvLw807dvXzN8%2BHCTnZ3tM7d7926TkJBgtmzZYioqKsyqVatMhw4dzOHDh40xxixbtsz07t3b7Nu3zxw/ftw89thjZuDAgaa6utoYY8xDDz1k7r//fnPkyBFTXFxshg0bZh5//PErJt/OnTtNYmKi%2Bdvf/mbOnDljtmzZYuLj4837779vjPnh4zVr1iyTlpZm/v3vfxu3220eeugh88ADD1wx%2Bb5t1qxZ5qabbjLjx4/3jtn9%2BFVXV5v09HTz%2BOOPm%2BPHj5t9%2B/aZu%2B66y2zbti0o8m3ZssV06NDB7Nixw1RVVZkdO3aYDh06mM2bN9smnzHG3HHHHWbChAnmxIkT5vDhwyYtLc08%2Buijpry83Nxyyy1m/vz5pqyszOzatct069bNrF%2B/3hhz6d9/LhUFy6aqqqrM3Xffbf77v//bW7CWLl1qBg8e7HO77OxsM2XKFGOMMffff7%2BZOXOmz2N0797drF271n8br6XU1FTvP5JvC4aM//73v02bNm3Mvn37vGP/%2B7//a2699VZb5nv%2B%2BedNaWmpGT9%2BfI1v8NOnTzeZmZk%2BY3fffbdZsmSJMcaYAQMGmOeff947d/z4cRMXF2c%2B/vhj43K5zI033mj27Nnjnd%2B6davp2LGjOX369GVM5Ot8%2BbZu3WoWLFjgMzZkyBCzaNEiY8z5j9eZM2fMTTfdZDZu3Oid37dvn2nbtq0pLi6%2BjIl8nS/fWXv27DHdu3c3M2bM8ClYdj9%2B7733nklKSjKnTp06533tnm/BggUmPT3dZ%2Bzuu%2B82CxcuNMbYI9%2BxY8fMhAkTjMvl8o698MILpm/fvmbdunXmpz/9qamsrPTOzZkzx9x7773GmEv7/mMFThHa1J///GeFh4dr4MCB3rHCwkLFxcX53C4uLs57SuK786GhoWrXrp0KCgr8s%2Bla%2Buqrr3Tw4EEdO3ZM/fv39546OXr0aFBkbNKkidq1a6f8/HyVlZXpyJEj2rBhg3r16mXLfCNGjFCjRo3OOfd9eQoKClRRUaF9%2B/b5zEdERKh58%2BYqKCjQnj17FBYW5nMKPD4%2BXidPntTnn39%2BecKcw/ny9ezZU5mZmd7PKysr5XK51KRJE0nnP1779%2B/X8ePHFR8f752/4YYbVK9ePRUWFl6mNDWdL58kGWM0bdo05eTkyOl0eseD4fh9%2BOGHatOmjebNm6ekpCT16dPHe4opGPLdcsst2rdvn9577z2dPn1aH3/8sYqKitSjRw/b5HM6nZo1a5auueYa79jhw4cVGxurwsJCtW3bVmFhYd65832/PDtfm%2B8/VqBg2dDXX3%2Bt%2BfPna%2BrUqT7jHo/H5xugJEVFRXmv6fB4PIqMjPSZj4yMDMg1H%2BdTXFwsSXrjjTf07LPPatWqVSouLtbkyZODImNoaKjmz5%2BvN998U507d1ZycrIqKys1duzYoMj3befb77Fjx2SM%2Bd55j8ejiIgIhYSE%2BMxJumLz5ubmqkGDBurfv7%2Bk8%2Bf3eDySVON4O53OKypffn6%2BQkJClJaW5jMeDMevuLhYO3bsUOPGjbVlyxb97ne/07x587Rx48agyNe%2BfXtNnDhR9957rxITE3XPPfcoOztb7du3t22%2BgoICvfjii3rwwQe/9/ulx%2BNRdXX1JX3/sQIFy4ZmzZqltLQ0tWrV6oLva4y5DDuy1tk9jho1Sk2aNNF1112nhx9%2BWJs2bbqg%2B1%2BpTp8%2BrdGjR%2Bv222/XBx98oL///e9q1KiRxo0bV6v7X%2Bn5vuuH9nu%2BebtkNcZozpw5Wrt2rRYtWqTw8HCfuR%2B675XqyJEjeuqppzRt2jSf/6P9NjsfP2OMYmJiNGrUKNWvX18pKSm67bbbtG7dOp/bnO/%2BV7J3331Xc%2BfO1dKlS/XJJ5/o%2Beef1%2BLFi7Vx40bvbeyU78MPP9R9992nsWPHKjk5%2BXtv9%2B3naiD//VGwbGb79u36%2BOOPfU5LnBUdHe39qfgst9utmJiY7533eDze%2BSvF2ZeCv/2TSdOmTWWM0ZkzZ2yfcfv27Tp48KAeeeQRNWrUSE2aNFFWVpb%2B9re/KTQ01Pb5vu18%2B42KijpnXo/Ho8aNGysmJkYnTpxQVVWVz5wkNW7c%2BPJvvpaqq6s1YcIEbdq0SS%2B99JJ%2B8pOfeOfOl//sMfvu/LFjx66YfLNnz9bgwYN9ThOdFQzH79prr61xeq1p06ZyuVxBke%2Bll15S3759dfPNNys8PFxdunTRgAEDtHLlStvl27Rpk%2B6//349%2BuijGjFihCQpJiamxqtNHo/Hm%2B1Svv9YgYJlM6tXr9aRI0fUu3dvJSUleV%2B2T0pKUps2bWr8yv6uXbvUoUMHSVJCQoLPtR1VVVXavXu3d/5Kcd111ykiIkJ79uzxjh06dEh16tRRSkqK7TNWVVWpurra5yen06dPS5KSk5Ntn%2B/bEhISauQpKChQhw4dFB4ertatW/vkKS0t1f79%2B9W%2BfXu1a9dOxhh9%2BumnPvd1Op1q2bKl3zL8kJkzZ%2Bqzzz7TSy%2B9pGbNmvnMne94NWvWTJGRkT7z//znP3X69GklJCT4bf/ns3r1aq1cuVJJSUlKSkrS0qVL9dprrykpKSkojt8NN9ygAwcOqKyszDt26NAhNW3aNCjyVVdX%2BxQk6f%2B%2B19gp30cffaTx48frqaee0uDBg73jCQkJ2rt3ryorK332%2BO3vlxf7/ccKFCybmTBhgtavX69Vq1Zp1apVysvLkyStWrVKAwcO1KFDh7RixQqdOnVKW7du1datWzV06FBJUkZGhl599VXt2LFD5eXlWrRokerWratevXoFMFFNDodD6enpWrx4sf71r3/pyJEjWrhwoQYOHKghQ4bYPmOnTp3UoEEDzZ8/X%2BXl5XK73Vq0aJG6du2qO%2B%2B80/b5vm3o0KHatm2btmzZolOnTmnlypX68ssvNWjQIEnf5Fm2bJmKiop04sQJ5ebmql27dkpMTFRMTIz69eunJ598UkePHlVxcbEWLlyo9PR0ORyOACf7xocffqjVq1crLy9PUVFRNebPd7zCwsI0dOhQLV68WIcPH5bb7dYf//hH3XbbbT4X9AbS1q1btWbNGu/3m%2BHDhys1NVWrVq2SZP/jl5qaKqfTqSeeeEInT57U9u3btXHjRu8PrsGQb/369frggw9UWVmpTz75ROvWrdNtt90myR75KisrNXnyZI0bN049evTwmUtJSVFERIQWLVqk8vJy7dy5UytXrlRGRoakS/v%2BYwlLfhcRAXPgwAHv2zQYY8w//vEPM2jQIBMfH2/69u1b460Oli9fblJSUkxCQoLJyMgwe/fu9feWa%2BXUqVNm2rRppmvXrqZjx45m/Pjx5sSJE8aY4MhYUFBg7rnnHtOlSxeTnJxssrOzvb%2Bab7d8CQkJJiEhwdx4443mxhtv9H5%2B1vr1603fvn1NfHy8ufPOO80//vEP71x1dbV56qmnzM0332zat29vfv3rX3vfo8YYY0pLS01OTo7p2LGj6dq1q5k%2Bffr3/kp9IPJNnDjRZ%2Bzsf7/61a%2B89z/f8fr287xTp07mkUceMaWlpVdMvu/605/%2B5PM2DXY/fsYYs3fvXjN8%2BHCTmJhoUlJSzCuvvBJU%2BZ577jnTt29f06FDB9O3b1%2BzdOlS7/s82SHf%2B%2B%2B/b9q0aVPj31hCQoI5ePCg9/glJCSYXr16meXLl/vc/1K%2B/1yqEGOusKvYAAAAbI5ThAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABb7f%2BKks9ukQr8nAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common346922975645130063”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>658.5372403943636</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>711.7697982264114</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>739.1472646451974</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>534.3835830301039</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>617.3390065182529</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>529.1379539937614</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>646.2592516337282</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>665.1112958335699</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>476.7934104357549</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>654.2676818853976</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (41)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme346922975645130063”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>371.8450513207563</td> <td class=”number”>82</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:83%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>434.9249921475118</td> <td class=”number”>55</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:56%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>444.9218223984439</td> <td class=”number”>75</td> <td class=”number”>2.3%</td> <td>

<div class=”bar” style=”width:76%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>476.7934104357549</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>484.3815070495379</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:68%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>900.3162095952791</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:95%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>919.5131037446367</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1205.7142799950157</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:26%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1340.0143784300437</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1930.1558056074637</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PC_FSRSALES12”><s>PC_FSRSALES12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PC_FSRSALES07”><code>PC_FSRSALES07</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.91163</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PC_SNAPBEN10”>PC_SNAPBEN10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3051</td>

</tr> <tr>

<th>Unique (%)</th> <td>95.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>4.9%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>158</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>18.607</td>

</tr> <tr>

<th>Minimum</th> <td>1.0136</td>

</tr> <tr>

<th>Maximum</th> <td>76.285</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-3755730262451959402”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-3755730262451959402,#minihistogram-3755730262451959402”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-3755730262451959402”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-3755730262451959402”

aria-controls=”quantiles-3755730262451959402” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-3755730262451959402” aria-controls=”histogram-3755730262451959402”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-3755730262451959402” aria-controls=”common-3755730262451959402”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-3755730262451959402” aria-controls=”extreme-3755730262451959402”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-3755730262451959402”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>1.0136</td>

</tr> <tr>

<th>5-th percentile</th> <td>5.8894</td>

</tr> <tr>

<th>Q1</th> <td>11.362</td>

</tr> <tr>

<th>Median</th> <td>17.426</td>

</tr> <tr>

<th>Q3</th> <td>24.118</td>

</tr> <tr>

<th>95-th percentile</th> <td>35.953</td>

</tr> <tr>

<th>Maximum</th> <td>76.285</td>

</tr> <tr>

<th>Range</th> <td>75.271</td>

</tr> <tr>

<th>Interquartile range</th> <td>12.756</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>9.578</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.51474</td>

</tr> <tr>

<th>Kurtosis</th> <td>1.6256</td>

</tr> <tr>

<th>Mean</th> <td>18.607</td>

</tr> <tr>

<th>MAD</th> <td>7.4651</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.9958</td>

</tr> <tr>

<th>Sum</th> <td>56790</td>

</tr> <tr>

<th>Variance</th> <td>91.737</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-3755730262451959402”>

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Bqc2bN%2Buzzz5TZWVlnfPvvvuuDVMBAADUn9cF1ooVK1RYWKiBAweqZcuWdo8DAABw27wusLKzs/Xuu%2B8qPDzc7lEAAAAaxOs%2BpuGee%2B7hmSsAANCkeV1gzZ49W1u2bJFlWXaPAgAA0CBe9xLhe%2B%2B9p1OnTumtt97SfffdJ19fzwZ88803bZoMAACgfrwusFq1aqXBgwfbPQbQ5Ix86QO7R6i3g4sG2D0CANxRXhdY69evt3sEAACARvG692BJ0hdffKGUlBQ9%2B%2Byz7mOffPKJjRMBAADUn9cFVmZmpsaOHasjR44oIyND0rcfPjpjxgw%2BZBQAADQJXhdYL774on75y1/qwIED8vHxkfTtzyfcsGGDXn31VZunAwAAuDWvC6z//u//1rRp0yTJHViSNGLECJ07d86usQAAAOrN6wIrODj4pj%2BDsLCwUAEBATZMBAAAcHu8LrB69%2B6tdevW6fLly%2B5j58%2Bf17Jly/Twww/bOBkAAED9eN3HNDz77LN68sknFRMTo5qaGvXu3VsVFRV66KGHtGHDBrvHAwAAuCWvC6w2bdooIyNDJ06c0Pnz59WiRQu1b99eAwYM8HhPFgAAgLfyusCSpLvuukuPPvqo3WMAAAA0iNcFVlxc3D98porPwgIAAN7O6wJr1KhRHoFVU1Oj8%2BfPKysrS08%2B%2BaSNkwEAANSP1wVWcnLyTY8fPnxYf/nLX37gaQAAAG6f131Mw/d59NFH9fbbb9s9BgAAwC01mcA6c%2BaMLMuyewwAAIBb8rqXCKdOnVrnWEVFhc6dO6f4%2BHgbJgIAALg9XhdY7dq1q/O3CAMDA5WQkKDJkyfbNBUAAED9eV1g8WntAACgqfO6wNq7d2%2B9Lzt%2B/Pg7OAkAAEDDeF1grVy5UrW1tXXe0O7j4%2BNxzMfHh8ACAABeyesC6/XXX9f27ds1Z84cderUSZZl6ezZs/r3f/93TZ8%2BXTExMXaPCAAA8A95XWBt2LBB27ZtU2RkpPtY3759df/992vmzJnKyMiwcToAAIBb87rPwbpw4YJCQ0PrHA8JCVF%2Bfr4NEwEAANwerwustm3basOGDSotLXUfKysr0%2BbNm/XAAw/YOBkAAED9eN1LhCtWrNDSpUuVlpamoKAg%2Bfr66vLly2rRooVeffVVu8cDAAC4Ja8LrIEDB%2Br48eM6ceKELl68KMuyFBkZqUGDBik4ONju8QAAAG7J6wJLku6%2B%2B2498sgjunjxou6//367xwEAALgtXvcerMrKSi1btky9evXSyJEjJX37HqxZs2aprKzM5ukAAABuzesCa%2BPGjcrNzdWmTZvk6/v/x6upqdGmTZtsnAwAAKB%2BvC6wDh8%2BrFdeeUUjRoxw/9DnkJAQrV%2B/XkeOHLF5OgAAgFvzusAqLy9Xu3bt6hwPDw/XlStXfviBAAAAbpPXBdYDDzygv/zlL5Lk8bMHDx06pJ/85Cd2jQUAAFBvXve3CJ944gktWLBAkyZNUm1trXbs2KHs7GwdPnxYK1eutHs8AACAW/K6wEpMTJS/v792794tPz8/vfbaa2rfvr02bdqkESNG2D0eAADALXldYJWUlGjSpEmaNGmSsdtct26ddu7cqbNnz0qSMjMztXnzZn3xxRf68Y9/rNmzZ2vs2LHuy%2B/atUtvvPGGioqK1KlTJ61cuVLR0dHG5gEAAM7mdYH1yCOP6NSpU%2B6/QdhYubm52rdvn/vPhYWFmjdvnlauXKkxY8bo448/1ty5c9W%2BfXt169ZNR48eVUpKil5//XV16tRJu3bt0pw5c3TkyBG1bNnSyEwmjXzpA7tHAAAAN/C6N7nHxMTo4MGDRm6rtrZWzz//vJKSktzHDhw4oHbt2ikhIUGBgYGKjY1VXFyc0tPTJUlpaWmaOHGievTooRYtWmjWrFmSpGPHjhmZCQAAOJ/XPYP14x//WL/%2B9a%2B1bds2PfDAA7rrrrs8zm/evLnet/Xmm28qMDBQY8aM0UsvvSRJysnJUVRUlMfloqKi3FGXk5OjUaNGuc/5%2BvqqS5cuysrK0ujRoxu6FgAAaEa8LrA%2B//xzPfjgg5Kk0tLSBt/O119/rZSUFP3ud7/zOO5yuRQZGelxrHXr1u77crlcCg0N9TgfGhp6W7MUFhaqqKjI45i/f0tFRETczgpufn6%2BHr8DTZ2/v3Mey83l%2B5M9naW57WkHrwmsxYsX68UXX/QIoldffVXz589v0O2tX79eEydOVIcOHZSXl3db1/3u5281RFpamrZs2eJxbP78%2BVq4cGGjbjck5O5GXR/wFmFhQXaPYFxz%2Bf5kT2dpLnvawWsC6%2BjRo3WObdu2rUGBlZmZqU8%2B%2BUQZGRl1zoWFhcnlcnkcKy0tVXh4%2BPeed7lceuihh%2Bp9/4mJiYqLi/M45u/fUqWl5fW%2Bje/y8/NVSMjdKiurUE1NbYNuA/AmDf1e8EbN5fuTPZ2lue1pB68JrJs9a9TQZ5L279%2Bv4uJiDRs2zON2YmJi9POf/7xOeGVnZ6tHjx6SpOjoaOXk5GjChAmSvv0h02fOnFFCQkK97z8iIqLOy4FFRZd07VrjHsQ1NbWNvg3AGzjxcdxcvj/Z01may5528JoXX2/2sQwN/aiG5cuX6/Dhw9q3b5/27dunbdu2SZL27dunMWPGKD8/X%2Bnp6aqqqtKJEyd04sQJTZkyRZI0bdo07d27V6dPn1ZFRYVSU1MVEBCgoUOHNng3AADQvHjNM1gmhYaGerxR/dq1a5KkNm3aSJK2bt2qF154QWvWrFHbtm21ceNGde7cWZI0ePBgLVmyRIsWLVJxcbG6deumbdu2qUWLFj/8IgAAoElyZGDd6L777nN/irsk9evXz%2BPDR2/0xBNP6IknnvghRgMAAA7kNYFVXV2tpUuX3vLY7XwOFgAAgB28JrD69OmjwsLCWx4DAADwdl4TWDd%2BICgAAEBT5TV/ixAAAMApCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDHBtY%2Bfn5mj9/vmJiYhQbG6vly5errKxMkpSbm6vp06erT58%2Bio%2BP1/bt2z2u%2B84772jMmDHq1auXJk6cqPfff9%2BOFQAAQBPl2MCaM2eOQkJCdPToUb311lv67LPP9K//%2Bq%2BqrKzU7Nmz9bOf/Ux//vOf9eKLL2rr1q06cuSIpG/ja9myZUpOTtZ//dd/KSkpSb/4xS908eJFmzcCAABNhSMDq6ysTNHR0Vq6dKmCgoLUpk0bTZgwQR999JGOHz%2Bu6upqzZ07Vy1btlTXrl01efJkpaWlSZLS09M1ZMgQDRkyRIGBgRo7dqw6duyo/fv327wVAABoKvztHuBOCAkJ0fr16z2OFRQUKCIiQjk5OerUqZP8/Pzc56KiopSeni5JysnJ0ZAhQzyuGxUVpaysrHrff2FhoYqKijyO%2Bfu3VERExO2uIkny8/P1%2BB1o6vz9nfNYbi7fn%2BzpLM1tTzs4MrBulJWVpd27dys1NVUHDx5USEiIx/nWrVvL5XKptrZWLpdLoaGhHudDQ0P1%2Beef1/v%2B0tLStGXLFo9j8%2BfP18KFCxu%2BhKSQkLsbdX3AW4SFBdk9gnHN5fuTPZ2luexpB8cH1scff6y5c%2Bdq6dKlio2N1cGDB296OR8fH/c/W5bVqPtMTExUXFycxzF//5YqLS1v0O35%2BfkqJORulZVVqKamtlGzAd6god8L3qi5fH%2Byp7M0tz3t4OjAOnr0qH75y1/queee0/jx4yVJ4eHhunDhgsflXC6XWrduLV9fX4WFhcnlctU5Hx4eXu/7jYiIqPNyYFHRJV271rgHcU1NbaNvA/AGTnwcN5fvT/Z0luaypx0c%2B%2BLrqVOntGzZMr388svuuJKk6OhonT17VteuXXMfy8rKUo8ePdzns7OzPW7ru%2BcBAABuxZGBde3aNa1atUrJyckaOHCgx7khQ4aoVatWSk1NVUVFhf72t79pz549mjZtmiRpypQpOnnypI4fP66qqirt2bNHFy5c0NixY%2B1YBQAANEGOfInw9OnTOnfunF544QW98MILHucOHTqk1157Tc8//7y2bdume%2B%2B9V4sXL9bQoUMlSR07dtSmTZu0fv165efnq0OHDtq6dat%2B9KMf2bAJAABoihwZWH379tXZs2f/4WX%2B8z//83vPxcfHKz4%2B3vRYAACgmXDkS4QAAAB2IrAAAAAMI7AAAAAMc%2BR7sAB4t5EvfWD3CLfl4KIBdo8AoInhGSwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADDCCwAAADD/O0eAAC83ciXPrB7hNtycNEAu0cAmj2ewQIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMn0UIAA7TlH52Ij83EU7FM1gAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVgAAACGEVg3kZ%2Bfr2eeeUYxMTEaNmyYNm7cqNraWrvHAgAATQSfg3UTCxYsUNeuXfWnP/1JxcXFmj17tu6991499dRTdo8GAACaAALrBllZWfr000%2B1Y8cOBQcHKzg4WElJSdq5cyeBBQCGNaUPRZX4YFTUH4F1g5ycHLVt21ahoaHuY127dtX58%2Bd1%2BfJltWrV6pa3UVhYqKKiIo9j/v4tFRER0aCZ/Px8PX4HANijqQXhH5MH3fR4c/nvip37EVg3cLlcCgkJ8Th2PbZKS0vrFVhpaWnasmWLx7Ff/OIXWrBgQYNmKiws1M6drysxMbFOpH306xENuk1vVFhYqLS0tJvu6STs6Szs6SzNac/v%2B%2B%2BKk9i5p7PTtYEsy2rU9RMTE/XWW295/EpMTGzw7RUVFWnLli11nhVzGvZ0FvZ0FvZ0Fva883gG6wbh4eFyuVwex1wul3x8fBQeHl6v24iIiHD0/xEAAIB/jGewbhAdHa2CggKVlJS4j2VlZalDhw4KCgqycTIAANBUEFg3iIqKUrdu3bR582ZdvnxZ586d044dOzRt2jS7RwMAAE2E3%2BrVq1fbPYS3GTRokDIyMrR27Vq9/fbbSkhI0MyZM%2BXj42PbTEFBQerfv7/jn0VjT2dhT2dhT2dhzzvLx2rsO7oBAADggZcIAQAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwvFx%2Bfr6eeeYZxcTEaNiwYdq4caNqa2vtHqvR/vznPys2NlaLFy%2Buc%2B6dd97RmDFj1KtXL02cOFHvv/%2B%2BDROakZ%2Bfr/nz5ysmJkaxsbFavny5ysrKJEm5ubmaPn26%2BvTpo/j4eG3fvt3maRvu008/1ZNPPqk%2BffooNjZWixYtUlFRkSQpMzNTCQkJ6t27t0aPHq39%2B/fbPG3jrVu3Tp06dXL/2Wk7durUSdHR0erWrZv719q1ayU5b9fU1FQNHDhQPXv2VFJSkvLy8iQ5Z8%2B//vWvHl/Hbt26KTo62v34dcqeknTmzBnNmDFDffv21YABA5ScnKySkhJJNu1pwatNmDDBWrVqlVVWVmadP3/eio%2BPt7Zv3273WI2ybds2Kz4%2B3po6daq1aNEij3NnzpyxoqOjrePHj1uVlZXWvn37rB49elgFBQU2Tds4jz/%2BuLV8%2BXLr8uXLVkFBgTVx4kRrxYoVVkVFhTVo0CArJSXFKi8vt7Kzs63%2B/ftbhw8ftnvk21ZVVWU9/PDD1pYtW6yqqiqruLhe9ULZAAAIM0lEQVTYmj59ujVv3jzrq6%2B%2Bsnr27Gmlp6dblZWV1gcffGB1797d%2Bvvf/2732A125swZq3///lbHjh0ty7IcuWPHjh2tL7/8ss5xp%2B26e/dua8SIEda5c%2BesS5cuWWvXrrXWrl3ruD1vlJqaav3Lv/yLo/asrq62BgwYYG3evNmqqqqySkpKrKeeespasGCBbXvyDJYXy8rK0qeffqrk5GQFBwerXbt2SkpKUlpamt2jNUpgYKD27Nmjn/70p3XOpaena8iQIRoyZIgCAwM1duxYdezYsUn%2BX1VZWZmio6O1dOlSBQUFqU2bNpowYYI%2B%2BugjHT9%2BXNXV1Zo7d65atmyprl27avLkyU3ya1tRUaHFixdr9uzZCggIUHh4uB577DF99tlnOnDggNq1a6eEhAQFBgYqNjZWcXFxSk9Pt3vsBqmtrdXzzz%2BvpKQk9zGn7fiPOG3X7du3a/HixXrwwQfVqlUrrVq1SqtWrXLcnt/1f//3f9qxY4d%2B9atfOWrPoqIiFRUVady4cQoICFBYWJgee%2Bwx5ebm2rYngeXFcnJy1LZtW4WGhrqPde3aVefPn9fly5dtnKxxZsyYoeDg4Juey8nJUVRUlMexqKgoZWVl/RCjGRUSEqL169fr3nvvdR8rKChQRESEcnJy1KlTJ/n5%2BbnPRUVFKTs7245RGyU0NFSTJ0%2BWv7%2B/JOmLL77QH/7wB40cOfJ7v55NcU9JevPNNxUYGKgxY8a4jzltx%2Bs2b96soUOHqm/fvnruuedUXl7uqF2/%2Buor5eXl6ZtvvtGoUaMUExOjhQsXqqSkxFF73ujll1/WpEmT9JOf/MRRe0ZGRqpLly5KS0tTeXm5iouLdeTIEQ0dOtS2PQksL%2BZyuRQSEuJx7HpslZaW2jHSHedyuTyCUvp2Zyfsm5WVpd27d2vu3Lk3/dq2bt1aLperyb7HLj8/X9HR0Ro1apS6deumhQsXfu%2BeTfHr%2BfXXXyslJUXPP/%2B8x3En7Xhdz549FRsbqyNHjigtLU2nT5/WmjVrHLXrxYsXJUmHDh3Sjh07tG/fPl28eFGrVq1y1J7flZeXpyNHjuipp56S5KzHrq%2Bvr1JSUvTuu%2B%2Bqd%2B/eio2N1bVr17R06VLb9iSwvJxlWXaP8INz4s4ff/yxZs6cqaVLlyo2NvZ7L%2Bfj4/MDTmVW27ZtlZWVpUOHDunChQv61a9%2BZfdIRq1fv14TJ05Uhw4d7B7ljktLS9PkyZMVEBCgf/qnf1JycrIyMjJUXV1t92jGXP/3zKxZsxQZGak2bdpowYIFOnr0qM2T3TlvvPGG4uPj9aMf/cjuUYy7evWq5syZoxEjRuijjz7Se%2B%2B9p%2BDgYCUnJ9s2E4HlxcLDw%2BVyuTyOuVwu%2Bfj4KDw83Kap7qywsLCb7tyU9z169KieeeYZrVixQjNmzJD07df2xv97crlcat26tXx9m%2B63pY%2BPj9q1a6fFixcrIyND/v7%2Bdb6epaWlTe7rmZmZqU8%2B%2BUTz58%2Bvc%2B5mj9mmuOM/ct9996mmpka%2Bvr6O2fX6S/fffWajbdu2sixL1dXVjtnzuw4fPqy4uDj3n5302M3MzFReXp6WLFmi4OBgRUZGauHChfrjH/9o2%2BO26f6bvBmIjo5WQUGB%2B6%2BZSt%2B%2BzNShQwcFBQXZONmdEx0dXed18aysLPXo0cOmiRrn1KlTWrZsmV5%2B%2BWWNHz/efTw6Olpnz57VtWvX3Mea6p6ZmZkaPny4x0ub1yOxe/fudb6e2dnZTW7P/fv3q7i4WMOGDVNMTIwmTpwoSYqJiVHHjh0dseN1Z86c0YYNGzyOnTt3TgEBARoyZIhjdm3Tpo1atWql3Nxc97H8/HzdddddjtrzutzcXOXn52vAgAHuY926dXPMnjU1NaqtrfV4BeTq1auSpNjYWHv2vKN/RxGNNnnyZGvFihXWpUuXrM8//9yKi4uzdu/ebfdYRixbtqzOxzScPXvW6tatm3Xs2DGrsrLSSk9Pt3r16mUVFhbaNGXDVVdXWyNHjrTefPPNOueqqqqsYcOGWa%2B88op15coV6/Tp01bfvn2tY8eO/fCDNlJZWZkVGxtrbdiwwbpy5YpVXFxszZw503riiSesr7/%2B2urVq5f1%2B9//3qqsrLSOHz9ude/e3crNzbV77NvicrmsgoIC969PPvnE6tixo1VQUGDl5%2Bc7YsfrLl68aPXs2dPaunWrVVVVZX3xxRfWqFGjrLVr1zrm63ndunXrrEceecS6cOGC9fXXX1uJiYnW8uXLHbenZVnWnj17rP79%2B3scc9KeJSUlVv/%2B/a1/%2B7d/s65cuWKVlJRYc%2BbMsf75n//Ztj0JLC9XUFBgzZo1y%2BrevbsVGxtrvfLKK1Ztba3dYzVKdHS0FR0dbXXu3Nnq3Lmz%2B8/XHT582IqPj7e6du1qjRs3zvrwww9tnLbh/vrXv1odO3Z07/fdX3l5edbZs2etqVOnWtHR0dbQoUOtN954w%2B6RG%2BzTTz%2B1pk%2BfbnXv3t362c9%2BZi1atMi6ePGiZVmW9eGHH1pjx461unbtasXHxzfJz/q60Zdffun%2BHCzLct6OH374oZWYmGj17NnT6t%2B/v7V%2B/XqrsrLSfc4pu1ZVVVmrV6%2B2%2BvXrZ/Xs2dNatmyZdfnyZcuynLWnZVnWa6%2B9Zo0ePbrOcSftmZWVZU2fPt3q27evFRsba/u/h3wsy4HvKAYAALAR78ECAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAw7P8B/8ErQAJAcxMAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-3755730262451959402”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>9.507699486700886</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.917355371900827</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.98455720500928</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>26.675220986598234</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>19.273165907645016</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.1429758935993348</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.61537314721091</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>36.800689578467356</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>39.67449048584951</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.283839940234392</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3040)</td> <td class=”number”>3040</td> <td class=”number”>94.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>158</td> <td class=”number”>4.9%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-3755730262451959402”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.0135875520490905</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.1429758935993348</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.374385999154224</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.9292823264131873</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0969635967119613</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>58.76249764195435</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>62.63303915237616</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>62.874133585381216</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>67.38100971185487</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>76.28485226799833</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PC_SNAPBEN15”><s>PC_SNAPBEN15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PC_SNAPBEN10”><code>PC_SNAPBEN10</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.94136</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PC_WIC_REDEMP08”>PC_WIC_REDEMP08<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2074</td>

</tr> <tr>

<th>Unique (%)</th> <td>64.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>35.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>1136</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>21.346</td>

</tr> <tr>

<th>Minimum</th> <td>1.7439</td>

</tr> <tr>

<th>Maximum</th> <td>110.63</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-3133903502351765901”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-3133903502351765901,#minihistogram-3133903502351765901”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-3133903502351765901”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-3133903502351765901”

aria-controls=”quantiles-3133903502351765901” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-3133903502351765901” aria-controls=”histogram-3133903502351765901”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-3133903502351765901” aria-controls=”common-3133903502351765901”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-3133903502351765901” aria-controls=”extreme-3133903502351765901”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-3133903502351765901”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>1.7439</td>

</tr> <tr>

<th>5-th percentile</th> <td>8.277</td>

</tr> <tr>

<th>Q1</th> <td>14.476</td>

</tr> <tr>

<th>Median</th> <td>19.623</td>

</tr> <tr>

<th>Q3</th> <td>25.965</td>

</tr> <tr>

<th>95-th percentile</th> <td>39.08</td>

</tr> <tr>

<th>Maximum</th> <td>110.63</td>

</tr> <tr>

<th>Range</th> <td>108.88</td>

</tr> <tr>

<th>Interquartile range</th> <td>11.488</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>10.439</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.48901</td>

</tr> <tr>

<th>Kurtosis</th> <td>7.0231</td>

</tr> <tr>

<th>Mean</th> <td>21.346</td>

</tr> <tr>

<th>MAD</th> <td>7.5955</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.7726</td>

</tr> <tr>

<th>Sum</th> <td>44272</td>

</tr> <tr>

<th>Variance</th> <td>108.96</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-3133903502351765901”>

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JEi1btkwFBQWSpIMHDyorK0u/%2BMUvdPToUY0cOVKzZs3SV199Zaw2AADg2ywPWO%2B8846ys7MVHR3dOBYdHa1169bp7bffNradLVu2aP78%2BbrjjjsUFBSkpUuXaunSpdq1a5eio6OVnJysgIAAxcfHKzExUfn5%2BZKkvLw8TZo0Sf3791eHDh00Y8YMSdKhQ4eM1QYAAHyb5QHr0qVLCggIaFqIzaaamhoj2/j888919uxZffnll3rggQcUFxen9PR0VVRUqKioSDExMW7Lx8TENF4G/Pa8zWZTnz59Gs9wAQAAXI/lN7kPHjxYa9asUUZGhkJDQyV9HYiee%2B45t0c1eOL8%2BfOSpH379mnr1q1yuVxKT0/X0qVLdenSJUVFRbktHxYWJofDIUlyOp2NdX0jNDS0cb45ysrKVF5e7jZmt3dUZGRki/fF39/m9hPWsdvpOcefZ%2BifZ%2Bif5%2Bih91gesBYvXqyf/vSn%2BuEPf6igoCA1NDSourpa3bp10%2B9%2B9zsj23C5XJKkGTNmNIapuXPn6vHHH1d8fHyz179ReXl5ys7OdhtLS0tTenr6Db9mSMgtHtWElgsPD/R2Ca0Gx59n6J9n6J/n6KH1LA9Y3bp10%2Buvv6633npLn332mWw2m7p3765hw4bJ39/fyDY6d%2B4sSQoJCWkc69q1q1wul2pra5t8WtHhcCgiIkKSFB4e3mTe6XSqZ8%2Bezd7%2BlClTlJiY6DZmt3eUw1Hdov2Qvn7XERJyiyora1Rf39Di9XHjbuTPy9dw/HmG/nmG/nmOHnrvzbLlAUuS2rdvr/vuu%2B%2BmvX6XLl0UFBSk4uJi9e3bV5JUWlqqdu3aKSEhQTt27HBbvrCwUP3795ckxcbGqqioSBMnTpQk1dfX68SJE0pOTm729iMjI5tcDiwvv6i6uhs/uOvrGzxaHy1Hv/%2BB488z9M8z9M9z9NB6lgesv//971q/fr3%2B9re/6dKlS03m33zzTY%2B3YbfblZycrBdffFGDBw9WUFCQNm7cqKSkJE2cOFH/9V//pfz8fI0fP17vvfeejhw5ory8PElSSkqKFixYoAcffFC9e/fWb37zG7Vv314jRozwuC4AAPDd4JV7sMrKyjRs2DB17Njxpm0nIyNDV65c0SOPPKLa2lqNGTNGS5cuVWBgoDZv3qyVK1fq6aefVteuXbV27VrdeeedkqThw4drwYIFmjdvni5cuKB%2B/fopJydHHTp0uGm1AgAA3%2BLn8vSO7hYaMGCA3nzzzcZ7nr4ryssv3tB6drtN4eGBcjiq2/zp3fuff9fbJbTI3nlDvV2C1/nS8ecN9M8z9M9z9FC69dZgr2zX8s9tdurU6aaeuQIAAPA2ywPWzJkzlZ2d7fGjEAAAAFory%2B/Beuutt/Thhx/q1Vdf1fe//33ZbO4Z75VXXrG6JAAAAKMsD1hBQUEaPny41ZsFAACwjOUBa/Xq1VZvEgAAwFJe%2BXKiTz/9VFlZWXrqqacaxz766CNvlAIAAGCc5QHr2LFjGj9%2BvA4cOKDdu3dL%2Bvrho9OmTTPykFEAAABvszxgbdiwQT/72c%2B0a9cu%2Bfn5Sfr6%2BwnXrFmjjRs3Wl0OAACAcZYHrP/93/9VSkqKJDUGLEkaO3asSkpKrC4HAADAOMsDVnBw8FW/g7CsrEzt27e3uhwAAADjLA9YAwcO1KpVq1RVVdU4dvr0aS1cuFA//OEPrS4HAADAOMsf0/DUU0/pxz/%2BseLi4lRfX6%2BBAweqpqZGPXv21Jo1a6wuBwAAwDjLA1aXLl20e/duHTlyRKdPn1aHDh3UvXt3DR061O2eLAAAgLbK8oAlSe3atdN9993njU0DAADcdJYHrMTExH95popnYQEAgLbO8oD1wAMPuAWs%2Bvp6nT59WgUFBfrxj39sdTkAAADGWR6wMjMzrzq%2Bf/9%2B/fnPf7a4GgAAAPO88l2EV3Pffffp9ddf93YZAAAAHms1AevEiRNyuVzeLgMAAMBjll8inDp1apOxmpoalZSUaPTo0VaXAwAAYJzlASs6OrrJpwgDAgKUnJysRx55xOpyAAAAjLM8YPG0drQV9z//rrdLaJG984Z6uwQAwP9necB67bXXmr3sQw89dBMrAQAAuDksD1hLlixRQ0NDkxva/fz83Mb8/PwIWAAAoE2yPGD9%2Bte/1pYtWzRr1iz17t1bLpdLJ0%2Be1K9%2B9Ss99thjiouLs7okAAAAo7xyD1ZOTo6ioqIax%2B655x5169ZN06dP1%2B7du60uCQAAwCjLn4N15swZhYaGNhkPCQlRaWmp1eUAAAAYZ3nA6tq1q9asWSOHw9E4VllZqfXr1%2Bu2226zuhwAAADjLL9EuHjxYmVkZCgvL0%2BBgYGy2WyqqqpShw4dtHHjRqvLAQAAMM7ygDVs2DAdPnxYR44c0fnz5%2BVyuRQVFaV7771XwcHBVpcDAABgnOUBS5JuueUWjRo1SufPn1e3bt28UQIAAMBNY/k9WJcuXdLChQs1YMAA3X///ZK%2BvgdrxowZqqystLocAAAA4ywPWGvXrlVxcbHWrVsnm%2B0fm6%2Bvr9e6deusLgcAAMA4ywPW/v379ctf/lJjx45t/NLnkJAQrV69WgcOHLC6HAAAAOMsD1jV1dWKjo5uMh4REaGvvvrK6nIAAACMszxg3Xbbbfrzn/8sSW7fPbhv3z7927/9m9XlAAAAGGf5pwgfffRRzZ07Vw8//LAaGhq0detWFRYWav/%2B/VqyZInV5QAAABhnecCaMmWK7Ha7tm3bJn9/f7344ovq3r271q1bp7Fjx1pdDgAAgHGWB6yKigo9/PDDevjhh63eNAAAgCUsvwdr1KhRbvdeAQAA%2BBrLA1ZcXJz27t1r9WYBAAAsY/klwu9973v6%2Bc9/rpycHN12221q166d2/z69eutLgkAAMAoywPWqVOndMcdd0iSHA6H1ZsHAAC46SwLWPPnz9eGDRv0u9/9rnFs48aNSktLs6oEAAAAS1h2D9bBgwebjOXk5Fi1eQAAAMtYFrCu9slBPk0IAAB8kWUB65svdr7eGAAAQFtn%2BWMaAAAAfB0BCwAAwDDLPkVYW1urjIyM647djOdgrVq1Si%2B99JJOnjwpSTp27JjWr1%2BvTz/9VN/73vc0c%2BZMjR8/vnH53NxcvfzyyyovL1fv3r21ZMkSxcbGGq8LAAD4JssC1qBBg1RWVnbdMdOKi4u1Y8eOxv8uKyvTnDlztGTJEiUlJemDDz7Q7Nmz1b17d/Xr108HDx5UVlaWfv3rX6t3797Kzc3VrFmzdODAAXXs2PGm1goAAHyDZQHrn59/ZZWGhgYtX75cqampev755yVJu3btUnR0tJKTkyVJ8fHxSkxMVH5%2Bvvr166e8vDxNmjRJ/fv3lyTNmDFDubm5OnTokMaNG2f5PgAAgLbHp%2B/BeuWVVxQQEKCkpKTGsaKiIsXExLgtFxMTo8LCwqvO22w29enTRwUFBdYUDQAA2jzLvyrHKl988YWysrKanDlzOp2KiopyGwsLC2v82h6n06nQ0FC3%2BdDQ0BZ9rU9ZWZnKy8vdxuz2joqMjGzJLkiS/P1tbj%2BBa7HbzR8jHH%2BeoX%2BeoX%2Beo4fe47MBa/Xq1Zo0aZJ69Oihs2fPtmhdTx%2BAmpeXp%2BzsbLextLQ0paen3/BrhoTc4lFN8H3h4YE37bU5/jxD/zxD/zxHD63nkwHr2LFj%2Buijj7R79%2B4mc%2BHh4XI6nW5jDodDERER15x3Op3q2bNns7c/ZcoUJSYmuo3Z7R3lcFQ3%2BzW%2B4e9vU0jILaqsrFF9fUOL18d3x40cX9fD8ecZ%2BucZ%2Buc5enhz33z%2BKz4ZsHbu3KkLFy5o5MiRkv5xRiouLk4//elPmwSvwsLCxpvaY2NjVVRUpIkTJ0qS6uvrdeLEicab4psjMjKyyeXA8vKLqqu78YO7vr7Bo/Xh%2B27m8cHx5xn65xn65zl6aD2fvCi7aNEi7d%2B/Xzt27NCOHTsav1R6x44dSkpKUmlpqfLz83X58mUdOXJER44c0eTJkyVJKSkpeu211/Txxx%2BrpqZGmzZtUvv27TVixAgv7hEAAGhLfPIMVmhoqNuN6nV1dZKkLl26SJI2b96slStX6umnn1bXrl21du1a3XnnnZKk4cOHa8GCBZo3b54uXLigfv36KScnRx06dLB%2BRwAAQJvkkwHr277//e83PsVdkgYPHuz28NFve/TRR/Xoo49aURoAAPBBPnmJEAAAwJsIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGDYd%2BIxDb7s/uff9XYJAADgWziDBQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMJ8NWKWlpUpLS1NcXJzi4%2BO1aNEiVVZWSpKKi4v12GOPadCgQRo9erS2bNnitu6ePXuUlJSkAQMGaNKkSXrnnXe8sQsAAKCN8tmANWvWLIWEhOjgwYN69dVX9be//U3PPfecLl26pJkzZ%2Brf//3f9fbbb2vDhg3avHmzDhw4IOnr8LVw4UJlZmbqvffeU2pqqp588kmdP3/ey3sEAADaCp8MWJWVlYqNjVVGRoYCAwPVpUsXTZw4Ue%2B//74OHz6s2tpazZ49Wx07dlTfvn31yCOPKC8vT5KUn5%2BvhIQEJSQkKCAgQOPHj1evXr20c%2BdOL%2B8VAABoK3wyYIWEhGj16tXq3Llz49i5c%2BcUGRmpoqIi9e7dW/7%2B/o1zMTExKiwslCQVFRUpJibG7fViYmJUUFBgTfEAAKDNs3u7ACsUFBRo27Zt2rRpk/bu3auQkBC3%2BbCwMDmdTjU0NMjpdCo0NNRtPjQ0VKdOnWr29srKylReXu42Zrd3VGRkZItr9/e3uf0ErsVuN3%2BMcPx5hv55hv55jh56j88HrA8%2B%2BECzZ89WRkaG4uPjtXfv3qsu5%2Bfn1/jvLpfLo23m5eUpOzvbbSwtLU3p6ek3/JohIbd4VBN8X3h44E17bY4/z9A/z9A/z9FD6/l0wDp48KB%2B9rOfadmyZXrooYckSRERETpz5ozbck6nU2FhYbLZbAoPD5fT6WwyHxER0eztTpkyRYmJiW5jdntHORzVLd4Hf3%2BbQkJuUWVljerrG1q8Pr47buT4uh6OP8/QP8/QP8/Rw5v75vNf8dmA9eGHH2rhwoV64YUXNGzYsMbx2NhY/f73v1ddXZ3s9q93v6CgQP3792%2Bc/%2BZ%2BrG8UFBRo3Lhxzd52ZGRkk8uB5eUXVVd34wd3fX2DR%2BvD993M44PjzzP0zzP0z3P00Ho%2BeVG2rq5OS5cuVWZmplu4kqSEhAQFBQVp06ZNqqmp0fHjx7V9%2B3alpKRIkiZPnqyjR4/q8OHDunz5srZv364zZ85o/Pjx3tgVAADQBvnkGayPP/5YJSUlWrlypVauXOk2t2/fPr344otavny5cnJy1LlzZ82fP18jRoyQJPXq1Uvr1q3T6tWrVVpaqh49emjz5s269dZbvbAnAACgLfLJgHXPPffo5MmT/3KZ3//%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%2B98cYbunDhgmbOnKnOnTvrJz/5ibdLA%2BAFPLcLQEsRsL6loKBAn3zyibZu3arg4GAFBwcrNTVVL730EgELQJvQlgIhYRC%2BioD1LUVFReratatCQ0Mbx/r27avTp0%2BrqqpKQUFB132NsrIylZeXu43Z7R0VGRnZ4nr8/W1uPwHAl7SlMChJf8q819sltAi/Q7yHgPUtTqdTISEhbmPfhC2Hw9GsgJWXl6fs7Gy3sSeffFJz585tcT1lZWV66aVfa8qUKVcNaO//fGyLX/O7pKysTHl5edfsH/41%2BucZ%2BucZ%2Bue56/0Owc1DpL0Kl8vl0fpTpkzRq6%2B%2B6vbPlClTbui1ysvLlZ2d3eSMGJqH/nmG/nmG/nmG/nmOHnoPZ7C%2BJSIiQk6n023M6XTKz89PERERzXqNyMhI3ikAAPAdxhmsb4mNjdW5c%2BdUUVHROFZQUKAePXooMDDQi5UBAIC2goD1LTExMerXr5/Wr1%2BvqqoqlZSUaOvWrUpJSfF2aQAAoI3wX7FixQpvF9Ha3Hvvvdq9e7eeffZZvf7660pOTtb06dPl5%2BfnlXoCAwM1ZMgQzqDdIPrnGfrnGfrnGfrnOXroHX4uT%2B/oBgAAgBsuEQIAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsBqpUpLS/XEE08oLi5OI0eO1Nq1a9XQ0ODtslq10tJSpaWlKS4uTvHx8Vq0aJEqKyslScXFxXrsscc0aNAgjR49Wlu2bPFyta3bqlWr1Lt378b/PnbsmJKTkzVw4ECNGzdOO3fu9GJ1rdemTZs0bNgw3X333UpNTdXZs2cl0b/mOHHihKZNm6Z77rlHQ4cOVWZmpioqKiTRv2t5%2B%2B23FR8fr/nz5zeZ27Nnj5KSkjRgwABNmjRJ77zzTuNcQ0ODNmzYoFGjRmnw4MGaPn26/v73v1tZ%2BneDC63SxIkTXUuXLnVVVla6Tp8%2B7Ro9erRry5Yt3i6rVXvwwQddixYtclVVVbnOnTvnmjRpkmvx4sWumpoa17333uvKyspyVVdXuwoLC11Dhgxx7d%2B/39slt0onTpxwDRkyxNWrVy%2BXy%2BVyff755667777blZ%2Bf77p06ZLr3Xffdd11112uv/71r16utHXZtm2ba%2BzYsa6SkhLXxYsXXc8%2B%2B6zr2WefpX/NUFtb6xo6dKhr/fr1rsuXL7sqKipcP/nJT1xz586lf9eQk5PjGj16tGvq1KmuefPmuc2dOHHCFRsb6zp8%2BLDr0qVLrh07drj69%2B/vOnfunMvlcrlyc3NdI0eOdJ06dcp18eJF1zPPPONKSkpyNTQ0eGNXfBZnsFqhgoICffLJJ8rMzFRwcLCio6OVmpqqvLw8b5fWalVWVio2NlYZGRkKDAxUly5dNHHiRL3//vs6fPiwamtrNXv2bHXs2FF9%2B/bVI488Qj%2BvoqGhQcuXL1dqamrj2K5duxQdHa3k5GQFBAQoPj5eiYmJys/P916hrdCWLVs0f/583XHHHQoKCtLSpUu1dOlS%2BtcM5eXlKi8v14QJE9S%2BfXuFh4frRz/6kYqLi%2BnfNQQEBGj79u26/fbbm8zl5%2BcrISFBCQkJCggI0Pjx49WrV6/GM395eXlKTU3VD37wAwUFBWn%2B/PkqKSnR8ePHrd4Nn0bAaoWKiorUtWtXhYaGNo717dtXp0%2BfVlVVlRcra71CQkK0evVqde7cuXHs3LlzioyMVFFRkXr37i1/f//GuZiYGBUWFnqj1FbtlVdeUUBAgJKSkhrHioqKFBMT47Yc/XP3%2Beef6%2BzZs/ryyy/1wAMPKC4uTunp6aqoqKB/zRAVFaU%2BffooLy9P1dXVunDhgg4cOKARI0bQv2uYNm2agoODrzp3rZ4VFBTo0qVLOnXqlNt8UFCQbr/9dhUUFNzUmr9rCFitkNPpVEhIiNvYN2HL4XB4o6Q2p6CgQNu2bdPs2bOv2s%2BwsDA5nU7ua/snX3zxhbKysrR8%2BXK38Wv1j2PxH86fPy9J2rdvn7Zu3aodO3bo/PnzWrp0Kf1rBpvNpqysLL355psaOHCg4uPjVVdXp4yMDPp3A5xOp9sbdOnr3yEOh0NffvmlXC7XNedhDgGrlXK5XN4uoc364IMPNH36dGVkZCg%2BPv6ay/n5%2BVlYVeu3evVqTZo0ST169PB2KW3ON39fZ8yYoaioKHXp0kVz587VwYMHvVxZ23DlyhXNmjVLY8eO1fvvv6%2B33npLwcHByszM9HZpbdb1fofwO%2BbmI2C1QhEREXI6nW5jTqdTfn5%2BioiI8FJVbcPBgwf1xBNPaPHixZo2bZqkr/v57XdmTqdTYWFhstn4KyB9/Smtjz76SGlpaU3mwsPDmxyPDoeDY/GffHNp%2Bp/PtHTt2lUul0u1tbX07zqOHTums2fPasGCBQoODlZUVJTS09P1pz/9STabjf610NX%2BzjqdTkVERDT%2Bf%2B9q8506dbKyTJ/Hb5dWKDY2VufOnWv8iLL09SWvHj16KDAw0IuVtW4ffvihFi5cqBdeeEEPPfRQ43hsbKxOnjypurq6xrGCggL179/fG2W2Sjt37tSFCxc0cuRIxcXFadKkSZKkuLg49erVq8n9LoWFhfTvn3Tp0kVBQUEqLi5uHCstLVW7du2UkJBA/66jvr5eDQ0NbmdVrly5IkmKj4%2Bnfy0UGxvbpGff/D8vICBAPXv2VFFRUeNcZWWlPvvsM911111Wl%2BrTCFitUExMjPr166f169erqqpKJSUl2rp1q1JSUrxdWqtVV1enpUuXKjMzU8OGDXObS0hIUFBQkDZt2qSamhodP35c27dvp5//ZNGiRdq/f7927NihHTt2KCcnR5K0Y8cOJSUlqbS0VPn5%2Bbp8%2BbKOHDmiI0eOaPLkyV6uuvWw2%2B1KTk7Wiy%2B%2BqP/7v//ThQsXtHHjRiUlJWnixIn07zoGDBigjh07KisrSzU1NXI4HNq0aZMGDx6sCRMm0L8Wmjx5so4eParDhw/r8uXL2r59u86cOaPx48dLklJSUpSbm6uSkhJVVVVp3bp16tOnj/r16%2Bflyn2Ln4sLsa3S%2BfPntWzZMv3lL39RUFCQpk6dqieffJL7hq7h/fff13/8x3%2Boffv2Teb27dun6upqLV%2B%2BXIWFhercubMef/xxPfroo16otG04e/asRo0apZMnT0qS/ud//kcrV65USUmJunbtqoyMDI0ePdrLVbYuV65c0erVq/X666%2BrtrZWY8aM0bJlyxQYGEj/mqGwsFDPPfecPvnkE7Vv315DhgzRokWLFBUVRf%2Bu4psw9M2ZebvdLkmNnwQ8cOCA1q9fr9LSUvXo0UNLlizR4MGDJX19/1VWVpZeeeUVVVdXKy4uTs8884y6dOnihT3xXQQsAAAAw7hECAAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACG/T9QXgbxoAAOZgAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-3133903502351765901”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>17.48462</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>24.92381</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.500142</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>15.97547</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14.5322</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>15.80326</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.31755</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>15.65315</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>23.48499</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.51293</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2063)</td> <td class=”number”>2063</td> <td class=”number”>64.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>1136</td> <td class=”number”>35.4%</td> <td>

<div class=”bar” style=”width:55%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-3133903502351765901”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.743872</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.113909</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.805049</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.113567</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.348181</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>78.72282</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>86.27953</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>87.59541</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>89.91983</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>110.6258</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PC_WIC_REDEMP12”><s>PC_WIC_REDEMP12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PC_WIC_REDEMP08”><code>PC_WIC_REDEMP08</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.931</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PERCHLDPOV10”>PERCHLDPOV10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.22542</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>75.6%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-8796731851960694140”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-8796731851960694140,#minihistogram-8796731851960694140”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-8796731851960694140”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-8796731851960694140”

aria-controls=”quantiles-8796731851960694140” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-8796731851960694140” aria-controls=”histogram-8796731851960694140”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-8796731851960694140” aria-controls=”common-8796731851960694140”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-8796731851960694140” aria-controls=”extreme-8796731851960694140”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-8796731851960694140”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>0</td>

</tr> <tr>

<th>95-th percentile</th> <td>1</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr>

<th>Range</th> <td>1</td>

</tr> <tr>

<th>Interquartile range</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.41792</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.854</td>

</tr> <tr>

<th>Kurtosis</th> <td>-0.27124</td>

</tr> <tr>

<th>Mean</th> <td>0.22542</td>

</tr> <tr>

<th>MAD</th> <td>0.34921</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.3149</td>

</tr> <tr>

<th>Sum</th> <td>706</td>

</tr> <tr>

<th>Variance</th> <td>0.17466</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-8796731851960694140”>

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QDABcX2gpWfn6%2BNGzfq9ddfV58%2BffS73/1OmZmZeuaZZ3TgwIFWHycyMlKrVq36zoL1fUpLS5WXl6devXopOjpaBQUF8ng82rNnj7744gtt3bpVBQUFSkhIUNeuXXX33Xdr9erVOnnyZJtfCwAAXHiCdg/WgAEDNHv2bG3fvl0PP/ywXn/9dd14442aMmWK/62873PHHXeoU6dO37n9yJEj%2BtWvfqW0tDRlZ2frjTfekCQ1NDSovLxcKSkp/n2jo6PVo0cPud1u7d%2B/XxEREerXr1/ArF999ZU%2B%2B%2ByzczhjAABwoXAF64VPnjypP//5z1qzZo0%2B/PBDJScna/r06aqsrFReXp6eeOIJjRkz5gcdOyEhQcnJybr//vvVu3dv/fnPf9Zvf/tbJSYm6vLLL5fP51NsbGzA58TGxqq6ulpxcXGKjo5WWFhYwDZJqq6ubtXrV1ZWqqqqKmDN5eqoxMTEH3Q%2B3yUiwlnfo%2BByOWfe09k6LWMnIFtrka91yNY6oZit7QXL4/Fo1apVWrt2rerq6jRy5Ej98Y9/1NChQ/37pKWl6fHHH//BBWvEiBEaMWKE/9ejR4/2l7lZs2ZJkv9%2BqzP5vm2tUVpaqqKiooC1/Px83Xvvved0XKeLj48K9ghtFhPTIdgjhCyytRb5WodsrRNK2dpesEaPHq2ePXtq6tSpGjdunOLi4lrsk5mZqWPHjhl93aSkJO3du1dxcXEKDw%2BX1%2BsN2O71etW5c2clJCSotrZWTU1NioiI8G%2BTpM6dO7fqtXJycpSVlRWw5nJ1VHV1nYEz%2BRenNX3T52%2BliIhwxcR0UE1NvZqamoM9TkghW2uRr3XI1jpWZhusv9zbXrBKSko0bNiws%2B63Z8%2BeH/war732mmJjY3XjjTf61zwej7p3767IyEj16dNHZWVl/jlqamp06NAhDRo0SElJSfL5fPrkk080YMAASZLb7VZMTIx69uzZqtdPTExs8XZgVdUJNTZe2F%2BQTjz/pqZmR87tBGRrLfK1DtlaJ5Sytf0SSL9%2B/XTXXXdp69at/rVXXnlFv/71r1tcVfqhvvnmGz355JNyu906efKkNmzYoHfffVeTJk2SJOXm5qqkpEQej0e1tbUqLCxU//79lZqaqoSEBI0cOVK///3vdezYMR09elRLly7VxIkT5XIF7ZY1AADgILY3hvnz5%2BvEiRPq3bu3f23EiBHauXOnFixYoAULFrTqOKmpqZLkf4bW6cLmdrt1xx13qK6uTvfdd5%2Bqqqp06aWXaunSpRo4cKAkadKkSaqqqtLkyZNVV1en9PT0gHum5s6dq8cee0zZ2dm66KKLdNNNN7V4mCkAAMB3CfOd6x3dbTR8%2BHCtX79e8fHxAevV1dW66aab9Je//MXOcWxTVXXC%2BDFdrnD9rHCn8eNaZdOMa4I9Qqu5XOGKj49SdXVdyFyuPl%2BQrbXI1zpkax0rs%2B3S5bsf6WQl298ibGhoUGRkZMtBwsNVX19v9zgAAADG2V6w0tLStGDBAh0/fty/9vnnn%2BuJJ54IeFQDAACAU9l%2BD9bDDz%2Bs//zP/9TVV1%2Bt6OhoNTc3q66uTt27d9ef/vQnu8cBAAAwzvaC1b17d7355pt69913dejQIYWHh6tnz54aPny4/7lTAAAAThaU5w60a9dO119/fTBeGgAAwHK2F6zDhw9r4cKF%2BvTTT9XQ0NBi%2B9tvv233SAAAAEYF5R6syspKDR8%2BXB07drT75QEAACxne8Hau3ev3n77bSUkJNj90gAAALaw/TENnTt35soVAAAIabYXrKlTp6qoqEg2P0AeAADANra/Rfjuu%2B/q448/1po1a3TppZcqPDyw461YscLukQAAAIyyvWBFR0fr2muvtftlAQAAbGN7wZo/f77dLwkAAGAr2%2B/BkqTPPvtMS5Ys0UMPPeRf%2B9vf/haMUQAAAIyzvWB98MEHGjt2rLZs2aINGzZIOvXw0TvuuIOHjAIAgJBge8FatGiRHnjgAa1fv15hYWGSTv18wgULFmjp0qV2jwMAAGCc7QXrH//4h3JzcyXJX7AkadSoUfJ4PHaPAwAAYJztBatTp05n/BmElZWVateund3jAAAAGGd7wbriiiv0u9/9TrW1tf61AwcOaPbs2br66qvtHgcAAMA42x/T8NBDD%2BmXv/yl0tPT1dTUpCuuuEL19fXq06ePFixYYPc4AAAAxtlesLp166YNGzZox44dOnDggNq3b6%2BePXvqmmuuCbgnCwAAwKlsL1iSdNFFF%2Bn6668PxksDAABYzvaClZWV9b1XqngWFgAAcDrbC9aNN94YULCampp04MABud1u/fKXv7R7HAAAAONsL1izZs064/pbb72lv/71rzZPAwAAYF5QfhbhmVx//fV68803gz0GAADAOTtvCta%2Bffvk8/mCPQYAAMA5s/0twkmTJrVYq6%2Bvl8fj0Q033GD3OAAAAMbZXrCSk5NbfBdhZGSkJk6cqFtvvdXucQAAAIyzvWDxtHYAABDqbC9Ya9eubfW%2B48aNs3ASAAAAa9hesObMmaPm5uYWN7SHhYUFrIWFhVGwAACAI9lesF588UW99NJLuuuuu9SvXz/5fD79/e9/1wsvvKBf/OIXSk9Pt3skAAAAo4JyD1ZxcbG6du3qX7vyyivVvXt3TZkyRRs2bLB7JAAAAKNsfw7WwYMHFRsb22I9JiZGFRUVdo8DAABgnO0FKykpSQsWLFB1dbV/raamRgsXLtRll11m9zgAAADG2f4W4cMPP6yZM2eqtLRUUVFRCg8PV21trdq3b6%2BlS5faPQ4AAIBxthes4cOH65133tGOHTt09OhR%2BXw%2Bde3aVT/96U/VqVMnu8cBAAAwzvaCJUkdOnRQdna2jh49qu7duwdjBAAAAMvYfg9WQ0ODZs%2BerSFDhujnP/%2B5pFP3YN15552qqamxexwAAADjbC9YzzzzjPbv36/CwkKFh//r5ZuamlRYWGj3OAAAAMbZXrDeeustLV68WKNGjfL/0OeYmBjNnz9fW7ZssXscAAAA42wvWHV1dUpOTm6xnpCQoK%2B%2B%2BsrucQAAAIyzvWBddtll%2Butf/ypJAT97cPPmzfrRj35k9zgAAADG2f5dhLfffrumT5%2BuW265Rc3NzXr55Ze1d%2B9evfXWW5ozZ47d4wAAABhne8HKycmRy%2BXSq6%2B%2BqoiICD3//PPq2bOnCgsLNWrUKLvHAQAAMM72gnXs2DHdcsstuuWWW%2Bx%2BaQAAAFvYfg9WdnZ2wL1XAAAAocb2gpWenq5NmzbZ/bIAAAC2sf0twksuuURPPfWUiouLddlll%2Bmiiy4K2L5w4UK7RwIAADDK9oJVXl6uyy%2B/XJJUXV1t98sDAABYzraCVVBQoEWLFulPf/qTf23p0qXKz8%2B3awQAAABb2HYP1rZt21qsFRcXn9Mxd%2B7cqYyMDBUUFLTYtnHjRo0ZM0ZDhgzRhAkT9N577/m3NTc3a9GiRcrOzlZaWpqmTJmiw4cP%2B7d7vV7NmDFDGRkZGj58uObMmaOGhoZzmhUAAFw4bCtYZ/rOwXP5bsIXXnhB8%2BbNU48ePVps279/v2bPnq1Zs2bpww8/VF5enu655x4dPXpUkrR8%2BXKtX79excXF2r59u5KTk5Wfn%2B%2Bf59FHH1V9fb02bNig1atXy%2BPx8IOoAQBAq9lWsE7/YOezrbVWZGSkVq1adcaCtXLlSmVmZiozM1ORkZEaO3as%2Bvbtq3Xr1kmSSktLlZeXp169eik6OloFBQXyeDzas2ePvvjiC23dulUFBQVKSEhQ165ddffdd2v16tU6efLkD54XAABcOGy/yd2UO%2B644zu3lZWVKTMzM2AtJSVFbrdbDQ0NKi8vV0pKin9bdHS0evToIbfbrRMnTigiIkL9%2BvXzbx8wYIC%2B%2BuorffbZZwHr36WyslJVVVUBay5XRyUmJrb29FolIsL2p2ycE5fLOfOeztZpGTsB2VqLfK1DttYJxWwdW7C%2Bj9frVWxsbMBabGysysvLdfz4cfl8vjNur66uVlxcnKKjowOurp3et7Xf9VhaWqqioqKAtfz8fN17770/5HRCRnx8VLBHaLOYmA7BHiFkka21yNc6ZGudUMrWtoJ18uRJzZw586xrpp6Ddbb7u75v%2B7k%2BaT4nJ0dZWVkBay5XR1VX153Tcb/NaU3f9PlbKSIiXDExHVRTU6%2BmpuZgjxNSyNZa5GsdsrWOldkG6y/3thWsoUOHqrKy8qxrJsTHx8vr9Qaseb1eJSQkKC4uTuHh4Wfc3rlzZyUkJKi2tlZNTU2KiIjwb5Okzp07t%2Br1ExMTW7wdWFV1Qo2NF/YXpBPPv6mp2ZFzOwHZWot8rUO21gmlbG0rWP/%2B/CurDRw4UHv37g1Yc7vdGj16tCIjI9WnTx%2BVlZVp2LBhkqSamhodOnRIgwYNUlJSknw%2Bnz755BMNGDDA/7kxMTHq2bOnbecAAACcy1nvMbXSbbfdpvfff1/vvPOOvv76a61atUoHDx7U2LFjJUm5ubkqKSmRx%2BNRbW2tCgsL1b9/f6WmpiohIUEjR47U73//ex07dkxHjx7V0qVLNXHiRLlcIXnLGgAAMMyxjSE1NVWS1NjYKEnaunWrpFNXm/r27avCwkLNnz9fFRUV6t27t5YtW6YuXbpIkiZNmqSqqipNnjxZdXV1Sk9PD7gpfe7cuXrssceUnZ2tiy66SDfddNMZH2YKAABwJmG%2Bc72jG61SVXXC%2BDFdrnD9rHCn8eNaZdOMa4I9Qqu5XOGKj49SdXVdyNwPcL4gW2uRr3XI1jpWZtulSyejx2utkHyLEAAAIJgoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGCYK9gDAAAAa/z8938J9gittuupUcEewSiuYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEhW7D69eungQMHKjU11f/x5JNPSpI%2B%2BOADTZw4UVdccYVGjx6tdevWBXxuSUmJRo4cqSuuuEK5ubnau3dvME4BAAA4lCvYA1hp8%2BbNuvTSSwPWKisrdffdd2vOnDkaM2aMPvroI02bNk09e/ZUamqqtm3bpiVLlujFF19Uv379VFJSorvuuktbtmxRx44dg3QmAADASUL2CtZ3Wb9%2BvZKTkzVx4kRFRkYqIyNDWVlZWrlypSSptLRUEyZM0ODBg9W%2BfXvdeeedkqTt27cHc2wAAOAgIX0Fa%2BHChfrb3/6m2tpa/fznP9eDDz6osrIypaSkBOyXkpKiTZs2SZLKysp04403%2BreFh4erf//%2BcrvdGj16dKtet7KyUlVVVQFrLldHJSYmnuMZBYqIcFY/drmcM%2B/pbJ2WsROQrbXI1zpka71QyjZkC9ZPfvITZWRk6Omnn9bhw4c1Y8YMPfHEE/J6veratWvAvnFxcaqurpYkeb1excbGBmyPjY31b2%2BN0tJSFRUVBazl5%2Bfr3nvv/YFnExri46OCPUKbxcR0CPYIIYtsrUW%2B1iFb64RStiFbsEpLS/3/3qtXL82aNUvTpk3T0KFDz/q5Pp/vnF47JydHWVlZAWsuV0dVV9ed03G/zWlN3/T5WykiIlwxMR1UU1OvpqbmYI8TUsjWWuRrHbK1nhXZBusv9yFbsL7t0ksvVVNTk8LDw%2BX1egO2VVdXKyEhQZIUHx/fYrvX61WfPn1a/VqJiYkt3g6sqjqhxsYL%2BwvSieff1NTsyLmdgGytRb7WIVvrhFK2zroE0kr79u3TggULAtY8Ho/atWunzMzMFo9d2Lt3rwYPHixJGjhwoMrKyvzbmpqatG/fPv92AACAswnJgtW5c2eVlpaquLhY33zzjQ4cOKBnn31WOTk5uvnmm1VRUaGVK1fq66%2B/1o4dO7Rjxw7ddtttkqTc3FytXbtWu3fvVn19vZ577jm1a9dOI0aMCO5JAQAAxwjJtwi7du2q4uJiLVy40F%2BQxo8fr4KCAkVGRmrZsmWaN29Gn/PxAAALdUlEQVSennjiCSUlJemZZ57Rj3/8Y0nStddeq/vvv18zZszQl19%2BqdTUVBUXF6t9%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%2Bu6667TM888o%2Bbm5mCPBQAAHMIV7AHOR9OnT9eAAQO0detWffnll5o6daouvvhi/epXvwr2aAAAwAG4gvUtbrdbn3zyiWbNmqVOnTopOTlZeXl5Ki0tDfZoAADAIbiC9S1lZWVKSkpSbGysf23AgAE6cOCAamtrFR0dfdZjVFZWqqqqKmDN5eqoxMREo7NGRDirH7tczpn3dLZOy9gJyNZa5GsdsrVeKGVLwfoWr9ermJiYgLXTZau6urpVBau0tFRFRUUBa/fcc4%2BmT59ublCdKnK/7PapcnJyjJe3C11lZaX%2B%2BMcXydYCZGst8rWOE7Pd9dSoYI/QKpWVlVqyZImjsj2b0KmKBvl8vnP6/JycHK1ZsybgIycnx9B0/1JVVaWioqIWV8tw7sjWOmRrLfK1DtlaJxSz5QrWtyQkJMjr9Qaseb1ehYWFKSEhoVXHSExMDJkGDgAA2o4rWN8ycOBAHTlyRMeOHfOvud1u9e7dW1FRUUGcDAAAOAUF61tSUlKUmpqqhQsXqra2Vh6PRy%2B//LJyc3ODPRoAAHCIiMcff/zxYA9xvvnpT3%2BqDRs26Mknn9Sbb76piRMnasqUKQoLCwv2aC1ERUVp2LBhXF2zANlah2ytRb7WIVvrhFq2Yb5zvaMbAAAAAXiLEAAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwCtZ5rqKiQr/5zW%2BUnp6u6667Ts8884yam5vPuG9JSYlGjhypK664Qrm5udq7d6/N0zpLW7J97bXXNHLkSA0ZMkQ333yztm7davO0ztKWbE/7/PPPNWTIEC1ZssSmKZ2rLfl6PB5NnjxZgwcPVmZmpl555RV7h3WY1mbb3NysxYsXKysrS0OGDNGYMWO0cePGIEzsLDt37lRGRoYKCgq%2Bd7/m5mYtWrRI2dnZSktL05QpU3T48GGbpjTEh/Pa%2BPHjfY888oivpqbGd%2BDAAd8NN9zge%2Bmll1rs9/bbb/uuvPJK3%2B7du3319fW%2BZcuW%2Ba655hpfXV1dEKZ2htZmu3nzZt/QoUN9u3bt8n3zzTe%2B119/3TdgwADfoUOHgjC1M7Q22393zz33%2BIYOHepbvHixTVM6V2vzra%2Bv940YMcL3wgsv%2BL766ivfnj17fKNHj/aVl5cHYWpnaG22r776qm/48OE%2Bj8fja2xs9G3bts2XkpLi279/fxCmdobi4mLfDTfc4Js0aZJvxowZ37tvSUmJ77rrrvOVl5f7Tpw44Zs7d65vzJgxvubmZpumPXdcwTqPud1uffLJJ5o1a5Y6deqk5ORk5eXlqbS0tMW%2BpaWlmjBhggYPHqz27dvrzjvvlCRt377d7rEdoS3ZNjQ06P7779fQoUN10UUX6dZbb1VUVJR2794dhMnPf23J9rQdO3aovLxcI0aMsG9Qh2pLvps2bVJ0dLTuvPNOdejQQYMGDdKGDRvUq1evIEx%2B/mtLtmVlZRo6dKguv/xyRURE6LrrrlNcXJz%2B/ve/B2FyZ4iMjNSqVavUo0ePs%2B5bWlqqvLw89erVS9HR0SooKJDH49GePXtsmNQMCtZ5rKysTElJSYqNjfWvDRgwQAcOHFBtbW2LfVNSUvy/Dg8PV//%2B/eV2u22b10naku3NN9%2Bs22%2B/3f/rmpoa1dXVqWvXrrbN6yRtyVY6VWDnzp2rxx57TC6Xy85RHakt%2BX700Ufq27evHnroIV155ZUaNWqU1q1bZ/fIjtGWbEeMGKH//u//1v79%2B/XNN9/o7bffVn19vYYNG2b32I5xxx13qFOnTmfdr6GhQeXl5QF/pkVHR6tHjx6O%2BjONgnUe83q9iomJCVg7/YVfXV3dYt9//5/C6X2/vR9OaUu2/87n8%2BmRRx7R4MGD%2BR/pd2hrtkuXLtVPfvITXXXVVbbM53Rtyffo0aN6%2B%2B23lZGRoZ07d2rq1KmaPXu29u3bZ9u8TtKWbG%2B44Qbl5ORo3LhxSk1N1cyZMzV//nxdcsklts0bqo4fPy6fz%2Bf4P9P46%2BJ5zufzWbIv2p7XyZMn9eCDD6q8vFwlJSUWTRUaWptteXm5Vq5cqfXr11s8UWhpbb4%2Bn08DBgzQmDFjJEnjx4/XihUrtHnz5oCrA/iX1ma7du1arV27VitXrlS/fv30wQcfaObMmbrkkks0aNAgi6e8MDj9zzSuYJ3HEhIS5PV6A9a8Xq/CwsKUkJAQsB4fH3/Gfb%2B9H05pS7bSqUvWU6dO1T//%2BU8tX75cF198sV2jOk5rs/X5fHr88cc1ffp0denSxe4xHastv3e7dOnS4i2ZpKQkVVVVWT6nE7Ul21dffVU5OTkaNGiQIiMjNWLECF111VW8BWtAXFycwsPDz/jfonPnzkGaqu0oWOexgQMH6siRIzp27Jh/ze12q3fv3oqKimqxb1lZmf/XTU1N2rdvnwYPHmzbvE7Slmx9Pp8KCgrkcrn0yiuvKD4%2B3u5xHaW12f7zn//U//zP/2jx4sVKT09Xenq63nzzTb344osaP358MEZ3hLb83u3Vq5f%2B8Y9/BFwJqKioUFJSkm3zOklbsm1ublZTU1PA2jfffGPLnKEuMjJSffr0CfgzraamRocOHXLU1UEK1nksJSVFqampWrhwoWpra%2BXxePTyyy8rNzdXkjRq1Cjt2rVLkpSbm6u1a9dq9%2B7dqq%2Bv13PPPad27drxXVnfoS3Zrl%2B/XuXl5Xr22WcVGRkZzLEdobXZduvWTTt27NAbb7zh/8jKytKkSZNUXFwc5LM4f7Xl9%2B7YsWNVXV2t559/Xg0NDdqwYYPKyso0duzYYJ7Ceast2WZlZWnVqlX65JNP1NjYqPfee08ffPCBsrOzg3kKjvX5559r1KhR/mdd5ebmqqSkRB6PR7W1tSosLFT//v2Vmpoa5Elbj3uwznOLFy/Wo48%2BqmuuuUbR0dGaNGmS/zvaDhw4oK%2B%2B%2BkqSdO211%2Br%2B%2B%2B/XjBkz9OWXXyo1NVXFxcVq3759MMc/r7U229WrV6uioqLFTe0333yz5s2bZ/vcTtCabCMiItStW7eAz%2BvQoYOio6N5y/AsWvt7t2vXrlq2bJmeeuop/dd//Zd%2B9KMfaenSpbrsssuCOf55rbXZTp06VY2NjcrPz9exY8eUlJSkefPm6eqrrw7m%2BOe10%2BWosbFRkvwPbHa73Tp58qQOHDjgvwo4adIkVVVVafLkyaqrq1N6erqKioqCM/gPFOZz%2Bl1kAAAA5xneIgQAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAw/4fxcBZqwosjI0AAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-8796731851960694140”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2426</td> <td class=”number”>75.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>706</td> <td class=”number”>22.0%</td> <td>

<div class=”bar” style=”width:29%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-8796731851960694140”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2426</td> <td class=”number”>75.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>706</td> <td class=”number”>22.0%</td> <td>

<div class=”bar” style=”width:29%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2426</td> <td class=”number”>75.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>706</td> <td class=”number”>22.0%</td> <td>

<div class=”bar” style=”width:29%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PERPOV10”>PERPOV10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.11207</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>86.6%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram9059720109187059977”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives9059720109187059977,#minihistogram9059720109187059977”
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</div> <div class=”row collapse col-md-12” id=”descriptives9059720109187059977”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles9059720109187059977”

aria-controls=”quantiles9059720109187059977” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram9059720109187059977” aria-controls=”histogram9059720109187059977”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common9059720109187059977” aria-controls=”common9059720109187059977”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme9059720109187059977” aria-controls=”extreme9059720109187059977”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles9059720109187059977”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>0</td>

</tr> <tr>

<th>95-th percentile</th> <td>1</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr>

<th>Range</th> <td>1</td>

</tr> <tr>

<th>Interquartile range</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.3155</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.8152</td>

</tr> <tr>

<th>Kurtosis</th> <td>4.0577</td>

</tr> <tr>

<th>Mean</th> <td>0.11207</td>

</tr> <tr>

<th>MAD</th> <td>0.19902</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.4607</td>

</tr> <tr>

<th>Sum</th> <td>351</td>

</tr> <tr>

<th>Variance</th> <td>0.099541</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram9059720109187059977”>

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g6upqjRw5Un/84x81ZMiQxn2GDh2qRx55RGPGjLF7PAAAgPNme8EaPXq0evTooalTp2rcuHGKi4trsk9GRoaOHz9u92gAAABG2F6wVq1apSuvvPKc%2B%2B3du9eGaQAAAMyz/Rmsvn376u6779a2bdsa11566SXddddd8vl8do8DAABgnO0Fa9GiRTpx4oR69erVuDZixAg1NDRo8eLFdo8DAABgnO1vEb7//vvauHGj4uPjG9eSk5NVUFCgm266ye5xAAAAjLP9DlZtba0iIyObDhIerpqaGrvHAQAAMM72gjV06FAtXrxYX3/9dePaF198oUcffTTgoxoAAACcyva3CB9%2B%2BGH9x3/8h66%2B%2BmpFR0eroaFB1dXV6tatm/70pz/ZPQ4AAIBxthesbt266c0339R7772nw4cPKzw8XD169NDw4cMVERFh9zgAAADG2V6wJKlt27a67rrrgnFqAAAAy9lesI4cOaKlS5fq008/VW1tbZPt77zzjt0jAQAAGBWUZ7DKy8s1fPhwdejQwe7TAwAAWM72grVv3z698847SkhIsPvUAAAAtrD9Yxo6derEnSsAABDSbC9YU6dOVWFhofx%2Bv92nBgAAsIXtbxG%2B9957%2BuSTT7R%2B/XpddNFFCg8P7HivvPKK3SMBAAAYZXvBio6O1jXXXGP3aQEAAGxje8FatGiR3acEAACwle3PYEnSZ599pmeeeUYPPfRQ49pf//rXFh9n165dSk9PV35%2BfsD6%2BvXrdemllyo1NTXg629/%2B5skqaGhQcuWLVNWVpaGDh2qyZMn68iRI42v9/l8mjlzptLT0zV8%2BHDNnTv3rJ/ZBQAAcDa2F6zdu3dr7Nixevvtt7Vp0yZJ33746B133NGiDxl97rnntHDhQnXv3v2s24cOHSq32x3wNXDgQEnSmjVrtHHjRhUVFWnHjh1KTk5WXl5e44P38%2BfPV01NjTZt2qTXXntNHo9HBQUF53nlAADgp8L2grVs2TI98MAD2rhxo8LCwiR9%2B/MJFy9erOXLlzf7OJGRkVq3bt33FqwfUlxcrNzcXPXs2VPR0dHKz8%2BXx%2BPR3r179eWXX2rbtm3Kz89XQkKCunTponvuuUevvfaaTp8%2B3eJzAQCAnx7bC9Y//vEP5eTkSFJjwZKkUaNGyePxNPs4d9xxhzp27Pi9248ePapf//rXGjp0qLKysvT6669Lkmpra1VaWqqUlJTGfaOjo9W9e3e53W4dOHBAERER6tu3b%2BP2/v376%2BTJk/rss8%2BaPR8AAPjpsv0h944dO6q2tlZt27YNWC8vL2%2By9mMlJCQoOTlZ999/v3r16qU///nP%2Bu1vf6vExERdcskl8vv9io2NDXhNbGysKioqFBcXp%2Bjo6IDyd2bfioqKZp2/vLxcXq83YM3l6qDExMTzvLJAERFBeYTuR3O5nDPvmWydlrETkK21yNc6ZGudUMzW9oJ1%2BeWX63e/%2B53mzZvXuHbo0CEtWLBAV199tZFzjBgxQiNGjGj89ejRo/XnP/9Z69ev1%2BzZsyXpBz/o9Hw/BLW4uFiFhYUBa3l5eZoxY8Z5Hdfp4uOjgj1Ci8XEtA/2CCGLbK1FvtYhW%2BuEUra2F6yHHnpId955p9LS0lRfX6/LL79cNTU16t27txYvXmzZeZOSkrRv3z7FxcUpPDxcPp8vYLvP51OnTp2UkJCgqqoq1dfXKyIionGb9O2P%2BWmO7OxsZWZmBqy5XB1UUVFt4Er%2BxWlN3/T1WykiIlwxMe1VWVmj%2BvqGYI8TUsjWWuRrHbK1jpXZBusf97YXrK5du2rTpk3auXOnDh06pHbt2qlHjx4aNmxYwNty5%2BPll19WbGysbrzxxsY1j8ejbt26KTIyUr1791ZJSYmuvPJKSVJlZaUOHz6sgQMHKikpSX6/XwcPHlT//v0lSW63WzExMerRo0ezzp%2BYmNjk7UCv94Tq6n7afyCdeP319Q2OnNsJyNZa5GsdsrVOKGVre8GSpDZt2ui6666z7PinTp3S448/rm7duunSSy/VW2%2B9pffee0%2BvvvqqJCknJ0dFRUW65ppr1KVLFxUUFKhfv35KTU2VJI0cOVK///3v9eSTT%2BrUqVNavny5JkyYIJcrKHEBAACHsb0xZGZm/uCdquZ%2BFtaZMlRXVydJ2rZtm6Rv7zbdcccdqq6u1n333Sev16uLLrpIy5cv14ABAyRJEydOlNfr1aRJk1RdXa20tLSAZ6Yee%2BwxLViwQFlZWWrTpo1uuummJh9mCgAA8H3C/Of7RHcLFRQUBBSs%2Bvp6HTp0SG63W3feeafuuusuO8exjdd7wvgxXa5wXV%2Bwy/hxrbJl5rBgj9BsLle44uOjVFFRHTK3q1sLsrUW%2BVqHbK1jZbadO3//RzpZyfY7WGe%2Bi%2B%2B73nrrLf3lL3%2BxeRoAAADzWs23oV133XV68803gz0GAADAeWs1BWv//v3n/flTAAAArYHtbxFOnDixyVpNTY08Ho9uuOEGu8cBAAAwzvaClZyc3OS7CCMjIzVhwgTddtttdo8DAABgnO0Fy8pPawcAAGgNbC9YGzZsaPa%2B48aNs3ASAAAAa9hesObOnauGhoYmD7SHhYUFrIWFhVGwAACAI9lesJ5//nm98MILuvvuu9W3b1/5/X79/e9/13PPPadf/epXSktLs3skAAAAo4LyDFZRUZG6dOnSuHbFFVeoW7dumjx5sjZt2mT3SAAAAEbZ/jlYn3/%2BuWJjY5usx8TEqKyszO5xAAAAjLO9YCUlJWnx4sWqqKhoXKusrNTSpUt18cUX2z0OAACAcba/Rfjwww9r1qxZKi4uVlRUlMLDw1VVVaV27dpp%2BfLldo8DAABgnO0Fa/jw4Xr33Xe1c%2BdOHTt2TH6/X126dNHPf/5zdewYnJ94DQAAYJLtBUuS2rdvr6ysLB07dkzdunULxggAAACWsf0ZrNraWs2ZM0eDBw/WL37xC0nfPoM1ZcoUVVZW2j0OAACAcbYXrCVLlujAgQMqKChQePi/Tl9fX6%2BCggK7xwEAADDO9oL11ltv6emnn9aoUaMaf%2BhzTEyMFi1apLffftvucQAAAIyzvWBVV1crOTm5yXpCQoJOnjxp9zgAAADG2V6wLr74Yv3lL3%2BRpICfPbh161b97Gc/s3scAAAA42z/LsJf/vKXuvfee3XrrbeqoaFBL774ovbt26e33npLc%2BfOtXscAAAA42wvWNnZ2XK5XFq9erUiIiL07LPPqkePHiooKNCoUaPsHgcAAMA42wvW8ePHdeutt%2BrWW2%2B1%2B9QAAAC2sP0ZrKysrIBnrwAAAEKN7QUrLS1NW7Zssfu0AAAAtrH9LcILL7xQTzzxhIqKinTxxRerTZs2AduXLl1q90gAAABG2V6wSktLdckll0iSKioq7D49AACA5WwrWPn5%2BVq2bJn%2B9Kc/Na4tX75ceXl5do0AAABgC9uewdq%2BfXuTtaKiIrtODwAAYBvbCtbZvnOQ7yYEAAChyLaCdeYHO59rDQAAwOls/5gGAACAUEfBAgAAMMy27yI8ffq0Zs2adc41PgcLAAA4nW0Fa8iQISovLz/nGgAAgNPZVrD%2B/fOvAAAAQhnPYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMc3TB2rVrl9LT05Wfn99k2%2BbNmzVmzBgNHjxYt9xyi95///3GbQ0NDVq2bJmysrI0dOhQTZ48WUeOHGnc7vP5NHPmTKWnp2v48OGaO3euamtrbbkmAADgfI4tWM8995wWLlyo7t27N9l24MABzZkzR7Nnz9aHH36o3NxcTZ8%2BXceOHZMkrVmzRhs3blRRUZF27Nih5ORk5eXlye/3S5Lmz5%2Bvmpoabdq0Sa%2B99po8Ho8KCgpsvT4AAOBcji1YkZGRWrdu3VkL1tq1a5WRkaGMjAxFRkZq7Nix6tOnj9544w1JUnFxsXJzc9WzZ09FR0crPz9fHo9He/fu1Zdffqlt27YpPz9fCQkJ6tKli%2B655x699tprOn36tN2XCQAAHMi2n0Vo2h133PG920pKSpSRkRGwlpKSIrfbrdraWpWWliolJaVxW3R0tLp37y63260TJ04oIiJCffv2bdzev39/nTx5Up999lnA%2BvcpLy%2BX1%2BsNWHO5OigxMbG5l9csERHO6scul3PmPZOt0zJ2ArK1Fvlah2ytE4rZOrZg/RCfz6fY2NiAtdjYWJWWlurrr7%2BW3%2B8/6/aKigrFxcUpOjpaYWFhAdskqaKiolnnLy4uVmFhYcBaXl6eZsyY8WMuJ2TEx0cFe4QWi4lpH%2BwRQhbZWot8rUO21gmlbEOyYElqfJ7qx2w/12vPJTs7W5mZmQFrLlcHVVRUn9dxv8tpTd/09VspIiJcMTHtVVlZo/r6hmCPE1LI1lrkax2ytY6V2QbrH/chWbDi4%2BPl8/kC1nw%2BnxISEhQXF6fw8PCzbu/UqZMSEhJUVVWl%2Bvp6RURENG6TpE6dOjXr/ImJiU3eDvR6T6iu7qf9B9KJ119f3%2BDIuZ2AbK1FvtYhW%2BuEUrbOugXSTAMGDNC%2BffsC1txutwYNGqTIyEj17t1bJSUljdsqKyt1%2BPBhDRw4UP369ZPf79fBgwcDXhsTE6MePXrYdg0AAMC5QrJg3X777frggw/07rvv6ptvvtG6dev0%2Beefa%2BzYsZKknJwcrVq1Sh6PR1VVVSooKFC/fv2UmpqqhIQEjRw5Ur///e91/PhxHTt2TMuXL9eECRPkcoXkDT8AAGCYYxtDamqqJKmurk6StG3bNknf3m3q06ePCgoKtGjRIpWVlalXr15auXKlOnfuLEmaOHGivF6vJk2apOrqaqWlpQU8lP7YY49pwYIFysrKUps2bXTTTTed9cNMAQAAzibMf75PdKNZvN4Txo/pcoXr%2BoJdxo9rlS0zhwV7hGZzucIVHx%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%2BqJKSEqWkpATsl5KSoi1btkiSSkpKdOONNzZuCw8PV79%2B/eR2uzV69Ohmnbe8vFxerzdgzeXqoMTExPO8okAREc7qxy6Xc%2BY9k63TMnYCsrUW%2BVqHbK0TitmGbMG67LLLlJ6erieffFJHjhzRzJkz9eijj8rn86lLly4B%2B8bFxamiokKS5PP5FBsbG7A9Nja2cXtzFBcXq7CwMGAtLy9PM2bM%2BJFXExri46OCPUKLxcS0D/YIIYtsrUW%2B1iFb64RStiFbsIqLixv/d8%2BePTV79mxNmzZNQ4YMOedr/X7/eZ07OztbmZmZAWsuVwdVVFSf13G/y2lN3/T1WykiIlwxMe1VWVmj%2BvqGYI8TUsjWWuRrHbK1jpXZBusf9yFbsL7roosuUn19vcLDw%2BXz%2BQK2VVRUKCEhQZIUHx/fZLvP51Pv3r2bfa7ExMQmbwd6vSdUV/fT/gPpxOuvr29w5NxOQLbWIl/rkK11QilbZ90Caab9%2B/dr8eLFAWsej0dt27ZVRkZGk49d2LdvnwYNGiRJGjBggEpKShq31dfXa//%2B/Y3bAQAAziUkC1anTp1UXFysoqIinTp1SocOHdJTTz2l7Oxs3XzzzSorK9PatWv1zTffaOcOqgCBAAALvklEQVTOndq5c6duv/12SVJOTo42bNigPXv2qKamRitWrFDbtm01YsSI4F4UAABwjJB8i7BLly4qKirS0qVLGwvS%2BPHjlZ%2Bfr8jISK1cuVILFy7Uo48%2BqqSkJC1ZskSXXnqpJOmaa67R/fffr5kzZ%2Bqrr75SamqqioqK1K5duyBfFQAAcIow//k%2B0Y1m8XpPGD%2BmyxWu6wt2GT%2BuVbbMHBbsEZrN5QpXfHyUKiqqQ%2BZ5gNaCbK1FvtYhW%2BtYmW3nzh2NHq%2B5QvItQgAAgGCiYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYFpI/ixAAAEi/%2BP1/B3uEZvvoiVHBHsEo7mABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBOouysjL95je/UVpamq699lotWbJEDQ0NwR4LAAA4hCvYA7RG9957r/r3769t27bpq6%2B%2B0tSpU3XBBRfo17/%2BdbBHAwAADsAdrO9wu906ePCgZs%2BerY4dOyo5OVm5ubkqLi4O9mgAAMAhuIP1HSUlJUpKSlJsbGzjWv/%2B/XXo0CFVVVUpOjr6nMcoLy%2BX1%2BsNWHO5OigxMdHorBERzurHLpdz5j2TrdMydgKytRb5WodsrRdK2VKwvsPn8ykmJiZg7UzZqqioaFbBKi4uVmFhYcDa9OnTde%2B995obVN8WuTu7fqrs7Gzj5e2nrry8XH/84/NkawGytRb5WseJ2X70xKhgj9As5eXleuaZZxyV7bmETlU0yO/3n9frs7OztX79%2BoCv7OxsQ9P9i9frVWFhYZO7ZTh/ZGsdsrUW%2BVqHbK0TitlyB%2Bs7EhIS5PP5AtZ8Pp/CwsKUkJDQrGMkJiaGTAMHAAAtxx2s7xgwYICOHj2q48ePN6653W716tVLUVFRQZwMAAA4BQXrO1JSUpSamqqlS5eqqqpKHo9HL774onJycoI9GgAAcIiIRx555JFgD9Ha/PznP9emTZv0%2BOOP680339SECRM0efJkhYWFBXu0JqKionTllVdyd80CZGsdsrUW%2BVqHbK0TatmG%2Bc/3iW4AAAAE4C1CAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWK1cWVmZfvOb3ygtLU3XXnutlixZooaGhrPuu2rVKo0cOVKXX365cnJytG/fPpundZaWZPvyyy9r5MiRGjx4sG6%2B%2BWZt27bN5mmdpSXZnvHFF19o8ODBeuaZZ2ya0rlakq/H49GkSZM0aNAgZWRk6KWXXrJ3WIdpbrYNDQ16%2BumnlZmZqcGDB2vMmDHavHlzECZ2ll27dik9PV35%2Bfk/uF9DQ4OWLVumrKwsDR06VJMnT9aRI0dsmtIQP1q18ePH%2B%2BfNm%2BevrKz0Hzp0yH/DDTf4X3jhhSb7vfPOO/4rrrjCv2fPHn9NTY1/5cqV/mHDhvmrq6uDMLUzNDfbrVu3%2BocMGeL/6KOP/KdOnfK/%2Buqr/v79%2B/sPHz4chKmdobnZ/rvp06f7hwwZ4n/66adtmtK5mptvTU2Nf8SIEf7nnnvOf/LkSf/evXv9o0eP9peWlgZhamdobrarV6/2Dx8%2B3O/xePx1dXX%2B7du3%2B1NSUvwHDhwIwtTOUFRU5L/hhhv8EydO9M%2BcOfMH9121apX/2muv9ZeWlvpPnDjhf%2Byxx/xjxozxNzQ02DTt%2BeMOVivmdrt18OBBzZ49Wx07dlRycrJyc3NVXFzcZN/i4mLdcsstGjRokNq1a6cpU6ZIknbs2GH32I7Qkmxra2t1//33a8iQIWrTpo1uu%2B02RUVFac%2BePUGYvPVrSbZn7Ny5U6WlpRoxYoR9gzpUS/LdsmWLoqOjNWXKFLVv314DBw7Upk2b1LNnzyBM3vq1JNuSkhINGTJEl1xyiSIiInTttdcqLi5Of//734MwuTNERkZq3bp16t69%2Bzn3LS4uVm5urnr27Kno6Gjl5%2BfL4/Fo7969NkxqBgWrFSspKVFSUpJiY2Mb1/r3769Dhw6pqqqqyb4pKSmNvw4PD1e/fv3kdrttm9dJWpLtzTffrF/%2B8peNv66srFR1dbW6dOli27xO0pJspW8L7GOPPaYFCxbI5XLZOaojtSTfjz/%2BWH369NFDDz2kK664QqNGjdIbb7xh98iO0ZJsR4wYof/5n//RgQMHdOrUKb3zzjuqqanRlVdeaffYjnHHHXeoY8eO59yvtrZWpaWlAX%2BnRUdHq3v37o76O42C1Yr5fD7FxMQErJ35g19RUdFk33//j8KZfb%2B7H77Vkmz/nd/v17x58zRo0CD%2BQ/o9Wprt8uXLddlll%2Bmqq66yZT6na0m%2Bx44d0zvvvKP09HTt2rVLU6dO1Zw5c7R//37b5nWSlmR7ww03KDs7W%2BPGjVNqaqpmzZqlRYsW6cILL7Rt3lD19ddfy%2B/3O/7vNP652Mr5/X5L9kXL8zp9%2BrQefPBBlZaWatWqVRZNFRqam21paanWrl2rjRs3WjxRaGluvn6/X/3799eYMWMkSePHj9crr7yirVu3BtwdwL80N9sNGzZow4YNWrt2rfr27avdu3dr1qxZuvDCCzVw4ECLp/xpcPrfadzBasUSEhLk8/kC1nw%2Bn8LCwpSQkBCwHh8ff9Z9v7sfvtWSbKVvb1lPnTpV//znP7VmzRpdcMEFdo3qOM3N1u/365FHHtG9996rzp072z2mY7Xk927nzp2bvCWTlJQkr9dr%2BZxO1JJsV69erezsbA0cOFCRkZEaMWKErrrqKt6CNSAuLk7h4eFn/f%2BiU6dOQZqq5ShYrdiAAQN09OhRHT9%2BvHHN7XarV69eioqKarJvSUlJ46/r6%2Bu1f/9%2BDRo0yLZ5naQl2fr9fuXn58vlcumll15SfHy83eM6SnOz/ec//6n//d//1dNPP620tDSlpaXpzTff1PPPP6/x48cHY3RHaMnv3Z49e%2Bof//hHwJ2AsrIyJSUl2Tavk7Qk24aGBtXX1wesnTp1ypY5Q11kZKR69%2B4d8HdaZWWlDh8%2B7Ki7gxSsViwlJUWpqalaunSpqqqq5PF49OKLLyonJ0eSNGrUKH300UeSpJycHG3YsEF79uxRTU2NVqxYobZt2/JdWd%2BjJdlu3LhRpaWleuqppxQZGRnMsR2hudl27dpVO3fu1Ouvv974lZmZqYkTJ6qoqCjIV9F6teT37tixY1VRUaFnn31WtbW12rRpk0pKSjR27NhgXkKr1ZJsMzMztW7dOh08eFB1dXV6//33tXv3bmVlZQXzEhzriy%2B%2B0KhRoxo/6yonJ0erVq2Sx%2BNRVVWVCgoK1K9fP6WmpgZ50ubjGaxW7umnn9b8%2BfM1bNgwRUdHa%2BLEiY3f0Xbo0CGdPHlSknTNNdfo/vvv18yZM/XVV18pNTVVRUVFateuXTDHb9Wam%2B1rr72msrKyJg%2B133zzzVq4cKHtcztBc7KNiIhQ165dA17Xvn17RUdH85bhOTT3926XLl20cuVKPfHEE/qv//ov/exnP9Py5ct18cUXB3P8Vq252U6dOlV1dXXKy8vT8ePHlZSUpIULF%2Brqq68O5vit2plyVFdXJ0mNH9jsdrt1%2BvRpHTp0qPEu4MSJE%2BX1ejVp0iRVV1crLS1NhYWFwRn8RwrzO/0pMgAAgFaGtwgBAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwLD/B1TONchjdppIAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common9059720109187059977”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2781</td> <td class=”number”>86.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>351</td> <td class=”number”>10.9%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme9059720109187059977”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2781</td> <td class=”number”>86.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>351</td> <td class=”number”>10.9%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2781</td> <td class=”number”>86.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>351</td> <td class=”number”>10.9%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_POPLOSS10”>POPLOSS10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.16794</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>81.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-5890118761517484934”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-5890118761517484934,#minihistogram-5890118761517484934”
aria-expanded=”false” aria-controls=”collapseExample”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-5890118761517484934”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-5890118761517484934”

aria-controls=”quantiles-5890118761517484934” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-5890118761517484934” aria-controls=”histogram-5890118761517484934”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-5890118761517484934” aria-controls=”common-5890118761517484934”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-5890118761517484934” aria-controls=”extreme-5890118761517484934”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-5890118761517484934”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>0</td>

</tr> <tr>

<th>95-th percentile</th> <td>1</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr>

<th>Range</th> <td>1</td>

</tr> <tr>

<th>Interquartile range</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.37388</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.2262</td>

</tr> <tr>

<th>Kurtosis</th> <td>1.16</td>

</tr> <tr>

<th>Mean</th> <td>0.16794</td>

</tr> <tr>

<th>MAD</th> <td>0.27948</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.7774</td>

</tr> <tr>

<th>Sum</th> <td>526</td>

</tr> <tr>

<th>Variance</th> <td>0.13978</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-5890118761517484934”>

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FwQl91PWFFRoZUrV2r16tWqq6vT6NGj9Yc//EFDhgzx7zNs2DA9/vjjGjt2rN3jAQAAXDDbC9bNN9%2BsHj16aOrUqbrtttsUFxd31j4ZGRk6duyY3aMBAAAYYXvBWr58uYYPH37e/fbu3WvDNAAAAObZfg9W3759dd9992nz5s3%2Btddff12/%2BtWv5PV67R4HAADAONsL1sKFC3X8%2BHH16tXLv5aZmanm5mY988wzdo8DAABgnO0vEe7atUtr165VfHy8fy05OVmFhYW65ZZb7B4HAADAONuvYDU0NCgyMvLsQcLDVV9fb/c4AAAAxtlesIYNG6ZnnnlG33zzjX/tq6%2B%2B0hNPPBHwVg0AAABOZXvBeuSRR7R7925de%2B21Gj58uIYOHarMzEyVlpZqwYIFrTrWzp07lZ6ervz8/ID1VatW6corr1RqamrA15l3iG9ubtbixYs1cuRIDRs2TFOmTNHhw4f9j/d6vZo9e7bS09M1YsQIzZs3Tw0NDRd%2B8gAA4EfB9nuwunXrpvfee08ffPCBDh06pPDwcPXo0UMjRoxQREREi4/z8ssva%2BXKlerevfs5tw8bNkx//OMfz7ntzTff1Nq1a/Xyyy%2BrS5cuWrx4sfLy8vTuu%2B8qLCxM8%2BfP16lTp7Ru3TqdPn1as2bNUmFhoR599NF/65wBAMCPi%2B0FS5Latm2rG2%2B88YKOERkZqZUrV%2Brpp5/WyZMnW/XYkpIS5ebmqmfPnpKk/Px8paWlae/evbrsssu0efNm/fnPf1ZCQoIk6f7779esWbNUUFCgNm3aXNDcAAAg9NlesA4fPqxFixbpiy%2B%2BOOfLblu2bGnRcSZPnvyD248cOaJf/vKXKi0tVUxMjGbOnKlbb71VDQ0NKi8vV0pKin/f6Ohode/eXW63W8ePH1dERIT69u3r396/f3%2BdOHFCX375ZcA6AADAudhesB555BFVVVVpxIgR6tChgyXPkZCQoOTkZD3wwAPq1auX/vKXv%2Bg3v/mNEhMTdcUVV8jn8yk2NjbgMbGxsaqurlZcXJyio6MVFhYWsE2SqqurW/T8VVVV8ng8AWsuVwclJiZe4JkFiogI2md1/1tcLufMeyZbp2XsBGRrLfK1DtlaJxSztb1glZaWasuWLf6X36yQmZmpzMxM/59vvvlm/eUvf9GqVas0d%2B5cSZLP5/vex//QtpYoKSlRUVFRwFpeXp5mzpx5Qcd1uvj4qGCP0GoxMe2DPULIIltrka91yNY6oZSt7QWrU6dOll25%2BiFJSUkqLS1VXFycwsPDz/pYHq/Xq06dOikhIUG1tbVqamry33R/Zt9OnTq16Lmys7OVlZUVsOZydVB1dZ2BM/knpzV90%2BdvpYiIcMXEtFdNTb2ampqDPU5IIVtrka91yNY6VmYbrB/ubS9YU6dOVVFRkebMmRPwMpxJf/rTnxQbG6ubbrrJv1ZRUaFu3bopMjJSvXv3VllZmf9Dp2tqanTo0CENHDhQSUlJ8vl82r9/v/r37y9JcrvdiomJUY8ePVr0/ImJiWe9HOjxHFdj44/7G9KJ59/U1OzIuZ2AbK1FvtYhW%2BuEUra2F6wPPvhAn376qVatWqXLLrtM4eGBV2HeeuutC36OU6dO6amnnlK3bt105ZVX6v3339cHH3ygt99%2BW5KUk5Oj4uJiXX/99erSpYsKCwvVr18/paamSpJGjx6t3/3ud3r22Wd16tQpLV26VBMmTJDLFZRfugQAAA5je2OIjo7W9ddff8HHOVOGGhsbJUmbN2%2BW9O3VpsmTJ6uurk6zZs2Sx%2BPRZZddpqVLl2rAgAGSpIkTJ8rj8WjSpEmqq6tTWlpawD1TTz75pB577DGNHDlSbdq00S233HLWm5kCAAB8nzDfhd7RjRbxeI4bP6bLFa6fFe40flyrbJh9XbBHaDGXK1zx8VGqrq4LmcvVFwuytRb5WodsrWNltp07dzR6vJYKyl3SX375pV544QU9/PDD/rX/%2BZ//CcYoAAAAxtlesHbv3q1x48Zp06ZNWrdunaRv33x08uTJLX6TUQAAgIuZ7QVr8eLFevDBB7V27Vr/bxF269ZNzzzzjJYuXWr3OAAAAMbZXrD%2B9re/KScnR5IC3qZhzJgxqqiosHscAAAA42wvWB07djznZxBWVVWpbdu2do8DAABgnO0F6%2Bqrr9Zvf/tb1dbW%2BtcOHDiggoICXXvttXaPAwAAYJzt74P18MMP65577lFaWpqampp09dVXq76%2BXr1799Yzzzxj9zgAAADG2V6wunbtqnXr1mnHjh06cOCA2rVrpx49eui6666z7KNzAAAA7BSUz35p06aNbrzxxmA8NQAAgOVsL1hZWVk/eKWK98ICAABOZ3vBuummmwIKVlNTkw4cOCC326177rnH7nEAAACMs71gzZ0795zr77//vv7617/aPA0AAIB5QfkswnO58cYb9d577wV7DAAAgAt20RSszz77TD6fL9hjAAAAXDDbXyKcOHHiWWv19fWqqKjQqFGj7B4HAADAONsLVnJy8lm/RRgZGakJEybozjvvtHscAAAA42wvWLxbOwAACHW2F6zVq1e3eN/bbrvNwkkAAACsYXvBmjdvnpqbm8%2B6oT0sLCxgLSwsjIIFAAAcyfaC9corr%2BjVV1/Vfffdp759%2B8rn8%2Bnzzz/Xyy%2B/rF/84hdKS0uzeyQAAACjgnIPVnFxsbp06eJfGzp0qLp166YpU6Zo3bp1do8EAABglO3vg3Xw4EHFxsaetR4TE6PKykq7xwEAADDO9oKVlJSkZ555RtXV1f61mpoaLVq0SJdffrnd4wAAABhn%2B0uEjzzyiObMmaOSkhJFRUUpPDxctbW1ateunZYuXWr3OAAAAMbZXrBGjBih7du3a8eOHTp69Kh8Pp%2B6dOmin/70p%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%2BLFy/WH//4R//a0qVLlZeXZ9cIAAAAtrDtHqytW7eetVZcXGzX0wMAANjGtoJ1rt8c5LcJAQBAKLKtYJ35YOfzrQEAADid7W/TAAAAEOooWAAAAIbZ9luEp0%2Bf1pw5c867xvtgAQAAp7OtYA0ZMkRVVVXnXQMAAHA62wrWv77/FQAAQCjjHiwAAADDHF2wdu7cqfT0dOXn55%2B1bf369Ro7dqwGDx6s22%2B/Xbt27fJva25u1uLFizVy5EgNGzZMU6ZM0eHDh/3bvV6vZs%2BerfT0dI0YMULz5s1TQ0ODLecEAACcz7EF6%2BWXX9aCBQvUvXv3s7bt27dPBQUFmjt3rj766CPl5uZq%2BvTpOnr0qCTpzTff1Nq1a1VcXKxt27YpOTlZeXl5/jc%2BnT9/vurr67Vu3Tq98847qqioUGFhoa3nBwAAnMuxBSsyMlIrV648Z8FasWKFMjIylJGRocjISI0bN059%2BvTRmjVrJEklJSXKzc1Vz549FR0drfz8fFVUVGjv3r36xz/%2Boc2bNys/P18JCQnq0qWL7r//fr3zzjs6ffq03acJAAAcyLab3E2bPHny924rKytTRkZGwFpKSorcbrcaGhpUXl6ulJQU/7bo6Gh1795dbrdbx48fV0REhPr27evf3r9/f504cUJffvllwPr3qaqqksfjCVhzuTooMTGxpafXIhERzurHLpdz5j2TrdMydgKytRb5WodsrROK2Tq2YP0Qr9er2NjYgLXY2FiVl5frm2%2B%2Bkc/nO%2Bf26upqxcXFKTo6OuBjfM7sW11d3aLnLykpUVFRUcBaXl6eZs6c%2Be%2BcTsiIj48K9gitFhPTPtgjhCyytRb5WodsrRNK2YZkwZLO/0HSP7T9Qj%2BEOjs7W1lZWQFrLlcHVVfXXdBxv8tpTd/0%2BVspIiJcMTHtVVNTr6am5mCPE1LI1lrkax2ytY6V2Qbrh/uQLFjx8fHyer0Ba16vVwkJCYqLi1N4ePg5t3fq1EkJCQmqra1VU1OTIiIi/NskqVOnTi16/sTExLNeDvR4jqux8cf9DenE829qanbk3E5AttYiX%2BuQrXVCKVtnXQJpoQEDBqi0tDRgze12a9CgQYqMjFTv3r1VVlbm31ZTU6NDhw5p4MCB6tevn3w%2Bn/bv3x/w2JiYGPXo0cO2cwAAAM4VkgXrrrvu0ocffqjt27fr5MmTWrlypQ4ePKhx48ZJknJycrR8%2BXJVVFSotrZWhYWF6tevn1JTU5WQkKDRo0frd7/7nY4dO6ajR49q6dKlmjBhglyukLzgBwAADHNsY0hNTZUkNTY2SpI2b94s6durTX369FFhYaEWLlyoyspK9erVS8uWLVPnzp0lSRMnTpTH49GkSZNUV1entLS0gJvSn3zyST322GMaOXKk2rRpo1tuueWcb2YKAABwLmG%2BC72jGy3i8Rw3fkyXK1w/K9xp/LhW2TD7umCP0GIuV7ji46NUXV0XMvcDXCzI1lrkax2ytY6V2Xbu3NHo8VoqJF8iBAAACCYKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMCxkC1bfvn01YMAApaam%2Br%2BeeuopSdLu3bs1YcIEXX311br55pu1Zs2agMcuX75co0eP1tVXX62cnByVlpYG4xQAAIBDuYI9gJU2btyoyy67LGCtqqpK999/v%2BbNm6exY8fqk08%2B0bRp09SjRw%2BlpqZq69ateuGFF/TKK6%2Bob9%2B%2BWr58ue677z5t2rRJHTp0CNKZAADQej//3f8L9ggt9vHTY4I9glEhewXr%2B6xdu1bJycmaMGGCIiMjlZ6erqysLK1YsUKSVFJSottvv12DBg1Su3btdO%2B990qStm3bFsyxAQCAg4R0wVq0aJEyMzM1dOhQzZ8/X3V1dSorK1NKSkrAfikpKf6XAb%2B7PTw8XP369ZPb7bZ1dgAA4Fwh%2BxLhVVddpfT0dD377LM6fPiwZs%2BerSeeeEJer1ddunQJ2DcuLk7V1dWSJK/Xq9jY2IDtsbGx/u0tUVVVJY/HE7DmcnVQYmLiv3k25xYR4ax%2B7HI5Z94z2TotYycgW2uRr3XI1nqhlG3IFqySkhL//%2B7Zs6fmzp2radOmaciQIed9rM/nu%2BDnLioqCljLy8vTzJkzL%2Bi4ThcfHxXsEVotJqZ9sEcIWWRrLfK1DtlaJ5SyDdmC9V2XXXaZmpqaFB4eLq/XG7CturpaCQkJkqT4%2BPiztnu9XvXu3bvFz5Wdna2srKyANZerg6qr6/7N6c/NaU3f9PlbKSIiXDEx7VVTU6%2BmpuZgjxNSyNZa5GsdsrWeFdkG64f7kCxYn332mdasWaOHHnrIv1ZRUaG2bdsqIyNDf/7znwP2Ly0t1aBBgyRJAwYMUFlZmcaPHy9Jampq0meffaYJEya0%2BPkTExPPejnQ4zmuxsYf9zekE8%2B/qanZkXM7Adlai3ytQ7bWCaVsnXUJpIU6deqkkpISFRcX69SpUzpw4ICef/55ZWdn69Zbb1VlZaVWrFihkydPaseOHdqxY4fuuusuSVJOTo5Wr16tPXtAPbhnAAALlUlEQVT2qL6%2BXi%2B%2B%2BKLatm2rzMzM4J4UAABwjJC8gtWlSxcVFxdr0aJF/oI0fvx45efnKzIyUsuWLdOCBQv0xBNPKCkpSc8995yuvPJKSdL111%2BvBx54QLNnz9bXX3%2Bt1NRUFRcXq127dkE%2BKwAA4BQhWbAkadiwYXrrrbe%2Bd9u77777vY%2B9%2B%2B67dffdd1s1GgAACHEh%2BRIhAABAMFGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhY51BZWalf//rXSktL0w033KDnnntOzc3NwR4LAAA4hCvYA1yMZsyYof79%2B2vz5s36%2BuuvNXXqVF1yySX65S9/GezRAACAA3AF6zvcbrf279%2BvuXPnqmPHjkpOTlZubq5KSkqCPRoAAHAIrmB9R1lZmZKSkhQbG%2Btf69%2B/vw4cOKDa2lpFR0ef9xhVVVXyeDwBay5XByUmJhqdNSLCWf3Y5XLOvGeydVrGTkC21iJf65Ct9UIpWwrWd3i9XsXExASsnSlb1dXVLSpYJSUlKioqClibPn26ZsyYYW5QfVvk7un6hbKzs42Xtx%2B7qqoq/eEPr5CtBcjWWuRrHSdm%2B/HTY4I9QotUVVXphRdecFS25xM6VdEgn893QY/Pzs7WqlWrAr6ys7MNTfdPHo9HRUVFZ10tw4UjW%2BuQrbXI1zpka51QzJYrWN%2BRkJAgr9cbsOb1ehUWFqaEhIQWHSMxMTFkGjgAAGg9rmB9x4ABA3TkyBEdO3bMv%2BZ2u9WrVy9FRUUFcTIAAOAUFKzvSElJUWpqqhYtWqTa2lpVVFTotddeU05OTrBHAwAADhHx%2BOOPPx7sIS42P/3pT7Vu3To99dRTeu%2B99zRhwgRNmTJFYWFhwR7tLFFRURo%2BfDhX1yxAttYhW2uRr3XI1jqhlm2Y70Lv6AYAAEAAXiIEAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCdZGrrKzUr3/9a6WlpemGG27Qc889p%2Bbm5nPuu3z5co0ePVpXX321cnJyVFpaavO0ztKabP/0pz9p9OjRGjx4sG699VZt3rzZ5mmdpTXZnvHVV19p8ODBeuGFF2ya0rlak29FRYUmTZqkQYMGKSMjQ6%2B//rq9wzpMS7Ntbm7WkiVLlJWVpcGDB2vs2LFav359ECZ2lp07dyo9PV35%2Bfk/uF9zc7MWL16skSNHatiwYZoyZYoOHz5s05SG%2BHBRGz9%2BvO/RRx/11dTU%2BA4cOOAbNWqU79VXXz1rvy1btviGDh3q27Nnj6%2B%2Bvt63bNky33XXXeerq6sLwtTO0NJsN27c6BsyZIjv448/9p06dcr39ttv%2B/r37%2B87dOhQEKZ2hpZm%2B6%2BmT5/uGzJkiG/JkiU2TelcLc23vr7el5mZ6Xv55Zd9J06c8O3du9d38803%2B8rLy4MwtTO0NNs33njDN2LECF9FRYWvsbHRt3XrVl9KSopv3759QZjaGYqLi32jRo3yTZw40Td79uwf3Hf58uW%2BG264wVdeXu47fvy478knn/SNHTvW19zcbNO0F44rWBcxt9ut/fv3a%2B7cuerYsaOSk5OVm5urkpKSs/YtKSnR7bffrkGDBqldu3a69957JUnbtm2ze2xHaE22DQ0NeuCBBzRkyBC1adNGd955p6KiorRnz54gTH7xa022Z%2BzYsUPl5eXKzMy0b1CHak2%2BGzZsUHR0tO699161b99eAwcO1Lp169SzZ88gTH7xa022ZWVlGjJkiK644gpFRETohhtuUFxcnD7//PMgTO4MkZGRWrlypbp3737efUtKSpSbm6uePXsqOjpa%2Bfn5qqio0N69e22Y1AwK1kWsrKxMSUlJio2N9a/1799fBw4cUG1t7Vn7pqSk%2BP8cHh6ufv36ye122zavk7Qm21tvvVV33323/881NTWqq6tTly5dbJvXSVqTrfRtgX3yySf12GOPyeVy2TmqI7Um308%2B%2BUR9%2BvTRww8/rKFDh2rMmDFas2aN3SM7RmuyzczM1H/9139p3759OnXqlLZs2aL6%2BnoNHz7c7rEdY/LkyerYseN592toaFB5eXnAv2nR0dHq3r27o/5No2BdxLxer2JiYgLWznzjV1dXn7Xvv/5H4cy%2B390P32pNtv/K5/Pp0Ucf1aBBg/gP6fdobbZLly7VVVddpWuuucaW%2BZyuNfkePXpUW7ZsUXp6unbu3KmpU6eqoKBAn332mW3zOklrsh01apSys7N12223KTU1VXPmzNHChQt16aWX2jZvqPrmm2/k8/kc/28aPy5e5Hw%2BnyX7ovV5nT59Wg899JDKy8u1fPlyi6YKDS3Ntry8XCtWrNDatWstnii0tDRfn8%2Bn/v37a%2BzYsZKk8ePH66233tLGjRsDrg7gn1qa7erVq7V69WqtWLFCffv21e7duzVnzhxdeumlGjhwoMVT/jg4/d80rmBdxBISEuT1egPWvF6vwsLClJCQELAeHx9/zn2/ux%2B%2B1ZpspW8vWU%2BdOlV///vf9eabb%2BqSSy6xa1THaWm2Pp9Pjz/%2BuGbMmKHOnTvbPaZjtebvbufOnc96SSYpKUkej8fyOZ2oNdm%2B8cYbys7O1sCBAxUZGanMzExdc801vARrQFxcnMLDw8/5/0WnTp2CNFXrUbAuYgMGDNCRI0d07Ngx/5rb7VavXr0UFRV11r5lZWX%2BPzc1Nemzzz7ToEGDbJvXSVqTrc/nU35%2Bvlwul15//XXFx8fbPa6jtDTbv//97/rv//5vLVmyRGlpaUpLS9N7772nV155RePHjw/G6I7Qmr%2B7PXv21N/%2B9reAKwGVlZVKSkqybV4naU22zc3NampqClg7deqULXOGusjISPXu3Tvg37SamhodOnTIUVcHKVgXsZSUFKWmpmrRokWqra1VRUWFXnvtNeXk5EiSxowZo48//liSlJOTo9WrV2vPnj2qr6/Xiy%2B%2BqLZt2/JbWd%2BjNdmuXbtW5eXlev755xUZGRnMsR2hpdl27dpVO3bs0Lvvvuv/ysrK0sSJE1VcXBzks7h4tebv7rhx41RdXa2XXnpJDQ0NWrduncrKyjRu3LhgnsJFqzXZZmVlaeXKldq/f78aGxu1a9cu7d69WyNHjgzmKTjWV199pTFjxvjf6yonJ0fLly9XRUWFamtrVVhYqH79%2Bik1NTXIk7Yc92Bd5JYsWaL58%2BfruuuuU3R0tCZOnOj/jbYDBw7oxIkTkqTrr79eDzzwgGbPnq2vv/5aqampKi4uVrt27YI5/kWtpdm%2B8847qqysPOum9ltvvVULFiywfW4naEm2ERER6tq1a8Dj2rdvr%2BjoaF4yPI%2BW/t3t0qWLli1bpqefflr/%2BZ//qZ/85CdaunSpLr/88mCOf1FrabZTp05VY2Oj8vLydOzYMSUlJWnBggW69tprgzn%2BRe1MOWpsbJQk/xs2u91unT59WgcOHPBfBZw4caI8Ho8mTZqkuro6paWlqaioKDiD/5vCfE6/iwwAAOAiw0uEAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGDY/wcgoAfAz2oUpwAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-5890118761517484934”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2606</td> <td class=”number”>81.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>526</td> <td class=”number”>16.4%</td> <td>

<div class=”bar” style=”width:20%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-5890118761517484934”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2606</td> <td class=”number”>81.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>526</td> <td class=”number”>16.4%</td> <td>

<div class=”bar” style=”width:20%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2606</td> <td class=”number”>81.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>526</td> <td class=”number”>16.4%</td> <td>

<div class=”bar” style=”width:20%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_POVRATE15”>POVRATE15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>333</td>

</tr> <tr>

<th>Unique (%)</th> <td>10.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.5%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>79</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>16.272</td>

</tr> <tr>

<th>Minimum</th> <td>3.4</td>

</tr> <tr>

<th>Maximum</th> <td>47.4</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-8118083890363668109”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-8118083890363668109,#minihistogram-8118083890363668109”
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</div> <div class=”row collapse col-md-12” id=”descriptives-8118083890363668109”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-8118083890363668109”

aria-controls=”quantiles-8118083890363668109” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-8118083890363668109” aria-controls=”histogram-8118083890363668109”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-8118083890363668109” aria-controls=”common-8118083890363668109”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-8118083890363668109” aria-controls=”extreme-8118083890363668109”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-8118083890363668109”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>3.4</td>

</tr> <tr>

<th>5-th percentile</th> <td>7.85</td>

</tr> <tr>

<th>Q1</th> <td>11.5</td>

</tr> <tr>

<th>Median</th> <td>15.2</td>

</tr> <tr>

<th>Q3</th> <td>19.7</td>

</tr> <tr>

<th>95-th percentile</th> <td>28.05</td>

</tr> <tr>

<th>Maximum</th> <td>47.4</td>

</tr> <tr>

<th>Range</th> <td>44</td>

</tr> <tr>

<th>Interquartile range</th> <td>8.2</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>6.4438</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.39602</td>

</tr> <tr>

<th>Kurtosis</th> <td>1.8466</td>

</tr> <tr>

<th>Mean</th> <td>16.272</td>

</tr> <tr>

<th>MAD</th> <td>4.9903</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.0769</td>

</tr> <tr>

<th>Sum</th> <td>50946</td>

</tr> <tr>

<th>Variance</th> <td>41.523</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-8118083890363668109”>

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hx/9913LZgKAACg8WwXWIsWLVJZWZl69%2B6t4OBgq8cBAAC4brYLrOLiYr377ruKjIy0ehQAAIAbYruPabjjjju4cgUAAJo02wXWtGnTlJWVJbfbbfUoAAAAN8R2LxG%2B9957OnLkiN588039%2Bte/lsPh2YCvv/66RZMBAAA0ju0Cq1mzZurbt6/VYwAAANww2wXWqlWrrB4BAADgptjuPViS9NVXXykzM1PPPPNM/drHH39s4UQAAACNZ7vAKiws1IgRI5Sfn6%2BdO3dK%2BuHDRydOnMiHjAIAgCbBdoH1/PPP6%2Bmnn9aOHTvk5%2Bcn6YffT7h69WqtX7/e4ukAAACuzXaB9Y9//ENpaWmSVB9YkjR48GCdOHHCqrEAAAAazXaB1bx586v%2BDsKysjIFBARYMBEAAMD1sV1gxcfHa%2BXKlbp48WL92smTJ7VgwQI98MADFk4GAADQOLb7mIZnnnlGjz/%2BuBISElRbW6v4%2BHhVV1erQ4cOWr16tdXjAQAAXJPtAqt169bauXOnCgoKdPLkSQUFBaldu3bq1auXx3uyAAAA7Mp2gSVJt912mwYMGGD1GAAAADfEdoGVnJz8X69U8VlYAADA7mwXWEOHDvUIrNraWp08eVJFRUV6/PHHLZwMAACgcWwXWPPnz7/q%2Bt69e/Xhhx/%2BwtMAAABcP9t9TMPPGTBggN5%2B%2B22rxwAAALimJhNYR48eldvttnoMAACAa7LdS4Tjx49vsFZdXa0TJ05o4MCBFkwEAABwfWwXWNHR0Q1%2BijAwMFCpqakaM2aMRVMBAAA0nu0Ci09rBwAATZ3tAmvbtm2Nvu3IkSNv4SQAAAA3xnaBtXjxYtXV1TV4Q7ufn5/Hmp%2BfH4EFAABsyXaB9ac//UmbNm3S9OnT1alTJ7ndbh0/flx//OMfNWHCBCUkJFg9IgAAwH9lu8BavXq1Nm7cqFatWtWv3XfffWrbtq0mT56snTt3WjgdAADAtdnuc7BOnTqlsLCwBuuhoaEqLS21YCIAAIDrY7vAatOmjVavXq2Kior6tcrKSq1bt0533nmnhZMBAAA0ju1eIly0aJEyMjKUk5OjkJAQORwOXbx4UUFBQVq/fr3V4wEAAFyT7QKrd%2B/eOnDggAoKCnT27Fm53W61atVKffr0UfPmza0eDwAA4JpsF1iSdPvtt%2BvBBx/U2bNn1bZtW6vHAQAAuC62ew/WpUuXtGDBAnXr1k1DhgyR9MN7sKZMmaLKysobus%2BVK1eqU6dO9f9dWFio1NRUxcfHa9iwYdq%2BfbvH7Tdv3qxBgwYpPj5eaWlpKi4uvvETAgAAPsd2gbVmzRodO3ZMa9eulcPxf%2BPV1tZq7dq1131/x44dU15eXv1/l5WVaebMmRo/frwKCwu1ePFiLV26VEVFRZKkffv2KTMzU88995wOHTqk/v37a/r06fruu%2B9u/uQAAIBPsF1g7d27V3/4wx80ePDg%2Bl/6HBoaqlWrVik/P/%2B67quurk7Lli3TpEmT6td27Nih6OhopaamKjAwUImJiUpOTlZubq4kKScnRykpKYqLi1NQUJCmTJkiSdq/f7%2BZEwQAAF7PdoFVVVWl6OjoBuuRkZHXfRXp9ddfV2BgoIYPH16/VlJSopiYGI/bxcTE1L8M%2BNPjDodDnTt3rr/CBQAAcC22e5P7nXfeqQ8//FAJCQkev3twz549%2BtWvftXo%2B/nmm2%2BUmZmp1157zWPd5XJ5fEq8JIWHh9d/7pbL5WrwQadhYWEen8t1LWVlZSovL/dYczqDFRUV1ej7ALyZ02m77%2B28jr%2B/w%2BNPWIe98E22C6xHH31Us2bN0ujRo1VXV6eXX35ZxcXF2rt3rxYvXtzo%2B1m1apVSUlLUvn17nT59%2Brpm%2BOkvmr5eOTk5ysrK8lhLT0/X7Nmzb%2Bp%2BAW8RERFi9Qg%2BIzT0dqtHwH%2BwF77FdoE1btw4OZ1ObdmyRf7%2B/nrppZfUrl07rV27VoMHD27UfRQWFurjjz%2B%2B6u8tjIiIkMvl8lirqKhQZGTkzx53uVzq0KHDdZ1DcnKyx5rTGayKiqpG3wfgzXgu3Hr%2B/g6Fht6uyspq1dbWWT2OT2MvrGXVN3S2C6zz589r9OjRGj169A3fx/bt23Xu3Dn1799f0v9dkUpISNATTzzRILyKi4sVFxcnSYqNjVVJSYlGjRol6YefXjx69KhSU1Mb/fhRUVENXg4sL7%2BgmhqeWIAkngu/oNraOv6%2BbYK98C22e0H4wQcfvOmX6BYuXKi9e/cqLy9PeXl52rhxoyQpLy9Pw4cPV2lpqXJzc3X58mUVFBSooKBAY8eOlSSlpaVp27Zt%2BuSTT1RdXa3s7GwFBASoX79%2BN3tqAADAR9juClZCQoJ2796toUOH3vB9hIWFebxRvaamRpLUunVrSdKGDRu0YsUKLV%2B%2BXG3atNGaNWt09913S5L69u2refPmac6cOTp37py6du2qjRs3Kigo6CbOCgAA%2BBI/981eLjJs%2BfLlys/PV8uWLXXnnXfqtttu8zi%2Bbt06iya7OeXlF6wewXJDXvjA6hFgE7vn9LJ6BK/ndDoUERGiiooqXpayGHthrZYtrfk9xra7gvXll1/qrrvukqTr%2BmgEAAAAu7BNYM2dO1fPP/%2B8x%2BdWrV%2B/Xunp6RZOBQAAcP1s8yb3ffv2NVj78c3pAAAATYltAutqbwWz2dvDAAAAGsU2gfXjL3a%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%2Bu0NBQ7du3T2%2B%2B%2Baa%2B%2BOIL/f73v9elS5c0bdo03X///Xr//ff1/PPPa8OGDcrPz5f0Q3wtWLBA8%2BfP19/%2B9jdNmjRJTz31lM6ePWvxGQEAgKbCKwOrsrJSsbGxysjIUEhIiFq3bq1Ro0bp8OHDOnDggK5cuaIZM2YoODhYXbp00ZgxY5STkyNJys3NVVJSkpKSkhQYGKgRI0aoY8eO2r59u8VnBQAAmgqvDKzQ0FCtWrVKLVq0qF87c%2BaMoqKiVFJSok6dOsnf37/%2BWExMjIqLiyVJJSUliomJ8bi/mJgYFRUV/TLDAwCAJs9p9QC/hKKiIm3ZskXZ2dnavXu3QkNDPY6Hh4fL5XKprq5OLpdLYWFhHsfDwsL05ZdfNvrxysrKVF5e7rHmdAYrKirqxk8CgGWczqb3vai/v8PjT1iHvfBNXh9YH330kWbMmKGMjAwlJiZq9%2B7dV72dn59f/f92u9039Zg5OTnKysryWEtPT9fs2bNv6n4BWCMiIsTqEW5YaOjtVo%2BA/2AvfItXB9a%2Bffv09NNPa%2BnSpRo5cqQkKTIyUqdOnfK4ncvlUnh4uBwOhyIiIuRyuRocj4yMbPTjjhs3TsnJyR5rTmewKiqqbuxEAFiqKT53/f0dCg29XZWV1aqtrbN6HJ/GXljLqm%2BQvDawjhw5ogULFujFF19U796969djY2P117/%2BVTU1NXI6fzj9oqIixcXF1R//8f1YPyoqKtKwYcMa/dhRUVENXg4sL7%2BgmhqeWEBT1JSfu7W1dU16fm/CXvgWr3xBuKamRkuWLNH8%2BfM94kqSkpKS1KxZM2VnZ6u6ulqffvqptm7dqrS0NEnS2LFjdejQIR04cECXL1/W1q1bderUKY0YMcKKUwEAAE2Qn/tm33BkQ4cPH9Zjjz2mgICABsf27NmjqqoqLVu2TMXFxWrRooWmTp2qRx99tP42%2Bfn5WrdunUpLS9W%2BfXstXrxYPXr0uKmZyssv3NTXe4MhL3xg9QjADdk9p5fVI1w3p9OhiIgQVVRUcdXEYuyFtVq2bG7J43plYNkRgUVgoekisHAz2AtrWRVYXvkSIQAAgJUILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMO89pc9%2Bwo%2BHR249Zra86wpfvI84G24ggUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGCY0%2BoBAABmDXnhA6tHaLTdc3pZPQJwS3AFCwAAwDACCwAAwDACCwAAwDACCwAAwDACCwAAwDACCwAAwDACCwAAwDACCwAAwDA%2BaBQAYJmm9KGoEh%2BMisbjChYAAIBhBBYAAIBhBBYAAIBhBBYAAIBhBBYAAIBh/BQhAACNxE89orG4gnUVpaWlevLJJ5WQkKD%2B/ftrzZo1qqurs3osAADQRHAF6ypmzZqlLl266J133tG5c%2Bc0bdo0tWjRQr/97W%2BtHg0AgEZrSlfcvO1qG1ewfqKoqEiff/655s%2Bfr%2BbNmys6OlqTJk1STk6O1aMBAIAmgitYP1FSUqI2bdooLCysfq1Lly46efKkLl68qGbNml3zPsrKylReXu6x5nQGKyoqyvi8AAB4A6fTu675EFg/4XK5FBoa6rH2Y2xVVFQ0KrBycnKUlZXlsfbUU09p1qxZ5gb9j8P/O9j4fTYlZWVlysnJ0bhx4whYi7EX9sJ%2B2Ad74ZsIrKtwu9039fXjxo1TcnKyx1rLli1v6j5xdeXl5crKylJycjL/cFmMvbAX9sM%2B2AvfRGD9RGRkpFwul8eay%2BWSn5%2BfIiMjG3UfUVFRPIkAAPBh3vWCpwGxsbE6c%2BaMzp8/X79WVFSk9u3bKyQkxMLJAABAU0Fg/URMTIy6du2qdevW6eLFizpx4oRefvllpaWlWT0aAABoIvyfffbZZ60ewm769OmjnTt36n/%2B53/09ttvKzU1VZMnT5afn5/Vo%2BEqQkJC1LNnT64w2gB7YS/sh32wF77Hz32z7%2BgGAACAB14iBAAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAQpPy/vvvKzExUXPnzm1wbNeuXRo%2BfLi6deumlJQUHTx40IIJfUNpaanS09OVkJCgxMRELVy4UJWVlZKkY8eOacKECerevbsGDhyoTZs2WTytd/v888/1%2BOOPq3v37kpMTNScOXNUXl4uSSosLFRqaqri4%2BM1bNgwbd%2B%2B3eJpfcfKlSvVqVOn%2Bv9mL3yQG2giNm7c6B44cKB7/Pjx7jlz5ngcO3r0qDs2NtZ94MAB96VLl9x5eXnuuLg495kzZyya1rs9/PDD7oULF7ovXrzoPnPmjDslJcW9aNEid3V1tbtPnz7uzMxMd1VVlbu4uNjds2dP9969e60e2StdvnzZ/cADD7izsrLcly9fdp87d849YcIE98yZM91ff/21%2B95773Xn5ua6L1265P7ggw/c99xzj/uzzz6zemyvd/ToUXfPnj3dHTt2dLvdbvbCR3EFC01GYGCgtm7dqt/85jcNjuXm5iopKUlJSUkKDAzUiBEj1LFjR75LvAUqKysVGxurjIwMhYSEqHXr1ho1apQOHz6sAwcO6MqVK5oxY4aCg4PVpUsXjRkzRjk5OVaP7ZWqq6s1d%2B5cTZs2TQEBAYqMjNRDDz2kL774Qjt27FB0dLRSU1MVGBioxMREJScnKzc31%2BqxvVpdXZ2WLVumSZMm1a%2BxF76JwEKTMXHiRDVv3vyqx0pKShRQsutVAAADg0lEQVQTE%2BOxFhMTo6Kiol9iNJ8SGhqqVatWqUWLFvVrZ86cUVRUlEpKStSpUyf5%2B/vXH4uJiVFxcbEVo3q9sLAwjRkzRk6nU5L01Vdf6a233tKQIUN%2B9jnBXtxar7/%2BugIDAzV8%2BPD6NfbCNxFY8Aoul0thYWEea2FhYaqoqLBoIt9RVFSkLVu2aMaMGXK5XAoNDfU4Hh4eLpfLpbq6Oosm9H6lpaWKjY3V0KFD1bVrV82ePftn94LnxK3zzTffKDMzU8uWLfNYZy98E4EFr%2BF2u60ewed89NFHmjx5sjIyMpSYmPizt/Pz8/sFp/I9bdq0UVFRkfbs2aNTp07pd7/7ndUj%2BaRVq1YpJSVF7du3t3oU2ACBBa8QEREhl8vlseZyuRQZGWnRRN5v3759evLJJ7Vo0SJNnDhRkhQZGdngu3KXy6Xw8HA5HPxzcyv5%2BfkpOjpac%2BfO1c6dO%2BV0Ohs8JyoqKnhO3CKFhYX6%2BOOPlZ6e3uDY1f59Yi%2B8H//iwSvExsY2eD9DUVGR4uLiLJrIux05ckQLFizQiy%2B%2BqJEjR9avx8bG6vjx46qpqalfYx9uncLCQg0aNMjj5dcfQ/aee%2B5p8JwoLi5mL26R7du369y5c%2Brfv78SEhKUkpIiSUpISFDHjh3ZCx9EYMErjB07VocOHdKBAwd0%2BfJlbd26VadOndKIESOsHs3r1NTUaMmSJZo/f7569%2B7tcSwpKUnNmjVTdna2qqur9emnn2rr1q1KS0uzaFrvFhsbq4sXL2rNmjWqrq7W%2BfPnlZmZqfvuu09paWkqLS1Vbm6uLl%2B%2BrIKCAhUUFGjs2LFWj%2B2VFi5cqL179yovL095eXnauHGjJCkvL0/Dhw9nL3yQn5s3rqCJ6Nq1qyTVXx358SenfvxJwfz8fK1bt06lpaVq3769Fi9erB49elgzrBc7fPiwHnvsMQUEBDQ4tmfPHlVVVWnZsmUqLi5WixYtNHXqVD366KMWTOobjh8/rhUrVuizzz5TcHCw7r//fi1cuFCtWrXS3//%2Bd61YsUInTpxQmzZtlJGRoYEDB1o9sk84ffq0HnzwQR0/flyS2AsfRGABAAAYxkuEAAAAhhFYAAAAhhFYAAAAhhFYAAAAhhFYAAAAhhFYAAAAhhFYAAAAhhFYAAAAhhFYAAAAhhFYAAAAhhFYAAAAhhFYAAAAhhFYAAAAhv0/g%2Bq%2BKCOgX6sAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-8118083890363668109”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>13.1</td> <td class=”number”>34</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.7</td> <td class=”number”>32</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>18.1</td> <td class=”number”>31</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.9</td> <td class=”number”>31</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.2</td> <td class=”number”>29</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.4</td> <td class=”number”>28</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.8</td> <td class=”number”>28</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.1</td> <td class=”number”>27</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14.8</td> <td class=”number”>26</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.7</td> <td class=”number”>26</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (322)</td> <td class=”number”>2839</td> <td class=”number”>88.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>79</td> <td class=”number”>2.5%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-8118083890363668109”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>3.4</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.7</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.3</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.4</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>44.7</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>46.3</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>46.8</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>47.1</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>47.4</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Population Estimate, 2011”><s>Population Estimate, 2011</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_2010 Census Population”><code>2010 Census Population</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99996</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Population Estimate, 2012”><s>Population Estimate, 2012</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_Population Estimate, 2011”><code>Population Estimate, 2011</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99998</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Population Estimate, 2013”><s>Population Estimate, 2013</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_Population Estimate, 2012”><code>Population Estimate, 2012</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99998</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Population Estimate, 2014”><s>Population Estimate, 2014</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_Population Estimate, 2013”><code>Population Estimate, 2013</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99997</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Population Estimate, 2015”><s>Population Estimate, 2015</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_Population Estimate, 2014”><code>Population Estimate, 2014</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99997</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Population Estimate, 2016”><s>Population Estimate, 2016</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_Population Estimate, 2015”><code>Population Estimate, 2015</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99997</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_RECFAC09”><s>RECFAC09</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_FSR14”><code>FSR14</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.95735</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_RECFAC14”><s>RECFAC14</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_RECFAC09”><code>RECFAC09</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99353</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_RECFACPTH09”>RECFACPTH09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2238</td>

</tr> <tr>

<th>Unique (%)</th> <td>69.7%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.077845</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>0.9901</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>27.6%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-8636230818473475261”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-8636230818473475261,#minihistogram-8636230818473475261”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-8636230818473475261”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-8636230818473475261”

aria-controls=”quantiles-8636230818473475261” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-8636230818473475261” aria-controls=”histogram-8636230818473475261”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-8636230818473475261” aria-controls=”common-8636230818473475261”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-8636230818473475261” aria-controls=”extreme-8636230818473475261”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-8636230818473475261”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0.070341</td>

</tr> <tr>

<th>Q3</th> <td>0.11293</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.20203</td>

</tr> <tr>

<th>Maximum</th> <td>0.9901</td>

</tr> <tr>

<th>Range</th> <td>0.9901</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.11293</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.078947</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.0141</td>

</tr> <tr>

<th>Kurtosis</th> <td>13.337</td>

</tr> <tr>

<th>Mean</th> <td>0.077845</td>

</tr> <tr>

<th>MAD</th> <td>0.056464</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.3889</td>

</tr> <tr>

<th>Sum</th> <td>243.81</td>

</tr> <tr>

<th>Variance</th> <td>0.0062326</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-8636230818473475261”>

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%2BbQ2RkZGh9PR0nzWHo4XKyiov6rjfZbdWb/r6rRYWFqro6OYqL69SbW1doMcJOuRrLfK1Fvlaz1TGgfrHfVAWrLNJTEzU3r17FRsbq9DQULndbp/tbrdbLVu2VHx8vCoqKlRbW6uwsDDvNklq2bJlg86VkJBQ7%2BlAl%2BuEamou7y9Cu15/bW2dbWe3A/K1Fvlai3ytZ9eM7XULpIHeeecdrVu3zmetpKRE7dq1U3h4uLp06aKioiLvtvLych06dEg9e/ZU9%2B7d5fF4dODAAe92p9Op6OhodezY0W/XAAAA7CsoC9apU6f07LPPyul0qrq6WmvXrtXvfvc7jRs3TpKUmZmpgoIClZSUqKKiQrm5uerevbtSUlIUHx%2BvoUOH6sUXX9Tx48d17NgxLVq0SGPHjpXDcdnc8AMAABfBto0hJSVFklRTUyNJ2rx5s6TTd5vGjx%2BvyspKTZ06VS6XS1dddZUWLVqk5ORkSdK4cePkcrl07733qrKyUqmpqT4vSp89e7aefvppDRo0SE2aNNGdd95Z781MAQAAziXEc7Gv6EaDuFwnjB/T4QjVrbk7jB/XKuun3RjoERrF4QhVXFyEysoqbfn8/6WOfK1FvtYiX%2BuZyrh16yiDUzVcUD5FCAAAEEgULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhvm9YKWnpysvL09Hjx7196kBAAD8wu8F66677tK6des0ePBg3X///dq0aZNqamr8PQYAAIBl/F6wsrKytG7dOv3mN79Rly5d9POf/1xpaWl6/vnndfDgQX%2BPAwAAYFzAXoPVo0cP5eTkaNu2bXriiSf0m9/8RnfccYcmTpyoTz75JFBjAQAAXLSAFazq6mqtW7dOP/3pT5WTk6M2bdro8ccfV/fu3TVhwgStWbMmUKMBAABcFIe/T1hSUqIVK1Zo1apVqqys1NChQ/X222%2BrT58%2B3n369eunWbNmafjw4f4eDwAA4KL5vWANGzZMHTt21KRJkzRq1CjFxsbW2yctLU3Hjx8/73F27NihnJwcpaamauHChT7bNm3apLy8PB0%2BfFgJCQmaOHGi7rnnHknSK6%2B8oldffVUOh%2B%2Blb9u2Ta1atdK3336r5557Ttu3b9e3336r1NRUPfPMM4qLi7vIKwcAAJcLvxesgoICXXfddRfcb8%2BePefc9vrrr2vFihVq3759vW2ffPKJZsyYoRdeeEEDBw7U73//e2VlZemaa65R3759JUkjR47UvHnzznrshQsXqqioSIWFhWrevLlmzpypxx9/XIsXL27gFQIAgMud31%2BD1a1bNz3wwAPavHmzd%2B2tt97ST3/6U7nd7gYdIzw8/JwFy%2B12a9KkSRo8eLAcDofS0tLUtWtX7dq164LHramp0YoVK/Tggw/qiiuuUGxsrKZNm6bt27friy%2B%2BaPhFAgCAy5rfC9bcuXN14sQJde7c2bs2cOBA1dXVnfOu0neNHz9eUVFRZ9128803Kysry/t5TU2NXC6X2rRp413729/%2BpnHjxunaa6/VsGHDtHPnTknSoUOHdOLECfXo0cO7b6dOndSsWTMVFRU16joBAMDly%2B9PEe7cuVNr1qzxeU1Thw4dlJubqzvvvNP4%2BXJzc9WiRQvdcccdkqS2bduqXbt2mj59uhISElRYWKgHHnhAq1ev9t5Bi46O9jlGdHS0ysrKGnzO0tJSuVwunzWHo4USEhIu8mp8hYXZ6ycdORz2mvdMvnbL2S7I11rkay3ytZ7dM/Z7wTp58qTCw8PrrYeGhqqqqsrYeTwej3Jzc7V27VoVFBR4z3n33Xfr7rvv9u43YcIEvffee1q9erVuvvlm72MvRmFhofLy8nzWsrKyNGXKlIs6rt3FxUUEeoTvJTq6eaBHCGrkay3ytRb5Ws%2BuGfu9YPXr10/z5s3T9OnTFRMTI0n64osvNH/%2BfJ%2B3argYdXV1evzxx/XJJ5/onXfeUbt27c67f2JiokpLSxUfHy/p9Ou4IiL%2BVQa%2B/vprtWzZssHnz8jIUHp6us%2Baw9FCZWWVjbiKC7Nbqzd9/VYLCwtVdHRzlZdXqba2LtDjBB3ytRb5Wot8rWcq40D9497vBeuJJ57QT37yE91www2KjIxUXV2dKisr1a5dO/3qV78yco6f//zn%2BvTTT/XOO%2B/UexuIV199Vb1799YNN9zgXSspKdEdd9yhdu3aKSYmRkVFRUpMTJQk/f3vf9epU6eUnJzc4PMnJCTUezrQ5TqhmprL%2B4vQrtdfW1tn29ntgHytRb7WIl/r2TVjvxesdu3a6b333tPvfvc7HTp0SKGhoerYsaMGDBigsLCwiz7%2Bxx9/rNWrV2vdunVnfY8tt9utZ555Rq%2B%2B%2BqoSExO1bNkyHTp0SKNHj1ZYWJjuueceLV68WCkpKWrWrJleeOEF3XrrrWrVqtVFzwYAAC4Pfi9YktS0aVMNHjz4ez8%2BJSVF0unvEJTkfcsHp9OplStX6sSJE7rlllt8HtOvXz/98pe/1PTp0yWdfu2V2%2B1W586d9dZbb6lt27aSpClTpqiyslIjR45UTU2NbrnlFs2aNet7zwoAAC4/IZ6LfUV3Ix0%2BfFgLFizQp59%2BqpMnT9bbvmXLFn%2BO4zcu1wnjx3Q4QnVr7g7jx7XK%2Bmk3BnqERnE4QhUXF6Gyskpb3p6%2B1JGvtcjXWuRrPVMZt2599rd1slpAXoNVWlqqAQMGqEWLFv4%2BPQAAgOX8XrD27t2rLVu2eL9jDwAAINj4/fv8W7ZsyZ0rAAAQ1PxesCZNmqS8vLyLfjNPAACAS5XfnyL83e9%2Bp7/85S969913ddVVVyk01Lfj/frXv/b3SAAAAEb5vWBFRkZ6fyQNAABAMPJ7wZo7d66/TwkAAOBXAflhdp999pleeeUVPf744961v/71r4EYBQAAwDi/F6wPP/xQI0aM0KZNm7R27VpJp998dPz48UH7JqMAAODy4veCtXDhQj366KNas2aNQkJCJJ3%2B%2BYTz5s3TokWL/D0OAACAcX4vWH//%2B9%2BVmZkpSd6CJUm33XabSkpK/D0OAACAcX4vWFFRUWf9GYSlpaVq2rSpv8cBAAAwzu8F69prr9XPf/5zVVRUeNcOHjyonJwc3XDDDf4eBwAAwDi/v03D448/rvvuu0%2Bpqamqra3Vtddeq6qqKnXp0kXz5s3z9zgAAADG%2Bb1gtW3bVmvXrtUHH3yggwcPqlmzZurYsaNuvPFGn9dkAQAA2JXfC5YkNWnSRIMHDw7EqQEAACzn94KVnp5%2B3jtVvBcWAACwO78XrDvuuMOnYNXW1urgwYNyOp267777/D0OAACAcX4vWDNmzDjr%2BsaNG/WnP/3Jz9MAAACYF5CfRXg2gwcP1nvvvRfoMQAAAC7aJVOw9u3bJ4/HE%2BgxAAAALprfnyIcN25cvbWqqiqVlJRoyJAh/h4HAADAOL8XrA4dOtT7LsLw8HCNHTtWd999t7/HAQAAMM7vBYt3awcAAMHO7wVr1apVDd531KhRFk4CAABgDb8XrCeffFJ1dXX1XtAeEhLisxYSEkLBAgAAtuT37yJ84403NGDAAC1btky7du3SRx99pKVLl%2Brmm2/W66%2B/rk8%2B%2BUSffPKJ9uzZc8Fj7dixQ/3791d2dna9bevWrdPw4cPVu3dvjRkzRjt37vRuq6ur08KFCzVo0CD169dPEydO1OHDh73b3W63pk2bpv79%2B2vAgAF68skndfLkSTMBAACAoOf3gjVv3jzNmTNHffr0UWRkpKKiotS3b1/Nnj1b8%2BfPV9OmTb0f5/P6669rzpw5at%2B%2Bfb1t%2B/fvV05OjmbMmKE//vGPmjBhgh566CEdO3ZMkrRs2TKtWbNG%2Bfn52rZtmzp06KCsrCzvHbSZM2eqqqpKa9eu1cqVK1VSUqLc3FzzYQAAgKDk94L1%2BeefKyYmpt56dHS0jhw50uDjhIeHa8WKFWctWMuXL1daWprS0tIUHh6uESNGqGvXrlq9erUkqbCwUBMmTFCnTp0UGRmp7OxslZSUaM%2BePfryyy%2B1efNmZWdnKz4%2BXm3atNGDDz6olStXqrq6%2BvtfOAAAuGz4/TVYiYmJmjdvnqZOnaq4uDhJUnl5uV5%2B%2BWVdffXVDT7O%2BPHjz7mtqKhIaWlpPmtJSUlyOp06efKkiouLlZSU5N0WGRmp9u3by%2Bl06sSJEwoLC1O3bt2823v06KFvvvlGn332mc/6uZSWlsrlcvmsORwtlJCQ0NDLa5CwsEvmfWIbxOGw17xn8rVbznZBvtYiX2uRr/XsnrHfC9YTTzyh6dOnq7CwUBEREQoNDVVFRYWaNWumRYsWGTmH2%2B2ud5csJiZGxcXF%2Bvrrr%2BXxeM66vaysTLGxsYqMjPR5r64z%2B5aVlTXo/IWFhcrLy/NZy8rK0pQpU77P5QSNuLiIQI/wvURHNw/0CEGNfK1FvtYiX%2BvZNWO/F6wBAwZo%2B/bt%2BuCDD3Ts2DF5PB61adNGN910k6Kiooyd50I/dud82y/2R/ZkZGQoPT3dZ83haKGyssqLOu532a3Vm75%2Bq4WFhSo6urnKy6tUW1sX6HGCDvlai3ytRb7WM5VxoP5x7/eCJUnNmzfXoEGDdOzYMbVr18748ePi4uR2u33W3G634uPjFRsbq9DQ0LNub9mypeLj41VRUaHa2lqFhYV5t0lSy5YtG3T%2BhISEek8HulwnVFNzeX8R2vX6a2vrbDu7HZCvtcjXWuRrPbtm7PdbICdPnlROTo569%2B6t22%2B/XdLp12Ddf//9Ki8vN3KO5ORk7d2712fN6XSqV69eCg8PV5cuXVRUVOTdVl5erkOHDqlnz57q3r27PB6PDhw44PPY6OhodezY0ch8AAAguPm9YD3//PPav3%2B/cnNzFRr6r9PX1tYaeyuEe%2B65R3/4wx%2B0fft2ffvtt1qxYoU%2B//xzjRgxQpKUmZmpgoIClZSUqKKiQrm5uerevbtSUlIUHx%2BvoUOH6sUXX9Tx48d17NgxLVq0SGPHjpXDEZAbfgAAwGb83hg2btyopUuXqkOHDsrJyZF0%2Bi0a5s6dq1GjRmn27NkNOk5KSookqaamRpK0efNmSafvNnXt2lW5ubmaO3eujhw5os6dO2vJkiVq3bq1JGncuHFyuVy69957VVlZqdTUVJ8Xpc%2BePVtPP/20Bg0apCZNmujOO%2B8865uZAgAAnI3fC1ZlZaU6dOhQbz0%2BPl7ffPNNg4/jdDrPu33IkCEaMmTIWbeFhIRoypQp5/yuvqioKL3wwgsNngUAAODf%2Bf0pwquvvlp/%2BtOfJPl%2Bt96GDRt05ZVX%2BnscAAAA4/x%2BB%2BtHP/qRHn74Yd11112qq6vTm2%2B%2Bqb1792rjxo168skn/T0OAACAcX4vWBkZGXI4HFq6dKnCwsK0ePFidezYUbm5ubrtttv8PQ4AAIBxfi9Yx48f11133aW77rrL36cGAADwC7%2B/BmvQoEEX/U7pAAAAlzK/F6zU1FStX7/e36cFAADwG78/RXjFFVfoueeeU35%2Bvq6%2B%2Bmo1adLEZ/uCBQv8PRIAAIBRfi9YxcXFuuaaayRJZWVl/j49AACA5fxWsLKzs7Vw4UL96le/8q4tWrRIWVlZ/hoBAADAL/z2GqytW7fWW8vPz/fX6QEAAPzGbwXrbN85yHcTAgCAYOS3ghUSEtKgNQAAALvz%2B4vccfm6/cXfB3qERnl/xk2BHgEAYFN%2Bfx8sAACAYOe3O1jV1dWaPn36Bdd4HywAAGB3fitYffr0UWlp6QXXAAAA7M5vBevf3/8KAAAgmPEaLAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhQfnDnj/66CP95Cc/8VnzeDyqrq5WQUGBxo8fr6ZNm/ps/%2B///m/dfvvtkqSCggItW7ZMLpdL3bp105NPPqnk5GS/zQ8AAOwtKAtWv3795HQ6fdYWL16sAwcOSJISExO1devWsz5269ateuWVV/TGG2%2BoW7duKigo0AMPPKBNmzapRYsWls8OAADs77J4ivCf//yn3nzzTf3sZz%2B74L6FhYUaM2aMevXqpWbNmun%2B%2B%2B%2BXJG3bts3qMQEAQJC4LArWSy%2B9pLvuuktXXnmlJKmyslJZWVlKTU3VTTfdpDfffFMej0eSVFRUpKSkJO9jQ0ND1b1793p3xAAAAM4lKJ8i/Hf/%2BMc/tGnTJm3atEmSFBkZqa7YetuyAAAU9ElEQVRdu%2Bq%2B%2B%2B7TwoUL9ec//1lTp05VVFSUxo4dK7fbrZiYGJ9jxMTEqKysrMHnLC0tlcvl8llzOFooISHh4i/o34SFXRb9OGDO5EvO1iBfa5GvtcjXenbPOOgL1rJlyzRkyBC1bt1aktSjRw/96le/8m4fMGCAxo0bp3fffVdjx46VJO/drO%2BrsLBQeXl5PmtZWVmaMmXKRR0X/hUd3dznv7AG%2BVqLfK1Fvtaza8ZBX7A2btyonJyc8%2B6TmJiojRs3SpLi4uLkdrt9trvdbnXp0qXB58zIyFB6errPmsPRQmVllQ0%2BRkPYtdXbRXl5laKjm6u8vEq1tXWBHifohIWFkq%2BFyNda5Gs9UxnHxUUYnKrhgrpg7d%2B/X0eOHNGNN97oXVu/fr3Kysr0ox/9yLv22WefqV27dpKk5ORkFRUVafTo0ZKk2tpa7du3z3t3qyESEhLqPR3ocp1QTQ1fhHZy5gu6traOXzsLka%2B1yNda5Gs9u2Yc1LdA9u3bp9jYWEVGRnrXmjRpovnz52vnzp2qrq7W73//e61cuVKZmZmSpMzMTK1atUq7d%2B9WVVWVXnvtNTVt2lQDBw4M0FUAAAC7Ceo7WF9%2B%2BaX3tVdnDB48WE888YSeffZZHT16VK1atdITTzyhIUOGSJJuvvlmPfLII5o2bZq%2B%2BuorpaSkKD8/X82aNQvEJQAAABsK8VzsK7rRIC7XCePHdDhCdWvuDuPHxWnvz7hJcXERKiurtOXt6UudwxFKvhYiX2uRr/VMZdy6dZTBqRouqJ8iBAAACAQKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGFBW7C6deum5ORkpaSkeD%2BeffZZSdKHH36osWPH6tprr9WwYcO0evVqn8cWFBRo6NChuvbaa5WZmam9e/cG4hIAAIBNOQI9gJU2bNigq666ymettLRUDz74oJ588kkNHz5cH3/8sSZPnqyOHTsqJSVFW7du1SuvvKI33nhD3bp1U0FBgR544AFt2rRJLVq0CNCVAAAAOwnaO1jnsmbNGnXo0EFjx45VeHi4%2Bvfvr/T0dC1fvlySVFhYqDFjxqhXr15q1qyZ7r//fknStm3bAjk2AACwkaC%2Bg7VgwQL99a9/VUVFhW6//XY99thjKioqUlJSks9%2BSUlJWr9%2BvSSpqKhId9xxh3dbaGiounfvLqfTqWHDhjXovKWlpXK5XD5rDkcLJSQkXOQV%2BQoLu%2Bz6sV%2BdyZecrUG%2B1iJfa5Gv9eyecdAWrB/%2B8Ifq37%2B/5s%2Bfr8OHD2vatGl65pln5Ha71aZNG599Y2NjVVZWJklyu92KiYnx2R4TE%2BPd3hCFhYXKy8vzWcvKytKUKVO%2B59UgEKKjm/v8F9YgX2uRr7XI13p2zThoC1ZhYaH3/zt16qQZM2Zo8uTJ6tOnzwUf6/F4LurcGRkZSk9P91lzOFqorKzyoo77XXZt9XZRXl6l6OjmKi%2BvUm1tXaDHCTphYaHkayHytRb5Ws9UxnFxEQanarigLVjfddVVV6m2tlahoaFyu90%2B28rKyhQfHy9JiouLq7fd7XarS5cuDT5XQkJCvacDXa4Tqqnhi9BOznxB19bW8WtnIfK1Fvlai3ytZ9eMg/IWyL59%2BzRv3jyftZKSEjVt2lRpaWn13nZh79696tWrlyQpOTlZRUVF3m21tbXat2%2BfdzsAAMCFBGXBatmypQoLC5Wfn69Tp07p4MGDeumll5SRkaGRI0fqyJEjWr58ub799lt98MEH%2BuCDD3TPPfdIkjIzM7Vq1Srt3r1bVVVVeu2119S0aVMNHDgwsBcFAABsIyifImzTpo3y8/O1YMECb0EaPXq0srOzFR4eriVLlmjOnDl65plnlJiYqOeff14/%2BMEPJEk333yzHnnkEU2bNk1fffWVUlJSlJ%2Bfr2bNmgX4qgAAgF2EeC72Fd1oEJfrhPFjOhyhujV3h/Hj4rT3Z9ykuLgIlZVV2vL5/0udwxFKvhYiX2uRr/VMZdy6dZTBqRouKJ8iBAAACCQKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwLCgLVhHjhxRVlaWUlNT1b9/fz322GMqLy/XP/7xD3Xr1k0pKSk%2BH7/4xS%2B8j123bp2GDx%2Bu3r17a8yYMdq5c2cArwQAANiNI9ADWOWBBx5QcnKytm7dqhMnTigrK0vz58/X5MmTJUlOp/Osj9u/f79ycnKUl5en66%2B/Xhs3btRDDz2kDRs2qG3btv68BAAAYFNBeQervLxcycnJmj59uiIiItS2bVuNHj1au3btuuBjly9frrS0NKWlpSk8PFwjRoxQ165dtXr1aj9MDgAAgkFQFqzo6GjNnTtXrVq18q4dPXpUCQkJ3s9/9rOfacCAAbr%2B%2Buu1YMECVVdXS5KKioqUlJTkc7ykpKRz3vECAAD4rqB9ivDfOZ1OLV26VK%2B99pqaNm2q3r1769Zbb9Vzzz2n/fv36%2BGHH5bD4dDUqVPldrsVExPj8/iYmBgVFxc3%2BHylpaVyuVw%2Baw5HC5%2BCZ0JYWFD240vGmXzJ2Rrkay3ytRb5Ws/uGQd9wfr44481efJkTZ8%2BXf3795ck/frXv/Zu79mzpyZNmqQlS5Zo6tSpkiSPx3NR5ywsLFReXp7PWlZWlqZMmXJRx4V/RUc39/kvrEG%2B1iJfa5Gv9eyacVAXrK1bt%2BrRRx/VzJkzNWrUqHPul5iYqC%2B//FIej0dxcXFyu90%2B291ut%2BLj4xt83oyMDKWnp/usORwtVFZW2bgLuAC7tnq7KC%2BvUnR0c5WXV6m2ti7Q4wSdsLBQ8rUQ%2BVqLfK1nKuO4uAiDUzVc0Basv/zlL8rJydFLL72kAQMGeNc//PBD7d692/vdhJL02WefKTExUSEhIUpOTtbevXt9juV0OjVs2LAGnzshIaHe04Eu1wnV1PBFaCdnvqBra%2Bv4tbMQ%2BVqLfK1Fvtaza8ZBeQukpqZGTz31lGbMmOFTriQpKipKixYt0m9/%2B1tVV1fL6XTqF7/4hTIzMyVJ99xzj/7whz9o%2B/bt%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%2Bo6ioSImJiYqJifGu9ejRQwcPHlRFRYUiIyMveIzS0lK5XC6fNYejhRISEozOGhZGPwb8wW533Ozk/Rk3BXqE7%2BXMn7/8OWwdu2dMwfoOt9ut6Ohon7UzZausrKxBBauwsFB5eXk%2Baw899JAefvhhc4PqdJG7r%2B2nysjIMF7ecDrfwsJC8rUI%2BVqLfK1VWlqqt99%2Bg3wtZPeM7VkLLebxeC7q8RkZGXr33Xd9PjIyMgxN9y8ul0t5eXn17pbBDPK1Fvlai3ytRb7Ws3vG3MH6jvj4eLndbp81t9utkJAQxcfHN%2BgYCQkJtmzbAADADO5gfUdycrKOHj2q48ePe9ecTqc6d%2B6siIiIAE4GAADsgoL1HUlJSUpJSdGCBQtUUVGhkpISvfnmm8rMzAz0aAAAwCbCZs2aNSvQQ1xqbrrpJq1du1bPPvus3nvvPY0dO1YTJ05USEhIoEerJyIiQtdddx131yxCvtYiX2uRr7XI13p2zjjEc7Gv6AYAAIAPniIEAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCdYk7cuSI/vM//1Opqam65ZZb9Pzzz6uuru6s%2BxYUFGjo0KG69tprlZmZqb179/p5WvtpTL7vvPOOhg4dqt69e2vkyJHavHmzn6e1n8bke8YXX3yh3r1765VXXvHTlPbVmHxLSkp07733qlevXkpLS9Nbb73l32FtqKH51tXV6eWXX1Z6erp69%2B6t4cOHa926dQGY2H527Nih/v37Kzs7%2B7z71dXVaeHChRo0aJD69euniRMn6vDhw36a8nvy4JI2evRoz1NPPeUpLy/3HDx40DNkyBDPL3/5y3r7bdmyxdO3b1/P7t27PVVVVZ4lS5Z4brzxRk9lZWUApraPhua7YcMGT58%2BfTy7du3ynDp1yvOb3/zG06NHD8%2BhQ4cCMLV9NDTff/fQQw95%2BvTp43n55Zf9NKV9NTTfqqoqz8CBAz2vv/6655tvvvHs2bPHM2zYME9xcXEApraPhua7dOlSz4ABAzwlJSWempoaz9atWz1JSUme/fv3B2Bq%2B8jPz/cMGTLEM27cOM%2B0adPOu29BQYHnlltu8RQXF3tOnDjhmT17tmf48OGeuro6P03beNzBuoQ5nU4dOHBAM2bMUFRUlDp06KAJEyaosLCw3r6FhYUaM2aMevXqpWbNmun%2B%2B%2B%2BXJG3bts3fY9tGY/I9efKkHnnkEfXp00dNmjTR3XffrYiICO3evTsAk9tDY/I944MPPlBxcbEGDhzov0FtqjH5rl%2B/XpGRkbr//vvVvHlz9ezZU2vXrlWnTp0CMLk9NCbfoqIi9enTR9dcc43CwsJ0yy23KDY2Vn/7298CMLl9hIeHa8WKFWrfvv0F9y0sLNSECRPUqVMnRUZGKjs7WyUlJdqzZ48fJv1%2BKFiXsKKiIiUmJiomJsa71qNHDx08eFAVFRX19k1KSvJ%2BHhoaqu7du8vpdPptXrtpTL4jR47Uj370I%2B/n5eXlqqysVJs2bfw2r900Jl/pdImdPXu2nn76aTkcDn%2BOakuNyffjjz9W165d9fjjj6tv37667bbbtHr1an%2BPbCuNyXfgwIH685//rP379%2BvUqVPasmWLqqqqdN111/l7bFsZP368oqKiLrjfyZMnVVxc7PN3XGRkpNq3b39J/x1HwbqEud1uRUdH%2B6yd%2BWIvKyurt%2B%2B//0FwZt/v7od/aUy%2B/87j8eipp55Sr169%2BAP0PBqb76JFi/TDH/5Q119/vV/ms7vG5Hvs2DFt2bJF/fv3144dOzRp0iTl5ORo3759fpvXbhqT75AhQ5SRkaFRo0YpJSVF06dP19y5c3XFFVf4bd5g9vXXX8vj8dju7zj%2BmXiJ83g8luyL0xqbWXV1tR577DEVFxeroKDAoqmCR0PzLS4u1vLly7VmzRqLJwouDc3X4/GoR48eGj58uCRp9OjR%2BvWvf60NGzb43BWAr4bmu2rVKq1atUrLly9Xt27d9OGHH2r69Om64oor1LNnT4unvHzY7e847mBdwuLj4%2BV2u33W3G63QkJCFB8f77MeFxd31n2/ux/%2BpTH5SqdvU0%2BaNEn//Oc/tWzZMrVq1cpfo9pSQ/P1eDyaNWuWHn74YbVu3drfY9pWY37/tm7dut5TMYmJiXK5XJbPaVeNyXfp0qXKyMhQz549FR4eroEDB%2Br666/naVhDYmNjFRoaetZfj5YtWwZoqgujYF3CkpOTdfToUR0/fty75nQ61blzZ0VERNTbt6ioyPt5bW2t9u3bp169evltXrtpTL4ej0fZ2dlyOBx66623FBcX5%2B9xbaeh%2Bf7zn//URx99pJdfflmpqalKTU3Ve%2B%2B9pzfeeEOjR48OxOi20Jjfv506ddLf//53nzsAR44cUWJiot/mtZvG5FtXV6fa2lqftVOnTvllzstBeHi4unTp4vN3XHl5uQ4dOnRJ3yGkYF3CkpKSlJKSogULFqiiokIlJSV68803lZmZKUm67bbbtGvXLklSZmamVq1apd27d6uqqkqvvfaamjZtyndjnUdj8l2zZo2Ki4v10ksvKTw8PJBj20ZD823btq0%2B%2BOAD/fa3v/V%2BpKena9y4ccrPzw/wVVy6GvP7d8SIESorK9PixYt18uRJrV27VkVFRRoxYkQgL%2BGS1ph809PTtWLFCh04cEA1NTXauXOnPvzwQw0aNCiQl2BrX3zxhW677Tbve11lZmaqoKBAJSUlqqioUG5urrp3766UlJQAT3puvAbrEvfyyy9r5syZuvHGGxUZGalx48Z5v5vt4MGD%2BuabbyRJN998sx555BFNmzZNX331lVJSUpSfn69mzZoFcvxLXkPzXblypY4cOVLvRe0jR47UnDlz/D63XTQk37CwMLVt29bncc2bN1dkZCRPGV5AQ3//tmnTRkuWLNFzzz2nV199VVdeeaUWLVqkq6%2B%2BOpDjX/Iamu%2BkSZNUU1OjrKwsHT9%2BXImJiZozZ45uuOGGQI5/yTtTjmpqaiTJ%2B%2BbNTqdT1dXVOnjwoPdO4Lhx4%2BRyuXTvvfeqsrJSqampysvLC8zgDRTisdurxgAAAC5xPEUIAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIb9H99ptXqYWFO6AAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-8636230818473475261”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>885</td> <td class=”number”>27.6%</td> <td>

<div class=”bar” style=”width:40%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0386548</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.285469597</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.19219681</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0524109</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.06762697</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.123092073</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.072511058</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.227531286</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.135666802</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2227)</td> <td class=”number”>2229</td> <td class=”number”>69.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-8636230818473475261”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>885</td> <td class=”number”>27.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.013326</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0141709</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0145467</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0155927</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.611995104</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.630119723</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.646754468</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.732869183</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.99009901</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_RECFACPTH14”>RECFACPTH14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2115</td>

</tr> <tr>

<th>Unique (%)</th> <td>65.9%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.068937</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>0.82237</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>31.4%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram1612852181654424184”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives1612852181654424184,#minihistogram1612852181654424184”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives1612852181654424184”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles1612852181654424184”

aria-controls=”quantiles1612852181654424184” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram1612852181654424184” aria-controls=”histogram1612852181654424184”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common1612852181654424184” aria-controls=”common1612852181654424184”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme1612852181654424184” aria-controls=”extreme1612852181654424184”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles1612852181654424184”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0.061047</td>

</tr> <tr>

<th>Q3</th> <td>0.10621</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.18653</td>

</tr> <tr>

<th>Maximum</th> <td>0.82237</td>

</tr> <tr>

<th>Range</th> <td>0.82237</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.10621</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.072179</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.047</td>

</tr> <tr>

<th>Kurtosis</th> <td>10.577</td>

</tr> <tr>

<th>Mean</th> <td>0.068937</td>

</tr> <tr>

<th>MAD</th> <td>0.054045</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.0911</td>

</tr> <tr>

<th>Sum</th> <td>215.91</td>

</tr> <tr>

<th>Variance</th> <td>0.0052098</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram1612852181654424184”>

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3L4NwX39NTW3Y/wzsgD7ZA32yh3Duk71OgdTT888/r1dffTVorKSkRElJSYqMjFTPnj1VXFwcmCsrK9OBAwfUp08f9e7dW36/X3v27AnMu91uxcTEqFu3bpatAQAA2FezDFjHjh3TAw88ILfbraqqKhUVFekvf/mLJkyYIEnKzs7WypUrVVJSovLycuXl5al3795KS0tTfHy8Ro4cqUceeUSHDx/WF198oWXLlmn8%2BPFyucLmhB8AAGgA2yaGtLQ0SVJ1dbUkaevWrZKOn22aOHGiKioqNH36dHk8Hp111llatmyZUlNTJUkTJkyQx%2BPRDTfcoIqKCqWnpwfdlH7//ffr3nvv1fDhw9WiRQtdddVVdR5mCgAA8EMi/A29oxv14vEcMb5Pl8uhy/K2G99vY9k4Y3CoSwgJl8uhuLgoeb0VYXsvgh3QJ3ugT/bQlPrUsWPbkBy3WV4iBAAACCUCFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIbZNmBt375dgwYN0syZM%2BvMbdmyRaNHj1a/fv00cuRI/elPfwrMPf744%2Brdu7fS0tKCvr766itJ0nfffaff/va3Gjp0qNLT0zVt2jR5vV7L1gUAAOzPlgFrxYoVWrhwobp06VJn7sMPP9Ts2bM1bdo0vfvuu7rnnnt0//33a8eOHYFtxowZI7fbHfTVoUMHSdLSpUtVXFyswsJCbd68WX6/X3PmzLFsbQAAwP5sGbAiIyO1du3akwYsn8%2BnyZMn69JLL5XL5VJGRobOPffcoID1Q6qrq7V27VrddtttOuOMM9SuXTvNmDFDb7zxhr788svGWAoAAGiGXKEu4KeYOHHiD84NHTpUQ4cODXxfXV0tj8ejTp06BcY%2B%2BugjTZgwQR9//LHOOOMMzZkzR0OGDNGBAwd05MgRpaSkBLbt3r27WrVqpeLi4qB9/JjS0lJ5PJ6gMZerjRISEuq7xHpxOu2Vj10ue9Vryok%2B2a1f4YY%2B2QN9sgf6ZNOAdTry8vLUpk0bXXnllZKkzp07KykpSbNmzVJCQoIKCws1ZcoUrV%2B/Xj6fT5IUExMTtI%2BYmJjTug%2BrsLBQ%2Bfn5QWM5OTmaNm1aA1djb3FxUaEuIaRiYlqHugTUA32yB/pkD%2BHcp2YbsPx%2Bv/Ly8lRUVKSVK1cqMjJSknTdddfpuuuuC2w3adIkvfLKK1q/fn3gzJff72/QsbOyspSZmRk05nK1kddb0aD9fp/d3hmYXr9dOJ0OxcS0VllZpWpqakNdDn4AfbIH%2BmQPTalPoXpz3ywDVm1trebMmaMPP/xQzz//vJKSkn50%2B8TERJWWlio%2BPl7S8fu4oqL%2B05BvvvlG7du3r/fxExIS6lwO9HiOqLo6vH8ZhPv6a2pqw/5nYAf0yR7okz2Ec5/sdQqknn73u9/pk08%2BOWm4%2Bv3vf6%2B33347aKykpERJSUlKSkpSbGysiouLA3Mff/yxjh07ptTUVEtqBwAA9md5wMrMzFR%2Bfr4OHTrUKPt/7733tH79ehUUFKhdu3Z15n0%2Bn%2B677z7t27dP3333nZ5%2B%2BmkdOHBA48aNk9Pp1PXXX68nnnhChw4dktfr1f/%2B7//qsssuCzzGAQAA4FQsv0R47bXX6pVXXtHy5ct18cUX6/rrr1dmZqZcrvqXkpaWJun4JwQlaevWrZIkt9utF198UUeOHNEll1wS9JoBAwbo6aef1qxZsyQdv/fK5/OpR48eevbZZ9W5c2dJ0rRp01RRUaExY8aourpal1xyiRYsWNDQZQMAgDAS4W/oHd0/UXFxsYqKirRx40ZVVVVp7NixGj9%2BvLp16xaKchqdx3PE%2BD5dLocuy9tufL%2BNZeOMwaEuISRcLofi4qLk9VaE7b0IdkCf7IE%2B2UNT6lPHjm1DctyQ3YOVkpKi3Nxcvf7667rnnnv0pz/9SVdeeaVuvvlmffjhh6EqCwAAoMFCFrCqqqr06quv6tZbb1Vubq46deqkOXPmqHfv3po0aZI2bNgQqtIAAAAaxPJ7sEpKSrR27VqtW7dOFRUVGjlypJ577jn1798/sM2AAQO0YMECXX311VaXBwAA0GCWB6xRo0apW7dumjx5ssaOHXvST/plZGTo8OHDVpcGAABghOUBa%2BXKlbroootOud0HH3xgQTUAAADmWX4PVq9evTRlypTAoxUk6dlnn9Wtt94a%2BFuAAAAAdmZ5wFq0aJGOHDmiHj16BMaGDRum2tpaLV682OpyAAAAjLP8EuFbb72lDRs2KC4uLjDWtWtX5eXl6aqrrrK6HAAAAOMsP4N19OhRRUZG1i3E4VBlZaXV5QAAABhnecAaMGCAFi9erG%2B%2B%2BSYw9uWXX%2Bq%2B%2B%2B4LelQDAACAXVl%2BifCee%2B7RTTfdpIsvvljR0dGqra1VRUWFkpKS9Mc//tHqcgAAAIyzPGAlJSXplVde0V/%2B8hcdOHBADodD3bp105AhQ%2BR0Oq0uBwAAwDjLA5YktWzZUpdeemkoDg0AANDoLA9Yn332mZYsWaJPPvlER48erTP/2muvWV0SAACAUSG5B6u0tFRDhgxRmzZtrD48AABAo7M8YO3cuVOvvfaa4uPjrT40AACAJSx/TEP79u05cwUAAJo1ywPW5MmTlZ%2BfL7/fb/WhAQAALGH5JcK//OUv%2Buc//6mXXnpJZ511lhyO4Iz3wgsvWF0SAACAUZYHrOjoaA0dOtTqwwIAAFjG8oC1aNEiqw8JAABgKcvvwZKkffv26fHHH9ecOXMCY//6179CUQoAAIBxlgest99%2BW6NHj9aWLVtUVFQk6fjDRydOnMhDRgEAQLNgecBaunSp7rrrLm3YsEERERGSjv99wsWLF2vZsmVWlwMAAGCc5QHr448/VnZ2tiQFApYkXX755SopKbG6HAAAAOMsD1ht27Y96d8gLC0tVcuWLa0uBwAAwDjLA9YFF1yg3/3udyovLw%2BM7d%2B/X7m5ubr44outLgcAAMA4yx/TMGfOHN14441KT09XTU2NLrjgAlVWVqpnz55avHix1eUAAAAYZ3nA6ty5s4qKivTmm29q//79atWqlbp166bBgwcH3ZNVH9u3b1dubq7S09O1dOnSoLlXX31Vy5cv1%2Beff65u3brpzjvv1JAhQyRJtbW1evTRR1VUVKSysjL16dNHCxYsUFJSkiTJ5/NpwYIFeuedd%2BRwOJSRkaH58%2BerVatWZn4IAACgWbM8YElSixYtdOmllzZoHytWrNDatWvVpUuXOnO7d%2B9Wbm6u8vPzNXDgQG3evFm33367Nm3apM6dO2v16tXasGGDVqxYoU6dOmnp0qXKycnRyy%2B/rIiICM2fP1/Hjh1TUVGRqqqqNH36dOXl5WnevHkNqhkAAIQHywNWZmbmj56pqu%2BzsCIjI7V27Vo9%2BOCD%2Bu6774Lm1qxZo4yMDGVkZEiSRo8erVWrVmn9%2BvX69a9/rcLCQk2aNEndu3eXJM2cOVPp6en64IMPdNZZZ2nr1q36f//v/yk%2BPl6SdNttt2n69OnKzc1VixYtfsqyAQBAGLE8YF155ZVBAaumpkb79%2B%2BX2%2B3WjTfeWO/9TJw48QfniouLA%2BHqhOTkZLndbh09elR79%2B5VcnJyYC46OlpdunSR2%2B3WkSNH5HQ61atXr8B8SkqKvv32W%2B3bty9oHAAA4GQsD1izZ88%2B6fjmzZv1j3/8w8gxfD6fYmNjg8ZiY2O1d%2B9effPNN/L7/Sed93q9ateunaKjo4NC4IltvV5vvY5fWloqj8cTNOZytVFCQsJPWc4PcjpD8peOfjKXy171mnKiT3brV7ihT/ZAn%2ByBPoXoHqyTufTSS/Xb3/5Wv/3tb43sz%2B/3/%2BT5U732VAoLC5Wfnx80lpOTo2nTpjVov3YXFxcV6hJCKiamdahLQD3QJ3ugT/YQzn1qMgFr165dDQ42J8TFxcnn8wWN%2BXw%2BxcfHq127dnI4HCedb9%2B%2BveLj41VeXq6amho5nc7AnCS1b9%2B%2BXsfPyspSZmZm0JjL1UZeb8VPXdJJ2e2dgen124XT6VBMTGuVlVWqpqY21OXgB9Ane6BP9tCU%2BhSqN/eWB6wJEybUGausrFRJSYlGjBhh5BipqanauXNn0Jjb7daoUaMUGRmpnj17qri4WBdddJEkqaysTAcOHFCfPn2UmJgov9%2BvPXv2KCUlJfDamJgYdevWrV7HT0hIqHM50OM5ourq8P5lEO7rr6mpDfufgR3QJ3ugT/YQzn2yPGB17dq1zqcIIyMjNX78eF133XVGjnH99ddr/PjxeuONN3TxxRdrw4YN%2BvTTTzV69GhJUnZ2tgoKCjR06FB16tRJeXl56t27t9LS0iRJI0eO1COPPKKHHnpIx44d07JlyzR%2B/Hi5XE3mhB8AAGjCLE8Mpp7WfiIMVVdXS5K2bt0q6fjZpnPPPVd5eXlatGiRDh48qB49eujJJ59Ux44dJR0/i%2BbxeHTDDTeooqJC6enpQfdM3X///br33ns1fPhwtWjRQldddZVmzpxppG4AAND8RfhN3fhUT%2BvWrav3tmPHjm3ESqzl8Rwxvk%2BXy6HL8rYb329j2ThjcKhLCAmXy6G4uCh5vRVhe6rcDuiTPdAne2hKferYsW1Ijmv5Gay5c%2Beqtra2zg3tERERQWMRERHNKmABAIDwYXnAeuqpp/T0009rypQp6tWrl/x%2Bvz766COtWLFCv/zlL5Wenm51SQAAAEaF5B6sgoICderUKTB24YUXKikpSTfffLOKioqsLgkWueKRv4a6hNMSrpc0AQANZ/mDlD799NM6T1GXpJiYGB08eNDqcgAAAIyzPGAlJiZq8eLFQX92pqysTEuWLNHZZ59tdTkAAADGWX6J8J577tGsWbNUWFioqKgoORwOlZeXq1WrVlq2bJnV5QAAABhnecAaMmSI3njjDb355pv64osv5Pf71alTJ/3sZz9T27ah%2BSglAACASSF5NHnr1q01fPhwffHFF0pKSgpFCQAAAI3G8nuwjh49qtzcXPXr109XXHGFpOP3YN1yyy0qKyuzuhwAAADjLA9YDz/8sHbv3q28vDw5HP85fE1NjfLy8qwuBwAAwDjLA9bmzZv12GOP6fLLLw/80eeYmBgtWrRIW7ZssbocAAAA4ywPWBUVFeratWud8fj4eH377bdWlwMAAGCc5QHr7LPP1j/%2B8Q9JCvrbg5s2bdKZZ55pdTkAAADGWf4pwp///Oe64447dO2116q2tlbPPPOMdu7cqc2bN2vu3LlWlwMAAGCc5QErKytLLpdLq1atktPp1BNPPKFu3bopLy9Pl19%2BudXlAAAAGGd5wDp8%2BLCuvfZaXXvttVYfGgAAwBKW34M1fPjwoHuvAAAAmhvLA1Z6ero2btxo9WEBAAAsY/klwjPOOEMPPvigCgoKdPbZZ6tFixZB80uWLLG6JAAAAKMsD1h79%2B7VOeecI0nyer1WHx4AAKDRWRawZs6cqaVLl%2BqPf/xjYGzZsmXKycmxqgQAAABLWHYP1rZt2%2BqMFRQUWHV4AAAAy1gWsE72yUE%2BTQgAAJojywLWiT/sfKoxAAAAu7P8MQ0AAADNHQELAADAMMs%2BRVhVVaVZs2adcoznYAEAALuzLGD1799fpaWlpxwDAACwO8sC1n8//woAAKA5s/xJ7lZ49913ddNNNwWN%2Bf1%2BVVVVaeXKlZo4caJatmwZNP8///M/uuKKKyRJK1eu1OrVq%2BXxeNSrVy/NnTtXqampltUPAADsrVkGrAEDBsjtdgeNPfHEE9qzZ48kKTEx8aQPPpWOPxD18ccf11NPPaVevXpp5cqVmjJlirZs2aI2bdo0eu0AAMD%2BwuJThP/%2B97/1zDPP6De/%2Bc0pty0sLNQ111yjvn37qlWrVrrlllskSa%2B//npjlwkAAJqJZnkG6/seffRRXXvttTrzzDP12WefqaKiQjk5OdqxY4datmypm266SZMmTVJERISKi4t15ZVXBl7rcDjUu3dvud1ujRo1ql7HKy0tlcfjCRpzudooISHB6LqczrDIxyHjcpn5%2BZ7oE/1q2uiTPdAne6BPYRCwPv/8c23ZskVbtmyRJEVHR%2Bvcc8/VjTfeqKVLl%2Bqdd97R9OnT1bZtW40fP14%2Bn0%2BxsbFB%2B4iNjZXX6633MQsLC5Wfnx92ONGOAAATv0lEQVQ0lpOTo2nTpjV8QbBMXFyU0f3FxLQ2uj80DvpkD/TJHsK5T80%2BYK1evVojRoxQx44dJUkpKSlBn2gcMmSIJkyYoJdeeknjx4%2BX1PC/kZiVlaXMzMygMZerjbzeigbt9/vC%2BZ2BFUz1y%2Bl0KCamtcrKKlVTU2tknzCPPtkDfbKHptQn02%2BW66vZB6zNmzcrNzf3R7dJTEzU5s2bJUlxcXHy%2BXxB8z6fTz179qz3MRMSEupcDvR4jqi6ml8GdmK6XzU1tfwbsAH6ZA/0yR7CuU/N%2BhTI7t27dfDgQQ0ePDgwtnHjRv3f//1f0Hb79u1TUlKSJCk1NVXFxcWBuZqaGu3atUt9%2B/a1pmgAAGB7zTpg7dq1S%2B3atVN0dHRgrEWLFnrooYf01ltvqaqqSn/961/14osvKjs7W5KUnZ2tdevW6f3331dlZaWWL1%2Buli1batiwYSFaBQAAsJtmfYnwq6%2B%2BCtx7dcKll16qe%2B65Rw888IAOHTqkDh066J577tGIESMkSUOHDtWdd96pGTNm6Ouvv1ZaWpoKCgrUqlWrUCwBAADYUIS/oXd0o148niPG9%2BlyOXRZ3nbj%2B8VxG2cMPvVG9eByORQXFyWvtyJs70WwA/pkD/TJHppSnzp2bBuS4zbrS4QAAAChQMACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAsGYbsHr16qXU1FSlpaUFvh544AFJ0ttvv63x48frggsu0KhRo7R%2B/fqg165cuVIjR47UBRdcoOzsbO3cuTMUSwAAADblCnUBjWnTpk0666yzgsZKS0t12223ae7cubr66qv13nvvaerUqerWrZvS0tK0bds2Pf7443rqqafUq1cvrVy5UlOmTNGWLVvUpk2bEK0EAADYSbM9g/VDNmzYoK5du2r8%2BPGKjIzUoEGDlJmZqTVr1kiSCgsLdc0116hv375q1aqVbrnlFknS66%2B/HsqyAQCAjTTrM1hLlizRv/71L5WXl%2BuKK67Q3XffreLiYiUnJwdtl5ycrI0bN0qSiouLdeWVVwbmHA6HevfuLbfbrVGjRtXruKWlpfJ4PEFjLlcbJSQkNHBFwZzOsMvHlnK5zPx8T/SJfjVt9Mke6JM90KdmHLDOP/98DRo0SA899JA%2B%2B%2BwzzZgxQ/fdd598Pp86deoUtG27du3k9XolST6fT7GxsUHzsbGxgfn6KCwsVH5%2BftBYTk6Opk2b9hNXg1CIi4syur%2BYmNZG94fGQZ/sgT7ZQzj3qdkGrMLCwsD/7969u2bPnq2pU6eqf//%2Bp3yt3%2B9v0LGzsrKUmZkZNOZytZHXW9Gg/X5fOL8zsIKpfjmdDsXEtFZZWaVqamqN7BPm0Sd7oE/20JT6ZPrNcn0124D1fWeddZZqamrkcDjk8/mC5rxer%2BLj4yVJcXFxdeZ9Pp969uxZ72MlJCTUuRzo8RxRdTW/DOzEdL9qamr5N2AD9Mke6JM9hHOfmuUpkF27dmnx4sVBYyUlJWrZsqUyMjLqPHZh586d6tu3ryQpNTVVxcXFgbmamhrt2rUrMA8AAHAqzTJgtW/fXoWFhSooKNCxY8e0f/9%2BPfroo8rKytKYMWN08OBBrVmzRt99953efPNNvfnmm7r%2B%2BuslSdnZ2Vq3bp3ef/99VVZWavny5WrZsqWGDRsW2kUBAADbaJaXCDt16qSCggItWbIkEJDGjRunmTNnKjIyUk8%2B%2BaQWLlyo%2B%2B67T4mJiXr44Yd13nnnSZKGDh2qO%2B%2B8UzNmzNDXX3%2BttLQ0FRQUqFWrViFeFQAAsIsIf0Pv6Ea9eDxHjO/T5XLosrztxveL4zbOGGxkPy6XQ3FxUfJ6K8L2XgQ7oE/2QJ/soSn1qWPHtiE5brO8RAgAABBKBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMa7YB6%2BDBg8rJyVF6eroGDRqku%2B%2B%2BW2VlZfr888/Vq1cvpaWlBX394Q9/CLz21Vdf1dVXX61%2B/frpmmuu0VtvvRXClQAAALtxhbqAxjJlyhSlpqZq27ZtOnLkiHJycvTQQw9p6tSpkiS3233S1%2B3evVu5ubnKz8/XwIEDtXnzZt1%2B%2B%2B3atGmTOnfubOUSAACATTXLM1hlZWVKTU3VrFmzFBUVpc6dO2vcuHHasWPHKV%2B7Zs0aZWRkKCMjQ5GRkRo9erTOPfdcrV%2B/3oLKAQBAc9AsA1ZMTIwWLVqkDh06BMYOHTqkhISEwPe/%2Bc1vNGTIEA0cOFBLlixRVVWVJKm4uFjJyclB%2B0tOTv7BM14AAADf12wvEf43t9utVatWafny5WrZsqX69eunyy67TA8%2B%2BKB2796tO%2B64Qy6XS9OnT5fP51NsbGzQ62NjY7V37956H6%2B0tFQejydozOVqExTwTHA6m2U%2BbjJcLjM/3xN9ol9NG32yB/pkD/QpDALWe%2B%2B9p6lTp2rWrFkaNGiQJOmFF14IzPfp00eTJ0/Wk08%2BqenTp0uS/H5/g45ZWFio/Pz8oLGcnBxNmzatQfuFteLioozuLyamtdH9oXHQJ3ugT/YQzn1q1gFr27ZtuuuuuzR//nyNHTv2B7dLTEzUV199Jb/fr7i4OPl8vqB5n8%2Bn%2BPj4eh83KytLmZmZQWMuVxt5vRWnt4BTCOd3BlYw1S%2Bn06GYmNYqK6tUTU2tkX3CPPpkD/TJHppSn0y/Wa6vZhuw/vnPfyo3N1ePPvqohgwZEhh/%2B%2B239f777wc%2BTShJ%2B/btU2JioiIiIpSamqqdO3cG7cvtdmvUqFH1PnZCQkKdy4EezxFVV/PLwE5M96umppZ/AzZAn%2ByBPtlDOPepWZ4Cqa6u1rx58zR79uygcCVJbdu21bJly/Tyyy%2BrqqpKbrdbf/jDH5SdnS1Juv766/W3v/1Nb7zxhr777jutXbtWn376qUaPHh2KpQAAABuK8Df0hqMmaMeOHfrFL36hli1b1pnbtGmTdu3apfz8fH366adq27atbrjhBt16661yOI7nzS1btmjJkiU6ePCgevTooblz52rAgAENqsnjOdKg15%2BMy%2BXQZXnbje8Xx22cMdjIflwuh%2BLiouT1VoTtOzk7oE/2QJ/soSn1qWPHtiE5brO8RHjhhRfqo48%2B%2BsH5xMREXXbZZT84P2LECI0YMaIxSgMAAGGgWV4iBAAACCUCFgAAgGEELAAAAMOa5T1YgAlXPPLXUJdwWkzdlA8AaDjOYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAxzhboAAGZc8chfQ11CvW2cMTjUJQBAo%2BIM1kkcPHhQv/71r5Wenq5LLrlEDz/8sGpra0NdFgAAsAnOYJ3EHXfcoZSUFG3dulVff/21Jk%2BerA4dOuhXv/pVqEsDAAA2QMD6HrfbrT179uiZZ55R27Zt1bZtW02aNEnPPfccAQswxE6XMyUuaQI4fQSs7ykuLlZiYqJiY2MDYykpKdq/f7/Ky8sVHR19yn2UlpbK4/EEjblcbZSQkGC0VqeTK7yAFewWCP88%2B2ehLqHRnPi9x%2B%2B/po0%2BEbDq8Pl8iomJCRo7Eba8Xm%2B9AlZhYaHy8/ODxm6//Xbdcccd5grV8SB3Y%2BdPlJWVZTy8wZzS0lIVFhbSpyaOPtlDaWmpnnvuKfrUxNEnbnI/Kb/f36DXZ2Vl6aWXXgr6ysrKMlTdf3g8HuXn59c5W4amhT7ZA32yB/pkD/SJM1h1xMfHy%2BfzBY35fD5FREQoPj6%2BXvtISEgI28QOAAA4g1VHamqqDh06pMOHDwfG3G63evTooaioqBBWBgAA7IKA9T3JyclKS0vTkiVLVF5erpKSEj3zzDPKzs4OdWkAAMAmnAsWLFgQ6iKamp/97GcqKirSAw88oFdeeUXjx4/XzTffrIiIiFCXVkdUVJQuuugizq41cfTJHuiTPdAnewj3PkX4G3pHNwAAAIJwiRAAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAJWE3fw4EH9%2Bte/Vnp6ui655BI9/PDDqq2tPem2K1eu1MiRI3XBBRcoOztbO3futLja8HU6fXr%2B%2Bec1cuRI9evXT2PGjNHWrVstrjZ8nU6fTvjyyy/Vr18/Pf744xZVidPpU0lJiW644Qb17dtXGRkZevbZZ60tNozVt0%2B1tbV67LHHlJmZqX79%2Bunqq6/Wq6%2B%2BGoKKLeZHkzZu3Dj/vHnz/GVlZf79%2B/f7R4wY4X/66afrbPfaa6/5L7zwQv/777/vr6ys9D/55JP%2BwYMH%2BysqKkJQdfipb582bdrk79%2B/v3/Hjh3%2BY8eO%2Bf/0pz/5U1JS/AcOHAhB1eGnvn36b7fffru/f//%2B/scee8yiKlHfPlVWVvqHDRvmX7Fihf/bb7/1f/DBB/5Ro0b59%2B7dG4Kqw099%2B7Rq1Sr/kCFD/CUlJf7q6mr/tm3b/MnJyf7du3eHoGrrcAarCXO73dqzZ49mz56ttm3bqmvXrpo0aZIKCwvrbFtYWKhrrrlGffv2VatWrXTLLbdIkl5//XWryw47p9Ono0eP6s4771T//v3VokULXXfddYqKitL7778fgsrDy%2Bn06YQ333xTe/fu1bBhw6wrNMydTp82btyo6Oho3XLLLWrdurX69OmjoqIide/ePQSVh5fT6VNxcbH69%2B%2Bvc845R06nU5dcconatWunjz76KASVW4eA1YQVFxcrMTFRsbGxgbGUlBTt379f5eXldbZNTk4OfO9wONS7d2%2B53W7L6g1Xp9OnMWPG6Oc//3ng%2B7KyMlVUVKhTp06W1RuuTqdP0vEwfP/99%2Bvee%2B%2BVy%2BWystSwdjp9eu%2B993Tuuedqzpw5uvDCC3X55Zdr/fr1Vpcclk6nT8OGDdM777yj3bt369ixY3rttddUWVmpiy66yOqyLUXAasJ8Pp9iYmKCxk78Y/Z6vXW2/e9/6Ce2/f52MO90%2BvTf/H6/5s2bp759%2Bzb7XzRNwen2admyZTr//PM1cOBAS%2BrDcafTpy%2B%2B%2BEKvvfaaBg0apO3bt2vy5MnKzc3Vrl27LKs3XJ1On0aMGKGsrCyNHTtWaWlpmjVrlhYtWqQzzjjDsnpDgbdlTZzf72%2BUbWHW6f7sq6qqdPfdd2vv3r1auXJlI1WF76tvn/bu3as1a9Zow4YNjVwRTqa%2BffL7/UpJSdHVV18tSRo3bpxeeOEFbdq0KeiMPhpHffu0bt06rVu3TmvWrFGvXr309ttva9asWTrjjDPUp0%2BfRq4ydDiD1YTFx8fL5/MFjfl8PkVERCg%2BPj5oPC4u7qTbfn87mHc6fZKOX3qaPHmy/v3vf2v16tXq0KGDVaWGtfr2ye/3a8GCBbrjjjvUsWNHq8sMe6fz31PHjh3Vtm3boLHExER5PJ5GrzPcnU6fVq1apaysLPXp00eRkZEaNmyYBg4c2Owv5xKwmrDU1FQdOnRIhw8fDoy53W716NFDUVFRdbYtLi4OfF9TU6Ndu3apb9%2B%2BltUbrk6nT36/XzNnzpTL5dKzzz6ruLg4q8sNW/Xt07///W%2B9%2B%2B67euyxx5Senq709HS98soreuqppzRu3LhQlB5WTue/p%2B7du%2Bvjjz8OOpNy8OBBJSYmWlZvuDqdPtXW1qqmpiZo7NixY5bUGUoErCYsOTlZaWlpWrJkicrLy1VSUqJnnnlG2dnZkqTLL79cO3bskCRlZ2dr3bp1ev/991VZWanly5erZcuWfPrJAqfTpw0bNmjv3r169NFHFRkZGcqyw059%2B9S5c2e9%2BeabevnllwNfmZmZmjBhggoKCkK8iubvdP57Gj16tLxer5544gkdPXpURUVFKi4u1ujRo0O5hLBwOn3KzMzU2rVrtWfPHlVXV%2Butt97S22%2B/reHDh4dyCY2Oe7CauMcee0zz58/X4MGDFR0drQkTJgQ%2BhbZ//359%2B%2B23kqShQ4fqzjvv1IwZM/T1118rLS1NBQUFatWqVSjLDxv17dOLL76ogwcP1rmpfcyYMVq4cKHldYeb%2BvTJ6XSqc%2BfOQa9r3bq1oqOjuWRokfr%2B99SpUyc9%2BeSTevDBB/X73/9eZ555ppYtW6azzz47lOWHjfr2afLkyaqurlZOTo4OHz6sxMRELVy4UBdffHEoy290EX7ujAYAADCKS4QAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYNj/B5DqR2ocMuRNAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common1612852181654424184”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1007</td> <td class=”number”>31.4%</td> <td>

<div class=”bar” style=”width:48%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.110213079</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.1447178</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.109289617</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.080853816</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.072432276</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.108849461</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0175821</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.094705938</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.070947144</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2104)</td> <td class=”number”>2107</td> <td class=”number”>65.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme1612852181654424184”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1007</td> <td class=”number”>31.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0112132</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0134956</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0138318</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0141579</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.571825348</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.61055386</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.635324015</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.651041667</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.822368421</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_REDEMP_SNAPS12”>REDEMP_SNAPS12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2873</td>

</tr> <tr>

<th>Unique (%)</th> <td>89.5%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>9.9%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>317</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>254980</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1253300</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.7%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram1765720055340471902”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives1765720055340471902,#minihistogram1765720055340471902”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives1765720055340471902”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles1765720055340471902”

aria-controls=”quantiles1765720055340471902” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram1765720055340471902” aria-controls=”histogram1765720055340471902”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common1765720055340471902” aria-controls=”common1765720055340471902”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme1765720055340471902” aria-controls=”extreme1765720055340471902”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles1765720055340471902”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>59973</td>

</tr> <tr>

<th>Q1</th> <td>164240</td>

</tr> <tr>

<th>Median</th> <td>258280</td>

</tr> <tr>

<th>Q3</th> <td>335200</td>

</tr> <tr>

<th>95-th percentile</th> <td>456700</td>

</tr> <tr>

<th>Maximum</th> <td>1253300</td>

</tr> <tr>

<th>Range</th> <td>1253300</td>

</tr> <tr>

<th>Interquartile range</th> <td>170960</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>125330</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.49154</td>

</tr> <tr>

<th>Kurtosis</th> <td>1.9304</td>

</tr> <tr>

<th>Mean</th> <td>254980</td>

</tr> <tr>

<th>MAD</th> <td>98875</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.56462</td>

</tr> <tr>

<th>Sum</th> <td>737660000</td>

</tr> <tr>

<th>Variance</th> <td>15708000000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram1765720055340471902”>

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Vxc7HQ5AAAA1jkesLp166bFixfr1KlT7rHvvvtOCxYs8PioBgAAgEDl%2BC3COXPm6KGHHtLtt9%2Bu8PBwlZeXq6ioSE2aNNHLL7/sdDkAAADWOR6wmjRporfeekvvvfeevv76awUHB6tZs2bq2bOnQkJCnC4HAADAOscDliTVqVNHd955pz%2BWBgAAqHaOB6xvvvlGy5Yt05dffqmSkhKv%2BXfffdfpkgAAAKzyy3uwcnJy1LNnT9WrV8/p5QEAAKqd4wErMzNT7777rqKjo51eGgAAwBGOf0zDDTfcwJUrAABQozkesCZNmqSVK1fKGOP00gAAAI5w/Bbhe%2B%2B9p08%2B%2BURvvPGGbr75ZgUHe2a8V1991emSAAAArHI8YIWHh6tXr16OrLVq1SqtX79eZ86cUceOHfXUU0/p5ptv1r59%2B7Rs2TJ99dVX%2BtnPfqZJkyZpyJAh7uetXbtW69evV25urlq1aqW5c%2BcqPj7ekZoBAEDgczxgLVq0yJF11q9fr02bNmnt2rWKiYnR8uXL9cc//lETJ07UI488orlz52rw4MH6%2BOOP9fDDD6tZs2Zq166ddu7cqRUrVuj5559Xq1attHbtWk2ePFk7duzgvWMAAKBSHH8PliR99dVXWrFihR577DH32P79%2B62u8cILL2jatGm67bbbFB4ernnz5mnevHnavHmzmjZtqpSUFIWFhSkxMVHJycnasGGDJCk9PV3Dhg1Thw4dVLduXY0fP16StGvXLqv1AQCAmsvxK1j79u3ThAkT1KxZMx07dkyLFi3SN998ozFjxmj58uXq27dvldf47rvvdPz4cZ06dUoDBw5UXl6eEhIS9MQTTygrK0txcXEej4%2BLi9O2bdskSVlZWRo4cKB7Ljg4WG3atFFGRoYGDRpUqfVzcnKUm5vrMRYaWk8xMTFVPDNPISHBHn%2BjdgsNvfw%2BYK/4Rl%2B80RPf6Itv9OXyHA9YTz/9tH7zm9/o17/%2Btdq3by/ph99PuHjxYj333HNWAtbJkyclSW%2B//bZefPFFGWM0depUzZs3TyUlJYqNjfV4fMOGDVVQUCBJcrlcioyM9JiPjIx0z1dGenq6Vq5c6TGWmpqqqVOnXs3pVCgi4vpqOS4CS1RU/Qofw17xjb54oye%2B0Rff6Is3xwPWP/7xD61bt06SFBQU5B4fMGCA5syZY2WNix8BMX78eHeYmjJliiZMmKDExMRKP/9qjRw5UsnJyR5joaH1VFBQVKXjXiokJFgREdersLBYZWXlVo%2BNwHOl/cVe8Y2%2BeKMnvtEX3wKhL5X55rM6OB6wGjRooJKSEtWpU8djPCcnx2vsat14442SpIiICPdY48aNZYzRhQsX5HK5PB5fUFDg/mT5qKgor3mXy6UWLVpUev2YmBiv24G5uadVWlo9m6%2BsrLzajo3AUZk9wF7xjb54oye%2B0Rff6Is3x2%2Badu7cWQsXLtSZM2fcY0ePHtWsWbN0%2B%2B23W1mjUaNGCg8P18GDB91j2dnZuu6665SUlKTMzEyPx2dmZqpDhw6SpPj4eGVlZbnnysrKdODAAfc8AABARRwPWI899pj279%2BvhIQEnTt3Tp07d9bAgQPlcrk0e/ZsK2uEhoYqJSVFv//97/U///M/ysvL03PPPafBgwdr6NChys7O1oYNG3Tu3Dnt2bNHe/bs0X333SdJGj16tN588019%2BumnKi4u1qpVq1SnTh317t3bSm0AAKDmc/wWYaNGjbRlyxbt2bNHR48eVd26ddWsWTP16NHD4z1ZVTVjxgydP39eI0aM0IULF9S/f3/NmzdP9evX1%2BrVq/XUU09pwYIFaty4sZYsWaLWrVtLknr16qXp06crLS1NeXl5ateundasWaO6detaqw0AANRsQYZfCuiI3NzT1o8ZGhqsqKj6KigoCoh733ct/9DfJdRo29J6XHYu0PaKU%2BiLN3riG33xLRD6ctNNDfyyruNXsJKTk694perdd991sBoAAAD7HA9YAwcO9AhYZWVlOnr0qDIyMvTrX//a6XIAAACsczxgzZw50%2Bf49u3b9de//tXhagAAAOy7Zj7b/s4779Rbb73l7zIAAACq7JoJWAcOHKjyJ6gDAABcCxy/RThq1CivseLiYh05ckT9%2BvVzuhwAAADrHA9YTZs29fopwrCwMKWkpGjEiBFOlwMAAGCd4wFr8eLFTi8JAADgKMcD1ptvvlnpx957773VWAkAAED1cDxgzZ07V%2BXl5V5vaA8KCvIYCwoKImABAICA5HjAev755/XCCy9o8uTJatWqlYwxOnTokP7whz/ogQceUEJCgtMlAQAAWOWX92CtWbNGsbGx7rGuXbuqSZMmGjdunLZs2eJ0SQAAAFY5/jlYx44dU2RkpNd4RESEsrOznS4HAADAOscDVuPGjbV48WIVFBS4xwoLC7Vs2TLdcsstTpcDAABgneO3COfMmaMZM2YoPT1d9evXV3BwsM6cOaO6devqueeec7ocAAAA6xwPWD179tTu3bu1Z88enTx5UsYYxcbG6o477lCDBg2cLgcAAMA6xwOWJF1//fXq27evTp48qSZNmvijBAAAgGrj%2BHuwSkpKNGvWLHXq1El33XWXpB/egzV%2B/HgVFhY6XQ4AAIB1jgesJUuW6ODBg1q6dKmCg/9/%2BbKyMi1dutTpcgAAAKxzPGBt375dzz77rAYMGOD%2Bpc8RERFatGiRduzY4XQ5AAAA1jkesIqKitS0aVOv8ejoaJ09e9bpcgAAAKxzPGDdcsst%2Butf/ypJHr978O2339Y//dM/OV0OAACAdY7/FOH999%2BvKVOmaPjw4SovL9eLL76ozMxMbd%2B%2BXXPnznW6HAAAAOscD1gjR45UaGio1q1bp5CQEP3%2B979Xs2bNtHTpUg0YMMDpcgJe17lv%2B7sEAABwCccDVn5%2BvoYPH67hw4c7vTQAAIAjHH8PVt%2B%2BfT3eewUAAFDTOB6wEhIStG3bNqeXBQAAcIzjtwh/9rOf6V//9V%2B1Zs0a3XLLLbruuus85pctW%2BZ0SQAAAFY5HrAOHz6s2267TZJUUFDg9PIAAADVzrGANW3aND399NN6%2BeWX3WPPPfecUlNTnSoBAADAEY69B2vnzp1eY2vWrHFqeQAAAMc4FrB8/eQgP00IAABqIscC1sVf7FzRGAAAQKBz/GMaAAAAajoCFgAAgGWO/RThhQsXNGPGjArH%2BBwsAAAQ6BwLWF26dFFOTk6FYwAAAIHOsYD148%2B/AgAAqMl4DxYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwrFYErIULF6pVq1buf%2B/bt08pKSnq3LmzBg0apE2bNnk8fu3aterfv786d%2B6s0aNHKzMz0%2BmSAQBAAKvxAevgwYPauHGj%2B985OTl65JFHNGrUKO3bt09z587V/PnzlZGRIUnauXOnVqxYod/97nfau3ev%2BvTpo8mTJ%2Bvs2bP%2BOgUAABBganTAKi8v1%2BOPP66xY8e6xzZv3qymTZsqJSVFYWFhSkxMVHJysjZs2CBJSk9P17Bhw9ShQwfVrVtX48ePlyTt2rXLH6cAAAACUI0OWK%2B%2B%2BqrCwsI0ePBg91hWVpbi4uI8HhcXF%2Be%2BDXjpfHBwsNq0aeO%2BwgUAAFARx35VjtO%2B//57rVixwutX9LhcLsXGxnqMNWzYUAUFBe75yMhIj/nIyEj3fGXk5OQoNzfXYyw0tJ5iYmJ%2ByilUKCSkRudj/EShoZffDxf3CnvGE33xRk98oy%2B%2B0ZfLq7EBa9GiRRo2bJiaN2%2Bu48eP/6TnGmOqtHZ6erpWrlzpMZaamqqpU6dW6bjAlURF1a/wMRER1ztQSeChL97oiW/0xTf64q1GBqx9%2B/Zp//792rJli9dcVFSUXC6Xx1hBQYGio6MvO%2B9yudSiRYtKrz9y5EglJyd7jIWG1lNBQVGlj1EZfMeAH7vS/goJCVZExPUqLCxWWVm5g1Vd2%2BiLN3riG33xLRD6UplvPqtDjQxYmzZtUl5envr06SPp/69IJSQk6KGHHvIKXpmZmerQoYMkKT4%2BXllZWRo6dKgkqaysTAcOHFBKSkql14%2BJifG6HZibe1qlpdfm5kPNUJn9VVZWzj70gb54oye%2B0Rff6Iu3GnkJZPbs2dq%2Bfbs2btyojRs3as2aNZKkjRs3avDgwcrOztaGDRt07tw57dmzR3v27NF9990nSRo9erTefPNNffrppyouLtaqVatUp04d9e7d249nBAAAAkmNvIIVGRnp8Ub10tJSSVKjRo0kSatXr9ZTTz2lBQsWqHHjxlqyZIlat24tSerVq5emT5%2ButLQ05eXlqV27dlqzZo3q1q3r/IkAAICAVCMD1qVuvvlmHTp0yP3vbt26eXz46KXuv/9%2B3X///U6UBgAAaqAaeYsQAADAnwhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACyrFb%2BLEKgN7lr%2Bob9LqLRtaT38XQIAVCuuYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYVmMDVnZ2tlJTU5WQkKDExETNnj1bhYWFkqSDBw/qgQceUJcuXdSvXz%2B98MILHs/dunWrBg8erE6dOmnYsGH64IMP/HEKAAAgQNXYgDV58mRFRERo586deuONN/Tll1/q3/7t31RSUqJJkybpF7/4hd5//309/fTTWr16tXbs2CHph/A1a9YszZw5U3/5y180duxYPfroozp58qSfzwgAAASKGhmwCgsLFR8frxkzZqh%2B/fpq1KiRhg4dqo8%2B%2Bki7d%2B/WhQsX9PDDD6tevXpq27atRowYofT0dEnShg0blJSUpKSkJIWFhWnIkCFq2bKlNm3a5OezAgAAgSLU3wVUh4iICC1atMhj7MSJE4qJiVFWVpZatWqlkJAQ91xcXJw2bNggScrKylJSUpLHc%2BPi4pSRkVHp9XNycpSbm%2BsxFhpaTzExMT/1VK4oJKRG5mPUAqGh18bevfg1xNfS/6MnvtEX3%2BjL5dXIgHWpjIwMrVu3TqtWrdK2bdsUERHhMd%2BwYUO5XC6Vl5fL5XIpMjLSYz4yMlKHDx%2Bu9Hrp6elauXKlx1hqaqqmTp169ScB1CBRUfX9XYKHiIjr/V3CNYee%2BEZffKMv3mp8wPr444/18MMPa8aMGUpMTNS2bdt8Pi4oKMj938aYKq05cuRIJScne4yFhtZTQUFRlY57Kb5jQKCy/bVwtUJCghURcb0KC4tVVlbu73KuCfTEN/riWyD0xV/f0NXogLVz50795je/0fz583XvvfdKkqKjo3Xs2DGPx7lcLjVs2FDBwcGKioqSy%2BXymo%2BOjq70ujExMV63A3NzT6u09NrcfIDTrrWvhbKy8muuJn%2BjJ77RF9/oi7caewnkk08%2B0axZs/TMM8%2B4w5UkxcfH69ChQyotLXWPZWRkqEOHDu75zMxMj2P9eB4AAKAiNTJglZaWat68eZo5c6Z69uzpMZeUlKTw8HCtWrVKxcXF%2Buyzz/T6669r9OjRkqT77rtPe/fu1e7du3Xu3Dm9/vrrOnbsmIYMGeKPUwEAAAEoyFT1DUfXoI8%2B%2Bki/%2BtWvVKdOHa%2B5t99%2BW0VFRXr88ceVmZmpG2%2B8URMmTND999/vfsyOHTu0bNkyZWdnq3nz5po7d666detWpZpyc09X6fm%2BhIYG65dL37d%2BXKC6bUvr4e8SJP3wNRQVVV8FBUXc3vg/9MQ3%2BuJbIPTlppsa%2BGXdGvkerK5du%2BrQoUNXfMwrr7xy2bl%2B/fqpX79%2BtssCAAC1RI28RQgAAOBPBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsD09x6eAAAOXUlEQVQCAACwjIAFAABgWai/CwBQ%2B9y1/EN/l/CTbEvr4e8SAAQYrmABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGWh/i7gWpSdna0FCxbos88%2BU7169TRw4EDNmDFDwcHkUaA2umv5h/4u4SfZltbD3yUAtR4By4cpU6aobdu2euedd5SXl6dJkybpxhtv1IMPPujv0gAAQADgkswlMjIy9MUXX2jmzJlq0KCBmjZtqrFjxyo9Pd3fpQEAgADBFaxLZGVlqXHjxoqMjHSPtW3bVkePHtWZM2cUHh5e4TFycnKUm5vrMRYaWk8xMTFWaw0JIR8D8BYaevWvDRdfV5x6ffnl0vcdWceWnbOS/F3CNcXp/RJICFiXcLlcioiI8Bi7GLYKCgoqFbDS09O1cuVKj7FHH31UU6ZMsVeofghyv270pUaOHGk9vAWqnJwcpaen05NL0Bff6Iu3nJwcvfTS84715KN/HVDta9hwca%2BUlHRmr/yI0/slkBA5fTDGVOn5I0eO1BtvvOHxZ%2BTIkZaq%2B3%2B5ublauXKl19Wy2oye%2BEZffKMv3uiJb/TFN/pyeVzBukR0dLRcLpfHmMvlUlBQkKKjoyt1jJiYGJI8AAC1GFewLhEfH68TJ04oPz/fPZaRkaHmzZurfv36fqwMAAAECgLWJeLi4tSuXTstW7ZMZ86c0ZEjR/Tiiy9q9OjR/i4NAAAEiJAnnnjiCX8Xca254447tGXLFj355JN66623lJKSonHjxikoKMjfpXmpX7%2B%2BunfvztW1H6EnvtEX3%2BiLN3riG33xjb74FmSq%2Bo5uAAAAeOAWIQAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBKwAlJ2drYkTJyohIUF9%2BvTRkiVLVF5e7u%2Byrkp2drZSU1OVkJCgxMREzZ49W4WFhZKkgwcP6oEHHlCXLl3Ur18/vfDCCx7P3bp1qwYPHqxOnTpp2LBh%2BuCDD9xz5eXlevrpp9W3b19169ZN48aN0zfffOOed7lcSktLU2Jionr27Km5c%2BeqpKTEPV/R2k5ZuHChWrVq5f73vn37lJKSos6dO2vQoEHatGmTx%2BPXrl2r/v37q3Pnzho9erQyMzPdc%2BfOndO//Mu/qFevXkpISNDUqVNVUFDgnq9oX1W0thNWrVqlnj17qmPHjho7dqyOHz9eqdpqcl8OHDigMWPGqGvXrurRo4dmzpzp/mX1takv77//vhITEzVt2jSvOX%2B%2BVlRl7ersyY4dOzRkyBB16tRJ/fv312uvveYx78%2B9caW1A4pBwBk6dKiZN2%2BeKSwsNEePHjX9%2BvUzL7zwgr/Luip33323mT17tjlz5ow5ceKEGTZsmJkzZ44pLi42d9xxh1mxYoUpKioymZmZpnv37mb79u3GGGMOHDhg4uPjze7du01JSYnZuHGj6dChgzlx4oQxxpi1a9eaPn36mMOHD5vTp0%2Bb3/72t2bw4MGmvLzcGGPMo48%2BaiZOnGjy8vLMyZMnzciRI82TTz5pjDEVru2UAwcOmO7du5uWLVsaY4z57rvvTMeOHc2GDRtMSUmJ%2BfDDD0379u3N559/bowx5t133zVdu3Y1n376qSkuLjarV682PXr0MEVFRcYYYxYtWmSGDRtmvv32W1NQUGAeffRRM2nSJPd6V9pXFa3thHXr1pkBAwaYI0eOmNOnT5snn3zSPPnkk7W6LxcuXDA9evQwy5YtM%2BfOnTP5%2BfnmwQcfNFOmTKlVfVmzZo3p16%2BfGTVqlElLS/OY8%2BdrRVXXrq6efPbZZ6Zdu3bmz3/%2Bs7lw4YLZvXu3adu2rfn73/9ujPHv3qho7UBCwAown3/%2BuWnTpo1xuVzusf/6r/8y/fv392NVV%2BfUqVNm9uzZJjc31z328ssvm379%2Bplt27aZX/ziF6a0tNQ9t2TJEvPQQw8ZY4xZsGCBSU1N9TjeiBEjzOrVq40xxgwaNMi89NJL7rnTp0%2BbuLg4s3//fpObm2tat25tDh486J7fs2eP6dixozl//nyFazuhrKzMjBgxwvzHf/yHO2A9//zz5t577/V4XFpampk/f74xxpiJEyeahQsXehyjR48eZsuWLebChQumS5cu5p133nHPHz582LRq1cqcPHmywn1V0dpOSE5O9hlya3Nfvv32W9OyZUtz%2BPBhj/ruvPPOWtWXl156yRQWFppZs2Z5hQl/vlZUZe2qulJP9uzZY1auXOkxNnToULNq1SpjjH/3xpXWDjTcIgwwWVlZaty4sSIjI91jbdu21dGjR3XmzBk/VvbTRUREaNGiRbrxxhvdYydOnFBMTIyysrLUqlUrhYSEuOfi4uLcl4qzsrIUFxfncby4uDhlZGSopKREhw8f9pgPDw/XrbfeqoyMDB08eFAhISEet97atm2rs2fP6quvvqpwbSe8%2BuqrCgsL0%2BDBg91jlzvny/UkODhYbdq0UUZGhr7%2B%2BmudPn1abdu2dc///Oc/V926dZWVlVXhvqpo7er23Xff6fjx4zp16pQGDhzovi2Rn59fq/sSGxurNm3aKD09XUVFRcrLy9OOHTvUu3fvWtWXMWPGqEGDBj7n/PlaUZW1q%2BpKPenVq5dSU1Pd/y4tLVVubq5iY2N91u3k3rjS2oGGgBVgXC6XIiIiPMYubuQf3wMPRBkZGVq3bp0efvhhn%2BfZsGFDuVwulZeXy%2BVyeXwBSz/0oaCgQKdOnZIx5rLzLpdL4eHhCgoK8piT5J6/0trV7fvvv9eKFSv0%2BOOPe4xfrq6L/9%2Bv1BOXyyVJXs%2BPiIi47DlXpidO7bmTJ09Kkt5%2B%2B229%2BOKL2rhxo06ePKl58%2BbV6r4EBwdrxYoVevfdd9W5c2clJiaqtLRUM2bMqNV9%2BTF/vlZUZW0nLV26VPXq1dPAgQMl%2BXdvXGntQEPACkDGGH%2BXYN3HH3%2BscePGacaMGUpMTLzs4378QldRH640fzU9/PHa1WnRokUaNmyYmjdv/pOf63RPnHKxtvHjxys2NlaNGjXSlClTtHPnzp/0/KuZv5b7cv78eU2ePFkDBgzQRx99pPfee08NGjTQzJkzK/X8mtqXS/nztaIqa1c3Y4yWLFmiLVu2aNWqVQoLC6t0XdW5NwJpb10JASvAREdHu7%2BDuMjlcikoKEjR0dF%2Bqqpqdu7cqYkTJ2rOnDkaM2aMpB/O89LvWFwulxo2bKjg4GBFRUX57EN0dLT7Mb7mb7jhBkVHR%2BvMmTMqKyvzmJPknr/S2tVp37592r9/v8fl%2B4t8nXNBQYH7//uVenLxMZfOnzp1yn3OV9pXFa1d3S7eRv7xd76NGzeWMUYXLlyotX3Zt2%2Bfjh8/runTp6tBgwaKjY3V1KlT9ec//9nn10Bt6cuP%2BfO1oiprV7fy8nLNnj1bO3fu1CuvvKLbbrvNPefPvXGltQMNASvAxMfH68SJE%2B4fw5Z%2BuLXWvHlz1a9f34%2BVXZ1PPvlEs2bN0jPPPKN7773XPR4fH69Dhw6ptLTUPZaRkaEOHTq45y99P8fF%2BbCwMLVo0UJZWVnuucLCQn399ddq37692rRpI2OMvvjiC4/nRkREqFmzZhWuXZ02bdqkvLw89enTRwkJCRo2bJgkKSEhQS1btvQ658zMTI%2Be/Picy8rKdODAAXXo0EFNmjRRZGSkx/w//vEPnT9/XvHx8RXuq3bt2l1x7erWqFEjhYeH6%2BDBg%2B6x7OxsXXfddUpKSqq1fSkrK1N5ebnHd/znz5%2BXJCUmJtbavvyYP18rqrJ2dVu4cKG%2B/PJLvfLKK2rSpInHnD/3xpXWDjgOvJEelo0YMcLMmTPHnD592hw%2BfNgkJyebdevW%2Bbusn%2BzChQvmrrvuMq%2B%2B%2BqrX3Llz50yfPn3Ms88%2Ba86ePWs%2B/fRT07VrV7Nr1y5jjDGHDh0y7dq1M7t27TIlJSVmw4YNplOnTiYnJ8cY88NPrfTu3dv948/z5883w4cPdx8/LS3NjB8/3uTl5ZkTJ06Y4cOHm8WLF1dq7erkcrnMiRMn3H/2799vWrZsaU6cOGGys7NNp06dzGuvvWZKSkrM7t27Tfv27d0/4bRnzx7TpUsXs3//fnP27FmzYsUKk5SUZIqLi40xP/x009ChQ823335r8vPzzaRJk8yUKVPca19pX33//fdXXNsJCxcuNH379jXHjh0z33//vRk5cqSZPXt2hbXV5L7k5%2Beb7t27m3//9383Z8%2BeNfn5%2BWby5MnmV7/6Va3si6%2BfmPPna0VV166unnz00UemW7duHj/B/WP%2B3BsVrR1ICFgB6MSJE2b8%2BPGmffv2JjEx0Tz77LNWPjfFaX//%2B99Ny5YtTXx8vNef48ePm0OHDplRo0aZ%2BPh407t3b7N%2B/XqP52/fvt3069fPtG3b1txzzz3mb3/7m3uuvLzcPPPMM%2Bb222837du3NxMmTHB/9owxxhQWFppp06aZjh07mm7dupkFCxaYc%2BfOuecrWtsp33zzjftjGowx5m9/%2B5sZMmSIadu2renXr5/XxxasX7/eJCUlmfj4eDN69Ghz6NAh99y5c%2BfME088Ybp162Y6depkpk%2BfbgoLC93zFe2ritaubj%2Buv2PHjmbWrFnmzJkzlaqtJvclIyPDPPDAA6Zr164mMTHRpKWlmZMnT1aqtprSl4uvG61btzatW7d2//sif75WVGXt6urJY4895jF28c%2BDDz7ofr4/98aV1g4kQcbUkHeTAQAAXCN4DxYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWPa/i4wPcC0lIpEAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common1765720055340471902”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>421944.9278146803</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>69775.086</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>288723.5276190477</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>283922.3532824428</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>248673.32219178084</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>239231.12197474172</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>302989.21151956334</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>239661.6951724138</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>91224.71294117646</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2862)</td> <td class=”number”>2862</td> <td class=”number”>89.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>317</td> <td class=”number”>9.9%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme1765720055340471902”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>15941.7</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16887.192857142858</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17750.12888888889</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17981.633898305085</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>751518.2690252708</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>829337.1494594592</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>882038.2474999998</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>931221.5129670331</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1253320.965</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_REDEMP_SNAPS16”>REDEMP_SNAPS16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2937</td>

</tr> <tr>

<th>Unique (%)</th> <td>91.5%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>7.8%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>250</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>196710</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>827700</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.8%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram7443623365229387548”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives7443623365229387548,#minihistogram7443623365229387548”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives7443623365229387548”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles7443623365229387548”

aria-controls=”quantiles7443623365229387548” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram7443623365229387548” aria-controls=”histogram7443623365229387548”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common7443623365229387548” aria-controls=”common7443623365229387548”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme7443623365229387548” aria-controls=”extreme7443623365229387548”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles7443623365229387548”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>42184</td>

</tr> <tr>

<th>Q1</th> <td>121710</td>

</tr> <tr>

<th>Median</th> <td>194520</td>

</tr> <tr>

<th>Q3</th> <td>259220</td>

</tr> <tr>

<th>95-th percentile</th> <td>368550</td>

</tr> <tr>

<th>Maximum</th> <td>827700</td>

</tr> <tr>

<th>Range</th> <td>827700</td>

</tr> <tr>

<th>Interquartile range</th> <td>137510</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>103310</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.52517</td>

</tr> <tr>

<th>Kurtosis</th> <td>1.4147</td>

</tr> <tr>

<th>Mean</th> <td>196710</td>

</tr> <tr>

<th>MAD</th> <td>81122</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.67408</td>

</tr> <tr>

<th>Sum</th> <td>582260000</td>

</tr> <tr>

<th>Variance</th> <td>10672000000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram7443623365229387548”>

<img 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yO40KDb2mSbYL3xIR0d7bJfg8jqXmjx75BvrkhYD1tZiYGMXExOjf/u3f9Nprr2np0qXauHGjEhMT9bOf/Ux9%2BvS54m0bYyRJ06dPd4WpWbNm6b777lNiYuJlP/9KpaWlKTk52W0sMLCdHI6qq9ruhQIC/BUaeo0qKqpVV1dvddvwPbZfX60Jx1LzR498Q3Psk7d%2B%2BfRawDp//rz%2B9Kc/6eWXX9Z7772nLl26aNasWSopKdG0adO0bNkyjR8//oq2fd1110mSQkNDXWOdO3eWMUbnz5%2BX0%2Bl0e7zD4VBkZKQkKSIiosG80%2BlU9%2B7dL3v9qKioBpcDS0srVVvbNC%2B2urr6Jts2fAevgavHsdT80SPfQJ%2B8ELCOHj2ql156Sa%2B88oqqqqo0atQoPffcc%2Brfv7/rMQMHDtTSpUuvOGB16tRJISEhOnTokGJiYiRJhYWFatOmjZKSkrRlyxa3x%2Bfn5ysuLk6SFBsbq4KCAk2cOFGSVFdXp4MHDyo1NfWKagEAAK2Px%2B9CGzt2rPbu3asZM2borbfe0qpVq9zClSQlJSWpvLz8itcIDAxUamqqfvOb3%2Bj//u//VFZWpqefflrjx4/XxIkTVVhYqJycHJ09e1b79u3Tvn37NGXKFElSenq6XnnlFX300Ueqrq7WunXr1LZtWw0bNuxqdhsAALQiHj%2BDtWnTJg0aNKjRx3388cdXtU5mZqbOnTunyZMn6/z58xo1apQWL16s9u3ba/369Xrssce0bNkyde7cWatWrdLNN98sSRo6dKjmzZunOXPmqKysTL1799aGDRsUHBx8VfUAAIDWw89c7R3d39GpU6e0YMECpaam6rbbbpMk/f73v9e7776rVatWKTw83JPleExpaaX1bQYG%2Bisior0cjiqfuNZ9%2BxPveruEFm3HnMHeLsFn%2Bdqx1BrRI9/QHPt0/fUdvLKuxy8RrlixQpWVlerWrZtrbNiwYaqvr9fKlSs9XQ4AAIB1Hr9E%2BM4772jbtm2KiIhwjXXp0kWrV6/WuHHjPF0OAACAdR4/g1VTU6OgoKCGhfj7q7q62tPlAAAAWOfxM1gDBw7UypUrlZmZ6frE9C%2B//FK/%2BtWvGrybEMDl86V73LhfDEBL5/GA9fDDD%2Buee%2B7RLbfcopCQENXX16uqqko33HCDnn/%2BeU%2BXAwAAYJ3HA9YNN9ygV199VW%2B99ZY%2B%2B%2Bwz%2Bfv7q2vXrhoyZIgCAgI8XQ4AAIB1XvmqnLZt27o%2BogEAAKCl8XjA%2Bvzzz7VmzRr9/e9/V01NTYP5N99809MlAQAAWOWVe7BKSko0ZMgQtWvXztPLAwAANDmPB6z8/Hy9%2BeabioyM9PTSAAAAHuHxz8G69tprOXMFAABaNI8HrBkzZmjt2rXy8FcgAgAAeIzHLxG%2B9dZb%2BvDDD/Xyyy/rX/7lX%2BTv757xXnzxRU%2BXBAAAYJXHA1ZISIiGDh3q6WUBAAA8xuMBa8WKFZ5eEgAAwKM8fg%2BWJH366afKysrSL37xC9fYgQMHvFEKAACAdR4PWLm5uZowYYJ27dql7du3S/rqw0fvvvtuPmQUAAC0CB4PWI8//rh%2B/vOfa9u2bfLz85P01fcTrly5Uk8//bSnywEAALDO4wHrb3/7m9LT0yXJFbAkafTo0Tp69KinywEAALDO4wGrQ4cOF/0OwpKSErVt29bT5QAAAFjn8YAVHx%2Bv5cuX6/Tp066xY8eOacGCBbrllls8XQ4AAIB1Hv%2BYhl/84hf6yU9%2BooSEBNXV1Sk%2BPl7V1dXq3r27Vq5c6elyAAAArPN4wOrUqZO2b9%2Buffv26dixYwoODlbXrl01ePBgt3uyAAAAfJXHA5YktWnTRrfddps3lgYAAGhyHg9YycnJlzxTxWdhAQAAX%2BfxgDVmzBi3gFVXV6djx44pLy9PP/nJTzxdDgAAgHUeD1jz58%2B/6PjOnTv15z//2cPVAAAA2OeV7yK8mNtuu02vvvqqt8sAAAC4as0mYB08eFDGGG%2BXAQAAcNU8folw6tSpDcaqq6t19OhRjRw50tPlAAAAWOfxgNWlS5cG7yIMCgpSamqqJk%2Be7OlyAAAArPN4wOLT2gEAQEvn8YD1yiuvXPZj77zzziasBAAAoGl4PGAtWrRI9fX1DW5o9/Pzcxvz8/MjYAEAAJ/k8YD1zDPPaOPGjZo5c6Z69uwpY4wOHz6s3/72t7rrrruUkJDg6ZIAAACs8so9WBs2bFDHjh1dYwMGDNANN9yge%2B%2B9V9u3b/d0SQAAAFZ5/HOwjh8/rrCwsAbjoaGhKiws9HQ5AAAA1nk8YHXu3FkrV66Uw%2BFwjVVUVGjNmjW68cYbPV0OAACAdR6/RPjwww8rMzNT2dnZat%2B%2Bvfz9/XX69GkFBwfr6aef9nQ5AAAA1nk8YA0ZMkR79%2B7Vvn37VFxcLGOMOnbsqFtvvVUdOnTwdDkAAADWeTxgSdI111yjESNGqLi4WDfccIM3SgAAAGgyHr8Hq6amRgsWLFC/fv10%2B%2B23S/rqHqzp06eroqLC0%2BUAAABY5/GAtWrVKh06dEirV6%2BWv/83y9fV1Wn16tWeLgcAAMA6jwesnTt36qmnntLo0aNdX/ocGhqqFStWaNeuXZ4uBwAAwDqPB6yqqip16dKlwXhkZKTOnDnj6XIAAACs83jAuvHGG/XnP/9Zkty%2Be/D111/XP//zP3u6HAAAAOs8/i7CH/3oR5o1a5YmTZqk%2Bvp6Pfvss8rPz9fOnTu1aNEiT5cDAABgnccDVlpamgIDA7V582YFBAToN7/5jbp27arVq1dr9OjRni7H5w1Y9Lq3SwAAABfweMAqLy/XpEmTNGnSJE8vDQAA4BEevwdrxIgRbvdeAQAAtDQeD1gJCQnasWOHp5cFAADwGI9fIvynf/on/cd//Ic2bNigG2%2B8UW3atHGbX7NmjadLAgAAsMrjZ7COHDmi733ve%2BrQoYMcDodKSkrc/jSF5cuXq2fPnq6/5%2BbmKjU1VfHx8Ro7dqy2bt3q9vhNmzZp1KhRio%2BPV3p6uvLz85ukLgAA0DJ57AzW3Llz9fjjj%2Bv55593jT399NPKyMho0nUPHTqkLVu2uP5eUlKiBx98UIsWLdL48eP1wQcf6IEHHlDXrl3Vu3dv7d69W1lZWXrmmWfUs2dPbdq0STNnztSuXbvUrl27Jq0VAAC0DB47g7V79%2B4GYxs2bGjSNevr6/XII49o2rRprrFt27apS5cuSk1NVVBQkBITE5WcnKycnBxJUnZ2tlJSUhQXF6fg4GBNnz5dkrRnz54mrRUAALQcHgtYF3vnYFO/m/DFF19UUFCQxo8f7xorKChQdHS02%2BOio6NdlwEvnPf391evXr2Ul5fXpLUCAICWw2OXCL/%2BYufGxmw5efKksrKy3C5JSpLT6VTHjh3dxsLDw%2BVwOFzzYWFhbvNhYWGu%2BctRUlKi0tJSt7HAwHaKior6LrvQqIAAj99CB1gRGNi8XrtfH0scU80XPfIN9OkbHn8XoaesWLFCKSkp6tatm06cOPGdnnu1Z9ays7O1du1at7GMjAzNnj37qrYLtBQREe29XcJFhYZe4%2B0S0Ah65BvoUwsNWLm5uTpw4IC2b9/eYC4iIkJOp9NtzOFwKDIy8lvnnU6nunfvftnrp6WlKTk52W0sMLCdHI6qy97G5eA3BPgq28fC1QoI8Fdo6DWqqKhWXV29t8vBRdAj39Ac%2B%2BStX%2Bg8FrDOnz%2BvzMzMRsdsfA7W1q1bVVZWpuHDh0v65oxUQkKC7rnnngbBKz8/X3FxcZKk2NhYFRQUaOLEiZKkuro6HTx4UKmpqZe9flRUVIPLgaWllaqtbR4vNsDbmuuxUFdX32xrw1fokW%2BgTx4MWP3792/wOVcXG7Nh4cKF%2BtnPfub6e3FxsdLS0rRlyxbV19dr/fr1ysnJ0YQJE/Tee%2B9p3759ys7OliSlp6dr3rx5GjdunHr27Knf/e53atu2rYYNG2a9TgAA0DJ5LGBdeLN5UwoLC3O7Ub22tlaS1KlTJ0nS%2BvXr9dhjj2nZsmXq3LmzVq1apZtvvlmSNHToUM2bN09z5sxRWVmZevfurQ0bNig4ONhj9QMAAN/mZ/jmZY8oLa20vs3AQH/9cPXb1rcLNLUdcwZ7uwQ3gYH%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%2B%2BvkSNHauPGjW7Pfe211zR%2B/Hj169dPKSkpeuedd7yxCwAAwEe12IA1c%2BZMhYaGavfu3Xr55Zf197//Xb/61a9UU1OjGTNm6Ac/%2BIHefvttPf7441q/fr127dol6avwtWDBAs2fP1/vvfeepk2bpoceekjFxcVe3iMAAOArWmTAqqioUGxsrDIzM9W%2BfXt16tRJEydO1Pvvv6%2B9e/fq/PnzeuCBB9SuXTvFxMRo8uTJys7OliTl5OQoKSlJSUlJCgoK0oQJE9SjRw9t3brVy3sFAAB8RaC3C2gKoaGhWrFihdtYUVGRoqKiVFBQoJ49eyogIMA1Fx0drZycHElSQUGBkpKS3J4bHR2tvLy8y16/pKREpaWlbmOBge0UFRX1XXflkgICWmQ%2BRisQGNi8XrtfH0scU80XPfIN9OkbLTJgXSgvL0%2BbN2/WunXrtGPHDoWGhrrNh4eHy%2Bl0qr6%2BXk6nU2FhYW7zYWFhOnLkyGWvl52drbVr17qNZWRkaPbs2Ve%2BE0ALEhHR3tslXFRo6DXeLgGNoEe%2BgT61goD1wQcf6IEHHlBmZqYSExO1Y8eOiz7Oz8/P9b%2BNMVe1ZlpampKTk93GAgPbyeGouqrtXojfEOCrbB8LVysgwF%2BhodeooqJadXX13i4HF0GPfENz7JO3fqFr0QFr9%2B7d%2BvnPf64lS5bozjvvlCRFRkbq%2BPHjbo9zOp0KDw%2BXv7%2B/IiIi5HQ6G8xHRkZe9rpRUVENLgeWllaqtrZ5vNgAb/vh6re9XcJ3smPOYG%2BXgP%2Bvrq6ef0t9AH1qoTe5S9KHH36oBQsW6Mknn3SFK0mKjY3V4cOHVVtb6xrLy8tTXFycaz4/P99tW/84DwAA0JgWGbBqa2u1ePFizZ8/X0OGDHGbS0pKUkhIiNatW6fq6mp9/PHHeumll5Seni5JmjJlivbv36%2B9e/fq7Nmzeumll3T8%2BHFNmDDBG7sCAAB8kJ%2B52huOmqH3339fP/7xj9W2bdsGc6%2B//rqqqqr0yCOPKD8/X9ddd53uu%2B8%2B/ehHP3I9ZteuXVqzZo0KCwvVrVs3LVq0SAMHDryqmkpLK6/q%2BRcTGOjvc5daAF/EJULvCwz0V0REezkcVa3%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%2BNDYAAAMBElEQVRjxoxRZmam/P3JowCaP1/67kS%2BNxEtFQHrImbNmqWYmBi98cYbKisr04wZM3Tdddfppz/9qbdLAwAAPoCAdYG8vDx98sknevbZZ9WhQwd16NBB06ZN03PPPUfAAgDLfOlsm8QZN1w%2BAtYFCgoK1LlzZ4WFhbnGYmJidOzYMZ0%2BfVohISGNbqOkpESlpaVuY4GB7RQVFWW11oAALlkCgCf5WiD0JX%2Baf6u3S7CKgHUBp9Op0NBQt7Gvw5bD4bisgJWdna21a9e6jT300EOaNWuWvUL1VZD7Sae/Ky0tzXp4gx0lJSXKzs6mR80cfWr%2B6JFvoE/f4BTIRRhjrur5aWlpevnll93%2BpKWlWaruG6WlpVq7dm2Ds2VoPuiRb6BPzR898g306RucwbpAZGSknE6n25jT6ZSfn58iIyMvaxtRUVGtPrkDANCacQbrArGxsSoqKlJ5eblrLC8vT926dVP79u29WBkAAPAVBKwLREdHq3fv3lqzZo1Onz6to0eP6tlnn1V6erq3SwMAAD4iYOnSpUu9XURzc%2Butt2r79u169NFH9eqrryo1NVX33nuv/Pz8vF1aA%2B3bt9egQYM4u9aM0SPfQJ%2BaP3rkG%2BjTV/zM1d7RDQAAADdcIgQAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIDlgwoLC3X//fcrISFBw4cP16pVq1RfX%2B/tslqEwsJCZWRkKCEhQYmJiVq4cKEqKiokSYcOHdJdd92l/v37a%2BTIkdq4caPbc1977TWNHz9e/fr1U0pKit555x3XXH19vR5//HGNGDFCAwcO1L333qvPP//cNe90OjVnzhwlJiZqyJAhWrRokWpqalzzja3dWi1fvlw9e/Z0/T03N1epqamKj4/X2LFjtXXrVrfHb9q0SaNGjVJ8fLzS09OVn5/vmjt79qz%2B/d//XUOHDlVCQoJmz54th8Phmm/suGts7dZo3bp1GjJkiPr27atp06bpxIkTkuhTc3Hw4EHdfffdGjBggAYPHqz58%2BervLxcEj2ywsDnTJw40SxevNhUVFSYY8eOmZEjR5qNGzd6u6wWYdy4cWbhwoXm9OnTpqioyKSkpJiHH37YVFdXm1tvvdVkZWWZqqoqk5%2BfbwYNGmR27txpjDHm4MGDJjY21uzdu9fU1NSYLVu2mLi4OFNUVGSMMWbTpk1m%2BPDh5siRI6aystL88pe/NOPHjzf19fXGGGMeeughc//995uysjJTXFxs0tLSzKOPPmqMMY2u3VodPHjQDBo0yPTo0cMYY8yXX35p%2Bvbta3JyckxNTY159913TZ8%2Bfcxf//pXY4wxb775phkwYID56KOPTHV1tVm/fr0ZPHiwqaqqMsYYs2LFCpOSkmK%2B%2BOIL43A4zEMPPWRmzJjhWu9Sx11ja7dGmzdvNqNHjzZHjx41lZWV5tFHHzWPPvoofWomzp8/bwYPHmzWrFljzp49a8rLy81Pf/pTM2vWLHpkCQHLx/z1r381vXr1Mk6n0zX2hz/8wYwaNcqLVbUMp06dMgsXLjSlpaWuseeff96MHDnS7Nixw/zgBz8wtbW1rrlVq1aZe%2B65xxhjzLJly0xGRobb9iZPnmzWr19vjDFm7Nix5rnnnnPNVVZWmujoaHPgwAFTWlpqbr75ZnPo0CHX/L59%2B0zfvn3NuXPnGl27NaqrqzOTJ082//Vf/%2BUKWM8884y588473R43Z84cs2TJEmOMMffff79Zvny52zYGDx5stm/fbs6fP2/69%2B9v3njjDdf8kSNHTM%2BePU1xcXGjx11ja7dGycnJF/0lgD41D1988YXp0aOHOXLkiGvsD3/4g7ntttvokSVcIvQxBQUF6ty5s8LCwlxjMTExOnbsmE6fPu3FynxfaGioVqxYoeuuu841VlRUpKioKBUUFKhnz54KCAhwzUVHR7tOixcUFCg6Otpte9HR0crLy1NNTY2OHDniNh8SEqKbbrpJeXl5OnTokAICAtwudcXExOjMmTP69NNPG127NXrxxRcVFBSk8ePHu8a%2BrQff1iN/f3/16tVLeXl5%2Buyzz1RZWamYmBjX/Pe//30FBweroKCg0eOusbVbmy%2B//FInTpzQqVOnNGbMGNdlovLycvrUTHTs2FG9evVSdna2qqqqVFZWpl27dmnYsGH0yBIClo9xOp0KDQ11G/v6hfqP17hx9fLy8rR582Y98MADF/25h4eHy%2Bl0qr6%2BXk6n0%2B0fDOmrvjgcDp06dUrGmG%2BddzqdCgkJkZ%2Bfn9ucJNf8pdZubU6ePKmsrCw98sgjbuPf9nP6%2Bri4VI%2BcTqckNXh%2BaGjot/bgcnrUWo/J4uJiSdLrr7%2BuZ599Vlu2bFFxcbEWL15Mn5oJf39/ZWVl6c0331R8fLwSExNVW1urzMxMemQJAcsHGWO8XUKL98EHH%2Bjee%2B9VZmamEhMTv/Vx/xiKGuvLpeavpKf/uHZrsmLFCqWkpKhbt27f%2Bbme7lFr9fXPavr06erYsaM6deqkWbNmaffu3d/p%2BVcyT58uz7lz5zRz5kyNHj1a77//vt566y116NBB8%2BfPv6zn06PGEbB8TGRkpOs3hK85nU75%2BfkpMjLSS1W1LLt379b999%2Bvhx9%2BWHfffbekr37uF/4G5XQ6FR4eLn9/f0VERFy0L5GRka7HXGz%2B2muvVWRkpE6fPq26ujq3OUmu%2BUut3Zrk5ubqwIEDysjIaDB3sR44HA7XcXGpHn39mAvnT5065erBpY67xtZubb6%2BzP6PZyI6d%2B4sY4zOnz9Pn5qB3NxcnThxQvPmzVOHDh3UsWNHzZ49W3/6058u%2Bu8VPfruWte/zi1AbGysioqKXG%2Bllb66lNWtWze1b9/ei5W1DB9%2B%2BKEWLFigJ598UnfeeadrPDY2VocPH1Ztba1rLC8vT3Fxca75C%2B8R%2BHo%2BKChI3bt3V0FBgWuuoqJCn332mfr06aNevXrJGKNPPvnE7bmhoaHq2rVro2u3Jlu3blVZWZmGDx%2BuhIQEpaSkSJISEhLUo0ePBj3Iz89369E/9qCurk4HDx5UXFycbrjhBoWFhbnN/%2B1vf9O5c%2BcUGxvb6HHXu3fvS67d2nTq1EkhISE6dOiQa6ywsFBt2rRRUlISfWoG6urqVF9f73Y26dy5c5KkxMREemSDp%2B%2Bqx9WbPHmyefjhh01lZaU5cuSISU5ONps3b/Z2WT7v/Pnz5vbbbzcvvvhig7mzZ8%2Ba4cOHm6eeesqcOXPGfPTRR2bAgAFmz549xhhjDh8%2BbHr37m327NljampqTE5OjunXr58pKSkxxnz1Lplhw4a5PqZhyZIlZtKkSa7tz5kzx0yfPt2UlZWZoqIiM2nSJLNy5crLWrs1cTqdpqioyPXnwIEDpkePHqaoqMgUFhaafv36mf/%2B7/82NTU1Zu/evaZPnz6ud2fu27fP9O/f3xw4cMCcOXPGZGVlmaSkJFNdXW2M%2BeqdmRMnTjRffPGFKS8vNzNmzDCzZs1yrX2p4%2B7kyZOXXLs1Wr58uRkxYoQ5fvy4OXnypElLSzMLFy5s9GdFnzyjvLzcDBo0yPznf/6nOXPmjCkvLzczZ840P/7xj%2BmRJQQsH1RUVGSmT59u%2BvTpYxITE81TTz3l%2BjwlXLn//d//NT169DCxsbEN/pw4ccIcPnzYTJ061cTGxpphw4aZF154we35O3fuNCNHjjQxMTHmjjvuMH/5y19cc/X19ebJJ580t9xyi%2BnTp4%2B57777XJ%2BRZYwxFRUVZu7cuaZv375m4MCBZtmyZebs2bOu%2BcbWbq0%2B//xz18c0GGPMX/7yFzNhwgQTExNjRo4c2eBjAl544QWTlJRkYmNjTXp6ujl8%2BLBr7uzZs2bp0qVm4MCBpl%2B/fmbevHmmoqLCNd/YcdfY2q3NP/48%2B/btaxYsWGBOnz5tjKFPzUVeXp656667zIABA0xiYqKZM2eOKS4uNsbQIxv8jGkld5sBAAB4CPdgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBl/w/qby%2BqhSvb6wAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common7443623365229387548”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>25</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>205756.5056719469</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>80778.1276119403</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>140499.85419558358</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>191150.0893670886</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>308227.58682352945</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>262063.49292775663</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>388320.55225806456</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>133246.84166666667</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>92521.90000000001</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2926)</td> <td class=”number”>2926</td> <td class=”number”>91.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>250</td> <td class=”number”>7.8%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme7443623365229387548”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>25</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7957.043389830507</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10570.650000000001</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11609.364590163937</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13764.03777777778</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>661416.1338237887</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>710641.882</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>721650.9440000001</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>765161.8561464157</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>827697.7405714287</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_REDEMP_WICS08”>REDEMP_WICS08<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2075</td>

</tr> <tr>

<th>Unique (%)</th> <td>64.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>35.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>1136</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>116700</td>

</tr> <tr>

<th>Minimum</th> <td>3464.5</td>

</tr> <tr>

<th>Maximum</th> <td>507450</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-556192277942373539”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-556192277942373539,#minihistogram-556192277942373539”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-556192277942373539”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-556192277942373539”

aria-controls=”quantiles-556192277942373539” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-556192277942373539” aria-controls=”histogram-556192277942373539”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-556192277942373539” aria-controls=”common-556192277942373539”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-556192277942373539” aria-controls=”extreme-556192277942373539”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-556192277942373539”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>3464.5</td>

</tr> <tr>

<th>5-th percentile</th> <td>32315</td>

</tr> <tr>

<th>Q1</th> <td>69789</td>

</tr> <tr>

<th>Median</th> <td>104240</td>

</tr> <tr>

<th>Q3</th> <td>148980</td>

</tr> <tr>

<th>95-th percentile</th> <td>240160</td>

</tr> <tr>

<th>Maximum</th> <td>507450</td>

</tr> <tr>

<th>Range</th> <td>503990</td>

</tr> <tr>

<th>Interquartile range</th> <td>79195</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>67481</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.57823</td>

</tr> <tr>

<th>Kurtosis</th> <td>3.2647</td>

</tr> <tr>

<th>Mean</th> <td>116700</td>

</tr> <tr>

<th>MAD</th> <td>50675</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.3851</td>

</tr> <tr>

<th>Sum</th> <td>242040000</td>

</tr> <tr>

<th>Variance</th> <td>4553600000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-556192277942373539”>

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w8KAAAEjZC8RdirVy%2BtW7dOq1at8gakKVOmaMGCBYqMjNTatWv16KOPaunSperdu7dWrlypgQMHSpLGjBmje%2B65R3l5eaqurlZSUpLWrVunzp07O3xUAAAgWISZ9r6iG21SVXXcyjouV7hiYrrK7a4NuvvXE594y%2BkSQtq2vNFOl3BGwXzOBjp66x/01X%2Bc6G3Pnt0vyn5OF5K3CAEAAJxEwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYFnABKz09Xfn5%2BTpy5IjTpQAAAFyQgAtYU6dO1SuvvKLvfe97mjVrlnbs2KHGxkanywIAAGizgAtYubm5euWVV/TSSy%2Bpf//%2BWrZsmdLS0rRy5UodOnTI6fIAAABaFXAB65TBgwdr4cKF2rVrlxYvXqyXXnpJkyZN0syZM/Xee%2B85XR4AAMBZBWzAOnnypF555RXNnj1bCxcuVK9evfTAAw9o0KBBuv3227VlyxanSwQAADgjl9MFnO7gwYN6%2BeWXtXHjRtXW1mrChAlav369RowY4d1m5MiRevjhh5WRkeFgpQAAAGcWcAFr8uTJ6tu3r%2BbMmaObb75ZPXr0aLFNWlqajh075kB1AAAArQu4gFVQUKBRo0a1ut277757EaoBAAA4fwH3Gqz4%2BHjNnTtXr732mnfsV7/6lWbPni2Px%2BNgZQAAAG0TcAFr%2BfLlOn78uPr16%2BcdGzt2rJqbm7VixQoHKwMAAGibgLtF%2BOabb2rLli2KiYnxjvXp00ePPfaYfvCDHzhYGQAAQNsE3BWs%2Bvp6RUZGthgPDw9XXV2dAxUBAACcn4ALWCNHjtSKFSv0%2Beefe8c%2B/fRTLV261OejGgAAAAJVwN0iXLx4sX784x/ruuuuU7du3dTc3Kza2lpdeeWV%2BvWvf%2B10eQAAAK0KuIB15ZVX6ve//73%2B8Ic/6KOPPlJ4eLj69u2r66%2B/XhEREU6XBwAA0KqAC1iS1KlTJ33ve99zugwAAIALEnAB6/Dhw1q1apX%2B8Y9/qL6%2BvsX866%2B/7kBVAAAAbRdwAWvx4sWqrKzU9ddfry5dujhdDgAAwHkLuIBVVlam119/XbGxsU6XAgAAcEEC7mMaLr30Uq5cAQCAoBZwAWvOnDnKz8%2BXMcbpUgAAAC5IwN0i/MMf/qC//e1v%2Bu1vf6srrrhC4eG%2BGfDFF190qDIAAIC2CbiA1a1bN40ZM8bqmsuWLdP69eu1f/9%2BSVJJSYlWrVqlDz74QN/85jc1Z84c3XTTTd7tCwoK9MILL6iqqkrx8fFasmSJEhMTrdYEAABCV8AFrOXLl1tdb9%2B%2Bfdq0aZP375WVlbrzzju1ZMkSZWRk6J133tG8efPUt29fJSUlaefOnVq9erWeffZZxcfHq6CgQHPnztWOHTt4bRgAAGiTgHsNliR98MEHWr16tR544AHv2O7du897nebmZv3sZz/T7bff7h3bsmWL%2BvTpo6ysLEVGRio1NVXp6ekqKiqSJBUWFiozM1PJycnq3LmzZs2aJUnatWtX%2Bw4KAAB0GAF3BaukpESzZ89W37599eGHH2r58uU6fPiwbr31Vj3xxBO64YYb2rzWiy%2B%2BqMjISGVkZOiJJ56QJJWXlyshIcFnu4SEBG3bts07P2nSJO9ceHi4Bg0apNLSUk2ePLlN%2B62srFRVVZXPmMvVRXFxcW2u/WwiIsJ9vgKnuFyBeU5wzvoPvfUP%2Buo/Ham3ARewHn/8cf3kJz/RbbfdpiFDhkj66vcTrlixQk899VSbA9Znn32m1atXt/gF0R6PR7169fIZ69Gjh9xut3c%2BOjraZz46Oto73xaFhYXKz8/3GcvNzdX8%2BfPbvEZroqIusbYWQkNMTFenSzgnzln/obf%2BQV/9pyP0NuAC1t///ndt2LBBkhQWFuYdv/HGG7V48eI2r7N8%2BXJlZmaqX79%2B%2Bvjjj8%2BrhvZ%2BRER2drbS09N9xlyuLnK7a9u1rvRV6o%2BKukQ1NXVqampu93oIHTbOL3/gnPUfeusf9NV/nOitUz98BlzA6t69u%2Brr69WpUyef8crKyhZjZ1NSUqLdu3dr69atLeZiYmLk8Xh8xtxut/eT48807/F41L9//zYfQ1xcXIvbgVVVx9XYaO9kampqtroegl%2Bgnw%2Bcs/5Db/2DvvpPR%2BhtwN0EHT58uJYtW6YTJ054xw4dOqSFCxfquuuua9MamzdvVnV1tcaNG6eUlBRlZmZKklJSUjRgwACVlZX5bF9WVqbk5GRJUmJiosrLy71zTU1N2rt3r3ceAACgNQEXsB544AHt3r1bKSkpamho0PDhwzVp0iR5PB4tWrSoTWssWrRI27dv16ZNm7Rp0yatW7dOkrRp0yZlZGSooqJCRUVFamhoUHFxsYqLizV9%2BnRJUk5OjjZu3Kg9e/aorq5Oa9asUadOnTR27Fh/HTIAAAgxAXeL8PLLL9fWrVtVXFysQ4cOqXPnzurbt69Gjx7t85qsc4mOjvZ5oXpjY6N3bUlau3atHn30US1dulS9e/fWypUrNXDgQEnSmDFjdM899ygvL0/V1dVKSkrSunXr1LlzZ8tHCgAAQlWY4Zf%2BXRRVVcetrONyhSsmpqvc7tqgu3898Ym3nC4hpG3LG%2B10CWcUzOdsoKO3/kFf/ceJ3vbs2f2i7Od0AXcFKz09/ZxXql5//fWLWA0AAMD5C7iANWnSJJ%2BA1dTUpEOHDqm0tFS33Xabg5UBAAC0TcAFrPvuu%2B%2BM49u3b9ef/vSni1wNAADA%2BQu4dxGezfe%2B9z39/ve/d7oMAACAVgVNwNq7d2%2B7P2EdAADgYgi4W4QzZsxoMVZXV6eDBw9q/PjxDlQEAABwfgIuYPXp06fFuwgjIyOVlZWladOmOVQVAABA2wVcwFqxYoXTJQAAALRLwAWsjRs3tnnbm2%2B%2B2Y%2BVAAAAXJiAC1hLlixRc3Nzixe0h4WF%2BYyFhYURsAAAQEAKuID17LPP6rnnntPcuXMVHx8vY4z279%2BvZ555RrfccotSUlKcLhEAAOCcAi5grVixQuvWrVOvXr28Y9dcc42uvPJKzZw5U1u3bnWwOgAAgNYF3Odgffjhh4qOjm4xHhUVpYqKCgcqAgAAOD8BF7B69%2B6tFStWyO12e8dqamq0atUqffvb33awMgAAgLYJuFuEixcv1r333qvCwkJ17dpV4eHhOnHihDp37qynnnrK6fIAAABaFXAB6/rrr9cbb7yh4uJiHT16VMYY9erVS9/97nfVvXt3p8sDAABoVcAFLEm65JJLdMMNN%2Bjo0aO68sornS4HAADgvATca7Dq6%2Bu1cOFCDRs2TBMnTpT01WuwZs2apZqaGoerAwAAaF3ABayVK1dq3759euyxxxQe/n/lNTU16bHHHnOwMgAAgLYJuIC1fft2/fKXv9SNN97o/aXPUVFRWr58uXbs2OFwdQAAAK0LuIBVW1urPn36tBiPjY3VF198cfELAgAAOE8BF7C%2B/e1v609/%2BpMk%2BfzuwVdffVXf%2Bta3nCoLAACgzQLuXYQ/%2BtGPdPfdd2vq1Klqbm7W888/r7KyMm3fvl1LlixxujwAAIBWBVzAys7Olsvl0oYNGxQREaGnn35affv21WOPPaYbb7zR6fIAAABaFXAB69ixY5o6daqmTp3qdCkAAAAXJOBeg3XDDTf4vPYKAAAg2ARcwEpJSdG2bducLgMAAOCCBdwtwm9%2B85v6j//4D61bt07f/va39Y1vfMNnftWqVQ5VBgAA0DYBF7AOHDig73znO5Ikt9vtcDUAAADnL2AC1oIFC/T444/r17/%2BtXfsqaeeUm5uroNVAQAAnL%2BAeQ3Wzp07W4ytW7fugtd7//33ddttt2nEiBFKTU1VXl6eqqqqJEklJSXKysrS8OHDNXnyZG3evNnnsQUFBZowYYKGDx%2BunJwclZWVXXAdAACg4wmYgHWmdw5e6LsJv/zyS/34xz/WqFGjVFJSoq1bt6q6uloPP/ywKisrdeedd2rGjBkqKSnRkiVL9NBDD6m0tFTSV0Fv9erV%2BsUvfqG3335b48aN09y5c/k1PQAAoM0C5hbhqV/s3NpYW9TV1WnBggWaMmWKXC6XYmNj9f3vf18bNmzQli1b1KdPH2VlZUmSUlNTlZ6erqKiIiUlJamwsFCZmZlKTk6WJM2aNUsFBQXatWuXJk%2BefOEH6CcTn3jL6RIAAMBpAuYKlk3R0dGaNm2aXK6v8uMHH3yg3/3ud5o4caLKy8uVkJDgs31CQoL3NuDp8%2BHh4Ro0aJD3ChcAAEBrAuYKlj9UVFRowoQJamxs1PTp0zV//nzNnj1bvXr18tmuR48e3ncsejweRUdH%2B8xHR0ef1zsaKysrva/3OsXl6qK4uLgLPJL/ExER7vMVOMXlCsxzgnPWf%2Bitf9BX/%2BlIvQ2YgHXy5Ende%2B%2B9rY6dz%2Bdg9e7dW6WlpfrnP/%2Bpn/70p7r//vvb9Lj2fpJ8YWGh8vPzfcZyc3M1f/78dq37dVFRl1hbC6EhJqar0yWcE%2Bes/9Bb/6Cv/tMRehswAWvEiBGqrKxsdex8hYWFqU%2BfPlqwYIFmzJihtLQ0eTwen23cbrdiY2MlSTExMS3mPR6P%2Bvfv3%2BZ9ZmdnKz093WfM5eoit7v2Ao/i/0REhCsq6hLV1NSpqam53eshdNg4v/yBc9Z/6K1/0Ff/caK3Tv3wGTAB6%2Buff9VeJSUlevjhh7Vt2zaFh391GfLU1yFDhmj79u0%2B25eVlXlf1J6YmKjy8nJNmTJFktTU1KS9e/d6XxTfFnFxcS1uB1ZVHVdjo72Tqamp2ep6CH6Bfj5wzvoPvfUP%2Buo/HaG3IXkTNDExUSdOnNDKlStVV1enY8eOafXq1brmmmuUk5OjiooKFRUVqaGhQcXFxSouLtb06dMlSTk5Odq4caP27Nmjuro6rVmzRp06ddLYsWOdPSgAABA0QjJgde/eXc8995zKysp07bXXavLkyerevbv%2B8z//U5deeqnWrl2rDRs2aMSIEVq2bJlWrlypgQMHSpLGjBmje%2B65R3l5eRo1apTefvttrVu3Tp07d3b4qAAAQLAIM%2B19RTfapKrquJV1XK5wxcR0ldtdq8bGZj4HC17b8kY7XcIZnX7Owh566x/01X%2Bc6G3Pnt0vyn5OF5JXsAAAAJxEwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlAfNBowDaJ5jeURqo73gEAFu4ggUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGBZyAasiooK5ebmKiUlRampqVq0aJFqamokSfv27dMtt9yiESNGaPz48Xruued8HvvKK68oIyNDw4YNU2Zmpt58800nDgEAAASpkA1Yc%2BfOVVRUlHbu3Knf/va3%2Bsc//qGf//znqq%2Bv15w5c3Tttdfqj3/8ox5//HGtXbtWO3bskPRV%2BFq4cKHuu%2B8%2B/e///q9uv/123XXXXTp69KjDRwQAAIJFSAasmpoaJSYm6t5771XXrl11%2BeWXa8qUKfrrX/%2BqN954QydPntS8efPUpUsXDR48WNOmTVNhYaEkqaioSGlpaUpLS1NkZKRuuukmDRgwQJs3b3b4qAAAQLAIyYAVFRWl5cuX67LLLvOOHTlyRHFxcSovL1d8fLwiIiK8cwkJCSorK5MklZeXKyEhwWe9hIQElZaWXpziAQBA0HM5XcDFUFpaqg0bNmjNmjXatm2boqKifOZ79Oghj8ej5uZmeTweRUdH%2B8xHR0frwIEDbd5fZWWlqqqqfMZcri6Ki4u78IP4/yIiwn2%2BAsHI5eL8tYHnA/%2Bgr/7TkXob8gHrnXfe0bx583TvvfcqNTVV27ZtO%2BN2YWFh3v82xrRrn4WFhcrPz/cZy83N1fz589u17tdFRV1ibS3gYouJ6ep0CSGF5wP/oK/%2B0xF6G9IBa%2BfOnfrJT36ihx56SDfffLMkKTY2Vh9%2B%2BKHPdh6PRz169FB4eLhiYmLk8XhazMfGxrZ5v9nZ2UpPT/cZc7m6yO2uvbAD%2BZqIiHBFRV2impo6NTU1t3s9wAk2vhfA84G/0Ff/caK3Tv1AF7IB629/%2B5sWLlyoJ598Utdff713PDExUb/5zW/U2Ngol%2Burwy8tLVVycrJ3/tTrsU4pLS3V5MmT27zvuLi4FrcDq6qFcjwzAAASMUlEQVSOq7HR3snU1NRsdT3gYuLctYvnA/%2Bgr/7TEXobkjdBGxsb9eCDD%2Bq%2B%2B%2B7zCVeSlJaWpm7dumnNmjWqq6vTu%2B%2B%2Bq5dfflk5OTmSpOnTp%2Bvtt9/WG2%2B8oYaGBr388sv68MMPddNNNzlxKAAAIAiFmfa%2B4CgA/fWvf9W//Mu/qFOnTi3mXn31VdXW1upnP/uZysrKdNlll2n27Nn60Y9%2B5N1mx44dWrVqlSoqKtSvXz8tWbJEI0eObFdNVVXH2/X4U1yucMXEdJXbXavGxmZNfOItK%2BsCF9O2vNFOlxASTn8%2BgB301X%2Bc6G3Pnt0vyn5OF5K3CK%2B55hrt37//nNv85je/Oevc%2BPHjNX78eNtlAQCADiIkbxECAAA4iYAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALAvZgPXHP/5RqampWrBgQYu5V155RRkZGRo2bJgyMzP15ptveueam5v1%2BOOP64YbbtDIkSM1c%2BZMHT58%2BGKWDgAAgpzL6QL84ZlnntHLL7%2Bsq666qsXcvn37tHDhQuXn5%2Bvaa6/V9u3bddddd%2BnVV1/V5ZdfrhdeeEFbtmzRM888o169eunxxx9Xbm6uNm3apLCwMAeOBgg9E594y%2BkSzsu2vNFOlwAgyITkFazIyMizBqyioiKlpaUpLS1NkZGRuummmzRgwABt3rxZklRYWKjbb79dV199tbp166YFCxbo4MGDevfddy/2YQAAgCAVklewbr311rPOlZeXKy0tzWcsISFBpaWlqq%2Bv14EDB5SQkOCd69atm6666iqVlpZq6NChbdp/ZWWlqqqqfMZcri6Ki4s7j6M4s4iIcJ%2BvAPzP5QrM7zeeD/yDvvpPR%2BptSAasc/F4PIqOjvYZi46O1oEDB/T555/LGHPGebfb3eZ9FBYWKj8/32csNzdX8%2BfPv/DCTxMVdYm1tQCcW0xMV6dLOCeeD/yDvvpPR%2BhthwtYkmSMadd8a7Kzs5Wenu4z5nJ1kdtd2651pa9Sf1TUJaqpqVNTU3O71wPQOhvfu/7A84F/0Ff/caK3Tv2A1OECVkxMjDwej8%2BYx%2BNRbGysevToofDw8DPOX3rppW3eR1xcXIvbgVVVx9XYaO9kampqtroegLML9O81ng/8g776T0fobejfBD1NYmKiysrKfMZKS0uVnJysyMhI9e/fX%2BXl5d65mpoaffTRRxoyZMjFLhUAAASpDhewpk%2BfrrfffltvvPGGGhoa9PLLL%2BvDDz/UTTfdJEnKyclRQUGBDh48qBMnTuixxx7ToEGDlJSU5HDlAAAgWITkLcJTYaixsVGS9Nprr0n66krVgAED9Nhjj2n58uWqqKhQv379tHbtWvXs2VOSNGPGDFVVVelf//VfVVtbq5SUlBYvWAcAADiXMNPeV3SjTaqqjltZx%2BUKV0xMV7ndtWpsbA66D2wEglGgftDo6c8HsIO%2B%2Bo8Tve3Zs/tF2c/pOtwtQgAAAH8jYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwLCR/2TMA2BRsv/MzUH93ItCRcAULAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlLqcLAADYNfGJt5wuIWRtyxvtdAkIElzBOoOKigrdcccdSklJ0bhx47Ry5Uo1Nzc7XRYAAAgSXME6g7vvvluDBw/Wa6%2B9purqas2ZM0eXXXaZ/u3f/s3p0gAAQBAgYJ2mtLRU77//vp5//nl1795d3bt31%2B23367169cTsACgg%2BP2q/%2BE2u1XAtZpysvL1bt3b0VHR3vHBg8erEOHDunEiRPq1q1bq2tUVlaqqqrKZ8zl6qK4uLh21xcREe7zFQCAUOByhdb/1whYp/F4PIqKivIZOxW23G53mwJWYWGh8vPzfcbuuusu3X333e2ur7KyUuvXP6vs7GzFxcXpr/9xY7vXxFd9LSws9PYV9tBb/6G3/kFf/acj9Ta04qIlxph2PT47O1u//e1vff5kZ2dbqa2qqkr5%2BfktrpChfeir/9Bb/6G3/kFf/acj9ZYrWKeJjY2Vx%2BPxGfN4PAoLC1NsbGyb1oiLiwv5ZA4AAM6OK1inSUxM1JEjR3Ts2DHvWGlpqfr166euXbs6WBkAAAgWBKzTJCQkKCkpSatWrdKJEyd08OBBPf/888rJyXG6NAAAECQiHn744YedLiLQfPe739XWrVv1yCOP6Pe//72ysrI0c%2BZMhYWFOV2aJKlr164aNWoUV9Qso6/%2BQ2/9h976B331n47S2zDT3ld0AwAAwAe3CAEAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2AFiYqKCt1xxx1KSUnRuHHjtHLlSjU3NztdlmP%2B%2BMc/KjU1VQsWLGgx98orrygjI0PDhg1TZmam3nzzTe9cc3OzHn/8cd1www0aOXKkZs6cqcOHD3vnPR6P8vLylJqaquuvv15LlixRfX29d37fvn265ZZbNGLECI0fP17PPfdcm/cdDCoqKpSbm6uUlBSlpqZq0aJFqqmpkdS%2BY/d334PB%2B%2B%2B/r9tuu00jRoxQamqq8vLyVFVVJUkqKSlRVlaWhg8frsmTJ2vz5s0%2Bjy0oKNCECRM0fPhw5eTkqKyszDvX0NCgn/70pxozZoxSUlI0f/58ud1u73xrzx2t7TuYLFu2TPHx8d6/09f2iY%2BPV2JiopKSkrx/HnnkEUn0tk0MgsKUKVPMgw8%2BaGpqasyhQ4fM%2BPHjzXPPPed0WY5Yt26dGT9%2BvJkxY4bJy8vzmdu7d69JTEw0b7zxhqmvrzebNm0yycnJ5siRI8YYYwoKCsy4cePMgQMHzPHjx82///u/m4yMDNPc3GyMMeauu%2B4yd9xxh6murjZHjx412dnZ5pFHHjHGGFNXV2e%2B%2B93vmtWrV5va2lpTVlZmRo0aZbZv396mfQeDH/zgB2bRokXmxIkT5siRIyYzM9MsXry43cfuz74Hg4aGBnPdddeZ/Px809DQYKqrq80tt9xi7rzzTvPpp5%2BaoUOHmqKiIlNfX2/eeustM2TIEPPee%2B8ZY4x5/fXXzTXXXGP27Nlj6urqzNq1a83o0aNNbW2tMcaY5cuXm8zMTPPJJ58Yt9tt7rrrLjNnzhzvvs/13NHavoPJ3r17zahRo8yAAQOMMa0fG31t3YABA8zhw4dbjNPbtiFgBYH33nvPDBo0yHg8Hu/Yf//3f5sJEyY4WJVz1q9fb2pqaszChQtbBKylS5ea3Nxcn7Fp06aZtWvXGmOMmTx5slm/fr137vjx4yYhIcHs3r3bVFVVmYEDB5p9%2B/Z554uLi83QoUPNl19%2BabZt22auvfZa09jY6J1fuXKl%2BfGPf9ymfQe6zz//3CxatMhUVVV5x37961%2Bb8ePHt/vY/dn3YODxeMxLL71kTp486R1bv369%2Bf73v2%2BeffZZc/PNN/tsn5eXZx566CFjjDF33HGHWbZsmXeuqanJjB492mzdutWcPHnSjBgxwrz22mve%2BQMHDpj4%2BHhz9OjRVp87Wtt3sGhqajLTpk0z//Vf/%2BUNWPS1/c4WsOht23CLMAiUl5erd%2B/eio6O9o4NHjxYhw4d0okTJxyszBm33nqrunfvfsa58vJyJSQk%2BIwlJCSotLRU9fX1OnDggM98t27ddNVVV6m0tFT79u1TRESEzy2GwYMH64svvtAHH3yg8vJyxcfHKyIiwmftU5e%2Bz7XvYBAVFaXly5frsssu844dOXJEcXFx7Tp2f/c9GERHR2vatGlyuVySpA8%2B%2BEC/%2B93vNHHixLP27my9DQ8P16BBg1RaWqqPPvpIx48f1%2BDBg73zV199tTp37qzy8vJWnzta23ewePHFFxUZGamMjAzvGH21Y9WqVRo7dqyuueYaPfTQQ6qtraW3bUTACgIej0dRUVE%2BY6dOvq/ft8ZXvfr6N6b0Va/cbrc%2B//xzGWPOOu/xeNStWzeFhYX5zEnyzp/%2B79CjRw95PB41Nzefc9/BqLS0VBs2bNC8efPadez%2B7nswqaioUGJioiZNmqSkpCTNnz//rMd36rw5V289Ho8ktXh8VFTUWXvXlt4G0zn72WefafXq1frZz37mM05f22/o0KFKTU3Vjh07VFhYqD179mjp0qX0to0IWEHCGON0CUGjtV6da/5C%2Bvz1YBAq/07vvPOOZs6cqXvvvVepqaln3e58jt2ffQ8WvXv3VmlpqV599VV9%2BOGHuv/%2B%2B9v0uIvd22CyfPlyZWZmql%2B/fuf9WPp6boWFhZo2bZo6deqkq6%2B%2BWvfdd5%2B2bt2qkydPtvpYekvACgqxsbHe1H%2BKx%2BNRWFiYYmNjHaoqMMXExJyxV7GxserRo4fCw8PPOH/ppZcqNjZWJ06cUFNTk8%2BcJO/86T8leTwe77rn2ncw2blzp%2B644w4tXrxYt956qyS169j93fdgExYWpj59%2BmjBggXaunWrXC5Xi9643W7veXOu3p7a5vT5zz//3Nu7cz13nGntr%2B870JWUlGj37t3Kzc1tMdfasdHX83fFFVeoqanpjN/P9Lal4Ht26oASExN15MgRHTt2zDtWWlqqfv36qWvXrg5WFngSExNb3IsvLS1VcnKyIiMj1b9/f5WXl3vnampq9NFHH2nIkCEaNGiQjDF6//33fR4bFRWlvn37KjExUfv371djY2OLtVvbd7D429/%2BpoULF%2BrJJ5/UzTff7B1vz7H7u%2B/BoKSkRBMmTPC5pXkqHA4ZMqRF78rKynx6%2B/XeNTU1ae/evUpOTtaVV16p6Ohon/m///3v%2BvLLL5WYmNjqc0dSUtI59x3oNm/erOrqao0bN04pKSnKzMyUJKWkpGjAgAH0tR327t2rFStW%2BIwdPHhQnTp1UlpaGr1ti4v6knpcsGnTppnFixeb48ePmwMHDpj09HSzYcMGp8ty1JneRbh//36TlJRkdu3aZerr601RUZEZNmyYqaysNMZ89W6UsWPHej8u4KGHHjJTp071Pj4vL8/MmjXLVFdXmyNHjpipU6eaFStWGGO%2Beqv9uHHjzC9/%2BUvzxRdfmD179phrrrnG7Nq1q037DnQnT540EydONC%2B%2B%2BGKLufYeuz/7HgxqampMamqqWbFihfniiy9MdXW1mTlzpvnRj35kPvvsMzNs2DDz0ksvmfr6evPGG2%2BYIUOGeN9VWVxcbEaMGGF2795tvvjiC7N69WqTlpZm6urqjDFfvaNyypQp5pNPPjHHjh0zc%2BbMMXfffbd33%2Bd67mht34HO4/GYI0eOeP/s3r3bDBgwwBw5csRUVFTQ13Y4evSoGTp0qFm7dq1paGgwH3zwgZk0aZJ55JFHOGfbiIAVJI4cOWJmzZplhgwZYlJTU80vf/lL72cIdTSJiYkmMTHRDBw40AwcOND791O2b99uxo8fbwYPHmx%2B%2BMMfmj//%2Bc/euebmZvPkk0%2Ba6667zgwZMsTMnj3b53OqampqzIIFC8zQoUPNyJEjzdKlS01DQ4N3fv/%2B/WbGjBkmMTHRjB071rzwwgs%2BtZ1r34HuL3/5ixkwYIC3n1//8/HHH7fr2P3d92Dw/vvvm1tuucUMGTLEXHvttSYvL88cPXrUGGPMn//8Z3PTTTeZwYMHm/Hjx7f4jK8XXnjBpKWlmcTERJOTk2P279/vnWtoaDAPP/ywGTlypBk2bJi55557TE1NjXe%2BteeO1vYdTA4fPuz9mAZj6Gt7/fnPfzbZ2dlm6NChZtSoUWb58uWmvr7eO0dvzy3MmBB5NRkAAECA4DVYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGDZ/wODv6ySkdOYkAAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-556192277942373539”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>295693.9</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>45938.67</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10614.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>139136.4</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>144510.1</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>47437.66</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>41840.27</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>317191.3</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>203960.8</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>62940.58</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2064)</td> <td class=”number”>2064</td> <td class=”number”>64.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>1136</td> <td class=”number”>35.4%</td> <td>

<div class=”bar” style=”width:55%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-556192277942373539”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>3464.5</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3596.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4620.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6513.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6561.423</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>454142.8</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>460623.6</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>460949.3</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>476280.4</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>507452.1</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_REDEMP_WICS12”><s>REDEMP_WICS12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_REDEMP_WICS08”><code>REDEMP_WICS08</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.91221</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SLHOUSE07”>SLHOUSE07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>14</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.60409</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>41</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>61.7%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram202087558769098752”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives202087558769098752,#minihistogram202087558769098752”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives202087558769098752”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles202087558769098752”

aria-controls=”quantiles202087558769098752” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram202087558769098752” aria-controls=”histogram202087558769098752”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common202087558769098752” aria-controls=”common202087558769098752”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme202087558769098752” aria-controls=”extreme202087558769098752”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles202087558769098752”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>1</td>

</tr> <tr>

<th>95-th percentile</th> <td>2</td>

</tr> <tr>

<th>Maximum</th> <td>41</td>

</tr> <tr>

<th>Range</th> <td>41</td>

</tr> <tr>

<th>Interquartile range</th> <td>1</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>1.3085</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.1661</td>

</tr> <tr>

<th>Kurtosis</th> <td>325.81</td>

</tr> <tr>

<th>Mean</th> <td>0.60409</td>

</tr> <tr>

<th>MAD</th> <td>0.7634</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>12.497</td>

</tr> <tr>

<th>Sum</th> <td>1892</td>

</tr> <tr>

<th>Variance</th> <td>1.7123</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram202087558769098752”>

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FVVVSk5OVlTpkxRRESErrrqKg0bNkw7d%2B7Uli1bdObMGU2YMEHNmzdXUlKSRowYocLCQklSUVGR0tPTlZ6ervDwcA0ZMkSdOnXSqlWrJEmFhYUaO3as2rdvr8jISOXm5qqsrEy7du2ycskAAMBGHFYX8EM4nU7NmzfPb%2BzIkSOKi4tTaWmpOnfurLCwMN9cYmKiioqKJEmlpaVKT0/3e21iYqJcLpdOnjyp/fv3KzEx0TcXGRmptm3byuVy6brrrmtUfRUVFaqsrPQbcziaKy4u7nut87uEhdkrHzsc9qrXlLN9slu/ghG9sgf6ZB/B3CtbBqxvcrlceu2117RkyRKtXbtWTqfTbz46Oloej0cNDQ3yeDyKiorym4%2BKitL%2B/fv15Zdfyuv1nnfe7XY3up7CwkLl5%2Bf7jWVnZysnJ%2Bd7ruzyEhMTYXUJlnI6r7C6BDQSvbIH%2BmQfwdgr2wesDz/8UBMmTNCUKVOUlpamtWvXnne7kJAQ39%2B/636qC73fatSoUcrIyPAbcziay%2B2uuaD9fpPdPhGYXr9dhIWFyum8QlVVtaqvb7C6HHwLemUP9Mk%2BLoVeWfXh3tYBa9OmTXrsscc0a9YsDR06VJIUGxurQ4cO%2BW3n8XgUHR2t0NBQxcTEyOPxnDMfGxvr2%2BZ88y1btmx0XXFxcedcDqysPKG6uuD%2BRhDs66%2Bvbwj6r4Fd0Ct7oE/2EYy9stcpkH/z0Ucfadq0aXrxxRd94UqSkpOT9cknn6iurs435nK51K1bN998SUmJ377OzoeHh6tjx44qLS31zVVVVenw4cPq2rXrRV4RAAC4XAQ8YGVkZCg/P19Hjhz5wfuoq6vTk08%2BqalTp6p3795%2Bc%2Bnp6YqMjNSSJUtUW1urXbt2qbi4WFlZWZKkkSNHavv27dqyZYtOnTql4uJiHTp0SEOGDJEkZWVladmyZSorK1N1dbXy8vLUpUsXpaSk/PBFAwCAoBLiDfADnhYvXqy3335b//znP3XTTTdp5MiRysjIkMPR%2BKuVO3fu1E9/%2BlM1bdr0nLl169appqZGs2fPVklJia688ko98MADuueee3zbbNiwQQsWLFB5ebk6dOigmTNnqmfPnpK%2Bvv9q0aJFev3111VTU6PU1FQ988wzuuqqqy5o3ZWVJy7o9efjcISqf9424/u9WNZOvtnqEizhcIQqJiZCbndN0J0itxt6ZQ/0yT4uhV61atXCkuMGPGCdVVpaqjVr1mjt2rU6c%2BaMhg4dqszMTLVr186Kci46AhYBi/8ZXProlT3QJ/u4FHplVcCy7B6spKQkTZs2TZs3b9aMGTP0xz/%2BUXfccYfGjRun3bt3W1UWAADABbMsYJ05c0bvvPOOHnjgAU2bNk2tW7fWE088oS5dumjs2LFavXq1VaUBAABckIA/pqGsrEzFxcV66623VFNTo4EDB%2BoPf/iDevTo4dumZ8%2BemjNnjgYPHhzo8gAAAC5YwAPWoEGD1K5dO40fP15Dhw5VdHT0Odukp6fr%2BPHjgS4NAADAiIAHrGXLlqlXr17fuR2/XBkAANhVwO/B6ty5sx566CFt3LjRN/Y///M/euCBB855gjoAAIAdBTxgzZs3TydOnFCHDh18Y3379lVDQ4Oef/75QJcDAABgXMAvEb733ntavXq1YmJifGMJCQnKy8vTnXfeGehyAAAAjAv4GayTJ08qPDz83EJCQ1VbWxvocgAAAIwLeMDq2bOnnn/%2BeX355Ze%2Bsc8//1xPP/2036MaAAAA7CrglwhnzJihn//857rpppsUGRmphoYG1dTUqE2bNnr11VcDXQ4AAIBxAQ9Ybdq00dtvv613331Xhw8fVmhoqNq1a6fevXsrLCws0OUAAAAYF/CAJUlNmzbVbbfdZsWhAQAALrqAB6zPPvtMCxYs0KeffqqTJ0%2BeM//nP/850CUBAAAYZck9WBUVFerdu7eaN28e6MMDAABcdAEPWCUlJfrzn/%2Bs2NjYQB8aAAAgIAL%2BmIaWLVty5goAAFzWAh6wxo8fr/z8fHm93kAfGgAAICACfonw3Xff1UcffaQVK1bo6quvVmiof8Z7/fXXA10SAACAUQEPWJGRkerTp0%2BgDwsAABAwAQ9Y8%2BbNC/QhAQAAAirg92BJ0oEDB7Ro0SI98cQTvrG///3vVpQCAABgXMAD1o4dOzRkyBBt2LBBa9askfT1w0fHjBnDQ0YBAMBlIeABa%2BHChXrssce0evVqhYSESPr69xM%2B//zzWrx4caDLAQAAMC7gAesf//iHsrKyJMkXsCTp9ttvV1lZWaDLAQAAMC7gAatFixbn/R2EFRUVatq0aaDLAQAAMC7gAat79%2B765S9/qerqat/YwYMHNW3aNN10002BLgcAAMC4gD%2Bm4YknntD999%2Bv1NRU1dfXq3v37qqtrVXHjh31/PPPB7ocAAAA4wIesK666iqtWbNGW7du1cGDB9WsWTO1a9dON998s989WQAAAHYV8IAlSU2aNNFtt91mxaEBAAAuuoAHrIyMjG89U8WzsAAAgN0FPGDdcccdfgGrvr5eBw8elMvl0v333x/ocgAAAIwLeMCaOnXqecfXr1%2Bvv/71rwGuBgAAwDxLfhfh%2Bdx22216%2B%2B23rS4DAADggl0yAWvPnj3yer1WlwEAAHDBAn6JcPTo0eeM1dbWqqysTAMGDAh0OQAAAMYFPGAlJCSc81OE4eHhyszM1IgRIwJdDgAAgHEBD1g8rR0AAFzuAh6w3nrrrUZvO3To0G%2Bd37Ztm6ZNm6bU1FQtXLjQN75ixQrNmDFDTZo08dt%2B%2BfLl6tq1qxoaGvTiiy9qzZo1qqqqUteuXTVnzhy1adNGkuTxeDRnzhz97W9/U2hoqNLT0zVr1iw1a9bse6wUAAAEq4AHrJkzZ6qhoeGcG9pDQkL8xkJCQr41YL300ksqLi5W27Ztzzvfs2dPvfrqq%2BedW758uVavXq2XXnpJrVu31sKFC5Wdna2VK1cqJCREs2bN0unTp7VmzRqdOXNGkyZNUl5enp588skfsGIAABBsAv5ThL/73e/Uu3dvLV%2B%2BXDt37tQHH3yg1157TX369NFLL72k3bt3a/fu3dq1a9e37ic8PPxbA9a3KSws1NixY9W%2BfXtFRkYqNzdXZWVl2rVrl7744gtt3LhRubm5io2NVevWrTVx4kS98cYbOnPmzA9dNgAACCKW3INVUFCg1q1b%2B8ZuuOEGtWnTRuPGjdOaNWsatZ8xY8Z86/yRI0f0s5/9TCUlJXI6ncrJydFdd92lkydPav/%2B/UpMTPRtGxkZqbZt28rlcunEiRMKCwtT586dffNJSUn66quvdODAAb/x/6SiokKVlZV%2BYw5Hc8XFxTVqbY0VFnbJPGWjURwOe9Vrytk%2B2a1fwYhe2QN9so9g7lXAA9ahQ4cUFRV1zrjT6VR5ebmRY8TGxiohIUGPPvqoOnTooD/96U96/PHHFRcXpx//%2BMfyer3n1BAVFSW3263o6GhFRkb6/aTj2W3dbnejjl9YWKj8/Hy/sezsbOXk5FzgyuwtJibC6hIs5XReYXUJaCR6ZQ/0yT6CsVcBD1jx8fF6/vnnNWnSJMXExEiSqqqq9Jvf/EbXXHONkWP07dtXffv29f170KBB%2BtOf/qQVK1b4flXPtz3U9EIfeDpq1ChlZGT4jTkczeV211zQfr/Jbp8ITK/fLsLCQuV0XqGqqlrV1zdYXQ6%2BBb2yB/pkH5dCr6z6cB/wgDVjxgxNmTJFhYWFioiIUGhoqKqrq9WsWTMtXrz4oh03Pj5eJSUlio6OVmhoqDwej9%2B8x%2BNRy5YtFRsbq%2BrqatXX1yssLMw3J0ktW7Zs1LHi4uLOuRxYWXlCdXXB/Y0g2NdfX98Q9F8Du6BX9kCf7CMYexXwgNW7d29t2bJFW7du1dGjR%2BX1etW6dWvdcsstatGihZFj/O///q%2BioqJ0xx13%2BMbKysrUpk0bhYeHq2PHjiotLVWvXr0kfX0G7fDhw%2Bratavi4%2BPl9Xq1b98%2BJSUlSZJcLpecTqfatWtnpD4AAHB5C3jAkqQrrrhC/fr109GjR33PnjLp9OnTevbZZ9WmTRtde%2B21Wr9%2Bvd5991398Y9/lCRlZWWpoKBAffr0UevWrZWXl6cuXbooJSVFkjRw4ED9%2Bte/1gsvvKDTp09r8eLFyszMlMNhyZcLAADYTMATw8mTJzV79my9/fbbkqSSkhJVVVXp0Ucf1a9%2B9Ss5nc5G7edsGKqrq5Mkbdy4UdLXZ5vGjBmjmpoaTZo0SZWVlbr66qu1ePFiJScnS/r69yFWVlbqvvvuU01NjVJTU/1uSn/mmWc0e/Zs9evXT02aNNGdd96p3NxcY18DAABweQvxXugd3d/Ts88%2Bqw8%2B%2BEATJ07U448/rt27d6uqqkqTJk1SmzZt9MwzzwSynICprDxhfJ8OR6j6520zvt%2BLZe3km60uwRIOR6hiYiLkdtcE3T0IdkOv7IE%2B2cel0KtWrczcfvR9BfzH0NavX6/f/OY3uv32232PQnA6nZo3b542bNgQ6HIAAACMC3jAqqmpUUJCwjnjsbGx%2BuqrrwJdDgAAgHEBD1jXXHON/vrXv0ryf97UunXr9F//9V%2BBLgcAAMC4gN/kfs899%2BiRRx7R3XffrYaGBr3yyisqKSnR%2BvXrNXPmzECXAwAAYFzAA9aoUaPkcDj02muvKSwsTL/97W/Vrl075eXl6fbbbw90OQAAAMYFPGAdP35cd999t%2B6%2B%2B%2B5AHxoAACAgAn4PVr9%2B/S74d/0BAABcygIesFJTU7V27dpAHxYAACBgAn6J8Ec/%2BpGee%2B45FRQU6JprrlGTJk385hcsWBDokgAAAIwKeMDav3%2B/fvzjH0uS3G53oA8PAABw0QUsYOXm5mrhwoV69dVXfWOLFy9WdnZ2oEoAAAAIiIDdg7Vp06ZzxgoKCgJ1eAAAgIAJWMA6308O8tOEAADgchSwgHX2Fzt/1xgAAIDdBfwxDQAAAJc7AhYAAIBhAfspwjNnzmjKlCnfOcZzsAAAgN0FLGD16NFDFRUV3zkGAABgdwELWP/%2B/CsAAIDLGfdgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgmK0D1rZt25SWlqbc3Nxz5t555x0NHjxY119/vYYPH6733nvPN9fQ0KCFCxeqX79%2B6tmzp8aNG6fPPvvMN%2B/xeDR58mSlpaWpd%2B/emjlzpk6ePBmQNQEAAPuzbcB66aWXNHfuXLVt2/acub1792ratGmaOnWq3n//fY0dO1YPP/ywjh49Kklavny5Vq9erYKCAm3evFkJCQnKzs6W1%2BuVJM2aNUu1tbVas2aN3njjDZWVlSkvLy%2Bg6wMAAPZl24AVHh6u4uLi8wasoqIipaenKz09XeHh4RoyZIg6deqkVatWSZIKCws1duxYtW/fXpGRkcrNzVVZWZl27dqlL774Qhs3blRubq5iY2PVunVrTZw4UW%2B88YbOnDkT6GUCAAAbsm3AGjNmjFq0aHHeudLSUiUmJvqNJSYmyuVy6eTJk9q/f7/ffGRkpNq2bSuXy6W9e/cqLCxMnTt39s0nJSXpq6%2B%2B0oEDBy7OYgAAwGXFYXUBF4PH41FUVJTfWFRUlPbv368vv/xSXq/3vPNut1vR0dGKjIxUSEiI35wkud3uRh2/oqJClZWVfmMOR3PFxcX9kOX8R2Fh9srHDoe96jXlbJ/s1q9gRK/sgT7ZRzD36rIMWJJ891P9kPnveu13KSwsVH5%2Bvt9Ydna2cnJyLmi/dhcTE2F1CZZyOq%2BwugQ0Er2yB/pkH8HYq8syYMXExMjj8fiNeTwexcbGKjo6WqGhoeedb9mypWJjY1VdXa36%2BnqFhYX55iSpZcuWjTr%2BqFGjlJGR4TfmcDSX213zQ5d0Xnb7RGB6/XYRFhYqp/MKVVXVqr6%2Bwepy8C3olT3QJ/u4FHpl1Yf7yzJgJScnq6SkxG/M5XJp0KBBCg8PV8eOHVVaWqpevXpJkqqqqnT48GF17dpV8fHx8nq92rdvn5KSknyvdTqdateuXaOOHxcXd87lwMrKE6qrC%2B5vBMG%2B/vr6hqD/GtgFvbIH%2BmQfwdgre50CaaSRI0dq%2B/bt2rJli06dOqXi4mIdOnRIQ4YMkSRlZWVp2bJlKisrU3V1tfLy8tSlSxelpKQoNjZWAwcO1K9//WsdP35cR48yGo31AAAOb0lEQVQe1eLFi5WZmSmH47LMowAAwDDbJoaUlBRJUl1dnSRp48aNkr4%2B29SpUyfl5eVp3rx5Ki8vV4cOHbR06VK1atVKkjR69GhVVlbqvvvuU01NjVJTU/3umXrmmWc0e/Zs9evXT02aNNGdd9553oeZAgAAnE%2BI90Lv6EajVFaeML5PhyNU/fO2Gd/vxbJ28s1Wl2AJhyNUMTERcrtrgu4Uud3QK3ugT/ZxKfSqVavzP9LpYrssLxECAABYiYAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIZdtgGrc%2BfOSk5OVkpKiu/Ps88%2BK0nasWOHMjMz1b17dw0aNEirVq3ye%2B2yZcs0cOBAde/eXVlZWSopKbFiCQAAwKYcVhdwMa1bt05XX32131hFRYUmTpyomTNnavDgwfrwww81YcIEtWvXTikpKdq0aZMWLVqk3/3ud%2BrcubOWLVumhx56SBs2bFDz5s0tWgkAALCTy/YM1n%2ByevVqJSQkKDMzU%2BHh4UpLS1NGRoaKiookSYWFhRo%2BfLi6deumZs2a6Re/%2BIUkafPmzVaWDQAAbOSyDlgLFixQ3759dcMNN2jWrFmqqalRaWmpEhMT/bZLTEz0XQb85nxoaKi6dOkil8sV0NoBAIB9XbaXCK%2B77jqlpaXphRde0GeffabJkyfr6aeflsfjUevWrf22jY6OltvtliR5PB5FRUX5zUdFRfnmG6OiokKVlZV%2BYw5Hc8XFxf3A1ZxfWJi98rHDYa96TTnbJ7v1KxjRK3ugT/YRzL26bANWYWGh7%2B/t27fX1KlTNWHCBPXo0eM7X%2Bv1ei/42Pn5%2BX5j2dnZysnJuaD92l1MTITVJVjK6bzC6hLQSPTKHuiTfQRjry7bgPVNV199terr6xUaGiqPx%2BM353a7FRsbK0mKiYk5Z97j8ahjx46NPtaoUaOUkZHhN%2BZwNJfbXfMDqz8/u30iML1%2BuwgLC5XTeYWqqmpVX99gdTn4FvTKHuiTfVwKvbLqw/1lGbD27NmjVatWafr06b6xsrIyNW3aVOnp6XrzzTf9ti8pKVG3bt0kScnJySotLdWwYcMkSfX19dqzZ48yMzMbffy4uLhzLgdWVp5QXV1wfyMI9vXX1zcE/dfALuiVPdAn%2BwjGXtnrFEgjtWzZUoWFhSooKNDp06d18OBBvfjiixo1apTuuusulZeXq6ioSKdOndLWrVu1detWjRw5UpKUlZWlt956Sx9//LFqa2u1ZMkSNW3aVH379rV2UQAAwDYuyzNYrVu3VkFBgRYsWOALSMOGDVNubq7Cw8O1dOlSzZ07V08//bTi4%2BM1f/58XXvttZKkPn366NFHH9XkyZN17NgxpaSkqKCgQM2aNbN4VQAAwC5CvBd6RzcapbLyhPF9Ohyh6p%2B3zfh%2BL5a1k2%2B2ugRLOByhiomJkNtdE3SnyO2GXtkDfbKPS6FXrVq1sOS4l%2BUlQgAAACsRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQSs8ygvL9eDDz6o1NRU3XrrrZo/f74aGhqsLgsAANiEw%2BoCLkWPPPKIkpKStHHjRh07dkzjx4/XlVdeqZ/97GdWl2ZrP/n1X6wu4XtZO/lmq0sAANgUZ7C%2BweVyad%2B%2BfZo6dapatGihhIQEjR07VoWFhVaXBgAAbIIzWN9QWlqq%2BPh4RUVF%2BcaSkpJ08OBBVVdXKzIy8jv3UVFRocrKSr8xh6O54uLijNYaFkY%2BvpgcDjNf37N9ol%2BXPnplD/TJPoK5VwSsb/B4PHI6nX5jZ8OW2%2B1uVMAqLCxUfn6%2B39jDDz%2BsRx55xFyh%2BjrI3X/Vpxo1apTx8AZzKioq9Ic//I4%2B2QC9sgf6ZB/B3Kvgi5SN4PV6L%2Bj1o0aN0ooVK/z%2BjBo1ylB1/6eyslL5%2BfnnnC3DpYU%2B2Qe9sgf6ZB/B3CvOYH1DbGysPB6P35jH41FISIhiY2MbtY%2B4uLigS%2BoAAOD/cAbrG5KTk3XkyBEdP37cN%2BZyudShQwdFRERYWBkAALALAtY3JCYmKiUlRQsWLFB1dbXKysr0yiuvKCsry%2BrSAACATYTNmTNnjtVFXGpuueUWrVmzRs8%2B%2B6zefvttZWZmaty4cQoJCbG6tHNERESoV69enF27xNEn%2B6BX9kCf7CNYexXivdA7ugEAAOCHS4QAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwbKi8vFwPPvigUlNTdeutt2r%2B/PlqaGiwuiz8f9u2bVNaWppyc3PPmXvnnXc0ePBgXX/99Ro%2BfLjee%2B89CyqE9PX7KDs7W6mpqUpLS9P06dNVVVUlSdq7d6/uvfde9ejRQwMGDNDLL79scbXBa9%2B%2Bfbr//vvVo0cPpaWlafLkyaqsrJQk7dixQ5mZmerevbsGDRqkVatWWVwtJOmXv/ylOnfu7Pt30PbJC9sZNmyY98knn/RWVVV5Dx486B0wYID35ZdftroseL3egoIC74ABA7yjR4/2Tp482W9uz5493uTkZO%2BWLVu8J0%2Be9K5cudLbrVs375EjRyyqNrjdeeed3unTp3urq6u9R44c8Q4fPtw7Y8YMb21trfeWW27xLlq0yFtTU%2BMtKSnx9urVy7t%2B/XqrSw46p06d8t50003e/Px876lTp7zHjh3z3nvvvd6JEyd6P//8c%2B91113nLSoq8p48edL7l7/8xdu1a1fv7t27rS47qO3Zs8fbq1cvb6dOnbxerzeo%2B8QZLJtxuVzat2%2Bfpk6dqhYtWighIUFjx45VYWGh1aVBUnh4uIqLi9W2bdtz5oqKipSenq709HSFh4dryJAh6tSpU/B8mruEVFVVKTk5WVOmTFFERISuuuoqDRs2TDt37tSWLVt05swZTZgwQc2bN1dSUpJGjBjBe8wCtbW1ys3N1fjx49W0aVPFxsaqf//%2B%2BvTTT7V69WolJCQoMzNT4eHhSktLU0ZGhoqKiqwuO2g1NDRo9uzZGjt2rG8smPtEwLKZ0tJSxcfHKyoqyjeWlJSkgwcPqrq62sLKIEljxoxRixYtzjtXWlqqxMREv7HExES5XK5AlIZ/43Q6NW/ePF155ZW%2BsSNHjiguLk6lpaXq3LmzwsLCfHOJiYkqKSmxotSgFhUVpREjRsjhcEiSDhw4oDfffFM/%2BclP/uP7iT5Z5/XXX1d4eLgGDx7sGwvmPhGwbMbj8cjpdPqNnQ1bbrfbipLQSB6Pxy8YS1/3jr5Zz%2BVy6bXXXtOECRPO%2Bx6Ljo6Wx%2BPhXkeLlJeXKzk5WXfccYdSUlKUk5PzH/vE%2B8kaX3zxhRYtWqTZs2f7jQdznwhYNuT1eq0uAT8Qvbv0fPjhhxo3bpymTJmitLS0/7hdSEhIAKvCv4uPj5fL5dK6det06NAhPf7441aXhG%2BYN2%2Behg8frg4dOlhdyiWDgGUzsbGx8ng8fmMej0chISGKjY21qCo0RkxMzHl7R9%2Bss2nTJj344IOaMWOGxowZI%2Bnr99g3P117PB5FR0crNJRvmVYJCQlRQkKCcnNztWbNGjkcjnPeT263m/eTBXbs2KG///3vys7OPmfufN/3gqVPfLewmeTkZB05ckTHjx/3jblcLnXo0EEREREWVobvkpycfM59By6XS926dbOoouD20Ucfadq0aXrxxRc1dOhQ33hycrI%2B%2BeQT1dXV%2BcbokzV27NihgQMH%2Bl2aPRtyu3btes77qaSkhD5ZYNWqVTp27JhuvfVWpaamavjw4ZKk1NRUderUKWj7RMCymcTERKWkpGjBggWqrq5WWVmZXnnlFWVlZVldGr7DyJEjtX37dm3ZskWnTp1ScXGxDh06pCFDhlhdWtCpq6vTk08%2BqalTp6p3795%2Bc%2Bnp6YqMjNSSJUtUW1urXbt2qbi4mPeYBZKTk1VdXa358%2BertrZWx48f16JFi3TDDTcoKytL5eXlKioq0qlTp7R161Zt3bpVI0eOtLrsoDN9%2BnStX79eK1eu1MqVK1VQUCBJWrlypQYPHhy0fQrxclOI7Rw9elSzZs3S3/72N0VGRmr06NF6%2BOGHuUfkEpCSkiJJvrMfZ3/66exPCm7YsEELFixQeXm5OnTooJkzZ6pnz57WFBvEdu7cqZ/%2B9Kdq2rTpOXPr1q1TTU2NZs%2BerZKSEl155ZV64IEHdM8991hQKT755BPNnTtXu3fvVvPmzXXjjTdq%2BvTpat26tT744APNnTtXZWVlio%2BP15QpUzRgwACrSw56//rXv9SvXz998sknkhS0fSJgAQAAGMYlQgAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAw7P8B%2B1loealrujIAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common202087558769098752”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1979</td> <td class=”number”>61.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>756</td> <td class=”number”>23.6%</td> <td>

<div class=”bar” style=”width:38%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>242</td> <td class=”number”>7.5%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>88</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>41.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3)</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme202087558769098752”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1979</td> <td class=”number”>61.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>756</td> <td class=”number”>23.6%</td> <td>

<div class=”bar” style=”width:38%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>242</td> <td class=”number”>7.5%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>88</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>24.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>41.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SLHOUSE12”>SLHOUSE12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>12</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.56833</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>23</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>63.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram1615091754380416239”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives1615091754380416239,#minihistogram1615091754380416239”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives1615091754380416239”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles1615091754380416239”

aria-controls=”quantiles1615091754380416239” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram1615091754380416239” aria-controls=”histogram1615091754380416239”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common1615091754380416239” aria-controls=”common1615091754380416239”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme1615091754380416239” aria-controls=”extreme1615091754380416239”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles1615091754380416239”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>1</td>

</tr> <tr>

<th>95-th percentile</th> <td>2</td>

</tr> <tr>

<th>Maximum</th> <td>23</td>

</tr> <tr>

<th>Range</th> <td>23</td>

</tr> <tr>

<th>Interquartile range</th> <td>1</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>1.1286</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.9859</td>

</tr> <tr>

<th>Kurtosis</th> <td>106.12</td>

</tr> <tr>

<th>Mean</th> <td>0.56833</td>

</tr> <tr>

<th>MAD</th> <td>0.73418</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>6.8455</td>

</tr> <tr>

<th>Sum</th> <td>1780</td>

</tr> <tr>

<th>Variance</th> <td>1.2738</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram1615091754380416239”>

<img 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s7GzFxMRo3Lhx6t27t9asWSNJ8ng8mjp1qnr06KG4uDjl5%2BeroqJCe/bssXPJAADAQVx2F/B9uN1uLVy4MGzs0KFDSklJUXl5ufr06aOoqKjQXFpamlatWiVJKi8vV3Z2dthz09LS5PV6dfz4ce3bt09paWmhubi4OHXr1k1er1cXXnhhk%2BqrqqqSz%2BcLG3O52iklJeU7rfPbREU5Kx%2B7XM6q92y%2B6r3TvgctAb23B323D713JkcGrK/zer166aWXtHz5cm3YsEFutztsPiEhQYFAQI2NjQoEAoqPjw%2Bbj4%2BP1759%2B/T5558rGAyecd7v9ze5Ho/Ho%2BLi4rCxvLw8zZ49%2BzuurGVJTIy1uwTj3O7z7C6h1aL39qDv9qH3zuL4gPXuu%2B9q5syZmjt3rrKysrRhw4YzPi4iIiL0/992PVVzr7fKycnR8OHDw8Zcrnby%2B2ubtd2vc9q7GdPrt1NUVKTc7vNUXV2nhoZGu8tpVei9Pei7feh989j15t7RAWvr1q269957NX/%2BfF133XWSpKSkJB04cCDscYFAQAkJCYqMjFRiYqICgcBp80lJSaHHnGk%2BOTm5yXWlpKScdjrQ5zum%2BvrW/cJoietvaGhsketyAnpvD/puH3rvLM46BPL/vPfeeyosLNRTTz0VCleSlJ6ero8//lj19fWhMa/XqwEDBoTmy8rKwrb11XxMTIx69eql8vLy0Fx1dbU%2B%2B%2Bwz9e/f/xyvCAAAtBSODFj19fV66KGHVFBQoKFDh4bNZWdnKy4uTsuXL1ddXZ327Nmj0tJS5ebmSpImTZqkXbt2afv27Tpx4oRKS0t14MABjRs3TpKUm5urlStXqqKiQjU1NSoqKlLfvn2VkZFh%2BToBAIAzOfIU4fvvv6%2BKigotWLBACxYsCJvbuHGjnnnmGT3yyCMqKSlRhw4dlJ%2Bfr2HDhkmSevfuraKiIi1cuFCVlZXq2bOnVqxYoY4dO0qSJk%2BeLJ/Pp5tuukm1tbXKzMw87YJ1AACAs4kIcgdNS/h8x4xv0%2BWK1Miinca3e65smHOp3SUY43JFKjExVn5/LddEWIze24O%2B24feN0/Hju1t2a8jTxECAAD8kBGwAAAADCNgAQAAGEbAAgAAMMzygDV8%2BHAVFxfr0KFDVu8aAADAEpYHrBtuuEHr16/XFVdcodtuu02bN28OuykoAACA01kesPLy8rR%2B/Xq98sor6tWrl375y18qOztbixcv1v79%2B60uBwAAwDjbrsHq16%2BfCgsLtW3bNj344IN65ZVXNHr0aE2bNk0ffPCBXWUBAAA0m20B69SpU1q/fr1uv/12FRYWqlOnTnrggQfUt29fTZ06VWvXrrWrNAAAgGax/KNyKioqVFpaqtdee021tbUaNWqUfvvb32rw4MGhxwwZMkSPPvqoxo4da3V5AAAAzWZ5wBozZoy6d%2B%2Bu6dOn67rrrlNCQsJpj8nOztbRo0etLg0AAMAIywPWypUrdfHFF3/r4/bs2WNBNQAAAOZZfg1Wnz59NGPGDG3ZsiU09l//9V%2B6/fbbFQgErC4HAADAOMsD1sKFC3Xs2DH17NkzNDZs2DA1NjZq0aJFVpcDAABgnOWnCN966y2tXbtWiYmJobHU1FQVFRXpmmuusbocAAAA4yw/gnX8%2BHHFxMScXkhkpOrq6qwuBwAAwDjLA9aQIUO0aNEiff7556Gxf/zjH3rsscfCbtUAAADgVJafInzwwQd166236qc//ani4uLU2Nio2tpade3aVS%2B%2B%2BKLV5QAAABhnecDq2rWr3njjDb355pv67LPPFBkZqe7du2vo0KGKioqyuhwAAADjLA9YkhQdHa0rrrjCjl0DAACcc5YHrIMHD2rJkiX65JNPdPz48dPm//SnP1ldEgAAgFG2XINVVVWloUOHql27dlbvHgAA4JyzPGCVlZXpT3/6k5KSkqzeNQAAgCUsv01DcnIyR64AAECLZnnAmj59uoqLixUMBq3eNQAAgCUsP0X45ptv6r333tPq1avVpUsXRUaGZ7zf//73VpcEAABglOUBKy4uTpdddpnVuwUAALCM5QFr4cKFVu8SAADAUpZfgyVJn376qZ5%2B%2Bmk98MADobG//e1vdpQCAABgnOUBa/fu3Ro3bpw2b96sdevWSfry5qNTpkzhJqMAAKBFsDxgLV26VPfee6/Wrl2riIgISV9%2BPuGiRYu0bNkyq8sBAAAwzvKA9fe//125ubmSFApYknTVVVepoqLC6nIAAACMszxgtW/f/oyfQVhVVaXo6GirywEAADDO8oA1aNAg/fKXv1RNTU1obP/%2B/SosLNRPf/pTq8sBAAAwzvLbNDzwwAO6%2BeablZmZqYaGBg0aNEh1dXXq1auXFi1aZHU5AAAAxlkesM4//3ytW7dOO3bs0P79%2B9W2bVt1795dl156adg1WQAAAE5lecCSpDZt2uiKK66wY9cAAADnnOUBa/jw4Wc9UsW9sAAAgNNZHrBGjx4dFrAaGhq0f/9%2Beb1e3XzzzVaXAwAAYJzlAaugoOCM45s2bdJf/vIXi6sBAAAwz5bPIjyTK664Qm%2B88cZ3es7OnTuVlZWl/Pz8sPHVq1frggsuUEZGRtjXBx98IElqbGzU0qVLNWLECA0ZMkTTpk3TwYMHQ88PBAKaM2eOsrKyNHToUM2bN%2B%2BM9%2B4CAAA4kx9MwPrwww8VDAab/Phnn31WCxYsULdu3c44P2TIEHm93rCv/v37S5JefvllrV27ViUlJdq2bZtSU1OVl5cX2v/8%2BfNVV1endevW6dVXX1VFRYWKioqav0gAANAqWH6KcPLkyaeN1dXVqaKiQldeeWWTtxMTE6PS0lI98cQTOnHixHeqwePxaOrUqerRo4ckKT8/X5mZmdqzZ4%2B6dOmiLVu26A9/%2BIOSkpIkSXfeeafuvvtuFRYWqk2bNt9pXwAAoPWxPGClpqae9luEMTExmjBhgiZOnNjk7UyZMuWs84cOHdItt9yisrIyud1uzZ49W9dee62OHz%2Buffv2KS0tLfTYuLg4devWTV6vV8eOHVNUVJT69OkTmu/Xr5%2B%2B%2BOILffrpp2Hj36Sqqko%2Bny9szOVqp5SUlCavrymion4wByCbxOVyVr1n81XvnfY9aAnovT3ou33ovTNZHrCsuFt7UlKSUlNTdc8996hnz5764x//qPvuu08pKSn6yU9%2BomAwqPj4%2BLDnxMfHy%2B/3KyEhQXFxcWEh8KvH%2Bv3%2BJu3f4/GouLg4bCwvL0%2BzZ89u5sqcLTEx1u4SjHO7z7O7hFaL3tuDvtuH3juL5QHrtddea/Jjr7vuuu%2B1j2HDhmnYsGGhP48ZM0Z//OMftXr16tBvMZ7teq/vci3YmeTk5Gj48OFhYy5XO/n9tc3a7tc57d2M6fXbKSoqUm73eaqurlNDQ6Pd5bQq9N4e9N0%2B9L557Hpzb3nAmjdvnhobG08LMREREWFjERER3ztgnUnnzp1VVlamhIQERUZGKhAIhM0HAgElJycrKSlJNTU1amhoUFRUVGhOkpKTk5u0r5SUlNNOB/p8x1Rf37pfGC1x/Q0NjS1yXU5A7%2B1B3%2B1D753F8oD13HPP6fnnn9eMGTPUp08fBYNBffzxx3r22Wd14403KjMzs9n7%2BN3vfqf4%2BHiNHj06NFZRUaGuXbsqJiZGvXr1Unl5uS6%2B%2BGJJUnV1tT777DP1799fnTt3VjAY1EcffaR%2B/fpJkrxer9xut7p3797s2gAAQMtnyzVYJSUl6tSpU2jsoosuUteuXTVt2jStW7eu2fs4efKkHn/8cXXt2lUXXHCBNm3apDfffFOvvPKKJCk3N1clJSW67LLL1KlTJxUVFalv377KyMiQJI0aNUq/%2BtWv9OSTT%2BrkyZNatmyZJkyYIJfLlo9uBAAADmN5Yjhw4MBpF5hLktvtVmVlZZO381UYqq%2BvlyRt2bJF0pdHm6ZMmaLa2lrdfffd8vl86tKli5YtW6b09HRJX94qwufz6aabblJtba0yMzPDLkr/xS9%2BoUceeUQjRoxQmzZtdM0115x2M1MAAIBvEhFs7hXd39Ho0aN18cUX6%2B6771ZiYqKkL0/R/frXv9Zf//pXvf7661aWYxmf75jxbbpckRpZtNP4ds%2BVDXMutbsEY1yuSCUmxsrvr%2BWaCIvRe3vQd/vQ%2B%2Bbp2LG9Lfu1/AjWgw8%2BqLlz58rj8Sg2NlaRkZGqqalR27ZttWzZMqvLAQAAMM7ygDV06FBt375dO3bs0OHDhxUMBtWpUyf97Gc/U/v29qRMAAAAk2y5avu8887TiBEjdPjwYXXt2tWOEgAAAM4Zy%2B9Uefz4cRUWFmrgwIG6%2BuqrJX15DdZtt92m6upqq8sBAAAwzvKAtXjxYu3du1dFRUWKjPzX7hsaGlRUVGR1OQAAAMZZHrA2bdqkX//617rqqqtCn/fndru1cOFCbd682epyAAAAjLM8YNXW1io1NfW08aSkJH3xxRdWlwMAAGCc5QHrxz/%2Bsf7yl79ICv9Q5Y0bN%2Brf/u3frC4HAADAOMt/i/DnP/%2B57rrrLt1www1qbGzUCy%2B8oLKyMm3atEnz5s2zuhwAAADjLA9YOTk5crlceumllxQVFaVnnnlG3bt3V1FRka666iqrywEAADDO8oB19OhR3XDDDbrhhhus3jUAAIAlLL8Ga8SIEbL44w8BAAAsZXnAyszM1IYNG6zeLQAAgGUsP0X4ox/9SE888YRKSkr04x//WG3atAmbX7JkidUlAQAAGGV5wNq3b59%2B8pOfSJL8fr/VuwcAADjnLAtY%2Bfn5Wrp0qV588cXQ2LJly5SXl2dVCQAAAJaw7BqsrVu3njZWUlJi1e4BAAAsY1nAOtNvDvLbhAAAoCWyLGB99cHO3zYGAADgdJbfpgEAAKClI2ABAAAYZtlvEZ46dUpz58791jHugwUAAJzOsoA1ePBgVVVVfesYAACA01kWsP7//a8AAABaMq7BAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAY5uiAtXPnTmVlZSk/P/%2B0ufXr12vs2LEaOHCgxo8fr7feeis019jYqKVLl2rEiBEaMmSIpk2bpoMHD4bmA4GA5syZo6ysLA0dOlTz5s3T8ePHLVkTAABwPscGrGeffVYLFixQt27dTpvbu3evCgsLVVBQoLfffltTp07VrFmzdPjwYUnSyy%2B/rLVr16qkpETbtm1Tamqq8vLyFAwGJUnz589XXV2d1q1bp1dffVUVFRUqKiqydH0AAMC5HBuwYmJiVFpaesaAtWrVKmVnZys7O1sxMTEaN26cevfurTVr1kiSPB6Ppk6dqh49eiguLk75%2BfmqqKjQnj179M9//lNbtmxRfn6%2BkpKS1KlTJ91555169dVXderUKauXCQAAHMhldwHf15QpU75xrry8XNnZ2WFjaWlp8nq9On78uPbt26e0tLTQXFxcnLp16yav16tjx44pKipKffr0Cc3369dPX3zxhT799NOw8W9SVVUln88XNuZytVNKSkpTl9ckUVHOyscul7PqPZuveu%2B070FLQO/tQd/tQ%2B%2BdybEB62wCgYDi4%2BPDxuLj47Vv3z59/vnnCgaDZ5z3%2B/1KSEhQXFycIiIiwuYkye/3N2n/Ho9HxcXFYWN5eXmaPXv291lOi5GYGGt3Cca53efZXUKrRe/tQd/tQ%2B%2BdpUUGLEmh66m%2Bz/y3Pffb5OTkaPjw4WFjLlc7%2Bf21zdru1znt3Yzp9dspKipSbvd5qq6uU0NDo93ltCr03h703T70vnnsenPfIgNWYmKiAoFA2FggEFBSUpISEhIUGRl5xvnk5GQlJSWppqZGDQ0NioqKCs1JUnJycpP2n5KSctrpQJ/vmOrrW/cLoyWuv6GhsUWuywnovT3ou33ovbM46xBIE6Wnp6usrCxszOv1asCAAYqJiVGvXr1UXl4emquurtZnn32m/v37q2/fvgoGg/roo4/Cnut2u9W9e3fL1gAAAJyrRQasSZMmadeuXdq%2BfbtOnDih0tJSHThwQOPGjZMk5ebmauXKlaqoqFBNTY2KiorUt29fZWRkKCkpSaNGjdKvfvUrHT16VIcPH9ayZcs0YcIEuVwt8oAfAAAwzLGJISMjQ5JUX18vSdqyZYukL4829e7dW0VFRVq4cKEqKyvVs2dPrVixQh07dpQkTZ48WT6fTzfddJNqa2uVmZkZdlH6L37xCz3yyCMaMWKE2rRpo2uuueaMNzO7crV6AAANiUlEQVQFAAA4k4hgc6/oRpP4fMeMb9PlitTIop3Gt3uubJhzqd0lGONyRSoxMVZ%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%2B/aV1%2BvVmDFjmrTfqqoq%2BXy%2BsDGXq51SUlKauaJwUVHOyscul7PqPZuveu%2B070FLQO/tQd/tQ%2B%2BdqcUGrAsvvFBZWVl68skndfDgQc2ZM0ePPfaYAoGAOnXqFPbYhIQE%2Bf1%2BSVIgEFB8fHzYfHx8fGi%2BKTwej4qLi8PG8vLyNHv27O%2B5mpYhMTHW7hKMc7vPs7uEVove24O%2B24feO0uLDVgejyf0/z169FBBQYFmzpypwYMHf%2Btzg8Fgs/adk5Oj4cOHh425XO3k99c2a7tf57R3M6bXb6eoqEi53eepurpODQ2NdpfTqtB7e9B3%2B9D75rHrzX2LDVhf16VLFzU0NCgyMlKBQCBszu/3KykpSZKUmJh42nwgEFCvXr2avK%2BUlJTTTgf6fMdUX9%2B6Xxgtcf0NDY0tcl1OQO/tQd/tQ%2B%2BdxVmHQJroww8/1KJFi8LGKioqFB0drezs7NNuu1BWVqYBAwZIktLT01VeXh6aa2ho0IcffhiaBwAA%2BDYtMmAlJyfL4/GopKREJ0%2Be1P79%2B/XUU08pJydH1157rSorK7Vq1SqdOHFCO3bs0I4dOzRp0iRJUm5url577TW9//77qqur0/LlyxUdHa1hw4bZuygAAOAYLfIUYadOnVRSUqIlS5aEAtL111%2Bv/Px8xcTEaMWKFVqwYIEee%2Bwxde7cWYsXL9YFF1wgSbrssst0zz33aM6cOTpy5IgyMjJUUlKitm3b2rwqAADgFBHB5l7RjSbx%2BY4Z36bLFamRRTuNb/dc2TDnUrtLMMblilRiYqz8/lquibAYvbcHfbcPvW%2Bejh3b27LfFnmKEAAAwE4ELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhLrsLQOtx9a/%2BbHcJ38mGOZfaXQIAwKE4ggUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2CdQWVlpe644w5lZmbq8ssv1%2BLFi9XY2Gh3WQAAwCG4D9YZ3HXXXerXr5%2B2bNmiI0eOaPr06erQoYNuueUWu0uDhbhvFwDg%2B%2BII1td4vV599NFHKigoUPv27ZWamqqpU6fK4/HYXRoAAHAIjmB9TXl5uTp37qz4%2BPjQWL9%2B/bR//37V1NQoLi7uW7dRVVUln88XNuZytVNKSorRWqOiyMf4Fycdcftjwc%2B%2B93O/%2Brnn599a9N0%2B9N6ZCFhfEwgE5Ha7w8a%2BClt%2Bv79JAcvj8ai4uDhsbNasWbrrrrvMFaovg9zN53%2BinJwc4%2BENZ1dVVSWPx0PvbVBVVaXf/vY5em8x%2Bm4feu9MxOEzCAaDzXp%2BTk6OVq9eHfaVk5NjqLp/8fl8Ki4uPu1oGc49em8fem8P%2Bm4feu9MHMH6mqSkJAUCgbCxQCCgiIgIJSUlNWkbKSkpvMsAAKAV4wjW16Snp%2BvQoUM6evRoaMzr9apnz56KjY21sTIAAOAUBKyvSUtLU0ZGhpYsWaKamhpVVFTohRdeUG5urt2lAQAAh4h69NFHH7W7iB%2Ban/3sZ1q3bp0ef/xxvfHGG5owYYKmTZumiIgIu0s7TWxsrC6%2B%2BGKOrtmA3tuH3tuDvtuH3jtPRLC5V3QDAAAgDKcIAQAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYDlQZWWl7rjjDmVmZuryyy/X4sWL1djYaHdZrUKfPn2Unp6ujIyM0Nfjjz9ud1kt1s6dO5WVlaX8/PzT5tavX6%2BxY8dq4MCBGj9%2BvN566y0bKmy5vqn3q1ev1gUXXBD2GsjIyNAHH3xgU6UtS2VlpfLy8pSZmamsrCzdf//9qq6uliTt3btXN954owYPHqwrr7xSzz//vM3V4mxcdheA7%2B6uu%2B5Sv379tGXLFh05ckTTp09Xhw4ddMstt9hdWquwceNGdenSxe4yWrxnn31WpaWl6tat22lze/fuVWFhoYqLi3XJJZdo06ZNmjVrljZu3Kjzzz/fhmpblrP1XpKGDBmiF1980eKqWocZM2YoPT1dW7du1bFjx5SXl6cnn3xS8%2BfP1/Tp0zVp0iSVlJRo//79uvXWW9WlSxddeeWVdpeNM%2BAIlsN4vV599NFHKigoUPv27ZWamqqpU6fK4/HYXRpgVExMzDf%2BI79q1SplZ2crOztbMTExGjdunHr37q01a9bYUGnLc7be49yprq5Wenq65s6dq9jYWJ1//vm6/vrr9c4772j79u06deqUZs6cqXbt2qlfv36aOHEif/f/gBGwHKa8vFydO3dWfHx8aKxfv37av3%2B/ampqbKys9ViyZImGDRumiy66SPPnz1dtba3dJbVIU6ZMUfv27c84V15errS0tLCxtLQ0eb1eK0pr8c7We0k6dOiQbrnlFg0ZMkQjRozQ66%2B/bmF1LZfb7dbChQvVoUOH0NihQ4eUkpKi8vJy9enTR1FRUaG5tLQ0lZWV2VEqmoCA5TCBQEButzts7Kuw5ff77SipVbnwwguVlZWlzZs3y%2BPx6P3339djjz1md1mtTiAQCHuTIX35OuA1cO4lJSUpNTVV9957r/785z/rnnvu0YMPPqjdu3fbXVqL4/V69dJLL2nmzJln/Ls/ISFBgUCAa3B/oAhYDhQMBu0uodXyeDyaOHGioqOj1aNHDxUUFGjdunU6efKk3aW1OrwO7DFs2DA999xzSktLU3R0tMaMGaORI0dq9erVdpfWorz77ruaNm2a5s6dq6ysrG98XEREhIVV4bsgYDlMUlKSAoFA2FggEFBERISSkpJsqqr16tKlixoaGnTkyBG7S2lVEhMTz/g64DVgj86dO6uqqsruMlqMrVu36o477tCDDz6oKVOmSPry7/6vH6ENBAJKSEhQZCT/lP8Q8V1xmPT0dB06dEhHjx4NjXm9XvXs2VOxsbE2Vtbyffjhh1q0aFHYWEVFhaKjo5WSkmJTVa1Tenr6adeeeL1eDRgwwKaKWo/f/e53Wr9%2BfdhYRUWFunbtalNFLct7772nwsJCPfXUU7ruuutC4%2Bnp6fr4449VX18fGuNn/oeNgOUwaWlpysjI0JIlS1RTU6OKigq98MILys3Ntbu0Fi85OVkej0clJSU6efKk9u/fr6eeeko5OTlhF57i3Js0aZJ27dql7du368SJEyotLdWBAwc0btw4u0tr8U6ePKnHH39cXq9Xp06d0rp16/Tmm29q8uTJdpfmePX19XrooYdUUFCgoUOHhs1lZ2crLi5Oy5cvV11dnfbs2aPS0lL%2B7v8BiwhyIYPjHD58WPPnz9d///d/Ky4uTpMnT9asWbM4F2%2BBv/71r1qyZIk%2B/vhjRUdH6/rrr1d%2Bfr5iYmLsLq3FycjIkKTQO3aX68vb9n31m4KbN2/WkiVLVFlZqZ49e2revHkaMmSIPcW2MGfrfTAY1PLly1VaWiqfz6cuXbrovvvu0%2BWXX25bvS3FO%2B%2B8o3//939XdHT0aXMbN25UbW2tHnnkEZWVlalDhw66/fbb9fOf/9yGStEUBCwAAADDOEUIAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIb9H%2BMeAg7e6yMqAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common1615091754380416239”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2023</td> <td class=”number”>63.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>738</td> <td class=”number”>23.0%</td> <td>

<div class=”bar” style=”width:37%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>227</td> <td class=”number”>7.1%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>79</td> <td class=”number”>2.5%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>32</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>23.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme1615091754380416239”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2023</td> <td class=”number”>63.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>738</td> <td class=”number”>23.0%</td> <td>

<div class=”bar” style=”width:37%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>227</td> <td class=”number”>7.1%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>79</td> <td class=”number”>2.5%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>32</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>23.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SNAPS12”><s>SNAPS12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_SPECS14”><code>SPECS14</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.92214</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SNAPS16”><s>SNAPS16</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_SNAPS12”><code>SNAPS12</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99663</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SNAPSPTH12”>SNAPSPTH12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3102</td>

</tr> <tr>

<th>Unique (%)</th> <td>96.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.87833</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>6.658</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.7%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram6506926068928033928”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives6506926068928033928,#minihistogram6506926068928033928”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives6506926068928033928”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles6506926068928033928”

aria-controls=”quantiles6506926068928033928” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram6506926068928033928” aria-controls=”histogram6506926068928033928”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common6506926068928033928” aria-controls=”common6506926068928033928”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme6506926068928033928” aria-controls=”extreme6506926068928033928”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles6506926068928033928”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.40094</td>

</tr> <tr>

<th>Q1</th> <td>0.62927</td>

</tr> <tr>

<th>Median</th> <td>0.81817</td>

</tr> <tr>

<th>Q3</th> <td>1.0665</td>

</tr> <tr>

<th>95-th percentile</th> <td>1.5592</td>

</tr> <tr>

<th>Maximum</th> <td>6.658</td>

</tr> <tr>

<th>Range</th> <td>6.658</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.43723</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.38363</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.43677</td>

</tr> <tr>

<th>Kurtosis</th> <td>20.063</td>

</tr> <tr>

<th>Mean</th> <td>0.87833</td>

</tr> <tr>

<th>MAD</th> <td>0.279</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.2384</td>

</tr> <tr>

<th>Sum</th> <td>2750.9</td>

</tr> <tr>

<th>Variance</th> <td>0.14717</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram6506926068928033928”>

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I%2BM%2BrIXEs0IMa9MGHPtCDGnb9stwoA1Z1dbWmT5%2Buzz//XH/4wx%2BUmJj4L5dPSEhQSUmJYmNjJfnu4woP/8cP5LvvvlPr1q3rvP24uLjTLge6XMdVVdUwB7jHU91g627KArGnHAv0oAZ98KEP9MAujfLC7JNPPqkvv/zyjOHqf//3f7Vt2za/seLiYiUmJioxMVHR0dEqKiqqnfviiy908uRJJScnW1I7AAAIfI0uYH388cdat26d8vPz1apVq9Pm3W635syZo7179%2BqHH37QCy%2B8oP3792vo0KEKCQnRHXfcoWeffVaHDh1SaWmpfvOb3%2Bj666%2BvfYwDAADATwnIS4QpKSmSfJ8QlKTNmzdLkpxOp1avXq3jx4/ruuuu83tPz5499cILL2jy5MmSfPdeud1ude7cWS%2B%2B%2BKLatm0rScrKylJFRYVuvfVWVVVV6brrrtOjjz5q0Z4BAIDGIMhb3zu6UScu13Hj63Q4ghUTE67S0oqAuL5%2B01Mf2F3COXlz0rV2l1BngXYsNAR64EMffOgDPajRpk2kLdttdJcIAQAA7EbAAgAAMIyABQAAYJjlAatfv37Ky8vToUOHrN40AACAJSwPWLfffrveeOMNDRgwQPfcc482bdpU%2B2lAAACAxsDygJWZmak33nhDr7zyirp06aInn3xSffr00cKFC7Vv3z6rywEAADDOtnuwunfvrqlTp%2Brtt9/WjBkz9Morr2jQoEEaM2aMPv/8c7vKAgAAqDfbAtapU6f0xhtv6N5779XUqVMVHx%2Bv6dOnq2vXrho9erTWr19vV2kAAAD1YvmT3IuLi7Vq1Sq99tprqqio0MCBA7V8%2BXJdeeWVtcv07NlTjz76qAYPHmx1eQAAAPVmecC6%2Beab1bFjR40dO1a33XbbGb8vsE%2BfPjp27JjVpQEAABhhecBasWKFrrrqqp9c7rPPPrOgGgAAAPMsvwcrKSlJ48aNq/2CZkl68cUXde%2B998rtdltdDgAAgHGWB6x58%2Bbp%2BPHj6ty5c%2B1Y3759VV1drfnz51tdDgAAgHGWXyJ8//33tX79esXExNSOdejQQTk5ObrlllusLgcAAMA4y89gnThxQmFhYacXEhysyspKq8sBAAAwzvKA1bNnT82fP1/fffdd7di3336rOXPm%2BD2qAQAAIFBZfolwxowZuvvuu3XNNdcoIiJC1dXVqqioUGJion73u99ZXQ4AAIBxlgesxMREvf7663rvvfe0f/9%2BBQcHq2PHjurVq5dCQkKsLgcAAMA4ywOWJIWGhmrAgAF2bBoAAKDBWR6wvvnmG%2BXm5urLL7/UiRMnTpt/6623rC4JAADAKFvuwSopKVGvXr3UsmVLqzcPAADQ4CwPWDt27NBbb72l2NhYqzcNAABgCcsf09C6dWvOXAEAgEbN8oA1duxY5eXlyev1Wr1pAAAAS1h%2BifC9997TJ598ojVr1qhdu3YKDvbPeC%2B//LLVJQEAABhlecCKiIhQ7969rd4sAACAZSwPWPPmzbN6kwAAAJay/B4sSdq7d68WL16s6dOn14797W9/s6MUAAAA4ywPWNu2bdOQIUO0adMmFRYWSvI9fHTUqFE8ZBQAADQKlgesRYsW6aGHHtL69esVFBQkyff9hPPnz9eSJUvOaV1bt25VWlqasrOzT5t74403NHjwYF1%2B%2BeUaNmyY3n///dq56upqLVq0SP3791fPnj01ZswYffPNN7XzbrdbkyZNUlpamnr16qWZM2ee8anzAAAAZ2J5wPriiy80cuRISaoNWJJ04403qri4uM7rWbp0qebOnav27dufNrdr1y5NnTpVU6ZM0V/%2B8heNHj1aDzzwgA4fPixJWrlypdavX6/8/Hy9/fbb6tChgzIzM2sfHTF79mxVVlaqsLBQq1evVnFxsXJycuqz2wAAoAmxPGBFRkae8WxQSUmJQkND67yesLAwrVq16owB69VXX1WfPn3Up08fhYWFaciQIbr44ou1bt06SVJBQYFGjx6tTp06KSIiQtnZ2SouLtZnn32mI0eOaPPmzcrOzlZsbKzi4%2BN1//33a/Xq1Tp16tS/v%2BMAAKDJsDxgXXHFFXryySdVXl5eO7Zv3z5NnTpV11xzTZ3XM2rUKEVGRp5xrqioSN26dfMb69atm5xOp06cOKGvvvrKbz4iIkLt27eX0%2BnUrl27FBISoqSkpNr57t276/vvv9fevXvrXB8AAGi6LH9Mw/Tp03XXXXcpNTVVHo9HV1xxhSorK9WlSxfNnz/fyDbcbreio6P9xqKjo/XVV1/pu%2B%2B%2Bk9frPeN8aWmpWrVqpYiICL/LlzXLlpaW1mn7JSUlcrlcfmMOR0vFxcX9O7tzViEhwX5/wiyHI3D6yrFAD2rQBx/6QA/sZnnAatu2rQoLC/Xuu%2B9q3759at68uTp27Khrr73WL9TU1099Fc%2B/mq/v1/gUFBQoLy/PbywzM1NZWVn1Wu/ZREW1aJD1NnUxMeF2l3DOOBboQQ364EMf6IFdLA9YktSsWTMNGDCgwdYfExMjt9vtN%2BZ2uxUbG6tWrVopODj4jPOtW7dWbGysysvL5fF4FBISUjsn%2Bb6oui4yMjLUr18/vzGHo6VKSyv%2B3V06o5CQYEVFtVBZWaU8nmqj64aM/7waEscCPahBH3zoAz2oYdcvy5YHrH79%2Bv3LM1UmnoWVnJysHTt2%2BI05nU7dfPPNCgsLU5cuXVRUVKSrrrpKklRWVqb9%2B/frkksuUUJCgrxer3bv3q3u3bvXvjcqKkodO3as0/bj4uJOuxzoch1XVVXDHOAeT3WDrbspC8SecizQgxr0wYc%2B0AO7WB6wBg0a5BewPB6P9u3bJ6fTqbvuusvINu644w6lp6frnXfe0TXXXKP169fr66%2B/1pAhQyRJI0eOVH5%2Bvnr37q34%2BHjl5OSoa9euSklJkSQNHDhQTz31lBYsWKCTJ09qyZIlSk9Pl8Nhywk/AAAQYCxPDFOmTDnj%2BMaNG/XXv/61zuupCUNVVVWSpM2bN0vynW26%2BOKLlZOTo3nz5ungwYPq3LmznnvuObVp00aSNGLECLlcLt15552qqKhQamqq3z1Tjz32mB555BH1799fzZo10y233HLGh5kCAACcSZC3vnd0G%2BLxeJSWlnZOISuQuFzHja/T4QhWTEy4SksrAuL0701PfWB3CefkzUnX2l1CnQXasdAQ6IEPffChD/SgRps2Z36kU0P72Xx2c%2BfOnfX%2B9B4AAMDPgeWXCEeMGHHaWGVlpYqLi3XDDTdYXQ4AAIBxlgesDh06nPYpwrCwMKWnp2v48OFWlwMAAGCc5QHL1NPaAQAAfq4sD1ivvfZanZe97bbbGrASAACAhmF5wJo5c6aqq6tPu6E9KCjIbywoKIiABQAAApLlAev555/XCy%2B8oHHjxikpKUler1d79uzR0qVL9Z//%2BZ9KTU21uqSA1mPmBrtLAAAAP2LLPVj5%2BfmKj4%2BvHevRo4cSExM1ZswYFRYWWl0SAACAUZY/B%2Bvrr79WdHT0aeNRUVE6ePCg1eUAAAAYZ3nASkhI0Pz581VaWlo7VlZWptzcXF144YVWlwMAAGCc5ZcIZ8yYocmTJ6ugoEDh4eEKDg5WeXm5mjdvriVLllhdDgAAgHGWB6xevXrpnXfe0bvvvqvDhw/L6/UqPj5ev/zlLxUZac/3BQEAAJhkecCSpBYtWqh///46fPiwEhMT7SgBAACgwVh%2BD9aJEyc0depUXX755brpppsk%2Be7Buueee1RWVmZ1OQAAAMZZHrAWLlyoXbt2KScnR8HB/9i8x%2BNRTk6O1eUAAAAYZ3nA2rhxo/7nf/5HN954Y%2B2XPkdFRWnevHnatGmT1eUAAAAYZ3nAqqioUIcOHU4bj42N1ffff291OQAAAMZZHrAuvPBC/fWvf5Ukv%2B8e3LBhgy644AKrywEAADDO8k8R/sd//IcmTJig22%2B/XdXV1Vq2bJl27NihjRs3aubMmVaXAwAAYJzlASsjI0MOh0MvvfSSQkJC9Oyzz6pjx47KycnRjTfeaHU5AAAAxlkesI4dO6bbb79dt99%2Bu9WbBgAAsITl92D179/f794rAACAxsbygJWamqo333zT6s0CAABYxvJLhOeff76eeOIJ5efn68ILL1SzZs385nNzc60uCQAAwCjLA9ZXX32liy66SJJUWlpq9eYBAAAanGUBKzs7W4sWLdLvfve72rElS5YoMzPTqhIAAAAsYdk9WFu2bDltLD8/36rNAwAAWMaygHWmTw7yaUIAANAYWRawar7Y%2BafGAAAAAp3lj2kAAABo7Cz/FKEVPvroI919991%2BY16vV6dOndKKFSs0atQohYaG%2Bs3/93//t2666SZJ0ooVK7Ry5Uq5XC4lJSVp5syZSk5Otqx%2BAAAQ2CwLWKdOndLkyZN/cszEc7B69uwpp9PpN/bss89q9%2B7dkqSEhIQz3nQv%2BW7GX7x4sZ5//nklJSVpxYoVGjdunDZt2qSWLVvWuzYAAND4WXaJ8Morr1RJSYnf60xjDeH//u//tGzZMj388MM/uWxBQYGGDRumSy%2B9VM2bN9c999wjSXr77bcbpDYAAND4WHYG65%2Bff2W1p59%2BWrfffrsuuOACffPNN6qoqFBmZqa2b9%2Bu0NBQ3X333Ro9erSCgoJUVFSkQYMG1b43ODhYXbt2ldPp1M0331yn7ZWUlMjlcvmNORwtFRcXZ3S/QkK4ha4hORyB09%2BaY6EpHxP0wIc%2B%2BNAHemC3RnkP1j87cOCANm3apE2bNkmSIiIidPHFF%2Buuu%2B7SokWL9OGHH2rixImKjIxUenq63G63oqOj/dYRHR19Tk%2BdLygoUF5ent9YZmamsrKy6r9DsExMTLjdJZyzqKgWdpdgO3rgQx986AM9sEujD1grV67UDTfcoDZt2kiSunfv7nc2rVevXhoxYoTWrFmj9PR0SfV/PldGRob69evnN%2BZwtFRpaUW91vtj/FbSsEz/vBpSSEiwoqJaqKysUh5Ptd3l2IIe%2BNAHH/pAD2rY9ctyow9YGzdu1NSpU//lMgkJCdq4caMkKSYmRm6322/e7XarS5cudd5mXFzcaZcDXa7jqqpqugd4IArEn5fHUx2QdZtED3zogw99oAd2adSnQHbt2qWDBw/q2muvrR1788039fvf/95vub179yoxMVGSlJycrKKioto5j8ejnTt36tJLL7WmaAAAEPAadcDauXOnWrVqpYiIiNqxZs2aacGCBXr//fd16tQpffDBB1q9erVGjhwpSRo5cqRee%2B01ffrpp6qsrNQzzzyj0NBQ9e3b16a9AAAAgaZRXyI8cuRI7b1XNQYMGKAZM2bo8ccf16FDh3TeeedpxowZuuGGGyRJvXv31oMPPqhJkybp6NGjSklJUX5%2Bvpo3b27HLgAAgAAU5OUbly3hch03vk6HI1jX52w1vl74vDnp2p9e6GfC4QhWTEy4Sksrmuy9FvTAhz740Ad6UKNNm0hbttuoLxECAADYgYAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwxptwEpKSlJycrJSUlJqX48//rgkadu2bUpPT9cVV1yhm2%2B%2BWevWrfN774oVKzRw4EBdccUVGjlypHbs2GHHLgAAgADlsLuAhrRhwwa1a9fOb6ykpET333%2B/Zs6cqcGDB%2Bvjjz/W%2BPHj1bFjR6WkpGjLli1avHixnn/%2BeSUlJWnFihUaN26cNm3apJYtW9q0JwAAIJA02jNYZ7N%2B/Xp16NBB6enpCgsLU1pamvr166dXX31VklRQUKBhw4bp0ksvVfPmzXXPPfdIkt5%2B%2B207ywYAAAGkUQes3Nxc9e3bVz169NDs2bNVUVGhoqIidevWzW%2B5bt261V4G/PF8cHCwunbtKqfTaWntAAAgcDXaS4SXXXaZ0tLStGDBAn3zzTeaNGmS5syZI7fbrfj4eL9lW7VqpdLSUkmS2%2B1WdHS033x0dHTtfF2UlJTI5XL5jTkcLRUXF/dv7s2ZhYQ06nxsO4cjcPpbcyw05WOCHvjQBx/6QA/s1mgDVkFBQe1/d%2BrUSVOmTNH48eN15ZVX/uR7vV5vvbedl5fnN5aZmamsrKx6rRegGuJNAAAPFElEQVTWiokJt7uEcxYV1cLuEmxHD3zogw99oAd2abQB68fatWsnj8ej4OBgud1uv7nS0lLFxsZKkmJiYk6bd7vd6tKlS523lZGRoX79%2BvmNORwtVVpa8W9Wf2b8VtKwTP%2B8GlJISLCiolqorKxSHk%2B13eXYgh740Acf%2BkAPatj1y3KjDFg7d%2B7UunXrNG3atNqx4uJihYaGqk%2BfPvrjH//ot/yOHTt06aWXSpKSk5NVVFSkoUOHSpI8Ho927typ9PT0Om8/Li7utMuBLtdxVVU13QM8EAXiz8vjqQ7Iuk2iBz70wYc%2B0AO7NMpTIK1bt1ZBQYHy8/N18uRJ7du3T08//bQyMjJ066236uDBg3r11Vf1ww8/6N1339W7776rO%2B64Q5I0cuRIvfbaa/r0009VWVmpZ555RqGhoerbt6%2B9OwUAAAJGozyDFR8fr/z8fOXm5tYGpKFDhyo7O1thYWF67rnnNHfuXM2ZM0cJCQlauHChfvGLX0iSevfurQcffFCTJk3S0aNHlZKSovz8fDVv3tzmvQIAAIEiyFvfO7pRJy7XcePrdDiCdX3OVuPrhc%2Bbk661u4Q6cziCFRMTrtLSiiZ7KYAe%2BNAHH/pAD2q0aRNpy3Yb5SVCAAAAOxGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADHPYXQDwc3XTUx/YXcI52f7EjXaXAAD4/ziDBQAAYBgBCwAAwDACFgAAgGGNNmAdPHhQmZmZSk1NVVpamqZNm6aysjIdOHBASUlJSklJ8Xv99re/rX3vG2%2B8ocGDB%2Bvyyy/XsGHD9P7779u4JwAAINA02pvcx40bp%2BTkZG3ZskXHjx9XZmamFixYoPHjx0uSnE7nGd%2B3a9cuTZ06VXl5ebr66qu1ceNGPfDAA9qwYYPatm1r5S4AAIAA1SjPYJWVlSk5OVmTJ09WeHi42rZtq6FDh2r79u0/%2Bd5XX31Vffr0UZ8%2BfRQWFqYhQ4bo4osv1rp16yyoHAAANAaN8gxWVFSU5s2b5zd26NAhxcXF1f794Ycf1p///GdVVVVp%2BPDhysrKUrNmzVRUVKQ%2Bffr4vbdbt25nPeN1JiUlJXK5XH5jDkdLv%2B2bEBLSKPMx6qEpHxM1%2B96UeyDRhxr0gR7YrVEGrB9zOp166aWX9Mwzzyg0NFSXX365rr/%2Bej3xxBPatWuXJkyYIIfDoYkTJ8rtdis6Otrv/dHR0frqq6/qvL2CggLl5eX5jWVmZiorK8vI/gBnExXVwu4SbEcPfOiDD32gB3Zp9AHr448/1vjx4zV58mSlpaVJkl5%2B%2BeXa%2BUsuuURjx47Vc889p4kTJ0qSvF5vvbaZkZGhfv36%2BY05HC1VWlpRr/X%2BGL%2BV4MfKyirl8VTbXYYtQkKCFRXVokn3QKIPNegDPagRExNuy3YbdcDasmWLHnroIc2ePVu33XbbWZdLSEjQkSNH5PV6FRMTI7fb7TfvdrsVGxtb5%2B3GxcWddjnQ5Tquqqqme4DDGh5PdZM/zuiBD33woQ/0wC6N9hTIJ598oqlTp%2Brpp5/2C1fbtm3TM88847fs3r17lZCQoKCgICUnJ2vHjh1%2B806nU5deeqkldQMAgMDXKANWVVWVZs2apSlTpqhXr15%2Bc5GRkVqyZInWrl2rU6dOyel06re//a1GjhwpSbrjjjv05z//We%2B8845%2B%2BOEHrVq1Sl9//bWGDBlix64AAIAAFOSt7w1HP0Pbt2/Xr371K4WGhp42t2HDBu3cuVN5eXn6%2BuuvFRkZqTvvvFP33nuvgoN9eXPTpk3Kzc3VwYMH1blzZ82cOVM9e/asV00u1/F6vf9MHI5gXZ%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%2BMl1lJSUyOVy%2BY05HC0VFxdntNaQEK7wIjDd9NQHdpdwTv405Zd2l1BnNf8uNPV/H%2BgDPbAbAetH3G63oqKi/MZqwlZpaWmdAlZBQYHy8vL8xh544AFNmDDBXKHyBbm72n6pjIwM4%2BEtUJSUlKigoKBJ90CiDxI9qFFSUqLly5%2BnD/SBHtiMWHsGXq%2B3Xu/PyMjQmjVr/F4ZGRmGqvsHl8ulvLy8086WNSX0wIc%2B0IMa9MGHPtADu3EG60diY2Pldrv9xtxut4KCghQbG1undcTFxfHbAgAATRhnsH4kOTlZhw4d0rFjx2rHnE6nOnfurPDwcBsrAwAAgYKA9SPdunVTSkqKcnNzVV5eruLiYi1btkwjR460uzQAABAgQh599NFH7S7i5%2BaXv/ylCgsL9fjjj%2Bv1119Xenq6xowZo6CgILtLO014eLiuuuqqJn12jR740Ad6UIM%2B%2BNAHemCnIG997%2BgGAACAHy4RAgAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwApABw8e1H333afU1FRdd911Wrhwoaqrq%2B0uyxZbt25VWlqasrOz7S7FNgcPHlRmZqZSU1OVlpamadOmqayszO6yLLV7927ddddduvLKK5WWlqZJkybJ5XLZXZZtnnzySSUlJdldhi2SkpKUnJyslJSU2tfjjz9ud1m2eOaZZ9SrVy9ddtllGj16tA4cOGB3SU0KASsATZgwQfHx8dq8ebOWLVumzZs3a/ny5XaXZbmlS5dq7ty5at%2B%2Bvd2l2GrcuHGKiorSli1btGbNGn355ZdasGCB3WVZ5uTJk7r77rt11VVXadu2bSosLNTRo0fVVL9mddeuXVq7dq3dZdhqw4YNcjqdta/Zs2fbXZLlVq5cqXXr1mnFihV6//331blzZ7344ot2l9WkELACjNPp1O7duzVlyhRFRkaqQ4cOGj16tAoKCuwuzXJhYWFatWpVkw5YZWVlSk5O1uTJkxUeHq62bdtq6NCh2r59u92lWaayslLZ2dkaO3asQkNDFRsbq%2Buvv15ffvml3aVZrrq6Wo888ohGjx5tdymw2QsvvKDs7GxddNFFioiI0KxZszRr1iy7y2pSCFgBpqioSAkJCYqOjq4d6969u/bt26fy8nIbK7PeqFGjFBkZaXcZtoqKitK8efN03nnn1Y4dOnRIcXFxNlZlrejoaA0fPlwOh0OStHfvXv3xj3/UTTfdZHNl1nv55ZcVFhamwYMH212KrXJzc9W3b1/16NFDs2fPVkVFhd0lWerbb7/VgQMH9N1332nQoEFKTU1VVlaWjh07ZndpTQoBK8C43W5FRUX5jdWErdLSUjtKws%2BI0%2BnUSy%2B9pPHjx9tdiuUOHjyo5ORkDRo0SCkpKcrKyrK7JEsdOXJEixcv1iOPPGJ3Kba67LLLlJaWpk2bNqmgoECffvqp5syZY3dZljp8%2BLAk36XSZcuWae3atTp8%2BDBnsCxGwApAXq/X7hLwM/Txxx9rzJgxmjx5stLS0uwux3IJCQlyOp3asGGDvv76az388MN2l2SpefPmadiwYercubPdpdiqoKBAw4cPV2hoqDp16qQpU6aosLBQJ0%2BetLs0y9T8P%2BKee%2B5RfHy82rZtqwkTJmjLli364YcfbK6u6SBgBZjY2Fi53W6/MbfbraCgIMXGxtpUFey2ZcsW3XfffZoxY4ZGjRpldzm2CQoKUocOHZSdna3CwsImc0lk27Zt%2Btvf/qbMzEy7S/nZadeunTwej44ePWp3KZapuWXgn692JCQkyOv1Nqk%2B2I2AFWCSk5N16NAhv/9xOJ1Ode7cWeHh4TZWBrt88sknmjp1qp5%2B%2BmnddtttdpdjuW3btmngwIF%2BjyoJDvb909asWTO7yrLUunXrdPToUV133XVKTU3VsGHDJEmpqal6/fXXba7OOjt37tT8%2BfP9xoqLixUaGtqk7kts27atIiIitGvXrtqxgwcPqlmzZk2qD3YjYAWYbt26KSUlRbm5uSovL1dxcbGWLVumkSNH2l0abFBVVaVZs2ZpypQp6tWrl93l2CI5OVnl5eVauHChKisrdezYMS1evFg9evRoMh%2BCmDZtmjZu3Ki1a9dq7dq1ys/PlyStXbtW/fr1s7k667Ru3VoFBQXKz8/XyZMntW/fPj399NPKyMhQSEiI3eVZxuFwKD09Xc8%2B%2B6z%2B/ve/6%2BjRo1qyZIkGDx5c%2B2EQNLwgLzf0BJzDhw9r9uzZ%2BvDDDxUREaERI0bogQceUFBQkN2lWSolJUWSL2RIqv2Hw%2Bl02laT1bZv365f/epXCg0NPW1uw4YNSkhIsKEq6%2B3Zs0dz587V559/rpYtW%2Brqq6/WtGnTFB8fb3dptjhw4ID69%2B%2BvPXv22F2K5T766CPl5uZqz549Cg0N1dChQ5Wdna2wsDC7S7PUyZMnNW/ePL3%2B%2Bus6deqUBg4cqNmzZ3Olw0IELAAAAMO4RAgAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhv0/8VUrye0W9WEAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common6506926068928033928”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.1918951132300357</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.8413967185527977</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5730936006849504</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.088139281828074</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.3196093956188968</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.1439553399835272</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6134526558891455</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.819672131147541</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.42992261392949266</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3091)</td> <td class=”number”>3092</td> <td class=”number”>96.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme6506926068928033928”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0410711352061771</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.13412017167381973</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.13576349998302956</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.1419244961680386</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>3.1979037529584136</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.199777406789093</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.5569105691056913</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.7787182587666264</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.658001155401502</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SNAPSPTH16”>SNAPSPTH16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3094</td>

</tr> <tr>

<th>Unique (%)</th> <td>96.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.2%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>104</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.93667</td>

</tr> <tr>

<th>Minimum</th> <td>0.1189</td>

</tr> <tr>

<th>Maximum</th> <td>6.0472</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram3381557328900682582”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives3381557328900682582,#minihistogram3381557328900682582”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives3381557328900682582”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles3381557328900682582”

aria-controls=”quantiles3381557328900682582” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram3381557328900682582” aria-controls=”histogram3381557328900682582”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common3381557328900682582” aria-controls=”common3381557328900682582”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme3381557328900682582” aria-controls=”extreme3381557328900682582”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles3381557328900682582”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0.1189</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.45336</td>

</tr> <tr>

<th>Q1</th> <td>0.69339</td>

</tr> <tr>

<th>Median</th> <td>0.88679</td>

</tr> <tr>

<th>Q3</th> <td>1.1159</td>

</tr> <tr>

<th>95-th percentile</th> <td>1.5692</td>

</tr> <tr>

<th>Maximum</th> <td>6.0472</td>

</tr> <tr>

<th>Range</th> <td>5.9283</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.42254</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.37506</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.40041</td>

</tr> <tr>

<th>Kurtosis</th> <td>20.834</td>

</tr> <tr>

<th>Mean</th> <td>0.93667</td>

</tr> <tr>

<th>MAD</th> <td>0.27001</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.4375</td>

</tr> <tr>

<th>Sum</th> <td>2909.3</td>

</tr> <tr>

<th>Variance</th> <td>0.14067</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram3381557328900682582”>

<img 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kVq1a/eTyCQkJKikpUWxsrKTvn%2BMKD////7h%2B8803atq0qeX9p6enq0%2BfPj5jDkdjlZaWW95GSEiwoqIaqazspKqqqi2vB3Nq8/2qyziXrKFPNaNH1tAna%2Bzqk79%2BWA7IgPX444/r888/10svvaQmTZr4zP33f/%2B3unbtqmuvvdY7VlxcrEGDBqlVq1aKjo5WUVGREhISJEl///vfdfr0aSUlJVnef1xc3Bm3A12u46qsrP0JVFVV/bPWw/kLtL5zLllDn2pGj6yhT9YEap8C7sbnRx99pHXr1ikvL%2B%2BMcCV9f3Vqzpw52rt3r7777js999xz2r9/v4YNG6aQkBDdfvvtevrpp3Xo0CGVlpbqiSee0A033OB9jQMAAEBN6uUVrOTkZEnf/4agJBUWFkqSnE6nVq9erePHj%2Bv666/3Wad79%2B567rnnNHnyZEnfP3vldrvVvn17Pf/882rRooUkKTMzU%2BXl5br55ptVWVmp66%2B/Xg8//LBNRwYAAAJBkOd8n%2BiGJS7X8Vot73AEKyYmXKWl5QFz6XTg4vf9XUKtvDHpOn%2BXYEQgnksXAn2qGT2yhj5ZY1efmjc/%2B2udLrSAu0UIAADgbwQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYJjtAatPnz7Kzc3VoUOH7N41AACALWwPWLfeeqtef/119evXT3fffbc2b97sfSM7AABAILA9YGVkZOj111/XK6%2B8og4dOujxxx9Xr169tGDBAu3bt8/ucgAAAIzz2zNYXbp00dSpU/XWW29p%2BvTpeuWVVzRo0CCNGTNGn332mb/KAgAAOG9%2BC1gVFRV6/fXX9dvf/lZTp05VfHy8HnroIXXq1EmjR4/W%2BvXr/VUaAADAeXHYvcPi4mKtWrVKr732msrLyzVgwAC98MILuuqqq7zLdO/eXQ8//LCGDBlid3kAAADnzfaANXjwYLVt21Zjx47VLbfcoiZNmpyxTK9evXTs2DG7SwMAADDC9oC1YsUKXX311TUu9%2Bmnn9pQDQAAgHm2P4OVmJiocePGqbCw0Dv2/PPP67e//a3cbrfd5QAAABhne8DKzs7W8ePH1b59e%2B9Y7969VV1drXnz5tldDgAAgHG23yLcunWr1q9fr5iYGO9YmzZtlJOTo5tuusnucgAAAIyz/QrWqVOnFBYWdmYhwcE6efKk3eUAAAAYZ3vA6t69u%2BbNm6dvvvnGO/b1119rzpw5Pq9qAAAAqK9sv0U4ffp03XXXXbr22msVERGh6upqlZeXq1WrVvrDH/5gdzkAAADG2R6wWrVqpQ0bNujdd9/V/v37FRwcrLZt26pHjx4KCQmxuxwAAADjbA9YkhQaGqp%2B/fr5Y9cAAAAXnO0B66uvvtLChQv1%2Beef69SpU2fMv/nmm3aXBAAAYJRfnsEqKSlRjx491LhxY7t3DwAAcMHZHrB27NihN998U7GxsXbvGgAAwBa2v6ahadOmXLkCAAABzfaANXbsWOXm5srj8di9awAAAFvYfovw3Xff1ccff6w1a9aoZcuWCg72zXgvv/yy3SUBAAAYZXvAioiIUM%2BePe3eLQAAgG1sD1jZ2dl27xIAAMBWtj%2BDJUl79%2B7VkiVL9NBDD3nH/va3v9V6O%2B%2B9955SU1OVlZV1xtzrr7%2BuIUOGqGvXrho%2BfLi2bt3qnauurtaiRYvUt29fde/eXWPGjNFXX33lnXe73Zo0aZJSU1PVo0cPzZgx46zv7AIAADgb2wPWtm3bNHToUG3evFkFBQWSvn/56KhRo2r1ktFly5Zp7ty5at269Rlzu3bt0tSpUzVlyhT95S9/0ejRo3Xffffp8OHDkqSVK1dq/fr1ysvL01tvvaU2bdooIyPD%2B%2BD9rFmzdPLkSRUUFGj16tUqLi5WTk6OgaMHAAC/BLYHrEWLFumBBx7Q%2BvXrFRQUJOn7v084b948LV261PJ2wsLCtGrVqrMGrFdffVW9evVSr169FBYWpqFDh6pjx45at26dJCk/P1%2BjR49Wu3btFBERoaysLBUXF%2BvTTz/VkSNHVFhYqKysLMXGxio%2BPl733nuvVq9erYqKCjNNAAAAAc32Z7D%2B/ve/68UXX5Qkb8CSpBtvvFHTp0%2B3vJ1Ro0adc66oqEi9evXyGevcubOcTqdOnTqlL774Qp07d/bORUREqHXr1nI6nTp%2B/LhCQkKUmJjone/SpYu%2B/fZb7d2712f8XEpKSuRyuXzGHI7GiouLs3p4CgkJ9vkM%2BzkcgdF7ziVr6FPN6JE19MmaQO%2BT7QErMjJSp06dUmhoqM94SUnJGWM/l9vtVnR0tM9YdHS0vvjiC33zzTfyeDxnnS8tLVWTJk0UERHhE/5%2BWLa0tNTS/vPz85Wbm%2BszlpGRoczMzFofS1RUo1qvAzNiYsL9XYJRnEvW0Kea0SNr6JM1gdon2wPWlVdeqccff1wzZ870ju3bt0%2BzZ8/Wtddea2w/Nb3I9Kfmz/clqOnp6erTp4/PmMPRWKWl5Za3ERISrKioRiorO6mqqurzqgc/T22%2BX3UZ55I19Klm9Mga%2BmSNXX3y1w/Ltgeshx56SHfeeadSUlJUVVWlK6%2B8UidPnlSHDh00b948I/uIiYmR2%2B32GXO73YqNjVWTJk0UHBx81vmmTZsqNjZWJ06cUFVVlUJCQrxz0vd/5seKuLi4M24HulzHVVlZ%2BxOoqqr6Z62H8xdofedcsoY%2B1YweWUOfrAnUPtkesFq0aKGCggK988472rdvnxo2bKi2bdvquuuu87ktdz6SkpK0Y8cOnzGn06nBgwcrLCxMHTp0UFFRka6%2B%2BmpJUllZmfbv36/LLrtMCQkJ8ng82r17t7p06eJdNyoqSm3btjVSHwAACGy2ByxJatCggfr163fBtn/77bcrLS1Nb7/9tq699lqtX79eX375pYYOHSpJGjlypPLy8tSzZ0/Fx8crJydHnTp1UnJysiRpwIABWrx4sebPn6/Tp09r6dKlSktLk8Phl3YBAIB6xvbE0KdPn5%2B8UmX1XVg/hKHKykpJUmFhoaTvrzZ17NhROTk5ys7O1sGDB9W%2BfXs988wzat68uSRpxIgRcrlcuuOOO1ReXq6UlBSfh9IfeeQRzZ49W3379lWDBg100003nfVlpgAAAGdje8AaNGiQT8CqqqrSvn375HQ6deedd1rejtPp/Mn5/v37q3///medCwoKUmZm5jl/qy8yMlJPPPGE5VoAAAD%2Ble0Ba8qUKWcd37Rpk/7617/aXA0AAIB5debtXv369dOGDRv8XQYAAMB5qzMBa%2BfOnef9/ikAAIC6wPZbhCNGjDhj7OTJkyouLj7nM1MAAAD1ie0Bq02bNmf8FmFYWJjS0tJ022232V0OAACAcbYHLFNvawcAAKirbA9Yr732muVlb7nllgtYCQAAwIVhe8CaMWOGqqurz3igPSgoyGcsKCiIgAUAAOol2wPWs88%2Bq%2Beee07jxo1TYmKiPB6P9uzZo2XLluk//uM/lJKSYndJAAAARvnlGay8vDzFx8d7x7p166ZWrVppzJgxKigosLskAAAAo2x/D9aXX36p6OjoM8ajoqJ08OBBu8sBAAAwzvaAlZCQoHnz5qm0tNQ7VlZWpoULF%2BqSSy6xuxwAAADjbL9FOH36dE2ePFn5%2BfkKDw9XcHCwTpw4oYYNG2rp0qV2lwMAAGCc7QGrR48eevvtt/XOO%2B/o8OHD8ng8io%2BP169//WtFRkbaXQ4AAIBxtgcsSWrUqJH69u2rw4cPq1WrVv4oAQAA4IKx/RmsU6dOaerUqeratasGDhwo6ftnsO6%2B%2B26VlZXZXQ4AAIBxtgesBQsWaNeuXcrJyVFw8P/vvqqqSjk5OXaXAwAAYJztAWvTpk36r//6L914443eP/ocFRWl7Oxsbd682e5yAAAAjLP9Gazy8nK1adPmjPHY2Fh9%2B%2B23dpdT7w1c/L6/SwAAAD9i%2BxWsSy65RH/9618lyedvD27cuFEXX3yx3eUAAAAYZ/sVrH/7t3/ThAkTdOutt6q6ulrLly/Xjh07tGnTJs2YMcPucgAAAIyzPWClp6fL4XDoxRdfVEhIiJ5%2B%2Bmm1bdtWOTk5uvHGG%2B0uBwAAwDjbA9axY8d066236tZbb7V71wAAALaw/Rmsvn37%2Bjx7BQAAEGhsD1gpKSl644037N4tAACAbWy/RXjRRRfpscceU15eni655BI1aNDAZ37hwoV2lwQAAGCU7QHriy%2B%2B0KWXXipJKi0ttXv3AAAAF5xtASsrK0uLFi3SH/7wB%2B/Y0qVLlZGRYVcJAAAAtrDtGawtW7acMZaXl2fX7gEAAGxjW8A6228O8tuEAAAgENl2i/CHP%2Bxc05gJH374oe666y6fMY/Ho4qKCq1YsUKjRo1SaGioz/x//ud/auDAgZKkFStWaOXKlXK5XEpMTNSMGTOUlJR0QWoFAACBx/aH3O3QvXt3OZ1On7Gnn35au3fvliQlJCSc9Zal9P2tzCVLlujZZ59VYmKiVqxYoXHjxmnz5s1q3LjxBa8dAADUf7a/B8sf/vd//1fLly/Xgw8%2BWOOy%2Bfn5Gj58uC6//HI1bNhQd999tyTprbfeutBlAgCAAGHbFayKigpNnjy5xrEL8R6sJ598UrfeeqsuvvhiffXVVyovL1dGRoa2b9%2Bu0NBQ3XXXXRo9erSCgoJUVFSkQYMGedcNDg5Wp06d5HQ6NXjwYEv7Kykpkcvl8hlzOBorLi7Ocs0hIcE%2Bn2E/hyMwes%2B5ZA19qhk9soY%2BWRPofbItYF111VUqKSmpccy0AwcOaPPmzdq8ebMkKSIiQh07dtSdd96pRYsW6YMPPtDEiRMVGRmptLQ0ud1uRUdH%2B2wjOjq6Vu/sys/PV25urs9YRkaGMjMza11/VFSjWq8DM2Jiwv1dglGcS9bQp5rRI2vokzWB2ifbAta/vv/KTitXrlT//v3VvHlzSVKXLl18aunRo4dGjBihNWvWKC0tTdL5/3Zjenq6%2BvTp4zPmcDRWaWm55W2EhAQrKqqRyspOqqqq%2Brzqwc9Tm%2B9XXca5ZA19qhk9soY%2BWWNXn/z1w3JAPuT%2BrzZt2qSpU6f%2B5DIJCQnatGmTJCkmJkZut9tn3u12q0OHDpb3GRcXd8btQJfruCora38CVVVV/6z1cP4Cre%2BcS9bQp5rRI2vokzWB2qfAvPH5T7t27dLBgwd13XXXecfeeOMN/fGPf/RZbu/evWrVqpUkKSkpSUVFRd65qqoq7dy5U5dffrk9RQMAgHovoAPWzp071aRJE0VERHjHGjRooPnz52vr1q2qqKjQ%2B%2B%2B/r9WrV2vkyJGSpJEjR%2Bq1117TJ598opMnT%2Bqpp55SaGioevfu7aejAAAA9U1A3yI8cuSI99mrH/Tr10/Tp0/Xo48%2BqkOHDqlZs2aaPn26%2BvfvL0nq2bOn7r//fk2aNElHjx5VcnKy8vLy1LBhQ38cAgAAqIeCPPy9Glu4XMdrtbzDEayYmHCVlpb/5L3pgYvfP9/ScA5vTLqu5oXqAavn0i8dfaoZPbKGPlljV5%2BaN4%2B8YNv%2BKQF9ixAAAMAfCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgWMAGrMTERCUlJSk5Odn78eijj0qStm3bprS0NF155ZUaPHiw1q1b57PuihUrNGDAAF155ZUaOXKkduzY4Y9DAAAA9ZTD3wVcSBs3blTLli19xkpKSnTvvfdqxowZGjJkiD766CONHz9ebdu2VXJysrZs2aIlS5bo2WefVWJiolasWKFx48Zp8%2BbNaty4sZ%2BOBAAA1CcBewXrXNavX682bdooLS1NYWFhSk1NVZ8%2BffTqq69KkvLz8zV8%2BHBdfvnlatiwoe6%2B%2B25J0ltvveXPsgEAQD0S0AFr4cKF6t27t7p166ZZs2apvLxcRUVF6ty5s89ynTt39t4G/PF8cHCwOnXqJKfTaWvtAACg/grYW4RXXHGFUlNTNX/%2BfH311VeaNGmS5syZI7fbrfj4eJ9lmzRpotLSUkmS2%2B1WdHS0z3x0dLR33oqSkhK5XC6fMYejseLi4ixvIyQk2Ocz7OdwBEbvOZesoU81o0fW0CdrAr1PARuw8vOYnyIqAAAPWklEQVTzvf%2B7Xbt2mjJlisaPH6%2BrrrqqxnU9Hs957zs3N9dnLCMjQ5mZmbXeVlRUo/OqBT9fTEy4v0swinPJGvpUM3pkDX2yJlD7FLAB68datmypqqoqBQcHy%2B12%2B8yVlpYqNjZWkhQTE3PGvNvtVocOHSzvKz09XX369PEZczgaq7S03PI2QkKCFRXVSGVlJ1VVVW15PZhTm%2B9XXca5ZA19qhk9soY%2BWWNXn/z1w3JABqydO3dq3bp1mjZtmnesuLhYoaGh6tWrl/70pz/5LL9jxw5dfvnlkqSkpCQVFRVp2LBhkqSqqirt3LlTaWlplvcfFxd3xu1Al%2Bu4KitrfwJVVVX/rPVw/gKt75xL1tCnmtEja%2BiTNYHap4C88dm0aVPl5%2BcrLy9Pp0%2Bf1r59%2B/Tkk08qPT1dN998sw4ePKhXX31V3333nd555x298847uv322yVJI0eO1GuvvaZPPvlEJ0%2Be1FNPPaXQ0FD17t3bvwcFAADqjYC8ghUfH6%2B8vDwtXLjQG5CGDRumrKwshYWF6ZlnntHcuXM1Z84cJSQkaMGCBfrVr34lSerZs6fuv/9%2BTZo0SUePHlVycrLy8vLUsGFDPx8VAACoL4I85/tENyxxuY7XanmHI1gxMeEqLS3/yUunAxe/f76l4RzemHSdv0swwuq59EtHn2pGj6yhT9bY1afmzSMv2LZ/SkDeIgQAAPAnAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYJjD3wUAddXAxe/7u4RaeWPSdf4uAQDwT1zBAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDAjZgHTx4UBkZGUpJSVFqaqqmTZumsrIyHThwQImJiUpOTvb5%2BP3vf%2B9d9/XXX9eQIUPUtWtXDR8%2BXFu3bvXjkQAAgPrG4e8CLpRx48YpKSlJW7Zs0fHjx5WRkaH58%2Bdr/PjxkiSn03nW9Xbt2qWpU6cqNzdX11xzjTZt2qT77rtPGzduVIsWLew8BAAAUE8F5BWssrIyJSUlafLkyQoPD1eLFi00bNgwbd%2B%2BvcZ1X331VfXq1Uu9evVSWFiYhg4dqo4dO2rdunU2VA4AAAJBQAasqKgoZWdnq1mzZt6xQ4cOKS4uzvv1gw8%2BqB49euiaa67RwoULVVFRIUkqKipS586dfbbXuXPnc17xAgAA%2BLGAvUX4r5xOp1588UU99dRTCg0NVdeuXXXDDTfoscce065duzRhwgQ5HA5NnDhRbrdb0dHRPutHR0friy%2B%2BsLy/kpISuVwunzGHo7FPwKtJSEiwz2egJg7H2c8VziVr6FPN6JE19MmaQO9TwAesjz76SOPHj9fkyZOVmpoqSXr55Ze985dddpnGjh2rZ555RhMnTpQkeTye89pnfn6%2BcnNzfcYyMjKUmZlZ621FRTU6r1rwyxETE/6T85xL1tCnmtEja%2BiTNYHap4AOWFu2bNEDDzygWbNm6ZZbbjnncgkJCTpy5Ig8Ho9iYmLkdrt95t1ut2JjYy3vNz09XX369PEZczgaq7S03PI2QkKCFRXVSGVlJ1VVVW15Pfxynev84lyyhj7VjB5ZQ5%2BssatPNf3weaEEbMD6%2BOOPNXXqVD355JPq0aOHd3zbtm365JNPvL9NKEl79%2B5VQkKCgoKClJSUpB07dvhsy%2Bl0avDgwZb3HRcXd8btQJfruCora38CVVVV/6z18MtT03nCuWQNfaoZPbKGPlkTqH0KyBuflZWVmjlzpqZMmeITriQpMjJSS5cu1dq1a1VRUSGn06nf//73GjlypCTp9ttv1//8z//o7bff1nfffadVq1bpyy%2B/1NChQ/1xKAAAoB4KyCtYn3zyiYqLizV37lzNnTvXZ27jxo1atGiRcnNz9bvf/U6RkZG64447dOedd0qSOnbsqJycHGVnZ%2BvgwYNq3769nnnmGTVv3twfhwIAAOqhgAxY3bp10549e845n5CQoBtuuOGc8/3791f//v0vRGkAAOAXICBvEQIAAPgTAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMc/i7AABmDFz8vr9LsOyNSdf5uwQAuKC4gnUWBw8e1D333KOUlBRdf/31WrBggaqrq/1dFgAAqCe4gnUWEyZMUJcuXVRYWKijR49q7NixatasmX7zm9/4uzQAAFAPcAXrR5xOp3bv3q0pU6YoMjJSbdq00ejRo5Wfn%2B/v0gAAQD3BFawfKSoqUkJCgqKjo71jXbp00b59%2B3TixAlFRETUuI2SkhK5XC6fMYejseLi4izXERIS7PMZCCT16XkxSfrzlF/7u4Q6gX%2BXrKFP1gR6nwhYP%2BJ2uxUVFeUz9kPYKi0ttRSw8vPzlZub6zN23333acKECZbrKCkp0QsvPKv09PSfDGbbH7vR8jYDUUlJifLz82vs0y8ZPbKGPtXM6r9Lv3T0yZpA71Ngxsbz5PF4zmv99PR0rVmzxucjPT29VttwuVzKzc0940oYfNGnmtEja%2BhTzeiRNfTJmkDvE1ewfiQ2NlZut9tnzO12KygoSLGxsZa2ERcXF5BpHAAAWMMVrB9JSkrSoUOHdOzYMe%2BY0%2BlU%2B/btFR4e7sfKAABAfUHA%2BpHOnTsrOTlZCxcu1IkTJ1RcXKzly5dr5MiR/i4NAADUEyEPP/zww/4uoq759a9/rYKCAj366KPasGGD0tLSNGbMGAUFBdlaR3h4uK6%2B%2BmqunNWAPtWMHllDn2pGj6yhT9YEcp%2BCPOf7RDcAAAB8cIsQAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACVh108OBB3XPPPUpJSdH111%2BvBQsWqLq62t9l1TnvvfeeUlNTlZWV5e9S6qyDBw8qIyNDKSkpSk1N1bRp01RWVubvsuqc3bt3684779RVV12l1NRUTZo0SS6Xy99l1VmPP/64EhMT/V1GnZSYmKikpCQlJyd7Px599FF/l1UnPfXUU%2BrRo4euuOIKjR49WgcOHPB3SUYRsOqgCRMmKD4%2BXoWFhVq%2BfLkKCwv1wgsv%2BLusOmXZsmWaO3euWrdu7e9S6rRx48YpKipKW7Zs0Zo1a/T5559r/vz5/i6rTjl9%2BrTuuusuXX311dq2bZsKCgp09OhR8Wdaz27Xrl1au3atv8uo0zZu3Cin0%2Bn9mDVrlr9LqnNWrlypdevWacWKFdq6davat2%2Bv559/3t9lGUXAqmOcTqd2796tKVOmKDIyUm3atNHo0aOVn5/v79LqlLCwMK1atYqA9RPKysqUlJSkyZMnKzw8XC1atNCwYcO0fft2f5dWp5w8eVJZWVkaO3asQkNDFRsbqxtuuEGff/65v0urc6qrqzV79myNHj3a36WgnnvuueeUlZWlSy%2B9VBEREZo5c6Zmzpzp77KMImDVMUVFRUpISFB0dLR3rEuXLtq3b59OnDjhx8rqllGjRikyMtLfZdRpUVFRys7OVrNmzbxjhw4dUlxcnB%2Brqnuio6N12223yeFwSJL27t2rP/3pTxo4cKCfK6t7Xn75ZYWFhWnIkCH%2BLqVOW7hwoXr37q1u3bpp1qxZKi8v93dJdcrXX3%2BtAwcO6JtvvtGgQYOUkpKizMxMHTt2zN%2BlGUXAqmPcbreioqJ8xn4IW6Wlpf4oCQHC6XTqxRdf1Pjx4/1dSp108OBBJSUladCgQUpOTlZmZqa/S6pTjhw5oiVLlmj27Nn%2BLqVOu%2BKKK5SamqrNmzcrPz9fn3zyiebMmePvsuqUw4cPS/r%2BVury5cu1du1aHT58mCtYuPA8Ho%2B/S0CA%2BeijjzRmzBhNnjxZqamp/i6nTkpISJDT6dTGjRv15Zdf6sEHH/R3SXVKdna2hg8frvbt2/u7lDotPz9ft912m0JDQ9WuXTtNmTJFBQUFOn36tL9LqzN%2B%2BG/c3Xffrfj4eLVo0UITJkzQli1b9N133/m5OnMIWHVMbGys3G63z5jb7VZQUJBiY2P9VBXqsy1btuiee%2B7R9OnTNWrUKH%2BXU6cFBQWpTZs2ysrKUkFBQcDdsvi5tm3bpr/97W/KyMjwdyn1TsuWLVVVVaWjR4/6u5Q644fHFv71bk1CQoI8Hk9A9YmAVcckJSXp0KFDPv%2BwO51OtW/fXuHh4X6sDPXRxx9/rKlTp%2BrJJ5/ULbfc4u9y6qRt27ZpwIABPq9CCQ7%2B/p/GBg0a%2BKusOmXdunU6evSorr/%2BeqWkpGj48OGSpJSUFG3YsMHP1dUdO3fu1Lx583zGiouLFRoayrOP/6JFixaKiIjQrl27vGMHDx5UgwYNAqpPBKw6pnPnzkpOTtbChQt14sQJFRcXa/ny5Ro5cqS/S0M9U1lZqZkzZ2rKlCnq0aOHv8ups5KSknTixAktWLBAJ0%2Be1LFjx7RkyRJ169aNX6T4p2nTpmnTpk1au3at1q5dq7y8PEnS2rVr1adPHz9XV3c0bdpU%2Bfn5ysvL0%2BnTp7Vv3z49%2BeSTSk9PV0hIiL/LqzMcDofS0tL09NNP6x//%2BIeOHj2qpUuXasiQId5fNgkEQR4e%2BKlzDh8%2BrFmzZumDDz5QRESERowYofvuu09BQUH%2BLq3OSE5OlvR9iJDk/T%2Bl0%2Bn0W011zfbt2/Xv//7vCg0NPWNu48aNSkhI8ENVddOePXs0d%2B5cffbZZ2rcuLGuueYaTZs2TfHx8f4urU46cOCA%2Bvbtqz179vi7lDrnww8/1MKFC7Vnzx6FhoZq2LBhysrKUlhYmL9Lq1NOnz6t7OxsbdiwQRUVFRowYIBmzZoVUHdqCFgAAACGcYsQAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAz7P1BZAyexAkbMAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common3381557328900682582”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.02564102564103</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.25339461040318</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.40252454417952</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.09784851164162</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.958772770853308</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.782472613458529</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.99535500995355</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.898600075671585</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.87260034904014</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.19047619047619</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3083)</td> <td class=”number”>3086</td> <td class=”number”>96.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>104</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme3381557328900682582”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.118901351512029</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.135354629128316</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.149186931224825</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.16860563142809</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.171781951442968</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>3.32778702163062</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.50877192982456</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.84857053094565</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.44174135723431</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.04717094945108</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SNAP_BBCE09”>SNAP_BBCE09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.46584</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>52.1%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-5276881443713412690”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-5276881443713412690”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-5276881443713412690”

aria-controls=”quantiles-5276881443713412690” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-5276881443713412690” aria-controls=”histogram-5276881443713412690”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-5276881443713412690” aria-controls=”common-5276881443713412690”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-5276881443713412690” aria-controls=”extreme-5276881443713412690”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-5276881443713412690”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>1</td>

</tr> <tr>

<th>95-th percentile</th> <td>1</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr>

<th>Range</th> <td>1</td>

</tr> <tr>

<th>Interquartile range</th> <td>1</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.49891</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.071</td>

</tr> <tr>

<th>Kurtosis</th> <td>-1.9825</td>

</tr> <tr>

<th>Mean</th> <td>0.46584</td>

</tr> <tr>

<th>MAD</th> <td>0.49767</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.13704</td>

</tr> <tr>

<th>Sum</th> <td>1459</td>

</tr> <tr>

<th>Variance</th> <td>0.24891</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-5276881443713412690”>

<img 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B0es3U0REuOz2DqqpqVNjY1Owywkp9NZc9Nc89NZ8ZvQ2WL/ch2TAampq0ty5c/Xhhx/qlVde0RVXXOGbi4%2BP952pOs/j8ahXr15KSEjwve7U6f//g3z11Vfq3Llzi4%2BfmJjY7HKgy3VKDQ2X9g%2BkFdff2NhkybqtgN6ai/6ah96aJ5R6a61TIC308MMP66OPPmoWriSpX79%2BKi8v971ubGzUoUOHlJ6eriuuuEKxsbF%2B83//%2B9919uxZ9evXL2D1AwAAawu5gPXBBx9o8%2BbNKi0tVVxcXLP5vLw8bdy4Ufv371ddXZ1Wr16t9u3ba9iwYYqIiNCdd96pp59%2BWp9//rncbrcef/xx3XzzzbrsssuCsBoAAGBFlrxEmJaWJumbTwhK0vbt2yVJTqdTGzZs0KlTpzR8%2BHC/9wwaNEjPPfecsrKyNHPmTM2YMUMnTpxQWlqaSktLFRUVJUkqLCxUbW2txo8fr4aGBg0fPlyLFi0K3OIAAIDlhXnbekc3WsTlOmX4Pm22cN1cvMfw/Zpl24wbg11Ci9ls4YqP7yS3uzZk7ge4WNBbc9Ff81ixt7944n%2BCXUKL7XtotCm97dIlxtD9tVTIXSIEAAAINgIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYLeMDKzs5WSUmJPv/880AfGgAAICACHrBuv/12vfHGG7rppps0ZcoUvf3222poaAh0GQAAAKYJeMAqKCjQG2%2B8oVdffVW9evXSww8/rKFDh2r58uU6evRooMsBAAAwXNDuwUpNTdWcOXO0c%2BdOPfDAA3r11Vd1yy236J577tGHH34YrLIAAADaLGgB69y5c3rjjTf0q1/9SnPmzFFSUpLmzp2rvn37Kj8/X1u2bAlWaQAAAG1iC/QBKysrtX79em3cuFG1tbUaNWqUfv/732vgwIG%2BbQYNGqRFixZp7NixgS4PAACgzQJ%2BBmvMmDHatWuXpk6dqj/%2B8Y9avny5X7iSpKFDh%2BrkyZM/uK89e/YoMzNTRUVFzebeeOMNjR07Vv3799fEiRP17rvv%2Buaampq0YsUKjRgxQoMGDdI999yjY8eO%2BeY9Ho9mzJihzMxMDRkyRPPmzVN9fX0bVg0AAC4lAQ9Ya9eu1bZt25Sfn6%2B4uLjv3O7AgQPfu59nnnlGS5YsUbdu3ZrNHT58WHPmzNHs2bP1pz/9Sfn5%2Bbr33nt1/PhxSdLLL7%2BsLVu2qLS0VDt37lT37t1VUFAgr9crSVqwYIHq6uq0detWbdiwQZWVlSouLm7DqgEAwKUk4AGrT58%2BmjZtmrZv3%2B4be%2BGFF/SrX/1KHo%2BnxfuJjIzU%2BvXrLxiw1q1bp6FDh2ro0KGKjIzUuHHj1Lt3b23evFmS5HA4lJ%2Bfrx49eig6OlpFRUWqrKzUgQMH9OWXX2r79u0qKipSQkKCkpKSNH36dG3YsEHnzp1rewMAAEDIC3jAWrp0qU6dOqWePXv6xoYNG6ampiYtW7asxfuZPHmyYmJiLjhXXl6ulJQUv7GUlBQ5nU7V19eroqLCbz46OlrdunWT0%2BnU4cOHFRERoT59%2BvjmU1NTdebMGX388cctrg8AAFy6An6T%2B7vvvqstW7YoPj7eN9a9e3cVFxfr1ltvNeQYHo9HsbGxfmOxsbGqqKjQV199Ja/Xe8F5t9utuLg4RUdHKywszG9Oktxud4uOX11dLZfL5Tdms3VUYmLij1nOd4qIsNY3Hdls1qn3fG%2Bt1mMroLfmor/mobfmC6XeBjxg1dfXKzIystl4eHi46urqDDvO%2Bfupfsz8D733hzgcDpWUlPiNFRQUqLCwsE37tbr4%2BE7BLqHV7PYOwS4hZNFbc9Ff89Bb84RSbwMesAYNGqRly5Zp1qxZvjNDX3zxhR555JFmnyb8seLj45vdz%2BXxeJSQkKC4uDiFh4dfcL5z585KSEjQ6dOn1djYqIiICN%2BcJHXu3LlFx8/NzVV2drbfmM3WUW537Y9d0gVZLekbvX4zRUSEy27voJqaOjU2NgW7nJBCb81Ff81Db81nRm%2BD9ct9wAPWAw88oH/7t3/TDTfcoOjoaDU1Nam2tlZXXHGFXnzxRUOO0a9fPx08eNBvzOl0asyYMYqMjFSvXr1UXl6uwYMHS5Jqamr06aef6pprrlFycrK8Xq%2BOHDmi1NRU33vtdruuuuqqFh0/MTGx2eVAl%2BuUGhou7R9IK66/sbHJknVbAb01F/01D701Tyj1NuAB64orrtDrr7%2BuP/7xj/r0008VHh6uq666SkOGDPGdMWqrO%2B%2B8Uzk5Odq1a5duuOEGbdmyRZ988onGjRsnScrLy1NpaamysrKUlJSk4uJi9e3bV2lpaZKkUaNG6YknntAjjzyis2fPatWqVcrJyZHNFvB2AQAACwpKYmjfvr1uuummNu3jfBhqaGiQJN9jH5xOp3r37q3i4mItXbpUVVVV6tmzp9asWaMuXbpIkiZNmiSXy6W7775btbW1ysjI8LtnavHixVq4cKFGjBihdu3a6dZbb73gw0wBAAAuJOAB69ixY3rsscf00UcfXfDp6O%2B8806L9uN0Or93fuTIkRo5cuQF58LCwlRYWPidN53HxMTo8ccfb1EdAAAA3xaUe7Cqq6s1ZMgQdezYMdCHBwAAMF3AA9bBgwf1zjvvKCEhIdCHBgAACIiAf86/c%2BfOnLkCAAAhLeABa%2BrUqSopKWnzwzwBAAAuVgG/RPjHP/5Rf/nLX/Taa6/ppz/9qcLD/TPeH/7wh0CXBAAAYKiAB6zo6GhlZWUF%2BrAAAAABE/CAtXTp0kAfEgAAIKCC8mV2H3/8sVauXKm5c%2Bf6xv76178GoxQAAADDBTxglZWVady4cXr77be1detWSd88fHTy5MktfsgoAADAxSzgAWvFihX63e9%2Bpy1btigsLEzSN99PuGzZMq1atSrQ5QAAABgu4AHr73//u/Ly8iTJF7AkafTo0aqsrAx0OQAAAIYLeMCKiYm54HcQVldXq3379oEuBwAAwHABD1gDBgzQww8/rNOnT/vGjh49qjlz5uiGG24IdDkAAACGC/hjGubOnatf/vKXysjIUGNjowYMGKC6ujr16tVLy5YtC3Q5AAAAhgt4wOratau2bt2q3bt36%2BjRo4qKitJVV12lG2%2B80e%2BeLAAAAKsKeMCSpHbt2ummm24KxqEBAABMF/CAlZ2d/b1nqngWFgAAsLqAB6xbbrnFL2A1Njbq6NGjcjqd%2BuUvfxnocgAAAAwX8IA1e/bsC46/9dZbeu%2B99wJcDQAAgPGC8l2EF3LTTTfp9ddfD3YZAAAAbXbRBKxDhw7J6/UGuwwAAIA2C/glwkmTJjUbq6urU2VlpUaOHBnocgAAAAwX8IDVvXv3Zp8ijIyMVE5Oju64445AlwMAAGC4gAcsntYOAABCXcAD1saNG1u87W233WZiJQAAAOYIeMCaN2%2Bempqamt3QHhYW5jcWFhZGwAIAAJYU8E8RPvvssxoyZIhefvll7du3T%2B%2B//75eeuklZWVl6ZlnntGHH36oDz/8UAcOHGjTcQ4dOqTJkyfruuuu04033qjZs2fr5MmTkqSysjLl5ORowIABGjNmjDZv3uz33rVr12rUqFEaMGCA8vLydPDgwTbVAgAALi0BD1jLli3TkiVLNHDgQEVHRysmJkbXXXedFi9erEceeUTt27f3/fNjNTQ06Ne//rWuvfZa7d27V1u3btXJkye1aNEiVVdXa/r06Zo0aZLKyso0b948LViwQE6nU5K0Y8cOrVy5Uo8%2B%2Bqj27t2r4cOHa9q0aTpz5oxRLQAAACEu4AHrk08%2BUWxsbLNxu92uqqoqQ47hcrnkcrk0fvx4tW/fXvHx8br55pt1%2BPBhbdmyRd27d1dOTo4iIyOVmZmp7OxsrVu3TpLkcDg0ceJEpaenKyoqSlOmTJEk7dy505DaAABA6Av4PVjJyclatmyZfvvb3yo%2BPl6SVFNTo6eeekpXXnmlIcdISkpS37595XA49Nvf/lb19fV6%2B%2B23NWzYMJWXlyslJcVv%2B5SUFG3btk2SVF5erltuucU3Fx4err59%2B8rpdGrMmDEtOn51dbVcLpffmM3WUYmJiW1cmb%2BIiIvmObEtYrNZp97zvbVaj62A3pqL/pqH3povlHob8ID1wAMPaNasWXI4HOrUqZPCw8N1%2BvRpRUVFadWqVYYcIzw8XCtXrlR%2Bfr5%2B//vfS5IGDx6sWbNmafr06UpKSvLbPi4uTm63W5Lk8XianWGLjY31zbeEw%2BFQSUmJ31hBQYEKCwt/zHJCRnx8p2CX0Gp2e4dglxCy6K256K956K15Qqm3AQ9YQ4YM0a5du7R7924dP35cXq9XSUlJ%2BvnPf66YmBhDjnH27FlNmzZNo0eP9t0/9eCDD37nF01/W1u/sic3N1fZ2dl%2BYzZbR7ndtW3a77dZLekbvX4zRUSEy27voJqaOjU2NgW7nJBCb81Ff81Db81nRm%2BD9ct9wAOWJHXo0EEjRozQ8ePHdcUVVxi%2B/7KyMn322WeaOXOmIiIiFBMTo8LCQo0fP14///nP5fF4/LZ3u91KSEiQJMXHxzeb93g86tWrV4uPn5iY2OxyoMt1Sg0Nl/YPpBXX39jYZMm6rYDemov%2BmofemieUehvwUyD19fWaM2eO%2Bvfvr1/84heSvrkHa8qUKaqpqTHkGI2Njc2etXX27FlJUmZmZrPHLhw8eFDp6emSpH79%2Bqm8vNxvX4cOHfLNAwAA/JCAB6zly5fr8OHDKi4uVnj4/x%2B%2BsbFRxcXFhhyjf//%2B6tixo1auXKm6ujq53W6tXr1agwYN0vjx41VVVaV169bp66%2B/1u7du7V7927deeedkqS8vDxt3LhR%2B/fvV11dnVavXq327dtr2LBhhtQGAABCX8AD1ltvvaWnnnpKo0eP9n3ps91u19KlS/X2228bcoz4%2BHj993//t/7yl78oKytLt956q6KiovTYY4%2Bpc%2BfOWrNmjV566SUNHDhQDz/8sJYvX66f/exnkqSsrCzNnDlTM2bM0ODBg7V3716VlpYqKirKkNoAAEDoC/g9WLW1terevXuz8YSEBEMf5tmvXz%2B9%2BOKLF5wbNGiQNm3a9J3vveuuu3TXXXcZVgsAALi0BPwM1pVXXqn33ntPkv%2Bn9d5880395Cc/CXQ5AAAAhgv4Gay77rpL9913n26//XY1NTXp%2Beef18GDB/XWW29p3rx5gS4HAADAcAEPWLm5ubLZbHrppZcUERGhp59%2BWldddZWKi4s1evToQJcDAABguIAHrJMnT%2Br222/X7bffHuhDAwAABETA78EaMWJEm5%2BUDgAAcDELeMDKyMjwfbEyAABAKAr4JcLLL79cDz30kEpLS3XllVeqXbt2fvOPPfZYoEsCAAAwVMADVkVFha6%2B%2BmpJ33wHIAAAQKgJWMAqKirSihUr/B7%2BuWrVKhUUFASqBAAAgIAI2D1YO3bsaDZWWloaqMMDAAAETMAC1oU%2BOcinCQEAQCgKWMA6/8XOPzQGAABgdQF/TAMAAECoI2ABAAAYLGCfIjx37pxmzZr1g2M8BwsAAFhdwALWwIEDVV1d/YNjAAAAVhewgPXPz78CAAAIZdyDBQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYLKQD1urVqzVkyBBde%2B21ys/P12effSZJKisrU05OjgYMGKAxY8Zo8%2BbNfu9bu3atRo0apQEDBigvL08HDx4MRvkAAMCiQjZgvfzyy9q8ebPWrl2rd999Vz179tQLL7yg6upqTZ8%2BXZMmTVJZWZnmzZunBQsWyOl0SpJ27NihlStX6tFHH9XevXs1fPhwTZs2TWfOnAnyigAAgFWEbMB67rnnVFRUpKuvvlrR0dGaP3%2B%2B5s%2Bfry1btqh79%2B7KyclRZGSkMjMzlZ2drXXr1kmSHA6HJk6cqPT0dEVFRWnKlCmSpJ07dwZzOQAAwEJCMmB98cUX%2Buyzz/TVV1/plltuUUZGhgoLC3Xy5EmVl5crJSXFb/uUlBTfZcBvz4eHh6tv376%2BM1wAAAA/JGBf9hxIx48flyS9%2Beabev755%2BX1elVYWKj58%2Bervr5eSUlJftvHxcXJ7XZLkjwej2JjY/3mY2NjffMtUV1dLZfL5Tdms3VUYmLij1nOd4qIsFY%2BttmsU%2B/53lqtx1YGOupVAAAUsElEQVRAb81Ff81Db80XSr0NyYDl9XolSVOmTPGFqfvuu0%2B/%2BtWvlJmZ2eL3/1gOh0MlJSV%2BYwUFBSosLGzTfq0uPr5TsEtoNbu9Q7BLCFn01lz01zz01jyh1NuQDFiXXXaZJMlut/vGkpOT5fV6de7cOXk8Hr/t3W63EhISJEnx8fHN5j0ej3r16tXi4%2Bfm5io7O9tvzGbrKLe7tlXr%2BCFWS/pGr99MERHhsts7qKamTo2NTcEuJ6TQW3PRX/PQW/OZ0dtg/XIfkgGra9euio6O1uHDh5WamipJqqqqUrt27TR06FBt2rTJb/uDBw8qPT1dktSvXz%2BVl5drwoQJkqTGxkYdOnRIOTk5LT5%2BYmJis8uBLtcpNTRc2j%2BQVlx/Y2OTJeu2AnprLvprHnprnlDqrbVOgbSQzWZTTk6Onn76af3v//6vTpw4oVWrVmns2LGaMGGCqqqqtG7dOn399dfavXu3du/erTvvvFOSlJeXp40bN2r//v2qq6vT6tWr1b59ew0bNiy4iwIAAJYRkmewJGnWrFk6e/as7rjjDp07d06jRo3S/Pnz1alTJ61Zs0ZLlizRgw8%2BqOTkZC1fvlw/%2B9nPJElZWVmaOXOmZsyYoRMnTigtLU2lpaWKiooK8ooAAIBVhHnbekc3WsTlOmX4Pm22cN1cvMfw/Zpl24wbg11Ci9ls4YqP7yS3uzZkTldfLOitueiveazY21888T/BLqHF9j002pTedukSY%2Bj%2BWiokLxECAAAEEwELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYJdEwHr44YfVp08f3%2BuysjLl5ORowIABGjNmjDZv3uy3/dq1azVq1CgNGDBAeXl5OnjwYKBLBgAAFhbyAevw4cPatGmT73V1dbWmT5%2BuSZMmqaysTPPmzdOCBQvkdDolSTt27NDKlSv16KOPau/evRo%2BfLimTZumM2fOBGsJAADAYkI6YDU1NWnhwoXKz8/3jW3ZskXdu3dXTk6OIiMjlZmZqezsbK1bt06S5HA4NHHiRKWnpysqKkpTpkyRJO3cuTMYSwAAABZkC3YBZvrDH/6gyMhIjR07Vk888YQkqby8XCkpKX7bpaSkaNu2bb75W265xTcXHh6uvn37yul0asyYMS06bnV1tVwul9%2BYzdZRiYmJbVlOMxER1srHNpt16j3fW6v12Arorbnor3norflCqbchG7C%2B/PJLrVy5Ui%2B%2B%2BKLfuMfjUVJSkt9YXFyc3G63bz42NtZvPjY21jffEg6HQyUlJX5jBQUFKiwsbM0SQk58fKdgl9BqdnuHYJcQsuitueiveeiteUKptyEbsJYuXaqJEyeqZ8%2Be%2Buyzz1r1Xq/X26Zj5%2BbmKjs722/MZusot7u2Tfv9NqslfaPXb6aIiHDZ7R1UU1OnxsamYJcTUuitueiveeit%2BczobbB%2BuQ/JgFVWVqa//vWv2rp1a7O5%2BPh4eTwevzG3262EhITvnPd4POrVq1eLj5%2BYmNjscqDLdUoNDZf2D6QV19/Y2GTJuq2A3pqL/pqH3ponlHprrVMgLbR582adOHFCw4cPV0ZGhiZOnChJysjIUO/evZs9duHgwYNKT0%2BXJPXr10/l5eW%2BucbGRh06dMg3DwAA8ENCMmDdf//9euutt7Rp0yZt2rRJpaWlkqRNmzZp7Nixqqqq0rp16/T1119r9%2B7d2r17t%2B68805JUl5enjZu3Kj9%2B/errq5Oq1evVvv27TVs2LAgrggAAFhJSF4ijI2N9btRvaGhQZLUtWtXSdKaNWu0ZMkSPfjgg0pOTtby5cv1s5/9TJKUlZWlmTNnasaMGTpx4oTS0tJUWlqqqKiowC8EAABYUkgGrG/76U9/qr/97W%2B%2B14MGDfJ7%2BOi33XXXXbrrrrsCURoAAAhBIXmJEAAAIJgIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMFCNmBVVVWpoKBAGRkZyszM1P3336%2BamhpJ0uHDh/Uv//IvGjhwoEaOHKnnnnvO771vvPGGxo4dq/79%2B2vixIl69913g7EEAABgUSEbsKZNmya73a4dO3botdde00cffaRHHnlE9fX1mjp1qq6//nrt2bNHK1as0Jo1a/T2229L%2BiZ8zZkzR7Nnz9af/vQn5efn695779Xx48eDvCIAAGAVIRmwampq1K9fP82aNUudOnVS165dNWHCBO3bt0%2B7du3SuXPn9Jvf/EYdO3ZUamqq7rjjDjkcDknSunXrNHToUA0dOlSRkZEaN26cevfurc2bNwd5VQAAwCpCMmDZ7XYtXbpUl112mW/s888/V2JiosrLy9WnTx9FRET45lJSUnTw4EFJUnl5uVJSUvz2l5KSIqfTGZjiAQCA5dmCXUAgOJ1OvfTSS1q9erW2bdsmu93uNx8XFyePx6OmpiZ5PB7Fxsb6zcfGxqqioqLFx6uurpbL5fIbs9k6KjEx8ccv4gIiIqyVj20269R7vrdW67EV0Ftz0V/z0FvzhVJvQz5gffDBB/rNb36jWbNmKTMzU9u2bbvgdmFhYb5/93q9bTqmw%2BFQSUmJ31hBQYEKCwvbtF%2Bri4/vFOwSWs1u7xDsEkIWvTUX/TUPvTVPKPU2pAPWjh079Lvf/U4LFizQbbfdJklKSEjQJ5984redx%2BNRXFycwsPDFR8fL4/H02w%2BISGhxcfNzc1Vdna235jN1lFud%2B2PW8h3sFrSN3r9ZoqICJfd3kE1NXVqbGwKdjkhhd6ai/6ah96az4zeBuuX%2B5ANWH/5y180Z84cPfnkkxoyZIhvvF%2B/fnrllVfU0NAgm%2B2b5TudTqWnp/vmz9%2BPdZ7T6dSYMWNafOzExMRmlwNdrlNqaLi0fyCtuP7GxiZL1m0F9NZc9Nc89NY8odRba50CaaGGhgbNnz9fs2fP9gtXkjR06FBFR0dr9erVqqur04EDB7R%2B/Xrl5eVJku68807t3btXu3bt0tdff63169frk08%2B0bhx44KxFAAAYEEheQZr//79qqys1JIlS7RkyRK/uTfffFNPP/20Fi5cqNLSUl122WUqKirSsGHDJEm9e/dWcXGxli5dqqqqKvXs2VNr1qxRly5dgrASAABgRSEZsK677jr97W9/%2B95tXnnlle%2BcGzlypEaOHGl0WQAA4BIRkpcIAQAAgomABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDAC1gVUVVXp17/%2BtTIyMjR8%2BHAtX75cTU1NwS4LAABYhC3YBVyM7rvvPqWmpmr79u06ceKEpk6dqssuu0z/%2Bq//GuzSAACABXAG61ucTqeOHDmi2bNnKyYmRt27d1d%2Bfr4cDkewSwMAABbBGaxvKS8vV3JysmJjY31jqampOnr0qE6fPq3o6Ogf3Ed1dbVcLpffmM3WUYmJiYbWGhFhrXxss1mn3vO9tVqPrYDemov%2Bmofemi%2BUekvA%2BhaPxyO73e43dj5sud3uFgUsh8OhkpISv7F7771X9913n3GF6psg98uuHyk3N9fw8Hapq66u1u9//yy9NQG9NRf9NY8Ve7vvodHBLqFFqqurtXLlSkv19oeETlQ0kNfrbdP7c3Nz9dprr/n9k5uba1B1/8/lcqmkpKTZ2TK0Hb01D701F/01D701Tyj2ljNY35KQkCCPx%2BM35vF4FBYWpoSEhBbtIzExMWQSOAAAaD3OYH1Lv3799Pnnn%2BvkyZO%2BMafTqZ49e6pTp05BrAwAAFgFAetbUlJSlJaWpscee0ynT59WZWWlnn/%2BeeXl5QW7NAAAYBERixYtWhTsIi42P//5z7V161b953/%2Bp15//XXl5OTonnvuUVhYWLBLa6ZTp04aPHgwZ9dMQG/NQ2/NRX/NQ2/NE2q9DfO29Y5uAAAA%2BOESIQAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYF7mqqir9%2Bte/VkZGhoYPH67ly5erqanpgtuuXbtWo0aN0oABA5SXl6eDBw8GuFpraU1vX3nlFY0aNUr9%2B/fX%2BPHjtX379gBXay2t6e15X3zxhfr376%2BVK1cGqErrak1/Kysrdffddys9PV1Dhw7VCy%2B8ENhiLaalvW1qatJTTz2l7Oxs9e/fX2PHjtUbb7wRhIqtZc%2BePcrMzFRRUdH3btfU1KQVK1ZoxIgRGjRokO655x4dO3YsQFUaxIuL2oQJE7zz58/31tTUeI8ePeodOXKk97nnnmu23TvvvOO97rrrvPv37/fW1dV516xZ473xxhu9tbW1QajaGlra2zfffNM7cOBA7759%2B7xnz571vvrqq97U1FTvp59%2BGoSqraGlvf1n9957r3fgwIHep556KkBVWldL%2B1tXV%2BcdNmyY95lnnvGeOXPGe%2BDAAe%2BYMWO8FRUVQajaGlra25deesk7ZMgQb2VlpbehocG7Y8cOb0pKivfw4cNBqNoaSktLvSNHjvROmjTJO2PGjO/ddu3atd7hw4d7KyoqvKdOnfIuXrzYO3bsWG9TU1OAqm07zmBdxJxOp44cOaLZs2crJiZG3bt3V35%2BvhwOR7NtHQ6HJk6cqPT0dEVFRWnKlCmSpJ07dwa6bEtoTW/r6%2Bs1c%2BZMDRw4UO3atdMdd9yhTp06af/%2B/UGo/OLXmt6et3v3blVUVGjYsGGBK9SiWtPfbdu2KTo6WlOmTFGHDh10zTXXaOvWrerRo0cQKr/4taa35eXlGjhwoK6%2B%2BmpFRERo%2BPDhiouL09/%2B9rcgVG4NkZGRWr9%2Bvbp16/aD2zocDuXn56tHjx6Kjo5WUVGRKisrdeDAgQBUagwC1kWsvLxcycnJio2N9Y2lpqbq6NGjOn36dLNtU1JSfK/Dw8PVt29fOZ3OgNVrJa3p7fjx43XXXXf5XtfU1Ki2tlZJSUkBq9dKWtNb6ZsAu3jxYi1cuFA2my2QpVpSa/r7wQcfqHfv3po7d66uu%2B46jR49Wps3bw50yZbRmt4OGzZMf/7zn3X48GGdPXtW77zzjurq6jR48OBAl20ZkydPVkxMzA9uV19fr4qKCr%2B/06Kjo9WtWzdL/Z1GwLqIeTwe2e12v7HzP/hut7vZtv/8P4Xz2357O3yjNb39Z16vV/Pnz1d6ejr/I/0Ore3tqlWrdO211%2Br6668PSH1W15r%2BHj9%2BXO%2B8844yMzO1Z88eTZ06VXPmzNGhQ4cCVq%2BVtKa3I0eOVG5urm677TalpaVp1qxZWrp0qS6//PKA1RuqvvrqK3m9Xsv/ncavixc5r9dryrZofb/OnTun%2B%2B%2B/XxUVFVq7dq1JVYWGlva2oqJC69at05YtW0yuKLS0tL9er1epqakaO3asJGnChAn6wx/%2BoDfffNPv7AD%2BX0t7u3HjRm3cuFHr1q1Tnz59VFZWplmzZunyyy/XNddcY3KVlwar/53GGayLWEJCgjwej9%2BYx%2BNRWFiYEhIS/Mbj4%2BMvuO23t8M3WtNb6ZtT1lOnTtU//vEPvfzyy7rssssCVarltLS3Xq9XixYt0n333acuXboEukzLas2f3S5dujS7JJOcnCyXy2V6nVbUmt6%2B9NJLys3N1TXXXKPIyEgNGzZM119/PZdgDRAXF6fw8PAL/rfo3LlzkKpqPQLWRaxfv376/PPPdfLkSd%2BY0%2BlUz5491alTp2bblpeX%2B143Njbq0KFDSk9PD1i9VtKa3nq9XhUVFclms%2BmFF15QfHx8oMu1lJb29h//%2BIfef/99PfXUU8rIyFBGRoZef/11Pfvss5owYUIwSreE1vzZ7dGjh/7%2B97/7nQmoqqpScnJywOq1ktb0tqmpSY2NjX5jZ8%2BeDUidoS4yMlK9evXy%2BzutpqZGn376qaXODhKwLmIpKSlKS0vTY489ptOnT6uyslLPP/%2B88vLyJEmjR4/Wvn37JEl5eXnauHGj9u/fr7q6Oq1evVrt27fnU1nfoTW93bJliyoqKvTkk08qMjIymGVbQkt727VrV%2B3evVubNm3y/ZOdna1JkyaptLQ0yKu4eLXmz%2B64cePkdrv19NNPq76%2BXlu3blV5ebnGjRsXzCVctFrT2%2BzsbK1fv15HjhxRQ0OD3n33XZWVlWnEiBHBXIJlffHFFxo9erTvWVd5eXlau3atKisrdfr0aRUXF6tv375KS0sLcqUtxz1YF7mnnnpKCxYs0I033qjo6GhNmjTJ94m2o0eP6syZM5KkrKwszZw5UzNmzNCJEyeUlpam0tJSRUVFBbP8i1pLe7thwwZVVVU1u6l9/PjxWrJkScDrtoKW9DYiIkJdu3b1e1%2BHDh0UHR3NJcMf0NI/u0lJSVqzZo0eeugh/dd//Zd%2B8pOfaNWqVbryyiuDWf5FraW9nTp1qhoaGlRQUKCTJ08qOTlZS5Ys0Q033BDM8i9q58NRQ0ODJPke2Ox0OnXu3DkdPXrUdxZw0qRJcrlcuvvuu1VbW6uMjAyVlJQEp/AfKcxr9bvIAAAALjJcIgQAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADDY/wHiW5JH7VCQmQAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-5276881443713412690”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1673</td> <td class=”number”>52.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>1459</td> <td class=”number”>45.5%</td> <td>

<div class=”bar” style=”width:87%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-5276881443713412690”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1673</td> <td class=”number”>52.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>1459</td> <td class=”number”>45.5%</td> <td>

<div class=”bar” style=”width:87%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1673</td> <td class=”number”>52.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>1459</td> <td class=”number”>45.5%</td> <td>

<div class=”bar” style=”width:87%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SNAP_BBCE16”>SNAP_BBCE16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.73851</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>25.5%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-8816026602969908808”>

</div> <div class=”col-md-12 text-right”>

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<li role=”presentation” class=”active”><a href=”#quantiles-8816026602969908808”

aria-controls=”quantiles-8816026602969908808” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-8816026602969908808” aria-controls=”histogram-8816026602969908808”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-8816026602969908808” aria-controls=”common-8816026602969908808”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-8816026602969908808” aria-controls=”extreme-8816026602969908808”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-8816026602969908808”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>1</td>

</tr> <tr>

<th>Q3</th> <td>1</td>

</tr> <tr>

<th>95-th percentile</th> <td>1</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr>

<th>Range</th> <td>1</td>

</tr> <tr>

<th>Interquartile range</th> <td>1</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.43952</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.59515</td>

</tr> <tr>

<th>Kurtosis</th> <td>-0.82113</td>

</tr> <tr>

<th>Mean</th> <td>0.73851</td>

</tr> <tr>

<th>MAD</th> <td>0.38623</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-1.086</td>

</tr> <tr>

<th>Sum</th> <td>2313</td>

</tr> <tr>

<th>Variance</th> <td>0.19318</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-8816026602969908808”>

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BaQAAAMxz/Bms4cOH65577vG%2Bb5Ukvfzyy/rZz37W4cfXAAAABCLHC9by5ct14sQJDRkyxLuWkZGh9vZ2rVixwulxAAAAjHP8FuG7776rsrIyxcbGetcSExNVWFiom2%2B%2B2elxAAAAjHP8ClZLS4siIiI6DhIaqubmZqfHAQAAMM7xgjVu3DitWLFCX3zxhXfts88%2B0%2BOPP%2B7zVg0AAACByvFbhA8//LB%2B%2BtOf6pprrlFkZKTa29vV1NSkAQMG6De/%2BY3T4wAAABjneMEaMGCAtm7dqnfeeUdHjhxRaGioBg0apAkTJigsLMzpcQAAAIxzvGBJUvfu3XXDDTf446UBAABs53jBOnr0qFatWqWPPvpILS0tHbbv2rXL6ZEAAACM8sszWLW1tZowYYJ69erl9MsDAADYzvGCVVFRoV27dikuLs7plwYAAHCE42/T0LdvX65cAQCAoOZ4wZo3b57Wrl0ry7KcfmkAAABHOH6L8J133tFf//pXbd68WZdeeqlCQ3073muvveb0SAAAAEY5XrAiIyN13XXXOf2yAAAAjnG8YC1fvtzplwQAAHCU489gSdLHH3%2BsZ599Vg899JB37W9/%2B5s/RgEAADDO8YL13nvvafr06XrrrbdUXl4u6as3H50zZw5vMgoAAIKC4wVr9erV%2BsUvfqGysjKFhIRI%2BurnE65YsUJFRUVOjwMAAGCc4wXr73//u3JyciTJW7AkacqUKXK73U6PAwAAYJzjBatPnz5n/RmEtbW16t69u9PjAAAAGOd4wRozZox%2B%2BctfqrGx0btWXV2tgoICXXPNNU6PAwAAYJzjb9Pw0EMP6a677lJaWpra2to0ZswYNTc3a%2BjQoVqxYoXT4wAAABjneMG6%2BOKLVV5err1796q6ulo9evTQoEGDdO211/o8kwUAABCoHC9YktStWzfdcMMN/nhpAAAA2zlesDIzM7/1ShXvhQUAAAKd4wXrpptu8ilYbW1tqq6ulsvl0l133eX0OAAAAMY5XrAWL1581vU333xTf/7znx2eBgAAwDy//CzCs7nhhhu0detWf48BAABw3i6YgnXw4EFZluXvMQAAAM6b47cIs7OzO6w1NzfL7XZr0qRJTo8DAABgnOMFKzExscN3EUZERGjWrFm6/fbbnR4HAADAOMcLFu/WDgAAgp3jBWvLli2d3veWW26xcRIAAAB7OF6wHnnkEbW3t3d4oD0kJMRnLSQkhIIFAAACkuMF64UXXtCLL76oe%2B65R8OHD5dlWfrwww/1/PPP68c//rHS0tKcHgkAAMAovzyDVVxcrISEBO/alVdeqQEDBmju3LkqLy93eiQAAACjHH8frE8%2B%2BUTR0dEd1qOiolRTU%2BP0OAAAAMY5XrD69%2B%2BvFStWqL6%2B3rvW0NCgVatW6bLLLnN6HAAAAOMcv0X48MMPa9GiRSotLVXv3r0VGhqqxsZG9ejRQ0VFRU6PAwAAYJzjBWvChAl6%2B%2B23tXfvXh07dkyWZSkhIUE/%2BtGP1KdPH6fHAQAAMM7xgiVJPXv21MSJE3Xs2DENGDDAHyMAAADYxvFnsFpaWlRQUKDRo0frxhtvlPTVM1h33323GhoanB4HAADAOMcL1sqVK3Xo0CEVFhYqNPT/X76trU2FhYVOjwMAAGCc4wXrzTff1Jo1azRlyhTvD32OiorS8uXL9dZbbzk9DgAAgHGOF6ympiYlJiZ2WI%2BLi9PJkyedHgcAAMA4xwvWZZddpj//%2Bc%2BS5POzB3fs2KEf/OAHTo8DAABgnOPfRXjnnXfqgQce0G233ab29na99NJLqqio0JtvvqlHHnnE6XEAAACMc7xgZWVlKTw8XK%2B88orCwsL03HPPadCgQSosLNSUKVOcHgcAAMA4xwvW8ePHddttt%2Bm2224772Pt27dPBQUFSktL0%2BrVq322bdu2TevWrdP//u//atCgQVq4cKEmTJggSWpvb9czzzyj8vJyNTQ0aOTIkVq6dKn3Pbk8Ho%2BWLl2qv/zlLwoNDVV6erqWLFmiHj16nPfMAAAg%2BDn%2BDNbEiRN9nr36rp5//nktW7ZMAwcO7LDt0KFDKigo0OLFi/WnP/1Jubm5uv/%2B%2B3Xs2DFJ0quvvqqysjIVFxdrz549SkxMVF5enneuJUuWqLm5WeXl5dq0aZPcbjdvIQEAADrN8YKVlpam7du3n/dxIiIitHHjxrMWrA0bNig9PV3p6emKiIjQ9OnTNWzYML3xxhuSpNLSUuXm5mrw4MGKjIxUfn6%2B3G63Dhw4oH/%2B85/auXOn8vPzFRcXp4SEBN13333atGmTTp8%2Bfd5zAwCA4Of4LcJLLrlETz31lIqLi3XZZZepW7duPttXrVrVqePMmTPnG7dVVlYqPT3dZy05OVkul0stLS2qqqpScnKyd1tkZKQGDhwol8ulEydOKCwsTMOHD/duT0lJ0cmTJ/Xxxx/7rH%2BT2tpa1dXV%2BayFh/dSfHx8p86ts8LCQn1%2Bhzlkax%2BytRf52ods7RdM2TpesKqqqnT55ZdLkurr6215DY/Ho%2BjoaJ%2B16OhoVVVV6YsvvpBlWWfdXl9fr5iYGEVGRnrfBPXrbV2Zt7S0VGvXrvVZy8vL0/z587/L6ZxTVFRPW44LsrUT2dqLfO1DtvYJpmwdK1j5%2BflavXq1fvOb33jXioqKlJeXZ8vrnes5r2/bfr7PiGVlZSkzM9NnLTy8l%2Brrm87ruGcKCwtVVFRPNTQ0q62t3eixv%2B/I1j5kay/ytQ/Z2s%2BObGNjexs9Xmc5VrB2797dYa24uNiWghUbGyuPx%2BOz5vF4FBcXp5iYGIWGhp51e9%2B%2BfRUXF6fGxka1tbUpLCzMu02S%2Bvbt26nXj4%2BP73A7sK7uhFpb7fmEbGtrt%2B3Y33dkax%2BytRf52ods7RNM2Tp2s/NsV4VMfDfh2YwYMUIVFRU%2Bay6XS6NGjVJERISGDh2qyspK77aGhgYdOXJEI0eOVFJSkizL0uHDh30%2BNioqSoMGDbJlXgAAEFwcK1j/%2BkzTt62ZcMcdd%2BiPf/yj3n77bX355ZfauHGjPvnkE02fPl2SlJOTo5KSErndbjU2NqqwsFBJSUlKTU1VXFycJk%2BerKefflrHjx/XsWPHVFRUpFmzZik83PFH1gAAQAAK2MaQmpoqSWptbZUk7dy5U9JXV5uGDRumwsJCLV%2B%2BXDU1NRoyZIjWr1%2Bvfv36SZKys7NVV1en2bNnq6mpSWlpaT4PpT/xxBN67LHHNHHiRHXr1k0333yz8vPzHT5DAAAQqAK2YLlcrm/dPmnSJE2aNOms20JCQjR//vxv/K6%2BPn366D/%2B4z/Oe0YAAPD95FjBOn36tBYtWnTOtc6%2BDxYAAMCFyrGCNXbsWNXW1p5zDQAAINA5VrD%2B9f2vAAAAglnwvCc9AADABYKCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGhft7AJyfKx/Z4e8ROm37gmv9PQIAAI7gChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGBa0BWv48OEaMWKEUlNTvb%2BefPJJSdJ7772nWbNmacyYMZo6dareeOMNn48tKSnR5MmTNWbMGOXk5KiiosIfpwAAAAJUuL8HsNOOHTt06aWX%2BqzV1tbqvvvu0yOPPKJp06bpgw8%2B0L333qtBgwYpNTVVu3fv1rPPPqsXXnhBw4cPV0lJie655x699dZb6tWrl5/OBAAABJKgvYL1TcrKypSYmKhZs2YpIiJC48ePV2ZmpjZs2CBJKi0t1a233qpRo0apR48euvvuuyVJe/bs8efYAAAggAT1FaxVq1bpb3/7mxobG3XjjTfqwQcfVGVlpZKTk332S05O1vbt2yVJlZWVuummm7zbQkNDlZSUJJfLpalTp3bqdWtra1VXV%2BezFh7eS/Hx8ed5Rr7CwgKrH4eHB868X2cbaBkHArK1F/nah2ztF0zZBm3BuuKKKzR%2B/Hj96le/0tGjR7VgwQI9/vjj8ng8SkhI8Nk3JiZG9fX1kiSPx6Po6Gif7dHR0d7tnVFaWqq1a9f6rOXl5Wn%2B/Pnf8WyCQ2xsb3%2BP0GVRUT39PULQIlt7ka99yNY%2BwZRt0Bas0tJS7z8PHjxYixcv1r333quxY8ee82Mtyzqv187KylJmZqbPWnh4L9XXN53Xcc8UaE3f9PnbKSwsVFFRPdXQ0Ky2tnZ/jxNUyNZe5GsfsrWfHdn663/ug7ZgnenSSy9VW1ubQkND5fF4fLbV19crLi5OkhQbG9thu8fj0dChQzv9WvHx8R1uB9bVnVBr6/f7EzIQz7%2BtrT0g5w4EZGsv8rUP2donmLINrEsgnXTw4EGtWLHCZ83tdqt79%2B5KT0/v8LYLFRUVGjVqlCRpxIgRqqys9G5ra2vTwYMHvdsBAADOJSgLVt%2B%2BfVVaWqri4mKdOnVK1dXVeuaZZ5SVlaUZM2aopqZGGzZs0Jdffqm9e/dq7969uuOOOyRJOTk52rJli/bv36/m5matW7dO3bt3V0ZGhn9PCgAABIygvEWYkJCg4uJirVq1yluQZs6cqfz8fEVERGj9%2BvVatmyZHn/8cfXv318rV67UD3/4Q0nSddddp4ULF2rBggX6/PPPlZqaquLiYvXo0cPPZwUAAAJFUBYsSRo3bpxee%2B21b9z2u9/97hs/9s4779Sdd95p12gAACDIBeUtQgAAAH%2BiYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFhnUVNTo5///OdKS0vT9ddfr5UrV6q9vd3fYwEAgAAR7u8BLkQPPPCAUlJStHPnTn3%2B%2BeeaN2%2BeLrroIv3kJz/x92gAACAAcAXrDC6XS4cPH9bixYvVp08fJSYmKjc3V6Wlpf4eDQAABAiuYJ2hsrJS/fv3V3R0tHctJSVF1dXVamxsVGRk5DmPUVtbq7q6Op%2B18PBeio%2BPNzprWFhg9ePw8MCZ9%2BtsAy3jQEC29iJf%2B5Ct/YIpWwrWGTwej6KionzWvi5b9fX1nSpYpaWlWrt2rc/a/fffrwceeMDcoPqqyN118UfKysoyXt6%2B72pra/XrX79AtjYgW3uRr30CMdv3n5ri7xE6pba2Vs8%2B%2B2xAZXsuwVMVDbIs67w%2BPisrS5s3b/b5lZWVZWi6/1dXV6e1a9d2uFqG80e29iFbe5GvfcjWPsGYLVewzhAXFyePx%2BOz5vF4FBISori4uE4dIz4%2BPmgaOAAA6DquYJ1hxIgR%2BvTTT3X8%2BHHvmsvl0pAhQ9S7d28/TgYAAAIFBesMycnJSk1N1apVq9TY2Ci3262XXnpJOTk5/h4NAAAEiLClS5cu9fcQF5of/ehHKi8v15NPPqmtW7dq1qxZmjt3rkJCQvw9Wge9e/fWVVddxdU1G5CtfcjWXuRrH7K1T7BlG2Kd7xPdAAAA8MEtQgAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgXuJqaGv385z9XWlqarr/%2Beq1cuVLt7e1n3bekpESTJ0/WmDFjlJOTo4qKCoenDSxdyfa3v/2tJk%2BerNGjR2vGjBnauXOnw9MGlq5k%2B7XPPvtMo0eP1rPPPusaID5AAAAHR0lEQVTQlIGrK/m63W7Nnj1bo0aNUnp6ul5%2B%2BWVnhw0wnc22vb1da9asUWZmpkaPHq1p06Zp27Ztfpg4sOzbt0/jx49Xfn7%2Bt%2B7X3t6u1atXa%2BLEiRo3bpzmzp2ro0ePOjSlIRYuaDNnzrQeffRRq6GhwaqurrYmTZpkvfjiix3227Vrl3XllVda%2B/fvt5qbm63169db1157rdXU1OSHqQNDZ7PdsWOHNXbsWOv999%2B3Tp06Zb3%2B%2ButWSkqKdeTIET9MHRg6m%2B2/uv/%2B%2B62xY8daa9ascWjKwNXZfJubm62MjAzr%2Beeft06ePGkdOHDAmjp1qlVVVeWHqQNDZ7N95ZVXrAkTJlhut9tqbW21du/ebSUnJ1uHDh3yw9SBobi42Jo0aZKVnZ1tLViw4Fv3LSkpsa6//nqrqqrKOnHihPXEE09Y06ZNs9rb2x2a9vxxBesC5nK5dPjwYS1evFh9%2BvRRYmKicnNzVVpa2mHf0tJS3XrrrRo1apR69Oihu%2B%2B%2BW5K0Z88ep8cOCF3JtqWlRQsXLtTYsWPVrVs33X777erdu7f279/vh8kvfF3J9mt79%2B5VVVWVMjIynBs0QHUl3%2B3btysyMlJ33323evbsqZEjR6q8vFyDBw/2w%2BQXvq5kW1lZqbFjx%2Bryyy9XWFiYrr/%2BesXExOjDDz/0w%2BSBISIiQhs3btTAgQPPuW9paalyc3M1ePBgRUZGKj8/X263WwcOHHBgUjMoWBewyspK9e/fX9HR0d61lJQUVVdXq7GxscO%2BycnJ3j%2BHhoYqKSlJLpfLsXkDSVeynTFjhu68807vnxsaGtTU1KSEhATH5g0kXclW%2BqrAPvHEE3rssccUHh7u5KgBqSv5fvDBBxo2bJgeeughXXnllZoyZYreeOMNp0cOGF3JNiMjQ3/5y1906NAhnTp1Srt27VJzc7Ouuuoqp8cOGHPmzFGfPn3OuV9LS4uqqqp8vqZFRkZq4MCBAfU1jYJ1AfN4PIqKivJZ%2B/oTv76%2BvsO%2B//ofha/3PXM/fKUr2f4ry7L06KOPatSoUfyH9Bt0NduioiJdccUVuvrqqx2ZL9B1Jd9jx45p165dGj9%2BvPbt26d58%2BapoKBABw8edGzeQNKVbCdNmqSsrCzdcsstSk1N1aJFi7R8%2BXJdcskljs0brL744gtZlhXwX9P438ULnGVZtuyLrud1%2BvRpPfjgg6qqqlJJSYlNUwWHzmZbVVWlDRs2qKyszOaJgktn87UsSykpKZo2bZokaebMmXrttde0Y8cOn6sD%2BH%2BdzXbLli3asmWLNmzYoOHDh%2Bu9997TokWLdMkll2jkyJE2T/n9EOhf07iCdQGLi4uTx%2BPxWfN4PAoJCVFcXJzPemxs7Fn3PXM/fKUr2UpfXbKeN2%2Be/vGPf%2BjVV1/VRRdd5NSoAaez2VqWpaVLl%2BqBBx5Qv379nB4zYHXl726/fv063JLp37%2B/6urqbJ8zEHUl21deeUVZWVkaOXKkIiIilJGRoauvvppbsAbExMQoNDT0rP8u%2Bvbt66epuo6CdQEbMWKEPv30Ux0/fty75nK5NGTIEPXu3bvDvpWVld4/t7W16eDBgxo1apRj8waSrmRrWZby8/MVHh6ul19%2BWbGxsU6PG1A6m%2B0//vEP/dd//ZfWrFmjtLQ0paWlaevWrXrhhRc0c%2BZMf4weELryd3fw4MH6%2B9//7nMloKamRv3793ds3kDSlWzb29vV1tbms3bq1ClH5gx2ERERGjp0qM/XtIaGBh05ciSgrg5SsC5gycnJSk1N1apVq9TY2Ci3262XXnpJOTk5kqQpU6bo/ffflyTl5ORoy5Yt2r9/v5qbm7Vu3Tp1796d78r6Bl3JtqysTFVVVXrmmWcUERHhz7EDQmezvfjii7V371797ne/8/7KzMxUdna2iouL/XwWF66u/N2dPn266uvr9dxzz6mlpUXl5eWqrKzU9OnT/XkKF6yuZJuZmamNGzfq8OHDam1t1bvvvqv33ntPEydO9OcpBKzPPvtMU6ZM8b7XVU5OjkpKSuR2u9XY2KjCwkIlJSUpNTXVz5N2Hs9gXeDWrFmjJUuW6Nprr1VkZKSys7O939FWXV2tkydPSpKuu%2B46LVy4UAsWLNDnn3%2Bu1NRUFRcXq0ePHv4c/4LW2Ww3bdqkmpqaDg%2B1z5gxQ8uWLXN87kDQmWzDwsJ08cUX%2B3xcz549FRkZyS3Dc%2Bjs392EhAStX79eTz31lP7zP/9TP/jBD1RUVKTLLrvMn%2BNf0Dqb7bx589Ta2qq8vDwdP35c/fv317Jly3TNNdf4c/wL2tflqLW1VZK8b9jscrl0%2BvRpVVdXe68CZmdnq66uTrNnz1ZTU5PS0tK0du1a/wz%2BHYVYgf4UGQAAwAWGW4QAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYNj/AcdZY6sY9HW1AAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-8816026602969908808”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>2313</td> <td class=”number”>72.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>819</td> <td class=”number”>25.5%</td> <td>

<div class=”bar” style=”width:36%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-8816026602969908808”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>819</td> <td class=”number”>25.5%</td> <td>

<div class=”bar” style=”width:36%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>2313</td> <td class=”number”>72.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>819</td> <td class=”number”>25.5%</td> <td>

<div class=”bar” style=”width:36%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>2313</td> <td class=”number”>72.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SNAP_CAP09”>SNAP_CAP09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.37165</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>61.3%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram4830369053988223188”>

</div> <div class=”col-md-12 text-right”>

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<li role=”presentation” class=”active”><a href=”#quantiles4830369053988223188”

aria-controls=”quantiles4830369053988223188” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram4830369053988223188” aria-controls=”histogram4830369053988223188”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common4830369053988223188” aria-controls=”common4830369053988223188”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme4830369053988223188” aria-controls=”extreme4830369053988223188”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles4830369053988223188”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>1</td>

</tr> <tr>

<th>95-th percentile</th> <td>1</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr>

<th>Range</th> <td>1</td>

</tr> <tr>

<th>Interquartile range</th> <td>1</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.48332</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.3005</td>

</tr> <tr>

<th>Kurtosis</th> <td>-1.7186</td>

</tr> <tr>

<th>Mean</th> <td>0.37165</td>

</tr> <tr>

<th>MAD</th> <td>0.46705</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.53147</td>

</tr> <tr>

<th>Sum</th> <td>1164</td>

</tr> <tr>

<th>Variance</th> <td>0.2336</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram4830369053988223188”>

<img 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rHbK1jpXZBusv9yFZsP74xz8qNjZWN910k2%2BtrKxMHTp0UEREhLp166aSkhJdffXVkqTKykodOnRIvXr1UlJSkrxer/bv36%2BUlBRJksvlUkxMjDp37tyo8ycmJp72dqDbfUx1dRf3F6Qdr7%2B%2BvsGWc9sB2VqLfK1DttYJpWztdQukkU6cOKHHHntMLpdLtbW12rBhg/785z9r7NixkqScnBwVFRWprKxMVVVVys/PV8%2BePZWWlqaEhAQNGTJETz/9tI4eParPPvtMS5Ys0ZgxY%2BR0hmQfBQAAhtm2MaSlpUmS6urqJElbt26VdPJu07hx41RdXa1p06bJ7Xbrsssu05IlS5SamipJGjt2rNxut%2B68805VV1crPT3d75mpefPm6eGHH9agQYPUrFkz3XLLLad9mCkAAMDZhHnP94luNIrbfcz4MZ3OcN2Yv9P4ca2ycfp1wR6h0ZzOcMXHR6qiojpkbldfKMjWWuRrHbK1jpXZtmlz9o90slJIvkUIAAAQTBQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADLNtwdq5c6cyMjKUl5d32rYtW7Zo%2BPDh6tOnj4YMGaJXX33Vt23x4sXq2bOn0tLS/H598cUXkqRvvvlGv/rVrzRgwAClp6dr6tSpqqioCNh1AQAA%2B7NlwVq%2BfLnmz5%2Bvjh07nrbtgw8%2B0MyZMzV16lS9%2B%2B67euihhzRv3jzt2rXLt8%2BIESPkcrn8fl1yySWSpEWLFqmkpETFxcXavHmzvF6vHnzwwYBdGwAAsD9bFqyIiAitWrXqjAXL4/Fo4sSJuuGGG%2BR0OpWZmanu3bv7Fayzqaur06pVq3TPPfeoffv2iouL0/Tp0/XWW2/p888/t%2BJSAABACHIGe4DvY9y4cWfdNmDAAA0YMMD3%2B7q6OrndbrVt29a39uGHH2rs2LH66KOP1L59ez344IPq37%2B/Dh06pGPHjiklJcW3b5cuXdSiRQuVlJT4HeO7lJeXy%2B12%2B605na2UmJjY2EtsFIfDXv3Y6bTPvKeytVvGdkC21iJf65CtdUIxW1sWrKbIz89Xq1atdNNNN0mS2rVrpw4dOmjGjBlKTExUcXGxJk2apHXr1snj8UiSYmJi/I4RExPTpOewiouLVVBQ4LeWm5urqVOnnufV2Ft8fGSwR2iymJiWwR4hZJGttcjXOmRrnVDKNuAFKysrS6NHj9att96q9u3bW3Yer9er/Px8bdiwQUVFRYqIiJAk3Xbbbbrtttt8%2B40fP16vv/661q1b57vz5fV6z%2Bvc2dnZysrK8ltzOlupoqL6vI77bXZr%2Bqav30oOR7hiYlqqsrJG9fUNwR4npJCttcjXOmRrHSuzDdZf7gNesG699Va9/vrrWrp0qa699lrdfvvtysrKktNpbpSGhgY9%2BOCD%2BuCDD/THP/5RHTp0%2BM79k5KSVF5eroSEBEknn%2BOKjPzPP5CvvvpKrVu3bvT5ExMTT3s70O0%2Bprq6i/sL0o7XX1/fYMu57YBsrUW%2B1iFb64RStgG/BZKbm6s33nhDr776qrp166Zf//rXyszM1FNPPaWDBw8aOcevf/1rffzxx2csV7/97W/1zjvv%2BK2VlZWpQ4cO6tChg2JjY1VSUuLb9tFHH%2BnEiRNKTU01MhsAAAh9QXuPKSUlRbNmzdL27dv10EMP6dVXX9VNN92kCRMm6IMPPvjex33vvfe0bt06FRYWKi4u7rTtHo9Hjz76qA4cOKBvvvlGL7zwgg4dOqRRo0bJ4XDo9ttv13PPPacjR46ooqJCv/nNb3TjjTf6PsYBAADgXIL2kHttba3%2B9Kc/ac2aNfrb3/6mTp06acqUKSovL9f48eP16KOPatiwYWd8bVpamqST3yEoSVu3bpUkuVwurV69WseOHdP111/v95p%2B/frphRde0IwZMySdfPbK4/Goa9eueumll9SuXTtJ0tSpU1VdXa0RI0aorq5O119/vR555BErIgAAACEqzHu%2BT3Q3UVlZmVatWqW1a9equrpaQ4YM0dixY9W3b1/fPjt27NAjjzyi7du3B3I0S7ndx4wf0%2BkM1435O40f1yobp18X7BEazekMV3x8pCoqqkPmeYALBdlai3ytQ7bWsTLbNm2ijR6vsQJ%2BB%2Bvmm29W586dNXHiRI0cOfKMb%2BNlZmbq6NGjgR4NAADAiIAXrKKiIl199dXn3G/37t0BmAYAAMC8gD/k3qNHD02aNMn33JQkvfTSS/r5z3/u%2B6BPAAAAOwt4wVqwYIGOHTumrl27%2BtYGDhyohoYGPfHEE4EeBwAAwLiAv0X49ttva/369YqPj/etderUSfn5%2BbrlllsCPQ4AAIBxAb%2BDdfz4cd%2BPrfEbJDxcNTU1gR4HAADAuIAXrH79%2BumJJ57QV1995Vv7/PPP9eijj/p9VAMAAIBdBfwtwoceekg/%2B9nPdO211yoqKkoNDQ2qrq5Whw4d9Ic//CHQ4wAAELJ%2B/PRfgj1Co%2B16fGiwRzAq4AWrQ4cOev311/XnP/9Zhw4dUnh4uDp37qz%2B/fvL4XAEehwAAADjgvKjcpo3b64bbrghGKcGAACwXMAL1qeffqqFCxfq448/1vHjx0/b/uabbwZ6JAAAAKOC8gxWeXm5%2Bvfvr1atWgX69AAAAJYLeMHas2eP3nzzTSUkJAT61AAAAAER8I9paN26NXeuAABASAt4wZo4caIKCgrk9XoDfWoAAICACPhbhH/%2B85/1z3/%2BU2vWrNFll12m8HD/jvfKK68EeiQAAACjAl6woqKiNGDAgECfFgAAIGACXrAWLFgQ6FMCAAAEVMCfwZKkAwcOaPHixXrwwQd9a//v//2/YIwCAABgXMAL1jvvvKPhw4dry5Yt2rBhg6STHz46btw4PmQUAACEhIAXrEWLFun%2B%2B%2B/X%2BvXrFRYWJunkzyd84okntGTJkkCPAwAAYFzAC9ZHH32knJwcSfIVLEkaOnSoysrKAj0OAACAcQEvWNHR0Wf8GYTl5eVq3rx5oMcBAAAwLuAF68orr9Svf/1rVVVV%2BdYOHjyoWbNm6dprrw30OAAAAMYF/GMaHnzwQd11111KT09XfX29rrzyStXU1Khbt2564oknAj0OAACAcQEvWO3atdOGDRu0Y8cOHTx4UC1atFDnzp113XXX%2BT2TBQAAYFcBL1iS1KxZM91www3BODUAAIDlAl6wsrKyvvNOFZ%2BFBQAA7C7gD7nfdNNNfr%2BGDBmi7t2765tvvtHYsWObdKydO3cqIyNDeXl5p2174403NGzYMPXp00ejR4/W22%2B/7dvW0NCgRYsWadCgQerXr58mTJigTz/91Lfd4/Fo%2BvTpysjIUP/%2B/TV79uwzfucjAADAmQT8DtbMmTPPuL5582b9/e9/b/Rxli9frlWrVqljx46nbdu3b59mzZqlgoICXXPNNdq8ebPuvfdebdq0Se3atdOKFSu0fv16LV%2B%2BXG3bttWiRYuUm5ur1157TWFhYZo7d65OnDihDRs2qLa2VtOmTVN%2Bfr7mzJnzva8bAABcPILyswjP5IYbbtDrr7/e6P0jIiLOWrBWrlypzMxMZWZmKiIiQsOHD1f37t21bt06SVJxcbHGjx%2BvLl26KCoqSnl5eSorK9Pu3bv1xRdfaOvWrcrLy1NCQoLatm2re%2B65R6tXr1Ztba2x6wUAAKErKA%2B5n8nevXvl9Xobvf%2B4cePOuq2kpESZmZl%2Ba8nJyXK5XDp%2B/LhKS0uVnJzs2xYVFaWOHTvK5XLp2LFjcjgc6tGjh297SkqKvv76ax04cMBv/WzKy8vldrv91pzOVkpMTGzs5TWKw3HB9ONGcTrtM%2B%2BpbO2WsR2QrbXI1zpka71QyjbgBetMz1nV1NSorKxMgwcPNnIOj8ej2NhYv7XY2FiVlpbqq6%2B%2BktfrPeP2iooKxcXFKSoqyu9B/FP7VlRUNOr8xcXFKigo8FvLzc3V1KlTv8/lhIz4%2BMhgj9BkMTEtgz1CyCJba5GvdcjWOqGUbcALVqdOnU77LsKIiAiNGTNGt912m7HznOtu2Hdtb8qdtDPJzs5WVlaW35rT2UoVFdXnddxvs1vTN339VnI4whUT01KVlTWqr28I9jghhWytRb7WIVvrWZFtsP5yH/CCFYhPa4%2BPj5fH4/Fb83g8SkhIUFxcnMLDw8%2B4vXXr1kpISFBVVZXq6%2BvlcDh82ySpdevWjTp/YmLiaW8Hut3HVFd3cX9B2vH66%2BsbbDm3HZCttcjXOmRrnVDKNuAFa%2B3atY3ed%2BTIkd/rHKmpqdqzZ4/fmsvl0s0336yIiAh169ZNJSUluvrqqyVJlZWVOnTokHr16qWkpCR5vV7t379fKSkpvtfGxMSoc%2BfO32seAABwcQl4wZo9e7YaGhpOexsuLCzMby0sLOx7F6zbb79dY8aM0VtvvaVrr71W69ev1yeffKLhw4dLknJyclRYWKgBAwaobdu2ys/PV8%2BePZWWliZJGjJkiJ5%2B%2Bmk9%2BeSTOnHihJYsWaIxY8bI6bxgvicAAABcwALeGJ5//nm98MILmjRpknr06CGv16sPP/xQy5cv109%2B8hOlp6c36jinylBdXZ0kaevWrZJO3m3q3r278vPztWDBAh0%2BfFhdu3bVsmXL1KZNG0knH7R3u9268847VV1drfT0dL%2BH0ufNm6eHH35YgwYNUrNmzXTLLbec8cNMAQAAziTMe75PdDfRiBEjVFhYqLZt2/qtf/7555owYYI2bNgQyHECxu0%2BZvyYTme4bszfafy4Vtk4/bpgj9BoTme44uMjVVFRHTLPA1woyNZa5GsdO2b746f/EuwRGm3X40MtybZNm2ijx2usgH8b2ieffHLaRyRIUkxMjA4fPhzocQAAAIwLeMFKSkrSE0884feZUpWVlVq4cKEuv/zyQI8DAABgXMCfwXrooYc0Y8YMFRcXKzIyUuHh4aqqqlKLFi20ZMmSQI8DAABgXMALVv/%2B/fXWW29px44d%2Buyzz%2BT1etW2bVv96Ec/UnR0cN4nBQAAMCkonzvQsmVLDRo0SJ999pk6dOgQjBEAAAAsE/BnsI4fP65Zs2apT58%2B%2BvGPfyzp5DNYd999tyorKwM9DgAAgHEBL1hPPfWU9u3bp/z8fIWH/%2Bf09fX1ys/PD/Q4AAAAxgW8YG3evFnPPvushg4d6vuhzzExMVqwYIG2bNkS6HEAAACMC3jBqq6uVqdOnU5bT0hI0Ndffx3ocQAAAIwLeMG6/PLL9fe//12S/H724KZNm3TppZcGehwAAADjAv5dhHfccYemTJmiW2%2B9VQ0NDXrxxRe1Z88ebd68WbNnzw70OAAAAMYFvGBlZ2fL6XTq5ZdflsPh0HPPPafOnTsrPz9fQ4cODfQ4AAAAxgW8YB09elS33nqrbr311kCfGgAAICAC/gzWoEGD/J69AgAACDUBL1jp6enauHFjoE8LAAAQMAF/i7B9%2B/Z6/PHHVVhYqMsvv1zNmjXz275w4cJAjwQAAGBUwAtWaWmpfvCDH0iSKioqAn16AAAAywWsYOXl5WnRokX6wx/%2B4FtbsmSJcnNzAzUCAABAQATsGaxt27adtlZYWBio0wMAAARMwArWmb5zkO8mBAAAoShgBevUD3Y%2B1xoAAIDdBfxjGgAAAEIdBQsAAMCwgH0XYW1trWbMmHHONT4HCwAA2F3AClbfvn1VXl5%2BzjUAAAC7C1jB%2Bu/PvwIAAAhlPIMFAABgWMB/VE4gvPvuu/rZz37mt%2Bb1elVbW6uioiKNGzdOzZs399v%2Bv//7v/rxj38sSSoqKtKKFSvkdrvVo0cPzZ49W6mpqQGbHwAA2FtIFqx%2B/frJ5XL5rT333HPav3%2B/JCkpKemMnywvnfzE%2BcWLF%2Bv5559Xjx49VFRUpEmTJmnLli1q1aqV5bMDAAD7uyjeIvz3v/%2BtF198Ub/85S/PuW9xcbFGjx6t3r17q0WLFrr77rslSdu3b7d6TAAAECIuioL1zDPP6NZbb9Wll14qSaqurlZubq7S09P1ox/9SC%2B%2B%2BKLvx/aUlJQoOTnZ99rw8HD17NnztDtiAAAAZxOSbxH%2Bt3/961/asmWLtmzZIkmKiopS9%2B7dddddd2nRokX6xz/%2BoWnTpik6OlpjxoyRx%2BPn0ueZAAAU6klEQVRRbGys3zFiY2NVUVHR6HOWl5fL7Xb7rTmdrZSYmHj%2BF/RfHA579WOn0z7znsrWbhnbAdlai3ytQ7bWC6VsQ75grVixQoMHD1abNm0kSSkpKX4fGdG/f3%2BNHTtWa9as0ZgxYySd/w%2BhLi4uVkFBgd9abm6upk6del7Htbv4%2BMhgj9BkMTEtgz1CyCJba5GvdcjWOqGUbcgXrM2bN2vWrFnfuU9SUpI2b94sSYqPj5fH4/Hb7vF41K1bt0afMzs7W1lZWX5rTmcrVVRUN/oYjWG3pm/6%2Bq3kcIQrJqalKitrVF/fEOxxQgrZWot8rUO21rMi22D95T6kC9a%2Bfft0%2BPBhXXfddb61jRs3qqKiQnfccYdv7cCBA%2BrQoYMkKTU1VSUlJRo1apQkqb6%2BXnv37vXd3WqMxMTE094OdLuPqa7u4v6CtOP119c32HJuOyBba5GvdcjWOqGUrb1ugTTR3r17FRcXp6ioKN9as2bN9OSTT%2Brtt99WbW2t/vKXv2j16tXKycmRJOXk5Gjt2rV6//33VVNTo6VLl6p58%2BYaOHBgkK4CAADYTUjfwfriiy98z16dcsMNN%2Bihhx7SY489piNHjuiSSy7RQw89pMGDB0uSBgwYoPvuu0/Tp0/Xl19%2BqbS0NBUWFqpFixbBuAQAAGBDYd7zfaIbjeJ2HzN%2BTKczXDfm7zR%2BXKtsnH7duXe6QDid4YqPj1RFRXXI3K6%2BUJCttcjXOnbM9sdP/yXYIzTarseHWpJtmzbRRo/XWCH9FiEAAEAwULAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwLGQLVo8ePZSamqq0tDTfr8cee0yS9M4772jMmDG68sordfPNN2vdunV%2Bry0qKtKQIUN05ZVXKicnR3v27AnGJQAAAJtyBnsAK23atEmXXXaZ31p5ebnuuecezZ49W8OGDdN7772nyZMnq3PnzkpLS9O2bdu0ePFiPf/88%2BrRo4eKioo0adIkbdmyRa1atQrSlQAAADsJ2TtYZ7N%2B/Xp16tRJY8aMUUREhDIyMpSVlaWVK1dKkoqLizV69Gj17t1bLVq00N133y1J2r59ezDHBgAANhLSBWvhwoUaOHCgrrrqKs2dO1fV1dUqKSlRcnKy337Jycm%2BtwG/vT08PFw9e/aUy%2BUK6OwAAMC%2BQvYtwiuuuEIZGRl68skn9emnn2r69Ol69NFH5fF41LZtW7994%2BLiVFFRIUnyeDyKjY312x4bG%2Bvb3hjl5eVyu91%2Ba05nKyUmJn7Pqzkzh8Ne/djptM%2B8p7K1W8Z2QLbWIl/rkK31QinbkC1YxcXFvv/fpUsXzZw5U5MnT1bfvn3P%2BVqv13ve5y4oKPBby83N1dSpU8/ruHYXHx8Z7BGaLCamZbBHCFlkay3ytQ7ZWieUsg3ZgvVtl112merr6xUeHi6Px%2BO3raKiQgkJCZKk%2BPj407Z7PB5169at0efKzs5WVlaW35rT2UoVFdXfc/ozs1vTN339VnI4whUT01KVlTWqr28I9jghhWytRb7WIVvrWZFtsP5yH5IFa%2B/evVq3bp0eeOAB31pZWZmaN2%2BuzMxM/d///Z/f/nv27FHv3r0lSampqSopKdGoUaMkSfX19dq7d6/GjBnT6PMnJiae9nag231MdXUX9xekHa%2B/vr7BlnPbAdlai3ytQ7bWCaVs7XULpJFat26t4uJiFRYW6sSJEzp48KCeeeYZZWdna8SIETp8%2BLBWrlypb775Rjt27NCOHTt0%2B%2B23S5JycnK0du1avf/%2B%2B6qpqdHSpUvVvHlzDRw4MLgXBQAAbCMk72C1bdtWhYWFWrhwoa8gjRo1Snl5eYqIiNCyZcs0f/58Pfroo0pKStJTTz2lH/7wh5KkAQMG6L777tP06dP15ZdfKi0tTYWFhWrRokWQrwoAANhFSBYsSerXr59eeeWVs2577bXXzvraO%2B64Q3fccYdVowEAgBAXkm8RAgAABBMFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMNCtmAdPnxYubm5Sk9PV0ZGhh544AFVVlbqX//6l3r06KG0tDS/X7/73e98r33jjTc0bNgw9enTR6NHj9bbb78dxCsBAAB24wz2AFaZNGmSUlNTtW3bNh07dky5ubl68sknNXnyZEmSy%2BU64%2Bv27dunWbNmqaCgQNdcc402b96se%2B%2B9V5s2bVK7du0CeQkAAMCmQvIOVmVlpVJTUzVjxgxFRkaqXbt2GjVqlHbt2nXO165cuVKZmZnKzMxURESEhg8fru7du2vdunUBmBwAAISCkLyDFRMTowULFvitHTlyRImJib7f//KXv9Rf//pX1dXV6bbbbtPUqVPVrFkzlZSUKDMz0%2B%2B1ycnJZ73jdSbl5eVyu91%2Ba05nK7/zm%2BBw2KsfO532mfdUtnbL2A7I1lrkax2ytV4oZRuSBevbXC6XXn75ZS1dulTNmzdXnz59dOONN%2Brxxx/Xvn37NGXKFDmdTk2bNk0ej0exsbF%2Br4%2BNjVVpaWmjz1dcXKyCggK/tdzcXE2dOtXI9dhVfHxksEdospiYlsEeIWSRrbXI1zpka51QyjbkC9Z7772nyZMna8aMGcrIyJAkvfLKK77tvXr10sSJE7Vs2TJNmzZNkuT1es/rnNnZ2crKyvJbczpbqaKi%2BryO%2B212a/qmr99KDke4YmJaqrKyRvX1DcEeJ6SQrbXI1zpkaz0rsg3WX%2B5DumBt27ZN999/v%2BbOnauRI0eedb%2BkpCR98cUX8nq9io%2BPl8fj8dvu8XiUkJDQ6PMmJiae9nag231MdXUX9xekHa%2B/vr7BlnPbAdlai3ytQ7bWCaVs7XULpAn%2B%2Bc9/atasWXrmmWf8ytU777yjpUuX%2Bu174MABJSUlKSwsTKmpqdqzZ4/fdpfLpd69ewdkbgAAYH8hWbDq6uo0Z84czZw5U/379/fbFh0drSVLlui1115TbW2tXC6Xfve73yknJ0eSdPvtt%2Buvf/2r3nrrLX3zzTdatWqVPvnkEw0fPjwYlwIAAGwoJN8ifP/991VWVqb58%2Bdr/vz5fts2bdqkRYsWqaCgQL/61a8UHR2tO%2B%2B8U3fddZckqXv37srPz9eCBQt0%2BPBhde3aVcuWLVObNm2CcSkAAMCGQrJgXXXVVfrwww/Puj0pKUk33njjWbcPHjxYgwcPtmI0AABwEQjJtwgBAACCiYIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRSsMzh8%2BLB%2B8YtfKD09Xddff72eeuopNTQ0BHssAABgE85gD3AhmjJlilJSUrR161Z9%2BeWXmjhxoi655BL99Kc/DfZoAADABriD9S0ul0v79%2B/XzJkzFR0drU6dOmn8%2BPEqLi4O9mgAAMAmuIP1LSUlJUpKSlJsbKxvLSUlRQcPHlRVVZWioqLOeYzy8nK53W6/NaezlRITE43O6nDYqx87nfaZ91S2dsvYDsjWWuRrHbK1XihlS8H6Fo/Ho5iYGL%2B1U2WroqKiUQWruLhYBQUFfmv33nuvpkyZYm5QnSxyd7X7WNnZ2cbL28WuvLxcv//982RrAbK1Fvlax47Z7np8aLBHaJTy8nItXrzYVtmeS%2BhURYO8Xu95vT47O1tr1qzx%2B5WdnW1ouv9wu90qKCg47W4Zzh/ZWodsrUW%2B1iFb64RittzB%2BpaEhAR5PB6/NY/Ho7CwMCUkJDTqGImJiSHTwAEAQNNxB%2BtbUlNTdeTIER09etS35nK51LVrV0VGRgZxMgAAYBcUrG9JTk5WWlqaFi5cqKqqKpWVlenFF19UTk5OsEcDAAA24XjkkUceCfYQF5of/ehH2rBhgx577DG9/vrrGjNmjCZMmKCwsLBgj3aayMhIXX311dxdswDZWodsrUW%2B1iFb64RatmHe832iGwAAAH54ixAAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMArWBe7w4cP6xS9%2BofT0dF1//fV66qmn1NDQcMZ9i4qKNGTIEF155ZXKycnRnj17AjytvTQl2z/%2B8Y8aMmSI%2BvTpoxEjRmjr1q0BntZempLtKZ9//rn69OmjxYsXB2hK%2B2pKvmVlZbrzzjvVu3dvZWZm6qWXXgrssDbT2GwbGhr07LPPKisrS3369NGwYcP0xhtvBGFie9m5c6cyMjKUl5f3nfs1NDRo0aJFGjRokPr166cJEybo008/DdCUhnhxQRs1apR3zpw53srKSu/Bgwe9gwcP9r7wwgun7ffmm296r7rqKu/777/vramp8S5btsx73XXXeaurq4MwtT00NttNmzZ5%2B/bt6921a5f3xIkT3ldffdWbkpLiPXToUBCmtofGZvvf7r33Xm/fvn29zz77bICmtK/G5ltTU%2BMdOHCgd/ny5d6vv/7au3v3bu/NN9/sLS0tDcLU9tDYbF9%2B%2BWVv//79vWVlZd66ujrvtm3bvMnJyd59%2B/YFYWp7KCws9A4ePNg7duxY7/Tp079z36KiIu/111/vLS0t9R47dsw7b94877Bhw7wNDQ0Bmvb8cQfrAuZyubR//37NnDlT0dHR6tSpk8aPH6/i4uLT9i0uLtbo0aPVu3dvtWjRQnfffbckafv27YEe2xaaku3x48d13333qW/fvmrWrJluu%2B02RUZG6v333w/C5Be%2BpmR7yo4dO1RaWqqBAwcGblCbakq%2BGzduVFRUlO6%2B%2B261bNlSvXr10oYNG9SlS5cgTH7ha0q2JSUl6tu3r37wgx/I4XDo%2BuuvV1xcnD788MMgTG4PERERWrVqlTp27HjOfYuLizV%2B/Hh16dJFUVFRysvLU1lZmXbv3h2ASc2gYF3ASkpKlJSUpNjYWN9aSkqKDh48qKqqqtP2TU5O9v0%2BPDxcPXv2lMvlCti8dtKUbEeMGKE77rjD9/vKykpVV1erbdu2AZvXTpqSrXSywM6bN08PP/ywnE5nIEe1pabk%2B95776l79%2B568MEHddVVV2no0KFat25doEe2jaZkO3DgQP3jH//Qvn37dOLECb355puqqanR1VdfHeixbWPcuHGKjo4%2B537Hjx9XaWmp359pUVFR6tixo63%2BTKNgXcA8Ho9iYmL81k594VdUVJy273//R%2BHUvt/eDyc1Jdv/5vV6NWfOHPXu3Zv/kJ5FU7NdsmSJrrjiCl1zzTUBmc/umpLvZ599pjfffFMZGRnauXOnJk6cqFmzZmnv3r0Bm9dOmpLt4MGDlZ2drZEjRyotLU0zZszQggUL1L59%2B4DNG6q%2B%2Buoreb1e2/%2BZxl8XL3Ber9eSfdH0vGpra/XAAw%2BotLRURUVFFk0VGhqbbWlpqVauXKn169dbPFFoaWy%2BXq9XKSkpGjZsmCRp1KhReuWVV7Rp0ya/uwP4j8Zmu3btWq1du1YrV65Ujx499M4772jGjBlq3769evXqZfGUFwe7/5nGHawLWEJCgjwej9%2Bax%2BNRWFiYEhIS/Nbj4%2BPPuO%2B398NJTclWOnnLeuLEifr3v/%2BtFStW6JJLLgnUqLbT2Gy9Xq8eeeQRTZkyRW3atAn0mLbVlH9327Rpc9pbMklJSXK73ZbPaUdNyfbll19Wdna2evXqpYiICA0cOFDXXHMNb8EaEBcXp/Dw8DP%2Bs2jdunWQpmo6CtYFLDU1VUeOHNHRo0d9ay6XS127dlVkZORp%2B5aUlPh%2BX19fr71796p3794Bm9dOmpKt1%2BtVXl6enE6nXnrpJcXHxwd6XFtpbLb//ve/9e677%2BrZZ59Venq60tPT9frrr%2Bv555/XqFGjgjG6LTTl390uXbroo48%2B8rsTcPjwYSUlJQVsXjtpSrYNDQ2qr6/3Wztx4kRA5gx1ERER6tatm9%2BfaZWVlTp06JCt7g5SsC5gycnJSktL08KFC1VVVaWysjK9%2BOKLysnJkSQNHTpUu3btkiTl5ORo7dq1ev/991VTU6OlS5eqefPmfFfWWTQl2/Xr16u0tFTPPPOMIiIigjm2LTQ223bt2mnHjh167bXXfL%2BysrI0duxYFRYWBvkqLlxN%2BXd3%2BPDhqqio0HPPPafjx49rw4YNKikp0fDhw4N5CRespmSblZWlVatWaf/%2B/aqrq9Pbb7%2Btd955R4MGDQrmJdjW559/rqFDh/o%2B6yonJ0dFRUUqKytTVVWV8vPz1bNnT6WlpQV50sbjGawL3LPPPqu5c%2BfquuuuU1RUlMaOHev7jraDBw/q66%2B/liQNGDBA9913n6ZPn64vv/xSaWlpKiwsVIsWLYI5/gWtsdmuXr1ahw8fPu2h9hEjRmj%2B/PkBn9sOGpOtw%2BFQu3bt/F7XsmVLRUVF8ZbhOTT23922bdtq2bJlevzxx/Xb3/5Wl156qZYsWaLLL788mONf0Bqb7cSJE1VXV6fc3FwdPXpUSUlJmj9/vq699tpgjn9BO1WO6urqJMn3gc0ul0u1tbU6ePCg7y7g2LFj5Xa7deedd6q6ulrp6ekqKCgIzuDfU5jX7k%2BRAQAAXGB4ixAAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADPv/uxLXTS4dWU4AAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common4830369053988223188”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1968</td> <td class=”number”>61.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>1164</td> <td class=”number”>36.3%</td> <td>

<div class=”bar” style=”width:59%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme4830369053988223188”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1968</td> <td class=”number”>61.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>1164</td> <td class=”number”>36.3%</td> <td>

<div class=”bar” style=”width:59%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1968</td> <td class=”number”>61.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>1164</td> <td class=”number”>36.3%</td> <td>

<div class=”bar” style=”width:59%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SNAP_CAP16”><s>SNAP_CAP16</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_SNAP_CAP09”><code>SNAP_CAP09</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.94179</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SNAP_OAPP09”>SNAP_OAPP09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>4</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.53736</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>41.8%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-1142572555709300592”>

</div> <div class=”col-md-12 text-right”>

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<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-1142572555709300592”

aria-controls=”quantiles-1142572555709300592” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-1142572555709300592” aria-controls=”histogram-1142572555709300592”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-1142572555709300592” aria-controls=”common-1142572555709300592”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-1142572555709300592” aria-controls=”extreme-1142572555709300592”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-1142572555709300592”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>1</td>

</tr> <tr>

<th>Q3</th> <td>1</td>

</tr> <tr>

<th>95-th percentile</th> <td>1</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr>

<th>Range</th> <td>1</td>

</tr> <tr>

<th>Interquartile range</th> <td>1</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.48141</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.89589</td>

</tr> <tr>

<th>Kurtosis</th> <td>-1.9053</td>

</tr> <tr>

<th>Mean</th> <td>0.53736</td>

</tr> <tr>

<th>MAD</th> <td>0.46589</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.14962</td>

</tr> <tr>

<th>Sum</th> <td>1683</td>

</tr> <tr>

<th>Variance</th> <td>0.23176</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-1142572555709300592”>

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p07t/r8ycnJLV4OdLtPqLHxwv6GtOP1NzU123JuOyBba5GvdcjWOuGUrb1ugbTSQw89pPfff79FuZKkfv36qaKiwv9xU1OT9u3bp4yMDF166aWKj48P2P6Pf/xDp06dUr9%2B/YI2PwAAsLewK1jvvvuuNm3apJKSEiUkJLTYnpeXp40bN2r37t2qr6/XmjVr1L59ew0fPlxRUVG69dZb9fjjj%2BuTTz6Rx%2BPRf/3Xf%2Bm6667TRRddFIKrAQAAdmTLlwjT09MlffETgpK0detWSVJ5ebk2bNigEydOaMSIEQGfM2jQID311FMaOnSoZs%2BerVmzZunYsWNKT09XSUmJYmJiJEkFBQWqq6vThAkT1NjYqBEjRmjx4sXBuzgAAGB7Eb5zfaIbreJ2nzB%2BTIcjUtcV7TR%2BXKtsmXVNqEdoNYcjUomJsfJ46sLmeYDzBdlai3ytY8dsr3/0T6EeodXeeXCMJdl26dLJ6PFaK%2BxeIgQAAAg1ChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDbF2wdu7cqaysLBUWFrbY9oc//EHjxo1T//79NWnSJL3xxhv%2Bbc3NzVq5cqVGjhypQYMGaerUqTp06JB/u9fr1axZs5SVlaUhQ4Zo/vz5amhoCMo1AQAA%2B7NtwXriiSe0dOlSdevWrcW2/fv3a968eZo7d67eeustTZkyRXfffbeOHDkiSXr%2B%2Bee1efNmlZSUaPv27erevbvy8/Pl8/kkSQsXLlR9fb3Kysq0YcMGVVVVqaioKKjXBwAA7Mu2BSs6Olrr168/a8Fat26dhg0bpmHDhik6Olrjx4/X5Zdfrk2bNkmSXC6XpkyZop49eyouLk6FhYWqqqrSnj179Omnn2rr1q0qLCxUUlKSunbtqpkzZ2rDhg06ffp0sC8TAADYkG0L1h133KFOnTqddVtFRYVSU1MD1lJTU1VeXq6GhgZVVlYGbI%2BLi1O3bt1UXl6u/fv3KyoqSn369PFvT0tL08mTJ/XBBx9YczEAACCsOEI9gBW8Xq/i4%2BMD1uLj41VZWanPPvtMPp/vrNs9Ho8SEhIUFxeniIiIgG2S5PF4WnX%2B6upqud3ugDWHo6OSk5O/y%2BV8ragoe/Vjh8M%2B857J1m4Z2wHZWot8rUO21gunbINesLKzszVp0iTdfPPNuuSSSyw7z5nnqb7L9m/73G/jcrlUXFwcsJafn6%2BCgoJzOq7dJSbGhnqENnM6O4R6hLBFttYiX%2BuQrXXCKdugF6ybb75ZL730ktasWaOrr75at956q7Kzs%2BVwmBslMTFRXq83YM3r9SopKUkJCQmKjIw86/bOnTsrKSlJtbW1ampqUlRUlH%2BbJHXu3LlV58/NzVV2dnbAmsPRUR5P3Xe9pLOyW9M3ff1WioqKlNPZQTU19Wpqag71OGGFbK1FvtYhW%2BtZkW2o/nIf9IKVn5%2Bv/Px8VVRUqKysTA899JCWLFmim266STk5OerRo8c5n6Nfv37au3dvwFp5ebnGjh2r6Oho9e7dWxUVFRo8eLAkqaamRh999JGuuOIKpaSkyOfz6cCBA0pLS/N/rtPpbPVsycnJLV4OdLtPqLHxwv6GtOP1NzU123JuOyBba5GvdcjWOuGUbchugaSlpWnevHnavn277r//fv3%2B97/XDTfcoKlTp%2Brvf//7OR371ltv1ZtvvqnXX39dn3/%2BudavX68PP/xQ48ePlyTl5eWptLRUVVVVqq2tVVFRkfr27av09HQlJSVp9OjRevTRR3X8%2BHEdOXJEq1evVk5OjtG7bAAAIHyFrDGcPn1a//u//6sXXnhBb731lrp376577rlH1dXVmjJlipYsWaJx48Z97eenp6dLkhobGyVJW7dulfTF3abLL79cRUVFWrZsmQ4fPqxevXpp7dq16tKliyRp8uTJcrvduv3221VXV6fMzMyAZ6YeeOABLVq0SCNHjlS7du104403nvXNTAEAAM4mwneuT3S3UVVVldavX6%2BNGzeqrq5Oo0eP1uTJkzVw4ED/Pjt27NDixYu1ffv2YI5mKbf7hPFjOhyRuq5op/HjWmXLrGtCPUKrORyRSkyMlcdTFza3q88XZGst8rWOHbO9/tE/hXqEVnvnwTGWZNuly9nf0slqQb%2BDNXbsWPXo0UPTp0/XTTfdpISEhBb7DBs2TMePHw/2aAAAAEYEvWCVlpb6Hy7/Jnv27AnCNAAAAOYF/SH3Pn36aMaMGf5npiTpmWee0c9//vMWb50AAABgR0EvWMuWLdOJEyfUq1cv/9rw4cPV3Nys5cuXB3scAAAA44L%2BEuEbb7yhzZs3KzEx0b/WvXt3FRUV6cYbbwz2OAAAAMYF/Q5WQ0ODoqOjWw4SGan6%2BvpgjwMAAGBc0AvWoEGDtHz5cn322Wf%2BtaNHj2rJkiUBb9UAAABgV0F/ifD%2B%2B%2B/Xz372M1199dWKi4tTc3Oz6urqdOmll%2BrZZ58N9jgAAADGBb1gXXrppXrppZf0xz/%2BUR999JEiIyPVo0cPDRkyxP/LlQEAAOwsJL8qp3379rr22mtDcWoAAADLBb1gHTp0SCtWrND777%2BvhoaGFttfe%2B21YI8EAABgVEiewaqurtaQIUPUsWPHYJ8eAADAckEvWHv37tVrr72mpKSkYJ8aAAAgKIL%2BNg2dO3fmzhUAAAhrQS9Y06dPV3FxsXw%2BX7BPDQAAEBRBf4nwj3/8o/7617/qhRde0Pe//31FRgZ2vN/97nfBHgkAAMCooBesuLg4DR06NNinBQAACJqgF6xly5YF%2B5QAAABBFfRnsCTpgw8%2B0KpVq3Tffff51/72t7%2BFYhQAAADjgl6wdu3apfHjx%2BvVV19VWVmZpC/efPSOO%2B7gTUYBAEBYCHrBWrlypX7xi19o8%2BbNioiIkPTF7ydcvny5Vq9eHexxAAAAjAt6wfrHP/6hvLw8SfIXLEkaM2aMqqqqgj0OAACAcUEvWJ06dTrr7yCsrq5W%2B/btgz0OAACAcUEvWAMGDNBDDz2k2tpa/9rBgwc1b948XX311cEeBwAAwLigv03Dfffdp5/%2B9KfKzMxUU1OTBgwYoPr6evXu3VvLly8P9jgAAADGBb1gXXzxxSorK9OOHTt08OBBxcTEqEePHrrmmmsCnskCAACwq6AXLElq166drr322lCcGgAAwHJBL1jZ2dnfeKeK98ICAAB2F/SCdcMNNwQUrKamJh08eFDl5eX66U9/auw8%2B/bt0/Lly7Vv3z5FR0fr6quv1v3336%2BkpCTt2rVLK1as0AcffKBLLrlE06dP1/jx4/2fW1paqueff15ut1t9%2BvTR/Pnz1a9fP2OzAQCA8Bb0gjV37tyzrr/yyit6%2B%2B23jZyjsbFRd955pyZNmqQnn3xSdXV1mjNnjhYvXqwFCxZo5syZmj9/vsaNG6d3331Xd911l3r06KH09HRt27ZNq1at0pNPPqk%2BffqotLRUM2bM0KuvvqqOHTsamQ8AAIS3kPwuwrO59tpr9dJLLxk5ltvtltvt1oQJE9S%2BfXslJibquuuu0/79%2B7V582Z1795dOTk5io6OVlZWlrKzs7Vu3TpJksvl0qRJk5SRkaGYmBhNmzZNkrR9%2B3YjswEAgPAXkofcz2bfvn3y%2BXxGjtW1a1f17dtXLpdL//7v/66Ghga9%2BuqrGj58uCoqKpSamhqwf2pqqrZs2SJJqqio0A033ODfFhkZqb59%2B6q8vFxjx45t1fmrq6vldrsD1hyOjkpOTj7HKwsUFXXe9ONWcTjsM%2B%2BZbO2WsR2QrbXI1zpka71wyjboBWvy5Mkt1urr61VVVaVRo0YZOUdkZKRWrVqlKVOm6De/%2BY0kafDgwZozZ45mzpyprl27BuyfkJAgj8cjSfJ6vYqPjw/YHh8f79/eGi6XS8XFxQFr%2Bfn5Kigo%2BC6XEzYSE2NDPUKbOZ0dQj1C2CJba5GvdcjWOuGUbdALVvfu3Vv8FGF0dLRycnJ0yy23GDnHqVOnNGPGDI0ZM0YzZszQyZMntWTJkq99/uurzvVOWm5urrKzswPWHI6O8njqzum4X2W3pm/6%2Bq0UFRUpp7ODamrq1dTUHOpxwgrZWot8rUO21rMi21D95T7oBSsY79a%2Ba9cuffzxx5o9e7aioqLUqVMnFRQUaMKECfrxj38sr9cbsL/H41FSUpIkKTExscV2r9er3r17t/r8ycnJLV4OdLtPqLHxwv6GtOP1NzU123JuOyBba5GvdcjWOuGUbdAL1saNG1u970033fSdztHU1KTm5uaAO1GnTp2SJGVlZel//ud/Avbfu3evMjIyJEn9%2BvVTRUWFJk6c6D/Wvn37lJOT851mAQAAF56gF6z58%2Be3KD%2BSFBEREbAWERHxnQtW//791bFjR61atUozZsxQQ0OD1qxZo0GDBmnChAkqLi7WunXrNH78eL311lvasWOHXC6XJCkvL0%2BzZ8/WjTfeqD59%2BujXv/612rdvr%2BHDh3/nawYAABeWoBesJ598Uk899ZRmzJihPn36yOfz6b333tMTTzyhn/zkJ8rMzDzncyQmJurXv/61fvnLX2ro0KFq3769Bg8erMWLF6tz585au3atli5dqiVLliglJUWPPPKIfvjDH0qShg4dqtmzZ2vWrFk6duyY0tPTVVJSopiYmHOeCwAAXBgifKbeG6GVJkyYoJKSkhY/yXf06FFNnTpVZWVlwRwnaNzuE8aP6XBE6rqincaPa5Uts64J9Qit5nBEKjExVh5PXdg8D3C%2BIFtrka917Jjt9Y/%2BKdQjtNo7D46xJNsuXToZPV5rBf3H0D788MMWb4MgSU6nU4cPHw72OAAAAMYFvWClpKRo%2BfLlAe8rVVNToxUrVuiyyy4L9jgAAADGBf0ZrPvvv19z5syRy%2BVSbGysIiMjVVtbq5iYGK1evTrY4wAAABgX9II1ZMgQvf7669qxY4eOHDkin8%2Bnrl276sc//rE6dQrN66QAAAAmheR3EXbo0EEjR47UkSNHdOmll4ZiBAAAAMsE/RmshoYGzZs3T/3799f1118v6YtnsKZNm6aamppgjwMAAGBc0AvWI488ov3796uoqEiRkf86fVNTk4qKioI9DgAAgHFBL1ivvPKKHnvsMY0ZM8b/S5%2BdTqeWLVumV199NdjjAAAAGBf0glVXV6fu3bu3WE9KStLJkyeDPQ4AAIBxQS9Yl112md5%2B%2B21JCvjdgy%2B//LK%2B973vBXscAAAA44L%2BU4S33Xab7rnnHt18881qbm7W008/rb179%2BqVV17R/Pnzgz0OAACAcUEvWLm5uXI4HHruuecUFRWlxx9/XD169FBRUZHGjBkT7HEAAACMC3rBOn78uG6%2B%2BWbdfPPNwT41AABAUAT9GayRI0cGPHsFAAAQboJesDIzM7Vly5ZgnxYAACBogv4S4SWXXKIHH3xQJSUluuyyy9SuXbuA7StWrAj2SAAAAEYFvWBVVlbqBz/4gSTJ4/EE%2B/QAAACWC1rBKiws1MqVK/Xss8/611avXq38/PxgjQAAABAUQXsGa9u2bS3WSkpKgnV6AACAoAlawTrbTw7y04QAACAcBa1gnfnFzt%2B2BgAAYHdBf5sGAACAcEfBAgAAMCxoP0V4%2BvRpzZkz51vXeB8sAABgd0ErWAMHDlR1dfW3rgEAANhd0ArWl9//CgAAIJzxDBYAAIBhFCwAAADDwrpgrVmzRkOGDNGPfvQjTZkyRR9//LEkadeuXcrJydGAAQM0duxYbdq0KeDzSktLNXr0aA0YMEB5eXnau3dvKMYHAAA2FbYF6/nnn9emTZtUWlqqN954Q7169dIzzzyj6upqzZw5U5MnT9auXbs0f/58LVy4UOXl5ZK%2B%2BJU%2Bq1at0sMPP6w333xTI0aM0IwZM3Ty5MkQXxEAALCLsC1YTz31lAoLC/WDH/xAcXFxWrBggRYsWKDNmzere/fuysnJUXR0tLKyspSdna1169ZJklwulyZNmqSMjAzFxMRo2rRpkqTt27eH8nIAAICNBO2nCIPp6NGj%2Bvjjj/XZZ5/phhtu0LFjx5SZmanFixeroqJCqampAfunpqZqy5YtkqSKigrdcMMN/m2RkZHq27evysvLNXbs2Fadv7q6Wm63O2DN4eio5OTkc7yyQFFR9urHDod95j2Trd34/NlBAAAVAElEQVQytgOytRb5WodsrRdO2YZlwTpy5Igk6eWXX9bTTz8tn8%2BngoICLViwQA0NDeratWvA/gkJCfJ4PJIkr9er%2BPj4gO3x8fH%2B7a3hcrlUXFwcsJafn6%2BCgoLvcjlhIzExNtQjtJnT2SHUI4QtsrUW%2BVqHbK0TTtmGZcHy%2BXySpGnTpvnL1D333KOf//znysrKavXnf1e5ubnKzs4OWHM4OsrjqTun436V3Zq%2B6eu3UlRUpJzODqqpqVdTU3OoxwkrZGst8rUO2VrPimxD9Zf7sCxYF110kSTJ6XT611JSUuTz%2BXT69Gl5vd6A/T0ej5KSkiRJiYmJLbZ7vV717t271edPTk5u8XKg231CjY0X9jekHa%2B/qanZlnPbAdlai3ytQ7bWCads7XULpJUuvvhixcXFaf/%2B/f61w4cPq127dho2bFiLt13Yu3evMjIyJEn9%2BvVTRUWFf1tTU5P27dvn3w4AAPBtwrJgORwO5eTk6PHHH9f//d//6dixY1q9erXGjRuniRMn6vDhw1q3bp0%2B//xz7dixQzt27NCtt94qScrLy9PGjRu1e/du1dfXa82aNWrfvr2GDx8e2osCAAC2EZYvEUrSnDlzdOrUKd1yyy06ffq0Ro8erQULFig2NlZr167V0qVLtWTJEqWkpOiRRx7RD3/4Q0nS0KFDNXv2bM2aNUvHjh1Tenq6SkpKFBMTE%2BIrAgAAdhHhO9cnutEqbvcJ48d0OCJ1XdFO48e1ypZZ14R6hFZzOCKVmBgrj6cubJ4HOF%2BQrbXI1zp2zPb6R/8U6hFa7Z0Hx1iSbZcunYwer7XC8iVCAACAUKJgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMOyCKFgPPfSQ%2BvTp4/94165dysnJ0YABAzR27Fht2rQpYP/S0lKNHj1aAwYMUF5envbu3RvskQEAgI2FfcHav3%2B/XnzxRf/H1dXVmjlzpiZPnqxdu3Zp/vz5WrhwocrLyyVJ27Zt06pVq/Twww/rzTff1IgRIzRjxgydPHkyVJcAAABsJqwLVnNzsxYtWqQpU6b41zZv3qzu3bsrJydH0dHRysrKUnZ2ttatWydJcrlcmjRpkjIyMhQTE6Np06ZJkrZv3x6KSwAAADYU1gXrd7/7naKjozVu3Dj/WkVFhVJTUwP2S01N9b8M%2BNXtkZGR6tu3r/8OFwAAwLdxhHoAq3z66adatWqVnn322YB1r9errl27BqwlJCTI4/H4t8fHxwdsj4%2BP929vjerqarnd7oA1h6OjkpOT23IJ3yoqyl792OGwz7xnsrVbxnZAttYiX%2BuQrfXCKduwLVjLli3TpEmT1KtXL3388cdt%2Blyfz3dO53a5XCouLg5Yy8/PV0FBwTkd1%2B4SE2NDPUKbOZ0dQj1C2CJba5GvdcjWOuGUbVgWrF27dulvf/ubysrKWmxLTEyU1%2BsNWPN4PEpKSvra7V6vV7179271%2BXNzc5WdnR2w5nB0lMdT1%2BpjtIbdmr7p67dSVFSknM4OqqmpV1NTc6jHCStkay3ytQ7ZWs%2BKbEP1l/uwLFibNm3SsWPHNGLECEn/uiOVmZmpn/3sZy2K1969e5WRkSFJ6tevnyoqKjRx4kRJUlNTk/bt26ecnJxWnz85ObnFy4Fu9wk1Nl7Y35B2vP6mpmZbzm0HZGst8rUO2VonnLK11y2QVrr33nv1yiuv6MUXX9SLL76okpISSdKLL76ocePG6fDhw1q3bp0%2B//xz7dixQzt27NCtt94qScrLy9PGjRu1e/du1dfXa82aNWrfvr2GDx8ewisCAAB2EpZ3sOLj4wMeVG9sbJQkXXzxxZKktWvXaunSpVqyZIlSUlL0yCOP6Ic//KEkaejQoZo9e7ZmzZqlY8eOKT09XSUlJYqJiQn%2BhQAAAFsKy4L1Vd///vf13nvv%2BT8eNGhQwJuPftVtt92m2267LRijAQCAMBSWLxECAACEEgULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAw8K2YB0%2BfFj5%2BfnKzMxUVlaW7r33XtXU1EiS9u/fr5/85CcaOHCgRo0apaeeeirgc//whz9o3Lhx6t%2B/vyZNmqQ33ngjFJcAAABsKmwL1owZM%2BR0OrVt2za98MILev/99/XLX/5SDQ0Nmj59uq666irt3LlTK1eu1Nq1a/Xqq69K%2BqJ8zZs3T3PnztVbb72lKVOm6O6779aRI0dCfEUAAMAuHKEewAo1NTXq16%2Bf5syZo9jYWMXGxmrixIl69tln9frrr%2Bv06dO66667FBUVpbS0NN1yyy1yuVwaNWqU1q1bp2HDhmnYsGGSpPHjx%2Bu5557Tpk2bdOedd4b4ygCEwvWP/inUI7TJOw%2BOCfUIwAUvLAuW0%2BnUsmXLAtY%2B%2BeQTJScnq6KiQn369FFUVJR/W2pqqtatWydJqqio8JerL28vLy9v9fmrq6vldrsD1hyOjkpOTm7rpXyjqCh73YB0OOwz75ls7ZaxHZBtcJCveXztWi%2Bcsg3LgvVV5eXleu6557RmzRpt2bJFTqczYHtCQoK8Xq%2Bam5vl9XoVHx8fsD0%2BPl6VlZWtPp/L5VJxcXHAWn5%2BvgoKCr77RYSBxMTYUI/QZk5nh1CPELbI1lrkax2ytU44ZRv2Bevdd9/VXXfdpTlz5igrK0tbtmw5634RERH%2B/%2B/z%2Bc7pnLm5ucrOzg5Yczg6yuOpO6fjfpXdmr7p67dSVFSknM4OqqmpV1NTc6jHCStkGxzkax5fu9azIttQ/eU%2BrAvWtm3b9Itf/EILFy7UTTfdJElKSkrShx9%2BGLCf1%2BtVQkKCIiMjlZiYKK/X22J7UlJSq8%2BbnJzc4uVAt/uEGhsv7G9IO15/U1OzLee2A7K1Fvlah2ytE07Z2usWSBv89a9/1bx58/SrX/3KX64kqV%2B/fnrvvffU2NjoXysvL1dGRoZ/%2B969ewOO9eXtAAAA3yYsC1ZjY6MWLFiguXPnasiQIQHbhg0bpri4OK1Zs0b19fXas2eP1q9fr7y8PEnSrbfeqjfffFOvv/66Pv/8c61fv14ffvihxo8fH4pLAQAANhSWLxHu3r1bVVVVWrp0qZYuXRqw7eWXX9bjjz%2BuRYsWqaSkRBdddJEKCws1fPhwSdLll1%2BuoqIiLVu2TIcPH1avXr20du1adenSJQRXAgAA7CgsC9aVV16p99577xv3%2Be1vf/u120aNGqVRo0aZHgsAAFwgwvIlQgAAgFCiYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBessDh8%2BrDvvvFOZmZkaMWKEHnnkETU3N4d6LAAAYBOOUA9wPrrnnnuUlpamrVu36tixY5o%2Bfbouuugi/du//VuoRwMAADbAHayvKC8v14EDBzR37lx16tRJ3bt315QpU%2BRyuUI9GgAAsAnuYH1FRUWFUlJSFB8f719LS0vTwYMHVVtbq7i4uG89RnV1tdxud8Caw9FRycnJRmeNirJXP3Y47DPvmWztlrEdkG1wkK95fO1aL5yypWB9hdfrldPpDFg7U7Y8Hk%2BrCpbL5VJxcXHA2t1336177rnH3KD6osj99OL3lZuba7y8Xeiqq6v1m988SbYWsGO27zw4JtQjtFp1dbVWrVplq3ztgq9d64Tj1234VEWDfD7fOX1%2Bbm6uXnjhhYB/cnNzDU33L263W8XFxS3uluHcka11yNZa5GsdsrVOOGbLHayvSEpKktfrDVjzer2KiIhQUlJSq46RnJwcNg0cAAC0HXewvqJfv3765JNPdPz4cf9aeXm5evXqpdjY2BBOBgAA7IKC9RWpqalKT0/XihUrVFtbq6qqKj399NPKy8sL9WgAAMAmohYvXrw41EOcb3784x%2BrrKxM//mf/6mXXnpJOTk5mjp1qiIiIkI9WguxsbEaPHgwd9csQLbWIVtrka91yNY64ZZthO9cn%2BgGAABAAF4iBAAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMgnWeO3z4sO68805lZmZqxIgReuSRR9Tc3HzWfUtLSzV69GgNGDBAeXl52rt3b5CntZe2ZPvb3/5Wo0ePVv/%2B/TVhwgRt3bo1yNPaS1uyPePo0aPq37%2B/Vq1aFaQp7ast%2BVZVVen2229XRkaGhg0bpmeeeSa4w9pMa7Ntbm7WY489puzsbPXv31/jxo3TH/7whxBMbC87d%2B5UVlaWCgsLv3G/5uZmrVy5UiNHjtSgQYM0depUHTp0KEhTGuLDeW3ixIm%2BBQsW%2BGpqanwHDx70jRo1yvfUU0%2B12O%2B1117zXXnllb7du3f76uvrfWvXrvVdc801vrq6uhBMbQ%2Btzfbll1/2DRw40PfOO%2B/4Tp065fv973/vS0tL83300UchmNoeWpvtl919992%2BgQMH%2Bh577LEgTWlfrc23vr7eN3z4cN8TTzzhO3nypG/Pnj2%2BsWPH%2BiorK0MwtT20NtvnnnvON2TIEF9VVZWvsbHRt23bNl9qaqpv//79IZjaHkpKSnyjRo3yTZ482Tdr1qxv3Le0tNQ3YsQIX2Vlpe/EiRO%2BBx54wDdu3Dhfc3NzkKY9d9zBOo%2BVl5frwIEDmjt3rjp16qTu3btrypQpcrlcLfZ1uVyaNGmSMjIyFBMTo2nTpkmStm/fHuyxbaEt2TY0NGj27NkaOHCg2rVrp1tuuUWxsbHavXt3CCY//7Ul2zN27NihyspKDR8%2BPHiD2lRb8t2yZYvi4uI0bdo0dejQQVdccYXKysrUs2fPEEx%2B/mtLthUVFRo4cKB%2B8IMfKCoqSiNGjFBCQoLee%2B%2B9EExuD9HR0Vq/fr26dev2rfu6XC5NmTJFPXv2VFxcnAoLC1VVVaU9e/YEYVIzKFjnsYqKCqWkpCg%2BPt6/lpaWpoMHD6q2trbFvqmpqf6PIyMj1bdvX5WXlwdtXjtpS7YTJkzQbbfd5v%2B4pqZGdXV16tq1a9DmtZO2ZCt9UWAfeOABLVq0SA6HI5ij2lJb8n333Xd1%2BeWX67777tOVV16pMWPGaNOmTcEe2Tbaku3w4cP15z//Wfv379epU6f02muvqb6%2BXoMHDw722LZxxx13qFOnTt%2B6X0NDgyorKwP%2BTIuLi1O3bt1s9WcaBes85vV65XQ6A9bOfON7PJ4W%2B375Pwpn9v3qfvhCW7L9Mp/PpwULFigjI4P/kH6Ntma7evVq/ehHP9JVV10VlPnsri35HjlyRK%2B99pqysrK0c%2BdOTZ8%2BXfPmzdO%2BffuCNq%2BdtCXbUaNGKTc3VzfddJPS09M1Z84cLVu2TJdccknQ5g1Xn332mXw%2Bn%2B3/TOOvi%2Bc5n89nyb5oe16nT5/Wvffeq8rKSpWWllo0VXhobbaVlZVat26dNm/ebPFE4aW1%2Bfp8PqWlpWncuHGSpIkTJ%2Bp3v/udXn755YC7A/iX1ma7ceNGbdy4UevWrVOfPn20a9cuzZkzR5dccomuuOIKi6e8MNj9zzTuYJ3HkpKS5PV6A9a8Xq8iIiKUlJQUsJ6YmHjWfb%2B6H77QlmylL25ZT58%2BXf/85z/1/PPP66KLLgrWqLbT2mx9Pp8WL16se%2B65R126dAn2mLbVlq/dLl26tHhJJiUlRW632/I57agt2T733HPKzc3VFVdcoejoaA0fPlxXXXUVL8EakJCQoMjIyLP%2Bu%2BjcuXOIpmo7CtZ5rF%2B/fvrkk090/Phx/1p5ebl69eql2NjYFvtWVFT4P25qatK%2BffuUkZERtHntpC3Z%2Bnw%2BFRYWyuFw6JlnnlFiYmKwx7WV1mb7z3/%2BU3/5y1/02GOPKTMzU5mZmXrppZf05JNPauLEiaEY3Rba8rXbs2dP/eMf/wi4E3D48GGlpKQEbV47aUu2zc3NampqClg7depUUOYMd9HR0erdu3fAn2k1NTX66KOPbHV3kIJ1HktNTVV6erpWrFih2tpaVVVV6emnn1ZeXp4kacyYMXrnnXckSXl5edq4caN2796t%2Bvp6rVmzRu3bt%2Bensr5GW7LdvHmzKisr9atf/UrR0dGhHNsWWpvtxRdfrB07dujFF1/0/5Odna3JkyerpKQkxFdx/mrL1%2B748ePl8Xj0%2BOOPq6GhQWVlZaqoqND48eNDeQnnrbZkm52drfXr1%2BvAgQNqbGzUG2%2B8oV27dmnkyJGhvATbOnr0qMaMGeN/r6u8vDyVlpaqqqpKtbW1KioqUt%2B%2BfZWenh7iSVuPZ7DOc4899pgWLlyoa665RnFxcZo8ebL/J9oOHjyokydPSpKGDh2q2bNna9asWTp27JjS09NVUlKimJiYUI5/Xmttths2bNDhw4dbPNQ%2BYcIELV26NOhz20Frso2KitLFF18c8HkdOnRQXFwcLxl%2Bi9Z%2B7Xbt2lVr167Vgw8%2BqP/%2B7//W9773Pa1evVqXXXZZKMc/r7U22%2BnTp6uxsVH5%2Bfk6fvy4UlJStHTpUl199dWhHP%2B8dqYcNTY2SpL/DZvLy8t1%2BvRpHTx40H8XcPLkyXK73br99ttVV1enzMxMFRcXh2bw7yjCZ/enyAAAAM4zvEQIAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIb9f6QbE9ioLHDgAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-1142572555709300592”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>1577</td> <td class=”number”>49.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1343</td> <td class=”number”>41.8%</td> <td>

<div class=”bar” style=”width:85%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5</td> <td class=”number”>212</td> <td class=”number”>6.6%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-1142572555709300592”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1343</td> <td class=”number”>41.8%</td> <td>

<div class=”bar” style=”width:85%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5</td> <td class=”number”>212</td> <td class=”number”>6.6%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>1577</td> <td class=”number”>49.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1343</td> <td class=”number”>41.8%</td> <td>

<div class=”bar” style=”width:85%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5</td> <td class=”number”>212</td> <td class=”number”>6.6%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>1577</td> <td class=”number”>49.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SNAP_OAPP16”>SNAP_OAPP16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>4</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.87612</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>10.7%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram7391772531951941159”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives7391772531951941159”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles7391772531951941159”

aria-controls=”quantiles7391772531951941159” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram7391772531951941159” aria-controls=”histogram7391772531951941159”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common7391772531951941159” aria-controls=”common7391772531951941159”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme7391772531951941159” aria-controls=”extreme7391772531951941159”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles7391772531951941159”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>1</td>

</tr> <tr>

<th>Median</th> <td>1</td>

</tr> <tr>

<th>Q3</th> <td>1</td>

</tr> <tr>

<th>95-th percentile</th> <td>1</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr>

<th>Range</th> <td>1</td>

</tr> <tr>

<th>Interquartile range</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.31816</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.36315</td>

</tr> <tr>

<th>Kurtosis</th> <td>3.3674</td>

</tr> <tr>

<th>Mean</th> <td>0.87612</td>

</tr> <tr>

<th>MAD</th> <td>0.21343</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-2.28</td>

</tr> <tr>

<th>Sum</th> <td>2744</td>

</tr> <tr>

<th>Variance</th> <td>0.10122</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram7391772531951941159”>

<img 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UlClTNHbsWEVHR5%2BzT3p6uk6dOmX3aAAAAEbYXrCKi4t1/fXXX3C/AwcO2DANAACAebY/g9WvXz9NnTpV27dv9669%2Buqruu%2B%2B%2B%2BR2u%2B0eBwAAwDjbC9bChQt1%2BvRp9e7d27uWkZGhpqYmLVq0yO5xAAAAjLP9FuHevXu1adMmxcTEeNcSEhJUUFCg22%2B/3e5xAAAAjLP9ClZ9fb3CwsLOHSQ4WHV1dXaPAwAAYJztBWvIkCFatGiRvvzyS%2B/a559/rqeeesrnrRoAAAACle23CB977DH9%2B7//u372s58pIiJCTU1Nqq2tVffu3fX73//e7nEAAACMs71gde/eXVu2bNG7776rY8eOKTg4WD179tTQoUMVEhJi9zgAAADG2V6wJKlt27a65ZZb/PHSAAAAlrO9YB0/flxLlizRJ598ovr6%2BnO279ixw%2B6RAAAAjPLLM1iVlZUaOnSoOnToYPfLAwAAWM72glVaWqodO3YoNjbW7pcGAACwhe1v09CpUydjV6727NmjtLQ05efn%2B6yvX79eP/3pT5WSkuLz8be//U2S1NTUpKVLlyorK0tDhgzR5MmTdfz4ce/nu91uzZw5U2lpaRo6dKjmzp173tuZAAAA52N7wZoyZYoKCwvl8Xgu6jgvvvii5s%2Bfrx49epx3%2B5AhQ%2BRwOHw%2BBgwYIElas2aNNm3apKKiIu3atUsJCQnKy8vzzjRv3jzV1dVp8%2BbNeuONN%2BR0OlVQUHBR8wIAgMuH7bcI3333XX300Udav369unXrpuBg34732muvNes4YWFhWrdunRYsWKCvvvqqRTOUlJQoNzdXvXr1kiTl5%2BcrNTVVBw4cULdu3bR9%2B3b98Y9/9N7GvP/%2B%2B/Xggw9qzpw5atOmTYteCwAAXH5sL1gRERG66aabLvo4d999949uP3HihH71q1%2BptLRUkZGRmjFjhsaMGaP6%2BnqVl5crMTHRZ6YePXrI4XDo9OnTCgkJUb9%2B/bzbk5KSdObMGX366ac%2B6wAAAOdje8FauHCh5a8RGxurhIQEPfTQQ%2Brdu7f%2B/Oc/6ze/%2BY3i4uJ09dVXy%2BPxKCoqyudzoqKiVFVVpejoaEVERCgoKMhnmyRVVVU16/UrKyvlcrl81kJDOyguLu4iz8xXSEiwz68wh2ytQ7bWIl/rkK31WlO2fnmj0U8//VRbtmzRZ5995i1c//u//6tBgwYZOX5GRoYyMjK8vx85cqT%2B/Oc/a/369Zo9e7Yk/egzYBf7fFhJSYkKCwt91vLy8jRjxoyLOu4PiYxsb8lxQbZWIltrka91yNY6rSlb2wvW%2B%2B%2B/r/vuu089e/bU0aNHtXDhQh0/flx33323li1bpqysLEteNz4%2BXqWlpYqOjlZwcLDcbrfPdrfbrU6dOik2NlY1NTVqbGz0/uieb/ft1KlTs14rOztbmZmZPmuhoR1UVVVr4Ez%2BKSQkWJGR7VVdXafGxiajx77cka11yNZa5GsdsrWeFdnGxIQbPV5z2V6wli5dqocfflj33HOP97v6unfvrkWLFmnFihVGCtYf/vAHRUVF6bbbbvOuOZ1Ode/eXWFhYerTp4/Kysp0/fXXS5Kqq6t17NgxDRgwQPHx8fJ4PDp8%2BLCSkpIkSQ6HQ5GRkerZs2ezXj8uLu6c24Eu12k1NFjzB7KxscmyY1/uyNY6ZGst8rUO2VqnNWVr%2B83Ov//978rJyZEkn%2BecRowYIafTaeQ1vv76az3zzDNyOBw6e/asNm/erHfffVcTJ06UJOXk5Ki4uFhOp1M1NTUqKChQ//79lZKSotjYWA0fPlzLli3TqVOndPLkSa1YsUITJkxQaKhf7qgCAIAAY3tj6Nixo%2Brr69W2bVuf9crKynPWfkxKSookqaGhQZK0fft2Sd9cbbr77rtVW1urBx98UC6XS926ddOKFSuUnJwsSZo4caJcLpcmTZqk2tpapaam%2Bjwz9fTTT%2BuJJ55QVlaW2rRpo9tvv/2cNzMFAAD4IUGei32iu4UefPBBtW/fXo8//rhuvPFGHThwQEeOHNETTzyh6OhoLV%2B%2B3M5xbONynTZ%2BzNDQYMXEhKuqqrbVXFK9VJCtdcjWWuRrnUDM9tZlf/H3CM22b8EIS7Lt3Lmj0eM1l%2B1XsB599FHdc889Sk1NVWNjo6699lrV1dWpT58%2BWrRokd3jAAAAGGd7weratas2b96s3bt368iRI2rXrp169uypG2%2B80eeZLAAAgEDll6e227Rpo1tuucUfLw0AAGA52wtWZmbmj16p2rFjh43TAAAAmGd7wbrtttt8ClZjY6OOHDkih8Ohe%2B65x%2B5xAAAAjLO9YH37o2q%2B76233tJf//pXm6cBAAAw75L5qYq33HKLtmzZ4u8xAAAALtolU7AOHjx40T9kGQAA4FJg%2By3Cb39czXfV1dXJ6XRq2LBhdo8DAABgnO0FKyEh4ZzvIgwLC9OECRN055132j0OAACAcbYXLN6tHQAAtHa2F6wNGzY0e9%2BxY8daOAkAAIA1bC9Yc%2BfOVVNT0zkPtAcFBfmsBQUFUbAAAEBAsr1gvfTSS3r55Zc1depU9evXTx6PRx9//LFefPFF/fKXv1RqaqrdIwEAABjll2ewioqK1KVLF%2B/addddp%2B7du2vy5MnavHmz3SMBAAAYZfv7YB09elRRUVHnrEdGRqqiosLucQAAAIyzvWDFx8dr0aJFqqqq8q5VV1dryZIluuqqq%2BweBwAAwDjbbxE%2B9thjmjVrlkpKShQeHq7g4GDV1NSoXbt2WrFihd3jAAAAGGd7wRo6dKjeeecd7d69WydPnpTH41GXLl3085//XB07drR7HAAAAONsL1iS1L59e2VlZenkyZPq3r27P0YAAACwjO3PYNXX12vOnDkaNGiQbr31VknfPIN17733qrq62u5xAAAAjLO9YC1evFiHDh1SQUGBgoP/%2BfKNjY0qKCiwexwAAADjbC9Yb731lpYvX64RI0Z4f%2BhzZGSkFi5cqLffftvucQAAAIyzvWDV1tYqISHhnPXY2FidOXPG7nEAAACMs71gXXXVVfrrX/8qST4/e3Dbtm36yU9%2BYvc4AAAAxtn%2BXYS/%2BMUv9MADD%2BiOO%2B5QU1OTXnnlFZWWluqtt97S3Llz7R4HAADAONsLVnZ2tkJDQ7V69WqFhITohRdeUM%2BePVVQUKARI0bYPQ4AAIBxthesU6dO6Y477tAdd9xh90sDAADYwvZnsLKysnyevQIAAGhtbC9YqampevPNN%2B1%2BWQAAANvYfovwyiuv1IIFC1RUVKSrrrpKbdq08dm%2BZMkSu0cCAAAwyvaCVV5erquvvlqSVFVVZffLAwAAWM62gpWfn6%2BlS5fq97//vXdtxYoVysvLs2sEAAAAW9j2DNbOnTvPWSsqKrLr5QEAAGxjW8E633cO8t2EAACgNbKtYH37g50vtAYAABDobH%2BbBgAAgNaOggUAAGCYbd9FePbsWc2aNeuCa7wPFgAACHS2FazBgwersrLygmsAAACBzraC9d33vwIAAGjNeAYLAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADAvogrVnzx6lpaUpPz//nG1bt27VqFGjNGjQII0fP1579%2B71bmtqatLSpUuVlZWlIUOGaPLkyTp%2B/Lh3u9vt1syZM5WWlqahQ4dq7ty5qq%2Bvt%2BWcAABA4AvYgvXiiy9q/vz56tGjxznbDh06pDlz5mj27Nn64IMPlJubq%2BnTp%2BvkyZOSpDVr1mjTpk0qKirSrl27lJCQoLy8PO8Pn543b57q6uq0efNmvfHGG3I6nSooKLD1/AAAQOAK2IIVFhamdevWnbdgrV27Vunp6UpPT1dYWJhGjx6tvn37auPGjZKkkpIS5ebmqlevXoqIiFB%2Bfr6cTqcOHDigf/zjH9q%2Bfbvy8/MVGxurLl266P7779cbb7yhs2fP2n2aAAAgANn2RqOm3X333T%2B4raysTOnp6T5riYmJcjgcqq%2BvV3l5uRITE73bIiIi1KNHDzkcDp0%2BfVohISHq16%2Bfd3tSUpLOnDmjTz/91Gf9h1RWVsrlcvmshYZ2UFxcXHNPr1lCQoJ9foU5ZGsdsrUW%2BVqHbK3XmrIN2IL1Y9xut6KionzWoqKiVF5eri%2B//FIej%2Be826uqqhQdHa2IiAgFBQX5bJOkqqqqZr1%2BSUmJCgsLfdby8vI0Y8aMf%2BV0Ligysr0lxwXZWolsrUW%2B1iFb67SmbFtlwZLkfZ7qX9l%2Boc%2B9kOzsbGVmZvqshYZ2UFVV7UUd9/tCQoIVGdle1dV1amxsMnrsyx3ZWodsrUW%2B1iFb61mRbUxMuNHjNVerLFgxMTFyu90%2Ba263W7GxsYqOjlZwcPB5t3fq1EmxsbGqqalRY2OjQkJCvNskqVOnTs16/bi4uHNuB7pcp9XQYM0fyMbGJsuOfbkjW%2BuQrbXI1zpka53WlG3rudn5HcnJySotLfVZczgcGjhwoMLCwtSnTx%2BVlZV5t1VXV%2BvYsWMaMGCA%2BvfvL4/Ho8OHD/t8bmRkpHr27GnbOQAAgMDVKgvWXXfdpffee0/vvPOOvvrqK61bt05Hjx7V6NGjJUk5OTkqLi6W0%2BlUTU2NCgoK1L9/f6WkpCg2NlbDhw/XsmXLdOrUKZ08eVIrVqzQhAkTFBraKi/4AQAAwwK2MaSkpEiSGhoaJEnbt2%2BX9M3Vpr59%2B6qgoEALFy5URUWFevfurVWrVqlz586SpIkTJ8rlcmnSpEmqra1Vamqqz0PpTz/9tJ544gllZWWpTZs2uv3228/7ZqYAAADnE%2BS52Ce60Swu12njxwwNDVZMTLiqqmpbzT3rSwXZWodsrUW%2B1gnEbG9d9hd/j9Bs%2BxaMsCTbzp07Gj1ec7XKW4QAAAD%2BRMECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhrbZg9evXT8nJyUpJSfF%2BPPPMM5Kk999/XxMmTNC1116rkSNHauPGjT6fW1xcrOHDh%2Bvaa69VTk6OSktL/XEKAAAgQIX6ewArbdu2Td26dfNZq6ys1P3336%2B5c%2Bdq1KhR%2BvDDDzVt2jT17NlTKSkp2rlzp55//nm99NJL6tevn4qLizV16lS9/fbb6tChg5/OBAAABJJWewXrh2zatEkJCQmaMGGCwsLClJaWpszMTK1du1aSVFJSovHjx2vgwIFq166d7r33XknSrl27/Dk2AAAIIK26YC1ZskQZGRm67rrrNG/ePNXW1qqsrEyJiYk%2B%2ByUmJnpvA35/e3BwsPr37y%2BHw2Hr7AAAIHC12luE11xzjdLS0vTss8/q%2BPHjmjlzpp566im53W516dLFZ9/o6GhVVVVJktxut6Kiony2R0VFebc3R2VlpVwul89aaGgHxcXF/Ytnc34hIcE%2Bv8IcsrUO2VqLfK1DttZrTdm22oJVUlLi/edevXpp9uzZmjZtmgYPHnzBz/V4PBf92oWFhT5reXl5mjFjxkUd94dERra35LggWyuRrbXI1zpka53WlG2rLVjf161bNzU2Nio4OFhut9tnW1VVlWJjYyVJMTEx52x3u93q06dPs18rOztbmZmZPmuhoR1UVVX7L05/fiEhwYqMbK/q6jo1NjYZPfbljmytQ7bWIl/rkK31rMg2Jibc6PGaq1UWrIMHD2rjxo165JFHvGtOp1Nt27ZVenq6/vjHP/rsX1paqoEDB0qSkpOTVVZWpnHjxkmSGhsbdfDgQU2YMKHZrx8XF3fO7UCX67QaGqz5A9nY2GTZsS93ZGsdsrUW%2BVqHbK3TmrJtPTc7v6NTp04qKSlRUVGRvv76ax05ckTPPfecsrOzNWbMGFVUVGjt2rX66quvtHv3bu3evVt33XWXJCknJ0cbNmzQ/v37VVdXp5UrV6pt27bKyMjw70kBAICA0SqvYHXp0kVFRUVasmSJtyCNGzdO%2Bfn5CgsL06pVqzR//nw99dRTio%2BP1%2BLFi/XTn/64yLt3AAALeklEQVRUknTTTTfpoYce0syZM/XFF18oJSVFRUVFateunZ/PCgAABIogz8U%2B0Y1mcblOGz9maGiwYmLCVVVV22ouqV4qyNY6ZGst8rVOIGZ767K/%2BHuEZtu3YIQl2Xbu3NHo8ZqrVd4iBAAA8CcKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDWuUPe76cXDd3m79HaLY3Z97o7xEAALAFV7AAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgWKi/BwCAS92ty/7i7xFaZN%2BCEf4eAbjscQULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgnUdFRYV%2B/etfKzU1VTfffLMWL16spqYmf48FAAACBG/TcB4PPPCAkpKStH37dn3xxReaMmWKrrjiCv3qV7/y92gAACAAcAXrexwOhw4fPqzZs2erY8eOSkhIUG5urkpKSvw9GgAACBBcwfqesrIyxcfHKyoqyruWlJSkI0eOqKamRhERERc8RmVlpVwul89aaGgHxcXFGZ01JCSw%2BnFoaODM%2B222gZZxICBbe5CveXztWq81ZUvB%2Bh63263IyEiftW/LVlVVVbMKVklJiQoLC33Wpk%2BfrgceeMDcoPqmyN3T9RNlZ2cbL2%2BXu8rKSv3udy%2BRrQUCMdtAemf0yspKPf/88wGVb6Dga9c6rfHrtvVURYM8Hs9FfX52drbWr1/v85GdnW1oun9yuVwqLCw852oZLh7ZWodsrUW%2B1iFb67TGbLmC9T2xsbFyu90%2Ba263W0FBQYqNjW3WMeLi4lpNAwcAAC3HFazvSU5O1okTJ3Tq1CnvmsPhUO/evRUeHu7HyQAAQKCgYH1PYmKiUlJStGTJEtXU1MjpdOqVV15RTk6Ov0cDAAABIuTJJ5980t9DXGp%2B/vOfa/PmzXrmmWe0ZcsWTZgwQZMnT1ZQUJC/RztHeHi4rr/%2Beq6uWYBsrUO21iJf65CtdVpbtkGei32iGwAAAD64RQgAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAXrEldRUaFf//rXSk1N1c0336zFixerqanpvPsWFxdr%2BPDhuvbaa5WTk6PS0lKbpw0sLcn2D3/4g4YPH65BgwZpzJgx2r59u83TBpaWZPutzz//XIMGDdLzzz9v05SBqyX5Op1OTZo0SQMHDlR6erpeffVVe4cNMM3NtqmpScuXL1dmZqYGDRqkUaNGaevWrX6YOLDs2bNHaWlpys/P/9H9mpqatHTpUmVlZWnIkCGaPHmyjh8/btOUhnhwSRs3bpzn8ccf91RXV3uOHDniGTZsmOfll18%2BZ78dO3Z4rrvuOs/%2B/fs9dXV1nlWrVnluvPFGT21trR%2BmDgzNzXbbtm2ewYMHe/bt2%2Bf5%2BuuvPa%2B//ronKSnJc%2BzYMT9MHRiam%2B13TZ8%2B3TN48GDP8uXLbZoycDU337q6Ok9GRobnxRdf9Jw5c8Zz4MABz8iRIz3l5eV%2BmDowNDfb1atXe4YOHepxOp2ehoYGz86dOz2JiYmeQ4cO%2BWHqwFBUVOQZNmyYZ%2BLEiZ6ZM2f%2B6L7FxcWem2%2B%2B2VNeXu45ffq05%2Bmnn/aMGjXK09TUZNO0F48rWJcwh8Ohw4cPa/bs2erYsaMSEhKUm5urkpKSc/YtKSnR%2BPHjNXDgQLVr10733nuvJGnXrl12jx0QWpJtfX29HnroIQ0ePFht2rTRnXfeqfDwcO3fv98Pk1/6WpLtt3bv3q3y8nJlZGTYN2iAakm%2Bb775piIiInTvvfeqffv2GjBggDZv3qxevXr5YfJLX0uyLSsr0%2BDBg3X11VcrJCREN998s6Kjo/Xxxx/7YfLAEBYWpnXr1qlHjx4X3LekpES5ubnq1auXIiIilJ%2BfL6fTqQMHDtgwqRkUrEtYWVmZ4uPjFRUV5V1LSkrSkSNHVFNTc86%2BiYmJ3t8HBwerf//%2Bcjgcts0bSFqS7ZgxY/SLX/zC%2B/vq6mrV1taqS5cuts0bSFqSrfRNgX366af1xBNPKDQ01M5RA1JL8v3www/Vt29fPfroo7ruuus0YsQIbdy40e6RA0ZLss3IyNB///d/69ChQ/r666%2B1Y8cO1dXV6frrr7d77IBx9913q2PHjhfcr76%2BXuXl5T5/p0VERKhHjx4B9XcaBesS5na7FRkZ6bP27R/8qqqqc/b97n8Uvt33%2B/vhGy3J9rs8Ho8ef/xxDRw4kP%2BQ/oCWZrtixQpdc801uuGGG2yZL9C1JN%2BTJ09qx44dSktL0549ezRlyhTNmTNHBw8etG3eQNKSbIcNG6bs7GyNHTtWKSkpmjVrlhYuXKgrr7zStnlbqy%2B//FIejyfg/07jfxcvcR6Px5J90fK8zp49q0ceeUTl5eUqLi62aKrWobnZlpeXa%2B3atdq0aZPFE7Uuzc3X4/EoKSlJo0aNkiSNGzdOr732mrZt2%2BZzdQD/1NxsN2zYoA0bNmjt2rXq16%2Bf3n//fc2aNUtXXnmlBgwYYPGUl4dA/zuNK1iXsNjYWLndbp81t9utoKAgxcbG%2BqzHxMScd9/v74dvtCRb6ZtL1lOmTNFnn32mNWvW6IorrrBr1IDT3Gw9Ho%2BefPJJPfDAA%2BrcubPdYwaslnztdu7c%2BZxbMvHx8XK5XJbPGYhaku3q1auVnZ2tAQMGKCwsTBkZGbrhhhu4BWtAdHS0goODz/vvolOnTn6aquUoWJew5ORknThxQqdOnfKuORwO9e7dW%2BHh4efsW1ZW5v19Y2OjDh48qIEDB9o2byBpSbYej0f5%2BfkKDQ3Vq6%2B%2BqpiYGLvHDSjNzfazzz7T//zP/2j58uVKTU1VamqqtmzZopdeeknjxo3zx%2BgBoSVfu7169dLf//53nysBFRUVio%2BPt23eQNKSbJuamtTY2Oiz9vXXX9syZ2sXFhamPn36%2BPydVl1drWPHjgXU1UEK1iUsMTFRKSkpWrJkiWpqauR0OvXKK68oJydHkjRixAjt27dPkpSTk6MNGzZo//79qqur08qVK9W2bVu%2BK%2BsHtCTbTZs2qby8XM8995zCwsL8OXZAaG62Xbt21e7du/WnP/3J%2B5GZmamJEyeqqKjIz2dx6WrJ1%2B7o0aNVVVWlF154QfX19dq8ebPKyso0evRof57CJasl2WZmZmrdunU6fPiwGhoatHfvXr3//vvKysry5ykErM8//1wjRozwvtdVTk6OiouL5XQ6VVNTo4KCAvXv318pKSl%2BnrT5eAbrErd8%2BXLNmzdPN954oyIiIjRx4kTvd7QdOXJEZ86ckSTddNNNeuihhzRz5kx98cUXSklJUVFRkdq1a%2BfP8S9pzc32jTfeUEVFxTkPtY8ZM0bz58%2B3fe5A0JxsQ0JC1LVrV5/Pa9%2B%2BvSIiIrhleAHN/drt0qWLVq1apQULFui//uu/9JOf/EQrVqzQVVdd5c/xL2nNzXbKlClqaGhQXl6eTp06pfj4eM2fP18/%2B9nP/Dn%2BJe3bctTQ0CBJ3jdsdjgcOnv2rI4cOeK9Cjhx4kS5XC5NmjRJtbW1Sk1NVWFhoX8G/xcFeQL9KTIAAIBLDLcIAQAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMCw/weeVoX7%2BFty1QAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common7391772531951941159”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>2698</td> <td class=”number”>84.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>342</td> <td class=”number”>10.7%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5</td> <td class=”number”>92</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme7391772531951941159”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>342</td> <td class=”number”>10.7%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5</td> <td class=”number”>92</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>2698</td> <td class=”number”>84.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>342</td> <td class=”number”>10.7%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5</td> <td class=”number”>92</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>2698</td> <td class=”number”>84.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SNAP_PART_RATE08”>SNAP_PART_RATE08<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>34</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>68.566</td>

</tr> <tr>

<th>Minimum</th> <td>48</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-3303690274262647668”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-3303690274262647668,#minihistogram-3303690274262647668”
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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-3303690274262647668”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-3303690274262647668”

aria-controls=”quantiles-3303690274262647668” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-3303690274262647668” aria-controls=”histogram-3303690274262647668”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-3303690274262647668” aria-controls=”common-3303690274262647668”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-3303690274262647668” aria-controls=”extreme-3303690274262647668”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-3303690274262647668”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>48</td>

</tr> <tr>

<th>5-th percentile</th> <td>52</td>

</tr> <tr>

<th>Q1</th> <td>62</td>

</tr> <tr>

<th>Median</th> <td>66</td>

</tr> <tr>

<th>Q3</th> <td>78</td>

</tr> <tr>

<th>95-th percentile</th> <td>85</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr>

<th>Range</th> <td>52</td>

</tr> <tr>

<th>Interquartile range</th> <td>16</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>10.406</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.15177</td>

</tr> <tr>

<th>Kurtosis</th> <td>-0.57125</td>

</tr> <tr>

<th>Mean</th> <td>68.566</td>

</tr> <tr>

<th>MAD</th> <td>8.6652</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.33297</td>

</tr> <tr>

<th>Sum</th> <td>214750</td>

</tr> <tr>

<th>Variance</th> <td>108.29</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-3303690274262647668”>

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ePHa%2BnSperRo4fWr1%2Bvxx9/XMuXL1efPn20atUq9e/fX5I0evRoLViwQEVFRTpy5IjS0tJUVlamqKgoi1cEAACCRZins3d0wy9O53GrSzgru92m%2BPgecrmaQua6t5Ws7OctT%2B25qMfrjO1FI8%2B7DT%2BbZtFPs%2BinWV3Zz969exrdn79C8hIhAACAlQhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgWMAFrOzsbJWUlOjTTz%2B1uhQAAIALEnABa9q0adq2bZtuvvlmzZ49W5WVlWppabG6LAAAAL8FXMCaN2%2Betm3bpl/96lfq27evnnjiCWVlZWnVqlU6ePCg1eUBAACcV8AFrH%2B64YYbtHjxYu3atUsPP/ywfvWrX2nixIm6%2B%2B679f7771tdHgAAwL8VsAHrzJkz2rZtm%2B655x4tXrxYSUlJ%2BsEPfqABAwZo1qxZ2rJlyzlf369fP6WmpiotLc3757HHHpMkVVdXKy8vTxkZGcrJydHmzZt9XlteXq7x48crIyNDBQUFqq2t7bJ1AgCA0GO3uoAvO3DggDZu3KhNmzapqalJ48eP1y9%2B8QsNGTLEu82wYcO0bNky5ebmnnNfr7zyiq688kqfscbGRs2dO1dLlixRbm6u3nnnHd1333265pprlJaWpp07d6q4uFjPPPOM%2BvXrp/Lycs2ZM0eVlZXq3r17l6wZAACEloA7g5WTk6Pdu3fr3nvv1WuvvaZVq1b5hCtJysrK0tGjRy9o/1u2bFFycrLy8vIUGRmpzMxMZWdnq6KiQpLkcDg0depUpaenKyoqSrNnz5Yk7dq1q3MLAwAAl4yAO4NVXl6u4cOHn3e7995777zbrFmzRn/4wx904sQJ3XLLLfr%2B97%2Bvuro6paSk%2BGyXkpKi7du3S5Lq6uo0ceJE75zNZtOAAQNUU1OjnJwcv9bQ2Ngop9PpM2a3d1diYqJfr7%2BYwsNtPl/ROfTTP3b7%2BftDL82in2bRT7NCsZ8BF7D69eunOXPmKC8vTzfffLMk6ec//7n27NmjVatWKS4uzq/9DBo0SJmZmfrRj36kTz75REVFRVq%2BfLncbreSkpJ8to2Li5PL5ZIkud1uxcbG%2BszHxsZ65/3hcDhUUlLiMzZv3jwVFhb6vY%2BLLSamm9UlhBT6eW7x8T383pZemkU/zaKfZoVSPwMuYK1YsULHjx/Xdddd5x0bM2aMXn/9da1cuVIrV670az8Oh8P739dee60WLVqk%2B%2B67r93lxrPxeDwdL/xf5OfnKzs722fMbu8ul6upU/vtCuHhNsXEdNOxY81qbW2zupygRz/94897gV6aRT/Nop9mdWU/O/IXOpMCLmC98cYb2rJli%2BLj471jycnJWr16tW699dYL3u%2BVV16p1tZW2Ww2ud1unzmXy6WEhARJUnx8fLt5t9utvn37%2Bn2sxMTEdpcDnc7jamkJ3Ddha2tbQNcXbOjnuXWkN/TSLPppFv00K5T6GXAXO0%2BePKnIyMh24zabTc3NzX7to76%2Bvt2ZrgMHDigiIkJZWVntHrtQW1ur9PR0SVJqaqrq6uq8c62traqvr/fOAwAAnE/ABaxhw4Zp5cqV%2Bvzzz71jn332mZYvX%2B7X5T1J6tWrlxwOh8rKynT69GkdPHhQa9euVX5%2BviZPnqyGhgZVVFTo1KlTqqqqUlVVlaZPny5JKigo0KZNm7Rv3z41NzertLRUERERGjNmTFcsFwAAhKCAu0T48MMP66677tI3v/lNRUdHq62tTU1NTbrqqqv0/PPP%2B7WPpKQklZWVac2aNd6ANGXKFM2fP1%2BRkZFav369Hn/8cS1fvlx9%2BvTRqlWr1L9/f0nS6NGjtWDBAhUVFenIkSNKS0tTWVmZoqKiunLZAAAghIR5OntHdxc4ffq0XnvtNX388cey2Wy65pprNGrUKIWHh1td2gVzOo9bXcJZ2e02xcf3kMvVFDLXva1kZT9veWrPRT1eZ2wvGnnebfjZNIt%2BmkU/zerKfvbu3dPo/vwVcGewJCkiIsL7iAYAAIBgE3AB65NPPtGaNWv0wQcf6OTJk%2B3mX331VQuqAgAA8F/ABayHH35YjY2NGjVqFL/7DwAABKWAC1i1tbV69dVXvc%2BlAgAACDYB95iGXr16ceYKAAAEtYALWPfee69KSko6/etqAAAArBJwlwhfe%2B01vfvuu/r1r3%2BtK6%2B8Ujabbwb85S9/aVFlAAAA/gm4gBUdHa3Ro0dbXQYAAMAFC7iAtWLFCqtLAAAA6JSAuwdLkv7yl7%2BouLhYP/jBD7xjf/jDHyysCAAAwH8BF7Cqq6s1adIkVVZWauvWrZL%2B8fDRmTNn8pBRAAAQFAIuYD355JP63ve%2Bpy1btigsLEySdNVVV2nlypVat26dxdUBAACcX8AFrD//%2Bc8qKCiQJG/AkqQJEybowIEDVpUFAADgt4C7yb1nz546efKkIiIifMYbGxvbjQEITrc8tcfqEjpke9FIq0sAEGQC7gxWRkaGnnjiCZ04ccI7dvDgQS1evFjf/OY3LawMAADAPwF3BusHP/iB7rjjDo0YMUKtra3KyMhQc3Oz%2Bvbtq5UrV1pdHgAAwHkFXMC64oortHXrVlVVVengwYOKiorSNddco5EjR/rckwUAABCoAi5gSdJll12mm2%2B%2B2eoyACAoBdM9btzfhlAVcAErOzv7nGeqeBYWAAAIdAEXsCZOnOgTsFpbW3Xw4EHV1NTojjvusLAyXGqC6SwAACCwBFzAWrRo0VnHd%2BzYod/97ncXuRoAAICOC7jHNPw7N998s15%2B%2BWWrywAAADivoAlY9fX18ng8VpcBAABwXgF3iXDGjBntxpqbm3XgwAGNGzfOgooAAAA6JuACVnJycrt/RRgZGam8vDzddtttFlUFAADgv4ALWDytHQAABLuAC1ibNm3ye9tvf/vbfm33xBNP6Be/%2BIX%2B9Kc/SZKqq6u1Zs0a/eUvf9FXvvIV3XvvvZo0aZJ3%2B/Lycr344otyOp3q16%2BflixZotTU1I4tBAAAXLICLmAtWbJEbW1t7W5oDwsL8xkLCwvzK2Dt379fL730kvf7xsZGzZ07V0uWLFFubq7eeecd3XfffbrmmmuUlpamnTt3qri4WM8884z69eun8vJyzZkzR5WVlerevbu5hQIAgJAVcP%2BK8JlnntGoUaP04osv6u2339Zbb72lF154QaNHj9bPfvYzvf/%2B%2B3r//ff13nvvnXdfbW1tevTRRzVr1izv2JYtW5ScnKy8vDxFRkYqMzNT2dnZqqiokCQ5HA5NnTpV6enpioqK0uzZsyVJu3bt6pL1AgCA0BNwZ7BWrlypsrIyJSUleceGDh2qq666Snfffbe2bt3q975%2B%2BctfKjIyUrm5uXrqqackSXV1dUpJSfHZLiUlRdu3b/fOT5w40Ttns9k0YMAA1dTUKCcnx6/jNjY2yul0%2BozZ7d2VmJjod%2B0XS3i4zecrgPbsdt4fXSVYe8tnp1mh2M%2BAC1gfffSRYmNj243HxMSooaHB7/38/e9/V3FxsZ5//nmfcbfb7RPeJCkuLk4ul8s7/%2BXjx8bGeuf94XA4VFJS4jM2b948FRYW%2Br2Piy0mppvVJQABKz6%2Bh9UlhKxg7y2fnWaFUj8DLmD16dNHK1eu1IMPPqj4%2BHhJ0rFjx/TTn/5UX/va1/zez4oVKzR16lRdd911OnToUIdq6OwDTfPz85Wdne0zZrd3l8vV1Kn9doXwcJtiYrrp2LFmtba2WV0OEJAC8b0bKoK1t3x2mtWV/bQqxAdcwHr44Ye1cOFCORwO9ejRQzabTSdOnFBUVJTWrVvn1z6qq6v1hz/84ayXE%2BPj4%2BV2u33GXC6XEhIS/u282%2B1W3759/V5DYmJiu8uBTudxtbQE7puwtbUtoOsDrMR7o%2BsEe2/57DQrlPoZcAFr1KhR2r17t6qqqnT48GF5PB4lJSXpxhtvVM%2BePf3ax%2BbNm3XkyBGNHTtW0v%2BdkRoxYoTuuuuudsGrtrZW6enpkqTU1FTV1dVpypQpkqTW1lbV19crLy/P1BIBAECIC7iAJUndunXTTTfdpMOHD%2Buqq67q8Ou///3v68EHH/R%2Bf/jwYeXn5%2Bull15SW1ub1q9fr4qKCk2aNElvvvmmqqqq5HA4JEkFBQVasGCBbr31VvXr10/PPvusIiIiNGbMGFPLAwAAIS7gAtbJkyf16KOP6uWXX5b0j7NLx44d04IFC/STn/xEMTEx591HbGysz43qLS0tkqQrrrhCkrR%2B/Xo9/vjjWr58ufr06aNVq1apf//%2BkqTRo0drwYIFKioq0pEjR5SWlqaysjJFRUWZXioAAAhRARewVq1apf3792v16tV66KGHvOOtra1avXq1fvjDH3Z4n1deeaX3Ke6SNGzYMJ%2BHj37Z7bffrttvv73DxwEAAJAC8EGjO3bs0E9/%2BlNNmDDB%2B0ufY2JitGLFClVWVlpcHQAAwPkFXMBqampScnJyu/GEhAR98cUXF78gAACADgq4gPW1r31Nv/vd7yT5Po/qlVde0Ve/%2BlWrygIAAPBbwN2Ddfvtt%2BuBBx7QtGnT1NbWpg0bNqi2tlY7duzQkiVLrC4PAADgvAIuYOXn58tut%2BuFF15QeHi4nn76aV1zzTVavXq1JkyYYHV5AAAA5xVwAevo0aOaNm2apk2bZnUpAAAAFyTg7sG66aabOv27AAEAAKwUcAFrxIgR2r59u9VlAAAAXLCAu0T4la98Rf/93/%2BtsrIyfe1rX9Nll13mM79mzRqLKkNn3fLUHqtLAADgogi4gPXhhx/q61//uiTJ5XJZXA0AAEDHBUzAmj9/vp588kk9//zz3rF169Zp3rx5FlYFAADQcQFzD9bOnTvbjZWVlVlQCQAAQOcETMA6278c5F8TAgCAYBQwAeufv9j5fGMAAACBLmACFgAAQKggYAEAABgWMP%2BK8MyZM1q4cOF5x3gOFgAACHQBE7CGDBmixsbG844BAAAEuoAJWP/6/CsAAIBgxj1YAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEhG7D%2B%2BMc/6o477tCQIUOUmZmpoqIiOZ1OSVJ1dbXy8vKUkZGhnJwcbd682ee15eXlGj9%2BvDIyMlRQUKDa2lorlgAAAIJUSAas06dP66677tLw4cNVXV2trVu36siRI1q2bJkaGxs1d%2B5czZgxQ9XV1VqyZIkeeeQR1dTUSJJ27typ4uJi/fjHP9bevXs1duxYzZkzR1988YXFqwIAAMEiJANWc3Oz5s%2Bfr3vvvVcRERFKSEjQt771LX3wwQfasmWLkpOTlZeXp8jISGVmZio7O1sVFRWSJIfDoalTpyo9PV1RUVGaPXu2JGnXrl1WLgkAAASRgPlVOSbFxsbqtttu837/l7/8Rb/5zW90yy23qK6uTikpKT7bp6SkaPv27ZKkuro6TZw40Ttns9k0YMAA1dTUKCcnx6/jNzY2ei9H/pPd3l2JiYkXuqQuEx5u8/kKoD27nfdHVwnW3vLZaVYo9jMkA9Y/NTQ0aPz48WppadH06dNVWFioe%2B65R0lJST7bxcXFyeVySZLcbrdiY2N95mNjY73z/nA4HCopKfEZmzdvngoLCy9wJV0vJqab1SUAASs%2BvofVJYSsYO8tn51mhVI/Qzpg9enTRzU1NfrrX/%2Bq//qv/9JDDz3k1%2Bs8Hk%2Bnjpufn6/s7GyfMbu9u1yupk7ttyuEh9sUE9NNx441q7W1zepygIAUiO/dUBGsveWz06yu7KdVIT6kA5YkhYWFKTk5WfPnz9eMGTOUlZUlt9vts43L5VJCQoIkKT4%2Bvt282%2B1W3759/T5mYmJiu8uBTudxtbQE7puwtbUtoOsDrMR7o%2BsEe2/57DQrlPoZOhc7/0V1dbXGjx%2Bvtrb/%2B59ks/1jqQMHDmz32IXa2lqlp6dLklJTU1VXV%2Beda21tVX19vXceAADgfEIyYKWmpurEiRNatWqVmpubdfToURUXF2vo0KEqKChQQ0ODKioqdOrUKVVVVamqqkrTp0%2BXJBUUFGjTpk3at2%2BfmpubVVpaqoiICI0ZM8baRQEAgKARkgGrZ8%2Beeu6551RbW6tvfOMbysnJUc%2BePfWTn/xEvXr10vr16/XCCy9oyJAheuKJJ7Rq1Sr1799fkjR69GjcsBxKAAASaUlEQVQtWLBARUVFGj58uPbu3auysjJFRUVZvCoAABAsQvYerH79%2Bun5558/69ywYcP00ksv/dvX3n777br99tu7qjQAABDiQvIMFgAAgJUIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGF2qwsAgEB3y1N7rC4BQJDhDBYAAIBhIRuwGhoaNG/ePI0YMUKZmZn6/ve/r2PHjkmS9u/fr%2B985zsaMmSIxo0bp%2Beee87ntdu2bVNubq4GDx6sqVOn6o033rBiCQAAIEiFbMCaM2eOYmJitHPnTv3617/WBx98oB/96Ec6efKk7r33Xn3jG9/Q66%2B/rieffFLr169XZWWlpH%2BEr8WLF2vRokV68803NWvWLN1///06fPiwxSsCAADBIiQD1rFjx5SamqqFCxeqR48euuKKKzRlyhS9/fbb2r17t86cOaP77rtP3bt31w033KDbbrtNDodDklRRUaGsrCxlZWUpMjJSkyZN0vXXX6/NmzdbvCoAABAsQvIm95iYGK1YscJn7NNPP1ViYqLq6urUr18/hYeHe%2BdSUlJUUVEhSaqrq1NWVpbPa1NSUlRTU%2BP38RsbG%2BV0On3G7PbuSkxM7OhSulx4uM3nKwBcTHZ7cH728NlpVij2MyQD1pfV1NTohRdeUGlpqbZv366YmBif%2Bbi4OLndbrW1tcntdis2NtZnPjY2Vh9%2B%2BKHfx3M4HCopKfEZmzdvngoLCy98EV0sJqab1SUAuATFx/ewuoRO4bPTrFDqZ8gHrHfeeUf33XefFi5cqMzMTG3fvv2s24WFhXn/2%2BPxdOqY%2Bfn5ys7O9hmz27vL5Wrq1H67Qni4TTEx3XTsWLNaW9usLgfAJSYQPxf9wWenWV3ZT6tCfEgHrJ07d%2Bp73/ueHnnkEX3729%2BWJCUkJOijjz7y2c7tdisuLk42m03x8fFyu93t5hMSEvw%2BbmJiYrvLgU7ncbW0BO6bsLW1LaDrAxCagv1zh89Os0Kpn6FzsfNL3n33XS1evFhr1671hitJSk1N1Z/%2B9Ce1tLR4x2pqapSenu6dr62t9dnXv84DAACcT0gGrJaWFi1dulSLFi3SqFGjfOaysrIUHR2t0tJSNTc367333tPGjRtVUFAgSZo%2Bfbr27t2r3bt369SpU9q4caM%2B%2BugjTZo0yYqlAACAIBTm6ewNRwHo7bff1n/8x38oIiKi3dwrr7yipqYmPfroo6qtrdXll1%2Bue%2B65R7fffrt3m8rKSq1Zs0YNDQ267rrrtGTJEg0bNqxTNTmdxzv1%2Bq5it9sUH99DLldTl5%2BW5deNAPiy7UUjrS7hglzMz85LQVf2s3fvnkb356%2BQDFiBqKsCFqEFAC6efwZCApZZoRiwQvISIQAAgJUIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIaFbMB6/fXXlZmZqfnz57eb27Ztm3JzczV48GBNnTpVb7zxhneura1NTz75pG666SYNGzZMd999tz755JOLWToAAAhyIRmwfvazn%2Bnxxx/X1Vdf3W5u//79Wrx4sRYtWqQ333xTs2bN0v3336/Dhw9Lkl588UVt2bJFZWVl2rVrl5KTkzVv3jx5PJ6LvQwAABCkQjJgRUZGauPGjWcNWBUVFcrKylJWVpYiIyM1adIkXX/99dq8ebMkyeFwaNasWbr22msVHR2t%2BfPn68CBA3rvvfcu9jIAAECQsltdQFeYOXPmv52rq6tTVlaWz1hKSopqamp08uRJffjhh0pJSfHORUdH6%2Bqrr1ZNTY0GDRrk1/EbGxvldDp9xuz27kpMTOzAKgAAgcZu/8d5ifBw36/onFDsZ0gGrHNxu92KjY31GYuNjdWHH36ozz//XB6P56zzLpfL72M4HA6VlJT4jM2bN0%2BFhYUXXjgAwHLx8T18vo%2BJ6WZRJaEplPp5yQUsSee9n6qz91vl5%2BcrOzvbZ8xu7y6Xq6lT%2BwUAWOufn%2BPh4TbFxHTTsWPNam1ts7iq4NeV/fxyKL5YLrmAFR8fL7fb7TPmdruVkJCguLg42Wy2s8736tXL72MkJia2uxzodB5XSwtvQgAIZl/%2BHG9tbeOz3aBQ6mfoXOz0U2pqqmpra33GampqlJ6ersjISPXt21d1dXXeuWPHjunjjz/WwIEDL3apAAAgSF1yAWv69Onau3evdu/erVOnTmnjxo366KOPNGnSJElSQUGBysvLdeDAAZ04cUKrV6/WgAEDlJaWZnHlAAAgWITkJcJ/hqGWlhZJ0m9/%2B1tJ/zhTdf3112v16tVasWKFGhoadN1112n9%2BvXq3bu3JGnGjBlyOp36z//8TzU1NWnEiBHtblgHAAA4lzAPT9C8KJzO412y31ue2tMl%2BwUAtLe9aKSkfzyuIT6%2Bh1yuppC5Z8hKXdnP3r17Gt2fvy65S4QAAABdjYAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADDMbnUBAAAEi1ue2mN1CR2yvWik1SVcsjiDBQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAzjMQ0AAISoYHqsxNv/PcHqEoziDBYAAIBhBKyzaGho0He/%2B12NGDFCY8eO1apVq9TW1mZ1WQAAIEhwifAsHnjgAd1www367W9/qyNHjujee%2B/V5ZdfrjvvvNPq0gAAQBDgDNaX1NTU6I9//KMWLVqknj17Kjk5WbNmzZLD4bC6NAAAECQ4g/UldXV16tOnj2JjY71jN9xwgw4ePKgTJ04oOjr6vPtobGyU0%2Bn0GbPbuysxMdF4vQAAhIrw8NA570PA%2BhK3262YmBifsX%2BGLZfL5VfAcjgcKikp8Rm7//779cADD5gr9P/r7L%2B6aGxslMPhUH5%2BPgHQAPppDr00i36aRT/NamxsVHFxcUj1M3SiokEej6dTr8/Pz9evf/1rnz/5%2BfmGqjPL6XSqpKSk3Rk3XBj6aQ69NIt%2BmkU/zQrFfnIG60sSEhLkdrt9xtxut8LCwpSQkODXPhITE0MmgQMAgI7jDNaXpKam6tNPP9XRo0e9YzU1NbruuuvUo0cPCysDAADBgoD1JSkpKUpLS9OaNWt04sQJHThwQBs2bFBBQYHVpQEAgCARvmzZsmVWFxFobrzxRm3dulWPPfaYXn75ZeXl5enuu%2B9WWFiY1aV1iR49emj48OGcoTOEfppDL82in2bRT7NCrZ9hns7e0Q0AAAAfXCIEAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyAdYno16%2BfUlNTlZaW5v3z2GOPSZKqq6uVl5enjIwM5eTkaPPmzRZXGzxKS0s1atQoDRo0SLNmzdKhQ4ck0dOOeOutt3x%2BLtPS0pSamqp%2B/fpJopcXor6%2BXjNnztTQoUM1cuRILVq0yPsL7Olnx9XW1mrmzJkaMmSIbrzxRj377LPeuW3btik3N1eDBw/W1KlT9cYbb1hYaeB6/fXXlZmZqfnz57ebO1cP29ra9OSTT%2Bqmm27SsGHDdPfdd%2BuTTz65mKVfOA8uCddff73nk08%2BaTf%2B2WefeQYNGuSpqKjwnDx50rNnzx7PwIEDPe%2B//74FVQaXF154wTNhwgTPgQMHPMePH/c89thjnscee4yeGlBaWup58MEH6eUFOHPmjGfkyJGeNWvWeE6dOuU5evSo58477/Q88MAD9PMCuFwuz4gRIzyrV6/2fPHFF54///nPnrFjx3q2bdvmqa%2Bv96Smpnp2797tOXnypOell17ypKenez799FOryw4oZWVlnnHjxnlmzJjhKSoq8pk7Xw/Ly8s9Y8eO9Xz44Yee48ePe374wx96cnNzPW1tbVYspUM4g3WJ27Jli5KTk5WXl6fIyEhlZmYqOztbFRUVVpcW8J577jnNnz9fX//61xUdHa2lS5dq6dKl9LST/va3v2nDhg166KGH6OUFcDqdcjqdmjx5siIiIhQfH69vfetb2r9/P/28APv27VNTU5OKiorUrVs39e3bV3fffbc2btyoiooKZWVlKSsrS5GRkZo0aZKuv/56zgp%2BSWRkpDZu3Kirr7663dz5euhwODRr1ixde%2B21io6O1vz583XgwAG99957F3sZHUbAuoSsWbNGY8aM0dChQ/XII4%2BoqalJdXV1SklJ8dkuJSVFtbW1FlUZHD777DMdOnRIn3/%2BuSZOnKgRI0aosLBQR48epaedtHbtWk2bNk1f/epX6eUFSEpK0oABA%2BRwONTU1KQjR46osrJSY8aMoZ8XKCwszOf72NhY7d%2B//9/2s6am5mKWF/Bmzpypnj17nnXuXD08efKkPvzwQ5/56OhoXX311UHRYwLWJWLQoEHKzMxUZWWlHA6H9u3bp%2BXLl8vtdismJsZn27i4OLlcLosqDQ6HDx%2BWJL3yyivasGGDXnrpJR0%2BfFhLly6lp51w6NAhVVZW6s4775QkenkBbDabiouL9eqrryojI0OZmZlqaWnRwoUL6ecFGDx4sLp166a1a9equblZH3/8sf7nf/5Hn3/%2Budxut2JjY322j42NpZ8dcK4efv755/J4PEHbYwLWJcLhcOi2225TRESErr32Wi1atEhbt27VmTNnrC4tKHk8HknS7NmzlZSUpCuuuEIPPPCAdu7caXFlwe3FF1/UuHHj1Lt3b6tLCVqnT5/WnDlzNGHCBL399tt67bXX1LNnTy1atMjq0oJSbGys1q1bp%2Brqao0cOVLf%2B973NHnyZIWHh0v6v88CXLjz9TBYe0zAukRdeeWVam1tlc1mk9vt9plzuVxKSEiwqLLgcPnll0uSz9mAPn36yOPx6MyZM/T0Au3YsUPZ2dne7%2BPj4%2BllB1VXV%2BvQoUNasGCBevbsqaSkJBUWFup///d/eb9foKFDh6qiokLvvvuuHA6H4uLilJSUdNafT7fbTT874Fw9jIuLO%2BvPrNvtVq9evS5mmReEgHUJqK%2Bv18qVK33GDhw4oIiICGVlZbW7/6K2tlbp6ekXs8Sgc8UVVyg6Olr79%2B/3jjU0NOiyyy6jpxdo//79amho0MiRI71jaWlp9LKDWltb1dbW5vO3/tOnT0uSMjMz6WcHnTp1Sr/5zW904sQJ79iePXs0ePBgpaamtutnTU0N/eyAc/UwMjJSffv2VV1dnXfu2LFj%2BvjjjzVw4MCLXWqHEbAuAb169ZLD4VBZWZlOnz6tgwcPau3atcrPz9fkyZPV0NCgiooKnTp1SlVVVaqqqtL06dOtLjug2e125eXl6emnn9Zf//pXHTlyROvWrVNubq6mTJlCTy9AfX294uLiFB0d7R3Lzc2llx00ePBgde/eXcXFxWpubpbL5VJpaamGDRvG%2B/0CXHbZZSopKVFpaalaWlr0xhtvaPPmzbrjjjs0ffp07d27V7t379apU6e0ceNGffTRR5o0aZLVZQeN8/WwoKBA5eXlOnDggE6cOKHVq1drwIABSktLs7hyP1j4iAhcRL///e89%2Bfn5nkGDBnmGDx/uWbFihefkyZPeuUmTJnluuOEGz7hx4zw7duywuNrgcOrUKc%2ByZcs8w4YN8wwaNMizePFiz4kTJzweDz29EE8//bQnJyen3Ti97LiamhrPd77zHc/QoUM9mZmZnqKiIs/hw4c9Hg/9vBDvv/%2B%2BZ8qUKZ6BAwd6xo0b56msrPTO7dixwzNu3DjPDTfc4Jk8ebLn97//vYWVBqbU1FRPamqqp3///p7%2B/ft7v/%2Bnc/Wwra3Ns3btWs83v/lNz8CBAz333HNP0DxnLMzjCdK7xwAAAAIUlwgBAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwLD/B%2BJ8QHxXSXepAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-3303690274262647668”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>65.0</td> <td class=”number”>407</td> <td class=”number”>12.7%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>56.0</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>66.0</td> <td class=”number”>206</td> <td class=”number”>6.4%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>62.0</td> <td class=”number”>188</td> <td class=”number”>5.9%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>85.0</td> <td class=”number”>170</td> <td class=”number”>5.3%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>72.0</td> <td class=”number”>163</td> <td class=”number”>5.1%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>81.0</td> <td class=”number”>138</td> <td class=”number”>4.3%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>70.0</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>64.0</td> <td class=”number”>132</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>61.0</td> <td class=”number”>131</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (23)</td> <td class=”number”>1210</td> <td class=”number”>37.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-3303690274262647668”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>48.0</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:23%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>51.0</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>52.0</td> <td class=”number”>87</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>56.0</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>84.0</td> <td class=”number”>83</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:49%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>85.0</td> <td class=”number”>170</td> <td class=”number”>5.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>88.0</td> <td class=”number”>14</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>92.0</td> <td class=”number”>36</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>16</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SNAP_PART_RATE13”>SNAP_PART_RATE13<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>47</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.5%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>86.446</td>

</tr> <tr>

<th>Minimum</th> <td>56.955</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram1402369884337458780”>

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<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles1402369884337458780”

aria-controls=”quantiles1402369884337458780” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram1402369884337458780” aria-controls=”histogram1402369884337458780”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common1402369884337458780” aria-controls=”common1402369884337458780”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme1402369884337458780” aria-controls=”extreme1402369884337458780”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

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<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles1402369884337458780”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>56.955</td>

</tr> <tr>

<th>5-th percentile</th> <td>73.941</td>

</tr> <tr>

<th>Q1</th> <td>79.024</td>

</tr> <tr>

<th>Median</th> <td>86.331</td>

</tr> <tr>

<th>Q3</th> <td>92.597</td>

</tr> <tr>

<th>95-th percentile</th> <td>100</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr>

<th>Range</th> <td>43.045</td>

</tr> <tr>

<th>Interquartile range</th> <td>13.573</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>8.9274</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.10327</td>

</tr> <tr>

<th>Kurtosis</th> <td>-0.18842</td>

</tr> <tr>

<th>Mean</th> <td>86.446</td>

</tr> <tr>

<th>MAD</th> <td>7.312</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.33564</td>

</tr> <tr>

<th>Sum</th> <td>270750</td>

</tr> <tr>

<th>Variance</th> <td>79.698</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram1402369884337458780”>

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1crVqy4ZOj6a//zP/%2BjzMxM/frXv/Yad7vdioqK8hoLCwtTdXV143xoaKjXfGhoaON8S1RUVKiystJrzOnsoMjIyBbvw%2B78/R1eX2E9enL1OJ1X9prSE99zpX8X0DK%2B9F6xXcBatmyZTp06pZ49ezaODRkyRB988IGWL1%2Bu5cuXt3g/Y8eOVc%2BePXX06NHLqsHj8VzW9hdyuVzKysryGps1a5bmzJnTqv3aUUhIe6tLwAXoiXnh4R1b9Xh64jta%2B3cBl%2BZL7xXbBaw9e/Zo8%2BbNCg8Pbxzr3r27Vq5cqb/9279t0T6Kior0ySefaMuWLU3mwsPD5Xa7vcaqq6sVERFx0Xm3261evXq1eA0TJkxQcnKy15jT2UHV1TUt3ofd%2Bfs7FBLSXidP1qq%2BvsHqciB6cjVd6XuXnvgeX/oct6Or8V6xKhTbLmCdOXNGgYGBTcYdDodqa2tbtI/8/HxVVVVp6NChkv7/iFRCQoIee%2ByxJsGrpKREcXFxkqSYmBiVlpZqzJgxkqT6%2BnodOHBAaWlpLV5DZGRkk9OBlZWnVFfnex%2Bw9fUNPrmutoyemNfa15Oe%2BA76eHX50nvFdic7%2B/fvr%2BXLl%2BvEiRONY998842WLl3a5KcJL%2Bbpp5/Wjh07tGnTJm3atEk5OTmSpE2bNik1NVXl5eXKy8vT2bNnVVhYqMLCQo0fP16SlJ6ero0bN2r//v2qra1Vdna2AgICNGTIEONrBQAAvsl2R7CeffZZPfbYY/rxj3%2Bs4OBgNTQ0qKamRjfddFOTC9YvJjQ01OtC9e9/l2HXrl0lSWvXrtWLL76opUuXqlu3blqxYoXuuOMOSdLgwYM1b948zZ07V1VVVYqNjVVOTo6CgoIMrxQAAPgq2wWsm266Se%2B%2B%2B67ef/99ffnll3I4HOrRo4cGDRokf3//K9rnjTfeqD/96U%2BNf%2B7fv7/XzUcvNGnSJE2aNOmKngsAAMB2AUuSAgICGm/RAAAA0NbYLmB99dVXWrVqlT7//HOdOXOmyfx7771nQVUAAAAtZ7uA9eyzz6qiokKDBg3id/8BAIA2yXYBq6SkRO%2B9917jfakAAADaGtvdpqFz584cuQIAAG2a7QLW9OnTlZWV1epfVwMAAGAV250ifP/99/Xxxx/rnXfe0Y033iiHwzsDvv322xZVBgAA0DK2C1jBwcEaPHiw1WUAAABcMdsFrGXLllldAgAAQKvY7hosSfrLX/6izMxMPfPMM41jn3zyiYUVAQAAtJztAlZRUZFGjx6tgoICbdmyRdJ3Nx%2BdPHkyNxkFAABtgu0C1urVq/XUU09p8%2BbN8vPzk/Td7ydcvny51qxZY3F1AAAAzbNdwPrzn/%2Bs9PR0SWoMWJI0YsQIlZWVWVUWAABAi9kuYHXq1OkHfwdhRUWFAgICLKgIAADg8tguYMXHx%2Bull17S6dOnG8cOHz6shQsX6sc//rGFlQEAALSM7W7T8Mwzz%2BiRRx5RQkKC6uvrFR8fr9raWvXq1UvLly%2B3ujwAAIBm2S5gde3aVVu2bFFhYaEOHz6soKAg9ejRQwMHDvS6JgsAgGtt5Ct7rS4BbYTtApYktWvXTg888IDVZQAAAFwR2wWs5OTkSx6p4l5YAADA7mwXsEaNGuUVsOrr63X48GEVFxfrkUcesbAyAACAlrFdwFqwYMEPju/YsUO///3vr3E1AAAAl892t2m4mAceeEDvvvuu1WUAAAA0q80ErAMHDsjj8VhdBgAAQLNsd4pw4sSJTcZqa2tVVlamYcOGWVARAADA5bFdwOrevXuTnyIMDAxUWlqaHn74YYuqAgAAaDnbBSzu1g4AANo62wWsjRs3tnjbn/zkJ1exEgAAgCtju4C1aNEiNTQ0NLmg3c/Pz2vMz8%2BPgAUAAGzJdgHrjTfe0Lp16zRjxgz17t1bHo9Hf/rTn/T666/rpz/9qRISEqwuEQAA4JJsF7CWL1%2BunJwcRUVFNY7dc889uummmzR16lRt2bLFwuoAAACaZ7v7YB05ckShoaFNxkNCQlReXm5BRQAAAJfHdgGrW7duWr58uaqrqxvHTp48qVWrVunmm2%2B2sDIAAICWsd0pwmeffVbz58%2BXy%2BVSx44d5XA4dPr0aQUFBWnNmjVWlwcAANAs2wWsQYMGaffu3SosLNSxY8fk8XgUFRWl%2B%2B67T506dbK6PAAAgGbZLmBJUvv27XX//ffr2LFjuummm6wuBwAA4LLY7hqsM2fOaOHCherbt69Gjhwp6btrsKZNm6aTJ0%2B2eD%2BfffaZHnnkEfXr10%2BJiYmaO3euKisrJUlFRUVKS0tTfHy8UlJSlJ%2Bf7/XY3NxcDR8%2BXPHx8UpPT1dJSYm5BQIAAJ9nu4C1YsUKHTx4UCtXrpTD8f/l1dfXa%2BXKlS3ax7lz5/TYY49pwIABKioq0pYtW1RVVaUlS5aooqJCTzzxhCZOnKiioiItWrRIzz33nIqLiyVJO3fuVGZmpn75y19q3759Gjp0qGbMmKFvv/32qqwXAAD4HtsFrB07duhf/uVfNGLEiMZf%2BhwSEqJly5apoKCgRfuora1VRkaGpk%2BfroCAAEVEROjBBx/U559/rs2bN6t79%2B5KS0tTYGCgEhMTlZycrLy8PEmSy%2BXS2LFjFRcXp6CgIE2bNk2StGvXrquzYAAA4HNsF7BqamrUvXv3JuMREREtPooUGhqqhx9%2BWE7nd5eY/eUvf9Fvf/tbjRw5UqWlpYqOjvbaPjo6uvE04IXzDodDffr0aTzCBQAA0BzbXeR%2B88036/e//70SEhK8fvfg9u3b9aMf/eiy9lVeXq7hw4errq5O48eP15w5c/T444973SVeksLCwhrvu%2BV2u5vc6DQ0NNTrvlzNqaioaLze63tOZwdFRkZeVv125u/v8PoK69GTq8fpvLLXlJ4Al8eX3iu2C1iTJk3S7NmzNW7cODU0NGj9%2BvUqKSnRjh07tGjRosvaV7du3VRcXKwvvvhC//RP/6Sf/exnLXrchb9o%2BnK5XC5lZWV5jc2aNUtz5sxp1X7tKCSkvdUl4AL0xLzw8I6tejw9AVrGl94rtgtYEyZMkNPp1Jtvvil/f3%2B99tpr6tGjh1auXKkRI0Zc9v78/PzUvXt3ZWRkaOLEiUpKSpLb7fbaprq6WhEREZKk8PDwJvNut1u9evW6rDUkJyd7jTmdHVRdXXPZ9duVv79DISHtdfJkrerrG6wuB6InV9OVvnfpCXB5rsZ7pbX/QLpStgtYx48f17hx4zRu3Lgr3kdRUZGWLFmibdu2Nf4k4vdf77rrLu3YscNr%2B5KSEsXFxUmSYmJiVFpaqjFjxkj67qcXDxw4oLS0tBY/f2RkZJPTgZWVp1RX53sfsPX1DT65rraMnpjX2teTngAt40vvFdud7Lz//vtbfYouJiZGp0%2Bf1ooVK1RbW6vjx48rMzNT99xzj9LT01VeXq68vDydPXtWhYWFKiws1Pjx4yVJ6enp2rhxo/bv36/a2lplZ2crICBAQ4YMMbA6AABwPbBdwEpISNC2bdtatY9OnTpp3bp1Kikp0b333quUlBR16tRJ//zP/6zOnTtr7dq1evPNN9WvXz%2B99NJLWrFihe644w5J0uDBgzVv3jzNnTtXAwYM0L59%2B5STk6OgoCATywMAANcB250i/Ju/%2BRv94he/UE5Ojm6%2B%2BWa1a9fOa37VqlUt2k/v3r3161//%2Bgfn%2Bvfvr02bNl30sZMmTdKkSZNaXjQAAMBfsV3AOnTokG699VZJuqxbIwAAANiFbQJWRkaGVq9e7XXUac2aNZo1a5aFVQEAAFw%2B21yDtXPnziZjOTk5FlQCAADQOrYJWD/0k4Ot/WlCAAAAK9jmFOH3v9i5uTEAuNZGvrLX6hIAtDG2OYIFAADgKwhYAAAAhtnmFOH58%2Bc1f/78Zsdaeh8sAAAAq9gmYPXr108VFRXNjgEAANidbQLWxe66DgAA0NZwDRYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhPhuwysvLNWvWLCUkJCgxMVFPP/20Tp48KUk6ePCgfvrTn6pfv34aNmyY1q1b5/XYrVu3KjU1VX379tXYsWO1Z88eK5YAAADaKJ8NWDNmzFBISIh27typd955R59//rlefvllnTlzRtOnT9e9996rDz74QKtXr9batWtVUFAg6bvwtXDhQi1YsED/9V//pSlTpujJJ5/UsWPHLF4RAABoK3wyYJ08eVIxMTGaP3%2B%2BOnbsqK5du2rMmDH68MMPtXv3bp0/f14zZ85Uhw4ddOedd%2Brhhx%2BWy%2BWSJOXl5SkpKUlJSUkKDAzU6NGjdfvttys/P9/iVQEAgLbCJwNWSEiIli1bpi5dujSOff3114qMjFRpaal69%2B4tf3//xrno6GiVlJRIkkpLSxUdHe21v%2BjoaBUXF1%2Bb4gEAQJvntLqAa6G4uFhvvvmmsrOztW3bNoWEhHjNh4WFye12q6GhQW63W6GhoV7zoaGhOnToUIufr6KiQpWVlV5jTmcHRUZGXvkibMbf3%2BH1FdajJwDaOl/6/PL5gPXRRx9p5syZmj9/vhITE7Vt27Yf3M7Pz6/x/z0eT6ue0%2BVyKSsry2ts1qxZmjNnTqv2a0chIe2tLgEXoCcA2ipf%2Bvzy6YC1c%2BdOPfXUU3ruuef0k5/8RJIUERGhI0eOeG3ndrsVFhYmh8Oh8PBwud3uJvMREREtft4JEyYoOTnZa8zp7KDq6porW4gN%2Bfs7FBLSXidP1qq%2BvsHqciB6AqDtuxqfX%2BHhHY3ur6V8NmB9/PHHWrhwoV599VUNGjSocTwmJkb/8R//obq6Ojmd3y2/uLhYcXFxjfPfX4/1veLiYqWkpLT4uSMjI5ucDqysPKW6Ot/7pldf3%2BCT62rL6AmAtsqXPr9852TnX6mrq9PixYu1YMECr3AlSUlJSQoODlZ2drZqa2v16aefasOGDUpPT5ckjR8/Xvv27dPu3bt19uxZbdiwQUeOHNHo0aOtWAoAAGiD/DytveDIhj788EP93d/9nQICAprMbd%2B%2BXTU1NXr%2B%2BedVUlKiLl266PHHH9ekSZMatykoKNCqVatUXl6unj17atGiRerfv3%2BraqqsPNWqx9uN0%2BlQeHhHVVfX%2BMy/Ntq6ttSTka/stboEADbz4S9GXJXPrxtu6GR0fy3lkwHLjghYuNraUk8IWAAu5GsByydPEQIAAFiJgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMJ8NWB988IESExOVkZHRZG7r1q1KTU1V3759NXbsWO3Zs6dxrqGhQatXr9b999%2Bv/v37a%2BrUqfrqq6%2BuZekAAKCNc1pdwNXw%2Buuva8OGDbrllluazB08eFALFy5UVlaW7r33Xu3YsUNPPvmktm/frq5du%2Bqtt97S5s2b9frrrysqKkqrV6/WrFmztGnTJvn5%2BVmwGqBlHlz5gdUlAAD%2Bj08ewQoMDLxowMrLy1NSUpKSkpIUGBio0aNH6/bbb1d%2Bfr4kyeVyacqUKbrtttsUHBysjIwMlZWV6dNPP73WywAAAG2UTwasyZMnq1OnTj84V1paqujoaK%2Bx6OhoFRcX68yZMzp06JDXfHBwsG655RYVFxdf1ZoBAIDv8MlThJfidrsVGhrqNRYaGqpDhw7pxIkT8ng8PzhfXV3d4ueoqKhQZWWl15jT2UGRkZFXXrjN%2BPs7vL4CANBavvQ95boLWJLk8XhaNd8cl8ulrKwsr7FZs2Zpzpw5rdqvHYWEtLe6BACAj/Cl7ynXXcAKDw%2BX2%2B32GnO73YqIiFBYWJgcDscPznfu3LnFzzFhwgQlJyd7jTmdHVRdXXPlhduMv79DISHtdfJkrerrG6wuBwDgA67G95Tw8I5G99dS113AiomJUUlJiddYcXGxUlJSFBgpg4F1AAAK8klEQVQYqF69eqm0tFQDBgyQJJ08eVJffvml7rrrrhY/R2RkZJPTgZWVp1RX53tBpL6%2BwSfXBQC49nzpe4rvnOxsofHjx2vfvn3avXu3zp49qw0bNujIkSMaPXq0JCk9PV25ubkqKyvT6dOntXLlSvXp00exsbEWVw4AANoKnzyC9X0YqqurkyT97ne/k/Tdkarbb79dK1eu1LJly1ReXq6ePXtq7dq1uuGGGyRJEydOVGVlpf7%2B7/9eNTU1SkhIaHI9FQAAwKX4eVp7RTdapLLylNUlGOV0OhQe3lHV1TU%2Bczi3rRv5yl6rSwCAK/bhL0Zcle8pN9zww7dtutquu1OEAAAAVxsBCwAAwDACFgAAgGEELAAAAMMIWAAAAIb55G0aABP4qTwAwJXiCBYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGCY0%2BoC0DojX9lrdQkttm3uQKtLAADgmuAIFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgPUDysvL9Q//8A9KSEjQ0KFDtWLFCjU0NFhdFgAAaCO4D9YPmD17tu6880797ne/U1VVlaZPn64uXbro0Ucftbo0AADQBnAE6wLFxcX67LPPtGDBAnXq1Endu3fXlClT5HK5rC4NAAC0ERzBukBpaam6deum0NDQxrE777xThw8f1unTpxUcHNzsPioqKlRZWek15nR2UGRkpPF62xKnkzwPALg4f3/f%2BT5BwLqA2%2B1WSEiI19j3Yau6urpFAcvlcikrK8tr7Mknn9Ts2bPNFfp/PvzFCOP7bImKigq5XC5NmDDBZ4OjVa/tlboeetLW0BN7oi/2U1FRoczMTJ/qie9ERYM8Hk%2BrHj9hwgS98847Xv9NmDDBUHX2UFlZqaysrCZH6mAdemI/9MSe6Iv9%2BGJPOIJ1gYiICLndbq8xt9stPz8/RUREtGgfkZGRPpPAAQDA5eMI1gViYmL09ddf6/jx441jxcXF6tmzpzp27GhhZQAAoK0gYF0gOjpasbGxWrVqlU6fPq2ysjKtX79e6enpVpcGAADaCP8lS5YssboIu7nvvvu0ZcsWvfDCC3r33XeVlpamqVOnys/Pz%2BrSbKVjx44aMGAAR/ZshJ7YDz2xJ/piP77WEz9Pa6/oBgAAgBdOEQIAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsBCi2RnZ2vQoEG6%2B%2B67NWXKFB09elSSVFRUpLS0NMXHxyslJUX5%2BfkWV%2Br7/vCHPyg2Ntbrv5iYGPXu3VsSPbHKgQMHNHnyZN1zzz0aOHCgFixY0PhL4%2BmJdUpKSjR58mT169dP9913n/71X/%2B1cW7r1q1KTU1V3759NXbsWO3Zs8fCSn3bBx98oMTERGVkZDSZu1QfGhoatHr1at1///3q37%2B/pk6dqq%2B%2B%2Bupaln7lPEAz3nzzTc%2BIESM8ZWVlnlOnTnleeOEFzwsvvOD55ptvPHfffbcnLy/Pc%2BbMGc/evXs9d911l%2BePf/yj1SVfd7Kzsz3/%2BI//SE8scv78ec/AgQM9q1at8pw9e9Zz/Phxz6OPPuqZPXs2PbFQdXW1JyEhwbNy5UrPt99%2B6/nzn//sGTp0qGfr1q2eAwcOeGJiYjy7d%2B/2nDlzxrNp0yZPXFyc5%2Buvv7a6bJ%2BTk5PjGTZsmGfixImeuXPnes0114fc3FzP0KFDPYcOHfKcOnXK8/Of/9yTmprqaWhosGIpl4UjWGjWunXrlJGRoVtvvVXBwcFavHixFi9erM2bN6t79%2B5KS0tTYGCgEhMTlZycrLy8PKtLvq7893//t9avX6%2Bf/exn9MQilZWVqqys1EMPPaSAgACFh4frwQcf1MGDB%2BmJhfbv36%2BamhrNnTtX7du3V69evTR16lRt2LBBeXl5SkpKUlJSkgIDAzV69GjdfvvtHF28CgIDA7VhwwbdcsstTeaa64PL5dKUKVN02223KTg4WBkZGSorK9Onn356rZdx2QhYuKRvvvlGR48e1YkTJzRq1CglJCRozpw5On78uEpLSxUdHe21fXR0tEpKSiyq9vr06quvaty4cfrRj35ETywSFRWlPn36yOVyqaamRlVVVSooKNCQIUPoicX8/Py8/hwaGqqDBw9etC/FxcXXsrzrwuTJk9WpU6cfnLtUH86cOaNDhw55zQcHB%2BuWW25pE30iYOGSjh07Jknavn271q9fr02bNunYsWNavHix3G63QkJCvLYPCwtTdXW1FaVel44ePaqCggI9%2BuijkkRPLOJwOJSZman33ntP8fHxSkxMVF1dnebPn09PLNS3b1%2B1b99er776qmpra/Xll1/q3//933XixAm53W6FhoZ6bR8aGkpfrrFL9eHEiRPyeDxttk8ELFySx%2BORJE2bNk1RUVHq2rWrZs%2BerZ07d1pcGSTprbfe0rBhw3TDDTdYXcp17dy5c5oxY4ZGjBihDz/8UO%2B//746deqkBQsWWF3adS00NFRr1qxRUVGRBg4cqKeeekoPPfSQ/P39Jf3/5xus1Vwf2mqfCFi4pC5dukiS17/Au3XrJo/Ho/Pnz8vtdnttX11drYiIiGta4/Vsx44dSk5ObvxzeHg4PbFAUVGRjh49qnnz5qlTp06KiorSnDlz9J//%2BZ9yOBz0xEL33HOP8vLy9PHHH8vlciksLExRUVE/%2BF5xu9305Rq7VB/CwsJ%2B8P3jdrvVuXPna1nmFSFg4ZK6du2q4OBgHTx4sHGsvLxc7dq1U1JSUpPrSEpKShQXF3ety7wuHTx4UOXl5Ro4cGDjWGxsLD2xQH19vRoaGrz%2BpX3u3DlJUmJiIj2xyNmzZ/Xb3/5Wp0%2Bfbhzbu3ev%2Bvbtq5iYmCZ9KS4upi/X2KX6EBgYqF69eqm0tLRx7uTJk/ryyy911113XetSLxsBC5fkdDqVlpam1157TV988YWqqqq0Zs0apaamasyYMSovL1deXp7Onj2rwsJCFRYWavz48VaXfV04cOCAwsLCFBwc3DiWmppKTyzQt29fdejQQZmZmaqtrVV1dbWys7PVv39/PfTQQ/TEIu3atVNWVpays7NVV1enPXv2KD8/X4888ojGjx%2Bvffv2affu3Tp79qw2bNigI0eOaPTo0VaXfV1prg/p6enKzc1VWVmZTp8%2BrZUrV6pPnz6KjY21uPLm%2BXna6slNXDPnzp3TsmXL9O677%2Br8%2BfMaPny4nnvuOXXs2FF/%2BMMf9OKLL6qsrEzdunXT/PnzNWzYMKtLvi6sXbtWmzdv1pYtW7zG6Yk1SkpK9PLLL%2Buzzz5TQECABgwYoKefflpRUVH0xELFxcV6/vnnVVZWpq5du2rBggV68MEHJUkFBQVatWqVysvL1bNnTy1atEj9%2B/e3uGLf830Yqqurk/TdP9wlNf4k4KX64PF4lJmZqbfffls1NTVKSEjQz3/%2Bc3Xt2tWClVweAhYAAIBhnCIEAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMP%2BF01aLda6LYBaAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common1402369884337458780”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>341</td> <td class=”number”>10.6%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>76.674</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>92.597</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>83.571</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>88.374</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>92.968</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>77.389</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>98.305</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>84.21</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>95.992</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (36)</td> <td class=”number”>1604</td> <td class=”number”>50.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme1402369884337458780”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>56.955</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:40%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>65.805</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:30%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>66.446</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>70.306</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:91%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>73.941</td> <td class=”number”>56</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:96%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>97.302</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>98.305</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:30%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>98.992</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>99.595</td> <td class=”number”>14</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>341</td> <td class=”number”>10.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SNAP_REPORTSIMPLE09”>SNAP_REPORTSIMPLE09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.97414</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>2.5%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-269587135149912573”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-269587135149912573,#minihistogram-269587135149912573”
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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-269587135149912573”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-269587135149912573”

aria-controls=”quantiles-269587135149912573” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-269587135149912573” aria-controls=”histogram-269587135149912573”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-269587135149912573” aria-controls=”common-269587135149912573”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-269587135149912573” aria-controls=”extreme-269587135149912573”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-269587135149912573”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>1</td>

</tr> <tr>

<th>Q1</th> <td>1</td>

</tr> <tr>

<th>Median</th> <td>1</td>

</tr> <tr>

<th>Q3</th> <td>1</td>

</tr> <tr>

<th>95-th percentile</th> <td>1</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr>

<th>Range</th> <td>1</td>

</tr> <tr>

<th>Interquartile range</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.15875</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.16296</td>

</tr> <tr>

<th>Kurtosis</th> <td>33.749</td>

</tr> <tr>

<th>Mean</th> <td>0.97414</td>

</tr> <tr>

<th>MAD</th> <td>0.050386</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-5.9772</td>

</tr> <tr>

<th>Sum</th> <td>3051</td>

</tr> <tr>

<th>Variance</th> <td>0.025201</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-269587135149912573”>

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0/q7rvvVps2bdSzZ0/dfvvtKioqkiStWLFCGRkZysjIUFhYmEaMGKHu3btrzZo1kqSioiJNmDBBXbp0UUREhHJzc1VWVqZdu3b585QBAEAACfX3AD9FZGSk5s6d67Pt4MGDiouLU0lJiXr06KGQkBDvWlJSklasWCFJKikpUUZGhs9rk5KS5HA4VFtbq9LSUiUlJXnXIiIi1KlTJzkcDl155ZWNmq%2BiokJOp9NnW2hoG8XFxTXpPM8mJCTY52eYQ7bWIVtrka91yNZ6zSnbgCxYp3I4HHrjjTe0ZMkSbdiwQZGRkT7r0dHRcrvdamhokNvtVlRUlM96VFSUSktLdeTIEXk8njOuu1yuRs9TVFSkgoICn205OTmaOnVqE8%2BscSIjW1tyXJCtlcjWWuRrHbK1TnPKNuAL1meffaa7775b06dPV3p6ujZs2HDG/YKCgrz/fLbnqc71eausrCxlZmb6bAsNbSOXq/qcjnuqkJBgRUa2VmVljerrG4we%2B0JHttYhW2uRr3XI1npWZBsTE270eI0V0AVr8%2BbNevDBBzVz5kzdeuutkqTY2Fh98803Pvu53W5FR0crODhYMTExcrvdp63HxsZ69znTert27Ro9V1xc3Gm3A53Oo6qrs%2BYLsr6%2BwbJjX%2BjI1jpkay3ytQ7ZWqc5ZRuwNzs///xz5eXl6fnnn/eWK0lKTk7WF198obq6Ou82h8Oh1NRU73pxcbHPsX5YDwsLU7du3VRSUuJdq6ys1P79%2B9WrVy%2BLzwgAADQXAVmw6urq9Pjjj2vGjBnq37%2B/z1pGRoYiIiK0ZMkS1dTUaNeuXVq5cqWys7MlSWPHjtUnn3yirVu36vjx41q5cqW%2B%2BeYbjRgxQpKUnZ2tZcuWqaysTFVVVcrPz1diYqJSUlJsP08AABCYAvIW4c6dO1VWVqbZs2dr9uzZPmsbN27Uiy%2B%2BqCeffFKFhYW66KKLlJubq4EDB0qSunfvrvz8fM2dO1fl5eXq2rWrli5dqvbt20uSxo0bJ6fTqfHjx6u6ulppaWmnPbAOAADwY4I8fIKmLZzOo8aPGRoarJiYcLlc1c3mnvX5gmytQ7bWIl/rBGK2Nz33Z3%2BP0Gg75gy1JNv27dsaPV5j2X6LMDMzUwUFBTp48KDdbw0AAGAL2wvWbbfdpvXr1%2Bv666/XpEmT9P777/s8kA4AABDobC9YOTk5Wr9%2Bvd566y1169ZNv/3tb5WRkaH58%2Bdr3759do8DAABgnN%2B%2Bi7Bnz57Ky8vTli1b9Oijj%2Bqtt97SzTffrIkTJ%2Bp//ud//DUWAADAOfNbwTp58qTWr1%2Bvu%2B66S3l5eerQoYMeeeQRJSYmasKECVq7dq2/RgMAADgntn9MQ1lZmVauXKnVq1erurpaQ4YM0e9//3v17dvXu0%2B/fv00a9YsDR8%2B3O7xAAAAzpntBWvYsGHq3LmzJk%2BerFtvvVXR0dGn7ZORkaHDhw/bPRoAAIARthesZcuW6eqrrz7rfrt27bJhGgAAAPNsfwarR48emjJlijZt2uTd9tprr%2Bmuu%2B467S9ZBgAACES2F6y5c%2Bfq6NGj6tq1q3fbwIED1dDQoHnz5tk9DgAAgHG23yL8%2BOOPtXbtWsXExHi3JSQkKD8/X7fccovd4wAAABhn%2BxWs2tpahYWFnT5IcLBqamrsHgcAAMA42wtWv379NG/ePB05csS77dtvv9VTTz3l81ENAAAAgcr2W4SPPvqofv3rX%2Bvaa69VRESEGhoaVF1drY4dO%2Br111%2B3exwAAADjbC9YHTt21LvvvqsPP/xQ%2B/fvV3BwsDp37qz%2B/fsrJCTE7nEAAACMs71gSVLLli11/fXX%2B%2BOtAQAALGd7wTpw4IAWLFigr776SrW1taetf/DBB3aPBAAAYJRfnsGqqKhQ//791aZNG7vfHgAAwHK2F6zi4mJ98MEHio2NtfutAQAAbGH7xzS0a9eOK1cAAKBZs71gTZ48WQUFBfJ4PHa/NQAAgC1sv0X44Ycf6vPPP9eqVat06aWXKjjYt%2BO9%2Beabdo8EAABglO0FKyIiQgMGDLD7bQEAAGxje8GaO3eu3W8JAABgK9ufwZKkr7/%2BWosWLdIjjzzi3fa3v/3NH6MAAAAYZ3vB2r59u0aMGKH3339f69atk/T9h4/ecccdfMgoAABoFmwvWAsXLtSDDz6otWvXKigoSNL3fz/hvHnztHjxYrvHAQAAMM72gvXll18qOztbkrwFS5KGDh2qsrIyu8cBAAAwzvaC1bZt2zP%2BHYQVFRVq2bKl3eMAAAAYZ3vB6tOnj37729%2BqqqrKu23fvn3Ky8vTtddea/c4AAAAxtn%2BMQ2PPPKI7rzzTqWlpam%2Bvl59%2BvRRTU2NunXrpnnz5tk9DgAAgHG2F6yLL75Y69at07Zt27Rv3z61atVKnTt31nXXXefzTBYAAECgsr1gSVKLFi10/fXX%2B%2BOtAQAALGd7wcrMzPzRK1V8FhYAAAh0thesm2%2B%2B2adg1dfXa9%2B%2BfXI4HLrzzjvtHgcAAMA42wvWjBkzzrj9vffe01/%2B8hebpwEAADDPL38X4Zlcf/31evfdd/09BgAAwDk7bwrW7t275fF4/D0GAADAObP9FuG4ceNO21ZTU6OysjLdeOONdo8DAABgnO0FKyEh4bTvIgwLC9OYMWN0%2B%2B23N%2BlYH330kfLy8pSWlqaFCxd6t69atUqPPvqoWrRo4bP/8uXL1atXLzU0NOj555/XunXrVFlZqV69emnWrFnq2LGjJMntdmvWrFn661//quDgYGVkZGjmzJlq1arVTzxrAABwIbG9YJn6tPaXXnpJK1euVKdOnc643q9fP73%2B%2ButnXFu%2BfLnWrl2rl156SR06dNDChQuVk5Ojd955R0FBQZo5c6ZOnDihdevW6eTJk7r//vuVn5%2Bvxx9/3MjsAACgebO9YK1evbrR%2B956663/di0sLEwrV67UnDlzdPz48SbNUFRUpAkTJqhLly6SpNzcXKWlpWnXrl269NJLtWnTJv3xj39UbGysJOmee%2B7R/fffr7y8vNOuigEAAJzK9oL12GOPqaGh4bQH2oOCgny2BQUF/WjBuuOOO370fQ4ePKhf/epXKi4uVmRkpKZOnaqRI0eqtrZWpaWlSkpK8u4bERGhTp06yeFw6OjRowoJCVGPHj286z179tSxY8f09ddf%2B2z/dyoqKuR0On22hYa2UVxc3Flf2xQhIcE%2BP8McsrUO2VqLfK1DttZrTtnaXrBefvllvfLKK5oyZYp69Oghj8ejL774Qi%2B99JJ%2B%2BctfKi0t7ZzfIzY2VgkJCXrggQfUtWtX/elPf9JDDz2kuLg4XX755fJ4PIqKivJ5TVRUlFwul6KjoxUREeHznNgP%2B7pcrka9f1FRkQoKCny25eTkaOrUqed4ZmcWGdnakuOCbK1EttYiX%2BuQrXWaU7Z%2BeQarsLBQHTp08G676qqr1LFjR02cOFHr1q075/cYOHCgBg4c6P31sGHD9Kc//UmrVq3yftDpj30kxLl%2BXERWVpYyMzN9toWGtpHLVX1Oxz1VSEiwIiNbq7KyRvX1DUaPfaEjW%2BuQrbXI1zpkaz0rso2JCTd6vMayvWB98803p109kqTIyEiVl5db9r7x8fEqLi5WdHS0goOD5Xa7fdbdbrfatWun2NhYVVVVqb6%2BXiEhId41SWrXrl2j3isuLu6024FO51HV1VnzBVlf32DZsS90ZGsdsrUW%2BVqHbK3TnLK1/WZnfHy85s2b53O7rbKyUgsWLNBll11m5D3%2B8Ic/aP369T7bysrK1LFjR4WFhalbt24qKSnxef/9%2B/erV69eSkxMlMfj0d69e73rDodDkZGR6ty5s5H5AABA82b7FaxHH31U06dPV1FRkcLDwxUcHKyqqiq1atVKixcvNvIeJ06c0DPPPKOOHTvqiiuu0HvvvacPP/xQb731liQpOztbhYWFGjBggDp06KD8/HwlJiYqJSVFkjRkyBA999xzevbZZ3XixAktXrxYY8aMUWio7XEBAIAAZHtj6N%2B/v7Zu3apt27bp0KFD8ng86tChg37%2B85%2Brbdu2jT7OD2Worq5OkrRp0yZJ319tuuOOO1RdXa37779fTqdTl156qRYvXqzk5GRJ33%2BavNPp1Pjx41VdXa20tDSfh9KffvppPfnkkxo8eLBatGihW265Rbm5uaYiAAAAzVyQx09/AeDJkyd16NAh76enN3dO51HjxwwNDVZMTLhcrupmc8/6fEG21iFba5GvdQIx25ue%2B7O/R2i0HXOGWpJt%2B/aNv3hjku3PYNXW1iovL0%2B9e/fWTTfdJOn7Z6AmTZqkyspKu8cBAAAwzvaCNX/%2BfO3Zs0f5%2BfkKDv7n29fX1ys/P9/ucQAAAIyzvWC99957euGFFzR06FDvh3lGRkZq7ty5ev/99%2B0eBwAAwDjbC1Z1dbUSEhJO2x4bG6tjx47ZPQ4AAIBxthesyy67TH/5y18k%2BX5i%2BsaNG/Wzn/3M7nEAAACMs/1jGn7xi1/ovvvu02233aaGhga9%2BuqrKi4u1nvvvafHHnvM7nEAAACMs71gZWVlKTQ0VG%2B88YZCQkL04osvqnPnzsrPz9fQoUPtHgcAAMA42wvW4cOHddttt%2Bm2226z%2B60BAABsYfszWIMHD5afPtsUAADAFrYXrLS0NG3YsMHutwUAALCN7bcIL7nkEs2ZM0eFhYW67LLL1KJFC5/1BQsW2D0SAACAUbYXrNLSUl1%2B%2BeWSJJfLZffbAwAAWM62gpWbm6uFCxfq9ddf925bvHixcnJy7BoBAADAFrY9g7V58%2BbTthUWFtr19gAAALaxrWCd6TsH%2BW5CAADQHNlWsH74i53Ptg0AACDQ2f4xDQAAAM0dBQsAAMAw276L8OTJk5o%2BffpZt/E5WAAAINDZVrD69u2rioqKs24DAAAIdLYVrH/9/CsAAIDmjGewAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYFtAF66OPPlJ6erpyc3NPW1u/fr2GDx%2Bu3r17a/To0fr444%2B9aw0NDVq4cKEGDx6sfv36aeLEiTpw4IB33e12a9q0aUpPT1f//v312GOPqba21pZzAgAAgS9gC9ZLL72k2bNnq1OnTqet7dmzR3l5eZoxY4Y%2B/fRTTZgwQffee68OHTokSVq%2BfLnWrl2rwsJCbdmyRQkJCcrJyZHH45EkzZw5UzU1NVq3bp3efvttlZWVKT8/39bzAwAAgStgC1ZYWJhWrlx5xoK1YsUKZWRkKCMjQ2FhYRoxYoS6d%2B%2BuNWvWSJKKioo0YcIEdenSRREREcrNzVVZWZl27dqlf/zjH9q0aZNyc3MVGxurDh066J577tHbb7%2BtkydP2n2aAAAgAIX6e4Cf6o477vi3ayUlJcrIyPDZlpSUJIfDodraWpWWliopKcm7FhERoU6dOsnhcOjo0aMKCQlRjx49vOs9e/bUsWPH9PXXX/ts/3cqKirkdDp9toWGtlFcXFxjT69RQkKCfX6GOWRrHbK1Fvlah2yt15yyDdiC9WPcbreioqJ8tkVFRam0tFRHjhyRx%2BM547rL5VJ0dLQiIiIUFBTksyZJLperUe9fVFSkgoICn205OTmaOnXqTzmds4qMbG3JcUG2ViJba5GvdcjWOs0p22ZZsCR5n6f6Ketne%2B3ZZGVlKTMz02dbaGgbuVzV53TcU4WEBCsysrUqK2tUX99g9NgXOrK1Dtlai3ytQ7bWsyLbmJhwo8drrGZZsGJiYuR2u322ud1uxcbGKjo6WsHBwWdcb9eunWJjY1VVVaX6%2BnqFhIR41ySpXbt2jXr/uLi4024HOp1HVVdnzRdkfX2DZce%2B0JGtdcjWWuRrHbK1TnPKtvnc7PwXycnJKi4u9tnmcDiUmpqqsLAwdevWTSUlJd61yspK7d%2B/X7169VJiYqI8Ho/27t3r89rIyEh17tzZtnMAAACBq1kWrLFjx%2BqTTz7R1q1bdfz4ca1cuVLffPONRowYIUnKzs7WsmXLVFZWpqqqKuXn5ysxMVEpKSmKjY3VkCFD9Nxzz%2Bnw4cM6dOiQFi9erDFjxig0tFle8AMAAIYFbGNISUmRJNXV1UmSNm3aJOn7q03du3dXfn6%2B5s6dq/LycnXt2lVLly5V%2B/btJUnjxo2T0%2BnU%2BPHjVV1drbS0NJ%2BH0p9%2B%2Bmk9%2BeSTGjx4sFq0aKFbbrnljB9mCgAAcCZouY52AAAPfUlEQVRBnnN9ohuN4nQeNX7M0NBgxcSEy%2BWqbjb3rM8XZGsdsrUW%2BVonELO96bk/%2B3uERtsxZ6gl2bZv39bo8RqrWd4iBAAA8CcKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwrNkWrB49eig5OVkpKSneH88884wkafv27RozZoz69OmjYcOGac2aNT6vXbZsmYYMGaI%2BffooOztbxcXF/jgFAAAQoEL9PYCVNm7cqEsvvdRnW0VFhe655x499thjGj58uD777DPdfffd6ty5s1JSUrR582YtWrRIL7/8snr06KFly5ZpypQpev/999WmTRs/nQkAAAgkzfYK1r%2Bzdu1aJSQkaMyYMQoLC1N6eroyMzO1YsUKSVJRUZFGjx6t1NRUtWrVSpMmTZIkbdmyxZ9jAwCAANKsr2AtWLBAf/vb31RVVaWbbrpJDz/8sEpKSpSUlOSzX1JSkjZs2CBJKikp0c033%2BxdCw4OVmJiohwOh4YNG9ao962oqJDT6fTZFhraRnFxced4Rr5CQoJ9foY5ZGsdsrUW%2BVqHbK3XnLJttgXryiuvVHp6up599lkdOHBA06ZN01NPPSW3260OHTr47BsdHS2XyyVJcrvdioqK8lmPioryrjdGUVGRCgoKfLbl5ORo6tSpP/FsflxkZGtLjguytRLZWot8rUO21mlO2TbbglVUVOT95y5dumjGjBm6%2B%2B671bdv37O%2B1uPxnNN7Z2VlKTMz02dbaGgbuVzV53TcU4WEBCsysrUqK2tUX99g9NgXOrK1Dtlai3ytQ7bWsyLbmJhwo8drrGZbsE516aWXqr6%2BXsHBwXK73T5rLpdLsbGxkqSYmJjT1t1ut7p169bo94qLizvtdqDTeVR1ddZ8QdbXN1h27Asd2VqHbK1FvtYhW%2Bs0p2ybz83Of7F7927NmzfPZ1tZWZlatmypjIyM0z52obi4WKmpqZKk5ORklZSUeNfq6%2Bu1e/du7zoAAMDZNMuC1a5dOxUVFamwsFAnTpzQvn379PzzzysrK0sjR45UeXm5VqxYoePHj2vbtm3atm2bxo4dK0nKzs7W6tWrtXPnTtXU1GjJkiVq2bKlBg4c6N%2BTAgAAAaNZ3iLs0KGDCgsLtWDBAm9BGjVqlHJzcxUWFqalS5dq9uzZeuqppxQfH6/58%2BfriiuukCQNGDBADzzwgKZNm6bvvvtOKSkpKiwsVKtWrfx8VgAAIFAEec71iW40itN51PgxQ0ODFRMTLperutncsz5fkK11yNZa5GudQMz2puf%2B7O8RGm3HnKGWZNu%2BfVujx2usZnmLEAAAwJ8oWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMC/X3ADg3Vz220d8jNNqGadf5ewQAAGzBFSwAAADDKFgAAACGUbAAAAAMo2CdQXl5uX7zm98oLS1NgwYN0vz589XQ0ODvsQAAQIDgIfczuO%2B%2B%2B9SzZ09t2rRJ3333nSZPnqyLLrpIv/rVr/w9GgAACABcwTqFw%2BHQ3r17NWPGDLVt21YJCQmaMGGCioqK/D0aAAAIEFzBOkVJSYni4%2BMVFRXl3dazZ0/t27dPVVVVioiIOOsxKioq5HQ6fbaFhrZRXFyc0VlDQgKrH4eGBs68P2QbaBkHArK1Fvlah2yt15yypWCdwu12KzIy0mfbD2XL5XI1qmAVFRWpoKDAZ9u9996r%2B%2B67z9yg%2Br7I3XnxV8rKyjJe3i50FRUV%2Bv3vXyZbC5CttcjXOoGY7Y45Q/09QqNUVFRo0aJFAZXt2TSfqmiQx%2BM5p9dnZWVp1apVPj%2BysrIMTfdPTqdTBQUFp10tw7kjW%2BuQrbXI1zpka53mmC1XsE4RGxsrt9vts83tdisoKEixsbGNOkZcXFyzaeAAAKDpuIJ1iuTkZB08eFCHDx/2bnM4HOratavCw8P9OBkAAAgUFKxTJCUlKSUlRQsWLFBVVZXKysr06quvKjs729%2BjAQCAABEya9asWf4e4nzz85//XOvWrdMzzzyjd999V2PGjNHEiRMVFBTk79FOEx4erquvvpqraxYgW%2BuQrbXI1zpka53mlm2Q51yf6AYAAIAPbhECAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBOs%2BVl5frN7/5jdLS0jRo0CDNnz9fDQ0NZ9x32bJlGjJkiPr06aPs7GwVFxfbPG1gaUq2f/jDHzRkyBD17t1bI0eO1KZNm2yeNrA0JdsffPvtt%2Brdu7cWLVpk05SBqyn5lpWVafz48UpNTVVGRoZee%2B01e4cNMI3NtqGhQS%2B88IIyMzPVu3dvDR8%2BXOvXr/fDxIHlo48%2BUnp6unJzc390v4aGBi1cuFCDBw9Wv379NHHiRB04cMCmKQ3x4Lw2atQoz%2BOPP%2B6prKz07Nu3z3PjjTd6XnnlldP2%2B%2BCDDzxXXXWVZ%2BfOnZ6amhrP0qVLPdddd52nurraD1MHhsZmu3HjRk/fvn09O3bs8Jw4ccLz1ltveXr27OnZv3%2B/H6YODI3N9l/de%2B%2B9nr59%2B3peeOEFm6YMXI3Nt6amxjNw4EDPSy%2B95Dl27Jhn165dnmHDhnlKS0v9MHVgaGy2b7zxhqd///6esrIyT11dnWfz5s2epKQkz549e/wwdWAoLCz03HjjjZ5x48Z5pk2b9qP7Llu2zDNo0CBPaWmp5%2BjRo56nn37aM3z4cE9DQ4NN0547rmCdxxwOh/bu3asZM2aobdu2SkhI0IQJE1RUVHTavkVFRRo9erRSU1PVqlUrTZo0SZK0ZcsWu8cOCE3Jtra2Vg888ID69u2rFi1a6Pbbb1d4eLh27tzph8nPf03J9gfbtm1TaWmpBg4caN%2BgAaop%2BW7YsEERERGaNGmSWrdurV69emndunXq0qWLHyY//zUl25KSEvXt21eXX365QkJCNGjQIEVHR%2BuLL77ww%2BSBISwsTCtXrlSnTp3Oum9RUZEmTJigLl26KCIiQrm5uSorK9OuXbtsmNQMCtZ5rKSkRPHx8YqKivJu69mzp/bt26eqqqrT9k1KSvL%2BOjg4WImJiXI4HLbNG0iaku3IkSP1i1/8wvvryspKVVdXq0OHDrbNG0iakq30fYF9%2Bumn9eSTTyo0NNTOUQNSU/L97LPP1L17dz3yyCO66qqrNHToUK1Zs8bukQNGU7IdOHCg/vrXv2rPnj06ceKEPvjgA9XU1Ojqq6%2B2e%2ByAcccdd6ht27Zn3a%2B2tlalpaU%2Bf6ZFRESoU6dOAfVnGgXrPOZ2uxUZGemz7YcvfJfLddq%2B//ofhR/2PXU/fK8p2f4rj8ejxx9/XKmpqfyH9N9oaraLFy/WlVdeqWuuucaW%2BQJdU/I9dOiQPvjgA6Wnp%2Bujjz7S5MmTlZeXp927d9s2byBpSrY33nijsrKydOuttyolJUXTp0/X3Llzdckll9g2b3N15MgReTyegP8zjf9dPM95PB5L9kXT8zp58qQefvhhlZaWatmyZRZN1Tw0NtvS0lKtWLFCa9eutXii5qWx%2BXo8HvXs2VPDhw%2BXJI0aNUpvvvmmNm7c6HN1AP/U2GxXr16t1atXa8WKFerRo4e2b9%2Bu6dOn65JLLlGvXr0snvLCEOh/pnEF6zwWGxsrt9vts83tdisoKEixsbE%2B22NiYs6476n74XtNyVb6/pL15MmT9fe//13Lly/XRRddZNeoAaex2Xo8Hs2aNUv33Xef2rdvb/eYAaspv3fbt29/2i2Z%2BPh4OZ1Oy%2BcMRE3J9o033lBWVpZ69eqlsLAwDRw4UNdccw23YA2Ijo5WcHDwGf9dtGvXzk9TNR0F6zyWnJysgwcP6vDhw95tDodDXbt2VXh4%2BGn7lpSUeH9dX1%2Bv3bt3KzU11bZ5A0lTsvV4PMrNzVVoaKhee%2B01xcTE2D1uQGlstn//%2B9/13//933rhhReUlpamtLQ0vfvuu3r55Zc1atQof4weEJrye7dLly768ssvfa4ElJeXKz4%2B3rZ5A0lTsm1oaFB9fb3PthMnTtgyZ3MXFhambt26%2BfyZVllZqf379wfU1UEK1nksKSlJKSkpWrBggaqqqlRWVqZXX31V2dnZkqShQ4dqx44dkqTs7GytXr1aO3fuVE1NjZYsWaKWLVvyXVn/RlOyXbt2rUpLS/X8888rLCzMn2MHhMZme/HFF2vbtm165513vD8yMzM1btw4FRYW%2Bvkszl9N%2Bb07YsQIuVwuvfjii6qtrdW6detUUlKiESNG%2BPMUzltNyTYzM1MrV67U3r17VVdXp48//ljbt2/X4MGD/XkKAevbb7/V0KFDvZ91lZ2drWXLlqmsrExVVVXKz89XYmKiUlJS/Dxp4/EM1nnuhRde0MyZM3XdddcpIiJC48aN835H2759%2B3Ts2DFJ0oABA/TAAw9o2rRp%2Bu6775SSkqLCwkK1atXKn%2BOf1xqb7dtvv63y8vLTHmofOXKkZs%2BebfvcgaAx2YaEhOjiiy/2eV3r1q0VERHBLcOzaOzv3Q4dOmjp0qWaM2eO/uu//ks/%2B9nPtHjxYl122WX%2BHP%2B81thsJ0%2BerLq6OuXk5Ojw4cOKj4/X7Nmzde211/pz/PPaD%2BWorq5Okrwf2OxwOHTy5Ent27fPexVw3LhxcjqdGj9%2BvKqrq5WWlqaCggL/DP4TBXkC/SkyAACA8wy3CAEAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAsP8HvjM5P3gxDukAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-269587135149912573”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>3051</td> <td class=”number”>95.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>81</td> <td class=”number”>2.5%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-269587135149912573”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>81</td> <td class=”number”>2.5%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>3051</td> <td class=”number”>95.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>81</td> <td class=”number”>2.5%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>3051</td> <td class=”number”>95.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SNAP_REPORTSIMPLE16”>SNAP_REPORTSIMPLE16<br/>

<small>Boolean</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr class=”“>

<th>Distinct count</th> <td>2</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>
<tr>

<th>Mean</th> <td>1</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minifreqtable-4564730128937271819”>

<table class=”mini freq”>

<tr class=”“>

<th>1.0</th> <td>

<div class=”bar” style=”width:100%” data-toggle=”tooltip” data-placement=”right” data-html=”true”

data-delay=500 title=”Percentage: 97.6%”>

3132

</div>

</td>

</tr><tr class=”missing”>

<th>(Missing)</th> <td>

<div class=”bar” style=”width:3%” data-toggle=”tooltip” data-placement=”right” data-html=”true”

data-delay=500 title=”Percentage: 2.4%”>

&nbsp;

</div> 78

</td>

</tr>

</table>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#freqtable-4564730128937271819, #minifreqtable-4564730128937271819”

aria-expanded=”true” aria-controls=”collapseExample”> Toggle details

</a>

</div> <div class=”col-md-12 extrapadding collapse” id=”freqtable-4564730128937271819”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>3132</td> <td class=”number”>97.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table> </div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SODATAX_STORES14”>SODATAX_STORES14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>20</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>3.9634</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>7</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>21.4%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram5225739628978085871”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives5225739628978085871,#minihistogram5225739628978085871”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives5225739628978085871”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles5225739628978085871”

aria-controls=”quantiles5225739628978085871” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram5225739628978085871” aria-controls=”histogram5225739628978085871”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common5225739628978085871” aria-controls=”common5225739628978085871”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme5225739628978085871” aria-controls=”extreme5225739628978085871”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles5225739628978085871”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>1.225</td>

</tr> <tr>

<th>Median</th> <td>5</td>

</tr> <tr>

<th>Q3</th> <td>6.15</td>

</tr> <tr>

<th>95-th percentile</th> <td>7</td>

</tr> <tr>

<th>Maximum</th> <td>7</td>

</tr> <tr>

<th>Range</th> <td>7</td>

</tr> <tr>

<th>Interquartile range</th> <td>4.925</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>2.611</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.65879</td>

</tr> <tr>

<th>Kurtosis</th> <td>-1.3964</td>

</tr> <tr>

<th>Mean</th> <td>3.9634</td>

</tr> <tr>

<th>MAD</th> <td>2.3485</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.51856</td>

</tr> <tr>

<th>Sum</th> <td>12413</td>

</tr> <tr>

<th>Variance</th> <td>6.8174</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram5225739628978085871”>

<img 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55ptG11q3bp0GDBigu%2B66S9nZ2Tpx4oQkqbS0VBkZGerdu7dGjRqlrVu3%2Bjxv48aNGj58uHr37q2srCyVlZUZrQsAAAQ3ywPWhQsXFBERcWUhoaGqq6szts6mTZu0detWbdy4UQcPHlTXrl310ksvqaqqSjNmzNDEiRNVWlqqhQsXavHixfJ4PJKkffv2qbCwUE8//bQOHTqkIUOGaPr06Tp//ryx2gAAQHCzPGD169dPK1as0GeffdY89sknn2jp0qU%2Bt2q4Xi%2B%2B%2BKLy8vJ0%2B%2B23KzIyUosWLdKiRYu0bds2JSYmKiMjQxEREUpLS1N6ero2b94sSXK73Ro3bpx69eql1q1ba%2BrUqZKk/fv3G6sNAAAEN8sD1oIFC1RaWqof/OAH6t%2B/v/r27avBgwerrKxMy5YtM7LGJ598ohMnTuizzz7TyJEjlZqaqtzcXJ05c0bl5eVKSkryeXxSUlLzacCvzoeGhqpHjx7NR7gAAAC%2BieW3abjlllu0fft2/e53v9Nf//pXhYaGqkuXLhowYIDCwsKMrHHq1ClJ0q5du7RhwwY1NTUpNzdXixYt0oULFxQfH%2B/z%2BPbt26umpkaS5PV6FR0d7TMfHR3dPN8SVVVVqq6u9hlzudooLi7u27TztcLCbLsR/7ficpmp98u%2BA63/6%2BXUviXn9u7UviVn926SqfddfwvG/W15wJKk8PBw3XfffX7bflNTkyRp6tSpzWFq1qxZeuyxx5SWltbi539bbrdba9as8RnLyclRbm7udW030MXEtDW6vaiom4xuL1A4tW/Jub07tW/J2b2bYPp919%2BCaX9bHrD%2B9re/afXq1frwww914cKFK%2BZ/%2B9vfXvcaHTt2lCRFRUU1jyUkJKipqUmXL1%2B%2B4n5bNTU1io2NlSTFxMRcMe/1etWtW7cWr5%2BZman09HSfMZerjWpqzl1TH98k0JK%2Bqf7DwkIVFXWTamvr1NDQaGSbgcCpfUvO7d2pfUvO7t0k0793/MWf%2B9uukGl5wFqwYIGqqqo0YMAAtWnTxi9rdO7cWZGRkTpy5Ih69uwpSaqsrFSrVq00aNAgFRcX%2Bzy%2BrKxMvXr1kiQlJyervLxcY8eOlSQ1NDTo8OHDysjIaPH6cXFxV5wOrK7%2BXPX1zn6TMN1/Q0OjI19Tp/YtObd3p/YtObt3EwLttQum/W15wCorK9Nvf/vb5iNG/uByuZSRkaHnn39e/fr1U2RkpNauXavRo0dr7Nix%2Bs///E9t3rxZY8aM0VtvvaUDBw7I7XZLkrKysjRnzhw9%2BOCD6t69u37xi18oPDxcgwcP9lu9AAAguFgesDp06OC3I1f/aO7cubp06ZImTJigy5cva/jw4Vq0aJHatm2r9evXa9myZVq6dKkSEhK0cuVK3XnnnZKkgQMHas6cOZo9e7ZOnz6tlJQUFRUVqXXr1n6vGQAABIeQpuu9ovsabd68WX/5y180d%2B5chYSEWLm0raqrPze%2BTZcrVPevMnv3e3/aOfseI9txuUIVE9NWNTXnguZQcks4tW/Jub07tW/pxu39gWd/b3cJ18TU%2B66/%2BXN/d%2BrUzuj2WsryI1i/%2B93v9M477%2Bi1117TzTffrNBQ3wu1X331VatLAgAAMMrygBUZGamBAwdavSwAAIBlLA9YBQUFVi8JAABgKVtupPTxxx%2BrsLBQTz75ZPPYu%2B%2B%2Ba0cpAAAAxlkesEpLSzVmzBjt2bNHJSUlkr64%2BejkyZON3GQUAADAbpYHrGeeeUY/%2BclPtG3btuZPEd5yyy1asWKF1q5da3U5AAAAxlkesP73f/9XWVlZkuRzm4YRI0aooqLC6nIAAACMszxgtWvX7qrfQVhVVaXw8HCrywEAADDO8oDVu3dvLV%2B%2BXGfPnm0eO3bsmObNm6cf/OAHVpcDAABgnOW3aXjyySf16KOPKjU1VQ0NDerdu7fq6urUrVs3rVixwupyAAAAjLM8YHXu3FklJSU6cOCAjh07ptatW6tLly665557HPXVOQAAIHhZHrAkqVWrVrrvvvvsWBoAAMDvLA9Y6enp/98jVdwLCwAABDrLA9bIkSN9AlZDQ4OOHTsmj8ejRx991OpyAAAAjLM8YOXn5191fPfu3frDH/5gcTUAAADm2fJdhFdz3333afv27XaXAQAAcN1umIB1%2BPBhNTU12V0GAADAdbP8FOHEiROvGKurq1NFRYWGDRtmdTkAAADGWR6wEhMTr/gUYUREhDIyMjRhwgSrywEAADDO8oDF3doBAECwszxgvf766y1%2B7MMPP%2BzHSgAAAPzD8oC1cOFCNTY2XnFBe0hIiM9YSEgIAQsAAAQkywPWf/3Xf%2BnFF1/U9OnT1b17dzU1Neno0aN64YUX9MMf/lCpqalWlwQAAGCULddgFRUVKT4%2Bvnmsb9%2B%2BuuWWWzRlyhSVlJRYXRIAAIBRlt8H6/jx44qOjr5iPCoqSpWVlVaXAwAAYJzlASshIUErVqxQTU1N81htba1Wr16tW2%2B91epyAAAAjLP8FOGCBQs0d%2B5cud1utW3bVqGhoTp79qxat26ttWvXWl0OAACAcZYHrAEDBuiNN97QgQMHdOrUKTU1NSk%2BPl733nuv2rVrZ3U5AAAAxlkesCTppptu0tChQ3Xq1CndcsstdpQAAADgN5Zfg3XhwgXNmzdPd999tx544AFJX1yDNXXqVNXW1lpdDgAAgHGWB6yVK1fqyJEjWrVqlUJD/758Q0ODVq1aZXU5AAAAxlkesHbv3q3/%2BI//0IgRI5q/9DkqKkoFBQXas2eP1eUAAAAYZ3nAOnfunBITE68Yj42N1fnz560uBwAAwDjLA9att96qP/zhD5Lk892Du3bt0ne/%2B12rywEAADDO8k8RTpo0SbNmzdL48ePV2NioDRs2qKysTLt379bChQutLgcAAMA4ywNWZmamXC6XXnnlFYWFhen5559Xly5dtGrVKo0YMcLqcgAAAIyzPGCdOXNG48eP1/jx461eGgAAwBKWX4M1dOhQn2uvAAAAgo3lASs1NVU7d%2B60elkAAADLWH6K8Dvf%2BY5%2B9rOfqaioSLfeeqtatWrlM7969WqrSwIAADDK8oD10Ucf6fbbb5ck1dTUWL08AACA31kWsPLy8vTMM8/ol7/8ZfPY2rVrlZOTY1UJAAAAlrDsGqx9%2B/ZdMVZUVGTV8gAAAJaxLGBd7ZODfJoQAAAEI8sC1pdf7PxNYwAAAIHO8ts0AAAABDsCFgAAgGGWfYrw8uXLmjt37jeOcR8sAAAQ6CwLWH369FFVVdU3jgEAAAQ6ywLWP97/ymrLly/Xyy%2B/rKNHj0qSSktLtXr1an388cf6zne%2Bo2nTpmnMmDHNj9%2B4caM2bdqk6upqde/eXQsXLlRycrJd5QMAgAAT9NdgHTlyRMXFxc3/v6qqSjNmzNDEiRNVWlqqhQsXavHixfJ4PJK%2BuF9XYWGhnn76aR06dEhDhgzR9OnTdf78ebtaAAAAASaoA1ZjY6OWLFmi7Ozs5rFt27YpMTFRGRkZioiIUFpamtLT07V582ZJktvt1rhx49SrVy%2B1bt1aU6dOlSTt37/fjhYAAEAAsvy7CK306quvKiIiQqNHj9azzz4rSSovL1dSUpLP45KSkrRz587m%2BZEjRzbPhYaGqkePHvJ4PBo1alSL1q2qqlJ1dbXPmMvVRnFxcdfTzhXCwgIrH7tcZur9su9A6/96ObVvybm9O7Vvydm9m2TqfdffgnF/B23A%2BvTTT1VYWHjFtV9er1fx8fE%2BY%2B3bt2/%2B4mmv16vo6Gif%2Bejo6Gv6Ymq32601a9b4jOXk5Cg3N/daWgg6MTFtjW4vKuomo9sLFE7tW3Ju707tW3J27yaYft/1t2Da30EbsAoKCjRu3Dh17dpVJ06cuKbnXu9X%2BGRmZio9Pd1nzOVqo5qac9e13a8KtKRvqv%2BwsFBFRd2k2to6NTQ0GtlmIHBq35Jze3dq35KzezfJ9O8df/Hn/rYrZAZlwCotLdW7776rkpKSK%2BZiYmLk9Xp9xmpqahQbG/u1816vV926dWvx%2BnFxcVecDqyu/lz19c5%2BkzDdf0NDoyNfU6f2LTm3d6f2LTm7dxMC7bULpv0dlAFr69atOn36tIYMGSLp70ekUlNT9eMf//iK4FVWVqZevXpJkpKTk1VeXq6xY8dKkhoaGnT48GFlZGRY2AEAANfvgWd/b3cJLfb2z0bYXYJRgXWOqYXmz5%2Bv3bt3q7i4WMXFxSoqKpIkFRcXa/To0aqsrNTmzZt18eJFHThwQAcOHNAjjzwiScrKytLrr7%2Bu9957T3V1dVq3bp3Cw8M1ePBgGzsCAACBJCiPYEVHR/tcqF5fXy9J6ty5syRp/fr1WrZsmZYuXaqEhAStXLlSd955pyRp4MCBmjNnjmbPnq3Tp08rJSVFRUVFat26tfWNAACAgBSUAeurbr755ua7uEtSv379fG4%2B%2BlWTJk3SpEmTrCgNAAAEoaA8RQgAAGAnAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEuuwsAblQPPPt7u0u4Jjtn32N3CcA1C7S/Z0BLcQQLAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADAvagFVZWamcnBylpqYqLS1N8%2BfPV21trSTpyJEj%2BuEPf6g%2Bffpo2LBhevHFF32eu2PHDo0ePVp33323xo0bp4MHD9rRAgAACFBBG7CmT5%2BuqKgo7du3T6%2B99po%2B/PBD/fznP9eFCxc0bdo0ff/739ebb76pZ555RuvXr9eePXskfRG%2B5s2bp/z8fL311lvKzs7WzJkzderUKZs7AgAAgSIoA1Ztba2Sk5M1d%2B5ctW3bVp07d9bYsWP19ttv64033tDly5f1xBNPqE2bNurZs6cmTJggt9stSdq8ebMGDRqkQYMGKSIiQmPGjNEdd9yhrVu32twVAAAIFEEZsKKiolRQUKCOHTs2j508eVJxcXEqLy9X9%2B7dFRYW1jyXlJSksrIySVJ5ebmSkpJ8tpeUlCSPx2NN8QAAIOA54k7uHo9Hr7zyitatW6edO3cqKirKZ759%2B/byer1qbGyU1%2BtVdHS0z3x0dLQ%2B%2BuijFq9XVVWl6upqnzGXq43i4uK%2BfRNXERYWWPnY5TJT75d9B1r//mbq9b0ROXWfO7VvOFcw/awHfcD605/%2BpCeeeEJz585VWlqadu7cedXHhYSENP/vpqam61rT7XZrzZo1PmM5OTnKzc29ru0GupiYtka3FxV1k9HtBTrTr%2B%2BNyK593nfhLlvW/bbe/tkIu0sAvpVgel8P6oC1b98%2B/eQnP9HixYv18MMPS5JiY2N1/Phxn8d5vV61b99eoaGhiomJkdfrvWI%2BNja2xetmZmYqPT3dZ8zlaqOamnPfrpGvEWhJ31T/YWGhioq6SbW1dWpoaDSyzWBg%2BufrRsI%2BvzbB/LOA4OaPv%2BN2/eMzaAPWO%2B%2B8o3nz5um5557TgAEDmseTk5P1q1/9SvX19XK5vmjf4/GoV69ezfNfXo/1JY/Ho1GjRrV47bi4uCtOB1ZXf676emf/YjDdf0NDo%2BNf03/khNeCfd4yvEYIVMH0dzywDoG0UH19vRYtWqT8/HyfcCVJgwYNUmRkpNatW6e6ujq9//772rJli7KysiRJjzzyiA4dOqQ33nhDFy9e1JYtW3T8%2BHGNGTPGjlYAAEAACsojWO%2B9954qKiq0bNkyLVu2zGdu165dev7557VkyRIVFRWpY8eOysvL0%2BDBgyVJd9xxh1atWqWCggJVVlaqa9euWr9%2BvTp16mRDJwAAIBAFZcDq27evjh49%2Bv99zK9%2B9auvnRs2bJiGDRtmuiwAAOAQQRmwAMDJHnj293aXADheUF6DBQAAYCcCFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYS67C4BzPPDs7%2B0uAQAAS3AECwAs7clBAAAJbklEQVQAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAY5rK7AADO88Czv7e7BADwK45gAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFhXUVlZqccff1ypqakaMmSIVq5cqcbGRrvLAgAAAcJldwE3olmzZqlnz57au3evTp8%2BrWnTpqljx4760Y9%2BZHdpAAAgAHAE6ys8Ho8%2B%2BOAD5efnq127dkpMTFR2drbcbrfdpQEAgADBEayvKC8vV0JCgqKjo5vHevbsqWPHjuns2bOKjIz8xm1UVVWpurraZ8zlaqO4uDijtYaFkY/xdy4XPw8AAlsw/V4jYH2F1%2BtVVFSUz9iXYaumpqZFAcvtdmvNmjU%2BYzNnztSsWbPMFaovgtyjnT9UZmam8fB2I6uqqpLb7abvAPb2z0Zc0%2BODqfdr4dS%2BJef27uS%2BCwsLg6rv4ImKBjU1NV3X8zMzM/Xaa6/5/MnMzDRU3d9VV1drzZo1VxwtC3b07ay%2BJef27tS%2BJef2Tt/B0zdHsL4iNjZWXq/XZ8zr9SokJESxsbEt2kZcXFzQJHAAAHDtOIL1FcnJyTp58qTOnDnTPObxeNS1a1e1bdvWxsoAAECgIGB9RVJSklJSUrR69WqdPXtWFRUV2rBhg7KysuwuDQAABIiwp5566im7i7jR3HvvvSopKdFPf/pTbd%2B%2BXRkZGZoyZYpCQkLsLu0Kbdu2Vf/%2B/R13dI2%2BndW35Nzendq35Nze6Ts4%2Bg5put4rugEAAOCDU4QAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAlBlZaUef/xxpaamasiQIVq5cqUaGxvtLssSb775ptLS0pSXl2d3KZaqrKxUTk6OUlNTlZaWpvnz56u2ttbusizxwQcf6NFHH1WfPn2Ulpam2bNnq7q62u6yLLV8%2BXJ1797d7jIs0b17dyUnJyslJaX5z09/%2BlO7y7LMunXrNGDAAN11113Kzs7WiRMn7C7Jr/74xz/67OuUlBQlJycHxc87ASsAzZo1S/Hx8dq7d682bNigvXv36uWXX7a7LL974YUXtGzZMt122212l2K56dOnKyoqSvv27dNrr72mDz/8UD//%2Bc/tLsvvLl26pB//%2BMfq37%2B/SktLVVJSotOnT8tJX6F65MgRFRcX212GpXbt2iWPx9P8Z/HixXaXZIlNmzZp69at2rhxow4ePKiuXbvqpZdesrssv%2BrXr5/PvvZ4PJo5c6YeeOABu0u7bgSsAOPxePTBBx8oPz9f7dq1U2JiorKzs%2BV2u%2B0uze8iIiK0ZcsWxwWs2tpaJScna%2B7cuWrbtq06d%2B6ssWPH6u2337a7NL%2Brq6tTXl6epk2bpvDwcMXGxur%2B%2B%2B/Xhx9%2BaHdplmhsbNSSJUuUnZ1tdymwwIsvvqi8vDzdfvvtioyM1KJFi7Ro0SK7y7LU//3f/2nDhg36t3/7N7tLuW4ErABTXl6uhIQERUdHN4/17NlTx44d09mzZ22szP8mT56sdu3a2V2G5aKiolRQUKCOHTs2j508eVJxcXE2VmWN6OhoTZgwQS6XS5L08ccf67//%2B7%2BD4l%2B3LfHqq68qIiJCo0ePtrsUS61evVqDBw9W3759tXjxYp07d87ukvzuk08%2B0YkTJ/TZZ59p5MiRSk1NVW5urs6cOWN3aZZ67rnnNH78eH33u9%2B1u5TrRsAKMF6vV1FRUT5jX4atmpoaO0qCxTwej1555RU98cQTdpdimcrKSiUnJ2vkyJFKSUlRbm6u3SX53aeffqrCwkItWbLE7lIsdddddyktLU179uyR2%2B3We%2B%2B9p6VLl9pdlt%2BdOnVK0henRzds2KDi4mKdOnXKUUewTpw4oT179uhHP/qR3aUYQcAKQE1NTXaXAJv86U9/0pQpUzR37lylpaXZXY5lEhIS5PF4tGvXLh0/fjwoTh98k4KCAo0bN05du3a1uxRLud1uTZgwQeHh4fre976n/Px8lZSU6NKlS3aX5ldfvq9PnTpV8fHx6ty5s2bNmqV9%2B/bp4sWLNldnjU2bNmnYsGHq1KmT3aUYQcAKMLGxsfJ6vT5jXq9XISEhio2NtakqWGHfvn16/PHHtWDBAk2ePNnuciwXEhKixMRE5eXlqaSkJKhPnZSWlurdd99VTk6O3aXY7uabb1ZDQ4NOnz5tdyl%2B9eUlAP94hiIhIUFNTU1B3/uXdu/erfT0dLvLMIaAFWCSk5N18uRJn18uHo9HXbt2Vdu2bW2sDP70zjvvaN68eXruuef08MMP212OZUpLSzV8%2BHCf25CEhn7xttWqVSu7yvK7rVu36vTp0xoyZIhSU1M1btw4SVJqaqq2b99uc3X%2Bc/jwYa1YscJnrKKiQuHh4UF/zWHnzp0VGRmpI0eONI9VVlaqVatWQd%2B79MWnZSsrK3XPPffYXYoxBKwAk5SUpJSUFK1evVpnz55VRUWFNmzYoKysLLtLg5/U19dr0aJFys/P14ABA%2Bwux1LJyck6e/asVq5cqbq6Op05c0aFhYXq27dvUH/gYf78%2Bdq9e7eKi4tVXFysoqIiSVJxcXFQ/Qv/qzp06CC3262ioiJdunRJx44d03PPPafMzEyFhYXZXZ5fuVwuZWRk6Pnnn9df/vIXnT59WmvXrtXo0aObP%2BQRzA4fPqz27dsrMjLS7lKMCWnigp6Ac%2BrUKS1evFj/8z//o8jISE2cOFEzZ85USEiI3aX5VUpKiqQvAoek5jcdj8djW01WePvtt/XP//zPCg8Pv2Ju165dSkhIsKEq6xw9elTLli3Tn//8Z7Vp00bf//73NX/%2BfMXHx9tdmmVOnDihoUOH6ujRo3aX4nd//OMftXr1ah09elTh4eEaO3as8vLyFBERYXdpfnfp0iUVFBRo%2B/btunz5soYPH67Fixc74uzE%2BvXrtW3bNpWUlNhdijEELAAAAMM4RQgAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhv0/mKCE6gKRehAAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common5225739628978085871”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>686</td> <td class=”number”>21.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>476</td> <td class=”number”>14.8%</td> <td>

<div class=”bar” style=”width:69%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.25</td> <td class=”number”>356</td> <td class=”number”>11.1%</td> <td>

<div class=”bar” style=”width:52%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>220</td> <td class=”number”>6.9%</td> <td>

<div class=”bar” style=”width:32%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.5</td> <td class=”number”>208</td> <td class=”number”>6.5%</td> <td>

<div class=”bar” style=”width:31%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>199</td> <td class=”number”>6.2%</td> <td>

<div class=”bar” style=”width:29%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.225</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.000000000000001</td> <td class=”number”>108</td> <td class=”number”>3.4%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.15</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.75</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (9)</td> <td class=”number”>559</td> <td class=”number”>17.4%</td> <td>

<div class=”bar” style=”width:81%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme5225739628978085871”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>686</td> <td class=”number”>21.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.225</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.5</td> <td class=”number”>208</td> <td class=”number”>6.5%</td> <td>

<div class=”bar” style=”width:31%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.7500000000000002</td> <td class=”number”>29</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.91</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>6.35</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.5</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:89%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.875</td> <td class=”number”>87</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:80%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>92</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:85%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.000000000000001</td> <td class=”number”>108</td> <td class=”number”>3.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SODATAX_VENDM14”>SODATAX_VENDM14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>23</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.7%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>4.6888</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>8</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>10.3%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram6212033776881856587”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives6212033776881856587,#minihistogram6212033776881856587”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives6212033776881856587”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles6212033776881856587”

aria-controls=”quantiles6212033776881856587” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram6212033776881856587” aria-controls=”histogram6212033776881856587”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common6212033776881856587” aria-controls=”common6212033776881856587”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme6212033776881856587” aria-controls=”extreme6212033776881856587”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles6212033776881856587”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>4</td>

</tr> <tr>

<th>Median</th> <td>5.5</td>

</tr> <tr>

<th>Q3</th> <td>6.15</td>

</tr> <tr>

<th>95-th percentile</th> <td>7</td>

</tr> <tr>

<th>Maximum</th> <td>8</td>

</tr> <tr>

<th>Range</th> <td>8</td>

</tr> <tr>

<th>Interquartile range</th> <td>2.15</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>2.1596</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.46058</td>

</tr> <tr>

<th>Kurtosis</th> <td>0.061672</td>

</tr> <tr>

<th>Mean</th> <td>4.6888</td>

</tr> <tr>

<th>MAD</th> <td>1.7051</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-1.0673</td>

</tr> <tr>

<th>Sum</th> <td>14685</td>

</tr> <tr>

<th>Variance</th> <td>4.6638</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram6212033776881856587”>

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zzxrWPvnkE61YscLroxoAAAB8leWXCB9//HH99Kc/1Y9%2B9COFhISorq5OFy9e1O23366XX37Z6nEAAACMs7xg3X777dq1a5feeust/e1vf5O/v786deqkQYMGKSAgwOpxAAAAjLO8YElSUFCQ7r33XjsODQAA0OIsL1gff/yx1q1bpw8%2B%2BECXL19utP2NN96weiQAAACjbLkHq6ysTIMGDVKbNm2sPjwAAECLs7xgFRYW6o033lBkZKTVhwYAALCE5R/T0L59e8vOXG3cuFGDBg1Sr169lJ6erjNnzkiSCgoKlJKSoj59%2BmjMmDHasWOH19dt2bJFI0eOVJ8%2BfZSWlqbCwkJL5gUAAM5gecGaOXOm1q9fr/r6%2BhY9zquvvqodO3Zoy5YtOnLkiDp37qwXX3xRZWVlmj17tiZPnqyCggItWbJEy5Ytk9vtliQdPHhQOTk5euqpp3T06FENGzZMs2bN0qVLl1p0XgAA4ByWXyJ866239O677%2Br111/XbbfdJn9/74732muvGTnOCy%2B8oEWLFumuu%2B6SJC1dulSS9Otf/1qxsbFKSUmRJCUlJSk5OVlbt25VQkKCXC6Xxo8fr549e0qSpk%2Bfri1btujQoUMaM2aMkdkAAICzWV6wQkJCNHjw4BY9xieffKIzZ87os88%2B0%2BjRo1VRUaHExEQ98cQTKioqUlxcnNfj4%2BLitGfPHklSUVGRRo8e3bDN399f3bt3l9vtpmABAIAmsbxgZWdnt/gxzp07J0nau3evNm/erPr6emVmZmrp0qW6fPmyoqOjvR4fHh6uyspKSZLH41FYWJjX9rCwsIbtTVFWVqby8nKvtcDANoqKivomcb5SQIC/189O4dRcknOzOTWXRDZ4Cwy099fKqc%2BZE3PZ8kGjH330kXbt2qW///3vDYXrvffeU%2B/evY3s/4v7u6ZPn95QpubOnatHH31USUlJTf76b8rlcmn9%2BvVeaxkZGcrMzGzWfr9KaOgtLbJfuzk1l%2BTcbE7NJZEN10VEtLV7BEnOfc6clMvyglVQUKBHH31UnTp10unTp5Wdna2PP/5YU6dO1TPPPKPhw4c3%2BxgdOnSQJIWGhjasxcTEqL6%2BXteuXZPH4/F6fGVlZcPHRkRERDTa7vF41KVLlyYfPzU1VcnJyV5rgYFtVFl58aZyfJ2AAH%2BFht6iqqpq1dbWGd23nZyaS3JuNqfmksgGb6bfx2%2BWU5%2BzlsxlVym2vGA9/fTT%2BtnPfqZHHnlEP/jBDyRd//6Eq1ev1oYNG4wUrI4dOyokJETFxcXq0aOHJKm0tFStWrXSkCFDlJeX5/X4wsLChpva4%2BPjVVRUpHHjxkmSamtrdfz48Yab4psiKiqq0eXA8vLPVVPTMi%2BG2tq6Ftu3nZyaS3JuNqfmksiG674tv05Ofc6clMvyi53/%2B7//q7S0NEmSn59fw/qoUaNUUlJi5BiBgYFKSUnRc889p7/%2B9a%2BqqKjQhg0bNHbsWI0bN06lpaXaunWrrly5osOHD%2Bvw4cOaNGmSJCktLU3bt2/XsWPHVF1drY0bNyooKEhDhw41MhsAAHA%2By89gtWvXTpcvX1ZQUJDXellZWaO15li4cKGuXr2qiRMn6tq1axo5cqSWLl2qtm3batOmTVq5cqVWrFihmJgYrVmzRnfffbckafDgwVqwYIHmzZuniooKJSQkKDc3V61btzY2GwAAcDbLC1afPn20atWqhs%2BlkqRTp05p%2BfLl%2BtGPfmTsOEFBQVq%2BfLmWL1/eaFv//v0bXSb8R1OmTNGUKVOMzQIAAL5bLC9YP//5z/XII48oMTFRtbW16tOnj6qrq9WlSxetXr3a6nEAAACMs7xgdezYUfn5%2BTp8%2BLBOnTql1q1bq1OnTho4cKDXPVkAAAC%2BypbPwWrVqpXuvfdeOw4NAADQ4iwvWMnJyf/nmao33njDwmkAAADMs7xgjR492qtg1dbW6tSpU3K73XrkkUesHgcAAMA4ywtWVlbWDdf37dunP/7xjxZPAwAAYJ4t92DdyL333qtf/OIX%2BsUvfmH3KADgpd%2BSvXaPcFP2zBto9wjAd9635ttWHz9%2BvNnfZBkAAODbwPIzWJMnT260Vl1drZKSEo0YMcLqcQAAAIyzvGDFxsY2%2BleEwcHBSklJ0cSJE60eBwAAwDjLCxaf1g4AAJzO8oK1ffv2Jj/24YcfbsFJAAAAWoblBWvJkiWqq6trdEO7n5%2Bf15qfnx8FCwAA%2BCTLC9Z//ud/6oUXXtCsWbPUrVs31dfX6%2BTJk3r%2B%2Bef14x//WImJiVaPBAAAYJQt92Dl5uYqOjq6Ya1fv366/fbbNW3aNOXn51s9EgAAgFGWfw7W6dOnFRYW1mg9NDRUpaWlVo8DAABgnOUFKyYmRqtXr1ZlZWXDWlVVldatW6c77rjD6nEAAACMs/wS4eOPP66FCxfK5XKpbdu28vf314ULF9S6dWtt2LDB6nEAAACMs7xgDRo0SG%2B%2B%2BaYOHz6sc%2BfOqb6%2BXtHR0brnnnvUrl07q8cBAAAwzpZv9nzLLbdo%2BPDhOnfunG6//XY7RgAAAGgxlt%2BDdfnyZS1atEi9e/fW/fffL%2Bn6PVjTp09XVVWV1eMAAAAYZ3nBWrNmjYqLi7V27Vr5%2B///w9fW1mrt2rVWjwMAAGCc5QVr3759%2Bo//%2BA%2BNGjWq4Zs%2Bh4aGKjs7W/v377d6HAAAAOMsvwfr4sWLio2NbbQeGRmpS5cuWT0OADjO/c/83u4RgO88y89g3XHHHfrjH/8oSV7fe3Dv3r363ve%2BZ/U4AAAAxll%2BBmvKlCmaO3euJkyYoLq6Om3evFmFhYXat2%2BflixZYvU4AAAAxllesFJTUxUYGKhXXnlFAQEBeu6559SpUyetXbtWo0aNsnocAAAA4ywvWOfPn9eECRM0YcIEqw8NAABgCcvvwRo%2BfLjXvVcAAABOY3nBSkxM1J49e6w%2BLAAAgGUsv0T4T//0T/rVr36l3Nxc3XHHHWrVqpXX9nXr1lk9EgAAgFGWF6wPP/xQd911lySpsrLS6sMDAAC0OMsK1vz58/X000/r5ZdfbljbsGGDMjIyrBoBAADAEpbdg3Xw4MFGa7m5uVYdHgAAwDKWFawb/ctB/jUhAABwIssK1hff2Pnr1gAAAHyd5R/TAAAA4HQULAAAAMMs%2B1eE165d08KFC792jc/BAgAAvs6ygtW3b1%2BVlZV97RoAAICvs6xg/ePnXwEAADgZ92ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYd%2BJgrVq1Sp169at4f8LCgqUkpKiPn36aMyYMdqxY4fX47ds2aKRI0eqT58%2BSktLU2FhodUjAwAAH%2Bb4glVcXKy8vLyG/y8rK9Ps2bM1efJkFRQUaMmSJVq2bJncbrck6eDBg8rJydFTTz2lo0ePatiwYZo1a5YuXbpkVwQAAOBjHF2w6urqtHz5cqWnpzes7dy5U7GxsUpJSVFwcLCSkpKUnJysrVu3SpJcLpfGjx%2Bvnj17qnXr1po%2Bfbok6dChQ3ZEAAAAPsiyDxq1w2uvvabg4GCNHTtWzzzzjCSpqKhIcXFxXo%2BLi4vTnj17GraPHj26YZu/v7%2B6d%2B8ut9utMWPGNOm4ZWVlKi8v91oLDGyjqKio5sRpJCDA3%2Btnp3BqLsm52ZyaS3JmJnxzgYH2/n5w6mvNibkcW7A%2B/fRT5eTkNPoEeY/Ho%2BjoaK%2B18PBwVVZWNmwPCwvz2h4WFtawvSlcLpfWr1/vtZaRkaHMzMybidBkoaG3tMh%2B7ebUXJJzszk1F/CFiIi2do8gybmvNSflcmzBys7O1vjx49W5c2edOXPmpr62vr6%2BWcdOTU1VcnKy11pgYBtVVl5s1n6/LCDAX6Ght6iqqlq1tXVG920np%2BaSnJvNqbkkZ/2NGs1n%2Bn38Zjn1tdaSuewqxY4sWAUFBXrvvfeUn5/faFtERIQ8Ho/XWmVlpSIjI79yu8fjUZcuXZp8/KioqEaXA8vLP1dNTcu8GGpr61ps33Zyai7Judmcmgv4wrfl97dTX2tOyuXIv5rt2LFDFRUVGjZsmBITEzV%2B/HhJUmJiorp27droYxcKCwvVs2dPSVJ8fLyKiooattXW1ur48eMN2wEAAL6OIwvW4sWLtW/fPuXl5SkvL0%2B5ubmSpLy8PI0dO1alpaXaunWrrly5osOHD%2Bvw4cOaNGmSJCktLU3bt2/XsWPHVF1drY0bNyooKEhDhw61MREAAPAljrxEGBYW5nWjek1NjSSpY8eOkqRNmzZp5cqVWrFihWJiYrRmzRrdfffdkqTBgwdrwYIFmjdvnioqKpSQkKDc3Fy1bt3a%2BiAAAMAnObJgfdltt92mkydPNvx///79vT589MumTJmiKVOmWDEaAABwIEdeIgQAALATBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAw74T3%2BzZyfot2Wv3CE22Z95Au0cAAMASnMECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGMYHjQIA0ET3P/N7u0e4KXzAs304gwUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABjm2IJVWlqqjIwMJSYmKikpSYsXL1ZVVZUkqbi4WD/%2B8Y/Vt29fjRgxQi%2B88ILX1%2B7evVtjx45V7969NX78eB05csSOCAAAwEc5tmDNmjVLoaGhOnjwoF5//XV98MEHevLJJ3X58mXNnDlTP/zhD/X222/r6aef1qZNm7R//35J18vXokWLlJWVpT/84Q9KT0/XnDlzdO7cOZsTAQAAX%2BHIglVVVaX4%2BHgtXLhQbdu2VceOHTVu3Di98847evPNN3Xt2jU99thjatOmjXr06KGJEyfK5XJJkrZu3aohQ4ZoyJAhCg4O1oMPPqiuXbtqx44dNqcCAAC%2BwpEFKzQ0VNnZ2erQoUPD2tmzZxUVFaWioiJ169ZNAQEBDdvi4uJUWFgoSSoqKlJcXJzX/uLi4uR2u60ZHgAA%2BLxAuwewgtvt1iuvvKKNGzdqz549Cg0N9doeHh4uj8ejuro6eTwehYWFeW0PCwvThx9%2B2OTjlZWVqby83GstMLCNoqKivnmIGwgI8K1%2BHBjYtHm/yOVr%2BZrCqdmcmktyZiZ8dzT1fdduTnwPcXzB%2BvOf/6zHHntMCxcuVFJSkvbs2XPDx/n5%2BTX8d319fbOO6XK5tH79eq%2B1jIwMZWZmNmu/vi4iou1NPT409JYWmsR%2BTs3m1FyAr7rZ9127Oek9xNEF6%2BDBg/rZz36mZcuW6eGHH5YkRUZG6vTp016P83g8Cg8Pl7%2B/vyIiIuTxeBptj4yMbPJxU1NTlZyc7LUWGNhGlZUXv1mQr%2BBrTb%2Bp%2BQMC/BUaeouqqqpVW1vXwlNZy6nZnJpL8r3XGfCPTP%2B501Ja8j3ErpLp2IL17rvvatGiRXr22Wc1aNCghvX4%2BHj95je/UU1NjQIDr8d3u93q2bNnw/Yv7sf6gtvt1pgxY5p87KioqEaXA8vLP1dNjbP%2B4LlZ96192%2B4RbsqeeQNbbN%2B1tXWO/P3g1FyAr/K116OT3kMc%2BVezmpoaLV26VFlZWV7lSpKGDBmikJAQbdy4UdXV1Xr//fe1bds2paWlSZImTZqko0eP6s0339SVK1e0bds2nT59Wg8%2B%2BKAdUQAAgA9y5BmsY8eOqaSkRCtXrtTKlSu9tu3du1fPPfecli9frtzcXHXo0EHz58/X0KFDJUldu3bV2rVrlZ2drdLSUnXu3FmbNm3SrbfeakMSAADgixxZsPr166eTJ0/%2Bn4/5zW9%2B85XbRowYoREjRpgeCwAAfEc48hIhAACAnShYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADAu0ewBuFqf9AAAKUElEQVQAANAy7n/m93aP0GTv/GqU3SMYxRksAAAAwyhYAAAAhlGwAAAADKNgAQAAGMZN7oBD%2BNLNrHvmDbR7BABoUZzBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMT3IHvoIvfTI6AODbhTNYAAAAhlGwAAAADKNgAQAAGEbBuoHS0lLNmDFDiYmJGjZsmNasWaO6ujq7xwIAAD6Cm9xvYO7cuerRo4cOHDigiooKzZw5Ux06dNBPfvITu0cDAAA%2BgDNYX%2BJ2u3XixAllZWWpXbt2io2NVXp6ulwul92jAQAAH8EZrC8pKipSTEyMwsLCGtZ69OihU6dO6cKFCwoJCfnafZSVlam8vNxrLTCwjaKioozOGhBAP4ZvCgz0nd%2B7vM4A6zjp9UbB%2BhKPx6PQ0FCvtS/KVmVlZZMKlsvl0vr1673W5syZo7lz55obVNeL3CMdP1Bqaqrx8mansrIyuVwux%2BWSnJvNqbkk577OJOc%2Bb07NJTk3W1lZmXJychyVyzlV0aD6%2BvpmfX1qaqpef/11rx%2BpqamGpvv/ysvLtX79%2BkZny3ydU3NJzs3m1FwS2XyRU3NJzs3mxFycwfqSyMhIeTwerzWPxyM/Pz9FRkY2aR9RUVGOaeAAAODmcQbrS%2BLj43X27FmdP3%2B%2BYc3tdqtz585q27atjZMBAABfQcH6kri4OCUkJGjdunW6cOGCSkpKtHnzZqWlpdk9GgAA8BEBTzzxxBN2D/Ftc8899yg/P1%2B//OUvtWvXLqWkpGjatGny8/Oze7RG2rZtqwEDBjju7JpTc0nOzebUXBLZfJFTc0nOzea0XH71zb2jGwAAAF64RAgAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAXLB5WWlmrGjBlKTEzUsGHDtGbNGtXV1dk9ljFvv/22kpKSNH/%2BfLtHMaq0tFQZGRlKTExUUlKSFi9erKqqKrvHarYTJ07okUceUd%2B%2BfZWUlKR58%2BapvLzc7rGMWrVqlbp162b3GMZ069ZN8fHxSkhIaPjxy1/%2B0u6xjNm4caMGDRqkXr16KT09XWfOnLF7pGb705/%2B5PV8JSQkKD4%2B3hG/L48fP66pU6eqX79%2BGjhwoLKysnT%2B/Hm7x2o2CpYPmjt3rqKjo3XgwAFt3rxZBw4c0EsvvWT3WEY8//zzWrlype688067RzFu1qxZCg0N1cGDB/X666/rgw8%2B0JNPPmn3WM1y9epV/fSnP9WAAQNUUFCg/Px8VVRUyEnf4rS4uFh5eXl2j2Hc3r175Xa7G34sW7bM7pGMePXVV7Vjxw5t2bJFR44cUefOnfXiiy/aPVaz9e/f3%2Bv5crvdmjNnju6//367R2uWmpoazZgxQ7169dLRo0eVn5%2Bv8%2BfPO%2BI9hILlY9xut06cOKGsrCy1a9dOsbGxSk9Pl8vlsns0I4KDg7Vt2zbHFayqqirFx8dr4cKFatu2rTp27Khx48bpnXfesXu0Zqmurtb8%2BfM1c%2BZMBQUFKTIyUvfdd58%2B%2BOADu0czoq6uTsuXL1d6errdo6CJXnjhBc2fP1933XWXQkJCtHTpUi1dutTusYz7%2B9//rs2bN%2Bvf/u3f7B6lWcrLy1VeXq6HHnpIQUFBioiI0H333afi4mK7R2s2CpaPKSoqUkxMjMLCwhrWevTooVOnTunChQs2TmbG1KlT1a5dO7vHMC40NFTZ2dnq0KFDw9rZs2cVFRVl41TNFxYWpokTJyowMFCS9NFHH%2Bm//uu/fP5v1V947bXXFBwcrLFjx9o9inHr1q3T0KFD1a9fPy1btkwXL160e6Rm%2B%2BSTT3TmzBl99tlnGj16tBITE5WZmemIy01f9uyzz2rChAn63ve%2BZ/cozRIdHa3u3bvL5XLp4sWLqqio0P79%2BzV06FC7R2s2CpaP8Xg8Cg0N9Vr7omxVVlbaMRK%2BAbfbrVdeeUWPPfaY3aMYUVpaqvj4eI0ePVoJCQnKzMy0e6Rm%2B/TTT5WTk6Ply5fbPYpxvXr1UlJSkvbv3y%2BXy6Vjx45pxYoVdo/VbOfOnZN0/fLn5s2blZeXp3PnzjnuDNaZM2e0f/9%2B/eQnP7F7lGbz9/dXTk6O3njjDfXp00dJSUmqqanRwoUL7R6t2ShYPqi%2Bvt7uEdAMf/7znzVt2jQtXLhQSUlJdo9jRExMjNxut/bu3avTp0/7/GULScrOztb48ePVuXNnu0cxzuVyaeLEiQoKCtL3v/99ZWVlKT8/X1evXrV7tGb54r1x%2BvTpio6OVseOHTV37lwdPHhQV65csXk6c1599VWNGDFCt956q92jNNvVq1c1a9YsjRo1Su%2B8847eeusttWvXTllZWXaP1mwULB8TGRkpj8fjtebxeOTn56fIyEibpkJTHTx4UDNmzNDjjz%2BuqVOn2j2OUX5%2BfoqNjdX8%2BfMbblT1VQUFBXrvvfeUkZFh9yiWuO2221RbW6uKigq7R2mWLy7B/%2BNZ/piYGNXX1/t8tn%2B0b98%2BJScn2z2GEQUFBTpz5owWLFigdu3aKTo6WpmZmfrv//7vRn/W%2BRoKlo%2BJj4/X2bNnvf7wcrvd6ty5s9q2bWvjZPg67777rhYtWqRnn31WDz/8sN3jGFFQUKCRI0d6fUyIv//1t5VWrVrZNVaz7dixQxUVFRo2bJgSExM1fvx4SVJiYqJ27dpl83TNc/z4ca1evdprraSkREFBQT5/T2DHjh0VEhLidYN0aWmpWrVq5fPZvlBcXKzS0lINHDjQ7lGMqK2tVV1dndeVGV8/k/oFCpaPiYuLU0JCgtatW6cLFy6opKREmzdvVlpamt2j4f9QU1OjpUuXKisrS4MGDbJ7HGPi4%2BN14cIFrVmzRtXV1Tp//rxycnLUr18/n/7HCosXL9a%2BffuUl5envLw85ebmSpLy8vJ8/sxB%2B/bt5XK5lJubq6tXr%2BrUqVN69tlnlZqaqoCAALvHa5bAwEClpKToueee01//%2BldVVFRow4YNGjt2bMM/xPB1x48fV3h4uEJCQuwexYjevXurTZs2ysnJUXV1tSorK7Vx40b1799f4eHhdo/XLH713NDjc86dO6dly5bpf/7nfxQSEqLJkydrzpw58vPzs3u0ZktISJB0vZBIanhTdLvdts1kwjvvvKN//ud/VlBQUKNte/fuVUxMjA1TmXHy5EmtXLlSf/nLX9SmTRv98Ic/1OLFixUdHW33aMacOXNGw4cP18mTJ%2B0exYg//elPWrdunU6ePKmgoCCNGzdO8%2BfPV3BwsN2jNdvVq1eVnZ2tXbt26dq1axo5cqSWLVvmmDP8mzZt0s6dO5Wfn2/3KMYUFhbqySef1IkTJxQUFKQBAwY44j2EggUAAGAYlwgBAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwLD/B/n%2BDEH1mN%2BYAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common6212033776881856587”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>522</td> <td class=”number”>16.3%</td> <td>

<div class=”bar” style=”width:69%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>381</td> <td class=”number”>11.9%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.25</td> <td class=”number”>356</td> <td class=”number”>11.1%</td> <td>

<div class=”bar” style=”width:47%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>332</td> <td class=”number”>10.3%</td> <td>

<div class=”bar” style=”width:44%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>220</td> <td class=”number”>6.9%</td> <td>

<div class=”bar” style=”width:29%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.3</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:18%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.225</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.5</td> <td class=”number”>109</td> <td class=”number”>3.4%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.15</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.75</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (12)</td> <td class=”number”>759</td> <td class=”number”>23.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme6212033776881856587”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>332</td> <td class=”number”>10.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.225</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:35%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.5</td> <td class=”number”>75</td> <td class=”number”>2.3%</td> <td>

<div class=”bar” style=”width:23%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.7500000000000002</td> <td class=”number”>29</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.91</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:20%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>6.5</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.875</td> <td class=”number”>87</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:89%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>92</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:94%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.000000000000001</td> <td class=”number”>26</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:27%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>82</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:84%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SODA_PRICE10”>SODA_PRICE10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>36</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.3%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>106</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.99173</td>

</tr> <tr>

<th>Minimum</th> <td>0.901</td>

</tr> <tr>

<th>Maximum</th> <td>1.2415</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram4325372065931097188”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives4325372065931097188,#minihistogram4325372065931097188”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives4325372065931097188”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles4325372065931097188”

aria-controls=”quantiles4325372065931097188” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram4325372065931097188” aria-controls=”histogram4325372065931097188”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common4325372065931097188” aria-controls=”common4325372065931097188”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme4325372065931097188” aria-controls=”extreme4325372065931097188”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles4325372065931097188”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0.901</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.93022</td>

</tr> <tr>

<th>Q1</th> <td>0.95023</td>

</tr> <tr>

<th>Median</th> <td>0.97482</td>

</tr> <tr>

<th>Q3</th> <td>1.0385</td>

</tr> <tr>

<th>95-th percentile</th> <td>1.1041</td>

</tr> <tr>

<th>Maximum</th> <td>1.2415</td>

</tr> <tr>

<th>Range</th> <td>0.34048</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.088293</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.058967</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.059459</td>

</tr> <tr>

<th>Kurtosis</th> <td>3.4634</td>

</tr> <tr>

<th>Mean</th> <td>0.99173</td>

</tr> <tr>

<th>MAD</th> <td>0.043872</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.4965</td>

</tr> <tr>

<th>Sum</th> <td>3078.3</td>

</tr> <tr>

<th>Variance</th> <td>0.0034771</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram4325372065931097188”>

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xs2fPat68eU6PagAAAPBUll8ifPHFF/XLX/5SPXr0UHBwsKqqqlRWVqaWLVvqnXfesbocAAAA4ywPWC1bttQHH3ygv/71rzp16pR8fX3VqlUr9ezZU35%2BflaXAwAAYJzlAUuSAgIC1L9/f1dsGgAAoN5ZHrC%2B%2BeYbLV26VP/85z916dKlGvMfffSR1SUBAAAY5ZJ7sAoLC9WzZ081btzY6s0DAADUO8sDVk5Ojj766CNFRERYvWkAAABLWP6YhmbNmnHmCgAAeDXLA9b48eOVmZmp6urqet/WqlWr1LNnTz344INKTU3V6dOnJUkHDx5UUlKSOnfurMGDB2vLli1O37du3ToNGDBAnTt3VkpKinJycuq9VgAA4D0sv0T417/%2BVV988YXef/993X333fL1dc54f/7zn41s57333tOWLVu0bt06RUZGavny5Xrrrbf0/PPPa9KkSZo5c6aGDBmizz//XBMnTlSrVq0UFxenPXv2aMWKFXr99dcVExOjdevWacKECdq1axdn3gAAQJ1YHrCCg4PVq1evet/Om2%2B%2BqRkzZugXv/iFJGnWrFmSpDfeeEPR0dFKSkqSJCUkJCgxMVHr169XXFycbDabhg8fro4dO0qSxo4dq3Xr1mnv3r0aPHhwvdcNAAA8n%2BUBa%2BHChfW%2BjbNnz%2Br06dM6f/68Bg0apHPnzik%2BPl5z585Vbm6uYmNjnZaPjY3Vjh07JEm5ubkaNGiQY87X11ft2rVTdnZ2nQNWYWGhioqKnMb8/RsrMjLyNjv7P35%2Bvk5fAX//hnMsNPTjn/7p//qvDY2n9O%2BSB41%2B/fXX%2BuCDD/S///u/jsD1j3/8Q506dTKy/oKCAknShx9%2BqLVr16q6ulrp6emaNWuWLl26pKioKKflw8LCVFJSIkmy2%2B0KDQ11mg8NDXXM14XNZlNmZqbTWFpamtLT02%2BlnZ8UEnKH8XXCM4WHN3F1CZZr6Mc//dN/Q%2Bbu/VsesA4ePKhx48apVatWOnnypBYuXKhvvvlGo0eP1vLly9WvX7/b3sa1G%2BjHjh3rCFOTJ0/WuHHjlJCQUOfvv1XJyclKTEx0GvP3b6ySkrLbWu/1/Px8FRJyhy5cKFdlZZWx9cJzmTy%2B3F1DP/7pn/7pv%2B79u%2BqHT8sD1rJly/TrX/9azz33nB544AFJ3/9%2BwkWLFmnlypVGAlbz5s0lSSEhIY6xFi1aqLq6WlevXpXdbndavqSkxPFcrvDw8Brzdrtdbdq0qfP2IyMja1wOLCr6ThUV5t8IlZVV9bJeeJ6GeBw09OOf/umf/t23f8svYH711VdKSUmRJPn4%2BDjGBw4cqLy8PCPbuOuuuxQcHKyjR486xvLz89WoUSP17t27xmMXcnJyHDe1d%2BjQQbm5uY65yspKHTlyxDEPAABwI5YHrKZNm9b6OwgLCwsVEBBgZBv%2B/v5KSkrS6tWr9a9//Uvnzp3TypUrNWTIEA0bNkz5%2Bflav369Ll%2B%2BrP3792v//v0aMWKEJCklJUWbNm3SoUOHVF5erlWrVikgIEB9%2BvQxUhsAAPB%2Bll8i7Ny5sxYsWOB4bIIknThxQnPmzFGPHj2MbWf69Om6cuWKnn76aV29elUDBgzQrFmz1KRJE61Zs0bz58/XvHnz1KJFCy1evFj333%2B/JKlXr16aNm2apkyZonPnzikuLk5ZWVkKCgoyVhsAAPBuPtVWPFL9OgUFBXruued0%2BvRpVVZWqnHjxiovL1ebNm20evVq/fznP7eyHMsUFX1ndH3%2B/r4KD2%2BikpIyt74Gfb3Hlv/N1SV4tR1THnJ1CZbxxOPfJPqnf/qve/933tnUgqpqsvwM1l133aVt27Zp//79OnHihIKCgtSqVSs99NBDTvdkAQAAeCqXPAerUaNG6t%2B/vys2DQAAUO8sD1iJiYk/eabqo48%2BsrAaAAAA8ywPWIMGDXIKWJWVlTpx4oSys7P13HPPWV0OAACAcZYHrIyMjFrHd%2B7cqc8%2B%2B8ziagAAAMxzm9%2BU2L9/f33wwQeuLgMAAOC2uU3AOnLkyG3/DkAAAAB3YPklwpEjR9YYKy8vV15enh599FGrywEAADDO8oAVHR1d418RBgYGKikpSU8//bTV5QAAABhnecBatGiR1ZsEAACwlOUBa9OmTXVe9sknn6zHSgAAAOqH5QFr5syZqqqqqnFDu4%2BPj9OYj48PAQsAAHgkywPW66%2B/rjfffFMTJkxQTEyMqqurdezYMb322mt65plnFB8fb3VJAAAARrnkHqysrCxFRUU5xrp27aqWLVtqzJgx2rZtm9UlAQAAGGX5c7BOnjyp0NDQGuMhISHKz8%2B3uhwAAADjLA9YLVq00KJFi1RSUuIYu3DhgpYuXap77rnH6nIAAACMs/wS4Ysvvqjp06fLZrOpSZMm8vX1VWlpqYKCgrRy5UqrywEAADDO8oDVs2dP7du3T/v371dBQYGqq6sVFRWlhx9%2BWE2bNrW6HAAAAOMsD1iSdMcdd6hfv34qKChQy5YtXVECAABAvbH8HqxLly5pxowZ6tSpkx577DFJ39%2BDNXbsWF24cMHqcgAAAIyzPGAtXrxYR48e1ZIlS%2BTr%2B3%2Bbr6ys1JIlS6wuBwAAwDjLA9bOnTv16quvauDAgY5f%2BhwSEqKFCxdq165dVpcDAABgnOUBq6ysTNHR0TXGIyIidPHiRavLAQAAMM7ygHXPPffos88%2BkySn3z344Ycf6uc//7nV5QAAABhn%2Bb8iHDVqlCZPnqynnnpKVVVVWrt2rXJycrRz507NnDnT6nIAAACMszxgJScny9/fX%2B%2B%2B%2B678/Py0evVqtWrVSkuWLNHAgQOtLgcAAMA4ywNWcXGxnnrqKT311FNWbxoAAMASlt%2BD1a9fP6d7rwAAALyN5QErPj5eO3bssHqzAAAAlrH8EuHPfvYz/e53v1NWVpbuueceNWrUyGl%2B6dKlVpcEAABglOUB6/jx4/rFL34hSSopKbF68wAAAPXOsoA1depULVu2TO%2B8845jbOXKlUpLS7OqBAAAAEtYdg/Wnj17aoxlZWVZtXkAAADLWBawavuXg/xrQgAA4I0sC1jXfrHzjcYAAAA8neWPaQAAAPB2BCwAAADDLPtXhFevXtX06dNvOMZzsADv99jyv7m6hJuyY8pDri4BgIexLGB16dJFhYWFNxwDAADwdJYFrOuffwUAAODNuAcLAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADGsQAWvBggWKiYlx/PngwYNKSkpS586dNXjwYG3ZssVp%2BXXr1mnAgAHq3LmzUlJSlJOTY3XJAADAg3l9wDp69Kg2b97s%2BHNhYaEmTZqkkSNH6uDBg5o5c6Zmz56t7OxsSdKePXu0YsUK/eEPf9CBAwfUt29fTZgwQRcvXnRVCwAAwMN4dcCqqqrSnDlzlJqa6hjbunWroqOjlZSUpMDAQCUkJCgxMVHr16%2BXJNlsNg0fPlwdO3ZUUFCQxo4dK0nau3evK1oAAAAeyLIHjbrCn//8ZwUGBmrIkCFavny5JCk3N1exsbFOy8XGxmrHjh2O%2BUGDBjnmfH191a5dO2VnZ2vw4MF12m5hYaGKioqcxvz9GysyMvJ22nHi5%2Bfr9BXw9%2BdYqC/u9to29Pc//dP/9V/dldcGrG%2B//VYrVqyo8QR5u92uqKgop7GwsDCVlJQ45kNDQ53mQ0NDHfN1YbPZlJmZ6TSWlpam9PT0m2mhThJ/v9/4OuGZwsObuLoEr%2BWur21IyB2uLsGl6J/%2B3ZnXBqyFCxdq%2BPDhat26tU6fPn1T31tdXX1b205OTlZiYqLTmL9/Y5WUlN3Weq/n5%2Bfr9gcXrGXy%2BIIzd3ttr73/L1woV2VllavLsRz90//N9O%2BqH5C8MmAdPHhQ//jHP7Rt27Yac%2BHh4bLb7U5jJSUlioiI%2BNF5u92uNm3a1Hn7kZGRNS4HFhV9p4qKhvdGgHU4vuqPu762lZVVblubFeif/t25f/e%2BgHmLtmzZonPnzqlv376Kj4/X8OHDJUnx8fFq27Ztjccu5OTkqGPHjpKkDh06KDc31zFXWVmpI0eOOOYBAABuxCsD1m9%2B8xvt3LlTmzdv1ubNm5WVlSVJ2rx5s4YMGaL8/HytX79ely9f1v79%2B7V//36NGDFCkpSSkqJNmzbp0KFDKi8v16pVqxQQEKA%2Bffq4sCMAAOBJvPISYWhoqNON6hUVFZKku%2B66S5K0Zs0azZ8/X/PmzVOLFi20ePFi3X///ZKkXr16adq0aZoyZYrOnTunuLg4ZWVlKSgoyPpGAACAR/LKgPVDd999t44dO%2Bb4c7du3ZwePvpDo0aN0qhRo6woDQAAeCGvvEQIAADgSgQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADDM39UFAIC7e2z531xdwk3ZMeUhV5cANHicwQIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGeW3Ays/PV1pamuLj45WQkKDf/OY3unDhgiTp6NGjeuaZZ9SlSxc9%2BuijevPNN52%2Bd/v27RoyZIg6deqk4cOH65NPPnFFCwAAwEN5bcCaMGGCQkJCtGfPHr3//vv65z//qd///ve6dOmSxo8fr3/7t3/Txx9/rGXLlmnNmjXatWuXpO/D14wZM5SRkaFPP/1UqampeuGFF1RQUODijgAAgKfwyoB14cIFdejQQdOnT1eTJk101113adiwYfr73/%2Buffv26erVq5o4caIaN26s9u3b6%2Bmnn5bNZpMkrV%2B/Xr1791bv3r0VGBiooUOHqm3bttqyZYuLuwIAAJ7CKwNWSEiIFi5cqObNmzvGzpw5o8jISOXm5iomJkZ%2Bfn6OudjYWOXk5EiScnNzFRsb67S%2B2NhYZWdnW1M8AADweA3iV%2BVkZ2fr3Xff1apVq7Rjxw6FhIQ4zYeFhclut6uqqkp2u12hoaFO86GhoTp%2B/Hidt1dYWKiioiKnMX//xoqMjLz1Jn7Az88rszFug78/xwS%2B5%2B3HwrXPv4b6OUj/ntG/1weszz//XBMnTtT06dOVkJCgHTt21Lqcj4%2BP4/%2Brq6tva5s2m02ZmZlOY2lpaUpPT7%2Bt9QI/JTy8iatLgJtoKMdCSMgdri7Bpejfvfv36oC1Z88e/frXv9bs2bP15JNPSpIiIiJ08uRJp%2BXsdrvCwsLk6%2Bur8PBw2e32GvMRERF13m5ycrISExOdxvz9G6ukpOzWGqmFn5%2Bv2x9csJbJ4wuezduPhWuffxculKuyssrV5ViO/m%2Buf1f9wOG1AeuLL77QjBkz9Morr6hnz56O8Q4dOuhPf/qTKioq5O//ffvZ2dnq2LGjY/7a/VjXZGdna/DgwXXedmRkZI3LgUVF36miouG9EWAdji9c01COhcrKqgbTa23o3737d%2B8LmLeooqJCs2bNUkZGhlO4kqTevXsrODhYq1atUnl5uQ4fPqwNGzYoJSVFkjRixAgdOHBA%2B/bt0%2BXLl7VhwwadPHlSQ4cOdUUrAADAA3nlGaxDhw4pLy9P8%2BfP1/z5853mPvzwQ61evVpz5sxRVlaWmjdvrqlTp6pPnz6SpLZt22rJkiVauHCh8vPz1bp1a61Zs0Z33nmnCzoBAACeyCsDVteuXXXs2LGfXOZPf/rTj849%2BuijevTRR02XBQAAGgivvEQIAADgSgQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwzN/VBQAw47Hlf3N1CQCA/48zWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjF/2DABexpN%2B8feOKQ%2B5ugSgXnAGCwAAwDDOYAEA4KU4m%2Bk6nMECAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAeiugYAAALvUlEQVQAwwhYAAAAhhGwAAAADCNgAQAAGEbAqkV%2Bfr6ef/55xcfHq2/fvlq8eLGqqqpcXRYAAPAQPMm9FpMnT1b79u21e/dunTt3TuPHj1fz5s317//%2B764uDQC8iic9aVzyvqeNo/5wBusHsrOz9eWXXyojI0NNmzZVdHS0UlNTZbPZXF0aAADwEJzB%2BoHc3Fy1aNFCoaGhjrH27dvrxIkTKi0tVXBw8A3XUVhYqKKiIqcxf//GioyMNFannx/ZGACs5u/v%2Bs/ea5//3vb3QF1fW0/pn4D1A3a7XSEhIU5j18JWSUlJnQKWzWZTZmam09gLL7ygyZMnG6uzsLBQb7/9urb/KtlocPMUhYWFstlsSk6mf/qn/4aG/r///K9L/3//3UCLqrLOzfTvSu4d/1ykurr6tr4/OTlZ77//vtN/ycnJhqr7XlFRkTIzM2ucKWso6J/%2B6Z/%2B6Z/%2B3RlnsH4gIiJCdrvdacxut8vHx0cRERF1WkdkZKRbp2oAAFC/OIP1Ax06dNCZM2dUXFzsGMvOzlbr1q3VpEkTF1YGAAA8BQHrB2JjYxUXF6elS5eqtLRUeXl5Wrt2rVJSUlxdGgAA8BB%2Bc%2BfOnevqItzNww8/rG3btunll1/WBx98oKSkJI0ZM0Y%2BPj6uLs1JkyZN1L179wZ7Zo3%2B6Z/%2B6Z/%2B6d9d%2BVTf7h3dAAAAcMIlQgAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFhuJD8/X88//7zi4%2BPVt29fLV68WFVVVTWWu3r1ql555RX169dPDz74oEaPHq1vvvnGMW%2B32zVlyhQlJCSoZ8%2Bemjlzpi5dumRlK7fEVP%2BJiYnq0KGD4uLiHP9NmDDBylZuyccff6yEhARNnTr1J5erqqrSsmXL1K9fP3Xr1k1jxozxiv1vqn9v3/%2BS9O2332rMmDGKiYnR5cuXnebq%2Bj5yR6Zeg5iYmBrHwMsvv1xfZRtzM%2B%2BBzMxMJSYmqlOnTkpOTtbf//53x/zly5f1n//5n%2BrVq5fi4%2BOVnp6ukpKS%2Bi7/tpnq/9lnn1X79u2d9v/QoUPru/waCFhuZPLkyYqKitLu3bu1du1a7d69W2%2B//XaN5bKysrRp0yatXLlSn376qbp06aJJkyY5PkRnz56t8vJybdu2TRs3blReXp6WLFlidTs3zVT/kvTGG28oOzvb8d/q1autbOWmvfbaa5o/f77uvffeGy773nvvaevWrcrKytLevXsVHR2ttLQ0XfutV564/032L3n3/j927JiSkpIUFhZW63xd30fuxuRrIEkffvih0zEwe/Zsk%2BUadzP9v/XWW9q4caPWrFmjzz77TD179lRaWppKS0slScuWLVNubq5sNpt27typ6upq/fa3v63vFm6Lyf4l6eWXX3ba/1u2bKnP8mtFwHIT2dnZ%2BvLLL5WRkaGmTZsqOjpaqampstlsNZbds2ePnn76ad1///0KCgrS5MmTVVxcrMOHD%2Bvbb7/V7t27NXXqVEVERCgqKkqTJk3Sxo0bdfXqVRd0Vjem%2BvdUgYGB2rBhQ50%2BXGw2m1JTU3XfffcpODhYU6dOVV5enkfvf1P9e6qb6b%2B4uFh//OMfNWLEiBpzN/M%2BcjemXgNPdTP9%2B/r66j/%2B4z/Upk0bBQQE6Je//KXsdru%2B%2BuorVVRUaMOGDZo0aZJ%2B9rOfKSwsTFOmTNG%2Bfft09uxZCzq5Nab6dycELDeRm5urFi1aKDQ01DHWvn17nThxwimVX%2BPj4%2BP4f19fXwUHB%2Bvo0aM6evSo/Pz8FBMT47Seixcv6uuvv67fJm6Dqf6vWbdunfr3769OnTopPT1d586dq98GbtPo0aPVtGnTGy536dIlHT9%2BXLGxsY6x4OBg3XvvvcrOzvbY/W%2Bq/2u8df9LUo8ePdS5c%2Bda5272feROTL0G1yxdulR9%2BvRR165dNXv2bJWVlZkos97cTP%2Bpqal67LHHHH8uKCiQJEVGRurUqVP67rvv1L59e8f8fffdp6CgIOXm5pot2iBT/V%2Bzfft2DRo0SJ06dVJqaqpOnTpltuA6IGC5CbvdrpCQEKexax%2BSP7x23rdvX9lsNh07dkxXrlzRe%2B%2B9p4KCAp0/f152u13BwcFOAeTH1uNOTPUvSe3atdMDDzygzZs3a/v27bLb7frVr35lTSP17Pz586qurnb6C1T6/rUqKSnx2P1fVzfqX/Lu/X8jN/M%2B8mYPPvigEhIStGvXLtlsNh06dEjz5s1zdVn14sqVK5o5c6aGDh2qu%2B%2B%2BW3a7XZJqHAchISFeeQz8sH/p%2B0DZpk0b/dd//Zc%2B%2BugjRUREaOzYsbpy5YqltflbujX8pOvvIfkp48aNk91u15gxY1RVVaWkpCR169ZNfn5%2BN7Ued2Oq/5UrVzqWbdKkiebMmaNBgwbp1KlTuueee%2Bqldqv91Gvlqfv/ZvxUjw1h//%2BUhrD/b%2BT6S6L33XefMjIyNHHiRM2fP18BAQEurMys0tJSpaWlyc/Pr0aAbAjHwY/1P3fuXKflXnrpJcXHx%2Bvzzz9Xjx49LKuPM1huIiIiwvGTxzV2u10%2BPj6KiIhwGg8MDNSsWbP0ySef6MCBA5o2bZrOnj2rqKgoRUREqLS0VJWVlU7rkaRmzZrVfyO3yFT/tWnRooUkqbCwsH6Kt1BYWJh8fX1rfa2aNWvmsfu/rm7Uf228af/fyM28jxqSu%2B%2B%2BW5WVlW5/qfhmFBcX65lnnlHTpk31xhtvqHHjxpLk2M8/PA7Onz/vFZ8B1/xY/7UJDg5WaGio5fegEbDcRIcOHXTmzBkVFxc7xrKzs9W6dWs1adLEadnc3FwdPHjQ8eezZ8/q%2BPHj6ty5s9q1a6fq6mp9%2BeWXTusJCQlRq1at6r%2BRW2Sq//z8fM2ZM8fpVHBeXp4kqWXLlvXcRf0LDAxUmzZtnO6luHDhgk6dOqUHHnjAY/d/Xd2of2/f/zdyM%2B8jb3XkyBEtWrTIaSwvL08BAQFO9%2Bh4ssuXL2v8%2BPFq3769Xn31VQUFBTnmWrZsqdDQUKf3yFdffaUrV66oQ4cOrijXuJ/qv7S0VHPnznUKU8XFxSouLrb8M4CA5SZiY2MVFxenpUuXqrS0VHl5eVq7dq1SUlIkSQMHDnQ85%2BPYsWPKyMjQv/71L8fB1K9fP7Vs2VIREREaMGCAli9fruLiYhUUFGjlypVKSkqSv7/7XhE21X%2BzZs20Z88eLVq0SBcvXtTZs2e1cOFC9e3b90fPcLm7s2fPauDAgY5nPaWkpGjdunXKy8tTaWmplixZonbt2ikuLs5j9/9PuZn%2BG8L%2B/yk3eh95qpt5DZo1ayabzaasrCxduXJFJ06c0CuvvKLk5GTHbQSe5of9v/nmm2rUqJFefvll%2Bfo6/zXu5%2BenESNGaPXq1Tpz5oxKSkr0xz/%2BUY888oiaN2/uivJv2830HxwcrMOHD2v%2B/Pmy2%2B06f/685s2bp5iYGHXq1MnSuj3zE9dLvfrqq5o9e7YeeughBQcHa%2BTIkRo1apQk6cSJE7p48aIkadiwYfrqq680YsQIVVRUqE%2BfPk7XnF966SXNmTNH/fr1U6NGjfT444/X6cF9rmai/6CgIL3%2B%2ButatGiRevXqJUl65JFH3P4ZMHFxcZKkiooKSdLu3bslfX/24erVqzpx4oTjrMzIkSNVVFSkZ599VmVlZYqPj1dmZqZjXZ64/0313xD2/6xZs7R582bHPTZdu3aV9P1zf5588smffB%2B5M5OvQVZWlpYuXapVq1YpICBAw4YN86r3wMaNG3XmzBl17NjRaR0TJ07UpEmTlJ6errKyMj3xxBOqqKhQ3759a9yX5G5M9r9y5UotWLBAAwYM0JUrV9SjRw9lZWXVCGP1zae6IdwJBwAAYCEuEQIAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYf8P0iaGgjHbTIMAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common4325372065931097188”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.038527</td> <td class=”number”>438</td> <td class=”number”>13.6%</td> <td>

<div class=”bar” style=”width:45%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9486629</td> <td class=”number”>275</td> <td class=”number”>8.6%</td> <td>

<div class=”bar” style=”width:28%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9343761</td> <td class=”number”>231</td> <td class=”number”>7.2%</td> <td>

<div class=”bar” style=”width:24%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9748207</td> <td class=”number”>225</td> <td class=”number”>7.0%</td> <td>

<div class=”bar” style=”width:23%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9722159</td> <td class=”number”>209</td> <td class=”number”>6.5%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9790413</td> <td class=”number”>200</td> <td class=”number”>6.2%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.079599</td> <td class=”number”>169</td> <td class=”number”>5.3%</td> <td>

<div class=”bar” style=”width:18%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9925836</td> <td class=”number”>143</td> <td class=”number”>4.5%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9009972</td> <td class=”number”>131</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9502338</td> <td class=”number”>106</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (25)</td> <td class=”number”>977</td> <td class=”number”>30.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>106</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme4325372065931097188”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.9009972</td> <td class=”number”>131</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:57%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9060665</td> <td class=”number”>19</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9302243</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9343761</td> <td class=”number”>231</td> <td class=”number”>7.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9392297</td> <td class=”number”>12</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.102873</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.104313</td> <td class=”number”>45</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:82%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.130546</td> <td class=”number”>55</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.164422</td> <td class=”number”>11</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:20%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.241477</td> <td class=”number”>45</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:82%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SPECS09”><s>SPECS09</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_GROC14”><code>GROC14</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.9563</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SPECS14”><s>SPECS14</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_SPECS09”><code>SPECS09</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99301</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SPECSPTH09”>SPECSPTH09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>1990</td>

</tr> <tr>

<th>Unique (%)</th> <td>62.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.056908</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1.3661</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>35.3%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram2214519207119624705”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives2214519207119624705,#minihistogram2214519207119624705”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives2214519207119624705”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles2214519207119624705”

aria-controls=”quantiles2214519207119624705” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram2214519207119624705” aria-controls=”histogram2214519207119624705”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common2214519207119624705” aria-controls=”common2214519207119624705”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme2214519207119624705” aria-controls=”extreme2214519207119624705”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles2214519207119624705”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0.043641</td>

</tr> <tr>

<th>Q3</th> <td>0.084023</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.17719</td>

</tr> <tr>

<th>Maximum</th> <td>1.3661</td>

</tr> <tr>

<th>Range</th> <td>1.3661</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.084023</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.075819</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.3323</td>

</tr> <tr>

<th>Kurtosis</th> <td>43.104</td>

</tr> <tr>

<th>Mean</th> <td>0.056908</td>

</tr> <tr>

<th>MAD</th> <td>0.04979</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>4.3216</td>

</tr> <tr>

<th>Sum</th> <td>178.24</td>

</tr> <tr>

<th>Variance</th> <td>0.0057485</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram2214519207119624705”>

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HVq5cqTVr1qiurk6jRo3Sn/70Jw0ZMsSzz9ChQ/XAAw9o3LhxVo8HAABwxiwvWGPHjlWvXr00depUXXPNNYqNjW22T2Zmpg4ePGj1aAAAAEZYXrCWLVumYcOGnXa/HTt2WDANAACAeZbfg9W/f39NmzZNGzdu9Ky99NJL%2BsMf/iCXy2X1OAAAAMZZXrDmz5%2BvQ4cOqU%2BfPp61ESNGqKmpSQsWLLB6HAAAAOMsv0T47rvvqqysTHFxcZ61pKQkFRUV6aqrrrJ6HAAAAOMsP4NVX1%2Bv8PDw5oOEhurw4cNWjwMAAGCc5QVr6NChWrBggb777jvP2tdff60HH3zQ66UaAAAAApXllwjvvfde/f73v9fFF1%2BsyMhINTU1qa6uTueee67%2B/Oc/Wz0OAACAcZYXrHPPPVdvvPGG3nnnHe3du1ehoaHq1auXhg8frrCwMKvHAQAAMM7ygiVJHTt21OWXX%2B6PpwYAAPA5ywvWvn37tHDhQn3xxReqr69vtv3tt9%2B2eiQAAACj/HIPVlVVlYYPH64uXbpY/fQAAAA%2BZ3nB2rlzp95%2B%2B23Fx8db/dQAAACWsPxlGrp27cqZKwAA0K5ZXrCmTp2q4uJiud1uq58aAADAEpZfInznnXf0ySefaPXq1TrnnHMUGurd8V599VWrRwIAADDK8oIVGRmpSy%2B91OqnBQAAsIzlBWv%2B/PlWPyUAAIClLL8HS5K%2B/PJLPf3007rnnns8a59%2B%2Bqk/RgEAADDO8oL1/vvva/z48Xrrrbe0du1aST%2B8%2BOgtt9zCi4wCAIB2wfKCtWjRIt15550qKytTSEiIpB9%2BP%2BGCBQu0ePFiq8cBAAAwzvKC9fnnnysnJ0eSPAVLkkaPHi2Hw2H1OAAAAMZZXrCioqJO%2BTsIq6qq1LFjx1Yda9u2bcrIyFB%2Bfr7X%2BurVq/XLX/5SaWlpXm//%2BMc/JElNTU1atGiRRo4cqaFDh2ry5Mnat2%2Bf5/Eul0uzZs1SRkaGhg8frrlz555yZgAAgFOxvGBdeOGFevTRR1VbW%2BtZ27NnjwoKCnTxxRe3%2BDjPPvusCgsL1bNnz1NuHzp0qOx2u9fbwIEDJUnLly9XWVmZSkpKtHnzZiUlJSkvL8/z4qfz5s3T4cOHtXbtWq1atUoOh0NFRUVnkBoAAAQTywvWPffco08//VTp6ek6cuSILrzwQo0ZM0Yul0t33313i48THh6ulStX/mjB%2BimlpaXKzc1V7969FRkZqfz8fDkcDu3YsUPffPONNm7cqPz8fMXHx6t79%2B669dZbtWrVKh07dqzVzwUAAIKP5a%2BD1aNHD61du1Zbt27Vnj171KlTJ/Xq1UuXXHKJ1z1Zp3PLLbf85Pb9%2B/frd7/7nXbu3Kno6GjNnDlTV199terr61VRUaHk5GTPvpGRkerZs6fsdrsOHTqksLAw9e/f37M9JSVF33//vb788kuv9R9TVVUlp9PptWazdVFCQkKL87VEWJhfXmXjZ7PZzM57PH%2BgfRxMCeb8wZxdIj/5yX/i%2B7bK8oIlSR06dNDll1/us%2BPHx8crKSlJd9xxh/r06aO//vWvuuuuu5SQkKDzzz9fbrdbMTExXo%2BJiYlRdXW1YmNjFRkZ6VX2ju9bXV3doucvLS1VcXGx11peXp5mzpx5hskCW1xchE%2BOGx3d2SfHDRTBnD%2BYs0vkJz/52zLLC1ZWVtZPnqky8VpYI0aM0IgRIzx/Hjt2rP76179q9erVmjNnjiT95C%2BbPtNfRJ2dna2srCyvNZuti6qr687ouCdr6%2B39ZL7IHx3dWTU1h9XY2GT02IEgmPMHc3aJ/OQnf2vy%2B%2Bqb%2B9OxvGCNGTPGq2A1NjZqz549stvt%2Bu1vf%2Buz501MTNTOnTsVGxur0NBQuVwur%2B0ul0tdu3ZVfHy8amtr1djYqLCwMM82SeratWuLnishIaHZ5UCn85AaGoLvE%2BFEvsrf2NgU1B/bYM4fzNkl8pOf/G05v%2BUF6/gZpJO9%2Beab%2BvDDD408xyuvvKKYmBiNGTPGs%2BZwOHTuuecqPDxcffv2VXl5uYYNGyZJqqmp0d69ezVw4EAlJibK7XZr9%2B7dSklJkSTZ7XZFR0erV69eRuYDAADtW5u5xnT55ZfrjTfeMHKso0eP6uGHH5bdbtexY8e0du1avfPOO5o4caIkKScnR8uWLZPD4VBtba2Kioo0YMAApaWlKT4%2BXqNGjdITTzyhgwcP6sCBA1q8eLEmTJggm80vt6wBAIAA02Yaw2effdaqe5/S0tIkSQ0NDZKkjRs3SvrhbNMtt9yiuro63X777XI6nTrnnHO0ePFipaamSpImTpwop9OpSZMmqa6uTunp6V43pT/00EO6//77NXLkSHXo0EFXXXVVsxczBQAA%2BDEh7jO9o7uVjp9FOtHhw4flcDh0xRVX6D/%2B4z%2BsHMcyTuch48e02UL1m6Jtxo/rK%2BtnXWL0eDZbqOLiIlRdXdemr8P7SjDnD%2BbsEvnJT/7W5O/WLcqCqZqz/AxWUlJSs58iDA8P14QJE3TDDTdYPQ4AAIBxlhesBQsWWP2UAAAAlrK8YK1Zs6bF%2B15zzTU%2BnAQAAMA3LC9Yc%2BfOVVNTU7Mb2kNCQrzWQkJCKFgAACAgWV6wnnvuOb3wwguaNm2a%2BvfvL7fbrX/%2B85969tlndfPNNys9Pd3qkQAAAIzyyz1YJSUl6t69u2ftV7/6lc4991xNnjxZa9eutXokAAAAoyx/odGvvvqq2S9alqTo6GhVVlZaPQ4AAIBxlhesxMRELViwQNXV1Z61mpoaLVy4UOedd57V4wAAABhn%2BSXCe%2B%2B9V7Nnz1ZpaakiIiIUGhqq2tpaderUSYsXL7Z6HAAAAOMsL1jDhw/Xli1btHXrVh04cEBut1vdu3fXr3/9a0VF%2BefVVgEAAEzyy%2B8i7Ny5s0aOHKkDBw7o3HPP9ccIAAAAPmP5PVj19fUqKCjQ4MGDdeWVV0r64R6sKVOmqKamxupxAAAAjLO8YD3%2B%2BOPatWuXioqKFBr6/0/f2NiooqIiq8cBAAAwzvKC9eabb%2Bqpp57S6NGjPb/0OTo6WvPnz9dbb71l9TgAAADGWV6w6urqlJSU1Gw9Pj5e33//vdXjAAAAGGd5wTrvvPP04YcfSpLX7x7csGGDfvGLX1g9DgAAgHGW/xThTTfdpBkzZuj6669XU1OTXnzxRe3cuVNvvvmm5s6da/U4AAAAxllesLKzs2Wz2fTyyy8rLCxMzzzzjHr16qWioiKNHj3a6nEAAACMs7xgHTx4UNdff72uv/56q58aAADAEpbfgzVy5Eive68AAADaG8sLVnp6utavX2/10wIAAFjG8kuEZ599th555BGVlJTovPPOU4cOHby2L1y40OqRAAAAjLK8YFVUVOj888%2BXJFVXV1v99AAAAD5nWcHKz8/XokWL9Oc//9mztnjxYuXl5Vk1AgAAgCUsuwdr06ZNzdZKSkqsenoAAADLWFawTvWTg/w0IQAAaI8sK1jHf7Hz6dYAAAACneUv0wAAANDeUbAAAAAMs%2BynCI8dO6bZs2efdo3XwQIAAIHOsoI1ZMgQVVVVnXYNAAAg0FlWsE58/SsAAID2jHuwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABgW0AVr27ZtysjIUH5%2BfrNt69at07hx4zR48GBdd911evfddz3bmpqatGjRIo0cOVJDhw7V5MmTtW/fPs92l8ulWbNmKSMjQ8OHD9fcuXNVX19vSSYAABD4ArZgPfvssyosLFTPnj2bbdu1a5cKCgo0Z84cffDBB8rNzdVtt92mAwcOSJKWL1%2BusrIylZSUaPPmzUpKSlJeXp7cbrckad68eTp8%2BLDWrl2rVatWyeFwqKioyNJ8AAAgcAVswQoPD9fKlStPWbBWrFihzMxMZWZmKjw8XOPHj1e/fv30%2BuuvS5JKS0uVm5ur3r17KzIyUvn5%2BXI4HNqxY4e%2B%2BeYbbdy4Ufn5%2BYqPj1f37t116623atWqVTp27JjVMQEAQAAK2IJ1yy23KCoq6pTbysvLlZyc7LWWnJwsu92u%2Bvp6VVRUeG2PjIxUz549ZbfbtWvXLoWFhal///6e7SkpKfr%2B%2B%2B/15Zdf%2BiYMAABoV2z%2BHsAXXC6XYmJivNZiYmJUUVGh7777Tm63%2B5Tbq6urFRsbq8jISIWEhHhtk6Tq6uoWPX9VVZWcTqfXms3WRQkJCT8nzo8KCwusfmyzmZ33eP5A%2BziYEsz5gzm7RH7yk//E921VuyxYkjz3U/2c7ad77OmUlpaquLjYay0vL08zZ848o%2BMGuri4CJ8cNzq6s0%2BOGyiCOX8wZ5fIT37yt2XtsmDFxcXJ5XJ5rblcLsXHxys2NlahoaGn3N61a1fFx8ertrZWjY2NCgsL82yTpK5du7bo%2BbOzs5WVleW1ZrN1UXV13c%2BNdEptvb2fzBf5o6M7q6bmsBobm4weOxAEc/5gzi6Rn/zkb01%2BX31zfzrtsmClpqZq586dXmt2u11jx45VeHi4%2Bvbtq/Lycg0bNkySVFNTo71792rgwIFKTEyU2%2B3W7t27lZKS4nlsdHS0evXq1aLnT0hIaHY50Ok8pIaG4PtEOJGv8jc2NgX1xzaY8wdzdon85Cd/W84fWKdAWujGG2/Ue%2B%2B9py1btujIkSNauXKlvvrqK40fP16SlJOTo2XLlsnhcKi2tlZFRUUaMGCA0tLSFB8fr1GjRumJJ57QwYMHdeDAAS1evFgTJkyQzdYu%2BygAADAsYBtDWlqaJKmhoUGStHHjRkk/nG3q16%2BfioqKNH/%2BfFVWVqpPnz5aunSpunXrJkmaOHGinE6nJk2apLq6OqWnp3vdM/XQQw/p/vvv18iRI9WhQwddddVVp3wxUwAAgFMJcZ/pHd1oEafzkPFj2myh%2Bk3RNuPH9ZX1sy4xejybLVRxcRGqrq5r06eJfSWY8wdzdon85Cd/a/J363bql3TytXZ5iRAAAMCfKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAxrtwWrf//%2BSk1NVVpamuft4YcfliS9//77mjBhgi688EKNHTtWr7/%2Butdjly1bplGjRunCCy9UTk6Odu7c6Y8IAAAgQNn8PYAvbdiwQeecc47XWlVVlW699VbNnTtX48aN08cff6zp06erV69eSktL06ZNm/T000/rueeeU//%2B/bVs2TJNmzZNb731lrp06eKnJAAAIJC02zNYP6asrExJSUmaMGGCwsPDlZGRoaysLK1YsUKSVFpaquuuu06DBg1Sp06dNGXKFEnS5s2b/Tk2AAAIIO36DNbChQv16aefqra2VldeeaXuvvtulZeXKzk52Wu/5ORkrV%2B/XpJUXl6uMWPGeLaFhoZqwIABstvtGjt2bIuet6qqSk6n02vNZuuihISEM0zkLSwssPqxzWZ23uP5A%2B3jYEow5w/m7BL5yU/%2BE9%2B3Ve22YF1wwQXKyMjQY489pn379mnWrFl68MEH5XK51L17d699Y2NjVV1dLUlyuVyKiYnx2h4TE%2BPZ3hKlpaUqLi72WsvLy9PMmTN/Zpr2IS4uwifHjY7u7JPjBopgzh/M2SXyk5/8bVm7LVilpaWe/%2B7du7fmzJmj6dOna8iQIad9rNvtPqPnzs7OVlZWlteazdZF1dV1Z3Tck7X19n4yX%2BSPju6smprDamxsMnrsQBDM%2BYM5u0R%2B8pO/Nfl99c396bTbgnWyc845R42NjQoNDZXL5fLaVl1drfj4eElSXFxcs%2B0ul0t9%2B/Zt8XMlJCQ0uxzodB5SQ0PwfSKcyFf5GxubgvpjG8z5gzm7RH7yk78t5w%2BsUyAt9Nlnn2nBggVeaw6HQx07dlRmZmazl13YuXOnBg0aJElKTU1VeXm5Z1tjY6M%2B%2B%2Bwzz3YAAIDTaZcFq2vXriotLVVJSYmOHj2qPXv26Mknn1R2drauvvpqVVZWasWKFTpy5IjrhWXKAAANIklEQVS2bt2qrVu36sYbb5Qk5eTkaM2aNdq%2BfbsOHz6sJUuWqGPHjhoxYoR/QwEAgIDRLi8Rdu/eXSUlJVq4cKGnIF177bXKz89XeHi4li5dqsLCQj344INKTEzU448/rl/%2B8peSpEsvvVR33HGHZs2apW%2B//VZpaWkqKSlRp06d/JwKAAAEinZZsCRp6NChevXVV39022uvvfajj73pppt00003%2BWo0AADQzrXLS4QAAAD%2BRMECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDbP4eAMHjyif%2B5u8RWmX9rEv8PQIAIEBxBgsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIbZ/D1AW1RZWakHH3xQO3bsUJcuXTRmzBjNnj1boaH00WBy5RN/8/cIrbJ%2B1iX%2BHgEA8H8oWKcwY8YMpaSkaOPGjfr22281depUnXXWWfrd737n79EAAEAA4JTMSex2u3bv3q05c%2BYoKipKSUlJys3NVWlpqb9HAwAAAYIzWCcpLy9XYmKiYmJiPGspKSnas2ePamtrFRkZedpjVFVVyel0eq3ZbF2UkJBgdNawMPox/l8gXdL865xfn9Hjj/%2B/H6yfA%2BQn/4nvg02g5KdgncTlcik6Otpr7XjZqq6ublHBKi0tVXFxsdfabbfdphkzZpgbVD8Uud/2%2BELZ2dnGy1sgqKqqUmlpKfmDMH9VVZX%2B9KfngjK7RH7ykz8Q8rft%2Bucnbrf7jB6fnZ2t1atXe71lZ2cbmu7/OZ1OFRcXNztbFizIH7z5gzm7RH7ykz8Q8nMG6yTx8fFyuVxeay6XSyEhIYqPj2/RMRISEtp0qwYAAL7FGayTpKamav/%2B/Tp48KBnzW63q0%2BfPoqIiPDjZAAAIFBQsE6SnJystLQ0LVy4ULW1tXI4HHrxxReVk5Pj79EAAECACHvggQce8PcQbc2vf/1rrV27Vg8//LDeeOMNTZgwQZMnT1ZISIi/R2smIiJCw4YNC9qza%2BQP3vzBnF0iP/nJ39bzh7jP9I5uAAAAeOESIQAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFKw2rrKyUv/2b/%2Bm9PR0XXbZZXr88cfV1NR0yn2XLVumUaNG6cILL1ROTo527txp8bTmtSb/K6%2B8olGjRmnw4MG6%2BuqrtXHjRounNas12Y/7%2BuuvNXjwYD399NMWTek7rcnvcDg0adIkDRo0SJmZmXrppZesHdYHWpq/qalJTz31lLKysjR48GCNGzdO69at88PE5m3btk0ZGRnKz8//yf2ampq0aNEijRw5UkOHDtXkyZO1b98%2Bi6b0jdZkLy4u9vz9Z2dn66OPPrJoSt9paf4TlZeXKzk5WatXr/bhZC1HwWrjZsyYoe7du2vjxo168cUXtXHjRv3pT39qtt%2BmTZv09NNP69///d/13nvv6bLLLtO0adP0/fff%2B2Fqc1qa/80339TChQv16KOP6u9//7tuvvlmzZo1K6C/yLY0%2B4kKCwsVFhZm0YS%2B1dL89fX1mjJlijIzM/XBBx/o6aef1sqVK%2BVwOPwwtTktzf/KK69oxYoVeu655/TRRx/pjjvu0J133qndu3f7YWpznn32WRUWFqpnz56n3Xf58uUqKytTSUmJNm/erKSkJOXl5SlQfxNca7K/9NJLWrVqlZYuXaoPP/xQw4cPV15enmpray2Y1Ddak/%2B4pqYm3X///erSpYsPJ2sdClYbZrfbtXv3bs2ZM0dRUVFKSkpSbm6uSktLm%2B1bWlqq6667ToMGDVKnTp00ZcoUSdLmzZutHtuY1uSvr6/XHXfcoSFDhqhDhw664YYbFBERoe3bt/th8jPXmuzHbd26VRUVFRoxYoR1g/pIa/KvX79ekZGRmjJlijp37qyBAwdq7dq16t27tx8mN6M1%2BcvLyzVkyBCdf/75CgsL02WXXabY2Fj985//9MPk5oSHh2vlypUt%2Bke2tLRUubm56t27tyIjI5Wfny%2BHw6EdO3ZYMKl5rckeGhqqu%2B66S3379lXHjh31%2B9//Xi6XS59//rkFk/pGa/If98orrygqKkoDBgzw4WStQ8Fqw8rLy5WYmKiYmBjPWkpKivbs2dPsu5Pjp0aPCw0N1YABA2S32y2b17TW5L/66qt10003ef5cU1Ojuro6de/e3bJ5TWpNdumHgvnQQw/p/vvvl81ms3JUn2hN/o8//lj9%2BvXTPffco1/96lcaPXq0Xn/9datHNqo1%2BUeMGKG///3v2rVrl44ePaq3335bhw8f1rBhw6we26hbbrlFUVFRp92vvr5eFRUVXl//IiMj1bNnz4D9%2BtfS7JKUm5urK6%2B80vPnAwcOSJISEhJ8MpsVWpNfkpxOpxYvXqx58%2Bb5cKrWo2C1YS6XS9HR0V5rx7/gVldXN9v3xC/Gx/c9eb9A0pr8J3K73brvvvs0aNCggP1HprXZFy9erAsuuEAXXXSRJfP5WmvyHzhwQG%2B//bYyMjK0bds2TZ06VQUFBfrss88sm9e01uS/4oorlJ2drWuuuUZpaWmaPXu25s%2Bfr7PPPtuyef3pu%2B%2B%2Bk9vtbndf/36Oo0ePau7cuRo/frzOOeccf49jmfnz5%2BuGG27Q%2Beef7%2B9RvAT%2Bt7rtXGvuIQjU%2Bw1%2BSmszHTt2THfffbcqKiq0bNkyH01ljZZmr6io0IoVK1RWVubjiazV0vxut1spKSkaN26cJOnaa6/Vq6%2B%2Bqg0bNnid1Qg0Lc2/Zs0arVmzRitWrFD//v31/vvva/bs2Tr77LM1cOBAH0/ZdrTHr3%2BtUVtbq7y8PIWFhenBBx/09ziW%2Bdvf/qbt27fr0Ucf9fcozXAGqw2Lj4%2BXy%2BXyWnO5XAoJCVF8fLzXelxc3Cn3PXm/QNKa/NIPlwqmTp2qf/3rX1q%2BfLnOOussq0Y1rqXZ3W63HnjgAc2YMUPdunWzekyfac3ffbdu3ZpdTkhMTJTT6fT5nL7Smvwvv/yysrOzNXDgQIWHh2vEiBG66KKLAv4yaUvFxsYqNDT0lB%2Bvrl27%2Bmkqax08eFA333yzoqKi9Pzzz7epG7196ejRo3rooYf0xz/%2BUZ06dfL3OM1QsNqw1NRU7d%2B/XwcPHvSs2e129enTRxEREc32LS8v9/y5sbFRn332mQYNGmTZvKa1Jr/b7VZ%2Bfr5sNpteeuklxcXFWT2uUS3N/q9//Uv/9V//paeeekrp6elKT0/XG2%2B8oeeee07XXnutP0Y3ojV/971799bnn3/udQajsrJSiYmJls1rWmvyNzU1qbGx0Wvt6NGjlszZFoSHh6tv375eX/9qamq0d%2B/eoDiDd%2BTIEU2dOlUpKSl66qmn2mTR8JXt27frv//7v1VQUOD5%2BvfJJ5/o4Ycf1vTp0/09HgWrLUtOTlZaWpoWLlyo2tpaORwOvfjii8rJyZEkjR492vN6Jzk5OVqzZo22b9%2Buw4cPa8mSJerYsWNA/0RZa/KXlZWpoqJCTz75pMLDw/05thEtzd6jRw9t3bpVr732muctKytLEydOVElJiZ9T/Hyt%2BbsfP368qqur9cwzz6i%2Bvl5r165VeXm5xo8f788IZ6Q1%2BbOysrRy5Urt3r1bDQ0Nevfdd/X%2B%2B%2B9r5MiR/ozgU19//bVGjx7teRmWnJwcLVu2TA6HQ7W1tSoqKtKAAQOUlpbm50nNOzn7Cy%2B8oA4dOujhhx9WaGj7/yf9xPwXXHCBtmzZ4vX1LzU1VbfffrseeeQRf4/KPVht3VNPPaV58%2BbpkksuUWRkpCZOnOj5abk9e/Z4Xufq0ksv1R133KFZs2bp22%2B/VVpamkpKSgL%2Bu5mW5l%2B1apUqKyub3dR%2B9dVXq7Cw0PK5TWhJ9rCwMPXo0cPrcZ07d1ZkZGTAXzJs6d999%2B7dtXTpUj3yyCP6z//8T/3iF7/Q4sWLdd555/lz/DPW0vxTp05VQ0OD8vLydPDgQSUmJqqwsFAXX3yxP8c/Y8fLUUNDgyR5XjjYbrfr2LFj2rNnj%2BdM3cSJE%2BV0OjVp0iTV1dUpPT1dxcXF/hncgNZkX7Vqlfbv39/sasX06dN16623Wji1OS3N37Fjx2Zf/zp27Kjo6Og2cXtMiDvY7wwEAAAwrP2fTwQAALAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhv0vdys2E/qccwUAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common2214519207119624705”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1132</td> <td class=”number”>35.3%</td> <td>

<div class=”bar” style=”width:57%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.096227868</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.074382624</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.078296273</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.093729497</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.063803994</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.133636242</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.135666802</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0572607</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0601341</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (1979)</td> <td class=”number”>1982</td> <td class=”number”>61.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme2214519207119624705”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1132</td> <td class=”number”>35.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00500947</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00710424</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00721678</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00761661</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.644722232</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.731528895</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.793990106</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.860215054</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.366120219</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SPECSPTH14”>SPECSPTH14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>1915</td>

</tr> <tr>

<th>Unique (%)</th> <td>59.7%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.051635</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1.3908</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>37.6%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram2785895467901356601”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives2785895467901356601,#minihistogram2785895467901356601”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives2785895467901356601”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles2785895467901356601”

aria-controls=”quantiles2785895467901356601” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram2785895467901356601” aria-controls=”histogram2785895467901356601”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common2785895467901356601” aria-controls=”common2785895467901356601”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme2785895467901356601” aria-controls=”extreme2785895467901356601”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles2785895467901356601”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0.037206</td>

</tr> <tr>

<th>Q3</th> <td>0.075043</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.16987</td>

</tr> <tr>

<th>Maximum</th> <td>1.3908</td>

</tr> <tr>

<th>Range</th> <td>1.3908</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.075043</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.073337</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.4203</td>

</tr> <tr>

<th>Kurtosis</th> <td>53.042</td>

</tr> <tr>

<th>Mean</th> <td>0.051635</td>

</tr> <tr>

<th>MAD</th> <td>0.047122</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>4.8407</td>

</tr> <tr>

<th>Sum</th> <td>161.72</td>

</tr> <tr>

<th>Variance</th> <td>0.0053783</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram2785895467901356601”>

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hKVOmqFWrVurRo4dGjx6tnJwcSdLq1auVlJSkpKQkBQcHa9iwYeratavefvttSVJOTo7Gjx%2BvTp06KSQkROnp6SosLNSePXt8GRkAAPgRvyxYYWFhWrBggc4777zatUOHDik6Olr5%2Bfnq1q2bgoKCavfi4uKUl5cnScrPz1dcXJzX%2BeLi4uR0OlVZWamCggKv/ZCQEHXo0EFOp7ORUwEAgKbC4esBTHA6nXr11Ve1fPlybdy4UWFhYV77ERERcrvdqqmpkdvtVnh4uNd%2BeHi4CgoK9P3338vj8Zxxv6SkpM7zFBcXy%2BVyea05HK0UHR1dz2S/LCjIv/qxw2Fu3pPZ/e1z0FB2zS2R/acf7cSu2e2aW2o62f2%2BYH322WeaMmWKZsyYof79%2B2vjxo1nPC4gIKD2f5/tfqqG3m%2BVk5OjrKwsr7W0tDRNmzatQef1d5GRrY2fMyyspfFz%2BgO75pbIbld2zW7X3JL/Z/frgrVlyxbdf//9mj17tm6%2B%2BWZJUlRUlL7%2B%2Bmuv49xutyIiIhQYGKjIyEi53e7T9qOiomqPOdN%2BmzZt6jxXSkqKkpOTvdYcjlYqKSmvR7qz87d2bzJ/UFCgwsJaqrS0QtXVNcbOe66za26J7GS3V3a75pbMZ2%2BMb%2B7rwm8L1ueff66MjAw9/fTTGjBgQO16fHy8XnvtNVVVVcnh%2BDGe0%2BlUr169avdP3o91ktPp1I033qjg4GB16dJF%2Bfn56tevn6Qfb6g/ePCgevbsWefZoqOjT3s50OU6qqoqe/0lOVVj5K%2BurrHl59WuuSWyk91e7Jpb8v/s/nUJ5P%2BrqqrSI488opkzZ3qVK0lKSkpSSEiIli9froqKCu3Zs0e5ublKTU2VJI0ZM0Y7d%2B7Utm3bdOzYMeXm5urrr7/WsGHDJEmpqalauXKlCgsLVVZWpszMTHXv3l0JCQmW5wQAAP7JL69g7d69W4WFhZo3b57mzZvntffuu%2B/q2Wef1WOPPabs7Gydd955Sk9P18CBAyVJXbt2VWZmphYsWKCioiJ17txZK1asUNu2bSVJY8eOlcvl0rhx41ReXq7ExMTT7qcCAAD4JQEe3kHTEi7XUePndDgCdU3mDuPnbSwbp19p7FwOR6AiI1urpKTcry8h15ddc0tkJ7u9sts1t2Q%2Be9u2oQamqj%2B/fIkQAADgXEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGCY5QUrOTlZWVlZOnTokNVPDQAAYAnLC9Ytt9yiDRs26Oqrr9bEiRP13nvvqaqqyuoxAAAAGo3lBSstLU0bNmzQG2%2B8oS5duujJJ59UUlKSFi1apAMHDlg9DgAAgHE%2BuwerR48eysjI0NatW/Xwww/rjTfe0A033KAJEybof//3f301FgAAQIP5rGCdOHFCGzZs0F133aWMjAy1a9dODz30kLp3767x48dr3bp1vhoNAACgQRxWP2FhYaFyc3O1du1alZeXa8iQIfrzn/%2BsPn361B7Tt29fzZkzR0OHDrV6PAAAgAazvGDdeOON6tixoyZNmqSbb75ZERERpx2TlJSkI0eOWD0aAACAEZYXrJUrV6pfv35nPW7Pnj0WTAMAAGCe5fdgdevWTZMnT9bmzZtr115%2B%2BWXdddddcrvdVo8DAABgnOUFa8GCBTp69Kg6d%2B5cuzZw4EDV1NRo4cKFVo8DAABgnOUvEX744Ydat26dIiMja9diY2OVmZmpm266yepxAAAAjLP8ClZlZaWCg4NPHyQwUBUVFVaPAwAAYJzlBatv375auHChvv/%2B%2B9q1b7/9VnPnzvV6qwYAAAB/ZflLhA8//LDuvPNO/fa3v1VISIhqampUXl6u9u3b65VXXrF6HAAAAOMsL1jt27fXO%2B%2B8ow8%2B%2BEAHDx5UYGCgOnbsqAEDBigoKMjqcQAAAIyzvGBJUvPmzXX11Vf74qkBAAAaneUF65tvvtHixYv11VdfqbKy8rT9999/3%2BqRAAAAjPLJPVjFxcUaMGCAWrVqZfXTAwAANDrLC1ZeXp7ef/99RUVFWf3UAAAAlrD8bRratGnDlSsAANCkWV6wJk2apKysLHk8HqufGgAAwBKWv0T4wQcf6PPPP9eaNWt04YUXKjDQu%2BO9/vrrVo8EAABglOUFKyQkRFdddZXVTwsAAGAZywvWggULrH5KAAAAS1l%2BD5Yk7d%2B/X0uXLtVDDz1Uu/b3v//dF6MAAAAYZ3nB2rVrl4YNG6b33ntP69evl/Tjm4/efvvtvMkoAABoEiwvWEuWLNH999%2BvdevWKSAgQNKP/z7hwoULtWzZMqvHAQAAMM7ygvXll18qNTVVkmoLliRdd911KiwstHocAAAA4ywvWKGhoWf8NwiLi4vVvHlzq8cBAAAwzvKCddlll%2BnJJ59UWVlZ7dqBAweUkZGh3/72t1aPAwAAYJzlb9Pw0EMP6Y477lBiYqKqq6t12WWXqaKiQl26dNHChQutHgcAAMA4ywvW%2Beefr/Xr12v79u06cOCAWrRooY4dO%2BrKK6/0uierLnbs2KGMjAwlJiZqyZIltetr1qzRww8/rGbNmnkdv2rVKvXs2VM1NTV6%2BumntX79epWWlqpnz56aM2eO2rdvL0lyu92aM2eOPvnkEwUGBiopKUmzZ89WixYtGv4JAAAATZ7lBUuSmjVrpquvvrpB53juueeUm5urDh06nHG/b9%2B%2BeuWVV864t2rVKq1bt07PPfec2rVrpyVLligtLU1vvfWWAgICNHv2bB0/flzr16/XiRMndO%2B99yozM1OPPPJIg2YGAAD2YHnBSk5O/sUrVXV9L6zg4GDl5uZq/vz5OnbsWL1myMnJ0fjx49WpUydJUnp6uhITE7Vnzx5deOGF2rx5s/7yl78oKipKknT33Xfr3nvvVUZGxmlXxQAAAE5lecG64YYbvApWdXW1Dhw4IKfTqTvuuKPO57n99tt/cf/QoUP6/e9/r7y8PIWFhWnatGkaPny4KisrVVBQoLi4uNpjQ0JC1KFDBzmdTh09elRBQUHq1q1b7X6PHj30ww8/aP/%2B/V7rP6e4uFgul8trzeFopejo6Drnq4ugIJ%2B8Ef%2Bv5nCYm/dkdn/7HDSUXXNLZP/pRzuxa3a75paaTnbLC9bMmTPPuL5p0yZ9/PHHRp4jKipKsbGxuu%2B%2B%2B9S5c2f99a9/1QMPPKDo6GhdfPHF8ng8Cg8P93pMeHi4SkpKFBERoZCQEK8SePLYkpKSOj1/Tk6OsrKyvNbS0tI0bdq0Bibzb5GRrY2fMyyspfFz%2BgO75pbIbld2zW7X3JL/Z/fJPVhncvXVV%2BvRRx/Vo48%2B2uBzDRw4UAMHDqz9/Y033qi//vWvWrNmTW3B83g8P/v4X9qri5SUFCUnJ3utORytVFJS3qDznsrf2r3J/EFBgQoLa6nS0gpVV9cYO%2B%2B5zq65JbKT3V7Z7ZpbMp%2B9Mb65r4tzpmB98cUXDS42vyQmJkZ5eXmKiIhQYGCg3G63177b7VabNm0UFRWlsrIyVVdXKygoqHZPktq0aVOn54qOjj7t5UCX66iqquz1l%2BRUjZG/urrGlp9Xu%2BaWyE52e7Frbsn/s1tesMaOHXvaWkVFhQoLC3XttdcaeY7XXntN4eHhuuGGG2rXCgsL1b59ewUHB6tLly7Kz89Xv379JEmlpaU6ePCgevbsqZiYGHk8Hu3bt089evSQJDmdToWFhaljx45G5gMAAE2b5QUrNjb2tJ8iDA4O1qhRozR69Ggjz3H8%2BHE98cQTat%2B%2BvS655BJt2rRJH3zwgd544w1JUmpqqrKzs3XVVVepXbt2yszMVPfu3ZWQkCBJGjJkiP70pz/pqaee0vHjx7Vs2TKNGjVKDsc5c8EPAACcwyxvDKberf1kGaqqqpIkbd68WdKPV5tuv/12lZeX695775XL5dKFF16oZcuWKT4%2BXtKPV9FcLpfGjRun8vJyJSYmet2U/vjjj%2Buxxx7T4MGD1axZM910001KT083MjcAAGj6AjyNeePTGaxdu7bOx958882NOIm1XK6jxs/pcATqmswdxs/bWDZOv9LYuRyOQEVGtlZJSblfv0ZfX3bNLZGd7PbKbtfckvnsbduGGpiq/iy/gjVr1izV1NScdkN7QECA11pAQECTKlgAAMA%2BLC9Yzz//vF588UVNnjxZ3bp1k8fj0T/%2B8Q8999xzuu2225SYmGj1SAAAAEb55B6s7OxstWvXrnbt8ssvV/v27TVhwgStX7/e6pEAAACMsvydKr/%2B%2BuvT3kVdksLCwlRUVGT1OAAAAMZZXrBiYmK0cOFCr392prS0VIsXL9ZFF11k9TgAAADGWf4S4cMPP6wZM2YoJydHrVu3VmBgoMrKytSiRQstW7bM6nEAAACMs7xgDRgwQNu2bdP27dt1%2BPBheTwetWvXTr/73e8UGuqbH6UEAAAwySdvTd6yZUsNHjxYhw8fVvv27X0xAgAAQKOx/B6syspKZWRkqHfv3rr%2B%2Busl/XgP1sSJE1VaWmr1OAAAAMZZXrAWLVqkvXv3KjMzU4GB/3n66upqZWZmWj0OAACAcZYXrE2bNumZZ57RddddV/uPPoeFhWnBggV67733rB4HAADAOMsLVnl5uWJjY09bj4qK0g8//GD1OAAAAMZZXrAuuugiffzxx5Lk9W8Pvvvuu/rNb35j9TgAAADGWf5ThLfeequmTp2qW265RTU1NXrppZeUl5enTZs2adasWVaPAwAAYJzlBSslJUUOh0OvvvqqgoKC9Oyzz6pjx47KzMzUddddZ/U4AAAAxllesI4cOaJbbrlFt9xyi9VPDQAAYAnL78EaPHiw171XAAAATY3lBSsxMVEbN260%2BmkBAAAsY/lLhBdccIHmz5%2Bv7OxsXXTRRWrWrJnX/uLFi60eCQAAwCjLC1ZBQYEuvvhiSVJJSYnVTw8AANDoLCtY6enpWrJkiV555ZXatWXLliktLc2qEQAAACxh2T1YW7ZsOW0tOzvbqqcHAACwjGUF60w/OchPEwIAgKbIsoJ18h92PtsaAACAv7P8bRoAAACaOgoWAACAYZb9FOGJEyc0Y8aMs67xPlgAAMDfWVaw%2BvTpo%2BLi4rOuAQAA%2BDvLCtZP3/8KAACgKeMeLAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAY5tcFa8eOHerfv7/S09NP29uwYYOGDh2q3r17a%2BTIkfrwww9r92pqarRkyRINHjxYffv21YQJE/TNN9/U7rvdbk2fPl39%2B/fXgAEDNGvWLFVWVlqSCQAA%2BD%2B/LVjPPfec5s2bpw4dOpy2t3fvXmVkZGjmzJn66KOPNH78eN1zzz06fPiwJGnVqlVat26dsrOztXXrVsXGxiotLU0ej0eSNHv2bFVUVGj9%2BvV68803VVhYqMzMTEvzAQAA/%2BW3BSs4OFi5ublnLFirV69WUlKSkpKSFBwcrGHDhqlr1656%2B%2B23JUk5OTkaP368OnXqpJCQEKWnp6uwsFB79uzRv//9b23evFnp6emKiopSu3btdPfdd%2BvNN9/UiRMnrI4JAAD8kN8WrNtvv12hoaFn3MvPz1dcXJzXWlxcnJxOpyorK1VQUOC1HxISog4dOsjpdGrv3r0KCgpSt27davd79OihH374Qfv372%2BcMAAAoElx%2BHqAxuB2uxUeHu61Fh4eroKCAn3//ffyeDxn3C8pKVFERIRCQkIUEBDgtSdJJSUldXr%2B4uJiuVwurzWHo5Wio6N/TZyfFRTkX/3Y4TA378ns/vY5aCi75pbI/tOPdmLX7HbNLTWd7E2yYEmqvZ/q1%2Byf7bFnk5OTo6ysLK%2B1tLQ0TZs2rUHn9XeRka2NnzMsrKXxc/oDu%2BaWyG5Xds1u19yS/2dvkgUrMjJSbrfba83tdisqKkoREREKDAw8436bNm0UFRWlsrIyVVdXKygoqHZPktq0aVOn509JSVFycrLXmsPRSiUl5b820hn5W7s3mT8oKFBhYS1VWlqh6uoaY%2Bc919k1t0R2stsru11zS%2BazN8Y393XRJAtWfHy88vLyvNacTqduvPFGBQcHq0uXLsrPz1e/fv0kSaWlpTp48KB69uypmJgYeTwe7du3Tz169Kh9bFhYmDp27Fin54%2BOjj7t5UCX66iqquz1l%2BRUjZG/urrGlp9Xu%2BaWyE52e7Frbsn/s/vXJZA6GjNmjHbu3Klt27bp2LFjys3N1ddff61hw4ZJklJTU7Vy5UoIK0ncAAARz0lEQVQVFhaqrKxMmZmZ6t69uxISEhQVFaUhQ4boT3/6k44cOaLDhw9r2bJlGjVqlByOJtlHAQCAYX7bGBISEiRJVVVVkqTNmzdL%2BvFqU9euXZWZmakFCxaoqKhInTt31ooVK9S2bVtJ0tixY%2BVyuTRu3DiVl5crMTHR656pxx9/XI899pgGDx6sZs2a6aabbjrjm5kCAACcSYCnoXd0o05crqPGz%2BlwBOqazB3Gz9tYNk6/0ti5HI5ARUa2VklJuV9fQq4vu%2BaWyE52e2W3a27JfPa2bc/8lk6NrUm%2BRAgAAOBLFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMOabMHq1q2b4uPjlZCQUPvriSeekCTt2rVLo0aN0mWXXaYbb7xRb7/9ttdjV65cqSFDhuiyyy5Tamqq8vLyfBEBAAD4KYevB2hM7777ri688EKvteLiYt19992aNWuWhg4dqs8%2B%2B0xTpkxRx44dlZCQoC1btmjp0qV6/vnn1a1bN61cuVKTJ0/We%2B%2B9p1atWvkoCQAA8CdN9grWz1m3bp1iY2M1atQoBQcHq3///kpOTtbq1aslSTk5ORo5cqR69eqlFi1aaOLEiZKkrVu3%2BnJsAADgR5r0FazFixfr73//u8rKynT99dfrwQcfVH5%2BvuLi4ryOi4uL08aNGyVJ%2Bfn5uuGGG2r3AgMD1b17dzmdTt144411et7i4mK5XC6vNYejlaKjoxuYyFtQkH/1Y4fD3Lwns/vb56Ch7JpbIvtPP9qJXbPbNbfUdLI32YJ16aWXqn///nrqqaf0zTffaPr06Zo7d67cbrfatWvndWxERIRKSkokSW63W%2BHh4V774eHhtft1kZOTo6ysLK%2B1tLQ0TZs27VemaRoiI1sbP2dYWEvj5/QHds0tkd2u7Jrdrrkl/8/eZAtWTk5O7f/u1KmTZs6cqSlTpqhPnz5nfazH42nQc6ekpCg5OdlrzeFopZKS8gad91T%2B1u5N5g8KClRYWEuVllaourrG2HnPdXbNLZGd7PbKbtfckvnsjfHNfV002YJ1qgsvvFDV1dUKDAyU2%2B322ispKVFUVJQkKTIy8rR9t9utLl261Pm5oqOjT3s50OU6qqoqe/0lOVVj5K%2BurrHl59WuuSWyk91e7Jpb8v/s/nUJpI6%2B%2BOILLVy40GutsLBQzZs3V1JS0mlvu5CXl6devXpJkuLj45Wfn1%2B7V11drS%2B%2B%2BKJ2HwAA4GyaZMFq06aNcnJylJ2drePHj%2BvAgQN6%2BumnlZKSouHDh6uoqEirV6/WsWPHtH37dm3fvl1jxoyRJKWmpmrt2rXavXu3KioqtHz5cjVv3lwDBw70bSgAAOA3muRLhO3atVN2drYWL15cW5BGjBih9PR0BQcHa8WKFZo3b57mzp2rmJgYLVq0SJdccokk6aqrrtJ9992n6dOn67vvvlNCQoKys7PVokULH6cCAAD%2BokkWLEnq27evXn/99Z/de%2Butt372sbfeeqtuvfXWxhoNAAA0cU3yJUIAAABfomABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAY5vD1ALCP6//0N1%2BPUC8bp1/p6xEAAH6KK1gAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEUrDMoKirSH/7wByUmJmrQoEFatGiRampqfD0WAADwEw5fD3Aumjp1qnr06KHNmzfru%2B%2B%2B06RJk3Teeefp97//va9Hg4Wu/9PffD1CvWycfqWvRwAA/H9cwTqF0%2BnUvn37NHPmTIWGhio2Nlbjx49XTk6Or0cDAAB%2BgitYp8jPz1dMTIzCw8Nr13r06KEDBw6orKxMISEhZz1HcXGxXC6X15rD0UrR0dFGZw0Koh/jP/zpittfZ/6u3o85%2Bf93O/7/nuz2y27X3FLTyU7BOoXb7VZYWJjX2smyVVJSUqeClZOTo6ysLK%2B1e%2B65R1OnTjU3qH4scnec/5VSUlKMl7dzXXFxsXJycmyX3a65pR%2Bz//nPz5Od7LZg19xS08nu3/WwkXg8ngY9PiUlRWvWrPH6lZKSYmi6/3C5XMrKyjrtapkd2DW7XXNLZCe7vbLbNbfUdLJzBesUUVFRcrvdXmtut1sBAQGKioqq0zmio6P9unUDAICG4QrWKeLj43Xo0CEdOXKkds3pdKpz585q3bq1DycDAAD%2BgoJ1iri4OCUkJGjx4sUqKytTYWGhXnrpJaWmpvp6NAAA4CeC5syZM8fXQ5xrfve732n9%2BvV64okn9M4772jUqFGaMGGCAgICfD3aaVq3bq1%2B/frZ8uqaXbPbNbdEdrLbK7tdc0tNI3uAp6F3dAMAAMALLxECAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBOscVFRXpD3/4gxITEzVo0CAtWrRINTU1Zzx25cqVGjJkiC677DKlpqYqLy/P4mnNqU/u1157TUOGDFHv3r01fPhwbd682eJpzapP9pO%2B/fZb9e7dW0uXLrVoysZRn%2ByFhYUaN26cevXqpaSkJL388svWDmtYXbPX1NTomWeeUXJysnr37q2hQ4dqw4YNPpjYnB07dqh///5KT0//xeNqamq0ZMkSDR48WH379tWECRP0zTffWDRl46hP9qysrNo/95SUFH366acWTWleXXP/VH5%2BvuLi4rRmzZpGnMwcCtY5burUqWrXrp02b96sl156SZs3b9af//zn047bsmWLli5dqj/%2B8Y/auXOnBg0apMmTJ%2BuHH37wwdQNV9fcmzZt0uLFi/Xkk0/qk08%2B0W233abp06f79Rfdumb/qXnz5ikoKMiiCRtPXbNXVlZq4sSJSkpK0kcffaSlS5cqNzdXhYWFPpjajLpmf%2B2117R69Wo9//zz%2BvTTT3Xffffp/vvv1759%2B3wwdcM999xzmjdvnjp06HDWY1etWqV169YpOztbW7duVWxsrNLS0uSv/%2BJbfbK//PLLevPNN7VixQp9/PHHGjBggNLS0lRWVmbBpGbVJ/dJNTU1euyxx9SqVatGnMwsCtY5zOl0at%2B%2BfZo5c6ZCQ0MVGxur8ePHKycn57Rjc3JyNHLkSPXq1UstWrTQxIkTJUlbt261euwGq0/uyspK3XffferTp4%2BaNWum0aNHq3Xr1tq9e7cPJm%2B4%2BmQ/afv27SooKNDAgQOtG7QR1Cf7xo0bFRISookTJ6ply5bq2bOn1q9fr06dOvlg8oarT/b8/Hz16dNHF198sYKCgjRo0CBFREToH//4hw8mb7jg4GDl5ubW6T%2B2OTk5Gj9%2BvDp16qSQkBClp6ersLBQe/bssWBS8%2BqTPTAwUA888IC6dOmi5s2b684775Tb7daXX35pwaRm1Sf3Sa%2B99ppCQ0PVvXv3RpzMLArWOSw/P18xMTEKDw%2BvXevRo4cOHDhw2nctJy%2BdnhQYGKju3bvL6XRaNq8p9ck9fPhw3XrrrbW/Ly0tVXl5udq1a2fZvCbVJ7v0Y8F8/PHH9dhjj8nhcFg5qnH1yf7ZZ5%2Bpa9eueuihh3T55Zfruuuu09tvv231yMbUJ/vAgQP1ySefaO/evTp%2B/Ljef/99VVRUqF%2B/flaPbcTtt9%2Bu0NDQsx5XWVmpgoICr69zISEh6tChg19%2BnZPqnl2Sxo8fr%2Buvv77294cPH5YkRUdHN8psjak%2BuSXJ5XJp2bJlmj17diNOZR4F6xzmdrsVFhbmtXbyC3BJSclpx/70i/PJY089zh/UJ/dPeTwePfLII%2BrVq5ff/semvtmXLVumSy%2B9VFdccYUl8zWm%2BmQ/fPiw3n//ffXv3187duzQpEmTlJGRoS%2B%2B%2BMKyeU2qT/Zrr71WKSkpuvnmm5WQkKAZM2ZowYIFuuCCCyyb1xe%2B//57eTyeJvN1riGOHz%2BuWbNmadiwYbrwwgt9PU6jW7BggUaPHq2LL77Y16PUi39/y2sD9bm3wF/vQziT%2BmY5ceKEHnzwQRUUFGjlypWNNJU16pq9oKBAq1ev1rp16xp5IuvUNbvH41GPHj00dOhQSdKIESP0%2Buuv69133/W6wuFP6pp97dq1Wrt2rVavXq1u3bpp165dmjFjhi644AL17Nmzkaf0vab0de7XKCsrU1pamoKCgjR37lxfj9Po/va3v2n37t168sknfT1KvXEF6xwWFRUlt9vtteZ2uxUQEKCoqCiv9cjIyDMee%2Bpx/qA%2BuaUfXzqYNGmS/vWvf2nVqlU677zzrBrVuLpm93g8mjNnjqZOnaq2bdtaPWajqM%2Bfe9u2bU97iSEmJkYul6vR52wM9cn%2B6quvKiUlRT179lRwcLAGDhyoK664wq9fIq2LiIgIBQYGnvHz1KZNGx9NZa0jR47otttuU2hoqF544QW/uuH71zh%2B/Lgef/xxPfroo2rRooWvx6k3CtY5LD4%2BXocOHdKRI0dq15xOpzp37qzWrVufdmx%2Bfn7t76urq/XFF1%2BoV69els1rSn1yezwepaeny%2BFw6OWXX1ZkZKTV4xpV1%2Bz/%2Bte/9D//8z965plnlJiYqMTERL3zzjt6/vnnNWLECF%2BM3mD1%2BXPv1KmTvvzyS6%2BrGUVFRYqJibFsXpPqk72mpkbV1dVea8ePH7dkTl8KDg5Wly5dvL7OlZaW6uDBg7a4cnfs2DFNmjRJPXr00DPPPOOXhaO%2Bdu/erX/%2B85/KyMio/Tr3%2Beef64knntCUKVN8Pd5ZUbDOYXFxcUpISNDixYtVVlamwsJCvfTSS0pNTZUkXXfddbXvg5Kamqq1a9dq9%2B7dqqio0PLly9W8eXO//Mmy%2BuRet26dCgoK9PTTTys4ONiXYxtR1%2Bznn3%2B%2Btm/frrfeeqv2V3JyssaOHavs7Gwfp/h16vPnPmzYMJWUlOjZZ59VZWWl1q9fr/z8fA0bNsyXEX61%2BmRPTk5Wbm6u9u3bp6qqKn344YfatWuXBg8e7MsIjeLbb7/VddddV/u2K6mpqVq5cqUKCwtVVlamzMxMde/eXQkJCT6e1LxTs7/44otq1qyZnnjiCQUGNt3/dP8096WXXqpt27Z5fZ2Lj4/Xvffeq/nz5/t61LPiHqxz3DPPPKPZs2fryiuvVEhIiMaOHVv7U3MHDhyofZ%2Brq666Svfdd5%2BmT5%2Bu7777TgkJCcrOzvbb73LqmvvNN99UUVHRaTe1Dx8%2BXPPmzbN8bhPqkj0oKEjnn3%2B%2B1%2BNatmypkJAQv37JsK5/7u3atdOKFSs0f/58/fd//7d%2B85vfaNmyZbrooot8OX6D1DX7pEmTVFVVpbS0NB05ckQxMTGaN2%2Befvvb3/py/F/tZDmqqqqSpNo3CnY6nTpx4oQOHDhQe4Vu7NixcrlcGjdunMrLy5WYmKisrCzfDG5AfbK/%2BeabOnTo0GmvSkyZMkV33323hVM3XF1zN2/e/LSvc82bN1dYWJhf3P4S4LH7HYMAAACGNd3rjAAAAD5CwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYf8P4ngbPNSEVVQAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common2785895467901356601”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1208</td> <td class=”number”>37.6%</td> <td>

<div class=”bar” style=”width:63%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0494438</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.138888889</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0484027</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0477099</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.109505037</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.120235662</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0389636</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.079076388</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.116144019</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (1904)</td> <td class=”number”>1906</td> <td class=”number”>59.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme2785895467901356601”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1208</td> <td class=”number”>37.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00718097</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0074206</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00768823</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00771712</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.633786587</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.681663258</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.838926174</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.865800866</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.390820584</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SUPERC09”>SUPERC09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>32</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1.4304</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>83</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>46.1%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-7348152909121889448”>

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<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-7348152909121889448”

aria-controls=”quantiles-7348152909121889448” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-7348152909121889448” aria-controls=”histogram-7348152909121889448”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-7348152909121889448” aria-controls=”common-7348152909121889448”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-7348152909121889448” aria-controls=”extreme-7348152909121889448”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

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<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>1</td>

</tr> <tr>

<th>Q3</th> <td>1</td>

</tr> <tr>

<th>95-th percentile</th> <td>6</td>

</tr> <tr>

<th>Maximum</th> <td>83</td>

</tr> <tr>

<th>Range</th> <td>83</td>

</tr> <tr>

<th>Interquartile range</th> <td>1</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>3.3659</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.3531</td>

</tr> <tr>

<th>Kurtosis</th> <td>145.88</td>

</tr> <tr>

<th>Mean</th> <td>1.4304</td>

</tr> <tr>

<th>MAD</th> <td>1.6151</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>8.8792</td>

</tr> <tr>

<th>Sum</th> <td>4480</td>

</tr> <tr>

<th>Variance</th> <td>11.329</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

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fDhw/Xhhx9qx44dOn36tCZOnKiWLVuqe/fuuvvuu1VcXCxJWrlypTIzM5WZmamIiAgNGzZMXbp00dq1ayVJxcXFys7OVseOHRUVFaW8vDyVlZVp9%2B7dgVwyAAAIISEZsBwOhwoKCtS6dWvf2KFDh5SQkKDS0lJ17dpV4eHhvrmUlBSVlJRIkkpLS5WSkuK3v5SUFDmdTp04cUL79%2B/3m4%2BKilL79u3ldDov8qoAAMClwh7oAkxwOp169dVXtWTJEm3cuFEOh8NvPjY2Vh6PR/X19fJ4PIqJifGbj4mJ0f79%2B3Xs2DF5vd5G591u93nXU1FRIZfL5Tdmt7dUQkLCv7myHxYeHlr52G4PrXpNONOjUOtVU0KPgh89Cn70qKGQD1gfffSRJk6cqClTpigjI0MbN25sdLuwsDDff5/rfqoLvd%2BquLhYixYt8hvLyclRbm7uBe031MXFRQa6hIBxOC4LdAk4B3oU/OhR8KNHZ4V0wNq2bZsef/xxzZo1S3feeackKT4%2BXp9//rnfdh6PR7GxsbLZbIqLi5PH42kwHx8f79umsflWrVqdd12jR4/WgAED/Mbs9pZyu6v/jdWdW6i9UzC9/lAQHm6Tw3GZKitrVFdXH%2Bhy0Ah6FPzoUfAL5h4F6s19yAasjz/%2BWPn5%2BXrhhRfUr18/33hqaqp%2B//vfq7a2Vnb7d8tzOp3q2bOnb/7M/VhnOJ1ODRkyRBEREercubNKS0vVt29fSd/dUP/FF1%2BoR48e511bQkJCg8uBLtdx1dYG1w%2Bd1Zry%2Buvq6pv0%2BkMBPQp%2B9Cj40aOzQusUyP9XW1urmTNnaurUqX7hSpIyMzMVFRWlJUuWqKamRrt379aqVauUlZUlSRo1apTef/997dixQydPntSqVav0%2Beefa9iwYZKkrKwsLV%2B%2BXGVlZaqqqlJhYaG6deumtLQ0y9cJAABCU0iewfrkk09UVlamuXPnau7cuX5zmzZt0osvvqjZs2erqKhIrVu3Vl5envr37y9J6tKliwoLC1VQUKDy8nJ16tRJS5cuVZs2bSRJY8aMkcvl0r333qvq6mqlp6c3uJ8KAADgh4R5eYKmJVyu48b3abfbNKjwXeP7vVg2Tr4%2B0CVYzm63KS4uUm53NafNgxQ9Cn70KPgFc4/atIkOyHFD8hIhAABAMLM8YA0YMECLFi3SoUOHrD40AACAJSwPWHfddZc2bNigm2%2B%2BWQ888IC2bNmi2tpaq8sAAAC4aCwPWDk5OdqwYYP%2B8Ic/qHPnznr22WeVmZmpefPm6eDBg1aXAwAAYFzA7sHq3r278vPztX37dk2fPl1/%2BMMfdNttt2ncuHH629/%2BFqiyAAAALljAAtbp06e1YcMGPfjgg8rPz1diYqKefPJJdevWTdnZ2Vq3bl2gSgMAALgglj8Hq6ysTKtWrdIbb7yh6upqDR48WL/73e90zTXX%2BLbp06eP5syZo6FDh1pdHgAAwAWzPGANGTJEHTp00Pjx43XnnXcqNja2wTaZmZk6evSo1aUBAAAYYXnAWr58ue/v/P2Q3bt3W1ANAACAeZbfg9W1a1dNmDBBW7du9Y397//%2Brx588EF5PB6rywEAADDO8oBVUFCg48ePq1OnTr6x/v37q76%2BXs8995zV5QAAABhn%2BSXC9957T%2BvWrVNcXJxvLDk5WYWFhbr99tutLgcAAMA4y89gnThxQhEREQ0LsdlUU1NjdTkAAADGWR6w%2BvTpo%2Beee07Hjh3zjX399dd66qmn/B7VAAAAEKosv0Q4ffp0/eIXv9BPf/pTRUVFqb6%2BXtXV1WrXrp1eeeUVq8sBAAAwzvKA1a5dO7355pt655139MUXX8hms6lDhw7q16%2BfwsPDrS4HAADAOMsDliQ1b95cN998cyAODQAAcNFZHrC%2B/PJLzZ8/X5999plOnDjRYP7tt9%2B2uiQAAACjAnIPVkVFhfr166eWLVtafXgAAICLzvKAVVJSorffflvx8fFWHxoAAMASlj%2BmoVWrVpy5AgAAlzTLA9b48eO1aNEieb1eqw8NAABgCcsvEb7zzjv6%2BOOPtXr1al1xxRWy2fwz3muvvWZ1SQAAAEZZHrCioqJ04403Wn1YAAAAy1gesAoKCqw%2BJAAAgKUsvwdLkg4cOKCFCxfqySef9I399a9/DUQpAAAAxlkesHbt2qVhw4Zpy5YtWr9%2BvaTvHj46duxYHjIKAAAuCZYHrAULFujxxx/XunXrFBYWJum7v0/43HPPafHixVaXAwAAYJzlAevvf/%2B7srKyJMkXsCTp1ltvVVlZmdXlAAAAGGd5wIqOjm70bxBWVFSoefPmVpcDAABgnOUBq3fv3nr22WdVVVXlGzt48KDy8/P105/%2B1OpyAAAAjLP8MQ1PPvmk7rvvPqWnp6uurk69e/dWTU2NOnfurOeee87qcgAAAIyzPGC1bdtW69ev186dO3Xw4EG1aNFCHTp00PXXX%2B93TxYAAECosjxgSVKzZs108803B%2BLQAAAAF53lAWvAgAE/eKaKZ2EBAIBQZ3nAuu222/wCVl1dnQ4ePCin06n77rvP6nIAAACMszxgTZ06tdHxzZs3689//rPF1QAAAJgXkL9F2Jibb75Zb775ZqDLAAAAuGBBE7D27Nkjr9cb6DIAAAAumOWXCMeMGdNgrKamRmVlZbrlllv%2BrX29%2B%2B67ys/PV3p6uhYsWOAbX716taZPn65mzZr5bb9ixQr16NFD9fX1euGFF7R%2B/XpVVlaqR48emjNnjtq1aydJ8ng8mjNnjv7yl7/IZrMpMzNTs2bNUosWLX7EigEAQFNjecBKTk5u8CnCiIgIjRw5Unffffd57%2Bell17SqlWr1L59%2B0bn%2B/Tpo1deeaXRuRUrVmjdunV66aWXlJiYqAULFignJ0dr1qxRWFiYZs2apVOnTmn9%2BvU6ffq0Hn30URUWFmrmzJnnv1AAANBkWR6wTD2tPSIiQqtWrdIzzzyjkydP/luvLS4uVnZ2tjp27ChJysvLU3p6unbv3q0rrrhCW7du1euvv674%2BHhJ0sMPP6xHH31U%2Bfn5Dc6KAQAAfJ/lAeuNN944723vvPPOfzk3duzYH3ztoUOHdP/996ukpEQOh0O5ubm64447dOLECe3fv18pKSm%2BbaOiotS%2BfXs5nU4dP35c4eHh6tq1q2%2B%2Be/fu%2Bvbbb3XgwAG/cQAAgMZYHrBmzJih%2Bvr6Bje0h4WF%2BY2FhYX9YMD6IfHx8UpOTtZjjz2mTp066a233tITTzyhhIQEXXXVVfJ6vYqJifF7TUxMjNxut2JjYxUVFeV3GfPMtm63%2B7yOX1FRIZfL5Tdmt7dUQkLCj1rPvxIeHjSfUTgvdnto1WvCmR6FWq%2BaEnoU/OhR8KNHDVkesH7zm99o2bJlmjBhgrp27Sqv16tPP/1UL730ku655x6lp6df8DH69%2B%2Bv/v37%2B74fMmSI3nrrLa1evdr3HK4f%2BsTihX6asbi4WIsWLfIby8nJUW5u7gXtN9TFxUUGuoSAcTguC3QJOAd6FPzoUfCjR2cF5B6soqIiJSYm%2BsauvfZatWvXTuPGjdP69esvynGTkpJUUlKi2NhY2Ww2eTwev3mPx6NWrVopPj5eVVVVqqurU3h4uG9Oklq1anVexxo9erQGDBjgN2a3t5TbXW1gJWeF2jsF0%2BsPBeHhNjkcl6myskZ1dfWBLgeNoEfBjx4Fv2DuUaDe3FsesD7//PMGl%2BckyeFwqLy83Mgxfv/73ysmJka33Xabb6ysrEzt2rVTRESEOnfurNLSUvXt21eSVFlZqS%2B%2B%2BEI9evRQUlKSvF6v9u3bp%2B7du0uSnE6nHA6HOnTocF7HT0hIaHA50OU6rtra4Pqhs1pTXn9dXX2TXn8ooEfBjx4FP3p0luWnQJKSkvTcc8/53c9UWVmp%2BfPn68orrzRyjFOnTunpp5%2BW0%2BnU6dOntX79er3zzju%2BZ3BlZWVp%2BfLlKisrU1VVlQoLC9WtWzelpaUpPj5egwcP1q9//WsdPXpUhw8f1uLFizVy5EjZ7ZbnUQAAEIIsTwzTp0/XlClTVFxcrMjISNlsNlVVValFixZavHjxee8nLS1NklRbWytJ2rp1q6TvzjaNHTtW1dXVevTRR%2BVyuXTFFVdo8eLFSk1NlfTdw05dLpfuvfdeVVdXKz093e%2BeqV/96leaPXu2Bg4cqGbNmun2229XXl6eqf8FAADgEhfmDcDfp6mpqdHOnTt1%2BPBheb1eJSYm6oYbblB0dLTVpVjG5TpufJ92u02DCt81vt%2BLZePk6wNdguXsdpvi4iLldldz2jxI0aPgR4%2BCXzD3qE2bwGSLgFzzuuyyyzRw4EAdPnzY9%2BdpAAAALhWW34N14sQJ5efnq1evXvrZz34m6bt7sB544AFVVlZaXQ4AAIBxlgesefPmae/evSosLJTNdvbwdXV1KiwstLocAAAA4ywPWJs3b9b//M//6NZbb/U9Ld3hcKigoEBbtmyxuhwAAADjLA9Y1dXVSk5ObjAeHx%2Bvb7/91upyAAAAjLM8YF155ZX685//LMn/T9Js2rRJ//Ef/2F1OQAAAMZZ/inCn//855o0aZLuuusu1dfX6%2BWXX1ZJSYk2b96sGTNmWF0OAACAcZYHrNGjR8tut%2BvVV19VeHi4XnzxRXXo0EGFhYW69dZbrS4HAADAOMsD1tGjR3XXXXfprrvusvrQAAAAlrD8HqyBAwcqAA%2BPBwAAsIzlASs9PV0bN260%2BrAAAACWsfwS4eWXX65nnnlGRUVFuvLKK9WsWTO/%2Bfnz51tdEgAAgFGWB6z9%2B/frqquukiS53W6rDw8AAHDRWRaw8vLytGDBAr3yyiu%2BscWLFysnJ8eqEgAAACxh2T1Y27ZtazBWVFRk1eEBAAAsY1nAauyTg3yaEAAAXIosC1hn/rDzucYAAABCneWPaQAAALjUEbAAAAAMs%2BxThKdPn9aUKVPOOcZzsAAAQKizLGBdc801qqioOOcYAABAqLMsYP3z868AAAAuZdyDBQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgWEgHrHfffVcZGRnKy8trMLdhwwYNHTpUvXr10ogRI/Tee%2B/55urr67VgwQINHDhQffr00bhx4/Tll1/65j0ejyZPnqyMjAz169dPM2bM0IkTJyxZEwAACH0hG7BeeuklzZ07V%2B3bt28wt3fvXuXn52vq1Kn64IMPlJ2drUceeUSHDx%2BWJK1YsULr1q1TUVGRtm/fruTkZOXk5Mjr9UqSZs2apZqaGq1fv15//OMfVVZWpsLCQkvXBwAAQlfIBqyIiAitWrWq0YC1cuVKZWZmKjMzUxERERo2bJi6dOmitWvXSpKKi4uVnZ2tjh07KioqSnl5eSorK9Pu3bv1zTffaOvWrcrLy1N8fLwSExP18MMP649//KNOnz5t9TIBAEAICtmANXbsWEVHRzc6V1paqpSUFL%2BxlJQUOZ1OnThxQvv37/ebj4qKUvv27eV0OrV3716Fh4era9euvvnu3bvr22%2B/1YEDBy7OYgAAwCXFHugCLgaPx6OYmBi/sZiYGO3fv1/Hjh2T1%2BttdN7tdis2NlZRUVEKCwvzm5Mkt9t9XsevqKiQy%2BXyG7PbWyohIeHHLOdfCg8PrXxst4dWvSac6VGo9aopoUfBjx4FP3rU0CUZsCT57qf6MfPneu25FBcXa9GiRX5jOTk5ys3NvaD9hrq4uMhAlxAwDsdlgS4B50CPgh89Cn706KxLMmDFxcXJ4/H4jXk8HsXHxys2NlY2m63R%2BVatWik%2BPl5VVVWqq6tTeHi4b06SWrVqdV7HHz16tAYMGOA3Zre3lNtd/WOX1KhQe6dgev2hIDzcJofjMlVW1qiurj7Q5aAR9Cj40aPgF8w9CtSb%2B0syYKWmpqqkpMRvzOl0asiQIYqIiFDnzp1VWlqqvn37SpIqKyv1xRdfqEePHkpKSpLX69W%2BffvUvXt332sdDoc6dOhwXsdPSEhocDnQ5Tqu2trg%2BqGzWlNef11dfZNefyigR8GPHgU/enRWaJ0COU%2BjRo3S%2B%2B%2B/rx07dujkyZNatWqVPv/8cw0bNkySlJWVpeXLl6usrExVVVUqLCxUt27dlJaWpvj4eA0ePFi//vWvdfToUR0%2BfFiLFy/WyJEjZbdfknkUAAAYFrKJIS0tTZJUW1srSdq6dauk7842denSRYWFhSqxdDKHAAAOh0lEQVQoKFB5ebk6deqkpUuXqk2bNpKkMWPGyOVy6d5771V1dbXS09P97pn61a9%2BpdmzZ2vgwIFq1qyZbr/99kYfZgoAANCYMO%2BF3tGN8%2BJyHTe%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%2B26WkpPguA35/3mazqVu3bnI6nZbWDgAAQtcle4nw6quvVkZGhp5//nl9%2BeWXmjx5sp566il5PB4lJib6bRsbGyu32y1J8ng8iomJ8ZuPiYnxzZ%2BPiooKuVwuvzG7vaUSEhJ%2B5GoaFx4eWvnYbg%2Btek0406NQ61VTQo%2BCHz0KfvSooUs2YBUXF/v%2Bu2PHjpo6daomTpyoa6655pyv9Xq9F3zsRYsW%2BY3l5OQoNzf3gvYb6uLiIgNdQsA4HJcFugScAz0KfvQo%2BNGjsy7ZgPV9V1xxherq6mSz2eTxePzm3G634uPjJUlxcXEN5j0ejzp37nzexxo9erQGDBjgN2a3t5TbXf0jq29cqL1TML3%2BUBAebpPDcZkqK2tUV1cf6HLQCHoU/OhR8AvmHgXqzf0lGbD27NmjtWvXatq0ab6xsrIyNW/eXJmZmXr99df9ti8pKVHPnj0lSampqSotLdXw4cMlSXV1ddqzZ49Gjhx53sdPSEhocDnQ5Tqu2trg%2BqGzWlNef11dfZNefyigR8GPHgU/enRWaJ0COU%2BtWrVScXGxioqKdOrUKR08eFAvvPCCRo8erTvuuEPl5eVauXKlTp48qZ07d2rnzp0aNWqUJCkrK0tvvPGGPvnkE9XU1GjJkiVq3ry5%2BvfvH9hFAQCAkHFJnsFKTExUUVGR5s%2Bf7wtIw4cPV15eniIiIrR06VLNnTtXTz31lJKSkjRv3jz95Cc/kSTdeOONeuyxxzR58mQdOXJEaWlpKioqUosWLQK8KgAAECrCvBd6RzfOi8t13Pg%2B7XabBhW%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%2B%2BWVFR0crOjpa2dnZ%2Bt3vfkfAamK4ZwwA8GMRsL6ntLRUSUlJiomJ8Y11795dBw8eVFVVlaKios65j4qKCrlcLr8xu72lEhISjNYaHs4VXpxlt/Pz8GOc%2BT3i9yl40aPgR48aImB9j8fjkcPh8Bs7E7bcbvd5Bazi4mItWrTIb%2ByRRx7RpEmTzBWq74LcfW0/0%2BjRo42HN5hRUVGh4uJiehTEKioq9Lvf/YYeBTF6FPzoUUNEzUZ4vd4Lev3o0aO1evVqv6/Ro0cbqu4sl8ulRYsWNThbhuBBj4IfPQp%2B9Cj40aOGOIP1PfHx8fJ4PH5jHo9HYWFhio%2BPP699JCQkkOABAGjCOIP1PampqTp06JCOHj3qG3M6nerUqZMiIyMDWBkAAAgVBKzvSUlJUVpamubPn6%2BqqiqVlZXp5ZdfVlZWVqBLAwAAISJ8zpw5cwJdRLC54YYbtH79ej399NN68803NXLkSI0bN05hYWGBLq2ByMhI9e3bl7NrQYweBT96FPzoUfCjR/7CvBd6RzcAAAD8cIkQAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACVggqLy/XQw89pPT0dN10002aN2%2Be6uvrA11Wk1deXq6cnBylp6crIyND06ZNU2VlpSRp7969uueee3TNNdfolltu0bJlywJcLZ599ll17drV9/2uXbs0cuRI9e7dW0OGDNHatWsDWF3TtmTJEvXr109XX321srOz9dVXX0miR8Fiz549Gjt2rK699lpdf/31mjp1qo4ePSqJHvnxIuQMHz7cO3PmTG9lZaX34MGD3ltuucW7bNmyQJfV5N1%2B%2B%2B3eadOmeauqqryHDh3yjhgxwjt9%2BnRvTU2N94YbbvAuXLjQW11d7S0pKfH27dvXu3nz5kCX3GTt2bPH27dvX2%2BXLl28Xq/X%2B/XXX3uvvvpq78qVK70nTpzw/ulPf/L26NHD%2B7e//S3AlTY9r776qvfWW2/1lpWVeY8fP%2B59%2BumnvU8//TQ9ChKnT5/2Xn/99d758%2Bd7T5486T169Kj3/vvv906aNIkefQ9nsEKM0%2BnUvn37NHXqVEVHRys5OVnZ2dkqLi4OdGlNWmVlpVJTUzVlyhRFRkaqbdu2Gj58uD788EPt2LFDp0%2Bf1sSJE9WyZUt1795dd999Nz0LkPr6es2ePVvZ2dm%2BsXXr1ik5OVkjR45URESEMjIyNGDAAK1cuTJwhTZRy5YtU15enq666ipFRUVp5syZmjlzJj0KEi6XSy6XS3fccYeaN2%2BuuLg4DRo0SHv37qVH30PACjGlpaVKSkpSTEyMb6x79%2B46ePCgqqqqAlhZ0%2BZwOFRQUKDWrVv7xg4dOqSEhASVlpaqa9euCg8P982lpKSopKQkEKU2ea%2B99poiIiI0dOhQ31hpaalSUlL8tqNH1vv666/11Vdf6dixY7rtttuUnp6u3NxcHT16lB4FicTERHXr1k3FxcWqrq7WkSNHtGXLFvXv358efQ8BK8R4PB45HA6/sTNhy%2B12B6IkNMLpdOrVV1/VxIkTG%2B1ZbGysPB4P985Z7JtvvtHChQs1e/Zsv/F/1SN%2Bp6x1%2BPBhSdKmTZv08ssva82aNTp8%2BLBmzpxJj4KEzWbTwoUL9fbbb6t3797KyMhQbW2tpkyZQo%2B%2Bh4AVgrxeb6BLwA/46KOPNG7cOE2ZMkUZGRn/cruwsDALq4IkFRQUaMSIEerUqVOgS0Ejzvzb9sADDygxMVFt27bVpEmTtG3btgBXhjNOnTqlCRMm6NZbb9WHH36od955R9HR0Zo6dWqgSws6BKwQEx8fL4/H4zfm8XgUFham%2BPj4AFWFM7Zt26aHHnpI06dP19ixYyV917Pvv4PzeDyKjY2VzcavoFV27dqlv/71r8rJyWkwFxcX1%2BD3yu128ztlsTOX2P/5LEhSUpK8Xq9Onz5Nj4LArl279NVXX%2Bmxxx5TdHS0EhMTlZubq7feeks2m40e/RP%2BdQ8xqampOnTokO8jsdJ3l6M6deqkyMjIAFaGjz/%2BWPn5%2BXrhhRd05513%2BsZTU1P16aefqra21jfmdDrVs2fPQJTZZK1du1ZHjhzRTTfdpPT0dI0YMUKSlJ6eri5dujS4T6SkpIQeWaxt27aKiorS3r17fWPl5eVq1qyZMjMz6VEQqKurU319vd%2BVlFOnTkmSMjIy6NE/IWCFmJSUFKWlpWn%2B/PmqqqpSWVmZXn75ZWVlZQW6tCattrZWM2fO1NSpU9WvXz%2B/uczMTEVFRWnJkiWqqanR7t27tWrVKnpmsWnTpmnz5s1as2aN1qxZo6KiIknSmjVrNHToUJWXl2vlypU6efKkdu7cqZ07d2rUqFEBrrppsdvtGjlypF588UX94x//0JEjR7R48WINHTpUw4cPp0dBoFevXmrZsqUWLlyompoaud1uLVmyRH369NEdd9xBj/5JmJcbekLO4cOHNWvWLP3lL39RVFSUxowZo0ceeYR7egLoww8/1H/%2B53%2BqefPmDeY2bdqk6upqzZ49WyUlJWrdurUefPBB/fznPw9ApTjjq6%2B%2B0sCBA/Xpp59Kkv7v//5Pc%2BfOVVlZmZKSkjRlyhTdcsstAa6y6Tl16pQKCgr05ptv6vTp0xo8eLBmzZqlyMhIehQkSkpK9Pzzz2vfvn1q3ry5%2Bvbtq2nTpikxMZEe/RMCFgAAgGFcIgQAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAw/4fbHqjToo8Ij0AAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-7348152909121889448”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1480</td> <td class=”number”>46.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>958</td> <td class=”number”>29.8%</td> <td>

<div class=”bar” style=”width:65%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>234</td> <td class=”number”>7.3%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>155</td> <td class=”number”>4.8%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>76</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>49</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>39</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>16</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (21)</td> <td class=”number”>75</td> <td class=”number”>2.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-7348152909121889448”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1480</td> <td class=”number”>46.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>958</td> <td class=”number”>29.8%</td> <td>

<div class=”bar” style=”width:65%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>234</td> <td class=”number”>7.3%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>155</td> <td class=”number”>4.8%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>76</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>27.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>39.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>51.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>83.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SUPERC14”><s>SUPERC14</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_SUPERC09”><code>SUPERC09</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.98347</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SUPERCPTH09”>SUPERCPTH09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>1646</td>

</tr> <tr>

<th>Unique (%)</th> <td>51.3%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.015629</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>0.25621</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>46.1%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram5073879343977118253”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives5073879343977118253,#minihistogram5073879343977118253”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives5073879343977118253”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles5073879343977118253”

aria-controls=”quantiles5073879343977118253” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram5073879343977118253” aria-controls=”histogram5073879343977118253”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common5073879343977118253” aria-controls=”common5073879343977118253”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme5073879343977118253” aria-controls=”extreme5073879343977118253”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles5073879343977118253”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0.0074821</td>

</tr> <tr>

<th>Q3</th> <td>0.025661</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.053383</td>

</tr> <tr>

<th>Maximum</th> <td>0.25621</td>

</tr> <tr>

<th>Range</th> <td>0.25621</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.025661</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.020955</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.3408</td>

</tr> <tr>

<th>Kurtosis</th> <td>12.401</td>

</tr> <tr>

<th>Mean</th> <td>0.015629</td>

</tr> <tr>

<th>MAD</th> <td>0.015898</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.403</td>

</tr> <tr>

<th>Sum</th> <td>48.951</td>

</tr> <tr>

<th>Variance</th> <td>0.00043911</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram5073879343977118253”>

<img 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ARtgesPLy8jRixIiLbnfgwAEbqgEAADDP9r8ijIuL09y5c7Vnzx7P2G9%2B8xv9/Oc/b/ZsKgAAgGBke8BavXq1qqur1a9fP8/Y6NGj1dTUpDVr1thdDgAAgHG2XyL88MMPtX37dkVHR3vGYmNjlZ2drdtuu83ucgAAAIyz/QxWfX29wsPDmxcSGqq6ujq7ywEAADDO9oA1fPhwrVmzRidOnPCMffPNN1q5cqXXoxoAAACCle2XCH/5y1/qZz/7ma6//npFRESoqalJtbW16tWrl37729/aXQ4AAIBxtgesXr166a233tIHH3yg0tJShYaGqk%2BfPho1apTCwsLsLgcAAMA4v7x7cfv27TV%2B/Hh/HBoAAMDnbA9YR48e1dq1a/Xll1%2Bqvr6%2B2fy7775rd0kAAABG%2BeUerPLyco0aNUodO3a0%2B/AAAAA%2BZ3vAOnjwoN599105nU67Dw0AAGAL2x/T0LVrV85cAQCAVs32gDVnzhytX79elmXZfWgAAABb2H6J8IMPPtDf/vY3bd26VVdffbVCQ70z3h/%2B8Ae7SwIAADDK9oAVERGhG2%2B80e7DAgAA2Mb2gLV69Wq7DwkAAGAr2%2B/BkqSvvvpKL7zwgn7xi194xj799FN/lAIAAGCc7QHrL3/5i6ZMmaLdu3eroKBA0ncPH73vvvt4yCgAAGgVbA9Y69at06OPPqrt27crJCRE0nfvT7hmzRrl5OTYXQ4AAIBxtgesf/zjH8rIyJAkT8CSpFtuuUUlJSV2lwMAAGCc7QGrc%2BfO530PwvLycrVv397ucgAAAIyzPWANHTpUTz/9tGpqajxjR44c0ZIlS3T99dfbXQ4AAIBxtj%2Bm4Re/%2BIXuv/9%2BpaSkqLGxUUOHDlVdXZ369%2B%2BvNWvW2F0OAACAcbYHrB49eqigoED79u3TkSNH1KFDB/Xp00cjR470uicLAAAgWNkesCSpXbt2Gj9%2BvD8ODQAA4HO2B6yxY8de8EwVz8ICAADBzvaANXHiRK%2BA1djYqCNHjqiwsFD333%2B/3eUAAAAYZ3vAWrx48XnH3377bf31r3%2B1uRoAAADz/PJehOczfvx4vfXWW/4uAwAA4LIFTMD6/PPPZVmWv8sAAAC4bLZfIrz77rubjdXV1amkpEQ333yz3eUAAAAYZ3vAio2NbfZXhOHh4Zo%2Bfbruuusuu8sBAAAwzvaAxdPaAQBAa2d7wNq2bVuLt73jjjt8WAkAAIBv2B6wli1bpqampmY3tIeEhHiNhYSEELAAAEBQsj1gvfzyy3rllVc0d%2B5cxcXFybIsffHFF3rppZd0zz33KCUlxe6SAAAAjPLLPVi5ubnq3r27Z%2By6665Tr169NGvWLBUUFNhdEgAAgFG2Pwfr66%2B/VlRUVLPxyMhIlZWV2V0OAACAcbYHrJ49e2rNmjVyuVyesaqqKq1du1bXXHON3eUAAAAYZ/slwl/%2B8pdatGiR8vPz1alTJ4WGhqqmpkYdOnRQTk6O3eUAAAAYZ3vAGjVqlN5//33t27dPx48fl2VZ6t69u2644QZ17tzZ7nIAAACMsz1gSdIVV1yhcePG6fjx4%2BrVq5c/SgAAAPAZ2%2B/Bqq%2Bv15IlSzRkyBDdeuutkr67B2v27NmqqqqyuxwAAADjbA9YzzzzjA4dOqTs7GyFhv778I2NjcrOzra7HAAAAONsD1hvv/22nn/%2Bed1yyy2eN32OjIzU6tWrtXv3brvLAQAAMM72gFVbW6vY2Nhm406nUydPnrS7HAAAAONsD1jXXHON/vrXv0qS13sP7tq1S//1X/9ldzkAAADG2f5XhD/5yU/00EMPadq0aWpqatKrr76qgwcP6u2339ayZcvsLgcAAMA42wNWenq6HA6Hfve73yksLEwvvvii%2BvTpo%2BzsbN1yyy12lwMAAGCc7QGrsrJS06ZN07Rp0%2Bw%2BNAAAgC1svwdr3LhxXvdeAQAAtDa2B6yUlBTt3LnTyL7279%2Bv1NRUZWVlNZvbsWOHJk%2BerCFDhmjq1Kn68MMPPXNNTU1at26dxo0bp%2BHDh2vWrFk6evSoZ97tdmvhwoVKTU3VqFGjtGzZMtXX1xupGQAAtH62XyK86qqr9Ktf/Uq5ubm65ppr1K5dO6/5tWvXtmg/L730krZs2aLevXs3mzt06JCWLFmi9evX68c//rHefvttzZ8/X7t27VKPHj20adMmbd%2B%2BXS%2B99JK6d%2B%2BudevWKTMzU3/6058UEhKiJ554QqdPn1ZBQYEaGhr08MMPKzs7W48//riRHgAAgNbN9jNYxcXF%2BtGPfqTOnTvL5XKpvLzc66OlwsPDvzdgbd68WWlpaUpLS1N4eLimTJmiAQMG6M0335Qk5efna%2BbMmerbt68iIiKUlZWlkpISHThwQP/617%2B0Z88eZWVlyel0qnv37nrwwQf1xhtvqKGhwVgfAABA62XbGaysrCytW7dOv/3tbz1jOTk5yszM/EH7u%2B%2B%2B%2B753rqioSGlpaV5jCQkJKiwsVH19vYqLi5WQkOCZi4iIUO/evVVYWKjq6mqFhYUpLi7OM5%2BYmKiTJ0/qq6%2B%2B8hr/PuXl5aqoqPAaczg6KiYmpqXLa5GwMNvz8WVxOAK/3rM9DbbeBjJ6ah49NY%2Be%2BkZb7qttAWvv3r3NxnJzc39wwLoQt9utqKgor7GoqCgVFxfrxIkTsizrvPMul0tdunRRRESE5218zs5JksvlatHx8/PztX79eq%2BxzMxMLViw4Icsp9WIju7k7xJaLDLyCn%2BX0OrQU/PoqXn01DfaYl9tC1jn%2B8tBX/414cX2faH5y60rPT1dY8eO9RpzODrK5aq9rP2eK9h%2BIzC9fl8ICwtVZOQVqqqqU2Njk7/LaRXoqXn01Dx66huB0Fd//XJvW8D6zzNCFxozITo6Wm6322vM7XbL6XSqS5cuCg0NPe98165d5XQ6VVNTo8bGRoWFhXnmJKlr164tOn5MTEyzy4EVFdU6c6Ztf9MG0/obG5uCqt5gQE/No6fm0VPfaIt9Da5TIC2UlJSkgwcPeo0VFhZq0KBBCg8PV//%2B/VVUVOSZq6qqUmlpqQYOHKj4%2BHhZlqXDhw97fW1kZKT69Olj2xoAAEDwapUBa8aMGfrzn/%2Bs999/X6dOndKWLVv09ddfa8qUKZKkjIwM5eXlqaSkRDU1NcrOzlZ8fLySk5PldDo1YcIEPfvss6qsrNTx48eVk5Oj6dOny%2BGw/akWAAAgCNmWGBoaGrRo0aKLjrX0OVjJycmSpDNnzkiS9uzZI%2Bm7s00DBgxQdna2Vq9erbKyMvXr108bN25Ut27dJEl33323KioqdO%2B996q2tlYpKSleN6U/%2BeSTWr58ucaNG6d27drptttuO%2B/DTAEAAM4nxLLpfWvuvffeFm33n49xaE0qKqqN79PhCNVN2fuN79dXdi4c6e8SLsrhCFV0dCe5XLVt7n4BX6Gn5tFT8%2BipbwRCX7t16%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%2BounTp2vo0KGaNGmS3nzzTa%2BvzcvL04QJEzR06FBlZGTo4MGD/lgCAAAIUq36vQh37dqlq6%2B%2B2musvLxcDz74oJYtW6bJkyfrk08%2B0bx589SnTx8lJydr7969euGFF/Tyyy8rLi5OeXl5mjt3rnbv3q2OHTv6aSUAACCYtNozWN9n%2B/btio2N1fTp0xUeHq7U1FSNHTtWmzdvliTl5%2Bdr6tSpGjRokDp06KDZs2dLkt577z1/lg0AAIJIqz6DtXbtWn366aeqqanRrbfeqqVLl6qoqEgJCQle2yUkJGjnzp2SpKKiIk2cONEzFxoaqvj4eBUWFmrSpEktOm55ebkqKiq8xhyOjoqJibnMFXkLC2tz%2BdhWDgf9NeHs65TXqzn01Dx66httua%2BtNmANHjxYqamp%2BvWvf62jR49q4cKFWrlypdxut7p37%2B61bZcuXeRyuSRJbrdbUVFRXvNRUVGe%2BZbIz8/X%2BvXrvcYyMzO1YMGCH7ga%2BEN0dCd/l9CqREZe4e8SWh16ah499Y222NdWG7Dy8/M9/%2B7bt68WL16sefPmadiwYRf9WsuyLuvY6enpGjt2rNeYw9FRLlftZe33XG3xNwI7mf7/aqvCwkIVGXmFqqrq1NjY5O9yWgV6ah499Y1A6Ku/fllutQHrXFdffbUaGxsVGhoqt9vtNedyueR0OiVJ0dHRzebdbrf69%2B/f4mPFxMQ0uxxYUVGtM2f4pg0m/H%2BZ1djYRE8No6fm0VPfaIt9bZWnQD7//HOtWbPGa6ykpETt27dXWlpas8cuHDx4UIMGDZIkJSUlqaioyDPX2Niozz//3DMPAABwMa0yYHXt2lX5%2BfnKzc3V6dOndeTIET333HNKT0/X7bffrrKyMm3evFmnTp3Svn37tG/fPs2YMUOSlJGRoW3btumzzz5TXV2dNmzYoPbt22v06NH%2BXRQAAAgarfISYffu3ZWbm6u1a9d6AtKdd96prKwshYeHa%2BPGjVq1apVWrlypnj176plnntG1114rSbrxxhv1yCOPaOHChfr222%2BVnJys3NxcdejQwc%2BrAgAAwSLEutw7utEiFRXVxvfpcITqpuz9xveL7%2BxcONLfJbQKDkeooqM7yeWqbXP3YPgKPTWPnvpGIPS1W7fOfjluq7xECAAA4E8ELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwxz%2BLgAIVLc%2B%2B7/%2BLuGS7Fw40t8lAAD%2BP85gAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAY70V4HmVlZVq5cqUOHDigjh07auLEiVq0aJFCQ8mjCFzB9N6JvG8igNaOgHUeDz30kBITE7Vnzx59%2B%2B23mjNnjq688kr99Kc/9XdpAAAgCHBK5hyFhYU6fPiwFi9erM6dOys2NlYzZ85Ufn6%2Bv0sDAABBgjNY5ygqKlLPnj0VFRXlGUtMTNSRI0dUU1OjiIiIi%2B6jvLxcFRUVXmMOR0fFxMQYrTUsjHyM4BRMlzOD0TuLb/B3CUHn7M9Tfq6a1Zb7SsA6h9vtVmRkpNfY2bDlcrlaFLDy8/O1fv16r7H58%2BfroYceMleovgty9/f4Uunp6cbDW1tVXl6u/Px8emoQPTWPnppXXl6u1157mZ4a1pb72vYiZQtYlnVZX5%2Benq6tW7d6faSnpxuq7t8qKiq0fv36ZmfL8MPRU/PoqXn01Dx66httua%2BcwTqH0%2BmU2%2B32GnO73QoJCZHT6WzRPmJiYtpcUgcAAP/GGaxzJCUl6dixY6qsrPSMFRYWql%2B/furUqZMfKwMAAMGCgHWOhIQEJScna%2B3ataqpqVFJSYleffVVZWRk%2BLs0AAAQJMJWrFixwt9FBJobbrhBBQUFeuqpp/TWW29p%2BvTpmjVrlkJCQvxdWjOdOnXSiBEjOLtmED01j56aR0/No6e%2B0Vb7GmJd7h3dAAAA8MIlQgAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAXXp8BRAAAIzUlEQVQAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgBpqysTA888IBSUlI0ZswYPfPMM2pqajrvtnl5eZowYYKGDh2qjIwMHTx40DN36tQp/fd//7duvPFGpaSkaMGCBXK5XHYtI6CY6um9996rxMREJScnez6mTJli1zICyqX0tLa2VosXL1ZcXJxKSkq85txutxYuXKjU1FSNGjVKy5YtU319vR1LCDimejp27FglJSV5vU7nzp1rxxICzqX09Pe//70mTJigIUOG6Pbbb9eePXs8c01NTVq3bp3GjRun4cOHa9asWTp69Khdywg4pvq6dOlSz/v/nv247rrr7FqG71kIKHfeeaf1%2BOOPW1VVVdaRI0esm2%2B%2B2XrllVeabffuu%2B9a1113nfXZZ59ZdXV11saNG62RI0datbW1lmVZ1urVq62pU6da//znPy2Xy2XNnz/fmjNnjt3LCQimenrPPfdYb7zxht3lB6SW9vT48ePWzTffbD322GPWgAEDrOLiYq/5%2BfPnWw888ID17bffWsePH7fS09Otp556yq5lBBRTPR0zZoz10Ucf2VV2QGtpT3ft2mUNGzbM%2Bvjjj63Tp09bf/zjH63ExESrtLTUsizLysvLs8aMGWMVFxdb1dXV1pNPPmlNnjzZampqsntJAcFUX5csWWI9//zzdpdvGwJWAPn73/9uxcfHW2632zP2%2BuuvWxMmTGi27QMPPGA9/fTTns8bGxutkSNHWgUFBVZDQ4M1bNgwa8%2BePZ754uJiKy4uzjp%2B/LhvFxFgTPXUsghYZ11KTw8dOmS988471tGjR5uFgYqKCuvaa6%2B1Dh065Bnbt2%2BfNXjwYOv06dO%2BXUSAMdVTyyJgnXUpPd22bZu1adMmr7ERI0ZYb775pmVZljVp0iTrtdde88xVV1dbCQkJ1qeffuqj6gOXyb629oDFJcIAUlRUpJ49eyoqKsozlpiYqCNHjqimpqbZtgkJCZ7PQ0NDFR8fr8LCQpWWlqq6ulqJiYme%2Bb59%2B6pDhw4qKiry/UICiKmenrVjxw5NnDhRQ4YM0cyZM1VaWur7RQSYS%2Bnptddeq/Hjx593P4cOHVJYWJji4uK89nPy5El99dVXvik%2BQJnq6Vl5eXkaP368hgwZogULFujbb7/1Sd2B7FJ6evvtt%2BsnP/mJ5/OqqirV1taqe/fuqq%2BvV3FxsdfPhoiICPXu3dvrZ0NbYaqvZ3300Ue64447NGTIEE2fPt3rtoxgR8AKIG63W5GRkV5jZ1/E594/5Xa7vV7gZ7d1uVxyu92S1GxfkZGRbe4%2BLFM9lb4Lqf3799frr7%2Bud999V06nU7Nnz9bp06d9uILAcyk9vdh%2BIiIiFBIScln7aQ1M9VSS4uPjNXDgQP3pT3/Sjh075Ha79fDDDxurNVj80J5alqXHH39cgwYN0ogRI3TixAlZlnXBnw1tiam%2BSlKvXr3Uu3dvbdy4Ufv379d1112nn/3sZ62mrw5/FwBvlmUZ2/ZS9tWamerpihUrvD5/8sknlZKSok8%2B%2BUTXX3/9Dy0vKJl6bfEa/TdTvcjJyfH8u1OnTlq%2BfLkmTpyo0tJSXXPNNUaOESwutacNDQ1aunSpiouLlZeXd1n7as1M9TUzM9Nru0cffVQFBQXas2eP7rrrLiO1%2BhNnsAKI0%2Bn0nH06y%2B12KyQkRE6n02s8Ojr6vNs6nU7PtufOnzhxQl27dvVB5YHLVE/PJyIiQlFRUfrmm2/MFh3gLqWnF9tPTU2NGhsbvfYjidepflhPz6dnz56SpPLy8svaT7C51J7W19drzpw5%2Buc//6lNmzbpyiuvlCR16dJFoaGh591XW3udSub6ej5hYWG66qqrWs1rlYAVQJKSknTs2DFVVlZ6xgoLC9WvXz916tSp2bb/eT9VY2OjPv/8cw0aNEi9evVSVFSU1/w//vEPnT59WklJSb5fSAAx1dOamhqtWLHCK0xVVlaqsrJSvXr18v1CAsil9PRC4uPjZVmWDh8%2B7LWfyMhI9enTx2jNgc5UT8vKyrR8%2BXKvy9ZnH%2BPA6/T7e2pZlrKysuRwOPSb3/xG0dHRnrnw8HD179/f62dDVVWVSktLNXDgQN8vJMCY6qtlWVq9erXX9//p06dVWlraal6rBKwAcvZ5IGvXrlVNTY1KSkr06quvKiMjQ5J0yy236OOPP5YkZWRkaNu2bfrss89UV1enDRs2qH379ho9erTCwsI0Y8YMvfjiizp27JhcLpf%2B53/%2BRzfddNMFf3tojUz1NCIiQgcOHNCqVavkdrt14sQJrVy5UnFxcRoyZIg/l2i7S%2BnphTidTk2YMEHPPvusKisrdfz4ceXk5Gj69OlyONrW3Qumetq1a1ft3btXa9as0cmTJ/XNN99o9erVGjNmjNeNxW3BpfR0%2B/btKi4u1nPPPafw8PBm%2B8rIyFBeXp5KSkpUU1Oj7OxsxcfHKzk52dY1BQJTfQ0JCdH//d//aeXKlfrmm29UW1ur7OxstWvX7qJ/xBE07P6zRVzYsWPHrNmzZ1sDBw60UlNTreeff97zrJUBAwZY%2B/bt82y7adMmKy0tzUpKSrIyMjKsL774wjN36tQpa8WKFdbw4cOtIUOGWI888ohVVVVl%2B3oCgamelpWVWZmZmdaIESOswYMHW/PmzWtzj704q6U9zcnJsZKSkqzExERrwIABVmJiopWUlGTl5ORYlmVZVVVVVlZWljV48GBr%2BPDh1sqVK61Tp075bV3%2BZKqnhw8ftmbOnGkNGzbMGjZsmLV06VLrxIkTfluXP7W0p/fdd58VHx9vJSUleX0sW7bMsizLampqsp577jnr%2BuuvtwYOHGj9/Oc/t44dO%2Ba3dfmbqb66XC5r6dKlVmpqqjVw4EDrnnvuafbYkWAWYlncuQcAAGASlwgBAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwLD/B8vRNYUH3CO9AAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common5073879343977118253”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1480</td> <td class=”number”>46.1%</td> <td>

<div class=”bar” style=”width:90%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0386548</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0275535</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0447147</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0249856</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0239057</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0362555</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0282845</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0202544</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0417362</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (1635)</td> <td class=”number”>1635</td> <td class=”number”>50.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme5073879343977118253”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1480</td> <td class=”number”>46.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.000803939</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.001247</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00135308</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00159667</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.156494523</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.16655563</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.170502984</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.202224469</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.256213169</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SUPERCPTH14”>SUPERCPTH14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>1776</td>

</tr> <tr>

<th>Unique (%)</th> <td>55.3%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.018324</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>0.24808</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>41.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram1549492813937386366”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives1549492813937386366,#minihistogram1549492813937386366”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives1549492813937386366”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles1549492813937386366”

aria-controls=”quantiles1549492813937386366” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram1549492813937386366” aria-controls=”histogram1549492813937386366”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common1549492813937386366” aria-controls=”common1549492813937386366”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme1549492813937386366” aria-controls=”extreme1549492813937386366”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles1549492813937386366”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0.014289</td>

</tr> <tr>

<th>Q3</th> <td>0.029034</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.058677</td>

</tr> <tr>

<th>Maximum</th> <td>0.24808</td>

</tr> <tr>

<th>Range</th> <td>0.24808</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.029034</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.023228</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.2676</td>

</tr> <tr>

<th>Kurtosis</th> <td>10.824</td>

</tr> <tr>

<th>Mean</th> <td>0.018324</td>

</tr> <tr>

<th>MAD</th> <td>0.017195</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.3571</td>

</tr> <tr>

<th>Sum</th> <td>57.39</td>

</tr> <tr>

<th>Variance</th> <td>0.00053954</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram1549492813937386366”>

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ckwDO3cuVPJycmSJJfLJafTqU6dOtXruHFxcXUuB7rd5aquPre/Ce26/pqaWtvWbgf011r011r011p27q%2B9ToGcoR07duiCCy7wGwsLC9M333yjuXPn6ttvv1VlZaXy8/PVpEkTDR48WLGxsRo6dKieeuopHT58WAcPHtSiRYuUmZnpF9QAAAB%2BjG0TQ2pqqqTvn3ElSZs2bZL0/dmmU9xut9q0aVPntY888ogef/xxjRw5UhUVFerevbteeuklNW/eXJI0b948zZ49W4MGDVKTJk10/fXXa/LkyVYvCQAAhAjbBqz/G6R%2BzIYNG0473qpVK%2BXl5f3o61q2bKknn3zyrGsDAADntpC%2BRAgAABAMBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBktg5YW7duVXp6uiZPnuw3vnLlSl188cVKTU31%2B/jss88kSbW1tVq4cKEGDRqkPn366M4779S%2Bfft8r/d6vZo0aZLS09PVr18/zZgxQ8eOHQvo2gAAgH3ZNmA999xzmj9/vjp06HDa%2BT59%2Bsjlcvl9dO/eXZK0bNkyrV69WkuXLtWWLVvUsWNHZWdnyzAMSdKsWbNUVVWlNWvWaMWKFSopKVF%2Bfn7A1gYAAOzNtgErMjJSy5cv/9GA9VOKioo0duxYXXTRRYqKitLkyZNVUlKiTz/9VN999502bdqkyZMnKzY2Vu3atdM999yjFStW6OTJkxasBAAAhBpHsAs4W2PGjPnJ%2BQMHDug3v/mNtm/fLqfTqZycHN144406duyYdu3apW7duvm2jYqKUocOHeRyuVReXq6IiAglJib65pOTk3X06FHt3r3bb/zHlJaWyu12%2B405HM0VFxd3hqv8aRER9srHDoe96j3VX7v12S7or7Xor7Xor7VCob%2B2DVg/JTY2Vh07dtT999%2Bvzp0766233tKDDz6ouLg4/fKXv5RhGIqOjvZ7TXR0tDwej1q1aqWoqCiFhYX5zUmSx%2BOp1/GLiopUUFDgN5adna2cnJwGrszeYmJaBLuEs%2BJ0nhfsEkIa/bUW/bUW/bWWnfsbkgGrf//%2B6t%2B/v%2B/z6667Tm%2B99ZZWrlypqVOnSpLvfqvT%2Bam5%2Bhg1apQGDhzoN%2BZwNJfHU9mg/f6Q3ZK92eu3WkREuJzO81RWVqWamtpglxNy6K%2B16K%2B16K%2B1zOxvsH65D8mAdTrx8fHavn27WrVqpfDwcHm9Xr95r9er1q1bKzY2VhUVFaqpqVFERIRvTpJat25dr2PFxcXVuRzodperuvrc/ia06/pramptW7sd0F9r0V9r0V9r2bm/9joFUk%2Bvvvqq1q5d6zdWUlKihIQERUZGqkuXLiouLvbNlZWVae/everevbuSkpJkGIZ27tzpm3e5XHI6nerUqVPA1gAAAOwrJAPWiRMn9PDDD8vlcunkyZNas2aN/v73v2v06NGSpKysLBUWFqqkpEQVFRXKz89XUlKSUlNTFRsbq6FDh%2Bqpp57S4cOHdfDgQS1atEiZmZlyOM6ZE34AAKABbJsYUlNTJUnV1dWSpE2bNkn6/mzTmDFjVFlZqYkTJ8rtduuCCy7QokWLlJKSIkkaPXq03G63brvtNlVWViotLc3vpvR58%2BZp9uzZGjRokJo0aaLrr7%2B%2BzsNMAQAAfkyY0dA7ulEvbne56ft0OMJ1df5W0/drlXWTrgh2CWfE4QhXTEwLeTyVtr0HoDGjv9aiv9aiv9Yys79t27Y0qaozE5KXCAEAAIKJgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGCygAesgQMHqqCgQAcOHAj0oQEAAAIi4AHrpptu0tq1azV48GDddddd2rhxo6qrqwNdBgAAgGUCHrCys7O1du1avfbaa%2BrSpYseffRRZWRk6IknntCePXsCXQ4AAIDpgnYPVnJysnJzc7VlyxZNnz5dr732mq699lrdeeed%2Buyzz4JVFgAAQIMFLWCdPHlSa9eu1W9/%2B1vl5uaqXbt2euihh5SUlKSxY8dq9erVwSoNAACgQRyBPmBJSYmWL1%2BuN954Q5WVlRo6dKheeukl9erVy7dNnz59NGfOHN1www2BLg8AAKDBAh6wrrvuOnXq1Enjxo3T8OHD1apVqzrbZGRk6PDhw4EuDQAAwBQBD1iFhYXq27fvz2736aefBqAaAAAA8wX8HqzExESNHz9emzZt8o396U9/0m9/%2B1t5vd5AlwMAAGC6gAesvLw8lZeXq3Pnzr6x/v37q7a2Vo899ligywEAADBdwC8Rvvfee1q9erViYmJ8Yx07dlR%2Bfr6uv/76QJcDAABguoCfwTp27JgiIyPrFhIerqqqqnrvZ%2BvWrUpPT9fkyZPrzG3cuFHDhg1Tz549NXToUL322mu%2BuWeeeUZJSUlKTU31%2B/juu%2B8kScePH9fvfvc7XXXVVUpLS1NOTo48Hs9ZrBQAAJyrAh6w%2BvTpo8cee0xHjhzxjX377beaO3eu36Mafspzzz2n%2BfPnq0OHDnXmPvvsM02dOlU5OTn697//renTp2vevHnatm2bb5sbb7xRLpfL76NNmzaSpIULF6q4uFhFRUXasGGDDMPQQw891MBVAwCAc0nAA9b06dP1wQcf6PLLL1ffvn3Vu3dv9e/fX9u3b9f8%2BfPrtY/IyEgtX778tAHL6/Vq3LhxGjx4sBwOhzIyMtS1a1e/gPVjqqurtXz5ct1zzz06//zz1apVK02aNEnvvPOOvv322zNeKwAAODcF/B6shIQEvfnmm/r73/%2BuvXv3Kjw8XJ06dVK/fv0UERFRr32MGTPmR%2BeuuuoqXXXVVb7Pq6ur5Xa71a5dO9/YF198odGjR%2BvLL7/U%2Beefr4ceekj9%2BvXT3r17VV5eruTkZN%2B2F110kZo1a6bi4mK/ffyU0tJSud1uvzGHo7ni4uLq9fr6iogI2oP4z4rDYa96T/XXbn22C/prLfprLfprrVDob8ADliQ1bdpUgwcPDsix8vPz1bx5c1177bWSpPbt2yshIUFTpkxRXFycioqKNH78eK1atcr3mAin0%2Bm3D6fTeUb3YRUVFamgoMBvLDs7Wzk5OQ1cjb3FxLQIdglnxek8L9glhDT6ay36ay36ay079zfgAWvfvn1asGCBvvrqKx07dqzO/Ntvv23KcQzDUH5%2BvtasWaPCwkLfjfU333yzbr75Zt92Y8eO1ZtvvqlVq1b5znwZhtGgY48aNUoDBw70G3M4msvjqWzQfn/Ibsne7PVbLSIiXE7neSorq1JNTW2wywk59Nda9Nda9NdaZvY3WL/cBzxgTZ8%2BXaWlperXr5%2BaN29uyTFqa2v10EMP6bPPPtOrr76qhISEn9w%2BPj5epaWlio2NlfT9fVwtWvzvP8iRI0fUunXreh8/Li6uzuVAt7tc1dXn9jehXddfU1Nr29rtgP5ai/5ai/5ay879DXjA2r59u95%2B%2B21fmLHCo48%2Bqq%2B%2B%2Bkqvvvpqnfc6/J//%2BR/17NlTl19%2BuW%2BspKRE1157rRISEhQdHa3i4mLFx8dLkr788kudOHFCKSkpltULAABCS8CvMbVu3dqyM1eS9NFHH2nVqlVaunTpad9I2uv1au7cudq9e7eOHz%2BuF154QXv37tWIESMUERGhW265Rc8%2B%2B6wOHDggj8ejJ598UldffbXvMQ4AAAA/J%2BBnsMaNG6eCggJNmTJFYWFhZ7WP1NRUSd//haAk3/saulwurVixQuXl5RowYIDfa/r06aMXXnhBU6ZMkfT9vVder1edO3fWn/70J7Vv316SlJOTo8rKSt14442qrq7WgAEDNGfOnLOqEwAAnJvCjIbe0X2G7rvvPn388ccyDEMXXHCBwsP9T6L95S9/CWQ5AeN2l5u%2BT4cjXFfnbzV9v1ZZN%2BmKYJdwRhyOcMXEtJDHU2nbewAaM/prLfprLfprLTP727ZtS5OqOjMBP4MVFRXl95wqAACAUBPwgJWXlxfoQwIAAARUUB6ktHv3bj3zzDN%2B7/H3n//8JxilAAAAmC7gAeuDDz7QsGHDtHHjRq1Zs0bS9w8fHTNmjGkPGQUAAAimgAeshQsX6oEHHtDq1at9f0WYkJCgxx57TIsWLQp0OQAAAKYLeMD68ssvlZWVJUl%2Bj2m45pprVFJSEuhyAAAATBfwgNWyZcvTvgdhaWmpmjZtGuhyAAAATBfwgHXppZfq0UcfVUVFhW9sz549ys3N9Xv7GgAAALsK%2BGMaHnroId1%2B%2B%2B1KS0tTTU2NLr30UlVVValLly567LHHAl0OAACA6QIesNq3b681a9bo3Xff1Z49e9SsWTN16tRJV1xxxVm/dQ4AAEBjEvCAJUlNmjTR4MGDg3FoAAAAywU8YA0cOPAnz1TxLCwAAGB3AQ9Y1157rV/Aqqmp0Z49e%2BRyuXT77bcHuhwAAADTBTxgTZ069bTjGzZs0D//%2Bc8AVwMAAGC%2BoLwX4ekMHjxYb775ZrDLAAAAaLBGE7A%2B//xzGYYR7DIAAAAaLOCXCEePHl1nrKqqSiUlJRoyZEigywEAADBdwANWx44d6/wVYWRkpDIzM3XzzTcHuhwAAADTBTxg8bR2AAAQ6gIesN544416bzt8%2BHALKwEAALBGwAPWjBkzVFtbW%2BeG9rCwML%2BxsLAwAhYAALClgAes559/Xi%2B88ILGjx%2BvxMREGYahL774Qs8995xuvfVWpaWlBbokAAAAUwXlHqylS5eqXbt2vrHevXsrISFBd955p9asWRPokgAAAEwV8Odgff3114qOjq4z7nQ6tX///kCXAwAAYLqAB6z4%2BHg99thj8ng8vrGysjItWLBAF1544Rnta%2BvWrUpPT9fkyZPrzK1du1Y33HCDevbsqZEjR%2Bq9997zzdXW1mrhwoUaNGiQ%2BvTpozvvvFP79u3zzXu9Xk2aNEnp6enq16%2BfZsyYoWPHjp3FagEAwLko4AFr%2BvTpWrdundLT09W7d2/17dtXl112mVauXKlp06bVez/PPfec5s%2Bfrw4dOtSZ27Fjh3JzczV16lR9%2BOGHGjt2rO69914dPHhQkrRs2TKtXr1aS5cu1ZYtW9SxY0dlZ2f7brKfNWuWqqqqtGbNGq1YsUIlJSXKz883pwEAACDkBfwerH79%2Bumdd97Ru%2B%2B%2Bq4MHD8owDLVr105XXnmlWrZsWe/9REZGavny5XrkkUd0/Phxv7nXX39dGRkZysjIkCQNGzZML7/8slatWqW7775bRUVFGjt2rC666CJJ0uTJk5WWlqZPP/1UF1xwgTZt2qS//vWvio2NlSTdc889mjhxonJzc9WkSROTOgEAAEJVwAOWJJ133nkaNGiQDh48qISEhLPax5gxY350rri42BeuTunWrZtcLpeOHTumXbt2qVu3br65qKgodejQQS6XS%2BXl5YqIiFBiYqJvPjk5WUePHtXu3bv9xgEAAE4n4AHr2LFjmj17tt58801J0vbt21VWVqb7779fTz75pJxOZ4OP4fV669xIHx0drV27dunIkSMyDOO08x6PR61atVJUVJTf2/mc2vb/3jf2U0pLS%2BV2u/3GHI7miouLO5vl/KiIiEbzXt314nDYq95T/bVbn%2B2C/lqL/lqL/lorFPob8ID1xBNPaMeOHcrPz9eDDz7oG6%2BpqVF%2Bfr7mzZtnynF%2B%2BCDTM5n/udf%2BnKKiIhUUFPiNZWdnKycnp0H7tbuYmBbBLuGsOJ3nBbuEkEZ/rUV/rUV/rWXn/gY8YG3YsEEvv/yyOnbsqNzcXEnfP6IhLy9Pw4cPNyVgxcTEyOv1%2Bo15vV7FxsaqVatWCg8PP%2B1869atFRsbq4qKCtXU1CgiIsI3J0mtW7eu1/FHjRqlgQMH%2Bo05HM3l8VSe7ZJOy27J3uz1Wy0iIlxO53kqK6tSTU1tsMsJOfTXWvTXWvTXWmb2N1i/3Ac8YFVWVqpjx451xmNjY3X06FFTjpGSkqLt27f7jblcLl133XWKjIxUly5dVFxcrL59%2B0r6/jERe/fuVffu3RUfHy/DMLRz504lJyf7Xut0OtWpU6d6HT8uLq7O5UC3u1zV1ef2N6Fd119TU2vb2u2A/lqL/lqL/lrLzv0N%2BCmQCy%2B8UP/85z8l%2BV9Iwb5QAAAZpUlEQVSKW79%2BvX7xi1%2BYcoxbbrlF77//vt555x0dP35cy5cv19dff61hw4ZJkrKyslRYWKiSkhJVVFQoPz9fSUlJSk1NVWxsrIYOHaqnnnpKhw8f1sGDB7Vo0SJlZmbK4QjK3wQAAACbCXhi%2BPWvf6377rtPN910k2pra/Xiiy9q%2B/bt2rBhg2bMmFHv/aSmpkqSqqurJUmbNm2S9P3Zpq5duyo/P195eXnav3%2B/OnfurCVLlqht27aSpNGjR8vtduu2225TZWWl0tLS/O6ZmjdvnmbPnq1BgwapSZMmuv7660/7MFMAAIDTCTMaekf3WVixYoVefvll7d69W82aNVOnTp00duxYXXPNNYEuJWDc7nLT9%2BlwhOvq/K2m79cq6yZdEewSzojDEa6YmBbyeCpte4q6MaO/1qK/1qK/1jKzv23b1v8Zm2YK%2BBmsw4cP66abbtJNN90U6EMDAAAERMDvwRo0aFCDH4MAAADQmAU8YKWlpWndunWBPiwAAEDABPwS4fnnn69HHnlES5cu1YUXXljnvf0WLFgQ6JIAAABMFfCAtWvXLv3yl7%2BUVP%2B3ngEAALCTgAWsyZMna%2BHChfrzn//sG1u0aJGys7MDVQIAAEBABOwerM2bN9cZW7p0aaAODwAAEDABC1in%2B8tB/poQAACEooAFrLCwsHqNAQAA2F3AH9MAAAAQ6ghYAAAAJgvYXxGePHlSU6ZM%2BdkxnoMFAADsLmABq1evXiotLf3ZMQAAALsLWMD6v8%2B/AgAACGXcgwUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYL2HsRBtK///1v3XHHHX5jhmHo5MmTKiws1JgxY9S0aVO/%2Bd///vf61a9%2BJUkqLCzUsmXL5Ha7lZiYqBkzZiglJSVg9QMAAHsLyYDVp08fuVwuv7Fnn31WO3fulCTFx8dr8%2BbNp33t5s2b9cwzz%2Bj5559XYmKiCgsLNX78eG3cuFHNmze3vHYAAGB/58Qlwv/%2B97968cUX9eCDD/7stkVFRRo5cqR69OihZs2a6a677pIkbdmyxeoyAQBAiAjJM1g/9PTTT%2Bumm27SL37xC%2B3bt0%2BVlZXKzs7Wtm3b1LRpU91xxx0aO3aswsLCVFxcrGuvvdb32vDwcCUlJcnlcum6666r1/FKS0vldrv9xhyO5oqLizN1XRER9srHDoe96j3VX7v12S7or7Xor7Xor7VCob8hH7C%2B%2BeYbbdy4URs3bpQkRUVFqWvXrrr99tu1cOFC/etf/9LEiRPVsmVLZWZmyuv1Kjo62m8f0dHR8ng89T5mUVGRCgoK/Mays7OVk5PT8AXZ2NX5W4NdwhnZ9sg1kiSn87wgVxLa6K%2B16K%2B16K%2B17NzfkA9Yy5Yt05AhQ9S2bVtJUnJysv785z/75vv166fRo0dr5cqVyszMlPT9DfENMWrUKA0cONBvzOFoLo%2BnskH7/SE7J3s7KCurktN5nsrKqlRTUxvsckJOREQ4/bUQ/bUW/bWWmf2NiWlhUlVnJuQD1oYNG5Sbm/uT28THx2vDhg2SpJiYGHm9Xr95r9erLl261PuYcXFxdS4Hut3lqq7mm9BOTn1T19TU8m9nIfprLfprLfprLTv3N6RPgezYsUP79%2B/XFVdc4Rtbt26dXnnlFb/tdu/erYSEBElSSkqKiouLfXM1NTX6/PPP1aNHj8AUDQAAbC%2BkA9bnn3%2BuVq1aKSoqyjfWpEkTPf7443rvvfd08uRJ/eMf/9CKFSuUlZUlScrKytIbb7yhTz75RFVVVVq8eLGaNm2q/v37B2kVAADAbkL6EuF3333nu/fqlMGDB2v69Ol6%2BOGHdeDAAbVp00bTp0/XkCFDJElXXXWV7r//fk2aNEmHDh1Samqqli5dqmbNmgVjCQAAwIbCjIbe0Y16cbvLTd%2BnwxFuu7/Ms5O3pl6pmJgW8ngqbXsPQGPmcITTXwvRX2vRX2uZ2d%2B2bVuaVNWZCelLhAAAAMFAwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJOFbMBKTExUSkqKUlNTfR8PP/ywJOmDDz5QZmamLr30Ul133XVatWqV32sLCws1dOhQXXrppcrKytL27duDsQQAAGBTjmAXYKX169frggsu8BsrLS3VPffcoxkzZuiGG27QRx99pAkTJqhTp05KTU3V5s2b9cwzz%2Bj5559XYmKiCgsLNX78eG3cuFHNmzcP0koAAICdhOwZrB%2BzevVqdezYUZmZmYqMjFR6eroGDhyo119/XZJUVFSkkSNHqkePHmrWrJnuuusuSdKWLVuCWTYAALCRkA5YCxYsUP/%2B/dW7d2/NmjVLlZWVKi4uVrdu3fy269atm%2B8y4A/nw8PDlZSUJJfLFdDaAQCAfYXsJcJLLrlE6enpevzxx7Vv3z5NmjRJc%2BfOldfrVbt27fy2bdWqlTwejyTJ6/UqOjrabz46Oto3Xx%2BlpaVyu91%2BYw5Hc8XFxZ3lak4vIiKk83HQneovfbYG/bUW/bUW/bVWKPQ3ZANWUVGR7/8vuugiTZ06VRMmTFCvXr1%2B9rWGYTT42AUFBX5j2dnZysnJadB%2BEVhO53l%2B/4U16K%2B16K%2B16K%2B17NzfkA1YP3TBBReopqZG4eHh8nq9fnMej0exsbGSpJiYmDrzXq9XXbp0qfexRo0apYEDB/qNORzN5fFUnmX1p2fnZG8HZWVVcjrPU1lZlWpqaoNdTsiJiAinvxaiv9aiv9Yys78xMS1MqurMhGTA%2Bvzzz7Vq1SpNmzbNN1ZSUqKmTZsqIyNDf/3rX/223759u3r06CFJSklJUXFxsUaMGCFJqqmp0eeff67MzMx6Hz8uLq7O5UC3u1zV1XwT2smpb%2Bqamlr%2B7SxEf61Ff61Ff61l5/6G5CmQ1q1bq6ioSEuXLtWJEye0Z88ePf300xo1apRuvPFG7d%2B/X6%2B//rqOHz%2Bud999V%2B%2B%2B%2B65uueUWSVJWVpbeeOMNffLJJ6qqqtLixYvVtGlT9e/fP7iLAgAAthGSZ7DatWunpUuXasGCBb6ANGLECE2ePFmRkZFasmSJ5s%2Bfr7lz5yo%2BPl5PPPGELr74YknSVVddpfvvv1%2BTJk3SoUOHlJqaqqVLl6pZs2ZBXhUAALCLMKOhd3SjXtzuctP36XCE6%2Br8rabvF997a%2BqViolpIY%2Bn0ranqBszhyOc/lqI/lqL/lrLzP62bdvSpKrOTEheIgQAAAgmAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGCykA1Y%2B/fvV3Z2ttLS0pSenq5p06aprKxM33zzjRITE5Wamur38cc//tH32rVr1%2BqGG25Qz549NXLkSL333ntBXAkAALAbR7ALsMr48eOVkpKizZs3q7y8XNnZ2Xr88cc1YcIESZLL5Trt63bs2KHc3FwVFBTosssu04YNG3Tvvfdq/fr1at%2B%2BfSCXAAAAbCokz2CVlZUpJSVFU6ZMUYsWLdS%2BfXuNGDFC27Zt%2B9nXvv7668rIyFBGRoYiIyM1bNgwde3aVatWrQpA5QAAIBSE5Bksp9OpvLw8v7EDBw4oLi7O9/mDDz6o999/X9XV1br55puVk5OjJk2aqLi4WBkZGX6v7dat24%2Be8Tqd0tJSud1uvzGHo7nf8c0QERGS%2BbjRONVf%2BmwN%2Bmst%2Bmst%2BmutUOhvSAasH3K5XHr55Ze1ePFiNW3aVD179tTVV1%2BtRx55RDt27NB9990nh8OhiRMnyuv1Kjo62u/10dHR2rVrV72PV1RUpIKCAr%2Bx7Oxs5eTkmLIeBIbTeZ7ff2EN%2Bmst%2Bmst%2BmstO/c35APWRx99pAkTJmjKlClKT0%2BXJP3lL3/xzXfv3l3jxo3TkiVLNHHiREmSYRgNOuaoUaM0cOBAvzGHo7k8nsoG7feH7Jzs7aCsrEpO53kqK6tSTU1tsMsJORER4fTXQvTXWvTXWmb2NyamhUlVnZmQDlibN2/WAw88oFmzZmn48OE/ul18fLy%2B%2B%2B47GYahmJgYeb1ev3mv16vY2Nh6HzcuLq7O5UC3u1zV1XwT2smpb%2Bqamlr%2B7SxEf61Ff61Ff61l5/6G7CmQjz/%2BWLm5uXr66af9wtUHH3ygxYsX%2B227e/duxcfHKywsTCkpKdq%2BfbvfvMvlUo8ePQJSNwAAsL%2BQDFjV1dWaOXOmpk6dqn79%2BvnNtWzZUosWLdLf/vY3nTx5Ui6XS3/84x%2BVlZUlSbrlllv0/vvv65133tHx48e1fPlyff311xo2bFgwlgIAAGwoJC8RfvLJJyopKdH8%2BfM1f/58v7n169dr4cKFKigo0O9%2B9zu1bNlSt912m26//XZJUteuXZWfn6%2B8vDzt379fnTt31pIlS9S2bdtgLAUAANhQSAas3r1764svvvjR%2Bfj4eF199dU/Oj9kyBANGTLEitIAAMA5ICQvEQIAAAQTAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZCH5mAbADFfnbw12CWdk3aQrgl0CAOD/xxksAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGSOYBcAwBy/euofwS6h3tZNuiLYJQCApTiDBQAAYDIC1mns379fd999t9LS0jRgwAA98cQTqq2tDXZZAADAJrhEeBr33XefkpOTtWnTJh06dEjjxo1TmzZt9Jvf/CbYpQEhwU6XMyUuaQI4c5zB%2BgGXy6WdO3dq6tSpatmypTp27KixY8eqqKgo2KUBAACb4AzWDxQXFys%2BPl7R0dG%2BseTkZO3Zs0cVFRWKior62X2UlpbK7Xb7jTkczRUXF2dqrRER5GMgEOx2xs1O3pp6ZbBLOCunfv7yc9gaodBfAtYPeL1eOZ1Ov7FTYcvj8dQrYBUVFamgoMBv7N5779V9991nXqH6Psjd3v4rjRo1yvTwhu/7W1RURH8tQn%2BtRX%2BtVVpaqpdeep7%2BWiQU%2BmvfaGghwzAa9PpRo0Zp5cqVfh%2BjRo0yqbr/5Xa7VVBQUOdsGcxBf61Ff61Ff61Ff60VCv3lDNYPxMbGyuv1%2Bo15vV6FhYUpNja2XvuIi4uzbeIGAAANxxmsH0hJSdGBAwd0%2BPBh35jL5VLnzp3VokWLIFYGAADsgoD1A926dVNqaqoWLFigiooKlZSU6MUXX1RWVlawSwMAADYRMWfOnDnBLqKxufLKK7VmzRo9/PDDevPNN5WZmak777xTYWFhwS6tjhYtWqhv376cXbMI/bUW/bUW/bUW/bWW3fsbZjT0jm4AAAD44RIhAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFiNzP79%2B3X33XcrLS1NAwYM0BNPPKHa2trTbltYWKihQ4fq0ksvVVZWlrZv3%2B6bO378uH73u9/pqquuUlpamnJycuTxeAK1jEbLrP7edtttSk5OVmpqqu9j2LBhgVpGo3Um/a2srNTUqVOVmJiokpISvzmv16tJkyYpPT1d/fr104wZM3Ts2LFALKFRM6u/AwcOVEpKit/X7/jx4wOxhEbtTPr76quvaujQoerZs6duvPFGbdq0yTdXW1urhQsXatCgQerTp4/uvPNO7du3L1DLaLTM6u%2B0adN87xt86qN3796BWkb9GWhURowYYcycOdMoKysz9uzZYwwZMsR44YUX6mz39ttvG7179zY%2B%2BeQTo6qqyliyZIlxxRVXGJWVlYZhGEZeXp4xcuRI47///a/h8XiMe%2B%2B91xg3blygl9PomNXfW2%2B91VixYkWgy2/06tvfgwcPGkOGDDEefPBBo2vXrsauXbv85u%2B9917j7rvvNg4dOmQcPHjQGDVqlPHwww8HahmNlln9HTBggPHhhx8GqmzbqG9/169fb/Tq1cvYtm2bceLECeO1114zkpOTjb179xqGYRiFhYXGgAEDjF27dhnl5eXGvHnzjBtuuMGora0N9JIaFbP6m5uba/zhD38IdPlnjIDViHz22WdGUlKS4fV6fWOvvPKKMXTo0Drb3n333cajjz7q%2B7ympsa44oorjDVr1hgnT540evXqZWzatMk3v2vXLiMxMdE4ePCgtYtoxMzqr2EQsE7nTPq7Y8cO46233jL27dtXJwC43W7j4osvNnbs2OEbe/fdd41LLrnEOHHihLWLaMTM6q9hELBO50z6%2B8YbbxjLli3zG%2Bvbt6%2BxatUqwzAM47rrrjNeeukl31x5ebnRrVs34z//%2BY9F1Td%2BZvbXLgGLS4SNSHFxseLj4xUdHe0bS05O1p49e1RRUVFn227duvk%2BDw8PV1JSklwul/bu3avy8nIlJyf75i%2B66CI1a9ZMxcXF1i%2BkkTKrv6esXbtW1157rXr27KmxY8dq79691i%2BiETuT/l588cUaPHjwafezY8cORUREKDEx0W8/R48e1e7du60p3gbM6u8phYWFGjx4sHr27KmcnBwdOnTIkrrt4kz6e%2BONN%2BrXv/617/OysjJVVlaqXbt2OnbsmHbt2uX38yMqKkodOnTw%2B/lxrjGrv6d8%2BOGHGj58uHr27KnMzEy/WzgaCwJWI%2BL1euV0Ov3GTn0x/vD%2BKa/X6/eFempbj8cjr9crSXX25XQ6z%2Bn7sMzqr/R9YO3SpYteeeUVvf3224qNjdVdd92lEydOWLiCxu1M%2Bvtz%2B4mKilJYWFiD9hNqzOqvJCUlJal79%2B7629/%2BprVr18rr9WrixImm1WpHZ9tfwzA0c%2BZM9ejRQ3379tWRI0dkGMZP/vw4F5nVX0lKSEhQhw4dtGTJEm3dulW9e/fWHXfc0ej66wh2AfBnGIZp257Jvs4VZvV3zpw5fp/PmzdPaWlp%2Buijj3T55ZefbXm2Z9bXHF%2B7p2dWXxYtWuT7/xYtWmj27Nm69tprtXfvXl144YWmHMOOzrS/J0%2Be1LRp07Rr1y4VFhY2aF/nArP6m52d7bfdAw88oDVr1mjTpk26%2BeabTanVDJzBakRiY2N9Z59O8Xq9CgsLU2xsrN94TEzMabeNjY31bfvD%2BSNHjqh169YWVG4PZvX3dKKiohQdHa1vv/3W3KJt5Ez6%2B3P7qaioUE1Njd9%2BJPH1a0J/Tyc%2BPl6SVFpa2qD92NmZ9vfYsWMaN26c/vvf/2rZsmVq06aNJKlVq1YKDw8/7b74%2Bm14f08nIiJC559/fqP7%2BiVgNSIpKSk6cOCADh8%2B7BtzuVzq3LmzWrRoUWfb/3s/VU1NjT7//HP16NFDCQkJio6O9pv/8ssvdeLECaWkpFi/kEbKrP5WVFRozpw5fmHq8OHDOnz4sBISEqxfSCN1Jv39KUlJSTIMQzt37vTbj9PpVKdOnUyt2U7M6u/%2B/fs1e/Zsv8vZpx7jwNdv/fprGIYmT54sh8OhP/3pT4qJifHNRUZGqkuXLn4/P8rKyrR37151797d%2BoU0Umb11zAM5eXl%2Bf18OHHihPbu3dvovn4JWI3Iqed6LFiwQBUVFSopKdGLL76orKwsSdI111yjbdu2SZKysrL0xhtv6JNPPlFVVZUWL16spk2bqn///oqIiNAtt9yiZ599VgcOHJDH49GTTz6pq6%2B%2B%2Bid/Cwh1ZvU3KipKn376qebPny%2Bv16sjR45o7ty5SkxMVM%2BePYO5xKA6k/7%2BlNjYWA0dOlRPPfWUDh8%2BrIMHD2rRokXKzMyUw3Hu3tVgVn9bt26tzZs367HHHtPRo0f17bffKi8vTwMGDPC7ifhccyb9Xb16tXbt2qWnn35akZGRdfaVlZWlwsJClZSUqKKiQvn5%2BUpKSlJqampA19SYmNXfsLAwffPNN5o7d66%2B/fZbVVZWKj8/X02aNPnZP%2BwIuED/2SJ%2B2oEDB4y77rrL6N69u5Genm784Q9/8D07pWvXrsa7777r23bZsmVGRkaGkZKSYmRlZRlffPGFb%2B748ePGnDlzjD59%2Bhg9e/Y07r//fqOsrCzg62lszOrv/v37jezsbKNv377GJZdcYkyYMOGcfgTGKfXt76JFi4yUlBQjOTnZ6Nq1q5GcnGykpKQYixYtMgzDMMrKyozJkycbl1xyidGnTx9j7ty5xvHjx4O2rsbCrP7u3LnTGDt2rNGrVy%2BjV69exrRp04wjR44EbV2NRX37O2bMGCMpKclISUnx%2B5gxY4ZhGIZRW1trPP3008bll19udO/e3fjtb39rHDhwIGjraizM6q/H4zGmTZtmpKenG927dzduvfXWOo8iaQzCDIM78QAAAMzEJUIAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACT/X9cbhufwqAFjwAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common1549492813937386366”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1346</td> <td class=”number”>41.9%</td> <td>

<div class=”bar” style=”width:76%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0305567</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.026936</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0494438</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0443675</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.026583</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0175821</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0367377</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0236552</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0447888</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (1765)</td> <td class=”number”>1768</td> <td class=”number”>55.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme1549492813937386366”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1346</td> <td class=”number”>41.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.000611147</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00117306</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00130335</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00139067</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.173340267</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.18308312</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.193685842</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.211237854</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.2480774</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_School Breakfast Program participants FY 2009”><s>School Breakfast Program participants FY 2009</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_National School Lunch Program participants, FY 2015”><code>National School Lunch Program participants, FY 2015</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.92183</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_School Breakfast Program participants FY 2011”><s>School Breakfast Program participants FY 2011</s><br/>

<small>Constant</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is constant and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Constant value</th> <td></td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_School Breakfast Program participants, FY 2012”><s>School Breakfast Program participants, FY 2012</s><br/>

<small>Constant</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is constant and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Constant value</th> <td></td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_School Breakfast Program participants, FY 2013”><s>School Breakfast Program participants, FY 2013</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_School Breakfast Program participants FY 2009”><code>School Breakfast Program participants FY 2009</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.98406</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_School Breakfast Program participants, FY 2014”><s>School Breakfast Program participants, FY 2014</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_School Breakfast Program participants, FY 2013”><code>School Breakfast Program participants, FY 2013</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99862</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_School Breakfast Program participants, FY 2015”><s>School Breakfast Program participants, FY 2015</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_School Breakfast Program participants, FY 2014”><code>School Breakfast Program participants, FY 2014</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99676</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_State Population, 2011”><s>State Population, 2011</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_State Population, 2010”><code>State Population, 2010</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99998</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_State Population, 2013”><s>State Population, 2013</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_State Population, 2012”><code>State Population, 2012</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99999</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_State Population, 2014”><s>State Population, 2014</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_State Population, 2013”><code>State Population, 2013</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99998</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_State Population, 2015”><s>State Population, 2015</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_State Population, 2014”><code>State Population, 2014</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99997</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_State Population, 2016”><s>State Population, 2016</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_State Population, 2015”><code>State Population, 2015</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99996</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_State Population, 2009”><s>State Population, 2009</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_Child and Adult Care participants, FY 2015”><code>Child and Adult Care participants, FY 2015</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.92968</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_State Population, 2010”><s>State Population, 2010</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_State Population, 2009”><code>State Population, 2009</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.9999</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_State Population, 2012”><s>State Population, 2012</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_State Population, 2011”><code>State Population, 2011</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99998</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_StateFIPS “>StateFIPS <br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>40</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.2%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>17.3%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>555</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>34.869</td>

</tr> <tr>

<th>Minimum</th> <td>13</td>

</tr> <tr>

<th>Maximum</th> <td>56</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram2848153906961446898”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives2848153906961446898,#minihistogram2848153906961446898”
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</div> <div class=”row collapse col-md-12” id=”descriptives2848153906961446898”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles2848153906961446898”

aria-controls=”quantiles2848153906961446898” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram2848153906961446898” aria-controls=”histogram2848153906961446898”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common2848153906961446898” aria-controls=”common2848153906961446898”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme2848153906961446898” aria-controls=”extreme2848153906961446898”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles2848153906961446898”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>13</td>

</tr> <tr>

<th>5-th percentile</th> <td>16</td>

</tr> <tr>

<th>Q1</th> <td>24</td>

</tr> <tr>

<th>Median</th> <td>33</td>

</tr> <tr>

<th>Q3</th> <td>45</td>

</tr> <tr>

<th>95-th percentile</th> <td>54</td>

</tr> <tr>

<th>Maximum</th> <td>56</td>

</tr> <tr>

<th>Range</th> <td>43</td>

</tr> <tr>

<th>Interquartile range</th> <td>21</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>12.849</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.36851</td>

</tr> <tr>

<th>Kurtosis</th> <td>-1.2503</td>

</tr> <tr>

<th>Mean</th> <td>34.869</td>

</tr> <tr>

<th>MAD</th> <td>11.184</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.14454</td>

</tr> <tr>

<th>Sum</th> <td>92576</td>

</tr> <tr>

<th>Variance</th> <td>165.1</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram2848153906961446898”>

<img 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Wr169VL9%2BfbvHAQAAuGyOC6z8/Hy9/fbbio6OtnsUAACAK%2BK4t2m44YYbeOQKAAD4NMcF1uTJk5WVlSXLsuweBQAA4Io47inCd955R59%2B%2Bqlef/113XTTTQoO9m7AdevW2TQZAABA7TgusBo2bKi7777b7jEAwGcNfP59u0cAAp7jAiszM9PuEQAAAK6K416DJUlHjx7VsmXL9OSTT3rW9u3bZ%2BNEAAAAtee4wMrLy9PQoUO1Y8cO5ebmSvrvm48%2B8MADvMkoAADwCY4LrKVLl%2Brxxx/X5s2bFRQUJOm/v59w4cKFeumll2yeDgAA4NIcF1j/%2Bte/lJaWJkmewJKke%2B%2B9V4WFhXaNBQAAUGuOC6xGjRpd9HcQFhcXKywszIaJAAAALo/jAqtr165asGCBzpw541k7duyYZs2apTvuuMPGyQAAAGrHcW/T8OSTT2rcuHFKSEhQVVWVunbtqoqKCrVu3VoLFy60ezwAAIBLclxg3XjjjcrNzdWePXt07Ngx1atXTy1bttSdd97p9ZosAAAAp3JcYEnSddddp379%2Btk9BgAAwBVxXGAlJyf/4iNVvBcWAABwOscF1qBBg7wCq6qqSseOHZPL5dK4ceNsnAwAAKB2HBdYM2fOvOj69u3b9eGHH9bxNAAAAJfPcW/T8HP69eunN9980%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%2BqqkqLFy%2Bu9fUUFRUpPT1dCQkJSkxM1OzZsz2BdvDgQY0dO1bdunVT//79tWrVKq%2BP3bJli4YMGaIuXbooJSVF7733npmTAwAAAcFxgbV9%2B3a9%2BOKLuvfeez2/9DkiIkKZmZnasWNHra9nypQpioiI0M6dO/X666/r8OHD%2BvOf/6xz585p8uTJuv322/Xuu%2B9q6dKlWrFihee6Dx48qFmzZmnmzJn64IMPNH78eD3yyCM6efLkNTlfAADgfxz3Gqzy8nLFxsbWWI%2BOjtbZs2drdR1lZWWKj4/XjBkz1KBBAzVo0ED333%2B/1qxZo927d%2BvChQuaOnWqQkJC1L59e40YMULZ2dnq37%2B/cnJylJSUpKSkJEnS0KFDtXbtWm3atEkPPfSQyVMFjBr4/Pt2jwAA%2BB/HBdbNN9%2BsDz/8UAkJCV6/e3Dbtm369a9/Xavr%2BOERrx87ceKEYmJiVFBQoLZt2yokJMRzLC4uTjk5OZKkgoICT1z9%2BLjL5ar1ORQXF6ukpMRrLTS0vmJiYmp9Hf4oNLTuHjANCQn2%2BhO4GnX5tQtnq4uvhUD9/uVv9zPHBdaYMWP06KOPavjw4aqurtarr76q/Px8bd%2B%2BXXPnzr2i63S5XFq7dq2WL1%2BurVu3KiIiwut448aN5Xa7VV1dLbfbXeOd5CMjI3XkyJFa3152draysrK81tLT0zVt2rQrmt9fREU1qPPbjIi4vs5vE/7Hjq9dOFNdfi0E2vcvf7ufOS6wRo0apdDQUK1du1YhISF6%2BeWX1bJlSy1evFj33nvvZV/fJ598oqlTp2rGjBlKTEzU1q1bL3q5H17vJcnrkbMrMWrUKCUnJ3uthYbWV2lp%2BVVdr6%2Bry/MPCQlWRMT1KiurUFVVdZ3dLvxToN938f/q4mshUL9/XavPrV3h5rjAOn36tIYPH67hw4df9XXt3LlTjz/%2BuJ566ikNGzZM0n9fy3X8%2BHGvy7ndbjVu3FjBwcGKioqS2%2B2ucTw6OrrWtxsTE1Pj6cCSku9VWRk4d5SLseP8q6qqA/7zjqvH1xB%2BUJdfC4H2/cvfztVxT3j27dv3qh9BkqRPP/1Us2bN0gsvvOCJK0mKj4/XoUOHVFlZ6VlzuVzq1KmT53h%2Bfr7Xdf34OAAAwKU4LrASEhJ%2B9mm82qqsrNQf/vAHzZw5U7169fI6lpSUpIYNG2r58uWqqKjQZ599pvXr1ystLU2SNHLkSO3du1e7d%2B/W%2BfPntX79eh0/flxDhw69qpkAAEDgcNxThL/61a/0pz/9SStXrtTNN9%2Bs6667zuv4kiVLLnkd%2B/fvV2FhoebPn6/58%2Bd7Hdu2bZtefvllzZs3TytXrlSTJk2UkZGh3r17S5LatGmjxYsXKzMzU0VFRWrVqpVWrFihpk2bGjtHAADg3xwXWEeOHNFvfvMbSVJpaekVXUf37t116NChX7zMP//5z5891r9/f/Xv3/%2BKbhsAAMAxgZWRkaGlS5dqzZo1nrWXXnpJ6enpNk4FAABw%2BRzzGqydO3fWWFu5cqUNkwAAAFwdxwTWxX5y0MRPEwIAANQ1xwTWj9/o85fWAAAAnM4xgQUAAOAvCCwAAADDHPNThBcuXNCMGTMuuVab98ECAACwk2MCq1u3biouLr7kGgAAgNM5JrB%2B/P5XAAAAvozXYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABgWavcACBwDn3/f7hEAAKgTPIIFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGG/TAACXwFuMALhcPIIFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgWKjdAwAA4CsGPv%2B%2B3SPAR/AIFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGEEFgAAgGF%2BG1jvvvuuEhMTlZGRUePYli1bNGTIEHXp0kUpKSl67733PMeqq6u1dOlS9e3bVz169NCECRP01Vdf1eXoAADAx/llYL3yyiuaP3%2B%2BbrnllhrHDh48qFmzZmnmzJn64IMPNH78eD3yyCM6efKkJOkf//iHNm/erJUrV2rXrl2KjY1Venq6LMuq69MAAAA%2Byi8DKzw8XOvXr79oYOXk5CgpKUlJSUkKDw/X0KFD1aZNG23atEmSlJ2drfHjx%2BvWW29Vw4YNlZGRocLCQn322Wd1fRoAAMBH%2BeU7uT/wwAM/e6ygoEBJSUlea3FxcXK5XDp37pyOHDmiuLg4z7GGDRvqlltukcvlUufOnWt1%2B8XFxSopKfFaCw2tr5iYmMs4CwAAAkdoqH895uOXgfVL3G63IiMjvdYiIyN15MgRfffdd7Is66LHS0tLa30b2dnZysrK8lpLT0/XtGnTrnxwAAD8WFRUA7tHMCrgAkvSJV9PdbWvtxo1apSSk5O91kJD66u0tPyqrhcAAH91rf6NtCvcAi6woqKi5Ha7vdbcbreio6PVuHFjBQcHX/T4DTfcUOvbiImJqfF0YEnJ96qsrL7ywQEA8GP%2B9m%2Bkfz3hWQvx8fHKz8/3WnO5XOrUqZPCw8PVunVrFRQUeI6VlZXpyy%2B/VMeOHet6VAAA4KMCLrBGjhypvXv3avfu3Tp//rzWr1%2Bv48ePa%2BjQoZKktLQ0rV69WoWFhTpz5owWL16sdu3aqUOHDjZPDgAAfIVfPkX4QwxVVlZKkt566y1J/32kqk2bNlq8eLEyMzNVVFSkVq1aacWKFWratKkkafTo0SopKdHvfvc7lZeXKyEhocYL1gEAAH5JkMU7aNaJkpLvr8n1Dnz%2B/WtyvQAA1KWt0%2B%2B8JtfbtGmja3K9lxJwTxECAABcawQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQTWRRQVFemhhx5SQkKC%2BvTpo0WLFqm6utrusQAAgI8ItXsAJ3r00UfVvn17vfXWWzp16pQmT56sJk2a6Pe//73dowEAAB/AI1g/4XK59MUXX2jmzJlq1KiRYmNjNX78eGVnZ9s9GgAA8BE8gvUTBQUFat68uSIjIz1r7du317Fjx3TmzBk1bNjwktdRXFyskpISr7XQ0PqKiYkxPi8AAP4gNNS/HvMhsH7C7XYrIiLCa%2B2H2CotLa1VYGVnZysrK8tr7ZFHHtGjjz5qbtD/%2BfhP9xq/Tn9QXFys7OxsjRo1irB1CPbEmdgX52FP/AOBdRGWZV3Vx48aNUrJyclea02bNr2q68TlKSkpUVZWlpKTk/kG5RDsiTOxL87DnvgHAusnoqOj5Xa7vdbcbreCgoIUHR1dq%2BuIiYnhTgEAQADzryc8DYiPj9eJEyd0%2BvRpz5rL5VKrVq3UoEEDGycDAAC%2BgsD6ibi4OHXo0EFLlizRmTNnVFhYqFdffVVpaWl2jwYAAHxEyNNPP/203UM4zV133aXc3Fw9%2B%2ByzevPNN5WamqoJEyYoKCjI7tFwGRo0aKCePXvyyKODsCfOxL44D3vi%2B4Ksq31FNwAAALzwFCEAAIBhBBYAAIBhBBYAAIBhBBYAAIBhBBYAAIBhBBYAAIBhBBYAAIBhBBYAAIBhBBYAAIBhBBZ8XlFRkdLT05WQkKDExETNnj1bZWVlkqSDBw9q7Nix6tatm/r3769Vq1bZPG1g%2BOKLLzRu3Dh169ZNiYmJmj59ukpKSiRJeXl5Sk1NVdeuXTV48GBt2rTJ5mkDz4IFC9S2bVvP39kT%2B7Rt21bx8fHq0KGD579nn31WEvvi8yzAx913333W7NmzrTNnzlgnTpywUlJSrDlz5lgVFRXWXXfdZS1btswqLy%2B38vPzrZ49e1rbt2%2B3e2S/dv78eeuOO%2B6wsrKyrPPnz1unTp2yxo4daz388MPWN998Y3Xu3NnKycmxzp07Z73//vtWx44drc8//9zusQPGgQMHrJ49e1pt2rSxLMtiT2zWpk0b66uvvqqxzr74Ph7Bgk8rKytTfHy8ZsyYoQYNGujGG2/U/fffr48//li7d%2B/WhQsXNHXqVNWvX1/t27fXiBEjlJ2dbffYfq2iokIZGRmaPHmywsLCFB0drXvuuUeHDx/W5s2bFRsbq9TUVIWHhysxMVHJycnKycmxe%2ByAUF1drXnz5mn8%2BPGeNfbEmdgX30dgwadFREQoMzNTTZo08aydOHFCMTExKigoUNu2bRUSEuI5FhcXp/z8fDtGDRiRkZEaMWKEQkNDJUlHjx7VG2%2B8oYEDB6qgoEBxcXFel2dP6s66desUHh6uIUOGeNbYE/stWbJEvXv3Vvfu3fXUU0%2BpvLycffEDBBb8isvl0tq1azV16lS53W5FRER4HW/cuLHcbreqq6ttmjBwFBUVKT4%2BXoMGDVKHDh00bdq0n92T0tJSm6YMHN9%2B%2B62WLVumefPmea2zJ/bq3LmzEhMTtWPHDmVnZ2v//v165pln2Bc/QGDBb3zyySeaMGGCZsyYocRo6Ky2AAAC0ElEQVTExJ%2B9XFBQUB1OFbiaN28ul8ulbdu26fjx43riiSfsHimgZWZmKiUlRa1atbJ7FPxIdna2RowYobCwMN16662aOXOmcnNzdeHCBbtHw1UisOAXdu7cqYceekhz5szRAw88IEmKjo6u8X97brdbjRs3VnAwX/p1ISgoSLGxscrIyFBubq5CQ0Pldru9LlNaWqro6GibJgwMeXl52rdvn9LT02sci4qKYk8c5KabblJVVZWCg4PZFx/HvzLweZ9%2B%2BqlmzZqlF154QcOGDfOsx8fH69ChQ6qsrPSsuVwuderUyY4xA0ZeXp4GDBjg9TTsD0HbsWPHGq8hyc/PZ0%2BusU2bNunUqVPq06ePEhISlJKSIklKSEhQmzZt2BObHDhwQAsXLvRaKywsVFhYmJKSktgXX2f3jzECV%2BPChQvWwIEDrXXr1tU4dv78eatPnz7Wiy%2B%2BaJ09e9bav3%2B/1b17d2vXrl11P2gAKSsrsxITE62FCxdaZ8%2BetU6dOmVNmDDBGjNmjPXtt99aXbp0sV577TXr3Llz1u7du62OHTtaBw8etHtsv%2BZ2u60TJ054/tu3b5/Vpk0b68SJE1ZRURF7YpOTJ09anTt3tlasWGGdP3/eOnr0qDVo0CDr2Wef5b7iB4Isy7LsjjzgSn388cf67W9/q7CwsBrHtm3bpvLycs2bN0/5%2Bflq0qSJJk2apDFjxtgwaWA5dOiQ5s%2Bfr88//1z169fX7bffrtmzZ6tZs2b66KOPNH/%2BfBUWFqp58%2BaaMWOG%2Bvfvb/fIAeXrr79W3759dejQIUliT2z00UcfacmSJTp06JDCwsJ0//33KyMjQ%2BHh4eyLjyOwAAAADOM1WAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIb9H2GzLCjUqQ/PAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common2848153906961446898”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>54.0</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17.0</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>130</td> <td class=”number”>4.0%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.0</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>24.0</td> <td class=”number”>113</td> <td class=”number”>3.5%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>21.0</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>41.0</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>23.0</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>53.0</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>35.0</td> <td class=”number”>93</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (29)</td> <td class=”number”>1395</td> <td class=”number”>43.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>555</td> <td class=”number”>17.3%</td> <td>

<div class=”bar” style=”width:40%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme2848153906961446898”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>13.0</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>15.0</td> <td class=”number”>18</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16.0</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:42%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17.0</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>19.0</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>51.0</td> <td class=”number”>65</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:26%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>53.0</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:38%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>54.0</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>55.0</td> <td class=”number”>29</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>56.0</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Summer Food participants FY 2011”><s>Summer Food participants FY 2011</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_Summer Food particpants FY 2009”><code>Summer Food particpants FY 2009</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.98245</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Summer Food participants, FY 2012”><s>Summer Food participants, FY 2012</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_Summer Food participants FY 2011”><code>Summer Food participants FY 2011</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99308</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Summer Food participants, FY 2013”><s>Summer Food participants, FY 2013</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_Summer Food participants, FY 2012”><code>Summer Food participants, FY 2012</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.98386</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Summer Food participants, FY 2014”><s>Summer Food participants, FY 2014</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_Summer Food participants, FY 2013”><code>Summer Food participants, FY 2013</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.98127</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Summer Food participants, FY 2015”><s>Summer Food participants, FY 2015</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_Summer Food participants, FY 2014”><code>Summer Food participants, FY 2014</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99324</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Summer Food particpants FY 2009”>Summer Food particpants FY 2009<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>40</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.2%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>17.3%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>555</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>34135</td>

</tr> <tr>

<th>Minimum</th> <td>2122</td>

</tr> <tr>

<th>Maximum</th> <td>436620</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram6925012981633540351”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives6925012981633540351,#minihistogram6925012981633540351”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives6925012981633540351”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles6925012981633540351”

aria-controls=”quantiles6925012981633540351” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram6925012981633540351” aria-controls=”histogram6925012981633540351”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common6925012981633540351” aria-controls=”common6925012981633540351”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme6925012981633540351” aria-controls=”extreme6925012981633540351”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles6925012981633540351”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>2122</td>

</tr> <tr>

<th>5-th percentile</th> <td>4418</td>

</tr> <tr>

<th>Q1</th> <td>8333</td>

</tr> <tr>

<th>Median</th> <td>28843</td>

</tr> <tr>

<th>Q3</th> <td>50389</td>

</tr> <tr>

<th>95-th percentile</th> <td>65014</td>

</tr> <tr>

<th>Maximum</th> <td>436620</td>

</tr> <tr>

<th>Range</th> <td>434500</td>

</tr> <tr>

<th>Interquartile range</th> <td>42056</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>39486</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.1568</td>

</tr> <tr>

<th>Kurtosis</th> <td>66.574</td>

</tr> <tr>

<th>Mean</th> <td>34135</td>

</tr> <tr>

<th>MAD</th> <td>22790</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>6.8122</td>

</tr> <tr>

<th>Sum</th> <td>90630000</td>

</tr> <tr>

<th>Variance</th> <td>1559200000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram6925012981633540351”>

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Vlpbm1WaztZLLVdmo/Z4XEREuu72lysurLNmfr1i1/kBzYT1qa%2Bv8PR2ImgQa6hFYmls9/PXDfUgGrLq6Oj3yyCP69NNP9Yc//EFXX321py8uLs5zpuo8t9utbt26KT4%2B3vN569b/V5CTJ0%2Bqbdu2DR4/ISGh3uVAp7NCNTXWvpGD7QvD6vUHmtraupBfY7ChJoGFegQW6tG0QvIC7MKFC/XPf/6zXriSpOTkZJWUlHg%2Br62t1d69e9WnTx9dffXViomJ8er/xz/%2BobNnzyo5Odln8wcAAMEt5ALWhx9%2BqA0bNqigoECxsbH1%2BjMzM/XWW29pz549qqqq0ooVK9SiRQsNHTpUERERuueee/T888/r6NGjcrlc%2Bu1vf6tbb71VV155pR9WAwAAglFQXiLs1auXpG9%2BQ1CStm7dKklyOBxat26dKioqNGzYMK/XDBgwQKtWrdLgwYM1c%2BZM5eTk6Pjx4%2BrVq5cKCgoUFRUlScrOzlZlZaXuvPNO1dTUaNiwYXr88cd9tzgAABD0wkxj7%2BhGgzidFZbty2YLV1xca7lclbo1b5dl%2B21qm3Nu8vcUmsSF9eB%2BhsBATQIL9Qgsza0e7dq18cu4IXeJEAAAwN8IWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFfB6w0tLSlJ%2Bfr6NHj/p6aAAAAJ/wecC6%2B%2B679c477%2BiWW27RlClT9N5776mmpsbX0wAAAGgyPg9YWVlZeuedd/TGG2%2BoW7duWrhwoYYMGaIlS5bo8OHDvp4OAACA5fx2D1ZSUpLmzJmj7du369FHH9Ubb7yh22%2B/XZMnT9ann37qr2kBAAA0mt8C1rlz5/TOO%2B/o/vvv15w5c9S%2BfXs98sgj6tmzpyZNmqSNGzf6a2oAAACNYvP1gIcOHdLatWv11ltvqbKyUiNHjtTLL7%2Bs/v37e7YZMGCAHn/8cY0ePdrX0wMAAGg0nwesUaNGqXPnzpo6daruuusuxcbG1ttmyJAhOnHihK%2BnBgAAYAmfB6zVq1dr4MCBl9zuk08%2B8cFsAAAArOfze7B69OihadOmaevWrZ623//%2B97r//vvldrsva1%2B7du1SamqqcnNz6/W98847Gj16tPr27av09HS9//77nr66ujotXbpUw4cP14ABAzR58mQdOXLE0%2B92u5WTk6PU1FQNGjRIc%2BfOVXV19Q9YLQAAaI58HrAWLVqkiooKde3a1dM2dOhQ1dXVafHixQ3ezwsvvKAFCxaoY8eO9fr27dunOXPmaPbs2frLX/6iSZMmacaMGTp27Jgk6bXXXtPGjRtVUFCg7du3q1OnTsrKypIxRpI0b948VVVVadOmTVq3bp0OHTqkvLy8Rq4cAAA0Fz4PWO%2B//77y8/PVqVMnT1unTp2Ul5enXbt2NXg/kZGRWrt27UUD1po1azRkyBANGTJEkZGRGjNmjLp3764NGzZIkgoLCzVp0iR16dJF0dHRys3N1aFDh/TJJ5/o66%2B/1tatW5Wbm6v4%2BHi1b99e06dP17p163Tu3LlGrx8AAIQ%2Bn9%2BDVV1drcjIyHrt4eHhqqqqavB%2BJk6c%2BJ19JSUlGjJkiFdbYmKiHA6HqqurdfDgQSUmJnr6oqOj1bFjRzkcDlVUVCgiIkI9evTw9CclJen06dP67LPPvNq/S1lZmZxOp1ebzdZKCQkJDV3e94qICPf6GCxstuCab0MFaz1CGTUJLNQjsFAP3/B5wBowYIAWL16sWbNmKSYmRpL01Vdf6amnnvJ6VENjuN1uz77Pi4mJ0cGDB3Xy5EkZYy7a73K5FBsbq%2BjoaIWFhXn1SZLL5WrQ%2BIWFhcrPz/dqy8rKUnZ29g9Zzney21taur%2BmFhfX2t9TaFLBVo/mgJoEFuoRWKhH0/J5wHr00Uf1i1/8QjfeeKOio6NVV1enyspKXX311XrllVcsG%2Bf8/VQ/pP9Sr72U8ePHKy0tzavNZmsll6uyUfs9LyIiXHZ7S5WXN/yMXyCwav2B5sJ61NbW%2BXs6EDUJNNQjsDS3evjrh3ufB6yrr75ab7/9tv70pz/piy%2B%2BUHh4uDp37qxBgwYpIiLCkjHi4uLq/Uai2%2B1WfHy8YmNjFR4eftH%2Btm3bKj4%2BXqdOnVJtba1nPue3bdu2bYPGT0hIqHc50OmsUE2NtW/kYPvCsHr9gaa2ti7k1xhsqElgoR6BhXo0LZ8HLElq0aKFbrnllibbf3JysoqLi73aHA6HRo0apcjISHXr1k0lJSWe53GVl5friy%2B%2BUO/evdWhQwcZY7R//34lJSV5Xmu329W5c%2BcmmzMAAAgdPg9YR44c0TPPPKN//vOfF3221B//%2BMdGj3HPPfcoIyNDO3bs0I033qiNGzfq888/15gxYyRJmZmZKigo0ODBg9W%2BfXvl5eWpZ8%2Be6tWrlyRp5MiRevbZZ/XUU0/p7NmzWr58uTIyMmSz%2BSWPAgCAIOOXe7DKyso0aNAgtWrV6gfv53wYqqmpkSTPg0sdDoe6d%2B%2BuvLw8LVq0SKWlperatatWrlypdu3aSZImTJggp9Opn/3sZ6qsrFRKSorXTelPPvmk5s%2Bfr%2BHDh%2BuKK67QHXfccdGHmQIAAFxMmGnsHd2XqW/fvvrjH/%2Bo%2BPh4Xw7rd05nhWX7stnCFRfXWi5XpW7Na/izw/xtc85N/p5Ck7iwHtzPEBioSWChHoGludWjXbs2fhnX5w/BaNu2baPOXAEAAAQ6nwesqVOnKj8/v9GPQgAAAAhUPr8H609/%2BpM%2B%2Bugjvfnmm/rxj3%2Bs8HDvjPf666/7ekoAAACW8nnAio6O1uDBg309LAAAgM/4PGAtWrTI10MCAAD4lF/%2B0uNnn32mZcuW6ZFHHvG0ffzxx/6YCgAAgOV8HrCKioo0ZswYvffee9q0aZOkbx4%2BOnHiREseMgoAAOBvPg9YS5cu1S9/%2BUtt3LhRYWFhkr75%2B4SLFy/W8uXLfT0dAAAAy/k8YP3jH/9QZmamJHkCliTddtttOnTokK%2BnAwAAYDmfB6w2bdpc9G8QlpWVqUWLFr6eDgAAgOV8HrD69eunhQsX6tSpU562w4cPa86cObrxxht9PR0AAADL%2BfwxDY888oh%2B/vOfKyUlRbW1terXr5%2BqqqrUrVs3LV682NfTAQAAsJzPA9ZVV12lTZs2aefOnTp8%2BLCioqLUuXNn3XTTTV73ZCH0/PTZP/t7CpclVP84NQCg6fk8YEnSFVdcoVtuucUfQwMAADQ5nwestLS07z1TxbOwAABAsPN5wLr99tu9AlZtba0OHz4sh8Ohn//8576eDgAAgOV8HrBmz5590fYtW7bogw8%2B8PFsAAAArOeXv0V4Mbfccovefvttf08DAACg0QImYO3du1fGGH9PAwAAoNF8folwwoQJ9dqqqqp06NAhjRgxwtfTAQAAsJzPA1anTp3q/RZhZGSkMjIyNG7cOF9PBwAAwHI%2BD1g8rR0AAIQ6nwest956q8Hb3nXXXU04EwAAgKbh84A1d%2B5c1dXV1buhPSwszKstLCyMgAUAAIKSzwPWiy%2B%2BqFWrVmnatGnq0aOHjDE6cOCAXnjhBd13331KSUnx9ZQAAAAs5fPHNCxevFgLFixQ//79FR0drTZt2uj666/Xk08%2BqaeeekotWrTw/GuMvXv3auLEibr%2B%2But10003afbs2Tpx4oQkqaioSBkZGerXr59GjRqlDRs2eL129erVGjlypPr166fMzEwVFxc3ai4AAKB58XnA%2BvzzzxUTE1Ov3W63q7S01JIxampq9MADD%2Bi6667T7t27tWnTJp04cUKPP/64ysrKNH36dE2YMEFFRUWaO3eu5s2bJ4fDIUnatm2bli1bpqefflq7d%2B/WsGHDNG3aNJ0%2BfdqSuQEAgNDn84DVoUMHLV68WC6Xy9NWXl6uZ555Rtdcc40lYzidTjmdTt15551q0aKF4uLidOutt2rfvn3auHGjOnXqpIyMDEVGRio1NVVpaWlas2aNJKmwsFDp6enq06ePoqKiNGXKFEnS9u3bLZkbAAAIfT6/B%2BvRRx/VrFmzVFhYqNatWys8PFynTp1SVFSUli9fbskY7du3V8%2BePVVYWKj/%2BI//UHV1td577z0NHTpUJSUlSkxM9No%2BMTFRmzdvliSVlJTo9ttv9/SFh4erZ8%2BecjgcGjVqVIPGLysrk9Pp9Gqz2VopISGhkSv7RkREuNdHNA2brWHHl3oEHmoSWKhHYKEevuHzgDVo0CDt2LFDO3fu1LFjx2SMUfv27XXzzTerTZs2lowRHh6uZcuWadKkSXr55ZclSQMHDtSsWbM0ffp0tW/f3mv72NhYzxk1t9td7xJmTEyM1xm3SyksLFR%2Bfr5XW1ZWlrKzs3/Icr6T3d7S0v3BW1xc68vannoEHmoSWKhHYKEeTcvnAUuSWrZsqeHDh%2BvYsWO6%2BuqrLd//2bNnNW3aNN12222e%2B6eeeOIJzZ49u0Gvb%2BzfRBw/frzS0tK82my2VnK5Khu13/MiIsJlt7dUeXmVJfvDxTW0XhfWo7a2rolnhYagJoGFegSW5laPy/1h2So%2BD1jV1dWaP3%2B%2B3n77bUlScXGxysvLNXPmTP32t7%2BV3W5v9BhFRUX68ssvNXPmTEVERKhNmzbKzs7WnXfeqZtvvllut9tre5fLpfj4eElSXFxcvX63261u3bo1ePyEhIR6lwOdzgrV1Fj7Rm4OXxj%2BdLn1qq2ts7zGaBxqElioR2ChHk3L5xdglyxZon379ikvL0/h4f83fG1trfLy8iwZo7a2tt7DTM%2BePStJSk1NrffYheLiYvXp00eSlJycrJKSEq997d2719MPAABwKT4PWFu2bNHvfvc73XbbbZ4/%2Bmy327Vo0SK99957lozRt29ftWrVSsuWLVNVVZVcLpdWrFihAQMG6M4771RpaanWrFmjM2fOaOfOndq5c6fuueceSVJmZqbeeust7dmzR1VVVVqxYoVatGihoUOHWjI3AAAQ%2BnwesCorK9WpU6d67fHx8ZY9ayouLk7//d//rY8%2B%2BkiDBw/WHXfcoaioKD3zzDNq27atVq5cqVdffVX9%2B/fXwoULtWTJEl177bWSpMGDB2vmzJnKycnRwIEDtXv3bhUUFCgqKsqSuQEAgNDn83uwrrnmGn3wwQdKSUnxuoT37rvv6t/%2B7d8sGyc5OVmvvPLKRfsGDBig9evXf%2Bdr7733Xt17772WzQUAADQvPg9Y9957rx566CHdfffdqqur00svvaTi4mJt2bJFc%2BfO9fV0AAAALOfzgDV%2B/HjZbDa9%2BuqrioiI0PPPP6/OnTsrLy9Pt912m6%2BnAwAAYDmfB6wTJ07o7rvv1t133%2B3roQEAAHzC5ze5Dx8%2BvNEP8gQAAAhkPg9YKSkpnr/7BwAAEIp8fonwRz/6kf7zP/9TBQUFuuaaa3TFFVd49T/zzDO%2BnhIAAIClfB6wDh48qJ/85CeSdFl/QBkAACBY%2BCxg5ebmaunSpV7Pplq%2BfLmysrJ8NQUAAACf8Nk9WNu2bavXVlBQ4KvhAQAAfMZnAetivznIbxMCAIBQ5LOAdf4PO1%2BqDQAAINj5/DENAAAAoY6ABQAAYDGf/RbhuXPnNGvWrEu28RwsAAAQ7HwWsPr376%2BysrJLtgEAAAQ7nwWsC59/BQAAEMq4BwsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIhHbBWrFihQYMG6brrrtOkSZP05ZdfSpKKioqUkZGhfv36adSoUdqwYYPX61avXq2RI0eqX79%2ByszMVHFxsT%2BmDwAAglTIBqzXXntNGzZs0OrVq/X%2B%2B%2B%2Bra9eu%2Bv3vf6%2BysjJNnz5dEyZMUFFRkebOnat58%2BbJ4XBIkrZt26Zly5bp6aef1u7duzVs2DBNmzZNp0%2Bf9vOKAABAsAjZgLVq1Srl5ubqJz/5iaKjo/XYY4/pscce08aNG9WpUydlZGQoMjJSqampSktL05o1ayRJhYWFSk9PV58%2BfRQVFaUpU6ZIkrZv3%2B7P5QAAgCASkgHrq6%2B%2B0pdffqmTJ0/q9ttvV0pKirKzs3XixAmVlJQoMTHRa/vExETPZcBv94eHh6tnz56eM1wAAACXYvP3BJrCsWPHJEnvvvuuXnrpJRljlJ2drccee0zV1dVq37691/axsbFyuVySJLfbrZiYGK/%2BmJgYT38GCC4MAAAWmElEQVRDlJWVyel0erXZbK2UkJDwQ5ZTT0REuNdHNA2brWHHl3oEHmoSWKhHYKEevhGSAcsYI0maMmWKJ0w99NBDuv/%2B%2B5Wamtrg1/9QhYWFys/P92rLyspSdnZ2o/b7bXZ7S0v3B29xca0va3vqEXioSWChHoGFejStkAxYV155pSTJbrd72jp06CBjjM6dOye32%2B21vcvlUnx8vCQpLi6uXr/b7Va3bt0aPP748eOVlpbm1WaztZLLVXlZ6/guERHhsttbqry8ypL94eIaWq8L61FbW9fEs0JDUJPAQj0CS3Orx%2BX%2BsGyVkAxYV111laKjo7Vv3z4lJSVJkkpLS3XFFVdoyJAhWr9%2Bvdf2xcXF6tOnjyQpOTlZJSUlGjt2rCSptrZWe/fuVUZGRoPHT0hIqHc50OmsUE2NtW/k5vCF4U%2BXW6/a2jrLa4zGoSaBhXoEFurRtELyAqzNZlNGRoaef/55/b//9/90/PhxLV%2B%2BXKNHj9bYsWNVWlqqNWvW6MyZM9q5c6d27type%2B65R5KUmZmpt956S3v27FFVVZVWrFihFi1aaOjQof5dFAAACBoheQZLkmbNmqWzZ89q3LhxOnfunEaOHKnHHntMrVu31sqVK7VgwQI98cQT6tChg5YsWaJrr71WkjR48GDNnDlTOTk5On78uHr16qWCggJFRUX5eUUAACBYhJnG3tGNBnE6Kyzbl80Wrri41nK5KnVr3i7L9gtvm3NuatB2F9aD0%2B2BgZoEFuoRWJpbPdq1a%2BOXcUPyEiEAAIA/EbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLNYuAtXDhQvXo0cPzeVFRkTIyMtSvXz%2BNGjVKGzZs8Np%2B9erVGjlypPr166fMzEwVFxf7esoAACCIhXzA2rdvn9avX%2B/5vKysTNOnT9eECRNUVFSkuXPnat68eXI4HJKkbdu2admyZXr66ae1e/duDRs2TNOmTdPp06f9tQQAABBkQjpg1dXVaf78%2BZo0aZKnbePGjerUqZMyMjIUGRmp1NRUpaWlac2aNZKkwsJCpaenq0%2BfPoqKitKUKVMkSdu3b/fHEgAAQBAK6YD1%2BuuvKzIyUqNHj/a0lZSUKDEx0Wu7xMREz2XAb/eHh4erZ8%2BenjNcAAAAl2Lz9wSaytdff61ly5bplVde8Wp3u91q3769V1tsbKxcLpenPyYmxqs/JibG098QZWVlcjqdXm02WyslJCRczhK%2BU0REuNdHNA2brWHHl3oEHmoSWKhHYKEevhGyAWvRokVKT09X165d9eWXX17Wa40xjRq7sLBQ%2Bfn5Xm1ZWVnKzs5u1H6/zW5vaen%2B4C0urvVlbU89Ag81CSzUI7BQj6YVkgGrqKhIH3/8sTZt2lSvLy4uTm6326vN5XIpPj7%2BO/vdbre6devW4PHHjx%2BvtLQ0rzabrZVcrsoG7%2BP7RESEy25vqfLyKkv2h4traL0urEdtbV0TzwoNQU0CC/UILM2tHpf7w7JVQjJgbdiwQcePH9ewYcMk/d8ZqZSUFP3iF7%2BoF7yKi4vVp08fSVJycrJKSko0duxYSVJtba327t2rjIyMBo%2BfkJBQ73Kg01mhmhpr38jN4QvDny63XrW1dZbXGI1DTQIL9Qgs1KNpheQF2IcfflhbtmzR%2BvXrtX79ehUUFEiS1q9fr9GjR6u0tFRr1qzRmTNntHPnTu3cuVP33HOPJCkzM1NvvfWW9uzZo6qqKq1YsUItWrTQ0KFD/bgiAAAQTELyDFZMTIzXjeo1NTWSpKuuukqStHLlSi1YsEBPPPGEOnTooCVLlujaa6%2BVJA0ePFgzZ85UTk6Ojh8/rl69eqmgoEBRUVG%2BXwgAAAhKIRmwvu3HP/6xDhw44Pl8wIABXg8f/bZ7771X9957ry%2BmBgAAQlBIXiIEAADwJwIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgsZANWKWlpcrKylJKSopSU1P18MMPq7y8XJK0b98%2B3Xffferfv79GjBihVatWeb32nXfe0ejRo9W3b1%2Blp6fr/fff98cSAABAkArZgDVt2jTZ7XZt27ZNb775pv75z3/qqaeeUnV1taZOnaobbrhBu3bt0tKlS7Vy5Uq99957kr4JX3PmzNHs2bP1l7/8RZMmTdKMGTN07NgxP68IAAAEi5AMWOXl5UpOTtasWbPUunVrXXXVVRo7dqz%2B/ve/a8eOHTp37pwefPBBtWrVSklJSRo3bpwKCwslSWvWrNGQIUM0ZMgQRUZGasyYMerevbs2bNjg51UBAIBgEZIBy263a9GiRbryyis9bUePHlVCQoJKSkrUo0cPRUREePoSExNVXFwsSSopKVFiYqLX/hITE%2BVwOHwzeQAAEPRs/p6ALzgcDr366qtasWKFNm/eLLvd7tUfGxsrt9uturo6ud1uxcTEePXHxMTo4MGDDR6vrKxMTqfTq81ma6WEhIQfvogLRESEe31E07DZGnZ8qUfgoSaBhXoEFurhGyEfsD788EM9%2BOCDmjVrllJTU7V58%2BaLbhcWFub5b2NMo8YsLCxUfn6%2BV1tWVpays7Mbtd9vs9tbWro/eIuLa31Z21OPwENNAgv1CCzUo2mFdMDatm2bfvnLX2revHm66667JEnx8fH6/PPPvbZzu92KjY1VeHi44uLi5Ha76/XHx8c3eNzx48crLS3Nq81mayWXq/KHLeRbIiLCZbe3VHl5lSX7w8U1tF4X1qO2tq6JZ4WGoCaBhXoEluZWj8v9YdkqIRuwPvroI82ZM0fPPfecBg0a5GlPTk7WH/7wB9XU1Mhm%2B2b5DodDffr08fSfvx/rPIfDoVGjRjV47ISEhHqXA53OCtXUWPtGbg5fGP50ufWqra2zvMZoHGoSWKhHYKEeTSskL8DW1NToscce0%2BzZs73ClSQNGTJE0dHRWrFihaqqqvTJJ59o7dq1yszMlCTdc8892r17t3bs2KEzZ85o7dq1%2BvzzzzVmzBh/LAUAAAShkDyDtWfPHh06dEgLFizQggULvPreffddPf/885o/f74KCgp05ZVXKjc3V0OHDpUkde/eXXl5eVq0aJFKS0vVtWtXrVy5Uu3atfPDSgAAQDAKyYB1/fXX68CBA9%2B7zR/%2B8Ifv7BsxYoRGjBhh9bQAAEAzEZKXCAEAAPyJgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFrP5ewJAoPrps3/29xQuy%2Bacm/w9BQDA/48zWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2BdRGlpqR544AGlpKRo2LBhWrJkierq6vw9LQAAECR4TMNFPPTQQ0pKStLWrVt1/PhxTZ06VVdeeaX%2B/d//3d9TAwCgwYLpcTOh9qgZzmB9i8Ph0P79%2BzV79my1adNGnTp10qRJk1RYWOjvqQEAgCDBGaxvKSkpUYcOHRQTE%2BNpS0pK0uHDh3Xq1ClFR0dfch9lZWVyOp1ebTZbKyUkJFgyx4iIcK%2BPgCTZbLwfzuNrJLBQDzREqH0PI2B9i9vtlt1u92o7H7ZcLleDAlZhYaHy8/O92mbMmKGHHnrIkjmWlZXp5Zdf1Pjx4/X3/7zNkn3ihysrK1NhYaHGjx9vWYhG41z4NUJN/I96%2BM/F/h/B9yzfCK24aBFjTKNeP378eL355pte/8aPH2/R7CSn06n8/Px6Z8ngH9Qj8FCTwEI9Agv18A3OYH1LfHy83G63V5vb7VZYWJji4%2BMbtI%2BEhAR%2BKgAAoBnjDNa3JCcn6%2BjRozpx4oSnzeFwqGvXrmrdurUfZwYAAIIFAetbEhMT1atXLz3zzDM6deqUDh06pJdeekmZmZn%2BnhoAAAgSEY8//vjj/p5EoLn55pu1adMm/eY3v9Hbb7%2BtjIwMTZ48WWFhYf6emkfr1q01cOBAzqoFCOoReKhJYKEegYV6NL0w09g7ugEAAOCFS4QAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAWR0tJSPfDAA0pJSdGwYcO0ZMkS1dXV%2BXtaQWfXrl1KTU1Vbm5uvb533nlHo0ePVt%2B%2BfZWenq7333/f01dXV6elS5dq%2BPDhGjBggCZPnqwjR454%2Bt1ut3JycpSamqpBgwZp7ty5qq6u9vTv27dP9913n/r3768RI0Zo1apVDR47lJWWliorK0spKSlKTU3Vww8/rPLyckmNO2ZNXa9QtX//fv385z9X//79lZqaqpycHDmdTklSUVGRMjIy1K9fP40aNUobNmzweu3q1as1cuRI9evXT5mZmSouLvb0nTlzRr/%2B9a81ePBgpaSkKDs7Wy6Xy9N/qe9vlxq7uVi4cKF69Ojh%2BZyaBDCDoDF27Fjz2GOPmfLycnP48GEzYsQIs2rVKn9PK6gUFBSYESNGmAkTJpicnByvvr1795rk5GSzY8cOU11dbdavX2/69Oljjh49aowxZvXq1WbYsGHm4MGDpqKiwjz55JNm9OjRpq6uzhhjzIwZM8wDDzxgjh8/bo4dO2bGjx9vfvOb3xhjjKmqqjI333yzWbZsmamsrDTFxcVm4MCBZsuWLQ0aO5Tdcccd5uGHHzanTp0yR48eNenp6ebRRx9t9DFrynqFqjNnzpgbb7zR5OfnmzNnzpjjx4%2Bb%2B%2B67z0yfPt189dVX5rrrrjNr1qwx1dXV5s9//rPp3bu3%2BfTTT40xxvzxj380119/vdmzZ4%2BpqqoyK1euNDfddJOprKw0xhizaNEik56ebv71r38Zl8tlZsyYYaZOneoZ%2B/u%2Bv11q7OZi7969ZuDAgaZ79%2B7GmEsfF2riXwSsIPHpp5%2Banj17Grfb7Wn7n//5HzNy5Eg/zir4vPzyy6a8vNzMmTOnXsB64oknTFZWllfbuHHjzMqVK40xxowaNcq8/PLLnr6KigqTmJhoPv74Y%2BN0Os21115r9u3b5%2BnfuXOnue6668zZs2fN5s2bzQ033GBqamo8/UuWLDG/%2BMUvGjR2qDp58qR5%2BOGHjdPp9LS98sorZsSIEY0%2BZk1Zr1DldrvNG2%2B8Yc6dO%2Bdpe/nll82tt95qXnzxRXPXXXd5bZ%2BTk2PmzZtnjDHmgQceMAsXLvT01dbWmptuusls2rTJnDt3zvTv399s3brV03/w4EHTo0cPc%2BzYsUt%2Bf7vU2M1BbW2tGTdunPmv//ovT8CiJoGNS4RBoqSkRB06dFBMTIynLSkpSYcPH9apU6f8OLPgMnHiRLVp0%2BaifSUlJUpMTPRqS0xMlMPhUHV1tQ4ePOjVHx0drY4dO8rhcGjfvn2KiIjwOnWflJSk06dP67PPPlNJSYl69OihiIgIr32fP13/fWOHMrvdrkWLFunKK6/0tB09elQJCQmNOmZNXa9QFRMTo3Hjxslms0mSPvvsM/3v//6vfvrTn37n8f6ueoSHh6tnz55yOBz64osvVFFRoaSkJE9/ly5dFBUVpZKSkkt%2Bf7vU2M3B66%2B/rsjISI0ePdrTRk0CGwErSLjdbtntdq%2B282/8C6%2BZ44dzu91e30ykb46xy%2BXSyZMnZYz5zn63263o6GiFhYV59Uny9H%2B7frGxsXK73aqrq/vesZsTh8OhV199VQ8%2B%2BGCjjllT1yvUlZaWKjk5Wbfffrt69eql7Ozs7zwm59%2Bj31cPt9stSfVeb7fbv/N4N6QezeXr4%2Buvv9ayZcs0f/58r3ZqEtgIWEHEGOPvKYS8Sx3j7%2Bv/IfW58H/wzb2%2BH374oSZPnqxZs2YpNTX1O7e7nGPWlPUKZR06dJDD4dC7776rzz//XL/61a8a9Dpf16O5WLRokdLT09W1a9fLfi018R8CVpCIj4/3/MRxntvtVlhYmOLj4/00q9ASFxd30WMcHx%2Bv2NhYhYeHX7S/bdu2io%2BP16lTp1RbW%2BvVJ8nT/%2B2f7Nxut2e/3zd2c7Bt2zY98MADevTRRzVx4kRJatQxa%2Bp6NQdhYWHq1KmTcnNztWnTJtlstnrH0%2BVyed6j31eP89t8u//kyZOe4/19398utu8Lxw5lRUVF%2Bvjjj5WVlVWv71LHhZr4V/P4ThECkpOTdfToUZ04ccLT5nA41LVrV7Vu3dqPMwsdycnJ9e4fcDgc6tOnjyIjI9WtWzeVlJR4%2BsrLy/XFF1%2Bod%2B/e6tmzp4wx2r9/v9dr7Xa7OnfurOTkZB04cEA1NTX19n2psUPdRx99pDlz5ui5557TXXfd5WlvzDFr6nqFqqKiIo0cOdLrMuj5QNm7d%2B96x7u4uNirHhce79raWu3du1d9%2BvTR1VdfrZiYGK/%2Bf/zjHzp79qySk5Mv%2Bf2tV69e3zt2KNuwYYOOHz%2BuYcOGKSUlRenp6ZKklJQUde/enZoEMl/fVY8fbty4cebRRx81FRUV5uDBgyYtLc28%2Buqr/p5WULrYbxEeOHDA9OrVy2zfvt1UV1ebNWvWmL59%2B5qysjJjzDe/QTN06FDPr/3PmzfP3H333Z7X5%2BTkmClTppjjx4%2Bbo0ePmrvvvtssXrzYGPPNr78PGzbM/O53vzOnT582e/bsMddff73Zvn17g8YOVefOnTM//elPzeuvv16vr7HHrCnrFarKy8tNamqqWbx4sTl9%2BrQ5fvy4mTx5srn33nvN119/bfr27WveeOMNU11dbXbs2GF69%2B7t%2BU3MnTt3mv79%2B5uPP/7YnD592ixbtswMGTLEVFVVGWO%2B%2BS3MsWPHmn/961/mxIkTZurUqeahhx7yjP19398uNXYoc7vd5ujRo55/H3/8senevbs5evSoKS0tpSYBjIAVRI4ePWqmTJlievfubVJTU83vfvc7zzN90DDJyckmOTnZXHvttebaa6/1fH7eli1bzIgRI0xSUpK58847zV//%2BldPX11dnXnuuefMjTfeaHr37m3uv/9%2Br%2BdUlZeXm9zcXHPdddeZAQMGmCeeeMKcOXPG03/gwAEzYcIEk5ycbIYOHWpee%2B01r7l939ih6m9/%2B5vp3r27pw4X/vvyyy8bdcyaul6hav/%2B/ea%2B%2B%2B4zvXv3NjfccIPJyckxx44dM8YY89e//tWMGTPGJCUlmREjRtR7Lthrr71mhgwZYpKTk01mZqY5cOCAp%2B/MmTPm8ccfNwMGDDB9%2B/Y1M2fONOXl5Z7%2BS31/u9TYzcWRI0c8j2kwhpoEsjBjuIsNAADAStyDBQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYLH/D64qQazGUPOQAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common6925012981633540351”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>15111.0</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50389.0</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50586.0</td> <td class=”number”>130</td> <td class=”number”>4.0%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4418.0</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50239.0</td> <td class=”number”>113</td> <td class=”number”>3.5%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>55312.0</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>36567.0</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6926.0</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>41455.0</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>43343.0</td> <td class=”number”>93</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (29)</td> <td class=”number”>1395</td> <td class=”number”>43.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>555</td> <td class=”number”>17.3%</td> <td>

<div class=”bar” style=”width:40%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme6925012981633540351”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>2122.0</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2572.0</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:56%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2964.0</td> <td class=”number”>46</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:40%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4418.0</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4459.0</td> <td class=”number”>36</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:31%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>59690.0</td> <td class=”number”>69</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>65014.0</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:48%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>68454.0</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:77%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100190.0</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:77%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>436620.0</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_USDA Model And”><s>USDA Model And</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_USDA Model Percent”><code>USDA Model Percent</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99358</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_USDA Model Count”>USDA Model Count<br/>

<small>Boolean</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr class=”“>

<th>Distinct count</th> <td>2</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.1%</td>

</tr> <tr class=”ignore”>

<th>Missing (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Missing (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>
<tr>

<th>Mean</th> <td>0.88723</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minifreqtable4792128543516815277”>

<table class=”mini freq”>

<tr class=”“>

<th>True</th> <td>

<div class=”bar” style=”width:100%” data-toggle=”tooltip” data-placement=”right” data-html=”true”

data-delay=500 title=”Percentage: 88.7%”>

2848

</div>

</td>

</tr><tr class=”missing”>

<th>(Missing)</th> <td>

<div class=”bar” style=”width:13%” data-toggle=”tooltip” data-placement=”right” data-html=”true”

data-delay=500 title=”Percentage: 11.3%”>

&nbsp;

</div> 362

</td>

</tr>

</table>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#freqtable4792128543516815277, #minifreqtable4792128543516815277”

aria-expanded=”true” aria-controls=”collapseExample”> Toggle details

</a>

</div> <div class=”col-md-12 extrapadding collapse” id=”freqtable4792128543516815277”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>True</td> <td class=”number”>2848</td> <td class=”number”>88.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>362</td> <td class=”number”>11.3%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr> </table> </div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_USDA Model Or”><s>USDA Model Or</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_USDA Model Count”><code>USDA Model Count</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99064</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_USDA Model Percent”>USDA Model Percent<br/>

<small>Boolean</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr class=”“>

<th>Distinct count</th> <td>2</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.1%</td>

</tr> <tr class=”ignore”>

<th>Missing (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Missing (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>
<tr>

<th>Mean</th> <td>0.17695</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minifreqtable5440154092395009667”>

<table class=”mini freq”>

<tr class=”“>

<th>True</th> <td>

<div class=”bar” style=”width:22%” data-toggle=”tooltip” data-placement=”right” data-html=”true”

data-delay=500 title=”Percentage: 17.7%”>

568

</div>

</td>

</tr><tr class=”missing”>

<th>(Missing)</th> <td>

<div class=”bar” style=”width:100%” data-toggle=”tooltip” data-placement=”right” data-html=”true”

data-delay=500 title=”Percentage: 82.3%”>

2642

</div>

</td>

</tr>

</table>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#freqtable5440154092395009667, #minifreqtable5440154092395009667”

aria-expanded=”true” aria-controls=”collapseExample”> Toggle details

</a>

</div> <div class=”col-md-12 extrapadding collapse” id=”freqtable5440154092395009667”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>True</td> <td class=”number”>568</td> <td class=”number”>17.7%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>2642</td> <td class=”number”>82.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table> </div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_VEG_ACRES07”>VEG_ACRES07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>950</td>

</tr> <tr>

<th>Unique (%)</th> <td>29.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>21.2%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>682</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1772.1</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>253700</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>8.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-4283743128318821332”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-4283743128318821332,#minihistogram-4283743128318821332”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-4283743128318821332”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-4283743128318821332”

aria-controls=”quantiles-4283743128318821332” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-4283743128318821332” aria-controls=”histogram-4283743128318821332”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-4283743128318821332” aria-controls=”common-4283743128318821332”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-4283743128318821332” aria-controls=”extreme-4283743128318821332”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-4283743128318821332”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>18</td>

</tr> <tr>

<th>Median</th> <td>81</td>

</tr> <tr>

<th>Q3</th> <td>524.75</td>

</tr> <tr>

<th>95-th percentile</th> <td>6770.5</td>

</tr> <tr>

<th>Maximum</th> <td>253700</td>

</tr> <tr>

<th>Range</th> <td>253700</td>

</tr> <tr>

<th>Interquartile range</th> <td>506.75</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>9160.2</td>

</tr> <tr>

<th>Coef of variation</th> <td>5.1693</td>

</tr> <tr>

<th>Kurtosis</th> <td>328.92</td>

</tr> <tr>

<th>Mean</th> <td>1772.1</td>

</tr> <tr>

<th>MAD</th> <td>2709.4</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>15.23</td>

</tr> <tr>

<th>Sum</th> <td>4479800</td>

</tr> <tr>

<th>Variance</th> <td>83910000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-4283743128318821332”>

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3799Oabb6pr166u13Tr1k3PPvusBgwYcMltbd%2B%2BXRkZGUpKStL8%2BfNd46tWrdK0adNUp04dt9cvX75ciYmJqqys1IIFC7R27VqVlJQoMTFRzz77rJo3by5JcjqdevbZZ/Xll18qMDBQKSkpmj59uurVq2cxCQAAUFN5vWD1799frVq10tixYzVo0CBFRERUeU1KSoqOHj16ye28%2BuqrWrlypVq0aHHB%2BW7duumtt9664Nzy5cu1Zs0avfrqq2rSpInmz5%2Bv9PR0vf/%2B%2BwoICND06dN1%2BvRprV27VmfOnNGjjz6qzMxMPf3007/%2BgAEAQK3j9UuES5cu1fr16zVq1KgLlqtzdu3adcnthISEXLJgXUp2drZGjRql1q1bKzQ0VJMmTVJ%2Bfr527dqlH3/8UZs2bdKkSZMUFRWlJk2a6OGHH9a7776rM2fO/Op9AQCA2sfrZ7BiYmL00EMPadiwYbr99tslSX/%2B85/12Wefae7cuZcsXb80cuTIS84XFBTogQceUE5OjsLCwjRhwgTdfffdKi8vV15enmJjY12vDQ0NVYsWLeRwOHT8%2BHEFBQUpJibGNR8XF6cTJ05o//79buMXU1hYqKKiIrex4OD6io6OrtaxVVdQkH89JzY42D/Wey5Xf8v3akeunkGunkGunlGbcvV6wZo9e7aOHz%2BuNm3auMZ69uyp7du3a86cOZozZ84V7yMqKkotW7bUY489pjZt2uijjz7SE088oejoaP3mN7%2BRMUbh4eFu7wkPD1dxcbEiIiIUGhqqgIAAtzlJKi4urtb%2Bs7OztXDhQrex9PR0TZgw4QqPzL9FRjbw9RJ%2BlbCwa3y9hBqJXD2DXD2DXD2jNuTq9YL16aefas2aNYqMjHSNtWzZUpmZmbrrrrus7KNnz57q2bOn69/79%2B%2Bvjz76SKtWrdKUKVMkScaYi77/UnPVkZqaql69ermNBQfXV3Fx2RVt93z%2B9hOA7eP3lKCgQIWFXaOSkpOqqKj09XJqDHL1DHL1DHL1DF/k6qsf7r1esMrLyxUSElJlPDAwUCdPnvTYfps2baqcnBxFREQoMDBQTqfTbd7pdKpRo0aKiopSaWmpKioqFBQU5JqTpEaNGlVrX9HR0VUuBxYVHdfZs7X7L6m/HX9FRaXfrdkfkKtnkKtnkKtn1IZcvX4KpFu3bpozZ46OHTvmGvvXv/6lGTNmuD2q4Ur85S9/0bp169zG8vPz1bx5c4WEhKht27bKzc11zZWUlOjgwYNKTExUhw4dZIzRt99%2B65p3OBwKCwtTq1atrKwPAADUbF4/gzVt2jT97ne/080336zQ0FBVVlaqrKxMzZs3v%2BhjFX6t06dPa%2BbMmWrevLnat2%2BvDRs26JNPPtE777wjSUpLS1NWVpZuvfVWNWnSRJmZmerQoYMSEhIkSf369dMf//hHvfjiizp9%2BrQWLVqkYcOGKTjY63EBAAA/5PXG0Lx5c33wwQf65JNPdPDgQQUGBqpVq1bq0aOH65JcdZwrQ%2Bd%2BUfSmTZsk/Xy2aeTIkSorK9Ojjz6qoqIiNWvWTIsWLVJ8fLwkacSIESoqKtJ9992nsrIyJSUlud2U/txzz%2BmZZ55R7969VadOHd11112aNGmSrQgAAEANF2Cu9I5uVEtR0XHr2wwODlSfzO3Wt%2Bsp6yd29/USqiU4OFCRkQ1UXFxW4%2B8R8CZy9Qxy9Qxy9Qxf5Nq4cUOv7Od8Xj%2BDdejQIc2bN0/fffedysvLq8x//PHH3l4SAACAVT65B6uwsFA9evRQ/fr1vb17AAAAj/N6wcrJydHHH3%2BsqKgob%2B8aAADAK7z%2BmIZGjRpx5goAANRoXi9YY8eO1cKFC6/4aekAAABXK69fIvzkk0/09ddfa9WqVWrWrJkCA9073ttvv%2B3tJQEAAFjl9YIVGhqqW2%2B91du7BQAA8BqvF6zZs2d7e5cAAABe5fV7sCRp//79evnll/Xkk0%2B6xr755htfLAUAAMA6rxesHTt2aODAgdq4caPWrl0r6eeHj44cOZKHjAIAgBrB6wVr/vz5evzxx7VmzRoFBARI%2Bvn3E86ZM0eLFi3y9nIAAACs83rB%2Bsc//qG0tDRJchUsSbrjjjuUn5/v7eUAAABY5/WC1bBhwwv%2BDsLCwkLVrVvX28sBAACwzusFq0uXLnrhhRdUWlrqGjtw4IAyMjJ08803e3s5AAAA1nn9MQ1PPvmk7r//fiUlJamiokJdunTRyZMn1bZtW82ZM8fbywEAALDO6wXruuuu09q1a7Vt2zYdOHBA9erVU6tWrdS9e3e3e7IAAAD8ldcLliTVqVNHt99%2Buy92DQAA4HFeL1i9evW65JkqnoUFAAD8ndcL1p133ulWsCoqKnTgwAE5HA7df//93l4OAACAdV4vWFOmTLng%2BIYNG/S3v/3Ny6sBAACwzye/i/BCbr/9dn3wwQe%2BXgYAAMAVu2oK1p49e2SM8fUyAAAArpjXLxGOGDGiytjJkyeVn5%2Bvvn37ens5AAAA1nm9YLVs2bLKpwhDQkI0bNgwDR8%2B3NvLAQAAsM7rBYuntQMAgJrO6wXrvffeq/ZrBw0a5MGVAAAAeIbXC9ZTTz2lysrKKje0BwQEuI0FBARQsAAAgF/yesF67bXX9MYbb%2Bihhx5STEyMjDHat2%2BfXn31Vd17771KSkry9pIAAACs8sk9WFlZWWrSpIlr7IYbblDz5s01evRorV271ttLAgAAsMrrz8H6/vvvFR4eXmU8LCxMhw8f9vZyAAAArPN6wWratKnmzJmj4uJi11hJSYnmzZun66%2B/3tvLAQAAsM7rlwinTZumyZMnKzs7Ww0aNFBgYKBKS0tVr149LVq0yNvLAQAAsM7rBatHjx7aunWrtm3bpiNHjsgYoyZNmuiWW25Rw4YNvb0cAAAA67xesCTpmmuuUe/evXXkyBE1b97cF0sAAADwGK/fg1VeXq6MjAx17txZv/3tbyX9fA/Wgw8%2BqJKSEm8vBwAAwDqvF6y5c%2Bdq7969yszMVGDg/%2B2%2BoqJCmZmZ3l4OAACAdV4vWBs2bNBLL72kO%2B64w/VLn8PCwjR79mxt3LjR28sBAACwzusFq6ysTC1btqwyHhUVpRMnTnh7OQAAANZ5vWBdf/31%2Btvf/iZJbr978MMPP9R//ud/ens5AAAA1nn9U4T/9V//pUceeURDhw5VZWWl/vSnPyknJ0cbNmzQU0895e3lAAAAWOf1gpWamqrg4GAtW7ZMQUFBeuWVV9SqVStlZmbqjjvu8PZyAAAArPN6wTp69KiGDh2qoUOHenvXAAAAXuH1e7B69%2B7tdu8VAABATeP1gpWUlKT169d7e7cAAABe4/VLhP/xH/%2Bh559/XllZWbr%2B%2ButVp04dt/l58%2BZ5e0kAAABWeb1g5eXl6Te/%2BY0kqbi42Nu7BwAA8DivFaxJkyZp/vz5euutt1xjixYtUnp6ureWAAAA4BVeuwdr8%2BbNVcaysrK8tXsAAACv8VrButAnB/k0IQAAqIm8VrDO/WLny40BAAD4O68/psGm7du3Kzk5WZMmTaoyt27dOg0YMECdO3fWkCFD9Omnn7rmKisrNX/%2BfPXu3VvdunXT6NGjdejQIde80%2BnUxIkTlZycrB49euipp55SeXm5V44JAAD4P78tWK%2B%2B%2BqpmzZqlFi1aVJnbu3evMjIyNGXKFH3xxRcaNWqUxo8fryNHjkiSli9frjVr1igrK0tbtmxRy5YtlZ6e7rpkOX36dJ08eVJr167Vu%2B%2B%2Bq/z8fGVmZnr1%2BAAAgP/y2qcIz5w5o8mTJ192rLrPwQoJCdHKlSv1/PPP69SpU25zK1asUEpKilJSUiRJAwcO1LJly7R69WqNGTNG2dnZGjVqlFq3bi3p5084JiUladeuXWrWrJk2bdqkv/71r4qKipIkPfzww3r00UeVkZFR5bldAAAA5/NaweratasKCwsvO1ZdI0eOvOhcbm6uq1ydExsbK4fDofLycuXl5Sk2NtY1FxoaqhYtWsjhcOj48eMKCgpSTEyMaz4uLk4nTpzQ/v373cYBAAAuxGsF65fPv/I0p9Op8PBwt7Hw8HDl5eXp2LFjMsZccL64uFgREREKDQ11uwH/3Gur%2B2DUwsJCFRUVuY0FB9dXdHT0v3M4FxUU5F9XeIOD/WO953L1t3yvduTqGeTqGeTqGbUpV68/yd1bLvcIiEvNX%2BnjI7Kzs7Vw4UK3sfT0dE2YMOGKtuvvIiMb%2BHoJv0pY2DW%2BXkKNRK6eQa6eQa6eURtyrZEFKzIyUk6n023M6XQqKipKERERCgwMvOB8o0aNFBUVpdLSUlVUVCgoKMg1J0mNGjWq1v5TU1PVq1cvt7Hg4PoqLi77dw/pgvztJwDbx%2B8pQUGBCgu7RiUlJ1VRUenr5dQY5OoZ5OoZ5OoZvsjVVz/c18iCFR8fr5ycHLcxh8Oh/v37KyQkRG3btlVubq5uvPFGSVJJSYkOHjyoxMRENW3aVMYYffvtt4qLi3O9NywsTK1atarW/qOjo6tcDiwqOq6zZ2v3X1J/O/6Kikq/W7M/IFfPIFfPIFfPqA25%2BtcpkGq655579Pnnn2vr1q06deqUVq5cqe%2B//14DBw6UJKWlpWnp0qXKz89XaWmpMjMz1aFDByUkJCgqKkr9%2BvXTH//4Rx09elRHjhzRokWLNGzYMAUH18g%2BCgAALPPbxpCQkCBJOnv2rCRp06ZNkn4%2B29SuXTtlZmZq9uzZOnz4sNq0aaMlS5aocePGkqQRI0aoqKhI9913n8rKypSUlOR2z9Rzzz2nZ555Rr1791adOnV01113XfBhpgAAABcSYPiFgF5RVHTc%2BjaDgwPVJ3O79e16yvqJ3X29hGoJDg5UZGQDFReX1fhT2N5Erp5Brp5Brp7hi1wbN27olf2cr0ZeIgQAAPAlChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlNbZgxcTEKD4%2BXgkJCa6vmTNnSpJ27NihYcOGqUuXLurfv79Wr17t9t6lS5eqX79%2B6tKli9LS0pSTk%2BOLQwAAAH4q2NcL8KQPP/xQzZo1cxsrLCzUww8/rKeeekoDBgzQV199pXHjxqlVq1ZKSEjQ5s2b9fLLL%2Bu1115TTEyMli5dqoceekgbN25U/fr1fXQkAADAn9TYM1gXs2bNGrVs2VLDhg1TSEiIkpOT1atXL61YsUKSlJ2drSFDhqhjx46qV6%2BeHnzwQUnSli1bfLlsAADgR2p0wZo3b5569uypG264QdOnT1dZWZlyc3MVGxvr9rrY2FjXZcDz5wMDA9WhQwc5HA6vrh0AAPivGnuJsFOnTkpOTtaLL76oQ4cOaeLEiZoxY4acTqeaNGni9tqIiAgVFxdLkpxOp8LDw93mw8PDXfPVUVhYqKKiIrex4OD6io6O/jeP5sKCgvyrHwcH%2B8d6z%2BXqb/le7cjVM8jVM8jVM2pTrjW2YGVnZ7v%2BuXXr1poyZYrGjRunrl27Xva9xpgr3vfChQvdxtLT0zVhwoQr2q6/i4xs4Osl/CphYdf4egk1Erl6Brl6Brl6Rm3ItcYWrPM1a9ZMFRUVCgwMlNPpdJsrLi5WVFSUJCkyMrLKvNPpVNu2bau9r9TUVPXq1cttLDi4voqLy/7N1V%2BYv/0EYPv4PSUoKFBhYdeopOSkKioqfb2cGoNcPYNcPYNcPcMXufrqh/saWbD27Nmj1atXa%2BrUqa6x/Px81a1bVykpKfrrX//q9vqcnBx17NhRkhQfH6/c3FwNHjxYklRRUaE9e/Zo2LBh1d5/dHR0lcuBRUXHdfZs7f5L6m/HX1FR6Xdr9gfk6hnk6hnk6hm1IVf/OgVSTY0aNVJ2draysrJ0%2BvRpHThwQAsWLFBqaqruvvtuHT58WCtWrNCpU6e0bds2bdu2Tffcc48kKS0tTe%2B995527typkyf6WH1dAAAOaUlEQVRPavHixapbt6569uzp24MCAAB%2Bo0aewWrSpImysrI0b948V0EaPHiwJk2apJCQEC1ZskSzZs3SjBkz1LRpU82dO1ft27eXJN1666167LHHNHHiRP30009KSEhQVlaW6tWr5%2BOjAgAA/iLAXOkd3aiWoqLj1rcZHByoPpnbrW/XU9ZP7O7rJVRLcHCgIiMbqLi4rMafwvYmcvUMcvUMcvUMX%2BTauHFDr%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%2B7bd//MzXS/hVPppyi6%2BXAADwU5zBOo/D4dC3336rKVOmqGHDhmrZsqVGjRql7OxsXy8NAAD4Cc5gnSc3N1dNmzZVeHi4aywuLk4HDhxQaWmpQkNDL7uNwsJCFRUVuY0FB9dXdHS01bUGBdGPPalP5nZfL%2BFXudrPuJ3788qfW7vI1TPI1TNqU64UrPM4nU6FhYW5jZ0rW8XFxdUqWNnZ2Vq4cKHb2Pjx4/XII4/YW6h%2BLnL3X/edUlNTrZe32qywsFDZ2dnkallhYaHefPM1crWMXD2DXD2jNuVa8yvkv8EYc0XvT01N1apVq9y%2BUlNTLa3u/xQVFWnhwoVVzpbhypCrZ5CrZ5CrZ5CrZ9SmXDmDdZ6oqCg5nU63MafTqYCAAEVFRVVrG9HR0TW%2BmQMAgIvjDNZ54uPjVVBQoKNHj7rGHA6H2rRpowYNGvhwZQAAwF9QsM4TGxurhIQEzZs3T6WlpcrPz9ef/vQnpaWl%2BXppAADATwQ9%2B%2Byzz/p6EVebW265RWvXrtXMmTP1wQcfaNiwYRo9erQCAgJ8vbQqGjRooBtvvJGza5aRq2eQq2eQq2eQq2fUllwDzJXe0Q0AAAA3XCIEAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyC5YcOHz6sMWPGKCkpSbfddpvmzp2ryspKXy/rqhATE6P4%2BHglJCS4vmbOnClJ2rFjh4YNG6YuXbqof//%2BWr16tdt7ly5dqn79%2BqlLly5KS0tTTk6Oa%2B7UqVP6wx/%2BoFtvvVVJSUmaMGGCiouLXfM18Xuyfft2JScna9KkSVXm1q1bpwEDBqhz584aMmSIPv30U9dcZWWl5s%2Bfr969e6tbt24aPXq0Dh065Jp3Op2aOHGikpOT1aNHDz311FMqLy93ze/du1f33nuvunbtqr59%2B%2BqNN96o9r79wcVyXbVqldq3b%2B/2ZzchIUG7d%2B%2BWRK6XcvjwYaWnpyspKUnJycmaOnWqSkpKJF3ZcXs686vdxXL94YcfFBMTU%2BXP6uuvv%2B56L7lKMvA7gwcPNk8//bQpKSkxBw4cMH379jVvvPGGr5d1VWjXrp05dOhQlfF//etfplOnTmbFihWmvLzcfPbZZyYxMdHs3r3bGGPMxx9/bG644Qazc%2BdOc/LkSbNkyRLTvXt3U1ZWZowxZvbs2WbIkCHmn//8pykuLjbjx483Y8eOdW2/pn1PsrKyTN%2B%2Bfc2IESPMxIkT3eb27Nlj4uPjzdatW015ebl5//33TceOHU1BQYExxpilS5ea2267zeTl5Znjx4%2Bb5557zgwYMMBUVlYaY4wZP368GTNmjPnpp5/MkSNHTGpqqpk5c6YxxpiTJ0%2BaW265xbz88sumrKzM5OTkmBtvvNFs2LChWvu%2B2l0q13fffdfce%2B%2B9F30vuV7cXXfdZaZOnWpKS0tNQUGBGTJkiJk2bdoVH7cnM/cHF8v10KFDpl27dhd9H7n%2BjILlZ3bv3m06dOhgnE6na%2Bx//ud/TL9%2B/Xy4qqvHxQrWa6%2B9ZgYNGuQ2NnHiRDN9%2BnRjjDFjxowxL7zwgmuuoqLCdO/e3axdu9acOXPGdO3a1WzatMk1n5eXZ2JiYsyRI0dq5PfkzTffNCUlJSYjI6NKEZgxY4ZJT093Gxs%2BfLhZsmSJMcaY/v37mzfffNM1d/z4cRMbG2u%2B%2BeYbU1RUZNq3b2/27t3rmt%2B2bZvp1KmTOX36tFm/fr256aabzNmzZ13zc%2BfONb/73e%2Bqte%2Br3aVyvVzBItcLO3bsmJk6daopKipyjb311lumb9%2B%2BV3zcnsz8anepXC9XsMj1Z1wi9DO5ublq2rSpwsPDXWNxcXE6cOCASktLfbiyq8e8efPUs2dP3XDDDZo%2BfbrKysqUm5ur2NhYt9fFxsa6LgOePx8YGKgOHTrI4XDo4MGDOn78uOLi4lzzrVu3Vr169ZSbm1sjvycjR45Uw4YNLzh3sSwdDofKy8uVl5fnNh8aGqoWLVrI4XBo7969CgoKUkxMjGs%2BLi5OJ06c0P79%2B5Wbm6uYmBgFBQW5bfti36df7tsfXCpXSSooKNADDzygbt26qXfv3nr//fcliVwvISwsTLNnz9a1117rGisoKFB0dPQVHbenM7/aXSrXc5544gn16NFDN910k%2BbNm6czZ85IItdzKFh%2Bxul0KiwszG3s3P%2Bx//KeoNqqU6dOSk5O1saNG5Wdna2dO3dqxowZF8wtIiLClZnT6XQrSNLPuRYXF8vpdEpSlfeHhYW55mvT9%2BRSWR07dkzGmEtmGRoaqoCAALc5SRfNMiIiQk6nU5WVlZfct7%2BLiopSy5Yt9fjjj%2Buzzz7TY489pmnTpmnHjh3k%2Bis4HA4tW7ZM48aNu6Lj9nTm/uaXudatW1edO3dWnz59tGXLFmVlZWn16tX67//%2Bb0m%2B/W/E1YSC5YeMMb5ewlUrOztbw4cPV926ddW6dWtNmTJFa9eudf1kdSmXy/VS87Xte%2BLtrH75H9uamnXPnj312muvKTY2VnXr1lX//v3Vp08frVq1yvUacr20r776SqNHj9bkyZOVnJx80df9muP2ZOb%2B4vxco6Oj9fbbb6tPnz6qU6eOEhMTNXbs2Gr/Wb3cfE3JlYLlZ6KiolxnVM5xOp0KCAhQVFSUj1Z19WrWrJkqKioUGBhYJbfi4mJXZpGRkRfMNSoqyvWa8%2BePHTumRo0a1brvyaWyioiIuGDWTqfTlVVpaakqKirc5iS55s8/a%2BJ0Ol3bvdS%2Ba6KmTZuqsLCQXKth8%2BbNGjNmjKZNm6aRI0dK0hUdt6cz9xcXyvVCmjZtqh9//FHGGHL9/66u1eCy4uPjVVBQoKNHj7rGHA6H2rRpowYNGvhwZb63Z88ezZkzx20sPz9fdevWVUpKSpVr9Dk5OerYsaOkn3PNzc11zVVUVGjPnj3q2LGjmjdvrvDwcLf5f/zjHzp9%2BrTi4%2BNr3fckPj6%2BSpYOh0MdO3ZUSEiI2rZt65ZVSUmJDh48qMTERHXo0EHGGH377bdu7w0LC1OrVq0UHx%2Bvffv26ezZs1W2fbl9%2B7u//OUvWrdundtYfn6%2BmjdvTq6X8fXXXysjI0MLFizQoEGDXONXctyeztwfXCzXHTt2aPHixW6v3b9/v5o2baqAgAByPcdbd9PDnuHDh5tp06aZ48ePm7y8PNOrVy%2BzbNkyXy/L544cOWI6depklixZYk6dOmX2799v7rzzTjNz5kzz448/ms6dO5t33nnHlJeXm61bt5rExETXJ1W2bdtmunbtar755htz4sQJ8/LLL5uUlBRz8uRJY8zPn1IZPHiw%2Bec//2mOHj1qxo4dax555BHXvmvq9%2BRCn3bbt2%2BfSUhIMFu2bDHl5eVmxYoVpnPnzqawsNAY8/MnKHv27On6CPb06dPN0KFDXe%2BfOHGiefDBB81PP/1kCgoKzNChQ82cOXOMMcacOnXK3Hbbbeall14yJ06cMDt37jQ33HCD2bJlS7X27S8ulOuf//xnc9NNN5ndu3eb06dPmzVr1pgOHToYh8NhjCHXizlz5oz57W9/a95%2B%2B%2B0qc1d63J7M/Gp3qVwdDoeJi4sz7733njl9%2BrTZvXu36d69u%2BvRNOT6MwqWHyooKDAPPvigSUxMNMnJyeall15yPT%2Bktvvyyy9Namqq6dSpk7nxxhvN7NmzTXl5uWtu4MCBJi4uzvTt27fKc1OWL19uUlJSTHx8vElLSzP79u1zzZ06dco8%2B%2Byzplu3bqZz587mscceMyUlJa75mvY9iY%2BPN/Hx8aZ9%2B/amffv2rn8/Z8OGDaZv374mLi7O3H333ebLL790zVVWVpoFCxaYm2%2B%2B2SQmJprf//73bs9TKikpMZMmTTKdOnUy3bp1MzNmzDCnTp1yze/bt8%2BMGDHCxMfHm549e5rly5e7re1S%2B77aXSrXyspKs2jRInPbbbeZ%2BPh4c8cdd5jNmze73kuuF/a///u/pl27dq4sf/n1ww8/XNFxezrzq9nlct24caMZOHCgSUxMNN27dzevvPKKqaiocL2fXI0JMKYG3NkIAABwFeEeLAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACw7P8Bg8Q8x3/xrbIAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-4283743128318821332”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>286</td> <td class=”number”>8.9%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>29</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>28</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.0</td> <td class=”number”>26</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>15.0</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.0</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (939)</td> <td class=”number”>2019</td> <td class=”number”>62.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>682</td> <td class=”number”>21.2%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-4283743128318821332”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>286</td> <td class=”number”>8.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>18</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>83755.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>89856.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>101663.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>195401.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>253704.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_VEG_ACRES12”><s>VEG_ACRES12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_VEG_ACRES07”><code>VEG_ACRES07</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.9886</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_VEG_ACRESPTH07”>VEG_ACRESPTH07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2244</td>

</tr> <tr>

<th>Unique (%)</th> <td>69.9%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>21.2%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>682</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>42.763</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>4666.9</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>8.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-4706068627650735215”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-4706068627650735215,#minihistogram-4706068627650735215”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-4706068627650735215”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-4706068627650735215”

aria-controls=”quantiles-4706068627650735215” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-4706068627650735215” aria-controls=”histogram-4706068627650735215”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-4706068627650735215” aria-controls=”common-4706068627650735215”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-4706068627650735215” aria-controls=”extreme-4706068627650735215”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-4706068627650735215”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0.49726</td>

</tr> <tr>

<th>Median</th> <td>2.0014</td>

</tr> <tr>

<th>Q3</th> <td>7.8394</td>

</tr> <tr>

<th>95-th percentile</th> <td>171.01</td>

</tr> <tr>

<th>Maximum</th> <td>4666.9</td>

</tr> <tr>

<th>Range</th> <td>4666.9</td>

</tr> <tr>

<th>Interquartile range</th> <td>7.3421</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>213.15</td>

</tr> <tr>

<th>Coef of variation</th> <td>4.9846</td>

</tr> <tr>

<th>Kurtosis</th> <td>162.26</td>

</tr> <tr>

<th>Mean</th> <td>42.763</td>

</tr> <tr>

<th>MAD</th> <td>67.469</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>11.048</td>

</tr> <tr>

<th>Sum</th> <td>108100</td>

</tr> <tr>

<th>Variance</th> <td>45434</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-4706068627650735215”>

<img 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HaWlpGjJkiObMmaP6%2Bnq7DQAAAK2Wy%2BkDlpeXa8WKFXrrrbdUV1enkSNH6pVXXtHAgQN9jxk0aJAef/xxjR49%2Bhv38%2BKLL2rFihXq1q1bs/ODBg3Sq6%2B%2B2uzcsmXLtHr1ar344ovq3LmzFi1apOzsbL399tsKCQnR3LlzdeLECa1Zs0YnT57UAw88oIKCAj366KMtWzwAAGgTHD%2BDNWrUKG3atElTpkzRBx98oAULFviFK0lKT0/XoUOHvnU/4eHh3xqwvk1xcbEmT56snj17KiIiQrm5uSovL9f27dv11Vdfaf369crNzVVsbKw6d%2B6s%2B%2B67T2%2B%2B%2BaZOnjz5vY8FAADaHsfPYC1dulSDBw/%2Bzsdt3779W%2BcnTZr0rfOVlZX6%2Bc9/rpKSEkVGRmr69Om65ZZbVF9fr7KyMiUkJPgeGxERoW7dusntduvIkSMKCwtTfHy8bz4xMVFHjx7V7t27/ca/SVVVlTwej9%2BYy9VOcXFx37nt9xEWFly3MXO5gqve5pzuebD1PtjRd%2BfR88Cg762H4wErPj5e9957r8aPH6/rrrtOkvSf//mf%2BvOf/6wFCxYoOjq6xceIjY1V9%2B7d9eCDD6pXr17605/%2BpIcfflhxcXH66U9/KmOMoqKi/LaJiopSdXW1oqOjFRERoZCQEL85Saqurj6r4xcXF6uwsNBvLDs7W9OnT2/hyoJbTEz7QJdgTWTkhYEuoU2i786j54FB34Of4wErPz9fR44cUa9evXxjQ4cO1ZYtWzR//nzNnz%2B/xccYOnSohg4d6vvzqFGj9Kc//UkrV67UzJkzJUnGmG/c/tvmzkZGRoaGDRvmN%2BZytVN1dV2L9vt1wfYKx/b6AyEsLFSRkReqpuaYGhubAl1Om0HfnUfPA4O%2B2xeoF/eOB6wPP/xQq1evVkxMjG%2Bse/fuKigo0M0333zOjtulSxeVlJQoOjpaoaGh8nq9fvNer1cdO3ZUbGysamtr1djYqLCwMN%2BcJHXs2PGsjhUXF3fG24EezxE1NLTtX5bWtP7GxqZWtZ5gQd%2BdR88Dg74HP8dPgdTX1ys8PPzMQkJDdezYMSvH%2BMMf/qC1a9f6jZWXl6tr164KDw9X7969VVpa6purqanRvn37lJKSor59%2B8oYo127dvnm3W63IiMj1aNHDyv1AQCA1s3xgDVo0CDNnz9fhw8f9o3961//0hNPPHHGpwl/qBMnTuipp56S2%2B3WyZMntWbNGn3wwQeaOHGiJCkzM1NLly5VeXm5amtrVVBQoL59%2Byo5OVmxsbEaOXKkfvOb3%2BjQoUM6cOCAFi9erPHjx8vlcvyEHwAACEKOJ4bZs2frF7/4ha688kpFRESoqalJdXV16tq16zfet6o5ycnJkuT7HsP169dLOnW2adKkSaqrq9MDDzwgj8ejSy65RIsXL1ZSUpIkaeLEifJ4PLrzzjtVV1en1NRUv4vSn3zyST322GMaPny4zjvvPN18881n3MwUAADgm4SYll7R/QOcOHFCH3zwgfbt26fQ0FD16NFDQ4YM8V3z1Bp5PEes79PlCtX1BVus7/dcWZdzVaBLaDGXK1QxMe1VXV3H9REOou/Oo%2BeBQd/t69SpQ0COG5D3vM4//3zfLRoAAABaG8cD1v79%2B7Vw4UJ9%2BeWXzX6/3/vvv%2B90SQAAAFYF5BqsqqoqDRkyRO3atXP68AAAAOec4wGrpKRE77//vmJjY50%2BNAAAgCMcv01Dx44dOXMFAABaNccD1pQpU1RYWNjir6MBAAD4sXL8LcIPPvhAn332mVauXKlLLrlEoaH%2BGe/11193uiQAAACrHA9YERERuuaaa5w%2BLAAAgGMcD1j5%2BflOHxIAAMBRjl%2BDJUm7d%2B/W888/r0ceecQ3tnXr1kCUAgAAYJ3jAevjjz/WmDFj9N5772nNmjWSTt18dNKkSdxkFAAAtAqOB6xFixbpoYce0urVqxUSEiJJ6tq1q%2BbPn6/Fixc7XQ4AAIB1jgesv//978rMzJQkX8CSpBtuuEHl5eVOlwMAAGCd4wGrQ4cOzX4HYVVVlc4//3ynywEAALDO8YA1YMAAPf3006qtrfWN7dmzR3l5ebryyiudLgcAAMA6x2/T8Mgjj%2Biuu%2B5SamqqGhsbNWDAAB07dky9e/fW/PnznS4HAADAOscD1sUXX6w1a9Zo8%2BbN2rNnjy644AL16NFDV111ld81WQAAAMHK8YAlSeedd56uu%2B66QBwaAADgnHM8YA0bNuxbz1RxLywAABDsHA9YN910k1/Aamxs1J49e%2BR2u3XXXXc5XQ4AAIB1jgesmTNnNjv%2B7rvv6i9/%2BYvD1QAAANgXkO8ibM51112nP/7xj4EuAwAAoMV%2BNAFrx44dMsYEugwAAIAWc/wtwokTJ54xduzYMZWXl2vEiBFOlwMAAGCd4wGre/fuZ3yKMDw8XOPHj9eECROcLgcAAMA6xwMWd2sHAACtneMB66233jrrx956663nsBIAAIBzw/GANWfOHDU1NZ1xQXtISIjfWEhICAELAAAEJccD1ksvvaSXX35Z9957r%2BLj42WM0RdffKEXX3xRd9xxh1JTU50uCQAAwKqAXINVVFSkzp07%2B8Yuv/xyde3aVVlZWVqzZo3TJQEAAFjl%2BH2w9u7dq6ioqDPGIyMjVVFR4XQ5AAAA1jkesLp06aL58%2BerurraN1ZTU6OFCxfqJz/5idPlAAAAWOf4W4SzZ8/WjBkzVFxcrPbt2ys0NFS1tbW64IILtHjxYqfLAQAAsM7xgDVkyBBt2rRJmzdv1oEDB2SMUefOnXX11VerQ4cOTpcDAABgneMBS5IuvPBCDR8%2BXAcOHFDXrl0DUQIAAMA54/g1WPX19crLy1P//v114403Sjp1Ddbdd9%2Btmpoap8sBAACwzvGAtWDBAu3cuVMFBQUKDf3fwzc2NqqgoMDpcgAAAKxzPGC9%2B%2B67eu6553TDDTf4vvQ5MjJS%2Bfn5eu%2B995wuBwAAwDrHA1ZdXZ26d%2B9%2BxnhsbKyOHj3qdDkAAADWOR6wfvKTn%2Bgvf/mLJPl99%2BA777yjf//3f3e6HAAAAOsc/xThz372M91///0aN26cmpqa9Pvf/14lJSV69913NWfOHKfLAQAAsM7xgJWRkSGXy6XXXntNYWFh%2Bu1vf6sePXqooKBAN9xwg9PlAAAAWOd4wDp06JDGjRuncePGOX1oAAAARzh%2BDdbw4cP9rr0CAABobRwPWKmpqVq3bp3ThwUAAHCM428R/tu//Zt%2B/etfq6ioSD/5yU903nnn%2Bc0vXLjQ6ZIAAACscjxglZWV6ac//akkqbq62unDAwAAnHOOBazc3FwtWrRIr776qm9s8eLFys7OdqoEAAAARzh2DdaGDRvOGCsqKnLq8AAAAI5xLGA198lBPk0IAABaI8cC1ukvdv6use9jy5YtSktLU25u7hlza9eu1ejRo9W/f3%2BNHTtWH374oW%2BuqalJixYt0vDhwzVo0CBlZWVp//79vnmv16ucnBylpaVpyJAhmjNnjurr61tUKwAAaDscv02DLS%2B%2B%2BKLmzZunbt26nTG3c%2BdO5eXlaebMmfrkk080efJkTZs2TQcOHJAkLVu2TKtXr1ZRUZE2btyo7t27Kzs723dGbe7cuTp27JjWrFmjN998U%2BXl5SooKHB0fQAAIHgFbcAKDw/XihUrmg1Yy5cvV3p6utLT0xUeHq4xY8aoT58%2BWrVqlSSpuLhYkydPVs%2BePRUREaHc3FyVl5dr%2B/bt%2Buqrr7R%2B/Xrl5uYqNjZWnTt31n333ac333xTJ0%2BedHqZAAAgCDn2KcKTJ09qxowZ3zl2tvfBmjRp0jfOlZaWKj093W8sISFBbrdb9fX1KisrU0JCgm8uIiJC3bp1k9vt1pEjRxQWFqb4%2BHjffGJioo4ePardu3f7jQMAADTHsYA1cOBAVVVVfeeYDV6vV1FRUX5jUVFRKisr0%2BHDh2WMaXa%2Burpa0dHRioiI8Ls%2B7PRjz/a%2BXVVVVfJ4PH5jLlc7xcXF/ZDlfKOwsOA6AelyBVe9zTnd82DrfbCj786j54FB31sPxwLW/73/lRO%2B6xOK3zbf0k83FhcXq7Cw0G8sOztb06dPb9F%2Bg11MTPtAl2BNZOSFgS6hTaLvzqPngUHfg5/jd3J3QkxMjLxer9%2BY1%2BtVbGysoqOjFRoa2ux8x44dFRsbq9raWjU2NiosLMw3J0kdO3Y8q%2BNnZGRo2LBhfmMuVztVV9f90CU1K9he4dhefyCEhYUqMvJC1dQcU2NjU6DLaTPou/PoeWDQd/sC9eK%2BVQaspKQklZSU%2BI253W6NGjVK4eHh6t27t0pLSzV48GBJUk1Njfbt26eUlBR16dJFxhjt2rVLiYmJvm0jIyPVo0ePszp%2BXFzcGW8HejxH1NDQtn9ZWtP6GxubWtV6ggV9dx49Dwz6HvyC6xTIWbr99tv10UcfadOmTTp%2B/LhWrFihvXv3asyYMZKkzMxMLV26VOXl5aqtrVVBQYH69u2r5ORkxcbGauTIkfrNb36jQ4cO6cCBA1q8eLHGjx8vl6tV5lEAAGBZ0CaG5ORkSVJDQ4Mkaf369ZJOnW3q06ePCgoKlJ%2Bfr4qKCvXq1UtLlixRp06dJEkTJ06Ux%2BPRnXfeqbq6OqWmpvpdM/Xkk0/qscce0/Dhw3Xeeefp5ptvbvZmpgAAAM0JMXxfjSM8niPW9%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%2BOOPNX78eA0YMECjRo3SqlWr/LZdunSpRo4cqQEDBigzM1MlJSWBWAIAAAhSrkAXcC698847uuSSS/zGqqqqdN9992nOnDkaPXq0Pv30U02dOlU9evRQcnKyNmzYoOeff14vvfSS4uPjtXTpUt17771677331K5duwCtBAAABJNWewbrm6xevVrdu3fX%2BPHjFR4errS0NA0bNkzLly%2BXJBUXF2vs2LHq16%2BfLrjgAt19992SpI0bNwaybAAAEERa9RmshQsXauvWraqtrdWNN96oWbNmqbS0VAkJCX6PS0hI0Lp16yRJpaWluummm3xzoaGh6tu3r9xut0aNGnVWx62qqpLH4/Ebc7naKS4uroUr8hcWFlz52OUKrnqbc7rnwdb7YEffnUfPA4O%2Btx6tNmBddtllSktL0zPPPKP9%2B/crJydHTzzxhLxerzp37uz32OjoaFVXV0uSvF6voqKi/OajoqJ882ejuLhYhYWFfmPZ2dmaPn36D1xN6xAT0z7QJVgTGXlhoEtok%2Bi78%2Bh5YND34NdqA1ZxcbHv33v27KmZM2dq6tSpGjhw4Hdua4xp0bEzMjI0bNgwvzGXq52qq%2BtatN%2BvC7ZXOLbXHwhhYaGKjLxQNTXH1NjYFOhy2gz67jx6Hhj03b5AvbhvtQHr6y655BI1NjYqNDRUXq/Xb666ulqxsbGSpJiYmDPmvV6vevfufdbHiouLO%2BPtQI/niBoa2vYvS2taf2NjU6taT7Cg786j54FB34NfcJ0COUs7duzQ/Pnz/cbKy8t1/vnnKz09/YzbLpSUlKhfv36SpKSkJJWWlvrmGhsbtWPHDt88AADAd2mVAatjx44qLi5WUVGRTpw4oT179ujZZ59VRkaGbrnlFlVUVGj58uU6fvy4Nm/erM2bN%2Bv222%2BXJGVmZuqtt97Stm3bdOzYMb3wwgs6//zzNXTo0MAuCgAABI1W%2BRZh586dVVRUpIULF/oC0m233abc3FyFh4dryZIlmjdvnp544gl16dJFCxYs0KWXXipJuuaaa/Tggw8qJydHBw8eVHJysoqKinTBBRcEeFUAACBYhJiWXtGNs%2BIePYsYAAAM1ElEQVTxHLG%2BT5crVNcXbLG%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%2B3Wrl27NHPmTHXo0EHdu3fX5MmTVVxcHOjSAABAkOAM1teUlpaqS5cuioqK8o0lJiZqz549qq2tVURExHfuo6qqSh6Px2/M5WqnuLg4q7WGhZGPz6VgO%2BMWTP408%2Brv9fjTf9f5O%2B8ceh4Y9L31IGB9jdfrVWRkpN/Y6bBVXV19VgGruLhYhYWFfmPTpk3T/fffb69QnQpyd138pTIyMqyHNzSvqqpKxcXF9NxhVVVVeuWVl%2Bi7g%2Bh5YND31oOI3AxjTIu2z8jI0MqVK/1%2BMjIyLFX3vzwejwoLC884W4Zzh54HBn13Hj0PDPreenAG62tiY2Pl9Xr9xrxer0JCQhQbG3tW%2B4iLi%2BOVBwAAbRhnsL4mKSlJlZWVOnTokG/M7XarV69eat%2B%2BfQArAwAAwYKA9TUJCQlKTk7WwoULVVtbq/Lycv3%2B979XZmZmoEsDAABBIuzxxx9/PNBF/NhcffXVWrNmjZ566in98Y9/1Pjx45WVlaWQkJBAl3aG9u3ba/DgwZxdcxA9Dwz67jx6Hhj0vXUIMS29ohsAAAB%2BeIsQAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICVhCqqKjQPffco9TUVF177bVasGCBmpqaAl1WUNqyZYvS0tKUm5t7xtzatWs1evRo9e/fX2PHjtWHH37om2tqatKiRYs0fPhwDRo0SFlZWdq/f79v3uv1KicnR2lpaRoyZIjmzJmj%2Bvp6R9b0Y1dRUaHs7GylpqYqLS1Ns2bNUk1NjSRp586duuOOOzRw4ECNGDFCL7/8st%2B2LXlO2rJdu3bprrvu0sCBA5WWlqacnBx5PB5J0scff6zx48drwIABGjVqlFatWuW37dKlSzVy5EgNGDBAmZmZKikp8c0dP35cv/rVr3TNNdcoNTVV06dPV3V1taNrCxZPP/204uPjfX%2Bm722AQdC57bbbzKOPPmpqamrMnj17zIgRI8zLL78c6LKCTlFRkRkxYoSZOHGiycnJ8ZvbsWOHSUpKMps2bTL19fXm7bffNv369TOVlZXGGGOWLl1qrr32WlNWVmaOHDlinnzySTN69GjT1NRkjDFm2rRp5p577jEHDx40Bw4cMBkZGeapp55yfI0/RjfffLOZNWuWqa2tNZWVlWbs2LFm9uzZ5tixY%2Bbqq682zz//vKmrqzMlJSVm8ODB5t133zXGtPw5aauOHz9urrzySlNYWGiOHz9uDh48aO644w5z3333mX/961/msssuM8uXLzf19fXmz3/%2Bs0lJSTGff/65McaY999/31x%2B%2BeVm27Zt5tixY2bJkiXmqquuMnV1dcYYY/Lz883YsWPNP//5T1NdXW2mTZtmpkyZEsjl/ijt2LHDDB482PTp08cYY%2Bh7G0HACjKff/656du3r/F6vb6x//qv/zIjR44MYFXB6ZVXXjE1NTUmLy/vjID1xBNPmOzsbL%2BxCRMmmCVLlhhjjBk1apR55ZVXfHNHjhwxCQkJZuvWrcbj8ZhLL73U7Ny50ze/efNmc9lll5kTJ06cwxX9%2BB0%2BfNjMmjXLeDwe39irr75qRowYYdatW2euuOIK09DQ4JtbsGCB%2BcUvfmGMadlz0pZ5vV7zxhtvmJMnT/rGXnnlFXP99debl156ydx6661%2Bj8/JyTFz5841xhhzzz33mKeffto319jYaK666iqzZs0ac/LkSTNw4ECzfv1633xZWZmJj483Bw4cOMerCh6NjY1mwoQJ5j/%2B4z98AYu%2Btw28RRhkSktL1aVLF0VFRfnGEhMTtWfPHtXW1gawsuAzadIkdejQodm50tJSJSQk%2BI0lJCTI7Xarvr5eZWVlfvMRERHq1q2b3G63du7cqbCwML%2B3AxITE3X06FHt3r373CwmSERGRio/P18XXXSRb6yyslJxcXEqLS1VfHy8wsLCfHMJCQm%2Bt0Za8py0ZVFRUZowYYJcLpckaffu3frv//5v3Xjjjd/Y02/qeWhoqPr27Su32619%2B/bpyJEjSkxM9M337NlTF1xwgUpLSx1YWXB4/fXXFR4ertGjR/vG6HvbQMAKMl6vV5GRkX5jp8MW78Hb4/V6/UKsdKrP1dXVOnz4sIwx3zjv9XoVERGhkJAQvzmJ5%2Bjr3G63XnvtNU2dOrXZv9vR0dHyer1qampq0XOCU9e%2BJSUl6aabblJycrKmT5/%2BjT0/3bNv67nX65WkM7aPjIyk5//fV199peeff16PPfaY3zh9bxsIWEHIGBPoEtqE7%2Brzt83zHH23Tz/9VFlZWZoxY4bS0tK%2B8XH/N6i25Dlp67p06SK326133nlHe/fu1cMPP3xW29HzHy4/P19jx45Vr169vve29D34EbCCTGxsrO8VzGler1chISGKjY0NUFWtT0xMTLN9jo2NVXR0tEJDQ5ud79ixo2JjY1VbW6vGxka/OUnq2LHjuS8%2BCGzYsEH33HOPZs%2BerUmTJkk69Xf766/AvV6vr98teU5wSkhIiLp3767c3FytWbNGLpfrjJ5VV1f7/lvybT0//Zivzx8%2BfJie69SnBLdu3ars7Owz5prrK31vfQhYQSYpKUmVlZU6dOiQb8ztdqtXr15q3759ACtrXZKSkvw%2BFi2d6nO/fv0UHh6u3r17%2B13vUFNTo3379iklJUV9%2B/aVMUa7du3y2zYyMlI9evRwbA0/Vp999pny8vL07LPP6tZbb/WNJyUl6YsvvlBDQ4Nv7HTPT8//0OekLfv44481cuRIv1u5hIae%2Bk9/SkrKGT0tKSnx6/n/7WljY6N27Nihfv36qWvXroqKivKb//vf/64TJ04oKSnpXC4pKKxatUoHDx7Utddeq9TUVI0dO1aSlJqaqj59%2BtD3tiAgl9ajRSZMmGBmz55tjhw5YsrKysywYcPMa6%2B9FuiyglZznyL84osvTHJystm4caOpr683y5cvN/379zdVVVXGmFOf3Bw6dKjvlgBz584148aN822fk5Nj7r77bnPw4EFTWVlpxo0bZ%2BbPn%2B/oun6MTp48aW688Ubz%2BuuvnzF3/Phxc%2B2115rnnnvOHD161Gzbts1cfvnlZuPGjcaYlj8nbVVNTY1JS0sz8%2BfPN0ePHjUHDx40WVlZ5mc/%2B5n56quvTP/%2B/c0bb7xh6uvrzaZNm0xKSorvE7CbN282AwcONFu3bjVHjx41zz//vElPTzfHjh0zxpz6lOdtt91m/vnPf5pDhw6ZKVOmmPvvvz%2BQy/3R8Hq9prKy0vezdetW06dPH1NZWWkqKiroextAwApClZWV5u677zYpKSkmLS3NPPfcc23%2BXj8/RFJSkklKSjKXXnqpufTSS31/Pu3dd981I0aMMImJieaWW24xf/3rX31zTU1N5tlnnzVXXnmlSUlJMb/85S9992My5tT/1HJzc81ll11mBg0aZJ544glz/PhxR9f3Y/Q///M/pk%2BfPr5e/9%2Bff/zjH%2BaLL74wEydONElJSWbo0KFm2bJlftu35Dlpy3bt2mXuuOMOk5KSYq644gqTk5Pj%2B0j/X//6VzNmzBiTmJhoRowY4bvv2GnLli0z6enpJikpyWRmZpovvvjCN3f8%2BHHz%2BOOPm0GDBpn%2B/fubBx980NTU1Di6tmCxf/9%2B320ajKHvbUGIMVwpBwAAYBPXYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZf8PVZlb91ndWT0AAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-4706068627650735215”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>286</td> <td class=”number”>8.9%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.407934281707073</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.346739945041219</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.099536060733867</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6639173678229895</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.1437578814627996</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>44.69164715066355</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>205.92302674494454</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.3100569722450721</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>239.35994114401325</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2233)</td> <td class=”number”>2233</td> <td class=”number”>69.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>682</td> <td class=”number”>21.2%</td> <td>

<div class=”bar” style=”width:31%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-4706068627650735215”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>286</td> <td class=”number”>8.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.005838126272833154</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.006026883920206736</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.011715384115227833</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.018761799754002263</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>2494.546126825661</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2806.0766182298544</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2947.462589459987</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3328.9795918367345</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4666.887944768986</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_VEG_ACRESPTH12”><s>VEG_ACRESPTH12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_VEG_ACRESPTH07”><code>VEG_ACRESPTH07</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99906</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_VEG_FARMS07”>VEG_FARMS07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>166</td>

</tr> <tr>

<th>Unique (%)</th> <td>5.2%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>4.2%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>134</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>22.386</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1100</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>8.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-5454999909659435391”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-5454999909659435391,#minihistogram-5454999909659435391”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-5454999909659435391”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-5454999909659435391”

aria-controls=”quantiles-5454999909659435391” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-5454999909659435391” aria-controls=”histogram-5454999909659435391”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-5454999909659435391” aria-controls=”common-5454999909659435391”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-5454999909659435391” aria-controls=”extreme-5454999909659435391”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-5454999909659435391”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>4</td>

</tr> <tr>

<th>Median</th> <td>11</td>

</tr> <tr>

<th>Q3</th> <td>26</td>

</tr> <tr>

<th>95-th percentile</th> <td>78</td>

</tr> <tr>

<th>Maximum</th> <td>1100</td>

</tr> <tr>

<th>Range</th> <td>1100</td>

</tr> <tr>

<th>Interquartile range</th> <td>22</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>43.537</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.9449</td>

</tr> <tr>

<th>Kurtosis</th> <td>216.21</td>

</tr> <tr>

<th>Mean</th> <td>22.386</td>

</tr> <tr>

<th>MAD</th> <td>20.876</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>11.393</td>

</tr> <tr>

<th>Sum</th> <td>68858</td>

</tr> <tr>

<th>Variance</th> <td>1895.5</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-5454999909659435391”>

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aM2eO2rZtq/PPP1/XXXedPvjgA23btk3Hjx/XrFmz1KZNG/Xt21c33HCDcnNzJUlr1qxRSkqKUlJSFB4ergkTJqhnz55at26dJCk3N1fTpk3ThRdeqIiICGVmZqqgoECffPJJIJcMAACCSFAGrMjISC1evFjnnXeed6yoqEhxcXHKz89Xr169FBYW5p2Lj49XXl6eJCk/P1/x8fE%2B%2B4uPj5fb7VZ1dbX27NnjMx8REaEuXbrI7Xaf4VUBAICWwhXoAmxwu9167rnn9NRTT2nTpk2KjIz0mY%2BOjpbH41FdXZ08Ho%2BioqJ85qOiorRnzx4dPnxYxpgG50tLSxtdT3FxsUpKSnzGXK42iouLO82VnVxYWHDlY5er%2BdR7onfB1sPmgN75h/41Hb3zD/1zVtAHrA8//FCzZs3SnDlzlJycrE2bNjW4XUhIiPe/T3U/lb/3W%2BXm5mrFihU%2BY%2Bnp6Zo9e7Zf%2Bw12MTFtA11CPZGR5wa6hKBF7/xD/5qO3vmH/jkjqAPWli1b9LOf/UwLFizQtddeK0mKjY3VV1995bOdx%2BNRdHS0QkNDFRMTI4/HU28%2BNjbWu01D8%2B3bt290XampqRo1apTPmMvVRqWllaexulMLtk8httfvj7CwUEVGnquysirV1tYFupygQu/8Q/%2Bajt7552ztX6A%2B3AdtwProo4%2BUlZWlJ554QsOHD/eOJyQk6E9/%2BpNqamrkcn23PLfbrf79%2B3vnT9yPdYLb7da4ceMUHh6uHj16KD8/X0OHDpX03Q31%2B/btU79%2B/RpdW1xcXL3LgSUl5aqpOXt%2BoRvSHNdfW1vXLOsKBvTOP/Sv6eidf%2BifM4LrFMj/qamp0QMPPKC5c%2Bf6hCtJSklJUUREhJ566ilVVVXpk08%2B0dq1a5WWliZJmjx5st5//31t27ZNR48e1dq1a/XVV19pwoQJkqS0tDStXr1aBQUFqqioUHZ2tvr06aPExETH1wkAAIJTUJ7B2rlzpwoKCrRo0SItWrTIZ%2B7111/Xb37zGz344IPKycnReeedp8zMTI0YMUKS1LNnT2VnZ2vx4sUqLCxU9%2B7dtWrVKnXo0EGSNGXKFJWUlOimm25SZWWlkpKS6t1PBQAAcDIhhidoOqKkpNz6Pl2uUF2e/a71/Z4pmzKGBboEL5crVDExbVVaWsmp8tNE7/xD/5qO3vnnbO1fhw7tAnLcoLxECAAA0JwRsAAAACwjYAEAAFhGwAIAALDM8YA1atQorVixQkVFRU4fGgAAwBGOB6zrr79eGzdu1GWXXaYZM2bojTfeUE1NjdNlAAAAnDGOB6z09HRt3LhRL774onr06KFHH31UKSkpWrJkifbu3et0OQAAANYF7B6svn37KisrS1u3btX999%2BvF198UVdddZWmT5%2BuTz/9NFBlAQAA%2BC1gAev48ePauHGjbrvtNmVlZaljx46677771KdPH02bNk3r168PVGkAAAB%2BcfxP5RQUFGjt2rV65ZVXVFlZqbFjx%2BrZZ5/VoEGDvNsMGTJECxcu1Pjx450uDwAAwG%2BOB6xx48apW7duuuOOO3TttdcqOjq63jYpKSk6dOiQ06UBAABY4XjAWr16tYYOHXrK7T755BMHqgEAALDP8XuwevXqpZkzZ%2Bqtt97yjv33f/%2B3brvtNnk8HqfLAQAAsM7xgLV48WKVl5ere/fu3rERI0aorq5Ojz32mNPlAAAAWOf4JcL33ntP69evV0xMjHesa9euys7O1tVXX%2B10OQAAANY5fgarurpa4eHh9QsJDVVVVZXT5QAAAFjneMAaMmSIHnvsMR0%2BfNg79s033%2Bihhx7yeVQDAABAsHL8EuH999%2BvW2%2B9VT/96U8VERGhuro6VVZWqnPnzvrDH/7gdDkAAADWOR6wOnfurNdee03vvPOO9u3bp9DQUHXr1k3Dhw9XWFiY0%2BUAAABY53jAkqRWrVrpsssuC8ShAQAAzjjHA9b%2B/fu1dOlSffHFF6qurq43//bbbztdEgAAgFUBuQeruLhYw4cPV5s2bZw%2BPAAAwBnneMDKy8vT22%2B/rdjYWKcPDQAA4AjHH9PQvn17zlwBAIAWzfGAdccdd2jFihUyxjh9aAAAAEc4fonwnXfe0UcffaSXX35ZF1xwgUJDfTPeCy%2B84HRJAAAAVjkesCIiInTppZc6fVgAAADHOB6wFi9e7PQhAQAAHOX4PViS9OWXX2r58uW67777vGMff/xxIEoBAACwzvGAtWPHDk2YMEFvvPGGNmzYIOm7h49OnTqVh4wCAIAWwfGAtWzZMv3sZz/T%2BvXrFRISIum7v0/42GOPaeXKlU6XAwAAYJ3jAeuf//yn0tLSJMkbsCTpiiuuUEFBgdPlAAAAWOd4wGrXrl2Df4OwuLhYrVq1crocAAAA6xwPWAMHDtSjjz6qiooK79jevXuVlZWln/70p06XAwAAYJ3jj2m47777dPPNNyspKUm1tbUaOHCgqqqq1KNHDz322GNOlwMAAGCd4wHr/PPP14YNG7R9%2B3bt3btXrVu3Vrdu3TRs2DCfe7IAAACCleMBS5LOOeccXXbZZYE4NAAAwBnneMAaNWrUSc9U8SwsAAAQ7BwPWFdddZVPwKqtrdXevXvldrt18803O10OAACAdY4HrLlz5zY4vnnzZv3tb39zuBoAAAD7AvK3CBty2WWX6bXXXjut17z77rtKTk5WZmamz/jLL7%2Bs3r17KzEx0efn008/lSTV1dVp2bJlGj16tIYMGaLp06dr//793td7PB5lZGQoOTlZw4cP1/z58xt8dhcAAEBDmk3A2rVrl4wxjd7%2Bt7/9rRYtWqQuXbo0OD9kyBC53W6fn379%2BkmSnn/%2Bea1fv145OTnaunWrunbtqvT0dO/xFyxYoKqqKm3YsEEvvfSSCgoKlJ2d7f8iAQDAWcHxS4RTpkypN1ZVVaWCggKNGTOm0fsJDw/X2rVr9cgjj%2Bjo0aOnVUNubq6mTZumCy%2B8UJKUmZmppKQkffLJJ7rgggv01ltv6c9//rNiY2MlSXfeeafuueceZWVl6ZxzzjmtYwEAgLOP4wGra9eu9b5FGB4erkmTJumGG25o9H6mTp160vmioiLdcsstysvLU2RkpGbPnq1rrrlG1dXV2rNnj%2BLj473bRkREqEuXLnK73SovL1dYWJh69erlne/bt6%2BOHDmiL7/80mf8hxQXF6ukpMRnzOVqo7i4uEavrzHCwprNCchGcbmaT70nehdsPWwO6J1/6F/T0Tv/0D9nOR6wnHhae2xsrLp27ap7771X3bt315tvvqmf//zniouL009%2B8hMZYxQVFeXzmqioKJWWlio6OloRERE%2BIfDEtqWlpY06fm5urlasWOEzlp6ertmzZ/u5suAWE9M20CXUExl5bqBLCFr0zj/0r%2BnonX/onzMcD1ivvPJKo7e99tprm3SMESNGaMSIEd5/jxs3Tm%2B%2B%2BaZefvll77cYT3a/1%2BncC9aQ1NRUjRo1ymfM5Wqj0tJKv/b7fcH2KcT2%2Bv0RFhaqyMhzVVZWpdraukCXE1TonX/oX9PRO/%2Bcrf0L1Id7xwPW/PnzVVdXVy/EhISE%2BIyFhIQ0OWA1pFOnTsrLy1N0dLRCQ0Pl8Xh85j0ej9q3b6/Y2FhVVFSotrZWYWFh3jlJat%2B%2BfaOOFRcXV%2B9yYElJuWpqzp5f6IY0x/XX1tY1y7qCAb3zD/1rOnrnH/rnDMcD1tNPP61nnnlGM2fOVK9evWSM0eeff67f/va3uvHGG5WUlOT3Mf70pz8pKipKV111lXesoKBAnTt3Vnh4uHr06KH8/HwNHTpUklRWVqZ9%2B/apX79%2B6tSpk4wx%2Buyzz9S3b19JktvtVmRkpLp16%2BZ3bQAAoOULyD1YOTk56tixo3ds8ODB6ty5s6ZPn64NGzb4fYxjx47p4YcfVufOndW7d29t3rxZ77zzjl588UVJUlpamnJycnTppZeqY8eOys7OVp8%2BfZSYmChJGjt2rH7961/r8ccf17Fjx7Ry5UpNmjRJLldA/nQjAAAIMo4nhq%2B%2B%2BqreDeaSFBkZqcLCwkbv50QYqqmpkSS99dZbkr472zR16lRVVlbqnnvuUUlJiS644AKtXLlSCQkJkr57VERJSYluuukmVVZWKikpyeem9F/%2B8pd68MEHNXr0aJ1zzjm6%2Buqr6z3MFAAA4IeEGH/v6D5NV111lYYOHap77rlHMTExkr67RPfkk0/qH//4h1599VUny3FMSUm59X26XKG6PPtd6/s9UzZlDAt0CV4uV6hiYtqqtLSSexFOE73zD/1rOnrnn7O1fx06tAvIcR0/g3X//fdrzpw5ys3NVdu2bRUaGqqKigq1bt1aK1eudLocAAAA6xwPWMOHD9e2bdu0fft2HThwQMYYdezYUZdcconatQtMygQAALApIHdtn3vuuRo9erQOHDigzp07B6IEAACAM8bxJ1VWV1crKytLAwYM0JVXXinpu3uwZsyYobKyMqfLAQAAsM7xgLVkyRLt3r1b2dnZCg39/4evra1Vdna20%2BUAAABY53jA2rx5s5588kldccUV3r/3FxkZqcWLF%2BuNN95wuhwAAADrHA9YlZWV6tq1a73x2NhYHTlyxOlyAAAArHM8YP34xz/W3/72N0m%2Bf1T59ddf17/92785XQ4AAIB1jn%2BL8N///d9199136/rrr1ddXZ1%2B//vfKy8vT5s3b9b8%2BfOdLgcAAMA6xwNWamqqXC6XnnvuOYWFhek3v/mNunXrpuzsbF1xxRVOlwMAAGCd4wHr0KFDuv7663X99dc7fWgAAABHOH4P1ujRo%2BXwnz8EAABwlOMBKykpSZs2bXL6sAAAAI5x/BLhj370Iz3yyCPKycnRj3/8Y51zzjk%2B80uXLnW6JAAAAKscD1h79uzRT37yE0lSaWmp04cHAAA44xwLWJmZmVq2bJn%2B8Ic/eMdWrlyp9PR0p0oAAABwhGP3YG3ZsqXeWE5OjlOHBwAAcIxjAauhbw7ybUIAANASORawTvxh51ONAQAABDvHH9MAAADQ0hGwAAAALHPsW4THjx/XnDlzTjnGc7AAAECwcyxgDRo0SMXFxaccAwAACHaOBax/ff4VAABAS8Y9WAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAy4I6YL377rtKTk5WZmZmvbmNGzdq/PjxGjBggCZOnKj33nvPO1dXV6dly5Zp9OjRGjJkiKZPn679%2B/d75z0ejzIyMpScnKzhw4dr/vz5qq6udmRNAAAg%2BAVtwPrtb3%2BrRYsWqUuXLvXmdu/eraysLM2dO1d//etfNW3aNN111106cOCAJOn555/X%2BvXrlZOTo61bt6pr165KT0%2BXMUaStGDBAlVVVWnDhg166aWXVFBQoOzsbEfXBwAAglfQBqzw8HCtXbu2wYC1Zs0apaSkKCUlReHh4ZowYYJ69uypdevWSZJyc3M1bdo0XXjhhYqIiFBmZqYKCgr0ySef6Ntvv9Vbb72lzMxMxcbGqmPHjrrzzjv10ksv6fjx404vEwAABCFXoAtoqqlTp/7gXH5%2BvlJSUnzG4uPj5Xa7VV1drT179ig%2BPt47FxERoS5dusjtdqu8vFxhYWHq1auXd75v3746cuSIvvzyS5/xH1JcXKySkhKfMZerjeLi4hq7vEYJCwuufOxyNZ96T/Qu2HrYHNA7/9C/pqN3/qF/zgragHUyHo9HUVFRPmNRUVHas2ePDh8%2BLGNMg/OlpaWKjo5WRESEQkJCfOYkqbS0tFHHz83N1YoVK3zG0tPTNXv27KYsp8WIiWkb6BLqiYw8N9AlBC165x/613T0zj/0zxktMmBJ8t5P1ZT5U732VFJTUzVq1CifMZerjUpLK/3a7/cF26cQ2%2Bv3R1hYqCIjz1VZWZVqa%2BsCXU5QoXf%2BoX9NR%2B/8c7b2L1Af7ltkwIqJiZHH4/EZ83g8io2NVXR0tEJDQxucb9%2B%2BvWJjY1VRUaHa2lqFhYV55ySpffv2jTp%2BXFxcvcuBJSXlqqk5e36hG9Ic119bW9cs6woG9M4/9K/p6J1/6J8zgusUSCMlJCQoLy/PZ8ztdqt///4KDw9Xjx49lJ%2Bf750rKyvTvn371K9fP/Xp00fGGH322Wc%2Br42MjFS3bt0cWwMAAAheLTJgTZ48We%2B//762bdumo0ePau3atfrqq680YcIESVJaWppWr16tgoICVVRUKDs7W3369FGm3QYqAAAR8UlEQVRiYqJiY2M1duxY/frXv9ahQ4d04MABrVy5UpMmTZLL1SJP%2BAEAAMuCNjEkJiZKkmpqaiRJb731lqTvzjb17NlT2dnZWrx4sQoLC9W9e3etWrVKHTp0kCRNmTJFJSUluummm1RZWamkpCSfm9J/%2Bctf6sEHH9To0aN1zjnn6Oqrr27wYaYAAAANCTH%2B3tGNRikpKbe%2BT5crVJdnv2t9v2fKpoxhgS7By%2BUKVUxMW5WWVnIvwmmid/6hf01H7/xztvavQ4d2ATlui7xECAAAEEgELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAshYbsHr16qWEhAQlJiZ6fx5%2B%2BGFJ0o4dOzRp0iQNHDhQ48aN07p163xeu3r1ao0dO1YDBw5UWlqa8vLyArEEAAAQpFyBLuBMev3113XBBRf4jBUXF%2BvOO%2B/U/PnzNX78eH344YeaNWuWunXrpsTERG3ZskXLly/X008/rV69emn16tWaOXOm3njjDbVp0yZAKwEAAMGkxZ7B%2BiHr169X165dNWnSJIWHhys5OVmjRo3SmjVrJEm5ubmaOHGi%2Bvfvr9atW2vGjBmSpK1btwaybAAAEERa9BmspUuX6uOPP1ZFRYWuvPJKzZs3T/n5%2BYqPj/fZLj4%2BXps2bZIk5efn66qrrvLOhYaGqk%2BfPnK73Ro3blyjjltcXKySkhKfMZerjeLi4vxcka%2BwsODKxy5X86n3RO%2BCrYfNAb3zD/1rOnrnH/rnrBYbsC666CIlJyfr8ccf1/79%2B5WRkaGHHnpIHo9HHTt29Nk2OjpapaWlkiSPx6OoqCif%2BaioKO98Y%2BTm5mrFihU%2BY%2Bnp6Zo9e3YTV9MyxMS0DXQJ9URGnhvoEoIWvfMP/Ws6eucf%2BueMFhuwcnNzvf994YUXau7cuZo1a5YGDRp0ytcaY/w6dmpqqkaNGuUz5nK1UWlppV/7/b5g%2BxRie/3%2BCAsLVWTkuSorq1JtbV2gywkq9M4/9K/p6J1/ztb%2BBerDfYsNWN93wQUXqLa2VqGhofJ4PD5zpaWlio2NlSTFxMTUm/d4POrRo0ejjxUXF1fvcmBJSblqas6eX%2BiGNMf119bWNcu6ggG98w/9azp65x/654zgOgXSSLt27dJjjz3mM1ZQUKBWrVopJSWl3mMX8vLy1L9/f0lSQkKC8vPzvXO1tbXatWuXdx4AAOBUWmTAat%2B%2BvXJzc5WTk6Njx45p7969euKJJ5SamqprrrlGhYWFWrNmjY4ePart27dr%2B/btmjx5siQpLS1Nr7zyinbu3Kmqqio99dRTatWqlUaMGBHYRQEAgKDRIi8RduzYUTk5OVq6dKk3IF133XXKzMxUeHi4Vq1apUWLFumhhx5Sp06dtGTJEvXu3VuSdOmll%2Bree%2B9VRkaGDh48qMTEROXk5Kh169YBXhUAAAgWIcbfO7rRKCUl5db36XKF6vLsd63v90zZlDEs0CV4uVyhiolpq9LSSu5FOE30zj/0r%2BnonX/O1v516NAuIMdtkZcIAQAAAomABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLXIEuAGePK3/9l0CXcFo2ZQwLdAkAgCDFGSwAAADLCFgAAACWEbAaUFhYqNtvv11JSUkaOXKklixZorq6ukCXBQAAggT3YDXg7rvvVt%2B%2BffXWW2/p4MGDuuOOO3TeeefplltuCXRpAAAgCHAG63vcbrc%2B%2B%2BwzzZ07V%2B3atVPXrl01bdo05ebmBro0AAAQJDiD9T35%2Bfnq1KmToqKivGN9%2B/bV3r17VVFRoYiIiFPuo7i4WCUlJT5jLlcbxcXFWa01LIx8fCYF27ce35x7iSPHOfF7x%2B9f09C/pqN3/qF/ziJgfY/H41FkZKTP2ImwVVpa2qiAlZubqxUrVviM3XXXXbr77rvtFarvgtzN53%2Bh1NRU6%2BGtpSsuLlZubi69a4Li4mI9%2B%2BzT9K6J6F/T0Tv/0D9nEWMbYIzx6/Wpqal6%2BeWXfX5SU1MtVff/lZSUaMWKFfXOluHU6F3T0Tv/0L%2Bmo3f%2BoX/O4gzW98TGxsrj8fiMeTwehYSEKDY2tlH7iIuL49MBAABnMc5gfU9CQoKKiop06NAh75jb7Vb37t3Vtm3bAFYGAACCBQHre%2BLj45WYmKilS5eqoqJCBQUF%2Bv3vf6%2B0tLRAlwYAAIJE2MKFCxcGuojm5pJLLtGGDRv08MMP67XXXtOkSZM0ffp0hYSEBLq0etq2bauhQ4dydq0J6F3T0Tv/0L%2Bmo3f%2BoX/OCTH%2B3tENAAAAH1wiBAAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgBWECgsLdfvttyspKUkjR47UkiVLVFdXF%2Biymo3CwkKlp6crKSlJycnJmjdvnsrKyiRJu3fv1o033qhBgwZpzJgxeuaZZ3xeu3HjRo0fP14DBgzQxIkT9d577wViCc3Co48%2Bql69enn/vWPHDk2aNEkDBw7UuHHjtG7dOp/tV69erbFjx2rgwIFKS0tTXl6e0yU3C0899ZSGDx%2Buiy66SNOmTdPXX38tif6dyq5duzR16lQNHjxYw4YN09y5c3Xo0CFJ9K4h7777rpKTk5WZmVlv7mTvY3V1dVq2bJlGjx6tIUOGaPr06dq/f7933uPxKCMjQ8nJyRo%2BfLjmz5%2Bv6upqR9bU4hgEneuuu8488MADpqyszOzdu9eMGTPGPPPMM4Euq9m4%2Buqrzbx580xFRYUpKioyEydONPfff7%2Bpqqoyl1xyiVm%2BfLmprKw0eXl5ZujQoWbz5s3GGGN27dplEhISzLZt20x1dbV59dVXTf/%2B/U1RUVGAV%2BS8Xbt2maFDh5qePXsaY4z55ptvzEUXXWTWrFljqqurzV/%2B8hfTr18/8%2BmnnxpjjHn77bfN4MGDzc6dO01VVZVZtWqVGTZsmKmsrAzkMhz33HPPmSuuuMIUFBSY8vJy8/DDD5uHH36Y/p3C8ePHzbBhw8zSpUvN0aNHzaFDh8wtt9xi7r77bnrXgJycHDNmzBgzZcoUk5GR4TN3qvex1atXm5EjR5o9e/aY8vJy88tf/tKMHz/e1NXVGWOMueuuu8ztt99uDh48aA4cOGBSU1PNww8/7PgaWwICVpD59NNPTZ8%2BfYzH4/GO/fGPfzRjx44NYFXNx%2BHDh828efNMSUmJd%2BwPf/iDGTNmjNm0aZO5%2BOKLTU1NjXduyZIl5tZbbzXGGPPQQw%2BZ9PR0n/3dcMMNZtWqVc4U30zU1taaG264wfzXf/2XN2A9/fTT5tprr/XZLiMjwyxYsMAYY8ztt99uHn30UZ99DBs2zGzYsMG5wpuBUaNGeQP7v6J/J/e///u/pmfPnmbPnj3esT/%2B8Y/msssuo3cNePbZZ01ZWZnJysqqF7BO9T42btw48%2Byzz3rnysvLTXx8vPn4449NSUmJ6d27t9m9e7d3fvv27eaiiy4yx44dO4Mrapm4RBhk8vPz1alTJ0VFRXnH%2Bvbtq71796qioiKAlTUPkZGRWrx4sc477zzvWFFRkeLi4pSfn69evXopLCzMOxcfH%2B%2B9nJCfn6/4%2BHif/cXHx8vtdjtTfDPxwgsvKDw8XOPHj/eO/VBvfqh3oaGh6tOnz1nVu2%2B%2B%2BUZff/21Dh8%2BrKuuukpJSUmaPXu2Dh06RP9OoWPHjurTp49yc3NVWVmpgwcP6o033tCIESPoXQOmTp2qdu3aNTh3svex6upq7dmzx2c%2BIiJCXbp0kdvt1u7duxUWFuZza0Dfvn115MgRffnll2dmMS0YASvIeDweRUZG%2BoydCFulpaWBKKlZc7vdeu655zRr1qwGexcdHS2Px6O6ujp5PB6f4Cp919uzqa/ffvutli9frgcffNBn/Id6d6I39E46cOCAJOn111/X73//e7366qs6cOCAHnjgAfp3CqGhoVq%2BfLnefvttDRw4UMnJyaqpqdGcOXPo3Wk6WT8OHz4sY8wPzns8HkVERCgkJMRnTuL/X5qCgBWEjDGBLiEofPjhh5o%2BfbrmzJmj5OTkH9zuX99MzvbeLl68WBMnTlT37t1P%2B7Vne%2B9OrH/GjBnq2LGjzj//fN19993asmXLab3%2BbHTs2DHNnDlTV1xxhT744AO98847ateunebOnduo15/NvWvIqfpxsnl6aQ8BK8jExsbK4/H4jHk8HoWEhCg2NjZAVTU/W7Zs0e233677779fU6dOlfRd777/Kczj8Sg6OlqhoaGKiYlpsLdnS1937Nihjz/%2BWOnp6fXmGupNaWmptzdne%2B8keS9L/%2BvZlk6dOskYo%2BPHj9O/k9ixY4e%2B/vpr3XvvvWrXrp06duyo2bNn680331RoaCi9Ow0n68eJ97qG5tu3b6/Y2FhVVFSotrbWZ06S2rdvf%2BaLb2EIWEEmISFBRUVF3q8vS99dBuvevbvatm0bwMqaj48%2B%2BkhZWVl64okndO2113rHExIS9Pnnn6umpsY75na71b9/f%2B/897/e/a/zLd26det08OBBjRw5UklJSZo4caIkKSkpST179qzXm7y8PJ/e5efne%2Bdqa2u1a9eus6Z3knT%2B%2BecrIiJCu3fv9o4VFhbqnHPOUUpKCv07idraWtXV1fmcPTl27JgkKTk5md6dhpO9j4WHh6tHjx4%2B/SorK9O%2BffvUr18/9enTR8YYffbZZz6vjYyMVLdu3RxbQ4sRkFvr4ZcbbrjB3H///aa8vNzs2bPHjBo1yjz33HOBLqtZOH78uLnyyivNCy%2B8UG/u6NGjZuTIkebJJ580R44cMTt37jSDBw82W7duNcYY8/nnn5vExESzdetWU11dbdasWWMGDBhgiouLHV5FYHg8HlNUVOT9%2Bfjjj03Pnj1NUVGRKSwsNAMGDDAvvviiqa6uNtu2bTP9%2BvXzftto%2B/btZtCgQebjjz82R44cMcuXLzcpKSmmqqoqwKty1qOPPmpGjx5tvvrqK/Ptt9%2Ba1NRUM2/ePPPtt9/Sv5M4dOiQGTp0qPnVr35ljhw5Yg4dOmRmzpxp/uM//oPenURD3yI81fvYH//4RzNixAjvYxoWLFhgrr/%2Beu/rMzIyzIwZM8zBgwdNUVGRuf76681jjz3m6LpaCgJWECoqKjIzZsww/fr1M8nJyebJJ5/0PsPkbPePf/zD9OzZ0yQkJNT7%2Bfrrr83nn39upkyZYhISEsyIESPM888/7/P6zZs3mzFjxpi%2Bffuaa665xvz9738P0EoCb//%2B/d7HNBhjzN///nczYcIE07dvXzNmzJh6jyN4/vnnTUpKiklISDBpaWnm888/d7rkgDt69KhZuHChGTJkiLnoootMVlaWqaioMMbQv1Nxu93mxhtvNIMHDzbJyckmIyPDHDhwwBhD777vxHta7969Te/evb3/PuFk72N1dXXmiSeeMD/96U9Nv379zG233ebzrL%2BysjKTmZlpLrroIjNkyBDz0EMPmaNHjzq6vpYixBjuaAMAALCJe7AAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwLL/BxwcjiJGcaDVAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-5454999909659435391”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>286</td> <td class=”number”>8.9%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>165</td> <td class=”number”>5.1%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>163</td> <td class=”number”>5.1%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>137</td> <td class=”number”>4.3%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>122</td> <td class=”number”>3.8%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>119</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>108</td> <td class=”number”>3.4%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>104</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.0</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (155)</td> <td class=”number”>1678</td> <td class=”number”>52.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>134</td> <td class=”number”>4.2%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-5454999909659435391”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>286</td> <td class=”number”>8.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>163</td> <td class=”number”>5.1%</td> <td>

<div class=”bar” style=”width:57%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>165</td> <td class=”number”>5.1%</td> <td>

<div class=”bar” style=”width:58%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>137</td> <td class=”number”>4.3%</td> <td>

<div class=”bar” style=”width:48%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>119</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:42%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>476.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>559.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>722.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>908.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1100.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_VEG_FARMS12”><s>VEG_FARMS12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_VEG_FARMS07”><code>VEG_FARMS07</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.91017</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_VLFOODSEC_10_12”>VLFOODSEC_10_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>32</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>5.8018</td>

</tr> <tr>

<th>Minimum</th> <td>3.2</td>

</tr> <tr>

<th>Maximum</th> <td>8.1</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram8457138207512358384”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives8457138207512358384,#minihistogram8457138207512358384”
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</div> <div class=”row collapse col-md-12” id=”descriptives8457138207512358384”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles8457138207512358384”

aria-controls=”quantiles8457138207512358384” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram8457138207512358384” aria-controls=”histogram8457138207512358384”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common8457138207512358384” aria-controls=”common8457138207512358384”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme8457138207512358384” aria-controls=”extreme8457138207512358384”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles8457138207512358384”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>3.2</td>

</tr> <tr>

<th>5-th percentile</th> <td>4.5</td>

</tr> <tr>

<th>Q1</th> <td>5.1</td>

</tr> <tr>

<th>Median</th> <td>5.7</td>

</tr> <tr>

<th>Q3</th> <td>6.5</td>

</tr> <tr>

<th>95-th percentile</th> <td>7.1</td>

</tr> <tr>

<th>Maximum</th> <td>8.1</td>

</tr> <tr>

<th>Range</th> <td>4.9</td>

</tr> <tr>

<th>Interquartile range</th> <td>1.4</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.8609</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.14838</td>

</tr> <tr>

<th>Kurtosis</th> <td>-0.40091</td>

</tr> <tr>

<th>Mean</th> <td>5.8018</td>

</tr> <tr>

<th>MAD</th> <td>0.72228</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.036458</td>

</tr> <tr>

<th>Sum</th> <td>18171</td>

</tr> <tr>

<th>Variance</th> <td>0.74114</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram8457138207512358384”>

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9GvTpd2JH/s8chn3zO7BJ%2B1tXCIKeu90P24M%2B0LZvX2NH88VnQ0s3vcvXt4h69T8tFLhAAAAGYiYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBfDZgJSQkKCkpScnJya5fTzzxhCSpqqpKOTk5Sk1N1ejRo7Vp0ya315aVlWnEiBFKTU1VXl6eampqzNgEAADQSVnNLsCTtm3bpp49e7qNNTQ0aObMmVqwYIHGjBmjDz/8UDNmzFCvXr2UnJysHTt2qLi4WC%2B88IISEhJUVlam6dOnq6KiQqGhoSZtCQAA6Ex89gzWT9m8ebPi4%2BOVk5Oj4OBgpaenKysrS%2BXl5ZIkm82m7OxspaSkKCQkRFOnTpUk7dy508yyAQBAJ%2BLTAWvFihXKzMzUwIEDtWjRIjU2Nqq2tlaJiYluyyUmJrouA/543mKxqF%2B/fqquru7Q2gEAQOfls5cI%2B/fvr/T0dD311FP65ptvVFhYqMWLF8vpdCouLs5t2aioKDkcDkmS0%2BlUZGSk23xkZKRrvj0aGhpkt9vdxqzWUMXGxkqSAgMtbl9hPHoMI1mt5uxH/rAfm9Xb0/yhx2bz1x77bMCy2Wyu76%2B55hrNmzdPM2bM0IABA8752ra2tote98qVK93G8vPzVVBQ4DYWEdHlotaDc6PHMEJ0dFdT1%2B/L%2B7HZvT3Nl3vsLfytx14XsLKyspSdna3x48fr8ssvN%2Bx9e/bsqZaWFlksFjmdTrc5h8OhmJgYSVJ0dPQZ806nU3369Gn3unJzc5WVleU2ZrWGyuFolHQqxUdEdNHhw01qaWm9kM3BOdBjGOn0392O5g/7sVm9Pc0femw2s3tsVoj3uoA1fvx4vfHGGyopKdGNN96oiRMnKisrS1Zr%2B0utq6vTpk2b9Mgjj7jG9u7dq6CgIGVkZOj11193W76mpkYpKSmSpKSkJNXW1mrcuHGSpJaWFtXV1SknJ6fd64%2BNjXVdDjzNbj%2Bi5mb3HaulpfWMMRiLHsMIZu9Dvrwfe8t2%2BXKPvYW/9djrLojm5%2BfrzTff1B//%2BEf16dNHS5YsUUZGhoqKirRv3752vUe3bt1ks9lUWlqqEydOaN%2B%2BfXruueeUm5urO%2B%2B8U/X19SovL9fx48dVWVmpyspKTZw4UZKUl5enDRs2aPfu3WpqalJJSYmCgoKUmZnpwa0GAAC%2BxOsC1mnXXXed5s%2Bfr507d%2BrRRx/VH//4R40aNUr33Xef/ud//uffvjYuLk6lpaXasWOH0tLSNGnSJN1000166KGH1K1bN61evVrr1q3TgAEDtGTJEhUVFalv376SpGHDhmnOnDkqLCzU4MGDtWvXLpWWliokJKQjNhsAAPgAr7tEeNrJkyf1pz/9Sa%2B99pref/99xcfHa9asWWpoaNCUKVO0ePFijRkz5idfP2jQIL3yyis/Obdx48affO3kyZM1efLki94GAADgn7wuYO3du1evvvqqNmzYoMbGRo0YMUIvvfSS26f/Bg0apMcff/zfBiwAAACzeF3AGj16tHr16qVp06bprrvuUlRU1BnLZGRk6NChQyZUBwAAcG5eF7DKyso0ePDgcy73ySefdEA1AAAA58/rAlZCQoKmT5%2BunJwc3XLLLZKkP/zhD3rvvfdUVFR01jNaAIDOaeSz75ldwnnZWjjE7BLQSXjdpwiXLl2qI0eOqHfv3q6xzMxMtba2atmyZSZWBgAA0D5edwbr3Xff1ebNmxUdHe0ai4%2BP1/Lly3XHHXeYWBkAAED7eN0ZrGPHjik4OPiMcYvFoqamJhMqAgAAOD9eF7AGDRqkZcuW6fvvv3eNffvtt1q8eHG7flAzAACA2bzuEuGjjz6qe%2B%2B9VzfeeKPCwsLU2tqqxsZGXXnllXr55ZfNLg8AAOCcvC5gXXnllXrjjTf0zjvv6O9//7ssFot69eqloUOHKjAw0OzyAAAAzsnrApYkBQUFuR7RAAAA0Nl4XcD65ptvtGLFCn3%2B%2Bec6duzYGfNvvfWWCVUBAAC0n9cFrEcffVQNDQ0aOnSoQkNDzS4HAADgvHldwKqpqdFbb72lmJgYs0sBAAC4IF73mIZu3bpx5goAAHRqXhewpk2bppUrV6qtrc3sUgAAAC6I110ifOedd/TRRx/ptddeU8%2BePWWxuGfAV155xaTKAAAA2sfrAlZYWJiGDRtmdhkAAAAXzOsC1tKlS80uAQAA4KJ43T1YkvTll1%2BquLhYv/nNb1xjH3/8sYkVAQAAtJ/XBayqqiqNHTtWFRUV2rJli6RTDx%2B9%2B%2B67ecgoAADoFLwuYD3zzDN66KGHtHnzZgUEBEg69fMJly1bplWrVplcHQAAwLl5XcD63//9X%2BXl5UmSK2BJ0u233669e/eaVRYAAEC7eV3ACg8PP%2BvPIGxoaFBQUJAJFQEAAJwfrwtYqampWrJkiY4ePeoa27dvn%2BbPn68bb7zRxMoAAADax%2Bse0/Cb3/xG99xzj9LS0tTS0qLU1FQ1NTWpT58%2BWrZsmdnlAQAAnJPXBazLLrtMW7ZsUWVlpfbt26eQkBD16tVLQ4YMcbsnCwAAwFt5XcCSpEsuuUS33HKL2WUAAABcEK8LWFlZWf/2TNWFPAtryZIleumll/TZZ59JOvWsrRUrVujLL7/U5ZdfrmnTpmns2LGu5cvKyrR%2B/XrZ7XYlJCRowYIFSkpKOv%2BNAQAAfsnrAtaoUaPcAlZLS4v27dun6upq3XPPPef9fnv27NHGjRtdv29oaNDMmTO1YMECjRkzRh9%2B%2BKFmzJihXr16KTk5WTt27FBxcbFeeOEFJSQkqKysTNOnT1dFRYVCQ0MN2UYAAODbvC5gzZs376zj27dv11//%2Btfzeq/W1lY99thjmjJlip599llJ0ubNmxUfH6%2BcnBxJUnp6urKyslReXq7k5GTZbDZlZ2crJSVFkjR16lSVlZVp586dGj169EVsGQAA8Bde95iGn3LLLbfojTfeOK/XvPLKKwoODtaYMWNcY7W1tUpMTHRbLjExUTU1NWedt1gs6tevn6qrqy%2BiegAA4E%2B87gzWT6mrq1NbW1u7l//uu%2B9UXFysl19%2B2W3c6XQqLi7ObSwqKkoOh8M1HxkZ6TYfGRnpmm%2BPhoYG2e12tzGrNVSxsbGSpMBAi9tXGI8ew0hWqzn7Efux9zFrX%2BjM/HU/9rqANWnSpDPGmpqatHfvXt12223tfp%2BlS5cqOztbvXv31v79%2B8%2BrhvMJcmdjs9m0cuVKt7H8/HwVFBS4jUVEdLmo9eDc6DGMEB3d1dT1sx97D7P3hc7M3/ZjrwtY8fHxZ3yKMDg4WDk5OZowYUK73qOqqkoff/yxtmzZcsZcdHS0nE6n25jD4VBMTMxPzjudTvXp06fd25Cbm6usrCy3Mas1VA5Ho6RTKT4ioosOH25SS0tru98X7UePYaTTf3c7Gvux9zFrX%2BjMzN6PzQrFXhewjHha%2B6ZNm3Tw4EENHz5c0v%2BfkUpLS9O99957RvCqqalx3dSelJSk2tpajRs3TtKpTzHW1dW5bopvj9jYWNflwNPs9iNqbnbfsVpaWs8Yg7HoMYxg9j7Efuw9%2BHO4cP62H3tdwNqwYUO7l73rrrvOOv7II4/owQcfdP3%2BwIEDys3N1caNG9Xa2qrVq1ervLxcY8eO1fvvv6/KykrZbDZJUl5enubMmaM77rhDCQkJevHFFxUUFKTMzMyL2i4AAOA/vC5gLViwQK2trWfcBxUQEOA2FhAQ8JMBKzIy0u1G9ebmZkmnfgyPJK1evVpPPvmkFi9erB49eqioqEh9%2B/aVJA0bNkxz5sxRYWGhDh48qOTkZJWWliokJMTQ7QQAAL7L6wLWCy%2B8oDVr1mj69OlKSEhQW1ubPvvsM/3%2B97/XL3/5S6WlpZ33e/bs2dP1FHdJGjRokNvDR39s8uTJmjx58gXVDwAA4HUBa9myZSotLXV7lMLAgQN15ZVX6r777jvrjesAAADexOseSvHVV1%2Bd8RwqSYqIiFB9fb0JFQEAAJwfrwtYPXr00LJly9we7Hn48GGtWLFCV111lYmVAQAAtI/XXSJ89NFHNXfuXNlsNnXt2lUWi0VHjx5VSEiIVq1aZXZ5AAAA5%2BR1AWvo0KF6%2B%2B23VVlZqQMHDqitrU1xcXG66aabFB4ebnZ5AAAA5%2BR1AUuSunTpoptvvlkHDhzQlVdeaXY5AAAA58Xr7sE6duyY5s%2Bfr%2Buvv14jR46UdOoerKlTp%2Brw4cMmVwcAAHBuXhewioqKtGfPHi1fvlwWy/%2BX19LSouXLl5tYGQAAQPt4XcDavn27fve73%2Bn22293/dDniIgILV26VBUVFSZXBwAAcG5eF7AaGxsVHx9/xnhMTIx%2B%2BOGHji8IAADgPHldwLrqqqv017/%2BVZLcfvbgtm3bdMUVV5hVFgAAQLt53acIJ0%2BerFmzZmn8%2BPFqbW3V2rVrVVNTo%2B3bt2vBggVmlwcAAHBOXhewcnNzZbVatW7dOgUGBur5559Xr169tHz5ct1%2B%2B%2B1mlwcAAHBOXhewDh06pPHjx2v8%2BPFmlwIAAHBBvO4erJtvvtnt3isAAIDOxusCVlpamrZu3Wp2GQAAABfM6y4RXn755frtb3%2Br0tJSXXXVVbrkkkvc5lesWGFSZQAAAO3jdQHriy%2B%2B0NVXXy1JcjgcJlcDAABw/rwmYM2ePVvPPPOMXn75ZdfYqlWrlJ%2Bfb2JVAAAA589r7sHasWPHGWOlpaUmVAIAAHBxvCZgne2Tg3yaEAAAdEZeE7BO/2Dnc40BAAB4O6%2B5BwsAvNXIZ98zuwQAnYzXnMECAADwFV5zBuvkyZOaO3fuOcd4DhYAAPB2XhOwBgwYoIaGhnOOAQAAeDuvCVj/%2BvwrAACAzox7sAAAAAzmswHr008/1T333KMBAwYoPT1dhYWFstvtkqSqqirl5OQoNTVVo0eP1qZNm9xeW1ZWphEjRig1NVV5eXmqqakxYxMAAEAn5ZMB68SJE7r33ns1ePBgVVVVacuWLTp48KAef/xxNTQ0aObMmZo0aZKqqqq0YMECLVq0SNXV1ZJOPVG%2BuLhYTz/9tHbt2qXhw4dr%2BvTp%2BuGHH0zeKgAA0Fn4ZMBqamrS7NmzNW3aNAUFBSkmJka33nqrPv/8c23evFnx8fHKyclRcHCw0tPTlZWVpfLyckmSzWZTdna2UlJSFBISoqlTp0qSdu7caeYmAQCATsRrbnI3UmRkpCZMmOD6/ZdffqnXX39dI0eOVG1trRITE92WT0xM1NatWyVJtbW1GjVqlGvOYrGoX79%2Bqq6u1ujRo9u1/oaGBtflyNOs1lDFxsZKkgIDLW5fYTx6DMATrFaOKefLX4/HPhmwTquvr9eIESPU3NysiRMnqqCgQPfff7/i4uLclouKipLD4ZAkOZ1ORUZGus1HRka65tvDZrNp5cqVbmP5%2BfkqKChwG4uI6HI%2Bm4MLQI8BGCk6uqvZJXRa/nY89umA1aNHD1VXV%2Bvrr7/Wf/7nf%2Brhhx9u1%2Bsu9odM5%2BbmKisry23Mag2Vw9Eo6VSKj4joosOHm9TS0npR68LZ0WMAnnD6OI72M/t4bFYo9umAJZ36gdHx8fGaPXu2Jk2apIyMDDmdTrdlHA6HYmJiJEnR0dFnzDudTvXp06fd64yNjXVdDjzNbj%2Bi5mb3HaulpfWMMRiLHgMwEseTC%2Bdvx2OfvCBaVVWlESNGqLX1//8gLZZTm/rzn//8jMcu1NTUKCUlRZKUlJSk2tpa11xLS4vq6upc8wAAAOfikwErKSlJR48eVVFRkZqamnTo0CEVFxdr4MCBysvLU319vcrLy3X8%2BHFVVlaqsrJSEydOlCTl5eVpw4YN2r17t5qamlRSUqKgoCBlZmaau1EAAKDT8MmAFR4erjVr1qimpkY33HCDRo8erfDwcP3Xf/2XunXrptWrV2vdunUaMGCAlixZoqKiIvXt21eSNGzYMM2ZM0eFhYUaPHiwdu3apdLSUoWEhJi8VQAAoLMIaLvYO7rRLnb7Edf3VqtF0dFd5XA0%2BtX16I7kjz0e%2Bex7ZpcA%2BLythUPMLqHTMft43L17eIevU/LRM1gAAABmImABAAAYjIAFAABgMAIWAACAwQhYAAAABiPKyXaaAAAQRklEQVRgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwXw2YNXX1ys/P19paWlKT0/XI488osOHD0uS9uzZo1/%2B8pcaMGCAbrvtNq1Zs8bttW%2B%2B%2BabGjBmj66%2B/XtnZ2Xr33XfN2AQAANBJ%2BWzAmj59uiIiIrRjxw699tpr%2Bvzzz/XUU0/p2LFjmjZtmm644Qb95S9/0TPPPKPVq1eroqJC0qnwNX/%2BfM2bN0/vv/%2B%2BpkyZogceeEAHDhwweYsAAEBn4ZMB6/Dhw0pKStLcuXPVtWtXXXbZZRo3bpw%2B%2BOADvf322zp58qRmzJih0NBQXXfddZowYYJsNpskqby8XBkZGcrIyFBwcLDGjh2ra6%2B9Vps2bTJ5qwAAQGdhNbsAT4iIiNDSpUvdxv75z38qNjZWtbW1SkhIUGBgoGsuMTFR5eXlkqTa2lplZGS4vTYxMVHV1dXtXn9DQ4PsdrvbmNUaqtjYWElSYKDF7SuMR48BeILVyjHlfPnr8dgnA9aPVVdXa926dSopKdHWrVsVERHhNh8VFSWn06nW1lY5nU5FRka6zUdGRuqLL75o9/psNptWrlzpNpafn6%2BCggK3sYiILue5JThf9BiAkaKju5pdQqflb8djnw9YH374oWbMmKG5c%2BcqPT1dW7duPetyAQEBru/b2touap25ubnKyspyG7NaQ%2BVwNEo6leIjIrro8OEmtbS0XtS6cHb0GIAnnD6Oo/3MPh6bFYp9OmDt2LFDDz30kBYtWqS77rpLkhQTE6OvvvrKbTmn06moqChZLBZFR0fL6XSeMR8TE9Pu9cbGxrouB55mtx9Rc7P7jtXS0nrGGIxFjwEYiePJhfO347HPXhD96KOPNH/%2BfD333HOucCVJSUlJ%2Buyzz9Tc3Owaq66uVkpKimu%2BpqbG7b3%2BdR4AAOBcfDJgNTc3a%2BHChZo3b56GDh3qNpeRkaGwsDCVlJSoqalJn3zyiV599VXl5eVJkiZOnKhdu3bp7bff1vHjx/Xqq6/qq6%2B%2B0tixY83YFAAA0AkFtF3sDUde6IMPPtAvfvELBQUFnTG3bds2NTY26rHHHlNNTY0uvfRS3X///Zo8ebJrmYqKCq1YsUL19fXq3bu3FixYoEGDBl1UTXb7Edf3VqtF0dFd5XA0%2BtXp0o5kRI9HPvuewVUB6Oy2Fg4xu4ROx%2Bx/87p3D%2B/wdUo%2BGrC8EQGrYxGwAHgCAev8mf1vnlkByycvEQIAAJiJgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAG89mA9Ze//EXp6emaPXv2GXNvvvmmxowZo%2Buvv17Z2dl69913XXOtra165plndPPNN2vQoEG677779M0333Rk6QAAoJPzyYD1%2B9//Xk8%2B%2BaR%2B9rOfnTG3Z88ezZ8/X/PmzdP777%2BvKVOm6IEHHtCBAwckSevXr9fmzZtVWlqqnTt3Kj4%2BXvn5%2BWpra%2BvozQAAAJ2UTwas4OBgvfrqq2cNWOXl5crIyFBGRoaCg4M1duxYXXvttdq0aZMkyWazacqUKbrmmmsUFham2bNna%2B/evfrkk086ejMAAEAnZTW7AE%2B4%2B%2B67f3KutrZWGRkZbmOJiYmqrq7WsWPH9MUXXygxMdE1FxYWpp/97Geqrq5W//7927X%2BhoYG2e12tzGrNVSxsbGSpMBAi9tXGI8eA/AEq5Vjyvny1%2BOxTwasf8fpdCoyMtJtLDIyUl988YW%2B//57tbW1nXXe4XC0ex02m00rV650G8vPz1dBQYHbWEREl/OsHueLHgMwUnR0V7NL6LT87XjsdwFL0jnvp7rY%2B61yc3OVlZXlNma1hsrhaJR0KsVHRHTR4cNNamlpvah14ezoMQBPOH0cR/uZfTw2KxT7XcCKjo6W0%2Bl0G3M6nYqJiVFUVJQsFstZ57t169budcTGxrouB55mtx9Rc7P7jtXS0nrGGIxFjwEYiePJhfO347F/XRCVlJSUpJqaGrex6upqpaSkKDg4WH369FFtba1r7vDhw/r73/%2Bun//85x1dKgAA6KT8LmBNnDhRu3bt0ttvv63jx4/r1Vdf1VdffaWxY8dKkvLy8lRWVqa9e/fq6NGjWr58ufr166fk5GSTKwcAAJ2FT14iPB2GmpubJUl//vOfJZ06U3Xttddq%2BfLlWrp0qerr69W7d2%2BtXr1a3bt3lyRNmjRJdrtd//Ef/6HGxkalpaWdccM6AADAvxPQxhM0O4TdfsT1vdVqUXR0VzkcjX51PbojGdHjkc%2B%2BZ3BVADq7rYVDzC6h0zH737zu3cM7fJ2Sj57BAgDAEzrbf7wIhObxu3uwAAAAPI2ABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBrGYXgIsz8tn3zC6h3bYWDjG7BAAAOgRnsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMxqcIAQDwUXzS3DycwQIAADAYAess6uvr9etf/1ppaWkaPny4ioqK1NraanZZAACgk%2BAS4VnMmjVL1113nf785z/r4MGDmjZtmi699FL96le/Mrs0AADQCXAG60eqq6v16aefat68eQoPD1d8fLymTJkim81mdmkAAKCT4AzWj9TW1qpHjx6KjIx0jV133XXat2%2Bfjh49qrCwsHO%2BR0NDg%2Bx2u9uY1Rqq2NhYSVJgoMXtq7%2BwWjtue/21xwDQWXXkvxEdgYD1I06nUxEREW5jp8OWw%2BFoV8Cy2WxauXKl29gDDzygWbNmSToVwF566QXl5ua6QteF%2BuC3t1/U632VET2mt/9eQ0ODbDabIfsxzo4eex499jx/7bFvxUWDtLW1XdTrc3Nz9dprr7n9ys3Ndc3b7XatXLnyjLNcMA499jx67Hn02PPosef5a485g/UjMTExcjqdbmNOp1MBAQGKiYlp13vExsb6VUoHAADuOIP1I0lJSfrnP/%2BpQ4cOucaqq6vVu3dvde3a1cTKAABAZ0HA%2BpHExEQlJydrxYoVOnr0qPbu3au1a9cqLy/P7NIAAEAnEfj4448/bnYR3uamm27Sli1b9MQTT%2BiNN95QTk6O7rvvPgUEBBi2jq5du2rw4MGcFfMgeux59Njz6LHn0WPP88ceB7Rd7B3dAAAAcMMlQgAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwOtinn36qe%2B65RwMGDFB6eroKCwtlt9vNLstnLVmyRAkJCWaX4XMSEhKUlJSk5ORk168nnnjC7LJ8TklJiYYOHar%2B/ftrypQp2r9/v9kl%2BYy//e1vbvtvcnKykpKSOF4YrK6uTnfffbcGDhyoIUOGaN68eTp06JDZZXUIflROBzpx4oQyMzP1i1/8Qvfff7%2BOHj2qBx98UBEREVq1apXZ5fmcPXv2aMqUKXI6nfrss8/MLsenJCQk6K233lLPnj3NLsVnrV%2B/XuvWrdOqVasUGxurZ599VpK0cOFCkyvzXc8//7w%2B/fRTV69xcZqbm5WZmans7Gw98MADamxs1Ny5cxUWFqbf/e53ZpfncZzB6kBNTU2aPXu2pk2bpqCgIMXExOjWW2/V559/bnZpPqe1tVWPPfaYpkyZYnYpwAVZs2aNZs%2BerauvvlphYWFauHAh4cqD/vGPf2jt2rV6%2BOGHzS7FZ9jtdtntdt15550KCgpSdHS0br31Vu3Zs8fs0joEAasDRUZGasKECbJarZKkL7/8Uq%2B//rpGjhxpcmW%2B55VXXlFwcLDGjBljdik%2Ba8WKFcrMzNTAgQO1aNEiNTY2ml2Sz/j222%2B1f/9%2Bff/99xo1apTS0tJUUFDgN5dWzPDcc89p/PjxuuKKK8wuxWfExcWpX79%2Bstlsamxs1MGDB1VRUaHMzEyzS%2BsQBCwT1NfXKykpSaNGjVJycrIKCgrMLsmnfPfddyouLtZjjz1mdik%2Bq3///kpPT1dFRYVsNpt2796txYsXm12Wzzhw4IAkadu2bVq7dq02btyoAwcOcAbLQ/bv36%2BKigr96le/MrsUn2KxWFRcXKy33npLqampSk9PV3Nzs%2BbOnWt2aR2CgGWCHj16qLq6Wtu2bdNXX33FKWmDLV26VNnZ2erdu7fZpfgsm82mCRMmKCgoSNdcc43mzZunLVu26MSJE2aX5hNO3xo7depUxcXF6bLLLtOsWbO0Y8cOHT9%2B3OTqfM/69et12223qXv37maX4lNOnDih6dOn6/bbb9cHH3ygd955R%2BHh4Zo3b57ZpXUIApZJAgICFB8fr9mzZ2vLli2c%2BjdIVVWVPv74Y%2BXn55tdil/p2bOnWlpadPDgQbNL8QmXXnqpJCkiIsI11qNHD7W1tdFjD9i%2BfbuysrLMLsPnVFVVaf/%2B/ZozZ47Cw8MVFxengoIC/elPf5LT6TS7PI8jYHWgqqoqjRgxQq2tra4xi%2BXUH8Ell1xiVlk%2BZdOmTTp48KCGDx%2ButLQ0ZWdnS5LS0tL0xhtvmFydb6irq9OyZcvcxvbu3augoCDFxsaaVJVvueyyyxQWFuZ2M3B9fb0uueQSemywPXv2qL6%2BXkOGDDG7FJ/T0tKi1tZW/evDCvzpLDcBqwMlJSXp6NGjKioqUlNTkw4dOqTi4mINHDhQ4eHhZpfnEx555BFt375dGzdu1MaNG1VaWipJ2rhxI/9DNUi3bt1ks9lUWlqqEydOaN%2B%2BfXruueeUm5urwMBAs8vzCVarVTk5OXr%2B%2Bef19ddf6%2BDBg1q1apXGjBnj%2BpAMjFFXV6eoqCiFhYWZXYrPuf766xUaGqri4mI1NTXJ4XCopKREgwYNUlRUlNnleRx/UztQeHi41qxZoyeffFI33HCDQkNDdcMNN%2Bi3v/2t2aX5jMjISEVGRrp%2B39zcLOnUGQEYIy4uTqWlpVqxYoVKSkoUFBSkcePGafbs2WaX5lPmzp2rEydOaMKECTp58qRGjBjBTe4e8N1333HvlYdER0frxRdf1FNPPaVhw4YpKChIgwcP1uOPP252aR2CB40CAAAYjEuEAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABvs/kXdHpG0yx30AAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common8457138207512358384”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>6.2</td> <td class=”number”>374</td> <td class=”number”>11.7%</td> <td>

<div class=”bar” style=”width:33%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.8</td> <td class=”number”>291</td> <td class=”number”>9.1%</td> <td>

<div class=”bar” style=”width:26%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.9</td> <td class=”number”>210</td> <td class=”number”>6.5%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.5</td> <td class=”number”>203</td> <td class=”number”>6.3%</td> <td>

<div class=”bar” style=”width:18%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.6</td> <td class=”number”>194</td> <td class=”number”>6.0%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.3</td> <td class=”number”>185</td> <td class=”number”>5.8%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.5</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.9</td> <td class=”number”>146</td> <td class=”number”>4.5%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.7</td> <td class=”number”>125</td> <td class=”number”>3.9%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.7</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (21)</td> <td class=”number”>1145</td> <td class=”number”>35.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme8457138207512358384”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>3.2</td> <td class=”number”>14</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:61%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.4</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:74%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.2</td> <td class=”number”>16</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:69%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.3</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:91%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.4</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>6.8</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:32%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.9</td> <td class=”number”>210</td> <td class=”number”>6.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.1</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:47%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.6</td> <td class=”number”>82</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:39%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.1</td> <td class=”number”>15</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_VLFOODSEC_13_15”><s>VLFOODSEC_13_15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_FOODINSEC_13_15”><code>FOODINSEC_13_15</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.93787</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_WIC participants FY 2009”>WIC participants FY 2009<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>40</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.2%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>17.3%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>555</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>124380</td>

</tr> <tr>

<th>Minimum</th> <td>13338</td>

</tr> <tr>

<th>Maximum</th> <td>518960</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-8338357513899598167”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-8338357513899598167”>

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<li role=”presentation” class=”active”><a href=”#quantiles-8338357513899598167”

aria-controls=”quantiles-8338357513899598167” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-8338357513899598167” aria-controls=”histogram-8338357513899598167”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-8338357513899598167” aria-controls=”common-8338357513899598167”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-8338357513899598167” aria-controls=”extreme-8338357513899598167”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-8338357513899598167”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>13338</td>

</tr> <tr>

<th>5-th percentile</th> <td>18362</td>

</tr> <tr>

<th>Q1</th> <td>46175</td>

</tr> <tr>

<th>Median</th> <td>113250</td>

</tr> <tr>

<th>Q3</th> <td>160150</td>

</tr> <tr>

<th>95-th percentile</th> <td>309870</td>

</tr> <tr>

<th>Maximum</th> <td>518960</td>

</tr> <tr>

<th>Range</th> <td>505620</td>

</tr> <tr>

<th>Interquartile range</th> <td>113980</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>104140</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.83726</td>

</tr> <tr>

<th>Kurtosis</th> <td>2.7778</td>

</tr> <tr>

<th>Mean</th> <td>124380</td>

</tr> <tr>

<th>MAD</th> <td>77309</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.5178</td>

</tr> <tr>

<th>Sum</th> <td>330230000</td>

</tr> <tr>

<th>Variance</th> <td>10845000000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-8338357513899598167”>

<img 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Rs0YMAAzzaDBg3S0qVLNW7cuB%2B1xhdffKHPP/9cX331lcaMGaOysjIlJSVp6dKlKiwsVHx8vNf28fHx2rFjhySpsLBQY8aM8cyFhoaqZ8%2BecrlcGjt2bJPWLykpUWlpqddYeHgbxcXFXXC/sLBQr6/BJjzcf8cV7L2V/NPfi6Gv/kBffYfe%2BgZ9bcjxgDV27Fh17txZM2bM0O2336727ds32CYlJUUnT5780WucOHFCkvTGG2/oueeekzFGs2fP1qJFi1RdXa0OHTp4bd%2B%2BfXuVl5dLktxut6Kjo73mo6OjPfNNkZeXp9zcXK%2BxrKwszZ49u0n7R0Vd2uS1AklMTFt/lxC0vZX8299g7qs/0Vffobe%2BQV%2B/43jA2rhxowYPHtzodh988MGPXsMYI0maNm2aJ0w98MADuvfee5WcnNzk/X%2BsjIwMpaameo2Fh7dReXnlBfcLCwtVVNSlqqioUl1dfbNqaIkaO35fCvbeSv7p78XQV3%2Bgr75Db32jJffVXz98Oh6wunfvrpkzZyo9PV0333yzJOm3v/2t3n33Xa1cufKcZ7R%2BqMsvv1ySFBUV5Rnr2LGjjDE6e/as3G631/bl5eWKjY2VJMXExDSYd7vd6tq1a5PXj4uLa3A5sLT0lGprm/aiq6urb/K2gaQlHFOw9lbyb3%2BDua/%2BRF99h976Bn39juMXS3NycnTq1Cl16dLFMzZ8%2BHDV19drxYoVVta48sorFRkZqcOHD3vGioqKdMkllyglJaXBYxcKCgqUmJgoSUpISFBhYaFnrq6uTocOHfLMAwAANMbxgPXOO%2B8oNzdXnTp18ox16tRJq1at0ttvv21ljfDwcKWnp%2BuZZ57R//3f/6msrExPP/20xo0bpzvuuENFRUXatGmTampqtG/fPu3bt0933nmnJCkzM1OvvfaaDh48qKqqKq1du1atWrXS8OHDrdQGAACCn%2BOXCKurqxUREdFgPDQ0VFVVVdbWmTt3rr7%2B%2BmtNnDhRZ8%2Be1ahRo7Ro0SK1bdtW69at06OPPqply5apY8eOWrlypXr06CFJGjZsmB588EFlZ2errKxMvXv31vr169W6dWtrtQEAgODmeMAaNGiQVqxYoblz53o%2BrffFF1/oV7/6ldejGpqrVatWWrJkiZYsWXLOGjZv3nzefSdPnqzJkydbqwUAAFxcHA9YCxYs0M9%2B9jPdcMMNioyMVH19vSorK3X11Vfr%2Beefd7ocAAAA6xwPWFdffbVef/11/fGPf9Snn36q0NBQde7cWUOHDlVYWJjT5QAAAFjneMCSvrl89%2B0jGgAAAIKN4wHrs88%2B0%2BrVq/X3v/9d1dXVDebfeustp0sCAACwyi/3YJWUlGjo0KFq06aN08sDAAD4nOMBq6CgQG%2B99ZbnyekAAADBxvEHjV522WWcuQIAAEHN8YA1Y8YM5ebmNvsXKgMAALRUjl8i/OMf/6j3339fr776qq666iqFhnpnvJdeesnpkgAAAKxyPGBFRkZq2LBhTi8LAADgGMcDVk5OjtNLAgAAOMrxe7Ak6eOPP9aaNWv08MMPe8YOHDjgj1IAAACsczxg5efna/z48dq1a5e2bdsm6ZuHj9599908ZBQAAAQFxwPW448/rp///OfaunWrQkJCJH3z%2BwlXrFihp59%2B2ulyAAAArHM8YP3tb39TZmamJHkCliTdcsstOnbsmNPlAAAAWOd4wGrXrt05fwdhSUmJWrVq5XQ5AAAA1jkesPr376/ly5fr9OnTnrHjx49r/vz5uuGGG5wuBwAAwDrHH9Pw8MMP65577lFSUpLq6urUv39/VVVVqWvXrlqxYoXT5QAAAFjneMC68sortW3bNu3bt0/Hjx9X69at1blzZw0ZMsTrniwAAIBA5XjAkqRLLrlEN998sz%2BWBgAA8DnHA1ZqauoFz1TxLCwAABDoHA9YY8aM8QpYdXV1On78uFwul%2B655x6nywEAALDO8YA1b968c47v3LlTf/7znx2uBgAAwD6//C7Cc7n55pv1%2Buuv%2B7sMAACAZmsxAevQoUMyxvi7DAAAgGZz/BLhpEmTGoxVVVXp2LFjGjlypNPlAAAAWOd4wOrUqVODTxFGREQoPT1dEydOdLocAAAA6xwPWDytHQAABDvHA9Zrr73W5G1vv/12H1YCAADgG44HrIULF6q%2Bvr7BDe0hISFeYyEhIQQsAAAQkBwPWL/5zW/07LPPaubMmerevbuMMTpy5Ih%2B/etf66677lJSUpLTJQEAAFjll3uw1q9frw4dOnjGBg4cqKuvvlpTp07Vtm3bnC4JAADAKsefg/XJJ58oOjq6wXhUVJSKioqcLgcAAMA6xwNWx44dtWLFCpWXl3vGKioqtHr1al1zzTVOlwMAAGCd45cIFyxYoLlz5yovL09t27ZVaGioTp8%2BrdatW%2Bvpp592uhwAAADrHA9YQ4cO1d69e7Vv3z6dOHFCxhh16NBBN954o9q1a%2Bd0OQAAANY5HrAk6dJLL9VNN92kEydO6Oqrr/ZHCQAAAD7j%2BD1Y1dXVmj9/vvr166fRo0dL%2BuYerGnTpqmiosLpcgAAAKxzPGCtXLlShw8f1qpVqxQa%2Bt3ydXV1WrVqldPlAAAAWOd4wNq5c6eeeuop3XLLLZ5f%2BhwVFaWcnBzt2rXL6XIAAACsczxgVVZWqlOnTg3GY2NjdebMGafLAQAAsM7xgHXNNdfoz3/%2BsyR5/e7BN954Q//6r//qdDkAAADWOf4pwsmTJ%2BuBBx7QhAkTVF9fr%2Beee04FBQXauXOnFi5c6HQ5AAAA1jkesDIyMhQeHq4XXnhBYWFheuaZZ9S5c2etWrVKt9xyi9PlAAAAWOd4wDp58qQmTJigCRMmOL00AACAIxy/B%2Bumm27yuvcKAAAg2DgesJKSkrRjxw6nlwUAAHCM45cI/%2BVf/kW//OUvtX79el1zzTW65JJLvOZXr17tdEkAAABWOR6wjh49qp/85CeSpPLycqeXBwAA8DnHAtacOXP0%2BOOP6/nnn/eMPf3008rKynKqBAAAAEc4dg/W7t27G4ytX7/eqeUBAAAc41jAOtcnB536NOHy5cvVvXt3z9/z8/OVnp6u/v37a%2BzYsdqyZYvX9hs3btSoUaPUv39/ZWZmqqCgwJE6AQBAcHAsYH37i50bG7Pt8OHD2rx5s%2BfvJSUlmjVrliZNmqT8/HwtXLhQixcvlsvlkvTNmbY1a9boscce0/79%2BzVixAjNnDmT35MIAACazPHHNDipvr5eS5Ys0ZQpUzxjW7duVadOnZSenq6IiAglJycrNTVVmzZtkiTl5eUpLS1NiYmJat26taZNmyZJ2rNnjz8OAQAABCDHP0XopJdeekkREREaN26cnnjiCUlSYWGh4uPjvbaLj4/3PJursLBQY8aM8cyFhoaqZ8%2BecrlcGjt2bJPWLSkpUWlpqddYeHgbxcXFXXC/sLBQr6/BJjzcf8cV7L2V/NPfi6Gv/kBffYfe%2BgZ9bcixgHX27FnNnTu30TFbz8H68ssvtWbNGq9PLUqS2%2B1Whw4dvMbat2/veWSE2%2B1WdHS013x0dPQPeqREXl6ecnNzvcaysrI0e/bsJu0fFXVpk9cKJDExbf1dQtD2VvJvf4O5r/5EX32H3voGff2OYwFrwIABKikpaXTMlpycHKWlpalLly76/PPPf9C%2Bzb35PiMjQ6mpqV5j4eFtVF5eecH9wsJCFRV1qSoqqlRXV9%2BsGlqixo7fl4K9t5J/%2Bnsx9NUf6Kvv0FvfaMl99dcPn44FrO%2BfSfKl/Px8HThwQNu2bWswFxMTI7fb7TVWXl6u2NjY88673W517dq1yevHxcU1uBxYWnpKtbVNe9HV1dU3edtA0hKOKVh7K/m3v8HcV3%2Bir75Db32Dvn4nKC%2BWbtmyRWVlZRoxYoSSkpKUlpYm6Zvfg9itW7cGj10oKChQYmKiJCkhIUGFhYWeubq6Oh06dMgzDwAA0JigDFgPPfSQdu7cqc2bN2vz5s2eB5pu3rxZ48aNU1FRkTZt2qSamhrt27dP%2B/bt05133ilJyszM1GuvvaaDBw%2BqqqpKa9euVatWrTR8%2BHA/HhEAAAgkQfkpwujoaK8b1WtrayVJV155pSRp3bp1evTRR7Vs2TJ17NhRK1euVI8ePSRJw4YN04MPPqjs7GyVlZWpd%2B/eWr9%2BvVq3bu38gQAAgIAUlAHr%2B6666iodOXLE8/dBgwZ5PXz0%2ByZPnqzJkyc7URoAAAhCQXmJEAAAwJ8IWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlF8WDRtEyjH7iXX%2BXAACAIziDBQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgWbi/CwBgx%2Bgn3vV3CU22I3uIv0sAAJ8K2jNYRUVFysrKUlJSkpKTk/XQQw%2BpoqJCknT48GHdddddGjBggEaOHKlnn33Wa9/t27dr3Lhx6tevn9LS0vTOO%2B/44xAAAECACtqANXPmTEVFRWn37t169dVX9fe//12/%2BtWvVF1drRkzZuj666/X22%2B/rccff1zr1q3Trl27JH0TvubPn6958%2BbpT3/6k6ZMmaL7779fJ06c8PMRAQCAQBGUAauiokIJCQmaO3eu2rZtqyuvvFJ33HGH/vKXv2jv3r06e/as7rvvPrVp00a9evXSxIkTlZeXJ0natGmTUlJSlJKSooiICI0fP17dunXTli1b/HxUAAAgUARlwIqKilJOTo4uv/xyz1hxcbHi4uJUWFio7t27KywszDMXHx%2BvgoICSVJhYaHi4%2BO9vl98fLxcLpczxQMAgIB3Udzk7nK59MILL2jt2rXasWOHoqKivObbt28vt9ut%2Bvp6ud1uRUdHe81HR0fr6NGjTV6vpKREpaWlXmPh4W0UFxd3wf3CwkK9vgLBKjw8sF7jP131tr9L%2BEH%2Bv3k3%2BruEFov3Wd%2Bgrw0FfcD661//qvvuu09z585VcnKyduzYcc7tQkJCPP/bGNOsNfPy8pSbm%2Bs1lpWVpdmzZzdp/6ioS5u1PtDSxcS09XcJQY3%2BNo73Wd%2Bgr98J6oC1e/du/fznP9fixYt1%2B%2B23S5JiY2P1ySefeG3ndrvVvn17hYaGKiYmRm63u8F8bGxsk9fNyMhQamqq11h4eBuVl1decL%2BwsFBFRV2qiooq1dXVN3k9INA09m8BzUN/z4/3Wd9oyX311w8cQRuw3n//fc2fP19PPvmkhg4d6hlPSEjQ7373O9XW1io8/JvDd7lcSkxM9Mx/ez/Wt1wul8aOHdvktePi4hpcDiwtPaXa2qa96Orq6pu8LRCIeH37Fv1tHO%2BzvkFfvxOUF0tra2u1aNEizZs3zytcSVJKSooiIyO1du1aVVVV6YMPPtArr7yizMxMSdKdd96p/fv3a%2B/evaqpqdErr7yiTz75ROPHj/fHoQAAgAAUlGewDh48qGPHjunRRx/Vo48%2B6jX3xhtv6JlnntGSJUu0fv16XX755ZozZ46GDx8uSerWrZtWrVqlnJwcFRUVqUuXLlq3bp2uuOIKPxwJAAAIREEZsAYOHKgjR45ccJvf/e53550bOXKkRo4cabssAABwkQjKS4QAAAD%2BRMACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALAs3N8FALj4jH7iXX%2BXAAA%2BxRksAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMCycH8XAAAAfGP0E%2B/6u4Qm25E9xN8lWMUZLAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsCPdP/NAAAMlUlEQVQCAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhY51BUVKTp06crKSlJI0aM0MqVK1VfX%2B/vsgAAQIAI93cBLdEDDzygXr166c0331RZWZlmzJihyy%2B/XP/%2B7//u79IAAEAA4AzW97hcLn300UeaN2%2Be2rVrp06dOmnKlCnKy8vzd2kAACBAcAbrewoLC9WxY0dFR0d7xnr16qXjx4/r9OnTioyMbPR7lJSUqLS01GssPLyN4uLiLrhfWFio11cA%2BDHCw3kPOR/eZ1uuYHvdErC%2Bx%2B12Kyoqymvs27BVXl7epICVl5en3Nxcr7H7779fDzzwwAX3Kykp0YYNv1FGRkajYexbf/nlLU3a7mJXUlKivLy8H9RbNI6%2B%2BgZ99Z0f8z4byJz6/whesw0FV1y0xBjTrP0zMjL06quvev3JyMhodL/S0lLl5uY2OPuF5qO3vkFffYO%2B%2Bg699Q362hBnsL4nNjZWbrfba8ztdiskJESxsbFN%2Bh5xcXEkeAAALmKcwfqehIQEFRcX6%2BTJk54xl8ulLl26qG3btn6sDAAABAoC1vfEx8erd%2B/eWr16tU6fPq1jx47pueeeU2Zmpr9LAwAAASJs6dKlS/1dREtz4403atu2bXrkkUf0%2BuuvKz09XVOnTlVISIjP127btq0GDx7M2TIfoLe%2BQV99g776Dr31DfrqLcQ0945uAAAAeOESIQAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBKwWoqioSNOnT1dSUpJGjBihlStXqr6%2B3t9l%2BcXbb7%2Bt5ORkzZkzp8Hc9u3bNW7cOPXr109paWl65513PHP19fV6/PHHddNNN2nQoEGaOnWqPvvsM8%2B82%2B1Wdna2kpOTNXToUC1cuFDV1dWe%2BcOHD%2Buuu%2B7SgAEDNHLkSD377LNNXjsQFBUVKSsrS0lJSUpOTtZDDz2kiooKSc07dl/3PRB89NFHuueeezRgwAAlJycrOztbpaWlkqT8/Hylp6erf//%2BGjt2rLZs2eK178aNGzVq1Cj1799fmZmZKigo8MzV1NToP//zPzVs2DAlJSVp9uzZKi8v98w39r7R2NqBZPny5erevbvn7/T1x%2BvevbsSEhLUu3dvz59HHnlEEn21yqBFuOOOO8yiRYtMRUWFOX78uBk5cqR59tln/V2W49avX29GjhxpJk2aZLKzs73mDh06ZBISEszevXtNdXW12bx5s0lMTDTFxcXGGGM2btxoRowYYY4ePWpOnTplfvGLX5hx48aZ%2Bvp6Y4wx999/v5k%2BfbopKyszJ06cMBkZGeaRRx4xxhhTVVVlbrzxRrNmzRpTWVlpCgoKzODBg83OnTubtHYguPXWW81DDz1kTp8%2BbYqLi01aWppZsGBBs4/dl30PBDU1NeaGG24wubm5pqamxpSVlZm77rrLzJo1y3zxxRemb9%2B%2BZtOmTaa6utq8%2B%2B67pk%2BfPubDDz80xhjz1ltvmYEDB5qDBw%2Baqqoqs27dOjNkyBBTWVlpjDEmJyfHpKWlmX/84x%2BmvLzc3H///WbGjBmetS/0vtHY2oHk0KFDZvDgwaZbt27GmMaPjb5eWLdu3cxnn33WYJy%2B2kXAagE%2B/PBD07NnT%2BN2uz1j//M//2NGjRrlx6r8Y8OGDaaiosLMnz%2B/QcBatmyZycrK8hqbOHGiWbdunTHGmLFjx5oNGzZ45k6dOmXi4%2BPNgQMHTGlpqenRo4c5fPiwZ37fvn2mb9%2B%2B5uuvvzY7duww119/vamtrfXMr1y50vzsZz9r0tot3VdffWUeeughU1pa6hl7/vnnzciRI5t97L7seyBwu93m5ZdfNmfPnvWMbdiwwfz0pz81v/nNb8ztt9/utX12drZZvHixMcaY6dOnm%2BXLl3vm6urqzJAhQ8y2bdvM2bNnzYABA8ybb77pmT969Kjp3r27OXHiRKPvG42tHSjq6urMxIkTzX/91395AhZ9bZ7zBSz6aheXCFuAwsJCdezYUdHR0Z6xXr166fjx4zp9%2BrQfK3Pe3XffrXbt2p1zrrCwUPHx8V5j8fHxcrlcqq6u1tGjR73mIyMjde2118rlcunw4cMKCwvzusTQq1cvnTlzRh9//LEKCwvVvXt3hYWFeX3vb09/X2jtQBAVFaWcnBxdfvnlnrHi4mLFxcU169h93fdAEB0drYkTJyo8PFyS9PHHH%2BsPf/iDRo8efd7ena%2B3oaGh6tmzp1wulz799FOdOnVKvXr18sxfd911at26tQoLCxt932hs7UDx0ksvKSIiQuPGjfOM0dfmW716tYYPH66BAwdq8eLFqqyspK%2BWEbBaALfbraioKK%2Bxb1%2BE/3z9%2BmLndru9/nFK3/SpvLxcX331lYwx5513u92KjIxUSEiI15wkz/z3/xu0b99ebrdb9fX1F1w7ELlcLr3wwgu67777mnXsvu57ICkqKlJCQoLGjBmj3r17a/bs2ec9vm9fNxfqrdvtlqQG%2B0dFRZ23d03pbSC9Zr/88kutWbNGS5Ys8Rqnr83Tt29fJScna9euXcrLy9PBgwe1bNky%2BmoZAauFMMb4u4SA0FifLjT/Y3r8z8EgWP4b/fWvf9XUqVM1d%2B5cJScnn3e7H3Lsvux7oOjYsaNcLpfeeOMNffLJJ/qP//iPJu3ndG8DSU5OjtLS0tSlS5cfvC99Pb%2B8vDxNnDhRrVq10nXXXad58%2BZp27ZtOnv2bKP70temI2C1ALGxsZ70/y23262QkBDFxsb6qaqWJyYm5px9io2NVfv27RUaGnrO%2Bcsuu0yxsbE6ffq06urqvOYkeea//5OS2%2B32fN8LrR1Idu/erenTp2vBggW6%2B%2B67JalZx%2B7rvgeakJAQderUSXPmzNG2bdsUHh7eoDfl5eWe182FevvtNt%2Bf/%2Bqrrzy9u9D7xrm%2B9z%2Bv3dLl5%2BfrwIEDysrKajDX2LHR1x/mqquuUl1d3Tn/LdPXHy/w3sGCUEJCgoqLi3Xy5EnPmMvlUpcuXdS2bVs/VtayJCQkNLge73K5lJiYqIiICHXt2lWFhYWeuYqKCn366afq06ePevbsKWOMPvroI699o6Ki1LlzZyUkJOjIkSOqra1t8L0bWztQvP/%2B%2B5o/f76efPJJ3X777Z7x5hy7r/seCPLz8zVq1CivS5rfhsM%2Bffo06F1BQYFXb/%2B5d3V1dTp06JASExN19dVXKzo62mv%2Bb3/7m77%2B%2BmslJCQ0%2Br7Ru3fvC67d0m3ZskVlZWUaMWKEkpKSlJaWJklKSkpSt27d6OuPdOjQIa1YscJr7NixY2rVqpVSUlLoq02O3lKP85o4caJZsGCBOXXqlDl69KhJTU01L7zwgr/L8ptzfYrwyJEjpnfv3mbPnj2murrabNq0yfTr18%2BUlJQYY775RMrw4cM9jwtYvHixmTBhgmf/7OxsM23aNFNWVmaKi4vNhAkTzIoVK4wx33zUfsSIEeapp54yZ86cMQcPHjQDBw40e/bsadLaLd3Zs2fN6NGjzUsvvdRgrrnH7su%2BB4KKigqTnJxsVqxYYc6cOWPKysrM1KlTzeTJk82XX35p%2BvXrZ15%2B%2BWVTXV1t9u7da/r06eP5VOW%2BffvMgAEDzIEDB8yZM2fMmjVrTEpKiqmqqjLGfPOJyjvuuMP84x//MCdPnjQzZswwDzzwgGftC71vNLZ2S%2Bd2u01xcbHnz4EDB0y3bt1McXGxKSoqoq8/0okTJ0zfvn3NunXrTE1Njfn444/NmDFjzCOPPMLr1TICVgtRXFxspk2bZvr06WOSk5PNU0895XmO0MUkISHBJCQkmB49epgePXp4/v6tnTt3mpEjR5pevXqZ2267zbz33nueufr6evPkk0%2BaG264wfTp08fce%2B%2B9Xs%2BpqqioMHPmzDF9%2B/Y1gwYNMsuWLTM1NTWe%2BSNHjphJkyaZhIQEM3z4cPPiiy961XahtVu6//3f/zXdunXz9POf/3z%2B%2BefNOnZf9z0QfPTRR%2Bauu%2B4yffr0Mddff73Jzs42J06cMMYY895775nx48ebXr16mZEjRzZ4xteLL75oUlJSTEJCgsnMzDRHjhzxzNXU1JilS5eaQYMGmX79%2BpkHH3zQVFRUeOYbe99obO1A8tlnn3ke02AMfW2O9957z2RkZJi%2BffuawYMHm5ycHFNdXe2Zo692hBhzkd11BgAA4GPcgwUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlv3/P8Neu0dSi8sAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-8338357513899598167”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>53060.0</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>309870.0</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>127944.0</td> <td class=”number”>130</td> <td class=”number”>4.0%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>18362.0</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>146411.0</td> <td class=”number”>113</td> <td class=”number”>3.5%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>141768.0</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>113248.0</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>26663.0</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>193387.0</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>70159.0</td> <td class=”number”>93</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (29)</td> <td class=”number”>1395</td> <td class=”number”>43.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>555</td> <td class=”number”>17.3%</td> <td>

<div class=”bar” style=”width:40%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-8338357513899598167”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>13338.0</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:56%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14573.0</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17496.0</td> <td class=”number”>46</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:40%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>18362.0</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20673.0</td> <td class=”number”>83</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:72%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>275039.0</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>303679.0</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>309870.0</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>499213.0</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>518961.0</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_WIC participants FY 2011”><s>WIC participants FY 2011</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_WIC participants FY 2009”><code>WIC participants FY 2009</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99857</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_WIC participants, FY 2012”><s>WIC participants, FY 2012</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_WIC participants FY 2011”><code>WIC participants FY 2011</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.9754</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_WIC participants, FY 2013”><s>WIC participants, FY 2013</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_WIC participants, FY 2012”><code>WIC participants, FY 2012</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99975</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_WIC participants, FY 2014”><s>WIC participants, FY 2014</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_WIC participants, FY 2013”><code>WIC participants, FY 2013</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99951</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_WIC participants, FY 2015”><s>WIC participants, FY 2015</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_WIC participants, FY 2014”><code>WIC participants, FY 2014</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.9992</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_WICS08”><s>WICS08</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_SNAPS16”><code>SNAPS16</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.91514</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_WICS12”><s>WICS12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_WICS08”><code>WICS08</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.98574</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_WICSPTH08”>WICSPTH08<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2963</td>

</tr> <tr>

<th>Unique (%)</th> <td>92.3%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.25423</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>4.6189</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>4.4%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram8590863561166491216”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives8590863561166491216,#minihistogram8590863561166491216”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives8590863561166491216”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles8590863561166491216”

aria-controls=”quantiles8590863561166491216” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram8590863561166491216” aria-controls=”histogram8590863561166491216”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common8590863561166491216” aria-controls=”common8590863561166491216”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme8590863561166491216” aria-controls=”extreme8590863561166491216”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles8590863561166491216”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.048939</td>

</tr> <tr>

<th>Q1</th> <td>0.12325</td>

</tr> <tr>

<th>Median</th> <td>0.18962</td>

</tr> <tr>

<th>Q3</th> <td>0.29478</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.68198</td>

</tr> <tr>

<th>Maximum</th> <td>4.6189</td>

</tr> <tr>

<th>Range</th> <td>4.6189</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.17152</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.25464</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.0016</td>

</tr> <tr>

<th>Kurtosis</th> <td>46.673</td>

</tr> <tr>

<th>Mean</th> <td>0.25423</td>

</tr> <tr>

<th>MAD</th> <td>0.14987</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>4.873</td>

</tr> <tr>

<th>Sum</th> <td>796.25</td>

</tr> <tr>

<th>Variance</th> <td>0.064839</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram8590863561166491216”>

<img 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w7Vo48%2BqpEjR1pdHgAAQLtZHrBuv/129e7dW1OmTNHo0aMVFxd3zmuys7N16tQpq0sDAAAwwvKAtWrVKg0bNuxbX7d3714LqgEAADDP8j1YKSkpmjp1qrZv39469m//9m/61a9%2BpUAgYHU5AAAAxlkesEpLS3X69Gn17du3dWz48OFqaWnRwoULrS4HAADAOMtvEb711lvasGGD4uPjW8eSk5NVVlamO%2B64w%2BpyAAAAjLP8CtaZM2cUGRl5biHh4WpsbLS6HAAAAOMsD1hDhw7VwoUL9dlnn7WO/fd//7cee%2ByxkEc1AAAAOJXltwgffvhh/fKXv9RPfvITRUdHq6WlRQ0NDerZs6eef/55q8sBAAAwzvKA1bNnT7322mt68803dezYMYWHh6t3797KyspSRESE1eUAAAAYZ3nAkqSuXbvqpptusuPUAAAAF53lAev48eNasmSJDh48qDNnzpwz/%2Bc//9nqkgAAAIyyZQ9WbW2tsrKy1K1bN6tPDwAAcNFZHrD27dunP//5z/J4PFafGgAAwBKWP6ahe/fuXLkCAAAdmuUBa8qUKSovL1cwGLT61AAAAJaw/Bbhm2%2B%2Bqffff19r167VFVdcofDw0Iz30ksvWV0SAACAUZYHrOjoaN1www1WnxYAAMAylges0tJSq08JAABgKcv3YEnS4cOH9fTTT%2Buhhx5qHfvrX/9qRykAAADGWR6w9uzZo1GjRmnbtm3auHGjpK8ePjpp0qTv/JDR3bt3KzMzU0VFRSHja9eu1VVXXaX09PSQnw8//FCS1NLSoqVLlyo3N1dDhw7V5MmTdfz48db3BwIBzZo1S5mZmcrKytK8efPO%2B1BUAACA87E8YC1dulQPPPCANmzYoLCwMElf/X7ChQsXatmyZW0%2BzjPPPKOSkhL16tXrvPNDhw6V1%2BsN%2BRk0aJAk6cUXX9SGDRtUUVGhnTt3Kjk5WYWFha3fbJw/f74aGxu1ceNGvfLKK6qurlZZWVk7Vw4AADoLywPWf/3Xfyk/P1%2BSWgOWJN1yyy2qrq5u83EiIyO1Zs2aCwasv6eyslIFBQXq06ePoqOjVVRUpOrqau3du1effPKJtm/frqKiInk8HiUmJuq%2B%2B%2B7TK6%2B8orNnz37ncwEAgM7H8oAVExNz3ttttbW16tq1a5uPM2nSJMXExFxw/sSJE/rFL36hoUOHKjc3V%2BvWrZMknTlzRocOHVJqamrra6Ojo9WrVy95vV7t379fERERSklJaZ0fOHCgPv/8cx0%2BfLjN9QEAgM7L8m8RXnPNNXriiSf0yCOPtI4dOXJECxYs0E9%2B8hMj5/B4PEpOTtavf/1r9e3bV6%2B//roefPBBJSQk6Ec/%2BpGCwaBiY2ND3hMbGyu/36%2B4uDhFR0eHXF37%2BrV%2Bv79N56%2BtrZXP5wsZc7m6KSEhoZ0rCxURYct3FL43l8tZ9V7I1313Wv87AnpvD/puH3rvXJYHrIceekh33323MjIy1NzcrGuuuUaNjY3q16%2BfFi5caOQcw4cP1/Dhw1v///bbb9frr7%2ButWvXqri4WJL%2B7pPk2/uU%2BcrKSpWXl4eMFRYWaubMme06rtPFx0fZXYJRbveldpfQadF7e9B3%2B9B757E8YF1%2B%2BeXauHGj3njjDR05ckSXXHKJevfureuvvz7kqpFpSUlJ2rdvn%2BLi4hQeHq5AIBAyHwgE1L17d3k8HtXX16u5uVkRERGtc9JXv0exLfLy8pSTkxMy5nJ1k9/fYGAl/8dpn2hMr98uERHhcrsvVV1do5qbW%2Bwup1Oh9/ag7/ah9%2B1n14d7ywOWJHXp0kU33XTTRTv%2Bv//7vys2Nla33XZb61h1dbV69uypyMhI9evXT1VVVRo2bJgkqa6uTseOHdOgQYOUlJSkYDCoAwcOaODAgZIkr9crt9ut3r17t%2Bn8CQkJ59wO9PlOq6mpc//h6Gjrb25u6XBrcgp6bw/6bh967zyWB6ycnJy/e6Xquz4L63y%2B/PJLPf744%2BrZs6euuuoqbd26VW%2B%2B%2BaZefvllSVJ%2Bfr4qKip0ww03KDExUWVlZRowYIDS09MlSSNGjNCTTz6pRYsW6csvv9SyZcs0btw4uVy25FEAAOAwlieG2267LSRgNTc368iRI/J6vbr77rvbfJyvw1BTU5Mkafv27ZK%2Buto0adIkNTQ06P7775fP59MVV1yhZcuWKS0tTZI0YcIE%2BXw%2BTZw4UQ0NDcrIyAjZM/Xb3/5WCxYsUG5urrp06aI77rjjnIeZAgAAXEhYsL07ug3ZunWr/vKXv%2Bg3v/mN3aVcFD7faePHdLnCdXPZbuPHvVg2z7re7hKMcLnCFR8fJb%2B/gUv2FqP39qDv9qH37dejx4Uf6XQx/WB2Sd9000167bXX7C4DAACg3X4wAeujjz5q9%2BMRAAAAfggs34M1YcKEc8YaGxtVXV2tn/3sZ1aXAwAAYJzlASs5OfmcbxFGRkZq3Lhxuuuuu6wuBwAAwDjLA5app7UDAAD8UFkesF599dU2v3b06NEXsRIAAICLw/KANW/ePLW0tJyzoT0sLCxkLCwsjIAFAAAcyfKA9eyzz%2BqPf/yjpk6dqpSUFAWDQX388cd65pln9POf/1wZGRlWlwQAAGCULXuwKioqlJiY2Dp27bXXqmfPnpo8ebI2btxodUkAAABGWf4crKNHjyo2NvaccbfbrZqaGqvLAQAAMM7ygJWUlKSFCxfK7/e3jtXV1WnJkiW68sorrS4HAADAOMtvET788MOaPXu2KisrFRUVpfDwcNXX1%2BuSSy7RsmXLrC4HAADAOMsDVlZWlnbt2qU33nhDJ0%2BeVDAYVGJion76058qJsaeX8gIAABgkuUBS5IuvfRS5ebm6uTJk%2BrZs6cdJQAAAFw0lu/BOnPmjObMmaPBgwfr1ltvlfTVHqx77rlHdXV1VpcDAABgnOUBa/Hixdq/f7/KysoUHv5/p29ublZZWZnV5QAAABhnecDaunWr/vVf/1W33HJL6y99drvdKi0t1bZt26wuBwAAwDjLA1ZDQ4OSk5PPGfd4PPr888%2BtLgcAAMA4ywPWlVdeqb/85S%2BSFPK7B7ds2aJ//Md/tLocAAAA4yz/FuE///M/a8aMGbrzzjvV0tKilStXat%2B%2Bfdq6davmzZtndTkAAADGWR6w8vLy5HK59MILLygiIkK///3v1bt3b5WVlemWW26xuhwAAADjLA9Yp06d0p133qk777zT6lMDAABYwvI9WLm5uSF7rwAAADoaywNWRkaGNm/ebPVpAQAALGP5LcJ/%2BId/0O9%2B9ztVVFToyiuvVJcuXULmlyxZYnVJAAAARlkesA4dOqQf/ehHkiS/32/16QEAAC46ywJWUVGRli5dqueff751bNmyZSosLLSqBAAAAEtYtgdrx44d54xVVFRYdXoAAADLWBawzvfNQb5NCAAAOiLLAtbXv9j528YAAACczvLHNAAAAHR0BCwAAADDLPsW4dmzZzV79uxvHeM5WAAAwOksC1hDhgxRbW3tt44BAAA4nWUB6/8//woAAKAjYw8WAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGGODli7d%2B9WZmamioqKzpnbtGmTRo4cqcGDB2vs2LF66623WudaWlq0dOlS5ebmaujQoZo8ebKOHz/eOh8IBDRr1ixlZmYqKytL8%2BbN05kzZyxZEwAAcD7HBqxnnnlGJSUl6tWr1zlz%2B/fv15w5c1RcXKx33nlHBQUFmj59uk6ePClJevHFF7VhwwZVVFRo586dSk5OVmFhoYLBoCRp/vz5amxs1MaNG/XKK6%2BourpaZWVllq4PAAA4l2MDVmRkpNasWXPegLV69WplZ2crOztbkZGRGjVqlPr376/169dLkiorK1VQUKA%2BffooOjpaRUVFqq6u1t69e/XJJ59o%2B/btKioqksfjUWJiou677z698sorOnv2rNXLBAAADmTZL3s2bdKkSRecq6qqUnZ2dshYamqqvF6vzpw5o0OHDik1NbV1Ljo6Wr169ZLX69Xp06cVERGhlJSU1vmBAwfq888/1%2BHDh0PGL6S2tlY%2Bny9kzOXqpoSEhLYur00iIpyVj10uZ9V7IV/33Wn97wjovT3ou33ovXM5NmD9PYFAQLGxsSFjsbGxOnTokD777DMFg8Hzzvv9fsXFxSk6OlphYWEhc5Lk9/vbdP7KykqVl5eHjBUWFmrmzJnfZzkdRnx8lN0lGOV2X2p3CZ0WvbcHfbcPvXeeDhmwJLXup/o%2B89/23m%2BTl5ennJyckDGXq5v8/oZ2HfebnPaJxvT67RIRES63%2B1LV1TWqubnF7nI6FXpvD/puH3rffnZ9uO%2BQASs%2BPl6BQCBkLBAIyOPxKC4uTuHh4eed7969uzwej%2Brr69Xc3KyIiIjWOUnq3r17m86fkJBwzu1An%2B%2B0mpo69x%2BOjrb%2B5uaWDrcmp6D39qDv9qH3zuOsSyBtlJaWpn379oWMeb1eXX311YqMjFS/fv1UVVXVOldXV6djx45p0KBBGjBggILBoA4cOBDyXrfbrd69e1u2BgAA4FwdMmCNHz9eb7/9tnbt2qUvvvhCa9as0dGjRzVq1ChJUn5%2BvlatWqXq6mrV19errKxMAwYMUHp6ujwej0aMGKEnn3xSp06d0smTJ7Vs2TKNGzdOLleHvOAHAAAMc2xiSE9PlyQ1NTVJkrZv3y7pq6tN/fv3V1lZmUpLS1VTU6O%2BfftqxYoV6tGjhyRpwoQJ8vl8mjhxohoaGpSRkRGyKf23v/2tFixYoNzcXHXp0kV33HHHeR9mCgAAcD5hwfbu6Eab%2BHynjR/T5QrXzWW7jR/3Ytk863q7SzDC5QpXfHyU/P4G9kRYjN7bg77bh963X48eMbact0PeIgQAALATAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEdNmClpKQoLS1N6enprT%2BPP/64JGnPnj0aN26crrnmGt1%2B%2B%2B1av359yHtXrVqlESNG6JprrlF%2Bfr727dtnxxIAAIBDuewu4GLasmWLrrjiipCx2tpa3XfffZo3b55Gjhyp9957T9OmTVPv3r2Vnp6uHTt26Omnn9azzz6rlJQUrVq1SlOnTtW2bdvUrVs3m1YCAACcpMNewbqQDRs2KDk5WePGjVNkZKQyMzOVk5Oj1atXS5IqKys1duxYXX311brkkkt0zz33SJJ27txpZ9kAAMBBOnTAWrJkiYYPH65rr71W8%2BfPV0NDg6qqqpSamhryutTU1NbbgN%2BcDw8P14ABA%2BT1ei2tHQAAOFeHvUX44x//WJmZmVq0aJGOHz%2BuWbNm6bHHHlMgEFBiYmLIa%2BPi4uT3%2ByVJgUBAsbGxIfOxsbGt821RW1srn88XMuZydVNCQsL3XM35RUQ4Kx%2B7XM6q90K%2B7rvT%2Bt8R0Ht70Hf70Hvn6rABq7KysvW/%2B/Tpo%2BLiYk2bNk1Dhgz51vcGg8F2n7u8vDxkrLCwUDNnzmzXcZ0uPj7K7hKMcrsvtbuETove24O%2B24feO0%2BHDVjfdMUVV6i5uVnh4eEKBAIhc36/Xx6PR5IUHx9/znwgEFC/fv3afK68vDzl5OSEjLlc3eT3N3zP6s/PaZ9oTK/fLhER4XK7L1VdXaOam1vsLqdToff2oO/2offtZ9eH%2Bw4ZsD766COtX79ec%2BfObR2rrq5W165dlZ2drT/96U8hr9%2B3b5%2BuvvpqSVJaWpqqqqo0ZswYSVJzc7M%2B%2BugjjRs3rs3nT0hIOOd2oM93Wk1NnfsPR0dbf3NzS4dbk1PQe3vQd/vQe%2Bdx1iWQNurevbsqKytVUVGhL7/8UkeOHNFTTz2lvLw8/dM//ZNqamq0evVqffHFF3rjjTf0xhtvaPz48ZKk/Px8vfrqq/rggw/U2Nio5cuXq2vXrho%2BfLi9iwIAAI7RIa9gJSYmqqKiQkuWLGkNSGPGjFFRUZEiIyO1YsUKlZSU6LHHHlNSUpIWL16sq666SpKhWgbsAAAH/ElEQVR0ww036Ne//rVmzZqlTz/9VOnp6aqoqNAll1xi86oAAIBThAXbu6MbbeLznTZ%2BTJcrXDeX7TZ%2B3Itl86zr7S7BCJcrXPHxUfL7G7hkbzF6bw/6bh963349esTYct4OeYsQAADATgQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMc9ldADqPW5/8D7tL%2BE42z7re7hIAAA7FFSwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADDMZXcBwA/VrU/%2Bh90lfCebZ11vdwkAgP/FFSwAAADDCFjnUVNTo3vvvVcZGRm68cYbtXjxYrW0tNhdFgAAcAhuEZ7HjBkzNHDgQG3fvl2ffvqppkyZossuu0y/%2BMUv7C4NAAA4AAHrG7xerw4cOKCVK1cqJiZGMTExKigo0HPPPUfAwg%2Bak/aMsV8MQEdHwPqGqqoqJSUlKTY2tnVs4MCBOnLkiOrr6xUdHf2tx6itrZXP5wsZc7m6KSEhwWitERHc4YUzOSkMStLrxT%2B1uwRbff13DX/nWI/eOxcB6xsCgYDcbnfI2Ndhy%2B/3tylgVVZWqry8PGRs%2BvTpmjFjhrlC9VWQu/vyg8rLyzMe3nBhtbW1qqyspO82oPf2qK2t1XPPPUvfbUDvnYtIfB7BYLBd78/Ly9PatWtDfvLy8gxV9398Pp/Ky8vPuVqGi4u%2B24fe24O%2B24feOxdXsL7B4/EoEAiEjAUCAYWFhcnj8bTpGAkJCXzSAACgE%2BMK1jekpaXpxIkTOnXqVOuY1%2BtV3759FRUVZWNlAADAKQhY35Camqr09HQtWbJE9fX1qq6u1sqVK5Wfn293aQAAwCEiHn300UftLuKH5qc//ak2btyoxx9/XK%2B99prGjRunyZMnKywszO7SzhEVFaVhw4Zxdc1i9N0%2B9N4e9N0%2B9N6ZwoLt3dENAACAENwiBAAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgOVANTU1uvfee5WRkaEbb7xRixcvVktLi91ldQq7d%2B9WZmamioqK7C6l06mpqVFhYaEyMjKUmZmpuXPnqq6uzu6yOrwDBw7o7rvv1pAhQ5SZmalZs2bJ5/PZXVan8sQTTyglJcXuMvAdEbAcaMaMGUpMTNT27du1cuVKbd%2B%2BXc8995zdZXV4zzzzjEpKStSrVy%2B7S%2BmUpk6dKrfbrR07dmjt2rU6ePCgFi1aZHdZHdqXX36pX/7ylxo2bJj27NmjjRs36tNPPxW/wtY6%2B/fv17p16%2BwuA98DActhvF6vDhw4oOLiYsXExCg5OVkFBQWqrKy0u7QOLzIyUmvWrCFg2aCurk5paWmaPXu2oqKidPnll2vMmDF699137S6tQ2tsbFRRUZGmTJmirl27yuPx6Oabb9bBgwftLq1TaGlp0YIFC1RQUGB3KfgeCFgOU1VVpaSkJMXGxraODRw4UEeOHFF9fb2NlXV8kyZNUkxMjN1ldEput1ulpaW67LLLWsdOnDihhIQEG6vq%2BGJjY3XXXXfJ5XJJkg4fPqw//elPuvXWW22urHN46aWXFBkZqZEjR9pdCr4Hl90F4LsJBAJyu90hY1%2BHLb/fr%2BjoaDvKAizl9Xr1wgsvaPny5XaX0inU1NRoxIgRampq0vjx4zVz5ky7S%2BrwPvnkEz399NN6/vnn7S4F3xNXsBwoGAzaXQJgm/fee0%2BTJ0/W7NmzlZmZaXc5nUJSUpK8Xq%2B2bNmio0eP6sEHH7S7pA6vtLRUY8eOVd%2B%2Bfe0uBd8TActhPB6PAoFAyFggEFBYWJg8Ho9NVQHW2LFjh%2B699149/PDDmjRpkt3ldCphYWFKTk5WUVGRNm7cqFOnTtldUoe1Z88e/fWvf1VhYaHdpaAdCFgOk5aWphMnToT85eb1etW3b19FRUXZWBlwcb3//vuaM2eOnnrqKY0ePdrucjqFPXv2aMSIESGPgQkP/%2BqfjS5duthVVoe3fv16ffrpp7rxxhuVkZGhsWPHSpIyMjL02muv2Vwd2oqA5TCpqalKT0/XkiVLVF9fr%2Brqaq1cuVL5%2Bfl2lwZcNE1NTXrkkUdUXFysrKwsu8vpNNLS0lRfX6/FixersbFRp06d0tNPP61rr72WL3xcRHPnztXWrVu1bt06rVu3ThUVFZKkdevWKScnx%2Bbq0FZhQTb0OM7Jkyc1f/58/ed//qeio6M1YcIETZ8%2BXWFhYXaX1qGlp6dL%2Buofe0mt36zyer221dRZvPvuu/qXf/kXde3a9Zy5LVu2KCkpyYaqOoePP/5YJSUl%2BvDDD9WtWzddd911mjt3rhITE%2B0urdP429/%2BptzcXH388cd2l4LvgIAFAABgGLcIAQAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMCw/wEifrdzRCLnFwAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common8590863561166491216”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>141</td> <td class=”number”>4.4%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.2398657</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.4830918</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.1979414</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6480882</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.3292723</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.3844675</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.218027</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.7168459</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.2198527</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2952)</td> <td class=”number”>2973</td> <td class=”number”>92.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme8590863561166491216”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>141</td> <td class=”number”>4.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0148223</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0320698</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.036135</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.038519</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>2.158895</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.188184</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.237693</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.076923</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.618937</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_WICSPTH12”>WICSPTH12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2946</td>

</tr> <tr>

<th>Unique (%)</th> <td>91.8%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.22931</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>2.994</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>4.6%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-1058795143025795673”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-1058795143025795673,#minihistogram-1058795143025795673”
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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-1058795143025795673”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-1058795143025795673”

aria-controls=”quantiles-1058795143025795673” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-1058795143025795673” aria-controls=”histogram-1058795143025795673”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-1058795143025795673” aria-controls=”common-1058795143025795673”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-1058795143025795673” aria-controls=”extreme-1058795143025795673”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-1058795143025795673”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.048561</td>

</tr> <tr>

<th>Q1</th> <td>0.11893</td>

</tr> <tr>

<th>Median</th> <td>0.17733</td>

</tr> <tr>

<th>Q3</th> <td>0.26777</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.57339</td>

</tr> <tr>

<th>Maximum</th> <td>2.994</td>

</tr> <tr>

<th>Range</th> <td>2.994</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.14884</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.20848</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.90918</td>

</tr> <tr>

<th>Kurtosis</th> <td>26.16</td>

</tr> <tr>

<th>Mean</th> <td>0.22931</td>

</tr> <tr>

<th>MAD</th> <td>0.12648</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>3.9089</td>

</tr> <tr>

<th>Sum</th> <td>718.2</td>

</tr> <tr>

<th>Variance</th> <td>0.043466</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-1058795143025795673”>

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RwIEDlZWVpc2bN/urNAAAgItit3rB8vJybdiwQa%2B//rpqa2s1ZswYvfTSS7r66qs924wYMUK//e1vNX78eKvLAwAAuGiWn8EaN26cdu3apdmzZ%2BuDDz7QypUrvcKVJKWnp%2BvEiRM%2B97V7926lpaUpJyfHa3zjxo268sorlZKS4vV19tJjU1OTioqKlJGRoREjRmjWrFk6duyY5/Uul0sLFixQWlqaRo4cqcWLF6u%2Bvt7A0QMAgPbA8jNY69at0zXXXONzu71797Y4/9xzz2nDhg3q1avXeedHjBihP/3pT%2Bede%2BWVV7R582Y999xzio%2BPV1FRkbKzs7Vp0yaFhITo0Ucf1enTp7VlyxadOXNG8%2BfPV2FhoZYsWeL7AAEAQLtn%2BRmsAQMGaM6cOdqxY4dn7I9//KN%2B/etfy%2BVytXo/YWFhLQaslpSWliorK0t9%2BvRRRESEcnJyVF5err179%2Brbb7/Vjh07lJOTo9jYWMXHx%2Bu%2B%2B%2B7Ta6%2B9pjNnzlzwWgAAoP2xPGAVFBTo5MmT6tu3r2ds1KhRampq0vLly1u9nxkzZigyMvJH548fP6577rlHI0aMUEZGhjZt2iRJqq%2Bv18GDB5WUlOTZNiIiQr169ZLD4dD%2B/fsVGhqqAQMGeOYHDRqkH374QYcOHbqQQwUAAO2U5ZcIP/zwQ23evFkxMTGescTERBUWFur22283skZsbKwSExP14IMPqm/fvnrnnXf08MMPKy4uTj//%2Bc/ldrsVHR3t9Zro6GhVVVWpa9euioiIUEhIiNecJFVVVbVq/crKSjmdTq8xu72L4uLiLvLIvIWGBtZvOrLbra/3bI8CrVdWokctoz%2B%2B0SPf6JFvwdYjywNWfX29wsLCmo3bbDbV1dUZWWPUqFEaNWqU58/jxo3TO%2B%2B8o40bNyovL0%2BS5Ha7f/T1Lc21RmlpqYqLi73GsrOzNW/evIvab6CLiQn329pRUZ39tnagoEctoz%2B%2B0SPf6JFvwdIjywPWiBEjtHz5cuXm5nrODH3zzTdasWJFs08TmpSQkKB9%2B/apa9eustlsze73crlc6tatm2JjY1VTU6PGxkaFhoZ65iSpW7durVpr6tSpGj16tNeY3d5FVVW1Bo7k/wRayjd9/K0RGmpTVFRnVVfXqbGxyfL1AwE9ahn98Y0e%2BUaPfGurHvnrH/eWB6xFixbpV7/6la677jpFRESoqalJtbW16tmz549%2B6u9C/fnPf1Z0dLTGjh3rGSsvL1fPnj0VFhamfv36qayszPNpxurqah09elSDBw9WQkKC3G63Dhw4oEGDBkmSHA6HoqKi1Lt371atHxcX1%2BxyoNN5Ug0N7fsvlT%2BPv7Gxqd333xd61DL64xs98o0e%2BRYsPbI8YPXs2VNvvvmmPvjgAx09elQ2m029e/fWyJEjPWeMLtbp06f1xBNPqGfPnrryyiv11ltv6YMPPtCrr74qScrMzFRJSYluvPFGxcfHq7CwUAMHDlRKSookacyYMfrd736nFStW6PTp01q9erUmT54su93ydgEAgADkl8TQsWNH3XTTTRe1j7NhqKGhQZI8j31wOByaMWOGamtrNX/%2BfDmdTl1%2B%2BeVavXq1kpOTJUnTpk2T0%2BnU9OnTVVtbq9TUVK97ph5//HE99thjysjIUIcOHXT77bc3e5gpAADAjwlxX%2Bwd3Rfo2LFjWrVqlb766qvzPh393XfftbIcyzidJ43v02636ebC3cb321a2Lbje8jXtdptiYsJVVVUbFKec2wI9ahn98Y0e%2BUaPfGurHnXv/uOPdGpLfrkHq7KyUiNHjlSXLl2sXh4AAKDNWR6w9u3bp3fffVexsbFWLw0AAGAJyz/n361bN85cAQCAoGZ5wJo9e7aKi4sv%2BmGeAAAAlyrLLxF%2B8MEH%2Bvzzz7Vx40Zdfvnlstm8M95f/vIXq0sCAAAwyvKAFRERoRtvvNHqZQEAACxjecAqKCiwekkAAABL%2BeWX2R06dEjPPvusHnnkEc/Y3//%2Bd3%2BUAgAAYJzlAevjjz/WhAkT9Pbbb2vLli2S/vXw0RkzZgTtQ0YBAED7YnnAKioq0kMPPaTNmzcrJCRE0r9%2BP%2BHy5cu1evVqq8sBAAAwzvKA9b//%2B7/KzMyUJE/AkqRbb71V5eXlVpcDAABgnOUBKzIy8ry/g7CyslIdO3a0uhwAAADjLA9Yw4YN01NPPaWamhrP2OHDh5Wfn6/rrrvO6nIAAACMs/wxDY888ohmzpyp1NRUNTY2atiwYaqrq1O/fv20fPlyq8sBAAAwzvKA1aNHD23ZskXvv/%2B%2BDh8%2BrE6dOql37966/vrrve7JAgAACFSWByxJ6tChg2666SZ/LA0AANDmLA9Yo0ePbvFMFc/CAgAAgc7ygDV27FivgNXY2KjDhw/L4XBo5syZVpcDAABgnOUBKy8v77zjb731lv72t79ZXA0AAIB5fvldhOdz00036c033/R3GQAAABftkglYX3zxhdxut7/LAAAAuGiWXyKcNm1as7G6ujqVl5frlltusbocAAAA4ywPWImJic0%2BRRgWFqbJkydrypQpVpcDAABgnOUBi6e1AwCAYGd5wHr99ddbve0dd9zRhpUAAAC0DcsD1uLFi9XU1NTshvaQkBCvsZCQEAIWAAAISJYHrOeff14vvPCC5syZowEDBsjtduvLL7/Uc889p7vvvlupqalWlwQAAGCUX%2B7BKikpUXx8vGds%2BPDh6tmzp2bNmqUtW7ZYXRIAAIBRlj8H68iRI4qOjm42HhUVpYqKCqvLAQAAMM7ygJWQkKDly5erqqrKM1ZdXa1Vq1bpiiuusLocAAAA4yy/RLho0SLl5uaqtLRU4eHhstlsqqmpUadOnbR69WqrywEAADDO8oA1cuRI7dq1S%2B%2B//76%2B/vprud1uxcfH64YbblBkZKTV5QAAABhnecCSpM6dOysjI0Nff/21evbs6Y8SAAAA2ozl92DV19crPz9fQ4cO1W233SbpX/dg3Xvvvaqurra6HAAAAOMsD1grV67U/v37VVhYKJvt/5ZvbGxUYWGh1eUAAAAYZ3nAeuutt/TMM8/o1ltv9fzS56ioKBUUFOjtt9%2B2uhwAAADjLA9YtbW1SkxMbDYeGxurH374wepyAAAAjLM8YF1xxRX629/%2BJklev3tw%2B/bt%2Bo//%2BA%2BrywEAADDO8k8R/vKXv9QDDzygO%2B%2B8U01NTXrxxRe1b98%2BvfXWW1q8eLHV5QAAABhnecCaOnWq7Ha7Xn75ZYWGhur3v/%2B9evfurcLCQt16661WlwMAAGCc5QHrxIkTuvPOO3XnnXdavTQAAIAlLL8HKyMjw%2BveKwAAgGBjecBKTU3Vtm3brF4WAADAMpZfIvzZz36mJ598UiUlJbriiivUoUMHr/lVq1ZZXRIAAIBRlgesgwcP6uc//7kkqaqqyurlAQAA2pxlASsnJ0dFRUX605/%2B5BlbvXq1srOzrSoBAADAEpbdg7Vz585mYyUlJVYtDwAAYBnLAtb5PjnIpwkBAEAwsixgnf3Fzr7GAAAAAp3lj2kAAAAIdgEdsHbv3q20tDTl5OQ0m9u6davGjx%2BvoUOHatKkSfrwww89c01NTSoqKlJGRoZGjBihWbNm6dixY555l8ulBQsWKC0tTSNHjtTixYtVX19vyTEBAIDAZ9mnCM%2BcOaPc3FyfY619DtZzzz2nDRs2qFevXs3m9u/fr/z8fBUXF%2Bvaa6/VW2%2B9pfvvv1/bt29Xjx499Morr2jz5s167rnnFB8fr6KiImVnZ2vTpk0KCQnRo48%2BqtOnT2vLli06c%2BaM5s%2Bfr8LCQi1ZsuSnNwAAALQblp3Buvrqq1VZWen1db6x1goLC/vRgLV%2B/Xqlp6crPT1dYWFhmjBhgvr376833nhDklRaWqqsrCz16dNHERERysnJUXl5ufbu3atvv/1WO3bsUE5OjmJjYxUfH6/77rtPr732ms6cOWOsHwAAIHhZdgbr359/ZcKMGTN%2BdK6srEzp6eleY0lJSXI4HKqvr9fBgweVlJTkmYuIiFCvXr3kcDh08uRJhYaGasCAAZ75QYMG6YcfftChQ4e8xgEAAM7H8ie5W8Hlcik6OtprLDo6WgcPHtT3338vt9t93vmqqip17dpVERERXp9wPLtta588X1lZKafT6TVmt3dRXFzcTzmcHxUaGli30Nnt1td7tkeB1isr0aOW0R/f6JFv9Mi3YOtRUAYsyfcztlqav9jnc5WWlqq4uNhrLDs7W/Pmzbuo/Qa6mJhwv60dFdXZb2sHCnrUMvrjGz3yjR75Fiw9CsqAFRMTI5fL5TXmcrkUGxurrl27ymaznXe%2BW7duio2NVU1NjRobGxUaGuqZk6Ru3bq1av2pU6dq9OjRXmN2exdVVdX%2B1EM6r0BL%2BaaPvzVCQ22Kiuqs6uo6NTY2Wb5%2BIKBHLaM/vtEj3%2BiRb23VI3/94z4oA1ZycrL27dvnNeZwODRu3DiFhYWpX79%2BKisr0zXXXCNJqq6u1tGjRzV48GAlJCTI7XbrwIEDGjRokOe1UVFR6t27d6vWj4uLa3Y50Ok8qYaG9v2Xyp/H39jY1O777ws9ahn98Y0e%2BUaPfAuWHgXWKZBWuuuuu/TRRx9p165dOnXqlDZs2KAjR45owoQJkqTMzEytW7dO5eXlqqmpUWFhoQYOHKiUlBTFxsZqzJgx%2Bt3vfqcTJ07o66%2B/1urVqzV58mTZ7UGZRwEAgGEBmxhSUlIkSQ0NDZKkHTt2SPrX2ab%2B/fursLBQBQUFqqioUN%2B%2BfbV27Vp1795dkjRt2jQ5nU5Nnz5dtbW1Sk1N9bpn6vHHH9djjz2mjIwMdejQQbfffvt5H2YKAABwPiFufuOyJZzOk8b3abfbdHPhbuP7bSvbFlxv%2BZp2u00xMeGqqqoNilPObYEetYz%2B%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%2BOAQAABCg7P4uoC1t375dl19%2BuddYZWWl7rvvPi1evFjjx4/XZ599prlz56p3795KSUnRzp079eyzz%2Br555/XgAEDtG7dOs2ZM0dvv/22unTp4qcjAQAAgSRoz2D9mM2bNysxMVGTJ09WWFiY0tLSNHr0aK1fv16SVFpaqkmTJmnIkCHq1KmT7r33XknSe%2B%2B958%2ByAQBAAAnqM1irVq3S3//%2Bd9XU1Oi2227TwoULVVZWpqSkJK/tkpKStG3bNklSWVmZxo4d65mz2WwaOHCgHA6Hxo0b16p1Kysr5XQ6vcbs9i6Ki4u7yCPyFhoaWPnYbre%2B3rM9CrReWYketYz%2B%2BEaPfKNHvgVbj4I2YF111VVKS0vTihUrdOzYMS1YsEBLly6Vy%2BVSfHy817Zdu3ZVVVWVJMnlcik6OtprPjo62jPfGqWlpSouLvYay87O1rx5837i0QSHmJhwv60dFdXZb2sHCnrUMvrjGz3yjR75Fiw9CtqAVVpa6vn/Pn36KC8vT3PnztXVV1/t87Vut/ui1p46dapGjx7tNWa3d1FVVe1F7fdcgZbyTR9/a4SG2hQV1VnV1XVqbGyyfP1AQI9aRn98o0e%2B0SPf2qpH/vrHfdAGrHNdfvnlamxslM1mk8vl8pqrqqpSbGysJCkmJqbZvMvlUr9%2B/Vq9VlxcXLPLgU7nSTU0tO%2B/VDcX7vZ3CRdk24Lr/V2CpRobm9r9e7Ql9Mc3euQbPfItWHoUWKdAWumLL77Q8uXLvcbKy8vVsWNHpaenN3vswr59%2BzRkyBBJUnJyssrKyjxzjY2N%2BuKLLzzzAAAAvgRlwOrWrZtKS0tVUlKi06dP6/Dhw3r66ac1depU/ed//qcqKiq0fv16nTp1Su%2B//77KGaq1AAAM4ElEQVTef/993XXXXZKkzMxMvf7669qzZ4/q6uq0Zs0adezYUaNGjfLvQQEAgIARlJcI4%2BPjVVJSolWrVnkC0sSJE5WTk6OwsDCtXbtWy5Yt09KlS5WQkKCVK1fqyiuvlCTdeOONevDBB7VgwQJ99913SklJUUlJiTp16uTnowIAAIEixH2xd3SjVZzOk8b3abfbAu6%2BpkDSXu7BstttiokJV1VVbVDc92Aa/fGNHvlGj3xrqx517x5pbF8XIigvEQIAAPgTAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMPs/i4AuFTd9ru/%2BruEC7JtwfX%2BLgEA8P9xBgsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAM41OE51FRUaGlS5dq79696tKli8aOHavc3FzZbORRXLoC6VOPfOIRQLAjYJ3HAw88oEGDBmnHjh367rvvNHv2bF122WW65557/F0aAAAIAJySOYfD4dCBAweUl5enyMhIJSYmKisrS6Wlpf4uDQAABAjOYJ2jrKxMCQkJio6O9owNGjRIhw8fVk1NjSIiInzuo7KyUk6n02vMbu%2BiuLg4o7WGhpKPEZgC6XIm2tY7eTf4uwRLnP1%2BzfftHxdsPSJgncPlcikqKspr7GzYqqqqalXAKi0tVXFxsdfY/fffrwceeMBcofpXkJvZ4ytNnTrVeHgLFpWVlSotLaVHLaBHLaM/vtEj3yorK/XSS8/ToxYEW4%2BCIyYa5na7L%2Br1U6dO1caNG72%2Bpk6daqi6/%2BN0OlVcXNzsbBn%2BDz3yjR61jP74Ro98o0e%2BBVuPOIN1jtjYWLlcLq8xl8ulkJAQxcbGtmofcXFxQZG%2BAQDAT8MZrHMkJyfr%2BPHjOnHihGfM4XCob9%2B%2BCg8P92NlAAAgUBCwzpGUlKSUlBStWrVKNTU1Ki8v14svvqjMzEx/lwYAAAJE6G9/%2B9vf%2BruIS80NN9ygLVu26IknntCbb76pyZMna9asWQoJCfF3ac2Eh4frmmuu4exaC%2BiRb/SoZfTHN3rkGz3yLZh6FOK%2B2Du6AQAA4IVLhAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAucRUVFfrNb36j1NRU/eIXv9DKlSvV1NR03m3XrVunMWPGaNiwYcrMzNS%2BffssrtY/WtujZ599VgMHDlRKSorX17fffuuHqq21e/dupaWlKScnp8XtmpqaVFRUpIyMDI0YMUKzZs3SsWPHLKrSv1rbo4ULF3p%2BZ%2BnZr%2BHDh1tUpf9UVFQoOztbqampSktL08KFC1VdXX3ebbdu3arx48dr6NChmjRpkj788EOLq/WP1vZo48aNuvLKK5t9L/rHP/7hh6qtdeDAAc2cOVNXX3210tLStGDBAjmdzvNuG/A/09y4pE2cONG9ZMkSd3V1tfvw4cPuW265xf3CCy802%2B7dd991Dx8%2B3L1nzx53XV2de%2B3ate7rr7/eXVtb64eqrdXaHj3zzDPu/Px8P1ToXyUlJe5bbrnFPW3aNPeCBQta3HbdunXuX/ziF%2B6DBw%2B6T5486X788cfd48ePdzc1NVlUrX9cSI/y8/PdzzzzjEWVXTpuv/1298KFC901NTXu48ePuydNmuRetGhRs%2B2%2B%2BOILd3JysnvXrl3u%2Bvp696ZNm9xDhgxxHz9%2B3A9VW6u1PXrttdfcd999tx8q9K9Tp065r7vuOndxcbH71KlT7u%2B%2B%2B8599913u%2B%2B7775m2wbDzzTOYF3CHA6HDhw4oLy8PEVGRioxMVFZWVkqLS1ttm1paakmTZqkIUOGqFOnTrr33nslSe%2B9957VZVvqQnrUXoWFhWnDhg3q1auXz21LS0uVlZWlPn36KCIiQjk5OSovL9fevXstqNR/LqRH7VF1dbWSk5OVm5ur8PBw9ejRQxMnTtSnn37abNv169crPT1d6enpCgsL04QJE9S/f3%2B98cYbfqjcOhfSo/aqrq5OOTk5mj17tjp27KjY2FjdfPPN%2Buqrr5ptGww/0whYl7CysjIlJCQoOjraMzZo0CAdPnxYNTU1zbZNSkry/Nlms2ngwIFyOByW1esPF9IjSfryyy81bdo0DRs2TOPGjWsXly5mzJihyMhIn9vV19fr4MGDXu%2BjiIgI9erVK%2BjfR63t0VmffPKJ7rjjDg0dOlSTJ08OvEsXFygqKkoFBQW67LLLPGPHjx9XXFxcs23P/V4kSUlJSUH/HrqQHp2du%2BeeezRixAhlZGRo06ZNVpXqN9HR0ZoyZYrsdrsk6dChQ/qf//kf3Xbbbc22DYafaQSsS5jL5VJUVJTX2NkgUVVV1Wzbfw8ZZ7c9d7tgcyE96tGjh3r27KkVK1bor3/9q6ZMmaI5c%2Bbo0KFDltV7Kfv%2B%2B%2B/ldrvb5fvoQvTs2VO9evXS2rVrtXv3bg0fPly/%2BtWv2lWPHA6HXn75Zc2dO7fZXHv9XnSulnoUGxurxMREPfTQQ/rrX/%2BqBx98UIsWLdLHH3/sh0qtV1FRoeTkZI0dO1YpKSmaN29es22C4X1EwLrEud3uNtk2mLT2uKdMmaJnnnlGvXr1UufOnZWVlaWBAwcG/aWLC9Ve30etlZ2draeeekrx8fGKiIjQQw89pI4dO2rHjh3%2BLs0Sn332mWbNmqXc3FylpaWdd5v2/h7y1aNRo0bp%2BeefV1JSkjp27Khx48bp5ptv1saNG/1QrfUSEhLkcDi0fft2HTlyRA8//PB5twv09xEB6xIWGxsrl8vlNeZyuRQSEqLY2Fiv8ZiYmPNue%2B52weZCenQ%2BCQkJqqysbKvyAkrXrl1ls9nO289u3br5qapLX2hoqH72s5%2B1i/fRzp079Zvf/EaLFi3SjBkzzrtNe/1edFZrenQ%2B7e17UUhIiBITE5WTk6MtW7boxIkTXvPB8D4iYF3CkpOTdfz4ca83nsPhUN%2B%2BfRUeHt5s27KyMs%2BfGxsb9cUXX2jIkCGW1esPF9Kj//7v/252Cr68vFw9e/a0pNZLXVhYmPr16%2Bf1PqqurtbRo0c1ePBgP1Z26XC73SooKNCBAwc8Y6dPn9bRo0eD/n30%2BeefKz8/X08//bTuuOOOH90uOTm52T1pDocj6L8XSa3v0Z///Gdt3brVa6w9fC/6%2BOOPNWbMGK/H6Nhs/4ohHTp08No2GH6mEbAuYWeftbNq1SrV1NSovLxcL774ojIzMyVJt956q%2BcTKpmZmXr99de1Z88e1dXVac2aNerYsaNGjRrlxyNoexfSI5fLpaVLl%2BrQoUM6deqUXnjhBR09elQTJ0705yH41TfffKNbb73V86yrzMxMrVu3TuXl5aqpqVFhYaHn2WHt1b/3KCQkRP/85z%2B1dOlSffPNN6qtrVVhYaE6dOigm266yd%2BltpmGhgYtWbJEeXl5GjlyZLP5mTNnegLDXXfdpY8%2B%2Bki7du3SqVOntGHDBh05ckQTJkywumxLXUiPTp8%2BrSeeeEIOh0NnzpzRli1b9MEHH2jatGlWl22p5ORk1dTUaOXKlaqrq9OJEyf07LPPavjw4YqMjAy6n2l2fxeAlj3zzDN69NFHdf311ysiIkLTpk3TL3/5S0nS4cOH9cMPP0iSbrzxRj344INasGCBvvvuO6WkpKikpESdOnXyZ/mWaG2PcnNzJUlZWVlyuVzq27ev/vjHP6pHjx5%2Bq90KZ8NRQ0ODJHnuFTr7zf3w4cM6ffq0JGnatGlyOp2aPn26amtrlZqaquLiYv8UbqEL6dGTTz6pFStWaNKkSaqpqdHgwYP10ksvqUuXLv4p3gJ79uxReXm5li1bpmXLlnnNbd%2B%2BXceOHdP3338vSerfv78KCwtVUFCgiooK9e3bV2vXrlX37t39UbplLqRHM2bMUG1trebPny%2Bn06nLL79cq1evVnJysj9Kt0xkZKReeOEFLVu2TNdee626dOmia6%2B9Vk8%2B%2BaSk4PuZFuIO9LvIAAAALjFcIgQAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAw/4foEPW9vXDLXcAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-1058795143025795673”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>149</td> <td class=”number”>4.6%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.1860638</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.244978</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.1943257</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.2351281</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.2141633</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.211342</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.2253267</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.1462844</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.2414293</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2935)</td> <td class=”number”>2962</td> <td class=”number”>92.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-1058795143025795673”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>149</td> <td class=”number”>4.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0145686</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0316678</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0379553</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.042924</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.948052</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.956947</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.968504</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.106741</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.994012</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_index”>index<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3210</td>

</tr> <tr>

<th>Unique (%)</th> <td>100.0%</td>

</tr> <tr class=”ignore”>

<th>Missing (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Missing (n)</th> <td>0</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>31450</td>

</tr> <tr>

<th>Minimum</th> <td>1001</td>

</tr> <tr>

<th>Maximum</th> <td>72153</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-3436787962180432786”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-3436787962180432786,#minihistogram-3436787962180432786”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-3436787962180432786”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-3436787962180432786”

aria-controls=”quantiles-3436787962180432786” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-3436787962180432786” aria-controls=”histogram-3436787962180432786”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-3436787962180432786” aria-controls=”common-3436787962180432786”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-3436787962180432786” aria-controls=”extreme-3436787962180432786”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-3436787962180432786”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>1001</td>

</tr> <tr>

<th>5-th percentile</th> <td>5111.9</td>

</tr> <tr>

<th>Q1</th> <td>19040</td>

</tr> <tr>

<th>Median</th> <td>30032</td>

</tr> <tr>

<th>Q3</th> <td>46110</td>

</tr> <tr>

<th>95-th percentile</th> <td>55024</td>

</tr> <tr>

<th>Maximum</th> <td>72153</td>

</tr> <tr>

<th>Range</th> <td>71152</td>

</tr> <tr>

<th>Interquartile range</th> <td>27071</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>16265</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.51718</td>

</tr> <tr>

<th>Kurtosis</th> <td>-0.62824</td>

</tr> <tr>

<th>Mean</th> <td>31450</td>

</tr> <tr>

<th>MAD</th> <td>13776</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.15897</td>

</tr> <tr>

<th>Sum</th> <td>100953117</td>

</tr> <tr>

<th>Variance</th> <td>264560000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-3436787962180432786”>

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YeHh1e5HVhYeFLnztXuRKuoqKz1NjyNL/bsCy51TDnWvslXjrkvnt%2B%2B2POF3O4G6bBhw7Rx40bdfffdGjNmjLZu3apz585d1TbWrVunoqIi9enTR7GxsUpMTJQkxcbGqnXr1lU%2BdiEnJ0cxMTGSpKioKOXm5jrmKioqtHfvXsc8AADAlbhdwEpNTdXGjRv11ltvqVWrVpo7d67i4%2BO1YMECHTp0qFrbmDp1qrZs2aK1a9dq7dq1Wr58uSRp7dq1GjRokPLy8pSZmakzZ85o586d2rlzp0aMGCFJSklJ0bvvvqs9e/aorKxMS5cuVb169dS7d%2B%2B6ahkAAHgZt7tFeF779u3Vvn17Pfnkk9q4caNmzZqlFStWKC4uTk888YQ6dOhw2XVDQkKcXqh%2B/gpYs2bNJEnLli3TM888o9mzZ6t58%2BZasGCB2rZtK0nq1auXJk6cqPT0dBUVFSk6OlrLly9X/fr167BbAADgTdw2YJ09e1b/%2B7//q7fffluffPKJIiIiNH78eBUUFGj06NGaPXu2Bg0aVK1t3Xzzzfr2228dP3fr1s3pw0cvNmrUKI0aNarWPQAAAN/kdgHrwIEDWrNmjd59912VlpaqX79%2Bev3119WlSxfHMt26ddOsWbOqHbAAAACuJbcLWAMHDlSLFi00duxYDRkyRI0bN66yTHx8vI4fP%2B6C6gAAAK7M7QLWypUr1b179ysu99VXX12DagAAAK6e272LsE2bNho3bpzef/99x9hf//pXPfzww1U%2BnwoAAMAduV3Amjdvnk6ePKmWLVs6xnr37q3KykrNnz/fhZUBAABUj9vdIvzoo4%2B0fv16hYaGOsYiIiK0cOFC3XvvvS6sDAAAoHrc7gpWeXm5AgMDq4z7%2B/urrKzMBRUBAABcHbcLWN26ddP8%2BfN14sQJx9gPP/yg2bNnO31UAwAAgLtyu1uE06ZN04MPPqg77rhDQUFBqqysVGlpqW655Ra98cYbri4PAADgitwuYN1yyy1677339Le//U3ff/%2B9/P391aJFC/Xs2VMBAQGuLg8AAOCK3C5gSVK9evV09913u7oMAACAGnG7gHXkyBEtWrRI3333ncrLy6vMf/DBBy6oCgAAoPrcLmBNmzZNBQUF6tmzpxo0aODqcgAAAK6a2wWsnJwcffDBBwoLC3N1KQAAADXidh/TcMMNN3DlCgAAeDS3C1hjx47VkiVLZIxxdSkAAAA14na3CP/2t7/pyy%2B/1Ntvv62bb75Z/v7OGfDNN990UWUAAADV43YBKygoSL169XJ1GQAAADXmdgFr3rx5ri4BAACgVtzuNViSdPDgQS1evFh//OMfHWO7d%2B92YUUAAADV53YBKysrS4MHD9bWrVu1YcMGST9/%2BOj999/Ph4wCAACP4HYB6/nnn9cf/vAHrV%2B/Xn5%2BfpJ%2B/vcJ58%2Bfr5dfftnF1QEAAFyZ2wWsf/zjH0pJSZEkR8CSpHvuuUcHDhxwVVkAAADV5nYBq1GjRpf8NwgLCgpUr149F1QEAABwddwuYHXu3Flz587VqVOnHGOHDh3SlClTdMcdd7iwMgAAgOpxu49p%2BOMf/6gHHnhAsbGxqqioUOfOnVVWVqZWrVpp/vz5ri4PAADgitwuYDVr1kwbNmzQzp07dejQIdWvX18tWrRQjx49nF6TBQAA4K7cLmBJ0nXXXae7777b1WUAAADUiNsFrISEhF%2B8UsVnYQEAAHfndgFrwIABTgGroqJChw4dUnZ2th544AEXVgYAAFA9bhewJk%2BefMnxLVu26O9///s1rgYAAODquV3Aupy7775bTz31lJ566ilXlwIAsEj/Fz52dQlXZVN6D1eXAA/hdp%2BDdTl79%2B6VMcbVZQAAAFyR213BGjlyZJWxsrIyHThwQH379nVBRQAAAFfH7QJWRERElXcRBgYGKikpScOHD3dRVQAAANXndgGLT2sHAACezu0C1rvvvlvtZYcMGVKHlQAAANSM2wWs6dOnq7KyssoL2v38/JzG/Pz8fjFgffPNN5o3b55ycnIUGBio7t27a/r06WrSpImysrK0aNEiHTx4UDfddJPGjh2rwYMHO9ZduXKlVq9ercLCQrVp00bTp09XVFSU9c0CAACv5HbvInz11VfVs2dPrV69Wp9//rk%2B%2B%2BwzrVq1Sr169dJf/vIXff311/r666/11VdfXXYbP/30kx588EF1795dWVlZ2rBhg4qKijRr1iwVFBToscce08iRI5WVlaXp06dr5syZys7OliRt27ZNixcv1nPPPaddu3apT58%2BGjdunE6fPn2tHgIAAODh3C5gzZ8/X88884y6dOmioKAgNWrUSF27dtWf/vQnPfvss6pXr57j63LKyso0YcIEjR07VvXq1VNYWJh%2B%2B9vf6rvvvtP69esVERGhpKQkBQYGKi4uTgkJCcrMzJQkZWRkKDExUTExMapfv77GjBkjSdq%2Bffs16R8AAHg%2BtwtYhw8fVkhISJXx4OBg5eXlVWsbISEhGj58uGy2n%2B%2BAHjx4UO%2B884769%2B%2Bv3NxcRUZGOi0fGRmpnJwcSaoy7%2B/vr3bt2jmucAEAAFyJ270Gq3nz5po/f76eeOIJhYaGSpJKSkr00ksv6de//vVVbSsvL0/9%2BvXTuXPnNGLECKWlpenhhx9W06ZNnZZr3LixiouLJUl2u71KwAsJCXHMV0dBQYEKCwudxmy2BgoPD7%2Bq%2Bs8LCPB3%2Bu4LfLFnX2Kz/d9x5VjDk1x47laHL57fvtjzpbhdwJo2bZomTZqkjIwMNWzYUP7%2B/jp16pTq16%2Bvl19%2B%2Baq21bx5c2VnZ%2Buf//ynnnrqKT355JPVWq%2B2nxifkZGhJUuWOI2lpqYqLS2tVtsNDr6%2BVut7Il/s2ReEhjasMsaxhie41LlbHb54fvtizxdyu4DVs2dP7dixQzt37lR%2Bfr6MMWratKnuvPNONWrU6Kq35%2Bfnp4iICE2YMEEjR45UfHy87Ha70zLFxcUKCwuTJIWGhlaZt9vtatWqVbX3mZycrISEBKcxm62BiotLr7p%2B6ee/AoKDr1dJSZkqKiqd5n678MMabdMV/nfyndVe9pd6hue78HeBYw1PcrXP4754frtbzzUNxbXldgFLkq6//nrdddddys/P1y233HLV62dlZWnWrFnatGmT/P1/vkR5/nuHDh20ZcsWp%2BVzcnIUExMjSYqKilJubq6GDh0qSaqoqNDevXuVlJRU7f2Hh4dXuR1YWHhS587V7kSrqKis9TZcqSa1e3rPuLRLHVOONTxBTc9RXzy/fbHnC7ldwCovL9fTTz%2Bt9957T9LP4aekpEQTJ07Un//8ZwUHB19xG1FRUTp16pQWLFigtLQ0lZWVafHixeratatSUlK0YsUKZWZmavDgwfrkk0%2B0c%2BdOZWRkSJJSUlI0ceJE3XvvvWrTpo3%2B67/%2BS/Xq1VPv3r3rsm3Ap/R/4WNXlwAAdcrtXoG2YMEC7du3TwsXLnRcdZJ%2BvpK0cOHCam2jUaNGWrFihXJycnT77bdr4MCBatSokf785z/rhhtu0LJly7Rq1Sp16dJFc%2BfO1YIFC9S2bVtJUq9evTRx4kSlp6ere/fu2rVrl5YvX6769evXSb8AAMD7%2BJnavqLbYj179tSqVasUERGhmJgYxweK5ufna8iQIfrkk09cXGHNFBaerPG6Npu/QkMbqri4tMrlVk%2B6ErApvUe1l/2lnq8VT3psAVwbV/M8JrnHc9m15m49N2ly9a/ftoLbXcEqLS1VRERElfGwsDA%2BTR0AAHgEtwtYv/71r/X3v/9dkvPHJWzevFm/%2BtWvXFUWAABAtbndi9xHjRql8ePHa9iwYaqsrNRrr72mnJwcbdmyRdOnT3d1eQAAAFfkdgErOTlZNptNq1atUkBAgF555RW1aNFCCxcu1D333OPq8gAAAK7I7QLW8ePHNWzYMA0bNszVpQAAANSI270G66677qr1P1UDAADgSm4XsGJjY7Vp0yZXlwEAAFBjbneL8KabbtJ//Md/aPny5fr1r3%2Bt6667zml%2B0aJFLqoMAACgetwuYO3fv1%2B/%2Bc1vJP38jzADAAB4GrcJWBMmTNDzzz%2BvN954wzH28ssvKzU11YVVAQAAXD23eQ3Wtm3bqowtX77cBZUAAADUjtsErEu9c5B3EwIAAE/kNgHLz8%2BvWmMAAADuzm0CFgAAgLcgYAEAAFjMbd5FePbsWU2aNOmKY3wOFgAAcHduE7C6dOmigoKCK44BAAC4O7cJWBd%2B/hUAAIAn4zVYAAAAFnObK1jwfv1f%2BNjVJQAAcE1wBQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAi3ltwMrLy1NqaqpiY2MVFxenqVOnqqSkRJK0b98%2B3XffferSpYv69u2rFStWOK27ceNGDRo0SJ06dVJiYqI%2B%2BugjV7QAAAA8lNcGrHHjxik4OFjbtm3T22%2B/re%2B%2B%2B07PPvusysvLNXbsWN1%2B%2B%2B368MMP9fzzz2vZsmXaunWrpJ/D15QpUzR58mR98sknGj16tB5//HHl5%2Be7uCMAAOApvDJglZSUKCoqSpMmTVLDhg3VrFkzDR06VJ9//rl27Nihs2fP6tFHH1WDBg3Uvn17DR8%2BXBkZGZKkzMxMxcfHKz4%2BXoGBgRo8eLBat26tdevWubgrAADgKbwyYAUHB2vevHm68cYbHWPHjh1TeHi4cnNz1aZNGwUEBDjmIiMjlZOTI0nKzc1VZGSk0/YiIyOVnZ19bYoHAAAez%2BbqAq6F7OxsrVq1SkuXLtWmTZsUHBzsNN%2B4cWPZ7XZVVlbKbrcrJCTEaT4kJET79%2B%2Bv9v4KCgpUWFjoNGazNVB4eHiN6g8I8Hf6DgBwDZvt6p6HffH52xd7vhSvD1hffPGFHn30UU2aNElxcXHatGnTJZfz8/Nz/Lcxplb7zMjI0JIlS5zGUlNTlZaWVqvtBgdfX6v1AQC1ExrasEbr%2BeLzty/2fCGvDljbtm3TH/7wB82cOVNDhgyRJIWFhenw4cNOy9ntdjVu3Fj%2B/v4KDQ2V3W6vMh8WFlbt/SYnJyshIcFpzGZroOLi0hr1ERDgr%2BDg61VSUqaKisoabQMAUHtX%2Bzzui8/f7tZzTUNxbXltwPryyy81ZcoUvfjii%2BrZs6djPCoqSv/zP/%2Bjc%2BfOyWb7uf3s7GzFxMQ45s%2B/Huu87OxsDRw4sNr7Dg8Pr3I7sLDwpM6dq92JVlFRWettAABqrqbPwb74/O2LPV/IK2%2BQnjt3TjNmzNDkyZOdwpUkxcfHKygoSEuXLlVZWZm%2B%2BuorrVmzRikpKZKkESNGaNeuXdqxY4fOnDmjNWvW6PDhwxo8eLArWgEAAB7Iz9T2BUdu6PPPP9e//du/qV69elXmNm/erNLSUj399NPKycnRjTfeqIcfflijRo1yLLN161YtWrRIeXl5atmypaZPn65u3brVqqbCwpM1Xtdm81doaEMVF5dW%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%2BTZkyRZMnT9Ynn3yi0aNH6/HHH1d%2Bfr4kafXq1Vq/fr2WL1%2Bu7du3KyIiQqmpqTLGXOs2AACAh/LKgBUYGKg1a9ZcMmBlZmYqPj5e8fHxCgwM1ODBg9W6dWutW7dOkpSRkaHRo0frtttuU1BQkCZMmKADBw7oq6%2B%2ButZtAAAAD2VzdQF14f7777/sXG5uruLj453GIiMjlZ090O6aAAAQxUlEQVSdrfLycu3fv1%2BRkZGOuaCgIN16663Kzs5Wx44dq7X/goICFRYWOo3ZbA0UHh5%2BFV38n4AAf6fvAADXsNmu7nnYF5%2B/fbHnS/HKgPVL7Ha7QkJCnMZCQkK0f/9%2BnThxQsaYS84XFxdXex8ZGRlasmSJ01hqaqrS0tJqXrik4ODra7U%2BAKB2QkMb1mg9X3z%2B9sWeL%2BRzAUvSFV9PVdvXWyUnJyshIcFpzGZroOLi0hptLyDAX8HB16ukpEwVFZW1qg0AUHNX%2Bzzui8/f7tZzTUNxbflcwAoNDZXdbncas9vtCgsLU%2BPGjeXv73/J%2BRtuuKHa%2BwgPD69yO7Cw8KTOnavdiVZRUVnrbQAAaq6mz8G%2B%2BPztiz1fyOdukEZFRSknJ8dpLDs7WzExMQoMDFSrVq2Um5vrmCspKdH333%2BvDh06XOtSAQCAh/K5gDVixAjt2rVLO3bs0JkzZ7RmzRodPnxYgwcPliSlpKRo5cqVOnDggE6dOqWFCxeqXbt2io6OdnHlAADAU3jlLcLzYejcuXOSpPfff1/Sz1eqWrdurYULF2revHnKy8tTy5YttWzZMjVp0kSSNHLkSBUWFurf//3fVVpaqtjY2CovWAcAAPglfoZP0LwmCgtP1nhdm81foaENVVxcWuV%2Bdv8XPq5taQCAatqU3uOqlv%2Bl529v5W49N2nSyCX79blbhAAAAHWNgAUAAGAxAhYAAIDFCFgAAAAW88p3EQIAAM96I9TVvoHA3XEFCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgSsS8jLy9Mjjzyi2NhY9enTRwsWLFBlZaWrywIAAB7C5uoC3NH48ePVvn17vf/%2B%2ByoqKtLYsWN144036ve//72rSwMAAB6AK1gXyc7O1jfffKPJkyerUaNGioiI0OjRo5WRkeHq0gAAgIfgCtZFcnNz1bx5c4WEhDjG2rdvr0OHDunUqVMKCgq64jYKCgpUWFjoNGazNVB4eHiNagoI8Hf6DgBwDZvt6p6Hef6uvqt9bN0dAesidrtdwcHBTmPnw1ZxcXG1AlZGRoaWLFniNPb4449r/PjxNaqpoKBAr7/%2BqpKTk6uEtM//454abdPdFRQUKCMj45I9eytf7Fnyzb7p2Td6ln75%2BftacMX/I3z1WF/Mu%2BKiRYwxtVo/OTlZb7/9ttNXcnJyjbdXWFioJUuWVLkq5s3o2Xf4Yt/07Dt8sW9f7PlSuIJ1kbCwMNntdqcxu90uPz8/hYWFVWsb4eHhPp3aAQDwdVzBukhUVJSOHTum48ePO8ays7PVsmVLNWzY0IWVAQAAT0HAukhkZKSio6O1aNEinTp1SgcOHNBrr72mlJQUV5cGAAA8RMCsWbNmuboId3PnnXdqw4YNmjNnjt577z0lJSXpoYcekp%2Bfn8tqatiwobp37%2B5TV9Ho2Xf4Yt/07Dt8sW9f7Plifqa2r%2BgGAACAE24RAgAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIDlxvLy8vTII48oNjZWffr00YIFC1RZWenqsqrlww8/VFxcnCZMmFBlbuPGjRo0aJA6deqkxMREffTRR465yspKPf/887rrrrvUrVs3PfTQQzpy5Ihj3m63Kz09XXFxcerZs6emT5%2Bu8vJyx/y%2Bfft03333qUuXLurbt69WrFhRt41eIC8vT6mpqYqNjVVcXJymTp2qkpKSatVVl49JXfrmm2/0wAMPqEuXLoqLi1N6eroKCwslSVlZWUpKSlLnzp01cOBArVu3zmndlStXql%2B/furcubNSUlKUk5PjmDtz5oyeeuop9erVS7GxsUpLS1NxcbFj3l1%2BN%2BbOnas2bdo4fvbmntu0aaOoqChFR0c7vubMmSPJu/teunSpevbsqY4dO2r06NE6evSoJO/s%2BbPPPnM6vtHR0YqKinKc497Yc50ycFtDhw41M2bMMCUlJebQoUOmb9%2B%2BZsWKFa4u64qWL19u%2Bvbta0aOHGnS09Od5vbu3WuioqLMjh07THl5uVm7dq2JiYkxx44dM8YYs3LlStOnTx%2Bzf/9%2Bc/LkSfOnP/3JDBo0yFRWVhpjjHn88cfNI488YoqKikx%2Bfr5JTk42c%2BbMMcYYU1ZWZu68806zePFiU1paanJyckz37t3Nli1brknf9957r5k6dao5deqUOXbsmElMTDTTpk27Yl11%2BZjUpTNnzpg77rjDLFmyxJw5c8YUFRWZ%2B%2B67zzz22GPmhx9%2BMB07djSZmZmmvLzcfPzxx6ZDhw7m66%2B/NsYY88EHH5iuXbuaPXv2mLKyMrNs2TLTo0cPU1paaowxZt68eSYxMdH861//MsXFxebxxx83Y8eOdezbHX439u7da7p3725at25tjDFe33Pr1q3NkSNHqox7c9%2BrVq0y99xzjzlw4IA5efKkmTNnjpkzZ45X93yxpUuXmieeeMKnerYKActNff3116Zdu3bGbrc7xv77v//b9OvXz4VVVc/rr79uSkpKzJQpU6oErNmzZ5vU1FSnseHDh5tly5YZY4wZOHCgef311x1zJ0%2BeNJGRkWb37t2msLDQtG3b1uzbt88xv3PnTtOxY0fz008/mU2bNpnbb7/dnDt3zjG/YMEC8%2BCDD9ZFm05OnDhhpk6dagoLCx1jb7zxhunbt%2B8V66rLx6Qu2e1289Zbb5mzZ886xl5//XXz29/%2B1rz66qtmyJAhTsunp6ebmTNnGmOMeeSRR8zcuXMdcxUVFaZHjx5mw4YN5uzZs6ZLly7m/fffd8zv37/ftGnTxuTn57vF70ZFRYUZPny4%2Bc///E9HwPL2ni8XsLy574SEhEv%2BgebNPV8oLy/PdO/e3eTl5flMz1biFqGbys3NVfPmzRUSEuIYa9%2B%2BvQ4dOqRTp065sLIru//%2B%2B9WoUaNLzuXm5ioyMtJpLDIyUtnZ2SovL9f%2B/fud5oOCgnTrrbcqOztb%2B/btU0BAgNMtmfbt2%2Bv06dM6ePCgcnNz1aZNGwUEBDht%2B8LL1HUlODhY8%2BbN04033ugYO3bsmMLDw69YV10%2BJnUpJCREw4cPl81mkyQdPHhQ77zzjvr373/Zni7Xs7%2B/v9q1a6fs7Gx9//33OnnypNq3b%2B%2BYv%2B2221S/fn3l5ua6xe/Gm2%2B%2BqcDAQA0aNMgx5u09S9KiRYvUu3dvde3aVTNnzlRpaanX9v3DDz/o6NGjOnHihAYMGOC4rXX8%2BHGv7fliL774ooYNG6Zf/epXPtOzlQhYbsputys4ONhp7PzJd%2BF9a09jt9udfomkn/sqLi7WiRMnZIy57LzdbldQUJD8/Pyc5iQ55i9%2BzBo3biy73X7N7%2BVnZ2dr1apVevTRR69YV10%2BJtdCXl6eoqKiNGDAAEVHRystLe2yPZ%2Bv6Zd6ttvtklRl/eDg4Mse52vZ848//qjFixfr6aefdhr35p4lqWPHjoqLi9PWrVuVkZGhPXv2aPbs2V7bd35%2BviRp8%2BbNeu2117R27Vrl5%2BdrxowZXtvzhY4ePaqtW7fq97//vSTvP7/rAgHLjRljXF1CnbhSX780X5PH5MLwcS188cUXeuihhzRp0iTFxcVddrkL67rWj4mVmjdvruzsbG3evFmHDx/Wk08%2BWa31PLXnefPmKTExUS1btrzqdT21Z0nKyMjQ8OHDVa9ePd12222aPHmyNmzYoLNnz15xXU/s%2B/x%2Bx4wZo6ZNm6pZs2YaP368tm3bdlXr12Te1cdaklavXq2%2BffuqSZMm1V7H03u2GgHLTYWFhTlS/3l2u11%2Bfn4KCwtzUVW1Fxoaesm%2BwsLC1LhxY/n7%2B19y/oYbblBYWJhOnTqliooKpzlJjvmL/9qx2%2B2O7V4L27Zt0yOPPKJp06bp/vvvl6Qr1lWXj8m14ufnp4iICE2YMEEbNmyQzWarUnNxcbHj3P2lns8vc/H8iRMnHD276ncjKytLu3fvVmpqapW5S/XkDT1fzs0336yKiopLnp/e0Pf52/0XXllp3ry5jDE6e/asV/Z8oS1btighIcHxs6%2Bd31YgYLmpqKgoHTt2TMePH3eMZWdnq2XLlmrYsKELK6udqKioKq%2BJys7OVkxMjAIDA9WqVSvl5uY65kpKSvT999%2BrQ4cOateunYwx%2Buabb5zWDQ4OVosWLRQVFaVvv/1W586dq7Lta%2BHLL7/UlClT9OKLL2rIkCGO8SvVVZePSV3KyspSv379nG6/ng%2ByHTp0qNJTTk6OU88X9lRRUaG9e/cqJiZGt9xyi0JCQpzm//GPf%2Binn35SVFSUS3831q1bp6KiIvXp00exsbFKTEyUJMXGxqp169Ze2bMk7d27V/Pnz3caO3DggOrVq6f4%2BHiv7LtZs2YKCgrSvn37HGN5eXm67rrrvLbn8/bt26e8vDz16NHDMRYdHe3VPdeJa/NaetTE8OHDzbRp08zJkyfN/v37TUJCglm1apWry6q2S72L8NtvvzXR0dFm%2B/btpry83GRmZppOnTqZgoICY8zP7xzp3bu34yMJZs6caYYNG%2BZYPz093YwZM8YUFRWZY8eOmWHDhpn58%2BcbY37%2B2IA%2BffqYl156yZw%2Bfdrs2bPHdO3a1Wzfvr3Oez179qzp37%2B/efPNN6vMXamuunxM6lJJSYmJi4sz8%2BfPN6dPnzZFRUXmoYceMqNGjTI//vij6dSpk3nrrbdMeXm52bFjh%2BnQoYPj3Y47d%2B40Xbp0Mbt37zanT582ixcvNvHx8aasrMwY8/O7LIcOHWr%2B9a9/mePHj5uxY8ea8ePHO/btqt8Nu91ujh075vjavXu3ad26tTl27JjJy8vzyp6NMSY/P9907NjRLFu2zJw5c8YcPHjQDBgwwMyZM8drj7UxxsydO9fcdddd5vDhw%2BbHH380ycnJZurUqV7dszHGrFmzxnTv3t1pzNt7rgsELDd27NgxM2bMGNOhQwcTFxdnXnrpJcdnH7mzqKgoExUVZdq2bWvatm3r%2BPm8LVu2mL59%2B5r27dub3/3ud%2BbTTz91zFVWVpoXX3zR3HHHHaZDhw7m4YcfdnwelDE//099woQJpmPHjqZbt25m9uzZ5syZM475b7/91owcOdJERUWZ3r17m9WrV1%2BTnj/77DPTunVrR68Xfh09evSKddXlY1KXvvnmG3PfffeZDh06mNtvv92kp6eb/Px8Y4wxn376qRk8eLBp37696du3b5W3u69evdrEx8ebqKgok5KSYr799lvH3JkzZ8ysWbNMt27dTKdOnczEiRNNSUmJY95dfjeOHDni%2BJgGY7y7508//dQkJyebjh07mu7du5t58%2BaZ8vJyx5w39n1hbR07djRTpkwxp06d8uqejTHmlVdeMQMHDqwy7s091wU/Y7zwlWUAAAAuxGuwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALPb/ADE958TOwaddAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-3436787962180432786”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>51199</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13095</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13025</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9013</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13107</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9009</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>54063</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>42089</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17197</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1071</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3200)</td> <td class=”number”>3200</td> <td class=”number”>99.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-3436787962180432786”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1001</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1003</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1005</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1007</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1009</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>72145</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>72147</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>72149</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>72151</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>72153</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div>

<div class=”row headerrow highlight”>

<h1>Correlations</h1>

</div> <div class=”row variablerow”> <img 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R73uXLlSuLj44mPj2fVqlV4eXkxdOhQbDZbmfWOHTvGrl27PO6nPPLy8njrrbf45ZdfqFGjBrfddhvvvPMO4DSj%2BM9//kNWVhZms5kLFy6wbNkyUTc%2BPp7x48eTkZFB/fr1mTJlCu%2B%2B%2By4HDhygUqVKjB07lvHjx9O4cWPeffddduzYwZtvvnndxi6RSCQSieTGkJqayrFjxzSf1NRUj%2Br%2B%2B9//JigoqNxyhYWFnDp1ikaNGolzFSpUoFatWsTHx5OQkIDZbCYqKkpcb9y4Mfn5%2BZw5c%2BaPT6oUpMAnueVISUnBbrdrzl25csXNZjo7O5vo6GhefPFFAgMDqVKlCr169WLfvn0AbNu2DavVynPPPUdAQACNGzemb9%2B%2BQsOTn5/Pww8/zOTJkw3HsWTJEoYNG0bVqlUJDQ1l9OjRbNu2TSNcLVq0iPnz52Oz2XjzzTd58skn%2BfDDDwkODsbb25uWLVsye/ZsatasKbSDx48fZ9CgQdx55520bduWSZMmibdAy5Yt46GHHhIauObNmzNmzBihWbPb7VitViwWCxaLheLiYjEWvRnkjh07xBeQzWbjjTfe4F//%2BhdNmzZl1qxZPP/88x7fk4oVK/Lyyy9z8uRJEUglJSWF5557jjZt2tCyZUvGjBlDVlYWc%2BfOZcaMGdxzzz0sXryYjh070qxZM15//XUxXlWLqlJUVERUVBS//vqrpt%2BdO3cKgbyoqIgTJ06we/duzpw5g91u56GHHgKgoKCA/Px80tLSyMvLIz8/H4CpU6ditVpJTU3lyJEjzJkzh06dOrFy5UqSkpJ47bXXuHbtGsXFxXzxxRcoivKH3sgZ/rHp0AGHA/HJy6PMY4cDrFY0JywW7XWsVnA43M65HuvriHIuB7m5BtdyczXHBQW6MpmZbm3q%2B8rLw318NpvhePT9l9UuDgcUFGjOqeNzreTWbmGh9tigjH6ebu3qbpZapsxGHA5sNjR1cnMN1iY9XXN85oy2DA4HHD6sbfvoUc317OwCfddcvapbu8JCTQGj%2B3n5svt90e/Ra9dwayc1tfS%2BHQ7Iztaup37eOByQk6O9XrL/XBtS95a%2BcddD/fNT8rho%2BnLdW/p%2B1PM2W%2Bn7XIzR5blT29XMwWCvqWthNBajvozW02i76cdrdO9cG1bXSl9H345bAYfD7VSpXzgOR6ntuD0/LmvhWkY/FtdzONz3NQ4HaWm6%2B6Qbh/4%2BORyIOq7rgM2m%2Be46dUpb5swZ9yknJGiPf6/jKPnAyZPaMmfPlt2GwwGZmdrjS5fcy%2BjbcT12XQvx/5uJolz3z/fff0/v3r01H/U33vXi2rVrOBwOQkJCNOdDQkLIzMwkKyuLChUqoCiK5hpQrlXYH0EKfJJbkvvuu09ol3bu3Mljjz2G1Wpl5cqVgFNA%2BPDDD9m9ezdxcXE89thjJCQkcPnyZUJCQoiKimL16tUUFBTQvHlzBg4cSFpaGo0aNWL//v20b9%2BeSpUq0a9fP9HnqlWrALh69SoZGRnk5OTQuHFj5s%2Bfz913302fPn0A%2BPrrrwH4/PPPmTt3LgcPHqRp06YkJyfz5ZdfMnbsWMApnM2cOZMnnniC1atXM3jwYLZt28aQIUNo164dQUFBPPnkkyxbtkyYKp44cYLk5GSOHj3KmjVrWLRoEZs3bxZCbHh4OG%2B//TYJCQn89NNPNG7cmLFjxxIZGQk4BaSuXbvStGlTJk6cKObWokULiouLuXbtGkOGDKFGjRq89NJLXL16VWgPt23bxn333cf9998PwOXLl%2BnXrx/NmjXj8ccfJysrCwBFUZg%2BfTpxcXFs376dyMhIpkyZQmpqKn379mX9%2BvUcOXKECxcuEB8fz%2BLFi2nbti2LFy%2BmefPmPPfccx772B09ehRwvk37%2BeefhfbN4XBgtVrJzc0FYPjw4ezZs4fWrVsDCIHv8OHDmpcHO3bsYOPGjVy5ckUI7jVr1gSgcuXK5ObmMmHCBI/GBjBv3jy3PzaL%2BvXX/E0KDKTMY0UBb280J3x8tNfx9gZFcTvneqyvI8q5HFSoYHCtQgXNsb%2B/rkxYmFub%2Br4CA3Efn9lc7t9rfR23eSsK%2BPtrzqnjc63k1q6fn/bYoIx%2Bnm7t6m6WWqbMRhQFsxlNnQoVDNamUiXNcd262jIoCjRtqm07OlpzPTjYX981FSvq1s7PT1PA6H5Wrep%2BX/R7NCQEt3YiIkrvW1EgOFi7nvp5oygQFKS9XrL/XBtS95a%2BcddD/fNT8rho%2BnLdW/p%2B1PNmc%2Bn7XIzR5blT29XMwWCvqWthNBajvozW02i76cdrdO9cG1bXSl9H345bAUVxO1XqF46ilNqO2/PjshauZfRjcT2H4r6vURQqV9bdJ9049PdJURB1XNcBs1nz3XX77doydeu6T7lhQ%2B3x73WUkg/Ur68tU6dO2W0oCoSFaY%2BrVXMvo2/H9dh1LcT/bzEeffRRli1bpvk8%2BuijN6Svsqy0/ooFl6dIgU9yy2Gz2Vi3bp0wV%2BzQoQObN2/GZDIJ9fi0adNYs2aNMC%2B8cOEC//73v/nqq6%2BEwLJnzx7UNy7Hjx/nlVdeITQ0lIKCAq5everWb2JiIuAUJlUtz8mTJ4WWCJxf4J999pkwjVQfcofDga%2BvLzabjXPnzgFOwXDevHlYLBbAKUiOGDECm83Gs88%2By5UrV1iwYAGVKlWiuLiYjIwMli1bRl5eHqNHjyYgIID69esTFRXFyZMnAUhLS%2BPNN9%2BkadOmxMXFkZiYyPTp07ly5QpFRUXs2LFDrIlrv67jVBRFRI%2BqWLEiKSkpAMyZMwe73S4EpHfffZeioiIcDgcHDhzgxRdfpHHjxhw6dIgFCxZQXFyMj48PGRkZjB8/niFDhrj5%2BI0ePZoJEyaQkJCAj48PxcXFHDt2jL1793q0F%2B644w78/PwwmUzcc889QtgGyM3NJS8vD3A6VHft2pVLly4BzjdyWVlZFBUV0aJFC3x9fTGZTKxZswaHw8GECRPEmzdVaExPT6dChQqcOnXKo7GVht1etsmrRCKRSCT/OEym6/6JiIigcePGmk9ERMR1HXZoaCgmk0m89FbJysqiYsWKhIeHk5ubq3F3UctWrFjxuo1DCnySWxLXtyUOh4NTp05hs9mEJuu7777D39%2BfhQsX8uuvv9KkSROys7Pp1asX0dHRom6rVq346aefqFKlCjt37nQzFTXCZrOJcna7HbPZzNy5czlw4ACBgYHY7Xb27dvH4MGDCQoKwtvbm2nTprm94Zk7dy6KojBnzhz27dvHE088gdVqJSMjg5iYGGw2G%2Bnp6SQlJeHv70%2BzZs24du0aoaGhVKhQQbTj7%2B/PtWvXxHFxcbEw6XQ167RYLFitVmbPns2hQ4cYNGiQqDNr1izx/9Js3BVFYcmSJUydOhVwCrsnT57EZrNRXFxMWloas2bNQlEUfHx8CAoKYv/%2B/dx7773k5OTg4%2BMj1svf35%2BQkBAcDgc7d%2B6kbt26PPfcc9SuXZuYmBiuXr3qUSAbHx8fCgsLyc7OJj8/Xwi%2BgMa01m6343A4hMBZWFgohL/Dhw9jtVqx2%2B1Cm3fx4kVMJpOoGxISgre3N9nZ2X8owM5DDz3EtGnTNJ9OnbppC338cdnH4G5qs2KF5jAnx71vN1fKjRvLb3fJErcyJfLu77gExAFK7OXKObV4sfsA9eto0I7bHD7/3K1MyfuI3/niC%2B1xiTa3rK6MbqnLI%2BVk5kz3QnpznNmzy%2BzHsE5ysnsZ/aIbmf3oB62rk5/vPil9Fbc5gtugDeegD85ktIC6ddcXMYxIrtfsGxQqeYfzOyUvrjSkp//hdt22yZo17u3u3l1%2B3/oFW7687OvgtOl0wfArpuRFocBgT%2BifFyOlglv3aWnaY4O1cXsOSwKYadCvhVHn%2Bryp%2Bn1utCHPn9cc6m8tAHPnao8Nnnn9HnBbYw/2hNvew309jW6v2zmDtdGvsSffUR58hbpNy%2Bi5k0Gr/xq%2Bvr7Ur1%2BfY8eOiXPZ2dlcuHCBJk2a0LBhQxwOB8ePHxfX4%2BPjCQ4Ovq6ppbyuW0sSyd8MVUNlsVgwm82AU/i7du0aVquVKlWqMHDgQNLT08WblcDAQNJK/sBFRkayZ88eOnbsSEREhBAIzGazYeCRdIO/NDVr1qRx48b07NlTU%2BfcuXN8/vnn5JT8En/ppZeEFlAV/NLS0oiKimLEiBFcuXKFevXqoSgKAQEBHDhwgKioKOrWrUuNGjXYu3evMF9UDOwuysrnovbn5eWFoij06tULk8lEYGAg4HzDNGzYMMAp3OTl5QlhKScnR6xXVFQUjzzyCMklP1CbN28OIL7EgoKCiIyMJDY2lhkzZpCamqpxYlY1hTabjYKCAgoLC8Wcdu/eLQK5qD6ArkJsaYFg1PoOhwOz2SwEO/hdMwfOPeLt7S3WSfVz1LetzmXfvn1ifTIzMzXC%2Bh/x4Vu1ahVf6ASQkSNH0axZg99PjBihraQ/BndTm549NYdGfuUlj8TvdO9efrslZsmuuLxbcNKhg/bY29utjtupvn3dB%2BjrW247bnMYPNitTJUquhNPPqk9Dggod3z6oUCJmaIrw4e7FwoL0x4PHVpmP4Z1qld3L6NfdH0dcB%2B0rk5AgPuk9FXc5ghugzacw223ld2wcwBlFvHzM2jXJXpuaYVKHsvf8fFxb6dSpT/crts2%2Bde/3Nu9667y%2B9YvWK9eZV8Hp02nC0bLSe3a2mODPaF/XoxM9Ny6r1xZe2ywNm7PYadO7g3r18Koc/2PW/0%2BN9qQugiJ%2BlsLwNNPa48Nnnn9HnBbYw/2hNvew309jW6v2zmDtdGvsSffUR58hbpNy%2Bi50z8eNw3T/46O6sqVKwwaNIi5c%2BdSo0YN%2BvfvL2IAREZGMn36dBo2bEhMTAyAiMg%2BZcoULBYLM2fOpE%2BfPnh5XT8x7X9n9SSSP4iPj4/QJFUv%2BdF07tw5IZglJiZSWFiI1WoluOQPakZGhjDXTEtLEyaJqlBz%2BPBhvA1/4fzu96X%2B%2BA8JCWHixIkcPHjQTXuXrXtjqygKt5X8SFIDyzgcDs6cOUNhYSEWi4UrV67gcDjIy8sTpohJSUlkZGTgcDg88mt7%2BOGHSUxMFJ%2BRI0fSpUsXIiMj8fHx0ZhuqgLR%2BfPnxXlFUWjdurUQdnbt2iXW69dff9WYdJ46dUqsn9VqFQLakCFDDDWEFy5cEFozdQyqxk2vsdWfu3DhQpnzDgoKIigoiNDQUHHOx%2BUHiI%2BPD1arlYCSv%2BA2m40aNWoAiLkCjCgRtvr27asxXVUURYzdZrN5LPTlG71p5mZ7xkskEolEIikL1W1o5cqVbNiwQRyD88Xv2bNnxYvjfv360atXLx5//HHat29PSkoKH7tY6/zf//0fQUFBxMXF8dBDD9GkSZNyk7n/UaSGT3LL43A4xI9zRVGEuaPVaqWwsBAvLy/xFqWoqEjYThcXF2MymTQmmhs2bBBmgiqqBkj/I/%2BRRx4ReftCQ0NF6H%2Br1cqlS5cI072BffPNNxkyZAgFBQXCx0814VQURWgpzWazSB1RXFzM2bNnNX0bOf%2BqZoZLly5l6dKlbtevXLlCQEAADoeD0NBQcnJyxLx%2B%2BuknYfaptq1eq1mzpjCTzM/P1whRetNGVSBVtWQmk0mYdxYUFLBy5Upq166tCUO8fft2wCl05efn4%2B3tTXBwMOnp6Zw%2BfZqUlBQCAwOZN2%2BeWB9waivffPNNAgICWLNmDdeuXRN9qajaVUVRsNvteHt7ExERQUpKCsHBwVSsWJHbb78dHx8ffvvtNwAaNWpE7dq1qVq1KlWrVgWcCVPVaGre3t40aNCg1JcCEolEIpFI/gR/Mw1ffHx8qdduu%2B02EdcBnL8zRo0axahRowzLBwUF8f7771/3Mbry91o9ieQGoWqrateuLX70W61WIbioWr9169aJty6FhYVMnz6dqKgoIfAMHTpUCIxTp04lOjpa%2BPzZbDbxdgdg1KhRQuuTkZEhUiKAU2s22MX87Oeff%2BauEnMgu91O9xLzOofDQc%2BePdmzZw%2B33347AFWrVhVCkd1up23btuzevZtKJbYs5UV7cjX5NJlMdO3alcjISJFPJisri8DAQFq0aCHGrpp0mkwm5s6dKzSUDRs2FG15eXlpfPjsdjsffvghmzdvFuujjk8VvFXzTYDg4GBNDjw1ASkgNJoOh0Pcq2rVqnHvvffSoUMHIiMj8TOwRYmPjxd%2BgWpqBnAKqqq2Tj0fEREhArGo923AgAEaAbRChQr85z//0fShvsHz9vbGarX%2BoeTyAQamRZ74iUokEolE8o/iBgRt%2BSfxz5qt5B%2BJoigEBweLf1XtmWqGV1xcLDQyHTtGft6SAAAgAElEQVR25N///jfg/AE/btw4fvvtNyEkxcbGCoFq4cKFBAYGCkHNZDJp3vj4%2BPiIIB96jh49yvjx48Vxq1atiI2NBZyCkxqFUlEUli9fzp133inOJSUl8dhjj4k%2BrFYrnTt3FvNq0aIF7dq1o2nTpsTGxhIVFUWTJk1EX64Cod1uZ%2BvWrbRt21bjE5ednc2BAwcAZ/TNjz76CHAKbUVFRRoN1sGDBwFITk6mVatWvPDCC4AzWMy4ceO45557RF9t27aluLgYh8PhZtZ66tQpTWJ6Ndm567hdNZlXrlyhf//%2B2O12MjMzOXDgAG3atGHdunVUqVKFadOmiaicap%2Bu/oquWlqr1crp06eFeaqq8f3444%2BFQAewceNGOnXqRHJyMkeOHNGMXx1bnpHnfincsKAtukAQ1y1oi0FwlesStGXRIvcB/pmgLfrgDPwPBm0peY4FNzFoi%2B4RdaJb9L990BajvssL2mJgHi%2BDtpQgg7YI9Hvt7xa0xZO%2BZdCWfw7SpFNyy6MoClWrVqWwsJCwsDDNj/6goCCKi4sJDAwUURlVzGYzkZGRJCcnoygKFosFk8kkNElqkJPWrVvz888/C5NCRVFEH/qkmSaTCbvdjqIo3HPPPaxcuRK73U6FChWEn6Cvi6e1oihUrFiR/Px8QkNDhfZodskPR5vNxogRI2jQoAFdu3YlPT2dU6dOCS2R1Wrlhx9%2BEMKNahrqmnBdbUcfMlhF1ZCpGjpFUfD29qaoqIgNGzawu%2BRHjmrSGBQUxNWrVykoKGDatGlkZmbStyQoh5H2UV0vRVF4%2BumnWb9%2BvThflsOyw%2BFg%2BfLlmjY//vhj5s2bJzSDrvfCFWPfOa22Ti1XmsbNNcWDURuecMOCtugCQVy3oC0GwVWuS9CWRx5xH%2BCfCdqiD87A/2DQlvBw7fFNDNqiixXiRLfof/ugLUZ9lxe0xSBKhQzaUoIM2iLQ77W/W9AWT/qWQVv%2BOcjVk9w0YmNjady4sXB0bdmyJQMGDGDPnj2iTHZ2NlOmTCEuLo4mTZrQoUMHnn/%2BeU6cOCHKjB8/3s25tVq1aqxYsQKLxcK3335L586dyc3NpXPnzsJ00c/Pj2XLlrFt2zZhvugqoJlMJr755ht27twpgrqEhIQI368BAwawY8cOITypgppqDtiuXTuhdbv33nv59ttvadmyJQDNmjWjTp06QhhRw/6DUzOmYrfbuXr1KhaLRSOQqn5wYWFhNGnShPXr14u%2BkpKSCAsL49VXX2XOnDncdtttQuBzOBwaYc9kMhEbG8uCBQvEuF9%2B%2BWWNnfmIESNEwnJwCrqqiexLL70kBMF%2B/fpx4MAB3n77bdFXjx49GDBgAOAUvjZt2iQEuWDdjxiHw8HIkSMBp7AdFhZGlZJf6/7%2B/iiKwuHDh4Xw%2BMEHH%2BDr64vVauXXX38lKiqKjz76CJPJxPPPP8/u3buZMGGCxoQ1PDycFi1a0KFDB3Hez8%2BPtWvXsnjxYiHoVS75kRMZGSmC6YDTRDQ%2BPp7q1auXGhkUnEnnPUEGbZFIJBKJRHKjkQKf5KYyceJE8SN6586d3H333TzzzDNcvHiR3Nxc%2Bvfvz8mTJ5kzZw6HDx9m8eLFhIeH8%2Bijj2ocYvVcunSJHj16AE5BZPv27cyfP5%2BqVasKH76ioiJ69%2B5Nly5dhLlkiMsbRIfDwWOPPUaHDh2E%2BaHdbhcC35w5c%2BjYsSP79%2B8XdRISEoQgsGrVKpE0c8OGDQwYMED0c/DgQXr16qUJxKIKIOnp6RQWForjgIAAfHx8RNk2bdqIa97e3mzYsIG33npLCIzr169n7NixvPvuu5w%2BfZo5c%2BZQt25dtzXy8vLCbrezbds2EhIS6NWrF4qiMGXKFE30KF9fX03yz969e1O75G2ya46Y0NBQvL29SUhIAJw%2Bebt372Z4iebD4XDw5ptv4u3tjd1udzPpBKcACYjcfaoAVlBQgMPhoGnTpiwuMSv09/enTZs24r6ovPfee9x77734%2Bvry/vvvazR8GRkZHDp0SHPPiouLeeyxxxg4cKA4d/vtt5Ofn09SUpJIMwG/R%2BVy9eurUaOGCCYDTjNbTxO3Sh8%2BiUQikUg8QPrw/SX%2BWbOV/K3x9/fnqaeeIiIigh07djB37lxyc3P55JNPRA66qlWr8sYbb9C/f3/DvHcq0dHRrFy5EoCVK1eyYMECmjZtCqBJNH716lWuXbsm8solJCQIgc7HxwebzaYJs3///fcLgc5ut1NQUCBMGnNzc%2Bnpkv%2Bsa9euwuxTNR9Vc9OBM%2BCJqkkqKCgQQlyFChVITU3VpChwDfhy4sQJIXCFh4eza9cujeA1YsQIxo4dC8AXX3zBr7/%2BSqtWrYDfA7YoiqKZx5w5c0hNTaVixYqapO3gDE6jBm0B%2BP7770WAmK5du4rzn3zyCY0aNRL%2BfvXq1cPX15f27duLMmvXrsVisWC32zGZTJjNZqH1NJlMQnhSCdebt5WgKApvvPEGW7duBRDj8fb2Ztu2bXTt2pWYmBhDfzq73c6FCxeEIFhcXExWVhYWi0VoQhs2bEh%2Bfr7QBuvJcXGKS0pKori4WOwrs9msiRhaFkY%2BfF266Hz4vvuu7GMMXD50CciN/DLcFJRffll%2BmU8/dSvj5juydq322MABxM2XxGBOHmUN1g9Q75%2BHgQvcjz/%2B4fEZ%2BcfoXZsM/RD131E6fy1D/zd9Hb1vFrg7Cxn58OlNi3WOnEY%2BfPp3MG5zxH2vGSqpXV6IAMYTLcdnytBPzQNHPzefUqO%2B9Y6d%2BrUyqOPmB6v31wNwSZxcat/6cz/8UPZYwG1Shv6N%2Br4N/N30XXvkw1eSJkjggc8rJflPNejunWFsMf3Dqp%2BDkVOpLiVPyZ9yLQsXlt9OOT6l7hvL/bEzavbP%2BPAZrY3%2Bq86T5%2BV6JV43vlk3ASnw/SX%2BWbOV/E9gs9kwm81s2rSJvn37akLpq7z00ksaQcIVs9lMLZ1dvysVK1YkMDBQo/lRBSGLxSIExQYNGpCSkiJ82MAZuVMVSNSUDaYyvjQCAgIIDw/Hy8uL2rVr8/jjj4trnTt35vbbb3fzM2vfvj01a9YUEUXVVA4qWVlZIsBLYGAgdevW1Zi4qtowLy8vcnJyGDVqlCbJOKAJYAJO09n169dTq1YtvL29DZO3u6IKnBs2bND42bmaOaq%2Bjsv1QQlKUHP2PfjggwBijdV0FSaTierVqxuur8PhIDExUWgJVcHQarWya9cuEaSlNIzMMV3XpEmTJpw8eZLs7GzDdly1fmo91TwzxDBbtTGrVq1i3Lhxms%2BOHTqBpF%2B/so8xcPnQJSA38stwk0kHDSq/zLPPupVx8x154AHtsYEDiJsvicGcPMoarB%2Bg3j8PAxe4uLg/PD4j/xi9a5OhH6LeoUjnr2Xo/6avo/fNAndnISMfPv33pu7lhZEPn95nz22OuO81I3co9BYFRhMtx2fK0E/NA0c/N59So771jp36tTKo4/buR%2B%2BvB9CggfbYE4etbroXPEZ%2Bf7pJGfo36vs2%2BB7Sd%2B2RD19JPlKBBz6vlESu1qC7d4Z/YvQPq34ORk6lusBoJe7bWvr3L7%2BdcnxK3TeW%2B2Nn1Oyf8eEzWhv9V50nz8v1SrxufLMk/2tIgU/ytyEvL4/PP/%2BcjIwMOnfuzMWLFzWaq7JwTXpps9nYsGGDMOnUYzKZ%2BFeJw31cXBwHDx6kb9%2B%2BmEwm7rrrLiEMqCH927RpQ8OGDencuTN2u134%2B7Vp04YqVaowfPhwQkNDRSTMuLg47r//fuLj42ndujW1atXil19%2B4d133%2BXVV18VAmOfPn2oXr06YWFhKIqCoij4%2B/tz7do1oqKiaNiwITabjZycHLc8e5%2BXaHD27t3LggULNFq5sLAwRo8ezYwZMwgODqa4uJgVK1Zo1sBsNtOzZ08iIiIwm81kZWVRXFzM/v37ycvLE0KOt7c3K1as4PXXXxd1vb29%2Bemnn/D39%2Bfs2bNiPv369dOkRti1axdTp05l9erV%2BPr60rlzZ7p06YK3t7cQ4h0OB9u2bQPgscce46mnniI3N5datWrxww8/EBoayv3336/pWxVGXYPIuPolZmZm0qVLFyZPnoy/vz/BwcFCmNT7Daptmc1mjf9eTEwMe/bsITAwUNQNDAwU5sdqgB1VS1m/fn1h9uqpOSdIHz6JRCKRSDxCavj%2BEv%2Bs2Ur%2BdkyaNEkIal26dNH42rmaHZbHvffeqwmo8dprrwlNnRHjx49HURR27txJ165dSUtLo3LlyrRv315olIqKinA4HBw4cAB/f38aNmxIVFQU8%2BbNIzo6mkOHDpGUlMTly5c1ppquGqFnn30Wm81G27ZtGT9%2BPMHBwdxxxx0A7NixgwcffJCcnByhJXI4HBw%2BfJgHHniArKws/P39NUKUXtvlcDiYOnWqRuC5/fbbmTVrFoMHD%2Ba2227jxRdfFBow18TpK1euJDU1FbvdzqRJk5gwYQL%2B/v74%2BvoKQahhw4acP3%2Be999/X2hNrVYraWlpIn%2BeOr6VK1fSsGFDzVps3bqVvLw8kcS%2BRo0aNGjQQKOB3blzJyaTic8//5zs7GzatWtHWloa3bp1w2az8dprr4myxcXFVK5cmS%2B//JLExESRX2%2B3i3lV27ZtRXoMq9VKXl6e2Ed6v8EuXbrQoEEDQkNDRRlVcL948SL5%2BfnifF5entirO0tSD9x3333cddddnDt3TqR0KM0MVSKRSCQSieRmINMySG4qEydOpL/e3KKEWrVqcerUqT/c5pYtWwA4ffp0qWUCAgIwmUzcfffdvP/%2B%2B4Dzx//PP/8shKKioiK6d%2B/Ohx9%2BCED37t1JSkoSAo2ax6%2BoqIjZs2fzySefcOTIEXr16kW3EjOd6tWrs8jFt6dRo0Y0bdqUY8eOERkZyZ133kmPHj1YunQpiqIQHR1NQkICa9euxWQyERERQUBAAGfOnEFRFO644w7sdjsnTpzA19eXoqIihg0bJjSAJpMJi8UiIkvu3LmTTZs2UVxcTKVKlcjLy6OgoACTycR9993HunXrcDgcvP7668IM0tvbW6zB0aNHGTNmDHa7XZiFmkwm6tWrR5s2bfj6669FZNOCggJOnz4t/NvUICvp6elkZ2ezdetWfH19qVq1Kv379%2Bebb77hwoUL7Nixg/DwcCZMmMDSpUuZqctnFhoaKsxeGzRoQPPmzXn55ZdJT0/HZrNRpUoVoW0E%2BOGHHzTmmXPmzOGZZ57BZrPh7%2B8vBFVwJrz39fVl27ZtxMXFkZWVJXw9mzRpQrdu3Xj%2B%2Beex2%2B20atVKpGJo164d4PRJ1BNpaFNU%2Bj7UI4O2SCQSiUSi4x%2BmkbveyNWT3HBKS79Q5OJRbJR%2BwW63s3DhQiFouKZfGDduHPPnzwecATTWrVsnctR5Mp6FJU7c69evF%2BO6fPky%2B/bt0/gMbty4kfXr17Nnzx6SkpL4/vvvWbFiBStWrOCjjz4iJiaGVatW0aRJEzGeL3XBL86cOcPo0aNp1KgRNpuNnTt3EhkZyZw5c0QZh8OBzWZj3759ItCI3W4nJSVFRIRs3Lgxx48f5%2BTJk4BTIFUUhfz8fCwWC15eXvj6%2BrJr1y6OHj1KVlYWOTk52O12goKC6NOnD1arVZQ7cOCAEIrCwsKoWrUqzZs354033hDmjaqfnSsOh4MKFSqwcOFCt%2BAkqs%2BboiiEhISwZcsWateuLTSGRUVFnDt3jsmTJ3Px4kXMZjPh4eEsWLCANWvW4HA4DNNsqONMSEjgu%2B%2B%2BIycnh2rVquHr64vZbOZCieN%2BjRo1RF%2BvvvoqxcXFjBo1SmjpCgoKCAgI0Phs5ubm0qVLF7HPHnroIcD5wmDkyJFi/nv37iUmJoannnqq1Dx%2BgIgc6glGQVtiY3U%2BPTpzXLdEzRj41OuTsxtoyj0JyOJW5ptv3Mq4BQvQj9cgkoEn7bo1bKTt10ch%2BOortyJuQRw8SI7tSdAWt5hRRom49VEddAE6DAOT6OsYvfTSR1YwCCjhNi9doBejoC36JdbngAeDvWYUCUIftMMt4on7ePTtGhp36OdksIBuj6YHicw9iazhNgWjwCS6ACIeRUVxSUNUWt8eJV7XJy03uHn6po3W2O2cB4nXPQraoitk6GKtD6ajD05ktI90gV6M4hdRkttVYJRJXLfGbnMy%2BB7TlzF6DP/uQVs8CpYkg7bcEiiOsiIbSCTXgdjYWJ5%2B%2BmmhySsoKGDhwoVMnTqVkSNHMmjQIB599FGqVq3KK6%2B8Qt26dUlJSeGTTz5h8eLF1K5dm/fee48FCxaQnZ1N5cqV2bJlCwsXLqRGjRoMHz6czZs38%2BOPP2pypp0%2BfZr777%2BfyMhIrl69KswhrVYrNWrUEAKil5cXDodDCEOKogiNWXh4OI0bNyYtLY0TJ05oygIiIXnPnj0JDw9n3rx5KIpC9%2B7deeWVV7h69SqPPfYYISEheHl5kZSURIcOHcjKyuLYsWN0794dk8nEunXrCAwMNIwqqaImbYffE4q7njObzbz11lu8/vrr4pyfnx9%2Bfn4iqbqa/mH27NksWLCAHTt2lHnv1NQNRlqnsLAwXnjhBS5fvsw333zDNV1ENTVB%2B/PPP09WVhZffPEFNptNY/Lq6%2BvLkSNHGDp0KGfOnOH8%2BfM0a9aMatWqMWPGDFEuKipK/F8VMlVtpLe3NzabjYKCAurXr88dd9zB2rVrNYFnFEURcwgICHAT2Pz8/LDb7VgsFtatW0e9evWYNGkSX3/9tWa8Pj4%2BtGrVigMHDlBQUIDZbMZut2vKJCQklBnIx5V3333XMPH6iBEGSbwlEolEIvmnYhRF6q9iFIr4FuWfJd5K/hao6RdMJhOnTp0qNf3C22%2B/zcCBA6lfvz4jR45k1apVIgLj4sWLqaGPHlYKaWlpbgJLdnY2drudzp07s3v3bm677TbMZjPFxcWaACkZGRns3LmTkydPih/xrkKXGoJ/3759wpSvc%2BfO5Ofn06tXLx5%2B%2BGEURaFPnz4iNUJISAhffvklcXFx5Ofns3nzZtGuj48Pd955p6HA4DoHVcAwmUwEBAQIwdNV2KtevTr%2B/v6YTCZMJhM%2BPj7Y7XaKi4t55plnhKYQnIKMl5cXJpNJE3Xz2LFjIjdeoC4yYGZmJpMnT2bkyJFCu2k2m8XYHQ4HFouFadOm8eOPP/L6669z11138dhjj3Hbbbfh5eUl0hns3btXJFk3SrehCm%2BtW7fGz8%2BPWrVq4e/vL0xNVTPNkydPsr7kba6fnx916tQhPDxcs3b5%2BfmYTCZNyoWhQ4cSGhoKOH33AGrWrGkYobNatWpijfQCrOtYPUEGbZFIJBKJxAOkhu8v8c%2BareRvRdWqVWnTpk2Z6RcmTpzIRx99xJYtW%2BjRowexsbG88847VK1aVZR54YUXDNuvV68eiYmJKIrCfffdJ4K6VKlShdatWwPOHGozZ87k4sWLtG3blnXr1nH8%2BHHatWsncruZzWYeeeQRjh49yqZNm2jYsCHwe6CY3377jR9dcntNmzaNuXPnChPFTz/9lJEjR2rMHytUqMDMmTOZO3euMCH09fXFYrGwb98%2BNyFCjfypUrlyZcLCwvDx8WHr1q0iabirsJGcnIzNZiM7O5uYmBh27drFv//9b1Hu1VdfFWXVROeuvm9qW6o/Yl5eHkFBQcL3UdWUvffee0RERODr64vNZhPClWsAnMDAQJo2bcqBAwd45JFHuHLlCjabjU6dOvHOO%2B%2BQm5vLr7/%2BKu6Ja9TViRMninHu2bMHk8nE888/z%2B7duzl06BDz588X9wqcQVvAqUk%2Be/asxmdPxeFwkJOTI1IoqKkhXKlXr56IYgpOH874%2BHgmTZokAtX4%2B/sTExOj2buuAXTKQ/rwSSQSiUQiudFIgU/yX%2Bd6pV9QP6WlXygNk8kkgm7k5uaybNky/Pz8mD17NvXq1SM9PZ09e/Ywe/ZsYYr5/fffExMTQ58%2BfTh//jwAffv2LbMfVVM0btw44TNYFqrZJeAm8HXv3l1zPT09HbvdTn5%2BPtu3b2fMmDH4%2B/vjcDg0gmV2djY2m4177rmHoKAgxowZI6Kfjhw5UpRThYwnnniCSZMm4evrK4KkqDn/AOET%2BOuvvwofzK%2B//pr27dtrfDJNJhOXLl0S7V6%2BfJlBgwZRVFREv379hCawdu3aLFq0SKSlcMVisRhGaX3vvfe499578fX1xcvLi7Fjx4rImIqisGvXLs065ufniwA7qqDmur7BwcEaU%2BDXXnuNjz/%2BmA8//JCrV6%2BKMWzcuJGYmBj27t0r8hAWFRURHx%2Bv0QqXZybripEPXzddnji924qRG4ubb4Z%2B3fQ%2BfQZFjPw0PPFlY9Wqsts9dsy9ji4xt2ECcg%2BcXzxx8eHy5TLLGCpZ9X0ZZSN20ZCX1rfejcqtjAd%2BQYZrrm/IA8cg/X0x8uFzG4%2BBH5jbePSJucHdkcpo/XT7TT8%2Boynpt6iRz5TbHAx8NN2W/U84Whkl2daf88SvypOlchuOwUssT7aEJ26x%2BpP68RnNyRNfNv2zauRMpJ%2BWvh0j1zv98%2BuJy6aRn5/bWuhjAhjdGH1DRnEE9IP2ZBJGZfQLqC9jdGP0ieuNvuz0i2x080oC4d10pIbvLyGjdEr%2BK0yaNIl33nkHcJraNWzY8E%2BnX3D17YLfffXKQv2xDk5B4q233gIgJSUFh8NBp06dhJZm%2BfLl3HHHHcTExLB582Y%2B%2BOADvv32W8Cp7YuIiCAnJ8ctGqOq%2BfMtyXaqCjCpqans3bsXf4MEzwDNmzdnyZIlZY5/woQJVHdJSms2myks%2BQP06quv8tlnn1G7dm0R1dR1PRVFYfr06SQlJXHq1CkRVAV%2BF3zUf/X%2BZKW5%2BNatW5djJT/kjcxPzWYzdevWJS0tjdzcXDp06MDmzZsxmUwoikJYWBjVqlUTOQZHjBhBSkoKS5Ys4bbbbqNJkyasX78eu93OypUr3VJdeHl50bNnT0aPHk16erqIFGoymdz2kslkokKFCuTl5REaGkqKS2CAa9euoSgKMTExWEp%2BGKamprJ06VLq1KlT6r7s1KkTe/fuFQKt6/jWr19PnD65dymsWrXKbc1HjRxJg8aNxbE%2B4bNbAmgMEurqs/SOGOFWxy2puoEpqicJyCnRUJfarstcBLrE3IYJyD3IWKzPP260NrhYAxiVMUwcru/LKBtx/frl9q3P0OFWxiBTs0drrm/Ig2zO%2BvtilHjdbTwGKUbcxmNkWq/PSG20frr9ph%2Bf0ZT0W9QgF7b7HAwsR9yW/U9kxzZKsq0/50kybE%2BWym04Bn9LPNkS%2Br7dnlWDk/rxGc1J35fRfdE/q0aW7/pp6dsx%2BhOqf36N5q2/L/o5gcFauLwEBIxvjL4hfR1wH7QnkzAqo19AfRmjG6NPXG/0ZadfZKOb5/LS96byDxPQrjdy9STXHX1UzkuXLlGtWjU%2B//xz4uPj2bt3Lx9//DEbNmwgLi4Oi8XCG2%2B8wfPPP8%2BJEydEO0bRGsEp4EVFRXkcldNms7n9cA8NDcXPz4%2BtW7cCzuiO77//PlFRUSxYsIATJ07QvHlzOnfuzPLly1EUhY4dO3LnnXdy6dIlAB5//HEmTZokNG9t2rQhMTGR5ORkXnzxRZ599lnAqT07duyYECjWr19PVFSU%2BEyYMEEztipVqvDDDz%2BwceNGES3TarUKjaHZbKZNmzaaOZ09e5aEhASsVis%2BPj4iYIqrWeV3330nhN677rqL48ePl7t2wcHBtG/fno4dO2o0cPHx8cJ0ceTIkQQGBtKkSRMhNFutVrZs2SKC0Nxzzz3UrVuX2rVr89prr/H000%2Bzb98%2BkRevY8eOou2kpCSRLgKcJpp9%2BvQBnMJb3759qVixIqtWrRLaXbVfu92Ol5eXRstpt9uFptNIgJ05cybx8fGahOkLFy5k7969hmvSqlUrkQJCxXVtLugj9ZWBkQ%2Bf9OCTSCQSiURyPZECn%2BSGMHHiRI3PXIMGDXjmmWe4ePEiubm59O/fn5MnTzJnzhyGDx%2BOt7c3QUFBPProoyQmJop2VB%2Bt64maMmDBggVCw3jp0iWWL19O27ZtSU9P16RfWLFiBcOHD2fLli2Eh4fz9NNPA86gKImJifTv319o2w4ePMgDDzyAoiisWbNGmAqeO3eOVSWmb67CQdeuXUWib9UPLTU1lZ49e/Lggw8KU8Fq1arx5JNPEhgYiM1mIy8vj9WrVwv/OlXTVKtWLYYMGYLJZMJqtRIaGsrq1avp2rUrAAcOHAAQAUpUrWOlSpVITEwkMTGRyZMniwAuqklkUlISQ4YMISAggLp16wpBFJxRKQGOHz9OTEwMlStXxsfHR6P5%2B%2Byzz0hISCC45FXrI488gqIoHDlyBICBAwey3CDdAMCbb74p/m%2B327l8%2BTILFizgyJEjfPbZZwBi/U0mE/7%2B/mK%2BgAiw4u/vL4Ru9ZyiKLRv3x5wJqwHp/BfpUoVfv31VyIjI4UgGBYWJhK6qwJljRo1OHr0KNOnTxf3VfrgSSQSiURynZEmnX8JadIpueGoPnOJiYns2LGD1NRUEZXTx8eHwYMHs3HjRo4ePUq3bt1IS0sjJCSEgwcPcvXqVY/N41RiY2O5cuWKRuDw9/fn008/pXXr1sTGxvLoo4%2ByYcMGXnjhBRwOh0gKrrJu3Tq%2B%2BuorocVSf8R/45Iv7PTp03Ts2JHw8HBSUlIoKChgyJAhBAcHM3nyZFavXm0YcdJVM6dqGMEZEVTtS6%2BVTE9PZ/HixUJjduLECfr06UNhYSE2m4169epx%2BvRprl27JhLFg9OfbOvWrRQWFuLr6yuEynXr1rFu3TpN%2B1FRUfj4%2BGj6zsnJ4eDBgyiKwrx587DZbCQlJWG32wkPDycjI0MIsgD79%2B/Hy8sLm82Gr68vERERJCcnc/ToUW6//XYhKHp5eVGnTh2RY9Bq6MSFyMt31CWv086dO4Wgq3LnnXcKE8vc3FzGjRsnop%2BqCdcLCwuFwFI6mgMAACAASURBVKneVzVtBTijuYLTzDc/P5/WrVtrgtBkZmYSExNDfHw8jz/%2BOIsXL%2BbixYtER0cDTi2jxWLRBBQqDxm0RSKRSCQSyY3mnyXeSm4qNpsNs9nsFpUzICCAb7/9ljZt2rB3716GDRvGo48%2BisPhoF27dh6nX1BJSUlx%2B9FstVoZPHgwFy9exG63M2XKFObNm6cxC7VYLPzyyy/UqFGDgwcPUlxcrMmx5przDmD%2B/PmYTCb27NlDjx49GDlyJLm5ubz33nt4e3uzf/9%2BioqKMJlMVK1aVcy3du3aBAUFMWHCBIYOHSrac23barVqzA8tFgvBwcFCs5Sfn09eXp4QYNTz2dnZIoAJOAW2/Px87rzzTtauXcvVq1dLXTeTyUR8fDxvv/222zU1MTzArFmzWLZsWan%2BbcXFxURGRrJq1SoRjdNqtZKYmMjevXuZOHEiMTExnDt3TtO3UZTWPn368NVXX2kibTocDry8vITpKiDML9Uoo0888QQtW7YUdYqKinA4HG77orCwUGgH1X/VMpMnTzYUvrp3787p06fdzqvaw3ADv6fS8CRoi36ZjZa9vKTVhrdKF8jlz7QLlBscxCg6gyft/pmExYbj%2BxPBQTxaQF0wEMO%2BdYEW3MoYda4rZJy5QzsnwzmUs8ieJF43atiTe%2BcWc%2BJPZJs2dB/24L7o63mSxNqTOvpzRjE8PEl0re9b344nfRsVKm9ORuP5M8%2B84X0p5xkzOufJ2njyHeDJ%2BDx5nMtrx5O1MgoQ9Gf6/jNlblS7gEy8fovwz5qt5KawevVq8vPzy4zKGRwczCuvvMKWLVs4cuQI27dvp0WLFmzfvl34Aq5du5bNmze7ReVU0y%2B4Rlp0TcMAULFiRaxWKytXruS%2B%2B%2B7Dy8uL9u3bs27dOn744QfgdzO/ixcvcujQIWrWrEnFihXp2bMnAGvXruX48eMiL12dOnX44IMP2L17N4cPH2b69OmANkk4OM0Gs7KyaFwSvOLSpUvk5OTw//7f/2P27NmGa9a2bVsOHz5Mo0aNxDm73S60cCqZmZn4%2BvpqfB%2BbNWumCUIDznQGvXv3FoKKa949ffuu6RrUe%2BPK4MGDeeihh0SidbOB539KSgoPPvig0GDNmTOHhIQEWrVqxaRJk4iPjxemoOD0mWvTpo1bO0uWLCEmJkYIv3Xr1sXX15dq1aqJJPLgnvsuzSWZqsPh4J577gG0voLqtZYtW9KiRQvhIxkdHU1AQACLFi1yGw9Ajx49yvR/zDEMFWnMqlWrGDdunOazees2TRn98hoFWtAHQPCkjj6Qy59pFyg3OIhRdAZP2v0TcTSMx/cngoN4tIC6FxSGfesCLbiVMepcV8gwqIxuToZzKGeRjYK2eDI%2BT%2B6dW8wJT26ermHDdJYe3Bd9PaN29NU8qaM/ZxTDQx83w4Pb69aOJ30bFSpvTkbj%2BTPPvOF9KecZMzrnydp48h3gyfg8eZzLa8eTtTIKEPRn%2Bv4zZW5Uu0ApN/0mIAW%2Bv4Q06ZTcEG52VE743bQTnH5xDoeDr7/%2BGqvVKtIwFBYW8txzz%2BHt7Y3dbtf4YdWoUYP69esLH8IHH3yQ4uJiYZbYvn173n77bSEQlubDZbfbKSgooFatWhw8eFCc12sMXfnll18oKirSaIsuX75MamoqxcXFwmxSTW4eEhLCtWvXCAgIEDkBK1asSHZ2tsivl5OTQ%2BXKlcnJycHhcIh2yjMhjIyMxGw2k5WVJQQvPz8/ioqKhFCpRsJUg6OAU0tZp04dfHx8WLx4MSNGjMBqtbJ//37eeOMNTR8DBgwQ%2BfPK4syZM/Tu3ZuzZ89qzGVdtaHqvXRda1dtoh6bzSa0e4CbuagRauAeVxRFweFwCLNbT5CJ1yUSiUQikdxo/lnireS/hmvQlr1797JgwQKaNm0KOAOLqOkDrjd33nknNpuNdevWkZyc7JZ2oH79%2BuTm5tK8eXMsFgv9%2B/fn2LFj2O12fHx88PX1FRors9nMyy%2B/LPLVDRs2DHD6qSUmJhIfHy%2BEPYCaNWsCcFKXn0ulWbNmol716tVFLjc1Cbpr2ga73c6SJUs4deqU0Dz6%2BPgQERFBdHQ0tWvXJiYmRgifNpsNLy8vcnNz8fPzIzg4mMzMTObPn68ppwqlqs%2Bca5Jws9lM3759OXr0qEbzd/HiRbp06cIDDzwgylWqVImKFStSVFREUVERVquViIgIxo4dKzStZrOZqVOnMnnyZPbu3cv27ds1Gr74%2BHgxHpvNJvLneXl5aTR2%2Bsiay5Yt4%2BDBg6UIS845dujQQSPIqoGAVLNL9R6rQprrC4jp06cTExPDgAEDaNWqldBw1qpVi/j4eIYNGya0eGazmbCwMEJDQ0XaDH26jrKQPnwSiUQikXiA1PD9Jf5Zs5X8LejevTuLFi0i1yDB57hx4/5SVM59%2B/ZpTP1U1DQMU6dOBZz%2BWnPnziUjIwOLxcKiRYtYuXIlq1atYt26dXTq1Imff/6ZnJwc8aN806ZNbv0VFBTQu3dv3nrrLTp16oSiKAwZMoSoqCiWLFkifMrAGW1STVeRnJwsApbUqlWLX375hcDAQCFwKorChx9%2BKBKsq2NOTU3l2LFjnDt3jvT0dKpWrUpcXBwFBQWacnl5edjtdsaMGUN6erpIbJ6amlrm%2Bp09e5b%2B/ftr1q%2BwsJDly5ezZs0aAO6%2B%2B26R186VxMREpkyZQnJyMv7%2B/rzzzjv4%2BPjQvn17WrduLXLXTZw4kejoaBo1aqQR5lx9JdX%2Bvb29hcZSvaZiZEoKTiFWjUYKWpPUjIwMzGazEPD0%2B8Tf35%2BAgADi4%2BOpVKkS%2B/fvF6kjzp8/T0xMDE899ZQYq81mIzMzk6ysLOEP6omGUMXIhy8uTld/48ayjzFwsfj003IKGPhqeJKcvSQfpStufjX6nJIGJq5u7eqStxs2bORHoveZWbjQrYjblt%2B/v%2Bw2DLo28tdyi8dk8P3gVmj79nLbdUvmbGQ%2B7J5F3b2Mfl6659Uw8bpujUsMJMoqYuxY9dtv2mOjRNL6yXvi5OWBU5fbUhi1U142cU%2BStZfkIdWgTxXkyRz27Su/ju7eGeXYRm/FYPDceeLD59a9S95SwHDTurXj4mJQWsOGbmHJydpj/RyMzOV1dYzyhuPyNwQw3o%2B6PeG2DgYN6%2BdgZNxxvXyR9WX0e8BoT3jiU%2BpJO8aOfZL/NaTAJ/mv89RTT1GpUiUGDhzIsWPHcDgcpKSk8Prrr7N79%2B4/HJWzPPRpGMAZ3GTTpk1UrlyZTp06ER0dTa1atcRn1qxZhIaGCiEHEAKaGhQmISGBIUOG4OfnJ9o1m82lJitXx6Ln3LlzbNmyhfT0dKxWqzDVzM/PF4Fj1LrqcUBAAL179%2Ba%2B%2B%2B5j8%2BbNGsHS1WTWz8%2BP3r17lyrguGKz2di3bx85OTn4%2B/uXKlD9%2BOOPWCwWjTbUFYfDgY%2BPDw899JDQdo0fP14TiKa4uLhUs95q1apRr149Uc5VYzZs2DDhQxn7/9n77vAoqv77M7ub3WwapJIQWmihhRIIRXqkBRQQ1AjSBEEREETqS0SEUGyI0gRMQORVINJCb0FKKKGGBDBA6IQ0UkjbbP39kcxl5s5NMgLvV/0x53l8zM7euXPvnTvLfvbzOecEByMkJER0LsdxUKvVyMvLI4GzMGN27949CW%2BRz6ACIPMCgNjYWNG55WUdhXjllVfKfI8Gi8N35Aj15aRnz/Jfg0GxKPWALLsBg6shx5x98GBJGwmvptQzkYDhSC7plzJvZ3bM2rs0Z2bQIEkTgb1iCQRiPsw%2BGJdm8bVKBW%2BfopQnWm6jzp0r7Fdi5tyggbSN1EVd2oaeF8UnZBqvU2vMSlZLbgOLWCXgHgNgG0nTk5dD8pJB6pIsBaufitzE5Zi1l3KyRaCNt%2BXMoVWris%2Bh7h3LYxulFSMEjOdODodPcnlvb/FrxqaV9FO/foUdM/8pKq2SIKDnwJgTfQ7LNxz0dwrWfqT2hGQdGB3Tc6C90Vn9PCsXmW5D7wHWnpDDKZXTD5vY9zdAyfA9F16u2Sr4R0CoyjlhwgQ0a9YMoaGhMJvNiIqK%2BsuqnCyEhIRArVajV69eqFq1Kh48eIB3332XiKAkJSXh7t27uHHjBo4cOYK9e/cCeJph1Gg06NevH3799Vfs2LEDwFM7hddffx3NmzfHpEmT0LZtW0RGRqJbt27o0aMHKlWqJLJ3AEq4dHzQWV6gAFRczqfT6aDT6RAWFoZ9%2B/ZhVWkmR6gmamdnB09PTzg6OmLFihXw8vKCxWJBSEgIBgwYAI7jiA8fK2BzdnbG4MGDER8fL/Lb4%2BdlNpvRpEkTFBUVYdasWVizZg1pw5el5ubmolOnTuS4r68vJk6cCAAYMWKEaB1UKpUo6Lp//z4xNhdm/XiMGjUKAHDy5ElJ%2BazNZiPlqt26dYOHh4cok9ykSZMyA1Wg5B7zGbpz1C/vfMBXtWpVJoePR05OTpnv0VA4fAoUKFCgQIEMKAHfc%2BHlmq2C/xPExMRgEOOXdiFYqpwLFiwQeZgtWrRIItgCsFU5y8LevXuRkZEB3pC7VatWeOONN6BWq2E2m6HX69G7d2/8%2BuuvmD17Nvbv30%2B%2BsL/22mt4%2BPAhKQnk/e0CAgLg6%2BuLHTt2YMKECbh9%2BzbefPNNeHt7Izo6GhcvXoRKpSIB0sSJE0mwwHMbeSVP3vQbKAnUhAGWWq3G2rVryWsfHx8cPXoUHTp0wKxZs0jGsVWrVggPDxetz65duxAcHIy9e/eK3hNCo9GgWrVqWLduHa5evYqmTZuC4zhcuXIFa9aswfLlyzF16lS4uLiQMkzhuhYXF2Pjxo2oVq0asXLo1q0baZOWlkZKWAMCArBy5UpotVpERkYCKLGn4IPyvXv3YvLkyVCr1WjYsCHxCwSeZlaF4DgOBQUFIuGWAQMGQK1Wk2Du8OHDIrVOoIR/SYMP5J1Kf8HlfxRwc3ND9erVSbA5e/ZsJCQkIDw8nASR48ePx6VLl7B582Zyjw/T5UPlQOHwKVCgQIECBQr%2B1%2BBsFaUcFCj4l4BX5TSbzUQB09XVFT/88ANat24NoKSUc%2BnSpfj111%2BJYAkv3FG9enVkZmbCbDaD4zhSgshzvjQaDcxmM06dOoWvv/4aY8eORY0aNTBo0CB4enqSgHDr1q2YOXMmfH198fDhQ5Eap1qtRlBQEE6fPg2gJMjjM1K06TkAjB49Gps3byYWCBzHkXP4oNXX1xfdunXDli1bSCDCcRwcHBzg7e2NBw8ewGg0lpld5PukTeaFcHJygoeHB1JTU0WKljRq1qyJlJQUBAYG4syZM5LsHfBUOMXPzw937twhpZdeXl6oVq0aEhMT0b59e2KXwd8ffv3UajWMRiNUKpWolLV%2B/fq4efOmqASWD9b4NXV1dUV%2Bfj5Zc75vlUqF8ePHY%2BnSpejTpw%2B%2B/fZb9OrVC/fv3yfrws8lMjIS48aNQ25urogPyOPjjz/GuHHjylwjIa5evSoRMKpduz6aNBGU8W3aBISGlv0aJVwSUXlRRARQmgkFSngZdKmOxUJV6qxdC7z3XvltqH6ZfUdHi0s0CwsldXYmE1Ve9N//Au%2B%2BKx4g3bHkJMYAGXN4%2BJCq%2BoqJAYKDn74uKpKUeNHdstYvIwPw9BQciIoC3npL3CgrCxD6MlJrw%2BoX2dni8rI7d6TlevSa5uYClSqJ29DrlZ8vKksrLCyWlHXm5Ymr5iRzhHSvMZYPuHkTqFv36WujUVomSZ1IrwVzbeg5MRoVFFCldax9k54urvWlx8c4h14bxMUBpf%2BuENy4AdSr9/S15AFi9H34sLjkkLVW1L1jrg19bXofMS4t%2BdxgtJE8QHKe58REQPADIWvQrKWRXOvJE3EtreQmALh/HxBUBaWlMUqR6c9Mul/GvCRrzFhP%2BhCrW3ptWNtRzn2h10vGoyDrI1TWc8ca0N%2BBhg1ffJ8Ce6j/36EEfAr%2B1WjVqhWKS1nGRgbR3t7eHlarFXv27IGrqyvefPNNGI1GmEwmiYBJ7dq10aVLF2zYsEHEneMDPf7/ly9fJtm7x48f45VXXsEvv/xCgspZs2bh999/R7NmzRAfHy8KCtRqNXx8fPDgwQO4uroiW0AU//LLL3Hr1i1ERkaSgESj0aBq1ap4%2BPChJLBo1KgRrl69Cp1OB6vVCh8fH0k7Dw8PZGdnl2uDwQd8vFJpWeqXXbt2RUhICGbNmkXGR6Nhw4Z4%2B%2B23sW7dOjx48AChoaESC4a2bdsSURn%2B%2BiaTCc7OzrC3t0dGRga6d%2B9OuInlQRhMu7q6wmAwoLi4GFarFfb29vj444/Rpk0bjBgxAnl5eXB1dUVubm6FWbT27dvDYrGQwFy4TpGRkZg8eXKZAjhff/01%2BrI4aQwsWrRIlMUFgAkTPsb48fICRgUKFChQoOClgBLwPReUkk4F/2qcO3eOSPyr1Wr06dMHSUlJSEpKgkqlgp%2BfHzFcX758Oe7fv49atWrhu%2B%2B%2Bg1qtRtWqVeFa%2BhNdamoqhg0bhjfeeEOk7Hjq1CkkJSURgRA%2B8wSAmHX7%2Bfnh1q1b%2BPTTT7Fz504AwPXr1zF06FD85z//EWWaeDXHbEoV7Pfff8f48eMRFBREjpnNZjx48ICpPMobl/O2CCkpKahduzbq169PrpeVlSUqj9RoNPD29iZ9abVaURlpYWEhPDw8MGvWLIl4zpEjRzBt2jRRsNemTRsRv%2B7atWv44osvcPfuXVgsFmzevBkBAQHEDzE4OBi5ubkwGo2wWCwYMWIEDhw4AGdnZ%2BTn5%2BPx48cASsoieT5g69atSZlk9erVRfdGaNbO8/eEaqWLFy/G0KFDUaNGDdSvX1/kJajT6dCzZ0%2B4u7tDo9GQUtA6deogMjJS8gOCMPjkLThYIjhNmzaVHCsLCodPgQIFChQokAGFw/dceLlmq%2BClAsdxqF27NtRqNW7duoWtW7cSw/ULFy7A398fO3bsQL9%2B/QCUfPl%2B4403YDabiWk8D4vFQmwZ1qxZg4iICAQEBODd0jK0Ll26ICQkBLt27SIZR6vVigMHDmDFihWibFZZOHv2LHJzc7F48WLRcavVCqPRSMoZVSoV1Go17AVqabyCZ2pqKu7cuYPevXuTUso0gbY6n02jx8EbsQNAZmYmmjZtihUrVmDMmDFlri2/Ln/88Qfc3d2Z7cxmM4xGI1599VViR8GvRaVKlTB16lRUrVoVw4cPB/CUR2e1Wsk6enp6Ii4uDiqVCvn5%2BaLA6/bt28T8neM4UbAsRE5OjqR0sri4mPjzcRxH1osXjBk1ahTzfq1cuRKBgYFk3WjExcUxx6BAgQIFChQoeEYoAd9z4eWarYKXCl5eXiST5O/vj5ycHLRv3x5arRZbtmzB9evX0blzZ2zevJmc07FjRyxYsIB8oedx5MgRGI1GBAcH4/r168R4mw8IhDwvHkFBQahWrRqMRiN27doFX19fElRUrVoVQElWb9KkSeSctWvXkowjAPTp0weHDx9Gp06d4Ofnh6ZNm8Le3h5r165FixYtSLuePXti5MiRKCgogM1mw/nz56HVajGYIaNPlzR%2B%2BumnSEhIQN26dcFxHLRaLd555x28/vrrxAwdAGbOnInXXnsNwFN1z0uXLuGLL74g/S1cuBBJSUkYP348VCoVtFottFotM3DKzc3FgQMHYDKZSJaxqKgIHMdBr9ejUiknac%2BePZg5cyasViuePHki4hC6u7uTjKNarUaXLl3Iew4ODggPD8eZM2cwceJE2Gw2eHh4iMbSrl07/Prrrxg/fjzph1c87datG5o2bUrmWqVKFSQkJBDRGaCEg1m7dm2RMmtiYqJkrmVBEW1RoECBAgUKFPyvoQR8LymCg4NFCootW7bE4MGDRdmJJ0%2Be4Msvv8Srr76Kpk2bokOHDpg4cSKuC0xVZ8yYgU8%2B%2BUTSf3JyMvz9/Un5opzx/MYwTaaxePFi%2BPv7ExsFGnv27EHDhg3h7%2B%2BPR48e4fDhw1CpVCQQ8PDwQFxcHO7fv4927doRoRT%2Bvf379xOuF/9Fv3379hg/fjysViuOHz8Om82GX375BevWrUNiYiJq1qwJoCTg0Gg0pOQwNjYW58%2BfR35%2BPvr164eHDx%2BSoCIlJQVeXl7w9/fHhx9%2BCMdSlYHffvsNly9fJvO5cOECBg4ciNjYWNy6dQtXr16FwWDAe%2B%2B9h7Nnz5J2ly5dwsaNG2G1WmEymfDo0SMUFRVJ1tRkMpHgFCjhPe7evZuIpvBtbDYbrl%2B/LgpeCgsL4erqSrKJQEmgGxMTQ8pTLRYLzpw5g2XLlsFms8FiscBoNBLeJPA0SLbZbJg6dSqaNm2K7777jpRkajQaUeAstGYQlrZyHIeffvqJtMvMzERycrJovDNnzkSzZs0wbdo02Gw2iWrnxo0bMWDAAGzfvh1qtVqkmtqzZ08kJCSQ/ZGWlkaylLxyqMlkwq1bt0g2Enha5isHLOP1Tp0o43XayDwqStKPJNH43/%2BKXrLMdOWYqssxZ5dc%2B8gR8WvGxSUUUJbxuhzXYPri1LwBqZezxISZ4ZZckckxUCJoIsK2bdJGWVni19Q8mVRY2u383j1pG9qAWuICD6lBNmUczTJep83FJXMEJGvO8sKmx8yk4lLm1/RwmQbQdAk0w0BbYpDOWuRHj8pvw%2BA8S%2BZ56pS0X9pwXo7LtoAiUOY51MWZa0ObndPm8oyuZRmv0/uP8bxI%2BmHxomiOPWtT0A8r/fxIbi4khvOsRwEbN4pfl4qgiUDtJckaM%2B4L3Q3L9J3e1yzNM/parKWhL0/3w/KSl/NM0Y8Um2XwD6EZKBm%2B54Ii2vKSIjg4GKNHjyb2CXxw8MMPP2Dnzp1wdXVFaGgofHx8MHPmTNSuXRupqalYvXo1tm/fjo0bN8Lf3x8zZsxAcXGxxD4hOTkZvXv3xuHDh5n2CbyiJv/F3mQyoXr16pg/f75IUXPlypU4cOAAMjIy4OzsjLy8PLRp0wY2mw2BgYFYuXIlALH/mhA%2BPj5EzOPWrVsIDg6GxWLBiRMn8N5778HLywurVq1CdnY2KleujNzcXMyYMQPDhg1Dy5YtkZ%2Bfj06dOuHYsWNQq9Xw9fVFeno6TCYTLl26hLCwMOLTVxZ4sRcederUEQUm/BpYrVY4OztDp9OJ7AZozJgxA99//z0CAgKY5YOenp4IDQ3F6NGjcf36dbxFKwdSaNOmDSIiIvDHH3/g008/RXFxMRmzUBRFCKG6qFBFkwW%2BXNJsNjPbqdVqklXTaDTw8vJCSkoKqlWrhgcPHhDfu1GjRuHQoUNIS0sjWb4NGzZg1KhRovJPOqgTgs86spRGK1euDE9PTyQnJ%2BPatWvo2bMn7lBfJrRaLfbt24fNmzfjxx9/ZF6jYcOG2L59e5ljEEIRbVGgQIECBQpkQFDV9MJw8eKL7/MfipcrvFVQJvR6PUaOHAkvLy8cO3YMa9asQX5%2BPlasWIE6deqA4zj4%2BPjg888/x6BBg8oNSOQgNTVVEkg8efIEo0aNwv3795Gfn4%2B3334b%2B/fvJ8GSwWCA2WzG6dOncfLkSZGhtrAvvozQ1dUVGRkZuHHjBvniHhMTg6NHj6Jp06aYOnUqrl%2B/jqysLNhsNnh6eqJ27dqIKs2g8Bm%2Bu3fvAijJXt27dw8NGzaE1WrF%2BfPnYW9vj/r16wOAJDPl5OQEjUaDN998E3q9npidC4M9fuz8%2BHv37g1PWgddAJVKBQcHB9SuXRvx8fHMNhkZGWjRogV27NiBTz75hGQPaWg0GnzwwQfIzs5Gq1atMGXKFJjNZvz222%2B4cuUKWrdujY4dO0psFfi1AEpKYIVlnyzYbDZRGSVd3ikMGCMiIohYiouLC7FgAID9%2B/eTv/kS0CFDhoiya%2BUFewAwbdo0EuzZ29uL7teTJ09w48YNMt%2BystO%2Bvr6YMGFCmdcoTxGVhiLaokCBAgUKFCj4X0MJ%2BBSIYLFYoFarcfDgQbz11ltEEEOIadOmiUrfnhUhISFEYdPb2xutW7dmKmpGRkYiPj4eTZs2hb%2B/P6xWK6pXrw4nJydyPu971qpVK3Ls9OnTuHLlChITE3Ht2jV8%2BOGH5Av%2BgAEDYLPZUFhYSLzcLBYLdu3aRVQ2efBG7HygEh8fD5vNhtWrV6N27dpE5MPJyQmRkZEYOHAggBJuHFBSvllUVEQMvQGgRYsW2Lt3L7Zt24YJEyYQEZZdu3aJ2jk5OWHp0qUkCO3UqRPmzZsHm82G4uJiBAQEACgJchs3bkzOmzJlCsLDw5GSkgJHR0d07doVQEmAxK8BbzGxc%2BdOxMfHY%2BjQobBYLDh27BgJ6GvUqEHaazQahIWFiRQtjx8/DqvVSgIwb29vEjDZ2dmR4JuGUGnTz8%2BPtFmyZInIcxAACdCqVauG1atX49KlS%2BjatSvJHKoF5kRCxVFhMMf//fbbb5M2HMdh/fr1%2BPHHH/H666%2BTNkajETdv3sTnn38Od3d3tGvXDgDg6OiIhIQEACWltkDJjwJ169aFg4MD4fH9FQ6ewuFToECBAgUKZEAp6XwuSH%2B6V/BSoqCgABs3bkRWVhY6d%2B6M%2BfPnw8/PT9a5%2B/btw6FDh0TH/mqlsEqlwiuvvIKYmBjcunULsbGxRFFTq9UiIyMDcXFx2L59O7Zt2war1YqtW7di7Nix4DiOBGV6iQPwU3zwwQeIjo5GSkoKlixZgnnz5omCguoC81Yh%2BACEnxP/hfz06dP45ptvEBERgfT0dOTk5GDBggVkDAsXLoTNZsOxY8egUqlEmTDeH7BRo0Zo1KgR3NzcsHjxYuTl5eHo0aMASgKSgoICTJ8%2BHWazGfb29jh69ChsNhvhzF29ehUqlYpwyXgILR/S09OJZ9yGDRtEczt16hQaNmyIevXqkUzmTz/9hJ9//hlFRUW4dOkSyaqp1WosX76clHry5Z7BwcEkC1unTh1J9tdqtYpKWqtXry4ypL9x4wZ57/r166RtQUEBLBYLzyER/QAAIABJREFUyYK1bduWZFP5UtaBAwdi06ZNomuV9/f06dNhb28Pk8mEoqIiDBkyBCwkJSXh3r17yMnJwalSrk5BQQECAgLQr18/%2BJaaA1ssFon6Z1kZVRb69u0rCvABoH6p5QMP2khaYiwNGea%2BDGNz2kuXZbgr6YdlwLt%2BPTBsWNn9PHgA0GXd1CRkmWwzXINpv3GWLzNt5kwbhTM9helGrEX/80%2BgQYNym9DGzBLPaso0mjWgR48AHx9qfLQjOsvFmp4DtX4s43WJf7vEtV46B8oTnNmG4dUtuedylpzeAgYDIBArLgFtXM7YXBKzeLpjGebnrDnR/cp5puScIznGeBZo029WP5J5MzY/vZXoZ0qOcTjr3lW45owxy1kbeg88637MygLc3MoeDMN3XTrPGzeAevXEjeQ4r9P7jbX/6MnTE2edQw9achMYx1gP1fHjQMeOUPDvhhLwvcQIDw8n9gP29vZo2LAh1q1bBx8fH3AcJ7s0rVevXmVy%2BOTCarUiNjaWKGru3r0bPXv2JJmfbdu2oX79%2BqhXrx6mTZuG/Px8/Pbbb%2BjQoQNycnLIF/ozZ85g8ODBmDRpUplcQABEzVKIo0ePokmTJjCZTNBqtSIfNo7jsHXrVty8eRPTpk1D3759sWPHDkRHR0OlUpGyxIyMDHKe0LydN%2B3meW%2BnTp3Ca6%2B9hkmTJqFz586Ijo4m5/Ft%2BPEJy/78/PzQoUMHIsbC3yOO41DEYm1XAD7ocnBwgF6vh8FggNVqJdcUjru4uJgEe3xWlO%2BDn/%2BZM2dIZk6ongk85TL27dsXq1atYnIutVotCkpFAfgyXH4NlyxZgqVLl5IAF4DIGJ0fS3n79o8//ij3RwEefJayrL6esMQDSsHK2pWF6OhoCYfv4wkT0KB5c/Ka/uLEiifp7w/0azrYA6RBjiTgYvXDshURBHvMfhgcXnoSrGtXPCkqOAEj2AMkAQt9%2B5lOKXQj1qILgr2ymtBfECVbg/UjEzUgSbAHiIM9QBrsAdI5UOtHB3uAdD3ptSs5T/ya/nLNasN6JOh7LmfJ6S0gCfYA6ZdexuaSfATQHTOqEuiJsuZE9yvnmZJzjuQY41kQBntl9SOZN2Pz01uJfqYYl5YcY927CtccFe8J1pzoPfCs%2B1EU7DEuRj/LAGOedLAHyPock%2Bw31v6jJ09PnHUOPWjWv330MdZD9U8J9l6yjNyLhrJ6LzHCwsJI%2BePZs2exfv16NGvWDABQs2ZNSebiRWPv3r1EJfTRo0c4d%2B4cdDod%2BvbtCwAiw3DeRqFFixZo0aIFOnbsCKPRiIKCAoSFhRFO1cKFC9GtWzeMGTOGcAEHDRqEGzduYPXq1Th27BiAEjl%2BOzs7dOzYERzHkaCED2LoYLB58%2BZo1KgRXn/9dTg6OiI%2BPh56vR7r168XccgsFgvh8vHG7i1atBDx7fiASKPR4LvvvsMHH3xABGzs7e3LDSbu3buHnTt3ikoX6fFyHEf87MoCx3Fo0KAB8a2Lj49HXl4eNBoNJk%2BejMDAQOh0OgwdOpQEzvz8eIEVvp%2BdO3eSwNxsNksCOb4tn7lbvXo1yYIJ1UEBoH///uRe8NYVAPDKK69Ao9HA19dXFIQVMBTj%2BH6F4Mf3yiuvMAVbhHB1dUXXrl3hw/ymXeKXKMxK0mCJFJUFFodPYfApUKBAgQIFFJSSzufCyzVbBbLRs2dPbN68WSSMwmPq1KlYt27dC70ex3GoVKkS1q9fTzKMj0qls%2BPi4vDgwQNs2rQJ27dvJ//Nnz8fRUVFMBgMJKsSFRUlEZ/Jy8tDZmYmcnJy4ObmRriCgwYNIl/%2Bvb29RXYDNK5cuYIWLVogICAA%2Bfn5uHPnDoqKipCSkoLs7GyJdYCQK9ivXz/s3LmTBEI1atSATqcjgYtWq0VQUBCZS8fSX9OEFgT8GvHlq0VFRYiIiCB8QSFsNhu0Wi30ej0cHR3x1VdfoUGDBrC3txdZIri4uOBiqUIVx3EICwuDi4sLli9fjn79%2BqFatWrIycnB%2BfPnSd8q6gOyZcuWMBqNJOilA1FAykkTKrHabDbRHrt48SJZS2GJrV6vx%2BrVq1G5cmVRf2%2B%2B%2BSbzWiqVSsLnc3V1FdlDaLVaNG/eXJKRq127NrRaLVJTUyVzEY4HAJydncma8P0IuZQKFChQoECBAgV/N5SATwETI0eOhIeHB4YMGYIrV67AZrMhNTUVs2fPxqlTp/Dqq68%2B9zWEoi1Vq1bFiBEjSIbRzc2NKHFGRUWhY8eOaNKkCWrWrIlly5bhyJEjWLp0KQDgq6%2B%2BwldffQWgJDgcPnw4jEYjVCoVoqOjkZ%2Bfj%2BTkZLz33nvo0KED7OzscP78eaSmpiItLQ02mw3p6emiQIIPjPhMGcdxoswWf4wOgPz8/LBixQr4%2BPiQwDMqKgqff/45aXP37l10794dVqsVTZs2xaFDh0hpYrVq1YgPn5OTEwIDA9G2bVtMnjwZer2eKHh6enpiypQp2LZtG9RqNVq2bCkyYi8oKEBRUREKCgowbdo0/PnnnzAYDGT8dnZ2uH79Opo2bUrKIOfMmYOsrCx4eHjA2dkZ7u7uSElJgdFoJGbwVqsVHMfBzc0NKpUKmZmZmDRpEnQ6HXx8fJCYmEiCIV60RU3VCM2aNQsxMTHkdWFhIexLy0ji4%2BPJfRDy6w4fPowPP/wQiYmJoiCYztZZLBb4%2BPhg%2BPDhqFKliuh4YGAgtFotEWExGo3E1xB4WnrKZ%2B88PT1x7NgxEsi1b98eCQkJ2L9/P%2BHw5eXlSUpv27RpA7lQRFsUKFCgQIECGVAyfM%2BFl2u2CmTDwcEBv/76K9q0aYMJEyagWbNmCA0NhdlsRlRUVJkCJ8%2BD8PBwUuKZnZ0Ng8GA/v37Y9%2B%2BfRg4cCAJOPfv34%2BcnBxwHAedTgdvb29MmzYNABAaGoozZ87g0aNHWLBgAVJSUmBvb4/NmzcjPj4eUVFRqFmzJmw2G3JzcwlPzNfXF/369SNjOXfuHKpUqYL8/HwS7FksFsJp0%2Bv1cHBwwIULFxAYGEjOi4%2BPR1hYGN544w14eHggPDwcdnZ2xJKBx/79%2BwEAN2/exEcffYS0UrPl7OxsNG3aFEBJkPPaa68hLi4OixcvRu/evUnWMyMjA9nZ2WjQoAG%2B/fZbGI1Gkq0DICoz5SHMPJpMJuTn5%2BPcuXOSAOPevXuYPHky4uLicPLkSVitVmRnZ8Pd3Z20ycrKgtVqxZ07d5CcnCwSV1m1ahUAIDIyEm3atCHBHI82bdqQTCpf2skHXTabjbTnjeX5AM9oNErGyso0P3r0CNu2bROtuclkwuHDh2G1WvHxxx%2BT48I%2B%2BYwrz9/z9vZGp06dyLxiY2MREBCAkSNHikpm6fLfv2LLwDJe79KFMl4/caL812D44lI%2BgKwhSY7RBu%2BsfmlTdTDMfGlzdsbFJfTN2FjpAOW4RNPHdu2SNJEYZl%2B5UsFgpN2yvLAlldeM%2ByIxv6Z8M5kG2jQXl2UPQt8YRmmwZLmoSbCM1%2Blrs9x3JHuCtTiUfyXTFZo%2BJud%2BU%2BfIMg5njY8uBadvBG0SzjhFYkgOSBaMqV1G9/3nnxVem54Dc9/QA2TNW/JZVeGlpPdJzvNM33/GaazhofTfNwK6/J61NpQVD7PfM2fEr1kLSFFYJE1Ye5i6GJMtQA%2BINcBnaUMvKGOzyWgibcS4v3/hnzQF/2AoxusK/hY0atQIvXr1wuLFi5nvFxYWYuDAgcjJyYFarUZubi4qVaoEJycnPHnyBJs2bcLw4cNRUFCA/Px8Yqvg7OwMo9GIIUOGwM7ODj/%2B%2BCMWLlyIAQMGAHhq%2BC70hjObzeRLPh%2BI%2BPr6IicnBwUFBahcuTLatm2LcePGoX79%2BlixYgWWLVtGLCzoL/gajQZOTk5wd3fHgwcPMGfOHMycORMA26S8cuXKRGU0MDAQtWvXxu%2B//w6tVitSuBSe26hRI9y8eRNubm6i0kN%2BTnyQygLfj6%2BvLx4%2BfFjufaKN11njd3JygtFohNFohFqtJkbqZZXH8qhZsybMZrNoDGq1Gu3atcOJEyfg6OiIgoICDBs2DKdOnUJqaiq518Lx%2BPj4ICUlBTVq1MC9e/eIiIyLiwuys7MJR9NsNiM4OBizZs1iZqh5MZo6depg9%2B7dGD9%2BPA4ePChqo9VqMW/ePAAlip8shIeHV2h2z0MxXlegQIECBQpk4AXYgUnA%2BrHxL%2BDhw4f44osvEB8fDwcHB/Tu3RuffvqppPpr5MiR5EdsHmazGePGjcP48eMxdOhQXLhwQXSen58foqOjn2t8QigZPgV/C7y9vYlgCAsODg7YtGkT%2BvbtC61WS760BwYGYsuWLaIMY1hYGPnSHBsbC09PT9SoUQMHDx6Eu7s7yeIJ21%2B9ehVXrlzBZ599BqDkwRMGboWFhejTpw/8/f2xfft2uLm54Y033oC/vz/8/Pxw6tQp6HQ6TJo0SdS3TqeD2WxGTk4OHjx4gK5du4qUJO3s7CSCIrxIi0ajwY0bN9CyZUsAJdknYdAk5Anev38fv/zyi8RonOM4NGvWjARp9vb2CA4OFrXRaDTo0qULhg4dCqAky/bRRx%2BhVatWZIz8OT/%2B%2BCMRVdHpdKKx85nN%2BvXrE4Edfg2F2TK%2BHJT33mvZsiWcnZ3x8OFDYnnBw2KxkGvz8/39999x9%2B5dkm0VwmazEeEWnuNoZ2cHlUpFrCmqV6%2BODh06ACjxMZw8eTI537lUgk6lUsHLyws2mw03b95EQUGB5FpCZGVllfkeb4EhB4rxugIFChQoUCAD/8CSzgkTJqBKlSo4dOgQ1q5di0OHDuHnn3%2BWtIuMjCQUpoSEBMTGxsLd3R3du3cnbebNmydq8yKDPUAJ%2BBT8H6BVq1akVJP/LyMjAwsWLEBAQECZWSYXFxfMnDkTMTExuHz5Mo4ePYoFCxYw1RPbtGmDCxcuYMOGDcRL8P79%2B2jVqlW54jMGgwFJSUlQq9UiNcqDBw9i3rx5iI6Oho%2BPD8LCwqDVatG6dWts2bIFlSpVwuXLlwnnUDjX2NhYXL16FZs2bUJWVpbkoRUGbsDTskaz2Yy8vDz85z//Eb1vZ2cHR0dHojRZt25d5OXlYdKkSbBYLBg%2BfDiioqIQGBgINzc3JCQkEMESg8FA%2BHK8kMnQoUNx79493L59G1WrVkVBQQFWrlyJ%2B/fvg%2BM4VK5cGcePH4dWq0VhYSHxiSsuLoZbqXa1SqUiv0ZptVps2bIFoaGhcHd3J5xMABg/fjwJivNKa%2BvOnz%2BPvLw8eHl5IT8/X8Q9VKvVGDRoEP78809iM1FUVET4k6ysJV%2B%2BqtfrYbPZYDKZYDKZiCm9Wq3G0qVL8eWXX0qsQvgxWa1WkX9g//79UatWLbi7uxNOYvPmzZGQkID%2B/fuTY3q9HgEBAaKy1b9S7qxw%2BBQoUKBAgYJ/HxISEvDnn39iypQpcHZ2Rq1atTBixAiRN3BZWLJkCbp37w5/f///g5GWQCnpVPC3gS%2Bv5AMbrVYLf3//cj30XFxc0LJlS4wbNw4ffvghM1jks2hGo1GUpeHL%2BoTX27VrF86ePYuZM2eibdu2JBt3%2BfJl6AS%2BNzExMZgzZw5ycnJQXFyMo0ePwtvbG2fOnMEwgQ8Z7VEnxAcffIATJ07A1dUVN27cILw9fmwqlQpBQUE4e/Ys06NO2LZr167E7J4vYeSzavXq1UNKSkqZlgVASRlpgwYNcPHiRSbfT3itsgItYZsZM2YAKLHFmD17No4dO4b4%2BHgUFBTAbDaTslkXFxdkZWWhTp06MJlMuEfxYDw9PXHixAlkZGSQrFx5EJac8vdPr9eLPAnt7OwwePBgHDlyBFu2bME777yD5OTkMvu0s7NDYmIi%2BvTpgzt37oiyrFqtFpGRkTh48CDzVzwAWLFihWxRo6tXr0rsT%2BpXrYoGpdlWALKMeyX%2BybQL85UrAK0eSp/EcC2X%2BHmznI9v3gTq1i37pGXLgPHjxefQc5A4fjPaMOYtOSTDFZqeNtO8%2B1muzTA/l9wXug1rvPRasMySqTasbiT3itoTLON1yR5gOVTTbtMsZ/jERKBJk6ev79wBatUSt6HGQ8%2BBtdXkmEQ/i8G3nPstWQo5zyGjjWSbUAeY95KeFMtdnO6YsW/o8cm5Fr3EjG0uy1ucvviz9MPwiZe0SUsDBHpdzPNY25re%2BpLxpacDAgshAJLFkWNKL6eNnMV5ljV/1vFJPuP/LnTu/MK7TI%2BKklRKeXp6iuyiysLGjRsREREhon5cvnwZb731Fs6fP1%2BmPdbdu3fxxhtv4NChQ%2BRH9KFDh0Kn0yElJQWPHj1Cs2bNMHfuXNSoUeM5ZieGkuFT8LdC6AV44sSJcj30eNEVNzc3hIaGwmQyoXLlypgzZw6SkpKwfv16ACUZpISEBHh6eoLjOGg0GpHlAh8gvPrqq3BzcyPefOX5p0VFRaFu3bpo2bIlNBoNVqxYwWxntVqhVqthZ2cHOzs70QP/888/48qVKzh58iQJ9hwdHaFWq%2BHo6AiTyYQHDx7ggw8%2BENVxu7i44MsvvyRZJIvFgtjYWMI75ANMZ2dn/Oc//0FeXh4z2OPHBQA5OTk4ffp0ucEefy062KNVNy0WCxYsWID58%2BfDarXiwIEDmD59OmrUqAGDwUACZ7PZTEoh%2BcwuXTbJl3%2B6CgxjyyutFI6tUqVKcHFxQVFRkWj9TCYTfvnlF9y7d4%2BouZYHs9mM%2B/fvY8aMGUwe4sqVK8vlJx44cKDCa/CIjo7G1KlTRf8dOk0JDMgw7pUsEe3CzLKKoE9iuJZL/LxZzsf0FwH6JDrYA6RzkDh%2BM9ow5i05JMMVmp4207z7Wa7NMD%2BX3Be6DWu89FqwzJKpNqxuJPeK2hMs43XJHmA5VNNu0yy/SmGwB0iDPcZ46Dmwtpock%2BhnMfiWc78lSyHnOWS0kWwT6gDzXtKTYn2RpDtm7Bt6fHKuRS8xY5vL8hanL/4s/bD%2BKaDb0MEe6zzWtqa3vmR8rACAWhw5pvRy2shZnGdZ82cd3z8i2PsfYdOmTRgwYIDoPzkZOqDke5QL9TlWqfSzOZsW7BJg9erVGDhwIAn2AKBOnTqoV68efv31Vxw%2BfBhubm54//33YWQJFT0jlIBPwT8Ger1e4qGXn5%2BPFStWoE6dOkSg4/PPP8egQYNgsVgwadIkDBo0SNRPr1690LhxY6IkqdVq0aJFC/zyyy/w9fWFRqOBzWbDnj170LJlS%2Bzbtw9AiTInUJKlioiIIOWnjRs3RkxMDE6ePIm4uDhYLBZs27aNmcXjTdc5jiNKmCqVCr6%2Bvvjyyy8BlARow4cPB8dxKCoqwqeffkp4fBkZGfj1119FgcyTJ08wffp0kQWBSqUiQYfNZiNKoAsXLizTWNxisZDMIS%2BuwvPehOjWrRt0Oh3UajUqVaokEoIBSoKs0NBQ1K9fn5wjXIuTJ08iJCQEV65cgZ2dnSjbxuOPP/6AVquVrOGNGzcQEBAgKZXlMWLECPTr1w8cx2HKlCmiD8yvv/6afDharVaoVCoS%2BPHX2U6pV7LQsWNHVK9eHWPHjmW%2BHxkZiYYNG5Z5Pmu%2BZUHh8ClQoECBAgUy8D/g8IWGhmLr1q2i/0JDQ2UP6a8WSebk5GDHjh2iyjAAmDNnDqZPn47KlSvDzc0Nc%2BfOxcOHD0U%2ByM8LJeBT8I8Dr3558OBBvPXWW4S7JsS0adMkcv9ChIWF4dy5c6hXrx6qVauGJk2aYMyYMSguLkaTJk2gUqnQqlUr7N69G/PnzwfHcaS/uLg4fPTRRyTzyCt8ajQanD59GqtWrYLRaCSZQR4tWrTAlStXyHkLFy6Eq6srrFYrXnvtNcI9bN68ORo0aED4W4cOHYJWqyXBEe9rx1%2BTB/83X77JB2DOzs6Es2iz2VBUVIQhQ4ZApVLhvffeI%2BdXq1aNlElaLBa8%2B%2B670Gq1cHd3x8qVK7Fw4UKoVCoYDAYUFxfD1dUVW7duJdm2xo0bw8HBARzHIS0tjZQi8sGhELxqKsdxGDhwIFlfXtylXr165G%2B1Wk0yod27d0dCQgIiIyNJX5MmTSJzX79%2BPa5evQoAcHd3lxi9CzN7rVq1Itk2PntrtVpFa8q3t7e3J7/U8W3XrFkDR0dHUtpRu3ZtJCQkAHgqWKPX6%2BHn5we9Xk/GUl6mWIECBQoUKFDwDPgfBHxeXl5o3Lix6D855ZxAiV80r7DOg7cME/4YLcThw4fh5%2BdXIdffyckJlSpVElF/nheaipsoUPB/g4KCAmzcuJGIrsyfPx9%2Bfn7P3B/vJbh8%2BXIcPHgQBoMBBoMBarUabm5uiIyMhFarRXx8PDiOQ9u2bfHnn3/i4MGDCAsLI0HInj17AJSUBrYScKs%2B%2BOADUaBz6dIlIhQCALVq1YLBYIBGo4GPjw%2BpxeYDC16MJCEhAW3btkWVKlWwb98%2BfP/996TUUmjJwP/Hq2yeOXMG2dnZyMvLI4GNTqeDXq8nWcINGzYQK4UHDx7gQamnl0ajwfr16%2BHh4YGcnByEhYXh8ePHAEqUTgEgMzMTPXv2JBYWDx8%2BRHFxMaxWK/744w8yT96OQQg%2B4LNYLIiKigIAsv4ARPLEVquViOocOHAAAQEBopLJJUuWkDEMGTKElO7ya8iD51fyiIuLQ7du3QAAKSkpAEoC3evXr4uuzfMn%2Bb62b9%2BOTz75BJs3b0ZxcTEpj7116xYCAgIwduxYImRTVFSE27dvi%2Bb%2B9ttvQy4U0RYFChQoUKDg34cmTZrg0aNHyMrKIgFeQkIC6tatS8T4aBw%2BfBjtKXuJ/Px8fPPNNxg7diyqlNYkZ2VlISsr64V6XisZPgV/K4Rm6126dMHRo0exbt06%2BPj4iIRIWIiJiZGUc9IQKn3ygippaWnIyspCu3btMHjwYNy6dQtASYB27NgxaDQatG3bFgEBAWjdujUJRhwdHTFgwAAi%2B2%2Bz2SS2CUJZ3ffee49kxO7duwdXV1dUqlQJFy9exPz580mfJpMJJ0%2BexO%2B//478/HwSeAkzUa1bt0ZiYiI%2B%2B%2Bwz2NnZ4f79%2B%2BjSpYukJLO4uBg5OTmwWq348ssvMWfOHEyYMEHiCcOPOzMzEzabTfQrlbBEgbersNlscHV1xfDhwyVrTK9DeeA4Dr169UK1atWg0Wjg7OwMPz8/DBgwACqVChqNBvHx8SLBnB07dsDBwQE2m00U7H322WekFBYo%2BdDk9wud%2BeOP04EpUMIXNBgMZB1fffVVuLi4YNy4ccx57dixo1ye3o0bN%2BQsBQC28fqrr4qN1%2BV48tJVJZLHpnSP/6VzGG2YWkKUA7ks42vKnJ3ZL3VxVuUMXT3LpKRSndPXYp0jZ81BBfpyzNnlXFvSiKEwLHF4ZpQGV%2BRrzjRepwbMqjiWjJlhQC5pw1gcug09Xta06XNYbSq638xjMsyn5TwLcrzj6VtX0TrIHJ7k3rHa0Ndi7j9qonQbVr/0MTn7WpbpO9XoWdecHg9rX0uObdggfs1SE6d5WqwB0h2zLk5z7uUMkN4orI1TqkJNwHpg6Db0hxYAlP5Q/LfjH2bL0KhRIwQEBODbb79Ffn4%2BkpOTsXbtWvK9tFevXoQqxOPatWuSKiAnJyfEx8cjPDwcOTk5yM3NxRdffAF/f3%2BRivnzQgn4FPytEIq2nD17FuvXryf8rZo1a0oUDJ8FBQUFiIiIgNVqhbOzM9RqNb7%2B%2BmsiErNu3TrYbDYUFxdj5MiRaNiwIfr06QMPDw9RQGE0GrFt2zYA0oAiJCQEKpUKx44dw9y5c%2BHv74%2BjR48iNzcXHMfB19cXANChQwc8efIEBQUFsFqtJHvEByRarZZwAIWB15kzZ9C5c2ckJSXBbDZj4sSJ2L17d7n14/wHzQ8//EBKDFgCKHQwWBZSUlIQGRkJJycn5q9XOkoJoUePHvDx8REdd3BwQEJCAtzc3IgNxe3bt7F3715i1j5lyhSSbdNqtRgxYgRZH%2BFYhX9zHIc%2BffqQ12WpnIaHhxPvPR6ZmZmwWq2E/8cHeWFhYaJ2/NqNHTu23DU7cuRIme/RYIm2HDlyWNRGDqmevq2SuLZ27b9%2BDqMNk9BfgfgG8yRKyEWO4ABLsIHWpWAKfVQgdsA6R5aQAVV9wGpDa%2BfIEiahG7EEOuhydkammF4bulumaItE2EXaRDJmhopchaIojDZydEnoc5gieBXcb%2BYxevPLEOCR0y/rmaJvXUXrIHN4knvHakNfi7n/qInSbVj90sfk7Gs5oi1yBG3krDk9Hta%2BlhwbMkT8uvTfcBEE4mJlDpDuWI4QkpwB0huFtXFoJRrWA0O3oT%2B0AEChKZSJH374Aenp6Wjfvj2GDRuG/v37Y/DgwQCA27dvS3j6GRkZhM4ixPLly2Gz2dCzZ0906dIFJpMJq1evlv39TA6Ukk4F/1j07NkTGzZsQHR0NDIyMkR2ClqtFiEhIZg9ezaAEmGT3377DUBJYDJnzhzMnTsXKpUKDg4OcHFxgc1mQ2FhISwWCyZPnky4gXxwsGjRIthsNty9e5fYOghhNptJqaIQly9fRnx8PPbu3YvMzEzs3r0bALBv3z44ODjAzs4Ov//%2BO4YMGYJ27dqR91ngr6nRaCTZpdTUVGzatAl6vR65ubnEdoIO%2BnirguPHj6NJqVqexWJBbm6uyAPQarWievXqOHToECIiIvDVV1%2BhTp06MJvNSE9PJ3YMLi4uePz4MSnHLCgowPTp0/HNN9%2BISk5pxc9Dhw5BrVZj//79CAkJIeWRRUVFyM7OJvYJfn5%2B%2BPHHH7Fnzx4sWbIEe/fuJX2YTCaRyXnz5s1x6dIleHh44Pjx48TDhrfc4KFWq2GxWKDRaKBWq8nYrFZrhSWTvNUH/WMDv3ZBQUEwGo2kVJVGeaqiNBTRFgUKFChQoEAGXmDw86Lg7e2NNWvWMN9LSkqSHEtMTGS2rVq1KpZRlS8vGv%2B81VOgoBQjR46Eh4cHMjMzMWrUKFy%2BfBkHDx5Ez55wpECpAAAgAElEQVQ9UVxcjC1btojsG/gv6j4%2BPvjkk0/wzjvvQKvVYsOGDTh8%2BDB8fX3x2WefkXT6uXPnkJCQAFdXVzg6OqJ27dqYMGECEhMTkZCQgKSkJFHpHh9Azp49G4mJiURlyWKxoHXr1hgwYAASExOh1%2BuJqIfFYsGTJ09I1nLr1q2ws7MjyprAU99A4ClXjw9eHBwcoNVqiULpjBkzUFxcjFWrVgEoCUI0Gg3efPNNODk5oU2bNhg9ejScnZ2xdetWkbBMs2bNMGPGDHAch549exKVUKvVSvzwkpOTCVeP98/LzMyEl5cXMYTnOI4ojvJKnrGxsVi4cKHo/vHZymnTphG1T5VKhf79%2BxNxGaCEG9ejRw8sWbIEgJjDRmcN4%2BPjYbVakZeXh%2BXLl4sEfYSBFh%2BUcxwnKuMsLi5GvXr1mPuNB8/xmzt3LmrVqkX61el0WLlyJXx9fcm4hCW1/A8Sf6UEQ%2BHwKVCgQIECBTLwDyvp/Lfh5Zqtgn8VeNEVrVaLTZs2oVmzZkQud/fu3ahSpYrIvmHixIkASr6EV6pUidg3ZGZmivoNCgoCAKSnpyMiIgIFBQUoKChAcnIyli1bRuqyR48ejS1btpRJmm3Xrh2AEhsCAJg/fz6qVq0Kg8GACxcuACgJMGw2G6KiotCkSRNcuHABJpMJer2eZBb1ej1atWqF0NBQjB07FnUFnjfFxcUwGo2w2Wx49OgRvv76a3AcJ8p62Ww2HDx4EPn5%2BThz5gy2b9%2BO77//Hq6urrh//z5pd%2BPGDXz99dew2Wy4cuUKFi1ahJkzZ8JkMsHT01M0Nz6TqVarSamkMMPKBzp85vDVV18l3DU%2BADIYDHB0dMTjx49JxpDjOJw7d042569Nmzai10KO3k8//SThUNLQ6/WiLFp4eDguXrwoaqPT6UhgzY87OTkZd%2B7cwZ07d0i/xcXFGDVqFCZPnkzWlfdABJ4Gau7u7rLmBrA5fJ06iTl82Ly5/Ndg8Nv%2B%2B1/RS1aFq4TrEh1dcb%2BMNhK%2BTqnoD0FuruQcyXhYv2zSjRj3V3KIsTbU4w8cP17BYKRrI4efhy1bpI1ozkypAFR5/UoGzOLP0LwbxhpLOqd4QiwOH72eLIcXyf1m%2BUT9%2BWfF46PGI4fLJocEKTmPtcgV8Z8YF5ccunRJ2i/F65T14MXFiV%2BzyvSpPSGHU4rUVGkbGbxYSec0T42xNpLPElYWg7pXzGvTe5%2B%2Bvyx%2BGTVPyfMOAIKqEQDsPVsR95Oxh%2BnhMexvZfGBJesng1Ati2cs434/0/gU/CuhlHQq%2BNsQExNTYRsXFxdUrlwZo0ePlgi00PYNHTp0QFJSEoKDg0mbadOmic4JDw8nf3fv3h2NGzfGL7/8Am9vbwQHB8PBwQHbtm0TBXl8bTYNnsfWsWNHAMDjx49x69YtaDQanDp1CpMnT8axY8fg7e1NxFF48NmtHj16YOnSpaJ%2BCwoKSPCk0%2BlQXFwsEh3hOI5wC%2B3t7WEwGIjZuNVqRXp6OkaNGgWtVis6Ly8vD/b29rBYLHjw4AHCwsJgtVolJarCIEqj0eDIkSMwm81Yvnw5dDod9u3bh7t37%2BLjjz%2BGyWRCYWEhDAaDyEoBKDGav379OqKjo4m6qMlkwnvvvYfBgwejYcOGsFqtcHNzQ3R0NDw8PPDw4UMMHTqUqGr26tULR48eJX0uX74ckyZNwrvvvov169eTIGvWrFmYO3cubDYbPDw8MHr0aCxatAhDhw7F2rVrSdDHrxH/9%2BTJkzF8%2BHBy3YyMDADAli1bJOqbQEkwm5iYiH79%2BgEoCTxpc3phkF0RoqOjsXbtWtGxjydMQPPmDZ4eoFU/GSqgkirSd98VvWRRSyRcl759K%2B6X0UbC16EUyFim6pLxyDFnZ5TKSg4x1kZClyh9XssejDx/dAnVZeBAaSOaM9O7d4X9SgbM4s/QvBs5xvUUT4jF4ZNjSi%2B53wzbHDRoIH7NGh81HjlcNjkkSDnG6xXynxgXlxxq3lzaL60qLefBa91a/JpVEk7tCTmcUnh7S9vI4MVKOqd5aoy1kXyWlFIJRKDuFfPa9N6n7y%2BLX0bNk0GPAkJCxK9Ze7Yi7idjD9PDYwkzyuEDS9ZPBqH6WYzX5RjXyxrf34WXLCP3oqGsnoJ/HXgRFt6%2B4f79%2B7LtG8LCwkhg8u677yI7OxvDhg1DaGgoHB0d0bdv32eWwd22bRtcXFwQHBwMZ2dndO3aFUBJJlFolC7En3/%2BiTjBL7xPnjzBcUEGorCwEDabjWTXjEYj8ZLT6/US3hogzjp5eXlBr9ejYcOGqFKlCuHhWa1WmEwmkZ8f8DSLx3vrFRUVwWAwwGg0IicnByaTCd27d8eYMWPg5OQEPz8/uLm5ketxHIcWLVqA4zi8//77%2BPbbb5GcnCzyqbNYLHj8%2BDEJktzc3NCjRw%2B0atUKkyZNQufOncl4eA9EoCRztnnzZnAchxo1aqBx48bkvaVLl5IxvP/%2B%2B9DpdMT7z1vwhWDYsGEiP8MffvgBgYGB6NWrl6h8dMyYMThz5ozkfnEcBy8vL7Rs2RIqlYoE4MKy0SesX6HLAIvDpzD4FChQoECBAgUvEkqGT8G/AuHh4ViwYAGAkqxWw4YNy7RvEGYOIyIiCDfMaDRi3rx5JMDZtGkT%2BvfvT7J%2By5Ytw4YNGzB58mTiwQeUlBX27dsXp0%2BfLneMUVFRyM7OxpEjR0Q8LldXV5w8eRKdO3dGamoq6tevj6ysLNjZ2aFBgwYYM2YMdu7cCVdXVwwaNIjw6Vq2bIlPPvkEmzZtwsGDB%2BHj44O0tDTUq1cP9vb2JCDRaDSkpLNPnz7w9fVFQUEBTpw4gYsXL2Lo0KFITk5G165dERISgqSkJMTExBADc41GI8pS0QIs/PFhw4bh1KlTMJlMuH37NlJTU/Hw4UNRqafJZCIlkxqNBrt370ZISAgpSwWA1atXEwNzAPjkk0/QqVMnmM1mLF68GBtK5bA5jsObb75J2uXn5yM2NhZmsxlhYWGiey7k8n377bdEnOXbb78V3aO1a9dCq9XCbDYTDz5%2BPwjLZJOSkuDq6koyrAUFBVCr1aRk9/jx49DpdKRMVTiWVFYplQIFChQoUKDg2aFk%2BJ4LSsCn4F%2BBsLCwMj33yrNvGDVqFEaNGgUACA4OxujRo1G7dm0MGzYM58%2BfF2V1Ro4ciX379mHIkCGYP38%2BGjVqhLS0NKxYsQKnTp3CiBEjRAqSQsTFxRFTc47jSJCk0Wjw%2BPFjXLt2DWq1Gs7Ozrh37x6cnJygUqkQExMDs9mMHj16iMoCnZyccP36dQQFBSEoKAhfffUVDAYDtm7dioSEBCISs3XrVlitVvTv35/4BaalpeHbb7%2BFnZ0dgoKCUL9%2BfTx8%2BBCPHj3C7NmzUVhYKLJVWL58Ofbv34%2BtW7eKxk5jw4YNUKvVaNq0KW7fvk2CHL69VquF0WiEnZ0duSfC8loe6enp2LFjB3nNm6OHhYWJjttsNuQKeBMmk4lciy5D5b0L%2BXYsvz2gJAvKK6HqdDrEx8cDAEaMGCEK6K9du4b8/HyRsqnFYsH9%2B/eRkpKCGzduiEzfhRAGsxVBEW1RoECBAgUKZEAJ%2BJ4Lyuop%2BNejZ8%2Be2Lx5Mwl4hJg6dSrWrVsnqx9eJKZNmzaYMGECEYkxm82IioqCm5sbaRsREYGRI0cCKMnEDR8%2BHFarlXDEunbtiujoaERHR0Oj0ZBMU9euXWEwGJCZmYn09HSSXbJareA4DpUrV0aDBg0wc%2BZM5OXlYcSIEUhNTcWUKVMQEhICg8EAOzs73L59G/Pnz4e%2BlE9RVFRE/l68eDF69eoF4GkJosViQWZmJgoLC6HX6%2BHj4wOgJCAtLCwkpaNCvzuh8TsPXgGUb6PT6eDi4oLt27fjlVdeIetYnlIl7SuzfPlyXLt2TRTs8fjkk0/I3/R4xpfyvt566y1R4FSjRg306NEDXl5eACAa74YNG5jiLsuXLwfHcaJMYVFREbOt1WoVlYkCQOXKlcnffyXDxxJt6dG9u7iRHLdkepx0GwYTX5ZBekX9sto8i1O8DHN2LF8uaSI5jTG%2BCtvIcYBmtJFz7QrXj3UOrZQix236GdaYJdryLAIOTLEIShTjWfqRI0LxzHuLFu2gB8N47iVbgPWDT0Vu90DF6jQyBEWYIhoyRI4qNJxnnfYszwJjDrLub0VzYKmOUONhCafIeqYq2gMy5s16nOnT5BjXv6jn5VmuLaeNgn8nOFt5zs0KFPwDwGfmysrwFRYW4u2334ZGo5Fk5mJiYvDbb7%2BJeHlnzpzBsGHDULVqVcKvA0oyVP7%2B/pg0aRJalxLpnzx5gpUrV%2BLAgQNITU2F2WwmnC0hrw4oCa62bNlCuGXJycno3bs3AgMDcfnyZXh5eWHMmDE4ceIEjh49Cq1WC4PBAKvViqCgIMTFxWHgwIGwWq2IjY0lhuD8tXx8fFCrVi3ExsZKMlxCaDQaqFQqvPbaa1i4cCE2btyIOXPmAAApQXR0dER%2Bfj4cHR1x8OBBODs7o1OnTsgWKLLx7QwGA8xmM0JDQ9GkSRN89tln5H2%2BP34thAbyQh9DYRnl3LlziX8ix3Gws7OTeB6qVCqiJtqoUSPmPPnrq1Qq2NnZicpQ1Wq1KJvHt2/cuDHxwVGr1ejcuTPOnz8Po9EIvV5PyjqnT5%2BOdevWIS0tTXJdFxcXvP/%2B%2B1i8eHGZ47p48SIJwMvDokWLJKItEyZ8jPHjx1V4rgIFChQoUPDS4I03Xnyf27a9%2BD7/oVAyfAr%2B9agoM1eeCEtYWBgSEhKQkJCAEydOoFu3bhgzZozI3%2B/GjRtYvXo15s6dCwDEJ48vvatSpQrq1KkDf39/kZAIj9mzZxPz7/DwcBw7dgxWq5VkkJycnGBvb084bx4eHti6dSsSExPx0UcfwcnJCSaTiQRj7UtVENesWUO87%2Bzs7EgZIz%2BuXbt24aeffsKXX36J0aNHo0OHDnB3d4darSZiId9//z3c3d2h1Wolwjc2mw2enp5EbGXr1q344osvyPtqtRpVqlTBmjVrEBgYKLIj2LNnD/E7bNSoERISEjB9%2BnTodDqcOHFCdA062ANKglabzYaIiAjRcaHADP9bVZMmTci1gJLgcuTIkZKMoUajwa1bt8hri8WCP/74A4WFhTCbzaLyUavVStRXaRiNRlIKyoLNZpMt3KIYrytQoECBAgUK/tdQOHwK/vGQa98wc%2BZMzJw5s8K2bdq0kdg3ACWebSNHjsTGjRtx7NgxpKenIz8/HytWrIBWq0WdOnUwsFR6/auvvkL79u3Rvn17Yob%2B3XffiURi%2BIDkrbfeAsdxePz4MQIDA/Hbb78xx9WsWTP4%2BvpiypQp5NjEiRMxceJErFu3Dq1bt0ajRo0wY8YM8v6AAQOIkiWfUaxXrx6io6ORn5%2BPTp06YerUqRg8eLDoWnfv3kWPHj1w/PhxdOzYEbdv3yZiKzt27MDVq1exbNkyzJs3DyNGjCDzcXNzQ3p6OoAS%2B4b09HS8//77or7btWsHrVZL/A9r1aoFoMRgvW3btoilfdoYMJlMcHd3x/79%2BwEAnTp1wrFjx6BSqSTZTT576u/vj6SkJBiNRqxZswZr1qwhbSpVqoTq1aujTp06iI6OJv3w4i40jEajSLGUzx6GhIQgNjYW7u7u0Gg0cHJyQk5OjuT8sjiENBQOnwIFChQoUCADCofvuaCsngIFFGh/Py3Ds2fatGkk0ybEqFGjSMaQzzDt27cPCQkJ8PX1RV%2BGjxlQIjpiMBhIkEnjypUrIvsGOThx4gSsViveeustyXs1a9aEt7c3GeOhQ4dIhu7WrVuioIMPfPgATwi6IlytVuPs2bOigMliseDJkyfYvXs3evfujT/%2B%2BIO012g0osxgpVKvo8qVK8NgMBAxFp4z9/HHHwMoyeLx/Ts5OSE4OJioewIgGVP%2Bb5PJhIyMDDx%2B/Bg6nY4EjT4%2BPtDr9XBxcUGDBg0It1Gv1xMunrBU9MCBA3jy5AnS0tJgsViQk5ND7Bl4cBwHV9q7qgwwOXw9KOP1Z%2BFrySCFSA6xqvvl8KEq4vgwzpFcinVtmrPHMGeXxUOsYDyyzKflzOFZeJIs0s8LGl9Fe4LF4aPPkeH/LKvNi%2BIkyVnzF7Jn5XDbWPdOBndWwm%2BjX8uYE7Oinzoog%2B4mb54y1kbOc/gsHD45FD45NGM5PMSKeJLM8VKN5NCBn5WS%2ByzPy//q2n8bVKoX/99LBIXDp%2BClhZAbGBERge%2B%2B%2B44YkdvZ2RG1R7VajX79%2BuHkyZNIS0uTcP4cHBwQGxtLyhBpI%2B7OnTtjypQp%2BPDDDzF69GjEx8eTjCCPS5cuITQ0FABQp04dfP3112VyEWfMmIFt27Zh5cqVSExMxO7du5GWlgaz2QyTyQQnJydYLBZSdhoUFISff/5ZlIksLi5G06ZNwXEcevToQTJpNNRqNTQaDYqLiwlnTg6EBudC6HQ6eHl5VWhOznP4HB0d8c0332Du3LnEjJ0Gx3Hw8fFBTk4OKZG0s7NjmsoDT83q1Wo1sabg8frrr%2BP69euYOHEiHj16hC1btkClUsFU%2Bq/gBx98gFWrVuHnn3/Ghg0bcPDgQdIfD3d3d5w8ebL8BSqFwuFToECBAgUKZIDx4/VzIyrqxff5D8XLFd4qUEAhPDwcAQEBWLJkCfR6PVq1aoXNmzcjMTEROp0OixYtQkJCAvHqY3H%2BTp48iU6dOuHUqVOoWbMm2rdvjz179mD37t0ASrJWoaGhJGhgQchNa968OSZMmICAgAB07twZUVFRyMnJIaqgPMaNG4cVK1bgwYMHJCADAIPBgO3bt%2BPDDz%2BESqVCXFxcmQGWq6sr7O3tyWs%2BU%2BXg4IBly5bBw8ODXLesYI/jODRr1kzkXejn5yeaU4sWLeDh4YGxY8di%2BPDhouvxHEEh1Go1GjVqhOLiYnz00UfIy8sTvX/27Fl88803xF7i0aNHIhP6ssbq4%2BNDOIOssks%2Bm%2Bvg4IDz58/DYrGI7tuqVasAAEFBQTh69CgAiII9AMjNzSX3viIoHD4FChQoUKBABpQM33Ph5ZqtAgUUhAHc2bNnsX79ejRr1gxA%2Bf5%2BwFPOn729PTIyMrBmzRrC%2BatTpw4JeCZOnIhBgwZh0aJFZSqN8jw3APjoo48QExODxMREJCUl4dq1a5g7dy7JVi1atAgA4OXlhU8//RRXrlzBnDlzYDKZEBQUBFdXV/Tr1w%2BrVq2CyWRCrVq1oNVqcf78ecLT41G9enWEhYUBKMmq7dy5EyqVCoWFhRg/fjzS0tKwcuVK2NnZSewUgKdKmfHx8SgQ6GHXqlULNWvWJK8vXryIzMxMLFmyhAjNAMDw4cPRrFkzcByH%2BvXrkzUzmUy4cuUKuWbLli1F1w0KCsKUKVNQUFAAnU4HtVqNZYKSP6vVyiw3bd%2B%2BPck8soLC5ORk8re9vT10Op1I9Ic/tnXrVlgsFrRo0UKixvnmm28y/QdZUDh8ChQoUKBAgYL/NZSAT4GCMiDX34%2B3JngWzh%2BPSpUqITAwsMz3WUFAlSpVsG3bNly7dg1vv/027OzscP78ecyaNQsXLlxATEwMVCoVbt%2B%2BLVHC5FUk27RpI7rG1atX0aRJE6LAqVariXUCKyMWExOD3bt3Y9asWejevTvhzeXl5ZGMn6%2BvL9q2bUvWxcXFhZw/ZMgQFBQUwGaz4fr166KsIM/Tq1u3Ltq2bSu67uXLl3Hq1CkAJb57ZrNZlGmz2WyigK5Jkya4evUqHj16RI6xgi3%2BHDc3N%2BTl5aG4uFiUHTUYDCguLkZkZCQsFgsuXryIoqIiiXqoHEsGQCaHTzBm5mswuC1UG5ZlmKTaVU6/jEwxbXEFgbUHsw8weCLUOQBkcZskhxhzkHRNt2EtDjVoVnKeSjoDLP9F2hSMasPkxtDjyciQtqHXhjUHetBUGxaHj%2BY/MT76JP0yOVP0iayJUifK4njJ2BOSpWBtQOrmyfEik/TLui8Ux1mOp55k37AmTqn%2BMteG3uilFjPlnieHFEdvdMYNl3TDeJ7pNWXuG7rigX5%2BWCdR5zD37MOH4tcynhc5Ppt0N3Iew2ehlLKO/V9y%2BCSf8X8XlAzfc0Hh8Cn41yI4OJjJqSvLRy8jIwMuLi5o2bIlxo0bVy6nDigRShkwYADh1I0fPx7vvPMOHj58iJiYGERGRv4/9s47PKoqf%2BOfaemUhCQkJDRBQwugoPSyKIJRqgsIiou4oAICK3ZRUEBFV9xVQQFpi1gAAZUSehFBqkBoARFMhCQQIJIyyWTK74/JPc6ceyAjsL/dlfs%2BDw/cO%2Beeds%2B9zHe%2B531fvv32W6ZMmUK7du3YunUrf/rTnzh27Bg5OTl4PB6Ki4t59913SUlJAeD5559n586dZGVl%2BRmJezwesXWwY8eO1KhRg/nz5wsumtPpJCwsjMcee4whQ4bQoEEDhgwZ4qdEKeNyPDrwiqU4nU5mz55NcnIyt99%2Bu7B/MJvN2O124Z0H3sBs4cKFuu2Lcp0ah9FkMmEymUT7vvw/33/v37%2Bfnj17cvLkSWrWrMmlS5e4ePEiYWFh1KpVi2PHjolsne%2BrKigoiCeeeILp06fjdruFSIxmYF%2B7dm2Sk5P9rBl81TbLQ58%2Bfdi%2BfTtnz57FYrFgt9sFr7B27dp88skndOnShfz8fIKDg3E4HKJ/b7zxhlBOLQ8qDt/IJ59keJmpvAEDBgwYMGAAePDB61/nggXXv87/UtxY4a2BPxx%2Bj4/e/v37WbRoEVFRUeVy6gDBbdM4dWfOnGHKlCmCU/fggw%2ByefNm2rRpQ2hoKC6Xi4MHD/Lee%2B%2BxZ88eoRr53HPP6Th0Xbt2Ff1OS0tjz5494rPg4GC%2B/PJLAMLDw2nZsiVvv/02c%2BbM4dNPPxXedJqi5dKlS3nppZeIiYkBvAHV448/zl133SWycr5bTOvUqSMCklWrVol2CwoKKCkpwV72M6XD4RC2BQsXLqRu3bqirBbApqSkEBcXh8Viwel0kpycLPqg8swD/Dh7drtd%2BN917tyZr7/%2BGvBaO8TExFCrVi127drlZ0Wh9e2f//wnxcXFfsb34A10z507p8tIasGelmmMj48nIiKC8PBwQkNDqVu3rvjx4L777uPcuXM4HA4xH1rgefbsWYqLiwWvsKSkxG98b7/9NoFCxeEzfoEzYMCAAQMGDFxPGAGfgT8MNE5dbGwsW7ZsUXLq4uPjGTduXLmcOl9onLqEhATGjx/PkSNHOHjwoOD8zZgxg3fffReLxUL16tVJSkrCYrGQnJxMeno6kyZNUm7zvByeffZZHnnkERo3bsz333/PrFmz6N69O02bNqV///6sWbOG9PR0GjduDHgDuIcffpiFCxfSqFEjPB4Pn3zyCSEhIYKPWLduXWrWrElsbCwrV65k1KhRAOzdu1cELhaLhcGDB3P77bcTFhZGeno66enprF27ltatW/sFrdp2yFWrVnH%2B/HkRKB04cADw2if4KlUGBQURGRmJxWLB4XCI4MyX%2B3f48GG6d%2B%2BOyWRi165d3HTTTRQWFjJ9%2BnTq168v6oqOjqZWrVqkpaURHBxMQkICNptNWCq4XC7y8/M5V7blas6cOdhsNv7617%2BK9sEr9FJQUEBhYSF2u50ff/wRt9tNcHAwtWvXpmbNmlitVkaPHi3mJyUlhYKCApxOp7CTuPnmm/28CKtWrRrwvTZgwIABAwYMBABjS%2Bc14cYarYEbAtfio3ctSExMZNeuXX7ZOvDytP7%2B978Lzt%2B1QBtbdna2X33Z2dnMmDGDefPmcccdd5CUlERxcTFpaWmAVzkyIiKC/Px8XC4X%2B/btw2w2ExwcLII0l8vFQw89BHgzT8nJySQnJ9OtWzd%2B/vlnkdWD37z5unfvTkxMjDi%2B6aabAGjYsKGfJYLD4eDixYtiu6d2Tz799FORecvPzyc1NZV69eoB3i21xcXFzJ49m5ycHFFXbm4up06dIjk5mZKSEk6fPk1iYqLog8lkwmw2CxuHkpISBg0axL/%2B9S%2B/uYyOjsZkMhEaGuqX4XM4HDgcDmrWrInT6eQf//iHmB8tI6oFugA//vgjn376qaj3SkI/MgzRFgMGDBgwYMDAvxtGwGfgD4PCwkJmzZrFhQsX6NChA5mZmdSuXTuga1NTU0WAo/3p0aPH727f7XYzYMAAkpKSaNiwodhi%2Bs033/D3v/%2BdZcuWcfr0aVauXElSUhJJSUnUr1/fLxs2efJknS%2Bey%2BVi5cqVvP/%2B%2B7Rq1YqoqCgOHToEeBUsO3fuzGeffUbz5s3ZuXMnlStX5q233uLhhx8GYMeOHRw%2BfBi73U7Hjh0ZPXo0breb9PR0xowZI9q5//77RcAaHx8vAhKHw%2BEneKLhq6%2B%2B4uabb6ZevXqEhISIIHTatGm88MILotyYMWOw2WzUrVvXL8umWRsApKWl0aJFC44cOUJ%2Bfj67du3i4sWLOJ1OXnzxRT/Oo4xTp05x%2BvRpGjdujMfjwe12C8XNxx9/nJkzZ%2BJwOPyC0NzcXDweD3a7HbvdLgznPR4PGzdupHPnzgB%2B7VqtVqKjowkKChJBa2hoqN/WzN8TsKlEWzp1kkRbyrbwCih4mzom9vz5foc6gREUwgCffVZ%2BGQXfQVf38uX%2Bx4qt0zoq5cKF%2Bg4GYmItd1Bhzq7T1ijbLi2gUFqQm1JRV3NzpROzZ%2BsLyY1L90W5q1wW2zh1Sl9G7pAk6gGUq8CiEm2R%2B6MbI%2BjmXDmGkyf9j1UTKG1nDki0Rd4CrRBFkXU%2BlEIfsriKXK9iTejW%2BYYN%2BnoPHvQ/DsR4feXKK38OuvurpCLLc64QTgnEyFzXZVlURjE3uveEyodUGpdSPUIeg7yuy7b/%2B0F6PlT6T0g/9imVXaS6dUtWMW75lOo9ezXCKarJKU%2B0RbUmAnmFymUC0MX5z8HI8F0TDNEWA/%2BzkEVbQkJCqF%2B/PmPGjKFJkyY0btyYiRMn0r1799PSRWIAACAASURBVCvW42tI7osTJ06QkpLC%2BvXrSUxM9DNqv1x/Bg4cyNGjR9m4cSOXLl0S3K4PPviAVq1a0bFjRxwOBy1atGDGjBkiM7d06VLBFbv77rs5efIkP/74o%2BC7mc1mYmJiyMzMZN26dVSvXp3U1FRGjRpFfHw8Fy5cwOl0UqlSJRHEwG88vMtBU%2BDUArCNGzcydOhQjh8/TlhYGMXFxdhsNiIjI3E4HFxQKL%2Bp6goKCqJSpUpiW%2BXNN99M27ZtmTt3LtWrV8flcnFaVk5T1Ge1WiktLWXevHlYrVYeLCNta1tCPR4PNptN8DE1MZpAEBMTQ8WKFRk1ahQ5OTns2rWLNWvWALBlyxbeffddli5dqrvOYrHQq1cvFi9erKzXbDZz5MiRgPpgGK8bMGDAgAEDAeCRR65/ndL/v39k3FjhrYE/HK7FR%2B/3YsOGDeVy/kJCQpg8eTI7d%2B7k6NGjbN26FYvFwoIFC5g5cyZOp5OWLVsyc%2BZMP05hr169AFi4cKEwE2/cuLEY2/79%2B4XptxbsREZGArB69Wp2795NYmIiTZs25dNPP%2BWHH36gXr16NGrUCIDRo0ezY8cOIWRiNpuZPn06H3/8MU6nU9gIpKeni0Bs6NChVK1alYcffpjZs2dTp04dMU6tbfBuSwwPD6d79%2B6kpaWxcOFCv0DTbDZz/PhxqlWrRsWKFXn33XfJln411rwFQ0NDiY2NZeDAgWKOwCtkoyEqKoovvvhCZNjuu%2B8%2BsU107Nix2Gw27r77bvFDwKhRo6hevbowiNfON2zYkIoVKzJy5Ejmzp1LSEiImId58%2BYJ0R7tflitVsaPH4/b7SYpKUlw/TQPQe3e%2BBrQlwfDeN2AAQMGDBgw8O%2BGEfAZ%2BMMiUB%2B964Hs7Gzy8vJ0sv8xMTHYbDacTidr166lWbNm/Pjjj35bCwHatGmDzWYLeAuqjKCgIObPn0%2BVKlV49NFHadq0Kfv27RM2ChqnzuVyUaVKFSwWC8OGDeO1117D7XazaNEiwsLC%2BNe//kVRURFVqlThiSeeYMCAAaxatYonn3ySJ554wq89DSUlJRQWFvLVV1%2BRnJzMQw89RFBQkGhby3LOmTOHbt260ahRI7FdUsPBgwcJCQnB6XSSm5vLZ599xuTJk7FarSIw9kWTJk1o0KABAN9%2B%2By1BQUG4XC5q1apFaWkpPXv2FAHnTz/9RFZWFvXq1cPtdosA7sSJE3z88cesWLGCQYMGUVxcLDKj58%2BfF//WRGWcTicTJkzA4/GQkZGBw%2BHg5MmTmEwmjh075jfWQGFw%2BAwYMGDAgIEAYGzpvCZcnhRjwMD/OAYPHsyHH35Is2bNsNlsYsthaGgopaWljBw5EvBuezxy5Ah33nmnn1ef71bQ8rZ9pqamUlxczJIlS2jfvj21atXi4sWLvP766xQXF9OnTx/Gjh3LoEGDmDJlCl27diU/Px%2B73U5ERAR2u52//vWvwpT8%2BPHjACQlJenG1aVLF7H1ELyZwDvvvJNp06YxceJEAPbt20e/fv2oU6cOx44d48knnxTbIcePH0%2BtWrXo3bs3Xbp0YdasWUyYMIEPPviARx99FPBm1Jo2bYrT6aS0tJQWLVpQs2ZN0eZZHw5MUFCQsCZYu3YtmZmZPPTQQ2KbqBYEnTlzhk8%2B%2BYRPP/1UnNO2Y2oWFho0Dp4W/HTu3Fls1bxw4YLfvOT6EI1GlPnXXbp0iYoVK3Lp0iW%2B%2BeYbAI4ePYrH4xHqn5mZmTRr1kw3vwBr1qzhscceIzIykkuXLokAPTY2lqysLPr06cPPP//Mli1bAH/Pwxo1aijrVKF79%2B4icNVwS7VqfsdZWeAb78rHgJfg4WNDkZ0NcXE%2Bn589C7Gx/tc4HOATuOfkgE5gtLQUfGw0Tp%2BGhASpjHThpUtQtoyVdQBejoqPbUduLkRHX3lMcn/ByzfxSf5y7hyUuZP8hi%2B/hPvvF4fyVNjtUJbYvXyX5b6AbpKV81dcDGWZYmUZxYTK/VHNeVER%2BP5WIB%2BruiyPqaiohLCwYP%2BL5IWjqFieczIyQFrzujUqzQPoloC%2BjGpQ%2BflQocLlDr0oKADfLLti3chFdGUU/ZUnVPUcyutPtfTl%2B5KZCdWrX/5z1ThV9cprSzdGxYWqeuQOXLwIPhs61NdIJy9cgKgo/yLylKrGWVgIZb/HAV5aXZn70GXb1i0TxY2RnyHlupFfXNIClfumbEv1IpMfGMV61JVRDVQ%2BJ9cTyDWqMvILR/VCVC4mA/9ruLHCWwM3FMLCwoiJiaFly5bExsaK7Xbx8fEiE1dQUMCWLVsoKCjQefU99dRTAbdls9mIiYnh2LFj3HPPPdSvX5/WrVuzefNmnn76aXr06IHJZOL8%2BfOUlJQQHByMxWLBZDIRHh5OlSpVWL16tciKVatWDbPZzJgxY9ixYwdHjx7lo48%2BAiAhIUF48GlYv349DRs2FIIzWnCnBVa33347mzZtArxqorfccguDBg1i4cKFuFwu/vznP9OmTRsRrOTl5QHe7ZMmk4l9%2B/b5ibY0a9aM9u3bA7B7927atGmDx%2BPh8ccf5/bbb%2BfWW28V4wB0JvManE4nsbGxft58crnQ0FDWrVtHu3btyr0PCWX/q7/88ssik%2BdbX2hoKNnZ2eVm0Xr06MH3338vlEU1aHNgtVrZvn07gPAq1HDkyBGdUuvl8PXXX/PMM8/4/Vn//fd%2BZeQvlbpgD3TfnPyCPdAHe6D70qF0k5Duiy7YU1zoF%2Bwp6gCkb/qKYA/03wYVarvBUryiC/bAL9gD/VTI321A0WW5L6CbZOX8SUGDroxiQuX%2BqOZcjoMUiWJdl%2BUx6YI90C8cRcXynMvBHijWqBw8oVsC%2BjKqQUnf0nVf2kH/xVSxbnTfXeUyiv7KE6p6DuX1p1r68n3xDfZUn4N%2BnKp65bWl/H4uXaiqR%2B6Ab7B32Wukk3KwB/opVY1TDqik/%2BaUbeuWieLGyM%2BQct3ILy5pgeqCPVVbqheZ/MCoLJrkMqqByufkegK5RlVGfuGoXoj/LcGekeG7JtxYozXwh0IgnDqz2UzXrl3ZsGEDBw4cYMuWLSxevJiqVasKr77w8HA2bNig8%2Bp78MEHmT17NomJiQH1x2KxMG7cOOFfl56ezu7duxkyZAjg5RR%2B/vnntGvXjuXLl/Pdd9%2Bxb98%2B1qxZw9KlS2natClnz57F4/GQl5fH4MGDGTp0KJUrV8ZkMolgrLi4mOnTp9OiRQvWr18PwGuvvUZiYiJ79uwRJvTgzXSlp6fz5JNP8tNPPwEI9cqnn36aR8pI0FqWSfPkW7ZsGfv27eOhhx6iTp06TJw40S8oi42NZcCAAQDs2rWLJk2aEB4eztGjR9m%2BfTtWq5WwsDAaNmwIILJzzZs3Z/HixX4WCkOHDqW0tJTw8HCWL19OeHg4VqtVBGx2u51p06b5bX8MCgoSQaTJZKJChQr06tVLGLS7XC6efPJJwBv4t2rViipVqojttRqioqL45JNPOHz4MKtXrxYZ1rZt2wouaEWfLwKa396SJUuIiYkRPn0hPt9m6tatKwzoy4NhvG7AgAEDBgwEACPguybcWKM1YKAM/wmvvnbt2pGdnc39UsYB4NVXXyUpKYkaNWqQl5dHUVGRsFSQ0a1bN51tQ58%2BfYiIiBCKj5GRkVSrVk2I1jRp0kR45M2aNYvevXuzZ88eduzYQYUKFfjkk0/o3bu3MA1//fXXcTqdLFy4kHvuuYfu3bsLoRZtriqU/Uw6ceJEnE4nkZGRmEwmXnzxRdxuNxEREezYscOvn7t372b48OG4XC5sNhv33nsvd999NzExMdjtdh588EGKiopwuVwMGDBAZOz2798v6oiPjyctLY1FixYB0Lp1axGoflkmuR8TEyPuXVFRESNGjBAZ0go%2BP%2B9aLBbGjBnDbbfdRq9evTCZTJhMJr9s4iUfafDZZdL7S5cuJSsrC6fTyT//%2BU%2BRmdWgWk8GDBgwYMCAAQP/CRgcPgM3FAoLC/n888%2BFV9%2BkSZMCEkr55ptvcDqdrJQ9k8rgawwOesuIoKAgsb3x7bffJjo6msTERKZMmcI333xDYWEhW7du5YcffuDcuXNYLBaqVq2q5A5Wr16dzMxMkpKSWFDmi2Y2m3nllVd45JFH6NGjB3FxcbRv357Fixfz1FNP8dRTTxEXF8eJEyeEZcDIkSPJzc3FarXymeTBtmnTJpGdmzp1KjNmzBCfORwOUlNTRdD5448/isBSMzzPysoiOjpaJ2IDCIXO0tJSvvnmG5YvXy62b/7q44U0e/ZskY07evQoR48eBbzbKn05fN999x1RUVGkpqYKoZWcnBzuvPNOUcbXzsF3e6lmG6EFelrGbcKECSQmJhIUFITH4xG2DxUqVODXX3/lww8/pG/fvoBeqEXLoAYCQ7TFgAEDBgwYCAA3WEbuesOYPQN/eEycOFFw2zp27MjmzZuZO3cu8fHxmEwmnWKmCt26dSMlJcVvu2Z6eroIAKsqCDy%2BlhFbt26lRYsWgHfL3/Dhw2nRogVLliyhdevWbNy4kSVLlhAVFcWRI0eEAfjlYNIRYLxZvK5du/LGG28AEB0dTceOHXG73aSkpAhzd7PZTEhIiBA7kX3rZINzt9tNSUmJn9eddl7uk/aZx%2BMhNzdXZMosFouSpwf%2Blgu%2B8O3X9OnTlZlRDRcuXCA4ONiPt6eaP%2B2eA9xyyy3iOCoqihgfAs5rr73GrFmzcDgcYtzwW0AaFRVF7969lX3R%2BIuBQGW83rGjZLwu%2BwQpfIN0Qy3jF2oIyHhdwTvU1Vu2hdgXul2phw5duQ4UBsDffqsvFIBrsO6UbKqOwnhdNmdXuIsHYryuM/hW%2BTnJLtCzZl25IdU1ugGgv6EqQ2rZwDsA43XZSFrl516eYTqgH4PqHStNqty2wudaP07FD0q6%2B6K6eT58ZCAgd2xdfxQenfiIaakvUtS9bFm5bcv3Wznnv/zif6y4efJ1qtuiq1v2XlXMZ3nvH1XFyv92MzL8j%2BV1rjJMl/xclVax8%2BaVX490r3RLS/Eik999gdzu/0/jdflWBVJG9bgon8X/BIwtndeEG2u0Bm5I/H959V2JUxgaGsrw4V4z7aioKHr06EHVqlXZs2cPH3zwAdWqVRPcwe7du%2BPxeMjMzPSro06dOqSnp3Pp0iWx1VHG008/zbZt24SgSIUKFfjHP/5BtWrVGDNmDPfcc4%2Bf4qXJZCIhIYGhQ4dSr149TCYTn3zyCVWrViU4OJjKlSuLLGXNmjUxm824XC66du1K165dAUTwe/ToUdLS0kQ2sHHjxiL4NJlMvPbaa9x1112AN%2BjV0KZNG5H9VM1bpUqVuOOOO8S5%2BPh40WZSUhIhISGYTCYGDhwosnqRkZE8%2B%2ByzANxzzz2i/Pr160UA17x5c9avX88nn3zCgAED/FRIN2/eTKtWrUTfNfh6IK5atQqbzcbLL7/sF7T%2BHh8%2BlWjLli1SUCWbzSrMZ3Xxf1nfNahECnSiCQrFUl29PllTDbokZVlm%2BLJ1oNAOUAnyBCBkoDul%2BFFAJ%2BRSpuQqoBBakOtVaXjoRBxUpsCy4kWZCu5lG1Jdo1KikW%2BorG4BelEHaV2qRFtkvQadAI/3Qr9D5e848hhUCh3SpAaiHaEbp%2BLHIt19Ud08WWwjAHELXX/KvFP9cLf0Y00gikA9e5bbtny/lXMuc80VN0%2B%2BTnVbdHXLCiyBCPBI7x9Vxaq2dQJA8jpXvVul/wtVgjH85S/l1yPdK93SUrzI5HdfILc7EG0VVVvliTCpfjeVb1UgZVSPi/JZNPA/B2NLp4EbAqotlklJSTRq1IiFCxcydOhQ3G43H374IWvWrBFb/WrXrs3bb78t6rmcPUNGRgZ33nkn69evv6zIS2RkJMHBwXz//fd4PB6qV69Ot27dxHZQk8nE008/zZtvvsm%2Bfft47LHH%2BOmnnzCbzaxbtw6LxUJCQgLnz58nJSWFBQsWsHnzZgA/kRCXy8UjjzxC5cqV6dixoxjHggULCAkJ4eTJk6LsgAEDGDt2LGazmYyMDHJycpg9eza5ubm4XC5CQkKoVq0adrudVq1akZ%2BfT25uLqtWrRLBT7NmzUhKSmL06NHccccdLC375fvIkSO0bNlS9GncuHEATJ48mebNm4vgbMeOHSIz16JFC/Ly8sjMzOSHH37gwoULbNu2Tdw3gNtuu03822w24/F48Hg8fPjhh2Lu7T4/SR44cIA2bdqIc1rmLy8vD4vFQvPmzWnevDkjRoygdevWnD9/nr179wr/vQceeIDPPvsMm81G%2B/bt2bp1K7/%2B%2Bit2ux2TySSsMDQEqtAJhvG6AQMGDBgwEBBusIzc9YYR8Bm4YTB27FiRgbPb7Xz22Wf885//JC4ujv79%2B2O326lVqxavv/46y5cvZ926ddxyyy3069ePli1b%2Bikx/l5o3EGTyURWVhZFRUUUFRUxffp0nE4nEydOJCcnh8mTJ9O%2BfXvGjx/P4MGDiYiIEEFBaWmpyEZq3L2PP/5Y15bZbKZmzZpCyAS8QdKmTZtwu92YzWaxlXXBggUsWbKEzp07k5OTQ15eHvv376dKlSqYTCZGjRrFSy%2B9hMfjITY2lgs%2B%2B2W0wKmgoIA9e/YwcOBAbDYbbrebqKgotm/fzl/%2B8he%2B//57bDYbcXFxuFwuxo8f77fdsrS0VAS9u3btwu12YzKZaNSoER6PB6fT6aeuuXLlStasWSO877SM3dChQ8nMzOTUqVMUFxfz5ptvAnD69GkiIiJwOp1%2BnMKVK1delpPpcrlwOp1YrVahhOp0Otm7dy8hISHElcnXq7aNqniLl4PB4TNgwIABAwYM/LthhMsGbkiEhoYyePBgqlatSt%2B%2BfQkJCeHMmTPs2LGDZ599FpfLxZIlS3j77bfp37//7/oSr0HFHfzXv/7F4sWLATh//jz9%2BvVjzJgxtGnThlWrVjFx4kSCgoJo1aoV7du3x%2Bl0Cr%2B%2BatWqMXToUHr27Enbtm2B32wCtC2raWlpHDx4kAkTJogs5aVLl5g2bRpjx45lxYoVtG3bFpfLJTJ0TqeTnTt38sMPP%2BDxeMjJyeHs2bPk5OTw4osv4vF4sFgsLF68WFzToEEDwakLCgoSqpRasOIbDGrIzMwkKysLu93up2o5btw4kcHzvT4yMlLwK%2BfMmSPa00RUfHmF4OX5HThwQPTRNxgrKCigpKQEq9Xqly28HB555BGKiopwOp3CZF6zyygoKMBisfDiiy8qr71b3tZ1Bag4fHd36uRfSM4CqrKCMqFD4m8FcomO86UqoyNIKcpIJ1ScEN3jpCK2SKQVJaVVvk5BNtGdkjl7MqdPVa%2Bi8UDmRkcoknlVp07pr5H4RUq%2BVgCENx1HSuJDqTh85VHbVBWrKIZyd2RKn6puuYzqmkB4VfJtUK4bSWSrPH6U8qSqg/I5xTOlaywQvqM8cFUZeeABcF6VcyP3WV6zqjHJFalIX1L/lHNcDm9X%2Bd%2Bw9HIr0wTzhzwGeaGj77LclurxlpaRckxyPaoxXA3PT57yQK5RtR1I/1Tv8P8IDA7fNcHI8Bn4Q2PDhg0AzJw5U/m5y%2BUiPDycwsJChg0bxgiZ3wOCCwYInzcNGq9Opczom1GUER4ejs1mY%2B7cudSvX1%2Bc7969u/h3ZGQkVapUoUmTJn5bSF944QUsZRv6K1WqRLSCg9S8eXPS09MBbybL6XQK%2BwltLs6dO0fPnj2pW7cuFStW5OzZs7jdbhITE/F4PJw%2BfVoEclarlQoVKtCqVSu%2B/PJL%2BvTpw8GDBzGZTKSlpQFw5513MmTIEKKiovjb3/5GUVGR8LCz2WyYzWYqVKggbA5KSkowm83cdNNNhIaGYrfbsVqtVKlShaysLCpXriyC1h9%2B%2BIFmzZqJYFmG1WolNTWV6tWr06xZMwoKCkhISBBjqFGjBufOncNut%2Bt8%2BDweDwUFBYSGhpKfn4/JZCIiIoIaNWpw4MABv3a0rKLH42Hu3LmYTCaCg4NZvny54CdmK79xqPH1118LKw0NTz45knqNGv12IhCX7XJMeQO5RGUIrCujcB/WlZFOqBLjOi5JAA7VKi6g7joF2UR3Sn5eFM%2B8rl5F44HMjY5QJPOqatXSXyPxi5R8rQAIbzqOlMSHUnH4yqO2qSpWUQzl7siUPlXdchnVNYHwquTboFw3kshWefwo5UlVB%2BVzKnsWubFA%2BI7ywANxLQ%2BA86qcG7nP8ppVjUmuSEX6kvqnnONyeLtKfS/p5Va2%2BcIf8hgU5uxyl%2BW2VI%2B3rNWmGpNcj2oMV8Pzk6c8kGtUbQfSv2vY3HR9cYMFaNcbxuwZuCFRWFjIrFmzhD1DZmZmQPYMAKmpqSJzp/3p0aOH%2BLxTp06cPn2aCRMmkJycTLNmzRgwYAA7d%2B4UZbp3786vv/5Kz549SUpKonHjxvTt25dDhw7xzDPPMHfuXPbs2eO3hbK0tJTvvvuOlStXCu4eQG5urq4/ycnJzJ07F4BDZaqJ9913H02bNqVp06a0a9eOo0ePkpyczE033UR4eLjIrmVnZ3P69GmsViv9%2BvXD4XDwzTffsGzZMsGfKy4uZunSpXg8Ht1c5ubm4nQ6ad68ufDh0/hwzzzzDImJiSxcuBDwZvQeeOABCgsLcbvdOBwOsrKysFgsDBs2TGxdPXbsGL1798ZisdC2bVtMJhNjx471uy%2Ba2byWFRw4cCDgzcydOXMGs9mMQ/p1%2Buabb%2Bbrr7/m4MGD7Nq1i9jYWCIjIzGbzSQnJ1OpUiWiyr60W61W7rrrLgYMGMCpU6c4c%2BYMHo%2BH4uJiOnfuLOr8%2BeefA1lGgMHhM2DAgAEDBgz8%2B2Fk%2BAzcMJg4cSKvv/46ACEhIdSvX/932zMAdO3aVSfacuLECVJSUujatavfNkPwZrL27dvHkCFDWL58OZGRkezYsYPQ0FCioqK4%2B%2B67OX78ONu2baN3795UrFiRkSNHsmDBAux2O6mpqaxbtw6r1UrNmjUZPnw477zzjqg/Ojqa77777rL93bVrFw6Hg6pVqzJ16lScTid//vOfxXbQoKAgscXT4/HQpk0bduzYQXFxMXv37gXgxRdfFAGk2%2B1m8uTJYstk27ZtqV%2B/Pk6nkyFDhgjuYXFxMffddx/LymTHi4uLRba0T58%2Bon8PPPAAqamp5OXlERISQnFxMS6XixdffFEERMuXL2f16tW4XC4R2PmKpZhMJpxOJ88%2B%2B6wQZ5k8eTLgDZRLS0uVwdWOHTto166d8OHTgt7mzZvz17/%2BlV9//ZXKlSsD3kBy8%2BbNfPHFF7osnu/2UV8FUgMGDBgwYMDAdYCR4bsmGLNn4IbB/4c9Q2pqKgkJCVSuXJmXX35ZcOoOHz5M1apV2bJlCzNnzqSoqIi1a9dy1113sXr1anbu3ElUVBRWq5XbbruN6tWrA16uYdeuXUlLS%2BOHH35g2bJlfobigSArK8vPfqFfv35%2BtgwOhwOXyyWClpiYGPG59veOHTv41scrzTfAKSoqYs%2BePTgcDo4fP85LL71EQUEBr732mjBLl6Fl2kwmExkZGeTl5QH4cfuKiorE1tUPPvhAbB3Vrgvy2VpUWlrKpEmT/DKfV/IxBOjZs%2Bdlffj2798vuH5a3zQBmVGjRvkZxMv4PRw%2BQ7TFgAEDBgwYMPDvhhHwGTAAdOnShYULF/qJjGjQtlj%2BHowePVrH33O5XFgsFlatWkV0dDQhISG88MILbNiwgQMHDrB161batWvn50lXuXJlXTZRw/r168V2wytB4wiGhYWxceNGDhw4QEREBCEhISJ7ZTabCQ0NxWQysWbNGoKCgjCbzcLCISwsjKKiImE2bjKZhM2CDC1gueOOO4Swiclk4qabbgK8fL7QMsKEx%2BMhJiZGjFkLerVrIsv4LbJCqs1m0wmw3HHHHWzbtk0EgtpnFouFmjVr0q9fP8DrTQheg/Qr%2BfBplhK%2BMJvNJCQksGLFisvO97Rp0y77mQylaMuf/uRXJhBCv8zgL09IBdAJP1xVvaBj%2BesS5QGIRyiT6wEMPBDhgquaG1nIZerUq%2BmeTu1AV0alkCD1V6UNcj3mRiXaovPqVrwP5XqV/Stv3JQvlBKIAEZAQj6KxVVeW4GI1ah0SeRzKrELuTu6MopByacCMUxXlZH7pxpneetGVW8goje6YQXwXpBPBFKvylM9kGe%2B3LlRDFz%2B3e9qRVHK0apRngtozQZQKBDjdXXl/wEYoi3XBGNLp4EbAtnZ2bz22mtiS6fmw6d5xw0ePJgVK1bQuXNnbDYbeXl5hIeHEx4eTkFBASNHjgS8Hmvylk1AmKSrBDs0SwaN4zZp0iQKCgq4//77KS0tFTy9atWqkZmZKQKG8%2BfPU1hYSMOGDf0CGy1zJbeVn59P27ZtqV69OsuXLxfnx48fz/3338%2BCBQtYsGCBEB7R1Cz/8pe/cOnSJUJCQli0aBGlpaUi06aN1e12%2B5mfezwe3njjDYKCgigtLcXj8TBw4EAyMzPZvHkzHo%2BHlJQUsU3W4/Fw8uRJzGYzEydOJDExkQcffBCAr776SmTywBv02e12SkpK6N27NzNmzGDv3r1CmRS8wbPD4fCblx07djBp0iQxrpCQEIqKivB4PGRnZ7No0SIsFgudO3dmyZIlzJ8/nzlz5pCTkyNsICpWrMilS5fYu3cvI0aMoFGjRhw9elRkOrds2UJkZKSOP%2BiL2NjYy34m47KiLT6%2BioEQ%2BmUGf3lCKoBO%2BOGq6gUdy1%2BnJxGAeITShDmAgQciXHBVcyMLuQwffjXd06kd6MqoFBKk/qq0Qa7H3KhEW3Re3QqDarleZf/KGzflC6UEIoARkJCPYnGV11YgYjUqXRL5nErsQu6OroxiUPKpQAzTVWXk/qnGWd66UdUbiOiNblgBvBfkE4HUq/JUD%2BSZL3duFAOvVMn/%2BGpFUcrRqlGeC2jNBlAoEON1deX/AdxgAdr1hjF7Bm4Y3HPPPWJL59atW7nrxML3JwAAIABJREFUrruEd5umvBgaGiqMvC0WCzabDbvdfhlxjctDZcngyxesXbs2eXl5OBwOkRGz2%2B14PB5q%2BSj3aeIhBw4cEH3/6quv/NrKzc0lKSmJ5s2bU1xczPHjx0lKShLG8h6Ph2eeeUYoT/raFhQXF7No0SJ2795Nbm4u8fHxhIWFiTnQMmtms5nIyEhhT5GUlCT6o2XGFi1axH333XfZrZSJiYksXbqUbt26%2BZm/33rrrcJ4/cKFC5w9e1bMxYwZMwBv1qyRj3Kl2%2B0mLCyMVq1a%2BQV927dvF8GZxlEMCwujpKQEt9tNfHy8UC/98ccfOXXqlLi/O3fuFAqis2bNYvjw4Rw8eNBP1bNVq1a6YK9OnTp%2BWzNrqZQXLwNDtMWAAQMGDBgw8O%2BGkeEzcEMgLi6O22%2B/XRxrPnyff/45W7ZsEUHG2rVr/bhhAG%2B99ZYIqpo1a6b05NM4d5oh95UsGTS%2B4F//%2BleGDBni99nXX38t2q9SpQpWq5Xc3FyWLl0qtlP6onfv3uJ879696d%2B/v%2BCfvfbaa35lQ0JCWLt2LeD17evfvz%2BlpaXY7XZyc3NZt24dHo%2BHjz76iGeffRa3282ePXtISkrCbrdz6NAhfvrpJ8AbLN166614PB4hkhIVFSUyhQ6Hg7S0NHbs2MHDDz8MeLOgvmqmGoYPH85rr72G3W4XGUWTyURYWJjgylWqVImuXbvy%2BeefA95gtaioiG3btokAFrwZySpVqnD27FlhSm%2B320lMTKRRo0a0adNGNy8qbNy4kXvvvVcI2WjweDwsW7aMAQMGiHOyJcfx48fLrV%2BDweEzYMCAAQMGAoCR4bsmGLNn4IaGxqtbu3at8KmT8eyzz9KmTZvr1maXLl0oLCzkyy%2B/5MiRI36fffvtt6xatUocm81mXnjhBf7%2B97%2BTLxkn%2B%2BLIkSMcP36crl270r17d1asWIHdbic7O5vx48f7iaEAJCcn07ZtW5o0aUJYWJiwIgBvdjI8PJySkhK%2B/vprwMuDGzdunPDB69mzJ8uWLeOJJ54QdZ45c4Y33nhDZNMyMjL82lQZnr/wwgu0a9eOZs2a0bhxY8xmMyaTiZiYGG6%2B%2BWZR7tKlS%2BJ6LcDT/rZarYKLmJmZycWLFwkNDWXQoEGAV0U0Pj6eY8eOYbfbRVmLxUKDBg2oUqUKNptNZDOjo6MpLCwkPz9fma30eDwkJSWJtmVoW1UDQSDG64FwQGR%2Bia5MAGQiJXdDGr%2BybYn8Eghf5mo4U6pC5fKhFNcFwpnScfZU5uzlzbmibV2ZAMzuZe92ZSFF4/Kw5CIqDp8u4awj9ekrViWpA%2BF0lccNu2oOnzyn14vDJ51UvY7luQhk3DLnLBCOXCDe7Kq2ZZ6aqp7yOHyB8DEDoKYGxuELgEN8NebiqoGXN3%2BBcCJVC/JquKlXw%2BG7Wh50QP%2B//Ldw%2BAxcE0ye8qTsDBj4A6BTp04MGTJEZN00Xt0HH3zAypUrufvuu3nzzTe59957r1jP888/z1dffaX7ou/xeCgtLWX9%2BvXcddddmM1mP16aL5YuXcrw4cPJyckR2TGz2SyCi4YNG/Lcc8/x0ksvERQUxGeffUavXr3Iy8ujtLSUsLAwLl68yJtvvkmvXr0A73bVoqIiwZ%2B78847GTlyJPXq1aNHjx7UqVOHCxcusG3bNjIyMhg/fjy7d%2B8mNjaWO%2B%2B8k/z8fLZs2cLFixcxm804nU5MJhNmsxmXy0Xr1q3ZtWuXyMDdfPPN5OXlERER4bc9UxuHx%2BOhZs2aXLx4UWyTDAkJwePxEB0dTb169Vi/fv1l59lisYhMnQY522Y2m0UQ6KsyajKZsNlsfkqg2hZdi8VCUFCQTpzHt9/gDYjT0tJo2LCh8DGMjo7GbDYzevRoGjduzH333afs%2ByOPPMLzzz9/2bH54s0331Ry%2BEaM0HPGDBgwYMCAgRsWL710/eucNOmaLj99%2BjSvvvoq%2B/fvJywsjJSUFMaMGaP7gfv9999n2rRpuu%2BOGzduJDo6mpKSEiZNmsSmTZsoKSmhRYsWvPrqq0K47nrAyPAZuGFQHq/u9/jwafw1Fa%2BuWrVqwpJB9adu3bosWrSIfv36iYyWzWbDbDZTtWpV2rdvz9ChQ3E6nbjdbvr3709cXBxOp5OFCxfyj3/8A4Bx48aRnp5OSUkJmZmZJCQkAN4Ap1u3bixevBhbGdk6PDycixcvUr9%2Bfbp06cL27dsxmUy0bduWp556CqfTScWKFXG73bRt25bY2Fg/M/bjx48LrzrtODc3l5MnT2IymbBarZhMJpo0aUKNGjUAb8bvscceE/NSv359qlWrxpQpU8jNzQW8W2u1jJsvOnToILbHgjcgi4uLw2QyUaFCBUwmE%2B%2B//z5OpxOn0%2BkXCEZHR%2BssHKxWqxB60bbkxsfH89577/HSSy9x1113iRdxtWrVhLqoFuyBlyupBaC%2BtgwWi4VKPux9zcYhEBgcPgMGDBgwYCAA/BeqdD755JNUrVqVdevWMWfOHNatW8e8efOUZXv06KH7PhgdHQ3Au%2B%2B%2By6FDh/jiiy9YvXo1Ho%2BHF1544Zr75wsj4DNww%2BD/w4cPYMOGDZfl72moWLEiL7zwAvHx8YwbN44DBw6wefNmTCYTJSUlxMbGEhMTQ1RUFAUFBcyZM4dBgwYxceJEYRfQo0cPcnNzSU1NpbS0lH379nHrrbdy6623MmfOHHbv3k1WVhbgDVKjo6NJT08nLS2NhIQEoqOj2bhxIy1atGDdunVi22ePHj0ICQkRgVSHDh04d%2B4cgwYN8gustH60b98et9tNREQEubm5nDlzhltuuYXS0lIWLFggyv/8889kZGQwatQoERSmpKSwY8cOEhIS/AJKzaoCvEHhLbfcQnZ2Nh6PR2xtfeqppwCvRUOHDh1EO%2BfOnSPZR%2BVSy1g2atSIlJQUkaUMDQ1l5MiRzJ07l5CQELFNMz8/XwSkqm2o8%2BfPZ8mSJeLY5XL5BYC%2BGc/yYHD4DBgwYMCAgf89pKWlcfToUZ5%2B%2BmkqVKhArVq1GDRoEF988cXvqsfpdLJ48WKGDRtGfHw8lStXZvTo0WzatImcnJzr1l8j4DNggOvvw3claLw6ua2YmBjq1auH3W7H5XJhMpk4ceKE4BY%2B/vjj/PLLL4JX99hjj9GmTRsWL15MzZo1adu2LcuWLWPZsmV888031K9fnzVr1uja17aJtmnTRmTmTCYTERERWK1W3G43zZo1E/59WmAXExPjJ5BSrVo16tWrh8fjwe12i8CvtLSUp556iqCgIM6cOQN4t3N%2B8803pKWl8dFHHwl%2B3/fff8%2BRI0dEFs43q6fhn//8J126dMHj8YhtpsOGDeO5557DbDZTWlrKuXPn/K7Zvn27%2BHdwcDAvv/wydrtdZDbBK8azYsUKBg0aRHFxMYcPHwa83odawKcKvkJDQ4VJe6VKlahSpYrf5927d9ddczmoOHydO0vG7bLZk8r0Xd6ZLxGMlD7x8jWFheUWUVWk4zJJxBYVJ0RHCQmEEKUg6OnqUXDOdBw4uS2FoVogvJsyJ5Yrtq0bg1RGSY2ROWgqwzd5LgKZZOn%2Bqjh8sqdeINWqfPjk6wLhF8nDVA1b1x/F%2B1q3TFSTLM1xIPxBXb2qhyoQEp9ckTyBKiKqvLZUTBy5jOKZCsQjU7f%2B5PdCAHxR1X2R21aSicpZBIFco3zXyXOs2FlRLo9OcV/kd4vqeZGnT/Ga1U2XauOHfK68YwjsmZKXiepVrLruP4J/Q4bv7NmzHDp0yO%2BPL5XkSjh06BAJCQl%2BO3waNmzIyZMnld8l09PTeeCBB7jtttu499572bp1KwAZGRnk5%2BfTsGFDUbZOnTqEhIT47TK6VhgqnQYMAIMHDyY1NZWWLVvidrsF/07jdflmkEpLS5k8eTJr1qzh3LlzVKxYUWSHNDz//POUlJToTNNPnDhBSkoKCQkJ5OTkCA87u93O%2BvXr2bZtG927d%2BfChQtcunSJ/Px8ateuDXgDjQEDBvDMM8%2BI%2Bm677TYKy/4H%2Bfnnn9m8ebNfe1oQc/78efLz82ndujWFhYVUqVKFTp06sXTpUrGl8siRI/Ts2ZMXXnhBcPfAm9GyWCz89NNPfjy6r776CpvNJpQ777zzTrZt2wbAyJEj/bKBbrebP/3pT/To0YOJEyeKzGNISAg9e/YU3DrtvK9X4PDhw0Vd2v2YOnUqFotFBGTaODX4KrIWFhYyYcIEADp27MimTZsA%2BPOf/4wKv/zyC7/88ouw6NAyn5pH31tvvUVaWhomk8kvswfe7aO/J%2BC7nA9f/fr1fjshmz3Jx96G/Y/LzOWvdInumjJrjCsVUVUkNaUztFL5TulsnXSVAHL2U2EQpatHZyQHUVHltKUwVAvE46pMmPeKbevGIJVR2lvJwlEqwzd5LgKZZOn%2Bqnz4ZLpIINWqKCbydYH4LMrDVA1b1x%2BF6ZpumagmWZrjQHzkdPWqHir5fgdSkTyBKiM0eW2pDAjlMopnKhCPTN36k98LikWh647ivshtKz0Uy1kEgVyjfNfJc6zYWVGuF57ivsjvFtXzIk%2Bf4jWrmy5F93TnyjuGwJ4peZmoXsWq6/4o%2BOKLL/hAEuUaMWIETz75ZLnX5uXlUVF67rTg7%2BLFi0T43Ni4uDiqV6/OmDFjiI2N5YsvvuDxxx/n66%2B/FjQQua6KFStyUfWL2lXCyPAZMIB3a92nn35KUFCQ4IhFRUWRkpLCkCFDGDt2LJmZmZSWlrJ9%2B3aOHz/OjBkz2L9/P4sWLRIPuRb8lIcpU6ZQpUoVzp8/z6uvvkrTpk15%2BumnMZvNZGRkCG6hx%2BPRcQt9txk%2B9NBD1KtXj7i4OBISEkhPT6dXr16kpKSwc%2BdOweGbP38%2BZrOZr7/%2Bmr179zJ16lTS09Pp37%2B/CGgWL15MlSpV8Hg8hIeHi62PZ86coVu3buzYsUNk2UwmEx06dGD37t2C%2B1ZYWChUTtetWycM5AF2795NWloaEydOBH5T2Hz//fdZsWIFN910k58IixbIVatWjfXr17N//36/ObBYLH7ZRhnallEZWrAXHh5Oz549qVatGsHBwYSEhIi2%2B/fvT8WKFXE4HH7qppr4zHPPPceJEyeUCp516tShgup/zMvA4PAZMGDAgAEDAeDfkOHr168fS5Ys8fvTr1%2B/gLsUqO5lnz59eO%2B996hZs6ZQEa9fv77YsfV76rpaGAGfgRsCgfLqtL3TGqfurbfeYvTo0cTGxrJlyxYSExOJiIhg2rRp1KlTB5PJRHx8PFOmTOHRRx%2B9rDKnjOjoaCZOnEhcXBzjx48nPT2do0ePsm/fPsEtfOWVVwD8BEi2b99O//79qVWrFomJiTz11FN8%2BeWX5Obmim2GGipVqiTEZFq2bMm%2BffuIjo7GYrFQv359PvzwQ5o2bSq2L3z33Xf8%2Bc9/prS0lPnz59O4cWOxrbNJkyacOnVKBHS1a9dmx44dOBwOioqKsFqtfPfddyL400ReAIYMGUKw9NOn1Wpl/Pjx1KlTh7p16zJjxgxuueUWEehp2ztdLhfz5s3DarUSEhIiAj1tG6lvfTVq1CCq7CdXX0sIm82GyWSiZs2awuC9sLCQZcuWcebMGUpKSiguLhb1derUSWyrnTx5sqhHM3KfOXOmeEkPHDiQChUqCEuH06dPB3T/DRgwYMCAAQO/A/%2BGgC82NpaGDRv6/YmNjQ2oO1FRUTqRtry8PJEwKA8JCQmcPXtWlJXr%2BvXXX3WUkWuBEfAZMBAA/hN%2BfU2bNiUoKIgvv/ySgoICHA4He/fu5eGHHyY7O5vhw4czd%2B5cDhw4gNPpFGpPvtBeID179tR9FhERwRtvvCGyYbVr12bdunXUqFGDLVu2iC2eVquVoqIi3G43YWFhWK1W7rvvPux2O0uWLMFkMhEaGupnezBnzhyxPUHbZmq32%2Bnduzd79uzB6XTy1ltvsXXrVrKzs5kxYwYff/wxw4cPx2Kx4HQ6yc3N5ddff%2BX8%2BfOAd/unZr/g%2B0tYgwYNOHToEGvXrhWBZZcuXcTn0dHRPP744zz11FMia1m1alX69Onjl%2BHTAtR27dqJa5977jnx79mzZwOwZ88ev%2B2u%2Bfn5IhNo/51kB0O0xYABAwYMGPjfQ6NGjcjKyuKCD5lTU2IPl/buTps2zU9bALwUn%2BrVq1O9enUqVarkx9c7duwYDoeDRo0aXbf%2BGgGfAQNXQGFhIbNmzeLChQt06NCBzMxMwam7HJo3b85XX33FypUrSUpK8vuTkpIScNtWq5V27dphsVho2bIlTZo0obi4mG7dulFSUsKGDRuIjo5m27ZtREZGYjKZmDx5MqtXryY1NZW2bduKDFX1MsLR888/z9/%2B9jddWydOnGDDhg0EBweTkZHB22%2B/TWhoKFarFafTKbaEFhUVER0dzWeffYbL5WLChAlCOXPFihVkZGTgcDjYunUrffv2BbwvrqSkJJo2bcqhQ4cYOHAgOTk5FBUV8eijj9KhQwc%2B%2B%2BwzmjVrxtSpU3G5XFitVqxWK3a7nWXLlpGcnOz365eW4TOZTPzyyy906dKFpk2bCg6gr8dfVlYWH374IaNGjRKBWk5ODsuWLSM3Nxe32%2B1n7VC/fn0%2B//xzbDYbrVu39msTvIGxtuVDDtjkl3x5UIm2dOwoibbIJuDyMQoxg%2BXL/Q5VRHydgMPateXXu3SproxOhKCMiH75hhSCCF9%2Bqe9gAG7EuqrLgnJf6ETOJM6kSkUhEDNnnTiEypy9TPxHwGebM6BWoZA5G2Vr2g%2ByIIBKqaIc8Q2VaIs8TpUOjTw5SuEZOdOtMJgvT9xHpV2iU49Q3BjdLmnVzZNEnnQXKX640dXrsxVL4Ntvy29bnrDFi6/8Oejut9Ic22dXA6BcE/JyUy0/XfMBKJPonkN5HlQVqxqXlbLlZ1O1IKVxy48cADNm%2BB8rBDXkG6wbpmINByKcEogAlO6c4p0pT9fV1BuI6btqbf2RRVuuBQ0aNCA5OZl33nmHgoICTpw4wZw5c8Rusq5du7J7927A%2B%2BP7q6%2B%2Byk8//URJSQmzZ88mIyODXr16YbFY6Nu3Lx999BFZWVlcvHiRKVOm0LlzZ%2BUP%2BVcLw3jdgAEfdOrUiZycHMHnCgkJoX79%2BowZM4YmTZrQuHFjJk6cWK4wR3miLevXrycxMVFnCC9j0aJFvPfeexQWFuJ0OiktLSU2NpbIyEhKS0s5ffo0devWpbi4mKysLG699VaCg4Ox2WzCvP27777jrbfeokePHgH1a/v27YwdO5aaNWty9uxZHA6HUA3VOHyq14YmtAJejl0gvoZVq1YFUEoP22w23G43NWvWZNWqVXTu3Nlvq6YMi8WCzWbz493J8O271WoVfQ4NDSU0NJSzZ89So0YN7r//fj766CNlxu5vf/sbcXFxftk/Xxw5ckRp56CCYbxuwIABAwYMBIBrNElX4hrN3LOzs3n55ZfZuXMnERERPPDAA4wYMQKTyURSUhIzZ86kffv2lJSU8M4775CamkpeXh5169bl5Zdf5tZbbwXA4XDwxhtvsGLFCpxOJ3/6058YP37879IEKA%2BGSqcBAxLGjh172QDsevr1gZdbeCW0a9eOsWPHEhsbi81m45lnnuGee%2B5h3759DB8%2BnOjoaA4dOkSPHj3Iz89n2rRpvPLKK5SUlBAfH88777xDy5Ytyc7ODrhPffr0oU%2BfPoDX065Lly6YzWYSExOpV68eJSUl7N%2B/n7CwMEpKSmjUqBGrV6%2Bma9euBAUFUVJSQrt27XjnnXfIzc0VQdXmzZuJjY0lPT2dvn374nK5GDp0KH379iU5OVlss/zkk08AL/fv7bffJrNM/75Zs2ZkZmbi8Xi49957MZlM/PTTT5w/f56zZ88ybNgwNm7cyMGDB%2BnXr5/wwnnssceEZ9%2BwYcNE9m/q1Kl07NiRLVu2iK2k2rbOqKgobDYbLpcLp9OJ2%2B2mcePGHDhwgIyMDG6//XZMJhPBwcFUrFhRZApNJhP79%2B8XL/HyYIi2GDBgwIABAwHgOhilX2/ExcUxc%2BZM5Wfp6eni38HBwbz44ou8%2BOKLyrJBQUGMGzeOcePG/Vv6CcaWTgMGfhf%2BP/36wPsyufnmm0UGr0WLFoB377jdbic/P5/g4GAOHDig5BZGRkZyxx13sHPnTl3dvpw6zXdODkBiYmKoW7cu%2Bfn5wo9Pw0033URGRoYf502Fpk2bAtCrVy9uvfVWRo8eTXBwMBEREYLzZzabCQoKYv78%2BeTl5ZGXl8eGDRto3bo1TqdTV%2BeqVatYtWoVhw8f5ty5c3g8Hj7//HMhYbxw4UJRdsaMGSQnJ7N3717B4QMYPnw4ycnJPPHEE7jdbpG91Ob30qVLOBwOkbXUjOCPHj3Kxx9/LCwbzp4968e7O3LkyBXnwxcGh8%2BAAQMGDBgIAP9lWzr/12Bk%2BAwY%2BB34//brW79%2BPe3atWPevHnExsYKNaeSkhKqVq3KyZMn6dixIxs3bmTq1KlMnz5dBEjr1q0jLi6O999/n759%2B/q1vXLlSvHvAQMGiH93795dmLGDl5N26dIlPvjgAzp37szzzz/Pxo0bsdvtZGdnExQUxKRJk3C5XJw/f5558%2BYxbdo0Xn75ZdGPvXv3Aogg%2BYknnmDKlCnk5OQIw3STyUTjxo1JTExkxowZ3HTTTRw5ckQEoMnJyWKewRsUaRk4LUDyNV8PCgoS2TktkHvkkUf8tnvKgaTmqZeRkcGxY8eIiIigUqVKZGVliTY0VVNNldPXL1BbA7I335XQvXt3GjRo4HfuFpkj%2Buuv/uZS8jFeboafb9TFi37eU9KhFx6Pv7GVopDLJflnKcpcuiRZgEn909WBl0Lj5z2lO6G/UFUPv/wCiYm/HV%2B4oDPH0s2NPH%2BKawJp226X/KlU9SxYAA8%2BePkyhw6Bj9kuoJ/QU6egVi3/MvKgVB2U7690TVFRic6Lr6jI38%2BrsFDhG%2BZw%2BHu16SYYL%2B8wPv6KReT%2ByW0XFyusz6SKVMMuKZH80FSNS%2BtYnir5WFlvbi7I/Bp5bakGIU/quXPgq7CsehakDunWnuqkYnLkcQU0f3K9qvmUK5JvpuqyQB4qaa0p15F0jTydgP5eZWSAbN0jVS7fBlXb8q1U3Rf5ukCmTzU15ZVRXRNI24GUUa43A/9zuLHCWwMGrhH/33594N3W6XK5yMnJITk5meTkZDp27CgUK%2B%2B%2B2yvy0bNnT9LS0ujRowddu3YlLS2NtWvXUq9ePYYNG0ZQUJDIptWqVYsRI0awf/9%2B0tPTRQDYoEEDYmJiMJvNWCwW4uPjiY2N5VuJhF%2B5cmURFJaUsby///57kpKS%2BPDDD5XjcDgcOBwOxo4dKzh7H374IcnJybhcLlasWMH06dPxeDxifipXrszAgQNJS0sjLS3Nz/aiVGKga/0FbzBn8/lfS7NeCEQquXLlyvTs2ZOCggJOnz7tl3Hbt28fDoeDPXv2AL9l43w5jYHy98BrvP7MM8/4/VlfpmoqEIDxuu5LkBSUqcyxdd9oFYV0AZaijM5vXOqfyqlE58usMGqWL1Q6nvgGe6BwWVfMjTx/qjURQNu6L0CqenyDPVUZOdgD/YTKwR7oB6XqoHx/pWtUxutywlmpQSQrFKvMu32CvcsVkfsnt63yH5crUg1bZ36talxax/JUqSw%2BdfWqxBTktaUahDypcnSiehakDim/fMsnFZMjjyug%2BZPrVc2nXJFi54LuskAeKmmtKdeRdI0u2AP9vVL5tEqVy7dB1bZ8K1X3Rb4ukOlTTU15ZVTXBNJ2IGX%2Ba4I9I8N3TbixRmvAQDn4b/PrA2jdujUJCQmMGzdOBD67du1izZo1pKen07t3b6xWq8hwvfnmm7qMYWxsLBUrVuTee%2B%2Bla9eurF69mieffFJkqjR07tyZjRs3sn//fvbt28fSpUv529/%2BxtoyBcc333xTBE1jx44lLS2NgwcPivHI42rcuLH4txZwmkwmEYxpQaovf077u6ioiJSUFJ5//nnlvGh1hYaGsnDhQg4fPkyzZs0Ar%2BXEo48%2BCsArr7xCYmIiHTp0YP78%2BeL6ESNGkJ6eTnp6Ops3bxbbK7Ws4s0334zZbBbCMnFxcSQlJeFyudi0aRPBwcEEBwdz//33%2B/kMligl9NRQcfgMBp8BAwYMGDBg4HrCCPgMGLiO%2BE/49QGEhoayZ8%2Bey3ILt8pS%2Bb8DcibtSn145ZVXSEtLY8KECURHR/P000%2BLz7VgNS0tjdTUVACRkQNISUnh3nvvJTIyklatWmE2m/2CSV80atSItLQ0wsLCqFSpEk2aNPHrq81mE5m2goICpk6dysMPP3zZYCwuLo5nn30W%2BG2r548//ojb7RbG9NnZ2aSnp2MymSguLqakpITS0lK%2B/PJLHD6S3b78QQMGDBgwYMDAdYCR4bsmGBw%2BAwauAwoLC/n888%2BFX9%2BkSZMC8usrLCzE7Xb7cepkdOrUidOnTzNhwgRef/11goKCSEpKYvTo0dxxxx2Ad6vppUuXaNmyJSaTiUqVKtGwYUOCg4PZu3cvgwYNYt26dezcuVMERxo0vqAMt9tNeno6M2fOpFu3buJ8dna28OB7/fXXRbBjt9uZOHGi4PS5XC6GD//NXqBJkya4XC6qVKkiMnFTp05lapmv3IoVK0TZ7du34/F4OHDgAA0aNMBqteLxeITVw8GDB0lOTsbhcFBYWEjLli1xOBwiWMvNzeW9994DYMqUKUyZMgXALxM3bdo0ZpT5M/maubvdbk6fPk3z5s3Zs2cPQUFBFBcXYzabMZlMWCwWsSVVtaVTJcRyORiiLQYMGDBgwEAAuMECtOsNY/YMGLhKTJw40Y9Tt3nzZubOnUt8fDwmk6lcH7rdu3fTo0cPUlJSxNZC7Y8cAFauXJmXX36ZtLQ0tm7dyl133cXQoUPJzMykoKCA8%2BfPY7fbcTqdOBwOzp07x6ZNm1izZg2TJ08mKiqKkpISTp8%2BTWpqqujozunJAAAgAElEQVR3cnIyhw8fFu28//77fp8NHDiQlJQUkf26EkwmkxAv0Xz7fLdjan5%2BpaWlbNq0CfAKxixYsEBcb7V6f4PyeDzElJExzGYz69at0ymg%2BmbVLl68SGFhYblzrtWvtafB6XQSWcbrCQ4OJiEhQVgtaEIvbrdbiNPcdtttwsxexr333nvFPvgiIOP1jz668jEKD%2BM1a/wOAzJel7mDqnoVZtM633JZETYQ43XVDx4BGK/rTvls2dWgs3icNcv/WGHmHIjxuu4y2VQd9C7Qsjm7ypBcvkbhUakzjpbN2kHfaekalfG6bGGpNF6X%2BqxMmst9Vu0SkDwu5baVZs/yYlM0rluPKl9OuX9yGUW9uv4sXaqvt%2By9dqV6dHMh7wj4Nxqvy49iQMbrskm94sboHvFt2/QVS%2BtG6QB96pT/sfziUi3In3/2O5S7C%2Bjfmap6pDUgz7Gqv/JjqFt7XKXxuqIxeY4DeUcFYqouL33V4/JfY7xu4JpgBHwGDFwlNA6bxqn717/%2BJbJn19uvb/To0YJbGBoayuDBgwVfcObMmXg8HsaOHcvRo0f9AsfBgweLOoKDg0lJSeHIkSN%2B2ys1lciqVauKoDItLY3p06dTWlpKjx49/AIl8AY1hw8fFvy70NBQwTGcPHkyoaGhHD16VFgUhIWFsWDBAqZPn87s2bNFZstX1bR///4cOnSIxMREqlWrxuuvv%2B5n5u4LzcbBF7fddhtpaWkEBQXRtm1bsV20WrVqgDfA07J%2B2rEv8vLyAO/2TkBYVcgiLDabjbNnz5KZmUnlypXp06cPoT6s9kRZSOQKUIm2fLtlnX%2Bhxx%2B/8jEKkYm7/YNGlXerbqesz724bL3du%2BvK6IQ9yrLOl29IIQygyDDr1CMUagK6UwMH6sqUUTB/Qxm3U0CnOqOvVydkobps2DB9IVksYsQI/2PFlm/dNboBoFeUUKnyyJ2WrlGJtsgaI4qp0fVZNTe6PgegBCG3rRSKkBebonHdelQJp8j9k8so6tX1p1cvfb0dO5Zbj24u%2Bva98uegu3fKOZeFSBTiTvKjqBKn0TUvq6AobozuEW/dWl%2BxtG5UbesEiuQXl2pB1qzpd6gUbZHfmap6pDUgz7Gqv/JjqBI5uhrhFFVj8hwH8o6Sz6nKyEtf9bgYoi1/DNxYozVg4DrAd4tlcnIyzZo1Y8CAAX5edx07dmTu3Ll06tSJxo0b07ZtW0aNGsWxY8eEX9/zzz%2Bv9Me73BZLGRpf8OOPP8blcvHGG2/4Zec6d%2B4s%2BIIZGRk4HA7WrVtHo0aN6NChA2%2B99ZZfliwnJ0eMKTk5mREjRmC1Whk6dKjYsvjLL7/gcrlYtWqVKOdyuahUqZLw45s3bx52u50GDRqI7F1JSQl/%2BctfGD58OBUqVKB9%2B/a68WjB35kzZygqKqJ9%2B/YkJiYSGxsrhFPAG6jdc889pKWlYbPZhIhMeno6999/P06nE6vVKgK%2B0NBQbLb/Y%2B%2B846Oq0j7%2BvTOTHgIJAQJID2QpASFRX5cgJUi1IBYWsbC4gMqyrLi4sCIsrIqu5X1dpYuCLiggLL2DdAsSSqjSewikENIzM/f9Y3KPc889kBF0V9f7%2B3zygbk5/Zx7c595nt/zC0LXdT788EPRzsSJE4VxO2DAADHH2267DfDxAaOiomjTpg3gC8GNjY2lQYMGwkN59epV5s%2Bfb1rHdeskg%2B06sJO22LBhw4YNGzZ%2BbNgGnw0bN4CKQizXrVuH0%2BkkJCSEOXPmMG/ePEJDQ3nggQfYsmULqampN9x3QUEBM2bMEHxBt9tNeHi4yTtnSDKAz0ibMWMGDodDJDW5fPkyM2bMoFWrVtx///2i7aefflrw3NxuN0VFRZw6dYqmTZuSmJhIt27dAJ9UhNGPw%2BHA4XDw9NNP4/F4mDdvHqNGjTLx5QyOXMuWLVm4cKFSusDtdrNhwwa8Xi/R0dF88MEHnD59WshRPPnkk6KtFStW0LVrV8rKysjOzgZ8nLwzZ87g9Xq5cuWK4POdPn2aRo0aAbBt2zbR3%2BDBg0lISCAhIYEZM2aI8gsWLCAxMZGysjLy8vJEIpzc3Fyys7OpWbMm9cq/VTZCSP1DSbOysgLeS5vDZ8OGDRs2bAQA28N3U9B0XRlJbcOGjWugU6dOFBQUmMIsAbp06cKTTz5JZmYmixYtYsGCBUyfPp21a9dy%2BfJloqOjiYqKYtCgQdx7773Cw9eqVSuTjIK/hy8oKEgYaUbCkPDwcJo2bcrTTz/Nli1b%2BOCDDwCfFEFKSgpDhgyhSZMmor3ly5fz5z//GYfDQWpqqujr0KFDDBgwgNzcXGGwOJ1OnE4ncXFxrF27loULFzJq1CjAJ4Og67oYj1G2VMVDwhdKWatWLb755hvB7ZPF0jVNw%2BFwXJd7N3HiRDp37sw333xDPz9dM2NtgoODhfh5tWrVOH/%2B/DXbqlKligjddDqdeL1e5EdgpUqV%2BOqrr2jRooXS%2BKpXrx7Lli2ja9euyr4MXl4gOHDggCX0t0n9%2BvzKT84iEGFc%2BaJFPNmijm6toxS6lpSalX1LItuBCF/LZZTtyoUsDQe4NhkZUB6mqyykEjaX%2B5LFxgEyM6F69e8%2Bq9S6Dx2CX/3q2u289541zFMSZ1cKScv7qRqffE36rBJetyyFJFAO1uU7dw5q1zZ3LR0JZZmrV80Re3JXsoY5WKetEoS2XFOtjSzEHYiKtVwngM6V51Eej9xOAOdcddZu5DmhKlRhX6qGA3lOSGuqul0s6y41FMicjh%2BHhg2vX8ZyQBWX5G04cQLkPGyWZhRnQr6kOjYBPOoqfGYq1yaAfQmkb4sK/X8K5QneflD4JZb7b4edpdOGjRuAbOyBVZIhNjaWUaNGCYNJBdnY84eRFRJ8/Ltq1aqRmZnJwoULiY6O5qGHHhLGlsFpO3DgAA8//DDz5s0jISEBgAYNGlBWVkazZs2EFwvgV7/6Fbm5udcVDTcExuXrTqeTl19%2Bmd69e4t%2BZGRmZhIVFUWrVq2YOXMmvXr14tSpU7hcLmFIVatWTXjEHA6HKVsmIIy466FRo0ZMnTpVhH2qxuNwOHC5XDRu3JgdO3YAvgQutWvXJjs7m4KCAqKiosjKyhJJWipVqsQVReKDgoIC8vPzyc7OJjo6moKCApxOJ0XlzPZevXpdd7z%2BWLJkiSnMFOAPQ4eaDL5AOCDyRcsfbBVnRaqjFLqW3siUfUsvToEIXwdAzwuIgBLQ2vgbe6pCKmFzuS8V187f2AM1ycff2FO1Ixt7YBFnVx5/eT9V45OvSZ9VHD7LUii4gfLyyYYcWI6EsoxMz5K7UlDQLNNWcYss1wLhSQaiYi3XCaBz5XmUxyO3E8A5V521G3lOBMSLlftSNRzIc0JaUyWHrwKiWiBzshh7qoryAVVckrdBlXTb0oziTMiXVMcmEK5dRc9M5doEsC%2BB9P2TMPZs3DR%2BWf5MGzZ%2BBMghlmfOnKlQksGAnDEzMTHRwt8zjJ527dpRVlbG4sWLefvttzl58qRILOL1emnWrBktWrTA7Xaze/duwKfB9/XXXzNq1Ci%2B/fZb1q1bR79%2B/Xj33Xdp164dHo/H4uEyvHulpaUcPnwY8IUe6rpOlSpVABg4cCC9e/fm7NmzpnEGBwcLXl1YWBjffvst2dnZhIeHCz27SpUqCUM2MzNTePcMgw8QwuyAkE4wUL38Rfujjz4CfEazYez5ewqNPgxjT9d1TvplgSspKRGZTTVNI7L8j1rVqlVxOp1Uq1bNlBwmOjoal8tFkyZNmD9/PsXFxeTk5FBaWiqMPf9xBQKbw2fDhg0bNmwEADuk86bwy5qtDRs/EG5WksFAt27dTLy79PR0YdB06NCB9PR0dF3n3LlzfPLJJ%2Bi6zrvvvsvs2bOF/IHhAdu6dSsrVqzA4/Ewfvx4OnTowBdffEFqair9%2B/ene/futG7dmsTERFatWiUMMDmBSkZGBomJibRs2ZL09HQANm3axN69e3nllVcAmDp1qonTFxwczKZNm0hPT%2Bell16ioKBAaAyePXuWDz74gKKiIjRN44UXXhAJVW6//XYhhzBx4kTef/99wJcwxTAAjUQzBofPGLdhCC5btozdu3dTVFTE5MmTAQgNDSU5ORmHwyGStsTHxwvjzsDFixcpLi4Wa2zMf8CAAWiahtfrFRxHI/Q0KyvLZKhFRUUR6uceM4xiGzZs2LBhw4aNnwJsg8%2BGjRvAjynJYHjtNm7cSGJiIpqmUbt2beGBuuOOO0Q/cXFxXLp0iYiICOrXr09YWBgul4vY2Fh%2B/etfM3/%2BfKEX53K5qFGjBiNHjmT58uXC8Nm8ebOp/7i4ONLT0%2BnUqZO41rlzZ1q2bMmLL74IILJkGtp4paWldO7cmcTERCZMmEBERIRJlNzgtOm6zqhRowT3LScnx%2BTRNDJ9gjl5yejRo5k1axaAMK4Mjb%2BnnnpKiNBvL9d/Ki4uZseOHXi9XmF8Hjx4kNjYWFOiFF3XcbvduN1uosrjxTp37swHH3wgDMD58%2BcDvmQsuq5z4sQJWrVqRWxsLFWrViUvL0%2BEgRrjCRR20hYbNmzYsGEjANgevpvCL2u2Nmz8G9C1a1emT59Os2bNTKGanTp1onv37sIztXr1apO8gfHjbyi53W50Xefs2bMiW%2BSePXsAn5E1bdo0atWqRb169Th16pQILczOzmbp0qV06dKFxx9/nEWLFnH77bezYsUKoUXn7%2BkydPbKysrIyMhg4MCB7Nq1S/w%2BJyeHsrIyLpWr2q5atYq0tDTl/EtKSigoV6ANDQ0lKirKouNn9H306FHmzJkj6pWUK8M6HA6TgPmHH34o9PEMDBkyhMqVKxMfH8/GjRtJT08nJSVFOSaHw0FQUBD169enSZMmIkzTP5zV4OsZHsQiSW3WGLOu66xdu5asrCxLRk5N0zhy5IhyDCqohNfvvlsSXpdFtcuzkpog596S6qjEfi1KvgrxboujWiUCLheSVXoVPEjLgGSxcbAqACsmYRESVqyNRTRYnqesngyW9VStn0WgWCV%2BLrctz1O1l7I4u2r95L7Kv5y47nCkvVMJr8t61KopyYuhPFuS%2BrVqmvLeyd%2BRlUt4XneAquNoqSeLdyvGZ5m4Qpjbco4Ua86FC6aPqqNlaejMmQo6UoxHpY4tz0k1b3mzVJEocl/y%2BVP1HYi6uDQv5bmRlcvlA6gSTC9/XhtQftcqH0DFxlS0NKpHlLxVStF3%2BaKqkHxNdc/L1%2BRzozqP8jVVGXliqokqN%2Bs/ANvguyn8smZrw8a/AQMGDMDr9Zr4cbquk5WVxYkTJ9i3bx/gMyiulyTXCCn0R3BwsPAmhYaG0qhRIzRN48KFC%2Bi6jtPpVLY5btw4/vWvfwHwz3/%2Bk1OnTpkyZaq8SkY2yxtBSUkJmqahaRoFBQVCK8%2BArusi66gx3okTJ7JkyRLA5%2BVav369KH/y5Em8Xi%2BHDx8W4bLHjx%2BnQ4cOTJkyhQsXLpCTk8PcuXOV4/F6vXi9XrZu3cro0aMJCQnBKSUIMMaxd%2B9e8vPzxXoaCWsGDx5M06ZNiYiI4J577gF8CXH8jVlN0%2BjYsWPA66QSXt%2BwXtLxk7NZSGtZ3vF16wSSREGVoMOSu0IlAi4XkrMSqLJvyAOSE2KANYtMAMlfVGtjSZIgz1OVkCCAZAeWJDcq8XO5bXmeqr2UE7mo1k/uS5EVxTIcae9USVvkpCiqKQWUSEPKNKOaprx38fHmz02bKtqVBqg6jpZ6cnYYxfgsE1ckObKcowCy1ShzXcgNlUdgXLsjxXhUGZbkOanmLW%2BWKjmN3Jd8/lR9B5JBJJCENrJyuXwAVcmnpORJ8jkCrAdQsTEVLY3qESVvlTLBknxRVUi%2Bprrn5WvyuVGdR/maqow8MdVElZtl4%2BcGO0unjR8EnTp14uLFi%2BLlODg4mISEBBFqB5CXl8fkyZNZs2YNly5dIioqiqSkJJOMwMiRIykpKbFkrjSkCtavX88tt9zyvccDvoyQd999N3/4wx%2BIKP/DoipnYMKECdxzzz2MHDmSxYsXi8QfHo%2BH2bNnU6tWLSEWbozdKAe%2Bl39DwiAsLIyUlBS%2B%2BOILVq9eLbxsNWvWFCLebreblJQUpcyBIWvgjxMnTnDgwAHx2WhrxIgRNG/enP79%2BwM%2Br90f//hHNmzYAPg8WXJiGH%2BDr6ysjC%2B//NJiBGqaRqVKlcjLy6NZs2b89re/Jbb8j4O/t9DfQ2l4yQxO3v/8z/8QExPD8uXLxZwNHD58mHHjxonP/r8Dn%2ByFv0yFMZfg4GA6dOhAcHCwSN4SGhrKrl27GDt2LAsXLsTtduPxeBg8eDDVq1fnavm33/6JYvznO2jQIMCcBGbatGl4vV6qVauG2%2B2matWqlJSUmMYZEhKiDNO8FuykLTZs2LBhw0YA%2BIV55H5o2Ktn4weDP69NJUbet29fjhw5wrRp09izZw/z588nJiaGPn36iGyQP9Z49u7dy9SpU9m2bRuvv/76Ncv5/xheHDAnV1m/fj39%2BvVj%2BPDhTJkyxdSWUc7pdNKjRw8OHz7M4cOH2b17N%2B%2B99x5Ny79%2BXrZsGQ6HgzZt2oi6W7duNRlO3bt3x%2Bl0EhUVxYgRIwgPDxceMAMDBgzg3XffJTIyEo/Hw5gxY1i9erXJO/faa69x7tw5UlNTiYyMFHw/A126dEHTNGGoHjx4UMzBSEASHBxMUlISTzzxBJqmsW/fPlq1aiX6SU5OFuvjnwTG5XIRHR3NkCFD0HWdL7/8khUrVlCjRg0qVaokjPewsDAqV65s0viTPWdhYWF88skn4tpdd93Fpk2bCA8Pp2rVqvzlL38R/Mbi4mJatGjBvHnzTOs1c%2BZM8vzCgrxeL1FRUbRv354nnnhCiMW/9957IlOo4Qns0aMHDoeDtm3bEhoaSk5ODiUlJTgcDho3bgxAUlIS3wc2h8%2BGDRs2bNgIAHZI503hlzVbG/82hIWFMWDAAKpXr87mzZuZPn06%2Bfn5TJo0SYQh1qxZk7Fjx9K3b1%2BRqOTHgqZpNG7cmIEDB7J27dqbaqdWrVr07duX//3f/%2BUf//iHKdX/teD1ejl48CAHDhxA0zTBzfPn8D3zzDMmQ23lypV4PB7y8vJEFtAGDRrgdDqFYZiXl8fgwYM5c%2BYMmZmZzJ07l5KSEt5//33R7ueff07Pnj2pXLkymqbRpUsXzvjF/69atUrwBcGXtMQYs4GIiAh27drFxIkThUesU6dOwsO3Y8cOEhISSEhIMCWBcbvdFBYWMn78eHRdx%2Bv1EhoaysWLF8nLyxOyDuHh4SZjt3Llytx7770A1K1bF13XKSwsZMCAAaLMli1b0HWdbdu2kZWVxT//%2BU/LmoOZp5ebm0u2xOe4cuUKGzdu5KOPPqKkpETIStSvX5877rhDePk2b95MREQEHTt2ZMmSJXi9XvLy8vB6vRw5cgSn08k777xT0VEwQcXhu%2BsuicMXAA/DQrGQiCwWrhtgcS4eOlRxu6dPW8pYeFR%2BUh2AmpMkc2jkOorOldHP5eHRAop7UaJVWfgyKnpKILRJiTqk6AifWrM/pMVScn5kro7M6QPrhqrIdjLXSiIlqTh88vrJw1e2u3evtcyWLRWPTz4X8oFUeL9lXpqqiOWsB0LAlAlbCm6bZa9UmydzzGROmmqA8t%2B%2BAA6k6n623EOKNZfvISU1S74ocwEVESiWezOAvpWJrOXngnxGVM8S%2BZqKYyjfm6p9kcvIc1Dt96lTpo%2Bq54SlHdW9cCNl5DkEsjaqMgH0rapm4%2BcH2%2BCz8aNCFiMPVojgvvDCC7Rt2/bfMp6yH5B8fNddd1G/fv1rGpD%2BxlyLFi149NFH8Xq9VK9enW7dupkMEcMYMkJLw8LCGDNmjPj9rl27eOyxx0hMTDRxA0NDQ02adeDTsPM3nmrVqsW//vUvYUz279/fEi4J33myzp49y8GDB9F1XdTJycnB4/GY2m3cuDH16tUDfKGRRn05GUy8RKooLCy06O1lZWWJhC3gSzpjZMc87WdkqITQrwWjD8NgM8ZVq1at69YrKytj0qRJtG7dmq%2B%2B%2Bkpcv3r1KlevXuWTTz7hwIED6LpuOk8ej4fk5GQmTZoU8BhVHL5tWyUOXwA8DAvFQlpzlZiuxbkoi4Sr2q1b11LGwqOSQ65VnCSZQ6MK05Y6Vwo1t2hh/qwQUbeII0t8GRU9JRDapKy7rhJztqg1S4ul5PzIXB2VOLu8oSqyncy1kkhJKg6fvH5KOVG53ZYtrWX8su1ec3zyuZAPpCo0WuKlqYpYznogBMwAhNcte6XaPJljJnPSVAOUOVMBHEilOLZ8DynWPBANdctFmQuo%2BBtuuTcD6FtFH7Q8FwJRLZevqTiG8r2p2he5jDwH1X6X//0zoHpOWNpR3Qs3UkaeQyBroyoTQN%2Bqav8R2B6%2Bm8Iva7Y2/m34ocXIDS20G4XhYZs%2BfbrwHBnw19QzfozQwIrQoEEDk/i4MXaPx4PX66W0tJTS0lLhoWrVqhV169blvvvuQ9d1U1IXp9NJw4YNAZ/3rG/fvkL4u3r16miaRlRUFFWrVgW%2B4whmZGQAPiPx7rvv5k9/%2BhPp6eksWrSI0tJShg8fTlRUFOvWrSM/P58ZM2YIDmNcXBxBQUEkJyeTnJws5vHAAw8o5%2BtvpB4%2BfJiBAwcC8MQTTwjDauTIkTgcDuLj40lISODYsWNUrVqV0NBQwvz%2BcjgcDpPB5J8V85ZbbhFZOV999VUlx1LWOjx69KgpXFM19rCwMBwOB5qmUadOHWrVqmUxmCMiInj22WctEg5xcXEsXbqUWbNmiYypBgxj1%2Bl08uyzz15zDDJsDp8NGzZs2LBh48eGbfDZ%2BMHwY4qRL168%2BKbGk5iYyOOPP06PHj144YUXTOVUHD5/z871YHgw5bE7nU569uwpOHzTp0/H4XAI4/BX5d6UunXrCt7bhAkTBAesUaNG5OfnC6Px4sWLeL1eiouLRX%2B6rvPQQw9x3333CW263bt3k5qaCsBDDz0E%2BIyx/fv3izpLly4VhpHhtatVq5bwpKmMq/fff59Dhw4JbT6n00mTJk2EgPo333wjyrpcLrxeL0ePHuXAgQOUlJSQnZ1NaWkpjz32GEuWLCEkJIRq1aqZDMDK5Z6NoKAgzp8/T9%2B%2BfQGf3qC/MWpA5d00ULt2bSIiIrjzzjsBqF69OrfeeivJycn8/ve/R9d1srOzeeONN0hLS2P//v389a9/Ff0DNGnShObNm4s2e/fuLZILGQazpmmMHTuWXbt2ERISgsfj%2BV7ZTW0Onw0bNmzYsBEAbA/fTeGXNVsbPyp%2BTDHymx3P1KlTKSsr4/7777dowt0ovF4vhw4dEl656yElJYVOnTpx7tw5ofMGPk7ZlStX2LVrF926daN79%2B6Az1s1ffp0ACpVqsS%2BffuoXr06RUVFJg7a/PnzWbx4sZBq%2BOCDD5gzZw6pqanCe%2BR2u/F6vSKkMSgoSKzB5cuXuf3223E6neSUx%2B57vV5hyBkYNGgQLVq0EPw8j8fDU089JcZrGJQAf/vb34iOjqZDhw7ouk58fLwwFGfPnk2/fv0oKSnh0qVLeDweYTwZ4ZplZWV4vV7effdd0abhHfYPFy0uLraEphrjO3fuHAUFBXzxxReAT1tv7969uN1ukdnU4XCIRC%2B33norr732GoAw8k6cOMHOnTtF25MnTyYxMZFJkyYJz6Su64wbN46WLVtSUlKCrus3rcOXmipx%2BALgF1kIMhLpQkVZsTSj0u2SCylCoi3UFpmHo%2BD8yBwkFT8vEGkvC4lLxd%2BROqtAshCwLoVKT80yb8UkLGsslZGltEAhwaUg0lk4NSrPtnxNWislh%2B/rr00flUdZHrSKuygvjtQuYF14eZ4KTqnct5JbJJ%2B3QA6XXEZRx0IzV92Hct8K3ldFt1QglEOlvp9UKBB%2BnnL95IpyIdW8pToqjqE85oD4g3Jfivvb0pffM/tazSi5dpKAo9yucrxSX0rdSplzHcj9YiEIYyVLyw8pxdpYtkq1MQHoBCrP238CtsF3U/hlzdbGfwxdu3Zl3rx5llA4gBEjRjBz5swftf%2BUlBRSU1N56aWXrqt9932wYMECsrKyuPvuuwMqP27cOJxOJ6dPn%2BZi%2BcP7ypUr3HnnnVy5coXk5GSRlOT8%2BfOsXbsWTdO4evUqiYmJnDt3Do/HI4yckJAQatSogaZphIaGUlRUxL333ssHH3xgCjP1F3A3sGbNGvG7L7/80mQ0appmER03QkojIyNp2rQp0eVx/jVr1mT16tUi%2B6gBj8fDkCFDAB/3bdu2bXi9XgoLC4mMjMTpdJKYmEiUSlepHP7G3Lfffgt8J6AOvsyhhrC7gWK/P3oGl9EwEgcNGiQykhrjOnXqFIWFhZSVlYn%2BDMmKiRMnmrxtRr1nn33Wsj7%2BZW677bZrzkmGisO38XOJwxcAv8hCkJFIFyrKiqUZlW6XXEjhUbVQW2QejoLzI3OQVPy8QKS9LCQuFX9H6qwCyULAuhQqPTXLvBWTsKyxVEaW0gKFBJciFN7CqVHdR/I1aa2UHL5yCR0D5clnzZAHreIuyosjtQtYF16ep4JTKvet5BbJ5y2QwyWXUdSxyJOp7kO5bwXvq6JbKhDKoVLfTyoUCD9PuX5yRbmQat5SHRXHUB5zQPxBuS/F/W3pS5EpWW5GybWT/obJ7SrHK/Wl1K2UOdeB3C8WgjBWsrT8kFKsjWWrVBsTgE6g8rzZ%2BNnBNvhs/FswYMAAYmNjeeyxx9i/fz%2B6rpORkcGYMWP44osvRBiiPzp16kTz5s1NHL5hw4bxtd%2B3xXl5ebz%2B%2BuukpqbSsmVLUlJSGDZsmIkbNnLkSJ577jn%2B8pe/cOjQISHOfezYMc6dOyc8WxXBfzzNmzdn9OjR1KhRg1NSpq68vDzCwsLYtm2baUzZ2dn07NlTGA5GaKbH4xF6cIaBUb16dU6cOCGMNEObT9M0br/9dp6HozoAACAASURBVKZNm0aXLl04d%2B6cCPW8Fgyjp0OHDjgcDurUqcOuXbvE7/216MBnBMpr0qNHD7xeLzk5ORw%2BfFj8/tSpU7zyyiv07t3bsgZ9%2BvRB0zQyMzNN4yssLKR58%2Bbs2bOHq1evCuNx/fr1InmPf6hnVFSUCH2cNGkS%2B/bto2fPnsL49U/2oqle7soxa9Ys3nzzTSE1Ia9RpfI/oIZkxfVCK%2BsrkoOAb%2B0uWtJWXhs2h8%2BGDRs2bNgIALaH76bwy5qtjf8YwsPDmTNnDnfccQdDhw6lVatW9OnTB7fbzfz586mj%2Bqqb78IyDQ5f27ZtA9L2u3TpkuXFOzY2luHDh/PGG2%2BYfvfee%2B9ZkrYkJiYyatQoUWblypWcO3cOt9tNaWkpXq%2BXxo0b065dOwYNGsTQoUP517/%2BxYoVK7j99tspKCigoKAAr9fLpUuX2Lx5M3369KF%2B/fokJycTHh5uMigcDgdhYWEkJCRQqVIl1qxZI37vdDpNmSVfffVV2rdvj8vlEsZLXFwcVapUITIykvT0dNO8jeQwGzduxOv1cuzYMSZNmoTD4aBnz56kp6dTrfxbvffee08kV/HnJs6aNcvi7TKMss2bN/PBBx9Y9s7r9eJ0OkU7bdu2pXLlyhQUFFCvXj1CQ0MpKSkR3sjU1FS2bdsG%2BLx7hvH2xhtv0Lp1a1PbI0eOBHzhn/4Gn7/hWlZWZkqMU1RUREFBgRBdNxAcHExQUBAF5XGP/fv3V2ZeNQzMvLw8k6FmGNRGBlpDVN6GDRs2bNiwYeOngB%2BGzGTjF48NGzZUWCYqKopRo0aZDCkZBo9KRqNGjYQ4%2B6pVq9i8eTOZmZlC28942Ta0/cLCwpShdX379hXJQIzw0jVr1ggB8GuNKSIigrlz5/K73/2OoUOHUlRUxMcff8zkyZOpWrUqFy9eJDo6Woxj3bp14v8JCQksXbqUOXPm0KpVK5599llmz55NcHAwDoeDWbNmCa4jwMKFCxk9ejTR0dE8%2BOCDPPfccxw7doyePXsSExMj2nW73TRp0oSvvvpKSCh07dpV/D4qKoq8vDzWr19P7dq16dmzJ/n5%2BURERJCcnCz0A8vKyigsLCQ4OJgFCxbQpk0b0tLSTEl2brnlFqZMmUL37t1p0KABx44dM4U1nj9/Xrl2QUFBwju5Y8cOSktL0TSNs2fPcscdd7Blyxaio6PJysqiTZs21KhRg5UrVzJ%2B/HjGjBlDWVkZTqdTeGWHDBmCw%2BHA4/GYxhcSEmIy/IKCgkSmVH/UqlWLgoICrly5gsvlQtd1HnjgAcaPHy/2aubMmSQnJzN%2B/Hi%2B%2BeYbli5disfj4YUXXmDHjh289NJLol3D8A0ODiYuLo5Dhw6xaNEik17g9WAnbbFhw4YNGzYCwC/MI/dDw149Gz87/Ce0/bZu3YqmacybN4%2BkpCTatWvH9u3bGTBgALquc/r0aXJzc7l8%2BTKJiYmWMWVkZJjGNG/ePJHkY8GCBaayO3fuxOPxcPnyZaZOnUrLli0ZNGgQ4NOr69y5M02bNmX58uUcPHgQh8MhErnE%2BhFNjEyc3bp1IzExkWPHjhEfH09mZibLly8Xnq%2BNGzcK%2BYhNmzYxfPhwwckzvGynTp0SCVqOHTsW0Jo5HA7cbjcej4fGjRuzbNkymjZtSlBQEEeOHMHlchERESGStaSlpbFy5UoARo0aRVlZmcjuekkiksuhm3LyFiPxi4zz588LQ98Y29y5c2nRogWJiYnAdx6%2B5cuXs2jRImFY/v3vf2fLli2sWbNGjNmQ28jNzeVQeZIJWdj9elAlbWnWTEraEogor5xRIIAMCZZLivBSC%2BnfkrlC0ZDUTkDJVlT6igFkswgkeYmlmtyXio8pV1IkO7BQgRVzsKyfvHeKhDaWJA%2Bq/fZLkgSoxdmlzuUjoUzaIs1beZTltVHNQVqvQJZYzgXjJ7/5HQI415atUoW7y%2BcvEIFquS9Vohw5kYZq7%2BRDIZVR3i/y2VIVku/NQJ4TgWSIkTM%2BKepYzrlqzStKlKOqJ%2B%2BDarxSGaUovXyQVWdW3k9pLErFH3m8qjnJCVhUAu7y3snPAIBy%2BaVr1lE9mwNIyGI5W4E8i/9TsEM6bwq2h8/GzwYFBQV8%2BumnnD17lpdfflkIZE%2BdOtVUbtWqVdSWiNKrVq1i3TpzMozvk7zFCOXs2LEj48ePp6ioiBkzZjBt2jRcLhcxMTEUFRVRXFzMF198wdWrVwUnTMbBgwc5cuQIwcHB6LrOkiVLGDVqlIm3pmkaiYmJ7N27V2TNPFf%2BR8DIuulyuUR4osFZnDNnDvPmzTP153Q6iYqKonr16hw7dozCwkK8Xi%2BVKlXitttuY%2B7cuWItvF4vn376KbNnz2bNmjW89NJLlhDIQOHPDTxy5AhdunQhKCiIsrIySktLKSwsJCQkhIiICKElKEPTNLZs2WK5HhMTQ2ZmJg6HA6/Xa/L21a9fX3gvDVSuXJm6dety4cIFrl69apEI8ed8er1emjVrpkzMYhiRt9xyCxcuXDB5FQ3UsCiRXxtLlizhww8/NF37w9ChdO7sl7AiEFFeOaNAABkSLJcU3kYL6d%2BSuULRkNROQMlWLJlKFBUVDQWSvMRSTe5LlblCrqRIdmChiyrmYFk/ee8UX1ZZkjyo9ttPLgRQi7NLnctHQpm0RZq3MrmFvDaqOUjrFcgSy1H9desq%2Bg7gXFu2SpXIRz5/gQhUy32pEuXIz3zV3smHQiqjvF/ks6UqJN%2BbgTwnAskQI2d8UtSxnHPVmleUKEdVT94H1XilMkpRevkgq86svJ/SWJT5xeTxquYkJ2BRCbjLeyc/AwDKdWmvWUf1bA4gIYvlbAXyLLbxs8Qvy7y18bODSttv3rx57Nu3j5CQEF577TWLhp5s7MHNa/tpmobT6eSzzz6jWbNm3HbbbXz44YfUrVuX//u//8PlctGsWTPAJ6PwzjvvXLOt%2BfPnExERQXh4OM2aNUPTNFavXq0sN3fuXFq3bs3evXtNc9m4cSP33nsvQ4YMoVevXrRv3x7whTL26tXL0lZmZib79%2B8XxiL4jJyOHTuybds2k7FpzPG5554Tkgn%2BePTRR4mLi8Plclk0%2B/x5f/fffz91y9/aQkJCCA0NNf3e0P0zwlkdDof4fUREBI0aNSIoKEhIMoAvc2Z6erqQjVB58WRjD3zZUNPT07l8%2BTIlJSXX5IwabcbFxQmPpvw7gPj4eKVeIZgTzlQEO2mLDRs2bNiwEQBsD99N4Zc1Wxs/O/xUtP0aN26Mpmk88sgjvPjii3Tu3BnwedsMQ89IrpKTk8Nnn30mOIf%2BKCkpYdGiRRQWFrJu3TqGDx%2BO2%2B0WHDUDuq4Lsfg9e/aYdPFq1qxp%2Bvzaa68xbdo0WrduTWlpqSlENCgoSGTI1HWdy35hH8XFxYwaNYqIiAiSkpJM4aCtWrVixYoVfP7555Y5fPrpp1y8eJGIiAiCg4NFeGWlSpVMxs7SpUs5fvw4mqYJL5zH4xGyCufOnaNDhw5iTLquC89bSUkJJ0%2BepKSkhBOSPldRURF79uwBvgvt1DSNmJgYnE4nt9xyC5qm0bp1awYOHAjAwIEDiYuLE8lxDEO2bdu27N69mypVqohruq5z9OhREZZrjDcsLEyIs7tcLuEVbN68ORMnThRjCTTkFWwOnw0bNmzYsGHjx4dt8Nn42eLfqe330ksv4XA4mDdvHjNnziQ0NJQXXniB0tJSZs2aJco9%2BOCDeDweGjRowN/%2B9jfLmF588UWRvfOuu%2B5i0KBBlJaWkpaWJjxehtfniy%2B%2BID09nb179zJp0iTAZ3RcSwNu4sSJOJ1Ok8FQpUoVQkNDCQ4O5pFHHuGWW24hIiKC5s2b43A42L59O1evXuXzzz83GYPffvstDzzwgODy%2BRt2I0eOFNy1un5xV08%2B%2BaTYC03TRLIWY0xlZWXix8D27duFxh4gJBpWr17Nxo0bCQoKQtd1qlatCvg08Pwzdhoho7quk52dja7rnD17Fl3X2bNnjxCvf//99wkNDcXr9RISEiLGtm3bNm699VZyc3NFlk7wcf1mzJgBfBfuWVRUxLhx40TmVYM3uH//foYMGYKu6zidTrKzs5WeOxVUHL4usv6YzBMJhB8jnRHlkZGJN6pCchmFgruF3lERBwgFH0ZFvAmAb2S5pJqDHLpdgRB7oH3f0BykMioNa7lh5d5JF5UUG3kfJE6XisMnP0oDEdBWbG9g509qSKYOqehGlokq7gULBUm1wRWcgUC02pWLXpFouapaIGdCGpDqERDIHG6IwxcI904etGISlmoBrF9AdQIRnJfKqKYgX5Npf6p25bMfwFFT3gvynwvlHCrg5KraDYT%2BLbejElm/jurTvxe2h%2B%2Bm8MuarY3/KtyItt%2BNICMjg2nTplG1alWeffZZ%2BvfvT3FxMX//%2B9%2B5cuUKWVlZpjHVrl1b8PSWLFkCwIwZM/jiiy84efIkQUFBjB07lkWLFrF48WL69u1LeHi48MyFlJMQbrvtNlq0aEHLli15%2BumnCQ0NpbS0lE6dOinHWbVqVV5%2B%2BWVTqOGlS5coLi6mtLSUefPmcf78eQoKCti/f7/JMHS5zHReo44BI7sm%2BGQhjDqNGzdG13WaNGli8u7puo7L5eJXv/oV%2B/fv54EHHgB8hqORnRR8iWXy8/NxOp1ER0cLfb9u3brRvn17YWwZa%2BzxeEzcS/8QUU3TiIiIMHn9/MdjcCDvvPPO6wq%2BA4wbN06p6Wf0bXh1ZcjcwIqgEl5f72cAA1aeSCD8GCmsVBllKhNvVIXkMooQXwu9oyIOEAo%2BjIp4EwDfyHJJNQd5HysQYg%2B07xuag1RGpWEtN6zcO%2BmikmIj74PE6VJx%2BGSKXCAC2ortDez8SQ3J1CEV3cgyUcW9YKEgqTa4gjMQiFa7ctErEi1XVQvkTEgDUj0CApnDDXH4AuHeyYNWTMJSLYD1C6hOIILzUhnVFORrMu1P1a589gM4asp7QQ70UM6hAk6uqt1A6N9yOyqRddV5%2B4/ANvhuCr%2Bs2dr4r8KNavt9X8TExLB9%2B3ZycnJwOBw89thj/P3vf%2BeFF16gqKjI5HELDw/ns88%2BIzExkfz8fEaMGAH4Qj//93//l/T0dMrKynjttdfo1asXvXr1YuHChRQWFjJ//nyRgVTTNB588EGqVauGw%2BEgKyuL5s2b849//IN27dpdc6y9e/fm1ltvFZ/9jZaQkBDeeecd6tatKzxmBuQENk6nk8p%2Bb06JiYkitNFAUVERq1atAnwewTfeeMP0e7fbzbFjx0hMTBTG7D333EN6ejr33Xcfjz76KJGRkei6TsOGDU3ZLV0uFw0bNhTGb9WqVS3tg9nA0nXdlGBGNr7cbjft27fn//7v/7j33nstbfmjb9%2B%2BFvF5f6SmpppCYAGToa0K1VTB5vDZsGHDhg0bNn5s2Fk6bfxk8e/U9rsesrOzRRjhwoULef/993E4HDRt2pQWLVpYjKeoqCg%2B%2BugjunfvTkREBEePHmXw4MFs3ryZ8PBwunfvzuDBg011hg4dytGjR9m6dau49vLLL193XNea1yeffEKPHj04duwYv/vd7zh58iSbN2%2BmtLSUoUOHKusY3DrDo%2Bb1eoWsA0B6erolSUlcXJwpDBR8hqJhaMXHx5OUlMT48eN58cUXWbBgARMmTADg9ddfF/18%2BumnXL16FYfDQc2aNdmwYQNut5u1a9fyxz/%2BEfDtgRFiCT7jygj3NDyR0dHRXLlyhZYtW7J7927LHGNiYjh9%2BrTgNF4PO3bsoFOnTixZsoQqVaqQm5sL%2BLJ9XrlyhZCQEBHSGRcXR35%2BPkFBQdc1Em3YsGHDhg0bN4hfmEfuh4a9ejZsVICYmBh27dpFXl4eM2bMYNeuXWzfvp1HH32Uo0eP0qlTJ5KSkrjjjjtEnbCwMP785z8Lvba4uDgefPBBioqK6NevH/Xq1TP99OnTh9jYWFJSUkhKSrIYkYEiIyODv/71r%2BTk5OByuVi7di1bt269ZkZJf/hr2UVERFi8fnIykdzcXJNgPPgMPqMvTdNYs2aNsi8jMcrGjRvRNI2MjAy8Xi8XLlwgMTGR2267jSlTpojEKbfeeisXL140jaWkpMTEB2zQoAFxcXGc8RP08pfGCA4Opnnz5mRKukhGmKl/qOmdd94pkrgMGjRIhI727NkT8CWcMTyeJSUlFBQU3JCxZydtsWHDhg0bNmz82LA9fDZ%2BtigrK2Py5MksX76cixcvomkaLVq0YNiwYSQnJzNy5EgWL17MZ599RnNJtyohIYH169dzyy23AJCcnExxcbEQ%2B5bDF5966ikmT55s8joGBwfTrl07kpKSWL58uWV83bt3Z%2B7cuUI0/NVXX0XXdXr37m0qp2kaDocDj8dDy5Ytufvuu4WAu4y6deuyfPlyzp49S2pqKkFBQSJsMyoqiqZNm7Jz505KS0txu90MGzaMatWq8eqrr3LgwAHAZ5Tpuo7X6zV59fwNvPz8fHRdx%2BFwEBwcrPSKFRcXs3PnTvE5JCSEvXv38tZbbzFt2jSOHDkC%2BMJBQ0NDReZRQGTsjI6O5s4772T79u2Az2hbunQpWVlZtG/fXhg/u3btsvRvGGGGR9Hw6vkbTD169GDu3Lk0bNiQ48ePs2/fPqpXr86UKVNEmVKFCO/69etp0aIF4BNcNzBnzhwAPv/8c6Kiorh06dJNefXuu%2B8%2BCx%2Bwicx/yc83Eyvkz/hI/qYjW1BgIph4PApajXxRqqMso%2Bjb0rn8OS/PwkuraLzKQopJFBdL/JJA1kYuc/WqRT9NrlNSYuXiWK4FMge579JSqybYxYvgr%2BWo2jypHdW2XLgAfsl8feLsfnp9hYUlFh6fvJ6WtQOfwLNfOLOuK7hL8kV5Toox5%2BSYOUaZmVYJM8t4FAM8ehTi4yuYw6VLJpKgPFzVnCz7LbWhvFZUZCFXWbYzO9tEGFOu55UrZnKi4szK7ajOo9x2YaFCflNuW56TqpJ8cBTztlxTnWu5HbmMqo60wfJSAdbDpjgUlntIKqPqWp6S8qxJi658FsvzDuQAyvukeEhV9GgOtJDyTP4nYHv4bgqa/n3Up23Y%2BAlh3LhxrFmzhpCQEMH/qlq1KhcvXmTlypUMGDCA06dPk5CQwOLFi018toSEBGbPnk1ycrK4NmPGDN58803AJz3g77168cUX%2Beyzz4THCXwhhEYSFSORyfWgaiM4OJjKlSuTmZnJihUrqFu3rrKcgbp169K7d2/efvttk0dOhuGpMowfTdNEFknD2AsJCSE2NpagoCBKS0s5f/68pZ2goCBTuGdF3qfw8HAhUu8/R3/9Pxkulwuv12v5vTHmVq1aCRkGMIeNytfkLKaaphEWFobH46GkpIRWrVrx5ptvsnnzZksWVX988sknbNiwgenTpxMRESEyeFatWpWsrCzGjh3Lpk2b2Lhxo2W8QEChwuALy5WF14cO/QO///2QgOrbsGHDhg0bvwisXPnDt6nQ2/1vhW0u2/jZYtmyZTgcDiZPnszOnTvZunUrDz30kElvLjQ0lBMnTrBw4cIK2/v4449p0KABLpeLadOmWX6vaZrQBNy7dy8ffPABISEhLFu2LOAx%2B7eRnp7O1q1b6du3L2632yQEL5czfpYvX85TTz0lpCCmTp3K4cOHOXz4MNu3bxeZSWNiYiwJW4KCgqhVqxa//vWvcTqdlJSU0KhRI06dOmUx9pxOJ06nk3r16onwyV9JcgEOh4Ma0rf2hYWFSo9Zw4YNAXj%2B%2Bec5fPiw4Fzu2LGD/fv3CzkGo%2B%2BIiAhq166Nw%2BEQWn4Gnn/%2BecBn/Ppr5wEWyQrDuDXq5%2BTkULduXZN%2BoxHOWbt2bQYMGMCuXbto06YN1cq/1faXazC%2BWHC5XKYQU/8xfB%2Bo5Rvs7%2BBs2LBhw4YNGz8cbIPPxs8WbrdbeI6cTieRkZE888wzTJgwgVq1agG%2B9PlRUVG88sorpgyOMg4ePEhGRgZ9%2B/bl17/%2BNRs3brym3h34DLL4%2BHg6dOigNHACRVhYGAMHDsThcAQs2D1jxgyefPJJAIYMGUJiYiKJiYl06NCBTZs2ATB8%2BHAaNWoE%2BPh4Xbp04S9/%2BQtlZWUkJCQIY2bbtm0EBQUJzuBTTz0FQI0aNfB6vaZQzkOHDlnWwJ8Pt2bNGtasWSO0%2BTRNw%2Bl0snv3brH277zzDomJiSJpy5133kliYiIlfmJFHo%2BHwsJCsrOz6dq1K1evXiUiIkJIRxghlqdPn6agoIBWrVrh9XpNHlGDG9egQQOWLFnCkiVL6N69O6dPn%2BbYsWMmbh/4jLXMzEw%2B/PBDwcU0vIgJCQlUL48tM4y6srIyEaobHx/Phg0baNmy5XX3TQWbw2fDhg0bNmwEAFuW4abwy5qtjf8q3HrrrRQWFtKrVy86derEiBEjWLZsGd26dROeJ6fTyfjx4yktLVWm9TcwdepUNE2jV69eDBgwAI/Hw9KlS69Z3j/pSOhNiNTk5%2BczceJEvF6vyYNm8N3kn5kzZ5o8fBMnThTev88//1xINmzdupW7774b8GkDyrqBQUFBgjdYWloqdO6MTJgOhwOHw0FGRob43KtXL6pUqSLGaHAADXTp0oUuXboIAXld1/F4PDRr1kx4w%2BTkMT179iQ9Pd20hi6Xi/vuu4%2BPP/5YhEwWFxcLY0vW3jPCPSP9CBiGJqCu6yIxjmHMvv7666aQy9LSUpMgvBEqu2/fPsDnFTQMW0PqIzY2ViTkMRL37N27l%2B8LlfB6SkoHUxlZIFslmF1RmRup89/Q9099fL/Uvn/q4/ul9v1TH98vte%2Bf2vhs/Dxhc/hs/Gxx/vx5hg8fzq5du6hcuTLh4eFcvnyZuLg4Zs%2BezWOPPUaNGjX45z//yeOPP05aWhoLFy4kISHBxOErKSkhKSmJFi1a8Omnn6LrOsnJyVSrVk3ozMm8Ol3XiY2NpaSkhO7duzNmzJgKx6vi5slct%2BDgYEJCQsjKyjL15fF48Hq9uFwuoqOjadiwIV999RUulwuHw4Hb7cblchEbG0tERAQlJSV4vV7Onj0LwPLly/n6668ZN24cXbt25eDBgxQVFQkvlQx/Ppo/QkNDrylrcPvttzNs2DD27NljSnTiD//xGqGWBodQ7ueVV14ROoYOh0OsQ0pKikm%2Bwh8Oh8OUjCYmJobY2FhOnTol5vToo4%2BSkZHBypUrRf/%2BnjtN0zh06BB9%2BvRRyjsAjBw58pqyGODzhqqE22XYHD4bNmzYsGEjAKxd%2B8O3Wf7F%2BI3i3LlzjBs3jj179hAeHk6PHj14/vnnlZnRP/nkE2bOnElmZiZ169Zl6NChdO7cGfC9UyxZssT0hXZISAjffPPNTY3PH7aHz8bPFrVq1eLTTz9l%2BfLl/P73vycxMRGn08n58%2BeZNGmSqayhaafS61u6dCllZWXs37%2Bf1q1b06ZNG4qKijhx4gR33XUXzZs3FxxAwxuk6zrBwcE88sgjPPPMM7z%2B%2BuukpqbSsmVLUlJSGDZsGN9%2B%2By0AkyZNIjEx0dSG8QM%2BQ2/evHmC01e7dm0AVqxYwRdffEG9evWIL08753A4yM3NZceOHYDPG2WEHpaWlnLhwgVOnjzJ2bNnOXfunJhj7969WbZsGS1atGDr1q2cPn3aZOzJxsm1vgfyeDzcc889wtMXGRlJSkoKAL/%2B9a8ZMGCACN%2BsWbMmlStX5qGHHqJq1ao4HA7q1KlDeno6f/7znwGfVzA9Pd0iYl5cXMyf//xnvF4vuq7jdrvFmIyMnjLq1KlD5cqVTZIS2dnZHDlyhNjYWNq3b09ZWRlbtmzh%2BPHjlnkaoZS6rjNq1CilVzIhIQHwZUQ11sz4N8IvI14gxh7YHD4bNmzYsGEjIPwEQzqHDh1KjRo1WLduHR9%2B%2BCHr1q0TEVj%2BWL16NW%2B99RavvvoqX3/9NY899hh//OMfTTJSzzzzjClnww9p7IFt8Nn4mcLQm8vPzyc%2BPp4nnniCd999l3Xr1uFyuUxZHQHq1avHk08%2ByZEjR1iyZInpd0YYo6ZpIluk8S1Lfn4%2Bo0ePpnfv3miaxu7du3nhhRcICgpixowZDBw4kP79%2B3PkyBGmTZvGnj17mD9/PjExMfTp04fDhw/z7LPPkp6ebpJj2Lt3L4cOHRLeLePFPywsjCZNmgCwefNmpk%2BfTn5%2BPo8//jixsbGkp6ezb98%2BPv74YwCGDRvGgQMHuP/%2B%2BwkLC2Ps2LGkpaVRp04d2rRpI/rr2LGj0A0MDQ2lZcuWhISEEBMTY/pGyR/%2Bhk5SUhK1a9dm3759vPXWW6JOw4YN6dmzJ7GxsTzzzDO8/PLLfPXVV4Av9DI8PJxXXnmFTZs2Ub9%2BfWUmUMCSdRO%2BC600jDLj33feeQdAGMH16tUjODiYM2fOUFBQYNq/oKAgunTpQn5%2BPhkZGVSuXNnEzfTXDDR4jLGxsUyYMEGEbN57772mcWmaZvKAGv8ae3it9bRhw4YNGzZs/HcgPT2dQ4cO8ac//YlKlSpRv359%2Bvfvz9y5cy1li4uLGT58OElJSQQFBfHwww8TERFxzSiiHwO2Dp%2BNnyVKS0tZunQp586dY9SoUdSvX5%2BSkhK%2B%2BuorysrKiIuLsyRBGTp0KAsWLGD8%2BPHi2qlTpzh%2B/DipqanC62TgiSeeICMjQ5TXdZ2UlBQSEhKIiopi8%2BbNZGZmkpeXR0pKCoMGDeLSpUtERUWRlJRE165duXz5MuvXr2fy5MkmGQVDDsLgjb3wwgusWbNGcM/AZzisXbuWhx9%2BmD179pi0%2BQwjY%2BLEicTFxQG%2BB8r48eOFRIT/N0erVq3iwoULvPnmm/zhD38QnD3DQ2h4pOSQSANpaWmCV9i7d2%2BaN2/O5s2bRTZUA/fee69IyHLp0iU0TcPtdrNv3z6ysrIsSUr8x1gRjDkPHz4cQGTavHr1Kn379mXz5s2cOHEC8HngDGH2tWvXEhoaSkJCAvHx8SxevFgYff5GmzGXw0%2Bw%2BgAAIABJREFU7OxsnnvuOQoLC3E6nabsqQb37/PPPxfXDOPPaMvYj0BgJ22xYcOGDRs2AsCPkGQlMzPTQm2pVq2aSNR2Pezfv5/atWtT2U/4sXnz5pw4cYL8/HxTXoH777/fVDcvL4%2BCggJTpvMvv/yS9evXc%2BrUKRo1asRf//pXoQf8Q8A2%2BGz85NGpUycuXrwovDHBwcE0adKE0NBQjh49ypNPPklubq54UfZ6vaSlpVFWViayMU6aNInJkyfj9XqF4dW/f3/hWWrevDn16tUz9fv444/zxhtvkJSURL169fjss8/Iz89n9%2B7deDweXnnlFeFN2rlzJ9OmTaNhw4Z8%2BOGHwuhZtmyZMrxv/vz54gFgZLts3749DzzwAPv37wd8YahlZWW8%2B%2B67prorVqzg4sWL9OvXj4YNG9K7d2%2B%2B/vprYXQYIacyTpw4wfDhw03afIZ%2BnVHe6/USHh4uDCL/34eGhjJz5kxat27N%2BfPnSU1N5fz587z22mu0b9%2BeZcuW0aVLF2rUqMHly5dF5s3ExETRp8PhIDExUezBgQMHGDVqFPHx8ezfv19wFQ1j05CNMMo3btyYxx9/nDFjxtC0aVMOHjxITEwMW7duFcYe%2BPTy8vLyxJwKCwv57LPPBG/QkFfQdd3CH9R1HZfLJQw5wwg20K1bN%2BrUqSMS3/hnGDXmGyhUwutxcU1Mn2U9XZUIeIXivgrxc0sZlbquJMKraMaq7yyrEatEoqWGAug6oEJKYWFJgVwenkWgXDFPpUC11JlqX%2BS1kfWVT56E%2BvWv37eqkGXMqolL6yX3rRJel8XZVdrilkmtXGnVspLHnJYGfhEHgFUo/Nw5KA9nB%2BDMGShPkiRw4gQ0aCA%2Bqo6WRbFdoXQti7wHorwu11EtubzGlJaCpKcq15P329KGqpKiXYvWuaKM3I6yL2mi8horhcOldlW667KwuapvuW3LvaBYdEtfmzZB%2B/amMnI70jFS15PPp0WZHfj4Y3j88esNz3pwLIdPMUDVAsr3nfzAUe23vKCqMgGMT7nn/yWYO3cu7733nuna73//e4YOHVph3dzcXKKkP4iG8ZeTk2My%2BPyh6zqjR4%2BmVatW3H777YCPkuJwOBg2bBgRERG89957DBgwgNWrV5tkq24GdkinjZ88MjIyLF4Pw7PWqlUrHA4HpaWluN1ukYTD6XSiaZrQqDMgE2mN8Lza/i8a5Xj44YfF/5OSkgixvGV/l1DFCM/UNE14Fg2Rc/%2B%2BDePPX8PNKHPlyhVmzZoluH/X44EZXiR/np5Rp1GjRkRFRREaGmp6UBhz9e9XNnaio6OZMmWK%2BOwfallcXMxvfvMb3nvvPRwOB1WrViU0NJTHHnuM4uJiRo8eTc%2BePbnrrrtM49c0jcaNGzNixAj27dtHeno6M2fOFO0uWbKEtLQ0iouLcbvdIvTV5XJx6dIlk2c0JyeHL7/8EvjOE1ZSUsIrr7ximoeKG%2Bf1ekXyl%2BtB13WKi4sJCQlRCsKvXr2au%2B66SwjMyyGcjRs3vm77/liyZAkjRoww/Xz99XpTGfnYKY6h5eXU8sdZttJUZVTnTXp7UTRjNvbA%2BqJieSO3NhRA1wEVstQBizUnD0829hTDsxp7is5U%2ByKvjfyCKxt7qr5VhSxjVk1cWi%2B5b4uxByZjDxTGHlgnpRIulscsG3tgfpkGs7EHVmMPLG/pqqOF/M284k3V8v4kny3FWZPrqJbcYjzJL9eKevJ%2BK5M%2By5UU7crnWlVGbkfZlzRReY2VL/5Su5axYLWVVH3LbVvuBcWiW/qSjD1VOxZjT1VPPp%2Bql3c/Y%2B8aw7MeHNXLuzxA1QLK9538wFHtt7ygqjIBjO8nY%2Bz9CBy%2BPn36sHDhQtNPnz59Ah7S9817WVZWxp/%2B9CeOHj0q6Cngk9h69dVXqVGjBpGRkYwYMYLg4GDWrVv3vdq/HmyDz8bPAt27dzeJlffr14%2BysjLi4%2BPp0aMHLpeLtm3bsnjxYtLT01m4cKHwoO3evVvw6NLT00Xilu3bt3PgwAEOHTpEr169LH1WrlxZeO/Gjh0rjKPk5GSqV68uMnPGx8crhdMPHDjA/v37Rb/79%2B9n5MiRpj5iYmL461//SlpaGnPnzqVdu3Y4nU66detGeno6t9xyCwBt27YVdXr06EH38pesgoICEW4JvofPiRMnKCoqoqysTHjqNE1j5cqVQjqgSZMmwhh2Op307NmTw4cPm0IMg4KCaNOmjSmrKMC7775L586dadq0Ke3atRM6h7169eLcuXPCYDSM6zvvvJPjx4/zxhtv0KxZMxISEnjiiSdEe16vl1dffZXevXsTHh4ujNlatWrxP//zP6Kcw%2BEgKyuLlStXEhwcLAz5M2fO8HvpRfV62Uf9jX4jM2pkZKTQbqxbty5dunRB13WTMWf8v0OHDtx2222EhYUJg98f3yck007aYsOGDRs2bASAH8Hgq169Os2bNzf9BBLOCb73t9zcXNO13NxcNE0jRv7CAN%2BX5oMHD%2Bb8%2BfPMnj3bkqzOH06nk5o1a5q0jm8WtsFn42eHsLAwBgwYgNPp5Pjx4yxcuJDQ0FCmTJlCo0aN0DSNmjVrMnbsWJ588klhNN0oDGPzwQcfRNM02rdvT35%2BPu3bt0fTNCE0PnHiRBITE1mwYIFFR2/06NHX7SMiIoJbb72VKVOmULt2bdatW4fH4xEi4Ib30j%2BDU5UqVQgJCaFNmzYiRLFOnTocOHCAffv2MWXKFGGM1KtXz%2BTd%2B/bbb8XYPB4Pq1atEn0ZKCsrIz093eQFNIylatWqsW3bNnbu3Mmdd95J3759OXv2LElJSXTs2BH4zsNXr149Dhw4IAzMjz76yNTPV199Ra9evUhKSqJp06ampCr%2BBnKnTp3QdZ2goCDcbjcPPfQQ4PMEtmrVytSmkYhnxIgRLFq0iCpVqtCmTRvq1KmjTKpSUlLChQsXxFrHxMRQUlJCUFCQEFQ3HuCnT59mwYIFFBYWomkagwcPNp0xOcTjerA5fDZs2LBhw8bPDy1atODChQvi/Qt8iVzi4%2BNNWbvB92X8c889h8vlYubMmaboK13XmTBhAocOHRLXSktLOX36tND%2B/SFgG3w2fnYoKChgxowZeDweEhISyM3NpW3bthZPFPiSofh7x24EK1euNBlymzZtYubMmdSsWRNN09i8eTO6rltCMP3lFxYsWEBiYiJff/31dftyOp089NBDuN1uLl%2B%2BTM%2BePQHfQ8TfiGzatCm5ubmUlJSQlpYmPFpnzpwRZZ555hlhPEydOlX5jZM/5LDWoKAg9u3bZ5qX0d758%2BfRdZ177rmH5cuXc%2BXKFTRNY8eOHWzevBn4LtShoKBASFMkJiby29/%2B1tRP27ZtWbRokfjsn2yndu3auFw%2BqrGRRbO0tBSv18tnn30G%2BAw0eV0HDhwIwBtvvEGvXr3Izc0lLS2NU6dOmQw%2BY3/8eY9FRUUiLLO4uFh4RY01Pn36NDt37hRznDp1qtA7NOoHCpXwepdyg9lAebJQgXJqogl%2B%2BXUAH/3k%2BheslfyijL%2BD5L1UOk4zMkwfpTw%2BPgJcBZdU0o7ynBSJXC1lLH2Djw/jB3n95DYAH1/nepXAshiqIvI8LWVycix15Et%2B1FSBCvdbUUb%2BrBJTtuyvxG0B617JSwWWJfdx4ioYn7y/qn2R7wVVu0jfiKvOjWUfAulc2kzLWPDx3SpqpqK9U0aIyZUUzxjLnFQNye0EUKagIIDxSRcDmXeJQstbnpbcdwDbonwGyPeU6nmzb9/121XNW2JUBHQ/K4rcUBl5v%2BW1goqfAYH2rXy4/SfwE5NlaNasGYmJibz11lvk5%2Bdz7NgxPvzwQ/r27Qv4%2BP6GtMLSpUtFGKdMD9I0jbNnzzJu3DguXrxIQUEBb775JkFBQUKn74eAnbTFxs8CK1euZG256GZISAjR0dGEhIRw33338fbbb1/XNX6z6N69O2%2B//bYQTt%2B1axePPfYYgCkByrRp00hJSWH48OGsWLECQPD6vg%2BMkMarV6%2BKB4MR%2BldaWirCBSIiIsjJyaFNmzYmWQev10tUVBT16tUTKX9XrlzJqVOnRIIR/zEZGTllPp8Bh8OBozz0YcOGDbRo0YKwsDDy8vJM3rqCggJGjx5Nv379GDZsGKtXrwYwGXNy38acRo0ahcvlIiIiwhIiYXj0OnXqJNIdh4eHExISQk75X6er0pvWmDFjeOSRR3j77beZPn26MMhTU1OFaLvB9wRMWn8ej4d81VtsOcLDw68bZvF9ZBmWLFliEV7/w9Ch/Mov8YtfAjBAzaOTuSMW215l7EuV/JKFfQdpLkpOl5SV1HIrKryY8iUVn0eek2pZ5TLKx4DEh5HXT8m7kfk6qkWXFkNVRJ6npYyCLyNfUvGNKtxvRRn5s4rDZ9lfKVQarHulojZZKEiKZ2BF%2B6vaF/leUPI6pXAs1bmx7EMgnUubaRkLVr6bqpmK9k7550KupOB4WeYUCDE2gDIydUw5PuliIPNWcV7lacl9B7AtymeAfE%2BpnjdyMkS5XdW8LdT/AO5nFYXvRsrI%2B23hUlPxMyDQvpUPNxsA/OMf/%2BCll16ibdu2REZG8pvf/IZHH30U8CXKM97NFixYwLlz50SSFgP3338/L7/8Mq%2B88gqvv/46vXv3Jj8/n5YtWzJr1ixlFNCNwjb4bPzsYIRsvvnmm8LLZoTj/Seg6zp33303Y8eO5fLly8Kgufvuu0lOTqZ///6i7MyZM1m//rukHBMmTKBDhw6m9gy5gZiYGGF4NGjQgJMnT3Lo0CGKior45JNP%2BPvf/05kZCT5%2BfmiXPPmzfnnP/9JRkYG06ZNY9euXdSsWZPp06fTrl07EZLpTzQ2MmIeOXIEh8PBs88%2BK7J9GiGfHo%2BHCxcuCOFxo06/fv2YM2cOTqeTwsJCXnvtNV577TWTpAN8l8DG6/XSrl07Nm/eTGxsLJcvXyY5OZmjR4%2Bi6zpVqlQhLy8Pj8fD6dOnhVg6%2BAxKl8uF2%2B2msLDQxH9zOp2EhISIa2PGjBEcS/CdmTp16gipBnkNNE0jODhYGMSG0emfoTM4OBhN04iMjDQlApKhSu5zLag4fDaDz4YNGzZs2JDwI8gy3Czi4uKYPn268nf%2B7wkqMXZ/VKlShQkTJvygY5Px01s9GzYU8E/asmPHDj766CPB24qJiWH79u1Kr8yIESNMGSG/L%2BLi4rjtttvEZ38e3d69e0lISEDXdQ4dOsS7777Lnj176NSpE%2BBLFpOammpqr2nTpgDK8FPwebs%2B/fRT4cU0NFqCgoJESGNYWBi//e1vhayC/8PG4JUZHMY%2BffqQk5NDeHi4KblIx44dxTwMgvL69es5ePAgW7duFYbhihUrTEai8f8qVaoQFhbG/Pnz0XWdjh078sYbb7B%2B/XrBnzOSoAwbNoznn39e1N2yZQuA0L3buXMnHTt2ZM6cOZw7d04YWG3atGHq1KmiXoMGDUwZO/0xbNgwoc8HPl6fIWfRsGFDdF3n9OnTtG7dWplVKyQkhJrlqQ%2BL/eJ9vF6v2LOUlBRKSkqIjo42ZdcCs9eyXbt2yjHasGHDhg0bNm4QP7GQzp8bflmztfFfiYcffpiysjL69u3L/v370XWdjIwMxowZw%2BrVqy0hgj8UNE3jzTffxOFwcOnSJYYMGUKrVq3YsGED4NPaC5Rwq%2Bs6hw8fZvDgwVy9epX7778fTdOEMXb8%2BHHBNcvKyhIagE2aNGHt2rX07dsXh8PBwYMHefXVV8nOzsbtdtOzZ08iIiK4dOkSGzZsYNWqVYBPODwhIYGEhAQRnpiamkrTpk1JSUkRRlfXrl3F/w0PHfgkJEpKSoRxe%2BzYMV588UU6d%2B4sOHrnz58H4J133uGtt94SGnjty9NfP//884DPmF27di0PPfQQpaWlhJbH26SlpXH//feLUNM%2BffpcMzz27bffJi0tTYRout1uoaNz/PhxUS8qKsqUFMXg8F29epWTJ0%2BKa/5yFwcPHgQQ%2BwoI4Xr//TNQUYIef9hJW2zYsGHDhg0bPzbskE4bP3sMHjyYNWvWcPnyZQYPHsyVK1eoXLkykZGRREZG8uCDD95w2/4v%2BSo0adKE/v37M2vWLDp37szo0aMF169Lly7KOuvWrRM6fIMHD1aWWbBggeC%2BuVwudF2nsLCQFi1aoGmaSGTyl7/8hUceeYSvvvqK5ORk0tLSWLduHQsWLMDj8VC5cmWysrJo3749MTExJukIGUFBQQwcOBC32y14b7LkAHwX5qjrujAgjx8/LtpQedCMdpxOp0jq8v777wPfGV0GioqKiIyMZOvWrZw6dYpnnnmG8%2BfPM3bsWM6cOcOUKVN4%2BumnmT17tom7l5aWRrNmzdizZ49YNyO7qTGmjz/%2BWIjJw3ee1pCQEOrXr8/%2B/fvxer306NFD8AqNJCwG17GsrEwYliq0V%2BhAXQsq4fUmssiarOarUMGVL8m60SpR8AC0pq3XFIVkjWBLEYsye2Bi8hXNSXlRsTaWtmUVdUWdQOZtaVeluiwvjiR8rBRqli8qFKqvXJE4ZIqGZN1oLl82EZyUwuvSXimFuSVxduUc5DVViDnL9WRda5X2tKXd/2fvvOOrqPL3/55bUkklhITQOyRAUJrSo9JFQHQXAWUpuoAg6KIgKIjwXUVdV0VdARFZC8WGIL1KkxaBQJDekkAIIaSRdsvvj2SOd2ZOyFX0t%2B4yz%2BuVl965Z86cc%2BbM5X7u5/M8j2TjGLabbIA6o2t9E9kphkvJDLT1hvO6NQeMi6qfuOTihi0qM9DWTVw2B8Mxb54X3QFZv/puvDFn96aNN3Py6jnUHfPmuavwcw0MrvSyPevN3tIvhuxahvO86DgjQ8vLNXwmeDk%2B6Zj/E7jNMnK/NczVM/GHh76sUo%2BAgACWLl1K3759Bc/KarVyxx138OWXXxqybGqJ3i/hWkGp%2BbpaVgmlNgEqZ87pdLJkyRIeeeQRg9yuatCt/s2cOVOj5Gi1WrHb7VSrVo3Bgwczfvx4fHx8WL16NfXq1RMcOrWcUeXX2Ww2YebucrnIy8ujUqVK1KtXj6KiIk227IUXXiA3N1cIwqjQG9Hv2LGDbdu2icyiZ0ZNtSdQz7Pb7Rozebvdznvvvcfjjz9e7ho6nU6Rwcq5ifJXUVERI0eOZMOGDeSXyY9Nnz5dePwtWLBAlF6qc7h8%2BTIXLlwQ45aVf6alpUlFVXx9fQkODhZrqt4Tz/ukcv/OnTuHv8e/6n5%2BfhoFVDVT6A1kxusb9%2BiUXL1QL9Ef0n9ZkG11L7ymjcckjfRfcAxNJGoC3pjJVzQn6UHpvdUd0GdVJed4M29Dv7JvRPrF0X1Jl36J0h%2BUKEwYBEMkHRmEXHSBh9R4XXevpMbcOiEX6Rz0aypRgtCfpxd/kXlPG/qVbBzDdpMNUCdC4Y24heFSMnULveG8TEGkItUbycUNW1RGCdBN3BvhFK%2BeF90BWb/6brwxZ/emjTdz8uo51B3z5rmr8HMNDCo9sj3rzd7SL4Y3ejvedKwXYfo14k7lHTPx3wczw2fiD4%2BKsmxQWqo3ZcoUYap%2BM7Rt2/amohvlYcCAAQwYMEC8vnz5suC1qQbe6enpfPzxx2zcuJGwsDBRpuh0OsnKyiIwMJBz587x5JNP8u233zJ69GiaNm3Ktm3buHr1KsuXLwdKs17du3cXmahp06YxdOhQAF5%2B%2BWU%2B%2BeQTqlatytWrV6lVqxa5ubmkpaVRUlLCvHnz6NatGyUlJbz33nv06tWL06dPs2XLFvz9/QkLC6OgoIDg4GAURRHZRoDDhw/j6%2BtLfHw8brebsWPHMn36dEJCQhg1ahTjxo3D7XZryjtVlJSUCDsEFWpAqAZZJSUl1KlThzNnzoism5plU1VHAdq1a0f9%2BvVZu3atsHxQ%2B3O5XJpgznMcAQEBZGVlCVVOz/9CKZ/P896rmcWrV6%2BSlZWFj48PQWX/iDscDvz9/UXQZ7PZ6NKlC8nJyWzbtk30UVhYqOH9/RJbBtN43YQJEyZMmPACZobvlmCungkTlBp1exql6//K42V5isns2LGDwYMHU1JSwooVK3j33Xe5ePEitWvXZuHChRw6dIivvvqKDh064HA4hGVC%2B/btNYbqSUlJVK9enRdffJFKZb/%2BFnmYFn3//ffYbDZycnJo37493bt35%2BzZs%2BTn59O8eXNNVu61114DYM6cObhcLiIjI1m1ahWJiYnMmzdPYxi%2Bdu1aEhISqFatGo0bNxbm8lCaZVWzWFWqVKFz5874%2BPhgt9s1GTO93UNoaCjr1q0T8%2Brfvz8hHukJq9VKUlISiYmJfPrpp0KyePv27Rw%2BfJgePXqIgO35558XpaxvvfUWM2bMwNfXl5iYGHx8fPD19eVymSdcbGysxhtRURThdeNZjqmawe/cuZNWrVpRUFDA3XffLQznLRaLGG/Lli05cOAA4eHhnDhxAigV0AkKChL3CRDiL97A5PCZMGHChAkTXsAUbbklmBk%2BE14jISGB9PR0UULn4%2BNDo0aNmDBhgviinpOTw/vvv8/69evJyMggODiYO%2B%2B8k7Fjx9KwYUMAJk%2BeTFFREW%2B%2B%2Baam/9OnT9OrVy82bdqkCURuZTzejulmKCkpEebbKo4ePSqsClT4%2B/sLvt3p06eFb%2BC//vUvkcUqKCggJycHm83GrFmzcDqdrFmzhp49exIaGkp%2Bfj5Llizh2rVrdO7cmZkzZwI/C4ecPXuWCxcuYLFYyM3NJS4uDqvVKjzkDhw4QLNmzUTmSh2famZ%2B7tw5unbtysaNG2nSpIkQVgGEoqiiKJw9e5YqVarwj3/8AygNjB599FEArly5ovGh8wzyPPl7DoeD69evM3bsWOrWrcuaNWuwWq088cQT/Pjjj0BpiWezMs85u91OUFCQ4NhFRUWxdu1a0WfPnj25cuUK//rXv3jqqafEdVSBFU9u3rFjxzRcQxX5%2BflERUWJwLCZh9%2BdGtB269aNo0ePYrFYRDkpwP79%2B3G73cTHxxMXF8eOHTtENs9zDUJkBl3lQMbhq1xZuye94VgYOB/6Rhcvgq602cChkXSs71dHWQG84NBcugT6IPjCBahZ8%2BfXEk6SF9RFA49KyjU5e1ZjZqdvo%2Be5SLo10P686RcwLKB%2BrVJTjV5ehn4OHwaPcmqQ8NQk66efg/5eSjl8a9ZAz57ipZ5eJh2fjtMHwLFjUFY6D8BPP0Hjxto2%2Bs2kH7Bss12%2BrPF9lNHo9Bw5GR9Kf8%2B94bPqh%2BMNl022afXn6eiEcu6ifqKSiXvDMdRfW3ot3Rrr%2BaJSLpsXHDn9%2Bsn4ofo56NdGdnHDs3nyJDRooGmj38eSj0M4cwbq1v359YkT4Pn9QNIv69ZB9%2B4/v5ZN3It7Z1hk2Y3RL6D%2Btexh1fcj4VMbeKYy3qmMZG3ivw5mwGfiF2HatGkMGjQIQPjBPf7446xcuZKwsDAGDRpEdHQ08%2BbNo27dusIP7k9/%2BhNLliwRPm7/P8ZTo0YN8vLyvBrTiBEjGDFihPQab775pqaED2Dnzp3Az9YCgAjWABo3bszq1auJiooSwd6xY8cYPHgwgwYNYtOmTYSFhdG5c2f27NlD27ZtRT%2BKohAREcGiRYuwWCwiKFQFUuDn0kaXy8Xs2bOZM2cOV69exeVyaQRQGjVqJEoY27RpQ0REBL6%2BvnTp0gWbzaZpa7fbue%2B%2B%2B4iKimLx4sVkZGRw5MgRkWELCgri%2BvXrtGzZUgRsoA3y1PGrZZ%2Bq%2Bqg6BkVRGDduHA8%2B%2BKDGskLlN3oGWKtXr9b03blzZ81rHx8fiouLqVatGpcvX9YIzPj6%2BuJ2uzWlliqCgoJEwFetWjVR0qq2rVevHmfOnCEgIEBj9aFeu6SkhE6dOjF37lxxzHNcMqGb8iAzXh83bjzt2//85dgbjoXhC5i%2BkUQt1hA8STrW96v//g1ecGhkGU/PYA%2BknCRvjNf1X5ykXBOdc7m%2BjcxMXv99TOp9W0G/gGEB9WtlMG6W9aML9kDCU5Osn34O%2Bnsp5fB5BHsgN1U3jE9izq4J9sAY7IFxM%2BkHLNtsOg6y1CRaF0XI%2BFD6e%2B4Nn9UbU3XDHpVsWv15ek9rKXfRC3dsbziGXvi3G9ZY//uVN8bwsrXRr5%2BMH6qfgzdm8oZnUx%2BUYdzHUvFsz2APtMFeOf1qgj349c7m%2BkWW3Rj9Aupfyx7WipzswRjcyXinf5Rg7zbLyP3WMFfPxK%2BGv78/w4cPJzIyku%2B//5758%2BeTl5fHe%2B%2B9R7169TR%2BcIMGDdIER/8/xgP8JmPq2LEjP/30E9euXRPHdu/ejaIoXLhwQZR9durUiTNnzmC32%2Bnbty%2BgFYaZOXMmHTp0YNKkSURERGC1WoXIDJQGXGqZpKIofPPNN/j7%2BxMRESHETtRyP/Ucl8uF1WrVBEqeHn%2BeKpZTpkzhzTff5MCBA4ayQZVrFxwczHPPPUe9evWA0oBWtVlQr%2BkZ7EEp986zH30AqJZhqkIqqg8fQIMGDUhKSqJKlSoEBgZy//33i/dkap%2Be8LSLqF69Oq1btxaiNFarVRrsqe%2BpOHfuHAUFBRQUFIi1Uks%2BXS6X8BL0nMeMGTNISkrC7XZL/RS9teIAk8NnwoQJEyZMeAWzpPOWcHvN1sTvAqfTidVqZcOGDTz00EPSL8HPPvss7du3//86HuA3GVN8fDyVKlVi165dQGk2KjExUQRAamCilg9%2B8MEHgsellvxlZmaSmJjIkCFDpNfo3bs3R44cEYbuCxcuxNfXl/z8fBHMdezYkdDQUABWrFhBrVq1sFgsJCQkUFBQgKIo%2BPn5kZSUJK7fpUsXcY0ffvhB/H90dDRJSUnExMQQGhrKiy%2B%2ByIYNG1AUhby8PBqU/ZqZn59P586dURSlnOCklF%2BoKArVq1dnzJgxQsm0f//%2BKIpC3759iYuLY8yYMdhsNmk/kZGR5Ofns3LlSuk1fHx8SEpK4rvvvgMgLi6Odu3aiXVXoQZtJSWSlEBcAAAgAElEQVQldOjQAShVKPVU1WzZsqX4f5vNJkzt69evj81mIzMzk5CQEG7cuEF%2Bfr4I9Pr16weU7q9169YBpdnChx9%2BWNwXgHvvvVc6BxlMDp8JEyZMmDBh4veG4q7oZ3QTJsqQkJDAqFGjRAmlWsI4d%2B5cVq9eTbdu3XjllVfo3bv3Tftp2bKl4Uu/oiiifHHTpk0EBwdXyLtr2bIlderU4auvvtKM5%2B2336awsJBNmzbRs2fPCsek5wJCqTDJfffdx/jx4wkMDGT8%2BPFs375dlEB6qkTabDZeffVVzp07xyeffEJ2dramXLJ9%2B/Z07NiRV155hR07dlClShXuvfdeUlJSRLBosVhEYDFgwABeeuklhgwZwr59%2BwgJCSE7O5uYmBjBV%2BvevTvr16/H7XbzzDPP8MYbb4jxqKWOMvj4%2BGC1WgkNDWXr1q106dKF9PR0hg0bxg8//EBycrIhkL0Z7Ha74A9Wr16dfv36kZiYyK5du4iOjubSpUsoikJQUBB%2Bfn5kZmayfft2CgoKuOeeewgPDyc4OJi0tDSKi4vx8fGhpKREem0fHx8cDgculwuLxUKVKlW4cuWKRsFTvS/h4eHk5%2BdrxG7UPt577z1Gjhwp7p3nfXc6nVgsFqHmqZbOqv27XC6aNGlCSEgIP/zwg%2BZ9KN3HP/30U4XrpiI5OZlTp05pjtWr15DYWI8SOD3fTf8aCVVIz/sykGEkNJGjRyE29ub96n3GkNBCzp%2BHWrV%2Bfn3wIMTHa84x8HeSk0HHZTQMUMaP0XNfTp2C%2BvU1Ta5cgTKXkVLoOCpS7p0XJlx794IHVVhODNLxofR8Rhm9kfR0qFr159e7d8Ndd2nb6BfQG0KZjuMj5fDp5iCjEhmWQrJv%2BPBD8CyRl/AQK%2BJxSrlievKdzI/OCz5ZRVtLdo6e4iXlYul4VVLeqf7e6c3RZHwp3byla6N/xiXkVC9sDI3PlBeLYzgk4YF55Y9Y0Y2RbUgdiU86J92DJn3mdW0Mn1GSzxb9HpDxgfVr7pXPnRdGfPql8IYT6Y2PoXT9pBvuP4CjR3/7PvWfXf/DMAM%2BE15DHxj5%2BfnRpEkTnnnmGVq0aEHz5s2ZNWuWKGcsD/pATeXe/fOf/6SoqIiVK1cyceJEoqOjmTJlioZ398033wjeXcuWLSkoKBAleOp4/vznPzNx4kQ2bdpEr169KhyTPpB1u92cOnWKiRMncscddzBz5kyWL1/Oiy%2B%2ByIsvvkhaWhrZ2dl88cUX1K9fnwYNGvDGG28wePBg8vLyqFu3Lm%2B%2B%2BSZ9%2BvTh5MmTREZGkp2dTVFREV988QXLly9n%2BfLl1K1bl4KCAtLS0ujVq5cQSHG5XBw/fpzBgweTn59PjRo1uHjxolf3KDg4mH379tG1a1fS0tIYOHAgX3zxhXi/d%2B/e/Pjjj6SlpQkjcSgN3JxOJzExMbRr145Vq1YZ7AU87Q1UfPjhh4wZM4aioiKqVKlC9erVOXv2LNevX9e0u1kQ6glVeCU8PJysrCzWrl1L9%2B7dady4MRMmTGD06NG43W727NnD008/ze7duzVlrm63W%2BwHdW6VKlUiLy9PlJv26tWL7777TgRrqpF9zZo1eeqpp%2BjevTuJiYliP%2BgxadIkGjRoIPUbDA8PZ/fu3RXOU8Urr7wi5fA9%2BeRYr/swYcKECRMm/udhBny3BLOk08QvwrRp04TE/r59%2B1i8eDEtWrQAoFatWoZshTdQuXdqKeCSJUu85t01bdrUMB5PYZhfMyZFUWjQoAGjRo0SSpsdO3bE5XKRkZHBDz/8wF1lv7hXrlyZH374gRs3bnDo0CGqevwy37hxYwIDA3E4HKKkdNiwYezduxe73U716tXZvHkzFouFNWvWCC5gXFwcjzzyCC6Xi6pVq0pL/NSMqApfX1%2Bpofjw4cOpVasW/v7%2BKIpCYmKiUO5UAyLP/lJSUnj66aeFHUOjRo00CpR6jB49mtiyD8yMjAx%2B/PFHQ7AH2jJFq9UqHatqIG%2B326lbty6BgYHULssm/fTTT4wdO1YEnB07dmTXrl2afmXKrjabjQ8%2B%2BAAovVeRkZHCDkOF2%2B3G4XBw%2BvRpxo8frwnAPNc4Pj4ei8VCYGCgdI6A8BH0FiaHz4QJEyZMmPACJofvlnB7zdbE74ru3buzbNkyjbKhikmTJrFo0aKbnq9%2Bed%2B%2BfftvxgW8lTF5BkRRUVHYbDZOnTrFsWPHaNu2LVFRUdxzzz0UFBTw1VdfERgYSJhHeYfNZuPuu%2B%2Bmb9%2B%2BBJeV2hQUFHDHHXfQpUsXDX9Lbx9w48YNWrRoQc2aNenRo4c4PnDgQKA0a/X2228LC4DAwEBN8BMXFyf%2B3%2Bl0UlJSQmxsLPHx8SKAq127NkFBQUydOpWkpCQOHTokuIpHjx5FURTOnz8v5lSzrPzqkUceAUoDtJKSknK5fZ7QK1fqM4UWi0Uoa77zzjucP3%2Be5mUlYOo63SzwVBRFrEVJSYlGOEXlLlarVo2srCxhrF65cmX%2B7//%2BjylTpgjVUoDPP/9cWDR4rumRI0dwuVykpKTw5ZdfiuOewavb7SYjI6PC9TBhwoQJEyZM/AKYAd8t4faarYnfFcOHDyciIoIhQ4Zw9OhR3G43ly9f5sUXX2T37t0aGX5P5Ofn8%2BGHH5KdnQ2UesfV0Umel4fk5GSDSfoDDzxwS2NyuVwcO3aM%2BfPna1Qj/fz8WLduHQ6Hg86dO5ORkcGrr75KUVERH3/8MXfffbchKLHb7UyZMoXNmzfTtm1bFEWhsLAQRVFYu3YtcXFxIhhSz506darItgGiHFVRFFasWAGUBoRvv/02drud48ePM2fOHNxut%2BDGbd26FYCxY8dy%2BfJlHA4HJ06cYN26dSKISUtLIzc3l1deeYW4uDhiY2Np2rQp77//Pmlpafj4%2BFC1alVR2piVlYWPjw8bNmwgMDAQi8WC1WrVBNOq/%2BHmzZuJjIwkODgYf39/FixYQOPGjYmJiSE5OZknnngCKM3AqlBLPt9%2B%2B20yMjKEp6DKw9NnOj1LLm02G0c9yj2Kiorw9fUlKCiId999F4BDhw5RXFwseH5ZWVnMmDGDN954g507d4r1P3/%2BvLBq6NKliwh4ExISAMjLy9P4PHqKBEFpYOgtTNEWEyZMmDBhwsTvDTPgM/GbISAggM8%2B%2B4y2bdsybtw4WrRowZ/%2B9CccDgfLly/XZF08A7UuXbqwbds2Zs%2BeDZQGNt56mXmWdKp/alD0S8Y0a9YsTdA4dOhQevXqxbPPPiv6Ui0Whg0bprnepEmTuHDhAh07dqxwfXr06IHL5WLz5s3Az%2BIvnoHiK6%2B8gtvtJiUlhbS0NBqXeVjVrFlTePFNnDiRO%2B%2B8k8zMTC5evCiUJ0tKSkhPTxfB09mzZ8U1PMVQQkJC2LlzJ5UqVRIZQIA9e/Zw6tQpXC4XRUVFnD9/XgRMOTk5FBcXk5GRQX5%2BPk6nE4fDobF%2BUMttbTYbnTt3xs/Pj%2B7du3Pu3DnOnj1LWloabdq0EffoypUrPPjgg4wbN04E38nJyQB89tlnQrXUarUSEhIiSizj4uI03ER1bmpmLjU1leLiYrKysjQBlMViESb0qtiMerxy5cooikLXrl2FL%2BLWrVtFmeb69euBUr%2B%2Bs2fPau6t534NNphHlY%2B%2Bffvy2muvaf7uvrubtpFOeMbwmlIyvgYe96Rs0MaL60/ScTaBUrJ%2B%2BS/lw9H3c%2B5cxdcu%2B7HnZk0Mc5QNSDZP/Xh0ny2yKlxvrm0YsoSjalgvXUdlGkwaeLi/lDtAfRvZrTOMuexHDBU3bhj3EYmJ2teyG64fj0ykSM%2B1mTvX2GbvXu1r3TNF2Q9eN20jy6brqjlka6NfP2/ud1kl/M0b6ddLUllS0bWk%2B1w3CVkbwz%2BZHlY9Aro9Kq1A162p/pGSXdubOeiXQvIxZjjPUDwieb4N05Q8VPrbYriXsoP6BZWddOyY5qXkdhv2n6wgRj8Hb9ZG//kj%2BQglJ%2BfmYwHjmGVzkH4O/CdgZvhuCaZoi4n/79CLpOhx//3307VrV55%2B%2Bumb9jN58mSKiop48803NcdPnz5Nr1692LRpk5TXVdF4duzYwdixY1mxYoXgkHkz7puNyeVykZCQwMiRIxkyZIhot337dnJzc1EURYiNQGlpYElJCYqisG3bNjp16gQghFbsdrvgnrVo0YK77rqLf/3rX4BcIMVut1O1alWRuSvPo%2B6Xws/Pj86dO7Nu3TqCgoIIDQ3l4sWLfPjhh7Rr1462bdvSqFEjzpw5Q3FxMYWFhQQGBmK1WsnKyqJJkyZ88803oj9VmRRKM3gzZswAoFevXpw%2BfZqqVauSnp5O5cqVyc3NFfP09/enoKCASZMm8dprr0nHqigKcXFxJCUlSd%2B32Ww4HA4ef/xxnnnmGZo0aSLM46G0jLakpIT77ruPGTNm0KpVK2k/y5YtE7zWiiATbRk/bhxjZWbWJkyYMGHCxO2Kkyd/%2Bz7LLKhuB9xe4a2J/wrcKhfwVtGhQwfuueceXnjhBa%2BsCWRISEggNjZWZAzj4%2BO5fPkyVTw0m0tKSnjooYeEiElISAgJCQl8%2BeWXHDlyhAceeAC3282MGTNEZku1JABtdnDDhg3CsF2FxWKhTZs2zJs3j3379onsot6qwGazCf6aWmKolloOGDCA48ePi7ae/atG7aqwjdPpJD09nRYtWvDRRx8xevRo8vLyOHDgAFlZWdy4cQOn08mNGzdEIH3s2DHi4uLEOqmCKj4%2BPiKbCfDwww8DP2cQ/f39GTu2VMkyMDBQBH7vvvuuGKM6JzWQttvtQmxFURT8/f2pVq0afn5%2B%2BPv7U6dOHSwWC3v27BFr7Xn/8/LyKCoqIiUlhdWrV8tvPIhyUG8g4z%2Bav8CZMGHChAkTOpgZvlvC7TVbE/8VGD58ONnZ2bRq1YrY2Fji4uKIi4ujSZMmfPvtt7z%2B%2BuvCj%2B63QFpaGi%2B//LKmpHP9%2BvXs3buX2NjYX32tadOmsXv3bqZNm4bFYuHee%2B/lueee4%2BLFi5SUlLB7925OnjzJypUrOXLkCDNnzmT9%2BvXcf//9xMbGsmrVKqC0rNDlcmGz2YiKiuLDDz9kyZIl4jr//Oc/uXjxIpUqVWL69OkkJSVhtVrp2bMn//73v%2BncuTPnzp3j888/B7SCKYqiaDwFV65cSbNmzbh8%2BTLBwcFs2rRJGKsDxMTE0K1bNwIDA4mMjGTRokXCAL2oqAiHw0HTpk05ePAgBw8e1JiWh4eHExYWxsGDB4Wyp348ammpoihkZWXRqFEjGjVqxN///nfg59LJ1NRUjpWV0%2BR71MPcuHFD9Dd69GjN/Pr16ycEVVS%2BY1paGoWFhRQUFIhS1kOHDpGamorNZiMkJESYtkdGRmK32wkNDRWG9larlYiICCpVqiTKSc%2BfP%2B/1HjE5fCZMmDBhwoQXMAO%2BW8LtNVsT/xUICAhgz549PPbYY1StWlXwq/r378/WrVs5cuQIMTExv9n1qlWrxgsvvKDh5R05coQZM2bg7%2B8vghYwcv3UvylTpog2a9euJTU1lZdffpmOHTuyatUq3nrrLebOnUtkZCTff/89J0%2BeJC8vjx9%2B%2BIF%2B/frRvHlznnrqKWw2G0FBQcybN49Zs2YRERFBjx49iImJweVykZqayhNPPMGjjz4KlPq%2BRUdH31TBEmDGjBmivFU1ru/evTv79%2B9nwIABwroA4PHHH6eoqIh7772XsLAwkcEDSElJoaSkRIiU1KtXj6tXrwremo%2BPD0lJSSxdupSCggIRbH3zzTdkZmZy/fp17rjjDj799FPRp8Ph0Hj8DR06VGQhO3XqxPHjx9m9ezetW7cW43e73ezcuVOTxfPz89PYKBw6dIiYmBiR4Vu2bBmNGjXStHnnnXc4cuQI33//vbDa8PPzIyYmBofDQXZ2tvAjvHLlCiUlJRQXF4sMn9Pp5OrVq%2BTl5XGtjBj0S0RbZBy%2BhAQth0/Pu5BV4%2BrpJgYOiMzHUXeSjPKj541I%2BR26H0Qq5BOCV5wkb3hB%2BrWQzUF/UL82UrqwNxfXZXJl/JiKODSybvWUOFm/%2BmtL563rXL9WUg6ffnFkHDn9oD1scgT0gz5zxthG/5zoCWUyIpNuPN7sG9n91Q9ZP1zpmuuy8d7QYmUDrIjTJbuX%2BqWQzVt/q7zhgUn5grqD3vBFvaAZG%2BYpu7b%2BPG%2B4bd600V/bK86r/iQJSc6wt2Q/9uk7lm0c/aBlnrUVLaAXD4NsbQwLKCMZmsrT/xMwOXwmTPwOSEhIIC8vj/z8fE2AUVxcLDJd3bt3x8/Pj5UrV5KSkoLNZiMuLo6RI0fSoUMH4GdOYGJiIjVq1CA1NZXMzEz8/f25fv06ixYt4q677hK8x1WrVpGenq7J2qnBVGRkJJmZmXTq1IktW7bQtGlTvv76awDhXbhhwwY2b97M6tWrWbZsGWPHjmXv3r3k5ubidrvZsmUL/fr1w%2Bl0kpeXR0BAAFarlZEjR7JixQquXr1Kjv5brgdUrqKngAyUCuKoIipvvPEGEydOxGaz4Xa72bdvH4GBgYwYMYLdu3cTEBAghGL8/f0pLCykRo0a%2BPr6curUqZuW4fbq1Yt169ZJRYFUDl/z5s1Zvnw5sbGxNGnShOTkZJxOJ3a7nfbt2xMaGspLL71ULk/v7rvvNvDyyoNpvG7ChAkTJkx4gV9QPeM1PJTC/9dhZvhMmPidkJOToynPU4MMPz8/Lly4wIYNG/j0009FBsntdnPlyhXGjBnD8uXLxXlr1qzh8uXL7Nu3j7S0NIqKigQXbfTo0SQnJwve4%2BXLlw0lgWoAFBwcTIcOHUSJYopEBW/VqlV88cUXwtrCbreTk5NDYGAgAFOmTCE7Oxur1SrKJXNzc1mwYAELFy6kX79%2B0rXw8fHBYrHQsGFDkpKSWLhwoXgvMjJSrI/L5WLixIlAaebP6XSKoDQ8PByn00mPHj0IDg7GarVisVjEuqWmporj5WH16tWaYM8zM%2Brr60t0dLTIJNpsNpKSkkT7kpIStm3bxoULF7goy5iVQV0rb2Aar5swYcKECRMmfm%2BYAZ%2BJ/2m0atVKWoKp/v2WXEA9PBUe/fz8aNWqFcuWLWPLli24XC5OnTpFWFgYb7zxBomJiWzfvp2RI0cCMHPmTBHUVa1aFYDmzZuzceNGjh8/zieffAKUZgyfe%2B454TfodDpp3749VquVrl270rNnT2EncerUKb7//nshhpKTkyMEU1S8//77nDx5Ungiqn57ajZrz549KIrCX//6V4YNG0ZERAShoaFC4GTq1KlYrVZ8fHw0ZbfqOpw8eZKWLVty4cIF8V5GRoZBVdQTKr/yu%2B%2B%2BA%2BCLL74gJyeHDRs2iIDN19eXgoICZsyYIa6llrCqJZ1qoOuZcbXb7fj4%2BGC32yksLOTSpUt8/PHHfPPNN0RFReHn5ydKYGNiYggPD%2Bexxx4TVg3VqlVjzZo17NixQ5SXWm4zXoAJEyZMmDDxu8Pk8N0Sbq/ZmrjtsH//fqlPX7du3QgKCqJXr14kJCQwa9YsEWAlJCQIgRNPfP7558J8uyLo/fUKCwv58ccfGTJkCFOmTBEZsjlz5rBixQruuece2rVrx%2Buvv07Dhg0pLi5m7dq1uN1u0tPTsVqttGvXjldffZV27doxYsQIACIiIjhx4gS5ubl89tlnAGzfvh2n08mWLVtYv369Rhm0Y8eO9OjRAx8fH6A0u%2BYpAOOpdtmyZUu%2B/vprXC4X%2B/fvF22sViufffYZX331lRA9iY2NFZlFm81GcXExaWlpYv6eJZzvvPMOcz18uTwDYz3U891uN/fff78IXitXrkxISAihoaEAZGdn43a7mThxophbcHAwd911l7BPKCgowGKxSEVRHA4HDRs2ZP78%2BRw9epR%2B/fpx9913U1hYyIkTJwBEOe0bb7whsohpaWn07NmTDh06iHt%2B5coV6VxkMEVbTJgwYcKECS9gBny3hNtrtiZuexw7doyBAwcSFRXFt99%2BS2JiIu%2B%2B%2By7Hjx9n0KBBt%2BRP9%2BijjzJt2jTNsY4dO5KUlMSsWbOw2%2B107dqVwsJCEeCMHDmSvLw8Fi9ezOHDh7Hb7cInbsaMGXz77be43W78/Pz497//TfPmzVm5ciV//vOfgZ%2BDi%2BHDh3P48GGRXbJYLPTu3ZvVq1fTtGlTETjt27cPi8UislHZ2dnC8sATgYGBtG/fnrvuugur1SoCwWrVquFwOLh48SI5OTlYrVaioqK4fv0606dPZ9KkSUJpUx/Iqf//zDPPCHsF%2BDnzZrPZDOIzamDlcDiw2%2B1Mnz4dKC2F7NixI5d0ZrijRo0SGUu3201OTo6wWQgJCdG0jYqKEj8C9O7dm7CwMOF3CDB9%2BnR69uwpXlssFlauXMmGDRtEoKmHr68vnTt3lr4ng0y0pV07rWiLN4II%2BnhZ/1oqTOJNI10b2eNhOKYTCpBpCehFE361wIROcMAb02r9tb0xXvdm/WRzqEhwR3btU6d0ByRlv/o20gpjjyy67FpS0RZdxYNs3gbhHtkk9EIQesN0MIr56M3Z580znqMbkPS3Fd0iy%2B6LnmasX2JZpbV%2BT3gjXuKNqIw3xub65ZQJu%2BiX0xthEm8EoPRtvHlWZW30%2B8Yb7RLDXpMsjoEyLtlr%2BrWQmZTrOzKMT7bo%2BgdP8sOlfnwyirv%2BEfLGRP3yZe1rmS%2B8vl%2BZfpY3xutSESMT/3UwRVtM3FYYNGgQVapU4e2339Ycz8vLY/bs2YwePZphw4ZJDdY///xz5s%2Bfz%2BbNm6V99%2BrVi/Pnz3PgwAG6detGenq6ECnxLFn09fXF5XLhcrmw2%2B189tlnNG3alPT0dLp06aIJktSMlMViYfjw4Tz99NMMHz6ckydPkpmZKUzW/f39cblcFBcXC4GWIUOGsHjxYipVqkROTo5QkaxVq5awDggNDeXGjRuGkkqr1UqdOnU4U6auFxUVRVpaGm3atGHv3r3S%2Bbdo0UJkwcr7WLFYLISEhHD9%2BnXRJjw8nGvXrhEUFER4eDipqanUqFGDrKws9uzZw0MPPUTr1q159tlnOX36NPfffz916tTh0qVLwgpCFabp0aMHW7ZsoaioiBYtWpCUlFRuxqx79%2B5iH1y9epWePXsyefJkHnzwQaC0rNZTTRRKuYijR49m4MCBdOzYUdrvtm3biIqKkr6nhynaYsKECRMmTHgBWVR7q4iO/u37/IPCzPCZuC1w5swZxo0bR2JiIlu2bDGUcfbt25fmzZtTs2ZNzXnelnHeuHGDlJQUHA6H4Jqp0Ac/quWA2%2B2mdevWjBs3jhYtWvCnP/3J0FZ97XK5SEtL4%2Buvv2bv3r1CGEQtCSwsLNSUTS5atIjk5GSys7OpWbMmderUEX16%2BsR17NiRffv2iddqhtDpdApfOvXagAj2qlSpIrhxKrKysvjiiy/Ea0VRsFgs%2BPj4iD%2Bbzca0adM058bGxlK9enWKi4vJzMwUiphut5tmzZrx008/sXjxYmJjY%2BnVqxdut5sLFy5QpUoVOnXqpLF02LhxI6%2B//jrHjx8XY1cVSPXYvHmz4HJ27dqV/Px8nn/%2BecFrLC9oW7FiBX5%2BftL3AOGf6A1M0RYTJkyYMGHCxO8NM%2BAz8T8PtYxT/ZK%2BYcOGCss4Vb%2B9l19%2BmdTUVJo1a8asWbPKvUZAQAC9e/dGURQ%2B%2Bugjwafr1KkTgwcPBmD8%2BPHUrFkTq9VKeHg4LpeLwYMHs3nzZg4fPsy2bdsMgh%2BLFy8WfL9//OMfLF68GEVRKCgooG/fvtSvXx8oDa5UHzkoDRA3btxIaGgo%2B/btE4EtoMlatW7dWhO8rFq1itdffx1FUfD19RXllr1792blypViLFlZWQae4oULF7jvvvtE8OV2uzUehgANGjSgT58%2BIuBTA9d58%2BZx6NAhVq1aRXR0tEY4BkoD0bp16zJlyhSOHDlCUlIS69atIyoqCl9fX1H6WbNmTbp1Ky2JtNvt2Gw2zp07B5QK0niu74EDBzTczqNHj9KyZUu6du1KUlISOTk5UgGW1q1bC99BKM2GegawZ2SeY%2BXA5PCZMGHChAkTXsDk8N0Sbq/ZmvivQUJCArGxsQZVzfvuuw8oFRf54IMP6NOnD/Hx8bRq1Yphw4axY8cOQ18vvPAC4eHh7Nq1C4B%2B/fqxYMECJk%2BeTHx8vODBvfnmm8TGxpKamorb7SYiIoK4uDgiIyNJSkoS/LyhQ4fy%2Buuvi/4bNWrEgAEDePjhh3G73Zw8eVL0uW3bNhYtWgTAe%2B%2B9x4ULF3A6nWSUGZmOHz%2Ber776CqfTyY0bNwxf9ocPHy4UMLt378758%2BcJCgqicuXKnD9/nv3796Moiij7VKEGZIWFhdSrV49z586JjFXdunVFkLds2TJefPFFcd7QoUPZtWsXderUEVw8KM2GZWdn43Q6cbvdOBwOIY7imZX09P8DDH53L7/8MvBzkJifn8/u3bsZMGAAPXr04Omnn%2BbKlSvUqVOHhQsXimDs8OHDrFy5kmHDhmlsF6ZPn06nTp3EGFJTU4XxuWr8fscdd9CkSRPGjRsn1nfChAlCAEaFoigsWbKEd955B4C//e1vDBkyRMwTSoPGWbNmsWnTJs0cPdfqwIEDeAsZh69DBy2HT/97hDe8G/1rGS/IUHUra6TryBtTY2/O0fNEvOEbSfl5OlLK78Xh%2BzVcLDDO3Rsu0bFjugOSLLDenF1H1ytFmdiQCj3fTcrh03GSvOFfSolB%2BhMlFjCG8/ScPT2nDwwLqvOfl/brDbdSvx%2B9MT%2BX7VnDHpCQIPXPnb4fWTW8fqlkPCv9MW94iN6Yx/9Wz4J%2BTb3hQBrmILm4oc3Jk4Y2%2BmtJrWJ1HwSGfmUbSecLJ1sbvXG97PNG/2zKfM49aO%2BAkT5Y9rumBvrnQ3Zt/TFZG2khyn8CZsB3SzA5fCb%2BkGjatKk0QxQVFcXatWsZPnw4Z8%2BexW63k5mZidVqpWrVqqSkpPDCCy/w0EMPAWm8I/4AACAASURBVPDTTz/xwAMPEBUVhcPh4OrVq1SqVImqVaty6dIlPv30U5o2bUpCQoJQlXS5XCILB6W2AYsWLeLcuXPMnz%2BfnJwc6taty7Jly4DSgM9qtTJ16lQWLVrEhQsXePjhh1m2bJng4MXHxxMUFMSuXbtwOp3C5Bt%2BNiP38fEhT/YveRkCAwPJL/tXUy2XVIPBmyE6OpqsrCyGDRvG0qVL2b17N3feeafoq3bt2iILFhoaSmBgILm5ucJsXcWOHTvo3r27OE%2BGKlWqcPXq1XLH1KxZM7744gvuuOMOQz8WiwWLxYLD4WDevHmEhYWJ7KgM1apV48aNG1JVzO%2B%2B%2B4433nijXL7l888/rwnaZWjSpAmHDh0yHH/rrbc4deqUCAz1iI6OZuvWrTftW4XJ4TNhwoQJEya8gCwSvlV4qJj/r%2BP2Cm9N/FehZ8%2BeBkuFDRs2CB6bNx52f//734FSQZElS5bQpk0bEeCpHnaACJw8yxGvX78u1CT/9re/3bTUztfXl9dee43%2B/ftjtVqF152nMuWOHTtEUDN16lT69OmjKaf0VKhUFAVFUQT3zc/Pj2rVqon33W43lSpV4q233qJ69eqMGDGCMWPGSMcWHx/PkiVL%2BO677%2BjTpw8ff/yxJtg65/HTYMeOHcnLy6OoqIi7775b04%2BiKERERBAcHFyu15zL5dJkxPQ4duwYQ4cO1Vxfnaufnx%2BhoaHY7XbGjx9vUA8NCAigTZs2LF26VJR0ZmZmSq8zY8YMDTfR81oAhw8fNmQjVQwYMICkpCTSJekDRVHYuXMnTz75pIHbqc77l/jwmRw%2BEyZMmDBhwguYGb5bwu01WxP/E1i8eDEWi4WFCxcSHx%2BPxWIhICCAAQMGMHnyZOFh53K5xJf%2BqVOnUqNGDaZOnUpycjJpaWnEx8dz4sQJduzYIQKHnj17EhMTw4svvsiRI0eYPn06gYGBXL16lRRZWVIZnnjiCRwOByfLyklyympG3G63CFTU11AaOEyePJmQkBAaNmzI559/rvG602c3HQ4HZ86cEVlHi8XC%2B%2B%2B/zyeffEJwcDDjxo3TiIyEhYVpxvb6669jtVoZN24cK1euNAiuqAgKCqJWrVr069eP0aNHa4K3q1evkp2dTW5uLpMnT6Zv375iLCrsdrswilcUhd69e3P8%2BHHxd/ToUQAxVh8fH9q0acOzzz5LSUkJzz33HIqiUFhYSNOmTQ3Bfp06dRg5cqRYX/WaAwcOBEoVSBVF4dSpUzz11FPYbDYiIyNp3bq1ZqwOh4MePXoYflBISkripZdeAkq9/vz8/IiLixPz%2BfOf/yx%2BTDh%2B/DgA999/v%2BgTIFdW4mbChAkTJkyYMPEfgq3iJiZM/LFw8eJFmjRpQuXKlQ3vPfzww7z88svs3LmThIQEwSE7efIkVatWpXHjxixbtox33nmHn8qIMJMnT8bX15eSkhKDDxyUBkH5%2BfmaTJgerVq1om3btqxfv55WrVoJ7zdAyPyrdghut5t33nmH5ORkJkyYQGJiImPHjiXLo3he70lnsVj44IMPePXVV7l27Roul4sJEybQp08f3G4399xzjybb5dnXwIEDqVGjBoWFhbRv317DN/Pz89OI1qiG8z/99BOrVq3S2DUMHDhQnPv3v/9dE7yqKCoq0nD7Vq9ezYYNGzRrVaVKFTG%2B4uJi9u7dy969e3G73UyZMkUETseOHStXYbNdu3Z8/PHHOJ1OnE6nUAdVFUizs7NZt24dDoeDK1euiLJPdT9s3bqVwsJCVq9eLe2/R48eZGVlYbPZBCfQ7XYLk/pvvvmG1NRU2rZty7p164CfxVby8vJwu93SvaSHKdpiwoQJEyZMeIHbLCP3W8NcPRN/WKxZs8Yg2jJq1ChKSkoMJtoqbDYbdrud7OxskYmpXbu2hidVr149/vnPf7J06VKg1D/PU3Vx8%2BbNDBo0iISEBGbPni2%2BuNeoUUNwwjIyMmjUqBFfffUVAIMHD2batGkoikJycrJmTE899RRQyv1Ss3aZmZksW7aMKVOmsHz5clq2bKnJ6MmChSZNmvD111%2BL14WFhSxcuJCPPvqIa9euoSgKtWvXpnr16kBpGSmUZp7OnTvH5cuXKSkp0VynRo0ammuoZa0lJSU4nU5N9s5zTHpDdVW9syI%2BYVRUFDk5OcKawhOKouBwOIQoiyzwUcfgdDpvGhi5XC4RqOnXskuXLjcdo8VioWvXruTk5Ej5irVr1xaB5Z49ewwehgEBAV4FeyAXbenWtq22kV5xQKJ2YBAL0I1Jelu8UHYxHJKUoBraeDFew%2B33QlVGNgeDOISsRFZ/D3VKKTItBm/M7g3T8kZ5RjcWqXiE/qAsY1yR6ggY1CL0Q5GKtuhMq6UG1fp9o3eABqMKhcx4XSd4YehXtuh6IRfZvtFvCpl7fAViP7JuDUss22u6e%2BeFDpLhXnpjbC6lUes6lj0v%2Bn6klHH9ftOfJFtPXRtvxH5k86zwWrJnTL8YejUTSRtpJb1O5MirNdftYemc9Pvx1ypUVfCZ7s1ek906w6C9UQX7T8Es6bwl3F6zNfE/AbvdLrh1epSUlFBSUkJ4eLgoa7zzzjs5ePAgTz/9NJcvX8blcnHs2DEmTJgg3pf1k5OTg8Ph4FKZ2eeWLVtE2WVWVhahoaGsWLFCnPPII49Qu3Zt8vLyRKBksVh44IEHiI2N5aGHHjKoVqpYv349b731lggUSkpKGDhwoKbU8NNPP6VFixbinBzdl4tq1apx7tw5nnrqKcLCwjQG6X5%2BftjtdiwWC1U8SMpq5ktf4hkYGEjVqlVFAGe32zl48CCNGzc2BDOq353b7SY/P5/atWuL99xuN8XFxeLvwoULmpLHoKAgbDabhj%2BprpGiKPTs2VOUg44fP14TUI4YMUK6lirU4FFvZH/s2DEiIiKk51gsFubNm0e/fv2EF6AelSpVom7dugaunrou3gZ7AN9%2B%2By2TJk3S/G3ct1/bqMwao9zXgKFCV8ejlA7JQ%2B1U3onkkCQjaWjjxXh1AqnSa%2BsHLZuDwQ5RMj7KrD8EdD8WGcYiOSZrY5iWZJ6GeenG4vE7U/kHg4IqvnilSsY2ZeXf5Q0lIEAyKQ%2B/TjAsVSn0%2B0bmVxkZedN%2BAahV6%2Bb9yhb9ySe1r2X7Rr8p9P2CYU313ci6NSyxbK/p7p2sH8NwdPdSNu0KtpG0Y9nzou9Htm0M%2B01/kmw9dW1k89ZvWdk8K7yW7BnTL4buR0xZG9mto2HDmw5Fuua6PSydk34/evM5IVvACj7TvdlrsltnGLTMY1Z6oon/NpgBn4k/LGSiLfPnz6dOnTqcOHFCKqrx1Vdf4XK56Ny5MxEREdjtdnbt2sXSpUtxuVz079%2Bfli1bMmHCBJFlatWqlaGfV155hcLCQhp6/CPQqVMnhg8fTmFhIQUFBVy/fp0ffvhBvJ%2Bdnc3p06dxu90i%2B%2BR2u0lNTcVut1OtWjW%2B%2BeYbKX/O6XQyevRo8bpVq1Z89913mkzYk08%2Byfvvv685z8fHR3jmXbp0CUVRePbZZ8nKymL79u2iXZ06dfD19cXtdovATx0zYCidzMvLIycnh7Zl2aaSkhLi4%2BNF1s8zqLFarYSGhhIZGYmvry87duwQpYqq8IyaVbTq/uHIzc3VlJh6wu12s2bNGho1akSjRo14%2B%2B23Ne%2BXJ7qiIi4ujubNm6Moiriu2%2B0mPT1dGMlDaTCreva5XC5GjhxJs2bNaNy4seFeud1ukpOTOXLkCC6XS4jMqO%2Bpc7qZ2qonTNEWEyZMmDBhwguYGb5bwu01WxP/E3jssceA0oza/v37hYfdkiVLmDVrFn5%2Bftx3330oikKPHj3IyMjglVdeYeLEiezevZtNmzbRsWNHLl26RPPmzalcuTKbN2/WZGx27tyJr6%2BvUMaMiYnhhRdeYNasWTidThRFwd/fn9mzZ4tzbDYb69evF6IlVquVgIAAYmJi6Nu3LxcuXGDAgAHUrl2bUaNG0a9fP3HuX/7yF3788UdhdJ6UlISfn5%2BB/7Z8%2BXKaN28uXg8fPpwWLVpQWFgoTMzr1q0LaMVUpk%2BfzsMPP8yECRP47rvvDNmpzMxMFEXhjjvuEMqhY8aM4cMPPxRtWrRoQVhYGJ07d8ZisQiuXkhICNnZ2dhsNipVqkT//v1FUNmrVy%2BSkpJEoBofHy%2BCK7VPRVF4//33hZCL1WotNwPnierVq4s5K4pCSEiImFdgYCBDhgzh8OHDtG7dmpkzZxIRESE8Bz0zd2pW2DOAHDp0KM8//zwOh0MjgBMQEMC2bduEeE50dLQ4z7NUNktmZiSByeEzYcKECRMmTPzeMH34TPwh0bRpU3r06ME//vEPw3sul4sRI0aQnJxMQEAAmZmZQh0yJSWF2bNnC%2BXEjIwMBgwYgNVqpaSkhNzcXAIDAwkJCeHq1assXbqUevXqiWu6XC7hxQcIr7tHHnmE6dOnk5CQQGpqKoqiUKdOHUaNGsWUKVOAUg%2B7b7/9lqFDhwqOV1BQEMHBwaSmppY7V0VR%2BPbbb2nYsCFNmzY1lH3abDYsFoso2bTb7dKsmN1ux%2Bl0irmq8PPzo02bNpw%2BfZrFixezZ88enn/%2BeTE3PaxWKz4%2BPtx11128//77NG7cGLfbzauvvsqXX37J%2BfPnycjIIDo6WsxL5e/Z7XbmzJnD1KlTKSgoqJDTV956%2BPr6asRkbta2vGv4%2BPgY%2BHXe4qmnnmLMmDFMnjyZLVu2CD7owoULad%2B%2BPZ9//jkzZsygZs2aXChzvfa8Lxs3bjTwI2VITk7m1KlTmmMNa9SgccuWPx8oLNSW2ehfQymBwzMbWVSkKdXJy5OUbzmd2lIdfR%2ByQwUFhpIk3aUM3crGaxiPvhPZxb0ZX3GxofQpO1tXmqi/ltttqI/SH5I0ISdHV8Enm4N%2BMXRtJMtJejqU/WYElPKNDL8LeLE2honr2ty4UWQo68zN1VX0ySaum0NWFnj8JiJrUuqfpfe70i3glSvaSlD9OkjnOXeusczz8mVNmanh/mNc94peg/FWypbGm/ui71vfj2wbGZ4h2f3WH5M8d95sG/0zZJinZOLefE4YTjN8UEiO6U6SjdfQjayRbi1k9%2B7aNW0VtOEzStKv4XmRrPnFi9oq05QUKKPZl9tG9kzpn4dz58CDPcGlSxAdrT1Hfyw1FWJitG3OntVWXOtfg/x5%2BI/gJh7AvxrSWt3/TZgBn4k/JBISEhg1ahSDBg2Svl9cXMyCBQtYtWqVKJls3rw5jz/%2BOO3atdO0vXLlCnPnzmXbtm1kZWURFBRE%2B/btefLJJ6lZs6Zo16FDBzIyMkTJohp4Wa1WTYmmJzwDDqvVyrRp0/i///s/ofjZunVrTpw4wfXr15k5cyZr165l165dor16Dbvdjsvl0vDXPK9ls9kICgqioKAAi8VCYWEhLpeL4OBgURaoZppkgZzFYiE%2BPp7PP/%2BcESNG8OOPPxq88NTrWSwWHnvsMVasWMFjjz3Gm2%2B%2BCUB4eDh%2Bfn6iHNJz/Op5MTExpKSkEBYWxo0bN3A4HFgsFqGmWREsFgs2m61CE3o1uKpSpQoZNzFj1Y9RNa/3NL4vr//hw4ezYMECzfl2u502bdrQv39/Jk2aVG6wmZiYKLKPN4PMeH38uHGM1X%2BJNWHChAkTJm5nyARvbhV/iEj2/w/Mkk4Tf0ioSpnlwcfHhzFjxrB69WoOHTrE/v37WbhwoSHYA4iMjGTmzJls27aNw4cPs3PnTubMmaMJ9qBUsbJ79%2B4cO3aM5ORkYmJi%2BPvf/y7EQtQv954llRaLRZRhAuTn54ssT58%2Bffj3v/8t3ouOjuatt94CSssuk5KSxHvDhg0jLi4Oi8XChg0beOyxx1AUhZo1a9KzZ0%2BGDx9Obm4ujzzyCHv37mXixIlAKV/sgw8%2B4OjRoxw4cIDY2FiNXUWDBg1ExjI%2BPp60tDR27dplUJ9U5%2Bbr64vL5eKjjz4iKytLBHtQGlCqWTcfHx%2BhzqmWUbpcLi6WKaRdu3aN4uJitm3bpvG284RnWamaVVTLImvXrq1539/fX1Myqa6xPthr0KCBUHANDQ0VwVpwcDCbNm0iKCgIq9WqCfb0FhgA27Ztw8/PTxqktmrVivDw8HLFWWrWrOlVsAdyDp/5C5wJEyZMmDBh4reEGfCZMEEphy0xMZEhQ4ZojsfExPDXv/6ViIgIwY2z2WyEhIQQExPDfffdJ3hYTqdTE0jIrCM8ves8ff0WLVpEZmYmLpeLPn36sHTpUtxuNykpKRw%2BfJgffvgBl8vFqFGjKCoqEmIxPj4%2BzJ8/nzNnzhAQEEBcXJxGwfTkyZPimh999BH33HOPIfunBi5Wq5UpU6aQnJxMdHQ0FotFBF2%2Bvr488cQTXLt2DZvNxtdff03v3r0JDg4mKCgIX19f6tevT9euXQkPDxcCMfPnzwfgzJkzYu3q169PYGAgwcHBov958%2BZpxHm6dOmCr0ddU1FRkWZt1Sys1WrFZrOJYLBbt24iiAoJCSE%2BPl6cM2TIEK5fv47NZtMEZLLAbdSoUYwZM4b69etzxx13iOMHDhxgzJgxNGnSRJT/6oO/Rx991NBfeTA5fCZMmDBhwoQXMEVbbgm312xNmCgHamaqTp06XL58mRkzZogv3pmZmVy9elUEaImJiWRnZ5Oamsq6deuE0iWUcrwAIerSqFEjwf8aPXo07du3B0rVMceMGSPOa9myJSkpKdhsNoqKiigoK11o2bIl6enpXL16FZfLRZcuXWjfvj07d%2B4ESjNf1apVY/jw4TRv3lwEiuoYPOGpHqo/DqUB68yZM2nWrBmXLl0yeN2dLfPSUq0WVHXL7OxsLBYLly5d4vvvv6d27dqEh4fjdrtZtGgRzZo1E2WLaomnoiiidBNKLRbmzZunGZfntfXjVm0gKlWqhMPhEMHgvffeK4Ko8%2BfPc/DgQaDUwuLSpUtER0ejKIom0CopKTGUZp44cYJjx46RlpZGYmKiON6qVSuaNWvGuHHjxFpcu3ZNc74a5HoDqQ9fx46aNvokozf%2BWt542Ok7lomlemMPVdG1kQjYGKgYMq6lFxP3xiJMXwakbyP1wvNi4vo5SK%2BtG7O%2BjUzbR39MZsOnbyO7ts6GD72TjdSHT3%2BSbIB6PrJsAfXn6bxJAYP/l966T1qpXfbDkYDHsymg82GT7Vn9kL3Z53pKsYxi7I2/pH4bG54FLx5w2f02jMcL80ipQLKu6kC//2TD88YTTn/MG7/BX7Oesj2rP032TOk3haGNbJ/raQeSxdE/UrJ9rT%2BmPweMz6%2B%2BjWx4%2BmdK/1p2bdn4yhHSNvFfBpPDZ8IEcOjQIR5%2B%2BGG%2B//57wsLC6NOnD%2Bnp6bz00kvUrFlTU16qBislJSX4%2BPjgcrkqtAgoD2FhYWRlZREdHc2lS5eExL/D4RBBhM1mw%2Bl00qBBA06cOGEQI/Hx8cFqtVK3bl3Onj0rxFJWrFhBTEwM7777Lh9//LE02CtPuEW9rsPhEPw%2Bi8VCrVq1SE9PZ9q0acydO5crV64wYsQIbDYb7777boXm64qi0KhRIzp27MjmzZs5ffq05n01AGzRogU//vgjTqcTt9uNj4%2BPNDBT%2B1SPP/rooxQXF7NkyRKRAVTvTbNmzYiPj2fZsmWGbKwe69evZ8CAAeTn5xu4lGPHjhXWDTJUqVKFHTt2lNu3J2QcvnHjxvPkk2O9Ot%2BECRMmTJi4LfB7RJ4yA8NfgNTUVF566SUOHTpEQEAAvXr14plnnjEooQMsXryYTz/9lIyMDBo1asTUqVOJi4sDSquYZs%2BezdatWykqKqJt27a89NJLGpXwW4WZ4TNRLhISEoiNjaVZs2Y0a9aMO%2B%2B8U3DIVOTk5PDqq69yzz330Lx5czp06MBTTz3FiRMnRJvJkycLzpknTp8%2BTaNGjUhJSflV4%2BnatSuTJ082qBwOHTqU119/Xbxu1KgRcXFx4rzmzZvTrVs33n33XfGlX%2BXzzZw5kx49enDp0iUcDgcvvvgiQ4cOFX3Z7XbCwsJo2bIliqJgt9s1gYOiKISFhaEoilBpVEsTZaWDqny/au6u%2BtwBNGnSRAieqKbmw4cPx%2BVyaewI2rRpw8GDB1mwYIGGEzZgwABatWrFRx99JA3qhg0bhq%2BvrxgvlPId1f/v3bs3lStXFhk5l8vF%2BfPnKSgoYPbs2TRu3BiHw8EXX3zBggULsFgsuN1uoqKiCA8PF/OoW7euGG9cXBwrVqzgb3/7G8XFxYYPRdUsvaCgQMP7S0pKEuW2vXv3FmP08fHRcCi/%2BuorjS%2Be571JSkri3//%2Bt7BgqFy5sqZsVPUMBPjwww%2BlAabT6WThwoVs2bJFjN1qtWpKRL/88kvDWpcH04fPhAkTJkyY8AJ/wJLOcePGUbVqVTZu3MhHH33Exo0b%2Bfjjjw3tNm/ezDvvvMOcOXPYtWsXXbt25a9//av4DvDmm29y9OhRli5dyrp163C73UIB/reCGfCZuCmmTZsmeFU7duzg3nvv5fHHH%2BfixYvk5eUxaNAgTp48ybx58zh06BDLly8nPDycP/3pTxw/fvx3G09iYiILFiwgLCyMBx98kN27d9/0vPfee0/M4%2BDBg8yZM4fPP/9c%2BMyFhYURGhrK/v37WbRoEYcPH2b79u08%2BOCDmuDKZrPxl7/8hezsbHx9fcnPz9eYifv4%2BIggRD1Pzca1bduWDRs2EBQURGBgIP7%2B/sToNJL9/f1JSEjg9ddf58yZMzz88MOEhoZSrVo15syZw/Lly3E6ncJPDmDXrl1cv36d8PBwmjVrJoKUnj170rhxY9avXy/sLdRASVEUFi1aREFBAVlZWeKczMxM8f9r1qxh7ty5hISEUL16dWrVqkXXrl1p2LAhDzzwgBCdmTt3LocPH6ZVq1ZUrlyZK1eucO3aNRFknjlzRojEJCUlkZqaSn5%2BPmlpadJSzcLCQgoLC7n33ns1AVdCQgJQ%2BsGpcvOKi4txuVwi%2BMrLy%2BOTTz4BSvmXvXr1ElzEwMBA/Pz8NIFa5cqVURQFRVH4y1/%2BwsCBAwG4cOECdrtdWhYLsG/fPjH2gIAAjQhO3759uSKrnTFhwoQJEyZM/E8gKSmJn376ib/97W8EBQVRu3Zthg0bxtKlSw1tly5dyoABA2jRogV%2Bfn6MHDkSgC1btogfzseMGUN0dDShoaFMmDCBrVu3kp6e/puN1wz4THgNf39/hg8fTmRkJN9//z3z588nLy%2BP9957j3r16qEoCtHR0UyfPp1BgwZpxEN%2Ba9jtdurVq8dzzz3H0KFDmTZtmley//CzRcEjjzyiMTbPz8%2BnoKCAf/7zn6SnpxMaGkr//v0JDw8nMDBQfNlXvf/ef/992rZtK025K4pCxzIulppF2r9/P7179yY3N5f8/Hz8/f3ZvHkzUFoGqI5hzZo1PPPMMxQVFbFs2TKysrJIS0tj8ODB5Obm4na7ee655wCEeujWrVuBUtVKNUgJDg7G19eXWrVq0bt3b6pUqSLmoP7XbreLgPWJJ57A6XRSqcx8qLi4mEGDBpGTk0NaWhoDBw7krbfeIiUlhTZt2ojMmmr%2BfvDgQbKzs8sVhQFo3LgxMTExnDx5stz7ZbVaqV69OkOGDBEG77m5uTzxxBNAqTCL%2BmNCSEgI3bp101xTndulS5dYvXo1LpcLl8tFfn4%2BhYWF%2BPv7Y7VauXLlCmlpaQQEBBAWFsbChQtZsWKF6DciIoIOHToY5rF3716RkYVSpVRPlMeVlMEUbTFhwoQJEya8wO%2BQ4bty5QpHjx7V/Hn7g%2B3Ro0eJiYnRCPTFxsZy9uxZg63U0aNHadq0qcdULDRp0oSkpCQuXLhAbm4usbGx4v169erh5%2BfH0aNHb3HRfoYZ8Jn4xVDNvTds2MBDDz0kyuA88eyzzwqBkt8bw4YNIyUl5Rc/GHrz8rp161K/fn1yc3Pp378/LVu2ZNKkSfTu3ZtFixYBpXXWBw8exGKxEBISQnp6uvjCX55Mv2pl4HA4NNy77OxsIRbjcrk0tgMqXC4XI0eOJCYmRpOdk8HpdHL16lVpP57neapyevLfsrKyUBTF8EHlcDhwOp288cYbNGvWjPz8fHx9fbl8%2BTIABQUFFBYWimyboig0btxYnO%2BZpTt%2B/Djp6elU93Ce1Y/L4XCQmJhIv3798PX1pbi4mDZt2oi1W7VqlRC1CQwMFCWcsrWTIS8vj9dee00E4vn5%2BVwrY8Dn5%2BejKArBwcFcu3ZNU77sacvRu3dvQGstoaJFixYigK8IMtGWTp26aRvpBU28UTvQv5aIohjokN4IIvwaZRfJOYZDv1IxxnDIm7XRqVt4I2gjVcnQX0t2bX3nemUNmViNXi3Cm7WRKIjoLav0p0hFW3TzlP4mo%2BvIGxEPmX1WRQIdsuXUD8hDL6v88%2BbONTbSC3scO3bz10hug0zQRqecId1b%2Bo70wh%2B/9jn0Rt1Jr/QhuTEVPXbePC/SfaObp5SOpd/Hv%2BJZkInpGDaFrB9deb03IjPePAuGTSrbtHrFFZnBuH6f6PefN/3K1Gq8GZ%2BXP6b/N0LNvHn%2ByTJ0Mly/fp3g4GDNMTX4y9Ldn%2BvXrxuU20NCQsjKyhLCfvq%2BgoODDf3cCuTfDE2YkCA/P58lS5Zw7do1OnfuzOzZs6lTp45X565du5aNGzdqjv1WekEREREEB/8/9q47PIpqfb%2BzLZsOIQRCICBEQkhCQq8CF5CqghBAkIsSfyBSlEvx0rHQvYJSgheRql4hCAGkCAZBeruQLAmEJj0JJb3sbrb8/tjMYebMCRkDXPXe8z5PHtjZM6fNzCbfft/7vj64ffu2zCOvLNhsNpw7dw4bN24kWSMAWLhwISZMmECUJrt06YKOHTuiY8eOxG%2BuWrVq2LNnDxwOB8aPH4%2BuXbuSem1vb29YSn8rhISEYOLEiTLFRoPBAJvNRgIRu92OLl26AHCVUgLA3Llz8fDhQ/z8889EHXLVqlXQ6/UwEIf14wAAIABJREFUGAywWCyKfRMzfjNmzCB8Pxp2ux15pR/%2BjRo1woMHD9CsWTMcPnwYdrsd3bp1Q0JCAgkAQ0NDUVRUhLZt22Lz5s2EC%2Bd0OiEIAt555x3m3opru3jxouy42K8oJjNixAgiPkNnHQEXN/Tzzz8nQZ40eLPb7QgPD0dKSgru3r1LlDil4wCPBGlGjBhBRGtEXt7HH38Mi8XCFK0RuX95eXnM4FoQBNy4cYO5fgBYsGCBrMz3cdi%2BfTvTeD06%2BlHADPoLFQnvkIAOeunXjC9lFEtjBM5q2pQ7NuMcxaGK9Ms6pGZvjEbZS%2Bb3J/T1Y11PeizW2HTn1Nis6wJvb/lrNXtD9wulnzB9iocHY77UOpm3MdURa//oYyxvY3o%2BaraTnhDD%2BUZ53pgxykZ0VUZY2ONfg3EZWGIK1B9szHuL7qi0oqLsgVQ%2Bh/TFYg3u5yd/zbgw5T12ap4X5n1DrZP5PR19H1fgWWA8CsqbgtUPVW1R3v3JasTcG/omZd201H0Dlo8rfZ/Q95%2Bafun9VDs/lb/PnjWcYH/Z/SQYOHAgoYuIUPuFLfDb/o4tr%2B2z1tDkGT6Ox2L27NlE7KRjx444ePAg1q5dS%2BTt1ZZRdu/eXeazZjKZSPnc04Ao918WRo0aJRNtGTduHEaMGIE33niDtGnQoAF27tyJ77//HjExMcjIyMB7772HAQMGkMxPw4YN4XA4sGXLFvz444%2BYMGECKlWqBAD45ptvmNlOQRDQq1cvmEwmzJo1S/aedM4GgwEtWrTA4MGDkZ2djWrVqpHzNRoNCSbLyvBJsWDBAvj5%2BSE5OZmoU4rnJycnw2634%2BbNm8jKyoJGoyElsUFBQfD29kbz5s3RtWtXbNq0SaFmKX4o%2Bfn54d///jcOHTqErl27KuYgxcmTJ0n54qJFixAeHk6COa1WqwiQpGWyYsZy2rRpaN68OQDgwYMHZB9Eu4iYmBgS8BuNRvJ%2BamoqrFYrKVU1GAwkmym1hhCh1WpJVpb1AdysWTOEh4djwIABsmyqIAioVq0a/Og/qh4DbrzOwcHBwcFRPhyOp/8TEBCA8PBw2U9AQICq%2Bfj5%2BZHsnIicnBzizytF5cqVmW39/PxIW/r93NxcVKlS5bduU5ngAR/HYyEVbTl16hTWr1%2BPqKgoAEDt2rUVCpm/B27cuIGioiJijM6CVLRl1qxZcDgc6NOnD7NtREQEhg8fji%2B//BLbt2/HtWvXkJCQAMDl12e32zFo0CBER0cjPDwcmZmZEAQBNWrUKHeuAwcOlL0eN24c%2Bb/VasXXX3%2BN5cuX4%2BbNmyRAeuONNzBnzhzSTgyOxKBTDDqOHDmCZs2awcPDg6ytUaNGZN39%2BvUjxw4ePIh69erB6XQiNzcXzZs3h81mw61bt5Cfn4%2Bvv/4aX3311WP5ZIIgwNPTEy%2B88AL27t1LjhkMBhKkiscyMzPJt2hGo1EWSI0cORK7d%2B/Gv/71L3IsJCQEJSUlskCwe/fuxP8uMzMTTqcTderUkc0xLCwMRqMRZrOZfBlx5MgROJ1OZGdnw2AwkPukZs2aCA8PJ2OIpaEbNmwgbeiADgDeffddNGnSBJs2bZKV6DqdTmRmZmLr1q1l7hkNzuHj4ODg4OD48yEiIgLp6emEEgK4hFxCQkJkyt1iWyntyG63IzU1FVFRUahVqxZ8fX1l71%2B6dAlWq5XYNjwN8ICPo8Lo1q0bNm3apOB8AcCkSZMI7%2B1ZY%2BnSpahfvz7q16%2Bvqv2AAQMQHByMefPmkWOXLl3C7NmzFX9s161bFzVr1iTedpcuXYJGo4HVaoVWq0W1atVgMBjg4%2BODXbt2lTnm7t27SXZRCrGkUww2vv32W6xevRp2ux2FhYXQ6/Xo168fNm/eDEEQ0K5dOxIs1apVC3Xq1CFE4GulpsRFRUWIjIxEXFwcyfBJM4viMdE%2BQBAEwmcsL3sYFhZG2jx8%2BFBGMgYeichISyX9/f3h5uZG%2BHBOp1OWGV6%2BfDl69eols784deoUaWez2VC7dm34%2BvqiRYsWsqyot7c3ydBt2bIFGzduJNm5sjB48GAAruA9KSmJ8AHFTObx48cJL1Oa3WzcuDEAl/jOpk2boNFoSKmvqPSp0%2BmIWI8aMI3XW7aUN6Jr%2BFk1/TThhOJuqKJlqOmXNt0GgwZEHVBDq2MSb2h%2BmxoOH0MoSrF2WvWM4VisxohbQYdhuSXTg9OuxozPTsU5LOsOer9YnB/6PGp%2BLA6fGjoPPWeWUTN9T7C2ht5T%2BvZjOpZQm67GVJ3ZiOLoqDFVVyT7WV923rwpf83iTNGdX74sf814YOhDLE4kJEJSQIVpscrhS7naBEzKQDkDAYpJM4uD6LHom4J1YajPJKapOv25xfiso7uml8C6h%2BnHVw1XlbVuxTE1e6ywDFL2q4YXW6F74nfCs8jwPQkaNmyIyMhIfPrppygoKMDVq1exZs0a4tvcvXt3nD59GgAwaNAgJCQk4Ny5cyguLsaKFStgMBgIZWjAgAH44osvkJ6ejuzsbCxatAgvvvgi/P39n3TbCHjAx1FhxMbGwt/fH0OGDEFKSgqcTicyMjIwc%2BZMHDt2DJ07d36m42dmZmLevHlITEyUZcDKgyAI%2BOijj7Bz504cOnQIgCso2bFjB2bOnIm7d%2B/C6XSioKAA69evx/Xr19G%2BfXtSZiiKneh0OjRq1Ahbt24lQVlYWJjCaoEVROl0OlStWhXVq1cHAAQGBgJ4FPgJgoDs7GyMGzcOwcHBqFKlClq0aIGcnBxFYNtSEiC0bNmy3KBNDJikXDfpOaKipzhPKYYPHw6jhCTRsWNH8v%2BQkBDMmzcPycnJ6NatGzneokULBAcHE%2BWrVq1akTJYEWKgWBZu3LiBqKgoNGnSRBaUp6WlkZIHug9BEPDNN99Aq9WiVatWCAsLg5ubmyzLylL3zMzMJOWvUohcwbNnz%2BL777%2BHw%2BGA3W6Hw%2BEgYzscjt/0Ab19%2B3ZMmjRJ9pN46pS8Ec3VYHGHaMKJgkiuPEVBy1DTL3VvAwwaEHVADa2OSbyhy6PVcPgYe69Ye2mpNAHNc2H0y6L8KOgwrFJeenCaG0LzcljnMLLAiv1icX7o86j5sTh8aug89JxZdBf6nmBtDb2n9O3HWja96azroricrEZUpQW9nazbUfGxGhKibFTq50rA4kzRnT//vPw144GhD7E4kSj9HSKigrRY5fClv6MIGL9fFJ8lrI6pSTNpYfRY9E3BujDUZxJryxWfW4zPOrpregmse5h%2BfNVwVVnrVhxTs8dUm4rSjCt0T3AQLFmyBPfu3UPbtm0xdOhQ9OnTh3yp/OuvvxLaRvv27TF%2B/HiMGzcOLVq0wNGjR7Fy5Ury99S7776LqKgo9O7dG507d4anp%2Bdv%2BrtWDbhoC0eF4eHhgW%2B//RbLly/H2LFj8eDBA1SuXBlt27ZFfHw8CWKeJmbPno25c%2BfC6XTC09MTrVu3Rnx8PEJYv3wfg/r16%2BPNN9/E22%2B/TcoPnU4ntm7dioSEBDidTri7u6NRo0ZYtmwZtm7diuLiYmg0GlSqVAlNmzbF6NGjSfB18%2BZNJCUlYefOndi9ezeWL1%2BOyMhI6PV6kj3r27cvPvzwQ4SGhsJms%2BH%2B/fto0qQJOR%2BALNPmcDjwj3/8A59%2B%2Bim0Wi0CAwPRo0cPeHt748KFC1i6dCmmTZtGAozXX38dGo2G%2BMd5e3vD3d0diYmJ0Ol0mDZtGmmXmJiIO6XferZt2xbjxo1DTEwMGb9fv3744osvYLfb4evrC5vNhsLCQnh5ecnKLEVbCQBEUTMmJkYWSO3evVtmf3Hjxg2cOHECq1atwieffAJBEEgmTVTOtFgs8Pb2Jpk2cV3SfqOiohAbG4sJEyaQY5GRkcT3z%2Bl04o033oDNZsOZM2dIcBYaGkraiyIvBoMBdrsddrsdffr0wdmzZxX3jBgQZmRkwN/fn1nO7HA4kJmZSfiX5YFz%2BDg4ODg4OMrHH5HtUL16dZk4nxS0F/XgwYNJMEjDYDBg1qxZCp2Hpwme4eMoE/v37yep6bLg4%2BODKVOmYP/%2B/UhOTsbBgwcxd%2B5cWbA3f/58LF68WHFuvXr1kJaWJpPoL28%2BKSkpMJlMOH/%2BPE6cOIHPPvtMEext2LABEydOJK/T0tLQvn17RX8TJ05E9erVCU/x/PnzOH36NMaPHw%2B9Xo%2BtW7diyZIlWLhwIS5fvozNmzcjNTWVaS7v7e2Nnj17IiQkBGPHjsXFixdlwjR/%2Bctf8OGHHwIAKQMUTdrFkkSxjhtwZcH69u0LnU6HyMhIaLVafPTRR2RuLHz66acyc/mVK1fCbrcTc3kRV65cwdq1awmnLzAwEG%2B%2B%2BSYpLz158iTJ3NWqVQv5%2BfmkTDI1NVWW/ZIGYKmpqZg1axbS0tJQWFhIsoM9evSAyWRCnTp1ALi%2B9erWrRsSExMBuGw1zp07h9TUVFLOqtVqZRxLh8OBadOmYfv27fAu/Qo3KSkJEydOJMGip6cnGjVqREo0AVdJpsgpBFwBpchBBUDKT5s1a4YXXngBGo0GTZo0Idlpo9FIMrve3t4QBAFWqxXr1q1DaGgotFotWWdAQAAEQVBkLzk4ODg4ODieDH%2B0ks4/G3jAx8EhwX/KXF4MgkwmE06cOIG6deuisLCQ2DMcPXoUJ0%2BehI%2BPDzIyMmCxWPDmm28iLCwMCxcuBADigydi27Zt6Ny5Mxo1aoT27dtjzZo16Nq1qyy7BgAnTpxAr169sGXLFgCu7OLkyZNx5swZAC7z9tdee428p9PpSFYvOztbodopQsyA2Ww23Lx5kwSDO3fuRHh4OPEcBIDr168T24ndu3fD4XDg7Nmz2LNnD2w2G6Kjo7FhwwYAj0pQb9%2B%2BDX9/f5nRuc1mI8FcQUEBWrdureBhWq1W4hFosVgwffp0aDQaEqg5HA4cPXoUBw8ehMPhwNatW1GrVi0Aj0prxX7EzPKZM2dw6dIlwjEEgHv37sHpdMrEZ8oDF23h4ODg4ODgeNbgAR/HHwLNmjUjtgmsnzsMsYhniadtLv84f0APDw9s3LgRr7zyChH/AFzB1bx582QWBKKxOQDs3bsXERERmD9/PgBXBvT27duwWCwoKChAamoqvvvuO5Kda9q0KelDynXTaDTo06cPCTZpWK1WUmfu4%2BNDzjUajejRo8dj1y3OlRZqEXmQgCtwXbduHW7dukWCPDH4FOcLuPwImzVrRo6Lmc4BAwagUqVKEASBeCJKodVqYTAYoNFo4O7ujho1asDhcJRpNzF58mQS4EqzheI%2B%2Bvr64vvvvy%2BTc1ieRYUULNGWzp2p82klAIYyQLnGzGrMnBm8xQoZr6sxOaYVBlhqByrMzxVds5Q%2BytkLltiBGiEDhpO5oomib1oZgjU4rTrCakML2qgw0KavJdN4nW6kYs/VmIuruW3o7WPp2dDnqBHJYJmoK4R7aHN2llm7ivtacagiyimMc9SIoqi4dIDkSzcAFTJeZ/VLj818XugTGYpA5a5TxZ4z163GIL2ce5bZr5qFqxHdUmOQTj8QtFoSSz2Jvt60qBDwpzJe5xm%2BJ4PgfNZOfxwcvxGdOnVCZmYmyewYDAaEhoYSsivgMuZesWIF9u7di/v378PHx0fBq5s8eTIsFouinPTq1avo2bMnEhMTMXToUAwfPpyUrorm8suWLcOuXbvQtWtXeHp6Ij8//7HzmTx5MjEul2ZoRI5YYmIiKV1t2LAhunfvjkWLFinW3rBhQyIAIlW71Ov1sNvt8PDwwHfffYd%2B/frBYrFg7NixOHnyJE6cOAEAhL8HuAKd4uJidOvWDUuWLAEA/Pvf/8bIkSORK/lQFwQBWq2WBEBVq1ZFWKnx8C%2B//CKbn4%2BPDzFwNxqNSEpKQmhoKBo1aoQrV67A19cX2dnZ5SplsiA1TWdh3LhxWLp0KQkcReN2Dw8PaDQaFBUVPbXs2IIFC/Dll1/KeHpiKW7r1q2xaNEitG7dmvACRej1epw/f171OPPnz2car49mmUVzcHBwcHD8j4KpwPqEYIr8/JeCZ/g4/pCQ%2Bv8dPnwYXbp0wYgRI3Dr1i0UFBRg0KBBuHz5MlauXImkpCQmr04tyjOXL28%2BgEtsxdPTE23btsWuXbtw8eJFHDhwAD179gTwyDIBcJF8RQNxFnr06IGTJ09i6NChRJBFFFOZOHEinn/%2BeZJxvHbtmsy7paSkhJQwitmpffv2Yfr06QCAlStXIjw8HMuXLydKmk6nU5btstvtOHLkCKpUqYLWrVvL5iaOK/XZA1xZys2bNyM6OhruEqkyrVaLGjVqYNiwYbLz9Hq9TPxFlCeWQqoc6ubmhs2bN5OgCwB%2B%2BuknAK7As0qVKmjVqpVsTqInoJTD165dO0RFRZHXGo2GnCOdT%2B/evWUlqOK%2BlJSUID09HYmJiSgpKVEEmOL7asFFWzg4ODg4ODieNXjAx/GHx7Pm1ZVnLl9ClWnQ8wGAy5cvw2azKeY0evRoAJAZc6oVw5k2bRoOHToErVaL4OBgOBwOREdHo7CwkBh%2Bh4aGEh9EqRCMGLyI6qMijhw5gv79%2B6NLly5YsmQJ0tLSsH37dlnJanZ2Nux2O7Zu3Ypjx46R41qtFj/%2B%2BGOZc65Xrx4%2B%2B%2BwzHD58mJSAimbkYqmlaF1QUlIiK/HU6XTEtkE0LO3ZsydZR8uWLXH79m3YbDYSZIlCPFlZWbh58yZOnz6NUxJLA6fTSYJfcQ8OHz6M2NhY1KtXD4CrXFTsT5yPRqPBtm3bYDQaZaIter0eQUFByMnJQZ8%2BfWTvASCBbnJycpl7RINz%2BDg4ODg4OMoHL%2Bl8MvCAj%2BNPg6fNq1ODbt26oaioiOnLdv/%2BfWImfvfuXdSqVYs5JwAy7llFINo2xMTEoEOHDrBarRAEAa%2B88gppM3bsWBK4jh8/XlZeum3bNoSHh8NqtRIvGBGhoaGoU6cOBEGA0WiE0%2BkkGTAvLy9SIqvX67Fr1y5otVpZNgxwKXtKOZdiSaeoqNmjRw8sW7YMXizfMbiCycLCQrRt2xaFhYXQaDT4%2BeefSeBYIOEviMeknoVilrK8YMnb2xvdu3fHjh070KpVK4VnoejR2KdPHxQXF8NsNiMnJweAK3t39%2B5dErybzWZkZ2eTPsSM6s6dOx87BylUcfjo8thnxeGjeWGsNmr4ZCoM01Vx%2BCrAJ2P2U858KmpQXREenYIH9kfn8KngTD0rDh/LS14Nh0%2BxXSwOX%2BmXSwS/J4ePnnAF%2B1XRzX%2BMw8c06qYbPSMOn5r7sSL3rCpe4h%2BNw3f7tvw1ff2BPxWHj%2BPJwH34OP7wEHl1WVlZ6NChA%2BbMmYPnnntO1bl79uwhpX8ifgttNTY2FitWrMCaNWvQvHlzNGzYEL/%2B%2BismT56M4uJiDB06lMxRzExJOYjiWO%2B99x7%2B/ve/l8tBlM5t8uTJyM3NVZiDi6IrtWvXJmWn9JpE/p/dbieZLul7u3fvxu7du8kcxfNr1KiBa9euISgoCF27dsWECRNIWWft2rXxwQcfkEyYzWYjnnYOh0M2hoiCggLY7Xbs3LkTe/bsAeDKXsbExOCbb74hgXR%2Bfj7y8/Nx/fp10p%2B03PHs2bPYsWMHXn75ZRJMioGWVNCmTZs2j72ekZGR2LhxI5YsWYIjR44gNzcXc%2BfORUJCAgIDA5Geno6PPvoI/fv3h5ubG9zc3CAIAvLy8uDm5oZ69eqR8twqVaoohG4EQSD8RzXYvn27gsM3duy7CA9v8OgA7QjMcPdV%2BPSqcNNVnMP4skLRhuXuS5%2BnwjBd0Q/LsZgenDG2omtWP%2BXMp6IG1Yp%2BGWMrzqMdjFn7STuHq9lzFWPT28kyXlc0UmF2z/CIrtAe08lulpc8fQ7rciu2i/U8BgTIX9OcWRaHlr52KvZG1cLpCVewXxXdAKXqwwQq7hs1xuH0PcA06qYblVoQPbZvFQ9iRe7HityzzP1Us%2Bm0yTvD9F3xzLOIZfSXpVWrPv41ANCWV/T1B5TXgXFdmBf9d8D/WkbuaYNn%2BDj%2BkCiPV2dX%2BY1T9%2B7dSdZL/BG98dTAw8MDVatWRWZmJvr27YsGDRqgZ8%2BeuHv3LpYvX06ULwF5ICmWiYpjtW3bVhUH0eFwYNeuXYiMjERCQgIOHToEAOjQoQM8PDywaNEixMfHk7kBgJ%2BfHwBg2bJlZM8WL14MPz8/%2BPv7QxAETJ06FWlpadi1axfc3d3h4eFBlDKdTifJ2GVlZUGv1%2BP27dvo3bs3Dh8%2BTDJXUqVNOpMpqovWrVuXlLHSEI3N8/LysG7dOtjtdtSuXfux%2By/Oy%2Bl0lllOKlUdlXo6arVamWdhdHQ0tFotdu3ahaKiIhw4cAC%2Bvr4kQNu2bRv0ej0pAbXb7SQQBVxm8KmpqYTvKAZ70uvudDpxgZVRKAMsDh9n8XFwcHBwcMjBSzqfDDzg4/hDojxenVQ98Umghk%2Bn0WiIoXhaWhouXryIw4cPE3NuAAgJCUGrVq0U54rm8uPHj1fFQfTy8sKQIUNgMpnQp08fvPjii9BoNBAEAUVFRahbty7pW9yP/v37A3CJwXz33XdITk7GuHHjkJeXh7y8PHh5eZEArV69erBarTCbzaQ0U6vVygI5kbM4YMAAjBw5kry%2BcuUK6Uc0MJdyBbVaLa5du4atW7cSIZRBgwbJyj87duyIuXPnwt3dHf369SN2G8HBwYq9k5Zb6vV6fPfdd4%2B9Tr1790ZgYCB5LYqsiP0kJycjNjYWp06dQlFRESZNmoSDBw/K1ifla4prFQV2AkqzAgEBASik6s00Gg2qVKkCAGjcuPFj58nBwcHBwcHB8Z8ED/g4/nTo1q0bNm3aJON1iZg0aRLWrl37h5yTGg6ir68vERSR4tKlS6hfvz7h00nx9ttvAwByc3Px9ttvo1GjRkQkRRAEkgm8dOkSZs%2BeDUBu/D579mwiOHJPwm0Rg0CdTofKlSvLPABpjBkzBh9%2B%2BCEqV66MlStXkoBv69atsgzYgQMHMHXqVBQUFGDjxo2k3ciRI0mb8PBwAMCcOXPw/vvvQ6vV4pVXXlGI8dAlpGazGZ9//rlibuL4DocDn376KVmX3W7Hxx9/TM4ReY01a9aEIAiwWCzQaDREBTU7Oxu%2Bvr7woctv4AqARdy9e5e5Ryxw0RYODg4ODo7ywTN8TwbO4eP40yE2NhZ79uzBkCFDMGfOHDRs2BCZmZmIi4vDsWPH8O6778ra075%2BOp3rtk9KSiIlgI/z9RPxOF%2B/pUuXok6dOhgyZAgJRDIyMhAXF4ejR4%2BievXquH37NmbPno2SkhIsXboUS5culfVTo0YNCIKAs2fP4oMPPkCDBg1w6dIlOBwO3L17F3q9HhEREQrV0JdeegmAqzxQDDizsrJQs2ZN3Llzh5QgDh8%2BnHACi4uL4XQ6sXLlSixbtgxWqxXu7u4yo3GpVUN2djYMBgN0Oh0sFgvy8/ORnJxMgqm9e/ciLy8PL7/8MqZNm0bOlWbCpOWjYtBVUlICNzc3kj3T6XQkwDpz5gy2bt0Kh8OB7du3K%2B4DaT%2BAy6YhJSWFeBeykJKSQuZRWFgom58Y7N65cwexsbEwm82knTjX3NxceHh4EL6miOLiYrJ3v8WH75VXXkHDhg1lx%2BrWlQf1RUVyfhP9GnBx6qU0i/x8igZSUqLglzidci5LXp6SSkK3UfTLmFBhoZx/xZov3W9xsZJORK%2BJPod1HnN%2B1NoVYzH2RjE4q839%2BzLeDGudZrOcgqkY%2B8EDoNR6paw21DDswVjzozqyWOTcqqIii4LHl51NUYwYi6L7Ye35lStASMij1%2BnpgCT5DkB5v9FLoMcBXLoVUioTfZlY/bC2ptzBWIMvWybj9rGut5qx792TUwjpNdHvM/thLFzVuul1MR48uoliKBWbzto%2BWK0y7qmaa0c/9Kxzyn2%2BwbhWjA87xb1Pja14Hy59ExnlLSsLKKVYlLko1oWhj7EWWt6HJv1hw1ona3Oo66J4XdYxjj8duPE6xx8OnTp1kpmhs5CXl4fly5dj3759ePDgASpXroy2bdti7NixpKxPDNCSkpJk/aWkpKBv374wGo344YcfULlyZQwcOBCBgYGYMmUK6tati4yMDKxcuRIJCQnw8vLCqFGjkJSU9Fgj923btmHr1q3YsGGDTOLfw8MD4eHhmDBhAqKiohAaGoq%2Bffti3rx5zLWnp6eTgEWv16NLly4YM2YMRowYIXtPo9FAp9MRbhwAjB49GmPHjsWVK1fwt7/9DVqtlgSN4jllBUNubm4kWO3Rowd27doFLy8vxMTE4F//%2BhcEQaiQoboU4vitW7fG5cuXERQUBJPJBJ1Op8jY/fWvf0X16tWxaNEi7N27l5TQCoJAuHn0OX5%2BftBoNHj48CEMBgMsFgu8vb2Rn59PeIbi%2BnU6HRG2sdvtiI6Oxrlz5xATE4M5c%2BYgLCxMsVeCICA4OBi7d%2B9WBGoioqOjsXHjRlX7wTJeHzv2XYwZw%2BZBcnBwcHBw/C8iM/Pp91mt2tPv848KXtLJ8YeDWp%2B6KVOmYP/%2B/UhOTsbBgwcxd%2B5cGYdr/vz5iuAMcJUMpqWloVq1auVy6sTgYe7cudi2bRv27t2LwYMHEzsG4JFlwMiRI/Gvf/0LTqcTDRs2xI4dO3DhwgW89dZbOHv2LIYMGYKIiAgArlJHUWAlISFBtvY5c%2BagRYsW8PDwQLNmzfDZZ58hOTkZd%2B7cIeqbwCNBE29vb/Tu3RsajQZxcXEoKirC888/j%2BHDh%2BPevXsyr7iyvt8RBEEmkCLy0MxmM7777jtYrVa0bt0anTp1AuCyaxg6dCgRfikL7u7u0Ov1pI3RaERkZCROnjyJkpISvPbaa4iMjJS9LyI%2BPh4//fQTqlevLuuzSZMmMJlMOHv2LARBICWW7733Hr7%2B%2Bms8ePAARqORBIN2ux0XLlzACy%2B8QHh2gCuDabVaSbB87tw5ACA2DKK4jXhPhYeHo127dujcuTOyJdJHM8RyAAAgAElEQVTa/v7%2BMnN32kD%2BceCiLRwcHBwcHOWDl3Q%2BGXjAx/E/C7WcuhkzZsBkMqF3797o3LkzunTpIlPcnDhxIgBg7ty5SEpKQtWqVeHp6YmBAwciLS0No0aNIny5r776CoArsDl27BgRZxExadIkHD58GAAQFBQk44NpNBoYjUZMnToVgMtywmQykfJCnU4HQRCwevVqACCln%2BygQonGjRuT4EpUmrTZbLDb7dBoNAgJCSGqoTVr1iTzEDNnIgRBQI0aNRAYGEhsKIBHAeq1a9dQr1495OXlITs7GwUFBWSuI0aMIO1ffvllmEwmWWkp4Cr1DA8PR3R0tMxS4ssvvyRcRGlgKypy/uMf/5CVrHp6eiItLQ3r168H4ArCAZcFBOASZykqKkJ6ejoAV2b40KFDOHLkCG5J/Iyys7NhtVrJ%2BipVqqRqvwHO4ePg4ODg4OB49uABH8f/HAoLC/HVV18hKysLCxcuxNWrVxEXFyczDhd/6GBj3759WLx4MYqLi9GtWzc0b96cmKIHBwdDEATodDr06tULgwYNkgmNNGvWDLGxsQBcnK9mzZohPDwcERERiIiIwMSJE3Hs2DFSKlirVi2kp6eTOTidTgQHB5dpXg64VDi///57tGvXDtOmTUNOTo4syJEGtXq9HmlpaZg3bx6aN2%2BO1atXk2yWGPwIgoCSkhLY7XZ8%2BeWXJDC7c%2BcO5s%2BfD6fTiU6dOpHMn3hObm4uiouLcfv2bWLkXrduXRQXF6OwsBCXLl2C0%2Bkk%2By8GOStWrJD143Q6kZWVhe7du8vWabfbSaAsZljj4%2BOxZcsWaDQaBAcHk6BPEAS888472L9/v6wEtLCwEA0aNCBein379gXgsluIjY3F1KlTodfrSRAcHByMwMBAbNq0CRcvXiT9iOOI10mtRyTANl7v2JEyXv/hh8e/BsMXl%2BIR0r6%2BgNLPHaXcycf2e%2ByYoo1Cp6j0eSBQYyRdml2VgTaFZrg5K3yj9%2B9XtCmN1x%2Bh9N4mYBgh0/2yfJAVe8rgmSrMkOmxWc7hpcq1ZfbBOo91gelNpvphGa/Tp7A8mOn9YhpS0/NhNaLWQC%2BJtTW0OzurYEFxP7KMrn/99fHzY8xX8b0Zy5ydvr6sa0dvaukXdASsm43aDOaeX74sf834ok9xnhoXdfp%2BZF0YuuPkZGUb6jyms1JSkvw1fR%2BxxqYUuzMyGP3SzyZDXI3um/64YZnJq7ln1Ri4K44xbmx6v9R4vqv4CK3QOn8v8Azfk4Fz%2BDj%2B60GLthiNRoSFhRFOXaNGjTB79my88sorzHNF/p9UtKVz584YPnw41q9fj1atWuGbb74hFgdWqxVubm6IiIjAuHHjZGbrM2fOxO7du5nz9PT0lHnAiY%2BmKDJjs9nQv39/NG3aFJMnT0ZiYiJq1qwp4/1JTdjFMk2bzUaOu7m5EbNzAEhLS8OWLVuwZcsWTJs2DTExMYogtzy4ubmhQYMGSCr9ZV2rVi1oNBrcuHEDgCvjlZ%2BfD6fTyeTqaTQaaDQa2Gw26PV62O12OBwObNy4EdHR0QCA27dvEw5f69atcezYMXzyySeYNGkS6Uen0yEqKgrXrl0jJZdarZb49AmCQDwdxQDTx8cHVqsVlSpVgl6vx61bt4hwzPr16zFnzhzZXHU6HV599VXUqFGDqQgKuDKvaoM%2BFofv3bFjMZpl/MzBwcHBwfE/Cvq7h6eBoKCn3%2BcfFTzDx/E/gafl62ez2Uh2MC4uDlevXlX4wwUHB2Pq1Kk4c%2BYMhg0bJjNbF33n3N3dUa1aNQwePBgeHh4ICAiAxWJR8OGaNm2KLl26kPLJ%2BPh48v8ePXogMjISGRkZ0Ov10Ov1aNKkCTlXEAQSvInlhg0bNpSN0bBhQ8yaNYtwDEU%2Bm1hqKAbJYmZQzOQNGzaMBKJdunRBt27dSJ9t27ZFZim7unLlyjCbzXA4HDAYDIiOjkZcXBzJmoWGhuL06dMYNWoUjEYjSkpKSDA2cOBALF%2B%2BXHENxOzaP/7xD9nx999/H4sXLyYcPBFVSyUOY2Ji8Oqrr8rKPYuLi4kZvDjnoNLfALdv34avry/Zr4EDByIlJQWzZ8%2BWqXTq9Xr4%2BPiQdvmsb%2BjLAKvcln8Dx8HBwcHBIQfP8D0ZeMDH8T%2BPx3noZWVl4aOPPkJkZCS2bduG/fv34%2BDBg1i7di10Oh10Oh3Gjx8PABg7dixMJhP27dsHg8FABEKkwjDvvfceAJDSz1mzZmHQoEGw2%2B0kKP3444%2BJ397Dhw%2BRmJiIZFaJDECyYa1bt4YgCGjSpAnxhBPHf%2B655wivjzY4l2YSCwoKZJ51wKNA0WazQaPRYH9pyVz//v1JUBgi1V%2BHi9MmZhFtNhvh840bNw49evTAuHHjSJlpUVERXnrpJWzbtg1ffPEFtm/fLvO0W7JkCa5duybrPycnB40bN0ZmZiYEQSDB15kzZzB06FBZQGe324ndwt69e3HkyBHZ%2ByL/0GazkX5effVVct1yc3NJ%2B02bNiEyMhJxcXGktFWn08HhcCBPUnr0W2wZOIePg4ODg4OjfPCA78nAAz6OPxU6deqE8PBwwrFr2rSpQjUzLy8PCxYsQOfOndGoUSOkp6dj06ZNuHTpEgDI%2BHoRERFYunQpcnJy0KxZM0RERCAhIQEZGRmYOXMmLBYLCeR69%2B6Nrl27Yv369bh69SrxuPv0008BAEuXLkVkZCS2bNkCwJXpsdlsuHHjBhGGEe0EfH19cefOHWzZsgXvv/8%2B7HY7PvjgA0RGRmLGjBlkrk6nEw6HAwcOHIAgCHB3d4fBYEBQUBCWLFlCArYDBw7AarXiq6%2B%2BIsHW/VL%2ByPXr1/HWW28BcHnV%2Bfr6yjwJ69Wrh1OnTkGv15PySrHsUszieXt7w%2BFwEBXNEydOID8/HzqdDt9%2B%2B60siNq7dy88PT0hCAJeeOEFZGVlwWKx4LPPPsOqVavQtGlTEtTMmDED6enp6NChAzZv3oxhw4bBYrGgcuXKxJuvV69eMg6f0%2BkkCp1S0Zbjx4/j%2BvXrZd47ubm55NqwIAZtS5cuRWxsLAYPHoxffvmFBL0DBgyAyWTCqFGjiKKnzWYjaxHn8fDhwzLHoMHi8HXtSnH46CwgS4SHJl5QZbOsUxScDxZ5g%2BqHReZQcAFplgDLyoNuQ4/DaqOGb8QYS0HhojOwjM2pEK%2BFtcl0I/pLJTX8RjUXj5VVps9TweGj18nUe6LqqlTtDYsERF0reizWbSOxzSzjAKPsi8VaKYeHqIpndfu2shHNX927V9mGriahSaY0vxBQbgbrWZDwxQEwF6E4jbVQeiz6AVJDVGPdONQxJg%2BRvp5UIyYBqRwuKADl88H4vKGnrIYjRx9jPQtqPr7pOau5LOW9BpQUSBbVl/5IYtEbmdeK408HHvBx/OkgLc88fPiwQjVz0KBBuHz5MlauXKlaNfPQoUN44403EBAQgJkzZ2LgwIGw2WyoWrWqzNZACo1Gg44dOxIRFTEw7Nu3LywWC8kCnT9/Hrdu3cJzzz1HgjGz2QyNRoMFCxYgPDxcUYYoBlo3btyA3W5HSUmJrBTTbDbjnXfegZ%2BfH/R6PWrVqgWAbbsgDYo8PT1J1kqr1aJy5cp4%2BPAhBg0aJCunFP8VM3G5pUIDog/fhx9%2BCMBVLtmuXTvZ3JxOJ8xmM/z8/LB7927k5uYiLy8PlSpVQvPmzVFSUkJKGUeMGAGn04n169dj586dJOjy8/Mje%2BVwOIhnoRiQhoeHkzXRcxSxZcsWfPbZZ7KMYVAZBfs%2BPj4IDQ2FIAiYPXs2Vq9ejaSkJLRv356UuYoZvoSEBNyW/LFH77mbwnG4bGzfvh2TJk2S/fz0U6K8EZ0FZGQFFS7HlOIs6xSFKTNr3rRyLW3aC6XXr8IdXdGA0YZl6ku3UTguqxtL8fjSLuGMzaG3grU1iq1gbTLdiBZcUlwExiE1F0/hNs84j3Jvp03XAeU6WUPTpBdVe8O4b%2BhrRY/Fum0kldRlHGBwcli2MdRNQW8n47Ioj9WsqWxEVVCA/vIGkDvSA0pHehb/l94M1rPg7y9/zViE4jTWQumx6AeIdS3pflg3DnWMNbTielKNmA5A1HxY01M8H4zPG3rK9F6puSdYz4Kaj296zmouS3mvAYW3vOI1oPxIYmnCMa/V7wCe4Xsy8ICP408Nd3d3xMbGIiAgoExPvV9%2B%2BQVff/01Uc2UZgnFzNfYsWPRuXNn4uu3Y8cO%2BPr64sGDB5g9ezbatWuH4uJivPPOOwBcfnkOhwOHDh0iQcpnn32G0NBQhIaG4sMPP4TT6URkZCQpx7Tb7UTyX/Svy8nJUQQMGo2GKEeK6NChg%2Bx1UVERBEFAVlYW6tevj5EjRwIo22fPbrdDp9Phb3/7GwkAxZLUoKAgojQaGBgIqen74%2BB0OpGTk4MXXnhBdrx%2B/foQBAEPHz6Et7c3goODodPpkJOTgz179sDDw4OpNOp0OkmQfPXqVdSpU4e8J4qniCIs/v7%2BJMgU1%2BxD/TabMmUKzpw5Q7wPAZB1irBarbBarXjw4AFSU1NJRhVwlYjS8wNcQaOF9VVuKUJDQ8t8jwb34ePg4ODg4OB41uABH8d/BdR46r3//vto27YtgEdZQjHD16lTJ2aWsEqVKpg2bRri4%2BPh5%2BdHsoSdOnWCRqMhXDwpDAYDmjRpAr1ej7fffhs2mw2VK1fGlStXcKxU1v7hw4ckc0Svw2q1Es6dWE545coVIoBiNptRr149EphcuHCBZNwAV8Do6elJgqrnnnsOTqcT1apVI21EBc/09HScO3eOlHBKyxHtdrssc7dz504ALs6eyHczm81kTwFXdsvPz4%2BIxeTl5cHNzQ2ffPIJjhw5gn79%2BuHIkSOKbJzRaESVKlVQpUoVGI1GeHh4yLKeouqmGBAeOnRIEdzmUfUqrVu3Rlpamixwo0VxxPtEp9ORMtcPPvgAANCmTRtZW/HcFStWkGvHMp2nOY0cHBwcHBwcTwae4Xsy8ICP408Nqadehw4dSOnkb4VYzklnCQ8dOoTBgwcjMDCQCKxIvfWmT59OgrNp06bBu7R05Ny5cygpKcG4cePgdDqRmZmJf/7zn7h69Sp0Oh1eeuklkuULDw%2BHu7s7PD09MXv2bFSpUgW%2Bvr4AQOwEcnJyMHfuXACuICMtLY3MoU2bNjCZTKhUqRIMBgN8fHxgsVhQo0YNAI/KGKOioojXnNiPVquFXq%2BHzWbDggULYDKZSHloYGAg4ZO5ubkhJCQEvr6%2BqFmzJoqLiyEIAoKCgmRG4waDAenp6STo0ev1yMnJwfjx4/Hjjz9i5syZ6NevHwlkRfzwww8YNmwYCgsLsWPHDpw9e5aYqLNAC5usWrVKUUopCutIhWpmz55NAjsAJNCVcvFKSkoQFxeH4OBgVK5cmQR1H3zwAUwmE1avXk36cDqdRGVVhLjvasBFWzg4ODg4OMoHD/ieDNyHj%2BNPhYp66knPk/rkdevWDXPnzkVycjK6d%2B%2BOOnXq4OTJk3A6nfDz80PTpk0xevRo1K9fH3FxccQY3G63KzJ0giBAr9dj3bp1uH79OmbNmoVVq1bh1q1bmDFjBvz9/ZGbmwuLxQKNRsP8w16r1Sr6HTp0KNavX48XX3wRiYmJxOIgICCAcMn0ej3JfgmCQLhuJSUl8PHxQV5eHgwGA0wmEyk5NBqNxPdPo9GQIEzqkycKo2i1WqSmpuKtt94iRun379%2BHVqtFYmIifvjhByxcuBAAyD6ZTCbF%2BsTgkv7Y0el0qFatGvLy8jB69GgMGzYMkydPlhnAi%2BdI905cN13eGRwcjJs3byIwMBAPHz4ka6pVqxaysrJQWCoOIO1XuuYNGzZgzZo1OHjwIMlWajQa6HQ6vPPOO/jpp5%2BQkpICnU6n8C38LT58qampCkuQevXqIzy8waMDVqucc0K/Zh1zOmWEF4tFyS8pKaG4GYoDjGOMNvQhamjmOXY7xZGp4NgoLpYTYFTMT3EOYz/NZjknRs30mJtMj0W1YfVbUEDxaAoLldymvDw5ISc7W8m1oo9Rcykqsih5fNResOaHBw/kfDHFxWScp1iU8pjiHDX9MiZIb5fifmTNpwL3Oe7fV/AikZ4u5%2BQtWwbQnpopKUB4%2BKPX9HW6fBl4/nn5OdTgzDXl58u5aoxGFXruytsr1lCsZ4E6xmqiePDojiv6OUENxri1yh2KOV%2BqEfN5oQ%2ByPr/pNkVFSrJfeR9Kan4v0H1AuW7WvcVc1%2B%2BAy5effp/0o/bfDJ7h4/jToaKeeuJ5QUFBmDBhArp06UK83FauXIn09HRiUr5w4UJFGadU7CU2NhaAK6u3fv16AMDUqVNhMplkXniAS4lRDFBZ3DUpWGWe69evh16vx8GDB8kxNzc3DBs2jLyuV6%2BeLCsoCr0AIHYTWq0Wp0%2Bflo3lcDjg5eWF6dOnY8KECfD29kbdunVJGzEYstvtiIyMxPHjx5Geno4HDx7A4XCga9euslJRLy8vTJo0iVwPGiUlJUyeoc1mw507d5Cfn4/58%2BejS5cu%2BOGHH8j7RskvKWkJrbjGhg0bonbt2uS46HeYnp5O2oigBWboMV5//XU0b94cv/zyiyyYE9uOGjWK8BtZJvXptOreY8ASbfn5Z0q0hf4lzhI4oY9Rv7FZYgKKX%2BBqVAlUiIwo/hCtqHiEGiUNFWoHikP0OYz9pAUQVIl4qFEvodqw%2BlV8RDCESRTqCyxhqXLENliiLfReMP/Io8VBGAIiivNYn3vUMcU5avplTJDeLqbQR3niOWruIzrYA5QCLHSwB8iDPUB5nVh/gaoRL6GFSRiNKvTcqRAaUgzFehaoY0xtK/rBozuu6OcENZga/Sc1gix0I%2BbzQh9kfX6rEb0p70NJze8FhrILvW7WvfVHCPYAnuF7UvCAj%2BO/Co/z1NuyZQvWrl0LAJg7dy4WLFhAMj/Lly%2BHVqvFyJEjSQbJ09MTRqMRWq0W/fr1Q7t27fDee%2B8RywQAmD9/PhF%2BmTNnDrF7mDFjBgneDAYDmjdvjsLCQtjtdiLKQpc1SiH6w4koKSmRZcaMRiPJUgEu64VChkQ58KhE0GKxYOTIkTJvvdatWyM/Px83b97EsmXLSOkmC%2BLcAVfwU6lSJRIwi8brFosF7dq1w4wZM8rNcvXv37/M927duiULJEUlTkEQ4HA44Ovri5dffhn%2BpX98Xr16lZSiinMVIZbFijCz9Kslx8NL/yCjA1Ppa%2Bm1o3l80pLR8sBFWzg4ODg4ODieNXjAx/FfhdjYWPj7%2B2PIkCFISUmB0%2BlERkYGsrOzce3aNXTu3BmAywfvgw8%2BQNXSb2lr166Nli1bYvTo0QgMDERqaioRbtm4cSNMJpMs45eVlQUAOHv2LBF%2BAYDTp0/DZDIhJSUF1atXJ8fHjRuHSpUqkSzc119/LSvp1Gg00Ov1mDdvHgwGA5YuXapYm8PhIPNt3Lgxjh8/Tt4zm82ybNOoUaMQGBgIjUYDo9EIvV4Ph8OBv/71ryQY8vDwwMqVK1GpUiWsWbMGvr6%2BaNWqFV5//XUArsCnquRb7DfeeAMmkwkdO3ZEzZo10aZNGxLc1KxZE1WrVkVJSQmGDBmCzZs3k8AxIiICBw8exIULFzBz5kzS3/fffy9bn7u7OwmeBEGQWR%2BI0Ov16NWrF7y9vdGtWzfCp5RmAL29vfHNN9/gu%2B%2B%2BI3srciul%2B1SrVi0cPnwYvr6%2BcHd3R48ePQC4rDAAFy/wxRdfJBnFxo0bkzJVaTAaEBAADw8PMvfMzEzFvMsC5/BxcHBwcHCUD57hezLwgI/jvwoeHh749ttv0bJlS4wdOxZRUVEYOHAgAGD48OGyLNCRI0eQX2rIev36dRw7dgzFxcW4ceMG1qxZgytXruD69etISkpCgwYNEB8fj4KCAkRGRpLMjM1mw%2BjRo2VzEG0f7ty5g7feeguRkZEYP3488vPziUXA9OnTSZZIEAS88sor8PX1xeHDh2G1WlFYWMgMBu7duwfAZW5%2B9OhRcpzOMhUUFCA3NxfPPfcczGYzCSLi4uJIG4fDgeTkZGIxcOfOHUybNo30VatWLWLeDriM5ENDQ3H16lXcvHlTYccg7qXRaERubi727dtH9qhPnz6IiorCJ598QgIoT0ntlSAIKC4uJlm0sqjFVqsV27Ztw%2B3bt3HmzBmispmbm0synGazGUOGDMFrr71G1imuUZpRu337Njp16oS8vDyUlJRgzJgx8PT0JOWaZ8%2BeRWJiIgkQz549i8jISMTFxaFhw4akn8zMTBQVFZE5S20gygPLeL1TJ8q7q5THWOZrMAyVJaW7AMN8HAyjXsqGgtkvw0hakViWZMDZnTAMiqn5AlC6ETOsMBQJ0l27FG1u3aIOUF80sDanPL905mmbNysb0cF/fLz8NSvbTE%2BYMkwHoNx01gWm3ZIpY26W8Tq9TtrLGwBAKewyCwsoxVymcTO1yfTlZSbiqQmyPiYU5tIsU3q67J%2B%2BRxn3rOIe%2BPJLZb%2BSMnQAQOkXgzJkZMhfL18uf8263uXsFQDg4kX5a0b1gOI6MNZZrsE8y9mc7icpSdmGmjTznii1MCKg72tWRQRF7GJ8Twhs3Ch/zXIgp9ZF33%2Bs%2BVKPAvNZoM9TY%2BDOui70va6mX/pSsZ4pNQbuTDP73wE84HsycNEWjt8NtACLwWBAaGgoxo0bhxYtWgBwSe2vWLECe/fuxf379%2BHj41OmkIro4ya1ZPj444/Rp08fhWgLANSpUwcLFy7E1atXMWXKFFLKKRVOGTNmDGrUqIHp06cT/7fvvvsOd%2B/exdChQ7Fs2TJMmDCBBBTJyclo3749cnJyZPPQ6XRwOByoU6cOLl68qBBn0Wq1GDFiBNasWQOz2YyuXbvC29tbkQUTIVpCiH3Q/Xl7e2PVqlVo1KgRBg0ahHPnzqm6Ju7u7vjnP/%2BJoUOHIjY2FllZWUhISADgKsGML/2DddGiRejVqxc5Lzc3Fx07dkRRURGio6OxYcMGREdHw263k%2ByiTqdD06ZNcfLkSdjtdmZQJ7YNCAhAVlYW2VdaHEWv15P1OhwOREdHIzU1VSY4w4KbmxvpUxTZEc/R6XRYuXIlsZlo2LChglMpCt9s3rwZ06ZNY47x1VdfoV27do%2Bdh4j58%2BdjzZo1smPvjh2L0SzuDwcHBwcHx/8oUlKefp80pfa/GTzDx/G7QirAcvjwYXTp0oXph7dy5UokJSU9VkhFLK0UyypNJhP69OmjGCsoKAi%2Bvr548803ZeIitWvXhoeHBxITE2EwGNC%2BfXts3boVM2fOJBmyXr16ybKEu3fvZhqUe3p6kjmYTCYcPXoUnTp1wsWLFyEIAmbMmEFsBzQaDex2O1asWEF4ZHv37sWWLVsU/Wq1Wmi1WoSGhqJKlSrkuLe3N%2BbNm0eyc/n5%2BRg4cCDCwsJkwV6DBg1k2UCpAIrBYJCZy587dw47duwg78dLshMTJkwggaA4X7HM0WQy4cCBA6hXrx4AF//QbrfDYrHg6NGjhIsotV0Q5yG2zc7OlgWE27dvhyAICAgIgCAIiImJgcPhIGu5ceMG0xOPhrRPur3NZkNsbCw2l2ZqmjdvzuwjLi5OZrxO90Mbtj8OLA4f/waOg4ODg4NDDp7hezLwgI/jDwN3d3fExsYy/fDq1asHQRDK9MP7Ldi/fz%2B8vLwwe/ZsREZGYvr06QBcQUNcXBx27doFq9WKI0eO4P79%2B7Isz7p160h7ADh48CDc3NwU5uustbVt21YWHLi5ucHNzU3G2RLf1%2Bv1%2BPbbb0nQJOW2aTQa/Prrr/D29pZ5z/Xt25dwBEXQ8woODpYFPdKsmZjpEn3koqOjSfAnRevWrSEIAnr27EmObd%2B%2BnRilu7m5YenSpTJxG51OR0zqxYwuzWGkhVVEMRwA6N27N5xOJ%2B7duwedTkf4eWIf2dnZJAhzc3PDG2%2B8galTp8JgMGDWrFnM/bhw4QJMJpOstBQAPvroIwDAr7/%2Bqli7OBcpv1AUhRH37RJd0sjBwcHBwcHB8TuCB3wcfzjY7XZotVrs27cP/fv3l5VGinj//fdJ6V1FkJGRQYIFMdDQaDSYOXMmWrVqhfr166Nv374ypUkxMJAGOgUFBcjOziaBU7NmzZCTk4PCwkKSAXM4HLhw4QK%2B/PJLGAwGeHh4YNCgQQDkAZdYYiji%2BvXrxPhcDNJsNhsCAgLg6%2BuLV155hYiRiMGOtFxRXJMUp0%2BfRoMGjzzeNBoN3n//fdkaL1%2B%2BjMjISKxbt46Uy0px/PhxOBwOREVFITIyEhERETh58iQJ%2BIqLi/H888%2Bjfv365BwxaPb19UXPnj2J2qY4pslkwpQpU2TjDBw4kIixjBkzhqxl8ODBssBYtGMQ7xOr1YpvvvkGc%2BfOhd1ux7x58wC4DOilYiuioqper4ebmxs2bNhAzgdcVh2jR49WiLYEBQURvqK4106nE3fv3iXrUQsu2sLBwcHBwVE%2BeIbvycADPo4/DAoLC/HVV18hKysLHTp0wK1bt1QbWJcFqYDKxx9/TP7It9vtiIiIgNVqRfv27Qkf7ubNm%2Bjbty8uXbqE%2BPh4pKWl4a233oJerycBxbBhw/Dmm28CcAVMrVq1IgHIkSNHyP///ve/IzQ0FGFhYejTpw%2BuX78Oi8WCwsJChIWFIScnh%2Bm7V7lyZXz11Vfo27cvgoODATwK4HQ6HTIyMlBYWIgOHTqQLGdxcTGaNWtGAr6pU6eiZs2aCA8Pl2XOsrKyZD6FERER%2BPTTT8nrkpISBAQE4NKlS4iNjZV58okQ%2B3M4HLBarTLDd8AVnO7atUuW6fLy8sLrr7%2BONWvWIDU1lfAtAdd1Dw0Nxdy5c2XjjBs3jgS6ixcvJoHQunXryP%2BbNGmCsLAwAI8CNdGH0NvbGz169MA333xD3veR%2BJeJfYiehUOHDiXnA8AXX3yB5cuXK0Rbpk%2BfLrN2oLmI0pLf8vDMRFtOnpS9VCXawhBOqXXLdl8AACAASURBVJBoCy0eoUa0hZovAKVSAIOfqRATUCPaIilFBsDcHHpvWKItCj0ONaItmzbJX7PUEOgJsyoZ6FJgNaIt1IRZoi20lgVLc4TeDJb%2BhSrRFmoN9J4zhSKog6pEW1gX79o1%2BWt6gmpEW1avVvZL33%2Bs60KLtixbJn/NUvktR1AEAHDhgvy1GtEWxoVRHKLvR5ZwihrRFmrSzHsiNVX%2BmlZFYY1Nfd4onndAKdrCui7UhxJ9/7HmS3fz1ERbGDc2vcX0OSwhHzWiLXQb1nPHRVv%2BO8BFWzieOtSKsfzlL3%2BR%2BeXpdDqEhYVhxowZOHLkCD7//HNSCliWGIsUJ06cwNChQ5GcnAw3Nzds2bIFU6ZMIbw3KUpKShAZGYnz58%2BTP/xF4ROtVguHw0H%2BkO/cuTMOHToEnU5HOFdiW1r449SpU4T7Jc7XZrPB4XBAr9fjxRdfxOLFixEZGckUGNHpdKhZsyZ%2B/PFHACBr8PT0RGFhIXQ6Hex2O6Kjo5GdnY3r16%2BTc41GIwlEunfvjs8//xx3797FX/7ylzKvlbe3N0pKSmA2m2EwGGC1WhESEoIrV67g448/xqpVq4hNQXno06cPEhISiMCKmPmiodfr4ePjg4cPHzL7EQQBbm5uMJvN8PX1RS79S78UPj4%2BOH78ODQaDSIjIxUG6zTCwsKQkJCA0NBQxXuiYI%2BItLQ0dOzYUWGibjAY8PHHH2PPnj34%2BeefmeOsXr1adfaZi7ZwcHBwcHCUj7Nnn36fjRs//T7/qOAZPo5nAjViLCUlJRg9ejQuXryIAwcOYMCAAbh69SqMRiNGjRqF%2BvXr46233ipXjOVx0Gg0mDFjhkxAxWQykWBUDCgBYMaMGahZsyZmzpwJT09Pwo87evQorFYriouLSeA4evRoPP/88xg9ejQal/GJYTKZsGTJEri7u0Oj0UCj0ZBsmslkQqVKlcg8xDJAm82GO3fukEzk7t27IQgCETjx8vJC1apVERwcjOvXr0Oj0RBe3Msvv0xKQPfs2YPw8HB06dLlsfuTn59PgkRPT09Mnz4dN2/eRFBQEGbOnImbN28CkAuT1K5dG2lpafDw8EBMTAyqVKmCtm3bYsGCBaSNp6cnKlWqRF4bDAayVtFEXoS/vz/q1KkDwJW9W79%2BPdlnMdhjCbLk5eXhzp07EAQBXl5esvf0ej30er2Cs3fixAnyuk2bNqSEVhrsicdEM3kpHA4HVqxYgWzWN8SlEIN1NeCiLRwcHBwcHOWDZ/ieDDzg43jmKEuMpUqVKqhatWqZYizdunXDpk2bUMyoJ5g0aRLWrl1b4TlVr16dlIsGBAQAcJVjZmVloWXLligoKCBlnL169YLBYICnpyeMRiOqV6%2BOtWvXIjs7G6%2B%2B%2BqrMd61Nmzbk/%2BHh4Rg5ciQKCwvhdDphsVgwf/582TzEIEgQBIwfPx4GgwHe3t44efIkTCYTvvzyS2IMDriyWi1atCD%2BeC%2B99BJCQkJgNBqxe/dumR3ACy%2B8QNYmjgEAMTExxFxdWn6YnZ2NBg0aoEePHiQQFs/x9/cn7W7cuIHIyEgUFxdj8%2BbNePjwIY4fP447d%2B48ds/Fvp577jmMHTuWHN%2B1axfJVL711lsYMWIECYTEc/r06QONRiNTVQWAoUOHwuFwKO4Rce7SQE4qcAO41DTnzZuHvXv3YujQoSQ4FK9JVFQUdDodOd6vXz/o9XqsW7eOZBO1Wi18fX1lYjnhv0HnmXP4ODg4ODg4OJ41eMDH8R8DLcbCytpIxVhiY2Ph7%2B%2BPOXPmAHBxpTIyMjBz5kwcO3YMnTt3fqL57NixA1arlQRPFy5cwNq1a5GamgpBEIhCJo2YmBgUFBSgVq1ayM/Px86dO8l7Ukl%2Bo9EIf39/tGnTBsnJyWjXrh3Wr1/PLJEsKCjA559/DofDgaysLDRu3Bj//ve/me1%2B%2BeUXJJca1E6cOBGAS6zFYrHg/PnzpO3PP/8sK0kUA5mtW7diYymn4datW7LrMGTIEGzbtg23bt2Cw%2BEgwYcoyCKC9tFzOBzo3r07AFeW0mKxyIRNrFYr4Stev34dn3/%2BOXnvs88%2BI//X6XTYtGkTXnvtNWi1WjJGQkICRKN4MVsKAOnp6UhJSZFx6kR4e3ujZcuWhLdnsVhwQcJzsVgsmDRpErp164YNGzaQtYoZvgkTJsBms5Fs5NatW2E2m7F9%2B3YSYNrtduTm5srKTtV68AFsDl9X%2Br6uCAmEes3ibiiqbSvQL7NvqlRZjSEwk4xFlzyzOqIzpIwy6XJ5Swy%2BFj0fVhMFV4w1P5r4VRHiDaNfetlqhqa3mMXho68lk/BBDcbk91BtmNXWVOd0G9bY9Haxtk9xjDU43UjFfX7vHnWAZehObwZlCg4AKK1YIaA5ezSnD8q9YF6XcvaTeZDxvJS7f6yHoTyCGeMYc37l9cNaOHXTsiibinUyblp6aLqJmvuRxWel%2B1Wzfaz7ulxzdjV8TMYi1NxbrPn8HuAZvicDD/g4njkqKsbi4eGBb7/9lohyNG/eHAMHDoTNZkN8fDzeeOMNhIeHk/LHt99%2BG4Cr9FOE0%2BnEhx9%2BiNDQUPITFhaGe/fu4eWXX4bBYCCBXdu2bREVFYUffvgBGo2GcPPq1q2LqlWrkrWsXLkSDocDSUlJGDJkCBFWESFm47y9vfHgwQMcO3YMkZGROH78OJxOJ7p27YrQ0FDk5ubKShsbN26MlJQUTJ8%2BHXq9Hvv370dMTAxycnJw584duLu7Izc3FyEhISgoKIAgCER1UsxenT59GhqNBr6%2BvgrVT5ZAjEajwSZaSAKuAE5aDknz4%2Bi%2BnE6nLDMlDZRo6HQ6FBcXk0Dz22%2B/Je%2BZzWa8/PLL%2BP7772VjtG7dmqionj17FiEhIeS9//u//1OModVqUVhYiOPHj8sCz4ULFzLbBgYGkj0U1yHNaoprFAQBCQkJsn2lv7gQ7RnUYPv27Zg0aZLs56efD8gb0T6PDN/H8tqUip3KoPi%2BpQL9MvumVHVZ3UosGMuYjLIfZkd0hpSh6Ks4jT5A8XtZ82E1kej/lD0/qtRY0YbKOgNQbg6jX3rZaoamt9jDQzk2fS2Z1pbUYIpryWjDmh/dOd2GNTa9XaztUxxjDU43UnGfS4olXChVSJaB3oznn1e2kdjNAAAkysEAAAZ/l94L5nUpZz%2BZBxnPS7n7x3oY6GMqPkuY8yuvH9bCqZuWvu8BKNfJuGnpoekmau5HxWcCo18128e6rxX7pWLPFYcYi1Bzb7Hm83uAB3xPBh7wcTwTiB53kZGR6NixIw4ePIi1a9ciMDCQKGLu37%2Bf2BOUBR8fH1J%2BePr0aRw8eBBz585FYGAgADlX8NixY/j73/%2BO0aNH49atW7BYLHA6nXA6nYTTJf6hXlJSgrzSr%2BOqVauGoKAg7NmzB7m5uTh16hR8fHyQKlEM279/PwmAbDYbtFotNBoNCgoKYDab8eqrrwJwBSxi4NCwYUO0adMGTqcTL7zwAlJSUvDqq6%2BiZ8%2BeSEtLw8WLFzF8%2BHAALm7eSy%2B9BAD461//ih49emDfvn2YMGECfH19ERISgs8//xxubm6kdFIqiiKu9dq1a3A4HCgsLJSVZAKPMnx2u50EYw6HA/379ydtRKGZevXqISwsTCF2A7gyWBcuXMDGjRtRtWpVjB8/HmlpaUhJSSHzehzEElWWYb20jVSkJz09HQ6HA7Nnz0bz5s1xTaKyl5eXpxizuLgYZrOZXH8RvXr1krVzOp2w2Wy4e/cuCfREq4sC6qtisR%2Bz2UzuHelxEU9qvM5ZfBwcHBwcHBxPEzzg43gmkAZip06dwvr16wn/qnbt2jJrgKcFmit48OBBMpfz58%2BTnwsXLsDLy0tWBli/fn3k5eXh66%2B/RvXq1fH6668TIZCMjAySwdLpdKhSpQp%2B/PFHnD9/HpUrV0Z6ejrx5ktMTATgyk4uX74cH374IQCX8Mstpl60CwMGDCDB79GjR7Fjxw7ExcWhdevW8PLywvTp09GhQwfMmjULZrMZ1atXh8PhQEREBNLS0uBwOGQZNZvNBqPRCKvVCp1OhwYNGpCAJjY2FvPmzYO/vz/S0tJkcxaVQ202GyZMmCDjJIq4f/8%2BNBoNoqOjMXjwYOzbt0/2viiWIgZOdDAmlndKj1etWhXx8fEkY9m0aVNZVlE0QRfVUY2Sb3X1er2CN9eiRQukpaVh2bJlMBqNpD2trPm3v/0N06ZNQ926dYmlRUBAAMxmMywWC55//nlUr14dgCuL2qVLF9SoUQPvvfceAJfyZ3x8vCx4zaBl1zk4ODg4ODieCDzD92TgAR/HfxyiGAudQQGeXIwFeMQVFDNO8%2BbNI9lG8aegoADHjx8nQZK7uzuqVasmE2MZNWoUABd3KyoqipQGxsfHo1atWhAEAUuWLMH9%2B/exudSDa9u2bQAeiYbs2rULdevWhVarxYABA8osc5Ri7969aNWqFZND2KdPH1SuXBnDhw%2BHu7s7bDabIoslQiybtNlsMvuG1atXY8qUKXjw4AFCQ0MJF7JGjRpERKRatWr49NNPcfToUXJeUFAQALmoCMsKQa/Xk3npdDrF3IKCgrBq1SpZ9jArKwsDBgxAZimfJTU1VabEKlXhdDqdsnvHYrGgWbNmsjFOnjyJ0NBQjBkzBmazmQT34hpELF68GHPmzJFlDK9du4YDBw7AbrfjypUrJICzWCzYu3cv/Pz80KNHDwiCgIsXL6J///6yfeCiLRwcHBwcHBx/JPCAj%2BM/DlGMZciQIUhJSamwGEtGRgY%2B%2BugjEsQ1bdoUnTt3xv3799GhQwdklboGt2zZEgEBARAE4f/ZO%2B/wKqqt/39OTQ8kgUAIEATpBMSAiAqRXoQgXSzIRQGliL4IgoIdxKvYiQjXe210qSJC6JiAgiAQWmhCAEOoSUhPzjm/Pw6zPTOzQ0bE36v3ne/z5JEzZ8/aZfYcZ81a6/ulQoUKtG/fnm%2B%2B%2BYZp06Zht9s5cuQIq1ev5sKFC%2BTk5HDp0iW6devGa6%2B9BsCDDz7Ivn37qFChAn5%2BfiKdFLyRpHvuuYe1a9dy9913s2PHDtUD%2B7Jly2jSpAlWq5XLly%2Bzdu1aVq9eLeoJ58yZA3idsKeeegrwEqnUrFmT%2BfPnixq%2BJ598kp49ezJ79my%2B/fZbHn74YT788ENsNhsVK1YU6aZK1KxWrVoEBgYKp8tXi2/o0KGkpaWJv8TEROA34hfwOkwWi0XlvCippEePHhXjT0xMJDU1lfr169OoUSPAW%2BcI3pRImYN7%2BfJl4uLiVDV6LpcLq9UqajsvXrzIqlWreOaZZ4Qt8EYHlfX1TfmcP3%2B%2Brh9ZOuvhw4dV0UGr1Yq/vz/h4eHiWElJiSC/8XVWFXvNmjVjy5YtWK1WnTMbGRlpqD5VgYy05Z57NMLrWiYACTOAzt83IHytO0eiGqxrIyGqKJfkwQBBglRc3ABhg86OZG1075W0c5AwEmi7khEt6E6TsUVoDWnaSIkrtHMwIBKtE6iW2da0kZG2GCEv0a2FbHza8ciYXTS2tdtPxomiG46EjUi7NEZIKG6Ac0Q%2BJ%2B14JIZ04zEgJq8jcvEh5xK4lgUh8Pnnuia6PSvr7Nr/NwW096ZGmxTQz1PGXmKACKlc1iDZSZq9Jutat7kk16U8Hh/ZPaYdnhF%2BICOELEbsGNmzN0JyJCP4MklbbgxZWVk8/fTT3HXXXdxzzz288MILUoI5BUlJSSQkJNC8eXMRGFHw4Ycf0rBhQ13w4qLs/51lwHT4TPx/h0LG0qpVK8aMGUOzZs1UZCy%2BUgG/BxaLRTzYKxEmq9WKzWZj9uzZ7N27l8WLFxMeHs7AgQNp3LixIGOJiori4MGD1KpVixdffJHU1FThCCl47rnnBCGLL2bOnEl4eLiIKt15550EBATQp08f0tPTycjIICEhgcmTJ1NcXExERAT//Oc/CQoKElpvNpuNTZs2ERsbS0pKCgsWLGDGjBmMGzeOHTt20KRJE44dO8aMGTNo3LgxTZs25Z///CcPPPAAzZo1Ezp%2BioMXFBQkSEZKS0tZs2aNGO%2B///1v4bA1aNCAUaNG4XA4yMnJUUXSsrOzBRuoESgOXMWKFbFYLNjtdoKDg1XSEwDnz58nNjZWOJeAiAQqqZv5%2BflYrVa2b98O6CUVAJVwveIs%2B0LLIqrYiY%2BPVx1X6h/bt2%2BP1WqltLSU7OxslUPpa%2B/SpUvs3LkTt9st5qbg/PnzLFu27Lrr5AsZacu2bRvUjbRMABJmAF3ZpKaNrFxSd861PXTdNhKiinJJHgwQJKAhyJHakdSG6uxI1kZH4qCdg2RvabuSES3oTpOxRWgN6fQi9afo5hAWVn7nPrIgZdrWtJGRthghL9GthWx82vHImF00trXbT8aJohuOhI1IuzRGSChugHNEPifteCSGdOPR2JGWQGuJXHykgAS0L5oefVTXRLdnZZ35vPwC9PemzwtPAe08ZewlBoiQymUNkp2k2WuyrnWbS3JdyuPxkd1j2uEZ4QcyQshixI6RPXsjJEcygi%2BTtOXGMGXKFAoKCli1ahVLlizh%2BPHjvP3229K2%2B/bt49lnn%2BWpp55i586dPP/887z66qsqEsJevXrpNKW15HLXg%2BnwmbjpMErGMmnSJDZu3Mi%2Bfft0ZCy%2BaNWqFWlpadKH/m7duomN/9NPP7Fu3TqqVq3K1q1bBTlIYmIiderUker9bdy4URXxWbt2LQ8%2B%2BKCqj7Zt2wLQp08fUlJSdGPw9/dn8%2BbNBAQE0KBBA44cOUJKSgrHjh3D7Xazc%2BdOli5dKhgiL126xIQJE8jPzyc1NRVAld6oOEZt2rShdevW2O12qlSpQsWKFUVKosfjIS8vj5UrV%2BJwONixYwcPPfSQcPQOHDggnN4KFSoQEREhnBObzSb6UNJBS0pKKC4uVskLlFVz2LZtW1599VXpd%2BB9q6WQoeTm5gpny3eOvlHQOnXqsGTJEkJCQqhSpYoYp9vtFrWe5f2oKc650odvdM/pdIrPjRo1on79%2BgDUrFmT6Oho7HY7WVlZbNy4UayHx%2BOhatWqqjErDuDDDz9MbGwsY8eOFXtSGbOfnx9t2rS57lh9YZK2mDBhwoQJE/9duHjxIuvXr%2BeZZ54hPDycKlWqMHLkSJYsWSIthcnKymLEiBF07NgRu91OfHw89erVUzl8fxSmw2fivw5KDZ/H48HlcqkiQQouXLjAUZlG0g3Cz8%2BPnj17cvbsWc6dO8fu3buFE1ClShUOHDhAamoqNWvWFGLo7du3x263ExUVpXpjo9TuJSUlERsbS%2BPGjVm5ciWXL18mMzMTf39/Jk%2BezEsvvYSfnx9nzpyhpKSE/v37k5%2Bfj9PppGnTptjtdqxWq3CiFMdW0dDzdYpatWrFihUriIyMVDk58%2BbNE%2BmNSvQvNzeXAQMGEBQUhM1mIzU1FYfDwezZswGEQ6Vl%2BFSITWw2G6tXrxbHrVYrWVlZZGdn0759e55//nkAXnrpJUGYEhgYKOrdxo4dK2omlUiikkaqaO5pI2/KfMeMGSOioUr0tV69egwdOpRBgwbh8XgIDQ0lIiKCgoICgoKCRLps06ZNsdlsrFq1il69erFixQoKCwtxOBxCe8/hcPyuN25mDZ8JEyZMmDBRPv6MCN/58%2Bc5cOCA6u%2B8TnTz9%2BPQoUPYbDbxPATe%2Bv78/HwVZ4CCtm3bMmrUKPG5tLSUCxcuCCI7gLS0NB544AFuv/127rvvPpKTk3/XmOzlNzFhwjjat29PZmameOB2Op3Ur1%2Bfp59%2BmjvuuAPw0uh//PHHJCUlceHCBUJDQ4mLi2PUqFHUq1cPgIkTJ1JUVMS7776rsn/8%2BHG6d%2B/OiBEjcLlcrF69WrBEKkyaLpeLHTt2kJWVhb%2B/Pw8//DBTp06lUaNGZGZmkpiYyKZNm1i/fj0DBgzg0qVL5OXlqW5MGTweDwsWLGDJkiXiho2OjqZnz54MHTqU/v3706dPHzp27Mgbb7xBWFgYBQUFOs065cdEIQYBiI2NZcGCBTRu3JgqVapw/PhxSktLsVqtUn27iIgI4uPj6dChAwsXLuT2228XbQoKCti3b59Ik8zLy6NatWrs2bNHNx8Fhw8fJiYmhrCwMDE%2Bt9utinYqdXS7d%2B8mNjZWRBCbN2%2BOxWIh7Fpql1K3p523Ilbu8XiYNWuWOH706FEmTZok/r1s2TKCg4P597//ze233y4cTiUa9uGHH/Lxxx8LWzabTfSpRCh3797N7bffrhKI93g8jBgxQrxdGzt2LEuXLuXIkSMcPHhQtIuKiqJJkyaqMcJv%2Bo4LFixg%2BPDhIv20pKSErVu3ijX64osvGDx4MEaQkJAgah8V1K5dT/W5qEidUqP97J2bOr1I16agQJ%2BCpjnphuyCt3jEJ59I1yY3V5fK6HKpU5I0JqRttJ9lfcnGp7Wt60ti2Ejf5c4bb%2BmQbzaZro12gcFbROOTdWBkTrIFvHJFk2159aoqTzI/v0if1llcrNYsk%2B0bbRtdR8aur9a0dq0k20bXxtDGkbTRTavcTaK3K2uiPSa7vNo2msstPYf9%2B9VpnB99pE/z1F6X9HTQaMQa2n%2BafWJkr2nt3Oj9rD1PO14jay7bN7p5Swxp2%2BjGp1kXqR2JXe0SS6%2Bv5jzZ2uiOaa%2B3gesi/R3THjSyaf%2BX8Ge8C124cCEfaWpkR48ezZgxY/6Q3aysLIKDg1Uv1itcSwu%2BIqt71uDtt98mMDBQMMBXrVqVGjVqMG7cOCIjI1m4cCFPPPEEK1eupHbt2obGZEb4TNx0%2BEoyJCcn07FjR4YPH87p06fJzc1l0KBBHD16VFpXl5aWZqiPAQMGqFITwcu02aJFCxYtWsQ777yDzWbD7XYTFxenqxWsV68ePXr0EBEWf39/0tLSRIqf0%2BkUkZ0hQ4bQqVMnBg0axMsvv8yhQ4coKSmhpKSE9PR0PvjgAwYPHkzDhg1p2LAhV65cYd%2B%2BfYLd8eLFizRp0oTY2FgyfArefR254uJi%2BvTpQ8OGDUlOTsbj8RAdHa0jBVHmO3fuXNq3b0/lypUJDg4Wud0vvvgigYGBREREUFpaKnT7br31Vl3Uy1dKICIigry8PNLT00U/AQEBQm8QftOnA6%2BTo4xNcUIVPb/jx49f99q5XC6WLl0KeElOrFYrv/76K%2BB1qgoLC3G73Zw%2BfVo4sr4yHhaLhapVq2K1WgkICFBFcH1r9Xr37o3FYhESCgCPPfYYERERAHzwwQciOuq7zlarlfj4%2BDL1BIvKqWA/dOjQdb/3hayGb/NmdQ2fkToM7VB1bWT1RpqTbsgu6B4EjNS2Gak/MSJYfFPqYySGjfRd7rzRlw4ZqqHSFNEYEmGWLKCutE7zsCqt4TMgUK1rI6nhM3J9taa1ayUridSVmRrZOJI2umkZKaLSZSzomxjRCde2MSR2r63Zk4iz666LxtkDg/tPs0%2BM7DUjou9G7inteeXW1UmOyfZNuXXGkja68RkpKjVQsym9vprzjIiz6663gesi/R3THjSyaf%2BLMHDgQJYuXar6GzhwoKFzV6xYIfgQtH9nz56VsqeXB4/Hw1tvvcWqVav4%2BOOPRdlI//79%2BeCDD4iJiSEgIIAhQ4bQsGFDVq5cadi2GeEz8adC0cZbsGABW7du5fz58%2BTm5pKYmChqopS6uoCAACEVYARVq1Zl2LBhZdYL1qpVi/T0dJo1a8aUKVPE8YyMDNq3b8/EiRMBr7OjRH0UzT2r1ap64C8sLOTnn38G1OmCit7czz//zPHjx%2BnXr5%2BqKFcRR3e73dhsNhwOByUlJTz11FN89NFHWCwWleOn/Nvj8XDp0iWRslizZk2RBtmyZUt27dpFUVERiYmJuN1ulixZwuTJk%2BnZsydvvPEGly5dEv2Dly1Umyrom0d%2B4sQJjh49KqJwyriUKJ3VahVpkwBz5sxh1qxZ7Nq1SziFir2QkBByc3MN/dh16NCBxYsX66QlCgsLcTqdIr3T9zuXyyUcU2W8QUFBqvE1adJERPdmzJghjn/11Ve0bNmSTZs2lTm%2BCxcu4HQ6CQsLE0yvADVq1CAjI0M44tWrV%2BfMmTMiCqtcW6PRPTBr%2BEyYMGHChAkj%2BDMifJGRkaLM5veiV69e9OrVS/pdSkoKubm5osQIvFE/QLx01sLtdjNp0iT27dvH/PnzyyUwjI6O/l3pp2aEz8T/Fyibft26dfTv31/HgAgwYcIE7r777pvWp6KVptR7KVixYgVBQUHs3r27zPNSU1OFpt6aNWvo0KGDkHbwrbdLTk7m0UcfxePxsHr1anr27Cn070JDQ/n3v/9NVFQUISEhpKamihqz4OBg4SSEhIQQExPDli1buPfee0Vq5J133onFYsFms6nIVIKCglTROfD%2BUHz77beEhITQrVs3mlx7I1xQUECtWrUYNGiQsKXA6XQSGBgonMKioiLxvdvt5rHHHuOxxx7joYceYtKkSSoH%2BJVXXmHXrl2A99r6Oo9Xr14V7KfVqlXDbrcTGBgoZCCGDh0q8tKfe%2B45oqKi8Hg8NGzYUERVw8LCVJG72NhYmjVrBnidT6UdeOvgfOmLwZsS3LdvXywWC9HR0WJe0dHRoi4Q4NVXX2XPnj1s2rRJkPMosFgs%2BPn5ib5uv/12KlSoQH5%2BPitXriQjI4OgoCB69eqlqsUzml5hwoQJEyZMmPjvQ8OGDfF4PBw%2BfFgcU54By5JumjZtGkePHpU6e4mJiYK1XMHx48d/F6u9xXMjMUcT/zW42TV3e/fuVUXd9u/fT9%2B%2BffH392fNmjV07tyZ6dOnc9999113PG63WxeRcjqduFwuXC6XiKbYbDYCAgKoV68eTz31FK1btwZg6dKloi4MEAQm4E1BDAgIYMWKFTidTrp166aKDsFv6Y4lJSVUqVKF8%2BfPi4iQ4qwqY3E6nRQXF1O9enU2bNhAp06dSE9PJzo6mg0bNvDss8%2ByatUq/vOf//CPf/wDFNc5LAAAIABJREFU%2BC3y54sOHTrw%2Buuvs23bNl566SUKCwspLS0VbatWrUpoaKhIdUxJSdHV%2BPmukfKdEq3U1tRpoaypApvNplprbVvFfnx8PPv27cNisQjh9ODgYAoKCqhWrRoZGRk4nU769u3Ll19%2ByfTp00V0VTt%2BBbfccguNGzfmu%2B%2B%2BE%2BNW1kFx%2BEJDQ7l48SI2m43g4GCVUyxb3%2Bv1p/0uLi5OOLTaNrfeequKQVSLrVu3qgqtr4fp06fzn//8R3Vs1KhRUpkJEyZMmDBh4v8qrtE13FR06nTzbSp45plnyM3N5c0336S4uJjRo0fTsmVLnnvuOQAeffRRBg4cSPfu3dm1axdPPvkkq1evlhK/TZs2ja1bt5KYmEh0dDRz587l/fffZ%2B3ataqX2NeDGeEzcdNr7l5//XUhCqmkt7399ttERUUZcjwmT55Mr1696N69u0ogPDU1VTwIr1mzhujoaKZMmUJycjLt27fniSeeEKl%2B4KXy37lzJxERESKSpTBNzps3j%2BrVq/P444/j8XiIjIxUvYnxFQzPzMzEbrfrbkLFOVAiUefOnaOwsFBo9Z09e5YGDRqwatUqAOHsAVJnZMOGDTz99NOibk1xLJW2586d48iRIxQWFrJt2zYA3ZsihZm0Q4cOKhmGxx9/vMyaNECkZPq2cblcWCwW4ez5zr%2BkpISioiJKSkpYv34958%2Bf58KFC%2BJ7JZXh9OnTlJaWkp%2Bfz5dffgl4U3G/%2BOKLMtcBvEXNXbt2pWvXrgAqYhPFmVMERxViGt8x%2BmoyKp8BevToQbVq1XT9ORwOmjRpgp%2BfHy1btuStt96SjsvtdhMYGEiOVN3Xi4oVK5b5nRYy4fX27TXC6ydPXv8zEv1kjQizTMRadxsasXvkiK6Nbim0Isey%2B117klY0GvRKwjI72mMSO9feQfyGM2fUnyWC89quZWWbWn1qfH57yrStaSMVXve5jwD9eEEv%2Bi0T39UK/GoGLBNe15o1ImItLWnVCMwbuXTaPSrTsTeiNq0bs6xzzaYwou%2Bt60rG8nz2rPqz5MbT3VPaml/Zb6J2X0uYp3Xi7JL9aOSW0l1QTVRBtz9lhmXi7Ndq2q/buZbMQruvZWL3mjWX8mFoWREl97xuH2vT7SWC2drhyPo2IqquOyZpVN4eldnVDtmI8LqsjWzZ/zfwd9Phe/XVVwkJCaFDhw4kJCTQtGlTnnnmGfH96dOnxYvqJUuWcPXqVdq1a6cSVh86dCgA48aNo23btgwZMoSWLVuyatUqPvvsM8POHpg1fCY0uBk1d5MnTxYRPoVVs2HDhgDExMSUGRn5vdi4caP497Bhw1i0aBHff/89Dz30kDgeGhrK6NGj%2Bfzzz1m7di0vvvgiV69epUGDBly4cIEjR44QHx9PUFCQytlRtOkUuFwu8ZCvlXlQImFWq5WkpKRyST3q1avHEcmDM8CPP/5Iu3btAFT6gL7RJ18nSXF6lDE8%2BOCDbNy4UZC22O124WBfL5gfEhJCdnY2AQEB5OfnY7fbcbvd0r7AW8M3e/Zs9uzZQ4MGDUTabP369YmKiuLcuXM4nU7pWgwZMkSkSSr2K1SooIrQZWVlMXr0aJEaevDgQfGdEsFUYLfbSUlJoWXLloSHh3Px4kWpKDrApk2b6NmzJ/PmzSMkJISr1x7ISktLSU1NFY5kRESEiBzm5OSIcbZs2RKA77//nvj4eC5fviz2g3KNsrKyDEf4Vq5cqYvwjRnzFE2aNPjtQK1a6pO0n5HU2WteBMj4BnQF/Ebs1quna6MTOtYya8iYArQnyVJctEQBRpgMJHZ0l6J6dfVnieB8eeQRoNenlpFk6Gxr2ki5EK7teQHteEHPOiKTAtGygWgGLCNt0Zo1ImItJfLRMGcYuXTaPSoj3zBCyKIbs6xzzaYwou%2Bt66puXX2jazqpApIbT3dPXft/Y9kN0O9rSUmEjshFsh%2BN3FK6C3ota0ZAuz9lhmXi7NoHU1nnWgIg7b6WkQhp1lzCIQTaNHvJPa/bx1rJHIkiuXY4sr6N8AEZIcYpb4/K7GqHbER4XdZGtuwmykdISAjvvPNOmd/7PsNOmzaNadOmldnWz8%2BP559/XvAa3AhMh%2B//OM6dO8err74qNpqS0pmfny9q7hISEnj33Xevm9K5a9cuSkpKdA/Zinj3uXPnqF69Ol26dOGrr75i%2BPDhItqmYPz48fz6668qUe/169cD3qjQunXrOHDgAABdu3YVDprT6aRevXqqtMxdu3Zx8eJFYmNjAa%2BT1qRJE0pKSkSEJywsjNDQUHbs2IHb7RZtwRvxUewXFxcTGhoqCm4Vx1dhd/Tz8xNO0rJly4SNtLQ0Zs2axfz588nKyqKwsJDQ0FCOHDly3dRCBYXXXs8pjJkKxe%2BFCxeEw1OrVi3Onz9PRkYGFSpUYMWKFbz99tsMGzYM8DoyMTExQlJBGZcWXbp0ITs7W5CguFwuUcicqQuRIOyDNy%2B9cePG4torxDflOb6%2B8HX2fFGxYkVV5FBZD4DHH3%2BcOXPmUFxcLHTwlHkqMhEK8Y3isF29elX01a1bNx5//HH279/Ppk2bWLNmDSUlJZw%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%2BAbYVWYP7UCpU6nUziWvrqCvXv3BhDyDgBPPvkkDodDpESAN2oaFxcHwKJFi7jvvvuYOHEia9euVclM7N%2B/XxDqKCm6LVq0wGaz8eCDD7Jz505sNhvNmzdXMbreddddhp09MIXXTZgwYcKECRN/PkzSlv/jaNSoER6PR6TY%2Bfv707BhQ06dOsXw4cOZOnUqwcHBJCcn65g1//nPf3L33Xdz991306lTJxHh8yVt2bx5MyNGjGDu3Lm0aNEC8JLAzJw5k3Xr1nHx4kXCwsK4%2B%2B67GTNmDB06dFClEdrtdoqLi7FYLDgcDkpLS1UPxEoqoxLFUUhc/Pz8uHTpknBIZ8yYwezZs7HZbCqmyuLiYgIDAykoKMBqterqC61WKx6Ph3bt2nHixAlOnjwpCF1cLpdqLIrQeUhICFlZWarIkkIao8gP2Gw2ioqKRBuLxaKTaOjatStr1qy57vVr3bo1eXl5VK9eXcg23AisVivJycl06dJFpDlqU1y17f39/cnPzxekNXa7ndLSUqKioigtLeXKlSuqGkDle2X9lbWuXbs2gwYNIiUlhW3btokUyZCQELp3787ChQuJjo7mypUr5Ofni33gO6aAgAARnbwemjdvztChQ4WoqsVioVGjRtx55500bNiQ559/npKSEvr168fGjRuFvIUvevToQdu2bZkwYYK0jwEDBvDaa6%2BVOxbwpqpqU5yrVq3HHXf4pHRqha0lQtc6rdzLl1UpfAY0eaXCwro2kr7LFTWWiHfrBIDPndOnfGk6l%2BkB6%2BxIxqcT687JUef9SRbHiHC4bt7Z2XBNWLdMQ5o2Uj1j7XWQzEmn1m1kT2gWwpDwumTRdWOWjU97THcR0O0L7TbJz9dn1emGo10H2fgMzMHI9dYdkw1QOwlJ37o9q7Ej3ROffw7XXlQCUlF13TGJOLuRtdHtvwsX1GmcJ0/q07%2B1hmXX24gIuPY8A8Lm2r0m61p3zxvYE7qutDYkk9D9Jhicws1oc6N2tWOWzUF27H8D3357822WwR/4XwkzpdME3bp10%2BUZd%2BjQQThHt956a5kyClr45iSXhdDQUCZNmqRi0bzeeBSn1BdOp5OqVavSsmVLlixZomNlVB7%2B09PTqVmzptBTs0nqBiwWC1u3bqVy5co0aNBA9Z3igG3evFnQ32rJTYKDg8nNzcXPz4%2B8vDwRhfMdj2/Uz2azCcfHt75OO0df8o%2ByWCf3799Pq1atGD58eJkOn69TWdb7HbfbTceOHaUphtpzEhISaNGihSr11hc5OTkUFBSItVNYPpXInNvtVtUWVq5cmb59%2B5KYmChYPYuKisjPz2fDht9EyJWxaRlDLRYLTqeTgoIC4XyWhQYNGqiigB6Ph4MHD4pUYfBGVC0WC/Xq1WPHjh1inIrtcePGUa1aNX744Qe2bdumSu0MCgpSCb2Xh7Jq%2BFQOn/ZhWlIooiv70dRrGdDkNVZvJOm7XFFjSQGI7jaUFZ5rOpeVNunsSMane/jTPrRJFsdIfYxu3lpnT2ZI00Zaw6e9DrLCIG2mgJE9oVkIQ8LrkkXXjVk2Pu0xSc2Udl9ot4kk%2BK0fjjRjoryTyq9lM1RnJRugdhKSvnV7VmNHuid8nT2Q14tqj0nE2Y2sjW7/aWv2JLW%2BOsOy621EBFx7npECOM1ek3Wtu%2BcN7AldV7KCVs0kZE7RjdTw3UibG7VrpIbvr%2BDsgZnS%2BUdhpnT%2BBdG%2BfXsaN24s0iLj4uJ48MEH2bFjh2iTk5PDm2%2B%2BSYcOHWjatCn33HMPY8eOVZGBTJw4UcUIpOD48ePUr1%2BfMxL2t7y8PD799FMuX75MfHw8LpeL3bt306hRIxVzUGxsLM2bN%2Bezzz5j6dKlpKenk5GRQf369cVfgwYNePLJJ4XtpUuXUr9%2BfZ2d2NhYlUxDYWEhkyZNom3btsTGxuJyuahatSpr166lT58%2BWCwWKleuzNChQ0VkDLxFrU8//TQhISHUqVMHgE6dOhEbG8uSJUuE/XHjxvH6669jtVrp1q0bbrebNm3aSNNBfZ2UU6dOieO%2BDprb7aZSpUrCQfN17sDr4DmdTpWcg9vt1rFmausfe/ToAXgdJkWOoGnTpoSHh4s6xNLSUtavX8%2BAAQNU5wYEBGCxWGjevDnVq1fntttuK5e0Zfny5URERIhxyJxQ8Dopr732mmqeDoeD%2BfPnAwjCFwWKg%2BbriE2ePFn8%2B8cff6Rv375cuXKFevXqidrB1q1bCwYq39RHq9WqegEREREhau2U8c6bN084975tv/76a9544w0Axo4dS40aNbDb7aprERUVRXh4OPv371dFfJXxK0yxS5cu1dXx5eXlkZycrFuzsmDW8JkwYcKECRMm/myYDt9fFDdbKuF6%2BO6774Tjde%2B997JlyxY%2B%2B%2BwzoqKixMNw5cqVWbBgAfv27WPdunX07t2boKAgOnToAHgfwqOiooSEwp49exg3bpwuolapUiWVcLny9%2B21WL3H42HDhg0sX76cy5cviwf4ixcvCgkFj8fD2bNnefnll/n666/xeDy43W6Kiop49913ycjIYP/%2B/YDX%2Bdi5c6cQ4U5NTWXIkCEsX76c%2BPh4Nm3aRI0aNYiLixO1fcp8kpOTOXToEFarldtuu01E%2BBRHUZEMyM/PJyYmhttuu026vr6OnFIP9sorr9CkSRPxeevWrRw6dIh58%2BYB3ujekCFDAAS5jTJ%2B32hlQkIC0dHR7N69m4kTJ2KxWPjXv/4lRNRLSko4c%2BYMDRo0wOl0EhUVJWrYPv30U/z9/bFYLDz22GNUqlSJ3Nxc4Yza7XamTp0q2vfs2VPMw9cRrF%2B/Pj169BDOfffu3cXa%2B0KpSfR4PBy9RmmuOFrVrzERJiUl8euvv1K3bl2ysrI4cOAAdrtdtPftXzlXWY%2BhQ4eKcd1%2B%2B%2B307t0bi8XCSy%2B9BEBKSgpvvPGGqCucOXMmmZmZIl1Y2asHDx5k8%2BbN5ObmEhISIuZRp04dqlWrRtQ1BjrFwbfb7URGRuJ37TVoay2r3XVg1vCZMGHChAkT5ePvJsvwV4OZ0vk3wM2QSrgeZCmdCmJiYjh79iy1atVizJgxqpq7xYsXExUVxc6dO6VjHjZsGJ9//rmOZdEIFi5cSNOmTQFvSmd8fDxdu3YlOTkZi8VCtWrVGDZsGKmpqSxZsoTevXuzc%2BdOatSowcmTJwkLC%2BPgwYOUlpZy%2B%2B23C4kB33VRnNmMjAxOnjzJvn37xHdut5sHHnhASCT4%2BfkJxlG32813332nGu%2BuXbvK1Lmz2WxUr16dhx56SNR2vfzyy6oH%2B7Zt26rOycrKEo7GlWviPjVr1qRt27YsWbJERNH27t1LpUqVcDgc9OvXj7feeotnn31W2M7IyMDj8fD1119TWlpKRkaGIHl54oknRPRt4cKFNG/eXDBrejweSktLmTJlinCEfIlzqlatKiLEqampHDp0SET8lEifNtJZ6CMK9NVXXwFeQpmff/6Z77//Xnyn6PcVFxdz11130aNHD1X6r8Viwc/PD5vNRmFhoWA8/fzzz0VEbu/evaL95s2bsVgs3HfffeTk5BAbG8vly5dFpFXrYMXExLB48WKaNm0q6hnBGxlX5uHxeASRT2lpKefPn1ddS6MvXRISElQagwC1aqmlD4zUYWhLUnRtJCeVe47BNto6Fl0bTT2h5BRpKVF5dVayNjdrbbR9SeuNyps3%2BtoXI%2BPVHpT1rbUra6MrOcrNVWkdGKrhkw1QW6cmq20yMFHtIW1JnJESOd14ZY0kdX7l1p3K7Bq43kaur3Y4RmqoOHRILd%2BwbBlcI6oS0C7YwYOg%2BW3R3WdGrq/2JNkANW1u6HdCYsfIPtce%2B9Pq6GS1qppGMruGfksMrF%2B5v1sSw0ZqU7WXV/ZbLD9o4u8G0%2BH7G8HlcgmphP79%2Bxuuq7selDq4stClSxdmzpxJu3btRMRJwfjx42ncuDGhoaE4HA6aN29%2B3X7SZcLEknZXrlxhzpw5TJkyRaT3bdiwgS1btgjH7ezZs3z11VciqrZs2TLsdjsZGRm4XC5Rw9e7d28yMzOpVq0aX3/9NU6nU5VS6Xa7qVWrFjVq1ODdd9%2BlYcOGOJ1OCgsLyc7OJikpCbfbzeHDh6lYsaKKrVOLsurkCgsLOXbsGK%2B88grgFUsfNGjQdTVXAJo1a8bu3bvxeDxUrVpVELgkJSWJ6KciFr98%2BXLuv/9%2B7rzzTsGWqcxx5MiR5OTksHDhQlX9m%2B%2B/MzIy%2BM9//iPq7RQoDpHVar1ubZzWuTOKhx9%2BGKfTyZ49e1QyDopzmJKSoqq5A%2B994CszUVpaSlBQEB07dmTFihUAPPjgg2KvKNIeyrVr1KgRe/fuLTNl9dSpU2RmZhIVFUVOTo7oy%2BFwUL16da5cucLKlSsJCAjA5XIRHh7OuXPnhOPZqVMnw/Mvq4avadPfaviM1GFo/198I7pON2QXdA8ZujY6wTr9A4/sWcKIZtiftTbavqT1RuXNG/2Dp5Hxag/K%2BtbaNSJ1qBW2M1TDJxugEbE%2BAxPVHtI6d0ZK5KR6dNpGkjq/cutOZXYNXG8j17c8bTRpvZS23EDr7IF%2BwTTOHkjuMyPXV3uSbICaNjf0OyGxY2Sfa4/9aXV0BkT2ZHYN/ZYYWL9yf7ckho3Upmovr9Sv%2B4s4e//XInI3G6bD9zdAXl4eCxYsEHV1U6dO5RaZULEEa9asEQ%2B8CowQs7Zv357MzEzxEDt9%2BnSWL1/OpEmTiImJITExkZSUFPz8/Fi/fj1FRUVs2LCBsWPHMmrUKKpVq8bnn3%2BuYzlUtPEUYXNF727cuHEMGTIEi8VC//79Wb9%2BPfHx8dStW1dEYNxut6qm6tixYyqGQ1%2BnQ3m4X7VqFSUlJSItMjU1lS5duvDggw9y11130bt3b44fP05qaqogPVGcjfz8fKpWrUpmZibZ2dki0mWxWOjYsSO//vorx48fJy4ujpSUFEJDQ1URIV8oDJUAJ0%2BeFHVkvggMDKR%2B/fr88ssvZGVlsWvXLvFdZmamiMhlZ2cTFhamcnp%2B%2Bukn7r//flq2bElKSooQFb948SJz5szRrZ0MmzdvJiwsTEQUFedVphl48eJFQVajdXL9/f1xOBzUrVuX3bt3Y7Va8fPzw9/fn8DAQObMmSPSPp999lnpfqxYsSKVK1fm6NGj/PLLL4DXAd67d6%2BQbPBlSS0tLVVFXX1F47X2FYfYZrPhdrux2WxUrFiRO%2B64g6SkJFwuF4cPH9Y5miUlJWRmZpKfn09iYqJ4qaDU8SljUVJ/jcCs4TNhwoQJEybKh%2Bnw/TGYNXx/Ubz%2B%2Butl1tVZLJZyH94V%2BGrZKX9KFKQ8KHWEUVFReDweDh06xODBg4mPj2fRokXk5%2Bdz7tw5EfkrLi4mKSmJnj17EhcXxw8//CDqvwAR4ZJhxowZnD17FvDS9K9fv54vv/ySzp07i0iM2%2B2mc%2BfOgrRk1KhRgtRj27ZtgpzEF0qapeIcNWzYkJMnTxIXF8eePXuw2WzY7XZRw%2BVwOPDz86Ndu3YcPHiQWbNmCVsul4tBgwYJ0pCTJ09SWFgoyHRyc3NVzkVMTIyoq/vyyy/F8bIiS8XFxSxYsIA5c%2BZgsVioX78%2BtWvXFufs2LGD7du389prr7Fx40ZVjdzixYupX78%2B7733HoDK8VyxYgVPPvkkzZo1E8eUejM/Pz8cPq/9fIXGAwIChMPt7%2B/P6NGjRc1hUVGRWNPg4GDsdjtt27bFYrFQWFjI1atXRYqs2%2B2moKCAK1euiPTgL774QvSh1LE5HA6CrlGsdevWjVatWqlqQA8dOiTWwmKx8I9//AP4TZrD6XQKEp/atWvTr18/cf2tVish19jnFHIel8slrkNOTg7r168Xcg%2BffPKJ7vqA17F0OByqNfOFv7//766hNWHChAkTJkyY%2BDNhOnx/UfiStuzcuZMvvvhCPLDHxMTotLtuFBs3bhSaeWXBarXy8ssvk5aWRkxMDC%2B%2B%2BCIjRoygQoUKJCYmEhkZSaVKlTh8%2BDCHDh3iscceo06dOtSsWZPq1avTvXt3qlevzksvvSRIWxSn01dAPTo6WozHZrPRokULRo8eLSJEbrebvn37CoH2ypUrc9dddwHw2WefsWHDBuLj4wGvoxcZGcmOHTt46aWXRIQvOjqa7t27U7NmTaZNm8aECRPo0KEDnTp1YurUqbjdbu677z7Cw8MpLi5WOUDgjXy6XC7WrVtHQUEBQUFBDB48GH9/f50%2B4KlTp5gxYwYej4dHHnkE8Eb6lLpALUpLS7nvvvvYtWuXYPVUGCHBG%2Bl1u92MGzeOJk2aUFpaKhyakSNHivpGxRkJ16TS%2BTqBbrcbh8OB2%2B0WjhBA3759ueOOOwBv9OnkyZNYrVYKCwv59ttvVRp5CvLz87FaraSkpAgHqmrVqnz22Wdi/yjjDA8PVzlxw4cPF9espKREvMhYunQpBQUFtG/fXlzPytfowRVbSipkrVq12LFjB127dsXpdOLxeDh%2B/DhLly4VGpNut1s4qMpLgTp16mCxWCgtLaW4uFjUBcbFxfH111/z8MMPq%2BZqsViwWq1UrFiRhIQEFRmPMialptAoTNIWEyZMmDBhonyYpC1/DKbD9zdEly5dWLRokSqlT8H48ePFg/afAaN1hO%2B99x4rVqxQkVkYwU8//SSiVL4oKioiMDBQlwKnPFyvXbtWdbx3795cunSJGTNm0LNnT5F%2Bd/r0aZKSkrjzzjvJz8/njTfeYMWKFeTn59OkSRNsNhu7du1i06ZNxMXFMXjwYFU/I0eOBH5LE3S73axdu1ZFSKKsE/xWJ6ekc/r5%2BbFnzx5q1KghdQyOHTvG9OnTKSoqIjU1VRCFKFD6Vewq0a527drx1VdfqdgrlbZ79uwBUF2LkpIS8aewXILXeTt48KCqneKAnD59WsyrrAiz0ve5c%2BfENfFNr7x8%2BTL169cXUeH33nuPpKQkcb7iUBYVFbF06VI2btyIn5%2BfqNtU%2Bi4uLhbjOnPmDK%2B//jqgZkRV%2BvTVOlScNmU%2Bvvs3Ly8Pl8vFnj17yMzMFNFj5dp6PB78/PywWCxERETQuXNn4bza7XYRcX366aelayNDQkICb731luqvU6fOqjbaYLA0I1tTX6m9PLLLZaQNGs3DG7GjMQF4iRV8IZ2TkQFq5i2zo%2B1fa%2BZGzpG1kdkp99rJFkfTmayJzpCkkXbM2uzh/HzNRcBLKHLdfiSGr/20XtfOtSxxFbR7QHuObix4eWd8IVsb3THJAHXT0raRTUp7zMjNYGSA2s%2ByWmkDPwK64ciundb2Rx/pmmivi5F9rjt4ozdMeWsjWU%2BtGe34pV1JNpfWtLaJzK62jfQ31MDaGPmN1512M/bajfZt4m8Ji8dIQZeJ/69o3749w4YNKzPylp%2Bfz4ABAwRtfqNGjcjMzCQxMZGNGzcyf/58atSowcSJE4VUgS%2BOHz9O9%2B7d2bBhg6DDl40hNzeXvLw8IRKuCIa/8847TJgwgenTp9OmTRv%2B53/%2Bh5SUFBwOB6GhocTFxTFq1Ci%2B%2BeYb5s6dS15enrQOTIFSx7dmzRry8/Pp168f//jHP3j44YepVKkSd9xxB1evXiUoKIj333%2Bf%2BfPns2HDBqxWq6ibc7lcHDx4kI8//pj3339f2FbGXVJSIhwAJbKlpAGWlJRQoUIFSktLxVzB63z4%2B/tL6/KsVisej4ewsDDy8vJUhCNlQXE2XC7XdUXQZbDZbAQFBVFcXExhYaGqJlCJ6pVFqnLbbbdxzz33MGfOHDFOxdEpLS0VpCy%2B2oCKcxQUFCReLMTExAgtQmX%2BCqFM165dqVWrFqtXr2bkyJGiHrOsOV5vPxhFQEAATqdTRGGVa%2BnxeHA6nYJM5Xo7QUdqAAAgAElEQVRQnDQtW%2BcDDzzAtm3bpERDFouFWbNmMWLECKnNN998k/vvv9/QHKZPny4lbRk9epSh802YMGHChIn/C1i48ObbHDjw5tv8q8KM8P0NERgYyLx582jVqhVjxoyhWbNmDBw4kNLSUhYvXvy7SCOuh%2BzsbJWT1LRpUx566CFBkZ%2BXl8egQYPIzMwkNDRUpweo1N8FBwfTtWtXQVTidDqlNVBut5u6devy%2Beefc/jwYXr16kVsbKyIZj377LOMGjWKpk2bUqVKFV544QW%2B%2BuoratSogdvt5s4772TmzJnCnt1u1zl78Jsz43A46NKlCzabjezsbIqKigTDZ15eHiUlJbooakREBA0aNBDOUU5OjhBBh99q44KCgujSpYvQ7FOcH99okwJf8XhAOJwOh0NVw5ebm6uKNimoV68en376qWqcvpGrPXv2kJiYqHJKXS6XcPZ8x925c2eqVKki%2BsjLyxPn%2BArPu91uWrZsSeXKlblw4QKff/45r7zyCjt37uSFF17AYrFw7733qsakRNYUJ1wLmRB9aGgojRs3xmKxYLPZhFSHxWKhoKCA7OxscV5xcbFYl%2BLiYp2zp/TfrVs3oaE3ffp0ISuRlpbGc889B8DOnTvLjE57PB4aN24s/Q4QNozAJG0xYcKECRMmyoeZ0vnHYDp8f0EYqasLDQ1l0qRJbNy4kX379rFlyxamTZsmRKHB%2BzCrje6Bt3YpLS2tzOieggoVKjBlyhTS0tLYu3cvc%2BfOZfLkyURGRlKxYkWWLl1Kbm4uS5Ys4ccff8RisQg9wEGDBpGTk0OXLl2oUKECAH369CEtLU1HHKPU8SmO6m233casWbNISUkhNTWVefPm4Xa7mTVrFg899BDDhw9n8%2BbNPPjgg5w7d4709HSsVisJCQlCugCgWrVqbN%2B%2BXazlihUr6NevHx07duTtt9/G5XLxzTffkJCQwOHDh9m/fz8vvfQS/v7%2BjBw5kgceeIDly5er1uTKlSuiBuyee%2B4hIiKCnJwc4XQoTlVeXp6QkfB9qLdYLFSoUEGlvebxeBg%2BfDjTpk0jJCRErJfL5RLOriIhoTgtiuNosVg4deoU06dPV/Whjax5PB4iIiLE53Xr1lGpUiVRF6qkvH7//fcqls7BgwfTtGlTQkNDVcLrVatWpWbNmjidTqZMmcKbb75JYGAgLVq04JNPPsFisfDTTz%2BJ9oGBgQwePBi73U5QUBCpqancd9994vsXX3yRF154QZC4KKye06ZN4/Dhw6r5VKpUiZUrV4rPAQEBgqjFX0LBrkB5yTBhwgRRD/jss8/SsmVLRowYwaeffioc/MqVK1NaWkrlypWFcxoUFISfnx9%2Bfn5UqlSJBx54gC5duoi1atOmDeB1KI3CrOEzYcKECRMmTPzZMFM6TUihpHQ%2B88wzOuezQ4cO1K1bly1btjBs2DD%2B53/%2BR/W9os83ZMgQJk6cyI4dO2jWrJnK%2BVTSSoHrppaC11lp164dGRkZbN26VUSgFNbOS5cu4e/vT1ZWFg6HQzhdlSpVok%2BfPrRu3Zp//OMfqvoup9NJUVERHo%2BHzp078%2BGHH5KTk0N8fDzt2rVj48aNIu2wwKd2w263Ex4eLqI/NpsNp9OJ3W4XaadKVMzhcAjWR1%2BEhIQQGRnJL7/8UibjqpL%2BabVaKSkpEUyUsnYJCQmsWLFCOHp2ux2r1aqK4AH06NGDVatWAfKUSovFwty5c6lUqRKdO3fWtfNNI%2B3YsSNbtmwRY1L6s1gsVKtWjQsXLqhSTH1TQLW2AJYsWcKiRYvYs2cPv/76qyCoqVatGoGBgRw7dqzMNQCvc3/x4kUsFku56bVpaWl06NCBs2fPlplyOmHCBN555x0cDgcVKlQQ0cLIyEjOnz/P1q1b2bZtGxMnThTyFAqSkpKIiYm57hgUHDx4UEfAVKdOPRo3/k2H72aIi8t0c29ElNeA5rLejkQQ%2BKYIIUsO3kibGxZLvhlC3BLD2vU00rfsApd3XaTC60Y6vwFh%2BBtZGyPnGBKJlhgqV3jdQOeyeRsRNi9PL1s2J90xSSMjIuW6006cgGtZJGXZKVfwG/RC9ZLx6U4z8qNk4EbUmTHwe3Mj97MRoXjpnG7Wj93NuGFusM1fRXd97tybb/Ohh26%2Bzb8qTB2%2BvxF8tfHA67TUr1%2Bfp59%2BWjAr5uTk8PHHH5OUlMSFCxdUNXX16tUDUNX2tWjRQjwg%2B2rjKZElX6FxXz3ACRMmsHnzZpYsWcKePXs4cuQI%2Bfn5ghFx6NCh4rzS0lJWr14tdO7KQ3JyMv/6179ITU3F7XZTvXp1brnlFjIyMvjyyy/p1q0bffr0YdKkSZw%2BfVqQlnz33Xe0bt2azZs3i36/%2B%2B47wQLpdrux2%2B28//773HvvvTzyyCPs3r2b9evX06hRI8HY%2BO233%2BLn50ffvn3p06cPffr0Uc3FN9XP5XIRGRlJSUkJV69eVTmHvgLmviguLiYoKEjqcIWHhzN%2B/Hjeeecdzp8/r4rkKfj3v//N2LFjKSgooLS0VERLtSQldrud4uJiIiIiuHTpkk6nTgt/f38OHjzIkiVLVO0UR7JVq1Zs374dt9vNpk2bxPdK1KtRo0aC9EQ7d21/ioi9kub6yCOPEBwcLNZ20qRJvPnmm5w7d06cW5azZ7PZyMjIwOPxiGhc06ZNiYqKEsQxvlHPRx55hIKCApHaK3P6Vq9eTWhoKJcvX1Zd0wsXLgCwcOFCkUarTfvt1q2bivjmeihLeN3X4bsZ4uKy/1nfiCivAc1lvR1J%2Bu5NEUKWHLyRNjcslnwzhLilqc2/v2/ZBS7vukiF1410fgPC8DeyNkbOMSQSLTFUrvC6gc5l8zYibF6eXrZsTrpjkkZGRMp1p2mcPZmdcgW/QS9ULxmf7jQjP0oGbkSdGQO/NzdyPxsRipfO6Wb92N2MG%2BYG2/wVnD0TfxxmSuffDL5yDcnJyXTs2JHhw4dz%2BvRpcnNzGTRoEEePHmX27Nm6mjqZPthPP/2k0%2Bdbs2YN0dHRVKhQgY8%2B%2BkiqB1i7dm3sdjuXL1/m0KFD5OXlUbFiRe666y6aNWvGs88%2Bq3sQXr16NWlpaaSlpfHNN9%2BI4xs3bhT/Xrx4MWPGjCEhIYHk5GR%2B%2BOEHJkyYwMGDB7FYLBw9epRHH30UgMTERAAGDx5Mt27ddKyXWVlZnD59WsW0qcgiuN1uatasCSAcBcWpSkhIEGmq2tq42NhY5mpeM506dUqIdCvOySeffEJaWpqKPfSha6%2BSiouLhUadLxStwdWrVwvHQoFvNOyxxx7j6tWrWK1WbDYbt956q86OIjUAXmZMu91Ojx49ALXz6Pvv5557jnfffVfl0I4cORKHw0FISAjDhg0T81Nq6sCbxmq324VDtnjxYl09nhYXL14UjK/grWXz7fe9997jnnvuEdp62prPLl268MQTT6jmDKhSOj/44ANCQkIIDAzE4/FQo0YN7rnnHrp166Z6yaHAd/9MmjRJVb/o209QUBAbN27UMbMquPPOO687d1%2BYNXwmTJgwYcJE%2BTBr%2BP4YTIfvb4yAgACGDh1KZGQkW7duZc6cOeTm5pKYmCg0xnxr6i5evGjY9saNGwkODi5TD1B58I%2BNjWXnzp2kpqaydetWPvroI%2BbMmUNBQQGffPIJ06dPVwl%2BK6hXr56I%2BCUnJwPe6OS0adN49tln6dOnDwEBAfj5%2BdGmTRuef/55PB4P48ePF3V1ChPi0KFD%2BeWXX6hVqxaBgYEiHVHRcBs7dizgdcYKCwtp1qwZzZo1IyUlBfASljz//POiHmvq1KnUrVuXYcOGsX37djZs2AB4HYL9%2B/czf/58wOvUtW3bVjgZI0aMEE7DU089RfPmzYWTZbPZmD9/Po0aNcLj8dC2bVvdmjgcDnJycti6dSsej0ekdCp92Ww2unTpQqdOnQBvHSegcviWLFlCUlISzzzzDP7%2B/jgcDuEAKumcCm699VZ%2B/vlnQdjy8ccfExcXJ6KzDRs2ZOzYsXz88cfMmDFDrKPFYhFroODy5cvs3LmTnTt3cv/996scqfvvvx%2Br1Uq7du1ISkoSEgoNGjQgNTWV0NBQkcKqkM0EBgZy4MABCgsLsVgszJ49G5vNJlJ/161bx6xZswBv5E%2BxoUSxfUlrgoKChJbjhQsX6N%2B/v3gZ8dFHH7Fv3z42bNhAixYtVI6qcl2V/WSxWAgJCSEoKIju3bvTuXNnUYPXqFEjAgICCAoKEinHJkyYMGHChImbA9Ph%2B2MwUzr/C2BUG%2B9m4sCBA5SUlHDy5Elyc3OFsDl4H7ZDQ0NZvHgx48aNM2wzOTmZ0tJS%2Bvfvr/suISGBxMREFixYIDTcFGcoPz%2BfRYsWcf/996t06xSnQ0nxrFSpEn5%2BfvTr14%2B8vDwRZVQe8pX/lpaWcvLkSd566y2V46JIQOzYsQNAR8v/r3/9SzjCkydPplWrVgwePJhz586J6JeS6vfzzz/Ttm1btm7dKs4vKSkRIuVKvzabDbfbzahRo4TguzJWJTr0yy%2B/CBtKvaVSGxgcHExWVhZWq5X69etz8OBBYfvYsWPcdtttYt6ZmZlkZ2eTmpoKwKFDh2jcuDGlpaU4HA4xB9n%2BUiCrs/vmm29EGuimTZtEVO/QoUPExcWJ9Er4LW3z0qVLQgjd5XIxdOhQPB4PZ86cAaBNmzZs2bIF8Kau5uTkAN59Cd7IdWxsLMXFxUJW49ixY9x1112qNOXRo0dL53HixAlxnq9OYGhoKJcuXaJevXpCHB7g8OHD4rr5pjOXB5O0xYQJEyZMmDDxZ8N0%2BP7G8K2pi4%2BPZ%2BrUqdxyyy2Gzl2zZg3r169XHfs9/D2nT5/G39%2BfyMhIHn74YZ0e4NmzZ8nNzdXZ9K1DVB7ut27dSuPGjUU91%2B23347L5RIPvuHh4dxxxx08/vjjvPbaa6I%2BTBGYV2QPPvnkE5UEw88//4zFYmHv3r2Al2HT5XLx/vvvi5REh8NBQUEBr776qnB8evToQX5%2BPm63m/vvv58333wTQDhOvumWdrud2rVrk56erkrxe/XVV7Hb7RQUFFC1alWKi4sJDw/n6tWrZGZmcvXqVbZv365bV9%2BHfbfbTXBwMCUlJaxcuZIePXoQGRnJuXPneOqpp/joo49wu90iVdeXGMXj8VC9enUhpeB2u6V1ZX5%2BfgwcOJAvvvgC8DqKV3xUkpVUUt%2BavKKiIh577DERsQN1/aAv0UvdunW59957%2BfTTT3G73fTu3Zvp06fTo0cPjh49qkr79ZWt8PPzU4m%2Ba/eR4uz5jlGpwQSEHIcW27ZtY968ebrjWqxbtw6n06m6pr7i71lZWSpHWxmn1WpVOZTlISEhQcXYClBPW1dz4QJcYxWVfkZSVK9pc%2BUKhIWpzeqIC86dg2ti82XaTU%2BHa6nQCq5ehZAQnwMFBRAQID4WFoKWPFVHmiEboBGmBW0byRx0pi9fhvDw3z7n5MC1aHlZZmVcB9nZcI1QV24XvGrnvk699trJ2BC0hnULLBlPXh4EBantaK6Dtm8ZaYu2K5lZ7cXTrQPorpVsD2inrm1jhIBHtnzayym1o1mLGyHy4exZiI5WN8rIAB%2BmbHJzwedlqNT46dPgK6Uk6/ziRahU6fpz2r4dWrcuc45G56mb6Ecfge%2BLsVWr4FrmisDBg%2BD7Oya7nzWbSdq3dr20F1PGGqWZp%2BR20V8XSSPt/tN2JetaOzzJT4nuNtR%2BNtK3kTaye0y7nNqfI9kxWRvp78D/Asx3oX8MZkrn3wyvv/66tKYuKiqqTMZHGbp27SpSNbU1fEbhdruZO3euVA9w/Pjxupq6Xr16CWbE0tJSkfo2fPhwHA4HQ4YMoUaNGsTExHD33XezevVqDh8%2BzNKlSwkPD2fatGlMmzaNkydPquxarVYGDx7Mli1bBDW%2BcjwsLEzIPSQmJtK5c2csFgvdu3cXZCv%2B/v4qZ%2BPs2bNkZWXRv39/nn76aZEKGxgYyIsvvshXX30FeNMsO3fuTOPGjcUDf1hYGDVr1mT//v20a9cOu93OuXPnyM7O5sSJEypn6oUXXihXj06JXHXv3h232y3YIj/44ANRk%2Bh7PUpKSoTTo0TDWrduTXR0NM2bN9f1VVRUJJw98NYXBmsfUK7NXdlj4I2qrlu3Do/Hg7%2B/P23atMFut%2BvOPXbsmHD2AJYvX86kSZNo3LgxwcHBojZPW6MXFRXFuHHjhHxC%2BLUHaWVP%2Ba5RrVq1gN9qIJV2Mq3H8PBwWrVqpTpmsViw2%2B3UrVtXSEVUrVq1zHspJiaGH374Qfrd743OrVy5kvHjx6v%2BNlyLSAtoHtp0n5EU1WvaaJ%2B9QEJcoHGUpHY1zh5IHq40TzMypQwdaYZsgEaYFrRtJHPQmdY6ZdonNIlZGdeBzsnR2gX9k5P22slqXbWGdQssGY/saUz7VKnpW0baou1K%2BpCnuXi6dQDdtZLtAe3UtW2MEPDIlk97OaV2NGtxI0Q%2BOmcP1E4F6J09mXGtbq6scx9nr6wmKmcPpL8ThohItBPVZkFonT1QO3sgv581m0nat3a9tBdTxhqlmafkdtFfF0kj7f7TdiXrWjs8yU%2BJ7jbUfjbSt5E2sntMu5yShBLdMVmbv4KzZ%2BKPw4zw/c0wefLkMjX6YmJidBTvfwS%2BZCpa3HLLLRQXF5Odnc2kSZOEGLuC2bNnU6tWLSwWC8899xxJSUmsWLGCYcOGMWzYMDIyMtiwYQOpqak4nU7WrFlDTk4OZ86cITIykmXLlonUQaUOMSAggLCwMKZOnUqHDh1o06YN6enpNGrUiPT0dLp160Z2djYAAwcOJDk5mby8PE6fPg146%2Bp8yVlsNptgspwyZQq9evUiPj6eypUr88svvzB37lwWLFiA3W6nXr16nDhxQkfU8e6773Lq1CmWLVtGxYoVRd3X1atXWb9%2BPW63m6pVq9KvXz8SEhJISkpixowZeDweXnnlFV3kSknhhN%2BYJWUSCuCNbNlsNkJDQ4VjqMDlcol5/vjjj1SuXLlMB8bXvtVqZcqUKUyZMoXi4mICAgIoKCggPz%2Bf4OBgIWfxxBNP8MknnwjZih9//FFoDmrHqhW9X7lyJS6XSxwPCAgQbJ8KmYqSUqvg8uXLALq1Aa%2BDHhERQe3atdm7d68QYJcRx7Rt25YWLVqIz%2BHh4eTk5OB2uzl69ChHjx4V3zkcDmrUqMEvv/yimsMHH3zA%2BfPnCQgIYMOGDWRmZorxVKlShZYtW0rXWQYZaYtJ2WLChAkTJkyoYUb4/hjMCN9/Ebp06cKiRYt07Jjg1cZTUiBvBho0aECtWrVU0SEFpaWlLFq06LoC1CNHjqSoqEhFvFG3bl3A60xq68SOHDnC5s2biY2NVR1/%2BeWX2bBhA7feeivfffediPAdPnwYu91Obm4uH330EQAzZ85k%2BfLlTJ06lQ0bNjB%2B/HhKS0sFqUdQUBBPPPEEv/zyCxERETRq1Iht27bRsGFD0tPT8ff3x9/fn6rXoghK5PDDDz%2BkSpUq2O12mjdvTlxcHCEhIXTt2pX4%2BHisVivh4eHExMSQnZ2Nx%2BOhYsWKjB49WhcFVVJaAeG0%2BDpQSo2fw%2BFgzpw5%2BPv7i1ozi8VCWloa3bt3V5GHeDwe8vLyVNHEtLQ0Dh8%2BTNOmTank8/a4UqVKLF%2B%2BnJdffhnwCrIrEbBOnTpRVFSExWLh7rvvVo3JF741kS1btiT62pvwoKAgnE4nBw4cICEhQZxXUFBAUVGRSrfvtddeIyIiApvNhtVqJSEhAYABAwYAXkIZpf7N7XaLNYiMjATggQce4MMPP8ThcBATE4O/v794%2BWC328VaXLlyhdq1azNgwAAh3wFeXb/8/HwuXbokoqhK9NLf35%2B4uDi%2B%2BeYbMjMzAcS5WsbU8mDW8JkwYcKECRPlwyRt%2BWMwhdf/Rmjfvj3Dhg0rM8KXn5/PgAEDsNvtupq6jRs3Mn/%2BfGrUqKHS4fOFIoZenhC6gu3btzNixAgGDx7M0KFDCQsL48SJE7z88svk5uYyb948AgICOHPmDB06dGD16tUMGzaM3NxcQXmvEIJoxcUVZ8A38hQTE8MHH3xAYGAgHTp0EMeV87QPyvfeey8Wi4XJkyeL/uvUqUNpaSn33nsvo0aN4s0331TprIE3yhYcHEx2djZ%2Bfn4EBASUWZelMGkOHz6cL774gtzcXJFqWVxcLHQJZ8yYQffu3Rk4cCCpqakiAqfcfsq/e/bsSXh4OJ9//rmqH0WewFdUPDAwsAxa/98QFBREYWEhDocDq9Uq2isOteJkde3alTVr1ojzAgICsFqtUmkC5XxF2L1y5cqMHDmSffv2ER0djcfjYebMmWWOSSvCrkBxwlwuF2WJ1vuiZs2a5Obmkp2dLc4Bb71h/fr1sVgsKjIVQKRuHjp0SNWn2%2B1W9dW8eXN%2B/vlnateuzYkTJ1T92u12Xn75ZV566SVp1NTpdArim/JgRHhdV5whKdbQ1TJp2hgRTDdkV1LMUZ6YuKz0TndMVthiQHRZ17fEjq6Ndp5a0WhJV0b0iqWLrG2kHZ/MsIHxGRLD1iutq/K1ZDV8RkTAb0T8XDY87XlGxNqNDFC7fEbGZ2Cr6Q/K9qyR61vOdZF2rq1Bk9nV1uydPAnX0t0VGNEJ1/WvrdnT1vTJ2hi4F4zUaN5IMa2slk13XQzcU9q1MVJCbKSNkfpQQ783mgH%2B/9R8/9/CnDk33%2BawYTff5l8VpsP3N0J5Dh94a75mzpzJunXruHjxImFhYdx9992MGTOGqGv/w7hZDh9AamoqM2fO5Oeff6agoIAqVarQtWtXRowYISIiWofv7Nmz2Gw2kb7odDrp3bs3S5cuxeVyMXDgQL7%2B%2BmuKi4vx8/PjlltuISEhgVOnTrF8%2BXLef/99hg0bRpUqVYiLi1PNY9iwYYL50ul08u6779KxY0fduN966y1%2B/PFHLl%2B%2BzK%2B//kr37t155513yMnJoV27duTm5gpHwel0YrPZRO2h2%2B1WOaK%2B3yn9ulwu4dQpUTqtg%2BZ0OlVRLS0iIyNV2nRKf263WziVAQEB5OTkiGigIsNQluh7WVDSKWvXrs3Zs2cF%2BU15Pw%2B%2Bsg9Wq5WKFSvSr18/Zs%2BeDUD16tU5c%2BaMyplTIoBaDTxtJLNFixacPHkSm83G22%2B/zUMPPaQTSvf9XFbqqwKbzUbr1q3Zu3evcMxdLhfR0dHExsZy8OBB0tPTefvtt4mOji7zPhs2bBjHjx8vM%2BVZpndZFqZPny4VXh89epRhGyZMmDBhwsR/Oz755Obb1JCt/1fDrOH7G%2BF6NXUKQkNDpTV1vpg%2Bfbr0eJ06dX7Xwyp4dfiUtMyyUL16dZXdChUq8Mwzz%2BgeqJOTkykoKGD//v1UqFBBMCX6IiAgAJvNRu/evYU8gi8mTpwoHL7vvvuuTMd1/PjxgNeJDg0NFXVXoaGhIuLn8Xjw8/PD4/FQVFRExYoV2b59O506dSI9PV01Nl/h77179/L888%2BzZMkSkQ5ZXFyMy%2BWiYsWKZGVlERYWxg8//ED9%2BvXLXLfz58/j7%2B%2BvqhtUHBq3201xcTGhoaGib99ooRI1Ba%2Bj07dvX3744QfS09OlfSmRw19//VU4tXXq1OHEiRMqiQiXy8W8efP%2BH3vnHR5Vmb7/z5mZ9F5IIXSEEEoCBFiKFEPHXaqACFjARlVUFBRFEBddRRcVUARUVL5Kk6J0oiJlEbJLMgkYkCIhIRBIQnommZnfH5PzOufMCRlRd93fnvu6csmcec/bzjnjPPM8933z4IMPUlZWpvALtNvtWCwW1qxZI/qV5%2B4cmDnz64KDgykpKWHw4MHCj7GgoACbzcalS5e4evWqEG5xXqP6dUBAADExMWRmZtYapNrtdvLy8igtLSUyMpKysjKKiorIzs4mJydHnOdcEh0QECDKRYOCgrBarTRt2pQxY8aQkZEhSjrlgNndH0pk6MbrOnTo0KFDh47fGzqHT8cfBlarlTZt2pCamsqwYcNcgr3Zs2cTERGh4I/91pCzWjJPLS0tjaSkJCRJUpR1RkdHKxROX3rpJfz9/UVWU87sGY1GVq5cSf369XniiSfo2rUr8HMg5FNTZiJJklBb7dWrlyhp9aspn5O5e5IksWbNGjIzM3n88ccpLCykV69ewjzdYrFgsVgUGT673c769espKCi4qYfe4sWLWbdunQjGevfuzcMPPyxey2vz8/Nj7NixgOPHA7PZzF/%2B8heio6O54447FAGirHAqB4Xyurp164bBYKC6uprq6mq%2B%2BeYbPDw8%2BPDDDzEajcTHxzNmzBgkSXLhxcnlrTLkbGe/fv2Ii4sDfjalnzJlCn379iUhIQEfHx/hC3n16lXGjx/PJ598wnPPPafg76Wmpop5BwQEiDnPmjWL0tJSwsLCOHLkCFeuXKF%2B/foEBQWJ/XbXFkWHDh06dOjQ4T50Dt%2Bvg17SqUPhjQeOL9SyWId8TLYtUHvjTZs2jZYtWwLulYree%2B%2B9lJSUKDJ8sp/gO%2B%2B8w6ZNm4TYi8w78/Pzw9/fn5KSEtavX0/Dhg1Flu1mmD17Nq%2B99poiyPH09KRly5bMnDmTbt26kZSURH5%2BvguPDyA2Npa5c%2Beybt06YUHgXDYo9/vcc88J373Kyko8PT1FSaRzZqtBgwZMmzaNuXPn4unpSZs2bfjXv/7lMm6HDh2YPXs299xzj2aZopw1dM7gyVwyHx8fYmJiGD9%2BPO%2B99x65ubnExcVx6tQpF1N0uW85i9W/f3%2BsVqvIJMsBq1yq2qJFC86cOaPg4BkMBiIjI8nLyyMxMZH69evzxRdf3PS6OK8jMDBQKKvKx%2BR%2BnX3wnOfu6emJzWYT85Lh7%2B8vVEK1ymHlNaszoloYPnw4W7Zs0Xxvz549zJgxo9ZseHJyshCqqQtaJZ3Tpk1j5syZbp2vQ4cOHTp0/C/gJtIAt4xp/0PsCT3DpwNw2D3I2aqDBw/y1FNP4eHhwY4dOzhy5Eit3nhjxxUpItYAACAASURBVI79xWWgULufYLMa0%2BnbbrtNKC4ajUbKy8tp2bKlUMYER3AzZMgQMjMzxd%2BOHTsU44SHhysycQcPHiQpKYlHH31UBIxysOfp6UmAkz9PZmYm999/P3v27BHBgXPwJWfT5s%2Bfj81mUwiqyJA5gH/%2B85/ZunUr%2B/fvF%2Beqgz253bJly5g/fz7gsNqQ5%2BTr6yuC8ISEBBGMb9%2B%2BnbvuugtJkjh%2B/DjPPPMMr732Gu3btwcQoiDbt2%2BnS5cuYqzWrVuLoAscJbXHjh0D4PbbbxceezLk0kZngRObzcbly5eprq7m6NGjiiDJOQtnMplc1Dyjo6MV11PeL7l9ZWUlgYGBHDt2jGeffRZwZBfNZjMZGRncd999inP9/PzEmPK1kLmDRqMRb29vQmr8oW4W7DVr1uymmTpJkqhfv77mex4eHrW%2Bp4WhQ4fy2muvKf769x%2BgaKPWhdFy11AvR03h1KR0qk5yp1%2BtNnWNpTW2O2typx/1MU3nEdVz6XLpNe4Fd%2BanPnhLbbTuQ9WitJqof5/SGtvl40hlKVNW5vp55bKhbuyN1oVRj6117epqo3nPqg66c09obY76kHqZmh8Pt3Bju9XPrTwMbtw3bj14Wv2oj508qXz95Zeu59QoYdc6jsZ83JmeW3ujfhjcuWfd%2BbBTvda8H9Uc/Fv9ILuVflTPs%2BsDr9GPVr/qsTV0BX6hJICOPyj0gE%2BHC3x8fJg0aRIREREcOHCA999/n5KSEpYvX07z5s2RJEl4440bN06Uv7mD5ORk/P39FQHmsWPHWLt2LQkJCYDjy350dDTJycmkpaVx8OBBpkyZwuXLl0U/iYmJwozbGc2bN3cJ%2BtRre%2Bihh4iIiOC7774jOTmZ4OBgEUgcP35cWAE4w9fXF09PT4YOHSrKJ2UxF3nOJpOJ4OBgzGYzo0aNAhzBQXV1NXv27CExMZFTp05hMpnw8vLiL3/5iwjAwBEEfvnll3Tv3l34wf31r38VQYqcdbVarRw/flxToMRkMtGrVy%2BGDBkCOGwM5EzgyJEjBe/RYrFw9913061bN7GvcuBrMplIS0sjIiKCdevW0bRpU7y8vESfCQkJIhiXy0zl/QoPDxc2BuqgymQyKYLAkpISkVmWeYfOe2G32ykvL1dkX0tLS8UPBR9//LE4LkkSX3/9tSjplLOGNpsNk8mE1WqloqKCRo0auVxbNeTgUm3eLpcSnzp1ihEjRiBJkrgHmzdvDjiyuFr%2Bf7VB03i95kcBGWrVNy3/cfWQbhksq05yp193vM9vxcT6VvrVOqbVj1ot0OXyaFwvd%2BanPnhLbbTuFTfMxdXikFpju5g3q6QLtYzXXTbUjb3RujDqsbWuXV1t3DFed%2Bee0Noc9SH1MjUf4Vu4sd3q51YeBjfuG7cePK1%2B1MfUpupaxutq1U431uCWub07e6N%2BGNy5Z935sFO91rwf1RSJW/0gu5V%2B3HFrd2f/1GNr0D7%2BCAqdoJd0/lroAZ%2BOWmG1WjEajezdu5fRo0dr8r%2Befvrp35xT5%2BPjQ0pKCiUlJdjtdrKysti6dSt//vOffzM/QXU5YFlZGW3atKFdu3ZYrVZRUujr68vTTz9N48aNsVgsZGVlieyRzGl76aWXSE9P56WXXqK8vJxZs2aJfmVbBrkcsaysjEmTJmGxWBg6dOhNs0G9evVi7ty5ZGdnA4hSRxnOgUViYiJhYWHidVVVFd988w25ubkiKF24cKGi/xdeeIGUlBQRFEmSRHFxMdXV1RQXF1NUVERGRgaFhYW0aNFCWGFcuXJFlEvKpZ1y8JmXl0dr9ReEmn3o0aMHt99%2BuzC8LysrIy8vD0BROgo/23JUVVWxfPlyxdrUkMs0e/Xq5VLG6ayeCg5%2Bnnxfh4aGUr9%2Bfby9vfH19SUkJASDwUDPnj0JCAhQXDdw2JDI/83Pz8dutwtD%2BLNnzwJw/vz5WuepBV20RYcOHTp06NDxe0MP%2BHS4oLS0lNWrV5Ofn0/v3r3JyspyW4xi165dIgMj/w0bNuwXjS9zsTp16kTbtm3p168fJpOJS5cuceTIERF4ZGdna44nlzAuXbqUa9euKd5r27YtrVu3Jicnh7Zt2yrGlbOORqORpk2bUl1dTVlZGR07dhQZnIsXLxIcHIwkSTRv3pyGDRuyePFizbJWSZJIT0/n4YcfFkqTBQUFIjjo0aOHyIaNHDmShx9%2BmIYNGwoj9AsXLvDWW29hNpt5%2BOGHiYqKEoImzpmnYcOGMX/%2BfIqKimjbti1t2rRh%2B/bt%2BPr6kpaWxkM1RjNz5sxRzM9ms4nsnxwUyeWdkiRRUVHB3LlzsVgsNGvWjO3btwMQFRUlMnzLly8XAW5AQAAmk8llX8ERuH377bd899132O12IiIixJgyl1DmbALMnTtXBLSfffYZTz31lKK/%2BvXr061bN5Fl9fPz48033%2BT69esAohQ1ODiYxo0bExoayoQJE0Tpq9VqFZYcFRUVlJWVCXXQtm3bCrGckJAQcY2efPJJvL29CQ0N5c9//jMGgwF/f39MJpOYu7OJvTvQjdd16NChQ4eOuqFn%2BH4d9IBPB1A7py46Olp8QXYHgwYNUnDmzGYzW7du/UVzMRgMzJkzh/vuu4/IyEg8PT3JzMxk9%2B7drFq1SvC%2BYmJiNMeTVR0fe%2Bwxl76rqqpITExk/fr1ooQUHJmgF198kdjYWKxWK2fOnCE2NpaoqCjS0tI4evQoAPn5%2BRQWFmK32zl27JjwFLxZWeu0adMUlgnffPMNXl5eZGVlCc7c5s2b2bx5M9euXROcvevXrzN06FAOHTqEp6cnkZGRLFq0SAibBAcHizXJ10f2lXvllVeQJIkbN264iI/IGT916eGCBQto0aIFoAw6SktL%2Beqrr/jss88AOHHihMikTZ06lb///e8AIju4bt06lz1wFvyx2WxIkiQypCEhIcJmIjw8nNDQUEaPHk2fPn0091POEF66dImqqio8PDwoLS1l4sSJeHt7I0mSEE2pqKggJyeH/Px8PvnkE/bt2ydEb7T2AOC7776jW7du4hrIgelrr70m%2Bvvyyy%2Bx2WyUlJRQXV1NVVUVNpuNa9euadqF1AYtDt%2BAAUoOHzU2I7W%2BRoOakZ6ueOnkNFH7OadP192mxrDeGTXUzp9x4YLipTv8LfV8AVcuiTuErcOHXZoUFKgO1GRqBTQEm9TdatFjXNb93XeujdQZXNX83OGpuS4AKC1VvlbzebT6Ua1Ti8On3gp3eFaaa1DN2Z1%2B1HvszpK0rp07/Ma6%2BGRa19vlWFqaa6NLl%2BruSD1YaurN3wcoKqqzCU6UB8D1HtE6zx0um/r%2Bc4crpub0gXvPc001i4D6JtDyrVXNz0kD7GeoP5Nv5ZnX%2BBBVfwZodOvSRnUpNbvW6kd9TH15tT7j1WNrtVHPx535/aegB3y/DnrApwPgppy6xo0bi6zZb4Hk5OSbmseDI0Mzd%2B5ckpOTMZvNpKSkEBYWxj//%2BU/RJiEhwUURFH72EwwNDVWItqSlpdGhQwcaNWqkCPaeeeYZDAYDL774IpmZmSLLlJmZSW5uLkuWLKGgoACTycSePXto1KgR8DN/rbKykkcffZT58%2BeLQEK9FlmJcciQIVitViorKxk4cCA//fSTaFdeXk54eDhZWVmAI9ACSE9PZ8aMGaxfv56hQ4cKY3dZRMXf35%2BAgADMZjMPPfQQHh4eDB48GG9vb7y9vUU/6syRh4eHCP7AUe64du1akc11fs854I%2BOjhbBmnN2yzmQqgvZ2dnC78651LSgoID8/Hy6deumCJzUfMDS0lIuXryI3W6nUaNGIiNXXl6O3W4X90ViYqK4XoDgoMpBpyyK4%2BnpKSwyDh06JLK4ziW0bdu2xWg0EhsbS8uWLfH39xd8TnBkOD08PIiPj3drD6AWDt%2B%2BfcpGvXrd/DUa1AxVlrXmct38nBq13Zu2qeFIOsNJ58iBJk0UL93hb6nnC7hySdwhbHXv7tKkhgL7M2qCeQE1B0ijWy16jMu6e/Z0baTO4Krm5w5PzXUBQM39LqDm82j1o1qnFodPvRXu8Kw016Caszv9qPfYnSVpXTt3%2BI118cm0rrfLMa3nXO3DqdWRejCn/xdpvg9QU3lxsyZERytfq%2B8RrfPc4bKp7z93uGJqTh%2B49zyr1Y3VN4GWrZBqfkFBrk1w4soDt/bMa3yIqj8DNLp1aaO6lJpda/WjPqa%2BvFqf8eqxtdqo5%2BPO/HT8d0IP%2BHTUiYEDB7J%2B/XqFIbWMX8KpGzBggMgiJiYmcs899yi%2B1BcVFfHqq6%2BSm5vLokWLuP3223nsscc47ZR52LRpk4IjJ%2BPs2bPExsZySf0LqxMkSWLhwoVs3bqVI0eOcOHCBebMmcPixYux2Wy8/vrrzJgxg7CwMIUdQmVlJdXV1Tz66KM0atSIxMREwBGEyPYLsrE6OMpat2zZgt1up127drRu3VoInsgZPeeMHyDsJ3rWfGn08PAQ7y1dulSUtr755pv8%2BOOPCvXMQYMGUVhYSEpKClOnTqWyspJ3332XyspKysvLRWAoQ%2Bawmc1m/va3v%2BHj40OrVq3YsWMH7dq148KFC/j7%2B9OiRQtN3ubly5dFEPnII49gNpuZPHky8fHxVFdXa4rpyMGxFqZMmSL%2BLZeY2u12KioqxDoXLVoEONRbjx8/rggSz507J4Rd5Gsmc%2BuOHj3KuXPnRNvjx49jtVoF93DevHkcPnyYf/7zn6L0OCcnBy8vLwU3ERyBtyz%2BsmnTJkpKShTKrMXFxUiSpLg2dUGLw6cz%2BHTo0KFDhw4l9Azfr4Me8OmoE5MmTSI8PJwJEyaQkZGB3W4nNzeXF154QcGpqwszZsxQ2CP069ePhx9%2BmKysLEpKShg3bhxnzpwhLCyM5557jg0bNhAaGsqYMWN49dVXKSgoIFr9K2YduH79uoLDN2rUKKxWK/fffz8DBw7Ey8uL6dOnI0kS5eXl7Nu3j7y8PHx8fKhfvz6RkZGAIwBr3Lgx4PBOmz59OvXq1aNz586kp6fz%2BOOP07BhQwYMGMCgQYPIyMggMzMTs9lMw4YNhYVAQUEBHTt2BBzm4z/88AOZmZls27aNq1evMmHCBJ588kkiIiJEUNi9e3d69OhBdnY27777LsXFxVRUVIhSQ9n3bvz48SQkJJCdnc2yZctE0PPggw8CjqAyPj6ekJAQkREDRwC4ePFiKioqFCIsOTk5CgN352xXYGAgISEhfPDBB2zZsoUTJ04IHmOBqsRm1KhRdO3aVRHkxsTEKPhuUVFRCp5eZWWlCK7AkYEGh8VEfHw8jzzyiGhrsVhcgsw1a9aIa6fODvr6%2BopjDz74oOB2yiWrS5YsYcKECSJwkzOdnp6eeHh4UFhYyMiRI9GCxWJh4sSJmu9pQefw6dChQ4cOHXVDD/h%2BHfSAT0ed8PX1Zd26dfzpT39ixowZJCQkMHbsWKqrq9mwYYOLl5o7uJn1g4eHBy%2B//DIDBgxg48aNVFdXc/jwYVatWiVKCUEpECNnZwYNGkT//v1Fm7CwMBeO37/%2B9S88PT1p2rQpCxYsICAgAEmSeP755zl16hRz587lk08%2BYefOnUIcJSIi4qZlrSNHjiQrK0uIhshISUkhKyuLfv36AY4MVlBNzYmzj1yLFi2Ij49n06ZNBAQEUFBQwBtvvAHA4cOH%2Beabb0QgUF1drQhiZHsA9TG5/Zo1awBHIJGWlkZBQQFFRUXExsYya9YsLBYLa9asoV%2B/fqKPsrIyioqKFH3K/cmZuoKCAsrLy3nmmWc4duyYCM7UZZ2DBg3izjvvFHsg91lRUSH4itXV1YILKLeRJEkEhQsXLuTJJ5/Ez89PBKTOc5M5lDL3zmw2c%2BXKFRc1Vpn7eLPSU0mSaNKkiViPnLm1WCxUVVVx/PhxNmzYUOv5gwcPrvU9Ndzi8Kl5LerXaFBxrlxRvNSigbmck5vr2kjN59F4BlwS/yryizs0Js2x3fGvUs%2BvphzaGS70WjXXSWNz3Bna5TR1v%2BBKvFFXIGh1XKNcK%2BBCFtQ4T0vtVb3xqglrcfjUQ2uKyKrGVt1qmtOr%2Be3ppm3U3CENCprLSVoUNJfrorXHTll/wD0jPjV/TIPz6nL/uXNdMjKUr7XIi6p%2BNDl8ajsijQ10WZZGRy6H1JxXrQ8T9UkqtWTAdb%2B09ljdt/qG1Nob1Z5rPYao1ZM11qC%2B/9zhdaqnp8UfVJ/nzufhrVgxusOV1hpbvaVaW6z5LOr4r4Nkv5kDsQ4dvxGSkpJ46KGHXLh7ffv25aGHHmLt2rUMGTKE6dOnk5SUJPzZwJFZiY2N5fHHH2fz5s1UVlayYMECVqxYwZ49e8jLy8PX15eCggJWrVolyiLnzJlDZWWlC88vJSWFe%2B65h6VLlzJo0KCbzg8gNjaWpKQkDh48SFBQEEVFRQQGBuLn54efnx/NmzenTZs2HDlyhG%2B%2B%2BYbOnTvzySefAPD888%2BTl5dHeHg4GzZsID4%2BntOnT1NRUcHWrVtp1aoV4Cj1nDRpElarVZRctm/fnpSUlFr31Gg0EhMTQ3Z2NhEREQqfQnW7oKAg8vPzRbBmt9uFGbrFYsHLy4sZM2bw%2Buuviz23aBHkgXHjxvGPf/yjVguCuXPnsnjx4lrnDfDJJ58wYcKEWt9v0aIFq1ev5u2332bDhg0YDAZMJpPmnKKiorh27RrV1dWEhIRQUFBAaGioyHCqMWPGDI4cOUJKSoqmAXtcXBxbtmwhNjZWcVzeO09PT9asWcP48eM1%2B3/zzTdFCW9deOWVVwS/8%2Bf5zWT69Gluna9Dhw4dOnT8L%2BDVV3/7Pp955rfv848KPcOn4z%2BCuqwfnEVknMs/S0tL2blzJ126dOGDDz7gypUr2Gw2wVObPn26pkWCM3JrsgkN1AT7myA5ORmLxUJeXh5Wq5XCwkJ%2B%2BuknMjIyOHDgAH379mX06NHAzxmhyspKdu7cyV133SX6kUVewFHqKGcoH3jgAex2u0JtU7ZvkEsyW7Rogbe3N6tWrSIhIQFJkmjdujUNGjRQCIhMmTJFlL56enry/PPPiyxsfHy84ED27NkTs9nMtGnTBO8PHNnXXr16iSBLhpzt/Oyzz4iMjOTFF18U78leduAQpnHm68nnyQF827Zt6dy5M%2B3atat1v%2B%2B%2B%2B24iIyMZPny42Dfn4MzDw0MEjP7%2B/iKoLygoQJIkSkpKMBqNeHt706xZM6E%2BGhQURP/%2B/fn000/54YcfOHjwIK/W/F/Ex8cHg8EgMnuenp5IkiS8Ejt27EhCQgKVlZUi8GzXrh133HGH2KeAgAAuXrxY67rU0H34dOjQoUOHDh2/N/QMn45/C%2BSsnXOJnSRJGI1GkWWSTd4nTpyomW0bMGAAwcHBlJSUUFpayt69e4WoyNmzZxkyZAhjxoxh0KBB9OjRo9YM344dO5g1axaff/457du3BxyeeAUFBRiNRux2O9HR0ezduxdwZPg6d%2B7M8uXLWbZsGXv37uXatWsYDAaqqqrw9/envLwck8kkxExkJU05WyeXISYkJNCiRQs2btyIt7c3J06coH///owaNYouXboIa4GysjLCw8OFMbka6gBIDqbq4n/JypM2mw2j0UhKSgrTpk0jICCAnTt3ulwbdUmk1hz8/f3p3bs3%2B/bto7KykrCwMHx8fLh06RImk0kYxhsMBiorK4UgTW0ZxNrGUs/H2Z5CtkfQgrMAT5s2bVi6dCn9%2BvXDaDQKfp7FYlG0S05OZvDgwQpRFmc8/PDDrFy5UvO9Hj16iDLauvDCCy/w%2BeefK47NmDGD6Voqdzp06NChQ8f/KOooHLolzJ372/f5R4We4dPxb8O8efPIzMwUfz/88AMZGRmYzWZatmzJgw8%2BKPzTtGC1WpEkiStXrjB69GhNBclHHnmEHj163HQe8hjOmZhDhw5x8uRJzGYzCxcuFJk2mZOXlJREYGCgsIr4/PPPhQecnN1xVmf89NNPBZ9s%2BvTpTJ48mS5duuDl5UVISIjIJM2aNYvCwkI%2B/PBDPv/8c2677TYqKiowGAwi4yn37%2BvrqyjJdEZAQAA71BwODYSHh3Py5EmWLl2K1Wrlk08%2B4fDhw%2BzevVvRzjnbqIWRI0fSvHlzUWaZkZEh1l9QUCDUUqurq2nUqBFr166tNSCTM2hGo7FWJc9OnTqxefNmAJc9kL33aoNzEHzq1CmefPJJwCE888EHH4iyWdmU3WAwEBMTwx133AEgOJfOQjdjxoxRcDzleUmSpKkiWxt00RYdOnTo0KGjbvy3ibYUFhby%2BOOP0717d26//Xaee%2B45UUGkxubNm2nVqpVCZLBdu3ak1fh92mw23nzzTfr27Uvnzp2ZPHmysPByF3rAp8MtJCUl0aZNG7dsFfr27Ut8fLymrcKcOXM0vxB37tyZ9957T9FWxqxZs5gyZQr5%2BflERkZSVlZG06ZNha1Cz549Renfiy%2B%2ByMmTJ8W5u3btIjY2VjF3mXv1xhtvMHHiROx2O/369WPFihWA8gu3/ECtWrUKu93OxIkTad26NSNGjKC6upry8nIhbjJixAhx3tixYzl48CDgsFV4//33ycjIEIblcsllWloaVVVVFBYWsnXrVm7cuCGyYjIHTQ6UysrKRJDjrEopSRLFxcWsXbtWsW%2B33347ERERzJ07V3jR5eXlERsby8yZM7Hb7bz22mtCAMXZCgJcg0pnz7mtW7fy448/Ul1djcVioaioSJSeqm0Jzp8/z3333ScCyCeffFKUgw4aNIivv/4agHfffZcffvhBBOT%2B/v4iqPfx8RFG882aNWPVqlWi//DwcIWaKFArv85ms5FaY3RcWVnJ7Nmz6VLj0bRr1y7g58BOXv%2BNGiZ%2BZWWlyAiHhoYyefJkxbhyJtO5NLkuaIq2JCUpG7khKFFXE606jrpEADSPudGROwIE6mPuzM8d33WtftRjuWUc7sbgLslfNwa/leui%2BbuLWgzmFhauJdqi/i7ijse25vcXVSPNRLlqzup%2BtMynXfrR2Bx37huXg2q1DQ31jbruI61u3TFwd2e%2B7oytPuiOp7rWfVPX8%2BHOc6jZSL2nGubsLqep5ufO3rgj7qT1uKivizufEy73vlbH7qiiqG92rZtfrZzizsOqbqOlvuLG2JrPkI468fzzz1NeXs6XX37Jpk2bOHv2rNBJ0ELnzp1dRAZlX99PP/2U7du3s3LlSr7%2B%2BmuaNGnCtGnTNHUIaoMe8OlwG7Xx6tS2CitXriQ1NVXYKowdO9blC7kao0aNAuDxxx/HYrGwaNEi2rVrR1xcHDt37iQ/P58PP/xQZEQuXrzIqFGjCA8PZ/PmzcKaICQkhLvvvlv8KiKLsqxYsULMfevWrYAje/fjjz%2BSnZ3NqFGj2LRpExs2bOCNN94QD5kcAHXu3Fn8OykpSWExEBUVhdlsZvbs2YBDGdTLy0sEDjNnzuT%2B%2B%2B8nMjKSrKwsVq9eTXaN0mJOTo7iF5/c3FyefPJJevfuLTh8UVFR4n0/Pz98fHy4ceMGCQkJgp9ms9kUPokGg4HDhw9z9epVMjIyRFYRHJk0mZ8mt5UDPqPR6BL4yZDVI2WTdR8nJ9ixY8fyTs3/wFeuXMl3332nONe5fDM5OVmUUu7du5fWrVsDjjLJ7t27C45ldXW1CL4PHDhAnz59AEf57pQpUwgKCsJoNJKenq6wjAAUgjDOdg9Lly6lXr16gCOAzsnJER5%2BcuBeXFzM8uXLOXXqlKLP6upqUlNTMRqNFBUVkZOT47JHVquV9evXuxyvDZrG6998o2ykvhYa16auJloJULXRsFYbl2NudKTuV8vbWX3Mnfm547uu1Y96LLeMw90Y3MWY2Y3Bb%2BW6aBqHq12Yb2HhWsbragtJdzy2NW0nVY20%2BlHPWd2Plvm0Sz8am%2BPOfeNyUO3WreHeXdd9pNWtOwbu7szXnbHVB93xVNe6b%2Bp6Ptx5DjUbqfdUo2zd5TTV/NzZG81Gqn60Hhf1dXHnc8Ll3tfqWN1I64FR3%2BxaN7/aad2dh1XdRt2Hm2NrPkP/Afw3ZfiuXbvGvn37mDVrFqGhoURGRjJ16lQ2bdpU5/dhLXz%2B%2Befcf//9NG/eHH9/f2bNmsXZs2fFD9juQA/4dNwSbmar0Lx5cyRJIjo6mvnz5zNu3LiblgfCz1mh9u3bi8xWaGgoI0aM4Ouvv%2Bbzzz8nISEBcAQ9n332Gb179%2Bapp56iXr16IkB57LHHeOKJJ24quy9j4MCBGAwG7rnnHpYvX05WVhbr16/n2WefFZYIcnZHzpDBz75sAwYMoEmTJi5ra9WqFeXl5aJ0cNmyZXz88cecP38eq9XqUrLnXJqamprKfffdx5IlS0RQef36dRHQlJaWiozfyZMnOX/%2BPKdPn0aSJF577TUAIiMjOXXqlAhYjtTIavv5%2BRETE8Pzzz%2BP2WwWZuZNmjRBkiQCAgKYN28e6enp/PDDDy77dfDgQRHoPfvssyJwBofqpnwNPT09GTp0qCJoTE5OFq8nTZpEp06dAMd1DwkJARwZstLSUhEoDxgwgFdffRWDwYC3tzcvvPCC6K9r16507NgRX19fBfcOHAHpqVOnxHjHjx8X98OKFSsEN1SSJDw9PcX%2By%2B0bNWrE1KlTNTmGsrrpzVCu9etsLdCN13Xo0KFDh47/v3Dq1CmMRqNC7btNmzaUlZVxTm0LU4PLly/zwAMP0LlzZ/r27Su%2BY1VUVPDjjz%2BKH8fBUQHVuHFjzGaz23PSAz4dvwqyKMnevXtr5dU9/fTTHD58WNPyQI2pU6cSFRXFvHnz%2BPbbb/nrX/%2BqMFt/5ZVXGDNmDLm5uQrz6%2BbNm5OZmcnSpUsBFA9GbfDz86NZs2YcOHAAs9lMnz59iI2N5c9//rMIsPbu3UtISIgiAJKFWaZNmybKGI8dOyZUJw8fPoyfn59mWaEcYEiShJ%2BfHwaDgY0bN4rxvvrqKwD27NkDOMoVGzVqxFtvvSX6qKqqwmq1UlVVOkmnmgAAIABJREFUJYLC2267Tbwvl17KQVBeXh6pqakUFxeTnZ3NhQsXAAdnrWnTppw7d44GDRpQUlJSq0gJOLh5clC2cOFChfVAcXGxCI7HjBmjyDbK65DXOGXKFJF5LS0tFaWaYWFhhIaGijls27aNt99%2BG6PRKIJAuY/vvvuOr7/%2BmuLiYrHOgIAAvLy88Pf3Z%2B3atWJvDhw4IPz%2BPD09%2BdOf/iTmZbfbRWCn5gaGhYXRsmVLRfawU6dOhIWFKe5J%2BTz5v02aNKl1D9XQOXw6dOjQoUNH3fg9MnxyFZTz31UtH8lfiMLCQvz9/RU/fMt6AAUaPpChoaE0adKE2bNnc%2BjQIZ544gmeffZZjhw5wo0bN7Db7eJ85/60%2BqoNesCn45ZQl63CzeBsmK42TncHvXr1AhzBX0ZGBna7ndzcXF544QWOHDlC3759Xc6ZOnWqy1jq0rvRo0ezc%2BdOUWIp2yrMmDGDtLQ0Zs2aRWVlJYcOHQJgxIgRPPfcc%2BJXGTlQsNvtiszN9OnTmTRpkqI%2BOz4%2BHoPBQIcOHZg3bx4dO3bEx8dHeLJ98MEHGAwG%2BvbtS2BgINOnTxcZqcDAQDw9PQkJCaFz586A48NFnpfNZuP48eM88MADInsml2HCz7YUAI8%2B%2Bqj4b0VFBZs3b%2BbcuXNuBR03qx0PDg524fLBz8qazufKhvY3btwgNzdXEXR6e3vz97//XbMvZ3h4eODp6SnOdc7wOQd1cukwODKIs2bNEgFds2bN8PHxEef5%2Bvpy%2BvRpxV4cP36cnJwcoeDqvA/yf39JSee/i8OndY47XLHfgsPnDu/m9%2BTwqY%2B5xeFzo5H6txHNx6EO7tDvyuGro6M/GodP3eaPxuGr6z7S6vY/yeFzg8r2h%2Bfwqafnzv2oNbY7z90fnsOnrga5FQ6flg3QfxGH7/cI%2BD7//HNGjhyp%2BFMrZ9eGrVu3Ehsbq/mXnZ39i/h1ffr0YdWqVbRu3RpPT0/uvPNO%2BvfvL8Tq4ObfudxB3XVvOnTUYNGiRfz1r38FHF/E4%2BLi%2BPDDD4mOjkaSpDrLNmUMGjTIxSpBtlVwBzJ3rEOHDsyYMYNr167h5%2BdHYWEhRqORfv36ASgyM3a7XXyRf//997n//vsZM2aMIrXep08fvL292bNnD0OHDmXv3r14e3szduxYevfuzbJlyxSCML6%2BvixYsIAhQ4aIsWJjY/Hx8XFRYlq/fj1FRUW0a9cOq9UqskCjR4/mmWee4dVXX6Vp06Zs2LCBzMxMTp8%2BTXh4OBs3bhS8uqqqKiwWizBKt1gsnDhxAnCUfU6ePBmAS5cuMX78eCZMmMCCBQsYOHAga9euJTExkbi4OPbs2aPwwPP09GT37t3Uq1cPPz8/Jk%2BeLNRJmzVrxpNPPsmSJUsYOXIkw4cP5/bbbwdQXG%2Bj0cjAgQN57rnn6NGjB8uXL9c0Vu/fvz979uzhtddeY9asWURERDB16lRefPFFVqxYQa9evZg1axY7duzAYDBw5swZZs2ahcViUfj%2ByWPKc6iqqiI/P5/AwEBKSkrw9PRk0qRJrF69WnjpnTlzhtjYWHHO7t27FeqkcuB58eJF5s2bx7Fjx1zmDw7bhU2bNmm%2BZzAY%2BOijjzTf08K2bdtcjNdnzphBq7Ztfz7wG3D4tM5xhyv2W3D43OHd/J4cPvUxtzh8bjRSU2Y0hWLr4A79rhy%2BOjr6o3H41G3%2BaBy%2Buu4jrW7/kxw%2BN6hsf3gOn3p67tyPWmO789z94Tl86mqQW%2BHwaVSU/Ddx%2BH4PjB07liTVj6wyz78uDBs2rNZkxaFDhygpKRFVcOD4YR4c1UPuICYmhvT0dIKDgzEYDOJ8GYWFhW73BXqGT8cvgLNoy7Fjx1i7dq3g1TVu3Fh8Yf61SE5Ovmn5p8w5GzJkCMnJyaSlpXHkyBFOnTpFeno6ixcvJiYmRsFjcxZt6datG5mZmaLMT4bJZGL48OFCAOaLL75g%2BPDhmEwmGjRowOLFi%2BnQoQOTJ0%2BmY8eOVFRUsH//fiHeUVhYyKJFi4SnmzO%2B//57fvjhB8xmsyJQGj58OJmZmQwfPlwEbDL375NPPiEqKgqbzaYQWImPj8fPz0%2BI0/j6%2BtK9e3diYmIEv8zLy4uWLVuKwO3tt98WdhUjRowQe7FlyxYsFgvz58/HaDSSlpbGtWvXRFbr/PnzIiCpqKjg6NGjgKN80vmDRuYAyli5cqWwrQCH4bvVahWlqrKQytWrV1m4cCFeXl787W9/U%2ByZ0WjkjTfewGw2ExMTI84JDQ0lMzOTkydPisyfnDmsqKgQc798%2BbK4rvK9OXHiRFFq261bNyRJYt68efTs2VNcs%2BjoaBYtWiREYWT%2B5m233YbBYCA8PJxHHnkE%2BPnHB0mS8PLywmaz1Sq7rAWdw6dDhw4dOnTUjd8jwxcREUGbNm0UfxEREb96rnFxcdjtdgUdyGw2ExgYqFkN93//938u1lpnz56lYcOGeHl50aJFCzIyMsR7RUVFXLx4UQgMugM94NPxm2DgwIGsX7/ehbsFMHv2bD788MPfbKygoKCbmlv/Gg7UXXfdxdGjR8nMzOQf//gHd911l2a7l19%2BGV9fX9LS0hg7dizx8fEMGDCADz/8UNgqaKk43mxuTZs2pWnTppw5c4YmTZrg7e1NYWEhs2fPxmw2ExAQgM1mIyMjg/z8fNasWUN8fDxlZWUcPnyYlStXYjab6dq1K3a7nfnz5zNhwgQMBgNms5ny8nIMBgMNGzasdQ5yUP/Pf/4TcKieXrx4kQsXLrBixQqWLFkCQMOGDdm2bRuLa5xQq6urWb9%2Bvcj%2BHThwAEmSmDJlCqD0sIOf%2BW7gyL62bdtWcA1lVFVV0axZM/Fa3k9njpxcghkYGIi/v7/4Jc1qtYoPz759%2B2IymTCZTEyfPl1w%2BM6dO4ckSQwfPpyHHnpI2CrI93BpaSmBgYHk5eUB8NNPP9GxY0ckSRK/AMolpKGhoQQGBgII0SEdOnTo0KFDx/8eQkNDGThwIH//%2B9/Jz88nNzeXZcuWcdddd4kf5u%2B77z7xPcVisfDSSy9hNpupqqriyy%2B/5MCBA9x9990AjBs3jrVr13L27FlKSkp4/fXXiYuLU1Rr1QU94NPxm2DSpEmEh4czYcIEt3l1vwbPPfec4NXJJt%2BFhYUutgp1ISUlRcElHDp0KDabjWHDhmG1WhXG2s5o1qwZX3zxBV26dBFBhsFgICoqipdffpnevXuLksa4uDgsFgvHjx8XXn2SJBEXF6fos7i4WAQzly5dYujQoZSWlvLRRx9x7tw5WrVqhcFgEBlCu90u5H1tNhsPPPAAaWlprF69mocffhhJkrDZbHh6elJWVkZ5eTk2m42///3vos5cLqNNT0%2BntLSUF198UfgsgoNXV1hYKEp2CwsL8fDwoFu3bsyZM4e//e1vCq6cXDprsViwWq2iZPLIkSPExMTQsGFDOnToILzvwsLCiIqKYsSIEfTv39%2BlVHLUqFG0a9eO7Oxs1q1bB8A///lPWrVqRb9%2B/QSZeeXKlRw8eFAEXc4m77t27aKqqgqbzcaaNWt47LHHALhy5Qo2m41OnTpx7733YrfbsdlsQtzGarVSUFAgVDerqqpISUnh0KFDwp9RDuCvX78uAsPayj21oIu26NChQ4cOHXXjv8mWAWDhwoUEBATQt29fhg4dSnx8vMKHOisrS4jd3XvvvUycOJHHHnuMjh07smzZMpYtW0bbGnrH3XffzYgRI5g4cSI9evQgNzdXWGG5C53Dp%2BM3ga%2BvL%2BvWrWPZsmWCVxcSEkKPHj3YsGGDQtXwt0CzZs3YtGkTy5Yt45577qGwsBBfX1/atGnDs88%2B6zYfMDExkY8//lhxbMuWLYJXJ5uAO2PNmjWaPK3Y2FhWr14NwODBg1m7di1btmzhiSeeABxZsaKiIsDBE3Mm45aUlDBmzBiqqqqIiYnh%2BvXr2O12jEYjOTk5jB07FovFgre3tyhbNJlMWK1WDAYDgYGBFBQUcM899wCOzJesWHnw4EF69uypKNUEB3dPDhrr1aunqdDp4eFBWFgYV69epU2bNqSlpREQEMBHH31EgwYNqKiowG63i%2Bya0WgkPz9f8OvOnDkDOAKipKQktm3bxuuvv87EiRMBR4assLCQQYMG0bhxYx599FHCwsIwGo189tlnInDv3r07BQUF2Gw2fH19sdlsIoCVJImZM2dSUFAgSjEkScJkMiGbysvBXFRUFImJifj7%2B2tmo8GR5YyJiVGIwMiQ/Qq7deuGj4%2BPpgWDUZNgo42hQ4e6KMq2bNlS2aikBPz9a3%2BNQ2BAMWxpqcJzSeMUqqpU3IziYlCVOWO3K3kp%2BfkQGqpoUl6uon1UVCi4Iy7jaE24stKVg6IaWz0VzX6KiqAm6K/1kHqdWhNUH9MYvKAAajSRHFDtOeCyF26Nre7YZYMBiwWcFZG12qj7Vu1VWVmlC49PvVdaS1If1Nhyl/0qK3OlD6mnp75Htc5xud4a18XlVtK6r2/cUHLK1JPRui7qsbQWrl6E1k2r7ruuuWj0o/W4cO0ahIf/sjVotHHpW71OrX7V10V932v1o7U36vPeeUfJ9dO6z1X7p95OzYMaH4guy3Jjz9VL0hxbvU6XD2uNrbiVNhr7qT7kzu3ozsfhfwr/bb%2BFBgQECIsvLSQnJ4t/S5LE1KlTmTp1qmZb%2BXvOzJkzb3k%2Bkv3Xyr7o0KGBpKQkrly5InhRnp6exMbG8vjjj4vsTlFREStWrGDPnj3k5eURGBhIYmIi06ZNE19658yZQ2VlZa0iL/v376dBgwZuz%2BvgwYNMnjyZe%2B65h/nz57u8X1xczPvvv8/u3bvJzc3F29ub2267jXvvvZeBAweKdhMnTiQlJUXx5V4OcJ966inBb3Oe/8SJE/n%2B%2B%2B8F30ySJJFBlLN2zZs35/Tp08LeQlaXDAsLY8mSJcTHx9OtWzcsFgvvvPMO06dPx9fXF4vFgt1up7q6mt69e%2BPr60tqaio5OTl07tyZBg0asHXrVkwmk6a/nCwKI1%2BrXbt2ERMTQ3l5OZ988glLliwR/Ebntl5eXiQlJTFhwgSeeeYZQkJCSE9P11STCg4O5o033uDs2bMsWbKEiooKhfCKjObNm/PWW28JqwmbzUabNm3Ev2NjY9m6dStffvml4PXJwWunTp04cuQIkZGR3Lhxg4qKChe7hS5duvD999/XdovQr18/li5dKsaUIfv9jRgxgueff56kpCQqKioUnL0//elPrF27tta%2B1XjllVc0RVumaQga6NChQ4cOHf%2BrmDv3t%2B%2BzhpXyPwG9pFPH7wZnkZeDBw/Sr18/Hn74YbKysigpKWHcuHGcOXOGlStXkpqayoYNGwgNDWXs2LFkZmb%2BLnPasGEDd955J1999ZVL9kae0w8//MDy5cs5ceIEu3fvZvDgwcyePdslEzhp0iSxPrPZzKeffsqlS5d4%2BumnNceWRUJefvllAMGtk/l5vr6%2BjB49Gh8fH%2BrXr6%2BwUsjPz%2Bf%2B%2B%2B%2BnV69eVFdX4%2BnpybRp07Db7VRWVgrLBnkde/fuFYFIamoqZWVlhNZkZiSXNAkigANHkDlw4EDatWtHly5dePfdd7Hb7aSkpAAOdVRw%2BO21bt2affv2MX78eMrKyoiJieH%2B%2B%2B9X9G0wGOjduzfFxcU88sgjfPTRR6IPq9XKtGnTOHnyJCkpKXTu3Jlz584JL0FwEJ1tNpsQSMnMzKR169bMmTNHrKeqqgqj0SgI0na7na5duxIWFoaXl5cIrP38/BQ%2BfCaTSeyz3Gbfvn2KgFAO6mUPnD59%2BnDp0iUKCwtdBFqOHj1602BSDV20RYcOHTp06Kgb/20lnX806AGfjn8LfHx8mDRpEhERERw4cID333%2BfkpISli9fTvPmzZEkiejoaObPn09VVRUjR46kXbt2bN261cW3Lzs7%2B5bmUFBQQHJyMjNnziQkJEThpQYOu4aysjLeeecdMafg4GAmTJjA66%2B/Tv369W/af8OGDXnsscc4fPiwMGd3xqeffkr9%2BvU5cOAAQUFBWK1W2rRpQ7t27SgsLKS4uJiXX36Z8vJysrOz%2Beqrr4iKimLw4MHCd%2B%2BJJ54QvC%2BTyUTLli2ZPHkyX375pRASKSsr47HHHhOGnGFhYWRlZdGlSxfMZjOdOnXCYDAIjz6A/fv3C4Ga8PBw0tPTMZvNHDlyRJRfXrx4EUCYpq9fv560tDRat26Nv78/iYmJnD592sW01GazMXHiRDZu3EhVVRXZ2dkKP7xVq1bRqVMnevbsSVpaGna7nby8PFavXk27du0Eabm0tFRkjG02m6I81W6306pVK/7yl78ADvXP77//nuLiYoUPX0VFheL%2Bqa6uFmWfzmqocp29p6enQiFVkiQKCgrw8fEhIiKCnj17Ao4MaUREhFBRdRc6h0%2BHDh06dOjQ8XtDD/h0/Fshe5Ls3buX0aNHi6yUM9LT08nIyMBsNjNs2DAGDRqkyKRp8ercwdatW4mLi6NJkyb85S9/YePGjYr3bzanAQMGuCU8U1VVpfD8k3HixAmuXbvGsGHD2LdvnyDqdu3aFbPZDEDbtm0JCgrCx8eHmJgY%2Bvbty%2BXLl9m5cycWi0URCFRWVlJdXc3p06d57733GDBgAFlZWYSGhvLDDz8wfvx4kSWrqKjg0qVLfP/997Rr147jx49js9lEQAiOIE4OUq9duyZEXRITE3n33XfFeho0aEBycrLwnrHZbKSlpYmsos1mc%2BGkAbzxxhsEBwcDjoycc0A8ffp0tmzZwpYtW9i%2BfTutW7fm22%2B/ZfLkyZjNZiF%2BI2faPDw8RHml3B84zOqdrSHKysqwWCyKTG6HDh3w9PRUGKzLsFqtouxTDjKrqqqEAExBQQF2u52NGzfi4eHB1atX%2Be6770S7q1evYrPZxPV0B1rG6336DFA2csMU2sUCs7hY8VLLx9fFTFd1DuBqJFzDQXWGC/VTVTLsjie4Vr91mZYDrgt3Z29U3E0tv2J3zKZd3DfcWIO6jaahsbofjYvnjtl0XW7dWsbr6n7cMtnWujB1mURrdeTOpqv2wq3rotXoFtzPXfZc66FSD%2B7OBqr3Sms/3TBVd5mPViP1D5HurLOuvdI6SYM24M61c1m6ek1aIhV1OaaD6zPlxoeSO7ejep1at4Q7pu91PKputdGcnxuD34op/X8Keobv10EXbdHxu%2BHNN99k0aJFCk%2B66upqgoKCyMrKomnTpm7x%2BHbs2MGuXbuElC38zMfq27ev2zy%2BjRs3Mm7cOA4ePMjbb78NONQw5XOzsrJo3Lix2zy%2BL7/8kjVr1ohARJ5TVFQU5eXlInuTmprKjBkzAIcfoDO/7eDBg0JWNz09HR8fHywWCxcuXFDM3W63c%2BPGDUVWTg2bzSZUKxcuXCgCnYKCAiRJokePHqxevZoxY8aQmpqKr6%2BvKCn88MMPhf8goOAQhoaGEhcXx8GDB8nPz%2BfSpUuKYMnLy4vKykrsdjsXLlwQZvDOOHnypNi7Nm3asH79evHekiVLhN2DbGlx8uRJRo0aRaNGjYQQi91uZ/DgwYIEvWLFCj744AMRPPfp00fBB5S5ks44fvw4x48fFyXDP/74I1OmTCE7O1twE0ePHs2IESN49913CQ4OpqKigvLycubPn8%2BCBQuIiooiOjqaoKAgqqqqKC8vF%2BPExcXRuXPnWq%2BRGrUZr8fHt/r5gBum0C46MSqRCi0fXxcSvlrYAlwZ/i7qHBriEaofTNzxBNfqty7TcsB14e7sjUqsQcuv2B2zaRf/ZDfWoG6jKYSg7kfj4rljNl2XW7eW8bq6H7dMtrUuTF0m0VodubPpqr1w67poNboF93OXPdd6qNSDu7OB6r3S2k83TNVd5qPVSK3A484669orrZM0fjR159q5LF29Ji1uc12O6eD6TLnxoeTO7ahep9Yt4Y7pex2PqlttNOfnxuC3Ykr/n8L/WoD2W0PP8On43SAHHwDe3t7Ex8czfvx45tYwb0tLS93i8cXExLhk%2BbZu3fqL5nLixAkuXLjA4MGDBY/PaDQqAg9JkliwYAGdOnVi5cqVZGdnU1FRQWFhIcePH2fmzJkiUDx58iSXL18WWSY5q9enTx8uX75Mjx49RElqTk6OKHOU98M5YFq4cKEox5TtD7SQkJDA3LlzsdlsGAwG4uLi8PX1VQSB8rlbtmxRBDsGg4GffvqJ9957T5h3avHH5DmYzWbS0tKIi4vj%2BvXrIng0Go306dNHZNJks3PnsTp27KjZr1xWmZOTo%2BBoJiQk0LNnT4KCgvDw8CA6OhoPDw/69%2B/PpUuXhGG8zWYjPT2dO%2B64g/j4eFauXKnIFMrZVYDGjRvTr18/QkJCxA8F3bt3JzY2lq5du/Liiy9y%2BvRp7rzzTgIDA/Hy8mLfvn2YTCZSU1OFV6GzLcP8%2BfOx2Wz89NNP5OTkcOPGDcrKyhRrP3XqlM7h06FDhw4dOnT8oaAHfDp%2BNwQFBfH8889jNps5duwYn376KfPmzSMiIoLg4GA2b95cK49v3LhxXLt27Teby4YNG6iuriYpKYldu3axf/9%2BrFYr69evF0Fb06ZNKSsrIzg4mLS0NNLT08nMzCQzM5P9%2B/cDEBkZyY8//kh5eTleXl506tQJs9nMF198gd1u58477xRj/uMf/2DYsGEiODKZTEiSRIsWLURJJDjKHceNGwc4AjZvb2%2BRFXVWATWbzYSGhooSw1OnTlFWVqbgtsllnG%2B88YaCc9izZ09ycnLYs2ePojRShizIAo4grl27dsTHx/Pjjz8C8K9//Uu8n56eLuwlHnzwQRYvXiz87wDhiyhjwYIFgCNr6OHhQUFBgcKI3cfHh4SEBJo0acLevXuxWq0MHTpUvB8XFycC4p9%2B%2Bom8vDzsdjtlZWViLzw9PfnXv/5FUlKSmEN8fDy7du1i586dSJJEZWUlZ86c4cEHH2T79u289NJLijUfPXqUqqoqoqKixB5JkiTUOqOiojAYDMTGxooMnxy4h4aGCuuRX5Lh06FDhw4dOnTUDb2k89dBD/h0/Nshi5WkpqYybNgwF87c7NmziYiIEJmdX4vS0lJ27NjBggULuPfee2nVqhXbtm1j3LhxFBYWcuTIEQCGDBmCxWLhxo0bJCYmKoRiZOGS7t27s3HjRhFAyKqQLVq04IEHHmBxjcavrBK5detWERwFBgaKssft27eL%2BV29epWRI0cCjpJX2WcPHAIqsrqmzWYTAY7dbicyMhJAEaDImbg5c%2BaQk5MjxmjSpAlWq5WIiAiRaXNW5ly%2BfLk4Lmf45L/w8HAxrtVqpby8HEmSMBgMrFq1irlz54o1hoSECIN0Ge%2B//z7gyPBFRkbi7%2B%2BvsIaQvfoA6tWrR6tWrUSwn5ubiyRJorzXZDJRXV3tYi0hB5xyxi8iIoK0tDQGDRrE4MGDMZlMFBYW4uPjw5EjRwgKCuLs2bOAI4AtLy/nnXfewWg0Mn78eMARRNarV4%2BTJ08CCH/AkpISrl69KkpJwWHnceXKFZd9rQu6aIsOHTp06NCh4/eGHvDp%2BLehtLSU1atXk5%2Bfz9NPP43dbmf79u1kZGRwxx130Lp1a%2BLi4ti2bRtLly7lnnvuEV/8d%2B3aRdu2bYmLiyM2NlZhrH7%2B/HnAEbTIAVrbtm2JjY0V1gJlZWWYTCaSk5MZM2YMjRs3ZvLkydjtduGbJtsJyEbd4MiCNWjQAJvNRqNGjfDz82Pz5s2idLKkpITjx48DMG3aNEXpY0pKCsOGDcNkMlGvXj3y8/PFnNXcsrS0NE3D7itXrijOk7NjdrtdBBgXL150CTJWrVqleC0bxctGn94qrklFRQXbtm0Tc5NFW5yDr9tuu01k1WQFUx8VaUEWNnHGypUrCa8xBM7OzqaoqEhRtiqLpdhsNoYMGcKBAwdISEgA4L777qNt27YcOnQIcATEWh5/165d4%2BjRoyJTefnyZZKTkykqKqK6uprq6mrOnj1LaWkpX3zxBUOHDuX69euKPs6fP091dTVTpkxh3rx5WCwWrl69KsbLyMigqqqKGzdusHPnTrFX4Ajy5EAtKyvLZX61QUu0pW9flWjLTz/d/DUaRHuVkq1W9a5L5bCW%2Bq26kYpbChoiGXl5ipduCX/k5tY5tlviLxrzcxHMVa9Tq6xWNZbWGlw0bmpUbBVQqzior51WxyqVW00lCLWKgjttVGNpibaou9G6b9TdalaGq8bSEC126Uitq6HZr6ojrep3l7G09vjy5Zs20fzNRn2wpvpBAdW975ZgTM2PhgJaAjcqMSLN%2BanvaydRrtrO0xRCUjdSr0lDGMnlxtEYW71uTeZCXWNpqYeohFy0hqbmO4KAGx%2BI6m1QaT1pdqN16dRttB7V36KN1pLU89HqV/28aD2rWuv6T0DP8P066MbrOn4XJCUlUVJSoig39Pb2Ji4ujieffJKEhATi4%2BPp0qUL586dE1L5BoMBo9GIl5cXwcHB5OTkUK9ePRISEjhz5gwWiwWr1Up%2Bfr7I8nh6erJx40b279/PihUrAEcQYbVa8fT0pKqqCkmSmDJlCitXruTAgQMiazZkyBDOnz/PkSNHCA4OJjY2FtAW/JCPe3l5IUmS4HYZDAYhNiIHXpIk8be//Y3Dhw9z9OhRPDw8%2BEnji7oMedxLly5pWjrISEhIoGXLlmzYsOGm%2B9%2B7d2%2B%2B/fZbzfe8vb2pV68ely9fFpk7cASpkiRRXV1NYGAgX3/9NX5%2BfowbN46ysjLKy8spKytzKbWVffaOHz9OsdO3YB8fH8rLy8nMzKRDhw4Kvpqvry%2BRkZEiWFdDzuR9%2BumnNGnSRGR7u3btykcffUT//v0pLCwUmUVwlBB36NCBb775RsxLDt7V13PPnj3ceeedVFVVCcXP8PBwLBYLgwcPZuHCheKaqBEfH8/YsWN57rnnNN9/9dVXGT58uOZ7amgZr8%2BYMZPp06e5db4OHTp06NDxv4Aa7bvfFDWyDP8V67SNAAAgAElEQVQT0DN8On4XJCcn4%2B/vrzBfP3bsGGvXrhXZm8aNG9O6dWuSk5MxGo3ceeednDp1ivT0dA4ePMj48eOx2%2B2EhIRw5swZsrKyaNKkCWvWrCEtLU1krWw2GydOnGDq1KliLH8nJT4PDw98fX357LPPqKqqom/fvnTo0IH27dtz/vx5JEmiV69e3H777YpztDJuskiJnMmTDbv/8Y9/KFQu7XY7s2fP5quvviInJ0d42IEj2GnRogXgKBMEhIiJOtgzGAw0a9ZMlG8OGDBABHtyWSUgfPpkXp4c7DkrpPr7%2B1OvXj1OnDjB9evXFcGePGc5S1VZWUlRURGSJLFw4ULOnTtHWVkZFRUVxMbGUq9ePbp3705YWBg2m42vv/6a4uJiReZQFj6ZM2eOItiTJIm2bdvSvn17xTEt3HfffS5B4dtvv01ubq7gK8p7%2BNRTT5Hn9Auxc6bWbrdjMBhECeXnn38ugnN5zZ06dcLX11cI7Kjn5OvrS3h4OMXFxdx1110K3qIkSeKe%2B1Hrl/9aoC2co/8Gp0OHDh06dOj47aAHfDr%2BYxg4cCDr16%2BnRKNW4oUXXhDZvpKSEi5duoS3tzfvvvuuEHiRhTxGjhxZqy3DoUOHMJvN7N69m6KiIjw8PFixYgXr1q0jIiKC9u3bI0kSy5cvV2TNPv74Y06ePClEW9566y1Fv3IgZbfbqaysZMeOHSJrCAjPvh07duDj46PILu3atYs77rjDpb/Y2FgX8ZX%2B/fuTm5srApUrV64QHh5Oly5d6NmzJ4mJiYJz98033yiCQIA//elPgCOrJ0kShYWFbNu2DZPJpBBtAUcw%2BfLLLxMWFsbChQuFsErLli154IEH8PX1JSAggMGDB%2BPj48Phw4ex2WxER0e7cAjlucowmUxERUURFRVFcHAwbdu2VRjfq7OpzsGoc%2BAVFxen6DMgIEBkFUNDQ%2BnUqZNYb9u2bTGZTKLsNCEhQQSkzjzDQYMG0apVK65cuUJMTAwTJ04UVguBgYG8%2BuqrACJTXFxczP79%2B8W4cvZQDtabNWuGu9A5fDp06NChQ0fd0Es6fx30kk4dvxuSkpJ46KGHhAKlGmVlZYwZMwaTyURmZiaDBw/m6aefZvny5ezfv5%2BRI0fy/vvvExcXJ3zcnAOvs2fPMmTIEPz8/Fw4bM6iHh4eHtjtdqqrq/Hy8mLs2LH4%2BvqyZcsWNm3axPjx47ly5Qo2m00RsHh4eGCz2VxsEgwGg7BPaNGiBefPn6d169b07NmTZcuWAT8HAc6lhLJfnRpym/bt25OamlprKWloaKgIMEtLSxV9y8I3zmWltUEOhlJTU7FarbUGGEajUQRyW7du5aGHHiI/P5/WrVvz3nvvsW7dOj777DOFPQU4SiudBU0GDRrE7t27Fevy8fHhtttuEyblLVq04Nq1axQWFhIUFMSrr77KI488IiwVnANAuQTTZDLxwAMP8PHHH1NRUcErr7zC2rVrOXnypKKc03k9tVleOO%2BzyWTirrvu4v/%2B7//EMed%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%2BPDDD8Uxg8GAj48PYWFhXL16ld69e7N7927Gjx/PCy%2B8INrJAZ%2BW8XqXLl24ceMGc%2BbM4YEHHuDuu%2B8mLi6Or776irKyMqKjoxk8eDD79%2B/Hy8uL06dP891339G1a1fAkT2KjIzk%2BvXrmEwmwsPDhRm6nBlz5n%2BBI9AxmUxcv34dg8FAVFQUV65cwdfXl9LSUtHOOQAFRyBYXV2Np6cnlZWVtQZgchaqpKSEoKAgDh06xIQJEzh27BhBQUEiKyUHQ5WVlZqBJzgCuJEjR7oEQB4eHlitVuLi4vj444%2BxWq2sWLGCPXv2kJeXR1VVFSaTifbt23PhwgWuXbv2izJS8jw8PDwYOnQomzZtqvMc5z2%2BGXx9fWnTpg3Hjh3TfK9FixZcvnwZb29vsrOzsdlsCl9EDw8PLBYLzZo1o0%2BfPqxdu9al7BUciqCzZs0SdhNqjB49mkWLFtU5X9A5fDp06NChQ4c7mDLlt%2B%2BzRvbhfwJ6SaeO3w3Jyck3DfbA8eV57ty5GI1GDAYDnp6emEwmjEYj0dHRrF27lrfeegtJkrisUlZr3rw5mZmZtZZzOmP16tU0aNCAqqoqIiIiyM7O5uzZs5SUlPDZZ5%2BRnp6uMDC/7777eOmll0hNTSUlJYXRo0eL97744gsmT54MwMaNG7n77rvx8vLixo0bFBYWAo7A6erVq1it/4%2B96wyvolq7a05NJYWTRggllFCSEAIhECACoUWaRGkKKmBBmiAfCFKVIveCoFQFUa6IdAxFOqEEiKGHGJIARlpIAmmk57T5fpzM9syeHXIE9ap31vPkkZkzs8s05533XWuZ0LRpU8JFBID4%2BHiSHdLpdKisrCR2B0JgI2TWPD09sWzZMgAWtctp06bBYDAgNzcXQUFBRJWyvLwcU6ZMQUhICLRaLaKjo8l4AwICEBERAbVajVdeeQWenp4YPHgwCfaEkkmFQkHUJq9fv45NmzZh2LBhuHnzJtatW4ekpCRs27YNvr6%2BuHTpEnJzc0XlozQ4joOjoyMJkDmOQ0xMDHr06IH/%2B7//w6JFi0S/CTxFZ2dnYm8BAO9QT3lrG48uXbrAwcEBnp6euHLlCtlPo9EQ/0MBd%2B/ehUqlQkREBMkACuB5nmSFMzIykJeXJ1JqtT5O/v7%2BRLCHni8Akal8TZA5fDJkyJAhQ0bNkEs6nw1ywCfjL4Po6OhqBV7c3d1x7tw5Jt9v6tSpogyhNZYsWYKgoCB06dIFp06dwsaNG8FxHDiOw7Vr1zBo0CBRAGFvbw%2BO4zBt2jSRD6B1MFivXj2y7Ofnh%2BHDh4vsGAQI/05JSUGnTp1ImaNSqYR3VS2Qu7s72c7Ozo4EKeOrSlJGjRqF999/H82aNSMBkZOTE3Q6HZKTkxETE0M4fK%2B//jrCw8PRpEkTojDZunVrdOzYEXq9HgaDAbt378Ynn3yCK1eukNJUlUqFpUuXIjU1Fenp6ahTpw54nsf333%2BPkpISrFmzhvAmmzVrBrPZDF9fX/A8/8QSSYHXJpSYCm0ePXoUS5cuxf79%2B0WlqALnr7i4GDNmzCDtrKU%2BwVln3U6fPo2ysjIMHz4cwK9Bl16vFymGfv755ygoKIC9vT3S0tJEHwloYRsAePToEUwmE7y8vEjAJ1hLBAcH49y5cwAs5//IkSNwc3MTZQtthczhkyFDhgwZMmT80ZADPhl/CwwaNAgGgwHDhg1DSkoKeJ5HdnY25syZg8OHD%2BOTTz4RGaULwZXwEh4bG4u1a9fi4sWLyM/Ph5%2BfH/Ly8tDQmjPCgMFgQHx8vCjjRMOnihzg7u5O/O9Wr16Nffv2oVatWqioqEBISAgJwqyDpPv375OAo6KiggQAq6q8hRYvXkwUOK0hZPh2794NnufJvK9evQrg1yDmwYMH2LhxI4qKitCjRw%2BUl5dj6tSpCA0NJSWNRqMRM2bMIN57gmH77du38ejRI2JCn5mZCY1Gg02bNiE8PFzCkasOwlg4jsPAgQNx8uRJxMfHSywovL29SQA4YMAAsp4OgKyX%2B/Xrh6NHj%2BLtt98WbaPVakUZvunTpwMAZs6ciatXr5LyXLo94YPCzZs3wXEccnJySBZOUACtrKxERkYGAMv569mzJwqszJ%2Bs1UdrAsuHr2eHDqJt6GQiy0uJ9pCSUEVZ9hcSX7Yam2V7o1FtS/zpWL6E1ABt8epjUlOpAUr6BiTGUvTxY7Zry0GnjONs2ES6Db0BpHNgnu9r10SLtti90SeP5cMncYS5eVOyjcSukdW5lXcowJ4DVbAh6Zt1LulLiSmIS9nGsL5J0VaHNl1s1LOONT7aA45xeiWeZjb5D9KTYBxQum/mo5laaYsPH22NxzqXEp82RsOSeTFOTE23HcuGjz4P1Oln9s06vZLHFH1AGSecuswZKyD1EmQZ%2BtHb0DcHa50tDwr65LFMCm0x4mP5pP4XIGf4ng0yh0/GXwItWrRA7969SfkijbKyMrz44osoLCyEUqnE48eP4eLiAicnJxQVFWHbtm3EBgD4lcNnLTxi7QMYHx%2BPlStXYv78%2BRg8eDDZb%2BbMmdi5c6doPw8PDxQWFpLgRPDcM5lMcHR0hLu7O%2B7du4fly5fj%2BeefR1FREeG9ZWVlwWQywd3dHTExMfjyyy8JT0649RQKBerUqYO2bdvi7NmzePjwIdPbD4BEqMXaixAQi9X8EXjuueegUqlw4sSJGjNR1uNjQafTSTz9WLCzs0NF1RtFdf6IAqyFcVjcvzp16pCAFrBkbisqKlBRUUHaValU6NWrFw4cOICJEydixYoVoj6PHTuGd999FykpKcw%2B1q5di27WRP4nQObwyZAhQ4YMGTXjzTd//zbXr//92/yrQs7wyfhLwNvbG2FhYdX%2B7uDggG3btqF///7QaDTgOA5KpRKhoaHYtWuXKNgDfuVdzZgxg1kmOmrUKGg0Gnz66aeijKHgl2edlXJ0dBRlnKZPn05e0uPi4tCiRQsAFlGVkpISEe9NyLgVFhaKVB2F7JiTkxM4jsPYsWNF46dVJgW7haioKFFJp6urKxwdHckc69Spg6FDh5KyQpVKRZQurREVFUUCWroE0dnZGfPnzwfHcfD390efPn1gb2%2BPDRs2wNfXFydPnkRERES1pYvCsRMCIXsreS%2BhjFSr1SIvL0/Ej3NwcCCBrr29PfG5q6ioQOfOnclxq65flUqF%2BvXrM%2BcUFBSEo0ePIjQ0lKzjOA7e3t5Qq9VQqVSk1NdoNMLNzQ0cx%2BGzzz6TBJgTJ07EsGHDoFQqmUGvUO5pC2QOnwwZMmTIkFEz5Azfs0EO%2BGT8JfBbBF7i4uJw7do1nDp1CosWLSIlldbQarVENZMFBwcHvP766ygpKcG4cePQqlUrDBkyBH5%2Bfjh58iSio6MxZcoUXLt2Dfv27UPv3r1F%2B4eHhyM9PR2urq5YsWIF6tevj4SEBKxfv17Ee9NoNPj444/BcRzs7e2hVqthZ2cHDw8PhIeHY/r06bC3t0fPnj0BQGT6rVKp4O/vTzz0eJ7HiRMnUFJSgtzcXAQEBKCwsBDl5eWkpDM7Oxvbt28nAY/RaCScN4VCgT179pDjLZSW0gFNcXExZs%2BeDZ7nkZGRgR9%2B%2BAHl5eUYPXo0MjMz4evri8rKSvA8T46vILQDWAI9IXACIBI4sS4j9ff3R9OmTclvoaGhaNSoEYxGI8rLywkHr06dOhLfQhYCAwNJ9s7aRB6w8Cj79OlDvBuFbVJTU1FUVESyqMJxc3Z2hoODg%2Bj6EUpEmzdvjpdeeokE4Wq1WhRgFrDKZmTIkCFDhgwZTw054Hs2yAGfjH8k4uLiiACHgA0bNoh4fl9//TX0ej2ys7NhMpnQqVMnTJw4EWvXrkVCQgIxTwcsAd4333xTbX8mkwlKpRJHjx6VCMG88MIL6NChA9zd3bFx40YkJiYiPDwcpaWlWLhwIebOnQsnJycsXrwY3bt3B2AxZ7969SpiY2Px%2Beefw87ODsuWLcP169fx8ccfQ6fTIT09Hb6%2BvpgzZw7J8HEch%2BjoaCL6MmbMGGzatAkKhQIKhQIHDhwAAHTv3h2%2Bvr5wdXXFvHnzkJ6ejkaNGmHChAlIT09Hw4YNiUm5YNKuVqsRHx%2BPu3fvEusD62BSgI%2BPD5KTk0lw1adPHxIU9enTBy4uLhgxYgR%2B/vln4kHH8zwuXryI9u3bo3bt2njppZfw9ddfg%2BM45Ofn48svvxRlCpVKJcnICcFW3bp1SdltvXr1MGbMGNH2586dE6mXchyH8ePHY/fu3Thy5AhWrlxJgtVu3bqhpKQE7u7uJNOoVCpRu3ZtpKWlYcuWLTAajbCzs4ObmxtcrMyiateuXe11QkMWbZEhQ4YMGTJk/NGQOXwy/tbo1q0bcnJySMCh0WgQEBCASZMmYfr06XjzzTfRp08frF27Frt27SJiLkIAJBioCy/Z3t7e6NixIyZMmAAfHx%2BR119mZiZeffVVzJo1CyNGjAAAlJaWYuvWrVi1ahUOHDiALl26kLJEAR4eHujWrRucnJxw5MgRZGZmEk5adRACJMGTELCU/9WqVQve3t5ITk4mHDma1yYsKxQKhIWFIT09ndhF1AS6HFOAYJsBgGns3qZNGzRp0gRbt26t1rCehalTp%2BLLL79EQUEBnJyc8M0332DlypVIT09HQUEBysvLodVqMXLkSGzfvh35%2BfkICQlBu3btsG7duie2rVarRWPVarVo0qQJfvrpJ7KOxQm0s7NDUlISWrduLSm5FEqJQ0JCcPHiRebxmjRpksRKojqwjNebNmyIZkFBv66gjXHpZUDqCkw55bIMySUexix3XdqBmuGoLNmNcnNmDVeyTq8HrD6SAJCaBDNMlyUTYzhSS3yO6W1oV2Yb%2B5YcCpYb9u3bQIMG1Q%2BG1TfdDsuEmd6GcfwkQ6bmxDJep73PWWbT9KFhmVjTht6sadLXDX08Wf7U9HXDMkinjcKZZtPUdUO3w2rXJvdzaoCsS1YycfoaZt2HNphsS64T1iToE8o4wRIzdvqAsu5VeqKsOVD7MY8xPR76GLN2oo4501T90SPAqrqDZc5OT5OeAus5Rt8vLCN7eh1rG/p02tIOfShYl8TTtMva5q9ivP7aa79/m1ZMm3885IBPxt8atLl7eXk5tmzZghUrVmDfvn1wc3PDkCFD4OPjgxkzZsDf3x/Z2dlYt24dYmNjsXXrVgQEBGD69OmorKzE8uXLRe3T5u7WAabJZILJZCJBgEKhIAGYWq3GgAEDMH/%2BfNy6dQuTJ09GaGgoPvroIwAWu4SGDRti9%2B7dov46d%2B6Mhw8fkoDPaDTCyckJM2fOxIABA5CdnY1Vq1YhNjYWtWvXJlYGgCUTxzIKZ8HDwwMajUZ07AIDA6FQKDBixAiiNmqr6TkLAsfN%2BhGjUqlEgjV%2Bfn5wd3dHUlISOnXqhIyMDDx48ID45Z0%2BfRq9e/fGZ599hh49ehCOJQt08KZUKiVBp6Ojo0gZlB6vsP3FixfRpUsXiQ2IUqmEm5sbcnNzme0DwJQpU/DWW2/VcHQsYIm2TJwwAeOolxEZMmTIkCHjfxlywPdskEs6ZfyjYG9vj1GjRsHT0xOnT5%2BWcOo4joOPjw/mzp2LYcOG2aQSSWPWrFlITk7G9evXkZ6ejrS0NKSkpCA5ORkqlQqdOnVCcnIyFixYAI7j0KRJE7z55ps4evToE9vt1q0b4fAJUCqViI6Oxrx583D//n0AFpVInueRl5cHhUIBZ2dnkaUBAFKCaQ2O4%2BDi4gKFQoFHjx7h4cOHWLRoESlxNRgM8PX1RaNGjQBYgj1HR0ccOXIEMTExOHr0KNq1aweO41CnTh24urqS7dLT01G/fn1MmTIFtWrVIplT%2BnuS0WgUrcvJySEB55kzZ/DKK68AACIiIggncOLEiQAsWUSO40i/I0eOhIeHB1nu0KEDunTpAq1WC61WS/avXbs2HB0dMWfOHGzfvl00Ho1GA41GQwJRAPD19YWzszMCAgJEmc0WLVpg0qRJ8Pb2xuHDh8n5EQJ8wBLQCn6JtoAl2iJ/gZMhQ4YMGTLEkDl8zwY54JPxj8STOHUCaHP1pwHNCzQajYiPj0dgYCCCgoIwa9YsAJYyyJKSkmoN4gUIwiCHDh2Cr68v2rVrh71798LNzQ1Hjx7FwIEDUVZWht27d%2BOnn34imbpHjx6hV69eJDihAysh8Bw8eDA4joO7uzvGjh1LuH8nT54EYBGNEcpNzWYzysrK0Lt3b8TGxqJ37964cOECeJ7HgwcPSJmo2WxGUFAQ7ty5g08//RQlJSXgeR52dnbQ0rUhEPP9lEqlyCZhzZo1AID4%2BHjkV3kaTZkyBZcuXQJg4foJ/W7cuBGPHj0iWbiLFy%2BiTp060Ov1cHNzIxw%2Bwf9wxYoVxLIBsHANnZycoFAoYGdnB51OB09PTxIoXr58WVTue/36dSxbtgytWrXC7t27SYbXWk3VaDTWWGpqDZnDJ0OGDBkyZNQMOeB7NsgBn4x/FEpLS7Fhwwbk5%2Bfjueeew71792o0Vxdw6NAhUfAWFBQksmNgYfTo0SRoSk5Oho%2BPDzw8PNC4cWNs3boVH374IeLj47F48WIolUqREMz169dFfWVmZhJlSgDIzMzEiy%2B%2BCE9PT5SVlSEhIQFFRUV444030KxZM6LkWbduXdSvXx/9%2BvUDx3Ho3r07nJ2dMXz4cNIWz/O4efMmmU/dunWJaTsA3KtynfXw8MCCBQvIfkLAw3EcPvjgA%2Bh0OrKdYJUAWDKKSqUS69evR9%2B%2BfaFUKlFRUSEKsARYBzTl5eVYsWIFWRbKLd3c3NCgivtkZ2eH4OBgLF68mGQXhSDNmiv5xRdfYNeuXQAAT09PfP/99wAsNglarRZ16tQR2V9kZWWhpKSEBOMKhQLFxcUk0/fuu%2B/CwcGBiLDUqlULWq0WgwcPxunTp4lgjIuLiyibWlNQbw2m8XqXLqJtaKNrifE1IDExvnNH/DNTOJTah1ktS/E1JYbVgMTVmPZ4t6JMEtAmxzZ4s7MN0qmVVUnwJ%2B4n8RBm8WnpdSxTY6rcl3Ve6N0kfsq0MTJj1fXrNXYt8W0GIDmA9FhYxuunT1Mr6JMJ6fFkuZDQ65KSpNvQ85SQSxgnPC2NWsHIkNO7scy66Y3ofZj0Y6vyeYB9rdEFIzb4t0uvCcZO9P3LuhxpA3eWB7ikbUZDkv0oL1XmfUgdMNb4JPcUoyHJ6aS2YZ1Leh3L15w6dcwb5vhx8fLt2%2BJlimoNALh8mVpBnwTGKoZ/u2TMrG3oa8CGwylZx9qG7pvFeGCeTxl/O8gcPhl/a9CiLdbm6q1atUJwcDAWLFiA/v37P7Gd38Lhs%2Ba9scaTnZ0tsgXgOA4BAQFYtWoV8Qtkcfi6deuGoqIiFBcX4/jx44iKihJx6ASO2tixY/Huu%2B8CALp06QKj0YhH1NvTb%2BHeCQI2RqMR3bt3R3x8vChQs%2BbGubm5VWs7wBJAocfRp08fpKenIy8vDwUFBYR3aL2dcC4dHBwkHDprCBw6f39/ZGRkkP4DAgJw48YNMhZ3d3eSLfTx8SHnx3p8arWa2EUIoi2ZmZkSA3UhwM7Pz0cR43/uABAZGYn1Nrq5ysbrMmTIkCFDRs2owbnrqbBly%2B/f5l8VcoZPxt8eAqeONlcHgPr160tUEJ8FtvgFzp49G6mpqUhPT8eGDRug1Wrx2Weficzh3dzcMGjQIMm%2BSqUSKpWK2AcIaptr1qyBSqWCQqHA%2BvXrce/ePZSUlMDR0REKhQKRkZFIS0uDl5cXwsLCYGdnh4ULF5J2re0LrCEoigqll6GhobC3t4e7uzvs7Ozg4OAg2k8IcrRarShbKYzVGlqtFv7%2B/qJ1hw8fRkZGBgkahX0aN25Mxtm5c2f069eP%2BPcFBgZi/vz5xGZDp9NBqVTCw8MDHMfhTlU665VXXiF2FQDQvn17AMCCBQuQnp6O5s2bo1evXqJxms1m%2BPr6gud5bKl68ldUVKC0tBSXLl2Ck5MTOTbe3t5ISUnB4cOHifqnTqeDt7e3yDKCPi5Pgmy8LkOGDBkyZMj4oyEHfDL%2B0ejVqxe2b9/OzBRNnTr1N5XfPQ06deqEqKgoYmReEwoLCyXcu6%2B%2B%2Bopk%2Bzw9PWFvb4/JkydjwoQJKCkpQXh4OJycnEiJZ58%2BfTBs2DBiQSFAKDv09PQkbQsqmvqqsp1///vfIuVJV1dXUYZO%2BLePjw/xDFQqlcSjsHbt2khPT4dOp4NKpcIvViVhoaGh6NmzJ1avXg1vb2%2ByXqFQ4ObNmwAAFxcXJCQkYO/evWQcX331FVQqFSorK%2BHg4IDnnnuOjMGaPyfMB7AEkj5VOvCCZ6CzszPKy8tFZaA9evRAZmYmgoODiRCLMKf09HSUlpaSbGd2djaCgoIwatQoEvDl5eWhtLRUxBHd8r/0yVCGDBkyZMj4EyBz%2BJ4Nqpo3kSHj74tRo0bh0KFDGD58OBYuXIgWLVogJycHa9asQUJCAlGA/CPxwQcfIDo6Gtu2bcPQoUOfuK2rqysmTZqEYcOGoVmzZjCbzRg5ciSUSiWMRiOyqwhIycnJqFOnDgYNGoT79%2B/DaDRi6tSpePXVV0kGMjExkbTbuXNnvPHGG3j11Vdx7NgxREdHY/To0Vi7di3GjRuHYcOGIS0tDQMGDBDxCKsruywsLMS///1vAJYgcNWqVWSfNWvWEPVTa4GWq1evEiGY8PBw7Nmzh2TqBOGWwYMH4/79%2B8jKysKVK1cAAO3atSNtGAwGwtMTfhfG5OHhgYKCAuJdKHD4vvnmG9y7dw/nz5/H%2BfPnRcdbUE69fPkyLlsRMsLCwpCcnAw7Ozt89dVXKCsrQ7169cj2oaGhMBqN8PPzQ3l5OQmYAUv21lbIoi0yZMiQIUNGzZD/1/hskDN8Mv7RcHBwwHfffYfw8HBMmDABrdng0IcAACAASURBVFq1wpAhQ2A0GrFjxw5RmeUfBZ1Oh/feew9LliwR%2BeYtWLBAItpSWFiI2NhYdOvWDTzPE9l/2u%2Btfv36ePjwIZKTk3Ho0CEcOHAA%2B/btw9mzZ3Hjxg0AwJ49e8j2Z8%2BexauvvgoAaNu2LTIzM5GcnIwBAwaQAKpZs2bEjF5AeXk56ds682gymfD666%2BT9RcvXgRgyXhZi7BYQyiNjI%2BPJ2WXZrMZLi4uJOsm9GVdgspxHMmgqVQqst663BIAPvvsM8KdtOYgmkwmHDt2DIBFsbNZs2bM8Vm3k5ycjO3bt2PVqlWk7PLu3btEeVUoL83KyoJSqRQZvD%2BJd0iDKdrSs6doG1qUgCVcQCePJeR8hsJEjfswtmEKYFCMfnobpgDGt9/W2DfdOasdWriApb9Ct2PL8bRF7IBWuWG1Q4sv2NK3ZJ4szixdCsyYuKRtahIs0RZasYNVcSwRcKh63jyxb8bJo28TegqsvunzwLzVqM5Z564mQSBbrsenvW7o/ejjyRLIsElUhjpgtvTNvF%2Boxul9mIUqNhwbekCsOUjGTLVrizAJa6OajjnAuJasPmICYKtaUfcL87zQKiisi5Z%2BULC2odfZcuHQzw4W75xex9qGIS4l4%2B8HWbRFhoynBC0Yo9FoEBAQgEmTJpGsVFFREdauXYsjR47g0aNHqFWrFtq0aYNx48ahadOmWLNmDdauXQsAJEtkbegtcOwELl9lZSV8fHwQEhKCgwcPArAEUjzPIzg4GBcvXoSdnR22b9%2BOwsJCEuS1atUKSVVSec899xzi4%2BOhVCrxxhtvIDY2Fjt37sSePXuwZMkSkk1UKpWIiIhAfHw8c/7WgicG6n%2ByCoUCSqUSkZGROF4lfxYZGUnULZ2cnFBcXAy1Wo2goCCS/fP19cXkyZPxf//3f6Tdjh074scff2SanFtj/vz5mD17tuQYWsPR0RE8z1fDnbOgSZMm2L9/P4YPH07KQQVoNBp89dVXWLRoEa6z5BNh8esTsos1QRZtkSFDhgwZMmrGiy/%2B/m1Wfe/%2Bn4Cc4ZMh4xlgLRhz5swZdO/eHW%2B99RYRVRk2bBhu3ryJdevWISkpCTt27IC7uzuGDBmC9PR0kReeYHlw5coV%2BPr6wtXVFXPmzEFycjJ%2B%2BuknXLt2DX5%2Bfqhfvz7OnDkDwJLBjIqKgpeXF%2BrWrQvAooRpbSgfGBhIMlKAxV7AbDbDYDBg3bp1yMrKQteuXXHq1CnodDoSKAnedAqFAmq1mmThBLGUN954AwBEGUEhE2dnZ4eGDRvixIkT5LfTVZrvKpWKlI0aDAZRKWVmZiamTp1KfgMsHDxhTG3atCH9CKIsAurUqUP%2BTQd7Wq0WHMfBYDCIMpUBAQHQaDTo2rUrKcUUymYF7z8aYWFhRKRF4E3Wq1eP%2BPcZjUbmfizIoi0yZMiQIUNGzZA5fM8GOeCTIcNGtG3bVlKCOX/%2BfLKcn5%2BPUaNGwdPTE6dPn8b69etRUlKCNWvWoFGjRuA4Dj4%2BPpg7dy6GDRsmCsoAYNmyZTWOged5REdHw9PTE%2B3atYNWq8WIESMwffp0TJgwAYDFGzA2NhapqalIS0tDWlqaKDgSFEBDQkLQoUMHbN68GTzPY/bs2SLOXWlpKWJjY0lwKAQyXbt2hYODA7Zu3QqVSiXirwn//uyzzzB//nwRH01Qrxw8eDCaNGlCyjEFLzvrOVrDun0hONTr9RI%2B3ltvvVXtcausrATP88TYXkB6ejr0ej1OnDhBlEOLi4sxatQoTJs2DSNHjiQ8Ozc3NyQnJwP4tWyT53lwHIfs7GwSZFqLwtQEmcMnQ4YMGTJkyPijIQd8MmTYiIsXL4pM1n19fTF79mzRMmDJLimVShw9ehSDBg0SKTgKmDZtGjp27Ghz3yxD%2BYEDB0Kn0%2BGjjz4iipUAsHLlSiQkJCAqKgocxyEyMlJUlrly5UoAwLVr13DmzBm8/vrrMJlM6NevH3JycqpVEx0yZAgAYPv27ejYsSOKioqqzWb98ssvWLp0qWidkO3bvHkznJycRNxAa28/66BTyB6ygijr7YBfA9kn4fHjx8TuAbAEn/v378fIkSNhZ2cHwFIOGhoaCnd3d3z99dckC1dQUICgoCD06tVLNC6DwSAKSllBXHVgcfi6dRNz%2BGwhqkmqV2kehi37MHgjtvBjJBQzmkvCcmunXcpZzuHUAJm8KhsM0iVJVOr6ZhkN032zqoklPCXKoBqQjpm%2BtRi%2B5hLzbhZ/hp6TLdQceg5MDh91DbAeBZJ1VQq7ItATk7iLQ3rg6WUGEUxyrlgXBTVxFp%2BMvmbpY8Nqlt6H1S59OT4N35b5%2BKXvD5YzN90546Kgbw8m1466uGzh5EqOF%2BNZYgtnmF5XE9eS1RWTlyjhr9a8jYSzR3P6ID0WTE4pvRHrBFN9M6%2BBmraxod2ndmdnPnz/fMgZvmeDzOGTIeMpQZuwl5aWYuvWrVi1ahUOHDiAnj17YvHixejTp49k34yMDKxevRoJCQkoLS1F7dq10bJlSxw5cgTXrl1D69atSeCoVCqJoXxQUBAOHjyI3NxcLFiwAFu2bIFer0dBQQEePXoEvV6P1q1bY/ny5cSWoLCwEIMHDyZ%2BdTQ4jiO8PQBMTp6wHW0ZwfM86tSpQ1Q2hXU6nQ4NGzYkHDitVovIyEiiclldH6x%2BaLi6usLe3h7NmzdHXFwcAIvVhMFgqNYUXoDAh6yJD/jNN99gypQpyM3NFY1Fo9Hg0KFDGDNmDBHHodGtWzfCy6wJLA7fxAkTMG78eJv2lyFDhgwZMv4XMGDA79%2BmlbbdPx5yhk%2BGjGeAtdJmly5dcOrUKWzcuBE%2BPj5MdU0ASE1NxUsvvQRvb2/s3bsXly9fxurVq3G36otiRdUnyj59%2BuD69esiQ3mBpycYyqtUKnTo0AFxcXFExGXgwIEk2AOAyZMnk%2BwjAGL4PnToUEycOBF2dnZo2rQpxo4dC8DCUQMsHoYeHh5kP0EhUxCSsbe3h0ajERmNDx8%2BHACQm5sryso5OzujRYsWAIADBw5g/PjxJEumVCpJSefs2bNFmcHmzZsDAOE3%2Bvn5wWAwICsrS8QP1Ol0%2BOCDDwBYMn/p6emkfzc3N7L/iBEjRBk4tVoNtVotGoO9vT1OnTqF5cuXS86dMEahDUFFFQDhSf6Wb2gsDp/8BU6GDBkyZMgQQ87wPRtkHz4ZMp4Bs2bNIhk%2BGkJQRuOjjz5Cp06diDgJYAlsJk%2BejLfffhuPqBIuazVQk8kEs9kMb29vbN68GQEBATh58iQ2bNhAREPmzp2LDz/8EEqlEt27d8e5c%2Bfw8ssv49y5cwB%2BtWv4/vvv0aJFCygUChQUFODkyZMAQII8IfsooFmzZkhOToZarYZer4fRaES9evWIoburqyteeOEFbNq0CQDw8OFDouSZm5uLTKsyvo0bN5LAyGQyEZP4%2BfPnk22EcknAkqUEgHv37gEQC8UAlkBQEFlRq9Xo27cvyVgWFRWhYcOGyM/Pl1gmmM1mclyFMVRUVKBr164ICwvD%2BPHjsW3bNjx8%2BBAuLi6EN/j222%2BT/V1cXMDzPIqqSqhqyh7KkCFDhgwZMn4b/tcCtN8bcoZPhow/CL169cL27dtFQUZeXh4uX76M4uJibNy4UbS9oPzI8gYU1EBnz54Nb29vDB06FKWlpUhJSUFlZSV69%2B5NAi1HR0ccPnwYycnJGDNmDABx1unw4cMAgKlTp6JWrVooLS1FVlYWsRkQAsKioiJR8CIIlljz9u7evYuvvvoKADBnzhySZQOAn3/%2BWSRAsnPnTgCWzKVQeuno6Fjt8TMajWjcuLGkTwHWczp58iS2bt0KwCLQcvv2bRIUms1mZGRkgOd53L17F%2B7u7qI2atWqhcaNG5OxKBQKtG7dGt988w1WrlyJh1UctMePHxMfvqZNm5L9CwsL8fjxYzIeIZtoC2TRFhkyZMiQIUPGHw054JMh4w/CqFGjoNPpMHz4cKSkpIDneVy9ehWARR3SuhSShYMHD0rUQBcsWACFQoExY8agbt26UKvVePDgAQ4dOoTxVbyvt99%2BmwSN/v7%2BaNCgATFGB4A1a9ZAqVTC398f69atg5eXFxwcHERG58J/hbJI64zaokWLmCIq7733Hn755ReS8asO1oFaTQInR48ehaurqySjR5dNVlZWioJHk8lEthHM2Hmex%2BPHjwnfELAEV3l5ebh16xbJZgrm89bHzBoPHjwgAi8sWJvB1wSWaEu7dmLRFrrqkyUyUqPoBIPbSFee2iIUwNJWqcngm6miQI3HFhNrW9QiWN7DqMoKC6CPZ06OdBd6nsx2qWwxSwiiJs9llpaJpC8G95bWvGEeY%2BqE0u0yRVuqPhpV2w8gFQyp%2BoAkAi00k5oq3Ya%2BJqnzxJo3vY6lXSJZyeAKS/RM6Iufca3Zck3UeC8wVtkiBiO5NxntSsZjw/3CFDihLlL6WDGp15RgEeu80PcHq2%2B6bcl5YggjSe67Kl63NehjzBJLQkKCeNkWU3VayIV1cOgLh6WwRB8w1sGhTzC9jQ2iUTaNj/GQ/6sUrcglnc8GWbRFhoynBC3awkJRURFWr16No0ePIjc3F46OjsjPz8eOHTsQHBws2jYxMRGvvvoqrl27hujoaNK2dT9btmzB%2BvXrERcXh6ioKOJpV1BQAKVSCYPBQIRe3nnnHYwdOxZ37tzBxIkTkZ6eDp7n0alTJ9y4cQNLly5FeHg4unbtiqKiIlRWVsJgMMDf3x8ZGRkSMReWmIqrqysWLlyIcePGQaVSiQIta%2Bh0OpENhSDaUrt2beTl5cHBwQFlZWVQqVSSbF737t2RlpaG%2B/fvi9Y3b94cqamp0Gq1qKysJOWjT4JOp0N%2Bfj7ZjjUnBwcHhIaG4vz58yL1TQFC6azJZGL22aZNG3z33XdPHIcA2XhdhgwZMmTIqBk2CHH/ZlRJH/xPQM7wyZDxlIiLi3tisAdYTM5nzJiBuLg4XLt2DQcOHAAAwveyRnh4ONLT02vMEJnNZmzYsAG5ubm4c%2BcOEQuhM3Rr165FSkoK4RIKJaNr1qxBfHw8WrZsidWrV6OwsBADBw5E06ZN0alTJ9SvXx%2BApSQ1JSUF6enpSE9PR1paGurXr485c%2BaQbR4/foxJkyZBo9FAoVCIgqdatWqR8RQWFsLZ2RmOjo5QKBSEm5eXlwdALF7Ss2dPqNVq1KtXD4ClXFPgMAIWjqFCoUBGRgYAS3ZPOC4CduzYgZSUFLL8ySefALBYKzRo0ICs79KlCwCLPYMg2vLFF1%2BgcePG0Gq12LFjBzF0V6vV2LZtG5KSkkh2lud5fP3113jvvfeeeM6qg2y8LkOGDBkyZNQMOcP3bJADPhn/aMTExODf//63aF1KSgoCAgJw5MgR0fpvvvkGnTp1As/zmD59OiZPniz6PSMjA1OmTEFERARatWqFbt26YcGCBURQxBa4ubmhXbt2kqwOAJSXlyMmJoaIj1hDUAP96KOPkJWVhVOnTpEgBgB69%2B5NOHZr164l3oAtW7Yk2wjBhWAg36ZNG6xcuRKVlZXYtGkT0tLScPbsWSLe8tNPP0nGIVhFZGdnAwB8fX1Ru3ZtcByHWrVqwdHRkWTAateuTYK%2B/v37o0%2BfPigtLSWBmUKhEPHpAAtX78iRIzAYDES11Gg0wmAwkP0ePXoEnueh1%2Bvh5ORE1D%2BBXwVn5s2bR8pnOY7D9OnTyfiLi4tJUCwofW7fvp2ItixZsgTTpk1DSUkJhg4dSkpADQYDhg4dimPHjpEsJM/zGDlyJJYtW0babNiwoeS4VQeZwydDhgwZMmTUjL9bwFdYWIhJkyYhIiICnTp1wsyZM4kKO41Zs2YRxXfhr0WLFpgxYwYAYPr06WjRooXo97Zt2/6m8cgBn4x/NB4%2BfIjvv/9etG737t0AgG3btonWb9%2B%2BnWTeLl26hCtXrpDfUlNTERMTg9TUVPJCbjAYcOTIEQwePLjam5iFmTNn4urVq3jvvfeQnZ0Ns9mM1NRUvPHGG7CzsxOVem7YsAGZmZngeZ5w0QDgypUrmDx5MnQ6HdPYPSgoCGfPniXL8%2BfPJxmsixcvEq%2B/Ll26oFmzZggNDcXVq1cxfPhwNGrUCADw%2Buuvk/2tjd87d%2B5MsmpZWVl47bXXcO7cOcTHx0On0xGhF0dHRxQVFYHneezZs4eItggQrB0AkGwex3HE%2BkFYFmD978WLFyMlJQXfffcd0tLSyHpB4TQlJQWvvPIKAEtQZp01zc3NRbNmzao7PUhNTcWDBw%2BgVCol5Z48z%2BODDz4gGUqFQkECXGFb2hD%2BSWBx%2BHr0EHP4avKnBmwwAKbKYQFIOB9P1S4AUEq09K3A5PxQK1l922L4TCfKmZwuik9GU2FY45PwqFjMB4oIxErW0uORzJNBBKPpbxSVyAKalMQaH9U2PU8Wh88W7qdkyIwDSJvHs64byW7UHGw6L6znLrUR69DUaODO6pw6ESwOny3N1HRNsChetnCxbOEC0rux7il6P3o8TD4XtZJ1bCTrGCeG3sYW3qTkEmBsRF9/T3NsmM8%2BeieGObtkmqwDSK%2Bzxd3elovNloedLe3QN7QMmzB79myUl5dj//792LVrF37%2B%2BWeR7ZQ1FixYQD7UJycn48qVK/D390fv3r3JNu%2B8845om%2Bp0BqqDzOGT8Y9CTEwM2rdvj2nTpgEApk2bRlQnV65ciZ49e6JHjx7khb5WrVqIj4/H%2B%2B%2B/j71796Jt27b49ttv0aNHD5SXlyM8PBwJCQnIz88Hz/No2bIlli5divr16%2BPGjRuYP38%2Bbty4gS1btqBJkyaisSQmJmLUqFFYtWoVFixYgEePHiEyMhL/%2Bte/kJ2djZUrV%2BLUqVMor3oou7q6YtasWejbty%2B6desGnU4HFxcXnDt3Dm5ubggMDCQZqfbt22PhwoWIiopC/fr10bJlS8yePRsdOnQg/QvBCMdx0Ov1hCcHWHhogpCJo6Mj1qxZg/bt2%2BP8%2BfMYMWKEaB4KhQKOjo7Q6XTQarW4d%2B8eSktLSfsqlQpKpZJwAAU4OzvDy8uLaU2hVqvxxhtvIDExEcnJyYiOjsbevXvJ2ISg0dqg3frf165dg1arxQ8//EDKKb28vJCTkwM3Nzfs2bMHvXv3Ru3atYmVA/ArZ8/d3R35zDdpCxo2bIiSkhKJRYYAmpNojQYNGhAl1JrANF4fPx7jJkywaX8ZMmTIkCHjfwE16Nw9FY4f//3bBCwfljt37ozvv/%2BefGA%2Bffo03n33XZw/f75GcbsNGzYgMTER69atA2DJ8Pn6%2BmLCM7wbyBk%2BGf8odO7cmfjNAcCLL74IANBoNDh37hz0ej3u37%2BP6OhoVFZWIjQ0FBzHkYCuf//%2BAAC9Xo/c3Fx4e3vjP//5Dwn2HB0dMW7cOBgMBjRv3hzr1q1Djx49mLy71NRUGI1GrFu3jgRX586dw9KlS9GoUSO88sorqKyshJeXF%2Bzt7aFSqfD%2B%2B%2B8jPz8fcXFxKCgoQHx8PMnqCVlEIdMllFUKHLNp06bBy8uLqFVqNBrMnj2blHoCFnVQpVIJBwcHBAYGQqlUoqysDOPHj4fRaES7du2IAqVarYarqyu0Wi3at2%2BPnJwctGzZkgR7wreiiooKvP7661iyZIkoC9exY0fcvn2bLHMcBxcXF9StWxc6nY5k9Xiex5kzZ8h2PM9DoVBgzpw5OHToEFkvZB4BoFWrVggMDBRx50JDQ9GkSRMUFBQgKioKlZWVomDPug2z2Yz09HRmdhQAcnJyyIPWOmN34MABeHh4iKw2XnjhBfTs2ZNsd/v2bRLE1wSm8TqlSCpDhgwZMmTI%2BPsgNTUVSqUSAQEBZF3Lli1RVlZG9AeqQ1FRET7//HORVzMA/Pjjj3jhhRfQunVrvPTSS0zazZMgB3wy/lE4fPgwUlNTSfamTZs2ACwB3LFjx/Djjz%2BC53m88847ACyCIDzP4/LlywAsgiGARdxDrVYjOzubCLNkZWXB398fLVq0IN5sTk5O%2BPjjj1GvXj0kJiaiZcuWOHHiBKKiorBkyRIAltLF2NhYHDt2DACwZcsWtG7dGlOnTgXHcVi/fj0uX76MunXrwmg0YsSIEejbty/hqpnNZjg5OZEvO23btsV//vMfUn56/vx5HDhwAPHx8cjJySHKkhUVFViyZAm6detGloODg2EymaDX6zFr1iwkJCTAzs4OxcXFCAsLQ0REBJRKJQn2SktLUV5ejvj4eLi6upIsnCAUYzQawfM8Vq9ejcDAQFGp5JkzZ0SKm4I5%2Bf3798n5MRqNMBqNomybEBwvXryYlDPY29vjxo0bxDJBoVAgMjISCoWC/On1ekRGRhLBF5VKBY7joNFo4O3tDcASyAFAcXExUlNTybESgjWBc6hUKrF//34yHgEDBgyASqUSzTM2NhZHjhyB2WwmAS9LlEeGDBkyZMiQ8XT4Izh8Dx8%2BREpKiuhPeL97FhQWFsLJyUn0EVyg1RQwSuWt8e233yIsLExUNebn54f69evjiy%2B%2BQHx8PNq2bYtRo0bV2JY15IBPxj8KxVUECSHLZ/2ynpeXhy1btsDR0ZEIdAgv/EKmxdXVFXl5eaisrIRer0dqaqrIfPzEiRNITk6Gp6cns3%2Bj0YjY2Fjs3r2bmJ67ubnBxcUFd%2B/eRXl5OQkwx44dC57nMXr0aAQHBxORkcDAQOzbt48IkHTs2FGU6RLw5ZdfArAoUArB5ejRo8lXnyFDhojKBV966SVcuHABHMfBZDIhMDAQLi4uRDhl1KhR2LlzJ7FXKCwsJFk8juMQGxuLt956CwDQo0cPeHl5wc3NDYAly1hcXEz4dB4eHli0aJGIi%2Bft7Y19%2B/ahXbt2pM%2BIiAjyuzUEURahhHPKlCkwm80kGDSbzTh%2B/DhZFuDs7AyDwYCePXsiLi4O77//PvR6PRHWEa4PnucxePBgsp9SqYRWqyWBWmVlJb755hvmOfbx8UH79u1J0EuPW2jPFsiiLTJkyJAhQ0bN%2BCMCvm3btiEmJkb0R%2Bs7VIc9e/YgICCA%2BSdoL/xWmEwmbN68Ga%2B%2B%2Bqpo/bhx47Bo0SJ4eXnByckJU6dOhUajIYkEW/CXC/jWrFmD4cOH/%2BH93L9/HwEBAfj555//kPZHjBhRLTnzWcFSkPxvIzExEQEBAUTM478FIUCIqzJfvXTpEgkmeJ7H%2BfPnERwcTEoIeZ5HWVkZeJ4n29FlgELQImSz8vLyJNsAlhQ%2BAISEhCAmJgaff/45AAvfDLBkG0NDQwFYyg%2BXLl0Kk8lESjWFh8OFCxfQr18/wh87e/YsXnvtNdHDo6CggKh5Tps2DR999BEAoHXr1mQbb29vBAUFkdLPEydOICwsDDzPw2g0IikpCYCFRwZY7AgGDhyIx48fk6BD%2BK9arcaQIUNImWNxcTHGjh1LMnOZmZkYOXIkGWNJSQnmzJlDBGiEIG3gwIEoLi5GSEgIoqKi8PLLL0OhUCA8PFx6Mq3g4uIiMjsX%2BnF2diYloB9%2B%2BCG8vLzg4uKCtLQ0DBgwgCi0GigiekhICMnmCr8bjUa4urqS9lkKWGazGVlZWXB3d0f37t2rHa8QCNcElmhLixZi0RZbOPU1Cl4wSkxrNGtnrGNqE9XQEHO8VCkra5unGR%2BzrxrES1iPLHods11KbeNpxses/KV5o6wvuDT/lPFFWvKIoq4RpvE61Rera1uEXejzy5onvRudFGdSbKkLkPEYlqprMIykJSbV9GBsmLhNojI2XNhPI6TCeo%2B05V6oSSiHtY0tuh/0SpZ4kmQ/xsNEMneJaJa0XUlVPKO6wpZ7XnLf0Wo6rBNDXVvM93tayIUh7II1a8TLGzZIt6n6fy/BF188eRkA6MCFFcjQfdPLQDUPqn8GhgwZgt27d4v%2BhgwZYtO%2BAwYMILZV9F9QUBBKSkpECQPhw3Pt2rWrbfPChQvQ6/U1KnAqlUr4%2BPj8pmzkMwV83bp1Q2RkpISHkpiYSMrIbMHXX39NSr/Gjh2Lb7/99lmG9bvAYDBgxYoV6NWrF0JCQtC6dWuMGDHiN6vi/NnYvXs3Onbs%2BN8eRo1YuXIlmjVrRuRlg4OD0atXL3z%2B%2BeeiG%2BS34rnnngNgIccCQHx8PAkOeJ5HSUkJnn/%2BedFXEeFrDqscr2vXruR3nU4HPz8/UTbK2krhzp07AICDBw%2BKMk9CwJWYmEiun7KyMpJlfOONN5CUlEQCmuoUP4UAMD8/X2RCbs0xS0xMJP%2B%2BcOEC4uLiyPGk1SOzsrIAWL4cAZZrXnggeXl54eLFi%2BTBV15eLprTrVu3SPmjgJ07d5I%2BysvLUVhYiKtXr8LHxwfDhw9HUFAQzGYziouLcfr0afTs2RM6nQ7vvfeeSGmThRkzZhCepKDkCYgzdkIbarUan3/%2BOc6ePYtXXnmFCNQI%2BwKWY/zuu%2B%2BS9r28vKDVakVehhs3boSzs7OIXK1SqZCVlYVPPvkEV69ehZ2dneTB3LhxY5szfHv37sXUqVNFf2lpYhY5ze1mcr2pDKlkmyo1VGvQQ2S1S6%2Bzirltbog5XiqzydrmacbH7IuaO70Ny/aSXsdst4or%2ByzjY5wWoCqzT8D6eEDZmYBRceDnR62grhEHB8bEqb5YXUvmydqIOr%2BsedK7UY8TyRQBSC5AyRwBgM68V5VRiUBzd%2BnB2DBx1vmWXEs2XNj0JqxHB90ui%2BZry70gWcdoqKZrlHkvUCudnW3om/EwkcydGh9r3pIiCfpCYvTNtLql7zvq/maeGOraYtKvx49/8jIAjB0rXh49WrpNVXUNwdtvP3kZAOjAhRXI0H3Ty0A1D6o/H39Ehs/T0xMtW7YU/VVXwfVb0Lx5c9F7CQAkJyejVq1aT7RuOn78ONq3bw%2BVSkXW8TyPjz/%2BWNSWXq/H3bt34cd8CLLxzBk%2BvV6PNawvAjYiPz8f//rXv8hL6Z%2BV4RNewlmZGsCS9YiLi8OKFStw6dIlxMfHIyIi3EdQKQAAIABJREFUAqNGjap2H2ukpqbi/Pnzv%2BuYBdCWAX8FCNmt35LhCw4OJvKyV69exZIlS/Ddd99hA%2Bvrlo146aWXAFgCgZ9//hnHqySYrF/aIyIikJ6eDsDyYr%2BO%2BnImBBFKpRJTp05F48aNERgYiDt37kCn06Fjx45Qq9VITU3FyJEj8csvv8BoNBJ%2BGP0BhOd5FBcX47vvviN92tvbk%2BCouLgYGRkZJNCrLuAVTMrz8vJE6pFms5k8HA4dOkSCtgsXLpDAF5Cem/PnzyMtLY0EuPb29iToLCkpQWVlJcmE0mOq7quSEFhpNBoypgYNGmDmzJn417/%2BRUzPL1%2B%2BjMTERCiVSrz55pskUFWr1cyvZSkpKdixYwdR2bQOzBwdHREdHY3OnTsjJiZGZEfh5OQEk8kEhUKBFi1aoFWrVgAsZZl6q6/8gwYNwqlTp4gRu3C91K9fHyaTiSzXrVsXdnZ2SEpKwty5c1FRUUH4nwICAwOZx4YF2XhdhgwZMmTI%2BGfB3d0dvXr1wqeffor8/HxkZ2dj9erVeOmll8i70WuvvYYDBw6I9ktNTUXdunVF6ziOw/379/Hhhx8iJycHpaWlWLp0KdRq9RMrjWg8U8CXnZ0No9GIb7/9Fr9YeRKlpqaSgAqwRLUvv/wy2rZti4iICMydOxcGgwG5ubno2LEjeJ5HmzZtsHv3biIoIeDixYsYPHgwWrdujU6dOmH58uXkpXLlypV45513sH79enTs2BFhYWFYsGAB2Tc/Px8TJ05Ehw4dEBoaioCAAPz44482ze3MmTNwdXXFxIkT0aZNG6L%2BOGrUKKLsV15ejtmzZyM8PBwtW7bEgAEDRGM3m82YM2cOQkND0aFDB9GJzc7OxjvvvIPw8HC0adMGkydPFhl4W887ODgYzz//vE3cnkuXLpHAgIVu3bphy5YtZPn06dMiFSHBkHzYsGEICQlBv379cP36ddFx6d%2B/P4KCghAQEIBTp06J2r9y5Qr69OmDwMBAvPnmmyR4opGYmIikpCSS4QsJCcHUqVPRqFEjImm/e/du9O3bF4sXL0ZISAhycnJgNpuxevVq9OjRA8HBwRg4cCASEhJIux5WX%2BneeustohJpXTIo8N0Aywu9QHo1m82Ijo5GfHw8%2BX369On48MMP4evrC5PJhGPHjuHIkSOIjo5GTEwMkpKSoNPpSKkmYAkWYmNj0a9fP7IuPDycGL1rNBoYjUZyPtevX4%2B%2BffuSbYuKijBp0iQSEGo0GuTl5WHTpk0ALGUB1tmpKVOmYOfOnVAqlcjNzSVzNRgMItJvUFAQLly4QJZ37dqFAQMGkGWh1BOw%2BO6Fh4eTTKLZbEbjxo3JOMvKyvDZZ5%2BRfb28vESKUXq9npSXJiQkoHnz5ggLC0NWVhbh7bFgMBgktfDNmzeHyWTCvHnzyNcs63uB53kcPnxYdN4ECFlXk8mE69evE57kuXPnRJnctWvXIiwsDPv27RPtX1lZibZt25KSUJ1OB4PBgKSkJJIhpe9L1jiqg8zhkyFDhgwZMmrG3814/aOPPoKzszOioqLQv39/BAcHi%2BhY9%2B7dI5VeAh49egSdTidpa%2BHChWjQoAFiYmIQERGB1NRU/Oc//2G%2BQ1SH3xTwVVfC6eXlJXpxpDF58mS0b98eiYmJ2LlzJ/bv34/vvvsOOp0Ozz//PADLC1hMTIxov9zcXIwePRoDBgwgfhQ7d%2B4UBSyXL1%2BG0WjEiRMnsGLFCmzatIlwppYsWYLS0lIcP34cK1asAAB8%2BumnzDHSJZx37tzBjz/%2BiH79%2BokyfF999RUJ6pYtW4Zbt27h4MGDsLOzw507d7B69WrS5s8//4wePXrgxx9/xKBBgzBv3jxR6aqzszOOHz%2BOw4cP4%2BHDh5g7dy5z3m5ubvjll19E8xbwR5Rwfvnll1i4cCESEhLg6emJ5cuXA7AoHE6YMAFjxozBpEmTAADvvvuuKFA9cOAAtmzZgoMHD%2BKnn34iZtt0CefFixfBcRzGjRuHq1evkgxfUlKSKGB9%2BPAhtFotLly4AC8vL2zevBk7duzAqlWrcPHiRfTr1w9jx44l%2B0yaNImIaVhfp6OtSiROnjwJwMKzEo6dUCb45ptvkpd%2Bk8mECxcu4IcffsCAAQOwbNkycBxH1Bj9/PwQFhaGgoICUQmfg4ODpHzSy8vrice8ZcuWJGulVqvBcRyp81ar1fjuu%2B9EAaQ1R8zDw4N44tnb2xMzc2tTcACSr0YODg7kd2ueowB3d3eSkVOr1dBoNGjevDnZbtu2beRh4%2BzsLDKMB34NttRqNbRaLezs7GAwGH5TCQJgCbKKi4uRkpKCzMxMkViKvb09GQPLTL26Dw7WZaHW8xaCLYFbefPmTVGm/vLly4RUXV3beXl5Ntsy/CM4fDUM8B/L4aO4Yn8qh4/2gGT4RWZmipfp4/mHcvioibES2TS1jqZeMcXnqHatKtt/BX1f/jc5fHQ/wF%2BLw8fo7Pfg8NlkHm8Lh49awZq35B56Wg4ffU/RXFBW57a40j8Nh%2B%2Brr6Tb0By%2Bmjh9ALB1q3j5b87h%2B7sFfM7Ozli2bBmuXLmC8%2BfPY86cOSIrqLi4OKICL%2BDw4cOi91UBrq6u%2BPjjj3H27FkkJSVh06ZNIqsqW/CbAr7s7Gw8fPhQ9GV/4MCBuH//PiorK3H06FHmfosWLUJCQgLCw8MRExODkpISXLlyBbm5uTh48CAAixLh7t27kZiYiJs3bwIA9u/fD3d3d%2BzZswfh4eF466234OvrSzJliYmJKC0thVKpRNeuXTFx4kRotVoixDJp0iTY2dkhKioK46vqpq0zkfQYt23bJhJ3UCgUWLlyJbp27YoPP/wQfn5%2BmDdvHjQaDXiex/fffw87OztER0ejpKQEarUa7dq1I/t7enri6NGjaN%2B%2BPbZs2YLHjx8jPz8fqampSElJQV5eHqKiotCrVy8AwLFjx6DX6yXzfvjwIZRKpST1C9Sc0aORnZ0tyvKwfDyaNm2KmTNnokOHDrhx4wYp1zx48CDc3d3x%2BeefY9myZQCAoUOHijISbdu2xbBhwxAdHQ2O40jpZGJiIlOxaP369Vi/fj14npf8funSJTx%2B/JhYBuTk5GDHjh1o2LAhxo8fj7Zt22Lfvn1QqVQ4efIkcnJyRN5q1lL/ERER6Nq1KwCQl/EmTZpg1qxZmDx5Mul/3bp1JChv0KAB8vPzUVRUhAkTJmDKlCkkQIqJiSGqlMXFxaJz0KpVK7zwwgvE8B0Q8%2BcqKythNBpFwdjNmzfJ/NetW4coK4fR0tJSkVInx3GirzrTp0/HoEGDoNfrUa9ePdKuu7s7WrRoQbY7c%2BYMwsLCyLJGoxEFejR38PHjxyTDFxkZKfpYwvM8hgwZQoLqjIwM0XwBiMpOlUoleJ6Hs7OzRH3KVgjnxdrzUDBApwNsAdY%2BgNZwdXUVfUUTjr1QumkymSRf3gRwHAdXV9cnlh7basvwj%2BDw1TDAfyyHj%2BKK/akcPvoLML0PAF9f8TJ9PP9QDh81MdZHaJpaR1OvmLpHVLvUNywLaALZf5PDx/L5/Ctx%2BBid/R4cPgaN7uk4fNQK1rwl99DTcvjoe4rmgrI6p/tinbyn4fCNGiXdhubw1cTpA4ChQ8XLf3MOn4xnw28u6bS3txeVcPr6%2BpLSrY8//hgVFRXIysqCyWQiJZxvvvkm7ty5A4PBQMrnDh48iNOnT8O36v9KZ8%2BeJS/RFRUVaN26NZYvX44HDx6gf//%2BRAjmp59%2Bwk8//YSOHTuS7J7JZMKJEyewcOFCVFZWYt68eWjbti0GDhyI8%2BfPw2AwEP5SdV/l9%2B/fD4VCgbVr1%2BLSpUvo3LkzXFxcoFQq0bt3b1RUVGDmzJlYsGAB%2Bvbti/bt25MX/YMHD0KhUJC62tDQUJSUlODBgwckwxcZGQkA6NOnD4YOHQqO40QZvry8PGJ8Tc9bo9HAYDCQQPi3gC7h5HkeP/zwA1kWgnehhFM4F0KGT6VS4dGjRwgJCcGaNWvw4MEDjBkzhnDlNm/ejJdffpmoISYkJJAMX3FxsWjMDg4OhLPXpk0bIqKyfPlyhIaGYty4caSsMCQkhARmN27cgE6nQ48ePXDjxg1cu3aNZPg6deqEoqIizJ49G29XPfCEQM9a1bFdu3YkKyYEB0lJSejWrZsokLEOEAQDbYVCgb59%2B8LZ2ZlcRyaTCadOnSJG7eXl5eTaPn36tIhnBlgysfZWD02tVksCM7VaLQqaP/roI%2BLDJyA4OJh40tWrVw/z5s0jvwkedIAl0BDKbAsLCyUkX2tERUWR8yiMyZrrqFaryQeQtLQ09O7dmyjPCtlQATzPk2BS8L4T5mcwGNCgQQO0bNkSjx8/rlHyWKPRiP5q1aoFV1dXkgEvLy8Xmc8rFArMnDlTVM4rQDB3d3Jygq%2BvLwkW/f39RcTstWvX4vTp0/Dx8SHzEeDu7k6umUuXLqF169ZwdnYmwS5tKQFYsvS2QObwyZAhQ4YMGTXj75bh%2B6vhNwV8gnADz/OYOXMmWS%2BUfWm1WnzxxRekjA%2BwvOxVVFSgVatWOHDggES9zrocMDc3F%2Bnp6eRlyzrbptFokJGRAbPZjMrKSpKR4XkeHTp0gEajIRkQQU2woKAAGo0GR44cIeOtThDDYDCgqKgIY8aMQZs2bXD%2B/Hno9XqYTCYcPnwYdnZ2CAwMRFlZGSoqKsgLuuBjJniECS/MPM/DYDCQsQkS%2BtZeYsXFxaQ87e7du6KxWM9byGywysRu3779RK8POqNX3bYVFRXkN71eD39/fxQVFYm4mGVlZTCbzYiIiCDBiJ%2Bfn0i90cXFBbVq1YKfnx9cXV1RUlUWkZmZibKyMlLSKdgl%2BPn5wd7eHpcvX0ZQUBDKyspE6kQAcP36dXJMBBsFnU4HjUZDssrC8RTg5OQkyljxPE9e8IVzp1Ao8PjxY/A8D61WC61WSz5ACFlCnudhMpmwd%2B9eMhcAeP7556HRaEhGt7CwkPR/7949ZGdnk36USiVcXFxEqfzKykoyJ4PBgOXLl5PAwfqeEJCSkkKyRgaDQVS%2BWFFRQfp2cnIi15SbmxspwQRA/AUF9OvXTxQkWY8JADp16kS4iQ8fPhSdZ0DMP%2BN5Hjdu3CD/Fu4dwPJcGD58OG7cuAGTyUSOsa3Q6XQoLCzE4sWL4ePjI7lWvb29sXTpUlGALED44FBSUoLMzEwy/%2BzsbNFcxo4di8jISGRW1cEJWTzAcj6Ec9mmTRtcvnwZSqUSI0eORNu2bZklqtbcySdB5vDJkCFDhgwZMv5o/OYMX1lZGZo0aYJr166RFyYho1JcXCwyek5MTMTEiRMBWDh6ffv2JS8zHh4eiImJIc7zgEUen%2Bd5qFQqJCYmkpcmawNkIXNy8uRJ8rugRCgEW127dsWuXbtgNpuRl5eHXr16YeHChdXOacSIEeA4DhUVFXjw4AHKy8tRWVkJtVoNlUqFF198EeXl5URSPyoqCluraqOtS7cE764ff/wRKpUKPM/jvffew%2BjRo4nAw6effkrqc8%2BePQu9Xo9t27aRl%2BPt27cz510dbM0kWI8RgMR/8JNPPiGCNrm5uXj%2B%2Beexfft2cr4uXLiAOnXqAADJZAGWF%2BPPPvuMZNcE/iQAkRw%2Bq8SN53ncu3cP5eXlCA0NJYFpdHQ0Lly4IOKirVu3jvD%2BzGYz9u/fj5ycHPzyyy/gOA6RkZGiWmia13jixAl06tSJLDdt2hRXrlyBt7c3AEvgUFlZSUoYO3ToIMrIKRQKeHp6kg8W/v7%2BAH4NDK2zZyqVCh4eHpLMj3Xw1ahRI1Fp4qRJk8i5iYiIEBmvAxb%2BnZCtcnR0FAUrSqWSBA7WGTqdTicKghwcHES/m0wm0XGys7MTBS9xcXFEDVahUGDHjh3kPCuVSkRHR8PZqnzKukRSo9HAxcWFZB83b94MFxcXcBwn%2BiDEgl6vF/0JfoGVlZXIyckRHVeFQoH8/HxJ9tL6dxZq164tUhoVjqdwH3IcR7KwtEAMYDmXCoUCFy9exIMHD0RjqlWrlii4fxJYHL5mzf4ADh8jk/g0HDkm9%2BVpOHw28N9sGZ9NXLsaOHy2tMucN/VMexoOn01cIhafjP64wfj/QEYGtYIq2WZy%2BKgKGCaPjj4xrNJnqi%2BaDsVqm15mCgFTfTMFs%2BmDzCqvpp8X9DY2kND%2BTB8%2BSTOMdp/mfraFC0hT7ZjfzKnjyTrfks5s4BXTu7D6ljzaGO3a9AylL0C6Goy1Ez1R1gBt4cjRvD4WXYDm6FX5/Fb7OwDQ/6/dtevpxsesRPnzIWf4ng2qmjcRQ61W49atW9BqtSgtLRVlBPz8/KDX68lXdWv%2BkFKpxObNm/HCCy8AsPB7du3aRQKB27dvIz4%2BHhzHwWQyITw8nLx83bt3D0ajkZSRajQadO3alQQe9%2B7dw4oVK0jAcvjwYaL0yPM89u7di08//RSxsbEALJwoGgaDASqVCkajEc7OzigtLSXtt2jRAi%2B%2B%2BCKOHj0Knudx7NgxogxZUFBA5Pg5jsPZs2exdetWkpWrqKgQ8bsmTZpEfuN5HkFBQaKXxf79%2B5OgQpi38MLpSPvC2ACTyUTEM4QxChkY6/UzZ84kXD3AUuL6yiuvQKFQwGw2IywsjLzEnjx5kozp9u3bmDx5MslQCtmpwsJClJWViQJ6a9CZRo7jyLVUWVmJsLAw4uXo6%2BuL8ePHIycnh%2Bx3/PhxIkLC8zzi4%2BPJeQAs3FLhGgAspa3Wdh%2B3bt0SZcnq16%2BPunXrIiEhASaTCQ8ePJCM2TpAyMvLqzZT1aFDB8TFxYmCiOLiYowbN45co7du3SLbKxQKTJ8%2BHQsXLiTm8NbG6wBI5gmwfFgRMsZC%2B9algcI5yMjIEB0TOoN36NAhzJgxg/DgKioqRCWtkZGR%2BPnnn3H37l0oFAoMGTKEBMRKpZKU7QqwDoyEYA0A5s%2BfD4VCgeXLlz8xG10dhIDW0dERZWVlaNiwITKq3mYbN26MW7dugeM4FBQUSMo6hXuNhnVW1BrCvShkmZVKpagqQGgvICAAjo6OUCgUMJlMonl9/PHHNs9t7969oo9kADBhwkT07Pnrtfm7cPgYmcSn4cgxuS9Pw%2BGzgf9my/hs4trVwOGzpV3mvCn%2BztNw%2BGziErH4ZHT5MkPVreqb1K%2BgOFNMDh/Ff2Py6OgTw3rGU33RdChW2/Qy0wqL6pup/0QfZBahjP4gQ29jAwntz/ThkzTDaPdp7mdbuIA01Y5pMUodT9b5lnRmA6%2BY3oXVt%2BTRxmjXpmfo/7P33fFRVOv7z/ZNr5BACC0gBJJQAgQiXVSagqAgKIpXriICAuq1XFSu7YKACipW1GsBQVFQiIBK6C2BgKGForRAIKQSkt3Nlt8fy3mZc%2BaEjEG/96d3ns/Hj5ndmdNmZpl33vd5HvECFLmgsoPEicoGqIUjJ/L6ZD58Ikdv/Pirfw8ACsoGAGD48LqN7zcoQf6R%2BF8L0H5v/OYMH/OxYg9WzKcLAJ599lkcOXJE6sV26dIl3H///Vxws3jxYtp31KhROHHiBCfewd7Os4CDBU42m43bjxlM1yReMnjwYC64uekm/g36wYMH4Xa7ERoaivnz52Ps2LGIiYmhwGvixImkJmo0GlUZBvadz%2BdTlWMdPHiQ6xvgOT%2BiWqDFYqGHSjZv1qbSPuFqYIFkcnIyAL8wC%2BNbsTndcccdeOKJJ%2BgYZZkc4F8zZSkbaxcAvvjiC1K6jIqKgtvtpu9YAL99%2B/Ya%2BEk8mjRpgoCAAGRnZ1NwKMra//LLLxQ8sPHv3r2bk7c1GAxclmfKlCmUhWNjv1qwcfLkSSrvBPwBmXLuaWlpVOIHyM3R2XxPnTrFXT8AMHToUIwZM6bG/lesWEHXwblz57jyUQCcAmZBQQFnP2Kz2Whsly5dInGa4OBgjqcWHBzMZRUzMzORmppaY5Z43759xIOsqqriAm6x7Bbw%2B88p58zmM2vWLLzwwgtwOp1ciXJNEDl8gwcPhtVqJbXYX3/9lfo5cuQIzGYzxo0bh%2Buuu07VVk0qVuylFUNsbCzMZjPxPFn7nTt3JrVUwH%2B9p6enIy4uDhUVFfD5fPQ7xeYrWpVcDTqHT4cOHTp06NDxR%2BM3B3xhYWEYOXIk8vPzYTQakZWVRQ%2BniYmJVLZmtVqxe/duvPHGGwD8D4jff/89lYwlJSWRkTLg9wRjD5ENGzbEjh07ODGNrKwsevhMS0vD%2BvXrSaa%2BsLAQAQEBlL2wWCxITU2lY7du3cplY0pKSvDss88CuMJBM5lMKC0txeTJk/HWW2%2BhuLgYCQkJsFgsaN68ObXn9XrRtm1bfPHFFwgODiZuEcPRo0cRExNDPCoxAFy8eDF%2B%2BOEHeqBUBolGoxHNmjXjHkSVNg85OTnIyclBamoqIiIiMGDAAAomf/zxRwB%2BjlFkZCT69OnDydezMlK2xq%2B99hpmzZpF3xcUFKBt27a07Xa7iTPIglw2zl69epFB9blz51BaWkqlglVVVfj%2B%2B%2B8xdepUVFdX4/Tp05xRPRNtCb38BvXEiROoqqpCp06dqDTYZDIhKyuLgqrq6mqUlZUhOzsb3bp1o3Vl4zEYDFi3bp0qs6LMZmVmZnIBILM6YEG01WrFkSNHqM277rqL4z4WFBSgqqqK1u%2BDDz5Ap06diBsaGxtLZZU%2Bnw/Lly%2BnNQJ40/NevXrhwIEDNfZ99913IzMzkzK6ZrMZNpuN7iWAt1j46KOPyIcvPz%2BfMlIlJSVcVnLYsGHcnBgnU7nmyj4uXbpEpb%2BhoaHIzMzEHXfcAcB/npV%2Bhv3798fatWupb5PJRPcME74B/FnqZs2a4bdg69atcLlcePbZZ9GkSRPiVTKEhIRg4cKFxCFUgtnFGI1GGI1GKtNlL2kYvF4vWUew8bvdbvz888%2BYNGkS/U59%2BOGH2LZtG86dO4ejR4%2BqxmK321Vefjp06NChQ4eOa4Ne0nltqJPx%2BrRp02C32ylrsnv3bvqOKfUxfhDza3O73Rg4cCA9FB89ehSTJk2iwOf222/nVO%2BMRiMiIyO5dlkAMGfOHLzzzjv0Jt3tdhPXiB3LtgF/id2OHTsA%2BLMcHo%2BHStMY3G43PexZLBa4XC4cPnwY1dXVOHbsGJVw2u12eDwezJ49G5GRkbDZbGT5YDAYUK9ePRw/fpwecEWRmFtuuQV9%2B/blPmcPk48%2B%2BijatWvHBQfjFWn7nTt3on379sjPz8fRo0cxe/Zs4nVNmTKFyhCLi4uxdu1armRt4sSJOHHiBD3gT506FY899hh9HxERgUWLFtH22rVraV%2BLxYLt27fTWFasWEEZvuTkZGzZsoWCR7fbjQEDBuCmm26CyWTCkCFDEB8fT%2BeOmWmzTKDNZsOTTz6J7OxsOt8hISEc18xgMGDp0qUAeJVVtm7Mf06ZORJtBoKDg2l/o9GIsLAw3HPPPWQb4XA40KJFC9rnyy%2B/xObNm2n7xRdfxObNm2lckZGR2Lp1Kzp16kR9MD6byWSC0WjkMorK%2BezatQvt2rW7at/r1q2j/e12O%2BbMmUOKoABf4unxeKg/ZT9er5cLDEXBJMYzZWvZs2dPztA9ICCAjnc4HJgxYwa9RPD5fFi5ciXdM/n5%2BaQiyvpmnD4WFLndbpSXl6v8%2BgBw96PI4TOZTDh79ixOnjyJkydPci9EjEYjnE4nAgMD6V5Q4ptvvqHxeL1eeomxdu1a7j4rLCzEpUuX6B4yGAw4efIkLl68iBkzZtA63XHHHfD5fFi4cCFxhxnYNafVgw/QRVt06NChQ4cOHX886hTwBQcHY/r06bBYLGjZsiWXSWG8KI/Hg6qqKpVHG3tAdrlcXEZp3759VCbKLB0mT55Mb%2BZzcnIo4zFz5kwsWbKE47SxAMvn88HpdHL92mw2TuGQ7ffhhx8iISEBBoOBK1FjKqBKMOsJh8OBffv24aeffkJFRQWuu%2B46EjPx%2BXxU9ibj27GHY1FggvU1e/ZsLF26lMo8jUYjpk2bRvulp6djz549CAgIQKdOnfD4448Td666uhr9%2BvXDc889B7PZjMjISE5Q4%2BLFi5gyZQoFBG%2B88QYnslFeXs5x3G6%2B%2BWZ6uHe73UhJSSHvvWHDhhG/rrCwED169MA//vEPOg/ff/891qxZA4/Hg1WrVuHUqVM1qqO63W5ER0erREjYmNj63HrrrejWrRsJeCgDKrfbjfT0dMraAv5zKJb4stJFr9eL8%2BfPq0oLlV54q1at4rzwPvroI3Tv3p3mkZ%2Bfj/T0dBLoadSoEZUDGgwG3HrrrVzQ5na7KaCrqKjQ1DcLSCoqKtCrVy98%2B%2B23tD%2B75gBg3LhxGDp0KLxeL%2BrXr0/9REVFcfutXr2aC1DZNcbGV1VVxZ2H0tJSUmkdOXIkXn/9deK1%2Bnw%2BDB48mILqAwcOcP6cPp%2BPVHOZRYPH44HJZMKgQYMgQqkGWxO8Xi/MZjNnYt%2BkSRNUVFTUKJKi5KUqERsbS1lmNl4lPB6PVC2VoX79%2Bhw/sq7QYryuUi6QlSbXZlCsQSBBi1iEtCq6NkUELUoLGgygNRlJywYoCnTUJnCjsW8tahaqj4S%2BpaItogiKTDlFtOgRXdZlhwl9S0VbhOtE1rWWawsCT142T5FKL05bIrqrOhFSURnxvEg4%2B6p9RD6vzL6pNkUR2UcaxJK0XBNabqm6iMpo%2BCnRJowkHCR956VBtKU2lRbZvFXNyM63lvtOPOcCrUL626dF0UYUYJGZqov7yMzZFdVeAIBPP%2BW3ZSJ/imcG6TYAKF70AwA%2B/1y9j4TC8t%2BAnuG7NvzmgI89bA0dOhQJCQk4duwY98adPQRZrVZER0dj%2BvTpCAwMpCCGPYAyr67Q0FB66AwNDYXNZkNkZCSp5DVp0oSCMbbvd999p%2BK%2BKHlwLMPC2i0pKaGH50ceeYSEFpSeeR6PB%2B3atUODBg24zBED40i1bt2aHg7LysqQk5NDWYHo6GjKfLIHY6PRSO0og0pRwdFor9l2AAAgAElEQVRkMqF169YUyNhsNni9Xsp8GAwG1K9fH2PHjsXmzZuxdetWnDx5kntQ9Xg8cLlccLvdKC0t5QJxwB/4Mp%2Bx2bNnIzMzk74LDAzEZ599RuM6f/48ZxUBXMk8fPLJJ5RdLS4u5nzgPB4PkpKS0KlTJ/LdUyo/Ml4W68fj8eDxxx9Hly5dKEArKCggOwDAHygbjUaO19alSxfy/gP8AS9TQgX8HEIWlAJAx44dVVldo9HI8fKUQdgzzzyD9u3bc5w1k8lE11FCQgK2bt2K5ORkOo5dB1FRUaoAZu3atVzGWin7L/Y9btw4PPDAA5y4kNlsxl133QXAXxI6adIk%2Bs7pdFJw3KxZMyy7rMSlDNgA/7oqXzYMGjSIstMMyhcVzAIF8GeX%2B/btS8JHRqMRI0aMoH09Hg9GjBjBlREzOBwOtG7dGq1atUJ1dTUXCDPILBUYlPe62%2B3G%2BfPn6fphQk4yLzxAndVUzk15PhhqUvWU4WoBofJavBq0GK%2BrlAtkc63NoFiDQIIWsQjpMtemiKBFaUGDAbQmI2nZAMWXAbUJ3GjsW4uaheojoW%2BpaIsogiJTThFtPyQCUqrDhL6loi3CdSLrWsu1BeGFp2ye4jtRcdoSW03ViZCKyojnRSZ2Ju4jCnSI20DtiiKyjzSIJWm5JrTcUnURldHwU6JNGEk4SOrTrUG0pTaVFtm8Vc3IzreW%2B04858K/ZdLfPi2KNqIAi8xUXdxHZs7%2B8MP8tqgJcM896mMUVUHSbQAYPZrfvvycwUGc538JesB3bfhNAZ/JZEKTJk1om3GmLBYLPVixcqalS5ciIyODOEXh4eF47bXXiEPzxRdfYNSoUYiLi%2BO4eU6nEyNHjkRWVhZmz56N/Px8ChjYvsw24fHHH4fRaETPnj3p4c1ut2PAgAFo0qQJBSjKTFdCQgKVdynL35gdxIcffkhS/crvmeBESUkJJybDHtStVivi4uJom/3fZDJR9oG1t3HjRlX5mcfjQV5eHq2jw%2BHgHmKZsibrFwB%2B%2BOEHMs42m80ICAhAcHAwjEYjIiIisHbtWi7InDFjBqkbXrx4kWv/zJkzqKys5AJI9n3Xrl2xe/duCsK8Xi%2BtQVhYGIYNG4bawNpiHL5GjRpRYB8WFoadO3dSeZsoCFJVVYWwsDDuxULfvn05lU2TycSdr379%2BqlK7pRgPn5ll18rDxkyBPv376dxFhcXc2tRVlbGrRcTEmHCOMCVACAnJ4fmBvjPu5JjCuCqfcuEPHr16kUcNZvNxs3dbrfTfaVUyU1OTqZrGfDfB8o1HDBgAFf2arPZ6BqzWq34%2BeefqfwyPz8fSh%2B%2B4OBgVdltZGQkKioqiCvHAtrg4GAMHjyYst/1pfJ7ftSrVw95eXncfxs3bkSTJk3w0EMPkX0KGwcTTZk5cyb9jijBuLuRkZFo2rQpeYaOHTuWU2L9%2BOOPsW7dOk60hf2mKPm/ubm5sNvtaNGiBUaPHo1hw4Zhw4YNqFevHt27zMJDC3TRFh06dOjQoUPHH406lXQyJCYmYujQoVwm4vrrr4fBYMCoUaMwYMAAHDx4EA0aNEB5eTmmTJlCSnqjRo3Cf/7zH669O%2B%2B8E2azGW%2B//TZX0vnAAw9w%2B5nNZni9XkyZMgVerxcbN25EUFAQWTpkZGTQm38AUvl1VvqpRF5eHkaPHk1v55Xfb968GbfffjtKS0s5A3qGmnzAAP/Dc%2BPGjekBv2fPnlLlzpiYGK700WQy4euvv6bt/Px8rs8bbriBvmcleRUVFfB6vbhw4QJuvvlmLnM0evRoLghWtqUs/WNgQd2WLVvQsWNH4moqg8/S0lLiSV0NrF%2BmHnr69GkKqkpLS9GhQwfKUoVKJLFLS0u587Fq1SpOkEQswdu1axd69uxZ43guXLjAle6uWLECSUlJXCAhg/i5MuhhD%2B9ut5sLGFmQxYINAL%2B57w0bNnDfK4MVh8NBfSs5YW63mytrFdcwNjYWXbt25cbErheXy4V27dqRr6LIKxNVSpVcXsaVY9d7RUUFVq1aRcHZihUrVPNj4ywsLCSeJ/uP3S/vv/%2B%2ByubDaDSiadOmmDFjhlQ5lVUcFBcX4/jx42Q7sXLlShw4cID2Gzt2LPr27UtjNhqNiI%2BPh8Fg4LJ1bdu2hcPhQFRUFMrLy/H111%2Bjb9%2B%2BKCwspHu3cePGNWYWRegcPh06dOjQoaN26Bm%2Ba8NvCvhiY2M582gA%2BMc//oHw8HDKJHTo0AF33303lVXeeOONeOmllyggS0lJQYcOHaikMy0tjYzG4%2BLisGDBAq6k8x//%2BAd597F9laVwDL/%2B%2Bit8Ph8CAgK4LEZAQAD3kKh8wGratCm2b99OD2dt2rThAqSEhASMHDmStocOHcqZcQ8YMADp6em03alTJ3pYY/1cvHgR586d4yTtAXXpWKtWrTj5d8AftDCJfQZloNCoUSM8//zzqrVg%2BPbbb7njL126RNlGk8lE6w6ACyyBK%2BWhbKzKMkwl4uPj8dxzz1G7VqsV8fHxMJvNcDgcaNWqFb777jvVsT6fjz5r1qwZqV0CfgGfrKwsOhcBAQGoV68ed%2B4OHTqkmruyD5fLxZWAjh07FhkZGaoxMBiNRhIYAvzB9JYtW2j7lltuwdatW%2BnaUmbOGNgatGrVivv%2B0qVLcDgcnFjJ1fq%2B/fbb8e6771J5JSuFZvN3u91cOa7NZqN9t23bRkFdXl4eFwQ1atSI47pt3ryZuwYAPtjo2bMnBd9msxkZGRlUJmuxWHD//fdTNpYJFinBvvvb3/6GgQMH0osamaWD0v9PRPPmzVFYWIjq6mp4vV6Vjcnx48dhs9m4%2B56hpsArODiYU49lYEEbq1pgY2ZgXMzIyEjy0BT5qYzrqgVajNe1UPhq5bpo4PDVmcv2e3D4JJP63TiGwouAP8rQXQsfStyWUmO0cPgUPp4AAEkJseoj4aWklMMn8J9kXavWQsZ3E/hPsvUTqVai17nULUZoSEqjFU%2BebHziC1rRQFvmHC6cYC2m5XXhzmq5D2X3gjie36sd8RrV8hughZ4nNfOu5ceurhy%2B2niJANQXuxZTdXEOssV5772rbwNq03SRrweoeX1CwgSCnysAbRw%2BsR1xG6iBlKnjzwaDry5OyP9l9OvXj8yxQ0JC4Ha7qcyLBZJOp5P8%2BoKCglBeXo6WLVvi9OnTqKiogMFgQKdOnXDgwAHKUL755ptYs2YNyaozRU9WphoYGEjCFgaDAQEBATAYDFQKGRgYSJmW5ORkKp90u90IDg7msi3M5F0J9sDqdDphMplgt9sxbNgwfCqScy/DYrFwhuXiqQwODkbLli05xdJ69eqhsLAQY8aMwe7du7F//37a980338TYsWOlfTVq1AgWi4XLnAJ%2BvtrFixe5DOePP/6IhQsXYvHixWT0zgzcWcDhcrko6G3Xrh2GDBmCGTNmAPAH0hs3bkSzZs2wb98%2BJCYmorS0FCUlJXC5XJwdA5uzyWRCYmIi9u3bR%2BcqODiYsoAGgwEtW7aUSvcD/vPRqVMnMkY3Go2wWCwUPI0ZMwbLly8n7zWmsLlgwQLMmzcPeXl56Nq1K0pKSjB16lR8/vnnKCoqomCgfv36XEbyan0bDAZ06NABe/bsoQDntttuQ1lZGX766SfuPAL%2Bks6QkBAUFhYiMTERhw8fhsfjQWJiIp5%2B%2Bmny/2vatCnOnj1Lc4qNjUVRUZGK66k85w6HAxcuXIDBYEBQUBAcDgfcbjfxSRmP0mAwYMeOHejSpQsdz/io7Bqx2%2B2orKzEHXfcgeeee47r68UXX6zxOjeZTJgwYQL27t2LjRs3Ij4%2BnoK1Ro0a4cyZM%2BjVqxdKS0vpmmNIT0%2BX%2BnO2bNkSN910E9566y3uvmNo1qwZli9fju7duyM%2BPp6ygax8dMiQIZg%2BfTq%2B%2BuorAPy9N3/%2BfNx8883SuYiYOXOm1Hh94sSHazhChw4dOnTo%2BN%2BDkG/6XVCDrttfEtdU0vnfQEFBAXlmzZ8/Hw8//DBnXt68eXOS8Hc6nZg1axYyMjIQHByM06dPU5aHPYhu376dshiiOifzzFN617FsYUpKCnbs2IFXX32VM4BnQczFixeRkpICp9MJp9OJp59%2BmviP0dHRaNCgAZ5%2B%2BmlubkqRjPj4eDzxxBOcMiODkhvGHqplcfvWrVs5ziVwRRyjcePGXFZv0KBBeOGFF2hbmYEMCgpCREQEZWGU3xUVFSE7O5vEOhITExEfH0/eaxEREZyHnzJryHDq1CnuoZyZc7MSz4KCAoSHh2Ps2LHk4Wa1WrlMr8fjwVHFW%2B8dO3aQQA3gDyqVgU1ISAgXGDz66KNcBjEuLg6fKFSvvvvuO1UwfOjQIaxZswaAn%2BfIgv3Dhw9j7dq1nMpn165daxQWeeKJJ8gnkc0tODiY%2BIFpaWn4%2BuuvsWfPHgB%2BwRqlWfrUqVMp82Y2m8ke4dChQ1y7Tz75JJ566inavnjxIhc822w2zkS8qKiIvBGbNm2KTZs2kWhTcXEx5xE5btw4TuXVarUiLCyMBIwYry8kJIQrbZXBYrFwxutTpkzBxIkTMWfOHFgsFi4zd%2BbMGdx0003YvHkzXnjhBdUasww8s%2BJg94OyzJspBmdmZpLPY2VlJdavXw%2BLxYJ27drRvsnJyXRP5ubmcrYWgD84ffvtt686PyV0Dp8OHTp06NBRO/SSzmvDny7gc7lcKCkpQVFRESZPnoxPPvkE3bp1I2GXY8eOURlmQEAAUlNTSRjEarWS4bLRaMSsWbNgtVqpHK6iooLjj7FAhX0/bdo0CkJuueUWGI1GznOrbdu29AB9/PhxFBUVkXjDo48%2BSvuWlZUhPz8fL7/8Mjc3ZUBy9913kx%2BgCCYm4XQ6uZI9FpixALVbt24qriDDSy%2B9xAV8X375JQWDLBvHcOnSJcqMKYM3hpSUFCqffO655%2BByuVBQUACLxYKSkhK0aNGC9g0PDyfBlPDwcOLwvasoaUhLS4PH4yGVUMaJU47X5XKpVBKVa5GcnExZGYPBgMOHD%2BPRRx8F4A8oFi9ejMWLF5OgzyuvvEJei4A/CL333nvpQT4oKAhvv/02lfROnz4d48aNI%2B6jy%2BWiYF0sOwT8Kp0sOOjfvz9XWjlz5kwu%2BHc6ndiyZQtlX9nasqB4586dXGnx3LlzKRg/dOgQvv/%2BewBX%2BG0MjI/HYDabKQMZExODjIwMKjtlPE12rvPz8/HWW2/RuH0%2BH1cuvH//frLmYOuhFBmqqqpCQUEBiouLOW9ABmbwDqgVN6Ojo7FixQr06dMHkydP5r4LDw/H%2BvXr0aRJE1V5KlsPNoaysjK6HwoKCjiLiZycHPTp04eM6S9cuIDc3Fy43W588cUX3Bpu3rwZWVlZJI6jfOHi8XhqzCLr0KFDhw4dOnT8N/CnC/gA/wNhYGAgIiMjce7cOWRmZlJwkJ6eTvwmn8%2BH/v37o3///igrK0NSUhJ27doFwF8%2ByVT9hg8fDsAfWLAH%2BOjoaIwePRrDhw%2Bn0stly5ahTZs2AICFCxeiY8eOmDp1Ko1J5OAdPnyYDMmBKyVjzPdP9A5T7ss8zGQcJPbgbzabceedd9Lnly5dQklJCY3Xbrdj8ODBAEAP2cpsKPOtM5lMCA0NpYBUJhoRFBTECZQokZCQQH%2BPHTsWmZmZyM7ORvv27TFu3Djcd9999H1paSnOnDkDh8OB8vJy9OzZE7t37%2BYsFMQHfofDgXPnzuH8%2BfNkMxAcHIzmzZtz%2B4kCGCyANhqN6N%2B/P2688UZYrVZ4PB4MHjwYo0aNomDKaDTSywC73Y53331XJerDLDcA/3Xm9XqRm5uLadOmYfv27aTwuHfvXvTr1w8//vgjAH%2BAOWDAAGpn9erVqkD%2Bzcv1%2Bcxz8sknn6RsGytTZUhMTMQtt9zCfZabmwvAf92yzKLH48FDDz0E4ErmScl7bNeuHW688UYA/gDohhtuoCwiAIwYMYLuh8DAQEybNo3zyhw6dCidq61bt9LLEAY2R5vNhm3btqFly5aIjo7GM888U2MZKTtO%2Bd/06dPh8XjQsGFDvP7661zWukWLFnC5XDQ%2BEWIJMoPRaOQsJJR%2BhGx%2BJ0%2BeRHl5OZc9Z3%2BnpqbW6C1Z0%2Bcy6KItOnTo0KFDR%2B3QM3zXhj9dwBcbG4vw8HAYjUaYzWaYzWbYbDYKaEJCQujhTSkOYTabKTAxmUzcwx57iI2KikKDBg04Dz/gihKjxWJBs2bN6CG3urqavpNlvgBe/ZJxBVnmRAxslCWIZ86cwcmTJ2so%2BQK1wzIeyrbY306nk4LQgoIC4ryxh9bq6mpaR6vVqipPU6K4uJjEeUTRDaViJABs2rQJzZs3R3Z2Nn744QcuyGDH2mw2uN1uzJ07FzabjTMEF6EMDtjDtNlsVpnbK8dtt9vp3Hg8HhgMBi4Dx/ZhAbzBYCDupsPhwEMPPcSt1ZkzZzjRm9OnT6OoqAiFhYV48803kZqaShmjc%2BfO4cKFC9SPTGSIrTvgDyKYEqTX60WHDh3w73//u0blV5vNxpmPu1wuCtbtdjtXesvOv9lsVmVu7733XlV5pdLaYMmSJXRuS0tLkZaWRkqcVqsVn3/%2Bucq6Qgl23pxOJ5577jmUlJTA4XBg8%2BbNqpcHMksFBnZeb7vtNng8HspuGgwGZGVl4frrr8f%2B/ftVATrjjsoQGBjIZYSVpa0MV1PeBYCHH35Yem6Bq/sKKiETbUlK4kVbtAiniBXdKlECDeIRMiEDTSIj4joJB2kRPKmrYExdxFW0CL1oEqtRXe/qXWoT5JC2K4oJyYykRYEJicKJ6M0uipdIRVuEBRWFVACoB63BpFzUoQHUgiuCdalqGQD1uZJpPamuY5nyjHg/iPer7P4VjtEiIqTlwlbtImlY9ZHkt0mL%2BEttxuaAev1U6ym7YYQfBtklqxqQ5MdENZy6GK9LJq5J3EkUadEgyqRJ0UYUZJGJtrzzDr8t47OLYiqiiItM6EU0VVdUq9Q4Htn4dNGWvwT%2BdAFfcXExOnfujC5dusBsNlPGqn79%2BkhKSkJUVBTCw8MREhKCpKQkCt46duxIpYWBgYFcIMYUFevVq4fGjRvD6/WiadOmMBgMMJlMaNeuHT1kt27dGrGxsQgNDSU/P%2BCKQqPBYOAeMpVm30qjZ7fbfdU3%2BdXV1Vi0aBE9gIpBmNlsRlhYGKcqyD5nD8QVFRVcWaMMRqMRgYGBKC0thdVqJU6gDC%2B//DJiY2O5%2BRkMBq6sFfAHfEeOHIHH48GpU6e4B3E2ZzZuxjdTrgXLjDEozxV7SFfOk0FpD%2BJwOLhA8eTJk3j//ffh8XhIpMPhcFD5ncfj4QJe5XjMZjMsFgt8Ph8FNWazGdHR0dJ1ZRxTBlE9kl2TLJvk8/nw0ksv0fdWq/Wq14bb7ebaVIrXVFZW0rWoPE9Op1NlvREfH09BqnK/msbdt29fGpfJZMI777zDzV%2B0WVG2s379ehQVFaGyslIVmAHyTJdyvnv27KH1ZmWaPp8PYWFhyMnJkV6zMuVQBmZfIkKZURd5nkoYjUbUq1cP9erVI4EfJVjFQW2QGa8fOMAbr2sxAReHqRIs1WC8LjMjFveRxuWisblwkLQwQBhPXc3PNZlC19KX7BRrMl4XLEJk%2B4jrp6ndy/QEgsxIWnQcj45W7aKqcBaMpaXG68KCShxy1IPWYFIumqoDgOKdFQBAFD4WlwFQnysZHVh1Hcvc2cX7QfydkP1uCMfIBIBV51PDha3aRdKw6iPxnpPsI722ajE2B9Trp1pP2Q0j/DDILlnVgCQ/Jqrh1MV4XTJxLfe8aLQuNqzJeF226A8%2ByG8LNmMAgPHj%2BW3RVB0A7r2X3xbN2UVjdkBtqq6oCKtxPLLxSf79%2BG9Az/BdG/50AV9kZCRycnJQXl6OhQsXIicnB1u3bsXo0aNx9OhRKg0cPnw4ysrKkJGRgYyMDFy4cIEyH4mJiZyaIOP/xcbGIjk5GQkJCWjUqBHWr1%2BPpUuXori4GIMGDaLSvKKiIvJ4Yw/taWlpWLlyJYYMGYL09HTKPik5dJWVlVx2rH379jXO0%2B12o6ysjLJT4gOt2%2B2G0%2BlEZGQkAgMD0aZNGwQEBMDtdnOBzgcffADgSjmi%2BHDKFERdLhcFoTVlW55//nn07dsX4xU/TizoZg/W3bt3x4ULFyi4VQZSwJXAjq0bK089duwYPVz/%2BuuvXGChzMQYDAbY7XbYbDaVJxuD0WjEwYMHqUwSAM6fP48HH3wQsbGx8Hg8iIqKgtFopLUymUw0TqvVStw%2BBmVmEQAOHDiAsrIyJCQkkJk8C1p8Ph%2BSk5MpID19%2BjTKyspgNBphs9kQERHBBathYWFcRig6Oho2m61GS4Fjx44hPz%2BftuvXr0/7njt3jtZeVIG12%2B3cmn388cdIS0ujY5VrwL5nIjmAn9fIzlFVVRXOnz/PBUTKQC48PBw2mw0WiwXV1dWYP38%2BoqOj4fV6MWLECNXYlNnan3/%2BWWW%2B/vzzz2PIkCGcgArgfxlQWVkJm82Gf/3rX6q1GjRoEABQcMbEZAA%2ByGQluEp%2BYatWrRAaGoomTZpQ8Gy329G7d28AwNmzZ/Hrr79i%2BPDhnDhOUFAQ2rZtqxqLDLpoiw4dOnTo0FE79IDv2vCnC/isVis%2B/fRTREVF4f7770eHDh2Qnp6ORYsWYe7cuejRowcAv0hKSkoKBg4ciIEDB6Jly5aYKL4RkcBgMGDBggU4f/48evfujREjRqBdu3bEd4uLi8Obb77JZaEaNWqE9957jzIQoaGh%2BOCDD2AwGPDaa6/Rfm63m3vQZQIRMng8Ho7jJz7822w2Mre%2B9957sXfvXsq0KQM%2BFjhVVVVR5krMbLDgweVywWAwSA2sAWDy5MnIyMjAG2%2B8QZ9VV1fDbreTwuePP/6Ipk2boqSkhIKa0NBQVWmdUvwjOzub85UrLy%2BnMbJSRAafz4ekpCT6TizrtFgsCA4OhtFo5IKiEydOoF27dsjPzydDciXXSnluqqurueDF7XbTvmyfTZs2ISQkBL/%2B%2Bituu%2B02zJw5kysB3LNnD6054Bfx8fl8cDqdKCoq4lQiS0tLuWxaaWkpPB5PjVwwh8PBqZqeO3eOXgwAV15gKMcLAEePHuXKLpcvX47mzZtTP2J/c%2BbMUWUAldizZw93XSqDSWby7vV6YTQakZycTGXUp06doiydDCkpKZzxelJSErZv344FCxYgPz%2BfCzIPHDhAnF5ZhvGzzz4D4C%2BxLCwshMvlojUIDAyka%2Bunn35Cv379uBc0Xbp0IaEXdj06HA5s3LgRgJ/H6Xa78fXXX%2BMdRUnOmDFjrlqiqoTO4dOhQ4cOHTp0/NH40wV8gF9R8MUXX0RmZib27t2LnJwcLFq0CP369aN9rFYrnn/%2BeezatQvbtm3DCy%2B8QA9hn376KR577DHaNyEhAXl5efR2v2nTpli4cCF27tyJDRs24JlnnuEyMr169cLixYtp%2B0FFyn7mzJl47bXX0LFjR7z55psq%2BfeBAwciLi5O0zzZw7rNZuNULAHgnXfegdFoRI8ePbB7924uoxAVFcVl8e6%2B%2B26uVLNt27ZcplH50B4fH68q5Vu1ahVsNhueeOIJGAwGTJo0ifve5XJxghlGoxGtW7em8Q8ZMgR79uzhxjhGUbIwf/583HjjjVyGjZ0Lp9OJDh060L4TJ07kApp58%2BZxZa9xcXEoLy9HcnIy5syZQ%2B0BfEAj8hAXLFjAbT/22GNSkRoWIJSWluL999%2BHyWTCgQMHsHjxYi5Af/bZZ5Gbm0vWCoWFhTXyyQCQByHgf%2BCfPHmy1KAcuGIpwtCrVy%2BMGzeOShCVpu/jxo2jvw0GA6ZNm0bnt7KyEiNGjKDzHxQUxPV5%2BvRptG7dmrYbN27MnYv9%2B/dzZcqvvvoqzRfwZ8O8Xi%2Bqq6txww03wGAwICUlBT6fT1VeGiqtHfOjuroa27ZtQ0xMDC5cuKDKnkVGRqKiokJaRlnTywslvxG4EmQpz3lpaSnmzZvHHcc4wwUFBejatStXVs3w3nvv4ZzUEVoNGYevVSuew1crpwZQEUxU%2B0gO0sLhEykpUipHLQSZP5LDJ34mXRvhQy28Py0G1SLBTQuHTxPnUPGiCoB80TW4lKveqQidSzl8AjdMExdLZlIu7CPj8Ik0OdEoXsbPE7uW%2BM2rz5Wsc/FEaOHwCb8lWjilMq6deKDqGA0cNC37aOH6ynYSxyP%2BhEoZH8JBsktCyw%2BO6txp%2BC1RFUlomJP0vhMbEu872cTF86uFwyduyz5TWEIRRGN1kbMncvoA4PPP%2BW2R0wf8qTh8eobv2vCnDPj%2Bf0BERAQFHqLiYFVVFYYNG4aIiAjOR8/n8%2BHAgQNo2bIlPVj/%2B9//lrZvMpnoITQwMBA9evTgZPDT09Px6aefonnz5ti7dy9OnTpFFhAisrOzuQf5ffv2cdnOaAX/Y8aMGXjxxRfxgKKOOysrC19//TXatm2Lo0ePYv78%2BargRcnjO3z4MC5dukTrcscdd8Bms3HZOOaVCAC7d%2B/GsmXLuPY6d%2B5MfyuzqcXFxZg5cya6dOmCjIwMPPLIIxQo%2Bnw%2BKtsFQAqlI0eOxM8//8yJ8SgzYgDwzDPP0N9erxe7du2SnhtlyWNSUhJuv/12yoqy4MJgMGDEiBGwWq0kjON0OuHxeGA0GqW8sGHDhtHfDocDb731FtLS0uiz%2BfPnc/srveg2bNhA7QYFBSEgIIDOt7Kve%2B65B/Xq1VOJ4LC1sNvt3P5nz57Fjh07aHvt2rWcfcSXX37JmckXFhaSlQRrm43B4XDgl19%2BQV5eHp566ikuUASgUlwV0bp1a%2BzevRt2u12lWlpcXAyn08kFm8oxyuD1elFYWKji4CrXxmAwYN26ddxxbrcbDocDQ4YMgdPpVAnVsLYfeeSRq86HQcbhO3KE5/DVyqnxD/bq%2B0gO0sLhE995SKkctRBk/kgOn/iZdG2ED7Xw/sTPpNXVwksKLQXu1RAAACAASURBVBw%2BTZxD8YWgbNHFFyQSDp/iXY20cymHT%2BCGaeJiidwnyT6y6nuRJqfQLAMg5%2BeJXYvHAJJzJetcPBFaOHxC1l4Lp1TGtRMPVB2jgYOmZR8tXF/ZTuJ4xGIFKZdNOEh2SWj5wVGdOw2/JaoiCQ1zkt53YkPifSebuHh%2BtXD4xG3ZZ/fco95HoXYOQM3Zk1Ww3XUXvy1y%2BgCdw/c/BD3guwb87W9/A%2BDPbDBD%2BIMHD2LcuHGwWq148cUXOa%2B4W2%2B9FWvWrEHv3r25B2tZJufLL7%2Bkz1euXIns7Gzs37%2BfC7Q6deqElStXYu/evejfvz9KSkpgMplU5YBnz55F165d6dj09HTOHLpVq1YUjLlcLsyePZvzvDty5AgGDRqEL7/8ksy2lVk2j8cDi8XCzenhhx%2BmzNGYMWPwyiuvcCWsEyZMoL8bNGiA%2B%2B67j8ZQXFxMthQAyFoAABYtWoSPPvoImzdvBsAHqwDooV/pe1dWVobXXnuNBHlYKZ8yeJs1axappNpsNpSWlnJZN6VwC0NqaiqWLl2KhIQEjB8/npQzfT4fqqur8cMPP%2BDgwYO0DYA4kmIWValSOnDgQKxevZrzzXv88ce59VUGYn/729/QunVrXLx4EREREXjjjTcouzplyhTab%2BnSpSgrK%2BNKBpWltS6Xi7tulDxCg8GAnJwcVXZNmZXdu3cv9x3jjfp8PgQFBRF/NCMjA1eD0nSd/RcTE4PHH38cVquVWwdm7t64cWOOR8fwyiuvwGQyUWYuMDCQ5jRhwgS6J/7%2B979j%2BfLlXNYvNjYWR48ehd1upwx/vXr1cOedd6JLly547/Kb0NDQULovWKayJv6lCJ3Dp0OHDh06dOj4o6EHfNcA5q1WUVGBG2%2B8Ee3bt8cjjzyChIQEyjaJ4hRaYbfbqf3u3btT2WNN/J4HH3yQuGaRkZGYP38%2BZfzcbjd27txJx2ZnZ%2BPWW2%2BlY9evX08P%2BuPHj8eFCxdwQVEi9PnlsoBhw4ZRoCaWrLlcLq4c7s0336SAtaysDMuXL%2BdKHg8cOEDjO3nyJP7zn//Qw6/L5SJRHMAf5CnxzTffYPny5QD8wSpwJZPF/Oh69%2B6N7du3A/CXpK5du5b4aJWVlQgMDMRTTz1FbU6cOJE8/pxOJxo3bowXXniBHtwZx4/J8NtsNgwYMABNmjRBfn4%2BzGYzJwSUnJyMiRMnkuCOsiS4qqoKHo%2BH428pM0k5OTkYPHgwV%2Bbocrm4YDM3N5d8/z788EM89thjMBqNOHXqFCmfAuAyzJWVlZg5cya3lsrvKyoqSKkV8Jc6M9ETn8%2BHBx54gCvpbNu2LZ1Txo1TlpMqy5nHjBlDlh5K5VqtqFevHmbPno2QkBDuBQnje5aUlGDJkiXcMSxD6fF4SOSosrISHo8HJpMJBoOBzstbb72FoUOHorq6mhMPYnYPLINdWFiITZs2oaSkhAzZy8vL8fDlt63Mi1BpJK9Dhw4dOnTouDboGb5rgx7wXQMiIiLI%2BiEqKgoBAQEoKCjAxo0bUVBQQA/oeXl53HGjRo2iQGXYsGGk1pmWlobRipT7kCFDAPizBnv27MGePXtq5HXFxcUhOjoaZrMZ%2Bfn5mDdvHmWcLl26xGUcvF4vvvzyS/reYrFwJZQsMBQFUd555x0899xzAHhulNlsRuPGjbFy5UoKQk6fPo3XX3%2Bd9ikpKaHv2FyVZuRer5fG4PP5MH36dPpOmSUF/HL5LLPHxiHjx7EAz%2BPxID8/n8umXLx4Ed988w1tOxwOfKrwvikvL8f58%2BdpzAaDARaLhR7oPR4PLly4gB49ekhtEBITExEYGEjXwLBhwzghD5PJRNcA4LenYGWAZ8%2Behcvl4kRnZGWgbA1sNhvMZjO9XBgxYgTtc/PNN9PfVquV83q02%2B1o1qwZpy7aqFEjElc5cOAAVq1aRfuPHz%2BeyyxGRERg5cqVtL1hwwZqKzAwEHa7na7Xzz//HHFxcSgrK0O3bt1Uc1GWtIrG6y6XCxcuXIDL5cLZs2cRFxdHa8mye5WVlSol2wYNGtSYTfR4PMjKyqJj2LlTGq0nJiaiXr16qnbPnDmDvLw8rFu3DqGhoSp7EAC44YYbpP2K0EVbdOjQoUOHDh1/NPSA7xrx0ksvSRX5mHBJx44d6TOlyXWTJk0oCGOef%2BxzwP8Q37lzZwQHB6sMxWsqF0tLS%2BOyaEprASXXiKlYskxQdXU1du3ahQYNGnBKi%2BLD6Pjx4znpexYYMGEONnbAH6AogzaDwcBZD1RVVXG2FE6nE9nZ2QD8D/FKviLjlzEwlU7lvFjGRgml0qbYhvg94C/rY8jKyiKrClb%2BKWbZXC4XvF4vJ%2BXPcPr0aS7ATE5ORmRkJI3R6XRyXLSioiISCAkPD4fVauXKZmXG4Gz8LChiaNCgAe2nzDoaDAYuM8vGzZRtAX9mqlzqtuwXtlHyCk0mE2666YrASFFREZWuVlZWwuFw0Di8Xi/2798Pn8%2BHzZs3Y8GCBbj77rul/cjAsq8%2Bnw9nzpyh4NZsNnN8RhFnZaoOClwtA8%2BCfBlYCXNVVRUcDofqRYzyZcbVIBNtSU%2B/umhLnYzXJVzD3814XWi7NuEFAGqzbsk%2BWoykNYm2CIPW4pWsSQFDULCt0/hka6NFtEW85iUiI7VNQSraIiy6bE6qrmTjE8QsZD8p4iUpCgLLdI/Ea0J6e4snWNa5uO7iesqqEIR2ZNNWidxoMEgX29EkBiO5brRc11pEW2rT5JHeL8KkxNtb2rfkx6RWg/Q6zlvLb6jqd0u8ILWI6cguCqHyRGp%2BLmgYQFF5U%2BNnogCLKNACaDNnF3nuMt67TPjovwA9w3dt0AO%2Ba0Tr1q3x1VdfoX379iScYbPZMGzYMHz88cfcA6MyUOncuTOVv6WmphJ3KDExEYA/4LPb7Xj33Xc5u4eJEydyZXNKTJs2DS1btqTg5umnn6YH7uHDhyM5ORn16tWjMjhl2ZnL5UJ6ejoXtE2dOpVrXzSLZ957Xq%2BXsnAu4R845QO/Mlg5efIkZ4rt8/m4tufMmUPBK8ueMqEPq4QIz3iEyiybMvMSFRUFq9XKnQ9RGVIZzBUXF5Pip/Jz9rfb7cb27dvx3XffoeLyv4hXUwFlfoXK8SkFQoKDgymQZ5%2BLmU1xjCyYUX5mMBjw1VdfSY3Ixc/YWigzucp9rFYrJxZz8eJF7vvGjRtz7RmNRlXwyzwP33zzTbq2CwoKMGHCBLJMECHy9wIDAzFgwAD885//REBAAKqrq0npls2hVatWuFNmKlsL2Dlj7Siv0cjISC4jyuD1ehESEgKr1YqgoCCEhYWpAkfli56rQSbasn371UVb6mS8LhGu%2BN2M14W2axNeAKA265bso8VIWpNoizBoLV7JmhQwBEPvOo1PtjZaRFtEV3KJyEhtU5CKtgiLLpuTqivZ%2BITfaJkIr3hJiv7oojG7ZHhS0RbVCZZ1Lq67uJ6Sl3hiO7Jpq0RuNBiki%2B1oEoORXDdarmstoi21afJI7xdhUuLtLe1b8mNSq0F6Heet5TdU9bslXpBaxHRkF8XIkfy27N%2Bp4cP5bQXlpsbPRAEWUaAF0GbOfscdV98G5MJH/wXoAd%2B1QQ/4fgckJCTg9ddfx7Zt27B3716sWbMGkyZN4jJKeXl5HC9t1KhRxNsaNmwYtmzZAsCfpcvLyyNRD6Uwy8cff4z77rtPpRzIEBcXh6VLl2Lv3r346quv0LJlSyqrW7ZsGQoLCxEUFITHHnuMykSZUb3JZILJZMK9995L7Q0fPpzLtLGMFuAPYBifDbhSWsmClWDhX4mEhAQuwPr1119VwZdyvRo0aECiOBUVFTAYDCTIIppvA36RltzcXHz88ccA/ObdSisLpcE6wz333MOJvihVPAGgZ8%2Be8Hq9UsP6oKAgvPjii7h48SJOnDiB4uJiLjiIi4uDzWbjfPiqq6spwFAqaQYFBeGDDz7gVC1dLhc3FmXfrE0WZAQEBFDQZjQasWjRItrnhx9%2BoH48Hg9CQ0PpuxMnTqC6upquJ4vFwgV/GzZs4LKUov2AGNSOGDECZ86cAQC67rxeL8rKytCsWTPs3r2bgva5c%2BeqMnxKHqbyv5ycHAD%2BzOeMGTNgNBrJL8/pdMJoNGL27NnSsteIiAgKtG02G%2B66/I9iQEAAOnfuTNdzly5dsGPHDk6FNCgoiAK3G2%2B8EYD/xYHBYMD7778PwB8EXxReZzdt2lRaqimDLtqiQ4cOHTp06PijoQd8f2EUFBQgLCyMgpzKykqcOnUKs2bNgsPhwPXXX08lnE6nEytWrMCiRYso8HI4HBipeDsVFBRED8Rer5fM6AF19igwMBDr168nDpzdbsfkyZMpEDx37hxXUnnp0iUuoFi1ahWVWDocDlRVVVEmTZbhu/nmm5GcnExKjZ06dcK2bdvo%2B8jISKxcuRJbtmyhMeTn53PZ0oCAAHTv3p22LRYLBTVGo5EUNtl4n3/%2Bedr%2B6quvuP7Onz%2BPrKwsCtzffvttVUkrC9guXbqEn376icbFAlzleixcuJBb69LSUlrzqqoqyqx6PB6Od7Z48WLqhymUskC9srISKSkplEmsrq7GJUVNUp8%2BfTi/yqNHj3L2G2JWq2fPnlR66XA4EBwcjODgYLjdbvTu3Rs%2Bnw/FxcWw2WywWCxcNjg7O5vG3KpVKyQmJiIpKQnJyclo27YtHnroIbz//vuYNWsWRNx///1o0aKF6nPgiuG91%2BuF0%2BkkAaKqqiqOI7l161akpaWprDiOHDmCHj16YMOGDQD85yYsLAy5ubl0zYqB8PHjx2v0/xOhc/h06NChQ4eO2qFn%2BK4NesD3F0ZkZCRMJhMFHQaDgTIsLpcLa9euxZo1awD4S0h37tyJf//73xTkpKWlcVmyZcuW4Z7L/jBmsxlt2rRBXl4el1lhhtoNGzaE1WpFSkoKAL9q5qhRo8h/z%2BFwqCwVlCWPS5YsoYxoQkIC9uzZo1LrzMvLozZk2Z0WLVpQUGqz2WA0GuHxeNCsWTMAfmXGgwcPAvCrSK5fvx5PPfUUBZRdu3aloIb5Iiof5K1WK5o1awaDwYA777yT83xjthcsqAkNDaWAxmKxICAggBvzN998QyWuFosFffv25VREW7VqxdlTKLl0iYmJyMrKwvDhwxEaGsp54SkN5Zs3b47nn3%2BeAjylJ6DBYEBISAiXVbxaiSoAzvMQACmFMpSWllIGi62pxWLBzJkzkZ2djaNHjwIALly4gPvvv5/GwjKDysBn9%2B7dcLvdyMzMxMsvv0yfh4SEcJ6SIuLFUi0Fzpw5o3pRIQZbu3btwqZNm%2Bg8Mu%2B9OXPm1Ngu4LeD0AIZh69jR57DVxdDZdU%2BkkyiFo6cFp6fyFupdSyAihgko7LVZXzSvmoxXpfOScuiC3OoC4evzsbrl1%2BsECQcvtq4YVqM12XjKy4WPpC5swuda%2BG7ibwvkUIFqM%2B3uFQA1KQzGaFMbOjkSX5bxgsWJiG7blS3mezFj9C3ahfJzSD2peKbSfb5vTh84py0cPhkxuuqMUuum7pw%2BFTXlgbjdU33vMj91HKDy/YROXziNgAoBOQAaOPwiVxAmam6FnN2kT8obgPSfz90/Pkgl3zU8ZdAcXExKV9u2bIFJSUllCUD/IHUqlWrSC0yLS0NiYmJaNSoEQ4dOkS%2Baq1atULXrl25tpVZNrvdzmV%2BgCtWCklJSVi/fj1llRISEgD4AygWHLBsFjMANxqNeOWVV9C/f3%2B0atWKCwxFxVMGZeCiHBt7gGc2F0ajkeZ7//33Y/369Thw4AD27duHbt26ITw8HA0aNMCJEydw2223YfHixTCZTAgKCqLyQdZmRUUFTpw4gZSUFIwbNw4vvPAC9Tt69GiOl6gs%2B6uurub4ewC48tgWLVpg48aN2L17NywWC6qrq9G7d28MHDgQgD8TuWfPHprHwYMH0b59exgMBgQGBsJsNpPh%2B%2BbNm2nMJ06cwN///ncu8LRarcTNE0sTWd%2BAn685a9YsCviVawv4g8e2bdvil19%2BoUBOuQ8LHqurq3Hq1CkqlQX8PpORkZFUDsqCbGaf4PF4SGm2T58%2BcLlcZHfxr3/9S%2BVpqMRPP/1U43dKZVr2MoD9n0FcE8B/jYWFhWGZ7B/GyxC5rDXh22%2B/xUcffcR9NmnSZHTvfsU1uy6Gyqp9JJlELRw5LTw/kbdS61gAFTFIRmWry/ikfdVivC6dk5ZFF%2BZQFw5fnY3XRR63hMNXGzdMi/G6bHyX3xlegcydXehcC99N5H2JFCpAfb7FpQKgJp3JCGViQwIfWcXpA1STkF03qttMRnoV%2BlbtIrkZxL5kum114fpq4aWJc9LC4ZMZr6vGLLlu6sLhU11bGozXNd3zIvdTyw0u20fk8InbAHDbbfy2Fg6fyAWUmaprMWcX%2BYPiNiD99%2BO/gf%2B1jNzvDT3D9xeGy%2BXCjz/%2BiOLiYnzyySfIzc3Frl27KOvFSioNBgOCgoKwY8cOzJo1C%2BfPn1e1FaNg0BuNRo6PpxQcEbN2LGslfh4dHU2fBQcHIyMjA/v27cOTTz6J4OBg9OrVi/atSaRGOa5169apuF%2BDBw8mYRG3243vv/8eGRkZlKVr2bIlfc9KQN9//33yINy2bRusVis8Hg/effddxMXFUWAZFBSE2NhYlJeXY%2B/evbjjjjvwzDPP0LjMZrNUvZXhvvvu45Qcz5w5g19//RUAcPjwYVRXV6O4uJgCrvXr15NvociPZJk6g8EAh8NBgZTX68UXX3xBQdf48ePRsmVLOs7pdNILgODgYHz//fdcu58rVL%2BioqIoG8qgVPO02WzYsGEDjh8/LrXIYGMPCAhAeno6Ll68CIfDgQ4dOuC1117DmTNnYLFYYLPZMHLkSJhMJtjtdoSGhsJoNJLHZGZmJubPn09zVBrWy8B8A00mE4n2GI1GLsAGgMGDByMrKwuTJk1SrW1ERASaN28OABg5ciQGDRqEVq1a4eeff66xX8YxrA06h0%2BHDh06dOioHXpJ57VBz/D9xeHz%2BXD48GHce%2B%2B9KCsrg9FoRGxsLAwGA/GebDYbysvL0aVLFzKN/79ASEgIbDYbXC4XRo4cSabxvyeHiSmjnjx5EjfffDMXjHTo0AEulwurV6/Gt99%2Bi0WLFhHnLDQ0FE2aNMHJkyfh8/lwx2XlKhbwORwOpKamwuPxoKioCH369OEyfEqBGwBkaM6C7G%2B//ZZ8AgG/SE1xcTFKSkoQGxuLkpISBAcHo7KyEhUVFejVqxcpqBYWFpKADXAlOxUVFQWHw0FZKZvNhuDgYBQVFQHwl3cqx9S9e3cSC6qsrFRZCdwlqH6JQczOnTvpb5bhNZlM0uuHKYpWVVXh66%2B/xqFDh2AwGLBjxw4MHz4chw8fhtvths/nw3fffQev1wuXy4XMzEz07t0bRUVFOH/%2BPGX4tIKVt4pcO1bWyjKF3333Hb755htV6arVakVpaSmdqyVLlsBgMKBDhw7o06cPvSypEGqYlGJHV4PO4dOhQ4cOHTp0/NHQM3x/YcTGxiI8PFyacTGbzfRAn5ycfFW7AoD3ELTb7SSLD/j5euzBVekpyMYAgLNgMBgM1N6YMWNUXmfieJV9i1D6FsoQFRWFtLQ0rg%2BLxYLU1FQ0aNAAN954I0JCQrgx22w2jBkzBmFhYarghW3HxMTA4XCgoKAA1dXVcDgcXIavdevWquPEMk6lEX3Lli1pDvn5%2BZThUwYSLPMo8hVZoFRYWIhLly6Rjx/jcDJ06tSJW9sxY8ZQW0ajUZWRVPbTpEkTzsMPAOfBp7TfENcsODiYMnyAP%2BhjvolWq5XmbTKZEBsbi1tuuYXaUH5/4cIFyvAxXK2ck83ramD9KLOiSkRHR0uvAXZv%2BXw%2BNGzYEAMHDiT%2BKgC0adPmqv0yyDh8SUk8h0/k%2BMhoQbX68ElINVr4ebXxeaQHavByE3lVdeXnaeLmCFwhcf1kx2jZRySZaVk/Te1evs9r6geA%2BnxKvOYuFwxcgfCiRMrhE/qSda06D%2BJ4AdXEZJwukXYoUrpkfYvt/vKLZB/xhZDMQ0ychLiP7BihXdm5E/lk0nenwoWt5RhVXxISn5Z7oVaOnGQfTRw%2BDb6LWjzraqPEycarogJKfqTErrTcz5o4fOJ1ItvnMp%2B/xm0AUHDtAcj5eP/5D7/93ntX3wbUnnoyGoLI65Px/GQ38H8Beobv2qAHfH9hWK1WLF68GNdffz35wDHhltdff50Mt6dPn474%2BHjyEJw2bZqqLaWHYMuWLTnRkC5dupDHmtJTELgimqEsy4yKikJqaioAYPLkybj11lspAIiNjcWHH37Ieegp%2Bxah9C2sCa%2B%2B%2BipSU1PJdqFLly546zKZOSgoCB9%2B%2BCGVa1osFtxzzz2YMGEC1q1bB7vdLs3CFBUVcXxCJn7D8PPPP3O2CWvWrOGCr6qqKs7kfvXq1aRwGRUVhezsbKxevRrh4eG0Nqz8j5nAi7j%2B%2BuvRpk0bVFZWwmg04vz58xQkAuBsCIxGI6ZMmULbVquVC/BYKSVDx44dVYbsvyietlhQFBcXR2WUDKNHj%2BbarqioIF5hhw4duFLR9u3bIzc3l5sTyw527doVVqsV3bp1o%2B9tNhs%2B%2B%2BwzlWckgxik/lYor2UlmjVrRmWhhw8fRkZGBnEQr3acCJkP34EDPO9QrAyWVQrX6sMnIdVo4efVxueRHqjBy03kVdWVn6eJmyNwhcT1kx2jZR%2BRZKZl/TS1K5S/S8ls4vmUvKS7rE11BQI/T8rhE/qSda06D%2BJ4AdXEZJwukXYoUrpkfYvtXq605iGqOMs8xMRJiPvIjhHalZ07kU8m46aKF7aWY1R9SUh8Wu6FWjlykn00cfg0%2BC5q8ayrjRInG6%2BKCij5kRK70nI/a%2BLwideJbB%2BF2Jp0GwAmTOC3ZXw8hWUWAOCBB66%2BDag99WT8PJHXJ%2BP5yW5gHX86GHz/V/V7OnT8ydC2bVv4fD7MnTsXKSkpmDt3Lg4fPowjR44AuFIOCPgDx8zMTHTp0oW%2BCwwM5GwOlIiJiUFiYiLWr18P4IoKpmh1YDQakZCQgJUrVyI1NRUVFRUUaPbu3ZuCIcCfefX5fHA6nWjYsCFKSkrgdDopqAsMDKSg0Ww2w2KxUClmQkICzpw5Q9uDBw/G3LlzSRgmICAAJpOJMo4ZGRkkIsO8%2B2qaa3JyMvLy8ihIbdWqFc0hMDAQLpcLbrcbBoMBAQEBcDqdJOATGRmJ4uJi%2BHw%2BjBs3Do8//ji1kZSUhGXLluGNN97Ali1b8IWoWgbg5Zdfxn/EN6OXkZeXR/Nr1KgRCgoKEB4eTkFyXl4eli5dipkzZ6Kqqgperxfx8fFYsGABrrvuOhQXF6Nbt25o27Ytp4xqtVqRk5MjVTYV8eyzz2KJoNo2adKkqyqP6tChQ4cOHf9rkOkvXStkIr5/VegZPh06JCgoKCBrgClTpuCGG27AqlWrkJ%2BfjwYNGmDIkCGcR6HX6%2BUyXmKZ6KZNm7gA4PbbbydeWEREBKxWK2UAo6KisHfvXvz4449o0aIFhgwZAuCKyE5ycjKSk5NRUFBAqqZmsxl79%2B6lTKqyVJMhKyuL/hbLaBs0aMBx8poJ6YHs7GyujFeZlVPO02KxcF6GADBq1Cj6u3Xr1lTKajAYUFVVRUGz0WhEVlYWqayy9WDts0CbfcfKeUNCQjhPPSX69u1LmV3gSnDKAj2G6upqWK1WVTY3Pj4ec%2BbMoePuv/9%2BXHfddQBAFie/CrVzt9xyi6ZgT4cOHTp06NChDXpJ57VBD/h06JAgMjJSlcGz2WyorKzE2bNnsXLlSnyr8MUZNGgQpkyZQtvl5eVcxqtHjx5cKeKyZctw4MABAEBJSQkqKyspA1ZUVIQOHTqgX79%2BOHr0KObNm4fOnTursn8%2Bn4%2BsH%2Bx2O3r16oWTl/2kbrnlFmzbto0rh1XaEFRVVeHuu%2B%2Bm7UOHDlF2EgA%2B%2B%2BwzrrSyU6dOXFClDP6Uhu3V1dXYuXMnQhSv4jIzMyngOnr0KI4dOwbAH%2BDZbDba1%2BPxoHPnzqSiarVaUVJSQudgw4YN6N69O6ZOncpx7fbs2cOVyyqxbds2CtwNBgON8/Dhw9x%2BbrcbTqcTxYLJ2A8//ICHHnqIhHBmzJiB5ORkzt9QVNpUBs61QRdt0aFDhw4dOnT80dADPh06JLBarYiJiaEH8oqKCsqwAf4A71aFL47Mk02slt62bRv9XVBQoApSlPs/8MADaN26NVq0aIHu3btz4iFMAdRkMpHNQEVFBc6dO0f7vPvuuxwfzmQycRk%2BAHj//ffp74CAAC5QKSkpwbx58yi7NnbsWAry6tevzwn7OAUGfGhoKOdfZzabKVhdtmwZBTTMyJ4pZgL%2BLCRbl/DwcJjNZi7j179/f7KPYAFs%2B/bt4fF4UF5ejlmzZuGGG25ASkoKunfvTnNUBu%2BAPNAyGAxc9hEATp06xWXr2OdKgRbl5%2BwYZYnn1SATbUlO/h8QbRH4oLpoiwJ1EW2RiIwcP371wfz/Jtoi7iMVbREWUCVMI9nnryDaUhdz9v9Z0RYJtaAu9/PvJtoiCrKI27LPFJZIBMGvVRdt0TN8vxU6h0%2BHDgkKCgowevRoWK1WTJ06FefOncOmTZuwZcsWkvdnnDir1Yrc3Fykp6eTBQLAG5cDfhsEZoQO%2BIMslnFigjpKURWTyYTU1FQcP34c8%2BbNw5133skFLSaTCWazGSEhIbhw4QLGjx%2BP9957D16vF126dMGuXbsAXDE9t1qtlEVUGsgzsM9CQkIQGhqKdevWoV27dpRxY7BarXjwwQfxxhtvSNdOGeAFBgaiadOmOHjwoCoAjouLQ1FRERo3boyjR4/C6/UiMDCQyjwTExPRpEkT2Gw2rFixAmazGdOnT8cHH3yA06dPY8aMGRg1ahRuu%2B02HDp0CD6fDwEBAXj00Udx1113oaCgAH369KnRZoSdP6PRSNnSRo0a4fTpS4ds%2BgAAIABJREFU0wD8gWvDhg1x7Ngx7jyyNS8vL%2BesHpSYMmUKHpKR8wXMnDlTarw%2BceLDNRyhQ4cOHTp0/O9BovNzzZC8d/jLQs/w6dAhgcvlQklJCYqKijB58mR89NFHCAkJIT5dfHw8cnJyAPiDtbKyMgqgevTogU2bNuGxxx7jOHQ333wzAJD/IcOkSZOwZ88erF27lkzVJ0yYgDZt2sBsNqNPnz5o37497d%2BzZ090794dK1euxKeffkrqmbfeeitlo/Ly8vDyyy9TsCP644l8tdatW%2BOll16iuft8PuTm5lKAaLPZSAk1JiYGK1asAAAyrr/hhhuoLWUQ1LBhQ5w%2BfZosH3r27EnfsYwhC7YA4NFHH6XvT58%2BjcLCQrzyyiu0HwAUFxfDbDZj1KhRWLt2Lc6ePYuYmBjExMTgxRdfxMyZM/HQQw9h3rx5tB4WiwWRkZEYO3YsAH%2Bwrcw0hoeHIzQ0lII9m82G1atXo7y8HOHh4dz8OnfujNDQUG49A4R/icTS0JqgG6/r0KFDhw4dtUPP8F0b9AyfDh0SnDx5EkOHDkVISAhcLhfKysooMPH5fEhPT8dHH32ExMREeL1ehIaGwmw2o7i4mDJlYhnhggULMOGy/LLJZKoxOwQAEydOxKZNm5CSkoLVq1fj1VdfxT333AOfz0cZKZvNhri4OFy8eBHnz59HXl4ejYepcHq9XjidTk6hE%2BCzcIA/uxcXF4dTp07RZzExMVJbg5iYGJw/f56bmzhXZTYxIiIC1dXVpPApZhdlY/F6vbBarXC73Zg6dSrmzp0LALjuuutw%2BPBhmEwmHDhwAA888AAKCgpw5MgRGI1GNG3aFEajEQaDAW3atMGKFSu4rCn7u0GDBli9ejVSU1PhdrtV2diGDRsiMzMTnTt3VtlRAH7hnNTUVHz88cequQP%2B0tWr2YkwyDJ8Dz/8MCZPnlzrsTp06NChQ8f/CqSWINcIaYny74jc3FxMmzYNERERWLp06VX3/eSTT/D555%2BjsLAQrVq1wj//%2BU96jnA6nXjppZewfv16OJ1OpKWl4V//%2Bhfn51wb9AyfDh0SKE3rzWYzzGYzbDYbGjVqBAAkNGKz2Yi/xbJJJpMJoaGhSE9PJyVHAGQ%2BzsRKkpOTyfsQuGLloMSkSZOQlJSEMWPGUFDRuXNnXH/99di7dy8yMjI4cRjGf3O73fB4PDQm5TgAtTDIgw8%2BSEqUMrA1AMBxBdncu3btSsqfLOBiKCkp4QzklQEYALJgMJlMuOuuu%2Bhzs9kMr9dLwR4AUiZlbRw8eBB5eXkkzHL8%2BHEcPnyY64%2BNUzlnm80Gu91OWUaRT3n27FkkJydLgz3AnzFkdgoGgwGbN29Gc4UpmMwnUQYZh69dO57Dp6o5kdWgiO/txHGLhClIeEES3pKqK9l6iC8FxKylLIspkmhkcxL/JZa9IBHHI9HYVnUvtqthPWWvRVXLJWtH5BMJiy6bkuozyblT8Y20ELaE8ck4fFoMqsVmpXwoYczSh6pauJ5auJXSd2ZaOKR16FvVjqxh8UKR7VPb9Sc7Rvw9kV2QIs9K0o6mOdRlfOJnMs5XXeYp7FNX/qCqby0kQ/EikHADxWZk1%2BPvxa2sjXMt43bXxomUHSdrR8rT1VErvv32W0yaNIkUxa%2BGdevW4Y033sArr7yCrVu3ok%2BfPhg/fjy9qH/ttdewf/9%2BLFmyBGvWrIHP58NTTz31m8ajB3w6dEhQXFyMzp07o0uXLpxpff369ZGUlETG2g0bNoTZbOak/wMCAmA0GtGuXTskJCRQAMTexLDt%2BPh4REVFISEhARaLBSaTiUo6GcLCwvDOO%2B/gxx9/JAP27OxsZGdn49ChQ8jOzqaAr0OHDlyWyuVyobKykoJVBmUwxj5v2LAhzp07B6PRSIGbzWajv1u3bk1BIwts2JoAQL9%2B/ehvi8WiUhRNSUmB0WhE/fr1MXLkSBgMBirz9Pl8aN%2B%2BPQVR7Fj2QxcZGUnrW11dTVnW0tJSXLp0CWlpaTCbzQgLC4PdbkdMTAwFr2xMYgaOtc3GwNYW8GckH3/8ceTm5tL6KEtzAX9Zbp8%2BfWj7pptuwi%2B//EJt/PDDD9ACmfH6vtwf%2BZ1E4oKMyCA6EoumwaLLNSQG7hKzaVVXMkflmBh%2BWxTEkbm1i87HsjmJr3NlSqzieCRGTaruxXY1rKfM8Fm1XLJ2xJcowqLLpqT6THLuVCbRWly2hfHJjNe1GFSLzUpNrIUxS9/MCxPV0q54zUrFeWtpt659q9qRNSxeKLJ9arv%2BZMeIZvKyC1I0x5a0o2kOdRmf%2BJnMqLsu8xT2qavpu6pvLc7w4kUgeSEqNiO7HsVmtFxbstMrti22o/o9l7QrWz/xOFk7iI6WfPh/jz9bSafT6cSSJUvQrl27WvddsmQJhg0bhnbt2sFut2PcuHEA/CrnbrcbX331FSZMmIAGDRogPDwcU6ZMwfr166VVWDVBD/h06JAgMjISOTk5KC8vx8KFC5GTk4OtW7di9OjROHr0KPr27QvAHyj169cPa9eupUBo5syZyMzMRFhYGCorKxEQEIC2bdtScNG9e3eEhYWhRYsWqKqqQr9%2B/ZCdnY1BgwahrKxM6uEWHx%2BPwMBAREVFwePxoGHDhnj77bexbNkyRF/%2BMV6%2BfDkiIiJgMBiQkpKCPn36IDo6Gm63m7hpAMjKAbhi9L5v3z706dMHLVq0QHR0NGw2G9LS0mi/fv36EY%2BN8Ql9Ph969eoFs9mMY8eOweVywWg0IjAwkMpV2Zzbt28Pk8mE%2BvXr47rrroPP50P9%2BvUpkPrll18QGRmJyZMnkyqn3W5HkyZN0LlzZ5hMJphMJkyZMuX/sXfe0VFV68N%2Bpqf3HlIIkISQ0HsNTXoR5aKCKKKICqhcG3pBRCwXRbhEBVHAAogiUqRKC52EXhIIJkA6pPcymfL9ke9sZ4ZQ9F5/oJ5nLRZzZk7ZZ%2B99Tt53vw3f/69gvPnmmwAkJCRgMBgoLS2ltraW/Px8q9IPCoUCFxcXvvjiC9EHxcXFXL16VfRLSEiIKLVQUlLC8OHDMZlMoj7fl19%2BKY719/cnKiqKUaNGiX7YsGGD%2BAzwyCOP3HqC/X8aiuGTfexlZGRkZGT%2B3IwePVrIK7cjKSmJqKgosa1UKmnevDnnzp0jIyOD8vJyWrRoIX5v0qQJdnZ2d5wRHGSFT0amQbRaLd988w2enp5MnDiRNm3a0LVrV1avXs38%2BfPp0aMHAKNGjWL//v3cd999TJ48GZVKxT//%2BU9iY2MpKCjgvvvuo7Kykurqary8vBgwYAD79u2joKCAM2fOEBcXx86dO2nXrh2bNm3C09OT5s2bA/WujnPnziUzM5Pa2lqMRiNFRUU88cQTdO3alfLycrZv306jRo1QKBSEhITg4ODAiBEjKCgoYO/eveTn5/PSSy/x4YcfintzcHAQljvJnXLt2rW4uLjw4osvUlBQQG1tLQkJCcLFNC4uTpSeWL9%2BPVBfIP7QoUMYDAaSk5MJCAjAZDJRUlKCWq3Gz89PlGzIzc3FYDBQVlbGBx98gJ2dHXl5eUKhnDp1KjExMezfv1%2B4vsKvyXPq6uqws7MTFjetVsvu3btFvURXV1dcXV2Ji4vD3t5elE0wmUyYzWbKy8utsmaaTCY%2B/PBDYR1NTk4Wv5vNZnJzc0lPT8dkMqHX65k4caI4Njc3lzNnzuDv7w/UWzRHjx5tNX9sLZw3Q67DJyMjIyMjc3v%2BCAtfXl4eSUlJVv/y8vL%2Bz%2B%2BtpKQEVxu3EVdXV4qLi0XpKhcbk7KLiwvFDdavaZgbTQkyMjIAIuvjrRg8eDCDBw8W26tWrSIuLo6kpCTWrl1L06ZNWbp0Kb169QJg0aJFVFRU8K9//Ytt27Zx%2BPBhVCoVzZo1IzIykq1bt/LBBx9w5MgR8fJZuXIlWq0WrVZLTEwMXbt25aWXXqJnz57odDqMRqOwpJlMJrKzs5kwYQLvvvsunTt35qmnnuLnn38G6leNJCuht7c38%2BfPZ9asWRw%2BfJh169axbt066urq8Pb2Zvjw4axYsQInJyciIiJISUnBbDajVqtxcHBApVLxxRdfMHXqVM6dO4dSqRTJayRFLzw8nNTUVFEDMCMjg0GDBnHkyBFcXV1JT08H6l9sY8aMYeLEifTv31/ECV67dg2j0Yirq6twd7W3t8doNFJQUEBoaChVVVVUVlZSW1vLtGnTGDt2LFlZWaK8g3TfCoVCJLwxmUzs3LkTlUqFyWTiueeeY8iQIYwaNQqDwcDDDz8sLI06nY6zZ8/y5Zdf8t577%2BHv70%2BrVq1wc3MD6pXPxMREIiIixDxISkqicePGt51jTzzxBMOGDbP6TqFQ8P7771NVVYWDgwPDhw9n06ZNDW4Dt93n9xzzv9rnbl77Xm/f3/Xa93r7/q7Xvtfb93e99r3UPqj/m2W5KPt/yR%2BRYjIu7js%2Btqk9OGXKFKZOnXrbYzdu3Mgrr7zS4G/vvfee8AK6U26XQ/O/zbEpK3wyMv9D2rRpw/Lly2%2B5j5OTEwsXLuSxxx4TymFubi6Ojo7ExcXRvn172rdvz9SpU6moqCAuLo49e/Zw7do1zpw5w8yZM5k2bRr3338/27ZtY%2BbMmRiNRmJiYtDr9Vy7do2kpCSmTZvGpEmT0Ov1IjtUUFAQ9913HzqdjgMHDhAUFMQXX3xBr1696Nq1K5s3byY0NJQdO3Ywf/58qqqqaNKkCatXrwbqY87mzZuHi4sLV69epbKyEo1Gg9FoRKPRoNfrKS8vx2w2U1lZSYsWLfD09CQ9PZ0FCxZw%2BfJlPvnkE0pKStBoNNx///1s3bqVRYsWsXLlSuzs7NizZw9GoxGVSsWUKVOIi4vDwcEBs9lMYmIiubm5BAUFUVBQwLBhwzh06BAxMTEsWbKE2bNnC/dLKTGLo6MjTzzxBE888QRVVVW8/fbbxMfHU1RURJs2bcjMzMTLy4uwsDDefvtt3nrrLSoqKujduzfbt28XL1nJ8irF6UnuoBqNRhSal7KvahoMNrkRHx%2BfG/54JiUlWWXujIqKuuX2nezze475K1z7Xm/f3/Xa93r7/q7Xvtfb93e99r3UvmHDht01he%2BPYMyYMSJER8K7objpBhgxYoQo1fXf4u7uLix5EiUlJTRr1kzkTygpKbFKrldaWirySdwJskunjMxdQlIOExISSEhIYNWqVcISKOHk5MSMGTPYuXMn586dY82aNYSFhbFo0SJ69%2B7Njz/%2ByOLFi0lJSeHcuXP07t2bgQMH8o9//IP169fTvn17OnbsSFJSEoGBgfj6%2BqLX69m/fz9nzpwhJiaG1q1bU1FRwZUrV%2BjUqZPIKHXp0iWUSqWIT4P6rJLz5s0jLS0NFxcXcnNzRV3ATZs2sWnTJqZPny5iCO3t7bn//vvR6/V07tyZcePGsXDhQtzc3GjcuLEoS5CTkyPqFD700EMsXboUX19fpkyZwsSJE6mqqqKkpAQXFxeGDBki4gwHDx5M9%2B7dSU5OJjY2ltTUVObNm0doaCiDBg3C3d0dLy8vUlJSgPrVyvfee4%2BBAwcSERHBqlWrrGImhw0bxu7du9FqtYwYMYKIiAhhFezUqRMpKSlC4Vu9ejVt2rQhMjJS1Bd86qmnAKx87WVkZGRkZGTuPXx8fGjRooXVv7uh0EZHR1vF4xmNRpKTk2nVqhVBQUG4urpa/X7p0iX0ev0dlX%2BSkBU%2BGZk/EbdTEp988kl27dpF48aN2bhxI4cPH2bmzJlUVlYyb948vvnmG15//XV0Oh1t2rTh%2B%2B%2B/59SpUyQmJjJt2jSSk5NFcpbo6Giio6OFtaq2tpZt27bxz3/%2BkyeeeIKjR48yYsQItm/fzpgxYwgJCSEkJIRJkyaxdu1aHnroIXbu3ElsbCwHDx4UCVo6derEq6%2B%2BKtrcrl07unTpgqenJ59%2B%2BikzZ860yir68ssv06RJEwYOHMirr77KCy%2B8QGVlJUOGDEGj0TBlyhQMBgOLFi1iw4YNlJeXi8xVe/bs4eWXX2br1q3Ex8djMBg4evQoGzduZM6cOQ32saurK2fPnqVXr14sXboUNzc3Jk2aJNxPv//%2Be9LS0oiPj%2Bedd97hyy%2B/5NSpU%2BJeUlJSRPkOGRkZGRkZGRlbBg4cyPHjxwF4%2BOGH2bBhA6dPn6a6uprFixej1WqJjY1FpVLxj3/8gyVLlpCbm0txcTEfffQR/fv3F0n77gTZpVNG5i9E%2B/bt%2Beabb4iLi2PBggVAfQkByVVUYsmSJcTFxTFt2jTy/3/NrLCwMF566SWrBCRnz54lJiYGQMQavvrqq4wcORKALVu2oNPprEoUSPTo0QNXV1d%2B%2Buknxo4de8t2jxgxgnnz5tG5c%2BcbflMoFHz66ae8/fbbxMbGYm9vT79%2B/XjppZcAaNWqFf/617%2BYPXs2ZWVlDBs2jIEDBwpXzG7duvHqq68yZ84cCgoKaNSoEbNnzxbZRm%2BFt7c3a9eu5eOPP2b8%2BPEUFxfj7OxMt27d%2BOGHHwgODr7tOWRkZGRkZGT%2BXgwYMICcnByMRiMmk0nIUtu3bycwMJArV66IWMmePXsyffp0XnjhBQoLC4mJiWHp0qUiP8O0adOorKxkxIgRGAwGevfuzezZs39TexTm/zYKUEZGRkbmf0ZeXh7Lly%2BXk7b8xdv3d732vd6%2Bv%2Bu17/X2/V2vfS%2B1D%2B5u0haZ/w5Z4ZORkZGRkZGRkZGRkfmLIsfwycjIyMjIyMjIyMjI/EWRFT4ZGRkZGRkZGRkZGZm/KLLCJyMjIyMjIyMjIyMj8xdFVvhkZGRkZGRkZGRkZGT%2BosgKn4yMjIyMjIyMjIyMzF8UWeGTkZGRkZGRkZGRkZH5iyIrfDIyMjIyMjIyMjIyMn9RZIVPRkZGRkZGRkZGRkbmL4r6bjdARkZGRubOuH79Oj4%2BPigUirvdFBkZGRmZ/09tbS2lpaWo1Wrc3d1veEfX1taSmpqKn58fHh4eDb7Df8/7PT8/n4sXL1JaWopKpcLX15eoqCjs7OwA%2BPjjj4mIiGDZsmXExMTQrFkz2rVrR%2BPGjVEq620%2BxcXFJCUlsXnzZkaMGIG9vT27du3ikUceoa6ujgsXLqBWq4mKigIgICDg93aTzF1EYTabzXe7ETIyMjJ/Z3Jycvjiiy84evQoubm5ADg6OhIQEEBmZiY1NTW4urqSm5uLVqtFqVQSHBzMU089xfDhw%2B9y6/87GhJYmjRpwr///W8mT55MUFAQCoWCrVu30qpVKy5fvtygcDN37lyef/55nJ2dATh16hTR0dFoNJr/STvLysoAcHFx%2BZ%2Bc74%2BipqYGQAh8f1bq6uooKirCaDQKAbOqqori4mIAPDw8sLe3v%2BG4goICzGYzXl5e8sJIAxw/fpyWLVui1Wpvu1/79u1v%2Bntubi7%2B/v63PffN9mvfvv1t2yL9fvbs2Vu25XZt/SNZvnw533//PRkZGRiNRgAUCgU%2BPj689dZbbNiwge3bt99wXEBAALNmzSI%2BPp41a9ZY/ebs7Ey/fv1o3rw5GzZsICUlBYVCgcFgIDAwkCeffJJBgwYxffp0jh49itlsxlaUHzFiBPv37xfPS0OMGTOGhIQErl69etN9lEolJpNJfAa4cOHCHfWNzL2FrPDJyMjI3GU6duyISqWipKQEk8mEQqHAbDajVqsxGAxW%2B2q1WnQ6HTU1Neh0Op577jnGjx/P8ePHycvLIygoiDZt2gBgNBq5fPmy%2BKNfV1eHTqcjODgYDw%2BPBo%2BRyM/PJykpieLiYoxGI4WFhbi4uBAZGUmbNm2shG9nZ2eqq6utVqczMzPFud3d3cnLyyMjI4P8/HyCgoIICgpi/PjxXL58GbVajUKhwN7enrKyMivhRaVSYW9vT2VlJWazGZVKJX4zm82YTCYhlDg6OgLQpEkTkpOTadasGQBubm4cP36cTp064eXlxfbt2xk0aBA5OTn07t2bxMREevfuzZYtW/jll1/QarXk5eXh7OyMv78/BQUF5OfnAxAWFsbQoUOJi4sjIiKC/v378/TTTzN58mSWLVt20zE2GAzMmDGDkSNH4uzsjK%2BvL59//jk9e/bkwoULjBs3jn379tG3b190Ot0Nx9sKtSUlJcydO5dDhw7h4OBAQEAAWVlZ5OTk3GqqCRQKBVqtlhYtWvDwww83uHBga3EwGAy3nDOW95qcnExZWRkuLi54enqSnp5Oeno6AQEBREZGotfrWbduHVlZWezbt4%2BoqCjMZjN1dXWkpKRQWVlpdU7LZ0GhUKBQKPDw8ODZZ5/l888/Z%2B3atYwePZrq6moAfvrpJ3x8fO6oL2wxGo2oVCoMBgOpqakkJiZSVlaGh4cHzZo1w9fXF61Wi4%2BPD0qlkvT0dPLz8wkMDMTf35/09HSuX79OUFAQer2e7OxsCgsLMZlM%2BPr6EhwcjJ%2BfH5mZmfz444%2B0bNkSV1dX7OzsWLlyJR06dMDBwYHCwkLy8/N57LHHOHjwIF26dMHV1RWtVsusWbOYM2eOaPOmTZsaHMPmzZszcOBAAgMDuXDhAgcPHsTb2xtPT08uXryIg4MDU6ZM4aOPPqJz5874%2BvoyceJERo0axZkzZ8S8a9WqFWfOnBHnjYyMJCYmBh8fH9LS0vD19SUhIQE/Pz9cXV25dOkSrq6ulJaW8uKLL3L27FnGjBlD27Zt6dy5Mzt27KBPnz5otVoaN27M1atXqampwcvLi5EjR3Lx4kXR1oKCAnQ6Hd7e3gwaNIivv/6aEydOkJGRQWlpKePHj2fXrl3s37%2Bfli1b0qRJE9Tq3%2BbAZrlgZPnuA9DpdLRv357Lly%2BTl5eHWq3m%2BPHjHDhwgPLycp588knS09NZu3YtUD8/JSXp92Jvb091dbWVwmWJWq3GZDLh5OQkFqP%2BKBQKhXjH9O/fn3bt2tG9e/c/9Joy/3tkhU9GRkbmLvLdd9/x5ptvAvXKnIuLC%2BXl5cJS4%2BPjQ15eHo6OjlRWVuLo6IharSY2NpbNmzeL1d175VWuUCiEsPxXJzw8nKysLDw8PMjKyqJv377U1tZy6NAhunfvzsmTJwkPD%2BfChQvo9Xor5dSWkJAQ0tPTcXZ2pm3btowbN44vv/ySgoICMjMzqaqqQqFQCGX/v8XZ2ZnKykqxwCApdSqVCqVSib29PSUlJahUKmFdkLC8B0lZr6ur%2B6/G3tfXl%2BvXr4ttd3f3W1onbJEWSSQ0Gg0ajYbq6moaNWpE9%2B7d2bt3L%2B3atcNgMNCnTx/WrFnD119/TWlpKX379uXBBx8EYM%2BePRiNRvLy8n7zffwRSP2tVCpRqVR07tyZo0ePcv78efr378%2BUKVOYOXMmW7du5eWXX6ZNmzb06dOHnJwcXnvtNZo3b05ycjKurq6iT6Vz6nQ6amtrAQgKCiIzM1PMhTFjxrBmzRpiYmI4f/48UN%2BvTZs2FW5%2BBoMBf39/cnNzxSKVyWSymuOWY6NSqYQlTPrNwcGB6urqG54Ly%2BMcHBxwcHCgoKDght9ssbe3x8vLi6KiImpqasTCkL29PXq9nqFDh7Jnzx68vLy4evXqLd%2BdN3teb0VDbQsICLjtYozttZydnSkvL7/tuW9Hv3792LVrFwDBwcHk5uZSV1fX4L4qlQqNRnPDO0aaE2azGaVSKVv5/oTICp%2BMjIzMXSQ6Ohqz2YzRaMRsNqPRaDAYDDf8Uff09KSwsBCo/%2BMbEBBAdnb2DedTKpW/WQH8PULE/4rfI1D9UdgKo9Iqu4RtP0kuTn9E%2B//bMZk0aRJLly61Otf999/P%2BvXrgXrB3WQyWd3vH9GmhqzUd4qfnx/Xrl2jRYsWJCUlYWdnR01NjVU7/i/nru18%2BK381rluu7/t/NRqtej1erF9q76wdc27mUIGv23MGnrftG7dmtOnT9/yOMtrSotaErb3CfUKX1VV1U3PcS8hLc5ZMmLECDZu3AjcfFHMtt9/z7Pz9ttvM2vWLKt%2Badu2LSdPngRg5MiRFBcXs2/fPqvjpAUXOzs7VCoVu3btok%2BfPuh0OhISEgCoqKigR48enDp16je1SebeQM7SKSMjI3MXefHFF2nRogXOzs7Y2dlhNpuxt7dHrVbj4uIiXJMk5QKsBT2FQkF4eLj4/Oijj1qtpkv7W%2BLg4CA%2BSxYdaQVX%2Bt8y8YBCoWD58uU3tN3W9dA2VgcQMXUSbm5u4rNCoUCpVFrt4%2BTkRJMmTcR206ZNrY6PiIiw2rb83dvb28qVy9PT02pf23i%2B4OBgq74JDQ21%2Bt12ldtWuLTdtuzHmyGNiW1bJEuZdA7Lc3t5eYnPtvd0M7Zs2XJDO7OyssR3tte3bbflfJOIjo622v/ZZ59t8FhLLAVWy7kl4eTkdNNjr127BkBSUhLw63hY9o3lZ8t22LapoXg/qI8FtMTyvqW2SWNWXV19Q79J88fV1fW217NNdiG5IIP1cyFhqxzaKkHSO6BLly7Ar30xd%2B5cq3bqdDqreaPT6Rq8noStkmH7nFvGhwYEBNCtWzexrVKprJQ9yYJni%2BW42VpSe/XqdcP%2BVVVVN7TDdux/a8ymNNaOjo7Cet6pUycGDhwo9pHcwlUqlXi3tGrVCm9vb7GP1B/SXDCZTFau54BYKNBoNOh0Ov71r3812CatVivitBt6Br///vtbxiW3a9fuhmufPXsWqO8jJycn8b613E%2Ba65Jbdbdu3aiurqaqqopOnTrx0EMPMW3atJu6ccvc%2B8gKn4yMjMxdZOLEiZw5c4aysjJqamowGAxUVVVhMBgoKysTwpcUQwb1Gd%2Bk7aCgIGHpM5vNPP/882I/SUD09fW1uqblttFoFNZF6RzS95bfderU6Ya22wrLtqvawA2xNJbCiuQqaGmlqKiooKioSGxfv37dSpCzVSDS09PFtuTuJQlgFRUV2NnZCcHJ29vbSsFzdna2UpxthWBbgUsS0C0VYUuhMzg4%2BIZjbBW8kJAQcR%2BS%2B5S0LVlKbIU96b4AYeW17APpi9A7AAAgAElEQVRbFAoFOTk5Vn2vVCo5ceKE2Pb29rZSIFQqFSNHjhTbDVmi3n//ffHZbDbzyCOPiM93gtQeyYUQbpwzkttiQwpbjx49cHNzIyAggLCwsBvOb6mI2LZJErht52NJSYnVtm38KNQ/YxLOzs5WYyMl8bG1/FoqBBKW4wjWCyQVFRVA/TxpaOGgoT6ROHLkiNX2nDlzrFz2amtrrfrGw8ODli1b3nCeVatWWW1LMaOWz6d0PomsrCxhAYL6uWep/ErulNJzZ9v/tvekUCiIjIwUfe/n5wfU94vlsTqdzmqxwDa%2BV0IaK2kxxfIYW8XJ2dmZCxcuCBd7gIyMDKB%2BnKV3T0pKipWrpdQfUj/V1taKZ0u6/s8//yzaWVNTw%2BzZs29or8FgIDw8HL1eL9xPbftn//79wn3a9v0L8MMPPwD1/Wz5bpH6YOXKlWzevBmwXkBIS0sTbZfcz6V7Kikp4dSpUyQlJYlFHpk/H3JZBhkZGZm7jIuLC5MmTaJ9%2B/acOXOGiIgI8vPz2bFjB/v372fixIksXbpU/PE3mUy4ublRVFSEv78/FRUVVFVVYTabrRQgyd3Ky8uLzMxM8b1lbJSkXBgMBrECXF5efkO82bZt26zarFQqady4scgqCjQocNl%2BZylkSfeg1%2BvFCnptba1V%2ByzjWBQKhRBMpHNbCi1hYWGkpaUJAcfV1dXKelBQUGAlvF68eFF8NpvNVgqRdD3LPqisrLRS8kJCQrhy5YrYPysrC6VSKdrk5OREXV0dRqMRk8mEg4ODsFpJiR2kfe3t7amrqxMC8s2UKMnNS3IvbGg/6TtL5dhWgbOcJyqVig8%2B%2BIDFixc3eE2JL7/8UvSHSqXi0UcfFfdied2bIQmq0v8NuR7fzOWxurqaAwcOAPVKmqWVQupDS3fLJk2akJWVZaWcQL2FOCkpSVzf9nqNGzfm8uXLAJSWlgK/Cv2A1WKESqUSSlx5ebloi1KptLKmQn0f2VqMLeeOwWBAqVRSV1eHm5sbJSUloo0KhQKj0XhDXKOHhwdFRUXC1VVyJbRV0ACr%2BLHs7GxhLVMoFGg0GvR6Pa%2B99hqAOJ%2BktNqOkWW7oF5xsHwuJOVVora29gZFXnJRN5vNhIWFiT43m8189dVXNyhMRqMROzs7sUBQW1vL0KFDWbdunbiOZTtdXV2F8iKhVCpF26Q%2BhV/nZcuWLTl48CAzZsywOgasFyak51SpVOLo6IiTk5OIPw0JCaGiooJmzZpx4sQJli5dSkZGBkuWLKGgoIAlS5awa9cuvL29adeuHZWVlZw%2BfZpvv/0WvV5Pjx49aNu2LeHh4Zw5c4aLFy%2BSmpqKXq/HaDSSlZVFREQEPj4%2BYtFmy5YtYt6VlZXRu3dv3nnnHVxdXamqqmL79u3s2bOH3r17U15ejq%2BvL507d7aytLdo0QKTycRrr73GggUL6Nq1K5MnT8bDw4Pa2lpKSkr47rvveO655/jhhx%2BsFkFk/hzIMXwyMjIyd5nCwkI8PT3Jzs4mOzsbhUJBcHAwvr6%2B/PLLLzg5ObFmzRo2bdrEtWvXhCXKUvj8syO5MbVp04aEhAQ8PT1RKpUUFxdTV1dHs2bNcHR0pLq6mpSUFMxmMx4eHowdO5avvvqKoKAg0tLS0Ov19OzZk0OHDuHv709OTg5ubm4UFBTg7e0tLKNOTk507NiRvXv3itjJmyUykHB1dcXDw0MI6gEBAajVaqEQWCofUiymvb29VeyRtI8kVIN1TM%2BoUaPYtm3bb44VkxRBSSmws7PDxcUFFxcXGjVqRH5%2BPtevXxcKimVbJaVcEhpvpnQ1FDPVULzVrbhZ3JXt9yqVir59%2B5KSkkJmZuZt22SpNEC9m%2BORI0dQqVRWiY0s48UUCgV%2Bfn5WixZqtdrKun03cXR0pKqqisjISC5cuICbmxulpaUolUrUajV1dXWYTCY6duxIYmKiGDsXFxcqKyvFuLRu3ZqLFy9alexQKpUiEdCDDz5IeXk5O3bswGw2ExcXx4svvij64YUXXmDhwoXAjfFnQ4YMIT4%2BXihETZo0ITMzUyhaHTp0oFGjRiJ29NVXX2X79u1cvHiR2tram8apKRQK1q5dyzPPPEPHjh15/PHHmTRpEsXFxSLBkeW%2BdzJe0rXu5Fn/PUgLbGq1Gjc3NyoqKjh9%2BjQff/wx7u7u4j3h7u5Oz549cXd3x9/fn5UrV%2BLg4IBCoaCkpAR3d3ciIiJo3rw5cPOyE9evX2fTpk2cO3eOoqIiKioqCA4Opk2bNgwfPhxHR0f%2B8Y9/0K1bN44ePQrUu/M//vjjpKSkMGXKFJYsWUJAQACLFy%2BmqKiIkJAQPDw8hGJXV1dHq1atxDW3bNlCQECAlbVf5s%2BBrPDJyMjI3GWys7OZNGkSqampwK/xKE2bNmXlypX07NmT06dPEx0d3aBw5Obmhkajobi4GJPJhFqtFu5Ler1euIha4uzsTG1tLQqFAi8vL2pra6mtrcXe3h6TyURNTQ0ODg4EBgaSlJQkVrWldP6Si1Z1dTVqtRoPDw%2BCgoKoq6sjLy8PlUolBEPJIhQQEECjRo0wGo0cPHhQXE9qh6enJ2q1mqKiIrRaLQMHDuTQoUO0bNmSCRMmsHPnTtasWUNpaSkhISGo1WpSU1OprKzktddew87OjlWrVvHRRx%2Bh0WhYu3YtGzZs4LPPPqOsrIyPP/6YU6dOER8fT3l5OW%2B99RbHjh3jP//5D3369GHfvn1s2rRJpJO/cuUKZWVlwoLh4eFBREQELVq0IDU1VViZOnTowI8//khmZibp6elUVVUJC5wktH/33Xd8/fXXHDlyhMLCQjZu3MjSpUvx9/fH3t6eqKgovvvuOx544AFiY2N566232Lp1K0FBQRiNRqqqqigsLBRZOiULg4%2BPD82bNyc0NBSdTkdpaSlBQUEMHTqU7Oxs9uzZg5%2BfH2q1murqalasWEFhYaEYdx8fH1q3bk1CQgJpaWmYzWZatmyJSqUiOTmZoUOH8u2332I0GnFwcMDJyYng4GDR/%2BfPn0ev1%2BPm5kZgYCA9evTAxcWF119/HTs7OwYOHIjRaGTfvn3o9Xo8PDxQq9XU1NTw0UcfUVRUxPr16zl27BgZGRk0atSIAQMGMHDgQFq0aMHXX3/NkCFDWL58OY0aNeLEiRMEBAQQGBhIUFAQzZs3Z%2BrUqaxcuZKtW7diMBj4/vvvSU1NpbS0lMmTJ7Nt2zYee%2BwxPvnkE1q1akV5eTnnz5%2BnUaNGdOzYEb1ez%2BbNm6msrMTb25uqqioqKytxcnKioqJClAZYv349mZmZrF%2B/nqtXr1JRUSEUB0kRMpvNomSDs7Mz9vb2NGvWjIyMDC5duiTctO3s7LC3t0en01FXV0ddXR1arZaysjKaNGnCokWLsLe35%2Beff6ampoYePXowb9481q9fj6urK/v37%2Bfpp5/m2LFjTJo0ifHjx/Piiy9y5MgRZs%2BezbFjx0Qc57PPPounpydffPEFdnZ2DBgwgF27dpGamopCoWDQoEHk5%2BcTHBzM2bNnefnll9m2bRtRUVEUFRXxwgsvsHPnTtLS0igpKaF///58%2BOGHGI1GhgwZgtFoJDExEbVaTUxMDDk5ORw6dEi4trZo0QKNRoNCoeC5557jyy%2B/RKfTUVVVxZNPPsl//vMfdu3aRVhYGBqNhuvXr3P16lVeeeUVamtr2bBhAzExMdTU1FBVVYVaraZXr15cvnyZo0ePUlRUxMCBA9mxYwelpaXY2dmh0WjIycnByclJWBcrKyupqqoStf2k8b3TRYubLXhIixEqlQonJyfs7OwoKCjAaDTSpEkTUlNTadq0qZV3gqSwd%2BvWjUOHDgG/JimSaNu2Lfb29uJ3qLeMFhcXi5jD8PBwLl%2B%2BLNxou3fvzk8//URVVRUuLi6UlJTQq1cvYR2HestwQUEBW7Zs4YEHHgDqC7RPmjRJZO2FXy2mAQEBVt4lBoOBgwcP3ra/ZO4tZIVPRkZG5i4zfPhwUlNT6du3L3v37uXUqVMcO3aMp59%2BmoiICC5dusTZs2dF3I3ZbGb37t307dsXgHPnzt1wzlmzZom6XO3bt6e2tla4qLm5uYkkLJMnT77pMdLKslQ8Wfp/1qxZAFZ1wCQ2bdoE1AsJlqvSS5YsEdfatGkTCoWC1157jejoaKKiosjPz2f//v3o9Xq0Wi11dXX4%2Bfk1mMq8UaNGQrGUlNBbuThaxsGYzWbc3NxwdHQkJyeHkSNH8sQTTxAeHs7EiROZP38%2BM2bMYM%2BePUC9EFZZWWkVsyNZUsLDw3nwwQfp168fAwYM4NixY1bJOnJycvD09OT48eMUFRWRnJxMYWEhCQkJDB48mLZt29KrVy9mz55N9%2B7dWblyJSEhIWRkZNC6dWtatWpFbGwsWq3Wqv%2Bg3oXwueee48SJE0Ih/S0ZIKVYq/Lyck6dOkX79u1FHUFPT0%2BhYBYXF4u4wWHDhon4z%2BPHjxMVFcXMmTOZPn06JpOJY8eOYTAYuHDhAgUFBSLuqry8nNLSUuzt7fHx8cHDw4Pr16/z7rvvioUDy/uzLMw9duxYUlJSRB04y3pwEydOpEOHDixevJhnnnmG5cuXk5iYSMeOHampqcFkMnHo0CERF5qTkyOsrR4eHsTExIg4KFsrSk1NDXv27OHcuXMizs/Dw4PWrVsTHx/PO%2B%2B8I9os1cCTzmE7VpbbS5YsYcSIESxevJg5c%2BawadMmOnXqxKZNm9i4cSPu7u6kpaUJ98Pa2lqxYOLh4YGHhweurq4kJSUxdepUhg8fjqen5w11%2BSRKS0t57bXXiI%2BPF0qmtDAEWFmaJfdFjUZDbW0t9913H76%2BvkyYMIGBAwcybdo0kUDIZDKxbNky0tPTCQwMFImksrKyMBqNuLi44O/vT0pKSoPzz83Njbq6OiorK7G3t6dFixZ4eXmxZ8%2BeG9wwJSXE398fJycn4fVgNpuJiIjg9OnTTJgwwWpOLVmyhPHjxwsXWA8PD/Fsnj59Gh8fHzw9PTl79iytW7cWruazZs3ijTfeYP/%2B/URGRgovg/DwcLRaLW%2B88QbPP/88jz/%2BOIWFhXTs2JFRo0YxefJkduzYwZYtW3jqqac4efIkEyZMwM/Pj/z8fE6ePEm7du3EfcXHx9O1a1e8vLyE1dbyHV9XV4dKpSIkJISrV6%2BKd9i8efN46aWXMBqNtG3blpEjR/LWW28JC/bw4cNFNlDJojlu3DhWrlwprj1jxgzee%2B89QkNDhaXU1hPBFstSDHeShVXm3kNW%2BGRkZGTuIg899BCnTp0SK/13KrRL8TqSJQbqBeBly5YxceJEjh8/LvaVBGWoV8S2bdsmtiUhd9myZVauO9IxkuBteQ7LfQYNGiTOa3sOSYlo6NpSXE9kZCTTp0/nySefBOoFi08//ZSnn35abEuCXkpKirAaFBQUCGtmREQEFy9exNvbm7y8PHx9fcnJyWHSpEksW7YMZ2dnSkpK6NatG0eOHBHxPbW1tfTr148DBw7g7OxMaWkpsbGxmM1mduzYgU6nY/z48ezcuZOrV6%2BiVqtp3749jo6O7N69Gy8vLwoKCqximrRaLbW1tWJ8JIFarVajUqno2rUr%2B/btE4qLFD8pCbaNGzcmPT2d7t27c%2BTIEXx8fFi6dKlYiZfGQRKKR44cyfr16wkLC%2BPKlSuYTCZRT61x48ZcuXJF1CTz8/MjJiaGHTt2iAywRUVFt3SJk9LhW8anScWeGzpOsk5blgBQq9Wivb1798ZoNLJ//35UKhXr1q0jODiYzp07o9FoOHDgAJ06deKZZ54hLi4Os9lMu3btOHnyJC%2B99BILFy4UKeYtFTQpHtKy1lxDSG2W%2Btvb25vIyEgOHjyInZ0dWq0Wo9Eoni2tVkunTp24fPkyZWVlIm5Pso5YKqF38rzYPmOWVnulUkmnTp04fPiwaKekSCkUCrH406NHD/bv34%2BPjw/Xr1%2BnUaNGZGVlCVdFpVKJnZ2d6BMnJyeio6NJSkqisrKSsWPHMnr0aB599FHKy8tp3bo1J0%2BeZOPGjQwbNgwfHx8rN1dLXFxcMJvNVgsglvOgc%2BfOPP/88zz88MPinhYsWMD58%2Bf5/PPPcXJyoqqqymqcJCtXVFQUFy5cYOnSpSxevJiTJ08KhSguLo4VK1aIBDGSy7fkon07N01JYZKs9wDjxo1j79697N69W7w7LccSfq2fd%2BbMGfFebd%2B%2BPT169OC999676fX%2BKG5XUuOP4PXXX6d58%2BZ07NiRjIwMxo8fT3x8/B96TZn/ParZs2fPvtuNkJGRkfm7snTpUkpLS3F0dBTujzqd7gbFTxJEJcLCwoSw3qpVKzIyMvjss89o2bIlS5YsEcWPTSYTLVu2ZMOGDRiNRkpKSlCr1SKznxQ32LJlSzZv3iyERJVKxZEjR8jKyhIWhsOHD4s2ms1mHn/8cebPn09paSnZ2dnExsby3XffCbefrKwsPD09RSHrkpISlEolR48eFefIz8/np59%2BssoIKmWRk7bLy8uFlclsNlNZWSn2N5lM5OXlYTKZqKiosBJGT506hclkEu6oUiZUhUIhrIHXrl1Dr9dTXl6OwWAgNTVVuF4ZjUZOnjxJWVmZSPCRn5/PL7/8AtTHKwUGBgpLY7du3TCZTGI8JVdVk8lEaGioKKJuMpkoLCzEaDSi1WoxGAw88sgjJCUl0bx5czIyMnjsscc4ePAgQ4cOZdWqVeL%2Bn3nmGRYvXiyyel66dAmTyURxcbHok7KyMuDXDJRSQp/y8nKrbHy2cYIqlUokAVIoFCKpg16vp2nTprRr1460tDQxdhERERQUFNC8eXNKS0sxmUy4urry4IMPcuHCBUwmE40aNSI8PJzr16%2BLWMlnn32Wbdu2YTAYWLNmDZ999hlGo5G6ujo%2B%2B%2BwzTCaTVeZHSfk4fPgwJpOJxYsXs3jxYpFhVkqKYzab0ev1VgKwdD9qtVoojzk5OTRr1ozCwkJqamqElcNoNBIQEEBhYSEmk4l27dqRnp5OVlYWJSUl6PV6JkyYQGJiIomJiRQXF9O8eXM2b95My5Yt2bhxI9nZ2RgMBtRqNYcOHRJtk5S6o0ePiu%2BioqKERVyyrpWXl4s5qlAoREyhQqGgY8eO/PLLL2RnZ2M0GvH19aW4uFjM9y5dupCZmYnZbMbT05PS0lLRP9euXaO8vByj0cipU6dYtWoVVVVVGI1GsrOzMZlMrFq1CrPZLBKbSFlspZqNarVaLFzYJqCRyMrKuiGRyvbt24WSLo2Pr6%2BvULykBDeS8vbTTz%2BJMa%2BqqsJkMrF582aruqNSyQAJqZ9cXFzE4sKwYcNwcXEhJyeHpk2bUlxcLBYfpIUIyQK5ceNG8W5q1aoVGzduFM%2BywWAgMTGRI0eOYDAYyMzMRK1Wc/XqVaBeCa6trRX91BBSfN/NtqHhjKW2/BblTmpPZGSkVYbYoKAg8Y6Q6Natm1WiodjYWLKyslCr1Xz22WcEBgYC8MorrxAdHU3v3r3vuB0y9wayhU9GRkbmLnLu3DlGjx5NcHAwJSUllJaW4uPjw1NPPcX7778vUpq/8cYbzJkzRyhxMrfn/7ows06nuyEr5L3E7Qo5/9bC4Pcitxtz23u818fstyBl7ZTiYi3v09J1E/4aY30vI80rydLv6upKRUWFWLSTsg9L2UqhPsOntGAA9aVTmjZtypEjR8QCwEsvvcQHH3wgPAokLwNpzlsWfZesw1KyK1s3XqkNGo2GhIQE2rZt2%2BC9SPVhJW%2BE%2BPh4q9qgMn8O5Dp8MjIyMneRmJgYRo8eTUZGhljxzsvL45133sFoNNK%2BfXucnZ15%2B%2B23G0wj/2fjZnXjbrUdFRX1u857s5p4lvu2a9fOartx48a3PIclloWzgTtSHCzrAN7u/L8XqV5Z69atgV/baZlKv6G6brZzq6EyG7fDst4b3Lqw%2Bs2QEolISCUEGjVqJPpLqn9ny%2B0UfNt7/K3K3p2O153Mx/81UtZeS%2BFewtYiZ/m7JLxLSYikeDapvb9nHlii0%2BnEuX7rfJD60cnJyerZkcpJ3KtIz0Hv3r1xcnIS1lYJpVKJg4MDw4cPF/2bnp5utSBTWlrKpUuX0Gq14t3/7bffijk%2BZMgQBgwYYHVdf39/Yb18%2BeWXiY6OFudXKpXodDpCQ0Pp1q0bq1evpl%2B/fnTt2pU5c%2Bbg4uJCcHAwUO8yK/V9TU0NRqORsLAwxo8fLyt7f1JkC5%2BMjIzMPcC5c%2BdYs2YNaWlpaDQamjRpwvjx40WB6WXLlomEFEePHiUvL4%2BsrCx0Oh0vvvgiH3/8MdeuXSM6OppLly5ZJT6IjY3lyJEjQriV3L0shYsvvvhCxNFBvXCdk5MjBMOePXuSkJBgJSB37NiRU6dOidiZ0NBQsrOzrWJpwsPDuXr1qmiPVBtK2kepVBIVFcX58%2Bdv2jcajQZnZ2eKioruKKOetLItIVm2JOuPZXkGX19fysrKhHvj1KlTOXLkiIiBtLUYSYlOpH4JCgoiPz%2BfmpoaYmNjycvLIzk5WcTNvfrqq7z33nsiqYwUr9S0aVNSU1PFarsUKye5bEoWGAcHB4xGY4NjZxsf91uRMktCvQLVsWNHdu3aJeZFq1atOH/%2BPEajEZ1Oh4ODA8XFxSKuSUr6INVxk8bKy8tLuORJmUSl7INDhw5l//79lJeXYzab6dKlC1FRUXz99dfU1dURFBREdnY2Fy5cIDIyErPZzCuvvEJcXBz9%2BvUjMTGR2tpaevToIbJQShkPAd555x2RIdRgMDB8%2BHB%2B/PFHcb/x8fH06tVLZFeU5qPk5nfu3Dn69%2B9PTk6OUFLs7e2F1UQq6xAbG0t8fDzBwcFkZGTg5%2BdHYWGhaEdUVBQpKSlWczUkJEQkNYH65yUzM9Nqn4iIiJsmOpHmomQVkqw5Q4cOZdu2bQQEBJCZmcmUKVP4%2Buuvhduej4%2BPqNX5W5DmerNmzUhNTcVoNOLl5YXRaBTxdw3FzjVkPbSMc7UtgyI9G7/V6hgdHU1ycjIqlYqIiAjOnz9vVWLE19eX8vJykbGypqYGJycnoRhLFlHJ3bdbt27s379fZL6UngOVSiXGXRqrhQsX8sILLwC/vm9WrVrFhAkTUCgUIgHL2bNnAW5ImmWpvBqNRjIyMqySA0kZj/fu3UtycrL4zd3dnZiYGHr27CnOkZOTw%2BXLlykvL6dly5ZcvXoVnU7XYCmHm9GpUye2bduGh4eHiGOU6rqOGjWKfv368e9//5sDBw7Qs2fPOz6vzL2DrPDJyMjI3AXS09MJCQkB6gswp6SkkJKSwuXLl0lPT6e4uFgoKVIK7uLiYry8vDCZTHTu3JkePXqQkpJCeHg4mZmZzJ8/n7Zt23Lq1ClmzJjBhg0byMjIwNPTk4yMDCHkTp48mStXrmBnZyeE4XfeeYdz586xc%2BdOnJycWL58Oa%2B99hrHjx9Ho9EQGBhIcXExFRUVjBgxgpqaGq5cuWJVvHzAgAFkZWXh7%2B9PUlISubm5dOrUiYSEBEaOHMmGDRuYOXMmW7ZsITMzE4VCIRSkiRMnEhsby4oVK9iyZQtPPPEEZ86cYdy4cdjZ2VFSUsLWrVtp3rw5jz32GM2bN2f69OlMnz4drVbLwYMH%2BfTTT3nzzTcpLS1l9erVhIaG0qZNG%2Bzt7fn4448JDQ3F19cXs9nMwYMHue%2B%2B%2B2jXrh3Dhw%2Bne/fujBs3jtdffx2oz5zapUsXBg4cCMDDDz9M7969ad26NQcOHODYsWMolUrc3NwwGAw3JDGRimdLwqxt0WyJ0NBQIYTm5uaK4yUl9ZlnnqFz5868/PLLFBQU8Oyzz7JkyRKg3hr5448/Mn78eM6ePSvurVevXmzevJnhw4ej0%2BnQaDQiEcfKlStRKBQ4OTnRuHFj8vPzRSzS7bBVOKRty0QYDWGpgCsUClFGpLCwkAULFjBgwAAWL17MwoULWbBgAf/85z/p378/Fy9e5IEHHqCyshKDwcCmTZuoqKigX79%2BfPjhhyxcuBCdTkd8fDzJycmirp5er6eoqOiOrHe2SoaPjw86nY7MzMwG78vZ2ZnGjRvz9ttv88EHH6DT6bh%2B/Trt2rXjwIEDZGVl0b59e%2B677z7effddzGYzQUFBXLlyheeff15kow0NDUWr1VJUVMS5c%2BeEMlVaWopKpaK6uhqFQkFQUBAVFRXY2dmhUqnIzMwU5QQkpUlyudNoNCL%2BztK1r1evXsTExPDJJ59gNpsZNmwYU6dOZejQoQCsWbMGBwcHDh06xPLly3FwcCAtLQ2dTodKpRJlBhpCKtORlpZGbW0tYWFhfPnll/Tv3x%2Bz2cy7775LVVUVO3fuJCUlhbZt21JZWcn58%2Bepq6sjJiaG2NhY5s%2BfL94/AQEBjBkzhg4dOuDp6YmTkxOzZs0Shcu7d%2B9OXFycSFZliWXyK8vFIcvFHoCCggKcnZ0pKyujS5cuHDt2jO7du6PRaKisrCQ7O5usrCzMZjNeXl5ERUVx4MABZsyYwbvvvktYWBgZGRkEBgbSv39/li5dKiyO0pgYDAZcXV3R6/XU1dWJuobSQotWqxXx2pJyKblO6nQ63N3d8fT0RKPRiO2QkBBcXV3RaDTk5%2BezevVqq4W534OlEu7o6EhpaSlTpkzh4YcfRqlUEhsbK%2BZnSUmJnKHzT4qs8MnIyMjcBSIjI0X6eCnN/J1guVIeFRXF4sWLrVLlW5ZTkJAy0NXU1JCbm2vltihZsmxXg6Vjblb0F7BSUHJycm7Yz/LYiooKysrKCAgIEL9//PHH7Nq1i759%2BxITE0NmZiYAK1asoKKigoiICHr06MGRI0dQq9Wkp6fj6OiIVqvl6tWrNG7cWPRFUVGREFAbN26Mh4cHvXr1onPnzsydO5c5c%2Bawf/9%2BTpw4gclkYvDgwTRp0gSoV0hycnJITU3lxIkTODs707x5c8LDw/H29ubRRx%2BlWbNmnD59Gj8/PwYNGoS/vz86nU6svldUVAHWTxgAACAASURBVJCRkSGERSlZjFKpxNPTk6CgIFxdXXnuuefw8PAgLy%2BPxYsXs3PnTiIiIoD61fvz58%2BzevVqPDw8OH78OMePH7dK8S%2Bxa9cuAPr160dOTg7Hjh3D3d2doKAg9u7dS9OmTVm8eDFeXl507dpVZE3My8vjp59%2B4ttvvyUkJESkuFer1YSEhJCfn09JSQne3t44OjrSqFEjnJycMBgM5OTkkJGRgaurKwqFAkdHR0JDQ3FwcKCkpITDhw8j5YHLzs5m8ODBwkqWm5uLm5sbVVVVeHp6smnTJtq3b281H7766isee%2BwxLly4wA8//EBSUhKXLl0SQvrVq1dZtWoVEydOJCEhgY4dO/L555/z7rvvUllZSbdu3UT7XFxcqKurQ6fTodfr0el0nDhxQtTOU6vVoti12WymurqaoqIi7OzsRLZOKUX%2B66%2B/zoEDBwgMDLzpcyLxr3/9i5EjR/4m60pOTo7oBykZiaenp9U%2BaWlp/PLLLyQnJzN27FiKiopYs2YNJ06coLq6WmS9VSgUhIWFUVZWxrVr1zAajdjZ2eHi4kLTpk154IEHyMjIYMOGDWRmZuLh4UGHDh2Ij4%2BnoKAAnU5Hv379aNeuHbt27cLPzw8nJydcXFxISEhg6tSpQH15AE9PT2JiYoD6ZCydO3cmPz%2BfnJwctm/fTl5eHl5eXlRXV5OdnY2joyNjxozh%2Beef57PPPruhJMycOXPIzs7m8OHD/Pzzz%2Bzfvx%2Bot7BFRkbi5%2BfHuHHj6Nq1KwAvvfQS4eHhJCUlUVRUJN4Pjo6O5Ofn07RpU9zc3ISiJdWxNBgMeHt7Ex0dTceOHYmKimrwPTdx4kTefvttMTarVq1i7NixJCYmMm3aNObOncv06dNFDcWbJbL5v%2BZ/FZ%2BpUqlEvUSNRsOMGTNEIhyZPx%2BywicjIyNzF3jrrbdwcnJi27ZtaDQa0tPTRbFyf39/CgoKKC8vx9nZmRYtWuDn58eRI0dQKpWEhYXh7e3NoUOHcHR0JDo6msjISFEn7cqVK2RlZbF582ZqamrYtWsXS5YsYerUqej1elq1asXgwYP597//zRtvvIFKpaJ9%2B/ZcunSJtLQ0Ll%2B%2BzObNm7G3t7cq8u3r60u/fv0oKyujZcuWvP/%2B%2B6xatYo333yTwsJC3N3dGTNmDJ9//jl6vZ6AgABatGjB5cuXSUpKol%2B/fuL%2BMzIyOH36tCgwf%2BnSpT%2Bkn2%2BVxEOpVIoixA3t5%2BbmJjIbNnSsFONiNBpp1KgRu3fvBupdIXfu3AlAnz59mDt3LidOnABg3bp1jBo1Slzvxx9/pEOHDri7uxMWFsbSpUtZsGAB8%2BfPJzs7W2TIDAoKQqlUct999xESEsL48eMBmD9/vhDCb0VKSgpdunQRwmtqamqDZQKgPmvipk2buHz5MpGRkSxatEi4pkG9FSItLY2cnByCg4PRaDQUFRUxduxY3nrrLa5fv87ChQvx9fXFy8sLBwcHEhMTadq0KZWVldx///0sX77caqHDsr6etD1q1ChWr17NI488wrp1635TvJ0Uozh27FgUCgUvv/yyiG20pFWrVkKx%2B%2BabbwDYsGGDGC%2BAffv20atXLyorK8nNzSU5OZmmTZvy8MMPM2vWLGHJW7ZsGb/88gsKhYKWLVvSunVroqKiMJlMjBo1CpPJxNSpU0lKSqKkpESUuTCZTCKmy8HBgevXr1u5PEt18SS0Wq14riIjIwGE8iv1YVpaGq%2B//joPPfQQM2fOBOoz%2Bz7yyCO8%2Beabd9yPliiVSubOnUtMTAzDhg1DoVDQp08fxo0bx4QJE1Cr1SiVSpRKpZXyY1nSA%2BpdRUtKSkS8GCCUNckqaYukqKnVav7zn//Qu3fvBudMYmIinTp1orq6WljCfXx8iIyMZM2aNezevZvExEROnDghYkOrq6tRqVS0bdtWHLNixQoOHjzI4MGD2blzJ9OnT2fevHn4%2BflRVlZGbW2tqLH35JNPsnjxYkwmE46OjgQEBIhFKakkS2BgoHDnValUBAUFkZOTI8ZVrVYLrwDpPqV%2BkuaCRqNBo9EI11ylUincjaWYRo1GQ3V1tfAsUCgUPP7443z11Vdiceyxxx4T2/b29oSEhDBu3DhmzJhx07F/4403xDtH5s%2BJrPDJyMjI3EU6deok0pxLAml8fDy9e/emtrZWuGH%2B/PPP9OrVSygftm5yllnbpPi0O0GygEhKnVTA1xKFQiHijW6W5VGj0YhSAf8NzZs3JzQ0lG3btgH1yQ/WrVuHn58fgLC8SNfp3bs3np6e/PDDD8TExJCUlGS1ui0JURKWQqVtfJl0H5KAZWdnh1KpFAKWr6%2BvKMEglVOwzHwnWVqlNP93k%2B7du3Pw4EGxnZKSQkxMjChR8XuxrMNniWXqfq1WS3l5uVXRbNssgpaFnKE%2BeVHv3r1ZtGgRAC1btkShUFBTU4OdnR16vd5qXBvKOKrT6Rg9ejTr1q27oeQEWCv/DSn47733Hu%2B8845wi7zZfrc6b0PY29sLV8DfagWyPHdDz7w0D93d3UW2x4aQ3iNSvKX0rPfp04fdu3cLpcwyhhSwygp5p9hamLy9vSksLMTDw4Pi4mIUCoXV2N3MImXpknn48GGOHDnCJ598gqenJytWrKBly5YsXbqUn3/%2BmeTkZM6fP3/H1i2dToenpyc5OTl3fF93gkKhEHUSLTPAHjx4kO7du4v9bLeh/r0fGxsrtnfv3k3fvn3FeR0cHHBxceHatWvifb9161aR4EihULBhwwZGjBhhNW9OnjxplYHTcluj0aBUKjl%2B/Liw1kpK4TfffINKpUKtVrNp0yaCgoL%2Bp30l83%2BLnKVTRkZG5i6RlJSEQqGgsLBQZGIzm82sWLEChUKBUqmkoqICBwcHFixYgNlsFqvoklCjUCgIDw/Hzc0NlUol3CZ9fHzw8fHBzc3N6po6nY7OnTvj6uoK1CcTMJvNIpYkNDQUPz8/FAqFsB64ubmRk5MjFBqwtnBBfdyKt7d3g5nz3N3d%2Beijj4TSZklgYCCOjo6inuCVK1c4deqU%2BF2tVtO0aVOcnJxEpj4p3gTqrVGS62RYWBj9%2B/e3uleDwYBKpcLX15dOnTqJemBQn6hk3759Vr9bJlNwcHAQ5xswYIAQ4qDehc3Hx4fRo0eLfpTq7Nmi0WhEfJnUH9L5FQqFVb/eDJ1Oh4eHB507d8bDw0O4Hkrujpb9BYhagZZIrm1QL0xL7YAbs0pK92mZoVGyyFRUVNC4cWPWrl0r5l2TJk1EfJikMDVu3Jinn37aKutjTU0NPj4%2BmEwmDh06BMD69evR6/VWCxWWAqtldlqpXQ3NJb1ez8iRIxu05EnXl6zoDSkwr7/%2BOlVVVXTr1o3g4GB8fHyE26FUB8/BwQGlUkloaKiIr42KiiI6OtrqXI6OjiJjosFgYPbs2WIBAbihje7u7sLtVmLJkiVijmi1Ws6dO3dDlteePXsSHR0tEpHYZkmVkMZeUhilbJmWccJQb3mX0Gg0bN68WSgXWq1WuB9L/Wk7b5RKpVWGVahfKFEoFIwbN%2B6GdlkeL8WQwY11R/v378/MmTO5fPkyx44do02bNiIe%2BYcffhDtDg0NbfD%2B4de5I/VHbm6umJtt2rQRY9lQhlVLa6Qtttl6O3TogLOzs7BqqlQqLly4IPpLWuyQrKHS91lZWWIsFAoF6enpVmOjVqvF4pP0XC5dutRqn1OnTolnR4oFXLRokVDsNBoNixYtEvUUXV1d8ff3t6oHKbmqS8qewWBg8%2BbNfPfdd%2BKfzJ8PWeGTkZGRuUuUl5eLeIjy8nLKysooKysjLi6O0tJSYYkyGAysW7cOhUKB0WikadOmYoV8zJgx1NbWsnfvXhwdHbG3t2fy5Mk8%2BuijfP/99yQkJGBnZ8fZs2d5//33CQ0NJSEhQSSjaNSoEWFhYUIQvHLlirA2FhYWYm9vT0REBM8995zVargkBFoKR1JyAkuGDBlCcXEx06dP5/r16%2BJ76bjly5ezdetWoD5Tqaurq5WScbPVeun47Oxsjh8/LlapO3bsaLWPVLfQaDTSuXNnq3NI1hbL3y0VgaqqKhHjaHle6dzLly9n7ty5aLVaHBwcqKystFLEv/jiC5HwRnLThV8Lo69Zs0bEFkmxV4CI35KEUelei4uLeffdd0XyF7PZfEMyDUmRaCiJitQ2rVaLRqPh/7H33tFRVev//2v6THonPYEACQIJhBBA%2BgVp0kEUK1hAih0RAQFBBSxYAUE%2BFBWxAApBRFREpEMgEEoikRJIo6SSNpny%2ByNrb89MEgTvvV%2B96zfvtVgkM2fO2WefcybPs5/3835/%2Bumncv8Gg8EhWTh06BAGg4GQkBCgVqzkhx9%2BkAsIFouF%2BPh4WXkrLCzEZrNRUVEh74u8vDwef/xxeW/Nnj0bm81GSkoKUEtHfeSRR5g/fz5arZZ169bVmWPl/8qfvby86tgF2O12xo4d22BSJQRsRNAthDOUnw8JCeH999/n9ddfR61WSyVQu91OTk4OWq1WVrNNJhNQ2xf50UcfORyroqKCvLw8Kchht9sdvPHsdjtDhgyR2zdt2lQG6%2BL8UlJSCAoKwm63y%2BRHnJuo7h07dszh3nzggQdo3rx5HfsD53tp8uTJ6PV60tLSZHIAyCTcYDCgUqkkRbVdu3ao1WoaN24s9yFER5Tw8PBg4cKFDq8Jpdd33nkHq9XqUN2z2%2B1SvEqoZYLjQoNKpaJly5a0atXKIUES11EItQAO5uFqtZqVK1fKhFNp7RAREYHJZCI4OBiDwcDixYtRqVR1hGC0Wq08jnL%2BlFDSUO12O2%2B99RZlZWWSAWC1Wnnsscew2Wxy8UL8Lv7Z7Xbuv/9%2BB4bFww8/7PB9pNPp5HeWVqtFq9XyzTffyM/Y7XbmzJkjP2Oz2aiurmb16tVSyKampobVq1djsViwWq1cvXqV8%2BfPM2PGDPmZqqoqDh48iNlslgq2a9euZdmyZSxbtozly5fXmQMX/vlwJXwuuOCCC38TOnbsyPfff09oaGidIEiZ6AhKolilV/a7vfzyy5w/f55OnTpRWlrK9evXWbRoEW%2B99RY9evQgOTkZs9nMvn37uPPOO9m8eTO//PKLDIYvXbrE5cuXHSqBxcXFqNVqSktLsdlsnD59mrvvvtvBx02MURmQOFcfwDEY0uv10utLfO7gwYMEBwfLAG/w4MEyAQRkEqSEM4VOWADk5eXRqVMn%2BbrNZsPLywu73U5RUVGdhE%2BIOyjfV1bbTCaTTHLFfpXHFaIvKpVK0m9FACxk3tVqNRcvXuTOO%2B90UOAEZLVEBGvdu3cHkHRE5fYiGA4LC8Nut1NTUyOrs9BwZUcJsa0IXps2bVrnnAREwC8SShFQV1RUYLPZHKpxdrtdJphpaWmyJ81ms8mkCGDIkCHY7XaZjJw%2BfRpfX1%2B%2B%2B%2B67BqtyDSEjI6NOwqfVaikrK3NIdpXzUlFRQVVVlQMV1zmAz8nJITExkXvuuYf8/HyHipfSFLuwsFA%2Bj2VlZfj5%2BTnsx3lOlVVpqL33xo8fL8dw5swZB7XUmpoa9uzZI48vni3lflUqFaWlpZw%2BfRq73Y5Go%2BHAgQP06tVLPnfOSbOSWmuz2SgrKwOQ4xcVpLCwMACZrAqhlPT09AbpnYJSfujQIYfX9%2B7di9Fo5LPPPiMsLEzedwaDgWnTpskkprKyUs6v8vtPp9PxySefMHfuXHx8fPDx8ZFsB7vdjsVioaqqCrVa7WBRolKp6Ny5MyUlJfI6iXu/oKAAq9VKv379pM2HMhkXEMcQar0%2BPj4YDAZMJpNUTv0rXpN/BuUYxPfD1atX5TNfXV19Q3p2fdXXm4FarSYgIIDu3bszcOBAWb3UarUsWbKEHTt2yF5lF/63cGvfsC644IILLvxHoVKp%2BPnnnykrK%2BPHH3/kwoULVFZW4u3tTUBAAPPnz2fjxo0MGTKEzz//nHvuuYfDhw/Tvn17h9XeiIgIzpw5g8Viobi4GH9/f3x9fSkvL6ekpITx48dLKlpUVJQDXaqystKhb0mv16NSqfD09KwTKN2ol%2BfOO%2B9k1apVckwWi4WdO3cCSCl5Z8%2B4%2BfPnExwcLAP%2BKVOmyKoK4FB9VApZiJ8rKysJCQkhLy%2BPNm3aOKgbKvu%2BgoODadu2bZ0xT5kyxeH9AQMGsHjxYqA2WRV9ODExMZSXlzuMQXkN60tYRNCl0%2BmYMmUKn3zyCVarFXd3d6qrq6XYghijuCYi4RP9Wd7e3nh4eFBeXi6FRZQVH7GSD38YiTv3iolkUrnNpEmT5GsiSbtVCJEgsRggEmQRbCqTmN27d8v7XZzfm2%2B%2B%2BafHUHooivlXqVR1qsnK7QCefvppPvjgA7m9l5cX169fl3NXX/V4%2BPDhxMXFYbVaefvttwHk/afX64mOjiYrKws3NzeZLDlD0B9Fb6xYBDh06BAJCQnYbDYpaiSgfP6g9hqJSrvVapVURTF2cb95eXk50JRPnz7tUBkS94fSJ9FisTBr1iysVqvsDevevTtffPGF3H/37t359NNP5X4iIiKw2WwOVX5xfkqcO3eOsWPHOsyFv78/NpuN559/noqKCoYPH87777%2BPSqVi7NixjB07ltjYWOlNKc5ffF6tVlNeXs6CBQskdbU%2B1Jdkf/rpp7IyZjQa5X6F2MmUKVNYtWqVw4LboEGDZBVavP7000/z4YcfUlVVJedMJJri2j399NOMGTOGjh07snTpUp5//nmWLl2Kp6enrKoLMZXq6mosFgslJSVSydfPz4%2BcnBwyMzMpLS2lqqqK8vJyioqKqKqqoqioiGvXrlFVVSXZFELcJTAwkObNm3PHHXfQokULoHYBLCcn56YUY9u2bYvdbufgwYNycSE/P5/o6GiWLFnClStXGDZsGFFRUWzbtu1P9%2BfCPw8u0RYXXHDBhb8Ze/bsYdasWfX6KYWGhuLr68uZM2cYNmwY69evZ%2BTIkWzcuJGamhqioqK4ePEiAwcOlP02wgxcKVwRGhoqrRGcIQyWNRqNDCJsNhuxsbFUVFRw8eJFBg0axKZNm%2BpU15QiJ86G587vC1pVQ4HyfxMtWrQgKSmJtLQ00tPT67w/YsQI2rZty969e2WFUa/X4%2B/vL/0EL126RE5ODlAbSAsfsy1btsjeMPgj2crMzJQqmEIRs6qqim7duvHrr78yffp0qfAIjkIcwkqgurpaBopKr7/6DK//HQhapxj74MGD2bJli6QRazQa7rzzTrZs2SI9zpYtW8amTZtkcCwgPAdFteDy5ctyzpTVP5VKRUhICImJiWzdutVBxCUhIUF6l90sTCYTXl5eFBQUoNVqCQ8PdzA2dxb0UAoWiTk4dOgQnp6e7Nq1i3nz5uHh4SFpfjqdjj59%2BvDtt9/SvHlzsrKypFDNpEmTePfdd%2BV%2BRAXKYrHIRHrAgAFs3bpVBtQLFy7kqaeeuqlzmzp1KhaLhUWLFgFIhcjY2Fg5b3a7HV9fX%2BLj4/n111/ldQoICJB06s6dO3PHHXfwww8/sGfPHrkoIbZxvgeVc1VdXS17v6qrq%2BUijoCnpyc1NTUO1y0pKYm1a9fSokULQkJCiIyMZOzYsYwbNw61Wi0rZ1u3bqVfv3789ttvnD171uHcVSoVo0ePJiUlBXd3d2bNmsWyZctIT0%2BndevWXLhwQVbxlAgJCcFms8lzb9KkCefPn8dms0lz9fvvv581a9ag0WhkcieSMucx/Fm43KJFC3Q6HSdPnpQ9x2VlZVRVVREdHc3169cJCwvDYrHQunVrUlNTKSgooLq6Gi8vL/r168epU6fw9vamsrKSUaNGsXz5cqnEbDQamTRpEh9%2B%2BCHV1dXs3buXjIwMqqqqMBgMsr9ZLNaJ5FxcJ3EOgYGBDlRw4dN56dIlwsPD0ev1TJkyhYkTJ0qxn6CgIN566y0efvhhFixYwJ133nnDuXDhnwdXwueCCy648Dfip59%2BYtKkSf%2B2uqUznFX5/slQBlNCKECv1%2BPr60tubm69yqB6vZ4RI0YQERHBhx9%2BKKmGYn9CMKE%2B1dF/B0rlwPrOA/6oNHTq1Il9%2B/bd1H5vJqCsDyLxu%2B222zCbzYSFhbF7925mzpwpt1m6dCkzZszg6aefdui9/E94dd0KxDm6ubnJqqQwrlcuAri5uVFRUSETNHd3d2n8nJub6zB2kViJXiiorX698cYbpKamyj7Fm8Htt9/OqlWraN%2B%2Bfb0LI/8u6lMW/W9BuSAwfPhwNm7ciEajkXRLnU5HfHw8ffr0kab1wki8oKDAYZzBwcFcu3YNq9UqE3VfX1%2B8vLxkBVer1TJgwAD5maNHj7JgwQJZXVKKvfwZNBoN7dq1o6qqiri4OL766ivWrVtHYGAgjRo1QqfT8fnnnzNv3jz0er2koTojKCiIq1ev/j%2B5z//q8/vvwnkR46%2Bqyt5oPyqVihEjRvDqq6%2Byf/9%2BXnnlFbZs2fIfPAsX/l/AlfC54IILLvyNGDZsmFzVVavVFBUV4e3tLSld4g9v8%2BbNHXr3mjVrVq8So9FoxM/PD5vN5mA3cCMoqYdWq9VBzVG5YmwymejevTsVFRWcO3cOvV5PZGQkcXFxclVeq9Wybds2h14hQK42x8bGcunSpToUthuNTSmu4evry/Xr14mIiCAjI8MhMNVoNHh5eUkhBh8fHzw9PfHz86O6ulpSPK1WK6dPn%2Bb8%2BfOyAqNSqaS8fe/evenQoQNarZagoCAA3nzzTYxGI506deKTTz6RVav/BIQfYGRkJG3btqWoqIjGjRuTlJREcHAwubm5rFy5UsrO63Q68vLyUKlUMrEXyX1MTAxPPfUUHTp0oKCggEaNGlFQUEBERATLly/ns88%2Bw9PTE39/f44fP05FRcVfDlSNRiMGg4GAgAD%2B9a9/kZ%2Bfz9GjR6VFxZkzZ7h27VqdntSbmQ/l9m5ubnTo0IFz5845UERF5UzcGyIhAWR/qaBTjhkzhtLSUioqKmjfvj133XUXaWlpzJs3j6ysLCIjI0lJScFsNnPnnXfK3rn6gmNB4W3IhkSj0cj%2BroqKCkk1FYnpjaqWyr4rk8nEiBEjKCoqIj4%2BntLSUinylJOTw44dO/D29iYmJoY2bdpQWFjI%2B%2B%2B/f0tzPHDgQKZOncrUqVOJiori5ZdfZubMmRw/fpyZM2eSlJSERqPhoYceomXLlmzcuLFONa2hxQODwcDbb7/Nzz//TLNmzfjqq6%2BA2kpnYmIiOp2OK1euUFpair%2B/P5MmTaJTp06YTCbOnDnjYOFQUVEhx%2Brp6Unv3r3r2FC4ubnRo0cP/P39%2BfHHH2Vv738Dgpbt5eXFc889x%2BLFi8nPz5fPodIvDyAuLo6MjAz5u6iCK3Hbbbc5iMY4W8pA3QUn52qkp6dnHQbF3Xff7aCsaTQa61C%2BH3vssTrCQ8pjpqamYjKZsFqttGvXjrS0tAbnxoV/JlwJnwsuuODC34jExETKy8ul/Hp5eTm9e/dmx44dslIF0K5dO44ePSoDq7Zt28oE4KWXXmLt2rX8/vvvdap5Go0GNzc3rl%2B/jslkYu7cuXTu3FnaMhQXF3P8%2BHGWLFlCeHi4rAD4%2BPhw5coV4uLiUKlUpKamYjabpQiGCDQsFgt9%2BvTh/vvvx2w24%2BvrS1VVFTExMahUKjZu3Ejnzp0ZPHgwAJs3byYmJobTp08zfPhwoFYpVATYgl5ktVopKSmhb9%2B%2BbN68WVbqGvLZElWH8PBwjh8/LhMhEYwKn7iG6H3O1UDRO2Q0GvH29iY3NxcvLy%2BHyo/BYKBr1678%2BOOPMmEWVEw3NzeKiorQarX4%2BfnRs2dPrFYr33zzDRqNhjFjxlBQUMCWLVuIi4tj/Pjx7N69m5SUFB5//HFWrlxJSUmJpD3W1NRQWlpKYGCg7K8S98XJkydlACeSwEaNGpGYmMjJkyfx9vamsLBQ%2BgMKXzsfHx969erFV199hVqt5o477uD48eOYzWamTZvGt99%2BS1FREffffz9paWl8/vnnREVFkZCQwLZt26isrJQ9ZuLcGzVqJM2ihWiL1WrFbDbXWx0V10EZqIok393dneLiYplc9enThx07dmCxWPDy8pKG2eHh4XTu3JlPPvnE4RqKe99qtTr0WJaWlqLRaGjRogUbNmwgNjaWoKAgli5dyrp168jOzmbSpEk0adKEyZMnc/HiRQICAqiuriY/P58uXbowdOhQeb2mTZvGxYsX2b59O127duXXX391OMfS0lJKSkrq3LddunRhyJAhUmTmlVdekVYbDz30EK%2B%2B%2Bio6nY6IiAhmzZrFmDFjaNOmDSdPnkStVlNdXU2bNm2YOXMmrVq1okWLFnh6enLnnXcSGRnJggUL6jwHyufFOfzr0KEDpaWlZGVlUVNTQ6NGjejcuTMPP/www4YNo0OHDly7do3nnnuOcePGyerqwIED2bx58y3RbyMiIhgxYgRLliwBant5p02bVm8SrdfrsdlsPPnkk6xcuRJA3is6nY6oqCgCAwM5ceIEFRUVxMTE8Oqrr5KTk8Mrr7xCaWkpJpOJ4uJi3N3dZe9dQEAAQ4cOpX379ly4cIHXXnuNMWPGMGLECNzc3Hj33XfZtm0bNpuNbdu2kZqaSlJSEtnZ2cyZM8dh8UHcb%2BL5E16HYk4SExMdei3ro787b1Nfwqf09gN48MEH%2Bfjjj%2BXv9dFRnam39R1bzLFYQIuIiCA/P18uhgmD%2B19%2B%2BYW5c%2Be6hFv%2BB%2BFK%2BFxwwQUX/kb069dPBoSBgYEUFRXRvHlzMjIypKiCqDwVFxfLYKi%2BP%2Bz1QafT4ePjQ1FRkZR3d1ZvExQ/ZcAmElDR0yfEHsBRedPb21tWMRo1aiSrim%2B88Qbz5s2jrKzslipIItCoqalBo9EQGBhIfn6%2BpKgdOXKE3r17ExgYSGZmZp3gpWXLlpw/f57y8nICAgL48MMPGTlyJFCr7Lh//37GjBkD1AZU3bt3Z9OmTUDtKvfly5dlgrlo0SLZO6VWyGfTnQAAIABJREFUqzly5AgdOnSguroalUrF4sWLmTx5Ms2bNycnJ4cVK1bwwAMP4ObmxoEDBygoKKBbt26Swrhz504GDBhAYWEh9957L7Nnz6Z169aYzWa%2B%2BOILvvnmGwdrArVazZAhQ/j6668B6N%2B/P7m5uTL4gtr7wG63U1VVRZMmTcjLy5NUvqCgIAcRlf79%2B/PII49w//3306NHD65evUpCQgKrVq3CZrORnp5OcnIylZWV6PV6ampqmD17Np999hmZmZnymDeitYaGhlJZWSmrF%2BK%2B8fPzo6SkBIvFwrRp01iwYAFQawLdqVMnampqUKlUJCYmcujQITw8PDAajVy9elUe74EHHuCrr76Sioyit%2Bjy5cu0bdtWVh1E754S7u7uREVF0bJlSzZs2EBISAjXrl3jX//6l%2BzZFBL8ItkXYxELDaGhoUyYMIHp06fz7bffcvz4cdatW8fRo0dv2vBbICAggMaNG8sFlj%2Bj4olk4pVXXmHatGnytYCAAFmlrayspH379ly7do3s7GwsFgtGo1Ea3YuePrvdjre3t%2ByzFH2Vbm5umEwmqcDqXIUKCwuTVhNQ%2B/z4%2BfnV23MnzqVFixZkZmY6zI1IQpXn6zx/rVq14sSJEwDyXrztttvw8PDg%2BPHjVFZW0qNHD3bu3Cn7T1NTUxk9ejSnTp2S31VeXl48%2BeSTPPDAAyQkJBAfH8%2BhQ4eIi4ujsLCQK1eusG7dOs6fP88LL7zQ4PyLhEiMWymKIxYklJRi4f8pnhPnypugLSvhvKBUX5%2Bu833iPG/x8fEcP37c4TPOVGLl7w3tT4xFVKanTJlCZmYmW7duZerUqdx3330NzpUL/0y4Ej4XXHDBhb8R69atY8GCBZKOpvRUUsJ5ZdcZDb1fXx%2Bb8LJS9j05rwK3bNmSFi1asHHjxjoVGIH6glStVkvXrl2ZMWOGNC2/lT8zgj4qAh%2BhVCnGOWXKFN59910ZCI0bN47ly5fL4EgZJHXu3JnJkydLQ2uRtMTFxUmabEpKiuwvio6Oxmq1cunSJex2O3379mXnzp1yXqdMmcLixYtlon3ixAlatWol595oNOLu7s61a9f46KOP%2BOmnn/j888/rnS%2BVSsWBAwfo0qWLrH41bdqU3377zWGbxo0b1wmolfDx8cFms8kk3DkRa9q0KRcvXkStVpOSkkJERAQJCQmEhIQwffp0xo8fL%2Bf2/vvvv2HPW7t27UhNTb3B1bt1dOvWjUuXLnHu3DkAh2svIOYtICBAik2IYLq%2B%2B14ZcIsFE6gVgpk0aRLjxo0Dap%2BZRx99VKqywh/VD5H0ifns3Lkz77zzDsnJybd0PwujdqVoxl/Ba6%2B9xsyZMzEajQ32rAkojyMWhry9vR3sKp5//nneeOMNua1KpaJr167s2rXrpsfkPEdQe77x8fHs37%2B/wTEpIRJNYQOjUqkICgqipKSkDu3Q3d1dJrcqlYqoqCiHKlvz5s0JCgoiNTUVf39/rl27Ju%2BN22%2B/nd27d8ttO3bsSHp6OuXl5Xz%2B%2BecsWrSI3NxcLl26VG8F7M/mITg4mLy8PJkAenl51fHI/E/gVhcW/iqcr1dQUBCBgYEkJSUxffr0//rxXfjPw%2BXD54ILLrjwN2L06NG4ubnh4eGBt7c3JpMJNzc3fH198fPzw2Aw0LRpUwYMGIBOp8PX1xe1Wo2HhwdarZbQ0FC%2B//57AgIC8Pb2xtfXl7CwMNzd3eVKOiBFULRarZQUV65IK5M9g8HAu%2B%2B%2BS0ZGhpSOV1olCDhbESQnJ6PX6xk6dChLlizB09PTwXtMjMPZrNnZv0%2BZWCr7sqC2l0656i36TuqzS8jKypLJHtQK5Pz444/y94sXL7J37175e25urvTlS0pK4sKFCw7H%2BuCDDxyCW1GlEsGh0mtt7dq1DtQ%2BkXgLqFQq5s6dK/dvtVodFDtFJcaZNiasNQSKi4spLS2VvZfO%2BP333%2Bv41UVFRZGTk%2BPQaymOKRAYGOjgLWcymTh27Bi%2Bvr51jiEqxh4eHuzYsYMnnnhCvjd//vw62yuxa9cuzp49KxclRNCvhAjwhak74EAjdYaYh9GjR9O3b1%2BpoGo2mx2sKCwWC08%2B%2BaTDZ5XXUjmfhw4dYsKECQ6VHeW91q1bNylQotPpGDZsGFDrayeeLXEeBoOBDRs24ObmJj/v7u5e57yV1%2BOll16SC0LO24WHhzv83r59%2Bzr7UVpAQG0FXoxJ4MSJEw5jEhBVT2cfS7vdzvfffy%2B/B7RaLd27d3fwpXO2cxHU52nTpsnzc3d3lwsWdrtdKlcqodfrmTlzJlqtVvZHxsXFyfd1Oh2DBg0iMzNTetYpxXxEsidEoc6ePSsrxeXl5bRt21YmaA356gmTd%2BfX7Ha7rIqKBarJkyfj5uZGcnIygwYNYsCAATRq1Ijg4GCCg4Pp0qULUVFRDBw4kNjYWAIDA2nZsqW0Z/Dx8SEuLo6IiAi8vb0lVTQ2NtbBY69Xr14O4xGWDFD7/bRq1SpWrFjB%2BPHjmTlzJhMmTOCdd95h/PjxPPfcc1L4StCoxT%2BdTid/FgsWxcXFLirn/zBcFT4XXHDBhb8BS5YskZ5xV65cuaFJ7qRJkxg%2BfDgvvPACCxcuZPLkyTz11FOYzWZ0Oh1eXl6YzWaZKBmNRubPn8%2BsWbNk9ejPqgJubm7cddddrFmzBq1Wy/PPPy99yKqrq/H39/9LK9aCCgXUoTmpVCqefPJJQkNDmTZt2k1XP%2BoTPPgn4Wbpts6IjIwkOzu7zhyJeRHBZFhYmANlUQTVziv/np6eDB48mM8%2B%2BwyoDQb1ej1Xr17l0qVLDvtWq9WSSgzw%2BuuvExYWVoe6pRxbfaqToldSq9VSXV1NVFSU7B00GAzU1NSgVqvl51q1akVZWRkXL16ssz%2BR3Oh0OlQqFStXruTNN9%2BUfU4tW7bk1KlT0nQcapM9JTVOeY7r16%2BntLSUhx9%2BWL6XkZHhoCBZn6CFM0TSJkSA9Ho9W7duZc2aNaSkpJCcnMy%2BffsatB8RipbBwcEsX74cqK0g7t2718Eaw/mYYj4aek7Ee8ICRK/Xs2DBAqZPn86xY8eIjY3FYDCgUqnYsmULvXv3dvi8p6cn8MeCi/N97Cwc5eHhwahRo1i9erXsE7z33nsJCgqS3x3imt9KVWrVqlVoNBoefPBB%2BZrBYOD48eN06tQJu91OSUkJPj4%2BN/Tlqw/KKvF/Cs2aNeP333%2BXlNlvvvmG5s2bk5CQ4EC//m9CUOmDg4Mdfr4ZdO3alfXr1/POO%2B/U%2B35lZSU7d%2B6kf//%2B8rU/W8hx4Z8HV8LnggsuuPA3IDY2lrCwMADp7dYQMjMzSUxM5PDhwyQlJXH48GESExOx2%2B0sXryY4uJibDYbM2bMoFu3bg5VrPpgMplkxQRqaX8LFiygdevWtGzZEovFUifYc6aEQd1k5JFHHmHt2rUy2Bb7EH1c9f25cbYIaNSoEStXrmT48OGSJmmxWNi4caMUfvH19aVLly4MGDCA/fv3s2bNGof9CcN4YVgugtSgoCAsFosM%2BMLDw%2BnZsydr166VAettt91GXl4edrudxo0bc/z4cWkNcN9995GWlibpar179yY8PJzVq1ffcL6VSE5O5uDBgw4JvpiXiRMnsmTJEgd/OCXVViQao0ePlgb3zjTRG4lzmEwmoDaBd6b5NmvWjEuXLslrLuivIhny8PDgvffek8mSWq1m5syZLFy4UPqzKSnCAoJiq9FoGDBgAFu2bJF9dy1atMBsNsveuaFDhzJ48GAWLVpEeHg469evlyI8ZrOZEydOcO%2B998qEb/bs2cyZMweopbZWVFTg6%2BtLaWkplZWVGAwGh8WO%2BpKl0NBQBzPxtWvXOiS5arWaNm3acP78%2BQaTC5HwTZw4kd9//73BZF1ZjfT29ua7777j9ttvB/5IRETCKfrWxOeFgu6IESPYuHEj8EdCPHToUL755huZZCvh7H0o4DwXISEhsjL47bffYrPZyMjIkD6Sffr04cqVKzdtM/JnUB7f09MTq9UqK7grVqygpKSEZ599Vm4fFRXFoEGD%2BOmnn7h69apcJFMmwX5%2BfvIaqVQqvLy86nxn3cyY6hNLUW4jKst6vZ7vv/%2Benj17Akg14bS0NK5du8aQIUMcaKT/VLRp04a0tDQmTJhAbm4uVVVVNG7cmN27d2M0Grn99tv54YcfCAkJobi4mP79%2B/Pqq6/%2B3cN24RbhonS64IILLvwN%2BPnnn9mxY8dN/QNkL5L438/PD29vbxo3bkxGRgYLFizAbDbXSfZEH5ig6SQlJWGxWKipqWHUqFFSdGHkyJHExsbKCotzhaq%2BwOn777%2BX1DaTyURMTAxDhw4lMDAQ%2BMOAXBm4ijEJDBkyhNmzZ0sp88GDB3PgwAFJdQwODkalUsnjGwwGZsyYQVFRERMnTpQiJ35%2BfqjVapYvX86MGTMwm81cvnyZUaNGodfrZXB27733ymPn5OTwySefOHi6nTx5ksLCQoqKikhPT5eiBXPnzmX37t0cPHgQqA3A33zzTV588UVJ4xR02aeeegq9Xk/btm2BPyh3MTExcm7UajV6vd6BFvj7778DyOsMtVLtAkKcpb4%2BKxGsOidcynmvrKyksrKyjmAGwJkzZxyu%2Be7du1m/fr38vaKios6qfufOnWnbtq28t2JiYtBqtWi1Wnx8fAAcKKspKSmSsme32zl16hRZWVlkZmaSmZnJmDFj8PPzY9KkSTz%2B%2BON06NCBffv2UV1dTU1NjTSrFsmqSPagltpqNpspKCiQ51FTUyNtDMRcKCnG4toKxVqAL774wuH%2BtNlsXLlyBS8vLzp16iRpy0oIK4fffvtNiiKNHj1abqdSqRyozGq1mrKyMlnhB7h%2B/TpGo1EmbEpDeKi93wwGA1u3bpUJ5U8//YROp%2BO1115Dp9PJZEzQMrVaLSNGjECj0cikRDkXYiwABQUFpKSkkJKSgs1mIyAggJycHHlPubu7y0ot1BqqN2/e3GGuNm3axI4dO2jatKnDuJOSkqTw0/Lly9m3bx8ZGRmcPHmSe%2B%2B9t87ixyOPPOKQ7AFcuHCBDz74gNOnT0v6pJubm/TsNBgM9OjRw%2BEzopos5kKr1XLXXXehVqvx8vLCYDDg5%2Bcn6e9iPurzStRqtTRv3hydTif3aTabpdIw1N4rZrOZLl260Lt371tKNv9OBAQEsG7dOvbs2YOvry%2BXLl3il19%2BITQ0lLKyMlq2bElgYCAlJSX069evzv3vwv8GXBU%2BF1xwwYW/GUKpryG0b9%2Be%2B%2B67jzFjxrB69WrGjBnDvn372Lt3LwkJCWRlZXHhwgV0Oh133XUX27dv59y5c3h6ekoD6549e5KWlsbly5dxd3dn1KhRhIWFyeQhIyPDwVtOVBhUKhVt2rShffv2fPTRRw7m5q1bt66jCCcofyIRrC94Uq7u63Q67r//frZs2SKl6729vet4bDUEcTznSs3NQKVSYTKZZGUhKCiI4uJimQjcrFfgzY5RJBvOynv/Dry9vWUVTdg4jBw5kg0bNuDh4dGggXijRo3qFcb4dyHO9WYEStzc3AgJCSE%2BPp6BAwfSpUsXoFZpcNKkSSxatOiGnxfVFjc3N/r3709paSkFBQUcPnxYbuNME23VqhUnT550uP9u5XpoNBpZRbyRsEfjxo05f/48drsdvV6Pj49PHRPwjh071hE3UUJZlfPy8nKo5AYEBNCzZ09WrlzZYCWrPsGmRo0aYTab69hEiKTUYrHckr1CQ%2BNtCOK%2B0Gq1UonVZDJhMpkwGAxcuHBBVunc3Nyk%2Buh/Gv7%2B/lK5WHwHCNTnbVdZWcmQIUOkgf3IkSMdBJkCAgKAWmsFi8XCr7/%2Byrhx4%2Br0iP4TsXDhQr744gvmz59P3759iYuLw2g0cvjwYdq0aYNarea%2B%2B%2B6je/fuzJs3r84z5sL/BlwJnwsuuODC3wyl%2BIASQqDg8OHDbN26lVdffZWRI0fyxRdfMH78eJYvXy7NmMvKymjdujXHjh1Dr9ffcpCkpH65u7tjMBj%2BI70uoqerIfXR/wS8vb0pLS1lwIAB3HXXXbzzzjscP36c9u3bU1paSkZGBna7HS8vL9zc3CgoKCAhIYFevXpx4sQJduzYQb9%2B/XjzzTex2WwsXryY77//ngkTJpCfn8/333/PsWPHeOGFF6isrGTp0qXU1NQQExNDUVGRDFAb6h2D2gRg/PjxvPjiiw4UNOEJ5u7ujtVqlUmm8GNT0uwqKyvlZ5XBv1LoJSAggP79%2B0uaqaisKO8HvV5PYGAgo0eP5qOPPpLWGdHR0WRnZ0tRCKV3nlDDDA8Px2QyyZ4luHHPopubG4GBgQ7VIWUf4K0mXMpzNhqNPProo/Tr148NGzawadMmSW9WwvkYN9OnB388E97e3ixevJh27dqxZMkS9u/fz6effsr%2B/fuZOHGig03JfxJizkUFzN/fX/pTajSaBudNmVT92YKLoCEKo/BWrVphtVo5ceKEpEuGhIRw5coV1Gq1THLFPSL6Qo8fP/5vLWQo%2B1DF/VlQUIBGo2Hq1Kls3ryZixcv0rx5c5577jmef/55KioqqKyslM9dYmIis2fPlvv86aefePXVV2UyK6qnwqxe2D4I/7m/oqKq7FHt0KEDr7/%2BOpcuXQKQx4uLi5OqoQL%2B/v5kZWUBtb274Ej7/bMq2ttvv012djYvvfSSA6tj1KhRfPnll4waNeqmz6GsrIz27dsTHBzMXXfdxXvvvYevry8dOnRg27ZtqFQqDh8%2BjMlkok2bNgQEBPDzzz/f9P5d%2BGfAlfC54IILLvzNcK4UWK1WLly4wKpVqwgODiY8PJysrCz27dvn4If230B8fDx5eXlALQWwqqpKyttDLU1T%2BG%2BJRDU0NJTVq1czdepU8vPzUalUhIaGotVqJY2tsrISk8lEYWEhb7zxxk0HVqmpqeTk5DB8%2BHCH4FUEZ6GhoRQUFEixC61WKymknTp1IjQ0lMOHD1NYWMjDDz/M2bNn2bZtG/7%2B/qxdu5bw8HDmzZvHt99%2BS1xcHJmZmTJp0Gq1TJw4kY8%2B%2BojKykopF6/X67l%2B/ToZGRkMGjSIqKgoduzYQdeuXTlw4AD%2B/v4899xzuLu7M3nyZPz9/fm///s/fvjhBz788EOqq6t55ZVXGDFiBFVVVXTo0EGKOxQVFdG9e3dmzJghz3Xbtm1ArXJmZGQkGzduJDc3ly5dusjAf8%2BePXWsMxISEkhPT5cJUGxsLNeuXePq1as0btyY7777jkWLFknhkIYg1F4fffRRVqxYIROBsLAwcnNzad%2B%2BPUeOHMFmsxEdHU1paSmFhYWy0qfsKwwNDcXT09PBfuLP0K5dO44cOULfvn3Ztm1bg4lMfejXrx/nzp0jKysLtVrN008/zTvvvCMTWqF4m5SUxJtvvkm3bt3o168fV69e5fnnn%2Bfuu%2B%2BmU6dOlJeXs3DhQry8vEhOTmb%2B/Pns3buX3bt3k5%2BfLy09hJhOy5YtKS8vr6Oy%2Bu/Aw8MDg8GAxWKRCwsajYaXXnqJ8PBwnn76aex2OytWrGDJkiWcPn2ampoaevbsiVarZdOmTej1egICAsjOzpbXRCS2zn2GYWFh5Ofnk5SUxJEjR7Db7TRr1gyA3377Tdq1AHLRwMPDQz7nVqtV0n379evHtm3bSE5OZu/evVitVmm14ePjI%2BnMP//8M8888wzHjx%2BvU2kU30Pp6emo1WoWLlzIypUrMRgMvPbaa8yaNcvBuLxz585069aNy5cvExoayoYNG2jfvj0HDx5Er9czY8YMli5dypgxY/j111%2Bx2Wzs3buXXr16UVpayqFDh6QvaGhoqLyWN%2BpJ1mg03HbbbZw5c0YqIWs0Gp5//nkWL14s53fmzJnMmTMHq9UqK52vv/46s2fPpqqqCm9vb9555x1eeuklPD090ev1vPzyy7z66quYzWYyMjIoKCiQC2qCAtyiRQuys7OJjY1l7ty5NGnSpN576dlnn2Xv3r3YbDa5IOPl5UVVVRXl5eUOizJGo5GuXbvy22%2B/ceHCBWbNmuXy4fsfhCvhc8EFF1z4h6KsrIwuXboQEBCAwWDg3LlzqFQqB8qcWq128OZSVnSE1LZYeReVmICAAEpKSoiOjubMmTP/tfE3adKE0NBQ0tPTKSsr46GHHkKj0bBq1SqsVit6vV7aPQwePBiDwUBgYCB9%2B/Zl6dKleHl50adPH3Jzc9m3b59UAVy3bh3%2B/v7k5eXh4eEhz10IryirX1AbhIkVfPEz1K6iC9rSX/G2yszMpF27dvz444907NhRJkYi4f3pp5/o1auXXPFXonXr1ixZsoS8vDyefvppuWKen59P9%2B7dHaTXb5Zi1xClTlT%2BCgoKHF4XtEOAe%2B65h2PHjskKX2lpKUajkZCQEKxWK9nZ2Q77s1qtNGrUiKtXrxIWFsalS5fkucMfPWKenp506dKF7777Dr1eT0hICNHR0aSmpspqplJhU8DHx4eysjKsVitt27b9S%2BbmN4NNmzbx4IMP4ufnR3Z2tqTe1let0mg0GAwGKioq8PT0RK1WU1VVJVVsvby8uHjxIjabjeTkZAoKCsjNzaW6uppWrVqRm5sraYRQ%2B6y6u7tjNptlhfRWktmbgU6nY9SoUUyZMoXbb7%2Bd6upqvL29ZQXfYrHUaxQOtcl5fn4%2Bvr6%2BsjplMBjQ6/UOSp6iJxhqlWDz8/Oliu6oUaPYunUr8%2BfP54knnqhTRdPr9bzwwgvMmzcPlUpFZGQkOTk5WCwWeb2dP9OvXz927txZb5U2Li6OkJAQHnzwQcaOHUt6ejpt2rSR5xUYGMjVq1friBvVB41GQ1pamhRpeeONN1i3bl29C0/OEEJE4rtYJLfl5eWyahsZGUl5ebmk%2BqrVapo0acK5c%2BckLb5JkyYyGTaZTAQFBXHx4kX0ej2dO3fml19%2BQaVS4ebmJmm9arWaZs2acf78eR599FHGjx9fp2K4du1afvnlF1q2bElsbCyrV68mIyOD%2B%2B67j5UrV2I0GgkKCuL8%2BfNERkaiVqu5dOkSDz30EFOnTr3hvLnwz4Qr4XPBBRdc%2BIeioKCAfv36cfToUYfXKyoqGD58OI888ghXrlxhw4YNVFZWEhkZibe3N1euXOHUqVM89thjFBcXc%2BTIEfLz8x3UHm/lqz86OprLly9jsVhuyZD4r%2BBf//oXjz32GPfdd58M9gQFT6VScfDgQebOncvmzZvlZ0wmExEREWRlZTn0rUVERKBSqWSyMm3aNMaOHcuQIUOk/YCzBQL8kawISwkR4AUHB3PlyhW5fWZmJsOHD8dqtcr9KfHUU0/x8ccfU1RU5KAgqNPpCA8Px2KxUFJSwhNPPMEdd9zByZMnWbp0KVqtlrS0tDr7E55YGo3mlnrvROBpt9sdEpmYmBguXrxIdHQ0KSkpXLhwgSFDhlBZWYnRaOSBBx6QPodQmwAYjUZpBG8ymWjatClXrlzh8uXL9SZ8oi/MZrMRGxvLq6%2B%2BypNPPskdd9zBmjVr8PDwoFWrVmRlZWEwGKisrKSkpIRp06bdkhKgSqWS1gbJycmEh4ejUqk4evQoZ8%2BelRWsgIAAaZCtVqv57rvvGDp0qHw2hGLhfxsDBgyQ1gV2u53CwkKGDRtGaWmpTAhiYmK4cuUKpaWluLu7O6jftmvXjoyMDPksl5SU3JBSqUyWAwIC%2BPjjjxk5ciRVVVV1evneffddnn32WRo1akRYWBhHjx6VSqtKwR9nFVJPT08eeeQRtm/fLucwKioKtVpNfn4%2Bdrsdd3d32T/onHCJMWq1WlkBhNrnRVQGnZPFqKgoHnjgAf7v//4Ps9ks2QnLly9n/Pjx3HXXXXz55Zd15mPs2LFS6favQKgWq9VqunTpwuHDh6moqKhDFxbVPiG2ZbPZaNKkiezvVKvVJCUlkZqaKhelDAYDwcHBcvHAy8sLjUZDUVERarWakJAQCgsLqampoUmTJnh4eHDixAmsVismk0lSjI1GI%2B7u7ri5ufHCCy/Qrl07KY4l5k%2BJ7du3s2HDBrKzszGbzVRXV6PRaPDw8CA0NJSHHnpI9tm68L8HV8LnggsuuPA347nnnpM/V1dXy16tY8eOERUVxZw5c/D19WXevHkcOXIEs9mM1Wqtk3yZTCZatGghe1O8vLwoLCz80945g8HAHXfcwZkzZyguLub69et4eXlJRcfr169jt9sJCgqioKBABvVqtZpu3brx8ssv061bt3r3XZ9wRH3bONPLGoJzQnEjOFdqhBqjUuCivuRXVDz0ej1arVYKRwjjeyFuozSMViIsLIycnJx69%2B1ctRCUR6jtd4uIiODChQsOVgLifPV6PXa7vQ6dzPk4yn2LiuYHH3zA5MmT/3TObgXKsTXkBXgzEEm3j48PkyZNYv78%2BYwZM4bPPvsMX19frl69WieZUSbQTZs2JSsri%2BDgYHx8fPjtt99o0qQJjRo1Ys%2BePUDtNQkICOC3336ja9eubN%2B%2BHY1Gw6JFi5g%2BfTrl5eWoVCruv/9%2BPv300xt63YlzFobjDVXk4uLiOHPmDFarFa1WS9OmTSkrKyMnJ4fWrVvz1VdfUVxczKxZs/jxxx8d5s7b25v4%2BHh%2B/fVXh32KYF08UyqVimbNmlFcXCznSdA%2Bb1QZVvab3gw8PT3R6XQNWlNoNBo8PT0btDNQor5nWFSpqqqq6h23cx%2BegFDcHDVqFE8%2B%2BSStWrUCYNiwYWzZssXhWdFoNERERHD%2B/Pk6fYx/VahGr9eTnp5OeXk57dq1q3Net4I/W4hzHqebm5usDHt4eDTo%2B9gQ4uPjiY6OJikpSb529913A7XfkXPmzOHHH3%2BU97fBYGDgwIG89NJLdQzoXfjnw5XwueCCCy78zXjxxReprKwkNTXVQSmzISgFAPz8/CgtLZU0QkGhBNi3bx96vZ5Zs2axefNmmbSkpqbSsWNHKioq0Ol0vPLKK%2BzevZu0tDQHQ%2B8bQQSAlZWVaLVaGWxW/4nHAAAgAElEQVRoNBr8/PwoLi5Gq9WSkpJCREQEUBtgHD9%2BnISEBDw8PNDpdLz33nvEx8fTunVr2VMlaKtQG2QIaXiLxUKvXr24fPmyPEeBkJAQ8vLyaNeuHceOHcNisdCvXz%2B2b98uA2lhcXD69GkZWPXp00fSotRqNTU1NYSFhdXpverZsyd9%2B/bFbDYza9YseT5qtZr09HQ5XuHbduTIEYxGowzKBYQp9pEjR2jTpg1du3blscce48UXX6SkpETK84s%2BGhEEms1mdDqdFLT4s94wpQdiTU0NzzzzjKwoCYh9e3t74%2BPjQ1VVlaR9CmqdMsAU/nkC48ePZ9WqVdjtdjp06EBWVhbNmjVjz549Mtns0qULO3fuxNPTk%2BvXr8u%2BLqvVSkxMDOfOnaNPnz7s2LGDuXPnMmzYMHbs2MGXX37JiRMnJDVQBJ1NmjTh7Nmz%2BPr6yvduRIMUQbHSi7CmpkZufzM00ZdffpnXXnsNu93Oxx9/DNQG9cuXL7%2BheIXRaGTkyJEYjUZWr15dZ4wBAQEUFxffNIXT19cXlUp1y2bj9cHZw06n0/HJJ59w33334eHhUUf1s3379hQWFkp6ISAFPHbs2CErU4LOevToUfmdoLw%2B8fHxeHp6cv78eek/qlwEUavVLF26lMjISAYMGIDNZiMxMZEhQ4ZQUlIilVtnzpxJUVERQ4YMISIiArVaTV5enrRm8PLywmw2U1NT4/BsCqsPjUZDYmIiWVlZXL9%2BnaZNm9K8eXNSUlLw9PSUiredO3cmPz%2BfixcvOiSbwivTYDAwe/ZssrOzWbp0KRqNhmbNmpGVlSXPub4FGbVa/ZeTzJuFM92/PjiPRVBRRaKs0WgwGo2Ehobi7%2B9PZmYmgwYNcugxduF/Ay4fPhdccMGFvxnz58%2BnpqaGjh07SspMYGCgg0ebqCZAbe%2BVWOlv3LgxdrsdX19fCgsLOX36NHFxcTRq1IhffvkFgK%2B//hoPDw%2BgNiH77rvvpIx4TU0Nc%2BfOJSUl5YbJnljR1el0eHt7U1ZWRmFhIZWVlQ4ry1arlStXrlBTU0NlZSVDhw6lU6dOJCYmUl1dTVxcHFVVVRQWFnL16lXOnj3LN998IwMim83Giy%2B%2BKPenDEyhVla/d%2B/edcaXl5cnRVbEXL377rsOtKWvv/6ar7/%2B2uG1H374gerqaqqrq2UlRqhS6nQ62V%2B2c%2BdOpk2bxiuvvCI/Gx0dTWRkpEwYRIIm6Gz1VWH79%2B8vt1Wr1fz88888%2B%2ByznD9/nvLycsxms6xyCFqV2IcIIJOSkqQYRmpqqvQUU0IEp%2BIzzske/FGJKCkp4eLFi7zwwgsy4c7NzXVIVP39/fHz83PwsXv22WdlUF1TU8PVq1dZtGiRNAlv3bo1fn5%2B%2BPj4SBqfTqeTVc2hQ4eSlJREy5YtUavVDBs2DKil9V64cIHdu3ejVquld6GYU8DBzkCco/IZgdoKrBDwEc9SZWWlQyAeHR0t585gMHD8%2BHGHuTQajdKqQ61W07p1a95//30effRRdu7cCdQmF126dCEoKMjh%2BFVVVRw9epQVK1ZgtVrp2bMnQ4YMke%2BLHjqoTUwTExMd5lf5/BuNRvbv38%2B%2BffvkeYq5NJlMQK3fo/IzKpVKiqocOnRI9iAqr31KSgpQez8eOHBA2hSo1WoHzznAIdmDWs/CxYsXS%2BqzGPOyZcsoLy%2BX41SKf3z11VesXLlSUi%2BV5%2Bnu7o67uzsvvvgiU6ZMkfsMDg7mnnvucVCeTEtLk4Ir8fHxxMXFOfjwlZaW1qkWqlQqlixZIsckxF8sFgvXr1%2BXHoSimmy32yktLZU9dWIfUPt8CYr0iy%2B%2ByIcffgjUVtoyMjLkdQ0PD5ffXyaTCb1eT0xMjHxNq9USGBgon2dBNW7WrJn8PtHr9TRp0gR3d3epqCr6ugXatGmD0WiUlO/IyEjZH2oymVi9ejWZmZl4e3vLnzMzMxkwYABqtZqIiAipFOzn5ycZBVFRUcTExFBaWkp6ejq33357Ha9XF/434KrwueCCCy78zbDZbLRp04bExEQOHDggld0Ah0qXCOJFNUYoZpaVlREZGcmFCxfQ6/XMnz%2BfWbNmoVKpePvtt3nssccIDg6moKBABgSA7IsLCwujrKyMxMRE9uzZQ3p6OrGxsbi5ucm%2BlGPHjkkj8aNHj/Lrr7/y%2BOOPO/TaNAQPD48GPe1E0OJM1fp3oFTwzMvLk%2BMLCwsDapNDEUx2795dJsZKiMRH9CwZjUagVlG1oYpQQ9QwEdD%2BVVsKZQXkyy%2B/JD4%2BnoSEBKC2Yigqpy1atKgzNlHpmz59Oq%2B99prDeyKJOXXqFBs3bmTGjBny856eng7J5t13302vXr2YMGGCFA1asWIF48aNc6ja3Qjx8fG0bNmSCxcusHfvXuAPJUi1Wi2pyGLbL774gqFDh9YR4rHb7Tz22GOyv1DMT2hoKEVFRfTt21fS%2BeqD8llyhjNNVwTlgLTSuHz5skOfmRiTcg40Gg0PPvggH3/8sRTgCAkJkc%2Bg80LAqFGjmDVrFklJSfK5jImJ4fz581JQRXi6ieRdJAfDhg3jq6%2B%2BchizoD%2BKnrLMzExiY2NlxVfM2Y3moj4oadIrVqwAYMKECdTU1EjK8bhx41i8eLHDeMQ8NW3alEuXLkkqolBJrY9eqqQ7Dxw4kFOnTkkrA1HRF159Nputznkor4darWbSpEk0btzYgUIvBFmcFW6doawo1wej0SiTQOX4xLG1Wq2keov59/f35%2BrVqzK5ErRms9ks/VMNBgPe3t5cvnxZfi4yMpJz584Btb6K3t7esk/V3d0dHx8fKisrmTZtGgsXLsRut7Nz5050Oh1dunTh559/lgsLXbt2pX///sTExACQlZXF%2BvXrsVgsTJ061WFxzGKxkJGRwfbt2zlw4ECDc%2BHCPxOaOXPmzPm7B%2BGCCy648P9nzJ49m/T0dLp168bZs2cxm80YjUaHoFAEmICsoAhFQRFMlZeXY7VaiYuLQ6fTkZGRwebNm7Hb7Vy/fl0KJdhsNqKioiQ1zNfXl5dffpknnniCZcuWMWHCBD744APc3Nxk4/6ECRP4%2Buuv0el0dO3alTZt2rBs2TJMJpP0a/Pw8KjTa6XT6ViyZIkUWVmzZo2sKqhUKt577z1KS0ulyiPA9OnTZe/SlClT6NSpEwcPHkSj0bBs2TK2bNlyU/PqHMCVlZVJ%2BXiBS5cu1ZuEOSdnFovFoR/I19eXoKAgKd6gUqkYOHAg2dnZdRKNW0n0NBoNvXv3pqCgwKHSALWB42233UZxcbGcw/j4eFJSUmjdujXffvttneOIsTj3ggmoVCo6dOjAzJkzqaqqwmAwyOqiMoE7efIkKSkpDvsX95bz%2BOujr9ntdgoKCjhx4oSsJKtUKkm3tNlsTJo0SQb4H374IefPnyc7O5vk5GRJ/xPjU8rvi%2BMJVc%2BMjAx5LyUnJ9OhQwc8PT3Jy8tDpVLRsmVLmcQpnzFhAO7sKah8bkQyqEys9Xq9XEhRzpmvr68UuLHb7YwePVqq7TpDVJeV1ZPCwkJ5HLvdzv79%2Bx0qm2LeTp065TD/QlxFfFYI1uzbtw8PDw%2BHxKShxQs3N7c6vaZardZBtGXz5s1s3rxZ7sNqtWKz2Th06BAAjz/%2BuLRzUJ6TuCdFBUtUYZUYNWoU165do6KiApvNxm%2B//eZAZVVeh6CgIPbv38%2BKFSscnj3lPu12O0ePHuW7774Dau%2B9Hj16UFBQgNlsdrCkEPerUMksKiq6YVIs7FpE1b5JkyaS%2BtymTRsqKiqYM2cOP/30E1B7v7zyyiucOHFCJuQ2m42JEyeyb98%2BqqursdlsDB06lBMnTlBeXo7NZqNt27bS9zMkJITHH3%2Bc999/n1OnThETE0NsbCwrVqwgOzubJUuWEB8fLxPhy5cvYzAYuHz5Mt9%2B%2By2ZmZmsWbOGkydPcvLkSXbt2sXOnTtJT0%2BXFg%2B7d%2B/m8OHDpKenc%2BTIEaZMmcL06dNp164dgwcPbnA%2BXPhnwlXhc8EFF1z4m9G2bVs6dOggg46ff/6ZgIAArl27Vm%2Bi0L59exlUhYWFkZeXR%2B/evdm%2BfXuDx2jcuLG0dejVq9ct0XK0Wi0nT57krbfeYuvWrURHR7Ns2TISEhIICwuTMuFNmzYlOztbrtaLVXMltevll19m3rx5MpnRarXo9XqqqqpkleTJJ5%2BUvTo3g88%2B%2B0xWUwB27drFsmXLJH3y%2B%2B%2B/Z/jw4XL7jRs3Mnz4cNq1a8ewYcNISEiQPng//vgjkydPln5%2BIvi75557GDx4MAkJCSQmJsrtodbfTlRNxXkqK7RiH8IOQ5iiA4wZM4bVq1cTHBwM1Ko3pqWl0aJFi79cDVSr1bRv354TJ05IwR3RY6Wk6AGSyqpSqfDz88PT05OKigo0Gg09e/akR48eTJ48mdWrV7Nz505WrFiBSqXik08%2BkeOHWq/Avn37cujQIdq3by%2BpbsuWLXNYWEhKSsLNzY0tW7bQsWNH3n//fWbMmMG2bduYMWMGDzzwAFDrIWgymSgqKsLX15eysrI/7XV76KGH6NmzJ%2BPGjcNsNqNSqXjppZcYMmQIer2e5ORkoJbGeO7cOVJTU1mxYgUBAQEyQBcJzl%2Btxt7ouoh9N5RkOQsciX7Q48ePk5iYKLc7fPiwrBqZzWYSEhLqKPneLJS%2BnqJqDH/0miqTw5uFENRZsWIFEyZMAGoTM%2BEpd9tttzFo0CDZCwswa9Ys5s2bx5kzZ%2BRz7IyQkBBJqy4qKsJisTBq1Ciqqqo4d%2B6c7OsV8%2Bzl5SUXupzh3LumFAEymUxUVVWhVqt59tlneeONN%2Bo8N8p93n777ZKVUV1dTXh4uLSbSU9Pp7S0VC4MCXh7e8vvQFEJjIyMlIrCly9fJikpicOHD5ObmwvUWteIhbPg4GByc3MZOnSotH0Rfy8CAgIIDw8HYOLEiezateuGCq71QVlV9/HxAZBJ7XfffSfN4l3434Er4XPBBRdc%2BJvRrVs3NmzYwEsvvXRDEYhbgbNQwK14mIkqhwg4AObMmcOAAQMYPXo0Fy5ckMGNMkjVarXExMTIIFJQQpX4K15qwlw9JCQEjUaDr68vJ06cYOrUqaxZs4a2bduyZcsWevfuzfTp09Hr9fTq1YsHH3yQa9eu8c0333DvvffSvXt3OnbsSFJSEmvWrCEtLY3MzEw2bdokq5UPPPCAPBfRG/TFF1%2BwYsUKmjVrRmBgIPHx8YwcOZK0tDSCg4PZtWuXAx1RmUAqf27o97S0NFQqFRcvXuSOO%2B5w8AoEaN68OYmJiVy6dEl60rVr145PP/1UivdAbZ9dbGws%2B/btIzg42KFPymAwEBIS4tCXp6w0CesGq9VKRESETI6gtvdRnF%2BrVq2w2WxSwEZULQYNGkRKSgotWrSQFSch3BIdHU1WVhbLly%2Bne/fu8rxtNhsPPfQQa9aswWKx0Lx5czZt2iTfh1rasaDTVlVV1RFoEb1PgrYIcP36dZKTkx2qbaISbrVa6dChg0OlTDwrarWa1NRU5s6dyy%2B//EJhYSFqtRq9Xs/WrVtZuHAhUNv3eccdd8if27VrR15eHjk5OTK57tOnD7t27XKoFmo0GpKTkx1o2%2BJZ6Ny5M1BLKf3hhx%2BA2kA7JiaGa9euMWPGDNnfm5CQIBPHYcOGERgYyNKlS6VgisDhw4fl/pXn6GzDALW0azFWq9UqvRWjo6PJzs6WizZisUIsVAiMHTtWVoZuFso%2BtNzcXEJDQwHIyckhICAAs9nM0KFDWbt2LRs3biQuLg6oTYa6du1608fRaDQ8/fTTjBs3rs574j7z9PTkypUrN73Pm4FIKgMDA/nll1/o3r27XAwbOnQoe/fu5dq1a7L3c%2BvWrfL5iImJoVOnTpjNZumV2qdPH8xmM8eOHWP9%2BvVAbULZunVrAMkMEecsXmvbti0HDx4kLi6OsrIymUAKSqlOp%2BPOO%2B8kIiKCkSNH0rNnT%2Bx2Oy1btsTLy4srV65QVVVFTk4OM2fOdJmu/4/ClfC54IILLvzNWL9%2BPenp6TzzzDPYbDYuXrzI1atX%2Bfzzz/H29iY5OZnff/%2Bdmpoazpw5g7u7O97e3jIIUwbxQhp82LBhbNy4kdTUVL7%2B%2Bmv8/PwApLDBxo0bsVqt3HXXXbLSBdCyZUt69%2B7Ntm3bGDx4cB0a342kwydOnMjDDz9MUlISKpWKhx9%2BmDVr1shep2eeeYZdu3bVoXk1BLVazenTp5kyZQpbtmyRfVCffvopffv2ZezYsaSkpEi6n4BQk9TpdJKGKSixjzzyiOw9ulX069ePmTNn0qVLFzQajUw%2BrFYrH374IT179gTqJnzz588nJSWFgoICTp06Rf/%2B/Rk8eDA9e/akVatWNGrUiLKysjrKiILy9umnn8rXhPrg4sWLOXbsGHFxcezZs8eBaqeEGOOtyvDfCDfr47hu3ToSEhLQaDTExsayf/9%2BjEYjW7duZfr06YBjz5zJZJKCNw0lfLc6PqEOWt94BQVTpVLh7%2B9PdHQ0R48elYniiBEjeOSRRxg%2BfHiDSXt8fDxms1mKJv030atXL9zd3es8k56enlKM5tlnn5XvffDBB8ybN4/U1FRKS0vZvn07PXr04MCBA1JoR6vV0rhxY7Kzs%2BulLTr7yoWGhmKxWLh8%2BTKbNm1i/fr1fPPNNw70abHQI76fHn/8cT744AMAh8qkwWDg3XffpWfPnre0SAJw9uxZ%2Bvfvj1arZdq0aZhMJmbMmIFKpaJNmzYcPXqUqKgomdz8f%2By9d3SUVdf%2B/5mamfROQgihBEIgISGEKi0JEEBphvpSNAgqCFhQQCkqKijSFAugYAFBqiggIAiCgkIADUE6IgRCJJXUSab9/sj3HGcmA8LzPu9PXWuutVhpdz33uYe9z772dSkUCiIiIoiIiKBVq1aMHj2azMxMJk%2BeTHl5ObNmzWLmzJlATV9cQUGBUzN6cQ8dO3bk0KFDdoJSYqFGq9WiUCjsxlP0UNqOq9Vq/ctt4M957%2Bw9cNzHmV3CyZMniYqK4tSpU7KPT/T1xcTE4ObmJtWPT5w4Iam8UVFRlJWVcf36dXr27MnYsWNp2bJlreO78O%2BAK%2BFzwQUXXPib0bNnT65evWqXuAlBBaPRiMVikVYMvr6%2BKJVKdu/eTY8ePVAqlfz444%2B1jjl79mzmzJnD7Nmz2bp1qwwsRBVE2CB4eXmh1WopKir6P6Gx2VYTdDodXl5esip13333ERMTQ1hYGB999BG///67DDbUajV16tTBZDKRm5uLp6cnFRUVNG3alNWrV9OmTRtCQkKorq6muLgYs9mMj48PJSUl6HQ6Wa2wDf7DwsIIDg6WJsVQQwcdPnw4SqUSf39/Jk2aZEc1i4iI4MqVK7WSHLVazcGDB/nxxx%2BZMmUKgYGBlJWVSQpceno6U6dOJTY2Vto9iP0TExPJysqSYgz/GzRo0IDff//drgdJ3LcQpBDPonfv3uzbt09eowhW33vvPbZs2cLevXtRKpWkpaUxYMAAoCZp27ZtG8HBwcydOxeVSsWYMWMAGD58OGvXrpXHhxr6bP369WsF6VFRUQwaNIht27bZBbnDhw9n8%2BbNcn/boB%2BQ1Dr4a48/W3EbnU5n1yMoFBIdk%2Bo7wZGWq9freemll5g5cyYnT55kxYoVLFy4UG4bGxt716btbm5ueHp6UlxcLEVLHN89Pz8/4uPjOXXqFCEhIcTGxrJhw4a7tnEAmDp1Kl999RXnzp2zO75Wq8VoNPLuu%2B8yd%2B5cBgwYwLJly%2BySG7FQIqr5YG/N4fh%2BO/6s0Wh47LHHmDRpkkxKVCoVw4cPZ/369YSHh%2BPv78/06dMZNmwYixcvpnHjxndMsHNycqR1yLBhw6SdhPjZFndamHBW6XS2sCVEZoxGIwkJCYSEhODt7c1nn30mt50wYQIrV65EoVDIPljxvYCzxOxutoE/Ba2cCVzdLkm0xfTp03nppZfkfBb3KYRiFAoFQUFBlJaWyp7tiooKO0Ecs9ksaeeArOy78O%2BBK%2BFzwQUXXPgb8eOPP/Lxxx9z8OBBu14isA9CbL9XKpU0bNiQK1euoNPpOH78eK3jxsXF8fLLL/PCCy8QExMjAyZnpsfOIOhmHTt2pG7durRu3RqTyUSfPn04fvw4CxYssAs2hHref%2BIt5Wi8PnDgQLZt22YnXiP8pNzc3Bg1ahQ7d%2B4kNTWV3377jYyMDBmgWCwWKTji5eVFUFCQFFKJjIwkNzdXVmQaNGjAp59%2BKu/Btvrg6%2BsrV8VFNUJU80Q/zLlz5zAYDDI5CQkJoXPnzmzatAkfHx/69evHp59%2BKmmvI0eOZN26dVKowvF5nTt3DrPZzM6dO%2BnVqxdms5nAwEAUCgXFxcW1lB3hzwrM66%2B/zqxZs4AaoZYlS5awfPly3N3dOXbsGOHh4RQVFbF69Wp0Oh0PP/wwN2/etJtfXbp04aWXXpLUOqjxH7xx4wZffvklUVFRvPPOO6xduxZ/f3/mzp3L0KFDZZIlIJLz6dOnc%2BvWLbZt28aVK1fw8vKSlSVBw3ziiSdYtmyZ3H/r1q3k5%2BeTnp4OOJ%2BnYr6I6ovo1Zo7dy5JSUmMGjWKBx98kPT0dOLi4uzGzWKx4O7ujlKprKUcK7zVRKLz1FNPAbBw4UISExP57bffKCsrw2QysX79egYNGgTUvCuiRys0NJSJEydy5MgRtm/fLu/VaDQSGRnJ4MGD8fHxoWPHjrz00kvs27ev1r1pNBoeeOAB5s6dS9u2bSkvL6dTp04sX76c4cOHk5mZKat6ixYt4oknnpDzIC4uDo1GQ0hICNu3b5f0bKHGKRKNhIQEDh8%2BbKfEW11dTUpKCt9//71UcRQV1tLSUhITE1Gr1WRnZ3Pjxg2Cg4PJy8uT7/zhw4f58ccfeeedd2S/8KhRo2jUqBG2%2BoAhISEYjUa5UGP77on3YtOmTdJEvWXLluzevZuQkJB76m318fGRPXzCFuGBBx7g4MGDREVFSa9Ax4UEx15KAccquZiz7u7ulJeX3/bzGv66enc329zLPgJGoxE3NzdZcRW05nuBuJdu3boxevRoNBqNHXXYhX8HXD58Lrjgggt/I3x8fKRXk2MS4KgyB8ieouzsbCly8vrrr9dKBqxWKwsWLJB%2BU7a/dwxk3NzcaNq0KTqdDpVKxYQJExg3bhxKpZKMjAyOHz/OunXrmDlzJgkJCYwbN05WCoWfk1ANFXj99deJiIigYcOG8neip8URtgGXxWKhSZMm6PV6VCqVFB/o2LEjULO6vW7dOlq0aMH69eupV68eVVVVqNVqKUAirsNkMnH9%2BnV5zpycHCwWC3q9nuLiYm7cuGHXj2KxWGjQoAEeHh5UVFRQVVVFRUUFRqORsrIy6eulVqtJS0tjwYIFzJ8/X97bqlWrMJvNJCcnU1ZWJsUnhAfivn37qFOnjt1zDg4ORqvVcvLkSQwGA0ajkVdffVUKvwhjblHViY6Opn79%2BjIgr6qqkjQ2Idii0WiIiYnh6tWrLF26FIDi4mIqKyvt%2BqAc59eBAwdISkoiKiqKqKgoWrRoQU5ODlarlREjRvD888/z5ZdfUllZSXZ2NoMHD7ZTkbT%2BP9uCuLg4AgICeO2113jnnXe4cuWKHCO9Xm8navPuu%2B9iNpvlPQ4ePJiFCxcSFBSE1WqVghGOc8jT05PWrVsTEhLCQw89RJs2bVi7di2%2Bvr5cv36d9957j7i4ODw9PaVipbjfqqoqSSNVKBS4u7vj5eVFfHy83MZkMrFgwQIWLFiA1WolIyODgoICOcdEsgcQGRnJ559/jtlsJjQ0lIEDB9K4cWNJrxbP%2B%2BLFi8ybN48ZM2bQpUsXmewJerBOp8Pb21v6sSUmJnLr1i1MJhMeHh4AUtHRYDAwaNAgNm7ciEqlorq6mt69e5OTk8OgQYPYvXs3devWJTo6Gi8vLxo0aCB7NA0Gg7TFEMqSSUlJKJVKAgMDMRgMlJaW0rdvX0wmE56enmi1Wnr27MmRI0e4fv26HM%2BAgABZkQ8ICKB79%2B5y0cpqrTGqdxSDz83NpaCgwO7zQizUAHTq1InRo0dz6dIlrl%2B/zpAhQ%2BjevTvdunVDo9HQq1cvkpKS5BzSaDR88MEH8p3w9vZm%2BfLlsoIFNf2QFouFkpIS8vLyOHz4MI0bN6ZZs2aSgqnRaHBzcyMgIMBpUulIiRZzXlz3P7F%2BYjab5Wfp%2BPHj0el0xMTESO8/8f%2BJu7s7gKyEA9Kfb%2BjQoVLU5rvvvnMle/9SuCp8Lrjgggt/M0pKShg0aBA3btyQwYNKpeLBBx9Eo9Fw69YtFi1axJkzZ4Aa4YQzZ87Qp08fLBYLO3fupHv37lLh7cqVK2RlZUnfJ/FVwFb4QqvVMmfOHN577z0qKytRqVT88ccf9xS8KBQKxo8fL4Pz6Ohou6DA1jMOagRg1q1bJ//u7e1NRUXFPVHVBOLj4yWNTvhWOcrq2%2BJOojEiwBEJiiNs%2B3lE4iGCbbG/SLjEKrztin/dunXlc3AUtLmTj50zqwBHOFYUhJjJzz//THR0NDqdDqPRiL%2B/fy1xCoVCISuA4pqNRqMddcyxlwvs/RWFZ5rVWuNZ%2BMorr/Dss8/abW/r4Sa29fT0pLy8nP79%2B3P48GGKioro06cP27dvrzUefn5%2BFBcXy5%2BPHj1K586dpaKkSqXiwIEDJCYm/uVYRUZGcunSJTtRE9sK053G1sPDg8rKSrt5JCotnp6erFu3TsrWC9qsqBo6znGlUskvv/xCu3btnD5j2ypSaGiorKTDn%2B%2BNqBhHRUXZqW6Kiupf9T46u67/BP7%2B/hiNxjv62bVp0waDwcDp06fl4kuvXr1qbSeEjC5fvizH/oUXXmD16tWYTCaioqKkwJXwCY2Li8NgMKDX6xk7diynTp2S2zRo0ACTyURcXJxU0IQayrawCRGqvosWLeLrr7/m6aef5qmnnmLJkiV24kCO8yEoKEj66d0Od2UrtkAAACAASURBVPMOBwYGkp%2Bfb/c723Op1epa7IB7eXZCfbR3794oFAq%2B%2B%2B47FAoFx48fx2w2Ex8fL5WVLRYLTz31FAcPHuTYsWPo9Xq6du3KkSNH5GKBC/8uuBI%2BF1xwwYW/Gc888ww3b95k7Nix0tfu5MmTbN68GV9fX86dOydV8cR/%2BFOmTOHdd99FpVLJHry7hbP%2BFWEyDjWBYmRkJGfOnMFgMBAVFcX58%2BdRq9U0atSIgoIC8vPzqVOnDnl5eXI/vV5PQEAAdevWlSvGUGOTADX0wO%2B%2B%2B84uaVKr1Zw6dYoePXqQm5sr6Wzr168HaoK5sWPHStEH8bt169bx8MMPk5yczBdffCEDI9ukIjQ0lKKiIruAV9ANLRYLGo0GX19fmQD5%2BflRXV0tqz8tWrSgqqpKBtki%2BBbnmDx5Mm%2B99ZY8dv/%2B/Tl06BAxMTH8%2BOOPsv9y1KhRbNy4kczMTPbt28eECRNo0qQJFy5cQKVS0bNnT/bu3SsTx9v1HokE083NrRYdUdDrxH6CnhgZGSlV/myPbWvOPXXqVL7//ns71UWADh06UF5ejtFoRKfT4ebmRlFREQqFQvaSigqUSqUiKCgIwG7BwMPDg6CgIH7//Xe7uSeuY/ny5Tz22GMcP36c3r17k5yczJdffimrsuXl5bWSYUE/fPjhh/nss884efKkFJMIDAy0W9xwNpa2c%2BRO6NWrFzqdzs5rTqvV8sMPP9ClSxcMBgMeHh4YjUaOHDlCq1atpE%2BgsyD82WefZcGCBfJnsW1KSgq7d%2B%2B221ar1WIymaSZduvWrfnhhx/srsMZxRdqLFjy8/OpqqqqtY2zBQ8xF8Ux73TsoKAg3NzcZLXcGWzP4Tj%2Bp0%2Bf5umnn2bPnj1YLBbq169PTk4OqampQE0lOjs7m5ycHEwmE23btiUrKwuLxcK8efOYOXMme/fu5ZNPPuGDDz6Q53H2TG3PHRAQwJYtWygsLOTRRx/FZDJRVFRk15OYmJhIYmIisbGxXLp0yc4aZu7cuVJoyBF3k8z9HXBsAxALGsJP0fZz2JGKKnqoY2JiGDZsGDExMXz55ZfMnTuXoKAg%2BZnuwr8HroTPBRdccOFvRps2bZg/fz4zZsygsLDwnqprPXv2JDk5mRkzZjj1mrP9ajab5Wr4uHHjOHjwoFSvEwG5l5cXHh4ejBw5kh07dpCTk0NlZSWPPvoon3/%2BOVATlFksFgYNGsSGDRvu%2BX6FMmNQUBAlJSWcPHmSNm3aMHToUD744IN7Ph4gE%2BXQ0FDpSyWM429XOVOpVAQHB9vZF8Cf1MNHHnkEo9HIsmXLgJpEo3Xr1nh4eEhlRyFaAjVVnjNnzjB69GhKSkpk4Ltz50769%2B/P%2B%2B%2B/j9Fo5PHHH6dPnz7SC/G9996jrKyM%2BfPnc/PmTZks%2BPj4EBQUJBNOQftbunSp7HETEKv/IpD7T%2BwvvL29gZoK7aeffkpcXBxqtbpWcunl5YVarcZgMNQKdIUaqtVqJTExUYpbREVF4ebmhsVisTOwb9SoEVeuXOGHH34gOTmZQYMGsX79esLCwvjjjz%2BoqKggKCioVlVSpVIREhJCQUEBTz75pLRMsH2GInCNjIyUlSIhQvHWW2%2Bxc%2BdO9u3bJ%2B%2BhS5cudxXIClGYyspKWrVqRVZWFvPnz2fKlCloNBqZADijaTv6vzmid%2B/e7Ny5U16/n58fgN2CjpubG%2BHh4XbVPtuK8dChQ8nLy%2BP48eNSpEbMB5Esi7kRFhZGbGwsBQUF/Pzzz3aJqkiixPuqUCgYOXIku3btoqCgAIvFYtf36jjnnCW/K1euZM2aNbLytnDhQqZPn86yZct44403yM/Pp0ePHvTt25eRI0fy888/c99992EwGPDx8cFqtdKwYUMuXLhQa146jjMgacb9%2BvVj8uTJ/M///A95eXl2diZ3wr2o2wrao0KhIDExkYyMDLuK6%2B1w/fp1cnNzAcjLy6OwsFB%2BHufm5nL9%2BnWKiorQaDRUVlZSWVkp2QRCyEWn0xEYGEiDBg0ApPLysWPHpGWG7SLP7SAWlAwGA1qtlmnTplGvXj02b97Mt99%2Bi6enJzNmzKBv3753NSYu/HPgSvhccMEFF/5mtG/fntDQULp3787OnTu5cOECvr6%2B3Lp1y878tri4WHqbNW/eHD8/P1599VWCg4OJi4vD29ubzp0707lzZ6ZNm8bJkyelwp2gO%2Bl0Ovr3709%2Bfn4tpTVhzi0EMURflegV/Ktm/9tVpoTHl%2Bjd%2BuKLLxg4cCCRkZFcvHiR06dPk5CQwNGjR8nOzmbt2rV25suOq/eCxmQb1O3fv58%2BffowePBgPv30U2nJEBgYSF5enqRUWq1WmeC5u7vj6ekpVf/CwsKwWq3cvHkTDw8PUlJSSEtLY8SIEWg0GulFl5yczJgxY%2BjRowddunSR19OvXz%2BsVivbt2%2BXVdP/VCDBmYfh3UBUYcvKyqQkvUajkZRO%2BFOIQwTn3t7eFBQUMGjQIK5cuULDhg2ZM2cOcXFxcruSkhKeeeYZFi1aJIUhRD%2BjSOLMZjOnT5%2BmefPmAMycOVMaqUdFRUlRnU8//ZQRI0ZgNBpllddqtRIaGopSqSQ3N1cqBUKNIXVJSQkmk8kuyBcKkkJopXHjxly%2BfFlWb8Xx/%2Bd//ofNmzcDsHr1agYPHszZs2fl/GnWrBm%2Bvr40b96cw4cPyzkskspp06ZJuwPxPFUqFaGhoVitVq5fvy5FjtRqtUzoHKt8r7zyCq%2B99prdM7BNvsCeJgvI68rOzpbUQ0c0atSI7t27s2LFChQKhUw8bmenIj5L/gpff/21tGsRScLXX3/NmjVr7BY6bJ%2BHUIYV4%2BRIBXYUjbL1B9RoNLRp04bevXvTq1cvWrduLSv5osrctGlTIiMjgRqBH1FdnjBhAlCjultUVHTHxQ6FQsFzzz0n%2B281Gg1%2Bfn4UFRXds0E51FQ9O3bsSFFRkVwwUCgUtGjRQs67vxP5%2Bfn07NmTiIgITCYTv/32GwqFQnqmindHpVJJsR1nixUNGzZkxYoVLtP1fylcoi0uuOCCC38zEhISOH/%2BPMOGDZN0NEdKla2SocVikdXAadOmyW3GjRtHUVERs2bNoqqqimHDhmE0Gjl58qRsxK%2BurmbDhg1OZbVtg8Lo6GgCAgJkIDVp0iQpJgF/Vpts4Zjs6fV6aS0hxChUKhUXL15EqVRy5coVKYDh4%2BPD559/zrlz5%2ByUC%2BPj42nVqhVQk6C1bduWhIQEoqKi0Gq18pxJSUlUVlbKRDEgIIDZs2ezZcsWoIbq16lTJyl%2BMWHCBD7%2B%2BGM%2B%2BeQToCYI7tKlC56enpjNZp544gnmzZsn%2B8F0Oh3Z2dmcPXuWGzdu0KFDB5lQCY/DHTt28PXXX9t5c0FNdUVIn69duxatVstbb73FK6%2B8glqtJiQkRIpFCO8yx2TPzc0NvV5PgwYNZNWnQYMGcv%2BYmBi8vb0xm82cP3%2BenJwcFAoFnTp14vjx4zJ4y8zMlFXerKwssrKySElJwc3NDaVSycmTJ6X5uUhGGjZsiEKhkMI5AiqVil9%2B%2BYWTJ0/KIPDll1%2BWf2/RooXd9n5%2BflRVVTFy5EipBKrT6RgzZgwBAQG0adOG7Oxs6tSpY0dvzc7O5pFHHqnlMWYymaTFBGDXkyfuV/QUisULYTa/a9cu5s%2Bfz9ixY7FarRQVFXHkyBG7ROSrr75i3759dOvWDb1ez65du4AaLzqz2UxkZKScH6J6KHofnVE6N2zYYJcMiQWQjIwMqQjqWLUqKysjIyND9nYJk2whWCKOI6wJhNCNEDuCGvqneHYqlcruHPXr1yc1NZVJkyahUqnw8/OT99SnTx%2Bqq6vtnvv9999/26q%2ByWSSCzsAffv2lRUngaeeeooff/xRUtGbNGkik2RhXbJo0SLZAzx8%2BHCqqqqwWCxUVFTwyy%2B/sGnTJjZt2iTH2Gw28/HHH7NixQoKCwtrqcY6wmq1ymQPap51QUGBtGF44403cHNzo0%2BfPri7u8tFCdvxtEVJSQm7du3iyJEj8ngHDhyQ5uh/NwIDAwHYuHEj27Ztw83NDa1WS2lpqZzvOTk5ZGdnYzQaqa6urjV/k5OT2bVrlyvZ%2BxfDVeFzwQUXXPib8ccff5CSkiL/k7XtuxCB5GeffWanKGlLc/P396eoqAg/Pz8qKipksKzRaO5I33EGUUEcNmwY06dP57vvvuP555%2BXCZugGgnYVv7UajWpqakcO3ZMVs2cbSe%2Bv5uqoV6vx8/Pj5ycHHx9fdHr9SgUCvz8/OjSpQsdO3aUVSTbexDnmTNnzm17b1QqFb6%2Bvk7pXQqFgqVLl9K2bVsZfMbGxmIwGPDy8mLdunU8%2BOCD/Prrr/j6%2BtK9e3dee%2B01Ll%2B%2BTL9%2B/fjqq6%2BAmj6wVatW2VUcHn/8cZlcm81m/Pz8qKyslB5YIlkZNWoUhw4d4rfffqNHjx6yj3HWrFls2LCBiIgInn/%2BeZKSkpg1axbFxcXs37%2BfwMBAunbtynfffUdVVRVpaWkyse3bty9ffPEFM2bM4Pjx41y9epVff/211tjVqVNH0swc%2B%2B4cxzAyMpL8/Pxa4/jggw8SFxdHcXExixcvplmzZpw/f14mYZWVlXdV0VQoFPj4%2BODr61tr/tlCiFL8N8Ma0YtqMBho06YNR44ckZXpiIgItm/fTsuWLf9r53TWP6dQKOjduzfff/89Pj4%2BpKenExsby5AhQ5y%2BQ4JKaTabcXd3Z/bs2fTv35/o6Gjc3d157LHHWLNmjZ0FjFjIEYwAi8Xi9FqCgoJkwtiwYUNWr17N6dOnGTVqFH5%2BfjRt2hSz2czx48elPYQQkxIQyZNjlf5eEB4eTm5u7l1V5NRqNTNnziQ6OpoLFy6wZMkS6TlnS3PUaDTy81aMqRAxEXBGyRULOvHx8QAMHjyYlJQUUlNTnXqk/l9DfB6uXr2aoUOHcvLkSeBPZoRtf6VAvXr1KCoqoqysDLVaTXh4ONevX0ej0RAUFERlZaVd8m7LwHDh3wFXwueCCy648A/AggUL2Lx5M3FxcRw4cMCORunsY9oxELGF6KNSqVS0a9eO4OBgO2ETEaA8%2BOCDXLx4kZ9//lmeq3fv3uzduxez2Yyvry8lJSXUrVu3VtDmCKVSibu7u1SBbNWqFfn5%2BVIwRPSXif44oS5oq2L4fwHHREL4kd0NgoOD8fPz49y5c3h7e%2BPl5YWbmxtdu3bFy8sLb29vXn31Vblt%2B/btZaL3n0JcnxgT2%2BvV6/VYLBa7n6GmmmW7Ij9ixAj8/f3p1asX999////qemwhzLr/SWGDqEyGhYUBNVXJa9euOfVWCwgIoKioiHbt2nHo0KHbHtM2CbHtBdRqtXdNsxX79ezZk2%2B%2B%2BQb4szIkKLB3Euex9RkUcJaAOVM%2BFXB3d6d3796MHj2ajz76iK1bt9KtWzcOHDhAt27d2L9/P88//zzz5s1z2vMpju1MCVKhUDBu3Dj27dvH1atXqa6uJjw8HJVKJVVuhYekoFQDfPDBB5JybUt/FNVRjUaDQqGgefPmjBs3jr179/LNN9/YqcGmpqZSWFhIRkYGw4cPZ%2BPGjUDN597w4cO5evUqP/zwA506dWLv3r3SckFQfP%2Bqt1Uk9M7g7DNL0McTEhJkH6y7uzsHDhxw6pH6f433338fg8HA008/zTvvvMMXX3yBl5cX586dk71/txPlcYS7uztarZbi4mLJLJg2bZqk%2Brrw74Er4XPBBRdc%2BBthtVr57bffJD2uf//%2BbNiwgS1btkivvdLSUhnUiaBb7BsVFcWFCxcICAggPz9fJnLR0dEsW7ZMJn5lZWW0b9%2Be9u3bs3jxYqCG3me1WmnevLldBeevpL6bNm0qAyKxIg41CYig2H333XeEhIRw/vx5HnroIUpKSrBYLHh6emIwGKiurqZTp04cO3aMl156if3797Nv3z4CAgK4efPmPQuOhIWFMXHiRIKCghg7dqzTbYTQiqBv6fV65s2bx3PPPYfRaESv1/PVV1/Rs2dPp4G4Y0AdFhbGjRs35LWqVCr5d1vlUnGvFy9eJCYmht27d9cKsEVwL3q4xN9DQkJkpa1evXrk5uYSEhKCSqUiNzfXLnkV1yfEe%2BbNm0dsbKzdecT12VKB33jjDaZNm8b8%2BfPvWuJdoVDUogULpUWj0cgTTzzBxo0bqa6udtovJsbFNqG51wUAW1GRkSNH8swzzwA1dNgLFy5Qr149srOzGTp0KFBD212yZAk7d%2B5k/fr1KBQKee%2B2ao7Dhw9n9%2B7dcrGiVatWNGjQgK1bt0rK5K1btwgJCaG8vJyysjK8vLyoW7eutE5xNl6OyYZKpSIpKYkLFy5QVFSEWq2WIitPP/0048aNIzY21mmSbVudceyHCw0NrSVEJPBXYj4JCQlkZmbSv39/oKZPrn///nz11Vfcf//9fP311/dk4aDT6WjWrBlnz54Faj5zmjVrdleJl6CAd%2BvWjTFjxmAwGHjzzTe5ceMG%2Bfn5aDQaQkNDMRgM%2BPr60rhxY/bs2YNKpeKJJ57g559/5vDhw07fEWcIDQ2lbt26nDp1iqqqKpRKJQ8%2B%2BCBNmjThjTfeuK0iqPA3FIt0Go1GLhD07t2bJ554gi%2B//BK1Ws2oUaP49ttvef/99ykuLqZr164888wz/PTTTwCkpKSQlZXFqlWryMvLo2fPngwfPlyeQ6FQcPbsWerUqSPVcN98801KSkrYsmUL8%2BfP55NPPsFkMuHj48OIESPw9vamRYsWXLt2jR07duDj40NFRQUXL17EZDKh1%2BspKCiQcw9q3uX27dszf/58UlJS%2BPHHH1myZAmAy4fvXwpXwueCCy648Dfh%2BvXrPP744zKwjImJ4b333iM4OLjWtklJSSxbtozHH3%2BcZcuWkZaWhslkolmzZly5ckVK5gcEBHDr1i2pnKdWq1mzZg2XLl2StK2ZM2cCyOrUzJkzpZGy1WolJCQEhUIhe9RsKX1arRaLxUJSUhKzZ88mNTUVk8nEW2%2B9xZYtW2Q1A%2BCRRx5h4sSJtG/fXoqaWCwWjh49Sm5uLvHx8axatYqqqipGjRrFxYsX2bx5M8OHD2fixIksX76cAwcOcN9997F8%2BXIefvhhIiIiuHnzpl2lpVGjRkyaNInu3buj1WqJi4ujqqqKLVu28MQTT8j7CA0N5Y8//pBjo9fr%2BeGHH0hMTJQ0w8zMTK5du0ZKSgqA7FP84osvKCwsJDQ0VJ43LCyMEydOMHz4cKBGpfOrr76iX79%2B0iYgICDA7rlt2bKFTz75BJVKJcVI8vPz7dQObWljzz33HG%2B%2B%2BSYeHh7Ur19f2ljAn2I4IkEXypm2yoDCA9ERwhNRbCOEfUTg2rlzZ77//nvMZjMdOnSgdevWeHl5ceLECS5duoSXlxcPPfQQ7u7uku7YrVs3aaD9xhtvMG/ePL799lu6dOnCiy%2B%2BSMOGDRk5ciQKhYLMzEzGjx9PZmYmycnJ7Nu3j/T0dPr06cPFixeJjIzk6aef5tSpU%2Bh0OsrLy2sJmoh5GRUVhVKpZOvWrdy4cYPQ0FBJV9y/fz%2BzZ89GqVRy//33o1Ao2LdvnzyOLf0WahRk161bx5AhQ6TYiKAgCh%2B2iIgIcnNzWbx4MRMnTiQ8PJzExESaNGnCggULSEtLY/369XaJWJMmTfj999/lvIuPjycwMJBt27ahUCho2LAh165d46OPPmLkyJHSXN5WZVNU5qFG7VIkL2KBY%2BXKlaSnp9OvXz%2B2bNlit3CjVCqZN28e165dY%2BnSpfJ%2BQ0NDycvLkwqQU6dOZfHixXKONW/enLVr1zJy5EgptuOsegpI1U6oSUK7du0q2QrC3HzXrl3k5uYyf/58srKyZBJjSzOMiYnh2WefpWHDhqxatYr169dL%2Bm9YWBh5eXlUVVXZVTzVajVdu3aVixDbt29nxIgRGAwGqqqq0Ol0VFVV4eHhUUv4JyMjg8TERB566CGOHz/O%2BfPnZZVdLKRpNBpGjRrFli1b5AKcsC6orKyksLAQlUoleystFgsREREYDAY0Gg05OTnyXsUzUavVUrBI/M7Ww1MkjyaTiaCgIG7evFnL91N8bkVGRhIYGMjBgwdRKpU0btyYgoICWV29U6iflZXFAw88IFkc1v9nbyMqejdv3mT27Nl2LQUu/PvgSvhccMEFF/4mPPnkk6jVaqZOncqtW7eYOnWqrBbk5ORQUVGB2WyWyZizfie1Wk1UVBRXrlyxoz3t3r2b/v37o1Ao%2BPnnn%2B363I4dOwYgxRmOHTsmAy43NzfZ8xEVFQXUNpoWgadarZar5469OLa0UhHMhISEUFhYiKenp6zuOMr6295bQEAALVu25IcffiArK4tmzZrJv4tAyGq1MmjQIHbv3o1KpaJv375s2bKF8vJyUlNT7fzNdDodjz76KEuXLkWhUBAYGIibm5ud%2BmFwcDD169fn2LFjMuAT9NaEhATUajVz5syR23ft2lVW4M6dO0dcXByffPIJDz30kByPgQMHsnnzZk6dOkV0dDRarVYGdT4%2BPnYr61DTJyVk70WyIeiEwl9v9erVhISE0Lt3bzkOQhHx3LlzzJgxg9dee00mcYIiKBLJ8PBwcnJycHd359atW/z666%2ByB0lAJG/i%2BYqKmUqlol69ely9elXOS/GcxRxVqVQoFApOnTolVTszMzNp1aoVarWaiRMnsmDBAvR6vTxGZWUl586dIzo6mjNnztCiRQtMJpM0NT937hwtWrRwSnNWqVRMnTqV%2BfPn06NHD/bs2YNCocDd3f2OsvqOFa/09HS2bNli90zc3d2prKyUX48cOULnzp2Ji4vj/PnzJCUlkZmZKZVde/ToQZ8%2BfeT%2Ber2etLQ0WrVqxZQpUwDYs2cPK1askHTE212PgFBZFeImOp2ODz/8kDFjxtRSVBTzoG7duuTk5KDValEqlQQGBkrLEoF69epx/fr1WuMpqsRC9MgZVCqVnR1IdXW1rMr9ldm7gBAW%2BuGHHyS7IC4ujkuXLrFkyRLGjRt32z49sbglxmTSpEm8%2B%2B670jJm27ZtmM1mu/0bNWrE1atX7aqUBw4coHv37jIhA%2B5ZXRf%2BpHZ6enrSpEkTVqxYIemnOp2O%2BPh4rl27RkFBAb6%2BvrRo0YITJ05QUFCAXq8nPT2djRs3UlpaioeHB08//TSzZ8%2BWYyySPsHy8PT0pKSkBKPRKOnCohrsqI56rxBsBdEHnpaWRt%2B%2BfenQocN/fEwX/l64VDpdcMEFF/4mHD9%2BnBkzZlCnTh1effVV8vLyuHbtGmfOnKG4uFgmRSIYE19tg3Cz2czAgQOlWXj//v2xWq3cf//9slq0e/duli5dyurVq1m9ejUajQaNRsPq1at5%2B%2B235aq0CCwcYZvsAdLrzZYqZUvttP3Z9ne5ublUV1dTWFhIWVmZ9JOyVRy0rdx5eHhQp04djEajrGKIc9pez2uvvcabb74J1Ji8i7FwNLMeO3YsDz/8sLym0tJSuz7IRo0aMXHiRJkQK5VKOnTogMViwWAwMHPmTKlgKSAqSbYJr0j2xO%2BPHDmCu7u7TOJEIObl5SWv1RZWa41XnI%2BPj1SXFPddr149rFYrixcvJjU1Vc4PW3%2Bt6OhoNm3aRHR0NAaDAaPRiNlsprq6Wv585coVwsLC5LMUpuu2z8226mC1WqUoi1qtlske1ASH/v7%2BUm1RPB%2BVSkV6ejqenp5YLBZZla6urpam1gaDAbPZbBecimfrSB2Mjo628/CzhdlsZt68eZjNZnbt2iVFSxo3bgzUJF0%2BPj54eXnZ7de0aVP5vZeXF9OnT6%2BllBkUFITVaqWqqorIyEheeeUVqqurycrKwmAw8NNPP7F48WIuXbrE6NGja3kCWq01tiFC5RNqBDVsq6y2CryOUCgU0k/Sls49atQoqqurayVEordTVLbFsZ3NNaFa63gdW7ZsYceOHXZ/S0lJkVUp223hz2cl3mXxNzEffHx87P4utlEqlXZWGO7u7qxdu5a%2BffsyefJkjEaj3T5Qs/ih1Wo5fPiwnHMajYbt27fLipno0RRjI67j5ZdfrpXMdevWTSZKCoWCWbNmkZqaKvsFlUolTz/9NGPGjKFRo0Z2%2ByYlJdn10lqtVgwGA6dOnSIjI4OKigrZN/3xxx/j6%2BuLv78/kydPZunSpfLzTqFQ8OSTTxIREUFwcDATJ05k8ODBsmqqUqmIjY1lzJgxdOnShfT0dAwGg3x%2BR48eJSgoCG9vbz7%2B%2BGMyMjLkcxIiLPXq1UOhUODh4YFWq8XHxwd3d3fZN%2Bnu7i7fj5iYGGJjY%2BnRowd169YlLi6OmTNn0qVLF1z4d8KV8Lngggsu/E0oLS2Vkv5ZWVl89dVXcnVWr9dz%2BPBh3N3d0ev1dOjQgRkzZtClSxd0Oh0qlUoGGu%2B//z5qtRoPDw%2B6du1Kw4YN5c9BQUEsX76cTp060aFDBzp27Eh4eDgDBgxg3bp19O7dm%2BTkZPbv328nPQ41lQKdTkfz5s2llxv8GbQ5BmK2sJWet4VGowH%2BrBJaLBbq1q0rxQ7q168vz7tnzx5efvllNBoNGRkZQA2N0fG4ycnJPP744xQXF8tqm6MtgkqlYsWKFTz66KPy3lQqlV0QfPnyZWbPnm13D9u3b%2BfWrVvcuHGDtm3bYjabOXfuHF9//TVpaWm1AnRxT7aJU1JSErGxsUyfPh2o6SUTybX45%2BfnR%2BPGjfHy8pKJ1ZAhQ1AoFMyePVvu89tvvwE1iwW2FUCo6dlTqVSkpaWhVCpJS0uTQZ2vr6%2Bk6kKNsuu2bdukqfyWLVuwWq2MHz%2Be8ePH29HsxHMW1ViFQkGfPn0kvVUk/3q9XgbWgn6XlZUltxOUMbGN%2BN5ZUO8MK1askPNn7ty5qNVqfH19b7u91WrlypUr1KlTh%2BbNm1NSUkJZWZkU8QBkbxkgEzXHOStESKxWKxcvXmTHjh2yIunh4UF%2Bfj5RUVFYrVYOHjzIgQMH7Pb38PCgoKCA0i6yhwAAIABJREFUTp06yd9t3bpV9kTBn%2B/F7e7DMckViq6O0Gq1sh/MEYcOHap1HuHDZnsuwKmwx61bt2SSBn/OdfEP/lwMUigU3HfffXY2LlqtlnXr1kl7jUOHDnH69GlOnz4t3/8NGzawcOFCduzYYVeZVSqV0sNQeB4uWrRILmJUV1fz22%2B/yUWPnJwcp4sIDz300G3pjWKR46WXXmL37t1YrVYyMjKwWCysWbOGVatWybkANe9QUlISAQEBdscxGo1ERkby3HPPyXutrKykVatWZGVlcf36dTZs2EB6eroUkjEajSQkJHDs2DGys7Pp2LGjvF%2BxqLNnzx727t3Lt99%2BS0JCgqz6KRQKunfvTm5uLqWlpcycOZOUlBT5rptMJrKzs8nNzZX3PnbsWDp16kTjxo3p2bMnM2fOlKI5ACdPniQzM5Pt27dTWVnJggULaNiwIZMmTWLdunVOx8%2BFfzZclE4XXHDBhb8JoncKYNiwYSxatIjk5GRJgXvnnXd45plnqK6uZsKECUydOlVWe0R/jk6nkyv/derU4datWzRu3NhOah/sqWKi9yMkJIS5c%2BdKny3R7yWuSfSh2Zopm81mli5dyqRJk6hfvz7Z2dkyCHz11VeZPn06AQEBeHt7Ex4eTlZWFsXFxTLQOHHiBB07diQ2NpbCwkLq1KmDj48PO3fulNcqjte7d2/MZrPd3%2B4WSqUSnU7H6NGjWb58%2BV2J0TgiLS0NQKrcOVIvxbU6imbcDcLCwlCpVHaKklqtFo1GQ3l5OXXr1sXDwwMvLy9KSkq4ePEiDRo0ICAggJ9//hmwrwYpFAq2bt3KkCFDOHnypF1fHtSM%2B44dO3juueeAmmTylVdewWw207JlSzQaDZMnT2bMmDFcuHCBAQMGyERDWEWI8XNzc6Nhw4akpqby1ltvSUEJx%2BR37Nix5OXlsW3bNru/OROsEb1LtvRNQSm2/V1cXJzczvEebSmoAkKkRwTH7du3l5Uri8WCm5sbBoMBpVLJAw88ANT0f4nKisVikTTi8PBwbty4QWRkJAUFBZSVlclFg%2BPHj9O/f3/y8vJqUelSU1M5fPgwixcvthMUcmZF4agi66jMGRoaSm5uLgqFgsTERI4ePSqN1m83vrc7NtR4JZ4/f75WlTA1NZUGDRrw4YcfyorzvaJ%2B/fpcv34ds9ks1Ur1er1cOBgxYgTt27enZcuW%2BPv7ExcXJ/vgEhISOHz48F2f6/9S6fdez/tXVhN6vb6Wsq7j/vXr15fJpbNziHl9O4h5I6jV4ly3G6e4uDiysrLkc7btlw0NDaVp06b4%2BPjwzTff4OvrW2tRw4V/PlwJnwsuuODC34SWLVvKqt6NGzdYtGgRv/76K8HBwZSVlUkfLUE71Ol0uLm5UVBQQGJiIj4%2BPnTv3p0dO3aQkZGBTqdj1KhR7Ny5UwrBOAaV7dq1IzMzE3d3dxo1asRnn30m/xYXF4fVauXAgQP4%2BfnJgFun0/Hkk0/KCogIwG37RPz9/dm7dy%2BtW7dGrVajUql49tlnmTt3rl2w2Lt3b3bu3IlOp8PDw4MJEyYwcuRI4uPja/XzOcP/tjfFFvfddx8tW7bkww8/RK1Ws2LFCgBGjx4taVjgnGYHdw4yu3XrBtRUMeLi4qioqCAsLIw9e/bQsmVLunbtyunTpzGbzRw8eBAPDw8MBoOskPj5%2BdGiRQuaN28ur6tVq1YUFBRIj8OqqiqeffZZFi5cKK%2BjTp067N69m9atW9t5DDqOW3x8PDdv3qSwsNBOnMRqtdaqcDm7X5VKxZo1a6RgjTOEh4czZcoU1Go1Tz75pDyu7ZwU/WWAXcI3Z84cWW0V38%2BZM4c5c%2BZgMpmYM2cOr7zyCgcPHqRz5863tS%2B53TNSqVQEBwcTGhrKiRMn5CKDqM4621en01GvXj2uXLmCVquVgjVlZWXUr1%2BfGzduOJW7/ytlTMfrEuOkUCiIiIigsLCQ8vLyWs9FeHHaenL%2BtxIfpVJJYmIiGRkZspKXmJhI48aN2b59u1N66J3w307I4uLiePbZZ1GpVISEhLBo0SKMRqO0lLFVfBX/bH9vC19fX5o0aSKr5kJQ6erVq7LH9qmnnmLr1q1cunTJbt%2BYmBiaNGnCF198Qb9%2B/fj2229llaykpASr1Yq/vz%2BRkZEcOXJE9jYLCna3bt04ffq0XW%2BebV9vgwYNCA0NlcmvoFyWlpZKoaeYmBh%2B%2BeUXuTgh%2Bk2F9c1/kqwLhIaGSgGv5ORkhg0bRufOne2q9C78O%2BBK%2BFxwwQUX/iYIeXJnlK2/gghi/P39pVhAly5dyMjIkNQ5d3d3XnjhBXbt2sWhQ4ewWq307duXp556ir59%2B1JdXc28efPo0KEDQUFBMsED7KphCkWNqbCoLImg3LZi0KVLFzp37szrr78uA5rw8HB8fHw4ceKEPKYIWoVq3vbt22nYsCHx8fG1qg%2Bir0TIhgNERERw5coVnnzySd5//31pDJ%2Bfn09ubi7l5eUycXR3d5fCBoLuqlAo2LFjh13/VIsWLWjRogWLFi2iXr16xMTEoFAomDdvHjt37mTfvn3SiBpq1BChRp5cVCWgpoomqk3t27cH4MiRIzzzzDN89tlnDBw4kMWLF9OxY0fq1q3Lli1b7NQJ69evz7Vr1%2BQYOXqsNW3alCtXrsiVe6PRyEcffcQjjzwixRXUajVNmzblzJkzNG3a1E6x03Zce/fuzalTp7h58%2BZ/lECLCpOg2vr4%2BFBaWkqLFi3IysoCoG7dupSWllJZWYnJZMLDw4Py8nJSUlJqWToIOPOaE0mQbaLo7%2B9PeXk5RqPxjgGteL9s1SOtViszZ86koqKCTz/9lPz8fKAm8Pfw8OD69etOjyFUGYuKiuR1CGEUZ/Dw8KjVn2j7Xtk%2BX/F74a8pvop%2BWw8PD/Ly8uwSQpGU2Hpy1q9fH3d3d3JyciQlUlAgIyIiyM7OZty4cQAsX74cPz8/iouLnVKT/5Pw0DG5FcJHwujcEVqtVorOqNVq/Pz8aNmypZwfCxcuBGoUjZcsWYLFYsHPz49bt27h5uYm1Shv3rwpRaTq169PSkoKn3/%2BueypExCfXWIuOTOut61C2n7vjCEQGRnJpUuXsFqtTJgwgVWrVslxMBqN8vmKZy2%2B2iofi3EWc1%2Bc0zFZhZrPNKvVKq/J8fqdiUCJ9%2B52z8h2G9EPaTKZMJvNaDQa3NzcKCsrIy4uDp1O5zJd/5fClfC54IILLvxNsA0s%2B/TpIz3BxH/Gb775pvQIW7VqFTdv3qRx48Zs2rSJrVu3olQqZdALtf8jV6vVbN26lccee0yeKy4ujurqaukXplAo8PLykr0wKpUKNzc3GVQIOfhHH32U9957D6jpN4yNjbWrGq1Zs4bHHntMBhYBAQHEx8cza9YsWe2yhZ%2Bfn6TZXblyBYPBIHurBg8ezMaNG9m1axcFBQU89NBDVFdXS/W9DRs2kJKSwv79%2Bzlz5gyTJk1iz549wJ9m2UIN0mKxcPz4caxWK15eXpKGN23aNIYMGcKnn34qg0qoMYsWCYAjxP2GhITIfh9bWpWotDgGy7ZJh6DTent7U1RUJP/u7u5OWloaBw8e5Pfff7c7rwjifHx8sFgsdkIztrgdvczxd/379%2BfFF1/k1KlTTJs2jYqKCoYNG8Zjjz1GamoqFouFL774giVLlkiRmpSUFA4ePHhPyeH333/P%2BfPnefvtt8nKysLHx4eioqJ7qniJexBfHaX/RXDs7u4u555CoSAqKopr167ZCbCoVCoaNWrEhQsXaiWAAs4SOHEOlUpFixYtOH36tOxB1ev1lJSUEB0dzfnz5zGZTPj6%2BtKuXTveeustNm/eDECnTp3w9/eXCxRlZWVMmTKFhQsXyr5Uoc5YUVFRyz7AEX9VfRaoU6cO5eXl1KlTh8LCQm7dukV6ejrZ2dnSQkUkMvf6XO50XVqtFn9/f%2BLj4zl8%2BLD0YlQqlWi1WhYuXMgTTzwBwKJFi9i7dy/Hjx/HZDJx69ate6JeCzgmNrYQ75B4vuK9cPbOCpVcrVbLAw88wLZt25wmq/9tOEs%2BbaFQKNDpdLWS2L%2BCSBrFXH/wwQe5evUqx44dQ6vV4ufnR2xsLHv37gWQiq5FRUWYTCZJH3322WcJCAhwma7/S%2BFK%2BFxwwQUX/gHo06cPGzZsYMiQITLo2rRpk/x55MiRLFq0iCFDhrBhwwa8vb25cePGHYM0Ly8vVCqVDLa0Wi0mk6nWCryjQuD8%2BfOJj4%2BnZ8%2BeMkGxpQY5O6dSqWTv3r1S3W7mzJmYTCZ27twpq0DdunXju%2B%2B%2Bs9tPJFGiWigUHUWAJWwEnJ3PYrEwefJkPvjgAwwGAy%2B88AILFiwAakQHfvrpJ1599VUuXrwoEy5brzu1Wo1er6dnz5588cUX0kbCbDaj0%2Bmorq7GarXi4%2BMjgyzHwE8Ey6IiuG/fPpKTk6XXoUKh4JlnnmHRokVs3LiRBQsWcOjQIerUqSP998S/iooKu7Ht3LkzP/74I7NmzeLFF1%2B84/xxBoVCweOPP87KlSudUg3vBFEZbNas2V1Xenx9ffHz8%2BPy5cvAn72ibdu2ZcaMGZjNZoYOHUpAQIA0BrdVzbStbAlz59WrVwPwyy%2B/kJ6eTlBQEJ07d%2Bazzz7Dz8%2BP8vJy/Pz8pFiPbfJiq27r7%2B/P8uXLeemll2r1t94OTZo04Y8//iA8PFzuo1QqpaDG5cuXmThxInXr1uXixYsMHDiQqKgoPvnkE4qKiujWrRsjRoxAr9ezdOlScnNzOXv2LFarVc53WxEVsXAjqH3i2qGG/v3rr7/KhEBU5sTffXx80Gq11KtXj7Nnz1JVVWVXPXZzcyMiIoJz587Z0R137NjBo48%2Bys2bN9mwYQMDBgwA/hTqETTIiRMnkpSUxMGDB3n33XcxGo2EhYXJ5GnatGlkZ2ezZs0aeU6r1Urz5s0ZOnQob7/9NkVFRYSEhJCXl8fKlSsZPXq0nANlZWW1FkliY2M5d%2B6c02Src%2BfOXL9%2BncLCQgIDA7l%2B/Tomk%2Bm2Fg4jRoxg9%2B7dFBQUyPM0atSI5557jqeffhqDwcBTTz3FsmXLyMzMtGM7OEIk%2B47JmTNKtLMFl/9GYu0I0fd4/vx5SktLcXd3p2vXrmRkZFBQUCCroiaTCZVKRWhoqJ0dzZ0quj4%2BPlRXV7Nt2zbCw8P/q9ftwv9/cCV8Lrjgggv/ABw4cIAvv/ySJk2asHfvXsrKynBzcyM8PJzTp0%2BTn59PQkICFy9epFevXmzYsIHq6moSEhL45ZdfZGLk7e3tVFwEbt//JiS/c3JyKCsro1GjRkyZMoX09HQAXnnlFfbv38%2B%2BffsAmDZtGlevXmXz5s1UV1ej1WpZuXKlpDgKEYy7CWp0Oh2%2Bvr7k5%2Bc77cO6F2qZqB68%2BeabfP311xw6dIh69epx%2BvRp1Go1hw8f5tChQzz33HNYrTX%2BfU2aNGHt2rVS/TIpKYnAwEBeffVVWrVqJb3vIiIiePjhh%2BUzERRMX1/fWmI3165dIzU1VY6trYgKIOmwtlRDYTfwwQcfMGHChHsaQ6hNhRR9lCdPnpT3Iehko0aN4vPPP6dPnz7S127FihXSF9FoNPLyyy8zbNgwYmJisFqtPProo1L8xtmCwYABA7h8%2BTKFhYVSbCItLY2pU6diMBjYv38/CxYsoLq6GpPJRFhYGCNHjqRHjx58%2BeWXtGvXjvT0dPbu3UuXLl0ICgqS1WY3NzdycnIICgqiefPmHDp0CC8vL65duyZpwkJa3mAwyGRG0H/F/HnjjTeorq5m1qxZ8vpbtmxJfn4%2B8fHx7N69W9LsLBYLgYGB9OvXjylTptCiRYu7eg6%2Bvr6YzWZKS0v/smJjC9t5npaWRmpqKk2aNCEpKQmoEZJ5//33%2Beabb2pV9FUqFe%2B//z5NmjRh6tSpUvAjLy%2BP2NhYKVLj2JuoUCho27YtGRkZtGnTBqvVytGjR2XSLGi6SqWS8ePHA7B27Vqqq6tp2bIlCQkJLFu2zE7d0Za2KPBX83j79u34%2B/tz3333YbXWeL/NnDmTd955R/Ym9uzZk6ysLP744w88PT0ZMGAA%2B/btq%2BUreLdQqVQkJydLZgD8WaEX1UBbr08BjUaDVquldevWHDx4kIEDB9KwYUPWrVvHd999x7Vr18jPz2ft2rUolUomTpzIqVOnZGU7ICBAzq38/Hx69uzJkiVLKC0tZcyYMWRlZVFVVUVwcDCJiYkMHDiQ6dOnM3v2bNauXcv%2B/fv55ZdfaNu2LeHh4fz0009kZ2cTERGB2WzmxIkTWK1Wli1bxocffkjnzp2ZPHkyhYWFTpVXncH2vsPCwsjPz0elUvHVV1%2B5Er5/MVwJnwsuuODCPwDNmjUDbq/sdiekpKSwb98%2Bu32FquHtYKskJ4L2e1l1tu0bghoqXGVlpaQpDh8%2BnG3btuHh4SFFRmwDP5VKhU6no7y83C4A9fDwwGq11qJmiSA0NTWV/fv34%2B3tLamXer2eqqoq%2BvXrx5dffolSqcTNzc3OLFuhUHD69GkAYmNjqa6upkGDBpjNZrnSLTy4%2BvfvT3FxMb169ZLVO9ux8fHxwdvbm4iICM6fPy97pTIzM8nIyJCiL2J8N23axMiRI8nMzCQnJ0cG8eKcWq1WPg%2BhOmkwGFi9ejXp6ek89thjQI0IxJkzZzhz5gzl5eV2CYWPjw/jxo2jsLBQ9hHBn72StlWl9u3b06FDBx5//HEAduzYwcyZM9m7dy8ffvghW7dulWqso0ePlmbajkbstvMtPDwci8XCH3/8YUfHE/vZ9imFh4ezcuVKTp06xeTJkwkODpYVOkc4nkcEyrZzSVD5vLy8iI6OJjMzk6qqKmJiYjh16pTc19vbmzp16khBI3F9er2eTZs2MWDAANkTqFAoeOWVV1i2bBkVFRV21N17rdCIZDQgIICqqiqKioqYNGkSy5cvJy0tjUcffZTQ0FBZVbK1FLnd54FjMnIv7/CYMWNk5TQhIYEjR47g4eEhWQPieC1btiQzMxOVSkX//v25du2arNa3a9cOi8XCsWPH5DnFNYk5LRaXHnjgAXbs2IFWq2XGjBnEx8ezf/9%2BFi9eDNQ8v169ekn6q1qt5tdff6V58%2BaYzWb8/f0pLS2tVb2zXURxNj5g78M3dOhQtmzZQlVVlaRHCo9Kq9XK5MmTefvtt%2BncubM0go%2BIiODq1avUr1%2BfoqIimjVrxs2bN7l27Romk4mgoCC%2B/vprOnToQHR0tJxvYjHHarXSs2dPGjRowPLly2vN57p169K8eXMOHDggmQy2n4dxcXG0bNmSzZs3U1lZSVRUFL///jtVVVVERUUxadIkFixYIBesnGHkyJGsWbNGMgnE%2B/nuu%2B9KWm1AQID87J41axbXrl0jKysLo9FIVlYWZrOZTp06MWzYMJKSku7KRsWFfxZcCZ8LLrjgwj8AoscuNzeXvLw8KbNeXFxMSUkJa9euZdy4cXz44Ye8%2BOKLPP/883Lfdu3aSb%2Bo6OhoLl%2B%2BzL59%2B6TdQlpaGt98841M0JzJszsiJCTktkG4M/j7%2B1NUVIRWqyUgIIBVq1bRq1ev2wZlzzzzjAymbbF9%2B3aaNGkigz1A9vaJ6sxrr73G6NGj%2Bfjjj9Hr9TRt2pTMzEwpJe7m5iYTOjc3N%2Bm1JhLgqKgoGVQCvPfee7z11lvyXM4oWG3atEGpVHL27FkZGDmiX79%2BHDp0SK6mQw0lztPTk/Lyclq2bEleXh45OTm1erBEALV8%2BXLGjx%2BP0Whk5cqVPP7447elqTnir%2BTgHbcNDAwEoGPHjhgMBt5%2B%2B21KS0sZNGgQv//%2Bu0zWRODq2B96r31Woj9z165dpKens2jRInx9famqqqKysvK2192sWTPOnj3r1MYAapJa8W788ssvUvH17NmztGzZ8o5VjdjYWF577TWioqKIi4ujqqpKKiieO3eO9PR0jh49Ku%2B1e/fu6PV67rvvvtvOA1ukpqYSFhZGSUkJu3fvZvv27fz0009280yj0dClSxcOHToklUrFtYlr12g0eHl52dkviKquTqcjMzOTM2fOMGDAAPl%2BC49P230AaWlhNBpp3Lgxly9fpl27dhw9elQKApnNZtzc3DCbzajVaqZPn878%2BfPtqNZCmETMi%2BDgYJkYt27dmqNHjwI1iXZFRQUhISEkJiaSnJzMk08%2Bafe8Hauhixcv5rnnnpNiP5GRkbWM6lUq1V3PQZ1OR2xsLJmZmfLdi4iIoLi4mFu3btkxH%2B4lodfr9SQkJEij938L1Go1hw4dol27dkBNZVpQ/8GeDeLr64tGo6Fz587s2rVLesS68O%2BCS1fVBRdccOEfAL1eT1hYGK1bt6ZTp04EBQWRkJDAhAkTmD59OlFRUbRu3ZqmTZuyYcMG4E8T8szMTBnsT5o0iXr16vHZZ59JxbUZM2bw/fffy3OlpqbWWqFVKpU0atRI/pyfn49Wq5VJgaenJwqFguTkZFQqFWq1mpSUFNzd3aXK5%2BLFi9HpdDz88MM8%2BOCDAFIcxhHvvfeeVJpbtGiR/P2IESMYMmSIXUXIarXKwDc%2BPl5WkqAmGBGmzaIq6Ovry6pVq3jppZfskkZb2N7/ww8/LH%2Bn1%2Bvt%2BhqhpgJx/vx5QkND5Up3v379iI2NtTvmjh077HqEBER/kqjwAXZm1YD0OBw7dqxM8B555BG7ZE%2Bv1zN16lRZvRAB/X%2BCtLQ0pkyZwpQpUzh27JjsmfPy8mLz5s0yABc9SY4B8JgxY4CawDA4OFiOmVarpVGjRjRs2BCdTif7SBUKBSEhIZw8eZLk5GRJPRs/fjwVFRVMmjQJQCqhNmvWTD5XUYFt1KiRnRy8QqGQyWrbtm3x8/MDahY0lEolN27ckP18AOPHj5fzUsDb25s33niDMWPGyJ5NMeb9%2B/cnIyPDLqlQKpXs2bOHw4cPc%2BbMGVauXMnFixcJDQ1l0KBBjBkzhgceeIAxY8YwZswYwsLC5HkGDx7MJ598gpubm938M5lMfPvtt7USU9tnf%2BLECTk2AmFhYXbjsWDBAlQqFT///DM6nQ6tVouHh4fdPgqFgnXr1kkVxl69emGxWPj/2Dvv6Kiqtu3/5kzJZJJJrySBQIAECEkg9A6KtADSJKCoFAGlF0WkixRp%2BjyigiIiRQSB0HsREARCC2CoAlICIZBCEkgmmZnvj6y9nQkJoO/zvvp8a661WCQzZ86cs/c5J/e97%2Bu%2BrpMnT8rrWdwzgiZeUFDAF198wb///W%2BpGurn54ezs7O81jUaDb6%2BvvJ9oWQLRZVpnU7H7du32bBhA0OHDrWrTLq6utoley%2B88ALjxo2T496mTRuuXbtmN2bLli17YgFCmJyXBpFI5%2BXlkZeXx%2BXLl0lNTX2C5i5oxLZo1aoV/fv3l9ez%2BE4PDw%2BOHTuGSqVi7Nixds%2BOkqwLxDUtYNvDKlB8nos/u/4TKCwslMkeYJfsKYpC9erVJaX6s88%2BY968eYwbN45hw4YxfPjw//jxOPC/D0eFzwEHHHDgb4KQp69fv75dVeivNvSrVCqCgoLw8vLizJkz8nWDwYBOp7P7ow5/rGQLMZfy5ctLnyknJycp9pCVlYWnpyfZ2dmMGjWKefPmYTabmThxIpUqVWLQoEF2%2BxYUNlsRCtvKgK2YhEgY9%2BzZI1UXe/fuLRVB/zdR0ngvX76cd955h71791K/fn0CAwOlzUXlypW5fv06VapU4cKFCzJBUKlUvP/%2B%2B7z55ptP9POV9LtQQwVk5awkGp9er6devXr079%2BfnJwcBgwYIGlzUETDEj1OarWasLAwoqKiyM/PZ9u2bXYCEmXLlsXFxYWUlBSys7Px8vIiODiYtLQ0bt%2B%2BTUxMDA0bNqRz587cvHmTIUOG8OjRI0aMGEH16tXtElFxbOI8BGUzLi6OPXv24O7uTmpqKlarFX9/f3JycjCZTPTp04dFixbZCfG88sorrFmzRqqv1q9fn19%2B%2BeW559Db25ucnBxZ6e3WrRvvv//%2BM4Uz/ipKqv6IRQmLxUJgYCBpaWksXryYPXv2MGbMmCf2Ia4Jq9VqV2kX%2By5%2BzUDJCpS2lgEGgwEoStZEz6gwhQd7iqPobYQ/qnLFew1LOs%2Bff/6ZF154AYvF8syqc3HK99P2a1vdCwwMpE%2BfPhw5cqRU6w5FUZg2bZpkORgMBoYPH46Pjw8jR4586nE9D0q6XoRn6Jw5c3jppZfYsWMHUOSNefr0afz9/dmyZQs///wzUFSpj4qKon79%2BvzrX//CYrHw9ttv8/LLL7N9%2B3a539atW9OiRQusVivx8fFotVr69Okjadmff/45x44do06dOtSuXRuVSsWXX34peyqzsrLQaDS89957REZGAnDs2DHOnTsnF5FEX%2BGxY8do1aoVV69e5fTp0881FkFBQfj6%2BpKdnc3Nmzfx8vJymK7/l8KR8DnggAMO/E3YsGEDHTt2pFGjRlStWpWAgAAuX74sJeydnJy4e/cuzs7OshcLitQMy5cvj5ubG23atGHfvn0cPXoUKKJSAZw4caLE71Sr1ajVartqwrRp0/jwww/tEpjifxpKE3x5VgBXHLZ%2BT1arleDgYNRqtbQiUKvVDB06VAZJtlCpVHTv3p1Vq1ZJ9czXX38di8XCggULgCI/wH379tG8eXO6d%2B%2BOm5sbvXr1wmKx0KZNGwC2bt1Kt27d5H7XrVuH2WymS5cufPDBBzRs2JCkpCRq1KjBmDFjmDp1ql2Vp2zZsjRs2JAOHTpIYZunJXhP%2B128Vvz3GjVqEBERQdWqValSpQodOnTAYrGUSscVCoEVKlRApVJx6dIluz4vUbEo6bNqtRoPDw9yc3NxcnKShtFPQ2nHYdt/VHwfO3bsoEOHDowcOZKZM2ditVoJDAyUipLFFySeBpFodujQgcLCQvbt24eiKHZWBuIYateuLXvPiiMmJoaZM2dSvnx54I%2B5sP0fihYFzp49y8yZM1myZAkD5%2BPIAAAgAElEQVT9%2B/dn8eLFWCwWqfxa3E9NURTatm1L48aNpfplQkIC48ePtxMoKu59Nnz4cEwmE1988QUVKlTg6tWrdnYhKpWK2rVrc/v2bW7fvo1WqyUmJkaapEdGRkovxJJgmzwGBwdjMBi4dOmSVAB2cnKiWbNm7Nq1iy%2B%2B%2BILCwkKGDh3Kyy%2B/TEJCwnMnz0JR9ejRowwYMIBJkyZRqVIlLl%2B%2BjF6vl%2BeekJDASy%2B9hLu7%2BxNKuIMHD8bT05Np06bJ/jMvLy969erFZ599JinHmzdvJiwsjMaNG3Pv3r1nHpttz6Ogo3t6esrFsatXr9pd4yEhIXa9vsXpqKJnT6vVYjAYyMzMxGAwUFBQIC1lxLZarRaNRiN7i8W1IGiyQj3Yzc2NlJQU6blpNBoJCgoiNzeXgIAAoqKiOHnypEzu3NzcePjw4TOTcfHsnTt3LkFBQRiNRnbs2MHWrVu5evVqiYsl7u7uPHz4kL59%2B/Luu%2B8%2B1/w78M%2BCI%2BFzwAEHHPibUaNGDfr168egQYNo06YN/v7%2B1K5dm0GDBlGtWjUiIyNZvHgxs2fPBmD16tUkJiYye/ZstFotR48e5eLFizRt2hRPT0927NjB48eP8fDw4LPPPmP69Omyj6179%2B4MGzaM%2BvXro1arCQ4OpkePHuzbt0%2BKgpSE0aNH8/nnn8uKgpOTEwaDwa6PSaPRoNPpZJ%2BVi4sLhYWFlCtXjitXrsiEJTAwkN9//x2VSsVnn31G06ZN5er0syDsFAA%2B/fRTGTgOGDBAVilv3rzJwoULadSoEfBkkhUeHs7UqVNl0Jaamsrnn3%2BOt7e3NKFu2bIl%2B/fvtwvkRSJbo0YNGjVqRJMmTejZsydWq5WJEycCMGXKFADatWsHwKZNmwBo3749UNSjaLVaadmyJREREZjNZj7//HOqVq1KTEwM3t7eHDx48IkVeNFTWTzBFsGbv7//n%2Bq59PT0lL5yU6ZMITAwkIkTJ7JmzRosFgvvvPMOAIsWLZJ9Wrbqi6X1OYlthN%2BZqB5brVZZ6S1fvjxpaWk4OTnx4MEDwsLC7CrLBQUFVKlSRfZYVqlSxe66FMG6Vqtlw4YNknqs0%2Blo3LgxCxYsICcnh5kzZ5Z6PYO9HYCzs7P0tRQJW0hICLdu3ZLzHx4ezp07d8jNzaVKlSrUrVuXmjVr8u6778prXlSNheKjGKdPPvmERYsWyURMURT0ej2PHj2S17DBYCA9PZ2LFy9iNpupWrWq3I8Q1RAG6jt37qRVq1akpKTIfVavXl3eG%2BJcioszCePyp/Ufzpw5k5kzZ/L48WOmTZsmK6cmkwmj0Sj9K22r00uXLuWNN94gNDSU%2B/fvk5mZSefOnencuTOffPIJ9%2B7d48aNG7Ru3Zp9%2B/ahVqupWrWq9ErMycmhXr16VKhQgbCwMHJzc5k3bx6JiYm4ubkRGRlJQECAFCBSFEXSumvWrMnKlSvJzc1lwIABHD9%2BXIqbJCUlYbVaiYmJsfssQFhYGJcvX%2Bbll1/m0qVLFBQU8PDhQ%2BrVq8eGDRtwc3OjadOm7Nmz54neUfEsKK5I%2Bt8If39/adlQt25djh49Kq%2BzjIwMMjMz8fb2Zs6cOYwePZqxY8fK55kD/z1wJHwOOOCAA38jMjMzad%2B%2BPRkZGUyePJlx48ZJul9cXJw0vo6NjeXkyZNAUfDm5%2BfHvXv3JPUyLy%2BPKlWqkJGRIQN/sVrs5eUlXzt06BBubm6yX8fX11cmOaVV5UQiZyusYTAYqFu3Lvv27bPbrriIglCIs4WTkxM%2BPj7k5eUxceJEXF1d6devH7Vq1cJoNPLll19K/7dnCSg8i65n%2B76Hh4fs39HpdPj6%2BgJ/COY8L0pSRNTpdKjValkFtRVtAXtfs%2BeFCCpFUme1WmnYsKFdX1nr1q3Zvn07W7dupU2bNnZm0iXtSxz/rl27JG1MePzt3LlT9tK1b98ejUZDQkKC/EyrVq2kWbegAoeHh9tVk8R8BQcHPyGZbzQaMZvN%2BPj44OLiwm%2B//WZXafbw8CAnJ0cqPdp6zD18%2BFCOu4%2BPjwz2NRoN3bp1Y%2BTIkRw4cIAxY8YwePBgSYWLjY2VSUmFChUwGAzye1UqlZ3SZUky/M8LYU7t5uZGQEAAv//%2Bu6wOlS1blhs3bkhvRzGePXv2ZNmyZXZVUShaKMnLyytVkMTJyYlp06YxZswYrFYr48ePJyQkhEGDBlFYWEjnzp2pXbu2pDwKE/fSzqtixYpcuXLluaivq1atwt3dXV5rUEQLLVu2LMePH39i%2B9I8O93d3Z9IOtVqNYqiEB4eTrly5di3bx%2BdO3cmNzeXhIQENBoNvXr14tdff%2BXMmTPk5eWh0WiIjo6mY8eObN68mZMnT%2BLk5MT27dtp1qwZbdq0ITs7m/379z9hXWKLks7daDTSqVMnNm7cSGZmJu7u7nzyySc0bNiQ1NRUmjRpwuTJk7lx4wYGgwGNRsPnn38uPz9w4EAWLFjAwIEDMRqNzJ49W75mu41QbhXV0xdffFFS3N3c3PD09CQtLU3OoRClslqthIaG4uvry/Lly6lRo4bcb2JiItWqVfuP0ZnHjh3Lm2%2B%2BybZt21iyZAmrVq36H%2B/Tgf9bOBI%2BBxxwwIG/EWvXrmXKlCnPVM18Gmz/qHt5eT2hylfatqVh%2BPDh1KtXj/j4eFQqFRUrViQqKoqEhAQsFgt6vR6VSoWnp6cUIdFoNHTs2NFOWr24r16zZs04evSorBJCUVBlMpmkn9%2BAAQN44YUXePjwIb169bJLVNzc3CgoKJC9Tx4eHrz11lvMmjXrL42b0WiU1ZUHDx7IalCLFi0AePToEdnZ2TLwFmIKojemOGwFQWbMmFHid546dUoaTm/fvp34%2BHgKCwtJT09HrVbj6ekpe95iYmK4cOGCNGSvW7cuV65ceSKZex6IZFyIamzcuJG4uDigqPK5ceNGO6qWbZIpsGDBAt5%2B%2B%2B0/HUDazmGvXr1Yu3atXcXEzc2NMWPGcPHiRTZt2kSdOnXYv38/gYGBXL9%2BnVq1anH8%2BHEiIyPJy8vD09NTJpP37t2TCaL4Hnd39xL7VYVYikql4tGjR0/MoVqtxmAw2CVHrq6uvPLKK%2BTl5XHz5k0OHjxImTJluHfvHp06dWLt2rV2UvqKokjK7%2BrVq6X32f91qPVnrSPUajWurq52Hp6Kokjq7J9ZqPiz8PPzo7CwUFKJbW08SoJ4/ojniFqtJiAgQNqGrF69muDgYMaOHUtqaiq//PILFouFypUrS6YB/EFL/rO2NCEhIYSEhHD48GFp7XLnzh3effddVq1aJfezdu1amjRpQsOGDfnss89o06YNP/74I926dZPbVK5cGa1Wy7x582Qv37Zt22jbti1Wq5XWrVuj0WgYNWoUdevWxWq18ssvv9CgQQMKCws5duwYly5dokqVKvTv319axIwYMYLevXuj0%2Bn46quvOH/%2BvOzbTE1N5datW9y8eRO1Wk2lSpVISkqyo5Z6enoSExND27ZtGTVqFLt37yYkJASTyUTdunU5derUX5tsB/42OBI%2BBxxwwIG/GWazmVq1ajFu3DimTZsmA%2B0GDRpIuW%2Bz2czkyZOZMmUKffv2ZcGCBZK%2BFRgYKPtLevXqJT22bCEUFEtLLMuVK0daWhoWi4UmTZowa9YsYmJiUKlUfPHFF4wYMUJWr8qXL096evoTBu/z589n8ODBdq9ptVop7T5t2jQmTJiAj48Pt27dwtPTkwEDBpCRkcHChQtlH9orr7zCiy%2B%2BSN%2B%2BfUvsB3xa0hoTE8OsWbPk2JjNZtq2bSuDmCpVqlChQgXWr1%2BPk5MTQ4cOlZ/19vYmLy%2BP2rVrc//%2Bfd5//30uXbqEs7MzdevW5bPPPuPu3bscPnyYnTt3cuTIERl0qlQqSZt9GqKjo2U1JykpiaioKKCot04EoP7%2B/ty/fx%2Bj0SgrkraKf5UqVeLSpUsljoFQULWVy1er1bz22mssXbpU9oLZUkB9fX25f/%2B%2B3f6Cg4PZtGmTrBqI/qTJkydTrlw5evXqRffu3UlMTLTr%2B2nYsCGHDx/Gz89PVuGeBTGfw4YNo7CwkPz8fCpUqEBoaCi9e/fmzJkzREVFsWLFCnr27EnNmjW5dOkSlSpVomvXrowdOxaLxSKTNVH9Lg2BgYHUr1%2BfiIgIGjZsSNeuXQE4fPgwW7Zs4ccffyQpKYnatWvLqpXVamXevHmMGTNGJkC2SVXVqlVJTk7m119/ldeeoBKLKj0UVWSfJ%2BxSFIVevXrx3XffydeGDBnCvXv32Lp1K48fP6awsNCuKlrSmIreTWdnZ5kEC/qq0Wjk3r17KIpC/fr1WbRoEVu3bmXkyJFYrVa8vb3R6XTMnz%2Bf3r17yyqrEN1xcnJi3bp1ODs7M3HiRH755RfMZrOdmAxgV1nz9/dHr9cTHBwsn23du3cnKCiIf/3rXyUmli4uLjx69KhEu5TevXszbNgweZ4xMTGcPn2aO3fuEBgYSJUqVWQSk5ycLEWNhJVF586dSU5OlgsGKpWKkJAQSTEuU6YMERER7N27V15XtiqcycnJmM1mOnXqxMWLF3F3d7djQhQUFKBWq%2BnVqxd79%2B4lLS1NsiXEwpHBYGDSpEksX76ca9eu4eTkhLOzM%2Bnp6Tx%2B/Bi9Xs%2BWLVuYP38%2Bly9fpk6dOly6dIlDhw4RHx/PDz/8IPv8ikNUxIVdTmhoKLVq1cJisfDxxx%2BjUqno27cv33zzjVxYqlevHomJiVSqVAmtVsvZs2fZuXOnNF1/mv%2BhA/9cOBI%2BBxxwwIF/CNLT02WFLjc3l%2BPHjxMaGsrFixeZOnUq7777LnPnzmX69OmMHj0anU6Hoih8%2BOGHvPfee8Af1RThSSfg6ur6BE1MURQZwOn1ehRF4dGjRxQWFsqkAXgqFUqgJJ8wsE/Oiv/s4%2BODp6cn3333HfXr17f7DJTsJxcVFUVOTg7Xrl2jSZMmHDlyBA8PD0aNGsXcuXNxcnIiKiqKkSNHStpmTEwMeXl5dOnShR07djBhwgTGjBmDTqfjzJkzpKSkMHbsWI4cOYLBYLCryHTr1o3KlStz/PhxDh48KMdUvO/p6Ul0dDQGgwEvLy8aNWrETz/9xO7du4Eib753331XJmzVqlWTVgGtW7fm4MGDaDQaMjIy7EQihNm0t7c3d%2B/eRa1W4%2BfnR9u2bXnttdekWqKAraiOCHoFbPcrKn2zZ89m3LhxWK1WtFotTk5OdhQ7nU5H69at2bhxo928GI1GmjVrxv79%2B8nJyflTlR8xr0KaPioqiszMTC5fvlyiIJCAbZ%2BU7QKAqD4ZDAasViuFhYV2PnLwR6XrWb1WItgXiYygSYukyWw2y3vHNtETP4v7TSxaNGvWrFSVyZLGxdfXVyYT4tqaNWuWvK%2B1Wi0LFiyQ/ZXiXPz8/MjKyiI/P99OMVWn01FQUIC7uzvZ2dl8%2Bumnkq4LJYvuGI1GNBoNubm5kvJqtVpltV5UwmznXDw3CgsLKSgokMq7ts%2BeZyE6OpqcnByZZLVr1465c%2BcSHR2NyWSSdFnb50HVqlW5cuWKHZ34zp079OrVi0WLFtGxY0eSkpKkmb2TkxMHDx6UVTKNRsPs2bMZMWJEifMhvqt48ireB%2BTzY/Lkyaxfv/6J7f5/gru7O9OnT6d58%2BbUrFnTkfD9F8KR8DnggAMO/M2IiIgA/hBWeNZj2Tbg1Gq1nDt3jsjISAoKCqhRowZnzpzh9OnTdj5xQj5cKDk%2BevSIe/fu/WULCFsUP%2BagoCBSUlLsaFIi0ahTpw7Hjh1DURTatWvHrl277AIzKKo4tWrViuXLl6PRaNBoNBw4cIA6derg7e1NgwYN2LRpEx9//DGTJk1CURQOHTpE7dq17aqdAQEBxMfH8%2BWXX8pVfVEtE0HxmTNnZL/gn0HTpk2ZOnUqnp6exMXFUVBQQHh4OAcPHsTPz48RI0ZgMplYtGgRcXFxdOjQgXHjxkkzarVaTfv27dm4cSO%2Bvr6kpqY%2BFw3vz/bkGAwG8vLySlRSLY06JxYBoPSeQ5FACf/GK1euyP1arVZ8fHyk1YjJZJIeaL169UKlUvHrr7%2BSm5v7p8ddiPaIBM8WRqMRnU4nBSgMBgMuLi52FFh/f38sFgvOzs4UFhZKSnJxiCodwLfffsvevXsxmUz8%2BOOPz1z8%2BN/GX%2BnLevPNN1mxYsUTPYpigUkwAF577TVWrFjx1ATcFiEhIdy7d69U5oBaraZVq1Zs3boV4InEW6VS4ezszJgxY5g0aZKsfIuFG7HfqlWrMmvWLLp06QIgq7629jPR0dH07NmT3377jV9%2B%2BYV169ZJ2rJer6dTp06sXLlS3v/wx7PUdiGqbdu2bN261a6H2HbMxbNMURTOnz9Po0aNMJvNkoqtVquJj4/n5MmTpKamkpWVhaIoRERESCGiFi1acObMGUllVRSFKlWqSE/VSpUqcfv2bR49eoSzszMFBQUUFBTIZ1hxGqpY5BAVSn9/f1xcXPjpp5%2Beax5tF9nE%2BKhUKmm6npOTw%2BPHj%2BV7ycnJz7VfB/45cBivO%2BCAAw78zfj666/x9/dnxIgR%2BPv7YzAYUBQFb29vO382Dw8P4I9gVIhOwB%2BiID/88ANardbOvFen09mpDaampnL37t2nJhfOzs6o1Wpp3Gw0GvHy8mLChAl4eHjg4uJCly5dmD59Onv27EGj0bBw4UL0ej0rV65Ep9Oh1WpRFAV/f3%2BZQLRq1QooEv3YtGkTVqtVekoJWCwWli9fTlxcnKS0iapQeno6O3fuRFEU2S/o5OREly5dsFgsdOzYkZCQEIxGI6%2B%2B%2Birr1q2TQWOzZs3Q6XQywBNj27ZtW5ydnXFycpIVQaF%2BGhwcLLcrX7687AX77bffOH36NOvXryctLY01a9awYMEC3Nzc8PLyokOHDnTt2pVPPvmE7777jo4dO9oZlJvNZjZu3GhnIv88yXdAQIDd76Iy1bZt2xK3F155lStXJiwsTPYsFu%2BvFOeo1Wq5cOECycnJJCcno9frJV1OXH9QRFXT6XTUrFmTu3fvys97eHig0%2Blk1c32O0wmE2FhYTx48ECqYUIRbU6lUhEWFvbE8dgaaguT95o1a/Lrr78%2BQR92cnLiyy%2B/lPfEBx98IK8RgR49euDu7s6NGzfskj3xfX379iUyMlJW9MxmM/fu3WPDhg2sWLHiuZI9EdzbolOnTgwZMkRSbitVqkSlSpVKvL8NBgPR0dHExMTI9/R6Pd27d5djJCpp8Me9IT4/evRoOXa28yr6X%2BGP%2B1DQsq1WK1FRUZw7d04m8rbjUvz8jEajrIY7OzvL98T5Ceh0OrZt2yZ/L15l1Wg0jB8/nnnz5sl9i/kW3%2B3k5ERCQoIcL/F68WOzWq2888473L59G5PJZFe9M5lMUmjENnnz9vaWrwkj%2BdOnT2MwGGRCpVKpCA0Nlfvq27cvANOnTweK6NgiARTH%2B/777/Pdd9/JhRVnZ2fKli0rj/%2BTTz4hJiZGJmoWi4XVq1dL4ZqKFStKgSRxjIqikJSUhFqtpkOHDtSrVw93d3e0Wi1z5szBYDDg7u7O22%2B/TXx8PMeOHcPZ2Zn169fj4uKCq6srbm5u7N69G3d3dy5evMiaNWto2rQpFy5c4McffyQwMJCQkBC6deuGv7%2B/VGXt06cPb731Fo0bN5YKvg78d8FR4XPAAQcc%2BAegRo0anDp1iho1apCfn4%2BiKNSqVUuaUNuKaDxthb%2Bk94SQhPC6K83cumPHjrLfSCgFBgcH8/vvv1OrVi1Onz5N3759WbRoERaLhS5duuDh4YFarebrr7%2BWynsvvfSSnaVBXFwcW7duxWKx0Lx5c/bt2ycDnwoVKvDbb7/ZrZ5v3bqVVq1aodVq8fPz49atW6X6volzhqLA18XFhaysLKxWK6dOnaKwsJBq1aoBRbTWwsJCWb1QqVQEBgbKwF%2BtVhMTE0NSUhKFhYUYjUby8/NlIPjGG2%2BwePFiJk%2BezIQJE6RCpaurK926deO9994jOjoajUYjfRCtVivVq1dn4cKFNGzYkPDwcGbMmCEVV81mM%2BvWrZMCGYIWJz77LCQkJHD48GH%2B9a9/oSgKBQUFBAYGkp6ebieMotVqCQwMpKCggDt37jyxHw8PDzIzM%2BWYCNy5c0f2bMXFxbF9%2B3b27t1Ljx49aNGiRan9osJeQVQINRoNarWawMBA2rdvT8eOHXnjjTd48OABHh4esgrn4uLCw4cPCQkJYfTo0QwfPpw6depw5swZVq1axdtvv82QIUPYtWsXJ06csBNnEYmguE6OHz/OxYsXefXVV0sdv7CwMK5duybHXohTzJgxgyVLlpT4mZISjQkTJjB9%2BvQSq6Zt27alTJkyQJFoz%2BnTp%2BnXrx9ly5Zl/Pjxsqpy/vx5qlWrhoeHB87OzgQGBpKRkcHVq1clfTsvL0/STsPCwkhOTsZgMGA0GvHx8SE5OZlVq1bRvXt3u%2BOoVasWp06d%2Bh%2BJr4hn0u3bt%2BU9YzQaKSwslJROV1dXcnNz5eKFoLqW9FxSqVRUrlyZgIAAaeYtqKsAgwYNIi8vT/bbgb3FSkn%2BlUlJSeTm5lK7dm3AvkItEuDiSadarUav10tvwtq1a3Py5Em7z2q1Wtm7KZ6TMTExBAUFcfbsWbKysmSPo1arlZW40p5ZJSkaF6ciBwcHc%2BPGDbtthB%2BgWOjx9fW1u5%2Bft/rr7e1Nr169%2BPbbb8nKyiIoKAiVSsXt27exWq12Xo2urq4sX76cKlWqPHO/Dvxz4Uj4HHDAAQf%2BAWjRogUrV67klVde4e7du3h5eZGbmysDBhGQC5T2h91gMMhAv06dOpw6deqZPlG9e/eWdC8ht6/RaHj99df5%2BuuvMZvN0rNMUEPFdiEhIeTn5z8hwW8L20CmuAdYcVNsIX4iKJ4zZ85k586d7N27F7VaLSloYWFhXLp0SZ6j2WyWKnxQVBFZv349S5cuZenSpXLf/v7%2BpKWlYTabKVu2LGazmdu3b6MoCs7Oznh5eUlKqJubG%2B7u7ty9exeDwcALL7zA9u3bOXnyJDExMSQkJBAfH4%2BiKGRlZXH%2B/Hmio6NRFMVOxc42MA0PD5f0xpLet/351q1b7N69m48//lj2Hbm7u3P//n0URSEmJoaVK1eSnp5O/fr18fHxQaVSSQEWWw88YRdw5cqVEoN%2BYdpsWxmGoqqjCEzFNafX68nPz0en01G/fn2MRiMFBQVs376d1q1bk5yczO3bt3nxxRfZuXOn3XW6a9cuypYty9KlS5kzZ4685sQ2Tk5ODBkyhPDwcD744APS0tLw8/MjMzOTuXPnMmLECLlYoCiK9FYrCXPmzGHWrFl2Ai4RERFUqlRJ%2BiH6%2BPjQokUL6T/o6%2BtLSEgIly5dkn1oTZo0oWnTpty6dUtWnF1cXLBarU%2B1T3gaxAJGWFgYmZmZPHjwgO3bt/Pyyy/j7e1NSkpKqeel1WpRqVSy2ujs7Ezv3r1ZuHAhVqsVo9EoFS9t503gz1JC/fz8ePjwoRSJEQbwPXr0YPz48Wg0GqpWrWo3j6Ky1bhxYw4ePFgqdVig%2BPGJY7SlXsIfVXBbqqWrqytjx45lypQpjB07lps3b7J48WLZo2xr89GoUSMsFguHDx%2B2G8/AwECZXD3Lp/BpKElk6p%2BEp819cV9Kk8kkWQ%2B///47ZcuWpX///nTr1u3/%2BKgd%2BE/AQel0wAEHHPibsWrVKqpWrUq7du3IzMxEURTS09NlsqdSqWSyp9Pp6Nq1K9HR0fj6%2BtKjRw%2BcnJzkH2ZBwQS4du2aHd1K7Euo40FRItmwYUNCQkKkYqRWq%2BXx48f8%2BOOP0stP/JGvUKECarUaDw8PmjdvTnx8/BN9UFqt1u53kYgAdt%2BtKAr9%2B/e329ZqtfLmm2/KAM/Z2ZkHDx7IY1%2B9ejXNmjWjQoUKKIrCsmXL0Gq1LFq0SErtFxYWkpOTw4svviiTPYCJEyfy448/8sILLwBFwf/Dhw/R6/XMmDGDN998k9zcXNRqNdWrV6ddu3ay12vQoEG89957uLq6MnDgQEwmE23btiUrK4vmzZtLTzur1Yqnp%2BdT57tVq1aSUteqVSvy8/MZMGAAAwYMAIpM5Lt3706bNm2YOXOmDKTVarUMtkNDQ5k3bx7nzp2jR48eANy/f5%2B0tDQZ0JlMJvnZrKwsaehdHAaDgaCgIOklJv45OTnJirJarcbZ2Rl3d3fMZjNqtZpXX32VhQsXMmXKFAoLC1Gr1UyePJkRI0ZQoUIFfvrpJ7sEQFT34uPjSUhIID8/345aqigKmzdv5rvvvmP69OkyoUlLS8NkMvHBBx9QWFgoe5qEuIq4VoxGI0ajUV5ro0ePluMFsGTJEjZs2EB2drY8pokTJ7Jnzx6ZSIjqjq3oSGJiIjNmzODbb7/FYrFgsVjIzs6W6phisUBApVIRERFBWFgYy5Ytk/%2BWLFkiqddeXl6sWLGC8PBwIiMjAWjTpg15eXncuXPniaBcnKtOp8NsNsuxURSFF154gQULFkhqoKhwC1itVtzd3e2OT1AHhSJkxYoVGT9%2BPGXKlMHV1RWVSsWSJUtwcXGhQoUKkhr88OFDBg0aBMDKlSuJjIykWrVqch6dnJzo1KmTTCwOHDggk1BRSRPnc%2BHCBS5cuMC2bdsICgqyO77ilVIx7lC0mPPiiy8CRSJI2dnZTJo0CZPJxPTp0/nmm2%2BwWq3yWSCqYQA///yzXbKn0Who06YNsbGx8rXiJuuKoqDT6WjUqJH8vWLFitSvXx9FUahcuTIqlYrOnTvj5eWFu7u7pOVD0TNb0EBr1aolqbharVZWvoUliFartaM2Dxs2zI7BIJgAAmLchPCW2JftM1h8n7gWSkN2drZ8//HjxwQGBjJv3jxycnIIDQ3l3r17zJgxg3Xr1pW6Dwf%2BuXBU%2BBxwwAEH/mYI3zdRrRABj7u7u5TA/%2B6773BxccHX15fBgwezYMECSXXcunUrWq2W3r17E5oary0AACAASURBVBISwgcffAAUCTVERETw/vvvA0UBxtSpU%2BnUqRPh4eGoVCo%2B/vhjpk6dKpMl2wqhQLNmzbh58yYFBQVyFdxoNLJlyxbeeustWTUqbfXYVozEVj1Uo9HQtGlTqWb4LNESZ2dnRo0axcGDByUF7OOPP2batGmsWbOGO3fusHTpUvbu3YuHh4fsSRIJo1arZcqUKWzZssUu8bRFTEwMZ8%2BeRavVyqBaVBKEYAgUqda5urri6enJypUrAcjNzSU2NpbY2Fg6dOgg9zl16lQmTpyI1Wpl4sSJMijbuHEjHTp0%2BFMiIGL8JkyYQEJCAufOnXvuz5amtqpSqfDz87PrvVKpVOzZs0cmI0ePHmXevHkcOnSImzdvYjab2bp1K8uWLSMxMZHU1FQ5r87OztSuXVtWdiwWC0ajkePHj5ORkcHkyZPZtWvXE8mn6Pc0m81kZWXh6%2BvLrVu35HUlLCUURcHFxQWTyUSDBg3Yv3%2B/VMq0Wq2SigbIiq7FYiE0NJTU1FSpvqkoCnFxcWzevBmLxSJpdLdv38bd3d1OcVan06FWq2XiI6p6gr4n%2BuNEVSQpKYlGjRrRoEEDPvzwQ3r27ElycrK8v%2BvXr09GRgZpaWnk5ubKKrytEqhGo2HYsGF8/vnnbNy4kTlz5hAcHEzfvn3p2bMn165dk3NlK%2BAhjqu4MInYt6%2BvL1lZWfJaUKvVDBgwgGXLlhEQEMDly5dldcxqtVKtWjXOnj2Lv78/6enprFixgl27drFlyxbc3Ny4cuUKhYWFlCtXjoSEBKCIXmv7PNDr9XZKpxqNhokTJ5KXl8fatWtxdXXl1KlTxMTESDq21WqlXbt2dgkLQGxsLDt37mT//v00adKEAwcOSDpzuXLlpN9cUFAQt2/fBooSTIvFgpOTE02aNKFLly4MGDBAJrzZ2dl2nny2tOqnPZMUReHVV1%2BlZcuWaDQaevfuLedYKL0aDAa2b99OkyZN7J6vtn2Dgrr6%2BPFju2dkcnIysbGxkoau1%2Bvx8vKS53Xx4kUp2GU712L/oiKfl5cnFUeTkpJo0KCBfK4JFkhUVBReXl5SMbZZs2a4ubmxbt06Bg0ahJ%2BfH//%2B979xcXFhzZo1pY6JA/9MOBI%2BBxxwwIG/Effu3XvCN8z2dz8/P9avX0/z5s2Ji4ujTJkypKSkEBYWxtGjR4E/1D0jIiJwdXWVSpBarZZBgwbx6aefAkj1t02bNknKZHR0NM7Ozhw7dkwGAGq1ml27dslE1MXFRcrfi%2BQJ/gio/syfkadRikSfohDnyMnJIT09/U/tX6vVUrduXUJDQzl8%2BDBpaWmyouPs7CwVMUWfZIsWLaSFgtFo5KWXXuLGjRuSZipk581mM%2BXLl6dJkyZ88803BAQEEBERQf/%2B/fHz8wPg008/ZfPmzVLZDrDryQHsvOmeRa2LjY0lKSlJ9uQ9fvwYFxcXeT5QlIhYrVbZ%2B/dXqXuKouDq6opGo5H9nSkpKTRv3hwoov2%2B/vrrLF26lO%2B//578/Hx2795Nu3btAKhYsaIcG5VKxdKlS%2BnevbsUC9FqtezcuZOBAwdy7949NBrNXzKQFyipB6pRo0b8/PPPKIpCx44d2bZtm%2Bwvs0Vp4yKENYTHoQjaFy9eTJ8%2BfTAYDLz99tv069eP8%2BfP88orrzyRQIserrNnz7JkyRLpdSauA5VKRVRUFElJSTI4L77I4u7uTo0aNbh%2B/ToPHjwgJyeHXbt2UVBQwOuvv243bsXPxcXFhcLCQrZs2UJBQQG9evWyq3ICslprW8UUFdinUbMFFEWhbdu25OXlsW/fPnlu1atXp06dOvz0009cv34dvV5P9erVZZJhC39/fzQaDZmZmeTn5%2BPr62un6Csorbb9e7aoVasWJpNJVjWLK2gKGrOTkxNpaWmyf2/IkCF8//330oNSzIG7uzv5%2BfkEBARgNpvJyMhgyZIlGAwG2rVrh9VqpV69ehw5ckReF76%2Bvjx48IDvvvuOt99%2BW5qeF4egSZe22GIwGOwWKWwhBHlK23dsbKzsFxb7K35t29pqCFXdW7du4eLiglqtpmLFiqSkpHDt2jVUKhXt2rWjZs2aXL58mVWrVmE2mwkICGDXrl3UqVNHJokO/HfBkfA54IADDvyNiI6OJjExkRo1aqDRaDCZTMyfP5933nkHlUpFixYtOHToEH5%2Bfty4cUPK3sfGxnL8%2BHGpimcymejTpw8nTpyQf4xte51sg15fX99Sg22dTodKpeLMmTNSIOHrr78GipKXAQMG4O/vz4MHD/j4448ZOXKkDGRspfr9/Py4e/eu7FXy9fXl0aNHTJgwgQkTJjB8%2BHDWr1/P5cuXZZAyfvx4Lly4wM2bN0lNTWXZsmXk5uayfft2mbQGBgZiNBpxcnLi7NmzBAYG2okWBAUFsWnTJlxcXLh58ybffvstK1asQKVS4eXlhZeXF3l5edy/fx9PT0/q1atHQkKCpEy2bduWpKQk0tLScHd3p379%2BlgsFjZv3oyzszMLFy7Ew8ODihUrAkW00OIiHqJaJH6GPxI%2B274k2%2BBMqIfaVjxtrSzS0tK4du0agYGB3L17Fz8/P9LT02WiV7NmTc6cOSONnm2rZ0FBQaSlpckqlKIoLF68mLfeektWBvbu3UuLFi1wcnKiUqVKrF271s4qQ5xDSdUOJycnzpw5w6lTp%2BjVq5fsrSt%2Bjs9jO1Ecs2fPthOGuX//PikpKbKXs/jx2Y5vtWrVGDZsGBERETRp0kRamEBR1VosqtgmYyUJHkVGRkr5/9q1a6MoCt99951U89Tr9Wg0Gho3biwVKWvWrCl7LItXzP8KDAYD77zzDteuXXtCedTHxwcXFxdGjhzJokWLOHv2LA0aNKBBgwbs3r3bzpPxr6JOnTpUqlSJFStWPPGeVqulXr16HDx4UL4WHR3N%2BfPnMZvNaLVa8vLy7PwhhWBTz5495cKAoJQvXbqU119/XfaJ7tixw26h5MSJE8yePbvUYy1OaRQ2Brb04t69e9O0aVP0ej2vvfYaJpOJMWPG0Lt3bwAWLFjAqlWrePDggbxvypYty82bN4mMjOS3335DURSys7PR6/XodDo6dOjAgwcPOHnypKT9lmRv8Tx9fkKc5c9AVAxNJhMGg6HUJPHP7rNhw4Z8/fXXKIpCVFQUarXakfD9F8KR8DnggAMO/I2IioqiQ4cOrFmzBp1OZ%2BcXB9gldLar2MLfTFR4ivtrPQ3FA1vhayZWgQEuXLhAdHS0VLlcvXo1b775Junp6Vy9epWCggKp5KZWq%2BWxiyphSEgIN27ckDTC5s2b89NPP/HRRx/x4YcfygA6KioKi8VCQUEBAwcOZMSIEaSmpuLl5WUXtLVv3x6NRiP772wh%2BucePXokExpBuwsNDeX27dsUFBSwbt06Ro0aJRO%2BmJgYli1bRo0aNaTfVVBQEKmpqajVaoYMGcJrr72G1WqlRo0acmXbZDKRmppKSEgIV69elceRnJzMe%2B%2B9h6Io0rD81q1b/PbbbzRt2hSr1crLL78sx3j9%2BvW8/PLLdnMi/MXy8/Np1qwZP/30E4GBgeTm5pKbm8s333zDwIEDpZLomjVrWLVqFefPny91/suXL09mZiaZmZlSkKFly5Zs2bJFBp4dOnRg48aNsrfr3LlzVK9eXSbzJSmHuru7y6Cyffv2JCYmcufOHTuK4aJFi%2BjXrx8AGzZsID09nYCAADp06MC8efOYOXMmgwcP5tNPP2XevHkMGjSIMWPGsHv3bvbs2SMTZdvvLu08XVxcpES%2BVqulR48ebNy4EY1Gw/3792XCd%2BnSJTp37kzFihVZv369pMTpdDo8PT1lciFEb2wXTGbOnAkU2SyIefLz8yMoKIhTp05RtmxZUlNTJX3R09MTFxcXu0q4SOSHDh3Kv//9bwCaN2/OkCFD6Ny5M1BUMb1x44adlULxhFlUjl5//XW%2B%2BeYbea%2BJa99WWfJ5kgyVSiVphW%2B%2B%2BSarV6%2BW/m8lJeuenp54e3tz7do1u32LfjKR6IaGhhIXF0dSUpJMCsWik1AnHTBgQKmecX9WZAaKklAvLy8yMjIwmUyyd1Dsx9a/T8zjhAkT2LhxI6tXr6Zp06bcu3eP5cuX06tXL8xmM6GhoVy/fh2j0SjPz2QyyWtE9NPt2rULKKLox8bGykWYgQMHUrFiRaxWK0FBQej1erp06UJhYaHsk/bw8KB%2B/frUrVuX7du3yx4%2BjUZDs2bNyMnJISYmhpYtW9K1a1e5wBEWFiaff7Vr12bYsGG8/PLLksqZk5Mjr%2BenLXAI2BrO7927V55b9erVCQwMlJYRDvz3wCHa4oADDjjwN0L0SonKGiDFAYASe%2BMEhU983tPT8wk1u6eha9eudo3/gvIjEj%2Br1SpX2wsLC0lKSuL06dP88ssvdOvWTX6XSEoFrco2IBRVN5EwCIrWuHHjMJlMfPLJJ1KtUvhMLVy4kJiYGEaPHi2rfDNnzqRevXpcuXKFrKwszp07R3BwMDqdjgMHDnDgwAH5HXq9Hr1eT%2BXKlSksLMTV1ZVGjRrh6%2BuL1WolOzublJQUUlJSMJlMGI1Gpk6diouLi%2BwLGzp0KNnZ2WRmZjJr1ixiYmLo16%2Bf7NfLysqiU6dOfPnll0CRiI34t3jxYsxms6RNVahQgSZNmvDpp59SoUIFwsLCpCx8QUEBX375JSaTifz8fCnhXr16dVkdu3jxIiqVisePH8sey759%2B5KXl8e7777L2LFjUalUxMfH8/777z9RaYSiwLewsJAaNWrYCTJs3LjRLkjfsmWLnEvbPjLba654cCjEQaxWK1u2bHmi78nV1ZVly5bJa23kyJFs3rxZCu7MmTOHoKAgPvjgA1JTU3n11VfJzMxk7NixssopKI%2BiP%2B9pao9isQGKFkrGjRuHRqORNOSCggLCw8Np3749BQUFnD9/nipVqsh7af369Rw4cEAe76ZNmxgzZozdwkOnTp3o1KkTq1atkvPk6uqKj4%2BPlNG3DapbtmzJ9u3b%2Bfbbb%2BU%2BCgsLiYqKwsfHR7526NAhXnnlFbv5%2BP777%2B3OV1EUKZhiOy/Lly/HarWyYsUKKZwizlcch%2B1ijhgfW8EbAXFO69ats6PDinu7adOmctuMjAyuX79O/fr15b6dnJxYvXo106ZNA5AVugEDBkgTdEAyDGbMmAHAkSNHpPhUcR/R5032vLy85M8FBQVkZ2fLZ0N%2Bfj4FBQVPPKc6duwo53/BggXUqVMHgC5duqAoihTDEVT3Jk2acPz4cX7%2B%2BWf69u2L2WzGw8MDs9lMbGys7Ac9ceIEM2fOlEIsHh4eZGRkoFKpePToESkpKaxevRqj0YhGo8FgMKDX62nevDmhoaEcPHhQWqIIQaVu3boxd%2B5chg8fTsWKFfH19SU%2BPh4nJyfatm3L3r17mTRpEhkZGXTs2FHaY4jFwvDwcF555RXZt3ju3DnZt1tcaMu2b3LUqFHEx8fTqVMnTCZTiYtuDvzz4ajwOeCAAw78jYiOjrZbfc/Ly2PmzJlSaAWwq/gJVKhQgWvXrqHVahkwYACbN2/m4cOHJCQk0KFDBxRFkdLiFy5cAJAUveKN/qLiULwvynZVX9DlatWqRWJiIoqicPjwYVJTU5k1axbHjx%2BXwU/btm25dOkSR44cITw8XB5n8T4Vb29vHj58yI4dO2S/YEkICAigTp06GI1G7ty5w4EDB1CpVHTs2BGr1cq6deukwmReXh4Wi4VevXpRpUoVli1bJlfzhZ2AOGdfX1/CwsJk1UGr1UqxDovFgoeHB35%2Bfly6dAlnZ2c6dOgg1QUXLlz4hJjE48ePiYmJQaPR8Ouvv8rXo6Ki5DFUrVpVVr9se7v%2BLISohpeXFw8ePJDJkK1/loCth6GiKPj4%2BGA2m%2B3UT0%2BePEmNGjVkJUT4nAFSUAKKAmd/f38WLlxIRESEnS8aFFUAxHWVmJhIfHy8tMrQaDQEBwcDSE/I/yTmzZvH6NGjpWXI2bNnSUhI4MSJE6xbtw6LxUJ4eDgXLlyQ49e5c2fWrl1LWFgYmzdvplWrVlKURixIxMXFcfnyZaCoEnfs2DE5xqJnr2LFinIsVq5cibe3t%2Bx96tmzJ5cvXy7Rs9AWtgs7vr6%2B/Pzzz/KeFQlVcXEW%2BGN%2BL168SHh4eInPC0G3FtDr9bzxxhssXLhQvubt7W3XMysSQuH7B8hqPBTRDvv27cumTZtkH5mg90LR80an09G4cWMOHDhgx0LYvn07KSkpNGzYECh6DgpV2ZLGRcCWlqlWq/%2BU4JHYl9VqZcSIEaxbt04KvEBRhV4k%2B6Lq36VLF9auXYtWq0Wr1fLee%2B%2Bh1WoJDg7GZDLx1ltvERwcLHviypYty%2BXLl/%2BSVcfz4q9Qo4t/ViSSgjaq0Wjk4pDoXxVVb19fXzp37syBAwe4f/%2B%2BpMw78N8FzbM3ccABBxxw4H8TlSpVsksQbJMfEezY/qG2WCy8%2BuqrTJ06FZPJxL59%2BwCIjIxk8uTJVK1aVUqPK4rCiRMniImJkfvs0aMHbm5uMuAXwUnxIMU2qKhTpw5Hjhzh%2BPHj8r169erZ0cYUReH%2B/fvcunWLxMREqUQZFRUlA%2BT4%2BHh%2B%2BOEH3Nzc5Pns3btXfk%2BNGjWkubZWqyUhIYGyZcsSEREhtxFBn61SXG5urqSkrV27lkqVKgFIKXyRDNpizZo1zJ8/307N7tGjR9JDbOvWrcyfP5/r16/z%2BPFjBg8eTM%2BePZk/f/4TyR4gpfmLJ3G2AavZbGbjxo0ykJ8/fz47duxg06ZNAOzYsYPhw4ej1WoxGAzk5%2Bfj4uJCRkYGCQkJGI1G9uzZw5o1a4iKimLVqlXAH1XaSpUq2fVsqdVq8vPzUavV%2BPn52a3ki%2BDearXK6pII5t99990S/RutVisPHz6U1hOiaiLEXWwpiC%2B%2B%2BKJMFKHomime6CmKIpMvQX0TFQhbgSDbMX78%2BLH8nO01O3bsWHl/mEwmOTahoaHyvDds2CATM4CPPvqINWvW0KlTJwCZ7IkxAPjtt9/k9uJesx0Pk8lEcnIyycnJ8nVx7JmZmUycOPGJ8yiOmTNnYrVaGTt2LFBUVYE/lFVLq%2BDbMgMEmjdvLnsJxbVd/N42mUyyCi%2B2KT7eQuhEVLgsFguRkZGyf%2Bv27dtMmTKF%2BPh4mfDl5%2BfzwgsvSKsWk8nEnj170Gq1MknLz8%2BnfPnylC9f3u77bM/N9ntDQ0N58OABjx8/lvTJffv2sW3bNjZv3kxWVhZxcXG0b9%2Be0NBQ6YVpq66blJREtWrVpMDL4cOHGThwoBxvgLVr13L69Gmio6Nl5Uv0S4qq/OTJk%2BXY%2BPj4SFYEFC2MnD9/vrQp/o/heZI9g8FAXl4e7u7udp6C4rMWi8WuR7AkcSNA2kYcPHiQ2NhYxowZ8x84Awf%2BDjgonQ444IADfyOsVit9%2BvSRAQUgZf4FRHUG/viDLeTPAc6dO8e1a9fYv38/e/futfOZEtUuW5royZMnSwymiweOtvQtEcjYkkKEH5roEZo1axYqlYr9%2B/djNpsxm82sXr3a7vhFYvPw4UMyMjLIz89n1qxZQFHAd%2BPGDRo1aoTJZEJRFMqWLcuoUaNo3bo1AQEBBAQE2NH2ivsMWq1WPvnkE3bv3s327dsxGAyYzWY8PT1xc3Pj008/lcmFt7c3AwcORK/XYzAYOH78uDRxV6vV8v3169cDRYqpDx48eELMpDieRZyx/fyRI0do1KgR8%2BfPp1WrVowfP16qBnbt2lUKR1gsFipXrkzr1q356KOPOH/%2BPMeOHZNBsvBBu3fv3hPzqFaradCgAe3ataNWrVpUq1aNJk2a4OXlJT8vKlgied%2B0aZOk6gLSIB2KqkMtW7YEiihxISEhZGRkoNFoKFOmjNxnVlYWiqLI5HD48OFMnTqVAwcOoNfrZRXL09OTffv2oVKpWLlyJadOneLw4cMMHjwYKEpqNBoNn3/%2BOf7%2B/vzyyy/MmzePevXq2Z2n6N8TmDp1KlOnTmX27NlS0RGKRE7Eudy8eVP2QIlxFNiyZQtbtmwpNcAWCou2Mv7i9achMDDQTlhEURSuXLnC%2BPHj5TYfffQRsbGxT1Swih%2BLrY%2BhgK0qZ2nXosVikX2mpW1ja02Sl5eHyWSyE%2BsQyZPt88qWXitQpkwZqX5pu4hw/Phx6VEo7jmdTodOp5PJs06nY/v27SQmJrJnzx58fHxQq9V069aNrKwsZs2axbBhw9i4cSNxcXEcO3YMX19fgoODZTUyPz%2BfYcOG2VWvxHVkiwULFrBu3ToWLlwohU8E3VzgyJEj7N%2B/n06dOlG5cmU0Gg3bt29Hr9eTnJwsaeV6vZ6zZ8/K/0XVU1BVRcVUvCc%2BY7uNXq/nww8/ZMqUKfKesvUUVKvVxMXF0a5dO/ma7aKTu7s7Li4u%2BPj4SJsP222eB48ePeLWrVucPXv2icUOB/674KB0OuCAAw78jRAqj3%2BVolMcQg1TBCw5OTnPpA2KSlLnzp358ccfJbVTWECAvadVadi3b5%2Bs9AgU/1xJEvQFBQVoNBqpOHns2DEiIyOlJLtYhRfVRRGkq1QqIiMjuXr1Ko8ePZKCEyVBq9XSokULjh49SmZmJhEREWzYsAFA9reJylh4eDjlypWTwgRWq5WIiAguXrxIw4YN2bRpk%2BwXEpUYgc2bNwPIfqWTJ0%2BSkpIif9%2B8eTOtW7eWFbkdO3ZIxVVvb28ZrAvza5VKJYVR3Nzc7MyRDQYDbm5u1K5dm0OHDpGens5rr73GqlWr7AJrQaU0mUxkZGSQl5dH9erVuXLlCvPmzWPgwIFPndficHV1JTc3V1oyzJ07l%2BjoaJKSkhg1apQ8Z0EJFCqNUBTEimqCoihMnjyZSZMmyeRCJIhxcXEMGTKEWrVq4e7uLs2fr169ysiRI6VFiRDceF4U9ymrVasWly9fJjIykm7dujFs2DA5f3PnzmXVqlVMmjQJ%2BGOBQaVSMWLECM6dO8f%2B/fvx8vKSCxm2OH78OAMHDiQnJ0cKdXz66aesXbuWH3744U%2BNuTheIYZUkuCGoHbaUnhL24%2BgaooxsVqtXLhwgYiIiOfumevSpQvr1q3D1dVVWoWoVCop4ARFVicC2dnZkr4%2BdepUJk2a9MRzTyQlEydOZPLkyTRo0IBvvvlGvr9w4UK%2B//57UlNTcXFxIScnh/Lly5OSkiL96wwGAx4eHtKnsDQUp0YWZ1HYig/ZbgMlq%2B2OGDGCtWvXyr7cmJgYrl69SsOGDVEURfbs5ebmotfryc3NpVu3bnh5ebF3714yMzNJS0vD39%2Bf%2B/fv4%2BPjw9tvv42HhweffPIJv//%2BO56enhgMBn7//Xf8/f2ZN28e77zzjhRsMRqNUr22fPnyXL9%2BHY1GQ2xsLIqikJiYyODBgykoKODbb79FpVJx4sQJzGYzNWvWlIqm4pxsrwW1Ws1bb73FiBEjnuv6cOCfBUfC54ADDjjwN%2BL27duYzWY2b97MpUuXZOAkRDygyHNK%2BDF5eHhw9%2B5dlixZIsVbXF1dcXZ25uuvv%2BbChQuMGTMGg8HAwYMHpaHx8OHDMRgMbNu2jZycHFq0aCETiXbt2rFt2zbatWsnV/2L41lKeYqi0K9fP7766iv5mk6n49ixY5JOqtFocHZ2xtXVlbt37%2BLv78%2BECRMYOnSorGABTJo0iddeew0vLy9pkH779m3Zh9SmTRt5LHFxcWzYsAGr1YpOp2Pfvn3cvHmThw8fkpmZycmTJ1m9ejWFhYW0bduWunXrMnnyZPR6PWPGjKFbt260atUKk8nErl27mDNnDsuWLeOjjz6iW7duAFy7do02bdpw4cIFRo8eTZkyZRg5ciSAHSUMisQuALlaLyqgtkIaov/wwoULXLp0iXbt2lG/fn2gqHKbmJhIy5YtcXNzIzMzk127dqEoCi%2B%2B%2BCK7d%2B%2BWya4IPgsLC%2BX8iAS7eLCq0%2BkYN24cH3/8Me%2B99x5btmwhPj6e5cuXS4l54esoMHbsWB4/fsyuXbukkXx2djZ79%2B7FbDbLHsgPPviAzp074%2BHhIY3RxfdGRERw//592SOqUqlwcXF5ag9fQECAPOZbt25JxcWEhARefPFFunfvzs6dO3FxceHixYvyWvD09JT0NT8/Px4%2BfEj79u1Zt26dVL8Uc3D06FHS0tKoWLEikyZN4quvvuLw4cPyntu2bRsVKlQgNjbWzq8OSr4XPD09KSwspEGDBlJtUtAXn6dP093dnaysrBLf8/Pzo1GjRmzdupWkpCTCw8PlMZSU3NkeX9myZblx4wYVK1bkypUrKIqCk5MThYWFBAcHc%2B3aNYKCgrhz5w5vvfUWhw8f5uzZs0CRx5ugal%2B4cIFHjx5Rrlw57t27R0FBAVu2bKFjx44sWLCA/v37ExcXx44dO%2Bx6SMVzy2QyyaS/uOJrUFAQ9%2B/fl8bnQknWZDLRpk0bua%2B0tDQuXbok51jsx8fHx85mJjIykuTkZCwWCz/88AOvvvoqZcuW5ffff%2BfXX38lKipKVhQFjXj06NFERkbSt29fFi5cyJAhQ5g7dy46nY6kpCTJDBDnI%2BbqryiI/hWIBSIBMUaPHz9GrVbj6upKQUHBX7IAEZRqIdzl4uJCuXLlKFOmDAcPHuTDDz/k6NGjpKens2fPHr7%2B%2BmsaN278nzw9B/4P4Ej4HHDAAQf%2BZowcOZLdu3dLwQVbqFRFJtaAtAPw9/fnzJkzbN68mTt37vDzzz%2Bj1Wr56quv%2BOyzz0hMTESlUvHFF19w/vx55s%2BfLwO38ePHc%2BrUKTZv3mwXKAvJ/tICGFuZbqFYZ0uf02g06HS6JwIO2yBcbBcbG8vRo0fR6/V2MvJGo5GuXbuyf/9%2B0tLSKFOmDBs2bOD48eP069ePPn36sHjxYknPs7WnEBg6dCh5eXkcOnSIixcvUqFCBdzd3Tl58iSjRo2SfXCdO3dmxowZknpqG0xZLBZ8fX2BosAqKyuLgoICvLy8MJvNPHz4NuTUhgAAIABJREFUEK1Wy5w5cyR17Pr162zYsIHExEQGDRokP18cN27c4JtvvkGtVpOXl8fYsWN5/fXXgSKVyA8//JAGDRqQkpJCu3btSEhI4MqVK7IHSsyBoARWqlSJ8%2BfPy7mw7UdUFIXy5ctLqtlLL71EXl4eI0aMoHPnznz//ffSFgKKKkQdOnSgZ8%2BeeHh4cPDgQT766CP0%2Bv/H3nuHRXmuUb%2B/6cDQi0hREVEkgtgbqFGxoUZjTIxt203RVONOMTGxRU2MiV2jcdtSVOxgF2OJWLCgYsGCMYCgiDSpU74/5jxPZgCT7O%2B6zpWdc2b9o8LMO2%2Bb13s9677XcmD58uX4%2B/sDcOvWLSZMmPBUx0xHR0cMBoO8thqNhs2bNzN06FDKysqkomQwGNBqtdIoR9wrHTt25PTp02g0GhQKBUVFRfTo0YO6devy888/8%2Byzz7J%2B/XqbcyHy3hQKBceOHcPDw0OqNZGRkZLwCbXOGmLGbsaMGYSGhqJWq%2BXPHjx4wMqVK20U01q1atkQDLPZjLu7O/n5%2BTbn4Y%2B%2BT0%2BDSqXC1dXV5juj1%2BvR6/U8fPhQms5YB9pbf4bYT3FOGjduTFpaGo0bN%2Bbq1auEhITw6NEjqSbn5%2Bfj6upaY2absOI3GAyUlJRQVFREq1atpNL%2BV45NtESKFvJ69erJFl8RPn/16lWSk5OZMGECarWakpIS%2BvXrx5kzZ8jNzbVpBVUoFLi4uFBYWFjNZCo0NJTbt28zZMgQDhw4QE5ODp988gkzZ86Ur9FoNPJZ%2BNZbb/HVV1/J34noGOuFkqcdY8uWLSktLZXf3cLCQlxdXZk3bx6FhYX/12ZM/wsQc6NgybIUi1iVlZUyX/LPDIjs%2BN%2BDnfDZYYcddvzNaNGiBZs3b5Zh3jWhoKCAoUOHEhYWxrVr1/Dx8eHs2bPA70HfLi4ulJaW4uzsTG5uriyIRKi3aNGxdoesW7cuCQkJbN%2B%2BnVmzZqFSqVi3bh3Dhg0Dfp/jeeONN1i4cOEftp62bNlSmjfUhOeff54DBw5Uc5EUGDhwID4%2BPvz888%2Bkp6ejUqn48MMPSUhIoEOHDiQlJdGmTRuZXfZn2LJlC02bNuWdd94hNTUVlUpF/fr1%2BeSTT/Dz86OoqIi33nqL1NRUAgMDefjwIWFhYbRr1w53d3fAQsJOnTpVo1NgVbRt25apU6fKGb3169fTrVs3SkpKyM3NlSpeZmYmKSkpXLlyhWHDhhEQEMD06dMJDw9n586drFmzhilTpnD9%2BnW8vb1JSUmRWWhiZkun00niDRb7fzFrCJZWOqPRyOrVqxk6dChgIQ7jx49n4cKFf4mEiPunfv36sj3OOlC%2BpmI4KCgIo9HI48ePKS4ulttYvXo13t7eNgRTwMnJCZVKhYeHBxkZGbLNt6ysTOZDVm3FtCZT/v7%2BZGdn4%2BDggNFotMlYA2xMWoSbaE2/t1bP/Pz8bK551XZm63Mv8vYeP35M/fr1ycvLo0GDBly%2BfFkSEpVKRf/%2B/auFpleFl5cXY8aMoVOnTuzcuZPVq1fLzxNqnnDLvXDhAiqVikaNGkmHzitXrqDValmwYAHz58/n3r17Mpvt3LlzDB06lOzsbFJSUmTwvPUxdejQgbt375KZmYmvry8hISGcOXPGZv7NbDZLN1bhAOzq6krfvn2lWzBYlNoHDx7YmOt88sknaDQa7t69y9atW9HpdHz88ce4u7szevRoTCYTfn5%2BAGRnZz/1PqsJSqUSrVZLUFAQubm55ObmyjZP69f81fb5%2BvXrc%2B/ePZo1a8b48eNZvHixnC0MCgpi1apV1KlTB7AQowkTJhATE0OTJk34%2BeefOX78uIxC2LJlC1lZWU/9fVJSEllZWTRv3pzg4GCSk5O5dOkS33zzDRcuXKBVq1YsW7aMtLQ0wNJ1sGnTJrRaLWVlZbi7u1OnTh18fX0BS9v7zZs3ee211/D392f27Nno9XpMJpOMh2jQoAHp6ekytuX8%2BfPodDqcnJxo3ry5NNOKioqSkTMtW7bEwcGBU6dO/aVzaMf/DuyEzw477LDjb0bXrl3Zs2dPNXXPGrNnz%2Bb69euyNSs3N5eysjImTJjAmTNnOHTokLTRFjMgQm3z9PSUhUFRUZGcqRIW/X379mXo0KEcPnyYH374AUdHR9nGVbU4qjqDZw1RkKpUKkkuGzduzPXr11GpVKSmprJ161ZOnz5NUVERp0%2Bfxs3NjYCAACorK6WrYnJyMu7u7ly6dInZs2dTXFws4was28HE%2B8CixHTu3JkzZ86QkJCARqPh1q1brFmzhpSUFDZv3lzNFRCoMeT9/v37GI1GAgMDycnJoaCgQM4Wmc1m%2Bvfvz7Rp0wgJCeHChQssXLiQXr168cUXX9CzZ0/2798PWFwef/nlFx4/fszYsWN57733bD57ypQpnD59mi1bttCjRw8ZhVD1T7PZLNv2hLIjWkRFMbx%2B/XoGDx4MWIraZs2a8fjxY959913eeOONp95XYhtBQUHUq1ePo0ePYjZbwuvbtm3LxYsXcXBw4LnnnuPo0aP07t2bfv36ERsbi7u7O35%2BfhQWFpKZmclLL73EmDFjAFi1ahVbt24lOjqaX375hTZt2rB%2B/XoaN25MVFSUVKU3bdrEhg0b/nAh4GkIDQ3lxo0bNGjQgIyMDKmaWhM8sBA5Ueo0a9YMs9lMt27diI%2BP58mTJ2RmZqJUKgkPD5dk0dnZmR07duDp6ck777zDsWPHZJD39u3bGTBgwF8mDg4ODsTFxRESEiLdZvV6PUuWLOH8%2BfMsXrzY5rWi9dHf35%2BcnBz69u3LxYsXycjIqKYcubu74%2BzsLFtfxfchKiqKhg0bYjAY8PLykoTjr8Ba4REQCn9V8iUU1Dt37lBcXMynn37Kr7/%2BajN3Zw2hGtZEoMV2PT09cXZ25t69e39pf0WIe1xcHMOGDbMh2bVq1ZKLCCqVijVr1lRbdKi6mGMdgWHtujl9%2BnSGDx%2BO0WgkODiYmzdvEhYWxuTJk4mPj8fV1RW1Wk2XLl3Iycnh7NmzVFZW2kSliFbJ/Px8rl%2B/LqNUBClt0KABTZs2JT8/X0ZZPPPMMzx8%2BJCYmBi6deuGQqHgypUr7N69W2b6ubi44O/vz507d6TxkoBQex0cHPD09CQnJwewGMYIpdLHxwe9Xk92djZqtZpJkyYxd%2B5ceY2/%2BOILGjZsyKRJkyguLrYTvn8g7ITPDjvssONvxo4dO0hNTeXdd999qoNaTEwMS5YsYdKkSfTv358lS5ag1%2Bs5duwYt27dksW%2Bn58f2dnZ1KtX7w/npHQ6nU3ItnCxzMzMlPNWomgwm80EBgYSGxvL2rVr/6vsK%2BtW0O7du%2BPn54dSqeSVV14hJyeHd999l08%2B%2BYTJkyfbuOZNnTpVGha0b9%2BekJAQrl%2B/TuvWrbl8%2BTKlpaU4OjrSuHFjevTowddff014eHg1hdHHx4emTZvi5uYmf3bo0CG6d%2B/O559/DliUONGaBVTLlhP47bff2Lp1K8uXL8fR0ZGLFy/y9ttv4%2BnpKVsArTP3wHLdMjIypBJiTQjB0k547Ngx8vLyuHTpkny/9Z//jSlJTRAzOiEhIXTv3p2dO3fi6urKtWvXZPvajz/%2ByEcffSRzyVatWsWWLVvw9PQkNjaWGTNmMG3aNF599VVcXFz%2BMnn4%2BOOPmTVrFmq1mtjYWHbt2kW/fv3YvXs3CoWCfv360apVK8xmM927d8dsNvPss88C2JAXo9HI%2BfPna7z33nrrLZYtW4ZKpaJnz57S8AaQ7YPLly8H4LXXXuPs2bOkpaVRWFhoE8T92muvydeJjEMXFxfu3r1Lly5diImJYerUqXTp0oXjx4/XaGPftm1bsrKybCzv9Xo969atIyIiQqq/devW5eDBgxQWFtK%2BfXs5U1bTNlUqFX5%2Bfri4uHDnzp1q94NGo8Hb25uKigrZOinaZp2cnNDr9dXUPKVSKYnAoUOH6Nq1q1QszWYz9erVY8KECXzyyScYjUYbIxYBDw8PwsPDcXFx4fTp01KFBEvkh8FgqBbtERgYyLBhw5g3bx59%2B/ZlxIgRzJ8/H71ez6VLl8jLy8PR0ZH4%2BHhJAvv370%2BLFi1ITU2lsLBQzqfu3r2bPn360KZNG3755Re5uKDT6fD09JTzpGVlZQQFBXHv3j2uXr0qSff777/PN998w2effUZBQQFNmjSRbeNgiUgZPXq0zI4UizACTZo0ITU1VV7Tmojy/xKEU6dQzBctWsTs2bPJzMxkzJgx5OTkkJCQIJ8JSqWS2rVrM2XKFL799lsZDq9QKJ5K6O3434Wd8Nlhhx12/M3o378/mZmZlJSUyAIVfneC27VrF927d%2BfAgQP06NFDBuaqVCpOnTrF5s2bWbp0qc3cjzWqzrpt2LCBNWvWSCt8hUJB7dq1MRgM5ObmYjabCQ8PR61Wc/36dSorK2nevDnt27fHzc2NmTNn0qhRI%2B7cuYOzszOlpaXScMG6WDCZTJLw%2Bfj4yG2DpfjYuXMngwcPlsYWS5cuJSYmBrAlXb169ZJkVyhWYv4vPj6e1atXc/r0ab777jtGjRpFx44d2bZtG2PGjKmmmhoMBpYvX45SqZSr91ULuaZNmwJw6dIlysvL2bdvH3FxcZw7dw5nZ2cKCgro2bMnGo2GgwcPEh0dLYn6nj17iI2NlbNBUVFR5ObmyhDvqoQwOzubZ599Fp1O91SFT6VSSRe%2BpyktNUGv18t5Ry8vL6ZMmcKxY8e4cuUKzZs3Z8eOHbKQv3r1Kq1atZLZZykpKXTs2JG4uDi8vb1p3bo1Z8%2BepWnTpvj7%2B3Pv3j18fX2lWiDaIAUePXpEeXm5jUurIBjiT3EfeHp6UlFRQWxsLJmZmfzyyy/VXBDd3d1xdHT8y0RT3Ivz5s0DLK2E4n7q2bMnfn5%2BZGZm4uHhIa%2B9j48PWq2WyMhIEhMTZabg0qVLZTZmdHS0zX0sIFpXxXURhKqyspK2bdtSXl5OZmamnP2bNm0aly5dIiEhQZIiZ2dnysrKpIOpj48PeXl51UiTyCDU6/X07NmTgwcPyhlccZ4HDRrErl27bFxrHRwcGDBgALt27bJR6cU9a03YfH19KSws5MmTJzYkJj4%2Bnr59%2B8qZ3T59%2BlBZWcnu3bv54osv6NWrF8uXL2fJkiX07duXQ4cOSaJY1fFRtEfu37%2BfN998E41GI0nxw4cP5aKPWCwR0Gq1NnmPfxVKpZLU1FTCwsIA5CKGXq9n6NChvPfeeyxfvpzXXnsNsFXgfX19q313hcLcqlUrioqKuHDhAu3bt5f3U0FBAZ07d5Zt9sePH7fJ7ywoKKBjx46Ul5fj5OTEypUrefz4sTShycnJkSpbTEwMFy5coLi4GK1WKxdxnJ2dycrKwtfXF39/f4KCgjCbzRQVFXH8%2BHFKS0tp06YNycnJMp7mzxxMrZ8tAwcOJDw8nFmzZuHh4UFxcTEbNmyopqLb8b8Pe/C6HXbYYcffjFGjRnHz5k3i4%2BN5%2BPBhtVYxkaEnnNFu3bolfxcZGcmzzz5LdHQ0%2B/btY9SoUfz44488efJEFp7Hjx/nhRdewGAwSHc50VolDD6MRiOlpaWy0L516xZarZZ27dpx5MgRBgwYwMqVK2W7WVpaGjqdDjc3N2lWIdqhOnfuzIkTJ1Cr1Wzfvp1JkybJfba2%2B/7yyy%2BlGx9YlJpFixYRFhYmM%2BAGDBhAUVERo0aNonbt2jIGwdPTk4cPH7J582a2b99OVFQU8%2BfPp3PnzsyYMYNdu3ZVa6E8deoUs2bNArAxHahaOArSOm3aNPbs2YObmxv9%2BvXj8uXLtG3bVjpEgoVAWquHwpZeoGqbolCebt68SUpKCgEBAXJOLSkpCYPBwKJFi2SbpsFgICEhge%2B//541a9bQunVrjh07Jotgg8FAeHi43GcPDw%2BUSiWhoaEEBQWxc%2BdOysrKePjwIf/%2B978Bi3PmiBEjZDg3QGlpKVqt1oYgFBUV4evrS15eHmq1moKCAvR6PQcPHiQyMpJjx47ZGA1Z53Q1a9YMBwcHEhMTbch7eHi4tNwX11hEUTwtqkC0706ePJlx48bJ6%2BXt7S2VK%2BtWX4VCgVqtRq1Wy/B0QCpYOTk5lJSUYDKZJGEFy/334osvYjQaiYiIkLEmguwBLF26lJdffrnaPaPRaCThA2yUw4sXL8qiXmDGjBnVjlPsk3i/9b55e3vj4uJCu3btpOom2nZHjRrFkiVLJDE0Go3s27dP3pfimpaVlXH8%2BHEGDRokjaAAGa9hHUuwcOFCm9ZHoV41bNhQfsaiRYvkMykhIYHFixfLZ4RKpaJBgwYkJCTUSMzMZjM9e/bEZDKRkZEhr6FQm8GixIPFwbRx48acPXsWk8lUTUVTqVTyvImculq1ahEQECCzKt944w0WLVoklWCj0ciCBQtQKBQMGDBAPitee%2B01bt68yZUrV3j99dcxGAy8%2B%2B67PP/881RWVrJq1SqbRbkTJ07QqFEjzp07x6RJkzAajaxatYobN25w5swZG3fe2NhYQkJCiIyM5Pz589y%2BfVsu4jg4ODBz5kz8/Pzw9PTkxRdf5Mcff0Sn0%2BHh4cHgwYMZOXIkZ86c4fLly3z%2B%2BeeoVCpmzJiBXq8nPT0dnU7H5cuXbVrAnZ2duXnzJmazWS4cmUwmgoODuXfvnpwJFgHrIhrFaDTi6enJ6dOnOX36NGazmcjISMaOHWsne/9Q2BU%2BO%2Byww47/AXTv3p02bdpw9OhRioqKMJlM0i5duPG5urqiVCp58OABXbt25eTJkzJDr379%2BqhUKi5duoTZbJYFkKOjI927d2fPnj3yP3KVSiUVvacpJgqFgubNm5OSkvJUx7mqKlOzZs1klp2DgwMRERHk5ubi4eHB%2BfPnUSgUfPDBB8yZMweFQoFGo6FVq1Y2QfFQPSvN%2BjOEOYaw9zeZTJSUlMig9K1bt1KrVi0b1S4nJ4c5c%2BaQlJTEm2%2B%2ByYwZM6TiBtUVPpFFNnjwYPr160fr1q0BS17frl27iImJke%2BPiYlh9erVBAUF1bitTp06kZOTI18fGRlJXFwcL774IgaDgU6dOnH48GEAxo8fz5o1a%2BSMZY8ePThw4AA%2BPj6sW7eO2NhYqST17t0bb29vsrKyOHz4sLTbd3d358MPP6Rnz57SpfDGjRs8ePAAtVpNVFQUN27coGXLluzbt0/O%2BzRp0oRHjx6Rk5ODUqmkT58%2BHDp0iHbt2lFWVoZOpyMoKIjvv/8epVJJeXk5PXr0sGlPtSa%2BhYWFmM1mGTcgMvdWrlwJWOYk/8oMnFCzxH1rMpmoW7cud%2B/eJSQkhFatWvHTTz%2Bh1%2BspKSmhadOmPHjwgNzcXJo3by6NjVq3bk3btm2ZNGkSkZGRNsRMFMdV7wmFQkFpaSk3btxgyZIl7Ny5k%2BLiYvLy8tBoNHh4eMgWyv79%2B0s3w/8WTk5O9OnTh%2BPHj5OdnW3zu6ioKNLT0zly5Ajt2rXj0KFDODs7A0jjlLp16zJt2jQmTpxYLdxcr9dTWVlJs2bNOHPmTLXP1mg0jB07lhUrVsifKRQKli5dyuuvvy6JnpgtFOYwANeuXZMmPhERERiNRsaPH09UVBQjRoz4y8cfHh7OlStXUKlUeHp64uPjw7Vr1%2BT3XhjUCOMp6/10c3OjUaNGlJWVcf36dRkB07ZtW7Zv3052djZms5kuXbqQmJhoYyAjHEe7d%2B9Ox44dGTx4MF999RUbN26kpKREtsX%2BUZn8Z0p77dq1KSsro7i4uMZ2XbVajZubG%2BXl5dXiP6p%2BhvVzsar5TNWoC4H%2B/ftjMplkTqRSqWTo0KH8%2BuuvnDt3DldXV3Jycnjttdfo37%2B/fI6JZ50wpan6XLPjnwfl370Ddthhhx3/f4fRaOT%2B/fucP3%2Behw8fylmLdu3ayUJ%2B69atlJSU0KhRI8xmM7m5uTRp0oTc3FyKioq4dOkSFy5ckKqJQGlpKbt27cJgMPDcc88RHBxMRUUF9%2B7dk2Sv6tygXq/no48%2B4vHjx5LsWRsbCFQtLmJjY%2BXfy8rKuH//Pnfv3uX8%2BfPy9Xv27JF/r6io4NSpU6hUKrRarSQsIrxcrVazZ88e9u7dS0hICI6OjjLL7b333pNqgIODA1OnTqVOnTrUqlVL7oPBYODbb78lNjYWZ2dn9u7dK91H/wiiuGratKk0ZZg8eTLl5eXyGIcMGSLb0EaMGCHDiI1GI5s3b2bTpk2sX79eFnE//vgjmzZtwmg08sEHH%2BDj48Pzzz8vCYlCoZAkSBTRJ06cACzzUIMGDQIspg8A%2B/btY8OGDZIsqlQqhg0bxr59%2B%2BjZsydgacu7evWqTQberFmzePToEQcPHkStVst7JTU1VRbHRqNRtv0lJiZy8uRJrl27xrp16wgICECj0WA2m23IHlha1IqLiyXZg9/Dth0cHPj2229JTExk9%2B7dhIWF4e/vz9y5c4mKimLu3Lk899xzPPfcczJqoEuXLvI4fH19ady4sXRtBAgODuazzz5Dp9MRHBxMUlISmzdv5ueff%2BbSpUts2LABnU6HTqfj/fffp3PnzlJx9fLywtXV9amk02Qy2cwCLlmyhN9%2B%2B03mZAYFBUknVKVSye7du6WCCBYzFXd3d5vvzYsvvsiLL77I3LlzcXR0ZObMmZI8b9myhby8PBljIOIZ1qxZI1u1RWuggHClXL58ORs3bqSsrMyG7CkUCqZMmUJSUhIbNmzAwcEBrVbLuHHjpAttZWWlXHCZOHGiJBdvv/22/D0glX3RDgmWGbawsDDCwsKoqKhAoVCwadMmSfZEW7hCoWDbtm3odDpu3LhBQEAAiYmJ3Lhxgxs3bjB58mR5noxGIx999JHN4oFoRxREUxzbCy%2B8QEFBAfn5%2BdKEasOGDTLDMjc3V7pWHjp0CJPJZKPCiniJxMREmR8aHx9PZWUl0dHRDBgwgMOHD6PT6UhMTESn09G3b19iY2Pl9%2B769evyOESOpbOzM25ubuzcuROTycS2bds4ePCgbMEU0RIKhYIDBw4QFxeHs7MzXl5e0v1VxJsMGTKEIUOGyO%2BciGcx/z%2B5o1FRUfTs2RN/f38UCoWcsRPHOHPmTBv3Z5PJxKFDhzh%2B/DglJSWyE2TFihX07NmT0NBQQkNDKSkpISYmhtDQUMLCwigrK5PPcTv%2BmbC3dNphhx12/M2YO3cuWq2Wvn37smTJEsLCwjAYDFy%2BfFk6IZaVlbFx40ZmzpyJ2WyWSlpVuLu7U1hYiJOTE%2B%2B99x6NGzdm1KhRlJWVERcXV%2B31opVHvG/cuHH89NNPrF27VqoNXbp0QaFQSJtusGSRWbfjOTo60q9fP%2BbMmSMJU0BAAK6urnJWB2yNUHQ6Hd99951s09Pr9ZSVldGgQQO5b8HBwYBFEaqoqOCtt95i1qxZpKWl0bFjR2nA8MILL7BgwQK5baPRSN%2B%2BfXF2duY///mPnMv7KxCGNj/88AMzZ87Ey8uLunXrAhaScf36dWm57%2BbmRklJCYcPH2bp0qU4Ozvz9ddfU1lZKV1F1Wo1M2fOxMnJicrKSlJTU1Gr1ezevVuqS02aNJEzZUJ1EjNw2dnZlJeX4%2B/vL9tF33vvPdRqNatWrSIvL489e/bIfbSGICiCRHp7e0uloX///vJ12dnZXLx48akOrI8ePaJhw4a4urqi1WpJS0tDo9FQu3ZtG0dYk8mERqNh8ODBJCUlUVJSwqBBg6hfvz4mk4mvv/6aLVu24OXlRWFhIW5ubpw%2BfZqbN2/y4MEDaZby6NEjPvjgA44ePcqjR48oKytj7ty5jBo1ivT0dJo2bcrFixe5e/cuXl5e3L59W7babdq0CUdHR2JiYiRReeGFF2yOR7QRWmPlypWYzWYqKyttgsJnz55Nhw4duHDhAmAhQWLmVhyzKODz8/PR6XTs2LGDF154QbbLGQwGZs2aRbdu3Th06BClpaUsWLBAEnwnJycZnwIWQlNZWcnjx4/l/Siwc%2BdOzpw5I/fPeqFFq9Xy/PPPM3ToUAYPHsyQIUNsjlGpVDJlyhSmTJlCREQECoVCzqUtX75cEnXRNll1UceaIFuf0x07dmAwGDCZTIwfP57169fz2WefsW7dOrKyspg0aRLl5eU0adIEo9FI9%2B7d6datG1OnTmXatGmyzTIvL49vv/2W6Ohom4UfJycnaSgk9ismJoa4uDj5c7FwZZ2tJ55ZwvxIqVRKEvjw4UMMBgMdO3bE2dmZyZMn8%2BjRI2rVqkVycjK1atXi0KFDeHt7ExAQgMFgoLS0lJMnT8o22cmTJ1NcXGzzvREq6/nz5ykqKpI5mWazWUaNVFRUYDabuXbtmsxBFWRbnAuFQkHDhg3p06cPW7ZskYTPZDLJ7SQnJ9OsWTNycnLkvWR9zYYOHcr9%2B/dtruGjR4%2BkQlhUVCSfYQqFAm9vb548eUJxcTE%2BPj4EBQXh4eFBTk4OY8aMYfHixfbQ9X8o7C2ddthhhx1/Mzp06MDo0aPZvn07d%2B7cQa/XSyIg8tfEiq%2Bbmxtubm6YTCby8vKoW7eutC9ftWoVixYt4tixYzg5OTFz5kzu3r1LfHw8t27dwmQyScMDrVaLwWAgLCxMxi%2BYTCa6du1KUlKSzSyXiFsQEJl/1kpO79698fHxIS4uThYPGo2GoUOHsnbtWsCiWuh0OsrKyjCZTISGhjJs2DBmzpyJwWCgTp06lJeXM3HiRKZNm4aDg4MkiM2bN5cZa5GRkXh6ejJq1CiGDBkiDUXatGnDmTNnePvttzl8%2BDCzZs3ixRdfrHa%2BQ0NDbYLRc3Nz8fb2lv8WKuu1a9e4du0acXFx7N69m8LCQgYNGsTOnTu5ePEiKSkpJCUlceLECRsCrlKpCA4OpkePHowdOxaTycSqVas4ePCgzfylCLxWKBQy1Bwsxb5oFbx48SLPPPOMPM9VFalnnnmG69ev2xjFWKOmiALxs%2BnTpwPIWS2hRoi2TpVKhY%2BPD%2BfOnePChQulvMtRAAAgAElEQVT89ttvKBQK6tWrx507dzh9%2BjRHjx4FLHOmbdq0kddXqVSSnJzMqlWr%2BP7776XZRL169WjevDlXr16tlpcnIIxemjRpIq3rTSYTDRs25NatW5jNZmbMmMGsWbNo1aoVAQEBJCQk8NFHH7F06VLu37%2BPQqGQraBgKfjbt2%2BPl5eXdGf9byHmXgWys7MxGo2o1WpiYmLo1KmTnK/7KxAFvqurq822xXxmgwYN6NKlC9u3b8fHx4ebN2%2BSmpoqW46ttyNMlM6fP49Wq8VoNNKiRQvOnz9PfHw8ZrOZjz76CJPJJGNIsrOz5XOlpKQEBwcHlEollZWVKBQKKioqcHNzo7CwUOZ/Wmcg7tq1S8aVPPfcc/JnZrOZvn37SpVp8uTJfPHFF1KdFeHxYPmutGjRgosXL0o1UalUMnLkSP7zn/8ASJVQ7BdYCF9SUhLt27eXM8jh4eH89NNPcnFHoVCwb98%2BAHr16iW3r9FocHd3l/mPwilVGEklJibSu3dvOnfuTFpaGnq9nvbt28sZ1Zs3b8p9aNSokczG%2B38bYlZWo9FQXFxsk4v4tJbOv7pd0SZdr1494uPjSU5OZuvWrezbtw9HR0dOnjzJ1q1b2bFjhz10/R8KO%2BGzww477Pib0bp1a1xcXIDq%2BVRPg2gf8/Pz49GjRxiNRry9vcnNza1maiBmYIxGI82bN8fFxYXjx4/LAO/o6GipxtQEQd7EdoVBhfV/H1u2bGHYsGGyHelp2xJzWNbFStXPsnb6FE6avXr1IjMzk8uXL8uCV6hWYlvW7qDW26oKUXjOmTOnxn387LPP%2BOyzz3j%2B%2Beflz65evcqePXu4dOkSp0%2BfpkmTJmzbto3169dLe/aUlBQuXLjA/fv3bfK7MjIyOH78OCqVis6dO8ussLVr19KrVy9q167N2rVrpXX8xo0bpXGFIFcmkwkHBwdCQkK4cuWKnG0T836hoaHS4VRg0qRJNG3a1GYOSbgQWrulLlu2jMDAQOrXr0%2BTJk3kLGjVfa9bty5ms5n27dsTGRnJ/v376datmzR%2BsIa7u7uc%2BxKmPoGBgXz33XcMGjSIli1b8vPPP%2BPu7o5er2fMmDHMnz%2Bf0tJSYmJiOHToEIGBgXL2qbi4mPbt20tr/oEDB7Jt2zbc3Nxo2bKljfpsfS%2BJe7wmcvnkyRNatWoljYfEfeTp6clLL73EK6%2B8IlWjpk2bMmPGDBsjk8jISEwmE5WVlajV6mpumk%2BD9X0vbPLHjh3LyJEjqVWrlpzNa9asGZcvX0ar1aLX63n06BGRkZFSaRTQ6XSyhTEyMhJfX19at27NrFmz/jA3syqsW1KFgY9KpZIt5qWlpcyYMYNFixZJZf/PZthEi6L1/Jp11mBNc3IjRowgKSnJZnHEOt7Fen%2BtybX1s6MqBDEVzwMHBwdeeeUVtFotmzdvlkr0999/z/jx4/nyyy8xm82EhITg4uIiz01%2Bfj63b9/Gz88PV1dXnJ2dKS8v59dff2XZsmUUFhayfPnyv2XeTSjJ4jirLgpU/bf1%2B5577jl27NiBTqfjwoULpKWlsXz5coqKivDy8mLevHmUlpbSpUsXewbfPxR2wmeHHXbY8Tdj1KhRREVFMX78eDkcX1payrx587h9%2B3a1FVVho37mzBm56loVCoWCiIgIrly5gpOTExUVFahUKlnEiUKpbt26JCQksH37dj799FOUSiXvv/8%2Bp06dIjExsZr7YU0FlUajITAwkHv37lUzVrDG888/z969e2WxVxUtW7aUitwHH3zA2LFj6dChA2BpGYuPj%2BfFF1/k8OHDODg4MGzYMCIiIigrK2PFihW4uroyZswYWYwKJcva7RBg5MiRrFu3jtq1a9v8XrR6VUV6ejoTJ05k%2BPDhvPfee/z6669s27aNIUOGMHbsWG7duiULV6VSyYgRI/jwww8BOHv2LBMmTKBWrVoYjUYeP35M165dOX/%2BPMHBwZw4cQJ/f3%2BCg4Np0aIFS5YskXN0VY0ZwJJPePXqVYxGI3v37kWhUNC3b98a93vOnDnV1KA/gouLi2wBFbNiT548QafToVQqKSkpoU2bNjg4OHDs2LEai31BRN9//33mz5%2BPh4eHbA1%2B5513uHLligzKPnr0KIGBgXTo0IHk5GSCg4NlzMWDBw%2B4du2abHXMy8v7S8dQE7RabY1F6oMHD%2BjTpw%2BxsbE0btyYfv36PVWd69WrF4BUjPz9/WnatKls2X3y5IlUFDUaDTExMXTo0IHDhw%2BTlJREeXk5QUFBDB48mOHDh6NQKBg%2BfDhXrlyxcfS0vub169eXM7mffvqpJK3Lli0DkDEKIrLDGn9W8FuTA7Ao6CNHjmTr1q24urqyYMECIiIiqKioQKvV4ujoyOrVqxkxYgRlZWW4uLhIV1/xna9fv768TsINGH43WhL7MX78eNatW0d5eTmenp7Ex8fTo0cPOe8aGBiIQqHg4cOH1Z4VNRFIcd5Ei6kwzxHnRrwPkPEwwhRLqJJvvPEGGRkZZGRkyIUjhUJBcnKyJP3Hjh3jlVdekTN6jx8/Zu3atSxYsIBTp07ZLPLk5%2Bdz4cIFpk2bRn5%2BPsHBwXz44YeSJBYWFpKbm8v06dN5/PgxYWFhvPPOO4SGhpKZmcnJkye5e/eubH9%2B/PgxXbp0oVatWvz222/cu3ePyspK8vPzKS0tJSQkBHd3d6Kiohg8eDBeXl4UFBTQpk0bG8OomiDuefFnVFQUZ8%2BeJSAggA4dOhAfH8%2B///1vevfubSd8/2DYCZ8ddthhx9%2BM69evM27cONmm6e7uLp0Na9WqhVarRaPRSKv5K1euyJm7qgWqUNgMBgMqlQq1Wo1er0er1VJcXExxcbFcYRfFk6urK71790apVLJx40bgd4In2ku9vb3Jzs6mbt26BAQEcPLkSZmPFhgYSGpqKgMHDiQ9PZ2rV6%2BiVCqpV68ev/32G8XFxfTq1YtvvvkGs9nMN998w40bN0hKSsLHxwc/Pz/S0tKIjo4mLy8PDw8PEhIS/vS8CQVw%2BvTpnDx5kg0bNtiYtgg0btzYZkVbKHw1kRVx7NYQ/z5y5AgnTpzg3LlznDt3jtzcXHl%2Bn3nmGZYsWcLatWs5fvy4bGMdPnw43bp1Y/To0QB89913HDlyhNzcXH777TcMBgN6vb5afAP8XqRbu/Q5OjqycOFCOnbsSGRkJAaDgQMHDlRrNxTIzMwkOzubkSNHArBu3ToWLVqEi4uLbMfs1asXer2ea9eukZKSgtlsxtPTU94jJpMJlUpFeXm5jYoVExMjiV/Xrl3Zt2%2BffL2rqytt2rTh66%2B/pnXr1jaF%2Bx%2BpQjWRXNH6WvV3Xbt2paCggHPnzgEWt8sZM2YwZcqU/6vWtj8jSdYIDAwkMzNTRkYMHTqUr7/%2BWsacNGjQgPv376NSqXjw4AEmk4mIiAhu3LiBwWDAz8%2BPt99%2BW16/69evk5eXJ/MsX331Vd5%2B%2B20ZSp%2BUlIRWq2XdunV88cUXT1VyrKHVannmmWdISUlhwIABbN%2B%2BXbpufvHFF/z73/%2B2OafiPIOFbL7yyit88MEHcnuvv/46q1evpqKiAp1Ox/79%2B5k8ebI8/yKqRMyqCVV60qRJLFmyBIPBwAcffECdOnV44403qKiowNHRkYsXLzJs2DBpomK9yCTaGEWLMVjamM%2BfP8/06dNt4i2EqmYdA2JNckwmE2q1mpCQENLS0ti2bRsNGjQgIyODd955hzt37rBgwQLeeOMNqWrOmDEDk8nEzZs3Wb16NR4eHuzbtw9nZ2dmz57N4cOHycrKwmw2y/iOvLw8zGYzjx49spkVrVOnDhqNRpoSPXr0SOYyKpVKOnfujNFolJmTFRUVbN%2B%2BHbVazahRoxg/fjwajQawLLLt3LmTjz/%2BGLC4gYrPFBD3iIgDEa324jwK1VPEaIhnjWiRN5lMjBs3jrZt27Jw4UJeeukl9u/fbw9d/4fCTvjssMMOO/4HcPToUd544w0Am3m5/xZCcfiz9jIxlydMAHQ6HQ4ODhQUFNhY4IuCMigoiIiICPbt24fBYCAqKorU1NRqyosoIEWrmjAaaNu2LWlpaSiVSvLz89FqtRQVFeHp6cn27dvp2rUrLi4uVFRUUFlZSYsWLWzm2gSEKczq1as5ePAgSqWSZs2aMXToUFnAANy7d48DBw5w7NgxsrKyUCgUBAYG0qlTJ77//nu0Wi2dO3fGZDJx9OhRhg8fzqBBgzCbzcTGxtqYH4g/rYtr4bD3wgsvEBcXx6hRozhy5AglJSV06dJFOva1bt2aEydOyDwu8fvExESWLVvG6tWrn3qNvLy8yM/PZ%2BjQoVLlnTFjBgkJCcyZM4fevXtjMBjw9vZm%2BfLl7N27l9LSUrp16yaVUYE1a9YAMGbMGBmo3qNHDwBJrgMDA4mMjMRsNuPl5UVxcTEnTpxg/vz5bNq0iV69epGQkMCQIUP%2B8hyPOAZAKs3bt28nNjYWNzc3vv32W06fPs3x48e5ffs2JSUl1VSdmizoHR0dSU5OlhmDPXv2RKPRSEVMXCuNRkPLli05f/68nBstKioiJyeHyspKnJ2dMRqNGAwG6TQpFJ3g4GAuX74s1TC1Ws3s2bMBi8I3atQojEajdI8Vph5Pg3WxrdfrKS4uRqFQMGTIEAoKCkhMTMTb25uCggLc3d1JSEggPDwcpVJJ8%2BbNef/99xk0aBBOTk7ScbJTp06ynTU6OpoTJ07IGcixY8eydu1aFApLLp2Yzazp2VC/fn0qKyvJysqSxyKC4wWCg4NJT0%2BXbbLh4eGkpKRI50uheApSK96rUqlkxltVglq7dm0OHjwoW4/F%2BV%2BwYAF37tyxWTz57bffSEhIoKioiHXr1rFjxw4GDhwoDVCq3is1LR4MGzaMd955h5iYGEpLS4mNjSUhIUG2xIprW15eLrNJKyoq5DPV399fLgyVlpbaEKz/L0Eo9SaTidjYWPbu3YtarbaHrv%2BDYSd8dthhhx1/M/Lz8xkzZgz9%2BvVj9%2B7dciZv9OjRrF27lvz8fLkaLgpK67ZMoeRBzWRR2O%2BL4mfDhg3MmzeP1NRUuZrerVs3Tpw4QXl5OWazmfDwcIqKisjMzESlUvHMM8/w66%2B/olQqZSRE//792bhxI0%2BePJHFUlVYm8p4e3vz%2Buuvk5aWxqZNm2SBp9VqKS8vR6/XYzQa8fX1JSsri%2B%2B//77G4mLlypX88MMPDBkyBIPBwOLFi23m9axngqxVsnfffZctW7Zw7949li9fLlfkf/nlF7766ivZClY1c8o6X3DFihV8%2B%2B23sgAsLS3l5ZdfZuvWrRw/fhx3d3eb99eUXxUZGUlCQgK7du1i8eLFjBs3jvXr1xMdHS2V06SkJHQ6HZGRkXTq1IkDBw4AFkfG/fv322QtCoiw64yMDBYsWECXLl3o168fgMzhWr9%2BPV988QVXrlz5Q0MXcd5SUlJ49OgRUVFR/PLLL3To0IEbN26QmZlJZWUlffv2xWg02mTSiX05ePAgLi4utG/fXpL/gIAANmzYQNeuXenYsSPt27dn7Nix8n337t1jxIgRDBkyhIULF6LT6fD39yc9PZ05c%2Bbg5eXFhAkTMJvNNGzYkKFDh1K7dm1effVVNmzYwObNm%2BWxWkOhUNCtWzdycnKIjo7m%2B%2B%2B/l/mNrq6ubNy4kZdeegmFQiHJOlhm9xYvXswrr7yC2WwmLCyMpk2bEhoaKtUl60B10eYo7kVr4xhHR0dMJpNUy61JkHBwrV27Nt27d%2Bff//43q1atYv78%2BfK7Lr7/U6ZMYdy4cbz11lucO3dOqkRiEUe0zAolX7x%2B3LhxHD9%2BHLAozxs3bpTXWajMopWxTZs2JCcnS9KmUCiYOHEiK1assGmnFPstyOysWbNo1KgRAwcOtDFhEfeq9XcJwNPTExcXF%2Bm0qVarmTdvHj/99BPffvstbdu2JTg4GL1ez%2BXLl6vNJ2s0GoYPH87mzZsBW8dS%2BL29c%2BDAgezfv5/4%2BHh8fX3Jzs5mwIABMtNTfJ8FIffy8mL8%2BPH07NlTmrXMmjWLqVOnMnv2bKmszZo1izp16nDnzh2OHDmC2WzmypUr8vmTnp7O3r17qayspHPnztJ0CyzP/YKCAnbu3CkjFnJzcwkKCqKwsJDi4mIuXLjAkydPcHV1JS8vT6r8lZWVGI3Gpy4OivtPhMFnZ2dz7do1cnNz0Wg05OfnS1JuNptxdnaWXQbCWVaoqmJxYf369bRq1arGz7Pjfx/2WAY77LDDjr8JFRUVjB8/nrp163Lnzh1GjBjBwoULAUubzZUrVygqKqKiooIxY8ZI1zqwzMXk5eXJ4Gyj0UhgYCAZGRk26oazszN79uyhc%2BfOgKXoFNbzYGlzcnR0JDU1lcrKSlmY3bp1i9LSUlmwpqenyzkcsLQKfvnll1RUVKDRaGjevDnLli0jKirKhogIsgfw008/odVqWbhwoY16JlrERowYQUREBAEBAYwdO5a5c%2Bfy6aefcubMGbp168aYMWMwGAxkZ2fTrl07Xn31VcDiZvj111%2BzYsUK6RAI2LgIgkWhGDNmDE2aNOHixYuS8LVt25a7d%2B8%2B9Tp5e3vz888/891335GZmcmcOXPo2bMnFRUVtGzZUioMHTt2pGHDhn%2BqropVc/E%2BkQF25MgRSQ7S0tKIiIgAsCmQ9%2B/f/9Q5M5HnOHPmTL777ju6dOliY2UPFst6g8HA3bt3JbkXCAsLq0Yiw8LC8PT0BJBKHdg6VioUCoKDgykoKJBkQOSpZWRkyJ9pNBqeffZZFi1ahEqlIjk5mXPnzrFy5Uri4%2BNZuHAhiYmJdOjQQZL4QYMG8fHHH/PVV19Jw5S5c%2BeSlZXF4sWL%2Bfrrr%2BW82I0bN%2BScm0KhoEmTJly9elXOmCmVSmbPns2bb74pVZuOHTuSkpLCZ599JhdT9u7dK8mzQqGgc%2BfO%2BPv7k5WVRU5ODjt37qS8vFwaoog4CKH6Hjt2jFWrVkmjJAGxfWHMJH5nNpt58uQJgYGBMi8TLJlwSqUSDw8PcnNzJeEbN26c/DxB9sR3vby8nAULFvCvf/1LEkDxeuvvgpjRFNdfFPvi%2BluTXrC0h%2B7evdvmfhHnSBAcPz8/%2Bd1zcXGxUf9r165Ndna2Tbg9WJ6B1kZVAQEBJCUlcfnyZdasWUNFRYV09LTeF9GqWllZyfr16xk3bhwTJ06USjrA0qVLpcHUrFmzJNkDS7vjq6%2B%2Bypdffom/v7%2Bc4xXXxMHBgfXr17Nw4ULWrl1LREQEc%2BbMYciQIcydO5eXX34ZsHQcCBdRf39/2boN8J///IfRo0fLe2/Tpk3UqVOHjIwMGTZ//vx5bt%2B%2BLe%2BVlJQUcnJyyMnJISYmRp7DBw8e2JyzqnjhhRfYsWOHXBwYPHgwcXFxTJo0iYKCAvn8VygU/Pzzzzz77LNcvnyZtm3b4uXlxdy5c2natClnz57lX//6l2wTr6iooHXr1gQGBtrJ3j8cdoXPDjvssONvwsKFCzl8%2BDArV65k5MiRLFu2jNdff12u3jo4OJCfn09xcTFLly7lk08%2BoaKiQs73VVWyrFs5FQoF0dHRZGZmUrt2bU6ePCmLIesWqKfB2qClqkunaNeE3wvGFi1aUFhYKJ31FAoFzzzzDFevXpXbiYiIsCnArfHyyy%2Bza9cuhg0bxuTJk4mMjKS8vJypU6dy%2BfJlTp8%2BzePHj5kxYwaffPIJbm5uTJ06ldjYWEwmEy1btpTuhY0bN0aj0XD58uUajy00NJSgoCCb4PCnqXLFxcWMGDFCzllOnDhRzhWBJT/Px8eH2rVr4%2Bvry5kzZ8jLy5N5idOnT6dPnz42xdKnn36Ku7s7bdq0Yf/%2B/bKQb9%2B%2BPWfPnsVoNNKuXTvWrl1LZGSkTRtcSkoKoaGhkhysWbOGlStXsmvXLvz8/NBqtezcuZOoqCjOnTtnM88EFsXKYDAQHR1Njx49uHjxoo0KYjAYOHLkCLVq1eLmzZu0aNECR0dHLly4gIeHBxkZGcycOZMnT55w9epVEhIS/jSGwHoB4p133mHFihVyVky0H9Y0i7Zo0SKmTJkiozjEMYwePZo333yTYcOG0aFDB8rLy0lPT5fkByztqbt27aJdu3aAJQTc39%2Bf%2BfPn06xZM%2BmuKWD92c7OzpjNZmn4ERcXR3h4OI0bN5bxHQ8fPpQmN2LW1WAw2BhguLu7U1ZWVqNLpog2sXaAzM7OplatWjg7OzN16lRee%2B01ysvL6d27Nzt37pTvDQkJoaCgQB6vSqWSM77FxcUEBwfz66%2B/yky35cuXA5Z4BGszlT%2BCmI8VYedVo1kErI1%2B%2BvTpQ6NGjQDLs%2B1pofZgMYm5ceMGFy5coGvXruTk5NjM2Qrlv7i4WMa56HQ6qchZX6/Q0FDUajXLli1jwoQJ7Nq1i%2BTkZIYPHy5ft3DhQubPn8/q1at5%2BPAhEyZMwMPDg8zMTHQ6HYsWLZIt9SaTidTUVMAycyvaSsPDw5k2bRrTp09n2rRpgCXYPDAwkPT0dOnWK45D3NuChNX0zA0ICOD%2B/fs1niulUkn79u0pKiqSCxnWaqkIhJ88efJ/ZaTi4eFBYWGhNHZ56623WLFiBX379uXw4cM0adJE3jMZGRnExMQwevRo3n///b/8GXb878FO%2BOywww47/ib07t2bzz//nObNm7N06VK2bt1K/fr1OXfunIw9EPMxYpVYzNXodDo5V1ReXi7z9Xx9fSkpKZEF9V%2BBdauoUqlEr9fz5ptvMm/ePAwGA56enqxYsYKXXnoJsMwfiXBqa1RtXxQFjzWEOYB1gaNSqYiOjiYwMJBp06aRk5NDt27dMJvNsvCaNm0acXFxXL16lcjISD7%2B%2BGMOHDjAqlWrqn12aGioTYZfVYSGhkqziJr2Xfx9%2B/btzJ8/n7CwMOmKJ0LEi4qKOHbsGIcOHZLbUKvVODg44ODgIMm1cKgUjqAAWVlZgCVSQ8wXCkJkHXfh5uYmyb04XxEREVy%2BfBmNRoNKpSIlJYWOHTvKoHawKIItWrTgwoUL1QifmNGrU6eOtKIXro89evTg%2BPHjuLq68uGHHzJt2jQZiaDT6Z46nyYKcg8PD5RKJb169WLnzp3SYMJkMnH//n2pCooWs6ehR48eHDhwgBs3bhAaGsqNGzdo2rSpLHrDw8Pldvz9/Xnw4AFmsxlvb28ePHiA0Whk4sSJ3L59m3379qFUKtm9ezchISGsWbOGBQsWyDbEHj16cPDgQZu5P5VKRadOnVAoFBw5cgQnJye2bNnCpUuXMJvNpKWlsXbtWnQ6HSqVqprhTps2bRg5ciQTJ060%2Bbm1KYo4b2Bpi1Sr1eTn5%2BPm5iYJZH5%2BPpGRkaxZs4bw8HCp5nt4eODk5GTjPiucJQ0Gg1T6/1usWLFCqua%2Bvr6oVCp5r9YEV1dX3N3dAYtq%2BUcOvVWxa9cuXn75ZX788Ueef/55uVjl4eFBREQEKSkpUlEWCtrAgQPZtGkTYFH5FAqFjdIqEBERQXp6upyRFDOHbdu2RaFQ8OTJExo1asSOHTsoLS3Fzc2NoqIijEaj3K6416xnckVHQFZWls188ePHj/9y9MXfBevWduv8QbVazdGjR%2Bnfvz%2BOjo506dKFN954A1dXV8DSsrpx40bOnTsnTXns%2BGfCTvjssMMOO/4mNG/enOTkZEmCNm7cSFxcHHfu3KmxbccaSqUSZ2dnXFxcKC8vp6SkhJKSkmp2638EUZCr1WoqKiowmUx89NFHLFu2DLPZLAuuPn36oFAoiI%2BPByzFQ%2B3atXn48KH8rA4dOnD79m2bIrQm0wRrCFt5UYRs374dX19fJk%2BezP79%2B6lTpw579%2B4FLMSpc%2BfO3Lhxg8jISI4dO0bv3r05efIkUJ3wWWevTZ482eZz4%2BPjUSgU%2BPn5AZaCrbS0VBZxWVlZaLVa1Go1TZo0wdfXF4PBQP369fnxxx8pKCiQBDw4OJhhw4bRrVs3OXv4Z%2BjVqxcKhcJmZk7McgUEBMg2WKEMWBN%2BBwcHOSfm4ODA2rVrefnllxk4cCB79uwBLBEWY8eOJTExsUbCB3Dy5Mkaw%2BCfBnd3d1kUi/tVpVJRr149tFotV69eBZDOsvXq1ZPnMiMjQxrBtG/fHrC0TZrNZrp168bhw4el4YjJZJKB2taELzIykrNnz3Lo0CHeeecdm3MSFhZGamoqer0eX19faSxiDY1GI1v0Tp06ZTPzKe5R64xI%2BL1IFgTf2dmZrKwsHBwcKCkpYeDAgezatYuysjKcnZ0lwYA/DsB2cHCQxjBgafVMSUmhZcuWnD17VpqCvPjii0yZMoU2bdoQGRmJv78/d%2B7cYcaMGSQnJ5OQkFAjyapJLRU/c3V15b333iMoKIiRI0eiVqulA6tQ4nU6HXq9XhroWG/P2dkZR0dH8vPzcXJyIiwsjHfffZf333%2BfJUuWUKdOHT766CPi4%2BMJCQkhISGBJk2a0LhxY65cuSL3Rxi56PV6dDqdnFs2m83yu2dNpMX3NSsrCycnJ/ncsTaC6dy5M8eOHcPBwQEnJycKCwu5cuUK4eHh1K5dGwcHB8xms8zRa926NfHx8bRr145ffvkF%2BJ1c9u/fXwa4z5w502bfqy5sffjhhyQmJuLq6mqjMotZSh8fHwD596qvKSkpoby8XB6vtWuvo6MjOp0OtVpt09ZZFaJl1sPDg/LycmrVqkVQUBCXLl1CqVSydu1aBg0aRFlZGfHx8Tg4OMjczubNm1NeXs7NmzflYoqA0WgkNTVVfr/t%2BOfCPsNnhx122PE3QYQbOzs7o1AoGDFiBCNGjJC/z8vLo1evXpw5c%2BYPt7N//36WL1/OtWvXZEuZdZG2YMEC2rdvT8eOHTEYDPTt25cPPvgAHx8f0tPTee655/Dy8uLBgwcsWbKkWtvX4cOHbdq5lEqlzPMThdfixYtxcnKiadOmNvOB1qia42c2m6U6WVhYyO3bt5k6dSrnzp2jsrKSoUOHyvfWrl0brfKWNukAACAASURBVFYLWIq8V155hcePH8tZmoqKCvl3sDWvEe8TcHV1lWHZ4nj0er3NjJPBYCAoKEhGWVRUVFBWVobRaKRp06aEhIQwc%2BZMm7BqsEQFWLemVYV1O5awc4ffyfe6detsCCFAUVERL7/8slTkrl%2B/zsGDB6moqGDYsGE4ODgwdepUSfjmz59PdHT0U/cBLCTX19eXkSNHMnPmTBQKhc2MqNgnDw8PdDod3t7esr30008/pXPnzvj6%2BjJ37lx27NiBm5sbjRo14tq1a1I5%2BPXXX22UpqCgIHktevfuzZ49e0hLS8NkMnHs2DH5uqioKMBieCFQWVlJx44dpaLk6enJo0ePbNrvlEolgwcPZs6cOfJ9oaGhZGdnU1FRwYwZM8jNzUWr1VKrVi1at27NtWvXSEtL4/nnn%2Bfzzz/nww8/pLS0lMOHD9O3b1%2BuXbvGvXv3ePLkCZ6ennTs2BE3Nze2b98ujUIAG8OLqlCr1URGRvLVV1/RtWtXpk%2BfXi3A/eLFi/j5%2BZGcnEybNm0AS4vtxIkT6dSpE2BpVb5z5w6LFi2S87s13WsKhQKlUinVqsTERNLT0xkyZAhdu3Zl27Zt3Lt3T3YQiNZwse/WarH1MalUKnx9fbl9%2BzZgydo7deoUL730El27dmXixInk5ORIJdhsNhMTE4PBYJCEydXVFbPZLBcPKioq8PT0JCwsjFu3blFRUYGfnx8bNmygU6dOBAQEsHr1agYMGEBiYiKNGzeupqaJ/RMxI6WlpZSWlkqVXRhNKRQKTpw4QZs2bcjOzmbv3r0YjUab56tQjbdu3UpSUpK894SyKF6zefNm%2BbnNmjWjWbNmDB48WJokgcUoqWvXrnz00UfSQGn37t02r3n33XcB6NKli1RXrZVWoQgXFRXx4MEDGjduTFpaGi4uLvj7%2B3Pz5k3i4uIICwsjNDRUuqt26dKFH374AYPBgIODAw0bNpSf2bBhQ3l91Wo10dHRGAwGGY1TFWL%2Bz45/NuwKnx122GHH34QBAwYwYMAAnJ2dOXv2LGAhBOfPn6e4uBiDwUBZWRkajUYWL3q9Hi8vL%2BrUqUNZWRmdO3fm008/lcXH0qVLmThxoo26tmvXLkJDQ2nevDklJSUcOnQIDw8Ptm3bxpo1aygtLaWwsPCpapy1k%2BbTUJXMWSswVW3ShatoTbOEzs7OlJSUoNVqSUxMxMvLC4CrV68yadIkEhMTWbJkCYWFhWzdupXRo0fbzOKBxfAELLNOQUFBcubu7bfflk6O33//PU2aNKnxWD744AObQjo3N5dffvlFGt6Ul5fTsWNHVqxYUe29d%2B7ckX8XEQ9CpQSLi%2BBf%2BW9X2NwLpSwiIoKtW7diMBi4fft2tbw463Nf08%2BEavRH1/j69euYzWabOSmlUom7uztnz56VgeGBgYEydFpkya1Zs4YGDRpw7do1hg0bxpMnT6QSKBz/BIGNi4tj9uzZT22Ds472MBgMvPXWWyxcuJAuXbrQrVs3Pv74YwIDA7l//z5arVaqUF5eXpSUlEjy1ahRI3744QcGDRokTXlCQkJIT0%2BXDplC1Tt9%2BrR0TwRkC2njxo3x8vLi0aNHtG7dWt4X1iRBo9HIWa9Vq1bZXJvo6GgGDBjAoUOHGD58OMOHD0etVrN27Vpat24tP0uYyNSpU4dly5ZRv359wJI5J1Re6zlasOS6PXnyhMePH2M2m4mKiiIjI4PMzEzZpq1UKuV7lEolGo3G5nssjsfHx4fS0tL/w957hkV5tV3/v2GGoXdEEFExKlZAlGBHsfeG4m2J3URN1ERNvNWQWGOiMTEmNtQkaOwdCbHEGruiIIqKYhSsCIrSZoaZeT9w7J0ZSsx9/5//myfvMesLzjXtKnsu99rnOteS/XivQ2BgIAMHDuSTTz4pt4ezbdu2FBYWSvMXtVotSaXoL%2B7cuTPff/%2B9/L2KTMuNGzdSu3Zt6tevj6%2BvL1WqVOHcuXO4urqaGUeVRqVKlcrsf%2BksPtPHplW6oKAgea8t3acXHR0tFxoESssbDQYDH3/8MZ999pkkvI0bN%2Bbw4cOo1WqOHz/OsGHDCAoK4uzZs3Lx6NWrV6jVahITE%2BnWrRv5%2BfmMHTuWH374QcY%2B2NvbY21tTW5uLp6enrx69QobGxt5XXv37k1AQADz5s3D1dVV5h%2BKimDlypUZM2YMixYtkhmQgYGBHDhwoNxqsOgPV6lU1K9fnxkzZhASElLhebfgnwEL4bPAAgss%2BJvQrl07nj9/Ls0JxH%2B%2BrzPBEBASwO7du7NhwwYKCgrkZHnUqFEye02EoD98%2BBCNRiNzySq6/QuCp1AoZLWuvBgA09eHh4dL6dKOHTsICQkhLy%2BPmJgYmZGWn5/P/PnzWbZsmQyjdnR0xMbGRoYVu7m5sXz5cnbt2oWXlxfvv/8%2B%2Bfn5DBs2jLCwMGkcMHXqVGxtbVmwYAH//ve/y%2BzT3r17sbW1JT8/X8ZSGI0leYKffvopfn5%2BqNVqEhISaN%2B%2BPZmZmWzZsgV3d3ezHLvY2Fji4uLo1q2bWXi6aT7Yn0FIEgXS09MZPny4jLH4M8mrKcR1dXV15cWLF7i5uUlJlikKCgqwtbWVBE9sM40OOHr0KL6%2BvtJg4%2BbNm7x8%2BZKQkBAOHDhQRkrn5uaGtbW1tHRPSkqS50AY5fTr108uPIh%2Bs7Vr1zJhwgT5We7u7gwePJhVq1bRv39/fvzxRzlxNY0v%2BCswDY8WVWJnZ2d69Oghr8uHH37I6NGjyc/Pp2nTpmZ9egLjx4/n%2B%2B%2B/N5v8p6Wl0bdvX1JSUti1axcKhYKPP/6YLl26UL9%2Bfdzc3Pj444/l60WvqSANFcF08cPd3Z0zZ87QsWNHKeH9%2BOOPSU5OJj4%2BnmbNmlG9enW2bNkix26VKlV49OgRUFJVevHiBU%2BePJHEWcgwSxNDAS8vLxnxMnr0aNatW4enpyfPnj1jyZIl9OjRg3r16sljcnFx4eXLlygUCkaNGkV4eLhUIAQGBrJ9%2B3YyMjLkOFQqlTg5OdGpUyfmzZsnq%2BHFxcWyv7OoqAg/Pz9WrVolJdW3bt1i6tSpkvi1bduWFStWUL9%2B/dfKwk0RFhYmQ%2BDFMXz22WfMmjVLjq9Dhw7RsWNHACZNmgSUyJXnzZsHIOXlL168MOvTE/dlIfn85ZdfAEhJSSErK4upU6e%2B1p33v4XpmFWr1Wi1WikNFbJvsf1/As2aNePy5csUFxfj7e1NTk4Oy5cvp3Xr1v8jn2/B3wML4bPAAgss%2BBtx4sQJPv30Ux4%2BfIidnZ2UOVWtWpW5c%2BcSGhrK06dP8fb2lpO9FStWkJGRQXR0NI6Ojvj4%2BGAwGKhXr560%2BzZFaGiorCCWB1E9ENDr9YwbN47hw4dz8OBBtm3bhp2dHUlJSZK4QckEs06dOlhbW/Po0SPefvttDhw4II0/QkNDuXv3LhkZGTRq1IjExET5HQ0bNiQtLY34%2BHj8/Px49eoVTZs2xc7OjjFjxuDk5MTSpUvx8/PjyZMnuLq6smDBArKysli7di337t1j2rRp0hIfSmSSb775ppnl/LVr1zh37hzLli2je/fufPbZZ1y8eJExY8Ywf/58jh8/zm%2B//cbz589xcHAgNDSUc%2BfOMWvWLMLDw/niiy%2BIi4tj8%2BbN/PrrrxQWFtKqVStmzJhRxrq%2BPJQmfKUh%2BoFERSkwMBB7e3ueP38ue8Q%2B%2BugjlixZIntznj59ypw5c8wkrKVhNBo5ceIECoWC9957z4zQlA6Df/z4Me3ataNRo0ZkZGTg7e1Nt27dKCoqYseOHej1ep49eybNL5KTk6WZhSnBMe0TLCoqkr13YkzpdDp8fX157733iIuLIzk5mQMHDrBo0SLOnTvH8uXLGTRoEF5eXuTn51NUVCSNPMT4fPbsWRkCICbhCoWCLl26sG/fPqCk2h0aGsqdO3cYNWoUBoOBSZMmsXjxYhwdHVGpVHzwwQfSSRVKJHQnT57EYDAwZ84c%2BR3z5s2ja9eunDt3ju3bt9OxY0ezKrC4hqZyZSF37t69O0eOHMHR0VH2b9na2vLtt9/KyAS1Wi1dZU%2BfPs2hQ4fIyMjg1KlTKBQKNm3aRHBwsDTd8fHxoaioiCpVqnD58uVyF28EWbKzs0Oj0dChQwdOnz5NXl4eV69epVGjRjRt2pSLFy%2BiUql46623WL9%2BvSQYI0eOlDLfvn37snfvXrNz36tXLxYvXiydPJVKJW5ubmzfvh2NRsPOnTuJjY2luLhYxhEoFApsbW3RaDTMmzeP9evXS6JXrVo1CgoK5G%2BxRo0a2NnZ/UcOlK9DeQRSVO9Ez7JYVBFjylTuarqQAiWxJenp6Tg5OTF58mQ%2B/vhjWWETCobyrk3p/lwov/fy/wucnJzw9fXF09OTM2fO4OTkhFqt5unTp6hUKtq2bStNp6ZOncq3336LRqPBysqKWrVqkZGRgUajoW/fvmRkZLBhw4b/sX2z4P8%2BLITPAgsssOBvhsFgICgoiM8//5wPP/xQ9uCISaRGo2HmzJl8%2BeWX6PV6iouLadmypSQcImx43759zJ07lzlz5uDk5GSWmwblT3ZsbGzM%2BtmEjC48PJzjx49TXFxs9h4XFxd8fX2pVq0ap0%2BfLuMGKr6j9HeJKAc/Pz/u379vFgz87rvvAsgJszAGEWHOFblDlge1Ws17773HiBEjePLkCUePHuXUqVP4%2BvoSHR3N5cuX%2Beqrr2jRooXsk2nRogU5OTmcOnUKDw8PvvnmG1auXMnixYvp0aMH9evXx87ODmdnZwICAjh37hw6na4MsS4Pf5Xwmf5NTEwkKCiIrl27sm/fPlxdXWnbti0JCQkkJyfToEEDQkNDy60wZmRk8P3337Nnzx5Z7dPr9YwfP57bt29Tr149li9fLg1tNBoNgwcPJiUlhbFjx5KWlsbKlSvlxFar1TJ%2B/HjOnDkj5XjCwEGYWoiJqvjMRo0aodVqzQhf48aNOX/%2BPI6Ojpw4cYI2bdqg0WgkSYyOjsZgMJCens6VK1do3Lgxly9flmPJ3d0df39/Lly4QNOmTbl8%2BTINGzYkOzubjIyMMudBpVLRokULzpw5I8eUqaGKcMJ0cXExcww1lcAKIwzhsHrkyBGio6M5ceIEUVFRfPfdd7IHzjSeoDTUajVjxoxhw4YN8jWmvw9BZk2rhlFRUcyZM0dGZiQlJZGamsqgQYPQarV4eXlRr149jh8/Lt05BURmoOk%2BqdVqHBwcpCTS1BgESgioo6OjrDyXBxE7IGBjY0Pv3r1lL6MYa4GBgVy6dIng4GCmT58OlPSN3b59m6FDh5rdF2xtbenVqxejR4%2BmRo0a5Ofn891337Fr164y8s3JkycTFRWFSqXi2rVrrF69mvPnz0ulQ0FBgextBGQFUeDRo0dSXj18%2BHCePn1K5cqVzXp3Hzx4IN2FK1WqxNKlS9m4cSP379/ngw8%2BMItkgZKxHhISglKpJCUlxUwuevv2bT744APS0tKwtbVl6dKlMvohJSWFn376ia1bt5KWliYr5yJ7c/ny5bi5ufHpp5%2BSnZ3N559/zurVq7l//7509oUSh1eDwSAdh4ODg5kzZw7Vq1fH39%2Bfdu3aMXr0aFasWEHnzp1JS0vj1q1bKJVKBg4cyObNmwFITEwkNDRUVtnPnj1LeHg4BoOBOnXq8PDhw/9R4m3B/31YCJ8FFlhgwd%2BIAwcOcODAARISEnBxcfnTHpX/FqVty8UEXVhxb9my5bWfIXL%2BhCmAkId5e3tTqVIlLl%2B%2BXK6kKCgoiBs3bhAaGsrZs2c5fvw4LVu2lD1G06ZNY%2BzYsUDJ5MlgMEj7%2Bk2bNhEUFEROTo4kt76%2BvqjVaiIjI9mxYwdQEu4%2Bbdo0OnfuTOfOnZk/fz46nY7w8HB0Oh379%2B9n%2B/btNGzYkH79%2BpGamsqZM2fkyr4gLoKwDBs2jCtXrsiKS4MGDdDr9ZIQ7t%2B/n%2BnTp5Oamvra8/bfED7Tv4GBgVhZWREWFsaxY8fo378/e/fuxdHRUZ4TjUZDXFwc69atkz2EPj4%2BDBs2DH9/f8aPHy8XEYQ8cMeOHRQUFDB9%2BnTprFq7dm0WLlwoz4fAzZs36d27t7TOv3nzJrm5uYSFhQF/VD1ElUxU2Hr06CGNZBwcHGTP3OXLl2ncuDFubm5s2LABX19fHj9%2BzL/%2B9S9%2B/fVXQkJCJOnVarUsW7aMyZMns3TpUukI%2BeWXXzJmzBjWr19PSEgI9%2B7dkxVwAA8PD168eCEnsOIcXrhwgeLiYuzs7Pjtt99kNVigdevW7NixQwZ0i14nQR5M3WJbt25NQUEBeXl5sipUHsTiRVRUFD/99JPZdr1eLx0%2BTXt1Bw0axPfffy8n9kIyqtFopNwyMDCQkydPmn2XQqFg7NixrFmzxmy7WNgxNQ3SaDRmJK60w2/p6pObm5tZmDqUSFPFNvH6vn378vPPP8vfE5T8hhwcHMoYQqlUKkJCQhg4cKCZmQmYV78NBgMNGzaUEmIouaZ169bFzc2NpKQkioqKcHFxoWfPngwePBhXV1ezvszSv8XSbpum20yf69ChA99%2B%2By1169alPISHh5OVlUVcXByjR49Go9Ewfvx4srOziYmJkZJLnU4nz2fXrl05ePCgmVReLLJBySLA8uXLZbSHlZUVVlZWzJkzh08%2B%2BQQrKysOHTqEu7s7Q4YM4fr16zKqRZzj1zk2m15f02qui4sLI0eOZNOmTXh6enL37l1sbW0thO8fDotLpwUWWGDB34j69euTmZlJQkIClSpVMiN8pSdcwnlPo9Hg6uqKra0tderUkb1z8fHxQEkFo7CwUP6Hb0r2RJ8PlPS51axZ87V9Mo0aNeL27dtysjl79mz69OnDqFGjePz4MWFhYcTGxrJ69Wq2bNnCw4cPsbKyon379iQnJ%2BPm5sbz58/R6/W0b98eQJLDLl26AHDhwgVpsW5tbY1er2fRokVs3rwZd3d3unbtarZPSqVSTsBWrVpFcXExixcvlsf122%2B/MXv2bOzt7YmPj2fLli3Mnz%2Bfn376iZCQEDZu3Cgri8IwQ%2BDatWtm7puCNAj5k5g4mzr1QUlVpnQEBJSNhQD48ssvKzzfphBjYPXq1QQGBhIUFMSuXbt48eIFycnJrF69WlZihUxRpVKxYcMG/Pz8GDJkCADTpk1jzJgxfPfdd3zzzTeMHDnSrNpkZWXFvXv3ZCVJHA%2BUTJSNRiMREREcOnSIrVu3SiInyLnRaJRxF4LIxMfHy/MjsvyqVq1KcnIyVatWxd/fn2%2B%2B%2BYbPP/8cb29vnj9/TmJiIj4%2BPmzdulWOh/DwcKysrOR5jImJobi4WJrmmMYsiO/Lzs42O48Gg4GzZ89K0qJWq8uQPShxQxRkT5z/Ll26yMm/qPalpaWRn5/P4MGD%2BeGHH5g6dSpz584FMMtShD/Gz7Fjx2TPnOn2vLw8oGRciT6wtWvXmu1Xaflsbm5uGbIHJb/9xo0bm20TeXWlzwdgVrErTQ5Mj8FoNJYhe4DZNvH63bt3l3mduA85OTnRpk0bfv31VwC2b9/O7Nmz%2BfDDD8sQPoPBwPz58%2BW%2Bm5I9IQ1NTU1FqVRSq1YtnJ2dOX/%2BPD/88IOsftevX581a9bIe%2BRfhemxZ2dnS9lqeRgxYgSLFi2ib9%2B%2BvPnmm5w8eZIFCxaYvab0YpipkZOAIHtQMhaEAgH%2BGCszZsyQ20o7apa%2Bxq%2BL5zE9RlOH3ry8PJYtW4adnR2LFy9m7NixFRpcWfDPgYXwWWCBBRb8jfDz82P06NHSGEEYrQDy8e3bt1m4cCFHjx4FYP369TRu3FhO7Nq1aweUhORCCcEwGo0sXboUg8HAkSNH5Epx6UqfqaskUCb8G%2BC9995jypQpcpLYs2dP7Ozs%2BPDDD5kyZQr79%2B/n1atXXLx4UebQGQwGfv31V2rWrEl6erqUzTk6OkqHOoCPPvqIsLAw4uPjJaE9fvw4DRs2lBW21%2BHSpUtmBC01NRWj0SiNSipXriwnU3Z2dvj4%2BLB7927effdd6Uzp5OQk36/X63F3dzc7T4J0mW4zdekUFZzSERBKpdJsm3B3FORFp9MxdepUNBoNkZGRaDQaIiIi0Ol0TJ8%2BXVYFhg8fjlarNYscGDRokLRVHz16NKGhoTRu3Njs2on%2BKGG2MXz4cL755hsz%2BaHRaJQZgmLiJ47HFCkpKfI8PH78mEqVKmFtbS3z6apUqSIn8o0aNaJq1aqS3Li4uPDkyRN69OjBvHnzaN%2B%2BPceOHSM7O5uoqCgiIiKwt7dn8uTJ5Obm8umnn0qp47Bhw8wWJMojHm5ubtjZ2cn9iI2NZd%2B%2BfSxbtgyVSkV8fDyVK1fGzs6OgICACh1nPT09%2Bf3336lRo4bZ%2BYGSymWtWrWAkv7APn36UKtWLRwdHWWY%2B5/hwYMHFT4nTDfGjRsnt61Zs4YuXbpw9erVMvEff9bv9cknn5g9Lu91f0YGTN10hby6vLgXKDFKqVKlCiqViry8PBISEmRV8sSJE/j4%2BHDnzh25TalUMmnSJA4ePIher6dfv34EBwfLca3Vavnll1%2BIiYlBq9Wa9Y2JjLmCggKMRiMFBQUyGiYtLc3M9Efs5/Xr12nbtq1cDPqrGD9%2BvPy36Kk1vSeYolu3bqxYsYKVK1cSHR2Nvb09RUVFZvmOtWvX5saNG9SqVYtHjx6Rn59vFoYuFBTCnVaQ%2B//EjEXI5k0r%2BVByT9fpdNSpU4c7d%2B7IiBtXV1euX78uDboEKffx8aFevXo0aNCA77//Hp1OJw1uLPjnwiLptMACCyz4X4Lnz5%2BTlpZGTk4Ot2/f5t69ezx8%2BJDExEQCAwNJSkqSlT6FQoFer8fa2pqjR4%2Bi1%2Bu5ffs2a9euJSkpiR07dkhrd0CuULdp04YLFy7IvjhHR0c0Go3ZZLW0BLRevXrcvHlTTmBu3LhBTEwMy5Yt%2B8sh7wKlJ4ymsqMqVaqQk5PDwYMH6d27N8%2BfP69QDmkquQoODsZoNJo9dnZ2ltlun3/%2BOT/88ANXr15FpVKxbNkyVq5cyfTp09m5cyf37t3D29ubAwcOoNFoaNWqFW3atGHZsmVcvnxZBpuLSendu3dlsPnrUNowpzxHUUC6/onrIqzRS5s%2BODs7Y2VlJU0lKlWqRGhoKMHBwbz11luS8MXFxeHn52dmoCJQWrIJJWSmZ8%2BerFy5slyCMH78eOLi4ujduzc7d%2B6kX79%2BrF69WrqZ1q9fn7p166LVavHz85MEW1Rmr1%2B/zt27d7l8%2BTITJkwgKSkJR0dHcnJycHBwKCOHVCgU7N27l/79%2BwMlBMV07ISEhJCWlsaWLVuYMGECv//%2Bu6yeeXp68vz58zKh5F5eXpw8eZKAgABsbW3LyPmgZKzcuXOHFStWyPw8%2BMPO/4033uDatWt4eXmhVCp59OiRjDsQ/bXwh1zTtHouiL9KpZLOqeKvjY0NeXl5Zn2hAQEBMqakffv2HDp0CCsrK1q2bCkzDsurzIs4DPHc66R9derUwWg08vTpU3Jzc3nnnXdYv349s2fP5tNPP2XSpEksW7YMGxsbec4GDRrE5cuX6d27N71795bZiUFBQfj6%2BsqsPlOISrLpvjg6OkpJcGFhobzOzs7OZcZE6dDyP4NCocDLy4thw4axZMkSbG1tKSoqYt68eXJMzJs3j%2BjoaLMxYhqynpaWxrNnz9i5cydVqlSRmXmxsbFERERQtWpVwHyBzbS6ajAYKCoqwtbWlp07d9K9e3cCAgJIS0uTvakirkKr1cp9rF27tlyoMZUmW1lZYWdnR40aNWRlVpA5UVkX51f8/2Bvb8/Bgwdp1aoVnp6eKJVK%2BvTpI6Mvbt%2B%2BTV5enuzpVCqVsl85NzcXnU5Hhw4d%2BOqrr/7Sebfgfy8shM8CCyyw4G9GVlYWGzduZN26df%2BRtbdpVhogHflatmzJd999Z/ZaQfh8fX2BstUG0YtTXt5XaZLWrVs3fv75ZzMTDNETVBojRoyQ0kshc8rLy8NoNMpJsfjuSpUqkZ2dTbdu3Xj06BGJiYncuHGj3GMv3WPz5MkTrl69itFoJDg4mPbt27N06VKgRC46YsQIvLy8iIyMxMXFhUWLFslJTpUqVVAqlRQVFeHh4cGdO3dwcXGhevXqXLhwAWtra06dOoWjoyO3b9/ms88%2Bw9XVtVypJpQQQisrK5o3b262PTY2lvbt28trUBG%2B/fZb4uLi0Ov1dOnShb1792JtbV3GDCc/P5%2B8vDz5uEmTJqSkpGBlZfWnhM80lwxKpGClc/zKg7jetra2FBYWMnToUGk08u9//5sLFy7w8OFDaZhy%2BvRp2den0%2Bk4duwYTk5O9O/fn2fPnhEfH09%2Bfn6Z6pHo73RxcSE3N1dWOURMgGm%2BY%2B3atcnOzpZjtzQ5NjUVUqlU1KhRg9u3b6NUKuW%2BATRt2pSoqKgyIffid9SyZUtOnDghXVKtra1lNVylUmFnZ0ezZs0kcX/rrbeIjY3lrbfeYuPGjYC5KcuCBQvo06cPe/bskb1uQUFB9O7dW0pDAwIC%2BOKLL/joo4/Mjku4bpper08%2B%2BYQ5c%2BYwdOhQNm/eLPMFRU%2BsWCQQFaWIiAiOHj2KQqGQeXMGg4G5c%2Bfi6Ohodt6EJBtKSJder6eoqIiCggJZVVIoFNjY2GA0GqXZ0OrVq3n69CmJiYmy39bZ2RkPDw/u3r0LlCzOCAjjkYiICP7973/TuXNnvL29pWrA0dGRgIAAaebTpUsXDh8%2BTN26daXp0Pr169mwYQPe3t506NCBbt26ER8fj4ODA3l5edjZ2ZUZ16Urd0eOHCEtLY2oqCgGDx5M//79iYyMpE2bNgwZMoTY2FguXbrEzJkz2b17N1euXKFdu3YcO3asjImVIK8tWrQoV8ZpZ2eHnZ0dOTk5ZpVUAWtra5ydnaVE2d3dnYKCAlkBbNKkiVkURenfgJBRl0fATSEqyKKyO3v2bOlSe%2BnSpTK5gxb882AhfBZYYIEFfxNevHjBawBUgwAAIABJREFUv//9b44dO4bRaMTZ2Zm8vLxy88j8/f0lkViyZAnbtm1DpVIxceJEjh49SmpqKvfv3ycvL4%2BwsDCznjT4w0hj0qRJTJw4UU76BUm7ceMGQUFBhISE8OjRI4qLi83cD2vUqEF2draZFNDa2lpKgUwnnwMGDGD79u1SqtS4cWP8/PxITU3l3r17MihbEMvZs2ezYMECRo0axcmTJ8nMzJTxD2Il25RcJSYm8ujRI2mkkZubS35%2BPl5eXqjVap49e0b//v2Jjo6WGX4hISE4Ojpy6NAhKWOtXbs2nTp1YvTo0RgMBmJiYkhISODhw4dSyubr68vcuXN58803gT%2BIc2l7dgFxHsaOHcu0adPMnps%2BfTrnzp3jiy%2B%2BwGg0mhHCwsJCRo0aRXp6uqxsWFtb4%2BHhQfXq1Xn69Cn379%2BnWrVq3L9/n6ZNmxIbGwuUSFjXrFnD4cOHJVHv2LEj7777LlFRURUSPtGT%2BVewadMmvL29mTt3Lunp6VhZWdG5c2dZ9RCIjo6WxiiNGjWSpBtKCGq7du2oVKmSnIA6OTlJGVlmZqbZQoAgdWISKwLcRW5feQS1tPtkRSgtkfTx8ZFy1Pz8fGJiYjh06BC3b98G/qik%2BPj48OjRI5RKJe7u7uj1etkvW6lSJX7//XezfTZdjDGNcbC1tTWrSmdkZMi8SrEtICCAHj16EB8fT0REhNy/0lAoFLz77rssX74cpVIpA8rFtW3bti0vX740IwbOzs68evVKEmyj0fin40FcE0HGTaFSqejQoQMpKSlkZmbK7cOHD8fW1pbNmzeTl5fH4MGDJXEQ8mgxLi9evEhycjJ79uwhLS1N7seHH37IsmXLKCoqMiOeAHPnzmXhwoXY2NiQm5tLUlISYWFhkjg2aNCA3r17s2fPHnm9S/cJAmYyaYEpU6bg7u4uyXBqairz5s0jMTHRLKohLCwMpVJJUlISdnZ2%2BPr6yoWnis7l6yquYE7cTImgKTEzGo1lJJ9inP5PoXPnznzzzTf/Y59nwd8HC%2BGzwAILLPibMHnyZIxGI1OmTKF///7SvbCgoKDMKn58fDw9evQASiR5c%2BbM4f3338fZ2Zlnz57x%2BPHjMrlOFcE0OkGtVktbb5HxBSUTdzHZAaST4OtgZWXFwYMH6dGjB1qttsykvHLlykyePJl69eoxevToMv1YIog7ODiYpKQk5syZg9FolEYQ/v7%2BXLx40ew9BoOBzMxMlEolLVq0oGnTpqxcuRJfX1%2BysrLw9PQkOjqaI0eOcPr0aR4/flxG8pqZmcnJkydRKpWEh4ebGXeYVhMvXLiAi4sLKpUKo9FInz59WLt2rTSF%2BOyzz3BxcWHJkiXlnp/Jkydz%2BPBhRo4caUYIFy1axI4dO2SIuEKhwM/Pj8ePH2MwGCgsLMTJyYn8/Hy6dOlCjRo1CAoKMiONWq2WGTNmcPXqVRnmbXpdTM9XaTg4ODB58uRy97levXpUqVKFqlWr0qJFCxQKBcuWLePtt9%2BWVQ9/f3/0ej0///wzX3zxBXq9nl69etG4cWM6deqEp6cnmZmZdO7cmU8%2B%2BYR58%2Bah1WqJiopi69atREVFsXv3bnQ6HYcPH%2BbHH3/k2rVrJCYmUq9ePVJTU%2BXjoUOHUq1aNfz8/Fi2bBmtWrWiqKgIKysrnJ2dKSwslHlic%2BfOZcCAAWbSzNeh9Fjo1KkTSUlJ1K1bt0wltLRZjNFoxNbWVsrqfH19adSoEWlpady4cQOFQiFlfEajkSFDhqBQKPjxxx/lb7Jbt27o9XoSEhJk9VvIBx88eECbNm24deuWdCU1Jb9CXiqqtmK/hHz0r2a91alTh99//50pU6bw4sULzp8/z/Xr16lSpQq5ubk0a9aM8PBwHj58yK5duygsLJS/5fr163Pr1i2Ki4vLVJOFrFXEKYgKvnDChZIqVmRkpFnPLJQQvFq1ajF//nwZDWJ6/ImJiTRr1owaNWqgVqtJS0vj4sWLNG3aVF4nQQYrwtOnT/Hy8irj1iqQk5NDRkYGOTk5fPrppxw/flxKyg8dOoSXl5e8Pz19%2BpSpU6fKa%2BPu7o69vT0%2BPj44ODhgMBik7FyY0KhUKt544w3mz5/PBx98IKWd/ylEduXTp0/lNkE0bWxsMBgM6HQ62WPcpk0btFotAwcOxMXFBRsbG3x8fPDw8Pivvt%2BC/32wED4LLLDAgr8JzZs358CBAzg7O9OxY0dWrVrFO%2B%2B8Q2ZmJu7u7hiNRinl%2Beqrr/jggw%2BkfNLX15d79%2B5hY2Mj3esaN25MjRo1KCwspHHjxmZVjJ07d8oqQ2RkJOnp6VSrVo24uDiKi4upUaMGz58/l5bef2YzL1DR5DE8PJyzZ89iZWXFhg0b5PdWq1ZNRiEIBAQE0KlTJw4ePIiNjQ0qlQp7e3vUarU04DD9voqqHIGBgYwaNUoGVotKUWFhodl%2B2tjYoFQqiY2NlZlXFy5cYNy4cXh5eaHX63n%2B/Dk//PCDfF6EopeH0kSioomiwDvvvMO5c%2BfMHAcB2rdvz5o1axgwYACtWrVi/PjxvPXWW2i1WvR6PSqVivbt2zNz5kxycnKk3KyiKuJXX31FXFwcjx8/xtPTs9x9ERlvDg4OzJw5k/Xr1xMXFyeliOKvOM6kpCQCAgKws7PjypUrFVY9oGQSLuILcnJyGDRoENu2baOwsJCqVauSkZGBUqmkWbNmnDp1yqxv1N/fnxEjRrB06VJevnxJQEAA6enpXL16lU6dOnHv3j0cHR2ZMWMGPj4%2BjB8/Hq1Wi7W1NW3atOHo0aMy/No0DF5cJ2FtXx7KGwtarZarV6/SsGFD1q9fz%2BjRo1m%2BfDlz586lqKiItm3b4uPjg8FgYNWqVbi4uMjKd35%2BPg4ODuh0OiIiImjRogVr164lMzNTTrj1ej2tWrXC3d2d%2BPh4OnbsyJkzZ8zkrn379pX7KHrMvL29JWGAkvvJ2bNn6du3L7t27ZJGIOVVfEaPHs26devMtimVSjw8PMjNzWXbtm0MGDCAmjVrsnfvXqCkGjdw4ECgRNp5%2BPBh8vLyaNKkifwM04UFIR81JchhYWE8ffqUu3fvolAoZOX8/Pnz3LhxA4PBwKhRowgJCSEpKYn33nuPVatWcfLkSVl1F1LVvwIxLn19fbGxseHNN9/k8OHDFBYW0q1bN%2BbOnSv3eevWrSxZsoQLFy4QHBz8WnIoXtOhQwcMBgP79u0zc37Ny8ujV69eMsvxz5738PDg119/NTN40mg08vx4eXmV%2BX6j0Yheryc7OxsXFxeaNGmC0Whk6NChXLx4kcTERE6ePIm1tTUTJkxg1apVFBUVSZm6qZy2dMXczs5OLljs37//L51rC/53w0L4LLDAAgv%2BJoSHh7Nt2zYqV67MDz/8wFdffcWECRP46quv5GSpolu06CEKCAiQOXotW7Zk27ZtfPTRRzRv3lw6c0LJqrvBYMDNzY0zZ84QEREBwMOHD6X0zNHRUUo2BdHKzs5Gq9UyefJkvv32W9q1a8fBgwdRq9XSRXPp0qV8//33UlpkOnlo3Lgxn376qezju3z5MuvXr6d///60bdtWZmMJQpGXl0d8fDw7duwgNTXVzMRCmCg0b96cBw8eoFarqVSpErGxsXz55Zc0adKE4uJioqKiZH9WTk4Oo0aNIjQ0lIkTJ%2BLq6sq6des4efKktG4fOHAgXbt2ZeTIkQBlni8vr0tAVEUFIXzdRLF169a8fPmywvyvwMBAlEoliYmJNGrUiFWrVvH222%2BzZs0aaYwxZcoUTpw4UWbCLhATE4PRaGTcuHFUrlzZjDSbwtS8JTk5mcDAQPr06cOOHTu4fv262XEL0hsQEICPjw8//PCDdLLMyckhLi6Or7/%2BmgkTJvDjjz%2BiVqs5cuSIzPyLjo4mMjKSffv2kZSURIMGDTAYDKSmphIYGCjHvSkcHByYMWMGAwcOpGHDhjRp0uQ/zgLr1asX8EdkSffu3dm/fz89evSQfXumGDp0KO3btzcbC19%2B%2BaXZ%2BRBGPKbST7HIIMw3NBoN3t7eREZGsnv3bl68eMHp06exsbEhNTWVqKgoNBqNrJx36dKFBQsW0Lx5c0kKnj9/TkREBCdPnpS/tSdPntCuXTsz2bf4vQmDl%2B3bt0uzmzp16nDr1i3atGljRg5Lv1fAxcVFZgIWFRXRpEkTevbsibu7Ox999JHsHTMajfTq1YvFixfTsGFDKXddv349RqORbt26MXLkSDp27MiePXvYsWMHkZGRuLq6EhMTI7979OjR8jyLqt3FixeZP38%2Be/bskfs1efJkMjMzuXbtGgqFgjp16uDm5kZmZqaUAosx07NnT2rWrElWVhaxsbEolUpsbGzw9/cnLy%2BPsWPH8tNPP3H16lXeeustBg8ezKxZs7h16xbvv/8%2BgwYNokOHDqxdu9bMrdUU169f59133%2BXIkSMcO3aMmJgYbGxsGD16NPv37ychIaGM9BX%2BiNcpT7YPJRVQoe4wjeFQKpXY2tri7u5OlSpVKCoqIjc3l7y8PAoLCykqKpJjqaLPNkV50lxTmMpI165dS%2BvWrV/7mRb874YllsECCyyw4G9CeHg47777LhMnTqRr167UqVNH9rtt3ryZ5ORkudJaUFCAo6Mjrq6u3L17VxoBmPZQNW/enK1btzJr1iw%2B%2BOADPD09pbxI/OdtZ2fH9OnTadKkCQcOHJATJYPBQH5%2BvnwsyJuQxq1YsQKdTsehQ4eAEtfES5cucf/%2BfTZu3EjdunWxsrLC3d2dM2fOyMnEtWvX6N27t6zErFy5Eq1Wy7FjxxgxYoTcd4PBwEcffcQvv/wiHSp1Op00bLh79y4TJ06kT58%2BvHz5kg8//JBp06YxbNgwjhw5QlFREWfPnsXa2ppz586RlZXFiBEjcHd358GDB2zdulWGWv/rX/8yC6ZOSkoys38v/fzrYNqbVZ6tvylyc3MrrLiJzxLSO6VSSatWrSguLpZkD0pMFPLz8xk0aNCf7teRI0dQKpVm8jdTCOt5ES8hnDHFWDFdbDC11VcqlSxYsICVK1eiUqnkNZ8yZQq7du3CYDDQuXNn%2Bb4BAwYwf/58adQBmFnPKxQK3nnnHVauXIleryckJISLFy%2BSn59PdHQ0S5cuxdbWVlaFqlSpYtanJyrRtra2tGzZ0qwKHBcXZ3YM8fHxGAwG9u/fz/nz5%2BVE9u7du%2BTk5JCamsr8%2BfNlddk03kOcD%2BG6%2Bvz5c9zc3KhWrRqvXr2SAfbdunVj0qRJssdUEBwx/urWrWsmwVSpVOTk5BAaGiqldrVr12bChAkcPXoUrVZL27Zt0ev1UqInpKHFxcWStEVHR0uCLCB66lavXk1QUJBZ%2BDeURD%2BMHTtWbhMVficnJ4qKinj48CExMTE8evSIIUOGsH37dgwGA1qtlgMHDmBra4tOp0OlUnHgwAF5jCqViqNHj7Jx40Z5joVZkai%2BKxQKHB0dZf/yqlWraNiwIWFhYWRkZJj17Zou/BiNRl69eiXNn0yPR6PRyH7mxMREKW0NCQnh999/Z82aNbzxxhv06tWL9957j40bN7J161YiIiL4%2BuuvpYSxY8eOLFy4ULq1miI/P5/Zs2fLMf7uu%2B/KKuqpU6f4M4iqXEXQarXlxjDo9Xry8/PJz88nIyOj3B5W01B7GxsbiouLUavVFBUVlVk4/DOyV6VKFV69eoWPjw89evRgzZo1FsL3/wAshM8CCyyw4G/C7NmzWbp0KdOnTy/TH%2Bfs7Mxbb73FpEmTZB6ZQOPGjWUAr2n1ZuLEiURGRpKRkcGDBw9YuXIlT58%2BNfvP/sGDBxXmgZlORKZOnUpBQQErVqzg2bNnODg4SPKn1%2BsxGAyMGTMGLy8vPvroI6AkB1CpVNKuXTt%2B/vlnrK2tUalUaLVaHj9%2BzKeffgr80UMmSFW9evUwGAzs3bsXhUJBvXr1pFmLCGoX79m6dSubN28GYOHChQwbNownT57I3qclS5YwdepUvvvuO0kotVqtnIgCMifLFK97/q/idRNFg8FAs2bNKnz/X5GqvXr1qsJYASghOdOmTUOtVtO0adMKP2f8%2BPGkpaXx3XffcebMGeAPUhMbG4teryczM1P2j0HJRN7a2pqTJ08SFBREpUqVsLGx4f79%2Bxw/fhyj0YiDgwMPHjxg6tSpZgHzqamp6PV6tm3bJifIQo4p/kJJlcbd3Z0XL16g0%2Bmku6TocapUqRJVq1aVvW9169bFaDRSVFRURvJbmrRev35dGu88ffqU9u3blyENXbp0QaFQkJqays2bNyWZM81mu3DhAiNHjpTHIXI0N27cSL9%2B/WTlCZC9eab7oVQq0el0fPHFF7zzzjtmLoh5eXkyp030hpr264nrMG/ePGbMmCH34dWrV1hbW3Pw4EHCw8OBP3I3w8PDyzjvOjs7M27cOKkmsLKyYvz48Xz77be8ePECa2trsrKyCA8P5%2BnTp9y4cQOdTicNdbRaLXfu3MHGxka6dJqOkwMHDnDp0iX27t2LwWAwq%2BCKe42pqc9XX32FlZUVKSkpGAwGM3mjkDYKeWlYWJh87ueff8ZoNBIdHc2qVatISUkxu5c5OTmxdu1agoODeeONN%2BT2mTNncvDgQVavXm22oAIwYcIEBg0aRMeOHYmMjMTf3x%2BDwUBaWhrbt2/Hw8NDKijWr19PamoqAIsXL5ZjacKECaxcuZKePXuSlZXFyZMnzQyIiouLmTRpEteuXePIkSNS/ipeo1Qq6dixI0ajkcOHD6PX62X/p06nw8nJiUWLFmE0GpkxY4aszOp0OhwcHCgoKOD8%2BfMYjUYCAwNlb7Aw6oqLi5MmNvv376dPnz4YDAb69euHs7MzJ0%2BeJD8/n7S0NCz458NC%2BCywwAIL/iaITLbSWXgGg4EXL16wZs0aSYpEhUUQrunTp6NQKDh%2B/LicVPv7%2B7Nx40Y%2B/vhjNBqNrDgYjUasra0ZOXKkmcsdQHp6OomJiWY9bnXq1OH06dOkpKRIIvrOO%2B/wxRdf4OTkRF5eHkqlkrp16/L48WOePXvGxIkT0el0bN68WebThYWFsW7dOgICAggICJAE7MSJExw6dEger1KpxMXFhVevXsmcPJ1OR%2B/evdm7d6%2BUiG3YsAFPT0/69evHpk2bsLW1BUocDlu2bCldCGfNmvX/W99J6SgGUTExDVK/dOlShRNFR0dHHj9%2BLO38BfR6PRs2bECr1aJQKGTo%2BvTp0wHk36ZNm2Jra1uuE2VaWhozZszg5s2b0s1VVCHKQ1paGgMGDECn08nKhBhnV69epbi4mEGDBrFz5075HoVCwc6dO4mKiuLevXuSiEDJ2KlRowYBAQFlyC6UVIW9vLzMAuvFWDHNNCydb%2Bjg4ICtrS0vX75Ep9ORkpIiCdPChQsZN24cq1ev5u233yYhIQEHBwfatGlTxkFUQK1WS%2BJgNBrp0aMHPXr0YMKECTIqQWDx4sXEx8ezfPly5s%2BfL2XF3333HTqdTlboiouL%2Bf777zEajVKmKKDT6czIkHDMBWjXrh1qtZpLly4xe/Zs5s%2BfT8uWLWnatCkbN27k3r17qNVqhg8fLsnBkCFD0Gq1LF%2B%2B3MykZePGjWg0GhYtWlTmmE3NO8Rx5%2Bbm0rRpUy5evCivV3BwsNzXDh06cPjwYdmDePHiRTNFACAjEqBk4UbANCqkSZMm7Nmzh4ULF7Jnzx5OnjxJgwYNGDlyZLmumadPn6ZatWrlumf%2B/PPPgLmzZnx8PHq9nq%2B%2B%2Bors7GxpSqJQKFi1ahWtW7c2q8ILMyoRf1Ga7EEJSdy2bRsxMTH8/PPPZGRkoFAoqF69OoMHD2b06NEy4uHNN9%2BUhFT0LEOJS%2Bnu3buZNWuWfJ2I9fDy8uLJkycMHz4cgD59%2BqDRaHj27Bne3t48efIEDw8PSSA7dOhAVlaWlHw%2Be/YMo9FI27Zt5T6Le/K1a9ekHLdTp05ygaFu3brSBEapVDJ06FD5XgcHB%2Bkc%2BtNPP3H06FFWrlxpVlm14J8NSw%2BfBRZYYMHfBEH4Ll68SHZ2tlx51ev1ZSQ4ppI6IQtSKBT07du3zMRo6tSpFBUVMW7cOGmWMn/%2BfLPwYIGIiAjZx/efQKFQULt2bdLT03F0dOTs2bPk5%2BfToUMHSSptbGxITk4uY2wiKjJQQmBjY2MJDQ2VoeEREREsXbrUrIdM9PpBSYVTGIWIz5s9ezZLlizhypUrGI1G6tatK1/fsGHDcgOWxbbo6Gh69eplVg0zfX7u3LlER0cTFRVVbnD63r176d27t3xcXFyMr6%2Bv7O0SE8VOnToxcOBARo4cKXPeBCH85JNPKCgokJNFU8MdYVYBJUYdubm5FBQUyCD5vLw8lixZIqMwnJ2d6d69u5xoVoQpU6Zw7do1Hjx4IHvUABnjUL9%2BfapXr06TJk2Ii4uT4ePx8fHs27eP9PR06tatS8%2BePaWTZUVo2LChPNcCc%2BfOZeLEiaSlpZGSkkJGRoasUFSqVIng4GDc3NzYv3%2B/lKCVVwEV8jZ7e3sKCwuxt7eX47M8JCcnm8lcAwICOHHiBJUrV65wrNjb2zNs2DBiYmIIDQ0lOTmZ6dOny%2Br7pk2bSE5ORqVSMX/%2BfPney5cvs2vXLoqLi2Vvnmm0SWmo1Wp0Oh329vZYWVnJfEhbW1ucnJyIjIxk5cqVGAwG6tWrZ3bOK4qqgNf3bJWHmzdvkpeXh6Ojo%2Bz3nDNnDpcuXZKGP/369Sv3vXv27DEjC0FBQWzcuJHff/%2BdDz/8EFtbW5njJ4yoIiIimD59OkOHDmXo0KFygaiwsJDnz5/z8ccf89tvvwEQEhJCSEgIR44ckTErgLwnxsfH4%2B7ubtZrKu4nYpxv3ryZfv36VZj1%2BWfOvRVB9PPl5%2Bfj4uJCaGgoZ8%2BepaioiJycHPLz82U/rK2tLR4eHtjb2xMcHMyvv/7Ko0ePsLOzo1WrVmRmZsqomaysLLRaLRkZGbKCByVjV8i29Xo9WVlZkrj92XgoDdH/J%2BJyFi5cyCeffILBYKBJkyYV9gtb8M%2BBhfBZYIEFFvwvRnmGIXfv3qVLly5YWVkRExND9erV0ev1pKen88MPP3D16lU2btxIjRo1sLa2Rq1WEx8fT3R0dBkb/du3b8scOltbWz755BMSExMB2L59O1CySp%2BUlCQnEj179uTAgQM0b96cU6dOodfr%2Beijjxg2bBjBwcHodDrpEJiSklLG2GTevHls3LgRhUJBzZo1%2Bfzzz2nUqJEkfHq9nqtXr5q5Y9atW5dffvmFGjVq0KVLF7799ltq1arFjRs36N27N8uXL2fRokUcOXKEu3fv0rVrVzmREwY1FeHBgwcolUq8vb3Lff7hw4f4%2BvpW6BBa%2Bhq9bqJY2uzDlBCOHj2aZs2amX1e6c9/9eoVoaGh%2BPj40KBBA06ePIlOp6Ny5coUFBTg5eXF5s2bzRwBy0Pr1q0xGAxkZ2fj6elJVlaWfK68yWKlSpV49uwZarXazPZfmEx069atwu/69ddfKSwsNAudf/78OYWFhZJcKRQKhgwZQkREBCkpKezevZsHDx5QvXp1iouLZQ9fo0aNKCoq4vfffzeTKbq6uqJWqyvMSPT19aVFixZASd%2BVgOliQkVj5eHDh9ja2mJlZYWbm5usxIwfP54%2BffrIz4ES2bFwQAwKCsLDw6NMz6HYRyEnVSgU2NnZmckYFQqFlEvXqFGDuXPnkpqa%2Bh8vzvwnMM2Iq169OlDy%2B9%2B7d69ZRmDpRZzSle%2BEhAS6du0KQFZWFufOnZPHJMaMcNCtXr26lL8qFAratGkjZaXTp0/n7Nmz6PV6mcP3V49/wIABPH36VMqrBZkXUS%2BiyiYeC0RFRb3WubciCAOb/1egUqnYtGlTmXgNC/55sBA%2BCyywwIK/CWK1Gv6Qed28eZNnz57J3puCggJplAElVTNnZ%2BcK%2B/BKw8rKitTUVLkSr1aruXLliqyOicmmSqXC2dmZuLg43N3dgT8msLVq1WLq1KlMmDABhUJBaGgo165dY9y4cSxbtgwADw8PIiIi2LJli3QphJLeEOEYOG/ePHr16sWoUaM4d%2B4cgYGBpKWl8erVK5ydncnPz8fa2hqDwcDVq1dZv349o0aNAkpcRlu1asWKFStYsWIFx44do3v37uzevZvbt2/j7e2Nv78/7dq1Y%2BfOndy6davciRwgjWAEunTpwi%2B//FLm3FXkbmnqFgpI18bY2Fg8PT2ZNWvWn04UX0cI69evLyfLYD55Fvj5559xc3MjJydHSnZr1qwpSaOQm/0ZBCkREQXlVW5MHzdp0oTVq1dTXFxMQEAABoOBU6dOodFoUKlU1K5dm8qVK8vxUx5Mq9HDhw8nMjKS7du3M2DAAPR6PXFxcZw9e5aaNWsSGRmJn58fs2fPlvEkV69eJSsri4SEBBwdHcnPz2fTpk1kZmbK6oQp4TPtf61fvz6xsbHcuXPHrJfLdDGhPAhHRp1OJyM3xCKGQqEwq0IDZv2V5UV6BAUFmckjmzRpYhaKLvD8%2BXN27NjB5s2byc7Oxmg04u/vL6ttpXt7CwsLSU9PJz09Xfag%2Bvn50bFjR1q1aoWtrS0Gg4Hz58%2BzZcuWMjJPwCzI28bGBgcHB5o3by5dToWMuqioCIVCIWNWxP2qe/fuHDx4EIBOnTqRnZ3N8ePHzb6jIvdhBwcH/Pz8pNx25syZZGZmMnnyZN599118fHyoW7cuDRs2ZN68eXJBwt7eno4dO9KmTRtmzZrFZ599xsyZM9m8eTPTpk2T1fQNGzagUChkXqBKpcLLy8tM7imiX8pzazV17q0I58%2BfJzU1lXv37gFIWT0glQ%2BvXr2S58ve3h4nJyd5/YQBkU6no6ioSMayWFtbU1hYWGHVTpjgANJ12d7eHr1eT%2BXKlaUUdv78%2BcyePZuXL1%2BSnp7O5MmTUSgUUpquUqlkj6YwDvqzPmAL/jmwED4LLLDAgr8JoueltGnE62A6YfL395cGDUlJSWSBQFbiAAAgAElEQVRnZzNw4EBZKahXrx5vvvkmhYWFjB07lubNm/Ovf/1LVpaqVatGmzZtgJIJmqnss169etI8IDk5mQYNGkjJnZj4lScTs7GxkYRPTELEX0dHR16%2BfEmnTp1Yvnw5UNKzs2zZMpKTk%2BVkZtasWTIDDkomzn5%2BfhQUFNCvXz%2BuXLnCrVu3sLe35969eyiVSry8vMjNzUWj0VCpUqUyRhmiQicCtAVKm2qI/p/SvWRg7hYqTGgEpk%2BfTkJCAuPGjWPSpElA2YniX6kclCcbLY3du3fLSkn9%2BvXL7ZmDkr4h02qWKTp06EBxcbGZ7E2QCSF7HDdunIxYgBJyazQaOXXqFDY2Nrx69Yrw8HD0ej2NGjXiypUrODo60rJlS1q3bi2rX38FTZo0oVu3bgwYMIDq1aszfvx4Ll26xBtvvCHlcDExMYwYMUIGaQPluh4KaeumTZuoVasWBQUFMu4jOTlZmmyA%2BWJC6fN4%2B/ZtpkyZQuPGjdm3bx8JCQlAiTkPlPx2J0%2BejKurq5SrKhQKPv74YwC%2B%2BOILTp8%2BbdZTWjoDsDz5tiCTwkylf//%2BDB48WMabCGi1WjZv3szWrVu5c%2BdOmc8wtdcvD56enmi1Wl69elXm9aby2qKiIjMpqq2trYxugJLFHCjJCwwMDKS4uJhq1aqRmZlJq1atOHPmDI6Ojmzfvl0upJiOtdzcXDp37oyVlRWnT58GoG3btvz000/4%2BvoSFBTEvn37ZM/f8OHDWbduHWFhYZw7d05KEoXU9datW/zyyy%2B4u7uXqaZrNBqUSiUXL16scGEkNDSU7du3y99FQUEB7dq1k5VK0/OfkZGBWq3Gz89Pbhe9jcHBwXKxLjs7m8ePH5dx4fyz6JQWLVrIBZG8vDwpkR8yZAibN2%2BW967SfaN6vZ5169bRu3dvJk2axNChQ1m8eDGbN29m//79KJVKFi5cSHR0NHl5eaxfv56GDRvSsWNHrKysOHDggPy3MHSy4J8NC%2BGzwAILLPib8Pnnn5Obm8vChQvLfd5gMNCoUSOmTJmCVqvl0qVLKBQKmjVrxtdff43BYGDq1KmMGTMGKJnAL168mO3bt3PhwgUZoSCQnJzMBx98wOHDh822z5s3Dx8fH9q3b09kZKSUfY4YMULKk5ydnXn58qWsZv23qFy5MhMnTiyTfwYlk6fvvvtOmno4OztLMpqQkEDHjh25c%2BcOjx49kivktWvXNqtqPXnyBHd3d7Pqx6NHj6RFPmDW8yMMYcREHkps9U0JoSlM7fRNSYNAw4YN6dmzp6xklZ4o/reVg9J47733uHDhgpQCmvb8maJp06blml9Ayfg7ePAgNWvWZNasWZw5c4bDhw%2BjVCplpuPz58/p3r07KpVKEh9TaR%2BUVKz0ej0pKSkUFhaya9cufvzxRzIyMszO0axZs1iwYEGFx1RYWIidnR1btmxh7ty5KJVKJk2axJdfflkhKforcHR0pLi4GCcnJ3r16sXt27dlVQVKqqWiGuLn5yejAsLCwuRYFJUVQcJKV1l8fX3/ctW9IpjKHWvUqMHNmzdxd3fHzs7O7Der1%2Bt5%2BPAhU6ZM4fr16xgMBknOAOnQ6OHhwbNnz8y%2Bw8bGBp1Oh1qtlsdQq1Yt3n//fenYCSXVL%2BHE6ePjQ//%2B/alatSqzZ88GSn434eHh/PbbbygUCpKTk2Xl%2B5133kGj0TBlyhT69evHL7/8wpdffolCoeDKlSvExMQwduxYGRlTu3ZtXFxcZMX5ypUrjBw5ksTERDMJaVJSEg0bNmTPnj3UqlVLbnvw4AGfffYZR48elXJUgFatWtGpUyeKi4uxtraW1fTSctTyUN5rSkurc3NziYqK4tmzZ3Tq1MnsPt6vXz/u3r1L/fr16dOnDwsWLKiwh1IEnD948KDcMa1QKHByckKj0UjCV7t2ba5duyZNvIQc2NfXF6PRyJ07d%2BSiW0REBMePH39tPp8YQ1ZWVvj7%2B3Pv3j1sbW3LrT5b8M%2BDhfBZYIEFFvxNKC4uZvDgwURGRpplZ5miUaNGODk5cfz4cUl%2BFAoFeXl5aDQaPDw8OH36NOfPn2fEiBGo1WoKCwvL5K/l5uYyePBg7t69WyaXrV%2B/fjg4OLBu3Tru3LnD3LlzuXz58msn1KbVAKVSSevWrTl27NifvkcQqaSkJE6dOkWrVq3KvCY0NJSioiICAgJo0KCB3J6fn8%2BTJ09QKBR4e3tjZ2dXLpn5TyWbpSdypQlhnz59pMxs6NChfP3113h6elKzZs0ynxkYGIiHhwdHjx6V20w/PzQ0lN9%2B%2B006IVZUOQC4f/8%2Bhw4dIjMzE6VSKSWbd%2B/eZdy4cbi7u6PVasnLyyM2NhYvLy9WrlxJUlISPj4%2BDBkyhJCQEBYsWEBcXJwMBY%2BMjGT69OloNBoGDBjAvXv3MBgMZpVZAU9PT9mjZ2dnx/3797GxsTE7X4GBgWg0Gnr27CkNKoqKipg2bZpZ1uKfBdgL9O/fn0ePHknZnel1KA9eXl5kZWWhVCopLi7mX//6F5s3b6Zq1aoYjUYeP36MwWDAaDSybt06WrVqVW4Ftbi4mJs3b/Lw4UMZiVCrVi1CQ0OJjIzExsaGtWvXsmvXLuLi4lAqlbJ6uWfPHipXrkyTJk0kGRS5jiNHjsTa2lq6slpZWVFYWEj9%2BvV58uQJer1eVqaVSiX%2B/v5cvnwZrVaLwWBg9OjRTJw4ERsbG/R6Pe%2B//z5eXl5s3bq13Ly2rl27cvjw4XL7yC5dukSTJk1QKpV4eHiQk5Mj8/YqV67M77//TrVq1bh//z6Ojo506tSJ/Px81Go1CQkJ7Nu3T0p9Z86cyY8//kh6ejoqlYrdu3fTs2dPxowZI49dXOvp06cTFxeHs7MzCQkJRERE4O3tLcnZw4cPOXz4MFFRUSiVSo4fP16GcJkarohtjRo1okWLFpw8eVKaWIWFheHk5MSJEydkpqGVlRW%2Bvr6ymi7cKV9H%2BEz7jk33QWDBggUcOHAAPz8/YmJiZF4jlCxejBo1isePH0uZZt%2B%2BfWnRooVZnyaULITk5eXRuHFjgoODzRardu7cyZtvvsnDhw%2B5cOEC165dIygoiL59%2B3Lr1i20Wi0pKSnUrFmTNm3aYDQaefToEYcOHUKlUlG3bl0CAwPZuHFjhcdqCisrK2xsbFCr1VSpUoW33367jJzcgn8mLITPAgsssOBvRGFhoVyNLw9Llixh27ZtfP3111Kap1KpaN%2B%2BPfv27ZNmI0%2BfPqWwsJDZs2ezePFiALPJyoIFC7h69Sr37t0rI9ExlXuKbKmcnBwpFZ05cyZLliyRk7/Q0FC%2B/PJLXF1dadSoETqdTgbIN2zYkClTppRp8l%2B/fr0kg6b/7ZRnsCEmRM2aNWP16tXAX5NCCrxOsinkcqL69DoiYvp8cHAwV65c%2BdPXikpGee8v77vK27Z69Wq%2B%2BeYbatSogb%2B/vyQkOTk5VK5cmVatWklTnU6dOpGVlcXjx4/JzMyUK/UqlYo2bdpw8eJFbG1tCQ8P59ixY2i1WgYNGsTkyZPJz8%2BnW7du5ObmmlUgVCoVrq6uZGdnExAQQHp6OiNHjmTdunUYjUZCQkLIysri2bNnMrqjXr16eHp64urqSkJCgsxSFCivn600TCWLouJV3jSlZcuWnD59GhcXF3Jzc2UPo7e3N8eOHcPZ2ZmwsDC%2B%2BOILli1bxo8//kjbtm3NIiH%2BU5ia5UT%2BH/bOMy6qc%2Bvb18ww9C5NsWGJBRVr1FgSsVfsBUussUYTFWOiITaMBRNN7IVYogn2giWIHcHeFSPYCyJIlTb1/cBv35mBQcx58ryec565vpjZzOzZ5Z6de91rrf%2B/d29WrFghjuXy5cuiB1WtVvPdd9%2Bh1%2BuZP38%2Bly5d4uzZsxw%2BfJhTp07x5s0bEYQkJSWxf/9%2BcnNzGTJkCGq1mtzcXPz8/AgJCTFSGl23bh3btm3D2dmZzMxMYe8h%2Bb0V/l0ZZqMlFVOpJzggIIDDhw%2BLEkcpKC5btqzoh5REeH744Qdq1KhBx44djYSLPD09ef78uVClTUxMJC4uDj8/P1QqFY0aNQIKFmpu3bqFTCajdOnSwsrDx8eHESNGMGPGDDGeGjduTEhISJGAz9fXlxEjRrBu3Trq1q3LnTt3yMvLQy6XU6ZMGRITE5HJZBw5coRy5crx5s0bevXqRY8ePXB1daVv374imy6JUr1tLJpSlTVU7oUC30ArKyvWrFlTpNQWCnwnx48fz8uXL3F2dub06dMmS6%2Bl6xQbG1vk73fv3mXkyJFoNBrS0tLEIpvkcVqqVCmRhU9OTkatVuPh4cHBgwdRKBRYWVlx7tw5qlevLrKbHh4enDlzRhi49%2B/fn6lTpxpZ25j578Psw2fGjBkz7xEbGxshww0FfR6mmvKlEkAJSUzD1taWNm3aMG7cOD788EPu378vsm7x8fEii3X8%2BHEqVKhAo0aNhGLgH3/8Qc%2BePbGxsWHGjBlMnjyZUaNGkZSURLly5UTQNGDAANLT0zlx4gSdOnUiMDAQlUrF5s2bRZYhJSWFBg0aoNFoWLFihVHAk5%2Bfz%2BnTp40mpLVq1WLChAlGhtMSI0eORCaTERMTQ2xsLE2bNmXZsmVMnDiRYcOGsXnzZlJSUliyZInJUkhJoAD%2BKtmURCf%2Bp7i5ufHo0aNiBT4k%2Bf3w8HCxTTIblxRIw8PDTZa0Spw%2BfZp169YJDzHDc9m5cyczZ87E2toaT09PevfuzZAhQ2jWrBkajQZLS0uOHTtGbGwsK1as4PTp07i4uLBhwwYqV65MQkICo0aN4tChQ0yaNIm0tDTS0tJo3bo1x44dIyIiAldXVxo2bEjZsmV5/fo1dnZ27N69m6pVqxIREcHLly9FWa/UY6bT6bh3757w%2BTI1hosrkzXElER%2B9erVhUCNTCajbNmywlBayp6oVCqCg4MZNGgQNjY2TJ06lY0bN2Jra8vw4cPZuHGjmOhDUeEdSUhHypSbUiV0cHDA0tKSgIAADh06JH5np0%2BfLtJPOHv2bPG5unXrFlHl1Gq1ZGRkoFKpGDRoEOXLlxc9imPHjmX79u1Fvj8iIoLZs2czZcoU9Hq9yJBJXpWGODo6imsjWR9UqlSJW7duIZfLRYWAJOQifd7d3Z1nz56h1%2Bvp0aMHUNC3qtPpjBaKpOyphYUFCoWC5ORkdDod/fv3Jz8/H71ez6NHjwgICBCl5dJnJOrXr4%2B9vT0ymYyrV6/i5OQkFpyg4LckGbO7u7tz4MABdDodV65cQaFQ4OjoiLW1tRAagYKMuZShff36NQMHDhTZ9AEDBrB27Vq%2B%2BOKLIte2MB4eHrx48UIsOEnbDBcM0tPTsbGxEWI9Uj8fFCxaVapUSZTUlilThu%2B%2B%2B47g4GBiY2OpXLkymZmZJCcnU6tWLZKSkli4cCEffvghbm5uoq%2BvevXqREVFcfLkSe7fvy/uTfXq1alYsSI3btxg7dq1nDhxAktLSzIzMwFEpj4nJ4f69euLa69QKKhfvz7Pnz8XWdHs7GwGDBhAdnY2Y8eOJSkpCS8vLwYNGiRUbc3852PO8JkxY8bMeyQqKorw8HAuXbokJOoVCgWWlpY4OzsLBTVLS0tcXFzw8vIiKioKHx8f0fMxdOhQpk6dip%2BfH3K5XPS3vQuSHL1er6devXpCIGH%2B/PmEhYUBMHz4cFH2OWbMGMLCwoiJiSkyqS9dujTJycnExsbi6OjIyZMnWb58OTdv3hTvkcqV3mboGxYWxo8//ohKpWLUqFFMnTrVqBRSkmrPy8t7p35CQ9l9U/ydDN/ChQu5f/%2B%2BSYGP7OxsGjdujJWVFU5OTib39fz5c5ydnY38EAtnDn799VcGDhxI//79Te6jVq1aom8nOjoaW1tbatSogZeXF7Vr1%2Bann34iLy%2BPjz76iOzsbCPVSOl8ANavX8%2BIESOEyqZGo8HW1pbNmzczcOBAbty4UWxW7s6dO5w9e5bY2FiuXr1Kbm4u/fv3p2nTpjRu3JiPP/74nTKZxSFN9KEgE5OQkEBycjLW1tZCKAT%2BKiu2srIiOjqaSZMmkZqayuvXr0lJSWHkyJF069aNrl27Ymlpyc2bN4mPj6dv374MHDiQqVOnGmWPpUz5mjVrRIbbkNq1a/PNN98IhVVJYAQKFlX8/f2ZMWOGENG4cuUK9evXNwrcoeA3tXTpUiZNmoStrS3Z2dnodDqRTTd1zRs2bEhMTIzICGk0GrRaLbNmzRJCMJaWluj1embOnCm2ubi4kJOTw40bN/joo48oVaoU3bt3Z9GiRe90L6RMq5WVFRqNxihALNxL6eDgQFZWFnK5nPHjxzNhwgRatmwpgs9KlSqZ7H21sLAgIiICHx8foGCsSKIuhty5c0dklN/12P9ONt%2BQkt7brFkzdDqdyKb169eP5ORkZDIZn3zyCTdv3uTFixfimv3TWFpaotVqS%2BzNKwlJpVkqA5aUki0sLFi2bBmtWrX6Jw7XzHvGnOEzY8aMmffEL7/8wpo1a%2BjTpw89e/YUEufPnj0jLi6O/fv3M3z4cDp37oyTkxN6vZ7MzEy8vb35/fff0Wq1VK9eXazEy%2BVyfvjhB8aOHVtkxV8ul4vJz7Rp0wBE6SfAq1ev0Ov1uLq6CmEGyRJh27ZtNGrUiC1btohtYDzZUygUbNmyhYiICGJjY/n666/Jzs7Gw8MDhUKBhYWFCCxUKpVRBqwwdnZ2qNVqFAoFU6dOBQpWz6WV%2BsWLFxMcHMzOnTv/pete2DNMrVYX2QawZMmSItvGjRtH//79adu2rZFxenx8PDt27MDHx%2BetHniSz9vbMgeJiYl88sknxR6/QqEQMuqSyqBerycnJ4cPP/wQKBDnKGmSuWzZMjw8PEhMTOT27dv4%2Bvri7u4uBDag%2BKxczZo1qVmzJqNGjUKlUtGgQQMcHR3ZsGEDU6dORa1Ws2jRIjHW3hUp0Dtw4IDRdqVSiaOjo5EoBxSU%2Bt24cQONRsP48eO5f/8%2Bw4cPR6vVsnbtWhISEsTvQ6fTkZqayooVK%2BjRo4cYW4bZY4C%2Bffsyc%2BZMzpw5Y/RdFy9eRKVSsXHjRrRaLQsXLjQqK%2B7Rowfx8fEoFAqj8RQfH8%2B2bdsYMWIEa9eupUuXLqjVaiZNmoRGo8HOzo5PP/2UdevWcfPmTfLz80lKSipi1yHJ5ktYWFhgYWFB//79jZQ/9Xq9UWmeRqNBpVIRFBSEr68v0dHRhIaGAgVjyc3NzciiQfqOTZs2AeDl5YW/vz8ymYxu3bpx%2BfJlnj59il6vF0bx0vV1cXHBxcWFhg0bihL0zMxM9Ho97u7u7N27lzp16ghlU6lPTBIKMUTqBSzM/fv3%2Bf777zl37pxRr6JCoWD06NHiObpo0SJ%2B%2BeUXk/v4J2jatClxcXFs3LhRlObb2tpSvnx5Efi6urqSmZkpFvMsLCzQ6XRCAMjd3Z3s7Gzy8vLE71WytZGywe7u7mRmZuLp6cmrV69QKpW8efMGlUol/AyhQJwoJydH7EehUAiLBul3Iy3sGIoPyWQyKleuLBbFfH19GTx4MCtWrGDDhg3mgO%2B/BHPAZ8aMGTPvid9//52wsDBq1qzJ69evmTVrFqdOnTISzli2bJnwuiuMhYUF9vb2BAQE8MEHH6BWq/Hy8hKTfXt7e%2BRyOQcPHmTevHkcOnQIhULBp59%2BChRMrqXAYcGCBUIB9MiRI3Tp0oWjR4%2Bydu1a7t69K1aBpdVkuVxOaGgoSUlJIhsHMHbsWNFD16hRI7p3786cOXOQyWRiAqLVao0CHlNI31cc48aNM1n29i4UFk0ICAgwen3hwgXgr8CwcEBYpUoV7t%2B/z6FDh4yM0wMDA0v0wDNl9VCYOnXqFGsCD3%2BVDVpaWrJ8%2BXKRccnIyCA9PZ3w8HBSUlKEIEdx/Pnnn0ZBhIWFBenp6WRkZJR4jIZIWaWyZcvSsmVLSpcuzZEjRwgLCxPm3YbHLSEFEYYYmr9L%2BPn5kZWVxcuXL/Hx8REm3ZJqobTva9euoVAoWL58OSqVCi8vLxwcHGjVqhVRUVFoNBpatmyJXC43UrD9888/2bBhg3i9cOFCIVRheN9jYmKQyWTUqlULKAg8xowZQ5MmTViyZAnx8fH069fP6DylbZ6enuzbt4/s7Gyx2OHg4EBaWhqZmZls3LiRvLw8IfbTunVrrK2thf1JdHQ0lSpVEsqWEvn5%2BUbCR9LvUPL1BISdwv79%2B4v0zGq1WiOvOCcnJ3r06MGvv/5KQkIC%2Bfn5JCQkiPcuWLAAKAj4tVot3bt3Z9SoUXzzzTdcuHCBjh070qdPHyOLAgcHB169ekX79u3FfjIyMsjIyODx48doNBqsra2NsrqmslZbt27F2tqapk2bMmfOHNq0aVPkXMLDw%2Bnfvz%2B9evVi0aJFYozfvXtXZIOl8mqJt5VXv43x48fTs2dPNmzYgEKhEGq5rVq1YunSpWi1WiHkIgVxktm7k5MTWVlZnDhxgkaNGmFpaYmFhQUymUyUsUNBKf/hw4epUKECly9fRqFQ8NFHHxk9R6Tzys7OFj2tjo6O4ndsZWUlnrsajUaIM0nBZEpKCk%2BfPsXd3Z3Xr1%2BTkJBAmzZtCA4ONunVaOY/E3NJpxkzZsy8Jxo2bEh0dDTW1tYMHjwYmUxGWlqaEEJwdHQUJUJSH45SqcTDw4MnT56g1%2BvFZHP37t3s2rVL9GZIWTUokDm/ffu2UNjbsGEDFSpUMPISK0xh0YcTJ07Qvn17EUBYWFiwe/duqlWrRr169cjJySEqKoq8vDx69OiBUqlEqVRiaWkpzkH6LoVCgYuLi9FkPzc3FysrKzEhTU5ONrI%2BqFWrllHZI0BwcDBz584tcfJWUklnYUrywZPUQnv16mXSOP1/SkmlZP7%2B/jx//ryIEI1SqcTd3V1kstzc3IRVQGGfwcKYMnSWtkmflYSFoqOjxXvS0tKIjY1lxowZ5Ofno9VqsbCwEMGKlJWVMJyo/v7770bHHxISIrLLhttr1arF119/TbVq1Rg3bhyVK1dm3bp1hIaGUqVKFR48eCCO3TDrLGW1Jby8vAgJCWHEiBFGSrWmrrc0ZgzHwoEDB%2BjUqZOwwNBoNGJx5Pvvv%2BeLL77A1dUVd3d3xo4dC8AXX3zBs2fPRAmzXq/H3t4eS0tLFAqFKHWUAjtpUSQzMxNnZ2e%2B%2BuoroCB7%2BNtvv7F582YePnxoVFIp7dcUDg4O4lwMffQkZDIZnp6epKSkoNFoxFhOSkrCzs6OvLw8bG1tycrKwsbGhmbNmnHt2jXRm1a5cmWePXtmtEhlYWHBhAkTKFeuHPHx8fzyyy/k5%2Bdz%2BfJl7O3tGTx4sNEx3L9/v9gM39atWylTpgxqtZqgoCAR0ErZr8mTJxsFiiXh7e1d5PwNhWgMeRcLmri4OL7%2B%2ButiLVrmz5/P1q1bSUxM5OzZs%2BJ%2BNW7cmNjYWCZMmMCdO3fIysrCzc2N9PR0unXrxu3bt7l//z537tzB1taWly9fGi22mRrvprCysmLLli2MGjVKBIB9%2B/Zl%2B/btwu4hJycHT09P3rx5Q05ODkqlkitXrlCvXj3RX2nmPx9zwGfGjBkz74nBgwfTtGlTxo4dS926dYmOjqZ58%2Bbif%2BBSmZder%2BfYsWNCNU8mk1GqVCmeP38u%2BpIkLly4QEhICAkJCbi4uKDX62nevDmRkZF/q7dvy5YtbN%2B%2BnaNHj5KXl4e9vT0tWrTgxIkTIuiTy%2BXUq1ePW7dukZ%2BfT1RUFEuWLBFloSdPnmTnzp0mJ1SFMzt6vR5HR0fGjx%2BPk5MTixYtYvjw4YwaNQr4qxRSQq1W8/r1a6NMmDR5K1yeGRERQZcuXYy2ZWVlMWzYMLGSLrF582Zat25dZGIo8XfUQv9VatasWaIU%2BuHDh%2BnXrx8RERFoNBrq1atHSEgIpUuXZvbs2Rw4cAAnJyeGDBnC9evXjSTjJSThn/r16wsRGmmbtDhgSIMGDYCC4CM0NJSYmBju3r2LnZ0dTZs2pUWLFrRs2fJfDoANAy%2B9Xs/vv//Ozz//TGpqKnq9XqhMSpkInU4nMhSG3nj29vasXLmSxo0bAwWLCXv37iU3N5eWLVsyZswY1q9fL865cMB3584devbsWURApiSF1RYtWrBz5048PT2FKMy0adOMFm2kgEUKhPV6vShXdnd3F7%2BV4tRgp02bxoEDB3B3dycpKUmoqUoBWIMGDUhISKBSpUpcu3aN2rVrc%2BvWLTw8PNDr9eTm5qLT6YrtgzMVQEp9k5KIT35%2BPjKZTAT3VlZWVKhQgSpVqpCSksLFixdRq9UolUo8PT2pVq0ax44do0mTJvj7%2B%2BPh4YFOpyMxMZGLFy9y48YNdu3aVcR8PDIykqCgIJYsWcKmTZvo0KEDc%2BfOxdvbm7CwMNq1a2dkJaJUKilVqhQvX77E1taWvLw8qlevLuwz3N3dhdff2yjOBP1tNG3aFLVajU6nIyQkhDlz5nDw4EFcXV2ZM2cOO3bsMGmj8XeRyWRYWlqKc5ZUVaEgOyuVz74NyXZBWgBwcXERCw8uLi7Y2NiwYcMGhgwZglKpfKeqBDP//pgDPjNmzJh5T9y9e5fRo0ej0WjIycmhSZMmXLhwQfTceHp6olAoSEpKYvfu3UycOBEoWEFv1KgR27dvx9LSkosXLxqVPz569IhevXoJ%2BXe5XM68efP49ttv0Wq1NGzYkHLlyrF3714mTpwoShD37dtHXFwcFhYWnDt3DijIBjVt2pRq1aoZCa0oFAqaNm3KhQsXxESmbdu2XL58WfiSSUgiI0ql0igTUKFCBebNmydeR0REoNPpmDdvXhFxlODgYObMmQNAQkKCMAGXMimGLFy4EMBkkAMFPUUnT55k2LBhoo9LIigoiPPnz7No0SL0en2RgLBt27Z07txZKP39q8bpb2P69OnvpGhZnKF6UlISarUaT09PlEplsRnDWrVqodPpaNiwIVAgsy%2BpmXbu3Fm8r2HDhkaZ0%2BDgYG7fvuWvv18AACAASURBVI1Op2PmzJnUrVu3WON3icTERBQKBR4eHsW%2Bp3r16nh7e5OXl0dqamqxPYhS4Dds2DCmT59OamoqT548YciQIaxevZpRo0bh7%2B/Pzz//jE6no1%2B/fiQkJFC2bFmePHlCmzZtyMrKEmPLMHucn59PWFgYSUlJzJ49G71ez6tXr8jKyiI8PNzoOm7evNlIYEUK0qQyzsDAQH799Vej/jrp3n722Wf4%2BPhw%2BfJldu/eTc%2BePcV9eP78OVu2bGH06NHic1I5JECfPn1EzxgU2JecO3dOLJpkZmaKIM3W1ha1Wm3Sl88U0qLJwoULSUlJISEhAV9fX%2BLi4mjSpAknTpxAr9fTunVrsrKyOHPmjLBDsLCw4OTJkyxevFgsCpWUgWzevDkzZ84skt2DggWxDh06MHDgQJo0aUJkZCSNGjXCysqKr776ijlz5uDl5SWUPx0dHbl48SL3799n7NixdOzYkcOHDzN48GAGDx5cxK6lMIXtWv4OU6dOFZ9r3bo1L168oEyZMkyePJmvv/6aI0eOmFxwk3rpirMfgb%2B88aytrcnMzHxnkRapXw8KgnaVSiXGiEqlIjc3VwSQ0r8ODg40btyY1NRU4uPjad68uXjumvnPxhzwmTFjxsx7JD8/nxMnThAREcH169dxdXUVxuGWlpYi%2BFOpVFhaWmJlZUWVKlV49OiRmBRbW1sLg/KHDx/y%2BvVrsf/AwED27t3L/v37hViEUqnEwcGBjIwMbt68yZs3bzh48CDbtm0zKYtfHFI/k6OjIzExMWLCUrNmTfbs2SPe5%2Bfnx6RJk/jll1%2BYMWMGX3zxhZj0fvnll%2BJ9L1%2B%2BZMCAAZw4cYKsrCz69%2B9PTk4OvXv3ZtWqVSxYsID4%2BHghcGLKww9KnrhJpXeGHluGTJo0iaioKJMBYa1atXBxcRGZnLcZp/%2B7YEpp8%2BnTpwQEBAiLDqDYoK106dJGWVpTJtg5OTksXLiQqKgoALp160ZQUJC4R76%2Bvtja2r61RM7X15cxY8awZs0aXFxcSE5OZtasWYSEhKBWq0X5mZ2dHRkZGfj5%2BREUFAQUBJRLly6lWrVqWFtbExcXx5EjRzh%2B/DgTJ04kIiKCihUrsnnzZo4dO0ZKSooYW1u2bBFquNnZ2cjlcjw8PMS25ORk7O3tycvLMyor3rlzJzdu3CAoKAgHBwd%2B/PFHhgwZwr1798T4atOmDTqdjokTJ/LBBx/Qs2dPPvjgA/7880/c3d1Fz6LUZ6XX64VZuGHA%2B7aSXG9vb54/f45MJsPa2prc3FwR8Lm6upKRkcE333xDSEgIrVu3Ji8vj3LlyhEeHs6hQ4dEuej69euJjo5GoVDQoUMHgoKCaNGiBWfPnhV9ZlLguHr1akaPHi0COysrK2QyGV5eXjx69AgoWEA4ffq00bGmpqaydOlSTp8%2BTXJyMi1btqRv3758/PHH4hwTExMpXbq0CPIcHR2pV68eV69eFfe3Ro0ajBw5kokTJ4oAyNbWVpQfnjlzhtDQUKZOnUpoaCj79u3jwYMH4jj0ej3du3dn3759RcZhpUqVih2jhkyZMkVYfWg0miLCNxqNRvjl5eXlCaVTKRNoY2PD4sWLsbCwoFWrVtSrVw%2B9Xk9MTAzNmjVDr9czZ84cVq9eLXodi6NGjRpYWVlRsWJFWrZsSatWrbCwsOCjjz4iICCAmJgYKlSogKOjIydOnBD3MS8vD2tra9FT6OLiQqNGjYiKisLJyYnffvvtrYs0Zv5zMAd8ZsyYMfNvgGTc%2B088kiXz5379%2BjFnzhzq1avH/v37Rdame/fuXLx4Eb1ej5%2BfH5GRkdjZ2dG9e3d%2B%2BeUXtFptkeNwdXWlZs2aODg4EBkZiV6vp2LFimISpVQqOXjwID179qRLly5GPmRSwFG3bl22bdvGhAkTePHiBR9//HER8RbDUjbJl%2Bzo0aMkJCRgbW0tvAR79%2B4tJsmmJm6PHz8WAg%2BGbN68mdWrV7Nnz55iSw/HjBnD%2BfPnTfau%2BPn5ERAQIDKR0rZ3lXp/Fw4dOiTKd4tjxowZhISEvNP%2BpOPLz8/nwIEDbNiwQdy30qVLM3jwYPr06YOjo%2BM77U%2B6n4aB5IIFCzh79qxQ7Vy/fj1dunShW7duzJgxgwsXLjB79uxirSak4%2BzUqRPOzs788ccfJCcnc/PmTfz9/cnMzCQvLw%2BZTIZKpRJqh6VLl0an05GWlibMxCMjI/H39%2Bfq1asEBweTlJQkxtmbN2/w9/fnxIkTYmwZCu%2B0a9fOSHjHcHFAKiuWemShwItNr9fj4uJCRkaG6L2VFgQkJU%2BlUkmVKlW4e/eu0W/RwsJC9LtKHmrwV2%2BWYUZKei2JDi1ZsoQpU6Zw48YNqlWrJoIJCwsLbG1tyczMZODAgVy4cAEvLy9evXpFWFgYnTp14tChQ3z00UfY2tqiUqno1asXV65cEfdv1qxZ%2BPr6cu/ePWrUqMHly5epUqUKCQkJyOVyqlevzpMnT4wWDDw8PPj4449Ff9iHH37I5s2bi73fly9fZt%2B%2BfRw%2BfBhra2t69OhB79696dq1K9evXxdBHkCHDh1YtWqVKM9UKpVERkbStGlTcS8qVKjA4cOHgYIArGHDhly6dImGDRuaLI/9n/5up0%2BfbqSGqlKpeP36dYnZVDs7O5ycnJDJZCQlJWFpaSmufUpKCr169eLQoUNkZGTw4MEDFAoFlSpV4t69e9StW5ebN2/i6OhI2bJlcXR0JD8/n2rVqvHJJ5%2BwZ88eTp48WWw2sVy5cvTs2ZPRo0eTnJyMq6srSqXSqCogNTXVqELAzH8HZpVOM2bMmHlPZGVlsWjRIi5evEjdunUZNGiQ8LJLTU0FYN68eQwZMsToc4YT8/j4eDIyMihfvjy5ubnExsaKrIFhWR4UKGhKvTuPHz9Gp9Px5MkThg0bxuTJk4W1goWFBWPGjKFKlSp4enoyceJE5HI5Dx8%2BBArEL54/f25Ununh4UGFChVwcXEhMjKSb7/9VpSZSmWXpUqVIigoiPbt27Nt2zZRNipx584dXF1dxeu0tDQ8PT359NNPmTdvXrGTM7lcbrQqHx8fz%2BTJkxk4cGCRgO/mzZtGGVBTSOWKxTFu3DgGDBjw1n38T/j666%2BNAr6mTZsamV5DQfnruwZ8Op2O8ePHc%2BrUKWEML0n6b9myxUhR8V0wZdkgKbpWrlwZKMjWDRkyhA0bNuDv74%2BVlVWRYK%2BwNYdWq%2BX48eMMGTJE2ISEh4dz/PhxNBoN33//PadPn%2BbJkyfs3LmTfv36cfz4cWbPnk1MTAwTJkxg3759Qq4eCnpa%2B/TpI77D3t6e/Px8o7H1NuGdy5cvC/sPqZfJMFCQstLHjx8XWen79%2B%2Bzc%2BdOfHx8qFSpEjqdjvz8fFF6aJhF1%2Bl05ObmkpeXh52dHQqFAmdnZ/z9/dm6dauR2mbHjh1FRg4KygilANhwf5Jio06nY/fu3WRnZxMfH49Op%2BPbb78lMzNT/M6ys7OpU6cO27dvp3fv3nTr1k3cCykD/vjxY5ycnHjy5In4jjt37lCuXDlhCaDX60lKSiIqKkqUkd%2B%2BfZu7d%2B%2BKxaz27duLLFXdunVZsmQJDRo0YObMmURGRrJ37146deqERqPhwIEDeHp68vDhQ3x8fGjXrp0o1YaCHt5nz54hk8lQKpXk5eVRvnx5oXApiVxt27YNlUrFs2fPKFu2rMl7/K8iKZYWJjU1ldu3b/PgwQNkMhmJiYls3rxZnHt2drZRoKzRaMQ1AozK3KHgXty7dw8oeDZptVry8/OJj49n4sSJrFy5kkuXLrF161ajz8nlcqGgq9PpUCqVVKxYkd27d4tngoTh%2BP%2BnRajM/HtgDvjMmDFj5j0xf/584uPjGTx4MC9evGDGjBn07duXatWq8eLFCxITE9FqtTx48AAHBwejXg43NzcWLVqESqVCoVAQExODjY0NGzduZNCgQahUKkaMGCHsGiIjI7ly5QoxMTFigq9UKlGpVGRlZbF161bS09OFL9ODBw/47bff2LFjBydPnhRln7t27WL79u34%2Bfnxxx9/0KBBA5E5ys7O5uXLl2g0Glq3bk2PHj2oVKkSpUuX5ptvvhEeUqNHj2bbtm3o9Xo2b96Mv78/Li4uzJw5U/QqFRZHyc/P5%2BbNm%2B8kjlLYZ82QxYsXc%2BzYMebPn1%2Bs3UVGRgZubm4m/6bVakU5mhSw/JNS71A0y2s4OSzuPcXRtWtXVCoVZ86coXnz5owYMYJGjRqJ8rF/ipSUFBHsQUHGWjIwb9asmTB7N6RwdtfDw4MXL16wc%2BdOMTleu3Yt/fr1w8LCgm%2B//RYoKF87fPiwCDjHjBnDN998g1KpxM3NjVmzZtGkSROePn3K48ePjYL%2BxMREbGxs6Nq1qxhbhf30DMnKyjLphydx4cIF0tLSgAJFzO3bt/PJJ5%2Bwd%2B9ekpKSRI%2BWTCYTkvmS8qdcLmfGjBm0bNmSW7duERERwbVr16hatSpJSUloNBq%2B/fZbunXrJsaT4cKGTqdDpVIxd%2B5cALEAY2jGnZ2dTY0aNYRK7ZkzZ3BwcOD169ciiyhlao8cOUKDBg3o0KED27ZtAwpKNV%2B/fo1CoWDo0KEcPnyYxMREdDodT58%2BFdk1aYEkIyMDmUwmSnIvXLgggpkXL14wZ84ctFotP/74o8hkW1pa0qVLF7p06cK9e/fo1q0b06ZNY9SoUSxcuJAVK1bw2Wef0b9/f5HlBRgyZAh6vV6UlZ46dYpnz55Rs2ZN0Q958%2BZNtFot/fv3Z9euXf9oMNOiRQsyMzPx8/Oje/futGnTBkdHR1xdXWnRogUtWrQAChbeXF1dKVu2LNeuXRNl%2BNK5FGdiL2G4TcoeSqX%2BixcvRq/X4%2BbmRmpqqtiHpPgZERGBWq0W/qmPHj1izZo1jB492ijgM/Pfj7mk04wZM2beE82bNxeTkLt37zJ8%2BPASs0%2BGyOVyWrRoQevWrYmOjubSpUs4OzuTlpZGWloav//%2BOxcvXmTXrl08evSIUqVKCRU3qVRHrVZz5MgRfvrpJyIjI5HL5QQGBhIUFCTK4aQSOxsbG3r16sXUqVOFAEJJ5t6GSH5qeXl5vHr1CplMRu3atfnzzz%2BxtLTE3d1dmJYPGjSI1q1bCzNsX19fGjVqZFIcpXBplqFaoim%2B%2B%2B479uzZw5UrV4p4/WVnZ9O4cWO6devG/Pnzi3zW39/fpEKoIW%2BTen8XCp9PSeqQJe0rLy%2BPKlWqEBgYSEBAAPb29iLgO3DgwN/O8Jnq4SvpGIs73vz8fF69eiWOQXpfzZo1kclkJvssZ8%2BeLRQvf/31V3x8fNBqtSQkJDBjxgySkpKYPn06kZGRZGRkGJm4BwcHc/ToUT777DMxtt4mvNOmTRsjRc/C51K7dm3c3d1F9u/Zs2d8%2B%2B23pKSksHr1ary9valTpw5arZbvvvuORo0aMWbMGB49eoRMJqNs2bJGSq%2BZmZkcPnyYlStX8vLlS8qWLYtSqeTIkSPCXkES25FKSy0tLUWmrTgkrzalUkmLFi2oUaMGq1evRiaTYWVlRXZ2drEBR1BQEIsXLxa2ClBgW%2BHk5CQ8K6VxJgWWEydOJCoqqlj7ix07dhAZGcm6detQqVRERkayZ88ezp8/j0ajYcGCBbRp04b%2B/fuTm5tL37598fLyIjg4%2BK3ekoWR%2BiQrVapEgwYNjLJn/9OSzs2bN7N9%2B3YePnxo1F8nWWtIgakUfEv3QCaT0blzZ%2BEtGR8fj1Kp5Pr162LR4eeff8bFxYUxY8aQn59vJJjzLnh4eJCcnCxKrj/66COg4Nl09uxZmjVrVqLlhJn/LswZPjNmzJh5T6jVapycnAAICQmhS5cunDt3jnv37uHk5ETbtm3ZsWMHlpaWeHt7k52djYWFhZBG1%2Bl0nDt3joSEBOG9lpaWJkrDVq5cyapVq/jss8%2BYOnUqBw8eRKfTYWVlhZWVleiFgoLMl42NDRcvXiQ%2BPp4JEyZw5coVUlNTkcvllCpVCi8vLywtLXn27BkWFhaEhYUJT62vvvoKhULBokWL0Gq11KxZEygQOTl//jzW1tb06tWL/Px8oqOjhTDNnTt3sLKyokyZMmzdulWU4hU2w1YoFO%2BsnmcqK2PI1KlTCQ8Pp23btvTu3RsfHx90Oh3x8fHs2LEDe3t7kaksHBAeOHCAwYMH06VLF%2BGR9u/M2bNn%2BfDDD8nOzmbu3LnMmzePevXqodVqixW9%2Bf9FRkYGgYGB%2BPn5MX/%2BfMLDw4XfmmQm//333xdRVtRqtUKMZNCgQUZKkA0aNMDb25vQ0FB8fHxYvHix%2BFxISAiRkZGo1WoCAwPF9gEDBrB27VqTx9i2bVvmz58vFD0Nyc7ORq1WF8lKu7m58eLFC1q3bs0nn3wiMm8hISHk5eVhYWGBXC5HoVAwatQooqOjCQoK4vPPP%2BeXX37hzp07oizyhx9%2BKJId9fDwEOWhkq2DFEyYwsXFhWXLljFkyBDUajUuLi6EhYVhZWWFVqvl1KlTNG3alJEjR7Ju3To6d%2B7MwYMHRb/gyJEjWbx4sQj2JAxLuqXvlrLesbGxyGQyo8y3YVDUrl07Fi1aRHBwMIcPH8bKyoru3bvz7bffEhAQIHr1wsPDWbt2Lfv37xe9ljVq1KBChQo0b96c69evc%2BDAAfR6PR9//LF4nko0aNCAjIwMwsLCiIiIMAqM1Wp1EQsXKOiNfBeGDBnCkCFD0Gq1jB49mitXrpCTk/NOKpqPHj1CpVKJZ3d%2Bfj6jR48WGbzPP/%2Bcn3/%2BmYyMDPR6PadOnUIul1O2bFkeP34MIMpWg4ODCQ8PNwqupQW10NBQLC0tsbOzQyaTUaZMGVatWsUHH3zwTudo5r8Hc8BnxowZM%2B%2BJRo0aMXfuXKZMmcKtW7fYsGEDjRs3RqlUkp6ejq%2Bvr/Bv%2Bv3332nfvj0qlQpbW1tkMpmQXTfsLQI4efIkffr0ISYmhiZNmtCgQQPKlSsnsiL5%2BfnCS8vCwkL09fn6%2BrJr1y7Re1KlShXS0tJE6VhycjKrVq1i/fr1aLVaduzYIdQEc3NzGT9%2BPKGhoWg0Gm7fvg0g/pXJZMyaNauIYfT169dFH5QU7AHCm8wQaWW/8CSt8MRNJpMxevToIiWDEk%2BfPsXLy4uAgAAOHTpkJNoRGBhI3759GTZsWLEBYalSpf5jyqHs7e2xs7Pj5MmTxMXFsXbtWqKiokTf18KFC5kwYYJRD1FJSPfQMNtgqqzVcJtGoyE8PNyo1HX58uW4uroyY8YM4K8Sz4MHDyKXy9FqteTl5RUJTL29vcWYT01NFfevfPnyODs7F3vcffv2ZezYsbRq1cpobEmebaYYN24c/fv3NxoLUknijh07APjss89QqVT8%2BOOPjBs3jk8//ZScnBwmTZrEqVOnxL6k79BoNEIB1DCDOXnyZKpVq8a8efPo2rUrderUKSJ6MmXKFOrVqwcU9HF26tRJBCi%2Bvr7o9Xru3LnDn3/%2BSbdu3VAqlZw9e1aUXjo7O9OzZ0%2BGDx9OSEgIFy9exMHBASgQZlIoFDRo0IBDhw4Jn0MpiAsKChKlktL5SK%2BlYG7WrFlAQYmnXC4XirpQEHhCwX3es2cPmZmZvHr1iu%2B//55WrVqZVIm1s7Pjyy%2B/NFLzNaR9%2B/bs27dPlFoXl7FbtGgRALdu3aJ%2B/foABAQEmHzv30WhULB%2B/XrxOjc3l/DwcNasWSPKLKVzkwJzQ4sbCcOxolarGTNmjHgtBZFSsNe6dWsWL17MzJkzWbJkCW/evMHa2horKythsK7X69m0aZPRcWZlZYmyTjP/tzCXdJoxY8bMe%2BLFixeMGTOG2rVrExMTw7Zt2wgMDESr1aJSqXB0dOTx48fI5XLCw8OF%2BIlCoSA9PZ38/Hwj6XMp4Lt%2B/TrZ2dmsXLmSXbt2iR4jibFjx9K7d2969uyJvb09r169omXLlkYGu4GBgRw9elRIjReWiTeF9B6ZTCY84k6fPs3hw4f5/vvvefDgARs3bsTR0ZHc3Fw0Gg1bt26ldu3aRYymC5dbhYWFsWzZMq5fvy56oIrj5s2bZGZmcvz4cZNZmcGDB1OzZk18fX1RKBQmRTsMFULfpuL4v4GhLxzA3LlzjV6np6fz008/cfTo0bfupzgDaZVKJdQ679%2B/DxQEC0OHDhWiHW8jLCyM4cOHi38BoWBZHC9evMDb29uo1LVNmzYsX77cKNg0LPGMi4tj8uTJbNq0iaioKKpWrUqVKlVE4PCv8ndLZAuPBUkVsV27dixfvlwsXkhj35SvmiTwkZ%2Bfz3fffUebNm24du0aKSkpwu6gQoUKlC5dWnwmNjYWd3d3oKCX7tmzZ9jY2FCqVCmgoHy0dOnSNGjQgLy8PKKiopDJZOzYsYN169bxxx9/YGVlRdu2bYGCANHR0ZGGDRtia2vL06dPuX79ulDjlLKqzs7OQjRKLpdjZ2dHVlYWcrlcCEapVCpycnKKBNjTp08HCvqTJ06cKCoADGnbti1t27Zl//79REdHv9P9KUxERASrV68mISEBvV6PtbU1M2fONBLokbhz5w49evTA29ub169f/6OKuhLNmjUjPT1dBGZSSaenpycDBgygYcOGTJs2jfHjx7No0SLS09ONnpfS4ptOpxM2DoCRsfzgwYPZvn27UWa1OCRlz/v371O3bl28vb3x8fGhcuXKtGzZsliPUjP/vZgDPjNmzJh5z2RlZbFs2TIuX76Mm5sb165dQ61Wi94cySZBoVAIeXepN6N8%2BfLUrVsXwMg0Oycnh2fPnmFpacnt27c5ceIEAwcO5OnTp/zxxx9UrFhRmJsPGzaM3bt3c/DgQTFh6devH5cvX6Zly5aEhYVx9OhRLl%2B%2BzJkzZzh27BharVaUXd25c4e7d%2B/Ss2dP9u7dS3BwMH379gUK/NkaNGjA4cOHmTlzpujL8/PzY%2BLEiZw5c4Zp06YxaNAg4WkFRYMeKBr4SMdp6noaevgVztDZ2Njw%2BvVrPD090Wq1Rj1UEs%2BePePMmTPFBoT/m5QUPD1//hz4130IDXn8%2BDEdO3Zk1KhRbNy48W9NhuPj40lJSTFpfeHv7/9WVcTExEQ6derElStXRMBUuMTz7NmzjBgxQpwTFJxz48aN%2Be6770yadet0OtavX2/kBzho0CDx9%2BjoaEaMGMHcuXPfeWwVHgvt2rUT16lWrVqEhYUBMGLECDZs2MDdu3cJDQ3FxcWFly9fYmNjw5YtW8T4qlatGjKZTPTUQkEAVdhrTy6XY21tDRTYEly6dAlAZNUuX76MXq%2BncuXK3Lt3j9TUVPLz88X99/b2Jj09nbZt2xIdHU1KSorouw0ICGDfvn1GwSr8ZekiIfnJ2djYGCnoShguEhkyadIkKlasWGxmbsqUKVhbW5tUmm3UqJHJ/rLHjx%2BzZMkSTp48SX5%2BPi4uLmRlZdGlSxcuXbpEpUqVWLVqldEiT3Z2NoGBgaJHesuWLaxbt87kmG3dujXe3t4mj9cUUVFRhIeHc/XqVbKysky%2BRwr%2BS5cuTUpKCtu2bRMZYFtbW9GLqVarsbGxIS8vj6pVqwpVzgEDBvDbb78BBerMcrlclBIb7h/gk08%2B4e7du5QqVYrAwEAePnzI1q1b%2Beqrr3B1dcXV1ZXy5cubVTj/j2IO%2BMyYMWPm3wCVSsUPP/zAzp07i508FEYmk5mc9Esy3IbY2trSpk0b9u/fT8uWLVm1ahW5ubkiMAoICGD79u1Cvc/CwgK1Wk2NGjWIi4tj06ZNNGnSBEAEmDt27CAlJYVKlSrRtWtXli1bxpgxY5gyZQr%2B/v6sXbuWe/fucePGDZo3b87Vq1eJjo7GysqKOnXqcO7cOT755BPKli3L3bt3jXpQSgp6JPPis2fPGm2XJm7Ozs7FZuhiYmJo27ZtsaIdhRVCTQWE75O/YyD94sULkpKS3rq/wYMHExkZSYcOHYoYtBdHfHw8/fr1IzAwsIgaalBQEOfPn2fHjh3FTi79/Pywt7fnwIEDIpAICQnh7t27TJ48mdmzZxsFrDKZDHt7e5o1a4aVlRWnTp1i586dRQRnVq5cyW%2B//UZgYCAqlYrt27czZswYAgICmD9/PhEREVhZWRXp9SqMJLxjaizo9XqxOGFKmMZQcKhatWpMmzbNaHxVq1aNnj17GgWX%2B/fvJyAgQGzbt28f8%2BfPp1atWiKolkyyoWCc161blxkzZhAfH4%2BzszPLli1j1KhRHD9%2BnJYtW6JUKtHpdELdds%2BePVy9epWZM2cKlUhPT0/hBVerVi1hvSL1wSoUCqZNm0ZqaqpQ6JV4%2BvSpyNDVrl2bWrVqib9Jv5X27dvToUMHypQpg1ar5cmTJ8yaNYvs7GyqV68uPAUBo/%2BWzlGlUhEVFcXChQt5%2BfIlVlZWaDQaVq1axccff0y9evXYtm0bX375JU%2BfPsXGxobmzZuLPsqYmBhUKhU6nY7w8HD69u3LqFGj/qUxW5jq1asLAazOnTszfvx4vL29uXr1qvDDS0lJMTm29Ho9Dg4OQgVVr9dTqlQpXr9%2BLe4NQP369bl7967w1bO3t0ej0Yi/jx07llWrVgFQsWJF1Gq1WBAyhVwup0mTJixdurTE34CZ/y7MAZ8ZM2bM/JuRmZmJSqVCqVSSmpoqJgeWlpakpqZib2%2BPm5tbETELiUGDBvHy5UsGDhzIsGHD8PX1xdvbGw8PDy5evIiFhYWYGDk5OXHu3DkeP34sJpsWFhYMGDCAoUOHcujQIVavXk1ubi6tWrWicePGrFu3DplMJlbP7927x9mzZxk2bBhbtmyhXbt2nD9/nvT0dBGYVaxYkcePH/P555/j4%2BPDtGnTGDp0KOvWraNq1ao8evTIKMMnUXgSCAXBRt%2B%2BfRk4cGCxE7effvqJuLg4kxm6Ro0aicATCrKQrVq14vz58%2BL6GSqEvk3F8X3x6tUrPDw8gLeXwEmT0pKQPLtiYmKKfY%2Bk7GppaWlkSG6K4OBgI3P6wtSpenMGVQAAIABJREFUU4d27dpRpkwZJk%2BeDBSUeH7zzTdMmzZNlA76%2BvoSEhLCgwcPWLVqFadPnyYyMpJNmzbx5s2bIl5o7du3Z/HixdSpUweAS5cu8cUXXwBQrlw55syZQ9WqVY0%2BIwkImWLSpEk0a9aMvn37UqZMmSJjwdB8XspKh4SEEBQUhFKpJDg4mM6dOxMZGUnHjh2BguBOKo9UqVRCVCMlJQUnJyfy8/PJyMggIiJCjHMPDw%2BWLl1K165d6dOnD5s2bRIBikKhwMXFBYVCIcZC7dq1RWngjh07CAwM5MaNGzx79oxOnTqxYMECvvzyS6ZMmcLy5cs5e/YsVlZW1K1bV5Trdu7cGb1ez7Fjx7Czs6N79%2B7i93bu3DnGjBmDh4cHGo2G58%2Bfs3PnTmrXrs358%2Bf59NNP8fT0xNvb28ic/G189tlnRq9zcnKIiIjA2dmZR48e4e/vz9dff01AQAD79%2B%2BnXLly1KtXj/379%2BPq6ipK2NPT00WW09XVlb59%2BxIWFoa/vz%2BRkZFGC0uGlDRmC3Pnzh12797N6dOnefr0KXq9Hnt7e/z8/OjcubPwn/zqq684evSoyZL44pRRDbGzsxPWLAqFAhsbG9F3/fvvvxfxt5RwdHSkatWq5Obm8vLlS5RKpRDTcnJyMvI1NPPfjzngM2PGjJn3jFQylpmZSWZmJg8fPkQul4tSxKSkJKpXr07Xrl3fuvosiStIdgLSpHP27NkolUohFDF06FCjiREUTCpyc3MZNGgQx48fNyqPevjwIT169MDZ2ZnExERcXFywt7enatWqqNVqYmNj6dixI6GhoWLSu2/fPi5fvoxCoUCn04mJcalSpUhLSzNS7JN6WUxlK02VJZYUbIwZM4bTp09Trlw5kxm6knq4SgoI/7fJyclh4cKFRmWJQUFB4vqEh4cTGhoqyt7%2BVXn5AwcOMGfOHFFWWBLS98fFxZVofSEJ8Zw4ccLk3/38/Ni7dy%2B9e/emZcuWDBw4kJEjR9KiRQssLCw4evQoVlZWwsAcCrKZtWrVomvXrkydOpXu3bsX6QEr3Auq1WqpU6cOs2bNMtnfBQiLEVNIk3S5XE5cXFyRsbBq1SrRWytlpV%2B8eCH6JyU1TclCAcDLy4vnz5/j7e1t1NsoBWn5%2BfkolUpatWolxnlQUBAnT57kgw8%2B4OrVq1StWhVbW1vKlSsnBEm6du3KkydPhGVGu3btOHDggPh9zZw5k0WLFiGTybh%2B/TrVqlXjxx9/ZMqUKXTo0IFu3boxadIkxo0bx9KlS7G0tCQ/P58FCxbQrVs3I1GVgQMH0qpVK0aOHAkgvO8qVKjA3r170Wq1XL16FSsrK65evSqug7e3NwMHDjTKSHfv3p1169bRuHHjIvelc%2BfOTJo0iSdPnrBz505OnDiBSqXi888/JzAwkFatWrF//35kMhn79%2B/nwYMHJp%2BVfn5%2BODo6kpGRUWwWu6Qx%2BzZUKhXnz59nz549nDt37m/Z67wLzs7OZGVlodVqjTKAhj1%2B8Nez1MvLi6ysLM6cOYNGoxGLDXK5nIMHD9K5c2eT/ZNm/nsxq3SaMWPGzHtEKhlzdHQUk6LCKBQKIiIiWLFiBZs3by62tFBSXpMmAFJvkST7DQWS7nZ2djRv3hw7Ozv27dsnetmePn1K69atOXPmjFD4lMqjateuLXqIJJ%2B/p0%2Bf0rhxYxwdHQkKChL7X716teg71Gq1eHp6cuPGDXQ6HW/evMHOzo43b94wZMgQNm/ezKeffsqmTZsYMmQIUHI55%2BXLl9m5c2exf09NTcXOzo4//vgDKMjQLVmy5J0zdIUVQt%2Bm4vi/wU8//cSVK1f46quvUKlUrF%2B/Hjs7O7p168aMGTO4d%2B%2BeSTn5dyU%2BPp7p06dz79497O3tUSgUbNy4kWHDhjFt2rRiP1ejRg3x3yVZX3h5eRURCyqMj48Pv/76K3PnzmXQoEHo9XoiIyOBgonptm3bjPr0Xr16hYODA9HR0SxYsEBkOQwpvIatUCiwsLAoNtgDOHTokNHnDUtku3fvzt69e8XfC4%2BFsWPHil5GQ7Xc48ePG/UymvJWPH78OHXq1BFCNhqNhj59%2BoixbTjOFy9eLDJQS5YsYe/evSxfvpyrV6%2Bi1Wrp06cPDx48MOpfi4yMRK/Xc/z4cVq3bs2vv/4qngPSb23GjBnIZDKSk5MZN26cKJOWev0UCgUNGzYsoqB579498XyBgqqACxcuoNFo2L17N7179xa/IUlVVEIulxsZyMtkMj777LMiixbr169n586ddO3alRo1ahAQEMCXX35J586d2bdvHytXrkSn0/Hbb7%2BxZcsW1Gr1W5%2BVWVlZxQb28G5jtjhOnz5NeHg4Fy5cMPms8PHxwdPTk06dOgnLBICmTZuSlJREcnKyKMX09vZmxYoVtGjRgtjYWL744gv69u2LSqVi1apVKBQKVq9ejUajEfdT6rWUFs5mzJjBnDlzUKvVJv1S/45/qpn/DswBnxkzZsy8R5YtW8bEiRM5duwYSqWSzMxMnJ2d0Wq1vHz5EgcHB6pXr06LFi0IDw9/a%2BBS2J6h8GudTicUHhUKBRqNBqVSSWhoKLVr18bX15fVq1eza9cuUR515MgRo/KotLQ0Nm3axMiRIzl9%2BjTOzs7UrVtXTP4NRRyqVauGlZUVoaGhDBo0CIVCgUqlIi8vT0zuvby8%2BOOPP9DpdERGRiKTyYTSX3GUFGw8fPhQ2A5AUZ%2B1kiwEtFptEQuB/58cPXqUtWvXUrlyZaCgrHHIkCFs2LABf39/li5dKpQa/w5v3rwhNDSUHTt2IJPJGDp0KBMmTKBx48Z8%2BOGHWFhY8Omnn77Tvtzc3Hj06JGRIbkhd%2B7cMSnyUZgaNWqwbds2UlNTCQoKIjY2lpiYGKEga8iiRYv46KOPOH78OE%2BePHmrBcPfwTD4AOOARCaTFfm7IYa9jJIQiDSWDhw4wE8//cTo0aOLjDlJHMkwANHpdIwePZpdu3YBRcf5uHHjGDBgAPPmzWPMmDGsXLkSpVKJnZ0dkyZNMsqaw1/Br5eXl7i3s2bNQiaTifLpSpUqce/ePSORlOfPn6PT6Zg0aRIrV67k6dOnaDQao%2BC78KKI5Nm3bdu2t17r4jBVbNa8eXOaN29OWloa%2B/btY9u2bYSEhKDT6Rg5ciSVKlVi9%2B7d/PLLL0CBmM2vv/7Khg0bijwr9Xo97u7uxS6qwbuP2cKkpaUxYcIEk%2BdQvnx5JkyYQEBAAI0bN2bVqlW0atVKvHf48OE0aNBAHD9ATEwMoaGhHDp0iOHDh7Nz507atm2Li4sLkyZN4sWLFyxfvhy5XE6ZMmXo0aMH48ePp0aNGuh0OpycnFi7di1VqlRh2LBh5Ofno9VqUSqVVK1alSlTphjZa5j5v4E54DNjxoyZ94hkML5y5Uoj2X2dTodGo0Gr1RIXF8fKlStZs2aN8Fh6G1qtFp1OJ7Ju0n70ej2WlpZ4eXnh7u7OjRs3sLe3F/10UFCuJ5m3N2/eXJRHubi4kJSURJcuXfjwww/x8PAgPT2dhw8fmpz837lzR0xm58%2Bfj6Ojo5hUbtiwgcWLFxsFh1LG410oKdjIy8sTcvZQNCsjZSENMdymVqv54YcfilzTwkHi/1ZAmJKSIoI9KChty83NZc2aNTRr1qxEH0IJQwPpPXv2MG/ePFQqFQ0bNmTu3LmUL18eMO2rVxIlGZLPnDlTGJK/C66ursycOZOOHTsyffp0atasyeLFi/nmm29ISEggLCyM69evs3TpUq5cuUJISAgff/xxkf1otVrOnj1rdC5S1spwW/Pmzd/puEpaHNi%2BfTu%2Bvr5GvaSGY0mn07Fy5coiY65wcCZhGOAVHueFM1BSEDNnzhx69OhB//790Wg0NG/e3Gi8S4GA5JGn1%2Buxs7MjPT3dpB%2BclGn68ccfARg2bJgoaX0bb8uelcTbPuvi4sLQoUMZOnQo165dY8eOHcydOxd7e3t8fX2xs7NDrVaLRZ4BAwawatUqo2flF198QXJyMidPnkSj0fwjYzY0NJQ9e/YIYRbJb8/w3j579oxp06aJzHnhbGfh14BRVlLy92vSpAk2NjY4OzuTlJQkFqY0Gg0JCQlcvXpVfEaqwDBFamoqLVq0EGPBzP8dzAGfGTNmzLxHpJVyw4xU4b9DQdAile%2B8TWQCCibPycnJwqYB/prMq1QqEhMTSUxMBAomB%2Bnp6axZswZPT0%2BSk5Pp1KkTKpVKlEctX76cUqVKGfXYSBP%2B5ORk1Go18%2BbNY/Xq1VhYWIjJk1wuR6/Xc/fuXSNp/AEDBvDTTz8ZHfM/GWyo1eq3TtxKCiylklJDc%2BLCE3aZTPb/LQMoTSSbNWsGFBWyeZuBdFxcHNOnT%2Bfhw4c4OjoSGhpKq1atjN4j9aBJ/74LpgzJ/6fm9D4%2BPnTs2JFz584JH7jDhw8DBVnOmTNnsnDhQvLy8rh3755JSX%2BNRiOsHAwx3PaulhVQ8uJAYmJikWyz4fiS%2BsIKj7latWqxfft2kU2WWLVqlVicKV%2B%2BvNE4N8xASePcy8uLDh06CCXW0aNH4%2BPjw%2BXLl9m9ezcAPXv2BGD37t1otVocHByE6qNMJqNbt2707NkThULB0KFD8fPz49q1a5QvX56nT5%2BycuVKqlSp8k7X613QarVG/WOS/1/hnjJTQXndunWpW7cuERERfPnll3z77bci07hs2TKg4FmZlZUlFE2hIJOWlZXF6dOn/7ExGxsbi0wmo3v37vTu3Zv69euLKobz58%2Bza9cuYmNjSU9P/1vXpzhyc3PJzc012vby5UsOHTpkVJZsSHBwMKVKlcLZ2RkbGxvKlSv3L2UxzfznYxZtMWPGjJn3iNTbI5VdGmIow274nvz8/LeuiBfnw2ZozA4FJZeG%2Bw8PDyc4OBgXFxfq1KlD69atiY6O5tSpU8hkMqZOncrChQu5deuW8LqT1OOysrKER6CkjpmVlSWMhS9dumRUoufn58fOnTuF5LyhiXdJPm4l%2Bey9fv2aGTNmGJWcvauH378Df9cY/G3UrFkTrVaLjY0NNWvWLBIgS1SuXJl27drh5uZm5K0n3Yvjx48XuSfZ2dmsXbuWqKiov21OX5zXWnJyMr1798bNzY0mTZpw5coV4uLiRBmwTCaja9eufPXVV7i5uf3t6/Eu/J1rXVgkprj3zJkzx8jUXlpUeP78uRDDMeyrkn7f0qLJ6NGjOXDgAD4%2BPlhaWnL69GnUajU1a9akb9%2B%2BdO3alQYNGvDnn38anYder2f%2B/Pns2bOH6OhoZDIZ3333HV27dqVZs2bo9XqGDRvGoUOHePbsmZFIjYuLi%2Bi7bdmyJb179xbHXb16daPrn5ycjEwmE9tSUlJwc3MjOjq6SPY5IiLC5HUyfKaVFJRLIlB16tQx%2BlzhZ1vh%2B5idnV2sXUtJY/Zf4cGDBwwfPpykpCSj%2BytZMJQtW5a0tDQcHR2FT2L//v3JzMzk0aNHPH78mJycHGQyGRqNBr1ej5OTE76%2Bvpw/f54NGzYQ8v/YO/O4qOr1j7/PzLAjICDgUqSWmkq4kXbVzCVcUlGzXNLr7XctvbSomaVltqiZqVS33FLS8pq5Zxpoalm4hIKWmGnuyioiiLLN%2BvuD1znNDDPDoKIs3/fr1auZs8x8z/nOwfOc53k%2Bn9mzSUtLw9fXl4yMDDw9PQkODqZ3797cf//99OvXj7i4OPr163dbj01QvRABn0AgENxFZFXLd955R7khsPVn2c/Pj7y8PCRJolmzZopBcKtWrVi6dKldHzZzbAV8cgAwY8YMYmJiLJ5G%2B/n5YTKZlNKounXrAqWS7PD3zdO2bdu4dOkSarUatVqNwWDAYDBQr149mjdvTlJSUpkbr7CwMFxcXG7ax83RjduGDRvsmpLLyD5rVRFnTOcvX75Mhw4dyjWQnjp1army%2BFqtlitXrhAVFcX27dst5kSeCzkIsjcn5WWdZWT1SkdcvnyZmJgYdu3axY0bNzCZTPj4%2BPDYY49VSqBnHZDEx8crqobmmJfIyvTq1Yvly5c77GV88cUXycnJsRlEbt68WXldXFzMokWL0Gq1REREUK9ePUpKSkhKSiI1NVXp%2B1OpVLRu3ZoTJ06QkpKi7N%2B8eXPq1auHwWBAq9VaiNrIyo6SJPHss88yceJE2rRpg8lkIjAwUDHyzs/Pp3v37hw%2BfBiVSoWLiwseHh40btyY/fv3KxnX7du3WxzHxx9/zMSJE5W/ETKDBw9m2rRpNs%2BNOVu2bLFrl2CL8PBwkpKS6NChgyJWYjKZ6NOnD1A6h2q1mr59%2B2IymRTxmc6dO5OQkGDTruV2kJuby65du9i0aRPHjh2zWbnRqFEjIiMj6dSpExMmTKCoqAgPDw9UKhUqlUoRx9JqtXTq1InCwkL8/PwoKSmhqKgIPz8/4uLi6N69O0lJSbRv316p/jBX7bR%2BWHgzD4wENQcR8AkEAsFdRH5inpmZqdzQOYOsmle/fn2uXLni1D/mrVu3xmg08sQTTwClfmAynp6eAEqpF5SqxUmSRGpqKm5ubsqTdPm7ZDsJlUrFrFmzeOONN5Qbqccff7yMP5n5Pzdvv/02Dz/8sDIWsMy4leeJJX93Zd243U2cMZ3Pzs5m7NixTgXLaWlp5OTkEBISQlBQEFlZWSxevJjff/%2Bd%2BvXrk5%2BfT7Nmzbh69apNuwt5LmTJd3lO5DnQaDS89dZbN5V1doTJZOLChQscOHCA3NxcJEmiadOmdOnSRfm9WmPde2kP2fsPcCogAZgzZ06ZZXPnzuXMmTN2y4tHjx5Nx44dWb16tVOm9tYPMkwmE3Xr1qVly5bk5ubi7u7OsmXLcHV1tZll8/DwKFP25%2B3tjU6nQ6fT4ePjg0qlwt3d3WaQLs%2Bh%2BbUqBwuOHqLczPyaU9GARLbScFZtUq1W4%2B/vT0FBAUFBQTbtWm4F634%2Bc8x7%2B5zx3XMGObCz/jxbn3/y5EkLv0hB7UQEfAKBQFDNcfZmqUePHhYqdebeYObLJEnCzc3NIhNoXh559OhRxU4iKCiI69evk5OTQ/369dFoNOTm5qLVajl8%2BDAmk4nevXuXCQYyMjIICgpSvt864%2BbIE8v8u2/3jVt1wNqHMCMjg/r16yvrzYPlpKQkxUB%2Bzpw5REZG0r9/f1JTU3FxcUGn02EwGJgzZw4fffSRTW89eS7WrFmjzIn1HOTk5DBnzhyaNWtWxtrAHEeKl%2Bbk5%2Bczb948Nm/ejCRJBAUF4enpSXp6Omq1mnfeecdmidro0aPL/WxJkvjqq6%2BcGkd5lFdeHBAQwJo1a%2BjcuTNJSUnl3uxfvnxZeZDh6%2BvL1KlTLX7nWq1WyeqZZwdllixZwoMPPsiff/7JxYsX6dmzJ/n5%2Bdy4cYM//vjDqWPq06dPmQyejHlAKEmS0jt25swZmjZtajG/vXv3VqxRZOsKOSOdkpKCi4sLBw8epGfPnvTq1UvJ/rZp08ZmNtWc1q1b88UXX1hYiXz44YdlXstWIg8//DCjRo2iZ8%2BeyvUQGxtLQkKC03YtjnjyySfJyspCp9PRrVs3jh49Su/evXn00UcJCAhg/PjxnDt3zua%2BclVERahXr56SwS0pKVEC3zfeeEPxYZV7fVNSUkSGTyACPoFAIKju3Ow/5rb2k7OAJpOJ/v37A6UG3fB3P1GfPn04duwYQ4cOVW6iNBoNjRs3ZsmSJaxevZoFCxY4zAjIGSNHGQF7/VGVeeNWHbA2PbeeR/NgecyYMaSlpTF06FDGjx/P999/z9SpU1Gr1ezevZsDBw7w6quv0qFDB44dO2a3H02eC/n/5c3Brdxgbtiwgffee4%2BSkhJCQkLw9fUlLS0NtVrNW2%2B9RUlJCe%2B//z5Lly4lIiLipr7DGuuARMa6RNYWzvSFOTJ3lzGZTHh4eCgBXlpaGqNHj%2BaNN94A/la3PXHihN3PaN%2B%2BPRMmTGDBggUUFxfTsGFD0tLScHV1RavVolKp8Pf3R6PRkJWVRXBwMHl5eRbH7enpyfbt2%2BnSpQsJCQl07dqVn3/%2BmW7duimWKX379iU%2BPl4J8KznOz09XQn4zp07xwsvvMCgQYN4/vnnOX36NC%2B99BJjx47l4sWLJCYmkpuby3vvvYfBYOCjjz7izTffdNhzZt77LH%2BvvdcyERER7N27V3l4VVhYSPfu3UlMTHQ4L7eLYcOG0a9fP9asWYNer8fd3V0Jzv773/9y%2BfJlzpw5o6yTBb2MRiMGgwEvLy/atGmj9FRPmjSJFStWKIqcarWaoKAgMjIy0Gg0SsZZlHQKQKh0CgQCQa3FWu0RShUIMzMzMRqNisqnXDolP4WWn%2BrHxMRYlNBlZ2fTrl075cb2yy%2B/tPvdkydPVmwjbOHIE0u2spCx9tmr6Vj7s1k/tzWX75ezQbLR9s8//0xgYCBhYWEEBATQq1cvAP744w%2B7dhfyXJjPSWXNwb59%2B5gzZw6NGjXi3//%2BN08%2B%2BSRQqr757bff8vbbb7N06VJee%2B01lixZYjfgM5lMHDt2jNTUVNRqNU2bNrWwujDn1KlTPP300zzzzDNlAr6UlBSWL1/usJ80NzeX4OBgxowZY7e8WKPRWBiV22LWrFkMHjxYCaIfeughi%2BBuxIgRfPjhhxb7XLp0SVG39Pf358aNGxYG6wEBAWRlZTFy5EhWrlyJr68vL730ElCaBXvmmWdYuHAhp0%2BfVrL/st/ezJkzGThwIHPnzqVTp05l5tc8m2f9G%2BzRowcmk4mePXsq2ae1a9eydu1a5f2iRYv4888/mTFjBhs2bGDw4MHKZ23evNlhwHczViLW3oHWdi2VydWrV/njjz%2BoW7cuFy5cAEoDtIYNG%2BLl5UVSUpJixQMo4yoqKsLLy4uSkhKuXbumzLXJZLJpHZORkYEkScrfbEmSuHz5MiaTiby8PFxcXHBxcQFs//0X1FxEwCcQCATVDHs%2BbNa9O9OnT7e5v1w6ZUsl0dys3fyJ8J9//sngwYPL3GDde%2B%2B9pKWl8frrr1uUUY0ePZqHH37Y7jH079%2Bfbdu20a9fvwp7Yt3NG7eqgHVgZp05Mg/M5PmS1QeTkpIoLCxU5sbNzQ21Wk1xcTG9evUqY3chz0WPHj0s5qSy5iA2Npbp06czZ84ci/5OjUbD0KFD0ev1LFmyhM8%2B%2B8xuv15iYiJvvvkmqamp%2BPj4oNfrKSwspHnz5syePZvWrVtbbL9w4UIGDx5cph8SYN68ecyYMYNPP/3UZj%2BpdWnr3LlzbZYXq9Vqh9cDlJbmjhw5UnkvSZKF6qZ13%2BKvv/7K%2BPHjCQoKQq/Xk5GRgVqt5ocffiAsLAytVsvJkycxmUyKIXp8fDwrV65k%2B/btFBYWsmDBAqXHbP78%2BWg0Gs6ePUtMTAxDhgwhPDycFStWOCWCZE5cXBxRUVFs2bKFUaNGkZ%2Bfr/QMjxo1ipiYGIKCgoBSiw9za4rIyEjFA9Ae5VmJVKVg5sCBA4wfPx6dTseePXuA0uvSaDTSpUsXmjVrpqjQ2kJWQgacKv00mUyKCA%2BUVgQAiq2OPJc3228pqJ6IgE8gEAiqGfZ82Kzlzq37325FWOHBBx/Ezc0Nk8nEqlWrGDVqFAkJCfj5%2BREeHs6YMWOIiYlhzJgxTn1eZfi41RYqYnoeHByMTqdTSsXS09ORJEnJZJ0/f15RgI2Pj0er1dKlSxc6duyIXq/n119/xdXVle%2B//57AwMBKn5Njx46xaNEi3nvvPQsfNZmoqChiYmLseleeOXOGcePGMXr0aP71r38REBAAwIULF/j000/55z//yfr16y2yfcnJyWzYsMHumKKjoxkxYoTNdZ988gkvv/yyRWnrggULypQXO5OJsg6iAYdB9KeffsqLL77I2LFjgdKMoE6n46233lLKN%2BVztGXLFvr378/o0aMpLCxkxIgRJCQkcOjQIVq0aMGff/7JzJkz%2Bfrrr2nevDkff/wxOTk5pKWlcfDgQbt2HvZo0qQJKpWKJk2aUFhYiFqtVjKChYWFdOrUSdk2JCTE4m%2Bar6%2BvhXgUlFWBjYqKYt%2B%2BfTRq1Ih9%2B/Zx9epVZYxfffUVmzdvLmPvYjAYWLduncVc2Fp2u%2B1a5s2bh5%2BfH4MGDeKnn36yCOKNRqPDEl17tG/fnjNnzlBcXExQUBAtW7akuLgYlUplYbou//6h9G9BaGio0tcoqF2IgE8gEAiqGbbUAgGee%2B455bUj4QxnsPWEXL4pkv29fvjhB4qLiykpKaFjx47odDqlTFOSJKZMmUKHDh2U/c1vpOrUqcO6detYtmwZcXFxFr1PI0eOdOiJdbdu3KoK1sGywWBg27ZtNoPlfv36sW7dOv7v//4Pg8GAm5sbLVu2pGnTphQUFPDBBx/g5ubGwIEDCQ4OZseOHVy4cEER21Cr1TRo0IDevXtbzIn1%2BV6/fj0lJSUMHToUKA1ehg4dSmhoqMXYyxPj0Ol0uLu706hRI8VnzRw50Dt8%2BLDNvrrly5czYsSIMlnw0NBQ5s%2Bfz5w5c1i4cKFFdtC6RNYa8xJZa5wtba2Iqb2MbMBunv0ClPN%2B7NixMvYRRqNREXMxV7CUPQBPnTqFJEl8%2BeWXXL16lZkzZ7J06VKg1DsvMjJS2UeSJM6fP09sbCzPPvtsmXNq/l6r1TJ58mSb8xsYGKgYwwP4%2BPiQk5OjBCPHjx%2BnXr16ynrZU848yOvRo4dFFtFkMpUJouXMlVyGGx4eblGOGxQUxJIlSyz2sV4mSdJt/7tx9uxZAF566SUmTZpEVlYW2dnZZGZmMnnyZIqLi3FxcWH%2B/Pm0aNGCy5cvk5yczOXLl1m7di1qtZqePXvStGlTwsPD6dChg5LtbdGiBRcvXuTSpUtlvvdWVFMFNQ8h2iIQCATVEGdEJszLMm%2BHT9rixYuB0pvXzp07U1hYSFFRkZI5tC77kiXl3d3dLVQ45SfvwE1ZK5RnWwBV22fvdmAuFHL69Gnc3d1tGkjr9Xpmz57Ntm3b0Ov1tG3bltla1UfOAAAgAElEQVSzZ1O/fn3effddtm7diq%2BvLzExMRw/fpz8/HwkScLPz8/hnFjPgb2ASPZFk7H3sEJmwIABzJ49mwMHDrBr1y4%2B//xzC2%2B3pKQk3nzzTSRJYtCgQYwfP77MuJYvX25XDTQrK4tBgwZx4MABZZmzXnpyubM5tsQw7AlkOLpme/ToQZ8%2BfSzsS2RfTD8/P2XbvLw8JdBNT0%2BnYcOGyu88PDwcvV7PrFmzSE5OZtOmTQAMGTIEKA3K%2B/TpQ506dWjfvj0zZswgJSUFk8nEzp07OXr0KN988w1PP/00X3zxBe3bt%2Bfw4cM8%2Buij7Nmzh5CQECWLlpqaioeHhxK0paWl0bBhQ9q0aaOMdfv27fzxxx9KmWtKSgoajYZp06YRFBTEpEmTLKwrXn/9daA0kHR3d2fjxo02bSKs3w8YMIDo6Gjg775Ce1Yid4PIyEh0Oh3Lli3D39%2Bfn3/%2BmdOnT/Pbb79x5MgRRS25Y8eO9O7dm61bt5Kbm0taWpqS4fX09FTsNMwfxrVr146dO3eyZ88euz3PAgGIgE8gEAiqHeYiE4582CIjI5Ubz/JUAp0p98zKymLZsmV8//333LhxQ7mRGjhwIKtXryYvL48hQ4bQqFEjTp06xbBhw%2BwaqyckJFBcXExwcHCttFa4Vcx9CGfNmnVTHluyjPylS5eIjo7Gx8dHycQEBQVRUFBw2%2BbE2jrCHkuXLmXXrl0sWrSI1157jaNHj/LEE08QGhrKtWvXWL16NcXFxTzyyCMsXrxYEaCQCQ8P5/Dhw2XsRsyx9iRz1ktPDkisv8%2BZgM/e9fDdd9%2BRkJBAYmKi0kvniBs3bnDw4EGb3xMeHk5JSQknTpygd%2B/eZGZmotfradKkCaNGjWLGjBnMnDlTCZbmzp3Lyy%2B/zL/%2B9S8LXztHXnFy8Cibg5tn8G0xZ84crl%2B/TkREBPXr12fo0KF4e3sTExPDPffcQ1ZWFvXq1WPRokVkZmayYsUKjh49ysaNGy3KWa0rFvr27cuqVauYOHEimzdvdspK5G6xcOFCVqxYgcFgKFNqezOo1WpCQkIUM/nTp09jMBho3bo1PXv2RK1Wo9PplGujS5cut%2BtQBNUYEfAJBAJBNcPah80a%2Ben21q1blRtCuawIHJd72sqM7Nixgw0bNrB//34MBgMqlYpRo0axfv16vvvuO%2B655x6mTJnCDz/8QJ06ddi4cSNz5sxxOMYuXbrQoEED1q1bB9Q%2Ba4VbwVoo5NKlS2zYsOGmAzPZYmH37t2KqqIsx1%2BROXGUwZo/f75TQalOp%2BP555/n6NGj9OvXj%2BvXr3Py5Emys7O5ceMG/v7%2BvPbaa0RFRdl8gOGM/Lz1Ns566Xl7e5f5rNatW1tk5aBU3dJ62YEDB2xeD/JYzL0T7WGdpbf2y5OVMevVq0d2draS3QKU15IkKVn8nJwc3NzcOHjwIGlpaVy7do0xY8bw7bffAqXlsefOnWP27NkAnDt3DrVa7ZR1hXX5Z1xcHE2bNiUjI4PCwkJMJhMqlUr5eyLzj3/8g%2BnTpytKobbOFZR6g548edKudQuUtRK5W8h9z3PnzlXKdKF0Trp06UKrVq3YtGmTRdmrTOPGjWnevDlHjx4lICAAX19fzp49S3BwMK%2B88goBAQEWaqbmwbqs1inKOgUgevgEAoGg2nEzIhPWgZwsqOAMEyZM4IknnqBr166cOXOGrl278sYbb7B%2B/Xplm3nz5uHh4cGhQ4f49NNPyx1jUVERly9fVt7XNmsFR5RXfvvhhx8yZswYJk6cCNgXCnEWuQ9t0aJFxMbGYjAY%2BPzzz1m0aJHTc1KetUFJSYni%2B%2BYIFxcXYmNj6dKlC9nZ2Vy8eBGVSkXHjh1JS0tTjNjtYauX09Y25txKP6mzfWFardbm9SCP05EwjIx1H5vRaKR79%2B5ltpMzY15eXkDptTZr1iymTZtG3759FQXMY8eOMXr0aGJiYhg1ahTbt2%2BnXbt2GI1GVq1axcaNG5k2bRq%2Bvr5kZGTw0ksv8cwzz3Dq1CmLAE/umfviiy9ISkqiuLgYnU6nfD%2BUCg1lZWXxwAMPEBISopzPKVOmKOf7vvvuw8fHp8zx2MoOy/2FFbESuVtcuXKFvn37EhMTw%2B%2B//26RFV28eDFGo5Gvv/4aDw8PioqK8PT0pLCwEA8PD/r27YvRaGTXrl2kp6ejUqlQqVSkp6czZswYGjRoQFhYGCdPnkSr1TJu3DiWLVuGJEnUr1%2BfXbt23cUjF1QlRIZPIBAIqhn2nlibP1WPj48HKCPqAKXCGRUx4o2JieHbb78lOzsbNzc3li5dSseOHWnbtq2S4YPSMqqnn34atVpNbm6uw6fq4eHhSJJksY0wBy6lvPJbOVMjq/vdqoG0IxNrZ%2BekvKxzy5YtGTJkSLm9VLJZt3nZ5bhx41i6dKlTY3GmvxMo049nXiJbkX5SZ7F3zYaHh5OUlITJZCIiIsKmVQqUiihZZ%2BkHDBjAhAkTLLb773//y6xZs3jrrbeYOXMmAG%2B//TZHjx4lLCwMjUZDt27d6Ny5M7169eL48eNMnz5decggZ9vq169PTk4Oo0eP5qeffsJoNPLII48wY8YMpkyZwv79%2B2nWrBnp6ekMGDCAS5cusWPHDqWnLy8vj9jYWNq0aVMmI13R8m1bv8WWLVvSpUsXmjRpwtmzZ8tYiYwePZp27dpx%2BPBhu%2BW4d4q2bdsCpeI1ixcvJjo6WrFNWLZsGU8//TSFhYUEBgaSk5ND3bp1ycvLIzAwkICAAHJycmxm/xxx3333ceHCBfbv33/XA15B1UBk%2BAQCgaCaYe%2BpttzMn5eXh5ubWxnBjJvllVdeYeLEiUqP0L///W%2BaNWuGTqfjxo0bynYhISHk5%2Bc7HKOM0Wi0UOYT/I1sbA%2B2y28HDRqklN1B1fAhLC%2Bjq9Fo2LdvX7mfk5GRAVj6uv3666%2BAc9YGtoRVyvu%2B1NRUp7z0bgV710NJSYmiRGo0GgkPD7e5/59//lkmI%2B/i4sK4ceOU971790aj0TB48GDeeecdOnXqRL9%2B/SzKOu%2B//37i4%2BOJj49n%2BvTpiuqpj48PRqOR5557jscee4wlS5bg7%2B/Pq6%2B%2BysCBA4mKiqJ///5AaTb/8ccf5/Tp04wfP57vvvsOjUaDRqNRBGRWrFjB%2B%2B%2B/z6RJk1i4cKGFdUV0dDTvv/8%2Ba9ascercmUwmJk%2BerCiBysuSk5NJTEzEaDTStm1bAgICaNWq1R23EimP4cOHk5ycjEajYezYsTRt2pTk5GT0ej2DBg3CYDCg0WjIycnBaDRy9epVpYdW9udzdXVV%2Bv7MS3UlScLb2xudTmch8JKbm4vJZKJv377cd999eHp6EhISQmhoqOJDKXr7ahci4BMIBIJqhj0ftjlz5ihPt4cPH35bn2qrVCqCg4PR6/V89NFHHDp0iIKCAp566ikeffRRhg4dSkhICN7e3ri6upbrFafT6bjvvvssJOdrk7WCI8orv5UkyelyXGeQz7u5rYb82nyZXIpma07KszaQJMmukqf1dtaY9yTdbvr06UNYWJhTXnq3gr3rQaPRsGjRImbPns2DDz5oYbxeHiUlJRbvMzIy0Gq1ylw%2B88wzFBYWolKpmDJlClqtlpSUFDp37kxYWBh79uzB1dWVmTNn8vrrr/Paa6/RuXNnoDSAX7hwIQDNmjVDo9Hw6aefKsFTQUEBGo2GZ555hkcffZTIyEgL9ciOHTvywQcfsG/fvjLWFW5ubhw5cqTcEl85uNPpdPz222%2BYTCYlS9qgQQOMRiPXr1/HZDJx48YNMjIyyMzMtGslcrfYu3cvp0%2BfVoI0%2BaGGOeYWGiaTyW5GT61WM2TIEJ544gkmTJhAv379%2BOabb%2BjTp49S1WHuYZiXl1cmsyx6%2B2onoqRTIBAIqhmORCbWrFlDnTp12LJli4XIxJAhQwgODlb8m%2BLj4%2B2We9pj7ty5/PDDDzRp0oTFixej0Wg4ePAg69evZ%2BfOnYSGhpKdnU1UVJTiFWdPCOPatWsEBQU5vImv6dYKzmJdyuisUIizwbJcBpmZmelwu5CQELtzUp61QVhYGPXq1Ss3A3c7yksrwkMPPYSbmxt79%2B5VTM9vtUTWFvau2ddffx1fX1%2BHwjD2kIVLZMLDwykuLqZhw4ZkZmYqvYqy8qfRaMRkMikqj0ajkZKSEg4cOED79u05cOCAErS1adMGk8mknO%2BePXuSmpqqfJ9svyAHE61btyYoKEiZ34kTJ/LDDz9w/Phxm/PmTInvtGnTgFLT%2BKioqDLrCwoKyMrKUnoby7MSuVts3ryZ5ORkiouLcXd3t8jGu7u7k5ubq1RGyOj1ekVBV6/X4%2BnpSX5%2BvlIKajKZ7Cqqyg%2BI1q9fz6OPPqooqgpqNyLDJxAIBNUMeyITISEhFBYWMmTIkDI3jvn5%2BZw5c4bu3bvj4eFh8waqPKKjo/npp5/Yv38/nTp1okuXLgQGBlJQUIDBYODUqVM0btyYF154AW9v75sWwhA45nYbSFe0DNIWzmR0e/fu7fTnmWd79Xo9a9eurZQMsCyoIgd7UDklsvauWYPBcFuvB3d3d2U%2B5fJQOdiKioqid%2B/eimed%2BTbWgUNgYKDFA4Du3buzatUq9Ho9Go0Gg8GglGQXFBSg1%2Bst5lcuYbSHMyW%2BsmdjXFwcc%2BbMsVCBlfsCfXx8iImJQZIkgoKCKqUc91YZPHgwgwcPpm3btiQnJ9O%2BfXtlXXJystI3KWfQDx06REREBFBqaG9dOm%2BOueKnu7s7RUVF1KtXDzc3Nz799FOMRiPJyclkZmbSq1cv3NzcKCkpsfi9C2oHIuATCASCakhubi7BwcGMGTNGeao9ceJEOnfuzNSpU8tsv2vXLqek3x0hWy4sWrSIjRs3sn37dsW/z2Qy8Z///Idx48YpN662xii4dW5HgHa7kTO6jz/%2BuM2MriRJTvVSyUGdl5eXkm3W6/XExMRYLPPy8qpQUFsVsHU9LF68mP/85z9O7W9tdWC9zNrbzc/PTymjzc7O5vTp07z99tvK%2BpycHOrUqQOUZmDj4%2BMZNGgQUBrAr1y5UgnwfH19leVRUVFotVq8vLx47rnnSE5OBkqFXuQSbfPyXVuButFoJDs726Kk29Zcnjp1SnmYZK4C%2B8YbbzBmzBiSkpIICAjgwoULNGrUiJ49e972ctybxWg0snz5cr7%2B%2BmuuXbuGSqXiueee48knn%2BTo0aMYjUZeeOEFrly5wpUrV3BxcVEelHXt2pWLFy/ywAMP0LFjR5KSkpRqCI1GQ2BgIE2aNCE0NJRly5Zx4sQJ2rVrx8aNG8nKyiIrK4s//vgDQCkT9vX15cMPP%2BSNN95g%2BfLltGzZ8q6dG8GdR5R0CgQCQTXDnupddHQ0GzZssBtYHThwgMmTJ7N//36L5bZ8tOyRmprKd999x9mzZ2nRogUDBgwgODi4jJn1rSrz1Wasb%2Bxvpvy2srFlHVFUVMSaNWv45ZdfyMjIUKT2IyMjnc5gOaOyeTtLfcPDwzEYDLe1RNYWjq4HR/6FPXr0oFGjRsDfJY4y1qWOmzdvBuDdd98F4Ntvv%2BXIkSOMGzeOhIQE0tLSmDRpEsOHDwdKFT3//PNPFi9ezN69e3n55ZeZMmUKTz31FEVFRURERBASEkLjxo1JTEzEZDLRpk0bfv/9d6Vc1MXFBQ8PD7y9vS3KszMzMwkMDOSXX36xOac6nY6cnBxCQkIA23MqB3klJSVERkZaqMC2bt2aunXrUlhYyP79%2B3n33XfZunUriYmJt70c92ZZunSpYmvSunVr5RzeLHLvHZQGfW5ubooFhj3UajWTJ09m3rx53HPPPYSEhCiem6tWrbrpsQiqHyLgEwgEgmqGbJRtLjKRkJDAb7/9ZtcKQb550mq1ypNfmSlTppCYmMj69esdZuEOHTrEv//9b7RaLWq1GoPBgIeHB1999RWjRo2y6NOxN8aq8OS9qmN9Y28PueTtbmDLOsL8dkI21q7qwhDh4eGKlYAjbjXItHc9vPnmmwwbNoyRI0fy6quvWuxT3nVp3csZExNDXl4efn5%2BQGmG6caNGxiNRlQqFf7%2B/ri7u7N7924%2B%2BeQTVqxYwVdffaWohK5fv545c%2BZgMBho2LAhZ86cUcoNVSoVer2e4OBgWrZsydSpU%2B32a0Jpv%2B%2BZM2eUEl9zLz1ZWKo8uwTZ6mPr1q24u7tbPMwKDw8nKiqKDRs2cPz4cTIzM3nsscc4ceJElbF36dOnDyUlJfz3v/8lLCyMTz75hG%2B%2B%2BQa9Xk/z5s0VIZrAwEA8PDzIz8/n6tWryrVjLuRSUeSqCzc3Nw4fPkyrVq2UoPzXX3%2BlU6dOorevliECPoFAIKhmRERE2BSZqFOnjl3hDNmk%2B%2BjRozZLAp0p9xw1ahSZmZk888wzPPvss8TGxrJ27VoaNGjAkSNHLG6y7I2xKjx5F9w61p5wAwcORKPREBAQgMFgID8/nzlz5tw2a5DK4k4FB/auh0ceecShf6H1dWmeDTTPnN24cQMPDw9FoEVGkiTWrl1L3bp1LdYlJSWh0%2BnKZBWvX79OQkICqampfPzxxwwdOpQHHniAoKAgXn75Ze677z6nMvbWIjWLFy/mgw8%2B4NSpU3zzzTfUrVuXTz/91MKc3Zphw4YpQZ61j2F4eDg7duxQgjz4W8SmqgR81sI2RqORdu3aIUkSR44cUfz55D4%2BvV5PREQEer2eN998U1FCTk5OpnHjxpw7d45mzZoppdJNmzYlJSWFjh07UlhYyMmTJzEajej1ekWgR6PRsHXrVvr27YuPjw9qtZqvv/6akSNHKnYngtqB6OETCASCaoY9kYkhQ4bYFc5ISkqibt26doUzoqOjGTFihM11cp9NSkoKUFrGtXbtWlxdXcnKyuLy5ctKiVd5YxQ4h6MyP2fLbysTa1sIg8HAq6%2B%2BapHBkuXiqzLmVgKVib3roTz/QvPr0rqPzfzBjZwNXLNmjd0svXmWrUOHDoSHh/P9998rJaNQ2qfbr18/Dh06hMFgIDExkf3795OWloaLiws7duwAyreusBap0el0vPnmm4SGhpKXl0d%2Bfj4DBw60ua/cF%2Bzm5qYci7WPocFg4JdffsFkMjFlyhSuXbumnAdnrUQqGzmfYjQaiY2N5YcffqCoqAgozc7qdDokSWL16tUEBATw1VdfKZYKK1eupKSkhBs3bvCf//yHffv2ERsbq2Rjk5KSWLRoEZMnT%2Ba///2v8rfVWnxHr9fz5JNPKq9btWrF%2BPHjeeKJJ%2B7YeRBUDUSGTyAQCKoZtp5gh4eHs3fvXrtWCEuWLKFZs2YOpd%2Btn6LLyJkEuW%2BrQYMGyjrzZeY3oPbGWBWevFd1zG/sK1rmd7do3rw5R48evesZXVu9hbYw/w3fCexdD5Ik2S3DlpGvS7nE0dls4IABA9i6dauyvlWrVhbl3GFhYUiSxPbt24FSMaC9e/cCcO7cOdLT09mxYwcNGjQgLCwMFxcXDh8%2BDDg3v6mpqSQkJKBWq5k1a5bS42udHR40aBBbtmwps//zzz%2BvVCxYl4j26NEDnU7H5cuXlfJySZKUElQZR1YilY2sgBodHc3XX39N06ZNFWVSecxQ6kuo1%2BuRJAm9Xq/sX69ePbKzs3F1dUWSJJKSkpQHFFqtlo4dOzJ58mRmzpzp9Jjq1q3LsGHDeOGFF%2B7Yww5B1UBk%2BAQCgaCaYUv1zmAwEB8fz7Bhw9i7dy9r1qwhPz9fsULw8fFh3rx5doO948eP4%2B/vb3Odtcy7dWBnvSwjI8PuGIWxevksXLiQwYMHlwn2AObNm8eMGTP49NNPb1pttbKoChld2ZPNHnL26E73Ftq7Hry9vVm0aJHSR2h9PZhflxXJBgJcuHDBYr1er7cIiGVVz549e2IymWwKivTo0YMXXngBrVZrsb68%2BbUWqSkpKSElJYWwsLAy2WHZN84ac6sPaxXYl156ic8%2B%2B4zw8HBSU1NvysewstFqtRiNRj766CPc3NwsxLLMKyJKSkoACAgIICcnByg9J3l5eajVaoxGI2q1mocffpji4mIkSVL6%2B%2BwFe/369WPw4MGEhobi7%2B9PSUkJ7u7uVer8CO4sIsMnEAgE1QxHSoZ6vV65gfjuu%2B%2BULJD1E3JznBVRaN26NUaj0aIc6PvvvwewWBYXF%2BdU9kkYq9uma9euDtVWMzMzGTFiBD/99NMdHpl9rE3A4e5kdJ3NHtkKMCoTe9fstWvX0Ol0BAQEoFKpLK4H6%2BvSXgbeHPNtrM9/8%2BbNLTJgctBgXgYoSRJxcXGK%2Bq6LiwtGo5HMzExUKpXF5zmaX2uRmlatWhEREWGzBNTe51j3ATZo0IBdu3aRnJxMbm4ukiRx//3306dPnyrp6/nZZ5%2BRmJhIUlISHTp0KCOS4u/vz5UrV5T399xzD6mpqcDfDybq1avH5cuXgbLlmjJBQUGoVCpat26Nl5cXKpWKq1evkp%2Bfr4j2yOIw9913n%2BIDKFtwCGoHIuATCASCGoIj6XfrmydrnzRnnpD36NHDwozZHgaDoczNv8B5Knpjfzewto7Ytm1bGQGPP/74g3ffffeuZnSrehlxRa7LXr162RVlgtJs4IsvvmiRkbcO%2BOLj44HSgKJfv364urqyZcsWhgwZgsFgYMuWLTRp0oTw8HB0Oh3PPfcc0dHRtG/fHpPJZFFO6si6wlqk5qGHHsLDw8NmCaijOSooKGDZsmXs3LmTixcvYjQaCQgIoF%2B/fkyYMKHKBXm2kI9ProaQkZfJmVJZcMZ6m4ceegiTycS8efM4e/YsJpOJBg0a0LBhQ06dOsUvv/yiCLyUh0qlUrLJcvmuoHYgSjoFAoGghvDJJ5/w8ssvWwhnyMIK1iIKly5dUso9R44c6dQTcmcNv61vWgQVw1qgwhpH5bd3Cuv%2BH09PT86cOWOxzN3dnSVLlijvq5tR%2Bp2gIteleYmjrSz99OnT7Yoyydgrp5StAOT1ck9cgwYN%2BPbbb/Hy8iIvL4%2BYmBhlXy8vL7vzay1SI0nSTZX4ymb13bp14%2Buvv6ZBgwYYDAbWr1/PE088UeN9PXv06KFUbEyYMKHC%2B3/44YcWXo2C2osI%2BAQCgaCGcPLkSWJjY5X3I0aMUIx/4e%2BbpzFjxtCtW7cqJfoh%2BJvbcWNf2Vh7AN5NT8DqjrPXpXUfm61s4AsvvHBbxhQUFER6ejpLly4FSoM7awuFipRku7q6Kj2D1tlhnU5XZhnA8OHDlYqFjIwMTCYT8%2BfPJywsrFyV0KqEVqtl%2BPDhZQLe5s2bO3wPzosQyabsRqMRSZLo3bs3SUlJREZG3vzABTUKUdIpEAgENQRHypiOyj3vxDgEznM7ym/vBFXdOgKq/m%2BxoteleYmjeTYwMjKyTJa%2BRYsWBAYGKu%2Bzs7OpV6%2BexXuVSsWff/6plA1%2B9913yvqoqCi%2B%2B%2B47i5LNxo0bA%2BUHIo8//jgTJkzA19dXWSaXgG7atKnM59ni0qVLSh9gREQEY8eO5cCBA6xcubJa%2BXp%2B9tlnACQmJpKWlkZ%2Bfr7F%2BqKiIgwGg2K2LvfvqdVqRUzHlgn7Pffcw7x589i5cydbt27F398ftVrNmTNncHV1ZeDAgZw8eZKuXbvi6%2BtLQEAAgYGBygOFO61UK7i7iIBPIBAIagiOAj5rEYXY2FgSEhIq5Ql5Vb/Jrg5U5Mb%2BblBVrSOsM0Xx8fH07du3zHYLFiy4U0NySEWvS3OrA%2BtsoHUQJtstyHz88cdMnDiRunXrAjBt2jQeeugh7rnnHrZt22Z3jCqVqoy6aYsWLRyqocoBiqOgv7wMoXkfYHh4OAcOHLAI8mrK3xlZqMXcD3HHjh2sW7eO/fv34%2BnpSVFRkRL4bdy4kSeffBIPDw%2B2bNnC008/TV5entPfJ2cD77RSreDuIko6BQKBoIbgyAohJSWFPn36sHbtWoYNG1am3FNQtajq5bdV1TrCurewqvcvlVeGbY51NnDOnDksWLCAFi1aAOVbUhiNRubOnUv//v2VZTk5OeTk5BASEqIsa9OmjcV%2Br732WpnPiouLU17fjBqqnB22xjw7bM%2Bsvrpx48YNVq9ezU8//UReXl6ZbF1qaiouLi4WweuECROIjIzEaDRSVFTEjz/%2ByLhx4zhx4gRff/21Ykw/fPhwtFotGo1G8fIzmUxKdrBFixY89thjaDQa6tWrR0hIiEVgKag9iIBPIBAIaghBQUEWIgrmy0pKSvjiiy8UYYXKvHkShr63hvWN/dy5cyut/PZmqagn3J2iuvUSViSosRZlat68Of/5z38srBbMH/bIr1988UUAjh49ipubm3J9Dhw4kOPHj5f5XTlzDp310rOFeXbYuhw4JSWF5cuXs379eovl8oMrvV7P2rVrLZZVdV/PV155RfHgk8suoTSLf%2BPGDQwGAwaDgXbt2in7aDQaduzYAZQG6k899RSdO3fmxIkTbNy4EcBmVk/OxH7xxRe8%2BuqrrFu3DhcXl8o%2BREE1QJR0CgQCQS3AUblneTgrHCB6Qm4Pd7L89mapytYR1aG3UKYi16W11cHx48f55z//qQQ91lm2gQMHolKpCAkJQa/Xk5eXx5dffklYWBiJiYm89dZblJSU8MEHH/DII49QWFjI0qVL2b59OxcvXkStVtO4cWP69evH//3f/1kEps4chz0mTpyIv7%2B/hcWDOTNmzMBoNPLtt98qtg%2ByOmheXh5%2Bfn4W28tiMlXV1zMsLAyTycSOHTssfnvXrl1j9%2B7dvPnmmwB06NDBYj%2B1Wo23tzfXr1/n0KFDeHp6cv36dRo0aEBmZib9%2BvXj0KFDPPDAA2RnZ5OdnY1Wq0WtVuPl5YWrqys6nY7t27eLh3ACkeETCASC2oCjcs/ynpCXVypm3d8juDUqUuZ3t6iq1hHOZo%2BqSolsRa5L62xgy5Yt0el0SmbNOsum1%2Bt59dVXGTt2LFBahvvuu%2B/i7%2B/PgQMHiIqK4vvvv2ffvn2kpKSwceNGdDodI0aM4DvBWbYAACAASURBVOeff%2Bavv/6ie/fubNq0iV27drFq1So8PT1v%2BZidzQ6bVyzIQd2tKIXeLUJDQzEajRYCNgC%2Bvr4MGTJEOaetW7cGSnv5zPv6tFotCQkJJCQksHfvXtLS0nBxcWH79u106NCBAwcOKOIu8m/m2rVryveYZ3CFD1/tRWT4BAKBoBbQo0ePcrexd/N09uxZ5fXN9OsIKsatZGPvFHPnzuXMmTN2rSNGjx5Nx44def311%2B/ouJzNHt3p3kJ7VOS6LO93YctoXS7jhFIT76effprg4GBiY2NZuHChcq6eeOIJLl68yPbt25UslHyu3nnnHZ599lnatWvHpEmTbI6xIr/Pqpwdvl1otVp0Oh0Ahw8f5quvvsLV1ZXQ0FCys7P566%2B/yMzMpLCwUCnplPHx8VGUPO%2B//35mz57Nv/71LyRJYs%2BePXTp0gW9Xm8R4Fnj4uKCJEkWGcXAwEDuu%2B8%2B2rdvD8DgwYMr6/AFVRCR4RMIBIJagLOm6ba4lX4dQc3kTnrCVYSq2ltoj4pcl%2BVlAw0GgyLKJGOeEVyxYgUqlYpffvkFsDxXOp2Onj17snjxYiUYls%2BVRqPhnXfeITo6Wgn4nPXSs6WGWlWzw7cT2ebiZjC3bTh9%2BrTFfHbq1MmmRQOU/l2eNWsWgwYNUvoEBQIZEfAJBAKBQFCFuJXy2ztFnTp1WLduHcuWLSMuLs7COmLkyJF3zTri%2BvXrDss1Q0JCyM3NvYMjun04EmWC0qBrzpw5HDx4UFlvHoT9%2BOOPFr8f83OVnZ3N1KlTLYJh83PVtGlTsrKylHW3oob6%2BOOP8/7779vNDk%2BfPp3evXs7/XlVka%2B%2B%2BqpMifvcuXMZP368xRyOHz%2BeL774AqPRiE6nw2g0Koqb5sjLjEaj4tcnM3LkSNasWQOU9vvOnj2b4uJixdNPRu7te/DBB5UxCmoPIuATCAQCgaAKUd6NPaCord5NqqJ1RE3OHllnA60zaunp6QQEBCilkJIk8dtvv/Hwww8DpT195oqe5ufK3d0dT09Pi2DY/Fzl5uZaZAtvRQ21qmaHbycPP/ywct6hNGv39ddfM2TIEH744QdKSkowGo0MGTKE1atXA6XndNasWahUKgYPHkyLFi146623KCwsRK/X06tXL3bv3o3RaKRXr17s3bsXg8HAtm3blPLOs2fPKj3VcrDn4eGBJEl4e3vToEEDwsPD78o5EdxdRA%2BfQCAQCCpEVesnE9x5rK0jcnNzq4R1RFXtLawMpk2b5nD9pk2bcHd3p06dOkCp557RaKRevXoAiiXAkSNHmDRpEvXr12fXrl38%2BOOPZc5VTEwMp06dYvHixcrn34oaakFBAcuWLWPnzp0W2eHIyMi7lh2uDLRaLQcPHiQ6Ohq9Xk94eDj9%2B/fnf//7H2fPnkWlUtGsWTPy8vJQq9Wkp6fj4uLCxo0beeaZZyzKO82xzvLZ4/Dhw2WEbgS1ExHwCQQCgcAh1pmE%2BPh4%2BvbtW2Y7W/06gppJVbWOuH79OsOHD6ewsNBu9mjNmjV4e3vf1XFWFuZB2ObNm5Xl%2B/btIysri2vXrvHPf/4TtVpNcXGxEhj/4x//YOPGjTRq1IjHHnuMuLg4AgICmD9/Phs3bmTdunWsXr2aVq1aKd8jq6G%2B%2BuqrFmOYMmUKiYmJDtVQU1NTSUhIQK1WV5nscGXQokWLm%2B7lk%2BnQoQNJSUlAqY/f1atXbW4nSRLNmzfH09OTt99%2Bm/nz5/Pqq6/SokWLW/p%2BQc1ABHwCgUAgcEh5mQSZ6mZ6Lbh5rD3hCgsL6d69O4mJiXd5ZLUnewSWAZ55EBYUFGSRZZsyZQoHDhzAy8sLrVarBMNFRUVs2LCB33//3UIp0sXFBbVajVarJTQ0lHfeeYdOnTop629FDbWqZocrg4MHDzJ27FiMRiNPP/20RR%2BkyWTip59%2BAkrVWn/%2B%2BWcARd3zZpEkCZVKhaenJ4WFhbi5uaFWq/Hx8aFRo0ZKqemLL754S98jqF6IgE8gEAgEAkGFqMrWEbUle2SdZTMPwqZMmcL333/PTz/9pBz/jBkz2LNnD0OGDFGCYZPJRN26dWnZsiVTp04lNDSUP/74g4sXLwKlCr22MkSdO3dm06ZNds9tZmYmI0aMUAIac6pqdriyiIqK4vr167z22mv84x//wGAwoFarWbNmDbGxsfj6%2BjJu3DjefvttTCYTjRo14sKFCzY/a9CgQWzduhWDwUDv3r3ZsWOH0%2BNQqVS4u7vTrFkzJEnim2%2B%2BuV2HKKgGiIBPIBAIBOVyK/06gppHVQ34alP2yDrL1rVrVzZs2KAEYS1btmTIkCFKli0zM5Nu3brxzjvvoFar8fX1ZerUqTd1rpo3b87JkycdbmPPS68qZ4crg7179xIdHY1Wq8VkMuHh4UFRUZGyXqVSOfTUkwVYWrZsSd%2B%2Bffnmm2/w9fVFo9Hw5JNPsnLlSl555RVeeeUVAIxGI02aNOH8%2BfPExcVx77333pHjFFRtVOVvIhAIBILajJxJ2LdvX5l1KSkpjBgxwqJUSVDzkW0i1q5dq/xna9md5pNPPuHll19mx44d7Nq1i%2Bjo6BrbW5qcnMy4ceOU99aWFBqNxuKavXTpEgArV67k888/Z%2BLEiTz11FM3fa7Onz9vd50jNVStVmuh%2BOnp6UlxcbHT31vd6NKlCzt37lSMz41GI66urtSpU4fp06fj4%2BODSqWyUCb18PAgKCgIKLVTkCSJ1157jQ0bNiBJEu%2B%2B%2By6nT59m0KBBZGVlERkZSfPmzWnatCkPPPAAs2bNwsXFRQi2CBREhk8gEAgEDrmVfh1BzaRHjx7lbiNJErt3774Do/mb2pQ9ss6g9erVi%2BXLlyuWFOHh4Yo1A8DgwYM5fvy4kpl76KGHaNOmjeLHVpFz1bJlS7p06XJTaqhVNTtc2bRt25akpCQ6dOigLEtKSiIiIoKCggKOHTtG69atgdKAb9euXXTv3h2NRoOHhwd%2Bfn588MEHrF69mpSUFDQaDa1atWLTpk0Os4Rt27bFw8ODkJAQQkNDle/o0qXLnTlwQZVA%2BPAJBAKBwCHJycls2LDB7vro6GgLw2ZBzcfaE66qUJuyR9aeg9aG5uYWDAUFBZw4ccLCh0%2BSJIuyzIqcK41GQ1pa2k156cmZYPPgxNayu%2B0zebs4ceIEM2fOpKioiFatWlkcY6tWrZAkCY1Gw1tvvaWUb%2Br1ekaMGIFWq0Wr1VJYWEhOTg5PPfWUsq8kSVy9epWBAwfi6%2BvLqlWrbH7/kSNHLN5LkoQkSWWM4QU1GxHwCQQCgcAh1qVi1oSEhFgYNgsEgsrHOsAzNzSPiopCq9Xi5eXFc889R3JyMkajERcXFyWwMhgMFBQU3NR3S5LEunXrWLZsGXFxcRZqqCNHjnSohhoUFMSSJUscLpMkqcYEfFOmTCE1NZX69etz%2BfJl3NzclPMuZ%2BWMRqOFjYZOp1OEc2QkSQLAz8%2BPf/7zn0RFRTF69GiSk5MBlD5qNzc3QkNDGTZsGJ9//jlr1qy5E4cpqOKIkk6BQCAQOMS6VMya48eP8%2BKLL1bZrI%2Bg9tC6dWtmzJhhkUWZOXNmmWU1IZiw5Tloy2bBxcUFDw8Prl%2B/ToMGDZT909LSAHjvvfeUZc6eq/DwcL7//vtaoYZ6q7Rt2xaTyURiYiJ5eXnEx8dz8uRJRQhLp9MhSRJ5eXm4uLgwYcKEW7a4UalU/Pbbb3Tq1KlMhk9QOxEBn0AgEAgcMnfuXM6cOXNT/ToCwZ2kqvYWVhbWnoPWNgvmD2msg%2BGYmBjy8vLw8/MDStUdVSqVhdCHvXMVFhaGRqOpFWqot8rIkSMpKSkhJiaG0NBQAPr37w/Atm3b6Nu3L4AifHX48GGaN28OwJNPPsnGjRvL/Q5XV1e0Wq1T45HLekVJZ%2B1CBHwCgUAgcIitTIJ1v86aNWvw9va%2B20MVCGoV5p6D5dkslBcMp6en07BhQ6eC4ZYtWzJlypRa46V3K%2BzcuZMFCxZQUFCARqMhPz%2BfGzduOLWvJEm4ubkpvZVNmzYlNTVVCe5ee%2B01VqxYwdWrV9Hr9Rb7enl5Ub9%2BfTIzM4HSHs2uXbty%2BvRpxo4dS2Rk5G08SkFVRwR8AoFAICgX60yC3K8TGRnpsF9HIBBUDtaeg2lpaYwePZo33ngDqFgQlp6e7pSRt1wSWpvUUG%2BVFi1a2PXYM0elUmE0Gsu8tkaj0aDX63FxcSE5ORmTyUSHDh3Q6XT8%2BOOPPPHEExgMBsLDw/nf//7HhQsXmDhxIgDu7u588MEHTJ06VfT21TKEaItAIBAIyiU3N5fg4GDGjBkj%2BnUEgiqA7DkoZ9keeughTpw4oawfMWIEn3/%2BuVOf1aNHD0wmEz179rS53mQyWSg71iY11FtFnpO2bdvy66%2B/0qlTJyUATExMZOzYsQCsWrVKMWT38PBgz549fPjhh3h7e2M0Gjl37hxarZb777%2BfEydO4OnpyciRI5Uyey8vL95%2B%2B22MRiN16tQhJSUFKBXVOnfuHFCaMQwJCbH4nQhqByLgEwgEAoFDrDMJc%2BfOFf06AsFd5uTJk8TGxirvb8VmIS4ujqioKLZs2YLJZGLQoEFs2bLlto%2B5NvPggw%2BSmZnJgw8%2BSElJCQADBgyguLiYSZMmKf54x44do2/fvphMJlQqFSdOnMDb25uSkhIMBgPHjx8H4Nq1a1y7dk35fJ1OR0JCAlCaBSwuLuaRRx6hpKQErVaLWq3Gz8%2BP8ePHc%2B%2B9997hoxfcbUTAJxAIBAKHWGcSYmNjWbBggejXEQjuItZZNuCms2xNmjRBpVLRpEkTAIvXtqhtXnq3gnlJp72%2BualTpyqvW7ZsqSisymi1WlQqFRqNRvHpU6lUBAcH4%2Bvry4kTJ/Dw8KCwsBBAsX24evUqUPowQK1W4%2BrqipubG3Pnzr3txymo2oiATyAQCAQOsc4kVKRUTCAQ3BkMBgMmk4m1a9daLKuMIKy2eendCsuWLePYsWN8/PHHTm1vHewByvy5uLhgMBjw8PAgPz8fHx8fzp49i7e3N3Xr1sXNzY3IyEj2799PkyZN2LFjh%2BLTJ6jdCNEWgUAgEDgkPDyc33//vdxlAoHgzlGezYKMMzYLYHlNi%2Bu78pHFVNzd3YmNjeWxxx7j2rVrijLn/fffz8aNG8nNzVUUkbdu3eqUAAxA3759iY%2BPJzQ0lHvuuQcfHx/8/f3p2bMn//jHPyr56ARVDZHhEwgEAoFAIKhmWGfUvLy8LII7qJjnoKur620dn8A%2B586d49y5c5w6dQpJkkhOTmbw4MHs3buX7OxsPDw8iIiI4JNPPmH58uV2PfZcXV3p1q0be/bsQafT4eLiwogRI1i1ahXbt28HSgPLCxcuAKXCPps2bSImJobu3bvfseMV3H1Ehk8gEAgEDrHOJADMnDmzzDJRviUQVB3S09Od2q5BgwZMnjzZYll8fLxiCG7OggULbsvYahNr164lKSmJ77777pY/S5IkRTFVrVbj4eGBt7c32dnZqFQqXnjhBbZt28aZM2cU0ZdBgwaxadMm7r33XoKCghg%2BfDjffPMN//vf/27D0QmqCyLgEwgEAoFDyjNshoplEgQCQeXTokULJEmyu97camHatGlOfeacOXNu1/BqDT169CAzM1N5OGYymZTXbm5uSJJEcHAwV65cUURXmjRpQkZGhvLe2qNvwIAB7N69m/vvv5/ffvtNEWTZv38/ERERuLi4oNVq6dq1K97e3sTHx%2BPu7o4kSezbt4/OnTuL3r5ahgj4BAKBQCAQCGoYZ8%2BeVV47slpwpMYpqHwuXLjA1KlT%2BeCDD3j55Ze5dOmSorJpjYuLi1K6qdPpyqyvX78%2BGRkZqNVqvL29efnll/niiy%2B4fPkyGo0GSZI4fPgw7dq148iRI5V9aIIqhOjhEwgEAoFAIKhhWAdy5VktnDp1iitXrvDII49YLP/qq6/o2bMnDRs2rJRx1mZGjx5NXl4ef/31F/369UOv19vcTg7wDAYDarXaZrAnSZKy3GAwcP36dYKDg8nIyMDPzw%2BNRoOLiwvnz5%2Bnbt26lXpcgqqHCPgEAoFAIBAIajGnTp3i6aef5plnnikT8KWkpLB8%2BXLWr19PcHDwXRph9Ue2y0hKSuL8%2BfPk5OSQlpamrLcX7AFKICeXdQJERETQpEkT3Nzc%2BPbbbykpKSE3N1dZX79%2BfT766CO8vb1xc3NDpVLRsWNH5s%2BfT5cuXW734QmqOKKkUyAQCAQCgaCG48hqYeLEifj7%2BzNjxgyb62fMmIHRaGTWrFmVOcQajdwLnZmZadNrz5zx48dz%2BfJlDh8%2BTEBAAP3798ff3x93d3f%2B%2Busvbty4gSRJpKenk5aWpvTjyaIuAPfeey9Go1EJAuvWrUtERARHjhxh1apVBAUFVeLRCqoaIuATCAQCgUAgqOE4Cvi6du3Khg0b7GbwMjMzGTFiBD/99FNlDrHWER4eTnFxMf7%2B/uzZs4euXbtiMpnYuXMnnp6eil%2Bem5sbWq2WoqIim%2BWc5ri6ujJixAjeeOMNsrKylO2Dg4O5evUq/v7%2BuLi4VPqxCaoWoqRTIBAIBAKBoIZhbbWg0%2BnKLINSqwW538seISEhFuWCgtuHSqXi%2BvXr/N///R8FBQXo9Xo6duxosc3169dt7jt%2B/HiWLFmCSqVi/vz5rFu3Do1Go1jkWM%2BpKMmtvYiATyAQCAQCgaCGYW2kHhUVZXfbwMBAzp8/z3333Wdz/fHjx/H397%2Bdw6vxdOnShZycHIfbGI1GJEnCYDBw6dIlh318KpUKk8lE69atSUlJAWDSpEmsWLECrVbLl19%2Byfvvv88rr7zCtGnTWLdu3W09HkH1RgR8AoFAIBAIBDWMinjmPf7447z//vssWrQIjcby1rCgoIDp06fTu3fv2z3EGs3kyZOd8rpr374977zzDsXFxUBpoK7VagFITk6mU6dOGAwGPD090Wg0qFQqJElCkiR27txJ3bp1yczM5OTJkzRq1IiLFy869F8U1E5ED59AIBAIBAJBDcRZq4Xr168zfPhwCgsLGTp0KI0bN8ZoNHLq1CnWr19PQEAAa9aswdvb%2B24cRo3noYceQqVSUVRUZLHc3HC9PFQqFYGBgRQUFNCsWTO%2B%2BeabyhiqoJoiMnwCgUAgEAgENYyKWC3UqVOHdevWsWzZMuLi4rh06RKSJBEaGsrIkSP597//jYeHx106kprN7t27KSkpsbnOOtjz9/cnNzcXk8mEn58f165dw8vLi5CQEAoKCjCZTPTo0YNXXnnlTgxdUI0QGT6BQCAQCASCGkZFrRZSU1NJSEhArVbTrVs3IfBxm5A973JycpzO1sHfZuvWuLm5MXbsWH777Teio6NZtGgRX3zxxW0br6BmIgI%2BgUAgEAgEghpGRawWDh06xPPPP09QUBAGg4Hc3FxWrlxJWFjYHR51zWPz5s1AaT/e%2BfPnuXLlisX68%2BfPA6W9fIcPH0aSJGbPns2AAQNo1aoVrq6uaDQatm3bhlqtpl69ehgMBjp27EhiYiIdO3bkyJEjd/qwBNUMEfAJBAKBQCAQ1DDatGnDb7/95tQ2o0aNomfPnjz77LMAxMbGkpCQwMqVK%2B/ASGs3/fr1o6SkhM8%2B%2B4xhw4ah1%2BsJDw/H3d2dAwcOAODl5cXIkSOVbOGBAwfYuXMnjz/%2BOLt372br1q138xAE1QAR8AkEAoFAIBDUMHr16sXy5csdWi28%2BOKL/Pjjj0RERLB3717c3NwAKCwspHv37iQmJt7BEdccJk%2BezOHDh53atm7dupw7dw69Xq%2Boc9pCkiTMb9lVKhW%2Bvr4sXLiQ9u3b3/KYBTUb1d0egEAgEAgEAoHg9iJbLdjydrO2WtBqtUqwB%2BDp6anYBAgqjlyG6cx/zZs3Z%2B7cuYrVgrktxksvvaS8Dw4OZvDgwdSpU4egoCC%2B/PJLfv75ZxHsCZxCZPgEAoFAIBAIahgVsVoIDw/n999/t9jf1jLB7Uc%2Bx2PGjAHg119/JTw8HIAGDRqQnp7u9Gep1WoA6tevz%2B7du2/zSAXVGWHLIBAIBAKBQFDDqIjVgsFgYN26dRYlg7aWDRs27I4fR01gzZo1rFy5kvT0dAwGAxqNhnvvvZfnn3%2Be9957DygN0rRaLW3atFH2czbY02g0BAcHc/XqVTQaDaNHj66U4xBUX0SGTyAQCAQCgaAG4qzVQo8ePcr9LEmSRNaogkyePJkTJ05w5swZJXC27sWTsbdcRq1WYzAYkCQJf39/3NzceP7559Hr9WzZsgUfHx%2Bio6P54IMP8PHxEVYNAgtEwCcQCAQCgUBQwxBWC3efadOmERcXp/RImvvqmUwmDAaDU5%2Bj0WjQ6/WoVCqWLl3KhAkTABRRnYcffhhJkkhMTFReC6sGgTki4BMIBAKBQCCoYQirhapBu3btADh48KCFIIvBYCAiIgKj0cikSZNIS0sDYMuWLURFRbFx40ZMJhMuLi7k5%2BfbNG13d3cHSkV2/P396datG5s3byYwMFBYNQgsEAGfQCAQCAQCQQ1DWC1UDYYNG8aVK1d47rnnMJlMxMXF8ddff9kM4lQqFUOHDiU%2BPh6dTsf06dPRaDQk/3979xocZX3%2Bf/x93/fuJrs5B0KOSFCBiCJJIISJEQ/TVotWlBGBsXQae3ggtY5WrS1Yq5UitTJqx7FWOlZlNKMM0CmVqYicRA0nA9RGA6gtgkma82E3e7rv/wOG/ZOiVvvLsmb9vJ7wZfe7u9d3hicX1/29rr176ejoYOfOnQSDwc/8vYyMDJ588kl175QhlPCJiIiIJBl13vxy2LNnD3V1dYRCIQzDwLKs00ZlpKWl4ff7T7vDN3bsWH79619z1llnkZqaimVZHDx4kP3799PZ2Ul/fz%2BGYeD1esnMzGT69OlMnz59yIgNEVDCJyIiIpJ0lPB9ebS3t/ONb3yDcePG0dzcDJxo0nLqnb7/RUFBAQBr1qzB7XbjdrsBcLvdeDye/1vQklQ0lkFEREQkyWjUwpfH6NGjAXj44YeZO3cuhmEwODhIcXExd9xxB3fffTe2bRMOh2P3/KqqqnjzzTc/83tbWloAqK2tHfK6aZo0NTXF4SQyUqnCJyIiIpJkNGrhy6Gnp4clS5awadOm094zTZOlS5fy0ksv8e677%2BI4DoWFheTm5rJ27VomTZoUSwDnzp1LZ2fnp/5Ofn4%2B48aNA%2BC8885jxowZ8TmQjEhK%2BERERERE4uD222/nzTffxOfz0draSmZmJh0dHUP2nDqDz%2BVyMXHiRA4fPkwoFBryvtvtZt68eSxZsmRIx0%2BR/0YJn4iIiIhIHFRVVeE4Dq%2B88gq2bbNhwwYOHz7MmjVrPnPQ%2Bv/CNE0ARo0axeuvvz6s3y0jm/57QEREREQkDizLAsDr9eL1ernxxhtpa2tjzZo1w/Yb48ePZ/To0ZSWlgJoJIOcRhU%2BEREREZE4uPnmm/nHP/7BlClTsG2bbdu2fa7unNdddx3r1q3DNE08Hg%2BGYdDY2Eh5eTkAjY2N8Q5dkogSPhERERGROGhtbeX73/9%2BbBzDqUzTxLZtDMOgsLCQ48ePAyeqgo2NjUybNm3I/b6KigoaGho%2B9bdOVhMLCwvVjEeGUMInIiIiIhJHlZWVfO973%2BP3v/89cKI5y%2BbNm7n00ksJhUJs27aNWbNmxfafe%2B65vP/%2B%2BziOE0sMU1NTCQQCsT2maTJmzBhM02TUqFGxLp1VVVUsWLDgzB5QvtR0h09EREREJE5s28blcjFz5kyefvrp2OvNzc2EQiEcx%2BHSSy8d8pnDhw/H1tFoFGBIsmdZFlOnTqW2tpbFixfH9wAy4qnCJyIiIiISBy%2B//DJLlizB7/d/6p68vDxSU1NpaWnBtm1s26aoqIjW1lby8/PxeDx0dHTgdruZMWMGf//732lra%2BORRx7hoYceYuPGjWfwRDISKeETEREREYmDiy66iIGBAS6//HIOHDjA0aNHh9zL%2B0%2BlpaW0tLTQ2NjI1KlTufnmmwF45513Yvf3ioqKaGpqIisri97eXmpqaoYMXr/ggguora09MweUEUEJn4iIiIhIHJwckdDQ0IDL5aKpqYkHHniA5uZmotEotm0DJ%2B70paam4na7aWlpIS8vj9bW1v/pN03TpKmpadjOICOf7vCJiIiIiMTB1VdfTWtrK7t27WLmzJnk5eUxatQofD4f0Wg0dj/PNE0cxyEajZKSkkIgEMDn81FSUsJf/vKXId/5k5/8BMdxYpXClStXJuJoMoKowiciIiIiMkxWrlwZe/zStm0OHz5MOBweUtH7LIZhkJaWRmVlJbt372bmzJnU1tZiGAYbN27kwIEDTJw4kSNHjnD//fcze/ZsDhw4gG3blJeXx8YziJykhE9EREREZJgsWrTotEcq%2B/v7cbvdhEKh2GuWZTFmzBjKyso4cOAAPT09sY6eBQUFWJbFBx988Im/kZeXR1ZWFsePH2fy5Mn4/X4%2B/PBDJk%2BezNNPP43H44nrGWVkUcInIiIiIhJH06ZNo6Ghgerq6thrs2bNYvv27QwMDMSauHxaQxfDMPB4PITDYTIzM9myZQuGYXDTTTfR0tLCnDlzaGhooKWlheuvv16jGmQIM9EBiIiIiIgko3A4zMMPP8zo0aNZvXo1V199NWPHjiUlJYX9%2B/cTCAQwDAOv14vL5cKyrNifp0pNTSUajWIYBtOmTcPn8%2BH1evnFL36BYRj87W9/i603bNiQoNPKl5UqfCIiIiIiw2zlypVs376d5uZm4MR9Po/HExu2fqq8vDwMw6CoqIjq6mpuvfVWqqqqGBgYwOfzsW/fPiorKwHYt28fhmEA4DgOFRUVGIbBvn37Yuu33377zB5WvtTUpVNEREREZJi9/fbbHDp0CNu2Y104g8HgJ%2B5tb28H4N///jf79%2B/nD3/4Qywp9Pv9XHjhhUSjUVJTU%2Bnq6iI3NxeAtrY2UlJSsCxryFrkVKrwiYiIgR4%2B5wAAEXFJREFUiIjEQXV1NY7j8Morr5CdnR17va2tjSuvvBLbtolGo0OauXzSPb7s7Gx6e3txHIfa2lpWrVoFnBjRcGpl7%2BRaoxrkVEr4RERERETi4J577uG1114jKyuLgoICpk6dyp49ezh48CChUIiSkhJuvPFGVqxYgW3bpKenMzg4iMvl4rHHHsPv93PrrbcCMGHCBA4dOhRbB4NBjh49isfjYdy4cRw5cgSv18uaNWsYP358Io8tXzJK%2BERERERE4mBwcJAVK1ZQX19/2gy%2B9PR0iouLKS0tZdOmTdi2TUZGBoFAgJSUFHbt2oVhGFRWVhIMBj%2Bxe%2BeppkyZwrJly5g0aVI8jyQjkBI%2BEREREZE4qqmp4fnnn2f%2B/PkAsSHqvb29XHXVVYTD4dM%2BU15ezpw5c3jqqafIycnhmmuu4cknnyQlJYWf/vSnwIn7fQUFBZx//vlDHhkVOZWatoiIiIiIDLNjx47R2dlJQUEB0WiUSCRCIBAgEolgGAa7d%2B9m/fr1RCIRioqKOH78%2BJDPNzY20tjYCEBnZycPPvggbrebFStWMGvWrEQcSUYoVfhERERERIbRnj17qKurA2D58uW88cYbbNiw4RO7dJqmSW5uLn6/P/ZYp9fr5ejRoxiGQWlpKZmZmcycOZP58%2BdTVFR0po8jI5wqfCIiIiIiw%2Bh3v/sd%2Bfn5XH/99Vx99dWEw2HWrVv3iXtt26a9vR2Px8P8%2BfNZuHAhbW1tLF68GMMwWLZsGXl5eRQVFWnkgvxPVOETERERERlGJ4ekv/766/h8Pu666y52797NlClTuO%2B%2B%2BwiHw1xxxRUEg0Fyc3M5//zzcRyHHTt2nNbc5STLspgzZw6//OUvSUlJOZPHkRFOFT4RERERkWF0sp7i9Xo5duwYDQ0N%2BP1%2BSkpKOHDgANu2bWNwcBDHcejp6WHr1q2nfYdpmmRmZmLbNueddx4tLS1s376d3/72tyxZsuQMn0hGMlX4RERERESG0ZVXXkk4HObHP/4x99xzT%2BzunmVZRKNRMjIy6OvrO%2B1zhmHgcrlwu91s3ryZcDjMvHnzsCyL%2Bvr62HrLli1n%2BkgygpmJDkBEREREJJnMnj2bYDDI0qVLsSwL0zTxeDxkZ2fjcrkoLCwETiR4hmHg9XoxTZP8/Hxs28ayLJqbmxkYGGBwcBC/309mZmZsLfJFqMInIiIiIjKMIpEIy5Yt4/nnn8fr9RIKhfB4PITDYfLz82ltbcVxHNLS0ujt7f3M78rPz6e0tJS8vDx2797NxIkTWbVq1Rk6iSQDJXwiIiIiInFQUVHBvn37Yk1cAoEAt9xyCy%2B%2B%2BCKBQIDe3l4cx2H27Nm8/PLL//X7SktLeeqppzjrrLPiHbokETVtERERERGJg/z8fI4cOUJ%2Bfj4DAwP4/X4yMjJobW0FTjR3MQyDxYsXs2fPHsLhMNFolOuvv55gMIjL5cKyLLKysrjooouYMmVKgk8kI5EqfCIiIiIicfDYY4%2BxdetWsrKyaGhoiCV4J5uz2LZNbm4uY8aMoa2tDcdxuPDCC6mrq6O7u5vu7m4ikQi5ubmMHj0aOJFEavi6fBFK%2BERERERE4iASibB8%2BXJ27txJKBSiu7s71nTF5TrxoJ3jOEQikc/9naZp0tTUFJd4JTkp4RMREREROUMWLVpEU1NTrNrnOA4DAwM4joPL5Yolf5ZlMWbMGObNm0deXh4FBQUAlJSUcPbZZyfyCDLCKOETERERERkmH3zwAR999NHn2ltSUsL48eOZNm0aAA0NDVRXV8fWJ6uAIv8X%2BlckIiIiIjJMvvnNb/J56ymGYfCDH/yA0aNHk5OTw65du7jgggs4dOgQd9xxB7/5zW/weDxxjliSnSp8IiIiIiLD5NixY7S0tPzXffX19ezYsSM2h8%2B2beDE3b5wOAycuK%2BXmppKenp6rFFLdXU1t99%2Be5yil2SkCp%2BIiIiIyDApLi6muLj4M/f4/X42btxIOBzGMAxM04xVBU8me3AiCfT7/QSDQQKBAAApKSnxC16SkhI%2BEREREZFh9sEHH9DR0cGDDz5IU1PTJ3biNAyDlJQUbr/9dnw%2BHwUFBZSUlJCWlsaVV16JaZrs2bMnAdFLMtEjnSIiIiIiw6ysrOxz3%2BX7T%2Bnp6QQCAbKzs7nuuus455xz2LRpE2effTa33nqr7vXJF2ImOgARERERkWSzefNmMjIy8Pl8PPTQQ7hcLlwuF/feey9z586ltLSUiooKqqqqMAxjyGf7%2B/vxer2kp6ezfv16tm7dyrFjx1i/fj3Lli1L0IlkpFKFT0REREQkDmpqanAch61bt3LxxRfjOA6bNm3C5/NRU1MDQGpqKtFolK6uLjIyMujr6yM7O5uNGzcCMHv2bEzTZMOGDbH1zp07E3ksGWF0h09EREREJA6mT5/O3r17uemmm7Asi66urticvZP6%2Bvpi65MdO/v6%2Bti9ezfl5eUEg0FM0yQcDsfWIl%2BEKnwiIiIiInHQ1dXFAw88wO7duykpKaGxsZFoNHraPpfLFWvqYhhG7O6fz%2BcDYMKECXR0dDAwMMAVV1zBfffdd%2BYOISOeEj4RERERkTiaMWMG27Zt45JLLqGnpwfLsohGo5imiWEYzJs3j/r6%2Bs/8Do/Hw8KFC7nzzjtxu91nKHJJBnqkU0REREQkTg4ePEgoFOLee%2B%2BNzdI7WeVzu90Eg0G6urowDAPLskhNTaW%2Bvp4bbrgBwzBYt24d2dnZZGVlJfIYMoKpwiciIiIiEgfLli3j2Wef/a/7TNPEtm3cbjder5evfe1rrFu3DoDLLruM1tZWCgoK%2BPa3vx1r9iLyeSnhExERERGJgxkzZhCJRFixYgVbtmzhtttuY8mSJWzfvv0zZ/Sdeo8PTlQCbdvG5XLx6KOPctlll52J8CVJKOETEREREYmD2tra2FiGU%2B/dPfroo9TX19Pf308kEsEwDFJTU0lNTaWrqwuAjIyMWNfOqVOnsmjRIh5//HFGjRrF6tWrE3IeGZmU8ImIiIiIxMGqVat46623qKqqoqamhiNHjvD%2B%2B%2B/z7rvv0tjYSE9PDwBpaWkEAgEcx8HlcuE4Dh6Ph/T0dDo6OvB6vezcuZOamhoMw2Dv3r0JPpmMJGraIiIiIiISB/v27eOtt95ix44drFy58lP3DQwMxNbhcJhbbrmFtWvX0t/fH7vfl5KSgm3bGIZxJkKXJKLJjSIiIiIicTB58mQqKiqwLIuysjIsy8I0TUzTpKysjOzsbEpKSvjrX/9KcXExcOL%2B3qRJkzh%2B/Di9vb1kZGSQk5PDhx9%2BGFuLfBF6pFNEREREJI5qa2vZsmULl112WawZy9atW3niiSd48cUXMU2T3t5eAoEAbrebrKws%2Bvv78Xg8ZGRkUF1dTWdnJ4cOHaK2tpb7778/wSeSkUQVPhERERGROHnhhReIRCJMnTqV9vZ2Ojo68Pv93Hnnndx88818/etfj83nKygoICUlBb/fT3Z2NtFoNNaxc%2B/evRiGwY9%2B9KMEn0hGGlX4RERERETi4JFHHuGZZ55hcHAwdhfPtu3T9pnmiRqM1%2Btl4sSJ1NfX09raSjgcJj8/n87Oztj61G6fIp%2BHmraIiIiIiMTB2rVr8Xq9XHPNNeTl5dHQ0MCxY8fo6uoiEAhgGAZpaWlkZmYCUFxcTHV1NQD5%2Bfmx7zl1LfJFqcInIiIiIhIHlZWVAOzatQuX6//XWaLRKFVVVRqxIGeEKnwiIiIiInEwYcIE2tvbWbNmDY7j8PLLL9Pc3Exvb2/s0c5JkybhcrkYP348P/zhD7nmmmsSHLUkG1X4RERERETiYM%2BePdTV1REKhTAMA8uyiEQisfcNw2DUqFF0d3fHhq3ffffdLFiwIIFRS7JRl04RERERkTiYPn06W7ZsIS0tjfPOOw8Al8uF1%2Bvlqaee4sUXXyQzM5MXXniB/Px8cnJyeOaZZxIctSQbPdIpIiIiIhIno0ePBuCll15ixowZsddramowDIO2tjbOP/98enp6AOjt7U1InJK8lPCJiIiIiAyTRYsWAfDcc88xf/58mpubGRwcpKKigkgkEhu8XlFRQX5%2BPuPGjWPt2rVkZWUBkJOTk7DYJTkp4RMRERERGSY1NTWx9cUXX4zH46Gvr4/33nsPl8tFKBQCIBQKcfToUUzTZOnSpRiGgdvt5r777ktU6JKk1LRFRERERCQO1q9fD8C1115Le3s7GzZs4PDhw7zzzjscPXqUaDSKz%2BdjzJgxzJo1i/nz51NUVJTgqCXZKOETERERERlGtm0TCoWYMWMGhmHwxhtvDHn//fffZ%2BHChZimyYEDBxIUpXxVKOETERERERlGf/rTn1i%2BfPnn3m%2BaJxrne71eJk6cSH19fbxCk68g3eETERERERlG3/3ud%2Bns7OSPf/wjtm0zduxYABzHoaenh2AwiGmaWJYFQGZmJgDFxcVUV1cnLG5JTqrwiYiIiIjEQUdHBwA7duzg2muvjd3pA9i%2BfTvRaJTy8nIWLFjAc889B4Db7aauri4h8UpyUsInIiIiIhIH4XCY1atX8%2BCDD1JYWMjHH3885H2fz0coFOK5555j0aJFGIahe30y7JTwiYiIiIjEwYIFC3j77be/0GcqKyt54YUX4hSRfBXpDp%2BIiIiISBx89NFHZGdnMzAwgOM4RCIRTNPEcRxycnIoKCigvLwcj8fD2LFjmTx5MhdeeGGiw5YkYyY6ABERERGRZBQMBrFtm23btuHxePB6vbz22mts3LgRy7Lo6Oigp6eHn/3sZ%2Bzbt4/Vq1dz1113JTpsSTJ6pFNEREREJA5uvPFGjh8/zpw5c1i3bh3d3d1kZ2fT399Pf39/bJ/H4yEcDmOaJhkZGTQ0NCQwakk2SvhEREREROLg4MGDLF68mP7%2BftxuN93d3Z%2B47%2BSIhrFjx7Js2TIqKyvPcKSSzPRIp4iIiIhIHEyZMoXt27fj8/n485//TE5ODnBiwLrL5cKyLFwuF3V1dVRVVVFYWMh7772X4Kgl2SjhExERERGJo2AwSEFBAdFoFACXy4XH48Hj8RCNRqmrq%2BPQoUM0NzfzxBNPJDhaSTbq0ikiIiIiMowuv/xyWlpaYn%2BPRqOUlZVx8iZVX18fAIZhAPDss88yMDAAgGVZZzhaSXa6wyciIiIiMox27dpFU1NT7O8ff/wx69ev56qrruKll14iGAx%2B4ucMw%2BA73/kOP//5z89UqPIVoIRPRERERCTOQqEQjz/%2BOKtWrcJxHKqqqvjWt75Fb28vx44dIzc3l4suuojy8vJEhypJRgmfiIiIiEgcvfrqq9xzzz309/dz7rnnsnz5csrKyhIdlnxFKOETEREREYmDWbNm0dXVRSgU%2Blz7LcuiqKiIV199Nc6RyVeJmraIiIiIiAyjwcFBHn30Udrb2wGYNm0a55xzDp2dnbE9/f397N%2B/n2g0imVZBAIBbNvG7/cnKmxJUkr4RERERESG0RVXXEFbWxter5cbbriB4uLi2HuRSISdO3eyd%2B9eSkpK%2BNe//kU4HMblcjF37lzuvPPOBEYuyUiPdIqIiIiIDKP/HMtwkuM42LY95DW3282ECRN0r0/iRgmfiIiIiEgc/fOf/%2BRXv/oV77zzDllZWXz00UfYtk1aWhpLly5lzpw5iQ5Rkpge6RQRERERiYPBwUEef/xxVq9ezdlnn01vby/d3d1YlsXChQu57bbbSE9PT3SYkuRU4RMRERERiYNLLrmEcDhMOBymr68Px3HweDxUVFSQlZUV25efnw/AuHHjKCsro6qqKlEhSxJShU9EREREJA4sy8KyLLq7uzlZYwmFQjQ0NHzq/sLCQjZv3nwmw5QkpwqfiIiIiIhIkjITHYCIiIiIiIjEhxI%2BERERERGRJKWET0REREREJEkp4RMREREREUlSSvhERERERESSlBI%2BERERERGRJKWET0REREREJEkp4RMREREREUlSSvhERERERESS1P8DICC6aiHGAUUAAAAASUVORK5CYII%3D” 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BfTvh4eGMHDlSROd9/fXXyc/PZ/369XzzzTecOHGCmTNn3tLxSoFPckdz7tw5Ro4cSXR0NG3btmXChAlkZWWJ8ykpKTz%2B%2BOM0b96c%2B%2B%2B/nwULFqiut1qtzJgxg/r167Nz507AGblu3LhxFBYWMnnyZDp27Eh0dDRjxozh%2BvXrZY6nc%2BfO9OrVi1atWtG0aVO6devGBx98QGFh4a2f/B/g22%2B/JSYmRvdceno6ERERNGrUiKioKKKiomjXrh1jxowhPT39LxlfcXExU6ZM4cSJE8TExLBixQqGDRtGs2bNuPfee4mPjxd1P/74Yzp37kyzZs149913GThwIN7e3uJ8XFwcvXr1omnTpnTt2pXly5cDcOXKFVq1asWOHTtE3RdeeIERI0b8JXOUSCQSiUTy9%2BDp6cnKlSvLJfAtX76cIUOGULduXfz8/Bg3bhwnTpzg0KFDXLlyha1btzJu3DiCg4MJCwvjueee45tvvsFqtd6y8UqBT3LbERMTQ2RkpBA2mjdvzsCBA9m7d69b3eHDhxMQEMC2bdtYtWoVaWlpzJgxA4CCggKeffZZWrduzY8//sisWbP45JNP%2BO677wDIy8tj4MCBZGZmqnIo7d%2B/n8TERGbNmsWRI0dYvnw5mzdvxuFw8Oqrr6r6P3v2LJs2bQJg586dXLx4kVq1arF161YSEhJ4%2B%2B23%2Bf777xk6dOgfytOkJ4S5fi5dunTTNlauXMm1a9fK3eeaNWtISkoiKSmJtWvXYjabGT58ODabrczrjhw5wq5du8rdz83Izc3lzTff5Oeff6ZGjRpUr16dt99%2BG3CaUfznP/8hMzMTk8nEmTNnWLVqlbg2KSmJCRMmcO3aNerVq8eMGTOYPn06Bw4coFKlSowfP54JEyYQGRnJ9OnT2blzJ1OmTLllY5dIJBKJRPLncPnyZY4cOaL6XL58uVzXPvHEE/j7%2B9%2B0XkFBAcePH6dhw4aizM/Pj1q1apGUlERKSgomk4mIiAhxPjIykry8PE6ePPn7J1UKUuCT3HZcvHgRu92uKrt06ZKbzXRWVhaNGjXixRdfxNfXl8qVK9OnTx/27dsHwI4dO7BarYwYMQIfHx8iIyPp16%2Bf0PDk5eXxr3/9i2nTpumOY%2BXKlTz33HNUqVKFoKAgxo4dy44dO1TC1ddff83ChQux2WxMmTKFJ598kg8//JCAgAAsFgvNmzdn3rx51KxZU2gHjx49yuDBg2nRogWtW7dm6tSp4i3QqlWreOihh4QGrlmzZowbN05o1ux2O1arlaKiIoqKiiguLhZj0ZpB7ty5U3wB2Ww23njjDf7v//6PJk2aMHfuXJ5//vly35OKFSvyyiuvkJaWJgKpXLx4kREjRhAdHU3z5s0ZN24cmZmZzJ8/n1mzZnHfffexYsUKOnToQNOmTZk8ebIYr6JFVSgsLCQiIoI9e/ao%2Bo2PjxcCeWFhIceOHWP37t2cPHkSu93OQw89BEB%2Bfj55eXlkZGSQm5tLXl4eAO%2B88w5Wq5XLly9z%2BPBhYmNj6dixI2vWrCE9PZ3XX3%2BdGzduUFxczOeff47BYPhdb%2BR0/9i0b4/DgfhYraiObTb1scOBW6WiIs35kv9or3P9FBXhVsetM73Oc3NVx8qha8NuY8nJ0e1HW3azrrVlbm04HJCfr7sOro24rad2zFarW53sbP11VB3k56uPtTfzxg33BlwaVppwm9fly6rjU6fUdXA4YP9%2BdbtHjqjOZ2Xlu63J9euauRQUuPednq46Pn%2B%2BzCm41VHay8hQ9629l1arzrwzMtRzdFlPpUzZx8onNxf3Aaanq4611%2Bg9P663EodzX7tWcjjUz4syJ91NUtae0MxJbxtpj7XrpBqn677QLLJr16WWlewBMUeXebveh7LWRjUG5T86ddw2gV4dzUepohyXtTZltas8imJeubmq67Kz3dfz2jX34WOzqb5TNFuNs2fdp3HqVGl1HCUf93YyMtTHaWnu7V65oj5OTnavo31%2BXfsRa1GyKRyOcv9p%2B3MwGG75Z/ny5Tz88MOqj/Ib71Zx48YNHA4HgYGBqvLAwECuX79OZmYmfn5%2BGAwG1TngplZhvwcp8EluSx544AGhXYqPj%2Bexxx7DarWyZs0awCkgfPjhh%2BzevZuuXbvy2GOPkZKSwoULFwgMDCQiIoJ169aRn59Ps2bNePzxx8nIyKBhw4bs37%2Bfdu3aUalSJR599FHR59q1awG4evUq165dIzs7m8jISBYuXMi9995L3759AViyZAkAn332GfPnzycxMZEmTZpw7tw5Fi1axPjx4wGncDZnzhyGDBnCunXrePrpp9mxYwdDhw6lbdu2%2BPv78%2BSTT7Jq1Sphqnjs2DHOnTtHcnIy69ev5%2Buvv2br1q1CiA0ODuatt94iJSWFH3/8kcjISMaPH09YWBjgFJC6dOlCkyZNmDRpkpjbPffcQ3FxMTdu3GDo0KHUqFGDl19%2BmatXrwrt4Y4dO3jggQfo0aMHABcuXODRRx%2BladOmDBo0iMzMTAAMBgMzZ86ka9eu/PDDD4SFhTFjxgwuX75Mv3792LhxI4cPH%2BbMmTMkJSWxYsUKWrduzYoVK2jWrBkjRowot49dcnIy4Hyb9tNPPwntm8PhwGq1kpOTA8DIkSPZu3cvrVq1AhAC36FDh1QvD3bu3MnmzZu5dOmSENxr1qwJQEhICDk5OUycOLFcYwNYsGCB2x%2Bbrx8doPqbZLGo/0aZTO5/t7SVPDw050v%2BU9bfPg8P3Oq4dabXua%2Bv6lg5dG3YbSx%2Bfrr9aMtu1rW2zK0NgwG8vXXXwbURt/XUjtlicavj76%2B/jqoDb2/1sfZmBga6N%2BDSsNKE27xCQ1XHtWur62AwQPPm6nYjI1XnAwK83dakQgXNXLy83PuuXl11XLVqmVNwq6O0FxKi7lt7Ly0WnXmHhKjn6LKeSpmyj5WPry/uA6xeXXWsvUbv%2BXG9lRic%2B9q1ksGgfl6UOelukrL2hGZOettIe6xdJ9U4XfeFZpFduy61rGQPiDm6zNv1PpS1NqoxKP/RqeO2CfTqaD5KFeW4rLUpq13lURTz8vVVXefv776ewcHuw8dkUn2naLYaNWq4T6N27dLqGEo%2B7u2EhKiP69Vzb7dSJfVxo0budbTPr2s/Yi1KNoXBUO4/bf8z9O/fn1WrVqk%2B/fv3/1P6KstK649YcJUXKfBJbjtsNhtxcXHCXLF9%2B/Zs3boVo9Eo1OPvvvsu69evF%2BaFZ86c4YknnuCLL74QAsvevXtR3rgcPXqUV199laCgIPLz87l69apbv6mpqYBTmFS0PGlpaUJLBM4v8E8//VSYRioPucPhwNPTE5vNxunTpwGnYLhgwQKKiooApyA5atQobDYbzz77LJcuXWLx4sVUqlSJ4uJirl27xqpVq8jNzWXs2LH4%2BPhQr149IiIiSEtLAyAjI4MpU6bQpEkTunbtSmpqKjNnzuTSpUsUFhayc%2BdOsSau/bqO02AwiOhRFStW5OLFiwDExsZit9uFgDR9%2BnQKCwtxOBwcOHCAF198kcjISA4ePMjixYspLi7Gw8ODa9euMWHCBIYOHerm4zd27FgmTpxISkoKHh4eFBcXc%2BTIERISEsq1F%2B6%2B%2B268vLwwGo3cd999QtgGyMnJITc3F3A6VHfp0oXz588DzjdymZmZFBYWcs899%2BDp6YnRaGT9%2BvU4HA4mTpwo3rwpQuOVK1fw8/Pj%2BPHj5RpbadjtZZu8SiQSiURyx2E03vJPaGgokZGRqk9oaOgtHXZQUBBGo1G89FbIzMykYsWKBAcHk5OTo3J3UepWrFjxlo1DCnyS2xLXtyUOh4Pjx49js9mEJuurr77C29ubZcuWsWfPHho3bkxWVhZ9%2BvShUaNG4tqWLVvy448/UrlyZeLj491MRfWw2Wyint1ux2QyMX/%2BfA4cOICvry92u519%2B/bx9NNP4%2B/vj8Vi4d1333V7wzN//nwMBgOxsbHs27ePIUOGYLVauXbtGlFRUdhsNq5cuUJ6ejre3t40bdqUGzduEBQUhJ%2Bfn2jH29ubGzduiOPi4mJh0ulq1llUVITVamXevHkcPHiQwYMHi2vmzp0r/l%2BajbvBYGDlypW88847gFPYTUtLw2azUVxcTEZGBnPnzsVgMODh4YG/vz/79%2B%2Bne/fuZGdn4%2BHhIdbL29ubwMBAHA4H8fHx1KlThxEjRhAeHk5UVBRXr14tVyAbDw8PCgoKyMrKIi8vTwi%2BgMq01m6343A4hMBZUFAghL9Dhw5htVqx2%2B1Cm3f27FmMRqO4NjAwEIvFQlZW1u8KsPPQQw/x7rvvqj5du96vrjR7dtnHOK2uVGhMUjIy3Pt2e6GoE9XUbSpff%2B1W58IFTcHq1epjnb3iZvX6zTfuA3TZs/oXARoTXubNc6uSmKgp0JrrHD78X/XtEven1L45eFB9/NlnqkNdRbX2ZrkEBVJwu3cugaYE2jFr5pSX575PSxTbgqNHdcZX8pJEQW9vofXB1XsmNJtWW0XXMrrkJZRAZwHdLKC09xLc94124nrh0LX%2By9o2ALQve/R8gbQ%2BzCXBvgR6a6Xtq%2BQlk4q4OPXxtm3udbTrp7c22oXXtvP99%2B7XaOetl/9Uc2N0FRraL5OUFPWx3nqeO1dWN04WLVIfa59LnbLsbM15nYa1y6f3vGi3lvZYr0xv72u3uvZY81gCTpNOV/S%2BJrRz0KvzF8Vau23x9PSkXr16HDlyRJRlZWVx5swZGjduTIMGDXA4HBx12UBJSUkEBATc0tRS5lvWkkTyD0PRUBUVFWEymQCn8Hfjxg2sViuVK1fm8ccf58qVK%2BLNiq%2BvLxklv2DCwsLYu3cvHTp0IDQ0VAgEJpNJN/DIFe23K05zv8jISHr37q265vTp03z22Wdkl/xVefnll4UWUBH8MjIyiIiIYNSoUVy6dIm6detiMBjw8fHhwIEDREREUKdOHWrUqEFCQoIwXzTo2F2Ulc9F6c9sNmMwGOjTpw9GoxFfX1/A%2BYbpueeeA5zCTW5urhCWsrOzxXpFRETwyCOPcK7kD3CzZs0AxJeYv78/YWFhxMTEMGvWLC5fvqxyYlY0hTabjfz8fAoKCsScdu/eLQK5KD6ArkJsaYFglOsdDgcmk0kIdvCbZg6ce8RisYh1UvwctW0rc9m3b59Yn%2BvXr6uE9d/jw7d27Vo%2B//xzVdno0WOIjKz/W8GoUeqLtMc4ra5UaExSQkLc%2B3bbJgMGuNXx9NQUPPKIW50qVTQFffqoj3XellosmoJ//ct9gBp/B/eLgOho9fHw4W5VSrbhb2jNdRo3/q/6bt9eU6DTN02bqo%2Bfflp16BIM9je0N6tzZ7cqbvcuIMC9He2YNXPy8dHeXPDxUR/Xr%2B9WxWnC64Le3qJtW/Wx20bCbdNqq%2BjdbqftpQs6C1ihgqZAey/Bfd9oJ%2B72QOG03yurDYC77lIf62kKSv4WCTp2VB/rrZW2L5cXeoISyxSBXhRl7frprY124bXtdO3qfo123no/UjU3Rtc8UPtl0qCB%2BlhvPatVK6sbJy4vLwH351KnzC0Wh07D2uXTe160W0t7rFemt/e1W117rHksAadJpyt6XxPaOejVqV7dvexvwfi/o6O6dOkSgwcPZv78%2BdSoUYMBAwaIGABhYWHMnDmTBg0aEBUVBSAiss%2BYMYOioiLmzJlD3759MZtvnZj2v7N6EsnvxMPDQ2iSqpX8UTh9%2BrQQzFJTUykoKMBqtRJQ8i137do1Ya6ZkZEhTBIVoebQoUNYdH%2BJ/Ob3pfz4DwwMZNKkSSQmJrpp77I0r9EMBgPVS75VlcAyDoeDkydPUlBQQFFREZcuXcLhcJCbmytMEdPT07l27RoOh6Ncfm3/%2Bte/SE1NFZ/Ro0fTuXNnwsLC8PDwUJluKgLRr7/%2BKsoNBgOtWrUSws6uXbvEeu3Zs0dl0nn8%2BHGxflarVQhoQ4cO1dUQnjlzRmjNlDEoGjetxlZbdubMmTLn7e/vj7%2B/P0FBQaLMw%2BXHj4eHB1arFZ%2BSv7w2m40aNWoAiLkCjCoRtvr166cyXTUYDGLsNput3EJfnt7rXv5uz3iJRCKRSCRlobgNrVmzhk2bNoljcL74PXXqlHhx/Oijj9KnTx8GDRpEu3btuHjxIrNdrHX%2B/e9/4%2B/vT9euXXnooYdo3LjxTZO5/16khk9y2%2BNwOMSPc4PBIMwdrVYrBQUFmM1m8RalsLBQ2E4XFxdjNBpVJpqbNm0SZoIKigZI%2ByP/kUceEXn7goKCROh/q9XK%2BfPnqaB5YzhlyhSGDh1Kfn6%2B8PFTTDgNBoPQUppMJpHK2ZkRAAAgAElEQVQ6ori4mFOnTqn61nP%2BVcwMv/nmG77RMZ%2B7dOkSPj4%2BOBwOgoKCyM7OFvP68ccfhdmn0rZyrmbNmsJMMi8vTyVEaU0bFYFU0ZIZjUZh3pmfn8%2BaNWsIDw9XhSH%2B4YcfAKfQlZeXh8ViISAggCtXrnDixAkuXryIr68vCxYsEOsDTm3llClT8PHxYf369dy4cUP0paBoVw0GA3a7HYvFQmhoKBcvXiQgIICKFSty11134eHhwS%2B//AJAw4YNCQ8Pp0qVKlQpeRv9%2Buuvi2hqFouF%2BvXrl/pSQCKRSCQSyX/BP0zDl5SUVOq56tWri7gO4PydMWbMGMaMGaNb39/fn/fff/%2BWj9GVf9bqSSR/Eoq2Kjw8XPzot1qtQnBRtH5xcXHirUtBQQEzZ84kIiJCCDzDhw8XAuM777xDo0aNhM%2BfzWYTb3cAxowZI7Q%2B165dEykRwKk1e9rFvOunn36iTZs2gNNsslu3boBTwOrduzd79%2B7lrhKzmSpVqgihyG6307p1a3bv3k2lEvuNm0V7cjX5NBqNdOnShbCwMJFPJjMzE19fX%2B655x4xdsWk02g0Mn/%2BfKGhbOBicmM2m1U%2BfHa7nQ8//JCtW7eK9VHGpwjeivkmQEBAgCoHnpKAFBAaTYfDIe5V1apV6d69O%2B3btycsLAwvHTOspKQk4ReopGYAp6CqaOuU8tDQUBGIRblvAwcOVAmgfn5%2B/Oc//1H1obzBs1gsWK3W35Vc3kfHvqc8fqISiUQikdxR/AlBW%2B4k7qzZSu5IDAYDAQEB4l9Fe6aY4RUXFwuNTIcOHXjiiScA5w/4l156iV9%2B%2BUUISTExMUKgWrZsGb6%2BvkJQMxqNqjc%2BHh4eIsiHluTkZCZMmCCOW7ZsSUyJr4TZbBZRKA0GA6tXr6ZFixaiLD09nccee0z0YbVa6dSpk5jXPffcQ9u2bWnSpAkxMTFERETQ2MVPyVUgtNvtbN%2B%2BndatW6t84rKysjhw4ADgjL750UcfAU6hrbCwUKXBSiyJinHu3DlatmzJCy%2B8ADiDxbz00kvcd999oq/WrVtTXFyMw%2BFwM2s9fvy4KjG9kuzcddyumsxLly4xYMAA7HY7169f58CBA0RHRxMXF0flypV59913RVROpU9Xf0VXLa3VauXEiRPCPFXR%2BM6ePVsIdACbN2%2BmY8eOnDt3jsOaYB/K2HL1vOdLQS9oy333/Y8Hbfn2W/XxXxm05ZNP3KrcNGjLoUPu7WoDNJQnaItLYCOBNjjE/PmqQ10rbO16/YVBW7RbtzxBW3RzFMugLU7%2B6UFbyrNvZNCWUhvWFmmHCzJoi%2BSfg8HxVyR/kEj%2BQpRk4Yomz8vLiwYNGnDq1ClGjhxJixYtePDBB4V/X3FxMb6%2Bvpw/f54OHTpQp04dFi1ahJ%2BfH5UqVeLcuXMYDAaKioqIi4tj5MiRnDp1ioCAAEwmE0OGDGHWrFlYLBaSk5OpX78%2BDoeD1NRU2rRpIwQxcAqFdrsdg8HAnDlzGDVqFHa7HT8/P/Ly8kTEx7179xIREYHRaKRSpUrk5eURFBQktEf169fn6NGjmEwmvvrqK%2BrXr0%2BXLl24cuUKNWvWJDs7m/z8fCHoFhQUCLNGk8mkSrgOTu2aXthgQJhKKho6o9GIj48POTk5/Oc//%2BGFF17AZrNhNBoxm834%2B/tz9epVzGYzmzZt4vr16/Tr1w/4zbTVFYPBgMPhwGw28/XXX/Pwww%2BL8smTJ/Pmm2/q3ufg4GDha9i7d2%2BmTp3K7NmzWbBgAWFhYVy6dIn8/HxdjVnlypVFkBhXzGYzxcXFVK9ene%2B//54mTZqoBENlT23atIn77ruv1GAxrqYcZTF9%2BnTdoC2jRo0s1/USiUQikdwR6EWm%2BaP8jhe0/%2BtIDZ/kbyMmJobIyEjh6Nq8eXMGDhzI3r17RZ2srCxmzJhB165dady4Me3bt%2Bf555/n2LFjos6ECRPcnFurVq3Kt99%2BS1FREV9%2B%2BSWdOnUiJyeHTp06CdNFLy8vVq1axY4dO4T54nWXV3ZGo5GlS5cSHx8vgroEBgYK36%2BBAweyc%2BdOITx5lkRXU8wB27ZtK4S97t278%2BWXX9K8eXMAmjZtSu3atYXGSQn7D07NmILdbufq1asUFRWJNAHwmx9chQoVaNy4MRs3bhR9paenU6FCBV577TViY2OpXr26EFocDodK2DMajcTExLB48WIx7ldeeUVlZz5q1CiRsBycQpFiIvvyyy8LoefRRx/lwIEDvPXWW6KvXr16MXDgQMApwG3ZsgWDwYDZbBZrquBwOBg9ejTgFDIrVKhA5cqVxZoYDAYOHTokhMcPPvgAT09PrFYre/bsISIigo8%2B%2Bgij0cjzzz/P7t27mThxosqENTg4mHvuuYf27duLci8vLzZs2MCKFSuE5jKkJPRgWFiYCKYDThPRpKQkqlWrVqqwB86k8%2BVBBm2RSCQSiUTyZyMFPsnfyqRJk8SP6Pj4eO69916GDRvG2bNnycnJYcCAAaSlpREbG8uhQ4dYsWIFwcHB9O/fv0wtyvnz5%2BnVqxfgFER%2B%2BOEHFi5cSJUqVYSWprCwkIcffpjOnTsLc8lAlxjFDoeDxx57jPbt2wvzQ7vdLgS%2B2NhYOnTowP79%2B8U1KSkpQhBYu3atSJq5adMmBg4cKPpJTEykT58%2BqkAsigBy5coVCgoKxLGPjw8eHh6ibnR0tDhnsVjYtGkTb775phAYN27cyPjx45k%2BfTonTpwgNjaWOnXquK2R2WzGbrezY8cOUlJS6NOnDwaDgRkzZqiiR3l6eqqSfz788MOEh4cDqHLEBAUFYbFYSCmxawkICGD37t2MHDlSrOeUKVOwWCzY7XY3k05wCpCAyN2nCGD5%2Bfk4HA6aNGnCihUrAKcQGF0SrtxVi/fee%2B/RvXt3PD09ef/991UmrNeuXePgwYOqe1ZcXMxjjz3G448/Lsruuusu8vLySE9PF2km4LeoXK5%2BfTVq1BDBZMCpBSxv4lbpwyeRSCQSSTmQPnx/iDtrtpJ/NN7e3jz11FOEhoayc%2BdO5s%2BfT05ODh9//LHIQVelShXeeOMNBgwYoJv3TqFRo0asWbMGgDVr1rB48WKaNGkCoEo0fvXqVW7cuCHyyqWkpAiBzsPDA5vNpgqz36NHDyHQ2e128vPz2b17N4AwLVTo0qWLyG2nmDo2c0kK1qBBA6FJys/PF0Kcn58fly9fVqUocA34cuzYMSFwBQcHs2vXLpXgNWrUKMaPHw/A559/zp49e2jZsiXwW8AWg8GgmkdsbCyXL1%2BmYsWKqqTt4AxOowRtAVi%2BfLkIENOlSxdR/vHHH9OwYUPh71e3bl08PT1p166dqLNhwwaKioqw2%2B0YjUZMJpPQehqNRiE8KQRrc1%2BVYDAYeOONN9i%2BfTuAGI/FYmHHjh106dKFqKgoXX86u93OmTNnhCBYXFxMZmYmRUVFQhPaoEED8vLyhDZYS7aLg0d6ejrFxcViX5lMJlXE0LLQ8%2BG7/36ND9/SpWUfg7vTR2ys%2Bljj5wK4O9ForwF3fyK9OtqUGFo/v5LnQ0VJZFeBJiE54O5MoufYUuJnKtD65wEuuW6drF%2BvPtZzVNP2raPN3bdPU/Dll%2B7tlOSBLHV8eh4VWl8xvfXT7ms9xyWtH5pmTno%2BfFq/JT1XLO190PWZcrHSAPT90tyi%2BKpP66YO1e4BHR8%2BN/c2PX%2B3kqi7pXamt9e0i%2BPmHIr7s6C9l%2BB%2Bz7Vrpde3dh%2BVx5/VbYPqXKdjYeDWtMZXWXfeWkewktRCZdXR2xJuzsa//qo%2B1ttsJX%2B7FXSNK1auVB9r7z%2B4fw9oF0LnXmp/guh9zWq3qJ5Rh7aO3u3Vrpf2K0Bvm2u/xtz8EnXq6Pnw6Q7670AKfH%2BIO2u2kv8JbDYbJpOJLVu20K9fP1UofYWXX35ZJUi4YjKZqFWrVqntV6xYEV9fX5XmRxGEioqKhKBYv359Ll68KKI8gjNypyKQKCkbjGV8afj4%2BBAcHIzZbCY8PJxBgwaJc506deKuu%2B4SPmwK7dq1o2bNmiKiqJLKQSEzM1MEePH19aVOnToqE1dFG2Y2m8nOzmbMmDGqJOOAKoAJOE1nN27cSK1atbBYLLrJ211RBM5NmzapEoO6mjkqUTNXr16t24aSs69nz57Ab76CSroKo9FItWrVdNdX8ZFUtISKYGi1Wtm1a5cI0lIaeuaYrmvSuHFj0tLSyMrK0m3HVeunXKeYZwbqJTMuhbVr1/LSSy%2BpPt%2BXRDUVlAToKfUY3DP1DhumPtYkJwbcMx9rrwH3JNB6dbSBibTJ2UuCGqlwCcYDuCUkB9wzAOuluigxxRZok6oDkZGagv/7P/WxXrZkbd86AnyLFpqCEtNlFSURfEsdn95zpn3Jobd%2BWl8WvWzT2qi1mjnpJV7Xvt/Qy5%2BtvQ%2B6ia5btVIf6yUT15RpE0nr5T532wM6idfdcpLrJSlv2LDszvT2mnZxXF7eCbTPgt4LK%2B09166VXt/afaRXR5uc3W2D6lynY2Hg1rRLwC9Af97a77ySHKZl1dHbEpS8ABVo/47rbbaSSM4K2tztAPTtqz7W3n9w/x7QLoTOvdQmNtf7mtVuUb3E69o6erdXu17arwC9ba79GtN7f6mto5d4XXfQkv85pMAn%2BceQm5vLZ599xrVr1%2BjUqRNnz55Vaa7KwjXppc1mY9OmTcKkU4vRaOT/Sn70de3alcTERPr164fRaKRNmzZCGFBC%2BkdHR9OgQQM6deqE3W4X/n7R0dFUrlyZkSNHEhQUJCJhdu3alR49epCUlESrVq2oVasWP//8M9OnT%2Be1114TAmPfvn2pVq0aFSpUwGAwYDAY8Pb25saNG0RERNCgQQNsNhvZ2dluefY%2BK9GIJCQksHjxYpVWrkKFCowdO5ZZs2YREBBAcXEx32oiJ5pMJnr37k1oaCgmk4nMzEyKi4vZv38/ubm5QsixWCx8%2B%2B23TJ48WVxrsVj48ccf8fb25tSpU2I%2Bjz76qCo1wq5du3jnnXdYt24dnp6edOrUic6dO2OxWIQQ73A42FEShfCxxx7jqaeeIicnh1q1avHdd98RFBREjx49VH0rwqhynwwGg8ov8fr163Tu3Jlp06bh7e0tgusAbn6DSlsmk0nlvxcVFcXevXvx9fUV1/r6%2Bgrz44ySN9GKlrJevXrC7LW85pyg78MnPfgkEolEItEgNXx/iDtrtpJ/HFOnThWCWufOnVW%2Bdq5mhzeje/fuqoAar7/%2ButDU6TFhwgQMBgPx8fF06dKFjIwMQkJCaNeundAoFRYW4nA4OHDgAN7e3jRo0ICIiAgWLFhAo0aNOHjwIOnp6Vy4cEFlqumqEXr22Wex2Wy0bt2aCRMmEBAQwN133w3Azp076dmzJ9nZ2UJL5HA4OHToEA8%2B%2BCCZmZl4e3urhCittsvhcPDOO%2B%2BoBJ677rqLuXPn8vTTT1O9enVefPFFoQFzTZy%2BZs0aLl%2B%2BjN1uZ%2BrUqUycOBFvb288PT2FINSgQQN%2B/fVX3n//faE1tVqtZGRkiPx5yvjWrFlDgwYNVGuxfft2cnNzRRL7GjVqUL9%2BfZUGNj4%2BHqPRyGeffUZWVhZt27YlIyOD%2B%2B%2B/H5vNxuuvvy7qFhcXExISwqJFi0hNTRX59Xa7mL21bt1apMewWq3k5uaKfaT1G%2BzcuTP169cnKChI1FEE97Nnz5KXlyfKc3NzxV6NL4nJ/8ADD9CmTRtOnz4tUjqUZoYqkUgkEolE8ndgvnkVieTPY9KkSQwYMED3XK1atTiuze9TDraV5A06ceJEqXV8fHwwGo3ce%2B%2B9vP/%2B%2B4Dzx/9PP/0khKLCwkK6devGhx9%2BCEC3bt1IT08XAo2Sx6%2BwsJB58%2Bbx8ccfc/jwYfr06SP8sKpVq8bXLj5NDRs2pEmTJhw5coSwsDBatGhBr169%2BOabbzAYDDRq1IiUlBQ2bNiA0WgkNDQUHx8fTp48icFg4O6778Zut3Ps2DE8PT0pLCzkueeeExpAo9FIUVGRiCwZHx/Pli1bKC4uplKlSuTm5pKfn4/RaOSBBx4gLi4Oh8PB5MmThRmkxWIRa5CcnMy4ceOw2%2B3CLNRoNFK3bl2io6NZsmSJiGyan5/PiRMnhH%2BbEmTlypUrZGVlsX37djw9PalSpQoDBgxg6dKlnDlzhp07dxIcHMzEiRP55ptvmDNnjupeBQUFCbPX%2BvXr06xZM1555RWuXLmCzWajcuXKQtsI8N1336nMM2NjYxk2bBg2mw1vb28hqIIz4b2npyc7duyga9euZGZmCl/Pxo0bc//99/P8889jt9tp2bKlyOvXtm1bwOmTqCVMY2JUFjJoi0QikUgk5eAO08jdauTqSf50Sku/UOjihayXfsFut7Ns2TIhaLimX3jppZdYuHAh4AygERcXJ3LUlWc8y0qSTG/cuFGM68KFC%2Bzbt0/lM7h582Y2btzI3r17SU9PZ/ny5Xz77bd8%2B%2B23fPTRR0RFRbF27VoaN24sxrNIk%2BT15MmTjB07loYNG2Kz2YiPjycsLIxYlwAYDocDm83Gvn37RKARu93OxYsXRUTIyMhIjh49SlpJwIvCwkIMBgN5eXkUFRVhNpvx9PRk165dJCcnk5mZSXZ2Nna7HX9/f/r27YvVahX1Dhw4IISiChUqUKVKFZo1a8Ybb7whzBsVPztXHA4Hfn5%2BLFu2zC04ieLzZjAYCAwMZNu2bYSHhwuNYWFhIadPn2batGmcPXsWk8lEcHAwixcvZv369TgcDt00G8o4U1JS%2BOqrr8jOzqZq1ap4enpiMpk4UxIsoUaNGqKv1157jeLiYsaMGSO0dPn5%2Bfj4%2BKh8NnNycujcubPYZw899BDgfGEwevRoMf%2BEhASioqJ46qmnSkmn4CRa60tTBnpBW7p21QRt0Qb60AlM4ha/SJuc3S1yiY6TvyYpOOjESNBJbO4WJUEbIEEveIT2Rc7ixe51tNEDtEmjwT2AREkEV1e08WHYvLnssYBbRAQ9d1C33M06SendMjFr/Fl1k4trF/3HH93raCM43KKgLdrgNNoYJIDboHXnoB2zXrQIzRy07ZQr8brOnnCL46L3rGo3hbad8mS%2B1gYzAfcM1Tr3xc1oRRt4SK9v7fOrV0cb/EXvudNuZJ2gN24D1PatN2/ts6oXtEXzhaMbtEX7RaZtR2%2Bfa5Kx68XJYe1a9bHeM699VrV7Qqdh7Rz0AsZoH0O9JS9P0JabxdvRSyen/Y7XC%2ByivU43LZ1uBKW/AWnS%2BYeQidclfzoxMTE888wzQpOXn5/PsmXLeOeddxg9ejSDBw%2Bmf//%2BVKlShVdffZU6depw8eJFPv74Y1asWEF4eDjvvfceixcvJisri5CQELZt28ayZcuoUaMGI0eOZOvWrXz//feqnGknTpygR48ehIWFcfXqVWEOabVaqVGjhhAQzWYzDodDCEMGg0FozIKDg4mMjCQjI4Njx46p6oLT98tms9G7d2%2BCg4NZsGABBoOBbt268eqrr3L16lUee%2BwxAgMDMZvNpKen0759ezIzMzly5AjdunXDaDQSFxeHr6%2BvblRJBSVpO/yWrNy1zGQy8eabbzJ58mRR5uXlhZeXl0h2rqR/mDdvHosXL2bnzp1l3jsldYOe1qlChQq88MILXLhwgaVLl3JDE4XNYDBgsVh4/vnnyczM5PPPP8dms6lMXj09PTl8%2BDDDhw/n5MmT/PrrrzRt2pSqVasya9YsUS8iIkL8XxEyFW2kxWLBZrORn59PvXr1uPvuu9mwYYMq8IzBYBBz8PHxcRPYvLy8sNvtFBUVERcXR926dZk6dSpLlixRjdfDw4OWLVty4MAB8vPzMZlM2O12VZ2UlJQyA/m4IhOvSyQSiURSDrRBfW4F2siwtzF3lngr%2BUegpF8wGo0cP3681PQLb731Fo8//jj16tVj9OjRrF27VkRgXLFiBTX0IoHpkJGR4SawZGVlYbfb6dSpE7t376Z69eqYTCaKi4tVAVKuXbtGfHw8aWlp4ke8q9ClhODft2%2BfMOXr1KkTeXl59OnTh3/9618YDAb69u0rUiMEBgayaNEiunbtSl5eHltLojLa7XY8PDxo0aKFrsDgOgdFwDAajfj4%2BAjB01XYq1atGt7e3hiNRoxGIx4eHtjtdoqLixk2bJjQFIJTkDGbzRiNRlXUzSNHjojceL6asGDXr19n2rRpjB49Wmg3TSaTGLvD4aCoqIh3332X77//nsmTJ9OmTRsee%2BwxqlevjtlsFukMEhISRJJ1vXQbivDWqlUrvLy8qFWrFt7e3sLUVDHTTEtLY%2BPGjYBTiKtduzbBwcGqtcvLy8NoNKpSLgwfPpygoCDA6bsHULNmTd0InVWrVhVrpBVgXcdaHmTidYlEIpFIyoHU8P0h7qzZSv5RVKlShejo6DLTL0yaNImPPvqIbdu20atXL2JiYnj77bep4hJ7%2BYUXXtBtv27duqSmpmIwGHjggQdEUJfKlSvTqiQUdnp6OnPmzOHs2bO0bt2auLg4jh49Stu2bUVuN5PJxCOPPEJycjJbtmyhQYMGwG%2BBYn755Re%2B//570e%2B7777L/PnzhYniJ598wujRo1Xmj35%2BfsyZM4f58%2BcLE0JPT0%2BKiorYt2%2BfmxChRP5UCAkJoUKFCnh4eLB9%2B3aRNNxV2Dh37hw2m42srCyioqLYtWsXTzzxhKj32muvibpKonNX3zelLcUfMTc3F39/f%2BH7qGjK3nvvPUJDQ/H09MRmswnhyjUAjq%2BvL02aNOHAgQM88sgjXLp0CZvNRseOHXn77bfJyclhT0keqfT0dFXU1UmTJolx7t27F6PRyPPPP8/u3bs5ePAgCxcuFPcKnEFbwKlJPnXqlMpnT8HhcJCdnS1SKCipIVypW7euiGIKTh/OpKQkpk6dKgLVeHt7ExUVpdq7rgF0bob04ZNIJBKJRPJnIwU%2ByV/OrUq/oHxKS79QGkajUQTdyMnJYdWqVXh5eTFv3jzq1q3LlStX2Lt3L/PmzROmmMuXLycqKoq%2Bffvya0ky2H79%2BpXZj6Ipeumll4TPYFkoZpeAm8DXrVs31fkrV65gt9vJy8vjhx9%2BYNy4cXh7e%2BNwOFSCZVZWFjabjfvuuw9/f3/GjRsnop%2BOHj1a1FOEjCFDhjB16lQ8PT1FkBQl5x8gfAL37NkjfDCXLFlCu3btVD6ZRqOR8%2BfPi3YvXLjA4MGDKSws5NFHHxWawPDwcL7%2B%2BmuRlsKVoqIi3Sit7733Ht27d8fT0xOz2cz48eNFZEyDwcCuXbtU65iXlycC7CiCmuv6BgQEqEyBX3/9dWbPns2HH37I1atXxRg2b95MVFQUCQkJIg9hYWEhSUlJKq3wzcxkXSlP4nWtxYmeBYqbUlTrd6P16UPHJUUna7Cby4ye74sm5YebP4/WtwigJLCSQMf/1k25qrMXtEW6ScA1hdoc0XqJhrW%2BObr%2BZBrza72%2Bte24rbnOywi3Ij0zb22ZXjRj7aA1dXR9%2BLST0C6W3vj0sk1ry3Sch9zWVOszpZsr8yZjAbe10XM/0g5Pe5/0/Mu0S6HXt9ZVUa9v7by11%2BjtNe1t0fNT0z5CFy%2B61/lv/MlOn1Yfl8TGUqH9WtBbP%2B0W0NvW2vFp11xvPcvj/1ae9XNbC63/oF7D2gFqksAD5dtc5XFg1Uy%2BPN%2BPbhPX61u7qHqL7PI39W9Favj%2BENKHT/KnExMTw6VLl4S2x8vLiwYNGvDiiy/SpEkTGjduzNSpU4WmqzQmTJhAYWGhyrcLfvPV0/rwKTRs2FD84AenIKH4wCn/duzYkfklQStiY2PZuHEjq1evJisriw8%2B%2BIAvv/wSs9kskrafOHGCDRs2cNddd4l%2B9uzZwxNPPMHhw4fx9PTk0KFDPPLIIyIipre3NytXruTBBx8UkUEBVq5cycSJE8ucu8VioVq1apwu%2BetrNpsxmUwUFhZiNpupU6cOJpNJRDV1FUAUP79HH32U48ePs2/fPiFc/bePvxJptLi4WNcnzmKx0LFjR/bs2UNOTg69e/dm69at5Ofn4%2BnpSVBQEFWrViU8PJyVK1cyatQoLl68yMqVK6levTqNGzcWppkWi4Uilx%2BDBoMBs9lM7969GTt2LO3atRMmrcq/rhiNRvz8/MjNzSUkJISLml9Ciq%2Bhax9Vq1aldu3a/PTTT6q6Hh4eLFiwgMTERN577z3dtenZsyczZ84s1zrq%2BfCNGT2akaNGlet6iUQikUjuCPQy2/9R9F5Y3abcWeKt5C9BG5Xz/PnzVK1alc8%2B%2B4ykpCQSEhKYPXs2mzZtomvXrhQVFfHGG2/w/PPPc%2BzYMdGOXrRGcAp4ERER5Y7KabPZ3ISAoKAgvLy82L59O%2BCM7vj%2B%2B%2B8TERHB4sWLOXbsGM2aNaNTp06sXr0ag8FAhw4daNGiBefPnwdg0KBBTJ06VWjeoqOjSU1N5dy5c7z44os8%2B%2ByzgFN7duTIESFQbNy4kYiICPHRCnuVK1fmu%2B%2B%2BY/PmzSJaptVqFRpDk8lEdHS0ak6nTp0iJSUFq9WKh4eHEGJczSq/%2BuoroqKiAGjTpg1Hjx696doFBATQrl07OnTooNLAJSUlCdPF0aNH4%2BvrS%2BPGjYVpo9VqZdu2bSIIzX333UedOnUIDw/n9ddf55lnnmHfvn0iL16HDh1E2%2Bnp6SJdBDhNNPv27Qs4hbd%2B/fpRsWJF1q5dK7S7Sr92u10Iwwp2u11oOvUE3Dlz5pCUlKRKmL5s2TISEhJ016Rly5YiBYSC69qc0Q1tqI9MvC6RSCQSieTPRgp8kj%2BFSZMmqXzm6tevz7Bhwzh79iw5OTkMGDCAtLQ0YmNjGTlyJBaLBX9/f/r3709qaqpoR/HRupUoKQMWL14sEryfP3%2Be1atX07p1a65cuaJKv/Dtt98ycuRItm3bRnBwMM888wzgDIqSmprKgAEDKCgxg0hMTOTBBx/EYDCwfv16oXE8ffo0a0tCQzNIfQoAACAASURBVLsKB126dBGJvhU/tMuXL9O7d2969uwpNHVVq1blySefxNfXF5vNRm5uLuvWrRPmf4rpZK1atRg6dChGoxGr1UpQUBDr1q2jS5cuABwoCQGuBCjx9vYWfaemppKamsq0adNEABfFJDI9PZ2hQ4fi4%2BNDnTp1hCAKzqiUAEePHiUqKoqQkBA8PDxUgWc%2B/fRTUlJSCAgIAOCRRx7BYDBwuCTE9%2BOPP85qTch6hSlTpoj/2%2B12Lly4wOLFizl8%2BDCffvopgFh/o9GIt7e3mC8gAqx4e3sLoVspMxgMtGvXDkBoa2vUqEHlypXZs2cPYWFhQhCsUKGCSOiuCJQ1atQgOTmZmTNnivsqffAkEolEIrnFSJPOP4RMvC7501F85lJTU9m5cyeXL18WUTk9PDx4%2Bumn2bx5M8nJydx///1kZGQQGBhIYmIiV69epWvXrr%2BrP60JKTh/7H/yySe0atWKmJgY%2Bvfvz6ZNm3jhhRdwOBwiKbhCXFwcX3zxhdBiKT/ily5dKuqcOHGCDh06EBwczMWLF8nPz2fo0KEEBAQwbdo01q1bpxtx0lUzp2gYwRkRVOlLq5W8cuUKK1asEBqzY8eO0bdvXwoKCrDZbNStW5cTJ05w48YNkSgenP5k27dvp6CgAE9PTyFUxsXFERcXp2o/IiICDw8PVd/Z2dkkJiZiMBhYsGABNpuN9PR07HY7wcHBXLt2TQiyAPv378dsNmOz2fD09CQ0NJRz586RnJzMXXfdJQRFs9lM7dq1RY5Bq66TFCIvX3JysiiLj49383Nr0aIFCQkJImrnSy%2B9JKKfKgnXCwoKhMCp3FclbQU4o7kCXLx4kby8PFq1aqUKQnP9%2BnWioqJISkpi0KBBrFixgrNnz9KoUSPAqWUsKipSBRS6GTJoi0QikUgkkj%2BbO0u8lfytKD5W2qicPj4%2BfPnll0RHR5OQkMBzzz1H//79cTgctG3bttzpFxQuXrzo9qPZarXy9NNPc/bsWex2OzNmzGDBggUqs9CioiJ%2B/vlnatSoQWJiIsXFxaoca6457wAWLlyI0Whk79699OrVi9GjR5OTk8N7772HxWJh//79FBYWYjQaqVKliphveHg4/v7%2BTJw4keHDh4v2XNu2Wq0q88OioiICAgKEZikvL4/c3FwhwCjlWVlZIoAJOAW2vLw8WrRowYYNG7h69Wqp62Y0GklKSuKtt95yO6ckhgeYO3cuq1at0g2oAk5hKiwsjLVr14ponFarldTUVBISEpg0aRJRUVHCH1HpWy9Ka9%2B%2Bffniiy9UkTYdDgdms1mYrgLC/FKJMjpkyBCaN28uriksLMThcLjti4KCAqEdVP5V6kybNk1X%2BOrWrRsnTpxwK1e0h67rfzPKE7RFa4Wq63apSSStDZCgm4xYG8hFT%2BjWtKubJvImibj1tkl55qQNoqAboEMT6KM8sUvKEx9B247ummsK9epo29bW0X3PUY7F0VoClydPuLYdvaAtbutXjgzQelXKE/TmZvdB717ebE66RXqd3yzRejkGXJ6ALLpfkZpCt67Ks4n1HoZyJKUv1wbUlpUn6Ih2POV5GHTm6dZ0OfaaW5lOpfIEOLnp8PTmdLN7qVP239a52TP1Zz2HQClfgH8DUsP3h5BBWyS3HG2i9dzcXL766itmz55NXFwc999/P9OnT%2BfBBx8ss50JEyawZs0aVV44QCQ%2BLytIS/fu3UVglIiICCpXrsylS5cYNWoUubm5LF68mOjoaCZOnIjZbOb%2B%2B%2B/HYrFgtVpFxMiaNWuSnZ1Nx44dWb16NRs3bqROnTosXLiQadOmkZCQIAQucJqf9u/fn/j4eEJCQpg4cSLffPONEGbq169PYmKi0ASVRdu2bfnkk0/o378/v/zyC%2BA0IQ0MDOT48eOq6z09PUWETKPRSJcuXSguLmb37t2qegEBASIdgZKTr7Sk6q4EBAQIXzs99AKlKOPy8fHh%2BvXrxMbG0qlTJwYNGkSfPn14%2BOGHxb0BWL58ObNnz%2BbHH390a8d1verUqcO5c%2BcICwsjIyODwsJC7Ha7CL6jYDabadKkCfv37wecQtrmzZt5%2BOGHWbVqlap9s9mMp6en0J42adKEr7/%2BmkGDBrFXE2HSw8ODESNGUFBQwCeffKK7HtqgPGUhE69LJBKJRFIOyhnN/XehF3b2NkWadEr%2BFKZOncrbb78N/BaVc%2BHChcJnrjTtkJbu3buXGpXzZiimneD0i3M4HCxZsgSr1SrSMBQUFDBixAgsFosQHMCp5alRowb16tUTPoQ9e/akuLhYmCW2a9eOt956i969ewOU6sNlt9vJz8%2BnVq1aJCYminKtxtCVn3/%2BmcLCQpW26MKFC1y%2BfJni4mJhNqkkNw8MDOTGjRv4%2BPiInIAVK1YkKytL5NfLzs4mJCSE7OxsHA6HaOdmAl9YWBgmk4nMzEwhVHl5eVFYWCi0ckokTCU4Cji1lLVr18bDw4MVK1YwatQorFYr%2B/fv54033lD1MXDgQJE/ryxOnjzJww8/zKlTp1Tmsq7CnnIvXdf6tDa2uAs2m01o9wA3DZseSuAeVxShM1dXDaaPTLwukUgkEonkz%2BbO0mdK/jJcg7YkJCSwePFimjRpAjgDixzXy%2Bd1C2jRogU2m424uDjOnTsnBAHl33r16pGTk0OzZs0oKipiwIABHDlyBLvdjoeHB56ensI80mQy8corr4h8dc899xzg9FNLTU0lKSlJCHsANWvWBCBNYwan0LRpU3FdtWrVRC43JQm6EkAFnELiypUrOX78uNBwenh4EBoaSqNGjQgPDycqKkoInzabDbPZTE5ODl5eXgQEBHD9%2BnUWLlyoqqcIpYrPnGuScJPJRL9%2B/UhOTlb5P549e5bOnTsLjazJZKJSpUpUrFiRwsJCCgsLsVqthIaGMn78eKF1NZlMvPPOO0Ib%2BsP/Z%2B%2B8w6uo1u//OSUnpEISWggdIYAEpKP0DhZUULkgTRBsIIiAcImoSFG8lisCChdE5CdNQCJIR0GKdEIoAtI7BBIg9dTfH8lszuzZgXPVe71%2BnfU8PGQme/bs2bPn5LzzvmutjRupX78%2B48aNE2tDG4/H4xH%2BeXa7XSdsIxchLFmyhL179xYQLOVdY5MmTXSBrCYEpGUKtXusBWn%2BLyD%2B8Y9/kJCQQPfu3alfv77I4pYrV46UlBRefPFFbuWbZ9lsNqKioihSpAhx%2BZLRJUqUUI5LBZPDZ8KECRMmTAQAs6TzN%2BGvdbUm/ifQvn17Fi5cSIbCjHf48OG/SZVz165d2Gw2g4m3ZsMwadIkII%2BvNWPGDK5fv47T6WThwoUsW7aMpKQkvvvuO5o1a8aWLVu4deuW%2BFK%2Bdu1aw/mys7Pp3Lkzb731Fs2aNcNisfDss88SHx/P119/LThlkKc2qdlVnD9/XgiWlCtXjp9%2B%2BomwsDARcFosFj7%2B%2BGNhsK6N%2BcqVKxw8eJBTp06RmppKbGwsrVu3Jjs7W9cuMzMTr9fLK6%2B8QmpqqihTvXLlyh3n7%2BTJk3Tr1k03fzk5OSxdupTly5cD0KZNG%2BFr548jR47w7rvvcv78eUJCQpgwYQIOh4PGjRvToEEDmjVrJjh8NWrUoHr16rpgzp8rqZ0/KChIZCy132nwt17wh9vtFmqkgK7s9vr167oSVHmdhISEEBoaSkpKCkWLFmX37t2inPX06dMkJCTQt29fMVaPx0NaWhrp6emCDxpIhlCDisPXurV0/Lx5d94GI4cm31NSQOU1JJM1FObshn6/%2BMLY5uJF/fb8%2BfrtffuMx5w%2Brd/%2B8ktjm0CIavkluwWeG0XFzpo1d2mAgaClOrXhshYsMDbKV7AVkJRolclg2SxZNqlXjUfl%2Bi7fO6ksW2m8Lg3IzyWn4H5Vk7N5s35bYeBuIMHJVR%2BBEBMVbQzvgVQvhg4e1G/fjdOnOne%2BwrAOslm3gjxrINHIC0l1bvlcgZhjS%2BXoyr4D4QL6VUoAsGuX8Rj574rq80ZymFddArLIWQDzKZ9bRTHET1gMgPy/vTokJ%2Bs2DfdJ9YxJF6G6bHmKVctR3hcITVKaTsM2GIesYmbI%2B5TsjQJE1Uz8uWBy%2BEz87pA5fDKysrJ46qmnsNvtjB8/nurVq3P58mWmTp3Khg0bmDdvHmXKlPlNRuuaQIfNZsPr9VKuXDkmTZpErVq1hP%2BdVtJYqlQpPv30U10fbrebFi1a8NJLL5Gbm8vEiRMFV27NmjWUKVOGI0eOMG7cOHw%2BHy1atGDy5Ml4vV5d1kxGUFAQFoulQA7fnco8/c3SIyMj6dWrFy6Xi88%2B%2B0xYKPibyUNe1rFTp05MmzZN7LtbBql8%2BfKkpqaSnZ2tLL212%2B2ULVuW69evc%2BvWLWWbwoUL6/hv58%2Bfp1WrVgDinhT00VO%2BfHmCgoI4duwYFouFn3/%2BWXD9Bg4cSGhoKJMmTaJt27Y4HA5WrFihmyO73Y7L5RLnCQsLEy8XSpcuzeXLl3WqoHa7Xdwzm81G4cKF2bZtGx999BHTpk3T9R0UFET9%2BvWx2Wxs2rRJOf7Dhw/rAtM7wTReN2HChAkTJgJA5cq/f58FVGT9X4SZ4TPxX4e/KuegQYOoVasWXbt2xe12s2jRon9blVOFjh07YrPZ6NChA6VKleLcuXM8/fTTVK9eHcjLRp0%2BfZpjx47x/fffs3LlSuB2htFut/Poo4/y1VdfsWzZMuC2ncIjjzzCfffdx5AhQ2jUqBGzZs2iTZs2tGvXjsKFC%2BvsHSCPS6d5/93t/crdgrHg4GCCg4NJTExk1apVQjjEP4AKCgqiWLFihIWFMXXqVIoXL47H46Fjx4507twZi8UifPhUWbKIiAi6d%2B9OcnKyzm9Puy63202NGjXIzs5m9OjRzPDLJmllqTdu3KBZs2Zif1xcHIMHDwagT58%2BunmwWq06YZ6zZ88KY3P/rJ%2BGfv36AbB161ZD%2Bawm6AN5mciiRYvqMsk1atTQle3K8Hg8IkO3S3qTrQXcpUqVUnL4NKSnpxf4Oxmm8boJEyZMmDARAMySzt%2BEv9bVmvivYMOGDQVm9zRERkYyatQoNmzYwP79%2B9m4cSMTJkzQeZi98847huweQKVKlThy5Igyuydj5cqVXL16VRhy16tXj8cffxybzYbb7SYkJIQHH3yQr776ijFjxrB69Wrxhf3hhx/m/PnzoiRQ87dLSEggLi6OZcuWMWjQIE6ePMkTTzxByZIlSUpKYu/evVitVhEgDR48WAQLGrdRy1hppt%2BQF6j5B1g2m02X/YmNjWXjxo00adKE0aNHi5LQevXqMW7cON38LF%2B%2BnFatWrFy5Urd7/xht9spXbo0s2fP5tChQ9SsWROLxcLBgweZMWMGU6ZMYfjw4URGRooyTP95zc3NZf78%2BZQuXVpYObRp00a0uXz5sihhTUhIYNq0aTgcDmbNmgXkZfK0oHzlypUMHToUm81GtWrVhF8gIK7THxaLhczMTJ1wS%2BfOnbHZbCKYW79%2BvfDW07BbLgHkdiAfHh4OIF4KREdHU6ZMGRFsjhkzhpSUFMaNGyeCyIEDB7Jv3z4WLlwo7rF/CerdYHL4TJgwYcKECRP/aZglnSb%2Bz0BT5XS73aI0Mioqio8//pgGDRoAeT51kydP5quvvhJlfFoJZJkyZUhNTcXtdmOxWIQXnsb50kr/tm3bxnvvvccLL7xA2bJl6datG8WKFRMB4ZIlSxg1ahRxcXGcP39eV6Zps9moX78%2BP/30E4CwggAMpucA/fv3Z%2BHChdzI58BoZYVayaLb7SYuLo42bdqwePFiEYhYLBZCQ0MpWbIk586dw%2Bl0Fphd1PqUTeb9ER4eTtGiRbl06ZJO0VJGuXLluHDhAnXq1GH79u2G7B3cFk6pUKECp06dEpzF4sWLU7p0aQ4cOEDjxo1Zk8%2B18i9R1QI6p9OpK2MFqFKlCr/88osYv8ViEcGaNqdRUVFkZGSIOdf6tlqtDBw4kMmTJ/PQQw/x/vvv06FDB86ePSvmRbuWWbNm8dJLL3Hjxg2lJcXLL7/MSy8FZqtw6NAhg4BR5cpVqFat6u0dX30F3bsXvA15HAu/lwVMnw4DBtzePn8e8kVlNPh8oKMwysdAHpnEP2M9cybkZ1gFzp4F/6z811/DE0/c3t62De6/X3/MsWP68hxVvzdvgh//0nCNAHv2QJ06t7cXLICuXXVNDh6Ee%2B/12/Hdd%2BCv8iuPRXVujwekrPCuXVCvnt%2BOefNAftEln3zRInjyyTteEmlpEBV1e3vrVnjgAX2b7GzwE3nixg0oXFjfRu5cuqasrFxCQ/XVCLduQUTE7e1TpyBfW6rAflWnNtzznBwoVEjfRlpbchPVIYb7IM8DeXwo3XuUzEwIC9P3c/QoVKlS4FiUNyYjA/JfCgF5nK98ITIB%2BVmQ7yWK505eSKpzHzgAfi/clG127ID8v3PKfiGg%2BTN0LZ97/36oWVPfr/y8yPOgaCNPOQBXr0KxYgX3o5hPrlyB4sXF5sWL4PfeOA%2BLF0OXLre3Dx%2BGatX0beTPAXkiFJ%2Bh8nAVTQxTrJhywz7V7ZXnS17WqmUuP8/yEla1kbcBxUP1B0G%2BZ78HZJ71/2GYAZ%2BJPzXq1asnPOhUvLhChQrh9Xr57rvviIqK4oknnsDpdOJyuQwCJhUrVqRFixbMnTtXmK7DbY6X9v/%2B/ftF9u7atWs88MADfPnllyKoHD16NF9//TW1atUiOTlZFxTYbDZiY2M5d%2B4cUVFRpPmxqt99911OnDjBrFmzRECicQzPnz9vCCyqV6/OoUOHCA4Oxuv1Ehsba2hXtGhR0tLS7miDoQV8mlJpQeqXLVu2pGPHjowePVrHgfNHtWrVeOqpp5g9ezbnzp2ja9euBguGRo0aCVEZ7fwul4uIiAgKFSrE1atXadu2LevWrbtrCax/MB0VFUVOTo7w5itUqBAvv/wyDRs2pE%2BfPty6dYuoqChu3Lhx1yxa48aN8Xg8IjD3n6dZs2YxdOjQAgVw3nvvPTp16nTH/jWYHD4TJkyYMGEiAJgB32%2BCWdJp4k%2BNXbt2CYl/m83GQw89xJEjRzhy5AhWq5UKFSrgcrlYtmwZU6ZM4ezZs5QvX54PP/wQm81GqVKliMp/Y3jp0iV69erF448/rlN23LZtG0eOHKFjx44AIvMEeXwzyMtWnThxgldffZVvv/0WgKNHj9KzZ0/%2B/ve/6zJNmppjmiSh9fXXXzNw4EDq168v9rndbs6dO6dUHj2c/0Gl2SJcuHCBihUrUqVKFXG%2B69ev68oj7XY7JUuWFH05HA5dGWlWVhZFixZl9OjRtG7dWne%2B77//nhEjRuiCvYYNG%2Br4dYcPH%2Batt97i9OnTeDweFi5cSEJCgvBDbNWqFTdu3MDpdOLxeOjTpw9r1qwhIiKCjIwMrl27BuSVRWp8wAYNGogyyTJlyujuTcOGDcXPGn/PX630gw8%2BoGfPnpQtW5YqVarovASDg4Np3749MTEx2O12UQpaqVIlZs2aZXiB4B98ahYc8j0BqCm//b4DTA6fCRMmTJgwEQBMDt9vwl/rak38pWCxWKhYsSI2m40TJ06wZMkSYbi%2BZ88e4uPjWbZsGY8%2B%2BiiQ9%2BX78ccfx%2B12C9N4DR6PR9gyzJgxg5kzZ5KQkMDTTz8NQIsWLejYsSPLly8XGUev18uaNWuYOnWqLptVEHbu3MmNGzf44IMPdPu9Xi9Op1OUM1qtVmw2G4X86p0sFgtut5tLly5x6tQpHnzwQVFKqQVbWjuXy2UYh2bEDpCamkrNmjWZOnUqA%2BQSP79%2BtHn54YcfiImJUbZzu904nU5at24t7Ci0uShcuDDDhw%2BnVKlS9O7dG7jNo/N6vWIeixUrxo4dO7BarWRkZOgCr5MnTwrzd4vFoguW/ZGenm4onczNzRX%2BfJq6p9Yn5InDqO7XtGnTqJNfRqjKQO5QyaGbMGHChAkTJn49zIDvN%2BGvdbUm/lIoXry4yCTFx8eTnp5O48aNcTgcLF68mKNHj9K8eXMWLlwojmnatCkTJkwQX%2Bg1fP/99zidTlq1asXRo0eF8bYWEPjzvDTUr1%2Bf0qVL43Q6Wb58OXFxcSKoKFWqFJCX1RsyZIg45vPPPxcZR4CHHnqI9evX06xZMypUqEDNmjUpVKgQn3/%2BObVr1xbt2rdvT9%2B%2BfcnMzMTn87F7924cDgfdZa4XGEoaX331VVJSUrjnnnuwWCw4HA7%2B9re/8cgjjwgzdIBRo0bx8MMPA7fVPfft28dbb70l%2Bps4cSJHjhxh4MCBWK1WHA4HDodDGTjduHGDNWvW4HK5RJYxOzsbi8VCSEgIhfOJQd999x2jRo3C6/Vy8%2BZNHYcwJiZGZBxtNhstWrQQvwsNDWXcuHFs376dwYMH4/P5KFq0qG4s999/P1999RUDBw4U/WiKp23atKFmzZriWkuUKEFKSooQnYE8DmbFihV1yqwHDhwwXGtBMEVbTJgwYcKECRP/aZgB318UrVq10iko1q1bl%2B7du%2BuyEzdv3uTdd9%2BldevW1KxZkyZNmjB48GCO%2Brnxjhw5kldeecXQ//Hjx4mPjxfli4GMZ57KUFrCBx98QHx8vLBRkPHdd99RrVo14uPjuXjxIuvXr8dqtYpAoGjRouzYsYOzZ89y//33C6EU7XerV68WXC/ti37jxo0ZOHAgXq%2BXH3/8EZ/Px5dffsns2bM5cOAA5cqVA/ICDrvdLkoOt2zZwu7du8nIyODRRx/l/PnzIqi4cOECxYsXJz4%2Bnueff56wfLb1vHnz2O9nsrtnzx66dOnCli1bOHHiBIcOHSInJ4dnnnmGnTt3inb79u1j/vz5eL1eXC4XFy9eJDs72zCnLpdL5xPodDpZsWKFEE3R2vh8Po4ePaoLXrKysoiKihLZRMgLdDds2CDKUz0eD9u3b%2BeTTz7B5/Ph8XhwOp2CNwl6P8Hhw4dTs2ZNPvzwQ1GSabfbdYGzvzWDf2mrxWLhX//6l2iXmprK8ePHdeMdNWoUtWrVYsSIEfh8PoNq5/z58%2BncuTPffPMNNptNp5ravn17UlJSxPq4fPmyyFJqyqEul4sTJ06IbCTcLvMNBCrj9XZt2%2Bob%2Bb2QUG6TJx6gQ75dh4CU3QSF6bfCVF32QVaas8vG5cuX67f37Ln7Mf/v/xnbyA7AKjdi2ZB68WJDE4N5uJ9vI6DmcMguxgoOrMF3e9EiYz%2BHDt25jcr4WnZLVhldy3OjMoWWy4WlNirjdbkblZG07EhtWHtgvOcqLq7EA5anQjU1hn4UjQzG0Sp3e/mey0JUKo6y3LFsmA5w%2BrR%2B2/AAKZaSXBGgOrc8XtV8yutE9dzJJ1eUlBtOL7/AUl13vqiYgDwPYJg/JQ1cXkxnzui3Vev80iXd5sWLin6XLtVvy88l5CkU3WmAirmSh6t6FuTl5%2BcSVGAb1dzIH39yPyrj9UBM1eUplW8lYHw%2B/iiYGb7fBFO05S8K2RxdCw4%2B/vhjvv32W6KioujatSuxsbGMGjWKihUrcunSJaZPn84333zD/PnziY%2BP/9Xm6JqipvbF3uVyUaZMGcaPH69T1Jw2bRpr1qzh6tWrREREcOvWLRo2bIjP56NOnTrCGNvff80fsbGxQszjxIkTtGrVCo/Hw%2BbNm3nmmWcoXrw4n332GWlpaRQpUoQbN24wcuRIevXqRd26dcnIyKBZs2Zs2rQJm81GXFwcV65cweVysW/fPhITE4VPX0HwN/aGPI6Yf2CizYHX6yUiIoLg4GCd3YCMkSNH8s9//pOEhARl%2BWCxYsXo2rUr/fv35%2BjRozzppwqoQsOGDZk5cyY//PADr776Krm5uWLMBRnB%2B6uL%2BqtoqqCVS7rdbmU7fyN2u91O8eLFuXDhAqVLl%2BbcuXPC965fv36sW7eOy5cviyzf3Llz6devn678Uw7q/KFlHVVKo0WKFKFYsWIcP36cw4cP0759e05JXwIcDgerVq1i4cKFfPrpp8pzVKtWjW%2B%2B%2BabAMfjDFG0xYcKECRMmAoBfVdPvhr17f/8%2B/0fx1wpvTRSIkJAQ%2BvbtS/Hixdm0aRMzZswgIyODqVOnUqlSJSwWC7Gxsbzxxht069btjgFJILh06ZIhkLh58yb9%2BvXj7NmzZGRk8NRTT7F69WoRLOXk5OB2u/npp5/YunWrzlDbvy%2BtjDAqKoqrV69y7Ngx8cV9w4YNbNy4kZo1azJ8%2BHCOHj3K9evX8fl8FCtWjIoVK7Io/y28luE7nf%2B20uPxcObMGapVq4bX62X37t0UKlSIKvny3nJmKjw8HLvdzhNPPEFISIgwO/cP9rSxa%2BN/8MEHKeav8yzBarUSGhpKxYoVSU5OVra5evUqtWvXZtmyZbzyyisieyjDbrfz3HPPkZaWRr169Rg2bBhut5t58%2BZx8OBBGjRoQNOmTQ22CtpcQF4JrH/Zpwo%2Bn09XRimXd/oHjDNnzhRiKZGRkcKCAWD16tXiZ60EtEePHrrs2p2CPYARI0aIYK9QoUK6%2B3Xz5k2OHTsmrreg7HRcXByDBg0q8Bx3UkSVYYq2mDBhwoQJEyb%2B0zADPhM6eDwebDYba9eu5cknnxSCGP4YMWKErvTt16Jjx45CYbNkyZI0aNBAqag5a9YskpOTqVmzJvHx8Xi9XsqUKUN4eLg4XvM9q1evntj3008/cfDgQQ4cOMDhw4d5/vnnxRf8zp074/P5yMrKEl5uHo%2BH5cuXC5VNDZoRuxaoJCcn4/P5mD59OhUrVhQiH%2BHh4cyaNYsu%2BX4/r776KpBXvpmdnS0MvQFq167NypUrWbp0KYMGDRIiLMuXL9e1Cw8PZ/LkySIIbdasGW%2B//TY%2Bn4/c3FwSEhKAvCD3Xj%2B/r2HDhjFu3DguXLhAWFgYLVu2BPICJG0ONIuJb7/9luTkZHr27InH42HTpk0ioC9btqxob7fbSUxM1Cla/vjjj3i9XhGAlSxZUgRMQUFBIviW4a%2B0WaFCBdHmo48%2B0nkOAiJA18BmiwAAIABJREFUK126NNOnT2ffvn20bNlSZA5tfr5S/oqj/sGc9vNTTz0l2lgsFubMmcOnn37KI488Ito4nU5%2B%2BeUX3njjDWJiYrg/308sLCyMlJQUIK/UFvJeCtxzzz2EhoYKHt%2B/w8EzOXwmTJgwYcJEADBLOn8T/lpXa6JAZGZmMnPmTK5fv07z5s05e/YsFSpUCOjYVatWCS6g9k9TvgwUVquVBx54QKmoWalSJVJTU9mxYweTJk2id%2B/etGzZkiVLlojMkBaUhciOpn547rnnKFmyJJAXWNSsWZMNGzaI35eRjWLzoQUg2rm0L%2BQ//fQTjzzyiBBZSU9PZ8KECUINcuLEifh8PjZt2oTVatVlwjR/wOrVqzNw4EASExMJCgoiMzOTjRs3AnkBSWZmJq%2B99hpPP/00hQoVYuPGjbhcLsGZO3ToEFarVXDJNKSlpeF0OvF6vVy5coXvv/8eyCuB9A8otm3bRrVq1ejUqRNffvklAP/6179o27YtO3fuZMGCBSKrZrPZmDJlCm63G4/HIwKkVq1aiSxspUqVDPOnqYxq81emTBkyMzPF8ceOHRPnOHr0qOgrMzMTj8cjsmCNGjWiSpUqWCwWUcrapUsXXUbN/9pUP7/22mtC3TQ7O5sePXrw/PPP8%2B233%2BrKbo8cOcKZM2dIT09n27ZtYjwJCQkkJiaye/duIO8FyS%2B//EJWVpbINBaUUVVByeFr1kzXRqKoGLZBQRUKgCNn4Gpcv25oY6DMqLgvUkmqoWpXVTKzebN%2BW%2BVpKGdKFSXbMoVLmeCVsqjyMSq%2BjCFJqypZlkgzqqIHuW8Dh0bB95SPUXLZ5EWg8CA1EIMkEpCKw2dYEwrOlHwNyvFJF6Gi0clV1TJHSdWv3EaZTJfOraJ%2ByktdnnPVmgiE5hfAkjWMJ5Bzy%2BP1E14WkAsSVPxL%2BdyquZHXgNyviqIsU4R/zXWr9slrQEUlk49R0fxkvpvq3Abun7RDxX8z8OYUkxPImpD3KRkSd1nXSk6k9Nmnel4CWX/k/w008eeGsVbLxF8G48aNE/YDhQoVolq1asyePZvY2FgsFkvApWkdOnQokMMXKLxeL1u2bBGKmitWrKB9%2B/Yi87N06VKqVKlC5cqVGTFiBBkZGcybN48mTZqQnp4uvtBv376d7t27M2TIkAK5gIBQs/THxo0bqVGjBi6XC4fDofNhs1gsLFmyhF9%2B%2BYURI0bQqVMnli1bRlJSElarVZQlXr16VRznb96umXZrvLdt27bx8MMPM2TIEJo3b05SUpI4Tmujjc%2B/7K9ChQo0adJEiLFo98hisZCt/OZ1Z2gcvdDQUEJCQsjJycHr9Ypz%2Bo87NzdX8Pq0rKjWh3b927dvF5k5f/VMuM1l7NSpE5999pmSc%2BlwOMjM/3aoleFqc/jRRx8xefJkEeACOmN0bSx3Wrc//PDDHV8KaNCylAX1dVP11z8fqqxdQUhKSlJy%2BKred5/Yzn9HUeA2gJ/VYh7klzX59iH%2ByBdBvY3oaEMbP8HYPPhlnwWeeUa3aRBkVfEumjTRb%2BcLBungl7kFwC97q0G%2BlcpqaOl%2ByMcoujWc2nhRQESEbtNwDxR9%2ByW286B4ySQfo1yu8iJQZNGRXzz4KckChIbqt0GxJgwLwHgNyvFJF6F6B%2BLnKqManrJfuY3hPinOLR8DxqUuz7lqTcjjDWTdqNrI4wnk3PJ4S5QwtpGp8nFxdz%2B3am7kNSD3q3oves89%2Bu1fc92qffIakO%2BB6hjFkiXf7eeO546NvfMOw7OL4SNAOTmBrAl5n9K96S7rWtWv/Nmnel4CWX/kV7n84fiLZeR%2Bb5iz9xdGYmKiKH/cuXMnc%2BbMoVatWgCUK1fO4Fv2e2PlypUiI3jx4kV27dpFcHAwnTp1AtAZhms2CrVr16Z27do0bdoUp9NJZmYmiYmJglM1ceJE2rRpw4ABAwQXsFu3bhw7dozp06ezadMmIE%2BOPygoiKZNm2KxWERQogUxcjB43333Ub16dR555BHCwsJITk4mJCSEOXPm6DhkHo9HcPk0Y/fatWvr%2BHZaQGS32/nwww957rnnhIBNoUKF7hhMnDlzhm%2B//VZXuiiP12KxCD%2B7gmCxWKhatarwrUtOTubWrVvY7XaGDh1KnTp1CA4OpmfPniJw1q5PE1jR%2Bvn2229FYO52uw2BnNZWy6BNnz5dZMH81UEBHnvsMXEvNOsKgAceeAC73U5cXJwuCMtUpQ4w8gS18T3wwANKwRZ/REVF0bJlS2IN3wDysHPnTo4dO1bg8SqRooJgcvhMmDBhwoSJAGCWdP4m/LWu1kTAaN%2B%2BPQsXLtQJo2gYPnw4s2fP/l3PZ7FYKFy4MHPmzBEZxov5JRU7duzg3LlzLFiwgG%2B%2B%2BUb8Gz9%2BPNnZ2eTk5IisyqJFiwziM7du3SI1NZX09HSio6MFV7Bbt27iy3/JkiV1dgMyDh48SO3atUlISCAjI4NTp06RnZ3NhQsXSEtLM1gH%2BHMFH330Ub799lsRCJUtW5bg4GARuDgcDurXry%2BupWnTpgA6CwJtjiwWC%2Bnp6WRnZzNz5kzBF/SHz%2BfD4XAQEhJCWFgYkyZNomrVqhQqVEhniRAZGcne/HI7i8VCYmIikZGRTJkyhUcffZTSpUuTnp4uyhe16/NH3bp1cTqdIuiVA1EwctL8lVh9Pp9uje3du1dX%2BqkhJCSE6dOnU6RIEV1/TzzxhPJcVqvVwOeLiorS2UM4HA7uu%2B8%2BQ0auYsWKOBwOLqlqJ/3GAxARESHmROvHn0tpwoQJEyZMmDDxR8MM%2BEwo0bdvX4oWLUqPHj04ePAgPp%2BPS5cuMWbMGLZt20br1q1/8zn8RVtKlSpFnz59RIYxOjpaKHEuWrSIpk2bUqNGDcqVK8cnn3zC999/z%2BTJkwGYNGkSkyZNAvKCw969e%2BN0OrFarSQlJZGRkcHx48d55plnaNKkCUFBQezevZtLly5x%2BfJlfD4fV65c0QUSWmCkZcosFosus6XtkwOgChUqMHXqVGJjY0XguWjRIt544w3R5vTp07Rt2xav10vNmjVZt26dKE0sXbq08OELDw%2BnTp06NGrUiKFDhxISEiIUPIsVK8awYcNYunQpNpuNunXr6ozYMzMzyc7OJjMzkxEjRvDzzz%2BTk5Mjxh8UFMTRo0epWbOmKIN88803uX79OkWLFiUiIoKYmBguXLiA0%2BkUPEWv14vFYiE6Ohqr1UpqaipDhgwhODiY2NhYDhw4IIIhTbTFJtWRjB49WsedzMrKEry65ORkcR969Ogh2qxfv57nn3%2BeAwcO6IJgOVvn8XiIjY2ld%2B/elPCre/J4PNSpUweHwyFEWJxOp/A1hNulp1r2rlixYmzatEkEco0bNyYlJYXVq1cTl18vdevWLUPpbcOGDQkUpmiLCRMmTJgwEQDMDN9vwl/rak0EjNDQUL766isaNmzIoEGDqFWrFl27dsXtdrNo0aICBU5%2BC8aNGydKPNPS0sjJyeGxxx5j1apVdOnSRQScq1evJj09HYvFQnBwMCVLlmTEiBEAdO3ale3bt3Px4kUmTJjAhQsXKFSoEAsXLiQ5OZlFixZRrlw5fD4fN27cEDyxuLg4ndDMrl27KFGiBBkZGSLY83g8gtMWEhJCaGgoe/bsoU6dOuK45ORkEhMTefzxxylatCjjxo0jKChIWDJoWL16NQC//PILL774IpfzWfhpaWnUrFkTyAtyHn74YXbs2MEHH3zAgw8%2BKLKeV69eJS0tjapVq/L%2B%2B%2B/jdDpFtg7QlZlq8M88ulwuMjIy2LVrlyHAOHPmDEOHDmXHjh1s3boVr9dLWloaMTExos3169fxer2cOnWK48eP68RVPss3/Z41axYNGzYUwZyGhg0bikyqVtqpBV0%2Bn0%2B014zltQBPE6HxhyrTfPHiRZYuXaqbc5fLxfr16/F6vbz88stiv3%2BfWsZV4%2B%2BVLFmSZs2aievasmULCQkJ9O3bV1cyK5f//ju2DCrRlmbN2ukbyabBKo8/%2BZxff63fVpQJGyiU8%2Bcb%2B5UbyabqqjayObvK2FyuHJCvEYyKDSolA1lERtGPQYxBFoxROTVL51adevt2aUdSkrGRXPq7fr1uU5lEltVf5PGiuHcqtRq5UQCiLXI/BnN5MN47RVkyP/yg31YpiMiKF4EoishG3AoesOHxU3GbZWNwuR/FM2w4lYryIN8HlRu23Lf8fARivK4qS5fFkRTG64bLUs2x/Fnx88/6bdWikJVSVMop0nWpDMgNx8liTqp%2B5edFdb%2Bl5065HhcsuPOpFKJW8v1VdWuYdMWHieGWB6AIFIigjcx4UE1NIKItqmVi4s8H03jdxB%2BC6tWr06FDBz744APl77OysujSpQvp6enYbDZu3LhB4cKFCQ8P5%2BbNmyxYsIDevXuTmZlJRkaGsFWIiIjA6XTSo0cPgoKC%2BPTTT5k4cSKdO3cGbhu%2B%2B3vDud1u8SVfC0Ti4uJIT08nMzOTIkWK0KhRI1566SWqVKnC1KlT%2BeSTT4SFhfwF3263Ex4eTkxMDOfOnePNN99k1KhRgNqkvEiRIkJltE6dOlSsWJGvv/4ah8OB1%2BsVY/I/tnr16vzyyy9ER0frSg%2B1a9KCVBW0fuLi4jiv/At1G7Lxumr84eHhOJ1OnE4nNptNGKkXVB6roVy5crjdbt0YbDYb999/P5s3byYsLIzMzEx69erFtm3buHTpkrjX/uOJjY3lwoULlC1bljNnzggRmcjISNLS0gRH0%2B1206pVK0aPHq3MUGtiNJUqVWLFihUMHDiQtWvX6to4HA7efvttIE/xU4Vx48bd1exeg8p4fdCglxk48KWAjjdhwoQJEyb%2BEvgd7MAM2LLlNx1%2B/vx53nrrLZKTkwkNDeXBBx/k1VdfNVR/9e3bV7zE1uB2u3nppZcYOHAgPXv2ZM%2BePbrjKlSoQJLqReKvhJnhM/GHoGTJkkIwRIXQ0FAWLFhAp06dcDgc4kt7nTp1WLx4sS7DmJiYKL40b9myhWLFilG2bFnWrl1LTEyMyOL5tz906BAHDx7k9ddfBxBWAxqysrJ46KGHiI%2BP55tvviE6OprHH3%2Bc%2BPh4KlSowLZt2wgODmbIkCG6voODg3G73aSnp3Pu3DlatmypU5IMCgoyCIpoIi12u51jx45Rt25dIC/75B80%2BfMEz549y5dffmkwGrdYLNSqVUsEaYUKFaJVq1a6Nna7nRYtWtCzZ08gL8v24osvUq9ePTFG7ZhPP/1UiKoEBwfrxq5lNqtUqSIEdrQ59M%2BWaeWgmvde3bp1iYiI4Pz588LyQoPH4xHn1q7366%2B/5vTp0yLb6g%2BfzyeEWzSOY1BQEFarVVhXlClThib5qpDNmjVj6NCh4viIfJk1q9VK8eLF8fl8/PLLL2RmZhrO5Y/rqre9%2BbiishgoACrRFlO2xYQJEyZMmJDwP1jSOWjQIEqUKMG6dev4/PPPWbduHV988YWh3axZswSFKSUlhS1bthATE0Pbtm1Fm7ffflvX5vcM9sAM%2BEz8F1CvXj2DT9/Vq1eZMGECCQkJBWaZIiMjGTVqFBs2bGD//v1s3LiRCRMmKNUTGzZsyJ49e5g7d67OS7BevXp3FJ/JycnhyJEj2Gw2nRrl2rVrefvtt0lKSiI2NpbExEQcDgcNGjRg8eLFFC5cmP379wvOof%2B1btmyhUOHDrFgwQKuX79ueGj9Aze4Xdbodru5desWf//733W/DwoKIiwsTChN3nPPPdy6dYshQ4bg8Xjo3bs3ixYtok6dOkRHR5OSkiIES3JycgRfThMy6dmzJ2fOnOHkyZOUKlWKzMxMpk2bxtmzZ7FYLBQpUoQff/wRh8NBVlaWMILPzc0lOl8f3Gq1irdRDoeDxYsX07VrV2JiYgQnE2DgwIEiKL6VX/6ye/dubt26RfHixcnIyNBxD202G926dePnn38WNhPZ2dmCP6nKWmrlqyEhIfh8PlwuFy6XS5jS22w2Jk%2BezLvvvmuwCtHG5PV6hdk85KmFli9fnpiYGMFJvO%2B%2B%2B0hJSeGxxx4T%2B0JCQkhISNCVrf475c4mh8%2BECRMmTJj48yElJYWff/6ZYcOGERERQfny5enTpw8LpPJgFT766CPatm1LfHz8f2GkeTBLOk38YdDKK7XAxuFwEB8ff0cPvcjISOrWrctLL73E888/rwwWtSya0%2BnUZWm0sj7/8y1fvpydO3cyatQoGjVqJLJx%2B/fvJ9jP6GbDhg28%2BeabpKenk5uby8aNGylZsiTbt2%2BnV69eop3sUeeP5557js2bNxMVFcWxY8cEb08bm9VqpX79%2BuzcuVPpUefftmXLlqxbt06c0983sXLlyly4cKFAywLIKyOtWrUqe/fuVfL9/M9VUKDl32bkyJFAni3GmDFj2LRpE8nJyWRmZuJ2u0XZbGRkJNevX6dSpUq4XC7OSJycYsWKsXnzZq5evSqycneCf8mpdv9CQkJ0noRBQUF0796d77//nsWLF/O3v/2N48ePF9hnUFAQBw4c4KGHHuLUqVO6LKvD4WDWrFmsXbtW%2BRYPYOrUqQGLGh06dMhgf1KlUiWq%2Bit9Xrmi96mTtyGPnOFvWrVrF%2BRnbAHYsQP8XmhAHiVEp6ezbx/4%2Bf8Befws/6B0zx7w46wq%2Bz58GKpVu739yScwcKD%2BmLVrwe/NJklJkG/HIpCZqTdwc7mMJlE3b%2BoNslJTDYZ4V69K/nwXL%2Bo9tuS5U/WrOPelS5Id3vnzRvMzeTxym1u3jGZeZ8/q/bwuXzYar12/rjdnU12DPOa0NJ1JWVZWrsGLz7AmFP0apkJ1bnn9qeYmI0NnkJabq/cWk4abB6dT7zkor0/FcAzXRB7fyZ9aLF%2BTPBbleBRrzdCxz2c0VZMHJN/fANa54rKN5wpgTaiuU%2B5HXqLyowFw8qRk/anqWLpu%2BfEGjPdXngt5fhXnkj9%2BAOMzrxqf3Lf8HMrbGOdCNbxA1pbhONUakOZPvr3Keyk1Us25fJxq2agn9Q9A8%2Ba/e5dXFi0yVEoVK1ZMZxdVEObPn8/MmTN11I/9%2B/fz5JNPsnv37gLtsU6fPs3jjz/OunXrxEv0nj17EhwczIULF7h48SK1atVi7NixlC1b9jdcnR5mhs/EHwp/L8DNmzff0UNPE12Jjo6ma9euuFwuihQpwptvvsmRI0eYM2cOkJdBSklJoVixYlgsFux2u85yQQsQWrduTXR0tPDmu5N/2qJFi7jnnnuoW7cudrudqVOnKtt5vV5sNhtBQUEEBQXpHvgvvviCgwcPsnXrVhHshYWFYbPZCAsLw%2BVyce7cOZ577jldHXdkZCTvvvuuyCJ5PB62bNkieIdagBkREcHf//53bt26pQz2tHEBpKen89NPP90x2NPOJQd7suqmx%2BNhwoQJjB8/Hq/Xy5o1a3jttdcoW7YsOTk5InB2u92iFFLL7Mplk1r5Z5TfN6s7lVb6j61w4cJERkaSnZ2tmz%2BXy8WXX37JmTNnhJrrneB2uzl79iwjR45U8hCnTZt2R37imjVr7noODUlJSQwfPlz3b933P%2BgbyX94VH%2BI5L/Q/l%2B2wRDsgcKEVw72wPitUg72VH3LXwzkYA/0wR4Ygz0wfjNROQLL3zwV7ucGM3a5QkDl8C33qzi37H2udLqWxyO3MTg3YzRvVrlsy07cqmu4iyO1ynjdsCYU/RqmQnVuef2p5kb6MiR/WVUZaBsM5hUZcnk4KrPpu5moqwzJDeNRrDVDx6rPLnlA8v0NYJ0rLtt4rgDWhOo65X7kJaoyINcFewV1LF23IdgD4/2V50LlvC6dSxmXyM%2B8anxy3/JzqKjckOciEGP4QE6tXAPS/Mm3V3kvpUaqOZePUy2b/4lg7z%2BEBQsW0LlzZ92/QDJ0kPc9KlJaBIULFwYQlBIVpk%2BfTpcuXUSwB1CpUiUqV67MV199xfr164mOjubZZ5/F6XT%2BiqtSwwz4TPzPICQkxOChl5GRwdSpU6lUqZIQ6HjjjTfo1q0bHo%2BHIUOG0K1bN10/HTp04N577xVKkg6Hg9q1a/Pll18SFxeH3W7H5/Px3XffUbduXVatWgXkKXNCXpZq5syZovz03nvvZcOGDWzdupUdO3bg8XhYunSpMounma5bLBahhGm1WomLi%2BPdd98F8gK03r17Y7FYyM7O5tVXXxU8vqtXr/LVV1/pApmbN2/y2muv6SwIrFarCDp8Pp9QAp04cWKBxuIej0dkDjVxFY335o82bdoQHByMzWajcOHCOiEYyAuyunbtSpUqVcQx/nOxdetWOnbsyMGDBwkKCtJl2zT88MMPOBwOwxweO3aMhIQEQ6mshj59%2BvDoo49isVgYNmyY7gPzvffeEx%2BOXq8Xq9UqAj/tPN%2BoFC4lNG3alDJlyvDCCy8ofz9r1iyq3eEPoOp6C4LJ4TNhwoQJEyYCwH%2BAw9e1a1eWLFmi%2B9e1a9eAh/TvFkmmp6ezbNkyXWUYwJtvvslrr71GkSJFiI6OZuzYsZw/f17ng/xbYQZ8Jv7noKlfrl27lieffFJw1/wxYsQIg9y/PxITE9m1axeVK1emdOnS1KhRgwEDBpCbm0uNGjWwWq3Uq1ePFStWMH78eCwWi%2Bhvx44dvPjiiyLzqCl82u12fvrpJz777DOcTqfIDGqoXbs2Bw8eFMdNnDiRqKgovF4vDz/8sOAe3nfffVStWlXwt9atW4fD4RDBkeZrp51Tg/azVr6pBWARERGCs%2Bjz%2BcjOzqZHjx5YrVaeeeYZcXzp0qVFmaTH4%2BHpp5/G4XAQExPDtGnTmDhxIlarlZycHHJzc4mKimLJkiUi23bvvfcSGhqKxWLh8uXLohRRCw79oammWiwWunTpIuZXE3epXLmy%2BNlms4lMaNu2bUlJSWHWrFmiryFDhohrnzNnDofypfhjYmIMRu/%2Bmb169eqJbJuWvfV6vbo51doXKlRIvKnT2s6YMYOwsDBR2lGxYkVSUlKA24I1ISEhVKhQgZCQEDGWO2WKTZgwYcKECRO/Av%2BBgK948eLce%2B%2B9un%2BBlHNCnl%2B0prCuQbMMi5arMPKxfv16KlSocFeuf3h4OIULF9ZRf34r7HdvYsLEfweZmZnMnz9fiK6MHz%2BeCoZakcCheQlOmTKFtWvXkpOTQ05ODjabjejoaGbNmoXD4SA5ORmLxUKjRo34%2BeefWbt2LYmJiSII%2Be6774C80sB6fqVKzz33nC7Q2bdvnxAKAShfvjw5OTnY7XZiY2NFLbYWWGhiJCkpKTRq1IgSJUqwatUq/vnPf4pSS39LBu2fprK5fft20tLSuHXrlghsgoODCQkJEVnCuXPnCiuFc%2BfOce7cOSAveJwzZw5FixYlPT2dxMRErl27BuQpnQKkpqbSvn17YWFx/vx5cnNz8Xq9/ODns6XZMfhDC/g8Hg%2BLFi0CEPMP6OSJvV6vENVZs2YNCQkJupLJjz76SIyhR48eonRXm0MNGr9Sw44dO2jTpg0AFy5cAPIC3aNHj%2BrOrfEntb6%2B%2BeYbXnnlFRYuXEhubq4ojz1x4gQJCQm88MILQsgmOzubkydP6q79qaeeIlCYoi0mTJgwYcLEnw81atTg4sWLXL9%2BXQR4KSkp3HPPPUKMT8b69etpLNlLZGRk8I9//IMXXniBEvkl3tevX%2Bf69eu/q%2Be1meEz8YfC32y9RYsWbNy4kdmzZxMbG6sTIlFhw4YNhnJOGf5Kn5qgyuXLl7l%2B/Tr3338/3bt358SJE0BegLZp0ybsdjuNGjUiISGBBg0aiGAkLCyMzp07C9l/n89nsE3wl9V95plnREbszJkzREVFUbhwYfbu3cv48eNFny6Xi61bt/L111%2BTkZEhAi//TFSDBg04cOAAr7/%2BOkFBQZw9e5YWLVoYSjJzc3NJT0/H6/Xy7rvv8uabbzJo0CCDJ4w27tTUVHw%2Bn%2B4tlX%2BJgmZX4fP5iIqKonfv3oY5lufhTrBYLHTo0IHSpUtjt9uJiIigQoUKdO7cGavVit1uJzk5WSeYs2zZMkJDQ/H5fLpg7/XXXxelsJD3oamtFznzp%2B2XA1PI4wvm5OSIeWzdujWRkZG89NJLyutatmzZHXl6x2Sz7TtAZbzerlkzXZtAjHENDruyKXS%2BUqs/DJUoshk1GI2jpeBW2besiCt5GQJGc/Z9%2B4xtZO6CqnRGmgwVJdWwTzpGOZ/STuXH0K9oY9iWbEkAo5G5bIQNBldo5TXIFy4dozRel%2BdYYehuoKaoHLTPntVvK65TXrLycFWaU3K1tFKXSlo3qrm5m2m1qirbsE/BrQnkWTWsE2mHah3J51bRg%2BQpDsTzXfW8yHMhO9AoloThdiufBelkKuaBvPwM/QRgWp7/TvOOx6nui%2BG6VqzQbyueQ8M1qC5cPlkgbVQ3RlpvhqlQ9Sub0qv6DWTRqj6f/wj8j9kyVK9enYSEBN5//30yMjI4fvw4n3/%2Bufhe2qFDB0EV0nD48GFDFVB4eDjJycmMGzeO9PR0bty4wVtvvUV8fLxOxfy3wgz4TPyh8Bdt2blzJ3PmzBH8rXLlyhkUDH8NMjMzmTlzJl6vl4iICGw2G%2B%2B9954QiZk9ezY%2Bn4/c3Fz69u1LtWrVeOihhyhatKguoHA6nSxduhQwBhQdO3bEarWyadMmxo4dS3x8PBs3buTGjRtYLBbi8kULmjRpws2bN8nMzMTr9YrskRaQOBwOwQH0D7y2b99O8%2BbNOXLkCG63m8GDB7NixYo71o9rHzQff/yxKDFQCaDIwWBBuHDhArNmzSI8PFz59ipYYn%2B3a9eO2NhY3f7Q0FBSUlKIjo4WNhQnT55k5cqVwqx92LBhItvmcDjo06ePmB//sfr/bLFYeOihh8R2QSqn48aNE957GlJTU/F6vYL/pwV5iYmJunba3L3wwgt3nLPvv/%2B%2BwN/JUIq2bNYbwcr8fRWf38DGv%2Bce/bbkxQgKPYly5Yz9yooNqoy73LesTCYLtIBRyEUlGCOXcqvxKsrCAAAgAElEQVQEMAIQoTDsk45Rzqe0UyX88WvaGLbzCf46yFlflUqGpCCivIa7qKCoRFsMc2xQvFGIl6iU6OS30orrlJesPFzVC3JZUCIQ4Q/V3Mj9yOwAlXCFYZ%2BCahDIs2pYJ9IO1TqSz60StJGnWKUHJPcdiICIXJmmWBKG2618FqSTqRgZ8vIz9KP4DJDnWFlRLx2nui%2BG6/L7ewIon0PDNaguXD5ZIG1UN0Zab4apUPUrCwup%2Bg1k0ao%2Bn00Aed%2Bvrly5QuPGjenVqxePPfYY3bt3B%2BDkyZMGnv7Vq1cFncUfU6ZMwefz0b59e1q0aIHL5WL69OkBfz8LBGZJp4n/WbRv3565c%2BeSlJTE1atXdXYKDoeDjh07MmbMGCBP2GTevHlAXmDy5ptvMnbsWKxWK6GhoURGRuLz%2BcjKysLj8TB06FDBDdSCg3feeQefz8fp06eFrYM/3G63KFX0x/79%2B0lOTmblypWkpqayIv/N4KpVqwgNDSUoKIivv/6aHj16cP/994vfq6Cd0263G7JLly5dYsGCBYSEhHDjxg1hOyEHfZpVwY8//kiNGjWAvIDyxo0bOg9Ar9dLmTJlWLduHTNnzmTSpElUqlQJt9vNlStXhB1DZGQk165dE%2BWYmZmZvPbaa/zjH//QlZzKip/r1q3DZrOxevVqOnbsKMojs7OzSUtLE/YJFSpU4NNPP%2BW7777jo48%2BYuXKlaIPl8ulMzm/77772LdvH0WLFuXHH38UHjaa5YYGm82Gx%2BPBbrdjs9nE2Lxe711LJjWrD/llgzZ39evXx%2Bl0ilJVGXdSFZVhiraYMGHChAkTAeB3DH5%2BL5QsWZIZM2Yof3fkyBHDvgMHDijblipVik/kypffGf97s2fCRD769u1L0aJFSU1NpV%2B/fuzfv5%2B1a9fSvn17cnNzWbx4sc6%2BQfuiHhsbyyuvvMLf/vY3HA4Hc%2BfOZf369cTFxfH666%2BLdPquXbtISUkhKiqKsLAwKlasyKBBgzhw4AApKSkcOXJEV7qnBZBjxozhwIEDQmXJ4/HQoEEDOnfuzIEDBwgJCRGiHh6Ph5s3b4qs5ZIlSwgKChLKmnDbNxBuc/W04CU0NBSHwyEUSkeOHElubi6fffYZkBeE2O12nnjiCcLDw2nYsCH9%2B/cnIiKCJUuW6IRlatWqxciRI7FYLLRv316ohHq9XuGHd/z4ccHV0/zzUlNTKV68uDCEt1gsQnFUU/LcsmULEydO1N0/LVs5YsQIofZptVp57LHHhLgM5HHj2rVrx0cffQToOWxy1jA5ORmv18utW7eYMmWKTtDHP9DSgnKLxaIr48zNzaVy5crK9aZB4/iNHTuW8uXLi36Dg4OZNm0acXFxYlz%2BJbXaC4l/pwTD5PCZMGHChAkTAeB/rKTzz4a/1tWa%2BFNBE11xOBwsWLCAWrVqCbncFStWUKJECZ19w%2BDBg4G8L%2BGFCxcW9g2pUh17/fr1Abhy5QozZ84kMzOTzMxMjh8/zieffCLqsvv378/ixYsLJM3ef//9QJ4NAcD48eMpVaoUOTk57NmzB8gLMHw%2BH4sWLaJGjRrs2bMHl8tFSEiIyCyGhIRQr149unbtygsvvMA9fuV4ubm5OJ1OfD4fFy9e5L333sNiseiyXj6fj7Vr15KRkcH27dv55ptv%2BOc//0lUVBRn/cgVx44d47333sPn83Hw4EHeeecdRo0ahcvlophUz6JlMm02myiV9M%2BwaoGOljls3bq14K5pAVBOTg5hYWFcu3ZNZAwtFgu7du0KmPPXsGFD3bY/R%2B9f//qXgUMpIyQkRJdFGzduHHv37tW1CQ4OFoG1Nu7jx49z6tQpTp06JfrNzc2lX79%2BDB06VMyr5oEItwO1mJiYgK4NCuDwySWQixffeRuMvAs/riOgJLYYpuvLL%2B/e7//7f8Y2%2BS9aBPLLngWSkozHyJwQ1ZtNmaik8iOS35YqzmV4oSrzL/M5vHc8t6JEOP8Rvw3VfZFPLs%2BNqvRYJkRt325sI/PmVDw/uW%2BJpKTi8MmHXLpk7DYQ3iQbN%2Bq3ZS4RGIhpMh9KRQ00nExBtjNw11SEPJmLKl2TslJenpz9%2B41t5GdBRbaTuVbyQlKtiYMH795GfqakzznlcYFwumRaxc8/G4%2BRn015HsBwY5TrRl4ngcyntK5VNGOWL9dvq6giMvdaPpfiM1Rukv/eVAd5%2BamWo1zoEQBd2cBfVRWLyM%2BU6mNCfl5U3E/1w2jizwaL7981kTBh4r%2BMVq1a0b9/f4NAS%2BvWrenfvz9z5szhwQcfZGA%2BL6ig9q1atRISt263G4vFwr333svrr79OyZIladWqFaGhoSxdulQX5G3fvp1evXrx9ttv88YbbzBmzBi6desm9u/fv5/g4GCuXr1K8%2BbNsVqtbNu2jaFDh7Jp0yZKliwpgk450GnXrh2TJ0/W7ZswYQJffPEFkBf05ubm6jh%2BWqDldrspVKgQOTk5OBwO3G43Xq9XBFwOh0O000octfZWqxWbzYbX672jME5ISIjIOIaFhZGVlcXq1as5ffo0L7/8Mi6Xi6ysLF1pqfbzF198wdGjR0lKSuLAgQPY7XZcLhdvvPEG3bt3p1q1ani9XqKjo0lKSqJo0aKcP3%2Benj17ClXNiRMnMmrUKDGeKVOmMGTIEJ599lnmzJkjOJBvvPEGY8eOxefzUbRoUfr3788777zDiy%2B%2ByOeffy6CPv9SWavVytChQ%2Bndu7c479X8Lw/9%2BvXj5MmTbJAESSwWC2XLlmX06NEMGDCAoKAggzn96NGjDR47BeGdd97h888/1%2B17edAgXlKZlZswYcKECRN/Vci8yt8Dd6DY/F%2BDmeEz8aeDJsKi2TecPXs2YPuGxMRE4fH29NNPk5aWRq9evejatSthYWF06tTpV8vgLl26lMjISFq1akVERAQtW7YE8jKJ/kbp/vj555/ZsWOH2L558yY//vij2M7KysLn84nsmtPpFF5yISEhBt4a6LNOxYsXJyQkhGrVqlGiRAnBw/N6vbhcLp2fH9zO4mneetnZ2eTk5OB0OklPT8flctG2bVsGDBhAeHg4FSpUIDo6Whfs1a5dG4vFwrPPPsv777/P8ePHdT51Ho%2BHa9euiSApOjqadu3aUa9ePYYMGULz5s3FeDQPRMjLnC1cuFAEXffee6/43eTJk8UYnn32WYKDg4X3X8mSJUW7Xr166fwMP/74Y%2BrUqUOHDh105aMDBgxguyKzYrFYKF68OHXr1hVBs1w2elP1GrUAqDh85hs4EyZMmDBhwsTvCVO0xcSfAuPGjWPChAlAnkl2tWrVCrRv8M/KzJw5U3DDnE4nb7/9tghwFixYwGOPPca4ceMA%2BOSTT5g7dy5Dhw4VHnyQV1bYqVMnfvrppzuOcdGiRaSlpfH999/reFxRUVFs3bqV5s2bc%2BnSJapUqcL169cJCgqiatWqDBgwgG%2B//ZaoqCi6desm%2BHR169bllVdeYcGCBaxdu5bY2FguX75M5cqVKVSokAhI7Ha7KOl86KGHiIuLIzMzk82bN7N371569uzJ8ePHadmyJR07duTIkSNs2LBBGJjb7XZdlkoWYNH29%2BrVi23btuFyuTh58iSXLl3i/PnzulJPl8slSibtdjsrVqygY8eOoiwVYPr06cLAHOCVV16hWbNmuN1uPvjgA%2BbOnSvG8cQTT4h2GRkZbNmyBbfbTWJiou6e%2B3P53n//fSHO8v777%2Bvu0eeff67LhmoBG6Arkz1y5AhRUVEEBwcLsRmbzSZKdn/88UeCg4NFmar/WC4p6%2BBMmDBhwoQJE78afzHO3e8NM%2BAz8adAYmJigZ57d7Jv6NevH/369QNul3pWrFiRXr16sXv3bl1Wp2/fvqxatYoePXowfvx4qlevzuXLl5k6dSrbtm2jT58%2BOgVJf%2BzYsUOYmlssFhEk2e12rl27xuHDh7HZbERERHDmzBnCw8OxWq1s2LABt9tNu3btdGWB4eHhHD16lPr161O/fn0mTZpETk4OS5YsISUlRYjELFmyBK/Xy2OPPSb8Ai9fvsz7779PUFAQ9evXp0qVKpw/f56LFy8yZswYsrKydLYKU6ZMYfXq1SxZskQ3dhlz587FZrNRs2ZNTp48KYIcrb3D4cDpdBIUFCTuSSuFHcCVK1dYtmyZ2NbM0RMTE3X7fT4fN/zMpVwulziXXIaqeRdq7VR%2Be5CXBdWUUIODg0lOTgagT58%2BuoD%2B8OHDZGRk6JRNPR4PZ8%2Be5cKFCxw7dkxn%2Bu4P/2D2bjBFW0yYMGHChIkAYAZ8vwnm7Jn406N9%2B/YsXLhQBDz%2BGD58OLNnzw6oH00kpmHDhgwaNEiIxLjdbhYtWkS0nyHRzJkz6du3L5CXievduzder1fYHbRs2ZKkpCSSkpKw2%2B0i09SyZUtycnJITU3lypUrIrukce%2BKFClC1apVGTVqFLdu3aJPnz5cunSJYcOG0bFjR3JycggKCuLkyZOMHz%2BekHyDpuzsbPHzBx98QIcOHYDbJYgej4fU1FSysrIICQkhNjYWyAtIs7KyROmov9%2Bdv/G7Bk0BVGsTHBxMZGQk33zzDQ888ICYxzspVcq%2BMlOmTOHw4cO6YE/DK6%2B8In6Wx6NxNp988kld4FS2bFnatWtH8eLFAXTjnTt3rlLcZcqUKVgsFl2mMDs7W9nW6/XqykQBihQpIn7%2BdzJ8AYm2yI7KChNrQ2WoVFaqYmob4lVFeend%2BgWFfkQg7tiyyINKjEEWcvn0U2MbmX%2BqmBtD1/IOVeAegOm74eNGZaJ%2BN8dsldLC3RzJwajGoGojj1m6USrRFnm4qm7lKVc6i0g7VSbb8roJRNzCsNYU4iV3XY%2BqfYGYT8v7VPdbVrwIxHk9AIEgeZ9qPg07Ff0YpiKQNvKzoDjGsE5UpeqBGIXfbRGohJuk65aN4pU7VZ9Jd3leVOMN5FGVu1V9Fv8ebX5tv/JlBeILb%2BLPCVO0xcT/PAoSYdGQlZXFU089hd1uN2TmNmzYwLx585QiLKVKlRL8OsjLUMXHxzNkyBAaNGgA5PGxpk2bxpo1a7h06ZIQe7HZbDpeHeQFV4sXLxbcsuPHj/Pggw9Sp04d9u/fT/HixRkwYACbN29m48aNOBwOcnJy8Hq91K9fnx07dtClSxe8Xi9btmwRhuDauWJjYylfvjxbtmy5o9CK3W7HarXy8MMPM3HiRObPn8%2Bbb74JIEoQw8LCyMjIICwsjLVr1xIREUGzZs1I8/vyobXLycnB7XbTtWtXatSoweuvvy5%2Br/WnzYW/uIy/j6F/GeXYsWOFf6LFYiEoKMjgeWi1WoWaaPXq1ZXXqZ3farUSFBSkK0O12Wy6bJ7W/t577xU%2BODabjebNm7N7926cTichISGirPO1115j9uzZQuTHH5GRkTz77LN88MEHBY5r7969IgC/E1SiLYMGvczAgS/d9VgTJkyYMGHiL4PHH//9%2B5SVk/8Pw8zwmfjT426ZuTuJsCQmJpKSkkJKSgqbN2%2BmTZs2DBgwQOfvd%2BzYMaZPn87YsWMBhGqlVnpXokQJKlWqRHx8vE5IRMOYMWOE%2Bfe4cePYtGkTXq9XZJDCw8MpVKiQ4LwVLVqUJUuWcODAAV588UXCw8NxuVwiGGvcuDEAM2bMEN53QUFBooxRG9fy5cv517/%2Bxbvvvkv//v1p0qQJMTEx2Gw2IRbyz3/%2Bk5iYGBwOh0H4xufzUaxYMSG2smTJEt566y3xe5vNRokSJZgxYwZ16tTR2RF89913wu%2BwevXqpKSk8NprrxEcHMzmzZt155CDPcgLWn0%2BHzNnztTt9xeY0d5V1ahRQ5wL8oLLvn37GjKGdrudE34S/B6Phx9%2B%2BIGsrCzcbreufNTr9dK0aVPDuCAvwNdKQVXw%2BXwBC7eYxusmTJgwYcKEif80TA6fif95yNL4KkRGRjJq1CidhH9BaNiwIUeOHDHwy0JCQujbty/z589n06ZNXLlyhYyMDKZOnYrD4aBSpUp06dIFgEmTJtG4cWMaN24szNA//PBDnUiMFpA8%2BeSTWCwWrl27Rp06dZg3b55yXLVq1SIuLo5hw4aJfYMHD2bw4MHMnj2bBg0aUL16dUaOHCl%2B37lzZ6FkqWUUK1euTFJSEhkZGTRr1ozhw4fTvXt33blOnz5Nu3bt%2BPHHH2natCknT54UYivLli3j0KFDfPLJJ7z99tv06dNHXE90dDRXrlwB8iwmrly5wrPPPqvr%2B/7778fhcAgrivLlywN5BuuNGjViy5Ytd71HLpeLmJgYVq9eDUCzZs3YtGkTVqvVkN3Usqfx8fEcOXIEp9PJjBkzmDFjhmhTuHBhypQpQ6VKlUhKShL9aOIuMpxOp06xVMseduzYkS1bthATE4Pdbic8PJz09HTD8QVxCGWYHD4TJkyYMGEiAJgcvt8Ec/ZMmJCgGY6vXbuWJ598Usfr0jBixAiRafNHv379RMZQyzCtWrWKlJQU4uLi6NSpk/Kc165dIycnRwSZMg4ePKizbwgEmzdvxuv18uSTTxp%2BV65cOUqWLCnGuG7dOpGhO3HihC7o0AIfLcDzh1wRbrPZ2Llzpy5g8ng83Lx5kxUrVvDggw/yww8/iPZ2u12XGSxcuDCQx4nLyckRYiwaZ%2B7ll18G8rJ4Wv/h4eG0atVKqHsCImOq/exyubh69SrXrl0jODhYBI2xsbGEhIQQGRlJ1apVBbcxJCREcPH8S0XXrFnDzZs3uXz5Mh6Ph/T0dGHPoMFisRAVFWWYcxVUHL62bdvpGwXALzJweuSsqaIEOBA%2Bj6HfX0NSURFA5DYqbo7M2VOYsxu6VvRjuM678bdU%2BwLhiqmuQW4kT6iKXybPsYp1IbdRZYp/BYcvECqb3K2STyY1CoSPFwgt8Vdx%2BAK5L7%2BGw6figQVC6pKfRfnzPoBJV5qWy/0q1k0gz7yh7wD6NXSjGKChjWpd340vGMB9URZNBMAFNHQtXXcgt1L1OftrltavafOf6regfX8IrNbf/99fCCaHz8RfFv7cwJkzZ/Lhhx8KI/KgoCCh9miz2Xj00UfZunUrly9fNnD%2BQkND2bJliyhDlI24mzdvzrBhw3j%2B%2Befp378/ycnJIiOoYd%2B%2BfXTt2hWASpUq8d577xXIRRw5ciRLly5l2rRpHDhwgBUrVnD58mXcbjcul4vw8HBhuK7xA7/44gtdJjI3N5eaNWtisVho166dyKTJsNls2O12cnNzdebqd4MmXiMjODiY4sWLc/bs2bse7/P5CAsL4x//%2BAdjx44VZuwyLBYLsbGxpKenixLJoKCgAk3lNfN5m80mrCk0PPLIIxw9epTBgwdz8eJFFi9ejNVqxZX/F%2B%2B5557js88%2B44svvmDu3LmsXbtW9KchJiaGrVu33nmC8mFy%2BEyYMGHChIkAoHh5/ZuxaNHv3%2Bf/KP5a4a0JExLGjRtHQkICH330ESEhIdSrV%2B//s3fe8VVU6Rv/3pZOSSUhgVANLQQEpJdVEQxSFRAUF90FlS4oiqIgoIKsZVVQQAFXFAUEBKnSRZBeQgvFkghJIEAk5abc8vsjmbP3njmQ6%2BL%2Bdlfm%2BXz4kJk795wzZ85M8s77Ps/DkiVLOHbsGP7%2B/kyfPp2UlBTh1afi/O3atYsOHTqwe/du4uPjadu2LWvXrmXNmjVAadaqf//%2BImhQwZOb1qRJE0aOHEliYiIdO3Zk6dKl5OTkCFVQDcOHD2f27Nn88ssvIiADKCwsZOXKlTzxxBOYzWb27t173QArNDSUgIAAsa1lqoKCgnjvvfeIiIgQ/V4v2DOZTCQlJXl5F9asWdPrnJo2bUpERARPPvkkf/7zn7360ziCnrBYLDRo0ICioiKGDRtGrqR%2Bt2/fPv72t78Je4mMjAwvE/rrjTUmJkZwBlVll1o2NygoiAMHDuB0Or2u25w5cwBo0aIF27dvB/AK9gB%2B/fVXce3Lg8HhM2DAgAEDBnyAkeG7KdxaZ2vAgATPAG7fvn384x//ICkpCbixvx/8k/MXEBDApUuXmDdvnuD81a5dWwQ8o0ePZsCAAUyfPv26SqMazw1g2LBhbNmyhWPHjpGamsrJkyeZMmWKyFZNnz4dgKioKMaNG8fx48eZPHkyJSUltGjRgtDQUHr27MmcOXMoKSmhRo0a%2BPn5ceDAAcHT01CtWjUmTpwIlGbVVq9ejdlspqCggBEjRpCVlcX777%2BPzWbT2SnAP5Uyjxw5Qr5HOVONGjWIj48X24cOHSI7O5u3335bCM0A/PnPfyYpKQmTycRtt90m5qykpITjx4%2BLPps1a%2BbVb4sWLXj66afJz8/H398fi8XCex4lfy6XS1lu2rZtW5F5VAWF586dEz8HBATg7%2B/vJfqj7Vu%2BfDlOp5OmTZvq1DgfeOABpf%2BgCgaHz4ABAwYMGDDw74YR8BkwcB346u%2BnWRP8K5w/DZUqVeL222%2B/7ueqIKBKlSqsWLGCkydP0q9fP2w2GwcOHOCFF17g4MGDbNmyBbPZzI8//qhTwtRUJFu2bOnVx4kTJ2jUqJFQ4LRYLMI6QZUR27JlC2vWrOGFF16gc%2BfOgjeXm5srMn6xsbG0atVKzEvFihXF9x9%2B%2BGHy8/Nxu92cPn3aKyuo8fTq1KlDq1atvPo9evQou3fvBkp99xwOh1emze12ewV0jRo14sSJE2RkZIh9qmBL%2B05YWBi5ubkUFRV5ZUcLCwspKipi/vz5OJ1ODh06hN1u16mH%2BmLJAGoO3513Shy%2Bw4dvvI3CHq/MekKDT7wM6TvKdo8eLb%2BdU6e8ty9d0ncuc2oUfet8zlREMPneVIxPl9yWj5F4qYCOsKOaP4%2BlVAqVcqvsySgfo2pYPm9Vdl7mIPlCkpP4Zb748KkunU%2BcKdnORHWe5cyxkqfmA%2BFI97hWzc358zduR1UhIB%2BjehkoLwoFz0835DNnbvw56OZTOTfyOtEtUHzizuo4mfJc%2BbIeVb6aUsPKc5DXvuyfp1I/lo5R%2BvDJ953qnpcXu0wHUFxL2XZR3gb9efrCTfXFL09u51/l58nXW8XJVd7j/wkYGb6bgsHhM/A/izvvvFPJqbuej96lS5eoWLEizZo1Y/jw4Tfk1EGpUEqfPn0Ep27EiBE8%2BOCDnD9/ni1btjB//ny%2B/fZb3nzzTdq3b8/OnTv505/%2BxOnTp8nKysLtdlNYWMhbb71FcnIyAM899xx79%2B4lIyPDy0jc7XaL0sFOnTpRvXp1PvnkE8FFczgcBAUF8fjjjzNkyBAaNGjAkCFDvJQoZVyPRwelYikOh4P58%2BeTmJhIixYthP2D2WzGbrcL7zwoDcyWLFmiK1%2BU29Q4jCaTCZPJJPr35P95/nzkyBF69erFjz/%2BSHx8PNeuXePq1asEBQVRo0YNTp8%2BLbJ1no8qPz8/nnzySebMmYPL5RIiMZqBfc2aNUlMTPSyZvBU2ywPffv2Zffu3Vy8eBGLxYLdbhe8wpo1a7Jo0SK6dOlCbm4u/v7%2BFBcXi/G99tprQjm1PBgcPgMGDBgwYMAHPPTQ79/mp5/%2B/m3%2Bl%2BLWCm8N/OHwW3z0jhw5wtKlSwkLCyuXUwcIbpvGqbtw4QJvvvmm4NQ99NBDbN%2B%2BnbZt2xIYGIjT6eTYsWO88847HDhwQKhGPvvsszoOXdeuXcW4U1JSOHDggPjM39%2BfL7/8EoDg4GBatWrFzJkzWbBgAZ999pnwptMULVesWMELL7xAZGQkUBpQPfHEE9x9990iK%2BdZYlq7dm0RkKxbt070m5eXR1FREfayN%2BLFxcXCtmDJkiXUqVNHHKsFsMnJyURHR2OxWHA4HCQmJooxqDzzAC/Ont1uF/53nTt3ZtWqVUCptUNkZCQ1atRg3759XlYU2tj%2B/ve/U1hY6GV8D6WB7qVLl3QZSS3Y0zKNMTExhISEEBwcTGBgIHXq1BEvD%2B677z4uXbpEcXGxmA8t8Lx48SKFhYWCV1hUVOR1fjNnzsRXGBw%2BAwYMGDBgwMC/G0bAZ%2BAPA41TFxUVxY4dO5ScupiYGCZNmlQup84TGqcuNjaWyZMnc/LkSY4dOyY4f3PnzuWtt97CYrFQrVo1EhISsFgsJCYmkpqayiuvvKIs87wexo8fz6OPPkrjxo35/vvv%2Beijj%2BjRowdNmjRhwIABbNy4kdTUVBo3bgyUBnCPPPIIS5YsoVGjRrjdbhYtWkRAQIDgI9apU4f4%2BHiioqJYu3Yto0ePBuDgwYMicLFYLDz22GO0aNGCoKAgUlNTSU1N5ZtvvqFNmzZeQatWDrlu3TouX74sAqWjZeVyo0aN8lKq9PPzIzQ0FIvFQnFxsQjOPLl/J06coEePHphMJvbt20etWrXIz89nzpw51K9fX7QVERFBjRo1SElJwd/fn9jYWGw2m7BUcDqd5ObmcqmsRGfBggXYbDb%2B%2Bte/iv6hVOglLy%2BP/Px87HY7Z8%2BexeVy4e/vT82aNYmPj8dqtTJmzBgxP8nJyeTl5eFwOISdRN26db28CKtUqeLztTZgwIABAwYM%2BACjpPOmcGudrYFbAjfjo3cziIuLY9%2B%2BfV7ZOijlaf3tb38TnL%2BbgXZumZmZXu1lZmYyd%2B5cPv74Y%2B644w4SEhIoLCwkJSUFKFWODAkJITc3F6fTyeHDhzGbzfj7%2B4sgzel08vDDDwOlmafExEQSExPp3r07P//8s8jqwT%2B9%2BXr06EFkZKTYrlWrFgANGzb0skQoLi7m6tWrotxTuyafffaZyLzl5uayfv166tWrB5SW1BYWFjJ//nyyPDgs2dnZ/PTTTyQmJlJUVMT58%2BeJi4sTYzCZTJjNZmHjUFRUxODBg/nHP/7hNZcRERGYTCYCAwO9MnzFxcUUFxcTHx%2BPw%2BHg7bffFvOjZUS1QBfg7NmzfPbZZ6LdGwn9yDBEWwwYMGDAgAED/24YAZ%2BBPwzy8/P56KOPuHLlCh07diQ9PZ2aNWv69N3169eLAEf717Nnz9/cv8vlYuDAgSQkJNCwYUNRYrp69XnKvXUAACAASURBVGr%2B9re/sXLlSs6fP8/atWtJSEggISGB%2BvXre2XDZsyYofPFczqdrF27lnfffZfWrVsTFhbG8ePHgVIFy86dO7N48WKaN2/O3r17qVy5Mq%2B//jqPPPIIAHv27OHEiRPY7XY6derEmDFjcLlcpKamMm7cONHP/fffLwLWmJgYEZAUFxd7CZ5o%2BOqrr6hbty716tUjICBABKGzZ89mwoQJ4rhx48Zhs9moU6eOV5ZNszYASElJoWXLlpw8eZLc3Fz27dvH1atXcTgcPP/8816cRxk//fQT58%2Bfp3Hjxrjdblwul1DcfOKJJ5g3bx7FxcVeQWh2djZutxu73Y7dbheG8263m61bt9K5c2cAr36tVisRERH4%2BfmJoDUwMNCrNPO3BGwq0ZZ77pFEW2QD8jJrCE/oqJWSt5BKQ0FnUfj557pjdJTHslJjT%2Bj0D1as8N7Ozi6/3bJSXi/IAg4qA%2B29e723FebssuaE7hxUgjGyOITCz1Humrlz9e0cPOi9LfE1lZRSWTzi2291h%2Biq0VViFnLj0nyqRFvk0zx5UjE%2BqQxZ5T9OmaCSgEo4RVq0vpiq685TcYxuKuR1BPqLJ49PdWHkdnQLAPjhB%2B9tleqNPOadO8vve/9%2B723VpG/c6L29bVv5fatURuSHicczGoAtW/TfkV9y/fST/hj5IeSLEpIkaKMTAwLdDa5aajqelkLcSZ5j3TpS3GPypZKHqxqPqopf3qeamvLaUV1K%2BdGrekzI%2B1TH/PKLft9/BEaG76ZgiLYY%2BJ%2BFLNoSEBBA/fr1GTduHElJSTRu3Jhp06bRo0ePG7bjaUjuiXPnzpGcnMzmzZuJi4vzMmq/3ngGDRrEqVOn2Lp1K9euXRPcrvfee4/WrVvTqVMniouLadmyJXPnzhWZuRUrVgiu2D333MOPP/7I2bNnBd/NbDYTGRlJeno6mzZtolq1aqxfv57Ro0cTExPDlStXcDgcVKpUSQQx8E8e3vWgKXBqAdjWrVsZOnQoZ86cISgoiMLCQmw2G6GhoRQXF3NFKYGmb8vPz49KlSqJssq6devSrl07Fi5cSLVq1XA6nZzX/SWub89qtVJSUsLHH3%2BM1WrloTLStlYS6na7sdlsgo%2BpidH4gsjISCpWrMjo0aPJyspi3759bCz7o2nHjh289dZbrJCDF0pLO3v37s2yZcuU7ZrNZk4q/1LWwxBtMWDAgAEDBnzAo4/%2B/m1Kv3//yLi1wlsDfzjcjI/eb8WWLVvK5fwFBAQwY8YM9u7dy6lTp9i5cycWi4VPP/2UefPm4XA4aNWqFfPmzfPiFPbu3RuAJUuWCDPxxo0bi3M7cuSIMP3Wgp3Q0FAANmzYwP79%2B4mLi6NJkyZ89tlnHDp0iHr16tGoUSMAxowZw549e4SQidlsZs6cOXz44Yc4HA5hI5CamioCsaFDh1KlShUeeeQR5s%2BfT%2B3atcV5an1DaVlicHAwPXr0ICUlhSVLlngFmmazmTNnzlC1alUqVqzIW2%2B9RaYkV695CwYGBhIVFcWgQYPEHEGpkI2GsLAwvvjiC5Fhu%2B%2B%2B%2B0SZ6MSJE7HZbNxzzz3iRcDo0aOpVq2aMIjX9jds2JCKFSsyatQoFi5cSEBAgJiHjz/%2BWIj2aNfDarUyefJkXC4XCQkJguuneQhq18bTgL48GKItBgwYMGDAgIF/N4yAz8AfFr766P0eyMzMJCcnRyf7HxkZic1mw%2BFw8M0339CsWTPOnj3rVVoI0LZtW2w2m88lqDL8/Pz45JNPCA8P5y9/%2BQtNmjTh8OHDwkZB49Q5nU7Cw8OxWCwMGzaMKVOm4HK5WLp0KUFBQfzjH/%2BgoKCA8PBwnnzySQYOHMi6desYOXIkTz75pFd/GoqKisjPz%2Berr74iMTGRhx9%2BGD8/P9G3luVcsGAB3bt3p1GjRqJcUsOxY8cICAjA4XCQnZ3N4sWLmTFjBlarVQTGnkhKSqJBgwYAfPvtt/j5%2BeF0OqlRowYlJSX06tVLBJw//PADGRkZ1KtXD5fLJQK4c%2BfO8eGHH7JmzRoGDx5MYWGhyIxevnxZ/KyJyjgcDqZOnYrb7SYtLY3i4mJ%2B/PFHTCYTp0%2Bf9jpXX2Fw%2BAwYMGDAgAEfYJR03hRurbM1cEvhscceIzc3l2bNmtGoUSMSExO5/fbbadu2Ldu3b%2Beuu%2B4CSsseT548yV133UXjxo1p164do0eP5icPHsJzzz3HU089pevj3LlzJCQkYLfbKSwsZPny5YILdvnyZcaNG0dhYSF9%2B/YlPT2dLl26UFBQQNeuXWnVqhVJSUm0bduW8ePH89e//lWYkp85c4YjR44Inl9CQoIQTenSpYtXOWTjxo0ZNmwYVapUYdq0aWzdulUIlGhZuZEjRwo7h8mTJ7N8%2BXLMZjNdunTBYrEwdepU3nvvPWFm7u/vT5MmTXjnnXf45ZdfiIiIID4%2BXvR50YO4pRmka%2Bbz8%2BfPp7i4WARKWhB04cIFFi1aRP369QVHUStZXbRoEYWFhZSUlAjfwXPnzlFQUIDb7aZz584MGjQIgCtXrpCQkMChQ4eAUi5eUVERTqeTESNGAKX%2Bi9pcrl69GofDwalTp0hJSREiM%2Bnp6TRr1oxu3brxyiuviHJOgI0bN1KzZk2hLKohKioKKPXpa9%2B%2Bvdhv9vjFUb16dd06uR6UHL42bbyOkT2DVf7eMr9Nl9hWEDNknsiPPyralQ4qi2u9IZFL5EpdH3zNOXFCf4z8nkbFa5FpQUqjcImbKFP2VLQgmSKloPDpCDtllFovyLwb3TEKX0uZi6OqfJbpZIp3WroJk%2BdcxeHj55/LbVh3HRRVFLLfvE8G5PLFVBFPpZJy1SHy5KgocfKc%2BmJiLV8XeapAv5ZUvCp5Mel4X4qXRrLXueqc5PtXRaPTrwHF%2BKRBy3OlWo9yu6rKf90jSLG25GPk%2BVSdt64ZxXqUKXuqe748DqmK26bjbKoWpC/u59I%2B5XtDeae0jpTPKPn5ouJByw8pldduORZWBv43YAR8Bv6wCAoKIjIyklatWhEVFSXK7WJiYkQmLi8vjx07dpCXl6fz6hs7dqzPfdlsNiIjIzl9%2BjT33nsv9evXp02bNmzfvp2nn36anj17YjKZuHz5MkVFRfj7%2B2OxWDCZTAQHBxMeHs6GDRtEVqxq1aqYzWbGjRvHnj17OHXqFB%2BUiXfExsYKDz4NmzdvpmHDhkJwRuO6acFWixYt2FZG4o%2BLi%2BO2225j8ODBLFmyBKfTyQMPPEDbtm1FsJKTkwOUlk%2BaTCYOHz7sJdrSrFkzOnToAMD%2B/ftp27YtbrebJ554ghYtWtC0aVNxHoDOZF6Dw%2BEgKirKy5tPPi4wMJBNmzZ5BVjXQ2xsLAAvvviiyOR5thcYGEhmZma5WbSePXvy/fffC2VRDdocWK1WERxrXoUaTp48qVNqvR5WrVrFM8884/Vv8/ffex1TVqF83W0AJCVaD8vEUpQFv56Qp1yZXJYOKqtc9UZZGayGsksgUBYje8GjQheAsmStF%2BTKWMUSwaOyGIAyK0pvPPig12ZZlbOAykVDWjpIlo6lqFvXa7NhQ/0h0tTojykrG/ZEhQre2/J8Aki3v26uAN2EyXMeFCTtAPB4qXO9hnXXQbfYIDpa2iFPqKod%2BWLK2wBhYeUeIk%2BOfN6gn1P5GNVak6%2BLPFWgX0vydwDdYpKWEXj4l2qoVs17W3VO8v1bo4b%2BGP0aUIxPGrQ8V6r1KLcrXSZA8QhSrC35GHk%2BVeeta0axHsscjK7bLiiuubRD8QiFMlVqAdWClBtWLS5pn2IJ6HdK60j5jJKfLyp7KPkhpXgmKcf8n4CR4bsp3Fpna%2BAPBV84dWazma5du7JlyxaOHj3Kjh07WLZsGVWqVBFefcHBwWzZskXn1ffQQw8xf/584uLifBqPxWJh0qRJwr8uNTWV/fv3M2TIEKCUU/j555/Tvn17vv76a7777jsOHz7Mxo0bWbFiBU2aNOHixYu43W5ycnJ47LHHGDp0KJUrV8ZkMolgrLCwkDlz5tCyZUs2b94MwJQpU4iLi%2BPAgQPChB5KM12pqamMHDmSH8reRmrqlU8//TSPlpGgtfJIzZNv5cqVHD58mIcffpjatWszbdo0r6AsKiqKgQMHArBv3z6SkpIIDg7m1KlT7N69G6vVSlBQEA3L/sLVhFSaN2/OsmXLvCwUhg4dSklJCcHBwXz99dcEBwdjtVpFwGa325k9e7ZX%2BaOfn58IIk0mExUqVKB3797CoN3pdDJy5EigNPBv3bo14eHhorxWQ1hYGIsWLeLEiRNs2LBBZAXbtWsnuKAVPX7Ta357y5cvJzIyUvj0BXj8kqxTp44woC8PKg6fweAzYMCAAQMGJBgB303h1jpbAwbK8J/w6mvfvj2ZmZncf//9us9efvllEhISqF69Ojk5ORQUFAhLBRndu3fX2Tb07duXkJAQofgYGhpK1apVhWhNUlKS8Mj76KOP6NOnDwcOHGDPnj1UqFCBRYsW0adPH2Ea/uqrr%2BJwOFiyZAn33nsvPXr0EEIt2lxVKHsTPG3aNBwOB6GhoZhMJp5//nlcLhchISHs2bPHa5z79%2B9n%2BPDhOJ1ObDYb3bp145577iEyMhK73c5DDz1EQUEBTqeTgQMHiozdEY86xpiYGFJSUlhaZj3Qpk0bEah%2BWSa5HxkZKa5dQUEBI0aMEBnSCh5vsC0WC%2BPGjeP222%2Bnd%2B/emEwmTCaTVzbxmkctz/z58wFYsWIFGRkZOBwO/v73v4vMrAbVejJgwIABAwYMGPhP4PrGVgYM/AGRn5/P559/Lrz6XnnlFZ%2BEUjQe2Nq1a5WfZ0mkANkyws/PT5Q3zpw5k4iICOLi4njzzTdZvXo1%2Bfn57Ny5k0OHDnHp0iUsFgtVqlRRWkZUq1aN9PR0EhIS%2BLTMX8hsNvPSSy/x6KOP0rNnT6Kjo%2BnQoQPLli1j7NixjB07lujoaM6dOycsA0aNGkV2djZWq5XFixd7jX/btm0iOzdr1izmeniMFRcXs379ehF0nj17VgSWmuF5RkYGEREROhEbQCh0lpSUsHr1ar7%2B%2BmtRvvmrB/9m/vz5Iht36tQpTp06BZSWVSYkJIjjvvvuO8LCwli/fr0QWsnKyhIcTcDLzsGzvFSzjdACPS3jNnXqVOLi4vDz88PtdgvbhwoVKvDrr7/y/vvv069fP0Av1KJlUH2BIdpiwIABAwYM%2BIBbLCP3e8OYPQN/eEybNk1w2zp16sT27dtZuHAhMTExmEwmnWKmCt27dyc5OdmrXDM1NVUEgFUUpABPy4idO3fSsmVLoLTkb/jw4bRs2ZLly5fTpk0btm7dyvLlywkLC%2BPkyZPCAPx6MCmK/JOSkujatSuvvfYaABEREXTq1AmXy0VycrIwdzebzQQEBJBd5soq%2B9bJBucul4uioiIvrzttvzwm7TO32012drbIlFksFiVPD7wtFzzhOa45c%2BYoM6Marly5gr%2B/vxdvTzV/2jUHuO2228R2WFgYkR4EsClTpvDRRx9RXFwszhv%2BGZCGhYXRp08f5Vg0/qIv8Ml4/YsvbryNQrhAygCrvKd1PHzZuBmF1saaNeX3LRtJy2bKqs4VfesEEFSOyrKKjMIYXuexLJuzSy87AP2EKUQLdOI5iuuiO0juS6VuIatiqDwd5VJglaKEPGZJ0UYl2iJ3nZamb1YWdVCaMktKJEozbGl88uVWrVnd%2ByOFwIROJEglpCFfF3mhqwYst6MyXpfXo0KxSPfr5ttvvbdVAhllAlUCKrWV774rf3zyfPkg5qQzfZeqNgD9Pe6Loo0vxuuyEo1qnUuKNrKOCgBl1SACBw/qj5EWv25pKdaEfJqqe8EXY3N5%2Bal0U%2BRLLg9HoYukM173QQdJeYxSDOs/AaOk86Zwa52tgVsS/19efTfiFAYGBjJ8eKmZdlhYGD179qRKlSocOHCA9957j6pVqwruYI8ePXC73aRLv8hq165Namoq165dE6WOMp5%2B%2Bml27dolBEUqVKjA22%2B/TdWqVRk3bhz33nsvnTt3FsGUyWQiNjaWoUOHUq9ePUwmE4sWLaJKlSr4%2B/tTuXJlkaWMj4/HbDbjdDrp2rUrXbt2BRDBr6aCqWUDGzduLIJPk8nElClTuPvuu4HSoFdD27ZtRfZTNW%2BVKlXijjvuEPtiYmJEnwkJCQQEBGAymRg0aJDI6oWGhjJ%2B/HgA7r33XnH85s2bRQDXvHlzNm/ezKJFixg4cKCXCun27dtp3bq1GLsGTw/EdevWYbPZePHFF72C1t/iw6cSbdm0abP3Qf3733gbhXBBly5em7LIByh4%2BHKgiUJro1u38vtu1857W7LUUHau6FsngCCLC4BeRUbxUkAWbKBMyVVAdc/KE6Z4WaETz1FcF91Bcl8qdQv53q5fX3%2BMnBlWKUrIY5YUbVSiLXLXSsFZSdRBSXGWlEhUl04en3y5VWtW925IITChEwlSCWnI10Ve6KoBy%2B14PJME5PWoUCzSiWvIYlSqF2PySySV2opMP1CNT54vH8ScaN7ce7vsxaUX5HvcF0Ub1XnK7cjVN6p1LinayDoqAPTt6719%2B%2B36Y6TFr1taijUhn6bqXpCHrDoFefmpdFPkSy4PR6GLRESE97YPOkjKY5RiWAb%2B52CUdBq4JaAqsUxISKBRo0YsWbKEoUOH4nK5eP/999m4caMo9atZsyYzZ84U7ahKLAHS0tK466672Lx583VFXkJDQ/H39%2Bf777/H7XZTrVo1unfvLspBTSYTTz/9NNOnT%2Bfw4cM8/vjj/PDDD5jNZjZt2oTFYiE2NpbLly%2BTnJzMp59%2Byvbt2wG8REKcTiePPvoolStXplOnTuI8Pv30UwICAvjR463pwIEDmThxImazmbS0NLKyspg/fz7Z2dk4nU4CAgKoWrUqdrud1q1bk5ubS3Z2NuvWrRPBT7NmzUhISGDMmDHccccdrFixAihVq2zVqpUY06RJkwCYMWMGzZs3F8HZnj17RGauZcuW5OTkkJ6ezqFDh7hy5Qq7du3ysj243eOXtdlsxu1243a7ef/998Xc2z1efx49epS2bduKfVrmLycnB4vFQvPmzWnevDkjRoygTZs2XL58mYMHDwpbiQcffJDFixdjs9no0KEDO3fu5Ndff8Vut2MymZg2bZrXdfZVoRMM43UDBgwYMGDAJ9xiGbnfG0bAZ%2BCWwcSJE0UGzm63s3jxYv7%2B978THR3NgAEDsNvt1KhRg1dffZWvv/6aTZs2cdttt9G/f39atWrlpcT4W6FxB00mExkZGRQUFFBQUMCcOXNwOBxMmzaNrKwsZsyYQYcOHZg8eTKPPfYYISEhIigoKSkR2UiNu/fhhx/q%2BjKbzcTHxwshEygNkrZt24bL5RKeeU6nk08//ZTly5fTuXNnsrKyyMnJ4ciRI4SHh2MymRg9ejQvvPACbrebqKgornjUf2iBU15eHgcOHGDQoEHYbDZcLhdhYWHs3r2bP//5z3z//ffYbDaio6NxOp1MnjzZq9yypKREBL379u3D5XJhMplo1KgRbrcbh8Phpa65du1aNm7ciNlsxuVyiYzd0KFDSU9P56effqKwsJDp06cDcP78eUJCQnA4HF6cwrVr116Xk%2Bl0OnE4HFitVqGE6nA4OHjwIAEBAUSX6c6rykZVvMXrweDwGTBgwIABAwb%2B3TDCZQO3JAIDA3nssceoUqUK/fr1IyAggAsXLrBnzx7Gjx%2BP0%2Blk%2BfLlzJw5kwEDBvymP%2BI1qLiD//jHP1i2bBkAly9fpn///owbN462bduybt06pk2bhp%2BfH61bt6ZDhw44HA7h11e1alWGDh1Kr169aFdWNqfZBGglqykpKRw7doypU6eKLOW1a9eYPXs2EydOZM2aNbRr1w6n0ykydA6Hg71793Lo0CHcbjdZWVlcvHiRrKwsnn/%2BedxuNxaLhWXLlonvNGjQQHDq/Pz8hCqlFqx4BoMa0tPTycjIECb1GiZNmiQyeJ7fDw0NFfzKBQsWiP40ERVPXiGU8vyOHj0qxugZjOXl5VFUVITVavXKFl4Pjz76KAUFBTgcDmEyr9ll5OXlYbFYeP7555Xf1XHwbgAlh69zZ%2B%2BDZBKawnldRy%2BRypR9oXipnNd17Sr4ZDq%2BiczDUXBfdH2riDfy91ScH9nhW3ZVVwxHx6OTOX2gJ8wo%2BtbRsw4f1rcju1/LvCqVi7VMolFdvPIIPZRvbO4Lh09pNi01rDKxlolqvvhRyzwh1Xd0CXHFddF9T%2BVKL5Ot5PnzhcMnu6GDfrEpTkLH4ZPXiIpTLt%2BbqsmR7yHVPSXfrCpbGPkdlkxUUzwndGQxeVu1T/U7VSZuygQz1TNAald1WXScvTIBMC9IpuS%2B%2BKXL94vqsvjC4fNl%2BcmXSp4%2BX5asqm9fuLOq8/qPwODw3RSMDJ%2BBPzS2bNkCwLx585SfO51OgoODyc/PZ9iwYYyQ%2BT0guGCA8HnToPHqVMqMnhlFGcHBwdhsNhYuXEh9D45Ojx49xM%2BhoaGEh4eTlJTkVUI6YcIELGVEkEqVKhEhF%2BpTyk9LTU0FSjNZDodD2E9oc3Hp0iV69epFnTp1qFixIhcvXsTlchEXF4fb7eb8%2BfMikLNarVSoUIHWrVvz5Zdf0rdvX44dO4bJZCIlJQWAu%2B66iyFDhhAWFsZTTz1FQUGB8LCz2WyYzWYqVKggbA6Kioowm83UqlWLwMBA7HY7VquV8PBwMjIyqFy5sghaDx06RLNmzUSwLMNqtbJ%2B/XqqVatGs2bNyMvLIzY2VpxD9erVuXTpEna7XefD53a7ycvLIzAwkNzcXEwmEyEhIVSvXp2jUrClZRXdbjcLFy7EZDLh7%2B/P119/LfiJmXIQcgOsWrVKWGloGDlyFPU8eVsyCU3hvK6jl0jmw75QvFTO67p2FXwyXeJb5uEouC%2B6vlXEG/l7Ks6P7PAtu6orhqPj0SnueR1hRtG3jp7VpIm%2BHdn9WuZVqbi4MolGdfHKI/RQvrG5Lxw%2Bpdm01LDKxFomqvniRy3zhFTf0SXEFddF9z0Vp1Yuu5fnzxcOn%2ByGDvrFpjgJHYdPXiMqB2353lRNjnwPqe4p%2BWZVVBjo9MBkoppK1Vr%2BHaT4naTbpxLrkombMsFM9QyQ2lVdFh1nr149/TGSlY4vfuny/aK6LL5w%2BHxZfvKlkqfPlyWr6tsX7qzqvP4juMUCtN8bxuwZuCWRn5/PRx99JOwZ0tPTfbJnAFi/fr3I3Gn/evbsKT6/8847OX/%2BPFOnTiUxMZFmzZoxcOBA9nq83e/Rowe//vorvXr1IiEhgcaNG9OvXz%2BOHz/OM888w8KFCzlw4IBXCWVJSQnfffcda9euFdw9gOzsbN14EhMTWbhwIQDHjx8H4L777qNJkyY0adKE9u3bc%2BrUKRITE6lVqxbBwcEiu5aZmcn58%2BexWq3079%2Bf4uJiVq9ezcqVKwV/rrCwkBUrVuB2u3VzmZ2djcPhoHnz5sKHT%2BPDPfPMM8TFxbFkyRKgNKP34IMPkp%2Bfj8vlori4mIyMDCwWC8OGDROlq6dPn6ZPnz5YLBbatWuHyWRi4sSJXtdFM5vXsoKDBg0CSjNzFy5cwGw2Uyy9xa1bty6rVq3i2LFj7Nu3j6ioKEJDQzGbzSQmJlKpUiXCyv7osFqt3H333QwcOJCffvqJCxcu4Ha7KSwspLNHVu5nlULddWBw%2BAwYMGDAgAED/24YGT4DtwymTZvGq6%2B%2BCkBAQAD169f/zfYMAF27dtWJtpw7d47k5GS6du3qVWYIpZmsw4cPM2TIEL7%2B%2BmtCQ0PZs2cPgYGBhIWFcc8993DmzBl27dpFnz59qFixIqNGjeLTTz/Fbrezfv16Nm3ahNVqJT4%2BnuHDh/PGG2%2BI9iMiIvhOluT2wL59%2ByguLqZKlSrMmjULh8PBAw88IMpB/fz8RImn2%2B2mbdu27Nmzh8LCQg6WlcI8//zzIoB0uVzMmDFDlEy2a9eO%2BvXr43A4GDJkiOAeFhYWct9997Fy5UqgNEjUsqV9PVTTHnzwQdavX09OTg4BAQEUFhbidDp5/vnnRUD09ddfs2HDBpxOpwjsPMVSTCYTDoeD8ePHC3GWGTNmAKWBcklJiTK42rNnD%2B3btxc%2BfFrQ27x5c/7617/y66%2B/UrlyZaA0kNy%2BfTtffPGFLovnWT5aR8quGTBgwIABAwZuEkaG76ZgzJ6BWwb/H/YM69evJzY2lsqVK/Piiy8KTt2JEyeoUqUKO3bsYN68eRQUFPDNN99w9913s2HDBvbu3UtYWBhWq5Xbb7%2BdamV1KYGBgXTt2pWUlBQOHTrEypUrvQzFfUFGRoaX/UL//v29bBmKi4txOp0iaImMjBSfa//v2bOHbz38ojwDnIKCAg4cOEBxcTFnzpzhhRdeIC8vjylTpgizdBlaps1kMpGWlkZOTg6AF7evoKBAlK6%2B9957onRU%2B56fRwlOSUkJr7zyilfm80Y%2BhgC9evW6rg/fkSNHBNdPG5smIDN69Ggvg3gZv4XDZ4i2GDBgwIABAwb%2B3TACPgMGgC5durBkyRIvkRENWonlb8GYMWN0/D2n04nFYmHdunVEREQQEBDAhAkT2LJlC0ePHmXnzp20b9/ey5OucuXKumyihs2bN4tywxtB4wgGBQWxdetWjh49SkhICAEBASJ7ZTabCQwMxGQysXHjRvz8/DCbzcLCISgoiIKCAmE2bjKZhM2CDC1gueOOO4SwiclkolYZp8RmsxFYRjhwu91ERkaKc9aCXu07oWXkAVkh1Waz6QRY7rjjDnbt2iUCQe0zi8VCfHw8/ct80ipUqACUGqTfyIdPs5TwhNlsJjY2ljUKA3INs2fPvu5nMpSiLXfe6X2QHFwqGPQ64RTJhVelj6BLaCvcfnW7PJRfNejEDGT1AF8UY1SqAFL5rVI1QXYbVvSlS%2BzK86kqq5WFXD74QHeIzjNddQ7yPlm4QqW0IL2oV888wgAAIABJREFUUA1PNxWKuSlPdEIl2qLT2lAtnHIM0wH9OlG8fClvfKrLrZsulUO1L%2BumvGNUL4ukY3wZn1LrS27blxOXGlIdoutMPkfF%2BHxZN7p7TOXw7cs5yOetmmN5n9yXD9dFuR7lnaqD5Lbl%2BVSsNVmwSPF4/JcEWf6VY1S3gi/CLvIU%2B/S8%2BU/BEG25KZjc5b0GN2DgD4AGDRrgdrtFMKH58GnecQUFBdx///3k5ORgs9nIyckhODiY4OBg8vLyWLp0KdWqVaNz586UlJSwbds2r/a3bdvG448/zqeffsr48eMZMmSICPg0S4b33nuPtWvXcs8991ChQgUqVKhASUmJ4OlVrVqV9PR0Zs%2BeTfv27bn99tvJz8/XBTaaSuWnn35KWloab7zxBt999x25ubm0a9eOatWq8fXXX4vjL1y4wP333y/60YRHNMGR/v37c%2B3aNQICAli6dCk2m80r0%2BZ2uwkICGDLli2Eh4eTkJAg5hBKs2tut5tBgwaRnp7O9u3bcbvdolRUK5XVyiZfe%2B014uLieOihh0QfFosFp9MpAj673U5RURGDBw9m7ty5DB8%2BnFGjRom%2BteO1c9Hw0EMPsWzZMoqKikSQajabsdlslJSUYDKZ6NmzJ8uXL6dOnTo4HA6ysrKEDcSpU6e4du0aQ4cOZcSIEQwcOJBTp06JTOf3339PaGgoEydOZOnSpcq11qhRI7788kuf1uX06dOVoi0jRgz36fsGDBgwYMDALYFnn/392yyjftwKuLXCWwO3NO69915R0rlz507uvvtu4d2mKS8GBgYKI2%2BLxYLNZsNut19HXOP6UFkyePIFa9asSU5ODsXFxSJgsdvtuN1uaniotmniIUePHhVj/%2Bqrr7z6ys7OJiEhgebNm1NYWMiZM2dISEgQxvJut5tnnnlGKE962hYUFhaydOlS9u/fT3Z2NjExMQQFBYk50DJrZrOZ0NBQYU%2BRkJAgxqNlxpYuXcp999133VLKuLg4VqxYQffu3b3M35s2bSqM169cucLFixfFXMydOxcozZo18lBgdLlcBAUF0bp1a69gePfu3SI40ziKQUFBFBUV4XK5iImJEeqlZ8%2Be5aeffhLXd%2B/evUJB9KOPPmL48OEcO3bMS9WzdevWOrGY2rVre5Vm1pBV924AQ7TFgAEDBgwYMPDvhiHaYuCWQHR0NC1atBDbmg/f559/zo4dO0SQ8c0333hxwwBef/11EVQ1a9ZM6cmnce40Q%2B4bWTJofMG//vWvDBkyxOuzVatWif7Dw8OxWq1kZ2ezYsUKUU7piT59%2Boj9ffr0YcCAAYJ/NmXKFK9jAwIC%2BOabb4BS374BAwZQUlKC3W4nOzubTZs24Xa7%2BeCDDxg/fjwul4sDBw6QkJCA3W7n%2BPHj/FDm7XT27FmaNm2K2%2B0WIilhYWEiU1hcXExKSgp79uzhkUceAUp9%2BDzVTDUMHz6cKVOmYLfbheCNyWQiKChIcOUqVapE165d%2Bfzzz4HSYLWgoIBdu3aJABZKs43h4eFcvHhRmNLb7Xbi4uJo1KgRbdu21c2LClu3bqVbt25CyEaD2%2B1m5cqVDBw4UOyTLTnOnDlTbvsaDA6fAQMGDBgw4ANusRLM3xvG7Bm4paHx6r755hvhUydj/PjxtG3b9nfrs0uXLuTn5/Pll19yUjKy/vbbb1m3bp3YNpvNTJgwgb/97W/kqggCZTh58iRnzpyha9eu9OjRgzVr1mC328nMzGTy5MleJZoAiYmJtGvXjqSkJIKCgoQVAZRmJ4ODgykqKmLVqlVAaQnlpEmThA9er169WLlyJU8%2B%2BaRo88KFC7z22msim5aWlubVp8rwfMKECbRv355mzZrRuHFjzGYzJpOJyMhI6tatK467du2a%2BL4W4Gn/W61WwUVMT0/n6tWrBAYGMnjwYKBURTQmJobTp09jt9vFsRaLhQYNGhAeHo7NZhPZzIiICPLz88nNzVVmK91utygt1UqEPeFZqloelBw%2BWZTHB35MeRQkXyg1qmN0%2BxRKtuVScxQN65r5Vwco85RUSrv/CmdK5uwpzNl94UPpTL/ll0WKl0cyF0dFgdTNsYKcI0%2BF3JWKw6frS0X6keZcdYg8Fz5Qr/THKL7kE4dPPkixJnS75B2qdSQNWEVlk7tWcp%2Bk89ItAR/69uWcVHPui3G97nvyPabgBvrCb/Tl%2BpbHTfXlGaXkTcoHKRZtuc8tRefyr2RfeJ2q%2B%2BX3OMaXW8EXbuB/NYfPwE3B4PAZuCVw5513lsurmz59Ot26dbthO8899xxfffWV7g99jVe3efNm7r77bsxms1CYlLFixQqGDx9OVlaWyI6ZzWYRXDRs2JBnn32WF154AT8/PxYvXkzv3r3JycmhpKSEoKAgrl69yvTp0%2BnduzdQWq5aUFAg%2BHN33XUXo0aNol69evTs2ZPatWtz5coVdu3aRVpaGpMnT2b//v1ERUVx1113kZuby44dO7h69SpmsxmHw4HJZMJsNuN0OmnTpg379u0TGbi6deuSk5NDSEiIV3mmdh5ut5v4%2BHiuXr0qyiQDAgJwu91ERERQr149Nm/efN15tlgsIlOnQc62mc1mEQR6qoyaTCZsNpuXEqhWomuxWPDz89OJ83iOG0oD4pSUFBo2bCh8DCMiIjCbzYwZM4bGjRtz3333Kcf%2B6KOP8txzz1333DxhcPgMGDBgwIABH/DCC79/m6%2B8clNfP3/%2BPC%2B//DJHjhwhKCiI5ORkxo0bp3vB/e677zJ79mzd345bt24lIiKCoqIiXnnlFbZt20ZRUREtW7bk5ZdfFsJ1vweMDJ%2BBWwbl8ep%2Biw%2Bfxl9T8eqqVq0qLBlU/%2BrUqcPSpUvp37%2B/yGjZbDbMZjNVqlShQ4cODB06FIfDgcvlYsCAAURHR%2BNwOFiyZAlvv/02AJMmTSI1NZWioiLS09OJjY0FSgOc7t27s2zZMmw2GwDBwcFcvXqV%2BvXr06VLF3bv3o3JZKJdu3aMHTsWh8NBxYoVcblctGvXjqioKC8z9jNnzgjRFW07OzubH3/8EZPJhNVqxWQykZSURPXq1YHSjN/jjz8u5qV%2B/fpUrVqVN998k%2BwyOcDAwECRcfNEx44dRXkslAZk0dHRmEwmKlSogMlk4t1338XhcOBwOLwCwYiICJ2Fg9Vqxel0UlxcLEpyY2JieOedd3jhhRe4%2B%2B67xYO4atWqQl1UC/aglCupBaCetgwWi4VKlSqJbc3GwRcYHD4DBgwYMGDAB/wXqnSOHDmSKlWqsGnTJhYsWMCmTZv4%2BOOPlcf27NlT9/dgREQEAG%2B99RbHjx/niy%2B%2BYMOGDbjdbiZMmHDT4/OEEfAZuGXw/%2BHDB7Bly5br8vc0VKxYkQkTJhATE8OkSZM4evQo27dvx2QyUVRURFRUFJGRkYSFhZGXl8eCBQsYPHgw06ZNE3YBPXv2JDs7m/Xr11NSUsLhw4dp2rQpTZs2ZcGCBezfv5%2BMjAygNEiNiIggNTWVlJQUYmNjiYiIYOvWrbRs2ZJNmzaJss%2BePXsSEBAgAqmOHTty6dIlBg8e7BVYaePo0KEDLpeLkJAQsrOzuXDhArfddptQEtXw888/k5aWxujRo0VQmJyczJ49e4iNjfUKKDWrCigNCm%2B77TYyMzNxu92itHXs2LFAqUVDx44dRT%2BXLl0iMTFRbGsZy0aNGpGcnCyylIGBgYwaNYqFCxcSEBAgyjRzc3NFQKoqQ/3kk09Yvny52HY6nV4BoGfGszwYHD4DBgwYMGDgfw8pKSmcOnWKp59%2BmgoVKlCjRg0GDx7MF1988ZvacTgcLFu2jGHDhhETE0PlypUZM2YM27ZtI0v2/rgJGAGfAQP8/j58N4LGq5P7ioyMpF69etjtdpxOJyaTiXPnzglu4RNPPMEvv/wieHWPP/44bdu2ZdmyZcTHx9OuXTtWrlzJypUrWb16NfXr12fjxo26/rUy0bZt24rMnMlkIiQkBKvVisvlolmzZsK/TwvsIiMjvQRSqlatSr169XC73bhcLhH4lZSUMHbsWPz8/Lhw4QJQWs65evVqUlJS%2BOCDDwS/7/vvv%2BfkyZMiC%2BeZ1dPw97//nS5duuB2u0WZ6bBhw3j22Wcxm82UlJRw6dIlr%2B/s3r1b/Ozv78%2BLL76I3W4XmU0oFeNZs2YNgwcPprCwkBMnTgCl3odawKcKvgIDA4VJe6VKlQgPD/f6vEePHrrvXA8qDl/nzpJxe1nQft1tFPwNySROlUjUJbQV3lQ6Pow0z8pj5HYU95Sub4/S3eu2oyKzyQ0p2tFN1/nzN%2B4H9BOqYD6cPl1eR%2BhJPnLfKj5Uebw/1T4VyUaeG4l0puLwyd6CKs6P3JViSfg0PHmnL1wiX3whdd9TzZ%2B8lnzgyOn26UwLFe2qiFVyX/K6UU2WfGFUx8jjUY1PJh4q5kbXdHlekvCvEcF84bzK11d1LaW%2BVMPjl1%2B8t1X3fHleh4q%2B5ceNanjyKaio%2BPKyUfFD5X3yVCkeszrLUdUj1BeLQiVP9z%2BBf0OG7%2BLFixw/ftzr30XV7yMFjh8/TmxsrFeFT8OGDfnxxx%2BVf0umpqby4IMPcvvtt9OtWzd27twJQFpaGrm5uTRs2FAcW7t2bQICAryqjG4WhkqnAQPAY489xvr162nVqhUul0vw7zRel2cGqaSkhBkzZrBx40YuXbpExYoVRXZIw3PPPUdRUZHONP3cuXMkJycTGxtLVlaW8LCz2%2B1s3ryZXbt20aNHD65cucK1a9fIzc2lZs2aQGmgMXDgQJ555hnRnubVB6UZtO3bt3v1pwUxly9fJjc3lzZt2pCfn094eDh33nknK1asECWVJ0%2BepFevXkyYMEFw96A0o2WxWPjhhx%2B8eHRfffUVNptNKHfedddd7Nq1C4BRo0Z5ZQNdLhd/%2BtOf6NmzJ9OmTROZx4CAAHr16iW4ddp%2BT6/A4cOHi7a06zFr1iwsFosIyLTz1OCpyJqfn8/UqVMB6NSpk/BQfOCBB1Dhl19%2B4ZdffhEWHVrms2LFily7do3XX3%2BdlJQUTCaTV2YPSstHf0vAt2rVKiWHr379ev/cERPj/SV5G5B86SEszGtTkUhERzFVcAX8/aUdZYHuDY%2BR2wkJKb/vqCj9AOV2KlbUHyM3pGhHN11lpc/X7Qf0E%2BrxokPDbbeV1xFQocKN%2B1aIROnmSzfBin1lpdtekOemzPpEQ1CQvl1p2RAYqG9W7kqxJHwanrxTXqOqNatb5/L8qr6nmj95LckDVPGv5X1lpVg3bFc3YEVf8rpRTZZ8YVTHyONRjU9aA6q50TUt3x%2Bqdn25eHLDqnOQ1758fVXXUupLNTzi4ry3Vfe81LZueIq%2B5ceNanjyKSiWrG7ZyJdJtU%2BeKsVjFo84RNkP6KdCNTWq58AfBV988QXvSaJcI0aMYOTIkeV%2BNycnh4rSpGrB39WrVwnxuCjR0dFUq1aNcePGERUVxRdffMETTzzBqlWrBA1EbqtixYpcVUXg/yKMDJ8BA5SW1n322Wf4%2BfkJjlhYWBjJyckMGTKEiRMnkp6eTklJCbt37%2BbMmTPMnTuXI0eOsHTpUnGTa8FPeXjzzTcJDw/n8uXLvPzyyzRp0oSnn34as9lMWlqa4Ba63W4dt9CzzPDhhx%2BmXr16REdHExsbS2pqKr179yY5OZm9e/cKDt8nn3yC2Wxm1apVHDx4kFmzZpGamsqAAQNEQLNs2TLCw8Nxu90EBweL0scLFy7QvXt39uzZI7JsJpOJjh07sn//fsF9y8/PFyqnmzZtYvbs2WKc%2B/fvJyUlhWnTpgH/VNh89913WbNmDbVq1fISYdECuapVq7J582aOHDniNQcWi8Ur2yhDKxmVoQV7wcHB9OrVi6pVq%2BLv709AQIDoe8CAAVSsWJHi4mIvdVNNfObZZ5/l3LlzSgXP2rVrU0H1G/06MDh8BgwYMGDAgA/4N2T4%2Bvfvz/Lly73%2B9e/f3%2Bch%2Bap72bdvX9555x3i4%2BOFinj9%2BvVFxdZvaetfhRHwGbgl4CuvTqud1jh1r7/%2BOmPGjCEqKoodO3YQFxdHSEgIs2fPpnbt2phMJmJiYnjzzTf5y1/%2Bcl1lThkRERFMmzaN6OhoJk%2BeTGpqKqdOneLw4cOCW/jSSy8BeAmQ7N69mwEDBlCjRg3i4uIYO3YsX375JdnZ2aLMUEOlSpWEmEyrVq04fPgwERERWCwW6tevz/vvv0%2BTJk1E%2BcJ3333HAw88QElJCZ988gmNGzcWZZ1JSUn89NNPIqCrWbMme/bsobi4mIKCAqxWK999950I/jSRF4AhQ4bgL736tFqtTJ48mdq1a1OnTh3mzp3LbbfdJgI9rbzT6XTy8ccfY7VaCQgIEIGeVkbq2V716tUJK3sT7mkJYbPZMJlMxMfHC4P3/Px8Vq5cyYULFygqKqKwsFC0d%2Bedd4qy2hkzZoh2NCP3efPmiYf0oEGDqFChgrB0OC%2BX7BkwYMCAAQMGbh7/hoAvKiqKhg0bev2LUlWcKBAWFqYTacvJyREJg/IQGxvLxYsXxbFyW7/%2B%2BquOMnIzMAI%2BAwZ8wH/Cr69Jkyb4%2Bfnx5ZdfkpeXR3FxMQcPHuSRRx4hMzOT4cOHs3DhQo4ePYrD4RBqT57QHiC9evXSfRYSEsJrr70msmE1a9Zk06ZNVK9enR07dogST6vVSkFBAS6Xi6CgIKxWK/fddx92u53ly5djMpkIDAz0sj1YsGCBKE/Qykztdjt9%2BvThwIEDOBwOXn/9dXbu3ElmZiZz587lww8/ZPjw4VgsFhwOB9nZ2fz6669cvnwZKC3/1OwXPN%2BENWjQgOPHj/PNN9%2BIwLJLly7i84iICJ544gnGjh0rspZVqlShb9%2B%2BXhk%2BLUBt3769%2BO6zzz4rfp4/fz4ABw4c8Cp3zc3NFZlA%2B28kOxiiLQYMGDBgwMD/Hho1akRGRgZXPDi2mhJ7sFSDO3v2bC9tASil%2BFSrVo1q1apRqVIlL77e6dOnKS4uplGjRr/beI2Az4CBGyA/P5%2BPPvqIK1eu0LFjR9LT0wWn7npo3rw5X331FWvXriUhIcHrX3Jyss99W61W2rdvj8VioVWrViQlJVFYWEj37t0pKipiy5YtREREsGvXLkJDQzGZTMyYMYMNGzawfv162rVrJzJU1apVA0q5hU899ZSur3PnzrFlyxb8/f1JS0tj5syZBAYGYrVacTgcoiS0oKCAiIgIFi9ejNPpZOrUqUI5c82aNaSlpVFcXMzOnTvp168fUPrgSkhIoEmTJhw/fpxBgwaRlZVFQUEBf/nLX%2BjYsSOLFy%2BmWbNmzJo1C6fTidVqxWq1YrfbWblyJYmJiV5vv7QMn8lk4pdffqFLly40adJEcAA9Pf4yMjJ4//33GT16tAjUsrKyWLlyJdnZ2bhcLi9rh/r16/P5559js9lo06aNV59QGhhrJR9ywCY/5MuD0nj9Hkm05f33b7yNQhtiyRKvTRUNQFc9olAW04kQLF2qO0Yn2uFRogLohUpQaGJ8%2BaV%2BgLLigErgpIwzKjB3ru6QY8ekHfJ5Hj6sb1eeMIXAxN690g6FOTtbttx4fCrhClmVTW4D9BOoUoKQ25bmUyXaIjejE6Yp/aLXppJiIl8X1XlKi1Zew0qhF3kNKERRdONRKVXs3%2B%2B9LZdWq8RWZDUQ6Y83AGSlZ5X4g3xiO3aU3/fBg%2BUfI/uafvtt%2BX2r1o38YCgTlhCQxwuQnu69rVIqltafsnotM9N7%2B8wZ722VIov0fFGJCCHL5OtuXqBMFVqDbmoU60heayqh73/FVN2H20W3rWrXF90reZ/qmP%2BawpX/MluGBg0akJiYyBtvvEFeXh7nzp1jwYIFopqsa9eu7C971uTk5PDyyy/zww8/UFRUxPz580lLS6N3795YLBb69evHBx98QEZGBlevXuXNN9%2Bkc%2BfOyhf5/yoM43UDBjxw5513kpWVJfhcAQEB1K9fn3HjxpGUlETjxo2ZNm1aucIc5Ym2bN68mbi4OJ0hvIylS5fyzjvvkJ%2Bfj8PhoKSkhKioKEJDQykpKeH8%2BfPUqVOHwsJCMjIyaNq0Kf7%2B/thsNmHe/t133/H666/Ts2dPn8a1e/duJk6cSHx8PBcvXqS4uFiohmocPtVjQxNagVKOnS%2B%2BhlWqVAFQSg/bbDZcLhfx8fGsW7eOzp07e5VqyrBYLNhsNi/enQzPsVutVjHmwMBAAgMDuXjxItWrV%2Bf%2B%2B%2B/ngw8%2BUGbsnnrqKaKjo72yf544efKk0s5BBcN43YABAwYMGPABN2mSrsRNmrlnZmby4osvsnfvXkJCQnjwwQcZMWIEJpOJhIQE5s2bR4cOHSgqKuKNN95g/fr15OTkUKdOHV588UWaNm0KQHFxMa%2B99hpr1qzB4XDwpz/9icmTJ/8mTYDyYKh0GjAgYeLEidcNwH5Pvz4o5RbeCO3bt2fixIlERUVhs9l45plnuPfeezl8%2BDDDhw8nIiKC48eP07NnT3Jzc5k9ezYvvfQSRUVFxMTE8MYbb9CqVSsy5TenN0Dfvn3p27cvUOpp16VLF8xmM3FxcdSrV4%2BioiKOHDlCUFAQRUVFNGrUiA0bNtC1a1f8/PwoKiqiffv2vPHGG2RnZ4ugavv27URFRZGamkq/fv1wOp0MHTqUfv36kZiYKMosFy1aBJRy/2bOnEl62dvjZs2akZ6ejtvtplu3bphMJn744QcuX77MxYsXGTZsGFu3buXYsWP0799feOE8/vjjwrNv2LBhIvs3a9YsOnXqxI4dO0QpqVbWGRYWhs1mw%2Bl04nA4cLlcNG7cmKNHj5KWlkaLFi0wmUz4%2B/tTsWJFkSk0mUwcOXJEPMTLgyHaYsCAAQMGDPiA38Eo/fdGdHQ08%2BbNU36Wmpoqfvb39%2Bf555/n%2BeefVx7r5%2BfHpEmTmDRp0r9lnGCUdBow8Jvw/%2BnXB6UPk7p164oMXsuWLYHS2nG73U5ubi7%2B/v4cPXpUyS0MDQ3ljjvuYK%2BihMWTU6f5zskBSGRkJHXq1CE3N1f48WmoVasWaWlpXpw3FZo0aQJA7969adq0KWPGjMHf35%2BQkBDB%2BTObzfj5%2BfHJJ5%2BQk5NDTk4OW7ZsoU2bNjgcDl2b69atY926dZw4cYJLly7hdrv5/PPPhYTxEo%2BSxrlz55KYmMjBgwcFhw9g%2BPDhJCYm8uSTT%2BJyuUT2Upvfa9euUVxcLLKWmhH8qVOn%2BPDDD4Vlw8WLF714dydPnrzhfHjC4PAZMGDAgAEDPuC/rKTzfw1Ghs%2BAgd%2BA/2%2B/vs2bN9O%2BfXs%2B/vhjoqKihJpTUVERVapU4ccff6RTp05s3bqVWbNmMWfOHBEgbdq0iejoaN5991369evn1ffatWvFzwMHDhQ/9%2BjRQ5ixQykn7dq1a7z33nt07tyZ5557jq1bt2K328nMzMTPz49XXnkFp9PJ5cuX%2Bfjjj5k9ezYvvviiGMfBMv6JFiQ/%2BeSTvPnmm2RlZQnDdJPJROPGjYmLi2Pu3LnUqlWLkydPigA0MTFRzDOUBkVaBk4LkDzN1/38/ER2TgvkHn30Ua9yTzmQ1Dz10tLSOH36NCEhIVSqVImMjAzRh6ZqqqlyevoFamtA9ua7EXr06EGDBg289t1Wq5b3QUePQuPG19%2BmlPPh5Rt16hTUq3f9zynl0Hg5W5w8CWWqrNf9ni/HnD0Lder8c/vSJb1ZW1GRt2nVsWMgk9Ptdm8DKNVJnD7tbYh3%2BDCUvWDQkJcneVTJ8/fzzxAf793utWveplXyWCjlx3h5Vm3eDHfd5d3O4sXgWS2wZw%2BUvbQBIC0NZAsRuW/5HKGUw%2BepCKy7mPpDKCz08oUrKCjSefEVFHjbmunOEUp5dJ4vluRt1XkpxidfT3lJyNuq76jWhO5SSecN6NekD%2B3qFpLq2l254u2ZJ19L1YnJ60%2Bx1uR2cnMVfm4%2B9K1bE4rz1H3t11%2B9Dd3kbcUpqRaO3LdqinUn5sszQPqO6nHD/v3QvPk/t0%2BcAOm5K39RPifVEpaXhOrS%2BbK05H2666T6njQg1fjkc/hXx6dcbwb%2B52Bw%2BAwY8EB5nDoo9WPr1KkTNpsNu91OaGgoLVu2pGrVqixcuJDVq1fz9ttvs23bNpo2bcqECROoVasWmZmZzJw5kzVr1oi6bl8CvrS0NB599FFMJpPw1QsICCA0NJSff/6Z1157jQkTJtCnTx9ee%2B01ZZsffPABs2bNAkoDnerVq3PfffcxZMgQAgICRH8NGzbk6tWrXLlyBZPJRM2aNbl69SodOnRgypQpPPfcc6xfvx5/f3/y8vIwm8243W6vzJmfn58QQlHBZrOJ481mM1arlWJJjEFT/vTz86N79%2B5MnDgRKCVJX48baLFYiIqKIiMjQ2QOZQ5eWFiYl6KWCpUrV2bPnj264FxDRESEyIiqMHbsWB5//PEb9qFBxeEbNXIkw0eM8On7BgwYMGDAwC2BmTN//zafeeb3b/O/FLdWPtOAgXLw3%2BbXB9CmTRtiY2OZNGkSKSkppKSksG/fPjZu3Ehqaip9%2BvTBarWKDNf06dN1AWRUVBQVK1akW7dudO3alQ0bNjBy5EiRqdLQuXNntm7dypEjRzh8%2BDArVqzgqaee4ptvvhFta1nGiRMnkpKSwrFjx8T5yOfV2COT4ufnh5%2Bfn1fg2qtXL1JSUrz4c9r/BQUFJCcn89xzzynnRWsrMDCQJUuWcOLECZo1awaUWk785S9/AeCll14iLi6Ojh078sknn4jvjxgxgtTUVFJTU9m%2Bfbsor9SyinXr1sVsNgthmejoaBISEnA6nWzbtg1/f3/8/f25//77vXwGi3TSlteHisNnvIEzYMCAAQMGDPyeMAI%2BAwZ%2BR/wn/PoAAgMDOXDgwHW5hTtlae3fgBKlPrp6DC%2B99BIpKSlMnTqViIgInn76afG5FqympKSwfv16AGJiYsTnycnJdOvWjdDQUFq3bo3ZbPYKJj3RqFEjUlJSCAoKolIDGEXRAAAgAElEQVSlSiQlJXmN1WazCaXMvLw8Zs2axSOPPHLdYCw6Oprx48cD/yz1PHv2LC6XSxjTZ2ZmkpqaislkorCwkKKiIkpKSvjyyy%2B9MpRLJEsEAwYMGDBgwMBNwuDw3RQMDp8BA78D8vPz%2Bfzzz4Vf3yuvvOKTX19%2Bfj4ul8uLUyfjzjvv5Pz580ydOpVXX30VPz8/EhISGDNmDHfccQdQKv5x7do1WrVqhclkolKlSjRs2BB/f38OHjzI4MGD2bRpE3v37hXBkQatnFOGy%2BUiNTWVefPm0b17d7E/MzNTePC9%2BuqrItix2%2B1MmzZNcPqcTifDh//TXiApKQmn00l4eLjIxM2aNUuUmq5Zs0Ycu3v3btxuN0ePHqVBgwZYrVbcbrco5zx27BiJiYkUFxeTn59Pq1atKC4uFsFa9v%2Bxd%2BbhUVRpF/9Vd2eHQEKAsG%2BBCKFBSdQPWQVFFhdEHQZXBgccRYaRGRwYUYRRcRtnHNkRBRUVEJR9V0AWlWFtVtmXQAgEQsjWSXfX90enrl23LqQVHXWo8zw8WpVbd6tb1f32fc85Z8/y73//G4A33niDN954A8C0Ezd%2B/Hgml/mihZq5BwIBMjMzycjIYPPmzURGRlJcXIzD4UDTNJxOp7CRMLh9oZnxKiGWS8EWbbFhw4YNGzbCwFUWoP3YsGfPho0fiBdeeAG3243b7aZjx46sWbOGadOmUaNGDTRNK9eH7j//%2BQ933XUX3bt3F6mFxj85AKxcuTLPPvssHo%2BHdevWccsttzBgwACOHz9Ofn4%2BOTk5FBUV4fP5KCkp4cyZM6xevZrly5fzyiuvkJiYiNfrJTMzk6VLl4p%2Bu91udu/eLdp56623TH976KGH6N69u9j9uhw0TRPiJYZvX2g6puHnV1payurVq4GgYMyMGTPE9S5X8DcoXdepWkagdzgcrFy50qKAGrqrdv78eQoKCsqdc6N%2Boz0DPp%2BPhDKRgaioKGrVqiWsFgyhl0AgIMRpWrVqJczsZfTo0eOyfQiF0nhdFv6YOtV8rJCAttgFfvqp6VBFW7Swt6VrlPUqjNctRteyAbTKtVfeNVa0balY5a8oG2hLfEhQGK9/9JH5WGXCLHM0FTvDshe2yvTdYs4tm7MfOWK9Rp4vVf/kuVC5Jct9lhaBynj91KnyuyfXq7TGlCZdZQotL0BZ66igoNymlffFQq9VOcN//bX5WHbZVtmlyJOzfr21jKzQq/AXtax9%2BXlRTdYPMV5XZXXIdSsEpiwJHbKB%2B5o11nqPHjUfHzpkLSPfGFXmiDzH8nyquNOS6btyzcrvhQ0brGWkCy1LS9Ff2W9e5dgUjrG5vPxUt1d%2BHuRjVb1lySkCqs8BeUpVZb6Hq5ONXzDsgM%2BGjR8Ig8NmcOree%2B89sXv2Y/v1/elPfxLcwpiYGPr16yf4glOmTEHXdUaMGMHevXtNgWO/fv1EHVFRUXTv3p09e/aY0isNlcjq1auLoNLj8TBp0iRKS0u56667TIESBIOa3bt3C/5dTEyM4Bi%2B8sorxMTEsHfvXmFREBsby4wZM5g0aRLvvPOO2NkKVTXt06cPu3btonbt2tSsWZOXXnrJZOYeCsPGIRStWrXC4/EQGRlJ27ZtRbpozZo1gWCAZ%2Bz6GcehyM3NBYLpnYAQZpFN1CMiIsjOzub48eNUrlyZ%2B%2B67j5gQ6bPatWtb%2BnspzJ8/n6FDh5r%2BrfriC3OhMi6iQP/%2Blnpk5TXuvtt0GCreZ0BWdJOvUdZb5s8YCouKoxyw1qplbVyWgVO0balYVloEs/IewO9%2BZykii38ic3TLdslNSEoyH1vkIqFVK%2BnEgAHWelq3Nh/LYjz161uvkedL1T95LmQlSLD2WVoEskInQEiG9SW7J9crC1UClkm3rCOwLEBJ%2BJG4uHKbVt4X%2BdZZFyhmpVSwShCqdunlyVGl5UsKtpTxf02Q1778vKgmS15sqmdBrqdtW2sZuW550hXdQ7bdCXlnC8gqt7LSMFhvjKUhrHMsz6fl5gLSD2/KNSu/F266yVpGutCytBT9lX/zCxUnNiA/mqpHVV5%2BqtsrPw/ysareatXMx6rPAXlKVWXKPhJ/ftgpnVeEq2u0Nmz8CAhNsXS73aSnp3P//febvO46duzItGnT6NSpEy1atKBt27YMHjyYb7/9Vvj1DRs2TOmPd6kUSxkGX/Dtt9/G7/czZswY0%2B7crbfeKviCx44do6SkhJUrV9K8eXM6dOjAq6%2B%2BatolO336tBiT2%2B3mySefxOVyMWDAAJGyeOLECfx%2BP0uWLBHl/H4/lSpVEn5806dPp6ioiGbNmondO6/XyyOPPMLAgQOpWLEi7du3t4zHCP5OnjxJYWEh7du3p3bt2lSrVk0Ip0AwUOvWrRsej4eIiAghIrNv3z7uuecefD4fLpdLBHwxMTFERESg67pJEXPcuHEiuO3Xr58Y4/XXXw8E%2BYDx8fG0KvuylZGRQVJSEg0aNBA7lBcvXmT27NmmeVy5cmW5986ALdpiw4YNGzZs2PipYQd8Nmz8AJSXYrly5UqcTidRUVF8%2BOGHzJo1i%2BjoaO6%2B%2B26%2B/PJLOsu/xn4PFBQUMHXqVMEX9Pl8xMbGmnbnPB6PUNY8ceIEU6dOxeFwCFGTs2fPMnXqVFq2bMldd90l6v7DH/4geG4%2Bn4%2BioiKOHj1K06ZNcbvddO3aFYB27dqJdhwOBw6Hgz/84Q/4/X5mzZrF8OHDTXw5gyPXokUL5s6da9k1M9r7/PPPCQQCJCQk8M4773Ds2DFOnz6N2%2B3mkUceEXUtXryY2267jdLSUmGzMH78eI4fP04gEODChQuCz3fs2DEaNWoEwPqQNKzHHnuM1NRUUlNTmTp1qig/Z84c3G43paWl5OXlCSGc3Nxczp07R40aNahX9ou2kUIamkqak5MT9r20OXw2bNiwYcNGGLB3%2BK4IV9dobdj4CaBKsSwsLGTFihW0b9%2BeP/7xj3Tt2pWvvvqKhg0b8swzz1yS/yWja9euZGZm8vzzz3PNNdeQlpYm%2BILjxo3jvffeA4I7Ra%2B//rrYRQzF9u3b0XUdl8tFt27d2LlzJ7t27WLevHkkJCSYAoyJEyfi9XpJTk5m586dvPDCC4BZlARgzZo1NGvWDLfbTSAQ4OTJk%2Bzfv59mzZqRmprKmDFjqFy5shBnMbz6tm/fzvjx45ldxgX7wx/%2BwMMPPwzAwIEDefzxxwE4fPgwr7zyCgBjx47F4/Ewffp00b6u62SWcZ0iIyNxuVz8/ve/p2JZbszWrVvZsWMHENyp27t3LwCVQlKYQq0gQlGhQgW2bdsmxmxca6h2Hjp0SPguqlBPTm%2B6DJQcPindSKbdqCg%2BMt/EQjdRXCSXCYszpWpc4t1Y%2BB4yQQWs3CEVn0fe/QxjDEoRVsnj0UJSUXEM5bZUFcvcMNUYZJKPTDCSOX2KelUUNMosWARU3FW5z5KCr4rDJ1OmVI3Lt%2BXwYWvT8pSqyshLQKa7qbhEchnVlFvWsereyWtCLqMiUckdUvHJ5DKqtuVOy7w1xTqXq1HxteT7onrsLONSTKC8tMKZGos4tKpQGM%2Bz5ZxUj/L5lgYaQkn/DvJiUxD99u%2B/fL0qhoblnOLGyMNW0UPl6VKNs7x3vOoauV7VO16%2Bdwqhb3Wnfw7YAd8VwVbptGHjByCUU2dAtmRISkpi%2BPDhDB8%2B/JL1tGzZ0uKZZ8BQhYQg/65q1apkZ2czd%2B5cEhISuPfee0UqocFp2717N/fddx%2BzZs0SxuENGjSgtLSUZs2amczQr7nmGnJzc03BnLzztnnzZuV5p9PJCy%2B8QK9evS5pUJ6dnU18fDwtW7Zk2rRp9OzZk6NHj%2BJyuUSQWbVqVbEjZpi4h/ZH0zQh3nIpNGrUiEmTJom0T1V/DIP3xo0bs2nTJiAo4FKrVi3OnTtHQUEB8fHx5OTkCJGWihUrckEhalBQUEB%2Bfj7nzp0jISGBgoICnE6nMHnv2bPnZfsbivnz5yuN168J4abJtBsVxUfmm1joJoqL5DJhcaZUjUu8GwvfQyaogJU7pOLzyLufYYxBQekC2RpFJqmoOIZyW6qKZW6YagzyDzsywUjm9CnqVVHQkJ8Jla%2Bn3OcKFUyHKg6fTJlSNS7fFpUYsTylqjLyEpDpbioukVxGNeWWday6d/KakMuoSFRyh1R8MrmMqm250zJvTbHO5WpUfC35vqgeO8u4FBMoL61wpkZaWupCYTzPlnNSPcrnWxpoGSXdDHmxKYh%2BjRtfvl4VP89yTnFj5GGr6KHydKnGWd47XnWNXK/qHS/fO8u9BHWnbfzqcHWFtzZs/ASQUyyPHz9eriWDAVkx0%2B12W/h7RtDTrl07SktLmTdvHm%2B88QZHjhwRwiKBQIBmzZrRvHlzfD4f27ZtA4IefN988w3Dhw/n22%2B/ZeXKlTzwwAO89dZbtGvXDr/fb9m9S05OZsWKFZSUlLBv3z4gmHqo6zqVK1cGoH///vTq1YsTJ06Y%2BhkZGSl4dTExMXz77becO3eO2NhY4WdXsWJFEchmZ2eLdEgj4AOEMTsgrBMMVCtjohu7my1bthTBXmhqpdGGEezpus6RkF92vV6vUDbVNI0KZZ90VapUwel0UrVqVZM4TEJCAi6XiyZNmjB79myKi4s5f/48JSUlItgL7Vc4sDl8NmzYsGHDRhiwd/iuCFfXaG3Y%2BJFwpZYMBrp27Wri3Xk8HhHQdOzYEY/HI9IXP/roI3Rd56233mLGjBnC/sDYAVu3bh2LFy/G7/czevRoOnbsyMaNG%2BncuTN9%2B/alW7duXHfddbjdbpYuXSoCMFlAJSsrC7fbTYsWLfB4PEAwhXPHjh28%2BOKLAEyaNMnE6YuMjGTNmjV4PB6effZZCgoKhMfgiRMneOeddygqKkLTNJ5%2B%2BmkhqHLDDTcIO4Rx48bx9ttvA8E0TCMANIRmDA6f0W8jEFy4cCHbtm2jqKiICRMmABAdHU1GRgYOh0OItqSkpIjgzsDp06cpLi42pYhmZWXRr18/NE0jEAgIjqPD4RC2DKGBWnx8PNEhP6UaQbENGzZs2LBhw8YvAXbAZ8PGD8BPaclg7NqtXr0at9uNpmnUqlVL7EDdWCYpXq9ePZKTkzlz5gxxcXHUr1%2BfmJgYXC4XSUlJ3HTTTcyePVvwBV0uF9WrV2fYsGEsWrRIBD5r1641tZ%2BcnIzH46FTp07i3C233EKLFi145plnAIRKpuGNV1JSwi233ILb7WbMmDHExcWZTMlfe%2B018f/Dhw/n5MmTQNA/L3RHs12IBHgot3DEiBGCw2cEV4bH36OPPipM6DeU%2BSsVFxezadMmAoGACD737NlDUlKSSShF13V8Ph8%2Bn4/4snScW265hXfeeUcEgAbfMCcnB13XOXz4MC1btiQpKYkqVaqQl5cn0kCN/oQLW7TFhg0bNmzYCAP2Dt8V4eoarQ0b/wXcdtttTJkyRYiaGP86depEt27dxM7UsmXLTPYGxr/QQMnn86HrOidOnBBqkdu3bweCQdbkyZOpWbMm9erV4%2BjRoyK18Ny5cyxYsIAuXbrw0EMP8dlnn3HDDTewePFi4UUXutNl%2BOyVlpaSlZVF//792bp1q/j7%2BfPnKS0t5UwZo3/p0qVssThPB%2BH1eikoY4dHR0cTHx9v8fEz2j5w4AAffvihuM5bxjx3OBwmA/N3331X%2BOMZGDhwIJUqVSIlJYXVq1fj8Xhoq/KeKqsvIiKC%2BvXr06RJE5GmGZrOavD1jB3EIklAwOizruusWLGCnJwciyKnpmnst7D/Lw2laIvseyW758rHKDQ7JLGNcIyvVQIdFk2HcMrIYhaycAlYRQBUZWSFAYUqgaVtWXEChXiFrCiiUkWR5kalQWG5TGWyLYs4hNO2LOSiUuiQ50YhIGKpWjLHVom2yOtENSR50pVCGpJ4iWoIclvyb2QWARkIS9DG8vipxFVCUtEBq1KFItXa8gzJYitgGbdSAENeTPLaVz2s8jOvEtGQx6lyyw7jmSrXnF01n/INlt8tqjKqmydPmNyW4vmW50b2gAes90rBzbb0T5pjlYhQefo7qnqU9zccF/Xyyiiukfun/ByQ16PqZae6nz8H7IDvinB1jdaGjf8C%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%2BY3JZKwEuaG6VIsnyvFIbzlv5Jc6wSESpPf0dVj/L%2BhuOiXl4ZxTVy/5SfA/J6VL3sVPfTxq8OtkqnjR8FnTp14vTp0%2BLLcWRkJKmpqSLVDiAvL48JEyawfPlyzpw5Q3x8POnp6QwcOJAmTZoAwTQ9r9drUa40zMhXrVpFbfnDOoz%2BQFAR8tZbb%2BWPf/wjcWUvT1U5A2PGjOH2229n2LBhzJs3Twh/%2BP1%2BZsyYQc2aNYVZuNF3oxwEv/wbvncxMTG0bduWjRs3smzZMrHLVqNGDWHi7fP5aNu2rcnE24CmaZZA7vDhw%2BwO0aA26ho6dChpaWn07dsXCO7a/elPf%2BLzzz8HgjtZsjBMaMBXWlrKV199ZQkCNU2jYsWK5OXl0axZM373u9%2BRVPaBHLpbGLpDaeySGZy8//u//yMxMZFFixaJMRvYt28fo0aNEsehfwPo0qULERERYk6NsURGRtKxY0ciIyOFeEt0dDRbt25l5MiRzJ07F5/Ph9/v57HHHqNatWpcLNv2CRWKCR3vgAEDALMIzOTJkwkEAlStWhWfz0eVKlXwer2mfkZFRSnTNC8FW7TFhg0bNmzYCANX2Y7cjw179mz8aAjltanMyPv06cP%2B/fuZPHky27dvZ/bs2SQmJtK7d2%2BhBvlT9WfHjh1MmjSJ9evXC383VbnQf8YuDpjFVVatWsUDDzzAkCFDmDhxoqkuo5zT6aR79%2B7s27ePffv2sW3bNsaOHUvTsp%2BtFy5ciMPhoFWrVuLadevWmQKnbt264XQ6iY%2BPZ%2BjQocTGxoodMAP9%2BvXjrbfeokKFCvj9fp577jmWLVtm2p17%2BeWXyczMpHPnzlSoUEHw/Qx06dIFTdNEoLpnzx4xBkOAJDIykvT0dB5%2B%2BGE0TWPnzp20bNlStJORkSHmJ1QExuVykZCQwMCBA9F1na%2B%2B%2BorFixdTvXp1KlasKIL3mJgYKlWqJII5p9Np2TmLiYnho48%2BEufat2/PmjVriI2NpUqVKvztb38T/Mbi4mKaN2/OrFmzTPM1bdo08kJSXwKBAPHx8XTo0IGHH35YmMWPHTtWKIUaO4Hdu3fH4XDQpk0boqOjOX/%2BPF6vF4fDQeOybQXDdzBc2Bw%2BGzZs2LBhIwzYKZ1XhKtrtDb%2Ba1CZkefn5zN%2B/HiRhlijRg1GjhxJnz59hFDJTwVN02jcuDH9%2B/dnxYoVV1RPzZo16dOnD//85z/597//bZL6vxQCgQB79uxh9%2B7daJomuHmhHL7HH3/cFKgtWbIEv99PXl6eUAFt0KCByTA8Ly%2BPxx57jOPHj5Odnc3MmTPxer28/fbbot4vvviCHj16UKlSJTRNo0uXLhwP4Y0sXbpU8AUhKFpi9NlAXFwcW7duZdy4cWJHrFOnTmKHb9OmTaSmppKammoSgfH5fBQWFjJ69Gh0XScQCBAdHc3p06fJy8sTtg6xsbGmYLdSpUrccccdANStWxdd1yksLKRfv36izJdffomu66xfv56cnBw%2B%2BOADy5yDmaeXm5vLOYmMceHCBVavXs17772H1%2BsVthL169fnxhtvFLt8a9euJS4ujptvvpn58%2BcTCATIy8sjEAiwf/9%2BnE4nb775ZnlLwQQVh69jxy7mQmU7wJc8Dg7SfPzll%2BZjleOufE3ZDvBly/znP5YiFhrQunXm43C4bV9/bS0jV6xKf5bbkseNglcjk8PCMEtWUWosFCnV/MltffON6VBpqi43pjJnD4N/aRmEROhRcfhkh/RDhxT9kwmjIVxfgS%2B%2BMB%2BruFcyV0gmW6rWrDQ3Kk6SZT2q3Nkl3pc8napLwiJsyWNQuZ/L9chzo1rn0mJT9k8mQSrc7i1cX1Vb5Y1T5j%2BC9V6GwfVV8hvlk/L8hUMGVfEbpedOxYOmjGohIHdYReKT5lj5NUbus2pNhMPhk8/J4w6Hj6maG7k/ihujvFc2fnWwAz4bPylkM/JI2fAWePrpp2nTps1/pT%2Blyk/KH4b27dtTv379SwaQocFc8%2BbNuf/%2B%2BwkEAlSrVo2uXbuaAhEjGDJSS2NiYnjuuefE37du3cqDDz6I2%2B02cQOjo6NNnnUQ9LALDZ5q1qzJp59%2BKoLJvn37WtIl4budrBMnTrBnzx50XRfXnD9/Hr/fb6q3cePG1CsjTDgcDnG9LAaTIpFzCgsLLX57OTk5QrAFgqIzhjrmsWPHxHmVEfqlYLRhBGxGv2rWrHnZ60pLSxk/fjzXXXcdX4cEIhcvXuTixYt89NFH7N69G13XTevJ7/eTkZHB%2BPHjw%2B6jisP35dqV5kIdO17%2BODg487Es/KJy3JWvCVFlvWSZEEN4AxYakCycEw63rWxn9rIVq3gkclvyuFHwamRyWBhmySpKjYUipZo/ua2y9HYDSlN1uTGVOXsY/EvLIKT3hIrDJzukN2yo6J9MGL3uOmsZmceq4l7JXCGZbKlas9LcqDhJlvWocmeXeF/ydKouCYuwJY9B5X4u1yPPjWqdS4tN2T%2BZBKnwgrVwfVVtlTdOFaVCvpdhcH2V/Eb5pDx/4ZBBVSn10nOn4kHTvLn5WO6wisQnzbGK3mjps2pNhMPhk8/J4w6Hj6maG7k/ihujvFc/B%2BwdvivC1TVaG/81/Nhm5IYX2g%2BFscM2ZcoUsXNkINRTz/h3o%2BoLqAINGjQwmY8bfff7/QQCAUpKSigpKRE7VC1btqRu3brceeed6LpuEnVxOp00LPuG1alTJ/r06YNh/F2tWjU0TSM%2BPp4qVaoA33EEs8p%2B/Y2JieHWW2/lL3/5Cx6Ph88%2B%2B4ySkhKGDBlCfHw8K1euJD8/n6lTpwoOY3JyMhEREWRkZJAR8mX%2B7rvvVo43NEjdt28f/fv3B%2BDhhx8WgdWwYcNwOBykpKSQmprKwYMHqVKlCtHR0cSEfAA5HA5TwBSqilm7dm2hyvnSSy8pOZay1%2BGBAwdM6ZqqvsfExOBwONA0jTp16lCzZk1LwBwXF8cTTzxhsXBITk5mwYIFTJ8%2BXSimGjCCXafTyRNPPHHJPsiwOXw2bNiwYcOGjZ8adsBn40fDT2lGPm/evCvqj9vt5qGHHqJ79%2B48/fTTpnIqDt/XqhQzBYwdTLnvTqeTHj16CA7flClTcDgcIji85pprgGC6osF7GzNmjOCANWrUiPz8fBE0nj59mkAgQHFxsWhP13Xuvfde7rzzTuFNt23bNjp37gzAvffeCwSDsV27dolrFixYIAIjY9euZs2aYidNFVy9/fbb7N27V3jzOZ1OmjRpIgzU/xOS6udyuQgEAhw4cIDdu3fj9Xo5d%2B4cJSUlPPjgg8yfP5%2BoqCiqVq1qCgArlSmnRUREcPLkSfr06QME/QYzFDtLqt1NA7Vq1SIuLo7WrVsDUK1aNa699loyMjJ48skn0XWdc%2BfO8dprr7FlyxZ27drF888/L9oHaNKkCWlpaaLOXr16CXEhI2DWNI2RI0eydetWoqKi8Pv930vd1Obw2bBhw4YNG2HA3uG7Ilxdo7Xxk%2BKnNCO/0v5MmjSJ0tJS7rrrLosn3A9FIBBg7969Ylfucmjbti2dOnUiMzNT%2BLxBkFN24cIFtm7dSteuXenWrRsQ3K2aMmUKABUrVmTnzp1Uq1aNoqIiEwdt9uzZzJs3T1g1vPPOO3z44Yd07txZ7B75fD4CgYBIaYyIiBBzcPbsWW644QacTifny3gXgUBABHIGBgwYQPPmzQU/z%2B/38%2Bijj4r%2BGgElwN///ncSEhLo2LEjuq6TkpIiAsUZM2bwwAMP4PV6OXPmDH6/XwRPRrpmaWkpgUCAt956S9Rp7A6HposWFxdbUlON/mVmZlJQUMDGjRuBoLfejh078Pl8QtnU4XAIoZdrr72Wl19%2BGUAEeYcPH2bz5s2i7gkTJuB2uxk/frzYmdR1nVGjRtGiRQu8Xi%2B6rl%2BxD1/79hKHT%2BbQqLhDMldD4tCorJUs/B2VF55cr4I0ZaFnffut6VC16Wq5RlFvOBQVC1dINQa5A1Jat4oqFo51lmVcKp6anEIuVaziyFnaUhSy8AdVHL5yeEFKDp/0Q5dyKcv8J9UgZN6mbDWi6p%2B0bpRGfJLJmmrY8pQrnW8kPpZ8jSrz33JfVIVkheUwuGzysXKdl8dtA%2BucK9ajPBfKtmTun%2BwdGZJiLyDx%2BlT1ylOhosRZSIby5CgeVktbYfCBlYkgIe96sPZXuY4kbqCSwye/r1X%2BiHJjKo6hXLm8JsIhtKpedvI6UaybyyTO/HdhB3xXhKtrtDZ%2BNtx2223MmjXLkgoHMHToUKZNm/aTtt%2B2bVs6d%2B7Ms88%2Be1nvu%2B%2BDOXPmkJOTw6233hpW%2BcTm78sAACAASURBVFGjRuF0Ojl27Binyz6cL1y4QOvWrblw4QIZGRlClOTkyZOsWLECTdO4ePEibrebzMxM/H6/CHKioqKoXr06mqYRHR1NUVERd9xxB%2B%2B8844pzTTUwN3A8uXLxd%2B%2B%2BuorU9CoaZrFdNxIKa1QoQJNmzYloYyjUaNGDZYtWybURw34/X4GDhwIBLlv69evJxAIUFhYSIUKFXA6nbjdbuJVXIUyhAZz35Z9GTQM1CGoHGoYuxsoDolsDC6jESQOGDBAKJIa/Tp69CiFhYWUlpaK9gzLinHjxpl224zrnnjiCcv8hJa5/vrrLzkmGSoO3/p1EodP5tCouEMyV0Pi0KislSz8HZUXnlyvgjRloWeV7YIaUN1iyzWKesOhqFi4QqoxyB2wpPBaLwnHOssyLhVPTd6BlipW/VZkaUtRyMIfVHH4yuEFKTl8Uiq7xdMOrPwn1SBk3qaKoy33T1o3SiM%2ByWRNNWx5ypUWYhIfS75GlThguS%2BqQjJHPQwum3ysXOflcdvAOueK9SjPhbItmXohe0fWrWu9RuL1qeqVp0JFibOQDOXJUTyslrbC4AMrP3YkhWW5v8p1JHEDlRw%2B%2BX2t8keUG1NxDOXK5TURDqFV9bKT14li3VzmY9rGrwh2wGfjv4J%2B/fqRlJTEgw8%2ByK5du9B1naysLJ577jk2btwo0hBD0alTJ9LS0kwcvsGDB/NNyK9qeXl5vPLKK3Tu3JkWLVrQtm1bBg8ebOKGDRs2jKeeeoq//e1v7N27V5hzHzx4kMzMTLGzVR5C%2B5OWlsaIESOoXr06R6VfnfPy8oiJiWH9%2BvWmPp07d44ePXqIwMFIzfT7/cIPzggwqlWrxuHDh0WQZnjzaZrGDTfcwOTJk%2BnSpQuZmZki1fNSMIKejh074nA4qFOnDltDlPVCveggGATKc9K9e3cCgQDnz59n37594u9Hjx7lxRdfpFevXpY56N27N5qmkZ2dbepfYWEhaWlpbN%2B%2BnYsXL4rgcdWqVUK8JzTVMz4%2BXqQ%2Bjh8/np07d9KjRw8R/IaKvWjKT%2BUgpk%2Bfzuuvvy6sJuQ5qlj2JcqwrLhcamWorUUodF0XwXw4sDl8NmzYsGHDRhiwd/iuCFfXaG38bIiNjeXDDz/kxhtvZNCgQbRs2ZLevXvj8/mYPXs2dVS/zPNdWqbB4WvTpk1Y3n5nzpyxfPFOSkpiyJAhvPbaa6a/jR071iLa4na7GT58uCizZMkSMjMz8fl8lJSUEAgEaNy4Me3atWPAgAEMGjSITz/9lMWLF3PDDTdQUFBAQUEBgUCAM2fOsHbtWnr37k39%2BvXJyMggNjbWFFA4HA5iYmJITU2lYsWKLF%2B%2BXPzd6XSalCVfeuklOnTogMvlEsFLcnIylStXpkKFCng8HtO4DXGY1atXEwgEOHjwIOPHj8fhcNCjRw88Hg9Vy37VGzt2rBBXCeUmTp8%2B3bLbZQRla9eu5Z133rHcu0AggNPpFPW0adOGSpUqUVBQQL169YiOjsbr9YrdyM6dO7O%2BLPXL5/OJ4O21117jOkkJcNiwYUAw/TM04AsNXEtLS03COEVFRRQUFAjTdQORkZFERERQUJbu0rdvX6XyqhFg5uXlmQI1I6A2FGgNU3kbNmzYsGHDho1fAn4cMpONqx6fqzyoJMTHxzN8%2BHBTICXD4FHJaNSokTBnX7p0KWvXriU7O1t4%2Bxlftg1vv5iYGGVqXZ8%2BfYQYiJFeunz5cmEAfqk%2BxcXFMXPmTH7/%2B98zaNAgioqKeP/995kwYQJVqlTh9OnTJCQkiH6sXLlS/H9qaioLFizgww8/pGXLljzxxBPMmDGDyMhIHA4H06dPF1xHgLlz5zJixAgSEhK45557eOqppzh48CA9evQgMTFR1Ovz%2BWjSpAlff/21sFC47bbbxN/j4%2BPJy8tj1apV1KpVix49epCfn09cXBwZGRnCP7C0tJTCwkIiIyOZM2cOrVq1YsuWLSaRndq1azNx4kS6detGgwYNOHjwoCmt8eTJk8q5i4iIELuTmzZtoqSkBE3TOHHiBDfeeCNffvklCQkJ5OTk0KpVK6pXr86SJUsYPXo0zz33HKWlpTidTrErO3DgQBwOB36/39S/qKgoU%2BAXEREhlFJDUbNmTQoKCrhw4QIulwtd17n77rsZPXq0uFfTpk0jIyOD0aNH85///IcFCxbg9/t5%2Bumn2bRpE88%2B%2B6yo1wh8IyMjSU5OZu/evXz22Wcmv8DLwRZtsWHDhg0bNsLAVbYj92PDnj0bvzr8HN5%2B69atQ9M0Zs2aRXp6Ou3atWPDhg3069cPXdc5duwYubm5nD17FrfbbelTVlaWqU%2BzZs0SIh9z5swxld28eTN%2Bv5%2BzZ88yadIkWrRowYABA4CgX90tt9xC06ZNWbRoEXv27MHhcAghl6SQPH9DibNr16643W4OHjxISkoK2dnZLFq0SOx8rV69WthHrFmzhiFDhghOnrHLdvToUSHQcvDgwbDmzOFw4PP58Pv9NG7cmIULF9K0aVMiIiLYv38/LpeLuLg4IdayZcsWlixZAsDw4cMpLS0V6q5nJCK5nLopi7cYwi8yTp48KQJ9o28zZ86kefPmuN1u4LsdvkWLFvHZZ5%2BJwPLVV1/lyy%2B/ZPny5aLPht1Gbm4ue/fuBbAYu18OKtEWt1sSbZEZ8yoGfXlG3AqFXIvmhCK12UJ3VaU/y0IVMulfwXe0tK0akyw4oEh/tQg2KOqxNF%2BegbGqg4oylrZVQhryBMptq5SLZXEG1ZzLgiYqc3bpvsjVKEVbpP6qdGjCMjaX5iucKS77/UlA5e9tEapQpLJbuqMSqpAHIdej6nA4wilh3DtL/6R6ldn51ptnLRPOe0JuXH52VWXkuVCNWy6jWhPSOaUlbnlKTarnRbpGWW84z7w0x3IRpXCJXOiHPs/yg6YSdpHFX%2BT1GI4pfTiG7uGsm58LdkrnFcHe4bPxq0FBQQEff/wxJ06c4IUXXhAG2ZMmTTKVW7p0KbUkovnSpUtZudIshvF9xFuMVM6bb76Z0aNHU1RUxNSpU5k8eTIul4vExESKioooLi5m48aNXLx4UXDCZOzZs4f9%2B/cTGRmJruvMnz%2Bf4cOHm3hrmqbhdrvZsWOHUM3MLFNLM1Q3XS6XSE80OIsffvghs2bNMrXndDqJj4%2BnWrVqHDx4kMLCQgKBABUrVuT6669n5syZYi4CgQAff/wxM2bMYPny5Tz77LOWFMhwEcoN3L9/P126dCEiIoLS0lJKSkooLCwkKiqKuLg44SUoQ9M0vvzyS8v5xMREsrOzcTgcBAIB025f/fr1xe6lgUqVKlG3bl1OnTrFxYsXLRYhoZzPQCBAs2bNlMIsRhBZu3ZtTp06ZdpVNFBdZTR%2BCcyfP593333XdO6PgwZx883XfHdCZsyrGPTlGXFbHJcVmhMKgQkLJVKlkiH/4CKT/hViApa2VWOSBQcUu6EWwQZFPZbmyzMwVnVQUcbStup5lydQbltxXyziDKo5lwVNVObs0n2Rq1GKtkj9VenQhGVsLs1XOFMs02KVSReyUIVCjcjSHZVQhTwIuR5Vh8MRTgnj3ln6J9WrFFiy3jxrmXDeE3Ljih9Ly137qnHLZVRrQjqndNQpT6lJ9bxI1yjrDeeZl%2BZYLqIULpEL/dDnWX7QVMIusviLvB7DMaUPx9A9nHVj41eJqyu8tfGrg8rbb9asWezcuZOoqChefvlli4eeHOzBlXv7aZqG0%2Bnkk08%2BoVmzZlx//fW8%2B%2B671K1bl3/961%2B4XC6aNWsGBG0U3nzzzUvWNXv2bOLi4oiNjaVZs2ZomsayZcuU5WbOnMl1113Hjh07TGNZvXo1d9xxBwMHDqRnz5506NABCKYy9uzZ01JXdnY2u3btEsEiBIOcm2%2B%2BmfXr15uCTWOMTz31lLBMCMX9999PcnIyLpfL4tkXyvu76667qFum6BYVFUV0dLTp74bvn5HO6nA4xN/j4uJo1KgRERERwpIBgsqZHo9H2EaodvHkYA%2BCaqgej4ezZ8/i9XovyRk16kxOThY7mvLfAFJSUpR%2BhWAWnCkPtmiLDRs2bNiwEQbsHb4rwtU1Whu/OvxSvP0aN26Mpmn85je/4ZlnnuGWW24BgrttRqBniKucP3%2BeTz75RHAOQ%2BH1evnss88oLCxk5cqVDBkyBJ/PJzhqBnRdF2bx27dvN/ni1ahRw3T88ssvM3nyZK677jpKSkpMKaIRERFCIVPXdc6GpJcUFxczfPhw4uLiSE9PN6WDtmzZksWLF/PFF19YxvDxxx9z%2BvRp4uLiiIyMFOmVFStWNAU7CxYs4NChQ2iaJnbh/H6/sFXIzMykY8eOok%2B6roudN6/Xy5EjR/B6vRyWfKGKiorYvn078F1qp6ZpJCYm4nQ6qV27Npqmcd1119G/f38A%2BvfvT3JyshDHMQLZNm3asG3bNipXrizO6brOgQMHRFqu0d%2BYmBhhzu5yucSuYFpaGuPGjRN9CTflFWwOnw0bNmzYsGHjp4cd8Nn41eK/6e337LPP4nA4mDVrFtOmTSM6Opqnn36akpISpk%2BfLsrdc889%2BP1%2BGjRowN///ndLn5555hmh3tm%2BfXsGDBhASUkJW7ZsETtexq7Pxo0b8Xg87Nixg/HjxwPBoONSHnDjxo3D6XSaAobKlSsTHR1NZGQkv/nNb6hduzZxcXGkpaXhcDjYsGEDFy9e5IsvvjAFg99%2B%2By1333234PKFBnbDhg0T3LW6IZ5MjzzyiLgXmqYJsRajT6WlpeKfgQ0bNgiPPUBYNCxbtozVq1cTERGBrutUqVIFCHrghSp2Gimjuq5z7tw5dF3nxIkT6LrO9u3bhXn922%2B/TXR0NIFAgKioKNG39evXc%2B2115KbmytUOiHI9Zs6dSrwXbpnUVERo0aNEsqrBm9w165dDBw4EF3XcTqdnDt3Trlzp4KKw9el7AcEATltVJFGaqGOSOnKyu7IFylSnC31Kjg/ljLy%2BlRwqCw0lnC4RAoeieWU6tmQxxUO30i6JgzqkNptWr5Qvkg1bmlylObYUp9V/bOQwaR6VRw%2BuXtKPplUSJnxLV2odL6RuE3ya1xJhZU7pOAbWfqjmhx5UuX7oLgvYXEX5f4pHjzLZVJbSrpUGPe7vHeAsnJVRfKcygNXPWNhjCEsg/nynjvVgpTOKecvHC6gtABlv3lV0/JUKFkiYbzHwnqXlMOLDWfOVfQ8eVyKr1Pq98DPAXuH74pwdY3Wxv8Ufoi33w9BVlYWkydPpkqVKjzxxBP07duX4uJiXn31VS5cuEBOTo6pT7Vq1RI8vfnz5wMwdepUNm7cyJEjR4iIiGDkyJF89tlnzJs3jz59%2BhAbGyt25qLKeAvXX389zZs3p0WLFvzhD38gOjqakpISOnXqpOxnlSpVeOGFF0yphmfOnKG4uJiSkhJmzZrFyZMnKSgoYNeuXabA0OUy03mNawwY6poQtIUwrmncuDG6rtOkSRPT7p6u67hcLq655hp27drF3XffDQQDR0OdFILCMvn5%2BTidThISEoS/X9euXenQoYMItow59vv9Ju5laIqopmnExcWZdv1C%2B2NwIFu3bn1Zw3eAUaNGKT39jLabyUFZGWRuYHlQGa%2BvKhN/EZB5LApXYwt1ROq7ivJjuUgxXku9Cs6PpYyc0qpIC7bQWMLhEil4JJZTqnRaeVzh8I2ka8KgDqndpuUL5YtU45YmR2mOLfVZ1T8LGUyqV8Xhk7un5JNJhZRUZelCFW1J5jbJlCSlMbfcIcVzbOmPanLkSZXvg%2BK%2BhMVdlPunePAsl0ltKelSYdzv8t4ByspVFclzKg9c9YyFMYawDObLe%2B5UC1I6p5y/cLiA0gKUmSGqpuWpUNrAhvEeC%2BtdUg4vNpw5V33syeOSn0NVmZ8NdsB3Rbi6Rmvjfwo/1Nvv%2ByIxMZENGzZw/vx5HA4HDz74IK%2B%2B%2BipPP/00RUVFph232NhYPvnkE9xuN/n5%2BQwdOhQIpn7%2B85//xOPxUFpayssvv0zPnj3p2bMnc%2BfOpbCwkNmzZwsFUk3TuOeee6hatSoOh4OcnBzS0tL497//Tbt27S7Z1169enHttdeK49CgJSoqijfffJO6deuKHTMDsoCN0%2BmkUqVK4tjtdovURgNFRUUsXboUCO4Ivvbaa6a/%2B3w%2BDh48iNvtFsHs7bffjsfj4c477%2BT%2B%2B%2B%2BnQoUK6LpOw4YNTeqWLpeLhg0biuC3SpUqlvrBHGDpum4SmJGDL5/PR4cOHfjXv/7FHXfcYakrFH369LGYz4eic%2BfOphRYwBRoq1I1VbA5fDZs2LBhw4aNnxq2SqeNXyz%2Bm95%2Bl8O5c%2BdEGuHcuXN5%2B%2B23cTgcNG3alObNm1uCp/j4eN577z26detGXFwcBw4c4LHHHmPt2rXExsbSrVs3HnvsMdM1gwYN4sCBA6xbt06ce%2BGFFy7br0uN66OPPqJ79%2B4cPHiQ3//%2B9xw5coS1a9dSUlLCoEGDlNcY3DpjRy0QCAhbBwCPx2MRKUlOTjalgUIwUDQCrZSUFNLT0xk9ejTPPPMMc%2BbMYcyYMQC88sorop2PP/6Yixcv4nA4qFGjBp9//jk%2Bn48VK1bwpz/9CQjeAyPFEoLBlZHuaexEJiQkcOHCBVq0aMG2bdssY0xMTOTYsWOC03g5bNq0iU6dOjF//nwqV65Mbm4uEFT7vHDhAlFRUSKlMzk5mfz8fCIiIi4bJNqwYcOGDRs2fiCush25Hxv27NmwUQ4SExPZunUreXl5TJ06la1bt7Jhwwbuv/9%2BDhw4QKdOnUhPT%2BfGG28U18TExPDXv/5V%2BLUlJydzzz33UFRUxAMPPEC9evVM/3r37k1SUhJt27YlPT3dEkSGi6ysLJ5//nnOnz%2BPy%2BVixYoVrFu37pKKkqEI9bKLi4uz7PrJYiK5ubkmw3gIBnxGW5qmsXz5cmVbhjDK6tWr0TSNrKwsAoEAp06dwu12c/311zNx4kQhnHLttddy%2BvRpU1%2B8Xq%2BJD9igQQOSk5M5fvy4OBdqjREZGUlaWhrZ2dmmvhhppqGppq1btxYiLgMGDBCpoz169ACCgjPGjqfX66WgoOAHBXu2aIsNGzZs2LBh46eGvcNn41eL0tJSJkyYwKJFizh9%2BjSaptG8eXMGDx5MRkYGw4YNY968eXzyySekpaWZrk1NTWXVqlXULjN7ysjIoLi4WJh9y%2BmLjz76KBMmTDDtOkZGRtKuXTvS09NZtGiRpX/dunVj5syZwjT8pZdeQtd1evXqZSqnaRoOhwO/30%2BLFi249dZbhYG7jLp167Jo0SJOnDhB586diYiIEGmb8fHxNG3alM2bN1NSUoLP52Pw4MFUrVqVl156id27dwPBoEzXdQKBgGlXLzTAy8/PR9d1HA4HkZGRyl2x4uJiNm/eLI6joqLYsWMH//jHP5g8eTL79%2B8Hgumg0dHRQnkUEIqdCQkJtG7dmg0bNgDBoG3BggXk5OTQoUMHEfxs3brV0r4RhBk7isauXmjA1L17d2bOnEnDhg05dOgQO3fupFq1akycOFGUKVEINKxatYrmzZsDQcN1Ax9%2B%2BCEAX3zxBfHx8Zw5c%2BaKdvXuvPNOCx%2BwicSn5MIFCEmvtRwTJOeblmxenpmw4fdbeSu6biadyNeo6j1/3krIKikxc3i8XjMHRXWNfO7MGasXldy4pTOKU4q5KS6WOChyW4r%2ByfXKQ1KeU7RNUZGZbHPunJmclp9vJc0cPQr16l1mkNZBqabYMqVjx5r8%2BgoLvRYenzwmVdMcOwYhYk2qMpZz8rgVp%2BS2FcvRWq%2Bi8a1bIUTXST2GrCyT15n8KMjHqv7JdQBBpY9Q8lc4z%2BrZsyY%2Bo7K/p06ZfdjCeabkaxSVq9a1ZeHIx6p6L140kyctDx3WG6qaZPl5keuR/64Yk2pqLG3L7ywUcyFVFMZjqCwj16uamnDWteUdLpVRXSLfluxsqFbNXEaeUtUUq27VzwJ7h%2B%2BKoOnfx33aho1fEEaNGsXy5cuJiooS/K8qVapw%2BvRplixZQr9%2B/Th27BipqanMmzfPxGdLTU1lxowZZGRkiHNTp07l9ddfB4LWA6G7V8888wyffPKJ2HGCYAqhIaJiCJlcDqo6IiMjqVSpEtnZ2SxevJi6desqyxmoW7cuvXr14o033jDtyMkwdqqM4EfTNKEiaQR7UVFRJCUlERERQUlJCSdPnrTUExERYUr3LG/3KTY2VpjUh44x1P9PhsvlIhAIWP5u9Llly5bChgHMaaPyOVnFVNM0YmJi8Pv9eL1eWrZsyeuvv87atWstKqqh%2BOijj/j888%2BZMmUKcXFxQsGzSpUq5OTkMHLkSNasWcPq1ast/QXCShWGYFqubLw%2BaNAfefLJgWFdb8OGDRs2bFwVWLLkx69T4bf7vwo7XLbxq8XChQtxOBxMmDCBzZs3s27dOu69916T31x0dDSHDx9m7ty55db3/vvv06BBA1wuF5MnT7b8XdM04Qm4Y8cO3nnnHaKioli4cGHYfQ6tw%2BPxsG7dOvr06YPP5zMZwcvljH%2BLFi3i0UcfFVYQkyZNYt%2B%2Bfezbt48NGzYIZdLExESLYEtERAQ1a9bkpptuwul04vV6adSoEUePHrUEe06nE6fTSb169UT65DXXXGMq43A4qF69uulcYWGhcsesYcOGAPz5z39m3759gnO5adMmdu3aJewYjLbj4uKoVasWDodDePkZ%2BPOf/wwEg99Q7zzAYllhBLfG9efPn6du3bom/0YjnbNWrVr069ePrVu30qpVK6qW/aodatdg/LDgcrlMKaahffg%2BUNs32L/B2bBhw4YNGzZ%2BPNgBn41fLXw%2Bn9g5cjqdVKhQgccff5wxY8ZQs2ZNICifHx8fz4svvmhScJSxZ88esrKy6NOnDzfddBOrV6%2B%2BpN8dBAOylJQUOnbsqAxwwkVMTAz9%2B/fH4XCEbdg9depUHnnkEQAGDhyI2%2B3G7XbTsWNH1qxZA8CQIUNo1KgREOTjdenShb/97W%2BUlpaSmpoqgpn169cTEREhOIOPPvooANWrVycQCJhSOfdKdgGappn4cMuXL2f58uXCm0/TNJxOJ9u2bRNz/%2Babb%2BJ2u4VoS%2BvWrXG73XhDfIf8fj%2BFhYWcO3eO2267jYsXLxIXFyesI4wUy2PHjlFQUEDLli0JBAKmHVGDG9egQQPmz5/P/Pnz6datG8eOHePgwYMmbh8Eg7Xs7GzeffddwcU0dhFTU1OpVpYHYwR1paWlIlU3JSWFzz//nBYtWlz2vqlgc/hs2LBhw4aNMGDbMlwRrq7R2vifwrXXXkthYSE9e/akU6dODB06lIULF9K1a1ex8%2BR0Ohk9ejQlJSVKWX8DkyZNQtM0evbsSb9%2B/fD7/SxYsOCS5UNFR6KvwKQmPz%2BfcePGEQgETDtoBt9N/jdt2jTTDt%2B4cePE7t8XX3whLBvWrVvHrbfeCgS9AWXfwIiICMEbLCkpET53hhKmw%2BHA4XCQlZUljnv27EnlypVFHw0OoIEuXbrQpUsXYSCv6zp%2Bv59mzZqJ3TBZPKZHjx54PB7THLpcLu68807ef/99kTJZXFwsgi3Ze89I96wQwoUyPAF1XRfCOEYw%2B8orr5hSLktKSkyG8Eaq7M6dO4HgrqAR2BpWH0lJSUKQxxDu2bFjB98XKuP1tm07msrIBtkqw%2BzyyvyQa/4X2v6l9%2B9qbfuX3r%2Brte1fev%2Bu1rZ/af2z8euEzeGz8avFyZMnGTJkCFu3bqVSpUrExsZy9uxZkpOTmTFjBg8%2B%2BCDVq1fngw8%2B4KGHHmLLli3MnTuX1NRUE4fP6/WSnp5O8%2BbN%2Bfjjj9F1nYyMDKpWrSp85mRena7rJCUl4fV66datG88991y5/VVx82SuW2RkJFFRUeTk5Jja8vv9BAIBXC4XCQkJNGzYkK%2B//hqXy4XD4cDn8%2BFyuUhKSiIuLg6v10sgEODEiRMALFq0iG%2B%2B%2BYZRo0Zx2223sWfPHoqKisQulYxQPloooqOjL2lrcMMNNzB48GC2b99uEjoJRWh/jVRLg0Mot/Piiy8KH0OHwyHmoW3btib7ilA4HA6TGE1iYiJJSUkcPXpUjOn%2B%2B%2B8nKyuLJUuWiPZDd%2B40TWPv3r307t1bae8AMGzYsEvaYkBwN1Rl3C7D5vDZsGHDhg0bYWDFih%2B/zrIfxn8oMjMzGTVqFNu3byc2Npbu3bvz5z//WamM/tFHHzFt2jSys7OpW7cugwYN4pZbbgGC3ynmz59v%2BkE7KiqK//znP1fUv1DYO3w2frWoWbMmH3/8MYsWLeLJJ5/E7XbjdDo5efIk48ePN5U1PO1Ufn0LFiygtLSUXbt2cd1119GqVSuKioo4fPgw7du3Jy0tTXAAjd0gXdeJjIzkN7/5DY8//jivvPIKnTt3pkWLFrRt25bBgwfz7bffAjB%2B/HjcbrepDuMfBAO9WbNmCU5frTKVt8WLF7Nx40bq1atHSkoKEAxocnNz2bRpExDcjTJSD0tKSjh16hRHjhzhxIkTZGZmijH26tWLhQsX0rx5c9atW8exY8dMwZ4cnFzqdyC/38/tt98udvoqVKhA27ZtAbjpppvo16%2BfSN%2BsUaMGlSpV4t5776VKlSo4HA7q1KmDx%2BPhr3/9KxDcFfR4PBYT8%2BLiYv76178SCATQdR2fzyf6ZCh6yqhTpw6VKlUyWUqcO3eO/fv3k5SURIcOHSgtLeXLL7/k0KFDlnEaqZS6rjN8%2BHDlrmRqaioQVEQ15sz4r8EnVM3npWBz%2BGzYsGHDho0w8AtM6Rw0aBDVq1dn5cqVvPvuu6xcuVJkYIVi2bJl/OMf/%2BCll17im2%2B%2B4cEHH%2BRPf/qTyUbq8ccfN2k2/JjBHtgBn41fKQy/ufz8fFJSUnj44Yd56623WLlyJS6Xy6TqCFCvXj0eeeQR9u/fz/z5801/M9IYNU0TapHGryz5%2BfmMGDGCXr16oWka27Zt4%2BmnnyYiIoKpMY474gAAIABJREFUU6fSv39/%2Bvbty/79%2B5k8eTLbt29n9uzZJCYm0rt3b/bt28cTTzyBx%2BMx2THs2LGDvXv3it0t44t/TEwMTZo0AWDt2rVMmTKF/Px8HnroIZKSkvB4POzcuZP3338fgMGDB7N7927uuusuYmJiGDlyJFu2bKFOnTq0atVKtHfzzTcL38Do6GhatGhBVFQUiYmJpl%2BUQhEa6KSnp1OrVi127tzJP/7xD3FNw4YN6dGjB0lJSTz%2B%2BOO88MILfP3110Aw9TI2NpYXX3yRNWvWUL9%2BfaUSKGBR3YTvUiuNoMz475tvvgkgguB69eoRGRnJ8ePHKSgoMN2/iIgIunTpQn5%2BPllZWVSqVMnEzQz1DDR4jElJSYwZM0akbN5xxx2mfmmaZtoBNf5r3MNLzacNGzZs2LBh438DHo%2BHvXv38pe//IWKFStSv359%2Bvbty8yZMy1li4uLGTJkCOnp6URERHDfffcRFxd3ySyinwK2D5%2BNXyVKSkpYsGABmZmZDB8%2BnPr16%2BP1evn6668pLS0lOTnZIoIyaNAg5syZw%2BjRo8W5o0ePcujQITp37ix2nQw8/PDDZGVlifK6rtO2bVtSU1OJj49n7dq1ZGdnk5eXR9u2bRkwYABnzpwhPj6e9PR0brvtNs6ePcuqVauYMGGCyUbBsIMweGNPP/00y5cvF9wzCAYOK1as4L777mP79u0mbz4jyBg3bhzJZV5QxcXFjB49WlhEhP5ytHTpUk6dOsXrr7/OH//4R8HZM3YIjR0pOSXSwJYtWwSvsFevXqSlpbF27VqhhmrgjjvuEIIsZ86cQdM0fD4fO3fuJCcnxyJSEtrH8mCMeciQIQBCafPixYv06dOHtWvXcvjwYSC4A2cYs69YsYLo6GhSU1NJSUlh3rx5IugLDdqMsZw7d46nnnqKwsJCnE6nST3V4P598cUX4pwR/Bl1JcveXJeBLdpiw4YNGzZshIGfQGQlOzvbQm2pWrWqEGq7HHbt2kWtWrWoFOK1mZaWxuHDh8nPzzfpCtx1112ma/Py8igoKDApnX/11VesWrWKo0eP0qhRI55//nnhB/xjwA74bPzi0alTJ06fPi12YyIjI2nSpAnR0dEcOHCARx55hNzcXPFFORAIsGXLFkpLS4Ua4/jx45kwYQKBQEAEXn379hU7S2lpadQLNTsGHnroIV577TXS09OpV68en3zyCfn5%2BWzbtg2/38%2BLL74odpM2b97M5MmTadiwIe%2B%2B%2B64IehYuXKhM75s9e7Z4ARhqlx06dODuu%2B9m165dQDANtbS0lLfeest07eLFizl9%2BjQPPPAADRs2pFevXnzzzTci6DBSTmUcPnyYIUOGmLz5DP86o3wgECA2NlYERKF/j46OZtq0aVx33XWcPHmSzp07c/LkSV5%2B%2BWU6dOjAwoUL6dKlC9WrV%2Bfs2bNCedPtdos2HQ4Hbrdb3IPdu3czfPhwUlJS2LVrl%2BAqGsGmYRthlG/cuDEPPfQQzz33HE2bNmXPnj0kJiaybt06EexB0C8vLy9PjKmwsJBPPvlE8AYNewVd1y38QV3XcblcIpAzgmADXbt2pU6dOkL4JlRh1BhvuFAZr9es2cR0XFgIoXFhQQGEZI8GIbvlyia9Cjdiixe7quIwDL4t3uGSUbPK81128lWZBstDUhpSS67GyrYOHYIyWxCwemPLx2DxwlaagMsDl02OwXrvZM/qPXugaVPzNRbj48OHoUEDUxnLKZUzcjkm2yrjddmcXWXCbFkEn34Kd99tLnPgAJTtwAOwcSO0bm0uI5uSHzkC9et/d3z8OJSJJF2qXnl%2BVf1TLWv5fspDUvilc/o0hLrQhFOv0rBaOin7t4dlZK9o3GIcrmhcrkf5LpGeF3mOVWbtclOqZ%2BHcOUhM/O5Y9S6R%2Bydfo2rcska//BLKxMsMyM%2B4vDwB2LEDQpWW5YpVi%2B299%2BDhhy/dF9V1qnrkgYexCCzvunBeoqqbJy9aywv9Eu/e/xHMnDmTsWPHms49%2BeSTDBo0qNxrc3NziZc%2BGIzg7/z586aALxS6rjNixAhatmzJDTfcAAQpKQ6Hg8GDBxMXF8fYsWPp168fy5YtM9lWXQnslE4bv3hkZWVZdj2MnbWWLVvicDgoKSnB5/MJEQ6n04mmacKjzoBMpDXS82rJ3/iA%2B%2B67T/x/eno6UfKLku8EVYz0TE3TxM6iYXIe2rYR/IV6uBllLly4wPTp0wX373I8MGMXKZSnZ1zTqFEj4uPjiY6ONr0ojLGGtisHOwkJCUycOFEch6ZaFhcX89vf/paxY8ficDioUqUK0dHRPPjggxQXFzNixAh69OhB%2B/btTf3XNI3GjRszdOhQdu7cicfjYdq0aaLe%2BfPns2XLFoqLi/H5fCL11eVycebMGdPO6Pnz5/nqq6%2BA73bCvF4vL774omkcKm5cIBAQ4i%2BXg67rFBcXExUVpTSEX7ZsGe3btxcG83IKZ%2BPGjS9bfyjmz5/P0KFDTf%2B%2B%2BmqVqYz83cDyBQ2s3zLkqEfxgWEJjFQVSwq0qs8dy2daSLCnbAcsX0RVXybkISm/cEjPpLKtkC%2BvYA3uFI8%2BEqXUGuyBZeDyF1yw3rvQYA%2BswR4ovjBKwZ7ylOpdIU2Y/PqyBHtgCvaUfQHrIpCDPbB%2Bm5aDPbBGVKHBHliDPUW9ig1yS/9Uy1q%2Bn/KQ5K6BOdgLt17lK1w6KScEqNa55ZyiccvHk6JxuR7lu0R6XuQ5VnwMWppSPQumwA31u0Tun3yNqnHLGpWCPbA%2B45ZgD8zBnqpi1WILCfaUfVFdp6pHHngYi8DyrgvnJaq6efKiVQQpv5hg7yfg8PXu3Zu5c%2Bea/vXu3TvsLn1f3cvS0lL%2B8pe/cODAAUFPgaDF1ksvvUT16tWpUKECQ4cOJTIykpUrV36v%2Bi8HO%2BCz8atAt27dTGblDzzwAKWlpaSkpNC9e3dcLhdt2rRh3rx5eDwe5s6dK3bQtm3bJnh0Ho9HCLds2LCB3bt3s3fvXnr27Glps1KlSmL3buTIkSI4ysjIoFq1akKZMyUlRWmcvnv3bnbt2iXa3bVrF8OGDTO1kZiYyPPPP8%2BWLVuYOXMm7dq1w%2Bl00rVrVzweD7Vr1wagTZs24pru3bvTrVs3IGgKbqRbQvDlc/jwYYqKiigtLRU7dZqmsWTJEmEd0KRJExEMO51OevTowb59%2B0wphhEREbRq1cqkKgrw1ltvccstt9C0aVPatWsnfA579uxJZmamCBiN4Lp169YcOnSI1157jWbNmpGamsrDIR%2BUgUCAl156iV69ehEbGyuC2Zo1a/J///d/opzD4SAnJ4clS5YQGRkpAvnjx4/zpPRF9XLqo6FBv6GMWqFCBeHdWLduXbp06YKu66Zgzvj/jh07cv311xMTEyMC/lB8n5RMW7TFhg0bNmzYCAM/QcBXrVo10tLSTP/CSeeE4Pe33Nxc07nc3Fw0TSPR8mtF8Efzxx57jJMnTzJjxgyLWF0onE4nNWrUMHkdXynsgM/Grw4xMTH069cPp9PJoUOHmDt3LtHR0UycOJFGjRqhaRo1atRg5MiRPPLIIyJo%2BqEwgs177rkHTdPo0KED%2Bfn5dOjQAU3ThNH4uHHjcLvdzJkzx%2BKjN2LEiMu2ERcXx7XXXsvEiROpVasWK1euxO/3CxNwY/cyVMGpcuXKREVF0apVK5GiWKdOHXbv3s3OnTuZOHGiCEbq1atn2t379ttvRd/8fj9Lly4VbRkoLS3F4/GYdgGNYKlq1aqsX7%2BezZs307p1a/r06cOJEydIT0/n5ptvBr7b4atXrx67d%2B8WAeZ7771naufrr7%2BmZ8%2BepKen07RpU5OoSmiA3KlTJ3RdJyIiAp/Px7333gsEdwJbtmxpqtMQ4hk6dCifffYZlStXplWrVtSpU0cpquL1ejl16pSY68TERLxeLxEREcJQ3XiBHzt2jDlz5lBYWIimaTz22GOmNSaneFwONofPhg0bNmzY%2BPWhefPmnDp1Snz/gqCQS0pKikm1G4I/xj/11FO4XC6mTZtmyr7SdZ0xY8awd%2B9eca6kpIRjx44J798fA3bAZ%2BNXh4KCAqZOnYrf7yc1NZXc3FzatGlj2YmCoBhK6O7YD8GSJUtMgdyaNWuYNm0aNWrUQNM01q5di67rlhTMUPuFOXPm4Ha7%2Beabby7bltPp5N5778Xn83H27Fl69OgBBF8ioUFk06ZNyc3Nxev1smXLFrGjdfz4cVHm8ccfF8HDpEmTlL84hUJOa42IiGDnzp2mcRn1nTx5El3Xuf3221m0aBEXLlxA0zQ2bdrE2rVrge9SHQoKCoQ1hdvt5ne/%2B52pnTZt2vDZZ5%2BJ41CxnVq1auFyBanGhopmSUkJgUCATz75BAgGaPK89u/fH4DXXnuNnj17kpuby5YtWzh69Kgp4DPuTyjvsaioSKRlFhcXi11RY46PHTvG5s2bxRgnTZok/A6N68OFyni9S1nAbCDkM0B5HOyH%2BfjYMalAmeKoCSGiPABlmcSXrVfKIFbWbfGfV82HtAOblWUtIlEj5e4CQa5QKPbsUfRPKiTPTRnV8/KNq8YQkpYN6vkL%2BR4ABGlqJpw/X161lDmIXLZ7qg1tucvy/KnMlC3DlLgtEKR1mqBQ2LXUo7h58hjkxaao1jpdqkUhTYbKNrTst51LVqOqNpw5l/unfBVIA7P8gK8YuPzcFRRYqz161HxsWWsAUgq/am7ksctzpdLZkl8vqnsn16vKhJP7Iz%2BbqvnMzzcfy%2B8ECHJeQ6F47Ni5UzohD1QxKMu7Q/EykU%2Bp3jfhlJH7LI9TlSwSzrqW65Hn89Infwb8wmwZmjVrhtvt5h//%2BAf5%2BfkcPHiQd999lz59%2BgBBvr9hrbBgwQKRxinTgzRN48SJE4waNYrTp09TUFDA66%2B/TkREhPDp%2BzFgi7bY%2BFVgyZIlrCgz3YyKiiIhIYGoqCjuvPNO3njjjctujV8punXrxhtvvCGM07du3cqDDz4IYBJAmTx5Mm3btmXIkCEsXrwYQPD6vg%2BMlMaLFy%2BKF4OR%2BldSUiLSBeLi4jh//jytWrUy2ToEAgHi4%2BOpV6%2BekPxdsmQJR48eFQIjoX0yFDllPp8Bh8OBoyz14fPPP6d58%2BbExMSQl5dn2q0rKChgxIgRPPDAAwwePJhly5YBmII5uW1jTMOHD8flchEXF2dJkTB29Dp16iTkjmNjY4mKiuJ82afgRelT67nnnuM3v/kNb7zxBlOmTBEBeefOnYVpu8H3BExef36/n/zLfMDFxsZeNs3i%2B9gyzJ8/32K8/sdBg7gmRPjlmmvM18jHYOXQlG06fwcVKUkiZjRpYi0i16viu8l1y1QYJbFF4vmphE1luomKRyJzhVScOLmQPDfKDVm5cdUYJFKXav7k31hkmpqKyCRzxSRKlbJ70nQC5XMgVRw%2ByzClVGmw0DqVxElLPYqbVx7nTPUYWaZLtSikybD0FyuXMhwKVThzLvdPyemSBmbJHlMMXH7uVNw7SXPMutYApB9FVXMjj12eK9WGg/x6Ud07uV7Vx6LcH/nZVM2nTDlT8QdlzquKP2gRQ5QHqhiU5d2heJnIp1Tvm3DKyH2Wx/lDqYFyPUqdkUuIj9iAf//73zz77LO0adOGChUq8Nvf/pb7778fCArlGd/N5syZQ2ZmphBpMXDXXXfxwgsv8OKLL/LKK6/Qq1cv8vPzadGiBdOnT1dmAf1Q2AGfjV8djJTN119/XeyynZJ/hvwvQtd1br31VkaOHMnZs2dFQHPrrbeSkZFB3759Rdlp06axatV3ohxjxoyhY8eOpvoMu4HExEQReDRo0IAjR46wd%2B9eioqK%2BOijj3j11VepUKEC%2Bfn5olxaWhoffPABWVlZTJ48ma1bt1KjRg2mTJlCu3btREpmKNHYUMTcv///2Tvz%2BJqu/f2/9xkySiQxhZiVIAmhphZFqLmqqnoVrSpVVNFevZSiqretq%2B3tfIuq6mDqpFRNpVpDUTHEPAuJKRGZZDrD74%2BTvXrO2gvnXu3v23vt5/Xy4uyz9tprrb32cT7n83me5ygWi4WRI0cKtU%2B95NPpdHLu3DlhPK6fM2DAAD7//HOsVitXr17llVde4ZVXXvGxdIDfBGxcLhdt27blp59%2Bonz58mRkZNCsWTOOHTuG2%2B0mIiKCnJwcnE4nqampwiwdPAGlzWbD4XBw9epVH/6b1WolMDBQHJsyZYrgWIJnz1SrVk1YNchroGkaAQEBIiDWg05vhc6AgAA0TaNMmTI%2BQkAyVOI%2B14KKw2cy%2BEyYMGHChAkJf4Atw80iOjqaOXPmKN/z/p6gMmP3RkREBC%2B//PLvOjYZf77VM2FCAW/Rlh07drBgwQLB24qKimLLli3KrMz48eN9FCH/XURHR9O8eXPx2ptHt3fvXmJjY3G73Rw6dIi3336bPXv2kJSUBHjEYjp27OjTX4PSFISq/BQ82a5FixaJLKbu0WK320VJY3BwMI8%2B%2BqiwVfD%2BsNF5ZTqH8cEHHyQrK4uQkBAfcZEOHTqIeegE5R9%2B%2BIGDBw%2ByadMmERiuXLnSJ0jU/x0REUFwcDBLly7F7XbToUMH/vGPf/DDDz8I/pwugjJmzBieeeYZce7PP/8MIHzvdu7cSYcOHfj8889JS0sTAVbTpk354IMPxHm1atXyUez0xpgxY4Q/H3h4fbqdRe3atXG73aSmptKkSROlqlZgYCCVS3/KLvSqK3K5XOKetWnThqKiIiIjI33UtcA3a9lWoRJnwoQJEyZMmLgJ/MlKOv/bcGvN1sT/JB544AFKSkro378/%2B/fvx%2B12c/78eaZMmcLq1asNJYK/FzRNY9asWVgsFi5dusSoUaNo3Lgx69evBzxee/4Sbt1uN4cPH2b48OHk5uZy7733ommaCMZOnDghuGaZmZnCA7BevXqsXbuW/v37Y7FYOHjwIH//%2B9%2B5fPkyDoeDHj16EBoayqVLl1i/fj2rVq0CPMbhsbGxxMbGivLEjh070qBBA9q0aSOCri5duoh/6xk68FhIFBUVieD2%2BPHjTJo0iU6dOgmOXnp6OgBvvvkmr732mvDAa9euHQDPPPMM4Alm165dS9%2B%2BfSkuLiaotK4nOTmZe%2B%2B9V5SaPvjgg9csj3399ddJTk4WJZoOh0P46Jw4cUKcFx4e7iOKonP4cnNzOVVKeikuLvaxuzhYSgzT7ysgjOu975%2BOGwn0eMMUbTFhwoQJEyZM/NEwAz4T//UYPnw4NWrUICMjg%2BHDh9OoUSP69u3L9u3bKVOmDPfff/9/3Pf69esFAVeFevXqMXjwYIqLi%2BnUqRN79%2B6lZ8%2BeAHTu3NlHqTMhIYGhQ4eybt06EZgMHz6c2NhY6tevT69evdiyZQsOh0OIvCQmJmKz2XC73Vy9epX4%2BHhat27NokWLAHjuuec4c%2BYM27Zto1mzZlitVtatWyfKSZ955hkyMzNp164dvXr1uu5c7XY7TzzxBA899JAIkGTLAUB42blcLhFA6gGpbv4uQzdUt1gsQtRl7ty5gCfAysvLE2InBQUFlClTht27d7Ns2TKRKZw6dSrDhw8H4IknniBMIh8kJyf7mJjrYi%2B6gTrAJ5984sOx020ZwsLCSEhIENnE7t27Y7fbCfYijej3rKSkRPxbBT2g9QdK0ZbERN9GskKDSrFBXnOppFYpHiEz%2BFWN5PuvuLeGxLp0QCUU4I/4hh%2BXVimR3LCJQRlCNUD5mILfatDBUakmyGsqKX8oXTnkayueQVmEwiCAglHEQ1arUYm2yDdGJephEHLxR6FDpfYjXUsWj1Btc8NBhdqPTK9V3V6D4oq8uRSbzfB4qBZdfhhUYknyTfdjDxtUPFSqI/IxRRvDrfJHxUOegx/XVm0J%2BVoqcRXDedIB1WeA4b6oFE%2Bkayv3hDQHgyiPHx9kKuq3PD7V7ZXnrWpjOCafpHhY5cdDFoQC/8Sx/g0dsj8WZobvpnBrzdbEfyXkskoZISEhLF68mF69egmeldVqpWnTpnz55ZeGLJteovfvcK3AY76ul1WCxyZA58w5nU4WLVrEQw89ZJDb1Q269T/Tp0/3UXK0Wq3Y7XaqVKnCgAEDeOqppwgICGDlypXUqVNHcOj0ckadX2ez2dDN3F0uF3l5eZQpU4Y6depQVFTkky17/vnnyc3NFYIwOmQj%2Bk2bNrFx40aRWfTOqDXyUuOwWCzY7Xa8zeTtdjvvvfcejz/%2B%2BDXXUA/8AHKU8ogeFBUVMXToUNauXUt%2B6Ze8qVOnCo%2B/uXPnitJLfQ7nz58ntfRLraZpyvLP9PR0pahKYGAg4eHhYk31e%2BJ9n3Tu36lTp3wCwaCgIB8F1FNKeTw1VMbr6/am%2BDaSFRpUig1y5lMKSJXiEXLQqmokr5Uiw2rg80sHVLGxP%2BIbflxapURywyYGZQh/nK4VJdgGHRwVuf4GxsdKPr58bcV%2BlUUoVB9lsoiHrFajNF6XboxK1MMg5OKPQodK7Ue6liweoTQFlw8q1H5kERTlbzOy4oq8uRSbzfB4qBZdfhhUYknyTfdjD9/QKV51TNHGL7Nu%2BUbIc/Dj2krdKulaKnEVw3nSAdVngOG%2BqBRPpGsr94Q0B4Mojx8fZCptE3/82%2BV5q9oYjsknKR5W%2BfGQBaHAP3Es5f8fJv7rYIq2mPjTw7uU7loIDw9n4sSJwlT9emjZsuV1RTeuhT59%2BtCnTx/x%2Bvz584LXpmeKLly4wMcff8y6deuIjIwUZYpOp5OsrCxCQ0M5deoUTz75JN9%2B%2By0jRoygYcOGbNy4kYyMDJYuXQp4sl5dunQRGbbJkyczaNAgAF588UU%2B/fRTKlWqREZGBjVq1CA3N5f09HRKSkqYPXs2nTt3pqSkhPfee4/u3btz/PhxNmzYQHBwMJGRkRQUFBAeHo6maVzw%2Btlv7969BAYGkpiYiNvtZtSoUUydOpWyZcsybNgwRo8eLbJ4culhSUmJsEPQoQeEepBVUlJCrVq1OHHihMi66XxGXXUUoFWrVtx2222sWrVKWD7o/blcLp9gznscISEhZGVlCVVO77/Bw%2Bfzvvd6uWhGRgZZWVki2weestDg4GAR9NlsNtq3b8%2BBAwfYuHGj6KOwsNCH9/fv2DKYxusmTJgwYcKEH7jFMnK/N8zVM2ECj1G3XH7pj3G6t5jMpk2bGDBgACUlJSxbtox3332XM2fOULNmTebNm8eePXv46quvaNOmDQ6HQ1gmtG7d2sdQPSUlhapVqzJlyhTKlP5kWORVd/HTTz9hs9nIycmhdevWdOnShZMnT5Kfn0%2BjRo18snL/%2BMc/AJg5cyYul4uKFSuyYsUKkpOTmT17to9h%2BKpVq0hKSqJKlSrUr19fmMuDJ8uqZ7EqVKhAu3btCAgIwG63%2B2TMZLuHiIgIVq9eLeZ13333UdbrF2Or1UpKSgrJycl89tlnQrL4559/Zu/evXTt2lUEbM8995wo03zzzTeZNm0agYGBxMTEEBAQQGBgIOdLa1ji4uJ8vBE1TRNeN97lmLoZ/ObNm2nWrBkFBQXceeedwnDeYrGI8TZp0oSdO3cSFRXFkVLTteDgYMLCwsR9AoT4iz8wOXwmTJgwYcKEHzBLOm8KZobPhN9ISkriwoULooQuICCA2NhYxo4dK76o5%2BTk8P7777NmzRouXbpEeHg4t99%2BO6NGjaJeqVHVhAkTKCoq4o033vDp//jx43Tv3p0ffvjBJxC5mfH4O6broaSkRJhv69i/f7%2BwKtARHBws%2BHbHjx8XvoH/%2Bte/RBaroKCAnJwcbDYbM2bMwOl08v3339OtWzciIiLIz89n0aJFXL58mXbt2jF9%2BnTgN%2BGQkydPkpqaisViITc3l/j4eKxWq/CQ27lzJwkJCSJzpY9PNzM/deoUHTp0YN26dTRo0EAIqwBCUVTTNE6ePEmFChV4/fXXAU9g9PDDDwNw8eJFHx867yDPm7/ncDi4cuUKo0aNonbt2nz//fdYrVaGDx/Orl27AE%2BJZ0Kp55zdbicsLAyr1YrT6SQ6OppVq1aJPrt168bFixf517/%2BxZgxY8R1dIEV/Tx9vfRsoPeY8vPziY6OFoFhgpffnR7Qdu7cmf3792OxWEQ5KcCvv/6K2%2B0mMTGR%2BPh4Nm3aJLJ53mtQVlXGdQ306tXLh3foGYfvnrx40bdMTX4NePgbXiU9JSVSaU5amqGsztDGcAAPwcOr5ufSJWOpk9TEQzbxDmQLCow1QXl5PvVPhj4U3UhT9CAjA7w8OPPzFaWAhw75mBeePQveHy8XLhhLnbKyfKu1VFPg6FGoW/ea5wAeno3XDyKpqb6VlSdPGsszDWuxaxc0aXLd8anuXW6ub9mc3OTq1SJjWefXX8N9911z/Mpj77xjLPPct8/X2Gz3bpC5qfJEz5/3rUHLzjaWE54752MMl5OjqOCT%2BlW1OXXK16dOvpTqHPm5U7WRtrWHdCbXIUrrJz%2BafjyGyodBHp/8/Kj6Vu5r6SGXllx%2B5ADjvFX9Xr7s60upWj95WvKWUC2nYZ7y3sPjoe7N7FA9d4ZG8gAVn6GsWAGlnH1AffPkDyXVh5Q8cdUCym3kTaE6R/6gUH1I%2BdNGNS8T/3XQ3CqFBRMmFEhKSmLYsGFCxET3g3vrrbdYvnw5kZGRPPjgg1SuXJmJEydSu3Zt4Qf3zTffsGjRImJjY3/XgO9646lWrRp5eXl%2Bjel6eOONN9i4caOPgfjs2bN57bXXqF69ugjs8vPzeeCBBzh%2B/DhPP/00r7/%2BOtHR0aL87%2BDBgwwYMID%2B/fvz6KOPEhkZSbt27bgkscM1TaN8%2BfL06NGDzz77jJKSEqxWqwgqHA6H8KOzWq28/PLLzJw5U9gceCM2NlaUMLZo0YLy5csTGBjIsmXLsNlsPmbrdrudu%2B%2B%2Bm%2BjoaBYsWIDD4aB27dqcPHlSeORduXKFJk2aiIBNBe8SShk2m42UlBTS09NFgBkQEKA0fZf7CQgIEPxF7/OqVKnoe0abAAAgAElEQVTC%2BfPnfTJjwcHBuN1un1JLHXXr1uXo0aMA1KxZU5S0BgYGcuXKFVauXMnatWuZM2eO0uqjTZs2jBkzhn79%2Binn%2BdRTTzFq1Khrro83XnnlFYPx%2BujRT/Hkk/6db8KECRMmTNwS2LPn9%2B%2Bz1N7rVsCtlc808bsiODiYIUOGULFiRX766SfxBfm9996jTp06Pn5w/fv3VwYkf%2BR4gN9lTG3btuXQoUNcvnxZHNu6dSuappGamirKPu%2B66y5OnDiB3W4XipjewjDTp0%2BnTZs2jB8/nvLly2O1WoXIDHgCLr1MUtM0vvnmG4KDgylfvrwQO9GDGv0cl8uF1Wr1yUR5e/zlekmhTZw4kTfeeIOdO3caygZ1rl14eDh/%2B9vfqFOnDuDhtuk2C/o15WAv1OvXSl3B0xveapkOh0P48IEn%2BEpJSaFChQqEhoZyzz33iPdu9FuUt11E1apVad68uRClsVqtymBPf0/HqVOnKCgooKCgQKyVXvLpcrmEQqj3PKZNm0ZKSgput1vpp%2BivFQeYHD4TJkyYMGHCL5glnTeFW2u2Jv4QOJ1OrFYra9eu5YEHHlB%2BCX722Wdp3br1/9fxAL/LmBITEylTpgxbtmwBPEIfycnJIgDSAxO9fPCDDz4QPC695C8zM5Pk5GQGDhyovEaPHj3Yt2%2BfMHSfN28egYGB5Ofni2Cubdu2REREALBs2TJq1KiBxWIhKSmJgoICNE0jKCiIlJQUcf327duLa/zyyy/i35UrVyYlJYWYmBgiIiKYMmUKa9euRdM08vLyqFtaqpafn0%2B7du3QNO0awYmHX6hpGlWrVmXkyJFCyfS%2B%2B%2B5D0zR69epFfHw8I0eOxGazKfupWLEi%2Bfn5LF%2B%2BXHmNgIAAUlJS%2BO677wCIj4%2BnVatWYt116EFbSUkJbdq0ATwKpd6qmk28yuNsNpswtb/tttuw2WxkZmZStmxZrl69Sn5%2Bvgj0evfuDXj21%2BrVqwEICwujX79%2B4r4AdOrUSTkHFUwOnwkTJkyYMGHij4YZ8Jn4j5Gfn8%2BHH34o%2BGZnzpyhlqEw3ojVq1ezcuVKYfyt%2B9B5Z3dycnJ49dVX6dixI40aNRJldLpYBkBWVpZQtfQeT0ZGBlOnTuXs2bN%2BjUm3V/AWaUlKSuLll18WX/jvvPNOnn/%2BeeLi4mjSpAmFhYU%2BGaaXX36Zxx57jMjISIYOHSq4YRcvXmTIkCF8%2B%2B23AGIsnTp1on79%2BqSlpeF2u/n%2B%2B%2B/FtadNm0bdunWpXr06TqdTBKvHjh0TWcY333yT1NRUnE4nn376qRhHYWEhCQkJgrv3%2Beefi3m%2B%2BuqrJCQk%2BJSQulwucnJySE1NZcSIESxcuJBmzZqJwMrpdPLII4/gdrt9hGN02O12nE6nTzZOL5H95ZdfcLvdfP3116SmprJ06VLcbrcPvzIzM5MuXbqIEkvvjKc3iouLSUhIoGvXrgAcOHCAo0ePomkaly5d4uzZs%2BzatUsEx6GhoezYsQPwqJrqgXdAQIAoJdWhaRq5ubmidHXQoEEsXLgQ8ASQuiLoV199BcDYsWPFGLOysliyZAlXrlwRfamCuGtB5cOXlNTZt5FXRlT5GoUlmNxGYcBk8Fvyp9/kZEMbgx/Ujz/e%2BBzZIMpL9VRALqdVGUTJFhibNhmaGPy0SvmwAio/NdkyRHHtUgvK36BYP4MZnsQFVtnTGQas6NeQ/FatjdxIUo9V%2BvBJ66n0wpOvJc0JgA8/9H1d%2Bnz7QK6ukMan9Erzx2RPKg9X9iO1uZH1HHgoXjdsJF9MVaUgl6/LvnaK8nZ5rZRCwPJ6evHLrzUc5drIe/9Gr1H4zynayIeUvx/KVRnys6lac3kxVJOS96iqjfw5JY/lxAnjOdKay9sTMCy6aksYPoIU45PPk/esak/I3ag%2B6v6TNv9nMDN8NwWTw2fCb8giKUFBQTRo0IBnnnmGxo0b06hRI2bMmHFDg%2B8mTZpQq1Yt8QVa597985//pKioiOXLlzNu3Lgb8u6aNGlCQUGBKMHTx/OXv/yFcePG8cMPP9C9e/cbjknmArrdbo4dO8a4ceNo2rQp06dPZ%2BnSpUyZMoUpU6aQnp5OdnY2X3zxBbfddht169bltddeY8CAAeTl5VG7dm3eeOMNevbsydGjR6lYsSLZ2dkUFRXxxRdfsHTpUpYuXUrt2rUpKCggPT2d7t27C4EUl8vF4cOHGTBgAPn5%2BVSrVo0zhm8baoSHh7Njxw46dOhAeno6ffv25YsvvhDv9%2BjRg127dpGeno7dbhd8OD1wi4mJoVWrVqxYscJgL6Di5n344YeMHDmSoqIiKlSoQNWqVTl58qQIgHRci6cnQxdeiYqKIisri1WrVtGlSxfq16/P2LFjGTFiBG63m23btvH000%2BzdetWnzJXt9vtY5AOUKZMGfLy8kS5affu3fnuu%2B%2BEqIumadhsNqpXr86YMWPo0qULycnJYj/IGD9%2BPHXr1lX6DUZFRbF169YbzlOHyeEzYcKECRMm/MD%2B/b9/n3Fxv3%2Bff1LcWuGtiZvG5MmThcT%2Bjh07WLBgAY1LSa81atTg2LFj/3afOvdOLwVctGiR37y7hg0bGsbjLcLyn4xJ0zTq1q3LsGHDhCBL27ZtcblcXLp0iV9%2B%2BYU77rgDgHLlyvHLL79w9epV9uzZQyUvub/69esTGhqKw%2BEQWbrBgwezfft27HY7VatWZf369VgsFp8MX3x8PA899BAul4tKlSopS/w0TfMxTQ8MDFQaig8ZMoQaNWoQHByMpmkkJyeL7F%2BJ1892en9nz57l6aefFnYMsbGxyoybjhEjRhBX%2BoF56dIldu3aZQj2wLdM0Wq1KseqG8jb7XZq165NaGgoNUul9A4dOsSoUaNEwNm2bVu2bNni069K6Mdms/HBBx8AnntVsWJFYYehw%2B1243A4OH78OE899ZRPAOa9xomJiVgsFkJDQ5VzBISPoL8wOXwmTJgwYcKEHzAzfDeFW2u2Jv5QdOnShSVLliiVDcePH8/8%2BfOve77%2B5f3nn3/%2B3biANzMm74AoOjoam83GsWPHOHjwIC1btiQ6OpqOHTtSUFDAV199RWhoKJFecsZ6KWivXr0IL5V3LigooGnTprRv396n9E%2B2D7h69SqNGzemevXqoowRoG/fvoAna/XWW28JC4DQ0FCf4CfeS5ba6XRSUlJCXFwciYmJIoCrWbMmYWFhTJo0iZSUFPbs2SO4ivv370fTNE6fPi3mVL1UT/6hhx4CPAFaSUnJNbl93nBKZX9yptBisRAYGIjb7ebtt9/m9OnTNGrUCPiN53a9wFPTNLEWJSUlPsIpOnexSpUqZGVlCWP1cuXK8fe//52JEyfSvn17wdVbuHChsGjwXtN9%2B/bhcrk4e/YsX375pTjuHby63W6D6qoJEyZMmDBh4iZhBnw3hVtrtib%2BUAwZMoTy5cszcOBA9u/fj9vt5vz580yZMoWtW7cauFM6dO5ddml9/rlz5/ziAoKHyyWbpN977703NSaXy8XBgweZM2eOD68wKCiI1atX43A4hJ3Cq6%2B%2BSlFRER9//DF33nmnISix2%2B1MnDiR9evX07JlSzRNo7CwEE3TWLVqFfHx8SIY0s%2BdNGmSyLYBohxV0zSWLVsGeALCt956C7vdzuHDh5k5cyZut5uCggI6duzIj6U8qlGjRnH%2B/HkcDgdHjhxh9erVIohJT08nNzeXV155hfj4eOLi4mjYsCHvv/8%2B6enpBAQEUKlSJVHamJWVRUBAAGvXriU0NBSLxYLVavUJpnV%2B3vr166lYsSLh4eEEBwczd%2B5c6tevT0xMDAcOHGD48OGAJwOrQy/5fOutt7h06ZLwFNS5g3Km07vk0mazsd%2Br3KOoqIjAwEDCwsJ49913AdizZw/FxcWCk5eVlcW0adN47bXX2Lx5s1j/06dPC6uG9u3bi4A3KSkJgLy8PB8eordIEHgCQ39hiraYMGHChAkTJv5omAGfid8NISEhfP7557Rs2ZLRo0fTuHFjHnzwQRwOB0uXLvXJungHau3bt2fjxo289NJLgCewkTNC14J3Saf%2BRw%2BK/p0xzZgxwydoHDRoEN27d%2BfZZ58VfekWC4MHD/a53vjx40lNTaVt27Y3XJ%2BuXbvicrlYv349gAg%2BvAPFV155BbfbzdmzZ0lPT6d%2BqWl09erVWVWqFDFu3Dhuv/12MjMzOXPmjFCeLCkp4cKFCyJ4OnnypLhGSUmJyKyVLVuWzZs3U6ZMGZEBBNi2bRvHjh3D5XJRVFTE6dOnRcCUk5NDcXExly5dIj8/H6fTicPh8LF%2B0MttbTYb7dq1IygoiC5dunDq1ClOnjxJeno6LVq0EPfo4sWL3H///YwePVoE3wcOHAA8gjO6aqnVaqVs2bKixDI%2BPt6Hm6jPTc/MpaWlUVxcTFZWlk8AZbFYhAm9blavHy9XrhyaptGhQwdatmwJwI8//ijKNNesWQNAYWEhJ0%2Be9Lm33vs13OAEfW2oRFuaNpVEW2QWvT%2BsepnRr8hwG4j4KkEEmXepoHwbxAJkJRKVMol8LYU9iqyZoBSqkNdC1Uiau9xEWYXrh9CCYblUygbSMfna0jYCFHoXioxxaqrva9W0Dce8bGXgGqItMv9UNSf5fkol0oDH7N4b77xjbCN7ask/lMiCPGAUf1HcPHnIiq1vWBv5Xqr2hDxtP263%2BuLSZpLHovyvzx%2B1FflElS2N1Ea59yWelGE8igHKw1GJ/chrrCqEuGE/ivU07HNZKAmjQIxCzwZk6oc8YNXnmCQG44%2BOj%2BrW%2BbMH5I86eW38EW1RaOkYLqa6tp9fx/54mBm%2Bm4Ip2mLi/ztkkRQZ99xzDx06dODpp5%2B%2Bbj9/lIH7pk2bGDVqFMuWLRMcMn/Gfb0xuVwukpKSGDp0KAMHDhTtfv75Z3Jzc9E0TYiNAMJgXNM0Nm7cyF133QUghFbsdrvgnjVu3Jg77riDf/3rX4BaIMVut1OpUiWRubuWR92/i6CgINq1a8fq1asJCwsjIiKCM2fO8OGHH9KqVStatmxJbGwsJ06coLi4mMLCQkJDQ7FarWRlZdGgQQMfQ/uBAwcKdc3%2B/fszbdo0ALp3787x48epVKkSFy5coFy5cuTm5op5BgcHU1BQwPjx4/nHP/6hHKumacTHx5OSkqJ8Xzezf/zxx3nmmWdo0KABbrdbBIVlypShpKSEu%2B%2B%2Bm2nTptGsWTNlP0uWLBG81hvBFG0xYcKECRMm/IBK7fdmUWpBdSvg1gpvTfxX4Ga5gDeLNm3a0LFjR55//vkbmn9fC7LVQ2JiIufPn6dChQqiTUlJCQ888IAQMSlbtixJSUl8%2BeWX7Nu3j3vvvRe32820adNEZsvlcol/e2cH165dKwzbdVgsFlq0aMHs2bPZsWOHyC7K9go2m03w1/QSQ73Usk%2BfPhw%2BfFi09e5fN2rXhW2cTicXLlygcePGfPTRR4wYMYK8vDx27txJVlYWV69exel0cvXqVRFIHzx4kPj4eLFOuqBKQECAyGYC9OvXD/gtgxgcHMyoUZ6gKDQ0VAR%2B7777rhijPic9kLbb7T72CcHBwVSpUoWgoCCCg4OpVasWFouFbdu2ibX2vv95eXkUFRVx9uxZVq5cqb7xIMpB/YEp2mLChAkTJkz4ATPDd1O4tWZr4r8CQ4YMITs7m2bNmhEXF0d8fDzx8fE0aNCAb7/9llmzZpGmNLD6z5Cens6LL77oU9K5Zs0atm/fTlxc3H98rcmTJ7N161YmT56MxWKhU6dO/O1vf%2BPMmTOUlJSwdetWjh49yvLly9m3bx/Tp09nzZo13HPPPcTFxbFixQrAU1bocrmw2WxER0fz4YcfsmjRInGdf/7zn5w5c4YyZcowdepUUlJSsFqtdOvWjU8%2B%2BYR27dpx6tQp4S3nHcRomiYCR4Dly5eTkJDA%2BfPnCQ8P54cffhDG6gAxMTF07tyZ0NBQKlasyPz584UBelFREQ6Hg4YNG7J79252797tY1oeFRVFZGQku3fvFsqe8nj00lJN08jKyhI%2BjS%2B//DLwW%2BlkWloaB0v91PK9aluuXr0q%2BhsxYoTP/Hr37i0EVXS%2BY3p6OoWFhRQUFIhS1j179pCWlobNZqNs2bLCtL1ixYrY7XYiIiJoX2pob7VaKV%2B%2BPGXKlBHlpKcVJUXXgsnhM2HChAkTJvyAGfDdFG6t2Zr4r0BISAjbtm3jkUceoVKlSoJfdd999/Hjjz%2Byb98%2BYmJifrfrValSheeff96Hl7dv3z6mTZtGcHCwCFrAyPXT/0ycOFG0WbVqFWlpabz44ou0bduWFStW8Oabb/LOO%2B9QsWJFfvrpJ44ePUpeXh6//PILvXv3plGjRowZMwabzUZYWBizZ89mxowZlC9fnq5duxITE4PL5SItLY3hw4fz8MMPAx7ft8qVK19XwRJg2rRpory1Xr16gCeT%2Buuvv9KnTx9hXQDw%2BOOPU1RURKdOnYiMjBQZPICzZ89SUlIiRErq1KlDRkaG4K0FBASQkpLC4sWLKSgoEMHWN998Q2ZmJleuXKFp06Z89tlnok%2BHw%2BHj8Tdo0CCRhbzrrrs4fPgwW7dupXnz5mL8brebzZs3%2B2TxgoKCfGwU9uzZQ0xMjMjwLVmyhNjYWJ82b7/9Nvv27eOnn34SVhtBQUHExMTgcDjIzs4WfoQXL16kpKSE4uJikeFzOp1kZGSQl5fH5VKO1L8j2qLi8HXs6MvhK9XuueZrMPI3DIlDhY%2Bj3EaieCn7VRpxS27DhsS8iqwjneMP/0TFfZE5Kaq1kfmMsjmyincjc1aUiX6pX9XvQvIay7fBHy95JXdRIiGpKrQNfUuTUnL45Iup7t1/0qaUl%2BsDmcAoE8r84Q8qbp68/1S8pdLfiq7ZjYrbJh9T9WsYsmLjyFwsuR/V/ZanrVpymYKmsm6V94lM9QUMvF35Nql4p/IxFc1YnpeqjXxM/kxS7XP5GVOtn8zZ8%2BezznBjFCcZuG2qDyA/jOsNH6yqNvIGlCeu4oueP%2B/7WrWAcr%2BqD0RTefp/AiaHz4SJPwBJSUnk5eWRn5/vE2AUFxeLTFeXLl0ICgpi%2BfLlnD17FpvNRnx8PEOHDqVNmzbAb5zA5ORkqlWrRlpaGpmZmQQHB3PlyhXmz5/PHXfcIXiPK1as4MKFCz5ZOz2YqlixIpmZmdx1111s2LCBhg0b8vXXXwMI78K1a9eyfv16Vq5cyZIlSxg1ahTbt28nNzcXt9vNhg0b6N27N06nk7y8PEJCQrBarQwdOpRly5aRkZFBjpIZ/ttYdB6i90dPYGCgEFF57bXXGDduHDabDbfbzY4dOwgNDeWxxx5j69athISECKGY4OBgCgsLqVatGoGBgRw7duy6Zbjdu3dn9erVSlEgncPXqFEjli5dSlxcHA0aNODAgQM4nU7sdjutW7cmIiKCF1544Zo8vTvvvNPAy7sWTA6fCRMmTJgw4Qf%2BjeoZv%2BGlFP6/DjPDZ8LEH4ScnByf8jw9yAgKCiI1NZW1a9fy2WefiQyS2%2B3m4sWLjBw5kqVLl4rzvv/%2Be86fP8%2BOHTtIT0%2BnqKhIcNFGjBjBgQMHBO/x/PnzhpJAPQAKDw%2BnTZs2okTxrOLXyBUrVvDFF18Iawu73U5OTg6hoaEATJw4kezsbKxWqyiXzM3NZe7cucybN4/evXsr1yIgIACLxUK9evVISUlh3rx54r2KFSuK9XG5XIwbNw7wZP6cTqcISqOionA6nXTt2pXw8HCsVisWi0WsW1pamjh%2BLaxcudIn2PPOjAYGBlK5cmWRSbTZbKSkpIj2JSUlbNy4kdTUVM6ofj4vhb5W/sDk8JkwYcKECRMm/miYAZ%2BJ/2k0a9ZMWYKp//k9uYAyvBUeg4KCaNasGUuWLGHDhg24XC6OHTtGZGQkr732GsnJyfz8888MHToUgOnTp4ugrlKlSgA0atSIdevWcfjwYT799FPAkzH829/%2BJvwGnU4nrVu3xmq10qFDB7p16ybsJI4dO8ZPP/0kxFBycnKEYIqO999/n6NHjwpPRN1vT89mbdu2DU3TeOKJJxg8eDDly5cnIiJCCJxMmjQJq9VKQECAT9mtvg5Hjx6lSZMmpHrpyl%2B6dMmgKuoNnV/53XffAfDFF1%2BQk5PD2rVrRcAWGBhIQUEB06ZNE9fSS1j1kk490PXOuNrtdgICArDb7RQWFnLu3Dk%2B/vhjvvnmG6KjowkKChIlsDExMURFRfHII48Iq4YqVarw/fffs2nTJlFearnFeAEmTJgwYcLEHw6Tw3dTuLVma%2BKWw6%2B//qr06evcuTNhYWF0796dpKQkZsyYIQKspKQkIXDijYULFwrz7RtB9tcrLCxk165dDBw4kIkTJ4oM2cyZM1m2bBkdO3akVatWzJo1i3r16lFcXMyqVatwu91cuHABq9VKq1atePXVV2nVqhWPPfYYAOXLl%2BfIkSPk5uby%2BeefA/Dzzz/jdDrZsGEDa9as8VEGbdu2LV27diUgIADwZNe8BWC81S6bNGnC119/jcvl4tdffxVtrFYrn3/%2BOV999ZUQPYmLixOZRZvNRnFxMenp6WL%2B3iWcb7/9Nu94%2BXJ5B8Yy9PPdbjf33HOPCF7LlStH2bJliYiIACA7Oxu32824cePE3MLDw7njjjuEfUJBQQEWi0UpiuJwOKhXrx5z5sxh//799O7dmzvvvJPCwkKOHDkCIMppX3vtNZFFTE9Pp1u3brRp00bc84sySew6MEVbTJgwYcKECT9gBnw3hVtrtiZueRw8eJC%2BffsSHR3Nt99%2BS3JyMu%2B%2B%2By6HDx%2Bmf//%2BN%2BVP9/DDDzN58mSfY23btiUlJYUZM2Zgt9vp0KEDhYWFIsAZOnQoeXl5LFiwgL1792K324VP3LRp0/j2229xu90EBQXxySef0KhRI5YvX85f/vIX4LfgYsiQIezdu1dklywWCz169GDlypU0bNhQBE47duzAYrGIbFR2drawPPBGaGgorVu35o477sBqtYpAsEqVKjgcDs6cOUNOTg5Wq5Xo6GiuXLnC1KlTGT9%2BvFDalAM5/d/PPPOMsFeA3zJvNpvNID6jB1YOhwO73c7UqVMBTylk27ZtOSex8YcNGyYylm63m5ycHGGzULZsWZ%2B20dHR4keAHj16EBkZKfwOAaZOnUq3bt3Ea4vFwvLly1m7dq0INGUEBgbSrl075XsqqERb7r7bV7TFH991WS3CIB6hyKDeSNRD1UgliGAYj3TAH3EG1WMnX0v5aEpiByqdD1mUQNYo8McAWvl7hNTIH%2BEUWQ9B5eAhi2%2BolDXkNiqPcllJQ15zpWiL3JFi4obhyMIQns59X6uEjGShCtmcffbsG/ar1JKQNo5KgEW%2BDzd6DcZ5%2B9PGH3N2mfKsel7ka6meQ3k5VYIsNzKcBwyqRv6IJ8ljVrWRNU9UVG95fIY5KDo29GN4gIx7wOu/n2s2MlxKpfTiVakCKJ8X%2BTTVfpQLjVS/Gcp7XWZkqFgG8mOo2lvy%2Bqn2hPL/HBP/dTBFW0zcUujfvz8VKlTgrbfe8jmel5fHSy%2B9xIgRIxg8eLDSYH3hwoXMmTOH9evXK/vu3r07p0%2BfZufOnXTu3JkLFy4IkRLvksXAwEBcLhculwu73c7nn39Ow4YNuXDhAu3bt/cJkvSMlMViYciQITz99NMMGTKEo0ePkpmZKUzWg4ODcblcFBcXC4GWgQMHsmDBAsqUKUNOTo5QkaxRo4awDoiIiODq1auGkkqr1UqtWrU4ceIE4AmO0tPTadGiBdu3b1fOv3HjxiILdq2PFYvFQtmyZbly5YpoExUVxeXLlwkLCyMqKoq0tDSqVatGVlYW27Zt44EHHqB58%2BY8%2B%2ByzHD9%2BnHvuuYdatWpx7tw5YQWhC9N07dqVDRs2UFRUROPGjUlJSblmxqxLly5iH2RkZNCtWzcmTJjA/fffD3jKar3VRMHDRRwxYgR9%2B/albdu2yn43btxIdHS08j0ZpmiLCRMmTJgw4QdkudXfA5Ur//59/klhZvhM3BI4ceIEo0ePJjk5mQ0bNhjKOHv16kWjRo2oXr26z3n%2BlnFevXqVs2fP4nA4BNdMhxz86JYDbreb5s2bM3r0aBo3bsyDDz5oaKu/drlcpKen8/XXX7N9%2B3YhDKKXBBYWFvqUTc6fP58DBw6QnZ1N9erVqVWrlujT2yeubdu27NixQ7zWM4ROp1P40unXBkSwV6FCBcGN05GVlcUXX3whXmuahsViISAgQPyx2WxMnjzZ59y4uDiqVq1KcXExmZmZQhHT7XaTkJDAoUOHWLBgAXFxcXTv3h23201qaioVKlTgrrvu8rF0WLduHbNmzeLw4cNi7LoCqYz169cLLmeHDh3Iz8/nueeeE7zGawVty5YtIygoSPkeIPwT/YEp2mLChAkTJkyY%2BKNhBnwm/uehl3HqX9LXrl17wzJO3W/vxRdfJC0tjYSEBGbMmHHNa4SEhNCjRw80TeOjjz4SfLq77rqLAQMGAPDUU09RvXp1rFYrUVFRuFwuBgwYwPr169m7dy8bN240CH4sWLBA8P1ef/11FixYgKZpFBQU0KtXL2677TbAE1zpPnLgCRDXrVtHREQEO3bsEIEt4JO1at68uU/wsmLFCmbNmoWmaQQGBopyyx49erB8%2BXIxlqysLANPMTU1lbvvvlsEX26328fDEKBu3br07NlTBHx64Dp79mz27NnDihUrqFy5so9wDHgC0dq1azNx4kT27dtHSkoKq1evJjo6msDAQFH6Wb16dTp39pRE2u12bDYbp0rL1N5//32f9d25c6cPt3P//v00adKEDh06kJKSQk5OjlKApXnz5sJ3EDzZUO8AVs%2BK%2BgOTw2fChAkTJkz4AZPDd1O4tWZr4r8GSUlJxMXFGVQ17777bsAjLvLBBx/Qs2dPEhMTadasGYMHD2bTpk2Gvp5//nmioqLYsmULAL1792bu3LlMmDCBxMREwYN74ytkcBwAACAASURBVI03iIuLIy0tDbfbTfny5YmPj6dixYqkpKQIft6gQYOYNWuW6D82NpY%2BffrQr18/3G43R48eFX1u3LiR%2BfPnA/Dee%2B%2BRmpqK0%2BnkUmlB/lNPPcVXX32F0%2Bnk6tWrhi/7Q4YMEQqYXbp04fTp04SFhVGuXDlOnz7Nr7/%2BiqZpouxThx6QFRYWUqdOHU6dOiUyVrVr1xZB3pIlS5gyZYo4b9CgQWzZsoVatWoJLh54smHZ2dk4nU7cbjcOh0OIo3hnJb39/wCD392LL74I/BYk5ufns3XrVvr06UPXrl15%2BumnuXjxIrVq1WLevHkiGNu7dy/Lly9n8ODBPrYLU6dO5a677hJjSEtLE8bnuvF706ZNadCgAaNHjxbrO3bsWCEAo0PTNBYtWsTbb78NwF//%2BlcGDhwo5gmeoHHGjBn88MMPPnP0XqudO3fiL/wxXveHQ3NDDp%2BCn2c45AfhSMUdMvxeIhE%2BVJwVmW%2Bk8gz2x6jZL9KUNAB/TLb9MdC%2B4ZorjsnzVtGCZFNwldv90aO%2Br5WG85LZuczVUXL4JCKQitZpGI6K9COvl4pkKN9QmbMnc/oU5yhFlqXN5A9XTG6j4r/d6F6CkTOlWkB5aeR%2B/Flzf/aa6lmVj6nmYHgg5I794Pqqxmfcf8Y28pYwzEHRsaEfBYdP/nxRWsVK8zZ8rqk2hfS8qOYt71HVnpV/I1Q9LjJdUJ7moUPGc2QuoGrechsVr1hZiPJ/ATPguymYHD4Tf0o0bNhQmSGKjo5m1apVDBkyhJMnT2K328nMzMRqtVKpUiXOnj3L888/zwMPPADAoUOHuPfee4mOjsbhcJCRkUGZMmWoVKkS586d47PPPqNhw4YkJSUJVUmXyyWycOCxDZg/fz6nTp1izpw55OTkULt2bZYsWQJ4Aj6r1cqkSZOYP38%2Bqamp9OvXjyVLlggOXmJiImFhYWzZsgWn0ylMvuE3M/KAgADyVN98SxEaGkp%2B6f/QermkHgxeD5UrVyYrK4vBgwezePFitm7dyu233y76qlmzpsiCRUREEBoaSm5urjBb17Fp0ya6dOkizlOhQoUKZGRkXHNMCQkJfPHFFzRt2tTQj8ViwWKx4HA4mD17NpGRkSI7qkKVKlW4evWqUhXzu%2B%2B%2B47XXXrsm3/K5557zCdpVaNCgAXv27DEcf/PNNzl27JgIDGVUrlyZH3/88bp961Bx%2BJ4aPZpRTz7p1/kmTJgwYcLELQGlStNNwkvF/H8dt1Z4a%2BK/Ct26dTNYKqxdu1bw2PzxsHv55ZcBj6DIokWLaNGihQjwdA87QARO3uWIV65cEWqSf/3rX69bahcYGMg//vEP7rvvPqxWq/C681am3LRpkwhqJk2aRM%2BePX3KKb0VKjVNQ9M0wX0LCgqiSpUq4n23202ZMmV48803qVq1Ko899hgjR45Uji0xMZFFixbx3Xff0bNnTz7%2B%2BGOfYOuU18%2BJbdu2JS8vj6KiIu68806ffjRNo3z58oSHh1/Ta87lcvlkxGQcPHiQQYMG%2BVxfn2tQUBARERHY7Xaeeuopg3poSEgILVq0YPHixaKkMzMzU3mdadOm%2BXATva8FsHfvXkM2UkefPn1ISUnhguKnTk3T2Lx5M08%2B%2BaSB26nP%2B9/x4VNx%2BMxf4EyYMGHChAkJZobvpnBrzdbE/wQWLFiAxWJh3rx5JCYmYrFYCAkJoU%2BfPkyYMEF42LlcLvGlf9KkSVSrVo1JkyZx4MAB0tPTSUxM5MiRI2zatEkEDt26dSMmJoYpU6awb98%2Bpk6dSmhoKBkZGZxV1k15MHz4cBwOB0dLa61ySmsn3G63CFT01%2BAJHCZMmEDZsmWpV68eCxcu9PG6k7ObDoeDEydOiKyjxWLh/fff59NPPyU8PJzRo0f7iIxERkb6jG3WrFlYrVZGjx7N8uXLDYIrOsLCwqhRowa9e/dmxIgRPsFbRkYG2dnZ5ObmMmHCBHr16iXGosNutwujeE3T6NGjB4cPHxZ/9u/fDyDGGhAQQIsWLXj22WcpKSnhb3/7G5qmUVhYSMOGDQ3Bfq1atRg6dKhYX/2affv2BTwKpJqmcezYMcaMGYPNZqNixYo0b97cZ6wOh4OuXbsaflBISUnhhRdeADxef0FBQcTHx4v5/OUvfxE/Jhw%2BfBiAe%2B65R/QJkKsq/TFhwoQJEyZMmPg/gu3GTUyY%2BHPhzJkzNGjQgHLlyhne69evHy%2B%2B%2BCKbN28mKSlJcMiOHj1KpUqVqF%2B/PkuWLOHtt9/mUGnR%2B4QJEwgMDKSkpMTgAweeICg/P98nEyajWbNmtGzZkjVr1tCsWTPh/QYImX/dDsHtdvP2229z4MABxo4dS3JyMqNGjSLLi0Mge9JZLBY%2B%2BOADXn31VS5fvozL5WLs2LH07NkTt9tNx44dfbJd3n317duXatWqUVhYSOvWrX34ZkFBQT6iNbrh/KFDh1ixYoWPXUPfvn3FuS%2B//LJP8KqjqKjIh9u3cuVK1q5d67NWFSpUEOMrLi5m%2B/btbN%2B%2BHbfbzcSJE0XgdPDgwWsqbLZq1YqPP/4Yp9OJ0%2BkU6qC6Aml2djarV6/G4XBw8eJFUfap74cff/yRwsJCVq5cqey/a9euZGVlYbPZBCfQ7XYLk/pvvvmGtLQ0WrZsyerVq4HfxFby8vJwu93KvSTDFG0xYcKECRMm/MAtlpH7vWGunok/Lb7//nuDaMuwYcMoKSkxmGjrsNls2O12srOzRSamZs2aPjypOnXq8M9//pPFixcDHv88b9XF9evX079/f5KSknjppZfEF/dq1aoJTtilS5eIjY3lq6%2B%2BAmDAgAFMnjwZTdM4IIkljBkzBvBwv/SsXWZmJkuWLGHixIksXbqUJk2a%2BGT0VMFCgwYN%2BPrrr8XrwsJC5s2bx0cffcTly5fRNI2aNWtStWpVwFNGCp7M06lTpzh//jwlJSU%2B16lWrZrPNfSy1pKSEpxOp0/2zntMsqG6rt55Iz5hdHQ0OTk5wprCG5qm4XA4hCiLKvDRx%2BB0Oq8bGLlcLhGoyWvZvn37647RYrHQoUMHcnJylHzFmjVrisBy27ZtBg/DkJAQv4I9UIu2dC7NBgvIpun%2BmKhLbVS3xSCIoFB58Ee8xHArZXUBxXgNl1Lsh//E8FmpSiCJMRgNyI2nyONTCWAYtEoU85QHLV9bJZBgEM1QzUleDBX39wYG2krRFkkJQiVoY1D6UHljyROTVWbAuE/8cYmWhVz8UBFSihxJ%2B1hePlWS3rAWfiinqKYga57Ir/05x585qRrJ/Sgp47KqiNyPQtVabuOPIMt/JP6iGrC8HxUO5HI/yiKMvXuveymlaNTJkzduI1/cn89v1Qe2wd7pBn0oGqmW3B91rD%2BN0odZ0nlTuLVma%2BJ/Ana7XXDrZJSUlFBSUkJUVJQoa7z99tvZvXs3Tz/9NOfPn8flcnHw4EHGjh0r3lf1k5OTg8Ph4FzpF5oNGzaIssusrCwiIiJYtmyZOOehhx6iZs2a5OXliUDJYrFw7733EhcXxwMPPGBQrdSxZs0a3nzzTREolJSU0LdvX59Sw88%2B%2B4zGjRuLc3KkL4JVqlTh1KlTjBkzhsjISB%2BD9KCgIOx2OxaLhQpeJGU98yWXeIaGhlKpUiURwNntdnbv3k39%2BvUNwYzud%2Bd2u8nPz6dmzZriPbfbTXFxsfiTmprqU/IYFhaGzWbz4U/qa6RpGt26dRPloE899ZRPQPnYY48p11KHHjzKRvYHDx6kfPnyynMsFguzZ8%2Bmd%2B/ewgtQRpkyZahdu7aBq6evi7/BHsC3337L%2BPHjff6s%2B1VS%2BZQ5kQqOpKFCV2qjGlKp48Z1Dij6VXQkiZ2C5GWpGq/hUoZOjNdWVSEbTvP64UbAq7xZdY4iyWoYn2JpMPzmpOKuSoOWr11a/ewLabzKOcmLUaaMsU3Fitc9JSTEuOaU2rxcaygAlFqpCKiMi%2BWJ1a1rbCPvE/lGKPYEspiR6sZIHpnK6nVpH8vLFxZmPMWwFqqOb3C/AbxEhpWv/TnHnzmpGsn9qLYNMTHX70flQSq1UT1ThnnJg1H0Y5iCasDyfpR%2BxFT1o7q/NGp03Uup7gte/rbXbCNf3J/Pb9UHtnRMvpZyT0iNVEtueIYUk/g3/ksz8SeGGfCZ%2BNNCJdoyZ84catWqxZEjR5SiGl999RUul4t27dpRvnx57HY7W7ZsYfHixbhcLu677z6aNGnC2LFjRZapWbNmhn5eeeUVCgsLqVevnjh21113MWTIEAoLCykoKODKlSv88ssv4v3s7GyOHz%2BO2%2B0W2Se3201aWhp2u50qVarwzTffKPlzTqeTESNGiNfNmjXju%2B%2B%2B88mEPfnkk7z//vs%2B5wUEBAjPvHPnzqFpGs8%2B%2ByxZWVn8/PPPol2tWrUIDAzE7XaLwE8fM2AonczLyyMnJ4eWLVsCngA0MTFRZP28gxqr1UpERAQVK1YkMDCQTZs2iVJFXXhGzypapf9xcnNzfUpMveF2u/n%2B%2B%2B%2BJjY0lNjaWt956y%2Bf9a4mu6IiPj6dRo0Zomiau63a7uXDhgjCSB08wq3v2uVwuhg4dSkJCAvXr1zfcK7fbzYEDB9i3bx8ul0uIzOjv6XO6ntqqN0zjdRMmTJgwYcIPmBm%2Bm8KtNVsT/xN45JFHAE9G7ddffxUedosWLWLGjBkEBQVx9913o2kaXbt25dKlS7zyyiuMGzeOrVu38sMPP9C2bVvOnTtHo0aNKFeuHOvXr/fJ2GzevJnAwEChjBkTE8Pzzz/PjBkzcDqdaJpGcHAwL730kjjHZrOxZs0aIVpitVoJCQkhJiaGXr16kZqaSp8%2BfahZsybDhg2jd%2B/e4txHH32UXbt2CaPzlJQUgoKCDPy3pUuX0sjrl8ghQ4bQuHFjCgsLhYl57dq1AV8xlalTp9KvXz/Gjh3Ld999Z8hOZWZmomkaTZs2FcqhI0eO5MMPPxRtGjduTGRkJO3atcNisQiuXtmyZcnOzsZms1GmTBnuu%2B8%2BEVR2796dlJQUEagmJiaK4ErvU9M03n//fSHkYrVar5mB80bVqlXFnDVNo2zZsmJeoaGhDBw4kL1799K8eXOmT59O%2BfLlheegd%2BZOzwp7B5CDBg3iueeew%2BFw%2BAjghISEsHHjRiGeU7lyZXGed6lslrIWzgiTw2fChAkTJkyY%2BKNh%2BvCZ%2BFOiYcOGdO3alddff93wnsvl4rHHHuPAgQOEhISQmZkp1CHPnj3LSy%2B9JJQTL126RJ8%2BfbBarZSUlJCbm0toaChly5YlIyODxYsXU6dOHXFNl8slvPgA4XX30EMPMXXqVJKSkkhLS0PTNGrVqsWwYcOYOHEi4PGw%2B/bbbxk0aJDgeIWFhREeHk6a0iHYA03T%2BPbbb6lXrx4NGzY0lH3abDYsFoso2bTb7cqsmN1ux%2Bl0irnqCAoKokWLFhw/fpwFCxawbds2nnvuOTE3GVarlYCAAO644w7ef/996tevj9vt5tVXX%2BXLL7/k9OnTXLp0icqVK4t56fw9u93OzJkzmTRpEgUFBTfk9F1rPQIDA33EZK7X9lrXCAgIMPDr/MWYMWMYOXIkEyZMYMOGDYIPOm/ePFq3bs3ChQuZNm0a1atXJ7WUi%2BR9X9atW2fgR6pw4MABjkm8qXp16lA/Lu63AwUFvmU38ms8/A2fZGRxsU/pkOIUDzHDu1bH0ImH8%2BGTlFV0JHdj6OfqVUONV36%2BVImlGqDcjz/jU7TJypLK8QoLfcvSFOcY2sivgYwM8PldQtHG0HdRkU/JlOrS8niV904%2BUXXtG9zfq1eLDGWdhlulGuD58%2BClCJyTY6w6laapuAl4%2BE9eJXGXLvlaYqWlGasLDYvxzjvGMk9pLXJzjSV8N9paqmnLe82w91TjU3Qkr40fj6GxX%2Bn5Vh7z45lSzuEGe9avayv2ozzPvDxFhaY0IH/WRp6Dcv3kOSgayYekba5cT8PeUqyNvPVVj4J8zPDZAly%2BDKW/MwIemqx35bRqn58751txffo01Kjh2%2BbiRd/qb/k1KJ7n/ytcxwP4P4ZcEvw/DDPgM/GnRFJSEsOGDaN///7K94uLi5k7dy4rVqwQJZONGjXi8ccfp1WrVj5tL168yDvvvMPGjRvJysoiLCyM1q1b8%2BSTT1Ldi0fSpk0bLl26JEoW9cDLarX6lGh6wzvgsFqtTJ48mb///e9C8bN58%2BYcOXKEK1euMH36dFatWsWWLVtEe/0adrsdl8vlw1/zvpbNZiMsLIyCggIsFguFhYW4XC7Cw8NFWaCeaVIFchaLhcTERBYuXMhjjz3Grl27DF54%2BvUsFguPPPIIy5Yt45FHHuGNN94AICoqiqCgIFEO6T1%2B/byYmBjOnj1LZGQkV69exeFwYLFYhJrmjWCxWLDZbDc0odeDqwoVKnDpOmas8hh183pv4/tr9T9kyBDmzp3rc77dbqdFixbcd999jB8//prBZnJyssg%2BXg%2Bm8boJEyZMmDDhB1QiTTcLFQ/4fxRmSaeJPyV0pcxrISAggJEjR7Jy5Ur27NnDr7/%2Byrx58wzBHkDFihWZPn06GzduZO/evWzevJmZM2f6BHvgUazs0qULBw8e5MCBA8TExPDyyy8LsRD9y713SaXFYhFlmAD5%2Bfkiy9OzZ08%2B%2BeQT8V7lypV58803AU/ZZUpKinhv8ODBxMfHY7FYWLt2LY888giaplG9enW6devGkCFDyM3N5aGHHmL79u2MGzcO8PDFPvjgA/bv38/OnTuJi4vzsauoW7euyFgmJiaSnp7Oli1bDOqT%2BtwCAwNxuVx89NFHZGVliWAPPAGlnnULCAgQ6px6GaXL5eJMqULa5cuXKS4uZuPGjT7edt7wLivVs4p6WWTNmjV93g8ODvYpmdTXWA726tatKxRcIyIiRLAWHh7ODz/8QFhYGFar1SfYky0wADZu3EhQUJAySG3WrBlRUVHXFGepXr26X8EemMbrJkyYMGHChIk/HmbAZ8IEHg5bcnIyAwcO9DkeExPDE088Qfny5QU3zmazUbZsWWJiYrj77rsFD8vpdPoEEirrCG/vOm9fv/nz55OZmYnL5aJnz54sXrwYt9vN2bNn2bt3L7/88gsul4thw4ZRVFQkxGICAgKYM2cOJ06cICQkhPj4eB8F06NHj4prfvTRR3Ts2NGQ/dMDF6vVysSJEzlw4ACVK1fGYrGIoCswMJDhw4dz%2BfJlbDYbX3/9NT169CA8PJywsDACAwO57bbb6NChA1FRUUIgZs6cOQCcOHFCrN1tt91GaGgo4eHhov/Zs2f7iPO0b9%2BeQK8akqKiIp%2B11bOwVqsVm80mgsHOnTuLIKps2bIkJiaKcwYOHMiVK1ew2Ww%2BAZkqcBs2bBgjR47ktttuo2nTpuL4zp07GTlyJA0aNBDlv3Lw9/DDDxv6uxZMDp8JEyZMmDDhB0zRlpvCrTVbEyauAT0zVatWLc6fP8%2B0adPEF%2B/MzEwyMjJEgJacnEx2djZpaWmsXr1aKF2Ch%2BMFCFGX2NhYwf8aMWIErVu3BjzqmCNHjhTnNWnShLNnz2Kz2SgqKqKgtHShSZMmXLhwgYyMDFwuF%2B3bt6d169Zs3rwZ8GS%2BqlSpwpAhQ2jUqJEIFPUxeMNbPVQ%2BDp6Adfr06SQkJHDu3DmD193JUs8h3WpBV7fMzs7GYrFw7tw5fvrpJ2rWrElUVBRut5v58%2BeTkJAgyhb1Ek9N00TpJngsFmbPnu0zLu9ry%2BPWbSDKlCmDw%2BEQwWCnTp1EEHX69Gl2794NeCwszp07R%2BXKldE0zSfQKikpMZRmHjlyhIMHD5Kenk5ycrI43qxZMxISEhg9erRYi8uXL/ucrwe5/kDpwyf5BMpURj9ssIyeTH54cqn69ccj7IbXUgjYGPpRKbXK5bJ%2BeAAqfbAkHqdMA1EKpcqLoeCCyj58qmvLQ/bH700%2B5oe1oNInS650lvtR%2BvDJjSQvP8DoqacqvZYX59AhYxvJL02e0/nzxlMo5UYLrF5tbCPdUNV9ka/lzz6Sp6mqLjOcp7ox0kNloCUpngU/rNIMbZTix1IjZRtpD8jzVu1H%2BXFR9Su3UW0bo1ek1ECxnoZDig8yeY2VbAFpbQzzVAlxyZ8LiooQ2UHq8mVjN/Kzqtr7st2lLFKu%2BhyTh3z27I3bqMbnBxvDxH8BTA6fCRPAnj176NevHz/99BORkZH07NmTCxcu8MILL1C9enWf8lI9WCkpKSEgIACXy3VDi4BrITIykqysLCpXrsy5c%2BeExL/D4RBBhM1mw%2Bl0UrduXY4cOWIQIwkICMBqtVK7dm1OnjwpxFKWLVtGTEwM7777Lh9//LEy2LuWcIt%2BXYfDIfh9FouFGjVqcOHCBSZPnsw777zDxYsXeeyxx7DZbLz77rs3NF/XNI3Y2Fjatm3L%2BvXrOX78uM/7egDYuHFjdu3ahdPpxO12ExAQoAzM9D714w8//DDFxcUsWrRIZAD1e5OQkEBiYiJLliwxZGNlrFmzhj59%2BpCfn2/gUo4aNUpYN6hQoUIFNm3adM2%2BvaHi8I0e/RRPPjnKr/NNmDBhwoSJWwLXsHC6KSgNDP1HWloaL7zwAnv27CEkJITu3bvzzDPPGJTQARYsWMBnn33GpUuXiI2NZdKkScTHxwOeKqaXXnqJH3/8kaKiIlq2bMkLL7zgoxJ%2BszAzfCauiaSkJOLi4khISCAhIYHbb79dcMh05OTk8Oqrr9KxY0caNWpEmzZtGDNmDEeOHBFtJkyYIDhn3jh%2B/DixsbGcVf3s5Md4OnTowIQJEwwqh4MGDWLWrFnidWxsLPHx8eK8Ro0a0blzZ959913xpV/n802fPp2uXbty7tw5HA4HU6ZMYdCgQaIvu91OZGQkTZo0QdM07Ha7T%2BCgaRqRkZFomiZUGvXSRFXpoC7fr5u76z53AA0aNBCCJ7qp%2BZAhQ3C5XD52BC1atGD37t3MnTvXhxPWp08fmjVrxkcffaQM6gYPHkxgYKAYL3j4jvq/e/ToQbly5URGzuVycfr0aQoKCnjppZeoX78%2BDoeDL774grlz52KxWHC73URHRxMVFSXmUbt2bTHe%2BPh4li1bxl//%2BleKi4sNH4q6WXpBQYEP7y8lJUWU2/bo0UOMMSAgwIdD%2BdVXX/n44nnfm5SUFD755BNhwVCuXDmfslHdMxDgww8/VAaYTqeTefPmsWHDBjF2q9XqUyL65ZdfGtb6WjB9%2BEyYMGHChAk/8Ccs6Rw9ejSVKlVi3bp1fPTRR6xbt46PP/7Y0G79%2BvW8/fbbzJw5ky1bttChQweeeOIJ8R3gjTfeYP/%2B/SxevJjVq1fjdruFAvzvBTPgM3FdTJ48WfCqNm3aRKdOnXj88cc5c%2BYMeXl59O/fn6NHjzJ79mz27NnD0qVLiYqK4sEHH%2BTw4cN/2HiSk5OZO3cukZGR3H///WzduvW657333ntiHrt372bmzJksXLhQ%2BMxFRkYSERHBr7/%2Byvz589m7dy8///wz999/v09wZbPZePTRR8nOziYwMJD8/HwfM/GAgAARhOjn6dm4li1bsnbtWsLCwggNDSU4OJgYSXc8ODiYpKQkZs2axYkTJ%2BjXrx8RERFUqVKFmTNnsnTpUpxOp/CTA9iyZQtXrlwhKiqKhIQEEaR069aN%2BvXrs2bNGmFvoQdKmqYxf/58CgoKyMrKEudkZmaKf3///fe88847lC1blqpVq1KjRg06dOhAvXr1uPfee4XozDvvvMPevXtp1qwZ5cqV4%2BLFi1y%2BfFkEmSdOnBAiMSkpKaSlpZGfn096erqyVLOwsJDCwkI6derkE3AlJSUBng9OnZtXXFyMy%2BUSwVdeXh6ffvop4OFfdu/eXXARQ0NDCQoK8gnUypUrh6ZpaJrGo48%2BSt%2B%2BfQFITU3Fbrcry2IBduzYIcYeEhLiI4LTq1cvLqpK4UyYMGHChAkT/xNISUnh0KFD/PWvfyUsLIyaNWsyePBgFi9ebGi7ePFi%2BvTpQ%2BPGjQkKCmLo0KEAbNiwQfxwPnLkSCpXrvz/2LvusCiu9f3ONhYQEEEUQTRCRAQEu8Z61VhiEo0SjcarEa8lGhOuJbGbxG6aFXONiS0aW6yxxASjUWzRKGxAscWCAhb6Arts%2Bf2xzHHmzAE2ll%2BSe8/7PDw6s6fPmdn95vve70XVqlURFxeHw4cPI4uO3X0McIOPw2m4uroiNjYWfn5%2B%2BPnnn/HFF1%2BgsLAQ8fHxCA4OhiAI8Pf3x8yZMzFgwABZ8pAnDa1Wi%2BDgYLz33nv45z//iWnTpjmV9h94KFEwcOBAmbC50WhEcXExFi1ahKysLFStWhWvvPIKqlWrBnd3d/JjX9T%2BW7FiBVq2bMl0uQuCgHbt2gF46OE7c%2BYMevbsiYKCAhiNRri6uuLQoUMAHGGA4hj279%2BP8ePHw2QyYcuWLcjJycGdO3fw%2Buuvo6CgAHa7He%2B99x4AkOyhhw8fBuDIWikaKZ6ennBxcUGdOnXQs2dPVK9encxB/Fer1RKDdeTIkbBarahSJo5kNpsxYMAA5Ofn486dO4iJicHixYuRnp6OFi1aEM%2BaKP5%2B/vx55OXllZsUBgAaNGiAgIAAXL58udzrpVarERgYiEGDBhGB94KCAowcORKAIzGL%2BDLBy8sLXbt2lfUpzi0jIwP79u2DzWaDzWaD0WhESUkJXF1doVarcffuXdy5cwdubm7w9vbGV199hV27dpF2fX190bZtW8U8Tp8%2BTTyygCNTqhTlcSVZ4ElbODg4ODg4nMBT8PDdvXsXKSkpsj9nX9impKQgICBAlqAvPDwcv//%2Bu0JWKiUlBQ0bNpRMRYWwsDAYDAbcvHkTBQUFCJfo7wYHB0Ov1yMlJeUxF%2B0huMHH8Ychinv/8MMPePXVV0kYnBTvvvsuSVDytPHGG28gPT39D98YtHh5vXr1EBISgoKCArzyyito3LgxJk6ciJ49e2LNmjUAHHHW58%2Bfh0qlgpeXF7KyssgP/vLSstbtwAAAIABJREFU9ItSBhaLRca9y8vLI8libDabTHZAhM1mw7/%2B9S8EBATIvHMsWK1W3L9/n9mOtJ40K6eU/5aTkwNBEBQPKovFAqvVik8%2B%2BQSRkZEwGo1wcXFBZhmzvLi4GCUlJcTbJggCGjRoQOpLvXRpaWnIyspCYGBgueOyWCz49ddf0bt3b7i4uMBsNqNFixZk7b777juS1Mbd3Z2EcLLWjoXCwkJ89NFHxBA3Go3ILmOqG41GCIIAT09PZGdny8KXpbIcPXv2BCCXlhARFRVFDPjKwEra0r59V3khOgEGfQxG4gIqVJSpYV9phgQGWZ8hfKtom0p%2BwLLrFVQMVgYM%2BhwjcYqiIcbaKMZHZylwJnsEYxKKdlnZLOg5VJYtBFBmeWDxVujxMK4L3bTyciv7pptxJkEQa9p0GWeWmF4aZ9plJb1RLOmyZcpCdAaMpCT5cXKysk5l1xJQLqAzm5%2BeKGufU%2BeYVCY60wfrhSs9b0aGDsX6OZM1ip4Da4D0%2BFjrRz%2BD6B/erE1BrTmr2UfZXPQUnHlEMSP06UKshuiKrDWmLwy9%2BVmi5PReYrVLn2OM72lQ5/4qED1v0j%2BWh46F3NxceHp6ys6Jxl8Otedyc3MVmdu9vLyQk5NDEvvRbXl6eiraeRywfxlycDBgNBqxadMmZGdno0OHDpgzZw6eeeYZp%2BoeOHAAP/74o%2Bzck8oX5OvrC09PT6Snp8s08sqDxWLB%2BfPnsXnzZuI1AoCFCxdi/PjxJNNkly5d0LFjR3Ts2JHozdWoUQMHDhyAzWbDuHHj0LVrVxKv7eHhAVPZQzkkJAQTJkyQZWzU6XSwWCzEELFarejSpQsARyglAMydOxcPHjzATz/9RLJDrlq1ClqtFjqdDiaTSbFuosdv%2BvTphO9Hw2q1Ir/sS65Ro0a4f/8%2BmjVrhmPHjsFqtaJbt27YuXMnMQBDQ0NRVFSENm3aYNu2bYQLZ7fbIQgC3nzzTebainO7SGXlE9sVk8mMGDGCJJ%2BhvY6Agxu6ePFiYuRJjTer1Yrw8HCkpKTgzp07JBOntB/gYUKaESNGkKQ1Ii9v1qxZMJlMzKQ1IvcvPz%2BfaVwLgoAbdLZACRYsWCAL860Iu3fvZgqvR0c/NJhBy3sw5D4Uw6Q8h3o9o3PaUGZ4GxXTYOgLKtqmhGxZS6Gw0Vnit/Q5xoslRUOMtVGMj/bIl3m0K2yXMQlFu9SXNQDlHOi%2BJRxSAl/fisfCGg/jutBNKy%2B3sm%2B6Gda%2BodthTZsu48wS00vjTLseHsoyiiV96y1lIX9/%2BXFUlPyY9T1S2bUElAvozOanJ8ra59Q55juumjXlx/Q%2BApTzrlZNUUSxfvQmYG0Keg6sAdLjY60f/Qzy86u4H0Cx5swcF4%2BwuegpOPOIYjxClYVYDdEVWWtMXxh687O0X%2Bm9xGqXPscY32PmNXlisIP9svtx0L9/f0IXEeHsC1vgj/2Orazs086hyT18HBVi9uzZJNlJx44dceTIEaxZs4akt3c2jLJ79%2B4ynTWDwUDC554ExHT/5WH06NGypC1xcXEYMWIEhgwZQso0aNAAe/fuxbfffouYmBhkZmbinXfeQb9%2B/Yjnp2HDhrDZbNi%2BfTu%2B//57jB8/HlWrVgUAbNiwgentFAQBPXv2hMFgwMyZM2WfSces0%2BnQokULDBw4EDk5OahRowapr1KpiDFZnodPigULFqBatWpITk4m2SnF%2BsnJybBarbh58yays7OhUqlISGxAQAA8PDzQvHlzdO3aFVu2bFFksxQfStWqVcOvv/6Ko0ePomvXrooxSHH69GkSvvjpp58iPDycGHNqtVphIEnDZEWP5dSpU9G8eXMAwP3798k6iHIRMTExxODX6/Xk89TUVJjNZhKqqtPpiDdTKg0hQq1WE68s6wHcrFkzhIeHo1%2B/fjJvqiAIqFGjBqoxfkSVBy68zsHBwcHBUTlstif/5%2Bfnh/DwcNmfH/2ioRxUq1aNeOdE5ObmEn1eKby9vZllq1WrRsrSn%2Bfl5cHHx%2BePLlO54AYfR4WQJm355ZdfsG7dOkSVvQ2tU6eOIkPmn4EbN26gqKiICKOzIE3aMnPmTNhsNvTu3ZtZNiIiAsOHD8cXX3yB3bt349q1a9i5cycAh16f1WrFgAEDEB0djfDwcGRlZUEQBNSqVavSsfbv3192HBcXR/5vNpvx9ddfY/ny5bh58yYxkIYMGYI5c%2BaQcqJxJBqdotGRmJiIZs2awc3NjcytUaNGZN59%2B/Yl544cOYLg4GDY7Xbk5eWhefPmsFgsuHXrFgoKCvD111/jyy%2B/rJBPJggC3N3d0a5dOxw8eJCc0%2Bl0xEgVz2VlZZG3aHq9XmZIjRo1Cvv378c333xDzoWEhKC0tFRmCHbv3p3o32VlZcFut6Nu3bqyMYaFhUGv16OkpIS8jEhMTITdbkdOTg50Oh3ZJ4GBgQgPDyd9iKGh69evJ2Vogw4A3n77bTRp0gRbtmyRheja7XZkZWVhx44d5a4ZDc7h4%2BDg4ODg%2BPshIiICGRkZhBICOBK5hISEyDJ3i2WltCOr1YrU1FRERUWhdu3a8PLykn1%2B6dIlmM1mItvwJMANPo5HRrdu3bBlyxYF5wsAJk6cSHhvTxtLly5F/fr1Ub9%2BfafK9%2BvXD0FBQZg3bx45d%2BnSJcyePVvxY7tevXoIDAwk2naXLl2CSqWC2WyGWq1GjRo1oNPp4OnpiX379pXb5/79%2B4l3UQoxpFM0NjZu3IivvvoKVqsVRqMRWq0Wffv2xbZt2yAIAtq2bUuMpdq1a6Nu3bqECHzt2jUADq9RZGQk4uPjiYdP6lkUz4nyAYIgED5jZd7DsLAwUubBgwcykjHwMImMNFTS19cXLi4uhA9nt9tlnuHly5ejZ8%2BeMvmLX375hZSzWCyoU6cOvLy80KJFC5lX1MPDg3jotm/fjs2bNxPvXHkYOHAgAIfxnpSURPiAoifz5MmThJcp9W42btwYgCP5zpYtW6BSqUior5jpU6PRkGQ9zoApvC4hdgMAzp6t%2BBgMqlDZXhDBFLGmiRkXLiiKKByctOg2GJwZSmbFKYFqVoisM0rSNBenLAxaitu3qRM015fmNQEK7gsr0oZaYkASWkxAKyrT43OG60S3warmzNpQ7TrD4WMtDT0eSj%2BdOR4WVZEuQ1OSnKnDmraClsbiNlFp050S5qY3OktM/uZN%2BTHrxqMHTbfD4vDRm5h1UyUmyo8Z97OiHutZSV%2BIo0crPgaUnEfW%2BGg%2BHmue9MWjXygz7gX6HJNvRo9P8VCA4qLTz1RW1/QeLQs2kYEO4mDx/Jyh%2BVVGD2VdSmeoi3S7rPExqNF/Cp6Gh%2B9x0LBhQ0RGRuKTTz5BYWEhrl69itWrVxPd5u7du%2BPMmTMAgAEDBmDnzp04f/48iouLsWLFCuh0OkIZ6tevHz7//HNkZGQgJycHn376KZ5//nn4skKzHxHc4ON4ZMTGxsLX1xeDBg1CSkoK7HY7MjMzMWPGDJw4cQKdO3d%2Bqv1nZWVh3rx5SEhIkHnAKoMgCPjwww%2Bxd%2B9eHC378vL19cWePXswY8YM3LlzB3a7HYWFhVi3bh2uX7%2BO9u3bkzBDMdmJRqNBo0aNsGPHDmKUhYWFKaQWWEaURqNB9erVUbOM1%2BBfxq0QDT9BEJCTk4O4uDgEBQXBx8cHLVq0QG5ursKwbdmypez/lRltosEk5bpJ64gZPcVxSjF8%2BHDoJTH/HTt2JP8PCQnBvHnzkJycjG7dupHzLVq0QFBQEMl81apVKxIGK0I0FMvDjRs3EBUVhSZNmsiM8rS0NBLyQLchCAI2bNgAtVqNVq1aISwsDC4uLjIvKyu7Z1ZWFgl/lULkCp47dw7ffvstbDYbrFYrbDYb6dtms/2hB/Tu3bsxceJE2V8C/QOwadOKj8GgClHebpo%2BA0BJzAgLUxRRbKVnn1WUUXBmJAl5ADZlRcETqlNHWYiuyCKS0FycJk0URajbEaBeUih4TYCC%2B8K6pRQBBWVSITLQXBB6fM5wnRh8EkU1Z9aGatcZDh9raejxlMmNVjgeFlWRLkNTkpypw5q2IqKaxW2ShPKzirA4h4qNLklMRVCm50rAuvHoQdPtsDh89CZm3VR0kjTG/ayox%2BJ00ReCfoHFeqFFcx5Z46PD5FjzpC9eSIj8mMWtos4x%2BWb0%2BBQPBSguOv1MZXVN71FWSgM6iIPF83OG5lcZPZR1KZ2hLtLtssbHoEZzlGHJkiW4e/cu2rRpg8GDB6N3797kpfLvv/9OaBvt27fHuHHjEBcXhxYtWuD48eNYuXIl%2BT319ttvIyoqCr169ULnzp3h7u7%2Bh37XOgOetIXjkeHm5oaNGzdi%2BfLlGDt2LO7fvw9vb2%2B0adMGW7duJUbMk8Ts2bMxd%2B5c2O12uLu7o3Xr1ti6dStC6C%2BGSlC/fn288cYbGDlyJAk/tNvt2LFjB3bu3Am73Q5XV1c0atQIy5Ytw44dO1BcXAyVSoWqVauiadOmGDNmDDG%2Bbt68iaSkJOzduxf79%2B/H8uXLERkZCa1WS7xnffr0wQcffIDQ0FBYLBbcu3cPTcp%2BAN4sezMs9bTZbDZ8/PHH%2BOSTT6BWq%2BHv748ePXrAw8MDFy5cwNKlSzF16lRiYLz%2B%2ButQqVREP87DwwOurq5ISEiARqPB1KlTSbmEhATcLnvL2aZNG8TFxSEmJob037dvX3z%2B%2BeewWq3w8vKCxWKB0WhElSpVZGGWoqwEAJJRMyYmRmZI7d%2B/XyZ/cePGDZw6dQqrVq3CRx99BEEQiCdNzJxpMpng4eFBPG3ivKTtRkVFITY2FuPHjyfnIiMjie6f3W7HkCFDYLFYcPbsWWKchYaGkvJikhedTger1Qqr1YrevXvj3Llzij0jGoSZmZnw9fVlhjPbbDZkZWUR/mVl4Bw%2BDg4ODg6OyvFXZDvUrFlTlpxPClqLeuDAgcQYpKHT6TBz5kxFnocnCe7h4ygXhw4dIq7p8uDp6YnJkyfj0KFDSE5OxpEjRzB37lyZsTd//nx89tlnirrBwcFIS0uTpeivbDwpKSkwGAz47bffcOrUKSxatEhh7K1fvx4TJkwgx2lpaWjfvr2ivQkTJqBmzZqEp/jbb7/hzJkzGDduHLRaLXbs2IElS5Zg4cKFuHz5MrZt24bU1FSmuLyHhwdeeOEFhISEYOzYsbh48aIsMc0//vEPfPDBBwBAwgBFkXYxJFGM4wYcXrA%2BffpAo9EgMjISarUaH374IRkbC5988olMXH7lypWwWq1EXF7ElStXsGbNGsLp8/f3xxtvvEHCS0%2BfPk08d7Vr10ZBQQEJk0xNTZV5v6QGWGpqKmbOnIm0tDQYjUbiHezRowcMBgPq1q0LwPHWq1u3bkhISADgkNU4f/48UlNTSTirWq2WcSxtNhumTp2K3bt3w6PsDXRSUhImTJhAjEV3d3c0atSIhGgCjpBMkVMIOAzKKElGPjH8tFmzZmjXrh1UKhWaNGlCvNN6vZ54dj08PCAIAsxmM9auXYvQ0FCo1WoyTz8/PwiCoPBecnBwcHBwcDwe/mohnX83cIOPg0OC/y9xedEIMhgMOHXqFOrVqwej0UjkGY4fP47Tp0/D09MTmZmZMJlMeOONNxAWFoaFCxcCANHBE7Fr1y507twZjRo1Qvv27bF69Wp07dpV5l0DgFOnTqFnz57Yvn07AId3cdKkSThbxg27d%2B8eXnvtNfKZRqMhXr2cnBxF1k4RogfMYrHg5s2bxBjcu3cvwsPDieYgAFy/fp3ITuzfvx82mw3nzp3DgQMHYLFYEB0djfXr1wN4GIKanp4OX19fmdC5xWIhxlxhYSFat26t4GGazWaiEWgymTBt2jSoVCpiqNlsNhw/fhxHjhyBzWbDjh07ULssTk0MrRXbET3LZ8%2BexaVLlwjHEADu3r0Lu90uSz5TGXjSFg4ODg4ODo6nDW7wcfwl0KxZMyKbwPq7zSJZP0U8aXH5ivQB3dzcsHnzZrz88ssk%2BQfgMK7mzZsnkyAQhc0B4ODBg4iIiMD8%2BfMBODyg6enpMJlMKCwsRGpqKjZt2kS8c03LuF8i50yESqVC7969ibFJw2w2kzhzT09PUlev16NHjx4VzlscK52oReRBAg7Dde3atbh16xYx8s5KEpOIBtCqVavQrFkzcl70dPbr1w9Vq1aFIAhEE1EKtVoNnU4HlUoFV1dX1KpVCzabrVy5iUmTJhEDV%2BotFNfRy8sL3377bbmcw8okKqRgJW3p3Jmq7wTrXzEUKiGCMwkwWEkUFO0yMgMo2nYisYaiGVYh%2BpwzZRjZDhSn6CwFrAwJTgivVyZ2z2zHmcwkVPII5jajTzKuC32KrsJK2kIneWAmwKAaYiV0oKfJKkO3TZdhJZigk6mwxqdYUlpUHVAmA6HF2Vli7fSkWPuGXnTW9aXr0ZNi7SP63mTJIdEvH1mCzXTmEYZyvWJa9Akn9ixTrYnui5UFpbJNwcpMQtVhip/Ta8yYd2VJUFjTppth3i/OPMfoxh/lWccYoOLZ8YjP2b%2BK8Dr38D0eBPvTVvrj4PiD6NSpE7KysohnR6fTITQ0lJBdAYcw94oVK3Dw4EHcu3cPnp6eCl7dpEmTYDKZFOGkV69exQsvvICEhAQMHjwYw4cPJ6Grorj8smXLsG/fPnTt2hXu7u4oKCiocDyTJk0iwuVSD43IEUtISCChqw0bNkT37t3x6aefKubesGFDkgBEmu1Sq9XCarXCzc0NmzZtQt%2B%2BfWEymTB27FicPn0ap06dAgDC3wMchk5xcTG6deuGJUuWAAB%2B/fVXjBo1CnmSL1JBEKBWq4kBVL16dYSVEf5//vln2fg8PT2JgLter0dSUhJCQ0PRqFEjXLlyBV5eXsjJyak0UyYLUtF0FuLi4rB06VJiOIrC7W5ublCpVCgqKnpi3rEFCxbgiy%2B%2BkPH0xFDc1q1b49NPP0Xr1q0JL1CEVqvFb7/95nQ/8%2BfPZwqvj2GJRXNwcHBwcPyPgmGnPzboHEX/zeAePo6/JKT6f8eOHUOXLl0wYsQI3Lp1C4WFhRgwYAAuX76MlStXIikpicmrcxaVictXNh7AkWzF3d0dbdq0wb59%2B3Dx4kUcPnwYL7zwAoCHkgmAg%2BQrCoiz0KNHD5w%2BfRqDBw8mCVnEZCoTJkzAs88%2BSzyO165dk2m3lJaWkhBG0Tv1ww8/YNq0aQCAlStXIjw8HMuXLyeZNO12u8zbZbVakZiYCB8fH7Ru3Vo2NrFfqc4e4PBSbtu2DdHR0XCVpBhTq9WoVasWhg4dKqun1WplyV/E9MRSSDOHuri4YNu2bcToAoAff/wRgMPw9PHxQatWrWRjEjUBpRy%2Btm3bIioqihyrVCpSRzqeXr16yUJQxXUpLS1FRkYGEhISUFpaqjAwxc%2BdBU/awsHBwcHBwfG0wQ0%2Bjr88njavrjJx%2BVIqnoEeDwBcvnwZFotFMaYxY8YAgEyY09lkOFOnTsXRo0ehVqsRFBQEm82G6OhoGI1GIvgdGhpKdBCliWBE40XMPioiMTERr776Krp06YIlS5YgLS0Nu3fvloWs5uTkwGq1YseOHThx4gQ5r1ar8f3335c75uDgYCxatAjHjh0jIaCiGLkYailKF5SWlspCPDUaDZFtEAVLX3jhBTKPli1bIj09HRaLhRhZYiKe7Oxs3Lx5E2fOnMEvv/xC2rTb7cT4Fdfg2LFjiI2NRXBwMABHuKjYnjgelUqFXbt2Qa/Xy5K2aLVaBAQEIDc3F71795Z9BoAYusm05lMF4Bw%2BDg4ODg6OysFDOh8P3ODj%2BNvgSfPqnEG3bt1QVFTE1GW7d%2B8eERO/c%2BcOateuzRwTABn37FEgyjbExMSgQ4cOMJvNEAQBL7/8MikzduxYYriOGzdOFl66a9cuhIeHw2w2Ey0YEaGhoahbty4EQYBer4fdbicesCpVqpAQWa1Wi3379kGtVsu8YYAjs6eUcymGdIoZNXv06IFly5ahClPgymFMGo1GtGnTBkajESqVCj/99BMxHAslHAzxnFSzUPRSVmYseXh4oHv37tizZw9atWql0CwUNRp79%2B6N4uJilJSUIDc3F4DDe3fnzh1ivJeUlCAnJ4e0IXpU9%2B7dW%2BEYpGBx%2BDp1ojh8dHgsI1y2Mq6GUxw%2BFlGDbtgZfgdFhnGGxsSM5H2U8TE6U5xyRuWYbpdFSnJG%2BZguQ3t0WXWoMs7w6FhzeBQOH02ZcqZvFteObodFJ6uMrsUK33KGM6W43qwXME%2BLw0d3zrq%2BdD1nlLmduRfoRX5aHD6WYDrFkWPum0fh8NGbi/UwoeqwLoviJM3jZXTtzGOCvlSP/Byj1/QJcfgUjy1Wu3ShvzCHj%2BPxwHX4OP7yEHl12dnZ6NChA%2BbMmYNnWAqnDBw4cICE/on4I7TV2NhYrFixAqtXr0bz5s3RsGFD/P7775g0aRKKi4sxePBgMkbRMyXlIIp9vfPOO3jvvfcq5SBKxzZp0iTk5eUpxMHFpCt16tQhYaf0nET%2Bn9VqJZ4u6Wf79%2B/H/v37yRjF%2BrVq1cK1a9cQEBCArl27Yvz48SSss06dOnj//feJJ8xisRBNO5vNJutDRGFhIaxWK/bu3YsDBw4AcHgvY2JisGHDBmJIFxQUoKCgANevXyftScMdz507hz179uCll14ixqRoaEkT2jz33HMVXs/IyEhs3rwZS5YsQWJiIvLy8jB37lzs3LkT/v7%2ByMjIwIcffohXX30VLi4ucHFxgSAIyM/Ph4uLC4KDg0l4ro%2BPjyLRjSAIhP/oDHbv3q3g8I0d%2BzYiIiRizLSiLkNhVyEM/gjC10zFYrphRhnFKeplAKtvWliYJWz%2BSONjdKY45YzKMd2uQtmecY6lfEyXoT26rDpUGaYKCz0%2BxhzopukqLOF1WmDZmb5ZYs50O7QANKttug6LW0OfY41Pcb1ZCbNo3VaaM8vi0NKds/YN3Tnr%2BtL1nFHmduZeoBeZtei0ejhjkRXTok%2BwXmpSL/KY%2B4bui6VkTlekNxfrYULVYV0WxUlabZzRtTOPCfpSPfJzjF5TVpnKBshYG8Vji9UuXciZZ/yfhP81j9yTBvfwcfwlURmvzspMA6ZE9%2B7diddL/BO18ZyBm5sbqlevjqysLPTp0wcNGjTACy%2B8gDt37mD58uUk8yUgNyTFMFGxrzZt2jjFQbTZbNi3bx8iIyOxc%2BdOHD16FADQoUMHuLm54dNPP8XWrVvJ2ACgWrVqAIBly5aRNfvss89QrVo1%2BPr6QhAETJkyBWlpadi3bx9cXV3h5uZGMmXa7XbiscvOzoZWq0V6ejp69eqFY8eOEc%2BVNNMm7ckUs4vWq1ePhLHSEIXN8/PzsXbtWlitVtSpU6fC9RfHZbfbyw0nlWYdlWo6qtVqmWZhdHQ01Go19u3bh6KiIhw%2BfBheXl7EQNu1axe0Wi0JAbVarcQQBRxi8KmpqYTvKBp70utut9tx4cKFCuckBYvDx1l8HBwcHBwccvCQzscDN/g4/pKojFd3hQ7JeUQ4w6dTqVREUDwtLQ0XL17EsWPHiDg3AISEhKBVq1aKuqK4/Lhx45ziIFapUgWDBg2CwWBA79698fzzz0OlUkEQBBQVFaFevXqkbXE9Xn31VQCOZDCbNm1CcnIy4uLikJ%2Bfj/z8fFSpUoUYaMHBwTCbzSgpKSGhmWq1WmbIiZzFfv36YdSoUeT4ypUrpB1RwFzKFVSr1bh27Rp27NhBEqEMGDBAFv7ZsWNHzJ07F66urujbty%2BR2wgKClKsnTTcUqvVYtOmTRVep169esHf358ci0lWxHaSk5MRGxuLX375BUVFRZg4cSKOHDkim5%2BUrynOVUyw4%2BfnR/41UiFBKpUKPj4%2BAIDGjRtXOE4ODg4ODg4Ojv9PcIOP42%2BHbt26YcuWLTJel4iJEydizZo1f8kxOcNB9PLyIglFpLh06RLq169P%2BHRSjBw5EgCQl5eHkSNHolGjRiRJiiAIxBN46dIlzJ49G4Bc%2BH327Nkk4YiYOAV4mMREo9HA29tbpgFI46233sIHH3wAb29vrFy5khh8O3bskHnADh8%2BjClTpqCwsBCbN28m5UaNGkXKhIeHAwDmzJmDd999F2q1Gi%2B//LIiGQ8dQlpSUoLFixcrxib2b7PZ8Mknn5B5Wa1WzJo1i9QReY2BgYEQBAEmkwkqlYpkQc3JyYGXlxc8GTFsLpJwmjt37jDXiAWetIWDg4ODg6NycA/f44Fz%2BDj%2BdoiNjcWBAwcwaNAgzJkzBw0bNkRWVhbi4%2BNx4sQJvP3227LytK6fRuPY9klJSSQEsCJdPxEV6fotXboUdevWxaBBg4ghkpmZifj4eBw/fhw1a9ZEeno6Zs%2BejdLSUixduhRLly6VtVOrVi0IgoBz587h/fffR4MGDXDp0iXYbDbcuXMHWq0WERERiqyhL774IgBHeKBocGZnZyMwMBC3b98mIYjDhw8nnMDi4mLY7XasXLkSy5Ytg9lshqurq0xoXCrVkJOTA51OB41GA5PJhIKCAiQnJxNj6uDBg8jPz8dLL72EqVOnkrpST5g0fFQ0ukpLS%2BHi4kK8ZxqNhhhYZ8%2BexY4dO2Cz2bB7927FPpC2AzhkGlJSUoh2IQspKSlkHEajUTY%2B0di9ffs2YmNjUVJSQsqJY83Ly4Obmxvha4ooLi4ma/dHdPhefvllNGzYUHaubl3KqM/OBsrCdpnHjsHJiRbFxTKOR16ekiNVWR1mmfx8Ba%2BmpEROVaKr5OQo6USKruhKrHOsMvR4GBNV9E%2BfYA3Qmb6tVjn/5f59oExGhaCoSE70ycgAJF5o1rV0am0KC%2BW8KfqYMS%2BTSU7zKSoyKXh8WVlAjRootw5rfHfvAmW3L0F6OiCJrsbt20BAgLwMvW8KCuQ0L9a06UtFXwLAkY9Ddnuy9jU9aLpz%2BhhwJHKRcPtYfSs6VwxGuQXoIvTnAJT7iDWnW7eA2rUfHrP2Nb2B5UzfAAAgAElEQVRPGPez4prTFy8zE6hZU94ufY61fvReZ42P3hR0GSfuBcaUlBubsch0PXodWMOlb3nW8OhLRU%2BR1RfrvqvsOcvqm26HtWfp8bG2FnNNOf524MLrHH85dOrUSSaGzkJ%2Bfj6WL1%2BOH374Affv34e3tzfatGmDsWPHkrA%2B0UBLSkqStZeSkoI%2BffpAr9fju%2B%2B%2Bg7e3N/r37w9/f39MnjwZ9erVQ2ZmJlauXImdO3eiSpUqGD16NJKSkioUct%2B1axd27NiB9evXy1L8u7m5ITw8HOPHj0dUVBRCQ0PRp08fzJs3jzn3jIwMYrBotVp06dIFb731FkaMGCH7TKVSQaPREG4cAIwZMwZjx47FlStX8O9//xtqtZoYjWKd8owhFxcXYqz26NED%2B/btQ5UqVRATE4NvvvkGgiA8kqC6FGL/rVu3xuXLlxEQEACDwQCNRqPw2P3zn/9EzZo18emnn%2BLgwYMkhFYQBMLNo%2BtUq1YNKpUKDx48gE6ng8lkgoeHBwoKCgjPUJy/RqMhiW2sViuio6Nx/vx5xMTEYM6cOQgLC1OslSAICAoKwv79%2BxWGmojo6Ghs3rzZqfVgCa%2BPHfs23nqLzYPk4ODg4OD4X0RW1pNvU/oe4L8dPKST4y8HZ3XqJk%2BejEOHDiE5ORlHjhzB3LlzZRyu%2BfPnK4wzwBEymJaWhho1alTKqRONh7lz52LXrl04ePAgBg4cSOQYgIeSAaNGjcI333wDu92Ohg0bYs%2BePbhw4QKGDRuGc%2BfOYdCgQYiIiADgCHUUE6zs3LlTNvc5c%2BagRYsWcHNzQ7NmzbBo0SIkJyfj9u3bJPsm8DChiYeHB3r16gWVSoX4%2BHgUFRXh2WefxfDhw3H37l2ZVlx573cEQZAlSBF5aCUlJdi0aRPMZjNat26NTp06AXDINQwePJgkfikPrq6u0Gq1pIxer0dkZCROnz6N0tJSvPbaa4iMjJR9LmLr1q348ccfUZN6m9ykSRMYDAacO3cOgiCQEMt33nkHX3/9Ne7fvw%2B9Xk%2BMQavVigsXLqBdu3aEZwc4PJhms5kYy%2BfPnwcAIsMgJrcR91R4eDjatm2Lzp07I0eS8tzX11cm7k4LyFcEnrSFg4ODg4OjcvCQzscDN/g4/mfhLKdu%2BvTpMBgM6NWrFzp37owuXbrIMm5OmDABADB37lwkJSWhevXqcHd3R//%2B/ZGWlobRo0cTvtyXX34JwGHYnDhxgiRnETFx4kQcO3YMABAQECDjg6lUKuj1ekyZMgWAQ3LCYDCQ8EKNRgNBEPDVV18BAAn9ZBsVSjRu3JgYV2KmSYvFAqvVCpVKhZCQEJI1NDAwkIxD9JyJEAQBtWrVgr%2B/P5GhAB4aqNeuXUNwcDDy8/ORk5ODwsJCMtYRI0aQ8i%2B99BIMBoMstBRwhHqGh4cjOjpaJinxxRdfEC6i1LAVM3J%2B/PHHspBVd3d3pKWlYd26dQAcRjjgkIAAHMlZioqKkJGRAcDhGT569CgSExNx69Yt0k5OTg7MZjOZX9WqVZ1ab4Bz%2BDg4ODg4ODiePrjBx/E/B6PRiC%2B//BLZ2dlYuHAhrl69ivj4eJlwuPhHGxs//PADPvvsMxQXF6Nbt25o3rw5EUUPCgqCIAjQaDTo2bMnBgwYIEs00qxZM8TGxgJwcL6aNWuG8PBwREREICIiAhMmTMCJEydIqGDt2rWRkZFBxmC32xEUFFSueDngyML57bffom3btpg6dSpyc3NlRo7UqNVqtUhLS8O8efPQvHlzfPXVV8SbJRo/giCgtLQUVqsVX3zxBTHMbt%2B%2Bjfnz58Nut6NTp07E8yfWycvLQ3FxMdLT04mQe7169VBcXAyj0YhLly7BbreT9ReNnBUrVsjasdvtyM7ORvfu3WXztFqtxFAWPaxbt27F9u3boVKpEBQURIw%2BQRDw5ptv4tChQ7IQUKPRiAYNGhAtxT59%2BgBwyC3ExsZiypQp0Gq1xAgOCgqCv78/tmzZgosXL5J2xH7E6%2BSsRiTAFl7vKsn%2BCgA4dariYyh0jxVi0yzRYEV0LkOgWvGuoMwLKoVC75fOoEsl2wGgFBpmiWPTStwMsWTFxCSedxE3bqDiMhLjXQQtNMzqOj2dOnHihLIQ3XliovyYtTZlLxjKPQaU68dSKacvTGam7JAlvE7Pk6WNTbfLEl53SoibmoMzl5tuiKXOk51NnWAJkNM8WydUthV9scTZ6b3F4vPS13PfPvkxYz8q9hFrQZOS5MeXLyvL0PNiJBlTzJNuR/LsI6A3AWvj0OdYcyjL2lzuMb1JAKDs%2B1eE4n4HlNeFdU9R%2B4QOhmFpvtPLx9pq9HOWxYqg22YtDV3GCb10xWVh3av0febMY/bPAvfwPR44h4/jvx500ha9Xo%2BwsDDCqWvUqBFmz56Nl19%2BmVlX5P9Jk7Z07twZw4cPx7p169CqVSts2LCBSByYzWa4uLggIiICcXFxMrH1GTNmYP/%2B/cxxuru7yzTgxFtTTDJjsVjw6quvomnTppg0aRISEhIQGBgo4/1JRdjFME2LxULOu7i4ELFzAEhLS8P27duxfft2TJ06FTExMQojtzK4uLigQYMGSCr7wVG7dm2oVCrcKPvmrVq1KgoKCmC325lcPZVKBZVKBYvFAq1WC6vVCpvNhs2bNyM6OhoAkJ6eTjh8rVu3xokTJ/DRRx9h4sSJpB2NRoOoqChcu3aNhFyq1Wqi0ycIAtF0FA1MT09PmM1mVK1aFVqtFrdu3SKJY9atW4c5c%2BbIxqrRaPDKK6%2BgVq1azIyggMPz6qzRx%2BLwvT12LMawhJ85ODg4ODj%2BR0Hb/08CdDKp/2ZwDx/H/wSelK6fxWIh3sH4%2BHhcvXpVoQ8XFBSEKVOm4OzZsxg6dKhMbF3UnXN1dUWNGjUwcOBAuLm5wc/PDyaTScGHa9q0Kbp06ULCJ7du3Ur%2B36NHD0RGRiIzMxNarRZarRZNmjQhdQVBIMabGG7YsGFDWR8NGzbEzJkzCcdQ5LOJoYaikSx6BkVP3tChQ4kh2qVLF3Tr1o202aZNG2SVsau9vb1RUlICm80GnU6H6OhoxMfHE69ZaGgozpw5g9GjR0Ov16O0tJQYY/3798fy5csV10D0rn388cey8%2B%2B%2B%2By4%2B%2B%2BwzwsETUb16dQBATEwMXnnlFVm4Z3FxMRGDF8ccUPYNkJ6eDi8vL7Je/fv3R0pKCmbPni3L0qnVauHp6UnKFbC8LeWAFW7L38BxcHBwcHDIwT18jwdu8HH8z6MiDb3s7Gx8%2BOGHiIyMxK5du3Do0CEcOXIEa9asgUajgUajwbhx4wAAY8eOhcFgwA8//ACdTkcShEgTw7zzzjsAQEI/Z86ciQEDBsBqtRKjdNasWURv78GDB0hISEAyK%2BQNIN6w1q1bQxAENGnShGjCif0/88wzhNdHC5xLPYmFhYUyzTrgoaFosVigUqlw6NAhAA6xd9EoDAkJkbWZk5NDvIgWi4Xw%2BeLi4tCjRw/ExcWRMNOioiK8%2BOKL2LVrFz7//HPs3r1bpmm3ZMkSXLt2TdZ%2Bbm4uGjdujKysLAiCQIyvs2fPYvDgwTKDzmq1ErmFgwcPIjExUfa5yD%2B0WCyknVdeeYVct7y8PFJ%2By5YtiIyMRHx8PAlt1Wg0sNlsyJfEyvwRWQbO4ePg4ODg4Kgc3OB7PHCDj%2BNvhU6dOiE8PJxw7Jo2barImpmfn48FCxagc%2BfOaNSoETIyMrBlyxZcunQJAGR8vYiICCxduhS5ublo1qwZIiIisHPnTmRmZmLGjBkwmUzEkOvVqxe6du2KdevW4erVq0Tj7pNPPgEALF26FJGRkdi%2BfTsAh6fHYrHgxo0bJDGMKCfg5eWF27dvY/v27Xj33XdhtVrx/vvvIzIyEtOnTydjtdvtsNlsOHz4MARBgKurK3Q6HQICArBkyRJisB0%2BfBhmsxlffvklMbbulXEmrl%2B/jmHDhgFwaNV5eXnJNAmDg4Pxyy%2B/QKvVkvBKMexS9OJ5eHjAZrORLJqnTp1CQUEBNBoNNm7cKDOiDh48CHd3dwiCgHbt2iE7OxsmkwmLFi3CqlWr0LRpU2LUTJ8%2BHRkZGejQoQO2bduGoUOHwmQywdvbm2jz9ezZU8bhs9vtJEOnNGnLyZMncf369XL3Tl5eHrk2LIhG29KlSxEbG4uBAwfi559/JkZvv379YDAYMHr0aJLR02KxkLmI43jw4EG5fdBgcvi6dpUXoufEmKOCd0PxtRjvMpQkFUa8jCLgn1FGwR2hiSIM8ouijjM8NfqY1TaD/1Rm7z/E779XUkA5PhY/RsF1Ye09mtRDvbxgcu/ozpwpwyLn0MQb6tqxOHzOcJLok6ycUArOD4u4RI2ZbodVRbGPGYQjBXWNdfHoa%2BXMXqMndeaMsgzN0TxwQFmGvofKkkSV%2Bzmg3NesC0Nz6yhuGwCnOHyKe4peUBY3kObnsbh2VF9MXhhdjz5mVaLKMPcsfb0ZhehnKL3/WNuI3rOs4dFblLXkdD1nOHx0GRbHkJ6mgt8K5RKz1o/VNsffD5zDx/G3Aq3RV1xcjG%2B%2B%2BQZLlizBnj17mJp6HTp0QFBQEFJSUrBp0yaEhoaS9k6dOoXBgwfj2LFjWLVqlULX79ixY3jzzTcVHD6R89a%2BfXv8%2BuuvyM/PR1xcHN58800AwDfffIMPPvgAdrsdTZs2hcFgwPz58%2BHh4YHhw4fD29sbeXl58PT0RGFhIQm91Ol0hIsnFVh3d3dHcXExXFxcYLfb4e7ujuzsbHh7e6OgoAA1a9bErVu3ZBw%2BKTQaDSwWC/z8/IghqFKpUK1MCNfHx4eESlak1SeFIAioWbMmWrVqhWeffRYLFy6U9efl5YXssm8YjUYDHx8ftGrVCunp6Th37pxMNF1sT6PRoLS0FMHBwbh//z7yyr6NPv74Y0yYMIEYqmFhYUSgvbw5b9%2B%2BHTdv3sR7771HjOC6devKjEIxVNXT0xN%2Bfn64cOEC5s6diz59%2BmD8%2BPH47rvvZOPTarWYNWsW1qxZQzKZ0hg/frws22hF4Bw%2BDg4ODg6OykG/q3sS%2BAM51v724B4%2Bjr81XF1dERsbCz8/v3I19X7%2B%2BWd8/fXXJGum1Esoer7Gjh2Lzp07E12/PXv2wMvLC/fv38fs2bPRtm1bFBcXE4Pu0KFDsNlsOHr0KPEMLVq0CKGhoQgNDSXGXmRkJAnHtFqtJOW/qF%2BXm5urMFZUKhXJHCmiQ4cOsuOioiIIgoDs7GzUr18fo0aNAlC%2Bzp7VaoVGo8G///1v4hUTQ1IDAgJIplF/f3%2BZ6HtFsNvtyM3NRbt27WTn69evD0EQ8ODBA3h4eCAoKAgajQa5ubk4cOAA3NzcmJlG7XY7MXKvXr2KunXrks/E5CliEhZfX1/CmRPnLOrxiZg8eTLOnj1LtA8BkHmKMJvNMJvNuH//PlJTU4lHFXCEiNLjAxwcP1MFrzylLxQqA%2BfwcXBwcHBwcDxtcIOP478Czmjqvfvuu2jTpg2Ah0lcRF28Tp06ybT1BgwYgMuXL8PHxwdTp07F1q1bUa1aNaKt16lTJ6hUKuKVkkKn06FJkybQarUYOXIkLBYLvL29ceXKFZwoS93%2B4MEDhYdLnIfZbCacOzGc8MqVKyQBSklJCYKDg4lhcuHCBXzwwQekDZVKBXd3d2JUPfPMM7Db7ahRowYpI2bwzMjIwPnz50kIpzQc0Wq1yhK87N27F4CDsyfy3UpKSsiaAo6MndWqVSMey/z8fLi4uOCjjz5CYmIi%2Bvbti8TEROK5E6HX6%2BHj4wMfHx/o9Xq4ubnJkq%2BIWTdFg/Do0aMK4zafChFr3bo10tLSZIYbnRRH3CcajYZ4D99//30AwHPPPScrK9ZdsWIFuXYs0Xma08jBwcHBwcHxeOAcvscDN/g4/taQaup16NABt27d%2BkM6aCKGDBnC9BIePXoUAwcOhL%2B/P0mwItXWmzZtGjHOpk6dCg8PDwDA%2BfPnUVpairi4ONjtdmRlZeE///kPrl69Co1GgxdffJF4%2BcLDw%2BHq6gp3d3fMnj0bPj4%2B8PLyAgAiJ5Cbm4u5c%2BcCcBgZaWlpZAzPPfccDAYDqlatCp1OB09PT5hMJtSqVQvAw6yTUVFRRGtObEetVkOr1cJisWDBggUwGAyoXbs2AIe3T%2BSTubi4ICQkBF5eXggMDERxcTEEQUBAQIBMaFyn0yEjI4MYPVqtFrm5uRg3bhy%2B//57zJgxA3379iWGrIjvvvsOQ4cOhdFoxJ49e3Du3Dkios4CHXK6atUqWbIXACSxjjRRzezZs4lhB4AYulIuXmlpKeLj4xEUFARvb29i1L3//vswGAz46quvSBt2u51kWRUhrrsz4ElbODg4ODg4Kgc3%2BB4PnMPH8bfCo2rqSetJdfK6deuGuXPnIjk5Gd27d0fdunVx%2BvRp2O12VKtWDU2bNsWYMWNQv359xMfHE2Fwq9XK5KBptVqsXbsW169fx8yZM7Fq1SrcunUL06dPh6%2BvL/Ly8mAymcrlyanVakW7gwcPxrp16/D8888jISGBSBz4%2BfkhvUz9WavVEu%2BXIAgk%2BUppaSk8PT2Rn58PnU4Hg8FAQg71ej3R/VOpVMQIk%2Brkifw4tVqN1NRUDBs2jAil37t3D2q1GgkJCfjuu%2B8Ih09cJ4PBoJifaFzSjx2NRoMaNWogPz8fY8aMwdChQzFp0iSZALxYR7p24rzp8M6goCDcvHkT/v7%2BePDgAZlT7dq1kZ2dDWMZk57F/xMEAevXr8fq1atx5MgR4q1UqVTQaDR488038eOPPyIlJYVwI6X4Izp8qampCkmQZ5%2Btj7CwBg9P3L0LSAxKxTEcBH5ZBG5ODuDtTQ5LSoCyfDsEVisgs7uzs4EyTqcIux2QOTHv3wd8fSvuu6AAKHvxwW6k8vEyC5lMAGXUo7gYkL4YuHcPKJPhEFFYCMgiiOn1Y/RNd6UYL%2Bsc47ogPx%2BQhhpnZQESTztrTorx0uvJOsdav7w8oOzFEeu4qMgENzd53/SlopeX2S7j%2Bir2lmJSynYU66lohLFcjAuTmQmUqb6U2w4yMgB///IbZt0wRiMgkWPBb78BknBxAA6B7zLdVQAOcXaaj3vtGlCv3sPjixeBBpL7/cYNoE4deR36%2BtJjARyJXcpe1gFg3s%2BKpWBdYPocvVbp6UBgYMXjKyoC6JdZVLv0rQFAua/psTgxXtZjQvHcYrVD7SV6a7GeAfTeZ/VN3x6s7ai4hcxmgI5SqmxAjAE6s63pMqw5sOr9GWDlC3pcPPvsk2/zrwru4eP42%2BFRNfXEegEBARg/fjy6dOlCtNxWrlyJjIwMIlK%2BcOFCRRjn6NGjSb%2BxsbEAHF69devWAQCmTJkCg8Eg08IDHJkYRQOVxV2TghXmuW7dOmi1Whw5coScc3FxwdChQ8lxcHCwzCtotVqJASjKTajVapyRZJYTJR2qVKmCadOmYfz48fDw8EA9yY8R0RiyWq2IjIzEyZMnkZGRgfv378Nms6Fr166yUNEqVapg4sSJ5HrQKC0tZfIMLRYLbt%2B%2BjYKCAsyfPx9dunSRJUzRS75tpCG04hwbNmyIOpIfSaLeYUZGhiz5DSAPw5SORezj9ddfR/PmzfHzzz/LjDmx7OjRowm/kSVSn8HKOFkOdu/ejYkTJ8r%2BDiX8KC9EGxH0MRg/RKgf/6wva/pHB/3jEFD8jlcYe8y%2BaeOEEfZa2XiZhRS/4qD80UYZe4DSzlCsH6NvuisWnVVxjnFdFL9opcYeqyMwxkuvJ%2Bsca/2kRhnjmDb2AOWlYjrZ6XYZ11ext1jPPaodxXoqGmEsF%2BPCyIy9ctqRGTCshlk3DG1g0cYeIDf2AKWxB8iNPUBu7AFKYw9QXl96LIDc2AOY97NiKVgXmD5HrxVt7LHGx4hcoNtVGHuAcl/TY3FivKzHhOK5xWqH2kv01mJS2qm9z%2Bqbvj1Y21FxCzEoKZUOiDFAZ7Y1XYY1h7%2BCsQdwD9/jght8HP9VqEhTb/v27VizZg0AYO7cuViwYAHx/CxfvhxqtRqjRo0iHiR3d3fo9Xqo1Wr07dsXbdu2xTvvvEMkEwBHlkUx8cucOXOI3MP06dOJ8abT6dC8eXMYjUZYrVaSlIUOa5RC1IcTUVpaKvOM6fV64qUCHNILRkaKcuBhiKDJZMKoUaNk2nqtW7dGQUEBbt68iWXLlpHQTRbEsQMO46dq1arEYBaF100mE9q2bYvp06dX6uV69dVXy/3s1q1bMkNS1O0TBAE2mw1eXl546aWX4Fv2RX716lUSiiqOVYQYFiuihJXvXXI%2BPDyc1JNCeiy9djSPTxoyWhl40hYODg4ODg6Opw1u8HH8VyE2Nha%2Bvr4YNGgQUlJSYLfbkZmZiZycHFy7dg2dO3cG4NDBe//991G9zCNQp04dtGzZEmPGjIG/vz9SU1NJ4pbNmzfDYDDIPH6i3MC5c%2BdI4hcAOHPmDAwGA1JSUlBT8qo5Li4OVatWJV64r7/%2BWhbSqVKpoNVqMW/ePOh0OixdulQxN5vNRsbbuHFjnDx5knxWUlIi8zaNHj0a/v7%2BUKlU0Ov10Gq1sNls%2BOc//0mMITc3N6xcuRJVq1bF6tWr4eXlhVatWuH1118H4DB8qks8JkOGDIHBYEDHjh0RGBiI5557jhg3gYGBqF69OkpLSzFo0CBs27aNGI4RERE4cuQILly4gBkzZpD2vv32W9n8XF1difEkCAIJV5VCq9WiZ8%2Be8PDwQLdu3QifUuoB9PDwwIYNG7Bp0yaytiK3UrpOtWvXxrFjx%2BDl5QVXV1f06NEDAHDjxg0ADl7g888/TzyKjRs3JmGqUmPUz88Pbm5uZOxZWVmKcZcHzuHj4ODg4OCoHNzD93jgBh/HfxXc3NywceNGtGzZEmPHjkVUVBT69%2B8PABg%2BfLjMC5SYmIiCMlHj69ev48SJEyguLsaNGzewevVqXLlyBdevX0dSUhIaNGiArVu3orCwEJGRkcQzY7FYMGbMGNkYRNmH27dvY9iwYYiMjMS4ceNQUFBAJAKmTZtGvESCIODll1%2BGl5cXjh07BrPZDKPRyDQG7paJRB88eBDHjx8n52kvU2FhIfLy8vDMM8%2BgpKSEGBHx8fGkjM1mQ3JyMpEYuH37NqZOnUraql27NtHsAxxC8qGhobh69Spu3rypkGMQ11Kv1yMvLw8//PADWaPevXsjKioKH330ETGg3CVhSYIgoLi4mHjRyqMWm81m7Nq1C%2Bnp6Th79izJspmXl0c8nCUlJRg0aBBee%2B01Mk9xjlKPWnp6Ojp16oT8/HyUlpbirbfegru7OwnXPHfuHBISEoiBeO7cOURGRiI%2BPh4NGzYk7WRlZaGoqIiMOYIV6lUOmMLrzz8vL3T4cMXHYOhuU5ISLLFfhbjvr78qyijqnT6tKKNQqCjTRyRghbjSnbNErGkFYNYkaK/20aOKIooIb7oMSzCdGh9LhUOhCSUJuS63UEKC/Jj1coAW2WaVoZWaWcLr9BpT4tgs4XVaP5v57oJaDGYEMzUepooJNT5aX54pzE09F1gC1ZKcWsyxAADKpHII6H3E8LwrAij27VO2S98fv/1WeZkyXjgBde8CUOwj5uMxKUl%2BzNIJpReMERWiuFb0HFhzoteYtSmo%2B5k5B7qeE4LptMA8k9FBPzNZovSUKjm9VKzx0vcLfQw8mvA6636prAzrfqGXi3Ur0OdYZZj34p8AbvA9HnjSFo4/DXQCFp1Oh9DQUMTFxaFFGRciPz8fK1aswMGDB3Hv3j14enqWm0hF1HGTSjLMmjULvXv3ViRtARwi3AsXLsTVq1cxefJkEsopTZzy1ltvoVatWpg2bRrRf9u0aRPu3LmDwYMHY9myZRg/fjwxKJKTk9G%2BfXvk5ubKxqHRaGCz2VC3bl1cvHhRkZxFrVZjxIgRWL16NUpKStC1a1d4eHgovGAiREkIsQ26PQ8PD6xatQqNGjXCgAEDcP78eaeuiaurK/7zn/9g8ODBiI2NRXZ2Nnbu3AnAEYK5detWAMCnn36Knj17knp5eXno2LEjioqKEB0djfXr1yM6OhpWq5V4FzUaDZo2bYrTp0/DarUyjTqxrJ%2BfH7Kzs8m60slRtFotma/NZkN0dDRSU1NlCWdYcHFxIW2KSXbEOhqNBitXriQyEw0bNlRwKsXEN9u2bcPUqVOZfXz55Zdo27ZtheMQwYXXOTg4ODg4Kgf9LvFJoIzB8T8B7uHj%2BFMhTcBy7NgxdOnShamHt3LlSiQlJVWYSEUMrRTDKg0GA3r37q3oKyAgAF5eXnjjjTdkyUXq1KkDNzc3JCQkQKfToX379tixYwdmzJhBPGQ9e/aUeQn379/PFCh3d3cnYzAYDDh%2B/Dg6deqEixcvQhAETJ8%2BncgOqFQqWK1WrFixgvDIDh48iO3btyvaVavVUKvVCA0NhY%2BPDznv4eGBefPmEe9cQUEB%2Bvfvj7CwMJmx16BBA5k3UJoARafTycTlz58/jz179pDPRWMPAMaPH08MQXG8YpijwWDA4cOHERwcDMDBP7RarTCZTDh%2B/DjhIkplF8RxiGVzcnJkBuHu3bshCAL8/PwgCAJiYmJgs9nIXG7cuMHUxKMhbZMub7FYEBsbi23btgEAmjdvzmwjPj5eJrxOt0MLtlcEzuHj4ODg4OCoHNzD93jgBh/HXwaurq6IjY1l6uEFBwdDEIRy9fD%2BCA4dOoQqVapg9uzZiIyMxLRp0wA4jIb4%2BHjs27cPZrMZiYmJuHfvnszLs3btWlIeAI4cOQIXFxeF%2BDprbm3atJEZBy4uLnBxcZFxtsTPtVotNm7cSIwmKbdNpVLh999/h4eHh0x7rk%2BfPoQjKIIeV1BQkMzokXrNRE%2BXqCMXHR1NjD8pWrduDUEQ8MILL5Bzu3fvJkLpLi4uWLp0qSy5jUajISL1okeX5jDSiVXEZDgA0KtXL9jtdty9excajYbw88Q2cnJyiBHm4uKCIUOGYILr0%2BQAACAASURBVMqUKdDpdJg5cyZzPS5cuACDwSALLQWADz/8EADwuyJm7%2BFYpPxCMSmMuG7SeXNwcHBwcHBw/NngBh/HXw5WqxVqtRo//PADXn31VVlopIh3332XhN49CjIzM4mxIBoaKpUKM2bMQKtWrVC/fn306dNHlmlSNAykhk5hYSFycnKI4dSsWTPk5ubCaDQSD5jNZsOFCxfwxRdfQKfTwc3NDQMGDAAgN7jEEEMR169fJ8LnopFmsVjg5%2BcHLy8vvPzyyyQZiWjsSMMVxTlJcebMGTSQpAFXqVR49913ZXO8fPkyIiMjsXbtWhIuK8XJkydhs9kQFRWFyMhIRERE4PTp08TgKy4uxrPPPov69euTOqLR7OXlhRdeeIFk2xT7NBgMmDx5sqyf/v37k2Qsb731FpnLwIEDZYaxKMcg7hOz2YwNGzZg7ty5sFqtmDdvHgCHAL002YqYUVWr1cLFxQXr168n9QGHVMeYMWMUSVsCAgIIX1Fca7vdjjt37pD5OAuetIWDg4ODg6NycA/f44EbfBx/GRiNRnz55ZfIzs5Ghw4dcOvWLacFrMuDNIHKrFmzyI98q9WKiIgImM1mtG/fnvDhbt68iT59%2BuDSpUvYunUr0tLSMGzYMGi1WmJQDB06FG%2B88QYAh8HUqlUrYoAkJiaS/7/33nsIDQ1FWFgYevfujevXr8NkMsFoNCIsLAy5ublM3T1vb298%2BeWX6NOnD4KCggA8NOA0Gg0yMzNhNBrRoUMH4uUsLi5Gs2bNiME3ZcoUBAYGIjw8XOY5y87OlukURkRE4JNPPiHHpaWl8PPzw6VLlxAbGyvT5BMhtmez2WA2m2WC74DDON23b5/M01WlShW8/vrrWL16NVJTUwnfEnBc99DQUMydO1fWT1xcHDF0P/vsM2IIrV27lvy/SZMmCAsLA/DQUBN1CD08PNCjRw9s2LCBfO4pEX8S2xA1CwcPHkzqA8Dnn3%2BO5cuXK5K2TJs2TSbtQHMRa9N6WBWAmbSlLJMswc8/V3wMBtGeSsBCJ8QAGAkvGDzPR0raQieLYGX%2BoPc9I2GMU0lb6JDYY8cURRRivU8raQvjuijappO2sJJb0ElbWJkgaGkR1trQHFkqIoKVtCUzU35cliNKDmptyiQvKxwPM%2BkDxbel9zBLZYbeNqwMBI%2BUtIVev0dN2kInH7p4UVmG3uvLlsmPGfcYvY8YXxvKObEiDWiOM2PfKFRraOLU00zaQm9AOrlKXp6yTllWZRFMcW763qTqsMZH3/OsNaeHS%2BV9AaBcYtazmL4/WMpB9JZ0JmkLfS%2Bwct7QS8q6XRi3w58CbvA9HnjSFo4nDmeTsfzjH/%2BQ6eVpNBqEhYVh%2BvTpSExMxOLFi0koYHnJWKQ4deoUBg8ejOTkZLi4uGD79u2YPHky4b1JUVpaisjISPz222/kh7%2BY%2BEStVsNms5Ef8p07d8bRo0eh0WgI50osSyf%2B%2BOWXXwj3SxyvxWKBzWaDVqvF888/j88%2B%2BwyRkZHMBCMajQaBgYH4/vvvAYDMwd3dHUajERqNBlarFdHR0cjJycF1yQ8BvV5PDJHu3btj8eLFuHPnDv7xj3%2BUe608PDxQWlqKkpIS6HQ6mM1mhISE4MqVK5g1axZWrVpFZAoqQ%2B/evbFz506SYEX0fNHQarXw9PTEgwcPmO0IggAXFxeUlJTAy8sLeawveQCenp44efIkVCoVIiMjFQLrNMLCwrBz506EhoYqPhMT9ohIS0tDx44dFSLqOp0Os2bNwoEDB/DTTz8x%2B/nqq6%2Bc9j7zpC0cHBwcHByV49y5J99m48ZPvs2/KriHj%2BOpwJlkLKWlpRgzZgwuXryIw4cPo1%2B/frh69Sr0ej1Gjx6N%2BvXrY9iwYZUmY6kIKpUK06dPlyVQMRgMxBgVDUoAmD59OgIDAzFjxgy4u7sTftzx48dhNptRXFxMDMcxY8bg2WefxZgxY9C4nCeGwWDAkiVL4OrqCpVKBZVKRbxpBoMBVatWJeMQwwAtFgtu375NPJH79%2B%2BHIAgkwUmVKlVQvXp1BAUF4fr161CpVIQX99JLL5EQ0AMHDiA8PBxdunSpcH0KCgqIkeju7o5p06bh5s2bCAgIwIwZM3Cz7A2r1EtYp04dpKWlwc3NDTExMfDx8UGbNm2wYMECUsbd3R1Vq1YlxzqdjsxVFJEX4evri7p16wJweO/WrVtH1lk09lgJWfLz83H79m0IgoAqVarIPtNqtdBqtQrO3qlTp8jxc889R0JopcaeeE4Uk5fCZrNhxYoVyGG9Ki2DaKw7A560hYODg4ODo3JwD9/jgRt8HE8d5SVj8fHxQfXq1ctNxtKtWzds2bIFxYxYhYkTJ2LNmjWPPKaaNWuScFE/Pz8AjnDM7OxstGzZEoWFhSSMs2fPntDpdHB3d4der0fNmjWxZs0a5OTk4JVXXpHprj333HPk/%2BHh4Rg1ahSMRiPsdjtMJhPmz58vG4doBAmCgHHjxkGn08HDwwOnT5%2BGwWDAF198QYTBAYdXq0WLFkQf78UXX0RISAj0ej32798vkwNo164dmZvYBwDExMQQcXVp%2BGFOTg4aNGiAHj16EENYrOPr60vK3bhxA5GRkSguLsa2bdvw4MEDnDx5EreZsV0PIbb1zDPPYOzYseT8vn37iKdy2LBhGDFiBDGExDq9e/eGSqWSZVUFgMGDB8Nmsyn2iDh2qSEnTXADOLJpzps3DwcPHsTgwYOJcShek6ioKGg0GnK%2Bb9%2B%2B0Gq1WLt2LfEmqtVqeHl5yZLlhP%2BBPM%2Bcw8fBwcHBwcHxtMENPo7/N9DJWFheG2kyltjYWPj6%2BmLOnDkAHFypzMxMzJgxAydOnEBnmuv0B7Fnzx6YzWZiPF24cAFr1qxBamoqBEEgGTJpxMTEoLCwELVr10ZBQQH27t1LPpOm5Nfr9fD19cVzzz2H5ORktG3bFuvWrWOGSBYWFmLx4sWw2WzIzs5G48aN8StTCLsQP//8M5LL%2BBoTJkwA4EjWYjKZ8JuEX/HTTz/JQhJFQ2bHjh3YvHkzAODWrVuy6zBo0CDs2rULt27dgs1mI8aHmJBFBK2jZ7PZ0L17dwAOL6XJZJIlNjGbzYSveP36dSxevJh8tmjRIvJ/jUaDLVu24LXXXoNarSZ97Ny5E6JQvOgtBYCMjAykpKTIOHUiPDw80LJlS8LbM5lMuCDhl5lMJkycOBHdunXD%2BvXryVxFD9/48eNhsViIN3LHjh0oKSnB7t27iYFptVqRl5cnCzt1VoMPcJLDR5M1GIQyRTQrdYIZ7UqTUhiFKmvXqTKMOgo%2BDGuATrSjIFYx1kYRVUy3wyLnOFFG4ZxlEW/oc/TLKxbxhj7HCIump83qmubi0FNicfgUS8xaG2rizL1FnXRmiZ3hTNFlnNrXLG1O%2BuJVdgwGLY3mWgLKRWeFw3/1lfyY5uzRnD5AsQeYRBwn7hdFPcbaKKrRZVibjd6zzlxw1iToc848A5zg2ypOsuZAj7myY8YpZ7jSzD1LzZtVRjEvJxp%2BlCVnFaqELfH/Bu7hezxwg4/jqeNRk7G4ublh48aNJClH8%2BbN0b9/f1gsFmzduhVDhgxBeHg4CX8cOXIkAEfopwi73Y4PPvgAoaGh5C8sLAx3797FSy%2B9BJ1ORwy7Nm3aICoqCt999x1UKhXh5tWrVw/Vq1cnc1m5ciVsNhuSkpIwaNAgklhFhOiN8/DwwP3793HixAlERkbi5MmTsNvt6Nq1K0JDQ5GXlycLbWzcuDFSUlIwbdo0aLVaHDp0CDExMcjNzcXt27fh6uqKvLw8hISEoLCwEIIgkKyTovfqzJkzUKlU8PLyUmT9ZCWIUalU2LJli%2BK8KJQugubH0W3Z7XaZZ0pqKNHQaDQoLi4mhubGjRvJZyUlJXjppZfw7bffyvpo3bo1yaJ67tw5hISEkM/%2B9a9/KfpQq9UwGo04efKkzPBcuHAhs6y/vz9ZQ3EeUq%2BmOEdBELBz507ZutIvLkR5Bmewe/duTJw4UfaXQHMDKc%2Bk4hiAQgqSOsGQigQoXiurUGXtOlWGUYfumjlAJ9oBnRGVsTaK90p0O4rBOFdG4Zwtyyhb4TmJ9iTzmHWO8WKMnjara0l%2BIgDKKbm5ObGPWGtDTZy5t6iT/8feeYdXUabv/3NaegJJICQECKAQCASEgEgvkRIUEAQUVHSjFEEERZAoiAVQfyuiq0aE1bXQEaQoQiChSEBpAqEk9GpCh5CeU35/nMzLmZk3kF3Z7%2Bru3NeVS2bOO2%2BfcZ55nue%2BKzLF2qWTXaMtU6F9LWF51i3e7Y6BiAjNCRk5k3bSyxiEVUhMVB%2BX5bULyPJ3NXtAKjlagftFd51kbnSXacvINpt2z1ZkwWWD0J6ryDNAc07yCNCflI1B2%2BfbHUtOlZFm36p78j2rGbesjG5cFaj4X5lyWSFpn/8DMAy%2B3wfD4DPwb4GicRcbG0unTp3YtGkTX375JREREYIRMy0tTcgTlIegoCARfrhz5042bdrE9OnTiSj7v69nruC2bdt4%2BeWXGTVqFGfOnKG4uBiXy4XL5RI5XcqLemlpKbllX2OrVatGZGQka9as4fr16%2BzYsYOgoCAOHjwo%2BpGWliYMILvdjsViwWw2k5eXR1FREX379gXcBotiOMTExNCmTRtcLhft27fnwIED9O3bl549e5KVlUVmZiZDhw4F3Ll5Dz74IABPPPEECQkJrFu3jnHjxlGpUiXuvvtuPvzwQ7y9vUXopCcpijLW48eP43Q6yc/PV4Vkwk0Pn8PhEMaY0%2BlkwIABooxCNHPXXXfRsGFDHdkNuD1Yhw4dYtGiRVStWpUXX3yRrKwsDpSxud1OAF0JUZUJ1nuW8STpyc7Oxul0MnXqVFq2bMnx48fFb7m5ubo2CwsLKSoqEuuv4IEHHlCVc7lc2O12fvvtN2HoKVIXeRp6NaWeoqIisXc8zyswhNcNGDBgwIABA38kGAafgX8LPA2xHTt28PXXX4v8q6ioKJU0wJ2CNldw06ZNoi/79%2B8Xf4cOHSIgIEAVBli/fn1yc3OZO3cu4eHhPPbYY4IIJCcnR3iwrFYroaGhrF27lv379xMcHEx2drbQ5ksto1338/Pjk08%2B4Y033gDcxC9nZCFAZRg4cKAwfrdu3cqqVatITk6mdevWBAQEMGnSJDp27MiUKVMoKioiPDwcp9NJ48aNycrKwul0qjxqdrsdHx8fSkpKsFqtNGjQQBg0iYmJvP3221SpUoWsrCxVnxXmULvdzrhx41Q5iQouXryI2WzmnnvuYfDgwaxbt071u0KWohhOWmNMCe/0PF%2B1alWWLFkiPJZxcXEqr6Iigq6wo/p4fKG12Wy6vLl7772XrKwsPv74Y3x8fER5LbPmCy%2B8wKuvvkrdunWFpEVYWBhFRUUUFxdTr149wsPDAbcX9f7776d69eqMGTMGcDN/LlmyRGW85mi5ug0YMGDAgAEDvwuGh%2B/3wTD4DPyfQyFj0XpQ4PeTscDNXEHF4/T2228Lb6Pyl5eXx88//yyMJF9fX6pVq6YiYxk5ciTgzt1q2rSpCA1csmQJNWvWxGQy8be//Y2LFy/y7bffArBixQrgJmnI6tWrqVu3LhaLhYEDB5Yb5uiJlJQU7rvvPmkO4UMPPURwcDBDhw7F19cXu92u82IpUMIm7Xa7Sr7hiy%2B%2BICkpiUuXLhEdHS1yIatXry5IRKpVq8aMGTPYunWruC4yMhJQk4rIpBBsNpvol9Vq1fUtMjKSv//97yrv4ZUrVxg4cCDnyzTbDh48qGJi9WThdLlcqr1TXFxMixYtVG1s376d6OhonnvuOYqKioRxr4xBwcyZM5k2bZrKY3j8%2BHE2btyIw%2BHg6NGjwoArLi4mJSWFkJAQEhISMJlMZGZmMmDAANU8GKQtBgwYMGDAgIE/EgyDz8D/ORQylscff5wDBw78y2QsOTk5vPnmm8KIi4uLIz4%2BnosXL9KxY0eulKmgtmrVirCwMEwmE5UqVaJLly6sWrWK6dOnY7VaOXz4MKtXr%2BbixYvk5uZy%2BfJlEhISeOuttwAYPHgw%2B/bto1KlSnh7e4twUnB7ktq1a8fatWtp27Yt27dvV72wf/fddzRu3Biz2cyVK1dYu3Ytq1evFvmEc%2BbMAdxG2PPPPw%2B4iVRq1arFggULRA7fs88%2BS69evZg9ezY//PADjz/%2BOB999BEWi4XKlSuLcFPFa1a7dm38/PyE0eWpxZeYmEhWVpb4S05OBm4Sv4DbYDKZTCrjRQklPXLkiOh/cnIyGRkZREdHExMTA7jzHMEdEikzcK9cuUJcXJwqR8/hcGA2m0Vu56VLl/j%2B%2B%2B954YUXRF3g9g4q8%2BsZ8rlgwQJdO7Jw1szMTJV30Gw24%2BPjQ0hIiDhXWloqyG88jVWlvqZNm7Jp0ybMZrPOmA0LC6tQfqoCGWlLp07d1IU8pCSkx0iS6jViyVIVCe1FWuFmWb1aUXVZ3doyEiVfHVeJVtxZ1nhFVLa1wtfA2bOaE1oxJ52COhViB9HVKxOP12pIaspICSa0aucSj7FuKiQLrKtbwzpSEdIWKfGuZj2lY9CuuWTttNdpp6oiwusy0hudTr2MtEU7MG0ZGUuFNvxaRsiiXSvJuujGoBFVl%2B5zLZHL11/ry3iQhwGg0feUdkc2ydpIFC2pzE8/6a/R3vMy4XXtvSojdtH2R3uNrL%2BaBZc%2B6/buvX3/NHtCt7UkzzHtHpbdL9r%2ByPqnrVrG5aQto50KmWB62fdTAd29ITlXkTL/KfzZPHzXrl1j7NixtGnThnbt2vHqq69KCeYUpKSk0Lt3b5o1ayYcIwo%2B%2BugjGjZsqHNeXNLeI7eAIbxu4I6jS5cuDB069Jb5ebm5uXzyySesW7eOS5cuERwcTNu2bRk9erTKoAK9oLqCmJgYYdCAO%2BQuODiY8%2BfP88MPP9C9e3dcLhdt27YlKSmJunXrkpOTw%2BzZs1m%2BfDkLFy7k2WefpbS0FIvFwsaNG%2BnevTtPPvkkgwcPZuPGjQwfPpzhw4fz4osvsmzZMmbMmEF6erqqf0VFRXTu3JkbN25w9913U7NmTXbv3k21atU4dOgQcXFx1KlTh/r16zN16lRCQ0N5%2BeWXeeONN4Sx6CkOX1paisvlIjAwkI8%2B%2BoiWLVuSmJjIjh07hLHj7e1NVFQULVu25OzZs%2BzZs0cwRVqtVqKjozlx4gSFhYWCbER2qytGjNVqFd4mpZ6oqKgKi657onLlyly/fh2LxYKPjw8FBQU4nU7RB61YvdK%2BJyuoMsZmzZrx888/4%2B3tLYxRGcaPH89f//rX2/bN29ubTp06qbTyTCYTlStXplmzZmzcuBFwM7EuX75c1UcFTz/9NCUlJcydO1cn/QBuMXUlp/N2kAmvjx79PM89N6pC1xswYMCAAQP/C5B9a/i9aN/%2BztepYPTo0ZSUlPD2229TWlrKmDFjaNy4MZMmTdKV3bdvH48//jjvv/8%2BnTp1Ij09nVGjRvHll1/SokULPvroI86dO6eT9vpnYHj4DNxxVJSMJSkpibS0NPbt26cjY/FEq1atyMrK0umoASQkJIhcwZ07d7Ju3TrCw8PZvHmzIAdJTk7mrrvukur9paWlqTw%2Ba9euZfDgwao2OnToAEC/fv10xh645Rc2btyIr68vDRo04PDhw6Snp3P06FGcTic7duxg2bJlgiHy8uXLTJgwgYKCAjIyMgBU4Y2KEda%2BfXtat26N1WqlWrVqVK5cWYQkulwu8vPzWblyJTabje3bt/PYY4/h7%2B%2BPy%2BXiwIEDIqyyUqVKhIaGCi%2BXxWIRbSjhoKWlpZSUlKjkBcrLOezQoQNvvvmm9Ddwf9VSyFDy8vKEQeQ5Rk8j6a677mLp0qUEBgZSrVo10U%2Bn0ylyPbWMmVooXkqlDU/vnpeXlziOiYkhOjoagFq1ahEZGYnVauXatWukpaWJ%2BXC5XISHh6v6rHgUH3/8cWJjYxkzZozYk0qfvb29af9P/B9ERtpi0LYYMGDAgAEDf15cunSJ9evX88ILLxASEkK1atUYOXIkS5culabCXLt2jeHDh3P//fdjtVrp2LEj9evXV7HO/14YBp%2BB/zooOXwulwuHwyH10ly8eJEjR47csTa9vb3p1asX586dIycnh927dwsjoFq1ahw4cICMjAxq1aolxNC7dOmC1WolIiJCGK0ZGRkidy8lJYXY2FgaNWrEypUruXLlCufPn8fHx4dJkyYxZcoUvL29OXv2LKWlpQwYMICCggK8vLxo0qQJVqsVs9ksjCjFsFU09DyNolatWrFixQrCwsJURs78%2BfNFeKOSR5eXl8fAgQPx9/fHYrGQkZGBzWZj9uzZAMKg0jJ8KsQmFouF1atXi/Nms5lr165x/fp1unTpwiuvvALAlClTBGGKn5%2Bf8ECOGTNG5EyaTCasVqsII1U095S5V6CMd/To0fiXcdqfPn2a7Oxs6tevT2JiIoMGDcLlchEUFERoaCiFhYX4%2B/sLD3KTJk2wWCx8//339OnThxUrVlBUVITNZhPaezab7bbGqSeMHD4DBgwYMGDg9vh3hHReuHCBAwcOqP4uaMPq/wUcOnQIi8Ui3ofAnd9fUFCg4gxQ0KFDB0aNuhnZY7fbuXjxoiCyA8jKyuLRRx%2BlefPmPPDAA2zZsuWf6pNh8Bm4o%2BjSpYtKGy8uLo7Bgwez3SMPIDc3l3fffZf4%2BHiaNGlCu3btGDNmDIcPHxZlJk6cKPK3PHHs2DGio6N5//33cTgcrF69WrTVuHFjYmJiOHv2LNu3b%2BfatWuYTCZpruCGDRv48MMPKSgo4PLly2RnZ6u0%2BqKjo4WunwKXyyXy6po3b07z5s1FXp3dbmfAgAHs3LmTDh068PbbbxMcHAzoNeuUh8nGjRuF0REbGytIZhQDTdGy07JvFhUVERoaSseOHYmPjyczM5PmzZszcOBAwE3Wsm/fPkpKSoQnsHr16jpvkmeIZ2ZmJlFRUQQHB4v%2BOp1OBg8eTOPGjYGbeXS7d%2B8mNjaWgoICHA4HzZo1AxDjVfqrHbciVu5yuZg1a5Y4f%2BTIEZKSksS/33vvPQICAvjiiy9o0KCBKKf0/6OPPhJeWJfLpTIsFQ/l7t27xXllnC6Xi%2BHDh/P2228DbsMxMjKSw4cP88UXX4g8wIiICBo3bixyOpXx7Ny5E4fDwcKFCwE3c6jiHd28ebOYo69l%2BTXloEI5fFpyIwnZ0e1EeMvSWdXQJorIcgsqkEeny%2B/Q1CNzYuryWCRfPLXdkUb0aq%2BT5PjoqtZ2SJJUUxEBcl1/JPOnWypNGVneTUWEzXU5PpJJ1pXRjFOWw6edP1nKlG6%2BJOPWjasC66vtbwU0tqX5ebcTnJc2rl3fikx6RdSxJROovYW0TUuTbLTPFJk4u9YT8N13uiK6e1V2U2nzabUvp7J8W22OnOym185FRQS%2BK6Ja/q9sHFkZzc2qu39kc6UZkyzXTbsfZfe8drpkOXxaUXftFpZNufa5L2tbeztXpMx/Cv8Og2/RokX069dP9bdo0aLf3ddr164REBCg%2BrBeqVIlAK5WYELfe%2B89/Pz8BAN8eHg4NWvW5N133yU9PZ0BAwYwYsQIqfFYHowcPgN3FNr8vcLCQhYsWMDf/vY3Vq1aRXBwMI888ggRERHl5tVFR0czceJEiouLmTlzpqr%2BY8eO0bNnT1JTU%2BnWrZsqh8/Hx4eGDRsybtw4mjZtSqNGjbBYLAwYMIANGzaocgVPnjzJXXfdxVtvvUXz5s1xOBzs3buXLl26cP78ecxms5A6sNlsREREEBoayq%2B//io8Z%2BD2JDkcDpo0acL8%2BfPp168ffn5%2B7NixQ9QBbs%2BPksOmuPNleXXKNSaTierVq/Pbb7/piENcLhdt2rTh1KlT9O3bly%2B//JKEhASmTp3K/Pnz%2Betf/4qvry%2BXL1/Gx8eHoqIi%2Bvfvz7Jly1TeI5vNJvpSt25dvv32W9q2bSvYPRVZB0WSIjAwUCVi7onycgRvh7CwMC5duqTzavn5%2BVFaWsobb7zBK6%2B8oqrfYrEQGRnJ2bNn8fb2Fnp7nsjMzGTSpEksXbqUcePG8d577wEwYsQIlixZwuXLl1VhrZ6IiYlh0aJFNGnSRDqmKlWqkJ6ervpy54l%2B/foJo/J2MHL4DBgwYMCAgdtDo6p0R9Co0QUuaiz1qlWrikisW2HFihVMmDBB%2BtsLL7zAP/7xD37xIFmz2%2B00atSIr776ivvuu096ncvl4r333mPFihV8/fXX1K1bt9z2BwwYQNu2bRk7duxt%2BwpgrVApAwb%2BRSjaeAsXLmTz5s1cuHCBvLw8kpOTRU6Uklfn6%2BsrpAIqgvDw8FuSw9SuXZvTp0/TtGlTJk%2BeLM5nZ2fTpUsXJk6cCEBoaKgwfBTNPbPZrPoyU1RUxK9l7H6e4YKK3tyvv/7KsWPH6N%2B/vzAu4KYh5HQ6sVgswsh6/vnn%2Bfjjj3WkH8q/XS6XMEqsViu1atUSYZAtW7Zk165dFBcXk5ycjNPpZOnSpUyaNIlevXrx9ttvc/nyZdE%2BuNlCtUaVZxz58ePHOXLkiPDCKf1SvHRms1mETQLMmTOHWbNmsWvXLiFCr9QXGBhIXl5ehQzA%2BPh4lixZopOWKCoqwsvLS4R3ev7mcDg4ffo0cNNr6O/vr%2Bpf48aNRejqjBkzxPm5c%2BfSsmVLNmzYUG7/Ll68iJeXF8HBwYLpFaBmzZpkZ2cTGRmJy%2BWiRo0anD17VhjpytoOGTLktuNWYOTwGTBgwIABA7fHvyPbISwsrELGnQx9%2BvShT58%2B0t/S09PJy8sTKUbg9vqB%2B51TBqfTSVJSEvv27WPBggXUrFnzlu1HRkb%2BU%2BGnRkingf8TKJt%2B3bp1DBgwQEWpr2DChAm0bdv2jrWpaKUp%2BV4KVqxYgb%2B/P7tlNOrcJIJRNPXWrFlDfHy8kHbwzLfbsmULTz75JC6Xi9WrV9OrVy/hIQsKCuKLa/VGCwAAIABJREFUL74gIiKCwMBAMjIyRI5ZQECAMBICAwOJiopi06ZNdOrUSYRG3nfffZhMJiwWi4pMxd/fXyX0De4HxQ8//EBgYCAJCQkiDLOwsJDatWszaNAgUZcCLy8v/Pz8hFFYXFwsfnc6nTz99NM8/fTTPPbYYyQlJakM4DfeeINdu3YB7rX1NB5v3LhB1apVAbe2n8IAqshAJCYmirj0l19%2BmYiICFwuFw0bNhTe2uDgYFXuZWxsLE2bNgXcxqdSDtzeQE/6YnCHBD/88MOYTCYiIyPFuCIjI0VeIMCbb77Jnj172LBhgyDnUWAymfD29hZtNW/enEqVKlFQUMDKlSvJzs7G39%2BfPn36qHLxbvVFzoABAwYMGDDw342GDRvicrnIzMwU55R3wPKkm6ZPn86RI0ekxl5ycjLbtm1TnTt27NhtjUJPGCGd/%2BPwDGEEtxEQHR3N2LFjuffeewF3zt2nn35KSkoKFy9eJCgoiLi4OEaNGkX9%2BvUBRAjm3r17VV63/fv38/DDD%2BPj48OaNWvo1q0b77zzDg888MAt%2B6Ol6Ff65nA4cDgcwptisVjw9fWlfv36PP/887Ru3RqAZcuWibwwQBWGabfb8fX1ZcWKFXh5eZGQkKDyDsFNgpHS0lKqVavGhQsXhEdIMVaVvnh5eVFSUkKNGjVITU2la9eunD59msjISFJTU3nppZf4/vvv%2Bcc//sFf/vIXQB4CGR8fz9SpU9m6dStTpkyhqKgIu90uyoaHhxMUFMSZM2do3rw56enpqrBR7RwpvyneSm1OnRaeIZ7gDp30nGttWaX%2Bjh07sm/fPkwmkxBODwgIoLCwkOrVq5OdnY2XlxcPP/ww33zzDe%2B8847wrmr7r6BOnTo0atSIH3/8UfRbmQfF4AsKCuLSpUtYLBYCAgJURnF5Iabltaf9LS4uThi02jJ33323ikFUi82bN6sSrW8FWUjnqFGjhCajAQMGDBgwYADWrbvzdXbteufrVPDCCy%2BQl5fHu%2B%2B%2BS0lJCc899xwtW7bk5ZdfBuDJJ5/kkUceoWfPnuzatYtnn32W1atXS4nfpk%2BfzubNm0lOTiYyMpJ58%2Bbx4YcfsnbtWtVH7FvB8PAZYNKkSSqP1f3338%2BwYcM4c%2BYMeXl5DBo0iCNHjjB79mz27t3LkiVLCAkJ4ZFHHiErK0tX39SpUwWRihLe9t577xEREVEhw2PSpEn06dOHnj17qgTCMzIyxIvwmjVriIyMZPLkyWzZsoUuXbowYsQIEeoH7lyrHTt2EBoaKjxZCtPk/PnzqVGjBs888wwul4uwsDDVlxhPopTz589jtVp1N6FiHCieqJycHIqKivD19QXcQuUNGjTg%2B%2B%2B/BxDGHujzxgBSU1MZO3YszZs3B24alkrZnJwcDh8%2BTFFREVu3bgXQfSlSmEnj4%2BNVMgzPPPOMykOnhRKS6VnG4XBgMpmEsec5/tLSUoqLiyktLWX9%2BvVcuKCOg1dCGc6cOYPdbqegoIBvvvkGcIfiKsQm5X1vunr1Kj169KBHjx4AQtQdbhpziuCoQkzj2UeTyaQKvVXG9eCDD1K9enVdezabjcaNG%2BPt7U3Lli3L1fRzOp34%2BfmRK2XbcKNy5crl/qaFjLSlWzcNaYsHo6n0GAkHgUYsWScSDnrCi7I95QndrSqhiNaJDWuFzSVrrBXZZs8eff9uR6wBetH0lJTbFkErrSJLetcQNMia9nhcuJGaqi%2BkZXHQlJFG9GqFdCUfFnTXyfajlmSiAqQt2kukMpyaeqQRRdr%2ByCZQsy%2B0nB5SshXtXpKQtuh0iCXC9ZSRLJV7kYxUQStToyUqAf1aSaRtdFOxb5/6WDZwrai6jKq9IuLsWsFxGcuI9mGxfPmtjwHK9EvLrQPg4MHbt609V8YQLSBTNr%2BdYDrApk3qY91DAd1a3Y5wCfT3i4z4uyKkLRXh5dI%2BM7XdkREsae9N3XMX/VaXlZFN%2B38Cfzbh9TfffJPAwEDi4%2BPp3bs3TZo0UZERnjlzRnyoXrp0KTdu3KBz584qYfXExEQAxo0bR4cOHXjqqado2bIl33//PV9%2B%2BWWFjT0wPHz/8yhPJL1bt248%2BeSTXLhwgeXLl7Nu3TpdGOb/%2B3//j7Zt29K2bdtyPXyeJCs1atSgV69edO7cmRdffPGW/dm7d6%2BUtGXWrFnMnDlT1OeJrl278tRTT/HYY4%2BpRNLnz5/PV199xdq1a3nttde4ceMGM2fO5OLFi7Rr146OHTvi7%2B/PzJkzy80fVLxKMokHxRPm5eXFtGnT%2BOSTTzh58mS5c16/fn0VI2l5UAhXlPZlnqlKlSoJkXOHw8GTTz5JWloarVu3ZvHixULQfOjQoXz22WfltqXU4%2BfnR0FBgbhOm1unYM6cOcyePZs9e/bQoEEDETYbHR1NREQEOTk5eHl5lSuWbrVaVUa10r4WVatW1SVUa%2BHl5UV6ejotW7ZUza3nnClGYmBgIL169WL%2B/PkqEhpPApc6deqwfPlymjdvTkBAALm5uWIOWrZsCbhzATt27MiVK1fEnlDa%2B70ePoO0xYABAwYMGFBj7do7X2f37ne%2Bzj8qDNKW/3Hk5OTw5ptvMn36dOBmSGdBQYHIuevduzczZ868ZUjnrl27KC0t1emfKeLdOTk51KhRg%2B7duzN37lyGDRsmvG0Kxo8fz2%2B//aYS9V6/fj3g9gqtW7dOSBf06NFDvKR7eXlRv359VVjmrl27uHTpErGxsYDbC9e4cWNKS0uFhyc4OJigoCC2b9%2BO0%2BkUZeEmq6ZybVBQkEi4VQzf0tJSXC4X3t7ewkj6zoMKOysri1mzZrFgwQKuXbtGUVERQUFBHD58%2BJahhQoUY89kMuHr6ysofi9evCi8pLVr1%2BbChQtkZ2dTqVIlVqxYwXvvvcfQoUMBt6cyKipKSCoo/dKie/fuXL9%2BXZCgOBwOkcishGl6Qqkf3HHpjRo1EmuvEN%2BUZ%2BzJIDP2wO0t0xp8yro888wzzJkzh5KSEqGDp4zTZDIRHBwsiG8Ug%2B3GjRuirYSEBJ555hn279/Phg0bWLNmDaWlpZw5c4aNGzficDh0/dqxYwc1atRg7dq15Gg8B8p6bt68mQEDBlRo3AZpiwEDBgwYMHB7GBK1vw9GSKcBQVKSkZHB2rVrCQwM5OLFi9SrV4/Tp0%2BzYsWKCoV0Zmdnc%2B7cOd566y3hjvYUkgRITEykSpUqUm28bdu2YTKZSEhIoE%2BfPvTo0UP0a50meHvNmjXitzVr1ggZgoYNG0rHqOjagTtuGtyeuaCgIEpKSiguLqZ27dqA29irVasWe/fuJSMjA4vFIow9cBuAilwB3HxpLyoqYtu2bSrP1XfffYePjw%2B9e/cmKytLiJ97wmaz4eXlRe/evUUfgoKCRNng4GB%2B%2BuknJkyYIAwaJXQxIyOD7LIwnevXr3Pt2jVGjBih0qe75557OOgRTqM14Pbs2cOpU6eoVauWGFPNmjUZOHAgly9fFkQ6ilEFbk/rvHnzCA4OpkWLFlSqVIn0slC5J554Al9fX3x9fQkJCQHcRDN16tTBZDLx9ddfk5ycrJsHz3BShflTqzHj5eUlDMs9HmGAffv2BeC3334T55599llsNpsIiQC31zQuLg6AxYsX88ADDzBx4kTWrl0rxm6329m/f78g1FFCdFu0aIHFYmHw4MHs2LEDi8VCs2bNVIyubdq0qbCxB4bwugEDBgwYMGDg3w8jpPN/HDExMVItu1OnTjFs2DCmTZtGQEAAW7ZsuWVIZ9euXYWHzzOkc%2BPGjQwfPpx58%2BbRokULwE0C88knn7Bu3TqVNt7o0aOJj49XhRAqYZQmkwmbzYbdble9ECuhjIoXRyFx8fb25vLly8IgnTFjBrNnz8ZisaiYKktKSvDz86OwsFBo6nnCbDbjcrno3Lkzx48f5%2BTJk4LQxeFwqPpitVpF2KAi%2Bq6MRSGNUUIkLRYLxcXFoozJZNJJNPTo0YM1a9bccv1at25Nfn4%2BNWrUELIN/wrMZjNbtmyhe/fuujBH0Ofamc1mfHx8KCgoEKQ1SphmREQEdrudq1evqnIAld%2BV%2BVfmum7dugwaNIj09HS2bt0qQiQDAwPp2bMnixYtIjIykqtXr1JQUCD2gWeffH19hXfyVmjWrBmJiYmMHj1ajDEmJob77ruPhg0b8sorr1BaWkr//v1JS0sT8haeePDBB%2BnQoUO5%2BjsDBw7krbfeum1fAA4ePKgjf6lfv75KcJ60NOjSpfxj3Gk/KuLWnTuh7H4DdxqTLtS/pAQ87%2Bnt26GMqKncenfvhrIcUwXnzkFk5C3KFBZCmdGs4MYNCAz0OPHrr9Csmbp/2sZdLtDmoZ49C56h3Rs3QqdOqiKnTkFUlMcJ7TiPHwcts6pmbmRNHz0Kd9/tceKnn6B9e3WhK1eg7KMHAFu2gMeHE908gH6xTpwATa5ucTF4e3ucyM2FMgbgcvucnQ0REeKwoKAYPz9vbnXRyZNQ9g3qJq5ehTImYXc9oPtuoR13URFoP3Y5HODxLNbOhWTb6PeEpJCme%2B78PC0Jwt69UMb6K71IV4nk3JEjUK%2Beuox2P2rnQTIEDh%2BGskgZeQHgH/8AjxxwvvsOyj5yCXz9NXhKwnz8MTz3nLqMZg9Ix6nd2KmpEB9/8zglBbR5xtp7SvdQwJ0j58koKNsT169DmTg1oL83L16EMgbo8sYk3Y%2B//AKtWpXfF9A9KPLyQBWEJLnHNFtY/0yQXCapRteWbAza67TPgPx88PdXX6N9lEi2Y4W2vmw5/xPQprLeCZTDH/hfCcPg%2Bx9HTEwMPXr04P3331edj4%2BPZ%2BjQoUybNo3Y2Fjmz59/y3oUg2%2BjJnlbZvD9s/3xNEoVg8/Ly4vw8HBatmzJ0qVLRYikYvA5nU4KCgpYt24dtWrV4tVXX%2BXbb7/VGa0lJSX4%2B/uzZs0aqlatqn7R9oDZbKZmzZqcKmMx8Mz5CggIIC8vT2jBafPTtPA0OG9FYPPoo4%2BycOFC0Z7sVg0MDKRVq1Y899xzPPTQQ9J6PI3KW93uSv6e53XKGD3Ru3dvWrRowZtvvondbtcZfP7%2B/hQWFqpE50tLS0U5RVtQMQRbtWrFp59%2BSnx8PJUqVSI7O1tIRAQHB3Pp0iUiIyM5V07muCKBcf36ddFGeRg0aBC1a9dWCaNr5yU4OJiuXbty5swZtm/fLtZIqXvDhg1Ur16dpKQktm7dqgrt9Pf3JyUlRcqyJYMsh%2B/50aMZpX1RM2DAgAEDBv6HsWrVna%2BzV687X%2BcfFUZI5x8QXbp0oVGjRiIsMi4ujsGDB7Pdg3kvNzeXd999l/j4eJo0aUK7du0YM2aMigxk4sSJKkYgBceOHSM6OpqzEiat/Px8Pv/8c65cuULHjh1xOBzs3r2bmJgYFXNQbGwszZo148svv2TZsmWcPn2a7OxsoqOjxV%2BDBg149tlnRd3Lli0jOjpaV09sbKxKpqGoqIikpCQ6dOhAbGwsDoeD8PBw1q5dS79%2B/TCZTFStWpXExEThGQPw9vZm7NixBAYGctdddwFuQzQ2NpalS5eK%2BseNG8fUqVMxm80kJCTgdDpp3769NBxUCR90Op3C2ANUZCZOp5MqVaoIdkatsWexWPDy8lLJOTidTh1rpjb/8cEHHwTcBpMiR9CkSRNCQkJEHqLdbmf9%2BvUMHDhQda2vry8mk4lmzZpRo0YN7rnnnlsae4GBgSxfvpzQ0FDRj/IIW1auXMlbb72lGqfNZmPBggUAgvBFgWLYeRpikyZNEv/%2B5ZdfePjhh7l69Sr169cXuYOtW7cWDFSeoY9ms1lluIeGhopcO6W/8%2BfPF4a1Z9lvv/1WGHtjxoyhZs2aWK1W1VpEREQQEhLC/v37VQa50n%2BFKXbZsmW6PL78/Hy2bNmim7PyIMvhM77AGTBgwIABAwbuJAyD7w%2BKOy2VcCv8%2BOOPwvDq1KkTmzZt4ssvvyQiIkK8DFetWpWFCxeyb98%2B1q1bR9%2B%2BffH39ye%2BLNTDbDYTEREhJBT27NnDuHHjVN4scOeeeQqXK38/lPnqXS4XqampLF%2B%2BnCtXrogX%2BEuXLgkJBZfLxblz53j99df59ttvcblcOJ1OweqZnZ3N/jI6Z6vVyo4dO4QId0ZGBk899RTLly%2BnY8eObNiwgZo1axIXFydy%2B5TxbNmyhUOHDmE2m7nnnnuEwKViKCqSAQUFBURFRXHPPfdI59fTkFPywd544w0aN24sjjdv3syhQ4eEJ7Vy5co89dRTAILcRun/lStXALch2bt3byIjI9m9ezcTJ07EZDLx97//XXjISktLOXv2LA0aNMDLy4uIiAiRw/b555/j4%2BODyWTi6aefpkqVKuTl5Qlj1Gq1Mm3aNFG%2Bl8enME9DMDo6mgcffFAY9z179hRz7wklJ9HlcnGkjL9aMbQUxtWUlBR%2B%2B%2B036tWrx7Vr1zhw4ABWq1WU92xfuVaZj8TERNGv5s2b07dvX0wmE1OmTAEgPT2dt99%2BW%2BQVfvLJJ5w/f16ECyt79eDBg2zcuJG8vDwCAwPFOO666y6qV69ORFn4kGLgW61WwsLC8C6Lr1G0ICsCI4fPgAEDBgwYuD3%2BbLIMfzQYBt%2BfAL6%2BviQmJhIWFsbmzZuZM2cOeXl5JCcnc9ddd2EymYiIiGDKlCkMGjRI6JNVFJ6kLTt27ODrr7%2BmaVl%2BQ1RUFL6%2BvtSuXZvRo0fTtGlTHnnkEex2O0uWLBFGkKzPQ4cO/ac0yTyxaNEi9u/fz/79%2B7FYLHTs2JGRI0cKYyQyMpLXX3%2Bdhx9%2BGHCTdtSoUYNWrVoREREhvHV2u53mzZuzdOlSXC6X8D5u3bqV1NRUHA4H2dnZZGRkMG/ePNG%2B0%2Bnk0UcfZerUqYDbe6gwjjqdTn788UdVft2uXbvKzaGzWCzUqlWLpKQkwVz5%2Buuvs2fPHpF31qFDB6Kjoxk8eDCAiiTmaplQTq1atRgyZAgBAQHCi7Z3716qVKmCzWajf//%2BmM1mXnrpJSFcn52djcvl4ttvv6WkpITs7GwhKD5ixAiKiopwuVwsWrSIjIwM0T%2BXy4Xdbmfy5MnsK9OJ8iTO8dR%2BycjIYNWqVWLfKZ4%2BraezyEM4aO7cuQDCSP7pp5/Eb4p%2B38GDB2nTpo0uH85kMuHt7S0MLMWg/uqrr0Sbez00sjZu3IjJZOKBBx5gwoQJYs86HA7sdrsg4VG8eVFRUSxZsgRws3oqdR47dozffvuNnTt34nK5xBrZ7XYuXLgg5q5Dhw5UFDIdvq5dNfkxWjElmfCZVrtLI%2BQkIVrVi7nJmFK1IceSPEmttJdWaEqmO6V7RFVA40qqy6btj0TCQ0cWqx2nRHOtIm3rtLIk9eimS1NGJvdGWQ7trRrXtS1ZF924yz6MKJDp8GkrlpLnajst6Z92CLL11V6mHYJ0vbUnJfXq5qYiwmLaMckWRluPjGFXe043ERJZSu1CycL8tdp3Zc9PFbT53rIy2qgemeCbVhdQqze4e7f%2BGq22pWTcFRKF045duw4VEbGT5XJrr6tAGZ2unYx1WjMGWdaBdipkU6PdNjJNPe10absj247ax6FsyrVTU5GlM/DnhCHL8CeCw%2BEQUgkDBgzQ5aMB5RJJlAclD648dO/enU8%2B%2BYTOnTsLj5OC8ePH06hRI4KCgrDZbDTTki5o2vEURb9VuatXrzJnzhwmT54swvtSU1PZtGmT0IY7d%2B4cc%2BfOFQbDd999h9VqJTs7G4fDIQypvn37cv78eapXry5y%2BDxDKp1OJ7Vr16ZmzZrMnDmThg0b4uXlRVFREdevXyclJQWn00lmZiaVK1dWGWJalJcnV1RUxNGjR3njjTcAt1j6oEGDhBRGeWjatCm7d%2B/G5XIRHh4uDMyUlBTh/VTE4pcvX85DDz3EfffdJ9gylTGOHDmS3NxcFi1aJMIrAdW/s7Oz%2Bcc//iHy7RQohqPZbL5lbtytchZvhccffxwvLy/27NmjknFQjMP09HQV8ya47wNPmQklb/D%2B%2B%2B9nxYoVAAwePFjsFUXaQ1m7mJgY9u7dW27I6qlTpzh//jwRERHk5uaKtmw2GzVq1ODq1ausXLkSX19fHA4HISEh5OTkiDzSrl27Vnj8K1eulObwNWzokUtadg%2BUe%2BzunPpYI3kilQXUehc9yRIUaDz0ehYNNQcEoGMk0BIUgJ5DQ0fegIaUBP0Qpf3REjpI6tGNU8tQUMG2NVMsrUc3XZoykkv0LC6SxnVtS9ZFN24NW4OOsEVSsWxL6Dot6Z%2BOiEbKTqw%2B1g5But7ak5J6dXMjG4SWgUI7JtnCaOuReOd153QToSf/0S2U9p4D0OZnP/64vkxZxMkty2i0a/WTBTRpoj72JLcBHWkTAGVMzgKSceueWxV53mjXQfYw0Y5Bci/orqtAGS0Biv6GQjcGGbGJdipkU6PdNrq29U3puiPbjtrHoWzKtVNTkaX7T%2BF/zSN3p2EYfH8C5Ofns3DhQpFXN23aNOpoWNvKw5o1a8QLr4KK8PR06dKF8%2BfPi5fYd955h%2BXLl5OUlERUVBTJycmkp6fj7e3N%2BvXrKS4uJjU1lTFjxjBq1CiqV6/OV199pWM5VLTxXC4XpaWlQu9u3LhxPPXUU5hMJgYMGMD69evp2LEj9erVEyFuTqdTlVN19OhRFcOhp9GhvNx///33lJaWirDIjIwMunfvzuDBg2nTpg19%2B/bl2LFjZGRkCA%2BdYmwUFBQQHh7O%2BfPnhbg5uD1M999/P7/99hvHjh0jLi6O9PR0goKCBMOlFp5ELidPnlSRhijw8/MjOjqaEydOcO3aNeGJA7eUwogRIygtLeX69esEBwerjJ6dO3fy0EMP0bJlS9LT04Wo%2BKVLl5gzZ45u7mTYuHEjwcHBwqOoGK8yzcBLly4Jshqtkevj44PNZqNevXrs3r0bs9mMt7c3Pj4%2B%2BPn5MWfOHBH2%2BdJLL0n3Y%2BXKlalatSpHjhzhxIkTgNsA3rt3r5Bs8GRJtdvt/Pjjj%2BJ6xdgD/X5XDGKLxSKIfipXrsy9995LSkoKDoeDzMxMnaFZWlrK%2BfPnKSgoIDk5WXxUUPL4lL6U5/WWwcjhM2DAgAEDBm4Pw%2BD7fTBCOv%2BgmDp1arl5dSaT6bYv7wo8teyUP8ULcjsoeYQRERG4XC4OHTrEkCFD6NixI4sXL6agoICcnBzh%2BSspKSElJYVevXoRFxfHzz//LPK/AOHhkmHGjBmChbFu3bqsX7%2Beb775hm7duglPjNPppFu3boK0ZNSoUSK0cOvWrYKcxBNKnpdiHDVs2JCTJ08SFxfHnj17sFgsWK1WkcNls9nw9vamc%2BfOHDx4kFmzZom6HA4HgwYNEqQhJ0%2BepKioSJDp5OXlqYyLqKgokVf3zTffiPPleZZKSkpYuHAhc%2BbMwWQyER0dTd0ySmqXy8X27dvZtm0bb731FmlpaaocuSVLlhAdHc0HH3wAoDI8V6xYwbPPPivCdAERDunt7S1kJkAtgO7r6ysMbh8fH5577jmRc1hcXCzmVAkx7dChAyaTiaKiIm7cuCFCQZ1OJ4WFhVy9epVz585Ru3Ztvv76a9GGksdms9nwL/u0mZCQQKtWrVQ5oIcOHRJzYTKZ%2BEsZTbkizeHl5SVIfOrWrUv//v3F%2BpvNZgLLPl0q4b4Oh0OsQ25uLuvXrxdyD5999plufcBtWNpsNtWcecLHx%2BefzqE1YMCAAQMGDBj4d8Iw%2BP6g8CRtkeXVabW7/lWkpaUJzbzyYDabef3118nKyiIqKorXXnuN4cOHU6lSJZKTkwkLC6NKlSpkZmZy6NAhnn76ae666y5q1apFjRo16NmzJzVq1GDKlCmCtEUxOj0F1CMjI0V/LBYLLVq04LnnnhMeIqfTycMPPywE2qtWrUqbNm0A%2BPLLL0lNTaVjx46A29ALCwtj%2B/btTJkyRXj4IiMj6dmzJ7Vq1WL69OlMmDCB%2BPh4unbtyrRp03A6nTzwwAOEhIRQUlKiMoDA7fl0OBysW7eOwsJC/P39GTJkCD4%2BPjp9wFOnTjFjxgxcLhdPPPEE4Pb0de7cWTrPdrudBx54gF27dglWT4UREtyeXqfTybhx42jcuDF2u10YNCNHjmTLli2CgAQQ5CQKPI1Ap9OJzWbD6XQKQwjg4Ycf5t4yPaWCggJOnjyJ2WymqKiIH374QaWRp6CgoACz2Ux6erowoMLDw/nyyy/F/lH6GRISojLihg0bJtastLRUfMhYtmwZhYWFdCnTm1PWW/k3IEIha9euzfbt2%2BnRowdeXl64XC6OHTvGsmXLhJyH0%2BkUBqryUUDJf1Xy%2BLy9vbFYLMTFxfHtt9/yeFlIlDJWk8mE2WymcuXK9O7dW0XGo/SpqKhIx7Z6KxikLQYMGDBgwMDtYZC2/D4YBt%2BfEN27d2fx4sWqkD4F48ePFy/a/w5UNI/wgw8%2BYMWKFVz4J7N9d%2B7cKbxUniguLtbpxMFNwo61a9eqzvft25fLly8zY8YMevXqJcLvzpw5Q0pKCvfddx8FBQW8/fbbrFixgoKCAho3bozFYmHXrl1s2LCBuLg4hpQJ2SrtjBw5ErgZJuh0Olm7dq2KkESZJ7iZJ6eEc3p7e7Nnzx5q1qwpNQyOHj3KO%2B%2B8Q3FxMRkZGRw7dkz1u9KuUq/i7ercuTNz585VsVcqZffs2QOgWovS0lLxd8WDzKGgoICDBw%2BqyikGyJkzZ8S4yvMwK23n5OSINfEMr7xy5QrR0dHCK/zBBx%2BQkpIirlcMyuLiYpYtW0ZaWhre3t4ib1Npu6SkRPTr7NmzglzHc06VNpW%2BKnOjlDlz5oxq/%2Bbn5%2BNwONizZw/nz58X3mNlbV0uF97e3phMJkJDQ%2BnWrZswXq1Wq/C4jh07Vjo3MshIW%2BLjNaQt2kx7GXGB1mOsISWQatLfjjVDUq2MGUDHZaA5IeM6uN010v7JWDy0fZaEVOuGpZ0/GdtBBdrW8YXIwrm112nXUjYm7VzIQvBJFkbrAAAgAElEQVS15yT5tdpq9CQP%2BjnXkkVIOFF0fZbtLe11Mg4UbX%2B0UyMjrtD%2BL0/2GNL1R0NWA9yeSUO2ltp9IyM80XZaNjm3IViSraWuXtn9omXokE26dq9//LGuiO7xou2fbNzatitCnCK777TXVWRDVmA/VmjtNPeUdplkU65tS9q2pl5pRo1mI1fklq/IM0p3f1SgjKxt2dgN/PlgCK//AdGlSxeGDh1aruetoKCAgQMHCtr8mJgYzp8/T3JyMmlpaSxYsICaNWsyceJEIVXgiWPHjtGzZ09SU1MFHb6sD3l5eeTn52O327FYLFgsFux2O%2B%2B//z4TJkzgnXfeoX379rz44oukp6djs9kICgoiLi6OUaNGsWrVKubNm0d%2Bfr40D0yBkse3Zs0aCgoK6N%2B/P3/5y194/PHHqVKlCvfeey83btzA39%2BfDz/8kAULFpCamorZbBZ5cw6Hg4MHD/Lpp5/y4YcfirqVfpeWlgoDQPFsKWGApaWlVKpUCbvdLsYKbuPDx8dHmpdnNptxuVwEBweTn5%2BvIhwpD4qx4XA4biuCroXFYsHf35%2BSkhKKiopUOYGKV688UpV77rmHdu3aMWfOHNFPxdBRhOyV9fE0Fk0mE/7%2B/uLDQlRUlNAiVMavEMr06NGD2rVrs3r1akaOHCnyMcsb4632Q0Xh6%2BuLl5eX8MIqa%2BlyufDy8hJkKreCYqQp5DQKHn30UbZu3SolGjKZTMyaNYvhw4dL63z33Xd5SEuyUA5kwuujRz/Pc8%2BNqtD1BgwYMGDAwP8CFi2683U%2B8sidr/OPCsPD9yeEn58f8%2BfPp1WrVv%2BUVMI/i%2BvXr6uMpCZNmvDYY4%2BRlJQEuD0igwYN4vz58wQFBen0AJX8u4CAAHr06CGISry8vKQ5UE6nk3r16vHVV1%2BRmZlJnz59iI2NFd6sl156iVGjRtGkSROqVavGq6%2B%2Byty5c6lZsyZOp5P77ruPTz75RNRntVp1xh7cNGZsNhvdu3fHYrFw/fp1iouLBcNnfn4%2BpaWlOi9qaGgoDRo0EMZRbm6uEEGHm7lx/v7%2BdO/eXWj2KcaPp7dJgad4PCAMTpvNpsrhy8vLU3mbFNSvX5/PP/9c1U9Pz9WePXtITk5WGaWKJIFi5Cj97tatG9XKKB1dLhf5Hl9YPYXnnU4nLVu2pGrVqly8eJGvvvqKN954gx07dvDqq69iMpno1KmTqk%2BKZ00xwrWQCdEHBQXRqFEjTCYTFouFJmUMciaTicLCQq5fvy6uKykpEfNSUlKiM/aU9hMSEoRcyDvvvENGRgaHDh0iKyuLl19%2BGYAdO3aU6512uVw0atRI%2Bhsg6qgIZKQtBm2LAQMGDBgwoIYR0vn7YBh8f0BUJK8uKCiIpKQk0tLS2LdvH5s2bWL69OlCFBrcL7Na7x64c5eysrLK9e4pqFSpEpMnTyYrK4u9e/cyb948Jk2aRFhYGJUrV2bZsmXk5eWxdOlSfvnlF50eYG5uLt27d6dSGRdwv379yMrK0hHHKHl8iqF6zz33MGvWLNLT08nIyGD%2B/Pk4nU5mzZrFY489xrBhw9i4cSODBw8mJyeH06dPYzab6d27t0obr3r16mzbtk3M5YoVK%2Bjfvz/3338/7733Hg6Hg1WrVtG7d28yMzPZv38/U6ZMwcfHh5EjR/Loo4%2ByXKN/dPXqVZED1q5dO0JDQ8nNzRVGh2JU5efnCxkJz5d6k8lEpUqViImJEedcLhfDhg1j%2BvTpBAYGivlyOBzC2FUkJBSjRTEcTSYTp06d4p133lG1ofWsuVwuQkNDxfG6deuoUqWKyAtVQl5/%2BuknFUvnkCFDaNKkCUFBQSrh9fDwcGrVqoWXlxeTJ0/m3Xffxc/PjxYtWvDZZ59hMpnYuXOnKO/n58eQIUOwWq34%2B/uTkZHBAw88IH5/7bXXePXVVwWJi8LqOX36dDIzM1XjqVKlCitXrhTHvr6%2BgqjFR0LTrkD5yDBhwgSRD/jSSy/RsmVLhg8fzueffy4M/KpVq2K326lataowTv39/YX%2BX5UqVXj00Ufp3r27mKv27dsDboOyojBy%2BAwYMGDAgAED/24YIZ0GpFBCOl944QWd8RkfH0%2B9evXYtGkTQ4cO5cUXX1T9rujzPfXUU0ycOJHt27fTtGlTlfGphJUCtwwtBbex0rlzZ7Kzs9m8ebPwQCmsnZcvX8bHx4dr165hs9mE0VWlShX69etH69at%2Bctf/qLK7/Ly8qK4uBiXy0W3bt346KOPyM3NpWPHjnTu3Jm0tDQRdljoEZhvtVoJCQkR3h%2BLxYKXlxdWq1WEnSpeMZvNJlgfPREYGEhYWBgnTpwol3FVCf80m82UlpYKJkpZud69e7NixQph6FmtVsxms8qDB/Dggw/y/fffA/KQSpPJxLx586hSpQrdunXTlfMMI73//vvZtGmT6JPSnslkonr16ly8eFEVYuoZAqqtC2Dp0qUsXryYPXv28NtvvwmCmurVq%2BPn58fRo0fLnQNwG/eXLl3CZDLdNrw2KyuL%2BPh4zp07V27I6YQJE3j//fex2WxUqlRJeAvDwsK4cOECmzdvZuvWrUycOFHIUyhISUkhKirqln1QcPDgQR0BU/369WnQwEOHr6QEPHNltcfgzs3w9JprjrU/V%2BSaipbRndL2T1avy6UWJPsX29ZWQ3GxTqBKV6YC/dNeo6ujomNwONTaYhWZm4IClaiWtgppPZJxa6/TNlVQUKzT4tOWkVSrG7asf9pzmiFJ27pxQ60Blpenl1grKtJI70ka1/W5sFCvu6a9TluxriHJOdmgtGUqcq9qr5FNuvYa2aRr65GNWzupW7dCGfGZgrNnNXJ9mrak94K2z7L505aRVXS751YF7jHZ9FXkfrndOGXd1S6DdG7u0HP2tjenbM7vUNvSZ9B/APPm3fk6H3vsztf5R4Whw/cngqc2HriNlujoaMaOHSuYFXNzc/n0009JSUnh4sWLqpy6%2BvXrA6hy%2B1q0aCFekD218RTPkqfQuKce4IQJE9i4cSNLly5lz549HD58mIKCAsGImJiYKK6z2%2B2sXr1a6NzdDlu2bOHvf/87GRkZOJ1OatSoQZ06dcjOzuabb74hISGBfv36kZSUxJkzZwRpyY8//kjr1q3ZuHGjaPfHH38ULJBOpxOr1cqHH35Ip06deOKJJ9i9ezfr168nJiZGMDb%2B8MMPeHt78/DDD9OvXz/69eunGotnqJ/D4SAsLIzS0lJu3LihMg49Bcw9UVJSgr%2B/v9TgCgkJYfz48bz//vtcuHBB5clT8MUXXzBmzBgKCwux2%2B3CW6olKbFarZSUlBAaGsrly5d1OnVa%2BPj4cPDgQZYuXaoqpxiSrVq1Ytu2bTidTjZs2CB%2BV7xeMTExgvREO3Zte4qIvRLm%2BsQTTxAQECDmNikpiXfffZecnBxxbXnGnsViITs7G5fLJbxxTZo0ISIiQhDHeHo9n3jiCQoLC0Vor8zoW716NUFBQVy5ckW1phfLyAkWLVokwmi1Yb8JCQkq4ptbQSa8Pnr082qDT/vCKCFK0v1PXHNcIRFrWaEKlNGd0vZPVq/2rehfbPu2ItayMhXon/YaXR2yk7IxaN%2BSKjI3GiNC%2BqKlrUcybu112qZkwuvaMjKtae2wZf3TnpOJQmvb0go%2ByzTBdQ58SeO6PstEtrXXaSuWRQpoz8kGpS1TkXtVe41s0rXXyCZdW49s3NpJ1Rh7oNdm17YlvRe0fZbNn7aMrKLbPbcqcI/Jpq8i98vtxinrrnYZpHNzh56zt705ZXN%2Bh9r%2BIxh7Bn4/jJDOPxk85Rq2bNnC/fffz7Bhwzhz5gx5eXkMGjSII0eOMHv2bF1OnUwfbOfOnTp9vjVr1hAZGUmlSpX4%2BOOPpXqAdevWxWq1cuXKFQ4dOkR%2Bfj6VK1emTZs2NG3alJdeekn3Irx69WqysrLIyspi1apV4nxaWpr495IlSxg9ejS9e/dmy5Yt/Pzzz0yYMIGDBw9iMpk4cuQITz75JADJyckADBkyhISEBB3r5bVr1zhz5oyKaVORRXA6ndSqVQtAGAqKUdW7d28RpqrNjYuNjWWe5jPTqVOnhEi3Ypx89tlnZGVlqdhDHyv7lFRSUiI06jyhaA2uXr1aGBYKPL1hTz/9NDdu3MBsNmOxWLj77rt19ShSA%2BBmxrRarTz44IOA2nj0/PfLL7/MzJkzVQbtyJEjsdlsBAYGMnToUDE%2BJacO3GGsVqtVGGRLlizR5eNpcenSJcH4Cu5cNs92P/jgA9q1aye09bQ5n927d2fEiBGqMQOqkM6//e1vBAYG4ufnh8vlombNmrRr146EhATVRw4FnvsnKSlJlb/o2Y6/vz9paWk6ZlYF99133y3H7gkjh8%2BAAQMGDBi4PYwcvt8Hw%2BD7E8PX15fExETCwsLYvHkzc%2BbMIS8vj%2BTkZKEx5plTd%2BnSpQrXnZaWRkBAQLl6gMqLf2xsLDt27CAjI4PNmzfz8ccfM2fOHAoLC/nss8945513VILfCurXry88flu2bAHc3snp06fz0ksv0a9fP3x9ffH29qZ9%2B/a88soruFwuxo8fL/LqFCbExMRETpw4Qe3atfHz8xPhiIqG25gxYwC3MVZUVETTpk1p2rQp6enpgJuw5JVXXhH5WNOmTaNevXoMHTqUbdu2kZqaCrgNgv3797NgwQLAbdR16NBBGBnDhw8XRsPzzz9Ps2bNhJFlsVhYsGABMTExuFwuOnTooJsTm81Gbm4umzdvxuVyiZBOpS2LxUL37t3p2rUr4M7jBFQG39KlS0lJSeGFF17Ax8cHm80mDEAlnFPB3Xffza%2B//ioIWz799FPi4uKEd7Zhw4aMGTOGTz/9lBkzZoh5NJlMYg4UXLlyhR07drBjxw4eeughlSH10EMPYTab6dy5MykpKUJCoUGDBmRkZBAUFCRCWBWyGT8/Pw4cOEBRUREmk4nZs2djsVhE6O%2B6deuYNWsW4Pb8KXUoXmxP0hp/f3%2Bh5Xjx4kUGDBggPkZ8/PHH7Nu3j9TUVFq0aKEyVJV1VfaTyWQiMDAQf39/evbsSbdu3UQOXkxMDL6%2Bvvj7%2B4uQYwMGDBgwYMDAnYFh8P0%2BGCGd/wWoqDbencSBAwcoLS3l5MmT5OXlCWFzcL9sBwUFsWTJEsaNG1fhOrds2YLdbmfAgAG633r37k1ycjILFy4UGm6KMVRQUMDixYt56KGHVLp1itGhhHhWqVIFb29v%2BvfvT35%2BvvAyKi/5yn/tdjsnT57kr3/9q8pwUSQgtm/fDqCj5f/73/8uDOFJkybRqlUrhgwZQk5OjvB%2BKaF%2Bv/76Kx06dGDz5s3i%2BtLSUiFSrrRrsVhwOp2MGjVKCL4rfVW8QydOnBB1KPmWSm5gQEAA165dw2w2Ex0dzcGDB0XdR48e5Z577hHjPn/%2BPNevXycjIwOAQ4cO0ahRI%2Bx2OzabTYxBtr8UyPLsVq1aJcJAN2zYILx6hw4dIi4uToRXws2wzcuXLwshdIfDQWJiIi6Xi7NnzwLQvn17Nm3aBLhDV3PLdJYOHDgAuD3XsbGxlJSUCFmNo0eP0qZNG1WY8nPPPScdx/Hjx8V1njqBQUFBXL58mfr16wtxeIDMzEyxbp7hzLeDQdpiwIABAwYMGPh3w/Dw/YmRn5/P559/zpUrV%2BjYsSNnzpyhTp06Fbp2zZo1IlRT%2BevTp0%2BF2z5z5gw%2BPj6EhYXx%2BOOPc%2BDAAVwuFzk5Obz22mucO3dOJeugoEuXLjRq1IjY2FjB%2Brh582YaNWrESy%2B9RElJCc2bN6dhw4ZER0cTHR1N69atGTt2LM888wyLFy9mzpw5AEJgPiEhgbNnz/LZZ5/x448/ihf0X3/9FZPJxN69ewE3w%2Ba5c%2Bf48MMP%2Bfzzz7l8%2BTI2m43CwkLefPNN4XF88MEHGTJkCCUlJfTu3Zt3330XQOSceYZbWq1W6tevj4%2BPj8rQefPNN%2BnTpw85OTmEh4cTEhLC3XffLbw/N27cYNu2bbp59RQpdzqd%2BJblYaxcuRKz2UxYWBhOp5PRo0dTVFSEw%2BEQoboKMYrT6cRutxMZGSmMG6fTKc0r8/b25oknnhDHJpNJsHTCzVBSTwH24uJinn76aTp37qyTlHA4HKrQyHr16pGYmCjO9e3bl4MHD1KvXj3Anf/mKVWh/Nvb25vS0lKpjAUgjD3PPio5mICQ49Bi69atzJ8/X3dei3Xr1ukMW0X8vaioiGvXrnHixAnVWil99TQobweZ8Hq3zp3VhTxCnqXHSPR0ywxfBSdPShrXktv8%2Buvt692/X1emzAa/icxM1aFMw1qnha0hrgH0AtSyHE5t4x77otz%2BebDHAuAhNyKgDeeVaFzqulwWqaCCdqA//aQ6lPILaRXIs7N1RXS60RVRP9eEicuE17URxpKmdf2TltH0R7Z02tRZ3V6T5UBXQA1bpzcuC73WrEOFFLTPnVMfHzmiL6O90SSTo0sZ1tyrUuH1so%2BMAseP68toGKUpi0xRQTsXMgFy7Q2jFWefPVt/jSZ6BJn%2BqVbTVNa2dvEOHVIfnz%2Bvv0bbloyI65dfbt0X0K2V9l6oiPC6bFm0t7P2GCqmC6%2B9Ttu27Dmr3X66ewP9I0pWRjZd/wkYHr7fB8Pg%2B5Nh6tSp0py6iIiIchkfZejRo4cI1dTm8FUUTqeTefPmSfUAx48fr8up69Onj2BGtNvtwvgZNmwYNpuNp556ipo1axIVFUXbtm1ZvXo1mZmZLFu2jJCQEKZPn8706dM5qfmfqtlsZsiQIWzatElQ4yvng4ODhdxDcnIy3bp1w2Qy0bNnT0G24uPjozI2zp07x7Vr1xgwYABjx44VobB%2Bfn689tprzJ07F3CHWXbr1o1GjRoJYyg4OJhatWqxf/9%2BOnfujNVqJScnh%2BvXr3P8%2BHGVMfXqq6/eVo9O8Vz17NkTp9Mp2CL/9re/iZxEz/UoLS0VRo/iDWvdujWRkZE0a9ZM11ZxcTFff/21OC4pKVF5axX4%2BfmJPQZur%2Bq6detwuVz4%2BPjQvn17rFar7tqjR4/y%2Beefi/lZvnw5SUlJNGrUiICAAJGbp83Ri4iIYNy4cUI%2BISQkBLjp1fWco9q1awM3cyCVcjKtx5CQEFq1aqU6ZzKZsFqt1KtXT0hFhIeHl3svRUVF8fPPP0t/%2B2e9cytXrmT8%2BPGqv1St0VIWmlzuMZI8e41OYNkUqaFN%2BpfsD129jRvryuhIHjwJZ9CTcQCULedNaPJQAT3Rgow5QNt4GUHTLfvXooX6WMao6u9/674g6XK7dvp6tAP1eD5BOQQTZbIsAh5SOwp0t6iMoENL4lB2LymQkbZoHc6SpnX9k5bR9Ee2dLflvPlXiCuA4GDNCRmZhWYddPMnm8/ISPVx2UcrFbQ3mmRydOnNWk1PWQRFGSmbQJk%2BqwplKQ4C8fH6Mtq5kDHjaG8YbRTEsGH6a8pSFwTCw/VlynLmb9m2dvEaNlQfy8LltW3J8sc1z3xdX0C3Vtp7QXavareJbFm0t7P2GKAsO0NANjXa67Rty56z2u2nuzfQP6JkZWTTZeDPByOk80%2BGSZMmlavRFxUVpaN4/z1Ik3gSFNSpU4eSkhKuX79OUlKSEGNXMHv2bGrXro3JZOLll18mJSWFFStWMHToUIYOHUp2djapqalkZGTg5eXFmjVryM3N5ezZs4SFhfHdd98JD4uSh%2Bjr60twcDDTpk0jPj6e9u3bc/r0aWJiYjh9%2BjQJCQlcL/sM9sgjj7Blyxby8/M5c%2BYM4M6r8yRnsVgsgsly8uTJ9OnTh44dO1K1alVOnDjBvHnzWLhwofDiHT9%2BXEfUMXPmTE6dOsV3331H5cqVRd7XjRs3WL9%2BPU6nk/DwcPr370/v3r1JSUlhxowZuFwu3njjDZ3nSgnhhJvMkjIJBXB7tiwWC0FBQcIwVOBwOMQ4f/nlF6pWrVquAeNZv9lsZvLkyUyePJmSkhJ8fX0pLCykoKCAgIAAIWcxYsQIPvvsMyFb8csvvwjNQW1ftaL3K1euxOFwiPO%2Bvr6C7VMhU1FCahVcKfsMqZ0bcBvooaGh1K1bl7179woBdhlxTIcOHWjh8cIfEhJCbm4uTqeTI0eOcMTjq73NZqNmzZoqTx64je0LFy7g6%2BtLamoq58%2BfF/2pVq0aLVu2lM6zDDLSFoOyxYABAwYMGFDjf80jd6dhePj%2Bi9C9e3cWL16sY8cEtzaeEgJ5J9CgQQNq166t8g4psNvtLF68%2BJYC1CNHjqS4uFhFvKGE%2BdWpU0cXTnf48GE2btxIbGys6vzrr79Oamoqd999Nz/%2B%2BKPw8GVmZmK1WsnLy%2BPjspCUTz75hOXLlzNt2jRSU1MZP348drtdkHr4%2B/szYsQITpw4QWhoKDExMWzdupWGDRty%2BvRpfHx88PHxIbzsi6LiOfzoo4%2BoVq0aVquVZs2a8f/ZO/PwKKr0%2B3%2B6O%2BnsIRsJEHaEECCJEGAEZDHsqKwCIuACgrIqKgqKsqiDjoLDKKAIqIzyVTZBVBQ0KgIRgRHSCRCQRULCnoSQtZPu/v3RqWtX1Q3pUZzlN3Weh0e7u%2BpudavTb73vOSc5OZmQkBD69etH9%2B7dMZvNRERE0KhRI1HmGhYWxtSpU3VZUIfDQbt27QBE0OIZQCkcP19fX95%2B%2B238/f0F18xkMpGVlcWAAQNU4iEul4vi4mJVNjErK4ujR4%2BSmJhIVFSUeD8qKorNmzczb948wG3IrmTAevfuTXl5OSaTiS5duqjG5AlPTmSHDh2IrXoyHhQUhNVqJTMzk4EDB4rzSktLKS8vV/n2Pf/880RGRmKxWDCbzQwcOBCAESNGAG5BGYX/5nQ6xRpER0cDcPfdd/P666/j6%2BtLo0aN8Pf3Fw8ffHx8xFrk5%2BfTtGlTRowYIew7wO3rV1JSwpUrV0QWVcle%2Bvv7k5yczNatW7lQVWKknKtVTK0JBofPgAEDBgwYqBlGSefvg2G8/l%2BElJQUJkyYUG2Gr6SkhBEjRuDj48OLL75Iq1atuHDhAsuWLSM1NZX/%2B7//o0GDBiofPk8oZug1GaErSEtL46GHHuLee%2B9l3LhxhIeHc/LkSebNm0dRURFr164lICCAs2fP0rNnTz7//HMmTJhAUVGRkLxXBEG05uJKMOCZeWrUqBF/%2B9vfCAwMpGdVuYpiTeBwOHQ/lHv06IHJZGLOnDmi/2bNmlFZWUmPHj2YMmUKL7/8sspnDdxZtuDgYK5evYqfnx8BAQHV8rIUJc2JEyeyZs0aioqKRKml3W4XvoSLFi1iwIABjBw5EpvNJjJwyu2n/P%2Bdd95JREQE7733nqofxZ7A01Q8MDCwGln/XxEUFERZWRm%2Bvr6YzWZxvBJQK0FWv379%2BOKLL8R5AQEBmM1mqTWBcr5i7F67dm0mT55Meno6sbGxuFwuli5dWu2YtCbsCpQgzOFwVGta74mGDRtSVFTE1atXxTng5hvGxcVhMplUYiqAKN08UsUNUfr05E4CtG3blp9%2B%2BommTZtyUkPM8PHxYd68ecydO1eaNbVarUL4piZIjdebNaOlZ5nX11%2Bry7O0r5F45aanQ2KieHnqFOjovVrj3v37deWOunYPHoSbb1Ydk50NVc8%2BpH0XFupLli5d0lQYHj2qKwXVdS5zNdZ2npqqK3nVGUnv3asu8ZItTnGxuqxTYqB9/Limqm/nTtCq716%2BDB4PVPjuO1XZqcwrWXeObgKSNdW6lsvGfO6cqsZLZryu3RK6awtuko9H3Zd2uIDe9Fvm3Ky5nrq95o25uOS6XLigqfyTLfKuXeoSXO3EZYbp58%2Brywdle/bnn9W1vpJrp5tXRoa6VFo23u%2B/V5ehZmbqS0E3b1aXdW7fDlUKw9VNU%2Bpur53DihXqMs433tCXea5fD55ia9q1AjcH0rMsVmZ%2Brt3Yx45BlfIyoNvDgP6Cy8zFd%2B8Gj4eU0nv%2Bl19U5d0lJeqyTm98zXXfCehuF91r0E9bdlm052nHJ/ue1d6/sntV%2B10sO%2Bb06WpoAf9iVMk33FBMmHDj2/xPhRHw/RehpoAP3JyvpUuXsmPHDi5fvkx4eDhdunRh2rRp1K36orxRAR%2BAzWZj6dKl/PTTT5SWlhITE0O/fv146KGHREZEG/Dl5ORgsVhE%2BaLVamXIkCFs2rQJh8PByJEj2bBhA3a7HT8/P5o0acLAgQP55Zdf2Lx5M0uWLGHChAnExMSQnJysmseECROE8qXVauW1116jV69eunG/8sor7N27l7y8PHJzcxkwYACLFy%2BmsLCQ2267jaKiIhEoWK1WLBaL4B46nU5VIOr5mdKvw%2BEQQZ2SpdMGaFarVZXV0iI6OlrlTaf053Q6RVAZEBBAYWGhyAYqNgzVmb5XB6WcsmnTpuTk5FBRUSENyrTwtH0wm82EhYVx1113saKK2F%2B/fn3Onj2rCuaUDKDWA0%2BbyWzfvj2nT5/GYrHw6quvMnr0aJ1Ruufr6kpfFVgsFjp16sShQ4dEYO5wOIiNjSUhIYHDhw9z5swZXn31VWJjY6u9zyZMmMCJEyeqLXmW%2BV1Wh5deeklnvD592jSmVKMeasCAAQMGDPwv4q23bnybGrH1/69hcPj%2Bi3A9Tp2C0NBQKafOEy%2B99JL0/WbNmv1TP1bB7cOnlGVWh/r166varVWrFjNmzND9oN61axelpaVkZGRQq1YtqVJiQEAAFouFIUOGCHsET8yaNUsEfNu2bas2cJ05cybgDqJDQ0MF7yo0NFRk/FwuF35%2BfrhcLsrLywkLCyMtLY3evXtz5swZ1dg8jb8PHTrE008/zcaNG0U5pN1ux%2BFwEBYWRkFBAeHh4fzwww/ExcVVu24XL17E399fxRtUAhqn04ndbic0NFT07ZktVLKm4A50hg0bxg8//MCZauS2lMxhbm6uCGqbNWvGyZMnVRYRDoeDtWvX8uCDD1JSUqLyC3S5XNjtdlavXi3aVcbuGZh58uvCwsIoKiqif8qz814AACAASURBVP/%2Bwo8xPz8fp9PJ2bNnuXjxohBu8Zyj9nVISAixsbFkZWVVG6QqCqvFxcXExMRQUlJCYWEhOTk55ObmivM8S6JDQkJEuWitWrVwOBw0adKEESNGkJmZKUo6lYDZ2wclCgwOnwEDBgwYMGDgj4bB4TPwHwOHw0Hr1q05dOgQgwYN0gV7M2fOJDo6WsUfu9FQsloKTy09PZ2UlBRMJpOqrLNu3boqhdPnn3%2Be4OBgkdVUMnsWi4UVK1ZQr149HnvsMW655Rbg10BIsV0wmUxCbbVbt26ipDWoqqxM4e6ZTCZWr15NVlYWjz76KAUFBXTr1k2Yp9vtdux2uyrD53K5WLduHfn5%2Bdf10Fu4cCFr164VwVj37t2ZOHGieK3MLSgoiJEjRwLuhwc2m40777yTunXrctttt6kCREXhVAkKlXl16tQJs9lMZWUllZWVfPvtt/j6%2BvLuu%2B9isVhITExkxIgRmEwmHS9OKW9VoGQ7e/XqRXyVqptiSj9p0iR69uxJUlISAQEBwhfy4sWLjB49mvfff59nnnlGxd87dOiQGHdISIgY84wZMyguLiYyMpK0tDQuXLhAvXr1qFWrllhvb21RDBgwYMCAAQPew%2BDw/T4YJZ0GSElJ4cKFC%2BKHrdVqFWIdynuKbYGSYYqIiKBjx45MmTKFFlU19t6Uit57770UFRWpMnzFxcV8%2BOGHvPHGG2zcuFGIvSi8s6CgIIKDgykqKmLdunU0aNBAZNmuh5kzZ/LKK6%2Boghyr1UqLFi2YPn06nTp1IiUlhby8PB2PDyAuLo7Zs2ezdu1aYUHgWTaotPvMM8%2BwYMECfHx8KC8vx2q1ipJIz8xW/fr1mTJlCrNnz8ZqtdK6dWt%2BkniftW3blpkzZ3LPPfdIyxSVrKFnBk/hkgUEBBAbG8vo0aN56623OH/%2BPPHx8Rw5ckRniq60rWSxevfujcPhEJlkJWBVSlWbN2/O8ePHVRw8s9lMTEwMly5dIjk5mXr16vHxxx9f97p4ziM0NFQoqyrvKe1arVYRGHuO3Wq1Cq9BTwQHBwuVUFk5rDJnbUZUhsGDB7NZ62tVhe3btzNt2rRqs%2BGpqalCqKYmyEo6p0yZwvTp070634ABAwYMGPhfwHWkAX4zpky58W3%2Bp8LI8BkA3HYPSrZq165dPPHEE/j6%2BvL555%2BTlpZWrTfeyJEj/%2BkyUKjeT7BplZHNTTfdJBQXLRYLpaWltGjRQihjgju4GTBgAFlZWeKfYp6uICoqSpWJ27VrFykpKTz88MMiYFSCPavVSoiH8EFWVhb3338/27dvVxlsK1CyaXPnzsXpdKoEVRQoHMA77riDLVu28HWVGa7dbtcFe8pxS5cuZe7cuYDbakMZU2BgoAjCk5KSRDC%2BdetW7rrrLkwmE/v37%2Bepp57ilVde4eYqcQ1FFGTr1q10rPJzslqttGrVSgRd4C6p3bdvHwC33nqr8NhToJQ2as3hz507R2VlJXv37lUFSZ5ZOB8fH52aZ926dVXXU1kv5fjy8nJCQ0PZt28fTz/9NODOLtpsNjIzM7nvvvtU5wYFBYk%2BlWuhcActFgv%2B/v6EV7HerxfsNW3a9LqZOpPJRL169aSf%2Bfr6VvuZDFLjda1/ltZqRWK9opuOxqZD6taifcghCZB17Upcg3Vmwxrn3lOn9F1XOaX8CgmXVXs7SWmpWpdgyUR1z3K0bsRV2dzrdibpXONjLpmUxHBcY/IuM1jWuTBLjMN1fUseWOmuncaZWWa8rj1HZhKtXXNv/LNl1067NrqvT2%2BM1yX7Rrt8%2BjdwCwt5QjsJ2aS0Bt%2BydrUTl7lYa%2BegbUdm%2Bq41ID90SH/Mt9%2BqX0soD7q2dRsJt1CKJ7Sm6uvX68/RmrPLXMC195lsc2lL3LUPdWX3qrYd2ZprzeRl89acpx2KbEraTSv7rtMOxxvjddnW0hqkezM%2B7bRlS%2BPNltV8bRn4L4UR8BnQISAggHHjxhEdHc3OnTt5%2B%2B23KSoqYtmyZTRr1gyTySS88UaNGiXK37xBamoqwcHBqgBz3759rFmzhqSkJMD9Y79u3bqkpqaSnp7Orl27mDRpEuc8fqglJycLM25PNGvWTBf0aec2YcIEoqOj%2Bf7770lNTSUsLEwEEvv37xdWAJ4IDAzEarUycOBAUT6piLkoY/bx8SEsLAybzcawYcMAd3BQWVnJ9u3bSU5O5siRI/j4%2BODn58edd94pAjBwB4GffvopnTt3Fn5wf/7zn0WQomRdHQ4H%2B/fvlwqU%2BPj40K1bNwYMGAC4bQyUTODQoUMF79Fut3P33XfTqVMnsa5K4Ovj40N6ejrR0dGsXbuWJk2a4OfnJ9pMSkoSwbhSZqqsV1RUlLAx0AZVPj4%2BqiCwqKhIZJYV3qHnWrhcLkpLS1XZ1%2BLiYvGg4O9//7t432Qy8c0334iSTiVr6HQ68fHxweFwUFZWRsOGDXXXVgsluNSatyulxEeOHGHIkCGYTCaxB5s1awa4s7gy/7/qIDNe/%2Bqbb9UHaa0eJNYPui41cm1Stwitc2/VNb1uuxLXYJ3ZsEaCThY765QfJaXGWgE/mQ%2B3Tu5OMlGdh7ZW5U8nMSnpTNK5xsdcMimJ4bjG5F1msKyT2pMYh%2Bv6lhiF666dRsVTZryuPUdmEq1dc2/8s2XXTrs2OmNrb4zXJftGu3z6N1CpyAL6ScgmpVWdlLWrnbjMxVo7B207MtN3rQF51d9KFXr0UL/WmrXL2tZtJNSqmKA3VfdU41SgFZmSuYBr7zPZ5tLa1Ggdv2X3qrYd2ZprudWyeWvO0w5FNiXtppV912mH443xumxraX/ueDM%2B7bRlS%2BPNltV8bf3bYJR0/j4YAZ%2BBauFwOLBYLOzYsYPhw4dL%2BV9PPvnkDefUBQQEcODAAYqKinC5XGRnZ7NlyxbuuOOOG%2BYnqC0HLCkpoXXr1iQkJOBwOERJYWBgIE8%2B%2BSSNGjXCbreTnZ0tskcKp%2B35558nIyOD559/ntLSUmbMmCHaVWwZlHLEkpISxo0bh91uZ%2BDAgdfNBnXr1o3Zs2eTk5MDIEodFXgGFsnJyURGRorXFRUVfPvtt5w/f14EpQsWLFC1/9xzz3HgwAERFJlMJq5du0ZlZSXXrl2jsLCQzMxMCgoKaN68ubDCuHDhgiiXVEo7leDz0qVLtGrVSjcXHx8funTpwq233ioM70tKSrhU9aTVs3QUfrXlqKioYNmyZaq5aaGUaXbr1k1Xxumpngpufp6yryMiIqhXrx7%2B/v4EBgYSHh6O2Wyma9euhISEqK4buG1IlP/m5eXhcrmEIfyJEycAOCV7xHsdyG01jCp7AwYMGDBgwMCNgxHwGdChuLiYVatWkZeXR/fu3cnOzvZajOKLL74QGRjl36BBg/6p/hUuVvv27WnTpg29evXCx8eHs2fPkpaWJgKPnJwcaX9KCeOSJUu4fPmy6rM2bdrQqlUrcnNzaePpfcSvZa0Wi4UmTZpQWVlJSUkJ7dq1ExmcM2fOEBYWhslkolmzZjRo0ICFCxdKy1pNJhMZGRlMnDhRKE3m5%2BeL4KBLly4iGzZ06FAmTpxIgwYNhBH66dOn%2Bdvf/obNZmPixInUqVNHCJp4Zp4GDRrE3LlzKSwspE2bNrRu3ZqtW7cSGBhIeno6E6qMZmbNmqUan9PpFNk/JShSyjtNJhNlZWXMnj0bu91O06ZN2bp1KwB16tQRGb5ly5aJADckJAQfHx/duoI7cPvuu%2B/4/vvvcblcREdHiz4VLqHC2QSYPXu2CGg//PBDnnjiCVV79erVo1OnTiLLGhQUxGuvvcaVK1cARClqWFgYjRo1IiIigjFjxojSV4fDISw5ysrKKCkpEeqgbdq0EWI54eHh4ho9/vjj%2BPv7ExERwR133IHZbCY4OBgfHx8x9ijZE%2BjrwDBeN2DAgAEDBmqGkeH7fTACPgNA9Zy6unXrih/I3qBfv34qzpzNZmPLli3/1FjMZjOzZs3ivvvuIyYmBqvVSlZWFl9%2B%2BSUrV64UvK/Y2Fhpf4qq4yOPPKJru6KiguTkZNatWydKSMGdCZo3bx5xcXE4HA6OHz9OXFwcderUIT09nb179wKQl5dHQUEBLpeLffv2CU/B65W1TpkyRWWZ8O233%2BLn50d2drbgzG3atIlNmzZx%2BfJlwdm7cuUKAwcOZPfu3VitVmJiYnjhhReEsElYWJiYk3J9FF%2B5l156CZPJxNWrV3XiI0rGT1t6OH/%2BfJpXucZ6Bh3FxcV89tlnfPjhhwAcPHhQZNImT57MX//6VwCRHVy7dq1uDTwFf5xOJyaTSWRIw8PDhc1EVFQUERERDB8%2BnB7aEiWP8SsPACoqKvD19aW4uJixY8fi7%2B%2BPyWQSoillZWXk5uaSl5fH%2B%2B%2B/z1dffSVEb2RrAPD999/TqVMncQ2UwPSVV14R7X366ac4nU6KioqorKykoqICp9PJ5cuXpXYh1cErDp82ayjJIupoPxo%2BmYReqidsVWWSPaGjj0nIHLpjtCQQSee6rxNJ3zqSimwS2r5On9YdouW%2B6ObgBYdPRifT8Wy8IfBoCY8Sfp7uushINdoL7g3fTdOXjMOn5QHJvva178koZ1oOXHGx/pCaOJqyKekS4pL1041H1pDmIO2cpHxR7eLIeGA1Ea2QcCu1nFLZomuPkVUGaAlbkntK17Q33E8td1H7GvRro%2BX0SfqSrrF2PF7wWbWbS8Z/092bXtzzurWSkOS0fcm%2Borzh53lDIdW%2Bp72nvKGUenOMjGMo5Rr/G2AEfL8PRsBnAOC6nLpGjRqJrNmNQGpq6nXN48GdoZk9ezapqanYbDYOHDhAZGQk//jHP8QxSUlJOkVQ%2BNVPMCIiQiXakp6eTtu2bWnYsKEq2Hvqqacwm83MmzePrKwskWXKysri/PnzLFq0iPz8fHx8fNi%2BfTsNq3gFCn%2BtvLychx9%2BmLlz54pAQjsXRYlxwIABOBwOysvL6du3L794/PgsLS0lKiqK7Kof68VV3%2BgZGRlMmzaNdevWMXDgQGHsroioBAcHExISgs1mY8KECfj6%2BtK/f3/8/f3x9/cX7WgzR76%2BviL4A3e545o1a0Q21/Mzz4C/bt26IljzzG55BlI1IScnR/jdeZaa5ufnk5eXR6dOnVSBk5YPWFxczJkzZ3C5XDRs2FBk5EpLS3G5XGJfJCcni%2BsFCA6qEnQqojhWq1VYZOzevVtkcT1LaNu0aYPFYiEuLo4WLVoQHBws%2BJzgznD6%2BvqSqOUHXQcyDt/X33yjPkibXZdk23W0Hw2fTMePAj1hS6IsqqOPScgcumO0JBBJ5zpum0zVVJv9lE1C21fjxrpDdFRf7Ry84PDJ6GQ6no03BB4t4VHCz9NdFxmpRnvBveG7afqScfi0PCDddZK8J6OcaTlwVbenCjVxNGVT0iXEJeunG4%2BsIc1B2jlJ%2BaLaxZHxwGoiWiHhVmo5pbJF1x4jaVdH2JLcU7qmveF%2BarmL2tegXxstp0/Sl3SNtePxgs%2Bq3Vwy/pvu3vTintetlYQkp%2B1L9hXlDT/PGwqp9j3tPeUNpdSbY2QcQynX2MB/HYyAz0CN6Nu3L%2BvWrVMZUiv4Zzh1ffr0EVnE5ORk7rnnHtWP%2BsLCQl5%2B%2BWXOnz/PCy%2B8wK233sojjzzCMQ/VsI0bN6o4cgpOnDhBXFwcZ7VPOT1gMplYsGABW7ZsIS0tjdOnTzNr1iwWLlyI0%2Bnk1VdfZdq0aURGRqrsEMrLy6msrOThhx%2BmYcOGJCcnA%2B4gRLFfUIzVwV3WunnzZlwuFwkJCbRq1UoInigZPc%2BMHyDsJ7p27Qq4gzHlsyVLlojS1tdee42ff/5ZpZ7Zr18/CgoKOHDgAJMnT6a8vJw333yT8vJySktLRWCoQOGw2Ww2/vKXvxAQEEDLli35/PPPSUhI4PTp0wQHB9O8eXMpb/PcuXMiiHzooYew2WyMHz%2BexMREKisrpWI6SnAsw6RJk8T/KyWmLpeLsrIyMc8XXngBcKu37t%2B/XxUknjx5Ugi7KNdM4dbt3buXkx5Zlf379%2BNwOAT3cM6cOezZs4d//OMfovQ4NzcXPz8/FTcR3IG3Iv6yceNGioqKVMqs165dw2Qyqa5NTTCM1w0YMGDAgIGaYWT4fh%2BMgM9AjRg3bhxRUVGMGTOGzMxMXC4X58%2Bf57nnnlNx6mrCtGnTVPYIvXr1YuLEiWRnZ1NUVMSoUaM4fvw4kZGRPPPMM6xfv56IiAhGjBjByy%2B/TH5%2BPnW1TzprwJUrV1QcvmHDhuFwOLj//vvp27cvfn5%2BTJ06FZPJRGlpKV999RWXLl0iICCAevXqERMTA7gDsEZVmYGXXnqJqVOnUrt2bTp06EBGRgaPPvooDRo0oE%2BfPvTr14/MzEyysrKw2Ww0aNBAWAjk5%2BfTrl07wG0%2BfvToUbKysvjkk0%2B4ePEiY8aM4fHHHyc6OloEhZ07d6ZLly7k5OTw5ptvcu3aNcrKykSpoeJ7N3r0aJKSksjJyWHp0qUi6HnwwQcBd1CZmJhIeHi4yIiBOwBcuHAhZWVlKhGW3NxclYG7Z7YrNDSU8PBw3nnnHTZv3szBgwcFjzFfUyMybNgwbrnlFlWQGxsbq%2BK71alTR8XTKy8vF8EVuDPQ4LaYSExM5KGHHhLH2u12XZC5evVqce202cHAwEDx3oMPPii4nUrJ6qJFixgzZowI3JRMp9VqxdfXl4KCAoYOHYoMdrudsWPHSj%2BTweDwGTBgwIABAzXDCPh%2BH4yAz0CNCAwMZO3atfzpT39i2rRpJCUlMXLkSCorK1m/fr3OS80bXM/6wdfXlxdffJE%2BffqwYcMGKisr2bNnDytXrhSlhKAWiFGyM/369aN3797imMjISB3H76effsJqtdKkSRPmz59PSEgIJpOJZ599liNHjjB79mzef/99tm3bJsRRoqOjr1vWOnToULKzs4VoiIIDBw6QnZ1Nr169AHcGq1ZVzYSnj1zz5s1JTExk48aNhISEkJ%2Bfz%2BLFiwHYs2cP3377rQgEKisrVUGMYg%2BgfU85fvXq1YA7kEhPTyc/P5/CwkLi4uKYMWMGdrud1atX06tXL9FGSUkJhYWFqjaV9pRMXX5%2BPqWlpTz11FPs27dPBGfass5%2B/fpx%2B%2B23izVQ2iwrKxN8xcrKSsEFVI4xmUwiKFywYAGPP/44QUFBIiD1HJvCoVS4dzabjQsXLujUWBXu4/VKT00mE40bNxbzUTK3drudiooK9u/fz3qZF1UV%2BvfvX%2B1nWnjF4dNyAiUcQR0fRuOlJaMb6U46erTmdrV%2BYEioLV5wknRcF9m9peXzSDzXuHBB/frgQd0hOp9Aj7JwQM6984IjpysmkPStI81or503JnbeeLnJiHTaRdaMRcbh09K%2BpD58mr5ll07btYwyVcPwpHQt3Xgke0vH2ZRtfu110A5Q1wh6T7jMTP0xWg6pxNtSt4%2B1noAyfl5Ghvq1jPO6fbv69Z49%2BmO82Vva/r//Xv161y79Odq9r/0OAKh6MCmgnbdsPIcPX/9z0HEKZUvD/v3q17JNq9lL2ltMxs/TTlP7dQTeLbn2a0DmqVeTXaPsHO3W/63WkbL3DPz3weS6ngOxAQM3CCkpKUyYMEHH3evZsycTJkxgzZo1DBgwgKlTp5KSkiL82cCdWYmLi%2BPRRx9l06ZNlJeXM3/%2BfJYvX8727du5dOkSgYGB5Ofns3LlSlEWOWvWLMrLy3U8vwMHDnDPPfewZMkS%2BvXrd93xAcTFxZGSksKuXbuoVasWhYWFhIaGEhQURFBQEM2aNaN169akpaXx7bff0qFDB95//30Ann32WS5dukRUVBTr168nMTGRY8eOUVZWxpYtW2jZsiXgLvUcN24cDodDlFzefPPNHDhwoNo1tVgsxMbGkpOTQ3R0tMqnUHtcrVq1yMvLE8Gay%2BUSZuh2ux0/Pz%2BmTZvGq6%2B%2BKtbcLvuBDYwaNYoffvihWguC2bNns3DhwmrHDfD%2B%2B%2B8zZsyYaj9v3rw5q1at4vXXX2f9%2BvWYzWZ8fHykY6pTpw6XL1%2BmsrKS8PBw8vPziYiIEBlOLaZNm0ZaWhoHDhyQGrDHx8ezefNm4uLiVO8ra2e1Wlm9ejWjR4%2BWtv/aa6%2BJEt6a8NJLLwl%2B56/jm87UqVO8Ot%2BAAQMGDBj4X8DLL9/4Np966sa3%2BZ8KI8Nn4N%2BCmqwfPEVkPMs/i4uL2bZtGx07duSdd97hwoULOJ1OwVObOnWq1CLBE%2BerngjW15Lcr4PU1FTsdjuXLl3C4XBQUFDAL7/8QmZmJjt37qRnz54MrzKkVTJC5eXlbNu2jbvuuku0o4i8gLvUUclQPvDAA7hcLpXapmLfoJRkNm/eHH9/f1auXElSUhImk4lWrVpRv359lYDIpEmTROmr1Wrl2WefFVnYxMREwYHs2rUrNpuNKVOmCN4fuLOv3bp1E0GWAiXb%2BeGHHxITE8O8efPEZ4qXHbiFaTz5esp5SgDfpk0bOnToQEJCQrXrfffddxMTE8PgwYPFunkGZ76%2BviJgDA4OFkF9fn4%2BJpOJoqIiLBYL/v7%2BNG3aVKiP1qpVi969e/PBBx9w9OhRdu3axctVf0UCAgIwm80is2e1WjGZTMIrsV27diQlJVFeXi4Cz4SEBG677TaxTiEhIZzRZgKuA8OHz4ABAwYMGDDwR8PI8Bn4l0DJ2nmW2JlMJiwWi8gyKSbvY8eOlWbb%2BvTpQ1hYGEVFRRQXF7Njxw4hKnLixAkGDBjAiBEj6NevH126dKk2w/f5558zY8YMPvroI26%2B%2BWbA7YmXn5%2BPxWLB5XJRt25dduzYAbgzfB06dGDZsmUsXbqUHTt2cPnyZcxmMxUVFQQHB1NaWoqPj48QM1GUNJVsnVKGmJSURPPmzdmwYQP%2B/v4cPHiQ3r17M2zYMDp27CisBUpKSoiKihLG5FpoAyAlmKqJ/6UoTzqdTiwWCwcOHGDKlCmEhISwbds23bXRlkTKxhAcHEz37t356quvKC8vJzIykoCAAM6ePYuPj48wjDebzZSXlwtBmuoyiNX1pR2Ppz2FYo8gg6cAT%2BvWrVmyZAm9evXCYrEIfp7dblcdl5qaSv/%2B/VWiLJ6YOHEiK1askH7WpUsXUUZbE5577jk%2B%2Bugj1XvTpk1jqkzlzoABAwYMGPgfRQ2FQ78Js2ff%2BDb/U2Fk%2BAz8yzBnzhyysrLEv6NHj5KZmYnNZqNFixY8%2BOCDwj9NBofDgclk4sKFCwwfPlyqIPnQQw/RpUuX645D6cMzE7N7924OHz6MzWZjwYIFItOmcPJSUlIIDQ0VVhEfffSR8IBTsjue6owffPCB4JNNnTqV8ePH07FjR/z8/AgPDxeZpBkzZlBQUMC7777LRx99xE033URZWRlms1lkPJX2AwMDVSWZnggJCeHzzz%2B/7rwBoqKiOHz4MEuWLMHhcPD%2B%2B%2B%2BzZ88evvzyS9VxntlGGYYOHUqzZs1EmWVmZqaYf35%2BvlBLrayspGHDhqxZs6bagEzJoFkslmqVPNu3b8%2BmTZsAdGugeO9VB88g%2BMiRIzz%2B%2BOOAW3jmnXfeEWWziim72WwmNjaW2267DUBwLj2FbkaMGKHieCrjMplMUhXZ6mCIthgwYMCAAQM1479NtKWgoIBHH32Uzp07c%2Butt/LMM8%2BICiItNm3aRMuWLVUigwkJCaRXcV2dTievvfYaPXv2pEOHDowfP15YeHkLI%2BAz4BVSUlJo3bq1V7YKPXv2JDExUWqrMGvWLOkP4g4dOvDWW2%2BpjlUwY8YMJk2aRF5eHjExMZSUlNCkSRNhq9C1a1dR%2Bjdv3jwOexC9v/jiC%2BLi4lRjV7hXixcvZuzYsbhcLnr16sXy5csB9Q9u5YZauXIlLpeLsWPH0qpVK4YMGUJlZSWlpaVC3GTIkCHivJEjR7Krity%2BZMkS3n77bTIzM4VhuVJymZ6eTkVFBQUFBWzZsoWrV6%2BKrJjCQVMCpZKSEhHkeKpSmkwmrl27xpo1a1TrduuttxIdHc3s2bOFF92lS5eIi4tj%2BvTpuFwuXnnlFSGA4mkFAfqg0tNzbsuWLfz8889UVlZit9spLCwUpadaW4JTp05x3333iQDy8ccfF%2BWg/fr145sq37k333yTo0ePioA8ODhYBPUBAQHCaL5p06asXLlStB8VFaVSEwWq5dc5nU4OHToEuEtuZ86cSceOHQH3XoFfAztl/lerlCLKy8tFRjgiIoLx48er%2BlUymU1kfmzVwBvRFm0NhqwmQ5uI1L6WGV9rRQpkf4e8McPWigfUZBAs60uaSNU%2BcJBMXNe2bIA1OXzLOvdi0b0Ynv49b8RWNAoJUtNjzZilJtbaRdYMRibaohVnkM1JKw4hE4LQvueNaJD2HGm1s2bRZWujG7NMcUK7cbQDlA1Y244XZvdSE3DNMbrxyhbdm82mHbPshtYqmsgWuSYXcNn9olHTke7H1FT1a5k5u3aemjWXtqtZZKnQkLZdyRx0l1zTmexW1e0/2dpo%2B5Y9SK3pO0r2Xg0iMyDZJrKqGi%2B%2B5KXrbqBGPPvss5SWlvLpp5%2ByceNGTpw4IXQSZOjQoYNOZFDx9f3ggw/YunUrK1as4JtvvqFx48ZMmTJFqkNQHYyAz4DXqI5Xp7VVWLFiBYcOHRK2CiNHjtT9INdi2LBhADz66KPY7XZeeOEFEhISiI%2BPZ9u2beTl5fHuu%2B%2BKjMiZM2cYNmwYUVFRbNq0SVgThIeHc/fdd4unIoooy/Lly8XYt2zZArizdz///DM5OTkMGzaMjRs3sn79ehYvXixuMiUA6tChg/j/lJQUlcVAnTp1sNlszJw5E3Arg/r5%2BYnAYfr06dx///3ExMSQnZ3NqlWryKn6w5ubm6t64nP%2B/Hkef/xxunfvLjh8dTyMboOCgggICODq1askJSUJfprT6VT5JJrNZvbs2cPFixfJzMwUWUVwZ9IUfppyrBLwWSwWXeCnoE%2BfPuJ4k8kkjMrBHeC%2B8DKwcQAAIABJREFUUfUHfMWKFXyvUXbzLN9MTU0VpZQ7duygVatWgLtMsnPnzoJjWVlZKYLvnTt30qNHD8Bdvjtp0iRq1aqFxWIhIyNDZRkBqARhPO0elixZQu0qw%2BSSkhJyc3OFh58SuF%2B7do1ly5ZxRKNIWVlZyaFDh7BYLBQWFpKbm6tbI4fDwbp163TvVwdvjNe1l0KWzNQa/mpfy4yvtc7CMvtAb8ywtV7JNRkEy/qSGsNrnY8lE9e1LRtgTQ7fss69WHQvhqd/T9u3zLVc44QsNT3WjFlqYq1dZM1gZMbrWhNm2Zy0/tMyM2ftezKPcu2gtefIvMW1iy5bG92YZcb12o2jHaBswNp2vDC7l5qAa47RjVe26N5sNu2YZTe0toJGtsg1uYDL7heNW7d0P6akqF/Lyta189SsubRdzSLLjMN17UrmoLvkms5kt6pu/8nWRtu3ztFdcp4Xe0vbjuwU3TaRVEV58yUvXfd/A/6bMnyXL1/mq6%2B%2BYsaMGURERBATE8PkyZPZuHFjjb%2BHZfjoo4%2B4//77adasGcHBwcyYMYMTJ06IB9jewAj4DPwmXM9WoVmzZphMJurWrcvcuXMZNWrUdcsD4des0M033ywyWxEREQwZMoRvvvmGjz76iKSkJMAd9Hz44Yd0796dJ554gtq1a4sA5ZFHHuGxxx67ruy%2Bgr59%2B2I2m7nnnntYtmwZ2dnZrFu3jqefflpYIijZHSVDBr/6svXp04fGjRvr5tayZUtKS0tF6eDSpUv5%2B9//zqlTp3A4HLqSPc/S1EOHDnHfffexaNEiEVReuXJFBDTFxcUi43f48GFOnTrFsWPHMJlMvPLKKwDExMRw5MgREbCkpaWJdYuNjeXZZ5/FZrMJM/PGjRtjMpkICQlhzpw5ZGRkcFQi0b9r1y4R6D399NMicAa36qZyDa1WKwMHDlQFjampqeL1uHHjaN%2B%2BPeC%2B7uFVf9RdLhfFxcUiUO7Tpw8vv/wyZrMZf39/nnvuOdHeLbfcQrt27QgMDFRx78AdkB45ckT0t3//frEfli9fLrihJpMJq9Uq1l85vmHDhkyePFnKMVTUTa%2BHUtmj4GpgGK8bMGDAgAED/3/hyJEjWCwWldp369atKSkp4aTOK8iNc%2BfO8cADD9ChQwd69uwpfmOVlZXx888/i4fj4K6AatSoETabzesxGQGfgd8FRZRkx44d1fLqnnzySfbs2SO1PNBi8uTJ1KlThzlz5vDdd9/x5z//WWW2/tJLLzFixAjOnz%2BvMr9u1qwZWVlZLFmyBEB1Y1SHoKAgmjZtys6dO7HZbPTo0YO4uDjuuOMOEWDt2LGD8PBwVQCkCLNMmTJFlDHu27dPqE7u2bOHoKAgaVmhEmCYTCaCgoIwm81s2LBB9PfZZ58BsL3KUykqKoqGDRvyt7/9TbRRUVGBw%2BGgoqJCBIU33XST%2BFwpvVSCoEuXLnHo0CGuXbtGTk4Op6u8ou666y6aNGnCyZMnqV%2B/PkVFRdWKlICbm6cEZQsWLFBZD1y7dk0ExyNGjFBlG5V5KHOcNGmSyLwWFxeLUs3IyEgiIiLEGD755BNef/11LBaLCAKVNr7//nu%2B%2BeYbrl27JuYZEhKCn58fwcHBrFmzRqzNzp07hd%2Bf1WrlT3/6kxiXy%2BUSgZ2WGxgZGUmLFi1U2cP27dsTGRmp2pPKecp/GzduXO0aamFw%2BAwYMGDAgIGa8Udk%2BJQqKM9/F2X%2Bmf8kCgoKCA4OVj34VvQA8iVl5hERETRu3JiZM2eye/duHnvsMZ5%2B%2BmnS0tK4evUqLpdLnO/Znqyt6mAEfAZ%2BE2qyVbgePA3Ttcbp3qBbt26AO/jLzMzE5XJx/vx5nnvuOdLS0uipNa7GHUhq%2B9KW3g0fPpxt27aJEkvFVmHatGmkp6czY8YMysvL2b17NwBDhgzhmWeeEU9llEDB5XKpMjdTp05l3LhxqvrsxMREzGYzbdu2Zc6cObRr146AgADhyfbOO%2B9gNpvp2bMnoaGhTJ06VWSkQkNDsVqthIeH06FDB8D95aKMy%2Bl0sn//fh544AGRPVPKMOFXWwqAhx9%2BWPy3rKyMTZs2cfLkSa%2BCjuvVjoeFhem4fPCrsqbnuYqh/dWrVzl//rwq6PT39%2Bevf/2rtC1P%2BPr6YrVaxbmeGT7PoE4pHQZ3BnHGjBkioGvatCkBAQHivMDAQI4dO6Zai/3795ObmysUXD3XQfnvP1PS6Q2HzxsKSE20NCmHzwuOijccPi1VSMvxkhkCa/uS8ti94NHpODTeDNALPo83vEnte7KuddfqN3D4ZOun5eJ4xeHTDMYbDp9s4t5w%2BLTtSH8/1cB3kybKNeORrY1uzW8Uh0/r63mDOHy68XrD4ZN9CXjD4asS0xKQcfi0i%2BoNh88brt2/iMMnXXPtmkqqN3R71IvvR933qmzNveFfasdzgzh8um0i4/B5sbf%2BUzh8f0TA99FHHzF06FDVP61ydnXYsmULcXFx0n85OTn/FL%2BuR48erFy5klatWmG1Wrn99tvp3bu3EKuD6//m8gY1170ZMFCFF154gT//%2Bc%2BA%2B4d4fHw87777LnXr1sVkMtVYtqmgX79%2BOqsExVbBGyjcsbZt2zJt2jQuX75MUFAQBQUFWCwWevXqBaDKzLhcLvFD/u233%2Bb%2B%2B%2B9nxIgRqtR6jx498Pf3Z/v27QwcOJAdO3bg7%2B/PyJEj6d69O0uXLlUJwgQGBjJ//nwGDBgg%2BoqLiyMgIECnxLRu3ToKCwtJSEjA4XCILNDw4cN56qmnePnll2nSpAnr168nKyuLY8eOERUVxYYNGwSvrqKiArvdLozS7XY7Bw8eBNxln%2BPHjwfg7NmzjB49mjFjxjB//nz69u3LmjVrSE5OJj4%2Bnu3bt6s88KxWK19%2B%2BSW1a9cmKCiI8ePHC3XSpk2b8vjjj7No0SKGDh3K4MGDufXWWwFU19tisdC3b1%2BeeeYZunTpwrJly6TG6r1792b79u288sorzJgxg%2BjoaCZPnsy8efNYvnw53bp1Y8aMGXz%2B%2BeeYzWaOHz/OjBkzsNvtKt8/pU9lDBUVFeTl5REaGkpRURFWq5Vx48axatUq4aV3/Phx4uLixDlffvmlSp1UCTzPnDnDnDlz2Ldvn2784LZd2Lhxo/Qzs9nMe%2B%2B9J/1Mhk8%2B%2BURnvD592jRatm7tMU/1OTIKSE20NCmHzwuOijccPm0sruV4aV/L%2BpLG817w6HQcGm8G6AWfxxvepPY9Wde6a/UbOHyy9dNycbzi8GkG4w2HTzZxbzh82naioyXjq4HvJlsa7Xhka6Nb8xvF4fMQygJuGIdPN15vOHyyLwFvOHxa71kZh0%2B7qN5w%2BLzh2v2LOHzSNdeuqaQSSbdHvfh%2B1H2vytbcG/6ldjw3iMOn2yYyDp8Xe%2Bs/hcP3R2DkyJGkaPZmbSnpWI9BgwZVm6zYvXs3RUVFogoO3A/mwV095A1iY2PJyMggLCwMs9kszldQUFDgdVtgZPgM/BPwFG3Zt28fa9asEby6Ro0aiR/MvxepqanXLf9UOGcDBgwgNTWV9PR00tLSOHLkCBkZGSxcuJDY2FgVj81TtKVTp05kZWWJMj8FPj4%2BDB48WAjAfPzxxwwePBgfHx/q16/PwoULadu2LePHj6ddu3aUlZXx9ddfC/GOgoICXnjhBeHp5okff/yRo0ePYrPZVIHS4MGDycrKYvDgwSJgU7h/77//PnXq1MHpdKoEVhITEwkKChLiNIGBgXTu3JnY2FjBL/Pz86NFixYicHv99deFXcWQIUPEWmzevBm73c7cuXOxWCykp6dz%2BfJlkdU6deqUCEjKysrYu3cv4C6f9PyiUTiAClasWCFsK8Bt%2BO5wOESpqiKkcvHiRRYsWICfnx9/%2BctfVGtmsVhYvHgxNpuN2NhYcU5ERARZWVkcPnxYZP6UzGFZWZkY%2B7lz58R1Vfbm2LFjRaltp06dMJlMzJkzh65du4prVrduXV544QUhCqPwN2%2B66SbMZjNRUVE89NBDwK8PH0wmE35%2Bfjidzmpll2UwOHwGDBgwYMBAzfgjMnzR0dG0bt1a9S9a%2BoTqn0N8fDwul0tFB7LZbISGhkqr4f7v//5PZ6114sQJGjRogJ%2BfH82bNyczM1N8VlhYyJkzZ4TAoDcwAj4DNwR9%2B/Zl3bp1Ou4WwMyZM3n33XdvWF%2B1atW6rrn17%2BFA3XXXXezdu5esrCx%2B%2BOEH7rrrLulxL774IoGBgaSnpzNy5EgSExPp06cP7777rrBVkKk4Xm9sTZo0oUmTJhw/fpzGjRvj7%2B9PQUEBM2fOxGazERISgtPpJDMzk7y8PFavXk1iYiIlJSXs2bOHFStWYLPZuOWWW3C5XMydO5cxY8ZgNpux2WyUlpZiNptp0KBBtWNQgvp//OMfgFv19MyZM5w%2BfZrly5ezaNEiABo0aMAnn3zCwion1MrKStatWyeyfzt37sRkMjFp0iRA7WEHqOraXS4Xbdq0EVxDBRUVFTRt2lS8VtbTkyOnlGCGhoYSHBwsnqQ5HA7x5dmzZ098fHzw8fFh6tSpgsN38uRJTCYTgwcPZsKECcJWQdnDxcXFhIaGcqmqVOqXX36hXbt2mEwm8QRQKSGNiIggtOrRcp629MuAAQMGDBgw8D%2BDiIgI%2Bvbty1//%2Blfy8vI4f/48S5cu5a677hIP5u%2B77z7xO8Vut/P8889js9moqKjg008/ZefOndx9990AjBo1ijVr1nDixAmKiop49dVXiY%2BPV1Vr1QQj4DNwQzBu3DiioqIYM2aM17y634NnnnlG8OoUk%2B%2BCggKdrUJNOHDggIpLOHDgQJxOJ4MGDcLhcKiMtT3RtGlTPv74Yzp27CiCDLPZTJ06dXjxxRfp3r27KGmMj4/Hbrezf/9%2B4dVnMpmIj49XtXnt2jURzJw9e5aBAwdSXFzMe%2B%2B9x8mTJ2nZsiVms1lkCF0ul5D3dTqdPPDAA6Snp7Nq1SomTpyIyWTC6XRitVopKSmhtLQUp9PJX//6V1FnrpTRZmRkUFxczLx584TPIrh5dQUFBaJkt6CgAF9fXzp16sSsWbP4y1/%2BouLKKaWzdrsdh8MhSibT0tKIjY2lQYMGtG3bVnjfRUZGUqdOHYYMGULv3r11pZLDhg0jISGBnJwc1q5dC8A//vEPWrZsSa9evQSZecWKFezatUsEXZ4m71988QUVFRU4nU5Wr17NI488AsCFCxdwOp20b9%2Bee%2B%2B9F5fLhdPpFOI2DoeD/Px8obpZUVHBgQMH2L17t/BnVAL4K1euiMCwunJPGQzRFgMGDBgwYKBm/DfZMgAsWLCAkJAQevbsycCBA0lMTFT5UGdnZwuxu3vvvZexY8fyyCOP0K5dO5YuXcrSpUtp06YNAHfffTdDhgxh7NixdOnShfPnzwsrLG9hcPgM3BAEBgaydu1ali5dKnh14eHhdOnShfXr16tUDW8EmjZtysaNG1m6dCn33HMPBQUFBAYG0rp1a55%2B%2Bmmv%2BYDJycn8/e9/V723efNmwauL1foWAatXr5bytOLi4li1ahUA/fv3Z82aNWzevJnHHnsMcGfFCqsY5WfOnFGRcYuKihgxYgQVFRXExsZy5coVXC4XFouF3NxcRo4cid1ux9/fX5Qt%2Bvj44HA4MJvNhIaGkp%2Bfzz333AO4M1%2BKYuWuXbvo2rWrqlQT3Nw9JWisXbu2VKHT19eXyMhILl68SOvWrUlPTyckJIT33nuP%2BvXrU1ZWhsvlEtk1i8VCXl6e4NcdP34ccAdEKSkpfPLJJ7z66quMHTsWcGfICgoK6NevH40aNeLhhx8mMjISi8XChx9%2BKAL3zp07k5%2Bfj9PpJDAwEKfTKQJYk8nE9OnTyc/PF6UYJpMJHx8fFFN5JZirU6cOycnJBAcHS7PR4M5yxsbGqkRgFCh%2BhZ06dSIgIEBqwWCR8WuqwcCBA3WKsi08spoAHDkCng8HtK9xk%2BpVzyays8Ejk3v1qsSfSnuS5hxpu2fOgIdFCbh1H1Rx68WLakKMrhG3LoBqmXJy9B5hpaVq4oykHV3nx49D8%2BaqQ7TD4dQp8CypycvT87OKi9UEHUnf586B6mtN0jdFRWoOlPbalZXpeT%2BXL0NU1K%2Bvr13T86q04ykv13OrtOun6aukpFzH49MuRWGhhBOluXjnz4OHVah0fNrllDSjm6Z2%2BJJmcbn0lKga9yPor4P2BpFNPD9fzSmT7dlLl9RcOtkktIPW3lOyfa7dE7JjMjKg6schACdPgva7RLs43lwY7VodOwYtWqjP0c5Btn7aG0a7nqC/X954Q831k52jeU%2B7VID%2B3tReJ9BfK811kt1i2uHItpr2UskunfY93fej7DzN%2BGTnaMcs69ubY3T31L8J/23PQkNCQoTFlwypHkJGJpOJyZMnM3nyZOmxyu%2Bc6dOn/%2BbxmFy/V/bFgAEJUlJSuHDhguBFWa1W4uLiePTRR0V2p7CwkOXLl7N9%2B3YuXbpEaGgoycnJTJkyhRZVf1BmzZpFeXl5tSIvX3/9NfW1RPTrYNeuXYwfP5577rmHuXPn6j6/du0ab7/9Nl9%2B%2BSXnz5/H39%2Bfm266iXvvvZe%2BffuK48aOHcuBAwdUP%2B6VAPeJJ54Q/DbP8Y8dO5Yff/xR8M1MJpPIICpZu2bNmnHs2DFhb6GoS0ZGRrJo0SISExPp1KkTdrudN954g6lTpxIYGIjdbsflclFZWUn37t0JDAzk0KFD5Obm0qFDB%2BrXr8%2BWLVvw8fGR%2BsspojDKtfriiy%2BIjY2ltLSU999/n0WLFgl%2Bo%2Bexfn5%2BpKSkMGbMGJ566inCw8PJyMiQqkmFhYWxePFiTpw4waJFiygrK1MJryho1qwZf/vb34TVhNPppHWViInT6SQuLo4tW7bw6aefCl6fEry2b9%2BetLQ0YmJiuHr1KmVlZTq7hY4dO/Ljjz9Wt0Xo1asXS5YsEX0qUPz%2BhgwZwrPPPktKSgplZWUqzt6f/vQn1qxZU23bWrz00ktS0ZYpMkEDAwYMGDBg4H8Us2ff%2BDarWCn/EzBKOg38YfAUedm1axe9evVi4sSJZGdnU1RUxKhRozh%2B/DgrVqzg0KFDrF%2B/noiICEaOHElWVtYfMqb169dz%2B%2B2389lnn%2BmyN8qYjh49yrJlyzh48CBffvkl/fv3Z%2BbMmbpM4Lhx48T8bDYbH3zwAWfPnuXJJ5%2BU9q2IhLz44osAglun8PMCAwMZPnw4AQEB1KtXT2WlkJeXx/3330%2B3bt2orKzEarUyZcoUXC4X5eXlwrJBmceOHTtEIHLo0CFKSkqIqHp0b9I%2BEgcRwIE7yOzbty8JCQl07NiRN998E5fLxYEDBwC3Oiq4/fZatWrFV199xejRoykpKSE2Npb7779f1bbZbKZ79%2B5cu3aNhx56iPfee0%2B04XA4mDJlCocPH%2BbAgQN06NCBkydPCi9BcBOdnU6nEEjJysqiVatWzJo1S8ynoqICi8UiCNIul4tbbrmFyMhI/Pz8RGAdFBSk8uHz8fER66wc89VXX6kCQiWoVzxwevTowdmzZykoKNAJtOzdu/e6waQWhmiLAQMGDBgwUDP%2B20o6/9NgBHwG/iUICAhg3LhxREdHs3PnTt5%2B%2B22KiopYtmwZzZo1w2QyUbduXebOnUtFRQVDhw4lISGBLVu26Hz7cnJyftMY8vPzSU1NZfr06YSHh6u81MBt11BSUsIbb7whxhQWFsaYMWN49dVXqVev3nXbb9CgAY888gh79uwR5uye%2BOCDD6hXrx47d%2B6kVq1aOBwOWrduTUJCAgUFBVy7do0XX3yR0tJScnJy%2BOyzz6hTpw79%2B/cXvnuPPfaY4H35%2BPjQokULxo8fz6effiqEREpKSnjkkUeEIWdkZCTZ2dl07NgRm81G%2B/btMZvNwqMP4OuvvxYCNVFRUWRkZGCz2UhLSxPll2fOnAEQpunr1q0jPT2dVq1aERwcTHJyMseOHdOZljqdTsaOHcuGDRuoqKggJydH5Ye3cuVK2rdvT9euXUlPT8flcnHp0iVWrVpFQkKCIC0XFxeLjLHT6VSVp7pcLlq2bMmdd94JuNU/f/zxR65du6by4SsrK1Ptn8rKSlH26amGqtTZW61WlUKqyWQiPz%2BfgIAAoqOj6dq1K%2BDOkEZHRwsVVW9hcPgMGDBgwIABA380jIDPwL8UiifJjh07GD58uMhKeSIjI4PMzExsNhuDBg2iX79%2BqkyajFfnDbZs2UJ8fDyNGzfmzjvvZMOGDarPrzemPn36eCU8U1FRofL8U3Dw4EEuX77MoEGD%2BOqrrwRR95ZbbsFmswHQpk0batWqRUBAALGxsfTs2ZNz586xbds27Ha7KhAoLy%2BnsrKSY8eO8dZbb9GnTx%2Bys7OJiIjg6NGjjB49WmTJysrKOHv2LD/%2B%2BCMJCQns378fp9MpAkJwB3FKkHr58mUh6pKcnMybb74p5lO/fn1SU1OF94zT6SQ9PV1kFZ1Op46TBrB48WLCwsIAd0bOMyCeOnUqmzdvZvPmzWzdupVWrVrx3XffMX78eGw2mxC/UTJtvr6%2BorxSaQ/cZvWe1hAlJSXY7XZVJrdt27ZYrVaVwboCh8Mhyj6VILOiokIIwOTn5%2BNyudiwYQO%2Bvr5cvHiR77//Xhx38eJFnE6nuJ7eQGq83qeP%2BiCtI7DExVpnjKsxLJY5RejOkaiLemNiraN%2Bao%2BRda4t%2BZWZY3tjRqxt%2B8KFmsendWaWtasZn%2ByQqlv4V1y%2BXPP4NMfIPKx1BtoSJ2ldxbTMA1W7fhq%2Bqcx4XTsgmdm0N6b0ujHL%2BLLaRa3ptaQd2SG6pLnMiVu7T7TXSXZhtJ3pNoCkc9kC1tSObFLaB4iyY7QPQmXm8dp5yu5N7Xpp16rK7kYF7d73ol2pmbf2Te33gkykQrPG0v2obUd2fbXz9sLYXDvP37LNZcOR3c7a87TnyKakbccbP3dvxvfvgpHh%2B30wOHwG/hCkpKRQVFSkysqAO6OyePFinnzySV566SW6du16XR7frFmz%2BPjjjzGbzULKFtQKld7y%2BO644w5GjRpFo0aNhOed57kJCQm8/PLLdO3atUYe39ixY8nOzubixYsiEFFupaioKDZu3EhkZCSzZs3ixx9/FAGBwt/zhNVqFRmogIAAoXApw2uvvcZzzz3HtWvXpJ8rnLjBgwezZcsW0ZfJZKJLly6sWrWKESNGcOjQIQIDA0VJ4QcffMDHH38sgmBPDmFkZCTx8fHs2rVLnOPJQ/Tz86O8vFz01bt3b1321HOeCQkJwl5CC8XSwuFwsGPHDho2bMjbb7/Nq6%2B%2Bitlspn///oIEvXz5ct555x0RPCtZU0WMRbbWCpSS4Z9//plJkyaRk5MjuInDhw/ngQceoG/fvoSHh1NWVkZpaSnz589n/vz5pKSksHTpUjp27EhFRQWlpaWin/j4eDZv3iztUwaDw2fAgAEDBgzUjCqL4huKKqep/wkYGT4DfxgUyXwAf39/EhMTGT16NLOrmLfFxcVe8fhiY2N1Wb4tW7b8U2M5ePAgp0%2Bfpn///oLHZ7FYWLdunTjGZDIxf/582rdvz4oVK8jJyaGsrIyCggL279/P9OnTef311wE4fPgw586dE1kmJfjp0aMH586do0uXLqIkNTc3V5Q5egZgChYsWCDKMRX7AxmSkpKYPXs2TqcTs9lMfHw8gYGBqtJM5dzNmzergh2z2cwvv/zCW2%2B9JQItGX9MGYPNZiM9PZ34%2BHiuXLkismQWi4UePXqITJpidu7ZV7t27aTtKkFtbm6uiqOZlJRE165dqVWrFr6%2BvtStWxdfX1969%2B7N2bNnhWG80%2BkkIyOD2267jcTERFasWKHKFCrZVYBGjRrRq1cvwsPDxYOCzp07ExcXxy233MK8efM4duwYt99%2BO6Ghofj5%2BfHVV1/h4%2BPDoUOHhFehpy3D3LlzcTqd/PLLL%2BTm5nL16lVKSkpUcz9y5IjB4TNgwIABAwYM/EfBCPgM/GGoVasWzz77LDabjX379vHBBx8wZ84coqOjCQsLY9OmTdXy%2BEaNGsVlWYnUb8T69euprKwkJSWFL774gq%2B//hqHw8G6detE0NakSRNKSkoICwsjPT2djIwMsrKyyMrK4uuvvwYgJiaGn3/%2BmdLSUvz8/Gjfvj02m42PP/4Yl8vF7bffLvr84YcfGDRokAiOfHx8MJlMNG/eXJREgrvccdSoUYA7YPP39xdZUU8VUJvNRkREhCgxPHLkCCUlJaosqlLGuXjxYhXnsGvXruTm5rJ9%2B3ZVaaQCRZAF3EFcQkICiYmJ/PzzzwD89NNP4vOMjAxhL/Hggw%2BycOFC4X8HCF9EBfPnzwfcGT5fX1/y8/NVRuwBAQEkJSXRuHFjduzYgcPhYODAgeLz%2BPh4ERD/8ssvXLp0CZfLRUlJiVgLq9XKTz/9REpKihhDYmIiX3zxBdu2bcNkMlFeXs7x48d58MEH2bp1K88//7xqznv37qWiooI6deqINTKZTEKts06dOpjNZuLi4qhbty61atUSgXtERISwHunQoQMGDBgwYMCAgRsHo6Tz98EI%2BAz8y6GIlRw6dIhBgwbpOHMzZ84kOjpaZHZ%2BL4qLi/n888%2BZP38%2B9957Ly1btuSTTz5h1KhRFBQUkJaWBsCAAQOw2%2B1cvXqV5ORklVCMIlzSuXNnNmzYIAIIRRWyefPmPPDAAyys0vhVVCK3bNkigqPQ0FBcLhenT59m69atYnwXL15k6NChgLvkVfHZA3d5qKKu6XQ6RYDjcrmIiYkBUAUoSiZu1qxZ5Obmij4aN26Mw%2BEgOjpaZNo8lTmXLVsm3lcyfMq/qKgo0a/D4aC0tBSTyYTZbGblypXMnj1bzDE8PFwYpCt4%2B%2B23AXeGLyYmhuDgYJU1hOLVB1C7dm1atmwpgv3z589jMpmETYePjw%2BVlZU6awkl4FQyftHR0aSnp9OvXz/69%2B%2BPj48PBQUFBAQEkJaWRq1atThx4gTgDmBQd6rJAAAgAElEQVRLS0t54403sFgsjB49GnAHkbVr1%2Bbw4cMAwh%2BwqKiIixcvilJScNt5XKjiulRIyR5yGKItBgwYMGDAgIE/GkbAZ%2BBfhuLiYlatWkVeXh5PPvkkLpeLrVu3kpmZyW233UarVq2Ij4/nk08%2BYcmSJdxzzz3ih/8XX3xBmzZtiI%2BPJy4uTmWsfurUKcAdtCgBWps2bYiLixPWAiUlJfj4%2BJCamsqIESMEj8/lcgnfNMVOQDHqBncWrH79%2BjidTho2bEhQUBCbNm0SpZNFRUXs378fgClTpqhKHw8cOMCgQYPw8fGhdu3a5HmIYmi5Zenp6VLD7gsXLqjOU7JjLpdLBBhnzpzRBRkrV65UvVaM4hWjT3%2BN2XNZWRmffPKJGJsi2uIZfN10000iq6YomAZoTIUVYRNPrFixgqgqJ9ycnBwKCwtVZauKWIrT6WTAgAHs3LmTpKQkAO677z7atGnD7t27AXdALOPlXb58mb1794pM5blz50hNTaWwsJDKykoqKys5ceIExcXFfPzxxwwcOJArV66o2jh16hSVlZVMmjSJOXPmYLfbuXjxougvMzOTiooKrl69yrZt28RagTvIUwK17Oxs3fiqg0y0pWdPjWiLVoxBolKr00jQCLt4ox0hE4PRtXv%2BvO4YHaFfm5mXMP51fXsjMOGN4oBkbXR0V%2B31kVUSaNqVda3TmZGpB2s71/Yt4%2BJqr4Ps4mnXVHZMDesnE23RNiMTodAeI60M1/QlG572Ta1mhrRdjXiJrPpdd55EMVknRPIbBGOoUi1WQbuPvVG9%2BeWXmvv2pl3t/pOJq3gzT63Yj/aelwgj6YRnZGuuvcAyMR3tvai9yWTz1gi5yPSfdGsjE9zRtK29xWS3qnavybRqtNtGdk9pl0s2Te0x3rSrHZ/snvLmssjm9e%2BAkeH7fTBEWwz8IZCJtvj7%2BxMfH8/jjz9OUlISiYmJdOzYkZMnTwqpfLPZjMViwc/Pj7CwMHJzc6lduzZJSUkcP35cCJrk5eWJLI/VamXDhg18/fXXLF%2B%2BHHAHEQ6HA6vVSkVFBSaTiUmTJrFixQp27twpsmYDBgzg1KlTpKWlERYWRlxcHFC94IciUmIymQS3SxGU8RSSMZlM/OUvf2HPnj3s3bsXX19fftH%2BYfeA0u/Zs2ellg4KkpKSaNGiBevXr7/u%2Bnfv3p3vvvtO%2Bpm/vz%2B1a9fm3LlzInMH7iDVZDJRWVlJaGgo33zzDUFBQYwaNYqSkhJKS0spKSnRldoqPnv79%2B9XickEBARQWlpKVlYWbdu2VfHVAgMDiYmJEcG6Fkom74MPPqBx48Yi23vLLbfw3nvv0bt3bwoKCkRmEdwlxG3btuXbb78V41KCd%2B313L59O7fffjsVFRVC8TMqKgq73U7//v1ZsGCBuCZaJCYmMnLkSJ555hnp5y%2B//DKDBw%2BWfqaFTLRl2rTpTJ06xavzDRgwYMCAgf8FTJt249uskmX4n4CR4TPwhyA1NZXg4GCV%2Bfq%2BfftYs2aNyN40atSIVq1akZqaisVi4fbbb%2BfIkSNkZGSwa9cuRo8ejcvlIjw8nOPHj5OdnU3jxo1ZvXo16enpImvldDo5ePAgkydPFn0FBweLsfj6%2BhIYGMiHH35IRUUFPXv2pG3bttx8882cOnUKk8lEt27duPXWW1XnyDJuikiJkslTDLt/%2BOEHPv74Y9VxM2fO5LPPPiM3N1d42IE72GnevDngLhOEX1UjtcGe2WymadOmonyzT58%2BIthTyioB4dOn8PKUYM9TITU4OJjatWtz8OBBrly5ogr2lDErWary8nIKCwsxmUwsWLCAkydPUlJSQllZGXFxcdSuXZvOnTsTGRmJ0%2Bnkm2%2B%2B4dq1a6rMoSJ8MmvWLFWwZzKZaNOmDTfffLPqPRnuu%2B8%2BXVD4%2Buuvc/78ecFXVNbwiSee4JLHk3DPTK3L5cJsNosSyo8%2B%2BkgE5/%2BPvfOOj6Ja3/h3W3pIIaQQegslQAgldClSIiISqVdQQbGAIMgPLl4EC6B48eIVBBREuXCliQgWeo%2BAoYcQQkBREEgCSQgphGQ32d8fmznunJ2QKHqLd57PJx%2Bd2TNnzjkzO%2Bw77/s8jzLnNm3a4OXlJQR25DF5eXkRFBREXl4egwYNUvEWDQaDuOcU3mNloC2co7%2BD06FDhw4dOnT8dtADPh3/NvTp04f169cLGX1nzJw5U2T78vPzuXLlCh4eHrz//vtC4EUR8oiLiyvXluHgwYMkJSWxfft2cnNzsVgsLFmyhNWrVxMcHExUVBQGg4HFixersmarVq3i7NmzQrRlwYIFqn6VQMput1NUVMSWLVtE1hAQnn1btmzB09NTlV3atm0b3bt3d%2BkvIiLCRXylV69epKeni0AlIyODoKAg2rVrR5cuXWjdurXg3O3bt08VBALExMQAjqyewWAgJyeHL774ArPZrBJtAUcwOWfOHKpWrcrrr78uhFUaNWrEqFGj8PLywtfXl9jYWDw9PTl06BClpaWEhYW5cAiVsSowm82EhoYSGhqKv78/kZGRKusGOZvqHIw6B15NmjRR9enr6yuyioGBgbRp00bMNzIyErPZLMpOW7ZsKQJSZ55h3759ady4MRkZGYSHhzNy5EhhtVClShXeeustAJEpzsvLY/fu3eK8SvZQCdbr1atHZaFz%2BHTo0KFDh46KoZd03hv0kk4dvxt69OjBmDFjhAKljNu3bzNkyBDMZjOpqanExsYydepUFi9ezO7du4mLi2PZsmU0adKEs2fP0qdPH1Xg9f333/PAAw/g7e3twmFzFvWwWCzY7XZsNhvu7u4MHToULy8vNm3axGeffcajjz5KRkYGpaWlqoDFYrFQWlrqYpNgNBqFfULDhg354YcfaNq0KV26dGHRokXAz0GAcymh4lcnQ2kTFRVFYmJiuaWkgYGBIsAsKChQ9a0I3ziXlZYHJRhKTEykpKSk3ADDZDKJQG7z5s2MGTOG7OxsmjZtygcffMDq1atZu3atyp4CHKWVzoImffv2Zfv27ap5eXp60qBBA2FS3rBhQzIzM8nJycHPz4%2B33nqLZ555RlgqOAeASgmm2Wxm1KhRrFq1ijt37jB37lxWrlzJ2bNnVeWczvMpz/LCeZ3NZjODBg1izZo1Yp9zP15eXgwePFhkmGUsX75clS2%2BG86ePeuSEaxTpxEtWjQW2zdvgpPzhss24CBeOGUcs7PB6f0DVitI8b3LzqIiKHvP8DPy8sDJzD4tDcrESAUKCsBJdNVBQHHidqakgFOcDsCVK%2BD8juaHH6BuXXUbeQ63b4NLfGy3g9MLAenUDly4AGUZdYCLF8E5Jv/pJyhLRgvIa6y5NtKa37gBZe%2Bgyu3nxx%2BhTp2ft8%2BehaZN1cdcugS1azvt0Ji4NCXt63vnDjhl3OWx3L5dhJeXNKn4eOjSRWxKl98Bad6cOQORkeo28oBcJoXron73HTRoUP42uFwszXlLN6nW90W%2BT%2BQl1rqP5GlrTen6dQgOLn%2BKWmOWr6XWMS5z0Jh4RXPS6lvz%2ByLfyNJ3THPRpQHKawUOGl14%2BM/bt26Bn5%2B6TX4%2BOBXnkJkJZfRvzaFonNrB6ZN8TOVrpXXt5AeD/PzRXKs9e6BMGRrQvnjypORtcF3T4mKQxOzkNiUloCpC0rou8j6tL7T8AHd5oJfz7P03YNzvwHQo%2B8n2PwE94NPxu6GigA8gNzeXRYsWsWLFCrHPaDTi6elJ1apVuX79Ovfddx/bt2/n0UcfZebMmaKdEvBpGa%2B3a9eOW7duMW3aNEaNGsWwYcNo0qQJX3/9Nbdv3yYsLIzY2Fh2796Nu7s758%2BfJz4%2Bnvbt2wOO7FFISAhZWVmYzWaCgoL48ccfgZ/tDJz5X%2BAIdMxmM1lZWRiNRkJDQ8nIyMDLy4uCggLRzjkABUcgaLPZcHNzo6ioqNwATMlC5efn4%2Bfnx8GDBxkxYgRHjx7Fz89PZKWUYKioqEgz8ARHABcXF%2BcSAFksFkpKSmjSpAmrVq2ipKSEJUuWsGPHDm7cuIHVasVsNhMVFcWPP/5IZmbmL8pIKeOwWCw89NBDfPbZZxUe47zGd4OXlxfNmjXj6NGjmp81bNiQtLQ0PDw8uHr1KqWlpSpfRIvFQnFxMfXq1aNbt26sXLnSpewVHIqgkyZNEnYTMgYPHszs2bMrHC/oHD4dOnTo0KGjMnjuud%2B%2BzzLZh/8J6CWdOn437Nmz567BHjh%2BPL/00kuYTCaMRiNubm6YzWZMJhNhYWGsXLmSBQsWYDAYSJNUx%2BrXr09qamq55ZzOWL58OTVq1MBqtRIcHMzVq1f5/vvvyc/PZ%2B3atZw5c0ZlYP74448za9YsEhMTOX78OIMHDxafff755zz55JMAbNiwgWHDhuHu7s6tW7fIyckBHIHT9evXKSkpoVGjRoKLCBAfHy%2ByQ0FBQRQVFQm7AyWwUTJrwcHBzJ8/H3CoXU6dOhWr1UpmZibNmzcXqpSFhYVMnjyZqKgo3N3diY2NFeONiIigY8eOWCwWHn30UYKDgxkyZIgI9pSSSaPRKNQmz549y6pVqxg%2BfDgXLlxg6dKlJCYmsm7dOsLDwzl%2B/DiZmZmq8lEZBoMBb29vESAbDAbi4uLo1asX//d//8cbb7yh%2BkzhKfr6%2Bgp7C4DnpKe8s41Ht27d8PLyIjg4mJMnT4rj3NzchP%2BhgsuXL2M2m%2BnYsaPIACqw2%2B0iK3zx4kWysrJUSq3O61SvXj0h2CPPF1CZylcEncOnQ4cOHTp0VAy9pPPeoAd8Ov5jEBsbW67AS2BgIIcOHdLk%2B02ZMkWVIXTGvHnzaN68Od26dWP//v2sWLECg8GAwWDg9OnTDB48WBVAeHp6YjAYmDp1qsoH0DkYrFWrltiuWbMmI0aMUNkxKFD%2BPzk5mc6dO4syR5PJRGhoqJiX0s7Dw0MEKc%2BXlaSMHj2aP//5zzRu3FgERD4%2BPgQFBZGUlERcXJzg8D3xxBPExMTQsGFDoTDZqlUrOnXqRHFxMVarlY0bN/K3v/2NkydPitJUs9nM22%2B/TUpKCqmpqVSvXh273c7nn39Ofn4%2BixcvFrzJxo0bU1paSnh4OHa7/a4lkgqvTSkxVfrcuXMnb7/9Nl999ZWqFFXh/OXl5fHSSy%2BJfpZIr%2BCcs24HDhzg9u3bjBgxAvg56CouLlYphr7//vvcvHkTT09Pzp07p3pJIAvbANy4cYOSkhJCQkJEwKdYS7Ro0YJDhw4Bjuu/Y8cOAgICVNnCykLn8OnQoUOHDh06fm/oAZ%2BO/woMHjwYq9XK8OHDSU5Oxm63k56ezsyZM9m%2BfTt/%2B9vfVEbpSnCl/AjftGkTS5Ys4dixY2RnZ1OzZk2ysrKoKxOHJFitVuLj41UZJxlhZXyRwMBA4X%2B3aNEivvzyS6pUqcKdO3eIiooSQZhzkHTlyhURcNy5c0cEAO%2BVeQvNnTtXKHA6Q8nwbdy4EbvdLuZ96tQp4Ocg5tq1a6xYsYLc3Fx69epFYWEhU6ZMITo6WpQ02mw2XnrpJeG9pxi2//jjj9y4cUOY0F%2B9ehU3NzdWrVpFTEyMC0euPChjMRgMDBw4kH379hEfH%2B9iQREaGioCwAEDBoj9cgDkvN2/f3927tzJM888o2rj7u6uyvBNmzYNgOnTp3Pq1ClRniv3p7xQuHDhAgaDgYyMDJGFUxRAi4qKuHjxIuC4fr179%2Bamk/mTs/poRdDy4evtxKECB5Xpbtvgaqfl8l5E4yD5GC17LbmNpp%2Ba1LeL04aWH1hKimpTg9rqcm6tdwvycRcuaIxPMpGqjCWXy8m1Bih15HRLCcieYFeuSA00TK/k66Dlr1WZ9avIxlDLh8/lXNJ5AMoeMXc/uTRxJytRARcbO%2BlZokVFPntWva3p6iLdj1r%2BafK05Clo%2BY7Jl0rrtpbvLQ1rS5f7WG6jtZzyPLUsH8uKPQTktQLXeWkVGMjfh8RE9faRIxWfW%2BvLKq%2Bf1vdZvv/ktdHyiJPvLS1bTXm9tL5Tp09LO86dq/DkLp5/WhdcHqCWUaDss1iZm0uahOY/xbKHotYCVsbQT2s8/wboGb57g87h0/EfgaZNm9K3b19Rvijj9u3bPPLII%2BTk5GAymbh16xZ%2Bfn74%2BPiQm5vLunXrhA0A/MzhcxYecfYBjI%2BPZ%2BHChcyaNYshQ4aI46ZPn86GDRtUx1WrVo2cnBwRnCieeyUlJXh7exMYGMhPP/3EO%2B%2B8wwMPPEBubq7gvaWlpVFSUkJgYCBxcXF8%2BOGHgienfPWMRiPVq1enTZs2HDx4kOvXr2t6%2BwEuQi3OXoSgFqv5PXDfffdhNpvZu3dvhZko5/FpISgoyMXTTwseHh7cKfulUp4/ogJnYRwt7l/16tVFQAuOzO2dO3e4c%2BeO6NdsNtOnTx%2B2bNnChAkTWLBggeqcu3bt4oUXXiA5OVnzHEuWLKGHM5H/LtA5fDp06NChQ0fFGDPmt%2B9z2bLfvs//VOgZPh3/EQgNDaVt27blfu7l5cW6det46KGHcHNzw2AwYDKZiI6O5rPPPlMFe/Az7%2Bqll17SLBMdPXo0bm5u/P3vf1dlDBW/POeslLe3tyrjNG3aNPEjfc%2BePTQtk9lr3Lgx%2Bfn5Kt6bknHLyclRqToq2TEfHx8MBgNjx45VjV9WmVTsFnr27Kkq6fT398fb21vMsXr16gwbNkyUFZrNZqF06YyePXuKgFYuQfT19WXWrFkYDAbq1atHv3798PT0ZPny5YSHh7Nv3z46duxYbumisnZKIOTpJG2mlJG6u7uTlZWl4sd5eXmJQNfT01P43N25c4cuZVkvRYBGC2azmdpl0mtym%2BbNm7Nz506io6PFPoPBQGhoKBaLBbPZLEp9bTYbAQEBGAwG3n33XZcAc8KECQwfPhyTyaQZ9CrlnpWBzuHToUOHDh06Koae4bs36AGfjv8I/BKBlz179nD69Gn279/PG2%2B8IUoqneHu7i5UM7Xg5eXFE088QX5%2BPuPGjaNly5YMHTqUmjVrsm/fPmJjY5k8eTKnT5/myy%2B/pG/fvqrjY2JiSE1Nxd/fnwULFlC7dm0OHz7MsmXLVLw3Nzc33nzzTQwGA56enlgsFjw8PKhWrRoxMTFMmzYNT09PevfuDaAy/TabzdSrV0946Nntdvbu3Ut%2Bfj6ZmZlERESQk5NDYWGhKOlMT09n/fr1IuCx2WyC82Y0Gtm8ebNYb6W0VA5o8vLymDFjBna7nYsXL/L1119TWFjIk08%2BydWrVwkPD6eoqAi73S7WVxHaAUegpwROgErgxLmMtF69ejRq1Eh8Fh0dTf369bHZbBQWFgoOXvXq1V18C7UQGRkpsnfOJvLg4FH269dPeDcqbVJSUsjNzRVZVGXdfH198fLyUt0/SolokyZNGDRokAjCLRaLKsC8qVWyo0OHDh06dOj41dADvnuDHvDp%2BENiz549QoBDwfLly1U8v48//pji4mLS09MpKSmhc%2BfOTJgwgSVLlnD48GFhng6OAG/lypXlnq%2BkpASTycTOnTtdhGAefvhhOnToQGBgICtWrCAhIYGYmBgKCgqYM2cOr7zyCj4%2BPsydO5f7778fcJiznzp1ik2bNvH%2B%2B%2B/j4eHB/PnzOXv2LG%2B%2B%2BSZBQUGkpqYSHh7OzJkzRYbPYDAQGxsrRF%2BeffZZVq1ahdFoxGg0smXLFgDuv/9%2BwsPD8ff359VXXyU1NZX69eszfvx4UlNTqVu3rjApV0zaLRYL8fHxXL58WVgfOAeTCsLCwkhKShLBVb9%2B/URQ1K9fP/z8/Bg5ciTff/%2B98KCz2%2B0cO3aM9u3bU7VqVQYNGsTHH3%2BMwWAgOzubDz/8UJUpNJlMIiOnBFs1atQQZbe1atXi2WefVbU/dOiQSr3UYDDw/PPPs3HjRnbs2MHChQtFsNqjRw/y8/MJDAwUmUaTyUTVqlU5d%2B4ca9aswWaz4eHhQUBAAH5OhlJVq1Yt9z6RoYu26NChQ4cOHTp%2Bb%2BgcPh3/1ejRowcZGRki4HBzcyMiIoKJEycybdo0xowZQ79%2B/ViyZAmfffaZEHNRAiDFQF35kR0aGkqnTp0YP348YWFhKq%2B/q1ev8thjj/Hyyy8zcuRIAAoKCli7di3vvfceW7ZsoVu3bqIsUUG1atXo0aMHPj4%2B7Nixg6tXrwpOWnlQAiTFkxAc5X9VqlQhNDSUpKQkwZGTeW3KttFopG3btqSmpgq7iIogl2MqUGwzAE1j99atW9OwYUPWrl1brmG9FqZMmcKHH37IzZs38fHxYeXKlSxcuJDU1FRu3rxJYWEh7u7ujBo1ivXr15OdnU1UVBTt2rVj6dKld%2B3bYrGoxuru7k7Dhg05c%2BaM2KfFCfTw8CAxMZFWrVq5lFwqpcRRUVEcO3ZMc70mTpzoYiVRHrSM1xvVr0/jZs1%2B3nHqFDgLwcjbuHhsu5hWyybCgKtzr%2BxIrtWvhlO4i9ew7LCsYUbs4hGsZbItGwBrmRHLTuanT0OLFqomLmbxJ06AU2kv585B48aqY1zcnDVcl136PXIE2rVT95OcDM7X8tAh6Nix/PGDqyO17OYNrs7WGuNzMamW1k/LeF2%2BDFqm5fK1%2B%2BEHkLWv5DYua4XrbSGbbGuZd7v4RmuYTbscp9WR7Lwtd6xhPu0yCa1JyablWm7d8n0sj8XlS6dxLi2TbflCSCb1muPRMAF3mfrly1CrVvnnAdd5yxcTXK%2BDlkm53EbuV%2BsY6fuiaar%2B2WfwyCM/b2uYs8t9y5dB69SV%2Bb5UxnddPpfWbSObn8u3gNYx8ni0bmt5ybW%2BLlrz%2Bnfg8cd/%2Bz6dmDZ/eOgBn47/asjm7oWFhaxZs4YFCxbw5ZdfEhAQwNChQwkLC%2BOll16iXr16pKens3TpUjZt2sTatWuJiIhg2rRpFBUV8c4776j6l83dnQPMkpISSkpKRBBgNBpFAGaxWBgwYACzZs3iu%2B%2B%2BY9KkSURHR/P6668DDruEunXrsnHjRtX5unTpwvXr10XAZ7PZ8PHxYfr06QwYMID09HTee%2B89Nm3aRNWqVYWVATgycVpG4VqoVq0abm5uqrWLjIzEaDQycuRIoTZaWdNzLSgcN%2BdHjNlsVgnW1KxZk8DAQBITE%2BncuTMXL17k2rVrwi/vwIED9O3bl3fffZdevXoJjqUW5ODNZDK5BJ3e3t4qZVB5vEr7Y8eO0a1bNxcbEJPJREBAAJmZmZr9A0yePJmnn366gtVxQEu0ZcL48YyTf4zo0KFDhw4d/8PQA757g17SqeMPBU9PT0aPHk1wcDAHDhxw4dQZDAbCwsJ45ZVXGD58eKVUImW8/PLLJCUlcfbsWVJTUzl37hzJyckkJSVhNpvp3LkzSUlJzJ49G4PBQMOGDRkzZgw7d%2B68a789evQQHD4FJpOJ2NhYXn31Va6Uabrv2rULu91OVlYWRqMRX19flaUBIEownWEwGPDz88NoNHLjxg2uX7/OG2%2B8IUpcrVYr4eHh1K9fH3AEe97e3uzYsYO4uDh27txJu3btMBgMVK9eHX9/f9EuNTWV2rVrM3nyZKpUqSIyp/L7JJvNptqXkZEhAs5vvvmGRx99FICOHTsKTuCECRMARxbRYDCI844aNYpq1aqJ7Q4dOtCtWzfc3d1xd3cXx1etWhVvb29mzpzJ%2BvXrVeNxc3PDzc1NBKIA4eHh%2BPr6EhERocpsNm3alIkTJxIaGsr27dvF9VECfHAEtIpfYmWgJdqiv4HToUOHDh061NA5fPcGPeDT8YfE3Th1CmRz9V8DmRdos9mIj48nMjKS5s2b8/LLLwOOMsj8/PxyDeIVKMIg27ZtIzw8nHbt2vHFF18QEBDAzp07GThwILdv32bjxo2cOXNGZOpu3LhBnz59RHAiB1ZK4DlkyBAMBgOBgYGMHTtWcP/27dsHOERjlHLT0tJSbt%2B%2BTd%2B%2Bfdm0aRN9%2B/bl6NGj2O12rl27JspES0tLad68OZcuXeLvf/87%2Bfn52O12PDw8cJdrYFDz/Uwmk8omYfHixQDEx8eTXeZfNHnyZI6XGT3Z7XZx3hUrVnDjxg2RhTt27BjVq1enuLiYgIAAweFT/A8XLFggLBvAwTX08fHBaDTi4eFBUFAQwcHBIlA8ceKEqtz37NmzzJ8/n5YtW7Jx40aR4XVWU7XZbBWWmjpD5/Dp0KFDhw4dFUMP%2BO4NesCn4w%2BFgoICli9fTnZ2Nvfddx8//fRThebqCrZt26YK3po3b66yY9DCk08%2BKYKmpKQkwsLCqFatGg0aNGDt2rW89tprxMfHM3fuXEwmk0oI5uzZs6pzXb16VShTAly9epVHHnmE4OBgbt%2B%2BzeHDh8nNzeWpp56icePGQsmzRo0a1K5dm/79%2B2MwGLj//vvx9fVlxIgRoi%2B73c6FCxfEfGrUqCFM2wF%2B%2BuknwFHqOXv2bHGcEvAYDAb%2B8pe/EFTGy6hWrZqwSgBHRtFkMrFs2TIefPBBTCYTd%2B7cUQVYCpwDmsLCQhYsWCC2lXLLgIAA6pRxmzw8PGjRogVz584V2UUlSHPmSn7wwQd89tlnAAQHB/P5558DDpsEd3d3qlevrrK/SEtLIz8/XwTjRqORvLw8kel74YUX8PLyEiIsVapUwd3dnSFDhnDgwAEhGOPn56fKplYU1DtD03i9WzdVm7JLU%2B424OJSLRtLayaypWuj4bHt0m9lzr13r/pjLaNmuR%2BtNrLJu5YRt2yqrWnELR0oexxr9etCdNDyk5QmoVVtLJ9L3tYyNJb7caKcCshj1hSGlRrJyWQt43WX66BxweWvtIthNbB/v3r7xAnXNrIHtMujQqNc2uUe1bh48i5N0orcSL7ZtMq%2BpWvlVE0vIK%2Bx1r0lT8vl2mkMuDLm7PKQXe41rQHJ80bjlpRPpsXNlh4wmvRtuR/5y6sxHLkfrctSmeeEy1pofJ937FBvy9dF69ly4YK0Q8N4Xb4nNJbcZcxa3ucuc5cWR2vNXc6lsThyG617S2ufjv8%2B6Jn/X7YAACAASURBVBw%2BHf/VkEVbnM3VW7ZsSYsWLZg9ezYPPfTQXfv5JRw%2BZ96b1njS09NVtgAGg4GIiAjee%2B894ReoxeHr0aMHubm55OXlsXv3bnr27Kni0CkctbFjx/LCCy8A0K1bN2w2Gzekf9F%2BCfdOEbCx2Wzcf//9xMfHqwI1Z25cQEBAubYDWgIo8jj69etHamoqWVlZ3Lx5U/AOndsp19LLy8uFQ%2BcMhUNXr149Ll68KM4fERHB%2BfPnxVgCAwNFtjAsLExcH%2BfxWSwWYRehiLZcvXrVxUBdCbCzs7PJlX%2BxlqFr164sq6Sbq268rkOHDh06dFSMCpy7fhXWrPnt%2B/xPhZ7h0/FfD4VTJ5urA9SuXdtFBfFeUBm/wBkzZpCSkkJqairLly/H3d2dd999V2UOHxAQwODBg12ONZlMmM1mYR%2BgqG0uXrwYs9mM0Whk2bJl/PTTT%2BTn5%2BPt7Y3RaKRr166cO3eOkJAQ2rZti4eHB3PmzBH9OtsXOENRFFVKL6Ojo/H09CQwMBAPDw%2B8vLxUxylBjru7uypbqYzVGe7u7tSTVB%2B3b9/OxYsXRdCoHNOgTOrMYDDQpUsX%2BvfvL/z7IiMjmTVrlrDZCAoKwmQyUa1aNQwGA5cuXQLg0UcfFXYVAO3btwdg9uzZpKam0qRJE/r06aMaZ2lpKeHh4djtdtaUPfnv3LlDQUEBx48fx8fHR6xNaGgoycnJbN%2B%2BXah/BgUFERoaqrKMkNflbtCN13Xo0KFDhw4dvzf0gE/HHxp9%2BvRh/fr1mpmiKVOm/KLyu1%2BDzp0707NnT2FkXhFycnJcuHcfffSRyPYFBwfj6enJpEmTGD9%2BPPn5%2BcTExODj4yNKPPv168fw4cOFBYUCpewwuEzi3WAwCBXN4rISl7/%2B9a8q5Ul/f39Vhk75/7CwMOEZaDKZhEdh1apVSU1NJSgoCLPZzA9OdTDR0dH07t2bRYsWERoaKvYbjUYulNXG%2BPn5cfjwYb744gsxjo8%2B%2Bgiz2UxRURFeXl7cd999YgzO/DllPuAIJMPKZMwVz0BfX18KCwtVZaC9evXi6tWrtGjRQgixKHNKTU2loKBAZDvT09Np3rw5o0ePFgFfVlYWBQUFKo7omv%2BlV4Y6dOjQoUPHvwA6h%2B/eYK64iQ4d/70YPXo027ZtY8SIEcyZM4emTZuSkZHB4sWLOXz4sFCA/D3xl7/8hdjYWNatW8ewYcPu2tbf35%2BJEycyfPhwGjduTGlpKaNGjcJkMmGz2UhPTwcgKSmJ6tWrM3jwYK5cuYLNZmPKlCk89thjIgOZkJAg%2Bu3SpQtPPfUUjz32GLt27SI2NpYnn3ySJUuWMG7cOIYPH865c%2BcYMGCAikdYXtllTk4Of/3rXwFHEPjee%2B%2BJYxYvXizUT50FWk6dOiWEYGJiYti8ebPI1CnCLUOGDOHKlSukpaVx8uRJANo5eZtZrVbB01M%2BV8ZUrVo1bt68KbwLFQ7fypUr%2Bemnnzhy5AhHJJKSopx64sQJTjgRjdq2bUtSUhIeHh589NFH3L59m1q1aon20dHR2Gw2atasSWFhoQiYwZG9rSx00RYdOnTo0KGjYuj/NN4b9Ayfjj80vLy8WL16NTExMYwfP56WLVsydOhQbDYbn376qarM8vdCUFAQL774IvPmzVP55s2ePdtFtCUnJ4dNmzbRo0cP7Ha7kP2X/d5q167N9evXSUpKYtu2bWzZsoUvv/ySgwcPcv78eQA2b94s2h88eJDHHnsMgDZt2nD16lWSkpIYMGCACKAaN24szOgVFBYWinM7Zx5LSkp44oknxP5jx44BjoyXswiLM5TSyPj4eFF2WVpaip%2Bfn8i6KedyLkE1GAwig2Y2m8V%2B53JLgHfffVdwJ505iCUlJezatQtwKHY2lk22Jbz77rskJSWxfv163nvvPVF2efnyZaG8qpSXpqWlYTKZVAbvd%2BMdytAUbenaVdVG1jbQ0DpwJezLogQa4hvyMVoJaJd9WooIklCBC8VTS%2Bnl66/V21q8UPlcGqoE8vg0aZVSP2UitOWeBnBVKdBqJJ1M67rI3biIH2gdVBnlD%2Bn6aglBVKTsoiXa4tKRxrVzEcDQWnR5n1RtAFBGqxWQhytfJ3AVsyh7/6WGtH5ayydfB/lcWuspH6P1famMgIi8szJiNXK/WuOT10brMSSPR3N80vldxX5cD5H3aYq2VGJxXHbJi6zRsbx%2B8n0FroInWveWi1iN00tM7cG5jqdS30OtfuR9WiJR8twr068sIqN1U8jn0lJo0VpUHf910EVbdOj4lZAFY9zc3IiIiGDixIkiK5Wbm8uSJUvYsWMHN27coEqVKrRu3Zpx48bRqFEjFi9ezJIlSwBElsjZ0Fvh2ClcvqKiIsLCwoiKimLr1q2AI5Cy2%2B20aNGCY8eO4eHhwfr168nJyRFBXsuWLUlMTATgvvvuIz4%2BHpPJxFNPPcWmTZvYsGEDmzdvZt68eSKbaDKZ6NixI/Hx8ZrzdxY8sUr/2BiNRkwmE127dmX37t2AQ8xEUbf08fEhLy8Pi8VC8%2BbNRfYvPDycSZMm8X//93%2Bi306dOvHtt99qmpw7Y9asWcyYMcNlDZ3h7e2N3W4vhzvnQMOGDfnqq68YMWKEKAdV4ObmxkcffcQbb7zB2bNnNY9v2rSpyC5WBF20RYcOHTp06KgYjzzy2/dZ9r77fwJ6hk%2BHjnuAs2DMN998w/3338/TTz8tRFWGDx/OhQsXWLp0KYmJiXz66acEBgYydOhQUlNTVV54iuXByZMnCQ8Px9/fn5kzZ5KUlMSZM2c4ffo0NWvWpHbt2nzzzTeAI4PZs2dPQkJCqFGjBuBQwnQ2lI%2BMjBQZKXDYC5SWlmK1Wlm6dClpaWl0796d/fv3ExQUJAIlxZvOaDRisVhEFk4RS3nqqacAVBlBJRPn4eFB3bp12euk0X/gwAHAkaVTykatVquqlPLq1atMmTJFfAYODp4yptatW4vzKKIsCqpXry7%2BXw723N3dMRgMWK1WVaYyIiICNzc3unfvLkoxlbJZxftPRtu2bYVIi8KbrFWrlvDvs9lsmsdpQRdt0aFDhw4dOiqGzuG7N%2BgBnw4dlUSbNm1cSjBnzZoltrOzsxk9ejTBwcEcOHCAZcuWkZ%2Bfz%2BLFi6lfvz4Gg4GwsDBeeeUVhg8frgrKAObPn1/hGOx2O7GxsQQHB9OuXTvc3d0ZOXIk06ZNY/z48YDDG3DTpk2kpKRw7tw5zp07pwqOFAXQqKgoOnTowCeffILdbmfGjBkqzl1BQQGbNm0SwaESyHTv3h0vLy/Wrl2L2WxW8deU/3/33XeZNWuWio%2BmqFcOGTKEhg0binJMxcvOeY7OcO5fCQ6Li4td%2BHhPP/10uetWVFSE3W4XxvYKUlNTKS4uZu/evUI5NC8vj9GjRzN16lRGjRoleHYBAQEkJSUBP5dt2u12DAYD6enpIsh0FoWpCDqHT4cOHTp06NDxe0MP%2BHToqCSOHTumMlkPDw9nxowZqm1wZJdMJhM7d%2B5k8ODBKgVHBVOnTqVTp06VPreWofzAgQMJCgri9ddfF4qVAAsXLuTw4cP07NkTg8FA165dVWWZCxcuBOD06dN88803PPHEE5SUlNC/f38yMjLKVRMdOnQoAOvXr6dTp07k5uaWm8364YcfePvtt1X7lGzfJ598go%2BPj4ob6Ozt5xx0KtlDrSDKuR38HMjeDbdu3RJ2D%2BAIPr/66itGjRqFh4cH4CgHjY6OJjAwkI8//lhk4W7evEnz5s3p06ePalxWq1UVlGoFceVBi8PXpUtvdSOZ36bBd3Ohb1REkAJXfoxGvy5VsRoZSRcKl3xuLQ6ffNCVK65tpJNrmv/K3DANPpkLTU4i2mjymCpB9HOhpWmcuyIOpJZjjAtdRoM/I89Ji0YnX055CpocvsqQseSOrl51bSO7VGu1kddYnqcGv9HlHtAiTUmELa0m8qnkfrWOkddT636sFD9U2umyxFprXqFLvcbJNW6KCjmlGjsr4juCxqXSuHaV8Hx3aVMZKq18Kk0enbRTc94VcQxlTh%2BVWnLXh0AlyNKa/wRLO%2BXhaR7za/iDGm0qYFP8y6Bn%2BO4NOodPh45fCdmEvaCggLVr1/Lee%2B%2BxZcsWevfuzdy5c%2BnXr5/LsRcvXmTRokUcPnyYgoICqlatSrNmzdixYwenT5%2BmVatWInA0mUzCUL558%2BZs3bqVzMxMZs%2BezZo1ayguLubmzZvcuHGD4uJiWrVqxTvvvCNsCXJychgyZIjwq5NhMBgEbw/Q5OQp7WTLCLvdTvXq1YXKprIvKCiIunXrCg6cu7s7Xbt2FSqX5Z1D6zwy/P398fT0pEmTJuzZswdwWE1YrdZyTeEVKHzIiviAK1euZPLkyWRmZqrG4ubmxrZt23j22WeFOI6MHj16CF5mRdDi8E0YP55xzz9fqeN16NChQ4eO/wUMGPDb9%2BmkbfeHh57h06HjHuCstNmtWzf279/PihUrCAsL01TXBEhJSWHQoEGEhobyxRdfcOLECRYtWsTly5cBh/E3OLh4Z8%2BeVRnKKzw9xVDebDbToUMH9uzZI0RcBg4cKII9gEmTJonsIyAM34cNG8aECRPw8PCgUaNGjB07FnBw1MDhYVitWjVxnKKQqQjJeHp64ubmpjIaHzFiBACZmZmqrJyvry9NmzYFYMuWLTz//PMiS2YymURJ54wZM1SZwSZNmgAIfmPNmjWxWq2kpaWp%2BIFBQUH85S9/ARyZv9TUVHH%2BgIAAcfzIkSNVGTiLxYLFYlGNwdPTk/379/POO%2B%2B4XDtljEofiooqIHiSv%2BQdmhaHT38Dp0OHDh06dKihZ/juDboPnw4d94CXX35ZZPhkKEGZjNdff53OnTsLcRJwBDaTJk3imWee4Yake%2B6sBlpSUkJpaSmhoaF88sknREREsG/fPpYvXy5EQ1555RVee%2B01TCYT999/P4cOHeJPf/oThw4dAn62a/j8889p2rQpRqORmzdvsm/fPgAR5CnZRwWNGzcmKSkJi8VCcXExNpuNWrVqCUN3f39/Hn74YVatWgXA9evXhZJnZmYmV53KulasWCECo5KSEmESP2vWLNFGKZcER5YS4Key8kBnoRhwBIKKyIrFYuHBBx8UGcvc3Fzq1q1Ldna2i2VCaWmpWFdlDHfu3KF79%2B60bduW559/nnXr1nH9%2BnX8/PwEb/CZZ54Rx/v5%2BWG328ktq%2BepKHuoQ4cOHTp06Phl%2BF8L0H5r6Bk%2BHTp%2BJ/Tp04f169ergoysrCxOnDhBXl4eK1asULVXlB%2B1vAEVNdAZM2YQGhrKsGHDKCgoIDk5maKiIvr27SsCLW9vb7Zv305SUhLPPvssoM46bd%2B%2BHYApU6ZQpUoVCgoKSEtLEzYDSkCYm5urCl4UwRJn3t7ly5f56KOPAJg5c6bIsgF8//33KgGSDRs2AI7MpVJ66e3tXe762Ww2GjRo4HJOBc5z2rdvH2vXrgUcAi0//vijCApLS0u5ePEidrudy5cvExgYqOqjSpUqNGjQQIzFaDTSqlUrVq5cycKFC7le5mV069Yt4cPXqFEjcXxOTg63bt0S41GyiZWBLtqiQ4cOHTp06Pi9oQd8OnT8Thg9ejRBQUGMGDGC5ORk7HY7p06dAhzqkM6lkFrYunWrixro7NmzMRqNPPvss9SoUQOLxcK1a9fYtm0bz5fxvp555hkRNNarV486deoIY3SAxYsXYzKZqFevHkuXLiUkJAQvLy%2BV0bnyX6Us0jmj9sYbb2iKqLz44ov88MMPIuNXHpwDtYoETnbu3Im/v79LRk8umywqKlIFjyUlJaKNYsZut9u5deuW4BuCI7jKysriu%2B%2B%2BE9lMxXzeec2cce3aNSHwogVnM/iKoCXa0qGDWrSlEpotLoa6LqIEGsbXLkx8LTUGqeTUxXQbDZEESUVBS8hAPpXWnGSzZM3EaUUm1gAXLqg2ZSqrlp5IRoZ6W8sfXV4MrTnIPseyfo2WaIsLtVUWQMF1DppKFZKhsrzmmqItK1eqNjVFKOSJfvWVaxvZEf30adc2cudlJe0CWmI/0lpoXpdKiLbI%2B%2BR%2BtNxSJFFlTQ9rl31a5d3SjSwvldZ97nJfy18OjTZa/cj7NAVOpJurUmby0skrIyKktX4VPhc0nlEu16qM1%2B0M%2Bfui9b2TnxPyYmk9HmUhF821kRdQq6PKiKvIF%2B9XCL1o9ivfSxo3fzl0%2B3859JLOe4Mu2qJDx6%2BELNqihdzcXBYtWsTOnTvJzMzE29ub7OxsPv30U1q0aKFqm5CQwGOPPcbp06eJjY0VfTufZ82aNSxbtow9e/bQs2dP4Wl38%2BZNTCYTVqtVCL0899xzjB07lkuXLjFhwgRSU1Ox2%2B107tyZ8%2BfP8/bbbxMTE0P37t3Jzc2lqKgIq9VKvXr1uHjxoouYi5aYir%2B/P3PmzGHcuHGYzWZVoOWMoKAglQ2FItpStWpVsrKy8PLy4vbt25jNZpds3v3338%2B5c%2Be4Iik5NmnShJSUFNzd3SkqKhLlo3dDUFAQ2dnZop3WnLy8vIiOjubIkSMq9U0FSulsSUmJ5jlbt27N6tWr7zoOBbrxug4dOnTo0FExKiHE/YtRJn3wPwE9w6dDx6/Enj177hrsgcPk/KWXXmLPnj2cPn2aLVu2AAi%2BlzNiYmJITU2tMENUWlrK8uXLyczM5NKlS0IsRM7QLVmyhOTkZMElVEpGFy9eTHx8PM2aNWPRokXk5OQwcOBAGjVqROfOnalduzbgKElNTk4mNTWV1NRUzp07R%2B3atZk5c6Zoc%2BvWLSZOnIibmxtGo1EVPFWpUkWMJycnB19fX7y9vTEajYKbl5WVBajFS3r37o3FYqFWrVqAo1xT4TCCg2NoNBq5ePEi4MjuKeui4NNPPyU5OVls/%2B1vfwMc1gp16tQR%2B7t16wY47BkU0ZYPPviABg0a4O7uzqeffioM3S0WC%2BvWrSMxMVFkZ%2B12Ox9//DEvvvjiXa9ZedCN13Xo0KFDh46KoWf47g16wKfjD424uDj%2B%2Bte/qvYlJycTERHBjh07VPtXrlxJ586dsdvtTJs2jUmTJqk%2Bv3jxIpMnT6Zjx460bNmSHj16MHv2bCEoUhkEBATQrl07l6wOQGFhIXFxcUJ8xBmKGujrr79OWloa%2B/fvF0EMQN%2B%2BfQXHbsmSJcIbsFmzZqKNElwoBvKtW7dm4cKFFBUVsWrVKs6dO8fBgweFeMuZM2dcxqFYRaSX1SGFh4dTtWpVDAYDVapUwdvbW2TAqlatKoK%2Bhx56iH79%2BlFQUCACM6PRqOLTgYOrt2PHDqxWq1AttdlsWK1WcdyNGzew2%2B0UFxfj4%2BMj1D/hZ8GZV199VZTPGgwGpk2bJsafl5cngmJF6XP9%2BvVCtGXevHlMnTqV/Px8hg0bJkpArVYrw4YNY9euXSILabfbGTVqFPPnzxd91q1b12XdyoPO4dOhQ4cOHToqxn9bwJeTk8PEiRPp2LEjnTt3Zvr06UKFXcbLL78sFN%2BVv6ZNm/LSSy8BMG3aNJo2bar6vE2bNr9oPHrAp%2BMPjevXr/P555%2Br9m3cuBGAdevWqfavX79eZN6OHz/OyZMnxWcpKSnExcWRkpIifpBbrVZ27NjBkCFDyv0Sa2H69OmcOnWKF198kfT0dEpLS0lJSeGpp57Cw8NDVeq5fPlyrl69it1uF1w0gJMnTzJp0iSCgoI0jd2bN2/OwYMHxfasWbNEBuvYsWPC669bt240btyY6OhoTp06xYgRI6hfvz4ATzzxhDje2fi9S5cuIquWlpbG448/zqFDh4iPjycoKEgIvXh7e5Obm4vdbmfz5s1CtEWBYu0AiGyewWAQ1g/KtgLn/587dy7JycmsXr2ac%2BfOif2KwmlycjKPPvoo4AjKnLOmmZmZNG7cuLzLQ0pKCteuXcNkMrmUe9rtdv7yl7%2BIDKXRaBQBrtJWNoS/G7Q4fL16qTl8TtMrG59GRxK/Iy1N%2BlwmpVE5/2y5Xy0On8y9kvvR/GpI/cr0LdDgLWmQkmT%2Bk0zDAVzIV7LHuxb3TuZ0aXJYyjLMCrR4QXIi36WNRqZfvnYydwxwdQ7XImxVYDCvxeFz4XRpjM%2BFe6WxOPJ9okUhdTmsAmNp0LgnNEqu5QO1kugyLbIyJtZyG637piLjcK3xVMJv3uU7pfU9lO99rfuxMgbzct/Sba75HZPXszK%2B8FqN5PGU6WUJVIb3p9WvTAfVunbyI1IeixYv0eUe1TBn/63Mzyt0odc6Rp5EJb4vmv1oPoR0VIQZM2ZQWFjIV199xWeffcb333%2Bvsp1yxuzZs8WL%2BqSkJE6ePEm9evXo27evaPPcc8%2Bp2pSnM1AedA6fjj8U4uLiaN%2B%2BPVOnTgVg6tSpQnVy4cKF9O7dm169eokf9FWqVCE%2BPp4///nPfPHFF7Rp04Z//vOf9OrVi8LCQmJiYjh8%2BDDZ2dnY7XaaNWvG22%2B/Te3atTl//jyzZs3i/PnzrFmzhoYNG6rGkpCQwOjRo3nvvfeYPXs2N27coGvXrrz11lukp6ezcOFC9u/fT2HZQ9nf35%2BXX36ZBx98kB49ehAUFISfnx%2BHDh0iICCAyMhIkZFq3749c%2BbMoWfPntSuXZtmzZoxY8YMOnToIM6vBCMGg4Hi4mLBkwMHD00RMvH29mbx4sW0b9%2BeI0eOMHLkSNU8jEYj3t7eBAUF4e7uzk8//URBQYHo32w2YzKZBAdQga%2BvLyEhIZrWFBaLhaeeeoqEhASSkpKIjY3liy%2B%2BEGNTgkZng3bn/z99%2BjTu7u58/fXXopwyJCSEjIwMAgIC2Lx5M3379qVq1arCygF%2B5uwFBgaSLf/SckLdunXJz893schQIHMSnVGnTh2hhFoRNI3Xn3%2BecePHV%2Bp4HTp06NCh438BFejc/Srs3v3b9wmOF8tdunTh888/Fy%2BYDxw4wAsvvMCRI0cqFLdbvnw5CQkJLF26FHBk%2BMLDwxl/D78N9Ayfjj8UunTpIvzmAB555BEA3NzcOHToEMXFxVy5coXY2FiKioqIjo7GYDCIgO6hhx4CoLi4mMzMTEJDQ/nHP/4hgj1vb2/GjRuH1WqlSZMmLF26lF69emny7lJSUrDZbCxdulQEV4cOHeLtt9%2Bmfv36PProoxQVFRESEoKnpydms5k///nPZGdns2fPHm7evEl8fLzI6ilZRCXTpZRVKhyzqVOnEhISItQq3dzcmDFjhij1BIc6qMlkwsvLi8jISEwmE7dv3%2Bb555/HZrPRrl07oUBpsVjw9/fH3d2d9u3bk5GRQbNmzUSwp7wrunPnDk888QTz5s1TZeE6derEjz/%2BKLYNBgN%2Bfn7UqFGDoKAgkdWz2%2B188803op3dbsdoNDJz5ky2bdsm9iuZR4CWLVsSGRmp4s5FR0fTsGFDbt68Sc%2BePSkqKlIFe859lJaWkpqaqpkdBcjIyBAPWueM3ZYtW6hWrZrKauPhhx%2Bmd%2B/eot2PP/4ogviKoGm8LimS6tChQ4cOHTr%2Be5CSkoLJZCIiIkLsa9asGbdv3xb6A%2BUhNzeX999/X%2BXVDPDtt9/y8MMP06pVKwYNGqRJu7kb9IBPxx8K27dvJyUlRWRvWrduDTgCuF27dvHtt99it9t57rnnAIcgiN1u58SJE4BDMAQc4h4Wi4X09HQhzJKWlka9evVo2rSp8Gbz8fHhzTffpFatWiQkJNCsWTP27t1Lz549mTdvHuAoXdy0aRO7du0CYM2aNbRq1YopU6ZgMBhYtmwZJ06coEaNGthsNkaOHMmDDz4ouGqlpaX4%2BPiINztt2rThH//4hyg/PXLkCFu2bCE%2BPp6MjAyhLHnnzh3mzZtHjx49xHaLFi0oKSmhuLiYl19%2BmcOHD%2BPh4UFeXh5t27alY8eOmEwmEewVFBRQWFhIfHw8/v7%2BIgunCMXYbDbsdjuLFi0iMjJSVSr5zTffqBQ3FXPyK1euiOtjs9mw2WyqbJsSHM%2BdO1eUM3h6enL%2B/HlhmWA0GunatStGo1H8FRcX07VrVyH4YjabMRgMuLm5ERoaCjgCOYC8vDxSUlLEWinBmsI5NJlMfFUmN%2B/MqRswYABms1k1z02bNrFjxw5KS0tFwKslyqNDhw4dOnTo%2BHX4PTh8169fJzk5WfV3Xa4l/hXIycnBx8dH9RJcodXc1PRW%2Bhn//Oc/adu2rapqrGbNmtSuXZsPPviA%2BPh42rRpw%2BjRoyvsyxl6wKfjD4W8MgKEkuVz/rGelZXFmjVr8Pb2FgIdyg9%2BJdPi7%2B9PVlYWRUVFFBcXk5KSojIf37t3L0lJSQQHB2ue32azsWnTJjZu3ChMzwMCAvDz8%2BPy5csUFhaKAHPs2LHY7XaefPJJWrRoIURGIiMj%2BfLLL4UASadOnVSZLgUffvgh4FCgVILLJ598Urz1GTp0qKpccNCgQRw9ehSDwUBJSQmRkZH4%2BfkJ4ZTRo0ezYcMGYa%2BQk5MjsngGg4FNmzbx9NNPA9CrVy9CQkIICAgAHFnGvLw8waerVq0ab7zxhoqLFxoaypdffkm7du3EOTt27Cg%2Bd4YiyqKUcE6ePJnS0lIRDJaWlrJ7926xrcDX1xer1Urv3r3Zs2cPf/7znykuLhbCOsr9YbfbGTJkiDjOZDLh7u4uArWioiJWSp5kCsLCwmjfvr0IeuVxK/1VBrpoiw4dOnTo0FExfo%2BAb926dcTFxan%2BZH2H8rB582YiIiI0/xTthV%2BKkpISPvnkEx577DHV/nHjxvHGG28QEhKCj48PU6ZMwc3NTSQSKoP/uIBv8eLFjBgx4nc/z5UrV4iIiOD777//XfofOXJkueTMe4WWguS/GwkJCURERAgxj38XlABhT5n56vHjx0UwYbfbOXLkCC1atBAlhHa7ndu3b2O320U7uQxQCVqUbFZWVpZLG3Ck8AGioqKIi4vj/fffBxx8M3BkG6OjowFH%2BeHbb79NSUmJKNVUHg5Hjx6lS9fkjgAAIABJREFUf//%2Bgj928OBBHn/8cdXD4%2BbNm0LNc%2BrUqbz%2B%2BusAtGrVSrQJDQ2lefPmovRz7969tG3bFrvdjs1mIzExEXDwyMBhRzBw4EBu3bolgg7lvxaLhaFDh4oyx7y8PMaOHSsyc1evXmXUqFFijPn5%2BcycOVMI0ChB2sCBA8nLyyMqKoqePXvypz/9CaPRSExMjOvFdIKfn5/K7Fw5j6%2BvrygBfe211wgJCcHPz49z584xYMAAodBqlYjoUVFRIpurfG6z2fD39xf9aylglZaWkpaWRmBgIPfff3%2B541UC4YqgJdoSFaUWbZGrPrVEKCr0UNf4XsrcfK0qVLlfDb9nV70QaYem4Im0U%2BvclfEMrowIRUWm0FrJWLkfzX6lAyuzfvK5NF055J2VcbHW4JPKQh/yWmkar0vnrszaaN4U0j6tayf3LVNqNUWEpI40X27LCjEaF8bl61AJ4YrK%2BGfLbSpjhi0LkWitlTxPretSmfu6Msbw8nHycmqteWXG57JeGkJDFV0GrbVxmZOGsou8S0v8pUKHea2LWRlXelnIRUvYpey3gsAHH7i2WbJEvb18uXq77N9mFf7xD/W21kvMxYvvfh4o5wH4x8DQoUPZuHGj6m/o0KGVOnbAgAHCtkr%2Ba968Ofn5%2BaqEgfLiuWrVquX2efToUYqLiytU4DSZTISFhf2ibOQ9BXw9evSga9euLjyUhIQEUUZWGXz88cei9Gvs2LH885//vJdh/SawWq0sWLCAPn36EBUVRatWrRg5cuQvVsX5V2Pjxo106tTp3z2MCrFw4UIaN24s5GVbtGhBnz59eP/991VfkF%2BK%2B%2B67D3CQYwHi4%2BNFcGC328nPz%2BeBBx5QvRVR3uZoleN1795dfB4UFETNmjVV2ShnK4VLly4BsHXrVlXmSQm4EhISxP1z%2B/ZtkWV86qmnSExMFAFNeYqfSgCYnZ2tMiF35pglJCSI/z969Ch79uwR6ymrR6aVyQGOG%2Bcw%2BbZareKBFBISwrFjx8SDr7CwUDWn7777TpQ/KtiwYYM4R2FhITk5OZw6dYqwsDBGjBhB8%2BbNKS0tJS8vjwMHDtC7d2%2BCgoJ48cUXVUqbWnjppZcET1JR8gR1xk7pw2Kx8P7773Pw4EEeffRRIVCjHAuONX7hhRdE/yEhIbi7u6u8DFesWIGvr6%2BKXG02m0lLS%2BNvf/sbp06dwsPDw%2BXB3KBBg0pn%2BL744gumTJmi%2Bjt9Ws0il5OAGklB5NM5xcYOaHBMZc54mWDqXfsto4fetY28Q5ObLu3UOrdMZdTqR56WVj9yI%2Bm2ddnW6kezX%2BnAyqyffC6ta%2BmyU2uA8guFoCCXJuHh6m15rby8NPw%2BpXNXZm00bwppn9a1k/uWHFpcxq/VkeZ7lbKyKQGNC%2BPydZAHqDFguRuX75hGG006rrRTLhTQWit5nlrXpTL3tUtRgsYA5ePk5dRa88qMz2W9NJ6RFV0GrbVxmZNG5YW8S6OJ68WTT6Z1MSs6BuD55%2B%2B%2BDVBWDSTwzDOubcpoKAJPPqneLqu%2BUeHxx9XbUtYIgLFj734eKOcB%2BK/H75HhCw4OplmzZqq/8iq4fgmaNGmi%2Bl0CkJSURJUqVe5q3bR7927at2%2BP2WwW%2B%2Bx2O2%2B%2B%2Baaqr%2BLiYi5fvkzNmjUrPaZ7zvAVFxezWH5D8AuQnZ3NW2%2B9JX6U/qsyfMqPcK1MDTiyHnv27GHBggUcP36c%2BPh4OnbsyOjRo8s9xhkpKSkcOXLkNx2zAtky4D8BSnbrl2T4WrRoIeRlT506xbx581i9ejXL5TdXvwCDBg0CHIHA999/z%2B4yCSbnH%2B0dO3YkNTUVcPywXyq9GVOCCJPJxJQpU2jQoAGRkZFcunSJoKAgOnXqhMViISUlhVGjRvHDDz9gs9kEP0x%2BAWK328nLy2P16tXinJ6eniI4ysvL4%2BLFiyLQKy/gVUzKs7KyVOqRpaWl4uGwbds2EbQdPXpUBL7gem2OHDnCuXPnRIDr6ekpgs78/HyKiopEJlQeU3lvlZTAys3NTYypTp06TJ8%2BnbfeekuYnp84cYKEhARMJhNjxowRgarFYtF8W5acnMynn34qVDadAzNvb29iY2Pp0qULcXFxKjsKHx8fSkpKMBqNNG3alJYtWwKOssxiJ4nqwYMHs3//fmHErtwvtWvXpqSkRGzXqFEDDw8PEhMTeeWVV7hz547gfyqIjIzUXBst6MbrOnTo0KFDxx8LgYGB9OnTh7///e9kZ2eTnp7OokWLGDRokPht9Pjjj7NlyxbVcSkpKdSoUUO1z2AwcOXKFV577TUyMjIoKCjg7bffxmKx3LXSSMY9BXzp6enYbDb%2B%2Bc9/8oOTEUtKSooIqMAR1f7pT3%2BiTZs2dOzYkVdeeQWr1UpmZiadOnXCbrfTunVrNm7cKAQlFBw7dowhQ4bQqlUrOnfuzDvvvCN%2BVC5cuJDnnnuOZcuW0alTJ9q2bcvs2bPFsdnZ2UyYMIEOHToQHR1NREQE3377baXm9s033%2BDv78%2BECRNo3bq1UH8cPXq0UPYrLCxkxowZxMTE0KxZMwYMGKAae2lpKTNnziQ6OpoOHTqoLmx6ejrPPfccMTExtG7dmkmTJqkMvJ3n3aJFCx544IFKcXuOHz8uAgMt9OjRgzVr1ojtAwcOqFSEFEPy4cOHExUVRf/%2B/Tl79qxqXR566CGaN29OREQE%2B/fvV/V/8uRJ%2BvXrR2RkJGPGjBHBk4yEhAQSExNFhi8qKoopU6ZQv359IWm/ceNGHnzwQebOnUtUVBQZGRmUlpayaNEievXqRYsWLRg4cCCHDx8W/Sq8N4Cnn35aqEQ6lwwqfDdw/KBXSK%2BlpaXExsYSHx8vPp82bRqvvfYa4eHhlJSUsGvXLnbs2EFsbCxxcXEkJiYSFBQkSjXBESxs2rSJ/v37i30xMTHC6N3NzQ2bzSau57Jly3jwwQdF29zcXCZOnCgCQjc3N7Kysli1ahXgKAtwzk5NnjyZDRs2YDKZyMzMFHO1Wq0q0m/z5s05evSo2P7ss88YMGCA2FZKPcHhuxcTEyMyiaWlpTRo0ECM8/bt27z77rvi2JCQEJViVHFxsSgvPXz4ME2aNKFt27akpaUJ3p4WrFarSy18kyZNKCkp4dVXXxVvs5y/C3a7ne3bt6uumwIl61pSUsLZs2cFT/LQoUOqTO6SJUto27YtX375per4oqIi2rRpI0pCg4KCsFqtJCYmigyp/L3UGkd50Dl8OnTo0KFDR8X4bzNef/311/H19aVnz5489NBDtGjRQkXH%2Bumnn0Sll4IbN24QpFGxMWfOHOrUqUNcXBwdO3YkJSWFf/zjH5q/IcrDLwr4yivhDAkJUf1wlDFp0iTat29PQkICGzZs4KuvvmL16tUEBQXxwAMPAI4fYHFxcarjMjMzefLJJxkwYIDwo9iwYYMqYDlx4gQ2m429e/eyYMECVq1aJThT8%2BbNo6CggN27d7NgwQIA/v73v2uOUS7hvHTpEt9%2B%2By39%2B/dXZfg%2B%2BugjEdTNnz%2Bf7777jq1bt%2BLh4cGlS5dYtGiR6PP777%2BnV69efPvttwwePJhXX31VVbrq6%2BvL7t272b59O9evX%2BeVV17RnHdAQAA//PCDat4Kfo8Szg8//JA5c%2BZw%2BPBhgoODeeeddwCHwuH48eN59tlnmThxIgAvvPCCKlDdsmULa9asYevWrZw5c0aYbcslnMeOHcNgMDBu3DhOnTolMnyJiYmqgPX69eu4u7tz9OhRQkJC%2BOSTT/j000957733OHbsGP3792fs2LHimIkTJwoxDef79Emn8od9%2B/YBDp6VsnZKmeCYMWPEj/6SkhKOHj3K119/zYABA5g/fz4Gg0GoMdasWZO2bdty8%2BZNVQmfl5eXS/lkSEjIXde8WbNmImtlsVgwGAyizttisbB69WpVAOnMEatWrZrwxPP09BRm5s6m4IDLWyMvLy/xuTPPUUFgYKDIyFksFtzc3GjSpIlot27dOvGw8fX1VRnGw8/BlsViwd3dHQ8PD6xW6y8qQQBHkJWXl0dycjJXr15ViaV4enqKMWiZqZf3wsG5LNR53kqwpXArL1y4oMrUnzhxQpCqy%2Bs7Kyur0rYMWhy%2Bli3vncPncvpKcPgq068W96UiA23NpL/U8a/l8MnVz5oJ09%2BLwyf9Q/2bcfjkjrTus0oQq2QDd9e1%2BnUcPpcxa3H4KjB91%2BpbnoImh08yjtbk8Mkd/0YcPnn9KsPh0yzWkG5s%2BfL%2BKzl8WrS0X8Phk9v8z3D45En9nhw%2Buc2v4fDJ2%2BDK4dOq2PsP4fD9twV8vr6%2BzJ8/n5MnT3LkyBFmzpypsoLas2ePUIFXsH37dtXvVQX%2B/v68%2BeabHDx4kMTERFatWqWyqqoMflHAl56ezvXr11Vv9gcOHMiVK1coKipi586dmse98cYbHD58mJiYGOLi4sjPz%2BfkyZNkZmaydetWwKFEuHHjRhISErhw4QIAX331FYGBgWzevJmYmBiefvppwsPDRaYsISGBgoICTCYT3bt3Z8KECbi7uwshlokTJ%2BLh4UHPnj15vqxu2jkTKY9x3bp1KnEHo9HIwoUL6d69O6%2B99ho1a9bk1Vdfxc3NDbvdzueff46HhwexsbHk5%2BdjsVho166dOD44OJidO3fSvn171qxZw61bt8jOziYlJYXk5GSysrLo2bMnffr0AWDXrl0UFxe7zPv69euYTCaX1C9UnNGTkZ6ersryaPl4NGrUiOnTp9OhQwfOnz8vyjW3bt1KYGAg77//PvPnzwdg2LBhqoxEmzZtGD58OLGxsRgMBlE6mZCQoKlYtGzZMpYtW4bdbnf5/Pjx49y6dUtYBmRkZPDpp59St25dnn/%2Bedq0acOXX36J2Wxm3759ZGRkqLzVnKX%2BO3bsSPfu3QHEj/GGDRvy8ssvM2nSJHH%2BpUuXiqC8Tp06ZGdnk5uby/jx45k8ebIIkOLi4oQqZV5enuoatGzZkocfflgYvoOaP1dUVITNZlMFYxcuXBDzX7p0KT2dHEYLCgpUSp0Gg0H1VmfatGkMHjyY4uJiatWqJfoNDAykadOmot0333xD27Ztxbabm5sq0JO5g7du3RIZvq5du6peltjtdoYOHSqC6osXL6rmC6jKTk0mE3a7HV9fXxf1qcpCuS7OnoeKAbocYCtw9gF0hr%2B/v%2BotmrL2SulmSUmJy5s3BQaDAX9//7uWHlfWlkGLw5eUdO8cPhfKRSU4fJXpV4v74kJbkbguGqd26fjXcvhkXpDmy87fi8MnkZt%2BMw6f3JGvb4Xn1iJWhYWpt13X6tdx%2BFzGrMXhk%2BZQGQ6fPAVNDp/km6nJ4ZM7/o04fPL6VYbDp0nllW5s%2BfL%2BKzl8WrS0X8Phk9v8z3D45En9nhw%2Buc2v4fDJ2%2BDK4ZO34T%2BGw6fj3vCLSzo9PT1VJZzh4eGidOvNN9/kzp07pKWlUVJSIko4x4wZw6VLl7BaraJ8buvWrRw4cIDwsqf6wYMHxY/oO3fu0KpVK9555x2uXbvGQw89JIRgzpw5w5kzZ%2BjUqZPI7pWUlLB3717mzJlDUVERr776Km3atGHgwIEcOXIEq9Uq%2BEvlvZX/6quvMBqNLFmyhOPHj9OlSxf8/PwwmUz07duXO3fuMH36dGbPns2DDz5I%2B/btxQ/9rVu3YjQaRV1tdHQ0%2Bfn5XLt2TWT4unbtCkC/fv0YNmwYBoNBleHLysoSxtfyvN3c3LBarSIQ/iWQSzjtdjtff/212FaCd6WEU7kWSobPbDZz48YNoqKiWLx4MdeuXePZZ58VXLlPPvmEP/3pT0IN8fDhwyLDl5eXpxqzl5eX4Oy1bt1aiKi88847REdHM27cOFFWGBUVJQKz8%2BfPExQURK9evTh//jynT58WGb7OnTuTm5vLjBkzeKbsIakEes6qju3atRNZMSU4SExMpEePHqpAxjlAUAy0jUYjDz74IL6%2BvuI%2BKikpYf/%2B/cKovbCwUNzbBw4cUPHMwJGJ9XR6aLq7u4vAzGKxqILm119/XfjwKWjRooXwpKtVqxavvvqq%2BEzxoANHoKGU2ebk5LiQfJ3Rs2dPcR2VMTlzHS0Wi3gBcu7cOfr27SuUZ5VsqAK73S6CScX7Tpmf1WqlTp06NGvWjFu3blUoeezm5qb6q1KlCv7%2B/iIDXlhYqDKfNxqNTJ8%2BXVXOq0Axd/fx8SE8PFwEi/Xq1VMRs5csWcKBAwf%2Bn73vjq%2BiSt9/Zm5ND0mAYAgtIEQILUAgIs1GUxAUBEVxYRUVEFDXsqisFQREsNd1LYAoighRUOk9oUkNRqkJgfQQktvv74/LeZlz5oTMyu5%2B1/3N8/n4MXPvlFNmLued932eB40urJLFTCe7Z3bu3IlOnTohKiqKgl3RUgIIZemNwOTwmTBhwoQJE3Xjj5bh%2B2/DPxXwMeGGYDCIv/71r/Q5K/tyOBx45513qIwPCC32XC4XOnTogKysLJ16nbYcsLi4GLm5ubTY0mbb7HY7fvvtNwQCAbjdbsrIBINB9OjRA3a7nTIgTE2wrKwMdrsdq1evpvbWJojh9XpRWVmJCRMmID09HTt27IDH44Hf78eqVavgdDrRrl07VFdXw%2BVy0QKd%2BZgxjzC2YA4Gg/B6vdQ2JqGv9RI7d%2B4claedOHGCa4u23yyzISsTO3bs2CW9PsSMXm37ulwu%2Bs7j8aBFixaorKzkuJjV1dUIBALIzMykYCQ5OZlTb4yJiUF0dDSSk5MRGxuLqgu1E/n5%2BaiurqaSTmaXkJycjLCwMOzatQtpaWmorq7m1IkA4ODBgzQmzEYhISEBdrudsspsPBkiIyO5jFUwGKQFPps7VVVRUVGBYDAIh8MBh8NBLyBYljAYDMLv92P58uXUFwAYOHAg7HY7ZXTLy8vp%2BidPnkRhYSFdx2KxICYmhkvlu91u6pPX68W8efMocNA%2BEwwHDhygrJHX6%2BXKF10uF107MjKS7ql69epRCSYA8hdkuOmmm7ggSdsmAOjZsydxE8%2BePcvNM8Dzz4LBII4cOUJ/s2cHCP0u3HnnnThy5Aj8fj%2BNsVEkJCSgvLwcM2fORKNGjXT3amJiIubMmcMFyAzshUNVVRXy8/Op/4WFhVxfHnjgAfTq1Qv5F%2BrIWBYPCM0Hm8v09HTs2rULFosF99xzD7p06SItUdVyJy8Fk8NnwoQJEyZMmPh345/O8FVXV6NVq1b4%2BeefacHEMirnzp3jjJ63b9%2BOyZMnAwhx9AYPHkyLmfr162PYsGHkPA%2BE5PGDwSCsViu2b99OiyatATLLnKxbt46%2BZ0qELNjq27cvli5dikAggJKSEtx444144YUXau3TmDFjoCgKXC4XCgoKUFNTA7fbDZvNBqvViuHDh6OmpoYk9a%2B99losXrwYAF%2B6xby7tm3bBqvVimAwiGnTpmHcuHEk8PDqq69Sfe7mzZvh8Xjw%2Beef0%2BJ4yZIl0n7XBqOZBG0bAej8B%2BfOnUuCNsXFxRg4cCCWLFlC85WdnY0rrrgCACiTBYQWxvPnz6fsGuNPAuDk8GUlbsFgECdPnkRNTQ06d%2B5MgemAAQOQnZ3NcdHeffdd4v0FAgGsWLECZ86cwdGjR6EoCnr16sXVQou8xrVr16Jnz560feWVV2L37t1ITEwEEAoc3G43lTD26NGDy8ipqooGDRrQC4sWLVoAuBgYarNnVqsV9evX12V%2BtMFXSkoKV5o4ZcoUmpvMzEzOeB0I8e9YtioiIoILViwWCwUO2gxdQkICFwSFh4dz3/v9fm6cnE4nF7ysWbOG1GBVVcUXX3xB82yxWDBgwABEaeqRtCWSdrsdMTExlH387LPPEBMTA0VRuBdCMng8Hu4/5hfodrtx5swZblxVVUVpaakue6n9Xob4%2BHhOaZSNJ3sOFUWhLKwoEAOE5lJVVeTk5KCgoIBrU3R0NBfcXwoyDl%2B7dv88h69OTz0Jt8QIh0/cR0bXqotnI3UZEXaSXdsIh0/spzRhKhwo9kFGTxHPI6WwCMZxRjh8YoGJdGzEBsoGXXy5IXnZkZfHb%2BvnW8LhE3aSVSbr5kHWCQM%2Bi%2BJYiJw9GftCvLbo3QdAZjhY53l0x0h%2BT8RdZNxU8TPp%2B9W6xsaAN57sPjfCU9PdSpIX4HXx8YzwB2XX1nXLAK9Y3JaNue5akh8Kcbykz11dPziyyTRCMhS5d%2BI2oOf1yTh84mfieWQcPtHiTLamFM/zX%2BzDZ2b4Lg/WunfhYbPZkJeXB4fDgfPnz3MZgeTkZHg8HnqrruUPWSwWfPbZZxg6dCiAEL9n6dKlFAgcO3YMGzduhKIo8Pv9yMjIoMXXyZMn4fP5qIzUbrejb9%2B%2BFHicPHkSCxYsoIBl1apVpPQYDAaxfPlyvPrqq1i2bBmAECdKhNfrhdVqhc/nQ1RUFM6fP0/nv%2BqqqzB8%2BHD88MMPCAaD%2BPHHH0kZsqysjOT4FUXB5s2bsXjxYsrKuVwujt81ZcoU%2Bi4YDCItLY1bLN58880UVLB%2BswVnhIwrUQf8fj%2BJZ7A2sgyM9vO//vWvxNUDQiWud9xxB1RVRSAQQNeuXWkRu27dOmrTsWPHMHXqVMpQsuxUeXk5qquruYBeCzHTqCgK3Ututxtdu3YlL8ekpCRMnDgRZ86coeN%2B%2BuknEiEJBoPYuHEjzQMQ4payewAIlbZq7T7y8vK4LFnTpk3RuHFjbN26FX6/HwUFBbo2awOEkpKSWjNVPXr0wJo1a7gg4ty5c3jwwQfpHs3TrMZUVcXjjz%2BOF154gczhtcbrACjzBIRerLCMMTu/tjSQzcFvv/3GjYmYwfv%2B%2B%2B/xxBNPEA/O5XJxJa29evXCr7/%2BihMnTkBVVYwcOZICYovFQmW7DNrAiAVrAPDcc89BVVXMmzfvktno2sAC2oiICFRXV6N58%2Bb47bffAIQ87/Ly8qAoCsrKynRlnexZE6HNimrBnkWWZbZYLFxVADtf69atERERAVVV4ff7uX699NJLhvu2fPly7iUZAEyaNBn9%2Bl28N41w%2BOr01JNwS4xw%2BMR9ZD9BdfFsZFwncSfZtY1w%2BMR%2BSjlxwoFiH2T0FPE8UgqLYBxnhMMn8rWkYyM2UDboYvmypJy5ZUt%2BWz/fEg6fsJOMi6WbB1knDPgsimMh/pzKrKrEa4vefQBkhoN1nkd3jOSFjbiLjJsqfib14atrbAx448nucyM8Nd2tJOHR1cXHM8IflF1b1y0DvGJxWzbmumtJfijE8ZI%2Bd3X94Mgm0wjJUOTeiduAntcn4/CJn4nnkXH4RIszGX9ePM9/uQ%2Bfid%2BPfzrDx3ys2MKK%2BXQBwNNPP41ffvlF6sV2/vx5jBs3jgtuFi1aRPuOGjUKx48f58Q72Nt5FnCwwMnhcHD7MYPp2sRLBg8ezAU3N9zAv0E/dOgQfD4foqOjsWDBAowdOxYNGzakwGvixImkJqqqqi7DwL4LBoO6cqxDhw5x1wZ4zo%2BoFmiz2WhRyfrNzqm1T7gUWCCZlpYGICTMwvhWrE%2B33XYbHnvsMTpGWyYHhMZMW8rGzgsAixcvJqXL%2BPh4%2BHw%2B%2Bo4F8Nu2bauFn8SjadOmCAsLQ05ODgWHoqz9b7/9RsEDa/%2BuXbs4eVtFUbgsz5QpUygLx9p%2BqWDjxIkTVN4JhAIybd8zMjKoxA%2BQm6Oz/p48eZK7fwBg6NChGDNmTK3X/%2Babb%2Bg%2BOHPmDFc%2BCoBTwCwsLOTsRxwOB7Xt/PnzJE4TGRnJ8dQiIyO5rOLatWuRnp5ea5Z4//79xIOsqanhAm6x7BYI%2Bc9p%2B8z6M2vWLDz33HNwu91ciXJtEDl8gwcPht1uJ7XYo0eP0nV%2B%2BeUXWK1WjB8/HldeeaXuXLWpWLGXVgyJiYmwWq3E82Tn79q1K6mlAqH7PTMzE0lJSaiqqkIwGKTfKdZf0arkUjA5fCZMmDBhwoSJfzf%2B6YAvJiYGI0eORH5%2BPlRVRXZ2Ni1OU1NTqWzNbrdj165deO211wCEFojfffcdlYy1a9eOjJSBkCcYW0ReccUV2L59OyemkZ2dTYvPjIwMrFu3jmTqi4qKEBYWRtkLm82G9PR0OnbLli1cNqasrAxPP/00gIscNIvFgvLyckyePBlvvPEGSktLkZKSApvNhhYtWtD5AoEA2rZti8WLFyMyMpK4RQx5eXlo2LAh8ajEAHDRokX44YcfaEGpDRJVVUXz5s25hajW5mH37t3YvXs30tPTUa9ePQwYMICCyR9//BFAiGMUFxeHvn37cvL1rIyUjfG8efMwa9Ys%2Br6wsBBt27albZ/PR5xBFuSydvbu3ZsMqs%2BcOYPy8nIqFaypqcF3332HqVOnwuv14tSpU5xRPRNtib7wSvD48eOoqalBly5dqDTYYrEgOzubgiqv14uKigrk5OSgR48eNK6sPYqiYM2aNbrMijabtXbtWi4AZFYHLIi22%2B345Zdf6Jx33HEHx30sLCxETU0Njd/777%2BPLl26EDc0MTGRyiqDwSCWLVtGYwTwpue9e/fGwYMHa732nXfeibVr11JG12q1wuFw0LME8BYLf//738mHLz8/nzJSZWVlXFZy2LBhXJ8YJ1M75tprnD9/nkp/o6OjsXbtWtx2220AQvOs9TPs378/Vq9eTde2WCz0zDDhGyCUpW4ue3V/CWzZsgUejwdPP/00mjZtSrxKhqioKHzwwQfEIdSC2cWoqgpVValMl72kYQgEAmQdwdrv8/nw888/Y9KkSfQ79eGHH2Lr1q04c%2BYM8vLydG1xOp06Lz8TJkyYMGHCxOXBLOm8PPwu4/Vp06bB6XRS1mTXrl30HVPqY/wg5tfm8/kwcOBAWhTn5eVh0qRJFPjceuutnOqdqqqI09RrdO3alQKAOXPm4O2336Y36T6fj7hG7Fi2DYRK7LZv3w4glOXw%2B/1Umsbg8/losWez2eDxeHDkyBF4vV78%2BuuvVMLpdDrh9/sxe/ZsxMXFweFwkOWDoiioX78%2Bjh07RgtcUSTmpptuQr9Jd60tAAAgAElEQVR%2B/bjP2WLy4YcfRocOHbjgYIIm3b5jxw507NgR%2Bfn5yMvLw%2BzZs4nXNWXKFCpDLC0txerVq7mStYkTJ%2BL48eO0wJ86dSoeeeQR%2Br5evXpYuHAhba9evZr2tdls2LZtG7Xlm2%2B%2BoQxfWloaNm/eTMGjz%2BfDgAEDcMMNN8BisWDIkCFITk6muWNm2iwT6HA48PjjjyMnJ4fmOyoqiuOaKYqCJUuWAOBVVtm4Mf85beZItBmIjIyk/VVVRUxMDO666y6yjXC5XGjZsiXt88UXX2DTpk20/fzzz2PTpk3Urri4OGzZsgVdunShazA%2Bm8VigaqqXEZR25%2BdO3eiQ4cOl7z2mjVraH%2Bn04k5c%2BaQIijAl3j6/X66nvY6gUCACwxFwSTGM2Vj2atXL87QPSwsjI53uVyYMWMGvUQIBoNYsWIFPTP5%2BfmkIsquzTh9LCjy%2BXyorKzU%2BfUB4J5HkcNnsVhw%2BvRpnDhxAidOnOBeiKiqCrfbjfDwcHoWtPj666%2BpPYFAgF5irF69mnvOioqKcP78eXqGFEXBiRMncO7cOcyYMYPG6bbbbkMwGMQHH3xA3GEGds8Z9eADTNEWEyZMmDBhwsS/H78r4IuMjMT06dNhs9nQqlUrLpPCeFF%2Bvx81NTU6jza2QPZ4PFxGaf/%2B/VQmyiwdJk%2BeTG/md%2B/eTRmPmTNn4vPPP%2Bc4bSzACgaDcLvd3HUdDgencMj2%2B/DDD5GSkgJFUbgSNaYCqgWznnC5XNi/fz9%2B%2BuknVFVV4corryQxk2AwSGVvMr4dWxyLAhPsWrNnz8aSJUuozFNVVUybNo32y8zMxJ49exAWFoYuXbrg0UcfJe6c1%2BvFddddh2eeeQZWqxVxcXGcoMa5c%2BcwZcoUCghee%2B01TmSjsrKS47jdeOONtLj3%2BXxo3749ee8NGzaM%2BHVFRUW45ppr8Je//IXm4bvvvsOqVavg9/uxcuVKnDx5slZ1VJ/Ph4SEBJ0ICWsTG5%2Bbb74ZPXr0IAEPbUDl8/mQmZlJWVsgNIdiiS8rXQwEAjh79qyutFDrhbdy5UrOC%2B/vf/87evbsSf3Iz89HZmYmCfQ0btyYygEVRcHNN9/MBW0%2Bn48CuqqqKkPXZgFJVVUVevfujeXLl9P%2B7J4DgPHjx2Po0KEIBAJo0KABXSc%2BPp7b7/vvv%2BcCVHaPsfbV1NRw81BeXk4qrSNHjsSrr75KvNZgMIjBgwdTUH3w4EHOnzMYDJJqLrNo8Pv9sFgsGDRoEERo1WBrQyAQgNVq5UzsmzZtiqqqqlpFUrS8VC0SExMpy8zaq4Xf75eqpTI0aNCA40f%2BXhgxXjeiC1DnPgZEW2SCCEaurauUNnKQcDFZtbUR0RaxzbJ9xA/FamxZfG7IeF1QqpBeW%2BiEeG2pKb0RlQxR0UTy/AjvNCVCOb9PtEVXhSwbHKEkXfbzL55bU4wAAJBaaArjKa1GF28cSfl9nc%2BHZDKN3Gu6fsrEpOq4J4yIExkYckPCLka8xI2IwRgRbTHymySOX13bgGQsDAjuSJ9nseO6dZrkGCO/daLYihFBFpk5u6baCwAgcL912wDwySf89mef6fcR/WRl/rKmaMv/BP7pgI8ttoYOHYqUlBT8%2Buuv3Bt3tgiy2%2B1ISEjA9OnTER4eTkEMW4Ayr67o6GhadEZHR8PhcCAuLo5U8po2bUrBGNv322%2B/1XFftDw4lmFh5y0rK6PF80MPPURCC1rPPL/fjw4dOqBRo0Zc5oiBcaTatGlDi8OKigrs3r2bsgIJCQmU%2BWQLY1VV6TzaoFJUcLRYLGjTpg0FMg6HA4FAgDIfiqKgQYMGGDt2LDZt2oQtW7bgxIkT3ELV7/fD4/HA5/OhvLycC8SBUODLfMZmz56NtWvX0nfh4eH49NNPqV1nz57lrCKAi5mHjz/%2BmLKrpaWlnA%2Bc3%2B9Hu3bt0KVLF/Ld0yo/Ml4Wu47f78ejjz6Kbt26UYBWWFhIdgBAKFBWVZXjtXXr1o28/4BQwMuUUIEQh5AFpQDQuXNnXVZXVVWOl6cNwp566il07NiR46xZLBa6j1JSUrBlyxakpaXRcew%2BiI%2BP1wUwq1ev5jLWWtl/8drjx4/Hvffey4kLWa1W3HHHHQBCJaGTJk2i79xuNwXHzZs3x9KlSwHwARsQGlfty4ZBgwZRdppB%2B6KCWaAAoexyv379SPhIVVWMGDGC9vX7/RgxYgRXRszgcrnQpk0btG7dGl6vlwuEGWSWCgzaZ93n8%2BHs2bN0/zAhJ5kXHqDPamr7FidRfKhN1VOGSwWE2nvxUjBivG5EF6DOfQyItsgEEYxcWzf0Rg4SLiabPiOiLWKbZfuIH4qCDTI9AkPG64JShfTaQifEa0tN6Y2oZIhl0RfUhrXQJPelDfy9oi26pLRscITfAdljKJ5bQzcGAFxwe%2BIhjKfmnWWt%2B8gUOup8PiSTaeRe0/VT9iKqjnvCiDiRgSE3JOxixEvciBiMEdEWI79J4vjVtQ1IxsKA4I70eRY7LpzHiACPdPJEsRUjgiwyc/YHH%2BS377nn0tsAIOoGXFhDcBAN3MVt4L9KtMUM%2BH4//qmAz2KxoGnTprTNOFM2m40WVqycacmSJcjKyiJOUWxsLObNm0ccmsWLF2PUqFFISkriuHlutxsjR45EdnY2Zs%2Bejfz8fAoY2L7MNuHRRx%2BFqqro1asXLd6cTicGDBiApk2bUoCizXSlpKRQeZe2/I3ZQXz44Yck1a/9nglOlJWVcWIybKFut9uRlJRE2%2Bz/FouFsg/sfBs2bNCVn/n9fuTm5tI4ulwubhHLlDXZdQHghx9%2BIONsq9WKsLAwREZGQlVV1KtXD6tXr%2BaCzBkzZpC64blz57jzFxQUoLq6mgsg2ffdu3fHrl27KAgLBAI0BjExMRg2bBjqAjsX4/A1btyYAvuYmBjs2LGDyttEQZCamhrExMRwLxb69evHqWxaLBZuvq677jpdyZ0WzMev4oIG9ZAhQ3DgwAFqZ2lpKTcWFRUV3HgxIREmjANcDAB2795NfQNC867lmAK45LVlQh69e/cmjprD4eD67nQ66bnSquSmpaXRvQyEngPtGA4YMIAre3U4HHSP2e12/Pzzz1R%2BmZ%2BfD60PX2RkpK7sNi4uDlVVVcSVYwFtZGQkBg8eTNnvBuLKToP69esjNzeX%2B2/Dhg1o2rQp7r//frJPYe1goikzZ86k3xEtGHc3Li4OzZo1I8/QsWPHckqsH330EdasWcOJtrDfFC3/d9%2B%2BfXA6nWjZsiVGjx6NYcOGYf369ahfvz49u8zCwwhM0RYTJkyYMGHCxL8bv6ukkyE1NRVDhw7lMhFXX301FEXBqFGjMGDAABw6dAiNGjVCZWUlpkyZQkp6o0aNwj/%2B8Q/ufLfffjusViveeustrqTzXkFu1mq1IhAIYMqUKQgEAtiwYQMiIiLI0iErK4ve/AOQyq%2Bz0k8tcnNzMXr0aHo7r/1%2B06ZNuPXWW1FeXs4Z0DPU5gMGhBbPTZo0oQV%2Br169pMqdDRs25EofLRYLvvrqK9rOz8/nrnnttdfS96wkr6qqCoFAAMXFxbjxxhu5zNHo0aO5IFh7Lm3pHwML6jZv3ozOnTsTV1MbfJaXlxNP6lJg12XqoadOnaKgqry8HJ06daIsVbTktXJ5eTk3HytXruQEScQSvJ07d6JXr161tqe4uJgr3f3mm2/Qrl07LpCQQfxcG/SwxbvP5%2BMCRhZkNdO8sv5nr71%2B/Xrue22w4nK56NpaTpjP5%2BPKWsUxTExMRPfu3bk2sfvF4/GgQ4cO5Kso8spElVItl5dx5dj9XlVVhZUrV1Jw9s033%2Bj6x9pZVFREPE/2H3te3nvvPZ3Nh6qqaNasGWbMmCFVTmUVB6WlpTh27BjZTqxYsQIHDx6k/caOHYt%2B/fpRm1VVRXJyMhRF4bJ1bdu2hcvlQnx8PCorK/HVV1%2BhX79%2BKCoqome3SZMmtWYWRZgcPhMmTJgwYaJumBm%2By8M/FfAlJiZy5tEA8Je//AWxsbGUSejUqRPuvPNOKqu8/vrr8cILL1BA1r59e3Tq1IlKOjMyMshoPCkpCW%2B%2B%2BSZX0vmXv/yFvPvYvtpSOIajR48iGAwiLCyMy2KEhYVxi0TtAqtZs2bYtm0bLc6uuuoqLkBKSUnByJEjaXvo0KGcGfeAAQOQmZlJ2126dKHFGrvOuXPncObMGU7SHtCXjrVu3ZqTfwdCQQuT2GfQBgqNGzfGs88%2BqxsLhuXLl3PHnz9/nrKNFouFxh0AF1gCF8tDWVu1ZZhaJCcn45lnnqHz2u12JCcnw2q1wuVyoXXr1vj22291xwaDQfqsefPmpHYJhAR8srOzaS7CwsJQv359bu4OHz6s67v2Gh6PhysBHTt2LLKysnRtYFBVlQSGgFAwvXnzZtq%2B6aabsGXLFrq3EiUlVGwMWrduzX1//vx5uFwuTqzkUte%2B9dZb8c4771B5JSuFZv33%2BXxcOa7D4aB9t27dSkFdbm4uFwQ1btyY47pt2rSJuwcAPtjo1asXBd9WqxVZWVlUJmuz2TBu3DjKxjLBIi3Yd3/6058wcOBAelEjs3Q4JiXshNCiRQsUFRXB6/UiEAjobEyOHTsGh8PBPfcMtQVekZGRnHosAwvaWNUCazMD42LGxcWRh6bIT2VcVyOQcfg6duQ5fEYMn0VuS53G0pJ9jHD4pJyzui4u47UIL8iMcANl%2B4gxvhGTdyN8KEPG6wJxSTY2Yh8MGUCLO8mIdBcqNQiSEmIdre93cPguMBMu2TxpJ4SdZHMn8rxEPp7k8dQNqKx9Rm5a3UcG7lkj/EvdYZIXweLjIo6n7LziO2sjPDojBulGOHziMaIxO6Cfh9/L4atrGmT3kc5WWbKT%2BPxKCyvEjosTZcR4XfbiXzQ2N8LhE/l6gJ7XJyRMpBw%2B0Wj9D87hM3F5UIK/xwn5/xjXXXcdmWNHRUXB5/NRmRcLJN1uN/n1RUREoLKyEq1atcKpU6dQVVUFRVHQpUsXHDx4kDKUr7/%2BOlatWkWy6kzRk5WphoeHk7CFoigICwuDoihUChkeHk6ZlrS0NCqf9Pl8iIyM5LItzORdC7ZgdbvdsFgscDqdGDZsGD4RibcXYLPZOMNycSojIyPRqlUrTrG0fv36KCoqwpgxY7Br1y4cOHCA9n399dcxduxY6bUaN24Mm83GZU6BEF/t3LlzXIbzxx9/xAcffIBFixaR0TszcGcBh8fjoaC3Q4cOGDJkCGbMmAEgFEhv2LABzZs3x/79%2B5Gamory8nKUlZXB4/FwdgyszxaLBampqdi/fz/NVWRkJGUBFUVBq1atpNL9QGg%2BunTpQsboqqrCZrNR8DRmzBgsW7aMvNeYwuabb76J%2BfPnIzc3F927d0dZWRmmTp2Kzz77DCUlJRQMNGjQgMtIXuraiqKgU6dO2LNnDwU4t9xyCyoqKvDTTz9x8wiESjqjoqJQVFSE1NRUHDlyBH6/H6mpqXjyySfJ/69Zs2Y4ffo09SkxMRElJSU6rqd2zl0uF4qLi6EoCiIiIuByueDz%2BYhPyniUiqJg%2B/bt6NatGx3P%2BKjsHnE6naiursZtt92GZ555hrvW888/X%2Bt9brFY8MADD2Dv3r3YsGEDkpOTKVhr3LgxCgoK0Lt3b5SXl9M9x5CZmSn152zVqhVuuOEGvPHGG9xzx9C8eXMsW7YMPXv2RHJyMmUDWfnokCFDMH36dHz55ZcA%2BGdvwYIFuPHGG6V9ETFz5kyp8frEiQ/WcoQJEyZMmDDx/x%2BEfNO/BLXouv1P4rJKOv8vUFhYSJ5ZCxYswIMPPsiZl7do0YIk/N1uN2bNmoWsrCxERkbi1KlTlOVhC9Ft27ZRFkNU52SeeVrvOpYtbN%2B%2BPbZv345XXnmFM4BnQcy5c%2BfQvn17uN1uuN1uPPnkk8R/TEhIQKNGjfDkk09yfdOKZCQnJ%2BOxxx7jlBkZtNwwtqiWxe1btmzhOJfARXGMJk2acFm9QYMG4bnnnqNtbQYyIiIC9erVoyyM9ruSkhLk5OSQWEdqaiqSk5PJe61evXqch582a8hw8uRJblHOzLlZiWdhYSFiY2MxduxY8nCz2%2B1cptfv9yMvL4%2B2t2/fTgI1QCio1AY2UVFRXGDw8MMPcxnEpKQkfKx5O/btt9/qguHDhw9j1apVAEI8RxbsHzlyBKtXr%2BZUPrt3716rsMhjjz1GPomsb5GRkcQPzMjIwFdffYU9e/YACAnWaM3Sp06dSpk3q9VK9giHDx/mzvv444/jiSeeoO1z585xwbPD4eBMxEtKSsgbsVmzZti4cSOJNpWWlnIekePHj%2BdUXu12O2JiYkjAiPH6oqKiuNJWGWw2G2e8PmXKFEycOBFz5syBzWbjMnMFBQW44YYbsGnTJjz33HO6MWYZeGbFwZ4HbZk3Uwxeu3Yt%2BTxWV1dj3bp1sNls6NChA%2B2blpZGz%2BS%2Bffs4WwsgFJy%2B9dZbl%2ByfFiaHz4QJEyZMmKgbZknn5eEPF/B5PB6UlZWhpKQEkydPxscff4wePXqQsMuvv/5KZZhhYWFIT08nYRC73U6Gy6qqYtasWbDb7VQOV1VVxfHHWKDCvp82bRoFITfddBNUVeU8t9q2bUsL6GPHjqGkpITEGx5%2B%2BGHat6KiAvn5%2BXjxxRe5vmkDkjvvvJP8AEUwMQm3282V7LHAjAWoPXr00HEFGV544QUu4Pviiy8oGGTZOIbz589TZkwbvDG0b9%2BeyiefeeYZeDweFBYWwmazoaysDC1btqR9Y2NjSTAlNjaWOHzvaEoaMjIy4Pf7SSWUceK07fV4PDqVRO1YpKWlUVZGURQcOXIEDz/8MIBQQLFo0SIsWrSIBH1efvll8loEQkHo3XffTQv5iIgIvPXWW1TSO336dIwfP564jx6Ph4J1sewQCKl0suCgf//%2BXGnlzJkzueDf7XZj8%2BbNlH1lY8uC4h07dnClxXPnzqVg/PDhw/juu%2B8AXOS3MTA%2BHoPVaqUMZMOGDZGVlUVlp4ynyeY6Pz8fb7zxBrU7GAxy5cIHDhwgaw42HlqRoZqaGhQWFqK0tJTzBmRgBu%2BAXnEzISEB33zzDfr27YvJkydz38XGxmLdunVo2rSprjyVjQdrQ0VFBT0PhYWFnMXE7t270bdvXzKmLy4uxr59%2B%2BDz%2BbB48WJuDDdt2oTs7GwSx9G%2BcPH7/bVmkU2YMGHChAkTJv4v8IcL%2BIDQgjA8PBxxcXE4c%2BYM1q5dS8FBZmYm8ZuCwSD69%2B%2BP/v37o6KiAu3atcPOnTsBhMonmarf8OHDAYQCC7aAT0hIwOjRozF8%2BHAqvVy6dCmuuuoqAMAHH3yAzp07Y%2BrUqdQmkYN35MgRMiQHLpaMMd8/0TtMuy/zMJNxkNjC32q14vbbb6fPz58/j7KyMmqv0%2BnE4MGDAYAW2dpsKPOts1gsiI6OpoBUJhoRERHBCZRokZKSQn%2BPHTsWa9euRU5ODjp27Ijx48fjHo1ccHl5OQoKCuByuVBZWYlevXph165dnIWCuOB3uVw4c%2BYMzp49SzYDkZGRaNGiBbefKIDBAmhVVdG/f39cf/31sNvt8Pv9GDx4MEaNGkXBlKqq9DLA6XTinXfe0Yn6MMsNIHSfBQIB7Nu3D9OmTcO2bdtI4XHv3r247rrr8OOPPwIIBZgDBgyg83z//fe6QP71C/X5zHPy8ccfp2wbK1NlSE1NxU033cR9tm/fPgCh%2B5ZlFv1%2BP%2B6//34AFzNPWt5jhw4dcP311wMIBUDXXnstZREBYMSIEfQ8hIeHY9q0aZxX5tChQ2mutmzZQi9DGFgfHQ4Htm7dilatWiEhIQFPPfVUrWWk7Djtf9OnT4ff78cVV1yBV199lctat2zZEh6Ph9onQixBZlBVlbOQ0PoRsv6dOHEClZWVXPac/Z2enl6rt2Rtn8tgiraYMGHChAkTdcPM8F0e/nABX2JiImJjY6GqKqxWK6xWKxwOBwU0UVFRtHjTikNYrVYKTCwWC7fYY4vY%2BPh4NGrUiPPwAy4qMdpsNjRv3pwWuV6vl76TZb4AXv2ScQVZ5kQMbLQliAUFBThx4kQtJV%2Bg87CMh/Zc7G%2B3201BaGFhIXHe2KLV6/XSONrtdl15mhalpaUkziOKbmgVIwFg48aNaNGiBXJycvDDDz9wQQY71uFwwOfzYe7cuXA4HJwhuAhtcMAW01arVWdur2230%2BmkufH7/VAUhcvAsX1YAK8oCnE3XS4X7r//fm6sCgoKONGbU6dOoaSkBEVFRXj99deRnp5OGaMzZ86guLiYriMTGWLjDoSCCKYEGQgE0KlTJ7z00ku1Kr86HA7OfNzj8VCw7nQ6udJbNv9Wq1WXub377rt15ZVaa4PPP/%2Bc5ra8vBwZGRmkxGm32/HZZ5/prCu0YPPmdrvxzDPPoKysDC6XC5s2bdK9PJBZKjCweb3lllvg9/spu6koCrKzs3H11VfjwIEDugCdcUdlCA8P5zLC2tJWhksp7wLAgw8%2BKJ1b4NK%2BglrIRFu6deNFW4yIjIjxs/izYURvQCJmrNtHJsZQl6mxrL1ie3TCC5J9jBhSS38u6xBtkYlkGDJev6AAy2BEMEbsp/S9h6iSIbu4AafrCwnoWi8mE20R31XI7gndTgYEgWTzK3ZBNIrXVOjXeilhCuTNMXJjG5hw8RDZex0jwiTiZ3UKyEA/frJ7zYiojO44ybXEny4jz5j4mWy%2BjQyg7iPhR0DWJ/HWN2LObmBadB9In1UjP1JGRFFEYReZAIv4mSjiIp4DADTVKQCAzz/X7/Pee/z2u%2B/q95H%2BuJn4o%2BEPF/CVlpaia9eu6NatG6xWK2WsGjRogHbt2iE%2BPh6xsbGIiopCu3btKHjr3LkzlRaGh4dzgRhTVKxfvz6aNGmCQCCAZs2aQVEUWCwWdOjQgRbZbdq0QWJiIqKjo8nPD7io0KgoCrfI1Jp9a42efT7fJd/ke71eLFy4kBagYhBmtVoRExPDqQqyz9mCuKqqiitrlEFVVYSHh6O8vBx2u504gTK8%2BOKLSExM5PqnKApX1gqEAr5ffvkFfr8fJ0%2Be5BbirM%2Bs3Yxvph0Llhlj0M4VW6Rr%2B8mgtQdxuVxcoHjixAm899578Pv9JNLhcrmo/M7v93MBr7Y9VqsVNpsNwWCQghqr1YqEhATpuDKOKYOoHsnuSZZNCgaDeOGFF%2Bh7u91%2ByXvD5/Nx59SK11RXV9O9qJ0nt9uts95ITk6mIFW7X23t7tevH7XLYrHg7bff5vov2qxoz7Nu3TqUlJSgurpaF5gB8kyXtr979uyh8WZlmsFgEDExMdi9e7f0npUphzIw%2BxIR2oy6yPPUQlVV1K9fH/Xr1yeBHy1YxUFdkBmv5%2BTwxutGTJfF5Ls4nLJuiMdo7Epr3UdmqFyXqbGsvWJ7hHc30n2MGFJLbyPhQPEYmfm5IeN1zUsXQOrvrbu22E9p0YRoSyO7uAGna12FswHjdbGgRHZP6HaSDKDYL9n8il0QK701TIBaLyVMgbw5Rm5sAxMuHiITADZiLi5%2Bpmuv5Bhx/GT3mviZ7L7WHSe5lviTaeQZEz%2BTzbeRAdR9JPwIyPok3vpGzNkNTIvuA%2BmzauRHyoix%2BYQJ/LbMRF38TDRnF88BAJoKMACARnWe8Oc/89uCDRqAWn7c/vMwM3yXhz9cwBcXF4fdu3ejsrISH3zwAXbv3o0tW7Zg9OjRyMvLo9LA4cOHo6KiAllZWcjKykJxcTFlPlJTUzk1Qcb/S0xMRFpaGlJSUtC4cWOsW7cOS5YsQWlpKQYNGkSleSUlJeTxxhbtGRkZWLFiBYYMGYLMzEzKPmk5dNXV1Vx2rGPHjrX20%2BfzoaKigrJT4oLW5/PB7XYjLi4O4eHhuOqqqxAWFgafz8cFOu%2B//z6Ai%2BWI4uKUKYh6PB4KQmvLtjz77LPo168fJmh%2BWFjQzRbWPXv2RHFxMQW32kAKuBjYsXFj5am//vorLa6PHj3KBRbaTIyiKHA6nXA4HDpPNgZVVXHo0CEqkwSAs2fP4r777kNiYiL8fj/i4%2BOhqiqNlcVioXba7Xbi9jFoM4sAcPDgQVRUVCAlJYXM5FnQEgwGkZaWRgHpqVOnUFFRAVVV4XA4UK9ePS5YjYmJ4TJCCQkJcDgctVoK/Prrr8jPz6ftBg0a0L5nzpyhsRdVYJ1OJzdmH330ETIyMuhY7Riw75lIDhDiNbI5qqmpwdmzZ7mASBvIxcbGwuFwwGazwev1YsGCBUhISEAgEMCIESN0bdNma3/%2B%2BWed%2Bfqzzz6LIUOGcAIqQOhlQHV1NRwOB/72t7/pxmrQoEEAQMEZE5MB%2BCCTleBq%2BYWtW7dGdHQ0mjZtSsGz0%2BlEnz59AACnT5/G0aNHMXz4cE4cJyIiAm3bttW1RQZTtMWECRMmTJioG2bAd3n4wwV8drsdn3zyCeLj4zFu3Dh06tQJmZmZWLhwIebOnYtrrrkGQEgkpX379hg4cCAGDhyIVq1aYaL4RkQCRVHw5ptv4uzZs%2BjTpw9GjBiBDh06EN8tKSkJr7/%2BOpeFaty4Md59913KQERHR%2BP999%2BHoiiYN28e7efz%2BbiFLhOIkMHv93McP3Hx73A4yNz67rvvxt69eynTpg34WOBUU1NDmSsxs8GCB4/HA0VRpAbWADB58mRkZWXhtddeo8%2B8Xi%2BcTicpfP74449o1qwZysrKKKiJjo7WldZpxT9ycnI4X7nKykpqIytFZAgGg2jXrh19J5Z12mw2REZGQlVVLig6fvw4OnTogPz8fDIk13KttHPj9Xq54MXn89G%2BbJ%2BNGzciKioKR48exS233IKZM2dyJYB79uyhMQdCIj7BYBButxslJSWcSmR5eTmXTSsvL4ff76%2BVC%2BZyuThV0zNnztCLAeDiCwxtewEgLy%2BPK7tctmwZWrRoQdcRrzdnzhxdBlCLPXv2cPelNphkJu%2BBQACqqiItLY3KqE%2BePElZOhnat2/PGa%2B3azZtfpIAACAASURBVNcO27Ztw5tvvon8/HwuyDx48CBxemUZxk8//RRAqMSyqKgIHo%2BHxiA8PJzurZ9%2B%2BgnXXXcd94KmW7duJPTC7keXy4UNGzYACPE4fT4fvvrqK7ytKacZM2bMJUtUtTA5fCZMmDBhwoSJfzf%2BcAEfEFIUfP7557F27Vrs3bsXu3fvxsKFC3HdddfRPna7Hc8%2B%2Byx27tyJrVu34rnnnqNF2CeffIJHHnmE9k1JSUFubi693W/WrBk%2B%2BOAD7NixA%2BvXr8dTTz3FZWR69%2B6NRYsW0fZ9991Hf8%2BcORPz5s1D586d8frrr%2Bvk3wcOHIikpCRD/WSLdYfDwalYAsDbb78NVVVxzTXXYNeuXVxGIT4%2Bnsvi3XnnnVypZtu2bblMo3bRnpycrCvlW7lyJRwOBx577DEoioJJkyZx33s8Hk4wQ1VVtGnThto/ZMgQ7Nmzh2sj84YDQr5l119/PZdhY3PhdrvRqVMn2nfixIlcQDN//nyu7DUpKQmVlZVIS0vDnDlz6HwAH9CIPMQ333yT237kkUekIjUsQCgvL8d7770Hi8WCgwcPYtGiRVyA/vTTT2Pfvn1krVBUVFQrnwwAeRACoQX/5MmTpQblwEVLEYbevXtj/PjxVIKoNX0fP348/a0oCqZNm0bzW11djREjRtD8R0REcNc8deoU2rRpQ9tNmjTh5uLAgQNcmfIrr7xC/QVC2bBAIACv14trr70WiqKgffv2CAaDuvLSaLG%2BSwOv14utW7eiYcOGKC4u1mXP4uLiUFVVJS2jrO3lhZbfCFwMsrRzXl5ejvnz53PHMc5wYWEhunfvzpVVM7z77rs4IyMXSSDj8LVpw3P4RD6MjB9TJ2dK8vLg93D4jOxjhEsktkeW6Pw9HD4p50w4kTg2MoqcIUN3gewk5fkJ1xZ5a9KxMbKTeDGJy7boL6XnedbtHG7IoFrWcWF%2BZbuI75JEo3XZOyEj96PuVpc8MLp7SRxjA7xE2f1ohI9XF6dUdow4vbJ5MfI7oTvOAIdPHGOZobuRfYw8z3XR/Ixw%2BGQnFvtt4JbVnUfGdNF9JuuUyImTceRE43UZh%2B/DD/ltkbMncvoA4MILT4LMeN1I%2B/5LOHxmhu/y8IcM%2BP4bUK9ePQo8RMXBmpoaDBs2DPXq1eN89ILBIA4ePIhWrVrRwvqll16Snt9isdAiNDw8HNdccw0ng5%2BZmYlPPvkELVq0wN69e3Hy5EmygBCRk5PDLeT379/PZTsTEhLo7xkzZuD555/HvZo67uzsbHz11Vdo27Yt8vLysGDBAl3wouXxHTlyBOfPn6dxue222%2BBwOLhsHPNKBIBdu3Zh6dKl3Pm6du1Kf2uzqaWlpZg5cya6deuGrKwsPPTQQxQoBoNBKtsFQAqlI0eOxM8//8yJ8WgzYgDw1FNP0d%2BBQAA7d%2B6Uzo225LFdu3a49dZbKSvKggtFUTBixAjY7XYSxnG73fD7/VBVVcoLGzZsGP3tcrnwxhtvICMjgz5bsGABt7/Wi279%2BvV03oiICISFhdF8a6911113oX79%2BjoRHDYWTqeT2//06dPYvn07ba9evZqzj/jiiy84M/mioiKykmDnZm1wuVz47bffkJubiyeeeIILFAHoFFdFtGnTBrt27YLT6dSplpaWlsLtdnPBpraNMgQCARQVFek4uNqxURQFa9as4Y7z%2BXxwuVwYMmQI3G63TqiGnfuhhx66ZH8YZBy%2BI0d4Dp/Ih5HxY%2BrkTEnKg38Ph8/IPka4RGJ7ZLyg38Phk3LOhBOJYyOjyIl9kCZsBbKTlOcnXFt8ryEdGyM7iReTvDARTYr1PE/JeXU8P0n7xMGQdVyYX9kuF0SNCZr3eAAAzbum2ponnW/drS55YHT3kjjGBniJsvvRCB%2BvLk6p7BhxemXzYuR3QnecAQ6fOMayd3NG9jHyPNdF8zPC4ZOdWOy3gVtWdx4ZD1r3maxTIidOxpHTJA0AyDl8f/oTvy1y9mQVbBpvXADAHXf8vvaZHL7/CZgB32XgTxcewFdeeYUM4Q8dOoTx48fDbrfj%2Beef57zibr75ZqxatQp9%2BvThFtayTM4XX3xBn69YsQI5OTk4cOAAF2h16dIFK1aswN69e9G/f3%2BUlZXBYrHoygFPnz6N7t2707GZmZmcOXTr1q0pGPN4PJg9ezbneffLL79g0KBB%2BOKLL8hsW5tl8/v9sNlsXJ8efPBByhyNGTMGL7/8MlfC%2BsADD9DfjRo1wj333ENtKC0tJVsKAGQtAAALFy7E3//%2Bd2zatAkAH6wCoEW/1veuoqIC8%2BbNI0EeVsqnDd5mzZpFKqkOhwPl5eVc1k0r3MKQnp6OJUuWICUlBRMmTCDlzGAwCK/Xix9%2B%2BAGHDh2ibQDEkRSzqFqV0oEDB%2BL777/nfPMeffRRbny1gdif/vQntGnTBufOnUO9evXw2muvUXZ1ypQptN%2BSJUtQUVHBlQxqS2s9Hg9332h5hIqiYPfu3brsmjYru3fvXu47xhsNBoOIiIgg/mhWVhYuBa3pOvuvYcOGePTRR2G327lxYObuTZo04Xh0DC%2B//DIsFgtl5sLDw6lPDzzwAD0Tf/7zn7Fs2TIu65eYmIi8vDw4nU7K8NevXx%2B33347unXrhncvvAmNjo6m54JlKmvjX4owOXwmTJgwYcKEiX83zIDvMsC81aqqqnD99dejY8eOeOihh5CSkkLZJlGcwiicTiedv2fPnlT2WBu/57777iOuWVxcHBYsWEAZP5/Phx07dtCxOTk5uPnmm%2BnYdevW0UJ/woQJKC4uRnFxMX3/2YUygGHDhlGgJpaseTwerhzu9ddfp4C1oqICy5Yt40oeDx48SO07ceIE/vGPf9Di1%2BPxkCgOEArytPj666%2BxbNkyAKFgFbiYyWJ%2BdH369MG2bdsAhEpSV69eTXy06upqhIeH44knnqBzTpw4kTz%2B3G43mjRpgueee44W7ozjx2T4HQ4HBgwYgKZNmyI/Px9Wq5UTAkpLS8PEiRNJcEdbElxTUwO/38/xt7SZpN27d2Pw4MFcmaPH4%2BGCzX379pHv34cffohHHnkEqqri5MmTpHwKgMswV1dXY%2BbMmdxYar%2BvqqoipVYgVOrMRE%2BCwSDuvfderqSzbdu2NKeMG6ctJ9WWM48ZM4YsPU6J%2BusGUL9%2BfcyePRtRUVHcCxLG9ywrK8PnguQ0y1D6/X4SOaqurobf74fFYoGiKDQvb7zxBoYOHQqv18uJBzG7B5bBLioqwsaNG1FWVkaG7JWVlXjwwQfpb4A3kjdhwoQJEyZMXB7MDN/lwQz4LgP16tUj64f4%2BHiEhYWhsLAQGzZsQGFhIS3Qc3NzueNGjRpFgcqwYcNIrTMjIwOjR4%2Bm/YYMGQIglDXYs2cP9uzZUyuvKykpCQkJCbBarcjPz8f8%2BfMp43T%2B/Hku4xAIBPDFF1/Q9zabjSuhZIGhKIjy9ttv45lnngHAc6OsViuaNGmCFStWUBBy6tQpvPrqq7RPWVkZfcf6qjUjDwQC1IZgMIjp06fTd9osKRCSy2eZPdYOGT%2BOBXh%2Bvx/5%2BflcNuXcuXP4%2BuuvadvlcuGTTz6h7crKSpw9e5barCgKbDYbLej9fj%2BKi4txzTXXSG0QUlNTER4eTvfAsGHDOCEPi8VC9wAQsqdgZYCnT5%2BGx%2BPhRGdkZaBsDBwOB6xWK71cGDFiBO1z44030t92u53zenQ6nWjevDmnLtq4cWMSVzl48CBWrlxJ%2B0%2BYMIHLLNarVw8rVqyg7fXr19O5wsPD4XQ66X797LPPkJSUhIqKCvTo0UPXF21Jq2i87vF4UFxcDI/Hg9OnTyMpKYnGkmX3qqurdUq2jRo1qjWb6Pf7kZ2dTcewudMaraempqJ%2B/fq68xYUFCA3Nxdr1qxBdHS0zh4EAK699lrpdUWYoi0mTJgwYcKEiX83zIDvMvHCCy9IFfmYcEnnzp3pM63JddOmTSkIY55/7HMgtIjv2rUrIiMjdYbitZWLZWRkcFk0rbWAlmvEVCxZJsjr9WLnzp1o1KgRp7QoLkYnTJjASd%2BzwIAJc7C2A6EARRu0KYrCWQ/U1NRwthRutxs5OTkAQot4LV%2BR8csYmEqntl8sY6OFVmlTPIf4PRAq62PIzs4mqwpW/ilm2TweDwKBACflz3Dq1CkuwExLS0NcXBy10e12c1y0kpISEgiJjY2F3W7nymZlxuCs/SwoYmjUqBHtp806KorCZWZZu5myLRDKTFXKGPcICdtoeYUWiwU33HBRYKSkpIRKV6urq%2BFyuagdgUAABw4cQDAYxKZNm/Dmm2/iTpFfcAmw7GswGERBQQEFt1arleMzimCG9rXhUhl4FuTLwEqYa2pq4HK5dC9itC8zLgWZaEtGBi/aokm2S7cBAwbpErVVUQBBJsgqnld6awgKCDqxCJlCgtAJmSaAEZEHsSJWNjbi9cV9ZP2uy0weAKB5IWP0PBrKKwB5v3X9lF1cPJFkH/EeEIU1pKItwnlkY37hUbwIWceFjsnOI%2BoaiXMpdhGAbkClj7d4A2p%2BGxnEJov3mkwgWZxL2bMgnlcmMiJeS7wfjYxVaal%2BH3G8dPMEQKQcy/op9kscY9nPrVi0YeRZkImg1KWdI2uvESGa3/PM6wZUNjFiJ2TPqqgeLfOrFcVVNC%2Bfaz1ONFUXzwHohVxEk3VALwYjE4y5hFr3fxJmhu/yYAZ8l4k2bdrgyy%2B/RMeOHUk4w%2BFwYNiwYfjoo4%2B4BaM2UOnatSuVv6WnpxN3KPUC097hcMDpdOKdd97h7B4mTpzIlc1pMW3aNLRq1YqCmyeffJIW3MOHD0daWhrq169PZXDasjOPx4PMzEwuaJs6dSp3ftEsnnnvBQIBysJ5hH9ctQt%2BbbBy4sQJzhQ7GAxy554zZw4Fryx7yoQ%2BtEEtA%2BMRarNs2sxLfHw87HY7Nx%2BiMqQ2mCstLSXFT%2B3n7G%2Bfz4dt27bh22%2B/RdWFldWlVECZX6G2fVqBkMjISArk2ediZlNsIwtmtJ8pioIvv/xSakQufsbGQpvJ1e5jt9s5sZhz585x3zdp0oQ7n6qquuCXeR6%2B/vrrdG8XFhbigQceIMsEESJ/Lzw8HAMGDMBf//pXhIWFwev1ktIt60Pr1q1xu2gyawBszth5tPdoXFwclxFlCAQCiIqKgt1uR0REBGJiYnSBo/ZFz6UgE23JzuZFWwSaqm471H5%2BWydkICpkQC%2BAINlFd16pmKqQ4dSJRcgUEoROyDQBjIg8iAlS2diI1xf3kfW7LjN5AICgtmzkPKKulqzfun7KLi6eSLKPeA%2BIwhpS0RbhPLIx1/3zI%2Bu40DHZeUTTdHEupRpkwoBKHk/9DSj590Jssnivyd6pinMpexbE88pERsRrifejkbESNK8A6MdLtkwQrWtl/RT7JY6xKK4DAOI7TyPPgkwEpS7tHFl7jQjR/J5nXjegsokROyF7Vu%2B%2B%2B9LbgF5cRaNiXutx4r93sheoopCLaLIO6MVgZIIxsgH7P4AZ8F0ezIDvX4CUlBS8%2Buqr2Lp1K/bu3YtVq1Zh0qRJXEYpNzeX46WNGjWKeFvDhg3D5s2bAYSydLm5uSTqoRVm%2Beijj3DPPffolAMZkpKSsGTJEuzduxdffvklWrVqRWV1S5cuRVFRESIiIvDII49QmSgzqrdYLLBYLLhb86MyfPhwLtPGMlpAKIBhfDbgYmklC1YihdVGSkoKF2AdPXpUF3xpx6tRo0YkilNVVQVFUUiQRTTfBkIiLfv27cNHH30EIGTerbWy0BqsM9x1112c6ItWxRMAevXqhUAgIDWsj4iIwPPPP49z587h%2BPHjKC0t5YKDpKQkOBwOzofP6/VSgKFV0oyIiMD777/PqVp6PB6uLdprs3OyICMsLIyCNlVVsXDhQtrnhx9%2BoOv4/X5ER0fTd8ePH4fX66X7yWazccHf%2BvXruSylaD8gBrUjRoxAQUEBANB9FwgEUFFRgebNm2PXrl0UtM%2BdO1eX4dPyMLX/7d69G0Ao8zljxgyoqkp%2BeW63G6qqYvbs2dKy13r16lGg7XA4cMcFlbKwsDB07dqV7udu3bph%2B/btnAppREQEBW7XX389gNCLA0VR8N6FN6Xnzp3juIpAKGMvK9WUwRRtMWHChAkTJkz8u2EGfP/DKCwsRExMDAU51dXVOHnyJGbNmgWXy4Wrr76aSjjdbje%2B%2BeYbLFy4kAIvl8uFkSNH0vkiIiJoQRwIBMiMHtBnj8LDw7Fu3TriwDmdTkyePJkCwTNnznAllefPn%2BcCipUrV1KJpcvlQk1NDWXSZBm%2BG2%2B8EWlpaaTU2KVLF2zdupW%2Bj4uLw4oVK7B582ZqQ35%2BPpctDQsLQ8%2BePWnbZrNRUKOqKilssvY%2B%2B%2ByztP3ll19y1zt79iyys7MpcH/rrbd0Ja0sYDt//jx%2B%2BuknahcLcLXj8cEHH3BjXV5eTmNeU1NDmVW/38/xzhYtWkTXYQqlLFCvrq5G%2B/btKZPo9XpxXlMO1bdvX86vMi8vj7PfELNavXr1otJLl8uFyMhIREZGwufzoU%2BfPggGgygtLYXD4YDNZuOywTk5OdTm1q1bIzU1Fe3atUNaWhratm2L%2B%2B%2B/H%2B%2B99x5mzZoFEePGjUPLli11nwMXDe8DgQDcbjcJENXU1HAcyS1btiAjI0NnxfHLL7/gmmuuwfr16wGE5iYmJgb79u2je1YMhI8dO1ar/58Ik8NnwoQJEyZM1A0zw3d5MAO%2B/2HExcXBYrFQ0KEoCmVYPB4PVq9ejVWrVgEIlZDu2LEDL730EgU5GRkZXJZs6dKluOuuuwCEsjtXXXUVcnNzucwKM9S%2B4oorYLfb0b59ewAh1cxRo0aR/57L5dJZKmhLHj///HPKiKakpGDPnj06tc7c3Fw6hyy707JlSwpKHQ4HVFWF3%2B9H8%2BbNAYSUGQ9dcCceM2YM1q1bhyeeeIICyu7du1NQw3wRtQt5u92O5s2bQ1EU3H777ZznG7O9YEFNdHQ0BTQ2mw1hYWFcm7/%2B%2BmsqcbXZbOjXrx%2BnItq6dWvOnkLLpUtNTUV2djaGDx%2BO6OhozgtPayjfokULPPvssxTgaT0BFUVBVFQUl1W8VIkqAM7zEAAphTKUl5dTBouNqc1mw8yZM5GTk4O8vDwAQHFxMcaNG0dtYZlBbeCza9cu%2BHw%2BrF27Fi%2B%2B%2BCJ9HhUVxXlKikiW1R9dQEFBge5FhRhs7dy5Exs3bqR5ZN57c%2BbMqfW8QMgOwghkHL6UFJ7DJ3JqjJga6zh8BoykjZxXto/IqxFpLDIDaLE9v9eoWYyrpRxDYSeRjiI7RuyDlMMnkJtk5xHbJ3KvZGOju5YsC2zA6Vo00BYpSVIOnzAvRvhkRghRsrERuWBGOHLivXYh0X/p9khevujG3YCrunhtIxw%2B6Xsf4dx18QkB/VzKri3y82Q8P51tqKSfYnvEbRlP1sgzpfsJknAr65oGIzxe3bMBfb9lz51u3MUfUSNO8ZI%2B4eOP%2BW0Zh0/cR2aQLn4mqFJLjxE5ezJzds3LZOk2UMsPoIk/GuSSjyb%2BJ1BaWkrKl5s3b0ZZWRllyYBQILVy5UpSi8zIyEBqaioaN26Mw4cPk69a69at0b17d%2B7c2iyb0%2BnkMj/ARSuFdu3aYd26dZRVSklJARAKoFhwwLJZzABcVVW8/PLL6N%2B/P1q3bs0FhqLiKYM2cNG2jS3gmc2FqqrU33HjxmHdunU4ePAg9u/fjx49eiA2NhaNGjXC8ePHccstt2DRokWwWCyIiIig8kF2zqqqKhw/fhzt27fH%2BPHj8dxzz9F1R48ezfEStWV/Xq%2BX4%2B8B4MpjW7ZsiQ0bNmDXrl2w2Wzwer3o06cPBg4cCCCUidyzZw/149ChQ%2BjYsSMURUF4eDisVisZvm/atInafPz4cfz5z3/mAk%2B73U7cPLE0kV0bCPE1Z82aRQG/dmyBUPDYtm1b/PbbbxTIafdhwaPX68XJkyepVBYI%2BUzGxcVROSgLspl9gt/vJ6XZvn37wuPxkN3F3/72N52noRY//fRTrd9plWnZywD2fwZxTIDQPRYTE4OlS5fWem6Ry1obli9fjr8LJPlJkyYDuOg6LXJqjJga6zh8BoykjZxXto/IqxFpLDIDaLE9v9eoWeTASTmGwk4iHUV2jNgHKYdPIDfJziO2T%2BReycZGdy1ZebABp2vRQFukJEk5fMK8GOGTGSFEycZGfBdjhCMn3msaynnt7ZEQJXXjbsBVXby2EQ6f1K9aOHddfEJAP5eya4v8PBnPT9xH1k%2BxPeK2jCdr5JnS/QRJKnXqmgYjPF7dswF9v2XPnW7cxR9RI07xkj5B828mADmHT9xHZpAufqapvqr1GJGzJ3s5KvrXSvxs5T%2BA/3n8/5aR%2B1fDzPD9D8Pj8eDHH39EaWkpPv74Y%2Bzbtw87d%2B6krBcrqVQUBREREdi%2BfTtmzZqFsxJ5tIaaf%2BVVVeX4eFrBETFrx7JW4ucJCQn0WWRkJLKysrB//348/vjjiIyMRO/evWnf2kRqtO1as2aNjvs1ePBgEhbx%2BXz47rvvkJWVRVm6Vq1a0fesBPS9994jD8KtW7fCbrfD7/fjnXfeQVJSEgWWERERSExMRGVlJfbu3YvbbrsNTz31FLXLarVK1VsZ7rnnHk7JsaCgAEePHgUAHDlyBF6vF6WlpRRwrVu3jnwLRX4ky9QpigKXy0WBVCAQwOLFiynomjBhAlq1akXHud1uegEQGRmJ7777jjvvZ5o3hvHx8ZQNZdCqeTocDqxfvx7Hjh2TWmSwtoeFhSEzMxPnzp2Dy%2BVCp06dMG/ePBQUFMBms8HhcGDkyJGwWCxwOp2Ijo6GqqrkMbl27VosWLCA%2Bqg1rJeB%2BQZaLBYS7VFVlQuwAWDw4MHIzs7GpEmTdGNbr149tGjRAgAwcuRIDBo0CK1bt8bPP/9c63WPS1MPepgcPhMmTJgwYaJumCWdlwczw/c/jmAwiCNHjuDuu%2B9GRUUFVFVFYmIiFEUh3pPD4UBlZSW6detGpvH/CURFRcHhcMDj8WDkyJFkGv%2Bv5DAxZdQTJ07gxhtv5IKRTp06wePx4Pvvv8fy5cuxcOFC4pxFR0ejadOmOHHiBILBIG677TYAFzOJLpcL6enp8Pv9KCkpQd%2B%2BfbkMn1bgBgAZmrMge/ny5eQTCIREakpLS1FWVobExESUlZUhMjIS1dXVqKqqQu/evUlBtaioiARsgIvZqfj4eLhcLspKORwOREZGoqSkBECovFPbpp49e5JYUHV1tc5K4A7hjaEYxOzYsYP%2BZhlei8UivX%2BYomhNTQ2%2B%2BuorHD58GIqiYPv27Rg%2BfDiOHDkCn8%2BHYDCIb7/9FoFAAB6PB2vXrkWfPn1QUlKCs2fPUobPKFh5q8i1Y2WtLFP47bff4uuvv9aVrtrtdpSXl9Ncff7551AUBZ06dULfvn3pZUmVUP6jFTu6FEwOnwkTJkyYMGHi3w0zw/c/jMTERMTGxkozLlarlRb0aWlpl7QrAHgPQafTSbL4QIivxxauWk9B1gYAnAWDoih0vjFjxui8zsT2aq8tQutbKEN8fDwyMjK4a9hsNqSnp6NRo0a4/vrrERUVxbXZ4XBgzJgxiImJ0QUvbLthw4ZwuVwoLCyE1%2BuFy%2BXiMnxt2rTRHSeWcWqN6Fu1akV9yM/PpwyfNpBgmUeRr8gCpaKiIpw/f558/BiHk6FLly7c2I4ZM4bOpaqqLiOpvU7Tpk05Dz8AnAef1n5DHLPIyEjK8AGhoI/5Jtrtduq3xWJBYmIibrrpJjqH9vvi4mLK8DFcqpyT9etSYNfRZkW1SEhIkN4D7NkKBoO44oorMHDgQOKvAsBVV111yesyyDh86ek8h0/kn%2Bh4ONDzi3QcFQmZyAiHTzyvEa8%2BcVvmRSbuJKmc1V1bRqERqSVGfAJFbpOsT%2BL4SXmIAplJdm2xzSL/Sce1BPRzJRscsUGSiws2gbqxknL4hPPILq2bTwM%2BfDIDtbq4qVIfPqET0n3EQZXc%2BzpKkjhRkvYa4aaKfTBy74vPsxEOn2zI6%2BLeAfqhkfna1cUFFNti9Np1jrnkI3H8ZL8BultfMjFGOIY6/p24Lbu4eC3ZPhoevXQbAN5%2Bm9%2BW8ehEPp64/e67%2BmNEjz3ZeUVen4znJ/0B/M/DzPBdHsyA738YdrsdixYtwtVXX00%2BcEy45dVXXyXD7enTpyM5OZk8BKdNm6Y7l9ZDsFWrVpxoSLdu3chjTespCFwUzdCWZcbHxyM9PR0AMHnyZNx8880UACQmJuLDDz/kPPS01xah9S2sDa%2B88grS09PJdqFbt2544403AIRKMz/88EMq17TZbLjrrrvwwAMPYM2aNXA6ndIsTElJCccnZOI3DD///DNnm7Bq1Sou%2BKqpqeFM7r///ntSuIyPj0dOTg6%2B//57xMbG0tiw8j9mAi/i6quvxlVXXYXq6mqoqoqzZ89SkAiAsyFQVRVTpkyhbbvdzgV4rJSSoXPnzjpD9t9%2B%2B43%2BZkFRUlISlVEyjB49mjt3VVUV8Qo7derElYp27NgR%2B/bt4/rEsoPdu3eH3W5Hjx496HuHw4FPP/1U5xnJIAap/yy097IWzZs3p7LQI0eOICsriziIlzpOhMyHb/dunnco8k90PBzo%2BUU6joqkvNgIh088rxGvPnFb%2BmgKO0loN7pryyg0IrXEiE%2BgyG2S9UkcPykPUShTl11bbLPIf9JxLQH9XMkGR2yQ5OKCTaBurKQcPuE8skvr5tOAD5/MQK0ubqrUh0/ohHQfcVAl976OkiROlKS9RripYh%2BM3Pvi82yEwycb8rq4d4B%2BaGS%2BdnVxAcW2GL12nWMu%2BUgcP9lvgO7Wl0yMEY6hjn8nbssuLl5Lts8DD1x6GwAmTOC3ZTw6kY8nbt97r/4Y0WNPdl6R1yfj%2BUl/AE380aAE/1P1eyZM/MHQtm1bBINBzJ07F%2B3bt8fcuXNx5MgR/PLLLwAulgMCocBxSDV%2B%2BwAAIABJREFU7dq16NatG30XHh7O2Rxo0bBhQ6SmpmLdunUALqpgilYHqqoiJSUFK1asQHp6OqqqqijQ7NOnDwVDQCjzGgwG4Xa7ccUVV6CsrAxut5uCuvDwcAoarVYrbDYblWKmpKSgoKCAtgcPHoy5c%2BeSMExYWBgsFgtlHLOyskhEhnn31dbXtLQ05ObmUpDaunVr6kN4eDg8Hg98Ph8URUFYWBjcbjcJ%2BMTFxaG0tBTBYBDjx4/Ho48%2BSudo164dli5ditdeew2bN2/G4sWLddd%2B8cUX8Q%2BZKhpCAkCsf40bN0ZhYSFiY2MpSM7NzcWSJUswc%2BZM1NTUIBAIIDk5GW%2B%2B%2BSauvPJKlJaWokePHmjbti2njGq327F7926psqmIp59%2BGp8LamuTJk26pPKoCRMmTJgw8f8bZC%2BgLheyKob/VZgZPhMmJCgsLCRrgClTpuDaa6/FypUrkZ%2Bfj0aNGmHIkCGcR2EgEOAyXmKZ6MaNG7kA4NZbbyVeWL169WC32ykDGB8fj7179%2BLHH39Ey5YtMWTIEAAXRXbS0tKQlpaGwsJCUjW1Wq3Yu3cvZVK1pZoM2dnZ9LdYRtuoUSOOk8esKxhycnK4Ml5tVk7bT5vNxnkZAsCoUaPo7zZt2lApq6IoqKmpoaBZVVVkZ2eTyiobD3Z%2BFmiz71g5b1RUFOepp0W/fv0oswtcDE5ZoMfg9Xpht9t12dzk5GTMmTOHjhs3bhyuvPJKACCLEya2w3DTTTcZCvZMmDBhwoQJE8ZglnReHsyAz4QJCeLi4nQZPIfDgerqapw%2BfRorVqzA8uXLaf9BgwZhypQptF1ZWcllvK655hquFHHp0qU4ePAgAKCsrAzV1dWUASspKUGnTp1w3XXXIS8vD/Pnz0fXrl112b9gMEjWD06nE71798aJEycAhIKOrVu3cuWwWhuCmpoa3HnnnbR9%2BPBhyk4CwKeffsqVVnbp0oULqrTBn9aw3ev1YseOHYjSvIpbu3YtBVx5eXn49ddfAYQCPIfDQfv6/X507dqVVFTtdjvKyspoDtavX4%2BePXti6tSpHNduz549XLmsFlu3bqXAXVEUaueRI0e4/Xw%2BH9xuN0oFwsoPP/yA%2B%2B%2B/n4RwZsyYgbS0NM7fUFTa1AbOdcEUbTFhwoQJEyZM/LthBnwmTEhgt9vRsGFDWpBXVVVRhg0IBXg333wzbcs82cRq6a1bt9LfhYWFuiBFu/%2B9996LNm3aoGXLlujZsycnHsIUQC0WC9kMVFVV4cyZM7TPO%2B%2B8w/HhLBYLl%2BEDgPc0pO%2BwsDAuUCkrK8P8%2BfMpuzZ27FgK8ho0aMAJ%2B7gFZn10dDTnX2e1WilYXbp0KQU0zMieKWYCoSwkG5fY2FhYrVYu49e/f3%2Byj2ABbMeOHeH3%2B1FZWYlZs2bh2muvRfv27dGzZ0/qozZ4B%2BSBlqIoXPYRAE6ePMll69jnWoEW7efsGG2J56UgE23p2PGfN14XtQL%2BVaIt4j4ywZi6RFuknr3/h6ItooCDrE%2BiW4bUPUNQrzAyfqIAhiHRFtlOYoMkFxeNzQ2JtgjXkl1aNw8GjNdl4iDiWIjzIKXfCueViraIYyG5AXWPgwGH73%2BXaItu6iQsm99jvC6bFvE5kxF6/hXG67Jnqs4xl3xkxHj9Xyba8nsUY8TnULbPW2/x2zLRFnEfmbiK%2BJko0iITbRE8Xk3Rlv%2B/M3wmh8%2BECQkKCwsxevRo2O12TJ06FWfOnMHGjRuxefNmkvdnnDi73Y59%2B/YhMzOTLBAA3rgcCNkgMCN0IBRksYwTE9TRiqpYLBakp6fj2LFjmD9/Pm6//XYuaLFYLLBarYiKikJxcTEmTJiAd999F4FAAN26dcPOnTsBXDQ9t9vtlEXUGsgzsM%2BioqIQHR2NNWvWoEOHDpRxY7Db7bjvvvvw2muvScdOG%2BCFh4ejWbNmOHTokC4ATkpKQklJCZo0aYK8vDwEAgGEh4dTmWdqaiqaNm0Kh8OBb775BlarFdOnT8f777%2BPU6dOYcaMGRg1ahRuueUWHD58GMFgEGFhYXj44Ydxxx13oLCwEH379q3VZoTNn6qqlC1t/P/YO%2B/wqIq9Ab/bsumbHhISQk0jBUjoLZHQpIp6UUEsKKICImJBr4gI6kURr/EKoiBXQUFUilRBiPTeAwQTIB3Se9ls%2Bf7IPePuZil6rx%2Bo530eHvbszjkzZ2bOyfzm14KCyMnJARoF18DAQDIyMqzGUerziooKq1QPlkydOpUnn3zS7m%2BWvP3223YTr0%2Ba9PQNz5WRkZGRkfmr8Hvkf7e7IfknRdbwycjYQa/XU1paSnFxMVOmTOGzzz7Dzc1N%2BNMFBwdz/PhxoFFYKy8vFwJU79692b17N9OnT7fyoRs4cCCAyH8oMXnyZE6cOMEPP/wgkqo/9dRTREZGolarSUxMpEOHDqJ8nz596NWrFxs2bOCLL74Q0TOHDx8utFFpaWm8%2BeabQtixzY9n668WHh7O3Llzxb2bzWZOnz4tBEStVisiofr7%2B7Nu3ToAkbi%2BX79%2B4lqWQlBgYCA5OTki5UOfPn3Eb5LGUBK2AJ577jnxe05ODoWFhcybN0%2BUAygpKUGtVnP//ffzww8/kJ%2Bfj7%2B/P/7%2B/syZM4e3336bJ598kn/%2B85%2BiPzQaDV5eXjz88MNAo7BtqWn08PDA3d1dCHtarZYtW7ZQUVGBh4eH1f117twZd3d3q/50svlLZGsaei3kxOsyMjIyMjI3Rtbw/XfIGj4ZGTtkZWUxcuRI3Nzc0Ov1lJeXC8HEbDbTo0cPPvvsMyIiIjCZTLi7u6NWqykpKRGaMlszwo8%2B%2Boin/hOSWaVSXVM7BDBp0iR2795NTEwMW7Zs4b333mPcuHGYzWahkdJqtTRv3pzKykoKCgpIS0sT7ZGicJpMJurr660idIK1Fg4atXvNmzcn28IOzN/f325aA39/fwoKCqzuzfZeLbWJnp6eNDQ0iAifttpFe20xmUw4ODhgMBh49tlnmT9/PgChoaFcuHABlUrF2bNnmTBhAleuXOHnn39GqVTSsmVLlEolCoWCyMhI1q1bZ6U1lT4HBASwZcsW4uLiMBgMTbSxgYGB7Ny5k86dOzdJRwGNgXPi4uJYtmxZk3uHRtPV66UTkbCn4Xv66aeZMmXKDc%2BVkZGRkZH5q2Av68V/iz0r3P8lp0%2BfZtq0aXh6evL1119ft%2Bznn3/OihUrKCwsJCwsjFdeeUWsI%2Brr65k7dy4pKSnU19fTtWtXXn/9dat8zjdC1vDJyNjBMmm9Wq1GrVaj1WoJCgoCEIFGtFqt8N%2BStEkqlQp3d3d69OghIjkCIvm4FKwkOjpa5D6EX1I5WDJ58mSioqJ48MEHhVDRuXNnevbsycmTJ9m0aZNVcBjJ/81gMGA0GkWbvGySKdmacz7xxBMiEqU9pD4ArHwFpXvv1q2biPwpCVwSpaWlVgnkLQUwQKRgUKlUjBkzRnyvVqsxmUxC2ANEZFLpGufOnSMtLU0EZrl8%2BTIXLlywqk9qp%2BU9a7VaHB0dhZbR1p8yPz%2Bf6Ohou8IeNGoMpXQKCoWCPXv20Lp1a/G7vTyJ9rDnwxcfb%2B3Dx%2BXL1seZmU0vZOtsdeyY9bFFhFOB7YaD7TnQ1OnHXpn/BB8SWERwBcAmQI7dMvaua/uX2N7epEVaEsBu3zRxP7GN6Hoz2eTtbM40Oc12nOwVsi1jx56oSVX2HNVuxlnsBs6U9nz4bJtj77K2ZewumGzGxV6ZG7lM2avb1g/M7na1bQfa21izqdz2OnavezMOozfTgbYXt50j9tprW8Zeh96Ms91NdHKTS9u%2BW36rs6%2BNNYPdPrZtj%2B2A27tvm3et3Vf2f4KZCez1ja21he27z441hm1d9obuRrdkr8zNPC%2B2U82eaaJtXfbqvil/ZXv9JXND1q9fz%2BTJk0VE8euxY8cOkpOTmTdvHvv27SMxMZGJEyeKjfoFCxaQmprKqlWr2Lp1K2azmRkzZvyq9sgCn4yMHUpKSujcuTNdunSxSlrv5%2BdHVFSUSKwdGBiIWq22Cv3v5OSEUqkkNjaWNm3aCAFI2omRjoODg/H29qZNmzZoNBpUKpUw6ZTQ6XQsWrSI7du3iwTsR44c4ciRI5w/f54jR44Iga9jx45WWiq9Xk9NTY0QViUshTHp%2B8DAQK5evYpSqRSCm1arFZ/Dw8OF0CgJNlKfACQlJYnPGo2mSUTRmJgYlEolfn5%2BjB49GoVCIcw8zWYzHTp0EEKUdK70ovPy8hL929DQILSsZWVlVFdX07VrV9RqNTqdDkdHR/z9/YXwKrXJVgMnXVtqg9S30KiRfP755zl9%2BrToH0vTXGg0y01MTBTHAwYM4OLFi%2BIa27Zt42awm3j92HbrQi1bWh/b%2B%2BNhm1G5Uyfr43btmp5jG9nU9hxomvjIXpnISOvjiAjr4/%2BksbhuGXvXtd3OtZeROiDA%2BthO3zTZx7DNSH4z2eTtRIFtcprtONkrZFvGjlNKk6rsZRe/mQzftvdgU5e9xOu2zbF3WdsydnfdbcbFXpkbJdm2V7dtDnV7U6JJB9qL4GtTue117F7X9sbtZe%2B%2BmQ60vbjtHLHXXtsy9jrUtoyPT9MyN9HJTS5t%2B26xd9%2B2J9lmbwew2cy028e27bEdcHv3bZMg3W5S9f%2B4Hgjs9Y1tIC/bd5%2BdQF%2B2ddkbuhvdkr0yN/O82E41e/5ttnXZq9v2tuzcpv3%2BugX80Uw66%2BvrWbVqFbGxsTcsu2rVKkaNGkVsbCyOjo489thjQGOUc4PBwDfffMNTTz1FQEAAHh4eTJ06lZSUFLtWWNdCFvhkZOzg5eXF8ePHqaioYMmSJRw/fpx9%2B/bxwAMPkJ6ezh133AE0CkpJSUn88MMPQhB6%2B%2B232blzJzqdjpqaGpycnGjfvr0QLnr16oVOp6Nt27bU1taSlJTEkSNHGDJkCOXl5XZzuAUHB%2BPs7Iy3tzdGo5HAwEAWLlzIt99%2Bi89/XsZr167F09MThUJBTEwMiYmJ%2BPj4YDAYhG8aIFI5wC%2BJ3s%2BcOUNiYiJt27bFx8cHrVZL165dRbmkpCThxyb5E5rNZvr27YtarSYjIwO9Xo9SqcTZ2VmYq0r33KFDB1QqFX5%2BfoSGhmI2m/Hz8xOC1MWLF/Hy8mLKlCkiKqejoyMhISF07twZlUqFSqVi6tSp%2BPv7A/Daa68BcPDgQQwGA%2BXl5dTX11NYWGiV%2BkGhUODu7s6nn34q%2BqC0tJTLly%2BLfgkJCRGpFsrKyhg%2BfDgmk0nk51u2bJk4NyAggMjISEaNGiX6Ye3ateIzwAMPPHD9CfYf7PnwyTb2MjIyMjIyf2zuvfdesV65EampqURabJ4qlUoiIiI4ffo0WVlZVFZW0r59e/F7mzZtcHR0vOmI4CALfDIydnFwcOCLL77A29ub8ePH07FjR3r06MGXX37J/Pnz6d27NwCjRo1i165dDBgwgIkTJ6JSqXjuuedISEigqKiIAQMGUF1dTW1tLT4%2BPgwcOJCffvqJoqIiTp48SXJyMtu2bSMuLo7169fj7e1NxH%2B0H0ajkTlz5pCdnU19fT1Go5GSkhIeffRRevToQWVlJVu2bCEoKAiFQkFISAjOzs6MGDGCoqIidu7cSWFhIdOnT%2Bfdd98V9%2Bbs7Cw0d5I55erVq3F3d%2BfZZ5%2BlqKiI%2Bvp6Dh48KExMk5OTReqJNWvWAI0J4vfu3YvBYODs2bMEBgZiMpkoKytDrVbTrFkzkbIhPz8fg8FARUUF77zzDo6OjhQUFAiBcvLkyURHR7Nr1y5h%2Bgq/BM9paGjA0dFRaNwcHBz48ccfRb5EnU6HTqcjOTkZJycnkTbBZDJhNpuprKy0ipppMpl49913hXb07Nmz4nez2Ux%2Bfj6ZmZmYTCb0ej3jx48X5%2Bbn53Py5EkC/qPF0Gg03HvvvVbzx1bDeS3kPHwyMjIyMjI35vfQ8BUUFJCammr1r8Buvpffl7KyMnQ2mnGdTkdpaalIXeVuo1J2d3en1J5LwjVoqkqQkZEBEFEfr8edd97JnXfeKY5XrFhBcnIyqamprF69mrZt27J48WL69u0LwAcffEBVVRV///vf2bx5M/v27UOlUtGuXTvCw8PZtGkT77zzDvv37xcvn%2BXLl%2BPg4ICDgwPR0dH06NGD6dOn06dPH7RaLUajUWjSTCYTubm5PPLII7z55pt069aNxx9/nB9%2B%2BAFo3DWStIS%2Bvr7Mnz%2BfmTNnsm/fPr799lu%2B/fZbGhoa8PX1Zfjw4Xz22We4uroSFhZGWloaZrMZtVqNs7MzKpWKTz/9lMmTJ3P69GmUSqUIXiMJeqGhoaSnp4scgFlZWQwePJj9%2B/ej0%2BnI/I/PlU6nY/To0YwfP57%2B/fsLP8ErV65gNBrR6XTC3NXJyQmj0UhRUREtW7akpqaG6upq6uvrmTJlCmPGjCEnJ0ekd5DuW6FQiIA3JpOJbdu2oVKpMJlMPP300wwZMoRRo0ZhMBi4//77haZRq9Vy6tQpli1bxltvvUVAQACxsbF4eHgAjcLnoUOHCAsLE/MgNTWVVq1a3XCOPfroowwbNszqO4VCwdtvv01NTQ3Ozs4MHz6c9evX2z0Gbljmt5zzvypzK%2Bu%2B3dv3V637dm/fX7Xu2719f9W6b6f2QePfLD97Zub/D/weISaTk1fxoU3uwUmTJjF58uQbnrtu3TpeeOEFu7%2B99dZbwgroZrlRDM3/NsamLPDJyPwP6dixI0uXLr1uGVdXV95//30eeughIRzm5%2Bfj4uJCcnIy8fHxxMfHM3nyZKqqqkhOTmbHjh1cuXKFkydP8uqrrzJlyhTuuusuNm/ezKuvvorRaCQ6Ohq9Xs%2BVK1dITU1lypQpTJgwAb1eL6JDBQcHM2DAALRaLbt37yY4OJhPP/2Uvn370qNHDzZs2EDLli3ZunUr8%2BfPp6amhjZt2vDll18CjT5n8%2BbNw93dncuXL1NdXY1Go8FoNKLRaNDr9VRWVmI2m6murqZ9%2B/Z4e3uTmZnJggULuHjxIv/6178oKytDo9Fw1113sWnTJj744AOWL1%2BOo6MjO3bswGg0olKpmDRpEsnJyTg7O2M2mzl06BD5%2BfkEBwdTVFTEsGHD2Lt3L9HR0SxatIhZs2YJ80spMIuLiwuPPvoojz76KDU1NbzxxhukpKRQUlJCx44dyc7OxsfHh9atW/PGG2/w%2BuuvU1VVRWJiIlu2bBEvWUnzKvnpSeagGo1GJJqXoq9q7Do2NcXPz6/JH8/U1FSryJ2RkZHXPb6ZMr/lnD9D3bd7%2B/6qdd/u7fur1n27t%2B%2BvWvft1L5hw4bdMoHv92D06NHCRUfC19f3ps4dMWKESNX13%2BLp6Sk0eRJlZWW0a9dOxE8oKyuzCq5XXl4u4kncDLJJp4zMLUISDg8ePMjBgwdZsWKF0ARKuLq6MmPGDLZt28bp06dZuXIlrVu35oMPPiAxMZHvvvuOhQsXkpaWxunTp0lMTGTQoEH87W9/Y82aNcTHx9OlSxdSU1Np3rw5/v7%2B6PV6du3axcmTJ4mOjqZDhw5UVVVx6dIlunbtKiJKXbhwAaVSKfzToDGq5Lx588jIyMDd3Z38/HyRF3D9%2BvWsX7%2BeadOmCR9CJycn7rrrLvR6Pd26dWPs2LG8//77eHh40KpVK5GWIC8vT%2BQpvO%2B%2B%2B1i8eDH%2B/v5MmjSJ8ePHU1NTQ1lZGe7u7gwZMkT4Gd5555306tWLs2fPkpCQQHp6OvPmzaNly5YMHjwYT09PfHx8SEtLAxp3K9966y0GDRpEWFgYK1assPKZHDZsGD/%2B%2BCMODg6MGDGCsLAwoRXs2rUraWlpQuD78ssv6dixI%2BHh4SK/4OOPPw5gZWsvIyMjIyMjc/vh5%2BdH%2B/btrf7dCoE2KirKyh/PaDRy9uxZYmNjCQ4ORqfTWf1%2B4cIF9Hr9TaV/kpAFPhmZPxA3EhIfe%2Bwxtm/fTqtWrVi3bh379u3j1Vdfpbq6mnnz5vHFF1/w8ssvo9Vq6dixI19//TXHjx/n0KFDTJkyhbNnz4rgLFFRUURFRQltVX19PZs3b%2Ba5557j0Ucf5cCBA4wYMYItW7YwevRoQkJCCAkJYcKECaxevZr77ruPbdu2kZCQwJ49e0SAlq5du/Liiy%2BKNsfFxdG9e3e8vb356KOPePXVV62iij7//PO0adOGQYMG8eKLLzJ16lSqq6sZMmQIGo2GSZMmYTAY%2BOCDD1i7di2VlZUictWOHTt4/vnn2bRpEykpKRgMBg4cOMC6deuYPXu23T7W6XScOnWKvn37snjxYjw8PJgwYYIwP/3666/JyMggJSWFuXPnsmzZMo4fPy7uJS0tTaTvkJGRkZGRkZGxZdCgQRw5cgSA%2B%2B%2B/n7Vr13LixAlqa2tZuHAhDg4OJCQkoFKp%2BNvf/saiRYvIz8%2BntLSU9957j/79%2B4ugfTeDbNIpI/MnIj4%2Bni%2B%2B%2BILk5GQWLFgANKYQkExFJRYtWkRycjJTpkyhsLAQaEy3MH36dKsAJKdOnSI6OhpA%2BBq%2B%2BOKLjBw5EoCNGzei1WqtUhRI9O7dG51Ox/fff8%2BYMWOu2%2B4RI0Ywb948unXr1uQ3hULBRx99xBtvvEFCQgJOTk4kJSUxffp0AGJjY/n73//OrFmzqKioYNiwYQwaNEiYYvbs2ZMXX3yR2bNnU1RURFBQELNmzRLRRq%2BHr68vq1ev5sMPP2TcuHGUlpbi5uZGz549%2Beabb2hhG%2B5bRkZGRkZG5i/PwIEDycvLw2g0YjKZxFpqy5YtNG/enEuXLglfyT59%2BjBt2jSmTp1KcXEx0dHRLF68WMRnmDJlCtXV1YwYMQKDwUBiYiKzZs36Ve1RmP9bL0AZGRkZmf8ZBQUFLF26VA7a8idv31%2B17tu9fX/Vum/39v1V676d2ge3NmiLzH%2BHLPDJyMjIyMjIyMjIyMj8SZF9%2BGRkZGRkZGRkZGRkZP6kyAKfjIyMjIyMjIyMjIzMnxRZ4JORkZGRkZGRkZGRkfmTIgt8MjIyMjIyMjIyMjIyf1JkgU9GRkZGRkZGRkZGRuZPiizwycjIyMjIyMjIyMjI/EmRBT4ZGRkZGRkZGRkZGZk/KbLAJyMjIyMjIyMjIyMj8ydFfasbICMjIyNzc1y9ehU/Pz8UCsWtboqMjIyMzH%2Bor6%2BnvLwctVqNp6dnk3d0fX096enpNGvWDC8vL7vv8N/yfi8sLOT8%2BfOUl5ejUqnw9/cnMjISR0dHAD788EPCwsJYsmQJ0dHRtGvXjri4OFq1aoVS2ajzKS0tJTU1lQ0bNjBixAicnJzYvn07DzzwAA0NDZw7dw61Wk1kZCQAgYGBv7WbZG4hCrPZbL7VjZCRkZH5K5OXl8enn37KgQMHyM/PB8DFxYXAwECys7Opq6tDp9ORn5%2BPg4MDSqWSFi1a8PjjjzN8%2BPBb3Pr/DnsLljZt2vCPf/yDiRMnEhwcjEKhYNOmTcTGxnLx4kW7i5s5c%2BbwzDPP4ObmBsDx48eJiopCo9H8T9pZUVEBgLu7%2B//ker8XdXV1AGLB90eloaGBkpISjEajWGDW1NRQWloKgJeXF05OTk3OKyoqwmw24%2BPjI2%2BM2OHIkSPExMTg4OBww3Lx8fHX/D0/P5%2BAgIAbXvta5eLj42/YFun3U6dOXbctN2rr78nSpUv5%2BuuvycrKwmg0AqBQKPDz8%2BP1119n7dq1bNmypcl5gYGBzJw5k5SUFFauXGn1m5ubG0lJSURERLB27VrS0tJQKBQYDAaaN2/OY489xuDBg5k2bRoHDhzAbDZju5QfMWIEu3btEs%2BLPUaPHs3Bgwe5fPnyNcsolUpMJpP4DHDu3Lmb6huZ2wtZ4JORkZG5xXTp0gWVSkVZWRkmkwmFQoHZbEatVmMwGKzKOjg4oNVqqaurQ6vV8vTTTzNu3DiOHDlCQUEBwcHBdOzYEQCj0cjFixfFH/2Ghga0Wi0tWrTAy8vL7jkShYWFpKamUlpaitFopLi4GHd3d8LDw%2BnYsaPV4tvNzY3a2lqr3ens7GxxbU9PTwoKCsjKyqKwsJDg4GCCg4MZN24cFy9eRK1Wo1AocHJyoqKiwmrxolKpcHJyorq6GrPZjEqlEr%2BZzWZMJpNYlLi4uADQpk0bzp49S7t27QDw8PDgyJEjdO3aFR8fH7Zs2cLgwYPJy8sjMTGRQ4cOkZiYyMaNG/n5559xcHCgoKAANzc3AgICKCoqorCwEIDWrVszdOhQkpOTCQsLo3///jzxxBNMnDiRJUuWXHOMDQYDM2bMYOTIkbi5ueHv788nn3xCnz59OHfuHGPHjuWnn36iX79%2BaLXaJufbLmrLysqYM2cOe/fuxdnZmcDAQHJycsjLy7veVBMoFAocHBxo3749999/v92NA1uNg8FguO6csbzXs2fPUlFRgbu7O97e3mRmZpKZmUlgYCDh4eHo9Xq%2B/fZbcnJy%2BOmnn4iMjMRsNtPQ0EBaWhrV1dVW17R8FhQKBQqFAi8vL5566ik%2B%2BeQTVq9ezb333kttbS0A33//PX5%2BfjfVF7YYjUZUKhUGg4H09HQOHTpERUUFXl5etGvXDn9/fxwcHPDz80OpVJKZmUlhYSHNmzcnICCAzMxMrl69SnBwMHq9ntzcXIqLizGZTPj7%2B9OiRQuaNWtGdnY23333HTExMeh0OhwdHVm%2BfDmdO3fG2dmZ4uJiCgsLeeihh9izZw/du3dHp9Ph4ODAzJkzmT17tmjz%2BvXr7Y5hREQEgwYNonnz5pw7d449e/bg6%2BuLt7c358%2Bfx9nZmUmTJvHee%2B/RrVs3/P39GT9%2BPKNGjeLkyZNi3sXGxnLy5Elx3fDwcKKjo/Hz8yMjIwN/f38OHjxIs2bN0Ol0XLhwAZ1OR3l5Oc8%2B%2ByynTp1i9OjRdOrUiW7durF161buuOMOHBwcaNWqFZcvX6aurg4fHx9GjhzJ%2BfPnRVuLiorQarX4%2BvoyePBgPv/8c44ePUpWVhbl5eWMGzdtlm8hAAAgAElEQVSO7du3s2vXLmJiYmjTpg1q9a8zYLPcMLJ89wFotVri4%2BO5ePEiBQUFqNVqjhw5wu7du6msrOSxxx4jMzOT1atXA43zUxKSfitOTk7U1tZaCVyWqNVqTCYTrq6uYjPq90KhUIh3TP/%2B/YmLi6NXr16/a50y/3tkgU9GRkbmFrJq1Spee%2B01oFGYc3d3p7KyUmhq/Pz8KCgowMXFherqalxcXFCr1SQkJLBhwwaxu3u7vMoVCoVYLP/ZCQ0NJScnBy8vL3JycujXrx/19fXs3buXXr16cezYMUJDQzl37hx6vd5KOLUlJCSEzMxM3Nzc6NSpE2PHjmXZsmUUFRWRnZ1NTU0NCoVCCPv/LW5ublRXV4sNBkmoU6lUKJVKnJycKCsrQ6VSCe2ChOU9SMJ6Q0PDfzX2/v7%2BXL16VRx7enpeVzthi7RJIqHRaNBoNNTW1hIUFESvXr3YuXMncXFxGAwG7rjjDlauXMnnn39OeXk5/fr145577gFgx44dGI1GCgoKfvV9/B5I/a1UKlGpVHTr1o0DBw5w5swZ%2Bvfvz6RJk3j11VfZtGkTzz//PB07duSOO%2B4gLy%2BPl156iYiICM6ePYtOpxN9Kl1Tq9VSX18PQHBwMNnZ2WIujB49mpUrVxIdHc2ZM2eAxn5t27atMPMzGAwEBASQn58vNqlMJpPVHLccG5VKJTRh0m/Ozs7U1tY2eS4sz3N2dsbZ2ZmioqImv9ni5OSEj48PJSUl1NXViY0hJycn9Ho9Q4cOZceOHfj4%2BHD58uXrvjuv9bxeD3ttCwwMvOFmjG1dbm5uVFZW3vDaNyIpKYnt27cD0KJFC/Lz82loaLBbVqVSodFomrxjpDlhNptRKpWylu8PiCzwycjIyNxCoqKiMJvNGI1GzGYzGo0Gg8HQ5I%2B6t7c3xcXFQOMf38DAQHJzc5tcT6lU/moB8LcsIv5X/JYF1e%2BF7WJU2mWXsO0nycTp92j/fzsmEyZMYPHixVbXuuuuu1izZg3QuHA3mUxW9/t7tMmelvpmadasGVeuXKF9%2B/akpqbi6OhIXV2dVTv%2BP%2Beu7Xz4tfzauW5b3nZ%2BOjg4oNfrxfH1%2BsLWNO9aAhn8ujGz977p0KEDJ06cuO55lnVKm1oStvcJjQJfTU3NNa9xOyFtzlkyYsQI1q1bB1x7U8y233/Ls/PGG28wc%2BZMq37p1KkTx44dA2DkyJGUlpby008/WZ0nbbg4OjqiUqnYvn07d9xxB1qtloMHDwJQVVVF7969OX78%2BK9qk8ztgRylU0ZGRuYW8uyzz9K%2BfXvc3NxwdHTEbDbj5OSEWq3G3d1dmCZJwgVYL/QUCgWhoaHi84MPPmi1my6Vt8TZ2Vl8ljQ60g6u9L9l4AGFQsHSpUubtN3W9NDWVwcQPnUSHh4e4rNCoUCpVFqVcXV1pU2bNuK4bdu2VueHhYVZHVv%2B7uvra2XK5e3tbVXW1p%2BvRYsWVn3TsmVLq99td7ltF5e2x5b9eC2kMbFti6Qpk65heW0fHx/x2faersXGjRubtDMnJ0d8Z1u/bbst55tEVFSUVfmnnnrK7rmWWC5YLeeWhKur6zXPvXLlCgCpqanAL%2BNh2TeWny3bYdsme/5%2B0OgLaInlfUttk8astra2Sb9J80en092wPttgF5IJMlg/FxK2wqGtECS9A7p37w780hdz5syxaqdWq7WaN1qt1m59ErZChu1zbukfGhgYSM%2BePcWxSqWyEvYkDZ4tluNmq0nt27dvk/I1NTVN2mE79r/WZ1MaaxcXF6E979q1K4MGDRJlJLNwlUol3i2xsbH4%2BvqKMlJ/SHPBZDJZmZ4DYqNAo9Gg1Wr5%2B9//brdNDg4Owk/b3jP49ddfX9cvOS4urkndp06dAhr7yNXVVbxvLctJc10yq%2B7Zsye1tbXU1NTQtWtX7rvvPqZMmXJNM26Z2x9Z4JORkZG5hYwfP56TJ09SUVFBXV0dBoOBmpoaDAYDFRUVYvEl%2BZBBY8Q36Tg4OFho%2BsxmM88884woJy0Q/f39req0PDYajUK7KF1D%2Bt7yu65duzZpu%2B1i2XZXG2jiS2O5WJFMBS21FFVVVZSUlIjjq1evWi3kbAWIzMxMcSyZe0kLsKqqKhwdHcXCydfX10rAc3NzsxKcbRfBtgsuaYFuKQhbLjpbtGjR5BxbAS8kJETch2Q%2BJR1LmhLbxZ50X4DQ8lr2gS0KhYK8vDyrvlcqlRw9elQc%2B/r6WgkQKpWKkSNHimN7mqi3335bfDabzTzwwAPi880gtUcyIYSmc0YyW7QnsPXu3RsPDw8CAwNp3bp1k%2BtbCiK2bZIW3LbzsayszOrY1n8UGp8xCTc3N6uxkYL42Gp%2BLQUCCctxBOsNkqqqKqBxntjbOLDXJxL79%2B%2B3Op49e7aVyV59fb1V33h5eRETE9PkOitWrLA6lnxGLZ9P6XoSOTk5QgMEjXPPUviVzCml5862/23vSaFQEB4eLvq%2BWbNmQGO/WJ6r1WqtNgts/XslpLGSNlMsz7EVnNzc3Dh37pwwsQfIysoCGsdZevekpaVZmVpK/SH1U319vXi2pPp/%2BOEH0c66ujpmzZrVpL0Gg4HQ0FD0er0wP7Xtn127dgnzadv3L8A333wDNPaz5btF6oPly5ezYcMGwHoDISMjQ7RdMj%2BX7qmsrIzjx4%2BTmpoqNnlk/njIaRlkZGRkbjHu7u5MmDCB%2BPh4Tp48SVhYGIWFhWzdupVdu3Yxfvx4Fi9eLP74m0wmPDw8KCkpISAggKqqKmpqajCbzVYCkGRu5ePjQ3Z2tvje0jdKEi4MBoPYAa6srGzib7Z582arNiuVSlq1aiWiigJ2F1y231kusqR70Ov1Yge9vr7eqn2WfiwKhUIsTKRrWy5aWrduTUZGhljg6HQ6K%2B1BUVGR1eL1/Pnz4rPZbLYSiKT6LPugurraSsgLCQnh0qVLonxOTg5KpVK0ydXVlYaGBoxGIyaTCWdnZ6G1kgI7SGWdnJxoaGgQC%2BRrCVGSmZdkXmivnPSdpXBsK8BZzhOVSsU777zDwoUL7dYpsWzZMtEfKpWKBx98UNyLZb3XQlqoSv/bMz2%2BlsljbW0tu3fvBhqFNEsthdSHluaWbdq0IScnx0o4gUYNcWpqqqjftr5WrVpx8eJFAMrLy4FfFv2A1WaESqUSQlxlZaVoi1KptNKmQmMf2WqMLeeOwWBAqVTS0NCAh4cHZWVloo0KhQKj0djEr9HLy4uSkhJh6iqZEtoKaICV/1hubq7QlikUCjQaDXq9npdeeglAXE8SWm3HyLJd0Cg4WD4XkvAqUV9f30SQl0zUzWYzrVu3Fn1uNpv597//3URgMhqNODo6ig2C%2Bvp6hg4dyrfffivqsWynTqcTwouEUqkUbZP6FH6ZlzExMezZs4cZM2ZYnQPWGxPSc6pUKnFxccHV1VX4n4aEhFBVVUW7du04evQoixcvJisri0WLFlFUVMSiRYvYvn07vr6%2BxMXFUV1dzYkTJ/jqq6/Q6/X07t2bTp06ERoaysmTJzl//jzp6eno9XqMRiM5OTmEhYXh5%2BcnNm02btwo5l1FRQWJiYnMnTsXnU5HTU0NW7ZsYceOHSQmJlJZWYm/vz/dunWz0rS3b98ek8nESy%2B9xIIFC%2BjRowcTJ07Ey8uL%2Bvp6ysrKWLVqFU8//TTffPON1SaIzB8D2YdPRkZG5hZTXFyMt7c3ubm55ObmolAoaNGiBf7%2B/vz888%2B4urqycuVK1q9fz5UrV4QmynLx%2BUdHMmPq2LEjBw8exNvbG6VSSWlpKQ0NDbRr1w4XFxdqa2tJS0vDbDbj5eXFmDFj%2BPe//01wcDAZGRno9Xr69OnD3r17CQgIIC8vDw8PD4qKivD19RWaUVdXV7p06cLOnTuF7%2BS1AhlI6HQ6vLy8xEI9MDAQtVotBAJL4UPyxXRycrLyPZLKSItqsPbpGTVqFJs3b/7VvmKSICgJBY6Ojri7u%2BPu7k5QUBCFhYVcvXpVCCiWbZWEcmnReC2hy57PlD1/q%2BtxLb8r2%2B9VKhX9%2BvUjLS2N7OzsG7bJUmiARjPH/fv3o1KprAIbWfqLKRQKmjVrZrVpoVarrbTbtxIXFxdqamoIDw/n3LlzeHh4UF5ejlKpRK1W09DQgMlkokuXLhw6dEiMnbu7O9XV1WJcOnTowPnz561SdiiVShEI6J577qGyspKtW7diNptJTk7m2WefFf0wdepU3n//faCp/9mQIUNISUkRAlGbNm3Izs4Wglbnzp0JCgoSvqMvvvgiW7Zs4fz589TX11/TT02hULB69WqefPJJunTpwsMPP8yECRMoLS0VAY4sy97MeEl13cyz/luQNtjUajUeHh5UVVVx4sQJPvzwQzw9PcV7wtPTkz59%2BuDp6UlAQADLly/H2dkZhUJBWVkZnp6ehIWFERERAVw77cTVq1dZv349p0%2BfpqSkhKqqKlq0aEHHjh0ZPnw4Li4u/O1vf6Nnz54cOHAAaDTnf/jhh0lLS2PSpEksWrSIwMBAFi5cSElJCSEhIXh5eQnBrqGhgdjYWFHnxo0bCQwMtNL2y/wxkAU%2BGRkZmVtMbm4uEyZMID09HfjFH6Vt27YsX76cPn36cOLECaKiouwujjw8PNBoNJSWlmIymVCr1cJ8Sa/XCxNRS9zc3Kivr0ehUODj40N9fT319fU4OTlhMpmoq6vD2dmZ5s2bk5qaKna1pXD%2BkolWbW0tarUaLy8vgoODaWhooKCgAJVKJRaGkkYoMDCQoKAgjEYje/bsEfVJ7fD29katVlNSUoKDgwODBg1i7969xMTE8Mgjj7Bt2zZWrlxJeXk5ISEhqNVq0tPTqa6u5qWXXsLR0ZEVK1bw3nvvodFoWL16NWvXruXjjz%2BmoqKCDz/8kOPHj5OSkkJlZSWvv/46hw8f5p///Cd33HEHP/30E%2BvXrxfh5C9dukRFRYXQYHh5eREWFkb79u1JT08XWqbOnTvz3XffkZ2dTWZmJjU1NUIDJy3aV61axeeff87%2B/fspLi5m3bp1LF68mICAAJycnIiMjGTVqlXcfffdJCQk8Prrr7Np0yaCg4MxGo3U1NRQXFwsonRKGgY/Pz8iIiJo2bIlWq2W8vJygoODGTp0KLm5uezYsYNmzZqhVqupra3ls88%2Bo7i4WIy7n58fHTp04ODBg2RkZGA2m4mJiUGlUnH27FmGDh3KV199hdFoxNnZGVdXV1q0aCH6/8yZM%2Bj1ejw8PGjevDm9e/fG3d2dl19%2BGUdHRwYNGoTRaOSnn35Cr9fj5eWFWq2mrq6O9957j5KSEtasWcPhw4fJysoiKCiIgQMHMmjQINq3b8/nn3/OkCFDWLp0KUFBQRw9epTAwECaN29OcHAwERERTJ48meXLl7Np0yYMBgNff/016enplJeXM3HiRDZv3sxDDz3Ev/71L2JjY6msrOTMmTMEBQXRpUsX9Ho9GzZsoLq6Gl9fX2pqaqiursbV1ZWqqiqRGmDNmjVkZ2ezZs0aLl%2B%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%2BzZk7179wK/BCmS6NSpE05OTuJ3aNSMlpaWCp/D0NBQLl68KMxoe/Xqxffff09NTQ3u7u6UlZXRt29foR2HRs1wUVERGzdu5O677wYaE7RPmDBBRO2FXzSmgYGBVtYlBoOBPXv23LC/ZG4vZIFPRkZG5hYzfPhw0tPT6devHzt37uT48eMcPnyYJ554grCwMC5cuMCpU6eE343ZbObHH3%2BkX79%2BAJw%2BfbrJNWfOnCnycsXHx1NfXy9M1Dw8PEQQlokTJ17zHGlnWUqeLP0/c%2BZMAKs8YBLr168HGhcJlrvSixYtEnWtX78ehULBSy%2B9RFRUFJGRkRQWFrJr1y70ej0ODg40NDTQrFkzu6HMg4KChGApCaHXM3G09IMxm814eHjg4uJCXl4eI0eO5NFHHyU0NJTx48czf/58ZsyYwY4dO4DGRVh1dbWVz46kSQkNDeWee%2B4hKSmJgQMHcvjwYatgHXl5eXh7e3PkyBFKSko4e/YsxcXFHDx4kDvvvJNOnTrRt29fZs2aRa9evVi%2BfDkhISFkZWXRoUMHYmNjSUhIwMHBwar/oNGE8Omnn%2Bbo0aNCIP01ESAlX6vKykqOHz9OfHy8yCPo7e0tBMzS0lLhNzhs2DDh/3nkyBEiIyN59dVXmTZtGiaTicOHD2MwGDh37hxFRUXC76qyspLy8nKcnJzw8/PDy8uLq1ev8uabb4qNA8v7s0zMPWbMGNLS0kQeOMt8cOPHj6dz584sXLiQJ598kqVLl3Lo0CG6dOlCXV0dJpOJvXv3Cr/QvLw8oW318vIiOjpa%2BEHZalHq6urYsWMHp0%2BfFn5%2BXl5edOjQgZSUFObOnSvaLOXAk65hO1aWx4sWLWLEiBEsXLiQ2bNns379erp27cr69etZt24dnp6eZGRkCPPD%2Bvp6sWHi5eWFl5cXOp2O1NRUJk%2BezPDhw/H29m6Sl0%2BivLycl156iZSUFCFkShtDgJWmWTJf1Gg01NfXM2DAAPz9/XnkkUcYNGgQU6ZMEQGETCYTS5YsITMzk%2BbNm4tAUjk5ORiNRtzd3QkICCAtLc3u/PPw8KChoYHq6mqcnJxo3749Pj4%2B7Nixo4kZpiSEBAQE4OrqKqwezGYzYWFhnDhxgkceecRqTi1atIhx48YJE1gvLy/xbJ44cQI/Pz%2B8vb05deoUHTp0EKbmM2fO5JVXXmHXrl2Eh4cLK4PQ0FAcHBx45ZVXeOaZZ3j44YcpLi6mS5cujBo1iokTJ7J161Y2btzI448/zrFjx3jkkUdo1qwZhYWFHDt2jLi4OHFfKSkp9OjRAx8fH6G1tXzHNzQ0oFKpCAkJ4fLly%2BIdNm/ePKZPn47RaKRTp06MHDmS119/XWiwhw8fLqKBShrNsWPHsnz5clH3jBkzeOutt2jZsqXQlNpaIthimYrhZqKwytx%2ByAKfjIyMzC3kvvvu4/jx42Kn/2YX7ZK/jqSJgcYF8JIlSxg/fjxHjhwRZaWFMjQKYps3bxbH0iJ3yZIlVqY70jnSwtvyGpZlBg8eLK5rew1JiLBXt%2BTXEx4ezrRp03jssceAxoXFRx99xBNPPCGOpYVeWlqa0BoUFRUJbWZYWBjnz5/H19eXgoIC/P39ycvLY8KECSxZsgQ3NzfKysro2bMn%2B/fvF/499fX1JCUlsXv3btzc3CgvLychIQGz2czWrVvRarWMGzeObdu2cfnyZdRqNfHx8bi4uPDjjz/i4%2BNDUVGRlU%2BTg4MD9fX1YnykBbVarUalUtGjRw9%2B%2BuknIbhI/pPSwrZVq1ZkZmbSq1cv9u/fj5%2BfH4sXLxY78dI4SIvikSNHsmbNGlq3bs2lS5cwmUwin1qrVq24dOmSyEnWrFkzoqOj2bp1q4gAW1JScl2TOCkcvqV/mpTs2d55knbaMgWAWq0W7U1MTMRoNLJr1y5UKhXffvstLVq0oFu3bmg0Gnbv3k3Xrl158sknSU5Oxmw2ExcXx7Fjx5g%2BfTrvv/%2B%2BCDFvKaBJ/pCWuebsIbVZ6m9fX1/Cw8PZs2cPjo6OODg4YDQaxbPl4OBA165duXjxIhUVFcJvT9KOWAqhN/O82D5jllp7pVJJ165d2bdvn2inJEgpFAqx%2BdO7d2927dqFn58fV69eJSgoiJycHGGqqFQqcXR0FH3i6upKVFQUqampVFdXM2bMGO69914efPBBKisr6dChA8eOHWPdunUMGzYMPz8/KzNXS9zd3TGbzVYbIJbzoFu3bjzzzDPcf//94p4WLFjAmTNn%2BOSTT3B1daWmpsZqnCQtV2RkJOfOnWPx4sUsXLiQY8eOCYEoOTmZzz77TASIkUy%2BJRPtG5lpSgKTpL0HGDt2LDt37uTHH38U707LsYRf8uedPHlSvFfj4%2BPp3bs3b7311jXr%2B724UUqN34OXX36ZiIgIunTpQlZWFuPGjSMlJeV3rVPmf49q1qxZs251I2RkZGT%2BqixevJjy8nJcXFyE%2BaNWq20i%2BEkLUYnWrVuLxXpsbCxZWVl8/PHHxMTEsGjRIpH82GQyERMTw9q1azEajZSVlaFWq0VkP8lvMCYmhg0bNohFokqlYv/%2B/eTk5AgNw759%2B0QbzWYzDz/8MPPnz6e8vJzc3FwSEhJYtWqVMPvJycnB29tbJLIuKytDqVRy4MABcY3CwkK%2B//57q4igUhQ56biyslJomcxmM9XV1aK8yWSioKAAk8lEVVWV1WL0%2BPHjmEwmYY4qRUJVKBRCG3jlyhX0ej2VlZUYDAbS09OF6ZXRaOTYsWNUVFSIAB%2BFhYX8/PPPQKO/UvPmzYWmsWfPnphMJjGekqmqyWSiZcuWIom6yWSiuLgYo9GIg4MDBoOBBx54gNTUVCIiIsjKyuKhhx5iz549DB06lBUrVoj7f/LJJ1m4cKGI6nnhwgVMJhOlpaWiTyoqKoBfIlBKAX0qKyutovHZ%2BgmqVCoRBEihUIigDnq9nrZt2xIXF0dGRoYYu7CwMIqKioiIiKC8vByTyYROp%2BOee%2B7h3LlzmEwmgoKCCA0N5erVq8JX8qmnnmLz5s0YDAZWrlzJxx9/jNFopKGhgY8//hiTyWQV%2BVESPvbt24fJZGLhwoUsXLhQRJiVguKYzWb0er3VAli6H7VaLYTHvLw82rVrR3FxMXV1dULLYTQaCQwMpLi4GJPJRFxcHJmZmeTk5FBWVoZer%2BeRRx7h0KFDHDp0iNLSUiIiItiwYQMxMTGsW7eO3NxcDAYDarWavXv3irZJQt2BAwfEd5GRkUIjLmnXKisrxRxVKBTCp1ChUNClSxd%2B/vlncnNzMRqN%2BPv7U1paKuZ79%2B7dyc7Oxmw24%2B3tTXl5ueifK1euUFlZidFo5Pjx46xYsYKamhqMRiO5ubmYTCZWrFiB2WwWgU2kKLZSzka1Wi02LmwD0Ejk5OQ0CaSyZcsWIaRL4%2BPv7y8ELynAjSS8ff/992LMa2pqMJlMbNiwwSrvqJQyQELqJ3d3d7G5MGzYMNzd3cnLy6Nt27aUlpaKzQdpI0LSQK5bt068m2JjY1m3bp14lg0GA4cOHWL//v0YDAays7NRq9VcvnwZaBSC6%2BvrRT/ZQ/Lvu9Yx2I9YasuvEe6k9oSHh1tFiA0ODhbvCImePXtaBRpKSEggJycHtVrNxx9/TPPmzQF44YUXiIqKIjEx8abbIXN7IGv4ZGRkZG4hp0%2Bf5t5776VFixaUlZVRXl6On58fjz/%2BOG%2B//bYIaf7KK68we/ZsIcTJ3Jj/78TMWq22SVTI24kbJXL%2BtYnBb0duNOa293i7j9mvQYraKfnFWt6npekm/DnG%2BnZGmleSpl%2Bn01FVVSU27aTow1K0UmiM8CltGEBj6pS2bduyf/9%2BsQEwffp03nnnHWFRIFkZSHPeMum7pB2Wgl3ZmvFKbdBoNBw8eJBOnTrZvRcpP6xkjZCSkmKVG1Tmj4Gch09GRkbmFhIdHc29995LVlaW2PEuKChg7ty5GI1G4uPjcXNz44033rAbRv6PxrXyxl3vODIy8jdd91o58SzLxsXFWR23atXqutewxDJxNnBTgoNlHsAbXf%2B3IuUr69ChA/BLOy1D6dvL62Y7t%2Byl2bgRlvne4PqJ1a%2BFFEhEQkohEBQUJPpLyn9ny40EfNt7/LXC3s2O183Mx/81UtRey8W9hK1GzvJ3afEuBSGS/Nmk9v6WeWCJVqsV1/q180HqR1dXV6tnR0oncbsiPQeJiYm4uroKbauEUqnE2dmZ4cOHi/7NzMy02pApLy/nwoULODg4iHf/V199Jeb4kCFDGDhwoFW9AQEBQnv5/PPPExUVJa6vVCrRarW0bNmSnj178uWXX5KUlESPHj2YPXs27u7utGjRAmg0mZX6vq6uDqPRSOvWrRk3bpws7P1BkTV8MjIyMrcBp0%2BfZuXKlWRkZKDRaGjTpg3jxo0TCaaXLFkiAlIcOHCAgoICcnJy0Gq1PPvss3z44YdcuXKFqKgoLly4YBX4ICEhgf3794vFrWTuZbm4%2BPTTT4UfHTQurvPy8sTCsE%2BfPhw8eNBqgdylSxeOHz8ufGdatmxJbm6ulS9NaGgoly9fFu2RckNJZZRKJZGRkZw5c%2BaafaPRaHBzc6OkpOSmIupJO9sSkmZL0v5Ypmfw9/enoqJCmDdOnjyZ/fv3Cx9IW42RFOhE6pfg4GAKCwupq6sjISGBgoICzp49K/zmXnzxRd566y0RVEbyV2rbti3p6elit13ylZNMNiUNjLOzM0aj0e7Y2frH/VqkyJLQKEB16dKF7du3i3kRGxvLmTNnMBqNaLVanJ2dKS0tFX5NUtAHKY%2BbNFY%2BPj7CJE%2BKJCpFHxw6dCi7du2isrISs9lM9%2B7diYyM5PPPP6ehoYHg4GByc3M5d%2B4c4eHhmM1mXnjhBZKTk0lKSuLQoUPU19fTu3dvEYVSingIMHfuXBEh1GAwMHz4cJfOX7IAACAASURBVL777jtxvykpKfTt21dEV5Tmo2Tmd/r0afr3709eXp4QUpycnITWRErrkJCQQEpKCi1atCArK4tmzZpRXFws2hEZGUlaWprVXA0JCRFBTaDxecnOzrYqExYWds1AJ9JclLRCkjZn6NChbN68mcDAQLKzs5k0aRKff/65MNvz8/MTuTp/DdJcb9euHenp6RiNRnx8fDAajcL/zp7vnD3toaWfq20aFOnZ%2BLVax6ioKM6ePYtKpSIsLIwzZ85YpRjx9/ensrJSRKysq6vD1dVVCMaSRlQy9%2B3Zsye7du0SkS%2Bl50ClUolxl8bq/fffZ%2BrUqcAv75sVK1bwyCOPoFAoRACWU6dOATQJmmUpvBqNRrKysqyCA0kRj3fu3MnZs2fFb56enkRHR9OnTx9xjby8PC5evEhlZSUxMTFcvnwZrVZrN5XDtejatSubN2/Gy8tL%2BDFKeV1HjRpFUlIS//jHP9i9ezd9%2BvS56evK3D7IAp%2BMjIzMLSAzM5OQkBCgMQFzWloaaWlpXLx4kczMTEpLS4WQIoXgLi0txcfHB5PJRLdu3ejduzdpaWmEhoaSnZ3N/Pnz6dSpE8ePH2fGjBmsXbuWrKwsvL29ycrKEovciRMncunSJRwdHcVieO7cuZw%2BfZpt27bh6urK0qVLeemllzhy5AgajYbmzZtTWlpKVVUVI0aMoK6ujkuXLlklLx84cCA5OTkEBASQmppKfn4%2BXbt25eDBg4wcOZK1a9fy6quvsnHjRrKzs1EoFEJAGj9%2BPAkJCXz22Wds3LiRRx99lJMnTzJ27FgcHR0pKytj06ZNRERE8NBDDxEREcG0adOYNm0aDg4O7Nmzh48%2B%2BojXXnuN8vJyvvzyS1q2bEnHjh1xcnLiww8/pGXLlvj7%2B2M2m9mzZw8DBgwgLi6O4cOH06tXL8aOHcvLL78MNEZO7d69O4MGDQLg/vvvJzExkQ4dOrB7924OHz6MUqnEw8MDg8HQJIiJlDxbWszaJs2WaNmypViE5ufni/MlIfXJJ5%2BkW7duPP/88xQVFfHUU0%2BxaNEioFEb%2Bd133zFu3DhOnTol7q1v375s2LCB4cOHo9Vq0Wg0IhDH8uXLUSgUuLq60qpVKwoLC4Uv0o2wFTikY8tAGPawFMAVCoVII1JcXMyCBQsYOHAgCxcu5P3332fBggU899xz9O/fn/Pnz3P33XdTXV2NwWBg/fr1VFVVkZSUxLvvvsv777%2BPVqslJSWFs2fPirx6er2ekpKSm9Le2QoZfn5%2BaLVasrOz7d6Xm5sbrVq14o033uCdd95Bq9Vy9epV4uLi2L17Nzk5OcTHxzNgwADefPNNzGYzwcHBXLp0iWeeeUZEo23ZsiUODg6UlJRw%2BvRpIUyVl5ejUqmora1FoVAQHBxMVVUVjo6OqFQqsrOzRToBSWiSTO40Go3wv7M07evbty/R0dH861//wmw2M2zYMCZPnszQoUMBWLlyJc7Ozuzdu5elS5fi7OxMRkYGWq0WlUol0gzYQ0rTkZGRQX19Pa1bt2bZsmX0798fs9nMm2%2B%2BSU1NDdu2bSMtLY1OnTpRXV3NmTNnaGhoIDo6moSEBObPny/eP4GBgYwePZrOnTvj7e2Nq6srM2fOFInLe/XqRXJysghWZYll8CvLzSHLzR6AoqIi3NzcqKiooHv37hw%2BfJhevXqh0Wiorq4mNzeXnJwczGYzPj4%2BREZGsnv3bmbMmMGbb75J69atycrKonnz5vTv35/FixcLjaM0JgaDAZ1Oh16vp6GhQeQ1lDZaHBwchL%2B2JFxKppNarRZPT0%2B8vb3RaDTiOCQkBJ1Oh0ajobCwkC%2B//NJqY%2B63YCmEu7i4UF5ezqRJk7j//vtRKpUkJCSI%2BVlWViZH6PyDIgt8MjIyMreA8PBwET5eCjN/M1julEdGRrJw4UKrUPmW6RQkpAh0dXV15OfnW5ktSpos291g6ZxrJf0FrASUvLy8JuUsz62qqqKiooLAwEDx%2B4cffsj27dvp168f0dHRZGdnA/DZZ59RVVVFWFgYvXv3Zv/%2B/ajVajIzM3FxccHBwYHLly/TqlUr0RclJSVigdqqVSu8vLzo27cv3bp1Y86cOcyePZtdu3Zx9OhRTCYTd955J23atAEaBZK8vDzS09M5evQobm5uREREEBoaiq%2BvLw8%2B%2BCDt2rXjxIkTNGvWjMGDBxMQEIBWqxW771VVVWRlZYnFohQsRqlU4u3tTXBwMDqdjqeffhovLy8KCgpYuHAh27ZtIywsDGjcvT9z5gxffvklXl5eHDlyhCNHjliF%2BJfYvn07AElJSeTl5XH48GE8PT0JDg5m586dtG3bloULF%2BLj40OPHj1E1MSCggK%2B//57vvrqK0JCQkSIe7VaTUhICIWFhZSVleHr64uLiwtBQUG4urpiMBjIy8sjKysLnU6HQqHAxcWFli1b4uzsTFlZGfv27UOKA5ebm8udd94ptGT5%2Bfl4eHhQU1ODt7c369evJz4%2B3mo%2B/Pvf/%2Bahhx7i3LlzfPPNN6SmpnLhwgWxSL98%2BTIrVqxg/PjxHDx4kC5duvDJJ5/w5ptvUl1dTc%2BePUX73N3daWhoQKvVotfr0Wq1HD16VOTOU6vVItm12WymtraWkpISHB0dRbROKUT%2Byy%2B/zO7du2nevPk1nxOJv//974wcOfJXaVfy8vJEP0jBSLy9va3KZGRk8PPPP3P27FnGjBlDSUkJK1eu5OjRo9TW1oqotwqFgtatW1NRUcGVK1cwGo04Ojri7u5O27Ztufvuu8nKymLt2rVkZ2fj5eVF586dSUlJoaioCK1WS1JSEnFxcWzfvp1mzZrh6uqKu7s7Bw8eZPLkyUBjegBvb2%2Bio6OBxmAs3bp1o7CwkLy8PLZs2UJBQQE%2BPj7U1taSm5uLi4sLo0eP5plnnuHjjz9ukhJm9uzZ5Obmsm/fPn744Qd27doFNGrYwsPDadasGWPHjqVHjx4ATJ8%2BndDQUFJTUykpKRHvBxcXFwoLC2nbti0eHh5C0JLyWBoMBnx9fYmKiqJLly5ERkbafc%2BNHz%2BeN954Q4zNihUrGDNmDIcOHWLKlCnMmTOHadOmiRyK1wpk8//N/8o/U6VSiXyJGo2GGTNmiEA4Mn88ZIFPRkZG5hbw%2Buuv4%2BrqyubNm9FoNGRmZopk5QEBARQVFVFZWYmbmxvt27enWbNm7N%2B/H6VSSevWrfH19WXv3r24uLgQFRVFeHi4yJN26dIlcnJy2LBhA3V1dWzfvp1FixYxefJk9Ho9sbGx3HnnnfzjH//glVdeQaVSER8fz4ULF8jIyODixYts2LABJycnqyTf/v7%2BJCUlUVFRQUxMDG%2B//TYrVqzgtddeo7i4GE9PT0aPHs0nn3yCXq8nMDCQ9u3bc/HiRVJTU0lKShL3n5WVxYkTJ0SC%2BQsXLvwu/Xy9IB5KpVIkIbZXzsPDQ0Q2tHeu5ONiNBoJCgrixx9/BBpNIbdt2wbAHXfcwZw5czh69CgA3377LaNGjRL1fffdd3Tu3BlPT09at27N4sWLWbBgAfPnzyc3N1dEyAwODkapVDJgwABCQkIYN24cAPPnzxeL8OuRlpZG9%2B7dxeI1Pf3/2Hvv6Kiq9f//dabPpPdeIECCQGiBgDQRFESUJiiCCjZEbB9FRUBABMFysQKCXMCC2EAliIhSvNI7hJJIpJNCSSVtMuX3R9benjNJELz3fvWu37zXcpmZOXPOPvucMzzv/TzP%2B51Tr00A1Komrlq1iuPHj5OSksI777wjS9OgNgvx22%2B/kZubS3x8PEajkcLCQkaMGMFLL71EQUEBb731FhEREYSGhmKz2di5cydNmjShvLycQYMGsXjxYs1Ch9pfT7wePHgwn376KXfffTcrVqy4pn470aM4YsQIFEXh2Weflb2NarRu3VoSu48//hiAb775Rl4vgJ9//pkePXpQXl5OXl4eR44coUmTJgwfPpwpU6bITN4///lPjh07hqIopKam0qZNG6677jpcLheDBw/G5XLx%2BOOPc/jwYYqLi6XNhcvlkj1dNpuNgoICTcmz8MUTMJlM8rlKSUkBkORXzOFvv/3GxIkTueuuu3jxxReBWmXfu%2B%2B%2Bm6lTp171PKqh0%2BmYMWMGrVq14rbbbkNRFG688UZGjhzJ6NGjMRgM6HQ6dDqdhvyoLT2gtlS0uLhY9osBkqyJrKQnBFEzGAy8/fbb9OzZs957ZufOnaSnp1NZWSkz4eHh4aSkpPDZZ5%2Bxfv16du7cyZ49e2RvaGVlJXq9nnbt2snvLFmyhM2bN9OvXz9%2B/PFHnn76aV577TUiIyMpLS2lurpaeuw9%2BOCDzJ8/H5fLhY%2BPD9HR0XJRSliyxMTEyHJevV5PXFwcubm58roaDAZZFSDOU8yTuBeMRiNGo1GW5up0OlluLHoajUYjlZWVsrJAURRGjRrFhx9%2BKBfH7rvvPvnaarWSkJDAyJEjeeGFFxq89pMmTZK/OV78b8JL%2BLzwwgsv/kKkp6dLmXMRkG7atImePXtSXV0tyzDXrVtHjx49JPnwLJNTq7aJ/rSrgciACFInDHzVUBRF9hs1pPJoNBqlVcC/g%2BbNm5OYmMj3338P1IofrFixgsjISACZeRHH6dmzJyEhIXz11Ve0atWKw4cPa1a3RRAloA4qPfvLxHmIAMtisaDT6WSAFRERIS0YhJ2CWvlOZFqFzP9fia5du7J582b5Ojs7m1atWkmLij8LtQ%2BfGmrpfpPJRFlZmcY021NFUG3kDLXiRT179uSdd94BIDU1FUVRqKqqwmKxYLfbNde1PsVRs9nM0KFDWbFiRR3LCdCS//oI/qxZs5g5c6Ysi2xouyvttz5YrVZZCnitWSD1vut75sV9GBQUJNUe64P4HRH9luJZv/HGG1m/fr0kZeoeUkCjCnm18MwwhYWFcenSJYKDgykqKkJRFM21aygjpS7J3Lp1K9u2bWPu3LmEhISwZMkSUlNTWbhwIevWrePIkSMcOnToqrNbZrOZkJAQcnNzr/q8rgaKokifRLUC7ObNm%2BnatavczvM11P7u33DDDfL1%2BvXr6dWrl9yvzWbD39%2Bf/Px8%2BXu/Zs0aKXCkKArffPMNAwYM0Nw3e/fu1Shwql8bjUZ0Oh27d%2B%2BW2VpBCj/%2B%2BGP0ej0Gg4FVq1YRFxf3H50rL/7fwqvS6YUXXnjxF%2BHw4cMoisKlS5ekEpvb7WbJkiUoioJOp%2BPy5cvYbDbefPNN3G63XEUXQY2iKDRr1ozAwED0er0smwwPDyc8PJzAwEDNMc1mM506dSIgIACoFRNwu92ylyQxMZHIyEgURZHZg8DAQHJzcyWhAW2GC2r7VsLCwupVzgsKCmLOnDmStKkRExODj4%2BP9BM8ceIE%2B/btk58bDAaaNGmCr6%2BvVOoT/SZQm40SpZONGzfmpptu0pyrw%2BFAr9cTERFBenq69AODWqGSn3/%2BWfO5WkzBZrPJ/fXp00cGcVBbwhYeHs7QoUPlPAqfPU8YjUbZXybmQ%2BxfURTNvDYEs9lMcHAwnTp1Ijg4WJYeinJH9XwB0itQDVHaBrXBtBgH1FWVFOepVmgUGZnLly/TqFEjvvzyS3nfJSUlyf4wQZgaNWrEmDFjNKqPVVVVhIeH43K52LJlCwBff/01drtds1ChDljV6rRiXPXdS3a7nYEDB9abyRPHF1n0%2BgjMxIkTqaiooEuXLsTHxxMeHi7LDoUPns1mQ6fTkZiYKPtrr7vuOlq2bKnZl4%2BPj1RMdDgcTJs2TS4gAHXGGBQUJMtuBd5//315j5hMJjIzM%2BuovHbv3p2WLVtKIRJPlVQBce0FYRRqmeo%2BYajNvAsYjUZWr14tyYXJZJLlx2I%2BPe8bnU6nUViF2oUSRVEYOXJknXGpvy96yKCu7%2BhNN93Eiy%2B%2ByPHjx9m1axdt27aV/chfffWVHHdiYmK95w%2B/3ztiPvLy8uS92bZtW3kt61NYVWcjPeGp1tuhQwf8/PxkVlOv13P06FE5X2KxQ2RDxftnz56V10JRFE6dOqW5NgaDQS4%2Biedy4cKFmm327dsnnx3RC/jOO%2B9IYmc0GnnnnXekn2JAQABRUVEaP0hRqi7InsPhYPXq1Xz%2B%2BefyPy/%2B9%2BAlfF544YUXfxHKyspkP0RZWRmlpaWUlpby7rvvUlJSIjNRDoeDFStWoCgKTqeTJk2ayBXyO%2B%2B8k%2BrqajZu3IiPjw9Wq5VHHnmEe%2B65hy%2B%2B%2BIIdO3ZgsVg4ePAgs2fPJjExkR07dkgxitjYWBo3biwDwRMnTshs46VLl7BarSQnJzNu3DjNargIAtXBkRAnUOPWW2%2BlqKiIp59%2BmoKCAvm%2B%2BN7ixYtZs2YNUKtUGhAQoCEZDa3Wi%2B%2BfO3eO3bt3y1Xqjh07arYRvoVOp5NOnTpp9iGyLerP1USgoqJC9jiq9yv2vXjxYmbMmIHJZMJms1FeXq4h4osWLZKCN6JMF343Rv/ss89kb5HovQJk/5YIRsW5FhUV8corr0jxF7fbXUdMQxCJ%2BkRUxNhMJhNGo5FPPvlE7t9sNmvIwq5duzCbzURFRQG1YiU//vijXEBwOBykpqbKzFthYSEul4uKigp5X%2BTl5fHII4/Ie2vq1Km4XC4yMjKA2nLUBx54gFmzZmEwGFi%2BfHmdOVb/X/23v79/HbsAt9vN6NGjGyRVQsBGBN1COEP9/aioKN59911ee%2B01dDqdVAJ1u92cO3cOg8Egs9lWqxWo7Yv84IMPNMeqqKggLy9PCnK43W6NN57b7WbAgAFy%2ByZNmshgXZxfRkYG4eHhuN1uSX7EuYns3oEDBzT35j333EOzZs3q2B943kuPPfYYJpOJ/fv3S3IASBJuNptRFEWWqLZv3x6dTkejRo3kPoToiBq%2Bvr68%2BuqrmveE0utbb72F0%2BnUZPfcbrcUrxJqmaBdaFAUhRYtWtCyZUsNQRLXUQi1ABrzcJ1Ox%2BLFiyXhVFs7xMXFYbVaiYyMxGw2M3fuXBRFqSMEYzAY5HHU86eGugzV7Xbzj3/8g7KyMlkB4HQ6eeihh3C5XHLxQrwW/7ndbkaOHKmpsLj//vs1v0dGo1H%2BZhkMBgwGA9988438jtvtZtq0afI7LpeL6upqli5dKoVsampqWLp0KQ6HA6fTycWLFzl58iSTJk2S36mqqmLnzp3Y7XapYLts2TIWLFjAggULWLhwYZ058OLvDy/h88ILL7z4i9CpUyd%2B%2BOEHoqOj6wRBaqIjShLFKr263%2B2ll17i5MmTdO7cmdLSUi5fvsycOXP4xz/%2BwQ033EDHjh2x2%2B1s27aNW2%2B9lVWrVvHzzz/LYPjs2bOcP39ekwksLi5Gp9NRWlqKy%2BXi6NGj3HnnnRofNzFGdUDimX0AbTBkMpmk15f43s6dO4mMjJQB3u233y4JICBJkBqeJXTCAiAvL4/OnTvL910uF/7%2B/rjdboqKiuoQPiHuoP5cnW2zWq2S5Ir9qo8rRF8URZHltyIAFjLvOp2OM2fOcOutt2oUOAGZLRHBWo8ePQBkOaJ6exEMx8TE4Ha7qampkdlZaDizo4bYVgSvTZo0qXNOAiLgF4RSBNQVFRW4XC5NNs7tdkuCuX//ftmT5nK5JCkCGDBgAG63W5KRo0ePEhQUxPfff99gVq4hZGVl1SF8BoOBsrIyDdlVz0tFRQVVVVWaUlzPAP7cuXO0a9eOu%2B66i/z8fE3GS22KXVhYKJ/HsrIygoODNfvxnFN1Vhpq770xY8bIMRw7dkyjllpTU8OWLVvk8cWzpd6voiiUlpZy9OhR3G43er2eHTt20KtXL/nceZJmdWmty%2BWirKwMQI5fZJBiYmIAJFkVQimZmZkNlneKkvJdu3Zp3t%2B6dSsWi4VPP/2UmJgYed%2BZzWYmTJggSUxlZaWcX/Xvn9Fo5OOPP2b69OkEBgYSGBgoqx3cbjcOh4Oqqip0Op3GokRRFLp06UJJSYm8TuLeLygowOl00rdvX2nzoSbjAuIYQq03MDAQs9mM1WqVyql/xmvyj6Aeg/h9uHjxonzmq6urr1ieXV/29Wqg0%2BkIDQ2lR48e9O/fX2YvDQYD8%2BbNY8OGDbJX2Yv/LVzbL6wXXnjhhRf/USiKwsaNGykrK%2BOnn37i1KlTVFZWEhAQQGhoKLNmzWLlypUMGDCAzz77jLvuuovdu3fToUMHzWpvXFwcx44dw%2BFwUFxcTEhICEFBQZSXl1NSUsKYMWNkKVpCQoKmXKqyslLTt2QymVAUBT8/vzqB0pV6eW699VaWLFkix%2BRwONi0aROAlJL39IybNWsWkZGRMuAfP368zKoAmuyjWshC/F1ZWUlUVBR5eXm0adNGo26o7vuKjIykbdu2dcY8fvx4zef9%2BvVj7ty5QC1ZFX04SUlJlJeXa8agvob1ERYRdBmNRsaPH8/HH3%2BM0%2BnEx8eH6upqKbYgxiiuiSB8oj8rICAAX19fysvLpbCIOuMjVvLhdyNxz14xQSbV24wbN06%2BJ0jatUKIBInFAEGQRbCpJjGbN2%2BW97s4vzfeeOMPj6H2UBTzryhKnWyyejuAp556ivfee09u7%2B/vz%2BXLl%2BXc1Zc9Hjx4MCkpKTidTt58800Aef%2BZTCYSExPJycnBZrNJsuQJUf4oemPFIsCuXbto3bo1LpdLihoJqJ8/qL1GItPudDplqaIYu7jf/P39NWXKR48e1WSGxP2h9kl0OBxMmTIFp9Mpe8N69OjB559/Lvffo0cPPvnkE7mfuLg4XC6XJssvzk%2BNEydOMHr0aM1chISE4HK5ePbZZ6moqGDw4MG8%2B%2B67KIrC6NGjGT16NMnJydKbUpy/%2BL5Op6O8vJzZs2fL0tX6UB/J/uSTT2RmzGKxyP0KsZPx48ezZMkSzYLbbbfdJrPQ4v2nnnqK999/n6qqKjlngmiKa/fUU08xatQoOnXqxPz583n22WeZP38%2Bfn5%2BMqsuxFSqq6txOByUlJRIJd/g4GDOnTtHdnY2paWlVFVVUV5eTlFREVVVVRQVFXHp0iWqqqpkNYUQdwkLC6NZs2bcdNNNNG/eHKhdADt37txVKca2bdsWt9vNzp075eJCfn4%2BiYmJzJs3jwsXLjBo0CASEhJYu3btH%2B7Pi78fvKItXnjhhRd/MbZs2cKUKVPq9VOKjo4mKCiIY8eOMWjQIL766ivuuOMOVq5cSU1NDQkJCZw5c4b%2B/fvLfhthBq4WroiOjpbWCJ4QBst6vV4GES6Xi%2BTkZCoqKjhz5gy33XYb3377bZ3smlrkxNPw3PNzUVbVUKD830Tz5s1JS0tj//79ZGZm1vl8yJAhtG3blq1bt8oMo8lkIiQkRPoJnj17lnPnzgG1gbTwMVu9erXsDYPfyVZ2drZUwRSKmFVVVXTv3p1ffvmFiRMnSoVH0ApxCCuB6upqGSiqvf7qM7z%2BdyDKOsXYb7/9dlavXi3LiPV6PbfeeiurV6%2BWHmcLFizg22%2B/lcGxgPAcFNmC8%2BfPyzlTZ/8URSEqKop27dqxZs0ajYhL69atpXfZ1cJqteLv709BQQEGg4HY2FiNsbmnoIdasEjMwa5du/Dz8%2BNf//oXL7/8Mr6%2BvrLMz2g0cvPNN/Pdd9/RrFkzcnJypFDNuHHjePvtt%2BV%2BRAbK4XBIIt2vXz/WrFkjA%2BpXX32VJ5988qrO7bnnnsPhcDBnzhwAqRCZnJws583tdhMUFERqaiq//PKLvE6hoaGynLpLly7cdNNN/Pjjj2zZskUuSohtPO9B9VxVV1fL3q/q6mq5iCPg5%2BdHTU2N5rqlpaWxbNkymjdvTlRUFPHx8YwePZqHH34YnU4nM2dr1qyhb9%2B%2B/Prrrxw/flxz7oqiMHz4cDIyMvDx8WHKlCksWLCAzMxMWrVqxalTp2QWT42oqChcLpc898aNG3Py5ElcLpc0Vx85ciQffvgher1ekjtByjzH8EfhcvPmzTEajRw%2BfFj2HJeVlVFVVUViYiKXL18mJiYGh8NBq1at2LNnDwUFBVRXV%2BPv70/fvn05cuQIAQEBVFZWMmzYMBYuXCiVmC0WC%2BPGjeP999%2BnurqarVu3kpWVRVVVFWazWfY3i8U6Qc7FdRLnEBYWpikFFz6dZ8%2BeJTY2FpPJxPjx43n00Uel2E94eDj/%2BMc/uP/%2B%2B5k9eza33nrrFefCi78fvITPCy%2B88OIvxPr16xk3bty/rW7pCU9Vvr8z1MGUEAowmUwEBQWRm5tbrzKoyWRiyJAhxMXF8f7778tSQ7E/IZhQn%2BrovwO1cmB95wG/Zxo6d%2B7Mtm3brmq/VxNQ1gdB/K677jrsdjsxMTFs3ryZyZMny23mz5/PpEmTeOqppzS9l/8Jr65rgThHm80ms5LCuF69CGCz2aioqJAEzcfHRxo/5%2BbmasYuiJXohYLa7Nfrr7/Onj17ZJ/i1eD6669nyZIldOjQod6FkX8X9SmL/regXhAYPHgwK1euRK/Xy3JLo9FIamoqN998szStF0biBQUFmnFGRkZy6dIlnE6nJOpBQUH4%2B/vLDK7BYKBfv37yO/v27WP27Nkyu6QWe/kj6PV62rdvT1VVFSkpKXz55ZcsX76csLAwIiIiMBqNfPbZZ7z88suYTCZZhuqJ8PBwLl68%2BP/kPv%2Bzz%2B%2B/C89FjD%2BrKnul/SiKwpAhQ5g5cybbt29nxowZrF69%2Bj94Fl78v4CX8HnhhRde/IUYNGiQXNXV6XQUFRUREBAgS7rEP7zNmjXT9O41bdq0XiVGi8VCcHAwLpdLYzdwJahLD51Op0bNUb1ibLVa6dGjBxUVFZw4cQKTyUR8fDwpKSlyVd5gMLB27VpNrxAgV5uTk5M5e/ZsnRK2K41NLa4RFBTE5cuXiYuLIysrSxOY6vV6/P39pRBDYGAgfn5%2BBAcHU11dLUs8nU4nR48e5eTJkzIDoyiKlLfv3bs36enpGAwGwsPDAXjjjTewWCx07tyZjz/%2BWGat/hMQfoDx8fG0bduWoqIiGjVqRFpaGpGRkeTm5rJ48dwDbgAAIABJREFU8WIpO280GsnLy0NRFEnsBblPSkriySefJD09nYKCAiIiIigoKCAuLo6FCxfy6aef4ufnR0hICAcPHqSiouJPB6oWiwWz2UxoaCg33ngj%2Bfn57Nu3T1pUHDt2jEuXLtXpSb2a%2BVBvb7PZSE9P58SJE5oSUZE5E/eGICSA7C8V5ZSjRo2itLSUiooKOnTowNChQ9m/fz8vv/wyOTk5xMfHk5GRgd1u59Zbb5W9c/UFx6KEtyEbEr1eL/u7KioqZKmpIKZXylqq%2B66sVitDhgyhqKiI1NRUSktLpcjTuXPn2LBhAwEBASQlJdGmTRsKCwt59913r2mO%2B/fvz3PPPcdzzz1HQkICL730EpMnT%2BbgwYNMnjyZtLQ09Ho99913Hy1atGDlypV1smkNLR6YzWbefPNNNm7cSNOmTfnyyy%2BB2kxnu3btMBqNXLhwgdLSUkJCQhg3bhydO3fGarVy7NgxjYVDRUWFHKufnx%2B9e/euY0Nhs9m44YYbCAkJ4aeffpK9vf8NiLJsf39/nnnmGebOnUt%2Bfr58DtV%2BeQApKSlkZWXJ1yILrsZ1112nEY3xtJSBugtOntlIPz%2B/OhUUd955p0ZZ02Kx1Cn5fuihh%2BoID6mPuWfPHqxWK06nk/bt27N///4G58aLvye8hM8LL7zw4i9Eu3btKC8vl/Lr5eXl9O7dmw0bNshMFUD79u3Zt2%2BfDKzatm0rCcCLL77IsmXL%2BO233%2Bpk8/R6PTabjcuXL2O1Wpk%2BfTpdunSRtgzFxcUcPHiQefPmERsbKzMAgYGBXLhwgZSUFBRFYc%2BePdjtdimCIQINh8PBzTffzMiRI7Hb7QQFBVFVVUVSUhKKorBy5Uq6dOnC7bffDsCqVatISkri6NGjDB48GKhVChUBtigvcjqdlJSU0KdPH1atWiUzdQ35bImsQ2xsLAcPHpRESASjwieuofI%2Bz2yg6B2yWCwEBASQm5uLv7%2B/JvNjNpvp1q0bP/30kyTMohTTZrNRVFSEwWAgODiYnj174nQ6%2Beabb9Dr9YwaNYqCggJWr15NSkoKY8aMYfPmzWRkZPDII4%2BwePFiSkpKZNljTU0NpaWlhIWFyf4qcV8cPnxYBnCCBEZERNCuXTsOHz5MQEAAhYWF0h9Q%2BNoFBgbSq1cvvvzyS3Q6HTfddBMHDx7EbrczYcIEvvvuO4qKihg5ciT79%2B/ns88%2BIyEhgdatW7N27VoqKytlj5k494iICGkWLURbnE4ndru93uyouA7qQFWQfB8fH4qLiyW5uvnmm9mwYQMOhwN/f39pmB0bG0uXLl34%2BOOPNddQ3PtOp1PTY1laWoper6d58%2BasWLGC5ORkwsPDmT9/PsuXL%2Bf06dOMGzeOxo0b89hjj3HmzBlCQ0Oprq4mPz%2Bfrl27MnDgQHm9JkyYwJkzZ1i3bh3dunXjl19%2B0ZxjaWkpJSUlde7brl27MmDAACkyM2PGDGm1cd999zFz5kyMRiNxcXFMmTKFUaNG0aZNGw4fPoxOp6O6upo2bdowefJkWrZsSfPmzfHz8%2BPWW28lPj6e2bNn13kO1M%2BLZ/iXnp5OaWkpOTk51NTUEBERQZcuXbj//vsZNGgQ6enpXLp0iWeeeYaHH35YZlf79%2B/PqlWrrqn8Ni4ujiFDhjBv3jygtpd3woQJ9ZJok8mEy%2BXiiSeeYPHixQDyXjEajSQkJBAWFsahQ4eoqKggKSmJmTNncu7cOWbMmEFpaSlWq5Xi4mJ8fHxk711oaCgDBw6kQ4cOnDp1ildeeYVRo0YxZMgQbDYbb7/9NmvXrsXlcrF27Vr27NlDWloap0%2BfZtq0aZrFB3G/iedPeB2KOWnXrp2m17K%2B8nfPbeojfGpvP4B7772Xjz76SL6urxzVs/S2vmOLORYLaHFxceTn58vFMGFw//PPPzN9%2BnSvcMv/ILyEzwsvvPDiL0Tfvn1lQBgWFkZRURHNmjUjKytLiiqIzFNxcbEMhur7h70%2BGI1GAgMDKSoqkvLunuptosRPHbAJAip6%2BoTYA2iVNwMCAmQWIyIiQmYVX3/9dV5%2B%2BWXKysquKYMkAo2amhr0ej1hYWHk5%2BfLErW9e/fSu3dvwsLCyM7OrhO8tGjRgpMnT1JeXk5oaCjvv/8%2Bd9xxB1Cr7Lh9%2B3ZGjRoF1AZUPXr04NtvvwVqV7nPnz8vCeacOXNk75ROp2Pv3r2kp6dTXV2NoijMnTuXxx57jGbNmnHu3DkWLVrEPffcg81mY8eOHRQUFNC9e3dZwrhp0yb69etHYWEhd999N1OnTqVVq1bY7XY%2B//xzvvnmG401gU6nY8CAAXz99dcA3HLLLeTm5srgC2rvA7fbTVVVFY0bNyYvL0%2BW8oWHh2tEVG655RYeeOABRo4cyQ033MDFixdp3bo1S5YsweVykZmZSceOHamsrMRkMlFTU8PUqVP59NNPyc7Olse8UllrdHQ0lZWVMnsh7pvg4GBKSkpwOBxMmDCB2bNnA7Um0J07d6ampgZFUWjXrh27du3C19cXi8XCxYsX5fHuuecevvzyS6nIKHqLzp8/T9u2bWXWQfTuqeHj40NCQgItWrRgxYoVREVFcenSJW688UbZsykk%2BAXZF2MRCw3R0dGMHTuWiRMn8t1333Hw4EGWL1/Ovn37rtrwWyA0NJRGjRrJBZY/KsUTZGLGjBlMmDBBvhcaGiqztJWVlXTo0IFLly5x%2BvRpHA4HFotFGt2Lnj63201AQIDssxR9lTabDavVKhVYPbNQMTEx0moCap%2Bf4ODgenvuxLk0b96c7OxszdwIEqo%2BX8/5a9myJYcOHQKQ9%2BJ1112Hr68vBw8epLKykhtuuIFNmzbJ/tM9e/YwfPhwjhw5In%2Br/P39eeKJJ7jnnnto3bo1qamp7Nq1i5SUFAoLC7lw4QLLly/n5MmTPP/88w3OvyBEYtxqURyxIKEuKRb%2Bn%2BI58cy8ibJlNTwXlOrr0/W8TzznLTU1lYMHD2q%2B41lKrH7d0P7EWERmevz48WRnZ7NmzRqee%2B45RowY0eBcefH3hJfweeGFF178hVi%2BfDmzZ8%2BW5WhqTyU1PFd2PdHQ5/X1sQkvK3Xfk%2BcqcIsWLWjevDkrV66sk4ERqC9INRgMdOvWjUmTJknT8mv5Z0aUj4rARyhVinGOHz%2Bet99%2BWwZCDz/8MAsXLpTBkTpI6tKlC4899pg0tBakJSUlRZbJZmRkyP6ixMREnE4nZ8%2Bexe1206dPHzZt2iTndfz48cydO1cS7UOHDtGyZUs59xaLBR8fHy5dusQHH3zA%2BvXr%2Beyzz%2BqdL0VR2LFjB127dpXZryZNmvDrr79qtmnUqFGdgFqNwMBAXC6XJOGeRKxJkyacOXMGnU5HRkYGcXFxtG7dmqioKCZOnMiYMWPk3I4cOfKKPW/t27dnz549V7h6147u3btz9uxZTpw4AaC59gJi3kJDQ6XYhAim67vv1QG3WDCBWiGYcePG8fDDDwO1z8yDDz4oVVnh9%2ByHIH1iPrt06cJbb71Fx44dr%2Bl%2BFkbtatGMP4NXXnmFyZMnY7FYGuxZE1AfRywMBQQEaOwqnn32WV5//XW5raIodOvWjX/9619XPSbPOYLa801NTWX79u0NjkkNQTSFDYyiKISHh1NSUlKn7NDHx0eSW0VRSEhI0GTZmjVrRnh4OHv27CEkJIRLly7Je%2BP6669n8%2BbNcttOnTqRmZlJeXk5n332GXPmzCE3N5ezZ8/WmwH7o3mIjIwkLy9PEkB/f/86Hpn/CVzrwsKfhef1Cg8PJywsjLS0NCZOnPhfP74X/3l4ffi88MILL/5CDB8%2BHJvNhq%2BvLwEBAVitVmw2G0FBQQQHB2M2m2nSpAn9%2BvXDaDQSFBSETqfD19cXg8FAdHQ0P/zwA6GhoQQEBBAUFERMTAw%2BPj5yJR2QIigGg0FKiqtXpNVkz2w28/bbb5OVlSWl49VWCQKeVgQdO3bEZDIxcOBA5s2bh5%2Bfn8Z7TIzD06zZ079PTSzVfVlQ20unXvUWfSf12SXk5ORIsge1Ajk//fSTfH3mzBm2bt0qX%2Bfm5kpfvrS0NE6dOqU51nvvvacJbkWWSgSHaq%2B1ZcuWaUr7BPEWUBSF6dOny/07nU6NYqfIxHiWjQlrDYHi4mJKS0tl76Unfvvttzp%2BdQkJCZw7d07TaymOKRAWFqbxlrNarRw4cICgoKA6xxAZY19fXzZs2MDjjz8uP5s1a1ad7dX417/%2BxfHjx%2BWihAj61RABvjB1BzRlpJ4Q8zB8%2BHD69OkjFVTtdrvGisLhcPDEE09ovqu%2Blur53LVrF2PHjtVkdtT3Wvfu3aVAidFoZNCgQUCtr514tsR5mM1mVqxYgc1mk9/38fGpc97q6/Hiiy/KBSHP7WJjYzWvO3ToUGc/agsIqM3AizEJHDp0SDMmAZH19PSxdLvd/PDDD/J3wGAw0KNHD40vnaediyh9njBhgjw/Hx8fuWDhdrulcqUaJpOJyZMnYzAYZH9kSkqK/NxoNHLbbbeRnZ0tPevUYj6C7AlRqOPHj8tMcXl5OW3btpUErSFfPWHy7vme2%2B2WWVGxQPXYY49hs9no2LEjt912G/369SMiIoLIyEgiIyPp2rUrCQkJ9O/fn%2BTkZMLCwmjRooW0ZwgMDCQlJYW4uDgCAgJkqWhycrLGY69Xr16a8QhLBqj9fVqyZAmLFi1izJgxTJ48mbFjx/LWW28xZswYnnnmGSl8JcqoxX9Go1H%2BLRYsiouLvaWc/8PwZvi88MILL/4CzJs3T3rGXbhw4YomuePGjWPw4ME8//zzvPrqqzz22GM8%2BeST2O12jEYj/v7%2B2O12SZQsFguzZs1iypQpMnv0R1kBm83G0KFD%2BfDDDzEYDDz77LPSh6y6upqQkJA/tWItSqGAOmVOiqLwxBNPEB0dzYQJE646%2B1Gf4MHfCVdbbuuJ%2BPh4Tp8%2BXWeOxLyIYDImJkZTsiiCas%2BVfz8/P26//XY%2B/fRToDYYNJlMXLx4kbNnz2r2rdPpZCkxwGuvvUZMTEyd0i312OpTnRS9kgaDgerqahISEmTvoNlspqamBp1OJ7/XsmVLysrKOHPmTJ39CXJjNBpRFIXFixfzxhtvyD6nFi1acOTIEWk6DrVkT10apz7Hr776itLSUu6//375WVZWlkZBsj5BC08I0iZEgEwmE2vWrOHDDz8kIyODjh07sm3btgbtR4SiZWRkJAsXLgRqM4hbt27VWGN4HlPMR0PPifhMWICYTCZmz57NxIkTOXDgAMnJyZjNZhRFYfXq1fTu3VvzfT8/P%2BD3BRfP%2B9hTOMrX15dhw4axdOlS2Sd49913Ex4eLn87xDW/lqzUkiVL0Ov13HvvvfI9s9nMwYMH6dy5M263m5KSEgIDA6/oy1cf1Fni/xSaNm3Kb7/9Jktmv/nmG5o1a0br1q015df/TYhS%2BsjISM3fV4Nu3brx1Vdf8dZbb9X7eWVlJZs2beKWW26R7/3RQo4Xfz94CZ8XXnjhxV%2BA5ORkYmJiAKS3W0PIzs6mXbt27N69m7S0NHbv3k27du1wu93MnTuX4uJiXC4XkyZNonv37posVn2wWq0yYwK1ZX%2BzZ8%2BmVatWtGjRAofDUSfY8ywJg7pk5IEHHmDZsmUy2Bb7EH1c9f1z42kREBERweLFixk8eLAsk3Q4HKxcuVIKvwQFBdG1a1f69evH9u3b%2BfDDDzX7E4bxwrBcBKnh4eE4HA4Z8MXGxtKzZ0%2BWLVsmA9brrruOvLw83G43jRo14uDBg9IaYMSIEezfv1%2BWq/Xu3ZvY2FiWLl16xflWo2PHjuzcuVND8MW8PProo8ybN0/jD6cutRVEY/jw4dLg3rNM9EriHFarFagl8J5lvk2bNuXs2bPymovyV0GGfH19eeeddyRZ0ul0TJ48mVdffVX6s6lLhAVEia1er6dfv36sXr1a9t01b94cu90ue%2BcGDhzI7bffzpw5c4iNjeWrr76SIjx2u51Dhw5x9913S8I3depUpk2bBtSWtlZUVBAUFERpaSmVlZWYzWbNYkd9ZCk6OlpjJr5s2TINydXpdLRp04aTJ082SC4E4Xv00Uf57bffGiTr6mxkQEAA33//Pddffz3wOxERhFP0rYnvCwXdIUOGsHLlSuB3Qjxw4EC%2B%2BeYbSbLV8PQ%2BFPCci6ioKJkZ/O6773C5XGRlZUkfyZtvvpkLFy5ctc3IH0F9fD8/P5xOp8zgLlq0iJKSEp5%2B%2Bmm5fUJCArfddhvr16/n4sWLcpFMTYKDg4PlNVIUBX9//zq/WVczpvrEUtTbiMyyyWTihx9%2BoGfPngBSTXj//v1cunSJAQMGaMpI/65o06YN%2B/fvZ%2BzYseTm5lJVVUWjRo3YvHkzFouF66%2B/nh9//JGoqCiKi4u55ZZbmDlz5l89bC%2BuEd6STi%2B88MKLvwAbN25kw4YNV/UfIHuRxP%2BDg4MJCAigUaNGZGVlMXv2bOx2ex2yJ/rARJlOWloaDoeDmpoahg0bJkUX7rjjDpKTk2WGxTNDVV/g9MMPP8jSNqvVSlJSEgMHDiQsLAz43YBcHbiKMQkMGDCAqVOnSinz22%2B/nR07dshSx8jISBRFkcc3m81MmjSJoqIiHn30USlyEhwcjE6nY%2BHChUyaNAm73c758%2BcZNmwYJpNJBmd33323PPa5c%2Bf4%2BOOPNZ5uhw8fprCwkKKiIjIzM6VowfTp09m8eTM7d%2B4EagPwN954gxdeeEGWcYpy2SeffBKTyUTbtm2B30vukpKS5NzodDpMJpOmLPC3334DkNcZaqXaBYQ4S319ViJY9SRc6nmvrKyksrKyjmAGwLFjxzTXfPPmzXz11VfydUVFRZ1V/S5dutC2bVt5byUlJWEwGDAYDAQGBgJoSlYzMjJkyZ7b7ebIkSPk5OSQnZ1NdnY2o0aNIjg4mHHjxvHII4%2BQnp7Otm3bqK6upqamRppVC7IqyB7Ulrba7XYKCgrkedTU1EgbAzEX6hJjcW2FYi3A559/rrk/XS4XFy5cwN/fn86dO8uyZTWElcOvv/4qRZGGDx8ut1MURVPKrNPpKCsrkxl%2BgMuXL2OxWCRhUxvCQ%2B39ZjabWbNmjSSU69evx2g08sorr2A0GiUZE2WZBoOBIUOGoNfrJSlRz4UYC0BBQQEZGRlkZGTgcrkIDQ3l3Llz8p7y8fGRmVqoNVRv1qyZZq6%2B/fZbNmzYQJMmTTTjTktLk8JPCxcuZNu2bWRlZXH48GHuvvvuOosfDzzwgIbsAZw6dYr33nuPo0ePyvJJm80mPTvNZjM33HCD5jsimyzmwmAwMHToUHQ6Hf7%2B/pjNZoKDg2X5u5iP%2BrwSDQYDzZo1w2g0yn3a7XapNAy194rdbqdr16707t37msjmX4nQ0FCWL1/Oli1bCAoK4uzZs/z8889ER0dTVlZGixYtCAsLo6SkhL59%2B9a5/73434A3w%2BeFF1548RdDKPU1hA4dOjBixAhGjRrF0qVLGTVqFNu2bWPr1q20bt2anJwcTp06hdFoZOjQoaxbt44TJ07g5%2BcnDax79uzJ/v37OX/%2BPD4%2BPgwbNoyYmBhJHrKysjTeciLDoCgKbdq0oUOHDnzwwQcac/NWrVrVUYQTJX%2BCCNYXPKlX941GIyNHjmT16tVSuj4gIKCOx1ZDEMfzzNRcDRRFwWq1ysxCeHg4xcXFkghcrVfg1Y5RkA1P5b1/BwEBATKLJmwc7rjjDlasWIGvr2%2BDBuIRERH1CmP8uxDnejUCJTabjaioKFJTU%2Bnfvz9du3YFapUGx40bx5w5c674fZFtsdls3HLLLZSWllJQUMDu3bvlNp5loi1btuTw4cOa%2B%2B9aroder5dZxCsJezRq1IiTJ0/idrsxmUwEBgbWMQHv1KlTHXETNdRZOX9/f00mNzQ0lJ49e7J48eIGM1n1CTZFRERgt9vr2EQIUupwOK7JXqGh8TYEcV8YDAapxGq1WrFarZjNZk6dOiWzdDabTaqP/qcREhIilYvFb4BAfd52lZWVDBgwQBrY33HHHRpBptDQUKDWWsHhcPDLL7/w8MMP1%2BkR/Tvi1Vdf5fPPP2fWrFn06dOHlJQULBYLu3fvpk2bNuh0OkaMGEGPHj14%2BeWX6zxjXvxvwEv4vPDCCy/%2BYqjFB9QQAgW7d%2B9mzZo1zJw5kzvuuIPPP/%2BcMWPGsHDhQmnGXFZWRqtWrThw4AAmk%2BmagyR16ZePjw9ms/k/0usieroaUh/9TyAgIIDS0lL69evH0KFDeeuttzh48CAdOnSgtLSUrKws3G43/v7%2B2Gw2CgoKaN26Nb169eLQoUNs2LCBvn378sYbb%2BByuZg7dy4//PADY8eOJT8/nx9%2B%2BIEDBw7w/PPPU1lZyfz586mpqSEpKYmioiIZoDbUOwa1BGDMmDG88MILmhI04Qnm4%2BOD0%2BmUJFP4sanL7CorK%2BV31cG/WuglNDSUW265RZaZisyK%2Bn4wmUyEhYUxfPhwPvjgA2mdkZiYyOnTp6UohNo7T6hhxsbGYrVaZc8SXLln0WazERYWpskOqfsAr5Vwqc/ZYrHw4IMP0rdvX1asWMG3334ry5vV8DzG1fTpwe/PREBAAHPnzqV9%2B/bMmzeP7du388knn7B9%2B3YeffRRjU3JfxJizkUGLCQkRPpT6vX6BudNTar%2BaMFFlCEKo/CWLVvidDo5dOiQLJeMioriwoUL6HQ6SXLFPSL6Qg8ePPhvLWSo%2B1DF/VlQUIBer%2Be5555j1apVnDlzhmbNmvHMM8/w7LPPUlFRQWVlpXzu2rVrx9SpU%2BU%2B169fz8yZMyWZFdlTYVYvbB%2BE/9yfUVFV96imp6fz2muvcfbsWQB5vJSUFKkaKhASEkJOTg5Q27sL2rLfP8qivfnmm5w%2BfZoXX3xRU9UxbNgwvvjiC4YNG3bV51BWVkaHDh2IjIxk6NChvPPOOwQFBZGens7atWtRFIXdu3djtVpp06YNoaGhbNy48ar378XfA17C54UXXnjxF8MzU%2BB0Ojl16hRLliwhMjKS2NhYcnJy2LZtm8YP7b%2BB1NRU8vLygNoSwKqqKilvD7VlmsJ/SxDV6Oholi5dynPPPUd%2Bfj6KohAdHY3BYJBlbJWVlVitVgoLC3n99devOrDas2cP586dY/DgwZrgVQRn0dHRFBQUSLELg8EgS0g7d%2B5MdHQ0u3fvprCwkPvvv5/jx4%2Bzdu1aQkJCWLZsGbGxsbz88st89913pKSkkJ2dLUmDwWDg0Ucf5YMPPqCyslLKxZtMJi5fvkxWVha33XYbCQkJbNiwgW7durFjxw5CQkJ45pln8PHx4bHHHiMkJIR//vOf/Pjjj7z//vtUV1czY8YMhgwZQlVVFenp6VLcoaioiB49ejBp0iR5rmvXrgVqlTPj4%2BNZuXIlubm5dO3aVQb%2BW7ZsqWOd0bp1azIzMyUBSk5O5tKlS1y8eJFGjRrx/fffM2fOHCkc0hCE2uuDDz7IokWLJBGIiYkhNzeXDh06sHfvXlwuF4mJiZSWllJYWCgzfeq%2BwujoaPz8/DT2E3%2BE9u3bs3fvXvr06cPatWsbJDL1oW/fvpw4cYKcnBx0Oh1PPfUUb731liS0QvE2LS2NN954g%2B7du9O3b18uXrzIs88%2By5133knnzp0pLy/n1Vdfxd/fn44dOzJr1iy2bt3K5s2byc/Pl5YeQkynRYsWlJeX11FZ/Xfg6%2BuL2WzG4XDIhQW9Xs%2BLL75IbGwsTz31FG63m0WLFjFv3jyOHj1KTU0NPXv2xGAw8O2332IymQgNDeX06dPymghi69lnGBMTQ35%2BPmlpaezduxe3203Tpk0B%2BPXXX6VdCyAXDXx9feVz7nQ6Zblv3759Wbt2LR07dmTr1q04nU5ptREYGCjLmTdu3Mj//d//cfDgwTqZRvE7lJmZiU6n49VXX2Xx4sWYzWZeeeUVpkyZojEu79KlC927d%2Bf8%2BfNER0ezYsUKOnTowM6dOzGZTEyaNIn58%2BczatQofvnlF1wuF1u3bqVXr16Ulpaya9cu6QsaHR0tr%2BWVepL1ej3XXXcdx44dk0rIer2eZ599lrlz58r5nTx5MtOmTcPpdMpM52uvvcbUqVOpqqoiICCAt956ixdffBE/Pz9MJhMvvfQSM2fOxG63k5WVRUFBgVxQEyXAzZs35/Tp0yQnJzN9%2BnQaN25c77309NNPs3XrVlwul1yQ8ff3p6qqivLycs2ijMVioVu3bvz666%2BcOnWKKVOmeH34/gfhJXxeeOGFF39TlJWV0bVrV0JDQzGbzZw4cQJFUTQlczqdTuPNpc7oCKltsfIuMjGhoaGUlJSQmJjIsWPH/mvjb9y4MdHR0WRmZlJWVsZ9992HXq9nyZIlOJ1OTCaTtHu4/fbbMZvNhIWF0adPH%2BbPn4%2B/vz8333wzubm5bNu2TaoALl%2B%2BnJCQEPLy8vD19ZXnLoRX1NkvqA3CxAq%2B%2BBtqV9FF2dKf8bbKzs6mffv2/PTTT3Tq1EkSI0F4169fT69eveSKvxqtWrVi3rx55OXl8dRTT8kV8/z8fHr06KGRXr/aEruGSupE5q%2BgoEDzvig7BLjrrrs4cOCAzPCVlpZisViIiorC6XRy%2BvRpzf6cTicRERFcvHiRmJgYzp49K88dfu8R8/Pzo2vXrnz//feYTCaioqJITExkz549MpupVtgUCAwMpKysDKfTSdu2bf%2BUufnV4Ntvv%2BXee%2B8lODiY06dPy9Lb%2BrJVer0es9lMRUUFfn5%2B6HQ6qqqqpIqtv78/Z86cweVy0bFjRwoKCsjNzaW6upqWLVuSm5srywih9ln18fHBbrfLDOm1kNmrgdFoZNiwYYwfP57rr7%2Be6upqAgICZAbf4XDUaxQOteQ8Pz%2BfoKAgmZ0ym82YTCaNkqfoCYZaJdj8/Hypojts2DDWrFnDrFmzePzxx%2Btk0UwmE88//zwvv/wyiqIQHx/PuXPncDgc8np7fqdv375s2rSp3ixtSkoKUVEGnEupAAAgAElEQVRR3HvvvYwePZrMzEzatGkjzyssLIyLFy/WETeqD3q9nv3790uRltdff53ly5fXu/DkCSFEJH6LBbktLy%2BXWdv4%2BHjKy8tlqa9Op6Nx48acOHFClsU3btxYkmGr1Up4eDhnzpzBZDLRpUsXfv75ZxRFwWazybJenU5H06ZNOXnyJA8%2B%2BCBjxoypkzFctmwZP//8My1atCA5OZmlS5eSlZXFiBEjWLx4MRaLhfDwcE6ePEl8fDw6nY6zZ89y33338dxzz11x3rz4e8JL%2BLzwwgsv/qYoKCigb9%2B%2B7Nu3T/N%2BRUUFgwcP5oEHHuDChQusWLGCyspK4uPjCQgI4MKFCxw5coSHHnqI4uJi9u7dS35%2Bvkbt8Vp%2B%2BhMTEzl//jwOh%2BOaDIn/DG688UYeeughRowYIYM9UYKnKAo7d%2B5k%2BvTprFq1Sn7HarUSFxdHTk6Opm8tLi4ORVEkWZkwYQKjR49mwIAB0n7A0wIBficrwlJCBHiRkZFcuHBBbp%2Bdnc3gwYNxOp1yf2o8%2BeSTfPTRRxQVFWkUBI1GI7GxsTgcDkpKSnj88ce56aabOHz4MPPnz8dgMLB///46%2BxOeWHq9/pp670Tg6Xa7NUQmKSmJM2fOkJiYSEZGBqdOnWLAgAFUVlZisVi45557pM8h1BIAi8UijeCtVitNmjThwoULnD9/vl7CJ/rCXC4XycnJzJw5kyeeeIKbbrqJDz/8EF9fX1q2bElOTg5ms5nKykpKSkqYMGHCNSkBKooirQ06duxIbGwsiqKwb98%2Bjh8/LjNYoaGh0iBbp9Px/fffM3DgQPlsCMXC/zb69esnrQvcbjeFhYUMGjSI0tJSSQiSkpK4cOECpaWl%2BPj4aNRv27dvT1ZWlnyWS0pKrlhSqSbLoaGhfPTRR9xxxx1UVVXV6eV7%2B%2B23efrpp4mIiCAmJoZ9%2B/ZJpVW14I%2BnCqmfnx8PPPAA69atk3OYkJCATqcjPz8ft9uNj4%2BP7B/0JFxijAaDQWYAofZ5EZlBT7KYkJDAPffcwz//%2BU/sdrusTli4cCFjxoxh6NChfPHFF3XmY/To0VLp9s9AqBbrdDq6du3K7t27qaioqFMuLLJ9QmzL5XLRuHFj2d%2Bp0%2BlIS0tjz549clHKbDYTGRkpFw/8/f3R6/UUFRWh0%2BmIioqisLCQmpoaGjdujK%2BvL4cOHcLpdGK1WmWJscViwcfHB5vNxvPPP0/79u2lOJaYPzXWrVvHihUrOH36NHa7nerqavR6Pb6%2BvkRHR3PffffJPlsv/vfgJXxeeOGFF38xnnnmGfl3dXW17NU6cOAACQkJTJs2jaCgIF5%2B%2BWX27t2L3W7H6XTWIV9Wq5XmzZvL3hR/f38KCwv/sHfObDZz0003cezYMYqLi7l8%2BTL%2B/v5S0fHy5cu43W7Cw8MpKCiQQb1Op6N79%2B689NJLdO/evd591yccUd82nuVlDcGTUFwJnpkaocaoFrioj/yKjIfJZMJgMEjhCGF8L8Rt1IbRasTExHDu3Ll69%2B2ZtRAlj1Db7xYXF8epU6c0VgLifE0mE263u045medx1PsWGc333nuPxx577A/n7FqgHltDXoBXA0G6AwMDGTduHLNmzWLUqFF8%2BumnBAUFcfHixTpkRk2gmzRpQk5ODpGRkQQGBvLrr7/SuHFjIiIi2LJlC1B7TUJDQ/n111/p1q0b69atQ6/XM2fOHCZOnEh5eTmKojBy5Eg%2B%2BeSTK3rdiXMWhuMNZeRSUlI4duwYTqcTg8FAkyZNKCsr49y5c7Rq1Yovv/yS4uJipkyZwk8//aSZu4CAAFJTU/nll180%2BxTBunimFEWhadOmFBcXy3kSZZ9Xygyr%2B02vBn5%2BfhiNxgatKfR6PX5%2Bfg3aGahR3zMsslRVVVX1jtuzD09AKG4OGzaMJ554gpYtWwIwaNAgVq9erXlW9Ho9cXFxnDx5sk4f458VqjGZTGRmZlJeXk779u3rnNe14I8W4jzHabPZZGbY19e3Qd/HhpCamkpiYiJpaWnyvTvvvBOo/Y2cNm0aP/30k7y/zWYz/fv358UXX6xjQO/F3x9ewueFF1548RfjhRdeoLKykj179miUMhuCWgAgODiY0tJSWUYoSigBtm3bhslkYsqUKaxatUqSlj179tCpUycqKiowGo3MmDGDzZs3s3//fo2h95UgAsDKykoMBoMMNvR6PcHBwRQXF2MwGMjIyCAuLg6oDTAOHjxI69at8fX1xWg08s4775CamkqrVq1kT5UoW4XaIENIwzscDnr16sX58%2BflOQpERUWRl5dH%2B/btOXDgAA6Hg759%2B7Ju3ToZSAuLg6NHj8rA6uabb5ZlUTqdjpqaGmJiYur0XvXs2ZM%2Bffpgt9uZMmWKPB%2BdTkdmZqYcr/Bt27t3LxaLRQblAsIUe%2B/evbRp04Zu3brx0EMP8cILL1BSUiLl%2BUUfjQgC7XY7RqNRClr8UW%2BY2gOxpqaG//u//5MZJQGx74CAAAIDA6mqqpJln6K0Th1gCv88gTFjxrBkyRLcbjfp6enk5OTQtGlTtmzZIslm165d2bRpE35%2Bfly%2BfFn2dTmdTpKSkjhx4gQ333wzGzZsYPr06QwaNIgNGzbwxRdfcOjQIVkaKILOxo0bc/z4cYKCguRnVyqDFEGx2ouwpqZGbn81ZaIvvfQSr7zyCm63m48%2B%2BgioDeoXLlx4RfEKi8XCHXfcgcViYenSpXXGGBoaSnFx8VWXcAYFBaEoyjWbjdcHTw87o9HIxx9/zIgRI/D19a2j%2BtmhQwcKCwtleSEgBTw2bNggM1OinHXfvn3yN0F9fVJTU/Hz8%2BPkyZPSf1S9CKLT6Zg/fz7x8fH069cPl8tFu3btGDBgACUlJVK5dfLkyRQVFTFgwADi4uLQ6XTk5eVJawZ/f3/sdjs1NTWaZ1NYfej1etq1a0dOTg6XL1%2BmSZMmNGvWjIyMDPz8/KTibZcuXcjPz%2BfMmTMasim8Ms1mM1OnTuX06dPMnz8fvV5P06ZNycnJkedc34KMTqf70yTzauFZ7l8fPMciSlEFUdbr9VgsFqKjowkJCSE7O5vbbrtN02Psxf8GvD58XnjhhRd/MWbNmkVNTQ2dOnWSJTNhYWEajzaRTYDa3iux0t%2BoUSPcbjdBQUEUFhZy9OhRUlJSiIiI4Oeffwbg66%2B/xtfXF6glZN9//72UEa%2BpqWH69OlkZGRckeyJFV2j0UhAQABlZWUUFhZSWVmpWVl2Op1cuHCBmpoaKisrGThwIJ07d6Zdu3ZUV1eTkpJCVVUVhYWFXLx4kePHj/PNN9/IgMjlcvHCCy/I/akDU6iV1e/du3ed8eXl5UmRFTFXb7/9tqZs6euvv%2Bbrr7/WvPfjjz9SXV1NdXW1zMQIVUqj0Sj7yzZt2sSECROYMWOG/G5iYiLx8fGSMAiCJsrZ6svC3nLLLXJbnU7Hxo0befrppzl58iTl5eXY7XaZ5RBlVWIfIoBMS0uTYhh79uyRnmJqiOBUfMeT7MHvmYiSkhLOnDnD888/Lwl3bm6uhqiGhIQQHBys8bF7%2BumnZVBdU1PDxYsXmTNnjjQJb9WqFcHBwQQGBsoyPqPRKLOaAwcOJC0tjRYtWqDT6Rg0aBBQW9Z76tQpNm/ejE6nk96FYk4BjZ2BOEf1MwK1GVgh4COepcrKSk0gnpiYKOfObDZz8OBBzVxaLBZp1aHT6WjVqhXvvvsuDz74IJs2bQJqyUXXrl0JDw/XHL%2Bqqop9%2B/axaNEinE4nPXv2ZMCAAfJz0UMHtcS0Xbt2mvlVP/8Wi4Xt27ezbds2eZ5iLq1WK1Dr96j%2BjqIoUlRl165dsgdRfe0zMjKA2vtxx44d0qZAp9NpPOcADdmDWs/CuXPnytJnMeYFCxZQXl4ux6kW//jyyy9ZvHixLL1Un6ePjw8%2BPj688MILjB8/Xu4zMjKSu%2B66S6M8uX//fim4kpqaSkpKisaHr7S0tE62UFEU5s2bJ8ckxF8cDgeXL1%2BWHoQim%2Bx2uyktLZU9dWIfUPt8iRLpF154gffffx%2BozbRlZWXJ6xobGyt/v6xWKyaTiaSkJPmewWAgLCxMPs%2Bi1Lhp06by98RkMtG4cWN8fHykoqro6xZo06YNFotFlnzHx8fL/lCr1crSpUvJzs4mICBA/p2dnU2/fv3Q6XTExcVJpeDg4GBZUZCQkEBSUhKlpaVkZmZy/fXX1/F69eJ/A94MnxdeeOHFXwyXy0WbNm1o164dO3bskMpugCbTJYJ4kY0RipllZWXEx8dz6tQpTCYTs2bNYsqUKSiKwptvvslDDz1EZGQkBQUFMiAAZF9cTEwMZWVltGvXji1btpCZmUlycjI2m032pRw4cEAaie/bt49ffvmFRx55RNNr0xB8fX0b9LQTQYtnqda/A7WCZ15enhxfTEwMUEsORTDZo0cPSYzVEMRH9CxZLBagVlG1oYxQQ6VhIqD9s7YU6gzIF198QWpqKq1btwZqM4Yic9q8efM6YxOZvokTJ/LKK69oPhMk5siRI6xcuZJJkybJ7/v5%2BWnI5p133kmvXr0YO3asFA1atGgRDz/8sCZrdyWkpqbSokULTp06xdatW4HflSB1Op0sRRbbfv755wwcOLCOEI/b7eahhx6S/YVifqKjoykqKqJPnz6ynK8%2BqJ8lT3iW6YqgHJBWGufPn9f0mYkxqedAr9dz77338tFHH0kBjqioKPkMei4EDBs2jClTppCWliafy6SkJE6ePCkFVYSnmyDvghwMGjSIL7/8UjNmUf4oesqys7NJTk6WGV8xZ1eai/qgLpNetGgRAGPHjqWmpkaWHD/88MPMnTtXMx4xT02aNOHs2bOyFFGopNZXXqoud%2B7fvz9HjhyRVgYioy%2B8%2BlwuV53zUF8PnU7HuHHjaNSokaaEXgiyeCrcekKdUa4PFotFkkD1%2BMSxDQaDLPUW8x8SEsLFixcluRJlzXa7Xfqnms1mAgICOH/%2BvPxefHw8J06cAGp9FQMCAmSfqo%2BPD4GBgVRWVjJhwgReffVV3G43mzZtwmg00rVrVzZu3CgXFrp168Ytt9xCUlISADk5OXz11Vc4HA6ee%2B45zeKYw%2BEgKyuLdevWsWPHjgbnwou/J/TTpk2b9lcPwgsvvPDi/8%2BYOnUqmZmZdO/enePHj2O327FYLJqgUASYgMygCEVBEUyVl5fjdDpJSUnBaDSSlZXFqlWrcLvdXL58WQoluFwuEhISZGlYUFAQL730Eo8//jgLFixg7NixvPfee9hsNtm4P3bsWL7%2B%2BmuMRiPdunWjTZs2LFiwAKvVKv3afH196/RaGY1G5s2bJ0VWPvzwQ5lVUBSFd955h9LSUqnyCDBx4kTZuzR%2B/Hg6d%2B7Mzp070ev1LFiwgNWrV1/VvHoGcGVlZVI%2BXuDs2bP1kjBPcuZwODT9QEFBQYSHh0vxBkVR6N%2B/P6dPn65DNK6F6On1enr37k1BQYEm0wC1geN1111HcXGxnMPU1FQyMjJo1aoV3333XZ3jiLF49oIJKIpCeno6kydPpqqqCrPZLLOLagJ3%2BPBhMjIyNPsX95bn%2BOsrX3O73RQUFHDo0CGZSVYURZZbulwuxo0bJwP8999/n5MnT3L69Gk6duwoy//E%2BNTy%2B%2BJ4QtUzKytL3ksdO3YkPT0dPz8/8vLyUBSFFi1aSBKnfsaEAbinp6D6uRFkUE2sTSaTXEhRz1lQUJAUuHG73QwfPlyq7XpCZJfV2ZPCwkJ5HLfbzfbt2zWZTTFvR44c0cy/EFcR3xWCNdu2bcPX11dDTBpavLDZbHV6TQ0Gg0a0ZdWqVaxatUruw%2Bl04nK52LVrFwCPPPKItHNQn5O4J0UGS2Rh1Rg2bBiXLl2ioqICl8vFr7/%2BqillVV%2BH8PBwtm/fzqJFizTPnnqfbrebffv28f333wO1994NN9xAQUEBdrtdY0kh7lehkllUVHRFUizsWkTWvnHjxrL0uU2bNlRUVDBt2jTWr18P1N4vM2bM4NChQ5KQu1wuHn30UbZt20Z1dTUul4uBAwdy6NAhysvLcblctG3bVvp%2BRkVF8cgjj/Duu%2B9y5MgRkpKSSE5OZtGiRZw%2BfZp58%2BaRmpoqifD58%2Bcxm82cP3%2Be7777juzsbD788EMOHz7M4cOH%2Bde//sWmTZvIzMyUFg%2BbN29m9%2B7dZGZmsnfvXsaPH8/EiRNp3749t99%2Be4Pz4cXfE94MnxdeeOHFX4y2bduSnp4ug46NGzcSGhrKpUuX6iUKHTp0kEFVTEwMeXl59O7dm3Xr1jV4jEaNGklbh169el1TWY7BYODw4cP84x//YM2aNSQmJrJgwQJat25NTEyMlAlv0uT/Y%2B/Nw2M%2B9///x6yZJLInJIhYQpBEIkJRWwRptPalHFujaKtoaYtT4bS0nKqtRxd0OVVKKUHtpZa0qEZqiVJbNSKRykbWyay/P%2BZ7352ZhHI%2Bn8%2Bvp9c1z%2BtyJZl5z3u53/d7vF736/l6PsO5ceOGXK0Xq%2Bb21K7XX3%2BdBQsWyGRGrVaj1WrR6/WySjJt2jTZq/Mg2LBhg6ymAKSnp7N69WpJn9y/fz%2BDBw%2BW26elpTF48GDatWvHoEGDiImJkT54Bw8eZMqUKdLPTwR/I0aMoH///sTExBAXFye3B5u/naiaiuu0r9CKfQg7DGGKDvDUU0/x6aefEhwcDNjUG8%2BcOUOrVq3%2B42qgUqmkffv2nD9/XgruiB4re4oeIKmsCoUCf39/vLy8qKysRKVSkZCQQI8ePZgyZQqffvopR44c4aOPPkKhULBu3Tp5/mDzCkxKSiIjI4P27dtLqtvq1asdFhbi4%2BPx8PBg165ddOzYkZUrVzJnzhz27dvHnDlzGDNmDGDzEHR3d6ekpAQ/Pz/Kysr%2BsNdt3LhxJCQkMGnSJAwGAwqFgrlz5zJgwAC0Wi0dOnQAbDTG69evk5mZyUcffURgYKAM0EWC859WY%2B93X8S%2B75VkOQsciX7Qc%2BfOERcXJ7c7deqUrBoZDAZiYmJqKPk%2BKOx9PUXVGH7vNbVPDh8UQlDno48%2B4rnnngNsiZnwlGvdujX9%2BvWTvbAA8%2BbNY8GCBVy5ckU%2Bx84ICQmRtOqSkhJMJhPDhw9Hr9dz/fp12dcrxtnb21sudDnDuXfNXgTI3d0dvV6PUqlkxowZvP322zWeG/t9du7cWbIyqquradiwobSbycrKorS0VC4MCfj4%2BMjvQFEJbNSokVQUvn37NvHx8Zw6dYq8vDzAZl0jFs6Cg4PJy8tj4MCB0vZF/H8RGBhIw4YNAZg8eTLp6en3VXCtDfZVdV9fXwCZ1O7du1eaxbvw14Er4XPBBRdc%2BJPRrVs3tm7dyty5c%2B8rAvEwcBYKeBgPM1HlEAEHwGuvvUbfvn0ZOXIk2dnZMrixD1LVajXNmjWTQaSghNrjP/FSE%2BbqISEhqFQq/Pz8OH/%2BPDNnzmTt2rW0bduWXbt20atXL1599VW0Wi2JiYmMHTuWoqIitm/fzt/%2B9je6d%2B9Ox44diY%2BPZ%2B3atZw5c4ZLly6xY8cOWa0cM2aMvBbRG7Rp0yY%2B%2BugjmjdvTlBQEG3atGHo0KGcOXOG4OBg0tPTHeiI9gmk/e/3%2BvvMmTMoFApycnLo3bu3g1cgQIsWLYiLi%2BPmzZvSk65du3asX79eiveArc8uIiKCEydOEBwc7NAn5ebmRkhIiENfnn2lSVg3mM1mQkNDZXIEtt5HcX1RUVFYLBYpYCOqFv369WPnzp20atVKVpyEcEvjxo25evUqa9asoXv37vK6LRYL48aNY%2B3atZhMJlq0aMGOHTvk%2B2CjHQs6rV6vryHQInqfBG0RoLy8nA4dOjhU20Ql3Gw288gjjzhUysSzolQqyczMZP78%2BRw9epTi4mKUSiVarZY9e/bw1ltvAba%2Bz969e8vf27Vrx61bt8jNzZXJdZ8%2BfUhPT3eoFqpUKjp06OBA2xbPwqOPPgrYKKUHDhwAbIF2s2bNKCoqYs6cObK/NyYmRiaOgwYNIigoiA8%2B%2BEAKpgicOnVK7t/%2BGp1tGMBGuxbnajabpbdi48aNuXHjhly0EYsVYqFCICUlRVaGHhT2fWh5eXnUr18fgNzcXAIDAzEYDAwcOJDPP/%2BctLQ0WrZsCdiSoa5duz7wcVQqFS%2B%2B%2BCKTJk2q8Z6YZ15eXhQUFDzwPh8EIqkMCgri6NGjdO/eXS6GDRw4kOPHj1NUVCR7P/fs2SOfj2bNmtGpUycMBoP0Su3Tpw8Gg4GzZ8%2ByZcsWwJZQRkdHA0hmiLhm8Vrbtm354YcfaNmyJWVlZTKBFJRSjUbD448/TmhoKEOHDiUhIQGr1UpkZCTe3t4UFBSg1%2BvJzc0lNTXVZbr%2BF4Ur4XPBBRdc%2BJOxZcsWsrKymD59OhaLhZycHAoLC/niiy/w8fGhQ4cOXLt2DaPRyJUrV/D09MTHx0cGYfZBvJAGHzRoEGlpaWRmZrJt2zb8/f0BpLBBWloaZrOZYcOGyUoXQGRkJL169WLfvn3079%2B/Bo3vftLhkydPZvz48cTHx6NQKBg/fjxr166VvU7Tp08nPT29Bs3rXlAqlVy8eJGXX36ZXbt2yT6o9evXk5SUREpKCjt37pR0PwGhJqnRaCQNU1Bin376adl79LB47LHHSE1NpUuXLqhUKpl8mM1mVq1aRUJCAlAz4Vu0aBE7d%2B7kt99%2B48KFCyQnJ9O/f38SEhKIioqiXr16lJWV1VBGFJS39evXy9eE%2BuB7773H2bNnadmyJceOHXOg2tlDnOPDyvDfDw/q47hx40ZiYmJQqVRERETw/fffo9Pp2LNnD6%2B%2B%2Birg2DPn7u4uBW/ulfA97PkJddDazldQMBUKBQEBATRu3JjTp0/LRHHIkCE8/fTTDB48%2BJ5Je5s2bTAYDFI06f8SiYmJeHp61ngmvby8pBjNjBkz5HvvvvsuCxYsIDMzk9LSUr7%2B%2Bmt69OjByZMnpdCOWq2mSZMm3Lhxo1baorOvXP369TGZTNy%2BfZsdO3awZcsWtm/f7kCfFgs94vvp2Wef5d133wVwqEy6ubnxzjvvkJCQ8FCLJAC//PILycnJqNVqZs%2Bejbu7O3PmzEGhUBAbG8vp06cJCwuTyY1CoSAsLIywsDDatm3L2LFjOXv2LNOmTaOiooK5c%2BeSmpoK2PriioqKajWjF9fQuXNnjh075iAoJRZqtFotCoXCYTxFD6X9uFqt1j/cBn6f97U9B86fqc0u4dy5c0RERHD%2B/HnZxyf6%2BqKionBzc5Pqxz/%2B%2BKOk8kZERFBeXk5ubi59%2BvRhwoQJtGnTpsb%2BXfhrwJXwueCCCy78yejTpw83btxwSNyEoILRaMRisUgrBl9fX5RKJfv376d3794olUpOnDhRY5/z5s1j/vz5zJs3j%2B3bt8vAQlRBhA2Cl5cXWq2WkpKS/xMam301QafT4eXlJatSjz76KFFRUTRo0IB///vf/PrrrzLYUKvV1KtXD5PJRH5%2BPnXq1KGyspIWLVqwbt062rdvT3BwMAaDgTt37mA2m/Hx8aG0tBSdTierFfbBf4MGDahbt640KQYbHXTkyJEolUr8/f2ZOnWqA9UsLCyM7OzsGkmOWq0mPT2dEydO8NJLLxEYGEh5ebmkwKWkpDBz5kyio6Ol3YP4fHx8PFlZWVKM4X%2BCxo0b8%2Buvvzr0IInrFoIU4l4kJydz6NAheY4iWH3//fdJS0vj4MGDKJVKhgwZwsCBAwFb0rZz507q1q3LwoULUalUjB8/HoCRI0eyYcMGuX%2Bw0WcbNWpUI0iPiIhg6NCh7Ny50yHIHTlyJFu3bpWftw/6AUmtgz/2%2BLMXt9HpdA49gkIh0Tmpvh%2Bcabnu7u689tprpKamcu7cOdasWcPSpUvlttHR0Q9s2u7m5kadOnW4c%2BeOFC1xfvb8/PyIjY3l/PnzBAcHEx0dzebNmx/YxgFg5syZfPXVV1y6dMlh/1qtFqPRyHvvvcfChQsZOHAgq1atckhuxEKJqOaDozWH8/Pt/LdGo%2BGZZ55h6tSpMilRqVSMHDmSTZs2ERoair%2B/P7Nnz2bEiBEsX76cZs2a3TfBzsvLk9YhI0aMkHYS4m973G9horZKZ20LW0Jkxmg0EhcXR3BwMN7e3nz%2B%2Bedy28mTJ/Pxxx%2BjUChkH6z4XaC2xOxBtoHfBa1qE7i6V5Joj9mzZ/Paa6/J%2BSyuUwjFKBQKgoKCKCsrkz3blZWVDoI4ZrNZ0s4BWdl34a8DV8LnggsuuPAn4sSJE3z66aekp6c79BKBYxBi/7tSqaRJkyZkZ2ej0%2BnIzMyssd%2BYmBhef/11Xn31VaKiomTAVJvpcW0QdLPOnTtTv3592rVrh8lkom/fvmRmZrJkyRKHYEOo5/0n3lLOxuuDBg1i586dDuI1wk/Kzc2NMWPGsHfvXpKSkvjll1/IyMiQAYrFYpGCI15eXgQFBUkhlfDwcPLz82VFpnHjxnz22WfyGuyrD76%2BvnJVXFQjRDVP9MNcunQJvV4vk5Pg4GC6du3Kli1b8PHxoX///nz22WeS9jp69Gg2btwohSqc79elS5cwm83s3buXxx57DLPZTGBgIAqFgjt37tRQdoTfKzD//Oc/mTt3LmATalmxYgWrV6/Gw8ODU6dOERoaSklJCevWrUOn0/HUU09x%2B/Zth/nVrVs3XnvtNUmtA5v/4K1bt9ixYwcRERG8%2B%2B67bNiwAX9/fxYuXMiTTz4pkywBkZzPnj2bu3fvsnPnTrKzs/Hy8pKVJUHDfP7551m1apX8/Pbt2yksLCQlJQWofZ6K%2BSKqL6JXa%2BHChSQkJDBmzBgGDx5MSkoKMTExDuNmsVjw8PBAqVTWUI4V3moi0XnxxRcBWLp0KfHx8fzyyy%2BUl5djMpnYtGkTQ4cOBWzPiujRCgkJYcqUKZw8eZJdu3bJazUajYSHhzNs2DB8fHzo3Lkzr732GocOHapxbRqNhieeeIKFCxfSoUMHKioq6NKlC6tXr2bkyJGcPSZj0bQAACAASURBVHtWVvWWLVvG888/L%2BdBTEwMGo2G4OBgdu3aJenZQo1TJBpxcXEcP37cQYnXYDCQmJjIt99%2BK1UcRYW1rKyM%2BPh41Go1OTk53Lp1i7p161JQUCCf%2BePHj3PixAneffdd2S88ZswYmjZtir0%2BYHBwMEajUS7U2D974rnYsmWLNFFv06YN%2B/fvJzg4%2BKF6W318fGQPn7BFeOKJJ0hPTyciIkJ6BTovJDj3Ugo4V8nFnPXw8KCiouKe39fwx9W7B9nmYT4jYDQacXNzkxVXQWt%2BGIhr6dGjB2PHjkWj0ThQh134a8Dlw%2BeCCy648CfCx8dHejU5JwHOKnOA7CnKycmRIif//Oc/ayQDVquVJUuWSL8p%2B9edAxk3NzdatGiBTqdDpVIxefJkJk6ciFKpJCMjg8zMTDZu3EhqaipxcXFMnDhRVgqFn5NQDRX45z//SVhYGE2aNJGviZ4WZ9gHXBaLhebNm%2BPu7o5KpZLiA507dwZsq9sbN24kMjKSTZs20bBhQ6qrq1Gr1VKARJyHyWQiNzdXHjMvLw%2BLxYK7uzt37tzh1q1bDv0oFouFxo0b4%2BnpSWVlJdXV1VRWVmI0GikvL5e%2BXmq1miFDhrBkyRIWL14sr%2B2TTz7BbDbTs2dPysvLpfiE8EA8dOgQ9erVc7jPdevWRavVcu7cOfR6PUajkTfeeEMKvwhjblHVadWqFY0aNZIBeXV1taSxCcEWjUZDVFQUN27cYOXKlQDcuXOHqqoqhz4o5/l19OhREhISiIiIICIigsjISPLy8rBarYwaNYq///3v7Nixg6qqKnJychg2bJiDiqT1/9kWxMTEEBAQwJtvvsm7775Ldna2HCN3d3cHUZv33nsPs9ksr3HYsGEsXbqUoKAgrFarFIxwnkN16tShXbt2BAcHM27cONq3b8%2BGDRvw9fUlNzeX999/n5iYGOrUqSMVK8X1VldXSxqpQqHAw8MDLy8vYmNj5TYmk4klS5awZMkSrFYrGRkZFBUVyTkmkj2A8PBwvvjiC8xmMyEhIQwaNIhmzZpJerW431evXmXRokXMmTOHbt26yWRP0IN1Oh3e3t7Sjy0%2BPp67d%2B9iMpnw9PQEkIqOer2eoUOH8uWXX6JSqTAYDCQnJ5OXl8fQoUPZv38/9evXp1WrVnh5edG4cWPZo6nX66UthlCWTEhIQKlUEhgYiF6vp6ysjH79%2BmEymahTpw5arZY%2Bffpw8uRJcnNz5XgGBATIinxAQAC9evWSi1ZWq82o3lkMPj8/n6KiIofvC7FQA9ClSxfGjh3LtWvXyM3NZfjw4fTq1YsePXqg0Wh47LHHSEhIkHNIo9Hw4YcfymfC29ub1atXywoW2PohLRYLpaWlFBQUcPz4cZo1a0bLli0lBVOj0eDm5kZAQECtSaUzJVrMeXHe/431E7PZLL9Ln3vuOXQ6HVFRUdL7T/x/4uHhASAr4YD053vyySelqM2RI0dcyd5fFK4KnwsuuODCn4zS0lKGDh3KrVu3ZPCgUqkYPHgwGo2Gu3fvsmzZMi5evAjYhBMuXrxI3759sVgs7N27l169ekmFt%2BzsbLKysqTvk/gpYC98odVqmT9/Pu%2B//z5VVVWoVCp%2B%2B%2B23hwpeFAoFzz33nAzOW7Vq5RAU2HvGgU0AZuPGjfJ9b29vKisrH4qqJhAbGytpdMK3yllW3x73E40RAY5IUJxh388jEg8RbIvPi4RLrMLbr/jXr19f3gdnQZv7%2BdjVZhXgDOeKghAzOX36NK1atUKn02E0GvH3968hTqFQKGQFUJyz0Wh0oI4593KBo7%2Bi8EyzWm2ehQsWLODll1922N7ew01sW6dOHSoqKhgwYADHjx%2BnpKSEvn37smvXrhrj4efnx507d%2BTfP/zwA127dpWKkiqViqNHjxIfH/%2BHYxUeHs61a9ccRE3sK0z3G1tPT0%2Bqqqoc5pGotNSpU4eNGzdK2XpBmxVVQ%2Bc5rlQqOXPmDI888kit99i%2BihQSEiIr6fD7cyMqxhEREQ6qm6Ki%2Bke9j7Wd138Cf39/jEbjff3s2rdvj16v58KFC3Lx5bHHHquxnRAyun79uhz7V199lXXr1mEymYiIiJACV8InNCYmBr1ej7u7OxMmTOD8%2BfNym8aNG2MymYiJiZEKmmCjbAubEKHqu2zZMvbs2cP06dN58cUXWbFihYM4kPN8CAoKkn5698KDPMOBgYEUFhY6vGZ/LLVaXYMd8DD3TqiPJicno1AoOHLkCAqFgszMTMxmM7GxsVJZ2WKx8OKLL5Kens6pU6dwd3ene/funDx5Ui4WuPDXgivhc8EFF1z4kzFjxgxu377NhAkTpK/duXPn2Lp1K76%2Bvly6dEmq4on/8F966SXee%2B89VCqV7MF7UNTWvyJMxsEWKIaHh3Px4kX0ej0RERFcvnwZtVpN06ZNKSoqorCwkHr16lFQUCA/5%2B7uTkBAAPXr15crxmCzSQAbPfDIkSMOSZNareb8%2BfP07t2b/Px8SWfbtGkTYAvmJkyYIEUfxGsbN27kqaeeomfPnmzbtk0GRvZJRUhICCUlJQ4Br6AbWiwWNBoNvr6%2BMgHy8/PDYDDI6k9kZCTV1dUyyBbBtzjGtGnTeOedd%2BS%2BBwwYwLFjx4iKiuLEiROy/3LMmDF8%2BeWXnD17lkOHDjF58mSaN2/OlStXUKlU9OnTh4MHD8rE8V69RyLBdHNzq0FHFPQ68TlBTwwPD5cqf/b7tjfnnjlzJt9%2B%2B62D6iJAp06dqKiowGg0otPpcHNzo6SkBIVCIXtJRQVKpVIRFBQE4LBg4OnpSVBQEL/%2B%2BqvD3BPnsXr1ap555hkyMzNJTk6mZ8%2Be7NixQ1ZlKyoqaiTDgn741FNP8fnnn3Pu3DkpJhEYGOiwuFHbWNrPkfvhscceQ6fTOXjNabVavvvuO7p164Zer8fT0xOj0cjJkydp27at9AmsLQh/%2BeWXWbJkifxbbJuYmMj%2B/fsdttVqtZhMJmmm3a5dO7777juH86iN4gs2C5bCwkKqq6trbFPbgoeYi2Kf99t3UFAQbm5uslpeG%2ByP4Tz%2BFy5cYPr06Rw4cACLxUKjRo3Iy8sjKSkJsFWic3JyyMvLw2Qy0aFDB7KysrBYLCxatIjU1FQOHjzI2rVr%2BfDDD%2BVxarun9scOCAggLS2N4uJiJk2ahMlkoqSkxKEnMT4%2Bnvj4eKKjo7l27ZqDNczChQul0JAzHiSZ%2BzPg3AYgFjSEn6L997AzFVX0UEdFRTFixAiioqLYsWMHCxcuJCgoSH6nu/DXgSvhc8EFF1z4k9G%2BfXsWL17MnDlzKC4ufqjqWp8%2BfejZsydz5syp1WvO/qfZbJar4RMnTiQ9PV2q14mA3MvLC09PT0aPHs3u3bvJy8ujqqqKSZMm8cUXXwC2oMxisTB06FA2b9780NcrlBmDgoIoLS3l3LlztG/fnieffJIPP/zwofcHyEQ5JCRE%2BlIJ4/h7Vc5UKhV169Z1sC%2BA36mHTz/9NEajkVWrVgG2RKNdu3Z4enpKZUchWgK2Ks/FixcZO3YspaWlMvDdu3cvAwYM4IMPPsBoNPLss8/St29f6YX4/vvvU15ezuLFi7l9%2B7ZMFnx8fAgKCpIJp6D9rVy5Uva4CYjVfxHI/Sf2F97e3oCtQvvZZ58RExODWq2ukVx6eXmhVqvR6/U1Al2hhmq1WomPj5fiFhEREbi5uWGxWBwM7Js2bUp2djbfffcdPXv2ZOjQoWzatIkGDRrw22%2B/UVlZSVBQUI2qpEqlIjg4mKKiIl544QVpmWB/D0XgGh4eLitFQoTinXfeYe/evRw6dEheQ7du3R4okBWiMFVVVbRt25asrCwWL17MSy%2B9hEajkQlAbTRtZ/83ZyQnJ7N37155/n5%2BfgAOCzpubm6EhoY6VPvsK8ZPPvkkBQUFZGZmSpEaMR9EsizmRoMGDYiOjqaoqIjTp087JKoiiRLPq0KhYPTo0ezbt4%2BioiIsFotD36vznKst%2Bf34449Zv369rLwtXbqU2bNns2rVKt566y0KCwvp3bs3/fr1Y/To0Zw%2BfZpHH30UvV6Pj48PVquVJk2acOXKlRrz0nmcAUkz7t%2B/P9OmTeNvf/sbBQUFDnYm98PDqNsK2qNCoSA%2BPp6MjAyHiuu9kJubS35%2BPgAFBQUUFxfL7%2BP8/Hxyc3MpKSlBo9FQVVVFVVWVZBMIIRedTkdgYCCNGzcGkMrLp06dkpYZ9os894JYUNLr9Wi1WmbNmkXDhg3ZunUr33zzDXXq1GHOnDn069fvgcbEhf8euBI%2BF1xwwYU/GR07diQkJIRevXqxd%2B9erly5gq%2BvL3fv3nUwv71z5470NmvdujV%2Bfn688cYb1K1bl5iYGLy9venatStdu3Zl1qxZnDt3TircCbqTTqdjwIABFBYW1lBaE%2BbcQhBD9FWJXsE/ava/V2VKeHyJ3q1t27YxaNAgwsPDuXr1KhcuXCAuLo4ffviBnJwcNmzY4GC%2B7Lx6L2hM9kHd4cOH6du3L8OGDeOzzz6TlgyBgYEUFBRISqXVapUJnoeHB3Xq1JGqfw0aNMBqtXL79m08PT1JTExkyJAhjBo1Co1GI73oevbsyfjx4%2BnduzfdunWT59O/f3%2BsViu7du2SVdP/VCChNg/DB4GowpaXl0tJeo1GIymd8LsQhwjOvb29KSoqYujQoWRnZ9OkSRPmz59PTEyM3K60tJQZM2awbNkyKQwh%2BhlFEmc2m7lw4QKtW7cGIDU1VRqpR0RESFGdzz77jFGjRmE0GmWV12q1EhISglKpJD8/XyoFgs2QurS0FJPJ5BDkCwVJIbTSrFkzrl%2B/Lqu3Yv9/%2B9vf2Lp1KwDr1q1j2LBh/Pzzz3L%2BtGzZEl9fX1q3bs3x48flHBZJ5axZs6TdgbifKpWKkJAQrFYrubm5UuRIrVbLhM65yrdgwQLefPNNh3tgn3yBI00WkOeVk5MjqYfOaNq0Kb169WLNmjUoFAqZeNzLTkV8l/wR9uzZI%2B1aRJKwZ88e1q9f77DQYX8/hDKsGCdnKrCzaJS9P6BGo6F9%2B/YkJyfz2GOP0a5dO1nJF1XmFi1aEB4eDtgEfkR1efLkyYBNdbekpOS%2Bix0KhYJXXnlF9t9qNBr8/PwoKSl5aINysFU9O3fuTElJiVwwUCgUREZGynn3Z6KwsJA%2BffoQFhaGyWTil19%2BQaFQSM9U8eyoVCoptlPbYkWTJk1Ys2aNy3T9LwqXaIsLLrjgwp%2BMuLg4Ll%2B%2BzIgRIyQdzZlSZa9kaLFYZDVw1qxZcpuJEydSUlLC3Llzqa6uZsSIERiNRs6dOycb8Q0GA5s3b65VVts%2BKGzVqhUBAQEykJo6daoUk4Dfq032cE723N3dpbWEEKNQqVRcvXoVpVJJdna2FMDw8fHhiy%2B%2B4NKlSw7KhbGxsbRt2xawJWgdOnQgLi6OiIgItFqtPGZCQgJVVVUyUQwICGDevHmkpaUBNqpfly5dpPjF5MmT%2BfTTT1m7di1gC4K7detGnTp1MJvNPP/88yxatEj2g%2Bl0OnJycvj555%2B5desWnTp1kgmV8DjcvXs3e/bscfDmAlt1RUifb9iwAa1WyzvvvMOCBQtQq9UEBwdLsQjhXeac7Lm5ueHu7k7jxo1l1adx48by81FRUXh7e2M2m7l8%2BTJ5eXkoFAq6dOlCZmamDN7Onj0rq7xZWVlkZWWRmJiIm5sbSqWSc%2BfOSfNzkYw0adIEhUIhhXMEVCoVZ86c4dy5czIIfP311%2BX7kZGRDtv7%2BflRXV3N6NGjpRKoTqdj/PjxBAQE0L59e3JycqhXr54DvTUnJ4enn366hseYyWSSFhOAQ0%2BeuF7RUygWL4TZ/L59%2B1i8eDETJkzAarVSUlLCyZMnHRKRr776ikOHDtGjRw/c3d3Zt28fYPOiM5vNhIeHy/khqoei97E2SufmzZsdkiGxAJKRkSEVQZ2rVuXl5WRkZMjeLmGSLQRLxH6ENYEQuhFiR2Cjf4p7p1KpHI7RqFEjkpKSmDp1KiqVCj8/P3lNffv2xWAwONz3xx9//J5VfZPJJBd2APr16ycrTgIvvvgiJ06ckFT05s2byyRZWJcsW7ZM9gCPHDmS6upqLBYLlZWVnDlzhi1btrBlyxY5xmazmU8//ZQ1a9ZQXFxcQzXWGVarVSZ7YLvXRUVF0obhrbfews3Njb59%2B%2BLh4SEXJezH0x6lpaXs27ePkydPyv0dPXpUmqP/2QgMDATgyy%2B/ZOfOnbi5uaHVaikrK5PzPS8vj5ycHIxGIwaDocb87dmzJ/v27XMle39huCp8Lrjgggt/Mn777TcSExPlf7L2fRcikPz8888dFCXtaW7%2B/v6UlJTg5%2BdHZWWlDJY1Gs196Tu1QVQQR4wYwezZszly5Ah///vfZcImqEYC9pU/tVpNUlISp06dklWz2rYTvz9I1dDd3R0/Pz/y8vLw9fXF3d0dhUKBn58f3bp1o3PnzrKKZH8N4jjz58%2B/Z%2B%2BNSqXC19e3VnqXQqFg5cqVdOjQQQaf0dHR6PV6vLy82LhxI4MHD%2Bann37C19eXXr168eabb3L9%2BnX69%2B/PV199Bdj6wD755BOHisOzzz4rk2uz2Yyfnx9VVVXSA0skK2PGjOHYsWP88ssv9O7dW/Yxzp07l82bNxMWFsbf//53EhISmDt3Lnfu3OHw4cMEBgbSvXt3jhw5QnV1NUOGDJGJbb9%2B/di2bRtz5swhMzOTGzdu8NNPP9UYu3r16kmamXPfnfMYhoeHU1hYWGMcBw8eTExMDHfu3GH58uW0bNmSy5cvyySsqqrqgSqaCoUCHx8ffH19a8w/ewhRiv/NsEb0our1etq3b8/JkydlZTosLIxdu3bRpk2b/7Vj1tY/p1AoSE5O5ttvv8XHx4eUlBSio6MZPnx4rc%2BQoFKazWY8PDyYN28eAwYMoFWrVnh4ePDMM8%2Bwfv16BwsYsZAjGAEWi6XWcwkKCpIJY5MmTVi3bh0XLlxgzJgx%2BPn50aJFC8xmM5mZmdIeQohJCYjkyblK/zAIDQ0lPz//gSpyarWa1NRUWrVqxZUrV1ixYoX0nLOnOWo0Gvl9K8ZUiJgI1EbJFQs6sbGxAAwbNozExESSkpJq9Uj9v4b4Ply3bh1PPvkk586dA35nRtj3Vwo0bNiQkpISysvLUavVhIaGkpubi0ajISgoiKqqKofk3Z6B4cJfA66EzwUXXHDhvwBLlixh69atxMTEcPToUQcaZW1f086BiD1EH5VKpeKRRx6hbt26DsImIkAZPHgwV69e5fTp0/JYycnJHDx4ELPZjK%2BvL6WlpdSvX79G0OYMpVKJh4eHVIFs27YthYWFUjBE9JeJ/jihLmivYvh/AedEQviRPQjq1q2Ln58fly5dwtvbGy8vL9zc3OjevTteXl54e3vzxhtvyG07duwoE73/FOL8xJjYn6%2B7uzsWi8Xhb7BVs%2BxX5EeNGoW/vz%2BPPfYYjz/%2B%2BP/ofOwhzLr/m8IGUZls0KABYKtK3rx5s1ZvtYCAAEpKSnjkkUc4duzYPfdpn4TY9wJqtdoHptmKz/Xp04evv/4a%2BL0yJCiw9xPnsfcZFKgtAatN%2BVTAw8OD5ORkxo4dy7///W%2B2b99Ojx49OHr0KD169ODw4cP8/e9/Z9GiRbX2fIp916YEqVAomDhxIocOHeLGjRsYDAZCQ0NRqVRS5VZ4SApKNcCHH34oKdf29EdRHdVoNCgUClq3bs3EiRM5ePAgX3/9tYMabFJSEsXFxWRkZDBy5Ei%2B/PJLwPa9N3LkSG7cuMF3331Hly5dOHjwoLRcEBTfP%2BptFQl9bajtO0vQx%2BPi4mQfrIeHB0ePHq3VI/X/Gh988AF6vZ7p06fz7rvvsm3bNry8vLh06ZLs/buXKI8zPDw80Gq13LlzRzILZs2aJam%2BLvx14Er4XHDBBRf%2BRFitVn755RdJjxswYACbN28mLS1Neu2VlZXJoE4E3eKzERERXLlyhYCAAAoLC2Ui16pVK1atWiUTv/Lycjp27EjHjh1Zvnw5YKP3Wa1WWrdu7VDB%2BSOp7xYtWsiASKyIgy0BERS7I0eOEBwczOXLlxk3bhylpaVYLBbq1KmDXq/HYDDQpUsXTp06xWuvvcbhw4c5dOgQAQEB3L59%2B6EFRxo0aMCUKVMICgpiwoQJtW4jhFYEfcvd3Z1FixbxyiuvYDQacXd356uvvqJPnz61BuLOAXWDBg24deuWPFeVSiXft1cuFdd69epVoqKi2L9/f40AWwT3oodLvB8cHCwrbQ0bNiQ/P5/g4GBUKhX5%2BfkOyas4PyHes2jRIqKjox2OI87Pngr81ltvMWvWLBYvXvzAEu8KhaIGLVgoLRqNRp5//nm%2B/PJLDAZDrf1iYlzsE5qHXQCwFxUZPXo0M2bMAGx02CtXrtCwYUNycnJ48sknARttd8WKFezdu5dNmzahUCjktdurOY4cOZL9%2B/fLxYq2bdvSuHFjtm/fLimTd%2B/eJTg4mIqKCsrLy/Hy8qJ%2B/frSOqW28XJONlQqFQkJCVy5coWSkhLUarUUWZk%2BfToTJ04kOjq61iTbvjrj3A8XEhJSQ4hI4I/EfOLi4jh79iwDBgwAbH1yAwYM4KuvvuLxxx9nz549D2XhoNPpaNmyJT///DNg%2B85p2bLlAyVeggLeo0cPxo8fj16v5%2B233%2BbWrVsUFhai0WgICQlBr9fj6%2BtLs2bNOHDgACqViueff57Tp09z/PjxWp%2BR2hASEkL9%2BvU5f/481dXVKJVKBg8eTPPmzXnrrbfuqQgq/A3FIp1Go5ELBMnJyTz//PPs2LEDtVrNmDFj%2BOabb/jggw%2B4c%2BcO3bt3Z8aMGXz//fcAJCYmkpWVxSeffEJBQQF9%2BvRh5MiR8hgKhYKff/6ZevXqSTXct99%2Bm9LSUtLS0li8eDFr167FZDLh4%2BPDqFGj8Pb2JjIykps3b7J79258fHyorKzk6tWrmEwm3N3dKSoqknMPbM9yx44dWbx4MYmJiZw4cYIVK1YAuHz4/qJwJXwuuOCCC38ScnNzefbZZ2VgGRUVxfvvv0/dunVrbJuQkMCqVat49tlnWbVqFUOGDMFkMtGyZUuys7OlZH5AQAB3796VynlqtZr169dz7do1SdtKTU0FkNWp1NRUaaRstVoJDg5GoVDIHjV7Sp9Wq8VisZCQkMC8efNISkrCZDLxzjvvkJaWJqsZAE8//TRTpkyhY8eOUtTEYrHwww8/kJ%2BfT2xsLJ988gnV1dWMGTOGq1evsnXrVkaOHMmUKVNYvXo1R48e5dFHH2X16tU89dRThIWFcfv2bYdKS9OmTZk6dSq9evVCq9USExNDdXU1aWlpPP/88/I6QkJC%2BO233%2BTYuLu789133xEfHy9phmfPnuXmzZskJiYCyD7Fbdu2UVxcTEhIiDxugwYN%2BPHHHxk5ciRgU%2Bn86quv6N%2B/v7QJCAgIcLhvaWlprF27FpVKJcVICgsLHdQO7Wljr7zyCm%2B//Taenp40atRI2ljA72I4IkEXypn2yoDCA9EZwhNRbCOEfUTg2rVrV7799lvMZjOdOnWiXbt2eHl58eOPP3Lt2jW8vLwYN24cHh4eku7Yo0cPaaD91ltvsWjRIr755hu6devGP/7xD5o0acLo0aNRKBScPXuW5557jrNnz9KzZ08OHTpESkoKffv25erVq4SHhzN9%2BnTOnz%2BPTqejoqKihqCJmJcREREolUq2b9/OrVu3CAkJkXTFw4cPM2/ePJRKJY8//jgKhYJDhw7J/djTb8GmILtx40aGDx8uxUYEBVH4sIWFhZGfn8/y5cuZMmUKoaGhxMfH07x5c5YsWcKQIUPYtGmTQyLWvHlzfv31VznvYmNjCQwMZOfOnSgUCpo0acLNmzf597//zejRo6W5vL3KpqjMg03tUiQvYoHj448/JiUlhf79%2B5OWluawcKNUKlm0aBE3b95k5cqV8npDQkIoKCiQCpAzZ85k%2BfLlco61bt2aDRs2MHr0aCm2U1v1FJCqnWBLQrt37y7ZCsLcfN%2B%2BfeTn57N48WKysrJkEmNPM4yKiuLll1%2BmSZMmfPLJJ2zatEnSfxs0aEBBQQHV1dUOFU%2B1Wk337t3lIsSuXbsYNWoUer2e6upqdDod1dXVeHp61hD%2BycjIID4%2BnnHjxpGZmcnly5dllV0spGk0GsaMGUNaWppcgBPWBVVVVRQXF6NSqWRvpcViISwsDL1ej0ajIS8vT16ruCdqtVoKFonX7D08RfJoMpkICgri9u3bNXw/xfdWeHg4gYGBpKeno1QqadasGUVFRbK6er9QPysriyeeeEKyOKz/z95GVPRu377NvHnzHFoKXPjrwZXwueCCCy78SXjhhRdQq9XMnDmTu3fvMnPmTFktyMvLo7KyErPZLJOx2vqd1Go1ERERZGdnO9Ce9u/fz4ABA1AoFJw%2Bfdqhz%2B3UqVMAUpzh1KlTMuByc3OTPR8RERFATaNpEXiq1Wq5eu7ci2NPKxXBTHBwMMXFxdSpU0dWd5xl/e2vLSAggDZt2vDdd9%2BRlZVFy5Yt5fsiELJarQwdOpT9%2B/ejUqno168faWlpVFRUkJSU5OBvptPpmDRpEitXrkShUBAYGIibm5uD%2BmHdunVp1KgRp06dkgGfoLfGxcWhVquZP3%2B%2B3L579%2B6yAnfp0iViYmJYu3Yt48aNk%2BMxaNAgtm7dyvnz52nVqhVarVYGdT4%2BPg4r62DrkxKy9yLZEHRC4a%2B3bt06goODSU5OluMgFBEvXbrEnDlzePPNN2USJyiCIpEMDQ0lLy8PDw8P7t69y08//SR7kARE8ibur6iYqVQqGjZsyI0bN%2BS8FPdZzFGVSoVCoeD8%2BfNStfPs2bO0bdsWtVrNlClTWLJkCe7u7nIfVVVVXLp0iVatWnHx4kUiIyMxmUzS1PzSpUtERkbWSnNWqVTMnDmTxYsX07t3bw4cOIBCocDDw%2BO%2BsvrOFa%2BUlBTSkkv/2AAAIABJREFU0tIc7omHhwdVVVXy58mTJ%2BnatSsxMTFcvnyZhIQEzp49K5Vde/fuTd%2B%2BfeXn3d3dGTJkCG3btuWll14C4MCBA6xZs0bSEe91PgJCZVWIm%2Bh0Oj766CPGjx9fQ1FRzIP69euTl5eHVqtFqVQSGBgoLUsEGjZsSG5ubo3xFFViIXpUG1QqlYMdiMFgkFW5PzJ7FxDCQt99951kF8TExHDt2jVWrFjBxIkT79mnJxa3xJhMnTqV9957T1rG7Ny5E7PZ7PD5pk2bcuPGDYcq5dGjR%2BnVq5dMyICHVteF36mdderUoXnz5qxZs0bST3U6HbGxsdy8eZOioiJ8fX2JjIzkxx9/pKioCHd3d1JSUvjyyy8pKyvD09OT6dOnM2/ePDnGIukTLI86depQWlqK0WiUdGFRDXZWR31YCLaC6AMfMmQI/fr1o1OnTv/xPl34c%2BFS6XTBBRdc%2BJOQmZnJnDlzqFevHm%2B88QYFBQXcvHmTixcvcufOHZkUiWBM/LQPws1mM4MGDZJm4QMGDMBqtfL444/LatH%2B/ftZuXIl69atY926dWg0GjQaDevWreNf//qXXJUWgYUz7JM9QHq92VOl7Kmd9n/bv5afn4/BYKC4uJjy8nLpJ2WvOGhfufP09KRevXoYjUZZxRDHtD%2BfN998k7fffhuwmbyLsXA2s54wYQJPPfWUPKeysjKHPsimTZsyZcoUmRArlUo6deqExWJBr9eTmpoqFSwFRCXJPuEVyZ54/eTJk3h4eMgkTgRiXl5e8lztYbXavOJ8fHykuqS47oYNG2K1Wlm%2BfDlJSUlyftj7a7Vq1YotW7bQqlUr9Ho9RqMRs9mMwWCQf2dnZ9OgQQN5L4Xpuv19s686WK1WKcqiVqtlsge24NDf31%2BqLYr7o1KpSElJoU6dOlgsFlmVNhgM0tRar9djNpsdglNxb52pg61atXLw8LOH2Wxm0aJFmM1m9u3bJ0VLmjVrBtiSLh8fH7y8vBw%2B16JFC/m7l5cXs2fPrqGUGRQUhNVqpbq6mvDwcBYsWIDBYCArKwu9Xs/333/P8uXLuXbtGmPHjq3hCWi12mxDhMon2AQ17Kus9gq8zlAoFNJP0p7OPWbMGAwGQ42ESPR2isq22Hdtc02o1jqfR1paGrt373Z4LzExUVal7LeF3%2B%2BVeJbFe2I%2B%2BPj4OLwvtlEqlQ5WGB4eHmzYsIF%2B/foxbdo0jEajw2fAtvih1Wo5fvy4nHMajYZdu3bJipno0RRjI87j9ddfr5HM9ejRQyZKCoWCuXPnkpSUJPsFlUol06dPZ/z48TRt2tThswkJCQ69tFarFb1ez/nz58nIyKCyslL2TX/66af4%2Bvri7%2B/PtGnTWLlypfy%2BUygUvPDCC4SFhVG3bl2mTJnCsGHDZNVUpVIRHR3N%2BPHj6datGykpKej1enn/fvjhB4KCgvD29ubTTz8lIyND3ichwtKwYUMUCgWenp5otVp8fHzw8PCQfZMeHh7y%2BYiKiiI6OprevXtTv359YmJiSE1NpVu3brjw14Qr4XPBBRdc%2BJNQVlYmJf2zsrL46quv5Oqsu7s7x48fx8PDA3d3dzp16sScOXPo1q0bOp0OlUolA40PPvgAtVqNp6cn3bt3p0mTJvLvoKAgVq9eTZcuXejUqROdO3cmNDSUgQMHsnHjRpKTk%2BnZsyeHDx92kB4HW6VAp9PRunVr6eUGvwdtzoGYPeyl5%2B2h0WiA36uEFouF%2BvXrS7GDRo0ayeMeOHCA119/HY1GQ0ZGBmCjMTrvt2fPnjz77LPcuXNHVtucbRFUKhVr1qxh0qRJ8tpUKpVDEHz9%2BnXmzZvncA27du3i7t273Lp1iw4dOmA2m7l06RJ79uxhyJAhNQJ0cU32iVNCQgLR0dHMnj0bsPWSieRa/PPz86NZs2Z4eXnJxGr48OEoFArmzZsnP/PLL78AtsUC%2Bwog2Hr2VCoVQ4YMQalUMmTIEBnU%2Bfr6Sqou2JRdd%2B7cKU3l09LSsFqtPPfcczz33HMONDtxn0U1VqFQ0LdvX0lvFcm/u7u7DKwF/S4rK0tuJyhjYhvxe21BfW1Ys2aNnD8LFy5ErVbj6%2Bt7z%2B2tVivZ2dnUq1eP1q1bU1paSnl5uRTxAGRvGSATNec5K0RIrFYrV69eZffu3bIi6enpSWFhIREREVitVtLT0zl69KjD5z09PSkqKqJLly7yte3bt8ueKPj9ubjXdTgnuULR1RlarVb2gznj2LFjNY4jfNjsjwXUKuxx9%2B5dmaTB73Nd/IPfF4MUCgWPPvqog42LVqtl48aN0l7j2LFjXLhwgQsXLsjnf/PmzSxdupTdu3c7VGaVSqX0MBSeh8uWLZOLGAaDgV9%2B%2BUUueuTl5dW6iDBu3Lh70hvFIsdrr73G/v37sVqtZGRkYLFYWL9%2BPZ988omcC2B7hhISEggICHDYj9FoJDw8nFdeeUVea1VVFW3btiUrK4vc3Fw2b95MSkqKFJIxGo3ExcVx6tQpcnJy6Ny5s7xesahz4MABDh48yDfffENcXJys%2BikUCnr16kV%2Bfj5lZWWkpqaSmJgon3WTyUROTg75%2Bfny2idMmECXLl1o1qwZffr0ITU1VYrmAJw7d46zZ8%2Bya9cuqqqqWLJkCU2aNGHq1Kls3Lix1vFz4b8bLkqnCy644MKfBNE7BTBixAiWLVtGz549JQXu3XffZcaMGRgMBiZPnszMmTNltUf05%2Bh0OrnyX69ePe7evUuzZs0cpPbBkSomej%2BCg4NZuHCh9NkS/V7inEQfmr2ZstlsZuXKlUydOpVGjRqRk5Mjg8A33niD2bNnExAQgLe3N6GhoWRlZXHnzh0ZaPz444907tyZ6OhoiouLqVevHj4%2BPuzdu1eeq9hfcnIyZrPZ4b0HhVKpRKfTMXbsWFavXv1AYjTOGDJkCIBUuXOmXopzdRbNeBA0aNAAlUrloCip1WrRaDRUVFRQv359PD098fLyorS0lKtXr9K4cWMCAgI4ffo04FgNUigUbN%2B%2BneHDh3Pu3DmHvjywjfvu3bt55ZVXAFsyuWDBAsxmM23atEGj0TBt2jTGjx/PlStXGDhwoEw0hFWEGD83NzeaNGlCUlIS77zzjhSUcE5%2BJ0yYQEFBATt37nR4rzbBGtG7ZE/fFJRi%2B9diYmLkds7XaE9BFRAiPSI47tixo6xcWSwW3Nzc0Ov1KJVKnnjiCcDW/yUqKxaLRdKIQ0NDuXXrFuHh4RQVFVFeXi4XDTIzMxkwYAAFBQU1qHRJSUkcP36c5cuXOwgK1WZF4awi66zMGRISQn5%2BPgqFgvj4eH744QdptH6v8b3XvsHmlXj58uUaVcKkpCQaN27MRx99JCvOD4tGjRqRm5uL2WyWaqXu7u5y4WDUqFF07NiRNm3a4O/vT0xMjOyDi4uL4/jx4w98rP9Lpd%2BHPe4fWU24u7vXUNZ1/nyjRo1kclnbMcS8vhfEvBHUanGse41TTEwMWVlZ8j7b98uGhITQokULfHx8%2BPrrr/H19a2xqOHCfz9cCZ8LLrjgwp%2BENm3ayKrerVu3WLZsGT/99BN169alvLxc%2BmgJ2qFOp8PNzY2ioiLi4%2BPx8fGhV69e7N69m4yMDHQ6HWPGjGHv3r1SCMY5qHzkkUc4e/YsHh4eNG3alM8//1y%2BFxMTg9Vq5ejRo/j5%2BcmAW6fT8cILL8gKiAjA7ftE/P39OXjwIO3atUOtVqNSqXj55ZdZuHChQ7CYnJzM3r170el0eHp6MnnyZEaPHk1sbGyNfr7a8D/tTbHHo48%2BSps2bfjoo49Qq9WsWbMGgLFjx0oaFtROs4P7B5k9evQAbFWMmJgYKisradCgAQcOHKBNmzZ0796dCxcuYDabSU9Px9PTE71eLyskfn5%2BREZG0rp1a3lebdu2paioSHocVldX8/LLL7N06VJ5HvXq1WP//v20a9fOwWPQedxiY2O5ffs2xcXFDuIkVqu1RoWrtutVqVSsX79eCtbUhtDQUF566SXUajUvvPCC3K/9nBT9ZYBDwjd//nxZbRW/z58/n/nz52MymZg/fz4LFiwgPT2drl273tO%2B5F73SKVSUbduXUJCQvjxxx/lIoOoztb2WZ1OR8OGDcnOzkar1UrBmvLycho1asStW7dqlbv/I2VM5/MS46RQKAgLC6O4uJiKiooa90V4cdp7cv5vJT5KpZL4%2BHgyMjJkJS8%2BPp5mzZqxa9euWumh98P/dkIWExPDyy%2B/jEqlIjg4mGXLlmE0GqWljL3iq/hn/7o9fH19ad68uayaC0GlGzduyB7bF198ke3bt3Pt2jWHz0ZFRdG8eXO2bdtG//79%2Beabb2SVrLS0FKvVir%2B/P%2BHh4Zw8eVL2NgsKdo8ePbhw4YJDb559X2/jxo0JCQmRya%2BgXJaVlUmhp6ioKM6cOSMXJ0S/qbC%2B%2BU%2BSdYGQkBAp4NWzZ09GjBhB165dHar0Lvw14Er4XHDBBRf%2BJAh58tooW38EEcT4%2B/tLsYBu3bqRkZEhqXMeHh68%2Buqr7Nu3j2PHjmG1WunXrx8vvvgi/fr1w2AwsGjRIjp16kRQUJBM8ACHaphCYTMVFpUlEZTbVwy6detG165d%2Bec//ykDmtDQUHx8fPjxxx/lPkXQKlTzdu3aRZMmTYiNja1RfRB9JUI2HCAsLIzs7GxeeOEFPvjgA2kMX1hYSH5%2BPhUVFTJx9PDwkMIGgu6qUCjYvXu3Q/9UZGQkkZGRLFu2jIYNGxIVFYVCoWDRokXs3buXQ4cOSSNqsKkhgk2eXFQlwFZFE9Wmjh07AnDy5ElmzJjB559/zqBBg1i%2BfDmdO3emfv36pKWlOagTNmrUiJs3b8oxcvZYa9GiBdnZ2XLl3mg08u9//5unn35aiiuo1WpatGjBxYsXadGihYNip/24Jicnc/78eW7fvv0fJdCiwiSotj4%2BPpSVlREZGUlWVhYA9evXp6ysjKqqKkwmE56enlRUVJCYmFjD0kGgNq85kQTZJ4r%2B/v5UVFRgNBrvG9CK58tePdJqtZKamkplZSWfffYZhYWFgC3w9/T0JDc3t9Z9CFXGkpISeR5CGKU2eHp61uhPtH%2Bu7O%2BveF34a4qfot/W09OTgoICh4RQJCX2npyNGjXCw8ODvLw8SYkUFMiwsDBycnKYOHEiAKtXr8bPz487d%2B7USk3%2BT8JD5%2BRWCB8Jo3NnaLVaKTqjVqvx8/OjTZs2cn4sXboUsCkar1ixAovFgp%2BfH3fv3sXNzU2qUd6%2BfVuKSDVq1IjExES%2B%2BOIL2VMnIL67xFyqzbjevgpp/3ttDIHw8HCuXbuG1Wpl8uTJfPLJJ3IcjEajvL/iXouf9srHYpzF3BfHdE5WwfadZrVa5Tk5n39tIlDiubvXPbLfRvRDmkwmzGYzGo0GNzc3ysvLiYmJQafTuUzX/6JwJXwuuOCCC38S7APLvn37Sk8w8Z/x22%2B/LT3CPvnkE27fvk2zZs3YsmUL27dvR6lUyqAXav5Hrlar2b59O88884w8VkxMDAaDQfqFKRQKvLy8ZC%2BMSqXCzc1NBhVCDn7SpEm8//77gK3fMDo62qFqtH79ep555hkZWAQEBBAbG8vcuXNltcsefn5%2BkmaXnZ2NXq%2BXvVXDhg3jyy%2B/ZN%2B%2BfRQVFTFu3DgMBoNU39u8eTOJiYkcPnyYixcvMnXqVA4cOAD8bpYt1CAtFguZmZlYrVa8vLwkDW/WrFkMHz6czz77TAaVYDOLFgmAM8T1BgcHy34fe1qVqLQ4B8v2SYeg03p7e1NSUiLf9/DwYMiQIaSnp/Prr786HFcEcT4%2BPlgsFgehGXvci17m/NqAAQP4xz/%2Bwfnz55k1axaVlZWMGDGCZ555hqSkJCwWC9u2bWPFihVSpCYxMZH09PSHSg6//fZbLl%2B%2BzL/%2B9S%2BysrLw8fGhpKTkoSpe4hrET2fpfxEce3h4yLmnUCiIiIjg5s2bDgIsKpWKpk2bcuXKlRoJoEBtCZw4hkqlIjIykgsXLsgeVHd3d0pLS2nVqhWXL1/GZDLh6%2BvLI488wjvvvMPWrVsB6NKlC/7%2B/nKBory8nJdeeomlS5fKvlShzlhZWVnDPsAZf1R9FqhXrx4VFRXUq1eP4uJi7t69S0pKCjk5OdJCRSQyD3tf7ndeWq0Wf39/YmNjOX78uPRiVCqVaLVali5dyvPPPw/AsmXLOHjwIJmZmZhMJu7evftQ1GsB58TGHuIZEvdXPBe1PbNCJVer1fLEE0%2Bwc%2BfOWpPV/23UlnzaQ6FQoNPpaiSxfwSRNIq5PnjwYG7cuMGpU6fQarX4%2BfkRHR3NwYMHAaSia0lJCSaTSdJHX375ZQICAlym639RuBI%2BF1xwwYX/AvTt25fNmzczfPhwGXRt2bJF/j169GiWLVvG8OHD2bx5M97e3ty6deu%2BQZqXlxcqlUoGW1qtFpPJVGMF3lkhcPHixcTGxtKnTx%2BZoNhTg2o7plKp5ODBg1LdLjU1FZPJxN69e2UVqEePHhw5csThcyKJEtVCoegoAixhI1Db8SwWC9OmTePDDz9Er9fz6quvsmTJEsAmOvD999/zxhtvcPXqVZlw2XvdqdVq3N3d6dOnD9u2bZM2EmazGZ1Oh8FgwGq14uPjI4Ms58BPBMuiInjo0CF69uwpvQ4VCgUzZsxg2bJlfPnllyxZsoRjx45Rr1496b8n/lVWVjqMbdeuXTlx4gRz587lH//4x33nT21QKBQ8%2B%2ByzfPzxx7VSDe8HURls2bLlA1d6fH198fPz4/r168DvvaIdOnRgzpw5mM1mnnzySQICAqQxuL1qpn1lS5g7r1u3DoAzZ86QkpJCUFAQXbt25fPPP8fPz4%2BKigr8/PykWI998mKvbuvv78/q1at57bXXavS33gvNmzfnt99%2BIzQ0VH5GqVRKQY3r168zZcoU6tevz9WrVxk0aBARERGsXbuWkpISevTowahRo3B3d2flypXk5%2Bfz888/Y7Va5Xy3F1ERCzeC2ifOHWz0759%2B%2BkkmBKIyJ9738fFBq9XSsGFDfv75Z6qrqx2qx25uboSFhXHp0iUHuuPu3buZNGkSt2/fZvPmzQwcOBD4XahH0CCnTJlCQkIC6enpvPfeexiNRho0aCCTp1mzZpGTk8P69evlMa1WK61bt%2BbJJ5/kX//6FyUlJQQHB1NQUMDHH3/M2LFj5RwoLy%2BvsUgSHR3NpUuXak22unbtSm5uLsXFxQQGBpKbm4vJZLqnhcOoUaPYv38/RUVF8jhNmzbllVdeYfr06ej1el588UVWrVrF2bNnHdgOzhDJvnNyVhslurYFl/%2BNxNoZou/x8uXLlJWV4eHhQffu3cnIyKCoqEhWRU0mEyqVipCQEAc7mvtVdH18fDAYDOzcuZPQ0ND/1fN24f8/uBI%2BF1xwwYX/Ahw9epQdO3bQvHlzDh48SHl5OW5uboSGhnLhwgUKCwuJi4vj6tWrPPbYY2zevBmDwUBcXBxnzpyRiZG3t3et4iJw7/43Ifmdl5dHeXk5TZs25aWXXiIlJQWABQsWcPjwYQ4dOgTArFmzuHHjBlu3bsVgMKDVavn4448lxVGIYDxIUKPT6fD19aWwsLDWPqyHoZaJ6sHbb7/Nnj17OHbsGA0bNuTChQuo1WqOHz/OsWPHeOWVV7Babf59zZs3Z8OGDVL9MiEhgcDAQN544w3atm0rve/CwsJ46qmn5D0RFExfX98aYjc3b94kKSlJjq29iAog6bD2VENhN/Dhhx8yefLkhxpDqEmFFH2U586dk9ch6GRjxozhiy%2B%2BoG/fvtLXbs2aNdIX0Wg08vrrrzNixAiioqKwWq1MmjRJit/UtmAwcOBArl%2B/TnFxsRSbGDJkCDNnzkSv13P48GGWLFmCwWDAZDLRoEEDRo8eTe/evdmxYwePPPIIKSkpHDx4kG7duhEUFCSrzW5ubuTl5REUFETr1q05duwYXl5e3Lx5U9KEhbS8Xq%2BXyYyg/4r589Zbb2EwGJg7d648/zZt2lBYWEhsbCz79%2B%2BXNDuLxUJgYCD9%2B/fnpZdeIjIy8oHug6%2BvL2azmbKysj%2Bs2NjDfp4PGTKEpKQkmjdvTkJCAmATkvnggw/4%2Buuva1T0VSoVH3zwAc2bN2fmzJlS8KOgoIDo6GgpUuPcm6hQKOjQoQMZGRm0b98eq9XKDz/8IJNmQdNVKpU899xzAGzYsAGDwUCbNm2Ii4tj1apVDuqO9rRFgT%2Bax7t27cLf359HH30Uq9Xm/Zaamsq7774rexP79OlDVlYWv/32G3Xq1GHgwIEcOnSohq/gg0KlUtGzZ0/JDIDfK/SiGmjv9Smg0WjQarW0a9eO9PR0Bg0aRJMmTdi4cSNHjhzh5s2bFBYWsmHDBpRKJVOmTOH8%2BfOysh0QECDnVmFhIX369GHFihWUlZUxfvx4srKyqK6upm7dusTHxzNo0CBmz57NvHnz2LBhA4cPH%2BbMmTN06NCB0NBQvv/%2Be3JycggLC8NsNvPjjz9itVpZtWoVH330EV27dmXatGkUFxfXqrxaG%2Byvu0GDBhQWFqJSqfjqq69cCd9fGK6EzwUXXHDhvwAtW7YE7q3sdj8kJiZy6NAhh88KVcN7wV5JTgTtD7PqbN83BDYqXFVVlaQpjhw5kp07d%2BLp6SlFRuwDP5VKhU6no6Ki4v9j772jo6ra9v/P9JJMejUJhARIaEkgtNDBQguhQ0BBilJEpImAFEFEEREsoPAKSJUHBEI3dAFBmkAAQ1WQHgIkIYVkkpn5/ZG1tzMpiH6f91V/a661WCQzc86cs88%2BJ/e97%2Bu%2BLocA1MXFBZvNVoqaJYLQ1q1bs2/fPtzc3CT10mAwUFBQQEJCAps2bUKpVKLT6RzMshUKBampqQDUqlULs9lMaGgoFotFrnQLD66OHTuSmZlJmzZtZPXOfmzc3d1xc3OjYsWKXLp0SfZKpaSkcPz4cSn6IsZ33bp1vPTSS6SkpHD79m0ZxIvv1Gq18noI1cn8/HxWrFhB//79GTx4MFAsAnH%2B/HnOnz9Pbm6uQ0Lh7u7Oq6%2B%2BysOHD2UfEfzeK2lfVWrYsCFxcXEMGTIEgG3btjFp0iR2797NokWL2Lhxo1Rj7du3rzTTLmnEbj/fQkJCsFqtpKWlOdDxxHb2fUohISEsXryYc%2BfO8cYbb%2BDn5ycrdCVR8ntEoGw/lwSVz2QyUa1aNVJSUigoKKBmzZqcO3dObuvm5oa/v78UNBLHZzAYWLduHZ06dZI9gQqFgunTp7NgwQLy8vIcqLt/tkIjklFvb28KCgrIyMhg%2BPDhLFy4kK5duzJo0CACAwNlVcneUqS850HJZOTP3MMDBgyQldM6depw9OhRXFxcJGtA7C8qKoqUlBRUKhUdO3bk5s2bslrfoEEDrFYrJ06ckN8pjknMabG4FB8fz7Zt29BqtUycOJGYmBj27dvH3LlzgeLr16ZNG0l/VavV/Pzzz1SvXh2LxYKXlxfZ2dmlqnf2iyhljQ84%2BvD17NmTDRs2UFBQIOmRwqPSZrPxxhtv8Nlnn9G0aVNpBF%2BxYkWuX79OhQoVyMjIIDIyknv37nHz5k2Kiorw9fVl%2B/btxMXFUa1aNTnfxGKOzWbjhRdeIDQ0lIULF5aaz8888wzVq1dn//79kslg/zyMjo4mKiqK9evX8/jxYyIiIrh27RoFBQVEREQwfPhwZs%2BeLResysJLL73EypUrJZNA3J/z58%2BXtFpvb2/57J48eTI3b97k7NmzFBYWcvbsWSwWC02aNCExMZGWLVs%2BlY2KE/8sOBM%2BJ5xwwol/AESP3d27d0lPT5cy65mZmTx69IhvvvmGV199lUWLFvHOO%2B8wYcIEuW2DBg2kX1S1atW4evUqe/fulXYLXbt2ZefOnTJBK0uevSQCAgLKDcLLgpeXFxkZGWi1Wry9vVmyZAlt2rQpNygbPXq0DKbtsXXrVqpUqSKDPUD29onqzIwZM%2Bjbty9Lly7FYDBQtWpVUlJSpJS4TqeTCZ1Op5NeayIBjoiIkEElwBdffMGnn34qv6ssCla9evVQKpVcuHBBBkYlkZCQwKFDh%2BRqOhRT4lxdXcnNzSUqKor09HRu375dqgdLBFALFy5k6NChFBYWsnjxYoYMGVIuTa0k/kgOvuRnfXx8AGjUqBH5%2Bfl89tlnZGdn061bN65duyaTNRG4luwP/bN9VqI/Mzk5mf79%2BzNnzhw8PDwoKCjg8ePH5R53ZGQkFy5cKNPGAIqTWnFvnD59Wiq%2BXrhwgaioqCdWNWrVqsWMGTOIiIggOjqagoICqaB48eJF%2Bvfvz7Fjx%2BS5PvfccxgMBho3blzuPLBH69atCQoK4tGjR%2BzYsYOtW7dy5MgRh3mm0Who1qwZhw4dkkql4tjEsWs0Gkwmk4P9gqjq6vV6UlJSOH/%2BPJ06dZL3t/D4tN8GkJYWhYWFhIeHc/XqVRo0aMCxY8ekIJDFYkGn02GxWFCr1YwfP55Zs2Y5UK2FMImYF35%2BfjIxjo2N5dixY0Bxop2Xl0dAQAB169alVatWjBgxwuF6l6yGzp07l7Fjx0qxn8qVK5cyqlepVE89B/V6PbVq1SIlJUXeexUrViQzM5OsrCwH5sOfSegNBgN16tSRRu//FqjVag4dOkSDBg2A4sq0oP6DIxvEw8MDjUZD06ZNSU5Olh6xTvy74NRVdcIJJ5z4B8BgMBAUFERsbCxNmjTB19eXOnXq8NprrzF%2B/HgiIiKIjY2latWqrF27FvjdhDwlJUUG%2B8OHDyc4OJhVq1ZJxbWJEydy8OBB%2BV2tW7cutUKrVCoJCwuTv9%2B/fx%2BtViuTAldXVxQKBa1atUKlUqFWq3n22WcxGo1S5XPu3Lno9Xr69etHly5dAKQ4TEl88cUXUmluzpw58vUXX3yRHj16OFSEbDabDHxjYmJkJQmKgxFh2iyqgh4eHixZsoSpU6c6JI32sD//fv36ydcMBoNDXyMUVyAuXbpEYGCgXOlOSEigVq0cZrFFAAAgAElEQVRaDvvctm2bQ4%2BQgOhPEhU%2BwMGsGpAeh6%2B88opM8AYOHOiQ7BkMBt566y1ZvRAB/V9B165dGTNmDGPGjOHEiROyZ85kMrF%2B/XoZgIuepJIB8IABA4DiwNDPz0%2BOmVarJSwsjEqVKqHX62UfqUKhICAggDNnztCqVStJPRs6dCh5eXkMHz4cQCqhRkZGyusqKrBhYWEOcvAKhUImq/Xr18fT0xMoXtBQKpXcuXNH9vMBDB06VM5LATc3Nz788EMGDBggezbFmHfs2JHjx487JBVKpZJdu3Zx%2BPBhzp8/z%2BLFi7ly5QqBgYF069aNAQMGEB8fz4ABAxgwYABBQUHye7p3786yZcvQ6XQO86%2BoqIg9e/aUSkztr/3Jkyfl2AgEBQU5jMfs2bNRqVScOnUKvV6PVqvFxcXFYRuFQsHq1aulCmObNm2wWq2cPHlSzmdxzwiaeGFhIV988QWfffaZVA318/PDYDDIua5Wq/H19ZXvCyVbKK5Ma7Vabt26xaZNm3jjjTccKpOurq4Oyd6zzz7LxIkT5bi3bduWq1evOozZihUrSi1ACJPz8iAS6fz8fPLz87l8%2BTJpaWmlaO6CRmyP1q1bM2jQIDmfxXd6eHhw7NgxFAoFEyZMcHh2lGVdIOa0gH0Pq0DJ61zy2fXfQFFRkUz2AIdkT6lUUqtWLUmp/vzzz5kzZw4TJ05kxIgRjBw58r9%2BPE7878NZ4XPCCSec%2BJsg5Onj4uIcqkJ/taFfoVAQFBSEl5cXZ86cka8bjUa0Wq3DH3X4fSVbiLlUqlRJ%2BkzpdDop9pCVlYWnpyfZ2dmMGTOGOXPmYLFYmDJlClWqVGHYsGEO%2BxYUNnsRCvvKgL2YhEgY9%2BzZI1UX%2B/fvLxVB/zdR1nivXLmS1157jb179xIXF0dgYKC0uahatSrXrl2jWrVqXLhwQSYICoWC8ePH069fv1L9fGX9LtRQAVk5K4vGp9fradiwIYMGDSInJ4fBgwdL2hwU07BEj5NKpSI8PJyoqCgKCgr47rvvHAQkKlSogIuLC7dv3yY7OxsvLy%2BCg4NJT0/n1q1bxMTE0LhxY7p06cKNGzcYPnw4eXl5jBo1ilq1ajkkouLYxHkIymZ8fDx79uzB3d2dtLQ0bDYb/v7%2B5OTkYDabGTBgAIsWLXIQ4unRowfr1q2T6qtxcXH8%2BOOPT30Nvb29ycnJkZXe7t27M378%2BD8UzvirKKv6IxYlrFYrgYGBpKens2TJEvbs2cO4ceNK7UPMCZvN5lBpF/suOWegbAVKe8sAo9EIFCdromdUmMKDI8VR9DbC71W5kr2GZZ3nDz/8wLPPPovVav3DqnNJyveT9mtf3QsMDGTAgAEcOXKkXOsOpVLJjBkzJMvBaDQycuRIfHx8GD169BOP62lQ1nwRnqGzZ8/mhRdeYMeOHUCxN%2Bbp06fx9/dn27Zt/PDDD0BxpT4qKoq4uDg%2B/fRTrFYrQ4cOpVOnTiQnJ8v9tmnThlatWmGz2UhMTESj0TBgwABJy54/fz7Hjh2jfv361KtXD4VCwZdffil7KrOyslCr1bz11lvUrFkTgGPHjnHu3Dm5iCT6Co8dO0br1q359ddfOX369FONRVBQEL6%2BvmRnZ3Pjxg28vLycpuv/UjgTPieccMKJvwmbNm2iY8eONGnShOrVqxMQEMDly5elhL1Op%2BPu3bsYDAbZiwXFaoaVKlXCzc2Ntm3bsm/fPo4ePQoUU6kAfvrppzK/U6VSoVKpHKoJM2bM4N1333VIYEr%2BaShP8OWPAriSsPd7stlsBAcHo1KppBWBSqXijTfekEGSPRQKBT179mTNmjVSPbNv375YrVYWLFgAFPsB7tu3j5YtW9KzZ0/c3Nzo06cPVquVtm3bArB9%2B3a6d%2B8u97thwwYsFgtdu3bl7bffpnHjxqSkpFC7dm3GjRvH9OnTHao8FSpUoHHjxiQkJEhhmycleE/6XbxW8vfatWsTGRlJ9erVqVatGgkJCVit1nLpuEIhMCwsDIVCwaVLlxz6vETFoqxtVSoVHh4e5ObmotPppGH0k1Decdj3H5Xcx44dO0hISGD06NHMnDkTm81GYGCgVJQsuSDxJIhEMyEhgaKiIvbt24dSqXSwMhDHUK9ePdl7VhIxMTHMnDmTSpUqAb9fC/v/oXhR4OzZs8ycOZOlS5cyaNAglixZgtVqlcqvJf3UlEol7dq1o2nTplL9MikpiUmTJjkIFJX0Phs5ciRms5kvvviCsLAwfv31Vwe7EIVCQb169bh16xa3bt1Co9EQExMjTdJr1qwpvRDLgn3yGBwcjNFo5NKlS1IBWKfT0aJFC3bt2sUXX3xBUVERb7zxBp06dSIpKempk2ehqHr06FEGDx7MO%2B%2B8Q5UqVbh8%2BTJ6vV6ee1JSEi%2B88ALu7u6llHBff/11PD09mTFjhuw/8/Lyok%2BfPnz%2B%2BeeScrx161bCw8Np2rQp9%2B7d%2B8Njs%2B95FHR0T09PuTj266%2B/OszxkJAQh17fknRU0bOn0WgwGo1kZmZiNBopLCyUljLisxqNBrVaLXuLxVwQNFmhHuzm5sbt27el56bJZCIoKIjc3FwCAgKIiori5MmTMrlzc3Pj0aNHf5iMi2fvxx9/TFBQECaTiR07drB9%2B3Z%2B/fXXMhdL3N3defToEQMHDmTs2LFPdf2d%2BGfBmfA54YQTTvzNqF27Nq%2B88grDhg2jbdu2%2BPv7U69ePYYNG0aNGjWoWbMmS5Ys4aOPPgJg7dq1HD9%2BnI8%2B%2BgiNRsPRo0e5ePEizZs3x9PTkx07dvD48WM8PDz4/PPPef/992UfW8%2BePRkxYgRxcXGoVCqCg4Pp1asX%2B/btk6IgZeHNN99k/vz5sqKg0%2BkwGo0OfUxqtRqtViv7rFxcXCgqKqJixYpcuXJFJiyBgYH89ttvKBQKPv/8c5o3by5Xp/8Iwk4B4JNPPpGB4%2BDBg2WV8saNGyxcuJAmTZoApZOsiIgIpk%2BfLoO2tLQ05s%2Bfj7e3tzShfv7559m/f79DIC8S2dq1a9OkSROaNWtG7969sdlsTJkyBYBp06YB0L59ewC2bNkCQIcOHYDiHkWbzcbzzz9PZGQkFouF%2BfPnU716dWJiYvD29ubgwYOlVuBFT2XJBFsEb/7%2B/n%2Bq59LT01P6yk2bNo3AwECmTJnCunXrsFqtvPbaawAsWrRI9mnZqy%2BW1%2BckPiP8zkT12GazyUpvpUqVSE9PR6fT8eDBA8LDwx0qy4WFhVSrVk32WFarVs1hXopgXaPRsGnTJkk91mq1NG3alAULFpCTk8PMmTPLnc/gaAdgMBikr6VI2EJCQrh586a8/hEREdy5c4fc3FyqVatGgwYNqFOnDmPHjpVzXlSNheKjGKe5c%2BeyaNEimYgplUr0ej15eXlyDhuNRh4%2BfMjFixexWCxUr15d7keIaggD9Z07d9K6dWtu374t91mrVi15b4hzKSnOJIzLn9R/OHPmTGbOnMnjx4%2BZMWOGrJyazWZMJpP0r7SvTi9fvpyXX36Z0NBQ7t%2B/T2ZmJl26dKFLly7MnTuXe/fucf36ddq0acO%2BfftQqVRUr15deiXm5OTQsGFDwsLCCA8PJzc3lzlz5nD8%2BHHc3NyoWbMmAQEBUoBIqVRKWnedOnVYvXo1ubm5DB48mBMnTkhxk5SUFGw2GzExMQ7bAoSHh3P58mU6derEpUuXKCws5NGjRzRs2JBNmzbh5uZG8%2BbN2bNnT6neUfEsKKlI%2Bm%2BEv7%2B/tGxo0KABR48elfMsIyODzMxMvL29mT17Nm%2B%2B%2BSYTJkyQzzMn/j1wJnxOOOGEE38jMjMz6dChAxkZGUydOpWJEydKul98fLw0vo6NjeXkyZNAcfDm5%2BfHvXv3JPUyPz%2BfatWqkZGRIQN/sVrs5eUlXzt06BBubm6yX8fX11cmOeVV5UQiZy%2BsYTQaadCgAfv27XP4XEkRBaEQZw%2BdToePjw/5%2BflMmTIFV1dXXnnlFerWrYvJZOLLL7%2BU/m9/JKDwR3Q9%2B/c9PDxk/45Wq8XX1xf4XTDnaVGWIqJWq0WlUskqqL1oCzj6mj0tRFApkjqbzUbjxo0d%2BsratGlDcnIy27dvp23btg5m0mXtSxz/rl27JG1MePzt3LlT9tJ16NABtVpNUlKS3KZ169bSrFtQgSMiIhyqSeJ6BQcHl5LMN5lMWCwWfHx8cHFx4ZdffnGoNHt4eJCTkyOVHu095h49eiTH3cfHRwb7arWa7t27M3r0aA4cOMC4ceN4/fXXJRUuNjZWJiVhYWEYjUb5vQqFwkHpsiwZ/qeFMKd2c3MjICCA3377TVaHKlSowPXr16W3oxjP3r17s2LFCoeqKBQvlOTn55crSKLT6ZgxYwbjxo3DZrMxadIkQkJCGDZsGEVFRXTp0oV69epJyqMwcS/vvCpXrsyVK1eeivq6Zs0a3N3d5VyDYlpohQoVOHHiRKnPl%2BfZ6e7uXirpVKlUKJVKIiIiqFixIvv27aNLly7k5uaSlJSEWq2mT58%2B/Pzzz5w5c4b8/HzUajXR0dF07NiRrVu3cvLkSXQ6HcnJybRo0YK2bduSnZ3N/v37S1mX2KOsczeZTHTu3JnNmzeTmZmJu7s7c%2BfOpXHjxqSlpdGsWTOmTp3K9evXMRqNqNVq5s%2BfL7cfMmQICxYsYMiQIZhMJj766CP5mv1nhHKrqJ4%2B99xzkuLu5uaGp6cn6enp8hoKUSqbzUZoaCi%2Bvr6sXLmS2rVry/0eP36cGjVq/NfozBMmTKBfv3589913LF26lDVr1vw/79OJ/1s4Ez4nnHDCib8R69evZ9q0aX%2Bomvkk2P9R9/LyKqXKV95ny8PIkSNp2LAhiYmJKBQKKleuTFRUFElJSVitVvR6PQqFAk9PTylColar6dixo4O0eklfvRYtWnD06FFZJYTioMpsNks/v8GDB/Pss8/y6NEj%2BvTp45CouLm5UVhYKHufPDw8ePXVV5k1a9ZfGjeTySSrKw8ePJDVoFatWgGQl5dHdna2DLyFmILojSkJe0GQDz74oMzvPHXqlDScTk5OJjExkaKiIh4%2BfIhKpcLT01P2vMXExHDhwgVpyN6gQQOuXLlSKpl7GohkXIhqbN68mfj4eKC48rl582YHqpZ9kimwYMEChg4d%2BqcDSPtr2KdPH9avX%2B9QMXFzc2PcuHFcvHiRLVu2UL9%2Bffbv309gYCDXrl2jbt26nDhxgpo1a5Kfn4%2Bnp6dMJu/duycTRPE97u7uZfarCrEUhUJBXl5eqWuoUqkwGo0OyZGrqys9evQgPz%2BfGzducPDgQZ555hnu3btH586dWb9%2BvYOUvlKplJTftWvXSu%2Bz/%2BtQ689aR6hUKlxdXR08PJVKpaTO/pmFij8LPz8/ioqKJJXY3sajLIjnj3iOqFQqAgICpG3I2rVrCQ4OZsKECaSlpfHjjz9itVqpWrWqZBrA77TkP2tLExISQkhICIcPH5bWLnfu3GHs2LGsWbNG7mf9%2BvU0a9aMxo0b8/nnn9O2bVu%2B/fZbunfvLj9TtWpVNBoNc%2BbMkb183333He3atcNms9GmTRvUajVjxoyhQYMG2Gw2fvzxRxo1akRRURHHjh3j0qVLVKtWjUGDBkmLmFGjRtG/f3%2B0Wi3/8z//w/nz52XfZlpaGjdv3uTGjRuoVCqqVKlCSkqKA7XU09OTmJgY2rVrx5gxY9i9ezchISGYzWYaNGjAqVOn/trFduJvgzPhc8IJJ5z4m2GxWKhbty4TJ05kxowZMtBu1KiRlPu2WCxMnTqVadOmMXDgQBYsWCDpW4GBgbK/pE%2BfPtJjyx5CQbG8xLJixYqkp6djtVpp1qwZs2bNIiYmBoVCwRdffMGoUaNk9apSpUo8fPiwlMH7vHnzeP311x1e02g0Utp9xowZTJ48GR8fH27evImnpyeDBw8mIyODhQsXyj60Hj168NxzzzFw4MAy%2BwGflLTGxMQwa9YsOTYWi4V27drJIKZatWqEhYWxceNGdDodb7zxhtzW29ub/Px86tWrx/379xk/fjyXLl3CYDDQoEEDPv/8c%2B7evcvhw4fZuXMnR44ckUGnQqGQtNknITo6WlZzUlJSiIqKAop760QA6u/vz/379zGZTLIiaa/4V6VKFS5dulTmGAgFVXu5fJVKxUsvvcTy5ctlL5g9BdTX15f79%2B877C84OJgtW7bIqoHoT5o6dSoVK1akT58%2B9OzZk%2BPHjzv0/TRu3JjDhw/j5%2Bcnq3B/BHE9R4wYQVFREQUFBYSFhREaGkr//v05c%2BYMUVFRrFq1it69e1OnTh0uXbpElSpV6NatGxMmTMBqtcpkTVS/y0NgYCBxcXFERkbSuHFjunXrBsDhw4fZtm0b3377LSkpKdSrV09WrWw2G3PmzGHcuHEyAbJPqqpXr05qaio///yznHuCSiyq9FBckX2asEupVNKnTx%2BWLVsmXxs%2BfDj37t1j%2B/btPH78mKKiIoeqaFljKno3DQaDTIIFfdVkMnHv3j2USiVxcXEsWrSI7du3M3r0aGw2G97e3mi1WubNm0f//v1llVWI7uh0OjZs2IDBYGDKlCn8%2BOOPWCwWBzEZwKGy5u/vj16vJzg4WD7bevbsSVBQEJ9%2B%2BmmZiaWLiwt5eXll2qX079%2BfESNGyPOMiYnh9OnT3Llzh8DAQKpVqyaTmNTUVClqJKwsunTpQmpqqlwwUCgUhISESIrxM888Q2RkJHv37pXzyl6FMzU1FYvFQufOnbl48SLu7u4OTIjCwkJUKhV9%2BvRh7969pKenS7aEWDgyGo288847rFy5kqtXr6LT6TAYDDx8%2BJDHjx%2Bj1%2BvZtm0b8%2BbN4/Lly9SvX59Lly5x6NAhEhMT%2Bc9//iP7/EpCVMSFXU5oaCh169bFarXy4YcfolAoGDhwIIsXL5YLSw0bNuT48eNUqVIFjUbD2bNn2blzpzRdf5L/oRP/XDgTPieccMKJfwgePnwoK3S5ubmcOHGC0NBQLl68yPTp0xk7diwff/wx77//Pm%2B%2B%2BSZarRalUsm7777LW2%2B9BfxeTRGedAKurq6laGJKpVIGcHq9HqVSSV5eHkVFRTJpAJ5IhRIoyycMHJOzkj/7%2BPjg6enJsmXLiIuLc9gGyvaTi4qKIicnh6tXr9KsWTOOHDmCh4cHY8aM4eOPP0an0xEVFcXo0aMlbTMmJob8/Hy6du3Kjh07mDx5MuPGjUOr1XLmzBlu377NhAkTOHLkCEaj0aEi0717d6pWrcqJEyc4ePCgHFPxvqenJ9HR0RiNRry8vGjSpAnff/89u3fvBoq9%2BcaOHSsTtho1akirgDZt2nDw4EHUajUZGRkOIhHCbNrb25u7d%2B%2BiUqnw8/OjXbt2vPTSS1ItUcBeVEcEvQL2%2BxWVvo8%2B%2BoiJEydis9nQaDTodDoHip1Wq6VNmzZs3rzZ4bqYTCZatGjB/v37ycnJ%2BVOVH3FdhTR9VFQUmZmZXL58uUxBIAH7Pin7BQBRfTIajdhsNoqKihx85OD3Stcf9VqJYF8kMoImLZImi8Ui7x37RE/8LO43sWjRokWLclUmyxoXX19fmUyIuTVr1ix5X2s0GhYsWCD7K8W5%2BPn5kZWVRUFBgYNiqlarpbCwEHd3d7Kzs/nkk08kXRfKFt0xmUyo1Wpyc3Ml5dVms8lqvaiE2V9z8dwoKiqisLBQKu/aP3v%2BCNHR0eTk5Mgkq3379nz88cdER0djNpslXdb%2BeVC9enWuXLniQCe%2Bc%2BcOffr0YdGiRXTs2JGUlBRpZq/T6Th48KCskqnVaj766CNGjRpV5vUQ31UyeRXvA/L5MXXqVDZu3Fjqc/9/gru7O%2B%2B//z4tW7akTp06zoTvXwhnwueEE0448TcjMjIS%2BF1Y4Y8ey/YBp0aj4dy5c9SsWZPCwkJq167NmTNnOH36tINPnJAPF0qOeXl53Lt37y9bQNij5DEHBQVx%2B/ZtB5qUSDTq16/PsWPHUCqVtG/fnl27djkEZlBccWrdujUrV65ErVajVqs5cOAA9evXx9vbm0aNGrFlyxY%2B/PBD3nnnHZRKJYcOHaJevXoO1c6AgAASExP58ssv5aq%2BqJaJoPjMmTOyX/DPoHnz5kyfPh1PT0/i4%2BMpLCwkIiKCgwcP4ufnx6hRozCbzSxatIj4%2BHgSEhKYOHGiNKNWqVR06NCBzZs34%2BvrS1pa2lPR8P5sT47RaCQ/P79MJdXyqHNiEQDK7zkUCZTwb7xy5Yrcr81mw8fHR1qNmM1m6YHWp08fFAoFP//8M7m5uX963IVoj0jw7GEymdBqtVKAwmg04uLi4kCB9ff3x2q1YjAYKCoqkpTkkhBVOoCvv/6avXv3Yjab%2Bfbbb/9w8eN/G3%2BlL6tfv36sWrWqVI%2BiWGASDICXXnqJVatWPTEBt0dISAj37t0rlzmgUqlo3bo127dvByiVeCsUCgwGA%2BPGjeOdd96RlW%2BxcCP2W716dWbNmkXXrl0BZNXX3n4mOjqa3r1788svv/Djjz%2ByYcMGSVvW6/V07tyZ1atXy/sffn%2BW2i9EtWvXju3btzv0ENuPuXiWKZVKzp8/T5MmTbBYLJKKrVKpSExM5OTJk6SlpZGVlYVSqSQyMlIKEbVq1YozZ85IKqtSqaRatWrSU7VKlSrcunWLvLw8DAYDhYWFFBYWymdYSRqqWOQQFUp/f39cXFz4/vvvn%2Bo62i%2ByifFRKBTSdD0nJ4fHjx/L91JTU59qv078c%2BA0XnfCCSec%2BJvx1Vdf4e/vz6hRo/D398doNKJUKvH29nbwZ/Pw8AB%2BD0aF6AT8Lgryn//8B41G42Deq9VqHdQG09LSuHv37hOTC4PBgEqlksbNJpMJLy8vJk%2BejIeHBy4uLnTt2pX333%2BfPXv2oFarWbhwIXq9ntWrV6PVatFoNCiVSvz9/WUC0bp1a6BY9GPLli3YbDbpKSVgtVpZuXIl8fHxktImqkIPHz5k586dKJVK2S%2Bo0%2Bno2rUrVquVjh07EhISgslk4sUXX2TDhg0yaGzRogVarVYGeGJs27Vrh8FgQKfTyYqgUD8NDg6Wn6tUqZLsBfvll184ffo0GzduJD09nXXr1rFgwQLc3Nzw8vIiISGBbt26MXfuXJYtW0bHjh0dDMotFgubN292MJF/muQ7ICDA4XdRmWrXrl2ZnxdeeVWrViU8PFz2LJbsrxTnqNFouHDhAqmpqaSmpqLX6yVdTsw/KKaqabVa6tSpw927d%2BX2Hh4eaLVaWXWz/w6z2Ux4eDgPHjyQaphQTJtTKBSEh4eXOh57Q21h8l6nTh1%2B/vnnUvRhnU7Hl19%2BKe%2BJt99%2BW84RgV69euHu7s7169cdkj3xfQMHDqRmzZqyomexWLh37x6bNm1i1apVT5XsieDeHp07d2b48OGSclulShWqVKlS5v1tNBqJjo4mJiZGvqfX6%2BnZs6ccI1FJg9/vDbH9m2%2B%2BKcfO/rqK/lf4/T4UtGybzUZUVBTnzp2Tibz9uJQ8P5PJJKvhBoNBvifOT0Cr1fLdd9/J30tWWdVqNZMmTWLOnDly3%2BJ6i%2B/W6XQkJSXJ8RKvlzw2m83Ga6%2B9xq1btzCbzQ7VO7PZLIVG7JM3b29v%2BZowkj99%2BjRGo1EmVAqFgtDQULmvgQMHAvD%2B%2B%2B8DxXRskQCK4x0/fjzLli2TCysGg4EKFSrI4587dy4xMTEyUbNaraxdu1YK11SuXFkKJIljVCqVpKSkoFKpSEhIoGHDhri7u6PRaJg9ezZGoxF3d3eGDh1KYmIix44dw2AwsHHjRlxcXHB1dcXNzY3du3fj7u7OxYsXWbduHc2bN%2BfChQt8%2B%2B23BAYGEhISQvfu3fH395eqrAMGDODVV1%2BladOmUsHXiX8XnBU%2BJ5xwwol/AGrXrs2pU6eoXbs2BQUFKJVK6tatK02o7UU0nrTCX9Z7QkhCeN2VZ27dsWNH2W8klAKDg4P57bffqFu3LqdPn2bgwIEsWrQIq9VK165d8fDwQKVS8dVXX0nlvRdeeMHB0iA%2BPp7t27djtVpp2bIl%2B/btk4FPWFgYv/zyi8Pq%2Bfbt22ndujUajQY/Pz9u3rxZru%2BbOGcoDnxdXFzIysrCZrNx6tQpioqKqFGjBlBMay0qKpLVC4VCQWBgoAz8VSoVMTExpKSkUFRUhMlkoqCgQAaCL7/8MkuWLGHq1KlMnjxZKlS6urrSvXt33nrrLaKjo1Gr1dIH0WazUatWLRYuXEjjxo2JiIjggw8%2BkIqrFouFDRs2SIEMQYsT2/4RkpKSOHz4MJ9%2B%2BilKpZLCwkICAwN5%2BPChgzCKRqMhMDCQwsJC7ty5U2o/Hh4eZGZmyjERuHPnjuzZio%2BPJzk5mb1799KrVy9atWpVbr%2BosFcQFUK1Wo1KpSIwMJAOHTrQsWNHXn75ZR48eICHh4eswrm4uPDo0SNCQkJ48803GTlyJPXr1%2BfMmTOsWbOGoUOHMnz4cHbt2sVPP/3kIM4iEkExT06cOMHFixd58cUXyx2/8PBwrl69KsdeiFN88MEHLF26tMxtyko0Jk%2BezPvvv19m1bRdu3Y888wzQLFoz%2BnTp3nllVeoUKECkyZNklWV8%2BfPU6NGDTw8PDAYDAQGBpKRkcGvv/4q6dv5%2BfmSdhoeHk5qaipGoxGTyYSPjw%2BpqamsWbOGnj17OhxH3bp1OXXq1P%2BT%2BIp4Jt26dUveMyaTiaKiIknpdHV1JTc3Vy5eCKprWc8lhUJB1apVCQgIkGbegroKMGzYMPLz82W/HTharJTlX5mSkkJubi716tUDHCvUIgEumXSqVCr0er30JqxXrx4nT5502Faj0cjeTfGcjImJISgoiLNnz5KVlSV7HDUajazElffMKkvRuCQVOTg4mOvXrzt8RvgBioUeX19fh/v5aau/3t7e9OnTh6%2B//pqsrCyCgoJQKBTcunULm83m4NXo6urKypUrqVat2h/u14l/LpwJnxNOOOHEPwCtWrVi9erV9OjRg7t37%2BLl5YP0kOoAACAASURBVEVubq4MGERALlDeH3aj0SgD/fr163Pq1Kk/9Inq37%2B/pHsJuX21Wk3fvn356quvsFgs0rNMUEPF50JCQigoKCglwW8P%2B0CmpAdYSVNsIX4iKJ4zZ85k586d7N27F5VKJSlo4eHhXLp0SZ6jxWKRKnxQXBHZuHEjy5cvZ/ny5XLf/v7%2BpKenY7FYqFChAhaLhVu3bqFUKjEYDHh5eUlKqJubG%2B7u7ty9exej0cizzz5LcnIyJ0%2BeJCYmhqSkJBITE1EqlWRlZXH%2B/Hmio6NRKpUOKnb2gWlERISkN5b1vv3PN2/eZPfu3Xz44Yey78jd3Z379%2B%2BjVCqJiYlh9erVPHz4kLi4OHx8fFAoFFKAxd4DT9gFXLlypcygX5g221eGobjqKAJTMef0ej0FBQVotVri4uIwmUwUFhaSnJxMmzZtSE1N5datWzz33HPs3LnTYZ7u2rWLChUqsHz5cmbPni3nnPiMTqdj%2BPDhRERE8Pbbb5Oeno6fnx%2BZmZl8/PHHjBo1Si4WKJVK6a1WFmbPns2sWbMcBFwiIyOpUqWK9EP08fGhVatW0n/Q19eXkJAQLl26JPvQmjVrRvPmzbl586asOLu4uGCz2Z5on/AkiAWM8PBwMjMzefDgAcnJyXTq1Alvb29u375d7nlpNBoUCoWsNhoMBvr378/ChQux2WyYTCapeGl/3QT%2BLCXUz8%2BPR48eSZEYYQDfq1cvJk2ahFqtpnr16g7XUVS2mjZtysGDB8ulDguUPD5xjPbUS/i9Cm5PtXR1dWXChAlMmzaNCRMmcOPGDZYsWSJ7lO1tPpo0aYLVauXw4cMO4xkYGCiTqz/yKXwSyhKZ%2BifhSde%2BpC%2Bl2WyWrIfffvuNChUqMGjQILp37/5/fNRO/DfgpHQ64YQTTvzNWLNmDdWrV6d9%2B/ZkZmaiVCp5%2BPChTPYUCoVM9rRaLd26dSM6OhpfX1969eqFTqeTf5gFBRPg6tWrDnQrsS%2BhjgfFiWTjxo0JCQmRipEajYbHjx/z7bffSi8/8Uc%2BLCwMlUqFh4cHLVu2JDExsVQflEajcfhdJCKAw3crlUoGDRrk8FmbzUa/fv1kgGcwGHjw4IE89rVr19KiRQvCwsJQKpWsWLECjUbDokWLpNR%2BUVEROTk5PPfcczLZA5gyZQrffvstzz77LFAc/D969Ai9Xs8HH3xAv379yM3NRaVSUatWLdq3by97vYYNG8Zbb72Fq6srQ4YMwWw2065dO7KysmjZsqX0tLPZbHh6ej7xerdu3VpS6lq3bk1BQQGDBw9m8ODBQLGJfM%2BePWnbti0zZ86UgbRKpZLBdmhoKHPmzOHcuXP06tULgPv375Oeni4DOrPZLLfNysqSht4lYTQaCQoKkl5i4p9Op5MVZZVKhcFgwN3dHYvFgkql4sUXX2ThwoVMmzaNoqIiVCoVU6dOZdSoUYSFhfH99987JACiupeYmEhSUhIFBQUO1FKlUsnWrVtZtmwZ77//vkxo0tPTMZvNvP322xQVFcmeJiGuIuaKyWTCZDLJufbmm2/K8QJYunQpmzZtIjs7Wx7TlClT2LNnj0wkRHXHXnTk%2BPHjfPDBB3z99ddYrVasVivZ2dlSHVMsFggoFAoiIyMJDw9nxYoV8t/SpUsl9drLy4tVq1YRERFBzZo1AWjbti35%2BfncuXOnVFAuzlWr1WKxWOTYKJVKnn32WRYsWCCpgaLCLWCz2XB3d3c4PkEdFIqQlStXZtKkSTzzzDO4urqiUChYunQpLi4uhIWFSWrwo0ePGDZsGACrV6%2BmZs2a1KhRQ15HnU5H586dZWJx4MABmYSKSpo4nwsXLnDhwgW%2B%2B%2B47goKCHI6vZKVUjDsUL%2BY899xzQLEIUnZ2Nu%2B88w5ms5n333%2BfxYsXY7PZ5LNAVMMAfvjhB4dkT61W07ZtW2JjY%2BVrJU3WlUolWq2WJk2ayN8rV65MXFwcSqWSqlWrolAo6NKlC15eXri7u0taPhQ/swUNtG7dupKKq9FoZOVbWIJoNBoHavOIESMcGAyCCSAgxk0Ib4l92T%2BDxfeJuVAesrOz5fuPHz8mMDCQOXPmkJOTQ2hoKPfu3eODDz5gw4YN5e7DiX8unBU%2BJ5xwwom/GcL3TVQrRMDj7u4uJfCXLVuGi4sLvr6%2BvP766yxYsEBSHbdv345Go6F///6EhITw9ttvA8VCDZGRkYwfPx4oDjCmT59O586diYiIQKFQ8OGHHzJ9%2BnSZLNlXCAVatGjBjRs3KCwslKvgJpOJbdu28eqrr8qqUXmrx/ZiJPbqoWq1mubNm0s1wz8SLTEYDIwZM4aDBw9KCtiHH37IjBkzWLduHXfu3GH58uXs3bsXDw8P2ZMkEkaNRsO0adPYtm2bQ%2BJpj5iYGM6ePYtGo5FBtagkCMEQKFatc3V1xdPTk9WrVwOQm5tLbGwssbGxJCQkyH1Onz6dKVOmYLPZmDJligzKNm/eTEJCwp8SARHjN3nyZJKSkjh37txTb1ue2qpCocDPz8%2Bh90qhULBnzx6ZjBw9epQ5c%2BZw6NAhbty4gcViYfv27axYsYLjx4%2BTlpYmr6vBYKBevXqysmO1WjGZTJw4cYKMjAymTp3Krl27SiWfot/TYrGQlZWFr68vN2/elPNKWEoolUpcXFwwm800atSI/fv3S6VMm80mqWiArOharVZCQ0NJS0uT6ptKpZL4%2BHi2bt2K1WqVNLpbt27h7u7uoDir1WpRqVQy8RFVPUHfE/1xoiqSkpJCkyZNaNSoEe%2B%2B%2By69e/cmNTVV3t9xcXFkZGSQnp5Obm6urMLbK4Gq1WpGjBjB/Pnz2bx5M7NnzyY4OJiBAwfSu3dvrl69Kq%2BVvYCHOK6SwiRi376%2BvmRlZcm5oFKpGDx4MCtWrCAgIIDLly/L6pjNZqNGjRqcPXsWf39/Hj58yKpVq9i1axfbtm3Dzc2NK1euUFRURMWKFUlKSgKK6bX2zwO9Xu%2BgdKpWq5kyZQr5%2BfmsX78eV1dXTp06RUxMjKRj22w22rdv75CwAMTGxrJz5072799Ps2bNOHDggKQzV6xYUfrNBQUFcevWLaA4wbRareh0Opo1a0bXrl0ZPHiwTHizs7MdPPnsadVPeiYplUpefPFFnn/%2BedRqNf3795fXWCi9Go1GkpOTadasmcPz1b5vUFBXHz9%2B7PCMTE1NJTY2VtLQ9Xo9Xl5e8rwuXrwoBbvsr7XYv6jI5%2BfnS8XRlJQUGjVqJJ9rggUSFRWFl5eXVIxt0aIFbm5ubNiwgWHDhuHn58dnn32Gi4sL69atK3dMnPhnwpnwOeGEE078jbh3714p3zD73/38/Ni4cSMtW7YkPj6eZ555htu3bxMeHs7Ro0eB39U9IyMjcXV1lUqQGo2GYcOG8cknnwBI9bctW7ZIymR0dDQGg4Fjx47JAEClUrFr1y6ZiLq4uEj5e5E8we8B1Z/5M/IkSpHoUxTiHDk5OTx8%2BPBP7V%2Bj0dCgQQNCQ0M5fPgw6enpsqJjMBikIqbok2zVqpW0UDCZTLzwwgtcv35d0kyF7LzFYqFSpUo0a9aMxYsXExAQQGRkJIMGDcLPzw%2BATz75hK1bt0plO8ChJwdw8Kb7I2pdbGwsKSkpsifv8ePHuLi4yPOB4kTEZrPJ3r%2B/St1TKpW4urqiVqtlf%2Bft27dp2bIlUEz77du3L8uXL%2Bebb76hoKCA3bt30759ewAqV64sx0ahULB8%2BXJ69uwpxUI0Gg07d%2B5kyJAh3Lt3D7Va/ZcM5AXK6oFq0qQJP/zwA0qlko4dO/Ldd9/J/jJ7lDcuQlhDeByKoH3JkiUMGDAAo9HI0KFDeeWVVzh//jw9evQolUCLHq6zZ8%2BydOlS6XUm5oFCoSAqKoqUlBQZnJdcZHF3d6d27dpcu3aNBw8ekJOTw65duygsLKRv374O41byXFxcXCgqKmLbtm0UFhbSp08fhyonIKu19lVMUYF9EjVbQKlU0q5dO/Lz89m3b588t1q1alG/fn2%2B//57rl27hl6vp1atWjLJsIe/vz9qtZrMzEwKCgrw9fV1UPQVlFb7/j171K1bF7PZLKuaJRU0BY1Zp9ORnp4u%2B/eGDx/ON998Iz0oxTVwd3enoKCAgIAALBYLGRkZLF26FKPRSPv27bHZbDRs2JAjR47IeeHr68uDBw9YtmwZQ4cOlabnJSFo0uUtthiNRodFCnsIQZ7y9h0bGyv7hcX%2BSs5te1sNoap78%2BZNXFxcUKlUVK5cmdu3b3P16lUUCgXt27enTp06XL58mTVr1mCxWAgICGDXrl3Ur19fJolO/LvgTPiccMIJJ/5GREdHc/z4cWrXro1arcZsNjNv3jxee%2B01FAoFrVq14tChQ/j5%2BXH9%2BnUpex8bG8uJEyekKp7ZbGbAgAH89NNP8o%2Bxfa%2BTfdDr6%2BtbbrCt1WpRKBScOXNGCiR89dVXQHHyMnjwYPz9/Xnw4AEffvgho0ePloGMvVS/n58fd%2B/elb1Kvr6%2B5OXlMXnyZCZPnszIkSPZuHEjly9flkHKpEmTuHDhAjdu3CAtLY0VK1aQm5tLcnKyTFoDAwMxmUzodDrOnj1LYGCgg2hBUFAQW7ZswcXFhRs3bvD111%2BzatUqFAoFXl5eeHl5kZ%2Bfz/379/H09KRhw4YkJSVJymS7du1ISUkhPT0dd3d34uLisFqtbN26FYPBwMKFC/Hw8KBy5cpAMS20pIiHqBaJn%2BH3hM%2B%2BL8k%2BOBPqofYVT3sri/T0dK5evUpgYCB3797Fz8%2BPhw8fykSvTp06nDlzRho921fPgoKCSE9Pl1UopVLJkiVLePXVV2VlYO/evbRq1QqdTkeVKlVYv369g1WGOIeyqh06nY4zZ85w6tQp%2BvTpI3vrSp7j09hOlMRHH33kIAxz//59bt%2B%2BLXs5Sx6f/fjWqFGDESNGEBkZSbNmzaSFCRRXrcWiin0yVpbgUc2aNaX8f7169VAqlSxbtkyqeer1etRqNU2bNpWKlHXq1JE9liUr5n8FRqOR1157jatXr5ZSHvXx8cHFxYXRo0ezaNEizp49S6NGjWjUqBG7d%2B928GT8q6hfvz5VqlRh1apVpd7TaDQ0bNiQgwcPyteio6M5f/48FosFjUZDfn6%2Bgz%2BkEGzq3bu3XBgQlPLly5fTt29f2Se6Y8cOh4WSn376iY8%2B%2BqjcYy1JaRQ2Bvb04v79%2B9O8eXP0ej0vvfQSZrOZcePG0b9/fwAWLFjAmjVrePDggbxvKlSowI0bN6hZsya//PILSqWS7Oxs9Ho9Wq2WhIQEHjx4wMmTJyXttyx7i6fp8xPiLH8GomJoNpsxGo3lJol/dp%2BNGzfmq6%2B%2BQqlUEhUVhUqlciZ8/0I4Ez4nnHDCib8RUVFRJCQksG7dOrRarYNfHOCQ0NmvYgt/M1HhKemv9SSUDGyFr5lYBQa4cOEC0dHRUuVy7dq19OvXj4cPH/Lrr79SWFgoldxUKpU8dlElDAkJ4fr165JG2LJlS77//nvee%2B893n33XRlAR0VFYbVaKSwsZMiQIYwaNYq0tDS8vLwcgrYOHTqgVqtl/509RP9cXl6eTGgE7S40NJRbt25RWFjIhg0bGDNmjEz4YmJiWLFiBbVr15Z%2BV0FBQaSlpaFSqRg%2BfDgvvfQSNpuN2rVry5Vts9lMWloaISEh/Prrr/I4UlNTeeutt1AqldKw/ObNm/zyyy80b94cm81Gp06d5Bhv3LiRTp06OVwT4S9WUFBAixYt%2BP777wkMDCQ3N5fc3FwWL17MkCFDpJLounXrWLNmDefPny/3%2BleqVInMzEwyMzOlIMPzzz/Ptm3bZOCZkJDA5s2bZW/XuXPnqFWrlkzmy1IOdXd3l0Flhw4dOH78OHfu3HGgGC5atIhXXnkFgE2bNvHw4UMCAgJISEhgzpw5zJw5k9dff51PPvmEOXPmMGzYMMaNG8fu3bvZs2ePTJTtv7u883RxcZES%2BRqNhl69erF582bUajX379%2BXCd%2BlS5fo0qULlStXZuPGjZISp9Vq8fT0lMmFEL2xXzCZOXMmUGyzIK6Tn58fQUFBnDp1igoVKpCWlibpi56enri4uDhUwkUi/8Ybb/DZZ58B0LJlS4YPH06XLl2A4orp9evXHawUSibMonLUt29fFi9eLO81MfftlSWfJslQKBSSVtivXz/Wrl0r/d/KStY9PT3x9vbm6tWrDvsW/WQi0Q0NDSU%2BPp6UlBSZFIpFJ6FOOnjw4HI94/6syAwUJ6FeXl5kZGRgNptl76DYj71/n7iOkydPZvPmzaxdu5bmzZtz7949Vq5cSZ8%2BfbBYLISGhnLt2jVMJpM8P7PZLOeI6KfbtWsXUEzRj42NlYswQ4YMoXLlythsNoKCgtDr9XTt2pWioiLZJ%2B3h4UFcXBwNGjQgOTlZ9vCp1WpatGhBTk4OMTExPP/883Tr1k0ucISHh8vnX7169RgxYgSdOnWSVM6cnBw5n5%2B0wCFgbzi/d%2B9eeW61atUiMDBQWkY48e%2BBU7TFCSeccOJvhOiVEpU1QIoDAGX2xgkKn9je09OzlJrdk9CtWzeHxn9B%2BRGJn81mk6vtRUVFpKSkcPr0aX788Ue6d%2B8uv0skpYJWZR8QiqqbSBgERWvixImYzWbmzp0r1SqFz9TChQuJiYnhzTfflFW%2BmTNn0rBhQ65cuUJWVhbnzp0jODgYrVbLgQMHOHDggPwOvV6PXq%2BnatWqFBUV4erqSpMmTfD19cVms5Gdnc3t27e5ffs2ZrMZk8nE9OnTcXFxkX1hb7zxBtnZ2WRmZjJr1ixiYmJ45ZVXZL9eVlYWnTt35ssvvwSKRWzEvyVLlmCxWCRtKiwsjGbNmvHJJ58QFhZGeHi4lIUvLCzkyy%2B/xGw2U1BQICXca9WqJatjFy9eRKFQ8PjxY9ljOXDgQPLz8xk7diwTJkxAoVCQmJjI%2BPHjS1UaoTjwLSoqonbt2g6CDJs3b3YI0rdt2yavpX0fmf2cKxkcCnEQm83Gtm3bSvU9ubq6smLFCjnXRo8ezdatW6XgzuzZswkKCuLtt98mLS2NF198kczMTCZMmCCrnILyKPrznqT2KBYboHihZOLEiajVaklDLiwsJCIigg4dOlBYWMj58%2BepVq2avJc2btzIgQMH5PFu2bKFcePGOSw8dO7cmc6dO7NmzRp5nVxdXfHx8ZEy%2BvZB9fPPP09ycjJff/213EdRURFRUVH4%2BPjI1w4dOkSPHj0crsc333zjcL5KpVIKpthfl5UrV2Kz2Vi1apUUThHnK47DfjFHjI%2B94I2AOKcNGzY40GHFvd28eXP52YyMDK5du0ZcXJzct06nY%2B3atcyYMQNAVugGDx4sTdAByTD44IMPADhy5IgUnyrpI/q0yZ6Xl5f8ubCwkOzsbPlsKCgooLCwsNRzqmPHjvL6L1iwgPr16wPQtWtXlEqlFMMRVPdmzZpx4sQJfvjhBwYOHIjFYsHDwwOLxUJsbKzsB/3pp5%2BYOXOmFGLx8PAgIyMDhUJBXl4et2/fZu3atZhMJtRqNUajEb1eT8uWLQkNDeXgwYPSEkUIKnXv3p2PP/6YkSNHUrlyZXx9fUlMTESn09GuXTv27t3LO%2B%2B8Q0ZGBh07dpT2GGKxMCIigh49esi%2BxXPnzsm%2B3ZJCW/Z9k2PGjCExMZHOnTtjNpvLXHRz4p8PZ4XPCSeccOJvRHR0tMPqe35%2BPjNnzpRCK4BDxU8gLCyMq1evotFoGDx4MFu3buXRo0ckJSWRkJCAUqmU0uIXLlwAkBS9ko3%2BouJQsi/KflVf0OXq1q3L8ePHUSqVHD58mLS0NGbNmsWJEydk8NOuXTsuXbrEkSNHiIiIkMdZsk/F29ubR48esWPHDtkvWBYCAgKoX78%2BJpOJO3fucODAARQKBR07dsRms7FhwwapMJmfn4/VaqVPnz5Uq1aNFStWyNV8YScgztnX15fw8HBZddBoNFKsw2q14uHhgZ%2BfH5cuXcJgMJCQkCDVBRcuXFhKTOLx48fExMSgVqv5%2Beef5etRUVHyGKpXry6rX/a9XX8WQlTDy8uLBw8eyGTI3j9LwN7DUKlU4uPjg8VicVA/PXnyJLVr15aVEOFzBkhBCSgOnP39/Vm4cCGRkZEOvmhQXAEQ8%2Br48eMkJiZKqwy1Wk1wcDCA9IT8b2LOnDm8%2Beab0jLk7NmzJCUl8dNPP7FhwwasVisRERFcuHBBjl%2BXLl1Yv3494eHhbN26ldatW0tRGrEgER8fz%2BXLl4HiStyxY8fkGIuevcqVK8uxWL16Nd7e3rL3qXfv3ly%2BfLlMz0J72C/s%2BPr68sMPP8h7ViRUJcVZ4Pfre/HiRSIiIsp8Xgi6tYBer%2Bfll19m4cKF8jVvb2%2BHnlmREArfP0BW46GYdjhw4EC2bNki%2B8gEvReKnzdarZamTZty4MABBxZCcnIyt2/fpnHjxkDxc1CoypY1LgL2tEyVSvWnBI/Evmw2G6NGjWLDhg1S4AWKK/Qi2RdV/65du7J%2B/Xo0Gg0ajYa33noLjUZDcHAwZrOZV199leDgYNkTV6FCBS5fvvyXrDqeFn%2BFGl1yW5FICtqoWq2Wi0Oif1VUvX19fenSpQsHDhzg/v37kjLvxL8L6j/%2BiBNOOOGEE/%2BbqFKlikOCYJ/8iGDH/g%2B11WrlxRdfZPr06ZjNZvbt2wdAzZo1mTp1KtWrV5fS40qlkp9%2B%2BomYmBi5z169euHm5iYDfhGclAxS7IOK%2BvXrc%2BTIEU6cOCHfa9iwoQNtTKlUcv/%2BfW7evMnx48elEmVUVJQMkBMTE/nPf/6Dm5ubPJ%2B9e/fK76ldu7Y019ZoNCQlJVGhQgUiIyPlZ0TQZ68Ul5ubKylp69evp0qVKgBSCl8kg/ZYt24d8%2BbNc1Czy8vLkx5i27dvZ968eVy7do3Hjx/z%2Buuv07t3b%2BbNm1cq2QOkNH/JJM4%2BYLVYLGzevFkG8vPmzWPHjh1s2bIFgB07djBy5Eg0Gg1Go5GCggJcXFzIyMggKSkJk8nEnj17WLduHVFRUaxZswb4vUpbpUoVh54tlUpFQUEBKpUKPz8/h5V8EdzbbDZZXRLB/NixY8v0b7TZbDx69EhaT4iqiRB3sacgPvfcczJRhOI5UzLRUyqVMvkS1DdRgbAXCLIf48ePH8vt7OfshAkT5P1hNpvl2ISGhsrz3rRpk0zMAN577z3WrVtH586dAWSyJ8YA4JdffpGfF/ea/XiYzWZSU1NJTU2Vr4tjz8zMZMqUKaXOoyRmzpyJzWZjwoQJQHFVBX5XVi2vgm/PDBBo2bKl7CUUc7vkvW02m2UVXnym5HgLoRNR4bJardSsWVP2b926dYtp06aRmJgoE76CggKeffZZadViNpvZs2cPGo1GJmkFBQVUqlSJSpUqOXyf/bnZf29oaCgPHjzg8ePHkj65b98%2BvvvuO7Zu3UpWVhbx8fF06NCB0NBQ6YVpr66bkpJCjRo1pMDL4cOHGTJkiBxvgPXr13P69Gmio6Nl5Uv0S4qq/NSpU%2BXY%2BPj4SFYEFC%2BMnD9/vrxL/F/D0yR7RqOR/Px83N3dHTwFxbZWq9WhR7AscSNA2kYcPHiQ2NhYxo0b9184Ayf%2BDjgpnU444YQTfyNsNhsDBgyQAQUgZf4FRHUGfv%2BDLeTPAc6dO8fVq1fZv38/e/fudfCZEtUue5royZMnywymSwaO9vQtEcjYk0KEH5roEZo1axYKhYL9%2B/djsViwWCysXbvW4fhFYvPo0SMyMjIoKChg1qxZQHHAd/36dZo0aYLZbEapVFKhQgXGjBlDmzZtCAgIICAgwIG2V9Jn0GazMXfuXHbv3k1ycjJGoxGLxYKnpydubm588sknMrnw9vZmyJAh6PV6jEYjJ06ckCbuKpVKvr9x40agWDH1wYMHpcRMSuKPiDP22x85coQmTZowb948WrduzaRJk6RqYLdu3aRwhNVqpWrVqrRp04b33nuP8%2BfPc%2BzYMRkkCx%2B0e/fulbqOKpWKRo0a0b59e%2BrWrUuNGjVo1qwZXl5ecntRwRLJ%2B5YtWyRVF5AG6VBcHXr%2B%2BeeBYkpcSEgIGRkZqNVqnnnmGbnPrKwslEqlTA5HjhzJ9OnTOXDgAHq9XlaxPD092bdvHwqFgtWrV3Pq1CkOHz7M66%2B/DhQnNWq1mvnz5%2BPv78%2BPP/7InDlzaNiwocN5iv49genTpzN9%2BnQ%2B%2BugjqegIxSIn4lxu3Lghe6DEOAps27aNbdu2lRtgC4VFexl/8fqTEBgY6CAsolQquXLlCpMmTZKfee%2B994iNjS1VwSp5LPY%2BhgL2qpzlzUWr1Sr7TMv7jL01SX5%2BPmaz2UGsQyRP9s8re3qtwDPPPCPVL%2B0XEU6cOCE9CsU9p9Vq0Wq1MnnWarUkJydz/Phx9uzZg4%2BPDyqViu7du5OVlcWsWbMYMWIEmzdvJj4%2BnmPHjuHr60twcLCsRhYUFDBixAiH6pWYR/ZYsGABGzZsYOHChVL4RNDNBY4cOcL%2B/fvp3LkzVatWRa1Wk5ycjF6vJzU1VdLK9Xo9Z8%2Belf%2BLqqegqoqKqXhPbGP/Gb1ez7vvvsu0adPkPWXvKahSqYiPj6d9%2B/byNftFJ3d3d1xcXPDx8ZE2H/afeRrk5eVx8%2BZNzp49W2qxw4l/F5yUTieccMKJvxFC5fGvUnRKQqhhioAlJyfnD2mDopLUpUsXvv32W0ntFBYQ4OhpVR727dsnKz0CJbcrS4K%2BsLAQtVotFSePHTtGzZo1pSS7WIUX1UURpCsUCmrWrMmvv/5KXl6eFJwoCxqNhlatWnH06FEyMzOJjIxk06ZNALK/7f9j7z3jojzX6N1rKmXoRRSwIYpGEHtDY1RsqDExGmPbsSdGTWJLM7HiVhNjYq9x21LsomAvUSxREcUK2BUQBJEmdcr/w5zn2TOASfb%2BnXOyc86sLyrMvPO2eb3vZ617LcGMBQUFUbNmTWlMYDKZqF%2B/PklJSYSFhbFv3z45LySYdG4LGgAAIABJREFUGIHo6GgAOa8UHx9PWlqa/Hd0dDTdu3eXjNyhQ4ek46qnp6cs1kX4tUKhkMYoLi4uVuHIjo6OuLi40KJFC86cOUN2djZDhgxh69atVoW1kFKWlpby/PlziouLCQkJ4c6dOyxatIj333//d69reTg5OfHixQsZyfDtt98SGhpKQkICkydPlscsJIHCpRHMRaxgE5RKJTNnzmTGjBmyuRANYq9evZgwYQLNmzfH1dVVhj/fu3ePSZMmyYgSYbjxZ1E%2Bp6x58%2Bbcvn2b4OBg%2Bvfvz0cffSSv37fffsvWrVuZMWMG8O8FBoVCwcSJE7l%2B/TonT57Ew8NDLmRYIi4ujvfff5%2BCggJp1PH999%2Bzc%2BdOfvnll//onIv9FWZIlRluCGmnpYT3ZdsRUk1xTkwmE4mJidSvX/9Pz8y99dZb7Nq1CycnJxkVolAopIETmKNOBPLz86V8fc6cOcyYMaPCc080JdOnT2fmzJm0bduWH374Qf5%2B9erV/PTTT2RkZKDT6SgoKKB27dqkpaXJ/DpHR0fc3NxkTuHLUF4aWV5FYWk%2BZPkaqNxtd%2BLEiezcuVPO5TZu3Jh79%2B4RFhaGUqmUM3svXrzA3t6eFy9e0L9/fzw8PDh%2B/Dg5OTlkZmbi4%2BNDVlYWXl5ejB07Fjc3N7777jsePnyIu7s7jo6OPHz4EB8fHxYtWsQHH3wgDVucnZ2le23t2rV58OABarWaZs2aoVQquXjxIuPHj6esrIx//etfKBQKLl26hMFgoGnTptLRVByT5b2gUqkYPXo0EydO/FP3hw3/W7A1fDbYYIMNfyFSU1MxGAxER0eTnJwsCydh4gHmzCmRx%2BTm5kZ6ejobNmyQ5i1OTk44ODiwdu1aEhMT%2BfTTT3F0dCQ2NlYGGn/88cc4Ojpy4MABCgoK6NSpk2wkevbsyYEDB%2BjZs6dc9S%2BPP3LKUyqVjBo1ijVr1sifabVaLly4IOWkarUaBwcHnJycSE9Px8fHh6%2B%2B%2BooPP/xQMlgAM2bMYMiQIXh4eMiA9NTUVDmH1KNHD7kvvXr1IioqCpPJhFar5cSJEzx%2B/Ji8vDxycnKIj49n27Zt6PV6IiIiaNWqFTNnzsTe3p5PP/2U/v37061bN0pLSzly5AgLFy5k8%2BbNREZG0r9/fwDu379Pjx49SExMZMqUKfj6%2BjJp0iQAK0kYmM0uALlaLxhQSyMNMX%2BYmJhIcnIyPXv2pE2bNoCZub148SJdunTBxcWFnJwcjhw5glKpJDw8nKNHj8pmVxSfer1eXh/RYJcvVrVaLdOmTWPBggV88sknxMTE8M4777BlyxZpMS9yHQU%2B//xzioqKOHLkiAySz8/P5/jx4xgMBjkD%2BcUXX9C3b1/c3NxkMLr43Pr165OVlSVnRBUKBTqd7ndn%2BKpWrSr3OSUlRTou7t69m/DwcAYMGMDhw4fR6XQkJSXJe8Hd3V3K16pUqUJeXh69e/dm165d0v1SXIPz58%2BTmZlJYGAgM2bMYM2aNZw9e1Z%2B5w4cOEBAQADNmjWzyquDyr8L7u7u6PV62rZtK90mhXzxz8xpurq6kpubW%2BnvqlSpQrt27di/fz8JCQkEBQXJfaisubPcvxo1avDo0SMCAwO5c%2BcOSqUSOzs79Ho9/v7%2B3L9/Hz8/P548ecLo0aM5e/Ys165dA8wZb0KqnZiYSGFhITVr1uTp06eUlZURExNDnz59WLVqFWPGjKFXr14cOnTIaoZUPLdKS0tl01/e8dXPz4%2BsrCwZfC6cZEtLS%2BnRo4fcVmZmJsnJyfIai%2B14eXlZxcwEBwdz8%2BZNjEYjv/zyC4MHD6ZGjRo8fPiQGzdu0KhRI8koChnxlClTCA4OZuTIkaxevZoJEybw7bffotVqSUhIkMoAcTziWv03DqL/DcQCkYA4R0VFRahUKpycnCgrK/uvIkCEpFoYd%2Bl0OmrWrImvry%2BxsbHMnj2b8%2BfPk52dzbFjx1i7di3t27f/v/PwbPh/AbaGzwYbbLDhL8akSZM4evSoNFywhEJhDrEGZByAj48PV69eJTo6midPnnD69Gk0Gg1r1qxh6dKlXLx4EYVCwYoVK7h16xbLli2ThduXX37J5cuXiY6OtiqUhWX/ywoYS5tu4VhnKZ9Tq9VotdoKBYdlES5e16xZM86fP4%2B9vb2VjbyzszP9%2BvXj5MmTZGZm4uvrS1RUFHFxcYwaNYoRI0awfv16Kc%2BzjKcQ%2BPDDDykuLubMmTMkJSUREBCAq6sr8fHxTJ48Wc7B9e3bl3nz5knpqWUxZTQa8fb2BsyFVW5uLmVlZXh4eGAwGMjLy0Oj0bBw4UIpHXvw4AFRUVFcvHiRcePGyfeXx6NHj/jhhx9QqVQUFxfz%2Beef849//AMwu0TOnj2btm3bkpaWRs%2BePdm9ezd37tyRM1DiGghJYN26dbl165a8FpbziEqlktq1a0upWdeuXSkuLmbixIn07duXn376ScZCgJkhev311xk0aBBubm7ExsYSGRmJvb09K1euxNfXF4A7d%2B4wZsyYlzpmOjg4oNfr5bXVaDRs27aNQYMGUVxcLBklvV6PVquVRjniXmnfvj3nz59Ho9GgUCjIz8%2Bna9eu1KhRg19//ZXXXnuNTZs2WZ0LkfemUCg4deoU7u7ukq0JDQ2VDZ9g6ywhZuxmz55NUFAQarVa/uzp06esXr3aijGtUqWKVYNhMplwc3MjJyfH6jz83vfpZVCpVLi4uFh9Z3Q6HTqdjszMTGk6Yxlob/kZYj/FOalfvz7JycnUr1%2BfmzdvEhgYyLNnzySbnJOTg4uLS6WZbcKKX6/XU1hYSH5%2BPs2bN5dM%2B585NiGJFBLymjVrSomvCJ%2B/efMmcXFxjBkzBrVaTWFhIb179%2BbChQtkZWVZSUEVCgXOzs7k5eVVMJkKCgri7t27DBw4kMOHD5ORkcFXX33FnDlz5Gs0Go18Fn700Ud8%2B%2B238nciOsZyoeRlx9isWTOKiorkdzcvLw8XFxcWLFhAXl7ef23G9L8AMTcK5ixLsYhVVlYm8yX/yIDIhv892Bo%2BG2ywwYa/GE2bNmXbtm0yzLsy5ObmMmjQIBo0aMCtW7fw9vbm4sWLwL%2BDvp2dnSkqKsLJyYmsrCxZEIlQbyHRsXSHrFGjBjExMezevZvIyEhUKhUbN25k8ODBwL/neCZMmMDixYt/V3rarFkzad5QGd58800OHz5cwUVSoG/fvnh7e/Prr79y//59VCoVn3/%2BOTExMbRt25Zz587RsmVLmV32R9i%2BfTuNGjVi4sSJ3LhxA5VKRe3atfnqq6%2BoVq0a%2Bfn5fPTRR9y4cQN/f38yMzNp0KABrVu3xs3NDTA3Yb/99lulToHl0apVK6ZNmyZn9DZt2kTnzp0pLCwkKytLsnipqakkJCRw/fp1Bg8ejJ%2BfH7NmzSI4OJioqCjWr1/P1KlTSUxMxMvLi4SEBJmFJma27OzsZOMNZvt/MWsIZimdwWBg3bp1DBo0CDA3DqNHj2bx4sV/qgkR90/t2rWlPM4yUL6yYrhWrVoYDAaeP39OQUGB3Ma6devw8vKyajAFHB0dUalUuLu7k5KSImW%2BxcXFMh%2ByvBTTspny9fUlPT0de3t7DAaDVcYaYGXSItxEK/u9JXtWrVo1q2teXs5see5F3t7z58%2BpXbs22dnZ1KlTh2vXrsmGRKVS0adPnwqh6eXh6enJiBEjePXVV4mKimLdunXy8wSbJ9xyL1%2B%2BjEqlol69etKh8/r162i1WhYtWsTChQt59OiRzGa7dOkSgwYNIj09nYSEBBk8b3lMbdu25cGDB6SmpuLj40NgYCAXLlywmn8zmUzSjVU4ALu4uNCrVy/pFgxmpvbp06dW5jpfffUVGo2GBw8esHPnTuzs7Pjyyy9xc3Nj%2BPDhGI1GqlWrBkB6evpL77PKoFQq0Wq11KpVi6ysLLKysqTM0/I1f1Y%2BX7t2bR49ekTjxo0ZPXo0S5culbOFtWrVYu3atVSvXh0wN0ZjxowhPDychg0b8uuvvxIbGyujELZv305aWtpLf3/u3DnS0tJo0qQJAQEBxMXFcfXqVb7//nsuX75M8%2BbNWbFiBcnJyYBZdbB161a0Wi3FxcW4ublRvXp1fHx8ALPs/fbt24wdOxZfX1/mzp2LTqfDaDTKeIg6depw//59GdsSHx%2BPnZ0djo6ONGnSRJpphYWFyciZZs2aYW9vz2%2B//fanzqEN/zuwNXw22GCDDX8xOnXqxP79%2Byuwe5aYO3cuiYmJUpqVlZVFcXExY8aM4cKFCxw9elTaaIsZEMG2eXh4yMIgPz9fzlQJi/5evXoxaNAgjh07xk8//YSDg4OUcZUvjsrP4FlCFKQqlUo2l/Xr1ycxMRGVSsWNGzfYuXMn58%2BfJz8/n/Pnz%2BPq6oqfnx9lZWXSVTEuLg43NzeuXr3K3LlzKSgokHEDlnIw8T4wMzEdOnTgwoULxMTEoNFouHPnDuvXrychIYFt27ZVcAUEKg15f/LkCQaDAX9/fzIyMsjNzZWzRSaTiT59%2BjB9%2BnQCAwO5fPkyixcvpnv37nz99dd069aNQ4cOAWaXxzNnzvD8%2BXNGjhzJlClTrD576tSpnD9/nu3bt9O1a1cZhVD%2BT5PJJGV7gtkRElFRDG/atIkBAwYA5qK2cePGPH/%2BnEmTJjFhwoSX3ldiG7Vq1aJmzZqcPHkSk8kcXt%2BqVSuuXLmCvb09r7/%2BOidPnqRHjx707t2biIgI3NzcqFatGnl5eaSmpvL2228zYsQIANauXcvOnTtp164dZ86coWXLlmzatIn69esTFhYmWemtW7eyefPm310IeBmCgoJISkqiTp06pKSkSNbUssEDcyMnSp3GjRtjMpno3Lkz0dHRvHjxgtTUVJRKJcHBwbJZdHJyYs%2BePXh4eDBx4kROnTolg7x3797NG2%2B88acbB3t7e3bs2EFgYKB0m9XpdCxbtoz4%2BHiWLl1q9VohffT19SUjI4NevXpx5coVUlJSKjBHbm5uODk5Semr%2BD6EhYVRt25d9Ho9np6esuH4M7BkeAQEw1%2B%2B%2BRIM6r179ygoKGDGjBk8fPjQau7OEoI1rKyBFtv18PDAycmJR48e/an9FSHuO3bsYPDgwVZNdpUqVeQigkqlYv369RUWHcov5lhGYFi6bs6aNYshQ4ZgMBgICAjg9u3bNGjQgMmTJxMdHY2LiwtqtZqOHTuSkZHBxYsXKSsrs4pKEVLJnJwcEhMTZZSKaErr1KlDo0aNyMnJkVEWr7zyCpmZmYSHh9O5c2cUCgXXr19n3759MtPP2dkZX19f7t27J42XBATba29vj4eHBxkZGYDZMEYwld7e3uh0OtLT01Gr1YwfP5758%2BfLa/z1119Tt25dxo8fT0FBga3h%2BxvC1vDZYIMNNvzF2LNnDzdu3GDSpEkvdVALDw9n2bJljB8/nj59%2BrBs2TJ0Oh2nTp3izp07stivVq0a6enp1KxZ83fnpOzs7KxCtoWLZWpqqpy3EkWDyWTC39%2BfiIgINmzY8B9lX1lKQbt06UK1atVQKpW89957ZGRkMGnSJL766ismT55s5Zo3bdo0aVjQpk0bAgMDSUxMpEWLFly7do2ioiIcHByoX78%2BXbt25bvvviM4OLgCw%2Bjt7U2jRo1wdXWVPzt69ChdunThn//8J2Bm4oQ0C6iQLSfw%2BPFjdu7cycqVK3FwcODKlSt8/PHHeHh4SAmgZeYemK9bSkqKZEIsG0IwywlPnTpFdnY2V69ele%2B3/PM/MSWpDGJGJzAwkC5duhAVFYWLiwu3bt2S8rWff/6ZL774QuaSrV27lu3bt%2BPh4UFERASzZ89m%2BvTpvP/%2B%2Bzg7O//p5uHLL78kMjIStVpNREQEe/fupXfv3uzbtw%2BFQkHv3r1p3rw5JpOJLl26YDKZeO211wCsmheDwUB8fHyl995HH33EihUrUKlUdOvWTRreAFI%2BuHLlSgDGjh3LxYsXSU5OJi8vzyqIe%2BzYsfJ1IuPQ2dmZBw8e0LFjR8LDw5k2bRodO3YkNja2Uhv7Vq1akZaWZmV5r9Pp2LhxIyEhIZL9rVGjBkeOHCEvL482bdrImbLKtqlSqahWrRrOzs7cu3evwv2g0Wjw8vKitLRUSieFbNbR0RGdTleBzVMqlbIROHr0KJ06dZKMpclkombNmowZM4avvvoKg8FgZcQi4O7uTnBwMM7Ozpw/f16ykGCO/NDr9RWiPfz9/Rk8eDALFiygV69eDB06lIULF6LT6bh69SrZ2dk4ODgQHR0tm8A%2BffrQtGlTbty4QV5enpxP3bdvHz179qRly5acOXNGLi7Y2dnh4eEh50mLi4upVasWjx494ubNm7Lp/vTTT/n%2B%2B%2B%2BZOXMmubm5NGzYUMrGwRyRMnz4cJkdKRZhBBo2bMiNGzfkNa2sUf5fgnDqFIz5kiVLmDt3LqmpqYwYMYKMjAxiYmLkM0GpVFK1alWmTp3KmjVrZDi8QqF4aUNvw/8ubA2fDTbYYMNfjD59%2BpCamkphYaEsUOHfTnB79%2B6lS5cuHD58mK5du8rAXJVKxW%2B//ca2bdtYvny51dyPJcrPum3evJn169dLK3yFQkHVqlXR6/VkZWVhMpkIDg5GrVaTmJhIWVkZTZo0oU2bNri6ujJnzhzq1avHvXv3cHJyoqioSBouWBYLRqNRNnze3t5y22AuPqKiohgwYIA0tli%2BfDnh4eGAddPVvXt32ewKxkrM/0VHR7Nu3TrOnz/PDz/8wLBhw2jfvj27du1ixIgRFVhTvV7PypUrUSqVcvW%2BfCHXqFEjAK5evUpJSQkHDx5kx44dXLp0CScnJ3Jzc%2BnWrRsajYYjR47Qrl072ajv37%2BfiIgIORsUFhZGVlaWDPEu3xCmp6fz2muvYWdn91KGT6VSSRe%2BlzEtlUGn08l5R09PT6ZOncqpU6e4fv06TZo0Yc%2BePbKQv3nzJs2bN5fZZwkJCbRv354dO3bg5eVFixYtuHjxIo0aNcLX15dHjx7h4%2BMj2QIhgxR49uwZJSUlVi6tosEQf4r7wMPDg9LSUiIiIkhNTeXMmTMVXBDd3NxwcHD4042muBcXLFgAmKWE4n7q1q0b1apVIzU1FXd3d3ntvb290Wq1hIaGcvz4cZkpuHz5cpmN2a5dO6v7WEBIV8V1EQ1VWVkZrVq1oqSkhNTUVDn7N336dK5evUpMTIxsipycnCguLpYOpt7e3mRnZ1domkQGoU6no1u3bhw5ckTO4Irz3K9fP/bu3WvlWmtvb88bb7zB3r17rVh6cc9aNmw%2BPj7k5eXx4sULqyYmOjqaXr16yZndnj17UlZWxr59%2B/j666/p3r07K1euZNmyZfTq1YujR4/KRrG846OQRx46dIgPP/wQjUYjm%2BLMzEy56CMWSwS0Wq1V3uOfhVKp5MaNGzRo0ABALmLodDoGDRrElClTWLlyJWPHjgWsGXgfH58K313BMDdv3pz8/HwuX75MmzZt5P2Um5tLhw4dpMw%2BNjbWKr8zNzeX9u3bU1JSgqOjI6tXr%2Bb58%2BfShCYjI0OybOHh4Vy%2BfJmCggK0Wq1cxHFyciItLQ0fHx98fX2pVasWJpOJ/Px8YmNjKSoqomXLlsTFxcl4mj9yMLV8tvTt25fg4GAiIyNxd3enoKCAzZs3V2DRbfjfhy143QYbbLDhL8awYcO4ffs20dHRZGZmVpCKiQw94Yx2584d%2BbvQ0FBee%2B012rVrx8GDBxk2bBg///wzL168kIVnbGwsb731Fnq9XrrLCWmVMPgwGAwUFRXJQvvOnTtotVpat27NiRMneOONN1i9erWUmyUnJ2NnZ4erq6s0qxByqA4dOnD69GnUajW7d%2B9m/Pjxcp8t7b6/%2BeYb6cYHZqZmyZIlNGjQQGbAvfHGG%2BTn5zNs2DCqVq0qYxA8PDzIzMxk27Zt7N69m7CwMBYuXEiHDh2YPXs2e/furSCh/O2334iMjASwMh0oXziKpnX69Ons378fV1dXevfuzbVr12jVqpV0iARzA2nJHgpbeoHyMkXBPN2%2BfZuEhAT8/PzknNq5c%2BfQ6/UsWbJEyjT1ej0xMTH8%2BOOPrF%2B/nhYtWnDq1ClZBOv1eoKDg%2BU%2Bu7u7o1QqCQoKolatWkRFRVFcXExmZiaffPIJYHbOHDp0qAznBigqKkKr1Vo1CPn5%2Bfj4%2BJCdnY1arSY3NxedTseRI0cIDQ3l1KlTVkZDljldjRs3xt7enuPHj1s178HBwdJyX1xjEUXxsqgCId%2BdPHkyo0aNktfLy8tLMleWUl%2BFQoFarUatVsvwdEAyWBkZGRQWFmI0GmXDCub7r3///hgMBkJCQmSsiWj2AJYvX84777xT4Z7RaDSy4QOsmMMrV67Iol5g9uzZFY5T7JN4v%2BW%2BeXl54ezsTOvWrSXrJmS7w4YNY9myZbIxNBgMHDx4UN6X4poWFxcTGxtLv379pBEUIOM1LGMJFi9ebCV9FOxV3bp15WcsWbJEPpNiYmJYunSpfEaoVCrq1KlDTExMpY2ZyWSiW7duGI1GUlJS5DUUbDOYmXgwO5jWr1%2BfixcvYjQaK7BoKpVKnjeRU1elShX8/PxkVuWECRNYsmSJZIINBgOLFi1CoVDwxhtvyGfF2LFjuX37NtevX%2BeDDz5Ar9czadIk3nzzTcrKyli7dq3Votzp06epV68ely5dYvz48RgMBtauXUtSUhIXLlywcueNiIggMDCQ0NBQ4uPjuXv3rlzEsbe3Z86cOVSrVg0PDw/69%2B/Pzz//jJ2dHe7u7gwYMIB3332XCxcucO3aNf75z3%2BiUqmYPXs2Op2O%2B/fvY2dnx7Vr16wk4E5OTty%2BfRuTySQXjoxGIwEBATx69EjOBIuAdRGNYjAY8PDw4Pz585w/fx6TyURoaCgjR460NXt/U9gYPhtssMGG/wF06dKFli1bcvLkSfLz8zEajdIuXbjxubi4oFQqefr0KZ06deLs2bMyQ6927dqoVCquXr2KyWSSBZCDgwNdunRh//798j9ylUolGb2XMSYKhYImTZqQkJDwUse58ixT48aNZZadvb09ISEhZGVl4e7uTnx8PAqFgs8%2B%2B4x58%2BahUCjQaDQ0b97cKigeKmalWX6GMMcQ9v5Go5HCwkIZlL5z506qVKlixdplZGQwb948zp07x4cffsjs2bMl4wYVGT6RRTZgwAB69%2B5NixYtAHNe3969ewkPD5fvDw8PZ926ddSqVavSbb366qtkZGTI14eGhrJjxw769%2B%2BPXq/n1Vdf5dixYwCMHj2a9evXyxnLrl27cvjwYby9vdm4cSMRERGSSerRowdeXl6kpaVx7Ngxabfv5ubG559/Trdu3aRLYVJSEk%2BfPkWtVhMWFkZSUhLNmjXj4MGDct6nYcOGPHv2jIyMDJRKJT179uTo0aO0bt2a4uJi7OzsqFWrFj/%2B%2BCNKpZKSkhK6du1qJU%2B1bHzz8vIwmUwybkBk7q1evRowz0n%2BmRk4wWaJ%2B9ZoNFKjRg0ePHhAYGAgzZs355dffkGn01FYWEijRo14%2BvQpWVlZNGnSRBobtWjRglatWjF%2B/HhCQ0OtGjNRHJe/JxQKBUVFRSQlJbFs2TKioqIoKCggOzsbjUaDu7u7lFD26dNHuhn%2Bp3B0dKRnz57ExsaSnp5u9buwsDDu37/PiRMnaN26NUePHsXJyQlAGqfUqFGD6dOnM27cuArh5jqdjrKyMho3bsyFCxcqfLZGo2HkyJGsWrVK/kyhULB8%2BXI%2B%2BOAD2eiJ2UJhDgNw69YtaeITEhKCwWBg9OjRhIWFMXTo0D99/MHBwVy/fh2VSoWHhwfe3t7cunVLfu%2BFQY0wnrLcT1dXV%2BrVq0dxcTGJiYkyAqZVq1bs3r2b9PR0TCYTHTt25Pjx41YGMsJxtEuXLrRv354BAwbw7bffsmXLFgoLC6Us9vfK5D9i2qtWrUpxcTEFBQWVynXVajWurq6UlJRUiP8o/xmWz8Xy5jPloy4E%2BvTpg9FolDmRSqWSQYMG8fDhQy5duoSLiwsZGRmMHTuWPn36yOeYeNYJU5ryzzUb/n5Q/tU7YIMNNtjw/3cYDAaePHlCfHw8mZmZctaidevWspDfuXMnhYWF1KtXD5PJRFZWFg0bNiQrK4v8/HyuXr3K5cuXJWsiUFRUxN69e9Hr9bz%2B%2BusEBARQWlrKo0ePZLNXfm5Qp9PxxRdf8Pz5c9nsWRobCJQvLiIiIuTfi4uLefLkCQ8ePCA%2BPl6%2Bfv/%2B/fLvpaWl/Pbbb6hUKrRarWxYRHi5Wq1m//79HDhwgMDAQBwcHGSW25QpUyQbYG9vz7Rp06hevTpVqlSR%2B6DX61mzZg0RERE4OTlx4MAB6T76exDFVaNGjaQpw%2BTJkykpKZHHOHDgQClDGzp0qAwjNhgMbNu2ja1bt7Jp0yZZxP38889s3boVg8HAZ599hre3N2%2B%2B%2BaZsSBQKhWyCRBF9%2BvRpwDwP1a9fP8Bs%2BgBw8OBBNm/eLJtFlUrF4MGDOXjwIN26dQPMsrybN29aZeBFRkby7Nkzjhw5glqtlvfKjRs3ZHFsMBik7O/48eOcPXuWW7dusXHjRvz8/NBoNJhMJqtmD8wStYKCAtnswb/Dtu3t7VmzZg3Hjx9n3759NGjQAF9fX%2BbPn09YWBjz58/n9ddf5/XXX5dRAx07dpTH4ePjQ/369aVrI0BAQAAzZ87Ezs6OgIAAzp07x7Zt2/j111%2B5evUqmzdvxs7ODjs7Oz799FM6dOggGVdPT09cXFxe2nQajUarWcBly5bx%2BPFjmZNZq1Yt6YSqVCrZt2%2BfZBDBbKbi5uZm9b3p378//fv3Z/78%2BTg4ODBnzhzZPG/fvp3s7GwZYyDiGdavXy%2Bl2kIaKCBcKVeuXMmWLVsoLi62avYUCgVTp07l3LlzbN68GXt7e7RaLaNGjZIutGVlZXLBZdy4cbK5%2BPjjj%2BXvAcnsCzkkmGfYGjRoQIMGDSgtLUWhULB161bZ7Al50ov7AAAgAElEQVRZuEKhYNeuXdjZ2ZGUlISfnx/Hjx8nKSmJpKQkJk%2BeLM%2BTwWDgiy%2B%2BsFo8EHJE0WiKY3vrrbfIzc0lJydHmlBt3rxZZlhmZWVJ18qjR49iNBqtWFgRL3H8%2BHGZHxodHU1ZWRnt2rXjjTfe4NixY9jZ2XH8%2BHHs7Ozo1asXERER8nuXmJgoj0PkWDo5OeHq6kpUVBRGo5Fdu3Zx5MgRKcEU0RIKhYLDhw%2BzY8cOnJyc8PT0lO6vIt5k4MCBDBw4UH7nRDyL6f/KHQ0LC6Nbt274%2BvqiUCjkjJ04xjlz5li5PxuNRo4ePUpsbCyFhYVSCbJq1Sq6detGUFAQQUFBFBYWEh4eTlBQEA0aNKC4uFg%2Bx234e8Im6bTBBhts%2BIsxf/58tFotvXr1YtmyZTRo0AC9Xs%2B1a9ekE2JxcTFbtmxhzpw5mEwmyaSVh5ubG3l5eTg6OjJlyhTq16/PsGHDKC4uZseOHRVeL6Q84n2jRo3il19%2BYcOGDZJt6NixIwqFQtp0gzmLzFKO5%2BDgQO/evZk3b55smPz8/HBxcZGzOmBthGJnZ8cPP/wgZXo6nY7i4mLq1Kkj9y0gIAAwM0KlpaV89NFHREZGkpycTPv27aUBw1tvvcWiRYvktg0GA7169cLJyYl//etfci7vz0AY2vz000/MmTMHT09PatSoAZibjMTERGm57%2BrqSmFhIceOHWP58uU4OTnx3XffUVZWJl1F1Wo1c%2BbMwdHRkbKyMm7cuIFarWbfvn2SXWrYsKGcKROsk5iBS09Pp6SkBF9fXykXnTJlCmq1mrVr15Kdnc3%2B/fvlPlpCNCiiifTy8pJMQ58%2BfeTr0tPTuXLlyksdWJ89e0bdunVxcXFBq9WSnJyMRqOhatWqVo6wRqMRjUbDgAEDOHfuHIWFhfTr14/atWtjNBr57rvv2L59O56enuTl5eHq6sr58%2Be5ffs2T58%2BlWYpz54947PPPuPkyZM8e/aM4uJi5s%2Bfz7Bhw7h//z6NGjXiypUrPHjwAE9PT%2B7evSuldlu3bsXBwYHw8HDZqLz11ltWxyNkhJZYvXo1JpOJsrIyq6DwuXPn0rZtWy5fvgyYmyAxcyuOWRTwOTk52NnZsWfPHt566y0pl9Pr9URGRtK5c2eOHj1KUVERixYtkg2%2Bo6OjjE8Bc0NTVlbG8%2BfP5f0oEBUVxYULF%2BT%2BWS60aLVa3nzzTQYNGsSAAQMYOHCg1TEqlUqmTp3K1KlTCQkJQaFQyLm0lStXykZdyCbLL%2BpYNsiW53TPnj3o9XqMRiOjR49m06ZNzJw5k40bN5KWlsb48eMpKSmhYcOGGAwGunTpQufOnZk2bRrTp0%2BXMsvs7GzWrFlDu3btrBZ%2BHB0dpaGQ2K/w8HB27Nghfy4Wriyz9cQzS5gfKZVK2QRmZmai1%2Btp3749Tk5OTJ48mWfPnlGlShXi4uKoUqUKR48excvLCz8/P/R6PUVFRZw9e1bKZCdPnkxBQYHV90awrPHx8eTn58ucTJPJJKNGSktLMZlM3Lp1S%2BagimZbnAuFQkHdunXp2bMn27dvlw2f0WiU24mLi6Nx48ZkZGTIe8nymg0aNIgnT55YXcNnz55JhjA/P18%2BwxQKBV5eXrx48YKCggK8vb2pVasW7u7uZGRkMGLECJYuXWoLXf%2BbwibptMEGG2z4i9G2bVuGDx/O7t27uXfvHjqdTjYCIn9NrPi6urri6uqK0WgkOzubGjVqSPvytWvXsmTJEk6dOoWjoyNz5szhwYMHREdHc%2BfOHYxGozQ80Gq16PV6GjRoIOMXjEYjnTp14ty5c1azXCJuQUBk/lkyOT169MDb25sdO3bI4kGj0TBo0CA2bNgAmFkLOzs7iouLMRqNBAUFMXjwYObMmYNer6d69eqUlJQwbtw4pk%2Bfjr29vWwQmzRpIjPWQkND8fDwYNiwYQwcOFAairRs2ZILFy7w8ccfc%2BzYMSIjI%2Bnfv3%2BF8x0UFGQVjJ6VlYWXl5f8t2BZb926xa1bt9ixYwf79u0jLy%2BPfv36ERUVxZUrV0hISODcuXOcPn3aqgFXqVQEBATQtWtXRo4cidFoZO3atRw5csRq/lIEXisUChlqDuZiX0gFr1y5wiuvvCLPc3lG6pVXXiExMdHKKMYSlUUUiJ/NmjULQM5qCTZCyDpVKhXe3t5cunSJy5cv8/jxYxQKBTVr1uTevXucP3%2BekydPAuY505YtW8rrq1QqiYuLY%2B3atfz444/SbKJmzZo0adKEmzdvVsjLExBGLw0bNpTW9Uajkbp163Lnzh1MJhOzZ88mMjKS5s2b4%2BfnR0xMDF988QXLly/nyZMnKBQKKQUFc8Hfpk0bPD09pTvrfwox9yqQnp6OwWBArVYTHh7Oq6%2B%2BKufr/gxEge/i4mK1bTGfWadOHTp27Mju3bvx9vbm9u3b3LhxQ0qOLbcjTJTi4%2BPRarUYDAaaNm1KfHw80dHRmEwmvvjiC4xGo4whSU9Pl8%2BVwsJC7O3tUSqVlJWVoVAoKC0txdXVlby8PJn/aZmBuHfvXhlX8vrrr8ufmUwmevXqJVmmyZMn8/XXX0t2VoTHg/m70rRpU65cuSLZRKVSybvvvsu//vUvAMkSiv0Cc8N37tw52rRpI2eQg4OD%2BeWXX%2BTijkKh4ODBgwB0795dbl%2Bj0eDm5ibzH4VTqjCSOn78OD169KBDhw4kJyej0%2Blo06aNnFG9ffu23Id69erJbLz/pyFmZTUaDQUFBVa5iC%2BTdP7Z7QqZdM2aNYmOjiYuLo6dO3dy8OBBHBwcOHv2LDt37mTPnj220PW/KWwNnw022GDDX4wWLVrg7OwMVMynehmEfKxatWo8e/YMg8GAl5cXWVlZFUwNxAyMwWCgSZMmODs7ExsbKwO827VrJ9mYyiCaN7FdYVBh%2Bd/H9u3bGTx4sJQjvWxbYg7Lslgp/1mWTp/CSbN79%2B6kpqZy7do1WfAK1kpsy9Id1HJb5SEKz3nz5lW6jzNnzmTmzJm8%2Beab8mc3b95k//79XL16lfPnz9OwYUN27drFpk2bpD17QkICly9f5smTJ1b5XSkpKcTGxqJSqejQoYPMCtuwYQPdu3enatWqbNiwQVrHb9myRRpXiObKaDRib29PYGAg169fl7NtYt4vKChIOpwKjB8/nkaNGlnNIQkXQku31BUrVuDv70/t2rVp2LChnAUtv%2B81atTAZDLRpk0bQkNDOXToEJ07d5bGD5Zwc3OTc1/C1Mff358ffviBfv360axZM3799Vfc3NzQ6XSMGDGChQsXUlRURHh4OEePHsXf31/OPhUUFNCmTRtpzd%2B3b1927dqFq6srzZo1s2KfLe8lcY9X1ly%2BePGC5s2bS%2BMhcR95eHjw9ttv895770nWqFGjRsyePdvKyCQ0NBSj0UhZWRlqtbqCm%2BbLYHnfC5v8kSNH8u6771KlShU5m9e4cWOuXbuGVqtFp9Px7NkzQkNDJdMoYGdnJyWMoaGh%2BPj40KJFCyIjI383N7M8LCWpwsBHpVJJiXlRURGzZ89myZIlktn/oxk2IVG0nF%2BzzBqsbE5u6NChnDt3zmpxxDLexXJ/LZtry2dHeYjGVDwP7O3tee%2B999BqtWzbtk0y0T/%2B%2BCOjR4/mm2%2B%2BwWQyERgYiLOzszw3OTk53L17l2rVquHi4oKTkxMlJSU8fPiQFStWkJeXx8qVK/%2BSeTfBJIvjLL8oUP7flu97/fXX2bNnD3Z2dly%2BfJnk5GRWrlxJfn4%2Bnp6eLFiwgKKiIjp27GjL4Pubwtbw2WCDDTb8xRg2bBhhYWGMHj1aDscXFRWxYMEC7t69W2FFVdioX7hwQa66lodCoSAkJITr16/j6OhIaWkpKpVKFnGiUKpRowYxMTHs3r2bGTNmoFQq%2BfTTT/ntt984fvx4BffDygoqjUaDv78/jx49qmCsYIk333yTAwcOyGKvPJo1ayYZuc8%2B%2B4yRI0fStm1bwCwZi46Opn///hw7dgx7e3sGDx5MSEgIxcXFrFq1ChcXF0aMGCGLUcFkWbodArz77rts3LiRqlWrWv1eSL3K4/79%2B4wbN44hQ4YwZcoUHj58yK5duxg4cCAjR47kzp07snBVKpUMHTqUzz//HICLFy8yZswYqlSpgsFg4Pnz53Tq1In4%2BHgCAgI4ffo0vr6%2BBAQE0LRpU5YtWybn6MobM4A5n/DmzZsYDAYOHDiAQqGgV69ele73vHnzKrBBvwdnZ2cpARWzYi9evMDOzg6lUklhYSEtW7bE3t6eU6dOVVrsi0b0008/ZeHChbi7u0tp8MSJE7l%2B/boMyj558iT%2B/v60bduWuLg4AgICZMzF06dPuXXrlpQ6Zmdn/6ljqAxarbbSIvXp06f07NmTiIgI6tevT%2B/evV/KznXv3h1AMka%2Bvr40atRISnZfvHghGUWNRkN4eDht27bl2LFjnDt3jpKSEmrVqsWAAQMYMmQICoWCIUOGcP36dStHT8trXrt2bTmTO2PGDNm0rlixAkDGKIjIDkv8UcFv2RyAmUF/99132blzJy4uLixatIiQkBBKS0vRarU4ODiwbt06hg4dSnFxMc7OztLVV3zna9euLa%2BTcAOGfxstif0YPXo0GzdupKSkBA8PD6Kjo%2Bnataucd/X390ehUJCZmVnhWVFZAynOm5CYCvMccW7E%2BwAZDyNMsQQrOWHCBFJSUkhJSZELRwqFgri4ONn0nzp1ivfee0/O6D1//pwNGzawaNEifvvtN6tFnpycHC5fvsz06dPJyckhICCAzz//XDaJeXl5ZGVlMWvWLJ4/f06DBg2YOHEiQUFBpKamcvbsWR48eCDlz8%2BfP6djx45UqVKFx48f8%2BjRI8rKysjJyaGoqIjAwEDc3NwICwtjwIABeHp6kpubS8uWLa0MoyqDuOfFn2FhYVy8eBE/Pz/atm1LdHQ0n3zyCT169LA1fH9j2Bo%2BG2ywwYa/GImJiYwaNUrKNN3c3KSzYZUqVdBqtWg0Gmk1f/36dTlzV75AFQybXq9HpVKhVqvR6XRotVoKCgooKCiQK%2ByieHJxcaFHjx4olUq2bNkC/LvBE/JSLy8v0tPTqVGjBn5%2Bfpw9e1bmo/n7%2B3Pjxg369u3L/fv3uXnzJkqlkpo1a/L48WMKCgro3r0733//PSaTie%2B//56kpCTOnTuHt7c31apVIzk5mXbt2pGdnY27uzsxMTF/eN4EAzhr1izOnj3L5s2brUxbBOrXr2%2B1oi0YvsqaFXHslhD/PnHiBKdPn%2BbSpUtcunSJrKwseX5feeUVli1bxoYNG4iNjZUy1iFDhtC5c2eGDx8OwA8//MCJEyfIysri8ePH6PV6dDpdhfgG%2BHeRbunS5%2BDgwOLFi2nfvj2hoaHo9XoOHz5cQW4okJqaSnp6Ou%2B%2B%2By4AGzduZMmSJTg7O0s5Zvfu3dHpdNy6dYuEhARMJhMeHh7yHjEajahUKkpKSqxYrPDwcNn4derUiYMHD8rXu7i40LJlS7777jtatGhhVbj/HitUWZMrpK/lf9epUydyc3O5dOkSYHa7nD17NlOnTv2vpG1/1CRZwt/fn9TUVBkZMWjQIL777jsZc1KnTh2ePHmCSqXi6dOnGI1GQkJCSEpKQq/XU61aNT7%2B%2BGN5/RITE8nOzpZ5lu%2B//z4ff/yxDKU/d%2B4cWq2WjRs38vXXX7%2BUybGEVqvllVdeISEhgTfeeIPdu3dL182vv/6aTz75xOqcivMM5mbzvffe47PPPpPb%2B%2BCDD1i3bh2lpaXY2dlx6NAhJk%2BeLM%2B/iCoRs2qClR4/fjzLli1Dr9fz2WefUb16dSZMmEBpaSkODg5cuXKFwYMHSxMVy0UmIWMUEmMwy5jj4%2BOZNWuWVbyFYNUsY0Asmxyj0YharSYwMJDk5GR27dpFnTp1SElJYeLEidy7d49FixYxYcIEyWrOnj0bo9HI7du3WbduHe7u7hw8eBAnJyfmzp3LsWPHSEtLw2QyyfiO7OxsTCYTz549s5oVrV69OhqNRpoSPXv2TOYyKpVKOnTogMFgkJmTpaWl7N69G7VazbBhwxg9ejQajQYwL7JFRUXx5ZdfAmY3UPGZAuIeEXEgQmovzqNgPUWMhnjWCIm80Whk1KhRtGrVisWLF/P2229z6NAhW%2Bj63xS2hs8GG2yw4X8AJ0%2BeZMKECQBW83L/KQTj8EfyMjGXJ0wA7OzssLe3Jzc318oCXxSUtWrVIiQkhIMHD6LX6wkLC%2BPGjRsVmBdRQAqpmjAaaNWqFcnJySiVSnJyctBqteTn5%2BPh4cHu3bvp1KkTzs7OlJaWUlZWRtOmTa3m2gSEKcy6des4cuQISqWSxo0bM2jQIFnAADx69IjDhw9z6tQp0tLSUCgU%2BPv78%2Bqrr/Ljjz%2Bi1Wrp0KEDRqORkydPMmTIEPr164fJZCIiIsLK/ED8aVlcC4e9t956ix07djBs2DBOnDhBYWEhHTt2lI59LVq04PTp0zKPS/z%2B%2BPHjrFixgnXr1r30Gnl6epKTk8OgQYMkyzt79mxiYmKYN28ePXr0QK/X4%2BXlxcqVKzlw4ABFRUV07txZMqMC69evB2DEiBEyUL1r164Asrn29/cnNDQUk8mEp6cnBQUFnD59moULF7J161a6d%2B9OTEwMAwcO/NNzPOIYAMk07969m4iICFxdXVmzZg3nz58nNjaWu3fvUlhYWIHVqcyC3sHBgbi4OJkx2K1bNzQajWTExLXSaDQ0a9aM%2BPh4OTean59PRkYGZWVlODk5YTAY0Ov10mlSMDoBAQFcu3ZNsmFqtZq5c%2BcCZoZv2LBhGAwG6R4rTD1eBstiW6fTUVBQgEKhYODAgeTm5nL8%2BHG8vLzIzc3Fzc2NmJgYgoODUSqVNGnShE8//ZR%2B/frh6OgoHSdfffVVKWdt164dp0%2BfljOQI0eOZMOGDSgU5lw6MZtZ2bOhdu3alJWVkZaWJo9FBMcLBAQEcP/%2BfSmTDQ4OJiEhQTpfCsZTNLXivSqVSma8lW9Qq1atypEjR6T0WJz/RYsWce/ePavFk8ePHxMTE0N%2Bfj4bN25kz5499O3bVxqglL9XKls8GDx4MBMnTiQ8PJyioiIiIiKIiYmRklhxbUtKSmQ2aWlpqXym%2Bvr6yoWhoqIiqwbr/0sQTL3RaCQiIoIDBw6gVqttoet/Y9gaPhtssMGGvxg5OTmMGDGC3r17s2/fPjmTN3z4cDZs2EBOTo5cDRcFpaUsUzB5UHmzKOz3RfGzefNmFixYwI0bN%2BRqeufOnTl9%2BjQlJSWYTCaCg4PJz88nNTUVlUrFK6%2B8wsOHD1EqlTISok%2BfPmzZsoUXL17IYqk8LE1lvLy8%2BOCDD0hOTmbr1q2ywNNqtZSUlKDT6TAYDPj4%2BJCWlsaPP/5YaXGxevVqfvrpJwYOHIher2fp0qVW83qWM0GWLNmkSZPYvn07jx49YuXKlXJF/syZM3z77bdSClY%2Bc8oyX3DVqlWsWbNGFoBFRUW888477Ny5k9jYWNzc3KzeX1l%2BVWhoKDExMezdu5elS5cyatQoNm3aRLt27SRzeu7cOezs7AgNDeXVV1/l8OHDgNmR8dChQ1ZZiwIi7DolJYVFixbRsWNHevfuDSBzuDZt2sTXX3/N9evXf9fQRZy3hIQEnj17RlhYGGfOnKFt27YkJSWRmppKWVkZvXr1wmAwWGXSiX05cuQIzs7OtGnTRjb/fn5%2BbN68mU6dOtG%2BfXvatGnDyJEj5fsePXrE0KFDGThwIIsXL8bOzg5fX1/u37/PvHnz8PT0ZMyYMZhMJurWrcugQYOoWrUq77//Pps3b2bbtm3yWC2hUCjo3LkzGRkZtGvXjh9//FHmN7q4uLBlyxbefvttFAqFbNbBPLu3dOlS3nvvPUwmEw0aNKBRo0YEBQVJdskyUF3IHMW9aGkc4%2BDggNFolGy5ZRMkHFyrVq1Kly5d%2BOSTT1i7di0LFy6U33Xx/Z86dSqjRo3io48%2B4tKlS5IlEos4QjIrmHzx%2BlGjRhEbGwuYmectW7bI6yxYZiFlbNmyJXFxcbJpUygUjBs3jlWrVlnJKcV%2Bi2Y2MjKSevXq0bdvXysTFnGvWn6XADw8PHB2dpZOm2q1mgULFvDLL7%2BwZs0aWrVqRUBAADqdjmvXrlWYT9ZoNAwZMoRt27YB1o6l8G95Z9%2B%2BfTl06BDR0dH4%2BPiQnp7OG2%2B8ITM9xfdZNOSenp6MHj2abt26SbOWyMhIpk2bxty5cyWzFhkZSfXq1bl37x4nTpzAZDJx/fp1%2Bfy5f/8%2BBw4coKysjA4dOkjTLTA/93Nzc4mKipIRC1lZWdSqVYu8vDwKCgq4fPkyL168wMXFhezsbMnyl5WVYTAYXro4KO4/EQafnp7OrVu3yMrKQqPRkJOTI5tyk8mEk5OTVBkIZ1nBqorFhU2bNtG8efNKP8%2BG/33YYhlssMEGG/4ilJaWMnr0aGrUqMG9e/cYOnQoixcvBswym%2BvXr5Ofn09paSkjRoyQrnVgnovJzs6WwdkGgwF/f39SUlKs2A0nJyf2799Phw4dAHPRKaznwSxzcnBw4MaNG5SVlcnC7M6dOxQVFcmC9f79%2B3IOB8xSwW%2B%2B%2BYbS0lI0Gg1NmjRhxYoVhIWFWTUiotkD%2BOWXX9BqtSxevNiKPRMSsaFDhxISEoKfnx8jR45k/vz5zJgxgwsXLtC5c2dGjBiBXq8nPT2d1q1b8/777wNmN8PvvvuOVatWSYdAwMpFEMwMxYgRI2jYsCFXrlyRDV%2BrVq148ODBS6%2BTl5cXv/76Kz/88AOpqanMmzePbt26UVpaSrNmzSTD0L59e%2BrWrfuH7KpYNRfvExlgJ06ckM1BcnIyISEhAFYF8qFDh146ZybyHOfMmcMPP/xAx44drazswWxZr9frefDggWzuBRo0aFChiWzQoAEeHh4AkqkDa8dKhUJBQEAAubm5shkQeWopKSnyZxqNhtdee40lS5agUqmIi4vj0qVLrF69mujoaBYvXszx48dp27atbOL79evHl19%2BybfffisNU%2BbPn09aWhpLly7lu%2B%2B%2Bk/NiSUlJcs5NoVDQsGFDbt68KWfMlEolc%2BfO5cMPP5SsTfv27UlISGDmzJlyMeXAgQOyeVYoFHTo0AFfX1/S0tLIyMggKiqKkpISaYgi4iAE63vq1CnWrl0rjZIExPaFMZP4nclk4sWLF/j7%2B8u8TDBnwimVStzd3cnKypIN36hRo%2BTniWZPfNdLSkpYtGgR//jHP2QDKF5v%2BV0QM5ri%2BotiX1x/y6YXzPLQffv2Wd0v4hyJBqdatWryu%2Bfs7GzF/letWpX09HSrcHswPwMtjar8/Pw4d%2B4c165dY/369ZSWlkpHT8t9EVLVsrIyNm3axKhRoxg3bpxk0gGWL18uDaYiIyNlswdmueP777/PN998g6%2Bvr5zjFdfE3t6eTZs2sXjxYjZs2EBISAjz5s1j4MCBzJ8/n3feeQcwKw6Ei6ivr6%2BUbgP861//Yvjw4fLe27p1K9WrVyclJUWGzcfHx3P37l15ryQkJJCRkUFGRgbh4eHyHD59%2BtTqnJXHW2%2B9xZ49e%2BTiwIABA9ixYwfjx48nNzdXPv8VCgW//vorr732GteuXaNVq1Z4enoyf/58GjVqxMWLF/nHP/4hZeKlpaW0aNECf39/W7P3N4eN4bPBBhts%2BIuwePFijh07xurVq3n33XdZsWIFH3zwgVy9tbe3Jycnh4KCApYvX85XX31FaWmpnO8rz2RZSjkVCgXt2rUjNTWVqlWrcvbsWVkMWUqgXgZLg5byLp1Crgn/LhibNm1KXl6edNZTKBS88sor3Lx5U24nJCTEqgC3xDvvvMPevXsZPHgwkydPJjQ0lJKSEqZNm8a1a9c4f/48z58/Z/bs2Xz11Ve4uroybdo0IiIiMBqNNGvWTLoX1q9fH41Gw7Vr1yo9tqCgIGrVqmUVHP4yVq6goIChQ4fKOctx48bJuSIw5%2Bd5e3tTtWpVfHx8uHDhAtnZ2TIvcdasWfTs2dOqWJoxYwZubm60bNmSQ4cOyUK%2BTZs2XLx4EYPBQOvWrdmwYQOhoaFWMriEhASCgoJkc7B%2B/XpWr17N3r17qVatGlqtlqioKMLCwrh06ZLVPBOYGSu9Xk%2B7du3o2rUrV65csWJB9Ho9J06coEqVKty%2BfZumTZvi4ODA5cuXcXd3JyUlhTlz5vDixQtu3rxJTEzMH8YQWC5ATJw4kVWrVslZMSE/rGwWbcmSJUydOlVGcYhjGD58OB9%2B%2BCGDBw%2Bmbdu2lJSUcP/%2Bfdn8gFmeunfvXlq3bg2YQ8B9fX1ZuHAhjRs3lu6aApaf7eTkhMlkkoYfO3bsIDg4mPr168v4jszMTGlyI2Zd9Xq9lQGGm5sbxcXFlbpkimgTSwfI9PR0qlSpgpOTE9OmTWPs2LGUlJTQo0cPoqKi5HsDAwPJzc2Vx6tSqeSMb0FBAQEBATx8%2BFBmuq1cuRIwxyNYmqn8HsR8rAg7Lx/NImBp9NOzZ0/q1asHmJ9tLwu1B7NJTFJSEpcvX6ZTp05kZGRYzdkK5r%2BgoEDGudjZ2UlGzvJ6BQUFoVarWbFiBWPGjGHv3r3ExcUxZMgQ%2BbrFixezcOFC1q1bR2ZmJmPGjMHd3Z3U1FTs7OxYsmSJlNQbjUZu3LgBmGduhaw0ODiY6dOnM2vWLKZPnw6Yg839/f25f/%2B%2BdOsVxyHubdGEVfbM9fPz48mTJ5WeK6VSSZs2bcjPz5cLGZZsqQiEnzx58n9kpOLu7k5eXp40dvnoo49YtWoVvXr14tixYzRs2FDeMykpKYSHhzN8%2BHA%2B/fTTP/0ZNvzvwdbw2WCDDTb8RejRowf//Oc/adKkCcuXL2fnzp3Url2bS5cuydgDMR8jVonFXI2dnZ2cKyopKZH5ej4%2BPhQWFsqC%2Bs/AUiqqVCrR6XR8%2BOGHLFiwAL1ej4eHB6tWreLtt98GzPNHIpzaEuXli6LgsYQwB7AscBK%2BjPEAACAASURBVFQqFe3atcPf35/p06eTkZFB586dMZlMsvCaPn06O3bs4ObNm4SGhvLll19y%2BPBh1q5dW%2BGzg4KCrDL8yiMoKEiaRVS27%2BLvu3fvZuHChTRo0EC64okQ8fz8fE6dOsXRo0flNtRqNfb29tjb28vmWjhUCkdQgLS0NMAcqSHmC0VDZBl34erqKpt7cb5CQkK4du0aGo0GlUpFQkIC7du3l0HtYGYEmzZtyuXLlys0fGJGr3r16tKKXrg%2Bdu3aldjYWFxcXPj888%2BZPn26jESws7N76XyaKMjd3d1RKpV0796dqKgoaTBhNBp58uSJZAWFxOxl6Nq1K4cPHyYpKYmgoCCSkpJo1KiRLHqDg4Pldnx9fXn69CkmkwkvLy%2BePn2KwWBg3Lhx3L17l4MHD6JUKtm3bx%2BBgYGsX7%2BeRYsWSRli165dOXLkiNXcn0ql4tVXX0WhUHDixAkcHR3Zvn07V69exWQykZyczIYNG7Czs0OlUlUw3GnZsiXvvvsu48aNs/q5pSmKOG9glkWq1WpycnJwdXWVDWROTg6hoaGsX7%2Be4OBgyea7u7vj6Oho5T4rnCX1er1k%2Bv9TrFq1SrLmPj4%2BqFQqea9WBhcXF9zc3AAza/l7Dr3lsXfvXt555x1%2B/vln3nzzTblY5e7uTkhICAkJCZJRFgxa37592bp1K2Bm%2BRQKhRXTKhASEsL9%2B/fljKSYOWzVqhUKhYIXL15Qr1499uzZQ1FREa6uruTn52MwGOR2xb1mOZMrFAFpaWlW88XPnz//09EXfxUspe2W%2BYNqtZqTJ0/Sp08fHBwc6NixIxMmTMDFxQUwS1a3bNnCpUuXpCmPDX9P2Bo%2BG2ywwYa/CE2aNCEuLk42QVu2bGHHjh3cu3evUtmOJZRKJU5OTjg7O1NSUkJhYSGFhYUV7NZ/D6IgV6vVlJaWYjQa%2BeKLL1ixYgUmk0kWXD179kShUBAdHQ2Yi4eqVauSmZkpP6tt27bcvXvXqgitzDTBEsJWXhQhu3fvxsfHh8mTJ3Po0CGqV6/OgQMHAHPj1KFDB5KSkggNDeXUqVP06NGDs2fPAhUbPsvstcmTJ1t9bnR0NAqFgmrVqgHmgq2oqEgWcWlpaWi1WtRqNQ0bNsTHxwe9Xk/t2rX5%2Beefyc3NlQ14QEAAgwcPpnPnznL28I/QvXt3FAqF1cycmOXy8/OTMljBDFg2/Pb29nJOzN7eng0bNvDOO%2B/Qt29f9u/fD5gjLEaOHMnx48crbfgAzp49W2kY/Mvg5uYmi2Jxv6pUKmrWrIlWq%2BXmzZsA0lm2Zs2a8lympKRII5g2bdoAZtmkyWSic%2BfOHDt2TBqOGI1GGaht2fCFhoZy8eJFjh79P%2By9eXyMZ//2/57MZDLZJZEQEaJFEksSS%2ByE2FXtRC1VlNZS3JZSNEpUtVRvpfaqotS%2BRKgtttoJiRAVoiS2kERkn8nMPH/M6zw7k6V6f3/37%2Bm3z2uOf5Jcs13LeU3O4/wcn%2BM4xr/%2B9S%2BLcxIYGMjNmzdxdHSkUqVK0ljEHLa2tlKid%2BHCBYueTzFGzTMi4Y9JsiD4Tk5OPH78GI1GQ35%2BPn369GH//v0UFhbi5OQkCQb8eQC2RqORxjBgknrGx8fTqFEjLl%2B%2BLE1B%2Bvfvz7Rp02jSpAnBwcFUqVKFlJQU5s2bx5UrV4iJiSmTZJVVLRXbXFxcmDp1Kn5%2BfgwbNgyVSiUdWEUl3s7ODkdHR2mgY/5%2BTk5O2Nvb8/LlSxwcHAgMDGTy5MlMnz6d5cuX4%2Bvry8yZMzlw4AA1a9YkJiaGunXrEhAQQGJiotwfYeTi6OiInZ2d7Fs2Go3y3jMn0uJ%2Bffz4MQ4ODvJ7x9wIJiwsjNOnT6PRaHBwcODVq1ckJiZSr149KleujEajwWg0yhy90NBQDhw4QLNmzTh79izwB7ns2bOnDHCPioqy2PeSC1uffPIJsbGxuLi4WFSZRS%2Blp6cngPy95HPy8/MpKiqSx2vu2mtvb4%2BdnR0qlcpC1lkSQjLr5uZGUVERXl5e%2BPn5kZCQgI2NDRs2bKBfv34UFhZy4MABNBqNzO1s0KABRUVFJCcny8UUAb1ez82bN%2BX9bcU/F9YePiussMKKvwki3NjJyQmFQsHQoUMZOnSofDwzM5MuXbpw6dKlP32fw4cPs3LlSpKSkqSkzHyStmTJEpo3b07r1q0pLi6me/fuzJgxA09PT%2B7fv0%2BPHj3w8PAgPT2d5cuXl5J9HT9%2B3ELOZWNjI/P8xMRr2bJlODg4EBQUZNEfaI6SOX5Go1FWJ1%2B9esW9e/eYNWsWV69eRafTMWjQIPnaypUro1arAdMk74MPPiArK0v20mi1Wvk7WJrXiNcJuLi4yLBscTyOjo4WPU7FxcX4%2BfnJKAutVkthYSF6vZ6goCBq1qxJVFSURVg1mKICzKVpJWEuxxJ27vAH%2Bf7xxx8tCCFATk4OAwcOlBW527dvc/ToUbRaLYMHD0aj0TBr1ixJ%2BBYvXkyrVq3K3QcwkdxKlSoxbNgwoqKiUCgUFj2iYp/c3Nyws7OjYsWKUl46Z84cwsLCqFSpEgsXLmTv3r24urpSu3ZtkpKSZOXgwYMHFpUmPz8/eS26du3KwYMHuXPnDgaDgdOnT8vntWzZEjAZXgjodDpat24tK0ru7u5kZGRYyO9sbGyIiIjgiy%2B%2BkK/z9/fn6dOnaLVa5s2bx4sXL1Cr1Xh5eREaGkpSUhJ37tyhd%2B/eLFiwgE8%2B%2BYSCggKOHz9O9%2B7dSUpK4uHDh%2BTl5eHu7k7r1q1xdXVlz5490igEsDC8KAmVSkVwcDBff/014eHhzJ07t1SA%2B/Xr1/H29ubKlSs0adIEMElsx40bR5s2bQCTVDklJYVvv/1W9u%2BWNdYUCgU2NjayWhUbG8v9%2B/d55513CA8PZ/fu3Tx8%2BFAqCIQ0XOy7ebXY/JiUSiWVKlXi3r17gClr78KFCwwYMIDw8HDGjRvHs2fPZCXYaDTSoUMHiouLJWFycXHBaDTKxQOtVou7uzuBgYHcvXsXrVaLt7c3mzZtok2bNvj4%2BLBu3Tp69epFbGwsAQEBpappYv9EzEhBQQEFBQWyyi6MphQKBb/%2B%2BitNmjTh6dOnHDp0CL1eb/H9KqrGu3bt4vz583LsicqieM727dvl54aEhBASEkJERIQ0SQKTUVJ4eDgzZ86UBkrR0dEWz5k8eTIA7dq1k9VV80qrqAjn5OSQnp5OQEAAd%2B7cwdnZmSpVqpCcnMzOnTsJDAzE399fuqu2a9eOLVu2UFxcjEajoVatWvIza9WqJa%2BvSqWiVatWFBcXy2ickhD9f1b8s2Gt8FlhhRVW/E3o1asXvXr1wsnJicuXLwMmQhAXF0dubi7FxcUUFhZia2srJy%2BOjo54eHjg6%2BtLYWEhYWFhzJkzR04%2BvvvuO8aNG2dRXdu/fz/%2B/v40aNCA/Px8jh07hpubG7t372b9%2BvUUFBTw6tWrcqtx5k6a5aEkmTOvwJS0SReuomX1Ejo5OZGfn49arSY2NhYPDw8Abt26xfjx44mNjWX58uW8evWKXbt2MXz4cItePDAZnoCp18nPz0/23E2aNEk6Of7000/UrVu3zGOZMWOGxUT6xYsXnD17VhreFBUV0bp1a1atWlXqtSkpKfJ3EfEgqpRgchH8K/92hc29qJTVr1%2BfXbt2UVxczL1790rlxZmf%2B7K2iarRn13j27dvYzQaLfqkbGxsqFChApcvX5aB4VWrVpWh0yJLbv369bz55pskJSUxePBg8vLyZCVQOP4JArtz504%2B//zzcmVw5tEexcXFTJw4kaVLl9KuXTvat2/P7NmzqVq1Kk%2BePEGtVssqlIeHB/n5%2BZJ81a5dmy1bttCvXz9pylOzZk3u378vHTJFVe/ixYvSPRGQEtKAgAA8PDzIyMggNDRUjgtzkmBrayt7vdauXWtxbVq1akWvXr04duwYQ4YMYciQIahUKjZs2EBoaKj8LGEi4%2Bvry4oVK6hRowZgypwTVV7zPlow5brl5eWRlZWF0WikZcuWpKWl8ejRIynTtrGxka%2BxsbHB1tbW4j4Wx%2BPp6UlBQYHsx3sdgoKCGDBgAHPmzCmzh7Nt27YUFBRI8xe1Wi1Jpegv7ty5Mz/88IO8X0Wm5ebNm6lVqxZ16tTBx8eHKlWqcPHiRSpUqGBhHFUSnp6epfa/ZBaf%2Bd/mVbrg4GD5XVuyTy8yMlIuNAiUlDcaDAY%2B/fRTvvjiC0l4GzRowLFjx1Cr1Zw6dYqhQ4cSHBzMhQsX5OJRTk4OarWauLg4unXrRl5eHqNGjWLDhg0y9sHBwQFbW1uys7OpWLEiOTk52NnZyevas2dP/P39iYqKokKFCjL/UFQEK1WqxPvvv8/ChQtlBmRQUBCHDx8usxos%2BsNVKhV16tRhxowZNGzYsNzzbsU/A1bCZ4UVVljxN6Fdu3ZkZWVJcwLxz/d1JhgCQgL41ltvsWnTJvLz8%2BVkecSIETJ7TYSgP378mKKiIplLVt7XvyB4CoVCVuvKigEwf35YWJiULu3cuZOGDRuSm5vL2rVrZUZaXl4e8%2BfPZ%2BnSpTKM2snJCTs7OxlW7ObmxrJly9i9ezdeXl7861//Ii8vj6FDh9K0aVNpHDBlyhQ0Gg2ff/45n3zySal92rdvHxqNhry8PBlLYTSa8gQ/%2B%2BwzfH19UavVHDp0iPbt25OWlsbPP/%2BMu7u7RY7dxo0biY6Oplu3bhbh6eb5YH8GIUkUSElJYdiwYTLG4s8kr%2BYQ17VChQq8fPkSNzc3KckyR35%2BPhqNRhI8sc08OuDEiRP4%2BPhIg43ffvuNV69e0bBhQw4fPlxKSufm5oatra20dI%2BPj5fnQBjl9OnTRy48iH6zdevWMXbsWPle7u7uDBo0iFWrVtG3b19%2B/PFHOXE1jy/4KzAPjxZVYhcXF7p37y6vy8cff8zIkSPJy8ujcePGFn16AmPGjOGHH36wmPwnJyfTu3dvEhMT2b17NwqFgk8//ZQuXbpQp04d3Nzc%2BPTTT%2BXzRa%2BpIA3lwXzxw93dnfPnz9OxY0cp4f30009JSEggJiaGZs2aUb16dX7%2B%2BWc5dqtUqcKTJ08AU1Xp5cuXPHv2TBJnIcMsSQwFvLy8ZMTLyJEj%2Bf7776lYsSIvXrxg8eLFdO/encDAQHlMrq6uvHr1CoVCwYgRIwgLC5MKhKCgIHbs2EFqaqoch0qlEmdnZzp16kRUVJSshhcXF8v%2BzsLCQnx9fVm1apWUVN%2B5c4cpU6ZI4te2bVtWrFhBnTp1XisLN0fTpk1lCLw4hi%2B%2B%2BIJZs2bJ8XX06FE6duwIwIQJEwCTXDkqKgpAystfvnxp0acnvpeF5POXX34BIDExkefPnzNlypTXuvP%2BT2E%2BZtVqNVqtVkpDhexbbP9voFmzZly7do3i4mIqV65MZmYmy5Yto3Xr1v%2BV97fi74GV8FlhhRVW/I04ffo0n332GY8fP8be3l7KnKpWrcq8efMIDQ0lPT2dypUry8neihUrSE1NJTIyEicnJ7y9vTEYDAQGBkq7b3OEhobKCmJZENUDAb1ez%2BjRoxk2bBhHjhxh%2B/bt2NvbEx8fL4kbmCaYtWvXxtbWlidPnvDBBx9w%2BPBhafwRGhrK/fv3SU1NpX79%2BsTFxcnPqFevHsnJycTExODr60tOTg6NGzfG3t6e999/H2dnZ5YsWYKvry/Pnj2jQoUKfP755zx//px169bx4MEDpk6dKi3xwSSTbNKkiYXl/M2bN7l48SJLly7lrbfe4osvvuDKlSu8//77zJ8/n1OnTvHrr7%2BSlZWFo6MjoaGhXLx4kVmzZhEWFsZXX31FdHQ0W7du5fjx4xQUFNCqVStmzJhRyrq%2BLJQkfCUh%2BoFERSkoKAgHBweysrJkj9j06dNZvHix7M1JT09n7ty5FhLWkjAajZw%2BfRqFQsFHH31kQWhKhsE/ffqUdu3aUb9%2BfVJTU6lcuTLdunWjsLCQnTt3otfrefHihTS/SEhIkGYW5gTHvE%2BwsLBQ9t6JMaXT6fDx8eGjjz4iOjqahIQEDh8%2BzMKFC7l48SLLli1j4MCBeHl5kZeXR2FhoTTyEOPzxYsXpQiAmIQrFAq6dOnC/v37AVO1OzQ0lHv37jFixAgMBgMTJkxg0aJFODk5oVKpmDx5snRSBZOE7syZMxgMBubOnSs/Iyoqiq5du3Lx4kV27NhBx44dLarA4hqay5WF3Pmtt94iNjYWJycn2b%2Bl0WhYvny5jExQq9XSVfbcuXMcPXqU1NRUzp49i0KhYMuWLYSEhEjTHW9vbwoLC6lSpQrXrl0rc/FGkCV7e3uKioro0KED586dIzc3lxs3blC/fn0aN27MlStXUKlUvPvuu6xfv14SjOHDh0uZb%2B/evdm3b5/Fue/RoweLFi2STp5KpRI3Nzd27NhBUVERu3btYuPGjRQXF8s4AoVCgUajoaioiKioKNavXy%2BJXrVq1cjPz5f3op%2BfH/b29v%2BRA%2BXrUBaBFNU70bMsFlXEmDKXu5ovpIAptiQlJQVnZ2cmTpzIp59%2BKitsQsFQ1rUp2Z8LZfde/n%2BBs7MzPj4%2BVKxYkfPnz%2BPs7IxarSY9PR2VSkXbtm2l6dSUKVNYvnw5RUVF2NjYULNmTVJTUykqKqJ3796kpqayadOm/9q%2BWfF/H1bCZ4UVVljxN8NgMBAcHMyXX37Jxx9/LHtwxCSyqKiImTNn8vXXX6PX6ykuLqZly5aScIiw4f379zNv3jzmzp2Ls7OzRW4alD3ZsbOzs%2BhnEzK6sLAwTp06RXFxscVrXF1d8fHxoVq1apw7d66UG6j4jJKfJaIcfH19efjwoUUw8Pjx4wHkhFkYg4gw5/LcIcuCWq3mo48%2B4r333uPZs2ecOHGCs2fP4uPjQ2RkJNeuXeObb76hRYsWsk%2BmRYsWZGZmcvbsWTw8PPj2229ZuXIlixYtonv37tSpUwd7e3tcXFzw9/fn4sWL6HS6UsS6LPxVwmf%2BMy4ujuDgYLp27cr%2B/fupUKECbdu25dChQyQkJFC3bl1CQ0PLrDCmpqbyww8/sHfvXlnt0%2Bv1jBkzhrt37xIYGMiyZcukoU1RURGDBg0iMTGRUaNGkZyczMqVK%2BXEVqvVMmbMGM6fPy/leMLAQZhaiImqeM/69euj1WotCF%2BDBg24dOkSTk5OnD59mjZt2lBUVCRJYmRkJAaDgZSUFK5fv06DBg24du2aHEvu7u7UqFGDy5cv07hxY65du0a9evXIyMggNTW11HlQqVS0aNGC8%2BfPyzFlbqginDBdXV0tHEPNJbDCCEM4rMbGxhIZGcnp06eJiIjgu%2B%2B%2Bkz1w5vEEJaFWq3n//ffZtGmTfI75/SHIrHnVMCIigrlz58rIjPj4eJKSkhg4cCBarRYvLy8CAwM5deqUdOcUEJmB5vukVqtxdHSUkkhzYxAwEVAnJydZeS4LInZAwM7Ojp49e8peRjHWgoKCuHr1KiEhIUybNg0w9Y3dvXuXIUOGWHwvaDQaevTowciRI/Hz8yMvL4/vvvuO3bt3l5JvTpw4kYiICFQqFTdv3mT16tVcunRJKh3y8/NlbyMgK4gCT548kfLqYcOGkZ6eTqVKlSx6dx89eiTdhT09PVmyZAmbN2/m4cOHTJ482SKSBUxjvWHDhiiVShITEy3konfv3mXy5MkkJyej0WhYsmSJjH5ITEzkp59%2BYtu2bSQnJ8vKucjeXLZsGW5ubnz22WdkZGTw5Zdfsnr1ah4%2BfCidfcHk8GowGKTjcEhICHPnzqV69erUqFGDdu3aMXLkSFasWEHnzp1JTk7mzp07KJVKBgwYwNatWwGIi4sjNDRUVtkvXLhAWFgYBoOB2rVr8/jx4/8q8bbi/z6shM8KK6yw4m/E4cOHOXz4MIcOHcLV1fVPe1T%2BpyhpWy4m6MKK%2B%2Beff37te4icP2EKIORhlStXxtPTk2vXrpUpKQoODub27duEhoZy4cIFTp06RcuWLWWP0dSpUxk1ahRgmjwZDAZpX79lyxaCg4PJzMyU5NbHxwe1Wk2/fv3YuXMnYAp3nzp1Kp07d6Zz587Mnz8fnU5HWFgYOp2OAwcOsGPHDurVq0efPn1ISkri/PnzcmVfEBdBWIYOHcr169dlxaVu3bro9XpJCA8cOMC0adNISkp67Xn7nxA%2B859BQUHY2NjQtGlTTp48Sd%2B%2Bfdm3bx9OTk7ynBQVFREdHc33338vewi9vb0ZOnQoNWrUYMyYMXIRQcgDd%2B7cSX5%2BPtOmTZPOqrVq1WLBggXyfAj89ttv9OzZU1rn//bbb2RnZ9O0aVPgj6qHqJKJClv37t2lkYyjo6Psmbt27RoNGjTAzc2NTZs24ePjw9OnT3nnnXc4fvw4DRs2lKRXq9WydOlSJk6cyJIlS6Qj5Ndff83777/P%2BvXradiwIQ8ePJAVcAAPDw9evnwpJ7DiHF6%2BfJni4mLs7e359ddfZTVYoHXr1uzcuVMGdIteJ0EezN1iW7duTX5%2BPrm5ubIqVBbE4kVERAQ//fSTxXa9Xi8dPs17dQcOHMgPP/wgJ/ZCMlpUVCTllkFBQZw5c8bisxQKBaNGjWLNmjUW28XCjrlpUFFRkQWJK%2BnwW7L65ObmZhGmDiZpqtgmnt%2B7d28OHjwo7ycw3UOOjo6lDKFUKhUNGzZkwIABFmYmYFn9NhgM1KtXT0qIwXRNAwICcHNzIz4%2BnsLCQlxdXXn77bcZNGgQFSpUsOjLLHkvlnTbNN9m/liHDh1Yvnw5AQEBlIWwsDCeP39OdHQ0I0eOpKioiDFjxpCRkcHatWul5FKn08nz2bVrV44cOWIhlReLbGBaBFi2bJmM9rCxscHGxoa5c%2BcyZ84cbGxsOHr0KO7u7gwePJhbt27JqBZxjl/n2Gx%2Bfc2rua6urgwfPpwtW7ZQsWJF7t%2B/j0ajsRK%2BfzisLp1WWGGFFX8j6tSpQ1paGocOHcLT09OC8JWccAnnvaKiIipUqIBGo6F27dqydy4mJgYwVTAKCgrkP3xzsif6fMDU5/bGG2%2B8tk%2Bmfv363L17V042Z8%2BeTa9evRgxYgRPnz6ladOmbNy4kdWrV/Pzzz/z%2BPFjbGxsaN%2B%2BPQkJCbi5uZGVlYVer6d9%2B/YAkhx26dIFgMuXL0uLdVtbW/R6PQsXLmTr1q24u7vTtWtXi31SKpVyArZq1SqKi4tZtGiRPK5ff/2V2bNn4%2BDgQExMDD///DPz58/np59%2BomHDhmzevFlWFoVhhsDNmzct3DcFaRDyJzFxNnfqA1NVpmQEBJSOhQD4%2Buuvyz3f5hBjYPXq1QQFBREcHMzu3bt5%2BfIlCQkJrF69WlZihUxRpVKxadMmfH19GTx4MABTp07l/fff57vvvuPbb79l%2BPDhFtUmGxsbHjx4ICtJ4njANFE2Go2Eh4dz9OhRtm3bJomcIOdGo1HGXQgiExMTI8%2BPyPKrWrUqCQkJVK1alRo1avDtt9/y5ZdfUrlyZbKysoiLi8Pb25tt27bJ8RAWFoaNjY08j2vXrqW4uFia5pjHLIjPy8jIsDiPBoOBCxcuSNKiVqtLkT0wuSEKsifOf5cuXeTkX1T7kpOTycvLY9CgQWzYsIEpU6Ywb948AIssRfhj/Jw8eVL2zJlvz83NBUzjSvSBrVu3zmK/Sspns7OzS5E9MN37DRo0sNgm8upKng/AomJXkhyYH4PRaCxF9gCLbeL5e/bsKfU88T3k7OxMmzZtOH78OAA7duxg9uzZfPzxx6UIn8FgYP78%2BXLfzcmekIYmJSWhVCqpWbMmLi4uXLp0iQ0bNsjqd506dVizZo38jvyrMD/2jIwMKVstC%2B%2B99x4LFy6kd%2B/eNGnShDNnzvD5559bPKfkYpi5kZOAIHtgGgtCgQB/jJUZM2bIbSUdNUte49fF85gfo7lDb25uLkuXLsXe3p5FixYxatSocg2urPjnwEr4rLDCCiv%2BRvj6%2BjJy5EhpjCCMVgD59927d1mwYAEnTpwAYP369TRo0EBO7Nq1aweYQnLBRDCMRiNLlizBYDAQGxsrV4pLVvrMXSWBUuHfAB999BGTJk2Sk8S3334be3t7Pv74YyZNmsSBAwfIycnhypUrMofOYDBw/Phx3njjDVJSUqRszsnJSTrUAUyfPp2mTZsSExMjCe2pU6eoV6%2BerLC9DlevXrUgaElJSRiNRmlUUqlSJTmZsre3x9vbmz179jB%2B/HjpTOns7Cxfr9frcXd3tzhPgnSZbzN36RQVnJIREEql0mKbcHcU5EWn0zFlyhSKioro168fRUVFhIeHo9PpmDZtmqwKDBs2DK1WaxE5MHDgQGmrPnLkSEJDQ2nQoIHFtRP9UcJsY9iwYXz77bcW8kOj0SgzBMXETxyPORITE%2BV5ePr0KZ6entja2sp8uipVqsiJfP369alataokN66urjx79ozu3bsTFRVF%2B/btOXnyJBkZGURERBAeHo6DgwMTJ04kOzubzz77TEodhw4darEgURbxcHNzw97eXu7Hxo0b2b9/P0uXLkWlUhETE0OlSpWwt7fH39%2B/XMfZihUr8vvvv%2BPn52dxfsBUuaxZsyZg6g/s1asXNWvWxMnJSYa5/xkePXpU7mPCdGP06NFy25o1a%2BjSpQs3btwoFf/xZ/1ec%2BbMsfi7rOf9GRkwd9MV8uqy4l7AZJRSpUoVVCoVubm5HDp0SFYlT58%2Bjbe3N/fu3ZPblEolEyZM4MiRI%2Bj1evr06UNISIgc11qtll9%2B%2BYW1a9ei1Wot%2BsZExlx%2Bfj5Go5H8/HwZDZOcnGxh%2BiP289atW7Rt21YuBv1VjBkzRv4uemrNvxPM0a1bN1asWMHKlSuJjIzEwcGBwsJCi3zHWrVqcfv2bWrWrMmTJ0/Iy8uzCEMXCgrhTivI/X9ixiJk8%2BaVfDB9p%2Bt0OmrXrs29e/dkxE2FChW4deuWNOgSpNzb25vAwEDq1q3LDz/8gE6nkwY3VvxzYZV0WmGFFVb8dzBnYgAAIABJREFUL0FWVhbJyclkZmZy9%2B5dHjx4wOPHj4mLiyMoKIj4%2BHhZ6VMoFOj1emxtbTlx4gR6vZ67d%2B%2Bybt064uPj2blzp7R2B%2BQKdZs2bbh8%2BbLsi3NycqKoqMhislpSAhoYGMhvv/0mJzC3b99m7dq1LF269C%2BHvAuUnDCay46qVKlCZmYmR44coWfPnmRlZZUrhzSXXIWEhGA0Gi3%2BdnFxkdluX375JRs2bODGjRuoVCqWLl3KypUrmTZtGrt27eLBgwdUrlyZw4cPU1RURKtWrWjTpg1Lly7l2rVrMthcTErv378vg81fh5KGOWU5igLS9U9cF2GNXtL0wcXFBRsbG2kq4enpSWhoKCEhIbz77ruS8EVHR%2BPr62thoCJQUrIJJjLz9ttvs3LlyjIJwpgxY4iOjqZnz57s2rWLPn36sHr1aulmWqdOHQICAtBqtfj6%2BkqCLSqzt27d4v79%2B1y7do2xY8cSHx%2BPk5MTmZmZODo6lpJDKhQK9u3bR9%2B%2BfQETQTEfOw0bNiQ5OZmff/6ZsWPH8vvvv8vqWcWKFcnKyioVSu7l5cWZM2fw9/dHo9GUkvOBaazcu3ePFStWyPw8%2BMPO/8033%2BTmzZt4eXmhVCp58uSJjDsQ/bXwh1zTvHouiL9KpZLOqeKnnZ0dubm5Fn2h/v7%2BMqakffv2HD16FBsbG1q2bCkzDsuqzIs4DPHY66R9tWvXxmg0kp6eTnZ2Nh9%2B%2BCHr169n9uzZfPbZZ0yYMIGlS5diZ2cnz9nAgQO5du0aPXv2pGfPnjI7MTg4GB8fH5nVZw5RSTbfFycnJykJLigokNfZxcWl1JgoGVr%2BZ1AoFHh5eTF06FAWL16MRqOhsLCQqKgoOSaioqKIjIy0GCPmIevJycm8ePGCXbt2UaVKFZmZt3HjRsLDw6latSpgucBmXl01GAwUFhai0WjYtWsXb731Fv7%2B/iQnJ8veVBFXodVq5T7WqlVLLtSYS5NtbGywt7fHz89PVmYFmROVdXF%2Bxf8HBwcHjhw5QqtWrahYsSJKpZJevXrJ6Iu7d%2B%2BSm5srezqVSqXsV87Ozkan09GhQwe%2B%2Beabv3TerfjfCyvhs8IKK6z4m/H8%2BXM2b97M999//x9Ze5tnpQHSka9ly5Z89913Fs8VhM/HxwcoXW0QvThl5X2VJGndunXj4MGDFiYYoieoJN577z0pvRQyp9zcXIxGo5wUi8/29PQkIyODbt268eTJE%2BLi4rh9%2B3aZx16yx%2BbZs2fcuHEDo9FISEgI7du3Z8mSJYBJLvree%2B/h5eVFv379cHV1ZeHChXKSU6VKFZRKJYWFhXh4eHDv3j1cXV2pXr06ly9fxtbWlrNnz%2BLk5MTdu3f54osvqFChQplSTTARQhsbG5o3b26xfePGjbRv315eg/KwfPlyoqOj0ev1dOnShX379mFra1vKDCcvL4/c3Fz5d6NGjUhMTMTGxuZPCZ95LhmYpGAlc/zKgrjeGo2GgoIChgwZIo1GPvnkEy5fvszjx4%2BlYcq5c%2BdkX59Op%2BPkyZM4OzvTt29fXrx4QUxMDHl5eaWqR6K/09XVlezsbFnlEDEB5vmOtWrVIiMjQ47dkuTY3FRIpVLh5%2BfH3bt3USqVct8AGjduTERERKmQe3EftWzZktOnT0uXVFtbW1kNV6lU2Nvb06xZM0nc3333XTZu3Mi7777L5s2bAUtTls8//5xevXqxd%2B9e2esWHBxMz549pTTU39%2Bfr776iunTp1scl3DdNL9ec%2BbMYe7cuQwZMoStW7fKfEHREysWCURFKTw8nBMnTqBQKGTenMFgYN68eTg5OVmcNyHJBhPp0uv1FBYWkp%2BfL6tKCoUCOzs7jEajNBtavXo16enpxMXFyX5bFxcXPDw8uH//PmBanBEQxiPh4eF88skndO7cmcqVK0vVgJOTE/7%2B/tLMp0uXLhw7doyAgABpOrR%2B/Xo2bdpE5cqV6dChA926dSMmJgZHR0dyc3Oxt7cvNa5LVu5iY2NJTk4mIiKCQYMG0bdvX/r160ebNm0YPHgwGzdu5OrVq8ycOZM9e/Zw/fp12rVrx8mTJ0uZWAny2qJFizJlnPb29tjb25OZmWlRSRWwtbXFxcVFSpTd3d3Jz8%2BXFcBGjRpZRFGUvAeEjLosAm4OUUEWld3Zs2dLl9qrV6%2BWyh204p8HK%2BGzwgorrPib8PLlSz755BNOnjyJ0WjExcWF3NzcMvPIatSoIYnE4sWL2b59OyqVinHjxnHixAmSkpJ4%2BPAhubm5NG3a1KInDf4w0pgwYQLjxo2Tk35B0m7fvk1wcDANGzbkyZMnFBcXW7gf%2Bvn5kZGRYSEFtLW1lVIg88ln//792bFjh5QqNWjQAF9fX5KSknjw4IEMyhbEcvbs2Xz%2B%2BeeMGDGCM2fOkJaWJuMfxEq2ObmKi4vjyZMn0kgjOzubvLw8vLy8UKvVvHjxgr59%2BxIZGSkz/Bo2bIiTkxNHjx6VMtZatWrRqVMnRo4cicFgYO3atRw6dIjHjx9LKZuPjw/z5s2jSZMmwB/EuaQ9u4A4D6NGjWLq1KkWj02bNo2LFy/y1VdfYTQaLQhhQUEBI0aMICUlRVY2bG1t8fDwoHr16qSnp/Pw4UOqVavGw4cPady4MRs3bgRMEtY1a9Zw7NgxSdQ7duzI%2BPHjiYiIKJfwiZ7Mv4ItW7ZQuXJl5s2bR0pKCjY2NnTu3FlWPQQiIyOlMUr9%2BvUl6QYTQW3Xrh2enp5yAurs7CxlZGlpaRYLAYLUiUmsCHAXuX1lEdSS7pPloaRE0tvbW8pR8/LyWLt2LUePHuXu3bvAH5UUb29vnjx5glKpxN3dHb1eL/tlPT09%2Bf333y322XwxxjzGQaPRWFSlU1NTZV6l2Obv70/37t2JiYkhPDxc7l9JKBQKxo8fz7Jly1AqlTKgXFzbtm3b8urVKwti4OLiQk5OjiTYRqPxT8eDuCaCjJtDpVLRoUMHEhMTSUtLk9uHDRuGRqNh69at5ObmMmjQIEkchDxajMsrV66QkJDA3r17SU5Olvvx8ccfs3TpUgoLCy2IJ8C8efNYsGABdnZ2ZGdnEx8fT9OmTSVxrFu3Lj179mTv3r3yepfsEwQsZNICkyZNwt3dXZLhpKQkoqKiiIuLs4hqaNq0KUqlkvj4eOzt7fHx8ZELT%2BWdy9dVXMGSuJkTQXNiZjQaS0k%2BxTj9b6Fz5858%2B%2B23/7X3s%2BLvg5XwWWGFFVb8TZg4cSJGo5FJkybRt29f6V6Yn59fahU/JiaG7t27AyZJ3ty5c/nXv/6Fi4sLL1684OnTp6VyncqDeXSCWq2Wtt4i4wtME3cx2QGkk%2BDrYGNjw5EjR%2BjevTtarbbUpLxSpUpMnDiRwMBARo4cWaofSwRxh4SEEB8fz9y5czEajdIIokaNGly5csXiNQaDgbS0NJRKJS1atKBx48asXLkSHx8fnj9/TsWKFYmMjCQ2NpZz587x9OnTUpLXtLQ0zpw5g1KpJCwszMK4w7yaePnyZVxdXVGpVBiNRnr16sW6deukKcQXX3yBq6srixcvLvP8TJw4kWPHjjF8%2BHALQrhw4UJ27twpQ8QVCgW%2Bvr48ffoUg8FAQUEBzs7O5OXl0aVLF/z8/AgODrYgjVqtlhkzZnDjxg0Z5m1%2BXczPV0k4OjoyceLEMvc5MDCQKlWqULVqVVq0aIFCoWDp0qV88MEHsupRo0YN9Ho9Bw8e5KuvvkKv19OjRw8aNGhAp06dqFixImlpaXTu3Jk5c%2BYQFRWFVqslIiKCbdu2ERERwZ49e9DpdBw7dowff/yRmzdvEhcXR2BgIElJSfLvIUOGUK1aNXx9fVm6dCmtWrWisLAQGxsbXFxcKCgokHli8%2BbNo3///hbSzNeh5Fjo1KkT8fHxBAQElKqEljSLMRqNaDQaKavz8fGhfv36JCcnc/v2bRQKhZTxGY1GBg8ejEKh4Mcff5T3ZLdu3dDr9Rw6dEhWv4V88NGjR7Rp04Y7d%2B5IV1Jz8ivkpaJqK/ZLyEf/atZb7dq1%2Bf3335k0aRIvX77k0qVL3Lp1iypVqpCdnU2zZs0ICwvj8ePH7N69m4KCAnkv16lThzt37lBcXFyqmixkrSJOQVTwhRMumKpY/fr1s%2BiZBRPBq1mzJvPnz5fRIObHHxcXR7NmzfDz80OtVpOcnMyVK1do3LixvE6CDJaH9PR0vLy8Srm1CmRmZpKamkpmZiafffYZp06dkpLyo0eP4uXlJb%2Bf0tPTmTJlirw27u7uODg44O3tjaOjIwaDQcrOhQmNSqXizTffZP78%2BUyePFlKO/9TiOzK9PR0uU0QTTs7OwwGAzqdTvYYt2nTBq1Wy4ABA3B1dcXOzg5vb288PDz%2BR59vxf8%2BWAmfFVZYYcXfhObNm3P48GFcXFzo2LEjq1at4sMPPyQtLQ13d3eMRqOU8nzzzTdMnjxZyid9fHx48OABdnZ20r2uQYMG%2BPn5UVBQQIMGDSyqGLt27ZJVhn79%2BpGSkkK1atWIjo6muLgYPz8/srKypKX3n9nMC5Q3eQwLC%2BPChQvY2NiwadMm%2BbnVqlWTUQgC/v7%2BdOrUiSNHjmBnZ4dKpcLBwQG1Wi0NOMw/r7wqR1BQECNGjJCB1aJSVFBQYLGfdnZ2KJVKNm7cKDOvLl%2B%2BzOjRo/Hy8kKv15OVlcWGDRvk4yIUvSyUJBLlTRQFPvzwQy5evGjhOAjQvn171qxZQ//%2B/WnVqhVjxozh3XffRavVotfrUalUtG/fnpkzZ5KZmSnlZuVVEb/55huio6N5%2BvQpFStWLHNfRMabo6MjM2fOZP369URHR0spovgpjjM%2BPh5/f3/s7e25fv16uVUPME3CRXxBZmYmAwcOZPv27RQUFFC1alVSU1NRKpU0a9aMs2fPWvSN1qhRg/fee48lS5bw6tUr/P39SUlJ4caNG3Tq1IkHDx7g5OTEjBkz8Pb2ZsyYMWi1WmxtbWnTpg0nTpyQ4dfmYfDiOglr%2B7JQ1ljQarXcuHGDevXqsX79ekaOHMmyZcuYN28ehYWFtG3bFm9vbwwGA6tWrcLV1VVWvvPy8nB0dESn0xEeHk6LFi1Yt24daWlpcsKt1%2Btp1aoV7u7uxMTE0LFjR86fP28hd%2B3du7fcR9FjVrlyZUkYwPR9cuHCBXr37s3u3bulEUhZFZ%2BRI0fy/fffW2xTKpV4eHiQnZ3N9u3b6d%2B/P2%2B88Qb79u0DTNW4AQMGACZp57Fjx8jNzaVRo0byPcwXFoR81JwgN23alPT0dO7fv49CoZCV80uXLnH79m0MBgMjRoygYcOGxMfH89FHH7Fq1SrOnDkjq%2B5CqvpXIMalj48PdnZ2NGnShGPHjlFQUEC3bt2YN2%2Be3Odt27axePFiLl%2B%2BTEhIyGvJoXhOhw4dMBgM7N%2B/38L5NTc3lx49esgsxz973MPDg%2BPHj1sYPBUVFcnz4%2BXlVerzjUYjer2ejIwMXF1dadSoEUajkSFDhnDlyhXi4uI4c%2BYMtra2jB07llWrVlFYWChl6uZy2pIVc3t7e7lgceDAgb90rq343w0r4bPCCius%2BJsQFhbG9u3bqVSpEhs2bOCbb75h7NixfPPNN3KyVN5XtOgh8vf3lzl6LVu2ZPv27UyfPp3mzZtLZ04wrbobDAbc3Nw4f/484eHhADx%2B/FhKz5ycnKRkUxCtjIwMtFotEydOZPny5bRr144jR46gVquli%2BaSJUv44YcfpLTIfPLQoEEDPvvsM9nHd%2B3aNdavX0/fvn1p27atzMYShCI3N5eYmBh27txJUlKShYmFMFFo3rw5jx49Qq1W4%2BnpycaNG/n6669p1KgRxcXFREREyP6szMxMRowYQWhoKOPGjaNChQp8//33nDlzRlq3DxgwgK5duzJ8%2BHCAUo%2BXldclIKqighC%2BbqLYunVrXr16VW7%2BV1BQEEqlkri4OOrXr8%2BqVav44IMPWLNmjTTGmDRpEqdPny41YRdYu3YtRqOR0aNHU6lSJQvSbA5z85aEhASCgoLo1asXO3fu5NatWxbHLUivv78/3t7ebNiwQTpZZmZmEh0dzb///W/Gjh3Ljz/%2BiFqtJjY2Vmb%2BRUZG0q9fP/bv3098fDx169bFYDCQlJREUFCQHPfmcHR0ZMaMGQwYMIB69erRqFGj/zgLrEePHsAfkSVvvfUWBw4coHv37rJvzxxDhgyhffv2FmPh66%2B/tjgfwojHXPopFhmE%2BUZRURGVK1emX79%2B7Nmzh5cvX3Lu3Dns7OxISkoiIiKCoqIiWTnv0qULn3/%2BOc2bN5ekICsri/DwcM6cOSPvtWfPntGuXTsL2be434TBy44dO6TZTe3atblz5w5t2rSxIIclXyvg6uoqMwELCwtp1KgRb7/9Nu7u7kyfPl32jhmNRnr06MGiRYuoV6%2BelLuuX78eo9FIt27dGD58OB07dmTv3r3s3LmTfv36UaFCBdauXSs/e%2BTIkfI8i6rdlStXmD9/Pnv37pX7NXHiRNLS0rh58yYKhYLatWvj5uZGWlqalAKLMfP222/zxhtv8Pz5czZu3IhSqcTOzo4aNWqQm5vLqFGj%2BOmnn7hx4wbvvvsugwYNYtasWdy5c4d//etfDBw4kA4dOrBu3ToLt1Zz3Lp1i/HjxxMbG8vJkydZu3YtdnZ2jBw5kgMHDnDo0KFS0lf4I16nLNk%2BmCqgQt1hHsOhVCrRaDS4u7tTpUoVCgsLyc7OJjc3l4KCAgoLC%2BVYKu%2B9zVGWNNcc5jLSdevW0bp169e%2BpxX/u2GNZbDCCius%2BJsQFhbG%2BPHjGTduHF27dqV27dqy323r1q0kJCTIldb8/HycnJyoUKEC9%2B/fl0YA5j1UzZs3Z9u2bcyaNYvJkydTsWJFKS8S/7zt7e2ZNm0ajRo14vDhw3KiZDAYyMvLk38L8iakcStWrECn03H06FHA5Jp49epVHj58yObNmwkICMDGxgZ3d3fOnz8vJxM3b96kZ8%2BeshKzcuVKtFotJ0%2Be5L333pP7bjAYmD59Or/88ot0qNTpdNKw4f79%2B4wbN45evXrx6tUrPv74Y6ZOncrQoUOJjY2lsLCQCxcuYGtry8WLF3n%2B/Dnvvfce7u7uPHr0iG3btslQ63feeccimDo%2BPt7C/r3k46%2BDeW9WWbb%2B5sjOzi634ibeS0jvlEolrVq1ori4WJI9MJko5OXlMXDgwD/dr9jYWJRKpYX8zRzCel7ESwhnTDFWzBcbzG31lUoln3/%2BOStXrkSlUslrPmnSJHbv3o3BYKBz587ydf3792f%2B/PnSqAOwsJ5XKBR8%2BOGHrFy5Er1eT8OGDbly5Qp5eXlERkayZMkSNBqNrApVqVLFok9PVKI1Gg0tW7a0qAJHR0dbHENMTAwGg4EDBw5w6dIlOZG9f/8%2BmZmZJCUlMX/%2BfFldNo/3EOdDuK5mZWXh5uZGtWrVyMnJkQH23bp1Y8KECbLHVBAcMf4CAgIsJJgqlYrMzExCQ0Ol1K5WrVqMHTuWEydOoNVqadu2LXq9Xkr0hDS0uLhYkrbIyEhJkAVET93q1asJDg62CP8GU/TDqFGj5DZR4Xd2dqawsJDHjx%2Bzdu1anjx5wuDBg9mxYwcGgwGtVsvhw4fRaDTodDpUKhWHDx%2BWx6hSqThx4gSbN2%2BW51iYFYnqu0KhwMnJSfYvr1q1inr16tG0aVNSU1Mt%2BnbNF36MRiM5OTnS/Mn8eIqKimQ/c1xcnJS2NmzYkN9//501a9bw5ptv0qNHDz766CM2b97Mtm3bCA8P59///reUMHbs2JEFCxZIt1Zz5OXlMXv2bDnGx48fL6uoZ8%2Be5c8gqnLlQavVlhnDoNfrycvLIy8vj9TU1DJ7WM1D7e3s7CguLkatVlNYWFhq4fDPyF6VKlXIycnB29ub7t27s2bNGivh%2B38AVsJnhRVWWPE3Yfbs2SxZsoRp06aV6o9zcXHh3XffZcKECTKPTKBBgwYygNe8ejNu3Dj69etHamoqjx49YuXKlaSnp1v8s3/06FG5eWDmE5EpU6aQn5/PihUrePHiBY6OjpL86fV6DAYD77//Pl5eXkyfPh0w5QAqlUratWvHwYMHsbW1RaVSodVqefr0KZ999hnwRw%2BZIFWBgYEYDAb27duHQqEgMDBQmrWIoHbxmm3btrF161YAFixYwNChQ3n27JnsfVq8eDFTpkzhu%2B%2B%2Bk4RSq9XKiSggc7LM8brH/ypeN1E0GAw0a9as3Nf/FalaTk5OubECYCI5U6dORa1W07hx43LfZ8yYMSQnJ/Pdd99x/vx54A9Ss3HjRvR6PWlpabJ/DEwTeVtbW86cOUNwcDCenp7Y2dnx8OFDTp06hdFoxNHRkUePHjFlyhSLgPmkpCT0ej3bt2%2BXE2QhxxQ/wVSlcXd35%2BXLl%2Bh0OukuKXqcPD09qVq1qux9CwgIwGg0UlhYWEryW5K03rp1SxrvpKen0759%2B1KkoUuXLigUCpKSkvjtt98kmTPPZrt8%2BTLDhw%2BXxyFyNDdv3kyfPn1k5QmQvXnm%2B6FUKtHpdHz11Vd8%2BOGHFi6Iubm5MqdN9Iaa9%2BuJ6xAVFcWMGTPkPuTk5GBra8uRI0cICwsD/sjdDAsLK%2BW86%2BLiwujRo6WawMbGhjFjxrB8%2BXJevnyJra0tz58/JywsjPT0dG7fvo1Op5OGOlqtlnv37mFnZyddOs3HyeHDh7l69Sr79u3DYDBYVHDFd425qc8333yDjY0NiYmJGAwGC3mjkDYKeWnTpk3lYwcPHsRoNBIZGcmqVatITEy0%2BC5zdnZm3bp1hISE8Oabb8rtM2fO5MiRI6xevdpiQQVg7NixDBw4kI4dO9KvXz9q1KiBwWAgOTmZHTt24OHhIRUU69evJykpCYBFixbJsTR27FhWrlzJ22%2B/zfPnzzlz5oyFAVFxcTETJkzg5s2bxMbGSvmreI5SqaRjx44YjUaOHTuGXq%2BX/Z86nQ5nZ2cWLlyI0WhkxowZsjKr0%2BlwdHQkPz%2BfS5cuYTQaCQoKkr3BwqgrOjpamtgcOHCAXr16YTAY6NOnDy4uLpw5c4a8vDySk5Ox4p8PK%2BGzwgorrPibIDLZSmbhGQwGXr58yZo1ayQpEhUWQbimTZuGQqHg1KlTclJdo0YNNm/ezKeffkpRUZGsOBiNRmxtbRk%2BfLiFyx1ASkoKcXFxFj1utWvX5ty5cyQmJkoi%2BuGHH/LVV1/h7OxMbm4uSqWSgIAAnj59yosXLxg3bhw6nY6tW7fKfLqmTZvy/fff4%2B/vj7%2B/vyRgp0%2Bf5ujRo/J4lUolrq6u5OTkyJw8nU5Hz5492bdvn5SIbdq0iYoVK9KnTx%2B2bNmCRqMBTA6HLVu2lC6Es2bN%2Bv%2Bt76RkFIOomJgHqV%2B9erXciaKTkxNPnz6Vdv4Cer2eTZs2odVqUSgUMnR92rRpAPJn48aN0Wg0ZTpRJicnM2PGDH777Tfp5iqqEGUhOTmZ/v37o9PpZGVCjLMbN25QXFzMwIED2bVrl3yNQqFg165dRERE8ODBA0lEwDR2/Pz88Pf3L0V2wVQV9vLysgisF2PFPNOwZL6ho6MjGo2GV69eodPpSExMlIRpwYIFjB49mtWrV/PBBx9w6NAhHB0dadOmTSkHUQG1Wi2Jg9FopHv37nTv3p2xY8fKqASBRYsWERMTw7Jly5g/f76UFX/33XfodDpZoSsuLuaHH37AaDRKmaKATqezIEPCMRegXbt2qNVqrl69yuzZs5k/fz4tW7akcePGbN68mQcPHqBWqxk2bJgkB4MHD0ar1bJs2TILk5bNmzdTVFTEwoULSx2zuXmHOO7s7GwaN27MlStX5PUKCQmR%2B9qhQweOHTsmexCvXLlioQgAZEQCmBZuBMyjQho1asTevXtZsGABe/fu5cyZM9StW5fhw4eX6Zp57tw5qlWrVqZ75sGDBwFLZ82YmBj0ej3ffPMNGRkZ0pREoVCwatUqWrdubVGFF2ZUIv6iJNkDE0ncvn07a9eu5eDBg6SmpqJQKKhevTqDBg1i5MiRMuKhSZMmkpCKnmUwuZTu2bOHWbNmyeeJWA8vLy%2BePXvGsGHDAOjVqxdFRUW8ePGCypUr8%2BzZMzw8PCSB7NChA8%2BfP5eSzxcvXmA0Gmnbtq3cZ/GdfPPmTSnH7dSpk1xgCAgIkCYwSqWSIUOGyNc6OjpK59CffvqJEydOsHLlSovKqhX/bFh7%2BKywwgor/iYIwnflyhUyMjLkyqtery8lwTGX1AlZkEKhoHfv3qUmRlOmTKGwsJDRo0dLs5T58%2BdbhAcLhIeHyz6%2B/wQKhYJatWqRkpKCk5MTFy5cIC8vjw4dOkhSaWdnR0JCQiljE1GRAROB3bhxI6GhoTI0PDw8nCVLllj0kIlePzBVOIVRiHi/2bNns3jxYq5fv47RaCQgIEA%2Bv169emUGLIttkZGR9OjRw6IaZv74vHnziIyMJCIioszg9H379tGzZ0/5d3FxMT4%2BPrK3S0wUO3XqxIABAxg%2BfLjMeROEcM6cOeTn58vJornhjjCrAJNRR3Z2Nvn5%2BTJIPjc3l8WLF8soDBcXF9566y050SwPkyZN4ubNmzx69Ej2qAEyxqFOnTpUr16dRo0aER0dLcPHY2Ji2L9/PykpKQQEBPD2229LJ8vyUK9ePXmuBebNm8e4ceNITk4mMTGR1NRUWaHw9PQkJCQENzc3Dhw4ICVoZVVAhbzNwcGBgoICHBwc5PgsCwkJCRYyV39/f06fPk2lSpXKHSsODg4MHTqUtWvXEhoaSkJCAtOmTZPV9y1btpCQkIBKpWL%2B/PnytdeuXWP37t0UFxfL3jzzaJOSUKvV6HQ6HBwcsLGxkfmQGo0GZ2dn%2BvXrx8qVKzEYDAQGBlqc8/KiKuD1PVtl4bfffiM3NxcnJyfZ7zl37lyuXr0qDX/69OlT5mv37t1rQRaCg4PZvHkzv//%2BOx9//DEajUbm%2BAkjqvDwcKZNm8aQIUMYMmSIXCAqKCjK1zcEAAAgAElEQVQgKyuLTz/9lF9//RWAhg0b0rBhQ2JjY2XMCiC/E2NiYnB3d7foNRXfJ2Kcb926lT59%2BpSb9flnzr3lQfTz5eXl4erqSmhoKBcuXKCwsJDMzEzy8vJkP6xGo8HDwwMHBwdCQkI4fvw4T548wd7enlatWpGWliajZp4/f45WqyU1NVVW8MA0doVsW6/X8/z5c0nc/mw8lITo/xNxOQsWLGDOnDkYDAYaNWpUbr%2BwFf8cWAmfFVZYYcX/YpRlGHL//n26dOmCjY0Na9eupXr16uj1elJSUtiwYQM3btxg8%2BbN%2BPn5YWtri1qtJiYmhsjIyFI2%2Bnfv3pU5dBqNhjlz5hAXFwfAjh07ANMqfXx8vJxIvP322xw%2BfJjmzZtz9uxZ9Ho906dPZ%2BjQoYSEhKDT6aRDYGJiYiljk6ioKDZv3oxCoeCNN97gyy%2B/pH79%2BpLw6fV6bty4YeGOGRAQwC%2B//IKfnx9dunRh%2BfLl1KxZk9u3b9OzZ0%2BWLVvGwoULiY2N5f79%2B3Tt2lVO5IRBTXl49OgRSqWSypUrl/n448eP8fHxKdchtOQ1et1EsaTZhzkhHDlyJM2aNbN4v5Lvn5OTQ2hoKN7e3tStW5czZ86g0%2BmoVKkS%2Bfn5eHl5sXXrVgtHwLLQunVrDAYDGRkZVKxYkefPn8vHyposenp68uLFC9RqtYXtvzCZ6NatW7mfdfz4cQoKCixC57OysigoKJDkSqFQMHjwYMLDw0lMTGTPnj08evSI6tWrU1xcLHv46tevT2FhIb///ruFTLFChQqo1epyMxJ9fHxo0aIFYOq7EjBfTChvrDx%2B/BiNRoONjQ1ubm6yEjNmzBh69eol3wdMsmPhgBgcHIyHh0epnkOxj0JOqlAosLe3t5AxKhQKKZf28/Nj3rx5JCUl/ceLM/8JzDPiqlevDpju/3379llkBJZcxClZ%2BT506BBdu3YF4Pnz51y8eFEekxgzwkG3evXqUv6qUCho06aNlJVOmzaNCxcuoNfrZQ7fXz3%2B/v37k56eLuXVgsyLqBdRZRN/C0RERLzWubc8CAOb/1egUqnYsmVLqXgNK/55sBI%2BK6ywwoq/CWK1Gv6Qef3222%2B8ePFC9t7k5%2BdLowwwVc1cXFzK7cMrCRsbG5KSkuRKvFqt5vr167I6JiabKpUKFxcXoqOjcXd3B/6YwNasWZMpU6YwduxYFAoFoaGh3Lx5k9GjR7N06VIAPDw8CA8P5%2Beff5YuhWDqDRGOgVFRUfTo0YMRI0Zw8eJFgoKCSE5OJicnBxcXF/Ly8rC1tcVgMHDjxg3Wr1/PiBEjAJPLaKtWrVixYgUrVqzg5MmTvPXWW%2BzZs4e7d%2B9SuXJlatSoQbt27di1axd37twpcyIHSCMYgS5duvDLL7%2BUOnfluVuau4UC0rVx48aNVKxYkVmzZv3pRPF1hLBOnTpysgyWk2eBgwcP4ubmRmZmppTsvvHGG5I0CrnZn0GQEhFRUFblxvzvRo0asXr1aoqLi/H398dgMHD27FmKiopQqVTUqlWLSpUqyfFTFsyr0cOGDaNfv37s2LGD/v37o9friY6O5sKFC7zxxhv069cPX19fZs%2BeLeNJbty4wfPnzzl06BBOTk7k5eWxZcsW0tLSZHXCnPCZ97/WqVOHjRs3cu/ePYteLvPFhLIgHBl1Op2M3BCLGAqFwqIKDVj0V5YV6REcHGwhj2zUqJFFKLpAVlYWO3fuZOvWrWRkZGA0GqlRo4astpXs7S0oKCAlJYWUlBTZg%2Brr60vHjh1p1aoVGo0Gg8HApUuX%2BPnnn0vJPAGLIG87OzscHR1p3ry5dDkVMurCwkIUCoWMWRHfV2%2B99RZHjhwBoFOnTmRkZHDq1CmLzyjPfdjR0RFfX18pt505cyZpaWlMnDiR8ePH4%2B3tTUBAAPXq1SMqKkouSDg4ONCxY0fatGnDrFmz%2BOKLL5g5cyZbt25l6tSpspq%2BadMmFAqFzAtUqVR4eXlZyD1F9EtZbq3mzr3l4dKlSyQlJfHgwQMAKasHpPIhJydHni8HBwecnZ3l9RMGRDqdjsLCQhnLYmtrS0FBQblVO2GCA0jXZQcHB/R6PZUqVZJS2Pnz5zN79mxevXpFSkoKEydORKFQSGm6SqWSPZrCOOjP%2BoCt%2BOfASvissMIKK/4miJ6XkqYRr4P5hKlGjRrSoCE%2BPp6MjAwGDBggKwWBgYE0adKEgoICRo0aRfPmzXnnnXdkZalatWq0adMGME3QzGWfgYGB0jwgISGBunXrSsmdmPiVJROzs7OThE9MQsRPJycnXr16RadOnVi2bBlg6tlZunQpCQkJcjIza9YsmQEHpomzr68v%2Bfn59OnTh%2BvXr3Pnzh0cHBx48OABSqUSLy8vsrOzKSoqwtPTs5RRhqjQiQBtgZKmGqL/p2QvGVi6hQoTGoFp06Zx6NAhRo8ezYQJE4DSE8W/UjkoSzZaEnv27JGVkjp16pTZMwemviHzapY5OnToQHFxsYXsTZAJIXscPXq0jFgAE7k1Go2cPXsWOzs7cnJyCAsLQ6/XU79%2Bfa5fv46TkxMtW7akdevWsvr1V9CoUSO6detG//79qV69OmPGjOHq1au8%2BeabUg63du1a3nvvPRmkDZTpeiikrVu2bKFmzZrk5%2BfLuI%2BEhARpsgGWiwklz%2BPdu3eZNGkSDRo0YP/%2B/Rw6dAgwmfOA6d6dOHEiFSpUkHJVhULBp59%2BCsBXX33FuXPnLHpKS2YAliXfFmRSmKn07duXQYMGyXgTAa1Wy9atW9m2bRv37t0r9R7m9vploWLFimi1WnJycko931xeW1hYaCFF1Wg0MroBTIs5YMoLDAoKori4mGrVqpGWlkarVq04f/48Tk5O7NixQy6kmI%2B17OxsOnfujI2NDefOnQOgbdu2/PTTT/j4%2BBAcHMz%2B/ftlz9%2BwYcP4/vvvadq0KRcvXpSSRCF1vXPnDr/88gvu7u6lqulFRUUolUquXLlS7sJIaGgoO3bskPdFfn4%2B7dq1k5VK8/OfmpqKWq3G19dXbhe9jSEhIXKxLiMjg6dPn5Zy4fyz6JQWLVrIBZHc3FwpkR88eDBbt26V310l%2B0b1ej3ff/89PXv2ZMKECQwZMoRFixaxdetWDhw4gFKpZMGCBURGRpKbm8v69eupV68eHTt2xMbGhsOHD8vfhaGTFf9sWAmfFVZYYcXfhC%2B//JLs7GwWLFhQ5uMGg4H69eszadIktFotV69eRaFQ0KxZM/79739jMBiYMmUK77//PmCawC9atIgdO3Zw%2BfJlGaEgkJCQwOTJkzl27JjF9qioKLy9vf8Pe%2BcZF9W59e1rGkNvKqDYsMSCio3YG/aKvWCJvUeNiknEGBvGmmiisRNLNGIvKIrYEXuvEewKIogCIsww5f3Ab99nBgY15znPk3POO9cXM5uZPbvce3Kve631/9O8eXO6d%2B8uyj4HDhwoypOcnZ1JT08X2ax/Fk9PT8aMGZPP/wxyJ0/Lly8Xoh7Ozs4iGI2MjKRly5Y8ePCAxMREsUJevnx5s6xWUlIS7u7uZtmPxMREIZEPmPX8SIIw0kQecmX1TQNCU0zl9E2DBokqVarQsWNHkcnKO1H8ZzMHefnyyy%2B5ePGiKAU07fkzpXbt2hbFLyB3/EVFRVGmTBlCQkI4e/Ys0dHRKBQK4en45s0b2rdvj1KpFIGPaWkf5Gas9Ho9t27dIisri127drFhwwaePXtmdo1CQkIIDQ0t8JyysrKws7Nj69atzJo1C4VCwbhx41i8eHGBQdGn4OjoiE6nw8nJiU6dOhEfHy%2ByKpCbLZWyISVKlBBWAXXq1BFjUcqsSEFY3iyLt7f3J2fdC8K03LF06dL8%2BeefuLu7Y2dnZ/bM6vV6EhISmDBhAnfu3MFgMIjgDBAKjYUKFSIlJcXsO9RqNTk5OdjY2IhzKFeuHF999ZVQ7ITc7JekxFm0aFG6detG8eLFmTZtGpD73DRp0oSYmBhkMhk3btwQme%2BRI0ei0WiYMGECXbt25dChQyxevBiZTMa1a9dYs2YNw4YNE5Yx5cuXx8XFRWScr127xqBBg7hy5YpZCen169epUqUKe/bsoVy5cmLbixcv%2BOGHHzh%2B/LgoRwVo2LAhrVq1QqfToVKpRDY9bzmqJSy9J29pdVpaGr169SIlJYVWrVqZ/Y537dqVR48eUblyZTp37kxoaGiBPZSSwfmLFy8sjmmZTIaTkxMajUYEfOXLl%2Bf27dtCxEsqB/b29sZoNPLgwQOx6BYQEMDJkyc/6s8njSG5XI6Pjw9PnjzB1tbWYvbZyn8e1oDPihUrVv4mdDodQUFBdO/e3cw7y5SqVavi5OTEyZMnRfAjk8l49%2B4dGo2GQoUKERsby4ULFxg4cCA2NjZkZWXl819LS0sjKCiIR48e5fNl69q1Kw4ODqxbt44HDx4wa9Ysrl69%2BtEJtWk2QKFQ0KhRI06cOPHBz0iB1PXr1zlz5gwNGzbM9x5/f3%2Bys7OpUKECvr6%2BYntmZiZJSUnIZDK8vLyws7OzGMz81ZLNvBO5vAFh586dRZlZv379WLJkCYULF6ZMmTL59lmtWjUKFSrE8ePHxTbT/fv7%2BxMTEyOUEAvKHAA8ffqUI0eO8Pz5cxQKhSjZfPToEcOHD8fd3R2tVsu7d%2B/YuHEjHh4erFixguvXr1O0aFH69u1LzZo1CQ0NZf/%2B/cIUvHv37gQHB6PRaOjRowdPnjzBYDCYZWYlChcuLHr07OzsePr0KWq12ux6VatWDY1GQ8eOHYVARXZ2NpMnTzbzWvyQgb1Et27dSExMFGV3pvfBEh4eHiQnJ6NQKNDpdPTp04c//viD4sWLYzQaefnyJQaDAaPRyLp162jYsKHFDKpOp%2BPPP/8kISFBWCKUK1cOf39/unfvjlqtZu3atezatYv9%2B/ejUChE9nLPnj14enpSq1YtEQxKvo6DBg1CpVIJVVa5XE5WVhaVK1cmKSkJvV4vMtMKhQIfHx%2BuXr2KVqvFYDAwZMgQxowZg1qtRq/X89VXX%2BHh4UF4eLhFv7a2bdsSHR1tsY/s8uXL1KpVC4VCQaFChUhNTRV%2Be56enjx%2B/JiSJUvy9OlTHB0dadWqFZmZmdjY2BAZGcm%2BfftEqe/UqVPZsGEDDx8%2BRKlUsnv3bjp27MjQoUPFuUv3Ojg4mP379%2BPs7ExkZCQBAQF4eXmJ4CwhIYHo6Gh69eqFQqHg5MmT%2BQIuU8EVaVvVqlWpX78%2Bp0%2BfFiJWderUwcnJiVOnTglPQ7lcjre3t8imS%2BqUHwv4TPuOTY9BIjQ0lMOHD1OiRAnWrFkj/Bohd/Fi8ODBvHz5UpRpdunShfr165v1aULuQsi7d%2B%2BoUaMG1atXN1us2rlzJ59//jkJCQlcvHiR27dv4%2BfnR5cuXbh//z5arZZbt25RpkwZGjdujNFoJDExkSNHjqBUKqlYsSLVqlXj999/L/BcTZHL5ajVamxsbChWrBgjRozIV05u5T8Ta8BnxYoVK38jWVlZYjXeEosWLWLbtm0sWbJElOYplUqaN2/Ovn37hNjIq1evyMrKYtq0aSxcuBDAbLISGhrKzZs3efLkSb4SHdNyT8lbKjU1VZSKTp06lUWLFonJn7%2B/P4sXL8bV1ZWqVauSk5MjDOSrVKnChAkT8jX5h4WFiWDQ9H87lgQ2pAlR3bp1WbVqFfBppZASHyvZlMrlpOzTxwIR079Xr16da9euffC9UibD0uctfZelbatWreLnn3%2BmdOnS%2BPj4iIAkNTUVT09PGjZsKER1WrVqRXJyMi9fvuT58%2BdipV6pVNK4cWMuXbqEra0tTZo04cSJE2i1Wnr37s348ePJzMykXbt2pKWlmWUglEolrq6uvH79mgoVKvDw4UMGDRrEunXrMBqN1KxZk%2BTkZFJSUoR1R6VKlShcuDCurq5ERkYKL0UJS/1seTEtWZQyXpamKQ0aNCA2NhYXFxfS0tJED6OXlxcnTpzA2dmZOnXqsGDBApYuXcqGDRto2rSpmSXEX8VULKd79%2B4sX75cHMvly5dFD2pOTg7ff/89RqORuXPncunSJc6cOUNkZCQnT57k3bt3IghJSkpi3759ZGVlMWDAAHJycsjKysLPz4/Q0FAzpdE1a9awZcsWXF1dSU9PF/Yekt9b3ufKNBstqZhKPcGBgYFERkaKEkcpKC5evLjoh5REeH788UcqVapE27ZtzYSLPD09efHihVClTUxM5O7du/j5%2BaHVavH39wdyF2pu3bqFTCajaNGiwsrDx8eHIUOGEBISIsZTnTp1CA0NzRfw%2Bfr6MmTIENasWUP16tW5c%2BcO2dnZyOVyihUrRmJiIjKZjEOHDlGiRAnevXtHt27d6NKlC%2B7u7vTs2VNk0yVRqg%2BNRUuqsqbKvZDrG6hWq1m1alW%2BUlvI9Z0cM2YML1%2B%2BxNXVlVOnTlksvZau09mzZ/P9/d69ewwdOhSdTsebN2/EIpvkcVqoUCGRhU9OTiYnJwcPDw8OHDiAQqFArVZz7tw5KlasKLKbHh4enD59Whi49%2B7dm8mTJ5tZ21j578Pqw2fFihUrfyN2dnZChhty%2BzwsNeVLJYASkpiGvb09LVq0YPTo0Xz%2B%2Bec8ePBAZN3i4uJEFuvYsWOUKlUKf39/oRh4%2BPBhunbtip2dHSEhIUycOJFhw4aRlJREiRIlRNDUp08f3r59y/Hjx2nXrh1BQUFotVo2btwosgwpKSnUqlULnU7H8uXLzQIejUbDqVOnzCakVapUYezYsWaG0xJDhw5FJpMRGxvL2bNnqVevHkuXLmXcuHEMGjSIjRs3kpKSwuLFiy2WQkoCBfCPkk1JdOJ/SuHChXn8%2BHGBAh%2BS/H54eLjYJpmNSwqk4eHhFktaJU6dOsWaNWuEh5jpuezYsYNp06Zha2uLp6cn3bt3Z8CAATRo0ACdToeNjQ1Hjx7l7NmzLF%2B%2BnFOnTuHm5sa6desoW7Ys8fHxDBs2jIMHDzJ%2B/HjevHnDmzdvaN68OUePHiUiIgJ3d3dq165N8eLFef36NQ4ODuzatYvy5csTERHBy5cvRVmv1GNmMBi4f/%2B%2B8PmyNIYLKpM1xZJEfsWKFYVAjUwmo3jx4sJQWsqeaLVapk%2BfTr9%2B/bCzs2Py5MmsX78ee3t7Bg8ezPr168VEH/IL70hCOlKm3JIqoZOTEzY2NgQGBnLw4EHxnJ06dSpfP%2BHMmTPF56pXr55PlVOv15OWloZWq6Vfv36ULFlS9CiOGjWKbdu25fv%2BiIgIZs6cyaRJkzAajSJDJnlVmuLs7CyujWR9UKZMGW7duoVcLhcVApKQi/T5IkWK8Pz5c4xGI126dAFy%2B1YNBoPZQpGUPVUqlSgUCpKTkzEYDPTu3RuNRoPRaOTx48cEBgaK0nLpMxI1a9bE0dERmUzG1atXcXFxEQtOkPssScbsRYoUYf/%2B/RgMBq5cuYJCocDZ2RlbW1shNAK5GXMpQ/v69Wv69u0rsul9%2BvRh9erVTJgwId%2B1zYuHhwcJCQliwUnaZrpg8PbtW%2Bzs7IRYj9TPB7mLVmXKlBEltcWKFeP7779n%2BvTpnD17lrJly5Kenk5ycjJVqlQhKSmJ%2BfPn8/nnn1O4cGHR11exYkWio6M5ceIEDx48EPemYsWKlC5dmhs3brB69WqOHz%2BOjY0N6enpACJT//79e2rWrCmuvUKhoGbNmrx48UJkRTMzM%2BnTpw%2BZmZmMGjWKpKQkvLy86Nevn1C1tfKfjzXDZ8WKFSt/I9HR0YSHh3Pp0iUhUa9QKLCxscHV1VUoqNnY2ODm5oaXlxfR0dH4%2BPiIno%2BBAwcyefJk/Pz8kMvlor/tU5Dk6I1GIzVq1BACCXPnziUsLAyAwYMHi7LPkSNHEhYWRmxsbL5JfdGiRUlOTubs2bM4Oztz4sQJli1bxs2bN8V7pHKlDxn6hoWF8dNPP6HVahk2bBiTJ082K4WUpNqzs7M/qZ/QVHbfEn8lwzd//nwePHhgUeAjMzOTOnXqoFarcXFxsbivFy9e4OrqauaHmDdz8Pvvv9O3b1969%2B5tcR9VqlQRfTsxMTHY29tTqVIlvLy8qFq1Kj///DPZ2dnUr1%2BfzMxMM9VI6XwA1q5dy5AhQ4TKpk6nw97eno0bN9K3b19u3LhRYFbuzp07nDlzhrNnz3L16lWysrLo3bs39erVo06dOjRp0uSTMpkFIU30ITcTEx8fT3JyMra2tkIoBP5RVqxWq4mJiWH8%2BPGkpqby%2BvVrUlJSGDp0KJ06daJjx47Y2Nhw8%2BZN4uLi6NmzJ3379mXy5Mlm2WMpU75q1SqR4TalatWqTJ06VSisSgIjkLuoEhAQQEhIiBDRuHLlCjVr1jQL3CH3mVqyZAnjx4/H3t6ezMxMDAaDyKZbuua1a9cmNjZWZIR0Oh16vZ4ZM2YIIRgbGxuMRiPTpk0T29zc3Hj//j03btygfv36FCpUiM6dO7NgwYJPuhdSplWtVqPT6cwCxLy9lE5OTmRkZCCXyxkzZgxjx46lcePGIvgsU6aMxd5XpVJJREQEPj4%2BQO5YkURdTLlz547IKH/qsf%2BVbL4pH3tvgwYNMBgMIpvWq1cvkpOTkclkNG3alJs3b5KQkCCu2b8aGxsb9Hr9R3vzPoak0iyVAUtKyUqlkqVLl9KsWbN/xeFa%2BZuxZvisWLFi5W/it99%2BY9WqVfTo0YOuXbsKifPnz59z9%2B5d9u3bx%2BDBg2nfvj0uLi4YjUbS09Px9vZm69at6PV6KlasKFbi5XI5P/74I6NGjcq34i%2BXy8XkZ8qUKQCi9BPg1atXGI1G3N3dhTCDZImwZcsW/P392bRpk9gG5pM9hULBpk2biIiI4OzZs3z77bdkZmbi4eGBQqFAqVSKwEKr1ZplwPLi4OBATk4OCoWCyZMnA7mr59JK/cKFC5k%2BfTo7duz4p657Xs%2BwnJycfNsAFi9enG/b6NGj6d27Ny1btjQzTo%2BLi2P79u34%2BPh80ANP8nn7UOYgMTGRpk2bFnj8CoVCyKhLKoNGo5H379/z%2BeefA7niHB%2BbZC5duhQPDw8SExO5ffs2vr6%2BFClSRAhsQMFZucqVK1O5cmWGDRuGVqulVq1aODs7s27dOiZPnkxOTg4LFiwQY%2B1TkQK9/fv3m21XqVQ4OzubiXJAbqnfjRs30Ol0jBkzhgcPHjB48GD0ej2rV68mPj5ePB8Gg4HU1FSWL19Oly5dxNgyzR4D9OzZk2nTpnH69Gmz77p48SJarZb169ej1%2BuZP3%2B%2BWVlxly5diIuLQ6FQmI2nuLg4tmzZwpAhQ1i9ejUdOnQgJyeH8ePHo9PpcHBw4IsvvmDNmjXcvHkTjUZDUlJSPrsOSTZfQqlUolQq6d27t5nyp9FoNCvN0%2Bl0aLVagoOD8fX1JSYmhkWLFgG5Y6lw4cJmFg3Sd2zYsAEALy8vAgICkMlkdOrUicuXL/Ps2TOMRqMwipeur5ubG25ubtSuXVuUoKenp2M0GilSpAh79uyhWrVqQtlU6hOThEJMkXoB8/LgwQN%2B%2BOEHzp07Z9arqFAoGDFihPgdXbBgAb/99pvFffwrqFevHnfv3mX9%2BvWiNN/e3p6SJUuKwNfd3Z309HSxmKdUKjEYDEIAqEiRImRmZpKdnS2eV8nWRsoGFylShPT0dDw9PXn16hUqlYp3796h1WqFnyHkihO9f/9e7EehUAiLBum5kRZ2TMWHZDIZZcuWFYtivr6%2B9O/fn%2BXLl7Nu3TprwPdfgjXgs2LFipW/ia1btxIWFkblypV5/fo1M2bM4OTJk2bCGUuXLhVed3lRKpU4OjoSGBjIZ599Rk5ODl5eXmKy7%2BjoiFwu58CBA8yZM4eDBw%2BiUCj44osvgNzJtRQ4zJs3TyiAHjp0iA4dOnDkyBFWr17NvXv3xCqwtJosl8tZtGgRSUlJIhsHMGrUKNFD5%2B/vT%2BfOnZk1axYymUxMQPR6vVnAYwnp%2Bwpi9OjRFsvePoW8ogmBgYFmry9cuAD8IzDMGxCWK1eOBw8ecPDgQTPj9KCgoI964FmyeshLtWrVCjSBh3%2BUDdrY2LBs2TKRcUlLS%2BPt27eEh4eTkpIiBDkK4s8//zQLIpRKJW/fviUtLe2jx2iKlFUqXrw4jRs3pmjRohw6dIiwsDBh3m163BJSEGGKqfm7hJ%2BfHxkZGbx8%2BRIfHx9h0i2pFkr7vnbtGgqFgmXLlqHVavHy8sLJyYlmzZoRHR2NTqejcePGyOVyMwXbP//8k3Xr1onX8%2BfPF0IVpvc9NjYWmUxGlSpVgNzAY%2BTIkdStW5fFixcTFxdHr169zM5T2ubp6cnevXvJzMwUix1OTk68efOG9PR01q9fT3Z2thD7ad68Oba2tsL%2BJCYmhjJlyghlSwmNRmMmfCQ9h5KvJyDsFPbt25evZ1av15t5xbm4uNClSxd%2B//134uPj0Wg0xMfHi/fOmzcPyA349Xo9nTt3ZtiwYUydOpULFy7Qtm1bevToYWZR4OTkxKtXr2jdurXYT1paGmlpaTx58gSdToetra1ZVtdS1mrz5s3Y2tpSr149Zs2aRYsWLfKdS3h4OL1796Zbt24sWLBAjPF79%2B6JbLBUXi3xofLqDzFmzBi6du3KunXrUCgUQi23WbNmLFmyBL1eL4RcpCBOMnt3cXEhIyOD48eP4%2B/vj42NDUqlEplMJsrYIbeUPzIyklKlSnH58mUUCgX169c3%2Bx2RziszM1P0tDo7O4vnWK1Wi99dnU4nxJmkYDIlJYVnz55RpEgRXr9%2BTXx8PC1atGD69OkWvRqt/GdiLem0YsWKlb%2BJ2rVrExMTg62tLf3790cmk/HmzRshhODs7CxKhKQ%2BHJVKhYeHB0%2BfPsVoNIrJ5q5du9i5c6fozZCyapArc3779m2hsLdu3TpKlSpl5iWWl7yiD8ePH6d169YigFAqlezatYsKFSpQo0YN3r9/T3R0NNnZ2XTp0gWVSoVKpcLGxkacg/RdCiQ%2BEQEAACAASURBVIUCNzc3s8l%2BVlYWarVaTEiTk5PNrA%2BqVKliVvYIMH36dGbPnv3RydvHSjrz8jEfPEkttFu3bhaN0/%2BnfKyULCAggBcvXuQTolGpVBQpUkRksgoXLiysAvL6DObFkqGztE36rCQsFBMTI97z5s0bzp49S0hICBqNBr1ej1KpFMGKlJWVMJ2obt261ez4Q0NDRXbZdHuVKlX49ttvqVChAqNHj6Zs2bKsWbOGRYsWUa5cOR4%2BfCiO3TTrLGW1Jby8vAgNDWXIkCFmSrWWrrc0ZkzHwv79%2B2nXrp2wwNDpdGJx5IcffmDChAm4u7tTpEgRRo0aBcCECRN4/vy5KGE2Go04OjpiY2ODQqEQpY5SYCctiqSnp%2BPq6srXX38N5GYP//jjDzZu3MijR4/MSiql/VrCyclJnIupj56ETCbD09OTlJQUdDqdGMtJSUk4ODiQnZ2Nvb09GRkZ2NnZ0aBBA65duyZ608qWLcvz58/NFqmUSiVjx46lRIkSxMXF8dtvv6HRaLh8%2BTKOjo7079/f7BgePHhQYIZv8%2BbNFCtWjJycHIKDg0VAK2W/Jk6caBYofgxvb%2B98528qRGPKp1jQ3L17l2%2B//bZAi5a5c%2BeyefNmEhMTOXPmjLhfderU4ezZs4wdO5Y7d%2B6QkZFB4cKFefv2LZ06deL27ds8ePCAO3fuYG9vz8uXL80W2yyNd0uo1Wo2bdrEsGHDRADYs2dPtm3bJuwe3r9/j6enJ%2B/eveP9%2B/eoVCquXLlCjRo1RH%2Bllf98rAGfFStWrPxN9O/fn3r16jFq1CiqV69OTEwMDRs2FP8Dl8q8jEYjR48eFap5MpmMQoUK8eLFC9GXJHHhwgVCQ0OJj4/Hzc0No9FIw4YNiYqK%2Bku9fZs2bWLbtm0cOXKE7OxsHB0dadSoEcePHxdBn1wup0aNGty6dQuNRkN0dDSLFy8WZaEnTpxgx44dFidUeTM7RqMRZ2dnxowZg4uLCwsWLGDw4MEMGzYM%2BEcppEROTg6vX782y4RJk7e85ZkRERF06NDBbFtGRgaDBg0SK%2BkSGzdupHnz5vkmhhJ/RS30n6Vy5coflUKPjIykV69eREREoNPpqFGjBqGhoRQtWpSZM2eyf/9%2BXFxcGDBgANevXzeTjJeQhH9q1qwpRGikbdLigCm1atUCcoOPRYsWERsby71793BwcKBevXo0atSIxo0b/9MBsGngZTQa2bp1K7/88gupqakYjUahMillIgwGg8hQmHrjOTo68uuvv1KnTh0gdzFhz549ZGVl0bhxY0aOHMnatWvFOecN%2BO7cuUPXrl3zCch8TGG1UaNG7NixA09PTyEKM2XKFLNFGylgkQJho9EoypWLFCkinpWC1GCnTJnC/v37KVKkCElJSUJNVQrAatWqRXx8PGXKlOHatWtUrVqVW7du4eHhgdFoJCsrC4PBUGAfnKUAUuqblER8NBoNMplMBPdqtZpSpUpRrlw5UlJSuHjxIjk5OahUKjw9PalQoQJHjx6lbt26BAQE4OHhgcFgIDExkYsXL3Ljxg127tyZz3w8KiqK4OBgFi9ezIYNG2jTpg2zZ8/G29ubsLAwWrVqZWYlolKpKFSoEC9fvsTe3p7s7GwqVqwo7DOKFCkivP4%2BREEm6B%2BiXr165OTkYDAYCA0NZdasWRw4cAB3d3dmzZrF9u3bLdpo/FVkMhk2NjbinCVVVcjNzkrlsx9Csl2QFgDc3NzEwoObmxt2dnasW7eOAQMGoFKpPqkqwcq/P9aAz4oVK1b%2BJu7du8eIESPQ6XS8f/%2BeunXrcuHCBdFz4%2BnpiUKhICkpiV27djFu3DggdwXd39%2Bfbdu2YWNjw8WLF83KHx8/fky3bt2E/LtcLmfOnDl899136PV6ateuTYkSJdizZw/jxo0TJYh79%2B7l7t27KJVKzp07B%2BRmg%2BrVq0eFChXMhFYUCgX16tXjwoULYiLTsmVLLl%2B%2BLHzJJCSREZVKZZYJKFWqFHPmzBGvIyIiMBgMzJkzJ584yvTp05k1axYA8fHxwgRcyqSYMn/%2BfACLQQ7k9hSdOHGCQYMGiT4uieDgYM6fP8%2BCBQswGo35AsKWLVvSvn17ofT3zxqnf4hvvvnmkxQtCzJUT0pKIicnB09PT1QqVYEZwypVqmAwGKhduzaQK7MvqZm2b99evK927dpmmdPp06dz%2B/ZtDAYD06ZNo3r16gUav0skJiaiUCjw8PAo8D0VK1bE29ub7OxsUlNTC%2BxBlAK/QYMG8c0335CamsrTp08ZMGAAK1euZNiwYQQEBPDLL79gMBjo1asX8fHxFC9enKdPn9KiRQsyMjLE2DLNHms0GsLCwkhKSmLmzJkYjUZevXpFRkYG4eHhZtdx48aNZgIrUpAmlXEGBQXx%2B%2B%2B/m/XXSfd2%2BPDh%2BPj4cPnyZXbt2kXXrl3FfXjx4gWbNm1ixIgR4nNSOSRAjx49RM8Y5NqXnDt3TiyapKeniyDN3t6enJwci758lpAWTebPn09KSgrx8fH4%2Bvpy9%2B5d6taty/HjxzEajTRv3pyMjAxOnz4t7BCUSiUnTpxg4cKFYlHoYxnIhg0bMm3atHzZPchdEGvTpg19%2B/albt26REVF4e/vj1qt5uuvv2bWrFl4eXkJ5U9nZ2cuXrzIgwcPGDVqFG3btiUyMpL%2B/fvTv3//fHYteclr1/JXmDx5svhc8%2BbNSUhIoFixYkycOJFvv/2WQ4cOWVxwk3rpCrIfgX9449na2pKenv7JIi1Svx7kBu1arVaMEa1WS1ZWlgggpX%2BdnJyoU6cOqampxMXF0bBhQ/G7a%2BU/G2vAZ8WKFSt/IxqNhuPHjxMREcH169dxd3cXxuE2NjYi%2BNNqtdjY2KBWqylXrhyPHz8Wk2JbW1thUP7o0SNev34t9h8UFMSePXvYt2%2BfEItQqVQ4OTmRlpbGzZs3effuHQcOHGDLli0WZfELQupncnZ2JjY2VkxYKleuzO7du8X7/Pz8GD9%2BPL/99hshISFMmDBBTHq/%2Buor8b6XL1/Sp08fjh8/TkZGBr179%2Bb9%2B/d0796dFStWMG/ePOLi4oTAiSUPP/j4xE0qvTP12DJl/PjxREdHWwwIq1Spgpubm8jkfMg4/d8FS0qbz549IzAwUFh0AAUGbUWLFjXL0loywX7//j3z588nOjoagE6dOhEcHCzuka%2BvL/b29h8skfP19WXkyJGsWrUKNzc3kpOTmTFjBqGhoeTk5IjyMwcHB9LS0vDz8yM4OBjIDSiXLFlChQoVsLW15e7duxw6dIhjx44xbtw4IiIiKF26NBs3buTo0aOkpKSIsbVp0yahhpuZmYlcLsfDw0NsS05OxtHRkezsbLOy4h07dnDjxg2Cg4NxcnLip59%2BYsCAAdy/f1%2BMrxYtWmAwGBg3bhyfffYZXbt25bPPPuPPP/%2BkSJEiomdR6rMyGo3CLNw04P1QSa63tzcvXrxAJpNha2tLVlaWCPjc3d1JS0tj6tSphIaG0rx5c7KzsylRogTh4eEcPHhQlIuuXbuWmJgYFAoFbdq0ITg4mEaNGnHmzBnRZyYFjitXrmTEiBEisFOr1chkMry8vHj8%2BDGQu4Bw6tQps2NNTU1lyZIlnDp1iuTkZBo3bkzPnj1p0qSJOMfExESKFi0qgjxnZ2dq1KjB1atXxf2tVKkSQ4cOZdy4cSIAsre3F%2BWHp0%2BfZtGiRUyePJlFixaxd%2B9eHj58KI7DaDTSuXNn9u7dm28clilTpsAxasqkSZOE1YdOp8snfKPT6YRfXnZ2tlA6lTKBdnZ2LFy4EKVSSbNmzahRowZGo5HY2FgaNGiA0Whk1qxZrFy5UvQ6FkSlSpVQq9WULl2axo0b06xZM5RKJfXr1ycwMJDY2FhKlSqFs7Mzx48fF/cxOzsbW1tb0VPo5uaGv78/0dHRuLi48Mcff3xwkcbKfw7WgM%2BKFStW/g2QjHv/FT/Jkvlzr169mDVrFjVq1GDfvn0ia9O5c2cuXryI0WjEz8%2BPqKgoHBwc6Ny5M7/99ht6vT7fcbi7u1O5cmWcnJyIiorCaDRSunRpMYlSqVQcOHCArl270qFDBzMfMingqF69Olu2bGHs2LEkJCTQpEmTfOItpqVski/ZkSNHiI%2BPx9bWVngJdu/eXUySLU3cnjx5IgQeTNm4cSMrV65k9%2B7dBZYejhw5kvPnz1vsXfHz8yMwMFBkIqVtnyr1/ikcPHhQlO8WREhICKGhoZ%2B0P%2Bn4NBoN%2B/fvZ926deK%2BFS1alP79%2B9OjRw%2BcnZ0/aX/S/TQNJOfNm8eZM2eEaufatWvp0KEDnTp1IiQkhAsXLjBz5swCrSak42zXrh2urq4cPnyY5ORkbt68SUBAAOnp6WRnZyOTydBqtULtsGjRohgMBt68eSPMxKOioggICODq1atMnz6dpKQkMc7evXtHQEAAx48fF2PLVHinVatWZsI7posDUlmx1CMLuV5sRqMRNzc30tLSRO%2BttCAgKXmqVCrKlSvHvXv3zJ5FpVIp%2Bl0lDzX4R2%2BWaUZKei2JDi1evJhJkyZx48YNKlSoIIIJpVKJvb096enp9O3blwsXLuDl5cWrV68ICwujXbt2HDx4kPr162Nvb49Wq6Vbt25cuXJF3L8ZM2bg6%2BvL/fv3qVSpEpcvX6ZcuXLEx8cjl8upWLEiT58%2BNVsw8PDwoEmTJqI/7PPPP2fjxo0F3u/Lly%2Bzd%2B9eIiMjsbW1pUuXLnTv3p2OHTty/fp1EeQBtGnThhUrVojyTJVKRVRUFPXq1RP3olSpUkRGRgK5AVjt2rW5dOkStWvXtlge%2Bz99br/55hszNVStVsvr168/mk11cHDAxcUFmUxGUlISNjY24tqnpKTQrVs3Dh48SFpaGg8fPkShUFCmTBnu379P9erVuXnzJs7OzhQvXhxnZ2c0Gg0VKlSgadOm7N69mxMnThSYTSxRogRdu3ZlxIgRJCcn4%2B7ujkqlMqsKSE1NNasQsPLfgVWl04oVK1b%2BJjIyMliwYAEXL16kevXq9OvXT3jZpaamAjBnzhwGDBhg9jnTiXlcXBxpaWmULFmSrKwszp49K7IGpmV5kKugKfXuPHnyBIPBwNOnTxk0aBATJ04U1gpKpZKRI0dSrlw5PD09GTduHHK5nEePHgG54hcvXrwwK8/08PCgVKlSuLm5ERUVxXfffSfKTKWyy0KFChEcHEzr1q3ZsmWLKBuVuHPnDu7u7uL1mzdv8PT05IsvvmDOnDkFTs7kcrnZqnxcXBwTJ06kb9%2B%2B%2BQK%2BmzdvmmVALSGVKxbE6NGj6dOnzwf38T/h22%2B/NQv46tWrZ2Z6Dbnlr58a8BkMBsaMGcPJkyeFMbwk6b9p0yYzRcVPwZJlg6ToWrZsWSA3WzdgwADWrVtHQEAAarU6X7CX15pDr9dz7NgxBgwYIGxCwsPDOXbsGDqdjh9%2B%2BIFTp07x9OlTduzYQa9evTh27BgzZ84kNjaWsWPHsnfvXiFXD7k9rT169BDf4ejoiEajMRtbHxLeuXz5srD/kHqZTAMFKSt97NgxkZV%2B8OABO3bswMfHhzJlymAwGNBoNKL00DSLbjAYyMrKIjs7GwcHBxQKBa6urgQEBLB582Yztc22bduKjBzklhFKAbDp/iTFRoPBwK5du8jMzCQuLg6DwcB3331Henq6eM4yMzOpVq0a27Zto3v37nTq1EncCykD/uTJE1xcXHj69Kn4jjt37lCiRAlhCWA0GklKSiI6OlqUkd%2B%2BfZt79%2B6JxazWrVuLLFX16tVZvHgxtWrVYtq0aURFRbFnzx7atWuHTqdj//79eHp68ujRI3x8fGjVqpUo1YbcHt7nz58jk8lQqVRkZ2dTsmRJoXApiVxt2bIFrVbL8%2BfPKV68uMV7/M8iKZbmJTU1ldu3b/Pw4UNkMhmJiYls3LhRnHtmZqZZoKzT6cQ1AszK3CH3Xty/fx/I/W3S6/VoNBri4uIYN24cv/76K5cuXWLz5s1mn5PL5UJB12AwoFKpKF26NLt27RK/CRKm4/9fLUJl5d8Da8BnxYoVK38Tc%2BfOJS4ujv79%2B5OQkEBISAg9e/akQoUKJCQkkJiYiF6v5%2BHDhzg5OZn1chQuXJgFCxag1WpRKBTExsZiZ2fH%2BvXr6devH1qtliFDhgi7hqioKK5cuUJsbKyY4KtUKrRaLRkZGWzevJm3b98KX6aHDx/yxx9/sH37dk6cOCHKPnfu3Mm2bdvw8/Pj8OHD1KpVS2SOMjMzefnyJTqdjubNm9OlSxfKlClD0aJFmTp1qvCQGjFiBFu2bMFoNLJx40YCAgJwc3Nj2rRpolcprziKRqPh5s2bnySOktdnzZSFCxdy9OhR5s6dW6DdRVpaGoULF7b4N71eL8rRpIDlXyn1DvmzvKaTw4LeUxAdO3ZEq9Vy%2BvRpGjZsyJAhQ/D39xflY/8qUlJSRLAHuRlrycC8QYMGwuzdlLzZXQ8PDxISEtixY4eYHK9evZpevXqhVCr57rvvgNzytcjISBFwjhw5kqlTp6JSqShcuDAzZsygbt26PHv2jCdPnpgF/YmJidjZ2dGxY0cxtvL66ZmSkZFh0Q9P4sKFC7x58wbIVcTctm0bTZs2Zc%2BePSQlJYkeLZlMJiTzJeVPuVxOSEgIjRs35tatW0RERHDt2jXKly9PUlISOp2O7777jk6dOonxZLqwYTAY0Gq1zJ49G0AswJiacWdmZlKpUiWhUnv69GmcnJx4/fq1yCJKmdpDhw5Rq1Yt2rRpw5YtW4DcUs3Xr1%2BjUCgYOHAgkZGRJCYmYjAYePbsmciuSQskaWlpyGQyUZJ74cIFEcwkJCQwa9Ys9Ho9P/30k8hk29jY0KFDBzp06MD9%2B/fp1KkTU6ZMYdiwYcyfP5/ly5czfPhwevfuLbK8AAMGDMBoNIqy0pMnT/L8%2BXMqV64s%2BiFv3ryJXq%2Bnd%2B/e7Ny5818azDRq1Ij09HT8/Pzo3LkzLVq0wNnZGXd3dxo1akSjRo2A3IU3d3d3ihcvzrVr10QZvnQuBZnYS5huk7KHUqn/woULMRqNFC5cmNTUVLEPSfEzIiKCnJwc4Z/6%2BPFjVq1axYgRI8wCPiv//VhLOq1YsWLlb6Jhw4ZiEnLv3j0GDx780eyTKXK5nEaNGtG8eXNiYmK4dOkSrq6uvHnzhjdv3rB161YuXrzIzp07efz4MYUKFRIqblKpTk5ODocOHeLnn38mKioKuVxOUFAQwcHBohxOKrGzs7OjW7duTJ48WQggfMzc2xTJTy07O5tXr14hk8moWrUqf/75JzY2NhQpUkSYlvfr14/mzZsLM2xfX1/8/f0tiqPkLc0yVUu0xPfff8/u3bu5cuVKPq%2B/zMxM6tSpQ6dOnZg7d26%2BzwYEBFhUCDXlQ1Lvn0Le8/mYOuTH9pWdnU25cuUICgoiMDAQR0dHEfDt37//L2f4LPXwfewYCzpejUbDq1evxDFI76tcuTIymcxin%2BXMmTOF4uXvv/%2BOj48Per2e%2BPh4QkJCSEpK4ptvviEqKoq0tDQzE/fp06dz5MgRhg8fLsbWh4R3WrRoYabomfdcqlatSpEiRUT27/nz53z33XekpKSwcuVKvL29qVatGnq9nu%2B//x5/f39GjhzJ48ePkclkFC9e3EzpNT09ncjISH799VdevnxJ8eLFUalUHDp0SNgrSGI7UmmpjY2NyLQVhOTVplKpaNSoEZUqVWLlypXIZDLUajWZmZkFBhzBwcEsXLhQ2CpArm2Fi4uL8KyUxpkUWI4bN47o6OgC7S%2B2b99OVFQUa9asQavVEhUVxe7duzl//jw6nY558%2BbRokULevfuTVZWFj179sTLy4vp06d/0FsyL1KfZJkyZahVq5ZZ9ux/WtK5ceNGtm3bxqNHj8z66yRrDSkwlYJv6R7IZDLat28vvCXj4uJQqVRcv35dLDr88ssvuLm5MXLkSDQajZlgzqfg4eFBcnKyKLmuX78%2BkPvbdObMGRo0aPBRywkr/11YM3xWrFix8jeRk5ODi4sLAKGhoXTo0IFz585x//59XFxcaNmyJdu3b8fGxgZvb28yMzNRKpVCGt1gMHDu3Dni4%2BOF99qbN29Eadivv/7KihUrGD58OJMnT%2BbAgQMYDAbUajVqtVr0QkFu5svOzo6LFy8SFxfH2LFjuXLlCqmpqcjlcgoVKoSXlxc2NjY8f/4cpVJJWFiY8NT6%2BuuvUSgULFiwAL1eT%2BXKlYFckZPz589ja2tLt27d0Gg0xMTECGGaO3fuoFarKVasGJs3bxaleHnNsBUKxSer51nKypgyefJkwsPDadmyJd27d8fHxweDwUBcXBzbt2/H0dFRZCrzBoT79%2B%2Bnf//%2BdOjQQXik/Ttz5swZPv/8czIzM5k9ezZz5syhRo0a6PX6AkVv/q9IS0sjKCgIPz8/5s6dS3h4uPBbk8zkf/jhh3zKinq9XoiR9OvXz0wJslatWnh7e7No0SJ8fHxYuHCh%2BFxoaChRUVHk5OQQFBQktvfp04fVq1dbPMaWLVsyd%2B5coehpSmZmJjk5Ofmy0oULFyYhIYHmzZvTtGlTkXkLDQ0lOzsbpVKJXC5HoVAwbNgwYmJiCA4O5ssvv%2BS3337jzp07oizyxx9/zJcd9fDwEOWhkq2DFExYws3NjaVLlzJgwABycnJwc3MjLCwMtVqNXq/n5MmT1KtXj6FDh7JmzRrat2/PgQMHRL/g0KFDWbhwoQj2JExLuqXvlrLeZ8%2BeRSaTmWW%2BTYOiVq1asWDBAqZPn05kZCRqtZrOnTvz3XffERgYKHr1wsPDWb16Nfv27RO9lpUqVaJUqVI0bNiQ69evs3//foxGI02aNBG/pxK1atUiLS2NsLAwIiIizALjnJycfBYukNsb%2BSkMGDCAAQMGoNfrGTFiBFeuXOH9%2B/efpKL5%2BPFjtFqt%2BO3WaDSMGDFCZPC%2B/PJLfvnlF9LS0jAajZw8eRK5XE7x4sV58uQJgChbnT59OuHh4WbBtbSgtmjRImxsbHBwcEAmk1GsWDFWrFjBZ5999knnaOW/B2vAZ8WKFSt/E/7%2B/syePZtJkyZx69Yt1q1bR506dVCpVLx9%2BxZfX1/h37R161Zat26NVqvF3t4emUwmZNdNe4sATpw4QY8ePYiNjaVu3brUqlWLEiVKiKyIRqMRXlpKpVL09fn6%2BrJz507Re1KuXDnevHkjSseSk5NZsWIFa9euRa/Xs337dqEmmJWVxZgxY1i0aBE6nY7bt28DiH9lMhkzZszIZxh9/fp10QclBXuA8CYzRVrZzztJyztxk8lkjBgxIl/JoMSzZ8/w8vIiMDCQgwcPmol2BAUF0bNnTwYNGlRgQFioUKH/mHIoR0dHHBwcOHHiBHfv3mX16tVER0eLvq/58%2BczduxYsx6ijyHdQ9Nsg6WyVtNtOp2O8PBws1LXZcuW4e7uTkhICPCPEs8DBw4gl8vR6/VkZ2fnC0y9vb3FmE9NTRX3r2TJkri6uhZ43D179mTUqFE0a9bMbGxJnm2WGD16NL179zYbC1JJ4vbt2wEYPnw4Wq2Wn376idGjR/PFF1/w/v17xo8fz8mTJ8W%2BpO/Q6XRCAdQ0gzlx4kQqVKjAnDlz6NixI9WqVcsnejJp0iRq1KgB5PZxtmvXTgQovr6%2BGI1G7ty5w59//kmnTp1QqVScOXNGlF66urrStWtXBg8eTGhoKBcvXsTJyQnIFWZSKBTUqlWLgwcPCp9DKYgLDg4WpZLS%2BUivpWBuxowZQG6Jp1wuF4q6kBt4Qu593r17N%2Bnp6bx69YoffviBZs2aWVSJdXBw4KuvvjJT8zWldevW7N27V5RaF5SxW7BgAQC3bt2iZs2aAAQGBlp8719FoVCwdu1a8TorK4vw8HBWrVolyiylc5MCc1OLGwnTsZKTk8PIkSPFaymIlIK95s2bs3DhQqZNm8bixYt59%2B4dtra2qNVqYbBuNBrZsGGD2XFmZGSIsk4r/39hLem0YsWKlb%2BJhIQERo4cSdWqVYmNjWXLli0EBQWh1%2BvRarU4Ozvz5MkT5HI54eHhQvxEoVDw9u1bNBqNmfS5FPBdv36dzMxMfv31V3bu3Cl6jCRGjRpF9%2B7d6dq1K46Ojrx69YrGjRubGewGBQVx5MgRITWeVybeEtJ7ZDKZ8Ig7deoUkZGR/PDDDzx8%2BJD169fj7OxMVlYWOp2OzZs3U7Vq1XxG03nLrcLCwli6dCnXr18XPVAFcfPmTdLT0zl27JjFrEz//v2pXLkyvr6%2BKBQKi6IdpgqhH1Jx/N/A1BcOYPbs2Wav3759y88//8yRI0c%2BuJ%2BCDKS1Wq1Q63zw4AGQGywMHDhQiHZ8iLCwMAYPHiz%2BBYSCZUEkJCTg7e1tVuraokULli1bZhZsmpZ43r17l4kTJ7Jhwwaio6MpX7485cqVE4HDP8tfLZHNOxYkVcRWrVqxbNkysXghjX1LvmqSwIdGo%2BH777%2BnRYsWXLt2jZSUFGF3UKpUKYoWLSo%2Bc/bsWYoUKQLk9tI9f/4cOzs7ChUqBOSWjxYtWpRatWqRnZ1NdHQ0MpmM7du3s2bNGg4fPoxaraZly5ZAboDo7OxM7dq1sbe359mzZ1y/fl2ocUpZVVdXVyEaJZfLcXBwICMjA7lcLgSjtFot79%2B/zxdgf/PNN0Buf/K4ceNEBYApLVu2pGXLluzbt4%2BYmJhPuj95iYiIYOXKlcTHx2M0GrG1tWXatGlmAj0Sd%2B7coUuXLnh7e/P69et/qaKuRIMGDXj79q0IzKSSTk9PT/r06UPt2rWZMmUKY8aMYcGCBbx9%2B9bs91JafDMYDMLGATAzlu/fvz/btm0zy6wWhKTs%2BeDBA6pXr463tzc%2BPj6ULVuWxo0bF%2BhRauW/F2vAZ8WKFSt/MxkZGSxdupTLly9TuHBhrl27Rk5OjujNkWwSFAqFkHeXejNKlixJ9erVAcxMs9%2B/f8/z58%2BxsbHh9u3bHD9%2BnL59rKSDewAAIABJREFU%2B/Ls2TMOHz5M6dKlhbn5oEGD2LVrFwcOHBATll69enH58mUaN25MWFgYR44c4fLly5w%2BfZqjR4%2Bi1%2BtF2dWdO3e4d%2B8eXbt2Zc%2BePUyfPp2ePXsCuf5stWrVIjIykmnTpom%2BPD8/P8aNG8fp06eZMmUK/fr1E55WkD/ogfyBj3Sclq6nqYdf3gydnZ0dr1%2B/xtPTE71eb9ZDJfH8%2BXNOnz5dYED4v8nHgqcXL14A/7wPoSlPnjyhbdu2DBs2jPXr1/%2BlyXBcXBwpKSkWrS8CAgI%2BqIqYmJhIu3btuHLligiY8pZ4njlzhiFDhohzgtxzrlOnDt9//71Fs26DwcDatWvN/AD79esn/h4TE8OQIUOYPXv2J4%2BtvGOhVatW4jpVqVKFsLAwAIYMGcK6deu4d%2B8eixYtws3NjZcvX2JnZ8emTZvE%2BKpQoQIymUz01EJuAJXXa08ul2Nrawvk2hJcunQJQGTVLl%2B%2BjNFopGzZsty/f5/U1FQ0Go24/97e3rx9%2B5aWLVsSExNDSkqK6LsNDAxk7969ZsEq/MPSRULyk7OzszNT0JUwXSQyZfz48ZQuXbrAzNykSZOwtbW1qDTr7%2B9vsb/syZMnLF68mBMnTqDRaHBzcyMjI4MOHTpw6dIlypQpw4oVK8wWeTIzMwkKChI90ps2bWLNmjUWx2zz5s3x9va2eLyWiI6OJjw8nKtXr5KRkWHxPVLwX7RoUVJSUtiyZYvIANvb24tezJycHOzs7MjOzqZ8%2BfJClbNPnz788ccfQK46s1wuF6XEpvsHaNq0Kffu3aNQoUIEBQXx6NEjNm/ezNdff427uzvu7u6ULFnSqsL5/ynWgM%2BKFStW/g3QarX8%2BOOP7Nixo8DJQ15kMpnFSb8kw22Kvb09LVq0YN%2B%2BfTRu3JgVK1aQlZUlAqPAwEC2bdsm1PuUSiU5OTlUqlSJu3fvsmHDBurWrQsgAszt27eTkpJCmTJl6NixI0uXLmXkyJFMmjSJgIAAVq9ezf3797lx4wYNGzbk6tWrxMTEoFarqVatGufOnaNp06YUL16ce/fumfWgfCzokcyLz5w5Y7Zdmri5uroWmKGLjY2lZcuWBYp25FUItRQQ/p38FQPphIQEkpKSPri//v37ExUVRZs2bfIZtBdEXFwcvXr1IigoKJ8aanBwMOfPn2f79u0FTi79/PxwdHRk//79IpAIDQ3l3r17TJw4kZkzZ5oFrDKZDEdHRxo0aIBarebkyZPs2LEjn%2BDMr7/%2Byh9//EFQUBBarZZt27YxcuRIAgMDmTt3LhEREajV6ny9XnmRhHcsjQWj0SgWJywJ05gKDlWoUIEpU6aYja8KFSrQtWtXs%2BBy3759BAYGim179%2B5l7ty5VKlSRQTVkkk25I7z6tWrExISQlxcHK6urixdupRhw4Zx7NgxGjdujEqlwmAwCHXb3bt3c/XqVaZNmyZUIj09PYUXXJUqVYT1itQHq1AomDJlCqmpqUKhV%2BLZs2ciQ1e1alWqVKki/iY9K61bt6ZNmzYUK1YMvV7P06dPmTFjBpmZmVSsWFF4CgJm/y2do1arJTo6mvnz5/Py5UvUajU6nY4VK1bQpEkTatSowZYtW/jqq6949uwZdnZ2NGzYUPRRxsbGotVqMRgMhIeH07NnT4YNG/ZPjdm8VKxYUQhgtW/fnjFjxuDt7c3Vq1eFH15KSorFsWU0GnFychIqqEajkUKFCvH69WtxbwBq1qzJvXv3hK%2Beo6MjOp1O/H3UqFGsWLECgNKlS5OTkyMWhCwhl8upW7cuS5Ys%2BegzYOW/C2vAZ8WKFSv/ZqSnp6PValGpVKSmporJgY2NDampqTg6OlK4cOF8YhYS/fr14%2BXLl/Tt25dBgwbh6%2BuLt7c3Hh4eXLx4EaVSKSZGLi4unDt3jidPnojJplKppE%2BfPgwcOJCDBw%2BycuVKsrKyaNasGXXq1GHNmjXIZDKxen7//n3OnDnDoEGD2LRpE61ateL8%2BfO8fftWBGalS5fmyZMnfPnll/j4%2BDBlyhQGDhzImjVrKF%2B%2BPI8fPzbL8EnknQRCbrDRs2dP%2BvbtW%2BDE7eeff%2Bbu3bsWM3T%2B/v4i8ITcLGSzZs04f/68uH6mCqEfUnH8u3j16hUeHh7Ah0vgpEnpx5A8u2JjYwt8j6TsamNjY2ZIbonp06ebmdPnpVq1arRq1YpixYoxceJEILfEc%2BrUqUyZMkWUDvr6%2BhIaGsrDhw9ZsWIFp06dIioqig0bNvDu3bt8XmitW7dm4cKFVKtWDYBLly4xYcIEAEqUKMGsWbMoX7682WckASFLjB8/ngYNGtCzZ0%2BKFSuWbyyYms9LWenQ0FCCg4NRqVRMnz6d9u3bExUVRdu2bYHc4E4qj9RqtUJUIyUlBRcXFzQaDWlpaURERIhx7uHhwZIlS%2BjYsSM9evRgw4YNIkBRKBS4ubmhUCjEWKhataooDdy%2BfTtBQUHcuHGD58%2Bf065dO%2BbNm8dXX33FpEmTWLZsGWfOnEGtVlO9enVRrtu%2BfXuMRiNHjx7FwcGBzp07i%2Bft3LlzjBw5Eg8PD3Q6HS9evGDHjh1UrVqV8%2BfP88UXX%2BDp6Ym3t7eZOfmHGD58uNnr9%2B/fExERgaurK48fPyYgIIBvv/2WwMBA9u3bR4kSJahRowb79u3D3d1dlLC/fftWZDnd3d3p2bMnYWFhBAQEEBUVZbawZMrHxmxe7ty5w65duzh16hTPnj3DaDTi6OiIn58f7du3F/6TX3/9NUeOHLFYEl%2BQMqopDg4OwppFoVBgZ2cn%2Bq63bt2az99SwtnZmfLly5OVlcXLly9RqVRCTMvFxcXM19DKfz/WgM%2BKFStW/makkrH09HTS09N59OgRcrlclCImJSVRsWJFOnbs%2BMHVZ0lcQbITkCadM2fORKVSCaGIgQMHmk2MIHdSkZWVRb9%2B/Th27JhZedSjR4/o0qULrq6uJCYm4ubmhqOjI%2BXLlycnJ4ezZ8/Stm1bFi1aJCa9e/fu5fLlyygUCgwGg5gYFypUiDdv3pgp9km9LJaylZbKEj8WbIwcOZJTp05RokQJixm6j/VwfSwg/N/m/fv3zJ8/36wsMTg4WFyf8PBwFi1aJMre/ll5%2Bf379zNr1ixRVvgxpO%2B/e/fuR60vJCGe48ePW/y7n58fe/bsoXv37jRu3Ji%2BffsydOhQGjVqhFKp5MiRI6jVamFgDrnZzCpVqtCxY0cmT55M586d8/WA5e0F1ev1VKtWjRkzZljs7wKExYglpEm6XC7n7t27%2BcbCihUrRG%2BtlJVOSEgQ/ZOSmqZkoQDg5eXFixcv8Pb2NuttlII0jUaDSqWiWbNmYpwHBwdz4sQJPvvsM65evUr58uWxt7enRIkSQpCkY8eOPH36VFhmtGrViv3794vna9q0aSxYsACZTMb169epUKECP/30E5MmTaJNmzZ06tSJ8ePHM3r0aJYsWYKNjQ0ajYZ58%2BbRqVMnM1GVvn370qxZM4YOHQogvO9KlSrFnj170Ov1XL16FbVazdWrV8V18Pb2pm/fvmYZ6c6dO7NmzRrq1KmT7760b9%2Be8ePH8/TpU3bs2MHx48fRarV8%2BeWXBAUF0axZM/bt24dMJmPfvn08fPjQ4m%2Bln58fzs7OpKWlFZjF/tiY/RBarZbz58%2Bze/duzp0795fsdT4FV1dXMjIy0Ov1ZhlA0x4/%2BMdvqZeXFxkZGZw%2BfRqdTicWG%2BRyOQcOHKB9%2B/YW%2Byet/PdiVem0YsWKlb8RqWTM2dlZTIryolAoiIiIYPny5WzcuLHA0kJJeU2aAEi9RZLsN%2BRKujs4ONCwYUMcHBzYu3ev6GV79uwZzZs35/Tp00LhUyqPqlq1qughknz%2Bnj17Rp06dXB2diY4OFjsf%2BXKlaLvUK/X4%2BnpyY0bNzAYDLx79w4HBwfevXvHgAED2LhxI1988QUbNmxgwIABwMfLOS9fvsyOHTsK/HtqaioODg4cPnwYyM3QLV68%2BJMzdHkVQj%2Bk4vi/wc8//8yVK1f4%2Buuv0Wq1rF27FgcHBzp16kRISAj379%2B3KCf/qcTFxfHNN99w//59HB0dUSgUrF%2B/nkGDBjFlypQCP1epUiXx3x%2BzvvDy8sonFpQXHx8ffv/9d2bPnk2/fv0wGo1ERUUBuRPTLVu2mPXpvXr1CicnJ2JiYpg3b57IcpiSdw1boVCgVCoLDPYADh48aPZ50xLZzp07s2fPHvH3vGNh1KhRopfRVC332LFjZr2MlrwVjx07RrVq1YSQjU6no0ePHmJsm47zhQsXigzU4sWL2bNnD8uWLePq1avo9Xp69OjBw4cPzfrXoqKiMBqNHDt2jObNm/P777%2BL3wHpWQsJCUEmk5GcnMzo0aNFmbTU66dQKKhdu3Y%2BBc379%2B%2BL3xfIrQq4cOECOp2OXbt20b17d/EMSaqiEnK53MxAXiaTMXz48HyLFmvXrmXHjh107NiRSpUqERgYyFdffUX79u3Zu3cvv/76KwaDgT/%2B%2BINNmzaRk5Pzwd/KjIyMAgN7%2BLQxWxCnTp0iPDycCxcuWPyt8PHxwdPTk3bt2gnLBIB69eqRlJREcnKyKMX09vZm%2BfLlNGrUiLNnzzJhwgR69uyJVqtlxYoVKBQKVq5ciU6nE/dT6rWUFs5CQkKYNWsWOTk5Fv1S/4p/qpX/DqwBnxUrVqz8jSxdupRx48Zx9OhRVCoV6enpuLq6otfrefnyJU5OTlSsWJFGjRoRHh7%2BwcAlrz1D3tcGg0EoPCoUCnQ6HSqVikWLFlG1alV8fX1ZuXIlO3fuFOVRhw4dMiuPevPmDRs2bGDo0KGcOnUKV1dXqlevLib/piIOFSpUQK1Ws2jRIvr164dCoUCr1ZKdnS0m915eXhw%2BfBiDwUBUVBQymUwo/RXEx4KNR48eCdsByO%2Bz9jELAb1en89C4P%2BSI0eOsHr1asqWLQvkljUOGDCAdf%2BPvTMPi6ps//jnzAw7sggCbpFLLrjgriVmLuEuauaWZvXaT181l1xSM63UzFRaRU3J3kzNPdNAy8pyIQq01FxySwUBEUFUlll/f3Cd08wwMwyuKM/nurpizjkzc855zhnP/dz3/f3GxtKxY0c%2B%2BOADRamxNNy4cYNFixaxceNGJEnihRdeYOzYsbRu3ZpWrVqh0WgYPny4U58VGBjIP//8Y2FIbs6xY8dsinxYU79%2BfdauXcvVq1eZMmUKCQkJHDhwQFGQNee9997jiSee4Mcff%2BTChQsOLRhKg3nwAZYBiSRJxdabY97LKAuByNfS9u3b%2Beijjxg5cmSxa04WRzIPQIxGIyNHjmTz5s1A8et89OjRDB48mLlz5zJq1ChiYmJwcXHBy8uL8ePHW2TN4d/gNyQkRBnbN998E0mSlPLpmjVr8vfff1uIpKSmpmI0Ghk/fjwxMTFcvHgRvV5vEXxbT4rInn1r1651eK7tYavYLCIigoiICLKzs9m2bRtr165l3rx5GI1GRowYQc2aNdmyZQurVq0CisRsvvzyS2JjY4v9VppMJipVqmR3Ug2cv2atyc7OZuzYsTaP4ZFHHmHs2LFERUXRunVrli5dSocOHZRtX3rpJZo3b67sP8CBAwdYtGgRcXFxvPTSS2zatImnn34af39/xo8fz6VLl/jkk09QqVRUqVKFvn37MmbMGOrXr4/RaMTX15dPP/2U2rVr8%2BKLL1JYWIjBYMDFxYXHHnuMSZMmWdhrCMoHIuATCASC%2B4hsMB4TE2Mhu280GtHr9RgMBo4fP05MTAzLly9XPJYcYTAYMBqNStZN/hyTyYSrqyshISFUqlSJw4cP4%2B3trfTTQVG5nmzeHhERoZRH%2Bfv7k5GRQc%2BePWnVqhVBQUHk5ORw7tw5mw//x44dUx5m33nnHXx8fJSHytjYWBYuXGgRHMoZD2coKdgoKChQ5OyheFZGzkKaY75Mp9MRHR1d7JxaB4l3KyC8cuWKEuxBUWlbfn4%2By5cvp23btiX6EMqYG0hv3bqVuXPnotVqadGiBXPmzOGRRx4BbPvqlURJhuQzZ85UDMmdoWLFisycOZNu3boxbdo0wsLCWLhwITNmzOD06dN89tln/Pnnn3zwwQccPHiQefPm0b59%2B2KfYzAY2L9/v8WxyFkr82URERFO7VdJkwMbNmygQYMGFr2k5teS0WgkJiam2DVnHZzJmAd41te5dQZKDmLefvtt%2Bvbty6BBg9Dr9URERFhc73IgIHvkmUwmvLy8yMnJsekHJ2ea3n//fQBefPFFpaTVEY6yZyXh6L3%2B/v688MILvPDCC/zxxx9s3LiROXPm4O3tTYMGDfDy8kKn0ymTPIMHD2bp0qUWv5UTJkwgMzOTPXv2oNfr78g1u2jRIrZu3aoIs8h%2Be%2BZjm5KSwtSpU5XMuXW20/o1YJGVlP392rRpg4eHB35%2BfmRkZCgTU3q9ntOnT3Po0CHlPXIFhi2uXr1Ku3btlGtBUH4QAZ9AIBDcR%2BSZcvOMlPV6KApa5PIdRyITUPTwnJmZqdg0wL8P81qtlrS0NNLS0oCih4OcnByWL19OcHAwmZmZdO/eHa1Wq5RHffLJJwQEBFj02MgP/JmZmeh0OubOncuyZcvQaDTKw5NKpcJkMnHixAkLafzBgwfz0UcfWezznQw2dDqdwwe3kgJLuaTU3JzY%2BoFdkqR7lgGUHyTbtm0LFBeycWQgffz4caZNm8a5c%2Bfw8fFh0aJFdOjQwWIbuQdN/r8z2DIkv11z%2Bho1atCtWzd%2B/fVXxQcuPj4eKMpyzpw5kwULFlBQUMDff/9tU9Jfr9crVg7mmC9z1rICSp4cSEtLK5ZtNr%2B%2B5L4w62uuYcOGbNiwQckmyyxdulSZnHnkkUcsrnPzDJR8nYeEhNC1a1dFiXXkyJHUqFGD5ORktmzZAkC/fv0A2LJlCwaDgQoVKiiqj5Ik0bt3b/r164dareaFF14gPDycP/74g0ceeYSLFy8SExND7dq1nTpfzmAwGCz6x2T/P%2BueMltBeZMmTWjSpAk7duxg4sSJvPHGG0qm8cMPPwSKfiuvX7%2BuKJpCUSbt%2BvXr/PLLL3fsmk1ISECSJPr06UP//v1p1qyZUsWQmJjI5s2bSUhIICcnp1Tnxx75%2Bfnk5%2BdbLEtPTycuLs6iLNmcWbNmERAQgJ%2BfHx4eHlSvXv2WspiCBx8h2iIQCAT3Ebm3Ry67NMdcht18m8LCQocz4vZ82MyN2aGo5NL889evX8%2BsWbPw9/encePGdOrUiX379vHzzz8jSRKTJ09mwYIFHD16VPG6k9Xjrl%2B/rngEyuqY169fV4yFk5KSLEr0wsPD2bRpkyI5b27iXZKPW0k%2Be1lZWbz%2B%2BusWJWfOeviVBUprDO6IsLAwDAYDHh4ehIWFFQuQZWrVqkVkZCSBgYEW3nryWPz444/FxuTmzZt8%2Bumn7N69u9Tm9Pa81jIzM%2Bnfvz%2BBgYG0adOGgwcPcvz4caUMWJIkevXqxWuvvUZgYGCpz4czlOZcW4vE2Nvm7bfftjC1lycVUlNTFTEc874q%2Bf6WJ01GjhzJ9u3bqVGjBq6urvzyyy/odDrCwsIYMGAAvXr1onnz5pw8edLiOEwmE%2B%2B88w5bt25l3759SJLE7Nmz6dWrF23btsVkMvHiiy8SFxdHSkqKhUiNv7%2B/0nf75JNP0r9/f2W/69WrZ3H%2BMzMzkSRJWXblyhUCAwPZt29fsezzjh07bJ4n89%2B0koJyWQSqcePGFu%2Bz/m2zHsebN2/atWsp6Zq9Fc6ePctLL71ERkaGxfjKFgzVqlUjOzsbHx8fxSdx0KBB5Obm8s8//3D%2B/Hny8vKQJAm9Xo/JZMLX15cGDRqQmJhIbGws8%2BbNIzU1FV9fX9LS0vD09CQ4OJguXbpQu3ZtunfvTlxcHN27d7%2BjxyZ4sBABn0AgENxHZFXLN998U3kgsPWz7OfnR05ODpIkUadOHcUguEGDBixfvtyuD5s5tgI%2BOQCYNWsW0dHRFrPRfn5%2BmEwmpTTK398fKJJkh38fnnbs2MHFixdRq9Wo1WoMBgMGg4FKlSpRt25dkpKSij14NWrUCBcXl1v2cXP04LZp0ya7puQyss9aWcQZ0/nLly/TokWLEg2kp02bVqIsvlar5cqVK0RFRbFz506LMZHHQg6C7I1JSVlnGVm90hGXL18mOjqa3bt3c%2BPGDUwmEz4%2BPjz11FN3JdCzDkji4%2BMVVUNzzEtkZTp37szKlSsd9jKOHTuWrKwsm0Hk1q1blb8LCgqIiYlBq9XSsmVLKlWqRGFhIUlJSaSkpCh9fyqVioYNG3LixAmOHDmivL9u3bpUqlQJg8GAVqu1ELWRlR0lSeLFF19kwoQJNGnSBJPJRGBgoGLknZubS4cOHTh48CAqlQoXFxc8PDyoUaMGBw4cUDKuO3futDiODz74gAkTJii/ETJ9%2B/Zl%2BvTpNs%2BNOdu2bbNrl2CL8PBwkpKSaNGihSJWYjKZ6Nq1K1A0hmq1mm7dumEymRTxmbZt27J3716bdi13guzsbHbv3s2WLVs4evSozcqNatWqERkZSZs2bRg/fjz5%2Bfl4eHigUqlQqVSKOJZWq6VNmzbk5eXh5%2BdHYWEh%2Bfn5%2BPn5ERcXR4cOHUhKSqJ58%2BZK9Ye5aqf1ZOGtTBgJHh5EwCcQCAT3EXnGPD09XXmgcwZZNa9y5cpcuXLFqX/MGzZsiNFopEePHkCRH5iMp6cngFLqBUVqcZIkkZKSgpubmzKTLn%2BXbCehUqmYO3cuM2bMUB6knn766WL%2BZOb/3MyePZtWrVop%2BwKWGbeSPLHk775bD273E2dM5zMzMxkxYoRTwXJqaipZWVmEhIQQFBRERkYGS5cu5c8//6Ry5crk5uZSp04drl69atPuQh4LWfJdHhN5DDQaDW%2B88cYtZZ0dYTKZOH/%2BPAkJCWRnZyNJErVq1SIiIkK5Xq2x7r20h%2Bz9BzgVkADMnz%2B/2LIFCxZw5swZu%2BXFw4YNo3Xr1qxZs8YpU3vriQyTyYS/vz9hYWFkZ2fj7u7OihUrcHV1tZll8/DwKFb25%2B3tjU6nQ6fT4ePjg0qlwt3d3WaQLo%2Bh%2Bb0qBwuOJlFuZXzNKW1AIltpOKs2qVarqVixIjdv3iQoKMimXcvtYN3PZ455b58zvnvOIAd21p9n6/NPnjxp4RcpKJ%2BIgE8gEAgecJx9WOrYsaOFSp25N5j5MkmScHNzs8gEmpdHHj58WLGTCAoK4vr162RlZVG5cmU0Gg3Z2dlotVoOHjyIyWSiS5cuxYKBtLQ0goKClO%2B3zrg58sQy/%2B47/eD2IGDtQ5iWlkblypWV9ebBclJSkmIgP3/%2BfCIjI%2BnZsycpKSm4uLig0%2BkwGAzMnz%2Bf999/36a3njwW69atU8bEegyysrKYP38%2BderUKWZtYI4jxUtzcnNzWbhwIVu3bkWSJIKCgvD09OTSpUuo1WrefPNNmyVqw4YNK/GzJUniiy%2B%2BcGo/SqKk8uKAgADWrVtH27ZtSUpKKvFh//Lly8pEhq%2BvL9OmTbO4zrVarZLVM88Oyixbtoz69etz/PhxLly4QKdOncjNzeXGjRv89ddfTh1T165di2XwZMwDQkmSlN6xM2fOUKtWLYvx7dKli2KNIltXyBnpI0eO4OLiwm%2B//UanTp3o3Lmzkv1t0qSJzWyqOQ0bNuSzzz6zsBJ57733iv0tW4m0atWKoUOH0qlTJ%2BV%2BiI2NZe/evU7btTjimWeeISMjA51OR/v27Tl8%2BDBdunThySefJCAggFGjRnHu3Dmb75WrIkpDpUqVlAxuYWGhEvjOmDFD8WGVe32PHDkiMnwCEfAJBALBg86t/mNu631yFtBkMtGzZ0%2BgyKAb/u0n6tq1K0ePHqV///7KQ5RGo6FGjRosW7aMNWvWsHjxYocZATlj5CgjYK8/6m4%2BuD0IWJueW4%2BjebA8fPhwUlNT6d%2B/P6NGjeLbb79l2rRpqNVqfvjhBxISEpg8eTItWrTg6NGjdvvR5LGQ/1/SGNzOA%2BamTZt4%2B%2B23KSwsJCQkBF9fX1JTU1Gr1bzxxhsUFhbyzjvvsHz5clq2bHlL32GNdUAiY10iawtn%2BsIcmbvLmEwmPDw8lAAvNTWVYcOGMWPGDOBfddsTJ07Y/YzmzZszfvx4Fi9eTEFBAVWrViU1NRVXV1e0Wi0qlYqKFSui0WjIyMggODiYnJwci%2BP29PRk586dREREsHfvXtq1a8fPP/9M%2B/btFcuUbt26ER8frwR41uN96dIlJeA7d%2B4cY8aMoU%2BfPvzf//0fp0%2Bf5pVXXmHEiBFcuHCBxMREsrOzefvttzEYDLz//vu8/vrrDnvOzHuf5e%2B197dMy5Yt2bdvnzJ5lZeXR4cOHUhMTHQ4LneKgQMH0r17d9atW4der8fd3V0Jzj766CMuX77MmTNnlHWyoJfRaMRgMODl5UWTJk2UnuqJEyeyatUqRZFTrVYTFBREWloaGo1GyTiLkk4BCJVOgUAgKLdYqz1CkQJheno6RqNRUfmUS6fkWWh5Vj86OtqihC4zM5NmzZopD7b/%2B9//7H73pEmTFNsIWzjyxJKtLGSsffYedqz92aznbc3l%2B%2BVskGy0/fPPPxMYGEijRo0ICAigc%2BfOAPz111927S7ksTAfk7s1Bvv372f%2B/PlUq1aN//znPzzzzDNAkfrm119/zezZs1m%2BfDlTp05l2bJldgM%2Bk8nE0aNHSUlJQa1WU6tWLQurC3NOnTrFgAEDeO6554oFfEeOHGHlypUO%2B0mzs7MJDg5m%2BPDhdsuLNRqNhVG5LebOnUvfvn2VILpx48YWwd3gwYN57733LN5z8eJFRd2yYsWK3Lhxw8JgPSAggIyMDIYMGcLnn3%2BOr68vr7zyClCUBXvuuedYsmQJp0%2BfVrL/st/enDlz6N27NwsWLKBNmzbFxtc8m2d9DXbs2BGTyUSnTp2U7NP69etZv3698jomJobjx48za9YsNm3aRN%2B%2BfZXP2rp1q8OA71a63e28AAAgAElEQVSsRKy9A63tWu4mV69e5a%2B//sLf35/z588DRQFa1apV8fLyIikpSbHiAZT9ys/Px8vLi8LCQq5du6aMtclksmkdk5aWhiRJym%2B2JElcvnwZk8lETk4OLi4uuLi4ALZ//wUPLyLgEwgEggcMez5s1r07M2fOtPl%2BuXTKlkqiuVm7%2BYzw8ePH6du3b7EHrEceeYTU1FRee%2B01izKqYcOG0apVK7vH0LNnT3bs2EH37t1L7Yl1Px/cygLWgZl15sg8MJPHS1YfTEpKIi8vTxkbNzc31Go1BQUFdO7cuZjdhTwWHTt2tBiTuzUGsbGxzJw5k/nz51v0d2o0Gvr3749er2fZsmV88skndvv1EhMTef3110lJScHHxwe9Xk9eXh5169Zl3rx5NGzY0GL7JUuW0Ldv32L9kAALFy5k1qxZfPzxxzb7Sa1LWxcsWGCzvFitVju8H6CoNHfIkCHKa0mSLFQ3rfsWf/31V0aNGkVQUBB6vZ60tDTUajXfffcdjRo1QqvVcvLkSUwmk2KIHh8fz%2Beff87OnTvJy8tj8eLFSo/ZokWL0Gg0nD17lujoaPr160d4eDirVq1ySgTJnLi4OKKioti2bRtDhw4lNzdX6RkeOnQo0dHRBAUFAUUWH%2BbWFJGRkYoHoD1KshIpS8FMQkICo0aNQqfTsWfPHqDovjQajURERFCnTh1FhdYWshIy4FTpp8lkUkR4oKgiAFBsdeSxvNV%2BS8GDiQj4BAKB4AHDng%2Bbtdy5df/b7Qgr1K9fHzc3N0wmE6tXr2bo0KHs3bsXPz8/wsPDGT58ONHR0QwfPtypz7sbPm7lhdKYngcHB6PT6ZRSsUuXLiFJkpLJ%2BueffxQF2Pj4eLRaLREREbRu3Rq9Xs%2Bvv/6Kq6sr3377LYGBgXd9TI4ePUpMTAxvv/22hY%2BaTFRUFNHR0Xa9K8%2BcOcPIkSMZNmwYL7zwAgEBAQCcP3%2Bejz/%2BmOeff56NGzdaZPuSk5PZtGmT3X0aPXo0gwcPtrnuww8/ZNy4cRalrYsXLy5WXuxMJso6iAYcBtEff/wxY8eOZcSIEUBRRlCn0/HGG28o5ZvyOdq2bRs9e/Zk2LBh5OXlMXjwYPbu3cvvv/9OvXr1OH78OHPmzGHt2rXUrVuXDz74gKysLFJTU/ntt9/s2nnYo2bNmqhUKmrWrEleXh5qtVrJCObl5dGmTRtl25CQEIvfNF9fXwvxKCiuAhsVFcX%2B/fupVq0a%2B/fv5%2BrVq8o%2BfvHFF2zdurWYvYvBYGDDhg0WY2Fr2Z22a1m4cCF%2Bfn706dOHn376ySKINxqNDkt07dG8eXPOnDlDQUEBQUFBhIWFUVBQgEqlsjBdl69/KPotCA0NVfoaBeULEfAJBALBA4YttUCAl19%2BWfnbkXCGM9iaIZcfimR/r%2B%2B%2B%2B46CggIKCwtp3bo1Op1OKdOUJIkpU6bQokUL5f3mD1IVKlRgw4YNrFixgri4OIvepyFDhjj0xLpfD25lBetg2WAwsGPHDpvBcvfu3dmwYQMvvfQSBoMBNzc3wsLCqFWrFjdv3uTdd9/Fzc2N3r17ExwczK5duzh//rwitqFWq6lSpQpdunSxGBPr871x40YKCwvp378/UBS89O/fn9DQUIt9L0mMQ6fT4e7uTrVq1RSfNXPkQO/gwYM2%2B%2BpWrlzJ4MGDi2XBQ0NDWbRoEfPnz2fJkiUW2UHrEllrzEtkrXG2tLU0pvYysgG7efYLUM770aNHi9lHGI1GRczFXMFS9gA8deoUkiTxv//9j6tXrzJnzhyWL18OFHnnRUZGKu%2BRJIl//vmH2NhYXnzxxWLn1Py1Vqtl0qRJNsc3MDBQMYYH8PHxISsrSwlGjh07RqVKlZT1sqeceZDXsWNHiyyiyWQqFkTLmSu5DDc8PNyiHDcoKIhly5ZZvMd6mSRJd/x34%2BzZswC88sorTJw4kYyMDDIzM0lPT2fSpEkUFBTg4uLCokWLqFevHpcvXyY5OZnLly%2Bzfv161Go1nTp1olatWoSHh9OiRQsl21uvXj0uXLjAxYsXi33v7aimCh4%2BhGiLQCAQPIA4IzJhXpZ5J3zSli5dChQ9vLZt25a8vDzy8/OVzKF12ZcsKe/u7m6hwinPvAO3ZK1Qkm0BlG2fvTuBuVDI6dOncXd3t2kgrdfrmTdvHjt27ECv19O0aVPmzZtH5cqVeeutt9i%2BfTu%2Bvr5ER0dz7NgxcnNzkSQJPz8/h2NiPQb2AiLZF03G3mSFTK9evZg3bx4JCQns3r2bTz/91MLbLSkpiddffx1JkujTpw%2BjRo0qtl8rV660qwaakZFBnz59SEhIUJY566UnlzubY0sMw55AhqN7tmPHjnTt2tXCvkT2xfTz81O2zcnJUQLdS5cuUbVqVeU6Dw8PR6/XM3fuXJKTk9myZQsA/fr1A4qC8q5du1KhQgWaN2/OrFmzOHLkCCaTie%2B//57Dhw/z1VdfMWDAAD777DOaN2/OwYMHefLJJ9mzZw8hISFKFi0lJQUPDw8laEtNTaVq1ao0adJE2dedO3fy119/KWWuR44cQaPRMH36dIKCgpg4caKFdcVrr70GFAWS7u7ubN682aZNhPXrXr16MXr0aODfvkJ7ViL3g8jISHQ6HStWrKBixYr8/PPPnD59mj/%2B%2BINDhw4pasmtW7emS5cubN%2B%2BnezsbFJTU5UMr6enp2KnYT4Z16xZM77//nv27Nljt%2BdZIAAR8AkEAsEDh7nIhCMftsjISOXBsySVQGfKPTMyMlixYgXffvstN27cUB6kevfuzZo1a8jJyaFfv35Uq1aNU6dOMXDgQLvG6nv37qWgoIDg4OByaa1wu5j7EM6dO/eWPLZkGfmLFy8yevRofHx8lExMUFAQN2/evGNjYm0dYY/ly5eze/duYmJimDp1KocPH6ZHjx6EhoZy7do11qxZQ0FBAY8//jhLly5VBChkwsPDOXjwYDG7EXOsPcmc9dKTAxLr73Mm4LN3P3zzzTfs3buXxMREpZfOETdu3OC3336z%2BT3h4eEUFhZy4sQJunTpQnp6Onq9npo1azJ06FBmzZrFnDlzlGBpwYIFjBs3jhdeeMHC186RV5wcPMrm4OYZfFvMnz%2Bf69ev07JlSypXrkz//v3x9vYmOjqa6tWrk5GRQaVKlYiJiSE9PZ1Vq1Zx%2BPBhNm/ebFHOal2x0K1bN1avXs2ECRPYunWrU1Yi94slS5awatUqDAZDsVLbW0GtVhMSEqKYyZ8%2BfRqDwUDDhg3p1KkTarUanU6n3BsRERF36lAEDzAi4BMIBIIHDGsfNmvk2e3t27crD4RyWRE4Lve0lRnZtWsXmzZt4sCBAxgMBlQqFUOHDmXjxo188803VK9enSlTpvDdd99RoUIFNm/ezPz58x3uY0REBFWqVGHDhg1A%2BbNWuB2shUIuXrzIpk2bbjkwky0WfvjhB0VVUZbjL82YOMpgLVq0yKmgVKfT8X//938cPnyY7t27c/36dU6ePElmZiY3btygYsWKTJ06laioKJsTGM7Iz1tv46yXnre3d7HPatiwoUVWDorULa2XJSQk2Lwf5H0x9060h3WW3tovT1bGrFSpEpmZmUp2C1D%2BliRJyeJnZWXh5ubGb7/9RmpqKteuXWP48OF8/fXXQFF57Llz55g3bx4A586dQ61WO2VdYV3%2BGRcXR61atUhLSyMvLw%2BTyYRKpVJ%2BT2SeeOIJZs6cqSiF2jpXUOQNevLkSbvWLVDcSuR%2BIfc9L1iwQCnThaIxiYiIoEGDBmzZssWi7FWmRo0a1K1bl8OHDxMQEICvry9nz54lODiYV199lYCAAAs1U/NgXVbrFGWdAhA9fAKBQPDAcSsiE9aBnCyo4Azjx4%2BnR48etGvXjjNnztCuXTtmzJjBxo0blW0WLlyIh4cHv//%2BOx9//HGJ%2B5ifn8/ly5eV1%2BXNWsERJZXfvvfeewwfPpwJEyYA9oVCnEXuQ4uJiSE2NhaDwcCnn35KTEyM02NSkrVBYWGh4vvmCBcXF2JjY4mIiCAzM5MLFy6gUqlo3bo1qampihG7PWz1ctraxpzb6Sd1ti9Mq9XavB/k/XQkDCNj3cdmNBrp0KFDse3kzJiXlxdQdK/NnTuX6dOn061bN0UB8%2BjRowwbNozo6GiGDh3Kzp07adasGUajkdWrV7N582amT5%2BOr68vaWlpvPLKKzz33HOcOnXKIsCTe%2BY%2B%2B%2BwzkpKSKCgoQKfTKd8PRUJDGRkZPPbYY4SEhCjnc8qUKcr5fvTRR/Hx8Sl2PLayw3J/YWmsRO4XV65coVu3bkRHR/Pnn39aZEWXLl2K0Whk7dq1eHh4kJ%2Bfj6enJ3l5eXh4eNCtWzeMRiO7d%2B/m0qVLqFQqVCoVly5dYvjw4VSpUoVGjRpx8uRJtFotI0eOZMWKFUiSROXKldm9e/d9PHJBWUJk%2BAQCgeABw96Mtfmsenx8PEAxUQcoEs4ojRFvdHQ0X3/9NZmZmbi5ubF8%2BXJat25N06ZNlQwfFJVRDRgwALVaTXZ2tsNZ9fDwcCRJsthGmAMXUVL5rZypkdX9btdA2pGJtbNjUlLWOSwsjH79%2BpXYSyWbdZuXXY4cOZLly5c7tS/O9HcCxfrxzEtkS9NP6iz27tnw8HCSkpIwmUy0bNnSplUKFIkoWWfpe/Xqxfjx4y22%2B%2Bijj5g7dy5vvPEGc%2BbMAWD27NkcPnyYRo0aodFoaN%2B%2BPW3btqVz584cO3aMmTNnKpMMcratcuXKZGVlMWzYMH766SeMRiOPP/44s2bNYsqUKRw4cIA6depw6dIlevXqxcWLF9m1a5fS05eTk0NsbCxNmjQplpEubfm2rWsxLCyMiIgIatasydmzZ4tZiQwbNoxmzZpx8OBBu%2BW494qmTZsCReI1S5cuZfTo0YptwooVKxgwYAB5eXkEBgaSlZWFv78/OTk5BAYGEhAQQFZWls3snyMeffRRzp8/z4EDB%2B57wCsoG4gMn0AgEDxg2JvVlpv5c3JycHNzKyaYcau8%2BuqrTJgwQekR%2Bs9//kOdOnXQ6XTcuHFD2S4kJITc3FyH%2ByhjNBotlPkE/yIb24Pt8ts%2BffooZXdQNnwIS8roajQa9u/fX%2BLnpKWlAZa%2Bbr/%2B%2BivgnLWBLWGVkr4vJSXFKS%2B928He/VBYWKgokRqNRsLDw22%2B//jx48Uy8i4uLowcOVJ53aVLFzQaDX379uXNN9%2BkTZs2dO/e3aKss3bt2sTHxxMfH8/MmTMV1VMfHx%2BMRiMvv/wyTz31FMuWLaNixYpMnjyZ3r17ExUVRc%2BePYGibP7TTz/N6dOnGTVqFN988w0ajQaNRqMIyKxatYp33nmHiRMnsmTJEgvritGjR/POO%2B%2Bwbt06p86dyWRi0qRJihKovCw5OZnExESMRiNNmzYlICCABg0a3HMrkZIYNGgQycnJaDQaRowYQa1atUhOTkav19OnTx8MBgMajYasrCyMRiNXr15Vemhlfz5XV1el78%2B8VFeSJLy9vdHpdBYCL9nZ2ZhMJrp168ajjz6Kp6cnISEhhIaGKj6UorevfCECPoFAIHjAsOfDNn/%2BfGV2e9CgQXd0VlulUhEcHIxer%2Bf999/n999/5%2BbNmzz77LM8%2BeST9O/fn5CQELy9vXF1dS3RK06n0/Hoo49aSM6XJ2sFR5RUfitJktPluM4gn3dzWw35b/NlcimarTEpydpAkiS7Sp7W21lj3pN0p%2BnatSuNGjVyykvvdrB3P2g0GmJiYpg3bx7169e3MF4vicLCQovXaWlpaLVaZSyfe%2B458vLyUKlUTJkyBa1Wy5EjR2jbti2NGjViz549uLq6MmfOHF577TWmTp1K27ZtgaIAfsmSJQDUqVMHjUbDxx9/rARPN2/eRKPR8Nxzz/Hkk08SGRlpoR7ZunVr3n33Xfbv31/MusLNzY1Dhw6VWOIrB3c6nY4//vgDk8mkZEmrVKmC0Wjk%2BvXrmEwmbty4QVpaGunp6XatRO4X%2B/bt4/Tp00qQJk9qmGNuoWEymexm9NRqNf369aNHjx6MHz%2Be7t2789VXX9G1a1elqsPcwzAnJ6dYZln09pVPREmnQCAQPGA4EplYt24dFSpUYNu2bRYiE/369SM4OFjxb4qPj7db7mmPBQsW8N1331GzZk2WLl2KRqPht99%2BY%2BPGjXz//feEhoaSmZlJVFSU4hVnTwjj2rVrBAUFOXyIf9itFZzFupTRWaEQZ4NluQwyPT3d4XYhISF2x6Qka4NGjRpRqVKlEjNwd6K8tDQ0btwYNzc39u3bp5ie326JrC3s3bOvvfYavr6%2BDoVh7CELl8iEh4dTUFBA1apVSU9PV3oVZeVPo9GIyWRSVB6NRiOFhYUkJCTQvHlzEhISlKCtSZMmmEwm5Xx36tSJlJQU5ftk%2BwU5mGjYsCFBQUHK%2BE6YMIHvvvuOY8eO2Rw3Z0p8p0%2BfDhSZxkdFRRVbf/PmTTIyMpTexpKsRO4XW7duJTk5mYKCAtzd3S2y8e7u7mRnZyuVETJ6vV5R0NXr9Xh6epKbm6uUgppMJruKqvIE0caNG3nyyScVRVVB%2BUZk%2BAQCgeABw57IREhICHl5efTr16/Yg2Nubi5nzpyhQ4cOeHh42HyAKonRo0fz008/ceDAAdq0aUNERASBgYHcvHkTg8HAqVOnqFGjBmPGjMHb2/uWhTAEjrnTBtKlLYO0hTMZ3S5dujj9eebZXr1ez/r16%2B9KBlgWVJGDPbg7JbL27lmDwXBH7wd3d3dlPOXyUDnYioqKokuXLopnnfk21oFDYGCgxQRAhw4dWL16NXq9Ho1Gg8FgUEqyb968iV6vtxhfuYTRHs6U%2BMqejXFxccyfP99CBVbuC/Tx8SE6OhpJkggKCror5bi3S9%2B%2Bfenbty9NmzYlOTmZ5s2bK%2BuSk5OVvkk5g/7777/TsmVLoMjQ3rp03hxzxU93d3fy8/OpVKkSbm5ufPzxxxiNRpKTk0lPT6dz5864ublRWFhocb0Lygci4BMIBIIHkOzsbIKDgxk%2BfLgyqz1hwgTatm3LtGnTim2/e/dup6TfHSFbLsTExLB582Z27typ%2BPeZTCb%2B%2B9//MnLkSOXB1dY%2BCm6fOxGg3WnkjO7TTz9tM6MrSZJTvVRyUOfl5aVkm/V6PdHR0RbLvLy8ShXUlgVs3Q9Lly7lv//9r1Pvt7Y6sF5m7e3m5%2BenlNFmZmZy%2BvRpZs%2BerazPysqiQoUKQFEGNj4%2Bnj59%2BgBFAfznn3%2BuBHi%2Bvr7K8qioKLRaLV5eXrz88sskJycDRUIvcom2efmurUDdaDSSmZlpUdJtayxPnTqlTCaZq8DOmDGD4cOHk5SUREBAAOfPn6datWp06tTpjpfj3ipGo5GVK1eydu1arl27hkql4uWXX%2BaZZ57h8OHDGI1GxowZw5UrV7hy5QouLi7KRFm7du24cOECjz32GK1btyYpKUmphtBoNAQGBlKzZk1CQ0NZsWIFJ06coFmzZmzevJmMjAwyMjL466%2B/AJQyYV9fX9577z1mzJjBypUrCQsLu2/nRnDvESWdAoFA8IBhT/Vu9OjRbNq0yW5glZCQwKRJkzhw4IDFcls%2BWvZISUnhm2%2B%2B4ezZs9SrV49evXoRHBxczMz6dpX5yjPWD/a3Un57t7FlHZGfn8%2B6dev45ZdfSEtLU6T2IyMjnc5gOaOyeSdLfcPDwzEYDHe0RNYWju4HR/6FHTt2pFq1asC/JY4y1qWOW7duBeCtt94C4Ouvv%2BbQoUOMHDmSvXv3kpqaysSJExk0aBBQpOh5/Phxli5dyr59%2Bxg3bhxTpkzh2WefJT8/n5YtWxISEkKNGjVITEzEZDLRpEkT/vzzT6Vc1MXFBQ8PD7y9vS3Ks9PT0wkMDOSXX36xOaY6nY6srCxCQkIA22MqB3mFhYVERkZaqMA2bNgQf39/8vLyOHDgAG%2B99Rbbt28nMTHxjpfj3irLly9XbE0aNmyonMNbRe69g6Kgz83NTbHAsIdarWbSpEksXLiQ6tWrExISonhurl69%2Bpb3RfDgIQI%2BgUAgeMCQjbLNRSb27t3LH3/8YdcKQX540mq1ysyvzJQpU0hMTGTjxo0Os3C///47//nPf9BqtajVagwGAx4eHnzxxRcMHTrUok/H3j6WhZn3so71g7095JK3%2B4Et6wjzxwnZWLusC0OEh4crVgKOuN0g09798PrrrzNw4ECGDBnC5MmTLd5T0n1p3csZHR1NTk4Ofn5%2BQFGG6caNGxiNRlQqFRUrVsTd3Z0ffviBDz/8kFWrVvHFF18oKqEbN25k/vz5GAwGqlatypkzZ5RyQ5VKhV6vJzg4mLCwMKZNm2a3XxOK%2Bn3PnDmjlPiae%2BnJwlIl2SXIVh/bt2/H3d3dYjIrPDycqKgoNm3axLFjx0hPT%2Bepp57ixIkTZcbepWvXrhQWFvLRRx/RqFEjPvzwQ7766iv0ej1169ZVhGgCAwPx8PAgNzeXq1evKveOuZBLaZGrLtzc3Dh48CANGjRQgvJff/2VNm3aiN6%2BcoYI%2BAQCgeABo2XLljZFJipUqGBXOEM26T58%2BLDNkkBnyj2HDh1Keno6zz33HC%2B%2B%2BCKxsbGsX7%2BeKlWqcOjQIYuHLHv7WBZm3gW3j7UnXO/evdFoNAQEBGAwGMjNzWX%2B/Pl3zBrkbnGvggN798Pjjz/u0L/Q%2Br40zwaaZ85u3LiBh4eHItAiI0kS69evx9/f32JdUlISOp2uWFbx%2BvXr7N27l5SUFD744AP69%2B/PY489RlBQEOPGjePRRx91KmNvLVKzdOlS3n33XU6dOsVXX32Fv78/H3/8sYU5uzUDBw5UgjxrH8Pw8HB27dqlBHnwr4hNWQn4rIVtjEYjzZo1Q5IkDh06pPjzyX18er2eli1botfref311xUl5OTkZGrUqMG5c%2BeoU6eOUipdq1Ytjhw5QuvWrcnLy%2BPkyZMYjUb0er0i0KPRaNi%2BfTvdunXDx8cHtVrN2rVrGTJkiGJ3IigfiB4%2BgUAgeMCwJzLRr18/u8IZSUlJ%2BPv72xXOGD16NIMHD7a5Tu6zOXLkCFBUxrV%2B/XpcXV3JyMjg8uXLSolXSfsocA5HZX7Olt/eTaxtIQwGA5MnT7bIYMly8WUZcyuBu4m9%2B6Ek/0Lz%2B9K6j8184kbOBq5bt85ult48y9aiRQvCw8P59ttvlZJRKOrT7d69O7///jsGg4HExEQOHDhAamoqLi4u7Nq1CyjZusJapEan0/H6668TGhpKTk4Oubm59O7d2%2BZ75b5gNzc35VisfQwNBgO//PILJpOJKVOmcO3aNeU8OGslcreR8ylGo5HY2Fi%2B%2B%2B478vPzgaLsrE6nQ5Ik1qxZQ0BAAF988YViqfD5559TWFjIjRs3%2BO9//8v%2B/fuJjY1VsrFJSUnExMQwadIkPvroI%2BW31Vp8R6/X88wzzyh/N2jQgFGjRtGjR497dh4EZQOR4RMIBIIHDFsz2OHh4ezbt8%2BuFcKyZcuoU6eOQ%2Bl361l0GTmTIPdtValSRVlnvsz8AdTePpaFmfeyjvmDfWnL/O4XdevW5fDhw/c9o2urt9AW5tfwvcDe/SBJkt0ybBn5vpRLHJ3NBvbq1Yvt27cr6xs0aGBRzt2oUSMkSWLnzp1AkRjQvn37ADh37hyXLl1i165dVKlShUaNGuHi4sLBgwcB58Y3JSWFvXv3olarmTt3rtLja50d7tOnD9u2bSv2/v/7v/9TKhasS0Q7duyITqfj8uXLSnm5JElKCaqMIyuRu42sgDp69GjWrl1LrVq1FGVSeZ%2BhyJdQr9cjSRJ6vV55f6VKlcjMzMTV1RVJkkhKSlImKLRaLa1bt2bSpEnMmTPH6X3y9/dn4MCBjBkz5p5NdgjKBiLDJxAIBA8YtlTvDAYD8fHxDBw4kH379rFu3Tpyc3MVKwQfHx8WLlxoN9g7duwYFStWtLnOWubdOrCzXpaWlmZ3H4WxesksWbKEvn37Fgv2ABYuXMisWbP4%2BOOPb1lt9W5RFjK6siebPeTs0b3uLbR3P3h7exMTE6P0EVrfD%2Bb3ZWmygQDnz5%2B3WK/X6y0CYlnVs1OnTphMJpuCIh07dmTMmDFotVqL9SWNr7VITWFhIUeOHKFRo0bFssOyb5w15lYf1iqwr7zyCp988gnh4eGkpKTcko/h3Uar1WI0Gnn//fdxc3OzEMsyr4goLCwEICAggKysLKDonOTk5KBWqzEajajValq1akVBQQGSJCn9ffaCve7du9O3b19CQ0OpWLEihYWFuLu7l6nzI7i3iAyfQCAQPGA4UjLU6/XKA8Q333yjZIGsZ8jNcVZEoWHDhhiNRotyoG%2B//RbAYllcXJxT2SdhrG6bdu3aOVRbTU9PZ/Dgwfz000/3eM/sY20CDvcno%2Bts9shWgHE3sXfPXrt2DZ1OR0BAACqVyuJ%2BsL4v7WXgzTHfxvr8161b1yIDJgcN5mWAkiQRFxenqO%2B6uLhgNBpJT09HpVJZfJ6j8bUWqWnQoAEtW7a0WQJq73Os%2BwCrVKnC7t27SU5OJjs7G0mSqF27Nl27di2Tvp6ffPIJiYmJJCUl0aJFi2IiKRUrVuTKlSvK6%2BrVq5OSkgL8OzFRqVIlLl%2B%2BDBQv15QJCgpCpVLRsGFDvLy8UKlUXL16ldzcXEW0RxaHefTRRxUfQNmCQ1A%2BEAGfQCAQPCQ4kn63fniy9klzZoa8Y8eOFmbM9jAYDMUe/gXOU9oH%2B/uBtXXEjh07igl4/PXXX7z11lv3NaNb1suIS3Nfdu7c2a4oExRlA8eOHWuRkbcO%2BOLj44GigKJ79%2B64urqybds2%2BvXrh8FgYNu2bdSsWZPw8HB0Oh0vv/wyo0ePpnnz5phMJotyUkfWFdYiNY0bN8bDw8NmCaijMbp58yYrVqzg%2B%2B%2B/58KFCxiNRgICAujevTvjx48vc0GeLeTjk6shZORlcqZUFpyx3qZx48aYTCYWLlzI2bNnMZlMVKlShapVq7Ncc1IAACAASURBVHLq1Cl%2B%2BeUXReClJFQqlZJNlst3BeUDUdIpEAgEDwkffvgh48aNsxDOkIUVrEUULl68qJR7DhkyxKkZcmcNv60fWgSlw1qgwhpH5bf3Cuv%2BH09PT86cOWOxzN3dnWXLlimvHzSj9HtBae5L8xJHW1n6mTNn2hVlkrFXTilbAcjr5Z64KlWq8PXXX%2BPl5UVOTg7R0dHKe728vOyOr7VIjSRJt1TiK5vVt2/fnrVr11KlShUMBgMbN26kR48eD72vZ8eOHZWKjfHjx5f6/e%2B9956FV6Og/CICPoFAIHhIOHnyJLGxscrrwYMHK8a/8O/D0/Dhw2nfvn2ZEv0Q/MudeLC/21h7AN5PT8AHHWfvS%2Bs%2BNlvZwDFjxtyRfQoKCuLSpUssX74cKArurC0USlOS7erqqvQMWmeHdTpdsWUAgwYNUioW0tLSMJlMLFq0iEaNGpWoElqW0Gq1DBo0qFjAW7duXYevwXkRItmU3Wg0IkkSXbp0ISkpicjIyFvfccFDhSjpFAgEgocER8qYjso978V%2BCJznTpTf3gvKunUElP1rsbT3pXmJo3k2MDIysliWvl69egQGBiqvMzMzqVSpksVrlUrF8ePHlbLBb775RlkfFRXFN998Y1GyWaNGDaDkQOTpp59m/Pjx%2BPr6KsvkEtAtW7YU%2BzxbXLx4UekDbNmyJSNGjCAhIYHPP//8gfL1/OSTTwBITEwkNTWV3Nxci/X5%2BfkYDAbFbF3u31Or1YqYji0T9urVq7Nw4UK%2B//57tm/fTsWKFVGr1Zw5cwZXV1d69%2B7NyZMnadeuHb6%2BvgQEBBAYGKhMKNxrpVrB/UUEfAKBQPCQ4CjgsxZRiI2NZe/evXdlhrysP2Q/CJTmwf5%2BUFatI6wzRfHx8XTr1q3YdosXL75Xu%2BSQ0t6X5lYH1tlA6yBMtluQ%2BeCDD5gwYQL%2B/v4ATJ8%2BncaNG1O9enV27Nhhdx9VKlUxddN69eo5VEOVAxRHQX9JGULzPsDw8HASEhIsgryH5XdGFmox90PctWsXGzZs4MCBA3h6epKfn68Efps3b%2BaZZ57Bw8ODbdu2MWDAAHJycpz%2BPjkbeK%2BVagX3F1HSKRAIBA8JjqwQjhw5QteuXVm/fj0DBw4sVu4pKFuU9fLbsmodYd1bWNb7l0oqwzbHOhs4f/58Fi9eTL169YCSLSmMRiMLFiygZ8%2BeyrKsrCyysrIICQlRljVp0sTifVOnTi32WXFxccrft6KGKmeHrTHPDtszq3/QuHHjBmvWrOGnn34iJyenWLYuJSUFFxcXi%2BB1/PjxREZGYjQayc/P58cff2TkyJGcOHGCtWvXKsb0gwYNQqvVotFoFC8/k8mkZAfr1avHU089hUajoVKlSoSEhFgEloLygwj4BAKB4CEhKCjIQkTBfFlhYSGfffaZIqxwNx%2BehKHv7WH9YL9gwYK7Vn57q5TWE%2B5e8aD1EpYmqLEWZapbty7//e9/LawWzCd75L/Hjh0LwOHDh3Fzc1Puz969e3Ps2LFi15Uz59BZLz1bmGeHrcuBjxw5wsqVK9m4caPFcnniSq/Xs379eotlZd3X89VXX1U8%2BOSySyjK4t%2B4cQODwYDBYKBZs2bKezQaDbt27QKKAvVnn32Wtm3bcuLECTZv3gxgM6snZ2I/%2B%2BwzJk%2BezIYNG3Bxcbnbhyh4ABAlnQKBQFAOcFTuWRLOCgeInpA7w70sv71VyrJ1xIPQWyhTmvvS2urg2LFjPP/880rQY51l6927NyqVipCQEPR6PTk5Ofzvf/%2BjUaNGJCYm8sYbb1BYWMi7777L448/Tl5eHsuXL2fnzp1cuHABtVpNjRo16N69Oy%2B99JJFYOrMcdhjwoQJVKxY0cLiwZxZs2ZhNBr5%2BuuvFdsHWR00JycHPz8/i%2B1lMZmy6uvZqFEjTCYTu3btsrj2rl27xg8//MDrr78OQIsWLSzep1ar8fb25vr16/z%2B%2B%2B94enpy/fp1qlSpQnp6Ot27d%2Bf333/nscceIzMzk8zMTLRaLWq1Gi8vL1xdXdHpdOzcuVNMwglEhk8gEAjKA47KPUuaIS%2BpVMy6v0dwe5SmzO9%2BUVatI5zNHpWVEtnS3JfW2cCwsDB0Op2SWbPOsun1eiZPnsyIESOAojLct956i4oVK5KQkEBUVBTffvst%2B/fv58iRI2zevBmdTsfgwYP5%2Beef%2Bfvvv%2BnQoQNbtmxh9%2B7drF69Gk9Pz9s%2BZmezw%2BYVC3JQdztKofeL0NBQjEajhYANgK%2BvL/369VPOacOGDYGiXj7zvj6tVsvevXvZu3cv%2B/btIzU1FRcXF3bu3EmLFi1ISEhQxF3ka%2BbatWvK95hncIUPX/lFZPgEAoGgHNCxY8cSt7H38HT27Fnl71vp1xGUjtvJxt4rFixYwJkzZ%2BxaRwwbNozWrVvz2muv3dP9cjZ7dK97C%2B1RmvuypOvCltG6XMYJRSbeAwYMIDg4mNjYWJYsWaKcqx49enDhwgV27typZKHkc/Xmm2/y4osv0qxZMyZOnGhzH0tzfZbl7PCdQqvVotPpADh48CBffPEFrq6uhIaGkpmZyd9//016ejp5eXlKSaeMj4%2BPouRZu3Zt5s2bxwsvvIAkSezZs4eIiAj0er1FgGeNi4sLkiRZZBQDAwN59NFHad68OQB9%2B/a9W4cvKIOIDJ9AIBCUA5w1TbfF7fTrCB5O7qUnXGkoq72F9ijNfVlSNtBgMCiiTDLmGcFVq1ahUqn45ZdfAMtzpdPp6NSpE0uXLlWCYflcaTQa3nzzTUaPHq0EfM566dlSQy2r2eE7iWxzcSuY2zacPn3aYjzbtGlj06IBin6X586dS58%2BfZQ%2BQYFARgR8AoFAIBCUIW6n/PZeUaFCBTZs2MCKFSuIi4uzsI4YMmTIfbOOuH79usNyzZCQELKzs%2B/hHt05HIkyQVHQNX/%2BfH777TdlvXkQ9uOPP1pcP%2BbnKjMzk2nTplkEw%2BbnqlatWmRkZCjrbkcN9emnn%2Badd96xmx2eOXMmXbp0cfrzyiJffPFFsRL3BQsWMGrUKIsxHDVqFJ999hlGoxGdTofRaFQUN82RlxmNRsWvT2bIkCGsW7cOKOr3nTdvHgUFBYqnn4zc21e/fn1lHwXlBxHwCQQCgUBQhijpwR5Q1FbvJ2XROuJhzh5ZZwOtM2qXLl0iICBAKYWUJIk//viDVq1aAUU9feaKnubnyt3dHU9PT4tg2PxcZWdnW2QLb0cNtaxmh%2B8krVq1Us47FGXt1q5dS79%2B/fjuu%2B8oLCzEaDTSr18/1qxZAxSd07lz56JSqejbty/16tXjjTfeIC8vD71eT%2BfOnfnhhx8wGo107tyZffv2YTAY2LFjh1LeefbsWaWnWg72PDw8kCQJb29vqlSpQnh4%2BH05J4L7i%2BjhEwgEAkGpKGv9ZIJ7j7V1RHZ2dpmwjiirvYV3g%2BnTpztcv2XLFtzd3alQoQJQ5LlnNBqpVKkSgGIJcOjQISZOnEjlypXZvXs3P/74Y7FzFR0dzalTp1i6dKny%2Bbejhnrz5k1WrFjB999/b5EdjoyMvG/Z4buBVqvlt99%2BY/To0ej1esLDw%2BnZsydffvklZ8%2BeRaVSUadOHXJyclCr1Vy6dAkXFxc2b97Mc889Z1HeaY51ls8eBw8eLCZ0IyifiIBPIBAIBA6xziTEx8fTrVu3YtvZ6tcRPJyUVeuI69evM2jQIPLy8uxmj9atW4e3t/d93c%2B7hXkQtnXrVmX5/v37ycjI4Nq1azz//POo1WoKCgqUwPiJJ55g8%2BbNVKtWjaeeeoq4uDgCAgJYtGgRmzdvZsOGDaxZs4YGDRoo3yOroU6ePNliH6ZMmUJiYqJDNdSUlBT27t2LWq0uM9nhu0G9evVuuZdPpkWLFiQlJQFFPn5Xr161uZ0kSdStWxdPT09mz57NokWLmDx5MvXq1but7xc8HIiATyAQCAQOKSmTIPOgmV4Lbh1rT7i8vDw6dOhAYmLifd6z8pM9AssAzzwICwoKssiyTZkyhYSEBLy8vNBqtUownJ%2Bfz6ZNm/jzzz8tlCJdXFxQq9VotVpCQ0N58803adOmjbL%2BdtRQy2p2%2BG7w22%2B/MWLECIxGIwMGDLDogzSZTPz0009AkVrrzz//DKCoe94qkiShUqnw9PQkLy8PNzc31Go1Pj4%2BVKtWTSk1HTt27G19j%2BDBQgR8AoFAIBAISkVZto4oL9kj6yybeRA2ZcoUvv32W3766Sfl%2BGfNmsWePXvo16%2BfEgybTCb8/f0JCwtj2rRphIaG8tdff3HhwgWgSKHXVoaobdu2bNmyxe65TU9PZ/DgwUpAY05ZzQ7fLaKiorh%2B/TpTp07liSeewGAwoFarWbduHbGxsfj6%2BjJy5Ehmz56NyWSiWrVqnD9/3uZn9enTh%2B3bt2MwGOjSpQu7du1yej9UKhXu7u7UqVMHSZL46quv7tQhCh4ARMAnEAgEghK5nX4dwcNHWQ34ylP2yDrL1q5dOzZt2qQEYWFhYfTr10/JsqWnp9O%2BfXvefPNN1Go1vr6%2BTJs27ZbOVd26dTl58qTDbex56ZXl7PDdYN%2B%2BfYwePRqtVovJZMLDw4P8/HxlvUqlcuipJwuwhIWF0a1bN7766it8fX3RaDQ888wzfP7557z66qu8%2BuqrABiNRmrWrMk///xDXFwcjzzyyD05TkHZRlXyJgKBQCAoz8iZhP379xdbd%2BTIEQYPHmxRqiR4%2BJFtItavX6/8Z2vZvebDDz9k3Lhx7Nq1i927dzN69OiHtrc0OTmZkSNHKq%2BtLSk0Go3FPXvx4kUAPv/8cz799FMmTJjAs88%2Be8vn6p9//rG7zpEaqlartVD89PT0pKCgwOnvfdCIiIjg%2B%2B%2B/V4zPjUYjrq6uVKhQgZkzZ%2BLj44NKpbJQJvXw8CAoKAgoslOQJImpU6eyadMmJEnirbfe4vTp0/Tp04eMjAwiIyOpW7cutWrV4rHHHmPu3Lm4uLgIwRaBgsjwCQQCgcAht9OvI3g46dixY4nbSJLEDz/8cA/25l/KU/bIOoPWuXNnVq5cqVhShIeHK9YMAH379uXYsWNKZq5x48Y0adJE8WMrzbkKCwsjIiLiltRQy2p2%2BG7TtGlTkpKSaNGihbIsKSmJli1bcvPmTY4ePUrDhg2BooBv9%2B7ddOjQAY1Gg4eHB35%2Bfrz77rusWbOGI0eOoNFoaNCgAVu2bHGYJWzatCkeHh6EhIQQGhqqfEdERMS9OXBBmUD48AkEAoHAIcnJyWzatMnu%2BtGjR1sYNgsefqw94coK5Sl7ZO05aG1obm7BcPPmTU6cOGHhwydJkkVZZmnOlUajITU19Za89ORMsHlwYmvZ/faZvFOcOHGCOXPmkJ%2BfT4MGDSyOsUGDBkiShEaj4Y033lDKN/V6PYMHD0ar1aLVasnLyyMrK4tnn31Wea8kSVy9epXevXvj6%2BvL6tWrbX7/oUOHLF5LkoQkScWM4QUPNyLgEwgEAoFDrEvFrAkJCbEwbBYIBHcf6wDP3NA8KioKrVaLl5cXL7/8MsnJyRiNRlxcXJTAymAwcPPmzVv6bkmS2LBhAytWrCAuLs5CDXXIkCEO1VCDgoJYtmyZw2WSJD00Ad%2BUKVNISUmhcuXKXL58GTc3N%2BW8y1k5o9FoYaOh0%2BkU4RwZSZIA8PPz4/nnnycqKophw4aRnJwMoPRRu7m5ERoaysCBA/n0009Zt27dvThMQRlHlHQKBAKBwCHWpWLWHDt2jLFjx5bZrI%2Bg/NCwYUNmzZplkUWZM2dOsWUPQzBhy3PQls2Ci4sLHh4eXL9%2BnSpVqijvT01NBeDtt99Wljl7rsLDw/n222/LhRrq7dK0aVNMJhOJiYnk5OQQHx/PyZMnFSEsnU6HJEnk5OTg4uLC%2BPHjb9viRqVS8ccff9CmTZtiGT5B%2BUQEfAKBQCBwyIIFCzhz5swt9esIBPeSstpbeLew9hy0tlkwn6SxDoajo6PJycnBz88PKFJ3VKlUFkIf9s5Vo0aN0Gg05UIN9XYZMmQIhYWFREdHExoaCkDPnj0B2LFjB926dQNQhK8OHjxI3bp1AXjmmWfYvHlzid/h6uqKVqt1an/ksl5R0lm%2BEAGfQCAQCBxiK5Ng3a%2Bzbt06vL297/euCgTlCnPPwZJsFkoKhi9dukTVqlWdCobDwsKYMmVKufHSux2%2B//57Fi9ezM2bN9FoNOTm5nLjxg2n3itJEm5ubkpvZa1atUhJSVGCu6lTp7Jq1SquXr2KXq%2B3eK%2BXlxeVK1cmPT0dKOrRbNeuHadPn2bEiBFERkbewaMUlHVEwCcQCASCErHOJMj9OpGRkQ77dQQCwd3B2nMwNTWVYcOGMWPGDKB0QdilS5ecMvKWS0LLkxrq7VKvXj27HnvmqFQqjEZjsb%2Bt0Wg06PV6XFxcSE5OxmQy0aJFC3Q6HT/%2B%2BCM9evTAYDAQHh7Ol19%2Byfnz55kwYQIA7u7uvPvuu0ybNk309pUzhGiLQCAQCEokOzub4OBghg8fLvp1BIIygOw5KGfZGjduzIkTJ5T1gwcP5tNPP3Xqszp27IjJZKJTp04215tMJgtlx/Kkhnq7yGPStGlTfv31V9q0aaMEgImJiYwYMQKA1atXK4bsHh4e7Nmzh/feew9vb2%2BMRiPnzp1Dq9VSu3ZtTpw4gaenJ0OGDFHK7L28vJg9ezZGo5EKFSpw5MgRoEhU69y5c0BRxjAkJMTiOhGUD0TAJxAIBAKHWGcSFixYIPp1BIL7zMmTJ4mNjVVe347NQlxcHFFRUWzbtg2TyUSfPn3Ytm3bHd/n8kz9%2BvVJT0%2Bnfv36FBYWAtCrVy8KCgqYOHGi4o939OhRunXrhslkQqVSceLECby9vSksLMRgMHDs2DEArl27xrVr15TP1%2Bl07N27FyjKAhYUFPD4449TWFiIVqtFrVbj5%2BfHqFGjeOSRR%2B7x0QvuNyLgEwgEAoFDrDMJsbGxLF68WPTrCAT3EessG3DLWbaaNWuiUqmoWbMmgMXftihvXnq3g3lJp72%2BuWnTpil/h4WFKQqrMlqtFpVKhUajUXz6VCoVwcHB%2BPr6cuLECTw8PMjLywNQbB%2BuXr0KFE0GqNVqXF1dcXNzY8GCBXf8OAVlGxHwCQQCgcAh1pmE0pSKCQSCe4PBYMBkMrF%2B/XqLZXcjCCtvXnq3w4oVKzh69CgffPCBU9tbB3uAMn4uLi4YDAY8PDzIzc3Fx8eHs2fP4u3tjb%2B/P25ubkRGRnLgwAFq1qzJrl27FJ8%2BQflGiLYIBAKBwCHh4eH8%2BeefJS4TCAT3jpJsFmScsVkAy3ta3N93H1lMxd3dndjYWJ566imuXbumKHPWrl2bzZs3k52drSgib9%2B%2B3SkBGIBu3boRHx9PaGgo1atXx8fHh4oVK9KpUyeeeOKJu3x0grKGyPAJBAKBQCAQPGBYZ9S8vLwsgjsoneegq6vrHd0/gX3OnTvHuXPnOHXqFJIkkZycTN%2B%2Bfdm3bx%2BZmZl4eHjQsmVLPvzwQ1auXGnXY8/V1ZX27duzZ88edDodLi4uDB48mNWrV7Nz506gKLA8f/48UCTss2XLFqKjo%2BnQocM9O17B/Udk%2BAQCgUDgEOtMAsCcOXOKLRPlWwJB2eHSpUtObVelShUmTZpksSw%2BPl4xBDdn8eLFd2TfyhPr168nKSmJb7755rY/S5IkRTFVrVbj4eGBt7c3mZmZqFQqxowZw44dOzhz5owi%2BtKnTx%2B2bNnCI488QlBQEIMGDeKrr77iyy%2B/vANHJ3hQEAGfQCAQCBxSkmEzlC6TIBAI7j716tVDkiS7682tFqZPn%2B7UZ86fP/9O7V65oWPHjqSnpyuTYyaTSfnbzc0NSZIIDg7mypUriuhKzZo1SUtLU15be/T16tWLH374gdq1a/PHH38ogiwHDhygZcuWuLi4oNVqadeuHd7e3sTHx%2BPu7o4kSezfv5%2B2bduK3r5yhgj4BAKBQCAQCB4yzp49q/ztyGrBkRqn4O5z/vx5pk2bxrvvvsu4ceO4ePGiorJpjYuLi1K6qdPpiq2vXLkyaWlpqNVqvL29GTduHJ999hmXL19Go9EgSRIHDx6kWbNmHDp06G4fmqAMIXr4BAKBQCAQCB4yrAO5kqwWTp06xZUrV3j88cctln/xxRd06tSJqlWr3pX9LM8MGzaMnJwc/v77b7p3745er7e5nRzgGQwG1Gq1zWBPkiRlucFg4Pr16wQHB5OWloafnx8ajQYXFxf%2B%2Becf/P397%2BpxCcoeIuATCAQCgUAgKMecOnWKAf/f3p0HV1Wffxx/n3PuvcnNnpBwkxAEXAAXTABDGIy4tFaLC%2BqggEpbbOt0sNaK1aqgVisqrbW1rVvFujGSASo4UpiKyCbVsIQAtWgApcXQJM1C9tztnN8fmdwfEaTQ5ibk%2BnnNOJ6c%2B70nz1f9g8fnfJ/nhhu46aabjkj4du3axYIFC1iyZAk%2Bn6%2BPIuz/usZlbN26lf3791NXV0dlZWXk8y9L9oBIItf1WidAYWEhp556KnFxcSxfvhy/309DQ0Pk85ycHH7961%2BTlJREXFwcpmlSVFTEk08%2BSXFxcU9vT05yeqVTREREJMYda9TCj3/8YzIyMnjwwQeP%2BvmDDz6Ibds8%2Buij0QwxpnWdha6qqjrqrL3D/eAHP6CmpoaysjIGDBjAlVdeSUZGBvHx8VRUVNDS0oJhGBw8eJDKysrIebyupi4Ap5xyCrZtR5LA9PR0CgsL2b59O6%2B//joDBw6M4m7lZKOET0RERCTGHSvhu%2BCCC1i6dOmXVvCqqqqYPn06a9eujWaIXzn5%2Bfl0dHSQkZHBunXruOCCC3Ach9WrV5OQkBCZlxcXF0cgEKC9vf2or3MezuPxMH36dO6//36qq6sj630%2BH/X19WRkZOB2u6O%2BNzm56JVOERERkRjzxVELwWDwiHvQOWqh67zXl8nOzu72uqD0HNM0aW5u5pZbbqG1tZVQKERRUVG3Nc3NzUf97g9%2B8AOef/55TNPkySefZPHixbhcrsiInC/%2BO9UruV9dSvhEREREYswXB6lPnjz5S9dmZmayf/9%2Bhg4detTP//73v5ORkdGT4cW84uJi6urqjrnGtm0MwyAcDnPgwIFjnuMzTRPHcTjnnHPYtWsXAHfeeScvv/wygUCAV199lccee4zZs2dz3333sXjx4h7dj/RvSvhEREREYsyJzMy79NJLeeyxx3j22Wdxubr/0bC1tZW5c%2Bdy2WWX9XSIMe2uu%2B46rll3Y8eO5Wc/%2BxkdHR1AZ6IeCAQA2LZtG%2BPHjyccDpOQkIDL5cI0TQzDwDAMVq9eTXp6OlVVVXzyySfk5eXxz3/%2B85jzF%2BWrSWf4RERERGLQ8Y5aaG5uZtq0abS1tTFlyhSGDRuGbdvs2bOHJUuWMGDAABYtWkRSUlJfbCPmnXvuuZimSXt7e7f7hw9c/09M0yQzM5PW1laGDx9OSUlJNEKVfkoVPhEREZEYcyKjFpKTk1m8eDEvvvgiK1eu5MCBAxiGwZAhQ7jxxhv57ne/i9fr7aOdxLY1a9bg9/uP%2BtkXk72MjAwaGhpwHIe0tDQaGxtJTEwkOzub1tZWHMfhkksuYfbs2b0RuvQjqvCJiIiIxJgTHbXw%2Beefs3HjRizL4sILL1SDjx7SNfOurq7uuKt18P/D1r8oLi6O733ve5SXlzNr1iyeffZZ/vjHP/ZYvBKblPCJiIiIxJgTGbWwZcsWbr31VgYOHEg4HKahoYFXXnmFUaNG9XLUsWfZsmVA53m8/fv3U1tb2%2B3z/fv3A51n%2BcrKyjAMg3nz5nHVVVdx9tln4/F4cLlcrFixAsuyyMrKIhwOU1RURGlpKUVFRWzfvr23tyX9jBI%2BERERkRhTUFBAeXn5ca25%2Beab%2BdrXvsbMmTMBeOmll9i4cSOvvPJKL0T61TZp0iT8fj%2B///3vmTp1KqFQiPz8fOLj4/nggw8ASExM5MYbb4xUCz/44ANWr17NpZdeypo1a3j77bf7cgvSDyjhExEREYkxX//611mwYMExRy388Ic/5L333qOwsJD333%2BfuLg4ANra2rj44ospLS3txYhjx1133UVZWdlxrU1PT%2Bezzz4jFApFunMejWEYHP5HdtM0SU1N5ZlnnmHs2LH/c8wS28y%2BDkBEREREelbXqIWjzXb74qiFQCAQSfYAEhISImMC5MR1vYZ5PH%2BNGDGC%2BfPnR0YtHD4W4/bbb4/87PP5uPbaa0lOTmbgwIG8%2BuqrrF%2B/XsmeHBdV%2BERERERizImMWsjPz2fHjh3dvn%2B0e9Lzuv4Zf/vb3wbgww8/JD8/H4Dc3FwOHjx43M%2ByLAuAnJwc1qxZ08ORSn%2BmsQwiIiIiMeZERi2Ew2EWL17c7ZXBo92bOnVqr%2B8jFixatIhXXnmFgwcPEg6HcblcnHLKKdx666088sgjQGeSFggEKCgoiHzveJM9l8uFz%2Bejvr4el8vFjBkzorIP6b9U4RMRERGJQcc7auGSSy75j88yDENVoxN011138fHHH7Nv375I4vzFs3hdvux%2BF8uyCIfDGIZBRkYGcXFx3HrrrYRCId566y1SUlKYNWsWTzzxBCkpKRrVIN0o4RMRERGJMRq10Pfuu%2B8%2BVq5cGTkjefhcPcdxCIfDx/Ucl8tFKBTCNE1eeOEF7rjjDoBIU51x48ZhGAalpaWRa41qkMMp4RMRERGJMRq1cHIYM2YMAJs3b%2B7WkCUcDlNYWIht29x5551UVlYC8NZbbzF58mT%2B9Kc/4TgObrebpqamow5tj4%2BPBzqb7GRkZHDhhReybNkyMjMzNapBulHCJyIiIhJjNGrh5DB16lRqa2v5/ve/j%2BM4rFy5koqKiqMmcaZpMmXKFFatWkUwGGTu3Lm4XC62bdtGXV0dmzZtwu/3H/P3JScn88ILL6h7p3SjhE9EREQkxqjz5slh69atzJw5k0AggGEYWJZ1xKiMxMRE2trajjjDN3jwYB57eq7COgAAF7tJREFU7DFOOeUU4uPjsSyLXbt2sWPHDurr62lpacEwDLxeLykpKZx33nmcd9553UZsiIASPhEREZGYo4Tv5FFbW8s3vvENhgwZQkVFBdDZpOXwM33/jezsbACWLl2K2%2B3G7XYD4Ha78Xg8/1vQElM0lkFEREQkxmjUwskjMzMTgF/96ldcd911GIZBR0cHgwYN4ic/%2BQn33nsvtm0TDAYj5/wKCwv54IMPjvncqqoqAIqLi7vdN02T3bt3R2En0l%2BpwiciIiISYzRq4eTQ2NjInDlzWL169RGfmabJ3LlzWbJkCR9//DGO45CTk0NGRgZvvvkmI0aMiCSA1113HfX19V/6e3w%2BH0OGDAHgzDPPZNy4cdHZkPRLSvhERERERKJg9uzZfPDBByQkJFBdXU1KSgp1dXXd1hw%2Bg8/lcjF8%2BHD27t1LIBDo9rnb7eb6669nzpw53Tp%2BivwnSvhERERERKKgsLAQx3F45513sG2bFStWsHfvXpYuXXrMQev/DdM0ARgwYADvv/9%2Bjz5b%2Bjf97wERERERkSiwLAsAr9eL1%2BvlpptuoqamhqVLl/bY7xg2bBiZmZkMHToUQCMZ5Aiq8ImIiIiIRMGsWbP4%2B9//zqhRo7Btm/Xr1x9Xd85rr72WZcuWYZomHo8HwzAoLy%2BnoKAAgPLy8miHLjFECZ%2BIiIiISBRUV1fzve99LzKO4XCmaWLbNoZhkJOTw8GDB4HOqmB5eTljx47tdr5v9OjRlJaWfunv6qom5uTkqBmPdKOET0REREQkisaMGcN3v/tdnn/%2BeaCzOcuaNWu46KKLCAQCrF%2B/nokTJ0bWn3766Xz66ac4jhNJDOPj42lvb4%2BsMU2TgQMHYpomAwYMiHTpLCwsZNq0ab27QTmp6QyfiIiIiEiU2LaNy%2BVi/PjxvPzyy5H7FRUVBAIBHMfhoosu6vadvXv3Rq7D4TBAt2TPsizy8/MpLi7mtttui%2B4GpN9ThU9EREREJApWrlzJnDlzaGtr%2B9I1WVlZxMfHU1VVhW3b2LZNbm4u1dXV%2BHw%2BPB4PdXV1uN1uxo0bx9/%2B9jdqamr4zW9%2Bwy9/%2BUtWrVrVizuS/kgJn4iIiIhIFJx//vm0trZyySWXsHPnTg4cONDtXN4XDR06lKqqKsrLy8nPz2fWrFkAfPTRR5Hze7m5uezevZvU1FSampqYMGFCt8Hr55xzDsXFxb2zQekXlPCJiIiIiERB14iE0tJSXC4Xu3fv5tFHH6WiooJwOIxt20Dnmb74%2BHjcbjdVVVVkZWVRXV39X/1O0zTZvXt3j%2B1B%2Bj%2Bd4RMRERERiYIrr7yS6upqNm/ezPjx48nKymLAgAEkJCQQDocj5/NM08RxHMLhMHFxcbS3t5OQkEBeXh5vv/12t2feddddOI4TqRQ%2B9dRTfbE16UdU4RMRERER6SFPPfVU5PVL27bZu3cvwWCwW0XvWAzDIDExkTFjxrBlyxbGjx9PcXExhmGwatUqdu7cyfDhw9m3bx%2BPPPIIkyZNYufOndi2TUFBQWQ8g0gXJXwiIiIiIj1kxowZR7xS2dLSgtvtJhAIRO5ZlsXAgQMZOXIkO3fupLGxMdLRMzs7G8uy%2BOyzz476O7KyskhNTeXgwYOcddZZtLW1sX//fs466yxefvllPB5PVPco/YsSPhERERGRKBo7diylpaUUFRVF7k2cOJENGzbQ2toaaeLyZQ1dDMPA4/EQDAZJSUlh7dq1GIbBLbfcQlVVFZMnT6a0tJSqqiqmTJmiUQ3SjdnXAYiIiIiIxKJgMMivfvUrMjMzWbhwIVdeeSWDBw8mLi6OHTt20N7ejmEYeL1eXC4XlmVF/n64%2BPh4wuEwhmEwduxYEhIS8Hq9PPjggxiGwV/%2B8pfI9YoVK/pot3KyUoVPRERERKSHPfXUU2zYsIGKigqg8zyfx%2BOJDFs/XFZWFoZhkJubS1FREXfccQeFhYW0traSkJBAWVkZY8aMAaCsrAzDMABwHIfRo0djGAZlZWWR6%2B3bt/fuZuWkpi6dIiIiIiI9bPv27ezZswfbtiNdOP1%2B/1HX1tbWAvDvf/%2BbHTt28Ic//CGSFLa1tXHuuecSDoeJj4%2BnoaGBjIwMAGpqaoiLi8OyrG7XIodThU9EREREJAqKiopwHId33nmHtLS0yP2amhouv/xybNsmHA53a%2BZytHN8aWlpNDU14TgOxcXFLFiwAOgc0XB4Za/rWqMa5HBK%2BEREREREouCBBx7gvffeIzU1lezsbPLz89m6dSu7du0iEAiQl5fHTTfdxPz587Ftm6SkJDo6OnC5XPz2t7%2Blra2NO%2B64A4AzzjiDPXv2RK79fj8HDhzA4/EwZMgQ9u3bh9frZenSpQwbNqwvty0nGSV8IiIiIiJR0NHRwfz58ykpKTliBl9SUhKDBg1i6NChrF69Gtu2SU5Opr29nbi4ODZv3oxhGIwZMwa/33/U7p2HGzVqFPPmzWPEiBHR3JL0Q0r4RERERESiaMKECbzxxhtMnToVIDJEvampiSuuuIJgMHjEdwoKCpg8eTIvvvgi6enpXH311bzwwgvExcXx05/%2BFOg835ednc3ZZ5/d7ZVRkcOpaYuIiIiISA%2BrrKykvr6e7OxswuEwoVCI9vZ2QqEQhmGwZcsWli9fTigUIjc3l4MHD3b7fnl5OeXl5QDU19fzxBNP4Ha7mT9/PhMnTuyLLUk/pQqfiIiIiEgP2rp1KzNnzgTg8ccf569//SsrVqw4apdO0zTJyMigra0t8lqn1%2BvlwIEDGIbB0KFDSUlJYfz48UydOpXc3Nze3o70c6rwiYiIiIj0oN/97nf4fD6mTJnClVdeSTAYZNmyZUdda9s2tbW1eDwepk6dyvTp06mpqeG2227DMAzmzZtHVlYWubm5Grkg/xVV%2BEREREREelDXkPT333%2BfhIQE7rnnHrZs2cKoUaN4%2BOGHCQaDXHbZZfj9fjIyMjj77LNxHIeNGzce0dyli2VZTJ48mZ/97GfExcX15nakn1OFT0RERESkB3XVU7xeL5WVlZSWltLW1kZeXh47d%2B5k/fr1dHR04DgOjY2NrFu37ohnmKZJSkoKtm1z5plnUlVVxYYNG3jyySeZM2dOL%2B9I%2BjNV%2BEREREREetDll19OMBjkRz/6EQ888EDk7J5lWYTDYZKTk2lubj7ie4Zh4HK5cLvdrFmzhmAwyPXXX49lWZSUlESu165d29tbkn7M7OsARERERERiyaRJk/D7/cydOxfLsjBNE4/HQ1paGi6Xi5ycHKAzwTMMA6/Xi2ma%2BHw%2BbNvGsiwqKipobW2lo6ODtrY2UlJSItciJ0IVPhERERGRHhQKhZg3bx5vvPEGXq%2BXQCCAx%2BMhGAzi8/morq7GcRwSExNpamo65rN8Ph9Dhw4lKyuLLVu2MHz4cBYsWNBLO5FYoIRPRERERCQKRo8eTVlZWaSJS3t7O7fffjuLFy%2Bmvb2dpqYmHMdh0qRJrFy58j8%2Bb%2BjQobz44ouccsop0Q5dYoiatoiIiIiIRIHP52Pfvn34fD5aW1tpa2sjOTmZ6upqoLO5i2EY3HbbbWzdupVgMEg4HGbKlCn4/X5cLheWZZGamsr555/PqFGj%2BnhH0h%2BpwiciIiIiEgW//e1vWbduHampqZSWlkYSvK7mLLZtk5GRwcCBA6mpqcFxHM4991xmzpzJoUOHOHToEKFQiIyMDDIzM4HOJFLD1%2BVEKOETEREREYmCUCjE448/zqZNmwgEAhw6dCjSdMXl6nzRznEcQqHQcT/TNE12794dlXglNinhExERERHpJTNmzGD37t2Rap/jOLS2tuI4Di6XK5L8WZbFwIEDuf7668nKyiI7OxuAvLw8Tj311L7cgvQzSvhERERERHrIZ599xueff35ca/Py8hg2bBhjx44FoLS0lKKiosh1VxVQ5H%2Bh/4pERERERHrIN7/5TY63nmIYBt///vfJzMwkPT2dzZs3c84557Bnzx5%2B8pOf8Itf/AKPxxPliCXWqcInIiIiItJDKisrqaqq%2Bo/rSkpK2LhxY2QOn23bQOfZvmAwCHSe14uPjycpKSnSqKWoqIjZs2dHKXqJRarwiYiIiIj0kEGDBjFo0KBjrmlra2PVqlUEg0EMw8A0zUhVsCvZg84ksK2tDb/fT3t7OwBxcXHRC15ikhI%2BEREREZEe9tlnn1FXV8cTTzzB7t27j9qJ0zAM4uLimD17NgkJCWRnZ5OXl0diYiKXX345pmmydevWPoheYole6RQRERER6WEjR4487rN8X5SUlER7eztpaWlce%2B21nHbaaaxevZpTTz2VO%2B64Q%2Bf65ISYfR2AiIiIiEisWbNmDcnJySQkJPDLX/4Sl8uFy%2BXioYce4rrrrmPo0KGMHj2awsJCDMPo9t2Wlha8Xi9JSUksX76cdevWUVlZyfLly5k3b14f7Uj6K1X4RERERESiYMKECTiOw7p167jgggtwHIfVq1eTkJDAhAkTAIiPjyccDtPQ0EBycjLNzc2kpaWxatUqACZNmoRpmqxYsSJyvWnTpr7clvQzOsMnIiIiIhIF5513Htu2beOWW27BsiwaGhoic/a6NDc3R667OnY2NzezZcsWCgoK8Pv9mKZJMBiMXIucCFX4RERERESioKGhgUcffZQtW7aQl5dHeXk54XD4iHUulyvS1MUwjMjZv4SEBADOOOMM6urqaG1t5bLLLuPhhx/uvU1Iv6eET0REREQkisaNG8f69eu58MILaWxsxLIswuEwpmliGAbXX389JSUlx3yGx%2BNh%2BvTp3H333bjd7l6KXGKBXukUEREREYmSXbt2EQgEeOihhyKz9LqqfG63G7/fT0NDA4ZhYFkW8fHxlJSUcMMNN2AYBsuWLSMtLY3U1NS%2B3Ib0Y6rwiYiIiIhEwbx583jttdf%2B4zrTNLFtG7fbjdfr5etf/zrLli0D4OKLL6a6uprs7GxuvvnmSLMXkeOlhE9EREREJArGjRtHKBRi/vz5rF27ljvvvJM5c%2BawYcOGY87oO/wcH3RWAm3bxuVy8fTTT3PxxRf3RvgSI5TwiYiIiIhEQXFxcWQsw%2BHn7p5%2B%2BmlKSkpoaWkhFAphGAbx8fHEx8fT0NAAQHJycqRrZ35%2BPjNmzOCZZ55hwIABLFy4sE/2I/2TEj4RERERkShYsGABH374IYWFhUyYMIF9%2B/bx6aef8vHHH1NeXk5jYyMAiYmJtLe34zgOLpcLx3HweDwkJSVRV1eH1%2Btl06ZNTJgwAcMw2LZtWx/vTPoTNW0REREREYmCsrIyPvzwQzZu3MhTTz31petaW1sj18FgkNtvv50333yTlpaWyPm%2BuLg4bNvGMIzeCF1iiCY3ioiIiIhEwVlnncXo0aOxLIuRI0diWRamaWKaJiNHjiQtLY28vDz%2B/Oc/M2jQIKDz/N6IESM4ePAgTU1NJCcnk56ezv79%2ByPXIidCr3SKiIiIiERRcXExa9eu5eKLL440Y1m3bh3PPfccixcvxjRNmpqaaG9vx%2B12k5qaSktLCx6Ph%2BTkZIqKiqivr2fPnj0UFxfzyCOP9PGOpD9RhU9EREREJEoWLVpEKBQiPz%2Bf2tpa6urqaGtr4%2B6772bWrFlceumlkfl82dnZxMXF0dbWRlpaGuFwONKxc9u2bRiGwQ9/%2BMM%2B3pH0N6rwiYiIiIhEwW9%2B8xteffVVOjo6ImfxbNs%2BYp1pdtZgvF4vw4cPp6SkhOrqaoLBID6fj/r6%2Bsj14d0%2BRY6HmraIiIiIiETBm2%2B%2Bidfr5eqrryYrK4vS0lIqKytpaGigvb0dwzBITEwkJSUFgEGDBlFUVASAz%2BeLPOfwa5ETpQqfiIiIiEgUjBkzBoDNmzfjcv1/nSUcDlNYWKgRC9IrVOETEREREYmCM844g9raWpYuXYrjOKxcuZKKigqampoir3aOGDECl8vFsGHDuPXWW7n66qv7OGqJNarwiYiIiIhEwdatW5k5cyaBQADDMLAsi1AoFPncMAwGDBjAoUOHIsPW7733XqZNm9aHUUusUZdOEREREZEoOO%2B881i7di2JiYmceeaZALhcLrxeLy%2B%2B%2BCKLFy8mJSWFRYsW4fP5SE9P59VXX%2B3jqCXW6JVOEREREZEoyczMBGDJkiWMGzcucn/ChAkYhkFNTQ1nn302jY2NADQ1NfVJnBK7lPCJiIiIiPSQGTNmAPD6668zdepUKioq6OjoYPTo0YRCocjg9dGjR%2BPz%2BRgyZAhvvvkmqampAKSnp/dZ7BKblPCJiIiIiPSQCRMmRK4vuOACPB4Pzc3NfPLJJ7hcLgKBAACBQIADBw5gmiZz587FMAzcbjcPP/xwX4UuMUpNW0REREREomD58uUAXHPNNdTW1rJixQr27t3LRx99xIEDBwiHwyQkJDBw4EAmTpzI1KlTyc3N7eOoJdYo4RMRERER6UG2bRMIBBg3bhyGYfDXv/612%2Beffvop06dPxzRNdu7c2UdRyleFEj4RERERkR70yiuv8Pjjjx/3etPsbJzv9XoZPnw4JSUl0QpNvoJ0hk9EREREpAd95zvfob6%2Bnpdeegnbthk8eDAAjuPQ2NiI3%2B/HNE0sywIgJSUFgEGDBlFUVNRncUtsUoVPRERERCQK6urqANi4cSPXXHNN5EwfwIYNGwiHwxQUFDBt2jRef/11ANxuNzNnzuyTeCU2KeETEREREYmCYDDIwoULeeKJJ8jJyeFf//pXt88TEhIIBAK8/vrrzJgxA8MwdK5PepwSPhERERGRKJg2bRrbt28/oe%2BMGTOGRYsWRSki%2BSrSGT4RERERkSj4/PPPSUtLo7W1FcdxCIVCmKaJ4zikp6eTnZ1NQUEBHo%2BHwYMHc9ZZZ3Huuef2ddgSY8y%2BDkBEREREJBb5/X5s22b9%2BvV4PB68Xi/vvfceq1atwrIs6urqaGxs5L777qOsrIyFCxdyzz339HXYEmP0SqeIiIiISBTcdNNNHDx4kMmTJ7Ns2TIOHTpEWloaLS0ttLS0RNZ5PB6CwSCmaZKcnExpaWkfRi2xRgmfiIiIiEgU7Nq1i9tuu42WlhbcbjeHDh066rquEQ2DBw9m3rx5jBkzppcjlVimVzpFRERERKJg1KhRbNiwgYSEBN566y3S09OBzgHrLpcLy7JwuVzMnDmTwsJCcnJy%2BOSTT/o4aok1SvhERERERKLI7/eTnZ1NOBwGwOVy4fF48Hg8hMNhZs6cyZ49e6ioqOC5557r42gl1qhLp4iIiIhID7rkkkuoqqqK/BwOhxk5ciRdJ6mam5sBMAwDgNdee43W1lYALMvq5Wgl1ukMn4iIiIhID9q8eTO7d%2B%2BO/Pyvf/2L5cuXc8UVV7BkyRL8fv9Rv2cYBt/61re4//77eytU%2BQpQwiciIiIiEmWBQIBnnnmGBQsW4DgOhYWFXHXVVTQ1NVFZWUlGRgbnn38%2BBQUFfR2qxBglfCIiIiIiUfTuu%2B/ywAMP0NLSwumnn87jjz/OyJEj%2Bzos%2BYpQwiciIiIiEgUTJ06koaGBQCBwXOstyyI3N5d33303ypHJV4matoiIiIiI9KCOjg6efvppamtrARg7diynnXYa9fX1kTUtLS3s2LGDcDiMZVm0t7dj2zZtbW19FbbEKCV8IiIiIiI96LLLLqOmpgav18sNN9zAoEGDIp%2BFQiE2bdrEtm3byMvL45///CfBYBCXy8V1113H3Xff3YeRSyzSK50iIiIiIj3oi2MZujiOg23b3e653W7OOOMMneuTqFHCJyIiIiISRf/4xz/4%2Bc9/zkcffURqaiqff/45tm2TmJjI3LlzmTx5cl%2BHKDFMr3SKiIiIiERBR0cHzzzzDAsXLuTUU0%2BlqamJQ4cOYVkW06dP58477yQpKamvw5QYpwqfiIiIiEgUXHjhhQSDQYLBIM3NzTiOg8fjYfTo0aSmpkbW%2BXw%2BAIYMGcLIkSMpLCzsq5AlBqnCJyIiIiISBZZlYVkWhw4doqvGEggEKC0t/dL1OTk5rFmzpjfDlBinCp%2BIiIiIiEiMMvs6ABEREREREYkOJXwiIiIiIiIxSgmfiIiIiIhIjFLCJyIiIiIiEqOU8ImIiIiIiMQoJXwiIiIiIiIxSgmfiIiIiIhIjFLCJyIiIiIiEqOU8ImIiIiIiMSo/wOmKw2rmUt1%2BwAAAABJRU5ErkJggg%3D%3D” class=”center-img”>

</div>
<div class=”row headerrow highlight”>

<h1>Sample</h1>

</div> <div class=”row variablerow”> <div class=”col-md-12” style=”overflow:scroll; width: 100%%; overflow-y: hidden;”>

<table border=”1” class=”dataframe sample”>

<thead>
<tr style=”text-align: right;”>

<th></th> <th>2010 Census Population</th> <th>Population Estimate, 2011</th> <th>Population Estimate, 2012</th> <th>Population Estimate, 2013</th> <th>Population Estimate, 2014</th> <th>Population Estimate, 2015</th> <th>Population Estimate, 2016</th> <th>LACCESS_POP10</th> <th>LACCESS_POP15</th> <th>PCH_LACCESS_POP_10_15</th> <th>PCT_LACCESS_POP10</th> <th>PCT_LACCESS_POP15</th> <th>LACCESS_LOWI10</th> <th>LACCESS_LOWI15</th> <th>PCH_LACCESS_LOWI_10_15</th> <th>PCT_LACCESS_LOWI10</th> <th>PCT_LACCESS_LOWI15</th> <th>LACCESS_HHNV10</th> <th>LACCESS_HHNV15</th> <th>PCH_LACCESS_HHNV_10_15</th> <th>PCT_LACCESS_HHNV10</th> <th>PCT_LACCESS_HHNV15</th> <th>LACCESS_SNAP15</th> <th>PCT_LACCESS_SNAP15</th> <th>LACCESS_CHILD10</th> <th>LACCESS_CHILD15</th> <th>LACCESS_CHILD_10_15</th> <th>PCT_LACCESS_CHILD10</th> <th>PCT_LACCESS_CHILD15</th> <th>LACCESS_SENIORS10</th> <th>LACCESS_SENIORS15</th> <th>PCH_LACCESS_SENIORS_10_15</th> <th>PCT_LACCESS_SENIORS10</th> <th>PCT_LACCESS_SENIORS15</th> <th>LACCESS_WHITE15</th> <th>PCT_LACCESS_WHITE15</th> <th>LACCESS_BLACK15</th> <th>PCT_LACCESS_BLACK15</th> <th>LACCESS_HISP15</th> <th>PCT_LACCESS_HISP15</th> <th>LACCESS_NHASIAN15</th> <th>PCT_LACCESS_NHASIAN15</th> <th>LACCESS_NHNA15</th> <th>PCT_LACCESS_NHNA15</th> <th>LACCESS_NHPI15</th> <th>PCT_LACCESS_NHPI15</th> <th>LACCESS_MULTIR15</th> <th>PCT_LACCESS_MULTIR15</th> <th>GROC09</th> <th>GROC14</th> <th>PCH_GROC_09_14</th> <th>GROCPTH09</th> <th>GROCPTH14</th> <th>PCH_GROCPTH_09_14</th> <th>SUPERC09</th> <th>SUPERC14</th> <th>PCH_SUPERC_09_14</th> <th>SUPERCPTH09</th> <th>SUPERCPTH14</th> <th>PCH_SUPERCPTH_09_14</th> <th>CONVS09</th> <th>CONVS14</th> <th>PCH_CONVS_09_14</th> <th>CONVSPTH09</th> <th>CONVSPTH14</th> <th>PCH_CONVSPTH_09_14</th> <th>SPECS09</th> <th>SPECS14</th> <th>PCH_SPECS_09_14</th> <th>SPECSPTH09</th> <th>SPECSPTH14</th> <th>PCH_SPECSPTH_09_14</th> <th>SNAPS12</th> <th>SNAPS16</th> <th>PCH_SNAPS_12_16</th> <th>SNAPSPTH12</th> <th>SNAPSPTH16</th> <th>PCH_SNAPSPTH_12_16</th> <th>WICS08</th> <th>WICS12</th> <th>PCH_WICS_08_12</th> <th>WICSPTH08</th> <th>WICSPTH12</th> <th>PCH_WICSPTH_08_12</th> <th>FFR09</th> <th>FFR14</th> <th>PCH_FFR_09_14</th> <th>FFRPTH09</th> <th>FFRPTH14</th> <th>PCH_FFRPTH_09_14</th> <th>FSR09</th> <th>FSR14</th> <th>PCH_FSR_09_14</th> <th>FSRPTH09</th> <th>FSRPTH14</th> <th>PCH_FSRPTH_09_14</th> <th>PC_FFRSALES07</th> <th>PC_FFRSALES12</th> <th>PC_FSRSALES07</th> <th>PC_FSRSALES12</th> <th>REDEMP_SNAPS12</th> <th>REDEMP_SNAPS16</th> <th>PCH_REDEMP_SNAPS_12_16</th> <th>PCT_SNAP12</th> <th>PCT_SNAP16</th> <th>PCH_SNAP_12_16</th> <th>PC_SNAPBEN10</th> <th>PC_SNAPBEN15</th> <th>PCH_PC_SNAPBEN_10_15</th> <th>SNAP_PART_RATE08</th> <th>SNAP_PART_RATE13</th> <th>SNAP_OAPP09</th> <th>SNAP_OAPP16</th> <th>SNAP_CAP09</th> <th>SNAP_CAP16</th> <th>SNAP_BBCE09</th> <th>SNAP_BBCE16</th> <th>SNAP_REPORTSIMPLE09</th> <th>SNAP_REPORTSIMPLE16</th> <th>PCT_NSLP09</th> <th>PCT_NSLP15</th> <th>PCH_NSLP_09_15</th> <th>PCT_FREE_LUNCH09</th> <th>PCT_FREE_LUNCH14</th> <th>PCT_REDUCED_LUNCH09</th> <th>PCT_REDUCED_LUNCH14</th> <th>PCT_SBP09</th> <th>PCT_SBP15</th> <th>PCH_SBP_09_15</th> <th>PCT_SFSP09</th> <th>PCT_SFSP15</th> <th>PCH_SFSP_09_15</th> <th>PC_WIC_REDEMP08</th> <th>PC_WIC_REDEMP12</th> <th>PCH_PC_WIC_REDEMP_08_12</th> <th>REDEMP_WICS08</th> <th>REDEMP_WICS12</th> <th>PCH_REDEMP_WICS_08_12</th> <th>PCT_WIC09</th> <th>PCT_WIC15</th> <th>PCH_WIC_09_15</th> <th>PCT_CACFP09</th> <th>PCT_CACFP15</th> <th>PCH_CACFP_09_15</th> <th>FDPIR12</th> <th>FOODINSEC_10_12</th> <th>FOODINSEC_13_15</th> <th>CH_FOODINSEC_12_15</th> <th>VLFOODSEC_10_12</th> <th>VLFOODSEC_13_15</th> <th>CH_VLFOODSEC_12_15</th> <th>FOODINSEC_CHILD_01_07</th> <th>FOODINSEC_CHILD_03_11</th> <th>MILK_PRICE10</th> <th>SODA_PRICE10</th> <th>MILK_SODA_PRICE10</th> <th>SODATAX_STORES14</th> <th>SODATAX_VENDM14</th> <th>CHIPSTAX_STORES14</th> <th>CHIPSTAX_VENDM14</th> <th>FOOD_TAX14</th> <th>DIRSALES_FARMS07</th> <th>DIRSALES_FARMS12</th> <th>PCH_DIRSALES_FARMS_07_12</th> <th>PCT_LOCLFARM07</th> <th>PCT_LOCLFARM12</th> <th>PCT_LOCLSALE07</th> <th>PCT_LOCLSALE12</th> <th>DIRSALES07</th> <th>DIRSALES12</th> <th>PCH_DIRSALES_07_12</th> <th>PC_DIRSALES07</th> <th>PC_DIRSALES12</th> <th>PCH_PC_DIRSALES_07_12</th> <th>FMRKT09</th> <th>FMRKT16</th> <th>PCH_FMRKT_09_16</th> <th>FMRKTPTH09</th> <th>FMRKTPTH16</th> <th>PCH_FMRKTPTH_09_16</th> <th>FMRKT_SNAP16</th> <th>PCT_FMRKT_SNAP16</th> <th>FMRKT_WIC16</th> <th>PCT_FMRKT_WIC16</th> <th>FMRKT_WICCASH16</th> <th>PCT_FMRKT_WICCASH16</th> <th>FMRKT_SFMNP16</th> <th>PCT_FMRKT_SFMNP16</th> <th>FMRKT_CREDIT16</th> <th>PCT_FMRKT_CREDIT16</th> <th>FMRKT_FRVEG16</th> <th>PCT_FMRKT_FRVEG16</th> <th>FMRKT_ANMLPROD16</th> <th>PCT_FMRKT_ANMLPROD16</th> <th>FMRKT_BAKED16</th> <th>PCT_FMRKT_BAKED16</th> <th>FMRKT_OTHERFOOD16</th> <th>PCT_FMRKT_OTHERFOOD16</th> <th>VEG_FARMS07</th> <th>VEG_FARMS12</th> <th>PCH_VEG_FARMS_07_12</th> <th>VEG_ACRES07</th> <th>VEG_ACRES12</th> <th>PCH_VEG_ACRES_07_12</th> <th>VEG_ACRESPTH07</th> <th>VEG_ACRESPTH12</th> <th>PCH_VEG_ACRESPTH_07_12</th> <th>FRESHVEG_FARMS07</th> <th>FRESHVEG_FARMS12</th> <th>PCH_FRESHVEG_FARMS_07_12</th> <th>FRESHVEG_ACRES07</th> <th>FRESHVEG_ACRES12</th> <th>PCH_FRESHVEG_ACRES_07_12</th> <th>FRESHVEG_ACRESPTH07</th> <th>FRESHVEG_ACRESPTH12</th> <th>PCH_FRESHVEG_ACRESPTH_07_12</th> <th>ORCHARD_FARMS07</th> <th>ORCHARD_FARMS12</th> <th>PCH_ORCHARD_FARMS_07_12</th> <th>ORCHARD_ACRES07</th> <th>ORCHARD_ACRES12</th> <th>PCH_ORCHARD_ACRES_07_12</th> <th>ORCHARD_ACRESPTH07</th> <th>ORCHARD_ACRESPTH12</th> <th>PCH_ORCHARD_ACRESPTH_07_12</th> <th>BERRY_FARMS07</th> <th>BERRY_FARMS12</th> <th>PCH_BERRY_FARMS_07_12</th> <th>BERRY_ACRES07</th> <th>BERRY_ACRES12</th> <th>PCH_BERRY_ACRES_07_12</th> <th>BERRY_ACRESPTH07</th> <th>BERRY_ACRESPTH12</th> <th>PCH_BERRY_ACRESPTH_07_12</th> <th>SLHOUSE07</th> <th>SLHOUSE12</th> <th>PCH_SLHOUSE_07_12</th> <th>GHVEG_FARMS07</th> <th>GHVEG_FARMS12</th> <th>PCH_GHVEG_FARMS_07_12</th> <th>GHVEG_SQFT07</th> <th>GHVEG_SQFT12</th> <th>PCH_GHVEG_SQFT_07_12</th> <th>GHVEG_SQFTPTH07</th> <th>GHVEG_SQFTPTH12</th> <th>PCH_GHVEG_SQFTPTH_07_12</th> <th>FOODHUB16</th> <th>CSA07</th> <th>CSA12</th> <th>PCH_CSA_07_12</th> <th>AGRITRSM_OPS07</th> <th>AGRITRSM_OPS12</th> <th>PCH_AGRITRSM_OPS_07_12</th> <th>AGRITRSM_RCT07</th> <th>AGRITRSM_RCT12</th> <th>PCH_AGRITRSM_RCT_07_12</th> <th>FARM_TO_SCHOOL09</th> <th>FARM_TO_SCHOOL13</th> <th>PCT_DIABETES_ADULTS08</th> <th>PCT_DIABETES_ADULTS13</th> <th>PCT_OBESE_ADULTS08</th> <th>PCT_OBESE_ADULTS13</th> <th>PCT_HSPA15</th> <th>RECFAC09</th> <th>RECFAC14</th> <th>PCH_RECFAC_09_14</th> <th>RECFACPTH09</th> <th>RECFACPTH14</th> <th>PCH_RECFACPTH_09_14</th> <th>PCT_NHWHITE10</th> <th>PCT_NHBLACK10</th> <th>PCT_HISP10</th> <th>PCT_NHASIAN10</th> <th>PCT_NHNA10</th> <th>PCT_NHPI10</th> <th>PCT_65OLDER10</th> <th>PCT_18YOUNGER10</th> <th>MEDHHINC15</th> <th>POVRATE15</th> <th>PERPOV10</th> <th>CHILDPOVRATE15</th> <th>PERCHLDPOV10</th> <th>METRO13</th> <th>POPLOSS10</th> <th>StateFIPS</th> <th>WIC participants FY 2009</th> <th>WIC participants FY 2011</th> <th>WIC participants, FY 2012</th> <th>WIC participants, FY 2013</th> <th>WIC participants, FY 2014</th> <th>WIC participants, FY 2015</th> <th>National School Lunch Program participants FY 2009</th> <th>National School Lunch Program participants FY 2011</th> <th>National School Lunch Program participants, FY 2012</th> <th>National School Lunch Program participants, FY 2013</th> <th>National School Lunch Program participants, FY 2014</th> <th>National School Lunch Program participants, FY 2015</th> <th>School Breakfast Program participants FY 2009</th> <th>School Breakfast Program participants FY 2011</th> <th>School Breakfast Program participants, FY 2012</th> <th>School Breakfast Program participants, FY 2013</th> <th>School Breakfast Program participants, FY 2014</th> <th>School Breakfast Program participants, FY 2015</th> <th>Child and Adult Care particpants FY 2009</th> <th>Child and Adult Care particpants FY 2011</th> <th>Child and Adult Care participants, FY 2012</th> <th>Child and Adult Care participants, FY 2013</th> <th>Child and Adult Care participants, FY 2014</th> <th>Child and Adult Care participants, FY 2015</th> <th>Summer Food particpants FY 2009</th> <th>Summer Food participants FY 2011</th> <th>Summer Food participants, FY 2012</th> <th>Summer Food participants, FY 2013</th> <th>Summer Food participants, FY 2014</th> <th>Summer Food participants, FY 2015</th> <th>State Population, 2009</th> <th>State Population, 2010</th> <th>State Population, 2011</th> <th>State Population, 2012</th> <th>State Population, 2013</th> <th>State Population, 2014</th> <th>State Population, 2015</th> <th>State Population, 2016</th> <th>USDA Model Percent</th> <th>USDA Model Count</th> <th>USDA Model And</th> <th>USDA Model Or</th>

</tr>

</thead> <tbody>

<tr>

<th>36103</th> <td>1493350.0</td> <td>1500259.0</td> <td>1499382.0</td> <td>1500776.0</td> <td>1500008.0</td> <td>1497903.0</td> <td>1492583.0</td> <td>453547.724578</td> <td>380951.607555</td> <td>-16.006280</td> <td>30.371160</td> <td>25.509868</td> <td>62976.169009</td> <td>58308.524326</td> <td>-7.411763</td> <td>4.217107</td> <td>3.904545</td> <td>6834.559750</td> <td>6336.216407</td> <td>-7.291521</td> <td>1.367125</td> <td>1.267441</td> <td>5994.100495</td> <td>1.199007</td> <td>109930.862572</td> <td>91481.202554</td> <td>-16.782967</td> <td>7.361360</td> <td>6.125905</td> <td>61974.502260</td> <td>53050.672115</td> <td>-14.399196</td> <td>4.150032</td> <td>3.552461</td> <td>323421.215247</td> <td>21.657429</td> <td>21665.346673</td> <td>1.450788</td> <td>43748.171640</td> <td>2.929532</td> <td>14770.842090</td> <td>0.989108</td> <td>1063.609224</td> <td>0.071223</td> <td>137.158374</td> <td>0.009185</td> <td>19893.435789</td> <td>1.332135</td> <td>508.0</td> <td>511.0</td> <td>0.590551</td> <td>0.341580</td> <td>0.339994</td> <td>-0.464367</td> <td>9.0</td> <td>12.0</td> <td>33.333333</td> <td>0.006052</td> <td>0.007984</td> <td>31.935033</td> <td>523.0</td> <td>543.0</td> <td>3.824092</td> <td>0.351666</td> <td>0.361285</td> <td>2.735263</td> <td>261.0</td> <td>248.0</td> <td>-4.980843</td> <td>0.175497</td> <td>0.165007</td> <td>-5.977332</td> <td>612.750000</td> <td>672.166667</td> <td>9.696722</td> <td>0.408698</td> <td>0.450338</td> <td>10.188401</td> <td>117.0</td> <td>142.0</td> <td>21.367520</td> <td>0.077308</td> <td>0.094713</td> <td>22.513950</td> <td>1101.0</td> <td>1314.0</td> <td>19.346049</td> <td>0.740314</td> <td>0.874270</td> <td>18.094437</td> <td>1235.0</td> <td>1375.0</td> <td>11.336032</td> <td>0.830416</td> <td>0.914856</td> <td>10.168424</td> <td>487.557187</td> <td>482.410232</td> <td>734.805659</td> <td>916.417933</td> <td>308787.220008</td> <td>274519.799702</td> <td>-11.097422</td> <td>15.74875</td> <td>15.017086</td> <td>-0.731664</td> <td>8.145166</td> <td>12.156773</td> <td>49.251387</td> <td>64.0</td> <td>85.595</td> <td>0.5</td> <td>1.0</td> <td>1.0</td> <td>1.0</td> <td>1.0</td> <td>1.0</td> <td>1.0</td> <td>1.0</td> <td>9.275093</td> <td>8.580161</td> <td>-0.694932</td> <td>17.517027</td> <td>27.678938</td> <td>5.964574</td> <td>4.919491</td> <td>2.932285</td> <td>3.401387</td> <td>0.469103</td> <td>2.234327</td> <td>2.217284</td> <td>-0.017043</td> <td>11.10993</td> <td>12.43724</td> <td>11.947090</td> <td>143710.80</td> <td>131315.70</td> <td>-8.625039</td> <td>2.655693</td> <td>2.383681</td> <td>-0.272012</td> <td>1.440968</td> <td>1.643918</td> <td>0.20295</td> <td>0.0</td> <td>15.2</td> <td>14.4</td> <td>-0.8</td> <td>5.9</td> <td>5.7</td> <td>-0.2</td> <td>8.0</td> <td>9.5</td> <td>1.217211</td> <td>0.906066</td> <td>1.242792</td> <td>4.0</td> <td>4.0</td> <td>0.0</td> <td>4.0</td> <td>0.0</td> <td>111.0</td> <td>153.0</td> <td>37.837838</td> <td>18.974359</td> <td>25.331126</td> <td>3.726542</td> <td>3.728661</td> <td>9053.0</td> <td>8942.0</td> <td>-1.226113</td> <td>6.136566</td> <td>5.961329</td> <td>-2.855618</td> <td>10.0</td> <td>23.0</td> <td>130.00000</td> <td>0.006586</td> <td>0.015410</td> <td>133.989835</td> <td>11.0</td> <td>47.826087</td> <td>12.0</td> <td>52.173913</td> <td>6.0</td> <td>26.086957</td> <td>11.0</td> <td>47.826087</td> <td>14.0</td> <td>60.869565</td> <td>17.0</td> <td>73.913043</td> <td>14.0</td> <td>60.869565</td> <td>13.0</td> <td>56.521739</td> <td>13.0</td> <td>56.521739</td> <td>140.0</td> <td>152.0</td> <td>8.571429</td> <td>6712.0</td> <td>6287.0</td> <td>6.759981</td> <td>4.549722</td> <td>4.474664</td> <td>-1.649732</td> <td>140.0</td> <td>151.0</td> <td>7.857143</td> <td>6464.0</td> <td>5686.0</td> <td>-12.035891</td> <td>4.381615</td> <td>3.790664</td> <td>-13.487063</td> <td>100.0</td> <td>98.0</td> <td>-2.0</td> <td>3161.0</td> <td>2810.0</td> <td>-11.104081</td> <td>2.142680</td> <td>1.873332</td> <td>-12.570626</td> <td>59.0</td> <td>70.0</td> <td>18.644068</td> <td>163.0</td> <td>161.0</td> <td>-1.226994</td> <td>0.110489</td> <td>0.107333</td> <td>-2.856484</td> <td>5.0</td> <td>5.0</td> <td>0.000000</td> <td>13.0</td> <td>27.0</td> <td>107.692308</td> <td>116733.0</td> <td>189385.0</td> <td>62.237756</td> <td>79.127337</td> <td>126.256582</td> <td>59.561268</td> <td>0.0</td> <td>17.0</td> <td>28.0</td> <td>64.705882</td> <td>32.0</td> <td>91.0</td> <td>184.375</td> <td>798000.0</td> <td>4247000.0</td> <td>432.205514</td> <td>0.0</td> <td>1.0</td> <td>8.0</td> <td>8.8</td> <td>24.7</td> <td>23.1</td> <td>23.3</td> <td>197.0</td> <td>215.0</td> <td>9.137056</td> <td>0.132463</td> <td>0.143050</td> <td>7.992508</td> <td>71.565808</td> <td>6.838116</td> <td>16.489035</td> <td>3.367931</td> <td>0.194596</td> <td>0.018415</td> <td>13.512773</td> <td>23.950849</td> <td>87634.0</td> <td>7.8</td> <td>0.0</td> <td>10.7</td> <td>0.0</td> <td>1.0</td> <td>0.0</td> <td>40.0</td> <td>130064.0</td> <td>126944.0</td> <td>123069.8332</td> <td>118552.3334</td> <td>114489.8334</td> <td>112892.1666</td> <td>437660.0</td> <td>452425.9859</td> <td>446144.1927</td> <td>437992.0892</td> <td>439385.8324</td> <td>440455.1121</td> <td>208249.0</td> <td>NaN</td> <td>NaN</td> <td>227754.4048</td> <td>226575.5723</td> <td>225681.4096</td> <td>57028.0</td> <td>56667.25</td> <td>54327.25</td> <td>53632.25</td> <td>52954.0</td> <td>53308.5</td> <td>12826.0</td> <td>10628.0</td> <td>11390.0</td> <td>11315.0</td> <td>16850.0</td> <td>16793.0</td> <td>3687050.0</td> <td>3760184.0</td> <td>3791508.0</td> <td>3815780.0</td> <td>3850568.0</td> <td>3878051.0</td> <td>3911338.0</td> <td>3923561.0</td> <td>False</td> <td>True</td> <td>False</td> <td>True</td>

</tr> <tr>

<th>36061</th> <td>1585873.0</td> <td>1609533.0</td> <td>1625121.0</td> <td>1630453.0</td> <td>1634468.0</td> <td>1641168.0</td> <td>1643734.0</td> <td>2.000000</td> <td>2.000000</td> <td>0.000000</td> <td>0.000126</td> <td>0.000126</td> <td>0.000000</td> <td>0.000000</td> <td>0.000000</td> <td>0.000000</td> <td>0.000000</td> <td>0.000000</td> <td>0.000000</td> <td>0.000000</td> <td>0.000000</td> <td>0.000000</td> <td>0.000000</td> <td>0.000000</td> <td>0.000000</td> <td>0.000000</td> <td>0.000000</td> <td>0.000000</td> <td>0.000000</td> <td>0.000000</td> <td>0.000000</td> <td>0.000000</td> <td>0.000000</td> <td>0.000000</td> <td>0.000000</td> <td>0.000000</td> <td>0.000000</td> <td>0.000000</td> <td>0.000000</td> <td>0.000000</td> <td>2.000000</td> <td>0.000126</td> <td>0.000000</td> <td>0.000000</td> <td>0.000000</td> <td>0.000000</td> <td>0.000000</td> <td>0.000000</td> <td>1230.0</td> <td>1342.0</td> <td>9.105691</td> <td>0.776794</td> <td>0.820159</td> <td>5.582541</td> 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</tr> <tr>

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</tr> <tr>

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</tr> <tr>

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</tr>

</tbody>

</table>

</div>

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